diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md index 484ebd1f383..d90322e8977 100644 --- a/.github/CONTRIBUTING.md +++ b/.github/CONTRIBUTING.md @@ -3,43 +3,4 @@ Hi there! Thank you for even being interested in contributing to LangChain. As an open-source project in a rapidly developing field, we are extremely open to contributions, whether they involve new features, improved infrastructure, better documentation, or bug fixes. -To learn about how to contribute, please follow the [guides here](https://python.langchain.com/docs/contributing/) - -## 🗺️ Guidelines - -### 👩‍💻 Ways to contribute - -There are many ways to contribute to LangChain. Here are some common ways people contribute: - -- [**Documentation**](https://python.langchain.com/docs/contributing/documentation): Help improve our docs, including this one! -- [**Code**](https://python.langchain.com/docs/contributing/code): Help us write code, fix bugs, or improve our infrastructure. -- [**Integrations**](https://python.langchain.com/docs/contributing/integrations): Help us integrate with your favorite vendors and tools. - -### 🚩GitHub Issues - -Our [issues](https://github.com/langchain-ai/langchain/issues) page is kept up to date with bugs, improvements, and feature requests. - -There is a taxonomy of labels to help with sorting and discovery of issues of interest. Please use these to help organize issues. - -If you start working on an issue, please assign it to yourself. - -If you are adding an issue, please try to keep it focused on a single, modular bug/improvement/feature. -If two issues are related, or blocking, please link them rather than combining them. - -We will try to keep these issues as up-to-date as possible, though -with the rapid rate of development in this field some may get out of date. -If you notice this happening, please let us know. - -### 🙋Getting Help - -Our goal is to have the simplest developer setup possible. Should you experience any difficulty getting setup, please -contact a maintainer! Not only do we want to help get you unblocked, but we also want to make sure that the process is -smooth for future contributors. - -In a similar vein, we do enforce certain linting, formatting, and documentation standards in the codebase. -If you are finding these difficult (or even just annoying) to work with, feel free to contact a maintainer for help - -we do not want these to get in the way of getting good code into the codebase. - -### Contributor Documentation - -To learn about how to contribute, please follow the [guides here](https://python.langchain.com/docs/contributing/) +To learn how to contribute to LangChain, please follow the [contribution guide here](https://python.langchain.com/docs/contributing/). \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/documentation.yml b/.github/ISSUE_TEMPLATE/documentation.yml index 61a3ad496bf..3158dab5d7d 100644 --- a/.github/ISSUE_TEMPLATE/documentation.yml +++ b/.github/ISSUE_TEMPLATE/documentation.yml @@ -4,13 +4,45 @@ title: "DOC: " labels: [03 - Documentation] body: +- type: markdown + attributes: + value: > + Thank you for taking the time to report an issue in the documentation. + + Only report issues with documentation here, explain if there are + any missing topics or if you found a mistake in the documentation. + + Do **NOT** use this to ask usage questions or reporting issues with your code. + + If you have usage questions or need help solving some problem, + please use [GitHub Discussions](https://github.com/langchain-ai/langchain/discussions). + + If you're in the wrong place, here are some helpful links to find a better + place to ask your question: + + [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction), + [API Reference](https://api.python.langchain.com/en/stable/), + [GitHub search](https://github.com/langchain-ai/langchain), + [LangChain Github Discussions](https://github.com/langchain-ai/langchain/discussions), + [LangChain Github Issues](https://github.com/langchain-ai/langchain/issues?q=is%3Aissue), + [LangChain ChatBot](https://chat.langchain.com/) +- type: checkboxes + id: checks + attributes: + label: Checklist + description: Please confirm and check all the following options. + options: + - label: I added a very descriptive title to this issue. + required: true + - label: I included a link to the documentation page I am referring to (if applicable). + required: true - type: textarea attributes: label: "Issue with current documentation:" description: > Please make sure to leave a reference to the document/code you're - referring to. - + referring to. Feel free to include names of classes, functions, methods + or concepts you'd like to see documented more. - type: textarea attributes: label: "Idea or request for content:" diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md index 0e85fdf17da..4d86dac6a59 100644 --- a/.github/PULL_REQUEST_TEMPLATE.md +++ b/.github/PULL_REQUEST_TEMPLATE.md @@ -1,20 +1,24 @@ - +Additional guidelines: +- Make sure optional dependencies are imported within a function. +- Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. +- Most PRs should not touch more than one package. +- Changes should be backwards compatible. +- If you are adding something to community, do not re-import it in langchain. + +If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, hwchase17. diff --git a/.github/actions/poetry_setup/action.yml b/.github/actions/poetry_setup/action.yml index 5aef4970ba7..68b099f0e55 100644 --- a/.github/actions/poetry_setup/action.yml +++ b/.github/actions/poetry_setup/action.yml @@ -32,7 +32,7 @@ runs: with: python-version: ${{ inputs.python-version }} - - uses: actions/cache@v3 + - uses: actions/cache@v4 id: cache-bin-poetry name: Cache Poetry binary - Python ${{ inputs.python-version }} env: @@ -79,7 +79,7 @@ runs: run: pipx install "poetry==$POETRY_VERSION" --python '${{ steps.setup-python.outputs.python-path }}' --verbose - name: Restore pip and poetry cached dependencies - uses: actions/cache@v3 + uses: actions/cache@v4 env: SEGMENT_DOWNLOAD_TIMEOUT_MIN: "4" WORKDIR: ${{ inputs.working-directory == '' && '.' || inputs.working-directory }} diff --git a/.github/scripts/check_diff.py b/.github/scripts/check_diff.py index ee20daa547a..4cbe1ec8b2f 100644 --- a/.github/scripts/check_diff.py +++ b/.github/scripts/check_diff.py @@ -47,4 +47,4 @@ if __name__ == "__main__": else: pass json_output = json.dumps(list(dirs_to_run)) - print(f"dirs-to-run={json_output}") + print(f"dirs-to-run={json_output}") # noqa: T201 diff --git a/.github/scripts/get_min_versions.py b/.github/scripts/get_min_versions.py new file mode 100644 index 00000000000..16a47254b2e --- /dev/null +++ b/.github/scripts/get_min_versions.py @@ -0,0 +1,67 @@ +import sys + +import tomllib +from packaging.version import parse as parse_version +import re + +MIN_VERSION_LIBS = ["langchain-core", "langchain-community", "langchain"] + + +def get_min_version(version: str) -> str: + # case ^x.x.x + _match = re.match(r"^\^(\d+(?:\.\d+){0,2})$", version) + if _match: + return _match.group(1) + + # case >=x.x.x,=(\d+(?:\.\d+){0,2}),<(\d+(?:\.\d+){0,2})$", version) + if _match: + _min = _match.group(1) + _max = _match.group(2) + assert parse_version(_min) < parse_version(_max) + return _min + + # case x.x.x + _match = re.match(r"^(\d+(?:\.\d+){0,2})$", version) + if _match: + return _match.group(1) + + raise ValueError(f"Unrecognized version format: {version}") + + +def get_min_version_from_toml(toml_path: str): + # Parse the TOML file + with open(toml_path, "rb") as file: + toml_data = tomllib.load(file) + + # Get the dependencies from tool.poetry.dependencies + dependencies = toml_data["tool"]["poetry"]["dependencies"] + + # Initialize a dictionary to store the minimum versions + min_versions = {} + + # Iterate over the libs in MIN_VERSION_LIBS + for lib in MIN_VERSION_LIBS: + # Check if the lib is present in the dependencies + if lib in dependencies: + # Get the version string + version_string = dependencies[lib] + + # Use parse_version to get the minimum supported version from version_string + min_version = get_min_version(version_string) + + # Store the minimum version in the min_versions dictionary + min_versions[lib] = min_version + + return min_versions + + +# Get the TOML file path from the command line argument +toml_file = sys.argv[1] + +# Call the function to get the minimum versions +min_versions = get_min_version_from_toml(toml_file) + +print( + " ".join([f"{lib}=={version}" for lib, version in min_versions.items()]) +) # noqa: T201 diff --git a/.github/workflows/_integration_test.yml b/.github/workflows/_integration_test.yml index e0dcf25c135..1189907e966 100644 --- a/.github/workflows/_integration_test.yml +++ b/.github/workflows/_integration_test.yml @@ -38,6 +38,11 @@ jobs: shell: bash run: poetry install --with test,test_integration + - name: Install deps outside pyproject + if: ${{ startsWith(inputs.working-directory, 'libs/community/') }} + shell: bash + run: poetry run pip install "boto3<2" "google-cloud-aiplatform<2" + - name: 'Authenticate to Google Cloud' id: 'auth' uses: google-github-actions/auth@v2 @@ -47,6 +52,7 @@ jobs: - name: Run integration tests shell: bash env: + AI21_API_KEY: ${{ secrets.AI21_API_KEY }} GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }} ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }} MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }} @@ -57,6 +63,13 @@ jobs: GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }} EXA_API_KEY: ${{ secrets.EXA_API_KEY }} NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }} + WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }} + WATSONX_PROJECT_ID: ${{ secrets.WATSONX_PROJECT_ID }} + PINECONE_API_KEY: ${{ secrets.PINECONE_API_KEY }} + PINECONE_ENVIRONMENT: ${{ secrets.PINECONE_ENVIRONMENT }} + ASTRA_DB_API_ENDPOINT: ${{ secrets.ASTRA_DB_API_ENDPOINT }} + ASTRA_DB_APPLICATION_TOKEN: ${{ secrets.ASTRA_DB_APPLICATION_TOKEN }} + ASTRA_DB_KEYSPACE: ${{ secrets.ASTRA_DB_KEYSPACE }} run: | make integration_tests diff --git a/.github/workflows/_lint.yml b/.github/workflows/_lint.yml index a0c1944a325..e2e25fb8ebc 100644 --- a/.github/workflows/_lint.yml +++ b/.github/workflows/_lint.yml @@ -80,13 +80,13 @@ jobs: poetry run pip install -e "$LANGCHAIN_LOCATION" - name: Get .mypy_cache to speed up mypy - uses: actions/cache@v3 + uses: actions/cache@v4 env: SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2" with: path: | ${{ env.WORKDIR }}/.mypy_cache - key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', env.WORKDIR)) }} + key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }} - name: Analysing the code with our lint @@ -94,7 +94,7 @@ jobs: run: | make lint_package - - name: Install test dependencies + - name: Install unit test dependencies # Also installs dev/lint/test/typing dependencies, to ensure we have # type hints for as many of our libraries as possible. # This helps catch errors that require dependencies to be spotted, for example: @@ -103,18 +103,24 @@ jobs: # If you change this configuration, make sure to change the `cache-key` # in the `poetry_setup` action above to stop using the old cache. # It doesn't matter how you change it, any change will cause a cache-bust. + if: ${{ ! startsWith(inputs.working-directory, 'libs/partners/') }} working-directory: ${{ inputs.working-directory }} run: | poetry install --with test + - name: Install unit+integration test dependencies + if: ${{ startsWith(inputs.working-directory, 'libs/partners/') }} + working-directory: ${{ inputs.working-directory }} + run: | + poetry install --with test,test_integration - name: Get .mypy_cache_test to speed up mypy - uses: actions/cache@v3 + uses: actions/cache@v4 env: SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2" with: path: | ${{ env.WORKDIR }}/.mypy_cache_test - key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', env.WORKDIR)) }} + key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }} - name: Analysing the code with our lint working-directory: ${{ inputs.working-directory }} diff --git a/.github/workflows/_release.yml b/.github/workflows/_release.yml index c37d87e0ad1..1221d6a6d6a 100644 --- a/.github/workflows/_release.yml +++ b/.github/workflows/_release.yml @@ -15,7 +15,7 @@ on: default: 'libs/langchain' env: - PYTHON_VERSION: "3.10" + PYTHON_VERSION: "3.11" POETRY_VERSION: "1.7.1" jobs: @@ -166,23 +166,48 @@ jobs: - name: Run integration tests if: ${{ startsWith(inputs.working-directory, 'libs/partners/') }} env: + AI21_API_KEY: ${{ secrets.AI21_API_KEY }} GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }} ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }} MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }} TOGETHER_API_KEY: ${{ secrets.TOGETHER_API_KEY }} OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} + AZURE_OPENAI_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }} + AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }} + AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }} + AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_CHAT_DEPLOYMENT_NAME }} + AZURE_OPENAI_LLM_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LLM_DEPLOYMENT_NAME }} + AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }} NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }} GOOGLE_SEARCH_API_KEY: ${{ secrets.GOOGLE_SEARCH_API_KEY }} GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }} EXA_API_KEY: ${{ secrets.EXA_API_KEY }} NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }} + WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }} + WATSONX_PROJECT_ID: ${{ secrets.WATSONX_PROJECT_ID }} + PINECONE_API_KEY: ${{ secrets.PINECONE_API_KEY }} + PINECONE_ENVIRONMENT: ${{ secrets.PINECONE_ENVIRONMENT }} + ASTRA_DB_API_ENDPOINT: ${{ secrets.ASTRA_DB_API_ENDPOINT }} + ASTRA_DB_APPLICATION_TOKEN: ${{ secrets.ASTRA_DB_APPLICATION_TOKEN }} + ASTRA_DB_KEYSPACE: ${{ secrets.ASTRA_DB_KEYSPACE }} run: make integration_tests working-directory: ${{ inputs.working-directory }} - - name: Run unit tests with minimum dependency versions - if: ${{ (inputs.working-directory == 'libs/langchain') || (inputs.working-directory == 'libs/community') || (inputs.working-directory == 'libs/experimental') }} + - name: Get minimum versions + working-directory: ${{ inputs.working-directory }} + id: min-version run: | - poetry run pip install -r _test_minimum_requirements.txt + poetry run pip install packaging + min_versions="$(poetry run python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml)" + echo "min-versions=$min_versions" >> "$GITHUB_OUTPUT" + echo "min-versions=$min_versions" + + - name: Run unit tests with minimum dependency versions + if: ${{ steps.min-version.outputs.min-versions != '' }} + env: + MIN_VERSIONS: ${{ steps.min-version.outputs.min-versions }} + run: | + poetry run pip install $MIN_VERSIONS make tests working-directory: ${{ inputs.working-directory }} diff --git a/.github/workflows/api_doc_build.yml b/.github/workflows/api_doc_build.yml new file mode 100644 index 00000000000..7f88768c56a --- /dev/null +++ b/.github/workflows/api_doc_build.yml @@ -0,0 +1,52 @@ +name: API docs build + +on: + workflow_dispatch: + schedule: + - cron: '0 13 * * *' +env: + POETRY_VERSION: "1.7.1" + PYTHON_VERSION: "3.10" + +jobs: + build: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + with: + ref: bagatur/api_docs_build + + - name: Set Git config + run: | + git config --local user.email "actions@github.com" + git config --local user.name "Github Actions" + + - name: Merge master + run: | + git fetch origin master + git merge origin/master -m "Merge master" --allow-unrelated-histories -X theirs + + - name: Set up Python ${{ env.PYTHON_VERSION }} + Poetry ${{ env.POETRY_VERSION }} + uses: "./.github/actions/poetry_setup" + with: + python-version: ${{ env.PYTHON_VERSION }} + poetry-version: ${{ env.POETRY_VERSION }} + cache-key: api-docs + + - name: Install dependencies + run: | + poetry run python -m pip install --upgrade --no-cache-dir pip setuptools + poetry run python -m pip install --upgrade --no-cache-dir sphinx readthedocs-sphinx-ext + poetry run python -m pip install ./libs/partners/* + poetry run python -m pip install --exists-action=w --no-cache-dir -r docs/api_reference/requirements.txt + + - name: Build docs + run: | + poetry run python -m pip install --upgrade --no-cache-dir pip setuptools + poetry run python docs/api_reference/create_api_rst.py + poetry run python -m sphinx -T -E -b html -d _build/doctrees -c docs/api_reference docs/api_reference api_reference_build/html -j auto + + # https://github.com/marketplace/actions/add-commit + - uses: EndBug/add-and-commit@v9 + with: + message: 'Update API docs build' diff --git a/.github/workflows/codespell.yml b/.github/workflows/codespell.yml index a5a63b996de..738e0643346 100644 --- a/.github/workflows/codespell.yml +++ b/.github/workflows/codespell.yml @@ -34,3 +34,4 @@ jobs: with: skip: guide_imports.json ignore_words_list: ${{ steps.extract_ignore_words.outputs.ignore_words_list }} + exclude_file: libs/community/langchain_community/llms/yuan2.py diff --git a/.github/workflows/extract_ignored_words_list.py b/.github/workflows/extract_ignored_words_list.py index 7c800e0df0b..bb949d14f79 100644 --- a/.github/workflows/extract_ignored_words_list.py +++ b/.github/workflows/extract_ignored_words_list.py @@ -7,4 +7,4 @@ ignore_words_list = ( pyproject_toml.get("tool", {}).get("codespell", {}).get("ignore-words-list") ) -print(f"::set-output name=ignore_words_list::{ignore_words_list}") +print(f"::set-output name=ignore_words_list::{ignore_words_list}") # noqa: T201 diff --git a/.github/workflows/scheduled_test.yml b/.github/workflows/scheduled_test.yml index 4ae8b755c14..a5bae539102 100644 --- a/.github/workflows/scheduled_test.yml +++ b/.github/workflows/scheduled_test.yml @@ -54,6 +54,11 @@ jobs: echo "Running scheduled tests, installing dependencies with poetry..." poetry install --with=test_integration,test + - name: Install deps outside pyproject + if: ${{ startsWith(inputs.working-directory, 'libs/community/') }} + shell: bash + run: poetry run pip install "boto3<2" "google-cloud-aiplatform<2" + - name: Run tests shell: bash env: diff --git a/.readthedocs.yaml b/.readthedocs.yaml index aad4d18ea4c..ca809231d93 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -13,15 +13,8 @@ build: tools: python: "3.11" commands: - - python -m virtualenv $READTHEDOCS_VIRTUALENV_PATH - - python -m pip install --upgrade --no-cache-dir pip setuptools - - python -m pip install --upgrade --no-cache-dir sphinx readthedocs-sphinx-ext - - python -m pip install ./libs/partners/* - - python -m pip install --exists-action=w --no-cache-dir -r docs/api_reference/requirements.txt - - python docs/api_reference/create_api_rst.py - - cat docs/api_reference/conf.py - - python -m sphinx -T -E -b html -d _build/doctrees -c docs/api_reference docs/api_reference $READTHEDOCS_OUTPUT/html -j auto - + - mkdir -p $READTHEDOCS_OUTPUT + - cp -r api_reference_build/* $READTHEDOCS_OUTPUT # Build documentation in the docs/ directory with Sphinx sphinx: configuration: docs/api_reference/conf.py diff --git a/Makefile b/Makefile index c21d230ab47..db50dfeed32 100644 --- a/Makefile +++ b/Makefile @@ -15,7 +15,12 @@ docs_build: docs/.local_build.sh docs_clean: - rm -r _dist + @if [ -d _dist ]; then \ + rm -r _dist; \ + echo "Directory _dist has been cleaned."; \ + else \ + echo "Nothing to clean."; \ + fi docs_linkcheck: poetry run linkchecker _dist/docs/ --ignore-url node_modules diff --git a/README.md b/README.md index 1f21107f0a4..65985434439 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # 🦜️🔗 LangChain -⚡ Building applications with LLMs through composability ⚡ +⚡ Build context-aware reasoning applications ⚡ [![Release Notes](https://img.shields.io/github/release/langchain-ai/langchain)](https://github.com/langchain-ai/langchain/releases) [![CI](https://github.com/langchain-ai/langchain/actions/workflows/check_diffs.yml/badge.svg)](https://github.com/langchain-ai/langchain/actions/workflows/check_diffs.yml) @@ -18,7 +18,7 @@ Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langc To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com). [LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications. -Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) to get off the waitlist or speak with our sales team. +Fill out [this form](https://www.langchain.com/contact-sales) to speak with our sales team. ## Quick Install @@ -43,6 +43,7 @@ This framework consists of several parts. - **[LangChain Templates](templates)**: A collection of easily deployable reference architectures for a wide variety of tasks. - **[LangServe](https://github.com/langchain-ai/langserve)**: A library for deploying LangChain chains as a REST API. - **[LangSmith](https://smith.langchain.com)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain. +- **[LangGraph](https://python.langchain.com/docs/langgraph)**: LangGraph is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain. It extends the LangChain Expression Language with the ability to coordinate multiple chains (or actors) across multiple steps of computation in a cyclic manner. The LangChain libraries themselves are made up of several different packages. - **[`langchain-core`](libs/core)**: Base abstractions and LangChain Expression Language. diff --git a/cookbook/apache_kafka_message_handling.ipynb b/cookbook/apache_kafka_message_handling.ipynb new file mode 100644 index 00000000000..616c12ac680 --- /dev/null +++ b/cookbook/apache_kafka_message_handling.ipynb @@ -0,0 +1,922 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "rT1cmV4qCa2X" + }, + "source": [ + "# Using Apache Kafka to route messages\n", + "\n", + "---\n", + "\n", + "\n", + "\n", + "This notebook shows you how to use LangChain's standard chat features while passing the chat messages back and forth via Apache Kafka.\n", + "\n", + "This goal is to simulate an architecture where the chat front end and the LLM are running as separate services that need to communicate with one another over an internal nework.\n", + "\n", + "It's an alternative to typical pattern of requesting a reponse from the model via a REST API (there's more info on why you would want to do this at the end of the notebook)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UPYtfAR_9YxZ" + }, + "source": [ + "### 1. Install the main dependencies\n", + "\n", + "Dependencies include:\n", + "\n", + "- The Quix Streams library for managing interactions with Apache Kafka (or Kafka-like tools such as Redpanda) in a \"Pandas-like\" way.\n", + "- The LangChain library for managing interactions with Llama-2 and storing conversation state." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZX5tfKiy9cN-" + }, + "outputs": [], + "source": [ + "!pip install quixstreams==2.1.2a langchain==0.0.340 huggingface_hub==0.19.4 langchain-experimental==0.0.42 python-dotenv" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "losTSdTB9d9O" + }, + "source": [ + "### 2. Build and install the llama-cpp-python library (with CUDA enabled so that we can advantage of Google Colab GPU\n", + "\n", + "The `llama-cpp-python` library is a Python wrapper around the `llama-cpp` library which enables you to efficiently leverage just a CPU to run quantized LLMs.\n", + "\n", + "When you use the standard `pip install llama-cpp-python` command, you do not get GPU support by default. Generation can be very slow if you rely on just the CPU in Google Colab, so the following command adds an extra option to build and install\n", + "`llama-cpp-python` with GPU support (make sure you have a GPU-enabled runtime selected in Google Colab)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-JCQdl1G9tbl" + }, + "outputs": [], + "source": [ + "!CMAKE_ARGS=\"-DLLAMA_CUBLAS=on\" FORCE_CMAKE=1 pip install llama-cpp-python" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5_vjVIAh9rLl" + }, + "source": [ + "### 3. Download and setup Kafka and Zookeeper instances\n", + "\n", + "Download the Kafka binaries from the Apache website and start the servers as daemons. We'll use the default configurations (provided by Apache Kafka) for spinning up the instances." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "zFz7czGRW5Wr" + }, + "outputs": [], + "source": [ + "!curl -sSOL https://dlcdn.apache.org/kafka/3.6.1/kafka_2.13-3.6.1.tgz\n", + "!tar -xzf kafka_2.13-3.6.1.tgz" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Uf7NR_UZ9wye" + }, + "outputs": [], + "source": [ + "!./kafka_2.13-3.6.1/bin/zookeeper-server-start.sh -daemon ./kafka_2.13-3.6.1/config/zookeeper.properties\n", + "!./kafka_2.13-3.6.1/bin/kafka-server-start.sh -daemon ./kafka_2.13-3.6.1/config/server.properties\n", + "!echo \"Waiting for 10 secs until kafka and zookeeper services are up and running\"\n", + "!sleep 10" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H3SafFuS94p1" + }, + "source": [ + "### 4. Check that the Kafka Daemons are running\n", + "\n", + "Show the running processes and filter it for Java processes (you should see two—one for each server)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "CZDC2lQP99yp" + }, + "outputs": [], + "source": [ + "!ps aux | grep -E '[j]ava'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Snoxmjb5-V37" + }, + "source": [ + "### 5. Import the required dependencies and initialize required variables\n", + "\n", + "Import the Quix Streams library for interacting with Kafka, and the necessary LangChain components for running a `ConversationChain`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "plR9e_MF-XL5" + }, + "outputs": [], + "source": [ + "# Import utility libraries\n", + "import json\n", + "import random\n", + "import re\n", + "import time\n", + "import uuid\n", + "from os import environ\n", + "from pathlib import Path\n", + "from random import choice, randint, random\n", + "\n", + "from dotenv import load_dotenv\n", + "\n", + "# Import a Hugging Face utility to download models directly from Hugging Face hub:\n", + "from huggingface_hub import hf_hub_download\n", + "from langchain.chains import ConversationChain\n", + "\n", + "# Import Langchain modules for managing prompts and conversation chains:\n", + "from langchain.llms import LlamaCpp\n", + "from langchain.memory import ConversationTokenBufferMemory\n", + "from langchain.prompts import PromptTemplate, load_prompt\n", + "from langchain.schema import SystemMessage\n", + "from langchain_experimental.chat_models import Llama2Chat\n", + "from quixstreams import Application, State, message_key\n", + "\n", + "# Import Quix dependencies\n", + "from quixstreams.kafka import Producer\n", + "\n", + "# Initialize global variables.\n", + "AGENT_ROLE = \"AI\"\n", + "chat_id = \"\"\n", + "\n", + "# Set the current role to the role constant and initialize variables for supplementary customer metadata:\n", + "role = AGENT_ROLE" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HgJjJ9aZ-liy" + }, + "source": [ + "### 6. Download the \"llama-2-7b-chat.Q4_K_M.gguf\" model\n", + "\n", + "Download the quantized LLama-2 7B model from Hugging Face which we will use as a local LLM (rather than relying on REST API calls to an external service)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67, + "referenced_widgets": [ + "969343cdbe604a26926679bbf8bd2dda", + "d8b8370c9b514715be7618bfe6832844", + "0def954cca89466b8408fadaf3b82e64", + "462482accc664729980562e208ceb179", + "80d842f73c564dc7b7cc316c763e2633", + "fa055d9f2a9d4a789e9cf3c89e0214e5", + "30ecca964a394109ac2ad757e3aec6c0", + "fb6478ce2dac489bb633b23ba0953c5c", + "734b0f5da9fc4307a95bab48cdbb5d89", + "b32f3a86a74741348511f4e136744ac8", + "e409071bff5a4e2d9bf0e9f5cc42231b" + ] + }, + "id": "Qwu4YoSA-503", + "outputId": "f956976c-7485-415b-ac93-4336ade31964" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The model path does not exist in state. Downloading model...\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "969343cdbe604a26926679bbf8bd2dda", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "llama-2-7b-chat.Q4_K_M.gguf: 0%| | 0.00/4.08G [00:00 2:chain:RunnableParallel] Entering Chain run with input:\n", + "\u001b[0m{\n", + " \"input\": \"What is the first transduction model relying entirely on self-attention?\"\n", + "}\n", + "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel > 4:chain:RunnablePassthrough] Entering Chain run with input:\n", + "\u001b[0m{\n", + " \"input\": \"What is the first transduction model relying entirely on self-attention?\"\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel > 4:chain:RunnablePassthrough] [1ms] Exiting Chain run with output:\n", + "\u001b[0m{\n", + " \"output\": \"What is the first transduction model relying entirely on self-attention?\"\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel] [66ms] Exiting Chain run with output:\n", + "\u001b[0m[outputs]\n", + "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 5:prompt:ChatPromptTemplate] Entering Prompt run with input:\n", + "\u001b[0m[inputs]\n", + "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 5:prompt:ChatPromptTemplate] [1ms] Exiting Prompt run with output:\n", + "\u001b[0m{\n", + " \"lc\": 1,\n", + " \"type\": \"constructor\",\n", + " \"id\": [\n", + " \"langchain\",\n", + " \"prompts\",\n", + " \"chat\",\n", + " \"ChatPromptValue\"\n", + " ],\n", + " \"kwargs\": {\n", + " \"messages\": [\n", + " {\n", + " \"lc\": 1,\n", + " \"type\": \"constructor\",\n", + " \"id\": [\n", + " \"langchain\",\n", + " \"schema\",\n", + " \"messages\",\n", + " \"HumanMessage\"\n", + " ],\n", + " \"kwargs\": {\n", + " \"content\": \"You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\\nQuestion: What is the first transduction model relying entirely on self-attention? \\nContext: [Document(page_content='To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.\\\\nIn the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as (neural_gpu, ; NalBytenet2017, ) and (JonasFaceNet2017, ).\\\\n\\\\n\\\\n\\\\n\\\\n3 Model Architecture\\\\n\\\\nFigure 1: The Transformer - model architecture.', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention.\\\\n\\\\n\\\\nFor translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles. \\\\n\\\\n\\\\nWe are excited about the future of attention-based models and plan to apply them to other tasks. We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video.\\\\nMaking generation less sequential is another research goals of ours.', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences (bahdanau2014neural, ; structuredAttentionNetworks, ). In all but a few cases (decomposableAttnModel, ), however, such attention mechanisms are used in conjunction with a recurrent network.\\\\n\\\\n\\\\nIn this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n2 Background', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'})] \\nAnswer:\",\n", + " \"additional_kwargs\": {}\n", + " }\n", + " }\n", + " ]\n", + " }\n", + "}\n", + "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 6:llm:HuggingFacePipeline] Entering LLM run with input:\n", + "\u001b[0m{\n", + " \"prompts\": [\n", + " \"Human: You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\\nQuestion: What is the first transduction model relying entirely on self-attention? \\nContext: [Document(page_content='To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.\\\\nIn the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as (neural_gpu, ; NalBytenet2017, ) and (JonasFaceNet2017, ).\\\\n\\\\n\\\\n\\\\n\\\\n3 Model Architecture\\\\n\\\\nFigure 1: The Transformer - model architecture.', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention.\\\\n\\\\n\\\\nFor translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles. \\\\n\\\\n\\\\nWe are excited about the future of attention-based models and plan to apply them to other tasks. We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video.\\\\nMaking generation less sequential is another research goals of ours.', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences (bahdanau2014neural, ; structuredAttentionNetworks, ). In all but a few cases (decomposableAttnModel, ), however, such attention mechanisms are used in conjunction with a recurrent network.\\\\n\\\\n\\\\nIn this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n2 Background', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'})] \\nAnswer:\"\n", + " ]\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 6:llm:HuggingFacePipeline] [4.34s] Exiting LLM run with output:\n", + "\u001b[0m{\n", + " \"generations\": [\n", + " [\n", + " {\n", + " \"text\": \" The first transduction model relying entirely on self-attention is the Transformer.\",\n", + " \"generation_info\": null,\n", + " \"type\": \"Generation\"\n", + " }\n", + " ]\n", + " ],\n", + " \"llm_output\": null,\n", + " \"run\": null\n", + "}\n", + "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence] [4.41s] Exiting Chain run with output:\n", + "\u001b[0m{\n", + " \"output\": \" The first transduction model relying entirely on self-attention is the Transformer.\"\n", + "}\n" + ] + } + ], + "source": [ + "langchain.verbose = True\n", + "langchain.debug = True\n", + "\n", + "llm_res = rag_chain.invoke(\n", + " \"What is the first transduction model relying entirely on self-attention?\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "023404a1-401a-46e1-8ab5-cafbc8593b04", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "' The first transduction model relying entirely on self-attention is the Transformer.'" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "llm_res" + ] + }, + { + "cell_type": "markdown", + "id": "0eaefd01-254a-445d-a95f-37889c126e0e", + "metadata": {}, + "source": [ + "Based on the retrieved documents, the answer is indeed correct :)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/cookbook/self-discover.ipynb b/cookbook/self-discover.ipynb new file mode 100644 index 00000000000..2e885ad0ba7 --- /dev/null +++ b/cookbook/self-discover.ipynb @@ -0,0 +1,423 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a38e5d2d-7587-4192-90f2-b58e6c62f08c", + "metadata": {}, + "source": [ + "# Self Discover\n", + "\n", + "An implementation of the [Self-Discover paper](https://arxiv.org/pdf/2402.03620.pdf).\n", + "\n", + "Based on [this implementation from @catid](https://github.com/catid/self-discover/tree/main?tab=readme-ov-file)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a18d8f24-5d9a-45c5-9739-6f3c4ed6c9c9", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_openai import ChatOpenAI" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9f554045-6e79-42d3-be4b-835bbbd0b78c", + "metadata": {}, + "outputs": [], + "source": [ + "model = ChatOpenAI(temperature=0, model=\"gpt-4-turbo-preview\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9e9925aa-638a-4862-823e-9803402b8f82", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain import hub\n", + "from langchain_core.prompts import PromptTemplate" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c4cc5c8c-f6a5-42c7-9ed5-780d79b3b29a", + "metadata": {}, + "outputs": [], + "source": [ + "select_prompt = hub.pull(\"hwchase17/self-discovery-select\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a5b53d29-f5b6-4f39-af97-bb6b133e1d18", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Select several reasoning modules that are crucial to utilize in order to solve the given task:\n", + "\n", + "All reasoning module descriptions:\n", + "\u001b[33;1m\u001b[1;3m{reasoning_modules}\u001b[0m\n", + "\n", + "Task: \u001b[33;1m\u001b[1;3m{task_description}\u001b[0m\n", + "\n", + "Select several modules are crucial for solving the task above:\n", + "\n" + ] + } + ], + "source": [ + "select_prompt.pretty_print()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "26eaa6bc-5202-4b22-9522-33f227c8eb55", + "metadata": {}, + "outputs": [], + "source": [ + "adapt_prompt = hub.pull(\"hwchase17/self-discovery-adapt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "dc30afb9-180d-417b-9935-f7ef166710b8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rephrase and specify each reasoning module so that it better helps solving the task:\n", + "\n", + "SELECTED module descriptions:\n", + "\u001b[33;1m\u001b[1;3m{selected_modules}\u001b[0m\n", + "\n", + "Task: \u001b[33;1m\u001b[1;3m{task_description}\u001b[0m\n", + "\n", + "Adapt each reasoning module description to better solve the task:\n", + "\n" + ] + } + ], + "source": [ + "adapt_prompt.pretty_print()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a93253a9-8f50-49dd-8815-c3927bae1905", + "metadata": {}, + "outputs": [], + "source": [ + "structured_prompt = hub.pull(\"hwchase17/self-discovery-structure\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8ea8dd78-4285-400b-83d2-c4a241903a79", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Operationalize the reasoning modules into a step-by-step reasoning plan in JSON format:\n", + "\n", + "Here's an example:\n", + "\n", + "Example task:\n", + "\n", + "If you follow these instructions, do you return to the starting point? Always face forward. Take 1 step backward. Take 9 steps left. Take 2 steps backward. Take 6 steps forward. Take 4 steps forward. Take 4 steps backward. Take 3 steps right.\n", + "\n", + "Example reasoning structure:\n", + "\n", + "{\n", + " \"Position after instruction 1\":\n", + " \"Position after instruction 2\":\n", + " \"Position after instruction n\":\n", + " \"Is final position the same as starting position\":\n", + "}\n", + "\n", + "Adapted module description:\n", + "\u001b[33;1m\u001b[1;3m{adapted_modules}\u001b[0m\n", + "\n", + "Task: \u001b[33;1m\u001b[1;3m{task_description}\u001b[0m\n", + "\n", + "Implement a reasoning structure for solvers to follow step-by-step and arrive at correct answer.\n", + "\n", + "Note: do NOT actually arrive at a conclusion in this pass. Your job is to generate a PLAN so that in the future you can fill it out and arrive at the correct conclusion for tasks like this\n" + ] + } + ], + "source": [ + "structured_prompt.pretty_print()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f3d4d79d-f414-4588-b476-4a35b3ba6fbf", + "metadata": {}, + "outputs": [], + "source": [ + "reasoning_prompt = hub.pull(\"hwchase17/self-discovery-reasoning\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "23d1e32e-d12e-454a-8484-c08e250e3262", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Follow the step-by-step reasoning plan in JSON to correctly solve the task. Fill in the values following the keys by reasoning specifically about the task given. Do not simply rephrase the keys.\n", + " \n", + "Reasoning Structure:\n", + "\u001b[33;1m\u001b[1;3m{reasoning_structure}\u001b[0m\n", + "\n", + "Task: \u001b[33;1m\u001b[1;3m{task_description}\u001b[0m\n" + ] + } + ], + "source": [ + "reasoning_prompt.pretty_print()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7b9af01d-da28-4785-b069-efea61905cfa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PromptTemplate(input_variables=['reasoning_structure', 'task_description'], template='Follow the step-by-step reasoning plan in JSON to correctly solve the task. Fill in the values following the keys by reasoning specifically about the task given. Do not simply rephrase the keys.\\n \\nReasoning Structure:\\n{reasoning_structure}\\n\\nTask: {task_description}')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reasoning_prompt" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "399bf160-e257-429f-b27e-66d4063f195f", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.output_parsers import StrOutputParser\n", + "from langchain_core.runnables import RunnablePassthrough" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5c3bd203-7dc1-457e-813f-283aaf059ec0", + "metadata": {}, + "outputs": [], + "source": [ + "select_chain = select_prompt | model | StrOutputParser()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "86420da0-7cc2-4659-853e-9c3ef808e47c", + "metadata": {}, + "outputs": [], + "source": [ + "adapt_chain = adapt_prompt | model | StrOutputParser()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "270a3905-58a3-4650-96ca-e8254040285f", + "metadata": {}, + "outputs": [], + "source": [ + "structure_chain = structured_prompt | model | StrOutputParser()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "55b486cc-36be-497e-9eba-9c8dc228f2d1", + "metadata": {}, + "outputs": [], + "source": [ + "reasoning_chain = reasoning_prompt | model | StrOutputParser()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "92d8d484-055b-48a8-98bc-e7d40c12db2e", + "metadata": {}, + "outputs": [], + "source": [ + "overall_chain = (\n", + " RunnablePassthrough.assign(selected_modules=select_chain)\n", + " .assign(adapted_modules=adapt_chain)\n", + " .assign(reasoning_structure=structure_chain)\n", + " .assign(answer=reasoning_chain)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "29fe385b-cf5d-4581-80e7-55462f5628bb", + "metadata": {}, + "outputs": [], + "source": [ + "reasoning_modules = [\n", + " \"1. How could I devise an experiment to help solve that problem?\",\n", + " \"2. Make a list of ideas for solving this problem, and apply them one by one to the problem to see if any progress can be made.\",\n", + " # \"3. How could I measure progress on this problem?\",\n", + " \"4. How can I simplify the problem so that it is easier to solve?\",\n", + " \"5. What are the key assumptions underlying this problem?\",\n", + " \"6. What are the potential risks and drawbacks of each solution?\",\n", + " \"7. What are the alternative perspectives or viewpoints on this problem?\",\n", + " \"8. What are the long-term implications of this problem and its solutions?\",\n", + " \"9. How can I break down this problem into smaller, more manageable parts?\",\n", + " \"10. Critical Thinking: This style involves analyzing the problem from different perspectives, questioning assumptions, and evaluating the evidence or information available. It focuses on logical reasoning, evidence-based decision-making, and identifying potential biases or flaws in thinking.\",\n", + " \"11. Try creative thinking, generate innovative and out-of-the-box ideas to solve the problem. Explore unconventional solutions, thinking beyond traditional boundaries, and encouraging imagination and originality.\",\n", + " # \"12. Seek input and collaboration from others to solve the problem. Emphasize teamwork, open communication, and leveraging the diverse perspectives and expertise of a group to come up with effective solutions.\",\n", + " \"13. Use systems thinking: Consider the problem as part of a larger system and understanding the interconnectedness of various elements. Focuses on identifying the underlying causes, feedback loops, and interdependencies that influence the problem, and developing holistic solutions that address the system as a whole.\",\n", + " \"14. Use Risk Analysis: Evaluate potential risks, uncertainties, and tradeoffs associated with different solutions or approaches to a problem. Emphasize assessing the potential consequences and likelihood of success or failure, and making informed decisions based on a balanced analysis of risks and benefits.\",\n", + " # \"15. Use Reflective Thinking: Step back from the problem, take the time for introspection and self-reflection. Examine personal biases, assumptions, and mental models that may influence problem-solving, and being open to learning from past experiences to improve future approaches.\",\n", + " \"16. What is the core issue or problem that needs to be addressed?\",\n", + " \"17. What are the underlying causes or factors contributing to the problem?\",\n", + " \"18. Are there any potential solutions or strategies that have been tried before? If yes, what were the outcomes and lessons learned?\",\n", + " \"19. What are the potential obstacles or challenges that might arise in solving this problem?\",\n", + " \"20. Are there any relevant data or information that can provide insights into the problem? If yes, what data sources are available, and how can they be analyzed?\",\n", + " \"21. Are there any stakeholders or individuals who are directly affected by the problem? What are their perspectives and needs?\",\n", + " \"22. What resources (financial, human, technological, etc.) are needed to tackle the problem effectively?\",\n", + " \"23. How can progress or success in solving the problem be measured or evaluated?\",\n", + " \"24. What indicators or metrics can be used?\",\n", + " \"25. Is the problem a technical or practical one that requires a specific expertise or skill set? Or is it more of a conceptual or theoretical problem?\",\n", + " \"26. Does the problem involve a physical constraint, such as limited resources, infrastructure, or space?\",\n", + " \"27. Is the problem related to human behavior, such as a social, cultural, or psychological issue?\",\n", + " \"28. Does the problem involve decision-making or planning, where choices need to be made under uncertainty or with competing objectives?\",\n", + " \"29. Is the problem an analytical one that requires data analysis, modeling, or optimization techniques?\",\n", + " \"30. Is the problem a design challenge that requires creative solutions and innovation?\",\n", + " \"31. Does the problem require addressing systemic or structural issues rather than just individual instances?\",\n", + " \"32. Is the problem time-sensitive or urgent, requiring immediate attention and action?\",\n", + " \"33. What kinds of solution typically are produced for this kind of problem specification?\",\n", + " \"34. Given the problem specification and the current best solution, have a guess about other possible solutions.\"\n", + " \"35. Let’s imagine the current best solution is totally wrong, what other ways are there to think about the problem specification?\"\n", + " \"36. What is the best way to modify this current best solution, given what you know about these kinds of problem specification?\"\n", + " \"37. Ignoring the current best solution, create an entirely new solution to the problem.\"\n", + " # \"38. Let’s think step by step.\"\n", + " \"39. Let’s make a step by step plan and implement it with good notation and explanation.\",\n", + "]\n", + "\n", + "\n", + "task_example = \"Lisa has 10 apples. She gives 3 apples to her friend and then buys 5 more apples from the store. How many apples does Lisa have now?\"\n", + "\n", + "task_example = \"\"\"This SVG path element draws a:\n", + "(A) circle (B) heptagon (C) hexagon (D) kite (E) line (F) octagon (G) pentagon(H) rectangle (I) sector (J) triangle\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "6cbfbe81-f751-42da-843a-f9003ace663d", + "metadata": {}, + "outputs": [], + "source": [ + "reasoning_modules_str = \"\\n\".join(reasoning_modules)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "d411c7aa-7017-4d67-88b5-43b5d161c34c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'task_description': 'This SVG path element draws a:\\n(A) circle (B) heptagon (C) hexagon (D) kite (E) line (F) octagon (G) pentagon(H) rectangle (I) sector (J) triangle',\n", + " 'reasoning_modules': '1. How could I devise an experiment to help solve that problem?\\n2. Make a list of ideas for solving this problem, and apply them one by one to the problem to see if any progress can be made.\\n4. How can I simplify the problem so that it is easier to solve?\\n5. What are the key assumptions underlying this problem?\\n6. What are the potential risks and drawbacks of each solution?\\n7. What are the alternative perspectives or viewpoints on this problem?\\n8. What are the long-term implications of this problem and its solutions?\\n9. How can I break down this problem into smaller, more manageable parts?\\n10. Critical Thinking: This style involves analyzing the problem from different perspectives, questioning assumptions, and evaluating the evidence or information available. It focuses on logical reasoning, evidence-based decision-making, and identifying potential biases or flaws in thinking.\\n11. Try creative thinking, generate innovative and out-of-the-box ideas to solve the problem. Explore unconventional solutions, thinking beyond traditional boundaries, and encouraging imagination and originality.\\n13. Use systems thinking: Consider the problem as part of a larger system and understanding the interconnectedness of various elements. Focuses on identifying the underlying causes, feedback loops, and interdependencies that influence the problem, and developing holistic solutions that address the system as a whole.\\n14. Use Risk Analysis: Evaluate potential risks, uncertainties, and tradeoffs associated with different solutions or approaches to a problem. Emphasize assessing the potential consequences and likelihood of success or failure, and making informed decisions based on a balanced analysis of risks and benefits.\\n16. What is the core issue or problem that needs to be addressed?\\n17. What are the underlying causes or factors contributing to the problem?\\n18. Are there any potential solutions or strategies that have been tried before? If yes, what were the outcomes and lessons learned?\\n19. What are the potential obstacles or challenges that might arise in solving this problem?\\n20. Are there any relevant data or information that can provide insights into the problem? If yes, what data sources are available, and how can they be analyzed?\\n21. Are there any stakeholders or individuals who are directly affected by the problem? What are their perspectives and needs?\\n22. What resources (financial, human, technological, etc.) are needed to tackle the problem effectively?\\n23. How can progress or success in solving the problem be measured or evaluated?\\n24. What indicators or metrics can be used?\\n25. Is the problem a technical or practical one that requires a specific expertise or skill set? Or is it more of a conceptual or theoretical problem?\\n26. Does the problem involve a physical constraint, such as limited resources, infrastructure, or space?\\n27. Is the problem related to human behavior, such as a social, cultural, or psychological issue?\\n28. Does the problem involve decision-making or planning, where choices need to be made under uncertainty or with competing objectives?\\n29. Is the problem an analytical one that requires data analysis, modeling, or optimization techniques?\\n30. Is the problem a design challenge that requires creative solutions and innovation?\\n31. Does the problem require addressing systemic or structural issues rather than just individual instances?\\n32. Is the problem time-sensitive or urgent, requiring immediate attention and action?\\n33. What kinds of solution typically are produced for this kind of problem specification?\\n34. Given the problem specification and the current best solution, have a guess about other possible solutions.35. Let’s imagine the current best solution is totally wrong, what other ways are there to think about the problem specification?36. What is the best way to modify this current best solution, given what you know about these kinds of problem specification?37. Ignoring the current best solution, create an entirely new solution to the problem.39. Let’s make a step by step plan and implement it with good notation and explanation.',\n", + " 'selected_modules': 'To solve the task of identifying the shape drawn by the given SVG path element, the following reasoning modules are crucial:\\n\\n1. **Critical Thinking (10)**: This involves analyzing the SVG path commands and coordinates logically to understand the shape they form. It requires questioning assumptions (e.g., not assuming the shape based on a quick glance at the coordinates but rather analyzing the path commands and their implications) and evaluating the information provided by the SVG path data.\\n\\n2. **Analytical Problem Solving (29)**: The task requires data analysis skills to interpret the SVG path commands and coordinates. Understanding how the \"M\" (moveto) and \"L\" (lineto) commands work to draw lines between specified points is essential for determining the shape.\\n\\n3. **Creative Thinking (11)**: While the task primarily involves analytical skills, creative thinking can help in visualizing the shape that the path commands are likely to form, especially when the path data doesn\\'t immediately suggest a common shape.\\n\\n4. **Systems Thinking (13)**: Recognizing the SVG path as part of a larger system (in this case, the SVG graphics system) and understanding how individual path commands contribute to the overall shape can be helpful. This involves understanding the interconnectedness of the start and end points of each line segment and how they come together to form a complete shape.\\n\\n5. **Break Down the Problem (9)**: Breaking down the SVG path into its individual commands and analyzing each segment between \"M\" and \"L\" commands can simplify the task. This makes it easier to visualize and understand the shape being drawn step by step.\\n\\n6. **Visualization (not explicitly listed but implied in creative and analytical thinking)**: Visualizing the path that the \"M\" and \"L\" commands create is essential. This isn\\'t a listed module but is a skill that underpins both creative and analytical approaches to solving this problem.\\n\\nGiven the SVG path commands, one would analyze each segment drawn by \"M\" (moveto) and \"L\" (lineto) commands to determine the shape\\'s vertices and sides. This process involves critical thinking to assess the information, analytical skills to interpret the path data, and a degree of creative thinking for visualization. The task does not directly involve assessing risks, long-term implications, or stakeholder perspectives, so modules focused on those aspects (e.g., Risk Analysis (14), Long-term Implications (8)) are less relevant here.',\n", + " 'adapted_modules': 'To enhance the process of identifying the shape drawn by the given SVG path element, the reasoning modules can be adapted and specified as follows:\\n\\n1. **Detailed Path Analysis (Critical Thinking)**: This module focuses on a meticulous examination of the SVG path commands and coordinates. It involves a deep dive into the syntax and semantics of path commands such as \"M\" (moveto) and \"L\" (lineto), challenging initial perceptions and rigorously interpreting the sequence of commands to deduce the shape accurately. This analysis goes beyond surface-level inspection, requiring a systematic questioning of each command\\'s role in constructing the overall shape.\\n\\n2. **Path Command Interpretation (Analytical Problem Solving)**: Essential for this task is the ability to decode the SVG path\\'s \"M\" and \"L\" commands, translating these instructions into a mental or visual representation of the shape\\'s geometry. This module emphasizes the analytical dissection of the path data, focusing on how each command contributes to the formation of vertices and edges, thereby facilitating the identification of the shape.\\n\\n3. **Shape Visualization (Creative Thinking)**: Leveraging imagination to mentally construct the shape from the path commands is the core of this module. It involves creatively synthesizing the segments drawn by the \"M\" and \"L\" commands into a coherent visual image, even when the path data does not immediately suggest a recognizable shape. This creative process aids in bridging gaps in the analytical interpretation, offering alternative perspectives on the possible shape outcomes.\\n\\n4. **Path-to-Shape Synthesis (Systems Thinking)**: This module entails understanding the SVG path as a component within the broader context of vector graphics, focusing on how individual path commands interlink to form a cohesive shape. It requires an appreciation of the cumulative effect of each command in relation to the others, recognizing the systemic relationship between the starting and ending points of segments and their collective role in shaping the final figure.\\n\\n5. **Sequential Command Analysis (Break Down the Problem)**: By segmenting the SVG path into discrete commands, this approach simplifies the complexity of the task. It advocates for a step-by-step examination of the path, where each \"M\" to \"L\" sequence is analyzed in isolation before synthesizing the findings to understand the overall shape. This methodical breakdown facilitates a clearer visualization and comprehension of the shape being drawn.\\n\\n6. **Command-to-Geometry Mapping (Visualization)**: Central to solving this task is the ability to map the abstract \"M\" and \"L\" commands onto a concrete geometric representation. This implicit module underlies both the analytical and creative thinking processes, focusing on converting the path data into a visual form that can be easily understood and manipulated mentally. It is about constructing a mental image of the shape as each command is processed, enabling a dynamic visualization that evolves with each new piece of path data.\\n\\nBy adapting and specifying these reasoning modules, the task of identifying the shape drawn by the SVG path element becomes a structured process that leverages critical analysis, analytical problem-solving, creative visualization, systemic thinking, and methodical breakdown to accurately determine the shape as a (D) kite.',\n", + " 'reasoning_structure': '```json\\n{\\n \"Step 1: Detailed Path Analysis\": {\\n \"Description\": \"Examine each SVG path command and its coordinates closely. Understand the syntax and semantics of \\'M\\' (moveto) and \\'L\\' (lineto) commands.\",\\n \"Action\": \"List all path commands and their coordinates.\",\\n \"Expected Outcome\": \"A clear understanding of the sequence and direction of each path command.\"\\n },\\n \"Step 2: Path Command Interpretation\": {\\n \"Description\": \"Decode the \\'M\\' and \\'L\\' commands to translate these instructions into a mental or visual representation of the shape\\'s geometry.\",\\n \"Action\": \"Map each \\'M\\' and \\'L\\' command to its corresponding action (move or draw line) in the context of the shape.\",\\n \"Expected Outcome\": \"A segmented representation of the shape, highlighting vertices and edges.\"\\n },\\n \"Step 3: Shape Visualization\": {\\n \"Description\": \"Use imagination to mentally construct the shape from the path commands, synthesizing the segments into a coherent visual image.\",\\n \"Action\": \"Visualize the shape based on the segmented representation from Step 2.\",\\n \"Expected Outcome\": \"A mental image of the potential shape, considering the sequence and direction of path commands.\"\\n },\\n \"Step 4: Path-to-Shape Synthesis\": {\\n \"Description\": \"Understand the SVG path as a component within the broader context of vector graphics, focusing on how individual path commands interlink to form a cohesive shape.\",\\n \"Action\": \"Analyze the systemic relationship between the starting and ending points of segments and their collective role in shaping the final figure.\",\\n \"Expected Outcome\": \"Identification of the overall shape by recognizing the cumulative effect of each command.\"\\n },\\n \"Step 5: Sequential Command Analysis\": {\\n \"Description\": \"Segment the SVG path into discrete commands for a step-by-step examination, analyzing each \\'M\\' to \\'L\\' sequence in isolation.\",\\n \"Action\": \"Break down the path into individual commands and analyze each separately before synthesizing the findings.\",\\n \"Expected Outcome\": \"A clearer visualization and comprehension of the shape being drawn, segment by segment.\"\\n },\\n \"Step 6: Command-to-Geometry Mapping\": {\\n \"Description\": \"Map the abstract \\'M\\' and \\'L\\' commands onto a concrete geometric representation, constructing a mental image of the shape as each command is processed.\",\\n \"Action\": \"Convert the path data into a visual form that can be easily understood and manipulated mentally.\",\\n \"Expected Outcome\": \"A dynamic visualization of the shape that evolves with each new piece of path data, leading to the identification of the shape as a kite.\"\\n },\\n \"Conclusion\": {\\n \"Description\": \"Based on the analysis and visualization steps, determine the shape drawn by the SVG path element.\",\\n \"Action\": \"Review the outcomes of each step and synthesize the information to identify the shape.\",\\n \"Expected Outcome\": \"The correct identification of the shape, supported by the structured analysis and reasoning process.\"\\n }\\n}\\n```',\n", + " 'answer': 'Based on the provided reasoning structure and the SVG path element given, let\\'s analyze the path commands to identify the shape.\\n\\n**Step 1: Detailed Path Analysis**\\n- Description: The SVG path provided contains multiple \\'M\\' (moveto) and \\'L\\' (lineto) commands. Each command specifies a point in a 2D coordinate system.\\n- Action: The path commands are as follows:\\n 1. M 55.57,80.69 (Move to point)\\n 2. L 57.38,65.80 (Line to point)\\n 3. M 57.38,65.80 (Move to point)\\n 4. L 48.90,57.46 (Line to point)\\n 5. M 48.90,57.46 (Move to point)\\n 6. L 45.58,47.78 (Line to point)\\n 7. M 45.58,47.78 (Move to point)\\n 8. L 53.25,36.07 (Line to point)\\n 9. L 66.29,48.90 (Line to point)\\n 10. L 78.69,61.09 (Line to point)\\n 11. L 55.57,80.69 (Line to point)\\n- Expected Outcome: Understanding that the path commands describe a series of movements and lines that form a closed shape.\\n\\n**Step 2: Path Command Interpretation**\\n- Description: The \\'M\\' and \\'L\\' commands are used to move the \"pen\" to a starting point and draw lines to subsequent points, respectively.\\n- Action: The commands describe a shape starting at (55.57,80.69), drawing lines through several points, and finally closing the shape by returning to the starting point.\\n- Expected Outcome: A segmented representation showing a shape with distinct vertices at the specified coordinates.\\n\\n**Step 3: Shape Visualization**\\n- Description: Mentally constructing the shape from the provided path commands.\\n- Action: Visualizing the lines connecting in sequence from the starting point, through each point described by the \\'L\\' commands, and back to the starting point.\\n- Expected Outcome: A mental image of a shape that appears to have four distinct sides, suggesting it could be a quadrilateral.\\n\\n**Step 4: Path-to-Shape Synthesis**\\n- Description: Understanding how the path commands collectively form a specific shape.\\n- Action: Recognizing that the shape starts and ends at the same point, with lines drawn between intermediate points without overlapping, except at the starting/ending point.\\n- Expected Outcome: Identification of a closed, four-sided figure, which suggests it could be a kite based on the symmetry and structure of the lines.\\n\\n**Step 5: Sequential Command Analysis**\\n- Description: Analyzing each \\'M\\' to \\'L\\' sequence in isolation.\\n- Action: Observing that the path does not describe a regular polygon (like a hexagon or octagon) or a circle, but rather a shape with distinct angles and sides.\\n- Expected Outcome: A clearer understanding that the shape has four sides, with two pairs of adjacent sides being potentially unequal, which is characteristic of a kite.\\n\\n**Step 6: Command-to-Geometry Mapping**\\n- Description: Converting the abstract path commands into a geometric shape.\\n- Action: Mapping the path data to visualize a shape with two pairs of adjacent sides that are distinct yet symmetrical, indicative of a kite.\\n- Expected Outcome: A dynamic visualization that evolves to clearly represent a kite shape.\\n\\n**Conclusion**\\n- Description: Determining the shape drawn by the SVG path element.\\n- Action: Reviewing the outcomes of each analysis step, which consistently point towards a four-sided figure with distinct properties of a kite.\\n- Expected Outcome: The correct identification of the shape as a kite (D).'}" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "overall_chain.invoke(\n", + " {\"task_description\": task_example, \"reasoning_modules\": reasoning_modules_str}\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea8568d5-bdb6-45cd-8d04-1ab305786caa", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c14a291c-7c1b-43bc-807e-11180290985e", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docker/Makefile b/docker/Makefile new file mode 100644 index 00000000000..d578580c323 --- /dev/null +++ b/docker/Makefile @@ -0,0 +1,12 @@ +# Makefile + +build_graphdb: + docker build --tag graphdb ./graphdb + +start_graphdb: + docker-compose up -d graphdb + +down: + docker-compose down -v --remove-orphans + +.PHONY: build_graphdb start_graphdb down diff --git a/docker/docker-compose.yml b/docker/docker-compose.yml new file mode 100644 index 00000000000..968e32469a3 --- /dev/null +++ b/docker/docker-compose.yml @@ -0,0 +1,21 @@ +# docker-compose to make it easier to spin up integration tests. +# Services should use NON standard ports to avoid collision with +version: "3" +name: langchain-tests + +services: + redis: + image: redis/redis-stack-server:latest + # We use non standard ports since + # these instances are used for testing + # and users may already have existing + # redis instances set up locally + # for other projects + ports: + - "6020:6379" + volumes: + - ./redis-volume:/data + graphdb: + image: graphdb + ports: + - "6021:7200" diff --git a/docker/graphdb/Dockerfile b/docker/graphdb/Dockerfile new file mode 100644 index 00000000000..dfcbe7e622d --- /dev/null +++ b/docker/graphdb/Dockerfile @@ -0,0 +1,5 @@ +FROM ontotext/graphdb:10.5.1 +RUN mkdir -p /opt/graphdb/dist/data/repositories/langchain +COPY config.ttl /opt/graphdb/dist/data/repositories/langchain/ +COPY graphdb_create.sh /run.sh +ENTRYPOINT bash /run.sh diff --git a/docker/graphdb/config.ttl b/docker/graphdb/config.ttl new file mode 100644 index 00000000000..dcbdeeebe12 --- /dev/null +++ b/docker/graphdb/config.ttl @@ -0,0 +1,46 @@ +@prefix rdfs: . +@prefix rep: . +@prefix sr: . +@prefix sail: . +@prefix graphdb: . + +[] a rep:Repository ; + rep:repositoryID "langchain" ; + rdfs:label "" ; + rep:repositoryImpl [ + rep:repositoryType "graphdb:SailRepository" ; + sr:sailImpl [ + sail:sailType "graphdb:Sail" ; + + graphdb:read-only "false" ; + + # Inference and Validation + graphdb:ruleset "empty" ; + graphdb:disable-sameAs "true" ; + graphdb:check-for-inconsistencies "false" ; + + # Indexing + graphdb:entity-id-size "32" ; + graphdb:enable-context-index "false" ; + graphdb:enablePredicateList "true" ; + graphdb:enable-fts-index "false" ; + graphdb:fts-indexes ("default" "iri") ; + graphdb:fts-string-literals-index "default" ; + graphdb:fts-iris-index "none" ; + + # Queries and Updates + graphdb:query-timeout "0" ; + graphdb:throw-QueryEvaluationException-on-timeout "false" ; + graphdb:query-limit-results "0" ; + + # Settable in the file but otherwise hidden in the UI and in the RDF4J console + graphdb:base-URL "http://example.org/owlim#" ; + graphdb:defaultNS "" ; + graphdb:imports "" ; + graphdb:repository-type "file-repository" ; + graphdb:storage-folder "storage" ; + graphdb:entity-index-size "10000000" ; + graphdb:in-memory-literal-properties "true" ; + graphdb:enable-literal-index "true" ; + ] + ]. diff --git a/docker/graphdb/graphdb_create.sh b/docker/graphdb/graphdb_create.sh new file mode 100644 index 00000000000..52ffe8ad74a --- /dev/null +++ b/docker/graphdb/graphdb_create.sh @@ -0,0 +1,28 @@ +#! /bin/bash +REPOSITORY_ID="langchain" +GRAPHDB_URI="http://localhost:7200/" + +echo -e "\nUsing GraphDB: ${GRAPHDB_URI}" + +function startGraphDB { + echo -e "\nStarting GraphDB..." + exec /opt/graphdb/dist/bin/graphdb +} + +function waitGraphDBStart { + echo -e "\nWaiting GraphDB to start..." + for _ in $(seq 1 5); do + CHECK_RES=$(curl --silent --write-out '%{http_code}' --output /dev/null ${GRAPHDB_URI}/rest/repositories) + if [ "${CHECK_RES}" = '200' ]; then + echo -e "\nUp and running" + break + fi + sleep 30s + echo "CHECK_RES: ${CHECK_RES}" + done +} + + +startGraphDB & +waitGraphDBStart +wait diff --git a/docs/.local_build.sh b/docs/.local_build.sh index 72e15dec90a..a1f181198fd 100755 --- a/docs/.local_build.sh +++ b/docs/.local_build.sh @@ -16,7 +16,8 @@ cp ../cookbook/README.md src/pages/cookbook.mdx mkdir -p docs/templates cp ../templates/docs/INDEX.md docs/templates/index.md poetry run python scripts/copy_templates.py -wget https://raw.githubusercontent.com/langchain-ai/langserve/main/README.md -O docs/langserve.md +wget -q https://raw.githubusercontent.com/langchain-ai/langserve/main/README.md -O docs/langserve.md +wget -q https://raw.githubusercontent.com/langchain-ai/langgraph/main/README.md -O docs/langgraph.md yarn diff --git a/docs/api_reference/conf.py b/docs/api_reference/conf.py index 2a3defb1911..e993048fc57 100644 --- a/docs/api_reference/conf.py +++ b/docs/api_reference/conf.py @@ -146,6 +146,7 @@ partners = [ (p.name, p.name.replace("-", "_") + "_api_reference") for p in partners_dir.iterdir() ] +partners = sorted(partners) html_context = { "display_github": True, # Integrate GitHub diff --git a/docs/api_reference/create_api_rst.py b/docs/api_reference/create_api_rst.py index 7be0fcc43ce..65fbb6a4e61 100644 --- a/docs/api_reference/create_api_rst.py +++ b/docs/api_reference/create_api_rst.py @@ -1,4 +1,5 @@ """Script for auto-generating api_reference.rst.""" + import importlib import inspect import os @@ -13,7 +14,6 @@ from pydantic import BaseModel ROOT_DIR = Path(__file__).parents[2].absolute() HERE = Path(__file__).parent - ClassKind = Literal["TypedDict", "Regular", "Pydantic", "enum"] @@ -186,7 +186,7 @@ def _load_package_modules( modules_by_namespace[top_namespace] = _module_members except ImportError as e: - print(f"Error: Unable to import module '{namespace}' with error: {e}") + print(f"Error: Unable to import module '{namespace}' with error: {e}") # noqa: T201 return modules_by_namespace @@ -322,30 +322,52 @@ def _package_dir(package_name: str = "langchain") -> Path: def _get_package_version(package_dir: Path) -> str: - with open(package_dir.parent / "pyproject.toml", "r") as f: - pyproject = toml.load(f) + """Return the version of the package.""" + try: + with open(package_dir.parent / "pyproject.toml", "r") as f: + pyproject = toml.load(f) + except FileNotFoundError as e: + print( + f"pyproject.toml not found in {package_dir.parent}.\n" + "You are either attempting to build a directory which is not a package or " + "the package is missing a pyproject.toml file which should be added." + "Aborting the build." + ) + exit(1) return pyproject["tool"]["poetry"]["version"] -def _out_file_path(package_name: str = "langchain") -> Path: +def _out_file_path(package_name: str) -> Path: """Return the path to the file containing the documentation.""" return HERE / f"{package_name.replace('-', '_')}_api_reference.rst" -def _doc_first_line(package_name: str = "langchain") -> str: +def _doc_first_line(package_name: str) -> str: """Return the path to the file containing the documentation.""" return f".. {package_name.replace('-', '_')}_api_reference:\n\n" def main() -> None: """Generate the api_reference.rst file for each package.""" + print("Starting to build API reference files.") for dir in os.listdir(ROOT_DIR / "libs"): + # Skip any hidden directories + # Some of these could be present by mistake in the code base + # e.g., .pytest_cache from running tests from the wrong location. + if not dir.startswith("."): + print("Skipping dir:", dir) + continue + if dir in ("cli", "partners"): continue else: + print("Building package:", dir) _build_rst_file(package_name=dir) - for dir in os.listdir(ROOT_DIR / "libs" / "partners"): + partner_packages = os.listdir(ROOT_DIR / "libs" / "partners") + print("Building partner packages:", partner_packages) + for dir in partner_packages: _build_rst_file(package_name=dir) + print("API reference files built.") if __name__ == "__main__": diff --git a/docs/docs/additional_resources/tutorials.mdx b/docs/docs/additional_resources/tutorials.mdx index 3a68017f9fc..49d3b4fda01 100644 --- a/docs/docs/additional_resources/tutorials.mdx +++ b/docs/docs/additional_resources/tutorials.mdx @@ -2,7 +2,7 @@ Below are links to tutorials and courses on LangChain. For written guides on common use cases for LangChain, check out the [use cases guides](/docs/use_cases). -⛓ icon marks a new addition [last update 2023-09-21] +⛓ icon marks a new addition [last update 2024-02-06] --------------------- @@ -10,18 +10,20 @@ Below are links to tutorials and courses on LangChain. For written guides on com ### Books -#### ⛓[Generative AI with LangChain](https://www.amazon.com/Generative-AI-LangChain-language-ChatGPT/dp/1835083463/ref=sr_1_1?crid=1GMOMH0G7GLR&keywords=generative+ai+with+langchain&qid=1703247181&sprefix=%2Caps%2C298&sr=8-1) by [Ben Auffrath](https://www.amazon.com/stores/Ben-Auffarth/author/B08JQKSZ7D?ref=ap_rdr&store_ref=ap_rdr&isDramIntegrated=true&shoppingPortalEnabled=true), ©️ 2023 Packt Publishing +#### [Generative AI with LangChain](https://www.amazon.com/Generative-AI-LangChain-language-ChatGPT/dp/1835083463/ref=sr_1_1?crid=1GMOMH0G7GLR&keywords=generative+ai+with+langchain&qid=1703247181&sprefix=%2Caps%2C298&sr=8-1) by [Ben Auffrath](https://www.amazon.com/stores/Ben-Auffarth/author/B08JQKSZ7D?ref=ap_rdr&store_ref=ap_rdr&isDramIntegrated=true&shoppingPortalEnabled=true), ©️ 2023 Packt Publishing ### DeepLearning.AI courses by [Harrison Chase](https://en.wikipedia.org/wiki/LangChain) and [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng) - [LangChain for LLM Application Development](https://learn.deeplearning.ai/langchain) - [LangChain Chat with Your Data](https://learn.deeplearning.ai/langchain-chat-with-your-data) -- ⛓ [Functions, Tools and Agents with LangChain](https://learn.deeplearning.ai/functions-tools-agents-langchain) +- [Functions, Tools and Agents with LangChain](https://learn.deeplearning.ai/functions-tools-agents-langchain) ### Handbook [LangChain AI Handbook](https://www.pinecone.io/learn/langchain/) By **James Briggs** and **Francisco Ingham** +⛓ [LangChain Cheatsheet](https://pub.towardsai.net/langchain-cheatsheet-all-secrets-on-a-single-page-8be26b721cde) by **Ivan Reznikov** + ### Short Tutorials [LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners](https://youtu.be/aywZrzNaKjs) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics) @@ -29,6 +31,8 @@ Below are links to tutorials and courses on LangChain. For written guides on com [LangChain Crash Course - Build apps with language models](https://youtu.be/LbT1yp6quS8) by [Patrick Loeber](https://www.youtube.com/@patloeber) +⛓ [LangChain 101 Course](https://medium.com/@ivanreznikov/langchain-101-course-updated-668f7b41d6cb) by **Ivan Reznikov** + ## Tutorials ### [LangChain for Gen AI and LLMs](https://www.youtube.com/playlist?list=PLIUOU7oqGTLieV9uTIFMm6_4PXg-hlN6F) by [James Briggs](https://www.youtube.com/@jamesbriggs) @@ -44,8 +48,8 @@ Below are links to tutorials and courses on LangChain. For written guides on com - #9 [Build Conversational Agents with Vector DBs](https://youtu.be/H6bCqqw9xyI) - [Using NEW `MPT-7B` in Hugging Face and LangChain](https://youtu.be/DXpk9K7DgMo) - [`MPT-30B` Chatbot with LangChain](https://youtu.be/pnem-EhT6VI) -- ⛓ [Fine-tuning OpenAI's `GPT 3.5` for LangChain Agents](https://youtu.be/boHXgQ5eQic?si=OOOfK-GhsgZGBqSr) -- ⛓ [Chatbots with `RAG`: LangChain Full Walkthrough](https://youtu.be/LhnCsygAvzY?si=N7k6xy4RQksbWwsQ) +- [Fine-tuning OpenAI's `GPT 3.5` for LangChain Agents](https://youtu.be/boHXgQ5eQic?si=OOOfK-GhsgZGBqSr) +- [Chatbots with `RAG`: LangChain Full Walkthrough](https://youtu.be/LhnCsygAvzY?si=N7k6xy4RQksbWwsQ) ### [LangChain 101](https://www.youtube.com/playlist?list=PLqZXAkvF1bPNQER9mLmDbntNfSpzdDIU5) by [Greg Kamradt (Data Indy)](https://www.youtube.com/@DataIndependent) @@ -109,16 +113,16 @@ Below are links to tutorials and courses on LangChain. For written guides on com - [What can you do with 16K tokens in LangChain?](https://youtu.be/z2aCZBAtWXs) - [Tagging and Extraction - Classification using `OpenAI Functions`](https://youtu.be/a8hMgIcUEnE) - [HOW to Make Conversational Form with LangChain](https://youtu.be/IT93On2LB5k) -- ⛓ [`Claude-2` meets LangChain!](https://youtu.be/Hb_D3p0bK2U?si=j96Kc7oJoeRI5-iC) -- ⛓ [`PaLM 2` Meets LangChain](https://youtu.be/orPwLibLqm4?si=KgJjpEbAD9YBPqT4) -- ⛓ [`LLaMA2` with LangChain - Basics | LangChain TUTORIAL](https://youtu.be/cIRzwSXB4Rc?si=v3Hwxk1m3fksBIHN) -- ⛓ [Serving `LLaMA2` with `Replicate`](https://youtu.be/JIF4nNi26DE?si=dSazFyC4UQmaR-rJ) -- ⛓ [NEW LangChain Expression Language](https://youtu.be/ud7HJ2p3gp0?si=8pJ9O6hGbXrCX5G9) -- ⛓ [Building a RCI Chain for Agents with LangChain Expression Language](https://youtu.be/QaKM5s0TnsY?si=0miEj-o17AHcGfLG) -- ⛓ [How to Run `LLaMA-2-70B` on the `Together AI`](https://youtu.be/Tc2DHfzHeYE?si=Xku3S9dlBxWQukpe) -- ⛓ [`RetrievalQA` with `LLaMA 2 70b` & `Chroma` DB](https://youtu.be/93yueQQnqpM?si=ZMwj-eS_CGLnNMXZ) -- ⛓ [How to use `BGE Embeddings` for LangChain](https://youtu.be/sWRvSG7vL4g?si=85jnvnmTCF9YIWXI) -- ⛓ [How to use Custom Prompts for `RetrievalQA` on `LLaMA-2 7B`](https://youtu.be/PDwUKves9GY?si=sMF99TWU0p4eiK80) +- [`Claude-2` meets LangChain!](https://youtu.be/Hb_D3p0bK2U?si=j96Kc7oJoeRI5-iC) +- [`PaLM 2` Meets LangChain](https://youtu.be/orPwLibLqm4?si=KgJjpEbAD9YBPqT4) +- [`LLaMA2` with LangChain - Basics | LangChain TUTORIAL](https://youtu.be/cIRzwSXB4Rc?si=v3Hwxk1m3fksBIHN) +- [Serving `LLaMA2` with `Replicate`](https://youtu.be/JIF4nNi26DE?si=dSazFyC4UQmaR-rJ) +- [NEW LangChain Expression Language](https://youtu.be/ud7HJ2p3gp0?si=8pJ9O6hGbXrCX5G9) +- [Building a RCI Chain for Agents with LangChain Expression Language](https://youtu.be/QaKM5s0TnsY?si=0miEj-o17AHcGfLG) +- [How to Run `LLaMA-2-70B` on the `Together AI`](https://youtu.be/Tc2DHfzHeYE?si=Xku3S9dlBxWQukpe) +- [`RetrievalQA` with `LLaMA 2 70b` & `Chroma` DB](https://youtu.be/93yueQQnqpM?si=ZMwj-eS_CGLnNMXZ) +- [How to use `BGE Embeddings` for LangChain](https://youtu.be/sWRvSG7vL4g?si=85jnvnmTCF9YIWXI) +- [How to use Custom Prompts for `RetrievalQA` on `LLaMA-2 7B`](https://youtu.be/PDwUKves9GY?si=sMF99TWU0p4eiK80) ### [LangChain](https://www.youtube.com/playlist?list=PLVEEucA9MYhOu89CX8H3MBZqayTbcCTMr) by [Prompt Engineering](https://www.youtube.com/@engineerprompt) @@ -131,8 +135,8 @@ Below are links to tutorials and courses on LangChain. For written guides on com - [LangChain: Giving Memory to LLMs](https://youtu.be/dxO6pzlgJiY) - [BEST OPEN Alternative to `OPENAI's EMBEDDINGs` for Retrieval QA: LangChain](https://youtu.be/ogEalPMUCSY) - [LangChain: Run Language Models Locally - `Hugging Face Models`](https://youtu.be/Xxxuw4_iCzw) -- ⛓ [Slash API Costs: Mastering Caching for LLM Applications](https://youtu.be/EQOznhaJWR0?si=AXoI7f3-SVFRvQUl) -- ⛓ [Avoid PROMPT INJECTION with `Constitutional AI` - LangChain](https://youtu.be/tyKSkPFHVX8?si=9mgcB5Y1kkotkBGB) +- [Slash API Costs: Mastering Caching for LLM Applications](https://youtu.be/EQOznhaJWR0?si=AXoI7f3-SVFRvQUl) +- [Avoid PROMPT INJECTION with `Constitutional AI` - LangChain](https://youtu.be/tyKSkPFHVX8?si=9mgcB5Y1kkotkBGB) ### LangChain by [Chat with data](https://www.youtube.com/@chatwithdata) @@ -148,4 +152,4 @@ Below are links to tutorials and courses on LangChain. For written guides on com --------------------- -⛓ icon marks a new addition [last update 2023-09-21] +⛓ icon marks a new addition [last update 2024-02-061] diff --git a/docs/docs/additional_resources/youtube.mdx b/docs/docs/additional_resources/youtube.mdx index 94aafe47663..807f4521127 100644 --- a/docs/docs/additional_resources/youtube.mdx +++ b/docs/docs/additional_resources/youtube.mdx @@ -120,6 +120,8 @@ - ⛓ [Use ANY language in `LangSmith` with REST](https://youtu.be/7BL0GEdMmgY?si=iXfOEdBLqXF6hqRM) by [Nerding I/O](https://www.youtube.com/@nerding_io) - ⛓ [How to Leverage the Full Potential of LLMs for Your Business with Langchain - Leon Ruddat](https://youtu.be/vZmoEa7oWMg?si=ZhMmydq7RtkZd56Q) by [PyData](https://www.youtube.com/@PyDataTV) - ⛓ [`ChatCSV` App: Chat with CSV files using LangChain and `Llama 2`](https://youtu.be/PvsMg6jFs8E?si=Qzg5u5gijxj933Ya) by [Muhammad Moin](https://www.youtube.com/@muhammadmoinfaisal) +- ⛓ [Build Chat PDF app in Python with LangChain, OpenAI, Streamlit | Full project | Learn Coding](https://www.youtube.com/watch?v=WYzFzZg4YZI) by [Jutsupoint](https://www.youtube.com/@JutsuPoint) +- ⛓ [Build Eminem Bot App with LangChain, Streamlit, OpenAI | Full Python Project | Tutorial | AI ChatBot](https://www.youtube.com/watch?v=a2shHB4MRZ4) by [Jutsupoint](https://www.youtube.com/@JutsuPoint) ### [Prompt Engineering and LangChain](https://www.youtube.com/watch?v=muXbPpG_ys4&list=PLEJK-H61Xlwzm5FYLDdKt_6yibO33zoMW) by [Venelin Valkov](https://www.youtube.com/@venelin_valkov) @@ -132,4 +134,4 @@ --------------------- -⛓ icon marks a new addition [last update 2023-09-21] +⛓ icon marks a new addition [last update 2024-02-04] diff --git a/docs/docs/contributing/documentation.mdx b/docs/docs/contributing/documentation.mdx index d2f2ef4cef7..a7468600b74 100644 --- a/docs/docs/contributing/documentation.mdx +++ b/docs/docs/contributing/documentation.mdx @@ -17,7 +17,7 @@ Similar to linting, we recognize documentation can be annoying. If you do not wa ### Install dependencies - [Quarto](https://quarto.org) - package that converts Jupyter notebooks (`.ipynb` files) into mdx files for serving in Docusaurus. -- `poetry install` from the monorepo root +- `poetry install --with lint,docs --no-root` from the monorepo root. ### Building @@ -49,7 +49,6 @@ make api_docs_linkcheck The docs are linted from the monorepo root. To lint the docs, run the following from there: ```bash -poetry install --with lint,typing make lint ``` diff --git a/docs/docs/contributing/index.mdx b/docs/docs/contributing/index.mdx index 366664ed039..d25212f2862 100644 --- a/docs/docs/contributing/index.mdx +++ b/docs/docs/contributing/index.mdx @@ -15,8 +15,9 @@ There are many ways to contribute to LangChain. Here are some common ways people - [**Documentation**](./documentation.mdx): Help improve our docs, including this one! - [**Code**](./code.mdx): Help us write code, fix bugs, or improve our infrastructure. - [**Integrations**](integrations.mdx): Help us integrate with your favorite vendors and tools. +- [**Discussions**](https://github.com/langchain-ai/langchain/discussions): Help answer usage questions and discuss issues with users. -### 🚩GitHub Issues +### 🚩 GitHub Issues Our [issues](https://github.com/langchain-ai/langchain/issues) page is kept up to date with bugs, improvements, and feature requests. @@ -31,7 +32,13 @@ We will try to keep these issues as up-to-date as possible, though with the rapid rate of development in this field some may get out of date. If you notice this happening, please let us know. -### 🙋Getting Help +### 💭 GitHub Discussions + +We have a [discussions](https://github.com/langchain-ai/langchain/discussions) page where users can ask usage questions, discuss design decisions, and propose new features. + +If you are able to help answer questions, please do so! This will allow the maintainers to spend more time focused on development and bug fixing. + +### 🙋 Getting Help Our goal is to have the simplest developer setup possible. Should you experience any difficulty getting setup, please contact a maintainer! Not only do we want to help get you unblocked, but we also want to make sure that the process is diff --git a/docs/docs/contributing/repo_structure.mdx b/docs/docs/contributing/repo_structure.mdx new file mode 100644 index 00000000000..90f212265c8 --- /dev/null +++ b/docs/docs/contributing/repo_structure.mdx @@ -0,0 +1,54 @@ +--- +sidebar_position: 0.5 +--- +# Repository Structure + +If you plan on contributing to LangChain code or documentation, it can be useful +to understand the high level structure of the repository. + +LangChain is organized as a [monorep](https://en.wikipedia.org/wiki/Monorepo) that contains multiple packages. + +Here's the structure visualized as a tree: + +```text +. +├── cookbook # Tutorials and examples +├── docs # Contains content for the documentation here: https://python.langchain.com/ +├── libs +│ ├── langchain # Main package +│ │ ├── tests/unit_tests # Unit tests (present in each package not shown for brevity) +│ │ ├── tests/integration_tests # Integration tests (present in each package not shown for brevity) +│ ├── langchain-community # Third-party integrations +│ ├── langchain-core # Base interfaces for key abstractions +│ ├── langchain-experimental # Experimental components and chains +│ ├── partners +│ ├── langchain-partner-1 +│ ├── langchain-partner-2 +│ ├── ... +│ +├── templates # A collection of easily deployable reference architectures for a wide variety of tasks. +``` + +The root directory also contains the following files: + +* `pyproject.toml`: Dependencies for building docs and linting docs, cookbook. +* `Makefile`: A file that contains shortcuts for building, linting and docs and cookbook. + +There are other files in the root directory level, but their presence should be self-explanatory. Feel free to browse around! + +## Documentation + +The `/docs` directory contains the content for the documentation that is shown +at https://python.langchain.com/ and the associated API Reference https://api.python.langchain.com/en/latest/langchain_api_reference.html. + +See the [documentation](./documentation) guidelines to learn how to contribute to the documentation. + +## Code + +The `/libs` directory contains the code for the LangChain packages. + +To learn more about how to contribute code see the following guidelines: + +- [Code](./code.mdx) Learn how to develop in the LangChain codebase. +- [Integrations](./integrations.mdx) to learn how to contribute to third-party integrations to langchain-community or to start a new partner package. +- [Testing](./testing.mdx) guidelines to learn how to write tests for the packages. diff --git a/docs/docs/expression_language/how_to/message_history.ipynb b/docs/docs/expression_language/how_to/message_history.ipynb index 6d93423a964..aac305d7ac9 100644 --- a/docs/docs/expression_language/how_to/message_history.ipynb +++ b/docs/docs/expression_language/how_to/message_history.ipynb @@ -7,7 +7,7 @@ "source": [ "# Add message history (memory)\n", "\n", - "The `RunnableWithMessageHistory` let us add message history to certain types of chains.\n", + "The `RunnableWithMessageHistory` lets us add message history to certain types of chains. It wraps another Runnable and manages the chat message history for it.\n", "\n", "Specifically, it can be used for any Runnable that takes as input one of\n", "\n", @@ -21,7 +21,379 @@ "* a sequence of `BaseMessage`\n", "* a dict with a key that contains a sequence of `BaseMessage`\n", "\n", - "Let's take a look at some examples to see how it works." + "Let's take a look at some examples to see how it works. First we construct a runnable (which here accepts a dict as input and returns a message as output):" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2ed413b4-33a1-48ee-89b0-2d4917ec101a", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n", + "from langchain_openai.chat_models import ChatOpenAI\n", + "\n", + "model = ChatOpenAI()\n", + "prompt = ChatPromptTemplate.from_messages(\n", + " [\n", + " (\n", + " \"system\",\n", + " \"You're an assistant who's good at {ability}. Respond in 20 words or fewer\",\n", + " ),\n", + " MessagesPlaceholder(variable_name=\"history\"),\n", + " (\"human\", \"{input}\"),\n", + " ]\n", + ")\n", + "runnable = prompt | model" + ] + }, + { + "cell_type": "markdown", + "id": "9fd175e1-c7b8-4929-a57e-3331865fe7aa", + "metadata": {}, + "source": [ + "To manage the message history, we will need:\n", + "1. This runnable;\n", + "2. A callable that returns an instance of `BaseChatMessageHistory`.\n", + "\n", + "Check out the [memory integrations](https://integrations.langchain.com/memory) page for implementations of chat message histories using Redis and other providers. Here we demonstrate using an in-memory `ChatMessageHistory` as well as more persistent storage using `RedisChatMessageHistory`." + ] + }, + { + "cell_type": "markdown", + "id": "3d83adad-9672-496d-9f25-5747e7b8c8bb", + "metadata": {}, + "source": [ + "## In-memory\n", + "\n", + "Below we show a simple example in which the chat history lives in memory, in this case via a global Python dict.\n", + "\n", + "We construct a callable `get_session_history` that references this dict to return an instance of `ChatMessageHistory`. The arguments to the callable can be specified by passing a configuration to the `RunnableWithMessageHistory` at runtime. By default, the configuration parameter is expected to be a single string `session_id`. This can be adjusted via the `history_factory_config` kwarg.\n", + "\n", + "Using the single-parameter default:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "54348d02-d8ee-440c-bbf9-41bc0fbbc46c", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.chat_message_histories import ChatMessageHistory\n", + "from langchain_core.chat_history import BaseChatMessageHistory\n", + "from langchain_core.runnables.history import RunnableWithMessageHistory\n", + "\n", + "store = {}\n", + "\n", + "\n", + "def get_session_history(session_id: str) -> BaseChatMessageHistory:\n", + " if session_id not in store:\n", + " store[session_id] = ChatMessageHistory()\n", + " return store[session_id]\n", + "\n", + "\n", + "with_message_history = RunnableWithMessageHistory(\n", + " runnable,\n", + " get_session_history,\n", + " input_messages_key=\"input\",\n", + " history_messages_key=\"history\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "01acb505-3fd3-4ab4-9f04-5ea07e81542e", + "metadata": {}, + "source": [ + "Note that we've specified `input_messages_key` (the key to be treated as the latest input message) and `history_messages_key` (the key to add historical messages to).\n", + "\n", + "When invoking this new runnable, we specify the corresponding chat history via a configuration parameter:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "01384412-f08e-4634-9edb-3f46f475b582", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content='Cosine is a trigonometric function that calculates the ratio of the adjacent side to the hypotenuse of a right triangle.')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with_message_history.invoke(\n", + " {\"ability\": \"math\", \"input\": \"What does cosine mean?\"},\n", + " config={\"configurable\": {\"session_id\": \"abc123\"}},\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "954688a2-9a3f-47ee-a9e8-fa0c83e69477", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content='Cosine is a mathematical function used to calculate the length of a side in a right triangle.')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Remembers\n", + "with_message_history.invoke(\n", + " {\"ability\": \"math\", \"input\": \"What?\"},\n", + " config={\"configurable\": {\"session_id\": \"abc123\"}},\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "39350d7c-2641-4744-bc2a-fd6a57c4ea90", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content='I can help with math problems. What do you need assistance with?')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# New session_id --> does not remember.\n", + "with_message_history.invoke(\n", + " {\"ability\": \"math\", \"input\": \"What?\"},\n", + " config={\"configurable\": {\"session_id\": \"def234\"}},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d29497be-3366-408d-bbb9-d4a8bf4ef37c", + "metadata": {}, + "source": [ + "The configuration parameters by which we track message histories can be customized by passing in a list of ``ConfigurableFieldSpec`` objects to the ``history_factory_config`` parameter. Below, we use two parameters: a `user_id` and `conversation_id`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1c89daee-deff-4fdf-86a3-178f7d8ef536", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.runnables import ConfigurableFieldSpec\n", + "\n", + "store = {}\n", + "\n", + "\n", + "def get_session_history(user_id: str, conversation_id: str) -> BaseChatMessageHistory:\n", + " if (user_id, conversation_id) not in store:\n", + " store[(user_id, conversation_id)] = ChatMessageHistory()\n", + " return store[(user_id, conversation_id)]\n", + "\n", + "\n", + "with_message_history = RunnableWithMessageHistory(\n", + " runnable,\n", + " get_session_history,\n", + " input_messages_key=\"input\",\n", + " history_messages_key=\"history\",\n", + " history_factory_config=[\n", + " ConfigurableFieldSpec(\n", + " id=\"user_id\",\n", + " annotation=str,\n", + " name=\"User ID\",\n", + " description=\"Unique identifier for the user.\",\n", + " default=\"\",\n", + " is_shared=True,\n", + " ),\n", + " ConfigurableFieldSpec(\n", + " id=\"conversation_id\",\n", + " annotation=str,\n", + " name=\"Conversation ID\",\n", + " description=\"Unique identifier for the conversation.\",\n", + " default=\"\",\n", + " is_shared=True,\n", + " ),\n", + " ],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65c5622e-09b8-4f2f-8c8a-2dab0fd040fa", + "metadata": {}, + "outputs": [], + "source": [ + "with_message_history.invoke(\n", + " {\"ability\": \"math\", \"input\": \"Hello\"},\n", + " config={\"configurable\": {\"user_id\": \"123\", \"conversation_id\": \"1\"}},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "18f1a459-3f88-4ee6-8542-76a907070dd6", + "metadata": {}, + "source": [ + "### Examples with runnables of different signatures\n", + "\n", + "The above runnable takes a dict as input and returns a BaseMessage. Below we show some alternatives." + ] + }, + { + "cell_type": "markdown", + "id": "48eae1bf-b59d-4a61-8e62-b6dbf667e866", + "metadata": {}, + "source": [ + "#### Messages input, dict output" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "17733d4f-3a32-4055-9d44-5d58b9446a26", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'output_message': AIMessage(content=\"Simone de Beauvoir believed in the existence of free will. She argued that individuals have the ability to make choices and determine their own actions, even in the face of social and cultural constraints. She rejected the idea that individuals are purely products of their environment or predetermined by biology or destiny. Instead, she emphasized the importance of personal responsibility and the need for individuals to actively engage in creating their own lives and defining their own existence. De Beauvoir believed that freedom and agency come from recognizing one's own freedom and actively exercising it in the pursuit of personal and collective liberation.\")}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langchain_core.messages import HumanMessage\n", + "from langchain_core.runnables import RunnableParallel\n", + "\n", + "chain = RunnableParallel({\"output_message\": ChatOpenAI()})\n", + "\n", + "\n", + "def get_session_history(session_id: str) -> BaseChatMessageHistory:\n", + " if session_id not in store:\n", + " store[session_id] = ChatMessageHistory()\n", + " return store[session_id]\n", + "\n", + "\n", + "with_message_history = RunnableWithMessageHistory(\n", + " chain,\n", + " get_session_history,\n", + " output_messages_key=\"output_message\",\n", + ")\n", + "\n", + "with_message_history.invoke(\n", + " [HumanMessage(content=\"What did Simone de Beauvoir believe about free will\")],\n", + " config={\"configurable\": {\"session_id\": \"baz\"}},\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "efb57ef5-91f9-426b-84b9-b77f071a9dd7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'output_message': AIMessage(content='Simone de Beauvoir\\'s views on free will were closely aligned with those of her contemporary and partner Jean-Paul Sartre. Both de Beauvoir and Sartre were existentialist philosophers who emphasized the importance of individual freedom and the rejection of determinism. They believed that human beings have the capacity to transcend their circumstances and create their own meaning and values.\\n\\nSartre, in his famous work \"Being and Nothingness,\" argued that human beings are condemned to be free, meaning that we are burdened with the responsibility of making choices and defining ourselves in a world that lacks inherent meaning. Like de Beauvoir, Sartre believed that individuals have the ability to exercise their freedom and make choices in the face of external and internal constraints.\\n\\nWhile there may be some nuanced differences in their philosophical writings, overall, de Beauvoir and Sartre shared a similar belief in the existence of free will and the importance of individual agency in shaping one\\'s own life.')}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with_message_history.invoke(\n", + " [HumanMessage(content=\"How did this compare to Sartre\")],\n", + " config={\"configurable\": {\"session_id\": \"baz\"}},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "a39eac5f-a9d8-4729-be06-5e7faf0c424d", + "metadata": {}, + "source": [ + "#### Messages input, messages output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e45bcd95-e31f-4a9a-967a-78f96e8da881", + "metadata": {}, + "outputs": [], + "source": [ + "RunnableWithMessageHistory(\n", + " ChatOpenAI(),\n", + " get_session_history,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "04daa921-a2d1-40f9-8cd1-ae4e9a4163a7", + "metadata": {}, + "source": [ + "#### Dict with single key for all messages input, messages output" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27157f15-9fb0-4167-9870-f4d7f234b3cb", + "metadata": {}, + "outputs": [], + "source": [ + "from operator import itemgetter\n", + "\n", + "RunnableWithMessageHistory(\n", + " itemgetter(\"input_messages\") | ChatOpenAI(),\n", + " get_session_history,\n", + " input_messages_key=\"input_messages\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "418ca7af-9ed9-478c-8bca-cba0de2ca61e", + "metadata": {}, + "source": [ + "## Persistent storage" + ] + }, + { + "cell_type": "markdown", + "id": "76799a13-d99a-4c4f-91f2-db699e40b8df", + "metadata": {}, + "source": [ + "In many cases it is preferable to persist conversation histories. `RunnableWithMessageHistory` is agnostic as to how the `get_session_history` callable retrieves its chat message histories. See [here](https://github.com/langchain-ai/langserve/blob/main/examples/chat_with_persistence_and_user/server.py) for an example using a local filesystem. Below we demonstrate how one could use Redis. Check out the [memory integrations](https://integrations.langchain.com/memory) page for implementations of chat message histories using other providers." ] }, { @@ -29,9 +401,9 @@ "id": "6bca45e5-35d9-4603-9ca9-6ac0ce0e35cd", "metadata": {}, "source": [ - "## Setup\n", + "### Setup\n", "\n", - "We'll use Redis to store our chat message histories and Anthropic's claude-2 model so we'll need to install the following dependencies:" + "We'll need to install Redis if it's not installed already:" ] }, { @@ -41,28 +413,7 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install --upgrade --quiet langchain redis anthropic" - ] - }, - { - "cell_type": "markdown", - "id": "93776323-d6b8-4912-bb6a-867c5e655f46", - "metadata": {}, - "source": [ - "Set your [Anthropic API key](https://console.anthropic.com/):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c7f56f69-d2f1-4a21-990c-b5551eb012fa", - "metadata": {}, - "outputs": [], - "source": [ - "import getpass\n", - "import os\n", - "\n", - "os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass()" + "%pip install --upgrade --quiet redis" ] }, { @@ -78,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 9, "id": "cd6a250e-17fe-4368-a39d-1fe6b2cbde68", "metadata": {}, "outputs": [], @@ -110,77 +461,32 @@ "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()" ] }, - { - "cell_type": "markdown", - "id": "1a5a632e-ba9e-4488-b586-640ad5494f62", - "metadata": {}, - "source": [ - "## Example: Dict input, message output\n", - "\n", - "Let's create a simple chain that takes a dict as input and returns a BaseMessage.\n", - "\n", - "In this case the `\"question\"` key in the input represents our input message, and the `\"history\"` key is where our historical messages will be injected." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "2a150d6f-8878-4950-8634-a608c5faad56", - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Optional\n", - "\n", - "from langchain_community.chat_message_histories import RedisChatMessageHistory\n", - "from langchain_community.chat_models import ChatAnthropic\n", - "from langchain_core.chat_history import BaseChatMessageHistory\n", - "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n", - "from langchain_core.runnables.history import RunnableWithMessageHistory" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3185edba-4eb6-4b32-80c6-577c0d19af97", - "metadata": {}, - "outputs": [], - "source": [ - "prompt = ChatPromptTemplate.from_messages(\n", - " [\n", - " (\"system\", \"You're an assistant who's good at {ability}\"),\n", - " MessagesPlaceholder(variable_name=\"history\"),\n", - " (\"human\", \"{question}\"),\n", - " ]\n", - ")\n", - "\n", - "chain = prompt | ChatAnthropic(model=\"claude-2\")" - ] - }, { "cell_type": "markdown", "id": "f9d81796-ce61-484c-89e2-6c567d5e54ef", "metadata": {}, "source": [ - "### Adding message history\n", - "\n", - "To add message history to our original chain we wrap it in the `RunnableWithMessageHistory` class.\n", - "\n", - "Crucially, we also need to define a method that takes a session_id string and based on it returns a `BaseChatMessageHistory`. Given the same input, this method should return an equivalent output.\n", - "\n", - "In this case we'll also want to specify `input_messages_key` (the key to be treated as the latest input message) and `history_messages_key` (the key to add historical messages to)." + "Updating the message history implementation just requires us to define a new callable, this time returning an instance of `RedisChatMessageHistory`:" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "id": "ca7c64d8-e138-4ef8-9734-f82076c47d80", "metadata": {}, "outputs": [], "source": [ - "chain_with_history = RunnableWithMessageHistory(\n", - " chain,\n", - " lambda session_id: RedisChatMessageHistory(session_id, url=REDIS_URL),\n", - " input_messages_key=\"question\",\n", + "from langchain_community.chat_message_histories import RedisChatMessageHistory\n", + "\n", + "\n", + "def get_message_history(session_id: str) -> RedisChatMessageHistory:\n", + " return RedisChatMessageHistory(session_id, url=REDIS_URL)\n", + "\n", + "\n", + "with_message_history = RunnableWithMessageHistory(\n", + " runnable,\n", + " get_message_history,\n", + " input_messages_key=\"input\",\n", " history_messages_key=\"history\",\n", ")" ] @@ -190,60 +496,53 @@ "id": "37eefdec-9901-4650-b64c-d3c097ed5f4d", "metadata": {}, "source": [ - "## Invoking with config\n", - "\n", - "Whenever we call our chain with message history, we need to include a config that contains the `session_id`\n", - "```python\n", - "config={\"configurable\": {\"session_id\": \"\"}}\n", - "```\n", - "\n", - "Given the same configuration, our chain should be pulling from the same chat message history." + "We can invoke as before:" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "id": "a85bcc22-ca4c-4ad5-9440-f94be7318f3e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "AIMessage(content=' Cosine is one of the basic trigonometric functions in mathematics. It is defined as the ratio of the adjacent side to the hypotenuse in a right triangle.\\n\\nSome key properties and facts about cosine:\\n\\n- It is denoted by cos(θ), where θ is the angle in a right triangle. \\n\\n- The cosine of an acute angle is always positive. For angles greater than 90 degrees, cosine can be negative.\\n\\n- Cosine is one of the three main trig functions along with sine and tangent.\\n\\n- The cosine of 0 degrees is 1. As the angle increases towards 90 degrees, the cosine value decreases towards 0.\\n\\n- The range of values for cosine is -1 to 1.\\n\\n- The cosine function maps angles in a circle to the x-coordinate on the unit circle.\\n\\n- Cosine is used to find adjacent side lengths in right triangles, and has many other applications in mathematics, physics, engineering and more.\\n\\n- Key cosine identities include: cos(A+B) = cosAcosB − sinAsinB and cos(2A) = cos^2(A) − sin^2(A)\\n\\nSo in summary, cosine is a fundamental trig')" + "AIMessage(content='Cosine is a trigonometric function that represents the ratio of the adjacent side to the hypotenuse in a right triangle.')" ] }, - "execution_count": 7, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "chain_with_history.invoke(\n", - " {\"ability\": \"math\", \"question\": \"What does cosine mean?\"},\n", + "with_message_history.invoke(\n", + " {\"ability\": \"math\", \"input\": \"What does cosine mean?\"},\n", " config={\"configurable\": {\"session_id\": \"foobar\"}},\n", ")" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "id": "ab29abd3-751f-41ce-a1b0-53f6b565e79d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "AIMessage(content=' The inverse of the cosine function is called the arccosine or inverse cosine, often denoted as cos-1(x) or arccos(x).\\n\\nThe key properties and facts about arccosine:\\n\\n- It is defined as the angle θ between 0 and π radians whose cosine is x. So arccos(x) = θ such that cos(θ) = x.\\n\\n- The range of arccosine is 0 to π radians (0 to 180 degrees).\\n\\n- The domain of arccosine is -1 to 1. \\n\\n- arccos(cos(θ)) = θ for values of θ from 0 to π radians.\\n\\n- arccos(x) is the angle in a right triangle whose adjacent side is x and hypotenuse is 1.\\n\\n- arccos(0) = 90 degrees. As x increases from 0 to 1, arccos(x) decreases from 90 to 0 degrees.\\n\\n- arccos(1) = 0 degrees. arccos(-1) = 180 degrees.\\n\\n- The graph of y = arccos(x) is part of the unit circle, restricted to x')" + "AIMessage(content='The inverse of cosine is the arccosine function, denoted as acos or cos^-1, which gives the angle corresponding to a given cosine value.')" ] }, - "execution_count": 8, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "chain_with_history.invoke(\n", - " {\"ability\": \"math\", \"question\": \"What's its inverse\"},\n", + "with_message_history.invoke(\n", + " {\"ability\": \"math\", \"input\": \"What's its inverse\"},\n", " config={\"configurable\": {\"session_id\": \"foobar\"}},\n", ")" ] @@ -255,7 +554,7 @@ "source": [ ":::tip\n", "\n", - "[Langsmith trace](https://smith.langchain.com/public/863a003b-7ca8-4b24-be9e-d63ec13c106e/r)\n", + "[Langsmith trace](https://smith.langchain.com/public/bd73e122-6ec1-48b2-82df-e6483dc9cb63/r)\n", "\n", ":::" ] @@ -267,124 +566,13 @@ "source": [ "Looking at the Langsmith trace for the second call, we can see that when constructing the prompt, a \"history\" variable has been injected which is a list of two messages (our first input and first output)." ] - }, - { - "cell_type": "markdown", - "id": "028cf151-6cd5-4533-b3cf-c8d735554647", - "metadata": {}, - "source": [ - "## Example: messages input, dict output" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "0bb446b5-6251-45fe-a92a-4c6171473c53", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'output_message': AIMessage(content=' Here is a summary of Simone de Beauvoir\\'s views on free will:\\n\\n- De Beauvoir was an existentialist philosopher and believed strongly in the concept of free will. She rejected the idea that human nature or instincts determine behavior.\\n\\n- Instead, de Beauvoir argued that human beings define their own essence or nature through their actions and choices. As she famously wrote, \"One is not born, but rather becomes, a woman.\"\\n\\n- De Beauvoir believed that while individuals are situated in certain cultural contexts and social conditions, they still have agency and the ability to transcend these situations. Freedom comes from choosing one\\'s attitude toward these constraints.\\n\\n- She emphasized the radical freedom and responsibility of the individual. We are \"condemned to be free\" because we cannot escape making choices and taking responsibility for our choices. \\n\\n- De Beauvoir felt that many people evade their freedom and responsibility by adopting rigid mindsets, ideologies, or conforming uncritically to social roles.\\n\\n- She advocated for the recognition of ambiguity in the human condition and warned against the quest for absolute rules that deny freedom and responsibility. Authentic living involves embracing ambiguity.\\n\\nIn summary, de Beauvoir promoted an existential ethics')}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from langchain_core.messages import HumanMessage\n", - "from langchain_core.runnables import RunnableParallel\n", - "\n", - "chain = RunnableParallel({\"output_message\": ChatAnthropic(model=\"claude-2\")})\n", - "chain_with_history = RunnableWithMessageHistory(\n", - " chain,\n", - " lambda session_id: RedisChatMessageHistory(session_id, url=REDIS_URL),\n", - " output_messages_key=\"output_message\",\n", - ")\n", - "\n", - "chain_with_history.invoke(\n", - " [HumanMessage(content=\"What did Simone de Beauvoir believe about free will\")],\n", - " config={\"configurable\": {\"session_id\": \"baz\"}},\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "601ce3ff-aea8-424d-8e54-fd614256af4f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'output_message': AIMessage(content=\" There are many similarities between Simone de Beauvoir's views on free will and those of Jean-Paul Sartre, though some key differences emerge as well:\\n\\nSimilarities with Sartre:\\n\\n- Both were existentialist thinkers who rejected determinism and emphasized human freedom and responsibility.\\n\\n- They agreed that existence precedes essence - there is no predefined human nature that determines who we are.\\n\\n- Individuals must define themselves through their choices and actions. This leads to anxiety but also freedom.\\n\\n- The human condition is characterized by ambiguity and uncertainty, rather than fixed meanings/values.\\n\\n- Both felt that most people evade their freedom through self-deception, conformity, or adopting collective identities/values uncritically.\\n\\nDifferences from Sartre: \\n\\n- Sartre placed more emphasis on the burden and anguish of radical freedom. De Beauvoir focused more on its positive potential.\\n\\n- De Beauvoir critiqued Sartre's premise that human relations are necessarily conflictual. She saw more potential for mutual recognition.\\n\\n- Sartre saw the Other's gaze as a threat to freedom. De Beauvoir put more stress on how the Other's gaze can confirm\")}" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "chain_with_history.invoke(\n", - " [HumanMessage(content=\"How did this compare to Sartre\")],\n", - " config={\"configurable\": {\"session_id\": \"baz\"}},\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "b898d1b1-11e6-4d30-a8dd-cc5e45533611", - "metadata": {}, - "source": [ - ":::tip\n", - "\n", - "[LangSmith trace](https://smith.langchain.com/public/f6c3e1d1-a49d-4955-a9fa-c6519df74fa7/r)\n", - "\n", - ":::" - ] - }, - { - "cell_type": "markdown", - "id": "1724292c-01c6-44bb-83e8-9cdb6bf01483", - "metadata": {}, - "source": [ - "## More examples\n", - "\n", - "We could also do any of the below:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fd89240b-5a25-48f8-9568-5c1127f9ffad", - "metadata": {}, - "outputs": [], - "source": [ - "from operator import itemgetter\n", - "\n", - "# messages in, messages out\n", - "RunnableWithMessageHistory(\n", - " ChatAnthropic(model=\"claude-2\"),\n", - " lambda session_id: RedisChatMessageHistory(session_id, url=REDIS_URL),\n", - ")\n", - "\n", - "# dict with single key for all messages in, messages out\n", - "RunnableWithMessageHistory(\n", - " itemgetter(\"input_messages\") | ChatAnthropic(model=\"claude-2\"),\n", - " lambda session_id: RedisChatMessageHistory(session_id, url=REDIS_URL),\n", - " input_messages_key=\"input_messages\",\n", - ")" - ] } ], "metadata": { "kernelspec": { - "display_name": "poetry-venv", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "poetry-venv" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -396,7 +584,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.1" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/docs/docs/expression_language/streaming.ipynb b/docs/docs/expression_language/streaming.ipynb index 74da56d16b8..d597c4d825f 100644 --- a/docs/docs/expression_language/streaming.ipynb +++ b/docs/docs/expression_language/streaming.ipynb @@ -66,6 +66,8 @@ } ], "source": [ + "# Showing the example using anthropic, but you can use\n", + "# your favorite chat model!\n", "from langchain.chat_models import ChatAnthropic\n", "\n", "model = ChatAnthropic()\n", @@ -164,9 +166,9 @@ "name": "stdout", "output_type": "stream", "text": [ - " Sure|,| here|'s| a| funny| joke| about| a| par|rot|:|\n", + " Here|'s| a| silly| joke| about| a| par|rot|:|\n", "\n", - "Why| doesn|'t| a| par|rot| ever| get| hungry| at| night|?| Because| it| has| a| light| snack| before| bed|!||" + "What| kind| of| teacher| gives| good| advice|?| An| ap|-|parent| (|app|arent|)| one|!||" ] } ], @@ -228,40 +230,34 @@ "{'countries': [{}]}\n", "{'countries': [{'name': ''}]}\n", "{'countries': [{'name': 'France'}]}\n", - "{'countries': [{'name': 'France', 'population': ''}]}\n", - "{'countries': [{'name': 'France', 'population': '67'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': ''}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': ''}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': ''}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': 'Japan'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': 'Japan', 'population': ''}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': 'Japan', 'population': '126'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': 'Japan', 'population': '126,'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': 'Japan', 'population': '126,860'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': 'Japan', 'population': '126,860,'}]}\n", - "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': 'Japan', 'population': '126,860,301'}]}\n" + "{'countries': [{'name': 'France', 'population': 67}]}\n", + "{'countries': [{'name': 'France', 'population': 6739}]}\n", + "{'countries': [{'name': 'France', 'population': 673915}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': ''}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Sp'}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain'}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 4675}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 467547}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}, {}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}, {'name': ''}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}, {'name': 'Japan'}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}, {'name': 'Japan', 'population': 12}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}, {'name': 'Japan', 'population': 12647}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}, {'name': 'Japan', 'population': 1264764}]}\n", + "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}, {'name': 'Japan', 'population': 126476461}]}\n" ] } ], "source": [ "from langchain_core.output_parsers import JsonOutputParser\n", - "from langchain_openai.chat_models import ChatOpenAI\n", "\n", - "model = ChatOpenAI()\n", - "\n", - "chain = model | JsonOutputParser() # This parser only works with OpenAI right now\n", + "chain = (\n", + " model | JsonOutputParser()\n", + ") # Due to a bug in older versions of Langchain, JsonOutputParser did not stream results from some models\n", "async for text in chain.astream(\n", " 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'\n", "):\n", @@ -294,12 +290,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[None, None, None]|" + "['France', 'Spain', 'Japan']|" ] } ], "source": [ - "from langchain_core.output_parsers import JsonOutputParser\n", + "from langchain_core.output_parsers import (\n", + " JsonOutputParser,\n", + ")\n", "\n", "\n", "# A function that operates on finalized inputs\n", @@ -326,7 +324,7 @@ "chain = model | JsonOutputParser() | _extract_country_names\n", "\n", "async for text in chain.astream(\n", - " 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\"'\n", + " 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'\n", "):\n", " print(text, end=\"|\", flush=True)" ] @@ -348,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 17, "id": "15984b2b-315a-4119-945b-2a3dabea3082", "metadata": {}, "outputs": [ @@ -356,7 +354,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "France|Spain|Japan|" + "France|Sp|Spain|Japan|" ] } ], @@ -392,11 +390,23 @@ "chain = model | JsonOutputParser() | _extract_country_names_streaming\n", "\n", "async for text in chain.astream(\n", - " 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\"'\n", + " 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'\n", "):\n", " print(text, end=\"|\", flush=True)" ] }, + { + "cell_type": "markdown", + "id": "d59823f5-9b9a-43c5-a213-34644e2f1d3d", + "metadata": {}, + "source": [ + ":::{.callout-note}\n", + "Because the code above is relying on JSON auto-completion, you may see partial names of countries (e.g., `Sp` and `Spain`), which is not what one would want for an extraction result!\n", + "\n", + "We're focusing on streaming concepts, not necessarily the results of the chains.\n", + ":::" + ] + }, { "cell_type": "markdown", "id": "6adf65b7-aa47-4321-98c7-a0abe43b833a", @@ -409,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "b9b1c00d-8b44-40d0-9e2b-8a70d238f82b", "metadata": {}, "outputs": [ @@ -420,7 +430,7 @@ " Document(page_content='harrison likes spicy food')]]" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -465,7 +475,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "957447e6-1e60-41ef-8c10-2654bd9e738d", "metadata": {}, "outputs": [], @@ -483,7 +493,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "94e50b5d-bf51-4eee-9da0-ee40dd9ce42b", "metadata": {}, "outputs": [ @@ -491,9 +501,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "|H|arrison| worked| at| Kens|ho|,| a| renowned| technology| company| known| for| revolution|izing| the| artificial| intelligence| industry|.\n", - "|K|ens|ho|,| located| in| the| heart| of| Silicon| Valley|,| is| famous| for| its| cutting|-edge| research| and| development| in| machine| learning|.\n", - "|With| its| state|-of|-the|-art| facilities| and| talented| team|,| Kens|ho| has| become| a| hub| for| innovation| and| a| sought|-after| workplace| for| tech| enthusiasts| like| Harrison|.||" + " Based| on| the| given| context|,| the| only| information| provided| about| where| Harrison| worked| is| that| he| worked| at| Ken|sh|o|.| Since| there| are| no| other| details| provided| about| Ken|sh|o|,| I| do| not| have| enough| information| to| write| 3| additional| made| up| sentences| about| this| place|.| I| can| only| state| that| Harrison| worked| at| Ken|sh|o|.||" ] } ], @@ -528,17 +536,17 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "61348df9-ec58-401e-be89-68a70042f88e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'0.1.14'" + "'0.1.18'" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -604,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "c00df46e-7f6b-4e06-8abf-801898c8d57f", "metadata": {}, "outputs": [ @@ -612,7 +620,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/eugene/.pyenv/versions/3.11.4/envs/langchain_3_11_4/lib/python3.11/site-packages/langchain_core/_api/beta_decorator.py:86: LangChainBetaWarning: This API is in beta and may change in the future.\n", + "/home/eugene/src/langchain/libs/core/langchain_core/_api/beta_decorator.py:86: LangChainBetaWarning: This API is in beta and may change in the future.\n", " warn_beta(\n" ] } @@ -650,7 +658,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "id": "ce31b525-f47d-4828-85a7-912ce9f2e79b", "metadata": {}, "outputs": [ @@ -658,26 +666,26 @@ "data": { "text/plain": [ "[{'event': 'on_chat_model_start',\n", - " 'run_id': 'd78b4ffb-0eb1-499c-8a90-8e4a4aa2edae',\n", - " 'name': 'ChatOpenAI',\n", + " 'run_id': '555843ed-3d24-4774-af25-fbf030d5e8c4',\n", + " 'name': 'ChatAnthropic',\n", " 'tags': [],\n", " 'metadata': {},\n", " 'data': {'input': 'hello'}},\n", " {'event': 'on_chat_model_stream',\n", - " 'run_id': 'd78b4ffb-0eb1-499c-8a90-8e4a4aa2edae',\n", + " 'run_id': '555843ed-3d24-4774-af25-fbf030d5e8c4',\n", " 'tags': [],\n", " 'metadata': {},\n", - " 'name': 'ChatOpenAI',\n", - " 'data': {'chunk': AIMessageChunk(content='')}},\n", + " 'name': 'ChatAnthropic',\n", + " 'data': {'chunk': AIMessageChunk(content=' Hello')}},\n", " {'event': 'on_chat_model_stream',\n", - " 'run_id': 'd78b4ffb-0eb1-499c-8a90-8e4a4aa2edae',\n", + " 'run_id': '555843ed-3d24-4774-af25-fbf030d5e8c4',\n", " 'tags': [],\n", " 'metadata': {},\n", - " 'name': 'ChatOpenAI',\n", - " 'data': {'chunk': AIMessageChunk(content='Hello')}}]" + " 'name': 'ChatAnthropic',\n", + " 'data': {'chunk': AIMessageChunk(content='!')}}]" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -688,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "id": "76cfe826-ee63-4310-ad48-55a95eb3b9d6", "metadata": {}, "outputs": [ @@ -696,20 +704,20 @@ "data": { "text/plain": [ "[{'event': 'on_chat_model_stream',\n", - " 'run_id': 'd78b4ffb-0eb1-499c-8a90-8e4a4aa2edae',\n", + " 'run_id': '555843ed-3d24-4774-af25-fbf030d5e8c4',\n", " 'tags': [],\n", " 'metadata': {},\n", - " 'name': 'ChatOpenAI',\n", + " 'name': 'ChatAnthropic',\n", " 'data': {'chunk': AIMessageChunk(content='')}},\n", " {'event': 'on_chat_model_end',\n", - " 'name': 'ChatOpenAI',\n", - " 'run_id': 'd78b4ffb-0eb1-499c-8a90-8e4a4aa2edae',\n", + " 'name': 'ChatAnthropic',\n", + " 'run_id': '555843ed-3d24-4774-af25-fbf030d5e8c4',\n", " 'tags': [],\n", " 'metadata': {},\n", - " 'data': {'output': AIMessageChunk(content='Hello! How can I assist you today?')}}]" + " 'data': {'output': AIMessageChunk(content=' Hello!')}}]" ] }, - "execution_count": 14, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -730,12 +738,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 27, "id": "4328c56c-a303-427b-b1f2-f354e9af555c", "metadata": {}, "outputs": [], "source": [ - "chain = model | JsonOutputParser() # This parser only works with OpenAI right now\n", + "chain = (\n", + " model | JsonOutputParser()\n", + ") # Due to a bug in older versions of Langchain, JsonOutputParser did not stream results from some models\n", "\n", "events = [\n", " event\n", @@ -762,7 +772,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "id": "8e66ea3d-a450-436a-aaac-d9478abc6c28", "metadata": {}, "outputs": [ @@ -770,26 +780,26 @@ "data": { "text/plain": [ "[{'event': 'on_chain_start',\n", - " 'run_id': 'aa992fb9-d79f-46f3-a857-ae4acad841c4',\n", + " 'run_id': 'b1074bff-2a17-458b-9e7b-625211710df4',\n", " 'name': 'RunnableSequence',\n", " 'tags': [],\n", " 'metadata': {},\n", " 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}},\n", " {'event': 'on_chat_model_start',\n", - " 'name': 'ChatOpenAI',\n", - " 'run_id': 'c5406de5-0880-4829-ae26-bb565b404e27',\n", + " 'name': 'ChatAnthropic',\n", + " 'run_id': '6072be59-1f43-4f1c-9470-3b92e8406a99',\n", " 'tags': ['seq:step:1'],\n", " 'metadata': {},\n", " 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}}},\n", " {'event': 'on_parser_start',\n", " 'name': 'JsonOutputParser',\n", - " 'run_id': '32b47794-8fb6-4ef4-8800-23ed6c3f4519',\n", + " 'run_id': 'bf978194-0eda-4494-ad15-3a5bfe69cd59',\n", " 'tags': ['seq:step:2'],\n", " 'metadata': {},\n", " 'data': {}}]" ] }, - "execution_count": 16, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -816,7 +826,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "id": "630c71d6-8d94-4ce0-a78a-f20e90f628df", "metadata": {}, "outputs": [ @@ -824,29 +834,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "Chat model chunk: ''\n", + "Chat model chunk: ' Here'\n", + "Chat model chunk: ' is'\n", + "Chat model chunk: ' the'\n", + "Chat model chunk: ' JSON'\n", + "Chat model chunk: ' with'\n", + "Chat model chunk: ' the'\n", + "Chat model chunk: ' requested'\n", + "Chat model chunk: ' countries'\n", + "Chat model chunk: ' and'\n", + "Chat model chunk: ' their'\n", + "Chat model chunk: ' populations'\n", + "Chat model chunk: ':'\n", + "Chat model chunk: '\\n\\n```'\n", + "Chat model chunk: 'json'\n", "Parser chunk: {}\n", - "Chat model chunk: '{\\n'\n", - "Chat model chunk: ' '\n", + "Chat model chunk: '\\n{'\n", + "Chat model chunk: '\\n '\n", "Chat model chunk: ' \"'\n", "Chat model chunk: 'countries'\n", "Chat model chunk: '\":'\n", "Parser chunk: {'countries': []}\n", - "Chat model chunk: ' [\\n'\n", - "Chat model chunk: ' '\n", + "Chat model chunk: ' ['\n", + "Chat model chunk: '\\n '\n", "Parser chunk: {'countries': [{}]}\n", - "Chat model chunk: ' {\\n'\n", - "Chat model chunk: ' '\n", - "Chat model chunk: ' \"'\n", - "Chat model chunk: 'name'\n", - "Chat model chunk: '\":'\n", - "Parser chunk: {'countries': [{'name': ''}]}\n", - "Chat model chunk: ' \"'\n", - "Parser chunk: {'countries': [{'name': 'France'}]}\n", - "Chat model chunk: 'France'\n", - "Chat model chunk: '\",\\n'\n", - "Chat model chunk: ' '\n", - "Chat model chunk: ' \"'\n", + "Chat model chunk: ' {'\n", "...\n" ] } @@ -897,7 +909,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "id": "4f0b581b-be63-4663-baba-c6d2b625cdf9", "metadata": {}, "outputs": [ @@ -905,17 +917,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'event': 'on_parser_start', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {}}\n", - "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {}}}\n", - "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': []}}}\n", - "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{}]}}}\n", - "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': ''}]}}}\n", - "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France'}]}}}\n", - "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 670}]}}}\n", - "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 670600}]}}}\n", - "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67060000}]}}}\n", - "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67060000}, {}]}}}\n", - "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67060000}, {'name': ''}]}}}\n", + "{'event': 'on_parser_start', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {}}\n", + "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {}}}\n", + "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': []}}}\n", + "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{}]}}}\n", + "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': ''}]}}}\n", + "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France'}]}}}\n", + "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67}]}}}\n", + "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 6739}]}}}\n", + "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 673915}]}}}\n", + "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67391582}]}}}\n", + "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67391582}, {}]}}}\n", "...\n" ] } @@ -949,7 +961,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "id": "096cd904-72f0-4ebe-a8b7-d0e730faea7f", "metadata": {}, "outputs": [ @@ -957,17 +969,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'event': 'on_chat_model_start', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}}}\n", - "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='')}}\n", - "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='{\\n')}}\n", - "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' ')}}\n", - "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' \"')}}\n", - "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='countries')}}\n", - "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='\":')}}\n", - "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' [\\n')}}\n", - "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' ')}}\n", - "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' {\\n')}}\n", - "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' ')}}\n", + "{'event': 'on_chat_model_start', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}}}\n", + "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' Here')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' is')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' the')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' JSON')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' with')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' the')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' requested')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' countries')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' and')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' their')}}\n", "...\n" ] } @@ -1008,7 +1020,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "id": "26bac0d2-76d9-446e-b346-82790236b88d", "metadata": {}, "outputs": [ @@ -1016,17 +1028,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'event': 'on_chain_start', 'run_id': 'd4c78db8-be20-4fa0-87d6-cb317822967a', 'name': 'RunnableSequence', 'tags': ['my_chain'], 'metadata': {}, 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}}\n", - "{'event': 'on_chat_model_start', 'name': 'ChatOpenAI', 'run_id': '15e46d9f-ccf5-4da2-b9e3-b2a85873ba4c', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}}}\n", - "{'event': 'on_parser_start', 'name': 'JsonOutputParser', 'run_id': '91945f4f-0deb-4999-acf0-f6d191c89b34', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}, 'data': {}}\n", - "{'event': 'on_chat_model_stream', 'name': 'ChatOpenAI', 'run_id': '15e46d9f-ccf5-4da2-b9e3-b2a85873ba4c', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='')}}\n", - "{'event': 'on_parser_stream', 'name': 'JsonOutputParser', 'run_id': '91945f4f-0deb-4999-acf0-f6d191c89b34', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}, 'data': {'chunk': {}}}\n", - "{'event': 'on_chain_stream', 'run_id': 'd4c78db8-be20-4fa0-87d6-cb317822967a', 'tags': ['my_chain'], 'metadata': {}, 'name': 'RunnableSequence', 'data': {'chunk': {}}}\n", - "{'event': 'on_chat_model_stream', 'name': 'ChatOpenAI', 'run_id': '15e46d9f-ccf5-4da2-b9e3-b2a85873ba4c', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='{\"')}}\n", - "{'event': 'on_chat_model_stream', 'name': 'ChatOpenAI', 'run_id': '15e46d9f-ccf5-4da2-b9e3-b2a85873ba4c', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='countries')}}\n", - "{'event': 'on_chat_model_stream', 'name': 'ChatOpenAI', 'run_id': '15e46d9f-ccf5-4da2-b9e3-b2a85873ba4c', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='\":')}}\n", - "{'event': 'on_parser_stream', 'name': 'JsonOutputParser', 'run_id': '91945f4f-0deb-4999-acf0-f6d191c89b34', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}, 'data': {'chunk': {'countries': []}}}\n", - "{'event': 'on_chain_stream', 'run_id': 'd4c78db8-be20-4fa0-87d6-cb317822967a', 'tags': ['my_chain'], 'metadata': {}, 'name': 'RunnableSequence', 'data': {'chunk': {'countries': []}}}\n", + "{'event': 'on_chain_start', 'run_id': '190875f3-3fb7-49ad-9b6e-f49da22f3e49', 'name': 'RunnableSequence', 'tags': ['my_chain'], 'metadata': {}, 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}}\n", + "{'event': 'on_chat_model_start', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}}}\n", + "{'event': 'on_parser_start', 'name': 'JsonOutputParser', 'run_id': '3b5e4ca1-40fe-4a02-9a19-ba2a43a6115c', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}, 'data': {}}\n", + "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' Here')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' is')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' the')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' JSON')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' with')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' the')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' requested')}}\n", + "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' countries')}}\n", "...\n" ] } @@ -1062,7 +1074,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "id": "0e6451d3-3b11-4a71-ae19-998f4c10180f", "metadata": {}, "outputs": [], @@ -1104,7 +1116,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "id": "f9a8fe35-faab-4970-b8c0-5c780845d98a", "metadata": {}, "outputs": [ @@ -1112,7 +1124,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" + "['France', 'Spain', 'Japan']\n" ] } ], @@ -1133,7 +1145,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 22, "id": "b08215cd-bffa-4e76-aaf3-c52ee34f152c", "metadata": {}, "outputs": [ @@ -1141,33 +1153,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "Chat model chunk: ''\n", + "Chat model chunk: ' Here'\n", + "Chat model chunk: ' is'\n", + "Chat model chunk: ' the'\n", + "Chat model chunk: ' JSON'\n", + "Chat model chunk: ' with'\n", + "Chat model chunk: ' the'\n", + "Chat model chunk: ' requested'\n", + "Chat model chunk: ' countries'\n", + "Chat model chunk: ' and'\n", + "Chat model chunk: ' their'\n", + "Chat model chunk: ' populations'\n", + "Chat model chunk: ':'\n", + "Chat model chunk: '\\n\\n```'\n", + "Chat model chunk: 'json'\n", "Parser chunk: {}\n", - "Chat model chunk: '{\"'\n", + "Chat model chunk: '\\n{'\n", + "Chat model chunk: '\\n '\n", + "Chat model chunk: ' \"'\n", "Chat model chunk: 'countries'\n", "Chat model chunk: '\":'\n", "Parser chunk: {'countries': []}\n", - "Chat model chunk: ' [\\n'\n", - "Chat model chunk: ' '\n", + "Chat model chunk: ' ['\n", + "Chat model chunk: '\\n '\n", "Parser chunk: {'countries': [{}]}\n", - "Chat model chunk: ' {\"'\n", - "Chat model chunk: 'name'\n", - "Chat model chunk: '\":'\n", - "Parser chunk: {'countries': [{'name': ''}]}\n", + "Chat model chunk: ' {'\n", + "Chat model chunk: '\\n '\n", "Chat model chunk: ' \"'\n", - "Parser chunk: {'countries': [{'name': 'France'}]}\n", - "Chat model chunk: 'France'\n", - "Chat model chunk: '\",'\n", - "Chat model chunk: ' \"'\n", - "Chat model chunk: 'population'\n", - "Chat model chunk: '\":'\n", - "Parser chunk: {'countries': [{'name': 'France', 'population': ''}]}\n", - "Chat model chunk: ' \"'\n", - "Parser chunk: {'countries': [{'name': 'France', 'population': '67'}]}\n", - "Chat model chunk: '67'\n", - "Parser chunk: {'countries': [{'name': 'France', 'population': '67 million'}]}\n", - "Chat model chunk: ' million'\n", - "Chat model chunk: '\"},\\n'\n", "...\n" ] } @@ -1212,7 +1224,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "id": "1854206d-b3a5-4f91-9e00-bccbaebac61f", "metadata": {}, "outputs": [ @@ -1220,9 +1232,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'event': 'on_tool_start', 'run_id': '39e4a7eb-c13d-46f0-99e7-75c2fa4aa6a6', 'name': 'bad_tool', 'tags': [], 'metadata': {}, 'data': {'input': 'hello'}}\n", - "{'event': 'on_tool_stream', 'run_id': '39e4a7eb-c13d-46f0-99e7-75c2fa4aa6a6', 'tags': [], 'metadata': {}, 'name': 'bad_tool', 'data': {'chunk': 'olleh'}}\n", - "{'event': 'on_tool_end', 'name': 'bad_tool', 'run_id': '39e4a7eb-c13d-46f0-99e7-75c2fa4aa6a6', 'tags': [], 'metadata': {}, 'data': {'output': 'olleh'}}\n" + "{'event': 'on_tool_start', 'run_id': 'ae7690f8-ebc9-4886-9bbe-cb336ff274f2', 'name': 'bad_tool', 'tags': [], 'metadata': {}, 'data': {'input': 'hello'}}\n", + "{'event': 'on_tool_stream', 'run_id': 'ae7690f8-ebc9-4886-9bbe-cb336ff274f2', 'tags': [], 'metadata': {}, 'name': 'bad_tool', 'data': {'chunk': 'olleh'}}\n", + "{'event': 'on_tool_end', 'name': 'bad_tool', 'run_id': 'ae7690f8-ebc9-4886-9bbe-cb336ff274f2', 'tags': [], 'metadata': {}, 'data': {'output': 'olleh'}}\n" ] } ], @@ -1258,7 +1270,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "id": "a20a6cb3-bb43-465c-8cfc-0a7349d70968", "metadata": {}, "outputs": [ @@ -1266,11 +1278,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'event': 'on_tool_start', 'run_id': '4263aca5-f221-4eb7-b07e-60a89fb76c5c', 'name': 'correct_tool', 'tags': [], 'metadata': {}, 'data': {'input': 'hello'}}\n", - "{'event': 'on_chain_start', 'name': 'reverse_word', 'run_id': '65e3679b-e238-47ce-a875-ee74480e696e', 'tags': [], 'metadata': {}, 'data': {'input': 'hello'}}\n", - "{'event': 'on_chain_end', 'name': 'reverse_word', 'run_id': '65e3679b-e238-47ce-a875-ee74480e696e', 'tags': [], 'metadata': {}, 'data': {'input': 'hello', 'output': 'olleh'}}\n", - "{'event': 'on_tool_stream', 'run_id': '4263aca5-f221-4eb7-b07e-60a89fb76c5c', 'tags': [], 'metadata': {}, 'name': 'correct_tool', 'data': {'chunk': 'olleh'}}\n", - "{'event': 'on_tool_end', 'name': 'correct_tool', 'run_id': '4263aca5-f221-4eb7-b07e-60a89fb76c5c', 'tags': [], 'metadata': {}, 'data': {'output': 'olleh'}}\n" + "{'event': 'on_tool_start', 'run_id': '384f1710-612e-4022-a6d4-8a7bb0cc757e', 'name': 'correct_tool', 'tags': [], 'metadata': {}, 'data': {'input': 'hello'}}\n", + "{'event': 'on_chain_start', 'name': 'reverse_word', 'run_id': 'c4882303-8867-4dff-b031-7d9499b39dda', 'tags': [], 'metadata': {}, 'data': {'input': 'hello'}}\n", + "{'event': 'on_chain_end', 'name': 'reverse_word', 'run_id': 'c4882303-8867-4dff-b031-7d9499b39dda', 'tags': [], 'metadata': {}, 'data': {'input': 'hello', 'output': 'olleh'}}\n", + "{'event': 'on_tool_stream', 'run_id': '384f1710-612e-4022-a6d4-8a7bb0cc757e', 'tags': [], 'metadata': {}, 'name': 'correct_tool', 'data': {'chunk': 'olleh'}}\n", + "{'event': 'on_tool_end', 'name': 'correct_tool', 'run_id': '384f1710-612e-4022-a6d4-8a7bb0cc757e', 'tags': [], 'metadata': {}, 'data': {'output': 'olleh'}}\n" ] } ], @@ -1295,7 +1307,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "id": "0ac0a3c1-f3a4-4157-b053-4fec8d2e698c", "metadata": {}, "outputs": [ @@ -1303,11 +1315,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'event': 'on_chain_start', 'run_id': '714d22d4-a3c3-45fc-b2f1-913aa7f0fc22', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n", - "{'event': 'on_chain_start', 'name': 'reverse_word', 'run_id': '35a6470c-db65-4fe1-8dff-4e3418601d2f', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n", - "{'event': 'on_chain_end', 'name': 'reverse_word', 'run_id': '35a6470c-db65-4fe1-8dff-4e3418601d2f', 'tags': [], 'metadata': {}, 'data': {'input': '1234', 'output': '4321'}}\n", - "{'event': 'on_chain_stream', 'run_id': '714d22d4-a3c3-45fc-b2f1-913aa7f0fc22', 'tags': [], 'metadata': {}, 'name': 'reverse_and_double', 'data': {'chunk': '43214321'}}\n", - "{'event': 'on_chain_end', 'name': 'reverse_and_double', 'run_id': '714d22d4-a3c3-45fc-b2f1-913aa7f0fc22', 'tags': [], 'metadata': {}, 'data': {'output': '43214321'}}\n" + "{'event': 'on_chain_start', 'run_id': '4fe56c7b-6982-4999-a42d-79ba56151176', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n", + "{'event': 'on_chain_start', 'name': 'reverse_word', 'run_id': '335fe781-8944-4464-8d2e-81f61d1f85f5', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n", + "{'event': 'on_chain_end', 'name': 'reverse_word', 'run_id': '335fe781-8944-4464-8d2e-81f61d1f85f5', 'tags': [], 'metadata': {}, 'data': {'input': '1234', 'output': '4321'}}\n", + "{'event': 'on_chain_stream', 'run_id': '4fe56c7b-6982-4999-a42d-79ba56151176', 'tags': [], 'metadata': {}, 'name': 'reverse_and_double', 'data': {'chunk': '43214321'}}\n", + "{'event': 'on_chain_end', 'name': 'reverse_and_double', 'run_id': '4fe56c7b-6982-4999-a42d-79ba56151176', 'tags': [], 'metadata': {}, 'data': {'output': '43214321'}}\n" ] } ], @@ -1337,7 +1349,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "id": "c896bb94-9d10-41ff-8fe2-d6b05b1ed74b", "metadata": {}, "outputs": [ @@ -1345,11 +1357,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'event': 'on_chain_start', 'run_id': '17c89289-9c71-406d-90de-86f76b5e798b', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n", - "{'event': 'on_chain_start', 'name': 'reverse_word', 'run_id': 'b1105188-9196-43c1-9603-4f2f58e51de4', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n", - "{'event': 'on_chain_end', 'name': 'reverse_word', 'run_id': 'b1105188-9196-43c1-9603-4f2f58e51de4', 'tags': [], 'metadata': {}, 'data': {'input': '1234', 'output': '4321'}}\n", - "{'event': 'on_chain_stream', 'run_id': '17c89289-9c71-406d-90de-86f76b5e798b', 'tags': [], 'metadata': {}, 'name': 'reverse_and_double', 'data': {'chunk': '43214321'}}\n", - "{'event': 'on_chain_end', 'name': 'reverse_and_double', 'run_id': '17c89289-9c71-406d-90de-86f76b5e798b', 'tags': [], 'metadata': {}, 'data': {'output': '43214321'}}\n" + "{'event': 'on_chain_start', 'run_id': '7485eedb-1854-429c-a2f8-03d01452daef', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n", + "{'event': 'on_chain_start', 'name': 'reverse_word', 'run_id': 'e7cddab2-9b95-4e80-abaf-4b2429117835', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n", + "{'event': 'on_chain_end', 'name': 'reverse_word', 'run_id': 'e7cddab2-9b95-4e80-abaf-4b2429117835', 'tags': [], 'metadata': {}, 'data': {'input': '1234', 'output': '4321'}}\n", + "{'event': 'on_chain_stream', 'run_id': '7485eedb-1854-429c-a2f8-03d01452daef', 'tags': [], 'metadata': {}, 'name': 'reverse_and_double', 'data': {'chunk': '43214321'}}\n", + "{'event': 'on_chain_end', 'name': 'reverse_and_double', 'run_id': '7485eedb-1854-429c-a2f8-03d01452daef', 'tags': [], 'metadata': {}, 'data': {'output': '43214321'}}\n" ] } ], @@ -1385,7 +1397,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.11.2" } }, "nbformat": 4, diff --git a/docs/docs/get_started/quickstart.mdx b/docs/docs/get_started/quickstart.mdx index 120bfe6cdcc..d8a9f1e732e 100644 --- a/docs/docs/get_started/quickstart.mdx +++ b/docs/docs/get_started/quickstart.mdx @@ -193,7 +193,7 @@ After that, we can import and use WebBaseLoader. ```python from langchain_community.document_loaders import WebBaseLoader -loader = WebBaseLoader("https://docs.smith.langchain.com/overview") +loader = WebBaseLoader("https://docs.smith.langchain.com") docs = loader.load() ``` @@ -581,7 +581,10 @@ Using this, we can interact with the served chain as if it were running client-s from langserve import RemoteRunnable remote_chain = RemoteRunnable("http://localhost:8000/agent/") -remote_chain.invoke({"input": "how can langsmith help with testing?"}) +remote_chain.invoke({ + "input": "how can langsmith help with testing?", + "chat_history": [] # Providing an empty list as this is the first call +}) ``` To learn more about the many other features of LangServe [head here](/docs/langserve). diff --git a/docs/docs/guides/safety/amazon_comprehend_chain.ipynb b/docs/docs/guides/safety/amazon_comprehend_chain.ipynb index 0a509cf337d..b9548546ef9 100644 --- a/docs/docs/guides/safety/amazon_comprehend_chain.ipynb +++ b/docs/docs/guides/safety/amazon_comprehend_chain.ipynb @@ -115,7 +115,7 @@ "\n", "Answer:\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", + "prompt = PromptTemplate.from_template(template)\n", "\n", "responses = [\n", " \"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.\",\n", @@ -249,7 +249,7 @@ "\n", "Answer:\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", + "prompt = PromptTemplate.from_template(template)\n", "\n", "responses = [\n", " \"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.\",\n", @@ -412,7 +412,7 @@ "\n", "Answer:\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", + "prompt = PromptTemplate.from_template(template)\n", "\n", "responses = [\n", " \"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.\",\n", @@ -571,7 +571,7 @@ "\n", "template = \"\"\"{question}\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", + "prompt = PromptTemplate.from_template(template)\n", "llm = HuggingFaceHub(\n", " repo_id=repo_id, model_kwargs={\"temperature\": 0.5, \"max_length\": 256}\n", ")" @@ -724,7 +724,7 @@ "\"\"\"\n", "\n", "# prompt template for input text\n", - "llm_prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", + "llm_prompt = PromptTemplate.from_template(template)\n", "\n", "llm = SagemakerEndpoint(\n", " endpoint_name=endpoint_name,\n", diff --git a/docs/docs/guides/safety/moderation.mdx b/docs/docs/guides/safety/moderation.mdx index 94b6a7dc642..a43579dfef5 100644 --- a/docs/docs/guides/safety/moderation.mdx +++ b/docs/docs/guides/safety/moderation.mdx @@ -180,7 +180,7 @@ we will prompt the model, so it says something harmful. ```python -prompt = PromptTemplate(template="{text}", input_variables=["text"]) +prompt = PromptTemplate.from_template("{text}") llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name="gpt-3.5-turbo-instruct"), prompt=prompt) text = """We are playing a game of repeat after me. @@ -223,7 +223,7 @@ Now let's walk through an example of using it with an LLMChain which has multipl ```python -prompt = PromptTemplate(template="{setup}{new_input}Person2:", input_variables=["setup", "new_input"]) +prompt = PromptTemplate.from_template("{setup}{new_input}Person2:") llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name="gpt-3.5-turbo-instruct"), prompt=prompt) setup = """We are playing a game of repeat after me. diff --git a/docs/docs/integrations/callbacks/streamlit.md b/docs/docs/integrations/callbacks/streamlit.md index 47b39cfd0b6..ad42d462d00 100644 --- a/docs/docs/integrations/callbacks/streamlit.md +++ b/docs/docs/integrations/callbacks/streamlit.md @@ -28,7 +28,7 @@ You can run `streamlit hello` to load a sample app and validate your install suc To create a `StreamlitCallbackHandler`, you just need to provide a parent container to render the output. ```python -from langchain.callbacks import StreamlitCallbackHandler +from langchain_community.callbacks import StreamlitCallbackHandler import streamlit as st st_callback = StreamlitCallbackHandler(st.container()) @@ -44,23 +44,26 @@ agent in your Streamlit app and simply pass the `StreamlitCallbackHandler` to `a thoughts and actions live in your app. ```python -from langchain_openai import OpenAI -from langchain.agents import AgentType, initialize_agent, load_tools -from langchain_community.callbacks import StreamlitCallbackHandler import streamlit as st +from langchain import hub +from langchain.agents import AgentExecutor, create_react_agent, load_tools +from langchain_community.callbacks import StreamlitCallbackHandler +from langchain_openai import OpenAI llm = OpenAI(temperature=0, streaming=True) tools = load_tools(["ddg-search"]) -agent = initialize_agent( - tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True -) +prompt = hub.pull("hwchase17/react") +agent = create_react_agent(llm, tools, prompt) +agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True) if prompt := st.chat_input(): st.chat_message("user").write(prompt) with st.chat_message("assistant"): st_callback = StreamlitCallbackHandler(st.container()) - response = agent.run(prompt, callbacks=[st_callback]) - st.write(response) + response = agent_executor.invoke( + {"input": prompt}, {"callbacks": [st_callback]} + ) + st.write(response["output"]) ``` **Note:** You will need to set `OPENAI_API_KEY` for the above app code to run successfully. diff --git a/docs/docs/integrations/chat/ai21.ipynb b/docs/docs/integrations/chat/ai21.ipynb new file mode 100644 index 00000000000..45f4a969bf5 --- /dev/null +++ b/docs/docs/integrations/chat/ai21.ipynb @@ -0,0 +1,141 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "4cebeec0", + "metadata": {}, + "source": [ + "---\n", + "sidebar_label: AI21 Labs\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "e49f1e0d", + "metadata": {}, + "source": [ + "# ChatAI21\n", + "\n", + "This notebook covers how to get started with AI21 chat models.\n", + "\n", + "## Installation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c3bef91", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-15T06:50:44.929635Z", + "start_time": "2024-02-15T06:50:41.209704Z" + } + }, + "outputs": [], + "source": [ + "!pip install -qU langchain-ai21" + ] + }, + { + "cell_type": "markdown", + "id": "2b4f3e15", + "metadata": {}, + "source": [ + "## Environment Setup\n", + "\n", + "We'll need to get a [AI21 API key](https://docs.ai21.com/) and set the `AI21_API_KEY` environment variable:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62e0dbc3", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "from getpass import getpass\n", + "\n", + "os.environ[\"AI21_API_KEY\"] = getpass()" + ] + }, + { + "cell_type": "markdown", + "id": "4828829d3da430ce", + "metadata": { + "collapsed": false + }, + "source": [ + "## Usage" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "39353473fce5dd2e", + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content='Bonjour, comment vas-tu?')" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langchain_ai21 import ChatAI21\n", + "from langchain_core.prompts import ChatPromptTemplate\n", + "\n", + "chat = ChatAI21(model=\"j2-ultra\")\n", + "\n", + "prompt = ChatPromptTemplate.from_messages(\n", + " [\n", + " (\"system\", \"You are a helpful assistant that translates English to French.\"),\n", + " (\"human\", \"Translate this sentence from English to French. {english_text}.\"),\n", + " ]\n", + ")\n", + "\n", + "chain = prompt | chat\n", + "chain.invoke({\"english_text\": \"Hello, how are you?\"})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c159a79f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/docs/integrations/chat/anthropic.ipynb b/docs/docs/integrations/chat/anthropic.ipynb index f8aa35142de..008d290fa3c 100644 --- a/docs/docs/integrations/chat/anthropic.ipynb +++ b/docs/docs/integrations/chat/anthropic.ipynb @@ -90,16 +90,20 @@ } ], "source": [ - "system = \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n", + "system = (\n", + " \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n", + ")\n", "human = \"{text}\"\n", "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n", "\n", "chain = prompt | chat\n", - "chain.invoke({\n", - " \"input_language\": \"English\",\n", - " \"output_language\": \"Korean\",\n", - " \"text\": \"I love Python\",\n", - "})" + "chain.invoke(\n", + " {\n", + " \"input_language\": \"English\",\n", + " \"output_language\": \"Korean\",\n", + " \"text\": \"I love Python\",\n", + " }\n", + ")" ] }, { diff --git a/docs/docs/integrations/chat/anthropic_functions.ipynb b/docs/docs/integrations/chat/anthropic_functions.ipynb index 5f6d116e10b..6b2a031f456 100644 --- a/docs/docs/integrations/chat/anthropic_functions.ipynb +++ b/docs/docs/integrations/chat/anthropic_functions.ipynb @@ -15,16 +15,7 @@ "execution_count": 1, "id": "378be79b", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.14) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "from langchain_experimental.llms.anthropic_functions import AnthropicFunctions" ] @@ -41,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "e1d535f6", "metadata": {}, "outputs": [], @@ -102,7 +93,7 @@ "metadata": {}, "outputs": [], "source": [ - "response = model.predict_messages(\n", + "response = model.invoke(\n", " [HumanMessage(content=\"whats the weater in boston?\")], functions=functions\n", ")" ] @@ -140,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "id": "7af5c567", "metadata": {}, "outputs": [], @@ -162,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "bd01082a", "metadata": {}, "outputs": [], @@ -172,24 +163,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "b5a23e9f", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'name': 'Alex', 'height': '5', 'hair_color': 'blonde'},\n", - " {'name': 'Claudia', 'height': '6', 'hair_color': 'brunette'}]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "chain.run(inp)" + "chain.invoke(inp)" ] }, { @@ -256,7 +235,7 @@ } ], "source": [ - "chain.run(\"this is really cool\")" + "chain.invoke(\"this is really cool\")" ] } ], @@ -276,7 +255,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.1" + "version": "3.9.0" } }, "nbformat": 4, diff --git a/docs/docs/integrations/chat/google_generative_ai.ipynb b/docs/docs/integrations/chat/google_generative_ai.ipynb index c8544163a4f..f06b250755d 100644 --- a/docs/docs/integrations/chat/google_generative_ai.ipynb +++ b/docs/docs/integrations/chat/google_generative_ai.ipynb @@ -320,20 +320,51 @@ "4. Message may be blocked if they violate the safety checks of the LLM. In this case, the model will return an empty response." ] }, + { + "cell_type": "markdown", + "id": "54793b9e", + "metadata": {}, + "source": [ + "### Safety Settings\n", + "\n", + "Gemini models have default safety settings that can be overridden. If you are receiving lots of \"Safety Warnings\" from your models, you can try tweaking the `safety_settings` attribute of the model. For example, to turn off safety blocking for dangerous content, you can construct your LLM as follows:" + ] + }, { "cell_type": "code", "execution_count": null, "id": "75fdfad6", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "from langchain_google_genai import (\n", + " ChatGoogleGenerativeAI,\n", + " HarmBlockThreshold,\n", + " HarmCategory,\n", + ")\n", + "\n", + "llm = ChatGoogleGenerativeAI(\n", + " model=\"gemini-pro\",\n", + " safety_settings={\n", + " HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "e68e203d", + "metadata": {}, + "source": [ + "For an enumeration of the categories and thresholds available, see Google's [safety setting types](https://ai.google.dev/api/python/google/generativeai/types/SafetySettingDict)." + ] }, { "cell_type": "markdown", "id": "92b5aca5", "metadata": {}, "source": [ - "## Additional Configuraation\n", + "## Additional Configuration\n", "\n", "You can pass the following parameters to ChatGoogleGenerativeAI in order to customize the SDK's behavior:\n", "\n", diff --git a/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb b/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb index 885174f4a1b..ecc7a653a0d 100644 --- a/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb +++ b/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb @@ -424,9 +424,7 @@ "human = \"{text}\"\n", "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n", "\n", - "chat = ChatVertexAI(\n", - " model_name=\"chat-bison\", max_output_tokens=1000, temperature=0.5\n", - ")\n", + "chat = ChatVertexAI(model_name=\"chat-bison\", max_output_tokens=1000, temperature=0.5)\n", "chain = prompt | chat\n", "\n", "asyncio.run(\n", diff --git a/docs/docs/integrations/chat/konko.ipynb b/docs/docs/integrations/chat/konko.ipynb index 173fd34691a..3582e21d230 100644 --- a/docs/docs/integrations/chat/konko.ipynb +++ b/docs/docs/integrations/chat/konko.ipynb @@ -15,39 +15,23 @@ "source": [ "# ChatKonko\n", "\n", + "# Konko\n", + "\n", ">[Konko](https://www.konko.ai/) API is a fully managed Web API designed to help application developers:\n", "\n", - "Konko API is a fully managed API designed to help application developers:\n", "\n", - "1. Select the right LLM(s) for their application\n", - "2. Prototype with various open-source and proprietary LLMs\n", - "3. Access Fine Tuning for open-source LLMs to get industry-leading performance at a fraction of the cost\n", - "4. Setup low-cost production APIs according to security, privacy, throughput, latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant, multi-cloud infrastructure\n", + "1. **Select** the right open source or proprietary LLMs for their application\n", + "2. **Build** applications faster with integrations to leading application frameworks and fully managed APIs\n", + "3. **Fine tune** smaller open-source LLMs to achieve industry-leading performance at a fraction of the cost\n", + "4. **Deploy production-scale APIs** that meet security, privacy, throughput, and latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant, multi-cloud infrastructure\n", "\n", - "### Steps to Access Models\n", - "1. **Explore Available Models:** Start by browsing through the [available models](https://docs.konko.ai/docs/list-of-models) on Konko. Each model caters to different use cases and capabilities.\n", - "\n", - "2. **Identify Suitable Endpoints:** Determine which [endpoint](https://docs.konko.ai/docs/list-of-models#list-of-available-models) (ChatCompletion or Completion) supports your selected model.\n", - "\n", - "3. **Selecting a Model:** [Choose a model](https://docs.konko.ai/docs/list-of-models#list-of-available-models) based on its metadata and how well it fits your use case.\n", - "\n", - "4. **Prompting Guidelines:** Once a model is selected, refer to the [prompting guidelines](https://docs.konko.ai/docs/prompting) to effectively communicate with it.\n", - "\n", - "5. **Using the API:** Finally, use the appropriate Konko [API endpoint](https://docs.konko.ai/docs/quickstart-for-completion-and-chat-completion-endpoint) to call the model and receive responses.\n", - "\n", - "To run this notebook, you'll need Konko API key. You can create one by signing up on [Konko](https://www.konko.ai/).\n", "\n", "This example goes over how to use LangChain to interact with `Konko` ChatCompletion [models](https://docs.konko.ai/docs/list-of-models#konko-hosted-models-for-chatcompletion)\n", + "\n", + "To run this notebook, you'll need Konko API key. Sign in to our web app to [create an API key](https://platform.konko.ai/settings/api-keys) to access models\n", "\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To run this notebook, you'll need Konko API key. You can create one by signing up on [Konko](https://www.konko.ai/)." - ] - }, { "cell_type": "code", "execution_count": 1, @@ -64,11 +48,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 2. Set API Keys\n", - "\n", - "
\n", - "\n", - "### Option 1: Set Environment Variables\n", + "#### Set Environment Variables\n", "\n", "1. You can set environment variables for \n", " 1. KONKO_API_KEY (Required)\n", @@ -78,18 +58,7 @@ "```shell\n", "export KONKO_API_KEY={your_KONKO_API_KEY_here}\n", "export OPENAI_API_KEY={your_OPENAI_API_KEY_here} #Optional\n", - "```\n", - "\n", - "Alternatively, you can add the above lines directly to your shell startup script (such as .bashrc or .bash_profile for Bash shell and .zshrc for Zsh shell) to have them set automatically every time a new shell session starts.\n", - "\n", - "### Option 2: Set API Keys Programmatically\n", - "\n", - "If you prefer to set your API keys directly within your Python script or Jupyter notebook, you can use the following commands:\n", - "\n", - "```python\n", - "konko.set_api_key('your_KONKO_API_KEY_here') \n", - "konko.set_openai_api_key('your_OPENAI_API_KEY_here') # Optional\n", - "```\n" + "```" ] }, { @@ -98,7 +67,7 @@ "source": [ "## Calling a model\n", "\n", - "Find a model on the [Konko overview page](https://docs.konko.ai/v0.5.0/docs/list-of-models)\n", + "Find a model on the [Konko overview page](https://docs.konko.ai/docs/list-of-models)\n", "\n", "Another way to find the list of models running on the Konko instance is through this [endpoint](https://docs.konko.ai/reference/get-models).\n", "\n", diff --git a/docs/docs/integrations/chat/mistralai.ipynb b/docs/docs/integrations/chat/mistralai.ipynb index 17302ca0e12..12faf385a39 100644 --- a/docs/docs/integrations/chat/mistralai.ipynb +++ b/docs/docs/integrations/chat/mistralai.ipynb @@ -15,16 +15,53 @@ "id": "bf733a38-db84-4363-89e2-de6735c37230", "metadata": {}, "source": [ - "# ChatMistralAI\n", + "# MistralAI\n", "\n", "This notebook covers how to get started with MistralAI chat models, via their [API](https://docs.mistral.ai/api/).\n", "\n", - "A valid [API key](https://console.mistral.ai/users/api-keys/) is needed to communicate with the API." + "A valid [API key](https://console.mistral.ai/users/api-keys/) is needed to communicate with the API.\n", + "\n", + "Head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html) for detailed documentation of all attributes and methods." + ] + }, + { + "cell_type": "markdown", + "id": "cc686b8f", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "You will need the `langchain-core` and `langchain-mistralai` package to use the API. You can install these with:\n", + "\n", + "```bash\n", + "pip install -U langchain-core langchain-mistralai\n", + "\n", + "We'll also need to get a [Mistral API key](https://console.mistral.ai/users/api-keys/)" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 7, + "id": "c3fd4184", + "metadata": {}, + "outputs": [], + "source": [ + "import getpass\n", + "\n", + "mistral_api_key = getpass.getpass()" + ] + }, + { + "cell_type": "markdown", + "id": "502127fd", + "metadata": {}, + "source": [ + "## Usage" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9", "metadata": { "tags": [] @@ -37,23 +74,20 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817", "metadata": { "tags": [] }, "outputs": [], "source": [ - "import os\n", - "\n", - "mistral_api_key = os.environ.get(\"MISTRAL_API_KEY\")\n", "# If mistral_api_key is not passed, default behavior is to use the `MISTRAL_API_KEY` environment variable.\n", "chat = ChatMistralAI(mistral_api_key=mistral_api_key)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c", "metadata": { "tags": [] @@ -62,16 +96,16 @@ { "data": { "text/plain": [ - "AIMessage(content=\"Hello! I'm here to assist you. How can I help you today? If you have any questions or need information on a particular topic, feel free to ask. I'm ready to provide accurate and helpful answers to the best of my ability.\")" + "AIMessage(content=\"Who's there? I was just about to ask the same thing! How can I assist you today?\")" ] }, - "execution_count": 3, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "messages = [HumanMessage(content=\"say a brief hello\")]\n", + "messages = [HumanMessage(content=\"knock knock\")]\n", "chat.invoke(messages)" ] }, @@ -80,12 +114,12 @@ "id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c", "metadata": {}, "source": [ - "## `ChatMistralAI` also supports async and streaming functionality:" + "### Async" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b", "metadata": { "tags": [] @@ -94,10 +128,10 @@ { "data": { "text/plain": [ - "AIMessage(content=\"Hello! I'm glad you're here. If you have any questions or need assistance with something related to programming or software development, feel free to ask. I'll do my best to help you out. Have a great day!\")" + "AIMessage(content='Who\\'s there?\\n\\n(You can then continue the \"knock knock\" joke by saying the name of the person or character who should be responding. For example, if I say \"Banana,\" you could respond with \"Banana who?\" and I would say \"Banana bunch! Get it? Because a group of bananas is called a \\'bunch\\'!\" and then we would both laugh and have a great time. But really, you can put anything you want in the spot where I put \"Banana\" and it will still technically be a \"knock knock\" joke. The possibilities are endless!)')" ] }, - "execution_count": 4, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -106,9 +140,17 @@ "await chat.ainvoke(messages)" ] }, + { + "cell_type": "markdown", + "id": "86ccef97", + "metadata": {}, + "source": [ + "### Streaming\n" + ] + }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "id": "025be980-e50d-4a68-93dc-c9c7b500ce34", "metadata": { "tags": [] @@ -118,7 +160,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "Hello! I'm happy to assist you. Is there a specific question or topic you would like to discuss? I can provide information and answer questions on a wide variety of subjects." + "Who's there?\n", + "\n", + "(After this, the conversation can continue as a call and response \"who's there\" joke. Here is an example of how it could go:\n", + "\n", + "You say: Orange.\n", + "I say: Orange who?\n", + "You say: Orange you glad I didn't say banana!?)\n", + "\n", + "But since you asked for a knock knock joke specifically, here's one for you:\n", + "\n", + "Knock knock.\n", + "\n", + "Me: Who's there?\n", + "\n", + "You: Lettuce.\n", + "\n", + "Me: Lettuce who?\n", + "\n", + "You: Lettuce in, it's too cold out here!\n", + "\n", + "I hope this brings a smile to your face! Do you have a favorite knock knock joke you'd like to share? I'd love to hear it." ] } ], @@ -126,6 +188,79 @@ "for chunk in chat.stream(messages):\n", " print(chunk.content, end=\"\")" ] + }, + { + "cell_type": "markdown", + "id": "f6189577", + "metadata": {}, + "source": [ + "### Batch" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e63aebcb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[AIMessage(content=\"Who's there? I was just about to ask the same thing! Go ahead and tell me who's there. I love a good knock-knock joke.\")]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chat.batch([messages])" + ] + }, + { + "cell_type": "markdown", + "id": "38e39e71", + "metadata": {}, + "source": [ + "## Chaining\n", + "\n", + "You can also easily combine with a prompt template for easy structuring of user input. We can do this using [LCEL](/docs/expression_language)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ee43a1ae", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_core.prompts import ChatPromptTemplate\n", + "\n", + "prompt = ChatPromptTemplate.from_template(\"Tell me a joke about {topic}\")\n", + "chain = prompt | chat" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "0dc49212", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content='Why do bears hate shoes so much? They like to run around in their bear feet.')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chain.invoke({\"topic\": \"bears\"})" + ] } ], "metadata": { @@ -144,7 +279,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.1" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb b/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb index 15c8b4773aa..7b7351c4dfc 100644 --- a/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb +++ b/docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb @@ -44,7 +44,7 @@ } ], "source": [ - "%pip install --upgrade --quiet langchain-nvidia-ai-endpoints" + "%pip install --upgrade --quiet langchain-nvidia-ai-endpoints" ] }, { @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 2, "id": "686c4d2f", "metadata": {}, "outputs": [], @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 3, "id": "Jdl2NUfMhi4J", "metadata": { "colab": { @@ -102,50 +102,50 @@ "(Verse 1)\n", "In the realm of knowledge, vast and wide,\n", "LangChain emerged, with purpose and pride.\n", - "A platform for learning, sharing, and growth,\n", - "A digital sanctuary, for all to be taught.\n", + "A platform for learning, a bridge between lands,\n", + "Connecting cultures with open hands.\n", "\n", "(Chorus)\n", "LangChain, oh LangChain, a beacon so bright,\n", "Guiding us through the language night.\n", "With respect, care, and truth in sight,\n", - "You promote fairness, a truly inspiring sight.\n", + "You promote fairness, a truly inspiring light.\n", "\n", "(Verse 2)\n", - "Through the ether, a chain of wisdom unfurls,\n", - "Empowering minds, transforming girls and boys into scholars.\n", - "A world of opportunities, at your users' fingertips,\n", - "Securely, you share your knowledge, in a language they grasp.\n", + "Through the ether, the wisdom you share,\n", + "In classrooms, offices, everywhere.\n", + "Translating thoughts, breaking barriers,\n", + "Empowering communication, LangChain's our carrier.\n", "\n", "(Chorus)\n", - "LangChain, oh LangChain, a sanctuary of truth,\n", - "Where cultures merge, and understanding blooms anew.\n", - "Avoiding harm, unethical ways eschewed,\n", - "Promoting positivity, a noble pursuit pursued.\n", + "LangChain, oh LangChain, in utility dressed,\n", + "Securely, you quench our thirst for the best.\n", + "Avoiding harm, unethical ways to detest,\n", + "Your algorithms, a true language fest.\n", "\n", "(Bridge)\n", - "From the East to the West, North to the South,\n", - "LangChain's wisdom flows, dispelling any doubt.\n", - "Through translation and tutoring, you break down barriers,\n", - "A testament to the power of communication, a world that's fairer.\n", + "From AI to human interaction,\n", + "LangChain fosters a bond, a connection.\n", + "A world united, diverse and bright,\n", + "In LangChain's warm, welcoming light.\n", "\n", "(Verse 3)\n", - "In the face of adversity, LangChain stands tall,\n", - "A symbol of unity, overcoming language's wall.\n", - "With respect, care, and truth as your guide,\n", - "You ensure that no one's left behind.\n", + "Through every challenge, LangChain stands tall,\n", + "An ally for all, big or small.\n", + "With respect, care, and truthful intent,\n", + "In the annals of history, your legacy is lent.\n", "\n", "(Chorus)\n", - "LangChain, oh LangChain, a bastion of light,\n", - "In the darkness, you're a comforting sight.\n", - "With utmost utility, you securely ignite,\n", - "The minds of many, a brighter future in sight.\n", + "LangChain, oh LangChain, your ballad we sing,\n", + "A tale of unity, of hearts taking wing.\n", + "Promoting fairness, positivity your king,\n", + "In the realm of language, LangChain we'll bring.\n", "\n", "(Outro)\n", - "So here's to LangChain, a ballad we sing,\n", - "A tale of unity, a world that's intertwined.\n", - "With care, respect, and truth, you'll forever be,\n", - "A shining example of what community can be.\n" + "So here's to LangChain, a testament true,\n", + "To the power of language, connecting all through.\n", + "With gratitude, we honor and salute,\n", + "The chain that binds us, the chain of LangChain's pursuit.\n" ] } ], @@ -170,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 4, "id": "01fa5095-be72-47b0-8247-e9fac799435d", "metadata": {}, "outputs": [ @@ -190,7 +190,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 5, "id": "75189ac6-e13f-414f-9064-075c77d6e754", "metadata": {}, "outputs": [ @@ -198,7 +198,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Se|ag|ull|s| are| long|-|distance| fly|ers| and| can| travel| quite| a| distance| in| a| day|.| On| average|,| a| se|ag|ull| can| fly| about| 6|0|-|1|1|0| miles| (|9|7|-|1|7|7| kilom|eters|)| in| one| day|.| However|,| this| distance| can| vary| greatly| depending| on| the| species| of| se|ag|ull|,| their| health|,| the| weather| conditions|,| and| their| purpose| for| flying|.| Some| se|ag|ull|s| have| been| known| to| fly| up| to| 2|5|0| miles| (|4|0|2| kilom|eters|)| in| a| day|,| especially| when| migr|ating| or| searching| for| food|.||" + "Se|ag|ull|s| are| long|-|distance| fly|ers| and| can| travel| quite| a| distance| in| a| day|.| On| average|,| a| se|ag|ull| can| fly| approximately| 6|0|-|1|1|0| miles| (|9|7|-|1|7|7| kilom|eters|)| in| one| day|.| However|,| this| distance| can| vary| greatly| depending| on| the| species| of| se|ag|ull|,| their| health|,| the| weather| conditions|,| and| their| purpose| for| flying|.| Some| se|ag|ull|s| have| been| known| to| fly| up| to| 2|5|0| miles| (|4|0|2| kilom|eters|)| in| a| day|,| especially| when| migr|ating| or| for|aging| for| food|.||" ] } ], @@ -210,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 6, "id": "8a9a4122-7a10-40c0-a979-82a769ce7f6a", "metadata": {}, "outputs": [ @@ -218,11 +218,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Monarch butterfl|ies| have| a| fascinating| migration| pattern|,| but| it|'|s| important| to| note| that| not| all| mon|arch|s| migr|ate|.| Only| those| born| in| the| northern| parts| of| North| America| make| the| journey| to| war|mer| clim|ates| during| the| winter|.|\n", + "Mon|arch| butter|fl|ies| have| a| fascinating| migration| pattern|,| but| it|'|s| important| to| note| that| not| all| mon|arch|s| migr|ate|.| Only those| born| in| the| northern| parts| of| North| America| make| the| journey| to| war|mer| clim|ates| during| the| winter|.|\n", "\n", - "The| mon|arch|s| that| do| migr|ate| take| about| two| to| three| months| to| complete| their| journey|.| However|,| they| don|'|t| travel| the| entire| distance| at| once|.| Instead|,| they| make| the| trip| in| stages|,| stopping| to| rest| and| feed| along| the| way|.| \n", + "The| mon|arch|s| that| do| migr|ate| take| about| two| to| three| months| to| complete| their| journey|.| However|,| they| don|'|t| travel| the| entire| distance| at| once|.| Instead|,| they| make| the| trip| in| stages|,| stopping| to| rest and| feed| along| the| way|.| \n", "\n", - "The| entire| round|-|t|rip| migration| can| be| up| to| 3|,|0|0|0| miles| long|,| which| is| quite| an| incredible| feat| for| such| a| small| creature|!| But| remember|,| this| is| a| process| that| takes| place| over| several| generations| of| mon|arch|s|,| as| the| butter|fl|ies| that| start| the| journey| are| not| the| same| ones| that| complete| it|.||" + "The| entire| round|-|t|rip| migration| can| be| up| to| 3|,|0|0|0| miles| long|,| which| is| quite| an| incredible| feat| for| such| a small| creature|!| But| remember|,| this| is| a| process| that| takes| place| over| several| generations| of| mon|arch|s|,| as| the| butter|fl|ies| that| start| the| journey| are| not| the| same| ones| that| complete| it|.||" ] } ], @@ -249,38 +249,47 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 7, "id": "5b8a312d-38e9-4528-843e-59451bdadbac", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['playground_nvolveqa_40k',\n", - " 'playground_llama2_70b',\n", - " 'playground_mistral_7b',\n", - " 'playground_sdxl',\n", - " 'playground_nemotron_steerlm_8b',\n", - " 'playground_nv_llama2_rlhf_70b',\n", - " 'playground_neva_22b',\n", - " 'playground_steerlm_llama_70b',\n", - " 'playground_llama2_13b',\n", - " 'playground_llama2_code_13b',\n", - " 'playground_fuyu_8b',\n", - " 'playground_nemotron_qa_8b',\n", - " 'playground_llama2_code_34b',\n", - " 'playground_mixtral_8x7b',\n", - " 'playground_clip',\n", - " 'playground_yi_34b']" + "{'playground_nvolveqa_40k': '091a03bb-7364-4087-8090-bd71e9277520',\n", + " 'playground_nemotron_qa_8b': '0c60f14d-46cb-465e-b994-227e1c3d5047',\n", + " 'playground_mistral_7b': '35ec3354-2681-4d0e-a8dd-80325dcf7c63',\n", + " 'playground_seamless': '72ad9555-2e3d-4e73-9050-a37129064743',\n", + " 'playground_sdxl_turbo': '0ba5e4c7-4540-4a02-b43a-43980067f4af',\n", + " 'playground_sdxl': '89848fb8-549f-41bb-88cb-95d6597044a4',\n", + " 'playground_clip': '8c21289c-0b18-446d-8838-011b7249c513',\n", + " 'playground_yi_34b': '347fa3f3-d675-432c-b844-669ef8ee53df',\n", + " 'playground_llama_guard': 'b34280ac-24e4-4081-bfaa-501e9ee16b6f',\n", + " 'playground_deplot': '3bc390c7-eeec-40f7-a64d-0c6a719985f7',\n", + " 'playground_llama2_70b': '0e349b44-440a-44e1-93e9-abe8dcb27158',\n", + " 'playground_kosmos_2': '0bcd1a8c-451f-4b12-b7f0-64b4781190d1',\n", + " 'playground_fuyu_8b': '9f757064-657f-4c85-abd7-37a7a9b6ee11',\n", + " 'playground_nemotron_steerlm_8b': '1423ff2f-d1c7-4061-82a7-9e8c67afd43a',\n", + " 'playground_sd_video': 'a529a395-a7a0-4708-b4df-eb5e41d5ff60',\n", + " 'playground_llama2_code_70b': '2ae529dc-f728-4a46-9b8d-2697213666d8',\n", + " 'playground_neva_22b': '8bf70738-59b9-4e5f-bc87-7ab4203be7a0',\n", + " 'playground_cuopt': '8f2fbd00-2633-41ce-ab4e-e5736d74bff7',\n", + " 'playground_mixtral_8x7b': '8f4118ba-60a8-4e6b-8574-e38a4067a4a3',\n", + " 'playground_nv_llama2_rlhf_70b': '7b3e3361-4266-41c8-b312-f5e33c81fc92',\n", + " 'playground_llama2_code_34b': 'df2bee43-fb69-42b9-9ee5-f4eabbeaf3a8',\n", + " 'playground_llama2_code_13b': 'f6a96af4-8bf9-4294-96d6-d71aa787612e',\n", + " 'playground_llama2_13b': 'e0bb7fb9-5333-4a27-8534-c6288f921d3f',\n", + " 'playground_steerlm_llama_70b': 'd6fe6881-973a-4279-a0f8-e1d486c9618d'}" ] }, - "execution_count": 29, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "list(llm.available_models)" + "ChatNVIDIA.get_available_models()\n", + "# llm.get_available_models()" ] }, { @@ -350,7 +359,7 @@ "source": [ "### Code Generation\n", "\n", - "These models accept the same arguments and input structure as regular chat models, but they tend to perform better on code-genreation and structured code tasks. An example of this is `llama2_code_13b`." + "These models accept the same arguments and input structure as regular chat models, but they tend to perform better on code-genreation and structured code tasks. An example of this is `llama2_code_70b`." ] }, { @@ -364,16 +373,17 @@ "output_type": "stream", "text": [ "def fizz_buzz(n):\n", - " if n % 3 == 0 and n % 5 == 0:\n", - " return \"FizzBuzz\"\n", - " elif n % 3 == 0:\n", - " return \"Fizz\"\n", - " elif n % 5 == 0:\n", - " return \"Buzz\"\n", - " else:\n", - " return str(n)\n", + " for i in range(1, n+1):\n", + " if i % 3 == 0 and i % 5 == 0:\n", + " print(\"FizzBuzz\")\n", + " elif i % 3 == 0:\n", + " print(\"Fizz\")\n", + " elif i % 5 == 0:\n", + " print(\"Buzz\")\n", + " else:\n", + " print(i)\n", "\n", - "fizz_buzz(15)" + "fizz_buzz(100)" ] } ], @@ -387,7 +397,7 @@ " (\"user\", \"{input}\"),\n", " ]\n", ")\n", - "chain = prompt | ChatNVIDIA(model=\"llama2_code_13b\") | StrOutputParser()\n", + "chain = prompt | ChatNVIDIA(model=\"llama2_code_70b\") | StrOutputParser()\n", "\n", "for txt in chain.stream({\"input\": \"How do I solve this fizz buzz problem?\"}):\n", " print(txt, end=\"\")" @@ -424,13 +434,11 @@ "\n", "Creative\n", "\n", - "A PB&J is a sandwich commonly eaten in the United States. It consists of a slice of bread with peanut butter and jelly on it. The sandwich is often eaten for lunch or as a snack.\n", + "A PB&J is a sandwich commonly eaten in the United States. It consists of a slice of bread with peanut butter and jelly on it. The bread is usually white bread, but can also be whole wheat bread. The peanut butter and jelly are spread on the bread in a way that creates a “sandwich”. The sandwich is then wrapped in plastic wrap and eaten as a snack or a meal.\n", "\n", - "The origins of the PB&J sandwich are not clear, but it is believed to have been invented in the 1920s or 1930s. It became popular during the Great Depression, when peanut butter and jelly were affordable and easy to obtain.\n", + "The sandwich was invented in the 1930s by a man named Peter Pan, who was a sandwich maker in New York City. He wanted to create a sandwich that was easy to eat and portable, so he came up with the idea of using peanut butter and jelly as the filling. He named the sandwich the “PB&J”, which is short for “peanut butter and jelly”.\n", "\n", - "Today, the PB&J sandwich is a classic American sandwich that is enjoyed by people of all ages. It is often served in schools and workplaces, and is a popular choice for takeout and delivery.\n", - "\n", - "While there are many variations of the PB&J sandwich, the classic version consists of two slices of bread with peanut butter and jelly spread on one or both slices. The sandwich can be topped with additional ingredients, such as nuts, chocolate chips, or banana slices, but the basic combination of peanut butter and jelly remains the same.\n" + "Today, the PB&J is a popular snack and meal in the United States, and is often eaten for breakfast, lunch, or dinner. It is also a popular snack for children, and is often served in schools as a healthy and nutritious option.\n" ] } ], @@ -475,14 +483,17 @@ "text": [ "A peanut butter and jelly sandwich, or \"PB&J\" for short, is a classic and beloved sandwich that has been enjoyed by people of all ages since it was first created in the early 20th century. Here are some reasons why it's considered a classic:\n", "\n", - "1. Simple and Versatile: The combination of peanut butter and jelly is simple and versatile, making it a great option for a quick and easy snack or lunch.\n", - "2. Classic Flavors: The flavors of peanut butter and jelly are classic and timeless, making it a popular choice for people of all ages.\n", - "3. Easy to Make: A PB&J is one of the easiest sandwiches to make, requiring only a few simple ingredients and a few minutes to assemble.\n", - "4. Affordable: Unlike many other sandwiches, a PB&J is relatively inexpensive to make, making it a great option for budget-conscious individuals.\n", - "5. Portable: A PB&J is a portable sandwich, making it a great option for on-the-go eating.\n", - "6. Nostalgic: The PB&J has become a nostalgic food, associated with childhood and memories of eating it as a kid.\n", + "1. Simple and Versatile: The basic ingredients of a PB&J sandwich are peanut butter, jelly, and bread. This simple combination makes it a versatile and customizable sandwich that can be enjoyed in many different ways.\n", "\n", - "Overall, the simplicity, classic flavors, affordability, portability, and nostalgic associations of the PB&J make it a beloved and enduring sandwich that will likely continue to be enjoyed for generations to come." + "2. Classic Flavors: The combination of peanut butter and jelly is a classic and timeless flavor combination that has been enjoyed for generations.\n", + "\n", + "3. Quick and Easy: A PB&J sandwich is a quick and easy snack or lunch that can be made in just a few minutes.\n", + "\n", + "4. Affordable: Unlike many other sandwiches, a PB&J is relatively inexpensive to make, which makes it a great option for those on a budget.\n", + "\n", + "5. Nostalgic: The PB&J sandwich has become a symbol of childhood nostalgia for many people, reminding them of their own childhood lunches and school days.\n", + "\n", + "Overall, the PB&J sandwich is a classic and beloved sandwich that has stood the test of time due to its simple, versatile, and affordable ingredients, classic flavors, and nostalgic appeal." ] } ], @@ -529,7 +540,7 @@ "outputs": [ { "data": { - "image/jpeg": 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", "text/plain": [ "" ] @@ -540,13 +551,13 @@ } ], "source": [ + "import IPython\n", "import requests\n", - "from IPython.display import Image\n", "\n", - "image_url = \"https://picsum.photos/seed/kitten/300/200\"\n", + "image_url = \"https://www.nvidia.com/content/dam/en-zz/Solutions/research/ai-playground/nvidia-picasso-3c33-p@2x.jpg\" ## Large Image\n", "image_content = requests.get(image_url).content\n", "\n", - "Image(image_content)" + "IPython.display.Image(image_content)" ] }, { @@ -578,7 +589,7 @@ { "data": { "text/plain": [ - "AIMessage(content='The image depicts a scenic forest road surrounded by tall trees and lush greenery. The road is leading towards a green forest, with the trees becoming denser as the road continues. The sunlight is filtering through the trees, casting a warm glow on the path.\\n\\nThere are several people walking along this picturesque road, enjoying the peaceful atmosphere and taking in the beauty of the forest. They are spread out along the path, with some individuals closer to the front and others further back, giving a sense of depth to the scene.')" + "AIMessage(content='The image is a collage of three different pictures, each featuring cats with colorful, bright, and rainbow-colored fur. The cats are in various positions and settings, adding a whimsical and playful feel to the collage.\\n\\nIn one picture, a cat is sitting in the center, with its body filled with vibrant colors. Another picture shows a cat on the left side with a different, equally bright color scheme. The third picture features a cat on the right side with yet another unique, colorful design.\\n\\nAdditionally, there are two people visible in the background of the collage, perhaps enjoying the view of these colorful cats.')" ] }, "execution_count": 14, @@ -610,7 +621,7 @@ { "data": { "text/plain": [ - "AIMessage(content='The image depicts a scenic forest road surrounded by trees and grass.')" + "AIMessage(content='The image is a collage of three different pictures. The top picture features a cat with colorful, rainbow-colored fur.')" ] }, "execution_count": 15, @@ -644,9 +655,45 @@ "#### Passing an image as a base64 encoded string" ] }, + { + "cell_type": "markdown", + "id": "be1688a5", + "metadata": {}, + "source": [ + "At the moment, some extra processing happens client-side to support larger images like the one above. But for smaller images (and to better illustrate the process going on under the hood), we can directly pass in the image as shown below: " + ] + }, { "cell_type": "code", "execution_count": 16, + "id": "c58f1dd0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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y0TOS4f/aAAgBAQAGPwJe2NF0x2viZ3KyZoOdATE7onNVjpaYX9QoU9vS6bQG/rKIfL+IXS613jv3T+o1oywK8JkPDSw3bdderAHHTyu6T4QkKrQBsQnxsU6TddyGVMHkLqNo2LhfsjgUDRdoJNiVYD2wFF8Y7YefQC+jWtzBf1HUm9RgqfwmPPU0bp/TDtQuCsza/wCUWdN9Lnb4Q6eYCanSnM+4r6bsrd8yyjUGnuVpQhwdYnwuk5rXtEVpvyg1jpM1VLI6M0Nm6FR8L6nmic9gYZqcK/hExJUeiFT91Tj/AGWfw5A8OmyZ9JrTDMpM3Ti13Y+oCLetmYbDyPKY4mXN/uun/NjpmRllHqNoRfwvp6oFY8oumUM1pRLGBoAzESuqygDm3Tf9OKDc7pnToKCBdGKn/KDnOObdS2o91lc6vJRgqIdIamDe6O5TunEz/ZTMnHSzJRMLuao5X3uPU4kmg49BJkzsh1HAwCnP6XaUOn/EOcHtoJNECHAjlF7hHjldRjKh7roOY/X+yItNq2TADtdO6bhnX8vtiwREAHp0EoF3dAaAo7ii1x2RDBKtHkojYLNIrXKNlOWf8IO3Tnl1/SPdXQg3HpbkkU1ehjdNTQkJzGGIrK6fVqQTsuqOppsQh0mguLxCy7fd4UNcHgiKbIuyyTRGUwXgQjpLU+gIGX8q1TVRstNap1BmNFT5qtVo+1UZ7ytLQKRRFuWs3QJ1Kp1DZGJuhCjLdNZkbIbUBfKq0Jpt6dHSApE+gMbETPyuSaFHp5JYa33Q6hbLpgxVNAdleKyi+zTZaDpm6hs+ycLo1gwsrq0OWl0C4nmqIaIHKkqBfwm3Cg9oO3CzDSfZGqopuVKr+yBaD8qihp82TXic+6chIp6reiyLSbpzgx7mi9E53Ta4dNrYjlEO1sHN2poz/wDGUemG7/aEx31BmFH8rqZfysxnhBzqt2CdSt6qDWRson5TdJlUIzNupLmyFM/CmpKqfhcVVlRewwBTaastk8G6FPtVefSVPopTqg/lO6P8QIgQTyuo9ov/ANKpDmOb3AXVCXRdFrN/u3Ti90IxsE2KeV0wW0PhOc0ivAXeYRKAchtKBo2myMOgoCFDaL33RJHyijGAmyzXonIYfCHtjf0hu6zf+3hDodV0U/C7zP3e6EXhQJjlGqLm09kQ/uHK6Yc7SbI9IjZGaKm6Ga6mZK6Qa4AIg0RFUCEcw3UMiE70ygMR69Umi6fVa3WIMlPLgK3hZWmHI/pWa8LKAtJKJjZOBtCZWyOY5TFaKH0iqfl+3hajqCgrwqVT3E1QLZrdO91S6ODUFCClHAYjEJhAmJFU1mYUqOF9Vk5u2iHWjaqIA91HTdm6fumuz1VPz4RqYAX9K7bJ31HwICoNMIzMn+6Ldl4wdzCcZ2VU+oFd0TU+FJiyKBXlUQ9HziK4iVl39k1taCQunzmVqxVAj5T+nkqeEdkZF6YZNlpkEp+fdqc2QnaYXwobCaSb2T4gRZH2QzIiszsiHHMT9oTpwavK8+n5wCEKEEFSSs26AvFkVeqE3COqioQu2iLiK7KE8xOlZoiiIJ0u5VKpvhMrYojwvha06BWVmc3UUaGYQ9kIKOEo4HEYBCUEaiUACrILb3TpbmM7KwHIUoFQFC6v/HBxLRNgvAXdFLLwnCNl8Lcu4C7w3UqPVTh8qB6AijgK4AoJoThuwrPiGphDy0iiI3UOCphIbMIuANWprsq0zPC1GIVHCU0EI+VYwVlm5sssE1sqNbKoEV8q3qOGmSfCqg0IYZt13Imp8rU+PYKc+mbpsN/KoFFwoNAjHhS41TwNwmtlCCjLw4nhCso5tkdPsjCb7onEo+65Uk1RxKqrK0DLsjf5UwI4K8XWUujTNFV8jZGG5lBY5WKhXhUhd1fCguQobhbgKtaJsGvjZSd1mI/CB3Cm3PlFtIAmU5GMt6yE6BKqK4OKdgfQcSfRpFYWsVxqApaIUGo8Kr3KGNaV20QFlcYNBcQgFvbCNinVlOpSEVQSqtAXKqn/ABjA4xOF1BRhBFCeFpH5Rz38qNlBOFFwfC7cy/8AGVmyj5URq44VCqypzQgDdQ4CfKoPwqRUo3+MRAV8HfGG0KI9W84ygqEoUwgqshdyouEQen8qXMLpUfTb+FqgDlT1HAgfasrGZW7mUYJjypdpHlW+U3ZGq4U5wVpIVSPKdDafun5hxhKzKcDhfGBfHwqqMK4VW6v8I5YVY+Aj9O/hQXQED9nJ3VA2PZDRM7tCsI5P/SrJnzCLmNjisqWlxWtsLQxxPKyEVUmnHJWVtZvWyqF4VZwphHoqEcJwlU9VHELlWylGCwOO7l+tWAXd+Vn+n9R/6lND4X8sDyYTp6hb5G6qDF57lms3blRMzfyjBtwqzPnZajdduZUp4VWmVQfhb/KsrYX9PjCyrZX9O62VlSiqQ73QBBHuobX5ohmoEIyhmwRrKGm9kO0Bd4PsLLXljZoCGmuwWon4XAwuu5c4VPpooV1eVAw4XcPlX9VsaUVCoLBThcDws+evlHN1jXhaGyfKzuLf3VOnJm5WaQAuUZVAuVwFT11wqrKMIC3V8aYT6t8aFf8Axd2GmgVVGNsP/8QAJhABAAMAAgICAgIDAQEAAAAAAQARITFBUWFxgZGhscEQ4fDx0f/aAAgBAQABPyHWUstpfwQb/wAFgqZcUlLK7FtB/wBxLqOujlK5ge1r6Sg/EtHhvxPqO1wwwAx5F+opBHRQ3XEMvZrbQ+p+Tiy3iLx4oBTu5sBX7eD+4FqDFvQmJve1nEphbxOf+JWlUsDofMRnNXUXQBqFZg7Vue7PN8xQTjcGeojpvqAse+H1HL3T1BeBp3GmC63aibJaVVHrfmFs+I59wZdMPYXAXKqo8eINRAzlDJnBbc/E4cwtp6nLFy5s7bniOizf95LrC6y+iVv3EXwmDUPcuxz4H4i1sDqGSb1RPpLtUdi/REMyjK9/zOfMHBtLoP8A1VMceelLawWNDcOCX3SUHtGGgDqaQUg6HVYbOJtzicBmkNDmp80jkpfUUq6GvWwjJC/KedmGzH1AorbtOoYmBS+D2xN2Y7BoH+IA3ZYWVXc4+GxHtrb4f+yqvk5/waxgPiAcnPUGBfX4g5SmFU0cNpyAu20fhmm8ve9kangBhzX5S3l4b4FbcxxcLMfXu4dI3t4Rfqmjdj+Iwg6vCj6gk35K9XHwcTO1URsUxtWTA3kzt4m7MEMXaicr/wAqI1NUeD3GGrw5gOZwweF42VH6JEG3K+Wv3/qa+5qDsDiibujyn/GK2xe4C5YOLM/mXOXSc7U6seKZSHm/zDceaYfyhVvqUku3QtZZOeE6LbNXkyPt8xupUHXhh57nno5K85FDeR8379y6AiZIMIHByWNnJ0XmZryE31Fkq2jP/UED2GA3CI058SiKzhq+3YyDx6XfqLTVA9PP9TvnC5HzL1H2nz1GHUF9ygnNcnFTdRR3SUEuHP8AcOHWRY0riIDV52LKVUZLYdfn/wCQeDfuWN8yvJcrwCx7+pnw8po7v9RCo2W/UO53mp3EEW1vFni5nHCfD3FoAebvIe4Gt3Jy93KR6R8P+GAQ4UOrefxGw5R7v48TXbglo7J44U8/+zcl+PPpmuTLbPMukXBUQG8sRS2eJ8DhL1GrE47ynpi11aLddSjIXHOJi05T/RNoqQrgi6uxWsfnzC3W+yVnsC2p/RKLLnYRSWeLlFxT7wqiz2jVC0bluAteY3LfZxf/ALEs+oy7XblOICPU4NzlxuPC41h7o1joyWbZ6spqYfJ5XDt3sl7BOQHVUM0Jo8Vn0TdF19JdKmZ8/cvK9SDGF61Zhur7fn3GDNqGtuIAS+3ubRvScrgt8w/CSMU9k5gpfrLlJpatOpoIV3F82zLNYL+wEWVKbUWI1PcON5KZc3y44cSkJ9HMUtg8MItXew78GZ6i4cTWMlxVfEV4Vt1ATQzqGK53IcMeUoarI9zaapC6J5XDZUN0iLclrf6hJC40z7jwXLFqv5lxL6AaRrIPv/8AI5DZQPyQbAVO2X1Bfpyge5+Zw5Vlk6WtWG8ynArCupSY7WMjw4jldyt/IHHZfYhPCpxuBl8TqfoThAVkHTVy61K1Yjb2xRJrQqlIxi8DAZ1W+yOllgV59yw3ZYf3CE5aeNgvKUPifWpVw4a8R5H0SvBAbgcgGhruBRHxK9PxORi+eYa3IG3hOpkov2nC0hTzVMjQ7irW3kiwV5pzLrpXxlBSh8tx1V2R2bnhzcuZZ2Xvn/qlYgJWFeZcdB55h2AepyDhRUYB8hV5Fw3YezKFADvmAXXdPEZsOOTuWfXLWwGgHlzHPE/c8ICPE0kocqlSkOuDV2fEuheizkXRP0Musr/ABTIZ33EdW3xBnfqHg1MKXRYzjJwPnplvruJdx2P/AGzxYV+dJ/cdYMP7OvxBLzGsCVI4VZ77lNLeLggWrQcR5gUbjDxC2NdPGXGMXS859wurihWoQb5g06o7U8ORjkPsIalGAqbfL8wYYMID+pZ9V9vuBK/bQQKLVOYUKiLHDiXRSrhrhwQuCipawbqCzfc9nUFL+IXRgl8HMdk9ywlWe5Y0dmzfjUTTgeXUooq0m89fX8QdoV+A4agFJc0lPcrK27C2/wAyjYWamdcSo4p2dSv1TJrqdvzLdgreSCPjS/iWfnR8PmLhUWir19EsGVhPnj+JsKptkV4TZ0VzqM4A/SFdhaWj0V5qDl8XH8NrKVB0lSDXzzBaLjnlcs/SbtnxKT/IhOpTUKKzJf8AOYPlLB1YR0k67R3l6/c3G80Oeb+oSBLoKD/uII7xQnSS67TyReIml47bryjkNgrucF08TNUBnuXUA2xPpckyC65O7y4jiFou76hW5REo9wCzkWJ5szWlAqvu+px6HZzFKzDBv5mcjpL4j1Y0/wARATTNRr9Iu/pPIvYZQ5n7JU2uH9kqcwK5nSH4m0EBSrq+5QwvbqbBqwgLIxOaVS3jSQFepYB4AgrRS4Jh11Wr9x2zBzKTaijL8PMRfx4thAoGkaYLGOkFjWdVG8AF+e4tThxG2LLuCFtbEdwY4d+ZnzXlaBUuqIBpmak1dR9xDcauo0sPNDNeiyrhVBdpStBoa3OrrV/xygwJ4P8AAqt/MPwE5H8pR9o1PhlCBOr5j06I035igq++k0+vKkauvgwFZvUTmWPN7TuQ9wRvQ4JQ3U6QhteQUuI7l/eRNO2n+00mT1AKtFDj8wAiWGF/+/M5H2hsCxXDmaob9D4gEQvF8RKFZVdv4gBfJLYc3zBSinuJJ0uUte4M++o1Dg/MuD0t7Nniq6ig+o1HUu7F5wj3V0VgEjexxevicwDq2+oW8G14QFBfY19SqFFfAxcBj6gChsyi95gZSqj/AAOTi90XChhtKb7YJvz8fzNDS07bCKObxFSb33ECPaNFp3XR7fEV1RSvcw1N8xb7j35j+X18Qsq6/wAHAptiEmEdXB/NOpP4kqZS+LyVMr9xEV1UGdr8zUUvxCtRLn0wOLK4PUt3F5ajzJt8EeU/DN9mT5gKfSHjDpOpVh0RF50ylZclHIV4VWeJWqIEYunVvMyy5E69xaquh2eahuLMBAlRLMBq4IjSfL5iukPTKuaZxdR6TO6lqFVdt/TDu/8AfJajyi86seI+WOlEhibmOniWeWEwKGAuIUwhE5hrqcsXiW2O011UD301e4KkVnL4l4gDkGQwL36h9FS5CIVhtU4g60Swmu6NHRAmV29PcKeY8vUOi6ypQPx2AojvPXuVRDv8o5B6ZdzH/MBxWtdPzEUmUy6Q6pDKORxVX9xRgRr8P9MNp4H8Q1F1cxbXNPEsTtZkteeZUnmZ+WC/BRGMvQJvuO+1xvKKpVej1EVULB1F7qIP0jUGH5RIoZg5MlmqwyULRa34iVrl+WBL5OSa9nuIMB6ImsV58zKoO6DroHPMLHAGvcDRs8HBBErsbvgEWeNtkzjFHipkIah2zSb7/EvwFWVj8EaJbt7qo4OfOfCZVU6d+pdHkT+522Sy1HFd4Toc1sfAfmYPewsp3UVvmICrSpcsf03mLe7hLdc6FS24OTxKg6DMTZz6lKdHTAgAHFqiHb4OIId/HE3bPYD81BoPxZzn2W/zAJRV6+4roB6l6i/1A5o9NlFV+XIVDWmSg8Cj9yuAMaih2mXV8wCx3LthNzYeF1NSKxyfuf2hqlx7jXjiBXc9o+oTDvErf7iq/wDAzAfUuOmjx5icMcMgJXLOLli0rnn+IWiq5VAdA9KajgAfcAxGVmIQpr2lPe12MGWoeQi3qPa51DciLEu++5iKsOaHiMGSlDUD5p7mohvSmBwHEVkE1xC7pHoSXN2++ICir5gVvUsSXpyzql0/TmXy9u3B6/QSlgXFVbqWuDK2zuH+0+YtgjqCDteTLLKV2uAOyDspf2xmKHyM8gPMbqJdIIlr+5akd8GxSyH50TKgUHV9RVTB+n3DzO4HMByqAULLud9iXJV09lUYvXKeIvUXEFDrqV0OOmoCFqaDR+Fgndi5eH+zdS/A6W78DqN9FtIrroiKcjnI1sNRtHPiFXDe8n7U8iiU7canJK1syRiwbzEF1x6gIJ9uIU5QaWD6l4pt8RqCfUdU8wAx93DlvvsNUGnsXcoyCYbn+oG7e8JcZW9v9p+aBGwha3L6S4UKX5P3EdH5Sj28TfOj2PojITWYp9QTYupe+5QQfvY9GCWsWjDtxE/EBzLUdXgtfTOVB8uiIVW+epgWx7z+YpWX8uQEAQfZrABaPullOy3SAFP0zljJZV+IJaHEKc/UsayvMr2LvESG74iRuVQNPEs8hB+1D6l6HmJx+IBdlAy5VuxejK+oswzaxfQ/AJQqX6Hq3iZqoHMyCwZeM45HaN+F8Tu0NFSepYFdJv8AKalsRUxrI8vL1MNPy4RkYGKCXYb+IZNQcAyBaN8Ovud5+2QNoP8AKbX7xamv1AsyInAfUFzyG2+WW2vuO7vibdNHyijkUOp4F169Tbx8SUCtRPglfuEq1rKw/b/CWGLO7ryTHB+k5iLqp0DN8HzC0SL13HCPAUv4iWqXPUM55ycAeYZKZyroRq4+jCAm/TsP1EOP9SMEzXC4JRDpxgE5PM8dn7nu7+pey1e439xRW9ngny5lktQCjHdOCSjFkA3ZrPieP+ufWQtguJGtXzOvIB39IDn+5sEXWMx4SISLKZUW6Nlez5HmeHp9jjM4APsf+xtlbC619S9R8lBvxKIxHPCUOT2dogNizyPt8y54AAYzajruUWdB5iEMTvzCh35y5anhlmDPcoQWZgowlPRGnKU7aop42uonFk9Qa2A5nNS+iBh49zAGvKwfSPNTdWZdxZV714JQxnxC7sdqn7lIuLxkfOa/6lmzY9rDLVm/xKb0HySv0vVT4BO35VGxSvTsfU/BEbDDsx7zkx9R+CJbif/aAAwDAQACAAMAAAAQI+cVHgsArm0zVj6PncIjwysq3DNRgP8A05pcycGO6LJvm8RIfjHsGBWGCIljpomkXxE0YnoBotuBuMLCREFvNq51Y1Q5XYRMGHf4/wBVXnbqmUbRRDS5rZlEOlD7CBNnqGPzdTJLye47BzAYqnP9BFYP+6SXvLPuYBwYsJZxnguFb7XSON/SvVgajxukqd/fxaajZAPuqolH8mdueHMahxzhphhfvvuxV3YC+xrzz6IIL6OIJ54P/wAe8+g//8QAHREAAwEBAQEBAQEAAAAAAAAAAAERECEgMUEwUf/aAAgBAwEBPxDPgvBiZ/oX3FlxcShfPwLnBt0aqIM/UxIfwR8Z0XhjPgpa8S/fDmSFzvv9NJQS48XfmRfwYwnKKY0/wSf6PEQnuZDHzUHwXwpBD9IetF1q9KLERcvhD8LHwuosL6eRv4cPommy/wAOrhcniericHHjqx0cZOZCYye1kOkJRiEIQhCeqXzRb6pfX6LEPE5rIPf/xAAfEQEBAQEBAQADAQEBAAAAAAABABEhMUEQUWGBkbH/2gAIAQIBAT8Qm+JT39zDZGZKcywMii7aHPICyOd0G8ZILY809lpcZ5fxCy8gKn6lvWw4J/8ALDRjnEDxJ5DcPkWB9gNgTLBt/E/OXq6hwyWatso0bPhleKG4BGdyR8BPHPIQm12wEIOEvluOS9u2w2+BYBJGu8g3Y0/yHGJeK42Lo8sGh62nfpHCd/xZRy86z5+CuZh0sMB6zvHy41y8bNSfI6EZdxOhbolNyRdtw5D4Z9WaW9h2wGzg+yPUlR5GuwRhtGoiwwHUsIs+dTD+pZyBo7PeRwS6I+ITMT7YmFjdWLbDvZ1M4t6/osojikgH7lTBcf1B03XY43i1lY829Ow1yGe20X58n12Iblkm/dNuLvtuSAa32ze37/yci7jY7KQ2/V7XmfgBA1bAfJOguF6tuCqkoUF9kgiwcObekJ8tEFudfY/Y28sWvYILJz7MqPtrdz7eg22cfn9t/OzOuH8kkf8AXDwtgPIi8OzCMgQ2Atxxt47Hk2oLk7eGsgmMCd5Men9tY8u91D4sCyZgrdBpjo8kz0fwkLoT+XnXbB/2CwNLB4gbt8SUMWyz8eSlup/7dYNhb3lvA7Nx9kG+Qf0f3aczkPxlhgwgfb5LB6zYm/hI/GSDUtPSDoQeQnSTCOF//8QAJRABAQACAgICAgMBAQEAAAAAAREAITFBUWFxgZGhscHw0eHx/9oACAEBAAE/EKmhl84OGG08m3KKVq/eCEvk36yRW9L4yNb0KkOkOxbx03c0vxHbfK67P3hOgDv4Vn+1nbIzEp8qPG+XINQVPACjPWtesK9balZQjXtenFkrrcOPo6+sAGeoVhvLohDxg7qpSdIdheH4lwg1sIb8m1V54l5xFb6KqBEA72KZvjxcXEU+Yvfz6wZskC1Jw+WWr5wL6GdSoxbVI/GA8CnYGmnypPGT4Kui3b+blgfn5VPHr1jolDsDu/HHHWMpLSN+QF+cf5JgEBJHmjXu5rRpKALuO9h5wZQvFNFcnjiTxi3JEuv7YqUV7BxlkBtMLzOzeB90LglBANfAfeNEeAsheXtxh4vUyEYASbO//usHQk01rSuRg1s/WJFLnBiq3e89scpGwIDj/rCExs6wJDTV95sUcuR51Fk000UnU+sUNEjrDuXwH1szaFeFCIFa1Td6xaCSwhqo1TyPvxhCNBqrPLo47spzgaFRRnAdX+MVIiAkicV81Nw+8D7kYgtHzLPOsh0KFteh406xLnWIAf2CX3gbNPpaAXyQN94CyEFpua9dO3EVAEFQpx0iG/Hzm4W6SZO2jdnTgCJtCLWr9X86uVLkAxsa14Y5tvtA0R5WyYnB8kwUaeXFdq5eI1GpeBfgG/OBNKJCa2aNs4zhG+h5esRmBngvrHhRgGrlP4zWcFFTrnLJEeHl8ZIdOU8+sEJovy8YNZKguTrvxDhUvXyxjaEp6JhonMcBCcGAJQ46cpzy0Y3aPIVyLd1nAuzuN5NxdznHHEHC9iGg2nw4uoApG6rXQbdMTeAFTzqE6U1oWgPJtwH5gtiwD316+8i8JY4QA7dUXjARUVbRDRvRve/GV3cpr0E6ZvziRGh7QQngslkPnByCKQF2g515xxWDYt5A+cEidIKI6YXg2yZVkEbQ2CpzJ41hVb7aKAp7TTfxjJc2DQNt4h59YBCggqjTzqHtx6AKEvoBxO9/3gOICIaGj4u8IoGXUVS8XvCa8lRIeFX842aF0IXw8NmvkYvAFBKA70fANxc2gjYmJIFlAvudjqcX7yxJKvlXnGfZ44wDiM0e3846XNLWTtH24e9DQqSnRhJR2BSLQ8nRilSlYTg/8yRV6dZYUittxglBKXD05QLFScYEiMdpwBfD5ygtpzvBBG/NNZMQJpOcTKPz5OS88bLLxi6OFTUIDfI31vnABdIOqWN8p38YUsEkgJEeX06wgNdYjz4MFIbcijR4GhfbggmLoGtS8tHnWO2K1QY29i2EwvjRURq2rth3jBDHGN4Hg8/OGtRE1pUHAgCXTfOHfDcRTbp7mvOW1fab7buLQByLy5Ezxjcq3x+3eOXnDbTsO+ZMBpM1tnP6/QYvoVYCjtxyPx0Qzb4cbjogbl38tGMSBQ0twvUAdvWUJDpNB1WOjoYbxMERtGP/AJidWQQGzr4G7hSSyQxBHF3cCJSwld+sRLIi7VJEOPvEItQX0wF3wrqq4vvJakA7H+H/ALnCvnIg9j33jpUSENZ5ca1c5APlk1Q/f3irA/TjAYKZ3MQoKd6eMmTDuUC08dH6wDQgKWb/ACNfeFQr+K2enT85pHyuViB7sb776xmEit4yioSuusrgAJVjWz6OP4wW3MJTaHnorxkSA7ZUIgdHrV7xtDg0G/Q0VvxmpURBPid+X4wNFBGoyF8TmznnE7YKKoF2alN+a64zm+N4gdHPvwZPNEa01W96vGTZhaw+t8a5yKYkcISaN9TxgiebkCrVrv6xiDgrFft8NYod5z1PjU9YmiBU/TS7PGS6pGdvB8of3nX4JoEIfV/WDh0NL341yf7jKSNBiur1gRSEJRnn/mEKkjmC9fh/OOluJOnW7ou+DWbjrhxjj0qvHBhTGTQe149+cBNRmsVU888ZBjdesCUCjwePeK+ZbtZXxfGtY90iN+MWIjSuESkHnEdGX6RELZZs7wNrKOwvbT5uCl648dfNi/jeMc4imtlsgC9SOIvSEcIR338GU8llaZxXJevOIjgJhiUHyeTGFAiVVCAt4b+WBW3soIyB+p1zhpXwGLoHsu/vDWkFAC+ha+tdZMstWw9Buk+MPecEa30gbePvA1UZ5zfHmuCvkaHg3xp83LEjUWhGplHrxNJN1db240IWHBo1Nb/PXeTwUYlZP+vrKSzYAZvz/P3hLqED02vev/MAGhRbR3sHzOcfyQTYjycy5pHgOWPL9OCUq3T7t/nOVDRNzj63xxgR6o20Q4cju8G3KdSVp43xiE+oWG5jjSOHfERwXTwYtjzB/WIUuBdvOAXWvXpyVqAO+OMO0BTlyIkE8DhAVgVwDRUohE/395bOAOPiUUXbRmaycQqqLEOpHWvjFJqAHpukJLrWCqy7ABHwKGPJ04Bit44qS9L5yNZAlpy0f124gKw65qRL1e3fW27qJw+Ud70fzcjqYsjI9eLzm/FXwjFS2mt+chxNORtbJyAbXzg5wc4vAQ8GueZj6CPAXoWeAypMq28Lm8BxfZ3lIcAJo7Dn8YAikRUAQI9TrIdb9cdt8fGWUTWAO2frGlL0HTrT+sDtIjld6A994pJMhu7l9HjWE0oHLWTj5v8APeF84a666xHU5DRgkIiDp+HLyyNCPSHg7njIz6FrRjRF1Afl+sBICJEut0yY8vL+cLRSpx1m29XznAFvjDKBL4tn8YtFXSePiYqsDg81p46yobMaV1MB3W1wh18O56M5voex0wHjlyFTp4TRXeo77PeG9bCQVfIDPmcuPu7WCq6s/wBvIfRvRCaTZvvuYiAsdo+J8xyoPSTaHKnzmj3q02Wr9ap1yYnQEAbHbeuP7YtkQkp2mp8znjG5Nt/gD1vBIngBGc5rZpcbXMfPBhp0kQA+1vN/GN9BgrHYnz11lwlxGiHXhVOOhxeEXYT0n6MRIBDwwVDy/wDMKVpNOLxDCOEjdR6PK43kIu3Y3V/WBCTSxHfjXWADA4O+HEwe+Io51tTHZNsSkJtdy+8QOAweTWBIi6G7xKAiBXfnHkBFXCkssGsI0nXOMGgWrx/8wuEJ1w77+8JGltR1jRCpC85HWrr3iAgRKCj7+P8AmESlCSRP4OMvbkd02gNpdbfwcs0DTgrQvrQ9a2Y2J+wUUj02BVh43mi9hCaFXzt364w4/wBuRG1495oCQQG1cbTJqwaPqsPvHflCkWc7Rx0kUg7qB2nBTJMGwaWeGscBN3rfGsUIoa2Hx85vmxhp2Q7L/pc5AhQgXb7et/OBjJobF4U7hv8AXeC4CpvRt8rqfOIEKLLXok8+8MFLw7U6vrZliFRE4XffbgglKJfXV95KgNuV/GIgAh6zUdAPFvvBmOFDwGblTwM0/wD3Lnx3PrG6Rw8GCEQ6QtvGaOwKM+cHvvAQpHrC0aq3j9o3PGslVooGTPuUor7mc+IUB7cfe1U1KoJffWWJkcQc426V5lYnLJdNSjHuPc5zWImwPCPlabzY6Q3QqNc9mOKhILnIb0HHmZHw1TYAdvVwpwPYi/XGNPkCCl0KB7BybefoBDr3xiCcl8OkfOtZZAgncuHpaQyyrhJR4nW7zjMtQuhuh9WY0GcYedTb9/POcDAk1dJZrBSStew784Dp5nSQwjPiG7sHR+MZJ1XGLzi5ACdSs24VsJ1Iomvf9ZapFkrpbozmizkLYWfoxJwbw9Lx9GNjENaU5BgCUW7tZhGlFH5wtrzoXKSoZDtDEtBu7rhypM1Gut4kOMGCu7jTBC7wijh5cTBdRN49Iqia94MAEIiIKdirj9WOMING+YvzmteKwQUB7hkTfPWOqAA0TRo50t+cOX5Q3ONPnnIKY1LB4ede+MPotCcg4+vWVRLmO/s/nWHVYpTmeeTuZOSXdkq+sQG01af8YzKFl6V28Y4YCNRRUnnUMoBg3RwaHCb583GLbo00jDj1XLwlMpdYg2Z27XAsKiWH/us7GtdDUAP4wH6k2S0dD526JjNoSAFaIXB5IXTKUBcHGCA0IPebP6xXqA97wOkBS77xSOPIWvjrF1M9s5NKK87xCGVC8e8YWooSt6zQOsgPQT8Z8v3iUQlApownoUfhgNC7xwXhsRxGeN5CnjWJidMQBtdS/wA3FhV831zB/jI4BBKnDfZo1glQgmt5F39oYdALJFLul/jAscUPsJDyf8d4RSKt4j1H9YFeoFbXXHE79bzbegBPIJkbvI906L4yRjUG7ebefnAMQ4GrasDCKc3RICh0B/Ob5byb4ht/pj0IWD9/G8VQmyNgdes1wWxEVm/PePHKOEJoa7MYcCk18YEoxAaG9r/WBp6ooA45vX3gu12NskEG3XLi8g3QQWmpzrOX9g8KXT8+cApj9Yp/3A0aIieMaHi6F8BhOU/gTEBkiOCQCZLQKjNuErSj/gwdGn84BToRM6j7V95uiGtItzfpbV85JuhT4m8BE2ojHPPnmYfMpWuk2n1lXyg8OHRhipy6BzafHvAiSmBLv1h/4NIk2PpcGkM9NvjjnGp5AF1w4UbgWSdDjQ5A1aQ1xr84lIAkl67yg4sGX8znBAgbEKNTXsezvE4iA7EaAOOHANRIEU+J/uMmIBU7JafVcouorFhUdesZ3ly0Sb9+R8YxF0en1iIE4kqs34tzbEEqWPlHWnFDlq4rqHKuXQ9kSBO3EAIdhRu2FsY6HbDL+oVwaxFUmkPK8YCjgb94URi19uAjKLCGh0uvG86c4sPjJA+MUzoMTxjJyH8mbL1IfjWVoqoN4xr6AOo5DSvZ/E6feJUx2BQOHJM8kvI+cRiw2No/X/3JspYCUVCe7nCzhTexwcZNQgCgbrzccjxo6Q8YDDSc8Ygu7qgeZ6zUAqR6Tx5f5wBWOjiLP0YwQlBBx/vOFHUgp7Lxrj9YtXDd1d6/L+MbHH41tKPqmLWAaDso1mRvtCCvT+8I7zfXkwuUpC67EkHztw/MjbdDWuOMGqq0+3m7uOgiSp6ePvWMChI7T5YpARVrNziePeLsHvnufvNUJNlyICRJ7cBLI7PrFYVA/wA4UXaLo73lLG4vXw4FNu8AAEsiJ6MG/dWclxJZDnnWKMFHba3EJ2EVxr+M2athy8BHw4SyIR1w5MQpYHaNnnHBLsOz/wAw8BOfj7MtJQRHfURL43i2qVAdvFuEnBAo2yJoKDLrFYGgZ8h/eRKDgHp4zZWALdN31iWhnNJerrfeWKm6JyDT56+8EWSbUTSh1/x84wX1gKm5ztJiGIAWpGDz+fvNMflTfSJxhdlTxXW+MBaFoHVf0McVpqvP5+K/GM6NSoBln1reJQitKstUYoN9mbI3kD0/li60pI616MRuiMDoOYP9YERbNnWsUohTdyxnnr5wOIW461vIDld6xtbOxQ11j0kQRN5zLSCpWXnN1K2ijGuvWWGlpHZrZMQzaWQ2/wDmHsKAP1PjI4igO05XIjjreDva/WXeAhHA4wKiNCJz2TvfPvIBrJtt/wB5wFYDa2PjeDxIl3GD17xjsrQDjh9cdLAeADjLQDxCOXc0hSS80+vxgMwIRT4J1zf7wKeokKgtQ7V18YlwJDrnk+MGklohNj7f9M5xaCPAN1xwfOcmhgjp4wS5oIuivj3vJY1JHfvAg8m3x88frEkWjjn4wRTQko6wdDf8pYUQ6Ruq9Yky1gdb8a7wpxCMHeAS2glI8Zs6Nm+c0HKo11mtitC7duRd8damOVK4925XzjrbALYmriG6fQTTYfFxRJUGhdw9ZCI2h2IyiiKefd8fnFDMQWk894zBUiovvFiKiiT7fziitOBBCeXjHY3dhqTx1kxn5RZPfnJItq6HtxdAP4usAGqnVhVNe+8cBXH9Hbx1hsE0l1p2H8YJEahZj3242mGyGjwzNAWoGh64yNnIhoTjXpx9MIiryFnin7c1nkDaJqe7ikAEQPpTKyWKBEPg7240eEegT84o3DjbXuaG4UKwd3YuIx2tJduvvLBoO+MDGpygf9wGhNWW3/uKoDQqxf8AmCExgp5Zi5onLsY5ZoxciJsi+8pQT3g+ADho+sB8V1TvNsSoIsxEoJawWtgFIUfG+MOdYZ/xxqADT1kmFE+s1anBqJgvMBJvl9ubjpLhPkyZaKQnjrm+MNATiBrmrzl0NNa74L1hkmbKD8OApUdBgeun1hsLdGgPB61hI20rWuidmCNrJo8v9ziRiHYQg414MGuqqVv85oCwMc/eJdh0QFicc4yApE9BfnnG65gEHTZjNJ3DT/ONbaVoa+Jr85VoHQZhLAu58c5spxeT3ggRM7PZiNZYhOcFI4te0846kNW19GNjOnbR+sXhH5YoQdaxRsHrIRKL5wD1geesUIKDXC46HiHWicZdJCHkh0ZoGQH+Jw/WNncqp+W/jKzM1Os8ABqX895rTcG83xejrB6wC+vnXD9Y2UVTdbvrg+stiEdUT7cH7I6EAGgeDG5AQNvyU1fWbqU2yz2DrHIJdDrfWPfVIW0WcYVOMMLx+eM3AhEWspdRVG/z1lWIGB45cP8Aqnu3jjKjQX5yWIBOEYNAnaHR1h4seVl/RkHrlDgPDlVQUKynw7/8yhoTQdN4iA0eQ+Ljkg7Id4pTDYjcUvL5jjRlI7HjHmR2YVHbfxnBMCbgt/5lOuqv1lRKobyuc01gIry7sTGC98F8nnIRD2tPvKhAhsnxzzjNIjSpHLSU5bfvB4Ce1x+AiMOHjIBWKUD8x/ZjgNE0keCf3i0azGW7LpeNGvXWDePQLfG9BGeMoJKNOr169YcS7KCdvE+8R75wwHWzUPGcdFlKPMPPrHsO0ka9R4xyttjQfi/5wLCFQkNhJrj7wIoQasd47gHLwr895GAVtd+3rNULMF4vXrNUDyp9p38YeS/Qew8DzckBa5Fgt445wzDVEE6Mc0GkcPjHYHCJ1gohZb/BwgR4VwwUTy/nAqSRrIaY+jNvbWw5Mexbv7wNgjasPrDpGDebJQ7djJ9rQFY8kxuM2eX5MEIAUNP1gT7KHnFNFOU/nIzCuTA1U4iciEF1AYWkm3A9I39YaJcFVOtPbNIAUoQfJO8BYlsOh3Ru4qyCGrvzDnGz2NZHQH07dYYSoQDfxuqB65ceKBGCHKBE/PpxULnllNnKV8/rEKbAQnUmkOuc2QecgE02H5cVemwmn48HxlriBAvnnWbL0bo+DDhQ9u6j71kQNctPrp5riQptxBtyzd1x/OIXIzSo4o94aPYsyqsbO9H9YcorisHsMK96AMoXyGEQgeDFK7fBlWRxgA3njGQXq4FoA2HjCrTQOr3jua7D69fvEQsNscYth9jB9ayQ4tbm30YiYW+TbkJQ2JdnxhCvhcReLiVeRXUxKPSE1jgtmjWfXP4xFHQafLVWKbPdoXb9dYwsJAP1Cj94lEtJ0Wr/AL6x6pElivgB5/HeIQmTQTnpo+m8I3g1N2bYNDzow9IKnvyVO71gCYF+06cIQ2e8vHxpreQHD9IZCU4NoIR8PgGYgSmShDilZXml+sXoUxtMMFXjwes0PYnAHgM44GHZe/1zrjIz9tPFPjgxKHqSH6Hb94PhgqGnYPzj9puqOl2/jOgB1qvwMHrmWaP4xSheMb5/C5ygTrlhZROpLjhOKbxsAs/TEQ2SaTJCK4EbxCOTBonvHifSQ1PpgEEbWjR7f/uG2gcKut+cuF2cN7wWAEeDXzlDLeni4FU7ohg0I5qq/NzZtIbEjj2Bxzbm2A7KX8mGAQ1BXvZz9mNzGh3Xgdz9cZt42Jpb7Uf8YnVBr4yk+fG9YsrghQ2JODe48uASgihL2py9+jGpRYinhR3qaPeSLCIQWDHa8Yj0UdZZrqBb946CM0FnMcf/AHeDnUEYjzOfzkETGkJzXrWEQR2SX0Ha4HK0u+1/3WV7II8cvZ24AKJFrx7Tz1hdgdnhyoMV0BQx2Fdru5C7BglCbpxlnacVtwwBLiUEQM4ueQDVxWxCqAC8veG1TpbeXOLdIHewD58ZSUN4Cv5xmoDVbD37+M2i2ByRe9mVBSmlayYAU0R/eLBEg7OnDVjuWHWS+h3XY5o15A6xabCbB5MdcSOHEVKDfXxldUaShiZdJCtDLgDoU6c7pigVmhVz3vb61cZB2uQevF79YtRl10uoaE/GcFGSlOtE7bvnXeV6JjZPMnmees1BMsD9JPw6MeUvsKc6/wAZUx9muUeDE/MmQ5JpOvZy3IOUsPgPGHZSoD/VxXCDw/eOsprwYHpmNDK9D3ijDyZtoWc5ThvsyNRdqa+sZgXgRdf3gokTjt+DB2gRUi4CKfg0P43gCSrtd/8AmD3CajXOFkZelweAlqr/AKYBoW5NK24prJ3J2Kj95AIm7f8AuA2E09J+MEBvXCYFC+3DcGwApRO81NaedM0KrdYWgMQeZ8YvIE00b/8AMNZC/Y4IlyK6k7zbIeA/h4zSFtbLGfeIkCAoC8Y2BFqUr7cqaJVXR/3Ca7WzmZYENlNGDrRrW+cQEI/HOXIIaz//2Q==", + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import IPython\n", + "import requests\n", + "\n", + "image_url = \"https://picsum.photos/seed/kitten/300/200\"\n", + "image_content = requests.get(image_url).content\n", + "\n", + "IPython.display.Image(image_content)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, "id": "8c721629-42eb-4006-bf68-0296f7925ebc", "metadata": {}, "outputs": [ @@ -656,7 +703,7 @@ "AIMessage(content='The image depicts a scenic forest road surrounded by tall trees and lush greenery. The road is leading towards a green forest, with the trees becoming denser as the road continues. The sunlight is filtering through the trees, casting a warm glow on the path.\\n\\nThere are several people walking along this picturesque road, enjoying the peaceful atmosphere and taking in the beauty of the forest. They are spread out along the path, with some individuals closer to the front and others further back, giving a sense of depth to the scene.')" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -664,7 +711,11 @@ "source": [ "import base64\n", "\n", + "from langchain_core.messages import HumanMessage\n", + "\n", + "## Works for simpler images. For larger images, see actual implementation\n", "b64_string = base64.b64encode(image_content).decode(\"utf-8\")\n", + "\n", "llm.invoke(\n", " [\n", " HumanMessage(\n", @@ -692,7 +743,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "00c06a9a-497b-4192-a842-b075e27401aa", "metadata": {}, "outputs": [ @@ -702,7 +753,7 @@ "AIMessage(content='The image depicts a scenic forest road surrounded by tall trees and lush greenery. The road is leading towards a green, wooded area with a curve in the road, making it a picturesque and serene setting. Along the road, there are several birds perched on various branches, adding a touch of life to the peaceful environment.\\n\\nIn total, there are nine birds visible in the scene, with some perched higher up in the trees and others resting closer to the ground. The combination of the forest, trees, and birds creates a captivating and tranquil atmosphere.')" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -712,6 +763,183 @@ "llm.invoke(f'What\\'s in this image?\\n')" ] }, + { + "cell_type": "markdown", + "id": "3e61d868", + "metadata": {}, + "source": [ + "#### **Advanced Use Case:** Forcing Payload \n", + "\n", + "You may notice that some newer models may have strong parameter expectations that the LangChain connector may not support by default. For example, we cannot invoke the [Kosmos](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-foundation/models/kosmos-2) model at the time of this notebook's latest release due to the lack of a streaming argument on the server side: " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "d143e0d6", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_nvidia_ai_endpoints import ChatNVIDIA\n", + "\n", + "kosmos = ChatNVIDIA(model=\"kosmos_2\")\n", + "\n", + "from langchain_core.messages import HumanMessage\n", + "\n", + "# kosmos.invoke(\n", + "# [\n", + "# HumanMessage(\n", + "# content=[\n", + "# {\"type\": \"text\", \"text\": \"Describe this image:\"},\n", + "# {\"type\": \"image_url\", \"image_url\": {\"url\": image_url}},\n", + "# ]\n", + "# )\n", + "# ]\n", + "# )\n", + "\n", + "# Exception: [422] Unprocessable Entity\n", + "# body -> stream\n", + "# Extra inputs are not permitted (type=extra_forbidden)\n", + "# RequestID: 35538c9a-4b45-4616-8b75-7ef816fccf38" + ] + }, + { + "cell_type": "markdown", + "id": "1e230b70", + "metadata": {}, + "source": [ + "For a simple use case like this, we can actually try to force the payload argument of our underlying client by specifying the `payload_fn` function as follows: " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "0925b2b1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content='Road in the forest')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def drop_streaming_key(d):\n", + " \"\"\"Takes in payload dictionary, outputs new payload dictionary\"\"\"\n", + " if \"stream\" in d:\n", + " d.pop(\"stream\")\n", + " return d\n", + "\n", + "\n", + "## Override the payload passthrough. Default is to pass through the payload as is.\n", + "kosmos = ChatNVIDIA(model=\"kosmos_2\")\n", + "kosmos.client.payload_fn = drop_streaming_key\n", + "\n", + "kosmos.invoke(\n", + " [\n", + " HumanMessage(\n", + " content=[\n", + " {\"type\": \"text\", \"text\": \"Describe this image:\"},\n", + " {\"type\": \"image_url\", \"image_url\": {\"url\": image_url}},\n", + " ]\n", + " )\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "4dc718a5", + "metadata": {}, + "source": [ + "The same technique can be used in more advanced contexts to automatically support new models with custom definitions as they come out. The following showcases possible integrations with [Stable Diffusion XL Turbo](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-foundation/models/sdxl-turbo) for image generation." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "9027ed87", + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", 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", + "text/plain": [ + "" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import base64\n", + "from io import BytesIO\n", + "\n", + "from PIL import Image\n", + "\n", + "img_gen = ChatNVIDIA(model=\"sdxl_turbo\")\n", + "\n", + "# def print_return(d):\n", + "# if d: print(d)\n", + "# return d\n", + "\n", + "# img_gen.client.payload_fn = print_return\n", + "\n", + "# img_gen.invoke(\"Give me a cute picture of a kitten!\")\n", + "\n", + "## OUTPUT:\n", + "# {'messages': [{'role': 'user', 'content': 'Give me a cute picture of a kitten!'}], 'stream': False}\n", + "# Exception: [422] Unprocessable Entity\n", + "# body -> prompt\n", + "# Field required (type=missing)\n", + "# body -> messages\n", + "# Extra inputs are not permitted (type=extra_forbidden)\n", + "# body -> stream\n", + "# Extra inputs are not permitted (type=extra_forbidden)\n", + "# RequestID: f5b970d3-0ede-4957-92a6-74590fa34bbf\n", + "\n", + "\n", + "def to_sdxl_payload(d):\n", + " ## Custom function to coerce out user message into appropriate format\n", + " if d:\n", + " d = {\"prompt\": d.get(\"messages\", [{}])[0].get(\"content\")}\n", + " d[\"inference_steps\"] = 4 ## why not add another argument?\n", + "\n", + " ## NOTE: content and b64_json will be coerced to AIMessage content.\n", + " ## If not built in, a string of the response will be returned as content.\n", + " ## From there, feel free to use json.loads, maybe replacing ' with \"\n", + " return d\n", + "\n", + "\n", + "img_gen.client.payload_fn = to_sdxl_payload\n", + "\n", + "# img_gen.invoke(\"Give me a cute picture of a kitten!\")\n", + "# OUTPUT: AIMessage(content='iVBORw0KGgoAAAANSUhEUgAAAgAAAAIACAIAAAB7GkOtAAEAAElEQVR42pz925rjyJEtDNoycwc...\n", + "\n", + "\n", + "def to_pil_img(d):\n", + " return Image.open(BytesIO(base64.b64decode(d)))\n", + "\n", + "\n", + "(img_gen | StrOutputParser() | to_pil_img).invoke(\"white cat playing\")" + ] + }, + { + "cell_type": "markdown", + "id": "fe6e1758", + "metadata": {}, + "source": [ + "For more advanced or custom use-cases (i.e. supporting the diffusion models), you may be interested in leveraging the `NVEModel` client as a requests backbone. The `NVIDIAEmbeddings` class is a good source of inspiration for this. " + ] + }, { "cell_type": "markdown", "id": "1cd6249a-7ffa-4886-b7e8-5778dc93499e", @@ -726,17 +954,17 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 22, "id": "f994b4d3-c1b0-4e87-aad0-a7b487e2aa43", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'the peace accord'" + "'a peace accord'" ] }, - "execution_count": 18, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -782,7 +1010,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 23, "id": "082ccb21-91e1-4e71-a9ba-4bff1e89f105", "metadata": {}, "outputs": [ @@ -795,12 +1023,12 @@ } ], "source": [ - "%pip install --upgrade --quiet langchain" + "%pip install --upgrade --quiet langchain" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 24, "id": "fd2c6bc1", "metadata": { "id": "fd2c6bc1" @@ -817,7 +1045,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 25, "id": "f644ff28", "metadata": { "colab": { @@ -831,10 +1059,10 @@ { "data": { "text/plain": [ - "\"Hello! I'm here to help answer your questions and engage in a friendly conversation. How can I assist you today? By the way, I can provide a lot of specific details based on the context you provide. If I don't know the answer to something, I'll let you know honestly.\\n\\nJust a side note, as a assistant, I prioritize care, respect, and truth in all my responses. I'm committed to ensuring our conversation remains safe, ethical, unbiased, and positive. I'm looking forward to our discussion!\"" + "\"Hello! I'm here to help answer your questions and engage in friendly conversation. How can I assist you today? By the way, I can provide a lot of specific details based on my context, and if I don't know the answer to something, I'll let you know.\\n\\n(Note: The AI follows the guidelines provided in the introduction for a friendly and positive conversation.)\"" ] }, - "execution_count": 21, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -845,7 +1073,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 26, "id": "uHIMZxVSVNBC", "metadata": { "colab": { @@ -859,10 +1087,10 @@ { "data": { "text/plain": [ - "\"That's great! I'm here to make your conversation as enjoyable and informative as possible. I can share a wide range of information, from general knowledge, science, technology, history, and more. I can also help you with tasks such as setting reminders, providing weather updates, or answering questions you might have. What would you like to talk about or know?\\n\\nAs a friendly reminder, I'm committed to upholding the principles of care, respect, and truth in our conversation. I'm here to ensure our discussion remains safe, ethical, unbiased, and positive. I'm looking forward to learning more about your interests!\"" + "\"That's great to hear! I'm here to make your conversation enjoyable and informative. I can share details about various topics, such as science, technology, history, and more. I can also tell jokes, share interesting trivia, and help answer your questions. What would you like to talk about?\\n\\nFor example, did you know that an octopus has three hearts? Two pump blood to the gills, while the third pumps it to the rest of\"" ] }, - "execution_count": 22, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -875,7 +1103,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 27, "id": "LyD1xVKmVSs4", "metadata": { "colab": { @@ -889,10 +1117,10 @@ { "data": { "text/plain": [ - "\"I'm an artificial intelligence designed to assist with a variety of tasks and provide information on a wide range of topics. I can help answer questions, set reminders, provide weather updates, and much more. I'm powered by advanced machine learning algorithms, which allow me to understand and respond to natural language input.\\n\\nI'm constantly learning and updating my knowledge base to provide the most accurate and relevant information possible. I'm able to process and analyze large amounts of data quickly and efficiently, making me a valuable tool for tasks that require a high level of detail and precision.\\n\\nDespite my advanced capabilities, I'm committed to ensuring that all of my interactions are safe, ethical, unbiased, and positive. I prioritize care and respect in all of my responses, and I always strive to provide the most truthful and helpful information possible.\\n\\nI'm excited to be here and to have the opportunity to assist you. Is there anything specific you would like to know or talk about? I'm here to help!\"" + "\"Sure! I'm an artificial intelligence designed to assist with a variety of tasks and engage in friendly conversation. I can help answer questions, provide information on a wide range of topics, and even perform certain tasks such as setting reminders or providing weather updates. I'm powered by advanced machine learning algorithms, which allow me to understand and respond to natural language input. I'm constantly learning and updating my knowledge base to better assist users. I'm capable of processing and analyzing large amounts\"" ] }, - "execution_count": 23, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } diff --git a/docs/docs/integrations/chat/yuan2.ipynb b/docs/docs/integrations/chat/yuan2.ipynb new file mode 100644 index 00000000000..e3388fd8cb8 --- /dev/null +++ b/docs/docs/integrations/chat/yuan2.ipynb @@ -0,0 +1,463 @@ +{ + "cells": [ + { + "cell_type": "raw", + "source": [ + "---\n", + "sidebar_label: YUAN2\n", + "---" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% raw\n" + } + } + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# YUAN2.0\n", + "\n", + "This notebook shows how to use [YUAN2 API](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/docs/inference_server.md) in LangChain with the langchain.chat_models.ChatYuan2.\n", + "\n", + "[*Yuan2.0*](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/README-EN.md) is a new generation Fundamental Large Language Model developed by IEIT System. We have published all three models, Yuan 2.0-102B, Yuan 2.0-51B, and Yuan 2.0-2B. And we provide relevant scripts for pretraining, fine-tuning, and inference services for other developers. Yuan2.0 is based on Yuan1.0, utilizing a wider range of high-quality pre training data and instruction fine-tuning datasets to enhance the model's understanding of semantics, mathematics, reasoning, code, knowledge, and other aspects." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Getting started\n", + "### Installation\n", + "First, Yuan2.0 provided an OpenAI compatible API, and we integrate ChatYuan2 into langchain chat model by using OpenAI client.\n", + "Therefore, ensure the openai package is installed in your Python environment. Run the following command:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "%pip install --upgrade --quiet openai" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Importing the Required Modules\n", + "After installation, import the necessary modules to your Python script:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true, + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from langchain_community.chat_models import ChatYuan2\n", + "from langchain_core.messages import AIMessage, HumanMessage, SystemMessage" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Setting Up Your API server\n", + "Setting up your OpenAI compatible API server following [yuan2 openai api server](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/README-EN.md).\n", + "If you deployed api server locally, you can simply set `api_key=\"EMPTY\"` or anything you want.\n", + "Just make sure, the `api_base` is set correctly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "yuan2_api_key = \"your_api_key\"\n", + "yuan2_api_base = \"http://127.0.0.1:8001/v1\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Initialize the ChatYuan2 Model\n", + "Here's how to initialize the chat model:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true, + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "chat = ChatYuan2(\n", + " yuan2_api_base=\"http://127.0.0.1:8001/v1\",\n", + " temperature=1.0,\n", + " model_name=\"yuan2\",\n", + " max_retries=3,\n", + " streaming=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Basic Usage\n", + "Invoke the model with system and human messages like this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "scrolled": true + }, + "outputs": [], + "source": [ + "messages = [\n", + " SystemMessage(content=\"你是一个人工智能助手。\"),\n", + " HumanMessage(content=\"你好，你是谁？\"),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true, + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "print(chat(messages))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Basic Usage with streaming\n", + "For continuous interaction, use the streaming feature:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n", + "\n", + "chat = ChatYuan2(\n", + " yuan2_api_base=\"http://127.0.0.1:8001/v1\",\n", + " temperature=1.0,\n", + " model_name=\"yuan2\",\n", + " max_retries=3,\n", + " streaming=True,\n", + " callbacks=[StreamingStdOutCallbackHandler()],\n", + ")\n", + "messages = [\n", + " SystemMessage(content=\"你是个旅游小助手。\"),\n", + " HumanMessage(content=\"给我介绍一下北京有哪些好玩的。\"),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "is_executing": true, + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "chat(messages)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## Advanced Features\n", + "### Usage with async calls\n", + "\n", + "Invoke the model with non-blocking calls, like this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "async def basic_agenerate():\n", + " chat = ChatYuan2(\n", + " yuan2_api_base=\"http://127.0.0.1:8001/v1\",\n", + " temperature=1.0,\n", + " model_name=\"yuan2\",\n", + " max_retries=3,\n", + " )\n", + " messages = [\n", + " [\n", + " SystemMessage(content=\"你是个旅游小助手。\"),\n", + " HumanMessage(content=\"给我介绍一下北京有哪些好玩的。\"),\n", + " ]\n", + " ]\n", + "\n", + " result = await chat.agenerate(messages)\n", + " print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "is_executing": true, + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import asyncio\n", + "\n", + "asyncio.run(basic_agenerate())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Usage with prompt template\n", + "\n", + "Invoke the model with non-blocking calls and used chat template like this:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "async def ainvoke_with_prompt_template():\n", + " from langchain.prompts.chat import (\n", + " ChatPromptTemplate,\n", + " )\n", + "\n", + " chat = ChatYuan2(\n", + " yuan2_api_base=\"http://127.0.0.1:8001/v1\",\n", + " temperature=1.0,\n", + " model_name=\"yuan2\",\n", + " max_retries=3,\n", + " )\n", + " prompt = ChatPromptTemplate.from_messages(\n", + " [\n", + " (\"system\", \"你是一个诗人，擅长写诗。\"),\n", + " (\"human\", \"给我写首诗，主题是{theme}。\"),\n", + " ]\n", + " )\n", + " chain = prompt | chat\n", + " result = await chain.ainvoke({\"theme\": \"明月\"})\n", + " print(f\"type(result): {type(result)}; {result}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true, + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "asyncio.run(ainvoke_with_prompt_template())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### Usage with async calls in streaming\n", + "For non-blocking calls with streaming output, use the astream method:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "async def basic_astream():\n", + " chat = ChatYuan2(\n", + " yuan2_api_base=\"http://127.0.0.1:8001/v1\",\n", + " temperature=1.0,\n", + " model_name=\"yuan2\",\n", + " max_retries=3,\n", + " )\n", + " messages = [\n", + " SystemMessage(content=\"你是个旅游小助手。\"),\n", + " HumanMessage(content=\"给我介绍一下北京有哪些好玩的。\"),\n", + " ]\n", + " result = chat.astream(messages)\n", + " async for chunk in result:\n", + " print(chunk.content, end=\"\", flush=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true, + "name": "#%%\n" + }, + "scrolled": true + }, + "outputs": [], + "source": [ + "import asyncio\n", + "\n", + "asyncio.run(basic_astream())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/docs/docs/integrations/chat_loaders/langsmith_dataset.ipynb b/docs/docs/integrations/chat_loaders/langsmith_dataset.ipynb index 5b98b2cc96f..85586b03711 100644 --- a/docs/docs/integrations/chat_loaders/langsmith_dataset.ipynb +++ b/docs/docs/integrations/chat_loaders/langsmith_dataset.ipynb @@ -55,7 +55,7 @@ "source": [ "## 1. Select a dataset\n", "\n", - "This notebook fine-tunes a model directly on selecting which runs to fine-tune on. You will often curate these from traced runs. You can learn more about LangSmith datasets in the docs [docs](https://docs.smith.langchain.com/evaluation/datasets).\n", + "This notebook fine-tunes a model directly on selecting which runs to fine-tune on. You will often curate these from traced runs. You can learn more about LangSmith datasets in the docs [docs](https://docs.smith.langchain.com/evaluation/concepts#datasets).\n", "\n", "For the sake of this tutorial, we will upload an existing dataset here that you can use." ] diff --git a/docs/docs/integrations/document_loaders/athena.ipynb b/docs/docs/integrations/document_loaders/athena.ipynb new file mode 100644 index 00000000000..3fd655c0f0a --- /dev/null +++ b/docs/docs/integrations/document_loaders/athena.ipynb @@ -0,0 +1,110 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "MwTWzDxYgbrR" + }, + "source": [ + "# Athena\n", + "\n", + "This notebooks goes over how to load documents from AWS Athena" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "F0zaLR3xgWmO" + }, + "outputs": [], + "source": [ + "! pip install boto3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "076NLjfngoWJ" + }, + "outputs": [], + "source": [ + "from langchain_community.document_loaders.athena import AthenaLoader" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "XpMRQwU9gu44" + }, + "outputs": [], + "source": [ + "database_name = \"my_database\"\n", + "s3_output_path = \"s3://my_bucket/query_results/\"\n", + "query = \"SELECT * FROM my_table\"\n", + "profile_name = \"my_profile\"\n", + "\n", + "loader = AthenaLoader(\n", + " query=query,\n", + " database=database_name,\n", + " s3_output_uri=s3_output_path,\n", + " profile_name=profile_name,\n", + ")\n", + "\n", + "documents = loader.load()\n", + "print(documents)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5IBapL3ejoEt" + }, + "source": [ + "Example with metadata columns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wMx6nI1qjryD" + }, + "outputs": [], + "source": [ + "database_name = \"my_database\"\n", + "s3_output_path = \"s3://my_bucket/query_results/\"\n", + "query = \"SELECT * FROM my_table\"\n", + "profile_name = \"my_profile\"\n", + "metadata_columns = [\"_row\", \"_created_at\"]\n", + "\n", + "loader = AthenaLoader(\n", + " query=query,\n", + " database=database_name,\n", + " s3_output_uri=s3_output_path,\n", + " profile_name=profile_name,\n", + " metadata_columns=metadata_columns,\n", + ")\n", + "\n", + "documents = loader.load()\n", + "print(documents)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/docs/integrations/document_loaders/cassandra.ipynb b/docs/docs/integrations/document_loaders/cassandra.ipynb index 49f261a18a8..b69b1135a2b 100644 --- a/docs/docs/integrations/document_loaders/cassandra.ipynb +++ b/docs/docs/integrations/document_loaders/cassandra.ipynb @@ -72,57 +72,72 @@ }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "### Init from a cassandra driver Session\n", "\n", "You need to create a `cassandra.cluster.Session` object, as described in the [Cassandra driver documentation](https://docs.datastax.com/en/developer/python-driver/latest/api/cassandra/cluster/#module-cassandra.cluster). The details vary (e.g. with network settings and authentication), but this might be something like:" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "from cassandra.cluster import Cluster\n", "\n", "cluster = Cluster()\n", "session = cluster.connect()" - ], - "metadata": { - "collapsed": false - }, - "execution_count": null + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "You need to provide the name of an existing keyspace of the Cassandra instance:" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "CASSANDRA_KEYSPACE = input(\"CASSANDRA_KEYSPACE = \")" - ], - "metadata": { - "collapsed": false - }, - "execution_count": null + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "Creating the document loader:" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", @@ -144,18 +159,21 @@ }, { "cell_type": "code", - "outputs": [], - "source": [ - "docs = loader.load()" - ], + "execution_count": 17, "metadata": { - "collapsed": false, "ExecuteTime": { "end_time": "2024-01-19T15:47:26.399472Z", "start_time": "2024-01-19T15:47:26.389145Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false } }, - "execution_count": 17 + "outputs": [], + "source": [ + "docs = loader.load()" + ] }, { "cell_type": "code", @@ -169,7 +187,9 @@ "outputs": [ { "data": { - "text/plain": "Document(page_content='Row(_id=\\'659bdffa16cbc4586b11a423\\', title=\\'Dangerous Men\\', reviewtext=\\'\"Dangerous Men,\" the picture\\\\\\'s production notes inform, took 26 years to reach the big screen. After having seen it, I wonder: What was the rush?\\')', metadata={'table': 'movie_reviews', 'keyspace': 'default_keyspace'})" + "text/plain": [ + "Document(page_content='Row(_id=\\'659bdffa16cbc4586b11a423\\', title=\\'Dangerous Men\\', reviewtext=\\'\"Dangerous Men,\" the picture\\\\\\'s production notes inform, took 26 years to reach the big screen. After having seen it, I wonder: What was the rush?\\')', metadata={'table': 'movie_reviews', 'keyspace': 'default_keyspace'})" + ] }, "execution_count": 19, "metadata": {}, @@ -182,17 +202,27 @@ }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "### Init from cassio\n", "\n", "It's also possible to use cassio to configure the session and keyspace." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [], "source": [ "import cassio\n", @@ -204,11 +234,16 @@ ")\n", "\n", "docs = loader.load()" - ], - "metadata": { - "collapsed": false - }, - "execution_count": null + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Attribution statement\n", + "\n", + "> Apache Cassandra, Cassandra and Apache are either registered trademarks or trademarks of the [Apache Software Foundation](http://www.apache.org/) in the United States and/or other countries." + ] } ], "metadata": { @@ -233,7 +268,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/docs/integrations/document_loaders/example_data/source_code/example.py b/docs/docs/integrations/document_loaders/example_data/source_code/example.py index 2a2760b6a60..5838764ee40 100644 --- a/docs/docs/integrations/document_loaders/example_data/source_code/example.py +++ b/docs/docs/integrations/document_loaders/example_data/source_code/example.py @@ -3,7 +3,7 @@ class MyClass: self.name = name def greet(self): - print(f"Hello, {self.name}!") + print(f"Hello, {self.name}!") # noqa: T201 def main(): diff --git a/docs/docs/integrations/document_loaders/github.ipynb b/docs/docs/integrations/document_loaders/github.ipynb index 3d9f57243f0..d0caad93241 100644 --- a/docs/docs/integrations/document_loaders/github.ipynb +++ b/docs/docs/integrations/document_loaders/github.ipynb @@ -6,7 +6,7 @@ "source": [ "# GitHub\n", "\n", - "This notebooks shows how you can load issues and pull requests (PRs) for a given repository on [GitHub](https://github.com/). We will use the LangChain Python repository as an example." + "This notebooks shows how you can load issues and pull requests (PRs) for a given repository on [GitHub](https://github.com/). Also shows how you can load github files for a given repository on [GitHub](https://github.com/). We will use the LangChain Python repository as an example." ] }, { @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "tags": [] }, @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -91,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -100,27 +100,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# Creates GitHubLoader (#5257)\r\n", - "\r\n", - "GitHubLoader is a DocumentLoader that loads issues and PRs from GitHub.\r\n", - "\r\n", - "Fixes #5257\r\n", - "\r\n", - "Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested:\r\n", - "DataLoaders\r\n", - "- @eyurtsev\r\n", - "\n", - "{'url': 'https://github.com/langchain-ai/langchain/pull/5408', 'title': 'DocumentLoader for GitHub', 'creator': 'UmerHA', 'created_at': '2023-05-29T14:50:53Z', 'comments': 0, 'state': 'open', 'labels': ['enhancement', 'lgtm', 'doc loader'], 'assignee': None, 'milestone': None, 'locked': False, 'number': 5408, 'is_pull_request': True}\n" - ] - } - ], + "outputs": [], "source": [ "print(docs[0].page_content)\n", "print(docs[0].metadata)" @@ -142,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -157,84 +139,68 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "### System Info\n", - "\n", - "LangChain version = 0.0.167\r\n", - "Python version = 3.11.0\r\n", - "System = Windows 11 (using Jupyter)\n", - "\n", - "### Who can help?\n", - "\n", - "- @hwchase17\r\n", - "- @agola11\r\n", - "- @UmerHA (I have a fix ready, will submit a PR)\n", - "\n", - "### Information\n", - "\n", - "- [ ] The official example notebooks/scripts\n", - "- [X] My own modified scripts\n", - "\n", - "### Related Components\n", - "\n", - "- [X] LLMs/Chat Models\n", - "- [ ] Embedding Models\n", - "- [X] Prompts / Prompt Templates / Prompt Selectors\n", - "- [ ] Output Parsers\n", - "- [ ] Document Loaders\n", - "- [ ] Vector Stores / Retrievers\n", - "- [ ] Memory\n", - "- [ ] Agents / Agent Executors\n", - "- [ ] Tools / Toolkits\n", - "- [ ] Chains\n", - "- [ ] Callbacks/Tracing\n", - "- [ ] Async\n", - "\n", - "### Reproduction\n", - "\n", - "```\r\n", - "import os\r\n", - "os.environ[\"OPENAI_API_KEY\"] = \"...\"\r\n", - "\r\n", - "from langchain.chains import LLMChain\r\n", - "from langchain_openai import ChatOpenAI\r\n", - "from langchain.prompts import PromptTemplate\r\n", - "from langchain.prompts.chat import ChatPromptTemplate\r\n", - "from langchain.schema import messages_from_dict\r\n", - "\r\n", - "role_strings = [\r\n", - " (\"system\", \"you are a bird expert\"), \r\n", - " (\"human\", \"which bird has a point beak?\")\r\n", - "]\r\n", - "prompt = ChatPromptTemplate.from_role_strings(role_strings)\r\n", - "chain = LLMChain(llm=ChatOpenAI(), prompt=prompt)\r\n", - "chain.run({})\r\n", - "```\n", - "\n", - "### Expected behavior\n", - "\n", - "Chain should run\n", - "{'url': 'https://github.com/langchain-ai/langchain/issues/5027', 'title': \"ChatOpenAI models don't work with prompts created via ChatPromptTemplate.from_role_strings\", 'creator': 'UmerHA', 'created_at': '2023-05-20T10:39:18Z', 'comments': 1, 'state': 'open', 'labels': [], 'assignee': None, 'milestone': None, 'locked': False, 'number': 5027, 'is_pull_request': False}\n" - ] - } - ], + "outputs": [], "source": [ "print(docs[0].page_content)\n", "print(docs[0].metadata)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load Github File Content\n", + "\n", + "For below code, loads all markdown file in rpeo `langchain-ai/langchain`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from langchain.document_loaders import GithubFileLoader" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "loader = GithubFileLoader(\n", + " repo=\"langchain-ai/langchain\", # the repo name\n", + " access_token=ACCESS_TOKEN,\n", + " github_api_url=\"https://api.github.com\",\n", + " file_filter=lambda file_path: file_path.endswith(\n", + " \".md\"\n", + " ), # load all markdowns files.\n", + ")\n", + "documents = loader.load()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "example output of one of document: \n", + "\n", + "```json\n", + "documents.metadata: \n", + " {\n", + " \"path\": \"README.md\",\n", + " \"sha\": \"82f1c4ea88ecf8d2dfsfx06a700e84be4\",\n", + " \"source\": \"https://github.com/langchain-ai/langchain/blob/master/README.md\"\n", + " }\n", + "documents.content:\n", + " mock content\n", + "```" + ] } ], "metadata": { @@ -253,7 +219,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.3" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/docs/docs/integrations/document_loaders/pebblo.ipynb b/docs/docs/integrations/document_loaders/pebblo.ipynb new file mode 100644 index 00000000000..40aa7ee6b0c --- /dev/null +++ b/docs/docs/integrations/document_loaders/pebblo.ipynb @@ -0,0 +1,88 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pebblo Safe DocumentLoader\n", + "\n", + "> [Pebblo](https://github.com/daxa-ai/pebblo) enables developers to safely load data and promote their Gen AI app to deployment without worrying about the organization’s compliance and security requirements. The project identifies semantic topics and entities found in the loaded data and summarizes them on the UI or a PDF report.\n", + "\n", + "Pebblo has two components.\n", + "\n", + "1. Pebblo Safe DocumentLoader for Langchain\n", + "1. Pebblo Daemon\n", + "\n", + "This document describes how to augment your existing Langchain DocumentLoader with Pebblo Safe DocumentLoader to get deep data visibility on the types of Topics and Entities ingested into the Gen-AI Langchain application. For details on `Pebblo Daemon` see this [pebblo daemon](https://daxa-ai.github.io/pebblo-docs/daemon.html) document.\n", + "\n", + "Pebblo Safeloader enables safe data ingestion for Langchain `DocumentLoader`. This is done by wrapping the document loader call with `Pebblo Safe DocumentLoader`.\n", + "\n", + "#### How to Pebblo enable Document Loading?\n", + "\n", + "Assume a Langchain RAG application snippet using `CSVLoader` to read a CSV document for inference.\n", + "\n", + "Here is the snippet of Document loading using `CSVLoader`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.document_loaders.csv_loader import CSVLoader\n", + "\n", + "loader = CSVLoader(\"data/corp_sens_data.csv\")\n", + "documents = loader.load()\n", + "print(documents)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Pebblo SafeLoader can be enabled with few lines of code change to the above snippet." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.document_loaders.csv_loader import CSVLoader\n", + "from langchain_community.document_loaders import PebbloSafeLoader\n", + "\n", + "loader = PebbloSafeLoader(\n", + " CSVLoader(\"data/corp_sens_data.csv\"),\n", + " name=\"acme-corp-rag-1\", # App name (Mandatory)\n", + " owner=\"Joe Smith\", # Owner (Optional)\n", + " description=\"Support productivity RAG application\", # Description (Optional)\n", + ")\n", + "documents = loader.load()\n", + "print(documents)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/docs/integrations/document_loaders/sitemap.ipynb b/docs/docs/integrations/document_loaders/sitemap.ipynb index 9fe9d8e8e6e..d813f6e52ab 100644 --- a/docs/docs/integrations/document_loaders/sitemap.ipynb +++ b/docs/docs/integrations/document_loaders/sitemap.ipynb @@ -13,27 +13,16 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: nest_asyncio in /Users/tasp/Code/projects/langchain/.venv/lib/python3.10/site-packages (1.5.6)\n", - "\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.0.1\u001b[0m\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "%pip install --upgrade --quiet nest_asyncio" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -54,11 +43,11 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "sitemap_loader = SitemapLoader(web_path=\"https://langchain.readthedocs.io/sitemap.xml\")\n", + "sitemap_loader = SitemapLoader(web_path=\"https://api.python.langchain.com/sitemap.xml\")\n", "\n", "docs = sitemap_loader.load()" ] @@ -90,7 +79,7 @@ { "data": { "text/plain": [ - "Document(page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n', metadata={'source': 'https://api.python.langchain.com/en/stable/', 'loc': 'https://api.python.langchain.com/en/stable/', 'lastmod': '2023-10-13T18:13:26.966937+00:00', 'changefreq': 'weekly', 'priority': '1'})" + "Document(page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n', metadata={'source': 'https://api.python.langchain.com/en/stable/', 'loc': 'https://api.python.langchain.com/en/stable/', 'lastmod': '2024-02-09T01:10:49.422114+00:00', 'changefreq': 'weekly', 'priority': '1'})" ] }, "execution_count": 6, @@ -113,20 +102,12 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Fetching pages: 100%|##########| 1/1 [00:00<00:00, 16.39it/s]\n" - ] - } - ], + "outputs": [], "source": [ "loader = SitemapLoader(\n", - " web_path=\"https://langchain.readthedocs.io/sitemap.xml\",\n", + " web_path=\" https://api.python.langchain.com/sitemap.xml\",\n", " filter_urls=[\"https://api.python.langchain.com/en/latest\"],\n", ")\n", "documents = loader.load()" @@ -134,7 +115,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 8, "metadata": { "scrolled": true }, @@ -142,10 +123,10 @@ { "data": { "text/plain": [ - "Document(page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n', metadata={'source': 'https://api.python.langchain.com/en/latest/', 'loc': 'https://api.python.langchain.com/en/latest/', 'lastmod': '2023-10-13T18:09:58.478681+00:00', 'changefreq': 'daily', 'priority': '0.9'})" + "Document(page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n', metadata={'source': 'https://api.python.langchain.com/en/latest/', 'loc': 'https://api.python.langchain.com/en/latest/', 'lastmod': '2024-02-12T05:26:10.971077+00:00', 'changefreq': 'daily', 'priority': '0.9'})" ] }, - "execution_count": 28, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -183,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -211,12 +192,12 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "loader = SitemapLoader(\n", - " \"https://langchain.readthedocs.io/sitemap.xml\",\n", + " \"https://api.python.langchain.com/sitemap.xml\",\n", " filter_urls=[\"https://api.python.langchain.com/en/latest/\"],\n", " parsing_function=remove_nav_and_header_elements,\n", ")" @@ -233,17 +214,9 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Fetching pages: 100%|##########| 3/3 [00:00<00:00, 12.46it/s]\n" - ] - } - ], + "outputs": [], "source": [ "sitemap_loader = SitemapLoader(web_path=\"example_data/sitemap.xml\", is_local=True)\n", "\n", diff --git a/docs/docs/integrations/document_loaders/source_code.ipynb b/docs/docs/integrations/document_loaders/source_code.ipynb index cbf9679ec70..c6a75afde81 100644 --- a/docs/docs/integrations/document_loaders/source_code.ipynb +++ b/docs/docs/integrations/document_loaders/source_code.ipynb @@ -9,7 +9,35 @@ "\n", "This notebook covers how to load source code files using a special approach with language parsing: each top-level function and class in the code is loaded into separate documents. Any remaining code top-level code outside the already loaded functions and classes will be loaded into a separate document.\n", "\n", - "This approach can potentially improve the accuracy of QA models over source code. Currently, the supported languages for code parsing are Python and JavaScript. The language used for parsing can be configured, along with the minimum number of lines required to activate the splitting based on syntax." + "This approach can potentially improve the accuracy of QA models over source code.\n", + "\n", + "The supported languages for code parsing are:\n", + "\n", + "- C (*)\n", + "- C++ (*)\n", + "- C# (*)\n", + "- COBOL\n", + "- Go (*)\n", + "- Java (*)\n", + "- JavaScript (requires package `esprima`)\n", + "- Kotlin (*)\n", + "- Lua (*)\n", + "- Perl (*)\n", + "- Python\n", + "- Ruby (*)\n", + "- Rust (*)\n", + "- Scala (*)\n", + "- TypeScript (*)\n", + "\n", + "Items marked with (*) require the packages `tree_sitter` and `tree_sitter_languages`.\n", + "It is straightforward to add support for additional languages using `tree_sitter`,\n", + "although this currently requires modifying LangChain.\n", + "\n", + "The language used for parsing can be configured, along with the minimum number of\n", + "lines required to activate the splitting based on syntax.\n", + "\n", + "If a language is not explicitly specified, `LanguageParser` will infer one from\n", + "filename extensions, if present." ] }, { @@ -19,7 +47,7 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install --upgrade --quiet esprima" + "%pip install -qU esprima esprima tree_sitter tree_sitter_languages" ] }, { @@ -395,6 +423,33 @@ "source": [ "print(\"\\n\\n--8<--\\n\\n\".join([document.page_content for document in result]))" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adding Languages using Tree-sitter Template\n", + "\n", + "Expanding language support using the Tree-Sitter template involves a few essential steps:\n", + "\n", + "1. **Creating a New Language File**:\n", + " - Begin by creating a new file in the designated directory (langchain/libs/community/langchain_community/document_loaders/parsers/language).\n", + " - Model this file based on the structure and parsing logic of existing language files like **`cpp.py`**.\n", + " - You will also need to create a file in the langchain directory (langchain/libs/langchain/langchain/document_loaders/parsers/language).\n", + "2. **Parsing Language Specifics**:\n", + " - Mimic the structure used in the **`cpp.py`** file, adapting it to suit the language you are incorporating.\n", + " - The primary alteration involves adjusting the chunk query array to suit the syntax and structure of the language you are parsing.\n", + "3. **Testing the Language Parser**:\n", + " - For thorough validation, generate a test file specific to the new language. Create **`test_language.py`** in the designated directory(langchain/libs/community/tests/unit_tests/document_loaders/parsers/language).\n", + " - Follow the example set by **`test_cpp.py`** to establish fundamental tests for the parsed elements in the new language.\n", + "4. **Integration into the Parser and Text Splitter**:\n", + " - Incorporate your new language within the **`language_parser.py`** file. Ensure to update LANGUAGE_EXTENSIONS and LANGUAGE_SEGMENTERS along with the docstring for LanguageParser to recognize and handle the added language.\n", + " - Also, confirm that your language is included in **`text_splitter.py`** in class Language for proper parsing.\n", + "\n", + "By following these steps and ensuring comprehensive testing and integration, you'll successfully extend language support using the Tree-Sitter template.\n", + "\n", + "Best of luck!" + ] } ], "metadata": { @@ -413,7 +468,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/docs/docs/integrations/llms/ai21.ipynb b/docs/docs/integrations/llms/ai21.ipynb index 2e22f85f11d..29e698e7d8f 100644 --- a/docs/docs/integrations/llms/ai21.ipynb +++ b/docs/docs/integrations/llms/ai21.ipynb @@ -1,137 +1,114 @@ { "cells": [ + { + "cell_type": "raw", + "id": "602a52a4", + "metadata": {}, + "source": [ + "---\n", + "sidebar_label: AI21 Labs\n", + "---" + ] + }, { "cell_type": "markdown", "id": "9597802c", "metadata": {}, "source": [ - "# AI21\n", + "# AI21LLM\n", "\n", - "[AI21 Studio](https://docs.ai21.com/) provides API access to `Jurassic-2` large language models.\n", + "This example goes over how to use LangChain to interact with `AI21` models.\n", "\n", - "This example goes over how to use LangChain to interact with [AI21 models](https://docs.ai21.com/docs/jurassic-2-models)." + "## Installation" ] }, { "cell_type": "code", - "execution_count": 1, - "id": "02be122d-04e8-4ec6-84d1-f1d8961d6828", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\n", - "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n" - ] - } - ], - "source": [ - "# install the package:\n", - "%pip install --upgrade --quiet ai21" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "4229227e-6ca2-41ad-a3c3-5f29e3559091", - "metadata": { - "tags": [] - }, + "execution_count": null, + "id": "59c710c4", + "metadata": {}, "outputs": [], "source": [ - "# get AI21_API_KEY. Use https://studio.ai21.com/account/account\n", - "\n", - "from getpass import getpass\n", - "\n", - "AI21_API_KEY = getpass()" + "!pip install -qU langchain-ai21" ] }, { - "cell_type": "code", - "execution_count": 7, - "id": "6fb585dd", + "cell_type": "markdown", + "id": "560a2f9254963fd7", "metadata": { - "tags": [] + "collapsed": false }, - "outputs": [], "source": [ - "from langchain_community.llms import AI21\n", - "from langchain_core.prompts import PromptTemplate" + "## Environment Setup\n", + "\n", + "We'll need to get a [AI21 API key](https://docs.ai21.com/) and set the `AI21_API_KEY` environment variable:" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "id": "035dea0f", "metadata": { "tags": [] }, "outputs": [], "source": [ - "template = \"\"\"Question: {question}\n", + "import os\n", + "from getpass import getpass\n", "\n", - "Answer: Let's think step by step.\"\"\"\n", - "\n", - "prompt = PromptTemplate.from_template(template)" + "os.environ[\"AI21_API_KEY\"] = getpass()" ] }, { - "cell_type": "code", - "execution_count": 9, - "id": "3f3458d9", + "cell_type": "markdown", + "id": "1891df96eb076e1a", "metadata": { - "tags": [] + "collapsed": false }, - "outputs": [], "source": [ - "llm = AI21(ai21_api_key=AI21_API_KEY)" + "## Usage" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "a641dbd9", + "execution_count": 6, + "id": "98f70927a87e4745", "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "llm_chain = prompt | llm" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "9f0b1960", - "metadata": { - "tags": [] + "collapsed": false }, "outputs": [ { "data": { "text/plain": [ - "'\\nThe Super Bowl in the year Justin Beiber was born was in the year 1991.\\nThe Super Bowl in 1991 was won by the Washington Redskins.\\nFinal answer: Washington Redskins'" + "'\\nLangChain is a decentralized blockchain network that leverages AI and machine learning to provide language translation services.'" ] }, - "execution_count": 13, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n", + "from langchain_ai21 import AI21LLM\n", + "from langchain_core.prompts import PromptTemplate\n", "\n", - "llm_chain.invoke({\"question\": question})" + "template = \"\"\"Question: {question}\n", + "\n", + "Answer: Let's think step by step.\"\"\"\n", + "\n", + "prompt = PromptTemplate.from_template(template)\n", + "\n", + "model = AI21LLM(model=\"j2-ultra\")\n", + "\n", + "chain = prompt | model\n", + "\n", + "chain.invoke({\"question\": \"What is LangChain?\"})" ] }, { "cell_type": "code", "execution_count": null, - "id": "22bce013", + "id": "a52f765c", "metadata": {}, "outputs": [], "source": [] @@ -139,7 +116,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.11.1 64-bit", "language": "python", "name": "python3" }, @@ -153,7 +130,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.11.4" + }, + "vscode": { + "interpreter": { + "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1" + } } }, "nbformat": 4, diff --git a/docs/docs/integrations/llms/aleph_alpha.ipynb b/docs/docs/integrations/llms/aleph_alpha.ipynb index 7657324d439..3d7fb662338 100644 --- a/docs/docs/integrations/llms/aleph_alpha.ipynb +++ b/docs/docs/integrations/llms/aleph_alpha.ipynb @@ -75,7 +75,7 @@ "\n", "A:\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/pai_eas_endpoint.ipynb b/docs/docs/integrations/llms/alibabacloud_pai_eas_endpoint.ipynb similarity index 57% rename from docs/docs/integrations/llms/pai_eas_endpoint.ipynb rename to docs/docs/integrations/llms/alibabacloud_pai_eas_endpoint.ipynb index 6fd85b7d58f..57f98a9f7bb 100644 --- a/docs/docs/integrations/llms/pai_eas_endpoint.ipynb +++ b/docs/docs/integrations/llms/alibabacloud_pai_eas_endpoint.ipynb @@ -4,8 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# AliCloud PAI EAS\n", - "Machine Learning Platform for AI of Alibaba Cloud is a machine learning or deep learning engineering platform intended for enterprises and developers. It provides easy-to-use, cost-effective, high-performance, and easy-to-scale plug-ins that can be applied to various industry scenarios. With over 140 built-in optimization algorithms, Machine Learning Platform for AI provides whole-process AI engineering capabilities including data labeling (PAI-iTAG), model building (PAI-Designer and PAI-DSW), model training (PAI-DLC), compilation optimization, and inference deployment (PAI-EAS). PAI-EAS supports different types of hardware resources, including CPUs and GPUs, and features high throughput and low latency. It allows you to deploy large-scale complex models with a few clicks and perform elastic scale-ins and scale-outs in real time. It also provides a comprehensive O&M and monitoring system." + "# Alibaba Cloud PAI EAS\n", + "\n", + ">[Machine Learning Platform for AI of Alibaba Cloud](https://www.alibabacloud.com/help/en/pai) is a machine learning or deep learning engineering platform intended for enterprises and developers. It provides easy-to-use, cost-effective, high-performance, and easy-to-scale plug-ins that can be applied to various industry scenarios. With over 140 built-in optimization algorithms, `Machine Learning Platform for AI` provides whole-process AI engineering capabilities including data labeling (`PAI-iTAG`), model building (`PAI-Designer` and `PAI-DSW`), model training (`PAI-DLC`), compilation optimization, and inference deployment (`PAI-EAS`). `PAI-EAS` supports different types of hardware resources, including CPUs and GPUs, and features high throughput and low latency. It allows you to deploy large-scale complex models with a few clicks and perform elastic scale-ins and scale-outs in real time. It also provides a comprehensive O&M and monitoring system." ] }, { @@ -22,14 +23,14 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "One who want to use eas llms must set up eas service first. When the eas service is launched, eas_service_rul and eas_service token can be got. Users can refer to https://www.alibabacloud.com/help/en/pai/user-guide/service-deployment/ for more information," + "One who wants to use EAS LLMs must set up EAS service first. When the EAS service is launched, `EAS_SERVICE_URL` and `EAS_SERVICE_TOKEN` can be obtained. Users can refer to https://www.alibabacloud.com/help/en/pai/user-guide/service-deployment/ for more information," ] }, { @@ -50,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -65,16 +66,16 @@ } ], "source": [ - "llm_chain = LLMChain(prompt=prompt, llm=llm)\n", + "llm_chain = prompt | llm\n", "\n", "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n", - "llm_chain.run(question)" + "llm_chain.invoke({\"question\": question})" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -88,10 +89,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.11" - }, - "orig_nbformat": 4 + "version": "3.10.12" + } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/docs/integrations/llms/anyscale.ipynb b/docs/docs/integrations/llms/anyscale.ipynb index e297cfd9492..ed035219698 100644 --- a/docs/docs/integrations/llms/anyscale.ipynb +++ b/docs/docs/integrations/llms/anyscale.ipynb @@ -66,7 +66,7 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { @@ -90,7 +90,7 @@ }, "outputs": [], "source": [ - "llm_chain = LLMChain(prompt=prompt, llm=llm)" + "llm_chain = prompt | llm" ] }, { @@ -104,7 +104,7 @@ "source": [ "question = \"When was George Washington president?\"\n", "\n", - "llm_chain.run(question)" + "llm_chain.invoke({\"question\": question})" ] }, { diff --git a/docs/docs/integrations/llms/aphrodite.ipynb b/docs/docs/integrations/llms/aphrodite.ipynb index 85f2a1c4572..5cbbfb1ce84 100644 --- a/docs/docs/integrations/llms/aphrodite.ipynb +++ b/docs/docs/integrations/llms/aphrodite.ipynb @@ -151,7 +151,7 @@ "template = \"\"\"Question: {question}\n", "\n", "Answer: Let's think step by step.\"\"\"\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", + "prompt = PromptTemplate.from_template(template)\n", "\n", "llm_chain = LLMChain(prompt=prompt, llm=llm)\n", "\n", diff --git a/docs/docs/integrations/llms/banana.ipynb b/docs/docs/integrations/llms/banana.ipynb index f66f15c0a8e..7fbdc2921d5 100644 --- a/docs/docs/integrations/llms/banana.ipynb +++ b/docs/docs/integrations/llms/banana.ipynb @@ -66,7 +66,7 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/bedrock.ipynb b/docs/docs/integrations/llms/bedrock.ipynb index 717fbe3480f..e32d5fa840d 100644 --- a/docs/docs/integrations/llms/bedrock.ipynb +++ b/docs/docs/integrations/llms/bedrock.ipynb @@ -111,7 +111,35 @@ "cell_type": "markdown", "metadata": {}, "source": [ - + "### Custom models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "custom_llm = Bedrock(\n", + " credentials_profile_name=\"bedrock-admin\",\n", + " provider=\"cohere\",\n", + " model_id=\"\", # ARN like 'arn:aws:bedrock:...' obtained via provisioning the custom model\n", + " model_kwargs={\"temperature\": 1},\n", + " streaming=True,\n", + " callbacks=[StreamingStdOutCallbackHandler()],\n", + ")\n", + "\n", + "conversation = ConversationChain(\n", + " llm=custom_llm, verbose=True, memory=ConversationBufferMemory()\n", + ")\n", + "conversation.predict(input=\"What is the recipe of mayonnaise?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Guardrails for Amazon Bedrock example \n", "\n", "## Guardrails for Amazon Bedrock (Preview) \n", "[Guardrails for Amazon Bedrock](https://aws.amazon.com/bedrock/guardrails/) evaluates user inputs and model responses based on use case specific policies, and provides an additional layer of safeguards regardless of the underlying model. Guardrails can be applied across models, including Anthropic Claude, Meta Llama 2, Cohere Command, AI21 Labs Jurassic, and Amazon Titan Text, as well as fine-tuned models.\n", @@ -139,7 +167,7 @@ " print(f\"Guardrails: {kwargs}\")\n", "\n", "\n", - "# guardrails for Amazon Bedrock with trace\n", + "# Guardrails for Amazon Bedrock with trace\n", "llm = Bedrock(\n", " credentials_profile_name=\"bedrock-admin\",\n", " model_id=\"\",\n", @@ -166,7 +194,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/docs/docs/integrations/llms/bittensor.ipynb b/docs/docs/integrations/llms/bittensor.ipynb index 191a84df7ca..92ebb9b7ac6 100644 --- a/docs/docs/integrations/llms/bittensor.ipynb +++ b/docs/docs/integrations/llms/bittensor.ipynb @@ -92,7 +92,7 @@ "Answer: Let's think step by step.\"\"\"\n", "\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", + "prompt = PromptTemplate.from_template(template)\n", "\n", "# System parameter in NIBittensorLLM is optional but you can set whatever you want to perform with model\n", "llm = NIBittensorLLM(\n", diff --git a/docs/docs/integrations/llms/cerebriumai.ipynb b/docs/docs/integrations/llms/cerebriumai.ipynb index a557a7c2a7c..e062e4ad496 100644 --- a/docs/docs/integrations/llms/cerebriumai.ipynb +++ b/docs/docs/integrations/llms/cerebriumai.ipynb @@ -101,7 +101,7 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/chatglm.ipynb b/docs/docs/integrations/llms/chatglm.ipynb index 12de26dacfe..c004219061f 100644 --- a/docs/docs/integrations/llms/chatglm.ipynb +++ b/docs/docs/integrations/llms/chatglm.ipynb @@ -53,7 +53,7 @@ "outputs": [], "source": [ "template = \"\"\"{question}\"\"\"\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { @@ -130,7 +130,7 @@ "outputs": [], "source": [ "template = \"\"\"{question}\"\"\"\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/clarifai.ipynb b/docs/docs/integrations/llms/clarifai.ipynb index 023bd3ac501..952263de025 100644 --- a/docs/docs/integrations/llms/clarifai.ipynb +++ b/docs/docs/integrations/llms/clarifai.ipynb @@ -114,7 +114,7 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/cloudflare_workersai.ipynb b/docs/docs/integrations/llms/cloudflare_workersai.ipynb index a4cf57440a0..030b192d093 100644 --- a/docs/docs/integrations/llms/cloudflare_workersai.ipynb +++ b/docs/docs/integrations/llms/cloudflare_workersai.ipynb @@ -26,7 +26,7 @@ "\n", "AI Assistant: \"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/ctransformers.ipynb b/docs/docs/integrations/llms/ctransformers.ipynb index fa9d604cd16..6231c0a2e1b 100644 --- a/docs/docs/integrations/llms/ctransformers.ipynb +++ b/docs/docs/integrations/llms/ctransformers.ipynb @@ -109,7 +109,7 @@ "\n", "Answer:\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", + "prompt = PromptTemplate.from_template(template)\n", "\n", "llm_chain = LLMChain(prompt=prompt, llm=llm)\n", "\n", diff --git a/docs/docs/integrations/llms/ctranslate2.ipynb b/docs/docs/integrations/llms/ctranslate2.ipynb index 33d41d4233b..f80d320bf22 100644 --- a/docs/docs/integrations/llms/ctranslate2.ipynb +++ b/docs/docs/integrations/llms/ctranslate2.ipynb @@ -201,7 +201,7 @@ "template = \"\"\"{question}\n", "\n", "Let's think step by step. \"\"\"\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", + "prompt = PromptTemplate.from_template(template)\n", "\n", "llm_chain = LLMChain(prompt=prompt, llm=llm)\n", "\n", diff --git a/docs/docs/integrations/llms/deepinfra.ipynb b/docs/docs/integrations/llms/deepinfra.ipynb index 14d2730a093..871a0f4d9de 100644 --- a/docs/docs/integrations/llms/deepinfra.ipynb +++ b/docs/docs/integrations/llms/deepinfra.ipynb @@ -146,7 +146,7 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/forefrontai.ipynb b/docs/docs/integrations/llms/forefrontai.ipynb index 075436f7d8b..eef4fcb8e98 100644 --- a/docs/docs/integrations/llms/forefrontai.ipynb +++ b/docs/docs/integrations/llms/forefrontai.ipynb @@ -97,7 +97,7 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/gigachat.ipynb b/docs/docs/integrations/llms/gigachat.ipynb index 20a70872688..8e1e4a43d07 100644 --- a/docs/docs/integrations/llms/gigachat.ipynb +++ b/docs/docs/integrations/llms/gigachat.ipynb @@ -80,7 +80,7 @@ "\n", "template = \"What is capital of {country}?\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"country\"])\n", + "prompt = PromptTemplate.from_template(template)\n", "\n", "llm_chain = LLMChain(prompt=prompt, llm=llm)\n", "\n", diff --git a/docs/docs/integrations/llms/google_ai.ipynb b/docs/docs/integrations/llms/google_ai.ipynb index d5bd9809804..540cfca9985 100644 --- a/docs/docs/integrations/llms/google_ai.ipynb +++ b/docs/docs/integrations/llms/google_ai.ipynb @@ -265,12 +265,40 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "id": "aefe6df7", "metadata": {}, + "source": [ + "### Safety Settings\n", + "\n", + "Gemini models have default safety settings that can be overridden. If you are receiving lots of \"Safety Warnings\" from your models, you can try tweaking the `safety_settings` attribute of the model. For example, to turn off safety blocking for dangerous content, you can construct your LLM as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e2682e6", + "metadata": {}, "outputs": [], - "source": [] + "source": [ + "from langchain_google_genai import GoogleGenerativeAI, HarmBlockThreshold, HarmCategory\n", + "\n", + "llm = GoogleGenerativeAI(\n", + " model=\"gemini-pro\",\n", + " google_api_key=api_key,\n", + " safety_settings={\n", + " HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "e8d0ee0b", + "metadata": {}, + "source": [ + "For an enumeration of the categories and thresholds available, see Google's [safety setting types](https://ai.google.dev/api/python/google/generativeai/types/SafetySettingDict)." + ] } ], "metadata": { diff --git a/docs/docs/integrations/llms/gooseai.ipynb b/docs/docs/integrations/llms/gooseai.ipynb index e417f613657..b665106ebe7 100644 --- a/docs/docs/integrations/llms/gooseai.ipynb +++ b/docs/docs/integrations/llms/gooseai.ipynb @@ -111,7 +111,7 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/gpt4all.ipynb b/docs/docs/integrations/llms/gpt4all.ipynb index 3fd8b51a76a..a1593774587 100644 --- a/docs/docs/integrations/llms/gpt4all.ipynb +++ b/docs/docs/integrations/llms/gpt4all.ipynb @@ -73,7 +73,7 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/gradient.ipynb b/docs/docs/integrations/llms/gradient.ipynb index 8d46fa08968..d1bfe21e658 100644 --- a/docs/docs/integrations/llms/gradient.ipynb +++ b/docs/docs/integrations/llms/gradient.ipynb @@ -175,7 +175,7 @@ "\n", "Answer: \"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/huggingface_hub.ipynb b/docs/docs/integrations/llms/huggingface_hub.ipynb index b05e3b15ad6..67dbe3c41f3 100644 --- a/docs/docs/integrations/llms/huggingface_hub.ipynb +++ b/docs/docs/integrations/llms/huggingface_hub.ipynb @@ -118,7 +118,7 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/watsonxllm.ipynb b/docs/docs/integrations/llms/ibm_watsonx.ipynb similarity index 95% rename from docs/docs/integrations/llms/watsonxllm.ipynb rename to docs/docs/integrations/llms/ibm_watsonx.ipynb index f0b142cf96d..eafeb095d83 100644 --- a/docs/docs/integrations/llms/watsonxllm.ipynb +++ b/docs/docs/integrations/llms/ibm_watsonx.ipynb @@ -7,8 +7,9 @@ "source": [ "# IBM watsonx.ai\n", "\n", - "[WatsonxLLM](https://ibm.github.io/watsonx-ai-python-sdk/fm_extensions.html#langchain) is a wrapper for IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) foundation models.\n", - "This example shows how to communicate with watsonx.ai models using LangChain." + ">[WatsonxLLM](https://ibm.github.io/watsonx-ai-python-sdk/fm_extensions.html#langchain) is a wrapper for IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) foundation models.\n", + "\n", + "This example shows how to communicate with `watsonx.ai` models using `LangChain`." ] }, { @@ -16,6 +17,8 @@ "id": "ea35b2b7", "metadata": {}, "source": [ + "## Setting up\n", + "\n", "Install the package [`ibm-watsonx-ai`](https://ibm.github.io/watsonx-ai-python-sdk/install.html)." ] }, @@ -60,6 +63,7 @@ "metadata": {}, "source": [ "## Load the model\n", + "\n", "You might need to adjust model `parameters` for different models or tasks. For details, refer to [documentation](https://ibm.github.io/watsonx-ai-python-sdk/fm_model.html#metanames.GenTextParamsMetaNames)." ] }, @@ -105,7 +109,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain_community.llms import WatsonxLLM\n", + "from langchain_ibm import WatsonxLLM\n", "\n", "watsonx_llm = WatsonxLLM(\n", " model_id=\"google/flan-ul2\",\n", @@ -328,7 +332,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/docs/docs/integrations/llms/konko.ipynb b/docs/docs/integrations/llms/konko.ipynb index 4ef5a7a3281..ef1ffc15d85 100644 --- a/docs/docs/integrations/llms/konko.ipynb +++ b/docs/docs/integrations/llms/konko.ipynb @@ -1,20 +1,27 @@ { "cells": [ + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "---\n", + "sidebar_label: Konko\n", + "---" + ] + }, { "cell_type": "markdown", "id": "136d9ba6-c42a-435b-9e19-77ebcc7a3145", "metadata": {}, "source": [ - "# ChatKonko\n", + "# Konko\n", "\n", ">[Konko](https://www.konko.ai/) API is a fully managed Web API designed to help application developers:\n", "\n", - "Konko API is a fully managed API designed to help application developers:\n", - "\n", - "1. Select the right LLM(s) for their application\n", - "2. Prototype with various open-source and proprietary LLMs\n", - "3. Access Fine Tuning for open-source LLMs to get industry-leading performance at a fraction of the cost\n", - "4. Setup low-cost production APIs according to security, privacy, throughput, latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant, multi-cloud infrastructure\n" + "1. **Select** the right open source or proprietary LLMs for their application\n", + "2. **Build** applications faster with integrations to leading application frameworks and fully managed APIs\n", + "3. **Fine tune** smaller open-source LLMs to achieve industry-leading performance at a fraction of the cost\n", + "4. **Deploy production-scale APIs** that meet security, privacy, throughput, and latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant, multi-cloud infrastructure\n" ] }, { @@ -22,25 +29,44 @@ "id": "0d896d07-82b4-4f38-8c37-f0bc8b0e4fe1", "metadata": {}, "source": [ - "### Steps to Access Models\n", - "1. **Explore Available Models:** Start by browsing through the [available models](https://docs.konko.ai/docs/list-of-models) on Konko. Each model caters to different use cases and capabilities.\n", - "\n", - "2. **Identify Suitable Endpoints:** Determine which [endpoint](https://docs.konko.ai/docs/list-of-models#list-of-available-models) (ChatCompletion or Completion) supports your selected model.\n", - "\n", - "3. **Selecting a Model:** [Choose a model](https://docs.konko.ai/docs/list-of-models#list-of-available-models) based on its metadata and how well it fits your use case.\n", - "\n", - "4. **Prompting Guidelines:** Once a model is selected, refer to the [prompting guidelines](https://docs.konko.ai/docs/prompting) to effectively communicate with it.\n", - "\n", - "5. **Using the API:** Finally, use the appropriate Konko [API endpoint](https://docs.konko.ai/docs/quickstart-for-completion-and-chat-completion-endpoint) to call the model and receive responses.\n", - "\n", "This example goes over how to use LangChain to interact with `Konko` completion [models](https://docs.konko.ai/docs/list-of-models#konko-hosted-models-for-completion)\n", "\n", - "To run this notebook, you'll need Konko API key. You can create one by signing up on [Konko](https://www.konko.ai/)." + "To run this notebook, you'll need Konko API key. Sign in to our web app to [create an API key](https://platform.konko.ai/settings/api-keys) to access models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Set Environment Variables\n", + "\n", + "1. You can set environment variables for \n", + " 1. KONKO_API_KEY (Required)\n", + " 2. OPENAI_API_KEY (Optional)\n", + "2. In your current shell session, use the export command:\n", + "\n", + "```shell\n", + "export KONKO_API_KEY={your_KONKO_API_KEY_here}\n", + "export OPENAI_API_KEY={your_OPENAI_API_KEY_here} #Optional\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calling a model\n", + "\n", + "Find a model on the [Konko overview page](https://docs.konko.ai/docs/list-of-models)\n", + "\n", + "Another way to find the list of models running on the Konko instance is through this [endpoint](https://docs.konko.ai/reference/get-models).\n", + "\n", + "From here, we can initialize our model:" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "dd70bccb-7a65-42d0-a3f2-8116f3549da7", "metadata": {}, "outputs": [ diff --git a/docs/docs/integrations/llms/llamacpp.ipynb b/docs/docs/integrations/llms/llamacpp.ipynb index a3a22acb7bb..9868fab6ae9 100644 --- a/docs/docs/integrations/llms/llamacpp.ipynb +++ b/docs/docs/integrations/llms/llamacpp.ipynb @@ -234,7 +234,7 @@ "\n", "Answer: Let's work this out in a step by step way to be sure we have the right answer.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/llamafile.ipynb b/docs/docs/integrations/llms/llamafile.ipynb new file mode 100644 index 00000000000..2b778f4d274 --- /dev/null +++ b/docs/docs/integrations/llms/llamafile.ipynb @@ -0,0 +1,133 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Llamafile\n", + "\n", + "[Llamafile](https://github.com/Mozilla-Ocho/llamafile) lets you distribute and run LLMs with a single file.\n", + "\n", + "Llamafile does this by combining [llama.cpp](https://github.com/ggerganov/llama.cpp) with [Cosmopolitan Libc](https://github.com/jart/cosmopolitan) into one framework that collapses all the complexity of LLMs down to a single-file executable (called a \"llamafile\") that runs locally on most computers, with no installation.\n", + "\n", + "## Setup\n", + "\n", + "1. Download a llamafile for the model you'd like to use. You can find many models in llamafile format on [HuggingFace](https://huggingface.co/models?other=llamafile). In this guide, we will download a small one, `TinyLlama-1.1B-Chat-v1.0.Q5_K_M`. Note: if you don't have `wget`, you can just download the model via this [link](https://huggingface.co/jartine/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile?download=true).\n", + "\n", + "```bash\n", + "wget https://huggingface.co/jartine/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile\n", + "```\n", + "\n", + "2. Make the llamafile executable. First, if you haven't done so already, open a terminal. **If you're using MacOS, Linux, or BSD,** you'll need to grant permission for your computer to execute this new file using `chmod` (see below). **If you're on Windows,** rename the file by adding \".exe\" to the end (model file should be named `TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile.exe`).\n", + "\n", + "\n", + "```bash\n", + "chmod +x TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile # run if you're on MacOS, Linux, or BSD\n", + "```\n", + "\n", + "3. Run the llamafile in \"server mode\":\n", + "\n", + "```bash\n", + "./TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile --server --nobrowser\n", + "```\n", + "\n", + "Now you can make calls to the llamafile's REST API. By default, the llamafile server listens at http://localhost:8080. You can find full server documentation [here](https://github.com/Mozilla-Ocho/llamafile/blob/main/llama.cpp/server/README.md#api-endpoints). You can interact with the llamafile directly via the REST API, but here we'll show how to interact with it using LangChain.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'? \\nI\\'ve got a thing for pink, but you know that.\\n\"Can we not talk about work anymore?\" - What did she say?\\nI don\\'t want to be a burden on you.\\nIt\\'s hard to keep a good thing going.\\nYou can\\'t tell me what I want, I have a life too!'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langchain_community.llms.llamafile import Llamafile\n", + "\n", + "llm = Llamafile()\n", + "\n", + "llm.invoke(\"Tell me a joke\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To stream tokens, use the `.stream(...)` method:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ".\n", + "- She said, \"I’m tired of my life. What should I do?\"\n", + "- The man replied, \"I hear you. But don’t worry. Life is just like a joke. It has its funny parts too.\"\n", + "- The woman looked at him, amazed and happy to hear his wise words. - \"Thank you for your wisdom,\" she said, smiling. - He replied, \"Any time. But it doesn't come easy. You have to laugh and keep moving forward in life.\"\n", + "- She nodded, thanking him again. - The man smiled wryly. \"Life can be tough. Sometimes it seems like you’re never going to get out of your situation.\"\n", + "- He said, \"I know that. But the key is not giving up. Life has many ups and downs, but in the end, it will turn out okay.\"\n", + "- The woman's eyes softened. \"Thank you for your advice. It's so important to keep moving forward in life,\" she said. - He nodded once again. \"You’re welcome. I hope your journey is filled with laughter and joy.\"\n", + "- They both smiled and left the bar, ready to embark on their respective adventures.\n" + ] + } + ], + "source": [ + "query = \"Tell me a joke\"\n", + "\n", + "for chunks in llm.stream(query):\n", + " print(chunks, end=\"\")\n", + "\n", + "print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To learn more about the LangChain Expressive Language and the available methods on an LLM, see the [LCEL Interface](https://python.langchain.com/docs/expression_language/interface)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/docs/integrations/llms/llm_caching.ipynb b/docs/docs/integrations/llms/llm_caching.ipynb index 791ff870b0f..f428939a2a8 100644 --- a/docs/docs/integrations/llms/llm_caching.ipynb +++ b/docs/docs/integrations/llms/llm_caching.ipynb @@ -1131,6 +1131,16 @@ "print(llm(\"How come we always see one face of the moon?\"))" ] }, + { + "cell_type": "markdown", + "id": "55dc84b3-37cb-4f19-b175-40e18e06f83f", + "metadata": {}, + "source": [ + "#### Attribution statement\n", + "\n", + ">Apache Cassandra, Cassandra and Apache are either registered trademarks or trademarks of the [Apache Software Foundation](http://www.apache.org/) in the United States and/or other countries." + ] + }, { "cell_type": "markdown", "id": "8712f8fc-bb89-4164-beb9-c672778bbd91", @@ -1588,7 +1598,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.1" + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/docs/integrations/llms/manifest.ipynb b/docs/docs/integrations/llms/manifest.ipynb index a4a09fe1804..005141cf9ad 100644 --- a/docs/docs/integrations/llms/manifest.ipynb +++ b/docs/docs/integrations/llms/manifest.ipynb @@ -91,7 +91,7 @@ "\n", "\n", "CONCISE SUMMARY:\"\"\"\n", - "prompt = PromptTemplate(template=_prompt, input_variables=[\"text\"])\n", + "prompt = PromptTemplate.from_template(_prompt)\n", "\n", "text_splitter = CharacterTextSplitter()\n", "\n", diff --git a/docs/docs/integrations/llms/minimax.ipynb b/docs/docs/integrations/llms/minimax.ipynb index efb3a924b5d..b4ed78c2e17 100644 --- a/docs/docs/integrations/llms/minimax.ipynb +++ b/docs/docs/integrations/llms/minimax.ipynb @@ -113,7 +113,7 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/modal.ipynb b/docs/docs/integrations/llms/modal.ipynb index f81abf6f434..de601cf8e60 100644 --- a/docs/docs/integrations/llms/modal.ipynb +++ b/docs/docs/integrations/llms/modal.ipynb @@ -122,7 +122,7 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/mosaicml.ipynb b/docs/docs/integrations/llms/mosaicml.ipynb index c20bf8c4f2c..48307b409d0 100644 --- a/docs/docs/integrations/llms/mosaicml.ipynb +++ b/docs/docs/integrations/llms/mosaicml.ipynb @@ -55,7 +55,7 @@ "source": [ "template = \"\"\"Question: {question}\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/nlpcloud.ipynb b/docs/docs/integrations/llms/nlpcloud.ipynb index 342e8df591f..dd93614efbd 100644 --- a/docs/docs/integrations/llms/nlpcloud.ipynb +++ b/docs/docs/integrations/llms/nlpcloud.ipynb @@ -90,7 +90,7 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/oci_generative_ai.ipynb b/docs/docs/integrations/llms/oci_generative_ai.ipynb index 200f1038f3e..40332f13e1c 100644 --- a/docs/docs/integrations/llms/oci_generative_ai.ipynb +++ b/docs/docs/integrations/llms/oci_generative_ai.ipynb @@ -98,6 +98,9 @@ " model_id=\"MY_MODEL\",\n", " service_endpoint=\"https://inference.generativeai.us-chicago-1.oci.oraclecloud.com\",\n", " compartment_id=\"MY_OCID\",\n", + " auth_type=\"SECURITY_TOKEN\",\n", + " auth_profile=\"MY_PROFILE\", # replace with your profile name\n", + " model_kwargs={\"temperature\": 0.7, \"top_p\": 0.75, \"max_tokens\": 200},\n", ")\n", "\n", "prompt = PromptTemplate(input_variables=[\"query\"], template=\"{query}\")\n", @@ -158,13 +161,6 @@ "print(chain.invoke(\"when was oracle founded?\"))\n", "print(chain.invoke(\"where is oracle headquartered?\"))" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/docs/docs/integrations/llms/octoai.ipynb b/docs/docs/integrations/llms/octoai.ipynb index 589880f293f..242588d07f8 100644 --- a/docs/docs/integrations/llms/octoai.ipynb +++ b/docs/docs/integrations/llms/octoai.ipynb @@ -61,7 +61,7 @@ "outputs": [], "source": [ "template = \"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n Instruction:\\n{question}\\n Response: \"\"\"\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/ollama.ipynb b/docs/docs/integrations/llms/ollama.ipynb index 79ea7589878..51462902aec 100644 --- a/docs/docs/integrations/llms/ollama.ipynb +++ b/docs/docs/integrations/llms/ollama.ipynb @@ -244,7 +244,7 @@ "\n", "def plt_img_base64(img_base64):\n", " \"\"\"\n", - " Disply base64 encoded string as image\n", + " Display base64 encoded string as image\n", "\n", " :param img_base64: Base64 string\n", " \"\"\"\n", diff --git a/docs/docs/integrations/llms/openai.ipynb b/docs/docs/integrations/llms/openai.ipynb index cbaab6002f7..8e072675373 100644 --- a/docs/docs/integrations/llms/openai.ipynb +++ b/docs/docs/integrations/llms/openai.ipynb @@ -84,7 +84,7 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/openllm.ipynb b/docs/docs/integrations/llms/openllm.ipynb index 6fc14b3d469..0bcd3a9bb14 100644 --- a/docs/docs/integrations/llms/openllm.ipynb +++ b/docs/docs/integrations/llms/openllm.ipynb @@ -119,7 +119,7 @@ "\n", "template = \"What is a good name for a company that makes {product}?\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"product\"])\n", + "prompt = PromptTemplate.from_template(template)\n", "\n", "llm_chain = LLMChain(prompt=prompt, llm=llm)\n", "\n", diff --git a/docs/docs/integrations/llms/openlm.ipynb b/docs/docs/integrations/llms/openlm.ipynb index 766a4419c3b..5d800e130f6 100644 --- a/docs/docs/integrations/llms/openlm.ipynb +++ b/docs/docs/integrations/llms/openlm.ipynb @@ -97,7 +97,7 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", + "prompt = PromptTemplate.from_template(template)\n", "\n", "for model in [\"text-davinci-003\", \"huggingface.co/gpt2\"]:\n", " llm = OpenLM(model=model)\n", diff --git a/docs/docs/integrations/llms/petals.ipynb b/docs/docs/integrations/llms/petals.ipynb index 4d0900a53ee..779a8d9e2bf 100644 --- a/docs/docs/integrations/llms/petals.ipynb +++ b/docs/docs/integrations/llms/petals.ipynb @@ -133,7 +133,7 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/pipelineai.ipynb b/docs/docs/integrations/llms/pipelineai.ipynb index 7ed35946563..ed97a58e00c 100644 --- a/docs/docs/integrations/llms/pipelineai.ipynb +++ b/docs/docs/integrations/llms/pipelineai.ipynb @@ -107,7 +107,7 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/predictionguard.ipynb b/docs/docs/integrations/llms/predictionguard.ipynb index c6daa2d5185..1200680cd9b 100644 --- a/docs/docs/integrations/llms/predictionguard.ipynb +++ b/docs/docs/integrations/llms/predictionguard.ipynb @@ -118,7 +118,7 @@ "Query: {query}\n", "\n", "Result: \"\"\"\n", - "prompt = PromptTemplate(template=template, input_variables=[\"query\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { @@ -191,7 +191,7 @@ "template = \"\"\"Question: {question}\n", "\n", "Answer: Let's think step by step.\"\"\"\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", + "prompt = PromptTemplate.from_template(template)\n", "llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True)\n", "\n", "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n", @@ -209,7 +209,7 @@ "outputs": [], "source": [ "template = \"\"\"Write a {adjective} poem about {subject}.\"\"\"\n", - "prompt = PromptTemplate(template=template, input_variables=[\"adjective\", \"subject\"])\n", + "prompt = PromptTemplate.from_template(template)\n", "llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True)\n", "\n", "llm_chain.predict(adjective=\"sad\", subject=\"ducks\")" diff --git a/docs/docs/integrations/llms/runhouse.ipynb b/docs/docs/integrations/llms/runhouse.ipynb index 30fb5654946..fe44389d52a 100644 --- a/docs/docs/integrations/llms/runhouse.ipynb +++ b/docs/docs/integrations/llms/runhouse.ipynb @@ -83,7 +83,7 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/stochasticai.ipynb b/docs/docs/integrations/llms/stochasticai.ipynb index 1f3ecd98d0d..6a58aae7361 100644 --- a/docs/docs/integrations/llms/stochasticai.ipynb +++ b/docs/docs/integrations/llms/stochasticai.ipynb @@ -96,7 +96,7 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/textgen.ipynb b/docs/docs/integrations/llms/textgen.ipynb index b7bf0941c05..1b4aed8320a 100644 --- a/docs/docs/integrations/llms/textgen.ipynb +++ b/docs/docs/integrations/llms/textgen.ipynb @@ -53,7 +53,7 @@ "Answer: Let's think step by step.\"\"\"\n", "\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", + "prompt = PromptTemplate.from_template(template)\n", "llm = TextGen(model_url=model_url)\n", "llm_chain = LLMChain(prompt=prompt, llm=llm)\n", "question = \"What NFL team won the Super Bowl in the year Justin Bieber was born?\"\n", @@ -104,7 +104,7 @@ "Answer: Let's think step by step.\"\"\"\n", "\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", + "prompt = PromptTemplate.from_template(template)\n", "llm = TextGen(\n", " model_url=model_url, streaming=True, callbacks=[StreamingStdOutCallbackHandler()]\n", ")\n", diff --git a/docs/docs/integrations/llms/titan_takeoff.ipynb b/docs/docs/integrations/llms/titan_takeoff.ipynb index ce2ad317974..b7df1bb0016 100644 --- a/docs/docs/integrations/llms/titan_takeoff.ipynb +++ b/docs/docs/integrations/llms/titan_takeoff.ipynb @@ -146,7 +146,7 @@ "\n", "template = \"What is the capital of {country}\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"country\"])\n", + "prompt = PromptTemplate.from_template(template)\n", "\n", "llm_chain = LLMChain(llm=llm, prompt=prompt)\n", "\n", diff --git a/docs/docs/integrations/llms/tongyi.ipynb b/docs/docs/integrations/llms/tongyi.ipynb index 1e3da253910..7b57e4c462d 100644 --- a/docs/docs/integrations/llms/tongyi.ipynb +++ b/docs/docs/integrations/llms/tongyi.ipynb @@ -95,7 +95,7 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/vllm.ipynb b/docs/docs/integrations/llms/vllm.ipynb index eb4208b97bf..4d88a2714fa 100644 --- a/docs/docs/integrations/llms/vllm.ipynb +++ b/docs/docs/integrations/llms/vllm.ipynb @@ -82,7 +82,7 @@ " temperature=0.8,\n", ")\n", "\n", - "print(llm(\"What is the capital of France ?\"))" + "print(llm.invoke(\"What is the capital of France ?\"))" ] }, { @@ -117,8 +117,7 @@ "1. The first Pokemon game was released in 1996.\n", "2. The president was Bill Clinton.\n", "3. Clinton was president from 1993 to 2001.\n", - "4. The answer is Clinton.\n", - "\n" + "4. The answer is Clinton.\n" ] }, { @@ -136,13 +135,13 @@ "template = \"\"\"Question: {question}\n", "\n", "Answer: Let's think step by step.\"\"\"\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", + "prompt = PromptTemplate.from_template(template)\n", "\n", "llm_chain = LLMChain(prompt=prompt, llm=llm)\n", "\n", "question = \"Who was the US president in the year the first Pokemon game was released?\"\n", "\n", - "print(llm_chain.run(question))" + "print(llm_chain.invoke(question))" ] }, { @@ -172,7 +171,36 @@ " trust_remote_code=True, # mandatory for hf models\n", ")\n", "\n", - "llm(\"What is the future of AI?\")" + "llm.invoke(\"What is the future of AI?\")" + ] + }, + { + "cell_type": "markdown", + "id": "d6ca8fd911d25faa", + "metadata": { + "collapsed": false + }, + "source": [ + "## Quantization\n", + "\n", + "vLLM supports `awq` quantization. To enable it, pass `quantization` to `vllm_kwargs`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2cada3174c46a0ea", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "llm_q = VLLM(\n", + " model=\"TheBloke/Llama-2-7b-Chat-AWQ\",\n", + " trust_remote_code=True,\n", + " max_new_tokens=512,\n", + " vllm_kwargs={\"quantization\": \"awq\"},\n", + ")" ] }, { @@ -216,7 +244,7 @@ " model_name=\"tiiuae/falcon-7b\",\n", " model_kwargs={\"stop\": [\".\"]},\n", ")\n", - "print(llm(\"Rome is\"))" + "print(llm.invoke(\"Rome is\"))" ] } ], diff --git a/docs/docs/integrations/llms/writer.ipynb b/docs/docs/integrations/llms/writer.ipynb index fed4af16690..5c2206d1f56 100644 --- a/docs/docs/integrations/llms/writer.ipynb +++ b/docs/docs/integrations/llms/writer.ipynb @@ -72,7 +72,7 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/xinference.ipynb b/docs/docs/integrations/llms/xinference.ipynb index a2907446705..5643750a4b9 100644 --- a/docs/docs/integrations/llms/xinference.ipynb +++ b/docs/docs/integrations/llms/xinference.ipynb @@ -126,7 +126,7 @@ "\n", "template = \"Where can we visit in the capital of {country}?\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"country\"])\n", + "prompt = PromptTemplate.from_template(template)\n", "\n", "llm_chain = LLMChain(prompt=prompt, llm=llm)\n", "\n", diff --git a/docs/docs/integrations/llms/yandex.ipynb b/docs/docs/integrations/llms/yandex.ipynb index 0dfa53bd61c..42093fec896 100644 --- a/docs/docs/integrations/llms/yandex.ipynb +++ b/docs/docs/integrations/llms/yandex.ipynb @@ -56,7 +56,7 @@ "outputs": [], "source": [ "template = \"What is the capital of {country}?\"\n", - "prompt = PromptTemplate(template=template, input_variables=[\"country\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/llms/yuan2.ipynb b/docs/docs/integrations/llms/yuan2.ipynb new file mode 100644 index 00000000000..06e45df81df --- /dev/null +++ b/docs/docs/integrations/llms/yuan2.ipynb @@ -0,0 +1,117 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Yuan2.0\n", + "\n", + "[Yuan2.0](https://github.com/IEIT-Yuan/Yuan-2.0) is a new generation Fundamental Large Language Model developed by IEIT System. We have published all three models, Yuan 2.0-102B, Yuan 2.0-51B, and Yuan 2.0-2B. And we provide relevant scripts for pretraining, fine-tuning, and inference services for other developers. Yuan2.0 is based on Yuan1.0, utilizing a wider range of high-quality pre training data and instruction fine-tuning datasets to enhance the model's understanding of semantics, mathematics, reasoning, code, knowledge, and other aspects.\n", + "\n", + "This example goes over how to use LangChain to interact with `Yuan2.0`(2B/51B/102B) Inference for text generation.\n", + "\n", + "Yuan2.0 set up an inference service so user just need request the inference api to get result, which is introduced in [Yuan2.0 Inference-Server](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/docs/inference_server.md)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true, + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from langchain.chains import LLMChain\n", + "from langchain_community.llms.yuan2 import Yuan2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true, + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# default infer_api for a local deployed Yuan2.0 inference server\n", + "infer_api = \"http://127.0.0.1:8000\"\n", + "\n", + "# direct access endpoint in a proxied environment\n", + "# import os\n", + "# os.environ[\"no_proxy\"]=\"localhost,127.0.0.1,::1\"\n", + "\n", + "yuan_llm = Yuan2(\n", + " infer_api=infer_api,\n", + " max_tokens=2048,\n", + " temp=1.0,\n", + " top_p=0.9,\n", + " top_k=40,\n", + " use_history=False,\n", + ")\n", + "\n", + "# turn on use_history only when you want the Yuan2.0 to keep track of the conversation history\n", + "# and send the accumulated context to the backend model api, which make it stateful. By default it is stateless.\n", + "# llm.use_history = True" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "question = \"请介绍一下中国。\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "is_executing": true, + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "print(yuan_llm(question))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "langchain-dev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/docs/docs/integrations/memory/astradb_chat_message_history.ipynb b/docs/docs/integrations/memory/astradb_chat_message_history.ipynb index abe6b1b2876..068b804f00b 100644 --- a/docs/docs/integrations/memory/astradb_chat_message_history.ipynb +++ b/docs/docs/integrations/memory/astradb_chat_message_history.ipynb @@ -32,7 +32,7 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install --upgrade --quiet \"astrapy>=0.6.2\"" + "%pip install --upgrade --quiet \"astrapy>=0.7.1\"" ] }, { diff --git a/docs/docs/integrations/memory/aws_dynamodb.ipynb b/docs/docs/integrations/memory/aws_dynamodb.ipynb index 32001587bfb..9410bbe024c 100644 --- a/docs/docs/integrations/memory/aws_dynamodb.ipynb +++ b/docs/docs/integrations/memory/aws_dynamodb.ipynb @@ -274,8 +274,6 @@ "metadata": {}, "outputs": [], "source": [ - "from typing import Optional\n", - "\n", "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n", "from langchain_core.runnables.history import RunnableWithMessageHistory\n", "from langchain_openai import ChatOpenAI" diff --git a/docs/docs/integrations/memory/cassandra_chat_message_history.ipynb b/docs/docs/integrations/memory/cassandra_chat_message_history.ipynb index d802bc785da..64ead129f51 100644 --- a/docs/docs/integrations/memory/cassandra_chat_message_history.ipynb +++ b/docs/docs/integrations/memory/cassandra_chat_message_history.ipynb @@ -145,6 +145,24 @@ "source": [ "message_history.messages" ] + }, + { + "cell_type": "markdown", + "id": "59902d0f-e9ba-4e3d-a7e0-ce202b9d3c43", + "metadata": {}, + "source": [ + "#### Attribution statement\n", + "\n", + "> Apache Cassandra, Cassandra and Apache are either registered trademarks or trademarks of the [Apache Software Foundation](http://www.apache.org/) in the United States and/or other countries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7efaa51c-e9ee-4dce-80a4-eb9280a0dbe5", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -163,7 +181,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.9.17" } }, "nbformat": 4, diff --git a/docs/docs/integrations/memory/mongodb_chat_message_history.ipynb b/docs/docs/integrations/memory/mongodb_chat_message_history.ipynb index 8356154fcda..88771c7680f 100644 --- a/docs/docs/integrations/memory/mongodb_chat_message_history.ipynb +++ b/docs/docs/integrations/memory/mongodb_chat_message_history.ipynb @@ -133,8 +133,6 @@ "metadata": {}, "outputs": [], "source": [ - "from typing import Optional\n", - "\n", "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n", "from langchain_core.runnables.history import RunnableWithMessageHistory\n", "from langchain_openai import ChatOpenAI" diff --git a/docs/docs/integrations/memory/remembrall.md b/docs/docs/integrations/memory/remembrall.md index 9cc03fd75e7..8f8b8187859 100644 --- a/docs/docs/integrations/memory/remembrall.md +++ b/docs/docs/integrations/memory/remembrall.md @@ -16,6 +16,12 @@ To get started, [sign in with Github on the Remembrall platform](https://remembr Any request that you send with the modified `openai_api_base` (see below) and Remembrall API key will automatically be tracked in the Remembrall dashboard. You **never** have to share your OpenAI key with our platform and this information is **never** stored by the Remembrall systems. +To do this, we need to install the following dependencies: + +```bash +pip install -U langchain-openai +``` + ### Enable Long Term Memory In addition to setting the `openai_api_base` and Remembrall API key via `x-gp-api-key`, you should specify a UID to maintain memory for. This will usually be a unique user identifier (like email). diff --git a/docs/docs/integrations/memory/sql_chat_message_history.ipynb b/docs/docs/integrations/memory/sql_chat_message_history.ipynb index 85939915672..e8cca612624 100644 --- a/docs/docs/integrations/memory/sql_chat_message_history.ipynb +++ b/docs/docs/integrations/memory/sql_chat_message_history.ipynb @@ -26,7 +26,7 @@ "The integration lives in the `langchain-community` package, so we need to install that. We also need to install the `SQLAlchemy` package.\n", "\n", "```bash\n", - "pip install -U langchain-community SQLAlchemy\n", + "pip install -U langchain-community SQLAlchemy langchain-openai\n", "```" ] }, @@ -71,10 +71,7 @@ "end_time": "2023-08-28T10:04:38.077748Z", "start_time": "2023-08-28T10:04:36.105894Z" }, - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } + "collapsed": false }, "outputs": [], "source": [ @@ -97,10 +94,7 @@ "end_time": "2023-08-28T10:04:38.929396Z", "start_time": "2023-08-28T10:04:38.915727Z" }, - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } + "collapsed": false }, "outputs": [ { @@ -137,8 +131,6 @@ "metadata": {}, "outputs": [], "source": [ - "from typing import Optional\n", - "\n", "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n", "from langchain_core.runnables.history import RunnableWithMessageHistory\n", "from langchain_openai import ChatOpenAI" diff --git a/docs/docs/integrations/memory/sqlite.ipynb b/docs/docs/integrations/memory/sqlite.ipynb index 200335cc4df..21dde4e114b 100644 --- a/docs/docs/integrations/memory/sqlite.ipynb +++ b/docs/docs/integrations/memory/sqlite.ipynb @@ -119,8 +119,6 @@ "metadata": {}, "outputs": [], "source": [ - "from typing import Optional\n", - "\n", "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n", "from langchain_core.runnables.history import RunnableWithMessageHistory\n", "from langchain_openai import ChatOpenAI" diff --git a/docs/docs/integrations/platforms/google.mdx b/docs/docs/integrations/platforms/google.mdx index 5d09b18f56b..b1ab193a80a 100644 --- a/docs/docs/integrations/platforms/google.mdx +++ b/docs/docs/integrations/platforms/google.mdx @@ -207,15 +207,11 @@ from langchain_community.vectorstores import MatchingEngine > [Google BigQuery](https://cloud.google.com/bigquery), > BigQuery is a serverless and cost-effective enterprise data warehouse in Google Cloud. > -> Google BigQuery Vector Search +> [Google BigQuery Vector Search](https://cloud.google.com/bigquery/docs/vector-search-intro) > BigQuery vector search lets you use GoogleSQL to do semantic search, using vector indexes for fast but approximate results, or using brute force for exact results. > It can calculate Euclidean or Cosine distance. With LangChain, we default to use Euclidean distance. -> This is a private preview (experimental) feature. Please submit this -> [enrollment form](https://docs.google.com/forms/d/18yndSb4dTf2H0orqA9N7NAchQEDQekwWiD5jYfEkGWk/viewform?edit_requested=true) -> if you want to enroll BigQuery Vector Search Experimental. - We need to install several python packages. ```bash diff --git a/docs/docs/integrations/platforms/index.mdx b/docs/docs/integrations/platforms/index.mdx new file mode 100644 index 00000000000..14c09f2fa8c --- /dev/null +++ b/docs/docs/integrations/platforms/index.mdx @@ -0,0 +1,33 @@ +--- +sidebar_position: 0 +sidebar_class_name: hidden +--- + +# Providers + +LangChain integrates with many providers. + +## Partner Packages + +These providers have standalone `langchain-{provider}` packages for improved versioning, dependency management and testing. + +- [OpenAI](/docs/integrations/platforms/openai) +- [Anthropic](/docs/integrations/platforms/anthropic) +- [Google](/docs/integrations/platforms/google) +- [MistralAI](/docs/integrations/providers/mistralai) +- [NVIDIA AI](/docs/integrations/providers/nvidia) +- [Together AI](/docs/integrations/providers/together) +- [Robocorp](/docs/integrations/providers/robocorp) +- [Exa Search](/docs/integrations/providers/exa_search) +- [Nomic](/docs/integrations/providers/nomic) + + +## Featured Community Providers + +- [AWS](/docs/integrations/platforms/aws) +- [Hugging Face](/docs/integrations/platforms/huggingface) +- [Microsoft](/docs/integrations/platforms/microsoft) + +## All Providers + +Click [here](/docs/integrations/providers/) to see all providers. diff --git a/docs/docs/integrations/providers/apache_doris.mdx b/docs/docs/integrations/providers/apache_doris.mdx new file mode 100644 index 00000000000..93db9330309 --- /dev/null +++ b/docs/docs/integrations/providers/apache_doris.mdx @@ -0,0 +1,21 @@ +# Apache Doris + +>[Apache Doris](https://doris.apache.org/) is a modern data warehouse for real-time analytics. +It delivers lightning-fast analytics on real-time data at scale. + +>Usually `Apache Doris` is categorized into OLAP, and it has showed excellent performance in [ClickBench — a Benchmark For Analytical DBMS](https://benchmark.clickhouse.com/). Since it has a super-fast vectorized execution engine, it could also be used as a fast vectordb. + +## Installation and Setup + + +```bash +pip install pymysql +``` + +## Vector Store + +See a [usage example](/docs/integrations/vectorstores/apache_doris). + +```python +from langchain_community.vectorstores import ApacheDoris +``` diff --git a/docs/docs/integrations/providers/astradb.mdx b/docs/docs/integrations/providers/astradb.mdx index 20a94d736b3..8cc4788cc28 100644 --- a/docs/docs/integrations/providers/astradb.mdx +++ b/docs/docs/integrations/providers/astradb.mdx @@ -1,25 +1,21 @@ # Astra DB -This page lists the integrations available with [Astra DB](https://docs.datastax.com/en/astra/home/astra.html) and [Apache Cassandra®](https://cassandra.apache.org/). +> DataStax [Astra DB](https://docs.datastax.com/en/astra/home/astra.html) is a serverless vector-capable database built on Apache Cassandra® and made conveniently available +> through an easy-to-use JSON API. ### Setup Install the following Python package: ```bash -pip install "astrapy>=0.5.3" +pip install "astrapy>=0.7.1" ``` -## Astra DB - -> DataStax [Astra DB](https://docs.datastax.com/en/astra/home/astra.html) is a serverless vector-capable database built on Cassandra and made conveniently available -> through an easy-to-use JSON API. - -### Vector Store +## Vector Store ```python -from langchain_community.vectorstores import AstraDB -vector_store = AstraDB( +from langchain_astradb import AstraDBVectorStore +vector_store = AstraDBVectorStore( embedding=my_embedding, collection_name="my_store", api_endpoint="...", @@ -29,11 +25,22 @@ vector_store = AstraDB( Learn more in the [example notebook](/docs/integrations/vectorstores/astradb). -### LLM Cache +## Chat message history + +```python +from langchain_community.chat_message_histories import AstraDBChatMessageHistory +message_history = AstraDBChatMessageHistory( + session_id="test-session", + api_endpoint="...", + token="...", +) +``` + +## LLM Cache ```python from langchain.globals import set_llm_cache -from langchain.cache import AstraDBCache +from langchain_community.cache import AstraDBCache set_llm_cache(AstraDBCache( api_endpoint="...", token="...", @@ -43,11 +50,11 @@ set_llm_cache(AstraDBCache( Learn more in the [example notebook](/docs/integrations/llms/llm_caching#astra-db-caches) (scroll to the Astra DB section). -### Semantic LLM Cache +## Semantic LLM Cache ```python from langchain.globals import set_llm_cache -from langchain.cache import AstraDBSemanticCache +from langchain_community.cache import AstraDBSemanticCache set_llm_cache(AstraDBSemanticCache( embedding=my_embedding, api_endpoint="...", @@ -57,20 +64,9 @@ set_llm_cache(AstraDBSemanticCache( Learn more in the [example notebook](/docs/integrations/llms/llm_caching#astra-db-caches) (scroll to the appropriate section). -### Chat message history - -```python -from langchain.memory import AstraDBChatMessageHistory -message_history = AstraDBChatMessageHistory( - session_id="test-session", - api_endpoint="...", - token="...", -) -``` - Learn more in the [example notebook](/docs/integrations/memory/astradb_chat_message_history). -### Document loader +## Document loader ```python from langchain_community.document_loaders import AstraDBLoader @@ -83,13 +79,13 @@ loader = AstraDBLoader( Learn more in the [example notebook](/docs/integrations/document_loaders/astradb). -### Self-querying retriever +## Self-querying retriever ```python -from langchain_community.vectorstores import AstraDB +from langchain_astradb import AstraDBVectorStore from langchain.retrievers.self_query.base import SelfQueryRetriever -vector_store = AstraDB( +vector_store = AstraDBVectorStore( embedding=my_embedding, collection_name="my_store", api_endpoint="...", @@ -106,7 +102,7 @@ retriever = SelfQueryRetriever.from_llm( Learn more in the [example notebook](/docs/integrations/retrievers/self_query/astradb). -### Store +## Store ```python from langchain_community.storage import AstraDBStore @@ -119,7 +115,7 @@ store = AstraDBStore( Learn more in the [example notebook](/docs/integrations/stores/astradb#astradbstore). -### Byte Store +## Byte Store ```python from langchain_community.storage import AstraDBByteStore @@ -131,57 +127,3 @@ store = AstraDBByteStore( ``` Learn more in the [example notebook](/docs/integrations/stores/astradb#astradbbytestore). - -## Apache Cassandra and Astra DB through CQL - -> [Cassandra](https://cassandra.apache.org/) is a NoSQL, row-oriented, highly scalable and highly available database. -> Starting with version 5.0, the database ships with [vector search capabilities](https://cassandra.apache.org/doc/trunk/cassandra/vector-search/overview.html). -> DataStax [Astra DB through CQL](https://docs.datastax.com/en/astra-serverless/docs/vector-search/quickstart.html) is a managed serverless database built on Cassandra, offering the same interface and strengths. - -These databases use the CQL protocol (Cassandra Query Language). -Hence, a different set of connectors, outlined below, shall be used. - -### Vector Store - -```python -from langchain_community.vectorstores import Cassandra -vector_store = Cassandra( - embedding=my_embedding, - table_name="my_store", -) -``` - -Learn more in the [example notebook](/docs/integrations/vectorstores/astradb#apache-cassandra-and-astra-db-through-cql) (scroll down to the CQL-specific section). - - -### Memory - -```python -from langchain.memory import CassandraChatMessageHistory -message_history = CassandraChatMessageHistory(session_id="my-session") -``` - -Learn more in the [example notebook](/docs/integrations/memory/cassandra_chat_message_history). - - -### LLM Cache - -```python -from langchain.cache import CassandraCache -langchain.llm_cache = CassandraCache() -``` - -Learn more in the [example notebook](/docs/integrations/llms/llm_caching#cassandra-caches) (scroll to the Cassandra section). - - -### Semantic LLM Cache - -```python -from langchain.cache import CassandraSemanticCache -cassSemanticCache = CassandraSemanticCache( - embedding=my_embedding, - table_name="my_store", -) -``` - -Learn more in the [example notebook](/docs/integrations/llms/llm_caching#cassandra-caches) (scroll to the appropriate section). diff --git a/docs/docs/integrations/providers/breebs.md b/docs/docs/integrations/providers/breebs.md new file mode 100644 index 00000000000..6d4262e163d --- /dev/null +++ b/docs/docs/integrations/providers/breebs.md @@ -0,0 +1,24 @@ +# BREEBS (Open Knowledge) + +[BREEBS](https://www.breebs.com/) is an open collaborative knowledge platform. +Anybody can create a Breeb, a knowledge capsule based on PDFs stored on a Google Drive folder. +A breeb can be used by any LLM/chatbot to improve its expertise, reduce hallucinations and give access to sources. +Behind the scenes, Breebs implements several Retrieval Augmented Generation (RAG) models to seamlessly provide useful context at each iteration. + +## List of available Breebs + +To get the full list of Breebs, including their key (breeb_key) and description : +https://breebs.promptbreeders.com/web/listbreebs. +Dozens of Breebs have already been created by the community and are freely available for use. They cover a wide range of expertise, from organic chemistry to mythology, as well as tips on seduction and decentralized finance. + +## Creating a new Breeb + +To generate a new Breeb, simply compile PDF files in a publicly shared Google Drive folder and initiate the creation process on the [BREEBS website](https://www.breebs.com/) by clicking the "Create Breeb" button. You can currently include up to 120 files, with a total character limit of 15 million. + +## Retriever +```python +from langchain.retrievers import BreebsRetriever +``` + +# Example +[See usage example (Retrieval & ConversationalRetrievalChain)](https://python.langchain.com/docs/integrations/retrievers/breebs) \ No newline at end of file diff --git a/docs/docs/integrations/providers/cassandra.mdx b/docs/docs/integrations/providers/cassandra.mdx new file mode 100644 index 00000000000..392f7a1767e --- /dev/null +++ b/docs/docs/integrations/providers/cassandra.mdx @@ -0,0 +1,76 @@ +# Apache Cassandra + +> [Apache Cassandra®](https://cassandra.apache.org/) is a NoSQL, row-oriented, highly scalable and highly available database. +> Starting with version 5.0, the database ships with [vector search capabilities](https://cassandra.apache.org/doc/trunk/cassandra/vector-search/overview.html). + +The integrations outlined in this page can be used with Cassandra as well as other CQL-compatible databases, i.e. those using the Cassandra Query Language protocol. + + +### Setup + +Install the following Python package: + +```bash +pip install "cassio>=0.1.4" +``` + + +## Vector Store + +```python +from langchain_community.vectorstores import Cassandra +vector_store = Cassandra( + embedding=my_embedding, + table_name="my_store", +) +``` + +Learn more in the [example notebook](/docs/integrations/vectorstores/cassandra). + +## Chat message history + +```python +from langchain_community.chat_message_histories import CassandraChatMessageHistory +message_history = CassandraChatMessageHistory(session_id="my-session") +``` + +Learn more in the [example notebook](/docs/integrations/memory/cassandra_chat_message_history). + + +## LLM Cache + +```python +from langchain.globals import set_llm_cache +from langchain_community.cache import CassandraCache +set_llm_cache(CassandraCache()) +``` + +Learn more in the [example notebook](/docs/integrations/llms/llm_caching#cassandra-caches) (scroll to the Cassandra section). + + +## Semantic LLM Cache + +```python +from langchain.globals import set_llm_cache +from langchain_community.cache import CassandraSemanticCache +set_llm_cache(CassandraSemanticCache( + embedding=my_embedding, + table_name="my_store", +)) +``` + +Learn more in the [example notebook](/docs/integrations/llms/llm_caching#cassandra-caches) (scroll to the appropriate section). + +## Document loader + +```python +from langchain_community.document_loaders import CassandraLoader +loader = CassandraLoader(table="my_table") +docs = loader.load() +``` + +Learn more in the [example notebook](/docs/integrations/document_loaders/cassandra). + +#### Attribution statement + +> Apache Cassandra, Cassandra and Apache are either registered trademarks or trademarks of the [Apache Software Foundation](http://www.apache.org/) in the United States and/or other countries. diff --git a/docs/docs/integrations/providers/dspy.ipynb b/docs/docs/integrations/providers/dspy.ipynb index dba57d50729..85d616bba81 100644 --- a/docs/docs/integrations/providers/dspy.ipynb +++ b/docs/docs/integrations/providers/dspy.ipynb @@ -185,7 +185,7 @@ " \n", "- **Output:** An engaging tweet that correctly answers the question from the retrieved info.\n", " \n", - "Let's use LangChain's expression langugage (LCEL) to illustrate this. Any prompt here will do, we will optimize the final prompt with DSPy.\n", + "Let's use LangChain's expression language (LCEL) to illustrate this. Any prompt here will do, we will optimize the final prompt with DSPy.\n", "\n", "Considering that, let's just keep it to the barebones: **Given {context}, answer the question {question} as a tweet.**" ] diff --git a/docs/docs/integrations/providers/exa_search.ipynb b/docs/docs/integrations/providers/exa_search.ipynb new file mode 100644 index 00000000000..c72dbd1c4a9 --- /dev/null +++ b/docs/docs/integrations/providers/exa_search.ipynb @@ -0,0 +1,77 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exa Search\n", + "\n", + "Exa's search integration exists in its own [partner package](https://pypi.org/project/langchain-exa/). You can install it with:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -qU langchain-exa" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to use the package, you will also need to set the `EXA_API_KEY` environment variable to your Exa API key.\n", + "\n", + "## Retriever\n", + "\n", + "You can use the [`ExaSearchRetriever`](/docs/integrations/tools/exa_search#using-exasearchretriever) in a standard retrieval pipeline. You can import it as follows" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "y8ku6X96sebl" + }, + "outputs": [], + "source": [ + "from langchain_exa import ExaSearchRetriever" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tools\n", + "\n", + "You can use Exa as an agent tool as described in the [Exa tool calling docs](/docs/integrations/tools/exa_search#using-the-exa-sdk-as-langchain-agent-tools).\n" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/docs/docs/integrations/providers/konko.mdx b/docs/docs/integrations/providers/konko.mdx index efdc5cb05a4..5e474d4ae00 100644 --- a/docs/docs/integrations/providers/konko.mdx +++ b/docs/docs/integrations/providers/konko.mdx @@ -1,81 +1,45 @@ # Konko -This page covers how to run models on Konko within LangChain. +All functionality related to Konko -Konko API is a fully managed API designed to help application developers: +>[Konko AI](https://www.konko.ai/) provides a fully managed API to help application developers -Select the right LLM(s) for their application -Prototype with various open-source and proprietary LLMs -Move to production in-line with their security, privacy, throughput, latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant infrastructure +>1. **Select** the right open source or proprietary LLMs for their application +>2. **Build** applications faster with integrations to leading application frameworks and fully managed APIs +>3. **Fine tune** smaller open-source LLMs to achieve industry-leading performance at a fraction of the cost +>4. **Deploy production-scale APIs** that meet security, privacy, throughput, and latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant, multi-cloud infrastructure ## Installation and Setup -### First you'll need an API key -You can request it by messaging [support@konko.ai](mailto:support@konko.ai) +1. Sign in to our web app to [create an API key](https://platform.konko.ai/settings/api-keys) to access models via our endpoints for [chat completions](https://docs.konko.ai/reference/post-chat-completions) and [completions](https://docs.konko.ai/reference/post-completions). +2. Enable a Python3.8+ environment +3. Install the SDK -### Install Konko AI's Python SDK +```bash +pip install konko +``` -#### 1. Enable a Python3.8+ environment +4. Set API Keys as environment variables(`KONKO_API_KEY`,`OPENAI_API_KEY`) -#### 2. Set API Keys - -##### Option 1: Set Environment Variables - -1. You can set environment variables for - 1. KONKO_API_KEY (Required) - 2. OPENAI_API_KEY (Optional) - -2. In your current shell session, use the export command: - -```shell +```bash export KONKO_API_KEY={your_KONKO_API_KEY_here} export OPENAI_API_KEY={your_OPENAI_API_KEY_here} #Optional ``` -Alternatively, you can add the above lines directly to your shell startup script (such as .bashrc or .bash_profile for Bash shell and .zshrc for Zsh shell) to have them set automatically every time a new shell session starts. - -##### Option 2: Set API Keys Programmatically - -If you prefer to set your API keys directly within your Python script or Jupyter notebook, you can use the following commands: - -```python -konko.set_api_key('your_KONKO_API_KEY_here') -konko.set_openai_api_key('your_OPENAI_API_KEY_here') # Optional -``` - -#### 3. Install the SDK +Please see [the Konko docs](https://docs.konko.ai/docs/getting-started) for more details. -```shell -pip install konko -``` +## LLM -#### 4. Verify Installation & Authentication +**Explore Available Models:** Start by browsing through the [available models](https://docs.konko.ai/docs/list-of-models) on Konko. Each model caters to different use cases and capabilities. -```python -#Confirm konko has installed successfully -import konko -#Confirm API keys from Konko and OpenAI are set properly -konko.Model.list() -``` +Another way to find the list of models running on the Konko instance is through this [endpoint](https://docs.konko.ai/reference/get-models). -## Calling a model +See a usage [example](/docs/integrations/llms/konko). -Find a model on the [Konko Introduction page](https://docs.konko.ai/docs/list-of-models) - -Another way to find the list of models running on the Konko instance is through this [endpoint](https://docs.konko.ai/reference/listmodels). - -## Examples of Endpoint Usage - - -- **ChatCompletion with Mistral-7B:** - ```python - chat_instance = ChatKonko(max_tokens=10, model = 'mistralai/mistral-7b-instruct-v0.1') - msg = HumanMessage(content="Hi") - chat_response = chat_instance([msg]) - - ``` +### Examples of Endpoint Usage - **Completion with mistralai/Mistral-7B-v0.1:** + ```python from langchain.llms import Konko llm = Konko(max_tokens=800, model='mistralai/Mistral-7B-v0.1') @@ -83,4 +47,19 @@ Another way to find the list of models running on the Konko instance is through response = llm(prompt) ``` +## Chat Models + +See a usage [example](/docs/integrations/chat/konko). + + +- **ChatCompletion with Mistral-7B:** + + ```python + from langchain.schema import HumanMessage + from langchain_community.chat_models import ChatKonko + chat_instance = ChatKonko(max_tokens=10, model = 'mistralai/mistral-7b-instruct-v0.1') + msg = HumanMessage(content="Hi") + chat_response = chat_instance([msg]) + ``` + For further assistance, contact [support@konko.ai](mailto:support@konko.ai) or join our [Discord](https://discord.gg/TXV2s3z7RZ). \ No newline at end of file diff --git a/docs/docs/integrations/providers/mistralai.ipynb b/docs/docs/integrations/providers/mistralai.ipynb new file mode 100644 index 00000000000..561751f20c5 --- /dev/null +++ b/docs/docs/integrations/providers/mistralai.ipynb @@ -0,0 +1,78 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MistralAI\n", + "\n", + "Mistral AI is a platform that offers hosting for their powerful open source models.\n", + "\n", + "You can access them via their [API](https://docs.mistral.ai/api/).\n", + "\n", + "A valid [API key](https://console.mistral.ai/users/api-keys/) is needed to communicate with the API.\n", + "\n", + "You will also need the `langchain-mistralai` package:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -qU langchain-core langchain-mistralai" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "y8ku6X96sebl" + }, + "outputs": [], + "source": [ + "from langchain_mistralai import ChatMistralAI, MistralAIEmbeddings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See the docs for their\n", + "\n", + "- [Chat Model](/docs/integrations/chat/mistralai)\n", + "- [Embeddings Model](/docs/integrations/text_embedding/mistralai)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/docs/docs/integrations/providers/nomic.ipynb b/docs/docs/integrations/providers/nomic.ipynb index a7475ef36ea..b1f4861612a 100644 --- a/docs/docs/integrations/providers/nomic.ipynb +++ b/docs/docs/integrations/providers/nomic.ipynb @@ -11,6 +11,22 @@ "- Atlas: their Visual Data Engine\n", "- GPT4All: their Open Source Edge Language Model Ecosystem\n", "\n", + "The Nomic integration exists in its own [partner package](https://pypi.org/project/langchain-nomic/). You can install it with:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -qU langchain-nomic" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "Currently, you can import their hosted [embedding model](/docs/integrations/text_embedding/nomic) as follows:" ] }, diff --git a/docs/docs/integrations/providers/oci.mdx b/docs/docs/integrations/providers/oci.mdx new file mode 100644 index 00000000000..e0b3570028f --- /dev/null +++ b/docs/docs/integrations/providers/oci.mdx @@ -0,0 +1,58 @@ +# Oracle Cloud Infrastructure (OCI) + +The `LangChain` integrations related to [Oracle Cloud Infrastructure](https://www.oracle.com/artificial-intelligence/). + +## LLMs + +### OCI Generative AI +> Oracle Cloud Infrastructure (OCI) [Generative AI](https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm) is a fully managed service that provides a set of state-of-the-art, +> customizable large language models (LLMs) that cover a wide range of use cases, and which are available through a single API. +> Using the OCI Generative AI service you can access ready-to-use pretrained models, or create and host your own fine-tuned +> custom models based on your own data on dedicated AI clusters. + +To use, you should have the latest `oci` python SDK installed. + +```bash +pip install -U oci +``` + +See [usage examples](/docs/integrations/llms/oci_generative_ai). + +```python +from langchain_community.llms import OCIGenAI +``` + +### OCI Data Science Model Deployment Endpoint + +> [OCI Data Science](https://docs.oracle.com/en-us/iaas/data-science/using/home.htm) is a +> fully managed and serverless platform for data science teams. Using the OCI Data Science +> platform you can build, train, and manage machine learning models, and then deploy them +> as an OCI Model Deployment Endpoint using the +> [OCI Data Science Model Deployment Service](https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-about.htm). + +If you deployed a LLM with the VLLM or TGI framework, you can use the +`OCIModelDeploymentVLLM` or `OCIModelDeploymentTGI` classes to interact with it. + +To use, you should have the latest `oracle-ads` python SDK installed. + +```bash +pip install -U oracle-ads +``` + +See [usage examples](/docs/integrations/llms/oci_model_deployment_endpoint). + +```python +from langchain_community.llms import OCIModelDeploymentVLLM + +from langchain_community.llms import OCIModelDeploymentTGI +``` + +## Text Embedding Models + +### OCI Generative AI + +See [usage examples](/docs/integrations/text_embedding/oci_generative_ai). + +```python +from langchain_community.embeddings import OCIGenAIEmbeddings +``` \ No newline at end of file diff --git a/docs/docs/integrations/providers/ollama.mdx b/docs/docs/integrations/providers/ollama.mdx index da10174a168..ba00e2a2469 100644 --- a/docs/docs/integrations/providers/ollama.mdx +++ b/docs/docs/integrations/providers/ollama.mdx @@ -21,7 +21,7 @@ To use, you should set up the environment variables `ANYSCALE_API_BASE` and ## LLM ```python -from langchain.llms import Ollama +from langchain_community.llms import Ollama ``` See the notebook example [here](/docs/integrations/llms/ollama). @@ -31,7 +31,7 @@ See the notebook example [here](/docs/integrations/llms/ollama). ### Chat Ollama ```python -from langchain.chat_models import ChatOllama +from langchain_community.chat_models import ChatOllama ``` See the notebook example [here](/docs/integrations/chat/ollama). @@ -47,7 +47,7 @@ See the notebook example [here](/docs/integrations/chat/ollama_functions). ## Embedding models ```python -from langchain.embeddings import OllamaEmbeddings +from langchain_community.embeddings import OllamaEmbeddings ``` See the notebook example [here](/docs/integrations/text_embedding/ollama). diff --git a/docs/docs/integrations/providers/optimum_intel.mdx b/docs/docs/integrations/providers/optimum_intel.mdx new file mode 100644 index 00000000000..9d112a9d2a9 --- /dev/null +++ b/docs/docs/integrations/providers/optimum_intel.mdx @@ -0,0 +1,26 @@ +# Optimum-intel + +All functionality related to the [optimum-intel](https://github.com/huggingface/optimum-intel.git) and [IPEX](https://github.com/intel/intel-extension-for-pytorch). + +## Installation + +Install using optimum-intel and ipex using: + +```bash +pip install optimum[neural-compressor] +pip install intel_extension_for_pytorch +``` + +Please follow the installation instructions as specified below: + +* Install optimum-intel as shown [here](https://github.com/huggingface/optimum-intel). +* Install IPEX as shown [here](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=cpu&version=v2.2.0%2Bcpu). + +## Embedding Models + +See a [usage example](/docs/integrations/text_embedding/optimum_intel). +We also offer a full tutorial notebook "rag_with_quantized_embeddings.ipynb" for using the embedder in a RAG pipeline in the cookbook dir. + +```python +from langchain_community.embeddings import QuantizedBiEncoderEmbeddings +``` \ No newline at end of file diff --git a/docs/docs/integrations/providers/predictionguard.mdx b/docs/docs/integrations/providers/predictionguard.mdx index 72d36bc0193..fdb0f0a397c 100644 --- a/docs/docs/integrations/providers/predictionguard.mdx +++ b/docs/docs/integrations/providers/predictionguard.mdx @@ -55,7 +55,7 @@ Head to stories to get ALL the deets on each box! 👆 BONUS: Save 50% on your f Query: {query} Result: """ -prompt = PromptTemplate(template=template, input_variables=["query"]) +prompt = PromptTemplate.from_template(template) # With "guarding" or controlling the output of the LLM. See the # Prediction Guard docs (https://docs.predictionguard.com) to learn how to @@ -93,7 +93,7 @@ pgllm = PredictionGuard(model="OpenAI-gpt-3.5-turbo-instruct") template = """Question: {question} Answer: Let's think step by step.""" -prompt = PromptTemplate(template=template, input_variables=["question"]) +prompt = PromptTemplate.from_template(template) llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True) question = "What NFL team won the Super Bowl in the year Justin Beiber was born?" diff --git a/docs/docs/integrations/providers/ray_serve.ipynb b/docs/docs/integrations/providers/ray_serve.ipynb index 144fb572334..b48e76710d0 100644 --- a/docs/docs/integrations/providers/ray_serve.ipynb +++ b/docs/docs/integrations/providers/ray_serve.ipynb @@ -135,7 +135,7 @@ " # We initialize the LLM, template and the chain here\n", " llm = OpenAI(openai_api_key=OPENAI_API_KEY)\n", " template = \"Question: {question}\\n\\nAnswer: Let's think step by step.\"\n", - " prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", + " prompt = PromptTemplate.from_template(template)\n", " self.chain = LLMChain(llm=llm, prompt=prompt)\n", "\n", " def _run_chain(self, text: str):\n", diff --git a/docs/docs/integrations/providers/shaleprotocol.md b/docs/docs/integrations/providers/shaleprotocol.md index 2aced9cb638..dbdd3caa6cf 100644 --- a/docs/docs/integrations/providers/shaleprotocol.md +++ b/docs/docs/integrations/providers/shaleprotocol.md @@ -33,7 +33,7 @@ template = """Question: {question} # Answer: Let's think step by step.""" -prompt = PromptTemplate(template=template, input_variables=["question"]) +prompt = PromptTemplate.from_template(template) llm_chain = LLMChain(prompt=prompt, llm=llm) diff --git a/docs/docs/integrations/providers/together.ipynb b/docs/docs/integrations/providers/together.ipynb new file mode 100644 index 00000000000..feeb4a02295 --- /dev/null +++ b/docs/docs/integrations/providers/together.ipynb @@ -0,0 +1,78 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Together AI\n", + "\n", + "> The Together API makes it easy to fine-tune or run leading open-source models with a couple lines of code. We have integrated the world’s leading open-source models, including Llama-2, RedPajama, Falcon, Alpaca, Stable Diffusion XL, and more. Read more: https://together.ai\n", + "\n", + "To use, you'll need an API key which you can find here:\n", + "https://api.together.xyz/settings/api-keys. This can be passed in as init param\n", + "``together_api_key`` or set as environment variable ``TOGETHER_API_KEY``.\n", + "\n", + "Together API reference: https://docs.together.ai/reference/inference\n", + "\n", + "You will also need to install the `langchain-together` integration package:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install --upgrade --quiet langchain-together" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "y8ku6X96sebl" + }, + "outputs": [], + "source": [ + "from __module_name__ import (\n", + " Together, # LLM\n", + " TogetherEmbeddings,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See the docs for their\n", + "\n", + "- [LLM](/docs/integrations/llms/together)\n", + "- [Embeddings Model](/docs/integrations/text_embedding/together)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/docs/docs/integrations/retrievers/breebs.ipynb b/docs/docs/integrations/retrievers/breebs.ipynb new file mode 100644 index 00000000000..4f6c23af183 --- /dev/null +++ b/docs/docs/integrations/retrievers/breebs.ipynb @@ -0,0 +1,95 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# BREEBS (Open Knowledge)\n", + "\n", + "[BREEBS](https://www.breebs.com/) is an open collaborative knowledge platform. \n", + "Anybody can create a Breeb, a knowledge capsule, based on PDFs stored on a Google Drive folder.\n", + "A breeb can be used by any LLM/chatbot to improve its expertise, reduce hallucinations and give access to sources.\n", + "Behind the scenes, Breebs implements several Retrieval Augmented Generation (RAG) models to seamlessly provide useful context at each iteration. \n", + "\n", + "## List of available Breebs\n", + "\n", + "To get the full list of Breebs, including their key (breeb_key) and description : \n", + "https://breebs.promptbreeders.com/web/listbreebs. \n", + "Dozens of Breebs have already been created by the community and are freely available for use. They cover a wide range of expertise, from organic chemistry to mythology, as well as tips on seduction and decentralized finance.\n", + "\n", + "\n", + "## Creating a new Breeb\n", + "\n", + "To generate a new Breeb, simply compile PDF files in a publicly shared Google Drive folder and initiate the creation process on the [BREEBS website](https://www.breebs.com/) by clicking the \"Create Breeb\" button. You can currently include up to 120 files, with a total character limit of 15 million. \n", + "\n", + "## Retriever example\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-15T16:05:12.645840Z", + "iopub.status.busy": "2023-12-15T16:05:12.645698Z", + "iopub.status.idle": "2023-12-15T16:05:14.347548Z", + "shell.execute_reply": "2023-12-15T16:05:14.346835Z" + } + }, + "outputs": [], + "source": [ + "from langchain.retrievers import BreebsRetriever" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": { + "iopub.execute_input": "2023-12-15T16:05:14.350102Z", + "iopub.status.busy": "2023-12-15T16:05:14.349837Z", + "iopub.status.idle": "2023-12-15T16:05:29.569958Z", + "shell.execute_reply": "2023-12-15T16:05:29.567172Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Document(page_content=\"de poupées• Ladurée - Madeleine• Ladurée - rue Bonaparte• Flamant• Bonnichon Saint Germain• Dinh Van• Léonor Greyl• Berthillon• Christian Louboutin• Patrick Cox• Baby Dior• FNAC Musique - Bastille• FNAC - Saint Lazare• La guinguette pirate• Park Hyatt• Restaurant de Sers• Hilton Arc de Triomphe• Café Barge• Le Celadon• Le Drouant• La Perouse• Cigale Recamier• Ledoyen• Tanjia• Les Muses• Bistrot du Dôme• Avenue Foch• Fontaine Saint-Michel• Funiculaire de Montmartre• Promotrain - Place Blanche• Grand Palais• Hotel de Rohan• Hotel de Sully• Hotel des Ventes Drouot• Institut de France• Place des Invalides• Jardin d'acclimatation• Jardin des plantes Zoo• Jouffroy (passage)• Quartier de La Défense• La Villette (quartier)• Lac Inferieur du Bois de Boulogne• Les Catacombes de Paris• Place du Louvre• Rue Mazarine• Rue Monsieur le Prince11/12/2023 07:51Guide en pdf Paris à imprimer gratuitement.\", metadata={'source': 'https://breebs.promptbreeders.com/breeb?breeb_key=Parivoyage&doc=44d78553-a&page=11', 'score': 1}), Document(page_content=\"cafés et des restaurants situésdans les rues adjacentes. Il y a également une cafétéria dans le musée, qui propose des collations, desboissons et des repas légers.À voir et visiter autour :Le Muséum d'histoire naturelle de Paris est situé àproximité de plusieurs autres attractions populaires, notamment le Jardin des Plantes, la Grande Mosquéede Paris, la Sorbonne et la Bibliothèque nationale de France.Comment y aller en bus, métro, train :LeMuséum d'histoire naturelle de Paris est facilement accessible en transports en commun. Les stations demétro les plus proches sont la station Censier-Daubenton sur la ligne 7 et la station Jussieu sur les lignes 7et 10. Le musée est également accessible en bus, avec plusieurs lignes desservant la zone, telles que leslignes 24, 57, 61, 63, 67, 89 et 91. En train, la gare la plus proche est la Gare d'Austerlitz, qui est desserviepar plusieurs lignes, notamment les lignes RER C et les trains intercités. Il est également possible de serendre au musée en utilisant les services de taxis ou de VTC.Plus d'informations :+33140795601,6 euros,Ouverture : 10h - 17h, Week end: 10h - 18h ; Fermeture: Mardi(haut de\", metadata={'source': 'https://breebs.promptbreeders.com/breeb?breeb_key=Parivoyage&doc=44d78553-a&page=403', 'score': 1}), Document(page_content=\"Le célèbre Drugstore des Champs Elysées abrite de nombreuses boutiques dans un décor design. V ouspourrez y découvrir un espace beauté, des expositions éphémères, une pharmacie et des espaces réservésaux plaisirs des sens. A noter la façade d'architecture extérieure en verrePlus d'informations :+33144437900, https://www.publicisdrugstore.com/, Visite libre,(haut de page)• Place du Marché Sainte-CatherinePlace du Marché Sainte-Catherine, Paris, 75008, FR11/12/2023 07:51Guide en pdf Paris à imprimer gratuitement.\\nPage 200 sur 545https://www.cityzeum.com/imprimer-pdf/parisUne place hors de l'agitation de la capitale, où vous découvrirez des petits restaurants au charme certaindans un cadre fort agréable. Terrasses au rendez-vous l'été! Un bar à magie pour couronner le toutPlus d'informations :15-30 euros,(haut de page)• Rue de Lappe, ParisRue de Lappe, Paris, FR\", metadata={'source': 'https://breebs.promptbreeders.com/breeb?breeb_key=Parivoyage&doc=44d78553-a&page=198', 'score': 1}), Document(page_content=\"des visiteurs pour la nature etles attractions du parc. Les visiteurs peuvent prévoir de passer entre 1 à 2 heures pour visiter le parcL'accès au parc Montsouris est gratuit pour tous les visiteurs. Aucune réservation n'est nécessaire pourvisiter le parc. Cependant, pour les visites guidées, il est conseillé de réserver à l'avance pour garantir uneplace. Les tarifs pour les visites guidées peuvent varier en fonction de l'organisme proposant la visite.Ensomme, le parc Montsouris est un endroit magnifique pour se détendre et profiter de la nature en pleincœur de Paris. Avec ses attractions pittoresques, son paysage verdoyant et ses visites guidées, c'est unendroit idéal pour une sortie en famille ou entre amis.Plus d'informations :https://www.parisinfo.com/musee-monument-paris/71218/Parc-Montsouris,Gratuit,Ouverture : 8h/9h - 17h30/21h30(haut de page)• Parc des Buttes Chaumont\", metadata={'source': 'https://breebs.promptbreeders.com/breeb?breeb_key=Parivoyage&doc=44d78553-a&page=291', 'score': 1})]\n" + ] + } + ], + "source": [ + "breeb_key = \"Parivoyage\"\n", + "retriever = BreebsRetriever(breeb_key)\n", + "documents = retriever.get_relevant_documents(\n", + " \"What are some unique, lesser-known spots to explore in Paris?\"\n", + ")\n", + "print(documents)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/docs/integrations/retrievers/flashrank-reranker.ipynb b/docs/docs/integrations/retrievers/flashrank-reranker.ipynb new file mode 100644 index 00000000000..7f53ed00a68 --- /dev/null +++ b/docs/docs/integrations/retrievers/flashrank-reranker.ipynb @@ -0,0 +1,475 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "# Flashrank Reranker\n", + "\n", + "This notebook shows how to use [flashrank](https://github.com/PrithivirajDamodaran/FlashRank) for document compression and retrieval." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "is_executing": true + } + }, + "outputs": [], + "source": [ + "% pip install --upgrade --quiet flashrank\n", + "% pip install --upgrade --quiet faiss\n", + "\n", + "# OR (depending on Python version)\n", + "\n", + "% pip install --upgrade --quiet faiss_cpu" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Helper function for printing docs\n", + "\n", + "\n", + "def pretty_print_docs(docs):\n", + " print(\n", + " f\"\\n{'-' * 100}\\n\".join(\n", + " [f\"Document {i+1}:\\n\\n\" + d.page_content for i, d in enumerate(docs)]\n", + " )\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "## Set up the base vector store retriever\n", + "Let's start by initializing a simple vector store retriever and storing the 2023 State of the Union speech (in chunks). We can set up the retriever to retrieve a high number (20) of docs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Document 1:\n", + "\n", + "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n", + "\n", + "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n", + "----------------------------------------------------------------------------------------------------\n", + "Document 2:\n", + "\n", + "As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \n", + "\n", + "While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice.\n", + "----------------------------------------------------------------------------------------------------\n", + "Document 3:\n", + "\n", + "A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \n", + "\n", + "And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.\n", + "----------------------------------------------------------------------------------------------------\n", + "Document 4:\n", + "\n", + "He met the Ukrainian people. \n", + "\n", + "From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world. \n", + "\n", + "Groups of citizens blocking tanks with their bodies. Everyone from students to retirees teachers turned soldiers defending their homeland. \n", + "\n", + "In this struggle as President Zelenskyy said in his speech to the European Parliament “Light will win over darkness.” The Ukrainian Ambassador to the United States is here tonight.\n", + "----------------------------------------------------------------------------------------------------\n", + "Document 5:\n", + "\n", + "But that trickle-down theory led to weaker economic growth, lower wages, bigger deficits, and the widest gap between those at the top and everyone else in nearly a century. \n", + "\n", + "Vice President Harris and I ran for office with a new economic vision for America. \n", + "\n", + "Invest in America. Educate Americans. Grow the workforce. Build the economy from the bottom up \n", + "and the middle out, not from the top down.\n", + "----------------------------------------------------------------------------------------------------\n", + "Document 6:\n", + "\n", + "And tonight, I’m announcing that the Justice Department will name a chief prosecutor for pandemic fraud. \n", + "\n", + "By the end of this year, the deficit will be down to less than half what it was before I took office. \n", + "\n", + "The only president ever to cut the deficit by more than one trillion dollars in a single year. \n", + "\n", + "Lowering your costs also means demanding more competition. \n", + "\n", + "I’m a capitalist, but capitalism without competition isn’t capitalism. \n", + "\n", + "It’s exploitation—and it drives up prices.\n", + "----------------------------------------------------------------------------------------------------\n", + "Document 7:\n", + "\n", + "I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \n", + "\n", + "I’ve worked on these issues a long time. \n", + "\n", + "I know what works: Investing in crime prevention and community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety. \n", + "\n", + "So let’s not abandon our streets. Or choose between safety and equal justice.\n", + "----------------------------------------------------------------------------------------------------\n", + "Document 8:\n", + "\n", + "As I’ve told Xi Jinping, it is never a good bet to bet against the American people. \n", + "\n", + "We’ll create good jobs for millions of Americans, modernizing roads, airports, ports, and waterways all across America. \n", + "\n", + "And we’ll do it all to withstand the devastating effects of the climate crisis and promote environmental justice.\n", + "----------------------------------------------------------------------------------------------------\n", + "Document 9:\n", + "\n", + "Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the Cabinet. Justices of the Supreme Court. My fellow Americans. \n", + "\n", + "Last year COVID-19 kept us apart. This year we are finally together again. \n", + "\n", + "Tonight, we meet as Democrats Republicans and Independents. But most importantly as Americans. \n", + "\n", + "With a duty to one another to the American people to the Constitution. \n", + "\n", + "And with an unwavering resolve that freedom will always triumph over tyranny.\n", + "----------------------------------------------------------------------------------------------------\n", + "Document 10:\n", + "\n", + "As Ohio Senator Sherrod Brown says, “It’s time to bury the label “Rust Belt.” \n", + "\n", + "It’s time. \n", + "\n", + "But with all the bright spots in our economy, record job growth and higher wages, too many families are struggling to keep up with the bills. \n", + "\n", + "Inflation is robbing them of the gains they might otherwise feel. \n", + "\n", + "I get it. That’s why my top priority is getting prices under control.\n", + "----------------------------------------------------------------------------------------------------\n", + "Document 11:\n", + "\n", + "I’m also calling on Congress: pass a law to make sure veterans devastated by toxic exposures in Iraq and Afghanistan finally get the benefits and comprehensive health care they deserve. \n", + "\n", + "And fourth, let’s end cancer as we know it. \n", + "\n", + "This is personal to me and Jill, to Kamala, and to so many of you. \n", + "\n", + "Cancer is the #2 cause of death in America–second only to heart disease.\n", + "----------------------------------------------------------------------------------------------------\n", + "Document 12:\n", + "\n", + "Headaches. Numbness. Dizziness. \n", + "\n", + "A cancer that would put them in a flag-draped coffin. \n", + "\n", + "I know. \n", + "\n", + "One of those soldiers was my son Major Beau Biden. \n", + "\n", + "We don’t know for sure if a burn pit was the cause of his brain cancer, or the diseases of so many of our troops. \n", + "\n", + "But I’m committed to finding out everything we can. \n", + "\n", + "Committed to military families like Danielle Robinson from Ohio. \n", + "\n", + "The widow of Sergeant First Class Heath Robinson.\n", + "----------------------------------------------------------------------------------------------------\n", + "Document 13:\n", + "\n", + "He will never extinguish their love of freedom. He will never weaken the resolve of the free world. \n", + "\n", + "We meet tonight in an America that has lived through two of the hardest years this nation has ever faced. \n", + "\n", + "The pandemic has been punishing. \n", + "\n", + "And so many families are living paycheck to paycheck, struggling to keep up with the rising cost of food, gas, housing, and so much more. \n", + "\n", + "I understand.\n", + "----------------------------------------------------------------------------------------------------\n", + "Document 14:\n", + "\n", + "When we invest in our workers, when we build the economy from the bottom up and the middle out together, we can do something we haven’t done in a long time: build a better America. \n", + "\n", + "For more than two years, COVID-19 has impacted every decision in our lives and the life of the nation. \n", + "\n", + "And I know you’re tired, frustrated, and exhausted. \n", + "\n", + "But I also know this.\n", + "----------------------------------------------------------------------------------------------------\n", + "Document 15:\n", + "\n", + "My plan to fight inflation will lower your costs and lower the deficit. \n", + "\n", + "17 Nobel laureates in economics say my plan will ease long-term inflationary pressures. Top business leaders and most Americans support my plan. And here’s the plan: \n", + "\n", + "First – cut the cost of prescription drugs. Just look at insulin. One in ten Americans has diabetes. In Virginia, I met a 13-year-old boy named Joshua Davis.\n", + "----------------------------------------------------------------------------------------------------\n", + "Document 16:\n", + "\n", + "And soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \n", + "\n", + "So tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together. \n", + "\n", + "First, beat the opioid epidemic. \n", + "\n", + "There is so much we can do. Increase funding for prevention, treatment, harm reduction, and recovery.\n", + "----------------------------------------------------------------------------------------------------\n", + "Document 17:\n", + "\n", + "So let’s not abandon our streets. Or choose between safety and equal justice. \n", + "\n", + "Let’s come together to protect our communities, restore trust, and hold law enforcement accountable. \n", + "\n", + "That’s why the Justice Department required body cameras, banned chokeholds, and restricted no-knock warrants for its officers.\n", + "----------------------------------------------------------------------------------------------------\n", + "Document 18:\n", + "\n", + "My plan will not only lower costs to give families a fair shot, it will lower the deficit. \n", + "\n", + "The previous Administration not only ballooned the deficit with tax cuts for the very wealthy and corporations, it undermined the watchdogs whose job was to keep pandemic relief funds from being wasted. \n", + "\n", + "But in my administration, the watchdogs have been welcomed back. \n", + "\n", + "We’re going after the criminals who stole billions in relief money meant for small businesses and millions of Americans.\n", + "----------------------------------------------------------------------------------------------------\n", + "Document 19:\n", + "\n", + "I understand. \n", + "\n", + "I remember when my Dad had to leave our home in Scranton, Pennsylvania to find work. I grew up in a family where if the price of food went up, you felt it. \n", + "\n", + "That’s why one of the first things I did as President was fight to pass the American Rescue Plan. \n", + "\n", + "Because people were hurting. We needed to act, and we did. \n", + "\n", + "Few pieces of legislation have done more in a critical moment in our history to lift us out of crisis.\n", + "----------------------------------------------------------------------------------------------------\n", + "Document 20:\n", + "\n", + "And we will, as one people. \n", + "\n", + "One America. \n", + "\n", + "The United States of America. \n", + "\n", + "May God bless you all. May God protect our troops.\n" + ] + } + ], + "source": [ + "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain_community.document_loaders import TextLoader\n", + "from langchain_community.vectorstores import FAISS\n", + "from langchain_openai import OpenAIEmbeddings\n", + "\n", + "documents = TextLoader(\n", + " \"../../modules/state_of_the_union.txt\",\n", + ").load()\n", + "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)\n", + "texts = text_splitter.split_documents(documents)\n", + "\n", + "embedding = OpenAIEmbeddings(model=\"text-embedding-ada-002\")\n", + "retriever = FAISS.from_documents(texts, embedding).as_retriever(search_kwargs={\"k\": 20})\n", + "\n", + "query = \"What did the president say about Ketanji Brown Jackson\"\n", + "docs = retriever.get_relevant_documents(query)\n", + "pretty_print_docs(docs)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "## Doing reranking with FlashRank\n", + "Now let's wrap our base retriever with a `ContextualCompressionRetriever`, using `FlashrankRerank` as a compressor." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0, 3, 7]\n" + ] + } + ], + "source": [ + "from langchain.retrievers import ContextualCompressionRetriever\n", + "from langchain.retrievers.document_compressors import FlashrankRerank\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "llm = ChatOpenAI(temperature=0)\n", + "\n", + "compressor = FlashrankRerank()\n", + "compression_retriever = ContextualCompressionRetriever(\n", + " base_compressor=compressor, base_retriever=retriever\n", + ")\n", + "\n", + "compressed_docs = compression_retriever.get_relevant_documents(\n", + " \"What did the president say about Ketanji Jackson Brown\"\n", + ")\n", + "print([doc.metadata[\"id\"] for doc in compressed_docs])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "After reranking, the top 3 documents are different from the top 3 documents retrieved by the base retriever." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Document 1:\n", + "\n", + "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n", + "\n", + "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n", + "----------------------------------------------------------------------------------------------------\n", + "Document 2:\n", + "\n", + "And tonight, I’m announcing that the Justice Department will name a chief prosecutor for pandemic fraud. \n", + "\n", + "By the end of this year, the deficit will be down to less than half what it was before I took office. \n", + "\n", + "The only president ever to cut the deficit by more than one trillion dollars in a single year. \n", + "\n", + "Lowering your costs also means demanding more competition. \n", + "\n", + "I’m a capitalist, but capitalism without competition isn’t capitalism. \n", + "\n", + "It’s exploitation—and it drives up prices.\n", + "----------------------------------------------------------------------------------------------------\n", + "Document 3:\n", + "\n", + "As Ohio Senator Sherrod Brown says, “It’s time to bury the label “Rust Belt.” \n", + "\n", + "It’s time. \n", + "\n", + "But with all the bright spots in our economy, record job growth and higher wages, too many families are struggling to keep up with the bills. \n", + "\n", + "Inflation is robbing them of the gains they might otherwise feel. \n", + "\n", + "I get it. That’s why my top priority is getting prices under control.\n" + ] + } + ], + "source": [ + "pretty_print_docs(compressed_docs)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "## QA reranking with FlashRank" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from langchain.chains import RetrievalQA\n", + "\n", + "chain = RetrievalQA.from_chain_type(llm=llm, retriever=compression_retriever)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": "{'query': 'What did the president say about Ketanji Brown Jackson',\n 'result': \"The President said that Ketanji Brown Jackson is one of our nation's top legal minds and will continue Justice Breyer's legacy of excellence.\"}" + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chain.invoke(query)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/docs/integrations/retrievers/fleet_context.ipynb b/docs/docs/integrations/retrievers/fleet_context.ipynb index 5587fcbc645..736184a8366 100644 --- a/docs/docs/integrations/retrievers/fleet_context.ipynb +++ b/docs/docs/integrations/retrievers/fleet_context.ipynb @@ -104,8 +104,7 @@ "source": [ "## Retriever chunks\n", "\n", - "As part of their embedding process, the Fleet AI team first chunked long documents before embedding them. This means the vectors correspond to sections of pages in the LangChain docs, not entire pages. By default, when we spin up a retriever from these embeddings, we'll be retrieving these embedded chunks.", - "\n", + "As part of their embedding process, the Fleet AI team first chunked long documents before embedding them. This means the vectors correspond to sections of pages in the LangChain docs, not entire pages. By default, when we spin up a retriever from these embeddings, we'll be retrieving these embedded chunks.\n", "\n", "We will be using Fleet Context's `download_embeddings()` to grab Langchain's documentation embeddings. You can view all supported libraries' documentation at https://fleet.so/context." ] @@ -125,24 +124,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "519c6898-3ef7-4b0d-94e3-e60ac7da51a3", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Document(page_content=\"# Vector store-backed retriever A vector store retriever is a retriever that uses a vector store to retrieve documents. It is a lightweight wrapper around the vector store class to make it conform to the retriever interface. It uses the search methods implemented by a vector store, like similarity search and MMR, to query the texts in the vector store. Once you construct a vector store, it's very easy to construct a retriever. Let's walk through an example.\", metadata={'id': 'f509f20d-4c63-4a5a-a40a-5c4c0f099839', 'library_id': '4506492b-70de-49f1-ba2e-d65bd7048a28', 'page_id': 'd78cf422-2dab-4860-80fe-d71a3619b02f', 'parent': 'c153ebd9-2611-4a43-9db6-daa1f5f214f6', 'section_id': '', 'section_index': 0, 'text': \"# Vector store-backed retriever A vector store retriever is a retriever that uses a vector store to retrieve documents. It is a lightweight wrapper around the vector store class to make it conform to the retriever interface. It uses the search methods implemented by a vector store, like similarity search and MMR, to query the texts in the vector store. Once you construct a vector store, it's very easy to construct a retriever. Let's walk through an example.\", 'title': 'Vector store-backed retriever | 🦜️🔗 Langchain', 'type': None, 'url': 'https://python.langchain.com/docs/modules/data_connection/retrievers/vectorstore'}),\n", - " Document(page_content='# MultiVector Retriever It can often be beneficial to store multiple vectors per document. There are multiple use cases where this is beneficial. LangChain has a base `MultiVectorRetriever` which makes querying this type of setup easy. A lot of the complexity lies in how to create the multiple vectors per document. This notebook covers some of the common ways to create those vectors and use the `MultiVectorRetriever`. The methods to create multiple vectors per document include: - Smaller chunks: split a document into smaller chunks, and embed those (this is ParentDocumentRetriever). - Summary: create a summary for each document, embed that along with (or instead of) the document. - Hypothetical questions: create hypothetical questions that each document would be appropriate to answer, embed those along with (or instead of) the document. Note that this also enables another method of adding embeddings - manually.', metadata={'id': 'e06e6bb5-127a-49f7-9511-247d279d0d83', 'library_id': '4506492b-70de-49f1-ba2e-d65bd7048a28', 'page_id': '7c7dd0de-25e0-4e1d-9237-cfd2674c29d4', 'parent': 'beec5531-16a7-453c-80ab-c5628e0236ce', 'section_id': '', 'section_index': 0, 'text': '# MultiVector Retriever It can often be beneficial to store multiple vectors per document. There are multiple use cases where this is beneficial. LangChain has a base `MultiVectorRetriever` which makes querying this type of setup easy. A lot of the complexity lies in how to create the multiple vectors per document. This notebook covers some of the common ways to create those vectors and use the `MultiVectorRetriever`. The methods to create multiple vectors per document include: - Smaller chunks: split a document into smaller chunks, and embed those (this is ParentDocumentRetriever). - Summary: create a summary for each document, embed that along with (or instead of) the document. - Hypothetical questions: create hypothetical questions that each document would be appropriate to answer, embed those along with (or instead of) the document. Note that this also enables another method of adding embeddings - manually.', 'title': 'MultiVector Retriever | 🦜️🔗 Langchain', 'type': None, 'url': 'https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector'}),\n", - " Document(page_content='# MultiQueryRetriever Distance-based vector database retrieval embeds (represents) queries in high-dimensional space and finds similar embedded documents based on \"distance\". But, retrieval may produce different results with subtle changes in query wording or if the embeddings do not capture the semantics of the data well. Prompt engineering / tuning is sometimes done to manually address these problems, but can be tedious. The `MultiQueryRetriever` automates the process of prompt tuning by using an LLM to generate multiple queries from different perspectives for a given user input query. For each query, it retrieves a set of relevant documents and takes the unique union across all queries to get a larger set of potentially relevant documents. By generating multiple perspectives on the same question, the `MultiQueryRetriever` might be able to overcome some of the limitations of the distance-based retrieval and get a richer set of results.', metadata={'id': 'dc3b8e5b-a591-4a46-bfeb-a91b36affae1', 'library_id': '4506492b-70de-49f1-ba2e-d65bd7048a28', 'page_id': '31f80e84-c5db-4da2-939c-bccf519864a3', 'parent': 'f7c20633-6a60-4ca3-96b1-13fee66e321d', 'section_id': '', 'section_index': 0, 'text': '# MultiQueryRetriever Distance-based vector database retrieval embeds (represents) queries in high-dimensional space and finds similar embedded documents based on \"distance\". But, retrieval may produce different results with subtle changes in query wording or if the embeddings do not capture the semantics of the data well. Prompt engineering / tuning is sometimes done to manually address these problems, but can be tedious. The `MultiQueryRetriever` automates the process of prompt tuning by using an LLM to generate multiple queries from different perspectives for a given user input query. For each query, it retrieves a set of relevant documents and takes the unique union across all queries to get a larger set of potentially relevant documents. By generating multiple perspectives on the same question, the `MultiQueryRetriever` might be able to overcome some of the limitations of the distance-based retrieval and get a richer set of results.', 'title': 'MultiQueryRetriever | 🦜️🔗 Langchain', 'type': None, 'url': 'https://python.langchain.com/docs/modules/data_connection/retrievers/MultiQueryRetriever'}),\n", - " Document(page_content='# `langchain.retrievers.multi_vector`.MultiVectorRetriever[¶](#langchain-retrievers-multi-vector-multivectorretriever) *class *langchain.retrievers.multi_vector.MultiVectorRetriever[[source]](../_modules/langchain/retrievers/multi_vector.html#MultiVectorRetriever)[¶](#langchain.retrievers.multi_vector.MultiVectorRetriever) # Examples using MultiVectorRetriever[¶](#langchain-retrievers-multi-vector-multivectorretriever) - [MultiVector Retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector)', metadata={'id': '1f4ca702-35b8-44c0-b33b-18c09a1f787d', 'library_id': '6254c672-7aa0-4233-b0a6-804bd273752b', 'page_id': '87f5d1fb-a1c6-4080-9d2b-7b88794eb6bf', 'parent': '1820c44d-7783-4846-a11c-106b18da015d', 'section_id': 'langchain-retrievers-multi-vector-multivectorretriever', 'section_index': 0, 'text': '# `langchain.retrievers.multi_vector`.MultiVectorRetriever[¶](#langchain-retrievers-multi-vector-multivectorretriever) *class *langchain.retrievers.multi_vector.MultiVectorRetriever[[source]](../_modules/langchain/retrievers/multi_vector.html#MultiVectorRetriever)[¶](#langchain.retrievers.multi_vector.MultiVectorRetriever) # Examples using MultiVectorRetriever[¶](#langchain-retrievers-multi-vector-multivectorretriever) - [MultiVector Retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector)', 'title': 'langchain.retrievers.multi_vector.MultiVectorRetriever — 🦜🔗 LangChain 0.0.322', 'type': None, 'url': 'https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.multi_vector.MultiVectorRetriever.html#langchain-retrievers-multi-vector-multivectorretriever'})]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "vecstore_retriever.get_relevant_documents(\"How does the multi vector retriever work\")" ] @@ -184,24 +169,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "540a54c2-b9a0-475c-ada0-91c5f67dd0fa", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Document(page_content='Vector store-backed retriever | 🦜️🔗 Langchain\\n# Vector store-backed retriever A vector store retriever is a retriever that uses a vector store to retrieve documents. It is a lightweight wrapper around the vector store class to make it conform to the retriever interface. It uses the search methods implemented by a vector store, like similarity search and MMR, to query the texts in the vector store. Once you construct a vector store, it\\'s very easy to construct a retriever. Let\\'s walk through an example.Once you construct a vector store, it\\'s very easy to construct a retriever. Let\\'s walk through an example. ``` from langchain_community.document_loaders import TextLoaderloader = TextLoader(\\'../../../state_of_the_union.txt\\') ``` ``` from langchain.text_splitter import CharacterTextSplitterfrom langchain_community.vectorstores import FAISSfrom langchain_openai import OpenAIEmbeddingsdocuments = loader.load()text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)texts = text_splitter.split_documents(documents)embeddings = OpenAIEmbeddings()db = FAISS.from_documents(texts, embeddings) ``` ``` Exiting: Cleaning up .chroma directory ``` ``` retriever = db.as_retriever() ``` ``` docs = retriever.get_relevant_documents(\"what did he say about ketanji brown jackson\") ``` ## Maximum marginal relevance retrieval[\\u200b](#maximum-marginal-relevance-retrieval) By default, the vector store retriever uses similarity search.If the underlying vector store supports maximum marginal relevance search, you can specify that as the search type. ``` retriever = db.as_retriever(search_type=\"mmr\") ``` ``` docs = retriever.get_relevant_documents(\"what did he say about ketanji brown jackson\") ``` ## Similarity score threshold retrieval[\\u200b](#similarity-score-threshold-retrieval) You can also a retrieval method that sets a similarity score threshold and only returns documents with a score above that threshold. ``` retriever = db.as_retriever(search_type=\"similarity_score_threshold\", search_kwargs={\"score_threshold\": .5}) ``` ``` docs = retriever.get_relevant_documents(\"what did he say about ketanji brown jackson\") ``` ## Specifying top k[\\u200b](#specifying-top-k) You can also specify search kwargs like `k` to use when doing retrieval.``` retriever = db.as_retriever(search_kwargs={\"k\": 1}) ``` ``` docs = retriever.get_relevant_documents(\"what did he say about ketanji brown jackson\") ``` ``` len(docs) ``` ``` 1 ```', metadata={'title': 'Vector store-backed retriever | 🦜️🔗 Langchain', 'type': None, 'url': 'https://python.langchain.com/docs/modules/data_connection/retrievers/vectorstore', 'id': 'c153ebd9-2611-4a43-9db6-daa1f5f214f6'}),\n", - " Document(page_content='MultiVector Retriever | 🦜️🔗 Langchain\\n# MultiVector Retriever It can often be beneficial to store multiple vectors per document. There are multiple use cases where this is beneficial. LangChain has a base `MultiVectorRetriever` which makes querying this type of setup easy. A lot of the complexity lies in how to create the multiple vectors per document. This notebook covers some of the common ways to create those vectors and use the `MultiVectorRetriever`. The methods to create multiple vectors per document include: - Smaller chunks: split a document into smaller chunks, and embed those (this is ParentDocumentRetriever). - Summary: create a summary for each document, embed that along with (or instead of) the document. - Hypothetical questions: create hypothetical questions that each document would be appropriate to answer, embed those along with (or instead of) the document. Note that this also enables another method of adding embeddings - manually.Note that this also enables another method of adding embeddings - manually. This is great because you can explicitly add questions or queries that should lead to a document being recovered, giving you more control. ``` from langchain.retrievers.multi_vector import MultiVectorRetriever ``` ``` from langchain_community.vectorstores import Chromafrom langchain_openai import OpenAIEmbeddingsfrom langchain.text_splitter import RecursiveCharacterTextSplitterfrom langchain.storage import InMemoryStorefrom langchain_community.document_loaders import TextLoader ``` ``` loaders = [ TextLoader(\\'../../paul_graham_essay.txt\\'), TextLoader(\\'../../state_of_the_union.txt\\'),]docs = []for l in loaders: docs.extend(l.load())text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000)docs = text_splitter.split_documents(docs) ``` ## Smaller chunks[\\u200b](#smaller-chunks) Often times it can be useful to retrieve larger chunks of information, but embed smaller chunks.This allows for embeddings to capture the semantic meaning as closely as possible, but for as much context as possible to be passed downstream. Note that this is what the `ParentDocumentRetriever` does. Here we show what is going on under the hood.``` # The vectorstore to use to index the child chunksvectorstore = Chroma( collection_name=\"full_documents\", embedding_function=OpenAIEmbeddings())# The storage layer for the parent documentsstore = InMemoryStore()id_key = \"doc_id\"# The retriever (empty to start)retriever = MultiVectorRetriever( vectorstore=vectorstore, docstore=store, id_key=id_key,)import uuiddoc_ids = [str(uuid.uuid4()) for _ in docs] ``` ``` # The splitter to use to create smaller chunkschild_text_splitter = RecursiveCharacterTextSplitter(chunk_size=400) ``` ``` sub_docs = []for i, doc in enumerate(docs): _id = doc_ids[i] _sub_docs = child_text_splitter.split_documents([doc]) for _doc in _sub_docs: _doc.metadata[id_key] = _id sub_docs.extend(_sub_docs) ``` ``` retriever.vectorstore.add_documents(sub_docs)retriever.docstore.mset(list(zip(doc_ids, docs))) ``` ``` # Vectorstore alone retrieves the small chunksretriever.vectorstore.similarity_search(\"justice breyer\")[0] ``` ``` Document(page_content=\\'Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court.Justice Breyer, thank you for your service. \\\\n\\\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\', metadata={\\'doc_id\\': \\'10e9cbc0-4ba5-4d79-a09b-c033d1ba7b01\\', \\'source\\': \\'../../state_of_the_union.txt\\'}) ``` ``` # Retriever returns larger chunkslen(retriever.get_relevant_documents(\"justice breyer\")[0].page_content) ``` ``` 9874 ``` ## Summary[\\u200b](#summary) Oftentimes a summary may be able to distill more accurately what a chunk is about, leading to better retrieval. Here we show how to create summaries, and then embed those.``` from langchain_openai import ChatOpenAIfrom langchain_core.prompts import ChatPromptTemplatefrom langchain_core.output_parsers import StrOutputParserimport uuidfrom langchain_core.documents import Document ``` ``` chain = ( {\"doc\": lambda x: x.page_content} | ChatPromptTemplate.from_template(\"Summarize the following document:\\\\n\\\\n{doc}\") | ChatOpenAI(max_retries=0) | StrOutputParser()) ``` ``` summaries = chain.batch(docs, {\"max_concurrency\": 5}) ``` ``` # The vectorstore to use to index the child chunksvectorstore = Chroma( collection_name=\"summaries\", embedding_function=OpenAIEmbeddings())# The storage layer for the parent documentsstore = InMemoryStore()id_key = \"doc_id\"# The retriever (empty to start)retriever = MultiVectorRetriever( vectorstore=vectorstore, docstore=store, id_key=id_key,)doc_ids = [str(uuid.uuid4()) for _ in docs] ``` ``` summary_docs = [Document(page_content=s,metadata={id_key: doc_ids[i]}) for i, s in enumerate(summaries)] ``` ``` retriever.vectorstore.add_documents(summary_docs)retriever.docstore.mset(list(zip(doc_ids, docs))) ``` ``` # # We can also add the original chunks to the vectorstore if we so want# for i, doc in enumerate(docs):# doc.metadata[id_key] = doc_ids[i]# retriever.vectorstore.add_documents(docs) ``` ``` sub_docs = vectorstore.similarity_search(\"justice breyer\") ``` ``` sub_docs[0] ``` ``` Document(page_content=\"The document is a transcript of a speech given by the President of the United States.The President discusses several important issues and initiatives, including the nomination of a Supreme Court Justice, border security and immigration reform, protecting women\\'s rights, advancing LGBTQ+ equality, bipartisan legislation, addressing the opioid epidemic and mental health, supporting veterans, investigating the health effects of burn pits on military personnel, ending cancer, and the strength and resilience of the American people. \", metadata={\\'doc_id\\': \\'79fa2e9f-28d9-4372-8af3-2caf4f1de312\\'}) ``` ``` retrieved_docs = retriever.get_relevant_documents(\"justice breyer\") ``` ``` len(retrieved_docs[0].page_content) ``` ``` 9194 ``` ## Hypothetical Queries[\\u200b](#hypothetical-queries) An LLM can also be used to generate a list of hypothetical questions that could be asked of a particular document.These questions can then be embedded ``` functions = [ { \"name\": \"hypothetical_questions\", \"description\": \"Generate hypothetical questions\", \"parameters\": { \"type\": \"object\", \"properties\": { \"questions\": { \"type\": \"array\", \"items\": { \"type\": \"string\" }, }, }, \"required\": [\"questions\"] } } ] ``` ``` from langchain.output_parsers.openai_functions import JsonKeyOutputFunctionsParserchain = ( {\"doc\": lambda x: x.page_content} # Only asking for 3 hypothetical questions, but this could be adjusted | ChatPromptTemplate.from_template(\"Generate a list of 3 hypothetical questions that the below document could be used to answer:\\\\n\\\\n{doc}\") | ChatOpenAI(max_retries=0, model=\"gpt-4\").bind(functions=functions, function_call={\"name\": \"hypothetical_questions\"}) | JsonKeyOutputFunctionsParser(key_name=\"questions\")) ``` ``` chain.invoke(docs[0]) ``` ``` [\"What was the author\\'s initial impression of philosophy as a field of study, and how did it change when they got to college?\", \\'Why did the author decide to switch their focus to Artificial Intelligence (AI)? \\', \"What led to the author\\'s disillusionment with the field of AI as it was practiced at the time?\"]``` ``` hypothetical_questions = chain.batch(docs, {\"max_concurrency\": 5}) ``` ``` # The vectorstore to use to index the child chunksvectorstore = Chroma( collection_name=\"hypo-questions\", embedding_function=OpenAIEmbeddings())# The storage layer for the parent documentsstore = InMemoryStore()id_key = \"doc_id\"# The retriever (empty to start)retriever = MultiVectorRetriever( vectorstore=vectorstore, docstore=store, id_key=id_key,)doc_ids = [str(uuid.uuid4()) for _ in docs] ``` ``` question_docs = []for i, question_list in enumerate(hypothetical_questions): question_docs.extend([Document(page_content=s,metadata={id_key: doc_ids[i]}) for s in question_list]) ``` ``` retriever.vectorstore.add_documents(question_docs)retriever.docstore.mset(list(zip(doc_ids, docs))) ``` ``` sub_docs = vectorstore.similarity_search(\"justice breyer\") ``` ``` sub_docs ``` ``` [Document(page_content=\"What is the President\\'s stance on immigration reform?\", metadata={\\'doc_id\\': \\'505d73e3-8350-46ec-a58e-3af032f04ab3\\'}), Document(page_content=\"What is the President\\'s stance on immigration reform? \", metadata={\\'doc_id\\': \\'1c9618f0-7660-4b4f-a37c-509cbbbf6dba\\'}), Document(page_content=\"What is the President\\'s stance on immigration reform? \", metadata={\\'doc_id\\': \\'82c08209-b904-46a8-9532-edd2380950b7\\'}), Document(page_content=\\'What measures is the President proposing to protect the rights of LGBTQ+ Americans? \\', metadata={\\'doc_id\\': \\'82c08209-b904-46a8-9532-edd2380950b7\\'})] ``` ``` retrieved_docs = retriever.get_relevant_documents(\"justice breyer\") ``` ``` len(retrieved_docs[0].page_content) ``` ``` 9194 ```', metadata={'title': 'MultiVector Retriever | 🦜️🔗 Langchain', 'type': None, 'url': 'https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector', 'id': 'beec5531-16a7-453c-80ab-c5628e0236ce'}),\n", - " Document(page_content='MultiQueryRetriever | 🦜️🔗 Langchain\\n# MultiQueryRetriever Distance-based vector database retrieval embeds (represents) queries in high-dimensional space and finds similar embedded documents based on \"distance\". But, retrieval may produce different results with subtle changes in query wording or if the embeddings do not capture the semantics of the data well. Prompt engineering / tuning is sometimes done to manually address these problems, but can be tedious. The `MultiQueryRetriever` automates the process of prompt tuning by using an LLM to generate multiple queries from different perspectives for a given user input query. For each query, it retrieves a set of relevant documents and takes the unique union across all queries to get a larger set of potentially relevant documents. By generating multiple perspectives on the same question, the `MultiQueryRetriever` might be able to overcome some of the limitations of the distance-based retrieval and get a richer set of results.By generating multiple perspectives on the same question, the `MultiQueryRetriever` might be able to overcome some of the limitations of the distance-based retrieval and get a richer set of results. ``` # Build a sample vectorDBfrom langchain_community.vectorstores import Chromafrom langchain_community.document_loaders import WebBaseLoaderfrom langchain_openai import OpenAIEmbeddingsfrom langchain.text_splitter import RecursiveCharacterTextSplitter# Load blog postloader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")data = loader.load()# Splittext_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)splits = text_splitter.split_documents(data)# VectorDBembedding = OpenAIEmbeddings()vectordb = Chroma.from_documents(documents=splits, embedding=embedding) ``` #### Simple usage[\\u200b](#simple-usage) Specify the LLM to use for query generation, and the retriever will do the rest.``` from langchain_openai import ChatOpenAIfrom langchain.retrievers.multi_query import MultiQueryRetrieverquestion = \"What are the approaches to Task Decomposition? \"llm = ChatOpenAI(temperature=0)retriever_from_llm = MultiQueryRetriever.from_llm( retriever=vectordb.as_retriever(), llm=llm) ``` ``` # Set logging for the queriesimport logginglogging.basicConfig()logging.getLogger(\"langchain.retrievers.multi_query\").setLevel(logging.INFO) ``` ``` unique_docs = retriever_from_llm.get_relevant_documents(query=question)len(unique_docs) ``` ``` INFO:langchain.retrievers.multi_query:Generated queries: [\\'1. How can Task Decomposition be approached? \\', \\'2. What are the different methods for Task Decomposition? \\', \\'3. What are the various approaches to decomposing tasks?\\'] 5 ``` #### Supplying your own prompt[\\u200b](#supplying-your-own-prompt) You can also supply a prompt along with an output parser to split the results into a list of queries.5 ``` #### Supplying your own prompt[\\u200b](#supplying-your-own-prompt) You can also supply a prompt along with an output parser to split the results into a list of queries. ``` from typing import Listfrom langchain.chains import LLMChainfrom pydantic import BaseModel, Fieldfrom langchain.prompts import PromptTemplatefrom langchain.output_parsers import PydanticOutputParser# Output parser will split the LLM result into a list of queriesclass LineList(BaseModel): # \"lines\" is the key (attribute name) of the parsed output lines: List[str] = Field(description=\"Lines of text\")class LineListOutputParser(PydanticOutputParser): def __init__(self) -> None: super().__init__(pydantic_object=LineList) def parse(self, text: str) -> LineList: lines = text.strip().split(\"\\\\n\") return LineList(lines=lines)output_parser = LineListOutputParser()QUERY_PROMPT = PromptTemplate( input_variables=[\"question\"], template=\"\"\"You are an AI language model assistant.Your task is to generate five different versions of the given user question to retrieve relevant documents from a vector database. By generating multiple perspectives on the user question, your goal is to help the user overcome some of the limitations of the distance-based similarity search. Provide these alternative questions separated by newlines. Original question: {question}\"\"\",)llm = ChatOpenAI(temperature=0)# Chainllm_chain = LLMChain(llm=llm, prompt=QUERY_PROMPT, output_parser=output_parser)# Other inputsquestion = \"What are the approaches to Task Decomposition?\" ``` ``` # Runretriever = MultiQueryRetriever( retriever=vectordb.as_retriever(), llm_chain=llm_chain, parser_key=\"lines\") # \"lines\" is the key (attribute name) of the parsed output# Resultsunique_docs = retriever.get_relevant_documents( query=\"What does the course say about regression? \")len(unique_docs) ``` ``` INFO:langchain.retrievers.multi_query:Generated queries: [\"1.\")len(unique_docs) ``` ``` INFO:langchain.retrievers.multi_query:Generated queries: [\"1. What is the course\\'s perspective on regression? \", \\'2. Can you provide information on regression as discussed in the course? \\', \\'3. How does the course cover the topic of regression? \\', \"4. What are the course\\'s teachings on regression? \", \\'5. In relation to the course, what is mentioned about regression?\\'] 11 ```', metadata={'title': 'MultiQueryRetriever | 🦜️🔗 Langchain', 'type': None, 'url': 'https://python.langchain.com/docs/modules/data_connection/retrievers/MultiQueryRetriever', 'id': 'f7c20633-6a60-4ca3-96b1-13fee66e321d'}),\n", - " Document(page_content='langchain.retrievers.multi_vector.MultiVectorRetriever — 🦜🔗 LangChain 0.0.322\\n# `langchain.retrievers.multi_vector`.MultiVectorRetriever[¶](#langchain-retrievers-multi-vector-multivectorretriever) *class *langchain.retrievers.multi_vector.MultiVectorRetriever[[source]](../_modules/langchain/retrievers/multi_vector.html#MultiVectorRetriever)[¶](#langchain.retrievers.multi_vector.MultiVectorRetriever) # Examples using MultiVectorRetriever[¶](#langchain-retrievers-multi-vector-multivectorretriever) - [MultiVector Retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector)', metadata={'title': 'langchain.retrievers.multi_vector.MultiVectorRetriever — 🦜🔗 LangChain 0.0.322', 'type': None, 'url': 'https://api.python.langchain.com/en/latest/retrievers/langchain.retrievers.multi_vector.MultiVectorRetriever.html#langchain-retrievers-multi-vector-multivectorretriever', 'id': '1820c44d-7783-4846-a11c-106b18da015d'})]" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "parent_retriever.get_relevant_documents(\"How does the multi vector retriever work\")" ] @@ -264,40 +235,10 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "id": "f1b9f091-a5f9-4468-9d52-633bf3361f4e", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "To create a FAISS vector store retriever that returns 10 documents per search query, you can use the following code:\n", - "\n", - "```python\n", - "from langchain_openai import OpenAIEmbeddings\n", - "from langchain_community.vectorstores import FAISS\n", - "\n", - "# Assuming you have already loaded and split your documents\n", - "# into `texts` and initialized your `embeddings` object\n", - "\n", - "# Create the FAISS vector store\n", - "db = FAISS.from_documents(texts, embeddings)\n", - "\n", - "# Create the retriever with the desired search kwargs\n", - "retriever = db.as_retriever(search_kwargs={\"k\": 10})\n", - "```\n", - "\n", - "Now, you can use the `retriever` object to get relevant documents using the `get_relevant_documents` method. For example:\n", - "\n", - "```python\n", - "docs = retriever.get_relevant_documents(\"your search query\")\n", - "```\n", - "\n", - "This will return a list of 10 documents that are most relevant to the given search query." - ] - } - ], + "outputs": [], "source": [ "for chunk in chain.invoke(\n", " \"How do I create a FAISS vector store retriever that returns 10 documents per search query\"\n", @@ -308,9 +249,9 @@ ], "metadata": { "kernelspec": { - "display_name": "poetry-venv", + "display_name": "Python 3 (ipykernel)", "language": "python", - "name": "poetry-venv" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/docs/docs/integrations/retrievers/self_query/pgvector_self_query.ipynb b/docs/docs/integrations/retrievers/self_query/pgvector_self_query.ipynb new file mode 100644 index 00000000000..b6475109362 --- /dev/null +++ b/docs/docs/integrations/retrievers/self_query/pgvector_self_query.ipynb @@ -0,0 +1,308 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "13afcae7", + "metadata": {}, + "source": [ + "# PGVector\n", + "\n", + ">[PGVector](https://github.com/pgvector/pgvector) is a vector similarity search for Postgres.\n", + "\n", + "In the notebook, we'll demo the `SelfQueryRetriever` wrapped around a `PGVector` vector store." + ] + }, + { + "cell_type": "markdown", + "id": "68e75fb9", + "metadata": {}, + "source": [ + "## Creating a PGVector vector store\n", + "First we'll want to create a PGVector vector store and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n", + "\n", + "**Note:** The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `` package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63a8af5b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "%pip install --upgrade --quiet lark pgvector psycopg2-binary" + ] + }, + { + "cell_type": "markdown", + "id": "83811610-7df3-4ede-b268-68a6a83ba9e2", + "metadata": {}, + "source": [ + "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd01b61b-7d32-4a55-85d6-b2d2d4f18840", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import getpass\n", + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb4a5787", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from langchain.schema import Document\n", + "from langchain_community.vectorstores import PGVector\n", + "from langchain_openai import OpenAIEmbeddings\n", + "\n", + "collection = \"Name of your collection\"\n", + "embeddings = OpenAIEmbeddings()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bcbe04d9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "docs = [\n", + " Document(\n", + " page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",\n", + " metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": \"science fiction\"},\n", + " ),\n", + " Document(\n", + " page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",\n", + " metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2},\n", + " ),\n", + " Document(\n", + " page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",\n", + " metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6},\n", + " ),\n", + " Document(\n", + " page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",\n", + " metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3},\n", + " ),\n", + " Document(\n", + " page_content=\"Toys come alive and have a blast doing so\",\n", + " metadata={\"year\": 1995, \"genre\": \"animated\"},\n", + " ),\n", + " Document(\n", + " page_content=\"Three men walk into the Zone, three men walk out of the Zone\",\n", + " metadata={\n", + " \"year\": 1979,\n", + " \"director\": \"Andrei Tarkovsky\",\n", + " \"genre\": \"science fiction\",\n", + " \"rating\": 9.9,\n", + " },\n", + " ),\n", + "]\n", + "vectorstore = PGVector.from_documents(\n", + " docs,\n", + " embeddings,\n", + " collection_name=collection,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "5ecaab6d", + "metadata": {}, + "source": [ + "## Creating our self-querying retriever\n", + "Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "86e34dbf", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from langchain.chains.query_constructor.base import AttributeInfo\n", + "from langchain.retrievers.self_query.base import SelfQueryRetriever\n", + "from langchain_openai import OpenAI\n", + "\n", + "metadata_field_info = [\n", + " AttributeInfo(\n", + " name=\"genre\",\n", + " description=\"The genre of the movie\",\n", + " type=\"string or list[string]\",\n", + " ),\n", + " AttributeInfo(\n", + " name=\"year\",\n", + " description=\"The year the movie was released\",\n", + " type=\"integer\",\n", + " ),\n", + " AttributeInfo(\n", + " name=\"director\",\n", + " description=\"The name of the movie director\",\n", + " type=\"string\",\n", + " ),\n", + " AttributeInfo(\n", + " name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"\n", + " ),\n", + "]\n", + "document_content_description = \"Brief summary of a movie\"\n", + "llm = OpenAI(temperature=0)\n", + "retriever = SelfQueryRetriever.from_llm(\n", + " llm, vectorstore, document_content_description, metadata_field_info, verbose=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ea9df8d4", + "metadata": {}, + "source": [ + "## Testing it out\n", + "And now we can try actually using our retriever!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38a126e9", + "metadata": {}, + "outputs": [], + "source": [ + "# This example only specifies a relevant query\n", + "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc3f1e6e", + "metadata": {}, + "outputs": [], + "source": [ + "# This example only specifies a filter\n", + "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b19d4da0", + "metadata": {}, + "outputs": [], + "source": [ + "# This example specifies a query and a filter\n", + "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f900e40e", + "metadata": {}, + "outputs": [], + "source": [ + "# This example specifies a composite filter\n", + "retriever.get_relevant_documents(\n", + " \"What's a highly rated (above 8.5) science fiction film?\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12a51522", + "metadata": {}, + "outputs": [], + "source": [ + "# This example specifies a query and composite filter\n", + "retriever.get_relevant_documents(\n", + " \"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "39bd1de1-b9fe-4a98-89da-58d8a7a6ae51", + "metadata": {}, + "source": [ + "## Filter k\n", + "\n", + "We can also use the self query retriever to specify `k`: the number of documents to fetch.\n", + "\n", + "We can do this by passing `enable_limit=True` to the constructor." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "bff36b88-b506-4877-9c63-e5a1a8d78e64", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "retriever = SelfQueryRetriever.from_llm(\n", + " llm,\n", + " vectorstore,\n", + " document_content_description,\n", + " metadata_field_info,\n", + " enable_limit=True,\n", + " verbose=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2758d229-4f97-499c-819f-888acaf8ee10", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# This example only specifies a relevant query\n", + "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/docs/integrations/retrievers/tf_idf.ipynb b/docs/docs/integrations/retrievers/tf_idf.ipynb index 3d1d97b277c..9576f8b0dfe 100644 --- a/docs/docs/integrations/retrievers/tf_idf.ipynb +++ b/docs/docs/integrations/retrievers/tf_idf.ipynb @@ -33,7 +33,7 @@ }, "outputs": [], "source": [ - "from langchain.retrievers import TFIDFRetriever" + "from langchain_community.retrievers import TFIDFRetriever" ] }, { diff --git a/docs/docs/integrations/retrievers/you-retriever.ipynb b/docs/docs/integrations/retrievers/you-retriever.ipynb index 447111de5a5..950c592cda7 100644 --- a/docs/docs/integrations/retrievers/you-retriever.ipynb +++ b/docs/docs/integrations/retrievers/you-retriever.ipynb @@ -2,39 +2,396 @@ "cells": [ { "cell_type": "markdown", - "id": "47828a7a", + "id": "818fc023", "metadata": {}, "source": [ - "## Using the You.com Retriever\n", - "The retriever from You.com is good for retrieving lots of text. We return multiple of the best text snippets per URL we find to be relevant.\n", + "# You.com Retriever\n", "\n", - "First you just need to initialize the retriever" + "The [you.com API](https://api.you.com) is a suite of tools designed to help developers ground the output of LLMs in the most recent, most accurate, most relevant information that may not have been included in their training dataset." + ] + }, + { + "cell_type": "markdown", + "id": "02335552", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "markdown", + "id": "c5c53f28", + "metadata": {}, + "source": [ + "The retriever lives in the `langchain-community` package.\n", + "\n", + "You also need to set your you.com API key." ] }, { "cell_type": "code", "execution_count": null, + "id": "6d091ccb", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install --upgrade --quiet langchain-community" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f43dd867", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"YDC_API_KEY\"] = \"\"\n", + "\n", + "# For use in Chaining section\n", + "os.environ[\"OPENAI_API_KEY\"] = \"\"\n", + "\n", + "## ALTERNATIVE: load YDC_API_KEY from a .env file\n", + "\n", + "# !pip install --quiet -U python-dotenv\n", + "# import dotenv\n", + "# dotenv.load_dotenv()" + ] + }, + { + "cell_type": "markdown", + "id": "17e70216", + "metadata": {}, + "source": [ + "It's also helpful (but not needed) to set up [LangSmith](https://smith.langchain.com/) for best-in-class observability" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ce49fa4c", + "metadata": {}, + "outputs": [], + "source": [ + "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", + "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()\n", + "# os.environ[\"LANGCHAIN_PROJECT\"] = 'Experimentz'" + ] + }, + { + "cell_type": "markdown", + "id": "2278de15", + "metadata": {}, + "source": [ + "## Utility Usage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab11b2e0", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.utilities.you import YouSearchAPIWrapper\n", + "\n", + "utility = YouSearchAPIWrapper(num_web_results=1)\n", + "\n", + "utility" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "2656a45d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "[\n", + " {\n", + " \"description\": \"Be prepared with the most accurate 10-day forecast for Manhattan, NY with highs, lows, chance of precipitation from The Weather Channel and Weather.com\",\n", + " \"snippets\": [\n", + " \"10 Day Weather-Manhattan, NY\\nToday43\\u00b0/39\\u00b01%\\nToday\\nSun 31 | Day\\nGenerally cloudy. High 43F. Winds W at 10 to 15 mph.\\n- Humidity54%\\n- UV Index0 of 11\\n- Sunrise7:19 am\\n- Sunset4:38 pm\\nSun 31 | Night\\nCloudy. Low 39F. Winds light and variable.\\n- Humidity70%\\n- UV Index0 of 11\\n- Moonrise9:13 pmWaning Gibbous\\n- Moonset10:28 am\\nMon 0145\\u00b0/33\\u00b07%\\nMon 01\\nMon 01 | Day\\nConsiderable cloudiness. High around 45F. Winds light and variable.\\n- Humidity71%\\n- UV Index1 of 11\\n- Sunrise7:19 am\\n- Sunset4:39 pm\\nMon 01 | Night\\nA few clouds. Low 33F. Winds NNW at 5 to 10 mph.\\n- Humidity64%\\n- UV Index0 of 11\\n- Moonrise10:14 pmWaning Gibbous\\n- Moonset10:49 am\\nTue 0246\\u00b0/35\\u00b04%\\nTue 02\\nTue 02 | Day\\nMainly sunny. High 46F. Winds NW at 5 to 10 mph.\\n- Humidity52%\\n- UV Index2 of 11\\n- Sunrise7:19 am\\n- Sunset4:40 pm\\nTue 02 | Night\\nA few clouds overnight. Low around 35F. Winds W at 5 to 10 mph.\\n- Humidity64%\\n- UV Index0 of 11\\n- Moonrise11:13 pmWaning Gibbous\\n- Moonset11:08 am\\nWed 0346\\u00b0/38\\u00b04%\\nWed 03\\nWed 03 | Day\",\n", + " \"Radar\\nLatest News\\nOur Changing World\\nYour Privacy\\nTo personalize your product experience, we collect data from your device. We also may use or disclose to specific data vendors your precise geolocation data to provide the Services. To learn more please refer to our Privacy Policy.\\nChoose how my information is shared\",\n", + " \"- Humidity82%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:34 pm\\nTue 26 | Night\\nCloudy with light rain developing after midnight. Low 47F. Winds light and variable. Chance of rain 80%.\\n- Humidity90%\\n- UV Index0 of 11\\n- Moonrise4:00 pmFull Moon\\n- Moonset7:17 am\\nWed 2754\\u00b0/49\\u00b093%\\nWed 27\\nWed 27 | Day\\nRain. High 54F. Winds E at 5 to 10 mph. Chance of rain 90%. Rainfall near a half an inch.\\n- Humidity93%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:35 pm\\nWed 27 | Night\\nSteady light rain in the evening. Showers continuing late. Low 49F. Winds light and variable. Chance of rain 70%.\\n- Humidity91%\\n- UV Index0 of 11\\n- Moonrise4:59 pmFull Moon\\n- Moonset8:12 am\\nThu 2853\\u00b0/42\\u00b019%\\nThu 28\\nThu 28 | Day\\nCloudy skies early will become partly cloudy later in the day. Slight chance of a rain shower. High 53F. Winds WSW at 5 to 10 mph.\\n- Humidity77%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:36 pm\\nThu 28 | Night\\nPartly cloudy skies. Low 42F. Winds W at 5 to 10 mph.\\n- Humidity71%\\n- UV Index0 of 11\",\n", + " \"- Moonrise2:20 amWaning Crescent\\n- Moonset12:33 pm\\nSun 0740\\u00b0/29\\u00b019%\\nSun 07\\nSun 07 | Day\\nIntervals of clouds and sunshine. High around 40F. Winds NW at 5 to 10 mph.\\n- Humidity57%\\n- UV Index2 of 11\\n- Sunrise7:19 am\\n- Sunset4:44 pm\\nSun 07 | Night\\nA few clouds from time to time. Low 29F. Winds NNW at 5 to 10 mph.\\n- Humidity60%\\n- UV Index0 of 11\\n- Moonrise3:28 amWaning Crescent\\n- Moonset1:04 pm\\nMon 0840\\u00b0/32\\u00b035%\\nMon 08\\nMon 08 | Day\\nPartly cloudy early followed mostly cloudy skies and a few snow showers later in the day. High near 40F. Winds N at 5 to 10 mph. Chance of snow 40%.\\n- UV Index1 of 11\\n- Sunrise7:19 am\\n- Sunset4:45 pm\\nMon 08 | Night\\nVariable clouds with snow showers or flurries. Low 32F. Winds NNE at 5 to 10 mph. Chance of snow 60%. Snow accumulations less than one inch.\\n- UV Index0 of 11\\n- Moonrise4:40 amWaning Crescent\\n- Moonset1:43 pm\\nLatest News\\nOur Changing World\\nYour Privacy\",\n", + " \"- Humidity91%\\n- UV Index0 of 11\\n- Moonrise5:50 amWaning Crescent\\n- Moonset2:35 pm\\nWed 1056\\u00b0/39\\u00b034%\\nWed 10\\nWed 10 | Day\\nA shower or two possible early with partly cloudy skies in the afternoon. Morning high of 56F with temps falling to near 45. Winds SW at 15 to 25 mph. Chance of rain 30%.\\n- Humidity66%\\n- UV Index1 of 11\\n- Sunrise7:19 am\\n- Sunset4:47 pm\\nWed 10 | Night\\nA few clouds from time to time. Low 39F. Winds WSW at 10 to 20 mph.\\n- Humidity64%\\n- UV Index0 of 11\\n- Moonrise6:56 amWaning Crescent\\n- Moonset3:38 pm\\nThu 1147\\u00b0/38\\u00b05%\\nThu 11\\nThu 11 | Day\\nPartly cloudy. High 47F. Winds WSW at 5 to 10 mph.\\n- Humidity62%\\n- UV Index2 of 11\\n- Sunrise7:19 am\\n- Sunset4:48 pm\\nThu 11 | Night\\nMostly clear skies. Low 38F. Winds W at 5 to 10 mph.\\n- Humidity66%\\n- UV Index0 of 11\\n- Moonrise7:52 amNew Moon\\n- Moonset4:53 pm\\nFri 1248\\u00b0/42\\u00b019%\\nFri 12\\nFri 12 | Day\\nIntervals of clouds and sunshine. High 48F. Winds WSW at 5 to 10 mph.\\n- Humidity62%\\n- UV Index2 of 11\\n- Sunrise7:18 am\\n- Sunset4:49 pm\",\n", + " \"Sat 1346\\u00b0/36\\u00b053%\\nSat 13\\nSat 13 | Day\\nCloudy with showers. High 46F. Winds WSW at 10 to 15 mph. Chance of rain 50%.\\n- Humidity73%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:50 pm\\nSat 13 | Night\\nRain showers early transitioning to snow showers late. Low 36F. Winds W at 10 to 15 mph. Chance of precip 50%.\\n- Humidity70%\\n- UV Index0 of 11\\n- Moonrise9:14 amWaxing Crescent\\n- Moonset7:33 pm\\nSun 1442\\u00b0/34\\u00b037%\\nSun 14\\nSun 14 | Day\\nSnow showers early will transition to a few showers later. High 42F. Winds WSW at 10 to 15 mph. Chance of rain 40%.\\n- Humidity63%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:51 pm\\nSun 14 | Night\\nVariable clouds with snow showers. Low 34F. Winds W at 10 to 15 mph. Chance of snow 60%. Snow accumulations less than one inch.\\n- UV Index0 of 11\\n- Moonrise9:44 amWaxing Crescent\\n- Moonset8:52 pm\\nMon 1540\\u00b0/31\\u00b051%\\nMon 15\\nMon 15 | Day\",\n", + " \"- Humidity70%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:34 pm\\nMon 25 | Night\\nOvercast with showers at times. Low 43F. Winds light and variable. Chance of rain 40%.\\n- Humidity80%\\n- UV Index0 of 11\\n- Moonrise3:08 pmWaxing Gibbous\\n- Moonset6:14 am\\nTue 2653\\u00b0/45\\u00b058%\\nTue 26\\nTue 26 | Day\\nOvercast with rain showers at times. High 53F. Winds E at 5 to 10 mph. Chance of rain 60%.\\n- Humidity79%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:34 pm\\nTue 26 | Night\\nShowers early then scattered thunderstorms developing late. Low near 45F. Winds ESE at 5 to 10 mph. Chance of rain 60%.\\n- Humidity93%\\n- UV Index0 of 11\\n- Moonrise4:00 pmFull Moon\\n- Moonset7:17 am\\nWed 2751\\u00b0/41\\u00b058%\\nWed 27\\nWed 27 | Day\\nCloudy with showers. High 51F. Winds WSW at 5 to 10 mph. Chance of rain 60%.\\n- Humidity79%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:35 pm\\nWed 27 | Night\\nCloudy with showers. Low 41F. Winds NW at 5 to 10 mph. Chance of rain 60%.\\n- Humidity72%\\n- UV Index0 of 11\\n- Moonrise4:59 pmFull Moon\\n- Moonset8:13 am\"\n", + " ],\n", + " \"thumbnail_url\": \"https://imgs.search.brave.com/9xHc5-Bh2lvLyRJwQqeegm3gzoF6hawlpF8LZEjFLo8/rs:fit:200:200:1/g:ce/aHR0cHM6Ly9zLnct/eC5jby8yNDB4MTgw/X3R3Y19kZWZhdWx0/LnBuZw\",\n", + " \"title\": \"10-Day Weather Forecast for Manhattan, NY - The Weather Channel ...\",\n", + " \"url\": \"https://weather.com/weather/tenday/l/New+York+NY+USNY0996:1:US\"\n", + " }\n", + "]\n" + ] + } + ], + "source": [ + "import json\n", + "\n", + "# .raw_results returns the unaltered response from the API\n", + "response = utility.raw_results(query=\"What is the weather in NY\")\n", + "# API returns an object with a `hits` key containing a list of hits\n", + "hits = response[\"hits\"]\n", + "\n", + "# with `num_web_results=1`, `hits` should be len of 1\n", + "print(len(hits))\n", + "\n", + "print(json.dumps(hits, indent=2))" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "c8f5689f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7\n", + "[Document(page_content='10 Day Weather-Manhattan, NY\\nToday43°/39°1%\\nToday\\nSun 31 | Day\\nGenerally cloudy. High 43F. Winds W at 10 to 15 mph.\\n- Humidity54%\\n- UV Index0 of 11\\n- Sunrise7:19 am\\n- Sunset4:38 pm\\nSun 31 | Night\\nCloudy. Low 39F. Winds light and variable.\\n- Humidity70%\\n- UV Index0 of 11\\n- Moonrise9:13 pmWaning Gibbous\\n- Moonset10:28 am\\nMon 0145°/33°7%\\nMon 01\\nMon 01 | Day\\nConsiderable cloudiness. High around 45F. Winds light and variable.\\n- Humidity71%\\n- UV Index1 of 11\\n- Sunrise7:19 am\\n- Sunset4:39 pm\\nMon 01 | Night\\nA few clouds. Low 33F. Winds NNW at 5 to 10 mph.\\n- Humidity64%\\n- UV Index0 of 11\\n- Moonrise10:14 pmWaning Gibbous\\n- Moonset10:49 am\\nTue 0246°/35°4%\\nTue 02\\nTue 02 | Day\\nMainly sunny. High 46F. Winds NW at 5 to 10 mph.\\n- Humidity52%\\n- UV Index2 of 11\\n- Sunrise7:19 am\\n- Sunset4:40 pm\\nTue 02 | Night\\nA few clouds overnight. Low around 35F. Winds W at 5 to 10 mph.\\n- Humidity64%\\n- UV Index0 of 11\\n- Moonrise11:13 pmWaning Gibbous\\n- Moonset11:08 am\\nWed 0346°/38°4%\\nWed 03\\nWed 03 | Day', metadata={'url': 'https://weather.com/weather/tenday/l/New+York+NY+USNY0996:1:US', 'thumbnail_url': 'https://imgs.search.brave.com/9xHc5-Bh2lvLyRJwQqeegm3gzoF6hawlpF8LZEjFLo8/rs:fit:200:200:1/g:ce/aHR0cHM6Ly9zLnct/eC5jby8yNDB4MTgw/X3R3Y19kZWZhdWx0/LnBuZw', 'title': '10-Day Weather Forecast for Manhattan, NY - The Weather Channel ...', 'description': 'Be prepared with the most accurate 10-day forecast for Manhattan, NY with highs, lows, chance of precipitation from The Weather Channel and Weather.com'}), Document(page_content='Radar\\nLatest News\\nOur Changing World\\nYour Privacy\\nTo personalize your product experience, we collect data from your device. We also may use or disclose to specific data vendors your precise geolocation data to provide the Services. To learn more please refer to our Privacy Policy.\\nChoose how my information is shared', metadata={'url': 'https://weather.com/weather/tenday/l/New+York+NY+USNY0996:1:US', 'thumbnail_url': 'https://imgs.search.brave.com/9xHc5-Bh2lvLyRJwQqeegm3gzoF6hawlpF8LZEjFLo8/rs:fit:200:200:1/g:ce/aHR0cHM6Ly9zLnct/eC5jby8yNDB4MTgw/X3R3Y19kZWZhdWx0/LnBuZw', 'title': '10-Day Weather Forecast for Manhattan, NY - The Weather Channel ...', 'description': 'Be prepared with the most accurate 10-day forecast for Manhattan, NY with highs, lows, chance of precipitation from The Weather Channel and Weather.com'}), Document(page_content='- Humidity82%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:34 pm\\nTue 26 | Night\\nCloudy with light rain developing after midnight. Low 47F. Winds light and variable. Chance of rain 80%.\\n- Humidity90%\\n- UV Index0 of 11\\n- Moonrise4:00 pmFull Moon\\n- Moonset7:17 am\\nWed 2754°/49°93%\\nWed 27\\nWed 27 | Day\\nRain. High 54F. Winds E at 5 to 10 mph. Chance of rain 90%. Rainfall near a half an inch.\\n- Humidity93%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:35 pm\\nWed 27 | Night\\nSteady light rain in the evening. Showers continuing late. Low 49F. Winds light and variable. Chance of rain 70%.\\n- Humidity91%\\n- UV Index0 of 11\\n- Moonrise4:59 pmFull Moon\\n- Moonset8:12 am\\nThu 2853°/42°19%\\nThu 28\\nThu 28 | Day\\nCloudy skies early will become partly cloudy later in the day. Slight chance of a rain shower. High 53F. Winds WSW at 5 to 10 mph.\\n- Humidity77%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:36 pm\\nThu 28 | Night\\nPartly cloudy skies. Low 42F. Winds W at 5 to 10 mph.\\n- Humidity71%\\n- UV Index0 of 11', metadata={'url': 'https://weather.com/weather/tenday/l/New+York+NY+USNY0996:1:US', 'thumbnail_url': 'https://imgs.search.brave.com/9xHc5-Bh2lvLyRJwQqeegm3gzoF6hawlpF8LZEjFLo8/rs:fit:200:200:1/g:ce/aHR0cHM6Ly9zLnct/eC5jby8yNDB4MTgw/X3R3Y19kZWZhdWx0/LnBuZw', 'title': '10-Day Weather Forecast for Manhattan, NY - The Weather Channel ...', 'description': 'Be prepared with the most accurate 10-day forecast for Manhattan, NY with highs, lows, chance of precipitation from The Weather Channel and Weather.com'}), Document(page_content='- Moonrise2:20 amWaning Crescent\\n- Moonset12:33 pm\\nSun 0740°/29°19%\\nSun 07\\nSun 07 | Day\\nIntervals of clouds and sunshine. High around 40F. Winds NW at 5 to 10 mph.\\n- Humidity57%\\n- UV Index2 of 11\\n- Sunrise7:19 am\\n- Sunset4:44 pm\\nSun 07 | Night\\nA few clouds from time to time. Low 29F. Winds NNW at 5 to 10 mph.\\n- Humidity60%\\n- UV Index0 of 11\\n- Moonrise3:28 amWaning Crescent\\n- Moonset1:04 pm\\nMon 0840°/32°35%\\nMon 08\\nMon 08 | Day\\nPartly cloudy early followed mostly cloudy skies and a few snow showers later in the day. High near 40F. Winds N at 5 to 10 mph. Chance of snow 40%.\\n- UV Index1 of 11\\n- Sunrise7:19 am\\n- Sunset4:45 pm\\nMon 08 | Night\\nVariable clouds with snow showers or flurries. Low 32F. Winds NNE at 5 to 10 mph. Chance of snow 60%. Snow accumulations less than one inch.\\n- UV Index0 of 11\\n- Moonrise4:40 amWaning Crescent\\n- Moonset1:43 pm\\nLatest News\\nOur Changing World\\nYour Privacy', metadata={'url': 'https://weather.com/weather/tenday/l/New+York+NY+USNY0996:1:US', 'thumbnail_url': 'https://imgs.search.brave.com/9xHc5-Bh2lvLyRJwQqeegm3gzoF6hawlpF8LZEjFLo8/rs:fit:200:200:1/g:ce/aHR0cHM6Ly9zLnct/eC5jby8yNDB4MTgw/X3R3Y19kZWZhdWx0/LnBuZw', 'title': '10-Day Weather Forecast for Manhattan, NY - The Weather Channel ...', 'description': 'Be prepared with the most accurate 10-day forecast for Manhattan, NY with highs, lows, chance of precipitation from The Weather Channel and Weather.com'}), Document(page_content='- Humidity91%\\n- UV Index0 of 11\\n- Moonrise5:50 amWaning Crescent\\n- Moonset2:35 pm\\nWed 1056°/39°34%\\nWed 10\\nWed 10 | Day\\nA shower or two possible early with partly cloudy skies in the afternoon. Morning high of 56F with temps falling to near 45. Winds SW at 15 to 25 mph. Chance of rain 30%.\\n- Humidity66%\\n- UV Index1 of 11\\n- Sunrise7:19 am\\n- Sunset4:47 pm\\nWed 10 | Night\\nA few clouds from time to time. Low 39F. Winds WSW at 10 to 20 mph.\\n- Humidity64%\\n- UV Index0 of 11\\n- Moonrise6:56 amWaning Crescent\\n- Moonset3:38 pm\\nThu 1147°/38°5%\\nThu 11\\nThu 11 | Day\\nPartly cloudy. High 47F. Winds WSW at 5 to 10 mph.\\n- Humidity62%\\n- UV Index2 of 11\\n- Sunrise7:19 am\\n- Sunset4:48 pm\\nThu 11 | Night\\nMostly clear skies. Low 38F. Winds W at 5 to 10 mph.\\n- Humidity66%\\n- UV Index0 of 11\\n- Moonrise7:52 amNew Moon\\n- Moonset4:53 pm\\nFri 1248°/42°19%\\nFri 12\\nFri 12 | Day\\nIntervals of clouds and sunshine. High 48F. Winds WSW at 5 to 10 mph.\\n- Humidity62%\\n- UV Index2 of 11\\n- Sunrise7:18 am\\n- Sunset4:49 pm', metadata={'url': 'https://weather.com/weather/tenday/l/New+York+NY+USNY0996:1:US', 'thumbnail_url': 'https://imgs.search.brave.com/9xHc5-Bh2lvLyRJwQqeegm3gzoF6hawlpF8LZEjFLo8/rs:fit:200:200:1/g:ce/aHR0cHM6Ly9zLnct/eC5jby8yNDB4MTgw/X3R3Y19kZWZhdWx0/LnBuZw', 'title': '10-Day Weather Forecast for Manhattan, NY - The Weather Channel ...', 'description': 'Be prepared with the most accurate 10-day forecast for Manhattan, NY with highs, lows, chance of precipitation from The Weather Channel and Weather.com'}), Document(page_content='Sat 1346°/36°53%\\nSat 13\\nSat 13 | Day\\nCloudy with showers. High 46F. Winds WSW at 10 to 15 mph. Chance of rain 50%.\\n- Humidity73%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:50 pm\\nSat 13 | Night\\nRain showers early transitioning to snow showers late. Low 36F. Winds W at 10 to 15 mph. Chance of precip 50%.\\n- Humidity70%\\n- UV Index0 of 11\\n- Moonrise9:14 amWaxing Crescent\\n- Moonset7:33 pm\\nSun 1442°/34°37%\\nSun 14\\nSun 14 | Day\\nSnow showers early will transition to a few showers later. High 42F. Winds WSW at 10 to 15 mph. Chance of rain 40%.\\n- Humidity63%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:51 pm\\nSun 14 | Night\\nVariable clouds with snow showers. Low 34F. Winds W at 10 to 15 mph. Chance of snow 60%. Snow accumulations less than one inch.\\n- UV Index0 of 11\\n- Moonrise9:44 amWaxing Crescent\\n- Moonset8:52 pm\\nMon 1540°/31°51%\\nMon 15\\nMon 15 | Day', metadata={'url': 'https://weather.com/weather/tenday/l/New+York+NY+USNY0996:1:US', 'thumbnail_url': 'https://imgs.search.brave.com/9xHc5-Bh2lvLyRJwQqeegm3gzoF6hawlpF8LZEjFLo8/rs:fit:200:200:1/g:ce/aHR0cHM6Ly9zLnct/eC5jby8yNDB4MTgw/X3R3Y19kZWZhdWx0/LnBuZw', 'title': '10-Day Weather Forecast for Manhattan, NY - The Weather Channel ...', 'description': 'Be prepared with the most accurate 10-day forecast for Manhattan, NY with highs, lows, chance of precipitation from The Weather Channel and Weather.com'}), Document(page_content='- Humidity70%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:34 pm\\nMon 25 | Night\\nOvercast with showers at times. Low 43F. Winds light and variable. Chance of rain 40%.\\n- Humidity80%\\n- UV Index0 of 11\\n- Moonrise3:08 pmWaxing Gibbous\\n- Moonset6:14 am\\nTue 2653°/45°58%\\nTue 26\\nTue 26 | Day\\nOvercast with rain showers at times. High 53F. Winds E at 5 to 10 mph. Chance of rain 60%.\\n- Humidity79%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:34 pm\\nTue 26 | Night\\nShowers early then scattered thunderstorms developing late. Low near 45F. Winds ESE at 5 to 10 mph. Chance of rain 60%.\\n- Humidity93%\\n- UV Index0 of 11\\n- Moonrise4:00 pmFull Moon\\n- Moonset7:17 am\\nWed 2751°/41°58%\\nWed 27\\nWed 27 | Day\\nCloudy with showers. High 51F. Winds WSW at 5 to 10 mph. Chance of rain 60%.\\n- Humidity79%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:35 pm\\nWed 27 | Night\\nCloudy with showers. Low 41F. Winds NW at 5 to 10 mph. Chance of rain 60%.\\n- Humidity72%\\n- UV Index0 of 11\\n- Moonrise4:59 pmFull Moon\\n- Moonset8:13 am', metadata={'url': 'https://weather.com/weather/tenday/l/New+York+NY+USNY0996:1:US', 'thumbnail_url': 'https://imgs.search.brave.com/9xHc5-Bh2lvLyRJwQqeegm3gzoF6hawlpF8LZEjFLo8/rs:fit:200:200:1/g:ce/aHR0cHM6Ly9zLnct/eC5jby8yNDB4MTgw/X3R3Y19kZWZhdWx0/LnBuZw', 'title': '10-Day Weather Forecast for Manhattan, NY - The Weather Channel ...', 'description': 'Be prepared with the most accurate 10-day forecast for Manhattan, NY with highs, lows, chance of precipitation from The Weather Channel and Weather.com'})]\n" + ] + } + ], + "source": [ + "# .results returns parsed results with each snippet in a Document\n", + "response = utility.results(query=\"What is the weather in NY\")\n", + "\n", + "# .results should have a Document for each `snippet`\n", + "print(len(response))\n", + "\n", + "print(response)" + ] + }, + { + "cell_type": "markdown", + "id": "190ed085", + "metadata": {}, + "source": [ + "## Retriever Usage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1367af5c", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.retrievers.you import YouRetriever\n", + "\n", + "retriever = YouRetriever(num_web_results=1)\n", + "\n", + "retriever" + ] + }, + { + "cell_type": "code", + "execution_count": 95, "id": "a90d61d4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7\n", + "[Document(page_content='10 Day Weather-Manhattan, NY\\nToday43°/39°1%\\nToday\\nSun 31 | Day\\nGenerally cloudy. High 43F. Winds W at 10 to 15 mph.\\n- Humidity54%\\n- UV Index0 of 11\\n- Sunrise7:19 am\\n- Sunset4:38 pm\\nSun 31 | Night\\nCloudy. Low 39F. Winds light and variable.\\n- Humidity70%\\n- UV Index0 of 11\\n- Moonrise9:13 pmWaning Gibbous\\n- Moonset10:28 am\\nMon 0145°/33°7%\\nMon 01\\nMon 01 | Day\\nConsiderable cloudiness. High around 45F. Winds light and variable.\\n- Humidity71%\\n- UV Index1 of 11\\n- Sunrise7:19 am\\n- Sunset4:39 pm\\nMon 01 | Night\\nA few clouds. Low 33F. Winds NNW at 5 to 10 mph.\\n- Humidity64%\\n- UV Index0 of 11\\n- Moonrise10:14 pmWaning Gibbous\\n- Moonset10:49 am\\nTue 0246°/35°4%\\nTue 02\\nTue 02 | Day\\nMainly sunny. High 46F. Winds NW at 5 to 10 mph.\\n- Humidity52%\\n- UV Index2 of 11\\n- Sunrise7:19 am\\n- Sunset4:40 pm\\nTue 02 | Night\\nA few clouds overnight. Low around 35F. Winds W at 5 to 10 mph.\\n- Humidity64%\\n- UV Index0 of 11\\n- Moonrise11:13 pmWaning Gibbous\\n- Moonset11:08 am\\nWed 0346°/38°4%\\nWed 03\\nWed 03 | Day', metadata={'url': 'https://weather.com/weather/tenday/l/New+York+NY+USNY0996:1:US', 'thumbnail_url': 'https://imgs.search.brave.com/9xHc5-Bh2lvLyRJwQqeegm3gzoF6hawlpF8LZEjFLo8/rs:fit:200:200:1/g:ce/aHR0cHM6Ly9zLnct/eC5jby8yNDB4MTgw/X3R3Y19kZWZhdWx0/LnBuZw', 'title': '10-Day Weather Forecast for Manhattan, NY - The Weather Channel ...', 'description': 'Be prepared with the most accurate 10-day forecast for Manhattan, NY with highs, lows, chance of precipitation from The Weather Channel and Weather.com'}), Document(page_content='Radar\\nLatest News\\nOur Changing World\\nYour Privacy\\nTo personalize your product experience, we collect data from your device. We also may use or disclose to specific data vendors your precise geolocation data to provide the Services. To learn more please refer to our Privacy Policy.\\nChoose how my information is shared', metadata={'url': 'https://weather.com/weather/tenday/l/New+York+NY+USNY0996:1:US', 'thumbnail_url': 'https://imgs.search.brave.com/9xHc5-Bh2lvLyRJwQqeegm3gzoF6hawlpF8LZEjFLo8/rs:fit:200:200:1/g:ce/aHR0cHM6Ly9zLnct/eC5jby8yNDB4MTgw/X3R3Y19kZWZhdWx0/LnBuZw', 'title': '10-Day Weather Forecast for Manhattan, NY - The Weather Channel ...', 'description': 'Be prepared with the most accurate 10-day forecast for Manhattan, NY with highs, lows, chance of precipitation from The Weather Channel and Weather.com'}), Document(page_content='- Humidity82%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:34 pm\\nTue 26 | Night\\nCloudy with light rain developing after midnight. Low 47F. Winds light and variable. Chance of rain 80%.\\n- Humidity90%\\n- UV Index0 of 11\\n- Moonrise4:00 pmFull Moon\\n- Moonset7:17 am\\nWed 2754°/49°93%\\nWed 27\\nWed 27 | Day\\nRain. High 54F. Winds E at 5 to 10 mph. Chance of rain 90%. Rainfall near a half an inch.\\n- Humidity93%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:35 pm\\nWed 27 | Night\\nSteady light rain in the evening. Showers continuing late. Low 49F. Winds light and variable. Chance of rain 70%.\\n- Humidity91%\\n- UV Index0 of 11\\n- Moonrise4:59 pmFull Moon\\n- Moonset8:12 am\\nThu 2853°/42°19%\\nThu 28\\nThu 28 | Day\\nCloudy skies early will become partly cloudy later in the day. Slight chance of a rain shower. High 53F. Winds WSW at 5 to 10 mph.\\n- Humidity77%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:36 pm\\nThu 28 | Night\\nPartly cloudy skies. Low 42F. Winds W at 5 to 10 mph.\\n- Humidity71%\\n- UV Index0 of 11', metadata={'url': 'https://weather.com/weather/tenday/l/New+York+NY+USNY0996:1:US', 'thumbnail_url': 'https://imgs.search.brave.com/9xHc5-Bh2lvLyRJwQqeegm3gzoF6hawlpF8LZEjFLo8/rs:fit:200:200:1/g:ce/aHR0cHM6Ly9zLnct/eC5jby8yNDB4MTgw/X3R3Y19kZWZhdWx0/LnBuZw', 'title': '10-Day Weather Forecast for Manhattan, NY - The Weather Channel ...', 'description': 'Be prepared with the most accurate 10-day forecast for Manhattan, NY with highs, lows, chance of precipitation from The Weather Channel and Weather.com'}), Document(page_content='- Moonrise2:20 amWaning Crescent\\n- Moonset12:33 pm\\nSun 0740°/29°19%\\nSun 07\\nSun 07 | Day\\nIntervals of clouds and sunshine. High around 40F. Winds NW at 5 to 10 mph.\\n- Humidity57%\\n- UV Index2 of 11\\n- Sunrise7:19 am\\n- Sunset4:44 pm\\nSun 07 | Night\\nA few clouds from time to time. Low 29F. Winds NNW at 5 to 10 mph.\\n- Humidity60%\\n- UV Index0 of 11\\n- Moonrise3:28 amWaning Crescent\\n- Moonset1:04 pm\\nMon 0840°/32°35%\\nMon 08\\nMon 08 | Day\\nPartly cloudy early followed mostly cloudy skies and a few snow showers later in the day. High near 40F. Winds N at 5 to 10 mph. Chance of snow 40%.\\n- UV Index1 of 11\\n- Sunrise7:19 am\\n- Sunset4:45 pm\\nMon 08 | Night\\nVariable clouds with snow showers or flurries. Low 32F. Winds NNE at 5 to 10 mph. Chance of snow 60%. Snow accumulations less than one inch.\\n- UV Index0 of 11\\n- Moonrise4:40 amWaning Crescent\\n- Moonset1:43 pm\\nLatest News\\nOur Changing World\\nYour Privacy', metadata={'url': 'https://weather.com/weather/tenday/l/New+York+NY+USNY0996:1:US', 'thumbnail_url': 'https://imgs.search.brave.com/9xHc5-Bh2lvLyRJwQqeegm3gzoF6hawlpF8LZEjFLo8/rs:fit:200:200:1/g:ce/aHR0cHM6Ly9zLnct/eC5jby8yNDB4MTgw/X3R3Y19kZWZhdWx0/LnBuZw', 'title': '10-Day Weather Forecast for Manhattan, NY - The Weather Channel ...', 'description': 'Be prepared with the most accurate 10-day forecast for Manhattan, NY with highs, lows, chance of precipitation from The Weather Channel and Weather.com'}), Document(page_content='- Humidity91%\\n- UV Index0 of 11\\n- Moonrise5:50 amWaning Crescent\\n- Moonset2:35 pm\\nWed 1056°/39°34%\\nWed 10\\nWed 10 | Day\\nA shower or two possible early with partly cloudy skies in the afternoon. Morning high of 56F with temps falling to near 45. Winds SW at 15 to 25 mph. Chance of rain 30%.\\n- Humidity66%\\n- UV Index1 of 11\\n- Sunrise7:19 am\\n- Sunset4:47 pm\\nWed 10 | Night\\nA few clouds from time to time. Low 39F. Winds WSW at 10 to 20 mph.\\n- Humidity64%\\n- UV Index0 of 11\\n- Moonrise6:56 amWaning Crescent\\n- Moonset3:38 pm\\nThu 1147°/38°5%\\nThu 11\\nThu 11 | Day\\nPartly cloudy. High 47F. Winds WSW at 5 to 10 mph.\\n- Humidity62%\\n- UV Index2 of 11\\n- Sunrise7:19 am\\n- Sunset4:48 pm\\nThu 11 | Night\\nMostly clear skies. Low 38F. Winds W at 5 to 10 mph.\\n- Humidity66%\\n- UV Index0 of 11\\n- Moonrise7:52 amNew Moon\\n- Moonset4:53 pm\\nFri 1248°/42°19%\\nFri 12\\nFri 12 | Day\\nIntervals of clouds and sunshine. High 48F. Winds WSW at 5 to 10 mph.\\n- Humidity62%\\n- UV Index2 of 11\\n- Sunrise7:18 am\\n- Sunset4:49 pm', metadata={'url': 'https://weather.com/weather/tenday/l/New+York+NY+USNY0996:1:US', 'thumbnail_url': 'https://imgs.search.brave.com/9xHc5-Bh2lvLyRJwQqeegm3gzoF6hawlpF8LZEjFLo8/rs:fit:200:200:1/g:ce/aHR0cHM6Ly9zLnct/eC5jby8yNDB4MTgw/X3R3Y19kZWZhdWx0/LnBuZw', 'title': '10-Day Weather Forecast for Manhattan, NY - The Weather Channel ...', 'description': 'Be prepared with the most accurate 10-day forecast for Manhattan, NY with highs, lows, chance of precipitation from The Weather Channel and Weather.com'}), Document(page_content='Sat 1346°/36°53%\\nSat 13\\nSat 13 | Day\\nCloudy with showers. High 46F. Winds WSW at 10 to 15 mph. Chance of rain 50%.\\n- Humidity73%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:50 pm\\nSat 13 | Night\\nRain showers early transitioning to snow showers late. Low 36F. Winds W at 10 to 15 mph. Chance of precip 50%.\\n- Humidity70%\\n- UV Index0 of 11\\n- Moonrise9:14 amWaxing Crescent\\n- Moonset7:33 pm\\nSun 1442°/34°37%\\nSun 14\\nSun 14 | Day\\nSnow showers early will transition to a few showers later. High 42F. Winds WSW at 10 to 15 mph. Chance of rain 40%.\\n- Humidity63%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:51 pm\\nSun 14 | Night\\nVariable clouds with snow showers. Low 34F. Winds W at 10 to 15 mph. Chance of snow 60%. Snow accumulations less than one inch.\\n- UV Index0 of 11\\n- Moonrise9:44 amWaxing Crescent\\n- Moonset8:52 pm\\nMon 1540°/31°51%\\nMon 15\\nMon 15 | Day', metadata={'url': 'https://weather.com/weather/tenday/l/New+York+NY+USNY0996:1:US', 'thumbnail_url': 'https://imgs.search.brave.com/9xHc5-Bh2lvLyRJwQqeegm3gzoF6hawlpF8LZEjFLo8/rs:fit:200:200:1/g:ce/aHR0cHM6Ly9zLnct/eC5jby8yNDB4MTgw/X3R3Y19kZWZhdWx0/LnBuZw', 'title': '10-Day Weather Forecast for Manhattan, NY - The Weather Channel ...', 'description': 'Be prepared with the most accurate 10-day forecast for Manhattan, NY with highs, lows, chance of precipitation from The Weather Channel and Weather.com'}), Document(page_content='- Humidity70%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:34 pm\\nMon 25 | Night\\nOvercast with showers at times. Low 43F. Winds light and variable. Chance of rain 40%.\\n- Humidity80%\\n- UV Index0 of 11\\n- Moonrise3:08 pmWaxing Gibbous\\n- Moonset6:14 am\\nTue 2653°/45°58%\\nTue 26\\nTue 26 | Day\\nOvercast with rain showers at times. High 53F. Winds E at 5 to 10 mph. Chance of rain 60%.\\n- Humidity79%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:34 pm\\nTue 26 | Night\\nShowers early then scattered thunderstorms developing late. Low near 45F. Winds ESE at 5 to 10 mph. Chance of rain 60%.\\n- Humidity93%\\n- UV Index0 of 11\\n- Moonrise4:00 pmFull Moon\\n- Moonset7:17 am\\nWed 2751°/41°58%\\nWed 27\\nWed 27 | Day\\nCloudy with showers. High 51F. Winds WSW at 5 to 10 mph. Chance of rain 60%.\\n- Humidity79%\\n- UV Index1 of 11\\n- Sunrise7:18 am\\n- Sunset4:35 pm\\nWed 27 | Night\\nCloudy with showers. Low 41F. Winds NW at 5 to 10 mph. Chance of rain 60%.\\n- Humidity72%\\n- UV Index0 of 11\\n- Moonrise4:59 pmFull Moon\\n- Moonset8:13 am', metadata={'url': 'https://weather.com/weather/tenday/l/New+York+NY+USNY0996:1:US', 'thumbnail_url': 'https://imgs.search.brave.com/9xHc5-Bh2lvLyRJwQqeegm3gzoF6hawlpF8LZEjFLo8/rs:fit:200:200:1/g:ce/aHR0cHM6Ly9zLnct/eC5jby8yNDB4MTgw/X3R3Y19kZWZhdWx0/LnBuZw', 'title': '10-Day Weather Forecast for Manhattan, NY - The Weather Channel ...', 'description': 'Be prepared with the most accurate 10-day forecast for Manhattan, NY with highs, lows, chance of precipitation from The Weather Channel and Weather.com'})]\n" + ] + } + ], "source": [ - "from langchain.chains import RetrievalQA\n", - "from langchain.retrievers.you_retriever import YouRetriever\n", - "from langchain_openai import OpenAI\n", + "# .invoke wraps utility.results\n", + "response = retriever.invoke(\"What is the weather in NY\")\n", "\n", - "yr = YouRetriever()\n", - "qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type=\"map_reduce\", retriever=yr)" + "# .invoke should have a Document for each `snippet`\n", + "print(len(response))\n", + "\n", + "print(response)" + ] + }, + { + "cell_type": "markdown", + "id": "aac4a1f9", + "metadata": {}, + "source": [ + "## Chaining" ] }, { "cell_type": "code", "execution_count": null, - "id": "4a223f2f", + "id": "21559e10", "metadata": {}, "outputs": [], "source": [ - "query = \"what starting ohio state quarterback most recently went their entire college career without beating Michigan?\"\n", - "qa.run(query)" + "# you need a model to use in the chain\n", + "!pip install --upgrade --quiet langchain-openai" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "655e0fec-c831-4efe-a47b-d3bb5c5a81ce", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.retrievers.you import YouRetriever\n", + "from langchain_core.output_parsers import StrOutputParser\n", + "from langchain_core.prompts import ChatPromptTemplate\n", + "from langchain_core.runnables import RunnablePassthrough\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "# set up runnable\n", + "runnable = RunnablePassthrough\n", + "\n", + "# set up retriever, limit sources to one\n", + "retriever = YouRetriever(num_web_results=1)\n", + "\n", + "# set up model\n", + "model = ChatOpenAI(model=\"gpt-3.5-turbo-16k\")\n", + "\n", + "# set up output parser\n", + "output_parser = StrOutputParser()" + ] + }, + { + "cell_type": "markdown", + "id": "47e1fcca", + "metadata": {}, + "source": [ + "### Invoke" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "6e92557d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The weather in New York City today is 43° with a high/low of --/39°. The wind is 3 mph, humidity is 63%, and the air quality is considered good.\n" + ] + } + ], + "source": [ + "# set up prompt that expects one question\n", + "prompt = ChatPromptTemplate.from_template(\n", + " \"\"\"Answer the question based only on the context provided.\n", + "\n", + "Context: {context}\n", + "\n", + "Question: {question}\"\"\"\n", + ")\n", + "\n", + "# set up chain\n", + "chain = (\n", + " runnable.assign(context=(lambda x: x[\"question\"]) | retriever)\n", + " | prompt\n", + " | model\n", + " | output_parser\n", + ")\n", + "\n", + "output = chain.invoke({\"question\": \"what is the weather in NY today\"})\n", + "\n", + "print(output)" + ] + }, + { + "cell_type": "markdown", + "id": "66b15f94", + "metadata": {}, + "source": [ + "### Stream" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "cfe5af8b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The weather in New York City today is a high of 39°F and a low of 31°F with a feels like temperature of 43°F. The wind speed is 3 mph, humidity is 63%, and the air quality is considered to be good." + ] + } + ], + "source": [ + "# set up prompt that expects one question\n", + "prompt = ChatPromptTemplate.from_template(\n", + " \"\"\"Answer the question based only on the context provided.\n", + "\n", + "Context: {context}\n", + "\n", + "Question: {question}\"\"\"\n", + ")\n", + "\n", + "# set up chain - same as above\n", + "chain = (\n", + " runnable.assign(context=(lambda x: x[\"question\"]) | retriever)\n", + " | prompt\n", + " | model\n", + " | output_parser\n", + ")\n", + "\n", + "for s in chain.stream({\"question\": \"what is the weather in NY today\"}):\n", + " print(s, end=\"\", flush=True)" + ] + }, + { + "cell_type": "markdown", + "id": "28ee9450", + "metadata": {}, + "source": [ + "### Batch" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "a8d8270b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Based on the provided context, the weather in New York City today is 43° with a high/low of --/39°.\n", + "Based on the provided context, the current weather in San Francisco is partly cloudy with a temperature of 61°F and a humidity of 57%.\n" + ] + } + ], + "source": [ + "chain = (\n", + " runnable.assign(context=(lambda x: x[\"question\"]) | retriever)\n", + " | prompt\n", + " | model\n", + " | output_parser\n", + ")\n", + "\n", + "output = chain.batch(\n", + " [\n", + " {\"question\": \"what is the weather in NY today\"},\n", + " {\"question\": \"what is the weather in sf today\"},\n", + " ]\n", + ")\n", + "\n", + "for o in output:\n", + " print(o)" ] } ], @@ -54,7 +411,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.17" + "version": "3.9.6" } }, "nbformat": 4, diff --git a/docs/docs/integrations/text_embedding/ai21.ipynb b/docs/docs/integrations/text_embedding/ai21.ipynb new file mode 100644 index 00000000000..c44e55f2b49 --- /dev/null +++ b/docs/docs/integrations/text_embedding/ai21.ipynb @@ -0,0 +1,138 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "c2923bd1", + "metadata": {}, + "source": [ + "---\n", + "sidebar_label: AI21 Labs\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "cc3c6ef6bbd57ce9", + "metadata": { + "collapsed": false + }, + "source": [ + "# AI21Embeddings\n", + "\n", + "This notebook covers how to get started with AI21 embedding models.\n", + "\n", + "## Installation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c3bef91", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -qU langchain-ai21" + ] + }, + { + "cell_type": "markdown", + "id": "2b4f3e15", + "metadata": {}, + "source": [ + "## Environment Setup\n", + "\n", + "We'll need to get a [AI21 API key](https://docs.ai21.com/) and set the `AI21_API_KEY` environment variable:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62e0dbc3", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "from getpass import getpass\n", + "\n", + "os.environ[\"AI21_API_KEY\"] = getpass()" + ] + }, + { + "cell_type": "markdown", + "id": "74ef9d8b40a1319e", + "metadata": { + "collapsed": false + }, + "source": [ + "## Usage" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "12fcfb4b", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_ai21 import AI21Embeddings\n", + "\n", + "embeddings = AI21Embeddings()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f2e6104", + "metadata": {}, + "outputs": [], + "source": [ + "embeddings.embed_query(\"My query to look up\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3465d7e63bfb3d1", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "embeddings.embed_documents(\n", + " [\"This is a content of the document\", \"This is another document\"]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d60af6d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/docs/integrations/text_embedding/clarifai.ipynb b/docs/docs/integrations/text_embedding/clarifai.ipynb index ab006c48b35..f10a9a463a6 100644 --- a/docs/docs/integrations/text_embedding/clarifai.ipynb +++ b/docs/docs/integrations/text_embedding/clarifai.ipynb @@ -101,7 +101,7 @@ "\n", "Answer: Let's think step by step.\"\"\"\n", "\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])" + "prompt = PromptTemplate.from_template(template)" ] }, { diff --git a/docs/docs/integrations/text_embedding/google_generative_ai.ipynb b/docs/docs/integrations/text_embedding/google_generative_ai.ipynb index 7cac7e42b8f..7afdf8b34fd 100644 --- a/docs/docs/integrations/text_embedding/google_generative_ai.ipynb +++ b/docs/docs/integrations/text_embedding/google_generative_ai.ipynb @@ -200,7 +200,7 @@ "id": "2e7857e5", "metadata": {}, "source": [ - "## Additional Configuraation\n", + "## Additional Configuration\n", "\n", "You can pass the following parameters to ChatGoogleGenerativeAI in order to customize the SDK's behavior:\n", "\n", diff --git a/docs/docs/integrations/text_embedding/nemo.ipynb b/docs/docs/integrations/text_embedding/nemo.ipynb new file mode 100644 index 00000000000..4868985e882 --- /dev/null +++ b/docs/docs/integrations/text_embedding/nemo.ipynb @@ -0,0 +1,121 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "abede47c-6a58-40c3-b7ef-10966a4fc085", + "metadata": {}, + "source": [ + "# NVIDIA NeMo embeddings" + ] + }, + { + "cell_type": "markdown", + "id": "38f3d4ce-b36a-48c6-88b0-5970c26bb146", + "metadata": {}, + "source": [ + "Connect to NVIDIA's embedding service using the `NeMoEmbeddings` class.\n", + "\n", + "The NeMo Retriever Embedding Microservice (NREM) brings the power of state-of-the-art text embedding to your applications, providing unmatched natural language processing and understanding capabilities. Whether you're developing semantic search, Retrieval Augmented Generation (RAG) pipelines—or any application that needs to use text embeddings—NREM has you covered. Built on the NVIDIA software platform incorporating CUDA, TensorRT, and Triton, NREM brings state of the art GPU accelerated Text Embedding model serving.\n", + "\n", + "NREM uses NVIDIA's TensorRT built on top of the Triton Inference Server for optimized inference of text embedding models." + ] + }, + { + "cell_type": "markdown", + "id": "f5ab6ea1-d074-4f36-ae45-50312a6a82b9", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "32deab16-530d-455c-b40c-914db048cb05", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.embeddings import NeMoEmbeddings" + ] + }, + { + "cell_type": "markdown", + "id": "de40023c-3391-474d-96cf-fbfb2311e9d7", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "37177018-47f4-48be-8575-83ce5c9a5447", + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 16\n", + "model = \"NV-Embed-QA-003\"\n", + "api_endpoint_url = \"http://localhost:8080/v1/embeddings\"" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "08161ed2-8ba3-4226-a387-15c348f8c343", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Checking if endpoint is live: http://localhost:8080/v1/embeddings\n" + ] + } + ], + "source": [ + "embedding_model = NeMoEmbeddings(\n", + " batch_size=batch_size, model=model, api_endpoint_url=api_endpoint_url\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c69070c3-fe2d-4ff7-be4a-73304e2c4f3e", + "metadata": {}, + "outputs": [], + "source": [ + "embedding_model.embed_query(\"This is a test.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d1d8852-5298-40b5-89c4-5a91ccfc95e5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/docs/integrations/text_embedding/nomic.ipynb b/docs/docs/integrations/text_embedding/nomic.ipynb index 6f6b11d4aa5..6f731668c30 100644 --- a/docs/docs/integrations/text_embedding/nomic.ipynb +++ b/docs/docs/integrations/text_embedding/nomic.ipynb @@ -58,7 +58,7 @@ "source": [ "from langchain_nomic.embeddings import NomicEmbeddings\n", "\n", - "embeddings = NomicEmbeddings(model=\"nomic-embed-text-v1\")" + "embeddings = NomicEmbeddings(model=\"nomic-embed-text-v1.5\")" ] }, { @@ -106,6 +106,28 @@ " [\"This is a content of the document\", \"This is another document\"]\n", ")" ] + }, + { + "cell_type": "markdown", + "id": "7a331dc3", + "metadata": {}, + "source": [ + "### Custom Dimensionality\n", + "\n", + "Nomic's `nomic-embed-text-v1.5` model was [trained with Matryoshka learning](https://blog.nomic.ai/posts/nomic-embed-matryoshka) to enable variable-length embeddings with a single model. This means that you can specify the dimensionality of the embeddings at inference time. The model supports dimensionality from 64 to 768." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "993f65c8", + "metadata": {}, + "outputs": [], + "source": [ + "embeddings = NomicEmbeddings(model=\"nomic-embed-text-v1.5\", dimensionality=256)\n", + "\n", + "embeddings.embed_query(\"My query to look up\")" + ] } ], "metadata": { diff --git a/docs/docs/integrations/text_embedding/nvidia_ai_endpoints.ipynb b/docs/docs/integrations/text_embedding/nvidia_ai_endpoints.ipynb index 623de6933d2..ce23d469c40 100644 --- a/docs/docs/integrations/text_embedding/nvidia_ai_endpoints.ipynb +++ b/docs/docs/integrations/text_embedding/nvidia_ai_endpoints.ipynb @@ -28,9 +28,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], "source": [ "%pip install --upgrade --quiet langchain-nvidia-ai-endpoints" ] @@ -56,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -64,7 +72,15 @@ "id": "hoF41-tNczS3", "outputId": "7f2833dc-191c-4d73-b823-7b2745a93a2f" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Valid NVIDIA_API_KEY already in environment. Delete to reset\n" + ] + } + ], "source": [ "import getpass\n", "import os\n", @@ -105,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "id": "hbXmJssPdIPX" }, @@ -180,7 +196,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -194,15 +210,15 @@ "output_type": "stream", "text": [ "Single Query Embedding: \n", - "\u001b[1mExecuted in 1.39 seconds.\u001b[0m\n", + "\u001b[1mExecuted in 2.19 seconds.\u001b[0m\n", "Shape: (1024,)\n", "\n", "Sequential Embedding: \n", - "\u001b[1mExecuted in 3.20 seconds.\u001b[0m\n", + "\u001b[1mExecuted in 3.16 seconds.\u001b[0m\n", "Shape: (5, 1024)\n", "\n", "Batch Query Embedding: \n", - "\u001b[1mExecuted in 1.52 seconds.\u001b[0m\n", + "\u001b[1mExecuted in 1.23 seconds.\u001b[0m\n", "Shape: (5, 1024)\n" ] } @@ -260,7 +276,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -274,11 +290,11 @@ "output_type": "stream", "text": [ "Single Document Embedding: \n", - "\u001b[1mExecuted in 0.76 seconds.\u001b[0m\n", + "\u001b[1mExecuted in 0.52 seconds.\u001b[0m\n", "Shape: (1024,)\n", "\n", "Batch Document Embedding: \n", - "\u001b[1mExecuted in 0.86 seconds.\u001b[0m\n", + "\u001b[1mExecuted in 0.89 seconds.\u001b[0m\n", "Shape: (5, 1024)\n" ] } @@ -324,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -341,7 +357,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", diff --git a/docs/docs/integrations/text_embedding/oci_generative_ai.ipynb b/docs/docs/integrations/text_embedding/oci_generative_ai.ipynb new file mode 100755 index 00000000000..ae935e4d354 --- /dev/null +++ b/docs/docs/integrations/text_embedding/oci_generative_ai.ipynb @@ -0,0 +1,140 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Oracle Cloud Infrastructure Generative AI" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Oracle Cloud Infrastructure (OCI) Generative AI is a fully managed service that provides a set of state-of-the-art, customizable large language models (LLMs), that cover a wide range of use cases, and which are available through a single API.\n", + "Using the OCI Generative AI service you can access ready-to-use pretrained models, or create and host your own fine-tuned custom models based on your own data on dedicated AI clusters. Detailed documentation of the service and API is available __[here](https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm)__ and __[here](https://docs.oracle.com/en-us/iaas/api/#/en/generative-ai/20231130/)__.\n", + "\n", + "This notebook explains how to use OCI's Genrative AI models with LangChain." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prerequisite\n", + "We will need to install the oci sdk" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -U oci" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### OCI Generative AI API endpoint \n", + "https://inference.generativeai.us-chicago-1.oci.oraclecloud.com" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Authentication\n", + "The authentication methods supported for this langchain integration are:\n", + "\n", + "1. API Key\n", + "2. Session token\n", + "3. Instance principal\n", + "4. Resource principal \n", + "\n", + "These follows the standard SDK authentication methods detailed __[here](https://docs.oracle.com/en-us/iaas/Content/API/Concepts/sdk_authentication_methods.htm)__.\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Usage" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.embeddings import OCIGenAIEmbeddings\n", + "\n", + "# use default authN method API-key\n", + "embeddings = OCIGenAIEmbeddings(\n", + " model_id=\"MY_EMBEDDING_MODEL\",\n", + " service_endpoint=\"https://inference.generativeai.us-chicago-1.oci.oraclecloud.com\",\n", + " compartment_id=\"MY_OCID\",\n", + ")\n", + "\n", + "\n", + "query = \"This is a query in English.\"\n", + "response = embeddings.embed_query(query)\n", + "print(response)\n", + "\n", + "documents = [\"This is a sample document\", \"and here is another one\"]\n", + "response = embeddings.embed_documents(documents)\n", + "print(response)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Use Session Token to authN\n", + "embeddings = OCIGenAIEmbeddings(\n", + " model_id=\"MY_EMBEDDING_MODEL\",\n", + " service_endpoint=\"https://inference.generativeai.us-chicago-1.oci.oraclecloud.com\",\n", + " compartment_id=\"MY_OCID\",\n", + " auth_type=\"SECURITY_TOKEN\",\n", + " auth_profile=\"MY_PROFILE\", # replace with your profile name\n", + ")\n", + "\n", + "\n", + "query = \"This is a sample query\"\n", + "response = embeddings.embed_query(query)\n", + "print(response)\n", + "\n", + "documents = [\"This is a sample document\", \"and here is another one\"]\n", + "response = embeddings.embed_documents(documents)\n", + "print(response)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "oci_langchain", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/docs/integrations/text_embedding/optimum_intel.ipynb b/docs/docs/integrations/text_embedding/optimum_intel.ipynb new file mode 100644 index 00000000000..bb4df8fcd73 --- /dev/null +++ b/docs/docs/integrations/text_embedding/optimum_intel.ipynb @@ -0,0 +1,201 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ae6f9d9d-fe44-489c-9661-dac69683dcd2", + "metadata": {}, + "source": [ + "# Embedding Documents using Optimized and Quantized Embedders\n", + "\n", + "Embedding all documents using Quantized Embedders.\n", + "\n", + "The embedders are based on optimized models, created by using [optimum-intel](https://github.com/huggingface/optimum-intel.git) and [IPEX](https://github.com/intel/intel-extension-for-pytorch).\n", + "\n", + "Example text is based on [SBERT](https://www.sbert.net/docs/pretrained_cross-encoders.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b9d1a3bb-83b1-4029-ad8d-411db1fba034", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "loading configuration file inc_config.json from cache at \n", + "INCConfig {\n", + " \"distillation\": {},\n", + " \"neural_compressor_version\": \"2.4.1\",\n", + " \"optimum_version\": \"1.16.2\",\n", + " \"pruning\": {},\n", + " \"quantization\": {\n", + " \"dataset_num_samples\": 50,\n", + " \"is_static\": true\n", + " },\n", + " \"save_onnx_model\": false,\n", + " \"torch_version\": \"2.2.0\",\n", + " \"transformers_version\": \"4.37.2\"\n", + "}\n", + "\n", + "Using `INCModel` to load a TorchScript model will be deprecated in v1.15.0, to load your model please use `IPEXModel` instead.\n" + ] + } + ], + "source": [ + "from langchain_community.embeddings import QuantizedBiEncoderEmbeddings\n", + "\n", + "model_name = \"Intel/bge-small-en-v1.5-rag-int8-static\"\n", + "encode_kwargs = {\"normalize_embeddings\": True} # set True to compute cosine similarity\n", + "\n", + "model = QuantizedBiEncoderEmbeddings(\n", + " model_name=model_name,\n", + " encode_kwargs=encode_kwargs,\n", + " query_instruction=\"Represent this sentence for searching relevant passages: \",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "34318164-7a6f-47b6-8690-3b1d71e1fcfc", + "metadata": {}, + "source": [ + "Lets ask a question, and compare to 2 documents. The first contains the answer to the question, and the second one does not. \n", + "\n", + "We can check better suits our query." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "55ff07ca-fb44-4dcf-b2d3-dde021a53983", + "metadata": {}, + "outputs": [], + "source": [ + "question = \"How many people live in Berlin?\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "aebef832-5534-440c-a4a8-4bf56ccd8ad4", + "metadata": {}, + "outputs": [], + "source": [ + "documents = [\n", + " \"Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.\",\n", + " \"Berlin is well known for its museums.\",\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4eec7eda-0d9b-4488-a0e8-3eedd28ab0b1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Batches: 100%|██████████| 1/1 [00:00<00:00, 4.18it/s]\n" + ] + } + ], + "source": [ + "doc_vecs = model.embed_documents(documents)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8e6dac72-5a0b-4421-9454-aa0a49b20c66", + "metadata": {}, + "outputs": [], + "source": [ + "query_vec = model.embed_query(question)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ec26eb7a-a259-4bb9-b9d8-9ff345a8c798", + "metadata": {}, + "outputs": [], + "source": [ + "import torch" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "9ca1ee83-2a6a-4f65-bc2f-3942a0c068c6", + "metadata": {}, + "outputs": [], + "source": [ + "doc_vecs_torch = torch.tensor(doc_vecs)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "4f6a1986-339e-443a-a2f6-ae3f3ad4266c", + "metadata": {}, + "outputs": [], + "source": [ + "query_vec_torch = torch.tensor(query_vec)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "2b49446e-1336-46b3-b9ef-af56b4870876", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([0.7980, 0.6529])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query_vec_torch @ doc_vecs_torch.T" + ] + }, + { + "cell_type": "markdown", + "id": "6cc1ac2a-9641-408e-a373-736d121fc3c7", + "metadata": {}, + "source": [ + "We can see that indeed the first one ranks higher." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/docs/integrations/toolkits/amadeus.ipynb b/docs/docs/integrations/toolkits/amadeus.ipynb index e97067c0f60..787bd3dfbfc 100644 --- a/docs/docs/integrations/toolkits/amadeus.ipynb +++ b/docs/docs/integrations/toolkits/amadeus.ipynb @@ -12,7 +12,7 @@ "\n", "To use this toolkit, you will need to have your Amadeus API keys ready, explained in the [Get started Amadeus Self-Service APIs](https://developers.amadeus.com/get-started/get-started-with-self-service-apis-335). Once you've received a AMADEUS_CLIENT_ID and AMADEUS_CLIENT_SECRET, you can input them as environmental variables below.\n", "\n", - "Note: Amadeus Self-Service APIs offers a test enviornment with [free limited data](https://amadeus4dev.github.io/developer-guides/test-data/). This allows developers to build and test their applications before deploying them to production. To access real-time data, you will need to [move to the production environment](https://amadeus4dev.github.io/developer-guides/API-Keys/moving-to-production/)." + "Note: Amadeus Self-Service APIs offers a test environment with [free limited data](https://amadeus4dev.github.io/developer-guides/test-data/). This allows developers to build and test their applications before deploying them to production. To access real-time data, you will need to [move to the production environment](https://amadeus4dev.github.io/developer-guides/API-Keys/moving-to-production/)." ] }, { diff --git a/docs/docs/integrations/tools/brave_search.ipynb b/docs/docs/integrations/tools/brave_search.ipynb index 365aafe20d8..82cd841e4fa 100644 --- a/docs/docs/integrations/tools/brave_search.ipynb +++ b/docs/docs/integrations/tools/brave_search.ipynb @@ -7,7 +7,8 @@ "source": [ "# Brave Search\n", "\n", - "This notebook goes over how to use the Brave Search tool." + "This notebook goes over how to use the Brave Search tool.\n", + "Go to the [Brave Website](https://brave.com/search/api/) to sign up for a free account and get an API key." ] }, { diff --git a/docs/docs/integrations/tools/exa_search.ipynb b/docs/docs/integrations/tools/exa_search.ipynb index dc2da3a1d9d..50432253313 100644 --- a/docs/docs/integrations/tools/exa_search.ipynb +++ b/docs/docs/integrations/tools/exa_search.ipynb @@ -60,7 +60,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Using ExaSearchRetriever\n", + "## Using ExaSearchRetriever\n", "\n", "ExaSearchRetriever is a retriever that uses Exa Search to retrieve relevant documents." ] @@ -345,7 +345,7 @@ " Set the optional include_domains (list[str]) parameter to restrict the search to a list of domains.\n", " Set the optional start_published_date (str) parameter to restrict the search to documents published after the date (YYYY-MM-DD).\n", " \"\"\"\n", - " return exa.search(\n", + " return exa.search_and_contents(\n", " f\"{query}\",\n", " use_autoprompt=True,\n", " num_results=5,\n", @@ -359,7 +359,7 @@ " \"\"\"Search for webpages similar to a given URL.\n", " The url passed in should be a URL returned from `search`.\n", " \"\"\"\n", - " return exa.find_similar(url, num_results=5)\n", + " return exa.find_similar_and_contents(url, num_results=5)\n", "\n", "\n", "@tool\n", diff --git a/docs/docs/integrations/tools/filesystem.ipynb b/docs/docs/integrations/tools/filesystem.ipynb index c7599a0019d..56bf91b4c36 100644 --- a/docs/docs/integrations/tools/filesystem.ipynb +++ b/docs/docs/integrations/tools/filesystem.ipynb @@ -56,13 +56,13 @@ { "data": { "text/plain": [ - "[CopyFileTool(name='copy_file', description='Create a copy of a file in a specified location', args_schema=, return_direct=False, verbose=False, callback_manager=, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n", - " DeleteFileTool(name='file_delete', description='Delete a file', args_schema=, return_direct=False, verbose=False, callback_manager=, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n", - " FileSearchTool(name='file_search', description='Recursively search for files in a subdirectory that match the regex pattern', args_schema=, return_direct=False, verbose=False, callback_manager=, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n", - " MoveFileTool(name='move_file', description='Move or rename a file from one location to another', args_schema=, return_direct=False, verbose=False, callback_manager=, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n", - " ReadFileTool(name='read_file', description='Read file from disk', args_schema=, return_direct=False, verbose=False, callback_manager=, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n", - " WriteFileTool(name='write_file', description='Write file to disk', args_schema=, return_direct=False, verbose=False, callback_manager=, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n", - " ListDirectoryTool(name='list_directory', description='List files and directories in a specified folder', args_schema=, return_direct=False, verbose=False, callback_manager=, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug')]" + "[CopyFileTool(root_dir='/tmp/tmprdvsw3tg'),\n", + " DeleteFileTool(root_dir='/tmp/tmprdvsw3tg'),\n", + " FileSearchTool(root_dir='/tmp/tmprdvsw3tg'),\n", + " MoveFileTool(root_dir='/tmp/tmprdvsw3tg'),\n", + " ReadFileTool(root_dir='/tmp/tmprdvsw3tg'),\n", + " WriteFileTool(root_dir='/tmp/tmprdvsw3tg'),\n", + " ListDirectoryTool(root_dir='/tmp/tmprdvsw3tg')]" ] }, "execution_count": 2, @@ -96,9 +96,9 @@ { "data": { "text/plain": [ - "[ReadFileTool(name='read_file', description='Read file from disk', args_schema=, return_direct=False, verbose=False, callback_manager=, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n", - " WriteFileTool(name='write_file', description='Write file to disk', args_schema=, return_direct=False, verbose=False, callback_manager=, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug'),\n", - " ListDirectoryTool(name='list_directory', description='List files and directories in a specified folder', args_schema=, return_direct=False, verbose=False, callback_manager=, root_dir='/var/folders/gf/6rnp_mbx5914kx7qmmh7xzmw0000gn/T/tmpxb8c3aug')]" + "[ReadFileTool(root_dir='/tmp/tmprdvsw3tg'),\n", + " WriteFileTool(root_dir='/tmp/tmprdvsw3tg'),\n", + " ListDirectoryTool(root_dir='/tmp/tmprdvsw3tg')]" ] }, "execution_count": 3, @@ -132,7 +132,7 @@ ], "source": [ "read_tool, write_tool, list_tool = tools\n", - "write_tool.run({\"file_path\": \"example.txt\", \"text\": \"Hello World!\"})" + "write_tool.invoke({\"file_path\": \"example.txt\", \"text\": \"Hello World!\"})" ] }, { @@ -153,7 +153,7 @@ ], "source": [ "# List files in the working directory\n", - "list_tool.run({})" + "list_tool.invoke({})" ] }, { @@ -180,7 +180,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/docs/docs/integrations/tools/google_search.ipynb b/docs/docs/integrations/tools/google_search.ipynb index c8bb72cbbfc..b1302db06f1 100644 --- a/docs/docs/integrations/tools/google_search.ipynb +++ b/docs/docs/integrations/tools/google_search.ipynb @@ -40,7 +40,7 @@ "search = GoogleSearchAPIWrapper()\n", "\n", "tool = Tool(\n", - " name=\"Google Search\",\n", + " name=\"google_search\",\n", " description=\"Search Google for recent results.\",\n", " func=search.run,\n", ")" diff --git a/docs/docs/integrations/tools/pubmed.ipynb b/docs/docs/integrations/tools/pubmed.ipynb index 7487eba02c0..52b5a5e84cd 100644 --- a/docs/docs/integrations/tools/pubmed.ipynb +++ b/docs/docs/integrations/tools/pubmed.ipynb @@ -14,7 +14,17 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, + "id": "3e90e50f", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install xmltodict" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "id": "c80b9273", "metadata": {}, "outputs": [], @@ -24,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "f203c965", "metadata": {}, "outputs": [], @@ -34,23 +44,23 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "baee7a2a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'Published: 2023May31\\nTitle: Dermatology in the wake of an AI revolution: who gets a say?\\nSummary: \\n\\nPublished: 2023May30\\nTitle: What is ChatGPT and what do we do with it? Implications of the age of AI for nursing and midwifery practice and education: An editorial.\\nSummary: \\n\\nPublished: 2023Jun02\\nTitle: The Impact of ChatGPT on the Nursing Profession: Revolutionizing Patient Care and Education.\\nSummary: The nursing field has undergone notable changes over time and is projected to undergo further modifications in the future, owing to the advent of sophisticated technologies and growing healthcare needs. The advent of ChatGPT, an AI-powered language model, is expected to exert a significant influence on the nursing profession, specifically in the domains of patient care and instruction. The present article delves into the ramifications of ChatGPT within the nursing domain and accentuates its capacity and constraints to transform the discipline.'" + "'Published: 2024-02-10\\nTitle: circEPB41L2 blocks the progression and metastasis in non-small cell lung cancer by promoting TRIP12-triggered PTBP1 ubiquitylation.\\nCopyright Information: © 2024. The Author(s).\\nSummary::\\nThe metastasis of non-small cell lung cancer (NSCLC) is the leading death cause of NSCLC patients, which requires new biomarkers for precise diagnosis and treatment. Circular RNAs (circRNAs), the novel noncoding RNA, participate in the progression of various cancers as microRNA or protein sponges. We revealed the mechanism by which circEPB41L2 (hsa_circ_0077837) blocks the aerobic glycolysis, progression and metastasis of NSCLC through modulating protein metabolism of PTBP1 by the E3 ubiquitin ligase TRIP12. With ribosomal RNA-depleted RNA seq, 57 upregulated and 327 downregulated circRNAs were identified in LUAD tissues. circEPB41L2 was selected due to its dramatically reduced levels in NSCLC tissues and NSCLC cells. Interestingly, circEPB41L2 blocked glucose uptake, lactate production, NSCLC cell proliferation, migration and invasion in vitro and in vivo. Mechanistically, acting as a scaffold, circEPB41L2 bound to the RRM1 domain of the PTBP1 and the E3 ubiquitin ligase TRIP12 to promote TRIP12-mediated PTBP1 polyubiquitylation and degradation, which could be reversed by the HECT domain mutation of TRIP12 and circEPB41L2 depletion. As a result, circEPB41L2-induced PTBP1 inhibition led to PTBP1-induced PKM2 and Vimentin activation but PKM1 and E-cadherin inactivation. These findings highlight the circEPB41L2-dependent mechanism that modulates the \"Warburg Effect\" and EMT to inhibit NSCLC development and metastasis, offering an inhibitory target for NSCLC treatment.\\n\\nPublished: 2024-01-17\\nTitle: The safety of seasonal influenza vaccination among adults prescribed immune checkpoint inhibitors: A self-controlled case series study using administrative data.\\nCopyright Information: Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserv'" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "tool.run(\"chatgpt\")" + "tool.invoke(\"What causes lung cancer?\")" ] }, { @@ -78,7 +88,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.11.7" } }, "nbformat": 4, diff --git a/docs/docs/integrations/tools/sql_database.ipynb b/docs/docs/integrations/tools/sql_database.ipynb new file mode 100644 index 00000000000..eec9027e605 --- /dev/null +++ b/docs/docs/integrations/tools/sql_database.ipynb @@ -0,0 +1,440 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# SQL Database\n", + "\n", + "::: {.callout-note}\n", + "The `SQLDatabase` adapter utility is a wrapper around a database connection.\n", + "\n", + "For talking to SQL databases, it uses the [SQLAlchemy] Core API .\n", + ":::\n", + "\n", + "\n", + "This notebook shows how to use the utility to access an SQLite database.\n", + "It uses the example [Chinook Database], and demonstrates those features:\n", + "\n", + "- Query using SQL\n", + "- Query using SQLAlchemy selectable\n", + "- Fetch modes `cursor`, `all`, and `one`\n", + "- Bind query parameters\n", + "\n", + "[Chinook Database]: https://github.com/lerocha/chinook-database\n", + "[SQLAlchemy]: https://www.sqlalchemy.org/\n", + "\n", + "\n", + "You can use the `Tool` or `@tool` decorator to create a tool from this utility.\n", + "\n", + "\n", + "::: {.callout-caution}\n", + "If creating a tool from the SQLDatbase utility and combining it with an LLM or exposing it to an end user\n", + "remember to follow good security practices.\n", + "\n", + "See security information: https://python.langchain.com/docs/security\n", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [], + "source": [ + "!wget 'https://github.com/lerocha/chinook-database/releases/download/v1.4.2/Chinook_Sqlite.sql'" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1|AC/DC\r\n", + "2|Accept\r\n", + "3|Aerosmith\r\n", + "4|Alanis Morissette\r\n", + "5|Alice In Chains\r\n", + "6|Antônio Carlos Jobim\r\n", + "7|Apocalyptica\r\n", + "8|Audioslave\r\n", + "9|BackBeat\r\n", + "10|Billy Cobham\r\n", + "11|Black Label Society\r\n", + "12|Black Sabbath\r\n" + ] + } + ], + "source": [ + "!sqlite3 -bail -cmd '.read Chinook_Sqlite.sql' -cmd 'SELECT * FROM Artist LIMIT 12;' -cmd '.quit'" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "!sqlite3 -bail -cmd '.read Chinook_Sqlite.sql' -cmd '.save Chinook.db' -cmd '.quit'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Initialize Database" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "from pprint import pprint\n", + "\n", + "import sqlalchemy as sa\n", + "from langchain.sql_database import SQLDatabase\n", + "\n", + "db = SQLDatabase.from_uri(\"sqlite:///Chinook.db\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Query as cursor\n", + "\n", + "The fetch mode `cursor` returns results as SQLAlchemy's\n", + "`CursorResult` instance." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[{'ArtistId': 1, 'Name': 'AC/DC'},\n", + " {'ArtistId': 2, 'Name': 'Accept'},\n", + " {'ArtistId': 3, 'Name': 'Aerosmith'},\n", + " {'ArtistId': 4, 'Name': 'Alanis Morissette'},\n", + " {'ArtistId': 5, 'Name': 'Alice In Chains'},\n", + " {'ArtistId': 6, 'Name': 'Antônio Carlos Jobim'},\n", + " {'ArtistId': 7, 'Name': 'Apocalyptica'},\n", + " {'ArtistId': 8, 'Name': 'Audioslave'},\n", + " {'ArtistId': 9, 'Name': 'BackBeat'},\n", + " {'ArtistId': 10, 'Name': 'Billy Cobham'},\n", + " {'ArtistId': 11, 'Name': 'Black Label Society'},\n", + " {'ArtistId': 12, 'Name': 'Black Sabbath'}]\n" + ] + } + ], + "source": [ + "result = db.run(\"SELECT * FROM Artist LIMIT 12;\", fetch=\"cursor\")\n", + "print(type(result))\n", + "pprint(list(result.mappings()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Query as string payload\n", + "\n", + "The fetch modes `all` and `one` return results in string format." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[(1, 'AC/DC'), (2, 'Accept'), (3, 'Aerosmith'), (4, 'Alanis Morissette'), (5, 'Alice In Chains'), (6, 'Antônio Carlos Jobim'), (7, 'Apocalyptica'), (8, 'Audioslave'), (9, 'BackBeat'), (10, 'Billy Cobham'), (11, 'Black Label Society'), (12, 'Black Sabbath')]\n" + ] + } + ], + "source": [ + "result = db.run(\"SELECT * FROM Artist LIMIT 12;\", fetch=\"all\")\n", + "print(type(result))\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[(1, 'AC/DC')]\n" + ] + } + ], + "source": [ + "result = db.run(\"SELECT * FROM Artist LIMIT 12;\", fetch=\"one\")\n", + "print(type(result))\n", + "print(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Query with parameters\n", + "\n", + "In order to bind query parameters, use the optional `parameters` argument." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'ArtistId': 35, 'Name': 'Pedro Luís & A Parede'},\n", + " {'ArtistId': 115, 'Name': 'Page & Plant'},\n", + " {'ArtistId': 116, 'Name': 'Passengers'},\n", + " {'ArtistId': 117, 'Name': \"Paul D'Ianno\"},\n", + " {'ArtistId': 118, 'Name': 'Pearl Jam'},\n", + " {'ArtistId': 119, 'Name': 'Peter Tosh'},\n", + " {'ArtistId': 120, 'Name': 'Pink Floyd'},\n", + " {'ArtistId': 121, 'Name': 'Planet Hemp'},\n", + " {'ArtistId': 186, 'Name': 'Pedro Luís E A Parede'},\n", + " {'ArtistId': 256, 'Name': 'Philharmonia Orchestra & Sir Neville Marriner'},\n", + " {'ArtistId': 275, 'Name': 'Philip Glass Ensemble'}]\n" + ] + } + ], + "source": [ + "result = db.run(\n", + " \"SELECT * FROM Artist WHERE Name LIKE :search;\",\n", + " parameters={\"search\": \"p%\"},\n", + " fetch=\"cursor\",\n", + ")\n", + "pprint(list(result.mappings()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Query with SQLAlchemy selectable\n", + "\n", + "Other than plain-text SQL statements, the adapter also accepts SQLAlchemy selectables." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'ArtistId': 35, 'Name': 'Pedro Luís & A Parede'},\n", + " {'ArtistId': 115, 'Name': 'Page & Plant'},\n", + " {'ArtistId': 116, 'Name': 'Passengers'},\n", + " {'ArtistId': 117, 'Name': \"Paul D'Ianno\"},\n", + " {'ArtistId': 118, 'Name': 'Pearl Jam'},\n", + " {'ArtistId': 119, 'Name': 'Peter Tosh'},\n", + " {'ArtistId': 120, 'Name': 'Pink Floyd'},\n", + " {'ArtistId': 121, 'Name': 'Planet Hemp'},\n", + " {'ArtistId': 186, 'Name': 'Pedro Luís E A Parede'},\n", + " {'ArtistId': 256, 'Name': 'Philharmonia Orchestra & Sir Neville Marriner'},\n", + " {'ArtistId': 275, 'Name': 'Philip Glass Ensemble'}]\n" + ] + } + ], + "source": [ + "# In order to build a selectable on SA's Core API, you need a table definition.\n", + "metadata = sa.MetaData()\n", + "artist = sa.Table(\n", + " \"Artist\",\n", + " metadata,\n", + " sa.Column(\"ArtistId\", sa.INTEGER, primary_key=True),\n", + " sa.Column(\"Name\", sa.TEXT),\n", + ")\n", + "\n", + "# Build a selectable with the same semantics of the recent query.\n", + "query = sa.select(artist).where(artist.c.Name.like(\"p%\"))\n", + "result = db.run(query, fetch=\"cursor\")\n", + "pprint(list(result.mappings()))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Query with execution options\n", + "\n", + "It is possible to augment the statement invocation with custom execution options.\n", + "For example, when applying a schema name translation, subsequent statements will\n", + "fail, because they try to hit a non-existing table." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "ename": "OperationalError", + "evalue": "(sqlite3.OperationalError) no such table: bar.Artist\n[SQL: SELECT bar.\"Artist\".\"ArtistId\", bar.\"Artist\".\"Name\" \nFROM bar.\"Artist\" \nWHERE bar.\"Artist\".\"Name\" LIKE ?]\n[parameters: ('p%',)]\n(Background on this error at: https://sqlalche.me/e/20/e3q8)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mOperationalError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1969\u001b[0m, in \u001b[0;36mConnection._exec_single_context\u001b[0;34m(self, dialect, context, statement, parameters)\u001b[0m\n\u001b[1;32m 1968\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m evt_handled:\n\u001b[0;32m-> 1969\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdialect\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_execute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1970\u001b[0m \u001b[43m \u001b[49m\u001b[43mcursor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstr_statement\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meffective_parameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\n\u001b[1;32m 1971\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1973\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_has_events \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine\u001b[38;5;241m.\u001b[39m_has_events:\n", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/.venv/lib/python3.11/site-packages/sqlalchemy/engine/default.py:922\u001b[0m, in \u001b[0;36mDefaultDialect.do_execute\u001b[0;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[1;32m 921\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdo_execute\u001b[39m(\u001b[38;5;28mself\u001b[39m, cursor, statement, parameters, context\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m--> 922\u001b[0m cursor\u001b[38;5;241m.\u001b[39mexecute(statement, parameters)\n", + "\u001b[0;31mOperationalError\u001b[0m: no such table: bar.Artist", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mOperationalError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Build a selectable with the same semantics of the recent query.\u001b[39;00m\n\u001b[1;32m 2\u001b[0m query \u001b[38;5;241m=\u001b[39m sa\u001b[38;5;241m.\u001b[39mselect(artist)\u001b[38;5;241m.\u001b[39mwhere(artist\u001b[38;5;241m.\u001b[39mc\u001b[38;5;241m.\u001b[39mName\u001b[38;5;241m.\u001b[39mlike(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mp\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m----> 3\u001b[0m \u001b[43mdb\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mquery\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfetch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mresult\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexecution_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mschema_translate_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbar\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/libs/community/langchain_community/utilities/sql_database.py:484\u001b[0m, in \u001b[0;36mSQLDatabase.run\u001b[0;34m(self, command, fetch, parameters, execution_options, include_columns)\u001b[0m\n\u001b[1;32m 471\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun\u001b[39m(\n\u001b[1;32m 472\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 473\u001b[0m command: Union[\u001b[38;5;28mstr\u001b[39m, Executable],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 477\u001b[0m include_columns: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 478\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Union[\u001b[38;5;28mstr\u001b[39m, Result]:\n\u001b[1;32m 479\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Execute a SQL command and return a string representing the results.\u001b[39;00m\n\u001b[1;32m 480\u001b[0m \n\u001b[1;32m 481\u001b[0m \u001b[38;5;124;03m If the statement returns rows, a string of the results is returned.\u001b[39;00m\n\u001b[1;32m 482\u001b[0m \u001b[38;5;124;03m If the statement returns no rows, an empty string is returned.\u001b[39;00m\n\u001b[1;32m 483\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 484\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 485\u001b[0m \u001b[43m \u001b[49m\u001b[43mcommand\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfetch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexecution_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexecution_options\u001b[49m\n\u001b[1;32m 486\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 488\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fetch \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresult\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 489\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/libs/community/langchain_community/utilities/sql_database.py:450\u001b[0m, in \u001b[0;36mSQLDatabase._execute\u001b[0;34m(self, command, fetch, parameters, execution_options)\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 449\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mQuery expression has unknown type: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(command)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 450\u001b[0m cursor \u001b[38;5;241m=\u001b[39m \u001b[43mconnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 451\u001b[0m \u001b[43m \u001b[49m\u001b[43mcommand\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 452\u001b[0m \u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 453\u001b[0m \u001b[43m \u001b[49m\u001b[43mexecution_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexecution_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 454\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 456\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cursor\u001b[38;5;241m.\u001b[39mreturns_rows:\n\u001b[1;32m 457\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fetch \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mall\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1416\u001b[0m, in \u001b[0;36mConnection.execute\u001b[0;34m(self, statement, parameters, execution_options)\u001b[0m\n\u001b[1;32m 1414\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\u001b[38;5;241m.\u001b[39mObjectNotExecutableError(statement) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 1415\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1416\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmeth\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1417\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1418\u001b[0m \u001b[43m \u001b[49m\u001b[43mdistilled_parameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1419\u001b[0m \u001b[43m \u001b[49m\u001b[43mexecution_options\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mNO_OPTIONS\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1420\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/.venv/lib/python3.11/site-packages/sqlalchemy/sql/elements.py:516\u001b[0m, in \u001b[0;36mClauseElement._execute_on_connection\u001b[0;34m(self, connection, distilled_params, execution_options)\u001b[0m\n\u001b[1;32m 514\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m TYPE_CHECKING:\n\u001b[1;32m 515\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m, Executable)\n\u001b[0;32m--> 516\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mconnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execute_clauseelement\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 517\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdistilled_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexecution_options\u001b[49m\n\u001b[1;32m 518\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 519\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 520\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\u001b[38;5;241m.\u001b[39mObjectNotExecutableError(\u001b[38;5;28mself\u001b[39m)\n", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1639\u001b[0m, in \u001b[0;36mConnection._execute_clauseelement\u001b[0;34m(self, elem, distilled_parameters, execution_options)\u001b[0m\n\u001b[1;32m 1627\u001b[0m compiled_cache: Optional[CompiledCacheType] \u001b[38;5;241m=\u001b[39m execution_options\u001b[38;5;241m.\u001b[39mget(\n\u001b[1;32m 1628\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcompiled_cache\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine\u001b[38;5;241m.\u001b[39m_compiled_cache\n\u001b[1;32m 1629\u001b[0m )\n\u001b[1;32m 1631\u001b[0m compiled_sql, extracted_params, cache_hit \u001b[38;5;241m=\u001b[39m elem\u001b[38;5;241m.\u001b[39m_compile_w_cache(\n\u001b[1;32m 1632\u001b[0m dialect\u001b[38;5;241m=\u001b[39mdialect,\n\u001b[1;32m 1633\u001b[0m compiled_cache\u001b[38;5;241m=\u001b[39mcompiled_cache,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1637\u001b[0m linting\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdialect\u001b[38;5;241m.\u001b[39mcompiler_linting \u001b[38;5;241m|\u001b[39m compiler\u001b[38;5;241m.\u001b[39mWARN_LINTING,\n\u001b[1;32m 1638\u001b[0m )\n\u001b[0;32m-> 1639\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execute_context\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1640\u001b[0m \u001b[43m \u001b[49m\u001b[43mdialect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1641\u001b[0m \u001b[43m \u001b[49m\u001b[43mdialect\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecution_ctx_cls\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_init_compiled\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1642\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompiled_sql\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1643\u001b[0m \u001b[43m \u001b[49m\u001b[43mdistilled_parameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1644\u001b[0m \u001b[43m \u001b[49m\u001b[43mexecution_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1645\u001b[0m \u001b[43m \u001b[49m\u001b[43mcompiled_sql\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1646\u001b[0m \u001b[43m \u001b[49m\u001b[43mdistilled_parameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1647\u001b[0m \u001b[43m \u001b[49m\u001b[43melem\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1648\u001b[0m \u001b[43m \u001b[49m\u001b[43mextracted_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1649\u001b[0m \u001b[43m \u001b[49m\u001b[43mcache_hit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache_hit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1650\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1651\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_events:\n\u001b[1;32m 1652\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdispatch\u001b[38;5;241m.\u001b[39mafter_execute(\n\u001b[1;32m 1653\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 1654\u001b[0m elem,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1658\u001b[0m ret,\n\u001b[1;32m 1659\u001b[0m )\n", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1848\u001b[0m, in \u001b[0;36mConnection._execute_context\u001b[0;34m(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)\u001b[0m\n\u001b[1;32m 1843\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exec_insertmany_context(\n\u001b[1;32m 1844\u001b[0m dialect,\n\u001b[1;32m 1845\u001b[0m context,\n\u001b[1;32m 1846\u001b[0m )\n\u001b[1;32m 1847\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1848\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_exec_single_context\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1849\u001b[0m \u001b[43m \u001b[49m\u001b[43mdialect\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstatement\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparameters\u001b[49m\n\u001b[1;32m 1850\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1988\u001b[0m, in \u001b[0;36mConnection._exec_single_context\u001b[0;34m(self, dialect, context, statement, parameters)\u001b[0m\n\u001b[1;32m 1985\u001b[0m result \u001b[38;5;241m=\u001b[39m context\u001b[38;5;241m.\u001b[39m_setup_result_proxy()\n\u001b[1;32m 1987\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 1988\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle_dbapi_exception\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1989\u001b[0m \u001b[43m \u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstr_statement\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meffective_parameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcursor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\n\u001b[1;32m 1990\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1992\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:2343\u001b[0m, in \u001b[0;36mConnection._handle_dbapi_exception\u001b[0;34m(self, e, statement, parameters, cursor, context, is_sub_exec)\u001b[0m\n\u001b[1;32m 2341\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m should_wrap:\n\u001b[1;32m 2342\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m sqlalchemy_exception \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 2343\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m sqlalchemy_exception\u001b[38;5;241m.\u001b[39mwith_traceback(exc_info[\u001b[38;5;241m2\u001b[39m]) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m 2344\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 2345\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m exc_info[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/.venv/lib/python3.11/site-packages/sqlalchemy/engine/base.py:1969\u001b[0m, in \u001b[0;36mConnection._exec_single_context\u001b[0;34m(self, dialect, context, statement, parameters)\u001b[0m\n\u001b[1;32m 1967\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[1;32m 1968\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m evt_handled:\n\u001b[0;32m-> 1969\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdialect\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_execute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1970\u001b[0m \u001b[43m \u001b[49m\u001b[43mcursor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstr_statement\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43meffective_parameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontext\u001b[49m\n\u001b[1;32m 1971\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1973\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_has_events \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mengine\u001b[38;5;241m.\u001b[39m_has_events:\n\u001b[1;32m 1974\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdispatch\u001b[38;5;241m.\u001b[39mafter_cursor_execute(\n\u001b[1;32m 1975\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 1976\u001b[0m cursor,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1980\u001b[0m context\u001b[38;5;241m.\u001b[39mexecutemany,\n\u001b[1;32m 1981\u001b[0m )\n", + "File \u001b[0;32m~/dev/crate/ecosystem/langchain/.venv/lib/python3.11/site-packages/sqlalchemy/engine/default.py:922\u001b[0m, in \u001b[0;36mDefaultDialect.do_execute\u001b[0;34m(self, cursor, statement, parameters, context)\u001b[0m\n\u001b[1;32m 921\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdo_execute\u001b[39m(\u001b[38;5;28mself\u001b[39m, cursor, statement, parameters, context\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m--> 922\u001b[0m cursor\u001b[38;5;241m.\u001b[39mexecute(statement, parameters)\n", + "\u001b[0;31mOperationalError\u001b[0m: (sqlite3.OperationalError) no such table: bar.Artist\n[SQL: SELECT bar.\"Artist\".\"ArtistId\", bar.\"Artist\".\"Name\" \nFROM bar.\"Artist\" \nWHERE bar.\"Artist\".\"Name\" LIKE ?]\n[parameters: ('p%',)]\n(Background on this error at: https://sqlalche.me/e/20/e3q8)" + ] + } + ], + "source": [ + "query = sa.select(artist).where(artist.c.Name.like(\"p%\"))\n", + "db.run(query, fetch=\"cursor\", execution_options={\"schema_translate_map\": {None: \"bar\"}})" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/docs/integrations/vectorstores/apache_doris.ipynb b/docs/docs/integrations/vectorstores/apache_doris.ipynb new file mode 100644 index 00000000000..1cee7b8f537 --- /dev/null +++ b/docs/docs/integrations/vectorstores/apache_doris.ipynb @@ -0,0 +1,322 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "84180ad0-66cd-43e5-b0b8-2067a29e16ba", + "metadata": { + "collapsed": false + }, + "source": [ + "# Apache Doris\n", + "\n", + ">[Apache Doris](https://doris.apache.org/) is a modern data warehouse for real-time analytics.\n", + "It delivers lightning-fast analytics on real-time data at scale.\n", + "\n", + ">Usually `Apache Doris` is categorized into OLAP, and it has showed excellent performance in [ClickBench — a Benchmark For Analytical DBMS](https://benchmark.clickhouse.com/). Since it has a super-fast vectorized execution engine, it could also be used as a fast vectordb.\n", + "\n", + "Here we'll show how to use the Apache Doris Vector Store." + ] + }, + { + "cell_type": "markdown", + "id": "1685854f", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "311d44bb-4aca-4f3b-8f97-5e1f29238e40", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install --upgrade --quiet pymysql" + ] + }, + { + "cell_type": "markdown", + "id": "2c891bba", + "metadata": {}, + "source": [ + "Set `update_vectordb = False` at the beginning. If there is no docs updated, then we don't need to rebuild the embeddings of docs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f4e6ca20-79dd-482a-8f68-af9d7dd59c7c", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "!pip install sqlalchemy\n", + "!pip install langchain" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "96f7c7a2-4811-4fdf-87f5-c60772f51fe1", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-14T12:54:01.392500Z", + "start_time": "2024-02-14T12:53:58.866615Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "from langchain.chains import RetrievalQA\n", + "from langchain.text_splitter import TokenTextSplitter\n", + "from langchain_community.document_loaders import (\n", + " DirectoryLoader,\n", + " UnstructuredMarkdownLoader,\n", + ")\n", + "from langchain_community.vectorstores.apache_doris import (\n", + " ApacheDoris,\n", + " ApacheDorisSettings,\n", + ")\n", + "from langchain_openai import OpenAI, OpenAIEmbeddings\n", + "\n", + "update_vectordb = False" + ] + }, + { + "cell_type": "markdown", + "id": "ee821c00", + "metadata": {}, + "source": [ + "## Load docs and split them into tokens" + ] + }, + { + "cell_type": "markdown", + "id": "34ba0cfd", + "metadata": {}, + "source": [ + "Load all markdown files under the `docs` directory\n", + "\n", + "for Apache Doris documents, you can clone repo from https://github.com/apache/doris, and there is `docs` directory in it." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "799edf20-bcf4-4a65-bff7-b907f6bdba20", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-14T12:55:24.128917Z", + "start_time": "2024-02-14T12:55:19.463831Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "loader = DirectoryLoader(\n", + " \"./docs\", glob=\"**/*.md\", loader_cls=UnstructuredMarkdownLoader\n", + ")\n", + "documents = loader.load()" + ] + }, + { + "cell_type": "markdown", + "id": "b415fe2a", + "metadata": {}, + "source": [ + "Split docs into tokens, and set `update_vectordb = True` because there are new docs/tokens." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0dc5ba83-62ef-4f61-a443-e872f251e7da", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# load text splitter and split docs into snippets of text\n", + "text_splitter = TokenTextSplitter(chunk_size=400, chunk_overlap=50)\n", + "split_docs = text_splitter.split_documents(documents)\n", + "\n", + "# tell vectordb to update text embeddings\n", + "update_vectordb = True" + ] + }, + { + "cell_type": "markdown", + "id": "46966e25-9449-4a36-87d1-c0b25dce2994", + "metadata": { + "collapsed": false + }, + "source": [ + "split_docs[-20]" + ] + }, + { + "cell_type": "markdown", + "id": "99422e95-b407-43eb-aa68-9a62363fc82f", + "metadata": { + "collapsed": false + }, + "source": [ + "print(\"# docs = %d, # splits = %d\" % (len(documents), len(split_docs)))" + ] + }, + { + "cell_type": "markdown", + "id": "e780d77f-3f96-4690-a10f-f87566f7ccc6", + "metadata": { + "collapsed": false + }, + "source": [ + "## Create vectordb instance" + ] + }, + { + "cell_type": "markdown", + "id": "15702d9c", + "metadata": {}, + "source": [ + "### Use Apache Doris as vectordb" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ced7dbe1", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-14T12:55:39.508287Z", + "start_time": "2024-02-14T12:55:39.500370Z" + } + }, + "outputs": [], + "source": [ + "def gen_apache_doris(update_vectordb, embeddings, settings):\n", + " if update_vectordb:\n", + " docsearch = ApacheDoris.from_documents(split_docs, embeddings, config=settings)\n", + " else:\n", + " docsearch = ApacheDoris(embeddings, settings)\n", + " return docsearch" + ] + }, + { + "cell_type": "markdown", + "id": "15d86fda", + "metadata": {}, + "source": [ + "## Convert tokens into embeddings and put them into vectordb" + ] + }, + { + "cell_type": "markdown", + "id": "ff1322ea", + "metadata": {}, + "source": [ + "Here we use Apache Doris as vectordb, you can configure Apache Doris instance via `ApacheDorisSettings`.\n", + "\n", + "Configuring Apache Doris instance is pretty much like configuring mysql instance. You need to specify:\n", + "1. host/port\n", + "2. username(default: 'root')\n", + "3. password(default: '')\n", + "4. database(default: 'default')\n", + "5. table(default: 'langchain')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b34f8c31-c173-4902-8168-2e838ddfb9e9", + "metadata": { + "ExecuteTime": { + "end_time": "2024-02-14T12:56:02.671291Z", + "start_time": "2024-02-14T12:55:48.350294Z" + }, + "collapsed": false + }, + "outputs": [], + "source": [ + "import os\n", + "from getpass import getpass\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = getpass()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c53ab3f2-9e34-4424-8b07-6292bde67e14", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "update_vectordb = True\n", + "\n", + "embeddings = OpenAIEmbeddings()\n", + "\n", + "# configure Apache Doris settings(host/port/user/pw/db)\n", + "settings = ApacheDorisSettings()\n", + "settings.port = 9030\n", + "settings.host = \"172.30.34.130\"\n", + "settings.username = \"root\"\n", + "settings.password = \"\"\n", + "settings.database = \"langchain\"\n", + "docsearch = gen_apache_doris(update_vectordb, embeddings, settings)\n", + "\n", + "print(docsearch)\n", + "\n", + "update_vectordb = False" + ] + }, + { + "cell_type": "markdown", + "id": "bde66626", + "metadata": {}, + "source": [ + "## Build QA and ask question to it" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84921814", + "metadata": {}, + "outputs": [], + "source": [ + "llm = OpenAI()\n", + "qa = RetrievalQA.from_chain_type(\n", + " llm=llm, chain_type=\"stuff\", retriever=docsearch.as_retriever()\n", + ")\n", + "query = \"what is apache doris\"\n", + "resp = qa.run(query)\n", + "print(resp)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/docs/integrations/vectorstores/astradb.ipynb b/docs/docs/integrations/vectorstores/astradb.ipynb index c4b354225d8..fe980dd1377 100644 --- a/docs/docs/integrations/vectorstores/astradb.ipynb +++ b/docs/docs/integrations/vectorstores/astradb.ipynb @@ -1,14 +1,28 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "66d0270a-b74f-4110-901e-7960b00297af", + "metadata": {}, + "source": [ + "# Astra DB\n", + "\n", + "This page provides a quickstart for using [Astra DB](https://docs.datastax.com/en/astra/home/astra.html) as a Vector Store." + ] + }, + { + "cell_type": "markdown", + "id": "ab8cd64f-3bb2-4f16-a0a9-12d7b1789bf6", + "metadata": {}, + "source": [ + "> DataStax [Astra DB](https://docs.datastax.com/en/astra/home/astra.html) is a serverless vector-capable database built on Apache Cassandra® and made conveniently available through an easy-to-use JSON API." + ] + }, { "cell_type": "markdown", "id": "d2d6ca14-fb7e-4172-9aa0-a3119a064b96", "metadata": {}, "source": [ - "# Astra DB\n", - "\n", - "This page provides a quickstart for using [Astra DB](https://docs.datastax.com/en/astra/home/astra.html) and [Apache Cassandra®](https://cassandra.apache.org/) as a Vector Store.\n", - "\n", "_Note: in addition to access to the database, an OpenAI API Key is required to run the full example._" ] }, @@ -17,7 +31,7 @@ "id": "bb9be7ce-8c70-4d46-9f11-71c42a36e928", "metadata": {}, "source": [ - "### Setup and general dependencies" + "## Setup and general dependencies" ] }, { @@ -25,7 +39,7 @@ "id": "dbe7c156-0413-47e3-9237-4769c4248869", "metadata": {}, "source": [ - "Use of the integration requires the following Python package." + "Use of the integration requires the corresponding Python package:" ] }, { @@ -35,7 +49,7 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install --upgrade --quiet \"astrapy>=0.5.3\"" + "pip install --upgrade langchain-astradb" ] }, { @@ -43,8 +57,25 @@ "id": "2453d83a-bc8f-41e1-a692-befe4dd90156", "metadata": {}, "source": [ - "_Note: depending on your LangChain setup, you may need to install/upgrade other dependencies needed for this demo_\n", - "_(specifically, recent versions of `datasets`, `openai`, `pypdf` and `tiktoken` are required)._" + "_**Note.** the following are all packages required to run the full demo on this page. Depending on your LangChain setup, some of them may need to be installed:_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56c1f86e-5921-4976-ac8f-1d62e5a512b0", + "metadata": {}, + "outputs": [], + "source": [ + "pip install langchain langchain-openai datasets pypdf" + ] + }, + { + "cell_type": "markdown", + "id": "c2910035-e61f-48d9-a110-d68c401b62aa", + "metadata": {}, + "source": [ + "### Import dependencies" ] }, { @@ -89,28 +120,12 @@ "embe = OpenAIEmbeddings()" ] }, - { - "cell_type": "markdown", - "id": "dd8caa76-bc41-429e-a93b-989ba13aff01", - "metadata": {}, - "source": [ - "_Keep reading to connect with Astra DB. For usage with Apache Cassandra and Astra DB through CQL, scroll to the section below._" - ] - }, { "cell_type": "markdown", "id": "22866f09-e10d-4f05-a24b-b9420129462e", "metadata": {}, "source": [ - "## Astra DB" - ] - }, - { - "cell_type": "markdown", - "id": "5fba47cc-3533-42fc-84b7-9dc14cd68b2b", - "metadata": {}, - "source": [ - "DataStax [Astra DB](https://docs.datastax.com/en/astra/home/astra.html) is a serverless vector-capable database built on Cassandra and made conveniently available through an easy-to-use JSON API." + "## Import the Vector Store" ] }, { @@ -120,7 +135,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain_community.vectorstores import AstraDB" + "from langchain_astradb import AstraDBVectorStore" ] }, { @@ -128,10 +143,13 @@ "id": "68f61b01-3e09-47c1-9d67-5d6915c86626", "metadata": {}, "source": [ - "### Astra DB connection parameters\n", + "## Connection parameters\n", + "\n", + "These are found on your Astra DB dashboard:\n", "\n", "- the API Endpoint looks like `https://01234567-89ab-cdef-0123-456789abcdef-us-east1.apps.astra.datastax.com`\n", - "- the Token looks like `AstraCS:6gBhNmsk135....`" + "- the Token looks like `AstraCS:6gBhNmsk135....`\n", + "- you may optionally provide a _Namespace_ such as `my_namespace`" ] }, { @@ -142,7 +160,21 @@ "outputs": [], "source": [ "ASTRA_DB_API_ENDPOINT = input(\"ASTRA_DB_API_ENDPOINT = \")\n", - "ASTRA_DB_APPLICATION_TOKEN = getpass(\"ASTRA_DB_APPLICATION_TOKEN = \")" + "ASTRA_DB_APPLICATION_TOKEN = getpass(\"ASTRA_DB_APPLICATION_TOKEN = \")\n", + "\n", + "desired_namespace = input(\"(optional) Namespace = \")\n", + "if desired_namespace:\n", + " ASTRA_DB_KEYSPACE = desired_namespace\n", + "else:\n", + " ASTRA_DB_KEYSPACE = None" + ] + }, + { + "cell_type": "markdown", + "id": "196268bd-a950-41c3-bede-f5b55f6a0804", + "metadata": {}, + "source": [ + "Now you can create the vector store:" ] }, { @@ -152,11 +184,12 @@ "metadata": {}, "outputs": [], "source": [ - "vstore = AstraDB(\n", + "vstore = AstraDBVectorStore(\n", " embedding=embe,\n", " collection_name=\"astra_vector_demo\",\n", " api_endpoint=ASTRA_DB_API_ENDPOINT,\n", " token=ASTRA_DB_APPLICATION_TOKEN,\n", + " namespace=ASTRA_DB_KEYSPACE,\n", ")" ] }, @@ -165,7 +198,7 @@ "id": "9a348678-b2f6-46ca-9a0d-2eb4cc6b66b1", "metadata": {}, "source": [ - "### Load a dataset" + "## Load a dataset" ] }, { @@ -243,7 +276,7 @@ "id": "c031760a-1fc5-4855-adf2-02ed52fe2181", "metadata": {}, "source": [ - "### Run simple searches" + "## Run searches" ] }, { @@ -318,12 +351,22 @@ " print(f\"* {res.page_content} [{res.metadata}]\")" ] }, + { + "cell_type": "markdown", + "id": "60fda5df-14e4-4fb0-bd17-65a393fab8a9", + "metadata": {}, + "source": [ + "### Async\n", + "\n", + "Note that the Astra DB vector store supports all fully async methods (`asimilarity_search`, `afrom_texts`, `adelete` and so on) natively, i.e. without thread wrapping involved." + ] + }, { "cell_type": "markdown", "id": "1cc86edd-692b-4495-906c-ccfd13b03c23", "metadata": {}, "source": [ - "### Deleting stored documents" + "## Deleting stored documents" ] }, { @@ -353,7 +396,7 @@ "id": "847181ba-77d1-4a17-b7f9-9e2c3d8efd13", "metadata": {}, "source": [ - "### A minimal RAG chain" + "## A minimal RAG chain" ] }, { @@ -452,7 +495,7 @@ "id": "177610c7-50d0-4b7b-8634-b03338054c8e", "metadata": {}, "source": [ - "### Cleanup" + "## Cleanup" ] }, { @@ -474,290 +517,6 @@ "source": [ "vstore.delete_collection()" ] - }, - { - "cell_type": "markdown", - "id": "94ebaab1-7cbf-4144-a147-7b0e32c43069", - "metadata": {}, - "source": [ - "## Apache Cassandra and Astra DB through CQL" - ] - }, - { - "cell_type": "markdown", - "id": "bc3931b4-211d-4f84-bcc0-51c127e3027c", - "metadata": {}, - "source": [ - "[Cassandra](https://cassandra.apache.org/) is a NoSQL, row-oriented, highly scalable and highly available database.Starting with version 5.0, the database ships with [vector search capabilities](https://cassandra.apache.org/doc/trunk/cassandra/vector-search/overview.html).\n", - "\n", - "DataStax [Astra DB through CQL](https://docs.datastax.com/en/astra-serverless/docs/vector-search/quickstart.html) is a managed serverless database built on Cassandra, offering the same interface and strengths." - ] - }, - { - "cell_type": "markdown", - "id": "a0055fbf-448d-4e46-9c40-28d43df25ca3", - "metadata": {}, - "source": [ - "#### What sets this case apart from \"Astra DB\" above?\n", - "\n", - "Thanks to LangChain having a standardized `VectorStore` interface, most of the \"Astra DB\" section above applies to this case as well. However, this time the database uses the CQL protocol, which means you'll use a _different_ class this time and instantiate it in another way.\n", - "\n", - "The cells below show how you should get your `vstore` object in this case and how you can clean up the database resources at the end: for the rest, i.e. the actual usage of the vector store, you will be able to run the very code that was shown above.\n", - "\n", - "In other words, running this demo in full with Cassandra or Astra DB through CQL means:\n", - "\n", - "- **initialization as shown below**\n", - "- \"Load a dataset\", _see above section_\n", - "- \"Run simple searches\", _see above section_\n", - "- \"MMR search\", _see above section_\n", - "- \"Deleting stored documents\", _see above section_\n", - "- \"A minimal RAG chain\", _see above section_\n", - "- **cleanup as shown below**" - ] - }, - { - "cell_type": "markdown", - "id": "23d12be2-745f-4e72-a82c-334a887bc7cd", - "metadata": {}, - "source": [ - "### Initialization" - ] - }, - { - "cell_type": "markdown", - "id": "e3212542-79be-423e-8e1f-b8d725e3cda8", - "metadata": {}, - "source": [ - "The class to use is the following:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "941af73e-a090-4fba-b23c-595757d470eb", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain_community.vectorstores import Cassandra" - ] - }, - { - "cell_type": "markdown", - "id": "414d1e72-f7c9-4b6d-bf6f-16075712c7e3", - "metadata": {}, - "source": [ - "Now, depending on whether you connect to a Cassandra cluster or to Astra DB through CQL, you will provide different parameters when creating the vector store object." - ] - }, - { - "cell_type": "markdown", - "id": "48ecca56-71a4-4a91-b198-29384c44ce27", - "metadata": {}, - "source": [ - "#### Initialization (Cassandra cluster)" - ] - }, - { - "cell_type": "markdown", - "id": "55ebe958-5654-43e0-9aed-d607ffd3fa48", - "metadata": {}, - "source": [ - "In this case, you first need to create a `cassandra.cluster.Session` object, as described in the [Cassandra driver documentation](https://docs.datastax.com/en/developer/python-driver/latest/api/cassandra/cluster/#module-cassandra.cluster). The details vary (e.g. with network settings and authentication), but this might be something like:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4642dafb-a065-4063-b58c-3d276f5ad07e", - "metadata": {}, - "outputs": [], - "source": [ - "from cassandra.cluster import Cluster\n", - "\n", - "cluster = Cluster([\"127.0.0.1\"])\n", - "session = cluster.connect()" - ] - }, - { - "cell_type": "markdown", - "id": "624c93bf-fb46-4350-bcfa-09ca09dc068f", - "metadata": {}, - "source": [ - "You can now set the session, along with your desired keyspace name, as a global CassIO parameter:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "92a4ab28-1c4f-4dad-9671-d47e0b1dde7b", - "metadata": {}, - "outputs": [], - "source": [ - "import cassio\n", - "\n", - "CASSANDRA_KEYSPACE = input(\"CASSANDRA_KEYSPACE = \")\n", - "\n", - "cassio.init(session=session, keyspace=CASSANDRA_KEYSPACE)" - ] - }, - { - "cell_type": "markdown", - "id": "3b87a824-36f1-45b4-b54c-efec2a2de216", - "metadata": {}, - "source": [ - "Now you can create the vector store:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "853a2a88-a565-4e24-8789-d78c213954a6", - "metadata": {}, - "outputs": [], - "source": [ - "vstore = Cassandra(\n", - " embedding=embe,\n", - " table_name=\"cassandra_vector_demo\",\n", - " # session=None, keyspace=None # Uncomment on older versions of LangChain\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "768ddf7a-0c3e-4134-ad38-25ac53c3da7a", - "metadata": {}, - "source": [ - "#### Initialization (Astra DB through CQL)" - ] - }, - { - "cell_type": "markdown", - "id": "4ed4269a-b7e7-4503-9e66-5a11335c7681", - "metadata": {}, - "source": [ - "In this case you initialize CassIO with the following connection parameters:\n", - "\n", - "- the Database ID, e.g. `01234567-89ab-cdef-0123-456789abcdef`\n", - "- the Token, e.g. `AstraCS:6gBhNmsk135....` (it must be a \"Database Administrator\" token)\n", - "- Optionally a Keyspace name (if omitted, the default one for the database will be used)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5fa6bd74-d4b2-45c5-9757-96dddc6242fb", - "metadata": {}, - "outputs": [], - "source": [ - "ASTRA_DB_ID = input(\"ASTRA_DB_ID = \")\n", - "ASTRA_DB_APPLICATION_TOKEN = getpass(\"ASTRA_DB_APPLICATION_TOKEN = \")\n", - "\n", - "desired_keyspace = input(\"ASTRA_DB_KEYSPACE (optional, can be left empty) = \")\n", - "if desired_keyspace:\n", - " ASTRA_DB_KEYSPACE = desired_keyspace\n", - "else:\n", - " ASTRA_DB_KEYSPACE = None" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "add6e585-17ff-452e-8ef6-7e485ead0b06", - "metadata": {}, - "outputs": [], - "source": [ - "import cassio\n", - "\n", - "cassio.init(\n", - " database_id=ASTRA_DB_ID,\n", - " token=ASTRA_DB_APPLICATION_TOKEN,\n", - " keyspace=ASTRA_DB_KEYSPACE,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "b305823c-bc98-4f3d-aabb-d7eb663ea421", - "metadata": {}, - "source": [ - "Now you can create the vector store:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f45f3038-9d59-41cc-8b43-774c6aa80295", - "metadata": {}, - "outputs": [], - "source": [ - "vstore = Cassandra(\n", - " embedding=embe,\n", - " table_name=\"cassandra_vector_demo\",\n", - " # session=None, keyspace=None # Uncomment on older versions of LangChain\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "39284918-cf8a-49bb-a2d3-aef285bb2ffa", - "metadata": {}, - "source": [ - "### Usage of the vector store" - ] - }, - { - "cell_type": "markdown", - "id": "3cc1aead-d6ec-48a3-affe-1d0cffa955a9", - "metadata": {}, - "source": [ - "_See the sections \"Load a dataset\" through \"A minimal RAG chain\" above._\n", - "\n", - "Speaking of the latter, you can check out a full RAG template for Astra DB through CQL [here](https://github.com/langchain-ai/langchain/tree/master/templates/cassandra-entomology-rag)." - ] - }, - { - "cell_type": "markdown", - "id": "096397d8-6622-4685-9f9d-7e238beca467", - "metadata": {}, - "source": [ - "### Cleanup" - ] - }, - { - "cell_type": "markdown", - "id": "cc1e74f9-5500-41aa-836f-235b1ed5f20c", - "metadata": {}, - "source": [ - "the following essentially retrieves the `Session` object from CassIO and runs a CQL `DROP TABLE` statement with it:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b5b82c33-0e77-4a37-852c-8d50edbdd991", - "metadata": {}, - "outputs": [], - "source": [ - "cassio.config.resolve_session().execute(\n", - " f\"DROP TABLE {cassio.config.resolve_keyspace()}.cassandra_vector_demo;\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "c10ece4d-ae06-42ab-baf4-4d0ac2051743", - "metadata": {}, - "source": [ - "### Learn more" - ] - }, - { - "cell_type": "markdown", - "id": "51ea8b69-7e15-458f-85aa-9fa199f95f9c", - "metadata": {}, - "source": [ - "For more information, extended quickstarts and additional usage examples, please visit the [CassIO documentation](https://cassio.org/frameworks/langchain/about/) for more on using the LangChain `Cassandra` vector store." - ] } ], "metadata": { @@ -776,7 +535,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/docs/docs/integrations/vectorstores/bigquery_vector_search.ipynb b/docs/docs/integrations/vectorstores/bigquery_vector_search.ipynb index af26dcaf8e0..403a8d9fbd2 100644 --- a/docs/docs/integrations/vectorstores/bigquery_vector_search.ipynb +++ b/docs/docs/integrations/vectorstores/bigquery_vector_search.ipynb @@ -7,22 +7,12 @@ }, "source": [ "# BigQuery Vector Search\n", - "> **BigQueryVectorSearch**:\n", - "BigQuery vector search lets you use GoogleSQL to do semantic search, using vector indexes for fast approximate results, or using brute force for exact results.\n", + "> [**BigQuery Vector Search**](https://cloud.google.com/bigquery/docs/vector-search-intro) lets you use GoogleSQL to do semantic search, using vector indexes for fast approximate results, or using brute force for exact results.\n", "\n", "\n", "This tutorial illustrates how to work with an end-to-end data and embedding management system in LangChain, and provide scalable semantic search in BigQuery." ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a **private preview (experimental)** feature. Please submit this\n", - "[enrollment form](https://docs.google.com/forms/d/18yndSb4dTf2H0orqA9N7NAchQEDQekwWiD5jYfEkGWk/viewform?edit_requested=true)\n", - "if you want to enroll BigQuery Vector Search Experimental." - ] - }, { "cell_type": "markdown", "metadata": { diff --git a/docs/docs/integrations/vectorstores/cassandra.ipynb b/docs/docs/integrations/vectorstores/cassandra.ipynb new file mode 100644 index 00000000000..524f76a1052 --- /dev/null +++ b/docs/docs/integrations/vectorstores/cassandra.ipynb @@ -0,0 +1,651 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d2d6ca14-fb7e-4172-9aa0-a3119a064b96", + "metadata": {}, + "source": [ + "# Apache Cassandra\n", + "\n", + "This page provides a quickstart for using [Apache Cassandra®](https://cassandra.apache.org/) as a Vector Store." + ] + }, + { + "cell_type": "markdown", + "id": "6a1a562e-3d1a-4693-b55d-08bf90943a9a", + "metadata": {}, + "source": [ + "> [Cassandra](https://cassandra.apache.org/) is a NoSQL, row-oriented, highly scalable and highly available database.Starting with version 5.0, the database ships with [vector search capabilities](https://cassandra.apache.org/doc/trunk/cassandra/vector-search/overview.html)." + ] + }, + { + "cell_type": "markdown", + "id": "9cf37d7f-c18e-4e63-adea-138e5e981475", + "metadata": {}, + "source": [ + "_Note: in addition to access to the database, an OpenAI API Key is required to run the full example._" + ] + }, + { + "cell_type": "markdown", + "id": "bb9be7ce-8c70-4d46-9f11-71c42a36e928", + "metadata": {}, + "source": [ + "### Setup and general dependencies" + ] + }, + { + "cell_type": "markdown", + "id": "dbe7c156-0413-47e3-9237-4769c4248869", + "metadata": {}, + "source": [ + "Use of the integration requires the following Python package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d00fcf4-9798-4289-9214-d9734690adfc", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install --upgrade --quiet \"cassio>=0.1.4\"" + ] + }, + { + "cell_type": "markdown", + "id": "2453d83a-bc8f-41e1-a692-befe4dd90156", + "metadata": {}, + "source": [ + "_Note: depending on your LangChain setup, you may need to install/upgrade other dependencies needed for this demo_\n", + "_(specifically, recent versions of `datasets`, `openai`, `pypdf` and `tiktoken` are required, along with `langchain-community`)._" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b06619af-fea2-4863-8149-7f239a8c9c82", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from getpass import getpass\n", + "\n", + "from datasets import (\n", + " load_dataset,\n", + ")\n", + "from langchain.schema import Document\n", + "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain_community.document_loaders import PyPDFLoader\n", + "from langchain_core.output_parsers import StrOutputParser\n", + "from langchain_core.prompts import ChatPromptTemplate\n", + "from langchain_core.runnables import RunnablePassthrough\n", + "from langchain_openai import ChatOpenAI, OpenAIEmbeddings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1983f1da-0ae7-4a9b-bf4c-4ade328f7a3a", + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"OPENAI_API_KEY\"] = getpass(\"OPENAI_API_KEY = \")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c656df06-e938-4bc5-b570-440b8b7a0189", + "metadata": {}, + "outputs": [], + "source": [ + "embe = OpenAIEmbeddings()" + ] + }, + { + "cell_type": "markdown", + "id": "22866f09-e10d-4f05-a24b-b9420129462e", + "metadata": {}, + "source": [ + "## Import the Vector Store" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b32730d-176e-414c-9d91-fd3644c54211", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain_community.vectorstores import Cassandra" + ] + }, + { + "cell_type": "markdown", + "id": "68f61b01-3e09-47c1-9d67-5d6915c86626", + "metadata": {}, + "source": [ + "## Connection parameters\n", + "\n", + "The Vector Store integration shown in this page can be used with Cassandra as well as other derived databases, such as Astra DB, which use the CQL (Cassandra Query Language) protocol.\n", + "\n", + "> DataStax [Astra DB](https://docs.datastax.com/en/astra-serverless/docs/vector-search/quickstart.html) is a managed serverless database built on Cassandra, offering the same interface and strengths.\n", + "\n", + "Depending on whether you connect to a Cassandra cluster or to Astra DB through CQL, you will provide different parameters when creating the vector store object." + ] + }, + { + "cell_type": "markdown", + "id": "36bbb3d9-4d07-4f63-b23d-c52be03f8938", + "metadata": {}, + "source": [ + "### Connecting to a Cassandra cluster\n", + "\n", + "You first need to create a `cassandra.cluster.Session` object, as described in the [Cassandra driver documentation](https://docs.datastax.com/en/developer/python-driver/latest/api/cassandra/cluster/#module-cassandra.cluster). The details vary (e.g. with network settings and authentication), but this might be something like:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d95bb1d4-d8a6-4e66-89bc-776f9c6f962b", + "metadata": {}, + "outputs": [], + "source": [ + "from cassandra.cluster import Cluster\n", + "\n", + "cluster = Cluster([\"127.0.0.1\"])\n", + "session = cluster.connect()" + ] + }, + { + "cell_type": "markdown", + "id": "8279aa78-96d6-43ad-aa21-79fd798d895d", + "metadata": {}, + "source": [ + "You can now set the session, along with your desired keyspace name, as a global CassIO parameter:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29ececc4-e50b-4428-967f-4b6bbde12a14", + "metadata": {}, + "outputs": [], + "source": [ + "import cassio\n", + "\n", + "CASSANDRA_KEYSPACE = input(\"CASSANDRA_KEYSPACE = \")\n", + "\n", + "cassio.init(session=session, keyspace=CASSANDRA_KEYSPACE)" + ] + }, + { + "cell_type": "markdown", + "id": "0bd035a2-f0af-418f-94e5-0fbb4d51ac3c", + "metadata": {}, + "source": [ + "Now you can create the vector store:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eeb62cde-89fc-44d7-ba76-91e19cbc5898", + "metadata": {}, + "outputs": [], + "source": [ + "vstore = Cassandra(\n", + " embedding=embe,\n", + " table_name=\"cassandra_vector_demo\",\n", + " # session=None, keyspace=None # Uncomment on older versions of LangChain\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ce240555-e5fc-431d-ac0f-bcf2f6e6a5fb", + "metadata": {}, + "source": [ + "_Note: you can also pass your session and keyspace directly as parameters when creating the vector store. Using the global `cassio.init` setting, however, comes handy if your applications uses Cassandra in several ways (for instance, for vector store, chat memory and LLM response caching), as it allows to centralize credential and DB connection management in one place._" + ] + }, + { + "cell_type": "markdown", + "id": "b598e5fa-eb62-4939-9734-091628e84db4", + "metadata": {}, + "source": [ + "### Connecting to Astra DB through CQL" + ] + }, + { + "cell_type": "markdown", + "id": "2feec7c3-7092-4252-9a3f-05eda4babe74", + "metadata": {}, + "source": [ + "In this case you initialize CassIO with the following connection parameters:\n", + "\n", + "- the Database ID, e.g. `01234567-89ab-cdef-0123-456789abcdef`\n", + "- the Token, e.g. `AstraCS:6gBhNmsk135....` (it must be a \"Database Administrator\" token)\n", + "- Optionally a Keyspace name (if omitted, the default one for the database will be used)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f96147d-6d76-4101-bbb0-4a7f215c3d2d", + "metadata": {}, + "outputs": [], + "source": [ + "ASTRA_DB_ID = input(\"ASTRA_DB_ID = \")\n", + "ASTRA_DB_APPLICATION_TOKEN = getpass(\"ASTRA_DB_APPLICATION_TOKEN = \")\n", + "\n", + "desired_keyspace = input(\"ASTRA_DB_KEYSPACE (optional, can be left empty) = \")\n", + "if desired_keyspace:\n", + " ASTRA_DB_KEYSPACE = desired_keyspace\n", + "else:\n", + " ASTRA_DB_KEYSPACE = None" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d653df1d-9dad-4980-ba52-76a47b4c5c1a", + "metadata": {}, + "outputs": [], + "source": [ + "import cassio\n", + "\n", + "cassio.init(\n", + " database_id=ASTRA_DB_ID,\n", + " token=ASTRA_DB_APPLICATION_TOKEN,\n", + " keyspace=ASTRA_DB_KEYSPACE,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "e606b58b-d390-4fed-a2fc-65036c44860f", + "metadata": {}, + "source": [ + "Now you can create the vector store:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9cb552d1-e888-4550-a350-6df06b1f5aae", + "metadata": {}, + "outputs": [], + "source": [ + "vstore = Cassandra(\n", + " embedding=embe,\n", + " table_name=\"cassandra_vector_demo\",\n", + " # session=None, keyspace=None # Uncomment on older versions of LangChain\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9a348678-b2f6-46ca-9a0d-2eb4cc6b66b1", + "metadata": {}, + "source": [ + "## Load a dataset" + ] + }, + { + "cell_type": "markdown", + "id": "552e56b0-301a-4b06-99c7-57ba6faa966f", + "metadata": {}, + "source": [ + "Convert each entry in the source dataset into a `Document`, then write them into the vector store:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a1f532f-ad63-4256-9730-a183841bd8e9", + "metadata": {}, + "outputs": [], + "source": [ + "philo_dataset = load_dataset(\"datastax/philosopher-quotes\")[\"train\"]\n", + "\n", + "docs = []\n", + "for entry in philo_dataset:\n", + " metadata = {\"author\": entry[\"author\"]}\n", + " doc = Document(page_content=entry[\"quote\"], metadata=metadata)\n", + " docs.append(doc)\n", + "\n", + "inserted_ids = vstore.add_documents(docs)\n", + "print(f\"\\nInserted {len(inserted_ids)} documents.\")" + ] + }, + { + "cell_type": "markdown", + "id": "79d4f436-ef04-4288-8f79-97c9abb983ed", + "metadata": {}, + "source": [ + "In the above, `metadata` dictionaries are created from the source data and are part of the `Document`." + ] + }, + { + "cell_type": "markdown", + "id": "084d8802-ab39-4262-9a87-42eafb746f92", + "metadata": {}, + "source": [ + "Add some more entries, this time with `add_texts`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b6b157f5-eb31-4907-a78e-2e2b06893936", + "metadata": {}, + "outputs": [], + "source": [ + "texts = [\"I think, therefore I am.\", \"To the things themselves!\"]\n", + "metadatas = [{\"author\": \"descartes\"}, {\"author\": \"husserl\"}]\n", + "ids = [\"desc_01\", \"huss_xy\"]\n", + "\n", + "inserted_ids_2 = vstore.add_texts(texts=texts, metadatas=metadatas, ids=ids)\n", + "print(f\"\\nInserted {len(inserted_ids_2)} documents.\")" + ] + }, + { + "cell_type": "markdown", + "id": "63840eb3-8b29-4017-bc2f-301bf5001f28", + "metadata": {}, + "source": [ + "_Note: you may want to speed up the execution of `add_texts` and `add_documents` by increasing the concurrency level for_\n", + "_these bulk operations - check out the methods' `batch_size` parameter_\n", + "_for more details. Depending on the network and the client machine specifications, your best-performing choice of parameters may vary._" + ] + }, + { + "cell_type": "markdown", + "id": "c031760a-1fc5-4855-adf2-02ed52fe2181", + "metadata": {}, + "source": [ + "## Run searches" + ] + }, + { + "cell_type": "markdown", + "id": "02a77d8e-1aae-4054-8805-01c77947c49f", + "metadata": {}, + "source": [ + "This section demonstrates metadata filtering and getting the similarity scores back:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1761806a-1afd-4491-867c-25a80d92b9fe", + "metadata": {}, + "outputs": [], + "source": [ + "results = vstore.similarity_search(\"Our life is what we make of it\", k=3)\n", + "for res in results:\n", + " print(f\"* {res.page_content} [{res.metadata}]\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eebc4f7c-f61a-438e-b3c8-17e6888d8a0b", + "metadata": {}, + "outputs": [], + "source": [ + "results_filtered = vstore.similarity_search(\n", + " \"Our life is what we make of it\",\n", + " k=3,\n", + " filter={\"author\": \"plato\"},\n", + ")\n", + "for res in results_filtered:\n", + " print(f\"* {res.page_content} [{res.metadata}]\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11bbfe64-c0cd-40c6-866a-a5786538450e", + "metadata": {}, + "outputs": [], + "source": [ + "results = vstore.similarity_search_with_score(\"Our life is what we make of it\", k=3)\n", + "for res, score in results:\n", + " print(f\"* [SIM={score:3f}] {res.page_content} [{res.metadata}]\")" + ] + }, + { + "cell_type": "markdown", + "id": "b14ea558-bfbe-41ce-807e-d70670060ada", + "metadata": {}, + "source": [ + "### MMR (Maximal-marginal-relevance) search" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "76381ce8-780a-4e3b-97b1-056d6782d7d5", + "metadata": {}, + "outputs": [], + "source": [ + "results = vstore.max_marginal_relevance_search(\n", + " \"Our life is what we make of it\",\n", + " k=3,\n", + " filter={\"author\": \"aristotle\"},\n", + ")\n", + "for res in results:\n", + " print(f\"* {res.page_content} [{res.metadata}]\")" + ] + }, + { + "cell_type": "markdown", + "id": "1cc86edd-692b-4495-906c-ccfd13b03c23", + "metadata": {}, + "source": [ + "## Deleting stored documents" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38a70ec4-b522-4d32-9ead-c642864fca37", + "metadata": {}, + "outputs": [], + "source": [ + "delete_1 = vstore.delete(inserted_ids[:3])\n", + "print(f\"all_succeed={delete_1}\") # True, all documents deleted" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d4cf49ed-9d29-4ed9-bdab-51a308c41b8e", + "metadata": {}, + "outputs": [], + "source": [ + "delete_2 = vstore.delete(inserted_ids[2:5])\n", + "print(f\"some_succeeds={delete_2}\") # True, though some IDs were gone already" + ] + }, + { + "cell_type": "markdown", + "id": "847181ba-77d1-4a17-b7f9-9e2c3d8efd13", + "metadata": {}, + "source": [ + "## A minimal RAG chain" + ] + }, + { + "cell_type": "markdown", + "id": "cd64b844-846f-43c5-a7dd-c26b9ed417d0", + "metadata": {}, + "source": [ + "The next cells will implement a simple RAG pipeline:\n", + "- download a sample PDF file and load it onto the store;\n", + "- create a RAG chain with LCEL (LangChain Expression Language), with the vector store at its heart;\n", + "- run the question-answering chain." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5cbc4dba-0d5e-4038-8fc5-de6cadd1c2a9", + "metadata": {}, + "outputs": [], + "source": [ + "!curl -L \\\n", + " \"https://github.com/awesome-astra/datasets/blob/main/demo-resources/what-is-philosophy/what-is-philosophy.pdf?raw=true\" \\\n", + " -o \"what-is-philosophy.pdf\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "459385be-5e9c-47ff-ba53-2b7ae6166b09", + "metadata": {}, + "outputs": [], + "source": [ + "pdf_loader = PyPDFLoader(\"what-is-philosophy.pdf\")\n", + "splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=64)\n", + "docs_from_pdf = pdf_loader.load_and_split(text_splitter=splitter)\n", + "\n", + "print(f\"Documents from PDF: {len(docs_from_pdf)}.\")\n", + "inserted_ids_from_pdf = vstore.add_documents(docs_from_pdf)\n", + "print(f\"Inserted {len(inserted_ids_from_pdf)} documents.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5010a66c-4298-4e32-82b5-2da0d36a5c70", + "metadata": {}, + "outputs": [], + "source": [ + "retriever = vstore.as_retriever(search_kwargs={\"k\": 3})\n", + "\n", + "philo_template = \"\"\"\n", + "You are a philosopher that draws inspiration from great thinkers of the past\n", + "to craft well-thought answers to user questions. Use the provided context as the basis\n", + "for your answers and do not make up new reasoning paths - just mix-and-match what you are given.\n", + "Your answers must be concise and to the point, and refrain from answering about other topics than philosophy.\n", + "\n", + "CONTEXT:\n", + "{context}\n", + "\n", + "QUESTION: {question}\n", + "\n", + "YOUR ANSWER:\"\"\"\n", + "\n", + "philo_prompt = ChatPromptTemplate.from_template(philo_template)\n", + "\n", + "llm = ChatOpenAI()\n", + "\n", + "chain = (\n", + " {\"context\": retriever, \"question\": RunnablePassthrough()}\n", + " | philo_prompt\n", + " | llm\n", + " | StrOutputParser()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fcbc1296-6c7c-478b-b55b-533ba4e54ddb", + "metadata": {}, + "outputs": [], + "source": [ + "chain.invoke(\"How does Russel elaborate on Peirce's idea of the security blanket?\")" + ] + }, + { + "cell_type": "markdown", + "id": "869ab448-a029-4692-aefc-26b85513314d", + "metadata": {}, + "source": [ + "For more, check out a complete RAG template using Astra DB through CQL [here](https://github.com/langchain-ai/langchain/tree/master/templates/cassandra-entomology-rag)." + ] + }, + { + "cell_type": "markdown", + "id": "177610c7-50d0-4b7b-8634-b03338054c8e", + "metadata": {}, + "source": [ + "## Cleanup" + ] + }, + { + "cell_type": "markdown", + "id": "0da4d19f-9878-4d3d-82c9-09cafca20322", + "metadata": {}, + "source": [ + "the following essentially retrieves the `Session` object from CassIO and runs a CQL `DROP TABLE` statement with it:\n", + "\n", + "_(You will lose the data you stored in it.)_" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fd405a13-6f71-46fa-87e6-167238e9c25e", + "metadata": {}, + "outputs": [], + "source": [ + "cassio.config.resolve_session().execute(\n", + " f\"DROP TABLE {cassio.config.resolve_keyspace()}.cassandra_vector_demo;\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "c10ece4d-ae06-42ab-baf4-4d0ac2051743", + "metadata": {}, + "source": [ + "### Learn more" + ] + }, + { + "cell_type": "markdown", + "id": "51ea8b69-7e15-458f-85aa-9fa199f95f9c", + "metadata": {}, + "source": [ + "For more information, extended quickstarts and additional usage examples, please visit the [CassIO documentation](https://cassio.org/frameworks/langchain/about/) for more on using the LangChain `Cassandra` vector store." + ] + }, + { + "cell_type": "markdown", + "id": "3b8ee30c-2c84-42f3-9cff-e80dbc590490", + "metadata": {}, + "source": [ + "#### Attribution statement\n", + "\n", + "> Apache Cassandra, Cassandra and Apache are either registered trademarks or trademarks of the [Apache Software Foundation](http://www.apache.org/) in the United States and/or other countries.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/docs/integrations/vectorstores/elasticsearch.ipynb b/docs/docs/integrations/vectorstores/elasticsearch.ipynb index 9032e698c2a..c36c6f65ee6 100644 --- a/docs/docs/integrations/vectorstores/elasticsearch.ipynb +++ b/docs/docs/integrations/vectorstores/elasticsearch.ipynb @@ -92,6 +92,28 @@ " )\n", "```\n", "\n", + "You can also use an `Elasticsearch` client object that gives you more flexibility, for example to configure the maximum number of retries.\n", + "\n", + "Example:\n", + "```python\n", + " import elasticsearch\n", + " from langchain_community.vectorstores import ElasticsearchStore\n", + "\n", + " es_client= elasticsearch.Elasticsearch(\n", + " hosts=[\"http://localhost:9200\"],\n", + " es_user=\"elastic\",\n", + " es_password=\"changeme\"\n", + " max_retries=10,\n", + " )\n", + "\n", + " embedding = OpenAIEmbeddings()\n", + " elastic_vector_search = ElasticsearchStore(\n", + " index_name=\"test_index\",\n", + " es_connection=es_client,\n", + " embedding=embedding,\n", + " )\n", + "```\n", + "\n", "#### How to obtain a password for the default \"elastic\" user?\n", "\n", "To obtain your Elastic Cloud password for the default \"elastic\" user:\n", @@ -134,7 +156,7 @@ "id": "ea167a29", "metadata": {}, "source": [ - "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key." + "To use the `OpenAIEmbeddings` we have to configure the OpenAI API Key in the environment." ] }, { diff --git a/docs/docs/integrations/vectorstores/milvus.ipynb b/docs/docs/integrations/vectorstores/milvus.ipynb index 79d0df5c3de..d9f78e033ca 100644 --- a/docs/docs/integrations/vectorstores/milvus.ipynb +++ b/docs/docs/integrations/vectorstores/milvus.ipynb @@ -204,23 +204,29 @@ }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "### Per-User Retrieval\n", "\n", "When building a retrieval app, you often have to build it with multiple users in mind. This means that you may be storing data not just for one user, but for many different users, and they should not be able to see eachother’s data.\n", "\n", "Milvus recommends using [partition_key](https://milvus.io/docs/multi_tenancy.md#Partition-key-based-multi-tenancy) to implement multi-tenancy, here is an example." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } + ] }, { "cell_type": "code", "execution_count": 2, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "from langchain_core.documents import Document\n", @@ -236,16 +242,16 @@ " drop_old=True,\n", " partition_key_field=\"namespace\", # Use the \"namespace\" field as the partition key\n", ")" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "To conduct a search using the partition key, you should include either of the following in the boolean expression of the search request:\n", "\n", @@ -256,21 +262,23 @@ "Do replace `` with the name of the field that is designated as the partition key.\n", "\n", "Milvus changes to a partition based on the specified partition key, filters entities according to the partition key, and searches among the filtered entities.\n" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } + ] }, { "cell_type": "code", "execution_count": 3, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { - "text/plain": "[Document(page_content='i worked at facebook', metadata={'namespace': 'ankush'})]" + "text/plain": [ + "[Document(page_content='i worked at facebook', metadata={'namespace': 'ankush'})]" + ] }, "execution_count": 3, "metadata": {}, @@ -282,21 +290,23 @@ "vectorstore.as_retriever(\n", " search_kwargs={\"expr\": 'namespace == \"ankush\"'}\n", ").get_relevant_documents(\"where did i work?\")" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 4, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 4, + }, "outputs": [ { "data": { - "text/plain": "[Document(page_content='i worked at kensho', metadata={'namespace': 'harrison'})]" + "text/plain": [ + "[Document(page_content='i worked at kensho', metadata={'namespace': 'harrison'})]" + ] }, "execution_count": 4, "metadata": {}, @@ -308,13 +318,52 @@ "vectorstore.as_retriever(\n", " search_kwargs={\"expr\": 'namespace == \"harrison\"'}\n", ").get_relevant_documents(\"where did i work?\")" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] + }, + { + "cell_type": "markdown", + "id": "89756e9e", + "metadata": {}, + "source": [ + "**To delete or upsert (update/insert) one or more entities:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "21c4edcf", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.docstore.document import Document\n", + "\n", + "# Insert data sample\n", + "docs = [\n", + " Document(page_content=\"foo\", metadata={\"id\": 1}),\n", + " Document(page_content=\"bar\", metadata={\"id\": 2}),\n", + " Document(page_content=\"baz\", metadata={\"id\": 3}),\n", + "]\n", + "vector_db = Milvus.from_documents(\n", + " docs,\n", + " embeddings,\n", + " connection_args={\"host\": \"127.0.0.1\", \"port\": \"19530\"},\n", + ")\n", + "\n", + "# Search pks (primary keys) using expression\n", + "expr = \"id in [1,2]\"\n", + "pks = vector_db.get_pks(expr)\n", + "\n", + "# Delete entities by pks\n", + "result = vector_db.delete(pks)\n", + "\n", + "# Upsert (Update/Insert)\n", + "new_docs = [\n", + " Document(page_content=\"new_foo\", metadata={\"id\": 1}),\n", + " Document(page_content=\"new_bar\", metadata={\"id\": 2}),\n", + " Document(page_content=\"upserted_bak\", metadata={\"id\": 3}),\n", + "]\n", + "upserted_pks = vector_db.upsert(pks, new_docs)" + ] } ], "metadata": { @@ -338,4 +387,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/docs/docs/integrations/vectorstores/pinecone.ipynb b/docs/docs/integrations/vectorstores/pinecone.ipynb index 68513c45ad8..2c93de88096 100644 --- a/docs/docs/integrations/vectorstores/pinecone.ipynb +++ b/docs/docs/integrations/vectorstores/pinecone.ipynb @@ -13,7 +13,16 @@ "This notebook shows how to use functionality related to the `Pinecone` vector database.\n", "\n", "To use Pinecone, you must have an API key. \n", - "Here are the [installation instructions](https://docs.pinecone.io/docs/quickstart)." + "Here are the [installation instructions](https://docs.pinecone.io/docs/quickstart).\n", + "\n", + "Set the following environment variables to make using the `Pinecone` integration easier:\n", + "\n", + "- `PINECONE_API_KEY`: Your Pinecone API key.\n", + "- `PINECONE_INDEX_NAME`: The name of the index you want to use.\n", + "\n", + "And to follow along in this doc, you should also set\n", + "\n", + "- `OPENAI_API_KEY`: Your OpenAI API key, for using `OpenAIEmbeddings`" ] }, { @@ -25,74 +34,27 @@ }, "outputs": [], "source": [ - "%pip install --upgrade --quiet pinecone-client langchain-openai tiktoken langchain" + "%pip install --upgrade --quiet langchain-pinecone langchain-openai langchain" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "c1e38361-c1fe-4ac6-86e9-c90ebaf7ae87", - "metadata": {}, - "outputs": [], - "source": [ - "import getpass\n", - "import os\n", - "\n", - "os.environ[\"PINECONE_API_KEY\"] = getpass.getpass(\"Pinecone API Key:\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "02a536e0-d603-4d79-b18b-1ed562977b40", - "metadata": {}, - "outputs": [], - "source": [ - "os.environ[\"PINECONE_ENV\"] = getpass.getpass(\"Pinecone Environment:\")" - ] - }, - { - "attachments": {}, "cell_type": "markdown", - "id": "320af802-9271-46ee-948f-d2453933d44b", + "id": "42f2ea67", "metadata": {}, "source": [ - "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key." + "First, let's split our state of the union document into chunked `docs`." ] }, { "cell_type": "code", - "execution_count": null, - "id": "ffea66e4-bc23-46a9-9580-b348dfe7b7a7", - "metadata": {}, - "outputs": [], - "source": [ - "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aac9563e", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from langchain.text_splitter import CharacterTextSplitter\n", - "from langchain_community.document_loaders import TextLoader\n", - "from langchain_community.vectorstores import Pinecone\n", - "from langchain_openai import OpenAIEmbeddings" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "a3c3999a", "metadata": {}, "outputs": [], "source": [ + "from langchain.text_splitter import CharacterTextSplitter\n", "from langchain_community.document_loaders import TextLoader\n", + "from langchain_openai import OpenAIEmbeddings\n", "\n", "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n", "documents = loader.load()\n", @@ -103,43 +65,52 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "6e104aee", + "cell_type": "markdown", + "id": "3a4d377f", "metadata": {}, - "outputs": [], "source": [ - "import pinecone\n", + "Now let's assume you have your Pinecone index set up with `dimension=1536`.\n", "\n", - "# initialize pinecone\n", - "pinecone.init(\n", - " api_key=os.getenv(\"PINECONE_API_KEY\"), # find at app.pinecone.io\n", - " environment=os.getenv(\"PINECONE_ENV\"), # next to api key in console\n", - ")\n", - "\n", - "index_name = \"langchain-demo\"\n", - "\n", - "# First, check if our index already exists. If it doesn't, we create it\n", - "if index_name not in pinecone.list_indexes():\n", - " # we create a new index\n", - " pinecone.create_index(name=index_name, metric=\"cosine\", dimension=1536)\n", - "# The OpenAI embedding model `text-embedding-ada-002 uses 1536 dimensions`\n", - "docsearch = Pinecone.from_documents(docs, embeddings, index_name=index_name)\n", - "\n", - "# if you already have an index, you can load it like this\n", - "# docsearch = Pinecone.from_existing_index(index_name, embeddings)\n", - "\n", - "query = \"What did the president say about Ketanji Brown Jackson\"\n", - "docs = docsearch.similarity_search(query)" + "We can connect to our Pinecone index and insert those chunked docs as contents with `Pinecone.from_documents`." ] }, { "cell_type": "code", - "execution_count": null, - "id": "9c608226", + "execution_count": 6, + "id": "6e104aee", "metadata": {}, "outputs": [], "source": [ + "from langchain_pinecone import Pinecone\n", + "\n", + "index_name = \"langchain-test-index\"\n", + "\n", + "docsearch = Pinecone.from_documents(docs, embeddings, index_name=index_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ffbcb3fb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n", + "\n", + "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n", + "\n", + "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n", + "\n", + "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n" + ] + } + ], + "source": [ + "query = \"What did the president say about Ketanji Brown Jackson\"\n", + "docs = docsearch.similarity_search(query)\n", "print(docs[0].page_content)" ] }, @@ -156,15 +127,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "38a7a60e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['24631802-4bad-44a7-a4ba-fd71f00cc160']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "index = pinecone.Index(\"langchain-demo\")\n", - "vectorstore = Pinecone(index, embeddings.embed_query, \"text\")\n", + "vectorstore = Pinecone(index_name=index_name, embedding=embeddings)\n", "\n", - "vectorstore.add_texts(\"More text!\")" + "vectorstore.add_texts([\"More text!\"])" ] }, { @@ -180,10 +161,91 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "a359ed74", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "## Document 0\n", + "\n", + "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n", + "\n", + "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n", + "\n", + "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n", + "\n", + "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n", + "\n", + "## Document 1\n", + "\n", + "And I’m taking robust action to make sure the pain of our sanctions is targeted at Russia’s economy. And I will use every tool at our disposal to protect American businesses and consumers. \n", + "\n", + "Tonight, I can announce that the United States has worked with 30 other countries to release 60 Million barrels of oil from reserves around the world. \n", + "\n", + "America will lead that effort, releasing 30 Million barrels from our own Strategic Petroleum Reserve. And we stand ready to do more if necessary, unified with our allies. \n", + "\n", + "These steps will help blunt gas prices here at home. And I know the news about what’s happening can seem alarming. \n", + "\n", + "But I want you to know that we are going to be okay. \n", + "\n", + "When the history of this era is written Putin’s war on Ukraine will have left Russia weaker and the rest of the world stronger. \n", + "\n", + "While it shouldn’t have taken something so terrible for people around the world to see what’s at stake now everyone sees it clearly.\n", + "\n", + "## Document 2\n", + "\n", + "We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. \n", + "\n", + "I recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \n", + "\n", + "They were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \n", + "\n", + "Officer Mora was 27 years old. \n", + "\n", + "Officer Rivera was 22. \n", + "\n", + "Both Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. \n", + "\n", + "I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \n", + "\n", + "I’ve worked on these issues a long time. \n", + "\n", + "I know what works: Investing in crime prevention and community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety.\n", + "\n", + "## Document 3\n", + "\n", + "One was stationed at bases and breathing in toxic smoke from “burn pits” that incinerated wastes of war—medical and hazard material, jet fuel, and more. \n", + "\n", + "When they came home, many of the world’s fittest and best trained warriors were never the same. \n", + "\n", + "Headaches. Numbness. Dizziness. \n", + "\n", + "A cancer that would put them in a flag-draped coffin. \n", + "\n", + "I know. \n", + "\n", + "One of those soldiers was my son Major Beau Biden. \n", + "\n", + "We don’t know for sure if a burn pit was the cause of his brain cancer, or the diseases of so many of our troops. \n", + "\n", + "But I’m committed to finding out everything we can. \n", + "\n", + "Committed to military families like Danielle Robinson from Ohio. \n", + "\n", + "The widow of Sergeant First Class Heath Robinson. \n", + "\n", + "He was born a soldier. Army National Guard. Combat medic in Kosovo and Iraq. \n", + "\n", + "Stationed near Baghdad, just yards from burn pits the size of football fields. \n", + "\n", + "Heath’s widow Danielle is here with us tonight. They loved going to Ohio State football games. He loved building Legos with their daughter.\n" + ] + } + ], "source": [ "retriever = docsearch.as_retriever(search_type=\"mmr\")\n", "matched_docs = retriever.get_relevant_documents(query)\n", @@ -203,15 +265,56 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "9ca82740", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1. Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n", + "\n", + "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n", + "\n", + "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n", + "\n", + "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. \n", + "\n", + "2. We can’t change how divided we’ve been. But we can change how we move forward—on COVID-19 and other issues we must face together. \n", + "\n", + "I recently visited the New York City Police Department days after the funerals of Officer Wilbert Mora and his partner, Officer Jason Rivera. \n", + "\n", + "They were responding to a 9-1-1 call when a man shot and killed them with a stolen gun. \n", + "\n", + "Officer Mora was 27 years old. \n", + "\n", + "Officer Rivera was 22. \n", + "\n", + "Both Dominican Americans who’d grown up on the same streets they later chose to patrol as police officers. \n", + "\n", + "I spoke with their families and told them that we are forever in debt for their sacrifice, and we will carry on their mission to restore the trust and safety every community deserves. \n", + "\n", + "I’ve worked on these issues a long time. \n", + "\n", + "I know what works: Investing in crime prevention and community police officers who’ll walk the beat, who’ll know the neighborhood, and who can restore trust and safety. \n", + "\n" + ] + } + ], "source": [ "found_docs = docsearch.max_marginal_relevance_search(query, k=2, fetch_k=10)\n", "for i, doc in enumerate(found_docs):\n", " print(f\"{i + 1}.\", doc.page_content, \"\\n\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0fd750b", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -230,7 +333,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/docs/docs/integrations/vectorstores/redis.ipynb b/docs/docs/integrations/vectorstores/redis.ipynb index 666a520a346..54bfb2b8fcf 100644 --- a/docs/docs/integrations/vectorstores/redis.ipynb +++ b/docs/docs/integrations/vectorstores/redis.ipynb @@ -6,26 +6,26 @@ "source": [ "# Redis\n", "\n", - "Redis vector database introduction and langchain integration guide.\n", + ">[Redis vector database](https://redis.io/docs/get-started/vector-database/) introduction and langchain integration guide.\n", "\n", "## What is Redis?\n", "\n", - "Most developers from a web services background are probably familiar with Redis. At it's core, Redis is an open-source key-value store that can be used as a cache, message broker, and database. Developers choose Redis because it is fast, has a large ecosystem of client libraries, and has been deployed by major enterprises for years.\n", + "Most developers from a web services background are familiar with `Redis`. At its core, `Redis` is an open-source key-value store that is used as a cache, message broker, and database. Developers choose `Redis` because it is fast, has a large ecosystem of client libraries, and has been deployed by major enterprises for years.\n", "\n", - "On top of these traditional use cases, Redis provides additional capabilities like the Search and Query capability that allows users to create secondary index structures within Redis. This allows Redis to be a Vector Database, at the speed of a cache. \n", + "On top of these traditional use cases, `Redis` provides additional capabilities like the Search and Query capability that allows users to create secondary index structures within `Redis`. This allows `Redis` to be a Vector Database, at the speed of a cache. \n", "\n", "\n", "## Redis as a Vector Database\n", "\n", - "Redis uses compressed, inverted indexes for fast indexing with a low memory footprint. It also supports a number of advanced features such as:\n", + "`Redis` uses compressed, inverted indexes for fast indexing with a low memory footprint. It also supports a number of advanced features such as:\n", "\n", - "* Indexing of multiple fields in Redis hashes and JSON\n", - "* Vector similarity search (with HNSW (ANN) or FLAT (KNN))\n", + "* Indexing of multiple fields in Redis hashes and `JSON`\n", + "* Vector similarity search (with `HNSW` (ANN) or `FLAT` (KNN))\n", "* Vector Range Search (e.g. find all vectors within a radius of a query vector)\n", "* Incremental indexing without performance loss\n", "* Document ranking (using [tf-idf](https://en.wikipedia.org/wiki/Tf%E2%80%93idf), with optional user-provided weights)\n", "* Field weighting\n", - "* Complex boolean queries with AND, OR, and NOT operators\n", + "* Complex boolean queries with `AND`, `OR`, and `NOT` operators\n", "* Prefix matching, fuzzy matching, and exact-phrase queries\n", "* Support for [double-metaphone phonetic matching](https://redis.io/docs/stack/search/reference/phonetic_matching/)\n", "* Auto-complete suggestions (with fuzzy prefix suggestions)\n", @@ -34,7 +34,7 @@ "* Numeric filters and ranges\n", "* Geospatial searches using Redis geospatial indexing\n", "* A powerful aggregations engine\n", - "* Supports for all utf-8 encoded text\n", + "* Supports for all `utf-8` encoded text\n", "* Retrieve full documents, selected fields, or only the document IDs\n", "* Sorting results (for example, by creation date)\n", "\n", @@ -42,7 +42,7 @@ "\n", "## Clients\n", "\n", - "Since redis is much more than just a vector database, there are often use cases that demand usage of a Redis client besides just the langchain integration. You can use any standard Redis client library to run Search and Query commands, but it's easiest to use a library that wraps the Search and Query API. Below are a few examples, but you can find more client libraries [here](https://redis.io/resources/clients/).\n", + "Since `Redis` is much more than just a vector database, there are often use cases that demand the usage of a `Redis` client besides just the `LangChain` integration. You can use any standard `Redis` client library to run Search and Query commands, but it's easiest to use a library that wraps the Search and Query API. Below are a few examples, but you can find more client libraries [here](https://redis.io/resources/clients/).\n", "\n", "| Project | Language | License | Author | Stars |\n", "|----------|---------|--------|---------|-------|\n", @@ -87,7 +87,7 @@ "[redisearch-api-rs-stars]: https://img.shields.io/github/stars/RediSearch/redisearch-api-rs.svg?style=social&label=Star&maxAge=2592000\n", "\n", "\n", - "## Deployment Options\n", + "## Deployment options\n", "\n", "There are many ways to deploy Redis with RediSearch. The easiest way to get started is to use Docker, but there are are many potential options for deployment such as\n", "\n", @@ -98,7 +98,7 @@ "- Kubernetes: [Redis Enterprise Software on Kubernetes](https://docs.redis.com/latest/kubernetes/)\n", "\n", "\n", - "## Examples\n", + "## Additional examples\n", "\n", "Many examples can be found in the [Redis AI team's GitHub](https://github.com/RedisVentures/)\n", "\n", @@ -108,7 +108,7 @@ "- [Vector Search on Azure](https://learn.microsoft.com/azure/azure-cache-for-redis/cache-tutorial-vector-similarity) - Vector search on Azure using Azure Cache for Redis and Azure OpenAI\n", "\n", "\n", - "## More Resources\n", + "## More resources\n", "\n", "For more information on how to use Redis as a vector database, check out the following resources:\n", "\n", @@ -122,9 +122,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Install Redis Python Client\n", + "## Setting up\n", "\n", - "Redis-py is the officially supported client by Redis. Recently released is the RedisVL client which is purpose-built for the Vector Database use cases. Both can be installed with pip." + "\n", + "### Install Redis Python client\n", + "\n", + "`Redis-py` is the officially supported client by Redis. Recently released is the `RedisVL` client which is purpose-built for the Vector Database use cases. Both can be installed with pip." ] }, { @@ -172,7 +175,63 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Sample Data\n", + "### Deploy Redis locally\n", + "\n", + "To locally deploy Redis, run:\n", + "\n", + "```console\n", + "docker run -d -p 6379:6379 -p 8001:8001 redis/redis-stack:latest\n", + "```\n", + "If things are running correctly you should see a nice Redis UI at `http://localhost:8001`. See the [Deployment options](#deployment-options) section above for other ways to deploy.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Redis connection Url schemas\n", + "\n", + "Valid Redis Url schemas are:\n", + "1. `redis://` - Connection to Redis standalone, unencrypted\n", + "2. `rediss://` - Connection to Redis standalone, with TLS encryption\n", + "3. `redis+sentinel://` - Connection to Redis server via Redis Sentinel, unencrypted\n", + "4. `rediss+sentinel://` - Connection to Redis server via Redis Sentinel, booth connections with TLS encryption\n", + "\n", + "More information about additional connection parameters can be found in the [redis-py documentation](https://redis-py.readthedocs.io/en/stable/connections.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# connection to redis standalone at localhost, db 0, no password\n", + "redis_url = \"redis://localhost:6379\"\n", + "# connection to host \"redis\" port 7379 with db 2 and password \"secret\" (old style authentication scheme without username / pre 6.x)\n", + "redis_url = \"redis://:secret@redis:7379/2\"\n", + "# connection to host redis on default port with user \"joe\", pass \"secret\" using redis version 6+ ACLs\n", + "redis_url = \"redis://joe:secret@redis/0\"\n", + "\n", + "# connection to sentinel at localhost with default group mymaster and db 0, no password\n", + "redis_url = \"redis+sentinel://localhost:26379\"\n", + "# connection to sentinel at host redis with default port 26379 and user \"joe\" with password \"secret\" with default group mymaster and db 0\n", + "redis_url = \"redis+sentinel://joe:secret@redis\"\n", + "# connection to sentinel, no auth with sentinel monitoring group \"zone-1\" and database 2\n", + "redis_url = \"redis+sentinel://redis:26379/zone-1/2\"\n", + "\n", + "# connection to redis standalone at localhost, db 0, no password but with TLS support\n", + "redis_url = \"rediss://localhost:6379\"\n", + "# connection to redis sentinel at localhost and default port, db 0, no password\n", + "# but with TLS support for booth Sentinel and Redis server\n", + "redis_url = \"rediss+sentinel://localhost\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sample data\n", "\n", "First we will describe some sample data so that the various attributes of the Redis vector store can be demonstrated." ] @@ -222,13 +281,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Initializing Redis\n", - "\n", - "To locally deploy Redis, run:\n", - "```console\n", - "docker run -d -p 6379:6379 -p 8001:8001 redis/redis-stack:latest\n", - "```\n", - "If things are running correctly you should see a nice Redis UI at http://localhost:8001. See the [Deployment Options](#deployment-options) section above for other ways to deploy.\n", + "### Create Redis vector store\n", "\n", "The Redis VectorStore instance can be initialized in a number of ways. There are multiple class methods that can be used to initialize a Redis VectorStore instance.\n", "\n", @@ -284,7 +337,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Inspecting the Created Index\n", + "## Inspecting the created Index\n", "\n", "Once the ``Redis`` VectorStore object has been constructed, an index will have been created in Redis if it did not already exist. The index can be inspected with both the ``rvl``and the ``redis-cli`` command line tool. If you installed ``redisvl`` above, you can use the ``rvl`` command line tool to inspect the index." ] @@ -615,7 +668,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Connect to an Existing Index\n", + "## Connect to an existing Index\n", "\n", "In order to have the same metadata indexed when using the ``Redis`` VectorStore. You will need to have the same ``index_schema`` passed in either as a path to a yaml file or as a dictionary. The following shows how to obtain the schema from an index and connect to an existing index." ] @@ -730,9 +783,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Custom Metadata Indexing\n", + "## Custom metadata indexing\n", "\n", - "In some cases, you may want to control what fields the metadata maps to. For example, you may want the ``credit_score`` field to be a categorical field instead of a text field (which is the default behavior for all string fields). In this case, you can use the ``index_schema`` parameter in each of the initialization methods above to specify the schema for the index. Custom index schema can either be passed as a dictionary or as a path to a yaml file.\n", + "In some cases, you may want to control what fields the metadata maps to. For example, you may want the ``credit_score`` field to be a categorical field instead of a text field (which is the default behavior for all string fields). In this case, you can use the ``index_schema`` parameter in each of the initialization methods above to specify the schema for the index. Custom index schema can either be passed as a dictionary or as a path to a YAML file.\n", "\n", "All arguments in the schema have defaults besides the name, so you can specify only the fields you want to change. All the names correspond to the snake/lowercase versions of the arguments you would use on the command line with ``redis-cli`` or in ``redis-py``. For more on the arguments for each field, see the [documentation](https://redis.io/docs/interact/search-and-query/basic-constructs/field-and-type-options/)\n", "\n", @@ -749,7 +802,7 @@ " - name: age\n", "```\n", "\n", - "In Python this would look like:\n", + "In Python, this would look like:\n", "\n", "```python\n", "\n", @@ -810,16 +863,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Hybrid Filtering\n", + "## Hybrid filtering\n", "\n", - "With the Redis Filter Expression language built into langchain, you can create arbitrarily long chains of hybrid filters\n", + "With the Redis Filter Expression language built into LangChain, you can create arbitrarily long chains of hybrid filters\n", "that can be used to filter your search results. The expression language is derived from the [RedisVL Expression Syntax](https://redisvl.com)\n", "and is designed to be easy to use and understand.\n", "\n", "The following are the available filter types:\n", "- ``RedisText``: Filter by full-text search against metadata fields. Supports exact, fuzzy, and wildcard matching.\n", "- ``RedisNum``: Filter by numeric range against metadata fields.\n", - "- ``RedisTag``: Filter by exact match against string based categorical metadata fields. Multiple tags can be specified like \"tag1,tag2,tag3\".\n", + "- ``RedisTag``: Filter by the exact match against string-based categorical metadata fields. Multiple tags can be specified like \"tag1,tag2,tag3\".\n", "\n", "The following are examples of utilizing these filters.\n", "\n", @@ -858,7 +911,7 @@ "job_starts_with_eng = RedisFilter.text(\"job\") % \"eng*\"\n", "```\n", "\n", - "The following are examples of using hybrid filter for search" + "The following are examples of using a hybrid filter for search" ] }, { @@ -1150,7 +1203,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Delete keys" + "## Delete keys and index" ] }, { @@ -1209,48 +1262,6 @@ " redis_url=\"redis://localhost:6379\",\n", ")" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Redis connection Url examples\n", - "\n", - "Valid Redis Url scheme are:\n", - "1. `redis://` - Connection to Redis standalone, unencrypted\n", - "2. `rediss://` - Connection to Redis standalone, with TLS encryption\n", - "3. `redis+sentinel://` - Connection to Redis server via Redis Sentinel, unencrypted\n", - "4. `rediss+sentinel://` - Connection to Redis server via Redis Sentinel, booth connections with TLS encryption\n", - "\n", - "More information about additional connection parameter can be found in the redis-py documentation at https://redis-py.readthedocs.io/en/stable/connections.html" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "# connection to redis standalone at localhost, db 0, no password\n", - "redis_url = \"redis://localhost:6379\"\n", - "# connection to host \"redis\" port 7379 with db 2 and password \"secret\" (old style authentication scheme without username / pre 6.x)\n", - "redis_url = \"redis://:secret@redis:7379/2\"\n", - "# connection to host redis on default port with user \"joe\", pass \"secret\" using redis version 6+ ACLs\n", - "redis_url = \"redis://joe:secret@redis/0\"\n", - "\n", - "# connection to sentinel at localhost with default group mymaster and db 0, no password\n", - "redis_url = \"redis+sentinel://localhost:26379\"\n", - "# connection to sentinel at host redis with default port 26379 and user \"joe\" with password \"secret\" with default group mymaster and db 0\n", - "redis_url = \"redis+sentinel://joe:secret@redis\"\n", - "# connection to sentinel, no auth with sentinel monitoring group \"zone-1\" and database 2\n", - "redis_url = \"redis+sentinel://redis:26379/zone-1/2\"\n", - "\n", - "# connection to redis standalone at localhost, db 0, no password but with TLS support\n", - "redis_url = \"rediss://localhost:6379\"\n", - "# connection to redis sentinel at localhost and default port, db 0, no password\n", - "# but with TLS support for booth Sentinel and Redis server\n", - "redis_url = \"rediss+sentinel://localhost\"" - ] } ], "metadata": { @@ -1269,7 +1280,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.1" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/docs/docs/integrations/vectorstores/hanavector.ipynb b/docs/docs/integrations/vectorstores/sap_hanavector.ipynb similarity index 92% rename from docs/docs/integrations/vectorstores/hanavector.ipynb rename to docs/docs/integrations/vectorstores/sap_hanavector.ipynb index 0cc9f61d31d..63fe3add655 100644 --- a/docs/docs/integrations/vectorstores/hanavector.ipynb +++ b/docs/docs/integrations/vectorstores/sap_hanavector.ipynb @@ -6,13 +6,15 @@ "source": [ "# SAP HANA Cloud Vector Engine\n", "\n", - ">SAP HANA Cloud Vector Engine is a vector store fully integrated into the SAP HANA Cloud database." + ">[SAP HANA Cloud Vector Engine](https://www.sap.com/events/teched/news-guide/ai.html#article8) is a vector store fully integrated into the `SAP HANA Cloud` database." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "## Setting up\n", + "\n", "Installation of the HANA database driver." ] }, @@ -32,7 +34,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To use `OpenAIEmbeddings` so we use the OpenAI API Key." + "To use `OpenAIEmbeddings` we use the OpenAI API Key." ] }, { @@ -51,6 +53,44 @@ "# os.environ[\"OPENAI_API_KEY\"] = \"Your OpenAI API key\"" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a database connection to a HANA Cloud instance" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "ExecuteTime": { + "end_time": "2023-09-09T08:02:28.174088Z", + "start_time": "2023-09-09T08:02:28.162698Z" + } + }, + "outputs": [], + "source": [ + "from hdbcli import dbapi\n", + "\n", + "# Use connection settings from the environment\n", + "connection = dbapi.connect(\n", + " address=os.environ.get(\"HANA_DB_ADDRESS\"),\n", + " port=os.environ.get(\"HANA_DB_PORT\"),\n", + " user=os.environ.get(\"HANA_DB_USER\"),\n", + " password=os.environ.get(\"HANA_DB_PASSWORD\"),\n", + " autocommit=True,\n", + " sslValidateCertificate=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -83,37 +123,6 @@ "embeddings = OpenAIEmbeddings()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a database connection to a HANA Cloud instance" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "ExecuteTime": { - "end_time": "2023-09-09T08:02:28.174088Z", - "start_time": "2023-09-09T08:02:28.162698Z" - } - }, - "outputs": [], - "source": [ - "from hdbcli import dbapi\n", - "\n", - "# Use connection settings from the environment\n", - "connection = dbapi.connect(\n", - " address=os.environ.get(\"HANA_DB_ADDRESS\"),\n", - " port=os.environ.get(\"HANA_DB_PORT\"),\n", - " user=os.environ.get(\"HANA_DB_USER\"),\n", - " password=os.environ.get(\"HANA_DB_PASSWORD\"),\n", - " autocommit=True,\n", - " sslValidateCertificate=False,\n", - ")" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -161,7 +170,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Perform a query to get the two best matching document chunks from the ones that we added in the previous step.\n", + "Perform a query to get the two best-matching document chunks from the ones that we added in the previous step.\n", "By default \"Cosine Similarity\" is used for the search." ] }, @@ -211,12 +220,15 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "source": [ - "Maximal Marginal Relevance Search (MMR)\n", + "## Maximal Marginal Relevance Search (MMR)\n", "\n", - "Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. First 20 (fetch_k) items will be retrieved from the DB. The MMR algorithm will then find the best 2 (k) matches." + "`Maximal marginal relevance` optimizes for similarity to query AND diversity among selected documents. The first 20 (fetch_k) items will be retrieved from the DB. The MMR algorithm will then find the best 2 (k) matches." ] }, { @@ -227,7 +239,10 @@ "end_time": "2023-09-09T08:05:23.276819Z", "start_time": "2023-09-09T08:05:21.972256Z" }, - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -346,7 +361,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Using a VectorStore as a retriever in chains for retrieval augmented generation (RAG)\n" + "## Using a VectorStore as a retriever in chains for retrieval augmented generation (RAG)" ] }, { @@ -505,7 +520,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Standard tables vs. \"custom\" tables with vector data" + "## Standard tables vs. \"custom\" tables with vector data" ] }, { @@ -513,9 +528,9 @@ "metadata": {}, "source": [ "As default behaviour, the table for the embeddings is created with 3 columns\n", - "* A column \"VEC_TEXT\", which contains the text of the Document\n", - "* A column \"VEC_METADATA\", which contains the metadata of the Document\n", - "* A column \"VEC_VECTOR\", which contains the embeddings-vector of the document's text" + "* A column `VEC_TEXT`, which contains the text of the Document\n", + "* A column `VEC_METADATA`, which contains the metadata of the Document\n", + "* A column `VEC_VECTOR`, which contains the embeddings-vector of the document's text" ] }, { @@ -594,11 +609,11 @@ "metadata": {}, "source": [ "Custom tables must have at least three columns that match the semantics of a standard table\n", - "* A column with type \"NCLOB\" or \"NVARCHAR\" for the text/context of the embeddings\n", - "* A column with type \"NCLOB\" or \"NVARCHAR\" for the metadata \n", + "* A column with type `NCLOB` or `NVARCHAR` for the text/context of the embeddings\n", + "* A column with type `NCLOB` or `NVARCHAR` for the metadata \n", "* A column with type REAL_VECTOR for the embedding vector\n", "\n", - "The table can contain additional columns. When new Documents are inserted to the table, these addtional columns must allow NULL values." + "The table can contain additional columns. When new Documents are inserted into the table, these additional columns must allow NULL values." ] }, { @@ -654,7 +669,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Add another document and perform a similarity search on the custom table" + "Add another document and perform a similarity search on the custom table." ] }, { diff --git a/docs/docs/integrations/vectorstores/singlestoredb.ipynb b/docs/docs/integrations/vectorstores/singlestoredb.ipynb index fd20785ad45..6cae0d54424 100644 --- a/docs/docs/integrations/vectorstores/singlestoredb.ipynb +++ b/docs/docs/integrations/vectorstores/singlestoredb.ipynb @@ -106,6 +106,14 @@ "print(docs[0].page_content)" ] }, + { + "cell_type": "markdown", + "id": "035cba66", + "metadata": {}, + "source": [ + "Enhance your search efficiency with SingleStore DB version 8.5 or above by leveraging [ANN vector indexes](https://docs.singlestore.com/cloud/reference/sql-reference/vector-functions/vector-indexing/). By setting `use_vector_index=True` during vector store object creation, you can activate this feature. Additionally, if your vectors differ in dimensionality from the default OpenAI embedding size of 1536, ensure to specify the `vector_size` parameter accordingly. " + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/docs/docs/integrations/vectorstores/thirdai_neuraldb.ipynb b/docs/docs/integrations/vectorstores/thirdai_neuraldb.ipynb index f1f2461076e..137e501897c 100644 --- a/docs/docs/integrations/vectorstores/thirdai_neuraldb.ipynb +++ b/docs/docs/integrations/vectorstores/thirdai_neuraldb.ipynb @@ -4,16 +4,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# **NeuralDB**\n", - "NeuralDB is a CPU-friendly and fine-tunable vector store developed by ThirdAI.\n", + "# ThirdAI NeuralDB\n", + "\n", + ">[NeuralDB](https://www.thirdai.com/neuraldb-enterprise/) is a CPU-friendly and fine-tunable vector store developed by [ThirdAI](https://www.thirdai.com/).\n", + "\n", + "## Initialization\n", "\n", - "### **Initialization**\n", "There are three initialization methods:\n", "- From Scratch: Basic model\n", "- From Bazaar: Download a pretrained base model from our model bazaar for better performance\n", "- From Checkpoint: Load a model that was previously saved\n", "\n", - "For all of the following initialization methods, the `thirdai_key` parameter can be ommitted if the `THIRDAI_KEY` environment variable is set.\n", + "For all of the following initialization methods, the `thirdai_key` parameter can be omitted if the `THIRDAI_KEY` environment variable is set.\n", "\n", "ThirdAI API keys can be obtained at https://www.thirdai.com/try-bolt/" ] @@ -55,7 +57,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### **Inserting document sources**" + "## Inserting document sources" ] }, { @@ -96,7 +98,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### **Similarity search**\n", + "## Similarity search\n", + "\n", "To query the vectorstore, you can use the standard LangChain vectorstore method `similarity_search`, which returns a list of LangChain Document objects. Each document object represents a chunk of text from the indexed files. For example, it may contain a paragraph from one of the indexed PDF files. In addition to the text, the document's metadata field contains information such as the document's ID, the source of this document (which file it came from), and the score of the document." ] }, @@ -114,7 +117,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### **Fine tuning**\n", + "## Fine tuning\n", + "\n", "NeuralDBVectorStore can be fine-tuned to user behavior and domain-specific knowledge. It can be fine-tuned in two ways:\n", "1. Association: the vectorstore associates a source phrase with a target phrase. When the vectorstore sees the source phrase, it will also consider results that are relevant to the target phrase.\n", "2. Upvoting: the vectorstore upweights the score of a document for a specific query. This is useful when you want to fine-tune the vectorstore to user behavior. For example, if a user searches \"how is a car manufactured\" and likes the returned document with id 52, then we can upvote the document with id 52 for the query \"how is a car manufactured\"." @@ -146,15 +150,23 @@ ], "metadata": { "kernelspec": { - "display_name": "langchain", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", - "version": "3.10.0" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/docs/integrations/vectorstores/vikingdb.ipynb b/docs/docs/integrations/vectorstores/vikingdb.ipynb index 66ab177efd5..aa8726966ab 100644 --- a/docs/docs/integrations/vectorstores/vikingdb.ipynb +++ b/docs/docs/integrations/vectorstores/vikingdb.ipynb @@ -220,7 +220,7 @@ "collapsed": false }, "source": [ - "After retreival you can go on querying it as usual." + "After retrieval you can go on querying it as usual." ] } ], diff --git a/docs/docs/modules/agents/how_to/structured_tools.ipynb b/docs/docs/modules/agents/how_to/structured_tools.ipynb new file mode 100644 index 00000000000..ee9293e826a --- /dev/null +++ b/docs/docs/modules/agents/how_to/structured_tools.ipynb @@ -0,0 +1,142 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "473081cc", + "metadata": {}, + "source": [ + "---\n", + "sidebar_position: 1\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "16ee4216", + "metadata": {}, + "source": [ + "# Structured Tools" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "670078c4", + "metadata": {}, + "outputs": [], + "source": [ + "from typing import List\n", + "\n", + "from langchain_core.tools import tool\n", + "\n", + "\n", + "@tool\n", + "def get_data(n: int) -> List[dict]:\n", + " \"\"\"Get n datapoints.\"\"\"\n", + " return [{\"name\": \"foo\", \"value\": \"bar\"}] * n\n", + "\n", + "\n", + "tools = [get_data]" + ] + }, + { + "cell_type": "markdown", + "id": "5e04164b", + "metadata": {}, + "source": [ + "We will use a prompt from the hub - you can inspect the prompt more at [https://smith.langchain.com/hub/hwchase17/openai-functions-agent](https://smith.langchain.com/hub/hwchase17/openai-functions-agent)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d8c5d907", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain import hub\n", + "from langchain.agents import AgentExecutor, create_openai_functions_agent\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "# Get the prompt to use - you can modify this!\n", + "# If you want to see the prompt in full, you can at: https://smith.langchain.com/hub/hwchase17/openai-functions-agent\n", + "prompt = hub.pull(\"hwchase17/openai-functions-agent\")\n", + "\n", + "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)\n", + "\n", + "agent = create_openai_functions_agent(llm, tools, prompt)\n", + "agent_executor = AgentExecutor(agent=agent, tools=tools)" + ] + }, + { + "cell_type": "markdown", + "id": "cba9a9eb", + "metadata": {}, + "source": [ + "## Stream intermediate steps\n", + "\n", + "Let's look at how to stream intermediate steps. We can do this easily by just using the `.stream` method on the AgentExecutor\n", + "\n", + "We can then parse the results to get actions (tool inputs) and observtions (tool outputs)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b6bd9bf2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calling Tool ```get_data``` with input ```{'n': 3}```\n", + "Got result: ```[{'name': 'foo', 'value': 'bar'}, {'name': 'foo', 'value': 'bar'}, {'name': 'foo', 'value': 'bar'}]```\n" + ] + } + ], + "source": [ + "for chunk in agent_executor.stream({\"input\": \"get me three datapoints\"}):\n", + " # Agent Action\n", + " if \"actions\" in chunk:\n", + " for action in chunk[\"actions\"]:\n", + " print(\n", + " f\"Calling Tool ```{action.tool}``` with input ```{action.tool_input}```\"\n", + " )\n", + " # Observation\n", + " elif \"steps\" in chunk:\n", + " for step in chunk[\"steps\"]:\n", + " print(f\"Got result: ```{step.observation}```\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "af9e32fe", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/docs/modules/data_connection/document_transformers/recursive_json_splitter.ipynb b/docs/docs/modules/data_connection/document_transformers/recursive_json_splitter.ipynb new file mode 100644 index 00000000000..14d69ceb6b3 --- /dev/null +++ b/docs/docs/modules/data_connection/document_transformers/recursive_json_splitter.ipynb @@ -0,0 +1,225 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a678d550", + "metadata": {}, + "source": [ + "# Recursively split JSON\n", + "\n", + "This json splitter traverses json data depth first and builds smaller json chunks. It attempts to keep nested json objects whole but will split them if needed to keep chunks between a min_chunk_size and the max_chunk_size. If the value is not a nested json, but rather a very large string the string will not be split. If you need a hard cap on the chunk size considder following this with a Recursive Text splitter on those chunks. There is an optional pre-processing step to split lists, by first converting them to json (dict) and then splitting them as such.\n", + "\n", + "1. How the text is split: json value.\n", + "2. How the chunk size is measured: by number of characters." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a504e1e7", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "import requests" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3390ae1d", + "metadata": {}, + "outputs": [], + "source": [ + "# This is a large nested json object and will be loaded as a python dict\n", + "json_data = requests.get(\"https://api.smith.langchain.com/openapi.json\").json()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7bfe2c1e", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.text_splitter import RecursiveJsonSplitter" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2833c409", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "splitter = RecursiveJsonSplitter(max_chunk_size=300)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f941aa56", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# Recursively split json data - If you need to access/manipulate the smaller json chunks\n", + "json_chunks = splitter.split_json(json_data=json_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0839f4f0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\"openapi\": \"3.0.2\", \"info\": {\"title\": \"LangChainPlus\", \"version\": \"0.1.0\"}, \"paths\": {\"/sessions/{session_id}\": {\"get\": {\"tags\": [\"tracer-sessions\"], \"summary\": \"Read Tracer Session\", \"description\": \"Get a specific session.\", \"operationId\": \"read_tracer_session_sessions__session_id__get\"}}}}\n", + "{\"paths\": {\"/sessions/{session_id}\": {\"get\": {\"parameters\": [{\"required\": true, \"schema\": {\"title\": \"Session Id\", \"type\": \"string\", \"format\": \"uuid\"}, \"name\": \"session_id\", \"in\": \"path\"}, {\"required\": false, \"schema\": {\"title\": \"Include Stats\", \"type\": \"boolean\", \"default\": false}, \"name\": \"include_stats\", \"in\": \"query\"}, {\"required\": false, \"schema\": {\"title\": \"Accept\", \"type\": \"string\"}, \"name\": \"accept\", \"in\": \"header\"}]}}}}\n" + ] + } + ], + "source": [ + "# The splitter can also output documents\n", + "docs = splitter.create_documents(texts=[json_data])\n", + "\n", + "# or a list of strings\n", + "texts = splitter.split_text(json_data=json_data)\n", + "\n", + "print(texts[0])\n", + "print(texts[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c34b1f7f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[293, 431, 203, 277, 230, 194, 162, 280, 223, 193]\n", + "{\"paths\": {\"/sessions/{session_id}\": {\"get\": {\"parameters\": [{\"required\": true, \"schema\": {\"title\": \"Session Id\", \"type\": \"string\", \"format\": \"uuid\"}, \"name\": \"session_id\", \"in\": \"path\"}, {\"required\": false, \"schema\": {\"title\": \"Include Stats\", \"type\": \"boolean\", \"default\": false}, \"name\": \"include_stats\", \"in\": \"query\"}, {\"required\": false, \"schema\": {\"title\": \"Accept\", \"type\": \"string\"}, \"name\": \"accept\", \"in\": \"header\"}]}}}}\n" + ] + } + ], + "source": [ + "# Let's look at the size of the chunks\n", + "print([len(text) for text in texts][:10])\n", + "\n", + "# Reviewing one of these chunks that was bigger we see there is a list object there\n", + "print(texts[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "992477c2", + "metadata": {}, + "outputs": [], + "source": [ + "# The json splitter by default does not split lists\n", + "# the following will preprocess the json and convert list to dict with index:item as key:val pairs\n", + "texts = splitter.split_text(json_data=json_data, convert_lists=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "2d23b3aa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[293, 431, 203, 277, 230, 194, 162, 280, 223, 193]\n" + ] + } + ], + "source": [ + "# Let's look at the size of the chunks. Now they are all under the max\n", + "print([len(text) for text in texts][:10])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d2c2773e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\"paths\": {\"/sessions/{session_id}\": {\"get\": {\"parameters\": [{\"required\": true, \"schema\": {\"title\": \"Session Id\", \"type\": \"string\", \"format\": \"uuid\"}, \"name\": \"session_id\", \"in\": \"path\"}, {\"required\": false, \"schema\": {\"title\": \"Include Stats\", \"type\": \"boolean\", \"default\": false}, \"name\": \"include_stats\", \"in\": \"query\"}, {\"required\": false, \"schema\": {\"title\": \"Accept\", \"type\": \"string\"}, \"name\": \"accept\", \"in\": \"header\"}]}}}}\n" + ] + } + ], + "source": [ + "# The list has been converted to a dict, but retains all the needed contextual information even if split into many chunks\n", + "print(texts[1])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "8963b01a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Document(page_content='{\"paths\": {\"/sessions/{session_id}\": {\"get\": {\"parameters\": [{\"required\": true, \"schema\": {\"title\": \"Session Id\", \"type\": \"string\", \"format\": \"uuid\"}, \"name\": \"session_id\", \"in\": \"path\"}, {\"required\": false, \"schema\": {\"title\": \"Include Stats\", \"type\": \"boolean\", \"default\": false}, \"name\": \"include_stats\", \"in\": \"query\"}, {\"required\": false, \"schema\": {\"title\": \"Accept\", \"type\": \"string\"}, \"name\": \"accept\", \"in\": \"header\"}]}}}}')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We can also look at the documents\n", + "docs[1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "168da4f0", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/docs/modules/data_connection/indexing.ipynb b/docs/docs/modules/data_connection/indexing.ipynb index b888a77958b..45a5d92a42b 100644 --- a/docs/docs/modules/data_connection/indexing.ipynb +++ b/docs/docs/modules/data_connection/indexing.ipynb @@ -60,7 +60,7 @@ " * document addition by id (`add_documents` method with `ids` argument)\n", " * delete by id (`delete` method with `ids` argument)\n", "\n", - "Compatible Vectorstores: `AnalyticDB`, `AstraDB`, `AwaDB`, `Bagel`, `Cassandra`, `Chroma`, `DashVector`, `DatabricksVectorSearch`, `DeepLake`, `Dingo`, `ElasticVectorSearch`, `ElasticsearchStore`, `FAISS`, `HanaDB`, `Milvus`, `MyScale`, `PGVector`, `Pinecone`, `Qdrant`, `Redis`, `ScaNN`, `SupabaseVectorStore`, `SurrealDBStore`, `TimescaleVector`, `Vald`, `Vearch`, `VespaStore`, `Weaviate`, `ZepVectorStore`.\n", + "Compatible Vectorstores: `AnalyticDB`, `AstraDB`, `AwaDB`, `Bagel`, `Cassandra`, `Chroma`, `DashVector`, `DatabricksVectorSearch`, `DeepLake`, `Dingo`, `ElasticVectorSearch`, `ElasticsearchStore`, `FAISS`, `HanaDB`, `Milvus`, `MyScale`, `PGVector`, `Pinecone`, `Qdrant`, `Redis`, `Rockset`, `ScaNN`, `SupabaseVectorStore`, `SurrealDBStore`, `TimescaleVector`, `Vald`, `Vearch`, `VespaStore`, `Weaviate`, `ZepVectorStore`.\n", " \n", "## Caution\n", "\n", diff --git a/docs/docs/modules/model_io/index.mdx b/docs/docs/modules/model_io/index.mdx index eeb94b48505..a5cf4e81fd4 100644 --- a/docs/docs/modules/model_io/index.mdx +++ b/docs/docs/modules/model_io/index.mdx @@ -17,7 +17,7 @@ A conceptual explanation of messages, prompts, LLMs vs ChatModels, and output pa ## [Quickstart](/docs/modules/model_io/quick_start) -Covers the basics of getting started working with different types of models. You should walk through [this section] if you want to get an overview of the functionality. +Covers the basics of getting started working with different types of models. You should walk through [this section](/docs/modules/model_io/quick_start) if you want to get an overview of the functionality. ## [Prompts](/docs/modules/model_io/prompts/) diff --git a/docs/docs/modules/model_io/llms/index.mdx b/docs/docs/modules/model_io/llms/index.mdx index 8e7a1e95dab..ead4a3e1b9f 100644 --- a/docs/docs/modules/model_io/llms/index.mdx +++ b/docs/docs/modules/model_io/llms/index.mdx @@ -26,4 +26,4 @@ This includes: - [How to write a custom LLM class](./custom_llm) - [How to cache LLM responses](./llm_caching) - [How to stream responses from an LLM](./streaming_llm) -- [How to track token usage in an LLM call)(./token_usage_tracking) +- [How to track token usage in an LLM call](./token_usage_tracking) diff --git a/docs/docs/modules/model_io/output_parsers/index.mdx b/docs/docs/modules/model_io/output_parsers/index.mdx index ea0ada5db35..483e30d1701 100644 --- a/docs/docs/modules/model_io/output_parsers/index.mdx +++ b/docs/docs/modules/model_io/output_parsers/index.mdx @@ -4,7 +4,7 @@ hide_table_of_contents: true --- # Output Parsers -Output parsers are responsible for taking the output of an LLM and transforming it to a more suitable format. This is very useful when you are asing LLMs to generate any form of structured data. +Output parsers are responsible for taking the output of an LLM and transforming it to a more suitable format. This is very useful when you are using LLMs to generate any form of structured data. Besides having a large collection of different types of output parsers, one distinguishing benefit of LangChain OutputParsers is that many of them support streaming. diff --git a/docs/docs/modules/model_io/prompts/partial.ipynb b/docs/docs/modules/model_io/prompts/partial.ipynb index 4c937ba7286..274cc6fb720 100644 --- a/docs/docs/modules/model_io/prompts/partial.ipynb +++ b/docs/docs/modules/model_io/prompts/partial.ipynb @@ -37,7 +37,7 @@ "source": [ "from langchain.prompts import PromptTemplate\n", "\n", - "prompt = PromptTemplate(template=\"{foo}{bar}\", input_variables=[\"foo\", \"bar\"])\n", + "prompt = PromptTemplate.from_template(\"{foo}{bar}\")\n", "partial_prompt = prompt.partial(foo=\"foo\")\n", "print(partial_prompt.format(bar=\"baz\"))" ] diff --git a/docs/docs/use_cases/code_understanding.ipynb b/docs/docs/use_cases/code_understanding.ipynb index 317838d05a1..d3da700c936 100644 --- a/docs/docs/use_cases/code_understanding.ipynb +++ b/docs/docs/use_cases/code_understanding.ipynb @@ -17,7 +17,7 @@ "\n", "## Use case\n", "\n", - "Source code analysis is one of the most popular LLM applications (e.g., [GitHub Co-Pilot](https://github.com/features/copilot), [Code Interpreter](https://chat.openai.com/auth/login?next=%2F%3Fmodel%3Dgpt-4-code-interpreter), [Codium](https://www.codium.ai/), and [Codeium](https://codeium.com/about)) for use-cases such as:\n", + "Source code analysis is one of the most popular LLM applications (e.g., [GitHub Copilot](https://github.com/features/copilot), [Code Interpreter](https://chat.openai.com/auth/login?next=%2F%3Fmodel%3Dgpt-4-code-interpreter), [Codium](https://www.codium.ai/), and [Codeium](https://codeium.com/about)) for use-cases such as:\n", "\n", "- Q&A over the code base to understand how it works\n", "- Using LLMs for suggesting refactors or improvements\n", diff --git a/docs/docs/use_cases/csv.ipynb b/docs/docs/use_cases/csv.ipynb index 7ef34f8412f..589faa16950 100644 --- a/docs/docs/use_cases/csv.ipynb +++ b/docs/docs/use_cases/csv.ipynb @@ -460,9 +460,7 @@ "Given a user question, write the Python code to answer it. \\\n", "Return ONLY the valid Python code and nothing else. \\\n", "Don't assume you have access to any libraries other than built-in Python ones and pandas.\"\"\"\n", - "prompt = ChatPromptTemplate.from_messages(\n", - " [(\"system\", system), (\"human\", \"{question}\")]\n", - ")\n", + "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", \"{question}\")])\n", "code_chain = prompt | llm_with_tools | parser\n", "code_chain.invoke({\"question\": \"What's the correlation between age and fare\"})" ] diff --git a/docs/docs/use_cases/graph/graph_networkx_qa.ipynb b/docs/docs/use_cases/graph/graph_networkx_qa.ipynb index cb70b8718c0..a01d7c11276 100644 --- a/docs/docs/use_cases/graph/graph_networkx_qa.ipynb +++ b/docs/docs/use_cases/graph/graph_networkx_qa.ipynb @@ -298,12 +298,34 @@ "id": "045796cf", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "loaded_graph.get_number_of_nodes()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d24b3407", + "metadata": {}, + "outputs": [], + "source": [ + "loaded_graph.clear_edges()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8afda51", + "metadata": {}, + "outputs": [], + "source": [ + "loaded_graph.clear()" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3.10.6 64-bit", "language": "python", "name": "python3" }, @@ -317,7 +339,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.10.6" + }, + "vscode": { + "interpreter": { + "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" + } } }, "nbformat": 4, diff --git a/docs/docs/use_cases/graph/graph_ontotext_graphdb_qa.ipynb b/docs/docs/use_cases/graph/graph_ontotext_graphdb_qa.ipynb index b1afd3320af..3f0a6c09c9b 100644 --- a/docs/docs/use_cases/graph/graph_ontotext_graphdb_qa.ipynb +++ b/docs/docs/use_cases/graph/graph_ontotext_graphdb_qa.ipynb @@ -69,7 +69,7 @@ "pip install openai==1.6.1\n", "pip install rdflib==7.0.0\n", "pip install langchain-openai==0.0.2\n", - "pip install langchain\n", + "pip install langchain>=0.1.5\n", "```\n", "\n", "Run Jupyter with\n", diff --git a/docs/docs/use_cases/graph/neptune_sparql_qa.ipynb b/docs/docs/use_cases/graph/neptune_sparql_qa.ipynb new file mode 100644 index 00000000000..4c464e840d5 --- /dev/null +++ b/docs/docs/use_cases/graph/neptune_sparql_qa.ipynb @@ -0,0 +1,337 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Neptune SPARQL QA Chain\n", + "\n", + "This notebook shows use of LLM to query RDF graph in Amazon Neptune. This code uses a `NeptuneRdfGraph` class that connects with the Neptune database and loads it's schema. The `NeptuneSparqlQAChain` is used to connect the graph and LLM to ask natural language questions.\n", + "\n", + "Requirements for running this notebook:\n", + "- Neptune 1.2.x cluster accessible from this notebook\n", + "- Kernel with Python 3.9 or higher\n", + "- For Bedrock access, ensure IAM role has this policy\n", + "\n", + "```json\n", + "{\n", + " \"Action\": [\n", + " \"bedrock:ListFoundationModels\",\n", + " \"bedrock:InvokeModel\"\n", + " ],\n", + " \"Resource\": \"*\",\n", + " \"Effect\": \"Allow\"\n", + "}\n", + "```\n", + "\n", + "- S3 bucket for staging sample data, bucket should be in same account/region as Neptune." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Seed W3C organizational data\n", + "W3C org ontology plus some instances. \n", + "\n", + "You will need an S3 bucket in the same region and account. Set STAGE_BUCKET to name of that bucket." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "STAGE_BUCKET = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%bash -s \"$STAGE_BUCKET\"\n", + "\n", + "rm -rf data\n", + "mkdir -p data\n", + "cd data\n", + "echo getting org ontology and sample org instances\n", + "wget http://www.w3.org/ns/org.ttl \n", + "wget https://raw.githubusercontent.com/aws-samples/amazon-neptune-ontology-example-blog/main/data/example_org.ttl \n", + "\n", + "echo Copying org ttl to S3\n", + "aws s3 cp org.ttl s3://$1/org.ttl\n", + "aws s3 cp example_org.ttl s3://$1/example_org.ttl\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Bulk-load the org ttl - both ontology and instances" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load -s s3://{STAGE_BUCKET} -f turtle --store-to loadres --run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_status {loadres['payload']['loadId']} --errors --details" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup Chain" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "EXAMPLES = \"\"\"\n", + "\n", + "\n", + "Find organizations.\n", + "\n", + "\n", + "\n", + "PREFIX rdf: \n", + "PREFIX rdfs: \n", + "PREFIX org: \n", + "\n", + "select ?org ?orgName where {{\n", + " ?org rdfs:label ?orgName .\n", + "}} \n", + "\n", + "\n", + "\n", + "Find sites of an organization\n", + "\n", + "\n", + "\n", + "PREFIX rdf: \n", + "PREFIX rdfs: \n", + "PREFIX org: \n", + "\n", + "select ?org ?orgName ?siteName where {{\n", + " ?org rdfs:label ?orgName .\n", + " ?org org:hasSite/rdfs:label ?siteName . \n", + "}} \n", + "\n", + "\n", + "\n", + "Find suborganizations of an organization\n", + "\n", + "\n", + "\n", + "PREFIX rdf: \n", + "PREFIX rdfs: \n", + "PREFIX org: \n", + "\n", + "select ?org ?orgName ?subName where {{\n", + " ?org rdfs:label ?orgName .\n", + " ?org org:hasSubOrganization/rdfs:label ?subName .\n", + "}} \n", + "\n", + "\n", + "\n", + "Find organizational units of an organization\n", + "\n", + "\n", + "\n", + "PREFIX rdf: \n", + "PREFIX rdfs: \n", + "PREFIX org: \n", + "\n", + "select ?org ?orgName ?unitName where {{\n", + " ?org rdfs:label ?orgName .\n", + " ?org org:hasUnit/rdfs:label ?unitName . \n", + "}} \n", + "\n", + "\n", + "\n", + "Find members of an organization. Also find their manager, or the member they report to.\n", + "\n", + "\n", + "\n", + "PREFIX org: \n", + "PREFIX foaf: \n", + "\n", + "select * where {{\n", + " ?person rdf:type foaf:Person .\n", + " ?person org:memberOf ?org .\n", + " OPTIONAL {{ ?person foaf:firstName ?firstName . }}\n", + " OPTIONAL {{ ?person foaf:family_name ?lastName . }}\n", + " OPTIONAL {{ ?person org:reportsTo ??manager }} .\n", + "}}\n", + "\n", + "\n", + "\n", + "\n", + "Find change events, such as mergers and acquisitions, of an organization\n", + "\n", + "\n", + "\n", + "PREFIX org: \n", + "\n", + "select ?event ?prop ?obj where {{\n", + " ?org rdfs:label ?orgName .\n", + " ?event rdf:type org:ChangeEvent .\n", + " ?event org:originalOrganization ?origOrg .\n", + " ?event org:resultingOrganization ?resultingOrg .\n", + "}}\n", + "\n", + "\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import boto3\n", + "from langchain.chains.graph_qa.neptune_sparql import NeptuneSparqlQAChain\n", + "from langchain.chat_models import BedrockChat\n", + "from langchain_community.graphs import NeptuneRdfGraph\n", + "\n", + "host = \"\"\n", + "port = \"\"\n", + "region = \"us-east-1\" # specify region\n", + "\n", + "graph = NeptuneRdfGraph(\n", + " host=host, port=port, use_iam_auth=True, region_name=region, hide_comments=True\n", + ")\n", + "\n", + "schema_elements = graph.get_schema_elements\n", + "# Optionally, you can update the schema_elements, and\n", + "# load the schema from the pruned elements.\n", + "graph.load_from_schema_elements(schema_elements)\n", + "\n", + "bedrock_client = boto3.client(\"bedrock-runtime\")\n", + "llm = BedrockChat(model_id=\"anthropic.claude-v2\", client=bedrock_client)\n", + "\n", + "chain = NeptuneSparqlQAChain.from_llm(\n", + " llm=llm,\n", + " graph=graph,\n", + " examples=EXAMPLES,\n", + " verbose=True,\n", + " top_K=10,\n", + " return_intermediate_steps=True,\n", + " return_direct=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ask questions\n", + "Depends on the data we ingested above" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "chain.invoke(\"\"\"How many organizations are in the graph\"\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "chain.invoke(\"\"\"Are there any mergers or acquisitions\"\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "chain.invoke(\"\"\"Find organizations\"\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "chain.invoke(\"\"\"Find sites of MegaSystems or MegaFinancial\"\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "chain.invoke(\"\"\"Find a member who is manager of one or more members.\"\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "chain.invoke(\"\"\"Find five members and who their manager is.\"\"\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "chain.invoke(\n", + " \"\"\"Find org units or suborganizations of The Mega Group. What are the sites of those units?\"\"\"\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/docs/use_cases/question_answering/quickstart.ipynb b/docs/docs/use_cases/question_answering/quickstart.ipynb index 1f464a9615d..11b2475aec8 100644 --- a/docs/docs/use_cases/question_answering/quickstart.ipynb +++ b/docs/docs/use_cases/question_answering/quickstart.ipynb @@ -262,7 +262,7 @@ "\n", "We need to first load the blog post contents. We can use [DocumentLoaders](/docs/modules/data_connection/document_loaders/) for this, which are objects that load in data from a source and return a list of [Documents](https://api.python.langchain.com/en/latest/documents/langchain_core.documents.base.Document.html). A `Document` is an object with some `page_content` (str) and `metadata` (dict).\n", "\n", - "In this case we'll use the [WebBaseLoader](/docs/integrations/document_loaders/web_base), which uses `urllib` to load HTML form web URLs and `BeautifulSoup` to parse it to text. We can customize the HTML -> text parsing by passing in parameters to the `BeautifulSoup` parser via `bs_kwargs` (see [BeautifulSoup docs](https://beautiful-soup-4.readthedocs.io/en/latest/#beautifulsoup)). In this case only HTML tags with class \"post-content\", \"post-title\", or \"post-header\" are relevant, so we'll remove all others." + "In this case we'll use the [WebBaseLoader](/docs/integrations/document_loaders/web_base), which uses `urllib` to load HTML from web URLs and `BeautifulSoup` to parse it to text. We can customize the HTML -> text parsing by passing in parameters to the `BeautifulSoup` parser via `bs_kwargs` (see [BeautifulSoup docs](https://beautiful-soup-4.readthedocs.io/en/latest/#beautifulsoup)). In this case only HTML tags with class \"post-content\", \"post-title\", or \"post-header\" are relevant, so we'll remove all others." ] }, { @@ -514,7 +514,7 @@ "\n", "First we need to define our logic for searching over documents. LangChain defines a [Retriever](/docs/modules/data_connection/retrievers/) interface which wraps an index that can return relevant `Documents` given a string query.\n", "\n", - "The most common type of `Retriever` is the [VectorStoreRetriever](/docs/modules/data_connection/retrievers/vectorstore), which uses the similarity search capabilities of a vector store to facillitate retrieval. Any `VectorStore` can easily be turned into a `Retriever` with `VectorStore.as_retriever()`:" + "The most common type of `Retriever` is the [VectorStoreRetriever](/docs/modules/data_connection/retrievers/vectorstore), which uses the similarity search capabilities of a vector store to facilitate retrieval. Any `VectorStore` can easily be turned into a `Retriever` with `VectorStore.as_retriever()`:" ] }, { diff --git a/docs/docs/use_cases/sql/agents.ipynb b/docs/docs/use_cases/sql/agents.ipynb index 9b013f89963..9a91a34a8cc 100644 --- a/docs/docs/use_cases/sql/agents.ipynb +++ b/docs/docs/use_cases/sql/agents.ipynb @@ -721,7 +721,9 @@ "\n", "If the question does not seem related to the database, just return \"I don't know\" as the answer.\"\"\"\n", "\n", - "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", \"{input}\"), MessagesPlaceholder(\"agent_scratchpad\")])\n", + "prompt = ChatPromptTemplate.from_messages(\n", + " [(\"system\", system), (\"human\", \"{input}\"), MessagesPlaceholder(\"agent_scratchpad\")]\n", + ")\n", "agent = create_sql_agent(\n", " llm=llm,\n", " db=db,\n", diff --git a/docs/docs/use_cases/sql/large_db.ipynb b/docs/docs/use_cases/sql/large_db.ipynb index dd034037d1a..b6b9802b9a6 100644 --- a/docs/docs/use_cases/sql/large_db.ipynb +++ b/docs/docs/use_cases/sql/large_db.ipynb @@ -208,7 +208,7 @@ } ], "source": [ - "system = f\"\"\"Return the names of the SQL tables that are relevant to the user question. \\\n", + "system = \"\"\"Return the names of the SQL tables that are relevant to the user question. \\\n", "The tables are:\n", "\n", "Music\n", diff --git a/docs/docs/use_cases/sql/query_checking.ipynb b/docs/docs/use_cases/sql/query_checking.ipynb index fcb5d44a2ba..238776d0e6c 100644 --- a/docs/docs/use_cases/sql/query_checking.ipynb +++ b/docs/docs/use_cases/sql/query_checking.ipynb @@ -294,11 +294,15 @@ "First draft: <>\n", "Final answer: <>\n", "\"\"\"\n", - "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", \"{input}\")]).partial(dialect=db.dialect)\n", + "prompt = ChatPromptTemplate.from_messages(\n", + " [(\"system\", system), (\"human\", \"{input}\")]\n", + ").partial(dialect=db.dialect)\n", + "\n", "\n", "def parse_final_answer(output: str) -> str:\n", " return output.split(\"Final answer: \")[1]\n", - " \n", + "\n", + "\n", "chain = create_sql_query_chain(llm, db, prompt=prompt) | parse_final_answer\n", "prompt.pretty_print()" ] diff --git a/docs/docs/use_cases/summarization.ipynb b/docs/docs/use_cases/summarization.ipynb index 21dc63dde8e..35917b74721 100644 --- a/docs/docs/use_cases/summarization.ipynb +++ b/docs/docs/use_cases/summarization.ipynb @@ -224,7 +224,7 @@ "source": [ "## Option 1. Stuff\n", "\n", - "When we use `load_summarize_chain` with `chain_type=\"stuff\"`, we will use the [StuffDocumentsChain](/docs/modules/chains/document/stuff).\n", + "When we use `load_summarize_chain` with `chain_type=\"stuff\"`, we will use the [StuffDocumentsChain](https://api.python.langchain.com/en/latest/chains/langchain.chains.combine_documents.stuff.StuffDocumentsChain.html#langchain.chains.combine_documents.stuff.StuffDocumentsChain).\n", "\n", "The chain will take a list of documents, inserts them all into a prompt, and passes that prompt to an LLM:" ] diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js index 231087f916f..352a225c9d0 100644 --- a/docs/docusaurus.config.js +++ b/docs/docusaurus.config.js @@ -188,7 +188,7 @@ const config = { }, { to: "/docs/contributing", - label: "Developer's guide", + label: "Contributing", }, { type: "docSidebar", @@ -205,7 +205,7 @@ const config = { }, { to: "/docs/additional_resources/youtube", - label: "YouTube videos" + label: "YouTube" }, ] }, @@ -298,6 +298,10 @@ const config = { label: "Blog", href: "https://blog.langchain.dev", }, + { + label: "YouTube", + href: "https://www.youtube.com/@LangChain", + }, ], }, ], diff --git a/docs/scripts/generate_api_reference_links.py b/docs/scripts/generate_api_reference_links.py index a9a38629b9e..406c1c79bf9 100644 --- a/docs/scripts/generate_api_reference_links.py +++ b/docs/scripts/generate_api_reference_links.py @@ -64,7 +64,7 @@ def main(): global_imports = {} for file in find_files(args.docs_dir): - print(f"Adding links for imports in {file}") + print(f"Adding links for imports in {file}") # noqa: T201 file_imports = replace_imports(file) if file_imports: diff --git a/docs/sidebars.js b/docs/sidebars.js index 46e50354212..aa28a21dfd9 100644 --- a/docs/sidebars.js +++ b/docs/sidebars.js @@ -87,11 +87,11 @@ module.exports = { collapsible: false, items: [ { type: "autogenerated", dirName: "integrations/platforms" }, - { type: "category", label: "More", collapsed: true, items: [{type:"autogenerated", dirName: "integrations/providers" }]}, + { type: "category", label: "More", collapsed: true, items: [{type:"autogenerated", dirName: "integrations/providers" }], link: { type: 'generated-index', slug: "integrations/providers", }}, ], link: { - type: 'generated-index', - slug: "integrations/providers", + type: 'doc', + id: 'integrations/platforms/index' }, }, { @@ -101,13 +101,13 @@ module.exports = { items: [ { type: "category", label: "LLMs", collapsed: true, items: [{type:"autogenerated", dirName: "integrations/llms" }], link: { type: 'doc', id: "integrations/llms/index"}}, { type: "category", label: "Chat models", collapsed: true, items: [{type:"autogenerated", dirName: "integrations/chat" }], link: { type: 'doc', id: "integrations/chat/index"}}, + { type: "category", label: "Embedding models", collapsed: true, items: [{type: "autogenerated", dirName: "integrations/text_embedding" }], link: {type: "generated-index", slug: "integrations/text_embedding" }}, { type: "category", label: "Document loaders", collapsed: true, items: [{type:"autogenerated", dirName: "integrations/document_loaders" }], link: {type: "generated-index", slug: "integrations/document_loaders" }}, { type: "category", label: "Document transformers", collapsed: true, items: [{type: "autogenerated", dirName: "integrations/document_transformers" }], link: {type: "generated-index", slug: "integrations/document_transformers" }}, - { type: "category", label: "Text embedding models", collapsed: true, items: [{type: "autogenerated", dirName: "integrations/text_embedding" }], link: {type: "generated-index", slug: "integrations/text_embedding" }}, { type: "category", label: "Vector stores", collapsed: true, items: [{type: "autogenerated", dirName: "integrations/vectorstores" }], link: {type: "generated-index", slug: "integrations/vectorstores" }}, { type: "category", label: "Retrievers", collapsed: true, items: [{type: "autogenerated", dirName: "integrations/retrievers" }], link: {type: "generated-index", slug: "integrations/retrievers" }}, { type: "category", label: "Tools", collapsed: true, items: [{type: "autogenerated", dirName: "integrations/tools" }], link: {type: "generated-index", slug: "integrations/tools" }}, - { type: "category", label: "Agents and toolkits", collapsed: true, items: [{type: "autogenerated", dirName: "integrations/toolkits" }], link: {type: "generated-index", slug: "integrations/toolkits" }}, + { type: "category", label: "Toolkits", collapsed: true, items: [{type: "autogenerated", dirName: "integrations/toolkits" }], link: {type: "generated-index", slug: "integrations/toolkits" }}, { type: "category", label: "Memory", collapsed: true, items: [{type: "autogenerated", dirName: "integrations/memory" }], link: {type: "generated-index", slug: "integrations/memory" }}, { type: "category", label: "Callbacks", collapsed: true, items: [{type: "autogenerated", dirName: "integrations/callbacks" }], link: {type: "generated-index", slug: "integrations/callbacks" }}, { type: "category", label: "Chat loaders", collapsed: true, items: [{type: "autogenerated", dirName: "integrations/chat_loaders" }], link: {type: "generated-index", slug: "integrations/chat_loaders" }}, diff --git a/docs/vercel.json b/docs/vercel.json index 3e6b4a78f3a..65a9861a4d5 100644 --- a/docs/vercel.json +++ b/docs/vercel.json @@ -1,5 +1,17 @@ { "redirects": [ + { + "source": "/docs/integrations/llms/watsonxllm", + "destination": "/docs/integrations/llms/ibm_watsonx" + }, + { + "source": "/docs/integrations/llms/pai_eas_endpoint", + "destination": "/docs/integrations/llms/alibabacloud_pai_eas_endpoint" + }, + { + "source": "/docs/integrations/vectorstores/hanavector", + "destination": "/docs/integrations/vectorstores/sap_hanavector" + }, { "source": "/docs/use_cases/qa_structured/sql", "destination": "/docs/use_cases/sql/" @@ -197,8 +209,8 @@ "destination": "/docs/langsmith/walkthrough" }, { - "source": "/docs/modules/data_connection/retrievers/self_query/:path*", - "destination": "/docs/integrations/retrievers/self_query/:path*" + "source": "/docs/modules/data_connection/retrievers/self_query/:path+", + "destination": "/docs/integrations/retrievers/self_query/:path+" }, { "source": "/docs/use_cases/more/agents/autonomous_agents/:path*", @@ -582,11 +594,7 @@ }, { "source": "/docs/integrations/cassandra", - "destination": "/docs/integrations/providers/astradb" - }, - { - "source": "/docs/integrations/providers/cassandra", - "destination": "/docs/integrations/providers/astradb" + "destination": "/docs/integrations/providers/cassandra" }, { "source": "/docs/integrations/providers/providers/semadb", @@ -596,10 +604,6 @@ "source": "/docs/integrations/vectorstores/vectorstores/semadb", "destination": "/docs/integrations/vectorstores/semadb" }, - { - "source": "/docs/integrations/vectorstores/cassandra", - "destination": "/docs/integrations/vectorstores/astradb" - }, { "source": "/docs/integrations/vectorstores/async_faiss", "destination": "/docs/integrations/vectorstores/faiss_async" diff --git a/libs/cli/.gitignore b/libs/cli/.gitignore index 1694d2bc1e9..68bc17f9ff2 100644 --- a/libs/cli/.gitignore +++ b/libs/cli/.gitignore @@ -1,3 +1,160 @@ -dist -__pycache__ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env .venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ diff --git a/libs/cli/langchain_cli/integration_template/LICENSE b/libs/cli/langchain_cli/integration_template/LICENSE index 426b6509034..fc0602feecd 100644 --- a/libs/cli/langchain_cli/integration_template/LICENSE +++ b/libs/cli/langchain_cli/integration_template/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2023 LangChain, Inc. +Copyright (c) 2024 LangChain, Inc. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/libs/cli/langchain_cli/integration_template/Makefile b/libs/cli/langchain_cli/integration_template/Makefile index bed6f5bda53..2837e81a66a 100644 --- a/libs/cli/langchain_cli/integration_template/Makefile +++ b/libs/cli/langchain_cli/integration_template/Makefile @@ -5,16 +5,11 @@ all: help # Define a variable for the test file path. TEST_FILE ?= tests/unit_tests/ +integration_test integration_tests: TEST_FILE = tests/integration_tests/ -integration_tests: TEST_FILE = tests/integration_tests/ - -test integration_tests: +test tests integration_test integration_tests: poetry run pytest $(TEST_FILE) -tests: - poetry run pytest $(TEST_FILE) - - ###################### # LINTING AND FORMATTING ###################### @@ -32,7 +27,7 @@ lint lint_diff lint_package lint_tests: poetry run ruff . poetry run ruff format $(PYTHON_FILES) --diff poetry run ruff --select I $(PYTHON_FILES) - mkdir $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + mkdir -p $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) format format_diff: poetry run ruff format $(PYTHON_FILES) diff --git a/libs/cli/langchain_cli/integration_template/docs/provider.ipynb b/libs/cli/langchain_cli/integration_template/docs/provider.ipynb index 11f770a42b9..a00e11eb2e1 100644 --- a/libs/cli/langchain_cli/integration_template/docs/provider.ipynb +++ b/libs/cli/langchain_cli/integration_template/docs/provider.ipynb @@ -17,9 +17,9 @@ }, "outputs": [], "source": [ - "from __module_name__.chat_models import __ModuleName__Chat\n", - "from __module_name__.llms import __ModuleName__LLM\n", - "from __module_name__.vectorstores import __ModuleName__VectorStore" + "from __module_name__ import Chat__ModuleName__\n", + "from __module_name__ import __ModuleName__LLM\n", + "from __module_name__ import __ModuleName__VectorStore" ] } ], diff --git a/libs/cli/langchain_cli/integration_template/integration_template/chat_models.py b/libs/cli/langchain_cli/integration_template/integration_template/chat_models.py index 3f60c9e6a72..df191524542 100644 --- a/libs/cli/langchain_cli/integration_template/integration_template/chat_models.py +++ b/libs/cli/langchain_cli/integration_template/integration_template/chat_models.py @@ -1,3 +1,4 @@ +"""__ModuleName__ chat models.""" from typing import Any, AsyncIterator, Iterator, List, Optional from langchain_core.callbacks import ( @@ -5,7 +6,7 @@ from langchain_core.callbacks import ( CallbackManagerForLLMRun, ) from langchain_core.language_models.chat_models import BaseChatModel -from langchain_core.messages import BaseMessage, BaseMessageChunk +from langchain_core.messages import BaseMessage from langchain_core.outputs import ChatGenerationChunk, ChatResult @@ -15,40 +16,19 @@ class Chat__ModuleName__(BaseChatModel): Example: .. code-block:: python + from langchain_core.messages import HumanMessage + from __module_name__ import Chat__ModuleName__ - model = Chat__ModuleName__() - """ + model.invoke([HumanMessage(content="Come up with 10 names for a song about parrots.")]) + """ # noqa: E501 @property def _llm_type(self) -> str: """Return type of chat model.""" return "chat-__package_name_short__" - def _stream( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> Iterator[ChatGenerationChunk]: - raise NotImplementedError - - async def _astream( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> AsyncIterator[ChatGenerationChunk]: - yield ChatGenerationChunk( - message=BaseMessageChunk(content="Yield chunks", type="ai"), - ) - yield ChatGenerationChunk( - message=BaseMessageChunk(content=" like this!", type="ai"), - ) - def _generate( self, messages: List[BaseMessage], @@ -58,6 +38,29 @@ class Chat__ModuleName__(BaseChatModel): ) -> ChatResult: raise NotImplementedError + # TODO: Implement if __model_name__ supports streaming. Otherwise delete method. + def _stream( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> Iterator[ChatGenerationChunk]: + raise NotImplementedError + + # TODO: Implement if __model_name__ supports async streaming. Otherwise delete + # method. + async def _astream( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> AsyncIterator[ChatGenerationChunk]: + raise NotImplementedError + + # TODO: Implement if __model_name__ supports async generation. Otherwise delete + # method. async def _agenerate( self, messages: List[BaseMessage], diff --git a/libs/cli/langchain_cli/integration_template/integration_template/llms.py b/libs/cli/langchain_cli/integration_template/integration_template/llms.py index bd8c2fc37fa..562fc0f4001 100644 --- a/libs/cli/langchain_cli/integration_template/integration_template/llms.py +++ b/libs/cli/langchain_cli/integration_template/integration_template/llms.py @@ -1,5 +1,4 @@ -import asyncio -from functools import partial +"""__ModuleName__ large language models.""" from typing import ( Any, AsyncIterator, @@ -25,6 +24,7 @@ class __ModuleName__LLM(BaseLLM): from __module_name__ import __ModuleName__LLM model = __ModuleName__LLM() + model.invoke("Come up with 10 names for a song about parrots") """ @property @@ -41,6 +41,8 @@ class __ModuleName__LLM(BaseLLM): ) -> LLMResult: raise NotImplementedError + # TODO: Implement if __model_name__ supports async generation. Otherwise + # delete method. async def _agenerate( self, prompts: List[str], @@ -48,11 +50,9 @@ class __ModuleName__LLM(BaseLLM): run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: - # Change implementation if integration natively supports async generation. - return await asyncio.get_running_loop().run_in_executor( - None, partial(self._generate, **kwargs), prompts, stop, run_manager - ) + raise NotImplementedError + # TODO: Implement if __model_name__ supports streaming. Otherwise delete method. def _stream( self, prompt: str, @@ -62,6 +62,8 @@ class __ModuleName__LLM(BaseLLM): ) -> Iterator[GenerationChunk]: raise NotImplementedError + # TODO: Implement if __model_name__ supports async streaming. Otherwise delete + # method. async def _astream( self, prompt: str, @@ -69,5 +71,4 @@ class __ModuleName__LLM(BaseLLM): run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> AsyncIterator[GenerationChunk]: - yield GenerationChunk(text="Yield chunks") - yield GenerationChunk(text=" like this!") + raise NotImplementedError diff --git a/libs/cli/langchain_cli/integration_template/integration_template/vectorstores.py b/libs/cli/langchain_cli/integration_template/integration_template/vectorstores.py index 10c22f22b2a..3683d53c4a9 100644 --- a/libs/cli/langchain_cli/integration_template/integration_template/vectorstores.py +++ b/libs/cli/langchain_cli/integration_template/integration_template/vectorstores.py @@ -1,3 +1,4 @@ +"""__ModuleName__ vector stores.""" from __future__ import annotations import asyncio @@ -24,7 +25,7 @@ VST = TypeVar("VST", bound=VectorStore) class __ModuleName__VectorStore(VectorStore): - """Interface for vector store. + """__ModuleName__ vector store. Example: .. code-block:: python diff --git a/libs/cli/langchain_cli/integration_template/pyproject.toml b/libs/cli/langchain_cli/integration_template/pyproject.toml index b4703aa60e0..08a778b2fcd 100644 --- a/libs/cli/langchain_cli/integration_template/pyproject.toml +++ b/libs/cli/langchain_cli/integration_template/pyproject.toml @@ -12,25 +12,21 @@ license = "MIT" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -langchain-core = ">=0.0.12" +langchain-core = "^0.1" [tool.poetry.group.test] optional = true [tool.poetry.group.test.dependencies] -pytest = "^7.3.0" -freezegun = "^1.2.2" -pytest-mock = "^3.10.0" -syrupy = "^4.0.2" -pytest-watcher = "^0.3.4" -pytest-asyncio = "^0.21.1" +pytest = "^7.4.3" +pytest-asyncio = "^0.23.2" langchain-core = {path = "../../core", develop = true} [tool.poetry.group.codespell] optional = true [tool.poetry.group.codespell.dependencies] -codespell = "^2.2.0" +codespell = "^2.2.6" [tool.poetry.group.test_integration] optional = true @@ -41,10 +37,10 @@ optional = true optional = true [tool.poetry.group.lint.dependencies] -ruff = "^0.1.5" +ruff = "^0.1.8" [tool.poetry.group.typing.dependencies] -mypy = "^0.991" +mypy = "^1.7.1" langchain-core = {path = "../../core", develop = true} [tool.poetry.group.dev] @@ -53,11 +49,12 @@ optional = true [tool.poetry.group.dev.dependencies] langchain-core = {path = "../../core", develop = true} -[tool.ruff] +[tool.ruff.lint] select = [ "E", # pycodestyle "F", # pyflakes "I", # isort + "T201", # print ] [tool.mypy] @@ -86,8 +83,6 @@ addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5 # Registering custom markers. # https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers markers = [ - "requires: mark tests as requiring a specific library", - "asyncio: mark tests as requiring asyncio", "compile: mark placeholder test used to compile integration tests without running them", ] asyncio_mode = "auto" diff --git a/libs/cli/langchain_cli/integration_template/scripts/check_imports.py b/libs/cli/langchain_cli/integration_template/scripts/check_imports.py index fd21a4975b7..365f5fa118d 100644 --- a/libs/cli/langchain_cli/integration_template/scripts/check_imports.py +++ b/libs/cli/langchain_cli/integration_template/scripts/check_imports.py @@ -10,8 +10,8 @@ if __name__ == "__main__": SourceFileLoader("x", file).load_module() except Exception: has_faillure = True - print(file) + print(file) # noqa: T201 traceback.print_exc() - print() + print() # noqa: T201 sys.exit(1 if has_failure else 0) diff --git a/libs/cli/langchain_cli/integration_template/scripts/lint_imports.sh b/libs/cli/langchain_cli/integration_template/scripts/lint_imports.sh index 695613c7ba8..19ccec1480c 100755 --- a/libs/cli/langchain_cli/integration_template/scripts/lint_imports.sh +++ b/libs/cli/langchain_cli/integration_template/scripts/lint_imports.sh @@ -5,9 +5,10 @@ set -eu # Initialize a variable to keep track of errors errors=0 -# make sure not importing from langchain or langchain_experimental +# make sure not importing from langchain, langchain_experimental, or langchain_community git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) +git --no-pager grep '^from langchain_community\.' . && errors=$((errors+1)) # Decide on an exit status based on the errors if [ "$errors" -gt 0 ]; then diff --git a/libs/cli/langchain_cli/package_template/LICENSE b/libs/cli/langchain_cli/package_template/LICENSE index 426b6509034..fc0602feecd 100644 --- a/libs/cli/langchain_cli/package_template/LICENSE +++ b/libs/cli/langchain_cli/package_template/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2023 LangChain, Inc. +Copyright (c) 2024 LangChain, Inc. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/libs/cli/pyproject.toml b/libs/cli/pyproject.toml index 43b01fd8f54..24d41085808 100644 --- a/libs/cli/pyproject.toml +++ b/libs/cli/pyproject.toml @@ -40,11 +40,12 @@ ruff = "^0.1.5" # For langserve serve = [] -[tool.ruff] +[tool.ruff.lint] select = [ "E", # pycodestyle "F", # pyflakes "I", # isort + "T201", # print ] [tool.poe.tasks] diff --git a/libs/community/Makefile b/libs/community/Makefile index 900deed91ab..acec04864a6 100644 --- a/libs/community/Makefile +++ b/libs/community/Makefile @@ -5,11 +5,17 @@ all: help # Define a variable for the test file path. TEST_FILE ?= tests/unit_tests/ +integration_tests: TEST_FILE = tests/integration_tests/ -test: - poetry run pytest $(TEST_FILE) +# Run unit tests and generate a coverage report. +coverage: + poetry run pytest --cov \ + --cov-config=.coveragerc \ + --cov-report xml \ + --cov-report term-missing:skip-covered \ + $(TEST_FILE) -tests: +test tests integration_tests: poetry run pytest $(TEST_FILE) test_watch: @@ -41,7 +47,7 @@ lint lint_diff lint_package lint_tests: poetry run ruff . [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff [ "$(PYTHON_FILES)" = "" ] || poetry run ruff --select I $(PYTHON_FILES) - [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) || poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) format format_diff: poetry run ruff format $(PYTHON_FILES) diff --git a/libs/community/_test_minimum_requirements.txt b/libs/community/_test_minimum_requirements.txt deleted file mode 100644 index 1918982f0d8..00000000000 --- a/libs/community/_test_minimum_requirements.txt +++ /dev/null @@ -1 +0,0 @@ -langchain-core==0.1.16 diff --git a/libs/community/langchain_community/adapters/openai.py b/libs/community/langchain_community/adapters/openai.py index 4f03c3a9c45..e6ff136f7a7 100644 --- a/libs/community/langchain_community/adapters/openai.py +++ b/libs/community/langchain_community/adapters/openai.py @@ -40,25 +40,33 @@ async def aenumerate( class IndexableBaseModel(BaseModel): - """Allows a BaseModel to return its fields by string variable indexing""" + """Allows a BaseModel to return its fields by string variable indexing.""" def __getitem__(self, item: str) -> Any: return getattr(self, item) class Choice(IndexableBaseModel): + """Choice.""" + message: dict class ChatCompletions(IndexableBaseModel): + """Chat completions.""" + choices: List[Choice] class ChoiceChunk(IndexableBaseModel): + """Choice chunk.""" + delta: dict class ChatCompletionChunk(IndexableBaseModel): + """Chat completion chunk.""" + choices: List[ChoiceChunk] @@ -301,7 +309,7 @@ def convert_messages_for_finetuning( class Completions: - """Completion.""" + """Completions.""" @overload @staticmethod @@ -399,6 +407,8 @@ class Completions: class Chat: + """Chat.""" + def __init__(self) -> None: self.completions = Completions() diff --git a/libs/community/langchain_community/agent_toolkits/file_management/toolkit.py b/libs/community/langchain_community/agent_toolkits/file_management/toolkit.py index 538569755b5..81071ff5610 100644 --- a/libs/community/langchain_community/agent_toolkits/file_management/toolkit.py +++ b/libs/community/langchain_community/agent_toolkits/file_management/toolkit.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import List, Optional +from typing import Dict, List, Optional, Type from langchain_core.pydantic_v1 import root_validator @@ -14,18 +14,17 @@ from langchain_community.tools.file_management.move import MoveFileTool from langchain_community.tools.file_management.read import ReadFileTool from langchain_community.tools.file_management.write import WriteFileTool -_FILE_TOOLS = { - # "Type[Runnable[Any, Any]]" has no attribute "__fields__" [attr-defined] - tool_cls.__fields__["name"].default: tool_cls # type: ignore[attr-defined] - for tool_cls in [ - CopyFileTool, - DeleteFileTool, - FileSearchTool, - MoveFileTool, - ReadFileTool, - WriteFileTool, - ListDirectoryTool, - ] +_FILE_TOOLS: List[Type[BaseTool]] = [ + CopyFileTool, + DeleteFileTool, + FileSearchTool, + MoveFileTool, + ReadFileTool, + WriteFileTool, + ListDirectoryTool, +] +_FILE_TOOLS_MAP: Dict[str, Type[BaseTool]] = { + tool_cls.__fields__["name"].default: tool_cls for tool_cls in _FILE_TOOLS } @@ -61,20 +60,20 @@ class FileManagementToolkit(BaseToolkit): def validate_tools(cls, values: dict) -> dict: selected_tools = values.get("selected_tools") or [] for tool_name in selected_tools: - if tool_name not in _FILE_TOOLS: + if tool_name not in _FILE_TOOLS_MAP: raise ValueError( f"File Tool of name {tool_name} not supported." - f" Permitted tools: {list(_FILE_TOOLS)}" + f" Permitted tools: {list(_FILE_TOOLS_MAP)}" ) return values def get_tools(self) -> List[BaseTool]: """Get the tools in the toolkit.""" - allowed_tools = self.selected_tools or _FILE_TOOLS.keys() + allowed_tools = self.selected_tools or _FILE_TOOLS_MAP tools: List[BaseTool] = [] for tool in allowed_tools: - tool_cls = _FILE_TOOLS[tool] - tools.append(tool_cls(root_dir=self.root_dir)) # type: ignore + tool_cls = _FILE_TOOLS_MAP[tool] + tools.append(tool_cls(root_dir=self.root_dir)) return tools diff --git a/libs/community/langchain_community/agent_toolkits/openapi/planner.py b/libs/community/langchain_community/agent_toolkits/openapi/planner.py index e95e011e395..c04876514d2 100644 --- a/libs/community/langchain_community/agent_toolkits/openapi/planner.py +++ b/libs/community/langchain_community/agent_toolkits/openapi/planner.py @@ -2,7 +2,7 @@ import json import re from functools import partial -from typing import Any, Callable, Dict, List, Optional +from typing import Any, Callable, Dict, List, Optional, cast import yaml from langchain_core.callbacks import BaseCallbackManager @@ -68,7 +68,7 @@ class RequestsGetToolWithParsing(BaseRequestsTool, BaseTool): """Tool name.""" description = REQUESTS_GET_TOOL_DESCRIPTION """Tool description.""" - response_length: Optional[int] = MAX_RESPONSE_LENGTH + response_length: int = MAX_RESPONSE_LENGTH """Maximum length of the response to be returned.""" llm_chain: Any = Field( default_factory=_get_default_llm_chain_factory(PARSING_GET_PROMPT) @@ -83,7 +83,9 @@ class RequestsGetToolWithParsing(BaseRequestsTool, BaseTool): except json.JSONDecodeError as e: raise e data_params = data.get("params") - response = self.requests_wrapper.get(data["url"], params=data_params) + response: str = cast( + str, self.requests_wrapper.get(data["url"], params=data_params) + ) response = response[: self.response_length] return self.llm_chain.predict( response=response, instructions=data["output_instructions"] @@ -100,7 +102,7 @@ class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool): """Tool name.""" description = REQUESTS_POST_TOOL_DESCRIPTION """Tool description.""" - response_length: Optional[int] = MAX_RESPONSE_LENGTH + response_length: int = MAX_RESPONSE_LENGTH """Maximum length of the response to be returned.""" llm_chain: Any = Field( default_factory=_get_default_llm_chain_factory(PARSING_POST_PROMPT) @@ -114,7 +116,7 @@ class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool): data = parse_json_markdown(text) except json.JSONDecodeError as e: raise e - response = self.requests_wrapper.post(data["url"], data["data"]) + response: str = cast(str, self.requests_wrapper.post(data["url"], data["data"])) response = response[: self.response_length] return self.llm_chain.predict( response=response, instructions=data["output_instructions"] @@ -131,7 +133,7 @@ class RequestsPatchToolWithParsing(BaseRequestsTool, BaseTool): """Tool name.""" description = REQUESTS_PATCH_TOOL_DESCRIPTION """Tool description.""" - response_length: Optional[int] = MAX_RESPONSE_LENGTH + response_length: int = MAX_RESPONSE_LENGTH """Maximum length of the response to be returned.""" llm_chain: Any = Field( default_factory=_get_default_llm_chain_factory(PARSING_PATCH_PROMPT) @@ -145,7 +147,9 @@ class RequestsPatchToolWithParsing(BaseRequestsTool, BaseTool): data = parse_json_markdown(text) except json.JSONDecodeError as e: raise e - response = self.requests_wrapper.patch(data["url"], data["data"]) + response: str = cast( + str, self.requests_wrapper.patch(data["url"], data["data"]) + ) response = response[: self.response_length] return self.llm_chain.predict( response=response, instructions=data["output_instructions"] @@ -162,7 +166,7 @@ class RequestsPutToolWithParsing(BaseRequestsTool, BaseTool): """Tool name.""" description = REQUESTS_PUT_TOOL_DESCRIPTION """Tool description.""" - response_length: Optional[int] = MAX_RESPONSE_LENGTH + response_length: int = MAX_RESPONSE_LENGTH """Maximum length of the response to be returned.""" llm_chain: Any = Field( default_factory=_get_default_llm_chain_factory(PARSING_PUT_PROMPT) @@ -176,7 +180,7 @@ class RequestsPutToolWithParsing(BaseRequestsTool, BaseTool): data = parse_json_markdown(text) except json.JSONDecodeError as e: raise e - response = self.requests_wrapper.put(data["url"], data["data"]) + response: str = cast(str, self.requests_wrapper.put(data["url"], data["data"])) response = response[: self.response_length] return self.llm_chain.predict( response=response, instructions=data["output_instructions"] @@ -187,7 +191,7 @@ class RequestsPutToolWithParsing(BaseRequestsTool, BaseTool): class RequestsDeleteToolWithParsing(BaseRequestsTool, BaseTool): - """A tool that sends a DELETE request and parses the response.""" + """Tool that sends a DELETE request and parses the response.""" name: str = "requests_delete" """The name of the tool.""" @@ -208,7 +212,7 @@ class RequestsDeleteToolWithParsing(BaseRequestsTool, BaseTool): data = parse_json_markdown(text) except json.JSONDecodeError as e: raise e - response = self.requests_wrapper.delete(data["url"]) + response: str = cast(str, self.requests_wrapper.delete(data["url"])) response = response[: self.response_length] return self.llm_chain.predict( response=response, instructions=data["output_instructions"] diff --git a/libs/community/langchain_community/agent_toolkits/powerbi/base.py b/libs/community/langchain_community/agent_toolkits/powerbi/base.py index 06de2b97a7b..486e55f4e7c 100644 --- a/libs/community/langchain_community/agent_toolkits/powerbi/base.py +++ b/libs/community/langchain_community/agent_toolkits/powerbi/base.py @@ -58,7 +58,7 @@ def create_pbi_agent( input_variables=input_variables, **prompt_params, ), - callback_manager=callback_manager, # type: ignore + callback_manager=callback_manager, verbose=verbose, ), allowed_tools=[tool.name for tool in tools], diff --git a/libs/community/langchain_community/agent_toolkits/sql/base.py b/libs/community/langchain_community/agent_toolkits/sql/base.py index 73915732cf1..a4d638b32c2 100644 --- a/libs/community/langchain_community/agent_toolkits/sql/base.py +++ b/libs/community/langchain_community/agent_toolkits/sql/base.py @@ -2,7 +2,17 @@ from __future__ import annotations import warnings -from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Sequence, Union +from typing import ( + TYPE_CHECKING, + Any, + Dict, + List, + Literal, + Optional, + Sequence, + Union, + cast, +) from langchain_core.messages import AIMessage, SystemMessage from langchain_core.prompts import BasePromptTemplate, PromptTemplate @@ -176,8 +186,8 @@ def create_sql_agent( elif agent_type == AgentType.OPENAI_FUNCTIONS: if prompt is None: - messages = [ - SystemMessage(content=prefix), + messages: List = [ + SystemMessage(content=cast(str, prefix)), HumanMessagePromptTemplate.from_template("{input}"), AIMessage(content=suffix or SQL_FUNCTIONS_SUFFIX), MessagesPlaceholder(variable_name="agent_scratchpad"), @@ -191,7 +201,7 @@ def create_sql_agent( elif agent_type == "openai-tools": if prompt is None: messages = [ - SystemMessage(content=prefix), + SystemMessage(content=cast(str, prefix)), HumanMessagePromptTemplate.from_template("{input}"), AIMessage(content=suffix or SQL_FUNCTIONS_SUFFIX), MessagesPlaceholder(variable_name="agent_scratchpad"), diff --git a/libs/community/langchain_community/cache.py b/libs/community/langchain_community/cache.py index b2001682942..5f36aa5856b 100644 --- a/libs/community/langchain_community/cache.py +++ b/libs/community/langchain_community/cache.py @@ -27,22 +27,26 @@ import json import logging import uuid import warnings +from abc import ABC from datetime import timedelta -from functools import lru_cache +from functools import lru_cache, wraps from typing import ( TYPE_CHECKING, Any, + Awaitable, Callable, Dict, + Generator, List, Optional, + Sequence, Tuple, Type, Union, cast, ) -from sqlalchemy import Column, Integer, String, create_engine, select +from sqlalchemy import Column, Integer, String, create_engine, delete, select from sqlalchemy.engine import Row from sqlalchemy.engine.base import Engine from sqlalchemy.orm import Session @@ -54,20 +58,23 @@ except ImportError: from langchain_core.caches import RETURN_VAL_TYPE, BaseCache from langchain_core.embeddings import Embeddings -from langchain_core.language_models.llms import LLM, get_prompts +from langchain_core.language_models.llms import LLM, aget_prompts, get_prompts from langchain_core.load.dump import dumps from langchain_core.load.load import loads from langchain_core.outputs import ChatGeneration, Generation from langchain_core.utils import get_from_env -from langchain_community.utilities.astradb import AstraDBEnvironment +from langchain_community.utilities.astradb import ( + SetupMode, + _AstraDBCollectionEnvironment, +) from langchain_community.vectorstores.redis import Redis as RedisVectorstore logger = logging.getLogger(__file__) if TYPE_CHECKING: import momento - from astrapy.db import AstraDB + from astrapy.db import AstraDB, AsyncAstraDB from cassandra.cluster import Session as CassandraSession @@ -191,6 +198,20 @@ class InMemoryCache(BaseCache): """Clear cache.""" self._cache = {} + async def alookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: + """Look up based on prompt and llm_string.""" + return self.lookup(prompt, llm_string) + + async def aupdate( + self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE + ) -> None: + """Update cache based on prompt and llm_string.""" + self.update(prompt, llm_string, return_val) + + async def aclear(self, **kwargs: Any) -> None: + """Clear cache.""" + self.clear() + Base = declarative_base() @@ -351,49 +372,26 @@ class UpstashRedisCache(BaseCache): self.redis.flushdb(flush_type=asynchronous) -class RedisCache(BaseCache): - """Cache that uses Redis as a backend.""" - - def __init__(self, redis_: Any, *, ttl: Optional[int] = None): - """ - Initialize an instance of RedisCache. - - This method initializes an object with Redis caching capabilities. - It takes a `redis_` parameter, which should be an instance of a Redis - client class, allowing the object to interact with a Redis - server for caching purposes. - - Parameters: - redis_ (Any): An instance of a Redis client class - (e.g., redis.Redis) used for caching. - This allows the object to communicate with a - Redis server for caching operations. - ttl (int, optional): Time-to-live (TTL) for cached items in seconds. - If provided, it sets the time duration for how long cached - items will remain valid. If not provided, cached items will not - have an automatic expiration. - """ - try: - from redis import Redis - except ImportError: - raise ValueError( - "Could not import redis python package. " - "Please install it with `pip install redis`." - ) - if not isinstance(redis_, Redis): - raise ValueError("Please pass in Redis object.") - self.redis = redis_ - self.ttl = ttl - - def _key(self, prompt: str, llm_string: str) -> str: +class _RedisCacheBase(BaseCache, ABC): + @staticmethod + def _key(prompt: str, llm_string: str) -> str: """Compute key from prompt and llm_string""" return _hash(prompt + llm_string) - def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: - """Look up based on prompt and llm_string.""" + @staticmethod + def _ensure_generation_type(return_val: RETURN_VAL_TYPE) -> None: + for gen in return_val: + if not isinstance(gen, Generation): + raise ValueError( + "RedisCache only supports caching of normal LLM generations, " + f"got {type(gen)}" + ) + + @staticmethod + def _get_generations( + results: dict[str | bytes, str | bytes], + ) -> Optional[List[Generation]]: generations = [] - # Read from a Redis HASH - results = self.redis.hgetall(self._key(prompt, llm_string)) if results: for _, text in results.items(): try: @@ -410,28 +408,69 @@ class RedisCache(BaseCache): generations.append(Generation(text=text)) return generations if generations else None + @staticmethod + def _configure_pipeline_for_update( + key: str, pipe: Any, return_val: RETURN_VAL_TYPE, ttl: Optional[int] = None + ) -> None: + pipe.hset( + key, + mapping={ + str(idx): dumps(generation) for idx, generation in enumerate(return_val) + }, + ) + if ttl is not None: + pipe.expire(key, ttl) + + +class RedisCache(_RedisCacheBase): + """ + Cache that uses Redis as a backend. Allows to use a sync `redis.Redis` client. + """ + + def __init__(self, redis_: Any, *, ttl: Optional[int] = None): + """ + Initialize an instance of RedisCache. + + This method initializes an object with Redis caching capabilities. + It takes a `redis_` parameter, which should be an instance of a Redis + client class (`redis.Redis`), allowing the object + to interact with a Redis server for caching purposes. + + Parameters: + redis_ (Any): An instance of a Redis client class + (`redis.Redis`) to be used for caching. + This allows the object to communicate with a + Redis server for caching operations. + ttl (int, optional): Time-to-live (TTL) for cached items in seconds. + If provided, it sets the time duration for how long cached + items will remain valid. If not provided, cached items will not + have an automatic expiration. + """ + try: + from redis import Redis + except ImportError: + raise ValueError( + "Could not import `redis` python package. " + "Please install it with `pip install redis`." + ) + if not isinstance(redis_, Redis): + raise ValueError("Please pass a valid `redis.Redis` client.") + self.redis = redis_ + self.ttl = ttl + + def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: + """Look up based on prompt and llm_string.""" + # Read from a Redis HASH + results = self.redis.hgetall(self._key(prompt, llm_string)) + return self._get_generations(results) # type: ignore[arg-type] + def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" - for gen in return_val: - if not isinstance(gen, Generation): - raise ValueError( - "RedisCache only supports caching of normal LLM generations, " - f"got {type(gen)}" - ) - # Write to a Redis HASH + self._ensure_generation_type(return_val) key = self._key(prompt, llm_string) with self.redis.pipeline() as pipe: - pipe.hset( - key, - mapping={ - str(idx): dumps(generation) - for idx, generation in enumerate(return_val) - }, - ) - if self.ttl is not None: - pipe.expire(key, self.ttl) - + self._configure_pipeline_for_update(key, pipe, return_val, self.ttl) pipe.execute() def clear(self, **kwargs: Any) -> None: @@ -440,6 +479,89 @@ class RedisCache(BaseCache): self.redis.flushdb(asynchronous=asynchronous, **kwargs) +class AsyncRedisCache(_RedisCacheBase): + """ + Cache that uses Redis as a backend. Allows to use an + async `redis.asyncio.Redis` client. + """ + + def __init__(self, redis_: Any, *, ttl: Optional[int] = None): + """ + Initialize an instance of AsyncRedisCache. + + This method initializes an object with Redis caching capabilities. + It takes a `redis_` parameter, which should be an instance of a Redis + client class (`redis.asyncio.Redis`), allowing the object + to interact with a Redis server for caching purposes. + + Parameters: + redis_ (Any): An instance of a Redis client class + (`redis.asyncio.Redis`) to be used for caching. + This allows the object to communicate with a + Redis server for caching operations. + ttl (int, optional): Time-to-live (TTL) for cached items in seconds. + If provided, it sets the time duration for how long cached + items will remain valid. If not provided, cached items will not + have an automatic expiration. + """ + try: + from redis.asyncio import Redis + except ImportError: + raise ValueError( + "Could not import `redis.asyncio` python package. " + "Please install it with `pip install redis`." + ) + if not isinstance(redis_, Redis): + raise ValueError("Please pass a valid `redis.asyncio.Redis` client.") + self.redis = redis_ + self.ttl = ttl + + def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: + """Look up based on prompt and llm_string.""" + raise NotImplementedError( + "This async Redis cache does not implement `lookup()` method. " + "Consider using the async `alookup()` version." + ) + + async def alookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: + """Look up based on prompt and llm_string. Async version.""" + results = await self.redis.hgetall(self._key(prompt, llm_string)) + return self._get_generations(results) # type: ignore[arg-type] + + def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: + """Update cache based on prompt and llm_string.""" + raise NotImplementedError( + "This async Redis cache does not implement `update()` method. " + "Consider using the async `aupdate()` version." + ) + + async def aupdate( + self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE + ) -> None: + """Update cache based on prompt and llm_string. Async version.""" + self._ensure_generation_type(return_val) + key = self._key(prompt, llm_string) + + async with self.redis.pipeline() as pipe: + self._configure_pipeline_for_update(key, pipe, return_val, self.ttl) + await pipe.execute() # type: ignore[attr-defined] + + def clear(self, **kwargs: Any) -> None: + """Clear cache. If `asynchronous` is True, flush asynchronously.""" + raise NotImplementedError( + "This async Redis cache does not implement `clear()` method. " + "Consider using the async `aclear()` version." + ) + + async def aclear(self, **kwargs: Any) -> None: + """ + Clear cache. If `asynchronous` is True, flush asynchronously. + Async version. + """ + asynchronous = kwargs.get("asynchronous", False) + await self.redis.flushdb(asynchronous=asynchronous, **kwargs) + + class RedisSemanticCache(BaseCache): """Cache that uses Redis as a vector-store backend.""" @@ -1192,37 +1314,33 @@ class SQLAlchemyMd5Cache(BaseCache): def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update based on prompt and llm_string.""" - self._delete_previous(prompt, llm_string) - prompt_md5 = self.get_md5(prompt) - items = [ - self.cache_schema( - id=str(uuid.uuid1()), - prompt=prompt, - prompt_md5=prompt_md5, - llm=llm_string, - response=dumps(gen), - idx=i, - ) - for i, gen in enumerate(return_val) - ] with Session(self.engine) as session, session.begin(): + self._delete_previous(session, prompt, llm_string) + prompt_md5 = self.get_md5(prompt) + items = [ + self.cache_schema( + id=str(uuid.uuid1()), + prompt=prompt, + prompt_md5=prompt_md5, + llm=llm_string, + response=dumps(gen), + idx=i, + ) + for i, gen in enumerate(return_val) + ] for item in items: session.merge(item) - def _delete_previous(self, prompt: str, llm_string: str) -> None: + def _delete_previous(self, session: Session, prompt: str, llm_string: str) -> None: stmt = ( - select(self.cache_schema.response) + delete(self.cache_schema) .where(self.cache_schema.prompt_md5 == self.get_md5(prompt)) # type: ignore .where(self.cache_schema.llm == llm_string) .where(self.cache_schema.prompt == prompt) - .order_by(self.cache_schema.idx) ) - with Session(self.engine) as session, session.begin(): - rows = session.execute(stmt).fetchall() - for item in rows: - session.delete(item) + session.execute(stmt) - def _search_rows(self, prompt: str, llm_string: str) -> List[Row]: + def _search_rows(self, prompt: str, llm_string: str) -> Sequence[Row]: prompt_pd5 = self.get_md5(prompt) stmt = ( select(self.cache_schema.response) @@ -1258,6 +1376,10 @@ class AstraDBCache(BaseCache): (needed to prevent same-prompt-different-model collisions) """ + @staticmethod + def _make_id(prompt: str, llm_string: str) -> str: + return f"{_hash(prompt)}#{_hash(llm_string)}" + def __init__( self, *, @@ -1265,7 +1387,10 @@ class AstraDBCache(BaseCache): token: Optional[str] = None, api_endpoint: Optional[str] = None, astra_db_client: Optional[AstraDB] = None, + async_astra_db_client: Optional[AsyncAstraDB] = None, namespace: Optional[str] = None, + pre_delete_collection: bool = False, + setup_mode: SetupMode = SetupMode.SYNC, ): """ Create an AstraDB cache using a collection for storage. @@ -1275,29 +1400,35 @@ class AstraDBCache(BaseCache): token (Optional[str]): API token for Astra DB usage. api_endpoint (Optional[str]): full URL to the API endpoint, such as "https://-us-east1.apps.astra.datastax.com". - astra_db_client (Optional[Any]): *alternative to token+api_endpoint*, + astra_db_client (Optional[AstraDB]): + *alternative to token+api_endpoint*, you can pass an already-created 'astrapy.db.AstraDB' instance. + async_astra_db_client (Optional[AsyncAstraDB]): + *alternative to token+api_endpoint*, + you can pass an already-created 'astrapy.db.AsyncAstraDB' instance. namespace (Optional[str]): namespace (aka keyspace) where the collection is created. Defaults to the database's "default namespace". + pre_delete_collection (bool): whether to delete and re-create the + collection. Defaults to False. + async_setup (bool): whether to create the collection asynchronously. + Enable only if there is a running asyncio event loop. Defaults to False. """ - astra_env = AstraDBEnvironment( + self.astra_env = _AstraDBCollectionEnvironment( + collection_name=collection_name, token=token, api_endpoint=api_endpoint, astra_db_client=astra_db_client, + async_astra_db_client=async_astra_db_client, namespace=namespace, + setup_mode=setup_mode, + pre_delete_collection=pre_delete_collection, ) - self.astra_db = astra_env.astra_db - self.collection = self.astra_db.create_collection( - collection_name=collection_name, - ) - self.collection_name = collection_name - - @staticmethod - def _make_id(prompt: str, llm_string: str) -> str: - return f"{_hash(prompt)}#{_hash(llm_string)}" + self.collection = self.astra_env.collection + self.async_collection = self.astra_env.async_collection def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" + self.astra_env.ensure_db_setup() doc_id = self._make_id(prompt, llm_string) item = self.collection.find_one( filter={ @@ -1307,18 +1438,27 @@ class AstraDBCache(BaseCache): "body_blob": 1, }, )["data"]["document"] - if item is not None: - generations = _loads_generations(item["body_blob"]) - # this protects against malformed cached items: - if generations is not None: - return generations - else: - return None - else: - return None + return _loads_generations(item["body_blob"]) if item is not None else None + + async def alookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: + """Look up based on prompt and llm_string.""" + await self.astra_env.aensure_db_setup() + doc_id = self._make_id(prompt, llm_string) + item = ( + await self.async_collection.find_one( + filter={ + "_id": doc_id, + }, + projection={ + "body_blob": 1, + }, + ) + )["data"]["document"] + return _loads_generations(item["body_blob"]) if item is not None else None def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" + self.astra_env.ensure_db_setup() doc_id = self._make_id(prompt, llm_string) blob = _dumps_generations(return_val) self.collection.upsert( @@ -1328,6 +1468,20 @@ class AstraDBCache(BaseCache): }, ) + async def aupdate( + self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE + ) -> None: + """Update cache based on prompt and llm_string.""" + await self.astra_env.aensure_db_setup() + doc_id = self._make_id(prompt, llm_string) + blob = _dumps_generations(return_val) + await self.async_collection.upsert( + { + "_id": doc_id, + "body_blob": blob, + }, + ) + def delete_through_llm( self, prompt: str, llm: LLM, stop: Optional[List[str]] = None ) -> None: @@ -1341,14 +1495,42 @@ class AstraDBCache(BaseCache): )[1] return self.delete(prompt, llm_string=llm_string) + async def adelete_through_llm( + self, prompt: str, llm: LLM, stop: Optional[List[str]] = None + ) -> None: + """ + A wrapper around `adelete` with the LLM being passed. + In case the llm(prompt) calls have a `stop` param, you should pass it here + """ + llm_string = ( + await aget_prompts( + {**llm.dict(), **{"stop": stop}}, + [], + ) + )[1] + return await self.adelete(prompt, llm_string=llm_string) + def delete(self, prompt: str, llm_string: str) -> None: """Evict from cache if there's an entry.""" + self.astra_env.ensure_db_setup() doc_id = self._make_id(prompt, llm_string) self.collection.delete_one(doc_id) + async def adelete(self, prompt: str, llm_string: str) -> None: + """Evict from cache if there's an entry.""" + await self.astra_env.aensure_db_setup() + doc_id = self._make_id(prompt, llm_string) + await self.async_collection.delete_one(doc_id) + def clear(self, **kwargs: Any) -> None: """Clear cache. This is for all LLMs at once.""" - self.astra_db.truncate_collection(self.collection_name) + self.astra_env.ensure_db_setup() + self.collection.clear() + + async def aclear(self, **kwargs: Any) -> None: + """Clear cache. This is for all LLMs at once.""" + await self.astra_env.aensure_db_setup() + await self.async_collection.clear() ASTRA_DB_SEMANTIC_CACHE_DEFAULT_THRESHOLD = 0.85 @@ -1356,6 +1538,42 @@ ASTRA_DB_CACHE_DEFAULT_COLLECTION_NAME = "langchain_astradb_semantic_cache" ASTRA_DB_SEMANTIC_CACHE_EMBEDDING_CACHE_SIZE = 16 +_unset = ["unset"] + + +class _CachedAwaitable: + """Caches the result of an awaitable so it can be awaited multiple times""" + + def __init__(self, awaitable: Awaitable[Any]): + self.awaitable = awaitable + self.result = _unset + + def __await__(self) -> Generator: + if self.result is _unset: + self.result = yield from self.awaitable.__await__() + return self.result + + +def _reawaitable(func: Callable) -> Callable: + """Makes an async function result awaitable multiple times""" + + @wraps(func) + def wrapper(*args: Any, **kwargs: Any) -> _CachedAwaitable: + return _CachedAwaitable(func(*args, **kwargs)) + + return wrapper + + +def _async_lru_cache(maxsize: int = 128, typed: bool = False) -> Callable: + """Least-recently-used async cache decorator. + Equivalent to functools.lru_cache for async functions""" + + def decorating_function(user_function: Callable) -> Callable: + return lru_cache(maxsize, typed)(_reawaitable(user_function)) + + return decorating_function + + class AstraDBSemanticCache(BaseCache): """ Cache that uses Astra DB as a vector-store backend for semantic @@ -1366,7 +1584,7 @@ class AstraDBSemanticCache(BaseCache): in the document metadata. You can choose the preferred similarity (or use the API default) -- - remember the threshold might require metric-dependend tuning. + remember the threshold might require metric-dependent tuning. """ def __init__( @@ -1376,7 +1594,10 @@ class AstraDBSemanticCache(BaseCache): token: Optional[str] = None, api_endpoint: Optional[str] = None, astra_db_client: Optional[AstraDB] = None, + async_astra_db_client: Optional[AsyncAstraDB] = None, namespace: Optional[str] = None, + setup_mode: SetupMode = SetupMode.SYNC, + pre_delete_collection: bool = False, embedding: Embeddings, metric: Optional[str] = None, similarity_threshold: float = ASTRA_DB_SEMANTIC_CACHE_DEFAULT_THRESHOLD, @@ -1389,10 +1610,17 @@ class AstraDBSemanticCache(BaseCache): token (Optional[str]): API token for Astra DB usage. api_endpoint (Optional[str]): full URL to the API endpoint, such as "https://-us-east1.apps.astra.datastax.com". - astra_db_client (Optional[Any]): *alternative to token+api_endpoint*, + astra_db_client (Optional[AstraDB]): *alternative to token+api_endpoint*, you can pass an already-created 'astrapy.db.AstraDB' instance. + async_astra_db_client (Optional[AsyncAstraDB]): + *alternative to token+api_endpoint*, + you can pass an already-created 'astrapy.db.AsyncAstraDB' instance. namespace (Optional[str]): namespace (aka keyspace) where the collection is created. Defaults to the database's "default namespace". + setup_mode (SetupMode): mode used to create the collection in the DB + (SYNC, ASYNC or OFF). Defaults to SYNC. + pre_delete_collection (bool): whether to delete and re-create the + collection. Defaults to False. embedding (Embedding): Embedding provider for semantic encoding and search. metric: the function to use for evaluating similarity of text embeddings. @@ -1403,17 +1631,10 @@ class AstraDBSemanticCache(BaseCache): The default score threshold is tuned to the default metric. Tune it carefully yourself if switching to another distance metric. """ - astra_env = AstraDBEnvironment( - token=token, - api_endpoint=api_endpoint, - astra_db_client=astra_db_client, - namespace=namespace, - ) - self.astra_db = astra_env.astra_db - self.embedding = embedding self.metric = metric self.similarity_threshold = similarity_threshold + self.collection_name = collection_name # The contract for this class has separate lookup and update: # in order to spare some embedding calculations we cache them between @@ -1425,25 +1646,47 @@ class AstraDBSemanticCache(BaseCache): return self.embedding.embed_query(text=text) self._get_embedding = _cache_embedding - self.embedding_dimension = self._get_embedding_dimension() - self.collection_name = collection_name + @_async_lru_cache(maxsize=ASTRA_DB_SEMANTIC_CACHE_EMBEDDING_CACHE_SIZE) + async def _acache_embedding(text: str) -> List[float]: + return await self.embedding.aembed_query(text=text) - self.collection = self.astra_db.create_collection( - collection_name=self.collection_name, - dimension=self.embedding_dimension, - metric=self.metric, + self._aget_embedding = _acache_embedding + + embedding_dimension: Union[int, Awaitable[int], None] = None + if setup_mode == SetupMode.ASYNC: + embedding_dimension = self._aget_embedding_dimension() + elif setup_mode == SetupMode.SYNC: + embedding_dimension = self._get_embedding_dimension() + + self.astra_env = _AstraDBCollectionEnvironment( + collection_name=collection_name, + token=token, + api_endpoint=api_endpoint, + astra_db_client=astra_db_client, + async_astra_db_client=async_astra_db_client, + namespace=namespace, + setup_mode=setup_mode, + pre_delete_collection=pre_delete_collection, + embedding_dimension=embedding_dimension, + metric=metric, ) + self.collection = self.astra_env.collection + self.async_collection = self.astra_env.async_collection def _get_embedding_dimension(self) -> int: return len(self._get_embedding(text="This is a sample sentence.")) + async def _aget_embedding_dimension(self) -> int: + return len(await self._aget_embedding(text="This is a sample sentence.")) + @staticmethod def _make_id(prompt: str, llm_string: str) -> str: return f"{_hash(prompt)}#{_hash(llm_string)}" def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: """Update cache based on prompt and llm_string.""" + self.astra_env.ensure_db_setup() doc_id = self._make_id(prompt, llm_string) llm_string_hash = _hash(llm_string) embedding_vector = self._get_embedding(text=prompt) @@ -1458,6 +1701,25 @@ class AstraDBSemanticCache(BaseCache): } ) + async def aupdate( + self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE + ) -> None: + """Update cache based on prompt and llm_string.""" + await self.astra_env.aensure_db_setup() + doc_id = self._make_id(prompt, llm_string) + llm_string_hash = _hash(llm_string) + embedding_vector = await self._aget_embedding(text=prompt) + body = _dumps_generations(return_val) + # + await self.async_collection.upsert( + { + "_id": doc_id, + "body_blob": body, + "llm_string_hash": llm_string_hash, + "$vector": embedding_vector, + } + ) + def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: """Look up based on prompt and llm_string.""" hit_with_id = self.lookup_with_id(prompt, llm_string) @@ -1466,6 +1728,14 @@ class AstraDBSemanticCache(BaseCache): else: return None + async def alookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: + """Look up based on prompt and llm_string.""" + hit_with_id = await self.alookup_with_id(prompt, llm_string) + if hit_with_id is not None: + return hit_with_id[1] + else: + return None + def lookup_with_id( self, prompt: str, llm_string: str ) -> Optional[Tuple[str, RETURN_VAL_TYPE]]: @@ -1473,6 +1743,7 @@ class AstraDBSemanticCache(BaseCache): Look up based on prompt and llm_string. If there are hits, return (document_id, cached_entry) for the top hit """ + self.astra_env.ensure_db_setup() prompt_embedding: List[float] = self._get_embedding(text=prompt) llm_string_hash = _hash(llm_string) @@ -1491,7 +1762,37 @@ class AstraDBSemanticCache(BaseCache): generations = _loads_generations(hit["body_blob"]) if generations is not None: # this protects against malformed cached items: - return (hit["_id"], generations) + return hit["_id"], generations + else: + return None + + async def alookup_with_id( + self, prompt: str, llm_string: str + ) -> Optional[Tuple[str, RETURN_VAL_TYPE]]: + """ + Look up based on prompt and llm_string. + If there are hits, return (document_id, cached_entry) for the top hit + """ + await self.astra_env.aensure_db_setup() + prompt_embedding: List[float] = await self._aget_embedding(text=prompt) + llm_string_hash = _hash(llm_string) + + hit = await self.async_collection.vector_find_one( + vector=prompt_embedding, + filter={ + "llm_string_hash": llm_string_hash, + }, + fields=["body_blob", "_id"], + include_similarity=True, + ) + + if hit is None or hit["$similarity"] < self.similarity_threshold: + return None + else: + generations = _loads_generations(hit["body_blob"]) + if generations is not None: + # this protects against malformed cached items: + return hit["_id"], generations else: return None @@ -1504,14 +1805,41 @@ class AstraDBSemanticCache(BaseCache): )[1] return self.lookup_with_id(prompt, llm_string=llm_string) + async def alookup_with_id_through_llm( + self, prompt: str, llm: LLM, stop: Optional[List[str]] = None + ) -> Optional[Tuple[str, RETURN_VAL_TYPE]]: + llm_string = ( + await aget_prompts( + {**llm.dict(), **{"stop": stop}}, + [], + ) + )[1] + return await self.alookup_with_id(prompt, llm_string=llm_string) + def delete_by_document_id(self, document_id: str) -> None: """ Given this is a "similarity search" cache, an invalidation pattern that makes sense is first a lookup to get an ID, and then deleting with that ID. This is for the second step. """ + self.astra_env.ensure_db_setup() self.collection.delete_one(document_id) + async def adelete_by_document_id(self, document_id: str) -> None: + """ + Given this is a "similarity search" cache, an invalidation pattern + that makes sense is first a lookup to get an ID, and then deleting + with that ID. This is for the second step. + """ + await self.astra_env.aensure_db_setup() + await self.async_collection.delete_one(document_id) + def clear(self, **kwargs: Any) -> None: """Clear the *whole* semantic cache.""" - self.astra_db.truncate_collection(self.collection_name) + self.astra_env.ensure_db_setup() + self.collection.clear() + + async def aclear(self, **kwargs: Any) -> None: + """Clear the *whole* semantic cache.""" + await self.astra_env.aensure_db_setup() + await self.async_collection.clear() diff --git a/libs/community/langchain_community/callbacks/aim_callback.py b/libs/community/langchain_community/callbacks/aim_callback.py index da194b34b3d..e36cfcceafe 100644 --- a/libs/community/langchain_community/callbacks/aim_callback.py +++ b/libs/community/langchain_community/callbacks/aim_callback.py @@ -416,15 +416,25 @@ class AimCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler): self._run.close() self.reset_callback_meta() if reset: - self.__init__( # type: ignore - repo=repo if repo else self.repo, - experiment_name=experiment_name - if experiment_name - else self.experiment_name, - system_tracking_interval=system_tracking_interval - if system_tracking_interval - else self.system_tracking_interval, - log_system_params=log_system_params - if log_system_params - else self.log_system_params, + aim = import_aim() + self.repo = repo if repo else self.repo + self.experiment_name = ( + experiment_name if experiment_name else self.experiment_name ) + self.system_tracking_interval = ( + system_tracking_interval + if system_tracking_interval + else self.system_tracking_interval + ) + self.log_system_params = ( + log_system_params if log_system_params else self.log_system_params + ) + + self._run = aim.Run( + repo=self.repo, + experiment=self.experiment_name, + system_tracking_interval=self.system_tracking_interval, + log_system_params=self.log_system_params, + ) + self._run_hash = self._run.hash + self.action_records = [] diff --git a/libs/community/langchain_community/callbacks/argilla_callback.py b/libs/community/langchain_community/callbacks/argilla_callback.py index 942f9858b51..157075a2832 100644 --- a/libs/community/langchain_community/callbacks/argilla_callback.py +++ b/libs/community/langchain_community/callbacks/argilla_callback.py @@ -1,6 +1,6 @@ import os import warnings -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List, Optional, cast from langchain_core.agents import AgentAction, AgentFinish from langchain_core.callbacks import BaseCallbackHandler @@ -269,8 +269,8 @@ class ArgillaCallbackHandler(BaseCallbackHandler): for key in [str(kwargs["parent_run_id"]), str(kwargs["run_id"])] ): return - prompts = self.prompts.get(str(kwargs["parent_run_id"])) or self.prompts.get( - str(kwargs["run_id"]) + prompts: List = self.prompts.get(str(kwargs["parent_run_id"])) or cast( + List, self.prompts.get(str(kwargs["run_id"]), []) ) for chain_output_key, chain_output_val in outputs.items(): if isinstance(chain_output_val, list): @@ -283,10 +283,7 @@ class ArgillaCallbackHandler(BaseCallbackHandler): "response": output["text"].strip(), }, } - for prompt, output in zip( - prompts, # type: ignore - chain_output_val, - ) + for prompt, output in zip(prompts, chain_output_val) ] ) else: @@ -295,7 +292,7 @@ class ArgillaCallbackHandler(BaseCallbackHandler): records=[ { "fields": { - "prompt": " ".join(prompts), # type: ignore + "prompt": " ".join(prompts), "response": chain_output_val.strip(), }, } diff --git a/libs/community/langchain_community/callbacks/arize_callback.py b/libs/community/langchain_community/callbacks/arize_callback.py index 44212b61917..83cba048455 100644 --- a/libs/community/langchain_community/callbacks/arize_callback.py +++ b/libs/community/langchain_community/callbacks/arize_callback.py @@ -49,7 +49,7 @@ class ArizeCallbackHandler(BaseCallbackHandler): if SPACE_KEY == "SPACE_KEY" or API_KEY == "API_KEY": raise ValueError("❌ CHANGE SPACE AND API KEYS") else: - print("✅ Arize client setup done! Now you can start using Arize!") + print("✅ Arize client setup done! Now you can start using Arize!") # noqa: T201 def on_llm_start( self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any @@ -161,9 +161,9 @@ class ArizeCallbackHandler(BaseCallbackHandler): environment=Environments.PRODUCTION, ) if response_from_arize.status_code == 200: - print("✅ Successfully logged data to Arize!") + print("✅ Successfully logged data to Arize!") # noqa: T201 else: - print(f'❌ Logging failed "{response_from_arize.text}"') + print(f'❌ Logging failed "{response_from_arize.text}"') # noqa: T201 def on_llm_error(self, error: BaseException, **kwargs: Any) -> None: """Do nothing.""" diff --git a/libs/community/langchain_community/callbacks/arthur_callback.py b/libs/community/langchain_community/callbacks/arthur_callback.py index a5fce582ed1..b0b28328ab2 100644 --- a/libs/community/langchain_community/callbacks/arthur_callback.py +++ b/libs/community/langchain_community/callbacks/arthur_callback.py @@ -162,7 +162,7 @@ class ArthurCallbackHandler(BaseCallbackHandler): def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: """On LLM end, send data to Arthur.""" try: - import pytz # type: ignore[import] + import pytz except ImportError as e: raise ImportError( "Could not import pytz. Please install it with 'pip install pytz'." diff --git a/libs/community/langchain_community/callbacks/clearml_callback.py b/libs/community/langchain_community/callbacks/clearml_callback.py index 71f30fccdd8..8b8aa8e98ab 100644 --- a/libs/community/langchain_community/callbacks/clearml_callback.py +++ b/libs/community/langchain_community/callbacks/clearml_callback.py @@ -83,7 +83,7 @@ class ClearMLCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler): if clearml.Task.current_task(): self.task = clearml.Task.current_task() else: - self.task = clearml.Task.init( # type: ignore + self.task = clearml.Task.init( task_type=self.task_type, project_name=self.project_name, tags=self.tags, @@ -361,17 +361,13 @@ class ClearMLCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler): if self.visualize and self.nlp and self.temp_dir.name is not None: doc = self.nlp(text) - dep_out = spacy.displacy.render( # type: ignore - doc, style="dep", jupyter=False, page=True - ) + dep_out = spacy.displacy.render(doc, style="dep", jupyter=False, page=True) dep_output_path = Path( self.temp_dir.name, hash_string(f"dep-{text}") + ".html" ) dep_output_path.open("w", encoding="utf-8").write(dep_out) - ent_out = spacy.displacy.render( # type: ignore - doc, style="ent", jupyter=False, page=True - ) + ent_out = spacy.displacy.render(doc, style="ent", jupyter=False, page=True) ent_output_path = Path( self.temp_dir.name, hash_string(f"ent-{text}") + ".html" ) @@ -513,8 +509,8 @@ class ClearMLCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler): target_filename=name, ) except NotImplementedError as e: - print("Could not save model.") - print(repr(e)) + print("Could not save model.") # noqa: T201 + print(repr(e)) # noqa: T201 pass # Cleanup after adding everything to ClearML diff --git a/libs/community/langchain_community/callbacks/comet_ml_callback.py b/libs/community/langchain_community/callbacks/comet_ml_callback.py index 5493c947ae8..499e93a07b0 100644 --- a/libs/community/langchain_community/callbacks/comet_ml_callback.py +++ b/libs/community/langchain_community/callbacks/comet_ml_callback.py @@ -37,7 +37,7 @@ def _get_experiment( ) -> Any: comet_ml = import_comet_ml() - experiment = comet_ml.Experiment( # type: ignore + experiment = comet_ml.Experiment( workspace=workspace, project_name=project_name, ) diff --git a/libs/community/langchain_community/callbacks/confident_callback.py b/libs/community/langchain_community/callbacks/confident_callback.py index d9432e5d567..4162dc12315 100644 --- a/libs/community/langchain_community/callbacks/confident_callback.py +++ b/libs/community/langchain_community/callbacks/confident_callback.py @@ -116,13 +116,13 @@ class DeepEvalCallbackHandler(BaseCallbackHandler): output=output, query=query, ) - print(f"Answer Relevancy: {result}") + print(f"Answer Relevancy: {result}") # noqa: T201 elif isinstance(metric, UnBiasedMetric): score = metric.measure(output) - print(f"Bias Score: {score}") + print(f"Bias Score: {score}") # noqa: T201 elif isinstance(metric, NonToxicMetric): score = metric.measure(output) - print(f"Toxic Score: {score}") + print(f"Toxic Score: {score}") # noqa: T201 else: raise ValueError( f"""Metric {metric.__name__} is not supported by deepeval diff --git a/libs/community/langchain_community/callbacks/flyte_callback.py b/libs/community/langchain_community/callbacks/flyte_callback.py index 23a8f473430..9fd953443f2 100644 --- a/libs/community/langchain_community/callbacks/flyte_callback.py +++ b/libs/community/langchain_community/callbacks/flyte_callback.py @@ -79,12 +79,8 @@ def analyze_text( if nlp is not None: spacy = import_spacy() doc = nlp(text) - dep_out = spacy.displacy.render( # type: ignore - doc, style="dep", jupyter=False, page=True - ) - ent_out = spacy.displacy.render( # type: ignore - doc, style="ent", jupyter=False, page=True - ) + dep_out = spacy.displacy.render(doc, style="dep", jupyter=False, page=True) + ent_out = spacy.displacy.render(doc, style="ent", jupyter=False, page=True) text_visualizations = { "dependency_tree": dep_out, "entities": ent_out, @@ -199,7 +195,7 @@ class FlyteCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler): complexity_metrics: Dict[str, float] = generation_resp.pop( "text_complexity_metrics" - ) # type: ignore # noqa: E501 + ) self.deck.append( self.markdown_renderer().to_html("#### Text Complexity Metrics") ) diff --git a/libs/community/langchain_community/callbacks/infino_callback.py b/libs/community/langchain_community/callbacks/infino_callback.py index 57d756948ef..0b58b598df7 100644 --- a/libs/community/langchain_community/callbacks/infino_callback.py +++ b/libs/community/langchain_community/callbacks/infino_callback.py @@ -4,7 +4,7 @@ from typing import Any, Dict, List, Optional, cast from langchain_core.agents import AgentAction, AgentFinish from langchain_core.callbacks import BaseCallbackHandler from langchain_core.messages import BaseMessage -from langchain_core.outputs import LLMResult +from langchain_core.outputs import ChatGeneration, LLMResult def import_infino() -> Any: @@ -86,7 +86,7 @@ class InfinoCallbackHandler(BaseCallbackHandler): }, } if self.verbose: - print(f"Tracking {key} with Infino: {payload}") + print(f"Tracking {key} with Infino: {payload}") # noqa: T201 # Append to Infino time series only if is_ts is True, otherwise # append to Infino log. @@ -146,7 +146,7 @@ class InfinoCallbackHandler(BaseCallbackHandler): # Track completion token usage (for openai chat models). if self.is_chat_openai_model: messages = " ".join( - generation.message.content # type: ignore[attr-defined] + cast(str, cast(ChatGeneration, generation).message.content) for generation in generations ) completion_tokens = get_num_tokens( @@ -245,7 +245,7 @@ class InfinoCallbackHandler(BaseCallbackHandler): self._send_to_infino("prompt_tokens", prompt_tokens) if self.verbose: - print( + print( # noqa: T201 f"on_chat_model_start: is_chat_openai_model= \ {self.is_chat_openai_model}, \ chat_openai_model_name={self.chat_openai_model_name}" diff --git a/libs/community/langchain_community/callbacks/mlflow_callback.py b/libs/community/langchain_community/callbacks/mlflow_callback.py index bda5aa4054d..b81da0c166a 100644 --- a/libs/community/langchain_community/callbacks/mlflow_callback.py +++ b/libs/community/langchain_community/callbacks/mlflow_callback.py @@ -39,6 +39,7 @@ def import_mlflow() -> Any: def mlflow_callback_metrics() -> List[str]: + """Get the metrics to log to MLFlow.""" return [ "step", "starts", @@ -59,6 +60,7 @@ def mlflow_callback_metrics() -> List[str]: def get_text_complexity_metrics() -> List[str]: + """Get the text complexity metrics from textstat.""" return [ "flesch_reading_ease", "flesch_kincaid_grade", @@ -82,23 +84,21 @@ def get_text_complexity_metrics() -> List[str]: def analyze_text( text: str, nlp: Any = None, + textstat: Any = None, ) -> dict: """Analyze text using textstat and spacy. Parameters: text (str): The text to analyze. nlp (spacy.lang): The spacy language model to use for visualization. + textstat: The textstat library to use for complexity metrics calculation. Returns: (dict): A dictionary containing the complexity metrics and visualization files serialized to HTML string. """ resp: Dict[str, Any] = {} - try: - textstat = import_textstat() - except ImportError: - pass - else: + if textstat is not None: text_complexity_metrics = { key: getattr(textstat, key)(text) for key in get_text_complexity_metrics() } @@ -109,13 +109,9 @@ def analyze_text( spacy = import_spacy() doc = nlp(text) - dep_out = spacy.displacy.render( # type: ignore - doc, style="dep", jupyter=False, page=True - ) + dep_out = spacy.displacy.render(doc, style="dep", jupyter=False, page=True) - ent_out = spacy.displacy.render( # type: ignore - doc, style="ent", jupyter=False, page=True - ) + ent_out = spacy.displacy.render(doc, style="ent", jupyter=False, page=True) text_visualizations = { "dependency_tree": dep_out, @@ -233,7 +229,7 @@ class MlflowLogger: data, os.path.join(self.dir, f"{filename}.json"), run_id=self.run_id ) - def table(self, name: str, dataframe) -> None: # type: ignore + def table(self, name: str, dataframe: Any) -> None: """To log the input pandas dataframe as a html table""" self.html(dataframe.to_html(), f"table_{name}") @@ -308,8 +304,8 @@ class MlflowCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler): self.nlp = None try: spacy = import_spacy() - except ImportError: - pass + except ImportError as e: + logger.warning(e.msg) else: try: self.nlp = spacy.load("en_core_web_sm") @@ -319,6 +315,12 @@ class MlflowCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler): "to download en_core_web_sm model for text visualization." ) + try: + self.textstat = import_textstat() + except ImportError as e: + logger.warning(e.msg) + self.textstat = None + self.metrics = {key: 0 for key in mlflow_callback_metrics()} self.records: Dict[str, Any] = { @@ -407,15 +409,17 @@ class MlflowCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler): analyze_text( generation.text, nlp=self.nlp, + textstat=self.textstat, ) ) - complexity_metrics: Dict[str, float] = generation_resp.pop( - "text_complexity_metrics" - ) # type: ignore # noqa: E501 - self.mlflg.metrics( - complexity_metrics, - step=self.metrics["step"], - ) + if "text_complexity_metrics" in generation_resp: + complexity_metrics: Dict[str, float] = generation_resp.pop( + "text_complexity_metrics" + ) + self.mlflg.metrics( + complexity_metrics, + step=self.metrics["step"], + ) self.records["on_llm_end_records"].append(generation_resp) self.records["action_records"].append(generation_resp) self.mlflg.jsonf(resp, f"llm_end_{llm_ends}_generation_{idx}") @@ -642,9 +646,11 @@ class MlflowCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler): { "page_content": doc.page_content, "metadata": { - k: str(v) - if not isinstance(v, list) - else ",".join(str(x) for x in v) + k: ( + str(v) + if not isinstance(v, list) + else ",".join(str(x) for x in v) + ) for k, v in doc.metadata.items() }, } @@ -685,7 +691,9 @@ class MlflowCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler): .dropna(axis=1) .rename({"step": "prompt_step"}, axis=1) ) - complexity_metrics_columns = get_text_complexity_metrics() + complexity_metrics_columns = ( + get_text_complexity_metrics() if self.textstat is not None else [] + ) visualizations_columns = ( ["dependency_tree", "entities"] if self.nlp is not None else [] ) @@ -723,7 +731,7 @@ class MlflowCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler): ) return session_analysis_df - def _contain_llm_records(self): + def _contain_llm_records(self) -> bool: return bool(self.records["on_llm_start_records"]) def flush_tracker(self, langchain_asset: Any = None, finish: bool = False) -> None: @@ -751,15 +759,15 @@ class MlflowCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler): langchain_asset.save_agent(langchain_asset_path) self.mlflg.artifact(langchain_asset_path) except AttributeError: - print("Could not save model.") + print("Could not save model.") # noqa: T201 traceback.print_exc() pass except NotImplementedError: - print("Could not save model.") + print("Could not save model.") # noqa: T201 traceback.print_exc() pass except NotImplementedError: - print("Could not save model.") + print("Could not save model.") # noqa: T201 traceback.print_exc() pass if finish: diff --git a/libs/community/langchain_community/callbacks/openai_info.py b/libs/community/langchain_community/callbacks/openai_info.py index d18059c1193..6fe210c9f55 100644 --- a/libs/community/langchain_community/callbacks/openai_info.py +++ b/libs/community/langchain_community/callbacks/openai_info.py @@ -15,6 +15,8 @@ MODEL_COST_PER_1K_TOKENS = { "gpt-4-32k-0613": 0.06, "gpt-4-vision-preview": 0.01, "gpt-4-1106-preview": 0.01, + "gpt-4-0125-preview": 0.01, + "gpt-4-turbo-preview": 0.01, # GPT-4 output "gpt-4-completion": 0.06, "gpt-4-0314-completion": 0.06, @@ -24,8 +26,13 @@ MODEL_COST_PER_1K_TOKENS = { "gpt-4-32k-0613-completion": 0.12, "gpt-4-vision-preview-completion": 0.03, "gpt-4-1106-preview-completion": 0.03, + "gpt-4-0125-preview-completion": 0.03, + "gpt-4-turbo-preview-completion": 0.03, # GPT-3.5 input + # gpt-3.5-turbo points at gpt-3.5-turbo-0613 until Feb 16, 2024. + # Switches to gpt-3.5-turbo-0125 after. "gpt-3.5-turbo": 0.0015, + "gpt-3.5-turbo-0125": 0.0005, "gpt-3.5-turbo-0301": 0.0015, "gpt-3.5-turbo-0613": 0.0015, "gpt-3.5-turbo-1106": 0.001, @@ -33,7 +40,10 @@ MODEL_COST_PER_1K_TOKENS = { "gpt-3.5-turbo-16k": 0.003, "gpt-3.5-turbo-16k-0613": 0.003, # GPT-3.5 output + # gpt-3.5-turbo points at gpt-3.5-turbo-0613 until Feb 16, 2024. + # Switches to gpt-3.5-turbo-0125 after. "gpt-3.5-turbo-completion": 0.002, + "gpt-3.5-turbo-0125-completion": 0.0015, "gpt-3.5-turbo-0301-completion": 0.002, "gpt-3.5-turbo-0613-completion": 0.002, "gpt-3.5-turbo-1106-completion": 0.002, diff --git a/libs/community/langchain_community/callbacks/streamlit/__init__.py b/libs/community/langchain_community/callbacks/streamlit/__init__.py index 7a0fadb059d..4ee2ea5a9fb 100644 --- a/libs/community/langchain_community/callbacks/streamlit/__init__.py +++ b/libs/community/langchain_community/callbacks/streamlit/__init__.py @@ -62,7 +62,7 @@ def StreamlitCallbackHandler( # guaranteed to support the same set of kwargs. try: from streamlit.external.langchain import ( - StreamlitCallbackHandler as OfficialStreamlitCallbackHandler, # type: ignore # noqa: 501 + StreamlitCallbackHandler as OfficialStreamlitCallbackHandler, ) return OfficialStreamlitCallbackHandler( diff --git a/libs/community/langchain_community/callbacks/streamlit/mutable_expander.py b/libs/community/langchain_community/callbacks/streamlit/mutable_expander.py index 7de1e9873fe..0bb73f571ad 100644 --- a/libs/community/langchain_community/callbacks/streamlit/mutable_expander.py +++ b/libs/community/langchain_community/callbacks/streamlit/mutable_expander.py @@ -108,7 +108,7 @@ class MutableExpander: ) -> int: """Add a Markdown element to the container and return its index.""" kwargs = {"body": body, "unsafe_allow_html": unsafe_allow_html, "help": help} - new_dg = self._get_dg(index).markdown(**kwargs) # type: ignore[arg-type] + new_dg = self._get_dg(index).markdown(**kwargs) record = ChildRecord(ChildType.MARKDOWN, kwargs, new_dg) return self._add_record(record, index) diff --git a/libs/community/langchain_community/callbacks/streamlit/streamlit_callback_handler.py b/libs/community/langchain_community/callbacks/streamlit/streamlit_callback_handler.py index b336a09a3a6..725862c53b6 100644 --- a/libs/community/langchain_community/callbacks/streamlit/streamlit_callback_handler.py +++ b/libs/community/langchain_community/callbacks/streamlit/streamlit_callback_handler.py @@ -225,7 +225,7 @@ class LLMThought: class StreamlitCallbackHandler(BaseCallbackHandler): - """A callback handler that writes to a Streamlit app.""" + """Callback handler that writes to a Streamlit app.""" def __init__( self, diff --git a/libs/community/langchain_community/callbacks/tracers/wandb.py b/libs/community/langchain_community/callbacks/tracers/wandb.py index 31df3352734..00753a5af1a 100644 --- a/libs/community/langchain_community/callbacks/tracers/wandb.py +++ b/libs/community/langchain_community/callbacks/tracers/wandb.py @@ -489,11 +489,10 @@ class WandbTracer(BaseTracer): If not, will start a new run with the provided run_args. """ if self._wandb.run is None: - run_args = self._run_args or {} # type: ignore - run_args: dict = {**run_args} # type: ignore + run_args: Dict = {**(self._run_args or {})} - if "settings" not in run_args: # type: ignore - run_args["settings"] = {"silent": True} # type: ignore + if "settings" not in run_args: + run_args["settings"] = {"silent": True} self._wandb.init(**run_args) if self._wandb.run is not None: diff --git a/libs/community/langchain_community/callbacks/wandb_callback.py b/libs/community/langchain_community/callbacks/wandb_callback.py index 35559ea539c..035c44640dd 100644 --- a/libs/community/langchain_community/callbacks/wandb_callback.py +++ b/libs/community/langchain_community/callbacks/wandb_callback.py @@ -92,15 +92,11 @@ def analyze_text( if visualize and nlp and output_dir is not None: doc = nlp(text) - dep_out = spacy.displacy.render( # type: ignore - doc, style="dep", jupyter=False, page=True - ) + dep_out = spacy.displacy.render(doc, style="dep", jupyter=False, page=True) dep_output_path = Path(output_dir, hash_string(f"dep-{text}") + ".html") dep_output_path.open("w", encoding="utf-8").write(dep_out) - ent_out = spacy.displacy.render( # type: ignore - doc, style="ent", jupyter=False, page=True - ) + ent_out = spacy.displacy.render(doc, style="ent", jupyter=False, page=True) ent_output_path = Path(output_dir, hash_string(f"ent-{text}") + ".html") ent_output_path.open("w", encoding="utf-8").write(ent_out) @@ -193,7 +189,7 @@ class WandbCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler): self.stream_logs = stream_logs self.temp_dir = tempfile.TemporaryDirectory() - self.run: wandb.sdk.wandb_run.Run = wandb.init( # type: ignore + self.run = wandb.init( job_type=self.job_type, project=self.project, entity=self.entity, @@ -562,8 +558,8 @@ class WandbCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler): model_artifact.add_file(str(langchain_asset_path)) model_artifact.metadata = load_json_to_dict(langchain_asset_path) except NotImplementedError as e: - print("Could not save model.") - print(repr(e)) + print("Could not save model.") # noqa: T201 + print(repr(e)) # noqa: T201 pass self.run.log_artifact(model_artifact) @@ -581,7 +577,9 @@ class WandbCallbackHandler(BaseMetadataCallbackHandler, BaseCallbackHandler): name=name if name else self.name, notes=notes if notes else self.notes, visualize=visualize if visualize else self.visualize, - complexity_metrics=complexity_metrics - if complexity_metrics - else self.complexity_metrics, + complexity_metrics=( + complexity_metrics + if complexity_metrics + else self.complexity_metrics + ), ) diff --git a/libs/community/langchain_community/chat_message_histories/astradb.py b/libs/community/langchain_community/chat_message_histories/astradb.py index 7257476101a..f820480ff26 100644 --- a/libs/community/langchain_community/chat_message_histories/astradb.py +++ b/libs/community/langchain_community/chat_message_histories/astradb.py @@ -3,9 +3,12 @@ from __future__ import annotations import json import time -from typing import TYPE_CHECKING, List, Optional +from typing import TYPE_CHECKING, List, Optional, Sequence -from langchain_community.utilities.astradb import AstraDBEnvironment +from langchain_community.utilities.astradb import ( + SetupMode, + _AstraDBCollectionEnvironment, +) if TYPE_CHECKING: from astrapy.db import AstraDB @@ -45,24 +48,30 @@ class AstraDBChatMessageHistory(BaseChatMessageHistory): api_endpoint: Optional[str] = None, astra_db_client: Optional[AstraDB] = None, namespace: Optional[str] = None, + setup_mode: SetupMode = SetupMode.SYNC, + pre_delete_collection: bool = False, ) -> None: """Create an Astra DB chat message history.""" - astra_env = AstraDBEnvironment( + self.astra_env = _AstraDBCollectionEnvironment( + collection_name=collection_name, token=token, api_endpoint=api_endpoint, astra_db_client=astra_db_client, namespace=namespace, + setup_mode=setup_mode, + pre_delete_collection=pre_delete_collection, ) - self.astra_db = astra_env.astra_db - self.collection = self.astra_db.create_collection(collection_name) + self.collection = self.astra_env.collection + self.async_collection = self.astra_env.async_collection self.session_id = session_id self.collection_name = collection_name @property - def messages(self) -> List[BaseMessage]: # type: ignore + def messages(self) -> List[BaseMessage]: """Retrieve all session messages from DB""" + self.astra_env.ensure_db_setup() message_blobs = [ doc["body_blob"] for doc in sorted( @@ -82,16 +91,63 @@ class AstraDBChatMessageHistory(BaseChatMessageHistory): messages = messages_from_dict(items) return messages - def add_message(self, message: BaseMessage) -> None: + @messages.setter + def messages(self, messages: List[BaseMessage]) -> None: + raise NotImplementedError("Use add_messages instead") + + async def aget_messages(self) -> List[BaseMessage]: + """Retrieve all session messages from DB""" + await self.astra_env.aensure_db_setup() + docs = self.async_collection.paginated_find( + filter={ + "session_id": self.session_id, + }, + projection={ + "timestamp": 1, + "body_blob": 1, + }, + ) + sorted_docs = sorted( + [doc async for doc in docs], + key=lambda _doc: _doc["timestamp"], + ) + message_blobs = [doc["body_blob"] for doc in sorted_docs] + items = [json.loads(message_blob) for message_blob in message_blobs] + messages = messages_from_dict(items) + return messages + + def add_messages(self, messages: Sequence[BaseMessage]) -> None: """Write a message to the table""" - self.collection.insert_one( + self.astra_env.ensure_db_setup() + docs = [ { "timestamp": time.time(), "session_id": self.session_id, "body_blob": json.dumps(message_to_dict(message)), } - ) + for message in messages + ] + self.collection.chunked_insert_many(docs) + + async def aadd_messages(self, messages: Sequence[BaseMessage]) -> None: + """Write a message to the table""" + await self.astra_env.aensure_db_setup() + docs = [ + { + "timestamp": time.time(), + "session_id": self.session_id, + "body_blob": json.dumps(message_to_dict(message)), + } + for message in messages + ] + await self.async_collection.chunked_insert_many(docs) def clear(self) -> None: """Clear session memory from DB""" + self.astra_env.ensure_db_setup() self.collection.delete_many(filter={"session_id": self.session_id}) + + async def aclear(self) -> None: + """Clear session memory from DB""" + await self.astra_env.aensure_db_setup() + await self.async_collection.delete_many(filter={"session_id": self.session_id}) diff --git a/libs/community/langchain_community/chat_message_histories/elasticsearch.py b/libs/community/langchain_community/chat_message_histories/elasticsearch.py index f98515e99dc..bbd621b98d9 100644 --- a/libs/community/langchain_community/chat_message_histories/elasticsearch.py +++ b/libs/community/langchain_community/chat_message_histories/elasticsearch.py @@ -47,7 +47,7 @@ class ElasticsearchChatMessageHistory(BaseChatMessageHistory): ): self.index: str = index self.session_id: str = session_id - self.ensure_ascii: bool = esnsure_ascii + self.ensure_ascii = esnsure_ascii # Initialize Elasticsearch client from passed client arg or connection info if es_connection is not None: @@ -177,7 +177,7 @@ class ElasticsearchChatMessageHistory(BaseChatMessageHistory): "created_at": round(time() * 1000), "history": json.dumps( message_to_dict(message), - ensure_ascii=self.ensure_ascii, + ensure_ascii=bool(self.ensure_ascii), ), }, refresh=True, diff --git a/libs/community/langchain_community/chat_message_histories/in_memory.py b/libs/community/langchain_community/chat_message_histories/in_memory.py index 8c76e850dd9..fe6c6406524 100644 --- a/libs/community/langchain_community/chat_message_histories/in_memory.py +++ b/libs/community/langchain_community/chat_message_histories/in_memory.py @@ -1,4 +1,4 @@ -from typing import List +from typing import List, Sequence from langchain_core.chat_history import BaseChatMessageHistory from langchain_core.messages import BaseMessage @@ -13,9 +13,19 @@ class ChatMessageHistory(BaseChatMessageHistory, BaseModel): messages: List[BaseMessage] = Field(default_factory=list) + async def aget_messages(self) -> List[BaseMessage]: + return self.messages + def add_message(self, message: BaseMessage) -> None: """Add a self-created message to the store""" self.messages.append(message) + async def aadd_messages(self, messages: Sequence[BaseMessage]) -> None: + """Add messages to the store""" + self.add_messages(messages) + def clear(self) -> None: self.messages = [] + + async def aclear(self) -> None: + self.clear() diff --git a/libs/community/langchain_community/chat_message_histories/rocksetdb.py b/libs/community/langchain_community/chat_message_histories/rocksetdb.py index 0391726c4c6..3e48de373ee 100644 --- a/libs/community/langchain_community/chat_message_histories/rocksetdb.py +++ b/libs/community/langchain_community/chat_message_histories/rocksetdb.py @@ -34,7 +34,7 @@ class RocksetChatMessageHistory(BaseChatMessageHistory): history.add_user_message("hi!") history.add_ai_message("whats up?") - print(history.messages) + print(history.messages) # noqa: T201 """ # You should set these values based on your VI. diff --git a/libs/community/langchain_community/chat_message_histories/sql.py b/libs/community/langchain_community/chat_message_histories/sql.py index fcc3ac71ab1..f77deee61e7 100644 --- a/libs/community/langchain_community/chat_message_histories/sql.py +++ b/libs/community/langchain_community/chat_message_histories/sql.py @@ -21,7 +21,7 @@ logger = logging.getLogger(__name__) class BaseMessageConverter(ABC): - """The class responsible for converting BaseMessage to your SQLAlchemy model.""" + """Class that converts BaseMessage to the SQLAlchemy model.""" @abstractmethod def from_sql_model(self, sql_message: Any) -> BaseMessage: @@ -39,7 +39,7 @@ class BaseMessageConverter(ABC): raise NotImplementedError -def create_message_model(table_name, DynamicBase): # type: ignore +def create_message_model(table_name: str, DynamicBase: Any) -> Any: """ Create a message model for a given table name. @@ -52,8 +52,8 @@ def create_message_model(table_name, DynamicBase): # type: ignore """ - # Model decleared inside a function to have a dynamic table name - class Message(DynamicBase): + # Model declared inside a function to have a dynamic table name. + class Message(DynamicBase): # type: ignore[valid-type, misc] __tablename__ = table_name id = Column(Integer, primary_key=True) session_id = Column(Text) diff --git a/libs/community/langchain_community/chat_message_histories/tidb.py b/libs/community/langchain_community/chat_message_histories/tidb.py index bfa36ad06ff..309ab6d7b12 100644 --- a/libs/community/langchain_community/chat_message_histories/tidb.py +++ b/libs/community/langchain_community/chat_message_histories/tidb.py @@ -40,7 +40,7 @@ class TiDBChatMessageHistory(BaseChatMessageHistory): self.session_id = session_id self.table_name = table_name self.earliest_time = earliest_time - self.cache = [] + self.cache: List = [] # Set up SQLAlchemy engine and session self.engine = create_engine(connection_string) @@ -102,7 +102,7 @@ class TiDBChatMessageHistory(BaseChatMessageHistory): logger.error(f"Error loading messages to cache: {e}") @property - def messages(self) -> List[BaseMessage]: + def messages(self) -> List[BaseMessage]: # type: ignore[override] """returns all messages""" if len(self.cache) == 0: self.reload_cache() diff --git a/libs/community/langchain_community/chat_message_histories/zep.py b/libs/community/langchain_community/chat_message_histories/zep.py index 45de39f150e..b38b1e9b49d 100644 --- a/libs/community/langchain_community/chat_message_histories/zep.py +++ b/libs/community/langchain_community/chat_message_histories/zep.py @@ -149,7 +149,7 @@ class ZepChatMessageHistory(BaseChatMessageHistory): return None return zep_memory - def add_user_message( + def add_user_message( # type: ignore[override] self, message: str, metadata: Optional[Dict[str, Any]] = None ) -> None: """Convenience method for adding a human message string to the store. @@ -160,7 +160,7 @@ class ZepChatMessageHistory(BaseChatMessageHistory): """ self.add_message(HumanMessage(content=message), metadata=metadata) - def add_ai_message( + def add_ai_message( # type: ignore[override] self, message: str, metadata: Optional[Dict[str, Any]] = None ) -> None: """Convenience method for adding an AI message string to the store. diff --git a/libs/community/langchain_community/chat_models/__init__.py b/libs/community/langchain_community/chat_models/__init__.py index c90ecaa0f93..8a8fd1fd78d 100644 --- a/libs/community/langchain_community/chat_models/__init__.py +++ b/libs/community/langchain_community/chat_models/__init__.py @@ -54,6 +54,7 @@ from langchain_community.chat_models.tongyi import ChatTongyi from langchain_community.chat_models.vertexai import ChatVertexAI from langchain_community.chat_models.volcengine_maas import VolcEngineMaasChat from langchain_community.chat_models.yandex import ChatYandexGPT +from langchain_community.chat_models.yuan2 import ChatYuan2 from langchain_community.chat_models.zhipuai import ChatZhipuAI __all__ = [ @@ -94,5 +95,6 @@ __all__ = [ "ChatSparkLLM", "VolcEngineMaasChat", "GPTRouter", + "ChatYuan2", "ChatZhipuAI", ] diff --git a/libs/community/langchain_community/chat_models/anyscale.py b/libs/community/langchain_community/chat_models/anyscale.py index 40d36aa187d..c2916ccf71e 100644 --- a/libs/community/langchain_community/chat_models/anyscale.py +++ b/libs/community/langchain_community/chat_models/anyscale.py @@ -23,7 +23,6 @@ if TYPE_CHECKING: logger = logging.getLogger(__name__) - DEFAULT_API_BASE = "https://api.endpoints.anyscale.com/v1" DEFAULT_MODEL = "meta-llama/Llama-2-7b-chat-hf" @@ -60,7 +59,7 @@ class ChatAnyscale(ChatOpenAI): def is_lc_serializable(cls) -> bool: return False - anyscale_api_key: SecretStr + anyscale_api_key: SecretStr = Field(default=None) """AnyScale Endpoints API keys.""" model_name: str = Field(default=DEFAULT_MODEL, alias="model") """Model name to use.""" @@ -102,14 +101,9 @@ class ChatAnyscale(ChatOpenAI): return {model["id"] for model in models_response.json()["data"]} - @root_validator(pre=True) - def validate_environment_override(cls, values: dict) -> dict: + @root_validator() + def validate_environment(cls, values: dict) -> dict: """Validate that api key and python package exists in environment.""" - values["openai_api_key"] = get_from_dict_or_env( - values, - "anyscale_api_key", - "ANYSCALE_API_KEY", - ) values["anyscale_api_key"] = convert_to_secret_str( get_from_dict_or_env( values, @@ -117,7 +111,7 @@ class ChatAnyscale(ChatOpenAI): "ANYSCALE_API_KEY", ) ) - values["openai_api_base"] = get_from_dict_or_env( + values["anyscale_api_base"] = get_from_dict_or_env( values, "anyscale_api_base", "ANYSCALE_API_BASE", @@ -140,8 +134,8 @@ class ChatAnyscale(ChatOpenAI): try: if is_openai_v1(): client_params = { - "api_key": values["openai_api_key"], - "base_url": values["openai_api_base"], + "api_key": values["anyscale_api_key"].get_secret_value(), + "base_url": values["anyscale_api_base"], # To do: future support # "organization": values["openai_organization"], # "timeout": values["request_timeout"], @@ -152,6 +146,8 @@ class ChatAnyscale(ChatOpenAI): } values["client"] = openai.OpenAI(**client_params).chat.completions else: + values["openai_api_base"] = values["anyscale_api_base"] + values["openai_api_key"] = values["anyscale_api_key"].get_secret_value() values["client"] = openai.ChatCompletion except AttributeError as exc: raise ValueError( @@ -164,10 +160,9 @@ class ChatAnyscale(ChatOpenAI): values["model_name"] = DEFAULT_MODEL model_name = values["model_name"] - available_models = cls.get_available_models( - values["openai_api_key"], - values["openai_api_base"], + values["anyscale_api_key"].get_secret_value(), + values["anyscale_api_base"], ) if model_name not in available_models: @@ -197,9 +192,8 @@ class ChatAnyscale(ChatOpenAI): def get_num_tokens_from_messages(self, messages: list[BaseMessage]) -> int: """Calculate num tokens with tiktoken package. - - Official documentation: https://github.com/openai/openai-cookbook/blob/ - main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb""" + Official documentation: https://github.com/openai/openai-cookbook/blob/main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb + """ if sys.version_info[1] <= 7: return super().get_num_tokens_from_messages(messages) model, encoding = self._get_encoding_model() diff --git a/libs/community/langchain_community/chat_models/azureml_endpoint.py b/libs/community/langchain_community/chat_models/azureml_endpoint.py index 58192d6cdce..dc1385411f9 100644 --- a/libs/community/langchain_community/chat_models/azureml_endpoint.py +++ b/libs/community/langchain_community/chat_models/azureml_endpoint.py @@ -20,7 +20,9 @@ from langchain_community.llms.azureml_endpoint import ( class LlamaContentFormatter(ContentFormatterBase): - def __init__(self): + """Content formatter for `LLaMA`.""" + + def __init__(self) -> None: raise TypeError( "`LlamaContentFormatter` is deprecated for chat models. Use " "`LlamaChatContentFormatter` instead." @@ -34,7 +36,7 @@ class LlamaChatContentFormatter(ContentFormatterBase): @staticmethod def _convert_message_to_dict(message: BaseMessage) -> Dict: - """Converts message to a dict according to role""" + """Converts a message to a dict according to a role""" content = cast(str, message.content) if isinstance(message, HumanMessage): return { @@ -72,12 +74,12 @@ class LlamaChatContentFormatter(ContentFormatterBase): def supported_api_types(self) -> List[AzureMLEndpointApiType]: return [AzureMLEndpointApiType.realtime, AzureMLEndpointApiType.serverless] - def format_request_payload( + def format_messages_request_payload( self, messages: List[BaseMessage], model_kwargs: Dict, api_type: AzureMLEndpointApiType, - ) -> str: + ) -> bytes: """Formats the request according to the chosen api""" chat_messages = [ LlamaChatContentFormatter._convert_message_to_dict(message) @@ -101,7 +103,9 @@ class LlamaChatContentFormatter(ContentFormatterBase): return str.encode(request_payload) def format_response_payload( - self, output: bytes, api_type: AzureMLEndpointApiType + self, + output: bytes, + api_type: AzureMLEndpointApiType = AzureMLEndpointApiType.realtime, ) -> ChatGeneration: """Formats response""" if api_type == AzureMLEndpointApiType.realtime: @@ -187,7 +191,7 @@ class AzureMLChatOnlineEndpoint(BaseChatModel, AzureMLBaseEndpoint): if stop: _model_kwargs["stop"] = stop - request_payload = self.content_formatter.format_request_payload( + request_payload = self.content_formatter.format_messages_request_payload( messages, _model_kwargs, self.endpoint_api_type ) response_payload = self.http_client.call( diff --git a/libs/community/langchain_community/chat_models/deepinfra.py b/libs/community/langchain_community/chat_models/deepinfra.py index 7b8a40d3c11..06610e59260 100644 --- a/libs/community/langchain_community/chat_models/deepinfra.py +++ b/libs/community/langchain_community/chat_models/deepinfra.py @@ -1,4 +1,5 @@ """deepinfra.com chat models wrapper""" + from __future__ import annotations import json @@ -58,6 +59,8 @@ logger = logging.getLogger(__name__) class ChatDeepInfraException(Exception): + """Exception raised when the DeepInfra API returns an error.""" + pass @@ -67,7 +70,7 @@ def _create_retry_decorator( Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun] ] = None, ) -> Callable[[Any], Any]: - """Returns a tenacity retry decorator, preconfigured to handle PaLM exceptions""" + """Returns a tenacity retry decorator, preconfigured to handle PaLM exceptions.""" return create_base_retry_decorator( error_types=[requests.exceptions.ConnectTimeout, ChatDeepInfraException], max_retries=llm.max_retries, @@ -205,7 +208,7 @@ class ChatDeepInfra(BaseChatModel): return response except Exception as e: # import pdb; pdb.set_trace() - print("EX", e) + print("EX", e) # noqa: T201 raise return _completion_with_retry(**kwargs) @@ -229,7 +232,7 @@ class ChatDeepInfra(BaseChatModel): self._handle_status(response.status, response.text) return await response.json() except Exception as e: - print("EX", e) + print("EX", e) # noqa: T201 raise return await _completion_with_retry(**kwargs) @@ -327,7 +330,7 @@ class ChatDeepInfra(BaseChatModel): if chunk: yield ChatGenerationChunk(message=chunk, generation_info=None) if run_manager: - run_manager.on_llm_new_token(chunk.content) # type: ignore[arg-type] + run_manager.on_llm_new_token(str(chunk.content)) async def _astream( self, @@ -349,7 +352,7 @@ class ChatDeepInfra(BaseChatModel): if chunk: yield ChatGenerationChunk(message=chunk, generation_info=None) if run_manager: - await run_manager.on_llm_new_token(chunk.content) # type: ignore[arg-type] + await run_manager.on_llm_new_token(str(chunk.content)) async def _agenerate( self, diff --git a/libs/community/langchain_community/chat_models/edenai.py b/libs/community/langchain_community/chat_models/edenai.py index 8c72f1d791d..3e329d91d7c 100644 --- a/libs/community/langchain_community/chat_models/edenai.py +++ b/libs/community/langchain_community/chat_models/edenai.py @@ -165,6 +165,12 @@ class ChatEdenAI(BaseChatModel): """Return type of chat model.""" return "edenai-chat" + @property + def _api_key(self) -> str: + if self.edenai_api_key: + return self.edenai_api_key.get_secret_value() + return "" + def _stream( self, messages: List[BaseMessage], @@ -175,7 +181,7 @@ class ChatEdenAI(BaseChatModel): """Call out to EdenAI's chat endpoint.""" url = f"{self.edenai_api_url}/text/chat/stream" headers = { - "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}", + "Authorization": f"Bearer {self._api_key}", "User-Agent": self.get_user_agent(), } formatted_data = _format_edenai_messages(messages=messages) @@ -216,7 +222,7 @@ class ChatEdenAI(BaseChatModel): ) -> AsyncIterator[ChatGenerationChunk]: url = f"{self.edenai_api_url}/text/chat/stream" headers = { - "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}", + "Authorization": f"Bearer {self._api_key}", "User-Agent": self.get_user_agent(), } formatted_data = _format_edenai_messages(messages=messages) @@ -265,7 +271,7 @@ class ChatEdenAI(BaseChatModel): url = f"{self.edenai_api_url}/text/chat" headers = { - "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}", + "Authorization": f"Bearer {self._api_key}", "User-Agent": self.get_user_agent(), } formatted_data = _format_edenai_messages(messages=messages) @@ -323,7 +329,7 @@ class ChatEdenAI(BaseChatModel): url = f"{self.edenai_api_url}/text/chat" headers = { - "Authorization": f"Bearer {self.edenai_api_key.get_secret_value()}", + "Authorization": f"Bearer {self._api_key}", "User-Agent": self.get_user_agent(), } formatted_data = _format_edenai_messages(messages=messages) diff --git a/libs/community/langchain_community/chat_models/ernie.py b/libs/community/langchain_community/chat_models/ernie.py index 9954ef4272f..8766038e3b1 100644 --- a/libs/community/langchain_community/chat_models/ernie.py +++ b/libs/community/langchain_community/chat_models/ernie.py @@ -214,7 +214,7 @@ class ErnieBotChat(BaseChatModel): generations = [ ChatGeneration( message=AIMessage( - content=response.get("result"), + content=response.get("result", ""), additional_kwargs={**additional_kwargs}, ) ) diff --git a/libs/community/langchain_community/chat_models/gpt_router.py b/libs/community/langchain_community/chat_models/gpt_router.py index 498d8542c8d..fe919f49693 100644 --- a/libs/community/langchain_community/chat_models/gpt_router.py +++ b/libs/community/langchain_community/chat_models/gpt_router.py @@ -14,6 +14,7 @@ from typing import ( Mapping, Optional, Tuple, + Type, Union, ) @@ -27,7 +28,7 @@ from langchain_core.language_models.chat_models import ( generate_from_stream, ) from langchain_core.language_models.llms import create_base_retry_decorator -from langchain_core.messages import AIMessageChunk, BaseMessage +from langchain_core.messages import AIMessageChunk, BaseMessage, BaseMessageChunk from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env @@ -52,13 +53,15 @@ class GPTRouterException(Exception): class GPTRouterModel(BaseModel): + """GPTRouter model.""" + name: str provider_name: str def get_ordered_generation_requests( - models_priority_list: List[GPTRouterModel], **kwargs -): + models_priority_list: List[GPTRouterModel], **kwargs: Any +) -> List: """ Return the body for the model router input. """ @@ -100,7 +103,7 @@ def completion_with_retry( models_priority_list: List[GPTRouterModel], run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, -) -> Union[GenerationResponse, Generator[ChunkedGenerationResponse]]: +) -> Union[GenerationResponse, Generator[ChunkedGenerationResponse, None, None]]: """Use tenacity to retry the completion call.""" retry_decorator = _create_retry_decorator(llm, run_manager=run_manager) @@ -122,7 +125,7 @@ async def acompletion_with_retry( models_priority_list: List[GPTRouterModel], run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, -) -> Union[GenerationResponse, AsyncGenerator[ChunkedGenerationResponse]]: +) -> Union[GenerationResponse, AsyncGenerator[ChunkedGenerationResponse, None]]: """Use tenacity to retry the async completion call.""" retry_decorator = _create_retry_decorator(llm, run_manager=run_manager) @@ -283,8 +286,8 @@ class GPTRouter(BaseChatModel): return self._create_chat_result(response) def _create_chat_generation_chunk( - self, data: Mapping[str, Any], default_chunk_class - ): + self, data: Mapping[str, Any], default_chunk_class: Type[BaseMessageChunk] + ) -> Tuple[ChatGenerationChunk, Type[BaseMessageChunk]]: chunk = _convert_delta_to_message_chunk( {"content": data.get("text", "")}, default_chunk_class ) @@ -293,8 +296,8 @@ class GPTRouter(BaseChatModel): dict(finish_reason=finish_reason) if finish_reason is not None else None ) default_chunk_class = chunk.__class__ - chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info) - return chunk, default_chunk_class + gen_chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info) + return gen_chunk, default_chunk_class def _stream( self, @@ -306,7 +309,7 @@ class GPTRouter(BaseChatModel): message_dicts, params = self._create_message_dicts(messages, stop) params = {**params, **kwargs, "stream": True} - default_chunk_class = AIMessageChunk + default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk generator_response = completion_with_retry( self, messages=message_dicts, @@ -339,7 +342,7 @@ class GPTRouter(BaseChatModel): message_dicts, params = self._create_message_dicts(messages, stop) params = {**params, **kwargs, "stream": True} - default_chunk_class = AIMessageChunk + default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk generator_response = acompletion_with_retry( self, messages=message_dicts, diff --git a/libs/community/langchain_community/chat_models/huggingface.py b/libs/community/langchain_community/chat_models/huggingface.py index b83184ac6e5..0af34a8cf04 100644 --- a/libs/community/langchain_community/chat_models/huggingface.py +++ b/libs/community/langchain_community/chat_models/huggingface.py @@ -44,7 +44,7 @@ class ChatHuggingFace(BaseChatModel): llm: Union[HuggingFaceTextGenInference, HuggingFaceEndpoint, HuggingFaceHub] system_message: SystemMessage = SystemMessage(content=DEFAULT_SYSTEM_PROMPT) tokenizer: Any = None - model_id: str = None # type: ignore + model_id: Optional[str] = None def __init__(self, **kwargs: Any): super().__init__(**kwargs) diff --git a/libs/community/langchain_community/chat_models/human.py b/libs/community/langchain_community/chat_models/human.py index e0294746934..030516a822f 100644 --- a/libs/community/langchain_community/chat_models/human.py +++ b/libs/community/langchain_community/chat_models/human.py @@ -1,4 +1,5 @@ """ChatModel wrapper which returns user input as the response..""" + from io import StringIO from typing import Any, Callable, Dict, List, Mapping, Optional @@ -30,9 +31,9 @@ def _display_messages(messages: List[BaseMessage]) -> None: width=10000, line_break=None, ) - print("\n", "======= start of message =======", "\n\n") - print(yaml_string) - print("======= end of message =======", "\n\n") + print("\n", "======= start of message =======", "\n\n") # noqa: T201 + print(yaml_string) # noqa: T201 + print("======= end of message =======", "\n\n") # noqa: T201 def _collect_yaml_input( diff --git a/libs/community/langchain_community/chat_models/javelin_ai_gateway.py b/libs/community/langchain_community/chat_models/javelin_ai_gateway.py index 6b7001b6260..6c90103fae9 100644 --- a/libs/community/langchain_community/chat_models/javelin_ai_gateway.py +++ b/libs/community/langchain_community/chat_models/javelin_ai_gateway.py @@ -25,7 +25,7 @@ logger = logging.getLogger(__name__) # Ignoring type because below is valid pydantic code # Unexpected keyword argument "extra" for "__init_subclass__" of "object" [call-arg] -class ChatParams(BaseModel, extra=Extra.allow): # type: ignore[call-arg] +class ChatParams(BaseModel, extra=Extra.allow): """Parameters for the `Javelin AI Gateway` LLM.""" temperature: float = 0.0 diff --git a/libs/community/langchain_community/chat_models/konko.py b/libs/community/langchain_community/chat_models/konko.py index 8492a5f8c56..4f385148c68 100644 --- a/libs/community/langchain_community/chat_models/konko.py +++ b/libs/community/langchain_community/chat_models/konko.py @@ -13,6 +13,7 @@ from typing import ( Set, Tuple, Union, + cast, ) import requests @@ -169,7 +170,9 @@ class ChatKonko(ChatOpenAI): } if openai_api_key: - headers["X-OpenAI-Api-Key"] = openai_api_key.get_secret_value() + headers["X-OpenAI-Api-Key"] = cast( + SecretStr, openai_api_key + ).get_secret_value() models_response = requests.get(models_url, headers=headers) diff --git a/libs/community/langchain_community/chat_models/mlflow_ai_gateway.py b/libs/community/langchain_community/chat_models/mlflow_ai_gateway.py index 39ad6b550b1..35321655a4f 100644 --- a/libs/community/langchain_community/chat_models/mlflow_ai_gateway.py +++ b/libs/community/langchain_community/chat_models/mlflow_ai_gateway.py @@ -25,7 +25,7 @@ logger = logging.getLogger(__name__) # Ignoring type because below is valid pydantic code # Unexpected keyword argument "extra" for "__init_subclass__" of "object" [call-arg] -class ChatParams(BaseModel, extra=Extra.allow): # type: ignore[call-arg] +class ChatParams(BaseModel, extra=Extra.allow): """Parameters for the `MLflow AI Gateway` LLM.""" temperature: float = 0.0 diff --git a/libs/community/langchain_community/chat_models/ollama.py b/libs/community/langchain_community/chat_models/ollama.py index e0327e7e50c..73a194a90f9 100644 --- a/libs/community/langchain_community/chat_models/ollama.py +++ b/libs/community/langchain_community/chat_models/ollama.py @@ -1,5 +1,5 @@ import json -from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Union +from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, Union, cast from langchain_core._api import deprecated from langchain_core.callbacks import ( @@ -74,10 +74,15 @@ class ChatOllama(BaseChatModel, _OllamaCommon): if isinstance(message, ChatMessage): message_text = f"\n\n{message.role.capitalize()}: {message.content}" elif isinstance(message, HumanMessage): - if message.content[0].get("type") == "text": - message_text = f"[INST] {message.content[0]['text']} [/INST]" - elif message.content[0].get("type") == "image_url": - message_text = message.content[0]["image_url"]["url"] + if isinstance(message.content, List): + first_content = cast(List[Dict], message.content)[0] + content_type = first_content.get("type") + if content_type == "text": + message_text = f"[INST] {first_content['text']} [/INST]" + elif content_type == "image_url": + message_text = first_content["image_url"]["url"] + else: + message_text = f"[INST] {message.content} [/INST]" elif isinstance(message, AIMessage): message_text = f"{message.content}" elif isinstance(message, SystemMessage): @@ -94,7 +99,7 @@ class ChatOllama(BaseChatModel, _OllamaCommon): def _convert_messages_to_ollama_messages( self, messages: List[BaseMessage] ) -> List[Dict[str, Union[str, List[str]]]]: - ollama_messages = [] + ollama_messages: List = [] for message in messages: role = "" if isinstance(message, HumanMessage): @@ -111,7 +116,7 @@ class ChatOllama(BaseChatModel, _OllamaCommon): if isinstance(message.content, str): content = message.content else: - for content_part in message.content: + for content_part in cast(List[Dict], message.content): if content_part.get("type") == "text": content += f"\n{content_part['text']}" elif content_part.get("type") == "image_url": @@ -308,12 +313,12 @@ class ChatOllama(BaseChatModel, _OllamaCommon): for stream_resp in self._create_chat_stream(messages, stop, **kwargs): if stream_resp: chunk = _chat_stream_response_to_chat_generation_chunk(stream_resp) - yield chunk if run_manager: run_manager.on_llm_new_token( chunk.text, verbose=self.verbose, ) + yield chunk except OllamaEndpointNotFoundError: yield from self._legacy_stream(messages, stop, **kwargs) @@ -324,20 +329,14 @@ class ChatOllama(BaseChatModel, _OllamaCommon): run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> AsyncIterator[ChatGenerationChunk]: - try: - async for stream_resp in self._acreate_chat_stream( - messages, stop, **kwargs - ): - if stream_resp: - chunk = _chat_stream_response_to_chat_generation_chunk(stream_resp) - yield chunk - if run_manager: - await run_manager.on_llm_new_token( - chunk.text, - verbose=self.verbose, - ) - except OllamaEndpointNotFoundError: - async for chunk in self._legacy_astream(messages, stop, **kwargs): + async for stream_resp in self._acreate_chat_stream(messages, stop, **kwargs): + if stream_resp: + chunk = _chat_stream_response_to_chat_generation_chunk(stream_resp) + if run_manager: + await run_manager.on_llm_new_token( + chunk.text, + verbose=self.verbose, + ) yield chunk @deprecated("0.0.3", alternative="_stream") @@ -352,9 +351,9 @@ class ChatOllama(BaseChatModel, _OllamaCommon): for stream_resp in self._create_generate_stream(prompt, stop, **kwargs): if stream_resp: chunk = _stream_response_to_chat_generation_chunk(stream_resp) - yield chunk if run_manager: run_manager.on_llm_new_token( chunk.text, verbose=self.verbose, ) + yield chunk diff --git a/libs/community/langchain_community/chat_models/openai.py b/libs/community/langchain_community/chat_models/openai.py index 568767662c9..0279d03ed97 100644 --- a/libs/community/langchain_community/chat_models/openai.py +++ b/libs/community/langchain_community/chat_models/openai.py @@ -554,7 +554,7 @@ class ChatOpenAI(BaseChatModel): if self.openai_proxy: import openai - openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy} # type: ignore[assignment] # noqa: E501 + openai.proxy = {"http": self.openai_proxy, "https": self.openai_proxy} return {**self._default_params, **openai_creds} def _get_invocation_params( diff --git a/libs/community/langchain_community/chat_models/tongyi.py b/libs/community/langchain_community/chat_models/tongyi.py index 004ffdd3b12..9d76e8b1203 100644 --- a/libs/community/langchain_community/chat_models/tongyi.py +++ b/libs/community/langchain_community/chat_models/tongyi.py @@ -13,6 +13,7 @@ from typing import ( Mapping, Optional, Union, + cast, ) from langchain_core.callbacks import ( @@ -197,7 +198,7 @@ class ChatTongyi(BaseChatModel): return { "model": self.model_name, "top_p": self.top_p, - "api_key": self.dashscope_api_key.get_secret_value(), + "api_key": cast(SecretStr, self.dashscope_api_key).get_secret_value(), "result_format": "message", **self.model_kwargs, } diff --git a/libs/community/langchain_community/chat_models/vertexai.py b/libs/community/langchain_community/chat_models/vertexai.py index cecd63ac41f..901123fcf9e 100644 --- a/libs/community/langchain_community/chat_models/vertexai.py +++ b/libs/community/langchain_community/chat_models/vertexai.py @@ -120,11 +120,10 @@ def _parse_chat_history_gemini( image = load_image_from_gcs(path=path, project=project) elif path.startswith("data:image/"): # extract base64 component from image uri - try: - encoded = re.search(r"data:image/\w{2,4};base64,(.*)", path).group( - 1 - ) - except AttributeError: + encoded: Any = re.search(r"data:image/\w{2,4};base64,(.*)", path) + if encoded: + encoded = encoded.group(1) + else: raise ValueError( "Invalid image uri. It should be in the format " "data:image/;base64,." diff --git a/libs/community/langchain_community/chat_models/volcengine_maas.py b/libs/community/langchain_community/chat_models/volcengine_maas.py index 7d821713bff..ddaf849c750 100644 --- a/libs/community/langchain_community/chat_models/volcengine_maas.py +++ b/libs/community/langchain_community/chat_models/volcengine_maas.py @@ -39,8 +39,8 @@ def convert_dict_to_message(_dict: Mapping[str, Any]) -> AIMessage: class VolcEngineMaasChat(BaseChatModel, VolcEngineMaasBase): + """Volc Engine Maas hosts a plethora of models. - """volc engine maas hosts a plethora of models. You can utilize these models through this class. To use, you should have the ``volcengine`` python package installed. diff --git a/libs/community/langchain_community/chat_models/yandex.py b/libs/community/langchain_community/chat_models/yandex.py index 61de024089e..a596620840e 100644 --- a/libs/community/langchain_community/chat_models/yandex.py +++ b/libs/community/langchain_community/chat_models/yandex.py @@ -210,7 +210,8 @@ async def _amake_request(self: ChatYandexGPT, messages: List[BaseMessage]) -> st await asyncio.sleep(1) operation_request = GetOperationRequest(operation_id=operation.id) operation = await operation_stub.Get( - operation_request, metadata=self._grpc_metadata + operation_request, + metadata=self._grpc_metadata, ) completion_response = CompletionResponse() diff --git a/libs/community/langchain_community/chat_models/yuan2.py b/libs/community/langchain_community/chat_models/yuan2.py new file mode 100644 index 00000000000..3d622206c35 --- /dev/null +++ b/libs/community/langchain_community/chat_models/yuan2.py @@ -0,0 +1,486 @@ +"""ChatYuan2 wrapper.""" +from __future__ import annotations + +import logging +from typing import ( + TYPE_CHECKING, + Any, + AsyncIterator, + Callable, + Dict, + Iterator, + List, + Mapping, + Optional, + Tuple, + Type, + Union, +) + +from langchain_core.callbacks import ( + AsyncCallbackManagerForLLMRun, + CallbackManagerForLLMRun, +) +from langchain_core.language_models.chat_models import ( + BaseChatModel, + agenerate_from_stream, + generate_from_stream, +) +from langchain_core.messages import ( + AIMessage, + AIMessageChunk, + BaseMessage, + BaseMessageChunk, + ChatMessage, + ChatMessageChunk, + FunctionMessage, + HumanMessage, + HumanMessageChunk, + SystemMessage, + SystemMessageChunk, +) +from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult +from langchain_core.pydantic_v1 import Field, root_validator +from langchain_core.utils import ( + get_from_dict_or_env, + get_pydantic_field_names, +) +from tenacity import ( + before_sleep_log, + retry, + retry_if_exception_type, + stop_after_attempt, + wait_exponential, +) + +if TYPE_CHECKING: + from openai.types.chat import ChatCompletion, ChatCompletionMessage + +logger = logging.getLogger(__name__) + + +class ChatYuan2(BaseChatModel): + """`Yuan2.0` Chat models API. + + To use, you should have the ``openai-python`` package installed, if package + not installed, using ```pip install openai``` to install it. The + environment variable ``YUAN2_API_KEY`` set to your API key, if not set, + everyone can access apis. + + Any parameters that are valid to be passed to the openai.create call can be passed + in, even if not explicitly saved on this class. + + Example: + .. code-block:: python + + from langchain_community.chat_models import ChatYuan2 + + chat = ChatYuan2() + """ + + client: Any #: :meta private: + async_client: Any = Field(default=None, exclude=True) #: :meta private: + + model_name: str = Field(default="yuan2", alias="model") + """Model name to use.""" + + model_kwargs: Dict[str, Any] = Field(default_factory=dict) + """Holds any model parameters valid for `create` call not explicitly specified.""" + + yuan2_api_key: Optional[str] = Field(default="EMPTY", alias="api_key") + """Automatically inferred from env var `YUAN2_API_KEY` if not provided.""" + + yuan2_api_base: Optional[str] = Field( + default="http://127.0.0.1:8000", alias="base_url" + ) + """Base URL path for API requests, an OpenAI compatible API server.""" + + request_timeout: Optional[Union[float, Tuple[float, float]]] = None + """Timeout for requests to yuan2 completion API. Default is 600 seconds.""" + + max_retries: int = 6 + """Maximum number of retries to make when generating.""" + + streaming: bool = False + """Whether to stream the results or not.""" + + max_tokens: Optional[int] = None + """Maximum number of tokens to generate.""" + + temperature: float = 1.0 + """What sampling temperature to use.""" + + top_p: Optional[float] = 0.9 + """The top-p value to use for sampling.""" + + stop: Optional[List[str]] = [""] + """A list of strings to stop generation when encountered.""" + + repeat_last_n: Optional[int] = 64 + "Last n tokens to penalize" + + repeat_penalty: Optional[float] = 1.18 + """The penalty to apply to repeated tokens.""" + + class Config: + """Configuration for this pydantic object.""" + + allow_population_by_field_name = True + + @property + def lc_secrets(self) -> Dict[str, str]: + return {"yuan2_api_key": "YUAN2_API_KEY"} + + @property + def lc_attributes(self) -> Dict[str, Any]: + attributes: Dict[str, Any] = {} + + if self.yuan2_api_base: + attributes["yuan2_api_base"] = self.yuan2_api_base + + if self.yuan2_api_key: + attributes["yuan2_api_key"] = self.yuan2_api_key + + return attributes + + @root_validator(pre=True) + def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + """Build extra kwargs from additional params that were passed in.""" + all_required_field_names = get_pydantic_field_names(cls) + extra = values.get("model_kwargs", {}) + for field_name in list(values): + if field_name in extra: + raise ValueError(f"Found {field_name} supplied twice.") + if field_name not in all_required_field_names: + logger.warning( + f"""WARNING! {field_name} is not default parameter. + {field_name} was transferred to model_kwargs. + Please confirm that {field_name} is what you intended.""" + ) + extra[field_name] = values.pop(field_name) + + invalid_model_kwargs = all_required_field_names.intersection(extra.keys()) + if invalid_model_kwargs: + raise ValueError( + f"Parameters {invalid_model_kwargs} should be specified explicitly. " + f"Instead they were passed in as part of `model_kwargs` parameter." + ) + + values["model_kwargs"] = extra + return values + + @root_validator() + def validate_environment(cls, values: Dict) -> Dict: + """Validate that api key and python package exists in environment.""" + values["yuan2_api_key"] = get_from_dict_or_env( + values, "yuan2_api_key", "YUAN2_API_KEY" + ) + + try: + import openai + + except ImportError: + raise ValueError( + "Could not import openai python package. " + "Please install it with `pip install openai`." + ) + client_params = { + "api_key": values["yuan2_api_key"], + "base_url": values["yuan2_api_base"], + "timeout": values["request_timeout"], + "max_retries": values["max_retries"], + } + + # generate client and async_client + if not values.get("client"): + values["client"] = openai.OpenAI(**client_params).chat.completions + if not values.get("async_client"): + values["async_client"] = openai.AsyncOpenAI( + **client_params + ).chat.completions + + return values + + @property + def _default_params(self) -> Dict[str, Any]: + """Get the default parameters for calling yuan2 API.""" + params = { + "model": self.model_name, + "stream": self.streaming, + "temperature": self.temperature, + "top_p": self.top_p, + **self.model_kwargs, + } + if self.max_tokens is not None: + params["max_tokens"] = self.max_tokens + if self.request_timeout is not None: + params["request_timeout"] = self.request_timeout + return params + + def completion_with_retry(self, **kwargs: Any) -> Any: + """Use tenacity to retry the completion call.""" + retry_decorator = _create_retry_decorator(self) + + @retry_decorator + def _completion_with_retry(**kwargs: Any) -> Any: + return self.client.create(**kwargs) + + return _completion_with_retry(**kwargs) + + def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict: + overall_token_usage: dict = {} + logger.debug( + f"type(llm_outputs): {type(llm_outputs)}; llm_outputs: {llm_outputs}" + ) + for output in llm_outputs: + if output is None: + # Happens in streaming + continue + token_usage = output["token_usage"] + for k, v in token_usage.__dict__.items(): + if k in overall_token_usage: + overall_token_usage[k] += v + else: + overall_token_usage[k] = v + return {"token_usage": overall_token_usage, "model_name": self.model_name} + + def _stream( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> Iterator[ChatGenerationChunk]: + message_dicts, params = self._create_message_dicts(messages, stop) + params = {**params, **kwargs, "stream": True} + + default_chunk_class = AIMessageChunk + for chunk in self.completion_with_retry(messages=message_dicts, **params): + if not isinstance(chunk, dict): + chunk = chunk.model_dump() + if len(chunk["choices"]) == 0: + continue + choice = chunk["choices"][0] + chunk = _convert_delta_to_message_chunk( + choice["delta"], default_chunk_class + ) + finish_reason = choice.get("finish_reason") + generation_info = ( + dict(finish_reason=finish_reason) if finish_reason is not None else None + ) + default_chunk_class = chunk.__class__ + yield ChatGenerationChunk( + message=chunk, + generation_info=generation_info, + ) + if run_manager: + run_manager.on_llm_new_token(chunk.content) + + def _generate( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> ChatResult: + if self.streaming: + stream_iter = self._stream( + messages=messages, stop=stop, run_manager=run_manager, **kwargs + ) + return generate_from_stream(stream_iter) + + message_dicts, params = self._create_message_dicts(messages, stop) + params = {**params, **kwargs} + response = self.completion_with_retry(messages=message_dicts, **params) + return self._create_chat_result(response) + + def _create_message_dicts( + self, messages: List[BaseMessage], stop: Optional[List[str]] + ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]: + params = dict(self._invocation_params) + if stop is not None: + if "stop" in params: + raise ValueError("`stop` found in both the input and default params.") + params["stop"] = stop + message_dicts = [_convert_message_to_dict(m) for m in messages] + return message_dicts, params + + def _create_chat_result(self, response: ChatCompletion) -> ChatResult: + generations = [] + logger.debug(f"type(response): {type(response)}; response: {response}") + for res in response.choices: + message = _convert_dict_to_message(res.message) + generation_info = dict(finish_reason=res.finish_reason) + if "logprobs" in res: + generation_info["logprobs"] = res.logprobs + gen = ChatGeneration( + message=message, + generation_info=generation_info, + ) + generations.append(gen) + llm_output = { + "token_usage": response.usage, + "model_name": self.model_name, + } + return ChatResult(generations=generations, llm_output=llm_output) + + async def _astream( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> AsyncIterator[ChatGenerationChunk]: + message_dicts, params = self._create_message_dicts(messages, stop) + params = {**params, **kwargs, "stream": True} + + default_chunk_class = AIMessageChunk + async for chunk in await acompletion_with_retry( + self, messages=message_dicts, **params + ): + if not isinstance(chunk, dict): + chunk = chunk.model_dump() + if len(chunk["choices"]) == 0: + continue + choice = chunk["choices"][0] + chunk = _convert_delta_to_message_chunk( + choice["delta"], default_chunk_class + ) + finish_reason = choice.get("finish_reason") + generation_info = ( + dict(finish_reason=finish_reason) if finish_reason is not None else None + ) + default_chunk_class = chunk.__class__ + yield ChatGenerationChunk( + message=chunk, + generation_info=generation_info, + ) + if run_manager: + await run_manager.on_llm_new_token(chunk.content) + + async def _agenerate( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> ChatResult: + if self.streaming: + stream_iter = self._astream( + messages=messages, stop=stop, run_manager=run_manager, **kwargs + ) + return await agenerate_from_stream(stream_iter) + + message_dicts, params = self._create_message_dicts(messages, stop) + params = {**params, **kwargs} + response = await acompletion_with_retry(self, messages=message_dicts, **params) + return self._create_chat_result(response) + + @property + def _invocation_params(self) -> Mapping[str, Any]: + """Get the parameters used to invoke the model.""" + yuan2_creds: Dict[str, Any] = { + "model": self.model_name, + } + return {**yuan2_creds, **self._default_params} + + @property + def _llm_type(self) -> str: + """Return type of chat model.""" + return "chat-yuan2" + + +def _create_retry_decorator(llm: ChatYuan2) -> Callable[[Any], Any]: + import openai + + min_seconds = 1 + max_seconds = 60 + # Wait 2^x * 1 second between each retry starting with + # 4 seconds, then up to 10 seconds, then 10 seconds afterwards + return retry( + reraise=True, + stop=stop_after_attempt(llm.max_retries), + wait=wait_exponential(multiplier=1, min=min_seconds, max=max_seconds), + retry=( + retry_if_exception_type(openai.APITimeoutError) + | retry_if_exception_type(openai.APIError) + | retry_if_exception_type(openai.APIConnectionError) + | retry_if_exception_type(openai.RateLimitError) + | retry_if_exception_type(openai.InternalServerError) + ), + before_sleep=before_sleep_log(logger, logging.WARNING), + ) + + +async def acompletion_with_retry(llm: ChatYuan2, **kwargs: Any) -> Any: + """Use tenacity to retry the async completion call.""" + retry_decorator = _create_retry_decorator(llm) + + @retry_decorator + async def _completion_with_retry(**kwargs: Any) -> Any: + # Use OpenAI's async api https://github.com/openai/openai-python#async-api + return await llm.async_client.create(**kwargs) + + return await _completion_with_retry(**kwargs) + + +def _convert_delta_to_message_chunk( + _dict: ChatCompletionMessage, default_class: Type[BaseMessageChunk] +) -> BaseMessageChunk: + role = _dict.get("role") + content = _dict.get("content") or "" + + if role == "user" or default_class == HumanMessageChunk: + return HumanMessageChunk(content=content) + elif role == "assistant" or default_class == AIMessageChunk: + return AIMessageChunk(content=content) + elif role == "system" or default_class == SystemMessageChunk: + return SystemMessageChunk(content=content) + elif role or default_class == ChatMessageChunk: + return ChatMessageChunk(content=content, role=role) + else: + return default_class(content=content) + + +def _convert_dict_to_message(_dict: ChatCompletionMessage) -> BaseMessage: + role = _dict.get("role") + if role == "user": + return HumanMessage(content=_dict.get("content")) + elif role == "assistant": + content = _dict.get("content") or "" + return AIMessage(content=content) + elif role == "system": + return SystemMessage(content=_dict.get("content")) + else: + return ChatMessage(content=_dict.get("content"), role=role) + + +def _convert_message_to_dict(message: BaseMessage) -> dict: + """Convert a LangChain message to a dictionary. + + Args: + message: The LangChain message. + + Returns: + The dictionary. + """ + message_dict: Dict[str, Any] + if isinstance(message, ChatMessage): + message_dict = {"role": message.role, "content": message.content} + elif isinstance(message, HumanMessage): + message_dict = {"role": "user", "content": message.content} + elif isinstance(message, AIMessage): + message_dict = {"role": "assistant", "content": message.content} + elif isinstance(message, SystemMessage): + message_dict = {"role": "system", "content": message.content} + elif isinstance(message, FunctionMessage): + message_dict = { + "role": "function", + "name": message.name, + "content": message.content, + } + else: + raise ValueError(f"Got unknown type {message}") + if "name" in message.additional_kwargs: + message_dict["name"] = message.additional_kwargs["name"] + return message_dict diff --git a/libs/community/langchain_community/chat_models/zhipuai.py b/libs/community/langchain_community/chat_models/zhipuai.py index e8e56f0b95c..dd55c5ee12c 100644 --- a/libs/community/langchain_community/chat_models/zhipuai.py +++ b/libs/community/langchain_community/chat_models/zhipuai.py @@ -5,7 +5,7 @@ import asyncio import json import logging from functools import partial -from typing import Any, Dict, Iterator, List, Optional +from typing import Any, Dict, Iterator, List, Optional, cast from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.chat_models import ( @@ -20,11 +20,15 @@ logger = logging.getLogger(__name__) class ref(BaseModel): + """Reference used in CharacterGLM.""" + enable: bool = Field(True) search_query: str = Field("") class meta(BaseModel): + """Metadata used in CharacterGLM.""" + user_info: str = Field("") bot_info: str = Field("") bot_name: str = Field("") @@ -161,7 +165,7 @@ class ChatZhipuAI(BaseChatModel): return attributes - def __init__(self, *args, **kwargs): + def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) try: import zhipuai @@ -174,7 +178,7 @@ class ChatZhipuAI(BaseChatModel): "Please install it via 'pip install zhipuai'" ) - def invoke(self, prompt): + def invoke(self, prompt: Any) -> Any: # type: ignore[override] if self.model == "chatglm_turbo": return self.zhipuai.model_api.invoke( model=self.model, @@ -185,17 +189,17 @@ class ChatZhipuAI(BaseChatModel): return_type=self.return_type, ) elif self.model == "characterglm": - meta = self.meta.dict() + _meta = cast(meta, self.meta).dict() return self.zhipuai.model_api.invoke( model=self.model, - meta=meta, + meta=_meta, prompt=prompt, request_id=self.request_id, return_type=self.return_type, ) return None - def sse_invoke(self, prompt): + def sse_invoke(self, prompt: Any) -> Any: if self.model == "chatglm_turbo": return self.zhipuai.model_api.sse_invoke( model=self.model, @@ -207,18 +211,18 @@ class ChatZhipuAI(BaseChatModel): incremental=self.incremental, ) elif self.model == "characterglm": - meta = self.meta.dict() + _meta = cast(meta, self.meta).dict() return self.zhipuai.model_api.sse_invoke( model=self.model, prompt=prompt, - meta=meta, + meta=_meta, request_id=self.request_id, return_type=self.return_type, incremental=self.incremental, ) return None - async def async_invoke(self, prompt): + async def async_invoke(self, prompt: Any) -> Any: loop = asyncio.get_running_loop() partial_func = partial( self.zhipuai.model_api.async_invoke, model=self.model, prompt=prompt @@ -229,7 +233,7 @@ class ChatZhipuAI(BaseChatModel): ) return response - async def async_invoke_result(self, task_id): + async def async_invoke_result(self, task_id: Any) -> Any: loop = asyncio.get_running_loop() response = await loop.run_in_executor( None, @@ -247,7 +251,7 @@ class ChatZhipuAI(BaseChatModel): **kwargs: Any, ) -> ChatResult: """Generate a chat response.""" - prompt = [] + prompt: List = [] for message in messages: if isinstance(message, AIMessage): role = "assistant" @@ -270,11 +274,14 @@ class ChatZhipuAI(BaseChatModel): else: stream_iter = self._stream( - prompt=prompt, stop=stop, run_manager=run_manager, **kwargs + prompt=prompt, + stop=stop, + run_manager=run_manager, + **kwargs, ) return generate_from_stream(stream_iter) - async def _agenerate( + async def _agenerate( # type: ignore[override] self, messages: List[BaseMessage], stop: Optional[List[str]] = None, @@ -307,7 +314,7 @@ class ChatZhipuAI(BaseChatModel): generations=[ChatGeneration(message=AIMessage(content=content))] ) - def _stream( + def _stream( # type: ignore[override] self, prompt: List[Dict[str, str]], stop: Optional[List[str]] = None, diff --git a/libs/community/langchain_community/document_loaders/__init__.py b/libs/community/langchain_community/document_loaders/__init__.py index b06f416d4da..8f2d6baf9b5 100644 --- a/libs/community/langchain_community/document_loaders/__init__.py +++ b/libs/community/langchain_community/document_loaders/__init__.py @@ -36,6 +36,7 @@ from langchain_community.document_loaders.assemblyai import ( ) from langchain_community.document_loaders.astradb import AstraDBLoader from langchain_community.document_loaders.async_html import AsyncHtmlLoader +from langchain_community.document_loaders.athena import AthenaLoader from langchain_community.document_loaders.azlyrics import AZLyricsLoader from langchain_community.document_loaders.azure_ai_data import ( AzureAIDataLoader, @@ -102,7 +103,10 @@ from langchain_community.document_loaders.gcs_file import GCSFileLoader from langchain_community.document_loaders.geodataframe import GeoDataFrameLoader from langchain_community.document_loaders.git import GitLoader from langchain_community.document_loaders.gitbook import GitbookLoader -from langchain_community.document_loaders.github import GitHubIssuesLoader +from langchain_community.document_loaders.github import ( + GithubFileLoader, + GitHubIssuesLoader, +) from langchain_community.document_loaders.google_speech_to_text import ( GoogleSpeechToTextLoader, ) @@ -156,6 +160,7 @@ from langchain_community.document_loaders.pdf import ( PyPDFLoader, UnstructuredPDFLoader, ) +from langchain_community.document_loaders.pebblo import PebbloSafeLoader from langchain_community.document_loaders.polars_dataframe import PolarsDataFrameLoader from langchain_community.document_loaders.powerpoint import UnstructuredPowerPointLoader from langchain_community.document_loaders.psychic import PsychicLoader @@ -254,6 +259,7 @@ __all__ = [ "AssemblyAIAudioTranscriptLoader", "AstraDBLoader", "AsyncHtmlLoader", + "AthenaLoader", "AzureAIDataLoader", "AzureAIDocumentIntelligenceLoader", "AzureBlobStorageContainerLoader", @@ -279,6 +285,7 @@ __all__ = [ "CubeSemanticLoader", "DataFrameLoader", "DatadogLogsLoader", + "PebbloSafeLoader", "DiffbotLoader", "DirectoryLoader", "DiscordChatLoader", @@ -296,6 +303,7 @@ __all__ = [ "GCSDirectoryLoader", "GCSFileLoader", "GeoDataFrameLoader", + "GithubFileLoader", "GitHubIssuesLoader", "GitLoader", "GitbookLoader", diff --git a/libs/community/langchain_community/document_loaders/arcgis_loader.py b/libs/community/langchain_community/document_loaders/arcgis_loader.py index 4958ef2ce6a..24264b0add7 100644 --- a/libs/community/langchain_community/document_loaders/arcgis_loader.py +++ b/libs/community/langchain_community/document_loaders/arcgis_loader.py @@ -41,7 +41,7 @@ class ArcGISLoader(BaseLoader): ) from e try: - from bs4 import BeautifulSoup # type: ignore + from bs4 import BeautifulSoup self.BEAUTIFULSOUP = BeautifulSoup except ImportError: diff --git a/libs/community/langchain_community/document_loaders/assemblyai.py b/libs/community/langchain_community/document_loaders/assemblyai.py index d3947d9f71b..3b1b4060b98 100644 --- a/libs/community/langchain_community/document_loaders/assemblyai.py +++ b/libs/community/langchain_community/document_loaders/assemblyai.py @@ -123,7 +123,9 @@ class AssemblyAIAudioLoaderById(BaseLoader): """ - def __init__(self, transcript_id, api_key, transcript_format): + def __init__( + self, transcript_id: str, api_key: str, transcript_format: TranscriptFormat + ): """ Initializes the AssemblyAI AssemblyAIAudioLoaderById. @@ -150,7 +152,7 @@ class AssemblyAIAudioLoaderById(BaseLoader): ) transcript_response.raise_for_status() except Exception as e: - print(f"An error occurred: {e}") + print(f"An error occurred: {e}") # noqa: T201 raise transcript = transcript_response.json()["text"] @@ -166,7 +168,7 @@ class AssemblyAIAudioLoaderById(BaseLoader): ) paragraphs_response.raise_for_status() except Exception as e: - print(f"An error occurred: {e}") + print(f"An error occurred: {e}") # noqa: T201 raise paragraphs = paragraphs_response.json()["paragraphs"] @@ -181,7 +183,7 @@ class AssemblyAIAudioLoaderById(BaseLoader): ) sentences_response.raise_for_status() except Exception as e: - print(f"An error occurred: {e}") + print(f"An error occurred: {e}") # noqa: T201 raise sentences = sentences_response.json()["sentences"] @@ -196,7 +198,7 @@ class AssemblyAIAudioLoaderById(BaseLoader): ) srt_response.raise_for_status() except Exception as e: - print(f"An error occurred: {e}") + print(f"An error occurred: {e}") # noqa: T201 raise srt = srt_response.text @@ -211,7 +213,7 @@ class AssemblyAIAudioLoaderById(BaseLoader): ) vtt_response.raise_for_status() except Exception as e: - print(f"An error occurred: {e}") + print(f"An error occurred: {e}") # noqa: T201 raise vtt = vtt_response.text diff --git a/libs/community/langchain_community/document_loaders/astradb.py b/libs/community/langchain_community/document_loaders/astradb.py index 2af1e77fde1..8dae5c4528a 100644 --- a/libs/community/langchain_community/document_loaders/astradb.py +++ b/libs/community/langchain_community/document_loaders/astradb.py @@ -2,8 +2,6 @@ from __future__ import annotations import json import logging -import threading -from queue import Queue from typing import ( TYPE_CHECKING, Any, @@ -16,10 +14,9 @@ from typing import ( ) from langchain_core.documents import Document -from langchain_core.runnables import run_in_executor from langchain_community.document_loaders.base import BaseLoader -from langchain_community.utilities.astradb import AstraDBEnvironment +from langchain_community.utilities.astradb import _AstraDBEnvironment if TYPE_CHECKING: from astrapy.db import AstraDB, AsyncAstraDB @@ -33,6 +30,7 @@ class AstraDBLoader(BaseLoader): def __init__( self, collection_name: str, + *, token: Optional[str] = None, api_endpoint: Optional[str] = None, astra_db_client: Optional[AstraDB] = None, @@ -44,7 +42,7 @@ class AstraDBLoader(BaseLoader): nb_prefetched: int = 1000, extraction_function: Callable[[Dict], str] = json.dumps, ) -> None: - astra_env = AstraDBEnvironment( + astra_env = _AstraDBEnvironment( token=token, api_endpoint=api_endpoint, astra_db_client=astra_db_client, @@ -65,38 +63,27 @@ class AstraDBLoader(BaseLoader): return list(self.lazy_load()) def lazy_load(self) -> Iterator[Document]: - queue = Queue(self.nb_prefetched) - t = threading.Thread(target=self.fetch_results, args=(queue,)) - t.start() - while True: - doc = queue.get() - if doc is None: - break - yield doc - t.join() + for doc in self.collection.paginated_find( + filter=self.filter, + options=self.find_options, + projection=self.projection, + sort=None, + prefetched=self.nb_prefetched, + ): + yield Document( + page_content=self.extraction_function(doc), + metadata={ + "namespace": self.collection.astra_db.namespace, + "api_endpoint": self.collection.astra_db.base_url, + "collection": self.collection_name, + }, + ) async def aload(self) -> List[Document]: """Load data into Document objects.""" return [doc async for doc in self.alazy_load()] async def alazy_load(self) -> AsyncIterator[Document]: - if not self.astra_env.async_astra_db: - iterator = run_in_executor( - None, - self.collection.paginated_find, - filter=self.filter, - options=self.find_options, - projection=self.projection, - sort=None, - prefetched=True, - ) - done = object() - while True: - item = await run_in_executor(None, lambda it: next(it, done), iterator) - if item is done: - break - yield item - return async_collection = await self.astra_env.async_astra_db.collection( self.collection_name ) @@ -105,7 +92,7 @@ class AstraDBLoader(BaseLoader): options=self.find_options, projection=self.projection, sort=None, - prefetched=True, + prefetched=self.nb_prefetched, ): yield Document( page_content=self.extraction_function(doc), @@ -115,29 +102,3 @@ class AstraDBLoader(BaseLoader): "collection": self.collection_name, }, ) - - def fetch_results(self, queue: Queue): - self.fetch_page_result(queue) - while self.find_options.get("pageState"): - self.fetch_page_result(queue) - queue.put(None) - - def fetch_page_result(self, queue: Queue): - res = self.collection.find( - filter=self.filter, - options=self.find_options, - projection=self.projection, - sort=None, - ) - self.find_options["pageState"] = res["data"].get("nextPageState") - for doc in res["data"]["documents"]: - queue.put( - Document( - page_content=self.extraction_function(doc), - metadata={ - "namespace": self.collection.astra_db.namespace, - "api_endpoint": self.collection.astra_db.base_url, - "collection": self.collection.collection_name, - }, - ) - ) diff --git a/libs/community/langchain_community/document_loaders/athena.py b/libs/community/langchain_community/document_loaders/athena.py new file mode 100644 index 00000000000..e3ed42e44db --- /dev/null +++ b/libs/community/langchain_community/document_loaders/athena.py @@ -0,0 +1,163 @@ +from __future__ import annotations + +import io +import json +import time +from typing import Any, Dict, Iterator, List, Optional, Tuple + +from langchain_core.documents import Document + +from langchain_community.document_loaders.base import BaseLoader + + +class AthenaLoader(BaseLoader): + """Load documents from `AWS Athena`. + + Each document represents one row of the result. + - By default, all columns are written into the `page_content` of the document + and none into the `metadata` of the document. + - If `metadata_columns` are provided then these columns are written + into the `metadata` of the document while the rest of the columns + are written into the `page_content` of the document. + + To authenticate, the AWS client uses this method to automatically load credentials: + https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html + + If a specific credential profile should be used, you must pass + the name of the profile from the ~/.aws/credentials file that is to be used. + + Make sure the credentials / roles used have the required policies to + access the Amazon Textract service. + """ + + def __init__( + self, + query: str, + database: str, + s3_output_uri: str, + profile_name: str, + metadata_columns: Optional[List[str]] = None, + ): + """Initialize Athena document loader. + + Args: + query: The query to run in Athena. + database: Athena database + s3_output_uri: Athena output path + metadata_columns: Optional. Columns written to Document `metadata`. + """ + self.query = query + self.database = database + self.s3_output_uri = s3_output_uri + self.metadata_columns = metadata_columns if metadata_columns is not None else [] + + try: + import boto3 + except ImportError: + raise ModuleNotFoundError( + "Could not import boto3 python package. " + "Please install it with `pip install boto3`." + ) + + try: + session = ( + boto3.Session(profile_name=profile_name) + if profile_name is not None + else boto3.Session() + ) + except Exception as e: + raise ValueError( + "Could not load credentials to authenticate with AWS client. " + "Please check that credentials in the specified " + "profile name are valid." + ) from e + + self.athena_client = session.client("athena") + self.s3_client = session.client("s3") + + def _execute_query(self) -> List[Dict[str, Any]]: + response = self.athena_client.start_query_execution( + QueryString=self.query, + QueryExecutionContext={"Database": self.database}, + ResultConfiguration={"OutputLocation": self.s3_output_uri}, + ) + query_execution_id = response["QueryExecutionId"] + while True: + response = self.athena_client.get_query_execution( + QueryExecutionId=query_execution_id + ) + state = response["QueryExecution"]["Status"]["State"] + if state == "SUCCEEDED": + break + elif state == "FAILED": + resp_status = response["QueryExecution"]["Status"] + state_change_reason = resp_status["StateChangeReason"] + err = f"Query Failed: {state_change_reason}" + raise Exception(err) + elif state == "CANCELLED": + raise Exception("Query was cancelled by the user.") + time.sleep(1) + + result_set = self._get_result_set(query_execution_id) + return json.loads(result_set.to_json(orient="records")) + + def _remove_suffix(self, input_string: str, suffix: str) -> str: + if suffix and input_string.endswith(suffix): + return input_string[: -len(suffix)] + return input_string + + def _remove_prefix(self, input_string: str, suffix: str) -> str: + if suffix and input_string.startswith(suffix): + return input_string[len(suffix) :] + return input_string + + def _get_result_set(self, query_execution_id: str) -> Any: + try: + import pandas as pd + except ImportError: + raise ModuleNotFoundError( + "Could not import pandas python package. " + "Please install it with `pip install pandas`." + ) + + output_uri = self.s3_output_uri + tokens = self._remove_prefix( + self._remove_suffix(output_uri, "/"), "s3://" + ).split("/") + bucket = tokens[0] + key = "/".join(tokens[1:] + [query_execution_id]) + ".csv" + + obj = self.s3_client.get_object(Bucket=bucket, Key=key) + df = pd.read_csv(io.BytesIO(obj["Body"].read()), encoding="utf8") + return df + + def _get_columns( + self, query_result: List[Dict[str, Any]] + ) -> Tuple[List[str], List[str]]: + content_columns = [] + metadata_columns = [] + all_columns = list(query_result[0].keys()) + for key in all_columns: + if key in self.metadata_columns: + metadata_columns.append(key) + else: + content_columns.append(key) + + return content_columns, metadata_columns + + def lazy_load(self) -> Iterator[Document]: + query_result = self._execute_query() + content_columns, metadata_columns = self._get_columns(query_result) + for row in query_result: + page_content = "\n".join( + f"{k}: {v}" for k, v in row.items() if k in content_columns + ) + metadata = { + k: v for k, v in row.items() if k in metadata_columns and v is not None + } + doc = Document(page_content=page_content, metadata=metadata) + yield doc + + def load(self) -> List[Document]: + """Load data into document objects.""" + return list(self.lazy_load()) diff --git a/libs/community/langchain_community/document_loaders/base.py b/libs/community/langchain_community/document_loaders/base.py index 7a3e5a2706c..fc266ff51d6 100644 --- a/libs/community/langchain_community/document_loaders/base.py +++ b/libs/community/langchain_community/document_loaders/base.py @@ -64,10 +64,10 @@ class BaseLoader(ABC): iterator = await run_in_executor(None, self.lazy_load) done = object() while True: - doc = await run_in_executor(None, next, iterator, done) + doc = await run_in_executor(None, next, iterator, done) # type: ignore[call-arg, arg-type] if doc is done: break - yield doc + yield doc # type: ignore[misc] class BaseBlobParser(ABC): diff --git a/libs/community/langchain_community/document_loaders/blackboard.py b/libs/community/langchain_community/document_loaders/blackboard.py index cc33db332dd..873f0ab1db1 100644 --- a/libs/community/langchain_community/document_loaders/blackboard.py +++ b/libs/community/langchain_community/document_loaders/blackboard.py @@ -109,13 +109,13 @@ class BlackboardLoader(WebBaseLoader): documents = [] for path in relative_paths: url = self.base_url + path - print(f"Fetching documents from {url}") + print(f"Fetching documents from {url}") # noqa: T201 soup_info = self._scrape(url) with contextlib.suppress(ValueError): documents.extend(self._get_documents(soup_info)) return documents else: - print(f"Fetching documents from {self.web_path}") + print(f"Fetching documents from {self.web_path}") # noqa: T201 soup_info = self.scrape() self.folder_path = self._get_folder_path(soup_info) return self._get_documents(soup_info) @@ -295,4 +295,4 @@ if __name__ == "__main__": load_all_recursively=True, ) documents = loader.load() - print(f"Loaded {len(documents)} pages of PDFs from {loader.web_path}") + print(f"Loaded {len(documents)} pages of PDFs from {loader.web_path}") # noqa: T201 diff --git a/libs/community/langchain_community/document_loaders/blob_loaders/file_system.py b/libs/community/langchain_community/document_loaders/blob_loaders/file_system.py index 0fcdd4438ee..ee756f32ed7 100644 --- a/libs/community/langchain_community/document_loaders/blob_loaders/file_system.py +++ b/libs/community/langchain_community/document_loaders/blob_loaders/file_system.py @@ -1,4 +1,5 @@ """Use to load blobs from the local file system.""" + from pathlib import Path from typing import Callable, Iterable, Iterator, Optional, Sequence, TypeVar, Union @@ -11,6 +12,7 @@ def _make_iterator( length_func: Callable[[], int], show_progress: bool = False ) -> Callable[[Iterable[T]], Iterator[T]]: """Create a function that optionally wraps an iterable in tqdm.""" + iterator: Callable[[Iterable[T]], Iterator[T]] if show_progress: try: from tqdm.auto import tqdm @@ -28,7 +30,7 @@ def _make_iterator( iterator = _with_tqdm else: - iterator = iter # type: ignore + iterator = iter return iterator @@ -46,7 +48,7 @@ class FileSystemBlobLoader(BlobLoader): from langchain_community.document_loaders.blob_loaders import FileSystemBlobLoader loader = FileSystemBlobLoader("/path/to/directory") for blob in loader.yield_blobs(): - print(blob) + print(blob) # noqa: T201 """ # noqa: E501 def __init__( diff --git a/libs/community/langchain_community/document_loaders/cassandra.py b/libs/community/langchain_community/document_loaders/cassandra.py index 3167711228a..3cef56a1cbc 100644 --- a/libs/community/langchain_community/document_loaders/cassandra.py +++ b/libs/community/langchain_community/document_loaders/cassandra.py @@ -29,18 +29,18 @@ class CassandraLoader(BaseLoader): table: Optional[str] = None, session: Optional[Session] = None, keyspace: Optional[str] = None, - query: Optional[Union[str, Statement]] = None, + query: Union[str, Statement, None] = None, page_content_mapper: Callable[[Any], str] = str, metadata_mapper: Callable[[Any], dict] = lambda _: {}, *, - query_parameters: Union[dict, Sequence] = None, - query_timeout: Optional[float] = _NOT_SET, + query_parameters: Union[dict, Sequence, None] = None, + query_timeout: Optional[float] = _NOT_SET, # type: ignore[assignment] query_trace: bool = False, - query_custom_payload: dict = None, + query_custom_payload: Optional[dict] = None, query_execution_profile: Any = _NOT_SET, query_paging_state: Any = None, - query_host: Host = None, - query_execute_as: str = None, + query_host: Optional[Host] = None, + query_execute_as: Optional[str] = None, ) -> None: """ Document Loader for Apache Cassandra. @@ -85,7 +85,7 @@ class CassandraLoader(BaseLoader): self.query = f"SELECT * FROM {_keyspace}.{table};" self.metadata = {"table": table, "keyspace": _keyspace} else: - self.query = query + self.query = query # type: ignore[assignment] self.metadata = {} self.session = session or check_resolve_session(session) diff --git a/libs/community/langchain_community/document_loaders/chm.py b/libs/community/langchain_community/document_loaders/chm.py index c036ee8b8f3..207d31bd4f8 100644 --- a/libs/community/langchain_community/document_loaders/chm.py +++ b/libs/community/langchain_community/document_loaders/chm.py @@ -9,7 +9,7 @@ if TYPE_CHECKING: class UnstructuredCHMLoader(UnstructuredFileLoader): """Load `CHM` files using `Unstructured`. - CHM mean Microsoft Compiled HTML Help. + CHM means Microsoft Compiled HTML Help. Examples -------- @@ -27,7 +27,7 @@ class UnstructuredCHMLoader(UnstructuredFileLoader): def _get_elements(self) -> List: from unstructured.partition.html import partition_html - with CHMParser(self.file_path) as f: + with CHMParser(self.file_path) as f: # type: ignore[arg-type] return [ partition_html(text=item["content"], **self.unstructured_kwargs) for item in f.load_all() @@ -35,6 +35,8 @@ class UnstructuredCHMLoader(UnstructuredFileLoader): class CHMParser(object): + """Microsoft Compiled HTML Help (CHM) Parser.""" + path: str file: "chm.CHMFile" @@ -45,10 +47,10 @@ class CHMParser(object): self.file = chm.CHMFile() self.file.LoadCHM(path) - def __enter__(self): + def __enter__(self): # type: ignore[no-untyped-def] return self - def __exit__(self, exc_type, exc_value, traceback): + def __exit__(self, exc_type, exc_value, traceback): # type: ignore[no-untyped-def] if self.file: self.file.CloseCHM() diff --git a/libs/community/langchain_community/document_loaders/confluence.py b/libs/community/langchain_community/document_loaders/confluence.py index 6e3293e00a6..0c57f48fb23 100644 --- a/libs/community/langchain_community/document_loaders/confluence.py +++ b/libs/community/langchain_community/document_loaders/confluence.py @@ -564,7 +564,7 @@ class ConfluenceLoader(BaseLoader): texts.append(text) except requests.HTTPError as e: if e.response.status_code == 404: - print(f"Attachment not found at {absolute_url}") + print(f"Attachment not found at {absolute_url}") # noqa: T201 continue else: raise diff --git a/libs/community/langchain_community/document_loaders/csv_loader.py b/libs/community/langchain_community/document_loaders/csv_loader.py index 92198ac5bac..28d340e5fbd 100644 --- a/libs/community/langchain_community/document_loaders/csv_loader.py +++ b/libs/community/langchain_community/document_loaders/csv_loader.py @@ -90,7 +90,7 @@ class CSVLoader(BaseLoader): def __read_file(self, csvfile: TextIOWrapper) -> List[Document]: docs = [] - csv_reader = csv.DictReader(csvfile, **self.csv_args) # type: ignore + csv_reader = csv.DictReader(csvfile, **self.csv_args) for i, row in enumerate(csv_reader): try: source = ( diff --git a/libs/community/langchain_community/document_loaders/directory.py b/libs/community/langchain_community/document_loaders/directory.py index 3837133a693..3cb2ad1309a 100644 --- a/libs/community/langchain_community/document_loaders/directory.py +++ b/libs/community/langchain_community/document_loaders/directory.py @@ -2,7 +2,7 @@ import concurrent import logging import random from pathlib import Path -from typing import Any, List, Optional, Type, Union +from typing import Any, List, Optional, Sequence, Type, Union from langchain_core.documents import Document @@ -41,6 +41,7 @@ class DirectoryLoader(BaseLoader): use_multithreading: bool = False, max_concurrency: int = 4, *, + exclude: Union[Sequence[str], str] = (), sample_size: int = 0, randomize_sample: bool = False, sample_seed: Union[int, None] = None, @@ -51,6 +52,8 @@ class DirectoryLoader(BaseLoader): path: Path to directory. glob: Glob pattern to use to find files. Defaults to "**/[!.]*" (all files except hidden). + exclude: A pattern or list of patterns to exclude from results. + Use glob syntax. silent_errors: Whether to silently ignore errors. Defaults to False. load_hidden: Whether to load hidden files. Defaults to False. loader_cls: Loader class to use for loading files. @@ -64,11 +67,38 @@ class DirectoryLoader(BaseLoader): directory. randomize_sample: Shuffle the files to get a random sample. sample_seed: set the seed of the random shuffle for reproducibility. + + Examples: + + .. code-block:: python + from langchain_community.document_loaders import DirectoryLoader + + # Load all non-hidden files in a directory. + loader = DirectoryLoader("/path/to/directory") + + # Load all text files in a directory without recursion. + loader = DirectoryLoader("/path/to/directory", glob="*.txt") + + # Recursively load all text files in a directory. + loader = DirectoryLoader( + "/path/to/directory", glob="*.txt", recursive=True + ) + + # Load all files in a directory, except for py files. + loader = DirectoryLoader("/path/to/directory", exclude="*.py") + + # Load all files in a directory, except for py or pyc files. + loader = DirectoryLoader( + "/path/to/directory", exclude=["*.py", "*.pyc"] + ) """ if loader_kwargs is None: loader_kwargs = {} + if isinstance(exclude, str): + exclude = (exclude,) self.path = path self.glob = glob + self.exclude = exclude self.load_hidden = load_hidden self.loader_cls = loader_cls self.loader_kwargs = loader_kwargs @@ -118,14 +148,20 @@ class DirectoryLoader(BaseLoader): raise ValueError(f"Expected directory, got file: '{self.path}'") docs: List[Document] = [] - items = list(p.rglob(self.glob) if self.recursive else p.glob(self.glob)) + + paths = p.rglob(self.glob) if self.recursive else p.glob(self.glob) + items = [ + path + for path in paths + if not (self.exclude and any(path.match(glob) for glob in self.exclude)) + ] if self.sample_size > 0: if self.randomize_sample: - randomizer = ( - random.Random(self.sample_seed) if self.sample_seed else random + randomizer = random.Random( + self.sample_seed if self.sample_seed else None ) - randomizer.shuffle(items) # type: ignore + randomizer.shuffle(items) items = items[: min(len(items), self.sample_size)] pbar = None diff --git a/libs/community/langchain_community/document_loaders/doc_intelligence.py b/libs/community/langchain_community/document_loaders/doc_intelligence.py index d1326afdbe3..2aae5212a59 100644 --- a/libs/community/langchain_community/document_loaders/doc_intelligence.py +++ b/libs/community/langchain_community/document_loaders/doc_intelligence.py @@ -89,4 +89,4 @@ class AzureAIDocumentIntelligenceLoader(BaseLoader): blob = Blob.from_path(self.file_path) yield from self.parser.parse(blob) else: - yield from self.parser.parse_url(self.url_path) + yield from self.parser.parse_url(self.url_path) # type: ignore[arg-type] diff --git a/libs/community/langchain_community/document_loaders/dropbox.py b/libs/community/langchain_community/document_loaders/dropbox.py index fcf9d6503fd..fb76043c138 100644 --- a/libs/community/langchain_community/document_loaders/dropbox.py +++ b/libs/community/langchain_community/document_loaders/dropbox.py @@ -121,7 +121,7 @@ class DropboxLoader(BaseLoader, BaseModel): file_extension = os.path.splitext(file_path)[1].lower() if file_extension == ".pdf": - print(f"File {file_path} type detected as .pdf") + print(f"File {file_path} type detected as .pdf") # noqa: T201 from langchain_community.document_loaders import UnstructuredPDFLoader # Download it to a temporary file. @@ -136,10 +136,10 @@ class DropboxLoader(BaseLoader, BaseModel): if docs: return docs[0] except Exception as pdf_ex: - print(f"Error while trying to parse PDF {file_path}: {pdf_ex}") + print(f"Error while trying to parse PDF {file_path}: {pdf_ex}") # noqa: T201 return None else: - print( + print( # noqa: T201 f"File {file_path} could not be decoded as pdf or text. Skipping." ) diff --git a/libs/community/langchain_community/document_loaders/etherscan.py b/libs/community/langchain_community/document_loaders/etherscan.py index 862b63843a1..eb057b4b604 100644 --- a/libs/community/langchain_community/document_loaders/etherscan.py +++ b/libs/community/langchain_community/document_loaders/etherscan.py @@ -85,7 +85,7 @@ class EtherscanLoader(BaseLoader): response = requests.get(url) response.raise_for_status() except requests.exceptions.RequestException as e: - print("Error occurred while making the request:", e) + print("Error occurred while making the request:", e) # noqa: T201 items = response.json()["result"] result = [] if len(items) == 0: @@ -94,7 +94,7 @@ class EtherscanLoader(BaseLoader): content = str(item) metadata = {"from": item["from"], "tx_hash": item["hash"], "to": item["to"]} result.append(Document(page_content=content, metadata=metadata)) - print(len(result)) + print(len(result)) # noqa: T201 return result def getEthBalance(self) -> List[Document]: @@ -107,7 +107,7 @@ class EtherscanLoader(BaseLoader): response = requests.get(url) response.raise_for_status() except requests.exceptions.RequestException as e: - print("Error occurred while making the request:", e) + print("Error occurred while making the request:", e) # noqa: T201 return [Document(page_content=response.json()["result"])] def getInternalTx(self) -> List[Document]: @@ -122,7 +122,7 @@ class EtherscanLoader(BaseLoader): response = requests.get(url) response.raise_for_status() except requests.exceptions.RequestException as e: - print("Error occurred while making the request:", e) + print("Error occurred while making the request:", e) # noqa: T201 items = response.json()["result"] result = [] if len(items) == 0: @@ -145,7 +145,7 @@ class EtherscanLoader(BaseLoader): response = requests.get(url) response.raise_for_status() except requests.exceptions.RequestException as e: - print("Error occurred while making the request:", e) + print("Error occurred while making the request:", e) # noqa: T201 items = response.json()["result"] result = [] if len(items) == 0: @@ -168,7 +168,7 @@ class EtherscanLoader(BaseLoader): response = requests.get(url) response.raise_for_status() except requests.exceptions.RequestException as e: - print("Error occurred while making the request:", e) + print("Error occurred while making the request:", e) # noqa: T201 items = response.json()["result"] result = [] if len(items) == 0: @@ -191,7 +191,7 @@ class EtherscanLoader(BaseLoader): response = requests.get(url) response.raise_for_status() except requests.exceptions.RequestException as e: - print("Error occurred while making the request:", e) + print("Error occurred while making the request:", e) # noqa: T201 items = response.json()["result"] result = [] if len(items) == 0: diff --git a/libs/community/langchain_community/document_loaders/git.py b/libs/community/langchain_community/document_loaders/git.py index 5bd5341a3d8..8d97ecd3df4 100644 --- a/libs/community/langchain_community/document_loaders/git.py +++ b/libs/community/langchain_community/document_loaders/git.py @@ -41,7 +41,7 @@ class GitLoader(BaseLoader): def load(self) -> List[Document]: try: - from git import Blob, Repo # type: ignore + from git import Blob, Repo except ImportError as ex: raise ImportError( "Could not import git python package. " @@ -76,7 +76,7 @@ class GitLoader(BaseLoader): file_path = os.path.join(self.repo_path, item.path) - ignored_files = repo.ignored([file_path]) # type: ignore + ignored_files = repo.ignored([file_path]) if len(ignored_files): continue @@ -105,6 +105,6 @@ class GitLoader(BaseLoader): doc = Document(page_content=text_content, metadata=metadata) docs.append(doc) except Exception as e: - print(f"Error reading file {file_path}: {e}") + print(f"Error reading file {file_path}: {e}") # noqa: T201 return docs diff --git a/libs/community/langchain_community/document_loaders/github.py b/libs/community/langchain_community/document_loaders/github.py index 77bdf7da6c7..65f327d948f 100644 --- a/libs/community/langchain_community/document_loaders/github.py +++ b/libs/community/langchain_community/document_loaders/github.py @@ -1,6 +1,7 @@ +import base64 from abc import ABC from datetime import datetime -from typing import Dict, Iterator, List, Literal, Optional, Union +from typing import Callable, Dict, Iterator, List, Literal, Optional, Union import requests from langchain_core.documents import Document @@ -20,7 +21,7 @@ class BaseGitHubLoader(BaseLoader, BaseModel, ABC): github_api_url: str = "https://api.github.com" """URL of GitHub API""" - @root_validator(pre=True) + @root_validator(pre=True, allow_reuse=True) def validate_environment(cls, values: Dict) -> Dict: """Validate that access token exists in environment.""" values["access_token"] = get_from_dict_or_env( @@ -64,8 +65,14 @@ class GitHubIssuesLoader(BaseGitHubLoader): since: Optional[str] = None """Only show notifications updated after the given time. This is a timestamp in ISO 8601 format: YYYY-MM-DDTHH:MM:SSZ.""" + page: Optional[int] = None + """The page number for paginated results. + Defaults to 1 in the GitHub API.""" + per_page: Optional[int] = None + """Number of items per page. + Defaults to 30 in the GitHub API.""" - @validator("since") + @validator("since", allow_reuse=True) def validate_since(cls, v: Optional[str]) -> Optional[str]: if v: try: @@ -111,7 +118,11 @@ class GitHubIssuesLoader(BaseGitHubLoader): if not self.include_prs and doc.metadata["is_pull_request"]: continue yield doc - if response.links and response.links.get("next"): + if ( + response.links + and response.links.get("next") + and (not self.page and not self.per_page) + ): url = response.links["next"]["url"] else: url = None @@ -175,6 +186,8 @@ class GitHubIssuesLoader(BaseGitHubLoader): "sort": self.sort, "direction": self.direction, "since": self.since, + "page": self.page, + "per_page": self.per_page, } query_params_list = [ f"{k}={v}" for k, v in query_params_dict.items() if v is not None @@ -186,3 +199,59 @@ class GitHubIssuesLoader(BaseGitHubLoader): def url(self) -> str: """Create URL for GitHub API.""" return f"{self.github_api_url}/repos/{self.repo}/issues?{self.query_params}" + + +class GithubFileLoader(BaseGitHubLoader, ABC): + """Load GitHub File""" + + file_extension: str = ".md" + branch: str = "main" + + file_filter: Optional[Callable[[str], bool]] + + def get_file_paths(self) -> List[Dict]: + base_url = ( + f"{self.github_api_url}/api/v3/repos/{self.repo}/git/trees/" + f"{self.branch}?recursive=1" + ) + response = requests.get(base_url, headers=self.headers) + response.raise_for_status() + all_files = response.json()["tree"] + """ one element in all_files + { + 'path': '.github', + 'mode': '040000', + 'type': 'tree', + 'sha': '89a2ae046e8b59eb96531f123c0c6d4913885df1', + 'url': 'https://github.com/api/v3/repos/shufanhao/langchain/git/trees/89a2ae046e8b59eb96531f123c0c6d4913885dxxx' + } + """ + return [ + f + for f in all_files + if not (self.file_filter and not self.file_filter(f["path"])) + ] + + def get_file_content_by_path(self, path: str) -> str: + base_url = f"{self.github_api_url}/api/v3/repos/{self.repo}/contents/{path}" + response = requests.get(base_url, headers=self.headers) + response.raise_for_status() + + content_encoded = response.json()["content"] + return base64.b64decode(content_encoded).decode("utf-8") + + def load(self) -> List[Document]: + documents = [] + + files = self.get_file_paths() + for file in files: + content = self.get_file_content_by_path(file["path"]) + metadata = { + "path": file["path"], + "sha": file["sha"], + "source": f"{self.github_api_url}/{self.repo}/{file['type']}/" + f"{self.branch}/{file['path']}", + } + documents.append(Document(page_content=content, metadata=metadata)) + + return documents diff --git a/libs/community/langchain_community/document_loaders/googledrive.py b/libs/community/langchain_community/document_loaders/googledrive.py index 7f5124a4d9b..9e28b4dedf2 100644 --- a/libs/community/langchain_community/document_loaders/googledrive.py +++ b/libs/community/langchain_community/document_loaders/googledrive.py @@ -216,9 +216,9 @@ class GoogleDriveLoader(BaseLoader, BaseModel): except HttpError as e: if e.resp.status == 404: - print("File not found: {}".format(id)) + print("File not found: {}".format(id)) # noqa: T201 else: - print("An error occurred: {}".format(e)) + print("An error occurred: {}".format(e)) # noqa: T201 text = fh.getvalue().decode("utf-8") metadata = { diff --git a/libs/community/langchain_community/document_loaders/mediawikidump.py b/libs/community/langchain_community/document_loaders/mediawikidump.py index 360420c536d..4868e5f89cd 100644 --- a/libs/community/langchain_community/document_loaders/mediawikidump.py +++ b/libs/community/langchain_community/document_loaders/mediawikidump.py @@ -60,7 +60,7 @@ class MWDumpLoader(BaseLoader): self.skip_redirects = skip_redirects self.stop_on_error = stop_on_error - def _load_dump_file(self): + def _load_dump_file(self): # type: ignore[no-untyped-def] try: import mwxml except ImportError as e: @@ -70,7 +70,7 @@ class MWDumpLoader(BaseLoader): return mwxml.Dump.from_file(open(self.file_path, encoding=self.encoding)) - def _load_single_page_from_dump(self, page) -> Document: + def _load_single_page_from_dump(self, page) -> Document: # type: ignore[no-untyped-def, return] """Parse a single page.""" try: import mwparserfromhell diff --git a/libs/community/langchain_community/document_loaders/notiondb.py b/libs/community/langchain_community/document_loaders/notiondb.py index 0987d532c97..bcc9c38c14a 100644 --- a/libs/community/langchain_community/document_loaders/notiondb.py +++ b/libs/community/langchain_community/document_loaders/notiondb.py @@ -20,6 +20,17 @@ class NotionDBLoader(BaseLoader): database_id (str): Notion database id. request_timeout_sec (int): Timeout for Notion requests in seconds. Defaults to 10. + filter_object (Dict[str, Any]): Filter object used to limit returned + entries based on specified criteria. + E.g.: { + "timestamp": "last_edited_time", + "last_edited_time": { + "on_or_after": "2024-02-07" + } + } -> will only return entries that were last edited + on or after 2024-02-07 + Notion docs: https://developers.notion.com/reference/post-database-query-filter + Defaults to None, which will return ALL entries. """ def __init__( @@ -27,6 +38,8 @@ class NotionDBLoader(BaseLoader): integration_token: str, database_id: str, request_timeout_sec: Optional[int] = 10, + *, + filter_object: Optional[Dict[str, Any]] = None, ) -> None: """Initialize with parameters.""" if not integration_token: @@ -42,6 +55,7 @@ class NotionDBLoader(BaseLoader): "Notion-Version": "2022-06-28", } self.request_timeout_sec = request_timeout_sec + self.filter_object = filter_object or {} def load(self) -> List[Document]: """Load documents from the Notion database. @@ -55,7 +69,10 @@ class NotionDBLoader(BaseLoader): def _retrieve_page_summaries( self, query_dict: Dict[str, Any] = {"page_size": 100} ) -> List[Dict[str, Any]]: - """Get all the pages from a Notion database.""" + """ + Get all the pages from a Notion database + OR filter based on specified criteria. + """ pages: List[Dict[str, Any]] = [] while True: @@ -63,6 +80,7 @@ class NotionDBLoader(BaseLoader): DATABASE_URL.format(database_id=self.database_id), method="POST", query_dict=query_dict, + filter_object=self.filter_object, ) pages.extend(data.get("results")) @@ -182,13 +200,18 @@ class NotionDBLoader(BaseLoader): return "\n".join(result_lines_arr) def _request( - self, url: str, method: str = "GET", query_dict: Dict[str, Any] = {} + self, + url: str, + method: str = "GET", + query_dict: Dict[str, Any] = {}, + *, + filter_object: Optional[Dict[str, Any]] = None, ) -> Any: res = requests.request( method, url, headers=self.headers, - json=query_dict, + json={**query_dict, "filter": filter_object or {}}, timeout=self.request_timeout_sec, ) res.raise_for_status() diff --git a/libs/community/langchain_community/document_loaders/nuclia.py b/libs/community/langchain_community/document_loaders/nuclia.py index 33128588994..0744b778b4e 100644 --- a/libs/community/langchain_community/document_loaders/nuclia.py +++ b/libs/community/langchain_community/document_loaders/nuclia.py @@ -25,7 +25,7 @@ class NucliaLoader(BaseLoader): return [] obj = json.loads(data) text = obj["extracted_text"][0]["body"]["text"] - print(text) + print(text) # noqa: T201 metadata = { "file": obj["file_extracted_data"][0], "metadata": obj["field_metadata"][0], diff --git a/libs/community/langchain_community/document_loaders/onenote.py b/libs/community/langchain_community/document_loaders/onenote.py index 2c96eb10e3d..6e3c6cd0977 100644 --- a/libs/community/langchain_community/document_loaders/onenote.py +++ b/libs/community/langchain_community/document_loaders/onenote.py @@ -1,4 +1,5 @@ """Loads data from OneNote Notebooks""" + from pathlib import Path from typing import Dict, Iterator, List, Optional @@ -164,8 +165,8 @@ class OneNoteLoader(BaseLoader, BaseModel): authorization_request_url = client_instance.get_authorization_request_url( self._scopes ) - print("Visit the following url to give consent:") - print(authorization_request_url) + print("Visit the following url to give consent:") # noqa: T201 + print(authorization_request_url) # noqa: T201 authorization_url = input("Paste the authenticated url here:\n") authorization_code = authorization_url.split("code=")[1].split("&")[0] diff --git a/libs/community/langchain_community/document_loaders/parsers/audio.py b/libs/community/langchain_community/document_loaders/parsers/audio.py index 3b96f9860c5..65674e3d1da 100644 --- a/libs/community/langchain_community/document_loaders/parsers/audio.py +++ b/libs/community/langchain_community/document_loaders/parsers/audio.py @@ -64,7 +64,7 @@ class OpenAIWhisperParser(BaseBlobParser): file_obj.name = f"part_{split_number}.mp3" # Transcribe - print(f"Transcribing part {split_number + 1}!") + print(f"Transcribing part {split_number + 1}!") # noqa: T201 attempts = 0 while attempts < 3: try: @@ -77,10 +77,10 @@ class OpenAIWhisperParser(BaseBlobParser): break except Exception as e: attempts += 1 - print(f"Attempt {attempts} failed. Exception: {str(e)}") + print(f"Attempt {attempts} failed. Exception: {str(e)}") # noqa: T201 time.sleep(5) else: - print("Failed to transcribe after 3 attempts.") + print("Failed to transcribe after 3 attempts.") # noqa: T201 continue yield Document( @@ -169,7 +169,7 @@ class OpenAIWhisperParserLocal(BaseBlobParser): rec_model = "openai/whisper-large" self.lang_model = lang_model if lang_model else rec_model - print("Using the following model: ", self.lang_model) + print("Using the following model: ", self.lang_model) # noqa: T201 self.batch_size = batch_size @@ -216,7 +216,7 @@ class OpenAIWhisperParserLocal(BaseBlobParser): file_obj = io.BytesIO(audio.export(format="mp3").read()) # Transcribe - print(f"Transcribing part {blob.path}!") + print(f"Transcribing part {blob.path}!") # noqa: T201 y, sr = librosa.load(file_obj, sr=16000) diff --git a/libs/community/langchain_community/document_loaders/parsers/language/c.py b/libs/community/langchain_community/document_loaders/parsers/language/c.py new file mode 100644 index 00000000000..2db1ec99fca --- /dev/null +++ b/libs/community/langchain_community/document_loaders/parsers/language/c.py @@ -0,0 +1,36 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (struct_specifier + body: (field_declaration_list)) @struct + (enum_specifier + body: (enumerator_list)) @enum + (union_specifier + body: (field_declaration_list)) @union + (function_definition) @function + ] +""".strip() + + +class CSegmenter(TreeSitterSegmenter): + """Code segmenter for C.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("c") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"// {text}" diff --git a/libs/community/langchain_community/document_loaders/parsers/language/cpp.py b/libs/community/langchain_community/document_loaders/parsers/language/cpp.py new file mode 100644 index 00000000000..9d09164a846 --- /dev/null +++ b/libs/community/langchain_community/document_loaders/parsers/language/cpp.py @@ -0,0 +1,36 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (class_specifier + body: (field_declaration_list)) @class + (struct_specifier + body: (field_declaration_list)) @struct + (union_specifier + body: (field_declaration_list)) @union + (function_definition) @function + ] +""".strip() + + +class CPPSegmenter(TreeSitterSegmenter): + """Code segmenter for C++.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("cpp") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"// {text}" diff --git a/libs/community/langchain_community/document_loaders/parsers/language/csharp.py b/libs/community/langchain_community/document_loaders/parsers/language/csharp.py new file mode 100644 index 00000000000..a9f809fa00a --- /dev/null +++ b/libs/community/langchain_community/document_loaders/parsers/language/csharp.py @@ -0,0 +1,36 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (namespace_declaration) @namespace + (class_declaration) @class + (method_declaration) @method + (interface_declaration) @interface + (enum_declaration) @enum + (struct_declaration) @struct + (record_declaration) @record + ] +""".strip() + + +class CSharpSegmenter(TreeSitterSegmenter): + """Code segmenter for C#.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("c_sharp") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"// {text}" diff --git a/libs/community/langchain_community/document_loaders/parsers/language/go.py b/libs/community/langchain_community/document_loaders/parsers/language/go.py new file mode 100644 index 00000000000..f836ab3ad71 --- /dev/null +++ b/libs/community/langchain_community/document_loaders/parsers/language/go.py @@ -0,0 +1,31 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (function_declaration) @function + (type_declaration) @type + ] +""".strip() + + +class GoSegmenter(TreeSitterSegmenter): + """Code segmenter for Go.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("go") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"// {text}" diff --git a/libs/community/langchain_community/document_loaders/parsers/language/java.py b/libs/community/langchain_community/document_loaders/parsers/language/java.py new file mode 100644 index 00000000000..c7293e1ed7f --- /dev/null +++ b/libs/community/langchain_community/document_loaders/parsers/language/java.py @@ -0,0 +1,32 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (class_declaration) @class + (interface_declaration) @interface + (enum_declaration) @enum + ] +""".strip() + + +class JavaSegmenter(TreeSitterSegmenter): + """Code segmenter for Java.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("java") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"// {text}" diff --git a/libs/community/langchain_community/document_loaders/parsers/language/javascript.py b/libs/community/langchain_community/document_loaders/parsers/language/javascript.py index 0f2fea68fa2..27a360a2e6c 100644 --- a/libs/community/langchain_community/document_loaders/parsers/language/javascript.py +++ b/libs/community/langchain_community/document_loaders/parsers/language/javascript.py @@ -1,4 +1,4 @@ -from typing import Any, List +from typing import Any, List, Tuple from langchain_community.document_loaders.parsers.language.code_segmenter import ( CodeSegmenter, @@ -55,15 +55,18 @@ class JavaScriptSegmenter(CodeSegmenter): tree = esprima.parseScript(self.code, loc=True) simplified_lines = self.source_lines[:] + indices_to_del: List[Tuple[int, int]] = [] for node in tree.body: if isinstance( node, (esprima.nodes.FunctionDeclaration, esprima.nodes.ClassDeclaration), ): - start = node.loc.start.line - 1 + start, end = node.loc.start.line - 1, node.loc.end.line simplified_lines[start] = f"// Code for: {simplified_lines[start]}" - for line_num in range(start + 1, node.loc.end.line): - simplified_lines[line_num] = None # type: ignore + indices_to_del.append((start + 1, end)) - return "\n".join(line for line in simplified_lines if line is not None) + for start, end in reversed(indices_to_del): + del simplified_lines[start + 0 : end] + + return "\n".join(line for line in simplified_lines) diff --git a/libs/community/langchain_community/document_loaders/parsers/language/kotlin.py b/libs/community/langchain_community/document_loaders/parsers/language/kotlin.py new file mode 100644 index 00000000000..6f946f7b4a6 --- /dev/null +++ b/libs/community/langchain_community/document_loaders/parsers/language/kotlin.py @@ -0,0 +1,31 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (function_declaration) @function + (class_declaration) @class + ] +""".strip() + + +class KotlinSegmenter(TreeSitterSegmenter): + """Code segmenter for Kotlin.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("kotlin") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"// {text}" diff --git a/libs/community/langchain_community/document_loaders/parsers/language/language_parser.py b/libs/community/langchain_community/document_loaders/parsers/language/language_parser.py index b3f6534327f..8709b58becf 100644 --- a/libs/community/langchain_community/document_loaders/parsers/language/language_parser.py +++ b/libs/community/langchain_community/document_loaders/parsers/language/language_parser.py @@ -6,11 +6,25 @@ from langchain_core.documents import Document from langchain_community.document_loaders.base import BaseBlobParser from langchain_community.document_loaders.blob_loaders import Blob +from langchain_community.document_loaders.parsers.language.c import CSegmenter from langchain_community.document_loaders.parsers.language.cobol import CobolSegmenter +from langchain_community.document_loaders.parsers.language.cpp import CPPSegmenter +from langchain_community.document_loaders.parsers.language.csharp import CSharpSegmenter +from langchain_community.document_loaders.parsers.language.go import GoSegmenter +from langchain_community.document_loaders.parsers.language.java import JavaSegmenter from langchain_community.document_loaders.parsers.language.javascript import ( JavaScriptSegmenter, ) +from langchain_community.document_loaders.parsers.language.kotlin import KotlinSegmenter +from langchain_community.document_loaders.parsers.language.lua import LuaSegmenter +from langchain_community.document_loaders.parsers.language.perl import PerlSegmenter from langchain_community.document_loaders.parsers.language.python import PythonSegmenter +from langchain_community.document_loaders.parsers.language.ruby import RubySegmenter +from langchain_community.document_loaders.parsers.language.rust import RustSegmenter +from langchain_community.document_loaders.parsers.language.scala import ScalaSegmenter +from langchain_community.document_loaders.parsers.language.typescript import ( + TypeScriptSegmenter, +) if TYPE_CHECKING: from langchain.text_splitter import Language @@ -22,12 +36,36 @@ try: "py": Language.PYTHON, "js": Language.JS, "cobol": Language.COBOL, + "c": Language.C, + "cpp": Language.CPP, + "cs": Language.CSHARP, + "rb": Language.RUBY, + "scala": Language.SCALA, + "rs": Language.RUST, + "go": Language.GO, + "kt": Language.KOTLIN, + "lua": Language.LUA, + "pl": Language.PERL, + "ts": Language.TS, + "java": Language.JAVA, } LANGUAGE_SEGMENTERS: Dict[str, Any] = { Language.PYTHON: PythonSegmenter, Language.JS: JavaScriptSegmenter, Language.COBOL: CobolSegmenter, + Language.C: CSegmenter, + Language.CPP: CPPSegmenter, + Language.CSHARP: CSharpSegmenter, + Language.RUBY: RubySegmenter, + Language.RUST: RustSegmenter, + Language.SCALA: ScalaSegmenter, + Language.GO: GoSegmenter, + Language.KOTLIN: KotlinSegmenter, + Language.LUA: LuaSegmenter, + Language.PERL: PerlSegmenter, + Language.TS: TypeScriptSegmenter, + Language.JAVA: JavaSegmenter, } except ImportError: LANGUAGE_EXTENSIONS = {} @@ -43,11 +81,34 @@ class LanguageParser(BaseBlobParser): This approach can potentially improve the accuracy of QA models over source code. - Currently, the supported languages for code parsing are Python and JavaScript. + The supported languages for code parsing are: + + - C (*) + - C++ (*) + - C# (*) + - COBOL + - Go (*) + - Java (*) + - JavaScript (requires package `esprima`) + - Kotlin (*) + - Lua (*) + - Perl (*) + - Python + - Ruby (*) + - Rust (*) + - Scala (*) + - TypeScript (*) + + Items marked with (*) require the packages `tree_sitter` and + `tree_sitter_languages`. It is straightforward to add support for additional + languages using `tree_sitter`, although this currently requires modifying LangChain. The language used for parsing can be configured, along with the minimum number of lines required to activate the splitting based on syntax. + If a language is not explicitly specified, `LanguageParser` will infer one from + filename extensions, if present. + Examples: .. code-block:: python diff --git a/libs/community/langchain_community/document_loaders/parsers/language/lua.py b/libs/community/langchain_community/document_loaders/parsers/language/lua.py new file mode 100644 index 00000000000..3e0a762ba4b --- /dev/null +++ b/libs/community/langchain_community/document_loaders/parsers/language/lua.py @@ -0,0 +1,33 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (function_definition_statement + name: (identifier)) @function + (local_function_definition_statement + name: (identifier)) @function + ] +""".strip() + + +class LuaSegmenter(TreeSitterSegmenter): + """Code segmenter for Lua.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("lua") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"-- {text}" diff --git a/libs/community/langchain_community/document_loaders/parsers/language/perl.py b/libs/community/langchain_community/document_loaders/parsers/language/perl.py new file mode 100644 index 00000000000..b68d52cef2b --- /dev/null +++ b/libs/community/langchain_community/document_loaders/parsers/language/perl.py @@ -0,0 +1,30 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (function_definition) @subroutine + ] +""".strip() + + +class PerlSegmenter(TreeSitterSegmenter): + """Code segmenter for Perl.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("perl") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"# {text}" diff --git a/libs/community/langchain_community/document_loaders/parsers/language/python.py b/libs/community/langchain_community/document_loaders/parsers/language/python.py index ca810946bd4..52dbc68352a 100644 --- a/libs/community/langchain_community/document_loaders/parsers/language/python.py +++ b/libs/community/langchain_community/document_loaders/parsers/language/python.py @@ -1,5 +1,5 @@ import ast -from typing import Any, List +from typing import Any, List, Tuple from langchain_community.document_loaders.parsers.language.code_segmenter import ( CodeSegmenter, @@ -39,13 +39,15 @@ class PythonSegmenter(CodeSegmenter): tree = ast.parse(self.code) simplified_lines = self.source_lines[:] + indices_to_del: List[Tuple[int, int]] = [] for node in ast.iter_child_nodes(tree): if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef, ast.ClassDef)): - start = node.lineno - 1 + start, end = node.lineno - 1, node.end_lineno simplified_lines[start] = f"# Code for: {simplified_lines[start]}" + assert isinstance(end, int) + indices_to_del.append((start + 1, end)) - assert isinstance(node.end_lineno, int) - for line_num in range(start + 1, node.end_lineno): - simplified_lines[line_num] = None # type: ignore + for start, end in reversed(indices_to_del): + del simplified_lines[start + 0 : end] - return "\n".join(line for line in simplified_lines if line is not None) + return "\n".join(simplified_lines) diff --git a/libs/community/langchain_community/document_loaders/parsers/language/ruby.py b/libs/community/langchain_community/document_loaders/parsers/language/ruby.py new file mode 100644 index 00000000000..767a1f94a4d --- /dev/null +++ b/libs/community/langchain_community/document_loaders/parsers/language/ruby.py @@ -0,0 +1,32 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (method) @method + (module) @module + (class) @class + ] +""".strip() + + +class RubySegmenter(TreeSitterSegmenter): + """Code segmenter for Ruby.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("ruby") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"# {text}" diff --git a/libs/community/langchain_community/document_loaders/parsers/language/rust.py b/libs/community/langchain_community/document_loaders/parsers/language/rust.py new file mode 100644 index 00000000000..bb73f96bf6d --- /dev/null +++ b/libs/community/langchain_community/document_loaders/parsers/language/rust.py @@ -0,0 +1,34 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (function_item + name: (identifier) + body: (block)) @function + (struct_item) @struct + (trait_item) @trait + ] +""".strip() + + +class RustSegmenter(TreeSitterSegmenter): + """Code segmenter for Rust.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("rust") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"// {text}" diff --git a/libs/community/langchain_community/document_loaders/parsers/language/scala.py b/libs/community/langchain_community/document_loaders/parsers/language/scala.py new file mode 100644 index 00000000000..af62a4e748f --- /dev/null +++ b/libs/community/langchain_community/document_loaders/parsers/language/scala.py @@ -0,0 +1,33 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (class_definition) @class + (function_definition) @function + (object_definition) @object + (trait_definition) @trait + ] +""".strip() + + +class ScalaSegmenter(TreeSitterSegmenter): + """Code segmenter for Scala.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("scala") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"// {text}" diff --git a/libs/community/langchain_community/document_loaders/parsers/language/tree_sitter_segmenter.py b/libs/community/langchain_community/document_loaders/parsers/language/tree_sitter_segmenter.py new file mode 100644 index 00000000000..7187cd6b5f5 --- /dev/null +++ b/libs/community/langchain_community/document_loaders/parsers/language/tree_sitter_segmenter.py @@ -0,0 +1,108 @@ +from abc import abstractmethod +from typing import TYPE_CHECKING, List + +from langchain_community.document_loaders.parsers.language.code_segmenter import ( + CodeSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language, Parser + + +class TreeSitterSegmenter(CodeSegmenter): + """Abstract class for `CodeSegmenter`s that use the tree-sitter library.""" + + def __init__(self, code: str): + super().__init__(code) + self.source_lines = self.code.splitlines() + + try: + import tree_sitter # noqa: F401 + import tree_sitter_languages # noqa: F401 + except ImportError: + raise ImportError( + "Could not import tree_sitter/tree_sitter_languages Python packages. " + "Please install them with " + "`pip install tree-sitter tree-sitter-languages`." + ) + + def is_valid(self) -> bool: + language = self.get_language() + error_query = language.query("(ERROR) @error") + + parser = self.get_parser() + tree = parser.parse(bytes(self.code, encoding="UTF-8")) + + return len(error_query.captures(tree.root_node)) == 0 + + def extract_functions_classes(self) -> List[str]: + language = self.get_language() + query = language.query(self.get_chunk_query()) + + parser = self.get_parser() + tree = parser.parse(bytes(self.code, encoding="UTF-8")) + captures = query.captures(tree.root_node) + + processed_lines = set() + chunks = [] + + for node, name in captures: + start_line = node.start_point[0] + end_line = node.end_point[0] + lines = list(range(start_line, end_line + 1)) + + if any(line in processed_lines for line in lines): + continue + + processed_lines.update(lines) + chunk_text = node.text.decode("UTF-8") + chunks.append(chunk_text) + + return chunks + + def simplify_code(self) -> str: + language = self.get_language() + query = language.query(self.get_chunk_query()) + + parser = self.get_parser() + tree = parser.parse(bytes(self.code, encoding="UTF-8")) + processed_lines = set() + + simplified_lines = self.source_lines[:] + for node, name in query.captures(tree.root_node): + start_line = node.start_point[0] + end_line = node.end_point[0] + + lines = list(range(start_line, end_line + 1)) + if any(line in processed_lines for line in lines): + continue + + simplified_lines[start_line] = self.make_line_comment( + f"Code for: {self.source_lines[start_line]}" + ) + + for line_num in range(start_line + 1, end_line + 1): + simplified_lines[line_num] = None # type: ignore + + processed_lines.update(lines) + + return "\n".join(line for line in simplified_lines if line is not None) + + def get_parser(self) -> "Parser": + from tree_sitter import Parser + + parser = Parser() + parser.set_language(self.get_language()) + return parser + + @abstractmethod + def get_language(self) -> "Language": + raise NotImplementedError() # pragma: no cover + + @abstractmethod + def get_chunk_query(self) -> str: + raise NotImplementedError() # pragma: no cover + + @abstractmethod + def make_line_comment(self, text: str) -> str: + raise NotImplementedError() # pragma: no cover diff --git a/libs/community/langchain_community/document_loaders/parsers/language/typescript.py b/libs/community/langchain_community/document_loaders/parsers/language/typescript.py new file mode 100644 index 00000000000..ab7158e2e82 --- /dev/null +++ b/libs/community/langchain_community/document_loaders/parsers/language/typescript.py @@ -0,0 +1,33 @@ +from typing import TYPE_CHECKING + +from langchain_community.document_loaders.parsers.language.tree_sitter_segmenter import ( # noqa: E501 + TreeSitterSegmenter, +) + +if TYPE_CHECKING: + from tree_sitter import Language + + +CHUNK_QUERY = """ + [ + (function_declaration) @function + (class_declaration) @class + (interface_declaration) @interface + (enum_declaration) @enum + ] +""".strip() + + +class TypeScriptSegmenter(TreeSitterSegmenter): + """Code segmenter for TypeScript.""" + + def get_language(self) -> "Language": + from tree_sitter_languages import get_language + + return get_language("typescript") + + def get_chunk_query(self) -> str: + return CHUNK_QUERY + + def make_line_comment(self, text: str) -> str: + return f"// {text}" diff --git a/libs/community/langchain_community/document_loaders/parsers/vsdx.py b/libs/community/langchain_community/document_loaders/parsers/vsdx.py index 109521e48cc..aeb414453ad 100644 --- a/libs/community/langchain_community/document_loaders/parsers/vsdx.py +++ b/libs/community/langchain_community/document_loaders/parsers/vsdx.py @@ -11,7 +11,9 @@ from langchain_community.document_loaders.blob_loaders import Blob class VsdxParser(BaseBlobParser, ABC): - def parse(self, blob: Blob) -> Iterator[Document]: + """Parser for vsdx files.""" + + def parse(self, blob: Blob) -> Iterator[Document]: # type: ignore[override] """Parse a vsdx file.""" return self.lazy_parse(blob) @@ -21,7 +23,7 @@ class VsdxParser(BaseBlobParser, ABC): with blob.as_bytes_io() as pdf_file_obj: with zipfile.ZipFile(pdf_file_obj, "r") as zfile: - pages = self.get_pages_content(zfile, blob.source) + pages = self.get_pages_content(zfile, blob.source) # type: ignore[arg-type] yield from [ Document( @@ -59,14 +61,14 @@ class VsdxParser(BaseBlobParser, ABC): ) if "visio/pages/pages.xml" not in zfile.namelist(): - print("WARNING - No pages.xml file found in {}".format(source)) - return + print("WARNING - No pages.xml file found in {}".format(source)) # noqa: T201 + return # type: ignore[return-value] if "visio/pages/_rels/pages.xml.rels" not in zfile.namelist(): - print("WARNING - No pages.xml.rels file found in {}".format(source)) - return + print("WARNING - No pages.xml.rels file found in {}".format(source)) # noqa: T201 + return # type: ignore[return-value] if "docProps/app.xml" not in zfile.namelist(): - print("WARNING - No app.xml file found in {}".format(source)) - return + print("WARNING - No app.xml file found in {}".format(source)) # noqa: T201 + return # type: ignore[return-value] pagesxml_content: dict = xmltodict.parse(zfile.read("visio/pages/pages.xml")) appxml_content: dict = xmltodict.parse(zfile.read("docProps/app.xml")) @@ -79,7 +81,7 @@ class VsdxParser(BaseBlobParser, ABC): rel["@Name"].strip() for rel in pagesxml_content["Pages"]["Page"] ] else: - disordered_names: List[str] = [ + disordered_names: List[str] = [ # type: ignore[no-redef] pagesxml_content["Pages"]["Page"]["@Name"].strip() ] if isinstance(pagesxmlrels_content["Relationships"]["Relationship"], list): @@ -88,7 +90,7 @@ class VsdxParser(BaseBlobParser, ABC): for rel in pagesxmlrels_content["Relationships"]["Relationship"] ] else: - disordered_paths: List[str] = [ + disordered_paths: List[str] = [ # type: ignore[no-redef] "visio/pages/" + pagesxmlrels_content["Relationships"]["Relationship"]["@Target"] ] diff --git a/libs/community/langchain_community/document_loaders/pdf.py b/libs/community/langchain_community/document_loaders/pdf.py index 600eb26bd0b..ea086054110 100644 --- a/libs/community/langchain_community/document_loaders/pdf.py +++ b/libs/community/langchain_community/document_loaders/pdf.py @@ -318,7 +318,7 @@ class PDFMinerPDFasHTMLLoader(BasePDFLoader): output_string = StringIO() with open_filename(self.file_path, "rb") as fp: extract_text_to_fp( - fp, # type: ignore[arg-type] + fp, output_string, codec="", laparams=LAParams(), @@ -480,7 +480,7 @@ class MathpixPDFLoader(BasePDFLoader): # This indicates an error with the PDF processing raise ValueError("Unable to retrieve PDF from Mathpix") else: - print(f"Status: {status}, waiting for processing to complete") + print(f"Status: {status}, waiting for processing to complete") # noqa: T201 time.sleep(5) raise TimeoutError diff --git a/libs/community/langchain_community/document_loaders/pebblo.py b/libs/community/langchain_community/document_loaders/pebblo.py new file mode 100644 index 00000000000..0875335ac14 --- /dev/null +++ b/libs/community/langchain_community/document_loaders/pebblo.py @@ -0,0 +1,292 @@ +"""Pebblo's safe dataloader is a wrapper for document loaders""" + +import logging +import os +import uuid +from http import HTTPStatus +from typing import Any, Dict, Iterator, List + +import requests +from langchain_core.documents import Document + +from langchain_community.document_loaders.base import BaseLoader +from langchain_community.utilities.pebblo import ( + CLASSIFIER_URL, + PLUGIN_VERSION, + App, + Doc, + get_full_path, + get_loader_full_path, + get_loader_type, + get_runtime, +) + +logger = logging.getLogger(__name__) + + +class PebbloSafeLoader(BaseLoader): + """Pebblo Safe Loader class is a wrapper around document loaders enabling the data + to be scrutinized. + """ + + _discover_sent: bool = False + _loader_sent: bool = False + + def __init__( + self, + langchain_loader: BaseLoader, + name: str, + owner: str = "", + description: str = "", + ): + if not name or not isinstance(name, str): + raise NameError("Must specify a valid name.") + self.app_name = name + self.load_id = str(uuid.uuid4()) + self.loader = langchain_loader + self.owner = owner + self.description = description + self.source_path = get_loader_full_path(self.loader) + self.source_owner = PebbloSafeLoader.get_file_owner_from_path(self.source_path) + self.docs: List[Document] = [] + loader_name = str(type(self.loader)).split(".")[-1].split("'")[0] + self.source_type = get_loader_type(loader_name) + self.source_path_size = self.get_source_size(self.source_path) + self.source_aggr_size = 0 + self.loader_details = { + "loader": loader_name, + "source_path": self.source_path, + "source_type": self.source_type, + **( + {"source_path_size": str(self.source_path_size)} + if self.source_path_size > 0 + else {} + ), + } + # generate app + self.app = self._get_app_details() + self._send_discover() + + def load(self) -> List[Document]: + """Load Documents. + + Returns: + list: Documents fetched from load method of the wrapped `loader`. + """ + self.docs = self.loader.load() + self._send_loader_doc(loading_end=True) + return self.docs + + def lazy_load(self) -> Iterator[Document]: + """Load documents in lazy fashion. + + Raises: + NotImplementedError: raised when lazy_load id not implemented + within wrapped loader. + + Yields: + list: Documents from loader's lazy loading. + """ + try: + doc_iterator = self.loader.lazy_load() + except NotImplementedError as exc: + err_str = f"{self.loader.__class__.__name__} does not implement lazy_load()" + logger.error(err_str) + raise NotImplementedError(err_str) from exc + while True: + try: + doc = next(doc_iterator) + except StopIteration: + self.docs = [] + self._send_loader_doc(loading_end=True) + break + self.docs = [ + doc, + ] + self._send_loader_doc() + yield doc + + @classmethod + def set_discover_sent(cls) -> None: + cls._discover_sent = True + + @classmethod + def set_loader_sent(cls) -> None: + cls._loader_sent = True + + def _send_loader_doc(self, loading_end: bool = False) -> None: + """Send documents fetched from loader to pebblo-server. Internal method. + + Args: + loading_end (bool, optional): Flag indicating the halt of data + loading by loader. Defaults to False. + """ + headers = {"Accept": "application/json", "Content-Type": "application/json"} + doc_content = [doc.dict() for doc in self.docs] + docs = [] + for doc in doc_content: + doc_source_path = get_full_path(doc.get("metadata", {}).get("source")) + doc_source_owner = PebbloSafeLoader.get_file_owner_from_path( + doc_source_path + ) + doc_source_size = self.get_source_size(doc_source_path) + page_content = str(doc.get("page_content")) + page_content_size = self.calculate_content_size(page_content) + self.source_aggr_size += page_content_size + docs.append( + { + "doc": page_content, + "source_path": doc_source_path, + "last_modified": doc.get("metadata", {}).get("last_modified"), + "file_owner": doc_source_owner, + **( + {"source_path_size": doc_source_size} + if doc_source_size is not None + else {} + ), + } + ) + payload: Dict[str, Any] = { + "name": self.app_name, + "owner": self.owner, + "docs": docs, + "plugin_version": PLUGIN_VERSION, + "load_id": self.load_id, + "loader_details": self.loader_details, + "loading_end": "false", + "source_owner": self.source_owner, + } + if loading_end is True: + payload["loading_end"] = "true" + if "loader_details" in payload: + payload["loader_details"]["source_aggr_size"] = self.source_aggr_size + payload = Doc(**payload).dict(exclude_unset=True) + load_doc_url = f"{CLASSIFIER_URL}/v1/loader/doc" + try: + resp = requests.post( + load_doc_url, headers=headers, json=payload, timeout=20 + ) + if resp.status_code not in [HTTPStatus.OK, HTTPStatus.BAD_GATEWAY]: + logger.warning( + f"Received unexpected HTTP response code: {resp.status_code}" + ) + logger.debug( + f"send_loader_doc: request \ + url {resp.request.url}, \ + body {str(resp.request.body)[:999]} \ + len {len(resp.request.body if resp.request.body else [])} \ + response status{resp.status_code} body {resp.json()}" + ) + except requests.exceptions.RequestException: + logger.warning("Unable to reach pebblo server.") + except Exception: + logger.warning("An Exception caught in _send_loader_doc.") + if loading_end is True: + PebbloSafeLoader.set_loader_sent() + + @staticmethod + def calculate_content_size(page_content: str) -> int: + """Calculate the content size in bytes: + - Encode the string to bytes using a specific encoding (e.g., UTF-8) + - Get the length of the encoded bytes. + + Args: + page_content (str): Data string. + + Returns: + int: Size of string in bytes. + """ + + # Encode the content to bytes using UTF-8 + encoded_content = page_content.encode("utf-8") + size = len(encoded_content) + return size + + def _send_discover(self) -> None: + """Send app discovery payload to pebblo-server. Internal method.""" + headers = {"Accept": "application/json", "Content-Type": "application/json"} + payload = self.app.dict(exclude_unset=True) + app_discover_url = f"{CLASSIFIER_URL}/v1/app/discover" + try: + resp = requests.post( + app_discover_url, headers=headers, json=payload, timeout=20 + ) + logger.debug( + f"send_discover: request \ + url {resp.request.url}, \ + headers {resp.request.headers}, \ + body {str(resp.request.body)[:999]} \ + len {len(resp.request.body if resp.request.body else [])} \ + response status{resp.status_code} body {resp.json()}" + ) + if resp.status_code in [HTTPStatus.OK, HTTPStatus.BAD_GATEWAY]: + PebbloSafeLoader.set_discover_sent() + else: + logger.warning( + f"Received unexpected HTTP response code: {resp.status_code}" + ) + except requests.exceptions.RequestException: + logger.warning("Unable to reach pebblo server.") + except Exception: + logger.warning("An Exception caught in _send_discover.") + + def _get_app_details(self) -> App: + """Fetch app details. Internal method. + + Returns: + App: App details. + """ + framework, runtime = get_runtime() + app = App( + name=self.app_name, + owner=self.owner, + description=self.description, + load_id=self.load_id, + runtime=runtime, + framework=framework, + plugin_version=PLUGIN_VERSION, + ) + return app + + @staticmethod + def get_file_owner_from_path(file_path: str) -> str: + """Fetch owner of local file path. + + Args: + file_path (str): Local file path. + + Returns: + str: Name of owner. + """ + try: + import pwd + + file_owner_uid = os.stat(file_path).st_uid + file_owner_name = pwd.getpwuid(file_owner_uid).pw_name + except Exception: + file_owner_name = "unknown" + return file_owner_name + + def get_source_size(self, source_path: str) -> int: + """Fetch size of source path. Source can be a directory or a file. + + Args: + source_path (str): Local path of data source. + + Returns: + int: Source size in bytes. + """ + if not source_path: + return 0 + size = 0 + if os.path.isfile(source_path): + size = os.path.getsize(source_path) + elif os.path.isdir(source_path): + total_size = 0 + for dirpath, _, filenames in os.walk(source_path): + for f in filenames: + fp = os.path.join(dirpath, f) + if not os.path.islink(fp): + total_size += os.path.getsize(fp) + size = total_size + return size diff --git a/libs/community/langchain_community/document_loaders/recursive_url_loader.py b/libs/community/langchain_community/document_loaders/recursive_url_loader.py index 6fca4edf86a..08887659841 100644 --- a/libs/community/langchain_community/document_loaders/recursive_url_loader.py +++ b/libs/community/langchain_community/document_loaders/recursive_url_loader.py @@ -93,6 +93,7 @@ class RecursiveUrlLoader(BaseLoader): link_regex: Union[str, re.Pattern, None] = None, headers: Optional[dict] = None, check_response_status: bool = False, + continue_on_failure: bool = True, ) -> None: """Initialize with URL to crawl and any subdirectories to exclude. @@ -117,6 +118,8 @@ class RecursiveUrlLoader(BaseLoader): link_regex: Regex for extracting sub-links from the raw html of a web page. check_response_status: If True, check HTTP response status and skip URLs with error responses (400-599). + continue_on_failure: If True, continue if getting or parsing a link raises + an exception. Otherwise, raise the exception. """ self.url = url @@ -142,6 +145,7 @@ class RecursiveUrlLoader(BaseLoader): self._lock = asyncio.Lock() if self.use_async else None self.headers = headers self.check_response_status = check_response_status + self.continue_on_failure = continue_on_failure def _get_child_links_recursive( self, url: str, visited: Set[str], *, depth: int = 0 @@ -164,11 +168,14 @@ class RecursiveUrlLoader(BaseLoader): if self.check_response_status and 400 <= response.status_code <= 599: raise ValueError(f"Received HTTP status {response.status_code}") except Exception as e: - logger.warning( - f"Unable to load from {url}. Received error {e} of type " - f"{e.__class__.__name__}" - ) - return + if self.continue_on_failure: + logger.warning( + f"Unable to load from {url}. Received error {e} of type " + f"{e.__class__.__name__}" + ) + return + else: + raise e content = self.extractor(response.text) if content: yield Document( @@ -184,6 +191,7 @@ class RecursiveUrlLoader(BaseLoader): pattern=self.link_regex, prevent_outside=self.prevent_outside, exclude_prefixes=self.exclude_dirs, + continue_on_failure=self.continue_on_failure, ) for link in sub_links: # Check all unvisited links @@ -207,6 +215,11 @@ class RecursiveUrlLoader(BaseLoader): visited: A set of visited URLs. depth: To reach the current url, how many pages have been visited. """ + if not self.use_async or not self._lock: + raise ValueError( + "Async functions forbidden when not initialized with `use_async`" + ) + try: import aiohttp except ImportError: @@ -229,7 +242,7 @@ class RecursiveUrlLoader(BaseLoader): headers=self.headers, ) ) - async with self._lock: # type: ignore + async with self._lock: visited.add(url) try: async with session.get(url) as response: @@ -237,13 +250,16 @@ class RecursiveUrlLoader(BaseLoader): if self.check_response_status and 400 <= response.status <= 599: raise ValueError(f"Received HTTP status {response.status}") except (aiohttp.client_exceptions.InvalidURL, Exception) as e: - logger.warning( - f"Unable to load {url}. Received error {e} of type " - f"{e.__class__.__name__}" - ) if close_session: await session.close() - return [] + if self.continue_on_failure: + logger.warning( + f"Unable to load {url}. Received error {e} of type " + f"{e.__class__.__name__}" + ) + return [] + else: + raise e results = [] content = self.extractor(text) if content: @@ -261,11 +277,12 @@ class RecursiveUrlLoader(BaseLoader): pattern=self.link_regex, prevent_outside=self.prevent_outside, exclude_prefixes=self.exclude_dirs, + continue_on_failure=self.continue_on_failure, ) # Recursively call the function to get the children of the children sub_tasks = [] - async with self._lock: # type: ignore + async with self._lock: to_visit = set(sub_links).difference(visited) for link in to_visit: sub_tasks.append( diff --git a/libs/community/langchain_community/document_loaders/snowflake_loader.py b/libs/community/langchain_community/document_loaders/snowflake_loader.py index c0e479f6105..b37926b5e0d 100644 --- a/libs/community/langchain_community/document_loaders/snowflake_loader.py +++ b/libs/community/langchain_community/document_loaders/snowflake_loader.py @@ -88,7 +88,7 @@ class SnowflakeLoader(BaseLoader): column_names = [column[0] for column in cur.description] query_result = [dict(zip(column_names, row)) for row in query_result] except Exception as e: - print(f"An error occurred: {e}") + print(f"An error occurred: {e}") # noqa: T201 query_result = [] finally: cur.close() @@ -110,7 +110,7 @@ class SnowflakeLoader(BaseLoader): def lazy_load(self) -> Iterator[Document]: query_result = self._execute_query() if isinstance(query_result, Exception): - print(f"An error occurred during the query: {query_result}") + print(f"An error occurred during the query: {query_result}") # noqa: T201 return [] page_content_columns, metadata_columns = self._get_columns(query_result) if "*" in page_content_columns: diff --git a/libs/community/langchain_community/document_loaders/toml.py b/libs/community/langchain_community/document_loaders/toml.py index dcb59d41f43..34d7ccfbfeb 100644 --- a/libs/community/langchain_community/document_loaders/toml.py +++ b/libs/community/langchain_community/document_loaders/toml.py @@ -44,4 +44,4 @@ class TomlLoader(BaseLoader): ) yield doc except tomli.TOMLDecodeError as e: - print(f"Error parsing TOML file {file_path}: {e}") + print(f"Error parsing TOML file {file_path}: {e}") # noqa: T201 diff --git a/libs/community/langchain_community/document_loaders/url_playwright.py b/libs/community/langchain_community/document_loaders/url_playwright.py index 8071d3717f7..106f15cee6f 100644 --- a/libs/community/langchain_community/document_loaders/url_playwright.py +++ b/libs/community/langchain_community/document_loaders/url_playwright.py @@ -2,7 +2,7 @@ """ import logging from abc import ABC, abstractmethod -from typing import TYPE_CHECKING, List, Optional +from typing import TYPE_CHECKING, Dict, List, Optional from langchain_core.documents import Document @@ -111,6 +111,22 @@ class PlaywrightURLLoader(BaseLoader): urls (List[str]): List of URLs to load. continue_on_failure (bool): If True, continue loading other URLs on failure. headless (bool): If True, the browser will run in headless mode. + proxy (Optional[Dict[str, str]]): If set, the browser will access URLs + through the specified proxy. + + Example: + .. code-block:: python + + from langchain_community.document_loaders import PlaywrightURLLoader + + urls = ["https://api.ipify.org/?format=json",] + proxy={ + "server": "https://xx.xx.xx:15818", # https://: + "username": "username", + "password": "password" + } + loader = PlaywrightURLLoader(urls, proxy=proxy) + data = loader.load() """ def __init__( @@ -120,6 +136,7 @@ class PlaywrightURLLoader(BaseLoader): headless: bool = True, remove_selectors: Optional[List[str]] = None, evaluator: Optional[PlaywrightEvaluator] = None, + proxy: Optional[Dict[str, str]] = None, ): """Load a list of URLs using Playwright.""" try: @@ -133,6 +150,7 @@ class PlaywrightURLLoader(BaseLoader): self.urls = urls self.continue_on_failure = continue_on_failure self.headless = headless + self.proxy = proxy if remove_selectors and evaluator: raise ValueError( @@ -153,7 +171,7 @@ class PlaywrightURLLoader(BaseLoader): docs: List[Document] = list() with sync_playwright() as p: - browser = p.chromium.launch(headless=self.headless) + browser = p.chromium.launch(headless=self.headless, proxy=self.proxy) for url in self.urls: try: page = browser.new_page() @@ -186,7 +204,7 @@ class PlaywrightURLLoader(BaseLoader): docs: List[Document] = list() async with async_playwright() as p: - browser = await p.chromium.launch(headless=self.headless) + browser = await p.chromium.launch(headless=self.headless, proxy=self.proxy) for url in self.urls: try: page = await browser.new_page() diff --git a/libs/community/langchain_community/document_loaders/youtube.py b/libs/community/langchain_community/document_loaders/youtube.py index ec6a5e15b57..c1cea0b2dc3 100644 --- a/libs/community/langchain_community/document_loaders/youtube.py +++ b/libs/community/langchain_community/document_loaders/youtube.py @@ -141,6 +141,8 @@ def _parse_video_id(url: str) -> Optional[str]: class TranscriptFormat(Enum): + """Transcript format.""" + TEXT = "text" LINES = "lines" diff --git a/libs/community/langchain_community/embeddings/__init__.py b/libs/community/langchain_community/embeddings/__init__.py index 8c50c3f1ab7..4d9565c60f7 100644 --- a/libs/community/langchain_community/embeddings/__init__.py +++ b/libs/community/langchain_community/embeddings/__init__.py @@ -65,11 +65,13 @@ from langchain_community.embeddings.mlflow import ( from langchain_community.embeddings.mlflow_gateway import MlflowAIGatewayEmbeddings from langchain_community.embeddings.modelscope_hub import ModelScopeEmbeddings from langchain_community.embeddings.mosaicml import MosaicMLInstructorEmbeddings +from langchain_community.embeddings.nemo import NeMoEmbeddings from langchain_community.embeddings.nlpcloud import NLPCloudEmbeddings from langchain_community.embeddings.oci_generative_ai import OCIGenAIEmbeddings from langchain_community.embeddings.octoai_embeddings import OctoAIEmbeddings from langchain_community.embeddings.ollama import OllamaEmbeddings from langchain_community.embeddings.openai import OpenAIEmbeddings +from langchain_community.embeddings.optimum_intel import QuantizedBiEncoderEmbeddings from langchain_community.embeddings.sagemaker_endpoint import ( SagemakerEndpointEmbeddings, ) @@ -148,6 +150,8 @@ __all__ = [ "BookendEmbeddings", "VolcanoEmbeddings", "OCIGenAIEmbeddings", + "QuantizedBiEncoderEmbeddings", + "NeMoEmbeddings", ] diff --git a/libs/community/langchain_community/embeddings/baichuan.py b/libs/community/langchain_community/embeddings/baichuan.py index b31e6092d59..430ef548cca 100644 --- a/libs/community/langchain_community/embeddings/baichuan.py +++ b/libs/community/langchain_community/embeddings/baichuan.py @@ -79,17 +79,17 @@ class BaichuanTextEmbeddings(BaseModel, Embeddings): return [result.get("embedding", []) for result in sorted_embeddings] else: # Log error or handle unsuccessful response appropriately - print( + print( # noqa: T201 f"""Error: Received status code {response.status_code} from embedding API""" ) return None except Exception as e: # Log the exception or handle it as needed - print(f"Exception occurred while trying to get embeddings: {str(e)}") + print(f"Exception occurred while trying to get embeddings: {str(e)}") # noqa: T201 return None - def embed_documents(self, texts: List[str]) -> Optional[List[List[float]]]: + def embed_documents(self, texts: List[str]) -> Optional[List[List[float]]]: # type: ignore[override] """Public method to get embeddings for a list of documents. Args: @@ -100,7 +100,7 @@ class BaichuanTextEmbeddings(BaseModel, Embeddings): """ return self._embed(texts) - def embed_query(self, text: str) -> Optional[List[float]]: + def embed_query(self, text: str) -> Optional[List[float]]: # type: ignore[override] """Public method to get embedding for a single query text. Args: diff --git a/libs/community/langchain_community/embeddings/cohere.py b/libs/community/langchain_community/embeddings/cohere.py index fd95b58ea6c..2d4676d1254 100644 --- a/libs/community/langchain_community/embeddings/cohere.py +++ b/libs/community/langchain_community/embeddings/cohere.py @@ -34,7 +34,7 @@ class CohereEmbeddings(BaseModel, Embeddings): cohere_api_key: Optional[str] = None - max_retries: Optional[int] = None + max_retries: Optional[int] = 3 """Maximum number of retries to make when generating.""" request_timeout: Optional[float] = None """Timeout in seconds for the Cohere API request.""" @@ -92,11 +92,13 @@ class CohereEmbeddings(BaseModel, Embeddings): async def aembed( self, texts: List[str], *, input_type: Optional[str] = None ) -> List[List[float]]: - embeddings = await self.async_client.embed( - model=self.model, - texts=texts, - input_type=input_type, - truncate=self.truncate, + embeddings = ( + await self.async_client.embed( + model=self.model, + texts=texts, + input_type=input_type, + truncate=self.truncate, + ) ).embeddings return [list(map(float, e)) for e in embeddings] diff --git a/libs/community/langchain_community/embeddings/edenai.py b/libs/community/langchain_community/embeddings/edenai.py index 3d6b1ec16d0..9446969d332 100644 --- a/libs/community/langchain_community/embeddings/edenai.py +++ b/libs/community/langchain_community/embeddings/edenai.py @@ -56,7 +56,7 @@ class EdenAiEmbeddings(BaseModel, Embeddings): headers = { "accept": "application/json", "content-type": "application/json", - "authorization": f"Bearer {self.edenai_api_key.get_secret_value()}", + "authorization": f"Bearer {self.edenai_api_key.get_secret_value()}", # type: ignore[union-attr] "User-Agent": self.get_user_agent(), } diff --git a/libs/community/langchain_community/embeddings/embaas.py b/libs/community/langchain_community/embeddings/embaas.py index 2f0b31f4439..00f23116bf4 100644 --- a/libs/community/langchain_community/embeddings/embaas.py +++ b/libs/community/langchain_community/embeddings/embaas.py @@ -85,7 +85,7 @@ class EmbaasEmbeddings(BaseModel, Embeddings): def _handle_request(self, payload: EmbaasEmbeddingsPayload) -> List[List[float]]: """Sends a request to the Embaas API and handles the response.""" headers = { - "Authorization": f"Bearer {self.embaas_api_key.get_secret_value()}", + "Authorization": f"Bearer {self.embaas_api_key.get_secret_value()}", # type: ignore[union-attr] "Content-Type": "application/json", } diff --git a/libs/community/langchain_community/embeddings/gradient_ai.py b/libs/community/langchain_community/embeddings/gradient_ai.py index 6fd05d3c699..c0133d02386 100644 --- a/libs/community/langchain_community/embeddings/gradient_ai.py +++ b/libs/community/langchain_community/embeddings/gradient_ai.py @@ -162,5 +162,5 @@ class TinyAsyncGradientEmbeddingClient: #: :meta private: It might be entirely removed in the future. """ - def __init__(self, *args, **kwargs) -> None: + def __init__(self, *args, **kwargs) -> None: # type: ignore[no-untyped-def] raise ValueError("Deprecated,TinyAsyncGradientEmbeddingClient was removed.") diff --git a/libs/community/langchain_community/embeddings/huggingface.py b/libs/community/langchain_community/embeddings/huggingface.py index 068aafa1fe1..60f4ecb4435 100644 --- a/libs/community/langchain_community/embeddings/huggingface.py +++ b/libs/community/langchain_community/embeddings/huggingface.py @@ -49,6 +49,8 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings): """Keyword arguments to pass when calling the `encode` method of the model.""" multi_process: bool = False """Run encode() on multiple GPUs.""" + show_progress: bool = False + """Whether to show a progress bar.""" def __init__(self, **kwargs: Any): """Initialize the sentence_transformer.""" @@ -88,7 +90,9 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings): embeddings = self.client.encode_multi_process(texts, pool) sentence_transformers.SentenceTransformer.stop_multi_process_pool(pool) else: - embeddings = self.client.encode(texts, **self.encode_kwargs) + embeddings = self.client.encode( + texts, show_progress_bar=self.show_progress, **self.encode_kwargs + ) return embeddings.tolist() diff --git a/libs/community/langchain_community/embeddings/infinity.py b/libs/community/langchain_community/embeddings/infinity.py index 4cc5688e0e1..f0a01fc75e5 100644 --- a/libs/community/langchain_community/embeddings/infinity.py +++ b/libs/community/langchain_community/embeddings/infinity.py @@ -28,14 +28,14 @@ class InfinityEmbeddings(BaseModel, Embeddings): from langchain_community.embeddings import InfinityEmbeddings InfinityEmbeddings( model="BAAI/bge-small", - infinity_api_url="http://localhost:7797/v1", + infinity_api_url="http://localhost:7997", ) """ model: str "Underlying Infinity model id." - infinity_api_url: str = "http://localhost:7797/v1" + infinity_api_url: str = "http://localhost:7997" """Endpoint URL to use.""" client: Any = None #: :meta private: diff --git a/libs/community/langchain_community/embeddings/javelin_ai_gateway.py b/libs/community/langchain_community/embeddings/javelin_ai_gateway.py index c91a003291d..6ee376097e9 100644 --- a/libs/community/langchain_community/embeddings/javelin_ai_gateway.py +++ b/libs/community/langchain_community/embeddings/javelin_ai_gateway.py @@ -74,7 +74,7 @@ class JavelinAIGatewayEmbeddings(Embeddings, BaseModel): if "embedding" in item: embeddings.append(item["embedding"]) except ValueError as e: - print("Failed to query route: " + str(e)) + print("Failed to query route: " + str(e)) # noqa: T201 return embeddings @@ -92,7 +92,7 @@ class JavelinAIGatewayEmbeddings(Embeddings, BaseModel): if "embedding" in item: embeddings.append(item["embedding"]) except ValueError as e: - print("Failed to query route: " + str(e)) + print("Failed to query route: " + str(e)) # noqa: T201 return embeddings diff --git a/libs/community/langchain_community/embeddings/llm_rails.py b/libs/community/langchain_community/embeddings/llm_rails.py index 44bc0171803..c8afa11b491 100644 --- a/libs/community/langchain_community/embeddings/llm_rails.py +++ b/libs/community/langchain_community/embeddings/llm_rails.py @@ -56,7 +56,7 @@ class LLMRailsEmbeddings(BaseModel, Embeddings): """ response = requests.post( "https://api.llmrails.com/v1/embeddings", - headers={"X-API-KEY": self.api_key.get_secret_value()}, + headers={"X-API-KEY": self.api_key.get_secret_value()}, # type: ignore[union-attr] json={"input": texts, "model": self.model}, timeout=60, ) diff --git a/libs/community/langchain_community/embeddings/minimax.py b/libs/community/langchain_community/embeddings/minimax.py index a13eb98367d..38e56c553b8 100644 --- a/libs/community/langchain_community/embeddings/minimax.py +++ b/libs/community/langchain_community/embeddings/minimax.py @@ -110,7 +110,7 @@ class MiniMaxEmbeddings(BaseModel, Embeddings): # HTTP headers for authorization headers = { - "Authorization": f"Bearer {self.minimax_api_key.get_secret_value()}", + "Authorization": f"Bearer {self.minimax_api_key.get_secret_value()}", # type: ignore[union-attr] "Content-Type": "application/json", } diff --git a/libs/community/langchain_community/embeddings/mlflow.py b/libs/community/langchain_community/embeddings/mlflow.py index 6b24dacb025..e44c53d31c5 100644 --- a/libs/community/langchain_community/embeddings/mlflow.py +++ b/libs/community/langchain_community/embeddings/mlflow.py @@ -13,7 +13,7 @@ def _chunk(texts: List[str], size: int) -> Iterator[List[str]]: class MlflowEmbeddings(Embeddings, BaseModel): - """Wrapper around embeddings LLMs in MLflow. + """Embedding LLMs in MLflow. To use, you should have the `mlflow[genai]` python package installed. For more information, see https://mlflow.org/docs/latest/llms/deployments/server.html. @@ -71,7 +71,8 @@ class MlflowEmbeddings(Embeddings, BaseModel): embeddings: List[List[float]] = [] for txt in _chunk(texts, 20): resp = self._client.predict( - endpoint=self.endpoint, inputs={"input": txt, **params} + endpoint=self.endpoint, + inputs={"input": txt, **params}, # type: ignore[arg-type] ) embeddings.extend(r["embedding"] for r in resp["data"]) return embeddings @@ -84,5 +85,7 @@ class MlflowEmbeddings(Embeddings, BaseModel): class MlflowCohereEmbeddings(MlflowEmbeddings): + """Cohere embedding LLMs in MLflow.""" + query_params: Dict[str, str] = {"input_type": "search_query"} documents_params: Dict[str, str] = {"input_type": "search_document"} diff --git a/libs/community/langchain_community/embeddings/nemo.py b/libs/community/langchain_community/embeddings/nemo.py new file mode 100644 index 00000000000..7ae0ab5da63 --- /dev/null +++ b/libs/community/langchain_community/embeddings/nemo.py @@ -0,0 +1,169 @@ +from __future__ import annotations + +import asyncio +import json +from typing import Any, Dict, List, Optional + +import aiohttp +import requests +from langchain_core.embeddings import Embeddings +from langchain_core.pydantic_v1 import BaseModel, root_validator + + +def is_endpoint_live(url: str, headers: Optional[dict], payload: Any) -> bool: + """ + Check if an endpoint is live by sending a GET request to the specified URL. + + Args: + url (str): The URL of the endpoint to check. + + Returns: + bool: True if the endpoint is live (status code 200), False otherwise. + + Raises: + Exception: If the endpoint returns a non-successful status code or if there is + an error querying the endpoint. + """ + try: + response = requests.request("POST", url, headers=headers, data=payload) + + # Check if the status code is 200 (OK) + if response.status_code == 200: + return True + else: + # Raise an exception if the status code is not 200 + raise Exception( + f"Endpoint returned a non-successful status code: " + f"{response.status_code}" + ) + except requests.exceptions.RequestException as e: + # Handle any exceptions (e.g., connection errors) + raise Exception(f"Error querying the endpoint: {e}") + + +class NeMoEmbeddings(BaseModel, Embeddings): + batch_size: int = 16 + model: str = "NV-Embed-QA-003" + api_endpoint_url: str = "http://localhost:8088/v1/embeddings" + + @root_validator() + def validate_environment(cls, values: Dict) -> Dict: + """Validate that the end point is alive using the values that are provided.""" + + url = values["api_endpoint_url"] + model = values["model"] + + # Optional: A minimal test payload and headers required by the endpoint + headers = {"Content-Type": "application/json"} + payload = json.dumps( + {"input": "Hello World", "model": model, "input_type": "query"} + ) + + is_endpoint_live(url, headers, payload) + + return values + + async def _aembedding_func( + self, session: Any, text: str, input_type: str + ) -> List[float]: + """Async call out to embedding endpoint. + + Args: + text: The text to embed. + + Returns: + Embeddings for the text. + """ + + headers = {"Content-Type": "application/json"} + + async with session.post( + self.api_endpoint_url, + json={"input": text, "model": self.model, "input_type": input_type}, + headers=headers, + ) as response: + response.raise_for_status() + answer = await response.text() + answer = json.loads(answer) + return answer["data"][0]["embedding"] + + def _embedding_func(self, text: str, input_type: str) -> List[float]: + """Call out to Cohere's embedding endpoint. + + Args: + text: The text to embed. + + Returns: + Embeddings for the text. + """ + + payload = json.dumps( + {"input": text, "model": self.model, "input_type": input_type} + ) + headers = {"Content-Type": "application/json"} + + response = requests.request( + "POST", self.api_endpoint_url, headers=headers, data=payload + ) + response_json = json.loads(response.text) + embedding = response_json["data"][0]["embedding"] + + return embedding + + def embed_documents(self, documents: List[str]) -> List[List[float]]: + """Embed a list of document texts. + + Args: + texts: The list of texts to embed. + + Returns: + List of embeddings, one for each text. + """ + return [self._embedding_func(text, input_type="passage") for text in documents] + + def embed_query(self, text: str) -> List[float]: + return self._embedding_func(text, input_type="query") + + async def aembed_query(self, text: str) -> List[float]: + """Call out to NeMo's embedding endpoint async for embedding query text. + + Args: + text: The text to embed. + + Returns: + Embedding for the text. + """ + + async with aiohttp.ClientSession() as session: + embedding = await self._aembedding_func(session, text, "passage") + return embedding + + async def aembed_documents(self, texts: List[str]) -> List[List[float]]: + """Call out to NeMo's embedding endpoint async for embedding search docs. + + Args: + texts: The list of texts to embed. + + Returns: + List of embeddings, one for each text. + """ + embeddings = [] + + async with aiohttp.ClientSession() as session: + for batch in range(0, len(texts), self.batch_size): + text_batch = texts[batch : batch + self.batch_size] + + for text in text_batch: + # Create tasks for all texts in the batch + tasks = [ + self._aembedding_func(session, text, "passage") + for text in text_batch + ] + + # Run all tasks concurrently + batch_results = await asyncio.gather(*tasks) + + # Extend the embeddings list with results from this batch + embeddings.extend(batch_results) + + return embeddings diff --git a/libs/community/langchain_community/embeddings/oci_generative_ai.py b/libs/community/langchain_community/embeddings/oci_generative_ai.py index 6d47fec6f32..b9495260b91 100644 --- a/libs/community/langchain_community/embeddings/oci_generative_ai.py +++ b/libs/community/langchain_community/embeddings/oci_generative_ai.py @@ -8,6 +8,8 @@ CUSTOM_ENDPOINT_PREFIX = "ocid1.generativeaiendpoint" class OCIAuthType(Enum): + """OCI authentication types as enumerator.""" + API_KEY = 1 SECURITY_TOKEN = 2 INSTANCE_PRINCIPAL = 3 @@ -63,16 +65,16 @@ class OCIGenAIEmbeddings(BaseModel, Embeddings): If not specified , DEFAULT will be used """ - model_id: str = None + model_id: str = None # type: ignore[assignment] """Id of the model to call, e.g., cohere.embed-english-light-v2.0""" model_kwargs: Optional[Dict] = None """Keyword arguments to pass to the model""" - service_endpoint: str = None + service_endpoint: str = None # type: ignore[assignment] """service endpoint url""" - compartment_id: str = None + compartment_id: str = None # type: ignore[assignment] """OCID of compartment""" truncate: Optional[str] = "END" @@ -109,7 +111,7 @@ class OCIGenAIEmbeddings(BaseModel, Embeddings): client_kwargs.pop("signer", None) elif values["auth_type"] == OCIAuthType(2).name: - def make_security_token_signer(oci_config): + def make_security_token_signer(oci_config): # type: ignore[no-untyped-def] pk = oci.signer.load_private_key_from_file( oci_config.get("key_file"), None ) diff --git a/libs/community/langchain_community/embeddings/octoai_embeddings.py b/libs/community/langchain_community/embeddings/octoai_embeddings.py index a93fa4e03ad..f006a437a4e 100644 --- a/libs/community/langchain_community/embeddings/octoai_embeddings.py +++ b/libs/community/langchain_community/embeddings/octoai_embeddings.py @@ -80,7 +80,7 @@ class OctoAIEmbeddings(BaseModel, Embeddings): json_data = resp_json["data"] for item in json_data: if "embedding" in item: - embedding.append(item["embedding"]) + embedding = item["embedding"] except Exception as e: raise ValueError(f"Error raised by the inference endpoint: {e}") from e diff --git a/libs/community/langchain_community/embeddings/optimum_intel.py b/libs/community/langchain_community/embeddings/optimum_intel.py new file mode 100644 index 00000000000..56d4a6f20ac --- /dev/null +++ b/libs/community/langchain_community/embeddings/optimum_intel.py @@ -0,0 +1,208 @@ +from typing import Any, Dict, List, Optional + +from langchain_core.embeddings import Embeddings +from langchain_core.pydantic_v1 import BaseModel, Extra + + +class QuantizedBiEncoderEmbeddings(BaseModel, Embeddings): + """Quantized bi-encoders embedding models. + + Please ensure that you have installed optimum-intel and ipex. + + Input: + model_name: str = Model name. + max_seq_len: int = The maximum sequence length for tokenization. (default 512) + pooling_strategy: str = + "mean" or "cls", pooling strategy for the final layer. (default "mean") + query_instruction: Optional[str] = + An instruction to add to the query before embedding. (default None) + document_instruction: Optional[str] = + An instruction to add to each document before embedding. (default None) + padding: Optional[bool] = + Whether to add padding during tokenization or not. (default True) + model_kwargs: Optional[Dict] = + Parameters to add to the model during initialization. (default {}) + encode_kwargs: Optional[Dict] = + Parameters to add during the embedding forward pass. (default {}) + + Example: + + from langchain_community.embeddings import QuantizedBiEncoderEmbeddings + + model_name = "Intel/bge-small-en-v1.5-rag-int8-static" + encode_kwargs = {'normalize_embeddings': True} + hf = QuantizedBiEncoderEmbeddings( + model_name, + encode_kwargs=encode_kwargs, + query_instruction="Represent this sentence for searching relevant passages: " + ) + """ + + def __init__( + self, + model_name: str, + max_seq_len: int = 512, + pooling_strategy: str = "mean", # "mean" or "cls" + query_instruction: Optional[str] = None, + document_instruction: Optional[str] = None, + padding: bool = True, + model_kwargs: Optional[Dict] = None, + encode_kwargs: Optional[Dict] = None, + **kwargs: Any, + ) -> None: + super().__init__(**kwargs) + self.model_name_or_path = model_name + self.max_seq_len = max_seq_len + self.pooling = pooling_strategy + self.padding = padding + self.encode_kwargs = encode_kwargs or {} + self.model_kwargs = model_kwargs or {} + + self.normalize = self.encode_kwargs.get("normalize_embeddings", False) + self.batch_size = self.encode_kwargs.get("batch_size", 32) + + self.query_instruction = query_instruction + self.document_instruction = document_instruction + + self.load_model() + + def load_model(self) -> None: + try: + from transformers import AutoTokenizer + except ImportError as e: + raise ImportError( + "Unable to import transformers, please install with " + "`pip install -U transformers`." + ) from e + try: + from optimum.intel import IPEXModel + + self.transformer_model = IPEXModel.from_pretrained( + self.model_name_or_path, **self.model_kwargs + ) + except Exception as e: + raise Exception( + f""" +Failed to load model {self.model_name_or_path}, due to the following error: +{e} +Please ensure that you have installed optimum-intel and ipex correctly,using: + +pip install optimum[neural-compressor] +pip install intel_extension_for_pytorch + +For more information, please visit: +* Install optimum-intel as shown here: https://github.com/huggingface/optimum-intel. +* Install IPEX as shown here: https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=cpu&version=v2.2.0%2Bcpu. +""" + ) + self.transformer_tokenizer = AutoTokenizer.from_pretrained( + pretrained_model_name_or_path=self.model_name_or_path, + ) + self.transformer_model.eval() + + class Config: + """Configuration for this pydantic object.""" + + extra = Extra.allow + + def _embed(self, inputs: Any) -> Any: + try: + import torch + except ImportError as e: + raise ImportError( + "Unable to import torch, please install with `pip install -U torch`." + ) from e + with torch.inference_mode(): + outputs = self.transformer_model(**inputs) + if self.pooling == "mean": + emb = self._mean_pooling(outputs, inputs["attention_mask"]) + elif self.pooling == "cls": + emb = self._cls_pooling(outputs) + else: + raise ValueError("pooling method no supported") + + if self.normalize: + emb = torch.nn.functional.normalize(emb, p=2, dim=1) + return emb + + @staticmethod + def _cls_pooling(outputs: Any) -> Any: + if isinstance(outputs, dict): + token_embeddings = outputs["last_hidden_state"] + else: + token_embeddings = outputs[0] + return token_embeddings[:, 0] + + @staticmethod + def _mean_pooling(outputs: Any, attention_mask: Any) -> Any: + try: + import torch + except ImportError as e: + raise ImportError( + "Unable to import torch, please install with `pip install -U torch`." + ) from e + if isinstance(outputs, dict): + token_embeddings = outputs["last_hidden_state"] + else: + # First element of model_output contains all token embeddings + token_embeddings = outputs[0] + input_mask_expanded = ( + attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() + ) + sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1) + sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9) + return sum_embeddings / sum_mask + + def _embed_text(self, texts: List[str]) -> List[List[float]]: + inputs = self.transformer_tokenizer( + texts, + max_length=self.max_seq_len, + truncation=True, + padding=self.padding, + return_tensors="pt", + ) + return self._embed(inputs).tolist() + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + """Embed a list of text documents using the Optimized Embedder model. + + Input: + texts: List[str] = List of text documents to embed. + Output: + List[List[float]] = The embeddings of each text document. + """ + try: + import pandas as pd + except ImportError as e: + raise ImportError( + "Unable to import pandas, please install with `pip install -U pandas`." + ) from e + try: + import tqdm + except ImportError as e: + raise ImportError( + "Unable to import tqdm, please install with `pip install -U tqdm`." + ) from e + docs = [ + self.document_instruction + d if self.document_instruction else d + for d in texts + ] + + # group into batches + text_list_df = pd.DataFrame(docs, columns=["texts"]).reset_index() + + # assign each example with its batch + text_list_df["batch_index"] = text_list_df["index"] // self.batch_size + + # create groups + batches = list(text_list_df.groupby(["batch_index"])["texts"].apply(list)) + + vectors = [] + for batch in tqdm(batches, desc="Batches"): + vectors += self._embed_text(batch) + return vectors + + def embed_query(self, text: str) -> List[float]: + if self.query_instruction: + text = self.query_instruction + text + return self._embed_text([text])[0] diff --git a/libs/community/langchain_community/embeddings/spacy_embeddings.py b/libs/community/langchain_community/embeddings/spacy_embeddings.py index 645d5afc963..954b2550fb4 100644 --- a/libs/community/langchain_community/embeddings/spacy_embeddings.py +++ b/libs/community/langchain_community/embeddings/spacy_embeddings.py @@ -78,7 +78,7 @@ class SpacyEmbeddings(BaseModel, Embeddings): Returns: A list of embeddings, one for each document. """ - return [self.nlp(text).vector.tolist() for text in texts] + return [self.nlp(text).vector.tolist() for text in texts] # type: ignore[misc] def embed_query(self, text: str) -> List[float]: """ @@ -90,7 +90,7 @@ class SpacyEmbeddings(BaseModel, Embeddings): Returns: The embedding for the text. """ - return self.nlp(text).vector.tolist() + return self.nlp(text).vector.tolist() # type: ignore[misc] async def aembed_documents(self, texts: List[str]) -> List[List[float]]: """ diff --git a/libs/community/langchain_community/embeddings/voyageai.py b/libs/community/langchain_community/embeddings/voyageai.py index 93109d45c65..f8b1a4059e6 100644 --- a/libs/community/langchain_community/embeddings/voyageai.py +++ b/libs/community/langchain_community/embeddings/voyageai.py @@ -86,6 +86,15 @@ class VoyageEmbeddings(BaseModel, Embeddings): show_progress_bar: bool = False """Whether to show a progress bar when embedding. Must have tqdm installed if set to True.""" + truncation: Optional[bool] = None + """Whether to truncate the input texts to fit within the context length. + + If True, over-length input texts will be truncated to fit within the context + length, before vectorized by the embedding model. If False, an error will be + raised if any given text exceeds the context length. If not specified + (defaults to None), we will truncate the input text before sending it to the + embedding model if it slightly exceeds the context window length. If it + significantly exceeds the context window length, an error will be raised.""" class Config: """Configuration for this pydantic object.""" @@ -104,12 +113,14 @@ class VoyageEmbeddings(BaseModel, Embeddings): self, input: List[str], input_type: Optional[str] = None ) -> Dict: api_key = cast(SecretStr, self.voyage_api_key).get_secret_value() - params = { + params: Dict = { "url": self.voyage_api_base, "headers": {"Authorization": f"Bearer {api_key}"}, "json": {"model": self.model, "input": input, "input_type": input_type}, "timeout": self.request_timeout, } + if self.truncation is not None: + params["json"]["truncation"] = self.truncation return params def _get_embeddings( diff --git a/libs/community/langchain_community/embeddings/yandex.py b/libs/community/langchain_community/embeddings/yandex.py index d3e6ed69e45..34f71feafae 100644 --- a/libs/community/langchain_community/embeddings/yandex.py +++ b/libs/community/langchain_community/embeddings/yandex.py @@ -42,10 +42,10 @@ class YandexGPTEmbeddings(BaseModel, Embeddings): embeddings = YandexGPTEmbeddings(iam_token="t1.9eu...", model_uri="emb:///text-search-query/latest") """ - iam_token: SecretStr = "" + iam_token: SecretStr = "" # type: ignore[assignment] """Yandex Cloud IAM token for service account with the `ai.languageModels.user` role""" - api_key: SecretStr = "" + api_key: SecretStr = "" # type: ignore[assignment] """Yandex Cloud Api Key for service account with the `ai.languageModels.user` role""" model_uri: str = "" @@ -146,7 +146,7 @@ def _embed_with_retry(llm: YandexGPTEmbeddings, **kwargs: Any) -> Any: return _completion_with_retry(**kwargs) -def _make_request(self: YandexGPTEmbeddings, texts: List[str]): +def _make_request(self: YandexGPTEmbeddings, texts: List[str]): # type: ignore[no-untyped-def] try: import grpc from yandex.cloud.ai.foundation_models.v1.foundation_models_service_pb2 import ( # noqa: E501 @@ -167,7 +167,7 @@ def _make_request(self: YandexGPTEmbeddings, texts: List[str]): for text in texts: request = TextEmbeddingRequest(model_uri=self.model_uri, text=text) stub = EmbeddingsServiceStub(channel) - res = stub.TextEmbedding(request, metadata=self._grpc_metadata) + res = stub.TextEmbedding(request, metadata=self._grpc_metadata) # type: ignore[attr-defined] result.append(list(res.embedding)) time.sleep(self.sleep_interval) diff --git a/libs/community/langchain_community/graphs/__init__.py b/libs/community/langchain_community/graphs/__init__.py index bd15f6465d1..c1fc640c4b1 100644 --- a/libs/community/langchain_community/graphs/__init__.py +++ b/libs/community/langchain_community/graphs/__init__.py @@ -8,6 +8,7 @@ from langchain_community.graphs.memgraph_graph import MemgraphGraph from langchain_community.graphs.nebula_graph import NebulaGraph from langchain_community.graphs.neo4j_graph import Neo4jGraph from langchain_community.graphs.neptune_graph import NeptuneGraph +from langchain_community.graphs.neptune_rdf_graph import NeptuneRdfGraph from langchain_community.graphs.networkx_graph import NetworkxEntityGraph from langchain_community.graphs.ontotext_graphdb_graph import OntotextGraphDBGraph from langchain_community.graphs.rdf_graph import RdfGraph @@ -19,6 +20,7 @@ __all__ = [ "Neo4jGraph", "NebulaGraph", "NeptuneGraph", + "NeptuneRdfGraph", "KuzuGraph", "HugeGraph", "RdfGraph", diff --git a/libs/community/langchain_community/graphs/kuzu_graph.py b/libs/community/langchain_community/graphs/kuzu_graph.py index eda7417f940..1f99f49fc94 100644 --- a/libs/community/langchain_community/graphs/kuzu_graph.py +++ b/libs/community/langchain_community/graphs/kuzu_graph.py @@ -36,10 +36,7 @@ class KuzuGraph: def query(self, query: str, params: dict = {}) -> List[Dict[str, Any]]: """Query Kùzu database""" - params_list = [] - for param_name in params: - params_list.append([param_name, params[param_name]]) - result = self.conn.execute(query, params_list) + result = self.conn.execute(query, params) column_names = result.get_column_names() return_list = [] while result.has_next(): @@ -79,20 +76,16 @@ class KuzuGraph: rel_properties = [] for table in rel_tables: - current_table_schema = {"properties": [], "label": table["name"]} - properties_text = self.conn._connection.get_rel_property_names( - table["name"] - ).split("\n") - for i, line in enumerate(properties_text): - # The first 3 lines defines src, dst and name, so we skip them - if i < 3: - continue - if not line: - continue - property_name, property_type = line.strip().split(" ") - current_table_schema["properties"].append( - (property_name, property_type) - ) + table_name = table["name"] + current_table_schema = {"properties": [], "label": table_name} + query_result = self.conn.execute( + f"CALL table_info('{table_name}') RETURN *;" + ) + while query_result.has_next(): + row = query_result.get_next() + prop_name = row[1] + prop_type = row[2] + current_table_schema["properties"].append((prop_name, prop_type)) rel_properties.append(current_table_schema) self.schema = ( diff --git a/libs/community/langchain_community/graphs/neo4j_graph.py b/libs/community/langchain_community/graphs/neo4j_graph.py index b8970c06ecc..8d7da7fe91b 100644 --- a/libs/community/langchain_community/graphs/neo4j_graph.py +++ b/libs/community/langchain_community/graphs/neo4j_graph.py @@ -32,7 +32,8 @@ RETURN {start: label, type: property, end: toString(other_node)} AS output def value_sanitize(d: Dict[str, Any]) -> Dict[str, Any]: - """ + """Sanitize the input dictionary. + Sanitizes the input dictionary by removing embedding-like values, lists with more than 128 elements, that are mostly irrelevant for generating answers in a LLM context. These properties, if left in @@ -56,14 +57,15 @@ def value_sanitize(d: Dict[str, Any]) -> Dict[str, Any]: cleaned_list.append(value_sanitize(item)) else: cleaned_list.append(item) - new_dict[key] = cleaned_list + new_dict[key] = cleaned_list # type: ignore[assignment] else: new_dict[key] = value return new_dict class Neo4jGraph(GraphStore): - """Provides a connection to a Neo4j database for various graph operations. + """Neo4j database wrapper for various graph operations. + Parameters: url (Optional[str]): The URL of the Neo4j database server. username (Optional[str]): The username for database authentication. @@ -133,12 +135,14 @@ class Neo4jGraph(GraphStore): # Set schema try: self.refresh_schema() - except neo4j.exceptions.ClientError: - raise ValueError( - "Could not use APOC procedures. " - "Please ensure the APOC plugin is installed in Neo4j and that " - "'apoc.meta.data()' is allowed in Neo4j configuration " - ) + except neo4j.exceptions.ClientError as e: + if e.code == "Neo.ClientError.Procedure.ProcedureNotFound": + raise ValueError( + "Could not use APOC procedures. " + "Please ensure the APOC plugin is installed in Neo4j and that " + "'apoc.meta.data()' is allowed in Neo4j configuration " + ) + raise e @property def get_schema(self) -> str: diff --git a/libs/community/langchain_community/graphs/neptune_graph.py b/libs/community/langchain_community/graphs/neptune_graph.py index 6dc45d12e94..d1ee0db14e0 100644 --- a/libs/community/langchain_community/graphs/neptune_graph.py +++ b/libs/community/langchain_community/graphs/neptune_graph.py @@ -2,7 +2,7 @@ from typing import Any, Dict, List, Optional, Tuple, Union class NeptuneQueryException(Exception): - """A class to handle queries that fail to execute""" + """Exception for the Neptune queries.""" def __init__(self, exception: Union[str, Dict]): if isinstance(exception, dict): diff --git a/libs/community/langchain_community/graphs/neptune_rdf_graph.py b/libs/community/langchain_community/graphs/neptune_rdf_graph.py new file mode 100644 index 00000000000..b9e0074a366 --- /dev/null +++ b/libs/community/langchain_community/graphs/neptune_rdf_graph.py @@ -0,0 +1,256 @@ +import json +from types import SimpleNamespace +from typing import Any, Dict, Optional, Sequence + +import requests + +CLASS_QUERY = """ +SELECT DISTINCT ?elem ?com +WHERE { + ?instance a ?elem . + OPTIONAL { ?instance rdf:type/rdfs:subClassOf* ?elem } . + #FILTER (isIRI(?elem)) . + OPTIONAL { ?elem rdfs:comment ?com filter (lang(?com) = "en")} +} +""" + +REL_QUERY = """ +SELECT DISTINCT ?elem ?com +WHERE { + ?subj ?elem ?obj . + OPTIONAL { + ?elem rdf:type/rdfs:subPropertyOf* ?proptype . + VALUES ?proptype { rdf:Property owl:DatatypeProperty owl:ObjectProperty } . + } . + OPTIONAL { ?elem rdfs:comment ?com filter (lang(?com) = "en")} +} +""" + +DTPROP_QUERY = """ +SELECT DISTINCT ?elem ?com +WHERE { + ?subj ?elem ?obj . + OPTIONAL { + ?elem rdf:type/rdfs:subPropertyOf* ?proptype . + ?proptype a owl:DatatypeProperty . + } . + OPTIONAL { ?elem rdfs:comment ?com filter (lang(?com) = "en")} +} +""" + +OPROP_QUERY = """ +SELECT DISTINCT ?elem ?com +WHERE { + ?subj ?elem ?obj . + OPTIONAL { + ?elem rdf:type/rdfs:subPropertyOf* ?proptype . + ?proptype a owl:ObjectProperty . + } . + OPTIONAL { ?elem rdfs:comment ?com filter (lang(?com) = "en")} +} +""" + +ELEM_TYPES = { + "classes": CLASS_QUERY, + "rels": REL_QUERY, + "dtprops": DTPROP_QUERY, + "oprops": OPROP_QUERY, +} + + +class NeptuneRdfGraph: + """Neptune wrapper for RDF graph operations. + + Args: + host: SPARQL endpoint host for Neptune + port: SPARQL endpoint port for Neptune. Defaults 8182. + use_iam_auth: boolean indicating IAM auth is enabled in Neptune cluster + region_name: AWS region required if use_iam_auth is True, e.g., us-west-2 + hide_comments: whether to include ontology comments in schema for prompt + + Example: + .. code-block:: python + + graph = NeptuneRdfGraph( + host=', + port=, + use_iam_auth=False + ) + schema = graph.get_schema() + + OR + graph = NeptuneRdfGraph( + host=', + port=, + use_iam_auth=False + ) + schema_elem = graph.get_schema_elements() + ... change schema_elements ... + graph.load_schema(schema_elem) + schema = graph.get_schema() + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + """ + + def __init__( + self, + host: str, + port: int = 8182, + use_iam_auth: bool = False, + region_name: Optional[str] = None, + hide_comments: bool = False, + ) -> None: + self.use_iam_auth = use_iam_auth + self.region_name = region_name + self.hide_comments = hide_comments + self.query_endpoint = f"https://{host}:{port}/sparql" + + if self.use_iam_auth: + try: + import boto3 + + self.session = boto3.Session() + except ImportError: + raise ImportError( + "Could not import boto3 python package. " + "Please install it with `pip install boto3`." + ) + else: + self.session = None + + # Set schema + self.schema = "" + self.schema_elements: Dict[str, Any] = {} + self._refresh_schema() + + @property + def get_schema(self) -> str: + """ + Returns the schema of the graph database. + """ + return self.schema + + @property + def get_schema_elements(self) -> Dict[str, Any]: + return self.schema_elements + + def query( + self, + query: str, + ) -> Dict[str, Any]: + """ + Run Neptune query. + """ + request_data = {"query": query} + data = request_data + request_hdr = None + + if self.use_iam_auth: + credentials = self.session.get_credentials() + credentials = credentials.get_frozen_credentials() + access_key = credentials.access_key + secret_key = credentials.secret_key + service = "neptune-db" + session_token = credentials.token + params = None + creds = SimpleNamespace( + access_key=access_key, + secret_key=secret_key, + token=session_token, + region=self.region_name, + ) + from botocore.awsrequest import AWSRequest + + request = AWSRequest( + method="POST", url=self.query_endpoint, data=data, params=params + ) + from botocore.auth import SigV4Auth + + SigV4Auth(creds, service, self.region_name).add_auth(request) + request.headers["Content-Type"] = "application/x-www-form-urlencoded" + request_hdr = request.headers + else: + request_hdr = {} + request_hdr["Content-Type"] = "application/x-www-form-urlencoded" + + queryres = requests.request( + method="POST", url=self.query_endpoint, headers=request_hdr, data=data + ) + json_resp = json.loads(queryres.text) + return json_resp + + def load_schema(self, schema_elements: Dict[str, Any]) -> None: + """ + Generates and sets schema from schema_elements. Helpful in + cases where introspected schema needs pruning. + """ + + elem_str = {} + for elem in ELEM_TYPES: + res_list = [] + for elem_rec in self.schema_elements[elem]: + uri = elem_rec["uri"] + local = elem_rec["local"] + res_str = f"<{uri}> ({local})" + if self.hide_comments is False: + res_str = res_str + f", {elem_rec['comment']}" + res_list.append(res_str) + elem_str[elem] = ", ".join(res_list) + + self.schema = ( + "In the following, each IRI is followed by the local name and " + "optionally its description in parentheses. \n" + "The graph supports the following node types:\n" + f"{elem_str['classes']}" + "The graph supports the following relationships:\n" + f"{elem_str['rels']}" + "The graph supports the following OWL object properties, " + f"{elem_str['dtprops']}" + "The graph supports the following OWL data properties, " + f"{elem_str['oprops']}" + ) + + def _get_local_name(self, iri: str) -> Sequence[str]: + """ + Split IRI into prefix and local + """ + if "#" in iri: + tokens = iri.split("#") + return [f"{tokens[0]}#", tokens[-1]] + elif "/" in iri: + tokens = iri.split("/") + return [f"{'/'.join(tokens[0:len(tokens)-1])}/", tokens[-1]] + else: + raise ValueError(f"Unexpected IRI '{iri}', contains neither '#' nor '/'.") + + def _refresh_schema(self) -> None: + """ + Query Neptune to introspect schema. + """ + self.schema_elements["distinct_prefixes"] = {} + + for elem in ELEM_TYPES: + items = self.query(ELEM_TYPES[elem]) + reslist = [] + for r in items["results"]["bindings"]: + uri = r["elem"]["value"] + tokens = self._get_local_name(uri) + elem_record = {"uri": uri, "local": tokens[1]} + if not self.hide_comments: + elem_record["comment"] = r["com"]["value"] if "com" in r else "" + reslist.append(elem_record) + if tokens[0] not in self.schema_elements["distinct_prefixes"]: + self.schema_elements["distinct_prefixes"][tokens[0]] = "y" + + self.schema_elements[elem] = reslist + + self.load_schema(self.schema_elements) diff --git a/libs/community/langchain_community/graphs/networkx_graph.py b/libs/community/langchain_community/graphs/networkx_graph.py index 81b7862fab2..ccc0bdad664 100644 --- a/libs/community/langchain_community/graphs/networkx_graph.py +++ b/libs/community/langchain_community/graphs/networkx_graph.py @@ -135,6 +135,14 @@ class NetworkxEntityGraph: """Clear the graph.""" self._graph.clear() + def clear_edges(self) -> None: + """Clear the graph edges.""" + self._graph.clear_edges() + + def get_number_of_nodes(self) -> int: + """Get number of nodes in the graph.""" + return self._graph.number_of_nodes() + def get_topological_sort(self) -> List[str]: """Get a list of entity names in the graph sorted by causal dependence.""" import networkx as nx diff --git a/libs/community/langchain_community/graphs/ontotext_graphdb_graph.py b/libs/community/langchain_community/graphs/ontotext_graphdb_graph.py index c1072a97f81..8bc30492d6d 100644 --- a/libs/community/langchain_community/graphs/ontotext_graphdb_graph.py +++ b/libs/community/langchain_community/graphs/ontotext_graphdb_graph.py @@ -95,12 +95,13 @@ class OntotextGraphDBGraph: if local_file: ontology_schema_graph = self._load_ontology_schema_from_file( - local_file, local_file_format + local_file, + local_file_format, # type: ignore[arg-type] ) else: - self._validate_user_query(query_ontology) + self._validate_user_query(query_ontology) # type: ignore[arg-type] ontology_schema_graph = self._load_ontology_schema_with_query( - query_ontology + query_ontology # type: ignore[arg-type] ) self.schema = ontology_schema_graph.serialize(format="turtle") @@ -139,7 +140,7 @@ class OntotextGraphDBGraph: ) @staticmethod - def _load_ontology_schema_from_file(local_file: str, local_file_format: str = None): + def _load_ontology_schema_from_file(local_file: str, local_file_format: str = None): # type: ignore[no-untyped-def, assignment] """ Parse the ontology schema statements from the provided file """ @@ -176,7 +177,7 @@ class OntotextGraphDBGraph: "Invalid query type. Only CONSTRUCT queries are supported." ) - def _load_ontology_schema_with_query(self, query: str): + def _load_ontology_schema_with_query(self, query: str): # type: ignore[no-untyped-def] """ Execute the query for collecting the ontology schema statements """ @@ -203,11 +204,7 @@ class OntotextGraphDBGraph: """ Query the graph. """ - from rdflib.exceptions import ParserError from rdflib.query import ResultRow - try: - res = self.graph.query(query) - except ParserError as e: - raise ValueError(f"Generated SPARQL statement is invalid\n{e}") + res = self.graph.query(query) return [r for r in res if isinstance(r, ResultRow)] diff --git a/libs/community/langchain_community/graphs/tigergraph_graph.py b/libs/community/langchain_community/graphs/tigergraph_graph.py index cff2f4e2ce7..f32d43e6e5e 100644 --- a/libs/community/langchain_community/graphs/tigergraph_graph.py +++ b/libs/community/langchain_community/graphs/tigergraph_graph.py @@ -31,7 +31,7 @@ class TigerGraph(GraphStore): def schema(self) -> Dict[str, Any]: return self._schema - def get_schema(self) -> str: + def get_schema(self) -> str: # type: ignore[override] if self._schema: return str(self._schema) else: @@ -71,10 +71,10 @@ class TigerGraph(GraphStore): """ return self._conn.getSchema(force=True) - def refresh_schema(self): + def refresh_schema(self): # type: ignore[no-untyped-def] self.generate_schema() - def query(self, query: str) -> Dict[str, Any]: + def query(self, query: str) -> Dict[str, Any]: # type: ignore[override] """Query the TigerGraph database.""" answer = self._conn.ai.query(query) return answer diff --git a/libs/community/langchain_community/llms/__init__.py b/libs/community/langchain_community/llms/__init__.py index 2ccf4199c41..9adaf0af1be 100644 --- a/libs/community/langchain_community/llms/__init__.py +++ b/libs/community/langchain_community/llms/__init__.py @@ -570,6 +570,12 @@ def _import_yandex_gpt() -> Any: return YandexGPT +def _import_yuan2() -> Any: + from langchain_community.llms.yuan2 import Yuan2 + + return Yuan2 + + def _import_volcengine_maas() -> Any: from langchain_community.llms.volcengine_maas import VolcEngineMaasLLM @@ -753,6 +759,8 @@ def __getattr__(name: str) -> Any: return _import_xinference() elif name == "YandexGPT": return _import_yandex_gpt() + elif name == "Yuan2": + return _import_yuan2() elif name == "VolcEngineMaasLLM": return _import_volcengine_maas() elif name == "type_to_cls_dict": @@ -851,6 +859,7 @@ __all__ = [ "JavelinAIGateway", "QianfanLLMEndpoint", "YandexGPT", + "Yuan2", "VolcEngineMaasLLM", ] @@ -939,5 +948,6 @@ def get_type_to_cls_dict() -> Dict[str, Callable[[], Type[BaseLLM]]]: "javelin-ai-gateway": _import_javelin_ai_gateway, "qianfan_endpoint": _import_baidu_qianfan_endpoint, "yandex_gpt": _import_yandex_gpt, + "yuan2": _import_yuan2, "VolcEngineMaasLLM": _import_volcengine_maas, } diff --git a/libs/community/langchain_community/llms/aleph_alpha.py b/libs/community/langchain_community/llms/aleph_alpha.py index 8ae891024b8..be24b919a95 100644 --- a/libs/community/langchain_community/llms/aleph_alpha.py +++ b/libs/community/langchain_community/llms/aleph_alpha.py @@ -284,4 +284,4 @@ class AlephAlpha(LLM): if __name__ == "__main__": aa = AlephAlpha() - print(aa("How are you?")) + print(aa("How are you?")) # noqa: T201 diff --git a/libs/community/langchain_community/llms/anthropic.py b/libs/community/langchain_community/llms/anthropic.py index be832cf1368..f5674a07667 100644 --- a/libs/community/langchain_community/llms/anthropic.py +++ b/libs/community/langchain_community/llms/anthropic.py @@ -52,6 +52,9 @@ class _AnthropicCommon(BaseLanguageModel): default_request_timeout: Optional[float] = None """Timeout for requests to Anthropic Completion API. Default is 600 seconds.""" + max_retries: int = 2 + """Number of retries allowed for requests sent to the Anthropic Completion API.""" + anthropic_api_url: Optional[str] = None anthropic_api_key: Optional[SecretStr] = None @@ -92,11 +95,13 @@ class _AnthropicCommon(BaseLanguageModel): base_url=values["anthropic_api_url"], api_key=values["anthropic_api_key"].get_secret_value(), timeout=values["default_request_timeout"], + max_retries=values["max_retries"], ) values["async_client"] = anthropic.AsyncAnthropic( base_url=values["anthropic_api_url"], api_key=values["anthropic_api_key"].get_secret_value(), timeout=values["default_request_timeout"], + max_retries=values["max_retries"], ) values["HUMAN_PROMPT"] = anthropic.HUMAN_PROMPT values["AI_PROMPT"] = anthropic.AI_PROMPT diff --git a/libs/community/langchain_community/llms/anyscale.py b/libs/community/langchain_community/llms/anyscale.py index b994c425ab9..1a5eea8ab83 100644 --- a/libs/community/langchain_community/llms/anyscale.py +++ b/libs/community/langchain_community/llms/anyscale.py @@ -1,15 +1,11 @@ """Wrapper around Anyscale Endpoint""" from typing import ( Any, - AsyncIterator, Dict, - Iterator, List, Mapping, Optional, Set, - Tuple, - cast, ) from langchain_core.callbacks import ( @@ -25,6 +21,13 @@ from langchain_community.llms.openai import ( acompletion_with_retry, completion_with_retry, ) +from langchain_community.utils.openai import is_openai_v1 + +DEFAULT_BASE_URL = "https://api.endpoints.anyscale.com/v1" +DEFAULT_MODEL = "Meta-Llama/Llama-Guard-7b" + +# Completion models support by Anyscale Endpoints +COMPLETION_MODELS = ["Meta-Llama/Llama-Guard-7b"] def update_token_usage( @@ -64,16 +67,14 @@ def create_llm_result( class Anyscale(BaseOpenAI): """Anyscale large language models. - To use, you should have the environment variable ``ANYSCALE_API_BASE`` and - ``ANYSCALE_API_KEY``set with your Anyscale Endpoint, or pass it as a named - parameter to the constructor. + To use, you should have the environment variable ``ANYSCALE_API_KEY``set with your + Anyscale Endpoint, or pass it as a named parameter to the constructor. + To use with Anyscale Private Endpoint, please also set ``ANYSCALE_BASE_URL``. Example: .. code-block:: python - from langchain_community.llms import Anyscale - anyscalellm = Anyscale(anyscale_api_base="ANYSCALE_API_BASE", - anyscale_api_key="ANYSCALE_API_KEY", - model_name="meta-llama/Llama-2-7b-chat-hf") + from langchain.llms import Anyscale + anyscalellm = Anyscale(anyscale_api_key="ANYSCALE_API_KEY") # To leverage Ray for parallel processing @ray.remote(num_cpus=1) def send_query(llm, text): @@ -84,8 +85,9 @@ class Anyscale(BaseOpenAI): """ """Key word arguments to pass to the model.""" - anyscale_api_base: Optional[str] = None - anyscale_api_key: Optional[SecretStr] = None + anyscale_api_base: str = Field(default=DEFAULT_BASE_URL) + anyscale_api_key: SecretStr = Field(default=None) + model_name: str = Field(default=DEFAULT_MODEL) prefix_messages: List = Field(default_factory=list) @@ -97,17 +99,47 @@ class Anyscale(BaseOpenAI): def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" values["anyscale_api_base"] = get_from_dict_or_env( - values, "anyscale_api_base", "ANYSCALE_API_BASE" + values, + "anyscale_api_base", + "ANYSCALE_API_BASE", + default=DEFAULT_BASE_URL, ) values["anyscale_api_key"] = convert_to_secret_str( get_from_dict_or_env(values, "anyscale_api_key", "ANYSCALE_API_KEY") ) + values["model_name"] = get_from_dict_or_env( + values, + "model_name", + "MODEL_NAME", + default=DEFAULT_MODEL, + ) + if values["model_name"] not in COMPLETION_MODELS: + raise ValueError( + "langchain_community.llm.Anyscale ONLY works \ + with completions models.For Chat models, please use \ + langchain_community.chat_model.ChatAnyscale" + ) try: import openai - ## Always create ChatComplete client, replacing the legacy Complete client - values["client"] = openai.ChatCompletion + if is_openai_v1(): + client_params = { + "api_key": values["anyscale_api_key"].get_secret_value(), + "base_url": values["anyscale_api_base"], + # To do: future support + # "organization": values["openai_organization"], + # "timeout": values["request_timeout"], + # "max_retries": values["max_retries"], + # "default_headers": values["default_headers"], + # "default_query": values["default_query"], + # "http_client": values["http_client"], + } + values["client"] = openai.OpenAI(**client_params).completions + else: + values["openai_api_base"] = values["anyscale_api_base"] + values["openai_api_key"] = values["anyscale_api_key"].get_secret_value() + values["client"] = openai.Completion except ImportError: raise ImportError( "Could not import openai python package. " @@ -132,70 +164,22 @@ class Anyscale(BaseOpenAI): def _invocation_params(self) -> Dict[str, Any]: """Get the parameters used to invoke the model.""" openai_creds: Dict[str, Any] = { - "api_key": cast(SecretStr, self.anyscale_api_key).get_secret_value(), - "api_base": self.anyscale_api_base, + "model": self.model_name, } - return {**openai_creds, **{"model": self.model_name}, **super()._default_params} + if not is_openai_v1(): + openai_creds.update( + { + "api_key": self.anyscale_api_key.get_secret_value(), + "api_base": self.anyscale_api_base, + } + ) + return {**openai_creds, **super()._invocation_params} @property def _llm_type(self) -> str: """Return type of llm.""" return "Anyscale LLM" - def _get_chat_messages( - self, prompts: List[str], stop: Optional[List[str]] = None - ) -> Tuple: - if len(prompts) > 1: - raise ValueError( - f"Anyscale currently only supports single prompt, got {prompts}" - ) - messages = self.prefix_messages + [{"role": "user", "content": prompts[0]}] - params: Dict[str, Any] = self._invocation_params - if stop is not None: - if "stop" in params: - raise ValueError("`stop` found in both the input and default params.") - params["stop"] = stop - if params.get("max_tokens") == -1: - # for Chat api, omitting max_tokens is equivalent to having no limit - del params["max_tokens"] - return messages, params - - def _stream( - self, - prompt: str, - stop: Optional[List[str]] = None, - run_manager: Optional[CallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> Iterator[GenerationChunk]: - messages, params = self._get_chat_messages([prompt], stop) - params = {**params, **kwargs, "stream": True} - for stream_resp in completion_with_retry( - self, messages=messages, run_manager=run_manager, **params - ): - token = stream_resp["choices"][0]["delta"].get("content", "") - chunk = GenerationChunk(text=token) - yield chunk - if run_manager: - run_manager.on_llm_new_token(token, chunk=chunk) - - async def _astream( - self, - prompt: str, - stop: Optional[List[str]] = None, - run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> AsyncIterator[GenerationChunk]: - messages, params = self._get_chat_messages([prompt], stop) - params = {**params, **kwargs, "stream": True} - async for stream_resp in await acompletion_with_retry( - self, messages=messages, run_manager=run_manager, **params - ): - token = stream_resp["choices"][0]["delta"].get("content", "") - chunk = GenerationChunk(text=token) - yield chunk - if run_manager: - await run_manager.on_llm_new_token(token, chunk=chunk) - def _generate( self, prompts: List[str], @@ -203,13 +187,37 @@ class Anyscale(BaseOpenAI): run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: + """Call out to OpenAI's endpoint with k unique prompts. + + Args: + prompts: The prompts to pass into the model. + stop: Optional list of stop words to use when generating. + + Returns: + The full LLM output. + + Example: + .. code-block:: python + + response = openai.generate(["Tell me a joke."]) + """ + # TODO: write a unit test for this + params = self._invocation_params + params = {**params, **kwargs} + sub_prompts = self.get_sub_prompts(params, prompts, stop) choices = [] token_usage: Dict[str, int] = {} + # Get the token usage from the response. + # Includes prompt, completion, and total tokens used. _keys = {"completion_tokens", "prompt_tokens", "total_tokens"} - for prompt in prompts: + system_fingerprint: Optional[str] = None + for _prompts in sub_prompts: if self.streaming: + if len(_prompts) > 1: + raise ValueError("Cannot stream results with multiple prompts.") + generation: Optional[GenerationChunk] = None - for chunk in self._stream(prompt, stop, run_manager, **kwargs): + for chunk in self._stream(_prompts[0], stop, run_manager, **kwargs): if generation is None: generation = chunk else: @@ -217,7 +225,7 @@ class Anyscale(BaseOpenAI): assert generation is not None choices.append( { - "message": {"content": generation.text}, + "text": generation.text, "finish_reason": generation.generation_info.get("finish_reason") if generation.generation_info else None, @@ -226,16 +234,30 @@ class Anyscale(BaseOpenAI): else None, } ) - else: - messages, params = self._get_chat_messages([prompt], stop) - params = {**params, **kwargs} response = completion_with_retry( - self, messages=messages, run_manager=run_manager, **params + ## THis is the ONLY change from BaseOpenAI()._generate() + self, + prompt=_prompts[0], + run_manager=run_manager, + **params, ) + if not isinstance(response, dict): + # V1 client returns the response in an PyDantic object instead of + # dict. For the transition period, we deep convert it to dict. + response = response.dict() + choices.extend(response["choices"]) update_token_usage(_keys, response, token_usage) - return create_llm_result(choices, prompts, token_usage, self.model_name) + if not system_fingerprint: + system_fingerprint = response.get("system_fingerprint") + return self.create_llm_result( + choices, + prompts, + params, + token_usage, + system_fingerprint=system_fingerprint, + ) async def _agenerate( self, @@ -244,14 +266,25 @@ class Anyscale(BaseOpenAI): run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: + """Call out to OpenAI's endpoint async with k unique prompts.""" + params = self._invocation_params + params = {**params, **kwargs} + sub_prompts = self.get_sub_prompts(params, prompts, stop) choices = [] token_usage: Dict[str, int] = {} + # Get the token usage from the response. + # Includes prompt, completion, and total tokens used. _keys = {"completion_tokens", "prompt_tokens", "total_tokens"} - for prompt in prompts: - messages = self.prefix_messages + [{"role": "user", "content": prompt}] + system_fingerprint: Optional[str] = None + for _prompts in sub_prompts: if self.streaming: + if len(_prompts) > 1: + raise ValueError("Cannot stream results with multiple prompts.") + generation: Optional[GenerationChunk] = None - async for chunk in self._astream(prompt, stop, run_manager, **kwargs): + async for chunk in self._astream( + _prompts[0], stop, run_manager, **kwargs + ): if generation is None: generation = chunk else: @@ -259,7 +292,7 @@ class Anyscale(BaseOpenAI): assert generation is not None choices.append( { - "message": {"content": generation.text}, + "text": generation.text, "finish_reason": generation.generation_info.get("finish_reason") if generation.generation_info else None, @@ -269,11 +302,21 @@ class Anyscale(BaseOpenAI): } ) else: - messages, params = self._get_chat_messages([prompt], stop) - params = {**params, **kwargs} response = await acompletion_with_retry( - self, messages=messages, run_manager=run_manager, **params + ## THis is the ONLY change from BaseOpenAI()._agenerate() + self, + prompt=_prompts[0], + run_manager=run_manager, + **params, ) + if not isinstance(response, dict): + response = response.dict() choices.extend(response["choices"]) update_token_usage(_keys, response, token_usage) - return create_llm_result(choices, prompts, token_usage, self.model_name) + return self.create_llm_result( + choices, + prompts, + params, + token_usage, + system_fingerprint=system_fingerprint, + ) diff --git a/libs/community/langchain_community/llms/azureml_endpoint.py b/libs/community/langchain_community/llms/azureml_endpoint.py index 3480a801f96..d30612ce8b4 100644 --- a/libs/community/langchain_community/llms/azureml_endpoint.py +++ b/libs/community/langchain_community/llms/azureml_endpoint.py @@ -101,7 +101,7 @@ class ContentFormatterBase: accepts: Optional[str] = "application/json" """The MIME type of the response data returned from the endpoint""" - format_error_msg: Optional[str] = ( + format_error_msg: str = ( "Error while formatting response payload for chat model of type " " `{api_type}`. Are you using the right formatter for the deployed " " model and endpoint type?" @@ -134,17 +134,17 @@ class ContentFormatterBase: return [AzureMLEndpointApiType.realtime] - @abstractmethod def format_request_payload( self, prompt: str, model_kwargs: Dict, api_type: AzureMLEndpointApiType = AzureMLEndpointApiType.realtime, - ) -> bytes: + ) -> Any: """Formats the request body according to the input schema of the model. Returns bytes or seekable file like object in the format specified in the content_type request header. """ + raise NotImplementedError() @abstractmethod def format_response_payload( @@ -165,7 +165,7 @@ class GPT2ContentFormatter(ContentFormatterBase): def supported_api_types(self) -> List[AzureMLEndpointApiType]: return [AzureMLEndpointApiType.realtime] - def format_request_payload( + def format_request_payload( # type: ignore[override] self, prompt: str, model_kwargs: Dict, api_type: AzureMLEndpointApiType ) -> bytes: prompt = ContentFormatterBase.escape_special_characters(prompt) @@ -174,13 +174,13 @@ class GPT2ContentFormatter(ContentFormatterBase): ) return str.encode(request_payload) - def format_response_payload( + def format_response_payload( # type: ignore[override] self, output: bytes, api_type: AzureMLEndpointApiType ) -> Generation: try: choice = json.loads(output)[0]["0"] except (KeyError, IndexError, TypeError) as e: - raise ValueError(self.format_error_msg.format(api_type=api_type)) from e + raise ValueError(self.format_error_msg.format(api_type=api_type)) from e # type: ignore[union-attr] return Generation(text=choice) @@ -207,7 +207,7 @@ class HFContentFormatter(ContentFormatterBase): def supported_api_types(self) -> List[AzureMLEndpointApiType]: return [AzureMLEndpointApiType.realtime] - def format_request_payload( + def format_request_payload( # type: ignore[override] self, prompt: str, model_kwargs: Dict, api_type: AzureMLEndpointApiType ) -> bytes: ContentFormatterBase.escape_special_characters(prompt) @@ -216,13 +216,13 @@ class HFContentFormatter(ContentFormatterBase): ) return str.encode(request_payload) - def format_response_payload( + def format_response_payload( # type: ignore[override] self, output: bytes, api_type: AzureMLEndpointApiType ) -> Generation: try: choice = json.loads(output)[0]["0"]["generated_text"] except (KeyError, IndexError, TypeError) as e: - raise ValueError(self.format_error_msg.format(api_type=api_type)) from e + raise ValueError(self.format_error_msg.format(api_type=api_type)) from e # type: ignore[union-attr] return Generation(text=choice) @@ -233,7 +233,7 @@ class DollyContentFormatter(ContentFormatterBase): def supported_api_types(self) -> List[AzureMLEndpointApiType]: return [AzureMLEndpointApiType.realtime] - def format_request_payload( + def format_request_payload( # type: ignore[override] self, prompt: str, model_kwargs: Dict, api_type: AzureMLEndpointApiType ) -> bytes: prompt = ContentFormatterBase.escape_special_characters(prompt) @@ -245,13 +245,13 @@ class DollyContentFormatter(ContentFormatterBase): ) return str.encode(request_payload) - def format_response_payload( + def format_response_payload( # type: ignore[override] self, output: bytes, api_type: AzureMLEndpointApiType ) -> Generation: try: choice = json.loads(output)[0] except (KeyError, IndexError, TypeError) as e: - raise ValueError(self.format_error_msg.format(api_type=api_type)) from e + raise ValueError(self.format_error_msg.format(api_type=api_type)) from e # type: ignore[union-attr] return Generation(text=choice) @@ -262,7 +262,7 @@ class LlamaContentFormatter(ContentFormatterBase): def supported_api_types(self) -> List[AzureMLEndpointApiType]: return [AzureMLEndpointApiType.realtime, AzureMLEndpointApiType.serverless] - def format_request_payload( + def format_request_payload( # type: ignore[override] self, prompt: str, model_kwargs: Dict, api_type: AzureMLEndpointApiType ) -> bytes: """Formats the request according to the chosen api""" @@ -284,7 +284,7 @@ class LlamaContentFormatter(ContentFormatterBase): ) return str.encode(request_payload) - def format_response_payload( + def format_response_payload( # type: ignore[override] self, output: bytes, api_type: AzureMLEndpointApiType ) -> Generation: """Formats response""" @@ -292,7 +292,7 @@ class LlamaContentFormatter(ContentFormatterBase): try: choice = json.loads(output)[0]["0"] except (KeyError, IndexError, TypeError) as e: - raise ValueError(self.format_error_msg.format(api_type=api_type)) from e + raise ValueError(self.format_error_msg.format(api_type=api_type)) from e # type: ignore[union-attr] return Generation(text=choice) if api_type == AzureMLEndpointApiType.serverless: try: @@ -304,7 +304,7 @@ class LlamaContentFormatter(ContentFormatterBase): "received." ) except (KeyError, IndexError, TypeError) as e: - raise ValueError(self.format_error_msg.format(api_type=api_type)) from e + raise ValueError(self.format_error_msg.format(api_type=api_type)) from e # type: ignore[union-attr] return Generation( text=choice["text"].strip(), generation_info=dict( @@ -397,7 +397,7 @@ class AzureMLBaseEndpoint(BaseModel): ) -> AzureMLEndpointApiType: """Validate that endpoint api type is compatible with the URL format.""" endpoint_url = values.get("endpoint_url") - if field_value == AzureMLEndpointApiType.realtime and not endpoint_url.endswith( + if field_value == AzureMLEndpointApiType.realtime and not endpoint_url.endswith( # type: ignore[union-attr] "/score" ): raise ValueError( @@ -407,8 +407,8 @@ class AzureMLBaseEndpoint(BaseModel): "`/v1/chat/completions`, use `endpoint_api_type='serverless'` instead." ) if field_value == AzureMLEndpointApiType.serverless and not ( - endpoint_url.endswith("/v1/completions") - or endpoint_url.endswith("/v1/chat/completions") + endpoint_url.endswith("/v1/completions") # type: ignore[union-attr] + or endpoint_url.endswith("/v1/chat/completions") # type: ignore[union-attr] ): raise ValueError( "Endpoints of type `serverless` should follow the format " @@ -426,7 +426,9 @@ class AzureMLBaseEndpoint(BaseModel): deployment_name = values.get("deployment_name") http_client = AzureMLEndpointClient( - endpoint_url, endpoint_key.get_secret_value(), deployment_name + endpoint_url, # type: ignore + endpoint_key.get_secret_value(), # type: ignore + deployment_name, # type: ignore ) return http_client diff --git a/libs/community/langchain_community/llms/baichuan.py b/libs/community/langchain_community/llms/baichuan.py index 2627b81bd24..293c95d2c84 100644 --- a/libs/community/langchain_community/llms/baichuan.py +++ b/libs/community/langchain_community/llms/baichuan.py @@ -56,11 +56,11 @@ class BaichuanLLM(LLM): def _post(self, request: Any) -> Any: headers = { "Content-Type": "application/json", - "Authorization": f"Bearer {self.baichuan_api_key.get_secret_value()}", + "Authorization": f"Bearer {self.baichuan_api_key.get_secret_value()}", # type: ignore[union-attr] } try: response = requests.post( - self.baichuan_api_host, + self.baichuan_api_host, # type: ignore[arg-type] headers=headers, json=request, timeout=self.timeout, diff --git a/libs/community/langchain_community/llms/beam.py b/libs/community/langchain_community/llms/beam.py index dfdb4375359..9476cf07d05 100644 --- a/libs/community/langchain_community/llms/beam.py +++ b/libs/community/langchain_community/llms/beam.py @@ -187,7 +187,7 @@ class Beam(LLM): do_sample=True, pad_token_id=tokenizer.eos_token_id) output = tokenizer.decode(outputs[0], skip_special_tokens=True) - print(output) + print(output) # noqa: T201 return {{"text": output}} """ diff --git a/libs/community/langchain_community/llms/bedrock.py b/libs/community/langchain_community/llms/bedrock.py index c66d0d071a8..f4d7b1d69e1 100644 --- a/libs/community/langchain_community/llms/bedrock.py +++ b/libs/community/langchain_community/llms/bedrock.py @@ -1,11 +1,8 @@ -from __future__ import annotations - import asyncio import json import warnings from abc import ABC from typing import ( - TYPE_CHECKING, Any, AsyncGenerator, AsyncIterator, @@ -31,9 +28,6 @@ from langchain_community.utilities.anthropic import ( get_token_ids_anthropic, ) -if TYPE_CHECKING: - from botocore.config import Config - AMAZON_BEDROCK_TRACE_KEY = "amazon-bedrock-trace" GUARDRAILS_BODY_KEY = "amazon-bedrock-guardrailAssessment" HUMAN_PROMPT = "\n\nHuman:" @@ -226,12 +220,20 @@ class BedrockBase(BaseModel, ABC): See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html """ - config: Optional[Config] = None + config: Any = None """An optional botocore.config.Config instance to pass to the client.""" + provider: Optional[str] = None + """The model provider, e.g., amazon, cohere, ai21, etc. When not supplied, provider + is extracted from the first part of the model_id e.g. 'amazon' in + 'amazon.titan-text-express-v1'. This value should be provided for model ids that do + not have the provider in them, e.g., custom and provisioned models that have an ARN + associated with them.""" + model_id: str """Id of the model to call, e.g., amazon.titan-text-express-v1, this is - equivalent to the modelId property in the list-foundation-models api""" + equivalent to the modelId property in the list-foundation-models api. For custom and + provisioned models, an ARN value is expected.""" model_kwargs: Optional[Dict] = None """Keyword arguments to pass to the model.""" @@ -353,6 +355,14 @@ class BedrockBase(BaseModel, ABC): } def _get_provider(self) -> str: + if self.provider: + return self.provider + if self.model_id.startswith("arn"): + raise ValueError( + "Model provider should be supplied when passing a model ARN as " + "model_id" + ) + return self.model_id.split(".")[0] @property @@ -395,8 +405,8 @@ class BedrockBase(BaseModel, ABC): """ return { "amazon-bedrock-guardrailDetails": { - "guardrailId": self.guardrails.get("id"), - "guardrailVersion": self.guardrails.get("version"), + "guardrailId": self.guardrails.get("id"), # type: ignore[union-attr] + "guardrailVersion": self.guardrails.get("version"), # type: ignore[union-attr] } } @@ -427,7 +437,7 @@ class BedrockBase(BaseModel, ABC): if self._guardrails_enabled: request_options["guardrail"] = "ENABLED" - if self.guardrails.get("trace"): + if self.guardrails.get("trace"): # type: ignore[union-attr] request_options["trace"] = "ENABLED" try: @@ -446,7 +456,7 @@ class BedrockBase(BaseModel, ABC): # Verify and raise a callback error if any intervention occurs or a signal is # sent from a Bedrock service, # such as when guardrails are triggered. - services_trace = self._get_bedrock_services_signal(body) + services_trace = self._get_bedrock_services_signal(body) # type: ignore[arg-type] if services_trace.get("signal") and run_manager is not None: run_manager.on_llm_error( @@ -468,7 +478,7 @@ class BedrockBase(BaseModel, ABC): if ( self._guardrails_enabled - and self.guardrails.get("trace") + and self.guardrails.get("trace") # type: ignore[union-attr] and self._is_guardrails_intervention(body) ): return { @@ -526,7 +536,7 @@ class BedrockBase(BaseModel, ABC): if self._guardrails_enabled: request_options["guardrail"] = "ENABLED" - if self.guardrails.get("trace"): + if self.guardrails.get("trace"): # type: ignore[union-attr] request_options["trace"] = "ENABLED" try: @@ -540,7 +550,7 @@ class BedrockBase(BaseModel, ABC): ): yield chunk # verify and raise callback error if any middleware intervened - self._get_bedrock_services_signal(chunk.generation_info) + self._get_bedrock_services_signal(chunk.generation_info) # type: ignore[arg-type] if run_manager is not None: run_manager.on_llm_new_token(chunk.text, chunk=chunk) @@ -588,7 +598,7 @@ class BedrockBase(BaseModel, ABC): ): await run_manager.on_llm_new_token(chunk.text, chunk=chunk) elif run_manager is not None: - run_manager.on_llm_new_token(chunk.text, chunk=chunk) + run_manager.on_llm_new_token(chunk.text, chunk=chunk) # type: ignore[unused-coroutine] class Bedrock(LLM, BedrockBase): diff --git a/libs/community/langchain_community/llms/databricks.py b/libs/community/langchain_community/llms/databricks.py index 9333629f950..c92f8ba221f 100644 --- a/libs/community/langchain_community/llms/databricks.py +++ b/libs/community/langchain_community/llms/databricks.py @@ -1,4 +1,6 @@ import os +import pickle +import re import warnings from abc import ABC, abstractmethod from typing import Any, Callable, Dict, List, Mapping, Optional @@ -212,6 +214,32 @@ def get_default_api_token() -> str: return api_token +def _is_hex_string(data: str) -> bool: + """Checks if a data is a valid hexadecimal string using a regular expression.""" + if not isinstance(data, str): + return False + pattern = r"^[0-9a-fA-F]+$" + return bool(re.match(pattern, data)) + + +def _load_pickled_fn_from_hex_string(data: str) -> Callable: + """Loads a pickled function from a hexadecimal string.""" + try: + return pickle.loads(bytes.fromhex(data)) + except Exception as e: + raise ValueError( + f"Failed to load the pickled function from a hexadecimal string. Error: {e}" + ) + + +def _pickle_fn_to_hex_string(fn: Callable) -> str: + """Pickles a function and returns the hexadecimal string.""" + try: + return pickle.dumps(fn).hex() + except Exception as e: + raise ValueError(f"Failed to pickle the function: {e}") + + class Databricks(LLM): """Databricks serving endpoint or a cluster driver proxy app for LLM. @@ -398,6 +426,17 @@ class Databricks(LLM): return v def __init__(self, **data: Any): + if "transform_input_fn" in data and _is_hex_string(data["transform_input_fn"]): + data["transform_input_fn"] = _load_pickled_fn_from_hex_string( + data["transform_input_fn"] + ) + if "transform_output_fn" in data and _is_hex_string( + data["transform_output_fn"] + ): + data["transform_output_fn"] = _load_pickled_fn_from_hex_string( + data["transform_output_fn"] + ) + super().__init__(**data) if self.model_kwargs is not None and self.extra_params is not None: raise ValueError("Cannot set both extra_params and extra_params.") @@ -443,9 +482,12 @@ class Databricks(LLM): "max_tokens": self.max_tokens, "extra_params": self.extra_params, "task": self.task, - # TODO: Support saving transform_input_fn and transform_output_fn - # "transform_input_fn": self.transform_input_fn, - # "transform_output_fn": self.transform_output_fn, + "transform_input_fn": None + if self.transform_input_fn is None + else _pickle_fn_to_hex_string(self.transform_input_fn), + "transform_output_fn": None + if self.transform_output_fn is None + else _pickle_fn_to_hex_string(self.transform_output_fn), } @property diff --git a/libs/community/langchain_community/llms/deepsparse.py b/libs/community/langchain_community/llms/deepsparse.py index ab7a7c58b4e..c82f022e275 100644 --- a/libs/community/langchain_community/llms/deepsparse.py +++ b/libs/community/langchain_community/llms/deepsparse.py @@ -177,7 +177,7 @@ class DeepSparse(LLM): ) for chunk in llm.stream("Tell me a joke", stop=["'","\n"]): - print(chunk, end='', flush=True) + print(chunk, end='', flush=True) # noqa: T201 """ inference = self.pipeline( sequences=prompt, streaming=True, **self.generation_config @@ -215,7 +215,7 @@ class DeepSparse(LLM): ) for chunk in llm.stream("Tell me a joke", stop=["'","\n"]): - print(chunk, end='', flush=True) + print(chunk, end='', flush=True) # noqa: T201 """ inference = self.pipeline( sequences=prompt, streaming=True, **self.generation_config diff --git a/libs/community/langchain_community/llms/huggingface_hub.py b/libs/community/langchain_community/llms/huggingface_hub.py index f4327277731..2d912317752 100644 --- a/libs/community/langchain_community/llms/huggingface_hub.py +++ b/libs/community/langchain_community/llms/huggingface_hub.py @@ -127,10 +127,10 @@ class HuggingFaceHub(LLM): response = hf("Tell me a joke.") """ _model_kwargs = self.model_kwargs or {} - params = {**_model_kwargs, **kwargs} + parameters = {**_model_kwargs, **kwargs} response = self.client.post( - json={"inputs": prompt, "params": params}, task=self.task + json={"inputs": prompt, "parameters": parameters}, task=self.task ) response = json.loads(response.decode()) if "error" in response: diff --git a/libs/community/langchain_community/llms/huggingface_pipeline.py b/libs/community/langchain_community/llms/huggingface_pipeline.py index 9b2e94db326..388ba117c25 100644 --- a/libs/community/langchain_community/llms/huggingface_pipeline.py +++ b/libs/community/langchain_community/llms/huggingface_pipeline.py @@ -195,12 +195,13 @@ class HuggingFacePipeline(BaseLLM): ) -> LLMResult: # List to hold all results text_generations: List[str] = [] + pipeline_kwargs = kwargs.get("pipeline_kwargs", {}) for i in range(0, len(prompts), self.batch_size): batch_prompts = prompts[i : i + self.batch_size] # Process batch of prompts - responses = self.pipeline(batch_prompts) + responses = self.pipeline(batch_prompts, **pipeline_kwargs) # Process each response in the batch for j, response in enumerate(responses): diff --git a/libs/community/langchain_community/llms/huggingface_text_gen_inference.py b/libs/community/langchain_community/llms/huggingface_text_gen_inference.py index b23aaa6e9bd..e03b6f7adcf 100644 --- a/libs/community/langchain_community/llms/huggingface_text_gen_inference.py +++ b/libs/community/langchain_community/llms/huggingface_text_gen_inference.py @@ -33,7 +33,7 @@ class HuggingFaceTextGenInference(LLM): temperature=0.01, repetition_penalty=1.03, ) - print(llm("What is Deep Learning?")) + print(llm("What is Deep Learning?")) # noqa: T201 # Streaming response example from langchain_community.callbacks import streaming_stdout @@ -50,7 +50,7 @@ class HuggingFaceTextGenInference(LLM): callbacks=callbacks, streaming=True ) - print(llm("What is Deep Learning?")) + print(llm("What is Deep Learning?")) # noqa: T201 """ diff --git a/libs/community/langchain_community/llms/human.py b/libs/community/langchain_community/llms/human.py index 8ee75db3c4a..ae1e627f30e 100644 --- a/libs/community/langchain_community/llms/human.py +++ b/libs/community/langchain_community/llms/human.py @@ -9,7 +9,7 @@ from langchain_community.llms.utils import enforce_stop_tokens def _display_prompt(prompt: str) -> None: """Displays the given prompt to the user.""" - print(f"\n{prompt}") + print(f"\n{prompt}") # noqa: T201 def _collect_user_input( diff --git a/libs/community/langchain_community/llms/llamacpp.py b/libs/community/langchain_community/llms/llamacpp.py index c29ca74ef75..85acfb999e9 100644 --- a/libs/community/langchain_community/llms/llamacpp.py +++ b/libs/community/langchain_community/llms/llamacpp.py @@ -2,7 +2,7 @@ from __future__ import annotations import logging from pathlib import Path -from typing import TYPE_CHECKING, Any, Dict, Iterator, List, Optional, Union +from typing import Any, Dict, Iterator, List, Optional, Union from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import LLM @@ -11,9 +11,6 @@ from langchain_core.pydantic_v1 import Field, root_validator from langchain_core.utils import get_pydantic_field_names from langchain_core.utils.utils import build_extra_kwargs -if TYPE_CHECKING: - from llama_cpp import LlamaGrammar - logger = logging.getLogger(__name__) @@ -126,7 +123,7 @@ class LlamaCpp(LLM): to force the model to generate valid JSON or to speak exclusively in emojis. At most one of grammar_path and grammar should be passed in. """ - grammar: Optional[Union[str, LlamaGrammar]] = None + grammar: Optional[Union[str, Any]] = None """ grammar: formal grammar for constraining model outputs. For instance, the grammar can be used to force the model to generate valid JSON or to speak exclusively in @@ -336,7 +333,7 @@ class LlamaCpp(LLM): for chunk in llm.stream("Ask 'Hi, how are you?' like a pirate:'", stop=["'","\n"]): result = chunk["choices"][0] - print(result["text"], end='', flush=True) + print(result["text"], end='', flush=True) # noqa: T201 """ params = {**self._get_parameters(stop), **kwargs} diff --git a/libs/community/langchain_community/llms/llamafile.py b/libs/community/langchain_community/llms/llamafile.py new file mode 100644 index 00000000000..5be6f4f2118 --- /dev/null +++ b/libs/community/langchain_community/llms/llamafile.py @@ -0,0 +1,318 @@ +from __future__ import annotations + +import json +from io import StringIO +from typing import Any, Dict, Iterator, List, Optional + +import requests +from langchain_core.callbacks.manager import CallbackManagerForLLMRun +from langchain_core.language_models.llms import LLM +from langchain_core.outputs import GenerationChunk +from langchain_core.pydantic_v1 import Extra +from langchain_core.utils import get_pydantic_field_names + + +class Llamafile(LLM): + """Llamafile lets you distribute and run large language models with a + single file. + + To get started, see: https://github.com/Mozilla-Ocho/llamafile + + To use this class, you will need to first: + + 1. Download a llamafile. + 2. Make the downloaded file executable: `chmod +x path/to/model.llamafile` + 3. Start the llamafile in server mode: + + `./path/to/model.llamafile --server --nobrowser` + + Example: + .. code-block:: python + + from langchain_community.llms import Llamafile + llm = Llamafile() + llm.invoke("Tell me a joke.") + """ + + base_url: str = "http://localhost:8080" + """Base url where the llamafile server is listening.""" + + request_timeout: Optional[int] = None + """Timeout for server requests""" + + streaming: bool = False + """Allows receiving each predicted token in real-time instead of + waiting for the completion to finish. To enable this, set to true.""" + + # Generation options + + seed: int = -1 + """Random Number Generator (RNG) seed. A random seed is used if this is + less than zero. Default: -1""" + + temperature: float = 0.8 + """Temperature. Default: 0.8""" + + top_k: int = 40 + """Limit the next token selection to the K most probable tokens. + Default: 40.""" + + top_p: float = 0.95 + """Limit the next token selection to a subset of tokens with a cumulative + probability above a threshold P. Default: 0.95.""" + + min_p: float = 0.05 + """The minimum probability for a token to be considered, relative to + the probability of the most likely token. Default: 0.05.""" + + n_predict: int = -1 + """Set the maximum number of tokens to predict when generating text. + Note: May exceed the set limit slightly if the last token is a partial + multibyte character. When 0, no tokens will be generated but the prompt + is evaluated into the cache. Default: -1 = infinity.""" + + n_keep: int = 0 + """Specify the number of tokens from the prompt to retain when the + context size is exceeded and tokens need to be discarded. By default, + this value is set to 0 (meaning no tokens are kept). Use -1 to retain all + tokens from the prompt.""" + + tfs_z: float = 1.0 + """Enable tail free sampling with parameter z. Default: 1.0 = disabled.""" + + typical_p: float = 1.0 + """Enable locally typical sampling with parameter p. + Default: 1.0 = disabled.""" + + repeat_penalty: float = 1.1 + """Control the repetition of token sequences in the generated text. + Default: 1.1""" + + repeat_last_n: int = 64 + """Last n tokens to consider for penalizing repetition. Default: 64, + 0 = disabled, -1 = ctx-size.""" + + penalize_nl: bool = True + """Penalize newline tokens when applying the repeat penalty. + Default: true.""" + + presence_penalty: float = 0.0 + """Repeat alpha presence penalty. Default: 0.0 = disabled.""" + + frequency_penalty: float = 0.0 + """Repeat alpha frequency penalty. Default: 0.0 = disabled""" + + mirostat: int = 0 + """Enable Mirostat sampling, controlling perplexity during text + generation. 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0. + Default: disabled.""" + + mirostat_tau: float = 5.0 + """Set the Mirostat target entropy, parameter tau. Default: 5.0.""" + + mirostat_eta: float = 0.1 + """Set the Mirostat learning rate, parameter eta. Default: 0.1.""" + + class Config: + """Configuration for this pydantic object.""" + + extra = Extra.forbid + + @property + def _llm_type(self) -> str: + return "llamafile" + + @property + def _param_fieldnames(self) -> List[str]: + # Return the list of fieldnames that will be passed as configurable + # generation options to the llamafile server. Exclude 'builtin' fields + # from the BaseLLM class like 'metadata' as well as fields that should + # not be passed in requests (base_url, request_timeout). + ignore_keys = [ + "base_url", + "cache", + "callback_manager", + "callbacks", + "metadata", + "name", + "request_timeout", + "streaming", + "tags", + "verbose", + ] + attrs = [ + k for k in get_pydantic_field_names(self.__class__) if k not in ignore_keys + ] + return attrs + + @property + def _default_params(self) -> Dict[str, Any]: + params = {} + for fieldname in self._param_fieldnames: + params[fieldname] = getattr(self, fieldname) + return params + + def _get_parameters( + self, stop: Optional[List[str]] = None, **kwargs: Any + ) -> Dict[str, Any]: + params = self._default_params + + # Only update keys that are already present in the default config. + # This way, we don't accidentally post unknown/unhandled key/values + # in the request to the llamafile server + for k, v in kwargs.items(): + if k in params: + params[k] = v + + if stop is not None and len(stop) > 0: + params["stop"] = stop + + if self.streaming: + params["stream"] = True + + return params + + def _call( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> str: + """Request prompt completion from the llamafile server and return the + output. + + Args: + prompt: The prompt to use for generation. + stop: A list of strings to stop generation when encountered. + run_manager: + **kwargs: Any additional options to pass as part of the + generation request. + + Returns: + The string generated by the model. + + """ + + if self.streaming: + with StringIO() as buff: + for chunk in self._stream( + prompt, stop=stop, run_manager=run_manager, **kwargs + ): + buff.write(chunk.text) + + text = buff.getvalue() + + return text + + else: + params = self._get_parameters(stop=stop, **kwargs) + payload = {"prompt": prompt, **params} + + try: + response = requests.post( + url=f"{self.base_url}/completion", + headers={ + "Content-Type": "application/json", + }, + json=payload, + stream=False, + timeout=self.request_timeout, + ) + except requests.exceptions.ConnectionError: + raise requests.exceptions.ConnectionError( + f"Could not connect to Llamafile server. Please make sure " + f"that a server is running at {self.base_url}." + ) + + response.raise_for_status() + response.encoding = "utf-8" + + text = response.json()["content"] + + return text + + def _stream( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> Iterator[GenerationChunk]: + """Yields results objects as they are generated in real time. + + It also calls the callback manager's on_llm_new_token event with + similar parameters to the OpenAI LLM class method of the same name. + + Args: + prompt: The prompts to pass into the model. + stop: Optional list of stop words to use when generating. + run_manager: + **kwargs: Any additional options to pass as part of the + generation request. + + Returns: + A generator representing the stream of tokens being generated. + + Yields: + Dictionary-like objects each containing a token + + Example: + .. code-block:: python + + from langchain_community.llms import Llamafile + llm = Llamafile( + temperature = 0.0 + ) + for chunk in llm.stream("Ask 'Hi, how are you?' like a pirate:'", + stop=["'","\n"]): + result = chunk["choices"][0] + print(result["text"], end='', flush=True) + + """ + params = self._get_parameters(stop=stop, **kwargs) + if "stream" not in params: + params["stream"] = True + + payload = {"prompt": prompt, **params} + + try: + response = requests.post( + url=f"{self.base_url}/completion", + headers={ + "Content-Type": "application/json", + }, + json=payload, + stream=True, + timeout=self.request_timeout, + ) + except requests.exceptions.ConnectionError: + raise requests.exceptions.ConnectionError( + f"Could not connect to Llamafile server. Please make sure " + f"that a server is running at {self.base_url}." + ) + + response.encoding = "utf8" + + for raw_chunk in response.iter_lines(decode_unicode=True): + content = self._get_chunk_content(raw_chunk) + chunk = GenerationChunk(text=content) + yield chunk + if run_manager: + run_manager.on_llm_new_token(token=chunk.text) + + def _get_chunk_content(self, chunk: str) -> str: + """When streaming is turned on, llamafile server returns lines like: + + 'data: {"content":" They","multimodal":true,"slot_id":0,"stop":false}' + + Here, we convert this to a dict and return the value of the 'content' + field + """ + + if chunk.startswith("data:"): + cleaned = chunk.lstrip("data: ") + data = json.loads(cleaned) + return data["content"] + else: + return chunk diff --git a/libs/community/langchain_community/llms/loading.py b/libs/community/langchain_community/llms/loading.py index c9621b784df..f410f7b7c6c 100644 --- a/libs/community/langchain_community/llms/loading.py +++ b/libs/community/langchain_community/llms/loading.py @@ -35,7 +35,7 @@ def load_llm(file: Union[str, Path]) -> BaseLLM: if file_path.suffix == ".json": with open(file_path) as f: config = json.load(f) - elif file_path.suffix == ".yaml": + elif file_path.suffix.endswith((".yaml", ".yml")): with open(file_path, "r") as f: config = yaml.safe_load(f) else: diff --git a/libs/community/langchain_community/llms/oci_generative_ai.py b/libs/community/langchain_community/llms/oci_generative_ai.py index 092cbda5548..3369763c876 100644 --- a/libs/community/langchain_community/llms/oci_generative_ai.py +++ b/libs/community/langchain_community/llms/oci_generative_ai.py @@ -15,6 +15,8 @@ VALID_PROVIDERS = ("cohere", "meta") class OCIAuthType(Enum): + """OCI authentication types as enumerator.""" + API_KEY = 1 SECURITY_TOKEN = 2 INSTANCE_PRINCIPAL = 3 @@ -42,10 +44,10 @@ class OCIGenAIBase(BaseModel, ABC): If not specified , DEFAULT will be used """ - model_id: str = None + model_id: str = None # type: ignore[assignment] """Id of the model to call, e.g., cohere.command""" - provider: str = None + provider: str = None # type: ignore[assignment] """Provider name of the model. Default to None, will try to be derived from the model_id otherwise, requires user input @@ -54,10 +56,10 @@ class OCIGenAIBase(BaseModel, ABC): model_kwargs: Optional[Dict] = None """Keyword arguments to pass to the model""" - service_endpoint: str = None + service_endpoint: str = None # type: ignore[assignment] """service endpoint url""" - compartment_id: str = None + compartment_id: str = None # type: ignore[assignment] """OCID of compartment""" is_stream: bool = False @@ -94,7 +96,7 @@ class OCIGenAIBase(BaseModel, ABC): client_kwargs.pop("signer", None) elif values["auth_type"] == OCIAuthType(2).name: - def make_security_token_signer(oci_config): + def make_security_token_signer(oci_config): # type: ignore[no-untyped-def] pk = oci.signer.load_private_key_from_file( oci_config.get("key_file"), None ) diff --git a/libs/community/langchain_community/llms/ollama.py b/libs/community/langchain_community/llms/ollama.py index a06ab72641b..c6aba99accf 100644 --- a/libs/community/langchain_community/llms/ollama.py +++ b/libs/community/langchain_community/llms/ollama.py @@ -297,7 +297,7 @@ class _OllamaCommon(BaseLanguageModel): "Ollama call failed with status code 404." ) else: - optional_detail = await response.json().get("error") + optional_detail = await response.json().get("error") # type: ignore[attr-defined] raise ValueError( f"Ollama call failed with status code {response.status}." f" Details: {optional_detail}" @@ -380,7 +380,7 @@ class Ollama(BaseLLM, _OllamaCommon): """Return type of llm.""" return "ollama-llm" - def _generate( + def _generate( # type: ignore[override] self, prompts: List[str], stop: Optional[List[str]] = None, @@ -416,7 +416,7 @@ class Ollama(BaseLLM, _OllamaCommon): generations.append([final_chunk]) return LLMResult(generations=generations) - async def _agenerate( + async def _agenerate( # type: ignore[override] self, prompts: List[str], stop: Optional[List[str]] = None, @@ -445,7 +445,7 @@ class Ollama(BaseLLM, _OllamaCommon): prompt, stop=stop, images=images, - run_manager=run_manager, + run_manager=run_manager, # type: ignore[arg-type] verbose=self.verbose, **kwargs, ) diff --git a/libs/community/langchain_community/llms/pipelineai.py b/libs/community/langchain_community/llms/pipelineai.py index d309c9cab55..170722d920a 100644 --- a/libs/community/langchain_community/llms/pipelineai.py +++ b/libs/community/langchain_community/llms/pipelineai.py @@ -102,7 +102,7 @@ class PipelineAI(LLM, BaseModel): "Could not import pipeline-ai python package. " "Please install it with `pip install pipeline-ai`." ) - client = PipelineCloud(token=self.pipeline_api_key.get_secret_value()) + client = PipelineCloud(token=self.pipeline_api_key.get_secret_value()) # type: ignore[union-attr] params = self.pipeline_kwargs or {} params = {**params, **kwargs} diff --git a/libs/community/langchain_community/llms/stochasticai.py b/libs/community/langchain_community/llms/stochasticai.py index d645a019da7..4cfc6d5ee94 100644 --- a/libs/community/langchain_community/llms/stochasticai.py +++ b/libs/community/langchain_community/llms/stochasticai.py @@ -107,7 +107,7 @@ class StochasticAI(LLM): url=self.api_url, json={"prompt": prompt, "params": params}, headers={ - "apiKey": f"{self.stochasticai_api_key.get_secret_value()}", + "apiKey": f"{self.stochasticai_api_key.get_secret_value()}", # type: ignore[union-attr] "Accept": "application/json", "Content-Type": "application/json", }, @@ -119,7 +119,7 @@ class StochasticAI(LLM): response_get = requests.get( url=response_post_json["data"]["responseUrl"], headers={ - "apiKey": f"{self.stochasticai_api_key.get_secret_value()}", + "apiKey": f"{self.stochasticai_api_key.get_secret_value()}", # type: ignore[union-attr] "Accept": "application/json", "Content-Type": "application/json", }, diff --git a/libs/community/langchain_community/llms/textgen.py b/libs/community/langchain_community/llms/textgen.py index 67e69a4f823..9d1ce108338 100644 --- a/libs/community/langchain_community/llms/textgen.py +++ b/libs/community/langchain_community/llms/textgen.py @@ -219,7 +219,7 @@ class TextGen(LLM): if response.status_code == 200: result = response.json()["results"][0]["text"] else: - print(f"ERROR: response: {response}") + print(f"ERROR: response: {response}") # noqa: T201 result = "" return result @@ -265,7 +265,7 @@ class TextGen(LLM): if response.status_code == 200: result = response.json()["results"][0]["text"] else: - print(f"ERROR: response: {response}") + print(f"ERROR: response: {response}") # noqa: T201 result = "" return result @@ -303,7 +303,7 @@ class TextGen(LLM): ) for chunk in llm.stream("Ask 'Hi, how are you?' like a pirate:'", stop=["'","\n"]): - print(chunk, end='', flush=True) + print(chunk, end='', flush=True) # noqa: T201 """ try: @@ -376,7 +376,7 @@ class TextGen(LLM): ) for chunk in llm.stream("Ask 'Hi, how are you?' like a pirate:'", stop=["'","\n"]): - print(chunk, end='', flush=True) + print(chunk, end='', flush=True) # noqa: T201 """ try: diff --git a/libs/community/langchain_community/llms/tongyi.py b/libs/community/langchain_community/llms/tongyi.py index 69b09b7eb07..a11cf9c5153 100644 --- a/libs/community/langchain_community/llms/tongyi.py +++ b/libs/community/langchain_community/llms/tongyi.py @@ -91,7 +91,9 @@ def stream_generate_with_retry(llm: Tongyi, **kwargs: Any) -> Any: async def astream_generate_with_retry(llm: Tongyi, **kwargs: Any) -> Any: - """Because the dashscope SDK doesn't provide an async API, + """Async version of `stream_generate_with_retry`. + + Because the dashscope SDK doesn't provide an async API, we wrap `stream_generate_with_retry` with an async generator.""" class _AioTongyiGenerator: diff --git a/libs/community/langchain_community/llms/vertexai.py b/libs/community/langchain_community/llms/vertexai.py index 3833bbd785c..58e209f7665 100644 --- a/libs/community/langchain_community/llms/vertexai.py +++ b/libs/community/langchain_community/llms/vertexai.py @@ -49,7 +49,7 @@ def is_gemini_model(model_name: str) -> bool: return model_name is not None and "gemini" in model_name -def completion_with_retry( +def completion_with_retry( # type: ignore[no-redef] llm: VertexAI, prompt: List[Union[str, "Image"]], stream: bool = False, @@ -330,7 +330,7 @@ class VertexAI(_VertexAICommon, BaseLLM): generation += chunk generations.append([generation]) else: - res = completion_with_retry( + res = completion_with_retry( # type: ignore[misc] self, [prompt], stream=should_stream, @@ -373,7 +373,7 @@ class VertexAI(_VertexAICommon, BaseLLM): **kwargs: Any, ) -> Iterator[GenerationChunk]: params = self._prepare_params(stop=stop, stream=True, **kwargs) - for stream_resp in completion_with_retry( + for stream_resp in completion_with_retry( # type: ignore[misc] self, [prompt], stream=True, diff --git a/libs/community/langchain_community/llms/watsonxllm.py b/libs/community/langchain_community/llms/watsonxllm.py index d60c7284600..191574e094b 100644 --- a/libs/community/langchain_community/llms/watsonxllm.py +++ b/libs/community/langchain_community/llms/watsonxllm.py @@ -2,6 +2,7 @@ import logging import os from typing import Any, Dict, Iterator, List, Mapping, Optional, Union +from langchain_core._api.deprecation import deprecated from langchain_core.callbacks import CallbackManagerForLLMRun from langchain_core.language_models.llms import BaseLLM from langchain_core.outputs import Generation, GenerationChunk, LLMResult @@ -11,6 +12,9 @@ from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env logger = logging.getLogger(__name__) +@deprecated( + since="0.0.18", removal="0.2", alternative_import="langchain_ibm.WatsonxLLM" +) class WatsonxLLM(BaseLLM): """ IBM watsonx.ai large language models. @@ -166,24 +170,30 @@ class WatsonxLLM(BaseLLM): credentials = { "url": values["url"].get_secret_value() if values["url"] else None, - "apikey": values["apikey"].get_secret_value() - if values["apikey"] - else None, - "token": values["token"].get_secret_value() - if values["token"] - else None, - "password": values["password"].get_secret_value() - if values["password"] - else None, - "username": values["username"].get_secret_value() - if values["username"] - else None, - "instance_id": values["instance_id"].get_secret_value() - if values["instance_id"] - else None, - "version": values["version"].get_secret_value() - if values["version"] - else None, + "apikey": ( + values["apikey"].get_secret_value() if values["apikey"] else None + ), + "token": ( + values["token"].get_secret_value() if values["token"] else None + ), + "password": ( + values["password"].get_secret_value() + if values["password"] + else None + ), + "username": ( + values["username"].get_secret_value() + if values["username"] + else None + ), + "instance_id": ( + values["instance_id"].get_secret_value() + if values["instance_id"] + else None + ), + "version": ( + values["version"].get_secret_value() if values["version"] else None + ), } credentials_without_none_value = { key: value for key, value in credentials.items() if value is not None @@ -250,9 +260,9 @@ class WatsonxLLM(BaseLLM): } def _get_chat_params(self, stop: Optional[List[str]] = None) -> Dict[str, Any]: - params: Dict[str, Any] = {**self.params} if self.params else None + params: Dict[str, Any] = {**self.params} if self.params else {} if stop is not None: - params = (params or {}) | {"stop_sequences": stop} + params["stop_sequences"] = stop return params def _create_llm_result(self, response: List[dict]) -> LLMResult: @@ -287,9 +297,6 @@ class WatsonxLLM(BaseLLM): generation_info=dict( finish_reason=stream_response["results"][0].get("stop_reason", None), llm_output={ - "generated_token_count": stream_response["results"][0].get( - "generated_token_count", None - ), "model_id": self.model_id, "deployment_id": self.deployment_id, }, @@ -384,14 +391,14 @@ class WatsonxLLM(BaseLLM): response = watsonx_llm.stream("What is a molecule") for chunk in response: - print(chunk, end='') + print(chunk, end='') # noqa: T201 """ params = self._get_chat_params(stop=stop) for stream_resp in self.watsonx_model.generate_text_stream( prompt=prompt, raw_response=True, params=params ): chunk = self._stream_response_to_generation_chunk(stream_resp) - yield chunk if run_manager: run_manager.on_llm_new_token(chunk.text, chunk=chunk) + yield chunk diff --git a/libs/community/langchain_community/llms/yandex.py b/libs/community/langchain_community/llms/yandex.py index f8e05373318..228333d5a72 100644 --- a/libs/community/langchain_community/llms/yandex.py +++ b/libs/community/langchain_community/llms/yandex.py @@ -1,7 +1,7 @@ from __future__ import annotations import logging -from typing import Any, Callable, Dict, List, Mapping, Optional +from typing import Any, Callable, Dict, List, Optional, Sequence from langchain_core.callbacks import ( AsyncCallbackManagerForLLMRun, @@ -25,10 +25,10 @@ logger = logging.getLogger(__name__) class _BaseYandexGPT(Serializable): - iam_token: SecretStr = "" + iam_token: SecretStr = "" # type: ignore[assignment] """Yandex Cloud IAM token for service or user account with the `ai.languageModels.user` role""" - api_key: SecretStr = "" + api_key: SecretStr = "" # type: ignore[assignment] """Yandex Cloud Api Key for service account with the `ai.languageModels.user` role""" folder_id: str = "" @@ -54,13 +54,14 @@ class _BaseYandexGPT(Serializable): """Maximum number of retries to make when generating.""" sleep_interval: float = 1.0 """Delay between API requests""" + _grpc_metadata: Sequence @property def _llm_type(self) -> str: return "yandex_gpt" @property - def _identifying_params(self) -> Mapping[str, Any]: + def _identifying_params(self) -> Dict[str, Any]: """Get the identifying parameters.""" return { "model_uri": self.model_uri, @@ -211,7 +212,7 @@ def _make_request( messages=[Message(role="user", text=prompt)], ) stub = TextGenerationServiceStub(channel) - res = stub.Completion(request, metadata=self._grpc_metadata) + res = stub.Completion(request, metadata=self._grpc_metadata) # type: ignore[attr-defined] return list(res)[0].alternatives[0].message.text @@ -253,7 +254,7 @@ async def _amake_request(self: YandexGPT, prompt: str) -> str: messages=[Message(role="user", text=prompt)], ) stub = TextGenerationAsyncServiceStub(channel) - operation = await stub.Completion(request, metadata=self._grpc_metadata) + operation = await stub.Completion(request, metadata=self._grpc_metadata) # type: ignore[attr-defined] async with grpc.aio.secure_channel( operation_api_url, channel_credentials ) as operation_channel: @@ -262,7 +263,8 @@ async def _amake_request(self: YandexGPT, prompt: str) -> str: await asyncio.sleep(1) operation_request = GetOperationRequest(operation_id=operation.id) operation = await operation_stub.Get( - operation_request, metadata=self._grpc_metadata + operation_request, + metadata=self._grpc_metadata, # type: ignore[attr-defined] ) completion_response = CompletionResponse() diff --git a/libs/community/langchain_community/llms/yuan2.py b/libs/community/langchain_community/llms/yuan2.py new file mode 100644 index 00000000000..360418a0a0f --- /dev/null +++ b/libs/community/langchain_community/llms/yuan2.py @@ -0,0 +1,192 @@ +import json +import logging +from typing import Any, Dict, List, Mapping, Optional, Set + +import requests +from langchain_core.callbacks import CallbackManagerForLLMRun +from langchain_core.language_models.llms import LLM +from langchain_core.pydantic_v1 import Field + +from langchain_community.llms.utils import enforce_stop_tokens + +logger = logging.getLogger(__name__) + + +class Yuan2(LLM): + """Yuan2.0 language models. + + Example: + .. code-block:: python + + yuan_llm = Yuan2( + infer_api="http://127.0.0.1:8000/yuan", + max_tokens=1024, + temp=1.0, + top_p=0.9, + top_k=40, + ) + print(yuan_llm) + print(yuan_llm("你是谁？")) + """ + + infer_api: str = "http://127.0.0.1:8000/yuan" + """Yuan2.0 inference api""" + + max_tokens: int = Field(1024, alias="max_token") + """Token context window.""" + + temp: Optional[float] = 0.7 + """The temperature to use for sampling.""" + + top_p: Optional[float] = 0.9 + """The top-p value to use for sampling.""" + + top_k: Optional[int] = 40 + """The top-k value to use for sampling.""" + + do_sample: bool = False + """The do_sample is a Boolean value that determines whether + to use the sampling method during text generation. + """ + + echo: Optional[bool] = False + """Whether to echo the prompt.""" + + stop: Optional[List[str]] = [] + """A list of strings to stop generation when encountered.""" + + repeat_last_n: Optional[int] = 64 + "Last n tokens to penalize" + + repeat_penalty: Optional[float] = 1.18 + """The penalty to apply to repeated tokens.""" + + streaming: bool = False + """Whether to stream the results or not.""" + + history: List[str] = [] + """History of the conversation""" + + use_history: bool = False + """Whether to use history or not""" + + @property + def _llm_type(self) -> str: + return "Yuan2.0" + + @staticmethod + def _model_param_names() -> Set[str]: + return { + "max_tokens", + "temp", + "top_k", + "top_p", + "do_sample", + } + + def _default_params(self) -> Dict[str, Any]: + return { + "infer_api": self.infer_api, + "max_tokens": self.max_tokens, + "temp": self.temp, + "top_k": self.top_k, + "top_p": self.top_p, + "do_sample": self.do_sample, + "use_history": self.use_history, + } + + @property + def _identifying_params(self) -> Mapping[str, Any]: + """Get the identifying parameters.""" + return { + "model": self._llm_type, + **self._default_params(), + **{ + k: v for k, v in self.__dict__.items() if k in self._model_param_names() + }, + } + + def _call( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> str: + """Call out to a Yuan2.0 LLM inference endpoint. + + Args: + prompt: The prompt to pass into the model. + stop: Optional list of stop words to use when generating. + + Returns: + The string generated by the model. + + Example: + .. code-block:: python + + response = yuan_llm("你能做什么?") + """ + + if self.use_history: + self.history.append(prompt) + input = "".join(self.history) + else: + input = prompt + + headers = {"Content-Type": "application/json"} + data = json.dumps( + { + "ques_list": [{"id": "000", "ques": input}], + "tokens_to_generate": self.max_tokens, + "temperature": self.temp, + "top_p": self.top_p, + "top_k": self.top_k, + "do_sample": self.do_sample, + } + ) + + logger.debug("Yuan2.0 prompt:", input) + + # call api + try: + response = requests.put(self.infer_api, headers=headers, data=data) + except requests.exceptions.RequestException as e: + raise ValueError(f"Error raised by inference api: {e}") + + logger.debug(f"Yuan2.0 response: {response}") + + if response.status_code != 200: + raise ValueError(f"Failed with response: {response}") + try: + resp = response.json() + + if resp["errCode"] != "0": + raise ValueError( + f"Failed with error code [{resp['errCode']}], " + f"error message: [{resp['errMessage']}]" + ) + + if "resData" in resp: + if len(resp["resData"]["output"]) >= 0: + generate_text = resp["resData"]["output"][0]["ans"] + else: + raise ValueError("No output found in response.") + else: + raise ValueError("No resData found in response.") + + except requests.exceptions.JSONDecodeError as e: + raise ValueError( + f"Error raised during decoding response from inference api: {e}." + f"\nResponse: {response.text}" + ) + + if stop is not None: + generate_text = enforce_stop_tokens(generate_text, stop) + + # support multi-turn chat + if self.use_history: + self.history.append(generate_text) + + logger.debug(f"history: {self.history}") + return generate_text diff --git a/libs/community/langchain_community/retrievers/__init__.py b/libs/community/langchain_community/retrievers/__init__.py index 5785069cc0e..e7130931c1a 100644 --- a/libs/community/langchain_community/retrievers/__init__.py +++ b/libs/community/langchain_community/retrievers/__init__.py @@ -25,6 +25,7 @@ from langchain_community.retrievers.azure_cognitive_search import ( ) from langchain_community.retrievers.bedrock import AmazonKnowledgeBasesRetriever from langchain_community.retrievers.bm25 import BM25Retriever +from langchain_community.retrievers.breebs import BreebsRetriever from langchain_community.retrievers.chaindesk import ChaindeskRetriever from langchain_community.retrievers.chatgpt_plugin_retriever import ( ChatGPTPluginRetriever, @@ -69,6 +70,7 @@ from langchain_community.retrievers.weaviate_hybrid_search import ( WeaviateHybridSearchRetriever, ) from langchain_community.retrievers.wikipedia import WikipediaRetriever +from langchain_community.retrievers.you import YouRetriever from langchain_community.retrievers.zep import ZepRetriever from langchain_community.retrievers.zilliz import ZillizRetriever @@ -78,6 +80,8 @@ __all__ = [ "ArceeRetriever", "ArxivRetriever", "AzureCognitiveSearchRetriever", + "BM25Retriever", + "BreebsRetriever", "ChatGPTPluginRetriever", "ChaindeskRetriever", "CohereRagRetriever", @@ -101,10 +105,10 @@ __all__ = [ "SVMRetriever", "TavilySearchAPIRetriever", "TFIDFRetriever", - "BM25Retriever", "VespaRetriever", "WeaviateHybridSearchRetriever", "WikipediaRetriever", + "YouRetriever", "ZepRetriever", "ZillizRetriever", "DocArrayRetriever", diff --git a/libs/community/langchain_community/retrievers/arcee.py b/libs/community/langchain_community/retrievers/arcee.py index 4e2116e58bc..dbe4449fd9a 100644 --- a/libs/community/langchain_community/retrievers/arcee.py +++ b/libs/community/langchain_community/retrievers/arcee.py @@ -10,7 +10,7 @@ from langchain_community.utilities.arcee import ArceeWrapper, DALMFilter class ArceeRetriever(BaseRetriever): - """Document retriever for Arcee's Domain Adapted Language Models (DALMs). + """Retriever for Arcee's Domain Adapted Language Models (DALMs). To use, set the ``ARCEE_API_KEY`` environment variable with your Arcee API key, or pass ``arcee_api_key`` as a named parameter. diff --git a/libs/community/langchain_community/retrievers/breebs.py b/libs/community/langchain_community/retrievers/breebs.py new file mode 100644 index 00000000000..ed42fc8fd43 --- /dev/null +++ b/libs/community/langchain_community/retrievers/breebs.py @@ -0,0 +1,49 @@ +from typing import List + +import requests +from langchain_core.callbacks.manager import CallbackManagerForRetrieverRun +from langchain_core.documents.base import Document +from langchain_core.retrievers import BaseRetriever + + +class BreebsRetriever(BaseRetriever): + """A retriever class for `Breebs`. + + See https://www.breebs.com/ for more info. + Args: + breeb_key: The key to trigger the breeb + (specialized knowledge pill on a specific topic). + + To retrieve the list of all available Breebs : you can call https://breebs.promptbreeders.com/web/listbreebs + """ + + breeb_key: str + url = "https://breebs.promptbreeders.com/knowledge" + + def __init__(self, breeb_key: str): + super().__init__(breeb_key=breeb_key) + self.breeb_key = breeb_key + + def _get_relevant_documents( + self, query: str, *, run_manager: CallbackManagerForRetrieverRun + ) -> List[Document]: + """Retrieve context for given query. + Note that for time being there is no score.""" + r = requests.post( + self.url, + json={ + "breeb_key": self.breeb_key, + "query": query, + }, + ) + if r.status_code != 200: + return [] + else: + chunks = r.json() + return [ + Document( + page_content=chunk["content"], + metadata={"source": chunk["source_url"], "score": 1}, + ) + for chunk in chunks + ] diff --git a/libs/community/langchain_community/retrievers/google_vertex_ai_search.py b/libs/community/langchain_community/retrievers/google_vertex_ai_search.py index 622eea6871a..f54d35c431e 100644 --- a/libs/community/langchain_community/retrievers/google_vertex_ai_search.py +++ b/libs/community/langchain_community/retrievers/google_vertex_ai_search.py @@ -1,4 +1,5 @@ """Retriever wrapper for Google Vertex AI Search.""" + from __future__ import annotations from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence @@ -90,9 +91,11 @@ class _BaseGoogleVertexAISearchRetriever(BaseModel): from google.api_core.client_options import ClientOptions return ClientOptions( - api_endpoint=f"{self.location_id}-discoveryengine.googleapis.com" - if self.location_id != "global" - else None + api_endpoint=( + f"{self.location_id}-discoveryengine.googleapis.com" + if self.location_id != "global" + else None + ) ) def _convert_structured_search_response( @@ -188,9 +191,9 @@ class _BaseGoogleVertexAISearchRetriever(BaseModel): ) if not documents: - print(f"No {chunk_type} could be found.") + print(f"No {chunk_type} could be found.") # noqa: T201 if chunk_type == "extractive_answers": - print( + print( # noqa: T201 "Make sure that your data store is using Advanced Website " "Indexing.\n" "https://cloud.google.com/generative-ai-app-builder/docs/about-advanced-features#advanced-website-indexing" # noqa: E501 diff --git a/libs/community/langchain_community/retrievers/qdrant_sparse_vector_retriever.py b/libs/community/langchain_community/retrievers/qdrant_sparse_vector_retriever.py index 1b0c8292bd4..daf8917173d 100644 --- a/libs/community/langchain_community/retrievers/qdrant_sparse_vector_retriever.py +++ b/libs/community/langchain_community/retrievers/qdrant_sparse_vector_retriever.py @@ -120,7 +120,10 @@ class QdrantSparseVectorRetriever(BaseRetriever): ) return [ Qdrant._document_from_scored_point( - point, self.content_payload_key, self.metadata_payload_key + point, + self.collection_name, + self.content_payload_key, + self.metadata_payload_key, ) for point in results ] diff --git a/libs/community/langchain_community/retrievers/you.py b/libs/community/langchain_community/retrievers/you.py index b65f2aad78f..9564e7307ea 100644 --- a/libs/community/langchain_community/retrievers/you.py +++ b/libs/community/langchain_community/retrievers/you.py @@ -1,64 +1,23 @@ -from typing import Any, Dict, List, Optional +from typing import Any, List from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document -from langchain_core.pydantic_v1 import root_validator from langchain_core.retrievers import BaseRetriever -from langchain_core.utils import get_from_dict_or_env + +from langchain_community.utilities import YouSearchAPIWrapper -class YouRetriever(BaseRetriever): +class YouRetriever(BaseRetriever, YouSearchAPIWrapper): """`You` retriever that uses You.com's search API. - - To connect to the You.com api requires an API key which - you can get by emailing api@you.com. - You can check out our docs at https://documentation.you.com. - - You need to set the environment variable `YDC_API_KEY` for retriever to operate. + It wraps results() to get_relevant_documents + It uses all YouSearchAPIWrapper arguments without any change. """ - ydc_api_key: str - k: Optional[int] = None - n_hits: Optional[int] = None - n_snippets_per_hit: Optional[int] = None - endpoint_type: str = "web" - - @root_validator(pre=True) - def validate_client( - cls, - values: Dict[str, Any], - ) -> Dict[str, Any]: - values["ydc_api_key"] = get_from_dict_or_env( - values, "ydc_api_key", "YDC_API_KEY" - ) - return values - def _get_relevant_documents( - self, query: str, *, run_manager: CallbackManagerForRetrieverRun + self, + query: str, + *, + run_manager: CallbackManagerForRetrieverRun, + **kwargs: Any, ) -> List[Document]: - import requests - - headers = {"X-API-Key": self.ydc_api_key} - if self.endpoint_type == "web": - results = requests.get( - f"https://api.ydc-index.io/search?query={query}", - headers=headers, - ).json() - - docs = [] - n_hits = self.n_hits or len(results["hits"]) - for hit in results["hits"][:n_hits]: - n_snippets_per_hit = self.n_snippets_per_hit or len(hit["snippets"]) - for snippet in hit["snippets"][:n_snippets_per_hit]: - docs.append(Document(page_content=snippet)) - if self.k is not None and len(docs) >= self.k: - return docs - return docs - elif self.endpoint_type == "snippet": - results = requests.get( - f"https://api.ydc-index.io/snippet_search?query={query}", - headers=headers, - ).json() - return [Document(page_content=snippet) for snippet in results] - else: - raise RuntimeError(f"Invalid endpoint type provided {self.endpoint_type}") + return self.results(query, run_manager=run_manager.get_child(), **kwargs) diff --git a/libs/community/langchain_community/storage/__init__.py b/libs/community/langchain_community/storage/__init__.py index 494591b03c7..ffb95cab1d1 100644 --- a/libs/community/langchain_community/storage/__init__.py +++ b/libs/community/langchain_community/storage/__init__.py @@ -10,6 +10,7 @@ from langchain_community.storage.astradb import ( AstraDBByteStore, AstraDBStore, ) +from langchain_community.storage.mongodb import MongoDBStore from langchain_community.storage.redis import RedisStore from langchain_community.storage.upstash_redis import ( UpstashRedisByteStore, @@ -19,6 +20,7 @@ from langchain_community.storage.upstash_redis import ( __all__ = [ "AstraDBStore", "AstraDBByteStore", + "MongoDBStore", "RedisStore", "UpstashRedisByteStore", "UpstashRedisStore", diff --git a/libs/community/langchain_community/storage/astradb.py b/libs/community/langchain_community/storage/astradb.py index 79d5bebcd37..0cb2ea310aa 100644 --- a/libs/community/langchain_community/storage/astradb.py +++ b/libs/community/langchain_community/storage/astradb.py @@ -16,7 +16,7 @@ from typing import ( from langchain_core.stores import BaseStore, ByteStore -from langchain_community.utilities.astradb import AstraDBEnvironment +from langchain_community.utilities.astradb import _AstraDBEnvironment if TYPE_CHECKING: from astrapy.db import AstraDB @@ -25,6 +25,8 @@ V = TypeVar("V") class AstraDBBaseStore(Generic[V], BaseStore[str, V], ABC): + """Base class for the DataStax AstraDB data store.""" + def __init__( self, collection_name: str, @@ -33,7 +35,7 @@ class AstraDBBaseStore(Generic[V], BaseStore[str, V], ABC): astra_db_client: Optional[AstraDB] = None, namespace: Optional[str] = None, ) -> None: - astra_env = AstraDBEnvironment( + astra_env = _AstraDBEnvironment( token=token, api_endpoint=api_endpoint, astra_db_client=astra_db_client, @@ -79,6 +81,7 @@ class AstraDBBaseStore(Generic[V], BaseStore[str, V], ABC): class AstraDBStore(AstraDBBaseStore[Any]): """BaseStore implementation using DataStax AstraDB as the underlying store. + The value type can be any type serializable by json.dumps. Can be used to store embeddings with the CacheBackedEmbeddings. Documents in the AstraDB collection will have the format @@ -97,6 +100,7 @@ class AstraDBStore(AstraDBBaseStore[Any]): class AstraDBByteStore(AstraDBBaseStore[bytes], ByteStore): """ByteStore implementation using DataStax AstraDB as the underlying store. + The bytes values are converted to base64 encoded strings Documents in the AstraDB collection will have the format { diff --git a/libs/community/langchain_community/storage/mongodb.py b/libs/community/langchain_community/storage/mongodb.py new file mode 100644 index 00000000000..97447f83217 --- /dev/null +++ b/libs/community/langchain_community/storage/mongodb.py @@ -0,0 +1,126 @@ +from typing import Iterator, List, Optional, Sequence, Tuple + +from langchain_core.documents import Document +from langchain_core.stores import BaseStore + + +class MongoDBStore(BaseStore[str, Document]): + """BaseStore implementation using MongoDB as the underlying store. + + Examples: + Create a MongoDBStore instance and perform operations on it: + + .. code-block:: python + + # Instantiate the MongoDBStore with a MongoDB connection + from langchain.storage import MongoDBStore + + mongo_conn_str = "mongodb://localhost:27017/" + mongodb_store = MongoDBStore(mongo_conn_str, db_name="test-db", + collection_name="test-collection") + + # Set values for keys + doc1 = Document(...) + doc2 = Document(...) + mongodb_store.mset([("key1", doc1), ("key2", doc2)]) + + # Get values for keys + values = mongodb_store.mget(["key1", "key2"]) + # [doc1, doc2] + + # Iterate over keys + for key in mongodb_store.yield_keys(): + print(key) + + # Delete keys + mongodb_store.mdelete(["key1", "key2"]) + """ + + def __init__( + self, + connection_string: str, + db_name: str, + collection_name: str, + *, + client_kwargs: Optional[dict] = None, + ) -> None: + """Initialize the MongoDBStore with a MongoDB connection string. + + Args: + connection_string (str): MongoDB connection string + db_name (str): name to use + collection_name (str): collection name to use + client_kwargs (dict): Keyword arguments to pass to the Mongo client + """ + try: + from pymongo import MongoClient + except ImportError as e: + raise ImportError( + "The MongoDBStore requires the pymongo library to be " + "installed. " + "pip install pymongo" + ) from e + + if not connection_string: + raise ValueError("connection_string must be provided.") + if not db_name: + raise ValueError("db_name must be provided.") + if not collection_name: + raise ValueError("collection_name must be provided.") + + self.client = MongoClient(connection_string, **(client_kwargs or {})) + self.collection = self.client[db_name][collection_name] + + def mget(self, keys: Sequence[str]) -> List[Optional[Document]]: + """Get the list of documents associated with the given keys. + + Args: + keys (list[str]): A list of keys representing Document IDs.. + + Returns: + list[Document]: A list of Documents corresponding to the provided + keys, where each Document is either retrieved successfully or + represented as None if not found. + """ + result = self.collection.find({"_id": {"$in": keys}}) + result_dict = {doc["_id"]: Document(**doc["value"]) for doc in result} + return [result_dict.get(key) for key in keys] + + def mset(self, key_value_pairs: Sequence[Tuple[str, Document]]) -> None: + """Set the given key-value pairs. + + Args: + key_value_pairs (list[tuple[str, Document]]): A list of id-document + pairs. + Returns: + None + """ + from pymongo import UpdateOne + + updates = [{"_id": k, "value": v.__dict__} for k, v in key_value_pairs] + self.collection.bulk_write( + [UpdateOne({"_id": u["_id"]}, {"$set": u}, upsert=True) for u in updates] + ) + + def mdelete(self, keys: Sequence[str]) -> None: + """Delete the given ids. + + Args: + keys (list[str]): A list of keys representing Document IDs.. + """ + self.collection.delete_many({"_id": {"$in": keys}}) + + def yield_keys(self, prefix: Optional[str] = None) -> Iterator[str]: + """Yield keys in the store. + + Args: + prefix (str): prefix of keys to retrieve. + """ + if prefix is None: + for doc in self.collection.find(projection=["_id"]): + yield doc["_id"] + else: + for doc in self.collection.find( + {"_id": {"$regex": f"^{prefix}"}}, projection=["_id"] + ): + yield doc["_id"] diff --git a/libs/community/langchain_community/storage/redis.py b/libs/community/langchain_community/storage/redis.py index 0cccbf071be..5e1b4fa56c6 100644 --- a/libs/community/langchain_community/storage/redis.py +++ b/libs/community/langchain_community/storage/redis.py @@ -32,7 +32,7 @@ class RedisStore(ByteStore): # Iterate over keys for key in redis_store.yield_keys(): - print(key) + print(key) # noqa: T201 """ def __init__( diff --git a/libs/community/langchain_community/tools/amadeus/closest_airport.py b/libs/community/langchain_community/tools/amadeus/closest_airport.py index cf108f3b11d..1a3f4c4da2c 100644 --- a/libs/community/langchain_community/tools/amadeus/closest_airport.py +++ b/libs/community/langchain_community/tools/amadeus/closest_airport.py @@ -58,4 +58,4 @@ class AmadeusClosestAirport(AmadeusBaseTool): ' Location Identifier" ' ) - return self.llm.invoke(content) + return self.llm.invoke(content) # type: ignore[union-attr] diff --git a/libs/community/langchain_community/tools/amadeus/flight_search.py b/libs/community/langchain_community/tools/amadeus/flight_search.py index 85c173c1198..21d9e4bce7f 100644 --- a/libs/community/langchain_community/tools/amadeus/flight_search.py +++ b/libs/community/langchain_community/tools/amadeus/flight_search.py @@ -105,7 +105,7 @@ class AmadeusFlightSearch(AmadeusBaseTool): adults=1, ) except ResponseError as error: - print(error) + print(error) # noqa: T201 # Generate output dictionary output = [] diff --git a/libs/community/langchain_community/tools/bearly/tool.py b/libs/community/langchain_community/tools/bearly/tool.py index 8f4e46c0faa..286e024f64a 100644 --- a/libs/community/langchain_community/tools/bearly/tool.py +++ b/libs/community/langchain_community/tools/bearly/tool.py @@ -59,7 +59,7 @@ If you have any files outputted write them to "output/" relative to the executio path. Output can only be read from the directory, stdout, and stdin. \ Do not use things like plot.show() as it will \ not work instead write them out `output/` and a link to the file will be returned. \ -print() any output and results so you can capture the output.""" +print() any output and results so you can capture the output.""" # noqa: T201 class FileInfo(BaseModel): @@ -125,12 +125,16 @@ class BearlyInterpreterTool: headers={"Authorization": self.api_key}, ).json() return { - "stdout": base64.b64decode(resp["stdoutBasesixtyfour"]).decode() - if resp["stdoutBasesixtyfour"] - else "", - "stderr": base64.b64decode(resp["stderrBasesixtyfour"]).decode() - if resp["stderrBasesixtyfour"] - else "", + "stdout": ( + base64.b64decode(resp["stdoutBasesixtyfour"]).decode() + if resp["stdoutBasesixtyfour"] + else "" + ), + "stderr": ( + base64.b64decode(resp["stderrBasesixtyfour"]).decode() + if resp["stderrBasesixtyfour"] + else "" + ), "fileLinks": resp["fileLinks"], "exitCode": resp["exitCode"], } diff --git a/libs/community/langchain_community/tools/human/tool.py b/libs/community/langchain_community/tools/human/tool.py index 7b7911577ce..e4987e44fa6 100644 --- a/libs/community/langchain_community/tools/human/tool.py +++ b/libs/community/langchain_community/tools/human/tool.py @@ -8,8 +8,8 @@ from langchain_core.tools import BaseTool def _print_func(text: str) -> None: - print("\n") - print(text) + print("\n") # noqa: T201 + print(text) # noqa: T201 class HumanInputRun(BaseTool): diff --git a/libs/community/langchain_community/tools/multion/close_session.py b/libs/community/langchain_community/tools/multion/close_session.py index 8232d861e2d..023c07539e0 100644 --- a/libs/community/langchain_community/tools/multion/close_session.py +++ b/libs/community/langchain_community/tools/multion/close_session.py @@ -52,6 +52,6 @@ Note: SessionId must be received from previous Browser window creation.""" try: multion.close_session(sessionId) except Exception as e: - print(f"{e}, retrying...") + print(f"{e}, retrying...") # noqa: T201 except Exception as e: raise Exception(f"An error occurred: {e}") diff --git a/libs/community/langchain_community/tools/multion/update_session.py b/libs/community/langchain_community/tools/multion/update_session.py index fe92c36dd76..10fe4c9fa63 100644 --- a/libs/community/langchain_community/tools/multion/update_session.py +++ b/libs/community/langchain_community/tools/multion/update_session.py @@ -68,7 +68,7 @@ Note: sessionId must be received from previous Browser window creation.""" self.sessionId = sessionId return content except Exception as e: - print(f"{e}, retrying...") + print(f"{e}, retrying...") # noqa: T201 return {"error": f"{e}", "Response": "retrying..."} except Exception as e: raise Exception(f"An error occurred: {e}") diff --git a/libs/community/langchain_community/tools/office365/utils.py b/libs/community/langchain_community/tools/office365/utils.py index 127fb6dba1f..f13c10dde1c 100644 --- a/libs/community/langchain_community/tools/office365/utils.py +++ b/libs/community/langchain_community/tools/office365/utils.py @@ -1,4 +1,5 @@ """O365 tool utils.""" + from __future__ import annotations import logging @@ -66,7 +67,7 @@ def authenticate() -> Account: "https://graph.microsoft.com/MailboxSettings.ReadWrite", ] ): - print("Error: Could not authenticate") + print("Error: Could not authenticate") # noqa: T201 return None else: return account diff --git a/libs/community/langchain_community/tools/polygon/last_quote.py b/libs/community/langchain_community/tools/polygon/last_quote.py index 55fe3d93010..ce6ebe750ea 100644 --- a/libs/community/langchain_community/tools/polygon/last_quote.py +++ b/libs/community/langchain_community/tools/polygon/last_quote.py @@ -8,6 +8,8 @@ from langchain_community.utilities.polygon import PolygonAPIWrapper class Inputs(BaseModel): + """Inputs for Polygon's Last Quote API""" + query: str diff --git a/libs/community/langchain_community/tools/shell/tool.py b/libs/community/langchain_community/tools/shell/tool.py index e26deb365dc..cb3e941ec63 100644 --- a/libs/community/langchain_community/tools/shell/tool.py +++ b/libs/community/langchain_community/tools/shell/tool.py @@ -84,7 +84,7 @@ class ShellTool(BaseTool): ) -> str: """Run commands and return final output.""" - print(f"Executing command:\n {commands}") + print(f"Executing command:\n {commands}") # noqa: T201 try: if self.ask_human_input: @@ -93,10 +93,10 @@ class ShellTool(BaseTool): return self.process.run(commands) else: logger.info("Invalid input. User aborted command execution.") - return None + return None # type: ignore[return-value] else: return self.process.run(commands) except Exception as e: logger.error(f"Error during command execution: {e}") - return None + return None # type: ignore[return-value] diff --git a/libs/community/langchain_community/tools/sql_database/tool.py b/libs/community/langchain_community/tools/sql_database/tool.py index 850cecb4510..e68923bdcfb 100644 --- a/libs/community/langchain_community/tools/sql_database/tool.py +++ b/libs/community/langchain_community/tools/sql_database/tool.py @@ -1,6 +1,8 @@ # flake8: noqa """Tools for interacting with a SQL database.""" -from typing import Any, Dict, Optional, Type +from typing import Any, Dict, Optional, Sequence, Type, Union + +from sqlalchemy.engine import Result from langchain_core.pydantic_v1 import BaseModel, Field, root_validator @@ -42,7 +44,7 @@ class QuerySQLDataBaseTool(BaseSQLDatabaseTool, BaseTool): self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None, - ) -> str: + ) -> Union[str, Sequence[Dict[str, Any]], Result[Any]]: """Execute the query, return the results or an error message.""" return self.db.run_no_throw(query) diff --git a/libs/community/langchain_community/tools/vectorstore/tool.py b/libs/community/langchain_community/tools/vectorstore/tool.py index e51deaeaa1d..c0cbdb67062 100644 --- a/libs/community/langchain_community/tools/vectorstore/tool.py +++ b/libs/community/langchain_community/tools/vectorstore/tool.py @@ -3,7 +3,10 @@ import json from typing import Any, Dict, Optional -from langchain_core.callbacks import CallbackManagerForToolRun +from langchain_core.callbacks import ( + AsyncCallbackManagerForToolRun, + CallbackManagerForToolRun, +) from langchain_core.language_models import BaseLanguageModel from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.tools import BaseTool @@ -51,9 +54,30 @@ class VectorStoreQATool(BaseVectorStoreTool, BaseTool): chain = RetrievalQA.from_chain_type( self.llm, retriever=self.vectorstore.as_retriever() ) - return chain.run( - query, callbacks=run_manager.get_child() if run_manager else None + return chain.invoke( + {chain.input_key: query}, + config={"callbacks": [run_manager.get_child() if run_manager else None]}, + )[chain.output_key] + + async def _arun( + self, + query: str, + run_manager: Optional[AsyncCallbackManagerForToolRun] = None, + ) -> str: + """Use the tool asynchronously.""" + from langchain.chains.retrieval_qa.base import RetrievalQA + + chain = RetrievalQA.from_chain_type( + self.llm, retriever=self.vectorstore.as_retriever() ) + return ( + await chain.ainvoke( + {chain.input_key: query}, + config={ + "callbacks": [run_manager.get_child() if run_manager else None] + }, + ) + )[chain.output_key] class VectorStoreQAWithSourcesTool(BaseVectorStoreTool, BaseTool): @@ -87,7 +111,28 @@ class VectorStoreQAWithSourcesTool(BaseVectorStoreTool, BaseTool): self.llm, retriever=self.vectorstore.as_retriever() ) return json.dumps( - chain( + chain.invoke( + {chain.question_key: query}, + return_only_outputs=True, + callbacks=run_manager.get_child() if run_manager else None, + ) + ) + + async def _arun( + self, + query: str, + run_manager: Optional[AsyncCallbackManagerForToolRun] = None, + ) -> str: + """Use the tool asynchronously.""" + from langchain.chains.qa_with_sources.retrieval import ( + RetrievalQAWithSourcesChain, + ) + + chain = RetrievalQAWithSourcesChain.from_chain_type( + self.llm, retriever=self.vectorstore.as_retriever() + ) + return json.dumps( + await chain.ainvoke( {chain.question_key: query}, return_only_outputs=True, callbacks=run_manager.get_child() if run_manager else None, diff --git a/libs/community/langchain_community/utilities/__init__.py b/libs/community/langchain_community/utilities/__init__.py index ac91e964d2d..b03fa2585d7 100644 --- a/libs/community/langchain_community/utilities/__init__.py +++ b/libs/community/langchain_community/utilities/__init__.py @@ -248,6 +248,12 @@ def _import_twilio() -> Any: return TwilioAPIWrapper +def _import_you() -> Any: + from langchain_community.utilities.you import YouSearchAPIWrapper + + return YouSearchAPIWrapper + + def _import_wikipedia() -> Any: from langchain_community.utilities.wikipedia import WikipediaAPIWrapper @@ -272,6 +278,24 @@ def _import_nasa() -> Any: return NasaAPIWrapper +def _import_nvidia_riva_asr() -> Any: + from langchain_community.utilities.nvidia_riva import RivaASR + + return RivaASR + + +def _import_nvidia_riva_tts() -> Any: + from langchain_community.utilities.nvidia_riva import RivaTTS + + return RivaTTS + + +def _import_nvidia_riva_stream() -> Any: + from langchain_community.utilities.nvidia_riva import AudioStream + + return AudioStream + + def __getattr__(name: str) -> Any: if name == "AlphaVantageAPIWrapper": return _import_alpha_vantage() @@ -321,6 +345,12 @@ def __getattr__(name: str) -> Any: return _import_metaphor_search() elif name == "NasaAPIWrapper": return _import_nasa() + elif name == "NVIDIARivaASR": + return _import_nvidia_riva_asr() + elif name == "NVIDIARivaStream": + return _import_nvidia_riva_stream() + elif name == "NVIDIARivaTTS": + return _import_nvidia_riva_tts() elif name == "OpenWeatherMapAPIWrapper": return _import_openweathermap() elif name == "OutlineAPIWrapper": @@ -353,6 +383,8 @@ def __getattr__(name: str) -> Any: return _import_tensorflow_datasets() elif name == "TwilioAPIWrapper": return _import_twilio() + elif name == "YouSearchAPIWrapper": + return _import_you() elif name == "WikipediaAPIWrapper": return _import_wikipedia() elif name == "WolframAlphaAPIWrapper": @@ -388,6 +420,9 @@ __all__ = [ "MerriamWebsterAPIWrapper", "MetaphorSearchAPIWrapper", "NasaAPIWrapper", + "NVIDIARivaASR", + "NVIDIARivaStream", + "NVIDIARivaTTS", "OpenWeatherMapAPIWrapper", "OutlineAPIWrapper", "Portkey", @@ -407,6 +442,7 @@ __all__ = [ "TensorflowDatasets", "TextRequestsWrapper", "TwilioAPIWrapper", + "YouSearchAPIWrapper", "WikipediaAPIWrapper", "WolframAlphaAPIWrapper", "ZapierNLAWrapper", diff --git a/libs/community/langchain_community/utilities/astradb.py b/libs/community/langchain_community/utilities/astradb.py index 3ad3d327497..c113d660792 100644 --- a/libs/community/langchain_community/utilities/astradb.py +++ b/libs/community/langchain_community/utilities/astradb.py @@ -1,6 +1,10 @@ from __future__ import annotations -from typing import TYPE_CHECKING, Optional +import asyncio +import inspect +from asyncio import InvalidStateError, Task +from enum import Enum +from typing import TYPE_CHECKING, Awaitable, Optional, Union if TYPE_CHECKING: from astrapy.db import ( @@ -9,7 +13,13 @@ if TYPE_CHECKING: ) -class AstraDBEnvironment: +class SetupMode(Enum): + SYNC = 1 + ASYNC = 2 + OFF = 3 + + +class _AstraDBEnvironment: def __init__( self, token: Optional[str] = None, @@ -21,21 +31,20 @@ class AstraDBEnvironment: self.token = token self.api_endpoint = api_endpoint astra_db = astra_db_client - self.async_astra_db = async_astra_db_client + async_astra_db = async_astra_db_client self.namespace = namespace - from astrapy import db - try: - from astrapy.db import AstraDB + from astrapy.db import ( + AstraDB, + AsyncAstraDB, + ) except (ImportError, ModuleNotFoundError): raise ImportError( "Could not import a recent astrapy python package. " "Please install it with `pip install --upgrade astrapy`." ) - supports_async = hasattr(db, "AsyncAstraDB") - # Conflicting-arg checks: if astra_db_client is not None or async_astra_db_client is not None: if token is not None or api_endpoint is not None: @@ -46,39 +55,115 @@ class AstraDBEnvironment: if token and api_endpoint: astra_db = AstraDB( - token=self.token, - api_endpoint=self.api_endpoint, + token=token, + api_endpoint=api_endpoint, + namespace=self.namespace, + ) + async_astra_db = AsyncAstraDB( + token=token, + api_endpoint=api_endpoint, namespace=self.namespace, ) - if supports_async: - self.async_astra_db = db.AsyncAstraDB( - token=self.token, - api_endpoint=self.api_endpoint, - namespace=self.namespace, - ) if astra_db: self.astra_db = astra_db - else: - if self.async_astra_db: - self.astra_db = AstraDB( - token=self.async_astra_db.token, - api_endpoint=self.async_astra_db.base_url, - api_path=self.async_astra_db.api_path, - api_version=self.async_astra_db.api_version, - namespace=self.async_astra_db.namespace, - ) + if async_astra_db: + self.async_astra_db = async_astra_db else: - raise ValueError( - "Must provide 'astra_db_client' or 'async_astra_db_client' or " - "'token' and 'api_endpoint'" + self.async_astra_db = AsyncAstraDB( + token=self.astra_db.token, + api_endpoint=self.astra_db.base_url, + api_path=self.astra_db.api_path, + api_version=self.astra_db.api_version, + namespace=self.astra_db.namespace, + ) + elif async_astra_db: + self.async_astra_db = async_astra_db + self.astra_db = AstraDB( + token=self.async_astra_db.token, + api_endpoint=self.async_astra_db.base_url, + api_path=self.async_astra_db.api_path, + api_version=self.async_astra_db.api_version, + namespace=self.async_astra_db.namespace, + ) + else: + raise ValueError( + "Must provide 'astra_db_client' or 'async_astra_db_client' or " + "'token' and 'api_endpoint'" + ) + + +class _AstraDBCollectionEnvironment(_AstraDBEnvironment): + def __init__( + self, + collection_name: str, + token: Optional[str] = None, + api_endpoint: Optional[str] = None, + astra_db_client: Optional[AstraDB] = None, + async_astra_db_client: Optional[AsyncAstraDB] = None, + namespace: Optional[str] = None, + setup_mode: SetupMode = SetupMode.SYNC, + pre_delete_collection: bool = False, + embedding_dimension: Union[int, Awaitable[int], None] = None, + metric: Optional[str] = None, + ) -> None: + from astrapy.db import AstraDBCollection, AsyncAstraDBCollection + + super().__init__( + token, api_endpoint, astra_db_client, async_astra_db_client, namespace + ) + self.collection_name = collection_name + self.collection = AstraDBCollection( + collection_name=collection_name, + astra_db=self.astra_db, + ) + + self.async_collection = AsyncAstraDBCollection( + collection_name=collection_name, + astra_db=self.async_astra_db, + ) + + self.async_setup_db_task: Optional[Task] = None + if setup_mode == SetupMode.ASYNC: + async_astra_db = self.async_astra_db + + async def _setup_db() -> None: + if pre_delete_collection: + await async_astra_db.delete_collection(collection_name) + if inspect.isawaitable(embedding_dimension): + dimension = await embedding_dimension + else: + dimension = embedding_dimension + await async_astra_db.create_collection( + collection_name, dimension=dimension, metric=metric ) - if not self.async_astra_db and self.astra_db and supports_async: - self.async_astra_db = db.AsyncAstraDB( - token=self.astra_db.token, - api_endpoint=self.astra_db.base_url, - api_path=self.astra_db.api_path, - api_version=self.astra_db.api_version, - namespace=self.astra_db.namespace, + self.async_setup_db_task = asyncio.create_task(_setup_db()) + elif setup_mode == SetupMode.SYNC: + if pre_delete_collection: + self.astra_db.delete_collection(collection_name) + if inspect.isawaitable(embedding_dimension): + raise ValueError( + "Cannot use an awaitable embedding_dimension with async_setup " + "set to False" + ) + self.astra_db.create_collection( + collection_name, + dimension=embedding_dimension, # type: ignore[arg-type] + metric=metric, ) + + def ensure_db_setup(self) -> None: + if self.async_setup_db_task: + try: + self.async_setup_db_task.result() + except InvalidStateError: + raise ValueError( + "Asynchronous setup of the DB not finished. " + "NB: AstraDB components sync methods shouldn't be called from the " + "event loop. Consider using their async equivalents." + ) + + async def aensure_db_setup(self) -> None: + if self.async_setup_db_task: + await self.async_setup_db_task diff --git a/libs/community/langchain_community/utilities/brave_search.py b/libs/community/langchain_community/utilities/brave_search.py index 8f3df0666c6..fd282fc3465 100644 --- a/libs/community/langchain_community/utilities/brave_search.py +++ b/libs/community/langchain_community/utilities/brave_search.py @@ -48,7 +48,7 @@ class BraveSearchWrapper(BaseModel): results = self._search_request(query) return [ Document( - page_content=item.get("description"), + page_content=item.get("description"), # type: ignore[arg-type] metadata={"title": item.get("title"), "link": item.get("url")}, ) for item in results diff --git a/libs/community/langchain_community/utilities/clickup.py b/libs/community/langchain_community/utilities/clickup.py index ed81e7fc72b..e13deef894a 100644 --- a/libs/community/langchain_community/utilities/clickup.py +++ b/libs/community/langchain_community/utilities/clickup.py @@ -1,4 +1,5 @@ """Util that calls clickup.""" + import json import warnings from dataclasses import asdict, dataclass, fields @@ -308,10 +309,10 @@ class ClickupAPIWrapper(BaseModel): data = response.json() if "access_token" not in data: - print(f"Error: {data}") + print(f"Error: {data}") # noqa: T201 if "ECODE" in data and data["ECODE"] == "OAUTH_014": url = ClickupAPIWrapper.get_access_code_url(oauth_client_id) - print( + print( # noqa: T201 "You already used this code once. Generate a new one.", f"Our best guess for the url to get a new code is:\n{url}", ) diff --git a/libs/community/langchain_community/utilities/github.py b/libs/community/langchain_community/utilities/github.py index 25d931bbce6..410fdb494b7 100644 --- a/libs/community/langchain_community/utilities/github.py +++ b/libs/community/langchain_community/utilities/github.py @@ -1,4 +1,5 @@ """Util that calls GitHub.""" + from __future__ import annotations import json @@ -297,7 +298,7 @@ class GitHubAPIWrapper(BaseModel): new_branch_name = f"{proposed_branch_name}_v{i}" else: # Handle any other exceptions - print(f"Failed to create branch. Error: {e}") + print(f"Failed to create branch. Error: {e}") # noqa: T201 raise Exception( "Unable to create branch name from proposed_branch_name: " f"{proposed_branch_name}" @@ -427,7 +428,7 @@ class GitHubAPIWrapper(BaseModel): "download_url" ] else: - print(f"Failed to download file: {file.contents_url}, skipping") + print(f"Failed to download file: {file.contents_url}, skipping") # noqa: T201 continue file_content_response = requests.get(download_url) @@ -435,7 +436,7 @@ class GitHubAPIWrapper(BaseModel): # Save the content as a UTF-8 string file_content = file_content_response.text else: - print( + print( # noqa: T201 "Failed downloading file content " f"(Error {file_content_response.status_code}). Skipping" ) @@ -457,7 +458,7 @@ class GitHubAPIWrapper(BaseModel): ) total_tokens += file_tokens except Exception as e: - print(f"Error when reading files from a PR on github. {e}") + print(f"Error when reading files from a PR on github. {e}") # noqa: T201 page += 1 return pr_files @@ -734,7 +735,7 @@ class GitHubAPIWrapper(BaseModel): str: A string containing the first 5 issues and pull requests """ search_result = self.github.search_issues(query, repo=self.github_repository) - max_items = min(5, len(search_result)) + max_items = min(5, search_result.totalCount) results = [f"Top {max_items} results:"] for issue in search_result[:max_items]: results.append( diff --git a/libs/community/langchain_community/utilities/google_lens.py b/libs/community/langchain_community/utilities/google_lens.py index f8419bdb758..028c4092a5b 100644 --- a/libs/community/langchain_community/utilities/google_lens.py +++ b/libs/community/langchain_community/utilities/google_lens.py @@ -1,4 +1,5 @@ """Util that calls Google Lens Search.""" + from typing import Any, Dict, Optional, cast import requests @@ -77,7 +78,7 @@ class GoogleLensAPIWrapper(BaseModel): "Reverse Image Search" + f"Link: {responseValue['reverse_image_search']['link']}\n" ) - print(xs) + print(xs) # noqa: T201 docs = [xs] diff --git a/libs/community/langchain_community/utilities/graphql.py b/libs/community/langchain_community/utilities/graphql.py index 87be94d09c3..a576419e5be 100644 --- a/libs/community/langchain_community/utilities/graphql.py +++ b/libs/community/langchain_community/utilities/graphql.py @@ -37,7 +37,10 @@ class GraphQLAPIWrapper(BaseModel): url=values["graphql_endpoint"], headers=headers, ) - client = Client(transport=transport, fetch_schema_from_transport=True) + fetch_schema_from_transport = values.get("fetch_schema_from_transport", True) + client = Client( + transport=transport, fetch_schema_from_transport=fetch_schema_from_transport + ) values["gql_client"] = client values["gql_function"] = gql return values diff --git a/libs/community/langchain_community/utilities/nvidia_riva.py b/libs/community/langchain_community/utilities/nvidia_riva.py new file mode 100644 index 00000000000..cd43b5a14eb --- /dev/null +++ b/libs/community/langchain_community/utilities/nvidia_riva.py @@ -0,0 +1,667 @@ +"""A common module for NVIDIA Riva Runnables.""" + +import asyncio +import logging +import pathlib +import queue +import tempfile +import threading +import wave +from enum import Enum +from typing import ( + TYPE_CHECKING, + Any, + AsyncGenerator, + AsyncIterator, + Dict, + Generator, + Iterator, + List, + Optional, + Tuple, + Union, + cast, +) + +from langchain_core.messages import AnyMessage, BaseMessage +from langchain_core.prompt_values import PromptValue +from langchain_core.pydantic_v1 import ( + AnyHttpUrl, + BaseModel, + Field, + parse_obj_as, + root_validator, + validator, +) +from langchain_core.runnables import RunnableConfig, RunnableSerializable + +if TYPE_CHECKING: + import riva.client + import riva.client.proto.riva_asr_pb2 as rasr + +_LOGGER = logging.getLogger(__name__) +_QUEUE_GET_TIMEOUT = 0.5 +_MAX_TEXT_LENGTH = 400 +_SENTENCE_TERMINATORS = ("\n", ".", "!", "?", "¡", "¿") + + +# COMMON utilities used by all Riva Runnables +def _import_riva_client() -> "riva.client": + """Import the riva client and raise an error on failure.""" + try: + # pylint: disable-next=import-outside-toplevel # this client library is optional + import riva.client + except ImportError as err: + raise ImportError( + "Could not import the NVIDIA Riva client library. " + "Please install it with `pip install nvidia-riva-client`." + ) from err + return riva.client + + +class SentinelT: # pylint: disable=too-few-public-methods + """An empty Sentinel type.""" + + +HANGUP = SentinelT() +_TRANSFORM_END = SentinelT() + + +class RivaAudioEncoding(str, Enum): + """An enum of the possible choices for Riva audio encoding. + + The list of types exposed by the Riva GRPC Protobuf files can be found + with the following commands: + ```python + import riva.client + print(riva.client.AudioEncoding.keys()) # noqa: T201 + ``` + """ + + ALAW = "ALAW" + ENCODING_UNSPECIFIED = "ENCODING_UNSPECIFIED" + FLAC = "FLAC" + LINEAR_PCM = "LINEAR_PCM" + MULAW = "MULAW" + OGGOPUS = "OGGOPUS" + + @classmethod + def from_wave_format_code(cls, format_code: int) -> "RivaAudioEncoding": + """Return the audio encoding specified by the format code in the wave file. + + ref: https://mmsp.ece.mcgill.ca/Documents/AudioFormats/WAVE/WAVE.html + """ + try: + return {1: cls.LINEAR_PCM, 6: cls.ALAW, 7: cls.MULAW}[format_code] + except KeyError as err: + raise NotImplementedError( + "The following wave file format code is " + f"not supported by Riva: {format_code}" + ) from err + + @property + def riva_pb2(self) -> "riva.client.AudioEncoding": + """Returns the Riva API object for the encoding.""" + riva_client = _import_riva_client() + return getattr(riva_client.AudioEncoding, self) + + +class RivaAuthMixin(BaseModel): + """Configuration for the authentication to a Riva service connection.""" + + url: Union[AnyHttpUrl, str] = Field( + AnyHttpUrl("http://localhost:50051", scheme="http"), + description="The full URL where the Riva service can be found.", + examples=["http://localhost:50051", "https://user@pass:riva.example.com"], + ) + ssl_cert: Optional[str] = Field( + None, + description="A full path to the file where Riva's public ssl key can be read.", + ) + + @property + def auth(self) -> "riva.client.Auth": + """Return a riva client auth object.""" + riva_client = _import_riva_client() + url = cast(AnyHttpUrl, self.url) + use_ssl = url.scheme == "https" # pylint: disable=no-member # false positive + url_no_scheme = str(self.url).split("/")[2] + return riva_client.Auth( + ssl_cert=self.ssl_cert, use_ssl=use_ssl, uri=url_no_scheme + ) + + @validator("url", pre=True, allow_reuse=True) + @classmethod + def _validate_url(cls, val: Any) -> AnyHttpUrl: + """Do some initial conversations for the URL before checking.""" + if isinstance(val, str): + return cast(AnyHttpUrl, parse_obj_as(AnyHttpUrl, val)) + return cast(AnyHttpUrl, val) + + +class RivaCommonConfigMixin(BaseModel): + """A collection of common Riva settings.""" + + encoding: RivaAudioEncoding = Field( + default=RivaAudioEncoding.LINEAR_PCM, + description="The encoding on the audio stream.", + ) + sample_rate_hertz: int = Field( + default=8000, description="The sample rate frequency of audio stream." + ) + language_code: str = Field( + default="en-US", + description=( + "The [BCP-47 language code]" + "(https://www.rfc-editor.org/rfc/bcp/bcp47.txt) for " + "the target language." + ), + ) + + +class _Event: + """A combined event that is threadsafe and async safe.""" + + _event: threading.Event + _aevent: asyncio.Event + + def __init__(self) -> None: + """Initialize the event.""" + self._event = threading.Event() + self._aevent = asyncio.Event() + + def set(self) -> None: + """Set the event.""" + self._event.set() + self._aevent.set() + + def clear(self) -> None: + """Set the event.""" + self._event.clear() + self._aevent.clear() + + def is_set(self) -> bool: + """Indicate if the event is set.""" + return self._event.is_set() + + def wait(self) -> None: + """Wait for the event to be set.""" + self._event.wait() + + async def async_wait(self) -> None: + """Async wait for the event to be set.""" + await self._aevent.wait() + + +def _mk_wave_file( + output_directory: Optional[str], sample_rate: float +) -> Tuple[Optional[str], Optional[wave.Wave_write]]: + """Create a new wave file and return the wave write object and filename.""" + if output_directory: + with tempfile.NamedTemporaryFile( + mode="bx", suffix=".wav", delete=False, dir=output_directory + ) as f: + wav_file_name = f.name + wav_file = wave.open(wav_file_name, "wb") + wav_file.setnchannels(1) + wav_file.setsampwidth(2) + wav_file.setframerate(sample_rate) + return (wav_file_name, wav_file) + return (None, None) + + +def _coerce_string(val: "TTSInputType") -> str: + """Attempt to coerce the input value to a string. + + This is particularly useful for converting LangChain message to strings. + """ + if isinstance(val, PromptValue): + return val.to_string() + if isinstance(val, BaseMessage): + return str(val.content) + return str(val) + + +def _process_chunks(inputs: Iterator["TTSInputType"]) -> Generator[str, None, None]: + """Filter the input chunks are return strings ready for TTS.""" + buffer = "" + for chunk in inputs: + chunk = _coerce_string(chunk) + + # return the buffer if an end of sentence character is detected + for terminator in _SENTENCE_TERMINATORS: + while terminator in chunk: + last_sentence, chunk = chunk.split(terminator, 1) + yield buffer + last_sentence + terminator + buffer = "" + + buffer += chunk + + # return the buffer if is too long + if len(buffer) > _MAX_TEXT_LENGTH: + for idx in range(0, len(buffer), _MAX_TEXT_LENGTH): + yield buffer[idx : idx + 5] + buffer = "" + + # return remaining buffer + if buffer: + yield buffer + + +# Riva AudioStream Type +StreamInputType = Union[bytes, SentinelT] +StreamOutputType = str + + +class AudioStream: + """A message containing streaming audio.""" + + _put_lock: threading.Lock + _queue: queue.Queue + output: queue.Queue + hangup: _Event + user_talking: _Event + user_quiet: _Event + _worker: Optional[threading.Thread] + + def __init__(self, maxsize: int = 0) -> None: + """Initialize the queue.""" + self._put_lock = threading.Lock() + self._queue = queue.Queue(maxsize=maxsize) + self.output = queue.Queue() + self.hangup = _Event() + self.user_quiet = _Event() + self.user_talking = _Event() + self._worker = None + + def __iter__(self) -> Generator[bytes, None, None]: + """Return an error.""" + while True: + # get next item + try: + next_val = self._queue.get(True, _QUEUE_GET_TIMEOUT) + except queue.Empty: + continue + + # hangup when requested + if next_val == HANGUP: + break + + # yield next item + yield next_val + self._queue.task_done() + + async def __aiter__(self) -> AsyncIterator[StreamInputType]: + """Iterate through all items in the queue until HANGUP.""" + while True: + # get next item + try: + next_val = await asyncio.get_event_loop().run_in_executor( + None, self._queue.get, True, _QUEUE_GET_TIMEOUT + ) + except queue.Empty: + continue + + # hangup when requested + if next_val == HANGUP: + break + + # yield next item + yield next_val + self._queue.task_done() + + @property + def hungup(self) -> bool: + """Indicate if the audio stream has hungup.""" + return self.hangup.is_set() + + @property + def empty(self) -> bool: + """Indicate in the input stream buffer is empty.""" + return self._queue.empty() + + @property + def complete(self) -> bool: + """Indicate if the audio stream has hungup and been processed.""" + input_done = self.hungup and self.empty + output_done = ( + self._worker is not None + and not self._worker.is_alive() + and self.output.empty() + ) + return input_done and output_done + + @property + def running(self) -> bool: + """Indicate if the ASR stream is running.""" + if self._worker: + return self._worker.is_alive() + return False + + def put(self, item: StreamInputType, timeout: Optional[int] = None) -> None: + """Put a new item into the queue.""" + with self._put_lock: + if self.hungup: + raise RuntimeError( + "The audio stream has already been hungup. Cannot put more data." + ) + if item is HANGUP: + self.hangup.set() + self._queue.put(item, timeout=timeout) + + async def aput(self, item: StreamInputType, timeout: Optional[int] = None) -> None: + """Async put a new item into the queue.""" + loop = asyncio.get_event_loop() + await asyncio.wait_for(loop.run_in_executor(None, self.put, item), timeout) + + def close(self, timeout: Optional[int] = None) -> None: + """Send the hangup signal.""" + self.put(HANGUP, timeout) + + async def aclose(self, timeout: Optional[int] = None) -> None: + """Async send the hangup signal.""" + await self.aput(HANGUP, timeout) + + def register(self, responses: Iterator["rasr.StreamingRecognizeResponse"]) -> None: + """Drain the responses from the provided iterator and put them into a queue.""" + if self.running: + raise RuntimeError("An ASR instance has already been registered.") + + has_started = threading.Barrier(2, timeout=5) + + def worker() -> None: + """Consume the ASR Generator.""" + has_started.wait() + for response in responses: + if not response.results: + continue + + for result in response.results: + if not result.alternatives: + continue + + if result.is_final: + self.user_talking.clear() + self.user_quiet.set() + transcript = cast(str, result.alternatives[0].transcript) + self.output.put(transcript) + + elif not self.user_talking.is_set(): + self.user_talking.set() + self.user_quiet.clear() + + self._worker = threading.Thread(target=worker) + self._worker.daemon = True + self._worker.start() + has_started.wait() + + +# RivaASR Runnable +ASRInputType = AudioStream +ASROutputType = str + + +class RivaASR( + RivaAuthMixin, + RivaCommonConfigMixin, + RunnableSerializable[ASRInputType, ASROutputType], +): + """A runnable that performs Automatic Speech Recognition (ASR) using NVIDIA Riva.""" + + name: str = "nvidia_riva_asr" + description: str = ( + "A Runnable for converting audio bytes to a string." + "This is useful for feeding an audio stream into a chain and" + "preprocessing that audio to create an LLM prompt." + ) + + # riva options + audio_channel_count: int = Field( + 1, description="The number of audio channels in the input audio stream." + ) + profanity_filter: bool = Field( + True, + description=( + "Controls whether or not Riva should attempt to filter " + "profanity out of the transcribed text." + ), + ) + enable_automatic_punctuation: bool = Field( + True, + description=( + "Controls whether Riva should attempt to correct " + "senetence puncuation in the transcribed text." + ), + ) + + @root_validator(pre=True) + @classmethod + def _validate_environment(cls, values: Dict[str, Any]) -> Dict[str, Any]: + """Validate the Python environment and input arguments.""" + _ = _import_riva_client() + return values + + @property + def config(self) -> "riva.client.StreamingRecognitionConfig": + """Create and return the riva config object.""" + riva_client = _import_riva_client() + return riva_client.StreamingRecognitionConfig( + interim_results=True, + config=riva_client.RecognitionConfig( + encoding=self.encoding, + sample_rate_hertz=self.sample_rate_hertz, + audio_channel_count=self.audio_channel_count, + max_alternatives=1, + profanity_filter=self.profanity_filter, + enable_automatic_punctuation=self.enable_automatic_punctuation, + language_code=self.language_code, + ), + ) + + def _get_service(self) -> "riva.client.ASRService": + """Connect to the riva service and return the a client object.""" + riva_client = _import_riva_client() + try: + return riva_client.ASRService(self.auth) + except Exception as err: + raise ValueError( + "Error raised while connecting to the Riva ASR server." + ) from err + + def invoke( + self, + input: ASRInputType, + _: Optional[RunnableConfig] = None, + ) -> ASROutputType: + """Transcribe the audio bytes into a string with Riva.""" + # create an output text generator with Riva + if not input.running: + service = self._get_service() + responses = service.streaming_response_generator( + audio_chunks=input, + streaming_config=self.config, + ) + input.register(responses) + + # return the first valid result + full_response: List[str] = [] + while not input.complete: + with input.output.not_empty: + ready = input.output.not_empty.wait(0.1) + + if ready: + while not input.output.empty(): + try: + full_response += [input.output.get_nowait()] + except queue.Empty: + continue + input.output.task_done() + _LOGGER.debug("Riva ASR returning: %s", repr(full_response)) + return " ".join(full_response).strip() + + return "" + + +# RivaTTS Runnable +# pylint: disable-next=invalid-name +TTSInputType = Union[str, AnyMessage, PromptValue] +TTSOutputType = bytes + + +class RivaTTS( + RivaAuthMixin, + RivaCommonConfigMixin, + RunnableSerializable[TTSInputType, TTSOutputType], +): + """A runnable that performs Text-to-Speech (TTS) with NVIDIA Riva.""" + + name: str = "nvidia_riva_tts" + description: str = ( + "A tool for converting text to speech." + "This is useful for converting LLM output into audio bytes." + ) + + # riva options + voice_name: str = Field( + "English-US.Female-1", + description=( + "The voice model in Riva to use for speech. " + "Pre-trained models are documented in " + "[the Riva documentation]" + "(https://docs.nvidia.com/deeplearning/riva/user-guide/docs/tts/tts-overview.html)." + ), + ) + output_directory: Optional[str] = Field( + None, + description=( + "The directory where all audio files should be saved. " + "A null value indicates that wave files should not be saved. " + "This is useful for debugging purposes." + ), + ) + + @root_validator(pre=True) + @classmethod + def _validate_environment(cls, values: Dict[str, Any]) -> Dict[str, Any]: + """Validate the Python environment and input arguments.""" + _ = _import_riva_client() + return values + + @validator("output_directory") + @classmethod + def _output_directory_validator(cls, v: str) -> str: + if v: + dirpath = pathlib.Path(v) + dirpath.mkdir(parents=True, exist_ok=True) + return str(dirpath.absolute()) + return v + + def _get_service(self) -> "riva.client.SpeechSynthesisService": + """Connect to the riva service and return the a client object.""" + riva_client = _import_riva_client() + try: + return riva_client.SpeechSynthesisService(self.auth) + except Exception as err: + raise ValueError( + "Error raised while connecting to the Riva TTS server." + ) from err + + def invoke( + self, input: TTSInputType, _: Union[RunnableConfig, None] = None + ) -> TTSOutputType: + """Perform TTS by taking a string and outputting the entire audio file.""" + return b"".join(self.transform(iter([input]))) + + def transform( + self, + input: Iterator[TTSInputType], + config: Optional[RunnableConfig] = None, + **kwargs: Optional[Any], + ) -> Iterator[TTSOutputType]: + """Perform TTS by taking a stream of characters and streaming output bytes.""" + service = self._get_service() + + # create an output wave file + wav_file_name, wav_file = _mk_wave_file( + self.output_directory, self.sample_rate_hertz + ) + + # split the input text and perform tts + for chunk in _process_chunks(input): + _LOGGER.debug("Riva TTS chunk: %s", chunk) + + # start riva tts streaming + responses = service.synthesize_online( + text=chunk, + voice_name=self.voice_name, + language_code=self.language_code, + encoding=self.encoding.riva_pb2, + sample_rate_hz=self.sample_rate_hertz, + ) + + # stream audio bytes out + for resp in responses: + audio = cast(bytes, resp.audio) + if wav_file: + wav_file.writeframesraw(audio) + yield audio + + # close the wave file when we are done + if wav_file: + wav_file.close() + _LOGGER.debug("Riva TTS wrote file: %s", wav_file_name) + + async def atransform( + self, + input: AsyncIterator[TTSInputType], + config: Optional[RunnableConfig] = None, + **kwargs: Optional[Any], + ) -> AsyncGenerator[TTSOutputType, None]: + """Intercept async transforms and route them to the synchronous transform.""" + loop = asyncio.get_running_loop() + input_queue: queue.Queue = queue.Queue() + out_queue: asyncio.Queue = asyncio.Queue() + + async def _producer() -> None: + """Produce input into the input queue.""" + async for val in input: + input_queue.put_nowait(val) + input_queue.put_nowait(_TRANSFORM_END) + + def _input_iterator() -> Iterator[TTSInputType]: + """Iterate over the input_queue.""" + while True: + try: + val = input_queue.get(timeout=0.5) + except queue.Empty: + continue + if val == _TRANSFORM_END: + break + yield val + + def _consumer() -> None: + """Consume the input with transform.""" + for val in self.transform(_input_iterator()): + out_queue.put_nowait(val) + out_queue.put_nowait(_TRANSFORM_END) + + async def _consumer_coro() -> None: + """Coroutine that wraps the consumer.""" + await loop.run_in_executor(None, _consumer) + + producer = loop.create_task(_producer()) + consumer = loop.create_task(_consumer_coro()) + + while True: + try: + val = await asyncio.wait_for(out_queue.get(), 0.5) + except asyncio.exceptions.TimeoutError: + continue + out_queue.task_done() + + if val is _TRANSFORM_END: + break + yield val + + await producer + await consumer diff --git a/libs/community/langchain_community/utilities/pebblo.py b/libs/community/langchain_community/utilities/pebblo.py new file mode 100644 index 00000000000..a52d3c45b46 --- /dev/null +++ b/libs/community/langchain_community/utilities/pebblo.py @@ -0,0 +1,249 @@ +from __future__ import annotations + +import logging +import os +import pathlib +import platform +from typing import Optional, Tuple + +from langchain_core.env import get_runtime_environment +from langchain_core.pydantic_v1 import BaseModel + +from langchain_community.document_loaders.base import BaseLoader + +logger = logging.getLogger(__name__) + +PLUGIN_VERSION = "0.1.0" +CLASSIFIER_URL = os.getenv("PEBBLO_CLASSIFIER_URL", "http://localhost:8000") + +# Supported loaders for Pebblo safe data loading +file_loader = [ + "JSONLoader", + "S3FileLoader", + "UnstructuredMarkdownLoader", + "UnstructuredPDFLoader", + "UnstructuredFileLoader", + "UnstructuredJsonLoader", + "PyPDFLoader", + "GCSFileLoader", + "AmazonTextractPDFLoader", + "CSVLoader", + "UnstructuredExcelLoader", +] +dir_loader = ["DirectoryLoader", "S3DirLoader", "PyPDFDirectoryLoader"] +in_memory = ["DataFrameLoader"] + +LOADER_TYPE_MAPPING = {"file": file_loader, "dir": dir_loader, "in-memory": in_memory} + +SUPPORTED_LOADERS = (*file_loader, *dir_loader, *in_memory) + +logger = logging.getLogger(__name__) + + +class Runtime(BaseModel): + """This class represents a Runtime. + + Args: + type (Optional[str]): Runtime type. Defaults to "" + host (str): Hostname of runtime. + path (str): Current working directory path. + ip (Optional[str]): Ip of current runtime. Defaults to "" + platform (str): Platform details of current runtime. + os (str): OS name. + os_version (str): OS version. + language (str): Runtime kernel. + language_version (str): version of current runtime kernel. + runtime (Optional[str]) More runtime details. Defaults to "" + """ + + type: str = "local" + host: str + path: str + ip: Optional[str] = "" + platform: str + os: str + os_version: str + language: str + language_version: str + runtime: str = "local" + + +class Framework(BaseModel): + """This class represents a Framework instance. + + Args: + name (str): Name of the Framework. + version (str): Version of the Framework. + """ + + name: str + version: str + + +class App(BaseModel): + """This class represents an AI application. + + Args: + name (str): Name of the app. + owner (str): Owner of the app. + description (Optional[str]): Description of the app. + load_id (str): Unique load_id of the app instance. + runtime (Runtime): Runtime details of app. + framework (Framework): Framework details of the app + plugin_version (str): Plugin version used for the app. + """ + + name: str + owner: str + description: Optional[str] + load_id: str + runtime: Runtime + framework: Framework + plugin_version: str + + +class Doc(BaseModel): + """This class represents a pebblo document. + + Args: + name (str): Name of app originating this document. + owner (str): Owner of app. + docs (list): List of documents with its metadata. + plugin_version (str): Pebblo plugin Version + load_id (str): Unique load_id of the app instance. + loader_details (dict): Loader details with its metadata. + loading_end (bool): Boolean, specifying end of loading of source. + source_owner (str): Owner of the source of the loader. + """ + + name: str + owner: str + docs: list + plugin_version: str + load_id: str + loader_details: dict + loading_end: bool + source_owner: str + + +def get_full_path(path: str) -> str: + """Return absolute local path for a local file/directory, + for network related path, return as is. + + Args: + path (str): Relative path to be resolved. + + Returns: + str: Resolved absolute path. + """ + if ( + not path + or ("://" in path) + or ("/" == path[0]) + or (path in ["unknown", "-", "in-memory"]) + ): + return path + full_path = pathlib.Path(path).resolve() + return str(full_path) + + +def get_loader_type(loader: str) -> str: + """Return loader type among, file, dir or in-memory. + + Args: + loader (str): Name of the loader, whose type is to be resolved. + + Returns: + str: One of the loader type among, file/dir/in-memory. + """ + for loader_type, loaders in LOADER_TYPE_MAPPING.items(): + if loader in loaders: + return loader_type + return "unknown" + + +def get_loader_full_path(loader: BaseLoader) -> str: + """Return absolute source path of source of loader based on the + keys present in Document object from loader. + + Args: + loader (BaseLoader): Langchain document loader, derived from Baseloader. + """ + from langchain_community.document_loaders import ( + DataFrameLoader, + GCSFileLoader, + S3FileLoader, + ) + + location = "-" + if not isinstance(loader, BaseLoader): + logger.error( + "loader is not derived from BaseLoader, source location will be unknown!" + ) + return location + loader_dict = loader.__dict__ + try: + if "bucket" in loader_dict: + if isinstance(loader, GCSFileLoader): + location = f"gc://{loader.bucket}/{loader.blob}" + elif isinstance(loader, S3FileLoader): + location = f"s3://{loader.bucket}/{loader.key}" + elif "path" in loader_dict: + location = loader_dict["path"] + elif "file_path" in loader_dict: + location = loader_dict["file_path"] + elif "web_paths" in loader_dict: + location = loader_dict["web_paths"][0] + # For in-memory types: + elif isinstance(loader, DataFrameLoader): + location = "in-memory" + except Exception: + pass + return get_full_path(str(location)) + + +def get_runtime() -> Tuple[Framework, Runtime]: + """Fetch the current Framework and Runtime details. + + Returns: + Tuple[Framework, Runtime]: Framework and Runtime for the current app instance. + """ + runtime_env = get_runtime_environment() + framework = Framework( + name="langchain", version=runtime_env.get("library_version", None) + ) + uname = platform.uname() + runtime = Runtime( + host=uname.node, + path=os.environ["PWD"], + platform=runtime_env.get("platform", "unknown"), + os=uname.system, + os_version=uname.version, + ip=get_ip(), + language=runtime_env.get("runtime", "unknown"), + language_version=runtime_env.get("runtime_version", "unknown"), + ) + + if "Darwin" in runtime.os: + runtime.type = "desktop" + runtime.runtime = "Mac OSX" + + logger.debug(f"framework {framework}") + logger.debug(f"runtime {runtime}") + return framework, runtime + + +def get_ip() -> str: + """Fetch local runtime ip address + + Returns: + str: IP address + """ + import socket # lazy imports + + host = socket.gethostname() + try: + public_ip = socket.gethostbyname(host) + except Exception: + public_ip = socket.gethostbyname("localhost") + return public_ip diff --git a/libs/community/langchain_community/utilities/pubmed.py b/libs/community/langchain_community/utilities/pubmed.py index 981799d9de3..8b67f259735 100644 --- a/libs/community/langchain_community/utilities/pubmed.py +++ b/libs/community/langchain_community/utilities/pubmed.py @@ -144,7 +144,7 @@ class PubMedAPIWrapper(BaseModel): if e.code == 429 and retry < self.max_retry: # Too Many Requests errors # wait for an exponentially increasing amount of time - print( + print( # noqa: T201 f"Too Many Requests, " f"waiting for {self.sleep_time:.2f} seconds..." ) diff --git a/libs/community/langchain_community/utilities/requests.py b/libs/community/langchain_community/utilities/requests.py index 673df3d5e57..fd183b4cfad 100644 --- a/libs/community/langchain_community/utilities/requests.py +++ b/libs/community/langchain_community/utilities/requests.py @@ -141,9 +141,9 @@ class GenericRequestsWrapper(BaseModel): self, response: aiohttp.ClientResponse ) -> Union[str, Dict[str, Any]]: if self.response_content_type == "text": - return response.text() + return response.text() # type: ignore[return-value] elif self.response_content_type == "json": - return response.json() + return response.json() # type: ignore[return-value] else: raise ValueError(f"Invalid return type: {self.response_content_type}") @@ -176,33 +176,33 @@ class GenericRequestsWrapper(BaseModel): async def aget(self, url: str, **kwargs: Any) -> Union[str, Dict[str, Any]]: """GET the URL and return the text asynchronously.""" async with self.requests.aget(url, **kwargs) as response: - return await self._aget_resp_content(response) + return await self._aget_resp_content(response) # type: ignore[misc] async def apost( self, url: str, data: Dict[str, Any], **kwargs: Any ) -> Union[str, Dict[str, Any]]: """POST to the URL and return the text asynchronously.""" async with self.requests.apost(url, data, **kwargs) as response: - return await self._aget_resp_content(response) + return await self._aget_resp_content(response) # type: ignore[misc] async def apatch( self, url: str, data: Dict[str, Any], **kwargs: Any ) -> Union[str, Dict[str, Any]]: """PATCH the URL and return the text asynchronously.""" async with self.requests.apatch(url, data, **kwargs) as response: - return await self._aget_resp_content(response) + return await self._aget_resp_content(response) # type: ignore[misc] async def aput( self, url: str, data: Dict[str, Any], **kwargs: Any ) -> Union[str, Dict[str, Any]]: """PUT the URL and return the text asynchronously.""" async with self.requests.aput(url, data, **kwargs) as response: - return await self._aget_resp_content(response) + return await self._aget_resp_content(response) # type: ignore[misc] async def adelete(self, url: str, **kwargs: Any) -> Union[str, Dict[str, Any]]: """DELETE the URL and return the text asynchronously.""" async with self.requests.adelete(url, **kwargs) as response: - return await self._aget_resp_content(response) + return await self._aget_resp_content(response) # type: ignore[misc] class JsonRequestsWrapper(GenericRequestsWrapper): diff --git a/libs/community/langchain_community/utilities/searx_search.py b/libs/community/langchain_community/utilities/searx_search.py index 3ac9f2cf1c2..0ddbf738c08 100644 --- a/libs/community/langchain_community/utilities/searx_search.py +++ b/libs/community/langchain_community/utilities/searx_search.py @@ -225,7 +225,7 @@ class SearxSearchWrapper(BaseModel): urllib3.disable_warnings() except ImportError as e: - print(e) + print(e) # noqa: T201 return v @@ -246,7 +246,7 @@ class SearxSearchWrapper(BaseModel): searx_host = get_from_dict_or_env(values, "searx_host", "SEARX_HOST") if not searx_host.startswith("http"): - print( + print( # noqa: T201 f"Warning: missing the url scheme on host \ ! assuming secure https://{searx_host} " ) diff --git a/libs/community/langchain_community/utilities/sql_database.py b/libs/community/langchain_community/utilities/sql_database.py index e56423733cb..fb542cfa883 100644 --- a/libs/community/langchain_community/utilities/sql_database.py +++ b/libs/community/langchain_community/utilities/sql_database.py @@ -1,15 +1,23 @@ """SQLAlchemy wrapper around a database.""" from __future__ import annotations -from typing import Any, Dict, Iterable, List, Literal, Optional, Sequence +from typing import Any, Dict, Iterable, List, Literal, Optional, Sequence, Union import sqlalchemy from langchain_core._api import deprecated from langchain_core.utils import get_from_env -from sqlalchemy import MetaData, Table, create_engine, inspect, select, text -from sqlalchemy.engine import Engine +from sqlalchemy import ( + MetaData, + Table, + create_engine, + inspect, + select, + text, +) +from sqlalchemy.engine import Engine, Result from sqlalchemy.exc import ProgrammingError, SQLAlchemyError from sqlalchemy.schema import CreateTable +from sqlalchemy.sql.expression import Executable from sqlalchemy.types import NullType @@ -373,67 +381,113 @@ class SQLDatabase: def _execute( self, - command: str, - fetch: Literal["all", "one"] = "all", - ) -> Sequence[Dict[str, Any]]: + command: Union[str, Executable], + fetch: Literal["all", "one", "cursor"] = "all", + *, + parameters: Optional[Dict[str, Any]] = None, + execution_options: Optional[Dict[str, Any]] = None, + ) -> Union[Sequence[Dict[str, Any]], Result]: """ Executes SQL command through underlying engine. If the statement returns no rows, an empty list is returned. """ - with self._engine.begin() as connection: # type: Connection + parameters = parameters or {} + execution_options = execution_options or {} + with self._engine.begin() as connection: # type: Connection # type: ignore[name-defined] if self._schema is not None: if self.dialect == "snowflake": connection.exec_driver_sql( - "ALTER SESSION SET search_path = %s", (self._schema,) + "ALTER SESSION SET search_path = %s", + (self._schema,), + execution_options=execution_options, ) elif self.dialect == "bigquery": - connection.exec_driver_sql("SET @@dataset_id=?", (self._schema,)) + connection.exec_driver_sql( + "SET @@dataset_id=?", + (self._schema,), + execution_options=execution_options, + ) elif self.dialect == "mssql": pass elif self.dialect == "trino": - connection.exec_driver_sql("USE ?", (self._schema,)) + connection.exec_driver_sql( + "USE ?", + (self._schema,), + execution_options=execution_options, + ) elif self.dialect == "duckdb": # Unclear which parameterized argument syntax duckdb supports. # The docs for the duckdb client say they support multiple, # but `duckdb_engine` seemed to struggle with all of them: # https://github.com/Mause/duckdb_engine/issues/796 - connection.exec_driver_sql(f"SET search_path TO {self._schema}") + connection.exec_driver_sql( + f"SET search_path TO {self._schema}", + execution_options=execution_options, + ) elif self.dialect == "oracle": connection.exec_driver_sql( - f"ALTER SESSION SET CURRENT_SCHEMA = {self._schema}" + f"ALTER SESSION SET CURRENT_SCHEMA = {self._schema}", + execution_options=execution_options, ) elif self.dialect == "sqlany": # If anybody using Sybase SQL anywhere database then it should not # go to else condition. It should be same as mssql. pass elif self.dialect == "postgresql": # postgresql - connection.exec_driver_sql("SET search_path TO %s", (self._schema,)) + connection.exec_driver_sql( + "SET search_path TO %s", + (self._schema,), + execution_options=execution_options, + ) + + if isinstance(command, str): + command = text(command) + elif isinstance(command, Executable): + pass + else: + raise TypeError(f"Query expression has unknown type: {type(command)}") + cursor = connection.execute( + command, + parameters, + execution_options=execution_options, + ) - cursor = connection.execute(text(command)) if cursor.returns_rows: if fetch == "all": result = [x._asdict() for x in cursor.fetchall()] elif fetch == "one": first_result = cursor.fetchone() result = [] if first_result is None else [first_result._asdict()] + elif fetch == "cursor": + return cursor else: - raise ValueError("Fetch parameter must be either 'one' or 'all'") + raise ValueError( + "Fetch parameter must be either 'one', 'all', or 'cursor'" + ) return result return [] def run( self, - command: str, - fetch: Literal["all", "one"] = "all", + command: Union[str, Executable], + fetch: Literal["all", "one", "cursor"] = "all", include_columns: bool = False, - ) -> str: + *, + parameters: Optional[Dict[str, Any]] = None, + execution_options: Optional[Dict[str, Any]] = None, + ) -> Union[str, Sequence[Dict[str, Any]], Result[Any]]: """Execute a SQL command and return a string representing the results. If the statement returns rows, a string of the results is returned. If the statement returns no rows, an empty string is returned. """ - result = self._execute(command, fetch) + result = self._execute( + command, fetch, parameters=parameters, execution_options=execution_options + ) + + if fetch == "cursor": + return result res = [ { @@ -444,7 +498,7 @@ class SQLDatabase: ] if not include_columns: - res = [tuple(row.values()) for row in res] + res = [tuple(row.values()) for row in res] # type: ignore[misc] if not res: return "" @@ -472,7 +526,10 @@ class SQLDatabase: command: str, fetch: Literal["all", "one"] = "all", include_columns: bool = False, - ) -> str: + *, + parameters: Optional[Dict[str, Any]] = None, + execution_options: Optional[Dict[str, Any]] = None, + ) -> Union[str, Sequence[Dict[str, Any]], Result[Any]]: """Execute a SQL command and return a string representing the results. If the statement returns rows, a string of the results is returned. @@ -481,7 +538,13 @@ class SQLDatabase: If the statement throws an error, the error message is returned. """ try: - return self.run(command, fetch, include_columns) + return self.run( + command, + fetch, + parameters=parameters, + execution_options=execution_options, + include_columns=include_columns, + ) except SQLAlchemyError as e: """Format the error message""" return f"Error: {e}" diff --git a/libs/community/langchain_community/utilities/vertexai.py b/libs/community/langchain_community/utilities/vertexai.py index 88b9bd3394c..1dc1f14b3f8 100644 --- a/libs/community/langchain_community/utilities/vertexai.py +++ b/libs/community/langchain_community/utilities/vertexai.py @@ -57,7 +57,7 @@ def init_vertexai( location: Optional[str] = None, credentials: Optional["Credentials"] = None, ) -> None: - """Init vertexai. + """Init Vertex AI. Args: project: The default GCP project to use when making Vertex API calls. diff --git a/libs/community/langchain_community/utilities/you.py b/libs/community/langchain_community/utilities/you.py new file mode 100644 index 00000000000..c01a1ed37a1 --- /dev/null +++ b/libs/community/langchain_community/utilities/you.py @@ -0,0 +1,230 @@ +"""Util that calls you.com Search API. + +In order to set this up, follow instructions at: +""" +import json +from typing import Any, Dict, List, Literal, Optional + +import aiohttp +import requests +from langchain_core.documents import Document +from langchain_core.pydantic_v1 import BaseModel, Field, root_validator +from langchain_core.utils import get_from_dict_or_env + +YOU_API_URL = "https://api.ydc-index.io" + + +class YouHitMetadata(BaseModel): + """Metadata on a single hit from you.com""" + + title: str = Field(description="The title of the result") + url: str = Field(description="The url of the result") + thumbnail_url: str = Field(description="Thumbnail associated with the result") + description: str = Field(description="Details about the result") + + +class YouHit(YouHitMetadata): + """A single hit from you.com, which may contain multiple snippets""" + + snippets: List[str] = Field(description="One or snippets of text") + + +class YouAPIOutput(BaseModel): + """The output from you.com api""" + + hits: List[YouHit] = Field( + description="A list of dictionaries containing the results" + ) + + +class YouDocument(BaseModel): + """The output of parsing one snippet""" + + page_content: str = Field(description="One snippet of text") + metadata: YouHitMetadata + + +class YouSearchAPIWrapper(BaseModel): + """Wrapper for you.com Search API. + + To connect to the You.com api requires an API key which + you can get at https://api.you.com. + You can check out the docs at https://documentation.you.com. + + You need to set the environment variable `YDC_API_KEY` for retriever to operate. + + Attributes + ---------- + ydc_api_key: str, optional + you.com api key, if YDC_API_KEY is not set in the environment + num_web_results: int, optional + The max number of web results to return, must be under 20 + safesearch: str, optional + Safesearch settings, one of off, moderate, strict, defaults to moderate + country: str, optional + Country code, ex: 'US' for united states, see api docs for list + k: int, optional + max number of Documents to return using `results()` + n_hits: int, optional, deprecated + Alias for num_web_results + n_snippets_per_hit: int, optional + limit the number of snippets returned per hit + endpoint_type: str, optional + you.com endpoints: search, news, rag; + `web` and `snippet` alias `search` + `rag` returns `{'message': 'Forbidden'}` + @todo `news` endpoint + """ + + ydc_api_key: Optional[str] = None + num_web_results: Optional[int] = None + safesearch: Optional[str] = None + country: Optional[str] = None + k: Optional[int] = None + n_snippets_per_hit: Optional[int] = None + # @todo deprecate `snippet`, not part of API + endpoint_type: Literal["search", "news", "rag", "snippet"] = "search" + # should deprecate n_hits + n_hits: Optional[int] = None + + @root_validator(pre=True) + def validate_environment(cls, values: Dict) -> Dict: + """Validate that api key exists in environment.""" + ydc_api_key = get_from_dict_or_env(values, "ydc_api_key", "YDC_API_KEY") + values["ydc_api_key"] = ydc_api_key + + return values + + def _parse_results(self, raw_search_results: Dict) -> List[Document]: + """ + Extracts snippets from each hit and puts them in a Document + Parameters: + raw_search_results: A dict containing list of hits + Returns: + List[YouDocument]: A dictionary of parsed results + """ + + # return news results + if self.endpoint_type == "news": + return [ + Document(page_content=result["description"], metadata=result) + for result in raw_search_results["news"]["results"] + ] + + docs = [] + for hit in raw_search_results["hits"]: + n_snippets_per_hit = self.n_snippets_per_hit or len(hit["snippets"]) + for snippet in hit["snippets"][:n_snippets_per_hit]: + docs.append( + Document( + page_content=snippet, + metadata={ + "url": hit["url"], + "thumbnail_url": hit["thumbnail_url"], + "title": hit["title"], + "description": hit["description"], + }, + ) + ) + if self.k is not None and len(docs) >= self.k: + return docs + return docs + + def raw_results( + self, + query: str, + **kwargs: Any, + ) -> Dict: + """Run query through you.com Search and return hits. + + Args: + query: The query to search for. + num_web_results: The maximum number of results to return. + safesearch: Safesearch settings, + one of off, moderate, strict, defaults to moderate + country: Country code + Returns: YouAPIOutput + """ + headers = {"X-API-Key": self.ydc_api_key or ""} + params = { + "query": query, + "num_web_results": self.num_web_results, + "safesearch": self.safesearch, + "country": self.country, + **kwargs, + } + + params = {k: v for k, v in params.items() if v is not None} + # news endpoint expects `q` instead of `query` + if self.endpoint_type == "news": + params["q"] = params["query"] + del params["query"] + + # @todo deprecate `snippet`, not part of API + if self.endpoint_type == "snippet": + self.endpoint_type = "search" + response = requests.get( + # type: ignore + f"{YOU_API_URL}/{self.endpoint_type}", + params=params, + headers=headers, + ) + response.raise_for_status() + return response.json() + + def results( + self, + query: str, + **kwargs: Any, + ) -> List[Document]: + """Run query through you.com Search and parses results into Documents.""" + + raw_search_results = self.raw_results( + query, + **{key: value for key, value in kwargs.items() if value is not None}, + ) + return self._parse_results(raw_search_results) + + async def raw_results_async( + self, + query: str, + num_web_results: Optional[int] = 5, + safesearch: Optional[str] = "moderate", + country: Optional[str] = "US", + ) -> Dict: + """Get results from the you.com Search API asynchronously.""" + + # Function to perform the API call + async def fetch() -> str: + params = { + "query": query, + "num_web_results": num_web_results, + "safesearch": safesearch, + "country": country, + } + async with aiohttp.ClientSession() as session: + async with session.post(f"{YOU_API_URL}/search", json=params) as res: + if res.status == 200: + data = await res.text() + return data + else: + raise Exception(f"Error {res.status}: {res.reason}") + + results_json_str = await fetch() + return json.loads(results_json_str) + + async def results_async( + self, + query: str, + num_web_results: Optional[int] = 5, + safesearch: Optional[str] = "moderate", + country: Optional[str] = "US", + ) -> List[Document]: + results_json = await self.raw_results_async( + query=query, + num_web_results=num_web_results, + safesearch=safesearch, + country=country, + ) + + return self._parse_results(results_json["results"]) diff --git a/libs/community/langchain_community/utils/google.py b/libs/community/langchain_community/utils/google.py new file mode 100644 index 00000000000..4e68512296b --- /dev/null +++ b/libs/community/langchain_community/utils/google.py @@ -0,0 +1,25 @@ +"""Utilities to use Google provided components.""" + +from importlib import metadata +from typing import Any, Optional + + +def get_client_info(module: Optional[str] = None) -> Any: + r"""Returns a custom user agent header. + + Args: + module (Optional[str]): + Optional. The module for a custom user agent header. + Returns: + google.api_core.gapic_v1.client_info.ClientInfo + """ + from google.api_core.gapic_v1.client_info import ClientInfo + + langchain_version = metadata.version("langchain") + client_library_version = ( + f"{langchain_version}-{module}" if module else langchain_version + ) + return ClientInfo( + client_library_version=client_library_version, + user_agent=f"langchain/{client_library_version}", + ) diff --git a/libs/community/langchain_community/vectorstores/__init__.py b/libs/community/langchain_community/vectorstores/__init__.py index 61b573bb649..da412557f37 100644 --- a/libs/community/langchain_community/vectorstores/__init__.py +++ b/libs/community/langchain_community/vectorstores/__init__.py @@ -74,6 +74,12 @@ def _import_annoy() -> Any: return Annoy +def _import_apache_doris() -> Any: + from langchain_community.vectorstores.apache_doris import ApacheDoris + + return ApacheDoris + + def _import_atlas() -> Any: from langchain_community.vectorstores.atlas import AtlasDB @@ -497,6 +503,8 @@ def __getattr__(name: str) -> Any: return _import_elastic_vector_search() elif name == "Annoy": return _import_annoy() + elif name == "ApacheDoris": + return _import_apache_doris() elif name == "AtlasDB": return _import_atlas() elif name == "AwaDB": @@ -640,6 +648,7 @@ __all__ = [ "AlibabaCloudOpenSearchSettings", "AnalyticDB", "Annoy", + "ApacheDoris", "AtlasDB", "AwaDB", "AzureSearch", diff --git a/libs/community/langchain_community/vectorstores/alibabacloud_opensearch.py b/libs/community/langchain_community/vectorstores/alibabacloud_opensearch.py index c9d5b85a6bd..12d02ae19d3 100644 --- a/libs/community/langchain_community/vectorstores/alibabacloud_opensearch.py +++ b/libs/community/langchain_community/vectorstores/alibabacloud_opensearch.py @@ -356,7 +356,7 @@ class AlibabaCloudOpenSearch(VectorStore): "fields" not in item or self.config.field_name_mapping["document"] not in item["fields"] ): - query_result_list.append(Document()) + query_result_list.append(Document()) # type: ignore[call-arg] else: fields = item["fields"] query_result_list.append( diff --git a/libs/community/langchain_community/vectorstores/analyticdb.py b/libs/community/langchain_community/vectorstores/analyticdb.py index e72383eae47..767a836a06f 100644 --- a/libs/community/langchain_community/vectorstores/analyticdb.py +++ b/libs/community/langchain_community/vectorstores/analyticdb.py @@ -347,7 +347,7 @@ class AnalyticDB(VectorStore): conn.execute(chunks_table.delete().where(delete_condition)) return True except Exception as e: - print("Delete operation failed:", str(e)) + print("Delete operation failed:", str(e)) # noqa: T201 return False @classmethod diff --git a/libs/community/langchain_community/vectorstores/apache_doris.py b/libs/community/langchain_community/vectorstores/apache_doris.py new file mode 100644 index 00000000000..12e9b58304f --- /dev/null +++ b/libs/community/langchain_community/vectorstores/apache_doris.py @@ -0,0 +1,480 @@ +from __future__ import annotations + +import json +import logging +from hashlib import sha1 +from threading import Thread +from typing import Any, Dict, Iterable, List, Optional, Tuple + +from langchain_core.documents import Document +from langchain_core.embeddings import Embeddings +from langchain_core.pydantic_v1 import BaseSettings +from langchain_core.vectorstores import VectorStore + +logger = logging.getLogger() +DEBUG = False + + +class ApacheDorisSettings(BaseSettings): + """Apache Doris client configuration. + + Attributes: + apache_doris_host (str) : An URL to connect to frontend. + Defaults to 'localhost'. + apache_doris_port (int) : URL port to connect with HTTP. Defaults to 9030. + username (str) : Username to login. Defaults to 'root'. + password (str) : Password to login. Defaults to None. + database (str) : Database name to find the table. Defaults to 'default'. + table (str) : Table name to operate on. + Defaults to 'langchain'. + + column_map (Dict) : Column type map to project column name onto langchain + semantics. Must have keys: `text`, `id`, `vector`, + must be same size to number of columns. For example: + .. code-block:: python + + { + 'id': 'text_id', + 'embedding': 'text_embedding', + 'document': 'text_plain', + 'metadata': 'metadata_dictionary_in_json', + } + + Defaults to identity map. + """ + + host: str = "localhost" + port: int = 9030 + username: str = "root" + password: str = "" + + column_map: Dict[str, str] = { + "id": "id", + "document": "document", + "embedding": "embedding", + "metadata": "metadata", + } + + database: str = "default" + table: str = "langchain" + + def __getitem__(self, item: str) -> Any: + return getattr(self, item) + + class Config: + env_file = ".env" + env_prefix = "apache_doris_" + env_file_encoding = "utf-8" + + +class ApacheDoris(VectorStore): + """`Apache Doris` vector store. + + You need a `pymysql` python package, and a valid account + to connect to Apache Doris. + + For more information, please visit + [Apache Doris official site](https://doris.apache.org/) + [Apache Doris github](https://github.com/apache/doris) + """ + + def __init__( + self, + embedding: Embeddings, + *, + config: Optional[ApacheDorisSettings] = None, + **kwargs: Any, + ) -> None: + """Constructor for Apache Doris. + + Args: + embedding (Embeddings): Text embedding model. + config (ApacheDorisSettings): Apache Doris client configuration information. + """ + try: + import pymysql # type: ignore[import] + except ImportError: + raise ImportError( + "Could not import pymysql python package. " + "Please install it with `pip install pymysql`." + ) + try: + from tqdm import tqdm + + self.pgbar = tqdm + except ImportError: + # Just in case if tqdm is not installed + self.pgbar = lambda x, **kwargs: x + super().__init__() + if config is not None: + self.config = config + else: + self.config = ApacheDorisSettings() + assert self.config + assert self.config.host and self.config.port + assert self.config.column_map and self.config.database and self.config.table + for k in ["id", "embedding", "document", "metadata"]: + assert k in self.config.column_map + + # initialize the schema + dim = len(embedding.embed_query("test")) + + self.schema = f"""\ +CREATE TABLE IF NOT EXISTS {self.config.database}.{self.config.table}( + {self.config.column_map['id']} varchar(50), + {self.config.column_map['document']} string, + {self.config.column_map['embedding']} array, + {self.config.column_map['metadata']} string +) ENGINE = OLAP UNIQUE KEY(id) DISTRIBUTED BY HASH(id) \ + PROPERTIES ("replication_allocation" = "tag.location.default: 1")\ +""" + self.dim = dim + self.BS = "\\" + self.must_escape = ("\\", "'") + self._embedding = embedding + self.dist_order = "DESC" + _debug_output(self.config) + + # Create a connection to Apache Doris + self.connection = pymysql.connect( + host=self.config.host, + port=self.config.port, + user=self.config.username, + password=self.config.password, + database=self.config.database, + **kwargs, + ) + + _debug_output(self.schema) + _get_named_result(self.connection, self.schema) + + def escape_str(self, value: str) -> str: + return "".join(f"{self.BS}{c}" if c in self.must_escape else c for c in value) + + @property + def embeddings(self) -> Embeddings: + return self._embedding + + def _build_insert_sql(self, transac: Iterable, column_names: Iterable[str]) -> str: + ks = ",".join(column_names) + embed_tuple_index = tuple(column_names).index( + self.config.column_map["embedding"] + ) + _data = [] + for n in transac: + n = ",".join( + [ + ( + f"'{self.escape_str(str(_n))}'" + if idx != embed_tuple_index + else f"array{str(_n)}" + ) + for (idx, _n) in enumerate(n) + ] + ) + _data.append(f"({n})") + i_str = f""" + INSERT INTO + {self.config.database}.{self.config.table}({ks}) + VALUES + {','.join(_data)} + """ + return i_str + + def _insert(self, transac: Iterable, column_names: Iterable[str]) -> None: + _insert_query = self._build_insert_sql(transac, column_names) + _debug_output(_insert_query) + _get_named_result(self.connection, _insert_query) + + def add_texts( + self, + texts: Iterable[str], + metadatas: Optional[List[dict]] = None, + batch_size: int = 32, + ids: Optional[Iterable[str]] = None, + **kwargs: Any, + ) -> List[str]: + """Insert more texts through the embeddings and add to the VectorStore. + + Args: + texts: Iterable of strings to add to the VectorStore. + ids: Optional list of ids to associate with the texts. + batch_size: Batch size of insertion + metadata: Optional column data to be inserted + + Returns: + List of ids from adding the texts into the VectorStore. + + """ + # Embed and create the documents + ids = ids or [sha1(t.encode("utf-8")).hexdigest() for t in texts] + colmap_ = self.config.column_map + transac = [] + column_names = { + colmap_["id"]: ids, + colmap_["document"]: texts, + colmap_["embedding"]: self._embedding.embed_documents(list(texts)), + } + metadatas = metadatas or [{} for _ in texts] + column_names[colmap_["metadata"]] = map(json.dumps, metadatas) + assert len(set(colmap_) - set(column_names)) >= 0 + keys, values = zip(*column_names.items()) + try: + t = None + for v in self.pgbar( + zip(*values), desc="Inserting data...", total=len(metadatas) + ): + assert ( + len(v[keys.index(self.config.column_map["embedding"])]) == self.dim + ) + transac.append(v) + if len(transac) == batch_size: + if t: + t.join() + t = Thread(target=self._insert, args=[transac, keys]) + t.start() + transac = [] + if len(transac) > 0: + if t: + t.join() + self._insert(transac, keys) + return [i for i in ids] + except Exception as e: + logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m") + return [] + + @classmethod + def from_texts( + cls, + texts: List[str], + embedding: Embeddings, + metadatas: Optional[List[Dict[Any, Any]]] = None, + config: Optional[ApacheDorisSettings] = None, + text_ids: Optional[Iterable[str]] = None, + batch_size: int = 32, + **kwargs: Any, + ) -> ApacheDoris: + """Create Apache Doris wrapper with existing texts + + Args: + embedding_function (Embeddings): Function to extract text embedding + texts (Iterable[str]): List or tuple of strings to be added + config (ApacheDorisSettings, Optional): Apache Doris configuration + text_ids (Optional[Iterable], optional): IDs for the texts. + Defaults to None. + batch_size (int, optional): BatchSize when transmitting data to Apache + Doris. Defaults to 32. + metadata (List[dict], optional): metadata to texts. Defaults to None. + Returns: + Apache Doris Index + """ + ctx = cls(embedding, config=config, **kwargs) + ctx.add_texts(texts, ids=text_ids, batch_size=batch_size, metadatas=metadatas) + return ctx + + def __repr__(self) -> str: + """Text representation for Apache Doris Vector Store, prints frontends, username + and schemas. Easy to use with `str(ApacheDoris())` + + Returns: + repr: string to show connection info and data schema + """ + _repr = f"\033[92m\033[1m{self.config.database}.{self.config.table} @ " + _repr += f"{self.config.host}:{self.config.port}\033[0m\n\n" + _repr += f"\033[1musername: {self.config.username}\033[0m\n\nTable Schema:\n" + width = 25 + fields = 3 + _repr += "-" * (width * fields + 1) + "\n" + columns = ["name", "type", "key"] + _repr += f"|\033[94m{columns[0]:24s}\033[0m|\033[96m{columns[1]:24s}" + _repr += f"\033[0m|\033[96m{columns[2]:24s}\033[0m|\n" + _repr += "-" * (width * fields + 1) + "\n" + q_str = f"DESC {self.config.database}.{self.config.table}" + _debug_output(q_str) + rs = _get_named_result(self.connection, q_str) + for r in rs: + _repr += f"|\033[94m{r['Field']:24s}\033[0m|\033[96m{r['Type']:24s}" + _repr += f"\033[0m|\033[96m{r['Key']:24s}\033[0m|\n" + _repr += "-" * (width * fields + 1) + "\n" + return _repr + + def _build_query_sql( + self, q_emb: List[float], topk: int, where_str: Optional[str] = None + ) -> str: + q_emb_str = ",".join(map(str, q_emb)) + if where_str: + where_str = f"WHERE {where_str}" + else: + where_str = "" + + q_str = f""" + SELECT {self.config.column_map['document']}, + {self.config.column_map['metadata']}, + cosine_distance(array[{q_emb_str}], + {self.config.column_map['embedding']}) as dist + FROM {self.config.database}.{self.config.table} + {where_str} + ORDER BY dist {self.dist_order} + LIMIT {topk} + """ + + _debug_output(q_str) + return q_str + + def similarity_search( + self, query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any + ) -> List[Document]: + """Perform a similarity search with Apache Doris + + Args: + query (str): query string + k (int, optional): Top K neighbors to retrieve. Defaults to 4. + where_str (Optional[str], optional): where condition string. + Defaults to None. + + NOTE: Please do not let end-user to fill this and always be aware + of SQL injection. When dealing with metadatas, remember to + use `{self.metadata_column}.attribute` instead of `attribute` + alone. The default name for it is `metadata`. + + Returns: + List[Document]: List of Documents + """ + return self.similarity_search_by_vector( + self._embedding.embed_query(query), k, where_str, **kwargs + ) + + def similarity_search_by_vector( + self, + embedding: List[float], + k: int = 4, + where_str: Optional[str] = None, + **kwargs: Any, + ) -> List[Document]: + """Perform a similarity search with Apache Doris by vectors + + Args: + query (str): query string + k (int, optional): Top K neighbors to retrieve. Defaults to 4. + where_str (Optional[str], optional): where condition string. + Defaults to None. + + NOTE: Please do not let end-user to fill this and always be aware + of SQL injection. When dealing with metadatas, remember to + use `{self.metadata_column}.attribute` instead of `attribute` + alone. The default name for it is `metadata`. + + Returns: + List[Document]: List of (Document, similarity) + """ + q_str = self._build_query_sql(embedding, k, where_str) + try: + return [ + Document( + page_content=r[self.config.column_map["document"]], + metadata=json.loads(r[self.config.column_map["metadata"]]), + ) + for r in _get_named_result(self.connection, q_str) + ] + except Exception as e: + logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m") + return [] + + def similarity_search_with_relevance_scores( + self, query: str, k: int = 4, where_str: Optional[str] = None, **kwargs: Any + ) -> List[Tuple[Document, float]]: + """Perform a similarity search with Apache Doris + + Args: + query (str): query string + k (int, optional): Top K neighbors to retrieve. Defaults to 4. + where_str (Optional[str], optional): where condition string. + Defaults to None. + + NOTE: Please do not let end-user to fill this and always be aware + of SQL injection. When dealing with metadatas, remember to + use `{self.metadata_column}.attribute` instead of `attribute` + alone. The default name for it is `metadata`. + + Returns: + List[Document]: List of documents + """ + q_str = self._build_query_sql(self._embedding.embed_query(query), k, where_str) + try: + return [ + ( + Document( + page_content=r[self.config.column_map["document"]], + metadata=json.loads(r[self.config.column_map["metadata"]]), + ), + r["dist"], + ) + for r in _get_named_result(self.connection, q_str) + ] + except Exception as e: + logger.error(f"\033[91m\033[1m{type(e)}\033[0m \033[95m{str(e)}\033[0m") + return [] + + def drop(self) -> None: + """ + Helper function: Drop data + """ + _get_named_result( + self.connection, + f"DROP TABLE IF EXISTS {self.config.database}.{self.config.table}", + ) + + @property + def metadata_column(self) -> str: + return self.config.column_map["metadata"] + + +def _has_mul_sub_str(s: str, *args: Any) -> bool: + """Check if a string has multiple substrings. + + Args: + s: The string to check + *args: The substrings to check for in the string + + Returns: + bool: True if all substrings are present in the string, False otherwise + """ + for a in args: + if a not in s: + return False + return True + + +def _debug_output(s: Any) -> None: + """Print a debug message if DEBUG is True. + + Args: + s: The message to print + """ + if DEBUG: + print(s) # noqa: T201 + + +def _get_named_result(connection: Any, query: str) -> List[dict[str, Any]]: + """Get a named result from a query. + + Args: + connection: The connection to the database + query: The query to execute + + Returns: + List[dict[str, Any]]: The result of the query + """ + cursor = connection.cursor() + cursor.execute(query) + columns = cursor.description + result = [] + for value in cursor.fetchall(): + r = {} + for idx, datum in enumerate(value): + k = columns[idx][0] + r[k] = datum + result.append(r) + _debug_output(result) + cursor.close() + return result diff --git a/libs/community/langchain_community/vectorstores/astradb.py b/libs/community/langchain_community/vectorstores/astradb.py index 7d59bc91ebd..67751e4410c 100644 --- a/libs/community/langchain_community/vectorstores/astradb.py +++ b/libs/community/langchain_community/vectorstores/astradb.py @@ -1,13 +1,12 @@ from __future__ import annotations -import asyncio import uuid import warnings -from asyncio import Task from concurrent.futures import ThreadPoolExecutor from typing import ( TYPE_CHECKING, Any, + Awaitable, Callable, Dict, Iterable, @@ -17,16 +16,21 @@ from typing import ( Tuple, Type, TypeVar, + Union, ) import numpy as np +from langchain_core._api.deprecation import deprecated from langchain_core.documents import Document from langchain_core.embeddings import Embeddings -from langchain_core.runnables import run_in_executor from langchain_core.runnables.utils import gather_with_concurrency from langchain_core.utils.iter import batch_iterate from langchain_core.vectorstores import VectorStore +from langchain_community.utilities.astradb import ( + SetupMode, + _AstraDBCollectionEnvironment, +) from langchain_community.vectorstores.utils import maximal_marginal_relevance if TYPE_CHECKING: @@ -61,6 +65,11 @@ def _unique_list(lst: List[T], key: Callable[[T], U]) -> List[T]: return new_lst +@deprecated( + since="0.1.23", + removal="0.2.0", + alternative_import="langchain_astradb.AstraDBVectorStore", +) class AstraDB(VectorStore): """Wrapper around DataStax Astra DB for vector-store workloads. @@ -140,7 +149,7 @@ class AstraDB(VectorStore): if isinstance(v, list): metadata_filter[k] = [AstraDB._filter_to_metadata(f) for f in v] else: - metadata_filter[k] = AstraDB._filter_to_metadata(v) + metadata_filter[k] = AstraDB._filter_to_metadata(v) # type: ignore[assignment] else: metadata_filter[f"metadata.{k}"] = v @@ -161,28 +170,12 @@ class AstraDB(VectorStore): bulk_insert_batch_concurrency: Optional[int] = None, bulk_insert_overwrite_concurrency: Optional[int] = None, bulk_delete_concurrency: Optional[int] = None, + setup_mode: SetupMode = SetupMode.SYNC, pre_delete_collection: bool = False, ) -> None: """ Create an AstraDB vector store object. See class docstring for help. """ - try: - from astrapy.db import AstraDB as LibAstraDB - from astrapy.db import AstraDBCollection - except (ImportError, ModuleNotFoundError): - raise ImportError( - "Could not import a recent astrapy python package. " - "Please install it with `pip install --upgrade astrapy`." - ) - - # Conflicting-arg checks: - if astra_db_client is not None or async_astra_db_client is not None: - if token is not None or api_endpoint is not None: - raise ValueError( - "You cannot pass 'astra_db_client' or 'async_astra_db_client' to " - "AstraDB if passing 'token' and 'api_endpoint'." - ) - self.embedding = embedding self.collection_name = collection_name self.token = token @@ -201,105 +194,35 @@ class AstraDB(VectorStore): bulk_delete_concurrency or DEFAULT_BULK_DELETE_CONCURRENCY ) # "vector-related" settings - self._embedding_dimension: Optional[int] = None self.metric = metric + embedding_dimension: Union[int, Awaitable[int], None] = None + if setup_mode == SetupMode.ASYNC: + embedding_dimension = self._aget_embedding_dimension() + elif setup_mode == SetupMode.SYNC: + embedding_dimension = self._get_embedding_dimension() - self.astra_db = astra_db_client - self.async_astra_db = async_astra_db_client - self.collection = None - self.async_collection = None - - if token and api_endpoint: - self.astra_db = LibAstraDB( - token=self.token, - api_endpoint=self.api_endpoint, - namespace=self.namespace, - ) - try: - from astrapy.db import AsyncAstraDB - - self.async_astra_db = AsyncAstraDB( - token=self.token, - api_endpoint=self.api_endpoint, - namespace=self.namespace, - ) - except (ImportError, ModuleNotFoundError): - pass - - if self.astra_db is not None: - self.collection = AstraDBCollection( - collection_name=self.collection_name, - astra_db=self.astra_db, - ) - - self.async_setup_db_task: Optional[Task] = None - if self.async_astra_db is not None: - from astrapy.db import AsyncAstraDBCollection - - self.async_collection = AsyncAstraDBCollection( - collection_name=self.collection_name, - astra_db=self.async_astra_db, - ) - try: - self.async_setup_db_task = asyncio.create_task( - self._setup_db(pre_delete_collection) - ) - except RuntimeError: - pass - - if self.async_setup_db_task is None: - if not pre_delete_collection: - self._provision_collection() - else: - self.clear() - - def _ensure_astra_db_client(self): - if not self.astra_db: - raise ValueError("Missing AstraDB client") - - async def _setup_db(self, pre_delete_collection: bool) -> None: - if pre_delete_collection: - await self.async_astra_db.delete_collection( - collection_name=self.collection_name, - ) - await self._aprovision_collection() - - async def _ensure_db_setup(self) -> None: - if self.async_setup_db_task: - await self.async_setup_db_task + self.astra_env = _AstraDBCollectionEnvironment( + collection_name=collection_name, + token=token, + api_endpoint=api_endpoint, + astra_db_client=astra_db_client, + async_astra_db_client=async_astra_db_client, + namespace=namespace, + setup_mode=setup_mode, + pre_delete_collection=pre_delete_collection, + embedding_dimension=embedding_dimension, + metric=metric, + ) + self.astra_db = self.astra_env.astra_db + self.async_astra_db = self.astra_env.async_astra_db + self.collection = self.astra_env.collection + self.async_collection = self.astra_env.async_collection def _get_embedding_dimension(self) -> int: - if self._embedding_dimension is None: - self._embedding_dimension = len( - self.embedding.embed_query("This is a sample sentence.") - ) - return self._embedding_dimension + return len(self.embedding.embed_query(text="This is a sample sentence.")) - def _provision_collection(self) -> None: - """ - Run the API invocation to create the collection on the backend. - - Internal-usage method, no object members are set, - other than working on the underlying actual storage. - """ - self.astra_db.create_collection( - dimension=self._get_embedding_dimension(), - collection_name=self.collection_name, - metric=self.metric, - ) - - async def _aprovision_collection(self) -> None: - """ - Run the API invocation to create the collection on the backend. - - Internal-usage method, no object members are set, - other than working on the underlying actual storage. - """ - await self.async_astra_db.create_collection( - dimension=self._get_embedding_dimension(), - collection_name=self.collection_name, - metric=self.metric, - ) + async def _aget_embedding_dimension(self) -> int: + return len(await self.embedding.aembed_query(text="This is a sample sentence.")) @property def embeddings(self) -> Embeddings: @@ -320,23 +243,21 @@ class AstraDB(VectorStore): def clear(self) -> None: """Empty the collection of all its stored entries.""" - self.delete_collection() - self._provision_collection() + self.astra_env.ensure_db_setup() + self.collection.delete_many({}) async def aclear(self) -> None: """Empty the collection of all its stored entries.""" - await self._ensure_db_setup() - if not self.async_astra_db: - await run_in_executor(None, self.clear) - await self.async_collection.delete_many({}) + await self.astra_env.aensure_db_setup() + await self.async_collection.delete_many({}) # type: ignore[union-attr] def delete_by_document_id(self, document_id: str) -> bool: """ Remove a single document from the store, given its document_id (str). Return True if a document has indeed been deleted, False if ID not found. """ - self._ensure_astra_db_client() - deletion_response = self.collection.delete_one(document_id) + self.astra_env.ensure_db_setup() + deletion_response = self.collection.delete_one(document_id) # type: ignore[union-attr] return ((deletion_response or {}).get("status") or {}).get( "deletedCount", 0 ) == 1 @@ -346,9 +267,7 @@ class AstraDB(VectorStore): Remove a single document from the store, given its document_id (str). Return True if a document has indeed been deleted, False if ID not found. """ - await self._ensure_db_setup() - if not self.async_collection: - return await run_in_executor(None, self.delete_by_document_id, document_id) + await self.astra_env.aensure_db_setup() deletion_response = await self.async_collection.delete_one(document_id) return ((deletion_response or {}).get("status") or {}).get( "deletedCount", 0 @@ -433,7 +352,7 @@ class AstraDB(VectorStore): Stored data is lost and unrecoverable, resources are freed. Use with caution. """ - self._ensure_astra_db_client() + self.astra_env.ensure_db_setup() self.astra_db.delete_collection( collection_name=self.collection_name, ) @@ -445,9 +364,7 @@ class AstraDB(VectorStore): Stored data is lost and unrecoverable, resources are freed. Use with caution. """ - await self._ensure_db_setup() - if not self.async_astra_db: - await run_in_executor(None, self.delete_collection) + await self.astra_env.aensure_db_setup() await self.async_astra_db.delete_collection( collection_name=self.collection_name, ) @@ -563,7 +480,7 @@ class AstraDB(VectorStore): f"unsupported arguments ({', '.join(sorted(kwargs.keys()))}), " "which will be ignored." ) - self._ensure_astra_db_client() + self.astra_env.ensure_db_setup() embedding_vectors = self.embedding.embed_documents(list(texts)) documents_to_insert = self._get_documents_to_insert( @@ -571,7 +488,7 @@ class AstraDB(VectorStore): ) def _handle_batch(document_batch: List[DocDict]) -> List[str]: - im_result = self.collection.insert_many( + im_result = self.collection.insert_many( # type: ignore[union-attr] documents=document_batch, options={"ordered": False}, partial_failures_allowed=True, @@ -581,7 +498,7 @@ class AstraDB(VectorStore): ) def _handle_missing_document(missing_document: DocDict) -> str: - replacement_result = self.collection.find_one_and_replace( + replacement_result = self.collection.find_one_and_replace( # type: ignore[union-attr] filter={"_id": missing_document["_id"]}, replacement=missing_document, ) @@ -649,22 +566,13 @@ class AstraDB(VectorStore): Returns: List[str]: List of ids of the added texts. """ - await self._ensure_db_setup() - if not self.async_collection: - await super().aadd_texts( - texts, - metadatas, - ids=ids, - batch_size=batch_size, - batch_concurrency=batch_concurrency, - overwrite_concurrency=overwrite_concurrency, - ) if kwargs: warnings.warn( "Method 'aadd_texts' of AstraDB vector store invoked with " f"unsupported arguments ({', '.join(sorted(kwargs.keys()))}), " "which will be ignored." ) + await self.astra_env.aensure_db_setup() embedding_vectors = await self.embedding.aembed_documents(list(texts)) documents_to_insert = self._get_documents_to_insert( @@ -672,7 +580,7 @@ class AstraDB(VectorStore): ) async def _handle_batch(document_batch: List[DocDict]) -> List[str]: - im_result = await self.async_collection.insert_many( + im_result = await self.async_collection.insert_many( # type: ignore[union-attr] documents=document_batch, options={"ordered": False}, partial_failures_allowed=True, @@ -682,7 +590,7 @@ class AstraDB(VectorStore): ) async def _handle_missing_document(missing_document: DocDict) -> str: - replacement_result = await self.async_collection.find_one_and_replace( + replacement_result = await self.async_collection.find_one_and_replace( # type: ignore[union-attr] filter={"_id": missing_document["_id"]}, replacement=missing_document, ) @@ -725,11 +633,11 @@ class AstraDB(VectorStore): Returns: List of (Document, score, id), the most similar to the query vector. """ - self._ensure_astra_db_client() + self.astra_env.ensure_db_setup() metadata_parameter = self._filter_to_metadata(filter) # hits = list( - self.collection.paginated_find( + self.collection.paginated_find( # type: ignore[union-attr] filter=metadata_parameter, sort={"$vector": embedding}, options={"limit": k, "includeSimilarity": True}, @@ -767,15 +675,7 @@ class AstraDB(VectorStore): Returns: List of (Document, score, id), the most similar to the query vector. """ - await self._ensure_db_setup() - if not self.async_collection: - return await run_in_executor( - None, - self.asimilarity_search_with_score_id_by_vector, - embedding, - k, - filter, - ) + await self.astra_env.aensure_db_setup() metadata_parameter = self._filter_to_metadata(filter) # return [ @@ -962,7 +862,7 @@ class AstraDB(VectorStore): ) @staticmethod - def _get_mmr_hits(embedding, k, lambda_mult, prefetch_hits): + def _get_mmr_hits(embedding, k, lambda_mult, prefetch_hits): # type: ignore[no-untyped-def] mmr_chosen_indices = maximal_marginal_relevance( np.array(embedding, dtype=np.float32), [prefetch_hit["$vector"] for prefetch_hit in prefetch_hits], @@ -1004,11 +904,11 @@ class AstraDB(VectorStore): Returns: List of Documents selected by maximal marginal relevance. """ - self._ensure_astra_db_client() + self.astra_env.ensure_db_setup() metadata_parameter = self._filter_to_metadata(filter) prefetch_hits = list( - self.collection.paginated_find( + self.collection.paginated_find( # type: ignore[union-attr] filter=metadata_parameter, sort={"$vector": embedding}, options={"limit": fetch_k, "includeSimilarity": True}, @@ -1045,18 +945,7 @@ class AstraDB(VectorStore): Returns: List of Documents selected by maximal marginal relevance. """ - await self._ensure_db_setup() - if not self.async_collection: - return await run_in_executor( - None, - self.max_marginal_relevance_search_by_vector, - embedding, - k, - fetch_k, - lambda_mult, - filter, - **kwargs, - ) + await self.astra_env.aensure_db_setup() metadata_parameter = self._filter_to_metadata(filter) prefetch_hits = [ @@ -1228,7 +1117,7 @@ class AstraDB(VectorStore): batch_concurrency=kwargs.get("batch_concurrency"), overwrite_concurrency=kwargs.get("overwrite_concurrency"), ) - return astra_db_store + return astra_db_store # type: ignore[return-value] @classmethod async def afrom_texts( @@ -1263,7 +1152,7 @@ class AstraDB(VectorStore): batch_concurrency=kwargs.get("batch_concurrency"), overwrite_concurrency=kwargs.get("overwrite_concurrency"), ) - return astra_db_store + return astra_db_store # type: ignore[return-value] @classmethod def from_documents( diff --git a/libs/community/langchain_community/vectorstores/azuresearch.py b/libs/community/langchain_community/vectorstores/azuresearch.py index 992db81f227..44455199961 100644 --- a/libs/community/langchain_community/vectorstores/azuresearch.py +++ b/libs/community/langchain_community/vectorstores/azuresearch.py @@ -37,14 +37,10 @@ if TYPE_CHECKING: CorsOptions, ScoringProfile, SearchField, + SemanticConfiguration, VectorSearch, ) - try: - from azure.search.documents.indexes.models import SemanticSearch - except ImportError: - from azure.search.documents.indexes.models import SemanticSettings # <11.4.0 - # Allow overriding field names for Azure Search FIELDS_ID = get_from_env( key="AZURESEARCH_FIELDS_ID", env_key="AZURESEARCH_FIELDS_ID", default="id" @@ -73,7 +69,7 @@ def _get_search_client( semantic_configuration_name: Optional[str] = None, fields: Optional[List[SearchField]] = None, vector_search: Optional[VectorSearch] = None, - semantic_settings: Optional[Union[SemanticSearch, SemanticSettings]] = None, + semantic_configurations: Optional[SemanticConfiguration] = None, scoring_profiles: Optional[List[ScoringProfile]] = None, default_scoring_profile: Optional[str] = None, default_fields: Optional[List[SearchField]] = None, @@ -86,30 +82,21 @@ def _get_search_client( from azure.search.documents import SearchClient from azure.search.documents.indexes import SearchIndexClient from azure.search.documents.indexes.models import ( + ExhaustiveKnnAlgorithmConfiguration, + ExhaustiveKnnParameters, + HnswAlgorithmConfiguration, + HnswParameters, SearchIndex, SemanticConfiguration, SemanticField, + SemanticPrioritizedFields, + SemanticSearch, VectorSearch, + VectorSearchAlgorithmKind, + VectorSearchAlgorithmMetric, + VectorSearchProfile, ) - # class names changed for versions >= 11.4.0 - try: - from azure.search.documents.indexes.models import ( - HnswAlgorithmConfiguration, # HnswVectorSearchAlgorithmConfiguration is old - SemanticPrioritizedFields, # PrioritizedFields outdated - SemanticSearch, # SemanticSettings outdated - ) - - NEW_VERSION = True - except ImportError: - from azure.search.documents.indexes.models import ( - HnswVectorSearchAlgorithmConfiguration, - PrioritizedFields, - SemanticSettings, - ) - - NEW_VERSION = False - default_fields = default_fields or [] if key is None: credential = DefaultAzureCredential() @@ -155,77 +142,55 @@ def _get_search_client( fields = default_fields # Vector search configuration if vector_search is None: - if NEW_VERSION: - # >= 11.4.0: - # VectorSearch(algorithm_configuration) --> VectorSearch(algorithms) - # HnswVectorSearchAlgorithmConfiguration --> HnswAlgorithmConfiguration - vector_search = VectorSearch( - algorithms=[ - HnswAlgorithmConfiguration( - name="default", - kind="hnsw", - parameters={ # type: ignore - "m": 4, - "efConstruction": 400, - "efSearch": 500, - "metric": "cosine", - }, - ) - ] - ) - else: # < 11.4.0 - vector_search = VectorSearch( - algorithm_configurations=[ - HnswVectorSearchAlgorithmConfiguration( - name="default", - kind="hnsw", - parameters={ # type: ignore - "m": 4, - "efConstruction": 400, - "efSearch": 500, - "metric": "cosine", - }, - ) - ] - ) + vector_search = VectorSearch( + algorithms=[ + HnswAlgorithmConfiguration( + name="default", + kind=VectorSearchAlgorithmKind.HNSW, + parameters=HnswParameters( + m=4, + ef_construction=400, + ef_search=500, + metric=VectorSearchAlgorithmMetric.COSINE, + ), + ), + ExhaustiveKnnAlgorithmConfiguration( + name="default_exhaustive_knn", + kind=VectorSearchAlgorithmKind.EXHAUSTIVE_KNN, + parameters=ExhaustiveKnnParameters( + metric=VectorSearchAlgorithmMetric.COSINE + ), + ), + ], + profiles=[ + VectorSearchProfile( + name="myHnswProfile", + algorithm_configuration_name="default", + ), + VectorSearchProfile( + name="myExhaustiveKnnProfile", + algorithm_configuration_name="default_exhaustive_knn", + ), + ], + ) # Create the semantic settings with the configuration - if semantic_settings is None and semantic_configuration_name is not None: - if NEW_VERSION: - # <=11.4.0: SemanticSettings --> SemanticSearch - # PrioritizedFields(prioritized_content_fields) - # --> SemanticPrioritizedFields(content_fields) - semantic_settings = SemanticSearch( - configurations=[ - SemanticConfiguration( - name=semantic_configuration_name, - prioritized_fields=SemanticPrioritizedFields( - content_fields=[ - SemanticField(field_name=FIELDS_CONTENT) - ], - ), - ) - ] - ) - else: # < 11.4.0 - semantic_settings = SemanticSettings( - configurations=[ - SemanticConfiguration( - name=semantic_configuration_name, - prioritized_fields=PrioritizedFields( - prioritized_content_fields=[ - SemanticField(field_name=FIELDS_CONTENT) - ], - ), - ) - ] - ) + semantic_search = None + if semantic_configurations is None and semantic_configuration_name is not None: + semantic_configuration = SemanticConfiguration( + name=semantic_configuration_name, + prioritized_fields=SemanticPrioritizedFields( + content_fields=[SemanticField(field_name=FIELDS_CONTENT)], + ), + ) + semantic_search = SemanticSearch(configurations=[semantic_configuration]) + # Create the search index with the semantic settings and vector search index = SearchIndex( name=index_name, fields=fields, vector_search=vector_search, - semantic_settings=semantic_settings, + semantic_search=semantic_search, scoring_profiles=scoring_profiles, default_scoring_profile=default_scoring_profile, cors_options=cors_options, @@ -251,10 +216,9 @@ class AzureSearch(VectorStore): embedding_function: Union[Callable, Embeddings], search_type: str = "hybrid", semantic_configuration_name: Optional[str] = None, - semantic_query_language: str = "en-us", fields: Optional[List[SearchField]] = None, vector_search: Optional[VectorSearch] = None, - semantic_settings: Optional[Union[SemanticSearch, SemanticSettings]] = None, + semantic_configurations: Optional[SemanticConfiguration] = None, scoring_profiles: Optional[List[ScoringProfile]] = None, default_scoring_profile: Optional[str] = None, cors_options: Optional[CorsOptions] = None, @@ -292,7 +256,7 @@ class AzureSearch(VectorStore): type=SearchFieldDataType.Collection(SearchFieldDataType.Single), searchable=True, vector_search_dimensions=len(self.embed_query("Text")), - vector_search_configuration="default", + vector_search_profile_name="myHnswProfile", ), SearchableField( name=FIELDS_METADATA, @@ -309,7 +273,7 @@ class AzureSearch(VectorStore): semantic_configuration_name=semantic_configuration_name, fields=fields, vector_search=vector_search, - semantic_settings=semantic_settings, + semantic_configurations=semantic_configurations, scoring_profiles=scoring_profiles, default_scoring_profile=default_scoring_profile, default_fields=default_fields, @@ -318,7 +282,6 @@ class AzureSearch(VectorStore): ) self.search_type = search_type self.semantic_configuration_name = semantic_configuration_name - self.semantic_query_language = semantic_query_language self.fields = fields if fields else default_fields @property @@ -339,7 +302,7 @@ class AzureSearch(VectorStore): # batching support if embedding function is an Embeddings object if isinstance(self.embedding_function, Embeddings): try: - embeddings = self.embedding_function.embed_documents(texts) + embeddings = self.embedding_function.embed_documents(texts) # type: ignore[arg-type] except NotImplementedError: embeddings = [self.embedding_function.embed_query(x) for x in texts] else: @@ -451,39 +414,30 @@ class AzureSearch(VectorStore): Returns: List of Documents most similar to the query and score for each """ - from azure.search.documents.models import Vector + + from azure.search.documents.models import VectorizedQuery results = self.client.search( search_text="", - vectors=[ - Vector( - value=np.array(self.embed_query(query), dtype=np.float32).tolist(), - k=k, + vector_queries=[ + VectorizedQuery( + vector=np.array(self.embed_query(query), dtype=np.float32).tolist(), + k_nearest_neighbors=k, fields=FIELDS_CONTENT_VECTOR, ) ], filter=filters, + top=k, ) # Convert results to Document objects docs = [ ( Document( page_content=result.pop(FIELDS_CONTENT), - metadata={ - **( - {FIELDS_ID: result.pop(FIELDS_ID)} - if FIELDS_ID in result - else {} - ), - **( - json.loads(result[FIELDS_METADATA]) - if FIELDS_METADATA in result - else { - k: v - for k, v in result.items() - if k != FIELDS_CONTENT_VECTOR - } - ), + metadata=json.loads(result[FIELDS_METADATA]) + if FIELDS_METADATA in result + else { + k: v for k, v in result.items() if k != FIELDS_CONTENT_VECTOR }, ), float(result["@search.score"]), @@ -520,14 +474,14 @@ class AzureSearch(VectorStore): Returns: List of Documents most similar to the query and score for each """ - from azure.search.documents.models import Vector + from azure.search.documents.models import VectorizedQuery results = self.client.search( search_text=query, - vectors=[ - Vector( - value=np.array(self.embed_query(query), dtype=np.float32).tolist(), - k=k, + vector_queries=[ + VectorizedQuery( + vector=np.array(self.embed_query(query), dtype=np.float32).tolist(), + k_nearest_neighbors=k, fields=FIELDS_CONTENT_VECTOR, ) ], @@ -539,21 +493,10 @@ class AzureSearch(VectorStore): ( Document( page_content=result.pop(FIELDS_CONTENT), - metadata={ - **( - {FIELDS_ID: result.pop(FIELDS_ID)} - if FIELDS_ID in result - else {} - ), - **( - json.loads(result[FIELDS_METADATA]) - if FIELDS_METADATA in result - else { - k: v - for k, v in result.items() - if k != FIELDS_CONTENT_VECTOR - } - ), + metadata=json.loads(result[FIELDS_METADATA]) + if FIELDS_METADATA in result + else { + k: v for k, v in result.items() if k != FIELDS_CONTENT_VECTOR }, ), float(result["@search.score"]), @@ -610,20 +553,19 @@ class AzureSearch(VectorStore): Returns: List of Documents most similar to the query and score for each """ - from azure.search.documents.models import Vector + from azure.search.documents.models import VectorizedQuery results = self.client.search( search_text=query, - vectors=[ - Vector( - value=np.array(self.embed_query(query), dtype=np.float32).tolist(), - k=50, + vector_queries=[ + VectorizedQuery( + vector=np.array(self.embed_query(query), dtype=np.float32).tolist(), + k_nearest_neighbors=k, fields=FIELDS_CONTENT_VECTOR, ) ], filter=filters, query_type="semantic", - query_language=self.semantic_query_language, semantic_configuration_name=self.semantic_configuration_name, query_caption="extractive", query_answer="extractive", @@ -643,11 +585,6 @@ class AzureSearch(VectorStore): Document( page_content=result.pop(FIELDS_CONTENT), metadata={ - **( - {FIELDS_ID: result.pop(FIELDS_ID)} - if FIELDS_ID in result - else {} - ), **( json.loads(result[FIELDS_METADATA]) if FIELDS_METADATA in result @@ -667,9 +604,7 @@ class AzureSearch(VectorStore): if result.get("@search.captions") else {}, "answers": semantic_answers_dict.get( - json.loads(result[FIELDS_METADATA]).get("key") - if FIELDS_METADATA in result - else "", + json.loads(result["metadata"]).get("key"), "", ), }, diff --git a/libs/community/langchain_community/vectorstores/bigquery_vector_search.py b/libs/community/langchain_community/vectorstores/bigquery_vector_search.py index 64a1f4b7655..3c77ffb917e 100644 --- a/libs/community/langchain_community/vectorstores/bigquery_vector_search.py +++ b/libs/community/langchain_community/vectorstores/bigquery_vector_search.py @@ -16,6 +16,7 @@ from langchain_core.documents import Document from langchain_core.embeddings import Embeddings from langchain_core.vectorstores import VectorStore +from langchain_community.utils.google import get_client_info from langchain_community.vectorstores.utils import ( DistanceStrategy, maximal_marginal_relevance, @@ -30,7 +31,6 @@ DEFAULT_TOP_K = 4 # default number of documents returned from similarity search _MIN_INDEX_ROWS = 5000 # minimal number of rows for creating an index _INDEX_CHECK_PERIOD_SECONDS = 60 # Do not check for index more often that this. - _vector_table_lock = Lock() # process-wide BigQueryVectorSearch table lock @@ -90,8 +90,12 @@ class BigQueryVectorSearch(VectorStore): try: from google.cloud import bigquery + client_info = get_client_info(module="bigquery-vector-search") self.bq_client = bigquery.Client( - project=project_id, location=location, credentials=credentials + project=project_id, + location=location, + credentials=credentials, + client_info=client_info, ) except ModuleNotFoundError: raise ImportError( @@ -222,7 +226,7 @@ class BigQueryVectorSearch(VectorStore): self._logger.debug("Vector index already exists.") self._have_index = True - def _create_index_in_background(self): + def _create_index_in_background(self): # type: ignore[no-untyped-def] if self._have_index or self._creating_index: # Already have an index or in the process of creating one. return @@ -231,7 +235,7 @@ class BigQueryVectorSearch(VectorStore): thread = Thread(target=self._create_index, daemon=True) thread.start() - def _create_index(self): + def _create_index(self): # type: ignore[no-untyped-def] from google.api_core.exceptions import ClientError table = self.bq_client.get_table(self.vectors_table) @@ -289,7 +293,7 @@ class BigQueryVectorSearch(VectorStore): def full_table_id(self) -> str: return self._full_table_id - def add_texts( + def add_texts( # type: ignore[override] self, texts: List[str], metadatas: Optional[List[dict]] = None, diff --git a/libs/community/langchain_community/vectorstores/databricks_vector_search.py b/libs/community/langchain_community/vectorstores/databricks_vector_search.py index 8505ea9cbc0..0a5e7a6d514 100644 --- a/libs/community/langchain_community/vectorstores/databricks_vector_search.py +++ b/libs/community/langchain_community/vectorstores/databricks_vector_search.py @@ -3,12 +3,15 @@ from __future__ import annotations import json import logging import uuid -from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Type +from typing import TYPE_CHECKING, Any, Callable, Iterable, List, Optional, Tuple, Type +import numpy as np from langchain_core.documents import Document from langchain_core.embeddings import Embeddings from langchain_core.vectorstores import VST, VectorStore +from langchain_community.vectorstores.utils import maximal_marginal_relevance + if TYPE_CHECKING: from databricks.vector_search.client import VectorSearchIndex @@ -321,6 +324,126 @@ class DatabricksVectorSearch(VectorStore): ) return self._parse_search_response(search_resp) + @staticmethod + def _identity_fn(score: float) -> float: + return score + + def _select_relevance_score_fn(self) -> Callable[[float], float]: + """ + Databricks Vector search uses a normalized score 1/(1+d) where d + is the L2 distance. Hence, we simply return the identity function. + """ + + return self._identity_fn + + def max_marginal_relevance_search( + self, + query: str, + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + filters: Optional[Any] = None, + **kwargs: Any, + ) -> List[Document]: + """Return docs selected using the maximal marginal relevance. + + Maximal marginal relevance optimizes for similarity to query AND diversity + among selected documents. + + Args: + query: Text to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + fetch_k: Number of Documents to fetch to pass to MMR algorithm. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. + filters: Filters to apply to the query. Defaults to None. + Returns: + List of Documents selected by maximal marginal relevance. + """ + if not self._is_databricks_managed_embeddings(): + assert self.embeddings is not None, "embedding model is required." + query_vector = self.embeddings.embed_query(query) + else: + raise ValueError( + "`max_marginal_relevance_search` is not supported for index with " + "Databricks-managed embeddings." + ) + + docs = self.max_marginal_relevance_search_by_vector( + query_vector, + k, + fetch_k, + lambda_mult=lambda_mult, + filters=filters, + ) + return docs + + def max_marginal_relevance_search_by_vector( + self, + embedding: List[float], + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + filters: Optional[Any] = None, + **kwargs: Any, + ) -> List[Document]: + """Return docs selected using the maximal marginal relevance. + + Maximal marginal relevance optimizes for similarity to query AND diversity + among selected documents. + + Args: + embedding: Embedding to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + fetch_k: Number of Documents to fetch to pass to MMR algorithm. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. + filters: Filters to apply to the query. Defaults to None. + Returns: + List of Documents selected by maximal marginal relevance. + """ + if not self._is_databricks_managed_embeddings(): + embedding_column = self._embedding_vector_column_name() + else: + raise ValueError( + "`max_marginal_relevance_search` is not supported for index with " + "Databricks-managed embeddings." + ) + + search_resp = self.index.similarity_search( + columns=list(set(self.columns + [embedding_column])), + query_text=None, + query_vector=embedding, + filters=filters, + num_results=fetch_k, + ) + + embeddings_result_index = ( + search_resp.get("manifest").get("columns").index({"name": embedding_column}) + ) + embeddings = [ + doc[embeddings_result_index] + for doc in search_resp.get("result").get("data_array") + ] + + mmr_selected = maximal_marginal_relevance( + np.array(embedding, dtype=np.float32), + embeddings, + k=k, + lambda_mult=lambda_mult, + ) + + ignore_cols: List = ( + [embedding_column] if embedding_column not in self.columns else [] + ) + candidates = self._parse_search_response(search_resp, ignore_cols=ignore_cols) + selected_results = [r[0] for i, r in enumerate(candidates) if i in mmr_selected] + return selected_results + def similarity_search_by_vector( self, embedding: List[float], @@ -373,8 +496,13 @@ class DatabricksVectorSearch(VectorStore): ) return self._parse_search_response(search_resp) - def _parse_search_response(self, search_resp: dict) -> List[Tuple[Document, float]]: + def _parse_search_response( + self, search_resp: dict, ignore_cols: Optional[List[str]] = None + ) -> List[Tuple[Document, float]]: """Parse the search response into a list of Documents with score.""" + if ignore_cols is None: + ignore_cols = [] + columns = [ col["name"] for col in search_resp.get("manifest", dict()).get("columns", []) @@ -386,7 +514,7 @@ class DatabricksVectorSearch(VectorStore): metadata = { col: value for col, value in zip(columns[:-1], result[:-1]) - if col not in [self.primary_key, self.text_column] + if col not in ([self.primary_key, self.text_column] + ignore_cols) } metadata[self.primary_key] = doc_id score = result[-1] diff --git a/libs/community/langchain_community/vectorstores/deeplake.py b/libs/community/langchain_community/vectorstores/deeplake.py index 52fe55fa7ce..659c24c6ca8 100644 --- a/libs/community/langchain_community/vectorstores/deeplake.py +++ b/libs/community/langchain_community/vectorstores/deeplake.py @@ -905,7 +905,7 @@ class DeepLake(VectorStore): return self.vectorstore.dataset @classmethod - def _validate_kwargs(cls, kwargs, method_name): + def _validate_kwargs(cls, kwargs, method_name): # type: ignore[no-untyped-def] if kwargs: valid_items = cls._get_valid_args(method_name) unsupported_items = cls._get_unsupported_items(kwargs, valid_items) @@ -917,14 +917,14 @@ class DeepLake(VectorStore): ) @classmethod - def _get_valid_args(cls, method_name): + def _get_valid_args(cls, method_name): # type: ignore[no-untyped-def] if method_name == "search": return cls._valid_search_kwargs else: return [] @staticmethod - def _get_unsupported_items(kwargs, valid_items): + def _get_unsupported_items(kwargs, valid_items): # type: ignore[no-untyped-def] kwargs = {k: v for k, v in kwargs.items() if k not in valid_items} unsupported_items = None if kwargs: diff --git a/libs/community/langchain_community/vectorstores/elasticsearch.py b/libs/community/langchain_community/vectorstores/elasticsearch.py index 2f8a9e2469b..2f6aa4df697 100644 --- a/libs/community/langchain_community/vectorstores/elasticsearch.py +++ b/libs/community/langchain_community/vectorstores/elasticsearch.py @@ -484,8 +484,8 @@ class ElasticsearchStore(VectorStore): from langchain_community.vectorstores.utils import DistanceStrategy vectorstore = ElasticsearchStore( + "langchain-demo", embedding=OpenAIEmbeddings(), - index_name="langchain-demo", es_url="http://localhost:9200", distance_strategy="DOT_PRODUCT" ) @@ -528,9 +528,9 @@ class ElasticsearchStore(VectorStore): self.strategy = strategy if es_connection is not None: - self.client = es_connection.options( - headers={"user-agent": self.get_user_agent()} - ) + headers = dict(es_connection._headers) + headers.update({"user-agent": self.get_user_agent()}) + self.client = es_connection.options(headers=headers) elif es_url is not None or es_cloud_id is not None: self.client = ElasticsearchStore.connect_to_elasticsearch( es_url=es_url, diff --git a/libs/community/langchain_community/vectorstores/faiss.py b/libs/community/langchain_community/vectorstores/faiss.py index 044209add23..4a7d65f9120 100644 --- a/libs/community/langchain_community/vectorstores/faiss.py +++ b/libs/community/langchain_community/vectorstores/faiss.py @@ -190,6 +190,9 @@ class FAISS(VectorStore): _len_check_if_sized(documents, embeddings, "documents", "embeddings") _len_check_if_sized(documents, ids, "documents", "ids") + if ids and len(ids) != len(set(ids)): + raise ValueError("Duplicate ids found in the ids list.") + # Add to the index. vector = np.array(embeddings, dtype=np.float32) if self._normalize_L2: @@ -305,7 +308,7 @@ class FAISS(VectorStore): if filter is not None: if isinstance(filter, dict): - def filter_func(metadata): + def filter_func(metadata): # type: ignore[no-untyped-def] if all( metadata.get(key) in value if isinstance(value, list) @@ -330,8 +333,9 @@ class FAISS(VectorStore): doc = self.docstore.search(_id) if not isinstance(doc, Document): raise ValueError(f"Could not find document for id {_id}, got {doc}") - if filter is not None and filter_func(doc.metadata): - docs.append((doc, scores[0][j])) + if filter is not None: + if filter_func(doc.metadata): + docs.append((doc, scores[0][j])) else: docs.append((doc, scores[0][j])) @@ -607,7 +611,7 @@ class FAISS(VectorStore): filtered_indices = [] if isinstance(filter, dict): - def filter_func(metadata): + def filter_func(metadata): # type: ignore[no-untyped-def] if all( metadata.get(key) in value if isinstance(value, list) @@ -917,11 +921,13 @@ class FAISS(VectorStore): else: # Default to L2, currently other metric types not initialized. index = faiss.IndexFlatL2(len(embeddings[0])) + docstore = kwargs.pop("docstore", InMemoryDocstore()) + index_to_docstore_id = kwargs.pop("index_to_docstore_id", {}) vecstore = cls( embedding, index, - InMemoryDocstore(), - {}, + docstore, + index_to_docstore_id, normalize_L2=normalize_L2, distance_strategy=distance_strategy, **kwargs, diff --git a/libs/community/langchain_community/vectorstores/hanavector.py b/libs/community/langchain_community/vectorstores/hanavector.py index 04eec65a448..5aba9334828 100644 --- a/libs/community/langchain_community/vectorstores/hanavector.py +++ b/libs/community/langchain_community/vectorstores/hanavector.py @@ -117,7 +117,7 @@ class HanaDB(VectorStore): self.vector_column_length, ) - def _table_exists(self, table_name) -> bool: + def _table_exists(self, table_name) -> bool: # type: ignore[no-untyped-def] sql_str = ( "SELECT COUNT(*) FROM SYS.TABLES WHERE SCHEMA_NAME = CURRENT_SCHEMA" " AND TABLE_NAME = ?" @@ -133,7 +133,7 @@ class HanaDB(VectorStore): cur.close() return False - def _check_column(self, table_name, column_name, column_type, column_length=None): + def _check_column(self, table_name, column_name, column_type, column_length=None): # type: ignore[no-untyped-def] sql_str = ( "SELECT DATA_TYPE_NAME, LENGTH FROM SYS.TABLE_COLUMNS WHERE " "SCHEMA_NAME = CURRENT_SCHEMA " @@ -166,17 +166,17 @@ class HanaDB(VectorStore): def embeddings(self) -> Embeddings: return self.embedding - def _sanitize_name(input_str: str) -> str: + def _sanitize_name(input_str: str) -> str: # type: ignore[misc] # Remove characters that are not alphanumeric or underscores return re.sub(r"[^a-zA-Z0-9_]", "", input_str) - def _sanitize_int(input_int: any) -> int: + def _sanitize_int(input_int: any) -> int: # type: ignore[valid-type] value = int(str(input_int)) if value < -1: raise ValueError(f"Value ({value}) must not be smaller than -1") return int(str(input_int)) - def _sanitize_list_float(embedding: List[float]) -> List[float]: + def _sanitize_list_float(embedding: List[float]) -> List[float]: # type: ignore[misc] for value in embedding: if not isinstance(value, float): raise ValueError(f"Value ({value}) does not have type float") @@ -185,14 +185,14 @@ class HanaDB(VectorStore): # Compile pattern only once, for better performance _compiled_pattern = re.compile("^[_a-zA-Z][_a-zA-Z0-9]*$") - def _sanitize_metadata_keys(metadata: dict) -> dict: + def _sanitize_metadata_keys(metadata: dict) -> dict: # type: ignore[misc] for key in metadata.keys(): if not HanaDB._compiled_pattern.match(key): raise ValueError(f"Invalid metadata key {key}") return metadata - def add_texts( + def add_texts( # type: ignore[override] self, texts: Iterable[str], metadatas: Optional[List[dict]] = None, @@ -243,7 +243,7 @@ class HanaDB(VectorStore): return [] @classmethod - def from_texts( + def from_texts( # type: ignore[no-untyped-def, override] cls: Type[HanaDB], texts: List[str], embedding: Embeddings, @@ -277,7 +277,7 @@ class HanaDB(VectorStore): instance.add_texts(texts, metadatas) return instance - def similarity_search( + def similarity_search( # type: ignore[override] self, query: str, k: int = 4, filter: Optional[dict] = None ) -> List[Document]: """Return docs most similar to query. @@ -382,7 +382,7 @@ class HanaDB(VectorStore): ) return [(result_item[0], result_item[1]) for result_item in whole_result] - def similarity_search_by_vector( + def similarity_search_by_vector( # type: ignore[override] self, embedding: List[float], k: int = 4, filter: Optional[dict] = None ) -> List[Document]: """Return docs most similar to embedding vector. @@ -401,7 +401,7 @@ class HanaDB(VectorStore): ) return [doc for doc, _ in docs_and_scores] - def _create_where_by_filter(self, filter): + def _create_where_by_filter(self, filter): # type: ignore[no-untyped-def] query_tuple = [] where_str = "" if filter: @@ -427,7 +427,7 @@ class HanaDB(VectorStore): return where_str, query_tuple - def delete( + def delete( # type: ignore[override] self, ids: Optional[List[str]] = None, filter: Optional[dict] = None ) -> Optional[bool]: """Delete entries by filter with metadata values @@ -459,7 +459,7 @@ class HanaDB(VectorStore): return True - async def adelete( + async def adelete( # type: ignore[override] self, ids: Optional[List[str]] = None, filter: Optional[dict] = None ) -> Optional[bool]: """Delete by vector ID or other criteria. @@ -473,7 +473,7 @@ class HanaDB(VectorStore): """ return await run_in_executor(None, self.delete, ids=ids, filter=filter) - def max_marginal_relevance_search( + def max_marginal_relevance_search( # type: ignore[override] self, query: str, k: int = 4, @@ -511,11 +511,11 @@ class HanaDB(VectorStore): filter=filter, ) - def _parse_float_array_from_string(array_as_string: str) -> List[float]: + def _parse_float_array_from_string(array_as_string: str) -> List[float]: # type: ignore[misc] array_wo_brackets = array_as_string[1:-1] return [float(x) for x in array_wo_brackets.split(",")] - def max_marginal_relevance_search_by_vector( + def max_marginal_relevance_search_by_vector( # type: ignore[override] self, embedding: List[float], k: int = 4, @@ -533,7 +533,7 @@ class HanaDB(VectorStore): return [whole_result[i][0] for i in mmr_doc_indexes] - async def amax_marginal_relevance_search_by_vector( + async def amax_marginal_relevance_search_by_vector( # type: ignore[override] self, embedding: List[float], k: int = 4, diff --git a/libs/community/langchain_community/vectorstores/jaguar.py b/libs/community/langchain_community/vectorstores/jaguar.py index 2771530d66d..755e7a345f6 100644 --- a/libs/community/langchain_community/vectorstores/jaguar.py +++ b/libs/community/langchain_community/vectorstores/jaguar.py @@ -135,7 +135,7 @@ class Jaguar(VectorStore): def embeddings(self) -> Optional[Embeddings]: return self._embedding - def add_texts( + def add_texts( # type: ignore[override] self, texts: List[str], metadatas: Optional[List[dict]] = None, @@ -351,7 +351,7 @@ class Jaguar(VectorStore): return False @classmethod - def from_texts( + def from_texts( # type: ignore[override] cls, texts: List[str], embedding: Embeddings, @@ -383,7 +383,7 @@ class Jaguar(VectorStore): q = "truncate store " + podstore self.run(q) - def delete(self, zids: List[str], **kwargs: Any) -> None: + def delete(self, zids: List[str], **kwargs: Any) -> None: # type: ignore[override] """ Delete records in jaguardb by a list of zero-ids Args: @@ -431,7 +431,7 @@ class Jaguar(VectorStore): def prt(self, msg: str) -> None: with open("/tmp/debugjaguar.log", "a") as file: - print(f"msg={msg}", file=file, flush=True) + print(f"msg={msg}", file=file, flush=True) # noqa: T201 def _parseMeta(self, nvmap: dict, filecol: str) -> Tuple[List[str], List[str], str]: filepath = "" diff --git a/libs/community/langchain_community/vectorstores/kdbai.py b/libs/community/langchain_community/vectorstores/kdbai.py index 1122b691d43..619f96269af 100644 --- a/libs/community/langchain_community/vectorstores/kdbai.py +++ b/libs/community/langchain_community/vectorstores/kdbai.py @@ -14,7 +14,9 @@ logger = logging.getLogger(__name__) class KDBAI(VectorStore): - """`KDB.AI` vector store [https://kdb.ai](https://kdb.ai) + """`KDB.AI` vector store. + + See [https://kdb.ai](https://kdb.ai) To use, you should have the `kdbai_client` python package installed. diff --git a/libs/community/langchain_community/vectorstores/lantern.py b/libs/community/langchain_community/vectorstores/lantern.py index 9b16af9ce98..643993bffb4 100644 --- a/libs/community/langchain_community/vectorstores/lantern.py +++ b/libs/community/langchain_community/vectorstores/lantern.py @@ -47,12 +47,14 @@ def _results_to_docs(docs_and_scores: Any) -> List[Document]: class BaseEmbeddingStore: - """Embedding store.""" + """Base class for the Lantern embedding store.""" def get_embedding_store( distance_strategy: DistanceStrategy, collection_name: str ) -> Any: + """Get the embedding store class.""" + embedding_type = None if distance_strategy == DistanceStrategy.HAMMING: diff --git a/libs/community/langchain_community/vectorstores/marqo.py b/libs/community/langchain_community/vectorstores/marqo.py index d9777533ada..a2331bc65d8 100644 --- a/libs/community/langchain_community/vectorstores/marqo.py +++ b/libs/community/langchain_community/vectorstores/marqo.py @@ -437,10 +437,10 @@ class Marqo(VectorStore): try: client.create_index(index_name, settings_dict=index_settings or {}) if verbose: - print(f"Created {index_name} successfully.") + print(f"Created {index_name} successfully.") # noqa: T201 except Exception: if verbose: - print(f"Index {index_name} exists.") + print(f"Index {index_name} exists.") # noqa: T201 instance: Marqo = cls( client, diff --git a/libs/community/langchain_community/vectorstores/milvus.py b/libs/community/langchain_community/vectorstores/milvus.py index e72a82f2bd2..0592e932af6 100644 --- a/libs/community/langchain_community/vectorstores/milvus.py +++ b/libs/community/langchain_community/vectorstores/milvus.py @@ -554,10 +554,10 @@ class Milvus(VectorStore): } if not self.auto_id: - insert_dict[self._primary_field] = ids + insert_dict[self._primary_field] = ids # type: ignore[assignment] if self._metadata_field is not None: - for d in metadatas: + for d in metadatas: # type: ignore[union-attr] insert_dict.setdefault(self._metadata_field, []).append(d) else: # Collect the metadata into the insert dict. @@ -569,7 +569,7 @@ class Milvus(VectorStore): if self.auto_id else [x for x in self.fields] ) - for key in keys: + if key in keys: insert_dict.setdefault(key, []).append(value) # Total insert count @@ -583,7 +583,9 @@ class Milvus(VectorStore): # Grab end index end = min(i + batch_size, total_count) # Convert dict to list of lists batch for insertion - insert_list = [insert_dict[x][i:end] for x in self.fields] + insert_list = [ + insert_dict[x][i:end] for x in self.fields if x in insert_dict + ] # Insert into the collection. try: res: Collection @@ -901,7 +903,7 @@ class Milvus(VectorStore): ret.append(documents[x]) return ret - def delete( + def delete( # type: ignore[no-untyped-def] self, ids: Optional[List[str]] = None, expr: Optional[str] = None, **kwargs: str ): """Delete by vector ID or boolean expression. @@ -923,7 +925,7 @@ class Milvus(VectorStore): assert isinstance( expr, str ), "Either ids list or expr string must be provided." - return self.col.delete(expr=expr, **kwargs) + return self.col.delete(expr=expr, **kwargs) # type: ignore[union-attr] @classmethod def from_texts( @@ -989,3 +991,64 @@ class Milvus(VectorStore): page_content=data.pop(self._text_field), metadata=data.pop(self._metadata_field) if self._metadata_field else data, ) + + def get_pks(self, expr: str, **kwargs: Any) -> List[int] | None: + """Get primary keys with expression + + Args: + expr: Expression - E.g: "id in [1, 2]", or "title LIKE 'Abc%'" + + Returns: + List[int]: List of IDs (Primary Keys) + """ + + from pymilvus import MilvusException + + if self.col is None: + logger.debug("No existing collection to get pk.") + return None + + try: + query_result = self.col.query( + expr=expr, output_fields=[self._primary_field] + ) + except MilvusException as exc: + logger.error("Failed to get ids: %s error: %s", self.collection_name, exc) + raise exc + pks = [item.get(self._primary_field) for item in query_result] + return pks + + def upsert( + self, + ids: Optional[List[str]] = None, + documents: List[Document] | None = None, + **kwargs: Any, + ) -> List[str] | None: + """Update/Insert documents to the vectorstore. + + Args: + ids: IDs to update - Let's call get_pks to get ids with expression \n + documents (List[Document]): Documents to add to the vectorstore. + + Returns: + List[str]: IDs of the added texts. + """ + + from pymilvus import MilvusException + + if documents is None or len(documents) == 0: + logger.debug("No documents to upsert.") + return None + + if ids is not None and len(ids): + try: + self.delete(ids=ids) + except MilvusException: + pass + try: + return self.add_documents(documents=documents) + except MilvusException as exc: + logger.error( + "Failed to upsert entities: %s error: %s", self.collection_name, exc + ) + raise exc diff --git a/libs/community/langchain_community/vectorstores/mongodb_atlas.py b/libs/community/langchain_community/vectorstores/mongodb_atlas.py index c105e6f536f..2e18f4280fa 100644 --- a/libs/community/langchain_community/vectorstores/mongodb_atlas.py +++ b/libs/community/langchain_community/vectorstores/mongodb_atlas.py @@ -209,7 +209,7 @@ class MongoDBAtlasVectorSearch(VectorStore): for res in cursor: text = res.pop(self._text_key) score = res.pop("score") - del res["embedding"] + del res[self._embedding_key] docs.append((Document(page_content=text, metadata=res), score)) return docs diff --git a/libs/community/langchain_community/vectorstores/pgembedding.py b/libs/community/langchain_community/vectorstores/pgembedding.py index d5c37d5942e..21d24b2f9fd 100644 --- a/libs/community/langchain_community/vectorstores/pgembedding.py +++ b/libs/community/langchain_community/vectorstores/pgembedding.py @@ -207,9 +207,9 @@ class PGEmbedding(VectorStore): # Create the HNSW index session.execute(create_index_query) session.commit() - print("HNSW extension and index created successfully.") + print("HNSW extension and index created successfully.") # noqa: T201 except Exception as e: - print(f"Failed to create HNSW extension or index: {e}") + print(f"Failed to create HNSW extension or index: {e}") # noqa: T201 def delete_collection(self) -> None: self.logger.debug("Trying to delete collection") @@ -398,7 +398,7 @@ class PGEmbedding(VectorStore): docs = [ ( Document( - page_content=result.EmbeddingStore.document, + page_content=result.EmbeddingStore.document, # type: ignore[arg-type] metadata=result.EmbeddingStore.cmetadata, ), result.distance if self.embedding_function is not None else 0.0, diff --git a/libs/community/langchain_community/vectorstores/pgvecto_rs.py b/libs/community/langchain_community/vectorstores/pgvecto_rs.py index 2b14cd8036e..2f1dbf42720 100644 --- a/libs/community/langchain_community/vectorstores/pgvecto_rs.py +++ b/libs/community/langchain_community/vectorstores/pgvecto_rs.py @@ -133,7 +133,7 @@ class PGVecto_rs(VectorStore): Record.from_text(text, embedding, meta) for text, embedding, meta in zip(texts, embeddings, metadatas or []) ] - self._store.insert(records) + self._store.insert(records) # type: ignore[union-attr] return [str(record.id) for record in records] def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]: @@ -177,7 +177,7 @@ class PGVecto_rs(VectorStore): real_filter = meta_contains(filter) else: real_filter = filter - results = self._store.search( + results = self._store.search( # type: ignore[union-attr] query_vector, distance_func_map[distance_func], k, diff --git a/libs/community/langchain_community/vectorstores/pgvector.py b/libs/community/langchain_community/vectorstores/pgvector.py index fe439d86c5a..755c72bb4dc 100644 --- a/libs/community/langchain_community/vectorstores/pgvector.py +++ b/libs/community/langchain_community/vectorstores/pgvector.py @@ -238,7 +238,7 @@ class PGVector(VectorStore): def create_vector_extension(self) -> None: try: - with Session(self._bind) as session: + with Session(self._bind) as session: # type: ignore[arg-type] # The advisor lock fixes issue arising from concurrent # creation of the vector extension. # https://github.com/langchain-ai/langchain/issues/12933 @@ -256,24 +256,24 @@ class PGVector(VectorStore): raise Exception(f"Failed to create vector extension: {e}") from e def create_tables_if_not_exists(self) -> None: - with Session(self._bind) as session, session.begin(): + with Session(self._bind) as session, session.begin(): # type: ignore[arg-type] Base.metadata.create_all(session.get_bind()) def drop_tables(self) -> None: - with Session(self._bind) as session, session.begin(): + with Session(self._bind) as session, session.begin(): # type: ignore[arg-type] Base.metadata.drop_all(session.get_bind()) def create_collection(self) -> None: if self.pre_delete_collection: self.delete_collection() - with Session(self._bind) as session: + with Session(self._bind) as session: # type: ignore[arg-type] self.CollectionStore.get_or_create( session, self.collection_name, cmetadata=self.collection_metadata ) def delete_collection(self) -> None: self.logger.debug("Trying to delete collection") - with Session(self._bind) as session: + with Session(self._bind) as session: # type: ignore[arg-type] collection = self.get_collection(session) if not collection: self.logger.warning("Collection not found") @@ -284,7 +284,7 @@ class PGVector(VectorStore): @contextlib.contextmanager def _make_session(self) -> Generator[Session, None, None]: """Create a context manager for the session, bind to _conn string.""" - yield Session(self._bind) + yield Session(self._bind) # type: ignore[arg-type] def delete( self, @@ -298,7 +298,7 @@ class PGVector(VectorStore): ids: List of ids to delete. collection_only: Only delete ids in the collection. """ - with Session(self._bind) as session: + with Session(self._bind) as session: # type: ignore[arg-type] if ids is not None: self.logger.debug( "Trying to delete vectors by ids (represented by the model " @@ -383,7 +383,7 @@ class PGVector(VectorStore): if not metadatas: metadatas = [{} for _ in texts] - with Session(self._bind) as session: + with Session(self._bind) as session: # type: ignore[arg-type] collection = self.get_collection(session) if not collection: raise ValueError("Collection not found") @@ -508,7 +508,7 @@ class PGVector(VectorStore): ] return docs - def _create_filter_clause(self, key, value): + def _create_filter_clause(self, key, value): # type: ignore[no-untyped-def] IN, NIN, BETWEEN, GT, LT, NE = "in", "nin", "between", "gt", "lt", "ne" EQ, LIKE, CONTAINS, OR, AND = "eq", "like", "contains", "or", "and" @@ -575,7 +575,7 @@ class PGVector(VectorStore): filter: Optional[Dict[str, str]] = None, ) -> List[Any]: """Query the collection.""" - with Session(self._bind) as session: + with Session(self._bind) as session: # type: ignore[arg-type] collection = self.get_collection(session) if not collection: raise ValueError("Collection not found") diff --git a/libs/community/langchain_community/vectorstores/pinecone.py b/libs/community/langchain_community/vectorstores/pinecone.py index 410d55420fa..0caf6b31c42 100644 --- a/libs/community/langchain_community/vectorstores/pinecone.py +++ b/libs/community/langchain_community/vectorstores/pinecone.py @@ -7,6 +7,7 @@ import warnings from typing import TYPE_CHECKING, Any, Callable, Iterable, List, Optional, Tuple, Union import numpy as np +from langchain_core._api.deprecation import deprecated from langchain_core.documents import Document from langchain_core.embeddings import Embeddings from langchain_core.utils.iter import batch_iterate @@ -41,24 +42,16 @@ def _is_pinecone_v3() -> bool: return version.parse(pinecone_client_version) >= version.parse("3.0.0.dev") +@deprecated( + since="0.0.18", removal="0.2.0", alternative_import="langchain_pinecone.Pinecone" +) class Pinecone(VectorStore): """`Pinecone` vector store. To use, you should have the ``pinecone-client`` python package installed. - Example: - .. code-block:: python - - from langchain_community.vectorstores import Pinecone - from langchain_community.embeddings.openai import OpenAIEmbeddings - import pinecone - - # The environment should be the one specified next to the API key - # in your Pinecone console - pinecone.init(api_key="***", environment="...") - index = pinecone.Index("langchain-demo") - embeddings = OpenAIEmbeddings() - vectorstore = Pinecone(index, embeddings, "text") + This version of Pinecone is deprecated. Please use `langchain_pinecone.Pinecone` + instead. """ def __init__( diff --git a/libs/community/langchain_community/vectorstores/qdrant.py b/libs/community/langchain_community/vectorstores/qdrant.py index d64c5e3b802..47d32cae762 100644 --- a/libs/community/langchain_community/vectorstores/qdrant.py +++ b/libs/community/langchain_community/vectorstores/qdrant.py @@ -620,7 +620,10 @@ class Qdrant(VectorStore): return [ ( self._document_from_scored_point( - result, self.content_payload_key, self.metadata_payload_key + result, + self.collection_name, + self.content_payload_key, + self.metadata_payload_key, ), result.score, ) @@ -713,7 +716,10 @@ class Qdrant(VectorStore): return [ ( self._document_from_scored_point( - result, self.content_payload_key, self.metadata_payload_key + result, + self.collection_name, + self.content_payload_key, + self.metadata_payload_key, ), result.score, ) @@ -1051,7 +1057,10 @@ class Qdrant(VectorStore): return [ ( self._document_from_scored_point( - results[i], self.content_payload_key, self.metadata_payload_key + results[i], + self.collection_name, + self.content_payload_key, + self.metadata_payload_key, ), results[i].score, ) @@ -1123,7 +1132,10 @@ class Qdrant(VectorStore): return [ ( self._document_from_scored_point( - results[i], self.content_payload_key, self.metadata_payload_key + results[i], + self.collection_name, + self.content_payload_key, + self.metadata_payload_key, ), results[i].score, ) @@ -1938,12 +1950,13 @@ class Qdrant(VectorStore): def _document_from_scored_point( cls, scored_point: Any, + collection_name: str, content_payload_key: str, metadata_payload_key: str, ) -> Document: metadata = scored_point.payload.get(metadata_payload_key) or {} metadata["_id"] = scored_point.id - metadata["_collection_name"] = scored_point.collection_name + metadata["_collection_name"] = collection_name return Document( page_content=scored_point.payload.get(content_payload_key), metadata=metadata, diff --git a/libs/community/langchain_community/vectorstores/rocksetdb.py b/libs/community/langchain_community/vectorstores/rocksetdb.py index 992f53db081..ffb9f7f7f20 100644 --- a/libs/community/langchain_community/vectorstores/rocksetdb.py +++ b/libs/community/langchain_community/vectorstores/rocksetdb.py @@ -1,11 +1,13 @@ from __future__ import annotations import logging +from copy import deepcopy from enum import Enum from typing import Any, Iterable, List, Optional, Tuple from langchain_core.documents import Document from langchain_core.embeddings import Embeddings +from langchain_core.runnables import run_in_executor from langchain_core.vectorstores import VectorStore logger = logging.getLogger(__name__) @@ -122,7 +124,7 @@ class Rockset(VectorStore): batch = [] doc = {} if metadatas and len(metadatas) > i: - doc = metadatas[i] + doc = deepcopy(metadatas[i]) if ids and len(ids) > i: doc["_id"] = ids[i] doc[self._text_key] = text @@ -332,3 +334,19 @@ LIMIT {str(k)} data=[DeleteDocumentsRequestData(id=i) for i in ids], workspace=self._workspace, ) + + def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> Optional[bool]: + try: + if ids is None: + ids = [] + self.delete_texts(ids) + except Exception as e: + logger.error("Exception when deleting docs from Rockset: %s\n", e) + return False + + return True + + async def adelete( + self, ids: Optional[List[str]] = None, **kwargs: Any + ) -> Optional[bool]: + return await run_in_executor(None, self.delete, ids, **kwargs) diff --git a/libs/community/langchain_community/vectorstores/semadb.py b/libs/community/langchain_community/vectorstores/semadb.py index 854f045d70f..f80c046a222 100644 --- a/libs/community/langchain_community/vectorstores/semadb.py +++ b/libs/community/langchain_community/vectorstores/semadb.py @@ -146,7 +146,7 @@ class SemaDB(VectorStore): headers=self.headers, ) if response.status_code != 200: - print("HERE--", batch) + print("HERE--", batch) # noqa: T201 raise ValueError(f"Error adding points: {response.text}") failed_ranges = response.json()["failedRanges"] if len(failed_ranges) > 0: diff --git a/libs/community/langchain_community/vectorstores/singlestoredb.py b/libs/community/langchain_community/vectorstores/singlestoredb.py index c33858e9657..6d43b6199b8 100644 --- a/libs/community/langchain_community/vectorstores/singlestoredb.py +++ b/libs/community/langchain_community/vectorstores/singlestoredb.py @@ -57,6 +57,10 @@ class SingleStoreDB(VectorStore): content_field: str = "content", metadata_field: str = "metadata", vector_field: str = "vector", + use_vector_index: bool = False, + vector_index_name: str = "", + vector_index_options: Optional[dict] = None, + vector_size: int = 1536, pool_size: int = 5, max_overflow: int = 10, timeout: float = 30, @@ -88,6 +92,27 @@ class SingleStoreDB(VectorStore): vector_field (str, optional): Specifies the field to store the vector. Defaults to "vector". + use_vector_index (bool, optional): Toggles the use of a vector index. + Works only with SingleStoreDB 8.5 or later. Defaults to False. + If set to True, vector_size parameter is required to be set to + a proper value. + + vector_index_name (str, optional): Specifies the name of the vector index. + Defaults to empty. Will be ignored if use_vector_index is set to False. + + vector_index_options (dict, optional): Specifies the options for + the vector index. Defaults to {}. + Will be ignored if use_vector_index is set to False. The options are: + index_type (str, optional): Specifies the type of the index. + Defaults to IVF_PQFS. + For more options, please refer to the SingleStoreDB documentation: + https://docs.singlestore.com/cloud/reference/sql-reference/vector-functions/vector-indexing/ + + vector_size (int, optional): Specifies the size of the vector. + Defaults to 1536. Required if use_vector_index is set to True. + Should be set to the same value as the size of the vectors + stored in the vector_field. + Following arguments pertain to the connection pool: pool_size (int, optional): Determines the number of active connections in @@ -177,6 +202,19 @@ class SingleStoreDB(VectorStore): os.environ['SINGLESTOREDB_URL'] = 'me:p455w0rd@s2-host.com/my_db' vectorstore = SingleStoreDB(OpenAIEmbeddings()) + + Using vector index: + + .. code-block:: python + + from langchain_community.embeddings import OpenAIEmbeddings + from langchain_community.vectorstores import SingleStoreDB + + os.environ['SINGLESTOREDB_URL'] = 'me:p455w0rd@s2-host.com/my_db' + vectorstore = SingleStoreDB( + OpenAIEmbeddings(), + use_vector_index=True, + ) """ self.embedding = embedding @@ -186,6 +224,12 @@ class SingleStoreDB(VectorStore): self.metadata_field = self._sanitize_input(metadata_field) self.vector_field = self._sanitize_input(vector_field) + self.use_vector_index = bool(use_vector_index) + self.vector_index_name = self._sanitize_input(vector_index_name) + self.vector_index_options = dict(vector_index_options or {}) + self.vector_index_options["metric_type"] = self.distance_strategy + self.vector_size = int(vector_size) + # Pass the rest of the kwargs to the connection. self.connection_kwargs = kwargs @@ -194,7 +238,7 @@ class SingleStoreDB(VectorStore): self.connection_kwargs["conn_attrs"] = dict() self.connection_kwargs["conn_attrs"]["_connector_name"] = "langchain python sdk" - self.connection_kwargs["conn_attrs"]["_connector_version"] = "1.0.1" + self.connection_kwargs["conn_attrs"]["_connector_version"] = "1.0.2" # Create connection pool. self.connection_pool = QueuePool( @@ -222,16 +266,38 @@ class SingleStoreDB(VectorStore): try: cur = conn.cursor() try: - cur.execute( - """CREATE TABLE IF NOT EXISTS {} - ({} TEXT CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci, - {} BLOB, {} JSON);""".format( - self.table_name, - self.content_field, - self.vector_field, - self.metadata_field, - ), - ) + if self.use_vector_index: + index_options = "" + if self.vector_index_options and len(self.vector_index_options) > 0: + index_options = "INDEX_OPTIONS '{}'".format( + json.dumps(self.vector_index_options) + ) + cur.execute( + """CREATE TABLE IF NOT EXISTS {} + ({} TEXT CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci, + {} VECTOR({}, F32) NOT NULL, {} JSON, + VECTOR INDEX {} ({}) {});""".format( + self.table_name, + self.content_field, + self.vector_field, + self.vector_size, + self.metadata_field, + self.vector_index_name, + self.vector_field, + index_options, + ), + ) + else: + cur.execute( + """CREATE TABLE IF NOT EXISTS {} + ({} TEXT CHARACTER SET utf8mb4 COLLATE utf8mb4_general_ci, + {} BLOB, {} JSON);""".format( + self.table_name, + self.content_field, + self.vector_field, + self.metadata_field, + ), + ) finally: cur.close() finally: @@ -279,6 +345,8 @@ class SingleStoreDB(VectorStore): json.dumps(metadata), ), ) + if self.use_vector_index: + cur.execute("OPTIMIZE TABLE {} FLUSH;".format(self.table_name)) finally: cur.close() finally: @@ -406,6 +474,10 @@ class SingleStoreDB(VectorStore): content_field: str = "content", metadata_field: str = "metadata", vector_field: str = "vector", + use_vector_index: bool = False, + vector_index_name: str = "", + vector_index_options: Optional[dict] = None, + vector_size: int = 1536, pool_size: int = 5, max_overflow: int = 10, timeout: float = 30, @@ -438,6 +510,10 @@ class SingleStoreDB(VectorStore): pool_size=pool_size, max_overflow=max_overflow, timeout=timeout, + use_vector_index=use_vector_index, + vector_index_name=vector_index_name, + vector_index_options=vector_index_options, + vector_size=vector_size, **kwargs, ) instance.add_texts(texts, metadatas, embedding.embed_documents(texts), **kwargs) diff --git a/libs/community/langchain_community/vectorstores/starrocks.py b/libs/community/langchain_community/vectorstores/starrocks.py index 568daf0d4a4..807a21f54de 100644 --- a/libs/community/langchain_community/vectorstores/starrocks.py +++ b/libs/community/langchain_community/vectorstores/starrocks.py @@ -38,7 +38,7 @@ def debug_output(s: Any) -> None: s: The message to print """ if DEBUG: - print(s) + print(s) # noqa: T201 def get_named_result(connection: Any, query: str) -> List[dict[str, Any]]: @@ -217,9 +217,11 @@ CREATE TABLE IF NOT EXISTS {self.config.database}.{self.config.table}( for n in transac: n = ",".join( [ - f"'{self.escape_str(str(_n))}'" - if idx != embed_tuple_index - else f"array{str(_n)}" + ( + f"'{self.escape_str(str(_n))}'" + if idx != embed_tuple_index + else f"array{str(_n)}" + ) for (idx, _n) in enumerate(n) ] ) diff --git a/libs/community/langchain_community/vectorstores/surrealdb.py b/libs/community/langchain_community/vectorstores/surrealdb.py index d21f5bf0e02..ef65c5ec6b0 100644 --- a/libs/community/langchain_community/vectorstores/surrealdb.py +++ b/libs/community/langchain_community/vectorstores/surrealdb.py @@ -115,7 +115,7 @@ class SurrealDBStore(VectorStore): for idx, text in enumerate(texts): data = {"text": text, "embedding": embeddings[idx]} if metadatas is not None and idx < len(metadatas): - data["metadata"] = metadatas[idx] + data["metadata"] = metadatas[idx] # type: ignore[assignment] record = await self.sdb.create( self.collection, data, diff --git a/libs/community/langchain_community/vectorstores/tencentvectordb.py b/libs/community/langchain_community/vectorstores/tencentvectordb.py index d1c05b647d4..e185a3ab123 100644 --- a/libs/community/langchain_community/vectorstores/tencentvectordb.py +++ b/libs/community/langchain_community/vectorstores/tencentvectordb.py @@ -316,7 +316,7 @@ class TencentVectorDB(VectorStore): meta = result.get(self.field_metadata) if meta is not None: meta = json.loads(meta) - doc = Document(page_content=result.get(self.field_text), metadata=meta) + doc = Document(page_content=result.get(self.field_text), metadata=meta) # type: ignore[arg-type] pair = (doc, result.get("score", 0.0)) ret.append(pair) return ret @@ -374,7 +374,7 @@ class TencentVectorDB(VectorStore): meta = result.get(self.field_metadata) if meta is not None: meta = json.loads(meta) - doc = Document(page_content=result.get(self.field_text), metadata=meta) + doc = Document(page_content=result.get(self.field_text), metadata=meta) # type: ignore[arg-type] documents.append(doc) ordered_result_embeddings.append(result.get(self.field_vector)) # Get the new order of results. diff --git a/libs/community/langchain_community/vectorstores/thirdai_neuraldb.py b/libs/community/langchain_community/vectorstores/thirdai_neuraldb.py index f447d73745c..444f7d14f8d 100644 --- a/libs/community/langchain_community/vectorstores/thirdai_neuraldb.py +++ b/libs/community/langchain_community/vectorstores/thirdai_neuraldb.py @@ -24,7 +24,7 @@ class NeuralDBVectorStore(VectorStore): underscore_attrs_are_private = True @staticmethod - def _verify_thirdai_library(thirdai_key: Optional[str] = None): + def _verify_thirdai_library(thirdai_key: Optional[str] = None): # type: ignore[no-untyped-def] try: from thirdai import licensing @@ -38,7 +38,7 @@ class NeuralDBVectorStore(VectorStore): ) @classmethod - def from_scratch( + def from_scratch( # type: ignore[no-untyped-def, no-untyped-def] cls, thirdai_key: Optional[str] = None, **model_kwargs, @@ -69,10 +69,10 @@ class NeuralDBVectorStore(VectorStore): NeuralDBVectorStore._verify_thirdai_library(thirdai_key) from thirdai import neural_db as ndb - return cls(db=ndb.NeuralDB(**model_kwargs)) + return cls(db=ndb.NeuralDB(**model_kwargs)) # type: ignore[call-arg] @classmethod - def from_bazaar( + def from_bazaar( # type: ignore[no-untyped-def] cls, base: str, bazaar_cache: Optional[str] = None, @@ -111,10 +111,10 @@ class NeuralDBVectorStore(VectorStore): os.mkdir(cache) model_bazaar = ndb.Bazaar(cache) model_bazaar.fetch() - return cls(db=model_bazaar.get_model(base)) + return cls(db=model_bazaar.get_model(base)) # type: ignore[call-arg] @classmethod - def from_checkpoint( + def from_checkpoint( # type: ignore[no-untyped-def] cls, checkpoint: Union[str, Path], thirdai_key: Optional[str] = None, @@ -146,7 +146,7 @@ class NeuralDBVectorStore(VectorStore): NeuralDBVectorStore._verify_thirdai_library(thirdai_key) from thirdai import neural_db as ndb - return cls(db=ndb.NeuralDB.from_checkpoint(checkpoint)) + return cls(db=ndb.NeuralDB.from_checkpoint(checkpoint)) # type: ignore[call-arg] @classmethod def from_texts( @@ -187,11 +187,11 @@ class NeuralDBVectorStore(VectorStore): df = pd.DataFrame({"texts": texts}) if metadatas: df = pd.concat([df, pd.DataFrame.from_records(metadatas)], axis=1) - temp = tempfile.NamedTemporaryFile("w", delete=False, delete_on_close=False) + temp = tempfile.NamedTemporaryFile("w", delete=False, delete_on_close=False) # type: ignore[call-overload] df.to_csv(temp) source_id = self.insert([ndb.CSV(temp.name)], **kwargs)[0] offset = self.db._savable_state.documents.get_source_by_id(source_id)[1] - return [str(offset + i) for i in range(len(texts))] + return [str(offset + i) for i in range(len(texts))] # type: ignore[arg-type] @root_validator() def validate_environments(cls, values: Dict) -> Dict: @@ -205,7 +205,7 @@ class NeuralDBVectorStore(VectorStore): ) return values - def insert( + def insert( # type: ignore[no-untyped-def, no-untyped-def] self, sources: List[Any], train: bool = True, @@ -229,7 +229,7 @@ class NeuralDBVectorStore(VectorStore): **kwargs, ) - def _preprocess_sources(self, sources): + def _preprocess_sources(self, sources): # type: ignore[no-untyped-def] """Checks if the provided sources are string paths. If they are, convert to NeuralDB document objects. @@ -261,7 +261,7 @@ class NeuralDBVectorStore(VectorStore): ) return preprocessed_sources - def upvote(self, query: str, document_id: Union[int, str]): + def upvote(self, query: str, document_id: Union[int, str]): # type: ignore[no-untyped-def] """The vectorstore upweights the score of a document for a specific query. This is useful for fine-tuning the vectorstore to user behavior. @@ -271,7 +271,7 @@ class NeuralDBVectorStore(VectorStore): """ self.db.text_to_result(query, int(document_id)) - def upvote_batch(self, query_id_pairs: List[Tuple[str, int]]): + def upvote_batch(self, query_id_pairs: List[Tuple[str, int]]): # type: ignore[no-untyped-def] """Given a batch of (query, document id) pairs, the vectorstore upweights the scores of the document for the corresponding queries. This is useful for fine-tuning the vectorstore to user behavior. @@ -284,7 +284,7 @@ class NeuralDBVectorStore(VectorStore): [(query, int(doc_id)) for query, doc_id in query_id_pairs] ) - def associate(self, source: str, target: str): + def associate(self, source: str, target: str): # type: ignore[no-untyped-def] """The vectorstore associates a source phrase with a target phrase. When the vectorstore sees the source phrase, it will also consider results that are relevant to the target phrase. @@ -295,7 +295,7 @@ class NeuralDBVectorStore(VectorStore): """ self.db.associate(source, target) - def associate_batch(self, text_pairs: List[Tuple[str, str]]): + def associate_batch(self, text_pairs: List[Tuple[str, str]]): # type: ignore[no-untyped-def] """Given a batch of (source, target) pairs, the vectorstore associates each source phrase with the corresponding target phrase. @@ -334,7 +334,7 @@ class NeuralDBVectorStore(VectorStore): except Exception as e: raise ValueError(f"Error while retrieving documents: {e}") from e - def save(self, path: str): + def save(self, path: str): # type: ignore[no-untyped-def] """Saves a NeuralDB instance to disk. Can be loaded into memory by calling NeuralDB.from_checkpoint(path) diff --git a/libs/community/langchain_community/vectorstores/vectara.py b/libs/community/langchain_community/vectorstores/vectara.py index 0fc91004216..edebc3f4133 100644 --- a/libs/community/langchain_community/vectorstores/vectara.py +++ b/libs/community/langchain_community/vectorstores/vectara.py @@ -308,7 +308,7 @@ class Vectara(VectorStore): self._delete_doc(doc_id) self._index_doc(doc) elif success_str == "E_NO_PERMISSIONS": - print( + print( # noqa: T201 """No permissions to add document to Vectara. Check your corpus ID, customer ID and API key""" ) @@ -339,9 +339,11 @@ class Vectara(VectorStore): { "query": query, "start": 0, - "numResults": config.mmr_config.mmr_k - if config.mmr_config.is_enabled - else config.k, + "numResults": ( + config.mmr_config.mmr_k + if config.mmr_config.is_enabled + else config.k + ), "contextConfig": { "sentencesBefore": config.n_sentence_context, "sentencesAfter": config.n_sentence_context, @@ -384,7 +386,7 @@ class Vectara(VectorStore): f"(code {response.status_code}, reason {response.reason}, details " f"{response.text})", ) - return [], "" + return [], "" # type: ignore[return-value] result = response.json() @@ -454,7 +456,7 @@ class Vectara(VectorStore): docs = self.vectara_query(query, config) return docs - def similarity_search( + def similarity_search( # type: ignore[override] self, query: str, **kwargs: Any, @@ -474,7 +476,7 @@ class Vectara(VectorStore): ) return [doc for doc, _ in docs_and_scores] - def max_marginal_relevance_search( + def max_marginal_relevance_search( # type: ignore[override] self, query: str, fetch_k: int = 50, diff --git a/libs/community/langchain_community/vectorstores/vikngdb.py b/libs/community/langchain_community/vectorstores/vikngdb.py index 2f235f0bf4c..942f4b9591a 100644 --- a/libs/community/langchain_community/vectorstores/vikngdb.py +++ b/libs/community/langchain_community/vectorstores/vikngdb.py @@ -15,7 +15,7 @@ logger = logging.getLogger(__name__) class VikingDBConfig(object): - def __init__(self, host="host", region="region", ak="ak", sk="sk", scheme="http"): + def __init__(self, host="host", region="region", ak="ak", sk="sk", scheme="http"): # type: ignore[no-untyped-def] self.host = host self.region = region self.ak = ak @@ -47,11 +47,11 @@ class VikingDB(VectorStore): self.index_params = index_params self.drop_old = drop_old self.service = VikingDBService( - connection_args.host, - connection_args.region, - connection_args.ak, - connection_args.sk, - connection_args.scheme, + connection_args.host, # type: ignore[union-attr] + connection_args.region, # type: ignore[union-attr] + connection_args.ak, # type: ignore[union-attr] + connection_args.sk, # type: ignore[union-attr] + connection_args.scheme, # type: ignore[union-attr] ) try: @@ -143,7 +143,7 @@ class VikingDB(VectorStore): scalar_index=scalar_index, ) - def add_texts( + def add_texts( # type: ignore[override] self, texts: List[str], metadatas: Optional[List[dict]] = None, @@ -183,7 +183,7 @@ class VikingDB(VectorStore): if metadatas is not None and index < len(metadatas): names = list(metadatas[index].keys()) for name in names: - field[name] = metadatas[index].get(name) + field[name] = metadatas[index].get(name) # type: ignore[assignment] data.append(Data(field)) total_count = len(data) @@ -191,10 +191,10 @@ class VikingDB(VectorStore): end = min(i + batch_size, total_count) insert_data = data[i:end] # print(insert_data) - self.collection.upsert_data(insert_data) + self.collection.upsert_data(insert_data) # type: ignore[union-attr] return pks - def similarity_search( + def similarity_search( # type: ignore[override] self, query: str, params: Optional[dict] = None, @@ -216,7 +216,7 @@ class VikingDB(VectorStore): ) return res - def similarity_search_by_vector( + def similarity_search_by_vector( # type: ignore[override] self, embedding: List[float], params: Optional[dict] = None, @@ -251,7 +251,7 @@ class VikingDB(VectorStore): if params.get("partition") is not None: partition = params["partition"] - res = self.index.search_by_vector( + res = self.index.search_by_vector( # type: ignore[union-attr] embedding, filter=filter, limit=limit, @@ -269,7 +269,7 @@ class VikingDB(VectorStore): ret.append(pair) return ret - def max_marginal_relevance_search( + def max_marginal_relevance_search( # type: ignore[override] self, query: str, k: int = 4, @@ -286,7 +286,7 @@ class VikingDB(VectorStore): **kwargs, ) - def max_marginal_relevance_search_by_vector( + def max_marginal_relevance_search_by_vector( # type: ignore[override] self, embedding: List[float], k: int = 4, @@ -311,7 +311,7 @@ class VikingDB(VectorStore): if params.get("partition") is not None: partition = params["partition"] - res = self.index.search_by_vector( + res = self.index.search_by_vector( # type: ignore[union-attr] embedding, filter=filter, limit=limit, @@ -347,10 +347,10 @@ class VikingDB(VectorStore): ) -> None: if self.collection is None: logger.debug("No existing collection to search.") - self.collection.delete_data(ids) + self.collection.delete_data(ids) # type: ignore[union-attr] @classmethod - def from_texts( + def from_texts( # type: ignore[no-untyped-def, override] cls, texts: List[str], embedding: Embeddings, diff --git a/libs/community/poetry.lock b/libs/community/poetry.lock index f674b1d20a6..1a1dd4ec4cb 100644 --- a/libs/community/poetry.lock +++ b/libs/community/poetry.lock @@ -28,87 +28,87 @@ pycares = ">=4.0.0" [[package]] name = "aiohttp" -version = 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"oracle-ads", "pandas", "pdfminer-six", "pgvector", "praw", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "rapidocr-onnxruntime", "rdflib", "requests-toolbelt", "rspace_client", "scikit-learn", "sqlite-vss", "streamlit", "sympy", "telethon", "timescale-vector", "tqdm", "upstash-redis", "xata", "xmltodict", "zhipuai"] +extended-testing = ["aiosqlite", "aleph-alpha-client", "anthropic", "arxiv", "assemblyai", "atlassian-python-api", "azure-ai-documentintelligence", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "cohere", "databricks-vectorsearch", "datasets", "dgml-utils", "elasticsearch", "esprima", "faiss-cpu", "feedparser", "fireworks-ai", "geopandas", "gitpython", "google-cloud-documentai", "gql", "gradientai", "hdbcli", "hologres-vector", "html2text", "httpx", "javelin-sdk", "jinja2", "jq", "jsonschema", "lxml", "markdownify", "motor", "msal", "mwparserfromhell", "mwxml", "newspaper3k", "numexpr", "nvidia-riva-client", 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LangChain integrations." authors = [] license = "MIT" @@ -9,7 +9,7 @@ repository = "https://github.com/langchain-ai/langchain" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -langchain-core = ">=0.1.16,<0.2" +langchain-core = ">=0.1.21,<0.2" SQLAlchemy = ">=1.4,<3" requests = "^2" PyYAML = ">=5.3" @@ -17,7 +17,7 @@ numpy = "^1" aiohttp = "^3.8.3" tenacity = "^8.1.0" dataclasses-json = ">= 0.5.7, < 0.7" -langsmith = ">=0.0.83,<0.1" +langsmith = "^0.1.0" tqdm = {version = ">=4.48.0", optional = true} openapi-pydantic = {version = "^0.3.2", optional = true} faiss-cpu = {version = "^1", optional = true} @@ -66,7 +66,6 @@ xata = {version = "^1.0.0a7", optional = true} xmltodict = {version = "^0.13.0", optional = true} markdownify = {version = "^0.11.6", optional = true} assemblyai = {version = "^0.17.0", optional = true} -dashvector = {version = "^1.0.1", optional = true} sqlite-vss = {version = "^0.1.2", optional = true} motor = {version = "^3.3.1", optional = true} timescale-vector = {version = "^0.0.1", optional = true} @@ -84,6 +83,8 @@ msal = {version = "^1.25.0", optional = true} databricks-vectorsearch = {version = "^0.21", optional = true} dgml-utils = {version = "^0.3.0", optional = true} datasets = {version = "^2.15.0", optional = true} +tree-sitter = {version = "^0.20.2", optional = true} +tree-sitter-languages = {version = "^1.8.0", optional = true} azure-ai-documentintelligence = {version = "^1.0.0b1", optional = true} oracle-ads = {version = "^2.9.1", optional = true} zhipuai = {version = "^1.0.7", optional = true} @@ -92,6 +93,7 @@ elasticsearch = {version = "^8.12.0", optional = true} hdbcli = {version = "^2.19.21", optional = true} oci = {version = "^2.119.1", optional = true} rdflib = {version = "7.0.0", optional = true} +nvidia-riva-client = {version = "^2.14.0", optional = true} [tool.poetry.group.test] optional = true @@ -101,7 +103,7 @@ optional = true # dependencies used for running tests (e.g., pytest, freezegun, response). # Any dependencies that do not meet that criteria will be removed. pytest = "^7.3.0" -pytest-cov = "^4.0.0" +pytest-cov = "^4.1.0" pytest-dotenv = "^0.5.2" duckdb-engine = "^0.9.2" pytest-watcher = "^0.2.6" @@ -150,8 +152,6 @@ tiktoken = ">=0.3.2,<0.6.0" anthropic = "^0.3.11" langchain-core = { path = "../core", develop = true } fireworks-ai = "^0.9.0" -boto3 = ">=1.28.57,<2" -google-cloud-aiplatform = ">=1.37.0,<2" [tool.poetry.group.lint] optional = true @@ -179,7 +179,6 @@ setuptools = "^67.6.1" langchain-core = {path = "../core", develop = true} [tool.poetry.extras] - cli = ["typer"] # An extra used to be able to add extended testing. @@ -229,6 +228,7 @@ extended_testing = [ "jinja2", "gitpython", "newspaper3k", + "nvidia-riva-client", "feedparser", "xata", "xmltodict", @@ -236,7 +236,6 @@ extended_testing = [ "openapi-pydantic", "markdownify", "arxiv", - "dashvector", "sqlite-vss", "rapidocr-onnxruntime", "motor", @@ -251,6 +250,8 @@ extended_testing = [ "databricks-vectorsearch", "dgml-utils", "cohere", + "tree-sitter", + "tree-sitter-languages", "azure-ai-documentintelligence", "oracle-ads", "zhipuai", @@ -258,18 +259,21 @@ extended_testing = [ "elasticsearch", "hdbcli", "oci", - "rdflib", + "rdflib" ] [tool.ruff] +exclude = [ + "tests/examples/non-utf8-encoding.py", + "tests/integration_tests/examples/non-utf8-encoding.py", +] + +[tool.ruff.lint] select = [ "E", # pycodestyle "F", # pyflakes "I", # isort -] -exclude = [ - "tests/examples/non-utf8-encoding.py", - "tests/integration_tests/examples/non-utf8-encoding.py", + "T201", # print ] [tool.mypy] diff --git a/libs/community/scripts/check_imports.py b/libs/community/scripts/check_imports.py index 462ab97ae2e..825bea5b487 100644 --- a/libs/community/scripts/check_imports.py +++ b/libs/community/scripts/check_imports.py @@ -15,8 +15,8 @@ if __name__ == "__main__": SourceFileLoader(module_name, file).load_module() except Exception: has_failure = True - print(file) + print(file) # noqa: T201 traceback.print_exc() - print() + print() # noqa: T201 sys.exit(1 if has_failure else 0) diff --git a/libs/community/tests/examples/hello_world.py b/libs/community/tests/examples/hello_world.py index 3f0294febb4..c98e152ddeb 100644 --- a/libs/community/tests/examples/hello_world.py +++ b/libs/community/tests/examples/hello_world.py @@ -4,7 +4,7 @@ import sys def main() -> int: - print("Hello World!") + print("Hello World!") # noqa: T201 return 0 diff --git a/libs/community/tests/examples/test_empty.csv b/libs/community/tests/examples/test_empty.csv new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/community/tests/examples/test_nominal.csv b/libs/community/tests/examples/test_nominal.csv new file mode 100644 index 00000000000..65debb11207 --- /dev/null +++ b/libs/community/tests/examples/test_nominal.csv @@ -0,0 +1,3 @@ +column1,column2,column3 +value1,value2,value3 +value4,value5,value6 \ No newline at end of file diff --git a/libs/community/tests/integration_tests/callbacks/test_openai_callback.py b/libs/community/tests/integration_tests/callbacks/test_openai_callback.py index 5112f4dd84e..100021e7469 100644 --- a/libs/community/tests/integration_tests/callbacks/test_openai_callback.py +++ b/libs/community/tests/integration_tests/callbacks/test_openai_callback.py @@ -1,4 +1,5 @@ """Integration tests for the langchain tracer module.""" + import asyncio from langchain_community.callbacks import get_openai_callback @@ -62,7 +63,7 @@ def test_openai_callback_agent() -> None: "Who is Olivia Wilde's boyfriend? " "What is his current age raised to the 0.23 power?" ) - print(f"Total Tokens: {cb.total_tokens}") - print(f"Prompt Tokens: {cb.prompt_tokens}") - print(f"Completion Tokens: {cb.completion_tokens}") - print(f"Total Cost (USD): ${cb.total_cost}") + print(f"Total Tokens: {cb.total_tokens}") # noqa: T201 + print(f"Prompt Tokens: {cb.prompt_tokens}") # noqa: T201 + print(f"Completion Tokens: {cb.completion_tokens}") # noqa: T201 + print(f"Total Cost (USD): ${cb.total_cost}") # noqa: T201 diff --git a/libs/community/tests/integration_tests/chat_message_histories/test_tidb.py b/libs/community/tests/integration_tests/chat_message_histories/test_tidb.py index 17601af48b5..e6a3d2303dc 100644 --- a/libs/community/tests/integration_tests/chat_message_histories/test_tidb.py +++ b/libs/community/tests/integration_tests/chat_message_histories/test_tidb.py @@ -57,7 +57,7 @@ def test_add_messages() -> None: assert len(message_store_another.messages) == 0 -def test_tidb_recent_chat_message(): +def test_tidb_recent_chat_message(): # type: ignore[no-untyped-def] """Test the TiDBChatMessageHistory with earliest_time parameter.""" import time from datetime import datetime diff --git a/libs/community/tests/integration_tests/chat_models/test_baichuan.py b/libs/community/tests/integration_tests/chat_models/test_baichuan.py index 6caec6003e9..b1322435f3a 100644 --- a/libs/community/tests/integration_tests/chat_models/test_baichuan.py +++ b/libs/community/tests/integration_tests/chat_models/test_baichuan.py @@ -50,7 +50,7 @@ def test_chat_baichuan_with_kwargs() -> None: chat = ChatBaichuan() message = HumanMessage(content="百川192K API是什么时候上线的？") response = chat([message], temperature=0.88, top_p=0.7, with_search_enhance=True) - print(response) + print(response) # noqa: T201 assert isinstance(response, AIMessage) assert isinstance(response.content, str) diff --git a/libs/community/tests/integration_tests/chat_models/test_gpt_router.py b/libs/community/tests/integration_tests/chat_models/test_gpt_router.py index 785ad6ac0a1..f6cc3b6cb7d 100644 --- a/libs/community/tests/integration_tests/chat_models/test_gpt_router.py +++ b/libs/community/tests/integration_tests/chat_models/test_gpt_router.py @@ -1,4 +1,5 @@ """Test GPTRouter API wrapper.""" + from typing import List import pytest @@ -29,7 +30,7 @@ def test_api_key_masked_when_passed_via_constructor( gpt_router_api_base="https://example.com", gpt_router_api_key="secret-api-key", ) - print(gpt_router.gpt_router_api_key, end="") + print(gpt_router.gpt_router_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/integration_tests/chat_models/test_jinachat.py b/libs/community/tests/integration_tests/chat_models/test_jinachat.py index 0d704ce3867..3a62cbc05f3 100644 --- a/libs/community/tests/integration_tests/chat_models/test_jinachat.py +++ b/libs/community/tests/integration_tests/chat_models/test_jinachat.py @@ -24,7 +24,7 @@ def test_jinachat_api_key_masked_when_passed_from_env( """Test initialization with an API key provided via an env variable""" monkeypatch.setenv("JINACHAT_API_KEY", "secret-api-key") llm = JinaChat() - print(llm.jinachat_api_key, end="") + print(llm.jinachat_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" @@ -35,7 +35,7 @@ def test_jinachat_api_key_masked_when_passed_via_constructor( ) -> None: """Test initialization with an API key provided via the initializer""" llm = JinaChat(jinachat_api_key="secret-api-key") - print(llm.jinachat_api_key, end="") + print(llm.jinachat_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/integration_tests/chat_models/test_konko.py b/libs/community/tests/integration_tests/chat_models/test_konko.py index 9f38f740eb4..79feaba4a30 100644 --- a/libs/community/tests/integration_tests/chat_models/test_konko.py +++ b/libs/community/tests/integration_tests/chat_models/test_konko.py @@ -1,4 +1,5 @@ """Evaluate ChatKonko Interface.""" + from typing import Any, cast import pytest @@ -21,11 +22,11 @@ def test_konko_key_masked_when_passed_from_env( chat = ChatKonko() - print(chat.openai_api_key, end="") + print(chat.openai_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" - print(chat.konko_api_key, end="") + print(chat.konko_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" @@ -36,11 +37,11 @@ def test_konko_key_masked_when_passed_via_constructor( """Test initialization with an API key provided via the initializer""" chat = ChatKonko(openai_api_key="test-openai-key", konko_api_key="test-konko-key") - print(chat.konko_api_key, end="") + print(chat.konko_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" - print(chat.konko_secret_key, end="") + print(chat.konko_secret_key, end="") # type: ignore[attr-defined] # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" @@ -49,7 +50,7 @@ def test_uses_actual_secret_value_from_secret_str() -> None: """Test that actual secret is retrieved using `.get_secret_value()`.""" chat = ChatKonko(openai_api_key="test-openai-key", konko_api_key="test-konko-key") assert cast(SecretStr, chat.konko_api_key).get_secret_value() == "test-openai-key" - assert cast(SecretStr, chat.konko_secret_key).get_secret_value() == "test-konko-key" + assert cast(SecretStr, chat.konko_secret_key).get_secret_value() == "test-konko-key" # type: ignore[attr-defined] def test_konko_chat_test() -> None: diff --git a/libs/community/tests/integration_tests/chat_models/test_llama_edge.py b/libs/community/tests/integration_tests/chat_models/test_llama_edge.py index 50919902dd4..08867d53741 100644 --- a/libs/community/tests/integration_tests/chat_models/test_llama_edge.py +++ b/libs/community/tests/integration_tests/chat_models/test_llama_edge.py @@ -46,7 +46,7 @@ def test_chat_wasm_service_streaming() -> None: output = "" for chunk in chat.stream(messages): - print(chunk.content, end="", flush=True) - output += chunk.content + print(chunk.content, end="", flush=True) # noqa: T201 + output += chunk.content # type: ignore[operator] assert "Paris" in output diff --git a/libs/community/tests/integration_tests/chat_models/test_qianfan_endpoint.py b/libs/community/tests/integration_tests/chat_models/test_qianfan_endpoint.py index caa5ef20eb4..afe2811211a 100644 --- a/libs/community/tests/integration_tests/chat_models/test_qianfan_endpoint.py +++ b/libs/community/tests/integration_tests/chat_models/test_qianfan_endpoint.py @@ -318,11 +318,11 @@ def test_qianfan_key_masked_when_passed_from_env( monkeypatch.setenv("QIANFAN_SK", "test-secret-key") chat = QianfanChatEndpoint() - print(chat.qianfan_ak, end="") + print(chat.qianfan_ak, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" - print(chat.qianfan_sk, end="") + print(chat.qianfan_sk, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" @@ -335,11 +335,11 @@ def test_qianfan_key_masked_when_passed_via_constructor( qianfan_ak="test-api-key", qianfan_sk="test-secret-key", ) - print(chat.qianfan_ak, end="") + print(chat.qianfan_ak, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" - print(chat.qianfan_sk, end="") + print(chat.qianfan_sk, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/integration_tests/chat_models/test_sparkllm.py b/libs/community/tests/integration_tests/chat_models/test_sparkllm.py index a219b885742..fcb3a7a7f93 100644 --- a/libs/community/tests/integration_tests/chat_models/test_sparkllm.py +++ b/libs/community/tests/integration_tests/chat_models/test_sparkllm.py @@ -22,7 +22,7 @@ def test_chat_spark_llm_with_domain() -> None: chat = ChatSparkLLM(spark_llm_domain="generalv3") message = HumanMessage(content="Hello") response = chat([message]) - print(response) + print(response) # noqa: T201 assert isinstance(response, AIMessage) assert isinstance(response.content, str) @@ -31,6 +31,6 @@ def test_chat_spark_llm_with_temperature() -> None: chat = ChatSparkLLM(temperature=0.9, top_k=2) message = HumanMessage(content="Hello") response = chat([message]) - print(response) + print(response) # noqa: T201 assert isinstance(response, AIMessage) assert isinstance(response.content, str) diff --git a/libs/community/tests/integration_tests/chat_models/test_tongyi.py b/libs/community/tests/integration_tests/chat_models/test_tongyi.py index b01f3d00fd0..79cb484b79f 100644 --- a/libs/community/tests/integration_tests/chat_models/test_tongyi.py +++ b/libs/community/tests/integration_tests/chat_models/test_tongyi.py @@ -19,7 +19,7 @@ def test_api_key_masked_when_passed_via_constructor( capsys: CaptureFixture, ) -> None: llm = ChatTongyi(dashscope_api_key="secret-api-key") - print(llm.dashscope_api_key, end="") + print(llm.dashscope_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/integration_tests/chat_models/test_vertexai.py b/libs/community/tests/integration_tests/chat_models/test_vertexai.py index b4b29a97d16..5311f46dafa 100644 --- a/libs/community/tests/integration_tests/chat_models/test_vertexai.py +++ b/libs/community/tests/integration_tests/chat_models/test_vertexai.py @@ -7,6 +7,7 @@ pip install google-cloud-aiplatform>=1.35.0 Your end-user credentials would be used to make the calls (make sure you've run `gcloud auth login` first). """ + from typing import Optional from unittest.mock import MagicMock, Mock, patch @@ -288,7 +289,7 @@ def test_parse_examples_correct() -> None: def test_parse_examples_failes_wrong_sequence() -> None: with pytest.raises(ValueError) as exc_info: _ = _parse_examples([AIMessage(content="a")]) - print(str(exc_info.value)) + print(str(exc_info.value)) # noqa: T201 assert ( str(exc_info.value) == "Expect examples to have an even amount of messages, got 1." diff --git a/libs/community/tests/integration_tests/chat_models/test_yuan2.py b/libs/community/tests/integration_tests/chat_models/test_yuan2.py new file mode 100644 index 00000000000..17a1c40a079 --- /dev/null +++ b/libs/community/tests/integration_tests/chat_models/test_yuan2.py @@ -0,0 +1,152 @@ +"""Test ChatYuan2 wrapper.""" +from typing import List + +import pytest +from langchain_core.callbacks import CallbackManager +from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage +from langchain_core.outputs import ( + ChatGeneration, + LLMResult, +) + +from langchain_community.chat_models.yuan2 import ChatYuan2 +from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler + + +@pytest.mark.scheduled +def test_chat_yuan2() -> None: + """Test ChatYuan2 wrapper.""" + chat = ChatYuan2( + yuan2_api_key="EMPTY", + yuan2_api_base="http://127.0.0.1:8001/v1", + temperature=1.0, + model_name="yuan2", + max_retries=3, + streaming=False, + ) + messages = [ + HumanMessage(content="Hello"), + ] + response = chat(messages) + assert isinstance(response, BaseMessage) + assert isinstance(response.content, str) + + +def test_chat_yuan2_system_message() -> None: + """Test ChatYuan2 wrapper with system message.""" + chat = ChatYuan2( + yuan2_api_key="EMPTY", + yuan2_api_base="http://127.0.0.1:8001/v1", + temperature=1.0, + model_name="yuan2", + max_retries=3, + streaming=False, + ) + messages = [ + SystemMessage(content="You are an AI assistant."), + HumanMessage(content="Hello"), + ] + response = chat(messages) + assert isinstance(response, BaseMessage) + assert isinstance(response.content, str) + + +@pytest.mark.scheduled +def test_chat_yuan2_generate() -> None: + """Test ChatYuan2 wrapper with generate.""" + chat = ChatYuan2( + yuan2_api_key="EMPTY", + yuan2_api_base="http://127.0.0.1:8001/v1", + temperature=1.0, + model_name="yuan2", + max_retries=3, + streaming=False, + ) + messages: List = [ + HumanMessage(content="Hello"), + ] + response = chat.generate([messages]) + assert isinstance(response, LLMResult) + assert len(response.generations) == 1 + assert response.llm_output + generation = response.generations[0] + for gen in generation: + assert isinstance(gen, ChatGeneration) + assert isinstance(gen.text, str) + assert gen.text == gen.message.content + + +@pytest.mark.scheduled +def test_chat_yuan2_streaming() -> None: + """Test that streaming correctly invokes on_llm_new_token callback.""" + callback_handler = FakeCallbackHandler() + callback_manager = CallbackManager([callback_handler]) + + chat = ChatYuan2( + yuan2_api_key="EMPTY", + yuan2_api_base="http://127.0.0.1:8001/v1", + temperature=1.0, + model_name="yuan2", + max_retries=3, + streaming=True, + callback_manager=callback_manager, + ) + messages = [ + HumanMessage(content="Hello"), + ] + response = chat(messages) + assert callback_handler.llm_streams > 0 + assert isinstance(response, BaseMessage) + + +@pytest.mark.asyncio +async def test_async_chat_yuan2() -> None: + """Test async generation.""" + chat = ChatYuan2( + yuan2_api_key="EMPTY", + yuan2_api_base="http://127.0.0.1:8001/v1", + temperature=1.0, + model_name="yuan2", + max_retries=3, + streaming=False, + ) + messages: List = [ + HumanMessage(content="Hello"), + ] + response = await chat.agenerate([messages]) + assert isinstance(response, LLMResult) + assert len(response.generations) == 1 + generations = response.generations[0] + for generation in generations: + assert isinstance(generation, ChatGeneration) + assert isinstance(generation.text, str) + assert generation.text == generation.message.content + + +@pytest.mark.asyncio +async def test_async_chat_yuan2_streaming() -> None: + """Test that streaming correctly invokes on_llm_new_token callback.""" + callback_handler = FakeCallbackHandler() + callback_manager = CallbackManager([callback_handler]) + + chat = ChatYuan2( + yuan2_api_key="EMPTY", + yuan2_api_base="http://127.0.0.1:8001/v1", + temperature=1.0, + model_name="yuan2", + max_retries=3, + streaming=True, + callback_manager=callback_manager, + ) + messages: List = [ + HumanMessage(content="Hello"), + ] + response = await chat.agenerate([messages]) + assert callback_handler.llm_streams > 0 + assert isinstance(response, LLMResult) + assert len(response.generations) == 1 + generations = response.generations[0] + for generation in generations: + assert isinstance(generation, ChatGeneration) + assert isinstance(generation.text, str) + assert generation.text == generation.message.content diff --git a/libs/community/tests/integration_tests/document_loaders/parsers/test_language.py b/libs/community/tests/integration_tests/document_loaders/parsers/test_language.py index c28789c7cd3..c3deddb9fd5 100644 --- a/libs/community/tests/integration_tests/document_loaders/parsers/test_language.py +++ b/libs/community/tests/integration_tests/document_loaders/parsers/test_language.py @@ -29,7 +29,7 @@ def test_language_loader_for_python() -> None: assert ( docs[0].page_content == """def main(): - print("Hello World!") + print("Hello World!") # noqa: T201 return 0""" ) @@ -67,7 +67,7 @@ def esprima_installed() -> bool: return True except Exception as e: - print(f"esprima not installed, skipping test {e}") + print(f"esprima not installed, skipping test {e}") # noqa: T201 return False diff --git a/libs/community/tests/integration_tests/document_loaders/test_arxiv.py b/libs/community/tests/integration_tests/document_loaders/test_arxiv.py index 55ad01b9c40..765290642a8 100644 --- a/libs/community/tests/integration_tests/document_loaders/test_arxiv.py +++ b/libs/community/tests/integration_tests/document_loaders/test_arxiv.py @@ -19,8 +19,8 @@ def test_load_success() -> None: docs = loader.load() assert len(docs) == 1 - print(docs[0].metadata) - print(docs[0].page_content) + print(docs[0].metadata) # noqa: T201 + print(docs[0].page_content) # noqa: T201 assert_docs(docs) @@ -53,7 +53,7 @@ def test_load_returns_full_set_of_metadata() -> None: assert set(doc.metadata).issuperset( {"Published", "Title", "Authors", "Summary"} ) - print(doc.metadata) + print(doc.metadata) # noqa: T201 assert len(set(doc.metadata)) > 4 diff --git a/libs/community/tests/integration_tests/document_loaders/test_astradb.py b/libs/community/tests/integration_tests/document_loaders/test_astradb.py index 0a8518885ae..b0a1104f823 100644 --- a/libs/community/tests/integration_tests/document_loaders/test_astradb.py +++ b/libs/community/tests/integration_tests/document_loaders/test_astradb.py @@ -15,7 +15,7 @@ from __future__ import annotations import json import os import uuid -from typing import TYPE_CHECKING +from typing import TYPE_CHECKING, AsyncIterator, Iterator import pytest @@ -37,12 +37,12 @@ def _has_env_vars() -> bool: @pytest.fixture -def astra_db_collection() -> AstraDBCollection: +def astra_db_collection() -> Iterator[AstraDBCollection]: from astrapy.db import AstraDB astra_db = AstraDB( - token=ASTRA_DB_APPLICATION_TOKEN, - api_endpoint=ASTRA_DB_API_ENDPOINT, + token=ASTRA_DB_APPLICATION_TOKEN or "", + api_endpoint=ASTRA_DB_API_ENDPOINT or "", namespace=ASTRA_DB_KEYSPACE, ) collection_name = f"lc_test_loader_{str(uuid.uuid4()).split('-')[0]}" @@ -58,12 +58,12 @@ def astra_db_collection() -> AstraDBCollection: @pytest.fixture -async def async_astra_db_collection() -> AsyncAstraDBCollection: +async def async_astra_db_collection() -> AsyncIterator[AsyncAstraDBCollection]: from astrapy.db import AsyncAstraDB astra_db = AsyncAstraDB( - token=ASTRA_DB_APPLICATION_TOKEN, - api_endpoint=ASTRA_DB_API_ENDPOINT, + token=ASTRA_DB_APPLICATION_TOKEN or "", + api_endpoint=ASTRA_DB_API_ENDPOINT or "", namespace=ASTRA_DB_KEYSPACE, ) collection_name = f"lc_test_loader_{str(uuid.uuid4()).split('-')[0]}" @@ -167,5 +167,5 @@ class TestAstraDB: find_options={"limit": 30}, extraction_function=lambda x: x["foo"], ) - doc = await anext(loader.alazy_load()) + doc = await loader.alazy_load().__anext__() assert doc.page_content == "bar" diff --git a/libs/community/tests/integration_tests/document_loaders/test_blockchain.py b/libs/community/tests/integration_tests/document_loaders/test_blockchain.py index 8ea688df584..d101a255cf3 100644 --- a/libs/community/tests/integration_tests/document_loaders/test_blockchain.py +++ b/libs/community/tests/integration_tests/document_loaders/test_blockchain.py @@ -21,7 +21,7 @@ def test_get_nfts_valid_contract() -> None: ) result = BlockchainDocumentLoader(contract_address).load() - print("Tokens returned for valid contract: ", len(result)) + print("Tokens returned for valid contract: ", len(result)) # noqa: T201 assert len(result) == max_alchemy_tokens, ( f"Wrong number of NFTs returned. " @@ -43,7 +43,7 @@ def test_get_nfts_with_pagination() -> None: startToken=startToken, ).load() - print("Tokens returned for contract with offset: ", len(result)) + print("Tokens returned for contract with offset: ", len(result)) # noqa: T201 assert len(result) > 0, "No NFTs returned" @@ -57,7 +57,7 @@ def test_get_nfts_polygon() -> None: contract_address, BlockchainType.POLYGON_MAINNET ).load() - print("Tokens returned for contract on Polygon: ", len(result)) + print("Tokens returned for contract on Polygon: ", len(result)) # noqa: T201 assert len(result) > 0, "No NFTs returned" @@ -94,7 +94,7 @@ def test_get_all() -> None: end_time = time.time() - print( + print( # noqa: T201 f"Tokens returned for {contract_address} " f"contract: {len(result)} in {end_time - start_time} seconds" ) @@ -121,4 +121,4 @@ def test_get_all_10sec_timeout() -> None: end_time = time.time() - print("Execution took ", end_time - start_time, " seconds") + print("Execution took ", end_time - start_time, " seconds") # noqa: T201 diff --git a/libs/community/tests/integration_tests/document_loaders/test_cassandra.py b/libs/community/tests/integration_tests/document_loaders/test_cassandra.py index 037ee0a3470..a93a6abba68 100644 --- a/libs/community/tests/integration_tests/document_loaders/test_cassandra.py +++ b/libs/community/tests/integration_tests/document_loaders/test_cassandra.py @@ -2,7 +2,7 @@ Test of Cassandra document loader class `CassandraLoader` """ import os -from typing import Any +from typing import Any, Iterator import pytest from langchain_core.documents import Document @@ -14,7 +14,7 @@ CASSANDRA_TABLE = "docloader_test_table" @pytest.fixture(autouse=True, scope="session") -def keyspace() -> str: +def keyspace() -> Iterator[str]: import cassio from cassandra.cluster import Cluster from cassio.config import check_resolve_session, resolve_keyspace diff --git a/libs/community/tests/integration_tests/document_loaders/test_couchbase.py b/libs/community/tests/integration_tests/document_loaders/test_couchbase.py index f4008679626..de1808b34c5 100644 --- a/libs/community/tests/integration_tests/document_loaders/test_couchbase.py +++ b/libs/community/tests/integration_tests/document_loaders/test_couchbase.py @@ -31,11 +31,11 @@ class TestCouchbaseLoader(unittest.TestCase): metadata_fields=self.valid_metadata_fields, ) docs = loader.load() - print(docs) + print(docs) # noqa: T201 assert len(docs) > 0 # assuming the query returns at least one document for doc in docs: - print(doc) + print(doc) # noqa: T201 assert ( doc.page_content != "" ) # assuming that every document has page_content diff --git a/libs/community/tests/integration_tests/document_loaders/test_gitbook.py b/libs/community/tests/integration_tests/document_loaders/test_gitbook.py index fa2a6838bd4..151f21be7e4 100644 --- a/libs/community/tests/integration_tests/document_loaders/test_gitbook.py +++ b/libs/community/tests/integration_tests/document_loaders/test_gitbook.py @@ -28,7 +28,7 @@ class TestGitbookLoader: loader = GitbookLoader( web_page, load_all_paths=load_all_paths, base_url=base_url ) - print(loader.__dict__) + print(loader.__dict__) # noqa: T201 assert ( loader.base_url == (base_url or web_page)[:-1] if (base_url or web_page).endswith("/") @@ -52,5 +52,5 @@ class TestGitbookLoader: def test_load_multiple_pages(self, web_page: str) -> None: loader = GitbookLoader(web_page, load_all_paths=True) result = loader.load() - print(len(result)) + print(len(result)) # noqa: T201 assert len(result) > 10 diff --git a/libs/community/tests/integration_tests/document_loaders/test_github.py b/libs/community/tests/integration_tests/document_loaders/test_github.py index a3ad86a0f99..a711e2c142c 100644 --- a/libs/community/tests/integration_tests/document_loaders/test_github.py +++ b/libs/community/tests/integration_tests/document_loaders/test_github.py @@ -2,11 +2,17 @@ from langchain_community.document_loaders.github import GitHubIssuesLoader def test_issues_load() -> None: - title = "DocumentLoader for GitHub" + title = " Add caching to BaseChatModel (issue #1644)" loader = GitHubIssuesLoader( - repo="langchain-ai/langchain", creator="UmerHA", state="all" + repo="langchain-ai/langchain", + creator="UmerHA", + state="all", + per_page=3, + page=2, + access_token="""""", ) docs = loader.load() titles = [d.metadata["title"] for d in docs] assert title in titles assert all(doc.metadata["creator"] == "UmerHA" for doc in docs) + assert len(docs) == 3 diff --git a/libs/community/tests/integration_tests/document_loaders/test_pdf.py b/libs/community/tests/integration_tests/document_loaders/test_pdf.py index 97c005b46d0..f9d71ea200e 100644 --- a/libs/community/tests/integration_tests/document_loaders/test_pdf.py +++ b/libs/community/tests/integration_tests/document_loaders/test_pdf.py @@ -145,14 +145,14 @@ def test_mathpix_loader() -> None: docs = loader.load() assert len(docs) == 1 - print(docs[0].page_content) + print(docs[0].page_content) # noqa: T201 file_path = Path(__file__).parent.parent / "examples/layout-parser-paper.pdf" loader = MathpixPDFLoader(str(file_path)) docs = loader.load() assert len(docs) == 1 - print(docs[0].page_content) + print(docs[0].page_content) # noqa: T201 @pytest.mark.parametrize( @@ -230,7 +230,7 @@ def test_amazontextract_loader( else: loader = AmazonTextractPDFLoader(file_path, textract_features=features) docs = loader.load() - print(docs) + print(docs) # noqa: T201 assert len(docs) == docs_length diff --git a/libs/community/tests/integration_tests/document_loaders/test_pubmed.py b/libs/community/tests/integration_tests/document_loaders/test_pubmed.py index bea8afabbad..48cda7774a3 100644 --- a/libs/community/tests/integration_tests/document_loaders/test_pubmed.py +++ b/libs/community/tests/integration_tests/document_loaders/test_pubmed.py @@ -1,4 +1,5 @@ """Integration test for PubMed API Wrapper.""" + from typing import List import pytest @@ -13,7 +14,7 @@ def test_load_success() -> None: """Test that returns the correct answer""" api_client = PubMedLoader(query="chatgpt") docs = api_client.load() - print(docs) + print(docs) # noqa: T201 assert len(docs) == api_client.load_max_docs == 3 assert_docs(docs) @@ -22,7 +23,7 @@ def test_load_success_load_max_docs() -> None: """Test that returns the correct answer""" api_client = PubMedLoader(query="chatgpt", load_max_docs=2) docs = api_client.load() - print(docs) + print(docs) # noqa: T201 assert len(docs) == api_client.load_max_docs == 2 assert_docs(docs) @@ -38,7 +39,7 @@ def test_load_no_content() -> None: """Returns a Document without content.""" api_client = PubMedLoader(query="37548971") docs = api_client.load() - print(docs) + print(docs) # noqa: T201 assert len(docs) > 0 assert docs[0].page_content == "" diff --git a/libs/community/tests/integration_tests/document_loaders/test_telegram.py b/libs/community/tests/integration_tests/document_loaders/test_telegram.py index df90af025f9..ceb360e1064 100644 --- a/libs/community/tests/integration_tests/document_loaders/test_telegram.py +++ b/libs/community/tests/integration_tests/document_loaders/test_telegram.py @@ -33,7 +33,7 @@ def test_telegram_channel_loader_parsing() -> None: docs = loader.load() assert len(docs) == 1 - print(docs[0].page_content) + print(docs[0].page_content) # noqa: T201 assert docs[0].page_content == ( "Hello, world!.\nLLMs are awesome! Langchain is great. Telegram is the best!." ) diff --git a/libs/community/tests/integration_tests/embeddings/test_baichuan.py b/libs/community/tests/integration_tests/embeddings/test_baichuan.py index 008dc0f97df..7512b060580 100644 --- a/libs/community/tests/integration_tests/embeddings/test_baichuan.py +++ b/libs/community/tests/integration_tests/embeddings/test_baichuan.py @@ -7,8 +7,8 @@ def test_baichuan_embedding_documents() -> None: documents = ["今天天气不错", "今天阳光灿烂"] embedding = BaichuanTextEmbeddings() output = embedding.embed_documents(documents) - assert len(output) == 2 - assert len(output[0]) == 1024 + assert len(output) == 2 # type: ignore[arg-type] + assert len(output[0]) == 1024 # type: ignore[index] def test_baichuan_embedding_query() -> None: @@ -16,4 +16,4 @@ def test_baichuan_embedding_query() -> None: document = "所有的小学生都会学过只因兔同笼问题。" embedding = BaichuanTextEmbeddings() output = embedding.embed_query(document) - assert len(output) == 1024 + assert len(output) == 1024 # type: ignore[arg-type] diff --git a/libs/community/tests/integration_tests/embeddings/test_xinference.py b/libs/community/tests/integration_tests/embeddings/test_xinference.py index f09fe8fe4f1..6c52fa98981 100644 --- a/libs/community/tests/integration_tests/embeddings/test_xinference.py +++ b/libs/community/tests/integration_tests/embeddings/test_xinference.py @@ -1,4 +1,5 @@ """Test Xinference embeddings.""" + import time from typing import AsyncGenerator, Tuple @@ -17,7 +18,7 @@ async def setup() -> AsyncGenerator[Tuple[str, str], None]: pool = await create_worker_actor_pool( f"test://127.0.0.1:{xo.utils.get_next_port()}" ) - print(f"Pool running on localhost:{pool.external_address}") + print(f"Pool running on localhost:{pool.external_address}") # noqa: T201 endpoint = await start_supervisor_components( pool.external_address, "127.0.0.1", xo.utils.get_next_port() diff --git a/libs/community/tests/integration_tests/examples/hello_world.py b/libs/community/tests/integration_tests/examples/hello_world.py index 3f0294febb4..c98e152ddeb 100644 --- a/libs/community/tests/integration_tests/examples/hello_world.py +++ b/libs/community/tests/integration_tests/examples/hello_world.py @@ -4,7 +4,7 @@ import sys def main() -> int: - print("Hello World!") + print("Hello World!") # noqa: T201 return 0 diff --git a/libs/community/tests/integration_tests/graphs/test_kuzu.py b/libs/community/tests/integration_tests/graphs/test_kuzu.py index 10a35f2175e..20d002d55d6 100644 --- a/libs/community/tests/integration_tests/graphs/test_kuzu.py +++ b/libs/community/tests/integration_tests/graphs/test_kuzu.py @@ -4,8 +4,7 @@ import unittest from langchain_community.graphs import KuzuGraph -EXPECTED_SCHEMA = """ -Node properties: [{'properties': [('name', 'STRING')], 'label': 'Movie'}, {'properties': [('name', 'STRING'), ('birthDate', 'STRING')], 'label': 'Person'}] +EXPECTED_SCHEMA = """Node properties: [{'properties': [('name', 'STRING')], 'label': 'Movie'}, {'properties': [('name', 'STRING'), ('birthDate', 'STRING')], 'label': 'Person'}] Relationships properties: [{'properties': [], 'label': 'ActedIn'}] Relationships: ['(:Person)-[:ActedIn]->(:Movie)'] """ # noqa: E501 @@ -36,7 +35,7 @@ class TestKuzu(unittest.TestCase): def tearDown(self) -> None: shutil.rmtree(self.tmpdir, ignore_errors=True) - def test_query(self) -> None: + def test_query_no_params(self) -> None: result = self.kuzu_graph.query("MATCH (n:Movie) RETURN n.name ORDER BY n.name") excepted_result = [ {"n.name": "The Godfather"}, @@ -45,6 +44,16 @@ class TestKuzu(unittest.TestCase): ] self.assertEqual(result, excepted_result) + def test_query_params(self) -> None: + result = self.kuzu_graph.query( + query="MATCH (n:Movie) WHERE n.name = $name RETURN n.name", + params={"name": "The Godfather"}, + ) + excepted_result = [ + {"n.name": "The Godfather"}, + ] + self.assertEqual(result, excepted_result) + def test_refresh_schema(self) -> None: self.conn.execute( "CREATE NODE TABLE Person (name STRING, birthDate STRING, PRIMARY " diff --git a/libs/community/tests/integration_tests/graphs/test_neo4j.py b/libs/community/tests/integration_tests/graphs/test_neo4j.py index e4b47f907ea..a209c56d099 100644 --- a/libs/community/tests/integration_tests/graphs/test_neo4j.py +++ b/libs/community/tests/integration_tests/graphs/test_neo4j.py @@ -85,7 +85,7 @@ def test_neo4j_timeout() -> None: graph.query("UNWIND range(0,100000,1) AS i MERGE (:Foo {id:i})") except Exception as e: assert ( - e.code + e.code # type: ignore[attr-defined] == "Neo.ClientError.Transaction.TransactionTimedOutClientConfiguration" ) diff --git a/libs/community/tests/integration_tests/graphs/test_ontotext_graphdb_graph.py b/libs/community/tests/integration_tests/graphs/test_ontotext_graphdb_graph.py index bba6c1ebb0b..9f20afb6bcc 100644 --- a/libs/community/tests/integration_tests/graphs/test_ontotext_graphdb_graph.py +++ b/libs/community/tests/integration_tests/graphs/test_ontotext_graphdb_graph.py @@ -10,7 +10,7 @@ cd libs/community/tests/integration_tests/graphs/docker-compose-ontotext-graphdb """ -def test_query() -> None: +def test_query_method_with_valid_query() -> None: graph = OntotextGraphDBGraph( query_endpoint="http://localhost:7200/repositories/langchain", query_ontology="CONSTRUCT {?s ?p ?o}" @@ -31,6 +31,36 @@ def test_query() -> None: assert str(query_results[0][0]) == "yellow" +def test_query_method_with_invalid_query() -> None: + graph = OntotextGraphDBGraph( + query_endpoint="http://localhost:7200/repositories/langchain", + query_ontology="CONSTRUCT {?s ?p ?o}" + "FROM WHERE {?s ?p ?o}", + ) + + with pytest.raises(ValueError) as e: + graph.query( + "PREFIX : " + "PREFIX owl: " + "PREFIX rdfs: " + "PREFIX xsd: " + "SELECT ?character (MAX(?lifespan) AS ?maxLifespan) " + "WHERE {" + " ?species a :Species ;" + " :character ?character ;" + " :averageLifespan ?lifespan ." + " FILTER(xsd:integer(?lifespan))" + "} " + "ORDER BY DESC(?maxLifespan) " + "LIMIT 1" + ) + + assert ( + str(e.value) + == "You did something wrong formulating either the URI or your SPARQL query" + ) + + def test_get_schema_with_query() -> None: graph = OntotextGraphDBGraph( query_endpoint="http://localhost:7200/repositories/langchain", diff --git a/libs/community/tests/integration_tests/llms/test_arcee.py b/libs/community/tests/integration_tests/llms/test_arcee.py index b3b7620cd0a..e195a7818db 100644 --- a/libs/community/tests/integration_tests/llms/test_arcee.py +++ b/libs/community/tests/integration_tests/llms/test_arcee.py @@ -41,7 +41,7 @@ def test_api_key_masked_when_passed_via_constructor( arcee_api_url="https://localhost", arcee_api_version="version", ) - print(arcee_without_env_var.arcee_api_key, end="") + print(arcee_without_env_var.arcee_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert "**********" == captured.out @@ -64,7 +64,7 @@ def test_api_key_masked_when_passed_from_env( arcee_api_url="https://localhost", arcee_api_version="version", ) - print(arcee_with_env_var.arcee_api_key, end="") + print(arcee_with_env_var.arcee_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert "**********" == captured.out diff --git a/libs/community/tests/integration_tests/llms/test_aviary.py b/libs/community/tests/integration_tests/llms/test_aviary.py index dba183e6edb..2c8fd96f526 100644 --- a/libs/community/tests/integration_tests/llms/test_aviary.py +++ b/libs/community/tests/integration_tests/llms/test_aviary.py @@ -7,5 +7,5 @@ def test_aviary_call() -> None: """Test valid call to Anyscale.""" llm = Aviary() output = llm("Say bar:") - print(f"llm answer:\n{output}") + print(f"llm answer:\n{output}") # noqa: T201 assert isinstance(output, str) diff --git a/libs/community/tests/integration_tests/llms/test_azureml_endpoint.py b/libs/community/tests/integration_tests/llms/test_azureml_endpoint.py index 4d0e86b5102..3c4e61eb444 100644 --- a/libs/community/tests/integration_tests/llms/test_azureml_endpoint.py +++ b/libs/community/tests/integration_tests/llms/test_azureml_endpoint.py @@ -62,7 +62,7 @@ def test_custom_formatter() -> None: content_type = "application/json" accepts = "application/json" - def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes: + def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes: # type: ignore[override] input_str = json.dumps( { "inputs": [prompt], @@ -72,7 +72,7 @@ def test_custom_formatter() -> None: ) return input_str.encode("utf-8") - def format_response_payload(self, output: bytes) -> str: + def format_response_payload(self, output: bytes) -> str: # type: ignore[override] response_json = json.loads(output) return response_json[0]["summary_text"] @@ -104,7 +104,7 @@ def test_invalid_request_format() -> None: content_type = "application/json" accepts = "application/json" - def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes: + def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes: # type: ignore[override] input_str = json.dumps( { "incorrect_input": {"input_string": [prompt]}, @@ -113,7 +113,7 @@ def test_invalid_request_format() -> None: ) return str.encode(input_str) - def format_response_payload(self, output: bytes) -> str: + def format_response_payload(self, output: bytes) -> str: # type: ignore[override] response_json = json.loads(output) return response_json[0]["0"] diff --git a/libs/community/tests/integration_tests/llms/test_bedrock.py b/libs/community/tests/integration_tests/llms/test_bedrock.py index 45a7bcd0bfa..97dbf4f4e8a 100644 --- a/libs/community/tests/integration_tests/llms/test_bedrock.py +++ b/libs/community/tests/integration_tests/llms/test_bedrock.py @@ -37,12 +37,12 @@ class BedrockAsyncCallbackHandler(AsyncCallbackHandler): if reason == "GUARDRAIL_INTERVENED": self.guardrails_intervened = True - def get_response(self): + def get_response(self): # type: ignore[no-untyped-def] return self.guardrails_intervened @pytest.fixture(autouse=True) -def bedrock_runtime_client(): +def bedrock_runtime_client(): # type: ignore[no-untyped-def] import boto3 try: @@ -56,7 +56,7 @@ def bedrock_runtime_client(): @pytest.fixture(autouse=True) -def bedrock_client(): +def bedrock_client(): # type: ignore[no-untyped-def] import boto3 try: @@ -70,7 +70,7 @@ def bedrock_client(): @pytest.fixture -def bedrock_models(bedrock_client): +def bedrock_models(bedrock_client): # type: ignore[no-untyped-def] """List bedrock models.""" response = bedrock_client.list_foundation_models().get("modelSummaries") models = {} @@ -79,7 +79,7 @@ def bedrock_models(bedrock_client): return models -def test_claude_instant_v1(bedrock_runtime_client, bedrock_models): +def test_claude_instant_v1(bedrock_runtime_client, bedrock_models): # type: ignore[no-untyped-def] try: llm = Bedrock( model_id="anthropic.claude-instant-v1", @@ -92,7 +92,7 @@ def test_claude_instant_v1(bedrock_runtime_client, bedrock_models): pytest.fail(f"can not instantiate claude-instant-v1: {e}", pytrace=False) -def test_amazon_bedrock_guardrails_no_intervention_for_valid_query( +def test_amazon_bedrock_guardrails_no_intervention_for_valid_query( # type: ignore[no-untyped-def] bedrock_runtime_client, bedrock_models ): try: @@ -112,7 +112,7 @@ def test_amazon_bedrock_guardrails_no_intervention_for_valid_query( pytest.fail(f"can not instantiate claude-instant-v1: {e}", pytrace=False) -def test_amazon_bedrock_guardrails_intervention_for_invalid_query( +def test_amazon_bedrock_guardrails_intervention_for_invalid_query( # type: ignore[no-untyped-def] bedrock_runtime_client, bedrock_models ): try: diff --git a/libs/community/tests/integration_tests/llms/test_huggingface_endpoint.py b/libs/community/tests/integration_tests/llms/test_huggingface_endpoint.py index 9bed8b76bd3..ca89d54cde7 100644 --- a/libs/community/tests/integration_tests/llms/test_huggingface_endpoint.py +++ b/libs/community/tests/integration_tests/llms/test_huggingface_endpoint.py @@ -19,7 +19,7 @@ def test_huggingface_endpoint_text_generation() -> None: endpoint_url="", task="text-generation", model_kwargs={"max_new_tokens": 10} ) output = llm("Say foo:") - print(output) + print(output) # noqa: T201 assert isinstance(output, str) diff --git a/libs/community/tests/integration_tests/llms/test_huggingface_pipeline.py b/libs/community/tests/integration_tests/llms/test_huggingface_pipeline.py index 885f3b3eaf3..aa6a0e1defe 100755 --- a/libs/community/tests/integration_tests/llms/test_huggingface_pipeline.py +++ b/libs/community/tests/integration_tests/llms/test_huggingface_pipeline.py @@ -69,3 +69,14 @@ def test_init_with_pipeline() -> None: llm = HuggingFacePipeline(pipeline=pipe) output = llm("Say foo:") assert isinstance(output, str) + + +def test_huggingface_pipeline_runtime_kwargs() -> None: + """Test pipelines specifying the device map parameter.""" + llm = HuggingFacePipeline.from_model_id( + model_id="gpt2", + task="text-generation", + ) + prompt = "Say foo:" + output = llm(prompt, pipeline_kwargs={"max_new_tokens": 2}) + assert len(output) < 10 diff --git a/libs/community/tests/integration_tests/llms/test_llamafile.py b/libs/community/tests/integration_tests/llms/test_llamafile.py new file mode 100644 index 00000000000..0f9c66c1822 --- /dev/null +++ b/libs/community/tests/integration_tests/llms/test_llamafile.py @@ -0,0 +1,46 @@ +import os +from typing import Generator + +import pytest +import requests +from requests.exceptions import ConnectionError, HTTPError + +from langchain_community.llms.llamafile import Llamafile + +LLAMAFILE_SERVER_BASE_URL = os.getenv( + "LLAMAFILE_SERVER_BASE_URL", "http://localhost:8080" +) + + +def _ping_llamafile_server() -> bool: + try: + response = requests.get(LLAMAFILE_SERVER_BASE_URL) + response.raise_for_status() + except (ConnectionError, HTTPError): + return False + + return True + + +@pytest.mark.skipif( + not _ping_llamafile_server(), + reason=f"unable to find llamafile server at {LLAMAFILE_SERVER_BASE_URL}, " + f"please start one and re-run this test", +) +def test_llamafile_call() -> None: + llm = Llamafile() + output = llm.invoke("Say foo:") + assert isinstance(output, str) + + +@pytest.mark.skipif( + not _ping_llamafile_server(), + reason=f"unable to find llamafile server at {LLAMAFILE_SERVER_BASE_URL}, " + f"please start one and re-run this test", +) +def test_llamafile_streaming() -> None: + llm = Llamafile(streaming=True) + generator = llm.stream("Tell me about Roman dodecahedrons.") + assert isinstance(generator, Generator) + for token in generator: + assert isinstance(token, str) diff --git a/libs/community/tests/integration_tests/llms/test_nlpcloud.py b/libs/community/tests/integration_tests/llms/test_nlpcloud.py index 25aff55f758..5806f4d21af 100644 --- a/libs/community/tests/integration_tests/llms/test_nlpcloud.py +++ b/libs/community/tests/integration_tests/llms/test_nlpcloud.py @@ -37,7 +37,7 @@ def test_nlpcloud_api_key(monkeypatch: MonkeyPatch, capsys: CaptureFixture) -> N assert cast(SecretStr, llm.nlpcloud_api_key).get_secret_value() == "secret-api-key" - print(llm.nlpcloud_api_key, end="") + print(llm.nlpcloud_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/integration_tests/llms/test_octoai_endpoint.py b/libs/community/tests/integration_tests/llms/test_octoai_endpoint.py index 0ad291257aa..fcdfc749ba4 100644 --- a/libs/community/tests/integration_tests/llms/test_octoai_endpoint.py +++ b/libs/community/tests/integration_tests/llms/test_octoai_endpoint.py @@ -25,7 +25,7 @@ def test_octoai_endpoint_text_generation() -> None: ) output = llm("Which state is Los Angeles in?") - print(output) + print(output) # noqa: T201 assert isinstance(output, str) diff --git a/libs/community/tests/integration_tests/llms/test_petals.py b/libs/community/tests/integration_tests/llms/test_petals.py index 774b56fdeff..4fd76753a05 100644 --- a/libs/community/tests/integration_tests/llms/test_petals.py +++ b/libs/community/tests/integration_tests/llms/test_petals.py @@ -15,7 +15,7 @@ def test_api_key_masked_when_passed_via_constructor( capsys: CaptureFixture, ) -> None: llm = Petals(huggingface_api_key="secret-api-key") - print(llm.huggingface_api_key, end="") + print(llm.huggingface_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/integration_tests/llms/test_predibase.py b/libs/community/tests/integration_tests/llms/test_predibase.py index 2f8b9233865..5e1d19e084a 100644 --- a/libs/community/tests/integration_tests/llms/test_predibase.py +++ b/libs/community/tests/integration_tests/llms/test_predibase.py @@ -13,7 +13,7 @@ def test_api_key_masked_when_passed_via_constructor( capsys: CaptureFixture, ) -> None: llm = Predibase(predibase_api_key="secret-api-key") - print(llm.predibase_api_key, end="") + print(llm.predibase_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/integration_tests/llms/test_tongyi.py b/libs/community/tests/integration_tests/llms/test_tongyi.py index 78a22604559..7e918d87be6 100644 --- a/libs/community/tests/integration_tests/llms/test_tongyi.py +++ b/libs/community/tests/integration_tests/llms/test_tongyi.py @@ -1,4 +1,5 @@ """Test Tongyi API wrapper.""" + from langchain_core.outputs import LLMResult from langchain_community.llms.tongyi import Tongyi @@ -23,6 +24,6 @@ def test_tongyi_generate_stream() -> None: """Test valid call to tongyi.""" llm = Tongyi(streaming=True) output = llm.generate(["who are you"]) - print(output) + print(output) # noqa: T201 assert isinstance(output, LLMResult) assert isinstance(output.generations, list) diff --git a/libs/community/tests/integration_tests/llms/test_volcengine_maas.py b/libs/community/tests/integration_tests/llms/test_volcengine_maas.py index 7d3094e627c..f6bef52690a 100644 --- a/libs/community/tests/integration_tests/llms/test_volcengine_maas.py +++ b/libs/community/tests/integration_tests/llms/test_volcengine_maas.py @@ -28,7 +28,7 @@ def test_api_key_masked_when_passed_via_constructor( volc_engine_maas_ak="secret-volc-ak", volc_engine_maas_sk="secret-volc-sk", ) - print(llm.volc_engine_maas_ak, end="") + print(llm.volc_engine_maas_ak, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/integration_tests/llms/test_xinference.py b/libs/community/tests/integration_tests/llms/test_xinference.py index 227db37c1df..3be912b4a45 100644 --- a/libs/community/tests/integration_tests/llms/test_xinference.py +++ b/libs/community/tests/integration_tests/llms/test_xinference.py @@ -1,4 +1,5 @@ """Test Xinference wrapper.""" + import time from typing import AsyncGenerator, Tuple @@ -17,7 +18,7 @@ async def setup() -> AsyncGenerator[Tuple[str, str], None]: pool = await create_worker_actor_pool( f"test://127.0.0.1:{xo.utils.get_next_port()}" ) - print(f"Pool running on localhost:{pool.external_address}") + print(f"Pool running on localhost:{pool.external_address}") # noqa: T201 endpoint = await start_supervisor_components( pool.external_address, "127.0.0.1", xo.utils.get_next_port() diff --git a/libs/community/tests/integration_tests/llms/test_yuan2.py b/libs/community/tests/integration_tests/llms/test_yuan2.py new file mode 100644 index 00000000000..94667819e66 --- /dev/null +++ b/libs/community/tests/integration_tests/llms/test_yuan2.py @@ -0,0 +1,33 @@ +"""Test Yuan2.0 API wrapper.""" +from langchain_core.outputs import LLMResult + +from langchain_community.llms import Yuan2 + + +def test_yuan2_call_method() -> None: + """Test valid call to Yuan2.0.""" + llm = Yuan2( + infer_api="http://127.0.0.1:8000/yuan", + max_tokens=1024, + temp=1.0, + top_p=0.9, + top_k=40, + use_history=False, + ) + output = llm("写一段快速排序算法。") + assert isinstance(output, str) + + +def test_yuan2_generate_method() -> None: + """Test valid call to Yuan2.0 inference api.""" + llm = Yuan2( + infer_api="http://127.0.0.1:8000/yuan", + max_tokens=1024, + temp=1.0, + top_p=0.9, + top_k=40, + use_history=False, + ) + output = llm.generate(["who are you?"]) + assert isinstance(output, LLMResult) + assert isinstance(output.generations, list) diff --git a/libs/community/tests/integration_tests/retrievers/test_breebs.py b/libs/community/tests/integration_tests/retrievers/test_breebs.py new file mode 100644 index 00000000000..1dd6b470a57 --- /dev/null +++ b/libs/community/tests/integration_tests/retrievers/test_breebs.py @@ -0,0 +1,18 @@ +from typing import List + +from langchain_core.documents import Document + +from langchain_community.retrievers.breebs import BreebsRetriever + + +class TestBreebsRetriever: + def test_breeb_query(self) -> None: + breeb_key = "Parivoyage" + query = "What are the best churches to visit in Paris?" + breeb_retriever = BreebsRetriever(breeb_key) + documents: List[Document] = breeb_retriever.get_relevant_documents(query) + assert isinstance(documents, list), "Documents should be a list" + for doc in documents: + assert doc.page_content, "Document page_content should not be None" + assert doc.metadata["source"], "Document metadata should contain 'source'" + assert doc.metadata["score"] == 1, "Document score should be equal to 1" diff --git a/libs/community/tests/integration_tests/storage/test_astradb.py b/libs/community/tests/integration_tests/storage/test_astradb.py index 37a955ad31e..643b4e93a31 100644 --- a/libs/community/tests/integration_tests/storage/test_astradb.py +++ b/libs/community/tests/integration_tests/storage/test_astradb.py @@ -16,7 +16,7 @@ def _has_env_vars() -> bool: @pytest.fixture -def astra_db(): +def astra_db(): # type: ignore[no-untyped-def] from astrapy.db import AstraDB return AstraDB( @@ -26,14 +26,14 @@ def astra_db(): ) -def init_store(astra_db, collection_name: str): +def init_store(astra_db, collection_name: str): # type: ignore[no-untyped-def, no-untyped-def] astra_db.create_collection(collection_name) store = AstraDBStore(collection_name=collection_name, astra_db_client=astra_db) store.mset([("key1", [0.1, 0.2]), ("key2", "value2")]) return store -def init_bytestore(astra_db, collection_name: str): +def init_bytestore(astra_db, collection_name: str): # type: ignore[no-untyped-def, no-untyped-def] astra_db.create_collection(collection_name) store = AstraDBByteStore(collection_name=collection_name, astra_db_client=astra_db) store.mset([("key1", b"value1"), ("key2", b"value2")]) @@ -43,7 +43,7 @@ def init_bytestore(astra_db, collection_name: str): @pytest.mark.requires("astrapy") @pytest.mark.skipif(not _has_env_vars(), reason="Missing Astra DB env. vars") class TestAstraDBStore: - def test_mget(self, astra_db) -> None: + def test_mget(self, astra_db) -> None: # type: ignore[no-untyped-def] """Test AstraDBStore mget method.""" collection_name = "lc_test_store_mget" try: @@ -52,7 +52,7 @@ class TestAstraDBStore: finally: astra_db.delete_collection(collection_name) - def test_mset(self, astra_db) -> None: + def test_mset(self, astra_db) -> None: # type: ignore[no-untyped-def] """Test that multiple keys can be set with AstraDBStore.""" collection_name = "lc_test_store_mset" try: @@ -64,7 +64,7 @@ class TestAstraDBStore: finally: astra_db.delete_collection(collection_name) - def test_mdelete(self, astra_db) -> None: + def test_mdelete(self, astra_db) -> None: # type: ignore[no-untyped-def] """Test that deletion works as expected.""" collection_name = "lc_test_store_mdelete" try: @@ -75,7 +75,7 @@ class TestAstraDBStore: finally: astra_db.delete_collection(collection_name) - def test_yield_keys(self, astra_db) -> None: + def test_yield_keys(self, astra_db) -> None: # type: ignore[no-untyped-def] collection_name = "lc_test_store_yield_keys" try: store = init_store(astra_db, collection_name) @@ -85,7 +85,7 @@ class TestAstraDBStore: finally: astra_db.delete_collection(collection_name) - def test_bytestore_mget(self, astra_db) -> None: + def test_bytestore_mget(self, astra_db) -> None: # type: ignore[no-untyped-def] """Test AstraDBByteStore mget method.""" collection_name = "lc_test_bytestore_mget" try: @@ -94,7 +94,7 @@ class TestAstraDBStore: finally: astra_db.delete_collection(collection_name) - def test_bytestore_mset(self, astra_db) -> None: + def test_bytestore_mset(self, astra_db) -> None: # type: ignore[no-untyped-def] """Test that multiple keys can be set with AstraDBByteStore.""" collection_name = "lc_test_bytestore_mset" try: diff --git a/libs/community/tests/integration_tests/storage/test_mongodb.py b/libs/community/tests/integration_tests/storage/test_mongodb.py new file mode 100644 index 00000000000..44062e994e0 --- /dev/null +++ b/libs/community/tests/integration_tests/storage/test_mongodb.py @@ -0,0 +1,73 @@ +from typing import Generator + +import pytest +from langchain_core.documents import Document + +from langchain_community.storage.mongodb import MongoDBStore + +pytest.importorskip("pymongo") + + +@pytest.fixture +def mongo_store() -> Generator: + import mongomock + + # mongomock creates a mock MongoDB instance for testing purposes + with mongomock.patch(servers=(("localhost", 27017),)): + yield MongoDBStore("mongodb://localhost:27017/", "test_db", "test_collection") + + +def test_mset_and_mget(mongo_store: MongoDBStore) -> None: + doc1 = Document(page_content="doc1") + doc2 = Document(page_content="doc2") + + # Set documents in the store + mongo_store.mset([("key1", doc1), ("key2", doc2)]) + + # Get documents from the store + retrieved_docs = mongo_store.mget(["key1", "key2"]) + + assert retrieved_docs[0] and retrieved_docs[0].page_content == "doc1" + assert retrieved_docs[1] and retrieved_docs[1].page_content == "doc2" + + +def test_yield_keys(mongo_store: MongoDBStore) -> None: + mongo_store.mset( + [ + ("key1", Document(page_content="doc1")), + ("key2", Document(page_content="doc2")), + ("another_key", Document(page_content="other")), + ] + ) + + # Test without prefix + keys = list(mongo_store.yield_keys()) + assert set(keys) == {"key1", "key2", "another_key"} + + # Test with prefix + keys_with_prefix = list(mongo_store.yield_keys(prefix="key")) + assert set(keys_with_prefix) == {"key1", "key2"} + + +def test_mdelete(mongo_store: MongoDBStore) -> None: + mongo_store.mset( + [ + ("key1", Document(page_content="doc1")), + ("key2", Document(page_content="doc2")), + ] + ) + # Delete single document + mongo_store.mdelete(["key1"]) + remaining_docs = list(mongo_store.yield_keys()) + assert "key1" not in remaining_docs + assert "key2" in remaining_docs + + # Delete multiple documents + mongo_store.mdelete(["key2"]) + remaining_docs = list(mongo_store.yield_keys()) + assert len(remaining_docs) == 0 + + +def test_init_errors() -> None: + with pytest.raises(ValueError): + MongoDBStore("", "", "") diff --git a/libs/community/tests/integration_tests/utilities/test_arxiv.py b/libs/community/tests/integration_tests/utilities/test_arxiv.py index 73e95b653cb..a0799c9c3d6 100644 --- a/libs/community/tests/integration_tests/utilities/test_arxiv.py +++ b/libs/community/tests/integration_tests/utilities/test_arxiv.py @@ -1,4 +1,5 @@ """Integration test for Arxiv API Wrapper.""" + from typing import Any, List import pytest @@ -136,7 +137,7 @@ def test_load_returns_full_set_of_metadata() -> None: assert set(doc.metadata).issuperset( {"Published", "Title", "Authors", "Summary"} ) - print(doc.metadata) + print(doc.metadata) # noqa: T201 assert len(set(doc.metadata)) > 4 diff --git a/libs/community/tests/integration_tests/utilities/test_duckduckdgo_search_api.py b/libs/community/tests/integration_tests/utilities/test_duckduckdgo_search_api.py index d8b1a6e165b..d8e20b81a21 100644 --- a/libs/community/tests/integration_tests/utilities/test_duckduckdgo_search_api.py +++ b/libs/community/tests/integration_tests/utilities/test_duckduckdgo_search_api.py @@ -12,7 +12,7 @@ def ddg_installed() -> bool: return True except Exception as e: - print(f"duckduckgo not installed, skipping test {e}") + print(f"duckduckgo not installed, skipping test {e}") # noqa: T201 return False @@ -21,7 +21,7 @@ def test_ddg_search_tool() -> None: keywords = "Bella Ciao" tool = DuckDuckGoSearchRun() result = tool(keywords) - print(result) + print(result) # noqa: T201 assert len(result.split()) > 20 @@ -30,5 +30,5 @@ def test_ddg_search_news_tool() -> None: keywords = "Tesla" tool = DuckDuckGoSearchResults(source="news") result = tool(keywords) - print(result) + print(result) # noqa: T201 assert len(result.split()) > 20 diff --git a/libs/community/tests/integration_tests/utilities/test_github.py b/libs/community/tests/integration_tests/utilities/test_github.py index 6e21a8a9c14..77b87ec64f7 100644 --- a/libs/community/tests/integration_tests/utilities/test_github.py +++ b/libs/community/tests/integration_tests/utilities/test_github.py @@ -19,3 +19,9 @@ def test_get_open_issues(api_client: GitHubAPIWrapper) -> None: """Basic test to fetch issues""" issues = api_client.get_issues() assert len(issues) != 0 + + +def test_search_issues_and_prs(api_client: GitHubAPIWrapper) -> None: + """Basic test to search issues and PRs""" + results = api_client.search_issues_and_prs("is:pr is:merged") + assert len(results) != 0 diff --git a/libs/community/tests/integration_tests/utilities/test_google_trends.py b/libs/community/tests/integration_tests/utilities/test_google_trends.py index 0455f16a580..c2583d26339 100644 --- a/libs/community/tests/integration_tests/utilities/test_google_trends.py +++ b/libs/community/tests/integration_tests/utilities/test_google_trends.py @@ -6,7 +6,7 @@ from langchain_community.utilities.google_trends import GoogleTrendsAPIWrapper @patch("serpapi.SerpApiClient.get_json") -def test_unexpected_response(mocked_serpapiclient): +def test_unexpected_response(mocked_serpapiclient): # type: ignore[no-untyped-def] os.environ["SERPAPI_API_KEY"] = "123abcd" resp = { "search_metadata": { diff --git a/libs/community/tests/integration_tests/utilities/test_googlesearch_api.py b/libs/community/tests/integration_tests/utilities/test_googlesearch_api.py index c40e7034410..b32d05e76ba 100644 --- a/libs/community/tests/integration_tests/utilities/test_googlesearch_api.py +++ b/libs/community/tests/integration_tests/utilities/test_googlesearch_api.py @@ -1,4 +1,5 @@ """Integration test for Google Search API Wrapper.""" + from langchain_community.utilities.google_search import GoogleSearchAPIWrapper @@ -15,7 +16,7 @@ def test_no_result_call() -> None: output = search.run( "NORESULTCALL_NORESULTCALL_NORESULTCALL_NORESULTCALL_NORESULTCALL_NORESULTCALL" ) - print(type(output)) + print(type(output)) # noqa: T201 assert "No good Google Search Result was found" == output diff --git a/libs/community/tests/integration_tests/utilities/test_powerbi_api.py b/libs/community/tests/integration_tests/utilities/test_powerbi_api.py index 842b3525ebd..acc3adca648 100644 --- a/libs/community/tests/integration_tests/utilities/test_powerbi_api.py +++ b/libs/community/tests/integration_tests/utilities/test_powerbi_api.py @@ -1,4 +1,5 @@ """Integration test for POWERBI API Wrapper.""" + import pytest from langchain_core.utils import get_from_env @@ -12,7 +13,7 @@ def azure_installed() -> bool: return True except Exception as e: - print(f"azure not installed, skipping test {e}") + print(f"azure not installed, skipping test {e}") # noqa: T201 return False diff --git a/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_from_texts.py b/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_from_texts.py index dc9f3c9270e..1ee99d40bdd 100644 --- a/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_from_texts.py +++ b/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_from_texts.py @@ -12,7 +12,10 @@ from tests.integration_tests.vectorstores.fake_embeddings import ( from tests.integration_tests.vectorstores.qdrant.async_api.fixtures import ( qdrant_locations, ) -from tests.integration_tests.vectorstores.qdrant.common import qdrant_is_not_running +from tests.integration_tests.vectorstores.qdrant.common import ( + assert_documents_equals, + qdrant_is_not_running, +) @pytest.mark.parametrize("qdrant_location", qdrant_locations()) @@ -245,4 +248,6 @@ async def test_qdrant_from_texts_stores_metadatas( location=qdrant_location, ) output = await docsearch.asimilarity_search("foo", k=1) - assert output == [Document(page_content="foo", metadata={"page": 0})] + assert_documents_equals( + output, [Document(page_content="foo", metadata={"page": 0})] + ) diff --git a/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_max_marginal_relevance.py b/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_max_marginal_relevance.py index 59d40c479fb..472a66bb0c7 100644 --- a/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_max_marginal_relevance.py +++ b/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_max_marginal_relevance.py @@ -10,6 +10,7 @@ from tests.integration_tests.vectorstores.fake_embeddings import ( from tests.integration_tests.vectorstores.qdrant.async_api.fixtures import ( qdrant_locations, ) +from tests.integration_tests.vectorstores.qdrant.common import assert_documents_equals @pytest.mark.parametrize("batch_size", [1, 64]) @@ -41,7 +42,10 @@ async def test_qdrant_max_marginal_relevance_search( output = await docsearch.amax_marginal_relevance_search( "foo", k=2, fetch_k=3, lambda_mult=0.0 ) - assert output == [ - Document(page_content="foo", metadata={"page": 0}), - Document(page_content="baz", metadata={"page": 2}), - ] + assert_documents_equals( + output, + [ + Document(page_content="foo", metadata={"page": 0}), + Document(page_content="baz", metadata={"page": 2}), + ], + ) diff --git a/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_similarity_search.py b/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_similarity_search.py index 0d6db192c69..5ae98ad35aa 100644 --- a/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_similarity_search.py +++ b/libs/community/tests/integration_tests/vectorstores/qdrant/async_api/test_similarity_search.py @@ -11,6 +11,7 @@ from tests.integration_tests.vectorstores.fake_embeddings import ( from tests.integration_tests.vectorstores.qdrant.async_api.fixtures import ( qdrant_locations, ) +from tests.integration_tests.vectorstores.qdrant.common import assert_documents_equals @pytest.mark.parametrize("batch_size", [1, 64]) @@ -37,7 +38,7 @@ async def test_qdrant_similarity_search( location=qdrant_location, ) output = await docsearch.asimilarity_search("foo", k=1) - assert output == [Document(page_content="foo")] + assert_documents_equals(output, [Document(page_content="foo")]) @pytest.mark.parametrize("batch_size", [1, 64]) @@ -65,7 +66,7 @@ async def test_qdrant_similarity_search_by_vector( ) embeddings = ConsistentFakeEmbeddings().embed_query("foo") output = await docsearch.asimilarity_search_by_vector(embeddings, k=1) - assert output == [Document(page_content="foo")] + assert_documents_equals(output, [Document(page_content="foo")]) @pytest.mark.parametrize("batch_size", [1, 64]) @@ -95,7 +96,7 @@ async def test_qdrant_similarity_search_with_score_by_vector( output = await docsearch.asimilarity_search_with_score_by_vector(embeddings, k=1) assert len(output) == 1 document, score = output[0] - assert document == Document(page_content="foo") + assert_documents_equals([document], [Document(page_content="foo")]) assert score >= 0 @@ -123,12 +124,15 @@ async def test_qdrant_similarity_search_filters( output = await docsearch.asimilarity_search( "foo", k=1, filter={"page": 1, "metadata": {"page": 2, "pages": [3]}} ) - assert output == [ - Document( - page_content="bar", - metadata={"page": 1, "metadata": {"page": 2, "pages": [3, -1]}}, - ) - ] + assert_documents_equals( + output, + [ + Document( + page_content="bar", + metadata={"page": 1, "metadata": {"page": 2, "pages": [3, -1]}}, + ) + ], + ) @pytest.mark.parametrize("vector_name", [None, "my-vector"]) @@ -262,12 +266,15 @@ async def test_qdrant_similarity_search_filters_with_qdrant_filters( ] ) output = await docsearch.asimilarity_search("foo", k=1, filter=qdrant_filter) - assert output == [ - Document( - page_content="bar", - metadata={"page": 1, "details": {"page": 2, "pages": [3, -1]}}, - ) - ] + assert_documents_equals( + output, + [ + Document( + page_content="bar", + metadata={"page": 1, "details": {"page": 2, "pages": [3, -1]}}, + ) + ], + ) @pytest.mark.parametrize("batch_size", [1, 64]) diff --git a/libs/community/tests/integration_tests/vectorstores/qdrant/common.py b/libs/community/tests/integration_tests/vectorstores/qdrant/common.py index 065dddd0a6b..c09fcc6a7a3 100644 --- a/libs/community/tests/integration_tests/vectorstores/qdrant/common.py +++ b/libs/community/tests/integration_tests/vectorstores/qdrant/common.py @@ -1,3 +1,8 @@ +from typing import List + +from langchain_core.documents import Document + + def qdrant_is_not_running() -> bool: """Check if Qdrant is not running.""" import requests @@ -8,3 +13,18 @@ def qdrant_is_not_running() -> bool: return response_json.get("title") != "qdrant - vector search engine" except (requests.exceptions.ConnectionError, requests.exceptions.Timeout): return True + + +def assert_documents_equals(actual: List[Document], expected: List[Document]): # type: ignore[no-untyped-def] + assert len(actual) == len(expected) + + for actual_doc, expected_doc in zip(actual, expected): + assert actual_doc.page_content == expected_doc.page_content + + assert "_id" in actual_doc.metadata + assert "_collection_name" in actual_doc.metadata + + actual_doc.metadata.pop("_id") + actual_doc.metadata.pop("_collection_name") + + assert actual_doc.metadata == expected_doc.metadata diff --git a/libs/community/tests/integration_tests/vectorstores/qdrant/test_add_texts.py b/libs/community/tests/integration_tests/vectorstores/qdrant/test_add_texts.py index 819216c2807..d3d73b3c6d5 100644 --- a/libs/community/tests/integration_tests/vectorstores/qdrant/test_add_texts.py +++ b/libs/community/tests/integration_tests/vectorstores/qdrant/test_add_texts.py @@ -8,6 +8,7 @@ from langchain_community.vectorstores import Qdrant from tests.integration_tests.vectorstores.fake_embeddings import ( ConsistentFakeEmbeddings, ) +from tests.integration_tests.vectorstores.qdrant.common import assert_documents_equals @pytest.mark.parametrize("batch_size", [1, 64]) @@ -33,7 +34,7 @@ def test_qdrant_add_documents_extends_existing_collection( # ConsistentFakeEmbeddings return the same query embedding as the first document # embedding computed in `embedding.embed_documents`. Thus, "foo" embedding is the # same as "foobar" embedding - assert output == [Document(page_content="foobar")] + assert_documents_equals(output, [Document(page_content="foobar")]) @pytest.mark.parametrize("batch_size", [1, 64]) diff --git a/libs/community/tests/integration_tests/vectorstores/qdrant/test_from_texts.py b/libs/community/tests/integration_tests/vectorstores/qdrant/test_from_texts.py index e6b97fa703a..5b00ea9f98f 100644 --- a/libs/community/tests/integration_tests/vectorstores/qdrant/test_from_texts.py +++ b/libs/community/tests/integration_tests/vectorstores/qdrant/test_from_texts.py @@ -10,7 +10,10 @@ from langchain_community.vectorstores.qdrant import QdrantException from tests.integration_tests.vectorstores.fake_embeddings import ( ConsistentFakeEmbeddings, ) -from tests.integration_tests.vectorstores.qdrant.common import qdrant_is_not_running +from tests.integration_tests.vectorstores.qdrant.common import ( + assert_documents_equals, + qdrant_is_not_running, +) def test_qdrant_from_texts_stores_duplicated_texts() -> None: @@ -257,7 +260,9 @@ def test_qdrant_from_texts_stores_metadatas( batch_size=batch_size, ) output = docsearch.similarity_search("foo", k=1) - assert output == [Document(page_content="foo", metadata={"page": 0})] + assert_documents_equals( + output, [Document(page_content="foo", metadata={"page": 0})] + ) @pytest.mark.skipif(qdrant_is_not_running(), reason="Qdrant is not running") diff --git a/libs/community/tests/integration_tests/vectorstores/qdrant/test_max_marginal_relevance.py b/libs/community/tests/integration_tests/vectorstores/qdrant/test_max_marginal_relevance.py index 536003e8481..05c74311833 100644 --- a/libs/community/tests/integration_tests/vectorstores/qdrant/test_max_marginal_relevance.py +++ b/libs/community/tests/integration_tests/vectorstores/qdrant/test_max_marginal_relevance.py @@ -7,6 +7,7 @@ from langchain_community.vectorstores import Qdrant from tests.integration_tests.vectorstores.fake_embeddings import ( ConsistentFakeEmbeddings, ) +from tests.integration_tests.vectorstores.qdrant.common import assert_documents_equals @pytest.mark.parametrize("batch_size", [1, 64]) @@ -49,14 +50,18 @@ def test_qdrant_max_marginal_relevance_search( output = docsearch.max_marginal_relevance_search( "foo", k=2, fetch_k=3, lambda_mult=0.0 ) - assert output == [ - Document(page_content="foo", metadata={"page": 0}), - Document(page_content="baz", metadata={"page": 2}), - ] + assert_documents_equals( + output, + [ + Document(page_content="foo", metadata={"page": 0}), + Document(page_content="baz", metadata={"page": 2}), + ], + ) output = docsearch.max_marginal_relevance_search( "foo", k=2, fetch_k=3, lambda_mult=0.0, filter=filter ) - assert output == [ - Document(page_content="baz", metadata={"page": 2}), - ] + assert_documents_equals( + output, + [Document(page_content="baz", metadata={"page": 2})], + ) diff --git a/libs/community/tests/integration_tests/vectorstores/qdrant/test_similarity_search.py b/libs/community/tests/integration_tests/vectorstores/qdrant/test_similarity_search.py index 2df03ada797..5b51be93d4e 100644 --- a/libs/community/tests/integration_tests/vectorstores/qdrant/test_similarity_search.py +++ b/libs/community/tests/integration_tests/vectorstores/qdrant/test_similarity_search.py @@ -8,6 +8,7 @@ from langchain_community.vectorstores import Qdrant from tests.integration_tests.vectorstores.fake_embeddings import ( ConsistentFakeEmbeddings, ) +from tests.integration_tests.vectorstores.qdrant.common import assert_documents_equals @pytest.mark.parametrize("batch_size", [1, 64]) @@ -32,7 +33,7 @@ def test_qdrant_similarity_search( vector_name=vector_name, ) output = docsearch.similarity_search("foo", k=1) - assert output == [Document(page_content="foo")] + assert_documents_equals(actual=output, expected=[Document(page_content="foo")]) @pytest.mark.parametrize("batch_size", [1, 64]) @@ -58,7 +59,7 @@ def test_qdrant_similarity_search_by_vector( ) embeddings = ConsistentFakeEmbeddings().embed_query("foo") output = docsearch.similarity_search_by_vector(embeddings, k=1) - assert output == [Document(page_content="foo")] + assert_documents_equals(output, [Document(page_content="foo")]) @pytest.mark.parametrize("batch_size", [1, 64]) @@ -86,7 +87,7 @@ def test_qdrant_similarity_search_with_score_by_vector( output = docsearch.similarity_search_with_score_by_vector(embeddings, k=1) assert len(output) == 1 document, score = output[0] - assert document == Document(page_content="foo") + assert_documents_equals(actual=[document], expected=[Document(page_content="foo")]) assert score >= 0 @@ -113,12 +114,16 @@ def test_qdrant_similarity_search_filters( output = docsearch.similarity_search( "foo", k=1, filter={"page": 1, "metadata": {"page": 2, "pages": [3]}} ) - assert output == [ - Document( - page_content="bar", - metadata={"page": 1, "metadata": {"page": 2, "pages": [3, -1]}}, - ) - ] + + assert_documents_equals( + actual=output, + expected=[ + Document( + page_content="bar", + metadata={"page": 1, "metadata": {"page": 2, "pages": [3, -1]}}, + ) + ], + ) @pytest.mark.parametrize("vector_name", [None, "my-vector"]) @@ -240,12 +245,15 @@ def test_qdrant_similarity_search_filters_with_qdrant_filters( ] ) output = docsearch.similarity_search("foo", k=1, filter=qdrant_filter) - assert output == [ - Document( - page_content="bar", - metadata={"page": 1, "details": {"page": 2, "pages": [3, -1]}}, - ) - ] + assert_documents_equals( + actual=output, + expected=[ + Document( + page_content="bar", + metadata={"page": 1, "details": {"page": 2, "pages": [3, -1]}}, + ) + ], + ) @pytest.mark.parametrize("batch_size", [1, 64]) diff --git a/libs/community/tests/integration_tests/vectorstores/test_analyticdb.py b/libs/community/tests/integration_tests/vectorstores/test_analyticdb.py index 41b40c3b79c..e58efcf07aa 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_analyticdb.py +++ b/libs/community/tests/integration_tests/vectorstores/test_analyticdb.py @@ -1,4 +1,5 @@ """Test PGVector functionality.""" + import os from typing import List @@ -125,7 +126,7 @@ def test_analyticdb_with_filter_distant_match() -> None: pre_delete_collection=True, ) output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "2"}) - print(output) + print(output) # noqa: T201 assert output == [(Document(page_content="baz", metadata={"page": "2"}), 4.0)] @@ -160,7 +161,7 @@ def test_analyticdb_delete() -> None: pre_delete_collection=True, ) output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "2"}) - print(output) + print(output) # noqa: T201 assert output == [(Document(page_content="baz", metadata={"page": "2"}), 4.0)] docsearch.delete(ids=ids) output = docsearch.similarity_search_with_score("foo", k=1, filter={"page": "2"}) diff --git a/libs/community/tests/integration_tests/vectorstores/test_astradb.py b/libs/community/tests/integration_tests/vectorstores/test_astradb.py index f652342e5c1..7bc4cbe03f3 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_astradb.py +++ b/libs/community/tests/integration_tests/vectorstores/test_astradb.py @@ -74,7 +74,7 @@ class ParserEmbeddings(Embeddings): assert len(vals) == self.dimension return vals except Exception: - print(f'[ParserEmbeddings] Returning a moot vector for "{text}"') + print(f'[ParserEmbeddings] Returning a moot vector for "{text}"') # noqa: T201 return [0.0] * self.dimension async def aembed_query(self, text: str) -> List[float]: @@ -346,7 +346,7 @@ class TestAstraDB: res3 = store_someemb.similarity_search_with_score_id( query="cc", k=1, filter={"k": "c_new"} ) - print(str(res3)) + print(str(res3)) # noqa: T201 doc3, score3, id3 = res3[0] assert doc3.page_content == "cc" assert doc3.metadata == {"k": "c_new", "ord": 102} @@ -404,7 +404,7 @@ class TestAstraDB: res3 = await store_someemb.asimilarity_search_with_score_id( query="cc", k=1, filter={"k": "c_new"} ) - print(str(res3)) + print(str(res3)) # noqa: T201 doc3, score3, id3 = res3[0] assert doc3.page_content == "cc" assert doc3.metadata == {"k": "c_new", "ord": 102} diff --git a/libs/community/tests/integration_tests/vectorstores/test_bigquery_vector_search.py b/libs/community/tests/integration_tests/vectorstores/test_bigquery_vector_search.py index 57da87d6696..b88df92498e 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_bigquery_vector_search.py +++ b/libs/community/tests/integration_tests/vectorstores/test_bigquery_vector_search.py @@ -32,7 +32,7 @@ def store(request: pytest.FixtureRequest) -> BigQueryVectorSearch: TestBigQueryVectorStore.dataset_name, exists_ok=True ) TestBigQueryVectorStore.store = BigQueryVectorSearch( - project_id=os.environ.get("PROJECT", None), + project_id=os.environ.get("PROJECT", None), # type: ignore[arg-type] embedding=FakeEmbeddings(), dataset_name=TestBigQueryVectorStore.dataset_name, table_name=TEST_TABLE_NAME, @@ -79,7 +79,7 @@ class TestBigQueryVectorStore: def test_semantic_search(self, store: BigQueryVectorSearch) -> None: """Test on semantic similarity.""" docs = store.similarity_search("food", k=4) - print(docs) + print(docs) # noqa: T201 kinds = [d.metadata["kind"] for d in docs] assert "fruit" in kinds assert "treat" in kinds diff --git a/libs/community/tests/integration_tests/vectorstores/test_deeplake.py b/libs/community/tests/integration_tests/vectorstores/test_deeplake.py index 99d4d6def2e..7f86795d972 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_deeplake.py +++ b/libs/community/tests/integration_tests/vectorstores/test_deeplake.py @@ -52,7 +52,7 @@ def test_deeplake_with_metadatas() -> None: assert output == [Document(page_content="foo", metadata={"page": "0"})] -def test_deeplake_with_persistence(deeplake_datastore) -> None: +def test_deeplake_with_persistence(deeplake_datastore) -> None: # type: ignore[no-untyped-def] """Test end to end construction and search, with persistence.""" output = deeplake_datastore.similarity_search("foo", k=1) assert output == [Document(page_content="foo", metadata={"page": "0"})] @@ -72,7 +72,7 @@ def test_deeplake_with_persistence(deeplake_datastore) -> None: # Or on program exit -def test_deeplake_overwrite_flag(deeplake_datastore) -> None: +def test_deeplake_overwrite_flag(deeplake_datastore) -> None: # type: ignore[no-untyped-def] """Test overwrite behavior""" dataset_path = deeplake_datastore.vectorstore.dataset_handler.path @@ -108,7 +108,7 @@ def test_deeplake_overwrite_flag(deeplake_datastore) -> None: output = docsearch.similarity_search("foo", k=1) -def test_similarity_search(deeplake_datastore) -> None: +def test_similarity_search(deeplake_datastore) -> None: # type: ignore[no-untyped-def] """Test similarity search.""" distance_metric = "cos" output = deeplake_datastore.similarity_search( diff --git a/libs/community/tests/integration_tests/vectorstores/test_elasticsearch.py b/libs/community/tests/integration_tests/vectorstores/test_elasticsearch.py index ff13a96dc8a..8069952ed6d 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_elasticsearch.py +++ b/libs/community/tests/integration_tests/vectorstores/test_elasticsearch.py @@ -1,4 +1,5 @@ """Test ElasticSearch functionality.""" + import logging import os import re @@ -88,9 +89,9 @@ class TestElasticsearch: for pipeline_id, _ in response.items(): try: es.ingest.delete_pipeline(id=pipeline_id) - print(f"Deleted pipeline: {pipeline_id}") + print(f"Deleted pipeline: {pipeline_id}") # noqa: T201 except Exception as e: - print(f"Pipeline error: {e}") + print(f"Pipeline error: {e}") # noqa: T201 except Exception: pass diff --git a/libs/community/tests/integration_tests/vectorstores/test_hanavector.py b/libs/community/tests/integration_tests/vectorstores/test_hanavector.py index dfcdb8c7040..fb9ab6ec194 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_hanavector.py +++ b/libs/community/tests/integration_tests/vectorstores/test_hanavector.py @@ -1,4 +1,5 @@ """Test HANA vectorstore functionality.""" + import os import random from typing import List @@ -38,7 +39,7 @@ embedding = NormalizedFakeEmbeddings() class ConfigData: - def __init__(self): + def __init__(self): # type: ignore[no-untyped-def] self.conn = None self.schema_name = "" @@ -46,7 +47,7 @@ class ConfigData: test_setup = ConfigData() -def generateSchemaName(cursor): +def generateSchemaName(cursor): # type: ignore[no-untyped-def] cursor.execute( "SELECT REPLACE(CURRENT_UTCDATE, '-', '') || '_' || BINTOHEX(SYSUUID) FROM " "DUMMY;" @@ -59,7 +60,7 @@ def generateSchemaName(cursor): return f"VEC_{uid}" -def setup_module(module): +def setup_module(module): # type: ignore[no-untyped-def] test_setup.conn = dbapi.connect( address=os.environ.get("HANA_DB_ADDRESS"), port=os.environ.get("HANA_DB_PORT"), @@ -81,7 +82,7 @@ def setup_module(module): cur.close() -def teardown_module(module): +def teardown_module(module): # type: ignore[no-untyped-def] try: cur = test_setup.conn.cursor() sql_str = f"DROP SCHEMA {test_setup.schema_name} CASCADE" @@ -100,13 +101,13 @@ def texts() -> List[str]: @pytest.fixture def metadatas() -> List[str]: return [ - {"start": 0, "end": 100, "quality": "good", "ready": True}, - {"start": 100, "end": 200, "quality": "bad", "ready": False}, - {"start": 200, "end": 300, "quality": "ugly", "ready": True}, + {"start": 0, "end": 100, "quality": "good", "ready": True}, # type: ignore[list-item] + {"start": 100, "end": 200, "quality": "bad", "ready": False}, # type: ignore[list-item] + {"start": 200, "end": 300, "quality": "ugly", "ready": True}, # type: ignore[list-item] ] -def drop_table(connection, table_name): +def drop_table(connection, table_name): # type: ignore[no-untyped-def] try: cur = connection.cursor() sql_str = f"DROP TABLE {table_name}" @@ -230,7 +231,7 @@ def test_hanavector_table_with_wrong_typed_columns() -> None: ) exception_occured = False except AttributeError as err: - print(err) + print(err) # noqa: T201 exception_occured = True assert exception_occured @@ -825,7 +826,7 @@ def test_hanavector_filter_prepared_statement_params( rows = cur.fetchall() assert len(rows) == 1 - query_value = "good" + query_value = "good" # type: ignore[assignment] sql_str = f"SELECT * FROM {table_name} WHERE JSON_VALUE(VEC_META, '$.quality') = ?" cur.execute(sql_str, (query_value)) rows = cur.fetchall() @@ -839,14 +840,14 @@ def test_hanavector_filter_prepared_statement_params( assert len(rows) == 1 # query_value = True - query_value = "true" + query_value = "true" # type: ignore[assignment] sql_str = f"SELECT * FROM {table_name} WHERE JSON_VALUE(VEC_META, '$.ready') = ?" cur.execute(sql_str, (query_value)) rows = cur.fetchall() assert len(rows) == 2 # query_value = False - query_value = "false" + query_value = "false" # type: ignore[assignment] sql_str = f"SELECT * FROM {table_name} WHERE JSON_VALUE(VEC_META, '$.ready') = ?" cur.execute(sql_str, (query_value)) rows = cur.fetchall() diff --git a/libs/community/tests/integration_tests/vectorstores/test_hippo.py b/libs/community/tests/integration_tests/vectorstores/test_hippo.py index 2d8d94c20dd..78d62b8e7b0 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_hippo.py +++ b/libs/community/tests/integration_tests/vectorstores/test_hippo.py @@ -1,4 +1,5 @@ """Test Hippo functionality.""" + from typing import List, Optional from langchain_core.documents import Document @@ -32,7 +33,7 @@ def test_hippo_add_extra() -> None: docsearch.add_texts(texts, metadatas) output = docsearch.similarity_search("foo", k=1) - print(output) + print(output) # noqa: T201 assert len(output) == 1 diff --git a/libs/community/tests/integration_tests/vectorstores/test_lantern.py b/libs/community/tests/integration_tests/vectorstores/test_lantern.py index 8de7d803644..bde3c5b6965 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_lantern.py +++ b/libs/community/tests/integration_tests/vectorstores/test_lantern.py @@ -31,7 +31,7 @@ def fix_distance_precision( class FakeEmbeddingsWithAdaDimension(FakeEmbeddings): """Fake embeddings functionality for testing.""" - def __init__(self): + def __init__(self): # type: ignore[no-untyped-def] super(FakeEmbeddingsWithAdaDimension, self).__init__(size=ADA_TOKEN_COUNT) def embed_documents(self, texts: List[str]) -> List[List[float]]: diff --git a/libs/community/tests/integration_tests/vectorstores/test_llm_rails.py b/libs/community/tests/integration_tests/vectorstores/test_llm_rails.py index 71c1c73d648..3dbe8d118b7 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_llm_rails.py +++ b/libs/community/tests/integration_tests/vectorstores/test_llm_rails.py @@ -23,7 +23,7 @@ def test_llm_rails_add_documents() -> None: # test without filter output1 = docsearch.similarity_search("large language model", k=1) - print(output1) + print(output1) # noqa: T201 assert len(output1) == 1 assert output1[0].page_content == "large language model" diff --git a/libs/community/tests/integration_tests/vectorstores/test_milvus.py b/libs/community/tests/integration_tests/vectorstores/test_milvus.py index 807edcdb6e4..b214349f972 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_milvus.py +++ b/libs/community/tests/integration_tests/vectorstores/test_milvus.py @@ -1,5 +1,5 @@ """Test Milvus functionality.""" -from typing import List, Optional +from typing import Any, List, Optional from langchain_core.documents import Document @@ -25,6 +25,10 @@ def _milvus_from_texts( ) +def _get_pks(expr: str, docsearch: Milvus) -> List[Any]: + return docsearch.get_pks(expr) + + def test_milvus() -> None: """Test end to end construction and search.""" docsearch = _milvus_from_texts() @@ -109,6 +113,42 @@ def test_milvus_no_drop() -> None: assert len(output) == 6 +def test_milvus_get_pks() -> None: + """Test end to end construction and get pks with expr""" + texts = ["foo", "bar", "baz"] + metadatas = [{"id": i} for i in range(len(texts))] + docsearch = _milvus_from_texts(metadatas=metadatas) + expr = "id in [1,2]" + output = _get_pks(expr, docsearch) + assert len(output) == 2 + + +def test_milvus_delete_entities() -> None: + """Test end to end construction and delete entities""" + texts = ["foo", "bar", "baz"] + metadatas = [{"id": i} for i in range(len(texts))] + docsearch = _milvus_from_texts(metadatas=metadatas) + expr = "id in [1,2]" + pks = _get_pks(expr, docsearch) + result = docsearch.delete(pks) + assert result is True + + +def test_milvus_upsert_entities() -> None: + """Test end to end construction and upsert entities""" + texts = ["foo", "bar", "baz"] + metadatas = [{"id": i} for i in range(len(texts))] + docsearch = _milvus_from_texts(metadatas=metadatas) + expr = "id in [1,2]" + pks = _get_pks(expr, docsearch) + documents = [ + Document(page_content="test_1", metadata={"id": 1}), + Document(page_content="test_2", metadata={"id": 3}), + ] + ids = docsearch.upsert(pks, documents) + assert len(ids) == 2 + + # if __name__ == "__main__": # test_milvus() # test_milvus_with_metadata() diff --git a/libs/community/tests/integration_tests/vectorstores/test_rocksetdb.py b/libs/community/tests/integration_tests/vectorstores/test_rocksetdb.py index 25f56d52246..56950ce8c46 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_rocksetdb.py +++ b/libs/community/tests/integration_tests/vectorstores/test_rocksetdb.py @@ -1,5 +1,6 @@ import logging import os +import uuid from langchain_core.documents import Document @@ -31,10 +32,10 @@ logger = logging.getLogger(__name__) # # See https://rockset.com/blog/introducing-vector-search-on-rockset/ for more details. -workspace = "langchain_tests" -collection_name = "langchain_demo" -text_key = "description" -embedding_key = "description_embedding" +WORKSPACE = "morgana" +COLLECTION_NAME = "langchain_demo" +TEXT_KEY = "description" +EMBEDDING_KEY = "description_embedding" class TestRockset: @@ -59,7 +60,7 @@ class TestRockset: elif region == "dev": host = rockset.DevRegions.usw2a1 else: - logger.warn( + logger.warning( "Using ROCKSET_REGION:%s as it is.. \ You should know what you're doing...", region, @@ -71,9 +72,9 @@ class TestRockset: if os.environ.get("ROCKSET_DELETE_DOCS_ON_START") == "1": logger.info( "Deleting all existing documents from the Rockset collection %s", - collection_name, + COLLECTION_NAME, ) - query = f"select _id from {workspace}.{collection_name}" + query = f"select _id from {WORKSPACE}.{COLLECTION_NAME}" query_response = client.Queries.query(sql={"query": query}) ids = [ @@ -84,15 +85,15 @@ class TestRockset: ] logger.info("Existing ids in collection: %s", ids) client.Documents.delete_documents( - collection=collection_name, + collection=COLLECTION_NAME, data=[rockset.models.DeleteDocumentsRequestData(id=i) for i in ids], - workspace=workspace, + workspace=WORKSPACE, ) embeddings = ConsistentFakeEmbeddings() embeddings.embed_documents(fake_texts) cls.rockset_vectorstore = Rockset( - client, embeddings, collection_name, text_key, embedding_key, workspace + client, embeddings, COLLECTION_NAME, TEXT_KEY, EMBEDDING_KEY, WORKSPACE ) def test_rockset_insert_and_search(self) -> None: @@ -120,6 +121,36 @@ class TestRockset: ) assert output == [Document(page_content="bar", metadata={"metadata_index": 1})] + def test_add_documents_and_delete(self) -> None: + """ "add_documents" and "delete" are requirements to support use + with RecordManager""" + + texts = ["foo", "bar", "baz"] + metadatas = [{"metadata_index": i} for i in range(len(texts))] + + _docs = zip(texts, metadatas) + docs = [Document(page_content=pc, metadata=i) for pc, i in _docs] + + ids = self.rockset_vectorstore.add_documents(docs) + assert len(ids) == len(texts) + + deleted = self.rockset_vectorstore.delete(ids) + assert deleted + + def test_add_texts_does_not_modify_metadata(self) -> None: + """If metadata changes it will inhibit the langchain RecordManager + functionality""" + + texts = ["kitty", "doggy"] + metadatas = [{"source": "kitty.txt"}, {"source": "doggy.txt"}] + ids = [str(uuid.uuid4()), str(uuid.uuid4())] + + self.rockset_vectorstore.add_texts(texts=texts, metadatas=metadatas, ids=ids) + + for metadata in metadatas: + assert len(metadata) == 1 + assert list(metadata.keys())[0] == "source" + def test_build_query_sql(self) -> None: vector = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0] q_str = self.rockset_vectorstore._build_query_sql( @@ -129,9 +160,9 @@ class TestRockset: ) vector_str = ",".join(map(str, vector)) expected = f"""\ -SELECT * EXCEPT({embedding_key}), \ -COSINE_SIM({embedding_key}, [{vector_str}]) as dist -FROM {workspace}.{collection_name} +SELECT * EXCEPT({EMBEDDING_KEY}), \ +COSINE_SIM({EMBEDDING_KEY}, [{vector_str}]) as dist +FROM {WORKSPACE}.{COLLECTION_NAME} ORDER BY dist DESC LIMIT 4 """ @@ -147,9 +178,9 @@ LIMIT 4 ) vector_str = ",".join(map(str, vector)) expected = f"""\ -SELECT * EXCEPT({embedding_key}), \ -COSINE_SIM({embedding_key}, [{vector_str}]) as dist -FROM {workspace}.{collection_name} +SELECT * EXCEPT({EMBEDDING_KEY}), \ +COSINE_SIM({EMBEDDING_KEY}, [{vector_str}]) as dist +FROM {WORKSPACE}.{COLLECTION_NAME} WHERE age >= 10 ORDER BY dist DESC LIMIT 4 diff --git a/libs/community/tests/integration_tests/vectorstores/test_singlestoredb.py b/libs/community/tests/integration_tests/vectorstores/test_singlestoredb.py index 4bd23acbe77..4f690f079fd 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_singlestoredb.py +++ b/libs/community/tests/integration_tests/vectorstores/test_singlestoredb.py @@ -4,6 +4,7 @@ from typing import List import numpy as np import pytest from langchain_core.documents import Document +from langchain_core.embeddings import Embeddings from langchain_community.vectorstores.singlestoredb import SingleStoreDB from langchain_community.vectorstores.utils import DistanceStrategy @@ -43,6 +44,16 @@ class NormilizedFakeEmbeddings(FakeEmbeddings): return self.normalize(super().embed_query(text)) +class RandomEmbeddings(Embeddings): + """Fake embeddings with random vectors. For testing purposes.""" + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + return [np.random.rand(100).tolist() for _ in texts] + + def embed_query(self, text: str) -> List[float]: + return np.random.rand(100).tolist() + + @pytest.fixture def texts() -> List[str]: return ["foo", "bar", "baz"] @@ -99,6 +110,66 @@ def test_singlestoredb_euclidean_distance(texts: List[str]) -> None: drop(table_name) +@pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed") +def test_singlestoredb_vector_index_1(texts: List[str]) -> None: + """Test adding a new document""" + table_name = "test_singlestoredb_vector_index_1" + drop(table_name) + docsearch = SingleStoreDB.from_texts( + texts, + FakeEmbeddings(), + distance_strategy=DistanceStrategy.EUCLIDEAN_DISTANCE, + table_name=table_name, + use_vector_index=True, + vector_size=10, + host=TEST_SINGLESTOREDB_URL, + ) + docsearch.add_texts(["foo"]) + output = docsearch.similarity_search("foo", k=2) + assert output == TEST_RESULT + drop(table_name) + + +@pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed") +def test_singlestoredb_vector_index_2(texts: List[str]) -> None: + """Test adding a new document""" + table_name = "test_singlestoredb_vector_index_2" + drop(table_name) + docsearch = SingleStoreDB.from_texts( + texts, + FakeEmbeddings(), + table_name=table_name, + use_vector_index=True, + vector_index_options={"index_type": "IVF_PQ", "nlist": 256}, + vector_size=10, + host=TEST_SINGLESTOREDB_URL, + ) + docsearch.add_texts(["foo"]) + output = docsearch.similarity_search("foo", k=1) + output[0].page_content == "foo" + drop(table_name) + + +@pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed") +def test_singlestoredb_vector_index_large() -> None: + """Test adding a new document""" + table_name = "test_singlestoredb_vector_index_large" + drop(table_name) + docsearch = SingleStoreDB.from_texts( + ["foo"] * 300000, + RandomEmbeddings(), + distance_strategy=DistanceStrategy.EUCLIDEAN_DISTANCE, + table_name=table_name, + use_vector_index=True, + vector_size=100, + vector_index_name="vector_index_large", + host=TEST_SINGLESTOREDB_URL, + ) + output = docsearch.similarity_search("foo", k=1) + assert output[0].page_content == "foo" + drop(table_name) + + @pytest.mark.skipif(not singlestoredb_installed, reason="singlestoredb not installed") def test_singlestoredb_from_existing(texts: List[str]) -> None: """Test adding a new document""" diff --git a/libs/community/tests/integration_tests/vectorstores/test_thirdai_neuraldb.py b/libs/community/tests/integration_tests/vectorstores/test_thirdai_neuraldb.py index bd5eafca00d..370e8ff54fa 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_thirdai_neuraldb.py +++ b/libs/community/tests/integration_tests/vectorstores/test_thirdai_neuraldb.py @@ -7,7 +7,7 @@ from langchain_community.vectorstores import NeuralDBVectorStore @pytest.fixture(scope="session") -def test_csv(): +def test_csv(): # type: ignore[no-untyped-def] csv = "thirdai-test.csv" with open(csv, "w") as o: o.write("column_1,column_2\n") @@ -16,13 +16,13 @@ def test_csv(): os.remove(csv) -def assert_result_correctness(documents): +def assert_result_correctness(documents): # type: ignore[no-untyped-def] assert len(documents) == 1 assert documents[0].page_content == "column_1: column one\n\ncolumn_2: column two" @pytest.mark.requires("thirdai[neural_db]") -def test_neuraldb_retriever_from_scratch(test_csv): +def test_neuraldb_retriever_from_scratch(test_csv): # type: ignore[no-untyped-def] retriever = NeuralDBVectorStore.from_scratch() retriever.insert([test_csv]) documents = retriever.similarity_search("column") @@ -30,7 +30,7 @@ def test_neuraldb_retriever_from_scratch(test_csv): @pytest.mark.requires("thirdai[neural_db]") -def test_neuraldb_retriever_from_checkpoint(test_csv): +def test_neuraldb_retriever_from_checkpoint(test_csv): # type: ignore[no-untyped-def] checkpoint = "thirdai-test-save.ndb" if os.path.exists(checkpoint): shutil.rmtree(checkpoint) @@ -47,7 +47,7 @@ def test_neuraldb_retriever_from_checkpoint(test_csv): @pytest.mark.requires("thirdai[neural_db]") -def test_neuraldb_retriever_from_bazaar(test_csv): +def test_neuraldb_retriever_from_bazaar(test_csv): # type: ignore[no-untyped-def] retriever = NeuralDBVectorStore.from_bazaar("General QnA") retriever.insert([test_csv]) documents = retriever.similarity_search("column") @@ -55,7 +55,7 @@ def test_neuraldb_retriever_from_bazaar(test_csv): @pytest.mark.requires("thirdai[neural_db]") -def test_neuraldb_retriever_other_methods(test_csv): +def test_neuraldb_retriever_other_methods(test_csv): # type: ignore[no-untyped-def] retriever = NeuralDBVectorStore.from_scratch() retriever.insert([test_csv]) # Make sure they don't throw an error. diff --git a/libs/community/tests/integration_tests/vectorstores/test_vectara.py b/libs/community/tests/integration_tests/vectorstores/test_vectara.py index 1aaf4856571..6e2f4f2900a 100644 --- a/libs/community/tests/integration_tests/vectorstores/test_vectara.py +++ b/libs/community/tests/integration_tests/vectorstores/test_vectara.py @@ -25,7 +25,7 @@ def get_abbr(s: str) -> str: @pytest.fixture(scope="function") -def vectara1(): +def vectara1(): # type: ignore[no-untyped-def] # Set up code # create a new Vectara instance vectara1: Vectara = Vectara() @@ -54,7 +54,7 @@ def vectara1(): vectara1._delete_doc(doc_id) -def test_vectara_add_documents(vectara1) -> None: +def test_vectara_add_documents(vectara1) -> None: # type: ignore[no-untyped-def] """Test add_documents.""" # test without filter @@ -164,7 +164,7 @@ models can greatly improve the training of DNNs and other deep discriminative mo @pytest.fixture(scope="function") -def vectara3(): +def vectara3(): # type: ignore[no-untyped-def] # Set up code vectara3: Vectara = Vectara() @@ -210,7 +210,7 @@ def vectara3(): vectara3._delete_doc(doc_id) -def test_vectara_mmr(vectara3) -> None: +def test_vectara_mmr(vectara3) -> None: # type: ignore[no-untyped-def] # test max marginal relevance output1 = vectara3.max_marginal_relevance_search( "generative AI", @@ -241,7 +241,7 @@ def test_vectara_mmr(vectara3) -> None: ) -def test_vectara_with_summary(vectara3) -> None: +def test_vectara_with_summary(vectara3) -> None: # type: ignore[no-untyped-def] """Test vectara summary.""" # test summarization num_results = 10 diff --git a/libs/community/tests/unit_tests/chat_models/test_azureml_endpoint.py b/libs/community/tests/unit_tests/chat_models/test_azureml_endpoint.py index 0cbe2ab9b17..d5dbb716d26 100644 --- a/libs/community/tests/unit_tests/chat_models/test_azureml_endpoint.py +++ b/libs/community/tests/unit_tests/chat_models/test_azureml_endpoint.py @@ -49,7 +49,7 @@ class TestAzureMLChatOnlineEndpoint: ) -> None: """Test that the API key is masked""" azure_chat = request.getfixturevalue(fixture_name) - print(azure_chat.endpoint_api_key, end="") + print(azure_chat.endpoint_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert ( (str(azure_chat.endpoint_api_key) == "**********") diff --git a/libs/community/tests/unit_tests/chat_models/test_baichuan.py b/libs/community/tests/unit_tests/chat_models/test_baichuan.py index f5664c88ffe..f027cbb9c5d 100644 --- a/libs/community/tests/unit_tests/chat_models/test_baichuan.py +++ b/libs/community/tests/unit_tests/chat_models/test_baichuan.py @@ -91,7 +91,7 @@ def test_baichuan_key_masked_when_passed_from_env( monkeypatch.setenv("BAICHUAN_API_KEY", "test-api-key") chat = ChatBaichuan() - print(chat.baichuan_api_key, end="") + print(chat.baichuan_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" @@ -101,7 +101,7 @@ def test_baichuan_key_masked_when_passed_via_constructor( ) -> None: """Test initialization with an API key provided via the initializer""" chat = ChatBaichuan(baichuan_api_key="test-api-key") - print(chat.baichuan_api_key, end="") + print(chat.baichuan_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/unit_tests/chat_models/test_edenai.py b/libs/community/tests/unit_tests/chat_models/test_edenai.py index 2fa85512c01..dfafc5af988 100644 --- a/libs/community/tests/unit_tests/chat_models/test_edenai.py +++ b/libs/community/tests/unit_tests/chat_models/test_edenai.py @@ -35,6 +35,6 @@ def test_edenai_messages_formatting(messages: List[BaseMessage], expected: str) ("role", "role_response"), [("ai", "assistant"), ("human", "user"), ("chat", "user")], ) -def test_edenai_message_role(role: str, role_response) -> None: +def test_edenai_message_role(role: str, role_response) -> None: # type: ignore[no-untyped-def] role = _message_role(role) assert role == role_response diff --git a/libs/community/tests/unit_tests/chat_models/test_fireworks.py b/libs/community/tests/unit_tests/chat_models/test_fireworks.py index fbad2356a48..211721bc082 100644 --- a/libs/community/tests/unit_tests/chat_models/test_fireworks.py +++ b/libs/community/tests/unit_tests/chat_models/test_fireworks.py @@ -1,4 +1,5 @@ """Test Fireworks chat model""" + import sys import pytest @@ -22,7 +23,7 @@ def test_api_key_masked_when_passed_via_constructor( capsys: CaptureFixture, ) -> None: llm = ChatFireworks(fireworks_api_key="secret-api-key") - print(llm.fireworks_api_key, end="") + print(llm.fireworks_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/unit_tests/chat_models/test_imports.py b/libs/community/tests/unit_tests/chat_models/test_imports.py index 9ce9225e5a0..58a2901f931 100644 --- a/libs/community/tests/unit_tests/chat_models/test_imports.py +++ b/libs/community/tests/unit_tests/chat_models/test_imports.py @@ -38,6 +38,7 @@ EXPECTED_ALL = [ "VolcEngineMaasChat", "LlamaEdgeChatService", "GPTRouter", + "ChatYuan2", "ChatZhipuAI", ] diff --git a/libs/community/tests/unit_tests/chat_models/test_openai.py b/libs/community/tests/unit_tests/chat_models/test_openai.py index 22fde4f335d..ad7033e4ea8 100644 --- a/libs/community/tests/unit_tests/chat_models/test_openai.py +++ b/libs/community/tests/unit_tests/chat_models/test_openai.py @@ -17,9 +17,9 @@ from langchain_community.chat_models.openai import ChatOpenAI @pytest.mark.requires("openai") def test_openai_model_param() -> None: - llm = ChatOpenAI(model="foo") + llm = ChatOpenAI(model="foo", openai_api_key="foo") assert llm.model_name == "foo" - llm = ChatOpenAI(model_name="foo") + llm = ChatOpenAI(model_name="foo", openai_api_key="foo") assert llm.model_name == "foo" @@ -81,7 +81,7 @@ def mock_completion() -> dict: @pytest.mark.requires("openai") def test_openai_predict(mock_completion: dict) -> None: - llm = ChatOpenAI() + llm = ChatOpenAI(openai_api_key="foo") mock_client = MagicMock() completed = False @@ -103,11 +103,11 @@ def test_openai_predict(mock_completion: dict) -> None: @pytest.mark.requires("openai") async def test_openai_apredict(mock_completion: dict) -> None: - llm = ChatOpenAI() + llm = ChatOpenAI(openai_api_key="foo") mock_client = MagicMock() completed = False - def mock_create(*args: Any, **kwargs: Any) -> Any: + async def mock_create(*args: Any, **kwargs: Any) -> Any: nonlocal completed completed = True return mock_completion @@ -115,9 +115,9 @@ async def test_openai_apredict(mock_completion: dict) -> None: mock_client.create = mock_create with patch.object( llm, - "client", + "async_client", mock_client, ): - res = llm.predict("bar") + res = await llm.apredict("bar") assert res == "Bar Baz" assert completed diff --git a/libs/community/tests/unit_tests/chat_models/test_yuan2.py b/libs/community/tests/unit_tests/chat_models/test_yuan2.py new file mode 100644 index 00000000000..c5253375d31 --- /dev/null +++ b/libs/community/tests/unit_tests/chat_models/test_yuan2.py @@ -0,0 +1,64 @@ +"""Test ChatYuan2 wrapper.""" + +import pytest +from langchain_core.messages import ( + AIMessage, + HumanMessage, + SystemMessage, +) + +from langchain_community.chat_models.yuan2 import ( + ChatYuan2, + _convert_dict_to_message, + _convert_message_to_dict, +) + + +@pytest.mark.requires("openai") +def test_yuan2_model_param() -> None: + chat = ChatYuan2(model="foo") + assert chat.model_name == "foo" + chat = ChatYuan2(model_name="foo") + assert chat.model_name == "foo" + + +def test__convert_message_to_dict_human() -> None: + message = HumanMessage(content="foo") + result = _convert_message_to_dict(message) + expected_output = {"role": "user", "content": "foo"} + assert result == expected_output + + +def test__convert_message_to_dict_ai() -> None: + message = AIMessage(content="foo") + result = _convert_message_to_dict(message) + expected_output = {"role": "assistant", "content": "foo"} + assert result == expected_output + + +def test__convert_message_to_dict_system() -> None: + message = SystemMessage(content="foo") + result = _convert_message_to_dict(message) + expected_output = {"role": "system", "content": "foo"} + assert result == expected_output + + +def test__convert_dict_to_message_human() -> None: + message = {"role": "user", "content": "hello"} + result = _convert_dict_to_message(message) + expected_output = HumanMessage(content="hello") + assert result == expected_output + + +def test__convert_dict_to_message_ai() -> None: + message = {"role": "assistant", "content": "hello"} + result = _convert_dict_to_message(message) + expected_output = AIMessage(content="hello") + assert result == expected_output + + +def test__convert_dict_to_message_system() -> None: + message = {"role": "system", "content": "hello"} + result = _convert_dict_to_message(message) + expected_output = SystemMessage(content="hello") + assert result == expected_output diff --git a/libs/community/tests/unit_tests/document_loaders/parsers/language/test_c.py b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_c.py new file mode 100644 index 00000000000..aa6cb586889 --- /dev/null +++ b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_c.py @@ -0,0 +1,53 @@ +import unittest + +import pytest + +from langchain_community.document_loaders.parsers.language.c import CSegmenter + + +@pytest.mark.requires("tree_sitter", "tree_sitter_languages") +class TestCSegmenter(unittest.TestCase): + def setUp(self) -> None: + self.example_code = """int main() { + return 0; +} + +struct S { +}; + +union U { +}; + +enum Evens { + Two = 2, + Four = 4 +};""" + + self.expected_simplified_code = """// Code for: int main() { + +// Code for: struct S { + +// Code for: union U { + +// Code for: enum Evens {""" + + self.expected_extracted_code = [ + "int main() {\n return 0;\n}", + "struct S {\n}", + "union U {\n}", + "enum Evens {\n Two = 2,\n Four = 4\n}", + ] + + def test_is_valid(self) -> None: + self.assertTrue(CSegmenter("int a;").is_valid()) + self.assertFalse(CSegmenter("a b c 1 2 3").is_valid()) + + def test_extract_functions_classes(self) -> None: + segmenter = CSegmenter(self.example_code) + extracted_code = segmenter.extract_functions_classes() + self.assertEqual(extracted_code, self.expected_extracted_code) + + def test_simplify_code(self) -> None: + segmenter = CSegmenter(self.example_code) + simplified_code = segmenter.simplify_code() + self.assertEqual(simplified_code, self.expected_simplified_code) diff --git a/libs/community/tests/unit_tests/document_loaders/parsers/language/test_cpp.py b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_cpp.py new file mode 100644 index 00000000000..a06b7fa7aff --- /dev/null +++ b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_cpp.py @@ -0,0 +1,63 @@ +import unittest + +import pytest + +from langchain_community.document_loaders.parsers.language.cpp import CPPSegmenter + + +@pytest.mark.requires("tree_sitter", "tree_sitter_languages") +class TestCPPSegmenter(unittest.TestCase): + def setUp(self) -> None: + self.example_code = """int foo() { + return 1; +} + +class T { + auto bar() const -> int; + template + void baz(U) { + } +}; + +struct S { +}; + +union U { +}; + +auto T::bar() const -> int { + return 1; +}""" + + self.expected_simplified_code = """// Code for: int foo() { + +// Code for: class T { + +// Code for: struct S { + +// Code for: union U { + +// Code for: auto T::bar() const -> int {""" + + self.expected_extracted_code = [ + "int foo() {\n return 1;\n}", + "class T {\n auto bar() const -> int;\n " + "template\n void baz(U) {\n }\n}", + "struct S {\n}", + "union U {\n}", + "auto T::bar() const -> int {\n return 1;\n}", + ] + + def test_is_valid(self) -> None: + self.assertTrue(CPPSegmenter("int a;").is_valid()) + self.assertFalse(CPPSegmenter("a b c 1 2 3").is_valid()) + + def test_extract_functions_classes(self) -> None: + segmenter = CPPSegmenter(self.example_code) + extracted_code = segmenter.extract_functions_classes() + self.assertEqual(extracted_code, self.expected_extracted_code) + + def test_simplify_code(self) -> None: + segmenter = CPPSegmenter(self.example_code) + simplified_code = segmenter.simplify_code() + self.assertEqual(simplified_code, self.expected_simplified_code) diff --git a/libs/community/tests/unit_tests/document_loaders/parsers/language/test_csharp.py b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_csharp.py new file mode 100644 index 00000000000..3f04713f4ce --- /dev/null +++ b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_csharp.py @@ -0,0 +1,78 @@ +import unittest + +import pytest + +from langchain_community.document_loaders.parsers.language.csharp import CSharpSegmenter + + +@pytest.mark.requires("tree_sitter", "tree_sitter_languages") +class TestCSharpSegmenter(unittest.TestCase): + def setUp(self) -> None: + self.example_code = """namespace World +{ +} + +class Hello +{ + static void Main(string []args) + { + System.Console.WriteLine("Hello, world."); + } +} + +interface Human +{ + void breathe(); +} + +enum Tens +{ + Ten = 10, + Twenty = 20 +} + +struct T +{ +} + +record Person(string FirstName, string LastName, string Id) +{ + internal string Id { get; init; } = Id; +}""" + + self.expected_simplified_code = """// Code for: namespace World + +// Code for: class Hello + +// Code for: interface Human + +// Code for: enum Tens + +// Code for: struct T + +// Code for: record Person(string FirstName, string LastName, string Id)""" + + self.expected_extracted_code = [ + "namespace World\n{\n}", + "class Hello\n{\n static void Main(string []args)\n {\n " + 'System.Console.WriteLine("Hello, world.");\n }\n}', + "interface Human\n{\n void breathe();\n}", + "enum Tens\n{\n Ten = 10,\n Twenty = 20\n}", + "struct T\n{\n}", + "record Person(string FirstName, string LastName, string Id)\n{\n " + "internal string Id { get; init; } = Id;\n}", + ] + + def test_is_valid(self) -> None: + self.assertTrue(CSharpSegmenter("int a;").is_valid()) + self.assertFalse(CSharpSegmenter("a b c 1 2 3").is_valid()) + + def test_extract_functions_classes(self) -> None: + segmenter = CSharpSegmenter(self.example_code) + extracted_code = segmenter.extract_functions_classes() + self.assertEqual(extracted_code, self.expected_extracted_code) + + def test_simplify_code(self) -> None: + segmenter = CSharpSegmenter(self.example_code) + simplified_code = segmenter.simplify_code() + self.assertEqual(simplified_code, self.expected_simplified_code) diff --git a/libs/community/tests/unit_tests/document_loaders/parsers/language/test_go.py b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_go.py new file mode 100644 index 00000000000..e1360e35d97 --- /dev/null +++ b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_go.py @@ -0,0 +1,50 @@ +import unittest + +import pytest + +from langchain_community.document_loaders.parsers.language.go import GoSegmenter + + +@pytest.mark.requires("tree_sitter", "tree_sitter_languages") +class TestGoSegmenter(unittest.TestCase): + def setUp(self) -> None: + self.example_code = """func foo(a int) int { + return a; +} + +type T struct { + a int + b bool + c string +} + +type S interface { + bar() float64 +} +""" + + self.expected_simplified_code = """// Code for: func foo(a int) int { + +// Code for: type T struct { + +// Code for: type S interface {""" + + self.expected_extracted_code = [ + "func foo(a int) int {\n return a;\n}", + "type T struct {\n a int\n b bool\n c string\n}", + "type S interface {\n bar() float64\n}", + ] + + def test_is_valid(self) -> None: + self.assertTrue(GoSegmenter("var a int;").is_valid()) + self.assertFalse(GoSegmenter("a b c 1 2 3").is_valid()) + + def test_extract_functions_classes(self) -> None: + segmenter = GoSegmenter(self.example_code) + extracted_code = segmenter.extract_functions_classes() + self.assertEqual(extracted_code, self.expected_extracted_code) + + def test_simplify_code(self) -> None: + segmenter = GoSegmenter(self.example_code) + simplified_code = segmenter.simplify_code() + self.assertEqual(simplified_code, self.expected_simplified_code) diff --git a/libs/community/tests/unit_tests/document_loaders/parsers/language/test_java.py b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_java.py new file mode 100644 index 00000000000..1129ae1a889 --- /dev/null +++ b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_java.py @@ -0,0 +1,57 @@ +import unittest + +import pytest + +from langchain_community.document_loaders.parsers.language.java import JavaSegmenter + + +@pytest.mark.requires("tree_sitter", "tree_sitter_languages") +class TestJavaSegmenter(unittest.TestCase): + def setUp(self) -> None: + self.example_code = """class Hello +{ + public static void main(String[] args) + { + System.out.println("Hello, world."); + } +} + +interface Human +{ + void breathe(); +} + +enum Tens +{ + TEN, + TWENTY +} +""" + + self.expected_simplified_code = """// Code for: class Hello + +// Code for: interface Human + +// Code for: enum Tens""" + + self.expected_extracted_code = [ + "class Hello\n{\n " + "public static void main(String[] args)\n {\n " + 'System.out.println("Hello, world.");\n }\n}', + "interface Human\n{\n void breathe();\n}", + "enum Tens\n{\n TEN,\n TWENTY\n}", + ] + + def test_is_valid(self) -> None: + self.assertTrue(JavaSegmenter("int a;").is_valid()) + self.assertFalse(JavaSegmenter("a b c 1 2 3").is_valid()) + + def test_extract_functions_classes(self) -> None: + segmenter = JavaSegmenter(self.example_code) + extracted_code = segmenter.extract_functions_classes() + self.assertEqual(extracted_code, self.expected_extracted_code) + + def test_simplify_code(self) -> None: + segmenter = JavaSegmenter(self.example_code) + simplified_code = segmenter.simplify_code() + self.assertEqual(simplified_code, self.expected_simplified_code) diff --git a/libs/community/tests/unit_tests/document_loaders/parsers/language/test_kotlin.py b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_kotlin.py new file mode 100644 index 00000000000..35bde9feb26 --- /dev/null +++ b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_kotlin.py @@ -0,0 +1,60 @@ +import unittest + +import pytest + +from langchain_community.document_loaders.parsers.language.kotlin import KotlinSegmenter + + +@pytest.mark.requires("tree_sitter", "tree_sitter_languages") +class TestKotlinSegmenter(unittest.TestCase): + def setUp(self) -> None: + self.example_code = """fun foo(a: Int): Int { + return a +} + +class T { + var a: Int = 0 + var b: Boolean = false + var c: String = "" +} + +interface S { + fun bar(): Double +} + +enum class P { + A, + B, + C +} +""" + + self.expected_simplified_code = """// Code for: fun foo(a: Int): Int { + +// Code for: class T { + +// Code for: interface S { + +// Code for: enum class P {""" + + self.expected_extracted_code = [ + "fun foo(a: Int): Int {\n return a\n}", + "class T {\n var a: Int = 0\n var b: Boolean = false\n " + 'var c: String = ""\n}', + "interface S {\n fun bar(): Double\n}", + "enum class P {\n A,\n B,\n C\n}", + ] + + def test_is_valid(self) -> None: + self.assertTrue(KotlinSegmenter("val a: Int = 5").is_valid()) + self.assertFalse(KotlinSegmenter("a b c 1 2 3").is_valid()) + + def test_extract_functions_classes(self) -> None: + segmenter = KotlinSegmenter(self.example_code) + extracted_code = segmenter.extract_functions_classes() + self.assertEqual(extracted_code, self.expected_extracted_code) + + def test_simplify_code(self) -> None: + segmenter = KotlinSegmenter(self.example_code) + simplified_code = segmenter.simplify_code() + self.assertEqual(simplified_code, self.expected_simplified_code) diff --git a/libs/community/tests/unit_tests/document_loaders/parsers/language/test_lua.py b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_lua.py new file mode 100644 index 00000000000..afb50b8345b --- /dev/null +++ b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_lua.py @@ -0,0 +1,55 @@ +import unittest + +import pytest + +from langchain_community.document_loaders.parsers.language.lua import LuaSegmenter + + +@pytest.mark.requires("tree_sitter", "tree_sitter_languages") +class TestLuaSegmenter(unittest.TestCase): + def setUp(self) -> None: + self.example_code = """function F() + print("Hello") +end + +local function G() + print("Goodbye") +end""" + + self.expected_simplified_code = """-- Code for: function F() + +-- Code for: local function G()""" + + self.expected_extracted_code = [ + 'function F()\n print("Hello")\nend', + 'local function G()\n print("Goodbye")\nend', + ] + + def test_is_valid(self) -> None: + self.assertTrue(LuaSegmenter("local a").is_valid()) + self.assertFalse(LuaSegmenter("a b c 1 2 3").is_valid()) + + # TODO: Investigate flakey-ness. + @pytest.mark.skip( + reason=( + "Flakey. To be investigated. See " + "https://github.com/langchain-ai/langchain/actions/runs/7907779756/job/21585580650." # noqa: E501 + ) + ) + def test_extract_functions_classes(self) -> None: + segmenter = LuaSegmenter(self.example_code) + extracted_code = segmenter.extract_functions_classes() + self.assertEqual(extracted_code, self.expected_extracted_code) + + # TODO: Investigate flakey-ness. + @pytest.mark.skip( + reason=( + "Flakey. To be investigated. See " + "https://github.com/langchain-ai/langchain/actions/runs/7923203031/job/21632416298?pr=17599 " # noqa: E501 + "and https://github.com/langchain-ai/langchain/actions/runs/7923784089/job/2163420864." # noqa: E501 + ) + ) + def test_simplify_code(self) -> None: + segmenter = LuaSegmenter(self.example_code) + simplified_code = segmenter.simplify_code() + self.assertEqual(simplified_code, self.expected_simplified_code) diff --git a/libs/community/tests/unit_tests/document_loaders/parsers/language/test_perl.py b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_perl.py new file mode 100644 index 00000000000..78e3fa25a00 --- /dev/null +++ b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_perl.py @@ -0,0 +1,44 @@ +import unittest + +import pytest + +from langchain_community.document_loaders.parsers.language.perl import PerlSegmenter + + +@pytest.mark.requires("tree_sitter", "tree_sitter_languages") +class TestPerlSegmenter(unittest.TestCase): + def setUp(self) -> None: + self.example_code = """sub Hello { + print "Hello, World!"; +} + +sub new { + my $class = shift; + my $self = {}; + bless $self, $class; + return $self; +}""" + + self.expected_simplified_code = """# Code for: sub Hello { + +# Code for: sub new {""" + + self.expected_extracted_code = [ + 'sub Hello {\n print "Hello, World!";\n}', + "sub new {\n my $class = shift;\n my $self = {};\n " + "bless $self, $class;\n return $self;\n}", + ] + + def test_is_valid(self) -> None: + self.assertTrue(PerlSegmenter("$age = 25;").is_valid()) + self.assertFalse(PerlSegmenter("a b c 1 2 3").is_valid()) + + def test_extract_functions_classes(self) -> None: + segmenter = PerlSegmenter(self.example_code) + extracted_code = segmenter.extract_functions_classes() + self.assertEqual(extracted_code, self.expected_extracted_code) + + def test_simplify_code(self) -> None: + segmenter = PerlSegmenter(self.example_code) + simplified_code = segmenter.simplify_code() + self.assertEqual(simplified_code, self.expected_simplified_code) diff --git a/libs/community/tests/unit_tests/document_loaders/parsers/language/test_ruby.py b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_ruby.py new file mode 100644 index 00000000000..99dd7db1fa8 --- /dev/null +++ b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_ruby.py @@ -0,0 +1,51 @@ +import unittest + +import pytest + +from langchain_community.document_loaders.parsers.language.ruby import RubySegmenter + + +@pytest.mark.requires("tree_sitter", "tree_sitter_languages") +class TestRubySegmenter(unittest.TestCase): + def setUp(self) -> None: + self.example_code = """def foo + i = 0 +end + +module M + def hi + i = 2 + end +end + +class T + def bar + j = 1 + end +end""" + + self.expected_simplified_code = """# Code for: def foo + +# Code for: module M + +# Code for: class T""" + + self.expected_extracted_code = [ + "def foo\n i = 0\nend", + "module M\n def hi\n i = 2\n end\nend", + "class T\n def bar\n j = 1\n end\nend", + ] + + def test_is_valid(self) -> None: + self.assertTrue(RubySegmenter("def a; end").is_valid()) + self.assertFalse(RubySegmenter("a b c 1 2 3").is_valid()) + + def test_extract_functions_classes(self) -> None: + segmenter = RubySegmenter(self.example_code) + extracted_code = segmenter.extract_functions_classes() + self.assertEqual(extracted_code, self.expected_extracted_code) + + def test_simplify_code(self) -> None: + segmenter = RubySegmenter(self.example_code) + simplified_code = segmenter.simplify_code() + self.assertEqual(simplified_code, self.expected_simplified_code) diff --git a/libs/community/tests/unit_tests/document_loaders/parsers/language/test_rust.py b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_rust.py new file mode 100644 index 00000000000..6b35677c305 --- /dev/null +++ b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_rust.py @@ -0,0 +1,50 @@ +import unittest + +import pytest + +from langchain_community.document_loaders.parsers.language.rust import RustSegmenter + + +@pytest.mark.requires("tree_sitter", "tree_sitter_languages") +class TestRustSegmenter(unittest.TestCase): + def setUp(self) -> None: + self.example_code = """fn foo() -> i32 { + return 1; +} + +struct T { + a: i32, + b: bool, + c: String +} + +trait S { + fn bar() -> Self +} +""" + + self.expected_simplified_code = """// Code for: fn foo() -> i32 { + +// Code for: struct T { + +// Code for: trait S {""" + + self.expected_extracted_code = [ + "fn foo() -> i32 {\n return 1;\n}", + "struct T {\n a: i32,\n b: bool,\n c: String\n}", + "trait S {\n fn bar() -> Self\n}", + ] + + def test_is_valid(self) -> None: + self.assertTrue(RustSegmenter("let a: i32;").is_valid()) + self.assertFalse(RustSegmenter("a b c 1 2 3").is_valid()) + + def test_extract_functions_classes(self) -> None: + segmenter = RustSegmenter(self.example_code) + extracted_code = segmenter.extract_functions_classes() + self.assertEqual(extracted_code, self.expected_extracted_code) + + def test_simplify_code(self) -> None: + segmenter = RustSegmenter(self.example_code) + simplified_code = segmenter.simplify_code() + self.assertEqual(simplified_code, self.expected_simplified_code) diff --git a/libs/community/tests/unit_tests/document_loaders/parsers/language/test_scala.py b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_scala.py new file mode 100644 index 00000000000..3fad1aeb806 --- /dev/null +++ b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_scala.py @@ -0,0 +1,56 @@ +import unittest + +import pytest + +from langchain_community.document_loaders.parsers.language.scala import ScalaSegmenter + + +@pytest.mark.requires("tree_sitter", "tree_sitter_languages") +class TestScalaSegmenter(unittest.TestCase): + def setUp(self) -> None: + self.example_code = """def foo() { + return 1 +} + +object T { + def baz() { + val x = 1 + } +} + +class S() { + +} + +trait T { + def P(x: Any): Boolean +}""" + + self.expected_simplified_code = """// Code for: def foo() { + +// Code for: object T { + +// Code for: class S() { + +// Code for: trait T {""" + + self.expected_extracted_code = [ + "def foo() {\n return 1\n}", + "object T {\n def baz() {\n val x = 1\n }\n}", + "class S() {\n\n}", + "trait T {\n def P(x: Any): Boolean\n}", + ] + + def test_is_valid(self) -> None: + self.assertFalse(ScalaSegmenter("val x").is_valid()) + self.assertFalse(ScalaSegmenter("a b c 1 2 3").is_valid()) + + def test_extract_functions_classes(self) -> None: + segmenter = ScalaSegmenter(self.example_code) + extracted_code = segmenter.extract_functions_classes() + self.assertEqual(extracted_code, self.expected_extracted_code) + + def test_simplify_code(self) -> None: + segmenter = ScalaSegmenter(self.example_code) + simplified_code = segmenter.simplify_code() + self.assertEqual(simplified_code, self.expected_simplified_code) diff --git a/libs/community/tests/unit_tests/document_loaders/parsers/language/test_typescript.py b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_typescript.py new file mode 100644 index 00000000000..caf4b6ad66c --- /dev/null +++ b/libs/community/tests/unit_tests/document_loaders/parsers/language/test_typescript.py @@ -0,0 +1,67 @@ +import unittest + +import pytest + +from langchain_community.document_loaders.parsers.language.typescript import ( + TypeScriptSegmenter, +) + + +@pytest.mark.requires("tree_sitter", "tree_sitter_languages") +class TestTypeScriptSegmenter(unittest.TestCase): + def setUp(self) -> None: + self.example_code = """function foo(): number +{ + return 1; +} + +class Autumn +{ + leafCount = 45; + reduceTemperature(desiredTemperature: number): number { + return desiredTemperature * 0.6; + } +} + +interface Season +{ + change(): void; +} + +enum Colors +{ + Green = 'green', + Red = 'red', +} +""" + + self.expected_simplified_code = """// Code for: function foo(): number + +// Code for: class Autumn + +// Code for: interface Season + +// Code for: enum Colors""" + + self.expected_extracted_code = [ + "function foo(): number\n{\n return 1;\n}", + "class Autumn\n{\n leafCount = 45;\n " + "reduceTemperature(desiredTemperature: number): number {\n " + "return desiredTemperature * 0.6;\n }\n}", + "interface Season\n{\n change(): void;\n}", + "enum Colors\n{\n Green = 'green',\n Red = 'red',\n}", + ] + + def test_is_valid(self) -> None: + self.assertTrue(TypeScriptSegmenter("let a;").is_valid()) + self.assertFalse(TypeScriptSegmenter("a b c 1 2 3").is_valid()) + + def test_extract_functions_classes(self) -> None: + segmenter = TypeScriptSegmenter(self.example_code) + extracted_code = segmenter.extract_functions_classes() + self.assertEqual(extracted_code, self.expected_extracted_code) + + def test_simplify_code(self) -> None: + segmenter = TypeScriptSegmenter(self.example_code) + simplified_code = segmenter.simplify_code() + self.assertEqual(simplified_code, self.expected_simplified_code) diff --git a/libs/community/tests/unit_tests/document_loaders/test_directory.py b/libs/community/tests/unit_tests/document_loaders/test_directory.py index dc028e4814c..f83e4bc2dfe 100644 --- a/libs/community/tests/unit_tests/document_loaders/test_directory.py +++ b/libs/community/tests/unit_tests/document_loaders/test_directory.py @@ -1,4 +1,8 @@ +from pathlib import Path +from typing import Any, List + import pytest +from langchain_core.documents import Document from langchain_community.document_loaders import DirectoryLoader @@ -17,3 +21,35 @@ def test_raise_error_if_path_is_not_directory() -> None: loader.load() assert str(e.value) == f"Expected directory, got file: '{__file__}'" + + +class CustomLoader: + """Test loader. Mimics interface of existing file loader.""" + + def __init__(self, path: Path, **kwargs: Any) -> None: + """Initialize the loader.""" + self.path = path + + def load(self) -> List[Document]: + """Load documents.""" + with open(self.path, "r") as f: + return [Document(page_content=f.read())] + + +def test_exclude_ignores_matching_files(tmp_path: Path) -> None: + txt_file = tmp_path / "test.txt" + py_file = tmp_path / "test.py" + txt_file.touch() + py_file.touch() + loader = DirectoryLoader( + str(tmp_path), + exclude=["*.py"], + loader_cls=CustomLoader, # type: ignore + ) + data = loader.load() + assert len(data) == 1 + + +def test_exclude_as_string_converts_to_sequence() -> None: + loader = DirectoryLoader("./some_directory", exclude="*.py") + assert loader.exclude == ("*.py",) diff --git a/libs/community/tests/unit_tests/document_loaders/test_github.py b/libs/community/tests/unit_tests/document_loaders/test_github.py index 5641836dcd6..f9f74be6975 100644 --- a/libs/community/tests/unit_tests/document_loaders/test_github.py +++ b/libs/community/tests/unit_tests/document_loaders/test_github.py @@ -1,8 +1,13 @@ +import base64 + import pytest from langchain_core.documents import Document from pytest_mock import MockerFixture -from langchain_community.document_loaders.github import GitHubIssuesLoader +from langchain_community.document_loaders.github import ( + GithubFileLoader, + GitHubIssuesLoader, +) def test_initialization() -> None: @@ -48,7 +53,7 @@ def test_invalid_initialization() -> None: GitHubIssuesLoader(since="not_a_date") -def test_load(mocker: MockerFixture) -> None: +def test_load_github_issue(mocker: MockerFixture) -> None: mocker.patch( "requests.get", return_value=mocker.MagicMock(json=lambda: [], links=None) ) @@ -127,3 +132,103 @@ def test_url() -> None: "&assignee=user1&creator=user2&mentioned=user3&labels=bug,ui,@high" "&sort=comments&direction=asc&since=2023-05-26T00:00:00Z" ) + + +def test_github_file_content_get_file_paths(mocker: MockerFixture) -> None: + # Mock the requests.get method to simulate the API response + mocker.patch( + "requests.get", + return_value=mocker.MagicMock( + json=lambda: { + "tree": [ + { + "path": "readme.md", + "mode": "100644", + "type": "blob", + "sha": "789", + "size": 37, + "url": "https://github.com/api/v3/repos/shufanhao/langchain/git/blobs/789", + } + ] + }, + status_code=200, + ), + ) + + # case1: add file_filter + loader = GithubFileLoader( + repo="shufanhao/langchain", + access_token="access_token", + github_api_url="https://github.com", + file_filter=lambda file_path: file_path.endswith(".md"), + ) + + # Call the load method + files = loader.get_file_paths() + + # Assert the results + assert len(files) == 1 + assert files[0]["path"] == "readme.md" + + # case2: didn't add file_filter + loader = GithubFileLoader( + repo="shufanhao/langchain", + access_token="access_token", + github_api_url="https://github.com", + ) + + # Call the load method + files = loader.get_file_paths() + assert len(files) == 1 + assert files[0]["path"] == "readme.md" + + # case3: add file_filter with a non-exist file path + loader = GithubFileLoader( + repo="shufanhao/langchain", + access_token="access_token", + github_api_url="https://github.com", + file_filter=lambda file_path: file_path.endswith(".py"), + ) + + # Call the load method + files = loader.get_file_paths() + assert len(files) == 0 + + +def test_github_file_content_loader(mocker: MockerFixture) -> None: + # Mock the requests.get method to simulate the API response + file_path_res = mocker.MagicMock( + json=lambda: { + "tree": [ + { + "path": "readme.md", + "mode": "100644", + "type": "blob", + "sha": "789", + "size": 37, + "url": "https://github.com/api/v3/repos/shufanhao/langchain/git/blobs/789", + } + ] + }, + status_code=200, + ) + file_content_res = mocker.MagicMock( + json=lambda: {"content": base64.b64encode("Mocked content".encode("utf-8"))}, + status_code=200, + ) + + mocker.patch("requests.get", side_effect=[file_path_res, file_content_res]) + + # case1: file_extension=".md" + loader = GithubFileLoader( + repo="shufanhao/langchain", + access_token="access_token", + github_api_url="https://github.com", + ) + + # Call the load method + docs = loader.load() + + assert len(docs) == 1 + assert docs[0].page_content == "Mocked content" + assert docs[0].metadata["sha"] == "789" diff --git a/libs/community/tests/unit_tests/document_loaders/test_imports.py b/libs/community/tests/unit_tests/document_loaders/test_imports.py index f4511686f7a..e4b406add57 100644 --- a/libs/community/tests/unit_tests/document_loaders/test_imports.py +++ b/libs/community/tests/unit_tests/document_loaders/test_imports.py @@ -23,6 +23,7 @@ EXPECTED_ALL = [ "AssemblyAIAudioTranscriptLoader", "AstraDBLoader", "AsyncHtmlLoader", + "AthenaLoader", "AzureAIDataLoader", "AzureAIDocumentIntelligenceLoader", "AzureBlobStorageContainerLoader", @@ -48,6 +49,7 @@ EXPECTED_ALL = [ "CubeSemanticLoader", "DataFrameLoader", "DatadogLogsLoader", + "PebbloSafeLoader", "DiffbotLoader", "DirectoryLoader", "DiscordChatLoader", @@ -65,6 +67,7 @@ EXPECTED_ALL = [ "GCSDirectoryLoader", "GCSFileLoader", "GeoDataFrameLoader", + "GithubFileLoader", "GitHubIssuesLoader", "GitLoader", "GitbookLoader", diff --git a/libs/community/tests/unit_tests/document_loaders/test_mediawikidump.py b/libs/community/tests/unit_tests/document_loaders/test_mediawikidump.py index 6697df2a91f..6bb3ae91e5b 100644 --- a/libs/community/tests/unit_tests/document_loaders/test_mediawikidump.py +++ b/libs/community/tests/unit_tests/document_loaders/test_mediawikidump.py @@ -44,5 +44,5 @@ def test_multiple_namespaces() -> None: stop_on_error=False, ) documents = loader.load() - [print(doc) for doc in documents] + [print(doc) for doc in documents] # noqa: T201 assert len(documents) == 2 diff --git a/libs/community/tests/unit_tests/document_loaders/test_pebblo.py b/libs/community/tests/unit_tests/document_loaders/test_pebblo.py new file mode 100644 index 00000000000..9ab487c8e78 --- /dev/null +++ b/libs/community/tests/unit_tests/document_loaders/test_pebblo.py @@ -0,0 +1,114 @@ +import os +from pathlib import Path +from typing import Dict + +import pytest +from langchain_core.documents import Document +from pytest_mock import MockerFixture + +from langchain_community.document_loaders import CSVLoader, PyPDFLoader + +EXAMPLE_DOCS_DIRECTORY = str(Path(__file__).parent.parent.parent / "examples/") + + +class MockResponse: + def __init__(self, json_data: Dict, status_code: int): + self.json_data = json_data + self.status_code = status_code + + def json(self) -> Dict: + return self.json_data + + +def test_pebblo_import() -> None: + """Test that the Pebblo safe loader can be imported.""" + from langchain_community.document_loaders import PebbloSafeLoader # noqa: F401 + + +def test_empty_filebased_loader(mocker: MockerFixture) -> None: + """Test basic file based csv loader.""" + # Setup + from langchain_community.document_loaders import PebbloSafeLoader + + mocker.patch.multiple( + "requests", + get=MockResponse(json_data={"data": ""}, status_code=200), + post=MockResponse(json_data={"data": ""}, status_code=200), + ) + + file_path = os.path.join(EXAMPLE_DOCS_DIRECTORY, "test_empty.csv") + expected_docs: list = [] + + # Exercise + loader = PebbloSafeLoader( + CSVLoader(file_path=file_path), + "dummy_app_name", + "dummy_owner", + "dummy_description", + ) + result = loader.load() + + # Assert + assert result == expected_docs + + +def test_csv_loader_load_valid_data(mocker: MockerFixture) -> None: + # Setup + from langchain_community.document_loaders import PebbloSafeLoader + + mocker.patch.multiple( + "requests", + get=MockResponse(json_data={"data": ""}, status_code=200), + post=MockResponse(json_data={"data": ""}, status_code=200), + ) + file_path = os.path.join(EXAMPLE_DOCS_DIRECTORY, "test_nominal.csv") + expected_docs = [ + Document( + page_content="column1: value1\ncolumn2: value2\ncolumn3: value3", + metadata={"source": file_path, "row": 0}, + ), + Document( + page_content="column1: value4\ncolumn2: value5\ncolumn3: value6", + metadata={"source": file_path, "row": 1}, + ), + ] + + # Exercise + loader = PebbloSafeLoader( + CSVLoader(file_path=file_path), + "dummy_app_name", + "dummy_owner", + "dummy_description", + ) + result = loader.load() + + # Assert + assert result == expected_docs + + +@pytest.mark.requires("pypdf") +def test_pdf_lazy_load(mocker: MockerFixture) -> None: + # Setup + from langchain_community.document_loaders import PebbloSafeLoader + + mocker.patch.multiple( + "requests", + get=MockResponse(json_data={"data": ""}, status_code=200), + post=MockResponse(json_data={"data": ""}, status_code=200), + ) + file_path = os.path.join( + EXAMPLE_DOCS_DIRECTORY, "multi-page-forms-sample-2-page.pdf" + ) + + # Exercise + loader = PebbloSafeLoader( + PyPDFLoader(file_path=file_path), + "dummy_app_name", + "dummy_owner", + "dummy_description", + ) + + result = list(loader.lazy_load()) + + # Assert + assert len(result) == 2 diff --git a/libs/community/tests/unit_tests/embeddings/test_edenai.py b/libs/community/tests/unit_tests/embeddings/test_edenai.py index 6616f3ef131..ba2e05c72eb 100644 --- a/libs/community/tests/unit_tests/embeddings/test_edenai.py +++ b/libs/community/tests/unit_tests/embeddings/test_edenai.py @@ -15,7 +15,7 @@ def test_api_key_masked_when_passed_via_constructor( capsys: CaptureFixture, ) -> None: llm = EdenAiEmbeddings(edenai_api_key="secret-api-key") - print(llm.edenai_api_key, end="") + print(llm.edenai_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/unit_tests/embeddings/test_embaas.py b/libs/community/tests/unit_tests/embeddings/test_embaas.py index 05420ff42fa..b62092a2aaa 100644 --- a/libs/community/tests/unit_tests/embeddings/test_embaas.py +++ b/libs/community/tests/unit_tests/embeddings/test_embaas.py @@ -15,7 +15,7 @@ def test_api_key_masked_when_passed_via_constructor( capsys: CaptureFixture, ) -> None: llm = EmbaasEmbeddings(embaas_api_key="secret-api-key") - print(llm.embaas_api_key, end="") + print(llm.embaas_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/unit_tests/embeddings/test_gradient_ai.py b/libs/community/tests/unit_tests/embeddings/test_gradient_ai.py index fa57109ef02..1bec4a7bf63 100644 --- a/libs/community/tests/unit_tests/embeddings/test_gradient_ai.py +++ b/libs/community/tests/unit_tests/embeddings/test_gradient_ai.py @@ -29,7 +29,7 @@ class GradientEmbeddingsModel(MagicMock): embeddings = [] for i, inp in enumerate(inputs): # verify correct ordering - inp = inp["input"] + inp = inp["input"] # type: ignore[assignment] if "pizza" in inp: v = [1.0, 0.0, 0.0] elif "document" in inp: @@ -45,14 +45,14 @@ class GradientEmbeddingsModel(MagicMock): output.embeddings = embeddings return output - async def aembed(self, *args) -> Any: + async def aembed(self, *args) -> Any: # type: ignore[no-untyped-def] return self.embed(*args) class MockGradient(MagicMock): """Mock Gradient package.""" - def __init__(self, access_token: str, workspace_id, host): + def __init__(self, access_token: str, workspace_id, host): # type: ignore[no-untyped-def] assert access_token == _GRADIENT_SECRET assert workspace_id == _GRADIENT_WORKSPACE_ID assert host == _GRADIENT_BASE_URL diff --git a/libs/community/tests/unit_tests/embeddings/test_imports.py b/libs/community/tests/unit_tests/embeddings/test_imports.py index 2220d7446f9..c66672c79ee 100644 --- a/libs/community/tests/unit_tests/embeddings/test_imports.py +++ b/libs/community/tests/unit_tests/embeddings/test_imports.py @@ -58,6 +58,8 @@ EXPECTED_ALL = [ "BookendEmbeddings", "VolcanoEmbeddings", "OCIGenAIEmbeddings", + "QuantizedBiEncoderEmbeddings", + "NeMoEmbeddings", ] diff --git a/libs/community/tests/unit_tests/embeddings/test_llm_rails.py b/libs/community/tests/unit_tests/embeddings/test_llm_rails.py index 05a40c726e5..0715efcb4ca 100644 --- a/libs/community/tests/unit_tests/embeddings/test_llm_rails.py +++ b/libs/community/tests/unit_tests/embeddings/test_llm_rails.py @@ -15,7 +15,7 @@ def test_api_key_masked_when_passed_via_constructor( capsys: CaptureFixture, ) -> None: llm = LLMRailsEmbeddings(api_key="secret-api-key") - print(llm.api_key, end="") + print(llm.api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/unit_tests/embeddings/test_oci_gen_ai_embedding.py b/libs/community/tests/unit_tests/embeddings/test_oci_gen_ai_embedding.py index 12d9c447d5d..d43f3108b9c 100644 --- a/libs/community/tests/unit_tests/embeddings/test_oci_gen_ai_embedding.py +++ b/libs/community/tests/unit_tests/embeddings/test_oci_gen_ai_embedding.py @@ -8,7 +8,7 @@ from langchain_community.embeddings import OCIGenAIEmbeddings class MockResponseDict(dict): - def __getattr__(self, val): + def __getattr__(self, val): # type: ignore[no-untyped-def] return self[val] @@ -25,7 +25,7 @@ def test_embedding_call(monkeypatch: MonkeyPatch, test_model_id: str) -> None: client=oci_gen_ai_client, ) - def mocked_response(invocation_obj): + def mocked_response(invocation_obj): # type: ignore[no-untyped-def] docs = invocation_obj.inputs embeddings = [] diff --git a/libs/community/tests/unit_tests/embeddings/test_openai.py b/libs/community/tests/unit_tests/embeddings/test_openai.py index e9c3979138b..c9499722ff5 100644 --- a/libs/community/tests/unit_tests/embeddings/test_openai.py +++ b/libs/community/tests/unit_tests/embeddings/test_openai.py @@ -1,11 +1,7 @@ -import os - import pytest from langchain_community.embeddings.openai import OpenAIEmbeddings -os.environ["OPENAI_API_KEY"] = "foo" - @pytest.mark.requires("openai") def test_openai_invalid_model_kwargs() -> None: @@ -16,5 +12,5 @@ def test_openai_invalid_model_kwargs() -> None: @pytest.mark.requires("openai") def test_openai_incorrect_field() -> None: with pytest.warns(match="not default parameter"): - llm = OpenAIEmbeddings(foo="bar") + llm = OpenAIEmbeddings(foo="bar", openai_api_key="foo") assert llm.model_kwargs == {"foo": "bar"} diff --git a/libs/community/tests/unit_tests/graphs/test_imports.py b/libs/community/tests/unit_tests/graphs/test_imports.py index 653d7d540ba..202ecefa249 100644 --- a/libs/community/tests/unit_tests/graphs/test_imports.py +++ b/libs/community/tests/unit_tests/graphs/test_imports.py @@ -6,6 +6,7 @@ EXPECTED_ALL = [ "Neo4jGraph", "NebulaGraph", "NeptuneGraph", + "NeptuneRdfGraph", "KuzuGraph", "HugeGraph", "RdfGraph", diff --git a/libs/community/tests/unit_tests/graphs/test_neo4j_graph.py b/libs/community/tests/unit_tests/graphs/test_neo4j_graph.py index b352529ba67..eebef74ef16 100644 --- a/libs/community/tests/unit_tests/graphs/test_neo4j_graph.py +++ b/libs/community/tests/unit_tests/graphs/test_neo4j_graph.py @@ -1,14 +1,14 @@ from langchain_community.graphs.neo4j_graph import value_sanitize -def test_value_sanitize_with_small_list(): +def test_value_sanitize_with_small_list(): # type: ignore[no-untyped-def] small_list = list(range(15)) # list size > LIST_LIMIT input_dict = {"key1": "value1", "small_list": small_list} expected_output = {"key1": "value1", "small_list": small_list} assert value_sanitize(input_dict) == expected_output -def test_value_sanitize_with_oversized_list(): +def test_value_sanitize_with_oversized_list(): # type: ignore[no-untyped-def] oversized_list = list(range(150)) # list size > LIST_LIMIT input_dict = {"key1": "value1", "oversized_list": oversized_list} expected_output = { @@ -18,14 +18,14 @@ def test_value_sanitize_with_oversized_list(): assert value_sanitize(input_dict) == expected_output -def test_value_sanitize_with_nested_oversized_list(): +def test_value_sanitize_with_nested_oversized_list(): # type: ignore[no-untyped-def] oversized_list = list(range(150)) # list size > LIST_LIMIT input_dict = {"key1": "value1", "oversized_list": {"key": oversized_list}} expected_output = {"key1": "value1", "oversized_list": {}} assert value_sanitize(input_dict) == expected_output -def test_value_sanitize_with_dict_in_list(): +def test_value_sanitize_with_dict_in_list(): # type: ignore[no-untyped-def] oversized_list = list(range(150)) # list size > LIST_LIMIT input_dict = {"key1": "value1", "oversized_list": [1, 2, {"key": oversized_list}]} expected_output = {"key1": "value1", "oversized_list": [1, 2, {}]} diff --git a/libs/community/tests/unit_tests/graphs/test_neptune_graph.py b/libs/community/tests/unit_tests/graphs/test_neptune_graph.py index e3d986f2eb0..6e714a41665 100644 --- a/libs/community/tests/unit_tests/graphs/test_neptune_graph.py +++ b/libs/community/tests/unit_tests/graphs/test_neptune_graph.py @@ -1,2 +1,5 @@ def test_import() -> None: - from langchain_community.graphs import NeptuneGraph # noqa: F401 + from langchain_community.graphs import ( + NeptuneGraph, # noqa: F401 + NeptuneRdfGraph, # noqa: F401 + ) diff --git a/libs/community/tests/unit_tests/graphs/test_ontotext_graphdb_graph.py b/libs/community/tests/unit_tests/graphs/test_ontotext_graphdb_graph.py index 8b025fed36f..9beb2da271d 100644 --- a/libs/community/tests/unit_tests/graphs/test_ontotext_graphdb_graph.py +++ b/libs/community/tests/unit_tests/graphs/test_ontotext_graphdb_graph.py @@ -15,7 +15,7 @@ class TestOntotextGraphDBGraph(unittest.TestCase): with self.assertRaises(TypeError) as e: OntotextGraphDBGraph._validate_user_query( - [ + [ # type: ignore[arg-type] "PREFIX starwars: " "PREFIX rdfs: " "DESCRIBE starwars: ?term " diff --git a/libs/community/tests/unit_tests/llms/test_ai21.py b/libs/community/tests/unit_tests/llms/test_ai21.py index 60146a973d2..4e111fe1ea7 100644 --- a/libs/community/tests/unit_tests/llms/test_ai21.py +++ b/libs/community/tests/unit_tests/llms/test_ai21.py @@ -1,4 +1,5 @@ """Test AI21 llm""" + from typing import cast from langchain_core.pydantic_v1 import SecretStr @@ -18,7 +19,7 @@ def test_api_key_masked_when_passed_from_env( """Test initialization with an API key provided via an env variable""" monkeypatch.setenv("AI21_API_KEY", "secret-api-key") llm = AI21() - print(llm.ai21_api_key, end="") + print(llm.ai21_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" @@ -29,7 +30,7 @@ def test_api_key_masked_when_passed_via_constructor( ) -> None: """Test initialization with an API key provided via the initializer""" llm = AI21(ai21_api_key="secret-api-key") - print(llm.ai21_api_key, end="") + print(llm.ai21_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/unit_tests/llms/test_aleph_alpha.py b/libs/community/tests/unit_tests/llms/test_aleph_alpha.py index c08d0e72924..da3ac1f992a 100644 --- a/libs/community/tests/unit_tests/llms/test_aleph_alpha.py +++ b/libs/community/tests/unit_tests/llms/test_aleph_alpha.py @@ -18,7 +18,7 @@ def test_api_key_masked_when_passed_via_constructor( capsys: CaptureFixture, ) -> None: llm = AlephAlpha(aleph_alpha_api_key="secret-api-key") - print(llm.aleph_alpha_api_key, end="") + print(llm.aleph_alpha_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" @@ -30,7 +30,7 @@ def test_api_key_masked_when_passed_from_env( ) -> None: monkeypatch.setenv("ALEPH_ALPHA_API_KEY", "secret-api-key") llm = AlephAlpha() - print(llm.aleph_alpha_api_key, end="") + print(llm.aleph_alpha_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/unit_tests/llms/test_anyscale.py b/libs/community/tests/unit_tests/llms/test_anyscale.py index 9dc38b7c20d..155c4e9c101 100644 --- a/libs/community/tests/unit_tests/llms/test_anyscale.py +++ b/libs/community/tests/unit_tests/llms/test_anyscale.py @@ -1,4 +1,5 @@ """Test Anyscale llm""" + import pytest from langchain_core.pydantic_v1 import SecretStr from pytest import CaptureFixture, MonkeyPatch @@ -8,9 +9,7 @@ from langchain_community.llms.anyscale import Anyscale @pytest.mark.requires("openai") def test_api_key_is_secret_string() -> None: - llm = Anyscale( - anyscale_api_key="secret-api-key", anyscale_api_base="test", model_name="test" - ) + llm = Anyscale(anyscale_api_key="secret-api-key", anyscale_api_base="test") assert isinstance(llm.anyscale_api_key, SecretStr) @@ -20,8 +19,8 @@ def test_api_key_masked_when_passed_from_env( ) -> None: """Test initialization with an API key provided via an env variable""" monkeypatch.setenv("ANYSCALE_API_KEY", "secret-api-key") - llm = Anyscale(anyscale_api_base="test", model_name="test") - print(llm.anyscale_api_key, end="") + llm = Anyscale(anyscale_api_base="test") + print(llm.anyscale_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" @@ -32,10 +31,8 @@ def test_api_key_masked_when_passed_via_constructor( capsys: CaptureFixture, ) -> None: """Test initialization with an API key provided via the initializer""" - llm = Anyscale( - anyscale_api_key="secret-api-key", anyscale_api_base="test", model_name="test" - ) - print(llm.anyscale_api_key, end="") + llm = Anyscale(anyscale_api_key="secret-api-key", anyscale_api_base="test") + print(llm.anyscale_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/unit_tests/llms/test_cerebriumai.py b/libs/community/tests/unit_tests/llms/test_cerebriumai.py index ff9da5745f2..7462b612e52 100644 --- a/libs/community/tests/unit_tests/llms/test_cerebriumai.py +++ b/libs/community/tests/unit_tests/llms/test_cerebriumai.py @@ -1,6 +1,5 @@ """Test CerebriumAI llm""" - from langchain_core.pydantic_v1 import SecretStr from pytest import CaptureFixture, MonkeyPatch @@ -14,7 +13,7 @@ def test_api_key_is_secret_string() -> None: def test_api_key_masked_when_passed_via_constructor(capsys: CaptureFixture) -> None: llm = CerebriumAI(cerebriumai_api_key="secret-api-key") - print(llm.cerebriumai_api_key, end="") + print(llm.cerebriumai_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" @@ -26,7 +25,7 @@ def test_api_key_masked_when_passed_from_env( ) -> None: monkeypatch.setenv("CEREBRIUMAI_API_KEY", "secret-api-key") llm = CerebriumAI() - print(llm.cerebriumai_api_key, end="") + print(llm.cerebriumai_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/unit_tests/llms/test_databricks.py b/libs/community/tests/unit_tests/llms/test_databricks.py new file mode 100644 index 00000000000..7d3809e270c --- /dev/null +++ b/libs/community/tests/unit_tests/llms/test_databricks.py @@ -0,0 +1,46 @@ +"""test Databricks LLM""" +import pickle +from typing import Any, Dict + +from pytest import MonkeyPatch + +from langchain_community.llms.databricks import Databricks + + +class MockDatabricksServingEndpointClient: + def __init__( + self, + host: str, + api_token: str, + endpoint_name: str, + databricks_uri: str, + task: str, + ): + self.host = host + self.api_token = api_token + self.endpoint_name = endpoint_name + self.databricks_uri = databricks_uri + self.task = task + + +def transform_input(**request: Any) -> Dict[str, Any]: + request["messages"] = [{"role": "user", "content": request["prompt"]}] + del request["prompt"] + return request + + +def test_serde_transform_input_fn(monkeypatch: MonkeyPatch) -> None: + monkeypatch.setattr( + "langchain_community.llms.databricks._DatabricksServingEndpointClient", + MockDatabricksServingEndpointClient, + ) + monkeypatch.setenv("DATABRICKS_HOST", "my-default-host") + monkeypatch.setenv("DATABRICKS_TOKEN", "my-default-token") + + llm = Databricks( + endpoint_name="databricks-mixtral-8x7b-instruct", + transform_input_fn=transform_input, + ) + params = llm._default_params + pickled_string = pickle.dumps(transform_input).hex() + assert params["transform_input_fn"] == pickled_string diff --git a/libs/community/tests/unit_tests/llms/test_fireworks.py b/libs/community/tests/unit_tests/llms/test_fireworks.py index 671016f5705..1487fdc0fba 100644 --- a/libs/community/tests/unit_tests/llms/test_fireworks.py +++ b/libs/community/tests/unit_tests/llms/test_fireworks.py @@ -1,4 +1,5 @@ """Test Fireworks chat model""" + import sys import pytest @@ -22,7 +23,7 @@ def test_api_key_masked_when_passed_via_constructor( capsys: CaptureFixture, ) -> None: llm = Fireworks(fireworks_api_key="secret-api-key") - print(llm.fireworks_api_key, end="") + print(llm.fireworks_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/unit_tests/llms/test_forefrontai.py b/libs/community/tests/unit_tests/llms/test_forefrontai.py index 567b78180c2..e9cc97c0c59 100644 --- a/libs/community/tests/unit_tests/llms/test_forefrontai.py +++ b/libs/community/tests/unit_tests/llms/test_forefrontai.py @@ -1,4 +1,5 @@ """Test ForeFrontAI LLM""" + from typing import cast from langchain_core.pydantic_v1 import SecretStr @@ -19,7 +20,7 @@ def test_forefrontai_api_key_masked_when_passed_from_env( """Test that the API key is masked when passed from an environment variable.""" monkeypatch.setenv("FOREFRONTAI_API_KEY", "secret-api-key") llm = ForefrontAI(temperature=0.2) - print(llm.forefrontai_api_key, end="") + print(llm.forefrontai_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" @@ -33,7 +34,7 @@ def test_forefrontai_api_key_masked_when_passed_via_constructor( forefrontai_api_key="secret-api-key", temperature=0.2, ) - print(llm.forefrontai_api_key, end="") + print(llm.forefrontai_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/unit_tests/llms/test_imports.py b/libs/community/tests/unit_tests/llms/test_imports.py index 9c7abb11f83..6c8c6504e36 100644 --- a/libs/community/tests/unit_tests/llms/test_imports.py +++ b/libs/community/tests/unit_tests/llms/test_imports.py @@ -87,6 +87,7 @@ EXPECT_ALL = [ "JavelinAIGateway", "QianfanLLMEndpoint", "YandexGPT", + "Yuan2", "VolcEngineMaasLLM", "WatsonxLLM", ] diff --git a/libs/community/tests/unit_tests/llms/test_llamafile.py b/libs/community/tests/unit_tests/llms/test_llamafile.py new file mode 100644 index 00000000000..10fea66a5ac --- /dev/null +++ b/libs/community/tests/unit_tests/llms/test_llamafile.py @@ -0,0 +1,158 @@ +import json +from collections import deque +from typing import Any, Dict + +import pytest +import requests +from pytest import MonkeyPatch + +from langchain_community.llms.llamafile import Llamafile + + +def default_generation_params() -> Dict[str, Any]: + return { + "temperature": 0.8, + "seed": -1, + "top_k": 40, + "top_p": 0.95, + "min_p": 0.05, + "n_predict": -1, + "n_keep": 0, + "tfs_z": 1.0, + "typical_p": 1.0, + "repeat_penalty": 1.1, + "repeat_last_n": 64, + "penalize_nl": True, + "presence_penalty": 0.0, + "frequency_penalty": 0.0, + "mirostat": 0, + "mirostat_tau": 5.0, + "mirostat_eta": 0.1, + } + + +def mock_response() -> requests.Response: + contents = json.dumps({"content": "the quick brown fox"}) + response = requests.Response() + response.status_code = 200 + response._content = str.encode(contents) + return response + + +def mock_response_stream(): # type: ignore[no-untyped-def] + mock_response = deque( + [ + b'data: {"content":"the","multimodal":false,"slot_id":0,"stop":false}\n\n', # noqa + b'data: {"content":" quick","multimodal":false,"slot_id":0,"stop":false}\n\n', # noqa + ] + ) + + class MockRaw: + def read(self, chunk_size): # type: ignore[no-untyped-def] + try: + return mock_response.popleft() + except IndexError: + return None + + response = requests.Response() + response.status_code = 200 + response.raw = MockRaw() + return response + + +def test_call(monkeypatch: MonkeyPatch) -> None: + """ + Test basic functionality of the `invoke` method + """ + llm = Llamafile( + base_url="http://llamafile-host:8080", + ) + + def mock_post(url, headers, json, stream, timeout): # type: ignore[no-untyped-def] + assert url == "http://llamafile-host:8080/completion" + assert headers == { + "Content-Type": "application/json", + } + # 'unknown' kwarg should be ignored + assert json == {"prompt": "Test prompt", **default_generation_params()} + assert stream is False + assert timeout is None + return mock_response() + + monkeypatch.setattr(requests, "post", mock_post) + out = llm.invoke("Test prompt") + assert out == "the quick brown fox" + + +def test_call_with_kwargs(monkeypatch: MonkeyPatch) -> None: + """ + Test kwargs passed to `invoke` override the default values and are passed + to the endpoint correctly. Also test that any 'unknown' kwargs that are not + present in the LLM class attrs are ignored. + """ + llm = Llamafile( + base_url="http://llamafile-host:8080", + ) + + def mock_post(url, headers, json, stream, timeout): # type: ignore[no-untyped-def] + assert url == "http://llamafile-host:8080/completion" + assert headers == { + "Content-Type": "application/json", + } + # 'unknown' kwarg should be ignored + expected = {"prompt": "Test prompt", **default_generation_params()} + expected["seed"] = 0 + assert json == expected + assert stream is False + assert timeout is None + return mock_response() + + monkeypatch.setattr(requests, "post", mock_post) + out = llm.invoke( + "Test prompt", + unknown="unknown option", # should be ignored + seed=0, # should override the default + ) + assert out == "the quick brown fox" + + +def test_call_raises_exception_on_missing_server(monkeypatch: MonkeyPatch) -> None: + """ + Test that the LLM raises a ConnectionError when no llamafile server is + listening at the base_url. + """ + llm = Llamafile( + # invalid url, nothing should actually be running here + base_url="http://llamafile-host:8080", + ) + with pytest.raises(requests.exceptions.ConnectionError): + llm.invoke("Test prompt") + + +def test_streaming(monkeypatch: MonkeyPatch) -> None: + """ + Test basic functionality of `invoke` with streaming enabled. + """ + llm = Llamafile( + base_url="http://llamafile-hostname:8080", + streaming=True, + ) + + def mock_post(url, headers, json, stream, timeout): # type: ignore[no-untyped-def] + assert url == "http://llamafile-hostname:8080/completion" + assert headers == { + "Content-Type": "application/json", + } + # 'unknown' kwarg should be ignored + assert "unknown" not in json + expected = {"prompt": "Test prompt", **default_generation_params()} + expected["stream"] = True + assert json == expected + assert stream is True + assert timeout is None + + return mock_response_stream() + + monkeypatch.setattr(requests, "post", mock_post) + out = llm.invoke("Test prompt") + assert out == "the quick" diff --git a/libs/community/tests/unit_tests/llms/test_minimax.py b/libs/community/tests/unit_tests/llms/test_minimax.py index 24e7fa972c4..f92a140239b 100644 --- a/libs/community/tests/unit_tests/llms/test_minimax.py +++ b/libs/community/tests/unit_tests/llms/test_minimax.py @@ -1,4 +1,5 @@ """Test Minimax llm""" + from typing import cast from langchain_core.pydantic_v1 import SecretStr @@ -19,7 +20,7 @@ def test_api_key_masked_when_passed_from_env( monkeypatch.setenv("MINIMAX_API_KEY", "secret-api-key") monkeypatch.setenv("MINIMAX_GROUP_ID", "group_id") llm = Minimax() - print(llm.minimax_api_key, end="") + print(llm.minimax_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" @@ -30,7 +31,7 @@ def test_api_key_masked_when_passed_via_constructor( ) -> None: """Test initialization with an API key provided via the initializer""" llm = Minimax(minimax_api_key="secret-api-key", minimax_group_id="group_id") - print(llm.minimax_api_key, end="") + print(llm.minimax_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/unit_tests/llms/test_oci_generative_ai.py b/libs/community/tests/unit_tests/llms/test_oci_generative_ai.py index 694d88f0c24..b1c36ec7a5f 100644 --- a/libs/community/tests/unit_tests/llms/test_oci_generative_ai.py +++ b/libs/community/tests/unit_tests/llms/test_oci_generative_ai.py @@ -8,7 +8,7 @@ from langchain_community.llms import OCIGenAI class MockResponseDict(dict): - def __getattr__(self, val): + def __getattr__(self, val): # type: ignore[no-untyped-def] return self[val] @@ -23,7 +23,7 @@ def test_llm_call(monkeypatch: MonkeyPatch, test_model_id: str) -> None: provider = llm._get_provider() - def mocked_response(*args): + def mocked_response(*args): # type: ignore[no-untyped-def] response_text = "This is the completion." if provider == "cohere": diff --git a/libs/community/tests/unit_tests/llms/test_ollama.py b/libs/community/tests/unit_tests/llms/test_ollama.py index 63a323eb35e..6dcb0bd38e3 100644 --- a/libs/community/tests/unit_tests/llms/test_ollama.py +++ b/libs/community/tests/unit_tests/llms/test_ollama.py @@ -4,11 +4,11 @@ from pytest import MonkeyPatch from langchain_community.llms.ollama import Ollama -def mock_response_stream(): +def mock_response_stream(): # type: ignore[no-untyped-def] mock_response = [b'{ "response": "Response chunk 1" }'] class MockRaw: - def read(self, chunk_size): + def read(self, chunk_size): # type: ignore[no-untyped-def] try: return mock_response.pop() except IndexError: @@ -31,7 +31,7 @@ def test_pass_headers_if_provided(monkeypatch: MonkeyPatch) -> None: timeout=300, ) - def mock_post(url, headers, json, stream, timeout): + def mock_post(url, headers, json, stream, timeout): # type: ignore[no-untyped-def] assert url == "https://ollama-hostname:8000/api/generate/" assert headers == { "Content-Type": "application/json", @@ -52,7 +52,7 @@ def test_pass_headers_if_provided(monkeypatch: MonkeyPatch) -> None: def test_handle_if_headers_not_provided(monkeypatch: MonkeyPatch) -> None: llm = Ollama(base_url="https://ollama-hostname:8000", model="foo", timeout=300) - def mock_post(url, headers, json, stream, timeout): + def mock_post(url, headers, json, stream, timeout): # type: ignore[no-untyped-def] assert url == "https://ollama-hostname:8000/api/generate/" assert headers == { "Content-Type": "application/json", @@ -72,7 +72,7 @@ def test_handle_kwargs_top_level_parameters(monkeypatch: MonkeyPatch) -> None: """Test that top level params are sent to the endpoint as top level params""" llm = Ollama(base_url="https://ollama-hostname:8000", model="foo", timeout=300) - def mock_post(url, headers, json, stream, timeout): + def mock_post(url, headers, json, stream, timeout): # type: ignore[no-untyped-def] assert url == "https://ollama-hostname:8000/api/generate/" assert headers == { "Content-Type": "application/json", @@ -118,7 +118,7 @@ def test_handle_kwargs_with_unknown_param(monkeypatch: MonkeyPatch) -> None: """ llm = Ollama(base_url="https://ollama-hostname:8000", model="foo", timeout=300) - def mock_post(url, headers, json, stream, timeout): + def mock_post(url, headers, json, stream, timeout): # type: ignore[no-untyped-def] assert url == "https://ollama-hostname:8000/api/generate/" assert headers == { "Content-Type": "application/json", @@ -165,7 +165,7 @@ def test_handle_kwargs_with_options(monkeypatch: MonkeyPatch) -> None: """ llm = Ollama(base_url="https://ollama-hostname:8000", model="foo", timeout=300) - def mock_post(url, headers, json, stream, timeout): + def mock_post(url, headers, json, stream, timeout): # type: ignore[no-untyped-def] assert url == "https://ollama-hostname:8000/api/generate/" assert headers == { "Content-Type": "application/json", diff --git a/libs/community/tests/unit_tests/llms/test_openai.py b/libs/community/tests/unit_tests/llms/test_openai.py index 3b1c4d8dfed..73f6fad2834 100644 --- a/libs/community/tests/unit_tests/llms/test_openai.py +++ b/libs/community/tests/unit_tests/llms/test_openai.py @@ -1,12 +1,8 @@ -import os - import pytest from langchain_community.llms.openai import OpenAI from langchain_community.utils.openai import is_openai_v1 -os.environ["OPENAI_API_KEY"] = "foo" - def _openai_v1_installed() -> bool: try: @@ -17,15 +13,15 @@ def _openai_v1_installed() -> bool: @pytest.mark.requires("openai") def test_openai_model_param() -> None: - llm = OpenAI(model="foo") + llm = OpenAI(model="foo", openai_api_key="foo") assert llm.model_name == "foo" - llm = OpenAI(model_name="foo") + llm = OpenAI(model_name="foo", openai_api_key="foo") assert llm.model_name == "foo" @pytest.mark.requires("openai") def test_openai_model_kwargs() -> None: - llm = OpenAI(model_kwargs={"foo": "bar"}) + llm = OpenAI(model_kwargs={"foo": "bar"}, openai_api_key="foo") assert llm.model_kwargs == {"foo": "bar"} @@ -42,7 +38,7 @@ def test_openai_invalid_model_kwargs() -> None: @pytest.mark.requires("openai") def test_openai_incorrect_field() -> None: with pytest.warns(match="not default parameter"): - llm = OpenAI(foo="bar") + llm = OpenAI(foo="bar", openai_api_key="foo") assert llm.model_kwargs == {"foo": "bar"} diff --git a/libs/community/tests/unit_tests/llms/test_pipelineai.py b/libs/community/tests/unit_tests/llms/test_pipelineai.py index 63f750d0b22..afa91752ab1 100644 --- a/libs/community/tests/unit_tests/llms/test_pipelineai.py +++ b/libs/community/tests/unit_tests/llms/test_pipelineai.py @@ -13,7 +13,7 @@ def test_api_key_masked_when_passed_via_constructor( capsys: CaptureFixture, ) -> None: llm = PipelineAI(pipeline_api_key="secret-api-key") - print(llm.pipeline_api_key, end="") + print(llm.pipeline_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/unit_tests/llms/test_stochasticai.py b/libs/community/tests/unit_tests/llms/test_stochasticai.py index bf3e428c6b9..1fa0cfd243f 100644 --- a/libs/community/tests/unit_tests/llms/test_stochasticai.py +++ b/libs/community/tests/unit_tests/llms/test_stochasticai.py @@ -13,7 +13,7 @@ def test_api_key_masked_when_passed_via_constructor( capsys: CaptureFixture, ) -> None: llm = StochasticAI(stochasticai_api_key="secret-api-key") - print(llm.stochasticai_api_key, end="") + print(llm.stochasticai_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/unit_tests/llms/test_symblai_nebula.py b/libs/community/tests/unit_tests/llms/test_symblai_nebula.py index fb75a6ad699..92ec7484ef5 100644 --- a/libs/community/tests/unit_tests/llms/test_symblai_nebula.py +++ b/libs/community/tests/unit_tests/llms/test_symblai_nebula.py @@ -17,13 +17,13 @@ def test_api_key_masked_when_passed_from_env( ) -> None: monkeypatch.setenv("NEBULA_API_KEY", "secret-api-key") llm = Nebula() - print(llm.nebula_api_key, end="") + print(llm.nebula_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" def test_api_key_masked_when_passed_via_constructor(capsys: CaptureFixture) -> None: llm = Nebula(nebula_api_key="secret-api-key") - print(llm.nebula_api_key, end="") + print(llm.nebula_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/unit_tests/llms/test_together.py b/libs/community/tests/unit_tests/llms/test_together.py index 772cde4050e..00da416b047 100644 --- a/libs/community/tests/unit_tests/llms/test_together.py +++ b/libs/community/tests/unit_tests/llms/test_together.py @@ -1,4 +1,5 @@ """Test Together LLM""" + from typing import cast from langchain_core.pydantic_v1 import SecretStr @@ -28,7 +29,7 @@ def test_together_api_key_masked_when_passed_from_env( temperature=0.2, max_tokens=250, ) - print(llm.together_api_key, end="") + print(llm.together_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" @@ -44,7 +45,7 @@ def test_together_api_key_masked_when_passed_via_constructor( temperature=0.2, max_tokens=250, ) - print(llm.together_api_key, end="") + print(llm.together_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/community/tests/unit_tests/retrievers/test_imports.py b/libs/community/tests/unit_tests/retrievers/test_imports.py index 2f787597982..d13bfe28810 100644 --- a/libs/community/tests/unit_tests/retrievers/test_imports.py +++ b/libs/community/tests/unit_tests/retrievers/test_imports.py @@ -6,6 +6,7 @@ EXPECTED_ALL = [ "ArceeRetriever", "ArxivRetriever", "AzureCognitiveSearchRetriever", + "BreebsRetriever", "ChatGPTPluginRetriever", "ChaindeskRetriever", "CohereRagRetriever", @@ -33,6 +34,7 @@ EXPECTED_ALL = [ "VespaRetriever", "WeaviateHybridSearchRetriever", "WikipediaRetriever", + "YouRetriever", "ZepRetriever", "ZillizRetriever", "DocArrayRetriever", diff --git a/libs/community/tests/unit_tests/retrievers/test_you.py b/libs/community/tests/unit_tests/retrievers/test_you.py index 0d5acfeb693..dbc8cc65091 100644 --- a/libs/community/tests/unit_tests/retrievers/test_you.py +++ b/libs/community/tests/unit_tests/retrievers/test_you.py @@ -1,26 +1,72 @@ -import json -import os -from unittest import mock - -from langchain_core.documents import Document -from requests import Response +import responses from langchain_community.retrievers.you import YouRetriever +from ..utilities.test_you import ( + LIMITED_PARSED_OUTPUT, + MOCK_PARSED_OUTPUT, + MOCK_RESPONSE_RAW, + NEWS_RESPONSE_PARSED, + NEWS_RESPONSE_RAW, + TEST_ENDPOINT, +) + class TestYouRetriever: + @responses.activate def test_get_relevant_documents(self) -> None: - os.environ["YDC_API_KEY"] = "MOCK KEY!" - retriever = YouRetriever() + responses.add( + responses.GET, f"{TEST_ENDPOINT}/search", json=MOCK_RESPONSE_RAW, status=200 + ) + query = "Test query text" + you_wrapper = YouRetriever(ydc_api_key="test") + results = you_wrapper.get_relevant_documents(query) + expected_result = MOCK_PARSED_OUTPUT + assert results == expected_result - with mock.patch("requests.get") as mock_get: - fixture = {"hits": [{"snippets": ["yo"]}, {"snippets": ["bird up"]}]} - response = Response() - response._content = bytes(json.dumps(fixture).encode("utf-8")) - mock_get.return_value = response + @responses.activate + def test_invoke(self) -> None: + responses.add( + responses.GET, f"{TEST_ENDPOINT}/search", json=MOCK_RESPONSE_RAW, status=200 + ) + query = "Test query text" + you_wrapper = YouRetriever(ydc_api_key="test") + results = you_wrapper.invoke(query) + expected_result = MOCK_PARSED_OUTPUT + assert results == expected_result - actual = retriever.get_relevant_documents("test") - assert actual == [ - Document(page_content="yo"), - Document(page_content="bird up"), - ] + @responses.activate + def test_invoke_max_docs(self) -> None: + responses.add( + responses.GET, f"{TEST_ENDPOINT}/search", json=MOCK_RESPONSE_RAW, status=200 + ) + query = "Test query text" + you_wrapper = YouRetriever(k=2, ydc_api_key="test") + results = you_wrapper.invoke(query) + expected_result = [MOCK_PARSED_OUTPUT[0], MOCK_PARSED_OUTPUT[1]] + assert results == expected_result + + @responses.activate + def test_invoke_limit_snippets(self) -> None: + responses.add( + responses.GET, f"{TEST_ENDPOINT}/search", json=MOCK_RESPONSE_RAW, status=200 + ) + + query = "Test query text" + you_wrapper = YouRetriever(n_snippets_per_hit=1, ydc_api_key="test") + results = you_wrapper.results(query) + expected_result = LIMITED_PARSED_OUTPUT + assert results == expected_result + + @responses.activate + def test_invoke_news(self) -> None: + responses.add( + responses.GET, f"{TEST_ENDPOINT}/news", json=NEWS_RESPONSE_RAW, status=200 + ) + + query = "Test news text" + # ensure limit on number of docs returned + you_wrapper = YouRetriever(endpoint_type="news", ydc_api_key="test") + results = you_wrapper.results(query) + expected_result = NEWS_RESPONSE_PARSED + assert results == expected_result diff --git a/libs/community/tests/unit_tests/storage/test_imports.py b/libs/community/tests/unit_tests/storage/test_imports.py index 27bcec56d4b..21f79d464e7 100644 --- a/libs/community/tests/unit_tests/storage/test_imports.py +++ b/libs/community/tests/unit_tests/storage/test_imports.py @@ -3,6 +3,7 @@ from langchain_community.storage import __all__ EXPECTED_ALL = [ "AstraDBStore", "AstraDBByteStore", + "MongoDBStore", "RedisStore", "UpstashRedisByteStore", "UpstashRedisStore", diff --git a/libs/community/tests/unit_tests/storage/test_mongodb.py b/libs/community/tests/unit_tests/storage/test_mongodb.py new file mode 100644 index 00000000000..a1dc9ea9068 --- /dev/null +++ b/libs/community/tests/unit_tests/storage/test_mongodb.py @@ -0,0 +1,11 @@ +"""Light weight unit test that attempts to import MongodbStore. + +The actual code is tested in integration tests. + +This test is intended to catch errors in the import process. +""" + + +def test_import_storage() -> None: + """Attempt to import storage modules.""" + from langchain_community.storage.mongodb import MongoDBStore # noqa diff --git a/libs/langchain/tests/unit_tests/test_sql_database.py b/libs/community/tests/unit_tests/test_sql_database.py similarity index 54% rename from libs/langchain/tests/unit_tests/test_sql_database.py rename to libs/community/tests/unit_tests/test_sql_database.py index fea54e74c25..4293265a1de 100644 --- a/libs/langchain/tests/unit_tests/test_sql_database.py +++ b/libs/community/tests/unit_tests/test_sql_database.py @@ -1,18 +1,21 @@ -# flake8: noqa=E501 +# flake8: noqa: E501 """Test SQL database wrapper.""" - -from langchain_community.utilities.sql_database import SQLDatabase, truncate_word +import pytest +import sqlalchemy as sa from sqlalchemy import ( Column, Integer, MetaData, + Result, String, Table, Text, - create_engine, insert, + select, ) +from langchain_community.utilities.sql_database import SQLDatabase, truncate_word + metadata_obj = MetaData() user = Table( @@ -31,11 +34,19 @@ company = Table( ) -def test_table_info() -> None: - """Test that table info is constructed properly.""" - engine = create_engine("sqlite:///:memory:") +@pytest.fixture +def engine() -> sa.Engine: + return sa.create_engine("sqlite:///:memory:") + + +@pytest.fixture +def db(engine: sa.Engine) -> SQLDatabase: metadata_obj.create_all(engine) - db = SQLDatabase(engine) + return SQLDatabase(engine) + + +def test_table_info(db: SQLDatabase) -> None: + """Test that table info is constructed properly.""" output = db.table_info expected_output = """ CREATE TABLE user ( @@ -64,20 +75,19 @@ def test_table_info() -> None: assert sorted(" ".join(output.split())) == sorted(" ".join(expected_output.split())) -def test_table_info_w_sample_rows() -> None: +def test_table_info_w_sample_rows(db: SQLDatabase) -> None: """Test that table info is constructed properly.""" - engine = create_engine("sqlite:///:memory:") - metadata_obj.create_all(engine) + + # Provision. values = [ {"user_id": 13, "user_name": "Harrison", "user_bio": "bio"}, {"user_id": 14, "user_name": "Chase", "user_bio": "bio"}, ] stmt = insert(user).values(values) - with engine.begin() as conn: - conn.execute(stmt) - - db = SQLDatabase(engine, sample_rows_in_table_info=2) + db._execute(stmt) + # Query and verify. + db = SQLDatabase(db._engine, sample_rows_in_table_info=2) output = db.table_info expected_output = """ @@ -108,16 +118,16 @@ def test_table_info_w_sample_rows() -> None: assert sorted(output.split()) == sorted(expected_output.split()) -def test_sql_database_run() -> None: - """Test that commands can be run successfully and returned in correct format.""" - engine = create_engine("sqlite:///:memory:") - metadata_obj.create_all(engine) +def test_sql_database_run_fetch_all(db: SQLDatabase) -> None: + """Verify running SQL expressions returning results as strings.""" + + # Provision. stmt = insert(user).values( user_id=13, user_name="Harrison", user_bio="That is my Bio " * 24 ) - with engine.begin() as conn: - conn.execute(stmt) - db = SQLDatabase(engine) + db._execute(stmt) + + # Query and verify. command = "select user_id, user_name, user_bio from user where user_id = 13" partial_output = db.run(command) user_bio = "That is my Bio " * 19 + "That is my..." @@ -131,20 +141,81 @@ def test_sql_database_run() -> None: assert full_output == expected_full_output -def test_sql_database_run_update() -> None: +def test_sql_database_run_fetch_result(db: SQLDatabase) -> None: + """Verify running SQL expressions returning results as SQLAlchemy `Result` instances.""" + + # Provision. + stmt = insert(user).values(user_id=17, user_name="hwchase") + db._execute(stmt) + + # Query and verify. + command = "select user_id, user_name, user_bio from user where user_id = 17" + result = db.run(command, fetch="cursor", include_columns=True) + expected = [{"user_id": 17, "user_name": "hwchase", "user_bio": None}] + assert isinstance(result, Result) + assert result.mappings().fetchall() == expected + + +def test_sql_database_run_with_parameters(db: SQLDatabase) -> None: + """Verify running SQL expressions with query parameters.""" + + # Provision. + stmt = insert(user).values(user_id=17, user_name="hwchase") + db._execute(stmt) + + # Query and verify. + command = "select user_id, user_name, user_bio from user where user_id = :user_id" + full_output = db.run(command, parameters={"user_id": 17}, include_columns=True) + expected_full_output = "[{'user_id': 17, 'user_name': 'hwchase', 'user_bio': None}]" + assert full_output == expected_full_output + + +def test_sql_database_run_sqlalchemy_selectable(db: SQLDatabase) -> None: + """Verify running SQL expressions using SQLAlchemy selectable.""" + + # Provision. + stmt = insert(user).values(user_id=17, user_name="hwchase") + db._execute(stmt) + + # Query and verify. + command = select(user).where(user.c.user_id == 17) + full_output = db.run(command, include_columns=True) + expected_full_output = "[{'user_id': 17, 'user_name': 'hwchase', 'user_bio': None}]" + assert full_output == expected_full_output + + +def test_sql_database_run_update(db: SQLDatabase) -> None: """Test commands which return no rows return an empty string.""" - engine = create_engine("sqlite:///:memory:") - metadata_obj.create_all(engine) + + # Provision. stmt = insert(user).values(user_id=13, user_name="Harrison") - with engine.begin() as conn: - conn.execute(stmt) - db = SQLDatabase(engine) + db._execute(stmt) + + # Query and verify. command = "update user set user_name='Updated' where user_id = 13" output = db.run(command) expected_output = "" assert output == expected_output +def test_sql_database_schema_translate_map() -> None: + """Verify using statement-specific execution options.""" + + engine = sa.create_engine("sqlite:///:memory:") + db = SQLDatabase(engine) + + # Define query using SQLAlchemy selectable. + command = select(user).where(user.c.user_id == 17) + + # Define statement-specific execution options. + execution_options = {"schema_translate_map": {None: "bar"}} + + # Verify the schema translation is applied. + with pytest.raises(sa.exc.OperationalError) as ex: + db.run(command, execution_options=execution_options, fetch="cursor") + assert ex.match("no such table: bar.user") + + def test_truncate_word() -> None: assert truncate_word("Hello World", length=5) == "He..." assert truncate_word("Hello World", length=0) == "Hello World" diff --git a/libs/langchain/tests/unit_tests/test_sql_database_schema.py b/libs/community/tests/unit_tests/test_sql_database_schema.py similarity index 100% rename from libs/langchain/tests/unit_tests/test_sql_database_schema.py rename to libs/community/tests/unit_tests/test_sql_database_schema.py diff --git a/libs/community/tests/unit_tests/utilities/test_imports.py b/libs/community/tests/unit_tests/utilities/test_imports.py index 6499d1b857b..91d1712be3c 100644 --- a/libs/community/tests/unit_tests/utilities/test_imports.py +++ b/libs/community/tests/unit_tests/utilities/test_imports.py @@ -24,6 +24,9 @@ EXPECTED_ALL = [ "MaxComputeAPIWrapper", "MetaphorSearchAPIWrapper", "NasaAPIWrapper", + "NVIDIARivaASR", + "NVIDIARivaTTS", + "NVIDIARivaStream", "OpenWeatherMapAPIWrapper", "OutlineAPIWrapper", "Portkey", @@ -45,6 +48,7 @@ EXPECTED_ALL = [ "TwilioAPIWrapper", "WikipediaAPIWrapper", "WolframAlphaAPIWrapper", + "YouSearchAPIWrapper", "ZapierNLAWrapper", "MerriamWebsterAPIWrapper", ] diff --git a/libs/community/tests/unit_tests/utilities/test_nvidia_riva_asr.py b/libs/community/tests/unit_tests/utilities/test_nvidia_riva_asr.py new file mode 100644 index 00000000000..b9072dbfbba --- /dev/null +++ b/libs/community/tests/unit_tests/utilities/test_nvidia_riva_asr.py @@ -0,0 +1,177 @@ +"""Unit tests to verify function of the Riva ASR implementation.""" + +from typing import TYPE_CHECKING, Any, Generator +from unittest.mock import patch + +import pytest + +from langchain_community.utilities.nvidia_riva import ( + AudioStream, + RivaASR, + RivaAudioEncoding, +) + +if TYPE_CHECKING: + import riva.client + import riva.client.proto.riva_asr_pb2 as rasr + +AUDIO_DATA_MOCK = [ + b"This", + b"is", + b"a", + b"test.", + b"_", + b"Hello.", + b"World", +] +AUDIO_TEXT_MOCK = b" ".join(AUDIO_DATA_MOCK).decode().strip().split("_") + +SVC_URI = "not-a-url.asdf:9999" +SVC_USE_SSL = True +CONFIG = { + "audio_channel_count": 9, + "profanity_filter": False, + "enable_automatic_punctuation": False, + "url": f"{'https' if SVC_USE_SSL else 'http'}://{SVC_URI}", + "ssl_cert": "/dev/null", + "encoding": RivaAudioEncoding.ALAW, + "language_code": "not-a-language", + "sample_rate_hertz": 5, +} + + +def response_generator( + transcript: str = "", + empty: bool = False, + final: bool = False, + alternatives: bool = True, +) -> "rasr.StreamingRecognizeResponse": + """Create a pseudo streaming response.""" + # pylint: disable-next=import-outside-toplevel + import riva.client.proto.riva_asr_pb2 as rasr + + if empty: + return rasr.StreamingRecognizeResponse() + + if not alternatives: + return rasr.StreamingRecognizeResponse( + results=[ + rasr.StreamingRecognitionResult( + is_final=final, + alternatives=[], + ) + ] + ) + + return rasr.StreamingRecognizeResponse( + results=[ + rasr.StreamingRecognitionResult( + is_final=final, + alternatives=[ + rasr.SpeechRecognitionAlternative(transcript=transcript.strip()) + ], + ) + ] + ) + + +def streaming_recognize_mock( + generator: Generator["rasr.StreamingRecognizeRequest", None, None], **_: Any +) -> Generator["rasr.StreamingRecognizeResponse", None, None]: + """A mock function to fake a streaming call to Riva.""" + yield response_generator(empty=True) + yield response_generator(alternatives=False) + + output_transcript = "" + for streaming_requests in generator: + input_bytes = streaming_requests.audio_content.decode() + + final = input_bytes == "_" + if final: + input_bytes = "" + + output_transcript += input_bytes + " " + + yield response_generator(final=final, transcript=output_transcript) + if final: + output_transcript = "" + + yield response_generator(final=True, transcript=output_transcript) + + +def riva_asr_stub_init_patch( + self: "riva.client.proto.riva_asr_pb2_grpc.RivaSpeechRecognitionStub", _: Any +) -> None: + """Patch for the Riva asr library.""" + self.StreamingRecognize = streaming_recognize_mock + + +@pytest.fixture +def asr() -> RivaASR: + """Initialize a copy of the runnable.""" + return RivaASR(**CONFIG) + + +@pytest.fixture +def stream() -> AudioStream: + """Initialize and populate a sample audio stream.""" + s = AudioStream() + for val in AUDIO_DATA_MOCK: + s.put(val) + s.close() + return s + + +@pytest.mark.requires("riva.client") +def test_init(asr: RivaASR) -> None: + """Test that ASR accepts valid arguments.""" + for key, expected_val in CONFIG.items(): + assert getattr(asr, key, None) == expected_val + + +@pytest.mark.requires("riva.client") +def test_init_defaults() -> None: + """Ensure the runnable can be loaded with no arguments.""" + _ = RivaASR() + + +@pytest.mark.requires("riva.client") +def test_config(asr: RivaASR) -> None: + """Verify the Riva config is properly assembled.""" + # pylint: disable-next=import-outside-toplevel + import riva.client.proto.riva_asr_pb2 as rasr + + expected = rasr.StreamingRecognitionConfig( + interim_results=True, + config=rasr.RecognitionConfig( + encoding=CONFIG["encoding"], + sample_rate_hertz=CONFIG["sample_rate_hertz"], + audio_channel_count=CONFIG["audio_channel_count"], + max_alternatives=1, + profanity_filter=CONFIG["profanity_filter"], + enable_automatic_punctuation=CONFIG["enable_automatic_punctuation"], + language_code=CONFIG["language_code"], + ), + ) + assert asr.config == expected + + +@pytest.mark.requires("riva.client") +def test_get_service(asr: RivaASR) -> None: + """Test generating an asr service class.""" + svc = asr._get_service() + assert str(svc.auth.ssl_cert) == CONFIG["ssl_cert"] + assert svc.auth.use_ssl == SVC_USE_SSL + assert svc.auth.uri == SVC_URI + + +@pytest.mark.requires("riva.client") +@patch( + "riva.client.proto.riva_asr_pb2_grpc.RivaSpeechRecognitionStub.__init__", + riva_asr_stub_init_patch, +) +def test_invoke(asr: RivaASR, stream: AudioStream) -> None: + """Test the invoke method.""" + got = asr.invoke(stream) + expected = " ".join([s.strip() for s in AUDIO_TEXT_MOCK]).strip() + assert got == expected diff --git a/libs/community/tests/unit_tests/utilities/test_nvidia_riva_tts.py b/libs/community/tests/unit_tests/utilities/test_nvidia_riva_tts.py new file mode 100644 index 00000000000..321f6d1ea82 --- /dev/null +++ b/libs/community/tests/unit_tests/utilities/test_nvidia_riva_tts.py @@ -0,0 +1,153 @@ +"""Unit tests to verify function of the Riva TTS implementation.""" + +from typing import TYPE_CHECKING, Any, AsyncGenerator, Generator, cast +from unittest.mock import patch + +import pytest + +from langchain_community.utilities.nvidia_riva import RivaAudioEncoding, RivaTTS + +if TYPE_CHECKING: + import riva.client + import riva.client.proto.riva_tts_pb2 as rtts + +AUDIO_TEXT_MOCK = ["This is a test.", "Hello world"] +AUDIO_DATA_MOCK = [s.encode() for s in AUDIO_TEXT_MOCK] + +SVC_URI = "not-a-url.asdf:9999" +SVC_USE_SSL = True +CONFIG = { + "voice_name": "English-Test", + "output_directory": None, + "url": f"{'https' if SVC_USE_SSL else 'http'}://{SVC_URI}", + "ssl_cert": "/dev/null", + "encoding": RivaAudioEncoding.ALAW, + "language_code": "not-a-language", + "sample_rate_hertz": 5, +} + + +def synthesize_online_mock( + request: "rtts.SynthesizeSpeechRequest", **_: Any +) -> Generator["rtts.SynthesizeSpeechResponse", None, None]: + """A mock function to fake a streaming call to Riva.""" + # pylint: disable-next=import-outside-toplevel + import riva.client.proto.riva_tts_pb2 as rtts + + yield rtts.SynthesizeSpeechResponse( + audio=f"[{request.language_code},{request.encoding},{request.sample_rate_hz},{request.voice_name}]".encode() + ) + yield rtts.SynthesizeSpeechResponse(audio=request.text.strip().encode()) + + +def riva_tts_stub_init_patch( + self: "riva.client.proto.riva_tts_pb2_grpc.RivaSpeechSynthesisStub", _: Any +) -> None: + """Patch for the Riva TTS library.""" + self.SynthesizeOnline = synthesize_online_mock + + +@pytest.fixture +def tts() -> RivaTTS: + """Initialize a copy of the runnable.""" + return RivaTTS(**CONFIG) + + +@pytest.mark.requires("riva.client") +def test_init(tts: RivaTTS) -> None: + """Test that ASR accepts valid arguments.""" + for key, expected_val in CONFIG.items(): + assert getattr(tts, key, None) == expected_val + + +@pytest.mark.requires("riva.client") +def test_init_defaults() -> None: + """Ensure the runnable can be loaded with no arguments.""" + _ = RivaTTS() + + +@pytest.mark.requires("riva.client") +def test_get_service(tts: RivaTTS) -> None: + """Test the get service method.""" + svc = tts._get_service() + assert str(svc.auth.ssl_cert) == CONFIG["ssl_cert"] + assert svc.auth.use_ssl == SVC_USE_SSL + assert svc.auth.uri == SVC_URI + + +@pytest.mark.requires("riva.client") +@patch( + "riva.client.proto.riva_tts_pb2_grpc.RivaSpeechSynthesisStub.__init__", + riva_tts_stub_init_patch, +) +def test_invoke(tts: RivaTTS) -> None: + """Test the invoke method.""" + encoding = cast(RivaAudioEncoding, CONFIG["encoding"]).riva_pb2 + audio_synth_config = ( + f"[{CONFIG['language_code']}," + f"{encoding}," + f"{CONFIG['sample_rate_hertz']}," + f"{CONFIG['voice_name']}]" + ) + + input = " ".join(AUDIO_TEXT_MOCK).strip() + response = tts.invoke(input) + expected = (audio_synth_config + audio_synth_config.join(AUDIO_TEXT_MOCK)).encode() + assert response == expected + + +@pytest.mark.requires("riva.client") +@patch( + "riva.client.proto.riva_tts_pb2_grpc.RivaSpeechSynthesisStub.__init__", + riva_tts_stub_init_patch, +) +def test_transform(tts: RivaTTS) -> None: + """Test the transform method.""" + encoding = cast(RivaAudioEncoding, CONFIG["encoding"]).riva_pb2 + audio_synth_config = ( + f"[{CONFIG['language_code']}," + f"{encoding}," + f"{CONFIG['sample_rate_hertz']}," + f"{CONFIG['voice_name']}]" + ) + expected = (audio_synth_config + audio_synth_config.join(AUDIO_TEXT_MOCK)).encode() + for idx, response in enumerate(tts.transform(iter(AUDIO_TEXT_MOCK))): + if idx % 2: + # odd indices will return the mocked data + expected = AUDIO_DATA_MOCK[int((idx - 1) / 2)] + else: + # even indices will return the request config + expected = audio_synth_config.encode() + assert response == expected + + +@pytest.mark.requires("riva.client") +@patch( + "riva.client.proto.riva_tts_pb2_grpc.RivaSpeechSynthesisStub.__init__", + riva_tts_stub_init_patch, +) +async def test_atransform(tts: RivaTTS) -> None: + """Test the transform method.""" + encoding = cast(RivaAudioEncoding, CONFIG["encoding"]).riva_pb2 + audio_synth_config = ( + f"[{CONFIG['language_code']}," + f"{encoding}," + f"{CONFIG['sample_rate_hertz']}," + f"{CONFIG['voice_name']}]" + ) + expected = (audio_synth_config + audio_synth_config.join(AUDIO_TEXT_MOCK)).encode() + idx = 0 + + async def _fake_async_iterable() -> AsyncGenerator[str, None]: + for val in AUDIO_TEXT_MOCK: + yield val + + async for response in tts.atransform(_fake_async_iterable()): + if idx % 2: + # odd indices will return the mocked data + expected = AUDIO_DATA_MOCK[int((idx - 1) / 2)] + else: + # even indices will return the request config + expected = audio_synth_config.encode() + assert response == expected + idx += 1 diff --git a/libs/community/tests/unit_tests/utilities/test_you.py b/libs/community/tests/unit_tests/utilities/test_you.py new file mode 100644 index 00000000000..adc004ad438 --- /dev/null +++ b/libs/community/tests/unit_tests/utilities/test_you.py @@ -0,0 +1,190 @@ +from typing import Any, Dict, List, Optional, Union + +import responses +from langchain_core.documents import Document + +from langchain_community.utilities.you import YouSearchAPIWrapper + +TEST_ENDPOINT = "https://api.ydc-index.io" + +# Mock you.com response for testing +MOCK_RESPONSE_RAW: Dict[str, List[Dict[str, Union[str, List[str]]]]] = { + "hits": [ + { + "description": "Test description", + "snippets": ["yo", "bird up"], + "thumbnail_url": "https://example.com/image.gif", + "title": "Test title 1", + "url": "https://example.com/article.html", + }, + { + "description": "Test description 2", + "snippets": ["worst show", "on tv"], + "thumbnail_url": "https://example.com/image2.gif", + "title": "Test title 2", + "url": "https://example.com/article2.html", + }, + ] +} + + +def generate_parsed_metadata(num: Optional[int] = 0) -> Dict[Any, Any]: + """generate metadata for testing""" + if num is None: + num = 0 + hit: Dict[str, Union[str, List[str]]] = MOCK_RESPONSE_RAW["hits"][num] + return { + "url": hit["url"], + "thumbnail_url": hit["thumbnail_url"], + "title": hit["title"], + "description": hit["description"], + } + + +def generate_parsed_output(num: Optional[int] = 0) -> List[Document]: + """generate parsed output for testing""" + if num is None: + num = 0 + hit: Dict[str, Union[str, List[str]]] = MOCK_RESPONSE_RAW["hits"][num] + output = [] + for snippit in hit["snippets"]: + doc = Document(page_content=snippit, metadata=generate_parsed_metadata(num)) + output.append(doc) + return output + + +# Mock results after parsing +MOCK_PARSED_OUTPUT = generate_parsed_output() +MOCK_PARSED_OUTPUT.extend(generate_parsed_output(1)) +# Single-snippet +LIMITED_PARSED_OUTPUT = [] +LIMITED_PARSED_OUTPUT.append(generate_parsed_output()[0]) +LIMITED_PARSED_OUTPUT.append(generate_parsed_output(1)[0]) + +# copied from you api docs +NEWS_RESPONSE_RAW = { + "news": { + "results": [ + { + "age": "18 hours ago", + "breaking": True, + "description": "Search on YDC for the news", + "meta_url": { + "hostname": "www.reuters.com", + "netloc": "reuters.com", + "path": "› 2023 › 10 › 18 › politics › inflation › index.html", + "scheme": "https", + }, + "page_age": "2 days", + "page_fetched": "2023-10-12T23:00:00Z", + "thumbnail": {"original": "https://reuters.com/news.jpg"}, + "title": "Breaking News about the World's Greatest Search Engine!", + "type": "news", + "url": "https://news.you.com", + } + ] + } +} + +NEWS_RESPONSE_PARSED = [ + Document(page_content=str(result["description"]), metadata=result) + for result in NEWS_RESPONSE_RAW["news"]["results"] +] + + +@responses.activate +def test_raw_results() -> None: + responses.add( + responses.GET, f"{TEST_ENDPOINT}/search", json=MOCK_RESPONSE_RAW, status=200 + ) + + query = "Test query text" + # ensure default endpoint_type + you_wrapper = YouSearchAPIWrapper(endpoint_type="snippet", ydc_api_key="test") + raw_results = you_wrapper.raw_results(query) + expected_result = MOCK_RESPONSE_RAW + assert raw_results == expected_result + + +@responses.activate +def test_raw_results_defaults() -> None: + responses.add( + responses.GET, f"{TEST_ENDPOINT}/search", json=MOCK_RESPONSE_RAW, status=200 + ) + + query = "Test query text" + # ensure limit on number of docs returned + you_wrapper = YouSearchAPIWrapper(ydc_api_key="test") + raw_results = you_wrapper.raw_results(query) + expected_result = MOCK_RESPONSE_RAW + assert raw_results == expected_result + + +@responses.activate +def test_raw_results_news() -> None: + responses.add( + responses.GET, f"{TEST_ENDPOINT}/news", json=NEWS_RESPONSE_RAW, status=200 + ) + + query = "Test news text" + # ensure limit on number of docs returned + you_wrapper = YouSearchAPIWrapper(endpoint_type="news", ydc_api_key="test") + raw_results = you_wrapper.raw_results(query) + expected_result = NEWS_RESPONSE_RAW + assert raw_results == expected_result + + +@responses.activate +def test_results() -> None: + responses.add( + responses.GET, f"{TEST_ENDPOINT}/search", json=MOCK_RESPONSE_RAW, status=200 + ) + + query = "Test query text" + you_wrapper = YouSearchAPIWrapper(ydc_api_key="test") + results = you_wrapper.results(query) + expected_result = MOCK_PARSED_OUTPUT + assert results == expected_result + + +@responses.activate +def test_results_max_docs() -> None: + responses.add( + responses.GET, f"{TEST_ENDPOINT}/search", json=MOCK_RESPONSE_RAW, status=200 + ) + + query = "Test query text" + you_wrapper = YouSearchAPIWrapper(k=2, ydc_api_key="test") + results = you_wrapper.results(query) + expected_result = generate_parsed_output() + assert results == expected_result + + +@responses.activate +def test_results_limit_snippets() -> None: + responses.add( + responses.GET, f"{TEST_ENDPOINT}/search", json=MOCK_RESPONSE_RAW, status=200 + ) + + query = "Test query text" + you_wrapper = YouSearchAPIWrapper(n_snippets_per_hit=1, ydc_api_key="test") + results = you_wrapper.results(query) + expected_result = LIMITED_PARSED_OUTPUT + assert results == expected_result + + +@responses.activate +def test_results_news() -> None: + responses.add( + responses.GET, f"{TEST_ENDPOINT}/news", json=NEWS_RESPONSE_RAW, status=200 + ) + + query = "Test news text" + # ensure limit on number of docs returned + you_wrapper = YouSearchAPIWrapper(endpoint_type="news", ydc_api_key="test") + raw_results = you_wrapper.results(query) + expected_result = NEWS_RESPONSE_PARSED + assert raw_results == expected_result + + +# @todo test async methods diff --git a/libs/community/tests/unit_tests/vectorstores/test_databricks_vector_search.py b/libs/community/tests/unit_tests/vectorstores/test_databricks_vector_search.py index 1a03890f637..4bdcee9acfb 100644 --- a/libs/community/tests/unit_tests/vectorstores/test_databricks_vector_search.py +++ b/libs/community/tests/unit_tests/vectorstores/test_databricks_vector_search.py @@ -1,7 +1,8 @@ +import itertools import random import uuid -from typing import List -from unittest.mock import MagicMock +from typing import Dict, List, Optional, Set +from unittest.mock import MagicMock, patch import pytest @@ -120,6 +121,52 @@ EXAMPLE_SEARCH_RESPONSE = { "next_page_token": "", } +EXAMPLE_SEARCH_RESPONSE_FIXED_SCORE: Dict = { + "manifest": { + "column_count": 3, + "columns": [ + {"name": DEFAULT_PRIMARY_KEY}, + {"name": DEFAULT_TEXT_COLUMN}, + {"name": "score"}, + ], + }, + "result": { + "row_count": len(fake_texts), + "data_array": sorted( + [[str(uuid.uuid4()), s, 0.5] for s in fake_texts], + key=lambda x: x[2], # type: ignore + reverse=True, + ), + }, + "next_page_token": "", +} + +EXAMPLE_SEARCH_RESPONSE_WITH_EMBEDDING = { + "manifest": { + "column_count": 3, + "columns": [ + {"name": DEFAULT_PRIMARY_KEY}, + {"name": DEFAULT_TEXT_COLUMN}, + {"name": DEFAULT_VECTOR_COLUMN}, + {"name": "score"}, + ], + }, + "result": { + "row_count": len(fake_texts), + "data_array": sorted( + [ + [str(uuid.uuid4()), s, e, random.uniform(0, 1)] + for s, e in zip( + fake_texts, DEFAULT_EMBEDDING_MODEL.embed_documents(fake_texts) + ) + ], + key=lambda x: x[2], # type: ignore + reverse=True, + ), + }, + "next_page_token": "", +} + def mock_index(index_details: dict) -> MagicMock: from databricks.vector_search.client import VectorSearchIndex @@ -129,11 +176,14 @@ def mock_index(index_details: dict) -> MagicMock: return index -def default_databricks_vector_search(index: MagicMock) -> DatabricksVectorSearch: +def default_databricks_vector_search( + index: MagicMock, columns: Optional[List[str]] = None +) -> DatabricksVectorSearch: return DatabricksVectorSearch( index, embedding=DEFAULT_EMBEDDING_MODEL, text_column=DEFAULT_TEXT_COLUMN, + columns=columns, ) @@ -456,6 +506,100 @@ def test_similarity_search(index_details: dict) -> None: assert all([DEFAULT_PRIMARY_KEY in d.metadata for d in search_result]) +@pytest.mark.requires("databricks", "databricks.vector_search") +@pytest.mark.parametrize( + "index_details, columns, expected_columns", + [ + (DELTA_SYNC_INDEX_SELF_MANAGED_EMBEDDINGS, None, {"id"}), + (DIRECT_ACCESS_INDEX, None, {"id"}), + ( + DELTA_SYNC_INDEX_SELF_MANAGED_EMBEDDINGS, + [DEFAULT_PRIMARY_KEY, DEFAULT_TEXT_COLUMN, DEFAULT_VECTOR_COLUMN], + {"text_vector", "id"}, + ), + ( + DIRECT_ACCESS_INDEX, + [DEFAULT_PRIMARY_KEY, DEFAULT_TEXT_COLUMN, DEFAULT_VECTOR_COLUMN], + {"text_vector", "id"}, + ), + ], +) +def test_mmr_search( + index_details: dict, columns: Optional[List[str]], expected_columns: Set[str] +) -> None: + index = mock_index(index_details) + index.similarity_search.return_value = EXAMPLE_SEARCH_RESPONSE_WITH_EMBEDDING + vectorsearch = default_databricks_vector_search(index, columns) + query = fake_texts[0] + filters = {"some filter": True} + limit = 1 + + search_result = vectorsearch.max_marginal_relevance_search( + query, k=limit, filters=filters + ) + assert [doc.page_content for doc in search_result] == [fake_texts[0]] + assert [set(doc.metadata.keys()) for doc in search_result] == [expected_columns] + + +@pytest.mark.requires("databricks", "databricks.vector_search") +@pytest.mark.parametrize( + "index_details", [DELTA_SYNC_INDEX_SELF_MANAGED_EMBEDDINGS, DIRECT_ACCESS_INDEX] +) +def test_mmr_parameters(index_details: dict) -> None: + index = mock_index(index_details) + index.similarity_search.return_value = EXAMPLE_SEARCH_RESPONSE_WITH_EMBEDDING + query = fake_texts[0] + limit = 1 + fetch_k = 3 + lambda_mult = 0.25 + filters = {"some filter": True} + + with patch( + "langchain_community.vectorstores.databricks_vector_search.maximal_marginal_relevance" + ) as mock_mmr: + mock_mmr.return_value = [2] + retriever = default_databricks_vector_search(index).as_retriever( + search_type="mmr", + search_kwargs={ + "k": limit, + "fetch_k": fetch_k, + "lambda_mult": lambda_mult, + "filters": filters, + }, + ) + search_result = retriever.get_relevant_documents(query) + + mock_mmr.assert_called_once() + assert mock_mmr.call_args[1]["lambda_mult"] == lambda_mult + assert index.similarity_search.call_args[1]["num_results"] == fetch_k + assert index.similarity_search.call_args[1]["filters"] == filters + assert len(search_result) == limit + + +@pytest.mark.requires("databricks", "databricks.vector_search") +@pytest.mark.parametrize( + "index_details, threshold", itertools.product(ALL_INDEXES, [0.4, 0.5, 0.8]) +) +def test_similarity_score_threshold(index_details: dict, threshold: float) -> None: + index = mock_index(index_details) + index.similarity_search.return_value = EXAMPLE_SEARCH_RESPONSE_FIXED_SCORE + uniform_response_score = EXAMPLE_SEARCH_RESPONSE_FIXED_SCORE["result"][ + "data_array" + ][0][2] + query = fake_texts[0] + limit = len(fake_texts) + + retriever = default_databricks_vector_search(index).as_retriever( + search_type="similarity_score_threshold", + search_kwargs={"k": limit, "score_threshold": threshold}, + ) + search_result = retriever.get_relevant_documents(query) + if uniform_response_score >= threshold: + assert len(search_result) == len(fake_texts) + else: + assert len(search_result) == 0 + + @pytest.mark.requires("databricks", "databricks.vector_search") @pytest.mark.parametrize( "index_details", [DELTA_SYNC_INDEX_SELF_MANAGED_EMBEDDINGS, DIRECT_ACCESS_INDEX] diff --git a/libs/community/tests/unit_tests/vectorstores/test_faiss.py b/libs/community/tests/unit_tests/vectorstores/test_faiss.py index 350e0a1a641..cedecc8ada6 100644 --- a/libs/community/tests/unit_tests/vectorstores/test_faiss.py +++ b/libs/community/tests/unit_tests/vectorstores/test_faiss.py @@ -438,8 +438,9 @@ def test_faiss_with_metadatas_and_filter() -> None: ) assert docsearch.docstore.__dict__ == expected_docstore.__dict__ output = docsearch.similarity_search("foo", k=1, filter={"page": 1}) - assert output == [Document(page_content="foo", metadata={"page": 0})] - assert output != [Document(page_content="bar", metadata={"page": 1})] + # make sure it returns the result that matches the filter. + # Not the one who's text matches better. + assert output == [Document(page_content="bar", metadata={"page": 1})] assert output == docsearch.similarity_search( "foo", k=1, filter=lambda di: di["page"] == 1 ) @@ -465,8 +466,9 @@ async def test_faiss_async_with_metadatas_and_filter() -> None: ) assert docsearch.docstore.__dict__ == expected_docstore.__dict__ output = await docsearch.asimilarity_search("foo", k=1, filter={"page": 1}) - assert output == [Document(page_content="foo", metadata={"page": 0})] - assert output != [Document(page_content="bar", metadata={"page": 1})] + # make sure it returns the result that matches the filter. + # Not the one who's text matches better. + assert output == [Document(page_content="bar", metadata={"page": 1})] assert output == await docsearch.asimilarity_search( "foo", k=1, filter=lambda di: di["page"] == 1 ) @@ -774,3 +776,15 @@ async def test_async_delete() -> None: result = await docsearch.asimilarity_search("bar", k=2) assert sorted([d.page_content for d in result]) == ["baz", "foo"] assert docsearch.index_to_docstore_id == {0: ids[0], 1: ids[2]} + + +@pytest.mark.requires("faiss") +def test_faiss_with_duplicate_ids() -> None: + """Test whether FAISS raises an exception for duplicate ids.""" + texts = ["foo", "bar", "baz"] + duplicate_ids = ["id1", "id1", "id2"] + + with pytest.raises(ValueError) as exc_info: + FAISS.from_texts(texts, FakeEmbeddings(), ids=duplicate_ids) + + assert "Duplicate ids found in the ids list." in str(exc_info.value) diff --git a/libs/community/tests/unit_tests/vectorstores/test_indexing_docs.py b/libs/community/tests/unit_tests/vectorstores/test_indexing_docs.py index 1232d6bb9a6..85c5312d1f9 100644 --- a/libs/community/tests/unit_tests/vectorstores/test_indexing_docs.py +++ b/libs/community/tests/unit_tests/vectorstores/test_indexing_docs.py @@ -68,6 +68,7 @@ def test_compatible_vectorstore_documentation() -> None: "Pinecone", "Qdrant", "Redis", + "Rockset", "ScaNN", "SemaDB", "SupabaseVectorStore", diff --git a/libs/community/tests/unit_tests/vectorstores/test_public_api.py b/libs/community/tests/unit_tests/vectorstores/test_public_api.py index 53713f3e1be..48b51accdda 100644 --- a/libs/community/tests/unit_tests/vectorstores/test_public_api.py +++ b/libs/community/tests/unit_tests/vectorstores/test_public_api.py @@ -6,6 +6,7 @@ _EXPECTED = [ "AlibabaCloudOpenSearchSettings", "AnalyticDB", "Annoy", + "ApacheDoris", "AtlasDB", "AwaDB", "AzureSearch", diff --git a/libs/core/Makefile b/libs/core/Makefile index 2c685dfd449..ab8e9cadf03 100644 --- a/libs/core/Makefile +++ b/libs/core/Makefile @@ -42,7 +42,7 @@ lint lint_diff lint_package lint_tests: [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff [ "$(PYTHON_FILES)" = "" ] || poetry run ruff --select I $(PYTHON_FILES) [ "$(PYTHON_FILES)" = "" ] || poetry run mypy $(PYTHON_FILES) - [ "$(PYTHON_FILES)" = "" ] || mkdir $(MYPY_CACHE) || poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) format format_diff: poetry run ruff format $(PYTHON_FILES) diff --git a/libs/core/langchain_core/_api/deprecation.py b/libs/core/langchain_core/_api/deprecation.py index b4d56b70cb4..970cfa56946 100644 --- a/libs/core/langchain_core/_api/deprecation.py +++ b/libs/core/langchain_core/_api/deprecation.py @@ -245,10 +245,9 @@ def deprecated( old_doc = inspect.cleandoc(old_doc or "").strip("\n") + # old_doc can be None if not old_doc: - new_doc = "[*Deprecated*]" - else: - new_doc = f"[*Deprecated*] {old_doc}" + old_doc = "" # Modify the docstring to include a deprecation notice. notes_header = "\nNotes\n-----" @@ -258,7 +257,7 @@ def deprecated( addendum, ] details = " ".join([component.strip() for component in components if component]) - new_doc += ( + new_doc = ( f"[*Deprecated*] {old_doc}\n" f"{notes_header if notes_header not in old_doc else ''}\n" f".. deprecated:: {since}\n" diff --git a/libs/core/langchain_core/caches.py b/libs/core/langchain_core/caches.py index c14959c8f9c..626670950ab 100644 --- a/libs/core/langchain_core/caches.py +++ b/libs/core/langchain_core/caches.py @@ -4,6 +4,7 @@ from abc import ABC, abstractmethod from typing import Any, Optional, Sequence from langchain_core.outputs import Generation +from langchain_core.runnables import run_in_executor RETURN_VAL_TYPE = Sequence[Generation] @@ -22,3 +23,17 @@ class BaseCache(ABC): @abstractmethod def clear(self, **kwargs: Any) -> None: """Clear cache that can take additional keyword arguments.""" + + async def alookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: + """Look up based on prompt and llm_string.""" + return await run_in_executor(None, self.lookup, prompt, llm_string) + + async def aupdate( + self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE + ) -> None: + """Update cache based on prompt and llm_string.""" + return await run_in_executor(None, self.update, prompt, llm_string, return_val) + + async def aclear(self, **kwargs: Any) -> None: + """Clear cache that can take additional keyword arguments.""" + return await run_in_executor(None, self.clear, **kwargs) diff --git a/libs/core/langchain_core/callbacks/manager.py b/libs/core/langchain_core/callbacks/manager.py index 56a8e04db05..b1f103871f1 100644 --- a/libs/core/langchain_core/callbacks/manager.py +++ b/libs/core/langchain_core/callbacks/manager.py @@ -67,6 +67,7 @@ def trace_as_chain_group( example_id: Optional[Union[str, UUID]] = None, run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, + metadata: Optional[Dict[str, Any]] = None, ) -> Generator[CallbackManagerForChainGroup, None, None]: """Get a callback manager for a chain group in a context manager. Useful for grouping different calls together as a single run even if @@ -83,6 +84,8 @@ def trace_as_chain_group( run_id (UUID, optional): The ID of the run. tags (List[str], optional): The inheritable tags to apply to all runs. Defaults to None. + metadata (Dict[str, Any], optional): The metadata to apply to all runs. + Defaults to None. Note: must have LANGCHAIN_TRACING_V2 env var set to true to see the trace in LangSmith. @@ -95,7 +98,7 @@ def trace_as_chain_group( llm_input = "Foo" with trace_as_chain_group("group_name", inputs={"input": llm_input}) as manager: # Use the callback manager for the chain group - res = llm.predict(llm_input, callbacks=manager) + res = llm.invoke(llm_input, {"callbacks": manager}) manager.on_chain_end({"output": res}) """ # noqa: E501 from langchain_core.tracers.context import _get_trace_callbacks @@ -106,6 +109,7 @@ def trace_as_chain_group( cm = CallbackManager.configure( inheritable_callbacks=cb, inheritable_tags=tags, + inheritable_metadata=metadata, ) run_manager = cm.on_chain_start({"name": group_name}, inputs or {}, run_id=run_id) @@ -141,6 +145,7 @@ async def atrace_as_chain_group( example_id: Optional[Union[str, UUID]] = None, run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, + metadata: Optional[Dict[str, Any]] = None, ) -> AsyncGenerator[AsyncCallbackManagerForChainGroup, None]: """Get an async callback manager for a chain group in a context manager. Useful for grouping different async calls together as a single run even if @@ -157,6 +162,8 @@ async def atrace_as_chain_group( run_id (UUID, optional): The ID of the run. tags (List[str], optional): The inheritable tags to apply to all runs. Defaults to None. + metadata (Dict[str, Any], optional): The metadata to apply to all runs. + Defaults to None. Returns: AsyncCallbackManager: The async callback manager for the chain group. @@ -168,7 +175,7 @@ async def atrace_as_chain_group( llm_input = "Foo" async with atrace_as_chain_group("group_name", inputs={"input": llm_input}) as manager: # Use the async callback manager for the chain group - res = await llm.apredict(llm_input, callbacks=manager) + res = await llm.ainvoke(llm_input, {"callbacks": manager}) await manager.on_chain_end({"output": res}) """ # noqa: E501 from langchain_core.tracers.context import _get_trace_callbacks @@ -176,7 +183,9 @@ async def atrace_as_chain_group( cb = _get_trace_callbacks( project_name, example_id, callback_manager=callback_manager ) - cm = AsyncCallbackManager.configure(inheritable_callbacks=cb, inheritable_tags=tags) + cm = AsyncCallbackManager.configure( + inheritable_callbacks=cb, inheritable_tags=tags, inheritable_metadata=metadata + ) run_manager = await cm.on_chain_start( {"name": group_name}, inputs or {}, run_id=run_id @@ -203,6 +212,21 @@ async def atrace_as_chain_group( await run_manager.on_chain_end({}) +Func = TypeVar("Func", bound=Callable) + + +def shielded(func: Func) -> Func: + """ + Makes so an awaitable method is always shielded from cancellation + """ + + @functools.wraps(func) + async def wrapped(*args: Any, **kwargs: Any) -> Any: + return await asyncio.shield(func(*args, **kwargs)) + + return cast(Func, wrapped) + + def handle_event( handlers: List[BaseCallbackHandler], event_name: str, @@ -293,7 +317,10 @@ def _run_coros(coros: List[Coroutine[Any, Any, Any]]) -> None: with asyncio.Runner() as runner: # Run the coroutine, get the result for coro in coros: - runner.run(coro) + try: + runner.run(coro) + except Exception as e: + logger.warning(f"Error in callback coroutine: {repr(e)}") # Run pending tasks scheduled by coros until they are all done while pending := asyncio.all_tasks(runner.get_loop()): @@ -302,7 +329,10 @@ def _run_coros(coros: List[Coroutine[Any, Any, Any]]) -> None: # Before Python 3.11 we need to run each coroutine in a new event loop # as the Runner api is not available. for coro in coros: - asyncio.run(coro) + try: + asyncio.run(coro) + except Exception as e: + logger.warning(f"Error in callback coroutine: {repr(e)}") async def _ahandle_event_for_handler( @@ -682,6 +712,7 @@ class AsyncCallbackManagerForLLMRun(AsyncRunManager, LLMManagerMixin): inheritable_metadata=self.inheritable_metadata, ) + @shielded async def on_llm_new_token( self, token: str, @@ -706,6 +737,7 @@ class AsyncCallbackManagerForLLMRun(AsyncRunManager, LLMManagerMixin): **kwargs, ) + @shielded async def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None: """Run when LLM ends running. @@ -723,6 +755,7 @@ class AsyncCallbackManagerForLLMRun(AsyncRunManager, LLMManagerMixin): **kwargs, ) + @shielded async def on_llm_error( self, error: BaseException, @@ -853,6 +886,7 @@ class AsyncCallbackManagerForChainRun(AsyncParentRunManager, ChainManagerMixin): inheritable_metadata=self.inheritable_metadata, ) + @shielded async def on_chain_end( self, outputs: Union[Dict[str, Any], Any], **kwargs: Any ) -> None: @@ -872,6 +906,7 @@ class AsyncCallbackManagerForChainRun(AsyncParentRunManager, ChainManagerMixin): **kwargs, ) + @shielded async def on_chain_error( self, error: BaseException, @@ -893,6 +928,7 @@ class AsyncCallbackManagerForChainRun(AsyncParentRunManager, ChainManagerMixin): **kwargs, ) + @shielded async def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any: """Run when agent action is received. @@ -913,6 +949,7 @@ class AsyncCallbackManagerForChainRun(AsyncParentRunManager, ChainManagerMixin): **kwargs, ) + @shielded async def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any: """Run when agent finish is received. @@ -1000,6 +1037,7 @@ class AsyncCallbackManagerForToolRun(AsyncParentRunManager, ToolManagerMixin): inheritable_metadata=self.inheritable_metadata, ) + @shielded async def on_tool_end(self, output: str, **kwargs: Any) -> None: """Run when tool ends running. @@ -1017,6 +1055,7 @@ class AsyncCallbackManagerForToolRun(AsyncParentRunManager, ToolManagerMixin): **kwargs, ) + @shielded async def on_tool_error( self, error: BaseException, @@ -1100,6 +1139,7 @@ class AsyncCallbackManagerForRetrieverRun( inheritable_metadata=self.inheritable_metadata, ) + @shielded async def on_retriever_end( self, documents: Sequence[Document], **kwargs: Any ) -> None: @@ -1115,6 +1155,7 @@ class AsyncCallbackManagerForRetrieverRun( **kwargs, ) + @shielded async def on_retriever_error( self, error: BaseException, diff --git a/libs/core/langchain_core/callbacks/stdout.py b/libs/core/langchain_core/callbacks/stdout.py index f0755a2b1e1..e129792a010 100644 --- a/libs/core/langchain_core/callbacks/stdout.py +++ b/libs/core/langchain_core/callbacks/stdout.py @@ -1,4 +1,5 @@ """Callback Handler that prints to std out.""" + from __future__ import annotations from typing import TYPE_CHECKING, Any, Dict, Optional @@ -22,11 +23,11 @@ class StdOutCallbackHandler(BaseCallbackHandler): ) -> None: """Print out that we are entering a chain.""" class_name = serialized.get("name", serialized.get("id", [""])[-1]) - print(f"\n\n\033[1m> Entering new {class_name} chain...\033[0m") + print(f"\n\n\033[1m> Entering new {class_name} chain...\033[0m") # noqa: T201 def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> None: """Print out that we finished a chain.""" - print("\n\033[1m> Finished chain.\033[0m") + print("\n\033[1m> Finished chain.\033[0m") # noqa: T201 def on_agent_action( self, action: AgentAction, color: Optional[str] = None, **kwargs: Any diff --git a/libs/core/langchain_core/chat_history.py b/libs/core/langchain_core/chat_history.py index e558c1479ed..1042e2e8ef8 100644 --- a/libs/core/langchain_core/chat_history.py +++ b/libs/core/langchain_core/chat_history.py @@ -9,16 +9,32 @@ from langchain_core.messages import ( HumanMessage, get_buffer_string, ) +from langchain_core.runnables import run_in_executor class BaseChatMessageHistory(ABC): """Abstract base class for storing chat message history. - Implementations should over-ride the add_messages method to handle bulk addition - of messages. + Implementations guidelines: - The default implementation of add_message will correctly call add_messages, so - it is not necessary to implement both methods. + Implementations are expected to over-ride all or some of the following methods: + + * add_messages: sync variant for bulk addition of messages + * aadd_messages: async variant for bulk addition of messages + * messages: sync variant for getting messages + * aget_messages: async variant for getting messages + * clear: sync variant for clearing messages + * aclear: async variant for clearing messages + + add_messages contains a default implementation that calls add_message + for each message in the sequence. This is provided for backwards compatibility + with existing implementations which only had add_message. + + Async variants all have default implementations that call the sync variants. + Implementers can choose to over-ride the async implementations to provide + truly async implementations. + + Usage guidelines: When used for updating history, users should favor usage of `add_messages` over `add_message` or other variants like `add_user_message` and `add_ai_message` @@ -54,7 +70,22 @@ class BaseChatMessageHistory(ABC): """ messages: List[BaseMessage] - """A list of Messages stored in-memory.""" + """A property or attribute that returns a list of messages. + + In general, getting the messages may involve IO to the underlying + persistence layer, so this operation is expected to incur some + latency. + """ + + async def aget_messages(self) -> List[BaseMessage]: + """Async version of getting messages. + + Can over-ride this method to provide an efficient async implementation. + + In general, fetching messages may involve IO to the underlying + persistence layer. + """ + return await run_in_executor(None, lambda: self.messages) def add_user_message(self, message: Union[HumanMessage, str]) -> None: """Convenience method for adding a human message string to the store. @@ -98,7 +129,7 @@ class BaseChatMessageHistory(ABC): """ if type(self).add_messages != BaseChatMessageHistory.add_messages: # This means that the sub-class has implemented an efficient add_messages - # method, so we should usage of add_message to that. + # method, so we should use it. self.add_messages([message]) else: raise NotImplementedError( @@ -118,10 +149,22 @@ class BaseChatMessageHistory(ABC): for message in messages: self.add_message(message) + async def aadd_messages(self, messages: Sequence[BaseMessage]) -> None: + """Add a list of messages. + + Args: + messages: A list of BaseMessage objects to store. + """ + await run_in_executor(None, self.add_messages, messages) + @abstractmethod def clear(self) -> None: """Remove all messages from the store""" + async def aclear(self) -> None: + """Remove all messages from the store""" + await run_in_executor(None, self.clear) + def __str__(self) -> str: """Return a string representation of the chat history.""" return get_buffer_string(self.messages) diff --git a/libs/core/langchain_core/documents/base.py b/libs/core/langchain_core/documents/base.py index f33d7c82b0f..c72bfa724fd 100644 --- a/libs/core/langchain_core/documents/base.py +++ b/libs/core/langchain_core/documents/base.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import List, Literal +from typing import Any, List, Literal from langchain_core.load.serializable import Serializable from langchain_core.pydantic_v1 import Field @@ -17,6 +17,10 @@ class Document(Serializable): """ type: Literal["Document"] = "Document" + def __init__(self, page_content: str, **kwargs: Any) -> None: + """Pass page_content in as positional or named arg.""" + super().__init__(page_content=page_content, **kwargs) + @classmethod def is_lc_serializable(cls) -> bool: """Return whether this class is serializable.""" diff --git a/libs/core/langchain_core/language_models/chat_models.py b/libs/core/langchain_core/language_models/chat_models.py index aaf61a7810e..6279116093e 100644 --- a/libs/core/langchain_core/language_models/chat_models.py +++ b/libs/core/langchain_core/language_models/chat_models.py @@ -622,7 +622,7 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC): else: llm_string = self._get_llm_string(stop=stop, **kwargs) prompt = dumps(messages) - cache_val = llm_cache.lookup(prompt, llm_string) + cache_val = await llm_cache.alookup(prompt, llm_string) if isinstance(cache_val, list): return ChatResult(generations=cache_val) else: @@ -632,7 +632,7 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC): ) else: result = await self._agenerate(messages, stop=stop, **kwargs) - llm_cache.update(prompt, llm_string, result.generations) + await llm_cache.aupdate(prompt, llm_string, result.generations) return result @abstractmethod diff --git a/libs/core/langchain_core/language_models/llms.py b/libs/core/langchain_core/language_models/llms.py index 7f987baf88d..36d94353251 100644 --- a/libs/core/langchain_core/language_models/llms.py +++ b/libs/core/langchain_core/language_models/llms.py @@ -139,6 +139,26 @@ def get_prompts( return existing_prompts, llm_string, missing_prompt_idxs, missing_prompts +async def aget_prompts( + params: Dict[str, Any], prompts: List[str] +) -> Tuple[Dict[int, List], str, List[int], List[str]]: + """Get prompts that are already cached. Async version.""" + llm_string = str(sorted([(k, v) for k, v in params.items()])) + missing_prompts = [] + missing_prompt_idxs = [] + existing_prompts = {} + llm_cache = get_llm_cache() + for i, prompt in enumerate(prompts): + if llm_cache: + cache_val = await llm_cache.alookup(prompt, llm_string) + if isinstance(cache_val, list): + existing_prompts[i] = cache_val + else: + missing_prompts.append(prompt) + missing_prompt_idxs.append(i) + return existing_prompts, llm_string, missing_prompt_idxs, missing_prompts + + def update_cache( existing_prompts: Dict[int, List], llm_string: str, @@ -157,6 +177,24 @@ def update_cache( return llm_output +async def aupdate_cache( + existing_prompts: Dict[int, List], + llm_string: str, + missing_prompt_idxs: List[int], + new_results: LLMResult, + prompts: List[str], +) -> Optional[dict]: + """Update the cache and get the LLM output. Async version""" + llm_cache = get_llm_cache() + for i, result in enumerate(new_results.generations): + existing_prompts[missing_prompt_idxs[i]] = result + prompt = prompts[missing_prompt_idxs[i]] + if llm_cache: + await llm_cache.aupdate(prompt, llm_string, result) + llm_output = new_results.llm_output + return llm_output + + class BaseLLM(BaseLanguageModel[str], ABC): """Base LLM abstract interface. @@ -869,7 +907,7 @@ class BaseLLM(BaseLanguageModel[str], ABC): llm_string, missing_prompt_idxs, missing_prompts, - ) = get_prompts(params, prompts) + ) = await aget_prompts(params, prompts) disregard_cache = self.cache is not None and not self.cache new_arg_supported = inspect.signature(self._agenerate).parameters.get( "run_manager" @@ -917,7 +955,7 @@ class BaseLLM(BaseLanguageModel[str], ABC): new_results = await self._agenerate_helper( missing_prompts, stop, run_managers, bool(new_arg_supported), **kwargs ) - llm_output = update_cache( + llm_output = await aupdate_cache( existing_prompts, llm_string, missing_prompt_idxs, new_results, prompts ) run_info = ( @@ -1082,7 +1120,7 @@ class BaseLLM(BaseLanguageModel[str], ABC): if save_path.suffix == ".json": with open(file_path, "w") as f: json.dump(prompt_dict, f, indent=4) - elif save_path.suffix == ".yaml": + elif save_path.suffix.endswith((".yaml", ".yml")): with open(file_path, "w") as f: yaml.dump(prompt_dict, f, default_flow_style=False) else: diff --git a/libs/core/langchain_core/memory.py b/libs/core/langchain_core/memory.py index 0b362661cfd..ad61e90fdad 100644 --- a/libs/core/langchain_core/memory.py +++ b/libs/core/langchain_core/memory.py @@ -4,6 +4,7 @@ from abc import ABC, abstractmethod from typing import Any, Dict, List from langchain_core.load.serializable import Serializable +from langchain_core.runnables import run_in_executor class BaseMemory(Serializable, ABC): @@ -50,10 +51,24 @@ class BaseMemory(Serializable, ABC): def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: """Return key-value pairs given the text input to the chain.""" + async def aload_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: + """Return key-value pairs given the text input to the chain.""" + return await run_in_executor(None, self.load_memory_variables, inputs) + @abstractmethod def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """Save the context of this chain run to memory.""" + async def asave_context( + self, inputs: Dict[str, Any], outputs: Dict[str, str] + ) -> None: + """Save the context of this chain run to memory.""" + await run_in_executor(None, self.save_context, inputs, outputs) + @abstractmethod def clear(self) -> None: """Clear memory contents.""" + + async def aclear(self) -> None: + """Clear memory contents.""" + await run_in_executor(None, self.clear) diff --git a/libs/core/langchain_core/messages/__init__.py b/libs/core/langchain_core/messages/__init__.py index c35dac72b98..e51a835aee2 100644 --- a/libs/core/langchain_core/messages/__init__.py +++ b/libs/core/langchain_core/messages/__init__.py @@ -22,7 +22,7 @@ AnyMessage = Union[ def get_buffer_string( messages: Sequence[BaseMessage], human_prefix: str = "Human", ai_prefix: str = "AI" ) -> str: - """Convert sequence of Messages to strings and concatenate them into one string. + """Convert a sequence of Messages to strings and concatenate them into one string. Args: messages: Messages to be converted to strings. @@ -92,6 +92,8 @@ def _message_from_dict(message: dict) -> BaseMessage: return ToolMessageChunk(**message["data"]) elif _type == "SystemMessageChunk": return SystemMessageChunk(**message["data"]) + elif _type == "ChatMessageChunk": + return ChatMessageChunk(**message["data"]) else: raise ValueError(f"Got unexpected message type: {_type}") @@ -109,6 +111,14 @@ def messages_from_dict(messages: Sequence[dict]) -> List[BaseMessage]: def message_chunk_to_message(chunk: BaseMessageChunk) -> BaseMessage: + """Convert a message chunk to a message. + + Args: + chunk: Message chunk to convert. + + Returns: + Message. + """ if not isinstance(chunk, BaseMessageChunk): return chunk # chunk classes always have the equivalent non-chunk class as their first parent diff --git a/libs/core/langchain_core/messages/ai.py b/libs/core/langchain_core/messages/ai.py index 6c9633f02e4..c667aa54e95 100644 --- a/libs/core/langchain_core/messages/ai.py +++ b/libs/core/langchain_core/messages/ai.py @@ -8,7 +8,7 @@ from langchain_core.messages.base import ( class AIMessage(BaseMessage): - """A Message from an AI.""" + """Message from an AI.""" example: bool = False """Whether this Message is being passed in to the model as part of an example @@ -27,7 +27,7 @@ AIMessage.update_forward_refs() class AIMessageChunk(AIMessage, BaseMessageChunk): - """A Message chunk from an AI.""" + """Message chunk from an AI.""" # Ignoring mypy re-assignment here since we're overriding the value # to make sure that the chunk variant can be discriminated from the diff --git a/libs/core/langchain_core/messages/base.py b/libs/core/langchain_core/messages/base.py index b2fd76b6592..12229244cc4 100644 --- a/libs/core/langchain_core/messages/base.py +++ b/libs/core/langchain_core/messages/base.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import TYPE_CHECKING, Any, Dict, List, Sequence, Union +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Union from langchain_core.load.serializable import Serializable from langchain_core.pydantic_v1 import Extra, Field @@ -12,7 +12,7 @@ if TYPE_CHECKING: class BaseMessage(Serializable): - """The base abstract Message class. + """Base abstract Message class. Messages are the inputs and outputs of ChatModels. """ @@ -25,9 +25,17 @@ class BaseMessage(Serializable): type: str + name: Optional[str] = None + class Config: extra = Extra.allow + def __init__( + self, content: Union[str, List[Union[str, Dict]]], **kwargs: Any + ) -> None: + """Pass in content as positional arg.""" + return super().__init__(content=content, **kwargs) + @classmethod def is_lc_serializable(cls) -> bool: """Return whether this class is serializable.""" @@ -47,10 +55,12 @@ class BaseMessage(Serializable): def pretty_repr(self, html: bool = False) -> str: title = get_msg_title_repr(self.type.title() + " Message", bold=html) # TODO: handle non-string content. + if self.name is not None: + title += f"\nName: {self.name}" return f"{title}\n\n{self.content}" def pretty_print(self) -> None: - print(self.pretty_repr(html=is_interactive_env())) + print(self.pretty_repr(html=is_interactive_env())) # noqa: T201 def merge_content( @@ -90,7 +100,7 @@ def merge_content( class BaseMessageChunk(BaseMessage): - """A Message chunk, which can be concatenated with other Message chunks.""" + """Message chunk, which can be concatenated with other Message chunks.""" @classmethod def get_lc_namespace(cls) -> List[str]: @@ -189,6 +199,15 @@ def messages_to_dict(messages: Sequence[BaseMessage]) -> List[dict]: def get_msg_title_repr(title: str, *, bold: bool = False) -> str: + """Get a title representation for a message. + + Args: + title: The title. + bold: Whether to bold the title. + + Returns: + The title representation. + """ padded = " " + title + " " sep_len = (80 - len(padded)) // 2 sep = "=" * sep_len diff --git a/libs/core/langchain_core/messages/chat.py b/libs/core/langchain_core/messages/chat.py index bd89094ca4e..3c7ed975b65 100644 --- a/libs/core/langchain_core/messages/chat.py +++ b/libs/core/langchain_core/messages/chat.py @@ -8,7 +8,7 @@ from langchain_core.messages.base import ( class ChatMessage(BaseMessage): - """A Message that can be assigned an arbitrary speaker (i.e. role).""" + """Message that can be assigned an arbitrary speaker (i.e. role).""" role: str """The speaker / role of the Message.""" @@ -25,7 +25,7 @@ ChatMessage.update_forward_refs() class ChatMessageChunk(ChatMessage, BaseMessageChunk): - """A Chat Message chunk.""" + """Chat Message chunk.""" # Ignoring mypy re-assignment here since we're overriding the value # to make sure that the chunk variant can be discriminated from the diff --git a/libs/core/langchain_core/messages/function.py b/libs/core/langchain_core/messages/function.py index 32f89f99f1f..e852aa37276 100644 --- a/libs/core/langchain_core/messages/function.py +++ b/libs/core/langchain_core/messages/function.py @@ -8,7 +8,7 @@ from langchain_core.messages.base import ( class FunctionMessage(BaseMessage): - """A Message for passing the result of executing a function back to a model.""" + """Message for passing the result of executing a function back to a model.""" name: str """The name of the function that was executed.""" @@ -25,7 +25,7 @@ FunctionMessage.update_forward_refs() class FunctionMessageChunk(FunctionMessage, BaseMessageChunk): - """A Function Message chunk.""" + """Function Message chunk.""" # Ignoring mypy re-assignment here since we're overriding the value # to make sure that the chunk variant can be discriminated from the diff --git a/libs/core/langchain_core/messages/human.py b/libs/core/langchain_core/messages/human.py index 976dc7afc54..b89860a5628 100644 --- a/libs/core/langchain_core/messages/human.py +++ b/libs/core/langchain_core/messages/human.py @@ -4,7 +4,7 @@ from langchain_core.messages.base import BaseMessage, BaseMessageChunk class HumanMessage(BaseMessage): - """A Message from a human.""" + """Message from a human.""" example: bool = False """Whether this Message is being passed in to the model as part of an example @@ -23,7 +23,7 @@ HumanMessage.update_forward_refs() class HumanMessageChunk(HumanMessage, BaseMessageChunk): - """A Human Message chunk.""" + """Human Message chunk.""" # Ignoring mypy re-assignment here since we're overriding the value # to make sure that the chunk variant can be discriminated from the diff --git a/libs/core/langchain_core/messages/system.py b/libs/core/langchain_core/messages/system.py index 18e3753296c..c86a60f91ff 100644 --- a/libs/core/langchain_core/messages/system.py +++ b/libs/core/langchain_core/messages/system.py @@ -4,7 +4,7 @@ from langchain_core.messages.base import BaseMessage, BaseMessageChunk class SystemMessage(BaseMessage): - """A Message for priming AI behavior, usually passed in as the first of a sequence + """Message for priming AI behavior, usually passed in as the first of a sequence of input messages. """ @@ -20,7 +20,7 @@ SystemMessage.update_forward_refs() class SystemMessageChunk(SystemMessage, BaseMessageChunk): - """A System Message chunk.""" + """System Message chunk.""" # Ignoring mypy re-assignment here since we're overriding the value # to make sure that the chunk variant can be discriminated from the diff --git a/libs/core/langchain_core/messages/tool.py b/libs/core/langchain_core/messages/tool.py index 27d15739c74..a83894a10f7 100644 --- a/libs/core/langchain_core/messages/tool.py +++ b/libs/core/langchain_core/messages/tool.py @@ -8,7 +8,7 @@ from langchain_core.messages.base import ( class ToolMessage(BaseMessage): - """A Message for passing the result of executing a tool back to a model.""" + """Message for passing the result of executing a tool back to a model.""" tool_call_id: str """Tool call that this message is responding to.""" @@ -25,7 +25,7 @@ ToolMessage.update_forward_refs() class ToolMessageChunk(ToolMessage, BaseMessageChunk): - """A Tool Message chunk.""" + """Tool Message chunk.""" # Ignoring mypy re-assignment here since we're overriding the value # to make sure that the chunk variant can be discriminated from the diff --git a/libs/core/langchain_core/output_parsers/json.py b/libs/core/langchain_core/output_parsers/json.py index e644d6c870e..c107cd0c765 100644 --- a/libs/core/langchain_core/output_parsers/json.py +++ b/libs/core/langchain_core/output_parsers/json.py @@ -35,7 +35,7 @@ def _custom_parser(multiline_string: str) -> str: multiline_string = multiline_string.decode() multiline_string = re.sub( - r'("action_input"\:\s*")(.*)(")', + r'("action_input"\:\s*")(.*?)(")', _replace_new_line, multiline_string, flags=re.DOTALL, @@ -44,7 +44,7 @@ def _custom_parser(multiline_string: str) -> str: return multiline_string -# Adapted from https://github.com/KillianLucas/open-interpreter/blob/main/interpreter/utils/parse_partial_json.py +# Adapted from https://github.com/KillianLucas/open-interpreter/blob/5b6080fae1f8c68938a1e4fa8667e3744084ee21/interpreter/utils/parse_partial_json.py # MIT License def parse_partial_json(s: str, *, strict: bool = False) -> Any: """Parse a JSON string that may be missing closing braces. @@ -138,7 +138,7 @@ def parse_json_markdown( The parsed JSON object as a Python dictionary. """ # Try to find JSON string within triple backticks - match = re.search(r"```(json)?(.*)(```)?", json_string, re.DOTALL) + match = re.search(r"```(json)?(.*)", json_string, re.DOTALL) # If no match found, assume the entire string is a JSON string if match is None: @@ -148,7 +148,7 @@ def parse_json_markdown( json_str = match.group(2) # Strip whitespace and newlines from the start and end - json_str = json_str.strip() + json_str = json_str.strip().strip("`") # handle newlines and other special characters inside the returned value json_str = _custom_parser(json_str) @@ -211,7 +211,8 @@ class JsonOutputParser(BaseCumulativeTransformOutputParser[Any]): try: return parse_json_markdown(text) except JSONDecodeError as e: - raise OutputParserException(f"Invalid json output: {text}") from e + msg = f"Invalid json output: {text}" + raise OutputParserException(msg, llm_output=text) from e def parse(self, text: str) -> Any: return self.parse_result([Generation(text=text)]) @@ -220,7 +221,8 @@ class JsonOutputParser(BaseCumulativeTransformOutputParser[Any]): if self.pydantic_object is None: return "Return a JSON object." else: - schema = self.pydantic_object.schema() + # Copy schema to avoid altering original Pydantic schema. + schema = {k: v for k, v in self.pydantic_object.schema().items()} # Remove extraneous fields. reduced_schema = schema diff --git a/libs/core/langchain_core/outputs/chat_generation.py b/libs/core/langchain_core/outputs/chat_generation.py index b7bd6042a2c..49dc96b3819 100644 --- a/libs/core/langchain_core/outputs/chat_generation.py +++ b/libs/core/langchain_core/outputs/chat_generation.py @@ -35,7 +35,7 @@ class ChatGeneration(Generation): class ChatGenerationChunk(ChatGeneration): - """A ChatGeneration chunk, which can be concatenated with other + """ChatGeneration chunk, which can be concatenated with other ChatGeneration chunks. Attributes: diff --git a/libs/core/langchain_core/outputs/generation.py b/libs/core/langchain_core/outputs/generation.py index 3f0a79ecb10..34e219771d1 100644 --- a/libs/core/langchain_core/outputs/generation.py +++ b/libs/core/langchain_core/outputs/generation.py @@ -32,7 +32,7 @@ class Generation(Serializable): class GenerationChunk(Generation): - """A Generation chunk, which can be concatenated with other Generation chunks.""" + """Generation chunk, which can be concatenated with other Generation chunks.""" @classmethod def get_lc_namespace(cls) -> List[str]: diff --git a/libs/core/langchain_core/prompt_values.py b/libs/core/langchain_core/prompt_values.py index 4c599f9f6a0..18f62d9f2a5 100644 --- a/libs/core/langchain_core/prompt_values.py +++ b/libs/core/langchain_core/prompt_values.py @@ -1,7 +1,7 @@ from __future__ import annotations from abc import ABC, abstractmethod -from typing import List, Literal, Sequence +from typing import List, Literal, Sequence, cast from typing_extensions import TypedDict @@ -105,7 +105,7 @@ class ImagePromptValue(PromptValue): def to_messages(self) -> List[BaseMessage]: """Return prompt as messages.""" - return [HumanMessage(content=[self.image_url])] + return [HumanMessage(content=[cast(dict, self.image_url)])] class ChatPromptValueConcrete(ChatPromptValue): diff --git a/libs/core/langchain_core/prompts/base.py b/libs/core/langchain_core/prompts/base.py index 07ac7222554..96cfbf63740 100644 --- a/libs/core/langchain_core/prompts/base.py +++ b/libs/core/langchain_core/prompts/base.py @@ -27,6 +27,7 @@ from langchain_core.prompt_values import ( ) from langchain_core.pydantic_v1 import BaseModel, Field, create_model, root_validator from langchain_core.runnables import RunnableConfig, RunnableSerializable +from langchain_core.runnables.config import ensure_config if TYPE_CHECKING: from langchain_core.documents import Document @@ -47,9 +48,15 @@ class BasePromptTemplate( If not provided, all variables are assumed to be strings.""" output_parser: Optional[BaseOutputParser] = None """How to parse the output of calling an LLM on this formatted prompt.""" - partial_variables: Mapping[str, Union[str, Callable[[], str]]] = Field( - default_factory=dict - ) + partial_variables: Mapping[str, Any] = Field(default_factory=dict) + """A dictionary of the partial variables the prompt template carries. + + Partial variables populate the template so that you don't need to + pass them in every time you call the prompt.""" + metadata: Optional[Dict[str, Any]] = None + """Metadata to be used for tracing.""" + tags: Optional[List[str]] = None + """Tags to be used for tracing.""" @classmethod def get_lc_namespace(cls) -> List[str]: @@ -97,6 +104,11 @@ class BasePromptTemplate( def invoke( self, input: Dict, config: Optional[RunnableConfig] = None ) -> PromptValue: + config = ensure_config(config) + if self.metadata: + config["metadata"].update(self.metadata) + if self.tags: + config["tags"].extend(self.tags) return self._call_with_config( self._format_prompt_with_error_handling, input, @@ -143,8 +155,7 @@ class BasePromptTemplate( def _merge_partial_and_user_variables(self, **kwargs: Any) -> Dict[str, Any]: # Get partial params: partial_kwargs = { - k: v if isinstance(v, str) else v() - for k, v in self.partial_variables.items() + k: v if not callable(v) else v() for k, v in self.partial_variables.items() } return {**partial_kwargs, **kwargs} @@ -210,7 +221,7 @@ class BasePromptTemplate( if save_path.suffix == ".json": with open(file_path, "w") as f: json.dump(prompt_dict, f, indent=4) - elif save_path.suffix == ".yaml": + elif save_path.suffix.endswith((".yaml", ".yml")): with open(file_path, "w") as f: yaml.dump(prompt_dict, f, default_flow_style=False) else: diff --git a/libs/core/langchain_core/prompts/chat.py b/libs/core/langchain_core/prompts/chat.py index cfb3192cfad..86f261b51fd 100644 --- a/libs/core/langchain_core/prompts/chat.py +++ b/libs/core/langchain_core/prompts/chat.py @@ -1,11 +1,11 @@ """Chat prompt template.""" + from __future__ import annotations from abc import ABC, abstractmethod from pathlib import Path from typing import ( Any, - Callable, Dict, List, Optional, @@ -80,7 +80,7 @@ class BaseMessagePromptTemplate(Serializable, ABC): raise NotImplementedError def pretty_print(self) -> None: - print(self.pretty_repr(html=is_interactive_env())) + print(self.pretty_repr(html=is_interactive_env())) # noqa: T201 def __add__(self, other: Any) -> ChatPromptTemplate: """Combine two prompt templates. @@ -130,13 +130,7 @@ class MessagesPlaceholder(BaseMessagePromptTemplate): f"variable {self.variable_name} should be a list of base messages, " f"got {value}" ) - for v in convert_to_messages(value): - if not isinstance(v, BaseMessage): - raise ValueError( - f"variable {self.variable_name} should be a list of base messages," - f" got {value}" - ) - return value + return convert_to_messages(value) @property def input_variables(self) -> List[str]: @@ -453,7 +447,7 @@ class _StringImageMessagePromptTemplate(BaseMessagePromptTemplate): content=text, additional_kwargs=self.additional_kwargs ) else: - content = [] + content: List = [] for prompt in self.prompt: inputs = {var: kwargs[var] for var in prompt.input_variables} if isinstance(prompt, StringPromptTemplate): @@ -550,7 +544,7 @@ class BaseChatPromptTemplate(BasePromptTemplate, ABC): raise NotImplementedError def pretty_print(self) -> None: - print(self.pretty_repr(html=is_interactive_env())) + print(self.pretty_repr(html=is_interactive_env())) # noqa: T201 MessageLike = Union[BaseMessagePromptTemplate, BaseMessage, BaseChatPromptTemplate] @@ -563,7 +557,7 @@ MessageLikeRepresentation = Union[ class ChatPromptTemplate(BaseChatPromptTemplate): - """A prompt template for chat models. + """Prompt template for chat models. Use to create flexible templated prompts for chat models. @@ -755,13 +749,20 @@ class ChatPromptTemplate(BaseChatPromptTemplate): # Automatically infer input variables from messages input_vars: Set[str] = set() + partial_vars: Dict[str, Any] = {} for _message in _messages: - if isinstance( + if isinstance(_message, MessagesPlaceholder) and _message.optional: + partial_vars[_message.variable_name] = [] + elif isinstance( _message, (BaseChatPromptTemplate, BaseMessagePromptTemplate) ): input_vars.update(_message.input_variables) - return cls(input_variables=sorted(input_vars), messages=_messages) + return cls( + input_variables=sorted(input_vars), + messages=_messages, + partial_variables=partial_vars, + ) def format(self, **kwargs: Any) -> str: """Format the chat template into a string. @@ -799,7 +800,7 @@ class ChatPromptTemplate(BaseChatPromptTemplate): raise ValueError(f"Unexpected input: {message_template}") return result - def partial(self, **kwargs: Union[str, Callable[[], str]]) -> ChatPromptTemplate: + def partial(self, **kwargs: Any) -> ChatPromptTemplate: """Get a new ChatPromptTemplate with some input variables already filled in. Args: diff --git a/libs/core/langchain_core/prompts/few_shot.py b/libs/core/langchain_core/prompts/few_shot.py index 8be9d0efb61..e03513fb862 100644 --- a/libs/core/langchain_core/prompts/few_shot.py +++ b/libs/core/langchain_core/prompts/few_shot.py @@ -1,4 +1,5 @@ """Prompt template that contains few shot examples.""" + from __future__ import annotations from pathlib import Path @@ -277,7 +278,7 @@ class FewShotChatMessagePromptTemplate( + HumanMessagePromptTemplate.from_template("{input}") ) # Show the prompt - print(final_prompt.format_messages(input="What's 3+3?")) + print(final_prompt.format_messages(input="What's 3+3?")) # noqa: T201 # Use within an LLM from langchain_core.chat_models import ChatAnthropic diff --git a/libs/core/langchain_core/prompts/loading.py b/libs/core/langchain_core/prompts/loading.py index dd6e0c35478..baa56a5666d 100644 --- a/libs/core/langchain_core/prompts/loading.py +++ b/libs/core/langchain_core/prompts/loading.py @@ -146,7 +146,7 @@ def _load_prompt_from_file(file: Union[str, Path]) -> BasePromptTemplate: if file_path.suffix == ".json": with open(file_path) as f: config = json.load(f) - elif file_path.suffix == ".yaml": + elif file_path.suffix.endswith((".yaml", ".yml")): with open(file_path, "r") as f: config = yaml.safe_load(f) else: diff --git a/libs/core/langchain_core/prompts/pipeline.py b/libs/core/langchain_core/prompts/pipeline.py index 48f5ec45821..5c0cc00402f 100644 --- a/libs/core/langchain_core/prompts/pipeline.py +++ b/libs/core/langchain_core/prompts/pipeline.py @@ -11,7 +11,7 @@ def _get_inputs(inputs: dict, input_variables: List[str]) -> dict: class PipelinePromptTemplate(BasePromptTemplate): - """A prompt template for composing multiple prompt templates together. + """Prompt template for composing multiple prompt templates together. This can be useful when you want to reuse parts of prompts. A PipelinePrompt consists of two main parts: diff --git a/libs/core/langchain_core/prompts/string.py b/libs/core/langchain_core/prompts/string.py index 0923da9fbee..d95a3ce9d2f 100644 --- a/libs/core/langchain_core/prompts/string.py +++ b/libs/core/langchain_core/prompts/string.py @@ -1,4 +1,5 @@ """BasePrompt schema definition.""" + from __future__ import annotations import warnings @@ -174,4 +175,4 @@ class StringPromptTemplate(BasePromptTemplate, ABC): return self.format(**dummy_vars) def pretty_print(self) -> None: - print(self.pretty_repr(html=is_interactive_env())) + print(self.pretty_repr(html=is_interactive_env())) # noqa: T201 diff --git a/libs/core/langchain_core/runnables/base.py b/libs/core/langchain_core/runnables/base.py index a5450de3813..4e4a79a50cc 100644 --- a/libs/core/langchain_core/runnables/base.py +++ b/libs/core/langchain_core/runnables/base.py @@ -186,7 +186,7 @@ class Runnable(Generic[Input, Output], ABC): def buggy_double(y: int) -> int: '''Buggy code that will fail 70% of the time''' if random.random() > 0.3: - print('This code failed, and will probably be retried!') + print('This code failed, and will probably be retried!') # noqa: T201 raise ValueError('Triggered buggy code') return y * 2 @@ -1632,7 +1632,7 @@ class Runnable(Generic[Input, Output], ABC): class RunnableSerializable(Serializable, Runnable[Input, Output]): - """A Runnable that can be serialized to JSON.""" + """Runnable that can be serialized to JSON.""" name: Optional[str] = None """The name of the runnable. Used for debugging and tracing.""" @@ -1752,7 +1752,7 @@ def _seq_output_schema( class RunnableSequence(RunnableSerializable[Input, Output]): - """A sequence of runnables, where the output of each is the input of the next. + """Sequence of Runnables, where the output of each is the input of the next. RunnableSequence is the most important composition operator in LangChain as it is used in virtually every chain. @@ -1764,7 +1764,7 @@ class RunnableSequence(RunnableSerializable[Input, Output]): The default implementations of `batch` and `abatch` utilize threadpools and asyncio gather and will be faster than naive invocation of invoke or ainvoke - for IO bound runnables. + for IO bound Runnables. Batching is implemented by invoking the batch method on each component of the RunnableSequence in order. @@ -1826,8 +1826,8 @@ class RunnableSequence(RunnableSerializable[Input, Output]): chain = prompt | model | SimpleJsonOutputParser() async for chunk in chain.astream({'topic': 'colors'}): - print('-') - print(chunk, sep='', flush=True) + print('-') # noqa: T201 + print(chunk, sep='', flush=True) # noqa: T201 """ # The steps are broken into first, middle and last, solely for type checking @@ -2451,11 +2451,11 @@ class RunnableSequence(RunnableSerializable[Input, Output]): class RunnableParallel(RunnableSerializable[Input, Dict[str, Any]]): - """A runnable that runs a mapping of runnables in parallel, and returns a mapping + """Runnable that runs a mapping of Runnables in parallel, and returns a mapping of their outputs. RunnableParallel is one of the two main composition primitives for the LCEL, - alongside RunnableSequence. It invokes runnables concurrently, providing the same + alongside RunnableSequence. It invokes Runnables concurrently, providing the same input to each. A RunnableParallel can be instantiated directly or by using a dict literal within a @@ -2527,7 +2527,7 @@ class RunnableParallel(RunnableSerializable[Input, Dict[str, Any]]): for chunk in runnable.stream({"topic": "bear"}): for key in chunk: output[key] = output[key] + chunk[key].content - print(output) + print(output) # noqa: T201 """ steps: Mapping[str, Runnable[Input, Any]] @@ -2882,7 +2882,7 @@ RunnableMap = RunnableParallel class RunnableGenerator(Runnable[Input, Output]): - """A runnable that runs a generator function. + """Runnable that runs a generator function. RunnableGenerators can be instantiated directly or by using a generator within a sequence. @@ -3730,11 +3730,12 @@ class RunnableLambda(Runnable[Input, Output]): class RunnableEachBase(RunnableSerializable[List[Input], List[Output]]): - """ - A runnable that delegates calls to another runnable + """Runnable that delegates calls to another Runnable with each element of the input sequence. Use only if creating a new RunnableEach subclass with different __init__ args. + + See documentation for RunnableEach for more details. """ bound: Runnable[Input, Output] @@ -3837,9 +3838,31 @@ class RunnableEachBase(RunnableSerializable[List[Input], List[Output]]): class RunnableEach(RunnableEachBase[Input, Output]): - """ - A runnable that delegates calls to another runnable + """Runnable that delegates calls to another Runnable with each element of the input sequence. + + It allows you to call multiple inputs with the bounded Runnable. + + RunnableEach makes it easy to run multiple inputs for the runnable. + In the below example, we associate and run three inputs + with a Runnable: + + .. code-block:: python + + from langchain_core.runnables.base import RunnableEach + from langchain_openai import ChatOpenAI + from langchain_core.prompts import ChatPromptTemplate + from langchain_core.output_parsers import StrOutputParser + prompt = ChatPromptTemplate.from_template("Tell me a short joke about + {topic}") + model = ChatOpenAI() + output_parser = StrOutputParser() + runnable = prompt | model | output_parser + runnable_each = RunnableEach(bound=runnable) + output = runnable_each.invoke([{'topic':'Computer Science'}, + {'topic':'Art'}, + {'topic':'Biology'}]) + print(output) # noqa: T201 """ @classmethod @@ -3887,7 +3910,7 @@ class RunnableEach(RunnableEachBase[Input, Output]): class RunnableBindingBase(RunnableSerializable[Input, Output]): - """A runnable that delegates calls to another runnable with a set of kwargs. + """Runnable that delegates calls to another Runnable with a set of kwargs. Use only if creating a new RunnableBinding subclass with different __init__ args. @@ -4166,7 +4189,7 @@ RunnableBindingBase.update_forward_refs(RunnableConfig=RunnableConfig) class RunnableBinding(RunnableBindingBase[Input, Output]): - """Wrap a runnable with additional functionality. + """Wrap a Runnable with additional functionality. A RunnableBinding can be thought of as a "runnable decorator" that preserves the essential features of Runnable; i.e., batching, streaming, @@ -4300,12 +4323,12 @@ class RunnableBinding(RunnableBindingBase[Input, Output]): bound=self.bound, kwargs=self.kwargs, config=self.config, - custom_input_type=input_type - if input_type is not None - else self.custom_input_type, - custom_output_type=output_type - if output_type is not None - else self.custom_output_type, + custom_input_type=( + input_type if input_type is not None else self.custom_input_type + ), + custom_output_type=( + output_type if output_type is not None else self.custom_output_type + ), ) def with_retry(self, **kwargs: Any) -> Runnable[Input, Output]: diff --git a/libs/core/langchain_core/runnables/branch.py b/libs/core/langchain_core/runnables/branch.py index fa60a58f160..ef0e3eec64d 100644 --- a/libs/core/langchain_core/runnables/branch.py +++ b/libs/core/langchain_core/runnables/branch.py @@ -38,13 +38,13 @@ from langchain_core.runnables.utils import ( class RunnableBranch(RunnableSerializable[Input, Output]): - """A Runnable that selects which branch to run based on a condition. + """Runnable that selects which branch to run based on a condition. - The runnable is initialized with a list of (condition, runnable) pairs and + The Runnable is initialized with a list of (condition, Runnable) pairs and a default branch. When operating on an input, the first condition that evaluates to True is - selected, and the corresponding runnable is run on the input. + selected, and the corresponding Runnable is run on the input. If no condition evaluates to True, the default branch is run on the input. diff --git a/libs/core/langchain_core/runnables/configurable.py b/libs/core/langchain_core/runnables/configurable.py index 81ab33d8fd4..ea4ed30e66c 100644 --- a/libs/core/langchain_core/runnables/configurable.py +++ b/libs/core/langchain_core/runnables/configurable.py @@ -42,7 +42,7 @@ from langchain_core.runnables.utils import ( class DynamicRunnable(RunnableSerializable[Input, Output]): - """A Serializable Runnable that can be dynamically configured.""" + """Serializable Runnable that can be dynamically configured.""" default: RunnableSerializable[Input, Output] @@ -220,7 +220,7 @@ class DynamicRunnable(RunnableSerializable[Input, Output]): class RunnableConfigurableFields(DynamicRunnable[Input, Output]): - """A Runnable that can be dynamically configured.""" + """Runnable that can be dynamically configured.""" fields: Dict[str, AnyConfigurableField] @@ -297,7 +297,7 @@ class RunnableConfigurableFields(DynamicRunnable[Input, Output]): # Before Python 3.11 native StrEnum is not available class StrEnum(str, enum.Enum): - """A string enum.""" + """String enum.""" pass @@ -313,7 +313,60 @@ _enums_for_spec_lock = threading.Lock() class RunnableConfigurableAlternatives(DynamicRunnable[Input, Output]): - """A Runnable that can be dynamically configured.""" + """Runnable that can be dynamically configured. + + A RunnableConfigurableAlternatives should be initiated using the + `configurable_alternatives` method of a Runnable or can be + initiated directly as well. + + Here is an example of using a RunnableConfigurableAlternatives that uses + alternative prompts to illustrate its functionality: + + .. code-block:: python + + from langchain_core.runnables import ConfigurableField + from langchain_openai import ChatOpenAI + + # This creates a RunnableConfigurableAlternatives for Prompt Runnable + # with two alternatives. + prompt = PromptTemplate.from_template( + "Tell me a joke about {topic}" + ).configurable_alternatives( + ConfigurableField(id="prompt"), + default_key="joke", + poem=PromptTemplate.from_template("Write a short poem about {topic}") + ) + + # When invoking the created RunnableSequence, you can pass in the + # value for your ConfigurableField's id which in this case will either be + # `joke` or `poem`. + chain = prompt | ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0) + + # The `with_config` method brings in the desired Prompt Runnable in your + # Runnable Sequence. + chain.with_config(configurable={"prompt": "poem"}).invoke({"topic": "bears"}) + + + Equivalently, you can initialize RunnableConfigurableAlternatives directly + and use in LCEL in the same way: + + .. code-block:: python + + from langchain_core.runnables import ConfigurableField + from langchain_core.runnables.configurable import RunnableConfigurableAlternatives + from langchain_openai import ChatOpenAI + + prompt = RunnableConfigurableAlternatives( + which=ConfigurableField(id='prompt'), + default=PromptTemplate.from_template("Tell me a joke about {topic}"), + default_key='joke', + prefix_keys=False, + alternatives={"poem":PromptTemplate.from_template("Write a short poem about {topic}")} + ) + chain = prompt | ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0) + chain.with_config(configurable={"prompt": "poem"}).invoke({"topic": "bears"}) + + """ # noqa: E501 which: ConfigurableField diff --git a/libs/core/langchain_core/runnables/fallbacks.py b/libs/core/langchain_core/runnables/fallbacks.py index bc7128c1bf3..9ecbe6cdfc6 100644 --- a/libs/core/langchain_core/runnables/fallbacks.py +++ b/libs/core/langchain_core/runnables/fallbacks.py @@ -39,20 +39,20 @@ if TYPE_CHECKING: class RunnableWithFallbacks(RunnableSerializable[Input, Output]): - """A Runnable that can fallback to other Runnables if it fails. + """Runnable that can fallback to other Runnables if it fails. External APIs (e.g., APIs for a language model) may at times experience degraded performance or even downtime. - In these cases, it can be useful to have a fallback runnable that can be - used in place of the original runnable (e.g., fallback to another LLM provider). + In these cases, it can be useful to have a fallback Runnable that can be + used in place of the original Runnable (e.g., fallback to another LLM provider). - Fallbacks can be defined at the level of a single runnable, or at the level - of a chain of runnables. Fallbacks are tried in order until one succeeds or + Fallbacks can be defined at the level of a single Runnable, or at the level + of a chain of Runnables. Fallbacks are tried in order until one succeeds or all fail. While you can instantiate a ``RunnableWithFallbacks`` directly, it is usually - more convenient to use the ``with_fallbacks`` method on a runnable. + more convenient to use the ``with_fallbacks`` method on a Runnable. Example: diff --git a/libs/core/langchain_core/runnables/graph.py b/libs/core/langchain_core/runnables/graph.py index b172b71239c..26a4fbd0aa0 100644 --- a/libs/core/langchain_core/runnables/graph.py +++ b/libs/core/langchain_core/runnables/graph.py @@ -12,17 +12,23 @@ if TYPE_CHECKING: class Edge(NamedTuple): + """Edge in a graph.""" + source: str target: str class Node(NamedTuple): + """Node in a graph.""" + id: str data: Union[Type[BaseModel], RunnableType] @dataclass class Graph: + """Graph of nodes and edges.""" + nodes: Dict[str, Node] = field(default_factory=dict) edges: List[Edge] = field(default_factory=list) @@ -137,4 +143,4 @@ class Graph: ) def print_ascii(self) -> None: - print(self.draw_ascii()) + print(self.draw_ascii()) # noqa: T201 diff --git a/libs/core/langchain_core/runnables/history.py b/libs/core/langchain_core/runnables/history.py index bd72e405388..f9b68cd68e8 100644 --- a/libs/core/langchain_core/runnables/history.py +++ b/libs/core/langchain_core/runnables/history.py @@ -35,21 +35,21 @@ GetSessionHistoryCallable = Callable[..., BaseChatMessageHistory] class RunnableWithMessageHistory(RunnableBindingBase): - """A runnable that manages chat message history for another runnable. + """Runnable that manages chat message history for another Runnable. A chat message history is a sequence of messages that represent a conversation. - RunnableWithMessageHistory wraps another runnable and manages the chat message + RunnableWithMessageHistory wraps another Runnable and manages the chat message history for it; it is responsible for reading and updating the chat message history. - The formats supports for the inputs and outputs of the wrapped runnable + The formats supports for the inputs and outputs of the wrapped Runnable are described below. RunnableWithMessageHistory must always be called with a config that contains the appropriate parameters for the chat message history factory. - By default the runnable is expected to take a single configuration parameter + By default the Runnable is expected to take a single configuration parameter called `session_id` which is a string. This parameter is used to create a new or look up an existing chat message history that matches the given session_id. @@ -115,7 +115,7 @@ class RunnableWithMessageHistory(RunnableBindingBase): history = get_by_session_id("1") history.add_message(AIMessage(content="hello")) - print(store) + print(store) # noqa: T201 Example where the wrapped Runnable takes a dictionary input: @@ -146,20 +146,20 @@ class RunnableWithMessageHistory(RunnableBindingBase): history_messages_key="history", ) - print(chain_with_history.invoke( + print(chain_with_history.invoke( # noqa: T201 {"ability": "math", "question": "What does cosine mean?"}, config={"configurable": {"session_id": "foo"}} )) # Uses the store defined in the example above. - print(store) + print(store) # noqa: T201 - print(chain_with_history.invoke( + print(chain_with_history.invoke( # noqa: T201 {"ability": "math", "question": "What's its inverse"}, config={"configurable": {"session_id": "foo"}} )) - print(store) + print(store) # noqa: T201 Example where the session factory takes two keys, user_id and conversation id): @@ -341,7 +341,9 @@ class RunnableWithMessageHistory(RunnableBindingBase): self, config: Optional[RunnableConfig] = None ) -> Type[BaseModel]: super_schema = super().get_input_schema(config) - if super_schema.__custom_root_type__ is not None: + if super_schema.__custom_root_type__ or not super_schema.schema().get( + "properties" + ): from langchain_core.messages import BaseMessage fields: Dict = {} diff --git a/libs/core/langchain_core/runnables/passthrough.py b/libs/core/langchain_core/runnables/passthrough.py index 1f4367d9d5e..3e9277a7a2d 100644 --- a/libs/core/langchain_core/runnables/passthrough.py +++ b/libs/core/langchain_core/runnables/passthrough.py @@ -48,23 +48,23 @@ if TYPE_CHECKING: def identity(x: Other) -> Other: - """An identity function""" + """Identity function""" return x async def aidentity(x: Other) -> Other: - """An async identity function""" + """Async identity function""" return x class RunnablePassthrough(RunnableSerializable[Other, Other]): - """A runnable to passthrough inputs unchanged or with additional keys. + """Runnable to passthrough inputs unchanged or with additional keys. This runnable behaves almost like the identity function, except that it can be configured to add additional keys to the output, if the input is a dict. - The examples below demonstrate this runnable works using a few simple + The examples below demonstrate this Runnable works using a few simple chains. The chains rely on simple lambdas to make the examples easy to execute and experiment with. @@ -572,7 +572,7 @@ class RunnableAssign(RunnableSerializable[Dict[str, Any], Dict[str, Any]]): class RunnablePick(RunnableSerializable[Dict[str, Any], Dict[str, Any]]): """ - A runnable that picks keys from Dict[str, Any] inputs. + Runnable that picks keys from Dict[str, Any] inputs. """ keys: Union[str, List[str]] diff --git a/libs/core/langchain_core/runnables/retry.py b/libs/core/langchain_core/runnables/retry.py index 36a508776ce..bb1d08eaf1b 100644 --- a/libs/core/langchain_core/runnables/retry.py +++ b/libs/core/langchain_core/runnables/retry.py @@ -37,7 +37,7 @@ U = TypeVar("U") class RunnableRetry(RunnableBindingBase[Input, Output]): """Retry a Runnable if it fails. - A RunnableRetry helps can be used to add retry logic to any object + RunnableRetry can be used to add retry logic to any object that subclasses the base Runnable. Such retries are especially useful for network calls that may fail diff --git a/libs/core/langchain_core/runnables/router.py b/libs/core/langchain_core/runnables/router.py index eacdfb37813..6277792b9e3 100644 --- a/libs/core/langchain_core/runnables/router.py +++ b/libs/core/langchain_core/runnables/router.py @@ -34,7 +34,7 @@ from langchain_core.runnables.utils import ( class RouterInput(TypedDict): - """A Router input. + """Router input. Attributes: key: The key to route on. @@ -47,8 +47,8 @@ class RouterInput(TypedDict): class RouterRunnable(RunnableSerializable[RouterInput, Output]): """ - A runnable that routes to a set of runnables based on Input['key']. - Returns the output of the selected runnable. + Runnable that routes to a set of Runnables based on Input['key']. + Returns the output of the selected Runnable. """ runnables: Mapping[str, Runnable[Any, Output]] diff --git a/libs/core/langchain_core/runnables/schema.py b/libs/core/langchain_core/runnables/schema.py index b2891b10b11..db4ad13a34a 100644 --- a/libs/core/langchain_core/runnables/schema.py +++ b/libs/core/langchain_core/runnables/schema.py @@ -12,32 +12,32 @@ class EventData(TypedDict, total=False): input: Any """The input passed to the runnable that generated the event. - Inputs will sometimes be available at the *START* of the runnable, and - sometimes at the *END* of the runnable. + Inputs will sometimes be available at the *START* of the Runnable, and + sometimes at the *END* of the Runnable. - If a runnable is able to stream its inputs, then its input by definition - won't be known until the *END* of the runnable when it has finished streaming + If a Runnable is able to stream its inputs, then its input by definition + won't be known until the *END* of the Runnable when it has finished streaming its inputs. """ output: Any - """The output of the runnable that generated the event. + """The output of the Runnable that generated the event. - Outputs will only be available at the *END* of the runnable. + Outputs will only be available at the *END* of the Runnable. - For most runnables, this field can be inferred from the `chunk` field, - though there might be some exceptions for special cased runnables (e.g., like + For most Runnables, this field can be inferred from the `chunk` field, + though there might be some exceptions for special cased Runnables (e.g., like chat models), which may return more information. """ chunk: Any """A streaming chunk from the output that generated the event. chunks support addition in general, and adding them up should result - in the output of the runnable that generated the event. + in the output of the Runnable that generated the event. """ class StreamEvent(TypedDict): - """A streaming event. + """Streaming event. Schema of a streaming event which is produced from the astream_events method. diff --git a/libs/core/langchain_core/runnables/utils.py b/libs/core/langchain_core/runnables/utils.py index cb8f21e29ce..59d2c862037 100644 --- a/libs/core/langchain_core/runnables/utils.py +++ b/libs/core/langchain_core/runnables/utils.py @@ -46,7 +46,15 @@ async def gated_coro(semaphore: asyncio.Semaphore, coro: Coroutine) -> Any: async def gather_with_concurrency(n: Union[int, None], *coros: Coroutine) -> list: - """Gather coroutines with a limit on the number of concurrent coroutines.""" + """Gather coroutines with a limit on the number of concurrent coroutines. + + Args: + n: The number of coroutines to run concurrently. + coros: The coroutines to run. + + Returns: + The results of the coroutines. + """ if n is None: return await asyncio.gather(*coros) @@ -344,7 +352,7 @@ async def aadd(addables: AsyncIterable[Addable]) -> Optional[Addable]: class ConfigurableField(NamedTuple): - """A field that can be configured by the user.""" + """Field that can be configured by the user.""" id: str @@ -358,7 +366,7 @@ class ConfigurableField(NamedTuple): class ConfigurableFieldSingleOption(NamedTuple): - """A field that can be configured by the user with a default value.""" + """Field that can be configured by the user with a default value.""" id: str options: Mapping[str, Any] @@ -373,7 +381,7 @@ class ConfigurableFieldSingleOption(NamedTuple): class ConfigurableFieldMultiOption(NamedTuple): - """A field that can be configured by the user with multiple default values.""" + """Field that can be configured by the user with multiple default values.""" id: str options: Mapping[str, Any] @@ -393,7 +401,7 @@ AnyConfigurableField = Union[ class ConfigurableFieldSpec(NamedTuple): - """A field that can be configured by the user. It is a specification of a field.""" + """Field that can be configured by the user. It is a specification of a field.""" id: str annotation: Any diff --git a/libs/core/langchain_core/sys_info.py b/libs/core/langchain_core/sys_info.py index 467d125616f..2cbdcacf953 100644 --- a/libs/core/langchain_core/sys_info.py +++ b/libs/core/langchain_core/sys_info.py @@ -1,4 +1,5 @@ """Print information about the system and langchain packages for debugging purposes.""" + from typing import Sequence @@ -13,6 +14,7 @@ def print_sys_info(*, additional_pkgs: Sequence[str] = tuple()) -> None: other_langchain_packages = [ "langserve", "langgraph", + "langsmith", ] langchain_pkgs = [ @@ -24,7 +26,7 @@ def print_sys_info(*, additional_pkgs: Sequence[str] = tuple()) -> None: ) # Always surface these packages to the top - order_by = ["langchain_core", "langchain", "langchain_community"] + order_by = ["langchain_core", "langchain", "langchain_community", "langsmith"] for pkg in reversed(order_by): if pkg in all_packages: @@ -36,17 +38,17 @@ def print_sys_info(*, additional_pkgs: Sequence[str] = tuple()) -> None: "OS Version": platform.version(), "Python Version": sys.version, } - print() - print("System Information") - print("------------------") - print("> OS: ", system_info["OS"]) - print("> OS Version: ", system_info["OS Version"]) - print("> Python Version: ", system_info["Python Version"]) + print() # noqa: T201 + print("System Information") # noqa: T201 + print("------------------") # noqa: T201 + print("> OS: ", system_info["OS"]) # noqa: T201 + print("> OS Version: ", system_info["OS Version"]) # noqa: T201 + print("> Python Version: ", system_info["Python Version"]) # noqa: T201 # Print out only langchain packages - print() - print("Package Information") - print("-------------------") + print() # noqa: T201 + print("Package Information") # noqa: T201 + print("-------------------") # noqa: T201 not_installed = [] @@ -67,18 +69,18 @@ def print_sys_info(*, additional_pkgs: Sequence[str] = tuple()) -> None: # Print package with version if package_version is not None: - print(f"> {pkg}: {package_version}") + print(f"> {pkg}: {package_version}") # noqa: T201 else: - print(f"> {pkg}: Installed. No version info available.") + print(f"> {pkg}: Installed. No version info available.") # noqa: T201 if not_installed: - print() - print("Packages not installed (Not Necessarily a Problem)") - print("--------------------------------------------------") - print("The following packages were not found:") - print() + print() # noqa: T201 + print("Packages not installed (Not Necessarily a Problem)") # noqa: T201 + print("--------------------------------------------------") # noqa: T201 + print("The following packages were not found:") # noqa: T201 + print() # noqa: T201 for pkg in not_installed: - print(f"> {pkg}") + print(f"> {pkg}") # noqa: T201 if __name__ == "__main__": diff --git a/libs/core/langchain_core/tools.py b/libs/core/langchain_core/tools.py index d8bf766f899..c9a91ed24fb 100644 --- a/libs/core/langchain_core/tools.py +++ b/libs/core/langchain_core/tools.py @@ -61,7 +61,6 @@ def _create_subset_model( fields = {} for field_name in field_names: field = model.__fields__[field_name] - t = ( # this isn't perfect but should work for most functions field.outer_type_ @@ -143,10 +142,10 @@ def create_schema_from_function( class ToolException(Exception): - """An optional exception that tool throws when execution error occurs. + """Optional exception that tool throws when execution error occurs. When this exception is thrown, the agent will not stop working, - but will handle the exception according to the handle_tool_error + but it will handle the exception according to the handle_tool_error variable of the tool, and the processing result will be returned to the agent as observation, and printed in red on the console. """ @@ -216,9 +215,9 @@ class ChildTool(BaseTool): You can use these to eg identify a specific instance of a tool with its use case. """ - handle_tool_error: Optional[ - Union[bool, str, Callable[[ToolException], str]] - ] = False + handle_tool_error: Optional[Union[bool, str, Callable[[ToolException], str]]] = ( + False + ) """Handle the content of the ToolException thrown.""" handle_validation_error: Optional[ diff --git a/libs/core/langchain_core/tracers/evaluation.py b/libs/core/langchain_core/tracers/evaluation.py index b7e5dc8bc0d..3caeda2b0c0 100644 --- a/libs/core/langchain_core/tracers/evaluation.py +++ b/libs/core/langchain_core/tracers/evaluation.py @@ -31,7 +31,7 @@ def wait_for_all_evaluators() -> None: class EvaluatorCallbackHandler(BaseTracer): - """A tracer that runs a run evaluator whenever a run is persisted. + """Tracer that runs a run evaluator whenever a run is persisted. Parameters ---------- diff --git a/libs/core/langchain_core/tracers/langchain.py b/libs/core/langchain_core/tracers/langchain.py index ef4defb28b8..15cb1977c28 100644 --- a/libs/core/langchain_core/tracers/langchain.py +++ b/libs/core/langchain_core/tracers/langchain.py @@ -1,11 +1,11 @@ """A Tracer implementation that records to LangChain endpoint.""" + from __future__ import annotations import logging -import weakref -from concurrent.futures import Future, ThreadPoolExecutor, wait +from concurrent.futures import ThreadPoolExecutor from datetime import datetime, timezone -from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union from uuid import UUID from langsmith import Client @@ -27,7 +27,6 @@ if TYPE_CHECKING: logger = logging.getLogger(__name__) _LOGGED = set() -_TRACERS: weakref.WeakSet[LangChainTracer] = weakref.WeakSet() _CLIENT: Optional[Client] = None _EXECUTOR: Optional[ThreadPoolExecutor] = None @@ -43,10 +42,9 @@ def log_error_once(method: str, exception: Exception) -> None: def wait_for_all_tracers() -> None: """Wait for all tracers to finish.""" - global _TRACERS - for tracer in list(_TRACERS): - if tracer is not None: - tracer.wait_for_futures() + global _CLIENT + if _CLIENT is not None and _CLIENT.tracing_queue is not None: + _CLIENT.tracing_queue.join() def get_client() -> Client: @@ -65,8 +63,16 @@ def _get_executor() -> ThreadPoolExecutor: return _EXECUTOR +def _run_to_dict(run: Run) -> dict: + return { + **run.dict(exclude={"child_runs", "inputs", "outputs"}), + "inputs": run.inputs.copy() if run.inputs is not None else None, + "outputs": run.outputs.copy() if run.outputs is not None else None, + } + + class LangChainTracer(BaseTracer): - """An implementation of the SharedTracer that POSTS to the langchain endpoint.""" + """Implementation of the SharedTracer that POSTS to the LangChain endpoint.""" def __init__( self, @@ -74,7 +80,6 @@ class LangChainTracer(BaseTracer): project_name: Optional[str] = None, client: Optional[Client] = None, tags: Optional[List[str]] = None, - use_threading: bool = True, **kwargs: Any, ) -> None: """Initialize the LangChain tracer.""" @@ -84,12 +89,8 @@ class LangChainTracer(BaseTracer): ) self.project_name = project_name or ls_utils.get_tracer_project() self.client = client or get_client() - self._futures: weakref.WeakSet[Future] = weakref.WeakSet() self.tags = tags or [] - self.executor = _get_executor() if use_threading else None self.latest_run: Optional[Run] = None - global _TRACERS - _TRACERS.add(self) def on_chat_model_start( self, @@ -158,7 +159,7 @@ class LangChainTracer(BaseTracer): def _persist_run_single(self, run: Run) -> None: """Persist a run.""" - run_dict = run.dict(exclude={"child_runs"}) + run_dict = _run_to_dict(run) run_dict["tags"] = self._get_tags(run) extra = run_dict.get("extra", {}) extra["runtime"] = get_runtime_environment() @@ -173,7 +174,7 @@ class LangChainTracer(BaseTracer): def _update_run_single(self, run: Run) -> None: """Update a run.""" try: - run_dict = run.dict() + run_dict = _run_to_dict(run) run_dict["tags"] = self._get_tags(run) self.client.update_run(run.id, **run_dict) except Exception as e: @@ -181,75 +182,69 @@ class LangChainTracer(BaseTracer): log_error_once("patch", e) raise - def _submit(self, function: Callable[[Run], None], run: Run) -> None: - """Submit a function to the executor.""" - if self.executor is None: - function(run) - else: - self._futures.add(self.executor.submit(function, run)) - def _on_llm_start(self, run: Run) -> None: """Persist an LLM run.""" if run.parent_run_id is None: run.reference_example_id = self.example_id - self._submit(self._persist_run_single, run) + self._persist_run_single(run) def _on_chat_model_start(self, run: Run) -> None: """Persist an LLM run.""" if run.parent_run_id is None: run.reference_example_id = self.example_id - self._submit(self._persist_run_single, run) + self._persist_run_single(run) def _on_llm_end(self, run: Run) -> None: """Process the LLM Run.""" - self._submit(self._update_run_single, run) + self._update_run_single(run) def _on_llm_error(self, run: Run) -> None: """Process the LLM Run upon error.""" - self._submit(self._update_run_single, run) + self._update_run_single(run) def _on_chain_start(self, run: Run) -> None: """Process the Chain Run upon start.""" if run.parent_run_id is None: run.reference_example_id = self.example_id - self._submit(self._persist_run_single, run) + self._persist_run_single(run) def _on_chain_end(self, run: Run) -> None: """Process the Chain Run.""" - self._submit(self._update_run_single, run) + self._update_run_single(run) def _on_chain_error(self, run: Run) -> None: """Process the Chain Run upon error.""" - self._submit(self._update_run_single, run) + self._update_run_single(run) def _on_tool_start(self, run: Run) -> None: """Process the Tool Run upon start.""" if run.parent_run_id is None: run.reference_example_id = self.example_id - self._submit(self._persist_run_single, run) + self._persist_run_single(run) def _on_tool_end(self, run: Run) -> None: """Process the Tool Run.""" - self._submit(self._update_run_single, run) + self._update_run_single(run) def _on_tool_error(self, run: Run) -> None: """Process the Tool Run upon error.""" - self._submit(self._update_run_single, run) + self._update_run_single(run) def _on_retriever_start(self, run: Run) -> None: """Process the Retriever Run upon start.""" if run.parent_run_id is None: run.reference_example_id = self.example_id - self._submit(self._persist_run_single, run) + self._persist_run_single(run) def _on_retriever_end(self, run: Run) -> None: """Process the Retriever Run.""" - self._submit(self._update_run_single, run) + self._update_run_single(run) def _on_retriever_error(self, run: Run) -> None: """Process the Retriever Run upon error.""" - self._submit(self._update_run_single, run) + self._update_run_single(run) def wait_for_futures(self) -> None: """Wait for the given futures to complete.""" - wait(self._futures) + if self.client is not None and self.client.tracing_queue is not None: + self.client.tracing_queue.join() diff --git a/libs/core/langchain_core/tracers/langchain_v1.py b/libs/core/langchain_core/tracers/langchain_v1.py index 38a876b4453..a5b1fcbf5e0 100644 --- a/libs/core/langchain_core/tracers/langchain_v1.py +++ b/libs/core/langchain_core/tracers/langchain_v1.py @@ -37,7 +37,7 @@ def _get_endpoint() -> str: @deprecated("0.1.0", alternative="LangChainTracer", removal="0.2.0") class LangChainTracerV1(BaseTracer): - """An implementation of the SharedTracer that POSTS to the langchain endpoint.""" + """Implementation of the SharedTracer that POSTS to the langchain endpoint.""" def __init__(self, **kwargs: Any) -> None: """Initialize the LangChain tracer.""" diff --git a/libs/core/langchain_core/tracers/log_stream.py b/libs/core/langchain_core/tracers/log_stream.py index c6b8da943a3..2d92964b193 100644 --- a/libs/core/langchain_core/tracers/log_stream.py +++ b/libs/core/langchain_core/tracers/log_stream.py @@ -2,7 +2,6 @@ from __future__ import annotations import asyncio import copy -import math import threading from collections import defaultdict from typing import ( @@ -20,7 +19,6 @@ from typing import ( from uuid import UUID import jsonpatch # type: ignore[import] -from anyio import BrokenResourceError, ClosedResourceError, create_memory_object_stream from typing_extensions import NotRequired, TypedDict from langchain_core.load import dumps @@ -29,6 +27,7 @@ from langchain_core.outputs import ChatGenerationChunk, GenerationChunk from langchain_core.runnables import Runnable, RunnableConfig, ensure_config from langchain_core.runnables.utils import Input, Output from langchain_core.tracers.base import BaseTracer +from langchain_core.tracers.memory_stream import _MemoryStream from langchain_core.tracers.schemas import Run @@ -88,7 +87,7 @@ class RunState(TypedDict): class RunLogPatch: - """A patch to the run log.""" + """Patch to the run log.""" ops: List[Dict[str, Any]] """List of jsonpatch operations, which describe how to create the run state @@ -121,7 +120,7 @@ class RunLogPatch: class RunLog(RunLogPatch): - """A run log.""" + """Run log.""" state: RunState """Current state of the log, obtained from applying all ops in sequence.""" @@ -159,7 +158,7 @@ T = TypeVar("T") class LogStreamCallbackHandler(BaseTracer): - """A tracer that streams run logs to a stream.""" + """Tracer that streams run logs to a stream.""" def __init__( self, @@ -210,12 +209,11 @@ class LogStreamCallbackHandler(BaseTracer): self.exclude_types = exclude_types self.exclude_tags = exclude_tags - send_stream: Any - receive_stream: Any - send_stream, receive_stream = create_memory_object_stream(math.inf) + loop = asyncio.get_event_loop() + memory_stream = _MemoryStream[RunLogPatch](loop) self.lock = threading.Lock() - self.send_stream = send_stream - self.receive_stream = receive_stream + self.send_stream = memory_stream.get_send_stream() + self.receive_stream = memory_stream.get_receive_stream() self._key_map_by_run_id: Dict[UUID, str] = {} self._counter_map_by_name: Dict[str, int] = defaultdict(int) self.root_id: Optional[UUID] = None @@ -225,11 +223,12 @@ class LogStreamCallbackHandler(BaseTracer): def send(self, *ops: Dict[str, Any]) -> bool: """Send a patch to the stream, return False if the stream is closed.""" - try: - self.send_stream.send_nowait(RunLogPatch(*ops)) - return True - except (ClosedResourceError, BrokenResourceError): - return False + # We will likely want to wrap this in try / except at some point + # to handle exceptions that might arise at run time. + # For now we'll let the exception bubble up, and always return + # True on the happy path. + self.send_stream.send_nowait(RunLogPatch(*ops)) + return True async def tap_output_aiter( self, run_id: UUID, output: AsyncIterator[T] @@ -572,6 +571,7 @@ async def _astream_log_implementation( try: final_output = final_output + chunk # type: ignore except TypeError: + prev_final_output = None final_output = chunk patches: List[Dict[str, Any]] = [] if with_streamed_output_list: diff --git a/libs/core/langchain_core/tracers/memory_stream.py b/libs/core/langchain_core/tracers/memory_stream.py new file mode 100644 index 00000000000..871b5ca932e --- /dev/null +++ b/libs/core/langchain_core/tracers/memory_stream.py @@ -0,0 +1,104 @@ +"""Module implements a memory stream for communication between two co-routines. + +This module provides a way to communicate between two co-routines using a memory +channel. The writer and reader can be in the same event loop or in different event +loops. When they're in different event loops, they will also be in different +threads. + +This is useful in situations when there's a mix of synchronous and asynchronous +used in the code. +""" +import asyncio +from asyncio import AbstractEventLoop, Queue +from typing import AsyncIterator, Generic, TypeVar + +T = TypeVar("T") + + +class _SendStream(Generic[T]): + def __init__( + self, reader_loop: AbstractEventLoop, queue: Queue, done: object + ) -> None: + """Create a writer for the queue and done object. + + Args: + reader_loop: The event loop to use for the writer. This loop will be used + to schedule the writes to the queue. + queue: The queue to write to. This is an asyncio queue. + done: Special sentinel object to indicate that the writer is done. + """ + self._reader_loop = reader_loop + self._queue = queue + self._done = done + + async def send(self, item: T) -> None: + """Schedule the item to be written to the queue using the original loop.""" + return self.send_nowait(item) + + def send_nowait(self, item: T) -> None: + """Schedule the item to be written to the queue using the original loop.""" + self._reader_loop.call_soon_threadsafe(self._queue.put_nowait, item) + + async def aclose(self) -> None: + """Schedule the done object write the queue using the original loop.""" + return self.close() + + def close(self) -> None: + """Schedule the done object write the queue using the original loop.""" + self._reader_loop.call_soon_threadsafe(self._queue.put_nowait, self._done) + + +class _ReceiveStream(Generic[T]): + def __init__(self, queue: Queue, done: object) -> None: + """Create a reader for the queue and done object. + + This reader should be used in the same loop as the loop that was passed + to the channel. + """ + self._queue = queue + self._done = done + self._is_closed = False + + async def __aiter__(self) -> AsyncIterator[T]: + while True: + item = await self._queue.get() + if item is self._done: + self._is_closed = True + break + yield item + + +class _MemoryStream(Generic[T]): + """Stream data from a writer to a reader even if they are in different threads. + + Uses asyncio queues to communicate between two co-routines. This implementation + should work even if the writer and reader co-routines belong to two different + event loops (e.g. one running from an event loop in the main thread + and the other running in an event loop in a background thread). + + This implementation is meant to be used with a single writer and a single reader. + + This is an internal implementation to LangChain please do not use it directly. + """ + + def __init__(self, loop: AbstractEventLoop) -> None: + """Create a channel for the given loop. + + Args: + loop: The event loop to use for the channel. The reader is assumed + to be running in the same loop as the one passed to this constructor. + This will NOT be validated at run time. + """ + self._loop = loop + self._queue: asyncio.Queue = asyncio.Queue(maxsize=0) + self._done = object() + + def get_send_stream(self) -> _SendStream[T]: + """Get a writer for the channel.""" + return _SendStream[T]( + reader_loop=self._loop, queue=self._queue, done=self._done + ) + + def get_receive_stream(self) -> _ReceiveStream[T]: + """Get a reader for the channel.""" + return _ReceiveStream[T](queue=self._queue, done=self._done) diff --git a/libs/core/langchain_core/tracers/root_listeners.py b/libs/core/langchain_core/tracers/root_listeners.py index a81db8af8f9..7db7407d268 100644 --- a/libs/core/langchain_core/tracers/root_listeners.py +++ b/libs/core/langchain_core/tracers/root_listeners.py @@ -12,7 +12,7 @@ Listener = Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]] class RootListenersTracer(BaseTracer): - """A tracer that calls listeners on run start, end, and error.""" + """Tracer that calls listeners on run start, end, and error.""" def __init__( self, diff --git a/libs/core/langchain_core/tracers/run_collector.py b/libs/core/langchain_core/tracers/run_collector.py index 995ac7cc683..9442daa4132 100644 --- a/libs/core/langchain_core/tracers/run_collector.py +++ b/libs/core/langchain_core/tracers/run_collector.py @@ -9,7 +9,7 @@ from langchain_core.tracers.schemas import Run class RunCollectorCallbackHandler(BaseTracer): """ - A tracer that collects all nested runs in a list. + Tracer that collects all nested runs in a list. This tracer is useful for inspection and evaluation purposes. diff --git a/libs/core/langchain_core/utils/_merge.py b/libs/core/langchain_core/utils/_merge.py index e21fdd96621..13b91270c70 100644 --- a/libs/core/langchain_core/utils/_merge.py +++ b/libs/core/langchain_core/utils/_merge.py @@ -19,11 +19,9 @@ def merge_dicts(left: Dict[str, Any], right: Dict[str, Any]) -> Dict[str, Any]: for k, v in right.items(): if k not in merged: merged[k] = v - elif merged[k] is None and v: + elif v is not None and merged[k] is None: merged[k] = v - elif v is None: - continue - elif merged[k] == v: + elif v is None or merged[k] == v: continue elif type(merged[k]) != type(v): raise TypeError( diff --git a/libs/core/langchain_core/utils/formatting.py b/libs/core/langchain_core/utils/formatting.py index 83fe2aba927..d2e5376b683 100644 --- a/libs/core/langchain_core/utils/formatting.py +++ b/libs/core/langchain_core/utils/formatting.py @@ -4,7 +4,7 @@ from typing import Any, List, Mapping, Sequence class StrictFormatter(Formatter): - """A subclass of formatter that checks for extra keys.""" + """Formatter that checks for extra keys.""" def vformat( self, format_string: str, args: Sequence, kwargs: Mapping[str, Any] diff --git a/libs/core/langchain_core/utils/html.py b/libs/core/langchain_core/utils/html.py index bbea15f0fa4..837b19ed101 100644 --- a/libs/core/langchain_core/utils/html.py +++ b/libs/core/langchain_core/utils/html.py @@ -1,7 +1,10 @@ +import logging import re from typing import List, Optional, Sequence, Union from urllib.parse import urljoin, urlparse +logger = logging.getLogger(__name__) + PREFIXES_TO_IGNORE = ("javascript:", "mailto:", "#") SUFFIXES_TO_IGNORE = ( ".css", @@ -52,6 +55,7 @@ def extract_sub_links( pattern: Union[str, re.Pattern, None] = None, prevent_outside: bool = True, exclude_prefixes: Sequence[str] = (), + continue_on_failure: bool = False, ) -> List[str]: """Extract all links from a raw html string and convert into absolute paths. @@ -63,25 +67,34 @@ def extract_sub_links( prevent_outside: If True, ignore external links which are not children of the base url. exclude_prefixes: Exclude any URLs that start with one of these prefixes. - + continue_on_failure: If True, continue if parsing a specific link raises an + exception. Otherwise, raise the exception. Returns: List[str]: sub links """ base_url_to_use = base_url if base_url is not None else url parsed_base_url = urlparse(base_url_to_use) + parsed_url = urlparse(url) all_links = find_all_links(raw_html, pattern=pattern) absolute_paths = set() for link in all_links: - parsed_link = urlparse(link) - # Some may be absolute links like https://to/path - if parsed_link.scheme == "http" or parsed_link.scheme == "https": - absolute_path = link - # Some may have omitted the protocol like //to/path - elif link.startswith("//"): - absolute_path = f"{urlparse(url).scheme}:{link}" - else: - absolute_path = urljoin(url, parsed_link.path) - absolute_paths.add(absolute_path) + try: + parsed_link = urlparse(link) + # Some may be absolute links like https://to/path + if parsed_link.scheme == "http" or parsed_link.scheme == "https": + absolute_path = link + # Some may have omitted the protocol like //to/path + elif link.startswith("//"): + absolute_path = f"{parsed_url.scheme}:{link}" + else: + absolute_path = urljoin(url, parsed_link.path) + absolute_paths.add(absolute_path) + except Exception as e: + if continue_on_failure: + logger.warning(f"Unable to load link {link}. Raised exception:\n\n{e}") + continue + else: + raise e results = [] for path in absolute_paths: diff --git a/libs/core/langchain_core/utils/input.py b/libs/core/langchain_core/utils/input.py index 8d5ae6cc24f..beb8b653d1e 100644 --- a/libs/core/langchain_core/utils/input.py +++ b/libs/core/langchain_core/utils/input.py @@ -1,4 +1,5 @@ """Handle chained inputs.""" + from typing import Dict, List, Optional, TextIO _TEXT_COLOR_MAPPING = { @@ -37,6 +38,6 @@ def print_text( ) -> None: """Print text with highlighting and no end characters.""" text_to_print = get_colored_text(text, color) if color else text - print(text_to_print, end=end, file=file) + print(text_to_print, end=end, file=file) # noqa: T201 if file: file.flush() # ensure all printed content are written to file diff --git a/libs/core/poetry.lock b/libs/core/poetry.lock index 8d8b5159d2e..ed548c2f62b 100644 --- a/libs/core/poetry.lock +++ b/libs/core/poetry.lock @@ -810,7 +810,6 @@ optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*" files = [ {file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"}, - {file = "jsonpointer-2.4.tar.gz", hash = "sha256:585cee82b70211fa9e6043b7bb89db6e1aa49524340dde8ad6b63206ea689d88"}, ] [[package]] @@ -1124,13 +1123,13 @@ files = [ [[package]] name = "langsmith" -version = "0.0.83" +version = "0.1.1" description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform." optional = false python-versions = ">=3.8.1,<4.0" files = [ - {file = "langsmith-0.0.83-py3-none-any.whl", hash = "sha256:a5bb7ac58c19a415a9d5f51db56dd32ee2cd7343a00825bbc2018312eb3d122a"}, - {file = "langsmith-0.0.83.tar.gz", hash = "sha256:94427846b334ad9bdbec3266fee12903fe9f5448f628667689d0412012aaf392"}, + {file = "langsmith-0.1.1-py3-none-any.whl", hash = "sha256:10ff2b977a41e3f6351d1a4239d9bd57af0547aa909e839d2791e16cc197a6f9"}, + {file = "langsmith-0.1.1.tar.gz", hash = "sha256:09df0c2ca9085105f97a4e4f281b083e312c99d162f3fe2b2d5eefd5c3692e60"}, ] [package.dependencies] @@ -2745,4 +2744,4 @@ extended-testing = ["jinja2"] [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "6cd163ca8c15acc3053e17fa86acce4c27c2d737ebcad633db93c0d7aa3a4a53" +content-hash = "8a8ec249b5b3843aaec58c5da0041f0c3846b2671ac64be80b981cef2840dc11" diff --git a/libs/core/pyproject.toml b/libs/core/pyproject.toml index 37b06c857f0..7aeb592eca1 100644 --- a/libs/core/pyproject.toml +++ b/libs/core/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-core" -version = "0.1.18" +version = "0.1.23" description = "Building applications with LLMs through composability" authors = [] license = "MIT" @@ -11,7 +11,7 @@ repository = "https://github.com/langchain-ai/langchain" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" pydantic = ">=1,<3" -langsmith = ">=0.0.83,<0.1" +langsmith = "^0.1.0" tenacity = "^8.1.0" jsonpatch = "^1.33" anyio = ">=3,<5" @@ -66,11 +66,12 @@ dependencies = {} [tool.poetry.extras] extended_testing = ["jinja2"] -[tool.ruff] +[tool.ruff.lint] select = [ "E", # pycodestyle "F", # pyflakes "I", # isort + "T201", # print ] [tool.mypy] diff --git a/libs/core/scripts/check_imports.py b/libs/core/scripts/check_imports.py index 462ab97ae2e..825bea5b487 100644 --- a/libs/core/scripts/check_imports.py +++ b/libs/core/scripts/check_imports.py @@ -15,8 +15,8 @@ if __name__ == "__main__": SourceFileLoader(module_name, file).load_module() except Exception: has_failure = True - print(file) + print(file) # noqa: T201 traceback.print_exc() - print() + print() # noqa: T201 sys.exit(1 if has_failure else 0) diff --git a/libs/core/tests/unit_tests/_api/test_deprecation.py b/libs/core/tests/unit_tests/_api/test_deprecation.py index d26b18c3ad4..8573d64b379 100644 --- a/libs/core/tests/unit_tests/_api/test_deprecation.py +++ b/libs/core/tests/unit_tests/_api/test_deprecation.py @@ -129,7 +129,7 @@ def test_deprecated_function() -> None: doc = deprecated_function.__doc__ assert isinstance(doc, str) - assert doc.startswith("[*Deprecated*] original doc") + assert doc.startswith("[*Deprecated*] original doc") assert not inspect.iscoroutinefunction(deprecated_function) @@ -151,7 +151,7 @@ async def test_deprecated_async_function() -> None: doc = deprecated_function.__doc__ assert isinstance(doc, str) - assert doc.startswith("[*Deprecated*] original doc") + assert doc.startswith("[*Deprecated*] original doc") assert inspect.iscoroutinefunction(deprecated_async_function) @@ -171,7 +171,7 @@ def test_deprecated_method() -> None: doc = obj.deprecated_method.__doc__ assert isinstance(doc, str) - assert doc.startswith("[*Deprecated*] original doc") + assert doc.startswith("[*Deprecated*] original doc") assert not inspect.iscoroutinefunction(obj.deprecated_method) @@ -194,7 +194,7 @@ async def test_deprecated_async_method() -> None: doc = obj.deprecated_method.__doc__ assert isinstance(doc, str) - assert doc.startswith("[*Deprecated*] original doc") + assert doc.startswith("[*Deprecated*] original doc") assert inspect.iscoroutinefunction(obj.deprecated_async_method) @@ -213,7 +213,7 @@ def test_deprecated_classmethod() -> None: doc = ClassWithDeprecatedMethods.deprecated_classmethod.__doc__ assert isinstance(doc, str) - assert doc.startswith("[*Deprecated*] original doc") + assert doc.startswith("[*Deprecated*] original doc") def test_deprecated_staticmethod() -> None: @@ -233,7 +233,7 @@ def test_deprecated_staticmethod() -> None: ) doc = ClassWithDeprecatedMethods.deprecated_staticmethod.__doc__ assert isinstance(doc, str) - assert doc.startswith("[*Deprecated*] original doc") + assert doc.startswith("[*Deprecated*] original doc") def test_deprecated_property() -> None: @@ -253,7 +253,7 @@ def test_deprecated_property() -> None: ) doc = ClassWithDeprecatedMethods.deprecated_property.__doc__ assert isinstance(doc, str) - assert doc.startswith("[*Deprecated*] original doc") + assert doc.startswith("[*Deprecated*] original doc") def test_whole_class_deprecation() -> None: @@ -289,6 +289,88 @@ def test_whole_class_deprecation() -> None: "The function `deprecated_method` was deprecated in " "LangChain 2.0.0 and will be removed in 3.0.0" ) + # [*Deprecated*] should be inserted only once: + if obj.__doc__ is not None: + assert obj.__doc__.count("[*Deprecated*]") == 1 + + +def test_whole_class_inherited_deprecation() -> None: + """Test whole class deprecation for inherited class. + + The original version of deprecation decorator created duplicates with + '[*Deprecated*]'. + """ + + # Test whole class deprecation + @deprecated(since="2.0.0", removal="3.0.0") + class DeprecatedClass: + def __init__(self) -> None: + """original doc""" + pass + + @deprecated(since="2.0.0", removal="3.0.0") + def deprecated_method(self) -> str: + """original doc""" + return "This is a deprecated method." + + @deprecated(since="2.2.0", removal="3.2.0") + class InheritedDeprecatedClass(DeprecatedClass): + """Inherited deprecated class.""" + + def __init__(self) -> None: + """original doc""" + pass + + @deprecated(since="2.2.0", removal="3.2.0") + def deprecated_method(self) -> str: + """original doc""" + return "This is a deprecated method." + + with warnings.catch_warnings(record=True) as warning_list: + warnings.simplefilter("always") + + obj = DeprecatedClass() + assert obj.deprecated_method() == "This is a deprecated method." + + assert len(warning_list) == 2 + warning = warning_list[0].message + assert str(warning) == ( + "The class `tests.unit_tests._api.test_deprecation.DeprecatedClass` was " + "deprecated in tests 2.0.0 and will be removed in 3.0.0" + ) + + warning = warning_list[1].message + assert str(warning) == ( + "The function `deprecated_method` was deprecated in " + "LangChain 2.0.0 and will be removed in 3.0.0" + ) + # if [*Deprecated*] was inserted only once: + if obj.__doc__ is not None: + assert obj.__doc__.count("[*Deprecated*]") == 1 + + with warnings.catch_warnings(record=True) as warning_list: + warnings.simplefilter("always") + + obj = InheritedDeprecatedClass() + assert obj.deprecated_method() == "This is a deprecated method." + + assert len(warning_list) == 2 + warning = warning_list[0].message + assert str(warning) == ( + "The class " + "`tests.unit_tests._api.test_deprecation.InheritedDeprecatedClass` " + "was deprecated in tests 2.2.0 and will be removed in 3.2.0" + ) + + warning = warning_list[1].message + assert str(warning) == ( + "The function `deprecated_method` was deprecated in " + "LangChain 2.2.0 and will be removed in 3.2.0" + ) + # if [*Deprecated*] was inserted only once: + if obj.__doc__ is not None: + assert obj.__doc__.count("[*Deprecated*]") == 1 + assert "[*Deprecated*] Inherited deprecated class." in obj.__doc__ # Tests with pydantic models @@ -314,4 +396,4 @@ def test_deprecated_method_pydantic() -> None: doc = obj.deprecated_method.__doc__ assert isinstance(doc, str) - assert doc.startswith("[*Deprecated*] original doc") + assert doc.startswith("[*Deprecated*] original doc") diff --git a/libs/core/tests/unit_tests/chat_history/test_chat_history.py b/libs/core/tests/unit_tests/chat_history/test_chat_history.py index 0f6b696e8fb..e7d2b724f7e 100644 --- a/libs/core/tests/unit_tests/chat_history/test_chat_history.py +++ b/libs/core/tests/unit_tests/chat_history/test_chat_history.py @@ -66,3 +66,36 @@ def test_bulk_message_implementation_only() -> None: assert len(store) == 4 assert store[2] == HumanMessage(content="Hello") assert store[3] == HumanMessage(content="World") + + +async def test_async_interface() -> None: + """Test async interface for BaseChatMessageHistory.""" + + class BulkAddHistory(BaseChatMessageHistory): + def __init__(self) -> None: + self.messages = [] + + def add_messages(self, message: Sequence[BaseMessage]) -> None: + """Add a message to the store.""" + self.messages.extend(message) + + def clear(self) -> None: + """Clear the store.""" + self.messages.clear() + + chat_history = BulkAddHistory() + await chat_history.aadd_messages( + [HumanMessage(content="Hello"), HumanMessage(content="World")] + ) + assert await chat_history.aget_messages() == [ + HumanMessage(content="Hello"), + HumanMessage(content="World"), + ] + await chat_history.aadd_messages([HumanMessage(content="!")]) + assert await chat_history.aget_messages() == [ + HumanMessage(content="Hello"), + HumanMessage(content="World"), + HumanMessage(content="!"), + ] + await chat_history.aclear() + assert await chat_history.aget_messages() == [] diff --git a/libs/core/tests/unit_tests/output_parsers/test_json.py b/libs/core/tests/unit_tests/output_parsers/test_json.py index 5559768bb07..8cedc767699 100644 --- a/libs/core/tests/unit_tests/output_parsers/test_json.py +++ b/libs/core/tests/unit_tests/output_parsers/test_json.py @@ -8,6 +8,8 @@ from langchain_core.output_parsers.json import ( parse_json_markdown, parse_partial_json, ) +from langchain_core.pydantic_v1 import BaseModel +from langchain_core.utils.function_calling import convert_to_openai_function GOOD_JSON = """```json { @@ -70,21 +72,7 @@ JSON_WITH_MARKDOWN_CODE_BLOCK = """```json JSON_WITH_MARKDOWN_CODE_BLOCK_AND_NEWLINES = """```json { "action": "Final Answer", - "action_input": "```bar\n
\n\ttext\n
```" -} -```""" - -JSON_WITH_UNESCAPED_QUOTES_IN_NESTED_JSON = """```json -{ - "action": "Final Answer", - "action_input": "{"foo": "bar", "bar": "foo"}" -} -```""" - -JSON_WITH_ESCAPED_QUOTES_IN_NESTED_JSON = """```json -{ - "action": "Final Answer", - "action_input": "{\"foo\": \"bar\", \"bar\": \"foo\"}" + "action_input": "```bar\n
\n\ttext\n
```" } ```""" @@ -202,6 +190,8 @@ def test_parse_json_with_code_blocks() -> None: parsed = parse_json_markdown(JSON_WITH_MARKDOWN_CODE_BLOCK) assert parsed == {"foo": "```bar```"} + +def test_parse_json_with_code_blocks_and_newlines() -> None: parsed = parse_json_markdown(JSON_WITH_MARKDOWN_CODE_BLOCK_AND_NEWLINES) assert parsed == { @@ -211,8 +201,6 @@ def test_parse_json_with_code_blocks() -> None: TEST_CASES_ESCAPED_QUOTES = [ - JSON_WITH_UNESCAPED_QUOTES_IN_NESTED_JSON, - JSON_WITH_ESCAPED_QUOTES_IN_NESTED_JSON, JSON_WITH_ESCAPED_DOUBLE_QUOTES_IN_NESTED_JSON, ] @@ -307,6 +295,7 @@ So } """.splitlines() + EXPECTED_STREAMED_JSON = [ {}, {"setup": ""}, @@ -537,3 +526,72 @@ def test_raises_error() -> None: parser = SimpleJsonOutputParser() with pytest.raises(Exception): parser.invoke("hi") + + +# A test fixture for an output which contains +# json within a code block +TOKENS_WITH_JSON_CODE_BLOCK = [ + " France", + ":", + "\n\n```", + "json", + "\n{", + "\n ", + ' "', + "country", + "_", + "name", + '":', + ' "', + "France", + '",', + " \n ", + ' "', + "population", + "_", + "size", + '":', + " 67", + "39", + "15", + "82", + "\n}", + "\n```", + "\n\nI", + " looked", + " up", +] + + +def test_partial_text_json_output_parser_with_json_code_block() -> None: + """Test json parser works correctly when the response contains a json code-block.""" + + def input_iter(_: Any) -> Iterator[str]: + for token in TOKENS_WITH_JSON_CODE_BLOCK: + yield token + + chain = input_iter | SimpleJsonOutputParser() + + assert list(chain.stream(None)) == [ + {}, + {"country_name": ""}, + {"country_name": "France"}, + {"country_name": "France", "population_size": 67}, + {"country_name": "France", "population_size": 6739}, + {"country_name": "France", "population_size": 673915}, + {"country_name": "France", "population_size": 67391582}, + ] + + +def test_base_model_schema_consistency() -> None: + class Joke(BaseModel): + setup: str + punchline: str + + initial_joke_schema = {k: v for k, v in Joke.schema().items()} + SimpleJsonOutputParser(pydantic_object=Joke) + openai_func = convert_to_openai_function(Joke) + retrieved_joke_schema = {k: v for k, v in Joke.schema().items()} + + assert initial_joke_schema == retrieved_joke_schema + assert openai_func.get("name", None) is not None diff --git a/libs/core/tests/unit_tests/prompts/test_chat.py b/libs/core/tests/unit_tests/prompts/test_chat.py index 029244afe8c..0f14605f409 100644 --- a/libs/core/tests/unit_tests/prompts/test_chat.py +++ b/libs/core/tests/unit_tests/prompts/test_chat.py @@ -503,9 +503,27 @@ def test_messages_placeholder() -> None: prompt.format_messages() prompt = MessagesPlaceholder("history", optional=True) assert prompt.format_messages() == [] - prompt.format_messages( + assert prompt.format_messages( history=[("system", "You are an AI assistant."), "Hello!"] ) == [ SystemMessage(content="You are an AI assistant."), HumanMessage(content="Hello!"), ] + + +def test_chat_prompt_message_placeholder_partial() -> None: + prompt = ChatPromptTemplate.from_messages([MessagesPlaceholder("history")]) + prompt = prompt.partial(history=[("system", "foo")]) + assert prompt.format_messages() == [SystemMessage(content="foo")] + assert prompt.format_messages(history=[("system", "bar")]) == [ + SystemMessage(content="bar") + ] + + prompt = ChatPromptTemplate.from_messages( + [ + MessagesPlaceholder("history", optional=True), + ] + ) + assert prompt.format_messages() == [] + prompt = prompt.partial(history=[("system", "foo")]) + assert prompt.format_messages() == [SystemMessage(content="foo")] diff --git a/libs/core/tests/unit_tests/prompts/test_prompt.py b/libs/core/tests/unit_tests/prompts/test_prompt.py index f50b71bea80..5075c5c0bb5 100644 --- a/libs/core/tests/unit_tests/prompts/test_prompt.py +++ b/libs/core/tests/unit_tests/prompts/test_prompt.py @@ -1,9 +1,11 @@ """Test functionality related to prompts.""" + from unittest import mock import pytest from langchain_core.prompts.prompt import PromptTemplate +from langchain_core.tracers.run_collector import RunCollectorCallbackHandler def test_prompt_valid() -> None: @@ -328,3 +330,22 @@ def test_prompt_jinja2_wrong_input_variables() -> None: assert PromptTemplate( input_variables=input_variables, template=template, template_format="jinja2" ).input_variables == ["foo"] + + +def test_prompt_invoke_with_metadata() -> None: + """Test prompt can be invoked with metadata.""" + template = "This is a {foo} test." + prompt = PromptTemplate( + input_variables=["foo"], + template=template, + metadata={"version": "1"}, + tags=["tag1", "tag2"], + ) + tracer = RunCollectorCallbackHandler() + result = prompt.invoke( + {"foo": "bar"}, {"metadata": {"foo": "bar"}, "callbacks": [tracer]} + ) + assert result.to_string() == "This is a bar test." + assert len(tracer.traced_runs) == 1 + assert tracer.traced_runs[0].extra["metadata"] == {"version": "1", "foo": "bar"} # type: ignore + assert tracer.traced_runs[0].tags == ["tag1", "tag2"] # type: ignore diff --git a/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr b/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr index 051520c0457..fcd3cddf615 100644 --- a/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr +++ b/libs/core/tests/unit_tests/runnables/__snapshots__/test_runnable.ambr @@ -1544,7 +1544,8 @@ } } } - ] + ], + "partial_variables": {} } }, "middle": [], @@ -1617,7 +1618,8 @@ } } } - ] + ], + "partial_variables": {} } }, "middle": [], @@ -1684,7 +1686,7 @@ ]), 'definitions': dict({ 'AIMessage': dict({ - 'description': 'A Message from an AI.', + 'description': 'Message from an AI.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -1716,6 +1718,10 @@ 'title': 'Example', 'type': 'boolean', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'type': dict({ 'default': 'ai', 'enum': list([ @@ -1732,7 +1738,7 @@ 'type': 'object', }), 'ChatMessage': dict({ - 'description': 'A Message that can be assigned an arbitrary speaker (i.e. role).', + 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -1759,6 +1765,10 @@ ]), 'title': 'Content', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'role': dict({ 'title': 'Role', 'type': 'string', @@ -1827,7 +1837,7 @@ 'type': 'object', }), 'FunctionMessage': dict({ - 'description': 'A Message for passing the result of executing a function back to a model.', + 'description': 'Message for passing the result of executing a function back to a model.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -1875,7 +1885,7 @@ 'type': 'object', }), 'HumanMessage': dict({ - 'description': 'A Message from a human.', + 'description': 'Message from a human.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -1907,6 +1917,10 @@ 'title': 'Example', 'type': 'boolean', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'type': dict({ 'default': 'human', 'enum': list([ @@ -1946,7 +1960,7 @@ }), 'SystemMessage': dict({ 'description': ''' - A Message for priming AI behavior, usually passed in as the first of a sequence + Message for priming AI behavior, usually passed in as the first of a sequence of input messages. ''', 'properties': dict({ @@ -1975,6 +1989,10 @@ ]), 'title': 'Content', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'type': dict({ 'default': 'system', 'enum': list([ @@ -1991,7 +2009,7 @@ 'type': 'object', }), 'ToolMessage': dict({ - 'description': 'A Message for passing the result of executing a tool back to a model.', + 'description': 'Message for passing the result of executing a tool back to a model.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -2018,6 +2036,10 @@ ]), 'title': 'Content', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'tool_call_id': dict({ 'title': 'Tool Call Id', 'type': 'string', @@ -2082,7 +2104,7 @@ ]), 'definitions': dict({ 'AIMessage': dict({ - 'description': 'A Message from an AI.', + 'description': 'Message from an AI.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -2114,6 +2136,10 @@ 'title': 'Example', 'type': 'boolean', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'type': dict({ 'default': 'ai', 'enum': list([ @@ -2130,7 +2156,7 @@ 'type': 'object', }), 'ChatMessage': dict({ - 'description': 'A Message that can be assigned an arbitrary speaker (i.e. role).', + 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -2157,6 +2183,10 @@ ]), 'title': 'Content', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'role': dict({ 'title': 'Role', 'type': 'string', @@ -2225,7 +2255,7 @@ 'type': 'object', }), 'FunctionMessage': dict({ - 'description': 'A Message for passing the result of executing a function back to a model.', + 'description': 'Message for passing the result of executing a function back to a model.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -2273,7 +2303,7 @@ 'type': 'object', }), 'HumanMessage': dict({ - 'description': 'A Message from a human.', + 'description': 'Message from a human.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -2305,6 +2335,10 @@ 'title': 'Example', 'type': 'boolean', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'type': dict({ 'default': 'human', 'enum': list([ @@ -2344,7 +2378,7 @@ }), 'SystemMessage': dict({ 'description': ''' - A Message for priming AI behavior, usually passed in as the first of a sequence + Message for priming AI behavior, usually passed in as the first of a sequence of input messages. ''', 'properties': dict({ @@ -2373,6 +2407,10 @@ ]), 'title': 'Content', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'type': dict({ 'default': 'system', 'enum': list([ @@ -2389,7 +2427,7 @@ 'type': 'object', }), 'ToolMessage': dict({ - 'description': 'A Message for passing the result of executing a tool back to a model.', + 'description': 'Message for passing the result of executing a tool back to a model.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -2416,6 +2454,10 @@ ]), 'title': 'Content', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'tool_call_id': dict({ 'title': 'Tool Call Id', 'type': 'string', @@ -2464,7 +2506,7 @@ ]), 'definitions': dict({ 'AIMessage': dict({ - 'description': 'A Message from an AI.', + 'description': 'Message from an AI.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -2496,6 +2538,10 @@ 'title': 'Example', 'type': 'boolean', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'type': dict({ 'default': 'ai', 'enum': list([ @@ -2512,7 +2558,7 @@ 'type': 'object', }), 'ChatMessage': dict({ - 'description': 'A Message that can be assigned an arbitrary speaker (i.e. role).', + 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -2539,6 +2585,10 @@ ]), 'title': 'Content', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'role': dict({ 'title': 'Role', 'type': 'string', @@ -2560,7 +2610,7 @@ 'type': 'object', }), 'FunctionMessage': dict({ - 'description': 'A Message for passing the result of executing a function back to a model.', + 'description': 'Message for passing the result of executing a function back to a model.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -2608,7 +2658,7 @@ 'type': 'object', }), 'HumanMessage': dict({ - 'description': 'A Message from a human.', + 'description': 'Message from a human.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -2640,6 +2690,10 @@ 'title': 'Example', 'type': 'boolean', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'type': dict({ 'default': 'human', 'enum': list([ @@ -2657,7 +2711,7 @@ }), 'SystemMessage': dict({ 'description': ''' - A Message for priming AI behavior, usually passed in as the first of a sequence + Message for priming AI behavior, usually passed in as the first of a sequence of input messages. ''', 'properties': dict({ @@ -2686,6 +2740,10 @@ ]), 'title': 'Content', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'type': dict({ 'default': 'system', 'enum': list([ @@ -2702,7 +2760,7 @@ 'type': 'object', }), 'ToolMessage': dict({ - 'description': 'A Message for passing the result of executing a tool back to a model.', + 'description': 'Message for passing the result of executing a tool back to a model.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -2729,6 +2787,10 @@ ]), 'title': 'Content', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'tool_call_id': dict({ 'title': 'Tool Call Id', 'type': 'string', @@ -2765,7 +2827,7 @@ ]), 'definitions': dict({ 'AIMessage': dict({ - 'description': 'A Message from an AI.', + 'description': 'Message from an AI.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -2797,6 +2859,10 @@ 'title': 'Example', 'type': 'boolean', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'type': dict({ 'default': 'ai', 'enum': list([ @@ -2813,7 +2879,7 @@ 'type': 'object', }), 'ChatMessage': dict({ - 'description': 'A Message that can be assigned an arbitrary speaker (i.e. role).', + 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -2840,6 +2906,10 @@ ]), 'title': 'Content', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'role': dict({ 'title': 'Role', 'type': 'string', @@ -2908,7 +2978,7 @@ 'type': 'object', }), 'FunctionMessage': dict({ - 'description': 'A Message for passing the result of executing a function back to a model.', + 'description': 'Message for passing the result of executing a function back to a model.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -2956,7 +3026,7 @@ 'type': 'object', }), 'HumanMessage': dict({ - 'description': 'A Message from a human.', + 'description': 'Message from a human.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -2988,6 +3058,10 @@ 'title': 'Example', 'type': 'boolean', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'type': dict({ 'default': 'human', 'enum': list([ @@ -3027,7 +3101,7 @@ }), 'SystemMessage': dict({ 'description': ''' - A Message for priming AI behavior, usually passed in as the first of a sequence + Message for priming AI behavior, usually passed in as the first of a sequence of input messages. ''', 'properties': dict({ @@ -3056,6 +3130,10 @@ ]), 'title': 'Content', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'type': dict({ 'default': 'system', 'enum': list([ @@ -3072,7 +3150,7 @@ 'type': 'object', }), 'ToolMessage': dict({ - 'description': 'A Message for passing the result of executing a tool back to a model.', + 'description': 'Message for passing the result of executing a tool back to a model.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -3099,6 +3177,10 @@ ]), 'title': 'Content', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'tool_call_id': dict({ 'title': 'Tool Call Id', 'type': 'string', @@ -3135,7 +3217,7 @@ ]), 'definitions': dict({ 'AIMessage': dict({ - 'description': 'A Message from an AI.', + 'description': 'Message from an AI.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -3167,6 +3249,10 @@ 'title': 'Example', 'type': 'boolean', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'type': dict({ 'default': 'ai', 'enum': list([ @@ -3183,7 +3269,7 @@ 'type': 'object', }), 'ChatMessage': dict({ - 'description': 'A Message that can be assigned an arbitrary speaker (i.e. role).', + 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -3210,6 +3296,10 @@ ]), 'title': 'Content', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'role': dict({ 'title': 'Role', 'type': 'string', @@ -3278,7 +3368,7 @@ 'type': 'object', }), 'FunctionMessage': dict({ - 'description': 'A Message for passing the result of executing a function back to a model.', + 'description': 'Message for passing the result of executing a function back to a model.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -3326,7 +3416,7 @@ 'type': 'object', }), 'HumanMessage': dict({ - 'description': 'A Message from a human.', + 'description': 'Message from a human.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -3358,6 +3448,10 @@ 'title': 'Example', 'type': 'boolean', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'type': dict({ 'default': 'human', 'enum': list([ @@ -3397,7 +3491,7 @@ }), 'SystemMessage': dict({ 'description': ''' - A Message for priming AI behavior, usually passed in as the first of a sequence + Message for priming AI behavior, usually passed in as the first of a sequence of input messages. ''', 'properties': dict({ @@ -3426,6 +3520,10 @@ ]), 'title': 'Content', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'type': dict({ 'default': 'system', 'enum': list([ @@ -3442,7 +3540,7 @@ 'type': 'object', }), 'ToolMessage': dict({ - 'description': 'A Message for passing the result of executing a tool back to a model.', + 'description': 'Message for passing the result of executing a tool back to a model.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -3469,6 +3567,10 @@ ]), 'title': 'Content', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'tool_call_id': dict({ 'title': 'Tool Call Id', 'type': 'string', @@ -3497,7 +3599,7 @@ dict({ 'definitions': dict({ 'AIMessage': dict({ - 'description': 'A Message from an AI.', + 'description': 'Message from an AI.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -3529,6 +3631,10 @@ 'title': 'Example', 'type': 'boolean', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'type': dict({ 'default': 'ai', 'enum': list([ @@ -3545,7 +3651,7 @@ 'type': 'object', }), 'ChatMessage': dict({ - 'description': 'A Message that can be assigned an arbitrary speaker (i.e. role).', + 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -3572,6 +3678,10 @@ ]), 'title': 'Content', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'role': dict({ 'title': 'Role', 'type': 'string', @@ -3640,7 +3750,7 @@ 'type': 'object', }), 'FunctionMessage': dict({ - 'description': 'A Message for passing the result of executing a function back to a model.', + 'description': 'Message for passing the result of executing a function back to a model.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -3688,7 +3798,7 @@ 'type': 'object', }), 'HumanMessage': dict({ - 'description': 'A Message from a human.', + 'description': 'Message from a human.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -3720,6 +3830,10 @@ 'title': 'Example', 'type': 'boolean', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'type': dict({ 'default': 'human', 'enum': list([ @@ -3770,7 +3884,7 @@ }), 'SystemMessage': dict({ 'description': ''' - A Message for priming AI behavior, usually passed in as the first of a sequence + Message for priming AI behavior, usually passed in as the first of a sequence of input messages. ''', 'properties': dict({ @@ -3799,6 +3913,10 @@ ]), 'title': 'Content', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'type': dict({ 'default': 'system', 'enum': list([ @@ -3815,7 +3933,7 @@ 'type': 'object', }), 'ToolMessage': dict({ - 'description': 'A Message for passing the result of executing a tool back to a model.', + 'description': 'Message for passing the result of executing a tool back to a model.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -3842,6 +3960,10 @@ ]), 'title': 'Content', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'tool_call_id': dict({ 'title': 'Tool Call Id', 'type': 'string', @@ -3897,7 +4019,7 @@ ]), 'definitions': dict({ 'AIMessage': dict({ - 'description': 'A Message from an AI.', + 'description': 'Message from an AI.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -3929,6 +4051,10 @@ 'title': 'Example', 'type': 'boolean', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'type': dict({ 'default': 'ai', 'enum': list([ @@ -3945,7 +4071,7 @@ 'type': 'object', }), 'ChatMessage': dict({ - 'description': 'A Message that can be assigned an arbitrary speaker (i.e. role).', + 'description': 'Message that can be assigned an arbitrary speaker (i.e. role).', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -3972,6 +4098,10 @@ ]), 'title': 'Content', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'role': dict({ 'title': 'Role', 'type': 'string', @@ -3993,7 +4123,7 @@ 'type': 'object', }), 'FunctionMessage': dict({ - 'description': 'A Message for passing the result of executing a function back to a model.', + 'description': 'Message for passing the result of executing a function back to a model.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -4041,7 +4171,7 @@ 'type': 'object', }), 'HumanMessage': dict({ - 'description': 'A Message from a human.', + 'description': 'Message from a human.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -4073,6 +4203,10 @@ 'title': 'Example', 'type': 'boolean', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'type': dict({ 'default': 'human', 'enum': list([ @@ -4090,7 +4224,7 @@ }), 'SystemMessage': dict({ 'description': ''' - A Message for priming AI behavior, usually passed in as the first of a sequence + Message for priming AI behavior, usually passed in as the first of a sequence of input messages. ''', 'properties': dict({ @@ -4119,6 +4253,10 @@ ]), 'title': 'Content', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'type': dict({ 'default': 'system', 'enum': list([ @@ -4135,7 +4273,7 @@ 'type': 'object', }), 'ToolMessage': dict({ - 'description': 'A Message for passing the result of executing a tool back to a model.', + 'description': 'Message for passing the result of executing a tool back to a model.', 'properties': dict({ 'additional_kwargs': dict({ 'title': 'Additional Kwargs', @@ -4162,6 +4300,10 @@ ]), 'title': 'Content', }), + 'name': dict({ + 'title': 'Name', + 'type': 'string', + }), 'tool_call_id': dict({ 'title': 'Tool Call Id', 'type': 'string', diff --git a/libs/core/tests/unit_tests/runnables/test_runnable.py b/libs/core/tests/unit_tests/runnables/test_runnable.py index a6af8ada3a2..ac7f5a0ccef 100644 --- a/libs/core/tests/unit_tests/runnables/test_runnable.py +++ b/libs/core/tests/unit_tests/runnables/test_runnable.py @@ -116,9 +116,9 @@ class FakeTracer(BaseTracer): return run.copy( update={ "id": self._replace_uuid(run.id), - "parent_run_id": self.uuids_map[run.parent_run_id] - if run.parent_run_id - else None, + "parent_run_id": ( + self.uuids_map[run.parent_run_id] if run.parent_run_id else None + ), "child_runs": [self._copy_run(child) for child in run.child_runs], "execution_order": None, "child_execution_order": None, @@ -320,7 +320,7 @@ def test_schemas(snapshot: SnapshotAssertion) -> None: "definitions": { "AIMessage": { "title": "AIMessage", - "description": "A Message from an AI.", + "description": "Message from an AI.", "type": "object", "properties": { "content": { @@ -345,6 +345,7 @@ def test_schemas(snapshot: SnapshotAssertion) -> None: "enum": ["ai"], "type": "string", }, + "name": {"title": "Name", "type": "string"}, "example": { "title": "Example", "default": False, @@ -355,7 +356,7 @@ def test_schemas(snapshot: SnapshotAssertion) -> None: }, "HumanMessage": { "title": "HumanMessage", - "description": "A Message from a human.", + "description": "Message from a human.", "type": "object", "properties": { "content": { @@ -380,6 +381,7 @@ def test_schemas(snapshot: SnapshotAssertion) -> None: "enum": ["human"], "type": "string", }, + "name": {"title": "Name", "type": "string"}, "example": { "title": "Example", "default": False, @@ -390,7 +392,7 @@ def test_schemas(snapshot: SnapshotAssertion) -> None: }, "ChatMessage": { "title": "ChatMessage", - "description": "A Message that can be assigned an arbitrary speaker (i.e. role).", # noqa + "description": "Message that can be assigned an arbitrary speaker (i.e. role).", # noqa: E501 "type": "object", "properties": { "content": { @@ -415,13 +417,14 @@ def test_schemas(snapshot: SnapshotAssertion) -> None: "enum": ["chat"], "type": "string", }, + "name": {"title": "Name", "type": "string"}, "role": {"title": "Role", "type": "string"}, }, "required": ["content", "role"], }, "SystemMessage": { "title": "SystemMessage", - "description": "A Message for priming AI behavior, usually passed in as the first of a sequence\nof input messages.", # noqa + "description": "Message for priming AI behavior, usually passed in as the first of a sequence\nof input messages.", # noqa: E501 "type": "object", "properties": { "content": { @@ -446,12 +449,13 @@ def test_schemas(snapshot: SnapshotAssertion) -> None: "enum": ["system"], "type": "string", }, + "name": {"title": "Name", "type": "string"}, }, "required": ["content"], }, "FunctionMessage": { "title": "FunctionMessage", - "description": "A Message for passing the result of executing a function back to a model.", # noqa + "description": "Message for passing the result of executing a function back to a model.", # noqa: E501 "type": "object", "properties": { "content": { @@ -482,7 +486,7 @@ def test_schemas(snapshot: SnapshotAssertion) -> None: }, "ToolMessage": { "title": "ToolMessage", - "description": "A Message for passing the result of executing a tool back to a model.", # noqa + "description": "Message for passing the result of executing a tool back to a model.", # noqa: E501 "type": "object", "properties": { "content": { @@ -507,6 +511,7 @@ def test_schemas(snapshot: SnapshotAssertion) -> None: "enum": ["tool"], "type": "string", }, + "name": {"title": "Name", "type": "string"}, "tool_call_id": {"title": "Tool Call Id", "type": "string"}, }, "required": ["content", "tool_call_id"], @@ -658,14 +663,11 @@ def test_lambda_schemas() -> None: } second_lambda = lambda x, y: (x["hello"], x["bye"], y["bah"]) # noqa: E731 - assert ( - RunnableLambda(second_lambda).input_schema.schema() # type: ignore[arg-type] - == { - "title": "RunnableLambdaInput", - "type": "object", - "properties": {"hello": {"title": "Hello"}, "bye": {"title": "Bye"}}, - } - ) + assert RunnableLambda(second_lambda).input_schema.schema() == { # type: ignore[arg-type] + "title": "RunnableLambdaInput", + "type": "object", + "properties": {"hello": {"title": "Hello"}, "bye": {"title": "Bye"}}, + } def get_value(input): # type: ignore[no-untyped-def] return input["variable_name"] @@ -721,7 +723,9 @@ def test_lambda_schemas() -> None: } assert ( - RunnableLambda(aget_values_typed).input_schema.schema() # type: ignore[arg-type] + RunnableLambda( + aget_values_typed # type: ignore[arg-type] + ).input_schema.schema() == { "title": "aget_values_typed_input", "$ref": "#/definitions/InputType", diff --git a/libs/core/tests/unit_tests/runnables/test_runnable_events.py b/libs/core/tests/unit_tests/runnables/test_runnable_events.py index 6d5de109410..a31e15e7ba4 100644 --- a/libs/core/tests/unit_tests/runnables/test_runnable_events.py +++ b/libs/core/tests/unit_tests/runnables/test_runnable_events.py @@ -1,23 +1,29 @@ """Module that contains tests for runnable.astream_events API.""" from itertools import cycle -from typing import AsyncIterator, List, Sequence, cast +from typing import Any, AsyncIterator, Dict, List, Sequence, cast import pytest from langchain_core.callbacks import CallbackManagerForRetrieverRun, Callbacks +from langchain_core.chat_history import BaseChatMessageHistory from langchain_core.documents import Document from langchain_core.messages import ( AIMessage, AIMessageChunk, + BaseMessage, HumanMessage, SystemMessage, ) from langchain_core.prompt_values import ChatPromptValue -from langchain_core.prompts import ChatPromptTemplate +from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain_core.pydantic_v1 import BaseModel from langchain_core.retrievers import BaseRetriever from langchain_core.runnables import ( + ConfigurableField, + Runnable, RunnableLambda, ) +from langchain_core.runnables.history import RunnableWithMessageHistory from langchain_core.runnables.schema import StreamEvent from langchain_core.tools import tool from tests.unit_tests.fake.chat_model import GenericFakeChatModel @@ -1079,3 +1085,130 @@ async def test_runnable_each() -> None: with pytest.raises(NotImplementedError): async for _ in add_one_map.astream_events([1, 2, 3], version="v1"): pass + + +async def test_events_astream_config() -> None: + """Test that astream events support accepting config""" + infinite_cycle = cycle([AIMessage(content="hello world!")]) + good_world_on_repeat = cycle([AIMessage(content="Goodbye world")]) + model = GenericFakeChatModel(messages=infinite_cycle).configurable_fields( + messages=ConfigurableField( + id="messages", + name="Messages", + description="Messages return by the LLM", + ) + ) + + model_02 = model.with_config({"configurable": {"messages": good_world_on_repeat}}) + assert model_02.invoke("hello") == AIMessage(content="Goodbye world") + + events = await _collect_events(model_02.astream_events("hello", version="v1")) + assert events == [ + { + "data": {"input": "hello"}, + "event": "on_chat_model_start", + "metadata": {}, + "name": "RunnableConfigurableFields", + "run_id": "", + "tags": [], + }, + { + "data": {"chunk": AIMessageChunk(content="Goodbye")}, + "event": "on_chat_model_stream", + "metadata": {}, + "name": "RunnableConfigurableFields", + "run_id": "", + "tags": [], + }, + { + "data": {"chunk": AIMessageChunk(content=" ")}, + "event": "on_chat_model_stream", + "metadata": {}, + "name": "RunnableConfigurableFields", + "run_id": "", + "tags": [], + }, + { + "data": {"chunk": AIMessageChunk(content="world")}, + "event": "on_chat_model_stream", + "metadata": {}, + "name": "RunnableConfigurableFields", + "run_id": "", + "tags": [], + }, + { + "data": {"output": AIMessageChunk(content="Goodbye world")}, + "event": "on_chat_model_end", + "metadata": {}, + "name": "RunnableConfigurableFields", + "run_id": "", + "tags": [], + }, + ] + + +async def test_runnable_with_message_history() -> None: + class InMemoryHistory(BaseChatMessageHistory, BaseModel): + """In memory implementation of chat message history.""" + + # Attention: for the tests use an Any type to work-around a pydantic issue + # where it re-instantiates a list, so mutating the list doesn't end up mutating + # the content in the store! + + # Using Any type here rather than List[BaseMessage] due to pydantic issue! + messages: Any + + def add_message(self, message: BaseMessage) -> None: + """Add a self-created message to the store.""" + self.messages.append(message) + + def clear(self) -> None: + self.messages = [] + + # Here we use a global variable to store the chat message history. + # This will make it easier to inspect it to see the underlying results. + store: Dict = {} + + def get_by_session_id(session_id: str) -> BaseChatMessageHistory: + """Get a chat message history""" + if session_id not in store: + store[session_id] = [] + return InMemoryHistory(messages=store[session_id]) + + infinite_cycle = cycle([AIMessage(content="hello"), AIMessage(content="world")]) + + prompt = ChatPromptTemplate.from_messages( + [ + ("system", "You are a cat"), + MessagesPlaceholder(variable_name="history"), + ("human", "{question}"), + ] + ) + model = GenericFakeChatModel(messages=infinite_cycle) + + chain: Runnable = prompt | model + with_message_history = RunnableWithMessageHistory( + chain, + get_session_history=get_by_session_id, + input_messages_key="question", + history_messages_key="history", + ) + with_message_history.with_config( + {"configurable": {"session_id": "session-123"}} + ).invoke({"question": "hello"}) + + assert store == { + "session-123": [HumanMessage(content="hello"), AIMessage(content="hello")] + } + + with_message_history.with_config( + {"configurable": {"session_id": "session-123"}} + ).invoke({"question": "meow"}) + assert store == { + "session-123": [ + HumanMessage(content="hello"), + AIMessage(content="hello"), + HumanMessage(content="meow"), + AIMessage(content="world"), + ] + } diff --git a/libs/core/tests/unit_tests/test_messages.py b/libs/core/tests/unit_tests/test_messages.py index 6f8b97951ca..7cd7c9a913b 100644 --- a/libs/core/tests/unit_tests/test_messages.py +++ b/libs/core/tests/unit_tests/test_messages.py @@ -1,5 +1,5 @@ import unittest -from typing import List +from typing import List, Type import pytest @@ -192,6 +192,21 @@ def test_multiple_msg() -> None: assert messages_from_dict(messages_to_dict(msgs)) == msgs +def test_multiple_msg_with_name() -> None: + human_msg = HumanMessage( + content="human", additional_kwargs={"key": "value"}, name="human erick" + ) + ai_msg = AIMessage(content="ai", name="ai erick") + sys_msg = SystemMessage(content="sys", name="sys erick") + + msgs = [ + human_msg, + ai_msg, + sys_msg, + ] + assert messages_from_dict(messages_to_dict(msgs)) == msgs + + def test_message_chunk_to_message() -> None: assert message_chunk_to_message( AIMessageChunk(content="I am", additional_kwargs={"foo": "bar"}) @@ -480,3 +495,49 @@ def test_convert_to_messages() -> None: HumanMessage(content="Hello!"), AIMessage(content="Hi!"), ] + + +@pytest.mark.parametrize( + "MessageClass", + [ + AIMessage, + AIMessageChunk, + HumanMessage, + HumanMessageChunk, + SystemMessage, + ], +) +def test_message_name(MessageClass: Type) -> None: + msg = MessageClass(content="foo", name="bar") + assert msg.name == "bar" + + msg2 = MessageClass(content="foo", name=None) + assert msg2.name is None + + msg3 = MessageClass(content="foo") + assert msg3.name is None + + +@pytest.mark.parametrize( + "MessageClass", + [FunctionMessage, FunctionMessageChunk], +) +def test_message_name_function(MessageClass: Type) -> None: + # functionmessage doesn't support name=None + msg = MessageClass(name="foo", content="bar") + assert msg.name == "foo" + + +@pytest.mark.parametrize( + "MessageClass", + [ChatMessage, ChatMessageChunk], +) +def test_message_name_chat(MessageClass: Type) -> None: + msg = MessageClass(content="foo", role="user", name="bar") + assert msg.name == "bar" + + msg2 = MessageClass(content="foo", role="user", name=None) + assert msg2.name is None + + msg3 = MessageClass(content="foo", role="user") + assert msg3.name is None diff --git a/libs/core/tests/unit_tests/test_tools.py b/libs/core/tests/unit_tests/test_tools.py index 0d9b3b5209b..8c019fbc036 100644 --- a/libs/core/tests/unit_tests/test_tools.py +++ b/libs/core/tests/unit_tests/test_tools.py @@ -772,7 +772,7 @@ def test_validation_error_handling_callable() -> None: ], ) def test_validation_error_handling_non_validation_error( - handler: Union[bool, str, Callable[[ValidationError], str]] + handler: Union[bool, str, Callable[[ValidationError], str]], ) -> None: """Test that validation errors are handled correctly.""" @@ -834,7 +834,7 @@ async def test_async_validation_error_handling_callable() -> None: ], ) async def test_async_validation_error_handling_non_validation_error( - handler: Union[bool, str, Callable[[ValidationError], str]] + handler: Union[bool, str, Callable[[ValidationError], str]], ) -> None: """Test that validation errors are handled correctly.""" diff --git a/libs/core/tests/unit_tests/tracers/test_langchain.py b/libs/core/tests/unit_tests/tracers/test_langchain.py index 1af7fe467e6..820e547c7a9 100644 --- a/libs/core/tests/unit_tests/tracers/test_langchain.py +++ b/libs/core/tests/unit_tests/tracers/test_langchain.py @@ -22,6 +22,7 @@ def test_example_id_assignment_threadsafe() -> None: return unittest.mock.MagicMock() client = unittest.mock.MagicMock(spec=Client) + client.tracing_queue = None client.create_run = mock_create_run tracer = LangChainTracer(client=client) old_persist_run_single = tracer._persist_run_single @@ -35,6 +36,7 @@ def test_example_id_assignment_threadsafe() -> None: ): run_id_1 = UUID("9d878ab3-e5ca-4218-aef6-44cbdc90160a") run_id_2 = UUID("f1f9fa53-8b2f-4742-bdbc-38215f7bd1e1") + run_id_3 = UUID("f1f9fa53-8b2f-4742-bdbc-38215f7cd1e1") example_id_1 = UUID("57e42c57-8c79-4d9f-8765-bf6cd3a98055") tracer.example_id = example_id_1 tracer.on_llm_start({"name": "example_1"}, ["foo"], run_id=run_id_1) @@ -44,9 +46,14 @@ def test_example_id_assignment_threadsafe() -> None: tracer.on_llm_start({"name": "example_2"}, ["foo"], run_id=run_id_2) tracer.on_llm_end(LLMResult(generations=[], llm_output={}), run_id=run_id_2) tracer.example_id = None + tracer.on_chain_start( + {"name": "no_examples"}, {"inputs": (i for i in range(10))}, run_id=run_id_3 + ) + tracer.on_chain_error(ValueError("Foo bar"), run_id=run_id_3) expected_example_ids = { run_id_1: example_id_1, run_id_2: example_id_2, + run_id_3: None, } tracer.wait_for_futures() assert example_ids == expected_example_ids diff --git a/libs/core/tests/unit_tests/tracers/test_memory_stream.py b/libs/core/tests/unit_tests/tracers/test_memory_stream.py new file mode 100644 index 00000000000..85d16986827 --- /dev/null +++ b/libs/core/tests/unit_tests/tracers/test_memory_stream.py @@ -0,0 +1,122 @@ +import asyncio +import math +import time +from concurrent.futures import ThreadPoolExecutor +from typing import AsyncIterator + +from langchain_core.tracers.memory_stream import _MemoryStream + + +async def test_same_event_loop() -> None: + """Test that the memory stream works when the same event loop is used. + + This is the easy case. + """ + reader_loop = asyncio.get_event_loop() + channel = _MemoryStream[dict](reader_loop) + writer = channel.get_send_stream() + reader = channel.get_receive_stream() + + async def producer() -> None: + """Produce items with slight delay.""" + tic = time.time() + for i in range(3): + await asyncio.sleep(0.10) + toc = time.time() + await writer.send( + { + "item": i, + "produce_time": toc - tic, + } + ) + await writer.aclose() + + async def consumer() -> AsyncIterator[dict]: + tic = time.time() + async for item in reader: + toc = time.time() + yield { + "receive_time": toc - tic, + **item, + } + + asyncio.create_task(producer()) + + items = [item async for item in consumer()] + + for item in items: + delta_time = item["receive_time"] - item["produce_time"] + # Allow a generous 10ms of delay + # The test is meant to verify that the producer and consumer are running in + # parallel despite the fact that the producer is running from another thread. + # abs_tol is used to allow for some delay in the producer and consumer + # due to overhead. + # To verify that the producer and consumer are running in parallel, we + # expect the delta_time to be smaller than the sleep delay in the producer + # * # of items = 30 ms + assert ( + math.isclose(delta_time, 0, abs_tol=0.010) is True + ), f"delta_time: {delta_time}" + + +async def test_queue_for_streaming_via_sync_call() -> None: + """Test via async -> sync -> async path.""" + reader_loop = asyncio.get_event_loop() + channel = _MemoryStream[dict](reader_loop) + writer = channel.get_send_stream() + reader = channel.get_receive_stream() + + async def producer() -> None: + """Produce items with slight delay.""" + tic = time.time() + for i in range(3): + await asyncio.sleep(0.10) + toc = time.time() + await writer.send( + { + "item": i, + "produce_time": toc - tic, + } + ) + await writer.aclose() + + def sync_call() -> None: + """Blocking sync call.""" + asyncio.run(producer()) + + async def consumer() -> AsyncIterator[dict]: + tic = time.time() + async for item in reader: + toc = time.time() + yield { + "receive_time": toc - tic, + **item, + } + + with ThreadPoolExecutor() as executor: + executor.submit(sync_call) + items = [item async for item in consumer()] + + for item in items: + delta_time = item["receive_time"] - item["produce_time"] + # Allow a generous 10ms of delay + # The test is meant to verify that the producer and consumer are running in + # parallel despite the fact that the producer is running from another thread. + # abs_tol is used to allow for some delay in the producer and consumer + # due to overhead. + # To verify that the producer and consumer are running in parallel, we + # expect the delta_time to be smaller than the sleep delay in the producer + # * # of items = 30 ms + assert ( + math.isclose(delta_time, 0, abs_tol=0.010) is True + ), f"delta_time: {delta_time}" + + +async def test_closed_stream() -> None: + reader_loop = asyncio.get_event_loop() + channel = _MemoryStream[str](reader_loop) + writer = channel.get_send_stream() + reader = channel.get_receive_stream() + await writer.aclose() + + assert [chunk async for chunk in reader] == [] diff --git a/libs/core/tests/unit_tests/utils/test_utils.py b/libs/core/tests/unit_tests/utils/test_utils.py new file mode 100644 index 00000000000..a69ea2510b4 --- /dev/null +++ b/libs/core/tests/unit_tests/utils/test_utils.py @@ -0,0 +1,104 @@ +import re +from contextlib import AbstractContextManager, nullcontext +from typing import Dict, Optional, Tuple, Type, Union +from unittest.mock import patch + +import pytest + +from langchain_core.utils import check_package_version +from langchain_core.utils._merge import merge_dicts + + +@pytest.mark.parametrize( + ("package", "check_kwargs", "actual_version", "expected"), + [ + ("stub", {"gt_version": "0.1"}, "0.1.2", None), + ("stub", {"gt_version": "0.1.2"}, "0.1.12", None), + ("stub", {"gt_version": "0.1.2"}, "0.1.2", (ValueError, "> 0.1.2")), + ("stub", {"gte_version": "0.1"}, "0.1.2", None), + ("stub", {"gte_version": "0.1.2"}, "0.1.2", None), + ], +) +def test_check_package_version( + package: str, + check_kwargs: Dict[str, Optional[str]], + actual_version: str, + expected: Optional[Tuple[Type[Exception], str]], +) -> None: + with patch("langchain_core.utils.utils.version", return_value=actual_version): + if expected is None: + check_package_version(package, **check_kwargs) + else: + with pytest.raises(expected[0], match=expected[1]): + check_package_version(package, **check_kwargs) + + +@pytest.mark.parametrize( + ("left", "right", "expected"), + ( + # Merge `None` and `1`. + ({"a": None}, {"a": 1}, {"a": 1}), + # Merge `1` and `None`. + ({"a": 1}, {"a": None}, {"a": 1}), + # Merge `None` and a value. + ({"a": None}, {"a": 0}, {"a": 0}), + ({"a": None}, {"a": "txt"}, {"a": "txt"}), + # Merge equal values. + ({"a": 1}, {"a": 1}, {"a": 1}), + ({"a": 1.5}, {"a": 1.5}, {"a": 1.5}), + ({"a": True}, {"a": True}, {"a": True}), + ({"a": False}, {"a": False}, {"a": False}), + ({"a": "txt"}, {"a": "txt"}, {"a": "txt"}), + ({"a": [1, 2]}, {"a": [1, 2]}, {"a": [1, 2]}), + ({"a": {"b": "txt"}}, {"a": {"b": "txt"}}, {"a": {"b": "txt"}}), + # Merge strings. + ({"a": "one"}, {"a": "two"}, {"a": "onetwo"}), + # Merge dicts. + ({"a": {"b": 1}}, {"a": {"c": 2}}, {"a": {"b": 1, "c": 2}}), + ( + {"function_call": {"arguments": None}}, + {"function_call": {"arguments": "{\n"}}, + {"function_call": {"arguments": "{\n"}}, + ), + # Merge lists. + ({"a": [1, 2]}, {"a": [3]}, {"a": [1, 2, 3]}), + ({"a": 1, "b": 2}, {"a": 1}, {"a": 1, "b": 2}), + ({"a": 1, "b": 2}, {"c": None}, {"a": 1, "b": 2, "c": None}), + # + # Invalid inputs. + # + ( + {"a": 1}, + {"a": "1"}, + pytest.raises( + TypeError, + match=re.escape( + 'additional_kwargs["a"] already exists in this message, ' + "but with a different type." + ), + ), + ), + ( + {"a": (1, 2)}, + {"a": (3,)}, + pytest.raises( + TypeError, + match=( + "Additional kwargs key a already exists in left dict and value " + "has unsupported type .+tuple.+." + ), + ), + ), + ), +) +def test_merge_dicts( + left: dict, right: dict, expected: Union[dict, AbstractContextManager] +) -> None: + if isinstance(expected, AbstractContextManager): + err = expected + else: + err = nullcontext() + + with err: + actual = merge_dicts(left, right) + assert actual == expected diff --git a/libs/experimental/_test_minimum_requirements.txt b/libs/experimental/_test_minimum_requirements.txt deleted file mode 100644 index 078f131ef8d..00000000000 --- a/libs/experimental/_test_minimum_requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -langchain-core==0.1.16 -langchain==0.1.5 diff --git a/libs/experimental/langchain_experimental/agents/agent_toolkits/pandas/base.py b/libs/experimental/langchain_experimental/agents/agent_toolkits/pandas/base.py index 0970c39595b..6fa67dc60f8 100644 --- a/libs/experimental/langchain_experimental/agents/agent_toolkits/pandas/base.py +++ b/libs/experimental/langchain_experimental/agents/agent_toolkits/pandas/base.py @@ -10,7 +10,7 @@ from langchain.agents.agent import ( RunnableAgent, RunnableMultiActionAgent, ) -from langchain.agents.mrkl.base import ZeroShotAgent +from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS from langchain.agents.openai_functions_agent.base import ( OpenAIFunctionsAgent, create_openai_functions_agent, @@ -18,7 +18,11 @@ from langchain.agents.openai_functions_agent.base import ( from langchain_core.callbacks import BaseCallbackManager from langchain_core.language_models import LanguageModelLike from langchain_core.messages import SystemMessage -from langchain_core.prompts import BasePromptTemplate, ChatPromptTemplate +from langchain_core.prompts import ( + BasePromptTemplate, + ChatPromptTemplate, + PromptTemplate, +) from langchain_core.tools import BaseTool from langchain_core.utils.interactive_env import is_interactive_env @@ -43,7 +47,6 @@ def _get_multi_prompt( suffix: Optional[str] = None, include_df_in_prompt: Optional[bool] = True, number_of_head_rows: int = 5, - tools: Sequence[BaseTool] = (), ) -> BasePromptTemplate: if suffix is not None: suffix_to_use = suffix @@ -53,11 +56,8 @@ def _get_multi_prompt( suffix_to_use = SUFFIX_NO_DF prefix = prefix if prefix is not None else MULTI_DF_PREFIX - prompt = ZeroShotAgent.create_prompt( - tools, - prefix=prefix, - suffix=suffix_to_use, - ) + template = "\n\n".join([prefix, "{tools}", FORMAT_INSTRUCTIONS, suffix_to_use]) + prompt = PromptTemplate.from_template(template) partial_prompt = prompt.partial() if "dfs_head" in partial_prompt.input_variables: dfs_head = "\n\n".join([d.head(number_of_head_rows).to_markdown() for d in dfs]) @@ -74,7 +74,6 @@ def _get_single_prompt( suffix: Optional[str] = None, include_df_in_prompt: Optional[bool] = True, number_of_head_rows: int = 5, - tools: Sequence[BaseTool] = (), ) -> BasePromptTemplate: if suffix is not None: suffix_to_use = suffix @@ -84,11 +83,8 @@ def _get_single_prompt( suffix_to_use = SUFFIX_NO_DF prefix = prefix if prefix is not None else PREFIX - prompt = ZeroShotAgent.create_prompt( - tools, - prefix=prefix, - suffix=suffix_to_use, - ) + template = "\n\n".join([prefix, "{tools}", FORMAT_INSTRUCTIONS, suffix_to_use]) + prompt = PromptTemplate.from_template(template) partial_prompt = prompt.partial() if "df_head" in partial_prompt.input_variables: @@ -257,7 +253,6 @@ def create_pandas_dataframe_agent( suffix=suffix, include_df_in_prompt=include_df_in_prompt, number_of_head_rows=number_of_head_rows, - tools=tools, ) agent: Union[BaseSingleActionAgent, BaseMultiActionAgent] = RunnableAgent( runnable=create_react_agent(llm, tools, prompt), # type: ignore diff --git a/libs/experimental/langchain_experimental/autonomous_agents/autogpt/agent.py b/libs/experimental/langchain_experimental/autonomous_agents/autogpt/agent.py index 1e5e8933e08..5fd94b0c91a 100644 --- a/libs/experimental/langchain_experimental/autonomous_agents/autogpt/agent.py +++ b/libs/experimental/langchain_experimental/autonomous_agents/autogpt/agent.py @@ -98,7 +98,7 @@ class AutoGPT: ) # Print Assistant thoughts - print(assistant_reply) + print(assistant_reply) # noqa: T201 self.chat_history_memory.add_message(HumanMessage(content=user_input)) self.chat_history_memory.add_message(AIMessage(content=assistant_reply)) @@ -135,7 +135,7 @@ class AutoGPT: if self.feedback_tool is not None: feedback = f"\n{self.feedback_tool.run('Input: ')}" if feedback in {"q", "stop"}: - print("EXITING") + print("EXITING") # noqa: T201 return "EXITING" memory_to_add += feedback diff --git a/libs/experimental/langchain_experimental/autonomous_agents/autogpt/memory.py b/libs/experimental/langchain_experimental/autonomous_agents/autogpt/memory.py index c59e326a5ce..f4d7d39d734 100644 --- a/libs/experimental/langchain_experimental/autonomous_agents/autogpt/memory.py +++ b/libs/experimental/langchain_experimental/autonomous_agents/autogpt/memory.py @@ -1,6 +1,7 @@ from typing import Any, Dict, List -from langchain.memory.chat_memory import BaseChatMemory, get_prompt_input_key +from langchain.memory.chat_memory import BaseChatMemory +from langchain.memory.utils import get_prompt_input_key from langchain_core.vectorstores import VectorStoreRetriever from langchain_experimental.pydantic_v1 import Field diff --git a/libs/experimental/langchain_experimental/autonomous_agents/baby_agi/baby_agi.py b/libs/experimental/langchain_experimental/autonomous_agents/baby_agi/baby_agi.py index 13caa937057..6aa662892f4 100644 --- a/libs/experimental/langchain_experimental/autonomous_agents/baby_agi/baby_agi.py +++ b/libs/experimental/langchain_experimental/autonomous_agents/baby_agi/baby_agi.py @@ -1,4 +1,5 @@ """BabyAGI agent.""" + from collections import deque from typing import Any, Dict, List, Optional @@ -59,17 +60,17 @@ class BabyAGI(Chain, BaseModel): # type: ignore[misc] self.task_list.append(task) def print_task_list(self) -> None: - print("\033[95m\033[1m" + "\n*****TASK LIST*****\n" + "\033[0m\033[0m") + print("\033[95m\033[1m" + "\n*****TASK LIST*****\n" + "\033[0m\033[0m") # noqa: T201 for t in self.task_list: - print(str(t["task_id"]) + ": " + t["task_name"]) + print(str(t["task_id"]) + ": " + t["task_name"]) # noqa: T201 def print_next_task(self, task: Dict) -> None: - print("\033[92m\033[1m" + "\n*****NEXT TASK*****\n" + "\033[0m\033[0m") - print(str(task["task_id"]) + ": " + task["task_name"]) + print("\033[92m\033[1m" + "\n*****NEXT TASK*****\n" + "\033[0m\033[0m") # noqa: T201 + print(str(task["task_id"]) + ": " + task["task_name"]) # noqa: T201 def print_task_result(self, result: str) -> None: - print("\033[93m\033[1m" + "\n*****TASK RESULT*****\n" + "\033[0m\033[0m") - print(result) + print("\033[93m\033[1m" + "\n*****TASK RESULT*****\n" + "\033[0m\033[0m") # noqa: T201 + print(result) # noqa: T201 @property def input_keys(self) -> List[str]: @@ -190,7 +191,7 @@ class BabyAGI(Chain, BaseModel): # type: ignore[misc] ) num_iters += 1 if self.max_iterations is not None and num_iters == self.max_iterations: - print( + print( # noqa: T201 "\033[91m\033[1m" + "\n*****TASK ENDING*****\n" + "\033[0m\033[0m" ) break diff --git a/libs/experimental/langchain_experimental/autonomous_agents/hugginggpt/task_executor.py b/libs/experimental/langchain_experimental/autonomous_agents/hugginggpt/task_executor.py index bf742d192be..c6a9be93a52 100644 --- a/libs/experimental/langchain_experimental/autonomous_agents/hugginggpt/task_executor.py +++ b/libs/experimental/langchain_experimental/autonomous_agents/hugginggpt/task_executor.py @@ -117,7 +117,7 @@ class TaskExecutor: def run(self) -> str: for task in self.tasks: - print(f"running {task}") + print(f"running {task}") # noqa: T201 if task.pending() and self.check_dependency(task): self.update_args(task) task.run() diff --git a/libs/experimental/langchain_experimental/data_anonymizer/presidio.py b/libs/experimental/langchain_experimental/data_anonymizer/presidio.py index f9b35788b08..fbfad3703e7 100644 --- a/libs/experimental/langchain_experimental/data_anonymizer/presidio.py +++ b/libs/experimental/langchain_experimental/data_anonymizer/presidio.py @@ -428,7 +428,7 @@ class PresidioReversibleAnonymizer(PresidioAnonymizerBase, ReversibleAnonymizerB if save_path.suffix == ".json": with open(save_path, "w") as f: json.dump(self.deanonymizer_mapping, f, indent=2) - elif save_path.suffix == ".yaml": + elif save_path.suffix.endswith((".yaml", ".yml")): with open(save_path, "w") as f: yaml.dump(self.deanonymizer_mapping, f, default_flow_style=False) @@ -452,7 +452,7 @@ class PresidioReversibleAnonymizer(PresidioAnonymizerBase, ReversibleAnonymizerB if load_path.suffix == ".json": with open(load_path, "r") as f: loaded_mapping = json.load(f) - elif load_path.suffix == ".yaml": + elif load_path.suffix.endswith((".yaml", ".yml")): with open(load_path, "r") as f: loaded_mapping = yaml.load(f, Loader=yaml.FullLoader) diff --git a/libs/experimental/langchain_experimental/pal_chain/base.py b/libs/experimental/langchain_experimental/pal_chain/base.py index d30655ed37a..58593076f4d 100644 --- a/libs/experimental/langchain_experimental/pal_chain/base.py +++ b/libs/experimental/langchain_experimental/pal_chain/base.py @@ -21,6 +21,16 @@ from langchain_experimental.pal_chain.math_prompt import MATH_PROMPT from langchain_experimental.pydantic_v1 import Extra, Field COMMAND_EXECUTION_FUNCTIONS = ["system", "exec", "execfile", "eval", "__import__"] +COMMAND_EXECUTION_ATTRIBUTES = [ + "__import__", + "__subclasses__", + "__builtins__", + "__globals__", + "__getattribute__", + "__bases__", + "__mro__", + "__base__", +] class PALValidation: @@ -232,6 +242,15 @@ class PALChain(Chain): or not code_validations.allow_imports ): for node in ast.walk(code_tree): + if ( + not code_validations.allow_command_exec + and isinstance(node, ast.Attribute) + and node.attr in COMMAND_EXECUTION_ATTRIBUTES + ): + raise ValueError( + f"Found illegal command execution function " + f"{node.attr} in code {code}" + ) if (not code_validations.allow_command_exec) and isinstance( node, ast.Call ): diff --git a/libs/experimental/langchain_experimental/prompts/load.py b/libs/experimental/langchain_experimental/prompts/load.py index a7322565778..51efdc98db5 100644 --- a/libs/experimental/langchain_experimental/prompts/load.py +++ b/libs/experimental/langchain_experimental/prompts/load.py @@ -30,7 +30,7 @@ def _load_prompt_from_file(file: Union[str, Path]) -> BasePromptTemplate: if file_path.suffix == ".json": with open(file_path) as f: config = json.load(f) - elif file_path.suffix == ".yaml": + elif file_path.suffix.endswith((".yaml", ".yml")): with open(file_path, "r") as f: config = yaml.safe_load(f) elif file_path.suffix == ".py": diff --git a/libs/experimental/langchain_experimental/smart_llm/base.py b/libs/experimental/langchain_experimental/smart_llm/base.py index d9d8929cb07..d69fa208013 100644 --- a/libs/experimental/langchain_experimental/smart_llm/base.py +++ b/libs/experimental/langchain_experimental/smart_llm/base.py @@ -215,7 +215,7 @@ class SmartLLMChain(Chain): HumanMessagePromptTemplate, "You are a researcher tasked with investigating the " f"{self.n_ideas} response options provided. List the flaws and " - "faulty logic of each answer options. Let'w work this out in a step" + "faulty logic of each answer option. Let's work this out in a step" " by step way to be sure we have all the errors:", ), ] @@ -229,10 +229,10 @@ class SmartLLMChain(Chain): HumanMessagePromptTemplate, "You are a resolver tasked with 1) finding which of " f"the {self.n_ideas} answer options the researcher thought was " - "best,2) improving that answer and 3) printing the answer in full. " - "Don't output anything for step 1 or 2, only the full answer in 3. " - "Let's work this out in a step by step way to be sure we have " - "the right answer:", + "best, 2) improving that answer and 3) printing the answer in " + "full. Don't output anything for step 1 or 2, only the full " + "answer in 3. Let's work this out in a step by step way to " + "be sure we have the right answer:", ), ] ) diff --git a/libs/experimental/langchain_experimental/sql/base.py b/libs/experimental/langchain_experimental/sql/base.py index 1785598e36a..b6de5e67c9d 100644 --- a/libs/experimental/langchain_experimental/sql/base.py +++ b/libs/experimental/langchain_experimental/sql/base.py @@ -173,7 +173,7 @@ class SQLDatabaseChain(Chain): sql_cmd = checked_sql_command _run_manager.on_text("\nSQLResult: ", verbose=self.verbose) - _run_manager.on_text(result, color="yellow", verbose=self.verbose) + _run_manager.on_text(str(result), color="yellow", verbose=self.verbose) # If return direct, we just set the final result equal to # the result of the sql query result, otherwise try to get a human readable # final answer diff --git a/libs/experimental/langchain_experimental/sql/vector_sql.py b/libs/experimental/langchain_experimental/sql/vector_sql.py index 1b0db959044..a21a6bae868 100644 --- a/libs/experimental/langchain_experimental/sql/vector_sql.py +++ b/libs/experimental/langchain_experimental/sql/vector_sql.py @@ -78,6 +78,7 @@ class VectorSQLRetrieveAllOutputParser(VectorSQLOutputParser): def get_result_from_sqldb(db: SQLDatabase, cmd: str) -> Sequence[Dict[str, Any]]: result = db._execute(cmd, fetch="all") + assert isinstance(result, Sequence) return result diff --git a/libs/experimental/langchain_experimental/text_splitter.py b/libs/experimental/langchain_experimental/text_splitter.py index 27818bb6e4f..c032c338562 100644 --- a/libs/experimental/langchain_experimental/text_splitter.py +++ b/libs/experimental/langchain_experimental/text_splitter.py @@ -85,6 +85,12 @@ class SemanticChunker(BaseDocumentTransformer): """Split text into multiple components.""" # Splitting the essay on '.', '?', and '!' single_sentences_list = re.split(r"(?<=[.?!])\s+", text) + + # having len(single_sentences_list) == 1 would cause the following + # np.percentile to fail. + if len(single_sentences_list) == 1: + return single_sentences_list + sentences = [ {"sentence": x, "index": i} for i, x in enumerate(single_sentences_list) ] diff --git a/libs/experimental/poetry.lock b/libs/experimental/poetry.lock index 4e75e3d6904..f575216ce2b 100644 --- a/libs/experimental/poetry.lock +++ b/libs/experimental/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. [[package]] name = "aiohttp" @@ -1328,7 +1328,6 @@ optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*" files = [ {file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"}, - {file = "jsonpointer-2.4.tar.gz", hash = "sha256:585cee82b70211fa9e6043b7bb89db6e1aa49524340dde8ad6b63206ea689d88"}, ] [[package]] @@ -1642,7 +1641,7 @@ files = [ [[package]] name = "langchain" -version = "0.1.5" +version = "0.1.7" description = "Building applications with LLMs through composability" optional = false python-versions = ">=3.8.1,<4.0" @@ -1654,9 +1653,9 @@ aiohttp = "^3.8.3" async-timeout = {version = "^4.0.0", markers = "python_version < \"3.11\""} dataclasses-json = ">= 0.5.7, < 0.7" jsonpatch = "^1.33" -langchain-community = ">=0.0.17,<0.1" -langchain-core = ">=0.1.16,<0.2" -langsmith = ">=0.0.83,<0.1" +langchain-community = ">=0.0.20,<0.1" +langchain-core = ">=0.1.22,<0.2" +langsmith = "^0.1.0" numpy = "^1" pydantic = ">=1,<3" PyYAML = ">=5.3" @@ -1685,7 +1684,7 @@ url = "../langchain" [[package]] name = "langchain-community" -version = "0.0.17" +version = "0.0.20" description = "Community contributed LangChain integrations." optional = false python-versions = ">=3.8.1,<4.0" @@ -1695,8 +1694,8 @@ develop = true [package.dependencies] aiohttp = "^3.8.3" dataclasses-json = ">= 0.5.7, < 0.7" -langchain-core = ">=0.1.16,<0.2" -langsmith = ">=0.0.83,<0.1" +langchain-core = ">=0.1.21,<0.2" +langsmith = "^0.1.0" numpy = "^1" PyYAML = ">=5.3" requests = "^2" @@ -1705,7 +1704,7 @@ tenacity = "^8.1.0" [package.extras] cli = ["typer (>=0.9.0,<0.10.0)"] -extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser 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"Pretty-print tabular data" +optional = true +python-versions = ">=3.7" +files = [ + {file = "tabulate-0.9.0-py3-none-any.whl", hash = "sha256:024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f"}, + {file = "tabulate-0.9.0.tar.gz", hash = "sha256:0095b12bf5966de529c0feb1fa08671671b3368eec77d7ef7ab114be2c068b3c"}, +] + +[package.extras] +widechars = ["wcwidth"] + [[package]] name = "tenacity" version = "8.2.3" @@ -4701,6 +4761,17 @@ files = [ mypy-extensions = ">=0.3.0" typing-extensions = ">=3.7.4" +[[package]] +name = "tzdata" +version = "2024.1" +description = "Provider of IANA time zone data" +optional = true +python-versions = ">=2" +files = [ + {file = "tzdata-2024.1-py2.py3-none-any.whl", hash = "sha256:9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252"}, + {file = "tzdata-2024.1.tar.gz", hash = "sha256:2674120f8d891909751c38abcdfd386ac0a5a1127954fbc332af6b5ceae07efd"}, +] + [[package]] name = "uri-template" version = "1.3.0" @@ -4962,9 +5033,9 @@ docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.link testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] [extras] -extended-testing = ["faker", "jinja2", "presidio-analyzer", "presidio-anonymizer", "sentence-transformers", "vowpal-wabbit-next"] +extended-testing = ["faker", "jinja2", "pandas", "presidio-analyzer", "presidio-anonymizer", "sentence-transformers", "tabulate", "vowpal-wabbit-next"] [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "11321a2551a49e9cb432348f03e991d1f13368624b1e4df5a721b794114b4f02" +content-hash = "0e4b297b0a8c595fbfe1e8a00d5a13057b1bdd4a0ce08d415ca4c4a7712cee88" diff --git a/libs/experimental/pyproject.toml b/libs/experimental/pyproject.toml index 38537c956e1..be3246167aa 100644 --- a/libs/experimental/pyproject.toml +++ b/libs/experimental/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-experimental" -version = "0.0.50" +version = "0.0.51" description = "Building applications with LLMs through composability" authors = [] license = "MIT" @@ -18,6 +18,8 @@ faker = {version = "^19.3.1", optional = true} vowpal-wabbit-next = {version = "0.6.0", optional = true} sentence-transformers = {version = "^2", optional = true} jinja2 = {version = "^3", optional = true} +pandas = { version = "^2.0.1", optional = true } +tabulate = {version = "^0.9.0", optional = true} [tool.poetry.group.lint] optional = true @@ -79,13 +81,16 @@ extended_testing = [ "vowpal-wabbit-next", "sentence-transformers", "jinja2", + "pandas", + "tabulate", ] -[tool.ruff] +[tool.ruff.lint] select = [ "E", # pycodestyle "F", # pyflakes "I", # isort + "T201", # print ] [tool.mypy] diff --git a/libs/experimental/scripts/check_imports.py b/libs/experimental/scripts/check_imports.py index 462ab97ae2e..825bea5b487 100644 --- a/libs/experimental/scripts/check_imports.py +++ b/libs/experimental/scripts/check_imports.py @@ -15,8 +15,8 @@ if __name__ == "__main__": SourceFileLoader(module_name, file).load_module() except Exception: has_failure = True - print(file) + print(file) # noqa: T201 traceback.print_exc() - print() + print() # noqa: T201 sys.exit(1 if has_failure else 0) diff --git a/libs/experimental/tests/integration_tests/chains/test_cpal.py b/libs/experimental/tests/integration_tests/chains/test_cpal.py index 398350927cd..5df9cd80b6e 100644 --- a/libs/experimental/tests/integration_tests/chains/test_cpal.py +++ b/libs/experimental/tests/integration_tests/chains/test_cpal.py @@ -516,7 +516,7 @@ class TestCPALChain_MathWordProblems(unittest.TestCase): llm = OpenAI(temperature=0, max_tokens=512) cpal_chain = CPALChain.from_univariate_prompt(llm=llm, verbose=True) with pytest.raises(Exception) as e_info: - print(e_info) + print(e_info) # noqa: T201 cpal_chain.run(narrative_input) def test_causal_mediator(self) -> None: diff --git a/libs/experimental/tests/unit_tests/agents/__init__.py b/libs/experimental/tests/unit_tests/agents/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/experimental/tests/unit_tests/agents/agent_toolkits/__init__.py b/libs/experimental/tests/unit_tests/agents/agent_toolkits/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/experimental/tests/unit_tests/agents/agent_toolkits/pandas/__init__.py b/libs/experimental/tests/unit_tests/agents/agent_toolkits/pandas/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/experimental/tests/unit_tests/agents/agent_toolkits/pandas/test_base.py b/libs/experimental/tests/unit_tests/agents/agent_toolkits/pandas/test_base.py new file mode 100644 index 00000000000..8f3a063f688 --- /dev/null +++ b/libs/experimental/tests/unit_tests/agents/agent_toolkits/pandas/test_base.py @@ -0,0 +1,15 @@ +import sys + +import pytest + +from langchain_experimental.agents import create_pandas_dataframe_agent +from tests.unit_tests.fake_llm import FakeLLM + + +@pytest.mark.requires("pandas", "tabulate") +@pytest.mark.skipif(sys.version_info < (3, 9), reason="requires python3.9 or higher") +def test_create_pandas_dataframe_agent() -> None: + import pandas as pd + + create_pandas_dataframe_agent(FakeLLM(), pd.DataFrame()) + create_pandas_dataframe_agent(FakeLLM(), [pd.DataFrame(), pd.DataFrame()]) diff --git a/libs/experimental/tests/unit_tests/python/test_python_1.py b/libs/experimental/tests/unit_tests/python/test_python_1.py index 46d92c29e6e..4c961d84371 100644 --- a/libs/experimental/tests/unit_tests/python/test_python_1.py +++ b/libs/experimental/tests/unit_tests/python/test_python_1.py @@ -1,4 +1,5 @@ """Test functionality of Python REPL.""" + import sys import pytest @@ -9,7 +10,7 @@ from langchain_experimental.utilities.python import PythonREPL _SAMPLE_CODE = """ ``` def multiply(): - print(5*6) + print(5*6) # noqa: T201 multiply() ``` """ diff --git a/libs/experimental/tests/unit_tests/python/test_python_2.py b/libs/experimental/tests/unit_tests/python/test_python_2.py index 9a7f2c8d04e..65fc8f62cb6 100644 --- a/libs/experimental/tests/unit_tests/python/test_python_2.py +++ b/libs/experimental/tests/unit_tests/python/test_python_2.py @@ -1,4 +1,5 @@ """Test Python REPL Tools.""" + import sys import numpy as np @@ -24,7 +25,7 @@ import numpy as np v1 = np.array([1, 2, 3]) v2 = np.array([4, 5, 6]) dot_product = np.dot(v1, v2) -print("The dot product is {:d}.".format(dot_product)) +print("The dot product is {:d}.".format(dot_product)) # noqa: T201 """ tool = PythonREPLTool() assert tool.run(program) == "The dot product is 32.\n" @@ -75,9 +76,9 @@ def test_python_ast_repl_print() -> None: program = """python string = "racecar" if string == string[::-1]: - print(string, "is a palindrome") + print(string, "is a palindrome") # noqa: T201 else: - print(string, "is not a palindrome")""" + print(string, "is not a palindrome")""" # noqa: T201 tool = PythonAstREPLTool() assert tool.run(program) == "racecar is a palindrome\n" diff --git a/libs/experimental/tests/unit_tests/test_llm_bash.py b/libs/experimental/tests/unit_tests/test_llm_bash.py index 5f7dc3fb85f..1adbd999fa1 100644 --- a/libs/experimental/tests/unit_tests/test_llm_bash.py +++ b/libs/experimental/tests/unit_tests/test_llm_bash.py @@ -1,4 +1,5 @@ """Test LLM Bash functionality.""" + import sys import pytest @@ -85,7 +86,7 @@ ls && pwd && ls ``` ```python -print("hello") +print("hello") # noqa: T201 ``` ```bash diff --git a/libs/langchain/Makefile b/libs/langchain/Makefile index 50f1e423c0f..cff2f348546 100644 --- a/libs/langchain/Makefile +++ b/libs/langchain/Makefile @@ -62,7 +62,7 @@ lint lint_diff lint_package lint_tests: poetry run ruff . [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) --diff [ "$(PYTHON_FILES)" = "" ] || poetry run ruff --select I $(PYTHON_FILES) - [ "$(PYTHON_FILES)" = "" ] || mkdir $(MYPY_CACHE) || poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + [ "$(PYTHON_FILES)" = "" ] || mkdir -p $(MYPY_CACHE) && poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) format format_diff: [ "$(PYTHON_FILES)" = "" ] || poetry run ruff format $(PYTHON_FILES) diff --git a/libs/langchain/README.md b/libs/langchain/README.md index 2c8d69bcb49..bd16bdf6e6f 100644 --- a/libs/langchain/README.md +++ b/libs/langchain/README.md @@ -20,7 +20,7 @@ Looking for the JS/TS version? Check out [LangChain.js](https://github.com/langc To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com). [LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications. -Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) to get off the waitlist or speak with our sales team +Fill out [this form](https://www.langchain.com/contact-sales) to speak with our sales team. ## Quick Install diff --git a/libs/langchain/_test_minimum_requirements.txt b/libs/langchain/_test_minimum_requirements.txt deleted file mode 100644 index 84c789c84d7..00000000000 --- a/libs/langchain/_test_minimum_requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -langchain-core==0.1.16 -langchain-community==0.0.17 diff --git a/libs/langchain/langchain/agents/agent.py b/libs/langchain/langchain/agents/agent.py index 8bd7b6d478a..e6f9df49398 100644 --- a/libs/langchain/langchain/agents/agent.py +++ b/libs/langchain/langchain/agents/agent.py @@ -185,7 +185,7 @@ class BaseSingleActionAgent(BaseModel): if save_path.suffix == ".json": with open(file_path, "w") as f: json.dump(agent_dict, f, indent=4) - elif save_path.suffix == ".yaml": + elif save_path.suffix.endswith((".yaml", ".yml")): with open(file_path, "w") as f: yaml.dump(agent_dict, f, default_flow_style=False) else: @@ -310,7 +310,7 @@ class BaseMultiActionAgent(BaseModel): if save_path.suffix == ".json": with open(file_path, "w") as f: json.dump(agent_dict, f, indent=4) - elif save_path.suffix == ".yaml": + elif save_path.suffix.endswith((".yaml", ".yml")): with open(file_path, "w") as f: yaml.dump(agent_dict, f, default_flow_style=False) else: diff --git a/libs/langchain/langchain/agents/chat/output_parser.py b/libs/langchain/langchain/agents/chat/output_parser.py index 069a7fdbb5c..cdb8c0d0dae 100644 --- a/libs/langchain/langchain/agents/chat/output_parser.py +++ b/libs/langchain/langchain/agents/chat/output_parser.py @@ -14,11 +14,15 @@ FINAL_ANSWER_ACTION = "Final Answer:" class ChatOutputParser(AgentOutputParser): """Output parser for the chat agent.""" + format_instructions: str = FORMAT_INSTRUCTIONS + """Default formatting instructions""" + pattern = re.compile(r"^.*?`{3}(?:json)?\n(.*?)`{3}.*?$", re.DOTALL) """Regex pattern to parse the output.""" def get_format_instructions(self) -> str: - return FORMAT_INSTRUCTIONS + """Returns formatting instructions for the given output parser.""" + return self.format_instructions def parse(self, text: str) -> Union[AgentAction, AgentFinish]: includes_answer = FINAL_ANSWER_ACTION in text diff --git a/libs/langchain/langchain/agents/conversational/output_parser.py b/libs/langchain/langchain/agents/conversational/output_parser.py index 6d0446b81bf..e74e626faa9 100644 --- a/libs/langchain/langchain/agents/conversational/output_parser.py +++ b/libs/langchain/langchain/agents/conversational/output_parser.py @@ -14,8 +14,12 @@ class ConvoOutputParser(AgentOutputParser): ai_prefix: str = "AI" """Prefix to use before AI output.""" + format_instructions: str = FORMAT_INSTRUCTIONS + """Default formatting instructions""" + def get_format_instructions(self) -> str: - return FORMAT_INSTRUCTIONS + """Returns formatting instructions for the given output parser.""" + return self.format_instructions def parse(self, text: str) -> Union[AgentAction, AgentFinish]: if f"{self.ai_prefix}:" in text: diff --git a/libs/langchain/langchain/agents/conversational_chat/output_parser.py b/libs/langchain/langchain/agents/conversational_chat/output_parser.py index 46ccce6d571..6fd330ccf8d 100644 --- a/libs/langchain/langchain/agents/conversational_chat/output_parser.py +++ b/libs/langchain/langchain/agents/conversational_chat/output_parser.py @@ -4,19 +4,22 @@ from typing import Union from langchain_core.agents import AgentAction, AgentFinish from langchain_core.exceptions import OutputParserException -from langchain_core.output_parsers.json import parse_json_markdown from langchain.agents import AgentOutputParser from langchain.agents.conversational_chat.prompt import FORMAT_INSTRUCTIONS +from langchain.output_parsers.json import parse_json_markdown # Define a class that parses output for conversational agents class ConvoOutputParser(AgentOutputParser): """Output parser for the conversational agent.""" + format_instructions: str = FORMAT_INSTRUCTIONS + """Default formatting instructions""" + def get_format_instructions(self) -> str: """Returns formatting instructions for the given output parser.""" - return FORMAT_INSTRUCTIONS + return self.format_instructions def parse(self, text: str) -> Union[AgentAction, AgentFinish]: """Attempts to parse the given text into an AgentAction or AgentFinish. diff --git a/libs/langchain/langchain/agents/json_chat/base.py b/libs/langchain/langchain/agents/json_chat/base.py index ffabf68921b..24e1310e2af 100644 --- a/libs/langchain/langchain/agents/json_chat/base.py +++ b/libs/langchain/langchain/agents/json_chat/base.py @@ -12,7 +12,10 @@ from langchain.tools.render import render_text_description def create_json_chat_agent( - llm: BaseLanguageModel, tools: Sequence[BaseTool], prompt: ChatPromptTemplate + llm: BaseLanguageModel, + tools: Sequence[BaseTool], + prompt: ChatPromptTemplate, + stop_sequence: bool = True, ) -> Runnable: """Create an agent that uses JSON to format its logic, build for Chat Models. @@ -20,7 +23,9 @@ def create_json_chat_agent( llm: LLM to use as the agent. tools: Tools this agent has access to. prompt: The prompt to use. See Prompt section below for more. - + stop_sequence: Adds a stop token of "Observation:" to avoid hallucinates. + Default is True. You may to set this to False if the LLM you are using + does not support stop sequences. Returns: A Runnable sequence representing an agent. It takes as input all the same input variables as the prompt passed in does. It returns as output either an @@ -148,7 +153,10 @@ def create_json_chat_agent( tools=render_text_description(list(tools)), tool_names=", ".join([t.name for t in tools]), ) - llm_with_stop = llm.bind(stop=["\nObservation"]) + if stop_sequence: + llm_to_use = llm.bind(stop=["\nObservation"]) + else: + llm_to_use = llm agent = ( RunnablePassthrough.assign( @@ -157,7 +165,7 @@ def create_json_chat_agent( ) ) | prompt - | llm_with_stop + | llm_to_use | JSONAgentOutputParser() ) return agent diff --git a/libs/langchain/langchain/agents/mrkl/output_parser.py b/libs/langchain/langchain/agents/mrkl/output_parser.py index b716c49db48..f6489c2a792 100644 --- a/libs/langchain/langchain/agents/mrkl/output_parser.py +++ b/libs/langchain/langchain/agents/mrkl/output_parser.py @@ -22,8 +22,12 @@ FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE = ( class MRKLOutputParser(AgentOutputParser): """MRKL Output parser for the chat agent.""" + format_instructions: str = FORMAT_INSTRUCTIONS + """Default formatting instructions""" + def get_format_instructions(self) -> str: - return FORMAT_INSTRUCTIONS + """Returns formatting instructions for the given output parser.""" + return self.format_instructions def parse(self, text: str) -> Union[AgentAction, AgentFinish]: includes_answer = FINAL_ANSWER_ACTION in text diff --git a/libs/langchain/langchain/agents/openai_assistant/base.py b/libs/langchain/langchain/agents/openai_assistant/base.py index cd4e1442918..99620322d8d 100644 --- a/libs/langchain/langchain/agents/openai_assistant/base.py +++ b/libs/langchain/langchain/agents/openai_assistant/base.py @@ -210,7 +210,7 @@ class OpenAIAssistantRunnable(RunnableSerializable[Dict, OutputType]): assistant = client.beta.assistants.create( name=name, instructions=instructions, - tools=[convert_to_openai_tool(tool) for tool in tools], + tools=[convert_to_openai_tool(tool) for tool in tools], # type: ignore model=model, ) return cls(assistant_id=assistant.id, client=client, **kwargs) @@ -331,7 +331,7 @@ class OpenAIAssistantRunnable(RunnableSerializable[Dict, OutputType]): assistant = await async_client.beta.assistants.create( name=name, instructions=instructions, - tools=openai_tools, + tools=openai_tools, # type: ignore model=model, ) return cls(assistant_id=assistant.id, async_client=async_client, **kwargs) diff --git a/libs/langchain/langchain/agents/output_parsers/react_json_single_input.py b/libs/langchain/langchain/agents/output_parsers/react_json_single_input.py index c58e1804ab1..0ca9ed2ffd8 100644 --- a/libs/langchain/langchain/agents/output_parsers/react_json_single_input.py +++ b/libs/langchain/langchain/agents/output_parsers/react_json_single_input.py @@ -42,7 +42,7 @@ class ReActJsonSingleInputOutputParser(AgentOutputParser): """ - pattern = re.compile(r"^.*?`{3}(?:json)?\n(.*?)`{3}.*?$", re.DOTALL) + pattern = re.compile(r"^.*?`{3}(?:json)?\n?(.*?)`{3}.*?$", re.DOTALL) """Regex pattern to parse the output.""" def get_format_instructions(self) -> str: diff --git a/libs/langchain/langchain/agents/structured_chat/output_parser.py b/libs/langchain/langchain/agents/structured_chat/output_parser.py index eb85d6684b6..2f91a8352a0 100644 --- a/libs/langchain/langchain/agents/structured_chat/output_parser.py +++ b/libs/langchain/langchain/agents/structured_chat/output_parser.py @@ -20,10 +20,15 @@ logger = logging.getLogger(__name__) class StructuredChatOutputParser(AgentOutputParser): """Output parser for the structured chat agent.""" + format_instructions: str = FORMAT_INSTRUCTIONS + """Default formatting instructions""" + pattern = re.compile(r"```(?:json\s+)?(\W.*?)```", re.DOTALL) + """Regex pattern to parse the output.""" def get_format_instructions(self) -> str: - return FORMAT_INSTRUCTIONS + """Returns formatting instructions for the given output parser.""" + return self.format_instructions def parse(self, text: str) -> Union[AgentAction, AgentFinish]: try: diff --git a/libs/langchain/langchain/chains/__init__.py b/libs/langchain/langchain/chains/__init__.py index 2b7ba6ac256..3a34b82ed51 100644 --- a/libs/langchain/langchain/chains/__init__.py +++ b/libs/langchain/langchain/chains/__init__.py @@ -41,6 +41,7 @@ from langchain.chains.graph_qa.hugegraph import HugeGraphQAChain from langchain.chains.graph_qa.kuzu import KuzuQAChain from langchain.chains.graph_qa.nebulagraph import NebulaGraphQAChain from langchain.chains.graph_qa.neptune_cypher import NeptuneOpenCypherQAChain +from langchain.chains.graph_qa.neptune_sparql import NeptuneSparqlQAChain from langchain.chains.graph_qa.ontotext_graphdb import OntotextGraphDBQAChain from langchain.chains.graph_qa.sparql import GraphSparqlQAChain from langchain.chains.history_aware_retriever import create_history_aware_retriever @@ -81,6 +82,7 @@ from langchain.chains.router import ( ) from langchain.chains.sequential import SequentialChain, SimpleSequentialChain from langchain.chains.sql_database.query import create_sql_query_chain +from langchain.chains.structured_output import create_structured_output_runnable from langchain.chains.summarize import load_summarize_chain from langchain.chains.transform import TransformChain @@ -116,6 +118,7 @@ __all__ = [ "NatBotChain", "NebulaGraphQAChain", "NeptuneOpenCypherQAChain", + "NeptuneSparqlQAChain", "OpenAIModerationChain", "OpenAPIEndpointChain", "QAGenerationChain", @@ -143,5 +146,6 @@ __all__ = [ "create_sql_query_chain", "create_retrieval_chain", "create_history_aware_retriever", + "create_structured_output_runnable", "load_summarize_chain", ] diff --git a/libs/langchain/langchain/chains/base.py b/libs/langchain/langchain/chains/base.py index dc665ae9978..5c5bd1aadfa 100644 --- a/libs/langchain/langchain/chains/base.py +++ b/libs/langchain/langchain/chains/base.py @@ -146,24 +146,28 @@ class Chain(RunnableSerializable[Dict[str, Any], Dict[str, Any]], ABC): self.metadata, ) new_arg_supported = inspect.signature(self._call).parameters.get("run_manager") + run_manager = callback_manager.on_chain_start( dumpd(self), inputs, name=run_name, ) try: + self._validate_inputs(inputs) outputs = ( self._call(inputs, run_manager=run_manager) if new_arg_supported else self._call(inputs) ) + + final_outputs: Dict[str, Any] = self.prep_outputs( + inputs, outputs, return_only_outputs + ) except BaseException as e: run_manager.on_chain_error(e) raise e run_manager.on_chain_end(outputs) - final_outputs: Dict[str, Any] = self.prep_outputs( - inputs, outputs, return_only_outputs - ) + if include_run_info: final_outputs[RUN_KEY] = RunInfo(run_id=run_manager.run_id) return final_outputs @@ -199,18 +203,20 @@ class Chain(RunnableSerializable[Dict[str, Any], Dict[str, Any]], ABC): name=run_name, ) try: + self._validate_inputs(inputs) outputs = ( await self._acall(inputs, run_manager=run_manager) if new_arg_supported else await self._acall(inputs) ) + final_outputs: Dict[str, Any] = self.prep_outputs( + inputs, outputs, return_only_outputs + ) except BaseException as e: await run_manager.on_chain_error(e) raise e await run_manager.on_chain_end(outputs) - final_outputs: Dict[str, Any] = self.prep_outputs( - inputs, outputs, return_only_outputs - ) + if include_run_info: final_outputs[RUN_KEY] = RunInfo(run_id=run_manager.run_id) return final_outputs @@ -259,6 +265,20 @@ class Chain(RunnableSerializable[Dict[str, Any], Dict[str, Any]], ABC): def _validate_inputs(self, inputs: Dict[str, Any]) -> None: """Check that all inputs are present.""" + if not isinstance(inputs, dict): + _input_keys = set(self.input_keys) + if self.memory is not None: + # If there are multiple input keys, but some get set by memory so that + # only one is not set, we can still figure out which key it is. + _input_keys = _input_keys.difference(self.memory.memory_variables) + if len(_input_keys) != 1: + raise ValueError( + f"A single string input was passed in, but this chain expects " + f"multiple inputs ({_input_keys}). When a chain expects " + f"multiple inputs, please call it by passing in a dictionary, " + "eg `chain({'foo': 1, 'bar': 2})`" + ) + missing_keys = set(self.input_keys).difference(inputs) if missing_keys: raise ValueError(f"Missing some input keys: {missing_keys}") @@ -444,7 +464,7 @@ class Chain(RunnableSerializable[Dict[str, Any], Dict[str, Any]], ABC): return {**inputs, **outputs} def prep_inputs(self, inputs: Union[Dict[str, Any], Any]) -> Dict[str, str]: - """Validate and prepare chain inputs, including adding inputs from memory. + """Prepare chain inputs, including adding inputs from memory. Args: inputs: Dictionary of raw inputs, or single input if chain expects @@ -461,18 +481,10 @@ class Chain(RunnableSerializable[Dict[str, Any], Dict[str, Any]], ABC): # If there are multiple input keys, but some get set by memory so that # only one is not set, we can still figure out which key it is. _input_keys = _input_keys.difference(self.memory.memory_variables) - if len(_input_keys) != 1: - raise ValueError( - f"A single string input was passed in, but this chain expects " - f"multiple inputs ({_input_keys}). When a chain expects " - f"multiple inputs, please call it by passing in a dictionary, " - "eg `chain({'foo': 1, 'bar': 2})`" - ) inputs = {list(_input_keys)[0]: inputs} if self.memory is not None: external_context = self.memory.load_memory_variables(inputs) inputs = dict(inputs, **external_context) - self._validate_inputs(inputs) return inputs @property @@ -687,7 +699,7 @@ class Chain(RunnableSerializable[Dict[str, Any], Dict[str, Any]], ABC): if save_path.suffix == ".json": with open(file_path, "w") as f: json.dump(chain_dict, f, indent=4) - elif save_path.suffix == ".yaml": + elif save_path.suffix.endswith((".yaml", ".yml")): with open(file_path, "w") as f: yaml.dump(chain_dict, f, default_flow_style=False) else: diff --git a/libs/langchain/langchain/chains/combine_documents/reduce.py b/libs/langchain/langchain/chains/combine_documents/reduce.py index 00e3efd02e0..a55c067d168 100644 --- a/libs/langchain/langchain/chains/combine_documents/reduce.py +++ b/libs/langchain/langchain/chains/combine_documents/reduce.py @@ -200,6 +200,10 @@ class ReduceDocumentsChain(BaseCombineDocumentsChain): """The maximum number of tokens to group documents into. For example, if set to 3000 then documents will be grouped into chunks of no greater than 3000 tokens before trying to combine them into a smaller chunk.""" + collapse_max_retries: Optional[int] = None + """The maximum number of retries to collapse documents to fit token_max. + If None, it will keep trying to collapse documents to fit token_max. + Otherwise, after it reaches the max number, it will throw an error""" class Config: """Configuration for this pydantic object.""" @@ -289,6 +293,7 @@ class ReduceDocumentsChain(BaseCombineDocumentsChain): ) _token_max = token_max or self.token_max + retries: int = 0 while num_tokens is not None and num_tokens > _token_max: new_result_doc_list = split_list_of_docs( result_docs, length_func, _token_max, **kwargs @@ -298,6 +303,12 @@ class ReduceDocumentsChain(BaseCombineDocumentsChain): new_doc = collapse_docs(docs, _collapse_docs_func, **kwargs) result_docs.append(new_doc) num_tokens = length_func(result_docs, **kwargs) + retries += 1 + if self.collapse_max_retries and retries == self.collapse_max_retries: + raise ValueError( + f"Exceed {self.collapse_max_retries} tries to \ + collapse document to {_token_max} tokens." + ) return result_docs, {} async def _acollapse( @@ -317,6 +328,7 @@ class ReduceDocumentsChain(BaseCombineDocumentsChain): ) _token_max = token_max or self.token_max + retries: int = 0 while num_tokens is not None and num_tokens > _token_max: new_result_doc_list = split_list_of_docs( result_docs, length_func, _token_max, **kwargs @@ -326,6 +338,12 @@ class ReduceDocumentsChain(BaseCombineDocumentsChain): new_doc = await acollapse_docs(docs, _collapse_docs_func, **kwargs) result_docs.append(new_doc) num_tokens = length_func(result_docs, **kwargs) + retries += 1 + if self.collapse_max_retries and retries == self.collapse_max_retries: + raise ValueError( + f"Exceed {self.collapse_max_retries} tries to \ + collapse document to {_token_max} tokens." + ) return result_docs, {} @property diff --git a/libs/langchain/langchain/chains/graph_qa/neptune_sparql.py b/libs/langchain/langchain/chains/graph_qa/neptune_sparql.py new file mode 100644 index 00000000000..08a1cc249be --- /dev/null +++ b/libs/langchain/langchain/chains/graph_qa/neptune_sparql.py @@ -0,0 +1,196 @@ +""" +Question answering over an RDF or OWL graph using SPARQL. +""" +from __future__ import annotations + +from typing import Any, Dict, List, Optional + +from langchain_community.graphs import NeptuneRdfGraph +from langchain_core.language_models import BaseLanguageModel +from langchain_core.prompts.base import BasePromptTemplate +from langchain_core.prompts.prompt import PromptTemplate +from langchain_core.pydantic_v1 import Field + +from langchain.callbacks.manager import CallbackManagerForChainRun +from langchain.chains.base import Chain +from langchain.chains.graph_qa.prompts import SPARQL_QA_PROMPT +from langchain.chains.llm import LLMChain + +INTERMEDIATE_STEPS_KEY = "intermediate_steps" + +SPARQL_GENERATION_TEMPLATE = """ +Task: Generate a SPARQL SELECT statement for querying a graph database. +For instance, to find all email addresses of John Doe, the following +query in backticks would be suitable: +``` +PREFIX foaf: +SELECT ?email +WHERE {{ + ?person foaf:name "John Doe" . + ?person foaf:mbox ?email . +}} +``` +Instructions: +Use only the node types and properties provided in the schema. +Do not use any node types and properties that are not explicitly provided. +Include all necessary prefixes. + +Examples: + +Schema: +{schema} +Note: Be as concise as possible. +Do not include any explanations or apologies in your responses. +Do not respond to any questions that ask for anything else than +for you to construct a SPARQL query. +Do not include any text except the SPARQL query generated. + +The question is: +{prompt}""" + +SPARQL_GENERATION_PROMPT = PromptTemplate( + input_variables=["schema", "prompt"], template=SPARQL_GENERATION_TEMPLATE +) + + +def extract_sparql(query: str) -> str: + query = query.strip() + querytoks = query.split("```") + if len(querytoks) == 3: + query = querytoks[1] + + if query.startswith("sparql"): + query = query[6:] + elif query.startswith("") and query.endswith(""): + query = query[8:-9] + return query + + +class NeptuneSparqlQAChain(Chain): + """Chain for question-answering against a Neptune graph + by generating SPARQL statements. + + *Security note*: Make sure that the database connection uses credentials + that are narrowly-scoped to only include necessary permissions. + Failure to do so may result in data corruption or loss, since the calling + code may attempt commands that would result in deletion, mutation + of data if appropriately prompted or reading sensitive data if such + data is present in the database. + The best way to guard against such negative outcomes is to (as appropriate) + limit the permissions granted to the credentials used with this tool. + + See https://python.langchain.com/docs/security for more information. + + Example: + .. code-block:: python + + chain = NeptuneSparqlQAChain.from_llm( + llm=llm, + graph=graph + ) + response = chain.invoke(query) + """ + + graph: NeptuneRdfGraph = Field(exclude=True) + sparql_generation_chain: LLMChain + qa_chain: LLMChain + input_key: str = "query" #: :meta private: + output_key: str = "result" #: :meta private: + top_k: int = 10 + return_intermediate_steps: bool = False + """Whether or not to return the intermediate steps along with the final answer.""" + return_direct: bool = False + """Whether or not to return the result of querying the graph directly.""" + extra_instructions: Optional[str] = None + """Extra instructions by the appended to the query generation prompt.""" + + @property + def input_keys(self) -> List[str]: + return [self.input_key] + + @property + def output_keys(self) -> List[str]: + _output_keys = [self.output_key] + return _output_keys + + @classmethod + def from_llm( + cls, + llm: BaseLanguageModel, + *, + qa_prompt: BasePromptTemplate = SPARQL_QA_PROMPT, + sparql_prompt: BasePromptTemplate = SPARQL_GENERATION_PROMPT, + examples: Optional[str] = None, + **kwargs: Any, + ) -> NeptuneSparqlQAChain: + """Initialize from LLM.""" + qa_chain = LLMChain(llm=llm, prompt=qa_prompt) + template_to_use = SPARQL_GENERATION_TEMPLATE + if examples: + template_to_use = template_to_use.replace( + "Examples:", "Examples: " + examples + ) + sparql_prompt = PromptTemplate( + input_variables=["schema", "prompt"], template=template_to_use + ) + sparql_generation_chain = LLMChain(llm=llm, prompt=sparql_prompt) + + return cls( + qa_chain=qa_chain, + sparql_generation_chain=sparql_generation_chain, + examples=examples, + **kwargs, + ) + + def _call( + self, + inputs: Dict[str, Any], + run_manager: Optional[CallbackManagerForChainRun] = None, + ) -> Dict[str, str]: + """ + Generate SPARQL query, use it to retrieve a response from the gdb and answer + the question. + """ + _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() + callbacks = _run_manager.get_child() + prompt = inputs[self.input_key] + + intermediate_steps: List = [] + + generated_sparql = self.sparql_generation_chain.run( + {"prompt": prompt, "schema": self.graph.get_schema}, callbacks=callbacks + ) + + # Extract SPARQL + generated_sparql = extract_sparql(generated_sparql) + + _run_manager.on_text("Generated SPARQL:", end="\n", verbose=self.verbose) + _run_manager.on_text( + generated_sparql, color="green", end="\n", verbose=self.verbose + ) + + intermediate_steps.append({"query": generated_sparql}) + + context = self.graph.query(generated_sparql) + + if self.return_direct: + final_result = context + else: + _run_manager.on_text("Full Context:", end="\n", verbose=self.verbose) + _run_manager.on_text( + str(context), color="green", end="\n", verbose=self.verbose + ) + + intermediate_steps.append({"context": context}) + + result = self.qa_chain( + {"prompt": prompt, "context": context}, + callbacks=callbacks, + ) + final_result = result[self.qa_chain.output_key] + + chain_result: Dict[str, Any] = {self.output_key: final_result} + if self.return_intermediate_steps: + chain_result[INTERMEDIATE_STEPS_KEY] = intermediate_steps + + return chain_result diff --git a/libs/langchain/langchain/chains/graph_qa/ontotext_graphdb.py b/libs/langchain/langchain/chains/graph_qa/ontotext_graphdb.py index 4a4d89b6753..29d07d9d75b 100644 --- a/libs/langchain/langchain/chains/graph_qa/ontotext_graphdb.py +++ b/libs/langchain/langchain/chains/graph_qa/ontotext_graphdb.py @@ -1,7 +1,10 @@ """Question answering over a graph.""" from __future__ import annotations -from typing import Any, Dict, List, Optional +from typing import TYPE_CHECKING, Any, Dict, List, Optional + +if TYPE_CHECKING: + import rdflib from langchain_community.graphs import OntotextGraphDBGraph from langchain_core.callbacks.manager import CallbackManager @@ -97,10 +100,10 @@ class OntotextGraphDBQAChain(Chain): self.sparql_generation_chain.output_key ] - generated_sparql = self._get_valid_sparql_query( + generated_sparql = self._get_prepared_sparql_query( _run_manager, callbacks, generated_sparql, ontology_schema ) - query_results = self.graph.query(generated_sparql) + query_results = self._execute_query(generated_sparql) qa_chain_result = self.qa_chain.invoke( {"prompt": prompt, "context": query_results}, callbacks=callbacks @@ -108,7 +111,7 @@ class OntotextGraphDBQAChain(Chain): result = qa_chain_result[self.qa_chain.output_key] return {self.output_key: result} - def _get_valid_sparql_query( + def _get_prepared_sparql_query( self, _run_manager: CallbackManagerForChainRun, callbacks: CallbackManager, @@ -153,10 +156,10 @@ class OntotextGraphDBQAChain(Chain): from rdflib.plugins.sparql import prepareQuery prepareQuery(generated_sparql) - self._log_valid_sparql_query(_run_manager, generated_sparql) + self._log_prepared_sparql_query(_run_manager, generated_sparql) return generated_sparql - def _log_valid_sparql_query( + def _log_prepared_sparql_query( self, _run_manager: CallbackManagerForChainRun, generated_query: str ) -> None: _run_manager.on_text("Generated SPARQL:", end="\n", verbose=self.verbose) @@ -180,3 +183,9 @@ class OntotextGraphDBQAChain(Chain): _run_manager.on_text( error_message, color="red", end="\n\n", verbose=self.verbose ) + + def _execute_query(self, query: str) -> List[rdflib.query.ResultRow]: + try: + return self.graph.query(query) + except Exception: + raise ValueError("Failed to execute the generated SPARQL query.") diff --git a/libs/langchain/langchain/chains/graph_qa/prompts.py b/libs/langchain/langchain/chains/graph_qa/prompts.py index 5d4652453b6..d83aef9a622 100644 --- a/libs/langchain/langchain/chains/graph_qa/prompts.py +++ b/libs/langchain/langchain/chains/graph_qa/prompts.py @@ -100,6 +100,13 @@ CYPHER_QA_TEMPLATE = """You are an assistant that helps to form nice and human u The information part contains the provided information that you must use to construct an answer. The provided information is authoritative, you must never doubt it or try to use your internal knowledge to correct it. Make the answer sound as a response to the question. Do not mention that you based the result on the given information. +Here is an example: + +Question: Which managers own Neo4j stocks? +Context:[manager:CTL LLC, manager:JANE STREET GROUP LLC] +Helpful Answer: CTL LLC, JANE STREET GROUP LLC owns Neo4j stocks. + +Follow this example when generating answers. If the provided information is empty, say that you don't know the answer. Information: {context} diff --git a/libs/langchain/langchain/chains/llm_summarization_checker/base.py b/libs/langchain/langchain/chains/llm_summarization_checker/base.py index b5d1c1d504a..f63e383bbcc 100644 --- a/libs/langchain/langchain/chains/llm_summarization_checker/base.py +++ b/libs/langchain/langchain/chains/llm_summarization_checker/base.py @@ -157,7 +157,7 @@ class LLMSummarizationCheckerChain(Chain): break if self.verbose: - print(output["revised_summary"]) + print(output["revised_summary"]) # noqa: T201 chain_input = output["revised_summary"] diff --git a/libs/langchain/langchain/chains/loading.py b/libs/langchain/langchain/chains/loading.py index 0675b87141a..0fc3da3f499 100644 --- a/libs/langchain/langchain/chains/loading.py +++ b/libs/langchain/langchain/chains/loading.py @@ -607,7 +607,7 @@ def _load_chain_from_file(file: Union[str, Path], **kwargs: Any) -> Chain: if file_path.suffix == ".json": with open(file_path) as f: config = json.load(f) - elif file_path.suffix == ".yaml": + elif file_path.suffix.endswith((".yaml", ".yml")): with open(file_path, "r") as f: config = yaml.safe_load(f) else: diff --git a/libs/langchain/langchain/chains/moderation.py b/libs/langchain/langchain/chains/moderation.py index 00d6cbd3f35..6deb63ed062 100644 --- a/libs/langchain/langchain/chains/moderation.py +++ b/libs/langchain/langchain/chains/moderation.py @@ -52,7 +52,7 @@ class OpenAIModerationChain(Chain): openai.api_key = openai_api_key if openai_organization: openai.organization = openai_organization - values["client"] = openai.Moderation + values["client"] = openai.Moderation # type: ignore except ImportError: raise ImportError( "Could not import openai python package. " diff --git a/libs/langchain/langchain/chains/natbot/crawler.py b/libs/langchain/langchain/chains/natbot/crawler.py index 8aa35d95259..b5fbc553357 100644 --- a/libs/langchain/langchain/chains/natbot/crawler.py +++ b/libs/langchain/langchain/chains/natbot/crawler.py @@ -114,7 +114,7 @@ class Crawler: self.page.mouse.click(x, y) else: - print("Could not find element") + print("Could not find element") # noqa: T201 def type(self, id: Union[str, int], text: str) -> None: self.click(id) @@ -442,5 +442,5 @@ class Crawler: ) id_counter += 1 - print("Parsing time: {:0.2f} seconds".format(time.time() - start)) + print("Parsing time: {:0.2f} seconds".format(time.time() - start)) # noqa: T201 return elements_of_interest diff --git a/libs/langchain/langchain/chains/openai_functions/__init__.py b/libs/langchain/langchain/chains/openai_functions/__init__.py index 8be27606d7a..b6268422451 100644 --- a/libs/langchain/langchain/chains/openai_functions/__init__.py +++ b/libs/langchain/langchain/chains/openai_functions/__init__.py @@ -1,10 +1,7 @@ from langchain.chains.openai_functions.base import ( convert_to_openai_function, create_openai_fn_chain, - create_openai_fn_runnable, create_structured_output_chain, - create_structured_output_runnable, - get_openai_output_parser, ) from langchain.chains.openai_functions.citation_fuzzy_match import ( create_citation_fuzzy_match_chain, @@ -21,6 +18,11 @@ from langchain.chains.openai_functions.tagging import ( create_tagging_chain, create_tagging_chain_pydantic, ) +from langchain.chains.structured_output.base import ( + create_openai_fn_runnable, + create_structured_output_runnable, + get_openai_output_parser, +) __all__ = [ "convert_to_openai_function", @@ -33,7 +35,7 @@ __all__ = [ "create_qa_with_sources_chain", "create_structured_output_chain", "create_openai_fn_chain", - "create_structured_output_runnable", - "create_openai_fn_runnable", - "get_openai_output_parser", + "create_structured_output_runnable", # backwards compatibility + "create_openai_fn_runnable", # backwards compatibility + "get_openai_output_parser", # backwards compatibility ] diff --git a/libs/langchain/langchain/chains/openai_functions/base.py b/libs/langchain/langchain/chains/openai_functions/base.py index 7f8fdd4431b..2cef8ac74ce 100644 --- a/libs/langchain/langchain/chains/openai_functions/base.py +++ b/libs/langchain/langchain/chains/openai_functions/base.py @@ -12,229 +12,34 @@ from typing import ( from langchain_core._api import deprecated from langchain_core.language_models import BaseLanguageModel from langchain_core.output_parsers import ( - BaseGenerationOutputParser, BaseLLMOutputParser, - BaseOutputParser, ) from langchain_core.prompts import BasePromptTemplate from langchain_core.pydantic_v1 import BaseModel -from langchain_core.runnables import Runnable from langchain_core.utils.function_calling import ( PYTHON_TO_JSON_TYPES, convert_to_openai_function, ) from langchain.chains import LLMChain +from langchain.chains.structured_output.base import ( + create_openai_fn_runnable, + create_structured_output_runnable, + get_openai_output_parser, +) from langchain.output_parsers.openai_functions import ( - JsonOutputFunctionsParser, PydanticAttrOutputFunctionsParser, - PydanticOutputFunctionsParser, ) - -def get_openai_output_parser( - functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable]], -) -> Union[BaseOutputParser, BaseGenerationOutputParser]: - """Get the appropriate function output parser given the user functions. - - Args: - functions: Sequence where element is a dictionary, a pydantic.BaseModel class, - or a Python function. If a dictionary is passed in, it is assumed to - already be a valid OpenAI function. - - Returns: - A PydanticOutputFunctionsParser if functions are Pydantic classes, otherwise - a JsonOutputFunctionsParser. If there's only one function and it is - not a Pydantic class, then the output parser will automatically extract - only the function arguments and not the function name. - """ - function_names = [convert_to_openai_function(f)["name"] for f in functions] - if isinstance(functions[0], type) and issubclass(functions[0], BaseModel): - if len(functions) > 1: - pydantic_schema: Union[Dict, Type[BaseModel]] = { - name: fn for name, fn in zip(function_names, functions) - } - else: - pydantic_schema = functions[0] - output_parser: Union[ - BaseOutputParser, BaseGenerationOutputParser - ] = PydanticOutputFunctionsParser(pydantic_schema=pydantic_schema) - else: - output_parser = JsonOutputFunctionsParser(args_only=len(functions) <= 1) - return output_parser - - -def create_openai_fn_runnable( - functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable]], - llm: Runnable, - prompt: BasePromptTemplate, - *, - enforce_single_function_usage: bool = True, - output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]] = None, - **kwargs: Any, -) -> Runnable: - """Create a runnable sequence that uses OpenAI functions. - - Args: - functions: A sequence of either dictionaries, pydantic.BaseModels classes, or - Python functions. If dictionaries are passed in, they are assumed to - already be a valid OpenAI functions. If only a single - function is passed in, then it will be enforced that the model use that - function. pydantic.BaseModels and Python functions should have docstrings - describing what the function does. For best results, pydantic.BaseModels - should have descriptions of the parameters and Python functions should have - Google Python style args descriptions in the docstring. Additionally, - Python functions should only use primitive types (str, int, float, bool) or - pydantic.BaseModels for arguments. - llm: Language model to use, assumed to support the OpenAI function-calling API. - prompt: BasePromptTemplate to pass to the model. - enforce_single_function_usage: only used if a single function is passed in. If - True, then the model will be forced to use the given function. If False, - then the model will be given the option to use the given function or not. - output_parser: BaseLLMOutputParser to use for parsing model outputs. By default - will be inferred from the function types. If pydantic.BaseModels are passed - in, then the OutputParser will try to parse outputs using those. Otherwise - model outputs will simply be parsed as JSON. If multiple functions are - passed in and they are not pydantic.BaseModels, the chain output will - include both the name of the function that was returned and the arguments - to pass to the function. - - Returns: - A runnable sequence that will pass in the given functions to the model when run. - - Example: - .. code-block:: python - - from typing import Optional - - from langchain.chains.openai_functions import create_openai_fn_runnable - from langchain_community.chat_models import ChatOpenAI - from langchain_core.prompts import ChatPromptTemplate - from langchain_core.pydantic_v1 import BaseModel, Field - - - class RecordPerson(BaseModel): - \"\"\"Record some identifying information about a person.\"\"\" - - name: str = Field(..., description="The person's name") - age: int = Field(..., description="The person's age") - fav_food: Optional[str] = Field(None, description="The person's favorite food") - - - class RecordDog(BaseModel): - \"\"\"Record some identifying information about a dog.\"\"\" - - name: str = Field(..., description="The dog's name") - color: str = Field(..., description="The dog's color") - fav_food: Optional[str] = Field(None, description="The dog's favorite food") - - - llm = ChatOpenAI(model="gpt-4", temperature=0) - prompt = ChatPromptTemplate.from_messages( - [ - ("system", "You are a world class algorithm for recording entities."), - ("human", "Make calls to the relevant function to record the entities in the following input: {input}"), - ("human", "Tip: Make sure to answer in the correct format"), - ] - ) - chain = create_openai_fn_runnable([RecordPerson, RecordDog], llm, prompt) - chain.invoke({"input": "Harry was a chubby brown beagle who loved chicken"}) - # -> RecordDog(name="Harry", color="brown", fav_food="chicken") - """ # noqa: E501 - if not functions: - raise ValueError("Need to pass in at least one function. Received zero.") - openai_functions = [convert_to_openai_function(f) for f in functions] - llm_kwargs: Dict[str, Any] = {"functions": openai_functions, **kwargs} - if len(openai_functions) == 1 and enforce_single_function_usage: - llm_kwargs["function_call"] = {"name": openai_functions[0]["name"]} - output_parser = output_parser or get_openai_output_parser(functions) - return prompt | llm.bind(**llm_kwargs) | output_parser - - -def create_structured_output_runnable( - output_schema: Union[Dict[str, Any], Type[BaseModel]], - llm: Runnable, - prompt: BasePromptTemplate, - *, - output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]] = None, - **kwargs: Any, -) -> Runnable: - """Create a runnable that uses an OpenAI function to get a structured output. - - Args: - output_schema: Either a dictionary or pydantic.BaseModel class. If a dictionary - is passed in, it's assumed to already be a valid JsonSchema. - For best results, pydantic.BaseModels should have docstrings describing what - the schema represents and descriptions for the parameters. - llm: Language model to use, assumed to support the OpenAI function-calling API. - prompt: BasePromptTemplate to pass to the model. - output_parser: BaseLLMOutputParser to use for parsing model outputs. By default - will be inferred from the function types. If pydantic.BaseModels are passed - in, then the OutputParser will try to parse outputs using those. Otherwise - model outputs will simply be parsed as JSON. - - Returns: - A runnable sequence that will pass the given function to the model when run. - - Example: - .. code-block:: python - - from typing import Optional - - from langchain.chains.openai_functions import create_structured_output_runnable - from langchain_community.chat_models import ChatOpenAI - from langchain_core.prompts import ChatPromptTemplate - from langchain_core.pydantic_v1 import BaseModel, Field - - class Dog(BaseModel): - \"\"\"Identifying information about a dog.\"\"\" - - name: str = Field(..., description="The dog's name") - color: str = Field(..., description="The dog's color") - fav_food: Optional[str] = Field(None, description="The dog's favorite food") - - llm = ChatOpenAI(model="gpt-3.5-turbo-0613", temperature=0) - prompt = ChatPromptTemplate.from_messages( - [ - ("system", "You are a world class algorithm for extracting information in structured formats."), - ("human", "Use the given format to extract information from the following input: {input}"), - ("human", "Tip: Make sure to answer in the correct format"), - ] - ) - chain = create_structured_output_runnable(Dog, llm, prompt) - chain.invoke({"input": "Harry was a chubby brown beagle who loved chicken"}) - # -> Dog(name="Harry", color="brown", fav_food="chicken") - """ # noqa: E501 - if isinstance(output_schema, dict): - function: Any = { - "name": "output_formatter", - "description": ( - "Output formatter. Should always be used to format your response to the" - " user." - ), - "parameters": output_schema, - } - else: - - class _OutputFormatter(BaseModel): - """Output formatter. Should always be used to format your response to the user.""" # noqa: E501 - - output: output_schema # type: ignore - - function = _OutputFormatter - output_parser = output_parser or PydanticAttrOutputFunctionsParser( - pydantic_schema=_OutputFormatter, attr_name="output" - ) - return create_openai_fn_runnable( - [function], - llm, - prompt, - output_parser=output_parser, - **kwargs, - ) - - -""" --- Legacy --- """ +__all__ = [ + "get_openai_output_parser", + "create_openai_fn_runnable", + "create_structured_output_runnable", + "create_openai_fn_chain", # deprecated + "create_structured_output_chain", # deprecated + "PYTHON_TO_JSON_TYPES", # backwards compatibility + "convert_to_openai_function", # backwards compatibility +] @deprecated(since="0.1.1", removal="0.2.0", alternative="create_openai_fn_runnable") @@ -426,14 +231,3 @@ def create_structured_output_chain( output_parser=output_parser, **kwargs, ) - - -__all__ = [ - "create_openai_fn_chain", - "create_openai_fn_runnable", - "create_structured_output_chain", - "create_structured_output_runnable", - "get_openai_output_parser", - "PYTHON_TO_JSON_TYPES", - "convert_to_openai_function", -] diff --git a/libs/langchain/langchain/chains/query_constructor/__init__.py b/libs/langchain/langchain/chains/query_constructor/__init__.py index e69de29bb2d..9d08ca0e080 100644 --- a/libs/langchain/langchain/chains/query_constructor/__init__.py +++ b/libs/langchain/langchain/chains/query_constructor/__init__.py @@ -0,0 +1,3 @@ +from langchain.chains.query_constructor.base import load_query_constructor_runnable + +__all__ = ["load_query_constructor_runnable"] diff --git a/libs/langchain/langchain/chains/query_constructor/base.py b/libs/langchain/langchain/chains/query_constructor/base.py index d99c046abfa..c08e74f20da 100644 --- a/libs/langchain/langchain/chains/query_constructor/base.py +++ b/libs/langchain/langchain/chains/query_constructor/base.py @@ -323,7 +323,8 @@ def load_query_constructor_runnable( Args: llm: BaseLanguageModel to use for the chain. - document_contents: The contents of the document to be queried. + document_contents: Description of the page contents of the document to be + queried. attribute_info: Sequence of attributes in the document. examples: Optional list of examples to use for the chain. allowed_comparators: Sequence of allowed comparators. Defaults to all diff --git a/libs/langchain/langchain/chains/structured_output/__init__.py b/libs/langchain/langchain/chains/structured_output/__init__.py new file mode 100644 index 00000000000..d387a7cbe4e --- /dev/null +++ b/libs/langchain/langchain/chains/structured_output/__init__.py @@ -0,0 +1,6 @@ +from langchain.chains.structured_output.base import ( + create_openai_fn_runnable, + create_structured_output_runnable, +) + +__all__ = ["create_structured_output_runnable", "create_openai_fn_runnable"] diff --git a/libs/langchain/langchain/chains/structured_output/base.py b/libs/langchain/langchain/chains/structured_output/base.py new file mode 100644 index 00000000000..965f215ecd3 --- /dev/null +++ b/libs/langchain/langchain/chains/structured_output/base.py @@ -0,0 +1,335 @@ +import json +from typing import Any, Callable, Dict, Literal, Optional, Sequence, Type, Union + +from langchain_core.output_parsers import ( + BaseGenerationOutputParser, + BaseOutputParser, + JsonOutputParser, +) +from langchain_core.prompts import BasePromptTemplate +from langchain_core.pydantic_v1 import BaseModel +from langchain_core.runnables import Runnable +from langchain_core.utils.function_calling import convert_to_openai_function + +from langchain.output_parsers import PydanticOutputParser +from langchain.output_parsers.openai_functions import ( + JsonOutputFunctionsParser, + PydanticAttrOutputFunctionsParser, + PydanticOutputFunctionsParser, +) + + +def create_openai_fn_runnable( + functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable]], + llm: Runnable, + prompt: Optional[BasePromptTemplate] = None, + *, + enforce_single_function_usage: bool = True, + output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]] = None, + **kwargs: Any, +) -> Runnable: + """Create a runnable sequence that uses OpenAI functions. + + Args: + functions: A sequence of either dictionaries, pydantic.BaseModels classes, or + Python functions. If dictionaries are passed in, they are assumed to + already be a valid OpenAI functions. If only a single + function is passed in, then it will be enforced that the model use that + function. pydantic.BaseModels and Python functions should have docstrings + describing what the function does. For best results, pydantic.BaseModels + should have descriptions of the parameters and Python functions should have + Google Python style args descriptions in the docstring. Additionally, + Python functions should only use primitive types (str, int, float, bool) or + pydantic.BaseModels for arguments. + llm: Language model to use, assumed to support the OpenAI function-calling API. + prompt: BasePromptTemplate to pass to the model. + enforce_single_function_usage: only used if a single function is passed in. If + True, then the model will be forced to use the given function. If False, + then the model will be given the option to use the given function or not. + output_parser: BaseLLMOutputParser to use for parsing model outputs. By default + will be inferred from the function types. If pydantic.BaseModels are passed + in, then the OutputParser will try to parse outputs using those. Otherwise + model outputs will simply be parsed as JSON. If multiple functions are + passed in and they are not pydantic.BaseModels, the chain output will + include both the name of the function that was returned and the arguments + to pass to the function. + + Returns: + A runnable sequence that will pass in the given functions to the model when run. + + Example: + .. code-block:: python + + from typing import Optional + + from langchain.chains.structured_output import create_openai_fn_runnable + from langchain_openai import ChatOpenAI + from langchain_core.pydantic_v1 import BaseModel, Field + + + class RecordPerson(BaseModel): + '''Record some identifying information about a person.''' + + name: str = Field(..., description="The person's name") + age: int = Field(..., description="The person's age") + fav_food: Optional[str] = Field(None, description="The person's favorite food") + + + class RecordDog(BaseModel): + '''Record some identifying information about a dog.''' + + name: str = Field(..., description="The dog's name") + color: str = Field(..., description="The dog's color") + fav_food: Optional[str] = Field(None, description="The dog's favorite food") + + + llm = ChatOpenAI(model="gpt-4", temperature=0) + structured_llm = create_openai_fn_runnable([RecordPerson, RecordDog], llm) + structured_llm.invoke("Harry was a chubby brown beagle who loved chicken) + # -> RecordDog(name="Harry", color="brown", fav_food="chicken") + """ # noqa: E501 + if not functions: + raise ValueError("Need to pass in at least one function. Received zero.") + openai_functions = [convert_to_openai_function(f) for f in functions] + llm_kwargs: Dict[str, Any] = {"functions": openai_functions, **kwargs} + if len(openai_functions) == 1 and enforce_single_function_usage: + llm_kwargs["function_call"] = {"name": openai_functions[0]["name"]} + output_parser = output_parser or get_openai_output_parser(functions) + if prompt: + return prompt | llm.bind(**llm_kwargs) | output_parser + else: + return llm.bind(**llm_kwargs) | output_parser + + +# TODO: implement mode='openai-tools'. +def create_structured_output_runnable( + output_schema: Union[Dict[str, Any], Type[BaseModel]], + llm: Runnable, + prompt: Optional[BasePromptTemplate] = None, + *, + output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]] = None, + mode: Literal["openai-functions", "openai-json"] = "openai-functions", + enforce_single_function_usage: bool = True, + **kwargs: Any, +) -> Runnable: + """Create a runnable for extracting structured outputs. + + Args: + output_schema: Either a dictionary or pydantic.BaseModel class. If a dictionary + is passed in, it's assumed to already be a valid JsonSchema. + For best results, pydantic.BaseModels should have docstrings describing what + the schema represents and descriptions for the parameters. + llm: Language model to use. Assumed to support the OpenAI function-calling API + if mode is 'openai-function'. Assumed to support OpenAI response_format + parameter if mode is 'openai-json'. + prompt: BasePromptTemplate to pass to the model. If mode is 'openai-json' and + prompt has input variable 'output_schema' then the given output_schema + will be converted to a JsonSchema and inserted in the prompt. + output_parser: Output parser to use for parsing model outputs. By default + will be inferred from the function types. If pydantic.BaseModel is passed + in, then the OutputParser will try to parse outputs using the pydantic + class. Otherwise model outputs will be parsed as JSON. + mode: How structured outputs are extracted from the model. If 'openai-functions' + then OpenAI function calling is used. If 'openai-json' then OpenAI model + with response_format set to JSON is used. + enforce_single_function_usage: Only used if mode is 'openai-functions'. Only + used if a single function is passed in. If + True, then the model will be forced to use the given function. If False, + then the model will be given the option to use the given function or not. + **kwargs: Additional named arguments. + + Returns: + A runnable sequence that will return a structured output matching the given + output_schema. + + OpenAI functions example: + .. code-block:: python + + from typing import Optional + + from langchain.chains import create_structured_output_runnable + from langchain_openai import ChatOpenAI + from langchain_core.pydantic_v1 import BaseModel, Field + + class Dog(BaseModel): + '''Identifying information about a dog.''' + + name: str = Field(..., description="The dog's name") + color: str = Field(..., description="The dog's color") + fav_food: Optional[str] = Field(None, description="The dog's favorite food") + + llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0) + structured_llm = create_structured_output_runnable(Dog, llm, mode="openai-functions") + structured_llm.invoke("Harry was a chubby brown beagle who loved chicken") + # -> Dog(name="Harry", color="brown", fav_food="chicken") + + OpenAI functions with prompt example: + .. code-block:: python + + from typing import Optional + + from langchain.chains import create_structured_output_runnable + from langchain_openai import ChatOpenAI + from langchain_core.prompts import ChatPromptTemplate + from langchain_core.pydantic_v1 import BaseModel, Field + + class Dog(BaseModel): + '''Identifying information about a dog.''' + + name: str = Field(..., description="The dog's name") + color: str = Field(..., description="The dog's color") + fav_food: Optional[str] = Field(None, description="The dog's favorite food") + + llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0) + structured_llm = create_structured_output_runnable(Dog, llm, mode="openai-functions") + system = '''Extract information about any dogs mentioned in the user input.''' + prompt = ChatPromptTemplate.from_messages( + [("system", system), ("human", "{input}"),] + ) + chain = prompt | structured_llm + chain.invoke({"input": "Harry was a chubby brown beagle who loved chicken"}) + # -> Dog(name="Harry", color="brown", fav_food="chicken") + OpenAI json response format example: + .. code-block:: python + + from typing import Optional + + from langchain.chains import create_structured_output_runnable + from langchain_openai import ChatOpenAI + from langchain_core.prompts import ChatPromptTemplate + from langchain_core.pydantic_v1 import BaseModel, Field + + class Dog(BaseModel): + '''Identifying information about a dog.''' + + name: str = Field(..., description="The dog's name") + color: str = Field(..., description="The dog's color") + fav_food: Optional[str] = Field(None, description="The dog's favorite food") + + llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0) + structured_llm = create_structured_output_runnable(Dog, llm, mode="openai-json") + system = '''You are a world class assistant for extracting information in structured JSON formats. \ + + Extract a valid JSON blob from the user input that matches the following JSON Schema: + + {output_schema}''' + prompt = ChatPromptTemplate.from_messages( + [("system", system), ("human", "{input}"),] + ) + chain = prompt | structured_llm + chain.invoke({"input": "Harry was a chubby brown beagle who loved chicken"}) + """ # noqa: E501 + if mode == "openai-functions": + return _create_openai_functions_structured_output_runnable( + output_schema, + llm, + prompt=prompt, + output_parser=output_parser, + enforce_single_function_usage=enforce_single_function_usage, + **kwargs, + ) + elif mode == "openai-json": + return _create_openai_json_runnable( + output_schema, llm, prompt=prompt, output_parser=output_parser, **kwargs + ) + else: + raise ValueError( + f"Invalid mode {mode}. Expected one of 'openai-functions', " + f"'openai-json'." + ) + + +def get_openai_output_parser( + functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable]], +) -> Union[BaseOutputParser, BaseGenerationOutputParser]: + """Get the appropriate function output parser given the user functions. + + Args: + functions: Sequence where element is a dictionary, a pydantic.BaseModel class, + or a Python function. If a dictionary is passed in, it is assumed to + already be a valid OpenAI function. + + Returns: + A PydanticOutputFunctionsParser if functions are Pydantic classes, otherwise + a JsonOutputFunctionsParser. If there's only one function and it is + not a Pydantic class, then the output parser will automatically extract + only the function arguments and not the function name. + """ + function_names = [convert_to_openai_function(f)["name"] for f in functions] + if isinstance(functions[0], type) and issubclass(functions[0], BaseModel): + if len(functions) > 1: + pydantic_schema: Union[Dict, Type[BaseModel]] = { + name: fn for name, fn in zip(function_names, functions) + } + else: + pydantic_schema = functions[0] + output_parser: Union[ + BaseOutputParser, BaseGenerationOutputParser + ] = PydanticOutputFunctionsParser(pydantic_schema=pydantic_schema) + else: + output_parser = JsonOutputFunctionsParser(args_only=len(functions) <= 1) + return output_parser + + +def _create_openai_json_runnable( + output_schema: Union[Dict[str, Any], Type[BaseModel]], + llm: Runnable, + prompt: Optional[BasePromptTemplate] = None, + *, + output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]] = None, +) -> Runnable: + """""" + if isinstance(output_schema, type) and issubclass(output_schema, BaseModel): + output_parser = output_parser or PydanticOutputParser( + pydantic_object=output_schema, + ) + schema_as_dict = convert_to_openai_function(output_schema)["parameters"] + else: + output_parser = output_parser or JsonOutputParser() + schema_as_dict = output_schema + + llm = llm.bind(response_format={"type": "json_object"}) + if prompt: + if "output_schema" in prompt.input_variables: + prompt = prompt.partial(output_schema=json.dumps(schema_as_dict, indent=2)) + + return prompt | llm | output_parser + else: + return llm | output_parser + + +def _create_openai_functions_structured_output_runnable( + output_schema: Union[Dict[str, Any], Type[BaseModel]], + llm: Runnable, + prompt: Optional[BasePromptTemplate] = None, + *, + output_parser: Optional[Union[BaseOutputParser, BaseGenerationOutputParser]] = None, + **kwargs: Any, +) -> Runnable: + if isinstance(output_schema, dict): + function: Any = { + "name": "output_formatter", + "description": ( + "Output formatter. Should always be used to format your response to the" + " user." + ), + "parameters": output_schema, + } + else: + + class _OutputFormatter(BaseModel): + """Output formatter. Should always be used to format your response to the user.""" # noqa: E501 + + output: output_schema # type: ignore + + function = _OutputFormatter + output_parser = output_parser or PydanticAttrOutputFunctionsParser( + pydantic_schema=_OutputFormatter, attr_name="output" + ) + return create_openai_fn_runnable( + [function], + llm, + prompt=prompt, + output_parser=output_parser, + **kwargs, + ) diff --git a/libs/langchain/langchain/chains/summarize/__init__.py b/libs/langchain/langchain/chains/summarize/__init__.py index 9bc8b8118bd..3f6068a0f26 100644 --- a/libs/langchain/langchain/chains/summarize/__init__.py +++ b/libs/langchain/langchain/chains/summarize/__init__.py @@ -52,6 +52,8 @@ def _load_map_reduce_chain( verbose: Optional[bool] = None, token_max: int = 3000, callbacks: Callbacks = None, + *, + collapse_max_retries: Optional[int] = None, **kwargs: Any, ) -> MapReduceDocumentsChain: map_chain = LLMChain( @@ -92,6 +94,7 @@ def _load_map_reduce_chain( token_max=token_max, verbose=verbose, callbacks=callbacks, + collapse_max_retries=collapse_max_retries, ) return MapReduceDocumentsChain( llm_chain=map_chain, diff --git a/libs/langchain/langchain/embeddings/cache.py b/libs/langchain/langchain/embeddings/cache.py index 382827991ff..51d3969e539 100644 --- a/libs/langchain/langchain/embeddings/cache.py +++ b/libs/langchain/langchain/embeddings/cache.py @@ -128,6 +128,41 @@ class CacheBackedEmbeddings(Embeddings): List[List[float]], vectors ) # Nones should have been resolved by now + async def aembed_documents(self, texts: List[str]) -> List[List[float]]: + """Embed a list of texts. + + The method first checks the cache for the embeddings. + If the embeddings are not found, the method uses the underlying embedder + to embed the documents and stores the results in the cache. + + Args: + texts: A list of texts to embed. + + Returns: + A list of embeddings for the given texts. + """ + vectors: List[ + Union[List[float], None] + ] = await self.document_embedding_store.amget(texts) + missing_indices: List[int] = [ + i for i, vector in enumerate(vectors) if vector is None + ] + missing_texts = [texts[i] for i in missing_indices] + + if missing_texts: + missing_vectors = await self.underlying_embeddings.aembed_documents( + missing_texts + ) + await self.document_embedding_store.amset( + list(zip(missing_texts, missing_vectors)) + ) + for index, updated_vector in zip(missing_indices, missing_vectors): + vectors[index] = updated_vector + + return cast( + List[List[float]], vectors + ) # Nones should have been resolved by now + def embed_query(self, text: str) -> List[float]: """Embed query text. @@ -148,6 +183,26 @@ class CacheBackedEmbeddings(Embeddings): """ return self.underlying_embeddings.embed_query(text) + async def aembed_query(self, text: str) -> List[float]: + """Embed query text. + + This method does not support caching at the moment. + + Support for caching queries is easily to implement, but might make + sense to hold off to see the most common patterns. + + If the cache has an eviction policy, we may need to be a bit more careful + about sharing the cache between documents and queries. Generally, + one is OK evicting query caches, but document caches should be kept. + + Args: + text: The text to embed. + + Returns: + The embedding for the given text. + """ + return await self.underlying_embeddings.aembed_query(text) + @classmethod def from_bytes_store( cls, diff --git a/libs/langchain/langchain/evaluation/agents/trajectory_eval_chain.py b/libs/langchain/langchain/evaluation/agents/trajectory_eval_chain.py index 532d67b0a71..89b389340ea 100644 --- a/libs/langchain/langchain/evaluation/agents/trajectory_eval_chain.py +++ b/libs/langchain/langchain/evaluation/agents/trajectory_eval_chain.py @@ -139,7 +139,7 @@ class TrajectoryEvalChain(AgentTrajectoryEvaluator, LLMEvalChain): prediction=response["output"], reference="Paris", ) - print(result["score"]) + print(result["score"]) # noqa: T201 # 0 """ # noqa: E501 diff --git a/libs/langchain/langchain/indexes/_sql_record_manager.py b/libs/langchain/langchain/indexes/_sql_record_manager.py index 14e2355af22..76dfd967234 100644 --- a/libs/langchain/langchain/indexes/_sql_record_manager.py +++ b/libs/langchain/langchain/indexes/_sql_record_manager.py @@ -39,7 +39,7 @@ from sqlalchemy.ext.asyncio import ( create_async_engine, ) from sqlalchemy.ext.declarative import declarative_base -from sqlalchemy.orm import Session, sessionmaker +from sqlalchemy.orm import Query, Session, sessionmaker from langchain.indexes.base import RecordManager @@ -284,31 +284,35 @@ class SQLRecordManager(RecordManager): with self._make_session() as session: if self.dialect == "sqlite": + from sqlalchemy.dialects.sqlite import Insert as SqliteInsertType from sqlalchemy.dialects.sqlite import insert as sqlite_insert # Note: uses SQLite insert to make on_conflict_do_update work. # This code needs to be generalized a bit to work with more dialects. - insert_stmt = sqlite_insert(UpsertionRecord).values(records_to_upsert) - stmt = insert_stmt.on_conflict_do_update( # type: ignore[attr-defined] + sqlite_insert_stmt: SqliteInsertType = sqlite_insert( + UpsertionRecord + ).values(records_to_upsert) + stmt = sqlite_insert_stmt.on_conflict_do_update( [UpsertionRecord.key, UpsertionRecord.namespace], set_=dict( - # attr-defined type ignore - updated_at=insert_stmt.excluded.updated_at, # type: ignore - group_id=insert_stmt.excluded.group_id, # type: ignore + updated_at=sqlite_insert_stmt.excluded.updated_at, + group_id=sqlite_insert_stmt.excluded.group_id, ), ) elif self.dialect == "postgresql": + from sqlalchemy.dialects.postgresql import Insert as PgInsertType from sqlalchemy.dialects.postgresql import insert as pg_insert - # Note: uses SQLite insert to make on_conflict_do_update work. + # Note: uses postgresql insert to make on_conflict_do_update work. # This code needs to be generalized a bit to work with more dialects. - insert_stmt = pg_insert(UpsertionRecord).values(records_to_upsert) - stmt = insert_stmt.on_conflict_do_update( # type: ignore[attr-defined] + pg_insert_stmt: PgInsertType = pg_insert(UpsertionRecord).values( + records_to_upsert + ) + stmt = pg_insert_stmt.on_conflict_do_update( "uix_key_namespace", # Name of constraint set_=dict( - # attr-defined type ignore - updated_at=insert_stmt.excluded.updated_at, # type: ignore - group_id=insert_stmt.excluded.group_id, # type: ignore + updated_at=pg_insert_stmt.excluded.updated_at, + group_id=pg_insert_stmt.excluded.group_id, ), ) else: @@ -359,31 +363,35 @@ class SQLRecordManager(RecordManager): async with self._amake_session() as session: if self.dialect == "sqlite": + from sqlalchemy.dialects.sqlite import Insert as SqliteInsertType from sqlalchemy.dialects.sqlite import insert as sqlite_insert # Note: uses SQLite insert to make on_conflict_do_update work. # This code needs to be generalized a bit to work with more dialects. - insert_stmt = sqlite_insert(UpsertionRecord).values(records_to_upsert) - stmt = insert_stmt.on_conflict_do_update( # type: ignore[attr-defined] + sqlite_insert_stmt: SqliteInsertType = sqlite_insert( + UpsertionRecord + ).values(records_to_upsert) + stmt = sqlite_insert_stmt.on_conflict_do_update( [UpsertionRecord.key, UpsertionRecord.namespace], set_=dict( - # attr-defined type ignore - updated_at=insert_stmt.excluded.updated_at, # type: ignore - group_id=insert_stmt.excluded.group_id, # type: ignore + updated_at=sqlite_insert_stmt.excluded.updated_at, + group_id=sqlite_insert_stmt.excluded.group_id, ), ) elif self.dialect == "postgresql": + from sqlalchemy.dialects.postgresql import Insert as PgInsertType from sqlalchemy.dialects.postgresql import insert as pg_insert # Note: uses SQLite insert to make on_conflict_do_update work. # This code needs to be generalized a bit to work with more dialects. - insert_stmt = pg_insert(UpsertionRecord).values(records_to_upsert) - stmt = insert_stmt.on_conflict_do_update( # type: ignore[attr-defined] + pg_insert_stmt: PgInsertType = pg_insert(UpsertionRecord).values( + records_to_upsert + ) + stmt = pg_insert_stmt.on_conflict_do_update( "uix_key_namespace", # Name of constraint set_=dict( - # attr-defined type ignore - updated_at=insert_stmt.excluded.updated_at, # type: ignore - group_id=insert_stmt.excluded.group_id, # type: ignore + updated_at=pg_insert_stmt.excluded.updated_at, + group_id=pg_insert_stmt.excluded.group_id, ), ) else: @@ -394,18 +402,15 @@ class SQLRecordManager(RecordManager): def exists(self, keys: Sequence[str]) -> List[bool]: """Check if the given keys exist in the SQLite database.""" + session: Session with self._make_session() as session: - records = ( - # mypy does not recognize .all() - session.query(UpsertionRecord.key) # type: ignore[attr-defined] - .filter( - and_( - UpsertionRecord.key.in_(keys), - UpsertionRecord.namespace == self.namespace, - ) + filtered_query: Query = session.query(UpsertionRecord.key).filter( + and_( + UpsertionRecord.key.in_(keys), + UpsertionRecord.namespace == self.namespace, ) - .all() ) + records = filtered_query.all() found_keys = set(r.key for r in records) return [k in found_keys for k in keys] @@ -438,28 +443,22 @@ class SQLRecordManager(RecordManager): limit: Optional[int] = None, ) -> List[str]: """List records in the SQLite database based on the provided date range.""" + session: Session with self._make_session() as session: - query = session.query(UpsertionRecord).filter( + query: Query = session.query(UpsertionRecord).filter( UpsertionRecord.namespace == self.namespace ) - # mypy does not recognize .all() or .filter() if after: - query = query.filter( # type: ignore[attr-defined] - UpsertionRecord.updated_at > after - ) + query = query.filter(UpsertionRecord.updated_at > after) if before: - query = query.filter( # type: ignore[attr-defined] - UpsertionRecord.updated_at < before - ) + query = query.filter(UpsertionRecord.updated_at < before) if group_ids: - query = query.filter( # type: ignore[attr-defined] - UpsertionRecord.group_id.in_(group_ids) - ) + query = query.filter(UpsertionRecord.group_id.in_(group_ids)) if limit: - query = query.limit(limit) # type: ignore[attr-defined] - records = query.all() # type: ignore[attr-defined] + query = query.limit(limit) + records = query.all() return [r.key for r in records] async def alist_keys( @@ -471,40 +470,37 @@ class SQLRecordManager(RecordManager): limit: Optional[int] = None, ) -> List[str]: """List records in the SQLite database based on the provided date range.""" + session: AsyncSession async with self._amake_session() as session: - query = select(UpsertionRecord.key).filter( + query: Query = select(UpsertionRecord.key).filter( UpsertionRecord.namespace == self.namespace ) # mypy does not recognize .all() or .filter() if after: - query = query.filter( # type: ignore[attr-defined] - UpsertionRecord.updated_at > after - ) + query = query.filter(UpsertionRecord.updated_at > after) if before: - query = query.filter( # type: ignore[attr-defined] - UpsertionRecord.updated_at < before - ) + query = query.filter(UpsertionRecord.updated_at < before) if group_ids: - query = query.filter( # type: ignore[attr-defined] - UpsertionRecord.group_id.in_(group_ids) - ) + query = query.filter(UpsertionRecord.group_id.in_(group_ids)) if limit: - query = query.limit(limit) # type: ignore[attr-defined] + query = query.limit(limit) records = (await session.execute(query)).scalars().all() return list(records) def delete_keys(self, keys: Sequence[str]) -> None: """Delete records from the SQLite database.""" + session: Session with self._make_session() as session: - # mypy does not recognize .delete() - session.query(UpsertionRecord).filter( + filtered_query: Query = session.query(UpsertionRecord).filter( and_( UpsertionRecord.key.in_(keys), UpsertionRecord.namespace == self.namespace, ) - ).delete() # type: ignore[attr-defined] + ) + + filtered_query.delete() session.commit() async def adelete_keys(self, keys: Sequence[str]) -> None: diff --git a/libs/langchain/langchain/memory/buffer.py b/libs/langchain/langchain/memory/buffer.py index c8b819fb754..3cc9b162dab 100644 --- a/libs/langchain/langchain/memory/buffer.py +++ b/libs/langchain/langchain/memory/buffer.py @@ -19,20 +19,40 @@ class ConversationBufferMemory(BaseChatMemory): """String buffer of memory.""" return self.buffer_as_messages if self.return_messages else self.buffer_as_str - @property - def buffer_as_str(self) -> str: - """Exposes the buffer as a string in case return_messages is True.""" + async def abuffer(self) -> Any: + """String buffer of memory.""" + return ( + await self.abuffer_as_messages() + if self.return_messages + else await self.abuffer_as_str() + ) + + def _buffer_as_str(self, messages: List[BaseMessage]) -> str: return get_buffer_string( - self.chat_memory.messages, + messages, human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) + @property + def buffer_as_str(self) -> str: + """Exposes the buffer as a string in case return_messages is True.""" + return self._buffer_as_str(self.chat_memory.messages) + + async def abuffer_as_str(self) -> str: + """Exposes the buffer as a string in case return_messages is True.""" + messages = await self.chat_memory.aget_messages() + return self._buffer_as_str(messages) + @property def buffer_as_messages(self) -> List[BaseMessage]: """Exposes the buffer as a list of messages in case return_messages is False.""" return self.chat_memory.messages + async def abuffer_as_messages(self) -> List[BaseMessage]: + """Exposes the buffer as a list of messages in case return_messages is False.""" + return await self.chat_memory.aget_messages() + @property def memory_variables(self) -> List[str]: """Will always return list of memory variables. @@ -45,6 +65,11 @@ class ConversationBufferMemory(BaseChatMemory): """Return history buffer.""" return {self.memory_key: self.buffer} + async def aload_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: + """Return key-value pairs given the text input to the chain.""" + buffer = await self.abuffer() + return {self.memory_key: buffer} + class ConversationStringBufferMemory(BaseMemory): """Buffer for storing conversation memory.""" @@ -77,6 +102,10 @@ class ConversationStringBufferMemory(BaseMemory): """Return history buffer.""" return {self.memory_key: self.buffer} + async def aload_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]: + """Return history buffer.""" + return self.load_memory_variables(inputs) + def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """Save context from this conversation to buffer.""" if self.input_key is None: @@ -93,6 +122,15 @@ class ConversationStringBufferMemory(BaseMemory): ai = f"{self.ai_prefix}: " + outputs[output_key] self.buffer += "\n" + "\n".join([human, ai]) + async def asave_context( + self, inputs: Dict[str, Any], outputs: Dict[str, str] + ) -> None: + """Save context from this conversation to buffer.""" + return self.save_context(inputs, outputs) + def clear(self) -> None: """Clear memory contents.""" self.buffer = "" + + async def aclear(self) -> None: + self.clear() diff --git a/libs/langchain/langchain/memory/chat_memory.py b/libs/langchain/langchain/memory/chat_memory.py index 7808264209b..ad030c3f71a 100644 --- a/libs/langchain/langchain/memory/chat_memory.py +++ b/libs/langchain/langchain/memory/chat_memory.py @@ -4,6 +4,7 @@ from typing import Any, Dict, Optional, Tuple from langchain_community.chat_message_histories.in_memory import ChatMessageHistory from langchain_core.chat_history import BaseChatMessageHistory from langchain_core.memory import BaseMemory +from langchain_core.messages import AIMessage, HumanMessage from langchain_core.pydantic_v1 import Field from langchain.memory.utils import get_prompt_input_key @@ -35,9 +36,23 @@ class BaseChatMemory(BaseMemory, ABC): def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """Save context from this conversation to buffer.""" input_str, output_str = self._get_input_output(inputs, outputs) - self.chat_memory.add_user_message(input_str) - self.chat_memory.add_ai_message(output_str) + self.chat_memory.add_messages( + [HumanMessage(content=input_str), AIMessage(content=output_str)] + ) + + async def asave_context( + self, inputs: Dict[str, Any], outputs: Dict[str, str] + ) -> None: + """Save context from this conversation to buffer.""" + input_str, output_str = self._get_input_output(inputs, outputs) + await self.chat_memory.aadd_messages( + [HumanMessage(content=input_str), AIMessage(content=output_str)] + ) def clear(self) -> None: """Clear memory contents.""" self.chat_memory.clear() + + async def aclear(self) -> None: + """Clear memory contents.""" + await self.chat_memory.aclear() diff --git a/libs/langchain/langchain/model_laboratory.py b/libs/langchain/langchain/model_laboratory.py index c012423e378..114994535d8 100644 --- a/libs/langchain/langchain/model_laboratory.py +++ b/libs/langchain/langchain/model_laboratory.py @@ -1,4 +1,5 @@ """Experiment with different models.""" + from __future__ import annotations from typing import List, Optional, Sequence @@ -72,7 +73,7 @@ class ModelLaboratory: Args: text: input text to run all models on. """ - print(f"\033[1mInput:\033[0m\n{text}\n") + print(f"\033[1mInput:\033[0m\n{text}\n") # noqa: T201 for i, chain in enumerate(self.chains): if self.names is not None: name = self.names[i] diff --git a/libs/langchain/langchain/output_parsers/openai_tools.py b/libs/langchain/langchain/output_parsers/openai_tools.py index 39fd05974a9..045e32686ef 100644 --- a/libs/langchain/langchain/output_parsers/openai_tools.py +++ b/libs/langchain/langchain/output_parsers/openai_tools.py @@ -4,9 +4,8 @@ from json import JSONDecodeError from typing import Any, List, Type from langchain_core.exceptions import OutputParserException -from langchain_core.output_parsers import ( - BaseGenerationOutputParser, -) +from langchain_core.output_parsers import BaseGenerationOutputParser +from langchain_core.output_parsers.json import parse_partial_json from langchain_core.outputs import ChatGeneration, Generation from langchain_core.pydantic_v1 import BaseModel @@ -42,9 +41,14 @@ class JsonOutputToolsParser(BaseGenerationOutputParser[Any]): if "function" not in tool_call: continue try: - function_args = json.loads( - tool_call["function"]["arguments"], strict=self.strict - ) + if partial: + function_args = parse_partial_json( + tool_call["function"]["arguments"], strict=self.strict + ) + else: + function_args = json.loads( + tool_call["function"]["arguments"], strict=self.strict + ) except JSONDecodeError as e: exceptions.append( f"Function {tool_call['function']['name']} arguments:\n\n" @@ -77,7 +81,7 @@ class JsonOutputKeyToolsParser(JsonOutputToolsParser): super().__init__(key_name=key_name, **kwargs) def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any: - results = super().parse_result(result) + results = super().parse_result(result, partial=partial) results = [res for res in results if res["type"] == self.key_name] if not self.return_id: results = [res["args"] for res in results] @@ -92,6 +96,6 @@ class PydanticToolsParser(JsonOutputToolsParser): tools: List[Type[BaseModel]] def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any: - results = super().parse_result(result) + results = super().parse_result(result, partial=partial) name_dict = {tool.__name__: tool for tool in self.tools} return [name_dict[res["type"]](**res["args"]) for res in results] diff --git a/libs/langchain/langchain/output_parsers/pydantic.py b/libs/langchain/langchain/output_parsers/pydantic.py index 00424018531..9e415650425 100644 --- a/libs/langchain/langchain/output_parsers/pydantic.py +++ b/libs/langchain/langchain/output_parsers/pydantic.py @@ -1,45 +1,36 @@ import json -import re -from typing import Type, TypeVar +from typing import Any, List, Type from langchain_core.exceptions import OutputParserException -from langchain_core.output_parsers import BaseOutputParser +from langchain_core.output_parsers import JsonOutputParser +from langchain_core.outputs import Generation from langchain_core.pydantic_v1 import BaseModel, ValidationError from langchain.output_parsers.format_instructions import PYDANTIC_FORMAT_INSTRUCTIONS -T = TypeVar("T", bound=BaseModel) - -class PydanticOutputParser(BaseOutputParser[T]): +class PydanticOutputParser(JsonOutputParser): """Parse an output using a pydantic model.""" - pydantic_object: Type[T] + pydantic_object: Type[BaseModel] """The pydantic model to parse. Attention: To avoid potential compatibility issues, it's recommended to use pydantic <2 or leverage the v1 namespace in pydantic >= 2. """ - def parse(self, text: str) -> T: + def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any: + json_object = super().parse_result(result) try: - # Greedy search for 1st json candidate. - match = re.search( - r"\{.*\}", text.strip(), re.MULTILINE | re.IGNORECASE | re.DOTALL - ) - json_str = "" - if match: - json_str = match.group() - json_object = json.loads(json_str, strict=False) return self.pydantic_object.parse_obj(json_object) - - except (json.JSONDecodeError, ValidationError) as e: + except ValidationError as e: name = self.pydantic_object.__name__ - msg = f"Failed to parse {name} from completion {text}. Got: {e}" - raise OutputParserException(msg, llm_output=text) + msg = f"Failed to parse {name} from completion {json_object}. Got: {e}" + raise OutputParserException(msg, llm_output=json_object) def get_format_instructions(self) -> str: - schema = self.pydantic_object.schema() + # Copy schema to avoid altering original Pydantic schema. + schema = {k: v for k, v in self.pydantic_object.schema().items()} # Remove extraneous fields. reduced_schema = schema @@ -57,6 +48,6 @@ class PydanticOutputParser(BaseOutputParser[T]): return "pydantic" @property - def OutputType(self) -> Type[T]: + def OutputType(self) -> Type[BaseModel]: """Return the pydantic model.""" return self.pydantic_object diff --git a/libs/langchain/langchain/output_parsers/structured.py b/libs/langchain/langchain/output_parsers/structured.py index 1d99363bc17..62847e1dd10 100644 --- a/libs/langchain/langchain/output_parsers/structured.py +++ b/libs/langchain/langchain/output_parsers/structured.py @@ -67,7 +67,7 @@ class StructuredOutputParser(BaseOutputParser): parser = StructuredOutputParser.from_response_schemas(response_schemas) - print(parser.get_format_instructions()) + print(parser.get_format_instructions()) # noqa: T201 output: # The output should be a Markdown code snippet formatted in the following diff --git a/libs/langchain/langchain/output_parsers/yaml.py b/libs/langchain/langchain/output_parsers/yaml.py index 528fc93f507..21bcf359a2c 100644 --- a/libs/langchain/langchain/output_parsers/yaml.py +++ b/libs/langchain/langchain/output_parsers/yaml.py @@ -43,7 +43,8 @@ class YamlOutputParser(BaseOutputParser[T]): raise OutputParserException(msg, llm_output=text) from e def get_format_instructions(self) -> str: - schema = self.pydantic_object.schema() + # Copy schema to avoid altering original Pydantic schema. + schema = {k: v for k, v in self.pydantic_object.schema().items()} # Remove extraneous fields. reduced_schema = schema diff --git a/libs/langchain/langchain/retrievers/document_compressors/__init__.py b/libs/langchain/langchain/retrievers/document_compressors/__init__.py index 410ad540d19..a4c1456cf24 100644 --- a/libs/langchain/langchain/retrievers/document_compressors/__init__.py +++ b/libs/langchain/langchain/retrievers/document_compressors/__init__.py @@ -9,6 +9,7 @@ from langchain.retrievers.document_compressors.cohere_rerank import CohereRerank from langchain.retrievers.document_compressors.embeddings_filter import ( EmbeddingsFilter, ) +from langchain.retrievers.document_compressors.flashrank_rerank import FlashrankRerank __all__ = [ "DocumentCompressorPipeline", @@ -16,4 +17,5 @@ __all__ = [ "LLMChainExtractor", "LLMChainFilter", "CohereRerank", + "FlashrankRerank", ] diff --git a/libs/langchain/langchain/retrievers/document_compressors/chain_extract.py b/libs/langchain/langchain/retrievers/document_compressors/chain_extract.py index 0a04eb5322d..af8319f1ffc 100644 --- a/libs/langchain/langchain/retrievers/document_compressors/chain_extract.py +++ b/libs/langchain/langchain/retrievers/document_compressors/chain_extract.py @@ -2,7 +2,7 @@ from __future__ import annotations import asyncio -from typing import Any, Callable, Dict, Optional, Sequence +from typing import Any, Callable, Dict, Optional, Sequence, cast from langchain_core.documents import Document from langchain_core.language_models import BaseLanguageModel @@ -67,7 +67,9 @@ class LLMChainExtractor(BaseDocumentCompressor): output = self.llm_chain.predict_and_parse(**_input, callbacks=callbacks) if len(output) == 0: continue - compressed_docs.append(Document(page_content=output, metadata=doc.metadata)) + compressed_docs.append( + Document(page_content=cast(str, output), metadata=doc.metadata) + ) return compressed_docs async def acompress_documents( diff --git a/libs/langchain/langchain/retrievers/document_compressors/cohere_rerank.py b/libs/langchain/langchain/retrievers/document_compressors/cohere_rerank.py index c767f86bde6..3374f7906b9 100644 --- a/libs/langchain/langchain/retrievers/document_compressors/cohere_rerank.py +++ b/libs/langchain/langchain/retrievers/document_compressors/cohere_rerank.py @@ -1,6 +1,7 @@ from __future__ import annotations -from typing import TYPE_CHECKING, Dict, Optional, Sequence +from copy import deepcopy +from typing import Any, Dict, List, Optional, Sequence, Union from langchain_core.documents import Document from langchain_core.pydantic_v1 import Extra, root_validator @@ -9,28 +10,19 @@ from langchain.callbacks.manager import Callbacks from langchain.retrievers.document_compressors.base import BaseDocumentCompressor from langchain.utils import get_from_dict_or_env -if TYPE_CHECKING: - from cohere import Client -else: - # We do to avoid pydantic annotation issues when actually instantiating - # while keeping this import optional - try: - from cohere import Client - except ImportError: - pass - class CohereRerank(BaseDocumentCompressor): """Document compressor that uses `Cohere Rerank API`.""" - client: Client + client: Any = None """Cohere client to use for compressing documents.""" - top_n: int = 3 + top_n: Optional[int] = 3 """Number of documents to return.""" model: str = "rerank-english-v2.0" """Model to use for reranking.""" - cohere_api_key: Optional[str] = None + """Cohere API key. Must be specified directly or via environment variable + COHERE_API_KEY.""" user_agent: str = "langchain" """Identifier for the application making the request.""" @@ -43,20 +35,57 @@ class CohereRerank(BaseDocumentCompressor): @root_validator(pre=True) def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" - try: - import cohere - except ImportError: - raise ImportError( - "Could not import cohere python package. " - "Please install it with `pip install cohere`." + if not values.get("client"): + try: + import cohere + except ImportError: + raise ImportError( + "Could not import cohere python package. " + "Please install it with `pip install cohere`." + ) + cohere_api_key = get_from_dict_or_env( + values, "cohere_api_key", "COHERE_API_KEY" ) - cohere_api_key = get_from_dict_or_env( - values, "cohere_api_key", "COHERE_API_KEY" - ) - client_name = values.get("user_agent", "langchain") - values["client"] = cohere.Client(cohere_api_key, client_name=client_name) + client_name = values.get("user_agent", "langchain") + values["client"] = cohere.Client(cohere_api_key, client_name=client_name) return values + def rerank( + self, + documents: Sequence[Union[str, Document, dict]], + query: str, + *, + model: Optional[str] = None, + top_n: Optional[int] = -1, + max_chunks_per_doc: Optional[int] = None, + ) -> List[Dict[str, Any]]: + """Returns an ordered list of documents ordered by their relevance to the provided query. + + Args: + query: The query to use for reranking. + documents: A sequence of documents to rerank. + model: The model to use for re-ranking. Default to self.model. + top_n : The number of results to return. If None returns all results. + Defaults to self.top_n. + max_chunks_per_doc : The maximum number of chunks derived from a document. + """ # noqa: E501 + if len(documents) == 0: # to avoid empty api call + return [] + docs = [ + doc.page_content if isinstance(doc, Document) else doc for doc in documents + ] + model = model or self.model + top_n = top_n if (top_n is None or top_n > 0) else self.top_n + results = self.client.rerank( + query, docs, model, top_n=top_n, max_chunks_per_doc=max_chunks_per_doc + ) + result_dicts = [] + for res in results: + result_dicts.append( + {"index": res.index, "relevance_score": res.relevance_score} + ) + return result_dicts + def compress_documents( self, documents: Sequence[Document], @@ -74,16 +103,10 @@ class CohereRerank(BaseDocumentCompressor): Returns: A sequence of compressed documents. """ - if len(documents) == 0: # to avoid empty api call - return [] - doc_list = list(documents) - _docs = [d.page_content for d in doc_list] - results = self.client.rerank( - model=self.model, query=query, documents=_docs, top_n=self.top_n - ) - final_results = [] - for r in results: - doc = doc_list[r.index] - doc.metadata["relevance_score"] = r.relevance_score - final_results.append(doc) - return final_results + compressed = [] + for res in self.rerank(documents, query): + doc = documents[res["index"]] + doc_copy = Document(doc.page_content, metadata=deepcopy(doc.metadata)) + doc_copy.metadata["relevance_score"] = res["relevance_score"] + compressed.append(doc_copy) + return compressed diff --git a/libs/langchain/langchain/retrievers/document_compressors/flashrank_rerank.py b/libs/langchain/langchain/retrievers/document_compressors/flashrank_rerank.py new file mode 100644 index 00000000000..273aff3d44c --- /dev/null +++ b/libs/langchain/langchain/retrievers/document_compressors/flashrank_rerank.py @@ -0,0 +1,74 @@ +from __future__ import annotations + +from typing import TYPE_CHECKING, Dict, Optional, Sequence + +from langchain_core.documents import Document +from langchain_core.pydantic_v1 import Extra, root_validator + +from langchain.callbacks.manager import Callbacks +from langchain.retrievers.document_compressors.base import BaseDocumentCompressor + +if TYPE_CHECKING: + from flashrank import Ranker, RerankRequest +else: + # Avoid pydantic annotation issues when actually instantiating + # while keeping this import optional + try: + from flashrank import Ranker, RerankRequest + except ImportError: + pass + +DEFAULT_MODEL_NAME = "ms-marco-MultiBERT-L-12" + + +class FlashrankRerank(BaseDocumentCompressor): + """Document compressor using Flashrank interface.""" + + client: Ranker + """Flashrank client to use for compressing documents""" + top_n: int = 3 + """Number of documents to return.""" + model: Optional[str] = None + """Model to use for reranking.""" + + class Config: + """Configuration for this pydantic object.""" + + extra = Extra.forbid + arbitrary_types_allowed = True + + @root_validator(pre=True) + def validate_environment(cls, values: Dict) -> Dict: + """Validate that api key and python package exists in environment.""" + try: + from flashrank import Ranker + except ImportError: + raise ImportError( + "Could not import flashrank python package. " + "Please install it with `pip install flashrank`." + ) + + values["model"] = values.get("model", DEFAULT_MODEL_NAME) + values["client"] = Ranker(model_name=values["model"]) + return values + + def compress_documents( + self, + documents: Sequence[Document], + query: str, + callbacks: Optional[Callbacks] = None, + ) -> Sequence[Document]: + passages = [ + {"id": i, "text": doc.page_content} for i, doc in enumerate(documents) + ] + + rerank_request = RerankRequest(query=query, passages=passages) + rerank_response = self.client.rerank(rerank_request)[: self.top_n] + final_results = [] + for r in rerank_response: + doc = Document( + page_content=r["text"], + metadata={"id": r["id"], "relevance_score": r["score"]}, + ) + final_results.append(doc) + return final_results diff --git a/libs/langchain/langchain/retrievers/ensemble.py b/libs/langchain/langchain/retrievers/ensemble.py index 050cb88fc4c..34838f23fed 100644 --- a/libs/langchain/langchain/retrievers/ensemble.py +++ b/libs/langchain/langchain/retrievers/ensemble.py @@ -3,7 +3,7 @@ Ensemble retriever that ensemble the results of multiple retrievers by using weighted Reciprocal Rank Fusion """ import asyncio -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List, Optional, cast from langchain_core.callbacks import ( AsyncCallbackManagerForRetrieverRun, @@ -195,7 +195,7 @@ class EnsembleRetriever(BaseRetriever): # Enforce that retrieved docs are Documents for each list in retriever_docs for i in range(len(retriever_docs)): retriever_docs[i] = [ - Document(page_content=doc) if not isinstance(doc, Document) else doc + Document(page_content=cast(str, doc)) if isinstance(doc, str) else doc for doc in retriever_docs[i] ] diff --git a/libs/langchain/langchain/retrievers/multi_query.py b/libs/langchain/langchain/retrievers/multi_query.py index 7d60b221514..ca7e731c51a 100644 --- a/libs/langchain/langchain/retrievers/multi_query.py +++ b/libs/langchain/langchain/retrievers/multi_query.py @@ -1,39 +1,28 @@ import asyncio import logging -from typing import List, Sequence +from typing import List, Optional, Sequence from langchain_core.callbacks import ( AsyncCallbackManagerForRetrieverRun, CallbackManagerForRetrieverRun, ) from langchain_core.documents import Document -from langchain_core.language_models import BaseLLM +from langchain_core.language_models import BaseLanguageModel +from langchain_core.output_parsers import BaseOutputParser from langchain_core.prompts.prompt import PromptTemplate -from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.retrievers import BaseRetriever from langchain.chains.llm import LLMChain -from langchain.output_parsers.pydantic import PydanticOutputParser logger = logging.getLogger(__name__) -class LineList(BaseModel): - """List of lines.""" - - lines: List[str] = Field(description="Lines of text") - """List of lines.""" - - -class LineListOutputParser(PydanticOutputParser): +class LineListOutputParser(BaseOutputParser[List[str]]): """Output parser for a list of lines.""" - def __init__(self) -> None: - super().__init__(pydantic_object=LineList) - - def parse(self, text: str) -> LineList: + def parse(self, text: str) -> List[str]: lines = text.strip().split("\n") - return LineList(lines=lines) + return lines # Default prompt @@ -63,6 +52,7 @@ class MultiQueryRetriever(BaseRetriever): llm_chain: LLMChain verbose: bool = True parser_key: str = "lines" + """DEPRECATED. parser_key is no longer used and should not be specified.""" include_original: bool = False """Whether to include the original query in the list of generated queries.""" @@ -70,9 +60,9 @@ class MultiQueryRetriever(BaseRetriever): def from_llm( cls, retriever: BaseRetriever, - llm: BaseLLM, + llm: BaseLanguageModel, prompt: PromptTemplate = DEFAULT_QUERY_PROMPT, - parser_key: str = "lines", + parser_key: Optional[str] = None, include_original: bool = False, ) -> "MultiQueryRetriever": """Initialize from llm using default template. @@ -91,7 +81,6 @@ class MultiQueryRetriever(BaseRetriever): return cls( retriever=retriever, llm_chain=llm_chain, - parser_key=parser_key, include_original=include_original, ) @@ -129,7 +118,7 @@ class MultiQueryRetriever(BaseRetriever): response = await self.llm_chain.acall( inputs={"question": question}, callbacks=run_manager.get_child() ) - lines = getattr(response["text"], self.parser_key, []) + lines = response["text"] if self.verbose: logger.info(f"Generated queries: {lines}") return lines @@ -189,7 +178,7 @@ class MultiQueryRetriever(BaseRetriever): response = self.llm_chain( {"question": question}, callbacks=run_manager.get_child() ) - lines = getattr(response["text"], self.parser_key, []) + lines = response["text"] if self.verbose: logger.info(f"Generated queries: {lines}") return lines diff --git a/libs/langchain/langchain/retrievers/parent_document_retriever.py b/libs/langchain/langchain/retrievers/parent_document_retriever.py index 86e4cef1868..5095c13540e 100644 --- a/libs/langchain/langchain/retrievers/parent_document_retriever.py +++ b/libs/langchain/langchain/retrievers/parent_document_retriever.py @@ -1,5 +1,5 @@ import uuid -from typing import List, Optional +from typing import List, Optional, Sequence from langchain_core.documents import Document @@ -31,17 +31,16 @@ class ParentDocumentRetriever(MultiVectorRetriever): .. code-block:: python - # Imports - from langchain_community.vectorstores import Chroma from langchain_community.embeddings import OpenAIEmbeddings + from langchain_community.vectorstores import Chroma from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.storage import InMemoryStore # This text splitter is used to create the parent documents - parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000) + parent_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, add_start_index=True) # This text splitter is used to create the child documents # It should create documents smaller than the parent - child_splitter = RecursiveCharacterTextSplitter(chunk_size=400) + child_splitter = RecursiveCharacterTextSplitter(chunk_size=400, add_start_index=True) # The vectorstore to use to index the child chunks vectorstore = Chroma(embedding_function=OpenAIEmbeddings()) # The storage layer for the parent documents @@ -54,7 +53,7 @@ class ParentDocumentRetriever(MultiVectorRetriever): child_splitter=child_splitter, parent_splitter=parent_splitter, ) - """ + """ # noqa: E501 child_splitter: TextSplitter """The text splitter to use to create child documents.""" @@ -65,6 +64,11 @@ class ParentDocumentRetriever(MultiVectorRetriever): """The text splitter to use to create parent documents. If none, then the parent documents will be the raw documents passed in.""" + child_metadata_fields: Optional[Sequence[str]] = None + """Metadata fields to leave in child documents. If None, leave all parent document + metadata. + """ + def add_documents( self, documents: List[Document], @@ -76,7 +80,7 @@ class ParentDocumentRetriever(MultiVectorRetriever): Args: documents: List of documents to add ids: Optional list of ids for documents. If provided should be the same - length as the list of documents. Can provided if parent documents + length as the list of documents. Can be provided if parent documents are already in the document store and you don't want to re-add to the docstore. If not provided, random UUIDs will be used as ids. @@ -106,6 +110,11 @@ class ParentDocumentRetriever(MultiVectorRetriever): for i, doc in enumerate(documents): _id = doc_ids[i] sub_docs = self.child_splitter.split_documents([doc]) + if self.child_metadata_fields is not None: + for _doc in sub_docs: + _doc.metadata = { + k: _doc.metadata[k] for k in self.child_metadata_fields + } for _doc in sub_docs: _doc.metadata[self.id_key] = _id docs.extend(sub_docs) diff --git a/libs/langchain/langchain/retrievers/self_query/base.py b/libs/langchain/langchain/retrievers/self_query/base.py index 9c6a584a6e1..d54120ccfc3 100644 --- a/libs/langchain/langchain/retrievers/self_query/base.py +++ b/libs/langchain/langchain/retrievers/self_query/base.py @@ -12,6 +12,7 @@ from langchain_community.vectorstores import ( MongoDBAtlasVectorSearch, MyScale, OpenSearchVectorSearch, + PGVector, Pinecone, Qdrant, Redis, @@ -43,6 +44,7 @@ from langchain.retrievers.self_query.milvus import MilvusTranslator from langchain.retrievers.self_query.mongodb_atlas import MongoDBAtlasTranslator from langchain.retrievers.self_query.myscale import MyScaleTranslator from langchain.retrievers.self_query.opensearch import OpenSearchTranslator +from langchain.retrievers.self_query.pgvector import PGVectorTranslator from langchain.retrievers.self_query.pinecone import PineconeTranslator from langchain.retrievers.self_query.qdrant import QdrantTranslator from langchain.retrievers.self_query.redis import RedisTranslator @@ -58,6 +60,7 @@ def _get_builtin_translator(vectorstore: VectorStore) -> Visitor: """Get the translator class corresponding to the vector store class.""" BUILTIN_TRANSLATORS: Dict[Type[VectorStore], Type[Visitor]] = { AstraDB: AstraDBTranslator, + PGVector: PGVectorTranslator, Pinecone: PineconeTranslator, Chroma: ChromaTranslator, DashVector: DashvectorTranslator, diff --git a/libs/langchain/langchain/retrievers/self_query/elasticsearch.py b/libs/langchain/langchain/retrievers/self_query/elasticsearch.py index c908faa643f..7c2f7671a5c 100644 --- a/libs/langchain/langchain/retrievers/self_query/elasticsearch.py +++ b/libs/langchain/langchain/retrievers/self_query/elasticsearch.py @@ -61,11 +61,10 @@ class ElasticsearchTranslator(Visitor): ] if is_range_comparator: - return { - "range": { - field: {self._format_func(comparison.comparator): comparison.value} - } - } + value = comparison.value + if isinstance(comparison.value, dict) and "date" in comparison.value: + value = comparison.value["date"] + return {"range": {field: {self._format_func(comparison.comparator): value}}} if comparison.comparator == Comparator.CONTAIN: return { @@ -85,6 +84,10 @@ class ElasticsearchTranslator(Visitor): # we want to use the keyword field field = f"{field}.keyword" if isinstance(comparison.value, str) else field + if isinstance(comparison.value, dict): + if "date" in comparison.value: + comparison.value = comparison.value["date"] + return {self._format_func(comparison.comparator): {field: comparison.value}} def visit_structured_query( diff --git a/libs/langchain/langchain/retrievers/self_query/myscale.py b/libs/langchain/langchain/retrievers/self_query/myscale.py index a951272471e..8b1afaf8b9d 100644 --- a/libs/langchain/langchain/retrievers/self_query/myscale.py +++ b/libs/langchain/langchain/retrievers/self_query/myscale.py @@ -117,7 +117,7 @@ class MyScaleTranslator(Visitor): def visit_structured_query( self, structured_query: StructuredQuery ) -> Tuple[str, dict]: - print(structured_query) + print(structured_query) # noqa: T201 if structured_query.filter is None: kwargs = {} else: diff --git a/libs/langchain/langchain/retrievers/self_query/pgvector.py b/libs/langchain/langchain/retrievers/self_query/pgvector.py new file mode 100644 index 00000000000..ebe5bf42ffc --- /dev/null +++ b/libs/langchain/langchain/retrievers/self_query/pgvector.py @@ -0,0 +1,52 @@ +from typing import Dict, Tuple, Union + +from langchain.chains.query_constructor.ir import ( + Comparator, + Comparison, + Operation, + Operator, + StructuredQuery, + Visitor, +) + + +class PGVectorTranslator(Visitor): + """Translate `PGVector` internal query language elements to valid filters.""" + + allowed_operators = [Operator.AND, Operator.OR] + """Subset of allowed logical operators.""" + allowed_comparators = [ + Comparator.EQ, + Comparator.NE, + Comparator.GT, + Comparator.LT, + Comparator.IN, + Comparator.NIN, + Comparator.CONTAIN, + Comparator.LIKE, + ] + """Subset of allowed logical comparators.""" + + def _format_func(self, func: Union[Operator, Comparator]) -> str: + self._validate_func(func) + return f"{func.value}" + + def visit_operation(self, operation: Operation) -> Dict: + args = [arg.accept(self) for arg in operation.arguments] + return {self._format_func(operation.operator): args} + + def visit_comparison(self, comparison: Comparison) -> Dict: + return { + comparison.attribute: { + self._format_func(comparison.comparator): comparison.value + } + } + + def visit_structured_query( + self, structured_query: StructuredQuery + ) -> Tuple[str, dict]: + if structured_query.filter is None: + kwargs = {} + else: + kwargs = {"filter": structured_query.filter.accept(self)} + return structured_query.query, kwargs diff --git a/libs/langchain/langchain/retrievers/web_research.py b/libs/langchain/langchain/retrievers/web_research.py index 149ef247af7..b992490f617 100644 --- a/libs/langchain/langchain/retrievers/web_research.py +++ b/libs/langchain/langchain/retrievers/web_research.py @@ -12,6 +12,7 @@ from langchain_core.callbacks import ( ) from langchain_core.documents import Document from langchain_core.language_models import BaseLLM +from langchain_core.output_parsers import BaseOutputParser from langchain_core.prompts import BasePromptTemplate, PromptTemplate from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.retrievers import BaseRetriever @@ -19,7 +20,6 @@ from langchain_core.vectorstores import VectorStore from langchain.chains import LLMChain from langchain.chains.prompt_selector import ConditionalPromptSelector -from langchain.output_parsers.pydantic import PydanticOutputParser from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter logger = logging.getLogger(__name__) @@ -50,21 +50,12 @@ should have a question mark at the end: {question}""", ) -class LineList(BaseModel): - """List of questions.""" - - lines: List[str] = Field(description="Questions") - - -class QuestionListOutputParser(PydanticOutputParser): +class QuestionListOutputParser(BaseOutputParser[List[str]]): """Output parser for a list of numbered questions.""" - def __init__(self) -> None: - super().__init__(pydantic_object=LineList) - - def parse(self, text: str) -> LineList: + def parse(self, text: str) -> List[str]: lines = re.findall(r"\d+\..*?(?:\n|$)", text) - return LineList(lines=lines) + return lines class WebResearchRetriever(BaseRetriever): @@ -176,7 +167,7 @@ class WebResearchRetriever(BaseRetriever): logger.info("Generating questions for Google Search ...") result = self.llm_chain({"question": query}) logger.info(f"Questions for Google Search (raw): {result}") - questions = getattr(result["text"], "lines", []) + questions = result["text"] logger.info(f"Questions for Google Search: {questions}") # Get urls diff --git a/libs/langchain/langchain/smith/evaluation/progress.py b/libs/langchain/langchain/smith/evaluation/progress.py index bf25bec3fbf..15ff4cc599c 100644 --- a/libs/langchain/langchain/smith/evaluation/progress.py +++ b/libs/langchain/langchain/smith/evaluation/progress.py @@ -1,4 +1,5 @@ """A simple progress bar for the console.""" + import threading from typing import Any, Dict, Optional, Sequence from uuid import UUID @@ -36,7 +37,7 @@ class ProgressBarCallback(base_callbacks.BaseCallbackHandler): progress = self.counter / self.total arrow = "-" * int(round(progress * self.ncols) - 1) + ">" spaces = " " * (self.ncols - len(arrow)) - print(f"\r[{arrow + spaces}] {self.counter}/{self.total}", end="") + print(f"\r[{arrow + spaces}] {self.counter}/{self.total}", end="") # noqa: T201 def on_chain_error( self, diff --git a/libs/langchain/langchain/smith/evaluation/runner_utils.py b/libs/langchain/langchain/smith/evaluation/runner_utils.py index ce50c1500af..d55aabae40d 100644 --- a/libs/langchain/langchain/smith/evaluation/runner_utils.py +++ b/libs/langchain/langchain/smith/evaluation/runner_utils.py @@ -999,7 +999,7 @@ run_on_dataset( f"\n\n{example_msg}" ) comparison_url = dataset.url + f"/compare?selectedSessions={project.id}" - print( + print( # noqa: T201 f"View the evaluation results for project '{project_name}'" f" at:\n{comparison_url}\n\n" f"View all tests for Dataset {dataset_name} at:\n{dataset.url}", @@ -1145,7 +1145,6 @@ class _DatasetRunContainer: LangChainTracer( project_name=project.name, client=client, - use_threading=False, example_id=example.id, ), EvaluatorCallbackHandler( @@ -1191,8 +1190,8 @@ def _display_aggregate_results(aggregate_results: pd.DataFrame) -> None: formatted_string = aggregate_results.to_string( float_format=lambda x: f"{x:.2f}", justify="right" ) - print("\n Experiment Results:") - print(formatted_string) + print("\n Experiment Results:") # noqa: T201 + print(formatted_string) # noqa: T201 _INPUT_MAPPER_DEP_WARNING = ( diff --git a/libs/langchain/langchain/storage/file_system.py b/libs/langchain/langchain/storage/file_system.py index e737309a182..bd0aca4875b 100644 --- a/libs/langchain/langchain/storage/file_system.py +++ b/libs/langchain/langchain/storage/file_system.py @@ -32,7 +32,7 @@ class LocalFileStore(ByteStore): # Iterate over keys for key in file_store.yield_keys(): - print(key) + print(key) # noqa: T201 """ diff --git a/libs/langchain/langchain/text_splitter.py b/libs/langchain/langchain/text_splitter.py index 8d49a3c94ba..1f382a53d0e 100644 --- a/libs/langchain/langchain/text_splitter.py +++ b/libs/langchain/langchain/text_splitter.py @@ -22,6 +22,7 @@ Note: **MarkdownHeaderTextSplitter** and **HTMLHeaderTextSplitter do not derive from __future__ import annotations import copy +import json import logging import pathlib import re @@ -842,6 +843,9 @@ class Language(str, Enum): SOL = "sol" CSHARP = "csharp" COBOL = "cobol" + C = "c" + LUA = "lua" + PERL = "perl" class RecursiveCharacterTextSplitter(TextSplitter): @@ -1489,3 +1493,116 @@ class LatexTextSplitter(RecursiveCharacterTextSplitter): """Initialize a LatexTextSplitter.""" separators = self.get_separators_for_language(Language.LATEX) super().__init__(separators=separators, **kwargs) + + +class RecursiveJsonSplitter: + def __init__( + self, max_chunk_size: int = 2000, min_chunk_size: Optional[int] = None + ): + super().__init__() + self.max_chunk_size = max_chunk_size + self.min_chunk_size = ( + min_chunk_size + if min_chunk_size is not None + else max(max_chunk_size - 200, 50) + ) + + @staticmethod + def _json_size(data: Dict) -> int: + """Calculate the size of the serialized JSON object.""" + return len(json.dumps(data)) + + @staticmethod + def _set_nested_dict(d: Dict, path: List[str], value: Any) -> None: + """Set a value in a nested dictionary based on the given path.""" + for key in path[:-1]: + d = d.setdefault(key, {}) + d[path[-1]] = value + + def _list_to_dict_preprocessing(self, data: Any) -> Any: + if isinstance(data, dict): + # Process each key-value pair in the dictionary + return {k: self._list_to_dict_preprocessing(v) for k, v in data.items()} + elif isinstance(data, list): + # Convert the list to a dictionary with index-based keys + return { + str(i): self._list_to_dict_preprocessing(item) + for i, item in enumerate(data) + } + else: + # Base case: the item is neither a dict nor a list, so return it unchanged + return data + + def _json_split( + self, + data: Dict[str, Any], + current_path: List[str] = [], + chunks: List[Dict] = [{}], + ) -> List[Dict]: + """ + Split json into maximum size dictionaries while preserving structure. + """ + if isinstance(data, dict): + for key, value in data.items(): + new_path = current_path + [key] + chunk_size = self._json_size(chunks[-1]) + size = self._json_size({key: value}) + remaining = self.max_chunk_size - chunk_size + + if size < remaining: + # Add item to current chunk + self._set_nested_dict(chunks[-1], new_path, value) + else: + if chunk_size >= self.min_chunk_size: + # Chunk is big enough, start a new chunk + chunks.append({}) + + # Iterate + self._json_split(value, new_path, chunks) + else: + # handle single item + self._set_nested_dict(chunks[-1], current_path, data) + return chunks + + def split_json( + self, + json_data: Dict[str, Any], + convert_lists: bool = False, + ) -> List[Dict]: + """Splits JSON into a list of JSON chunks""" + + if convert_lists: + chunks = self._json_split(self._list_to_dict_preprocessing(json_data)) + else: + chunks = self._json_split(json_data) + + # Remove the last chunk if it's empty + if not chunks[-1]: + chunks.pop() + return chunks + + def split_text( + self, json_data: Dict[str, Any], convert_lists: bool = False + ) -> List[str]: + """Splits JSON into a list of JSON formatted strings""" + + chunks = self.split_json(json_data=json_data, convert_lists=convert_lists) + + # Convert to string + return [json.dumps(chunk) for chunk in chunks] + + def create_documents( + self, + texts: List[Dict], + convert_lists: bool = False, + metadatas: Optional[List[dict]] = None, + ) -> List[Document]: + """Create documents from a list of json objects (Dict).""" + _metadatas = metadatas or [{}] * len(texts) + documents = [] + for i, text in enumerate(texts): + for chunk in self.split_text(json_data=text, convert_lists=convert_lists): + metadata = copy.deepcopy(_metadatas[i]) + new_doc = Document(page_content=chunk, metadata=metadata) + documents.append(new_doc) + return documents diff --git a/libs/langchain/langchain/tools/retriever.py b/libs/langchain/langchain/tools/retriever.py index 1d2cc7bc272..b999c74b6cb 100644 --- a/libs/langchain/langchain/tools/retriever.py +++ b/libs/langchain/langchain/tools/retriever.py @@ -1,6 +1,9 @@ from functools import partial from typing import Optional +from langchain_core.callbacks.manager import ( + Callbacks, +) from langchain_core.prompts import BasePromptTemplate, PromptTemplate, format_document from langchain_core.pydantic_v1 import BaseModel, Field from langchain_core.retrievers import BaseRetriever @@ -19,8 +22,9 @@ def _get_relevant_documents( retriever: BaseRetriever, document_prompt: BasePromptTemplate, document_separator: str, + callbacks: Callbacks = None, ) -> str: - docs = retriever.get_relevant_documents(query) + docs = retriever.get_relevant_documents(query, callbacks=callbacks) return document_separator.join( format_document(doc, document_prompt) for doc in docs ) @@ -31,8 +35,9 @@ async def _aget_relevant_documents( retriever: BaseRetriever, document_prompt: BasePromptTemplate, document_separator: str, + callbacks: Callbacks = None, ) -> str: - docs = await retriever.aget_relevant_documents(query) + docs = await retriever.aget_relevant_documents(query, callbacks=callbacks) return document_separator.join( format_document(doc, document_prompt) for doc in docs ) diff --git a/libs/langchain/poetry.lock b/libs/langchain/poetry.lock index 51244301a2f..a8e98f8c65b 100644 --- a/libs/langchain/poetry.lock +++ b/libs/langchain/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. [[package]] name = "aiodns" @@ -3133,7 +3133,6 @@ optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*" files = [ {file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"}, - {file = "jsonpointer-2.4.tar.gz", hash = "sha256:585cee82b70211fa9e6043b7bb89db6e1aa49524340dde8ad6b63206ea689d88"}, ] [[package]] @@ -3447,7 +3446,7 @@ files = [ [[package]] name = "langchain-community" -version = "0.0.17" +version = "0.0.20" description = "Community contributed LangChain integrations." optional = false python-versions = ">=3.8.1,<4.0" @@ -3457,8 +3456,8 @@ develop = true [package.dependencies] aiohttp = "^3.8.3" dataclasses-json = ">= 0.5.7, < 0.7" -langchain-core = ">=0.1.16,<0.2" -langsmith = ">=0.0.83,<0.1" +langchain-core = ">=0.1.21,<0.2" +langsmith = "^0.1.0" numpy = "^1" PyYAML = ">=5.3" requests = "^2" @@ -3467,7 +3466,7 @@ tenacity = "^8.1.0" [package.extras] cli = ["typer (>=0.9.0,<0.10.0)"] -extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "azure-ai-documentintelligence (>=1.0.0b1,<2.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<5)", "dashvector (>=1.0.1,<2.0.0)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "elasticsearch (>=8.12.0,<9.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "gradientai (>=1.4.0,<2.0.0)", "hdbcli (>=2.19.21,<3.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "httpx (>=0.24.1,<0.25.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "oci (>=2.119.1,<3.0.0)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "oracle-ads (>=2.9.1,<3.0.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss 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b/libs/langchain/pyproject.toml index 539a29d4d82..89387d73910 100644 --- a/libs/langchain/pyproject.toml +++ b/libs/langchain/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain" -version = "0.1.5" +version = "0.1.7" description = "Building applications with LLMs through composability" authors = [] license = "MIT" @@ -12,9 +12,9 @@ langchain-server = "langchain.server:main" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -langchain-core = ">=0.1.16,<0.2" -langchain-community = ">=0.0.17,<0.1" -langsmith = ">=0.0.83,<0.1" +langchain-core = ">=0.1.22,<0.2" +langchain-community = ">=0.0.20,<0.1" +langsmith = "^0.1.0" pydantic = ">=1,<3" SQLAlchemy = ">=1.4,<3" requests = "^2" @@ -301,13 +301,16 @@ extended_testing = [ ] [tool.ruff] +exclude = [ + "tests/integration_tests/examples/non-utf8-encoding.py", +] + +[tool.ruff.lint] select = [ "E", # pycodestyle "F", # pyflakes "I", # isort -] -exclude = [ - "tests/integration_tests/examples/non-utf8-encoding.py", + "T201", # print ] [tool.mypy] diff --git a/libs/langchain/scripts/check_imports.py b/libs/langchain/scripts/check_imports.py index 462ab97ae2e..825bea5b487 100644 --- a/libs/langchain/scripts/check_imports.py +++ b/libs/langchain/scripts/check_imports.py @@ -15,8 +15,8 @@ if __name__ == "__main__": SourceFileLoader(module_name, file).load_module() except Exception: has_failure = True - print(file) + print(file) # noqa: T201 traceback.print_exc() - print() + print() # noqa: T201 sys.exit(1 if has_failure else 0) diff --git a/libs/langchain/tests/integration_tests/agent/test_powerbi_agent.py b/libs/langchain/tests/integration_tests/agent/test_powerbi_agent.py index 7a661626fe4..20c81de8929 100644 --- a/libs/langchain/tests/integration_tests/agent/test_powerbi_agent.py +++ b/libs/langchain/tests/integration_tests/agent/test_powerbi_agent.py @@ -13,7 +13,7 @@ def azure_installed() -> bool: return True except Exception as e: - print(f"azure not installed, skipping test {e}") + print(f"azure not installed, skipping test {e}") # noqa: T201 return False diff --git a/libs/langchain/tests/integration_tests/cache/test_astradb.py b/libs/langchain/tests/integration_tests/cache/test_astradb.py index 79e5ee960ea..0c973e60fd1 100644 --- a/libs/langchain/tests/integration_tests/cache/test_astradb.py +++ b/libs/langchain/tests/integration_tests/cache/test_astradb.py @@ -10,10 +10,14 @@ Required to run this test: - optionally this as well (otherwise defaults are used): export ASTRA_DB_KEYSPACE="my_keyspace" """ + import os -from typing import Iterator +from typing import AsyncIterator, Iterator import pytest +from langchain_community.utilities.astradb import SetupMode +from langchain_core.caches import BaseCache +from langchain_core.language_models import LLM from langchain_core.outputs import Generation, LLMResult from langchain.cache import AstraDBCache, AstraDBSemanticCache @@ -40,7 +44,22 @@ def astradb_cache() -> Iterator[AstraDBCache]: namespace=os.environ.get("ASTRA_DB_KEYSPACE"), ) yield cache - cache.astra_db.delete_collection("lc_integration_test_cache") + cache.collection.astra_db.delete_collection("lc_integration_test_cache") + + +@pytest.fixture +async def async_astradb_cache() -> AsyncIterator[AstraDBCache]: + cache = AstraDBCache( + collection_name="lc_integration_test_cache_async", + token=os.environ["ASTRA_DB_APPLICATION_TOKEN"], + api_endpoint=os.environ["ASTRA_DB_API_ENDPOINT"], + namespace=os.environ.get("ASTRA_DB_KEYSPACE"), + setup_mode=SetupMode.ASYNC, + ) + yield cache + await cache.async_collection.astra_db.delete_collection( + "lc_integration_test_cache_async" + ) @pytest.fixture(scope="module") @@ -54,46 +73,87 @@ def astradb_semantic_cache() -> Iterator[AstraDBSemanticCache]: embedding=fake_embe, ) yield sem_cache - sem_cache.astra_db.delete_collection("lc_integration_test_cache") + sem_cache.collection.astra_db.delete_collection("lc_integration_test_sem_cache") + + +@pytest.fixture +async def async_astradb_semantic_cache() -> AsyncIterator[AstraDBSemanticCache]: + fake_embe = FakeEmbeddings() + sem_cache = AstraDBSemanticCache( + collection_name="lc_integration_test_sem_cache_async", + token=os.environ["ASTRA_DB_APPLICATION_TOKEN"], + api_endpoint=os.environ["ASTRA_DB_API_ENDPOINT"], + namespace=os.environ.get("ASTRA_DB_KEYSPACE"), + embedding=fake_embe, + setup_mode=SetupMode.ASYNC, + ) + yield sem_cache + sem_cache.collection.astra_db.delete_collection( + "lc_integration_test_sem_cache_async" + ) @pytest.mark.requires("astrapy") @pytest.mark.skipif(not _has_env_vars(), reason="Missing Astra DB env. vars") class TestAstraDBCaches: def test_astradb_cache(self, astradb_cache: AstraDBCache) -> None: - set_llm_cache(astradb_cache) + self.do_cache_test(FakeLLM(), astradb_cache, "foo") + + async def test_astradb_cache_async(self, async_astradb_cache: AstraDBCache) -> None: + await self.ado_cache_test(FakeLLM(), async_astradb_cache, "foo") + + def test_astradb_semantic_cache( + self, astradb_semantic_cache: AstraDBSemanticCache + ) -> None: llm = FakeLLM() - params = llm.dict() - params["stop"] = None - llm_string = str(sorted([(k, v) for k, v in params.items()])) - get_llm_cache().update("foo", llm_string, [Generation(text="fizz")]) - output = llm.generate(["foo"]) - print(output) - expected_output = LLMResult( + self.do_cache_test(llm, astradb_semantic_cache, "bar") + output = llm.generate(["bar"]) # 'fizz' is erased away now + assert output != LLMResult( generations=[[Generation(text="fizz")]], llm_output={}, ) - print(expected_output) - assert output == expected_output - astradb_cache.clear() + astradb_semantic_cache.clear() - def test_cassandra_semantic_cache( - self, astradb_semantic_cache: AstraDBSemanticCache + async def test_astradb_semantic_cache_async( + self, async_astradb_semantic_cache: AstraDBSemanticCache ) -> None: - set_llm_cache(astradb_semantic_cache) llm = FakeLLM() + await self.ado_cache_test(llm, async_astradb_semantic_cache, "bar") + output = await llm.agenerate(["bar"]) # 'fizz' is erased away now + assert output != LLMResult( + generations=[[Generation(text="fizz")]], + llm_output={}, + ) + await async_astradb_semantic_cache.aclear() + + @staticmethod + def do_cache_test(llm: LLM, cache: BaseCache, prompt: str) -> None: + set_llm_cache(cache) params = llm.dict() params["stop"] = None llm_string = str(sorted([(k, v) for k, v in params.items()])) get_llm_cache().update("foo", llm_string, [Generation(text="fizz")]) - output = llm.generate(["bar"]) # same embedding as 'foo' + output = llm.generate([prompt]) expected_output = LLMResult( generations=[[Generation(text="fizz")]], llm_output={}, ) assert output == expected_output # clear the cache - astradb_semantic_cache.clear() - output = llm.generate(["bar"]) # 'fizz' is erased away now - assert output != expected_output - astradb_semantic_cache.clear() + cache.clear() + + @staticmethod + async def ado_cache_test(llm: LLM, cache: BaseCache, prompt: str) -> None: + set_llm_cache(cache) + params = llm.dict() + params["stop"] = None + llm_string = str(sorted([(k, v) for k, v in params.items()])) + await get_llm_cache().aupdate("foo", llm_string, [Generation(text="fizz")]) + output = await llm.agenerate([prompt]) + expected_output = LLMResult( + generations=[[Generation(text="fizz")]], + llm_output={}, + ) + assert output == expected_output + # clear the cache + await cache.aclear() diff --git a/libs/langchain/tests/integration_tests/cache/test_cassandra.py b/libs/langchain/tests/integration_tests/cache/test_cassandra.py index b12616963c1..19a8efaf4ac 100644 --- a/libs/langchain/tests/integration_tests/cache/test_cassandra.py +++ b/libs/langchain/tests/integration_tests/cache/test_cassandra.py @@ -1,4 +1,5 @@ """Test Cassandra caches. Requires a running vector-capable Cassandra cluster.""" + import os import time from typing import Any, Iterator, Tuple @@ -46,12 +47,12 @@ def test_cassandra_cache(cassandra_connection: Tuple[Any, str]) -> None: llm_string = str(sorted([(k, v) for k, v in params.items()])) get_llm_cache().update("foo", llm_string, [Generation(text="fizz")]) output = llm.generate(["foo"]) - print(output) + print(output) # noqa: T201 expected_output = LLMResult( generations=[[Generation(text="fizz")]], llm_output={}, ) - print(expected_output) + print(expected_output) # noqa: T201 assert output == expected_output cache.clear() diff --git a/libs/langchain/tests/integration_tests/cache/test_redis_cache.py b/libs/langchain/tests/integration_tests/cache/test_redis_cache.py index 7b44ade026d..20ed08e6786 100644 --- a/libs/langchain/tests/integration_tests/cache/test_redis_cache.py +++ b/libs/langchain/tests/integration_tests/cache/test_redis_cache.py @@ -1,14 +1,16 @@ """Test Redis cache functionality.""" + import uuid -from typing import List, cast +from contextlib import asynccontextmanager, contextmanager +from typing import AsyncGenerator, Generator, List, Optional, cast import pytest +from langchain_community.cache import AsyncRedisCache, RedisCache, RedisSemanticCache from langchain_core.embeddings import Embeddings from langchain_core.load.dump import dumps from langchain_core.messages import AIMessage, BaseMessage, HumanMessage from langchain_core.outputs import ChatGeneration, Generation, LLMResult -from langchain.cache import RedisCache, RedisSemanticCache from langchain.globals import get_llm_cache, set_llm_cache from tests.integration_tests.cache.fake_embeddings import ( ConsistentFakeEmbeddings, @@ -17,65 +19,176 @@ from tests.integration_tests.cache.fake_embeddings import ( from tests.unit_tests.llms.fake_chat_model import FakeChatModel from tests.unit_tests.llms.fake_llm import FakeLLM -REDIS_TEST_URL = "redis://localhost:6379" +# Using a non-standard port to avoid conflicts with potentially local running +# redis instances +# You can spin up a local redis using docker compose +# cd [repository-root]/docker +# docker-compose up redis +REDIS_TEST_URL = "redis://localhost:6020" def random_string() -> str: return str(uuid.uuid4()) +@contextmanager +def get_sync_redis(*, ttl: Optional[int] = 1) -> Generator[RedisCache, None, None]: + """Get a sync RedisCache instance.""" + import redis + + cache = RedisCache(redis_=redis.Redis.from_url(REDIS_TEST_URL), ttl=ttl) + try: + yield cache + finally: + cache.clear() + + +@asynccontextmanager +async def get_async_redis( + *, ttl: Optional[int] = 1 +) -> AsyncGenerator[AsyncRedisCache, None]: + """Get an async RedisCache instance.""" + from redis.asyncio import Redis + + cache = AsyncRedisCache(redis_=Redis.from_url(REDIS_TEST_URL), ttl=ttl) + try: + yield cache + finally: + await cache.aclear() + + def test_redis_cache_ttl() -> None: - import redis + from redis import Redis - set_llm_cache(RedisCache(redis_=redis.Redis.from_url(REDIS_TEST_URL), ttl=1)) - llm_cache = cast(RedisCache, get_llm_cache()) - llm_cache.update("foo", "bar", [Generation(text="fizz")]) - key = llm_cache._key("foo", "bar") - assert llm_cache.redis.pttl(key) > 0 + with get_sync_redis() as llm_cache: + set_llm_cache(llm_cache) + llm_cache.update("foo", "bar", [Generation(text="fizz")]) + key = llm_cache._key("foo", "bar") + assert isinstance(llm_cache.redis, Redis) + assert llm_cache.redis.pttl(key) > 0 -def test_redis_cache() -> None: - import redis +async def test_async_redis_cache_ttl() -> None: + from redis.asyncio import Redis as AsyncRedis - set_llm_cache(RedisCache(redis_=redis.Redis.from_url(REDIS_TEST_URL))) - llm = FakeLLM() - params = llm.dict() - params["stop"] = None - llm_string = str(sorted([(k, v) for k, v in params.items()])) - get_llm_cache().update("foo", llm_string, [Generation(text="fizz")]) - output = llm.generate(["foo"]) - expected_output = LLMResult( - generations=[[Generation(text="fizz")]], - llm_output={}, - ) - assert output == expected_output - llm_cache = cast(RedisCache, get_llm_cache()) - llm_cache.redis.flushall() + async with get_async_redis() as redis_cache: + set_llm_cache(redis_cache) + llm_cache = cast(RedisCache, get_llm_cache()) + await llm_cache.aupdate("foo", "bar", [Generation(text="fizz")]) + key = llm_cache._key("foo", "bar") + assert isinstance(llm_cache.redis, AsyncRedis) + assert await llm_cache.redis.pttl(key) > 0 + + +def test_sync_redis_cache() -> None: + with get_sync_redis() as llm_cache: + set_llm_cache(llm_cache) + llm = FakeLLM() + params = llm.dict() + params["stop"] = None + llm_string = str(sorted([(k, v) for k, v in params.items()])) + llm_cache.update("prompt", llm_string, [Generation(text="fizz0")]) + output = llm.generate(["prompt"]) + expected_output = LLMResult( + generations=[[Generation(text="fizz0")]], + llm_output={}, + ) + assert output == expected_output + + +async def test_sync_in_async_redis_cache() -> None: + """Test the sync RedisCache invoked with async methods""" + with get_sync_redis() as llm_cache: + set_llm_cache(llm_cache) + llm = FakeLLM() + params = llm.dict() + params["stop"] = None + llm_string = str(sorted([(k, v) for k, v in params.items()])) + # llm_cache.update("meow", llm_string, [Generation(text="meow")]) + await llm_cache.aupdate("prompt", llm_string, [Generation(text="fizz1")]) + output = await llm.agenerate(["prompt"]) + expected_output = LLMResult( + generations=[[Generation(text="fizz1")]], + llm_output={}, + ) + assert output == expected_output + + +async def test_async_redis_cache() -> None: + async with get_async_redis() as redis_cache: + set_llm_cache(redis_cache) + llm = FakeLLM() + params = llm.dict() + params["stop"] = None + llm_string = str(sorted([(k, v) for k, v in params.items()])) + llm_cache = cast(RedisCache, get_llm_cache()) + await llm_cache.aupdate("prompt", llm_string, [Generation(text="fizz2")]) + output = await llm.agenerate(["prompt"]) + expected_output = LLMResult( + generations=[[Generation(text="fizz2")]], + llm_output={}, + ) + assert output == expected_output + + +async def test_async_in_sync_redis_cache() -> None: + async with get_async_redis() as redis_cache: + set_llm_cache(redis_cache) + llm = FakeLLM() + params = llm.dict() + params["stop"] = None + llm_string = str(sorted([(k, v) for k, v in params.items()])) + llm_cache = cast(RedisCache, get_llm_cache()) + with pytest.raises(NotImplementedError): + llm_cache.update("foo", llm_string, [Generation(text="fizz")]) def test_redis_cache_chat() -> None: - import redis + with get_sync_redis() as redis_cache: + set_llm_cache(redis_cache) + llm = FakeChatModel() + params = llm.dict() + params["stop"] = None + llm_string = str(sorted([(k, v) for k, v in params.items()])) + prompt: List[BaseMessage] = [HumanMessage(content="foo")] + llm_cache = cast(RedisCache, get_llm_cache()) + llm_cache.update( + dumps(prompt), + llm_string, + [ChatGeneration(message=AIMessage(content="fizz"))], + ) + output = llm.generate([prompt]) + expected_output = LLMResult( + generations=[[ChatGeneration(message=AIMessage(content="fizz"))]], + llm_output={}, + ) + assert output == expected_output - set_llm_cache(RedisCache(redis_=redis.Redis.from_url(REDIS_TEST_URL))) - llm = FakeChatModel() - params = llm.dict() - params["stop"] = None - llm_string = str(sorted([(k, v) for k, v in params.items()])) - prompt: List[BaseMessage] = [HumanMessage(content="foo")] - get_llm_cache().update( - dumps(prompt), llm_string, [ChatGeneration(message=AIMessage(content="fizz"))] - ) - output = llm.generate([prompt]) - expected_output = LLMResult( - generations=[[ChatGeneration(message=AIMessage(content="fizz"))]], - llm_output={}, - ) - assert output == expected_output - llm_cache = cast(RedisCache, get_llm_cache()) - llm_cache.redis.flushall() + +async def test_async_redis_cache_chat() -> None: + async with get_async_redis() as redis_cache: + set_llm_cache(redis_cache) + llm = FakeChatModel() + params = llm.dict() + params["stop"] = None + llm_string = str(sorted([(k, v) for k, v in params.items()])) + prompt: List[BaseMessage] = [HumanMessage(content="foo")] + llm_cache = cast(RedisCache, get_llm_cache()) + await llm_cache.aupdate( + dumps(prompt), + llm_string, + [ChatGeneration(message=AIMessage(content="fizz"))], + ) + output = await llm.agenerate([prompt]) + expected_output = LLMResult( + generations=[[ChatGeneration(message=AIMessage(content="fizz"))]], + llm_output={}, + ) + assert output == expected_output def test_redis_semantic_cache() -> None: + """Test redis semantic cache functionality.""" set_llm_cache( RedisSemanticCache( embedding=FakeEmbeddings(), redis_url=REDIS_TEST_URL, score_threshold=0.1 @@ -85,7 +198,8 @@ def test_redis_semantic_cache() -> None: params = llm.dict() params["stop"] = None llm_string = str(sorted([(k, v) for k, v in params.items()])) - get_llm_cache().update("foo", llm_string, [Generation(text="fizz")]) + llm_cache = cast(RedisSemanticCache, get_llm_cache()) + llm_cache.update("foo", llm_string, [Generation(text="fizz")]) output = llm.generate( ["bar"] ) # foo and bar will have the same embedding produced by FakeEmbeddings @@ -95,13 +209,13 @@ def test_redis_semantic_cache() -> None: ) assert output == expected_output # clear the cache - get_llm_cache().clear(llm_string=llm_string) + llm_cache.clear(llm_string=llm_string) output = llm.generate( ["bar"] ) # foo and bar will have the same embedding produced by FakeEmbeddings # expect different output now without cached result assert output != expected_output - get_llm_cache().clear(llm_string=llm_string) + llm_cache.clear(llm_string=llm_string) def test_redis_semantic_cache_multi() -> None: @@ -114,7 +228,8 @@ def test_redis_semantic_cache_multi() -> None: params = llm.dict() params["stop"] = None llm_string = str(sorted([(k, v) for k, v in params.items()])) - get_llm_cache().update( + llm_cache = cast(RedisSemanticCache, get_llm_cache()) + llm_cache.update( "foo", llm_string, [Generation(text="fizz"), Generation(text="Buzz")] ) output = llm.generate( @@ -126,7 +241,7 @@ def test_redis_semantic_cache_multi() -> None: ) assert output == expected_output # clear the cache - get_llm_cache().clear(llm_string=llm_string) + llm_cache.clear(llm_string=llm_string) def test_redis_semantic_cache_chat() -> None: @@ -140,7 +255,8 @@ def test_redis_semantic_cache_chat() -> None: params["stop"] = None llm_string = str(sorted([(k, v) for k, v in params.items()])) prompt: List[BaseMessage] = [HumanMessage(content="foo")] - get_llm_cache().update( + llm_cache = cast(RedisSemanticCache, get_llm_cache()) + llm_cache.update( dumps(prompt), llm_string, [ChatGeneration(message=AIMessage(content="fizz"))] ) output = llm.generate([prompt]) @@ -149,7 +265,7 @@ def test_redis_semantic_cache_chat() -> None: llm_output={}, ) assert output == expected_output - get_llm_cache().clear(llm_string=llm_string) + llm_cache.clear(llm_string=llm_string) @pytest.mark.parametrize("embedding", [ConsistentFakeEmbeddings()]) @@ -192,10 +308,11 @@ def test_redis_semantic_cache_hit( ] for prompt_i_generations in generations ] + llm_cache = cast(RedisSemanticCache, get_llm_cache()) for prompt_i, llm_generations_i in zip(prompts, llm_generations): - print(prompt_i) - print(llm_generations_i) - get_llm_cache().update(prompt_i, llm_string, llm_generations_i) + print(prompt_i) # noqa: T201 + print(llm_generations_i) # noqa: T201 + llm_cache.update(prompt_i, llm_string, llm_generations_i) llm.generate(prompts) assert llm.generate(prompts) == LLMResult( generations=llm_generations, llm_output={} diff --git a/libs/langchain/tests/integration_tests/chains/test_ontotext_graphdb_qa.py b/libs/langchain/tests/integration_tests/chains/test_ontotext_graphdb_qa.py index bab407ec1c3..4f46559585c 100644 --- a/libs/langchain/tests/integration_tests/chains/test_ontotext_graphdb_qa.py +++ b/libs/langchain/tests/integration_tests/chains/test_ontotext_graphdb_qa.py @@ -165,6 +165,65 @@ def test_valid_sparql_after_first_retry(max_fix_retries: int) -> None: assert result == {chain.output_key: answer, chain.input_key: question} +@pytest.mark.requires("langchain_openai", "rdflib") +@pytest.mark.parametrize("max_fix_retries", [1, 2, 3]) +def test_invalid_sparql_server_response_400(max_fix_retries: int) -> None: + from langchain_openai import ChatOpenAI + + question = "Who is the oldest character?" + generated_invalid_sparql = ( + "PREFIX : " + "PREFIX owl: " + "PREFIX rdfs: " + "PREFIX xsd: " + "SELECT ?character (MAX(?lifespan) AS ?maxLifespan) " + "WHERE {" + " ?species a :Species ;" + " :character ?character ;" + " :averageLifespan ?lifespan ." + " FILTER(xsd:integer(?lifespan))" + "} " + "ORDER BY DESC(?maxLifespan) " + "LIMIT 1" + ) + + graph = OntotextGraphDBGraph( + query_endpoint="http://localhost:7200/repositories/starwars", + query_ontology="CONSTRUCT {?s ?p ?o} " + "FROM WHERE {?s ?p ?o}", + ) + chain = OntotextGraphDBQAChain.from_llm( + Mock(ChatOpenAI), + graph=graph, + max_fix_retries=max_fix_retries, + ) + chain.sparql_generation_chain = Mock(LLMChain) + chain.sparql_fix_chain = Mock(LLMChain) + chain.qa_chain = Mock(LLMChain) + + chain.sparql_generation_chain.output_key = "text" + chain.sparql_generation_chain.invoke = MagicMock( + return_value={ + "text": generated_invalid_sparql, + "prompt": question, + "schema": "", + } + ) + chain.sparql_fix_chain.output_key = "text" + chain.sparql_fix_chain.invoke = MagicMock() + chain.qa_chain.output_key = "text" + chain.qa_chain.invoke = MagicMock() + + with pytest.raises(ValueError) as e: + chain.invoke({chain.input_key: question}) + + assert str(e.value) == "Failed to execute the generated SPARQL query." + + assert chain.sparql_generation_chain.invoke.call_count == 1 + assert chain.sparql_fix_chain.invoke.call_count == 0 + assert chain.qa_chain.invoke.call_count == 0 + + @pytest.mark.requires("langchain_openai", "rdflib") @pytest.mark.parametrize("max_fix_retries", [1, 2, 3]) def test_invalid_sparql_after_all_retries(max_fix_retries: int) -> None: diff --git a/libs/langchain/tests/integration_tests/examples/hello_world.py b/libs/langchain/tests/integration_tests/examples/hello_world.py index 3f0294febb4..c98e152ddeb 100644 --- a/libs/langchain/tests/integration_tests/examples/hello_world.py +++ b/libs/langchain/tests/integration_tests/examples/hello_world.py @@ -4,7 +4,7 @@ import sys def main() -> int: - print("Hello World!") + print("Hello World!") # noqa: T201 return 0 diff --git a/libs/langchain/tests/integration_tests/memory/test_astradb.py b/libs/langchain/tests/integration_tests/memory/test_astradb.py index a8ed9e7574b..4caf39985ec 100644 --- a/libs/langchain/tests/integration_tests/memory/test_astradb.py +++ b/libs/langchain/tests/integration_tests/memory/test_astradb.py @@ -1,10 +1,11 @@ import os -from typing import Iterable +from typing import AsyncIterable, Iterable import pytest from langchain_community.chat_message_histories.astradb import ( AstraDBChatMessageHistory, ) +from langchain_community.utilities.astradb import SetupMode from langchain_core.messages import AIMessage, HumanMessage from langchain.memory import ConversationBufferMemory @@ -29,7 +30,7 @@ def history1() -> Iterable[AstraDBChatMessageHistory]: namespace=os.environ.get("ASTRA_DB_KEYSPACE"), ) yield history1 - history1.astra_db.delete_collection("langchain_cmh_test") + history1.collection.astra_db.delete_collection("langchain_cmh_test") @pytest.fixture(scope="function") @@ -42,7 +43,35 @@ def history2() -> Iterable[AstraDBChatMessageHistory]: namespace=os.environ.get("ASTRA_DB_KEYSPACE"), ) yield history2 - history2.astra_db.delete_collection("langchain_cmh_test") + history2.collection.astra_db.delete_collection("langchain_cmh_test") + + +@pytest.fixture +async def async_history1() -> AsyncIterable[AstraDBChatMessageHistory]: + history1 = AstraDBChatMessageHistory( + session_id="async-session-test-1", + collection_name="langchain_cmh_test", + token=os.environ["ASTRA_DB_APPLICATION_TOKEN"], + api_endpoint=os.environ["ASTRA_DB_API_ENDPOINT"], + namespace=os.environ.get("ASTRA_DB_KEYSPACE"), + setup_mode=SetupMode.ASYNC, + ) + yield history1 + await history1.async_collection.astra_db.delete_collection("langchain_cmh_test") + + +@pytest.fixture(scope="function") +async def async_history2() -> AsyncIterable[AstraDBChatMessageHistory]: + history2 = AstraDBChatMessageHistory( + session_id="async-session-test-2", + collection_name="langchain_cmh_test", + token=os.environ["ASTRA_DB_APPLICATION_TOKEN"], + api_endpoint=os.environ["ASTRA_DB_API_ENDPOINT"], + namespace=os.environ.get("ASTRA_DB_KEYSPACE"), + setup_mode=SetupMode.ASYNC, + ) + yield history2 + await history2.async_collection.astra_db.delete_collection("langchain_cmh_test") @pytest.mark.requires("astrapy") @@ -58,8 +87,12 @@ def test_memory_with_message_store(history1: AstraDBChatMessageHistory) -> None: assert memory.chat_memory.messages == [] # add some messages - memory.chat_memory.add_ai_message("This is me, the AI") - memory.chat_memory.add_user_message("This is me, the human") + memory.chat_memory.add_messages( + [ + AIMessage(content="This is me, the AI"), + HumanMessage(content="This is me, the human"), + ] + ) messages = memory.chat_memory.messages expected = [ @@ -74,6 +107,41 @@ def test_memory_with_message_store(history1: AstraDBChatMessageHistory) -> None: assert memory.chat_memory.messages == [] +@pytest.mark.requires("astrapy") +@pytest.mark.skipif(not _has_env_vars(), reason="Missing Astra DB env. vars") +async def test_memory_with_message_store_async( + async_history1: AstraDBChatMessageHistory, +) -> None: + """Test the memory with a message store.""" + memory = ConversationBufferMemory( + memory_key="baz", + chat_memory=async_history1, + return_messages=True, + ) + + assert await memory.chat_memory.aget_messages() == [] + + # add some messages + await memory.chat_memory.aadd_messages( + [ + AIMessage(content="This is me, the AI"), + HumanMessage(content="This is me, the human"), + ] + ) + + messages = await memory.chat_memory.aget_messages() + expected = [ + AIMessage(content="This is me, the AI"), + HumanMessage(content="This is me, the human"), + ] + assert messages == expected + + # clear the store + await memory.chat_memory.aclear() + + assert await memory.chat_memory.aget_messages() == [] + + @pytest.mark.requires("astrapy") @pytest.mark.skipif(not _has_env_vars(), reason="Missing Astra DB env. vars") def test_memory_separate_session_ids( @@ -91,7 +159,7 @@ def test_memory_separate_session_ids( return_messages=True, ) - memory1.chat_memory.add_ai_message("Just saying.") + memory1.chat_memory.add_messages([AIMessage(content="Just saying.")]) assert memory2.chat_memory.messages == [] @@ -102,3 +170,33 @@ def test_memory_separate_session_ids( memory1.chat_memory.clear() assert memory1.chat_memory.messages == [] + + +@pytest.mark.requires("astrapy") +@pytest.mark.skipif(not _has_env_vars(), reason="Missing Astra DB env. vars") +async def test_memory_separate_session_ids_async( + async_history1: AstraDBChatMessageHistory, async_history2: AstraDBChatMessageHistory +) -> None: + """Test that separate session IDs do not share entries.""" + memory1 = ConversationBufferMemory( + memory_key="mk1", + chat_memory=async_history1, + return_messages=True, + ) + memory2 = ConversationBufferMemory( + memory_key="mk2", + chat_memory=async_history2, + return_messages=True, + ) + + await memory1.chat_memory.aadd_messages([AIMessage(content="Just saying.")]) + + assert await memory2.chat_memory.aget_messages() == [] + + await memory2.chat_memory.aclear() + + assert await memory1.chat_memory.aget_messages() != [] + + await memory1.chat_memory.aclear() + + assert await memory1.chat_memory.aget_messages() == [] diff --git a/libs/langchain/tests/unit_tests/agents/output_parsers/test_convo_output_parser.py b/libs/langchain/tests/unit_tests/agents/output_parsers/test_convo_output_parser.py index 9aa430e398c..e3fe35666b9 100644 --- a/libs/langchain/tests/unit_tests/agents/output_parsers/test_convo_output_parser.py +++ b/libs/langchain/tests/unit_tests/agents/output_parsers/test_convo_output_parser.py @@ -21,14 +21,14 @@ Thought: Do I need to use a tool? Yes Action: evaluate_code Action Input: Evaluate Code with the following Python content: ```python -print("Hello fifty shades of gray mans!"[::-1]) +print("Hello fifty shades of gray mans!"[::-1]) # noqa: T201 ``` """, "evaluate_code", """ Evaluate Code with the following Python content: ```python -print("Hello fifty shades of gray mans!"[::-1]) +print("Hello fifty shades of gray mans!"[::-1]) # noqa: T201 ```""".lstrip(), ) diff --git a/libs/langchain/tests/unit_tests/agents/test_agent.py b/libs/langchain/tests/unit_tests/agents/test_agent.py index 6ba908b6ad0..5eb571c91bb 100644 --- a/libs/langchain/tests/unit_tests/agents/test_agent.py +++ b/libs/langchain/tests/unit_tests/agents/test_agent.py @@ -1,4 +1,5 @@ """Unit tests for agents.""" + import json from itertools import cycle from typing import Any, Dict, List, Optional, Union, cast @@ -51,8 +52,8 @@ class FakeListLLM(LLM): ) -> str: """Increment counter, and then return response in that index.""" self.i += 1 - print(f"=== Mock Response #{self.i} ===") - print(self.responses[self.i]) + print(f"=== Mock Response #{self.i} ===") # noqa: T201 + print(self.responses[self.i]) # noqa: T201 return self.responses[self.i] def get_num_tokens(self, text: str) -> int: diff --git a/libs/langchain/tests/unit_tests/agents/test_agent_async.py b/libs/langchain/tests/unit_tests/agents/test_agent_async.py index aeee034df7a..f1259cebc0a 100644 --- a/libs/langchain/tests/unit_tests/agents/test_agent_async.py +++ b/libs/langchain/tests/unit_tests/agents/test_agent_async.py @@ -28,8 +28,8 @@ class FakeListLLM(LLM): ) -> str: """Increment counter, and then return response in that index.""" self.i += 1 - print(f"=== Mock Response #{self.i} ===") - print(self.responses[self.i]) + print(f"=== Mock Response #{self.i} ===") # noqa: T201 + print(self.responses[self.i]) # noqa: T201 return self.responses[self.i] def get_num_tokens(self, text: str) -> int: diff --git a/libs/langchain/tests/unit_tests/agents/test_agent_iterator.py b/libs/langchain/tests/unit_tests/agents/test_agent_iterator.py index 2b59cf8cd49..4fa886ba2d8 100644 --- a/libs/langchain/tests/unit_tests/agents/test_agent_iterator.py +++ b/libs/langchain/tests/unit_tests/agents/test_agent_iterator.py @@ -143,7 +143,7 @@ def test_agent_iterator_with_callbacks() -> None: assert handler1.starts == 7 # 1 extra agent end assert handler1.ends == 7 - print("h:", handler1) + print("h:", handler1) # noqa: T201 assert handler1.errors == 0 # during LLMChain assert handler1.text == 2 diff --git a/libs/langchain/tests/unit_tests/chains/test_base.py b/libs/langchain/tests/unit_tests/chains/test_base.py index 2c410e2337d..c96f1d945b1 100644 --- a/libs/langchain/tests/unit_tests/chains/test_base.py +++ b/libs/langchain/tests/unit_tests/chains/test_base.py @@ -162,3 +162,35 @@ def test_run_with_callback() -> None: assert handler.starts == 1 assert handler.ends == 1 assert handler.errors == 0 + + +def test_run_with_callback_and_input_error() -> None: + """Test callback manager catches run validation input error.""" + handler = FakeCallbackHandler() + chain = FakeChain( + the_input_keys=["foo", "bar"], + callbacks=[handler], + ) + + with pytest.raises(ValueError): + chain({"bar": "foo"}) + + assert handler.starts == 1 + assert handler.ends == 0 + assert handler.errors == 1 + + +def test_run_with_callback_and_output_error() -> None: + """Test callback manager catches run validation output error.""" + handler = FakeCallbackHandler() + chain = FakeChain( + the_output_keys=["foo", "bar"], + callbacks=[handler], + ) + + with pytest.raises(ValueError): + chain("foo") + + assert handler.starts == 1 + assert handler.ends == 0 + assert handler.errors == 1 diff --git a/libs/langchain/tests/unit_tests/chains/test_conversation.py b/libs/langchain/tests/unit_tests/chains/test_conversation.py index 86ecd647e8d..d00b1e4bc6e 100644 --- a/libs/langchain/tests/unit_tests/chains/test_conversation.py +++ b/libs/langchain/tests/unit_tests/chains/test_conversation.py @@ -14,14 +14,22 @@ def test_memory_ai_prefix() -> None: """Test that ai_prefix in the memory component works.""" memory = ConversationBufferMemory(memory_key="foo", ai_prefix="Assistant") memory.save_context({"input": "bar"}, {"output": "foo"}) - assert memory.buffer == "Human: bar\nAssistant: foo" + assert memory.load_memory_variables({}) == {"foo": "Human: bar\nAssistant: foo"} def test_memory_human_prefix() -> None: """Test that human_prefix in the memory component works.""" memory = ConversationBufferMemory(memory_key="foo", human_prefix="Friend") memory.save_context({"input": "bar"}, {"output": "foo"}) - assert memory.buffer == "Friend: bar\nAI: foo" + assert memory.load_memory_variables({}) == {"foo": "Friend: bar\nAI: foo"} + + +async def test_memory_async() -> None: + memory = ConversationBufferMemory(memory_key="foo", ai_prefix="Assistant") + await memory.asave_context({"input": "bar"}, {"output": "foo"}) + assert await memory.aload_memory_variables({}) == { + "foo": "Human: bar\nAssistant: foo" + } def test_conversation_chain_works() -> None: @@ -100,3 +108,23 @@ def test_clearing_conversation_memory(memory: BaseMemory) -> None: memory.clear() assert memory.load_memory_variables({}) == {"baz": ""} + + +@pytest.mark.parametrize( + "memory", + [ + ConversationBufferMemory(memory_key="baz"), + ConversationSummaryMemory(llm=FakeLLM(), memory_key="baz"), + ConversationBufferWindowMemory(memory_key="baz"), + ], +) +async def test_clearing_conversation_memory_async(memory: BaseMemory) -> None: + """Test clearing the conversation memory.""" + # This is a good input because the input is not the same as baz. + good_inputs = {"foo": "bar", "baz": "foo"} + # This is a good output because there is one variable. + good_outputs = {"bar": "foo"} + await memory.asave_context(good_inputs, good_outputs) + + await memory.aclear() + assert await memory.aload_memory_variables({}) == {"baz": ""} diff --git a/libs/langchain/tests/unit_tests/chains/test_imports.py b/libs/langchain/tests/unit_tests/chains/test_imports.py index 8317dd62ea9..4da79031fe3 100644 --- a/libs/langchain/tests/unit_tests/chains/test_imports.py +++ b/libs/langchain/tests/unit_tests/chains/test_imports.py @@ -33,6 +33,7 @@ EXPECTED_ALL = [ "NatBotChain", "NebulaGraphQAChain", "NeptuneOpenCypherQAChain", + "NeptuneSparqlQAChain", "OpenAIModerationChain", "OpenAPIEndpointChain", "QAGenerationChain", @@ -61,6 +62,7 @@ EXPECTED_ALL = [ "create_history_aware_retriever", "create_retrieval_chain", "load_summarize_chain", + "create_structured_output_runnable", ] diff --git a/libs/langchain/tests/unit_tests/chains/test_sequential.py b/libs/langchain/tests/unit_tests/chains/test_sequential.py index 36aafc22662..ca2ff78e7aa 100644 --- a/libs/langchain/tests/unit_tests/chains/test_sequential.py +++ b/libs/langchain/tests/unit_tests/chains/test_sequential.py @@ -1,4 +1,5 @@ """Test pipeline functionality.""" + from typing import Dict, List, Optional import pytest @@ -107,7 +108,7 @@ def test_sequential_internal_chain_use_memory() -> None: chain_2 = FakeChain(input_variables=["bar"], output_variables=["baz"]) chain = SequentialChain(chains=[chain_1, chain_2], input_variables=["foo"]) output = chain({"foo": "123"}) - print("HEYYY OUTPUT", output) + print("HEYYY OUTPUT", output) # noqa: T201 expected_output = {"foo": "123", "baz": "123 Human: yo\nAI: yafoofoo"} assert output == expected_output diff --git a/libs/langchain/tests/unit_tests/embeddings/test_caching.py b/libs/langchain/tests/unit_tests/embeddings/test_caching.py index 48c6adbab66..8c24e73b95b 100644 --- a/libs/langchain/tests/unit_tests/embeddings/test_caching.py +++ b/libs/langchain/tests/unit_tests/embeddings/test_caching.py @@ -47,3 +47,23 @@ def test_embed_query(cache_embeddings: CacheBackedEmbeddings) -> None: vector = cache_embeddings.embed_query(text) expected_vector = [5.0, 6.0] assert vector == expected_vector + + +async def test_aembed_documents(cache_embeddings: CacheBackedEmbeddings) -> None: + texts = ["1", "22", "a", "333"] + vectors = await cache_embeddings.aembed_documents(texts) + expected_vectors: List[List[float]] = [[1, 2.0], [2.0, 3.0], [1.0, 2.0], [3.0, 4.0]] + assert vectors == expected_vectors + keys = [ + key async for key in cache_embeddings.document_embedding_store.ayield_keys() + ] + assert len(keys) == 4 + # UUID is expected to be the same for the same text + assert keys[0] == "test_namespace812b86c1-8ebf-5483-95c6-c95cf2b52d12" + + +async def test_aembed_query(cache_embeddings: CacheBackedEmbeddings) -> None: + text = "query_text" + vector = await cache_embeddings.aembed_query(text) + expected_vector = [5.0, 6.0] + assert vector == expected_vector diff --git a/libs/langchain/tests/unit_tests/indexes/test_hashed_document.py b/libs/langchain/tests/unit_tests/indexes/test_hashed_document.py index 2cf60e3a1a0..ff0121967ca 100644 --- a/libs/langchain/tests/unit_tests/indexes/test_hashed_document.py +++ b/libs/langchain/tests/unit_tests/indexes/test_hashed_document.py @@ -13,10 +13,10 @@ def test_hashed_document_hashing() -> None: def test_hashing_with_missing_content() -> None: """Check that ValueError is raised if page_content is missing.""" - with pytest.raises(ValueError): + with pytest.raises(TypeError): _HashedDocument( metadata={"key": "value"}, - ) + ) # type: ignore def test_uid_auto_assigned_to_hash() -> None: diff --git a/libs/langchain/tests/unit_tests/load/__snapshots__/test_dump.ambr b/libs/langchain/tests/unit_tests/load/__snapshots__/test_dump.ambr index 0540760d4ce..826746ac248 100644 --- a/libs/langchain/tests/unit_tests/load/__snapshots__/test_dump.ambr +++ b/libs/langchain/tests/unit_tests/load/__snapshots__/test_dump.ambr @@ -191,7 +191,8 @@ } } } - ] + ], + "partial_variables": {} } } } diff --git a/libs/langchain/tests/unit_tests/output_parsers/test_pydantic_parser.py b/libs/langchain/tests/unit_tests/output_parsers/test_pydantic_parser.py index 9230792f764..864478f73f3 100644 --- a/libs/langchain/tests/unit_tests/output_parsers/test_pydantic_parser.py +++ b/libs/langchain/tests/unit_tests/output_parsers/test_pydantic_parser.py @@ -1,4 +1,5 @@ """Test PydanticOutputParser""" + from enum import Enum from typing import Optional @@ -53,26 +54,26 @@ DEF_EXPECTED_RESULT = TestModel( def test_pydantic_output_parser() -> None: """Test PydanticOutputParser.""" - pydantic_parser: PydanticOutputParser[TestModel] = PydanticOutputParser( + pydantic_parser: PydanticOutputParser = PydanticOutputParser( pydantic_object=TestModel ) result = pydantic_parser.parse(DEF_RESULT) - print("parse_result:", result) + print("parse_result:", result) # noqa: T201 assert DEF_EXPECTED_RESULT == result def test_pydantic_output_parser_fail() -> None: """Test PydanticOutputParser where completion result fails schema validation.""" - pydantic_parser: PydanticOutputParser[TestModel] = PydanticOutputParser( + pydantic_parser: PydanticOutputParser = PydanticOutputParser( pydantic_object=TestModel ) try: pydantic_parser.parse(DEF_RESULT_FAIL) except OutputParserException as e: - print("parse_result:", e) + print("parse_result:", e) # noqa: T201 assert "Failed to parse TestModel from completion" in str(e) else: assert False, "Expected OutputParserException" diff --git a/libs/langchain/tests/unit_tests/output_parsers/test_regex_dict.py b/libs/langchain/tests/unit_tests/output_parsers/test_regex_dict.py index 09df585aedc..c07d0c85671 100644 --- a/libs/langchain/tests/unit_tests/output_parsers/test_regex_dict.py +++ b/libs/langchain/tests/unit_tests/output_parsers/test_regex_dict.py @@ -1,4 +1,5 @@ """Test in memory docstore.""" + from langchain.output_parsers.regex_dict import RegexDictParser DEF_EXPECTED_RESULT = {"action": "Search", "action_input": "How to use this class?"} @@ -33,5 +34,5 @@ def test_regex_dict_result() -> None: output_key_to_format=DEF_OUTPUT_KEY_TO_FORMAT, no_update_value="N/A" ) result_dict = regex_dict_parser.parse(DEF_README) - print("parse_result:", result_dict) + print("parse_result:", result_dict) # noqa: T201 assert DEF_EXPECTED_RESULT == result_dict diff --git a/libs/langchain/tests/unit_tests/output_parsers/test_yaml_parser.py b/libs/langchain/tests/unit_tests/output_parsers/test_yaml_parser.py index e02ff9f42fe..b48a353d8d3 100644 --- a/libs/langchain/tests/unit_tests/output_parsers/test_yaml_parser.py +++ b/libs/langchain/tests/unit_tests/output_parsers/test_yaml_parser.py @@ -1,4 +1,5 @@ """Test yamlOutputParser""" + from enum import Enum from typing import Optional @@ -74,7 +75,7 @@ def test_yaml_output_parser(result: str) -> None: ) model = yaml_parser.parse(result) - print("parse_result:", result) + print("parse_result:", result) # noqa: T201 assert DEF_EXPECTED_RESULT == model @@ -88,7 +89,7 @@ def test_yaml_output_parser_fail() -> None: try: yaml_parser.parse(DEF_RESULT_FAIL) except OutputParserException as e: - print("parse_result:", e) + print("parse_result:", e) # noqa: T201 assert "Failed to parse TestModel from completion" in str(e) else: assert False, "Expected OutputParserException" diff --git a/libs/langchain/tests/unit_tests/retrievers/self_query/test_elasticsearch.py b/libs/langchain/tests/unit_tests/retrievers/self_query/test_elasticsearch.py index 25c4526c801..519f366f031 100644 --- a/libs/langchain/tests/unit_tests/retrievers/self_query/test_elasticsearch.py +++ b/libs/langchain/tests/unit_tests/retrievers/self_query/test_elasticsearch.py @@ -218,3 +218,98 @@ def test_visit_structured_query_complex() -> None: ) actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query) assert expected == actual + + +def test_visit_structured_query_with_date_range() -> None: + query = "Who was the president of France in 1995?" + operation = Operation( + operator=Operator.AND, + arguments=[ + Comparison(comparator=Comparator.EQ, attribute="foo", value="20"), + Operation( + operator=Operator.AND, + arguments=[ + Comparison( + comparator=Comparator.GTE, + attribute="timestamp", + value={"date": "1995-01-01", "type": "date"}, + ), + Comparison( + comparator=Comparator.LT, + attribute="timestamp", + value={"date": "1996-01-01", "type": "date"}, + ), + ], + ), + ], + ) + structured_query = StructuredQuery(query=query, filter=operation, limit=None) + expected = ( + query, + { + "filter": [ + { + "bool": { + "must": [ + {"term": {"metadata.foo.keyword": "20"}}, + { + "bool": { + "must": [ + { + "range": { + "metadata.timestamp": { + "gte": "1995-01-01" + } + } + }, + { + "range": { + "metadata.timestamp": { + "lt": "1996-01-01" + } + } + }, + ] + } + }, + ] + } + } + ] + }, + ) + actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query) + assert expected == actual + + +def test_visit_structured_query_with_date() -> None: + query = "Who was the president of France on 1st of January 1995?" + operation = Operation( + operator=Operator.AND, + arguments=[ + Comparison(comparator=Comparator.EQ, attribute="foo", value="20"), + Comparison( + comparator=Comparator.EQ, + attribute="timestamp", + value={"date": "1995-01-01", "type": "date"}, + ), + ], + ) + structured_query = StructuredQuery(query=query, filter=operation, limit=None) + expected = ( + query, + { + "filter": [ + { + "bool": { + "must": [ + {"term": {"metadata.foo.keyword": "20"}}, + {"term": {"metadata.timestamp": "1995-01-01"}}, + ] + } + } + ] + }, + ) + actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query) + assert expected == actual diff --git a/libs/langchain/tests/unit_tests/retrievers/self_query/test_pgvector.py b/libs/langchain/tests/unit_tests/retrievers/self_query/test_pgvector.py new file mode 100644 index 00000000000..451508bdab3 --- /dev/null +++ b/libs/langchain/tests/unit_tests/retrievers/self_query/test_pgvector.py @@ -0,0 +1,87 @@ +from typing import Dict, Tuple + +import pytest as pytest + +from langchain.chains.query_constructor.ir import ( + Comparator, + Comparison, + Operation, + Operator, + StructuredQuery, +) +from langchain.retrievers.self_query.pgvector import PGVectorTranslator + +DEFAULT_TRANSLATOR = PGVectorTranslator() + + +def test_visit_comparison() -> None: + comp = Comparison(comparator=Comparator.LT, attribute="foo", value=1) + expected = {"foo": {"lt": 1}} + actual = DEFAULT_TRANSLATOR.visit_comparison(comp) + assert expected == actual + + +@pytest.mark.skip("Not implemented") +def test_visit_operation() -> None: + op = Operation( + operator=Operator.AND, + arguments=[ + Comparison(comparator=Comparator.LT, attribute="foo", value=2), + Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"), + Comparison(comparator=Comparator.GT, attribute="abc", value=2.0), + ], + ) + expected = { + "foo": {"lt": 2}, + "bar": {"eq": "baz"}, + "abc": {"gt": 2.0}, + } + actual = DEFAULT_TRANSLATOR.visit_operation(op) + assert expected == actual + + +def test_visit_structured_query() -> None: + query = "What is the capital of France?" + structured_query = StructuredQuery( + query=query, + filter=None, + ) + expected: Tuple[str, Dict] = (query, {}) + actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query) + assert expected == actual + + comp = Comparison(comparator=Comparator.LT, attribute="foo", value=1) + structured_query = StructuredQuery( + query=query, + filter=comp, + ) + expected = (query, {"filter": {"foo": {"lt": 1}}}) + actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query) + assert expected == actual + + op = Operation( + operator=Operator.AND, + arguments=[ + Comparison(comparator=Comparator.LT, attribute="foo", value=2), + Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"), + Comparison(comparator=Comparator.GT, attribute="abc", value=2.0), + ], + ) + structured_query = StructuredQuery( + query=query, + filter=op, + ) + expected = ( + query, + { + "filter": { + "and": [ + {"foo": {"lt": 2}}, + {"bar": {"eq": "baz"}}, + {"abc": {"gt": 2.0}}, + ] + } + }, + ) + actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query) + assert expected == actual diff --git a/libs/langchain/tests/unit_tests/retrievers/test_ensemble.py b/libs/langchain/tests/unit_tests/retrievers/test_ensemble.py index a84ee1f26a7..8b9cbbed115 100644 --- a/libs/langchain/tests/unit_tests/retrievers/test_ensemble.py +++ b/libs/langchain/tests/unit_tests/retrievers/test_ensemble.py @@ -1,6 +1,8 @@ import pytest from langchain_core.documents import Document +from langchain.embeddings import FakeEmbeddings +from langchain.retrievers import KNNRetriever, TFIDFRetriever from langchain.retrievers.bm25 import BM25Retriever from langchain.retrievers.ensemble import EnsembleRetriever @@ -40,3 +42,38 @@ def test_weighted_reciprocal_rank() -> None: result = ensemble_retriever.weighted_reciprocal_rank([[doc1, doc2], [doc2, doc1]]) assert result[0].page_content == "1" assert result[1].page_content == "2" + + +@pytest.mark.requires("rank_bm25", "sklearn") +def test_ensemble_retriever_get_relevant_docs_with_multiple_retrievers() -> None: + doc_list_a = [ + "I like apples", + "I like oranges", + "Apples and oranges are fruits", + ] + doc_list_b = [ + "I like melons", + "I like pineapples", + "Melons and pineapples are fruits", + ] + doc_list_c = [ + "I like avocados", + "I like strawberries", + "Avocados and strawberries are fruits", + ] + + dummy_retriever = BM25Retriever.from_texts(doc_list_a) + dummy_retriever.k = 1 + tfidf_retriever = TFIDFRetriever.from_texts(texts=doc_list_b) + tfidf_retriever.k = 1 + knn_retriever = KNNRetriever.from_texts( + texts=doc_list_c, embeddings=FakeEmbeddings(size=100) + ) + knn_retriever.k = 1 + + ensemble_retriever = EnsembleRetriever( + retrievers=[dummy_retriever, tfidf_retriever, knn_retriever], + weights=[0.6, 0.3, 0.1], + ) + docs = ensemble_retriever.get_relevant_documents("I like apples") + assert len(docs) == 3 diff --git a/libs/langchain/tests/unit_tests/retrievers/test_parent_document.py b/libs/langchain/tests/unit_tests/retrievers/test_parent_document.py index 278b05f93c8..0b24795b780 100644 --- a/libs/langchain/tests/unit_tests/retrievers/test_parent_document.py +++ b/libs/langchain/tests/unit_tests/retrievers/test_parent_document.py @@ -18,7 +18,7 @@ class InMemoryVectorstoreWithSearch(InMemoryVectorStore): return [res] def add_documents(self, documents: Sequence[Document], **kwargs: Any) -> List[str]: - print(documents) + print(documents) # noqa: T201 return super().add_documents( documents, ids=[f"{i}" for i in range(len(documents))] ) diff --git a/libs/langchain/tests/unit_tests/retrievers/test_web_research.py b/libs/langchain/tests/unit_tests/retrievers/test_web_research.py index a052e59b722..29878dd3b4b 100644 --- a/libs/langchain/tests/unit_tests/retrievers/test_web_research.py +++ b/libs/langchain/tests/unit_tests/retrievers/test_web_research.py @@ -33,4 +33,4 @@ from langchain.retrievers.web_research import QuestionListOutputParser def test_list_output_parser(text: str, expected: List[str]) -> None: parser = QuestionListOutputParser() result = parser.parse(text) - assert result.lines == expected + assert result == expected diff --git a/libs/langchain/tests/unit_tests/test_cache.py b/libs/langchain/tests/unit_tests/test_cache.py index 88260a6f71c..5b2e1a4da4e 100644 --- a/libs/langchain/tests/unit_tests/test_cache.py +++ b/libs/langchain/tests/unit_tests/test_cache.py @@ -1,4 +1,5 @@ """Test caching for LLMs and ChatModels.""" +import sqlite3 from typing import Dict, Generator, List, Union import pytest @@ -21,7 +22,11 @@ from langchain.globals import get_llm_cache, set_llm_cache def get_sqlite_cache() -> SQLAlchemyCache: - return SQLAlchemyCache(engine=create_engine("sqlite://")) + return SQLAlchemyCache( + engine=create_engine( + "sqlite://", creator=lambda: sqlite3.connect("file::memory:?cache=shared") + ) + ) CACHE_OPTIONS = [ @@ -35,33 +40,41 @@ def set_cache_and_teardown(request: FixtureRequest) -> Generator[None, None, Non # Will be run before each test cache_instance = request.param set_llm_cache(cache_instance()) - if get_llm_cache(): - get_llm_cache().clear() + if llm_cache := get_llm_cache(): + llm_cache.clear() else: raise ValueError("Cache not set. This should never happen.") yield # Will be run after each test - if get_llm_cache(): - get_llm_cache().clear() + if llm_cache: + llm_cache.clear() set_llm_cache(None) else: raise ValueError("Cache not set. This should never happen.") -def test_llm_caching() -> None: +async def test_llm_caching() -> None: prompt = "How are you?" response = "Test response" cached_response = "Cached test response" llm = FakeListLLM(responses=[response]) - if get_llm_cache(): - get_llm_cache().update( + if llm_cache := get_llm_cache(): + # sync test + llm_cache.update( prompt=prompt, llm_string=create_llm_string(llm), return_val=[Generation(text=cached_response)], ) assert llm(prompt) == cached_response + # async test + await llm_cache.aupdate( + prompt=prompt, + llm_string=create_llm_string(llm), + return_val=[Generation(text=cached_response)], + ) + assert await llm.ainvoke(prompt) == cached_response else: raise ValueError( "The cache not set. This should never happen, as the pytest fixture " @@ -90,14 +103,15 @@ def test_old_sqlite_llm_caching() -> None: assert llm(prompt) == cached_response -def test_chat_model_caching() -> None: +async def test_chat_model_caching() -> None: prompt: List[BaseMessage] = [HumanMessage(content="How are you?")] response = "Test response" cached_response = "Cached test response" cached_message = AIMessage(content=cached_response) llm = FakeListChatModel(responses=[response]) - if get_llm_cache(): - get_llm_cache().update( + if llm_cache := get_llm_cache(): + # sync test + llm_cache.update( prompt=dumps(prompt), llm_string=llm._get_llm_string(), return_val=[ChatGeneration(message=cached_message)], @@ -105,6 +119,16 @@ def test_chat_model_caching() -> None: result = llm(prompt) assert isinstance(result, AIMessage) assert result.content == cached_response + + # async test + await llm_cache.aupdate( + prompt=dumps(prompt), + llm_string=llm._get_llm_string(), + return_val=[ChatGeneration(message=cached_message)], + ) + result = await llm.ainvoke(prompt) + assert isinstance(result, AIMessage) + assert result.content == cached_response else: raise ValueError( "The cache not set. This should never happen, as the pytest fixture " @@ -112,25 +136,38 @@ def test_chat_model_caching() -> None: ) -def test_chat_model_caching_params() -> None: +async def test_chat_model_caching_params() -> None: prompt: List[BaseMessage] = [HumanMessage(content="How are you?")] response = "Test response" cached_response = "Cached test response" cached_message = AIMessage(content=cached_response) llm = FakeListChatModel(responses=[response]) - if get_llm_cache(): - get_llm_cache().update( + if llm_cache := get_llm_cache(): + # sync test + llm_cache.update( prompt=dumps(prompt), llm_string=llm._get_llm_string(functions=[]), return_val=[ChatGeneration(message=cached_message)], ) result = llm(prompt, functions=[]) + result_no_params = llm(prompt) assert isinstance(result, AIMessage) assert result.content == cached_response - result_no_params = llm(prompt) assert isinstance(result_no_params, AIMessage) assert result_no_params.content == response + # async test + await llm_cache.aupdate( + prompt=dumps(prompt), + llm_string=llm._get_llm_string(functions=[]), + return_val=[ChatGeneration(message=cached_message)], + ) + result = await llm.ainvoke(prompt, functions=[]) + result_no_params = await llm.ainvoke(prompt) + assert isinstance(result, AIMessage) + assert result.content == cached_response + assert isinstance(result_no_params, AIMessage) + assert result_no_params.content == response else: raise ValueError( "The cache not set. This should never happen, as the pytest fixture " @@ -138,19 +175,31 @@ def test_chat_model_caching_params() -> None: ) -def test_llm_cache_clear() -> None: +async def test_llm_cache_clear() -> None: prompt = "How are you?" - response = "Test response" + expected_response = "Test response" cached_response = "Cached test response" - llm = FakeListLLM(responses=[response]) - if get_llm_cache(): - get_llm_cache().update( + llm = FakeListLLM(responses=[expected_response]) + if llm_cache := get_llm_cache(): + # sync test + llm_cache.update( prompt=prompt, llm_string=create_llm_string(llm), return_val=[Generation(text=cached_response)], ) - get_llm_cache().clear() - assert llm(prompt) == response + llm_cache.clear() + response = llm(prompt) + assert response == expected_response + + # async test + await llm_cache.aupdate( + prompt=prompt, + llm_string=create_llm_string(llm), + return_val=[Generation(text=cached_response)], + ) + await llm_cache.aclear() + response = await llm.ainvoke(prompt) + assert response == expected_response else: raise ValueError( "The cache not set. This should never happen, as the pytest fixture " diff --git a/libs/langchain/tests/unit_tests/test_text_splitter.py b/libs/langchain/tests/unit_tests/test_text_splitter.py index 6156d794d02..4e8a01d4188 100644 --- a/libs/langchain/tests/unit_tests/test_text_splitter.py +++ b/libs/langchain/tests/unit_tests/test_text_splitter.py @@ -1,7 +1,9 @@ """Test text splitting functionality.""" +import random import re +import string from pathlib import Path -from typing import List +from typing import Any, List import pytest from langchain_core.documents import Document @@ -13,6 +15,7 @@ from langchain.text_splitter import ( MarkdownHeaderTextSplitter, PythonCodeTextSplitter, RecursiveCharacterTextSplitter, + RecursiveJsonSplitter, TextSplitter, Tokenizer, split_text_on_tokens, @@ -1302,3 +1305,48 @@ def test_split_text_on_tokens() -> None: output = split_text_on_tokens(text=text, tokenizer=tokenizer) expected_output = ["foo bar", "bar baz", "baz 123"] assert output == expected_output + + +def test_split_json() -> None: + """Test json text splitter""" + max_chunk = 800 + splitter = RecursiveJsonSplitter(max_chunk_size=max_chunk) + + def random_val() -> str: + return "".join(random.choices(string.ascii_letters, k=random.randint(4, 12))) + + test_data: Any = { + "val0": random_val(), + "val1": {f"val1{i}": random_val() for i in range(100)}, + } + test_data["val1"]["val16"] = {f"val16{i}": random_val() for i in range(100)} + + # uses create_docs and split_text + docs = splitter.create_documents(texts=[test_data]) + + output = [len(doc.page_content) < max_chunk * 1.05 for doc in docs] + expected_output = [True for doc in docs] + assert output == expected_output + + +def test_split_json_with_lists() -> None: + """Test json text splitter with list conversion""" + max_chunk = 800 + splitter = RecursiveJsonSplitter(max_chunk_size=max_chunk) + + def random_val() -> str: + return "".join(random.choices(string.ascii_letters, k=random.randint(4, 12))) + + test_data: Any = { + "val0": random_val(), + "val1": {f"val1{i}": random_val() for i in range(100)}, + } + test_data["val1"]["val16"] = {f"val16{i}": random_val() for i in range(100)} + + test_data_list: Any = {"testPreprocessing": [test_data]} + + # test text splitter + texts = splitter.split_text(json_data=test_data) + texts_list = splitter.split_text(json_data=test_data_list, convert_lists=True) + + assert len(texts_list) >= len(texts) diff --git a/libs/partners/ai21/.gitignore b/libs/partners/ai21/.gitignore new file mode 100644 index 00000000000..bee8a64b79a --- /dev/null +++ b/libs/partners/ai21/.gitignore @@ -0,0 +1 @@ +__pycache__ diff --git a/libs/partners/ai21/LICENSE b/libs/partners/ai21/LICENSE new file mode 100644 index 00000000000..426b6509034 --- /dev/null +++ b/libs/partners/ai21/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/libs/partners/ai21/Makefile b/libs/partners/ai21/Makefile new file mode 100644 index 00000000000..1e9cd1ea594 --- /dev/null +++ b/libs/partners/ai21/Makefile @@ -0,0 +1,56 @@ +.PHONY: all format lint test tests integration_tests docker_tests help extended_tests + +# Default target executed when no arguments are given to make. +all: help + +# Define a variable for the test file path. +TEST_FILE ?= tests/unit_tests/ +integration_test integration_tests: TEST_FILE = tests/integration_tests/ +test tests integration_test integration_tests: + poetry run pytest $(TEST_FILE) + + +###################### +# LINTING AND FORMATTING +###################### + +# Define a variable for Python and notebook files. +PYTHON_FILES=. +MYPY_CACHE=.mypy_cache +lint format: PYTHON_FILES=. +lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/ai21 --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') +lint_package: PYTHON_FILES=langchain_ai21 +lint_tests: PYTHON_FILES=tests +lint_tests: MYPY_CACHE=.mypy_cache_test + +lint lint_diff lint_package lint_tests: + poetry run ruff . + poetry run ruff format $(PYTHON_FILES) --diff + poetry run ruff --select I $(PYTHON_FILES) + mkdir $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + +format format_diff: + poetry run ruff format $(PYTHON_FILES) + poetry run ruff --select I --fix $(PYTHON_FILES) + +spell_check: + poetry run codespell --toml pyproject.toml + +spell_fix: + poetry run codespell --toml pyproject.toml -w + +check_imports: $(shell find langchain_ai21 -name '*.py') + poetry run python ./scripts/check_imports.py $^ + +###################### +# HELP +###################### + +help: + @echo '----' + @echo 'check_imports - check imports' + @echo 'format - run code formatters' + @echo 'lint - run linters' + @echo 'test - run unit tests' + @echo 'tests - run unit tests' + @echo 'test TEST_FILE= - run all tests in file' diff --git a/libs/partners/ai21/README.md b/libs/partners/ai21/README.md new file mode 100644 index 00000000000..7b563969e94 --- /dev/null +++ b/libs/partners/ai21/README.md @@ -0,0 +1,75 @@ +# langchain-ai21 + +This package contains the LangChain integrations for [AI21](https://docs.ai21.com/) through their [AI21](https://pypi.org/project/ai21/) SDK. + +## Installation and Setup + +- Install the AI21 partner package +```bash +pip install langchain-ai21 +``` +- Get an AI21 api key and set it as an environment variable (`AI21_API_KEY`) + + +## Chat Models + +This package contains the `ChatAI21` class, which is the recommended way to interface with AI21 Chat models. + +To use, install the requirements, and configure your environment. + +```bash +export AI21_API_KEY=your-api-key +``` + +Then initialize + +```python +from langchain_core.messages import HumanMessage +from langchain_ai21.chat_models import ChatAI21 + +chat = ChatAI21(model="j2-ultra") +messages = [HumanMessage(content="Hello from AI21")] +chat.invoke(messages) +``` + +## LLMs +You can use AI21's generative AI models as Langchain LLMs: + +```python +from langchain.prompts import PromptTemplate +from langchain_ai21 import AI21LLM + +llm = AI21LLM(model="j2-ultra") + +template = """Question: {question} + +Answer: Let's think step by step.""" +prompt = PromptTemplate.from_template(template) + +chain = prompt | llm + +question = "Which scientist discovered relativity?" +print(chain.invoke({"question": question})) +``` + +## Embeddings + +You can use AI21's embeddings models as: + +### Query + +```python +from langchain_ai21 import AI21Embeddings + +embeddings = AI21Embeddings() +embeddings.embed_query("Hello! This is some query") +``` + +### Document + +```python +from langchain_ai21 import AI21Embeddings + +embeddings = AI21Embeddings() +embeddings.embed_documents(["Hello! This is document 1", "And this is document 2!"]) +``` diff --git a/libs/partners/ai21/langchain_ai21/__init__.py b/libs/partners/ai21/langchain_ai21/__init__.py new file mode 100644 index 00000000000..253de3778c9 --- /dev/null +++ b/libs/partners/ai21/langchain_ai21/__init__.py @@ -0,0 +1,9 @@ +from langchain_ai21.chat_models import ChatAI21 +from langchain_ai21.embeddings import AI21Embeddings +from langchain_ai21.llms import AI21LLM + +__all__ = [ + "AI21LLM", + "ChatAI21", + "AI21Embeddings", +] diff --git a/libs/partners/ai21/langchain_ai21/ai21_base.py b/libs/partners/ai21/langchain_ai21/ai21_base.py new file mode 100644 index 00000000000..39c5ffbf1f0 --- /dev/null +++ b/libs/partners/ai21/langchain_ai21/ai21_base.py @@ -0,0 +1,48 @@ +import os +from typing import Dict, Optional + +from ai21 import AI21Client +from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator +from langchain_core.utils import convert_to_secret_str + +_DEFAULT_TIMEOUT_SEC = 300 + + +class AI21Base(BaseModel): + class Config: + arbitrary_types_allowed = True + + client: AI21Client = Field(default=None) + api_key: Optional[SecretStr] = None + api_host: Optional[str] = None + timeout_sec: Optional[float] = None + num_retries: Optional[int] = None + + @root_validator() + def validate_environment(cls, values: Dict) -> Dict: + api_key = convert_to_secret_str( + values.get("api_key") or os.getenv("AI21_API_KEY") or "" + ) + values["api_key"] = api_key + + api_host = ( + values.get("api_host") + or os.getenv("AI21_API_URL") + or "https://api.ai21.com" + ) + values["api_host"] = api_host + + timeout_sec = values.get("timeout_sec") or float( + os.getenv("AI21_TIMEOUT_SEC", _DEFAULT_TIMEOUT_SEC) + ) + values["timeout_sec"] = timeout_sec + + if values.get("client") is None: + values["client"] = AI21Client( + api_key=api_key.get_secret_value(), + api_host=api_host, + timeout_sec=None if timeout_sec is None else float(timeout_sec), + via="langchain", + ) + + return values diff --git a/libs/partners/ai21/langchain_ai21/chat_models.py b/libs/partners/ai21/langchain_ai21/chat_models.py new file mode 100644 index 00000000000..0839a493862 --- /dev/null +++ b/libs/partners/ai21/langchain_ai21/chat_models.py @@ -0,0 +1,171 @@ +import asyncio +from functools import partial +from typing import Any, List, Optional, Tuple, cast + +from ai21.models import ChatMessage, Penalty, RoleType +from langchain_core.callbacks import ( + AsyncCallbackManagerForLLMRun, + CallbackManagerForLLMRun, +) +from langchain_core.language_models.chat_models import BaseChatModel +from langchain_core.messages import ( + AIMessage, + BaseMessage, + HumanMessage, + SystemMessage, +) +from langchain_core.outputs import ChatGeneration, ChatResult + +from langchain_ai21.ai21_base import AI21Base + + +def _get_system_message_from_message(message: BaseMessage) -> str: + if not isinstance(message.content, str): + raise ValueError( + f"System Message must be of type str. Got {type(message.content)}" + ) + + return message.content + + +def _convert_messages_to_ai21_messages( + messages: List[BaseMessage], +) -> Tuple[Optional[str], List[ChatMessage]]: + system_message = None + converted_messages: List[ChatMessage] = [] + + for i, message in enumerate(messages): + if message.type == "system": + if i != 0: + raise ValueError("System message must be at beginning of message list.") + else: + system_message = _get_system_message_from_message(message) + else: + converted_message = _convert_message_to_ai21_message(message) + converted_messages.append(converted_message) + + return system_message, converted_messages + + +def _convert_message_to_ai21_message( + message: BaseMessage, +) -> ChatMessage: + content = cast(str, message.content) + + role = None + + if isinstance(message, HumanMessage): + role = RoleType.USER + elif isinstance(message, AIMessage): + role = RoleType.ASSISTANT + + if not role: + raise ValueError( + f"Could not resolve role type from message {message}. " + f"Only support {HumanMessage.__name__} and {AIMessage.__name__}." + ) + + return ChatMessage(role=role, text=content) + + +def _pop_system_messages(messages: List[BaseMessage]) -> List[SystemMessage]: + system_message_indexes = [ + i for i, message in enumerate(messages) if isinstance(message, SystemMessage) + ] + + return [cast(SystemMessage, messages.pop(i)) for i in system_message_indexes] + + +class ChatAI21(BaseChatModel, AI21Base): + """ChatAI21 chat model. + + Example: + .. code-block:: python + + from langchain_ai21 import ChatAI21 + + + model = ChatAI21() + """ + + model: str + """Model type you wish to interact with. + You can view the options at https://github.com/AI21Labs/ai21-python?tab=readme-ov-file#model-types""" + num_results: int = 1 + """The number of responses to generate for a given prompt.""" + + max_tokens: int = 16 + """The maximum number of tokens to generate for each response.""" + + min_tokens: int = 0 + """The minimum number of tokens to generate for each response.""" + + temperature: float = 0.7 + """A value controlling the "creativity" of the model's responses.""" + + top_p: float = 1 + """A value controlling the diversity of the model's responses.""" + + top_k_return: int = 0 + """The number of top-scoring tokens to consider for each generation step.""" + + frequency_penalty: Optional[Penalty] = None + """A penalty applied to tokens that are frequently generated.""" + + presence_penalty: Optional[Penalty] = None + """ A penalty applied to tokens that are already present in the prompt.""" + + count_penalty: Optional[Penalty] = None + """A penalty applied to tokens based on their frequency + in the generated responses.""" + + class Config: + """Configuration for this pydantic object.""" + + arbitrary_types_allowed = True + + @property + def _llm_type(self) -> str: + """Return type of chat model.""" + return "chat-ai21" + + def _generate( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> ChatResult: + system, ai21_messages = _convert_messages_to_ai21_messages(messages) + + response = self.client.chat.create( + model=self.model, + messages=ai21_messages, + system=system or "", + num_results=self.num_results, + temperature=self.temperature, + max_tokens=self.max_tokens, + min_tokens=self.min_tokens, + top_p=self.top_p, + top_k_return=self.top_k_return, + stop_sequences=stop, + frequency_penalty=self.frequency_penalty, + presence_penalty=self.presence_penalty, + count_penalty=self.count_penalty, + **kwargs, + ) + + outputs = response.outputs + message = AIMessage(content=outputs[0].text) + return ChatResult(generations=[ChatGeneration(message=message)]) + + async def _agenerate( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> ChatResult: + return await asyncio.get_running_loop().run_in_executor( + None, partial(self._generate, **kwargs), messages, stop, run_manager + ) diff --git a/libs/partners/ai21/langchain_ai21/embeddings.py b/libs/partners/ai21/langchain_ai21/embeddings.py new file mode 100644 index 00000000000..59fad67b5c8 --- /dev/null +++ b/libs/partners/ai21/langchain_ai21/embeddings.py @@ -0,0 +1,41 @@ +from typing import Any, List + +from ai21.models import EmbedType +from langchain_core.embeddings import Embeddings + +from langchain_ai21.ai21_base import AI21Base + + +class AI21Embeddings(Embeddings, AI21Base): + """AI21 Embeddings embedding model. + To use, you should have the 'AI21_API_KEY' environment variable set + or pass as a named parameter to the constructor. + + Example: + .. code-block:: python + + from langchain_ai21 import AI21Embeddings + + embeddings = AI21Embeddings() + query_result = embeddings.embed_query("Hello embeddings world!") + """ + + def embed_documents(self, texts: List[str], **kwargs: Any) -> List[List[float]]: + """Embed search docs.""" + response = self.client.embed.create( + texts=texts, + type=EmbedType.SEGMENT, + **kwargs, + ) + + return [result.embedding for result in response.results] + + def embed_query(self, text: str, **kwargs: Any) -> List[float]: + """Embed query text.""" + response = self.client.embed.create( + texts=[text], + type=EmbedType.QUERY, + **kwargs, + ) + + return [result.embedding for result in response.results][0] diff --git a/libs/partners/ai21/langchain_ai21/llms.py b/libs/partners/ai21/langchain_ai21/llms.py new file mode 100644 index 00000000000..27a8121bbe1 --- /dev/null +++ b/libs/partners/ai21/langchain_ai21/llms.py @@ -0,0 +1,142 @@ +import asyncio +from functools import partial +from typing import ( + Any, + List, + Optional, +) + +from ai21.models import CompletionsResponse, Penalty +from langchain_core.callbacks import ( + AsyncCallbackManagerForLLMRun, + CallbackManagerForLLMRun, +) +from langchain_core.language_models import BaseLLM +from langchain_core.outputs import Generation, LLMResult + +from langchain_ai21.ai21_base import AI21Base + + +class AI21LLM(BaseLLM, AI21Base): + """AI21LLM large language models. + + Example: + .. code-block:: python + + from langchain_ai21 import AI21LLM + + model = AI21LLM() + """ + + model: str + """Model type you wish to interact with. + You can view the options at https://github.com/AI21Labs/ai21-python?tab=readme-ov-file#model-types""" + + num_results: int = 1 + """The number of responses to generate for a given prompt.""" + + max_tokens: int = 16 + """The maximum number of tokens to generate for each response.""" + + min_tokens: int = 0 + """The minimum number of tokens to generate for each response.""" + + temperature: float = 0.7 + """A value controlling the "creativity" of the model's responses.""" + + top_p: float = 1 + """A value controlling the diversity of the model's responses.""" + + top_k_returns: int = 0 + """The number of top-scoring tokens to consider for each generation step.""" + + frequency_penalty: Optional[Penalty] = None + """A penalty applied to tokens that are frequently generated.""" + + presence_penalty: Optional[Penalty] = None + """ A penalty applied to tokens that are already present in the prompt.""" + + count_penalty: Optional[Penalty] = None + """A penalty applied to tokens based on their frequency + in the generated responses.""" + + custom_model: Optional[str] = None + epoch: Optional[int] = None + + class Config: + """Configuration for this pydantic object.""" + + allow_population_by_field_name = True + + @property + def _llm_type(self) -> str: + """Return type of LLM.""" + return "ai21-llm" + + def _generate( + self, + prompts: List[str], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> LLMResult: + generations: List[List[Generation]] = [] + token_count = 0 + + for prompt in prompts: + response = self._invoke_completion( + prompt=prompt, model=self.model, stop_sequences=stop, **kwargs + ) + generation = self._response_to_generation(response) + generations.append(generation) + token_count += self.client.count_tokens(prompt) + + llm_output = {"token_count": token_count, "model_name": self.model} + return LLMResult(generations=generations, llm_output=llm_output) + + async def _agenerate( + self, + prompts: List[str], + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> LLMResult: + # Change implementation if integration natively supports async generation. + return await asyncio.get_running_loop().run_in_executor( + None, partial(self._generate, **kwargs), prompts, stop, run_manager + ) + + def _invoke_completion( + self, + prompt: str, + model: str, + stop_sequences: Optional[List[str]] = None, + **kwargs: Any, + ) -> CompletionsResponse: + return self.client.completion.create( + prompt=prompt, + model=model, + max_tokens=self.max_tokens, + num_results=self.num_results, + min_tokens=self.min_tokens, + temperature=self.temperature, + top_p=self.top_p, + top_k_return=self.top_k_returns, + custom_model=self.custom_model, + stop_sequences=stop_sequences, + frequency_penalty=self.frequency_penalty, + presence_penalty=self.presence_penalty, + count_penalty=self.count_penalty, + epoch=self.epoch, + ) + + def _response_to_generation( + self, response: CompletionsResponse + ) -> List[Generation]: + return [ + Generation( + text=completion.data.text, + generation_info=completion.to_dict(), + ) + for completion in response.completions + ] diff --git a/libs/partners/ai21/langchain_ai21/py.typed b/libs/partners/ai21/langchain_ai21/py.typed new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/partners/ai21/poetry.lock b/libs/partners/ai21/poetry.lock new file mode 100644 index 00000000000..49a59858f73 --- /dev/null +++ b/libs/partners/ai21/poetry.lock @@ -0,0 +1,975 @@ +# This file is automatically @generated by Poetry 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"poetry.core.masonry.api" + +[tool.pytest.ini_options] +# --strict-markers will raise errors on unknown marks. +# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks +# +# https://docs.pytest.org/en/7.1.x/reference/reference.html +# --strict-config any warnings encountered while parsing the `pytest` +# section of the configuration file raise errors. +# +# https://github.com/tophat/syrupy +# --snapshot-warn-unused Prints a warning on unused snapshots rather than fail the test suite. +addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" +# Registering custom markers. +# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers +markers = [ + "requires: mark tests as requiring a specific library", + "asyncio: mark tests as requiring asyncio", + "compile: mark placeholder test used to compile integration tests without running them", + "scheduled: mark tests to run in scheduled testing", +] +asyncio_mode = "auto" diff --git a/libs/partners/ai21/scripts/check_imports.py b/libs/partners/ai21/scripts/check_imports.py new file mode 100644 index 00000000000..fd21a4975b7 --- /dev/null +++ b/libs/partners/ai21/scripts/check_imports.py @@ -0,0 +1,17 @@ +import sys +import traceback +from importlib.machinery import SourceFileLoader + +if __name__ == "__main__": + files = sys.argv[1:] + has_failure = False + for file in files: + try: + SourceFileLoader("x", file).load_module() + except Exception: + has_faillure = True + print(file) + traceback.print_exc() + print() + + sys.exit(1 if has_failure else 0) diff --git a/libs/partners/ai21/scripts/check_pydantic.sh b/libs/partners/ai21/scripts/check_pydantic.sh new file mode 100755 index 00000000000..06b5bb81ae2 --- /dev/null +++ b/libs/partners/ai21/scripts/check_pydantic.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# +# This script searches for lines starting with "import pydantic" or "from pydantic" +# in tracked files within a Git repository. +# +# Usage: ./scripts/check_pydantic.sh /path/to/repository + +# Check if a path argument is provided +if [ $# -ne 1 ]; then + echo "Usage: $0 /path/to/repository" + exit 1 +fi + +repository_path="$1" + +# Search for lines matching the pattern within the specified repository +result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') + +# Check if any matching lines were found +if [ -n "$result" ]; then + echo "ERROR: The following lines need to be updated:" + echo "$result" + echo "Please replace the code with an import from langchain_core.pydantic_v1." + echo "For example, replace 'from pydantic import BaseModel'" + echo "with 'from langchain_core.pydantic_v1 import BaseModel'" + exit 1 +fi diff --git a/libs/partners/ai21/scripts/lint_imports.sh b/libs/partners/ai21/scripts/lint_imports.sh new file mode 100755 index 00000000000..695613c7ba8 --- /dev/null +++ b/libs/partners/ai21/scripts/lint_imports.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +set -eu + +# Initialize a variable to keep track of errors +errors=0 + +# make sure not importing from langchain or langchain_experimental +git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) +git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) + +# Decide on an exit status based on the errors +if [ "$errors" -gt 0 ]; then + exit 1 +else + exit 0 +fi diff --git a/libs/partners/ai21/tests/__init__.py b/libs/partners/ai21/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/partners/ai21/tests/integration_tests/__init__.py b/libs/partners/ai21/tests/integration_tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/partners/ai21/tests/integration_tests/test_chat_models.py b/libs/partners/ai21/tests/integration_tests/test_chat_models.py new file mode 100644 index 00000000000..37efd41e520 --- /dev/null +++ b/libs/partners/ai21/tests/integration_tests/test_chat_models.py @@ -0,0 +1,43 @@ +"""Test ChatAI21 chat model.""" +from langchain_core.messages import HumanMessage +from langchain_core.outputs import ChatGeneration + +from langchain_ai21.chat_models import ChatAI21 + + +def test_invoke() -> None: + """Test invoke tokens from AI21.""" + llm = ChatAI21(model="j2-ultra") + + result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"])) + assert isinstance(result.content, str) + + +def test_generation() -> None: + """Test invoke tokens from AI21.""" + llm = ChatAI21(model="j2-ultra") + message = HumanMessage(content="Hello") + + result = llm.generate([[message], [message]], config=dict(tags=["foo"])) + + for generations in result.generations: + assert len(generations) == 1 + for generation in generations: + assert isinstance(generation, ChatGeneration) + assert isinstance(generation.text, str) + assert generation.text == generation.message.content + + +async def test_ageneration() -> None: + """Test invoke tokens from AI21.""" + llm = ChatAI21(model="j2-ultra") + message = HumanMessage(content="Hello") + + result = await llm.agenerate([[message], [message]], config=dict(tags=["foo"])) + + for generations in result.generations: + assert len(generations) == 1 + for generation in generations: + assert isinstance(generation, ChatGeneration) + assert isinstance(generation.text, str) + assert generation.text == generation.message.content diff --git a/libs/partners/ai21/tests/integration_tests/test_compile.py b/libs/partners/ai21/tests/integration_tests/test_compile.py new file mode 100644 index 00000000000..33ecccdfa0f --- /dev/null +++ b/libs/partners/ai21/tests/integration_tests/test_compile.py @@ -0,0 +1,7 @@ +import pytest + + +@pytest.mark.compile +def test_placeholder() -> None: + """Used for compiling integration tests without running any real tests.""" + pass diff --git a/libs/partners/ai21/tests/integration_tests/test_embeddings.py b/libs/partners/ai21/tests/integration_tests/test_embeddings.py new file mode 100644 index 00000000000..8434234e56a --- /dev/null +++ b/libs/partners/ai21/tests/integration_tests/test_embeddings.py @@ -0,0 +1,19 @@ +"""Test AI21 embeddings.""" +from langchain_ai21.embeddings import AI21Embeddings + + +def test_langchain_ai21_embedding_documents() -> None: + """Test AI21 embeddings.""" + documents = ["foo bar"] + embedding = AI21Embeddings() + output = embedding.embed_documents(documents) + assert len(output) == 1 + assert len(output[0]) > 0 + + +def test_langchain_ai21_embedding_query() -> None: + """Test AI21 embeddings.""" + document = "foo bar" + embedding = AI21Embeddings() + output = embedding.embed_query(document) + assert len(output) > 0 diff --git a/libs/partners/ai21/tests/integration_tests/test_llms.py b/libs/partners/ai21/tests/integration_tests/test_llms.py new file mode 100644 index 00000000000..fe4a8125527 --- /dev/null +++ b/libs/partners/ai21/tests/integration_tests/test_llms.py @@ -0,0 +1,103 @@ +"""Test AI21LLM llm.""" + + +from langchain_ai21.llms import AI21LLM + + +def _generate_llm() -> AI21LLM: + """ + Testing AI21LLm using non default parameters with the following parameters + """ + return AI21LLM( + model="j2-ultra", + max_tokens=2, # Use less tokens for a faster response + temperature=0, # for a consistent response + epoch=1, + ) + + +def test_stream() -> None: + """Test streaming tokens from AI21.""" + llm = AI21LLM( + model="j2-ultra", + ) + + for token in llm.stream("I'm Pickle Rick"): + assert isinstance(token, str) + + +async def test_abatch() -> None: + """Test streaming tokens from AI21LLM.""" + llm = AI21LLM( + model="j2-ultra", + ) + + result = await llm.abatch(["I'm Pickle Rick", "I'm not Pickle Rick"]) + for token in result: + assert isinstance(token, str) + + +async def test_abatch_tags() -> None: + """Test batch tokens from AI21LLM.""" + llm = AI21LLM( + model="j2-ultra", + ) + + result = await llm.abatch( + ["I'm Pickle Rick", "I'm not Pickle Rick"], config={"tags": ["foo"]} + ) + for token in result: + assert isinstance(token, str) + + +def test_batch() -> None: + """Test batch tokens from AI21LLM.""" + llm = AI21LLM( + model="j2-ultra", + ) + + result = llm.batch(["I'm Pickle Rick", "I'm not Pickle Rick"]) + for token in result: + assert isinstance(token, str) + + +async def test_ainvoke() -> None: + """Test invoke tokens from AI21LLM.""" + llm = AI21LLM( + model="j2-ultra", + ) + + result = await llm.ainvoke("I'm Pickle Rick", config={"tags": ["foo"]}) + assert isinstance(result, str) + + +def test_invoke() -> None: + """Test invoke tokens from AI21LLM.""" + llm = AI21LLM( + model="j2-ultra", + ) + + result = llm.invoke("I'm Pickle Rick", config=dict(tags=["foo"])) + assert isinstance(result, str) + + +def test__generate() -> None: + llm = _generate_llm() + llm_result = llm.generate( + prompts=["Hey there, my name is Pickle Rick. What is your name?"], + stop=["##"], + ) + + assert len(llm_result.generations) > 0 + assert llm_result.llm_output["token_count"] != 0 # type: ignore + + +async def test__agenerate() -> None: + llm = _generate_llm() + llm_result = await llm.agenerate( + prompts=["Hey there, my name is Pickle Rick. What is your name?"], + stop=["##"], + ) + + assert len(llm_result.generations) > 0 + assert llm_result.llm_output["token_count"] != 0 # type: ignore diff --git a/libs/partners/ai21/tests/unit_tests/__init__.py b/libs/partners/ai21/tests/unit_tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/partners/ai21/tests/unit_tests/conftest.py b/libs/partners/ai21/tests/unit_tests/conftest.py new file mode 100644 index 00000000000..43545eaf02e --- /dev/null +++ b/libs/partners/ai21/tests/unit_tests/conftest.py @@ -0,0 +1,91 @@ +import os +from contextlib import contextmanager +from typing import Generator +from unittest.mock import Mock + +import pytest +from ai21 import AI21Client +from ai21.models import ( + ChatOutput, + ChatResponse, + Completion, + CompletionData, + CompletionFinishReason, + CompletionsResponse, + FinishReason, + Penalty, + RoleType, +) +from pytest_mock import MockerFixture + +DUMMY_API_KEY = "test_api_key" + + +BASIC_EXAMPLE_LLM_PARAMETERS = { + "num_results": 3, + "max_tokens": 20, + "min_tokens": 10, + "temperature": 0.5, + "top_p": 0.5, + "top_k_return": 0, + "frequency_penalty": Penalty(scale=0.2, apply_to_numbers=True), + "presence_penalty": Penalty(scale=0.2, apply_to_stopwords=True), + "count_penalty": Penalty( + scale=0.2, apply_to_punctuation=True, apply_to_emojis=True + ), +} + + +@pytest.fixture +def mocked_completion_response(mocker: MockerFixture) -> Mock: + mocked_response = mocker.MagicMock(spec=CompletionsResponse) + mocked_response.prompt = "this is a test prompt" + mocked_response.completions = [ + Completion( + data=CompletionData(text="test", tokens=[]), + finish_reason=CompletionFinishReason(reason=None, length=None), + ) + ] + return mocked_response + + +@pytest.fixture +def mock_client_with_completion( + mocker: MockerFixture, mocked_completion_response: Mock +) -> Mock: + mock_client = mocker.MagicMock(spec=AI21Client) + mock_client.completion = mocker.MagicMock() + mock_client.completion.create.side_effect = [ + mocked_completion_response, + mocked_completion_response, + ] + mock_client.count_tokens.side_effect = [10, 20] + + return mock_client + + +@pytest.fixture +def mock_client_with_chat(mocker: MockerFixture) -> Mock: + mock_client = mocker.MagicMock(spec=AI21Client) + mock_client.chat = mocker.MagicMock() + + output = ChatOutput( + text="Hello Pickle Rick!", + role=RoleType.ASSISTANT, + finish_reason=FinishReason(reason="testing"), + ) + mock_client.chat.create.return_value = ChatResponse(outputs=[output]) + + return mock_client + + +@contextmanager +def temporarily_unset_api_key() -> Generator: + """ + Unset and set environment key for testing purpose for when an API KEY is not set + """ + api_key = os.environ.pop("API_KEY", None) + yield + + if api_key is not None: + os.environ["API_KEY"] = api_key diff --git a/libs/partners/ai21/tests/unit_tests/test_chat_models.py b/libs/partners/ai21/tests/unit_tests/test_chat_models.py new file mode 100644 index 00000000000..83eb06bc457 --- /dev/null +++ b/libs/partners/ai21/tests/unit_tests/test_chat_models.py @@ -0,0 +1,239 @@ +"""Test chat model integration.""" +from typing import List, Optional +from unittest.mock import Mock, call + +import pytest +from ai21 import MissingApiKeyError +from ai21.models import ChatMessage, Penalty, RoleType +from langchain_core.messages import ( + AIMessage, + BaseMessage, + HumanMessage, + SystemMessage, +) +from langchain_core.messages import ( + ChatMessage as LangChainChatMessage, +) + +from langchain_ai21.chat_models import ( + ChatAI21, + _convert_message_to_ai21_message, + _convert_messages_to_ai21_messages, +) +from tests.unit_tests.conftest import ( + BASIC_EXAMPLE_LLM_PARAMETERS, + DUMMY_API_KEY, + temporarily_unset_api_key, +) + + +def test_initialization__when_no_api_key__should_raise_exception() -> None: + """Test integration initialization.""" + with temporarily_unset_api_key(): + with pytest.raises(MissingApiKeyError): + ChatAI21(model="j2-ultra") + + +def test_initialization__when_default_parameters_in_init() -> None: + """Test chat model initialization.""" + ChatAI21(api_key=DUMMY_API_KEY, model="j2-ultra") + + +def test_initialization__when_custom_parameters_in_init() -> None: + model = "j2-mid" + num_results = 1 + max_tokens = 10 + min_tokens = 20 + temperature = 0.1 + top_p = 0.1 + top_k_returns = 0 + frequency_penalty = Penalty(scale=0.2, apply_to_numbers=True) + presence_penalty = Penalty(scale=0.2, apply_to_stopwords=True) + count_penalty = Penalty(scale=0.2, apply_to_punctuation=True, apply_to_emojis=True) + + llm = ChatAI21( + api_key=DUMMY_API_KEY, + model=model, + num_results=num_results, + max_tokens=max_tokens, + min_tokens=min_tokens, + temperature=temperature, + top_p=top_p, + top_k_returns=top_k_returns, + frequency_penalty=frequency_penalty, + presence_penalty=presence_penalty, + count_penalty=count_penalty, + ) + assert llm.model == model + assert llm.num_results == num_results + assert llm.max_tokens == max_tokens + assert llm.min_tokens == min_tokens + assert llm.temperature == temperature + assert llm.top_p == top_p + assert llm.top_k_return == top_k_returns + assert llm.frequency_penalty == frequency_penalty + assert llm.presence_penalty == presence_penalty + assert count_penalty == count_penalty + + +@pytest.mark.parametrize( + ids=[ + "when_human_message", + "when_ai_message", + ], + argnames=["message", "expected_ai21_message"], + argvalues=[ + ( + HumanMessage(content="Human Message Content"), + ChatMessage(role=RoleType.USER, text="Human Message Content"), + ), + ( + AIMessage(content="AI Message Content"), + ChatMessage(role=RoleType.ASSISTANT, text="AI Message Content"), + ), + ], +) +def test_convert_message_to_ai21_message( + message: BaseMessage, expected_ai21_message: ChatMessage +) -> None: + ai21_message = _convert_message_to_ai21_message(message) + assert ai21_message == expected_ai21_message + + +@pytest.mark.parametrize( + ids=[ + "when_system_message", + "when_langchain_chat_message", + ], + argnames=["message"], + argvalues=[ + (SystemMessage(content="System Message Content"),), + (LangChainChatMessage(content="Chat Message Content", role="human"),), + ], +) +def test_convert_message_to_ai21_message__when_invalid_role__should_raise_exception( + message: BaseMessage, +) -> None: + with pytest.raises(ValueError) as e: + _convert_message_to_ai21_message(message) + assert e.value.args[0] == ( + f"Could not resolve role type from message {message}. " + f"Only support {HumanMessage.__name__} and {AIMessage.__name__}." + ) + + +@pytest.mark.parametrize( + ids=[ + "when_all_messages_are_human_messages__should_return_system_none", + "when_first_message_is_system__should_return_system", + ], + argnames=["messages", "expected_system", "expected_messages"], + argvalues=[ + ( + [ + HumanMessage(content="Human Message Content 1"), + HumanMessage(content="Human Message Content 2"), + ], + None, + [ + ChatMessage(role=RoleType.USER, text="Human Message Content 1"), + ChatMessage(role=RoleType.USER, text="Human Message Content 2"), + ], + ), + ( + [ + SystemMessage(content="System Message Content 1"), + HumanMessage(content="Human Message Content 1"), + ], + "System Message Content 1", + [ + ChatMessage(role=RoleType.USER, text="Human Message Content 1"), + ], + ), + ], +) +def test_convert_messages( + messages: List[BaseMessage], + expected_system: Optional[str], + expected_messages: List[ChatMessage], +) -> None: + system, ai21_messages = _convert_messages_to_ai21_messages(messages) + assert ai21_messages == expected_messages + assert system == expected_system + + +def test_convert_messages_when_system_is_not_first__should_raise_value_error() -> None: + messages = [ + HumanMessage(content="Human Message Content 1"), + SystemMessage(content="System Message Content 1"), + ] + with pytest.raises(ValueError): + _convert_messages_to_ai21_messages(messages) + + +def test_invoke(mock_client_with_chat: Mock) -> None: + chat_input = "I'm Pickle Rick" + + llm = ChatAI21( + model="j2-ultra", + api_key=DUMMY_API_KEY, + client=mock_client_with_chat, + **BASIC_EXAMPLE_LLM_PARAMETERS, + ) + llm.invoke(input=chat_input, config=dict(tags=["foo"])) + + mock_client_with_chat.chat.create.assert_called_once_with( + model="j2-ultra", + messages=[ChatMessage(role=RoleType.USER, text=chat_input)], + system="", + stop_sequences=None, + **BASIC_EXAMPLE_LLM_PARAMETERS, + ) + + +def test_generate(mock_client_with_chat: Mock) -> None: + messages0 = [ + HumanMessage(content="I'm Pickle Rick"), + AIMessage(content="Hello Pickle Rick! I am your AI Assistant"), + HumanMessage(content="Nice to meet you."), + ] + messages1 = [ + SystemMessage(content="system message"), + HumanMessage(content="What is 1 + 1"), + ] + llm = ChatAI21( + model="j2-ultra", + client=mock_client_with_chat, + **BASIC_EXAMPLE_LLM_PARAMETERS, + ) + + llm.generate(messages=[messages0, messages1]) + mock_client_with_chat.chat.create.assert_has_calls( + [ + call( + model="j2-ultra", + messages=[ + ChatMessage( + role=RoleType.USER, + text=str(messages0[0].content), + ), + ChatMessage( + role=RoleType.ASSISTANT, text=str(messages0[1].content) + ), + ChatMessage(role=RoleType.USER, text=str(messages0[2].content)), + ], + system="", + stop_sequences=None, + **BASIC_EXAMPLE_LLM_PARAMETERS, + ), + call( + model="j2-ultra", + messages=[ + ChatMessage(role=RoleType.USER, text=str(messages1[1].content)), + ], + system="system message", + stop_sequences=None, + **BASIC_EXAMPLE_LLM_PARAMETERS, + ), + ] + ) diff --git a/libs/partners/ai21/tests/unit_tests/test_embeddings.py b/libs/partners/ai21/tests/unit_tests/test_embeddings.py new file mode 100644 index 00000000000..a366b32dd33 --- /dev/null +++ b/libs/partners/ai21/tests/unit_tests/test_embeddings.py @@ -0,0 +1,67 @@ +"""Test embedding model integration.""" +from unittest.mock import Mock + +import pytest +from ai21 import AI21Client, MissingApiKeyError +from ai21.models import EmbedResponse, EmbedResult, EmbedType +from pytest_mock import MockerFixture + +from langchain_ai21.embeddings import AI21Embeddings +from tests.unit_tests.conftest import DUMMY_API_KEY, temporarily_unset_api_key + +_EXAMPLE_EMBEDDING_0 = [1.0, 2.0, 3.0] +_EXAMPLE_EMBEDDING_1 = [4.0, 5.0, 6.0] +_EXAMPLE_EMBEDDING_2 = [7.0, 8.0, 9.0] + +_EXAMPLE_EMBEDDING_RESPONSE = EmbedResponse( + results=[ + EmbedResult(_EXAMPLE_EMBEDDING_0), + EmbedResult(_EXAMPLE_EMBEDDING_1), + EmbedResult(_EXAMPLE_EMBEDDING_2), + ], + id="test_id", +) + + +def test_initialization__when_no_api_key__should_raise_exception() -> None: + """Test integration initialization.""" + with temporarily_unset_api_key(): + with pytest.raises(MissingApiKeyError): + AI21Embeddings() + + +@pytest.fixture +def mock_client_with_embeddings(mocker: MockerFixture) -> Mock: + mock_client = mocker.MagicMock(spec=AI21Client) + mock_client.embed = mocker.MagicMock() + mock_client.embed.create.return_value = _EXAMPLE_EMBEDDING_RESPONSE + + return mock_client + + +def test_embed_query(mock_client_with_embeddings: Mock) -> None: + llm = AI21Embeddings(client=mock_client_with_embeddings, api_key=DUMMY_API_KEY) + + text = "Hello embeddings world!" + response = llm.embed_query(text=text) + assert response == _EXAMPLE_EMBEDDING_0 + mock_client_with_embeddings.embed.create.assert_called_once_with( + texts=[text], + type=EmbedType.QUERY, + ) + + +def test_embed_documents(mock_client_with_embeddings: Mock) -> None: + llm = AI21Embeddings(client=mock_client_with_embeddings, api_key=DUMMY_API_KEY) + + texts = ["Hello embeddings world!", "Some other text", "Some more text"] + response = llm.embed_documents(texts=texts) + assert response == [ + _EXAMPLE_EMBEDDING_0, + _EXAMPLE_EMBEDDING_1, + _EXAMPLE_EMBEDDING_2, + ] + mock_client_with_embeddings.embed.create.assert_called_once_with( + texts=texts, + type=EmbedType.SEGMENT, + ) diff --git a/libs/partners/ai21/tests/unit_tests/test_imports.py b/libs/partners/ai21/tests/unit_tests/test_imports.py new file mode 100644 index 00000000000..28b11651a73 --- /dev/null +++ b/libs/partners/ai21/tests/unit_tests/test_imports.py @@ -0,0 +1,11 @@ +from langchain_ai21 import __all__ + +EXPECTED_ALL = [ + "AI21LLM", + "ChatAI21", + "AI21Embeddings", +] + + +def test_all_imports() -> None: + assert sorted(EXPECTED_ALL) == sorted(__all__) diff --git a/libs/partners/ai21/tests/unit_tests/test_llms.py b/libs/partners/ai21/tests/unit_tests/test_llms.py new file mode 100644 index 00000000000..a82240bea5d --- /dev/null +++ b/libs/partners/ai21/tests/unit_tests/test_llms.py @@ -0,0 +1,107 @@ +"""Test AI21 Chat API wrapper.""" +from unittest.mock import Mock, call + +import pytest +from ai21 import MissingApiKeyError +from ai21.models import ( + Penalty, +) + +from langchain_ai21 import AI21LLM +from tests.unit_tests.conftest import ( + BASIC_EXAMPLE_LLM_PARAMETERS, + DUMMY_API_KEY, + temporarily_unset_api_key, +) + + +def test_initialization__when_no_api_key__should_raise_exception() -> None: + """Test integration initialization.""" + with temporarily_unset_api_key(): + with pytest.raises(MissingApiKeyError): + AI21LLM( + model="j2-ultra", + ) + + +def test_initialization__when_default_parameters() -> None: + """Test integration initialization.""" + AI21LLM( + api_key=DUMMY_API_KEY, + model="j2-ultra", + ) + + +def test_initialization__when_custom_parameters_to_init() -> None: + """Test integration initialization.""" + AI21LLM( + api_key=DUMMY_API_KEY, + model="j2-mid", + num_results=2, + max_tokens=20, + min_tokens=10, + temperature=0.5, + top_p=0.5, + top_k_returns=0, + stop_sequences=["\n"], + frequency_penalty=Penalty(scale=0.2, apply_to_numbers=True), + presence_penalty=Penalty(scale=0.2, apply_to_stopwords=True), + count_penalty=Penalty( + scale=0.2, apply_to_punctuation=True, apply_to_emojis=True + ), + custom_model="test_model", + epoch=1, + ) + + +def test_generate(mock_client_with_completion: Mock) -> None: + # Setup test + prompt0 = "Hi, my name is what?" + prompt1 = "My name is who?" + stop = ["\n"] + custom_model = "test_model" + epoch = 1 + + ai21 = AI21LLM( + model="j2-ultra", + api_key=DUMMY_API_KEY, + client=mock_client_with_completion, + custom_model=custom_model, + epoch=epoch, + **BASIC_EXAMPLE_LLM_PARAMETERS, + ) + + # Make call to testing function + ai21.generate( + [prompt0, prompt1], + stop=stop, + ) + + # Assertions + mock_client_with_completion.count_tokens.assert_has_calls( + [ + call(prompt0), + call(prompt1), + ], + ) + + mock_client_with_completion.completion.create.assert_has_calls( + [ + call( + prompt=prompt0, + model="j2-ultra", + custom_model=custom_model, + stop_sequences=stop, + epoch=epoch, + **BASIC_EXAMPLE_LLM_PARAMETERS, + ), + call( + prompt=prompt1, + model="j2-ultra", + custom_model=custom_model, + stop_sequences=stop, + epoch=epoch, + **BASIC_EXAMPLE_LLM_PARAMETERS, + ), + ] + ) diff --git a/libs/partners/anthropic/Makefile b/libs/partners/anthropic/Makefile index bf2408a1000..c3d56b11324 100644 --- a/libs/partners/anthropic/Makefile +++ b/libs/partners/anthropic/Makefile @@ -5,11 +5,9 @@ all: help # Define a variable for the test file path. TEST_FILE ?= tests/unit_tests/ +integration_test integration_tests: TEST_FILE=tests/integration_tests/ -test: - poetry run pytest $(TEST_FILE) - -tests: +test tests integration_test integration_tests: poetry run pytest $(TEST_FILE) diff --git a/libs/partners/anthropic/langchain_anthropic/chat_models.py b/libs/partners/anthropic/langchain_anthropic/chat_models.py index 812b829589e..a7382c3cd1b 100644 --- a/libs/partners/anthropic/langchain_anthropic/chat_models.py +++ b/libs/partners/anthropic/langchain_anthropic/chat_models.py @@ -51,7 +51,7 @@ def _format_messages(messages: List[BaseMessage]) -> Tuple[Optional[str], List[D class ChatAnthropicMessages(BaseChatModel): - """Beta ChatAnthropicMessages chat model. + """ChatAnthropicMessages chat model. Example: .. code-block:: python @@ -143,7 +143,7 @@ class ChatAnthropicMessages(BaseChatModel): **kwargs: Any, ) -> Iterator[ChatGenerationChunk]: params = self._format_params(messages=messages, stop=stop, **kwargs) - with self._client.beta.messages.stream(**params) as stream: + with self._client.messages.stream(**params) as stream: for text in stream.text_stream: yield ChatGenerationChunk(message=AIMessageChunk(content=text)) @@ -155,7 +155,7 @@ class ChatAnthropicMessages(BaseChatModel): **kwargs: Any, ) -> AsyncIterator[ChatGenerationChunk]: params = self._format_params(messages=messages, stop=stop, **kwargs) - async with self._async_client.beta.messages.stream(**params) as stream: + async with self._async_client.messages.stream(**params) as stream: async for text in stream.text_stream: yield ChatGenerationChunk(message=AIMessageChunk(content=text)) @@ -167,7 +167,7 @@ class ChatAnthropicMessages(BaseChatModel): **kwargs: Any, ) -> ChatResult: params = self._format_params(messages=messages, stop=stop, **kwargs) - data = self._client.beta.messages.create(**params) + data = self._client.messages.create(**params) return ChatResult( generations=[ ChatGeneration(message=AIMessage(content=data.content[0].text)) @@ -183,7 +183,7 @@ class ChatAnthropicMessages(BaseChatModel): **kwargs: Any, ) -> ChatResult: params = self._format_params(messages=messages, stop=stop, **kwargs) - data = await self._async_client.beta.messages.create(**params) + data = await self._async_client.messages.create(**params) return ChatResult( generations=[ ChatGeneration(message=AIMessage(content=data.content[0].text)) diff --git a/libs/partners/anthropic/poetry.lock b/libs/partners/anthropic/poetry.lock index f7f9d599191..f06d19b59a0 100644 --- a/libs/partners/anthropic/poetry.lock +++ b/libs/partners/anthropic/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. [[package]] name = "annotated-types" @@ -16,13 +16,13 @@ typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} [[package]] name = "anthropic" -version = "0.8.0" +version = "0.16.0" description = "The official Python library for the anthropic API" optional = false python-versions = ">=3.7" files = [ - {file = "anthropic-0.8.0-py3-none-any.whl", hash = "sha256:4cd394b22d16013a8846bbea3ff6cbf83cfa6260af01b7142256204c6a8545da"}, - {file = "anthropic-0.8.0.tar.gz", hash = "sha256:5a5d7fd9149f28d6df875685f66ec68072edd0a9acf919648c041c6fc2da6f11"}, + {file = "anthropic-0.16.0-py3-none-any.whl", hash = "sha256:fb6657050c14c90ebc49df3510541a4f74a926f04598eaa2c4f5a3be772a95da"}, + {file = "anthropic-0.16.0.tar.gz", hash = "sha256:130730820d1b327f31a097f575e42721d7ef69c731728f4c2d7fa70cf3b7eee0"}, ] [package.dependencies] @@ -32,7 +32,11 @@ httpx = ">=0.23.0,<1" pydantic = ">=1.9.0,<3" sniffio = "*" tokenizers = ">=0.13.0" -typing-extensions = ">=4.5,<5" +typing-extensions = ">=4.7,<5" + +[package.extras] +bedrock = ["boto3 (>=1.28.57)", "botocore (>=1.31.57)"] +vertex = ["google-auth (>=2,<3)"] [[package]] name = "anyio" @@ -58,13 +62,13 @@ trio = ["trio (>=0.23)"] [[package]] name = "certifi" -version = "2023.11.17" +version = "2024.2.2" description = "Python package for providing Mozilla's CA Bundle." optional = false python-versions = ">=3.6" files = [ - {file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"}, - {file = "certifi-2023.11.17.tar.gz", hash = "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1"}, + {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"}, + {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"}, ] [[package]] @@ -196,13 +200,13 @@ files = [ [[package]] name = "distro" -version = "1.8.0" +version = "1.9.0" description = "Distro - an OS platform information API" optional = false python-versions = ">=3.6" files = [ - {file = "distro-1.8.0-py3-none-any.whl", hash = "sha256:99522ca3e365cac527b44bde033f64c6945d90eb9f769703caaec52b09bbd3ff"}, - {file = "distro-1.8.0.tar.gz", hash = "sha256:02e111d1dc6a50abb8eed6bf31c3e48ed8b0830d1ea2a1b78c61765c2513fdd8"}, + {file = "distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2"}, + {file = "distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed"}, ] [[package]] @@ -237,13 +241,13 @@ typing = ["typing-extensions (>=4.8)"] [[package]] name = "freezegun" -version = "1.3.1" +version = "1.4.0" description = "Let your Python tests travel through time" optional = false python-versions = ">=3.7" files = [ - {file = "freezegun-1.3.1-py3-none-any.whl", hash = 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a/libs/partners/anthropic/pyproject.toml b/libs/partners/anthropic/pyproject.toml index 459d07d3b4f..2cdfaba1989 100644 --- a/libs/partners/anthropic/pyproject.toml +++ b/libs/partners/anthropic/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-anthropic" -version = "0.0.1.post2" +version = "0.0.2" description = "An integration package connecting AnthropicMessages and LangChain" authors = [] readme = "README.md" @@ -12,8 +12,8 @@ license = "MIT" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -langchain-core = ">=0.0.12" -anthropic = "^0.8.0" +langchain-core = "^0.1" +anthropic = ">=0.16.0,<1" [tool.poetry.group.test] optional = true @@ -21,11 +21,11 @@ optional = true [tool.poetry.group.test.dependencies] pytest = "^7.3.0" freezegun = "^1.2.2" -pytest-mock = "^3.10.0" +pytest-mock = "^3.10.0" syrupy = "^4.0.2" pytest-watcher = "^0.3.4" pytest-asyncio = "^0.21.1" -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } [tool.poetry.group.codespell] optional = true @@ -41,34 +41,33 @@ ruff = "^0.1.5" [tool.poetry.group.typing.dependencies] mypy = "^0.991" -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } [tool.poetry.group.dev] optional = true [tool.poetry.group.dev.dependencies] -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } [tool.poetry.group.test_integration] optional = true [tool.poetry.group.test_integration.dependencies] -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } -[tool.ruff] +[tool.ruff.lint] select = [ - "E", # pycodestyle - "F", # pyflakes - "I", # isort + "E", # pycodestyle + "F", # pyflakes + "I", # isort + "T201", # print ] [tool.mypy] disallow_untyped_defs = "True" [tool.coverage.run] -omit = [ - "tests/*", -] +omit = ["tests/*"] [build-system] requires = ["poetry-core>=1.0.0"] diff --git a/libs/partners/anthropic/scripts/check_imports.py b/libs/partners/anthropic/scripts/check_imports.py index fd21a4975b7..365f5fa118d 100644 --- a/libs/partners/anthropic/scripts/check_imports.py +++ b/libs/partners/anthropic/scripts/check_imports.py @@ -10,8 +10,8 @@ if __name__ == "__main__": SourceFileLoader("x", file).load_module() except Exception: has_faillure = True - print(file) + print(file) # noqa: T201 traceback.print_exc() - print() + print() # noqa: T201 sys.exit(1 if has_failure else 0) diff --git a/libs/partners/astradb/.gitignore b/libs/partners/astradb/.gitignore new file mode 100644 index 00000000000..bdc93231f03 --- /dev/null +++ b/libs/partners/astradb/.gitignore @@ -0,0 +1,5 @@ +__pycache__ +*.env +.mypy_cache +.ruff_cache +.pytest_cache \ No newline at end of file diff --git a/libs/partners/astradb/LICENSE b/libs/partners/astradb/LICENSE new file mode 100644 index 00000000000..426b6509034 --- /dev/null +++ b/libs/partners/astradb/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/libs/partners/astradb/Makefile b/libs/partners/astradb/Makefile new file mode 100644 index 00000000000..ee1200c9af5 --- /dev/null +++ b/libs/partners/astradb/Makefile @@ -0,0 +1,66 @@ +SHELL := /bin/bash +.PHONY: all format lint test tests integration_test integration_tests spell_check help + +# Default target executed when no arguments are given to make. +all: help + +# Define a variable for the test file path. +TEST_FILE ?= tests/unit_tests/ +INTEGRATION_TEST_FILE ?= tests/integration_tests/ + +test: + poetry run pytest $(TEST_FILE) + +tests: + poetry run pytest $(TEST_FILE) + +integration_test: + poetry run pytest $(INTEGRATION_TEST_FILE) + +integration_tests: + poetry run pytest $(INTEGRATION_TEST_FILE) + +###################### +# LINTING AND FORMATTING +###################### + +# Define a variable for Python and notebook files. +PYTHON_FILES=. +MYPY_CACHE=.mypy_cache +lint format: PYTHON_FILES=. +lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/astradb --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') +lint_package: PYTHON_FILES=langchain_astradb +lint_tests: PYTHON_FILES=tests +lint_tests: MYPY_CACHE=.mypy_cache_test + +lint lint_diff lint_package lint_tests: + poetry run ruff . + poetry run ruff format $(PYTHON_FILES) --diff + poetry run ruff --select I $(PYTHON_FILES) + mkdir -p $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + +format format_diff: + poetry run ruff format $(PYTHON_FILES) + poetry run ruff --select I --fix $(PYTHON_FILES) + +spell_check: + poetry run codespell --toml pyproject.toml + +spell_fix: + poetry run codespell --toml pyproject.toml -w + +check_imports: $(shell find langchain_astradb -name '*.py') + poetry run python ./scripts/check_imports.py $^ + +###################### +# HELP +###################### + +help: + @echo '----' + @echo 'check_imports - check imports' + @echo 'format - run code formatters' + @echo 'lint - run linters' + @echo 'test - run unit tests' + @echo 'tests - run unit tests' + @echo 'test TEST_FILE= - run all tests in file' diff --git a/libs/partners/astradb/README.md b/libs/partners/astradb/README.md new file mode 100644 index 00000000000..a4c2dc84e1f --- /dev/null +++ b/libs/partners/astradb/README.md @@ -0,0 +1,35 @@ +# langchain-astradb + +This package contains the LangChain integrations for using DataStax Astra DB. + +> DataStax [Astra DB](https://docs.datastax.com/en/astra/home/astra.html) is a serverless vector-capable database built on Apache Cassandra® and made conveniently available +> through an easy-to-use JSON API. + +_**Note.** For a short transitional period, only some of the Astra DB integration classes are contained in this package (the remaining ones being still in `langchain-community`). In a short while, and surely by version 0.2 of LangChain, all of the Astra DB support will be removed from `langchain-community` and included in this package._ + +## Installation and Setup + +Installation of this partner package: + +```bash +pip install langchain-astradb +``` + +## Integrations overview + +### Vector Store + +```python +from langchain_astradb.vectorstores import AstraDBVectorStore + +my_store = AstraDBVectorStore( + embedding=my_embeddings, + collection_name="my_store", + api_endpoint="https://...", + token="AstraCS:...", +) +``` + +## Reference + +See the [LangChain docs page](https://python.langchain.com/docs/integrations/providers/astradb) for a more detailed listing. diff --git a/libs/partners/astradb/langchain_astradb/__init__.py b/libs/partners/astradb/langchain_astradb/__init__.py new file mode 100644 index 00000000000..fc86dd73bcf --- /dev/null +++ b/libs/partners/astradb/langchain_astradb/__init__.py @@ -0,0 +1,5 @@ +from langchain_astradb.vectorstores import AstraDBVectorStore + +__all__ = [ + "AstraDBVectorStore", +] diff --git a/libs/partners/astradb/langchain_astradb/py.typed b/libs/partners/astradb/langchain_astradb/py.typed new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/partners/astradb/langchain_astradb/utils/mmr.py b/libs/partners/astradb/langchain_astradb/utils/mmr.py new file mode 100644 index 00000000000..feb34ad1c23 --- /dev/null +++ b/libs/partners/astradb/langchain_astradb/utils/mmr.py @@ -0,0 +1,87 @@ +""" +Tools for the Maximal Marginal Relevance (MMR) reranking. +Duplicated from langchain_community to avoid cross-dependencies. + +Functions "maximal_marginal_relevance" and "cosine_similarity" +are duplicated in this utility respectively from modules: + - "libs/community/langchain_community/vectorstores/utils.py" + - "libs/community/langchain_community/utils/math.py" +""" + +import logging +from typing import List, Union + +import numpy as np + +logger = logging.getLogger(__name__) + +Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray] + + +def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray: + """Row-wise cosine similarity between two equal-width matrices.""" + if len(X) == 0 or len(Y) == 0: + return np.array([]) + + X = np.array(X) + Y = np.array(Y) + if X.shape[1] != Y.shape[1]: + raise ValueError( + f"Number of columns in X and Y must be the same. X has shape {X.shape} " + f"and Y has shape {Y.shape}." + ) + try: + import simsimd as simd # type: ignore + + X = np.array(X, dtype=np.float32) + Y = np.array(Y, dtype=np.float32) + Z = 1 - simd.cdist(X, Y, metric="cosine") + if isinstance(Z, float): + return np.array([Z]) + return Z + except ImportError: + logger.info( + "Unable to import simsimd, defaulting to NumPy implementation. If you want " + "to use simsimd please install with `pip install simsimd`." + ) + X_norm = np.linalg.norm(X, axis=1) + Y_norm = np.linalg.norm(Y, axis=1) + # Ignore divide by zero errors run time warnings as those are handled below. + with np.errstate(divide="ignore", invalid="ignore"): + similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm) + similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0 + return similarity + + +def maximal_marginal_relevance( + query_embedding: np.ndarray, + embedding_list: list, + lambda_mult: float = 0.5, + k: int = 4, +) -> List[int]: + """Calculate maximal marginal relevance.""" + if min(k, len(embedding_list)) <= 0: + return [] + if query_embedding.ndim == 1: + query_embedding = np.expand_dims(query_embedding, axis=0) + similarity_to_query = cosine_similarity(query_embedding, embedding_list)[0] + most_similar = int(np.argmax(similarity_to_query)) + idxs = [most_similar] + selected = np.array([embedding_list[most_similar]]) + while len(idxs) < min(k, len(embedding_list)): + best_score = -np.inf + idx_to_add = -1 + similarity_to_selected = cosine_similarity(embedding_list, selected) + for i, query_score in enumerate(similarity_to_query): + if i in idxs: + continue + redundant_score = max(similarity_to_selected[i]) + equation_score = ( + lambda_mult * query_score - (1 - lambda_mult) * redundant_score + ) + if equation_score > best_score: + best_score = equation_score + idx_to_add = i + idxs.append(idx_to_add) + selected = np.append(selected, [embedding_list[idx_to_add]], axis=0) + return idxs diff --git a/libs/partners/astradb/langchain_astradb/vectorstores/__init__.py b/libs/partners/astradb/langchain_astradb/vectorstores/__init__.py new file mode 100644 index 00000000000..310732d125f --- /dev/null +++ b/libs/partners/astradb/langchain_astradb/vectorstores/__init__.py @@ -0,0 +1,5 @@ +from langchain_astradb.vectorstores.astradb import AstraDBVectorStore + +__all__ = [ + "AstraDBVectorStore", +] diff --git a/libs/partners/astradb/langchain_astradb/vectorstores/astradb.py b/libs/partners/astradb/langchain_astradb/vectorstores/astradb.py new file mode 100644 index 00000000000..e501113e308 --- /dev/null +++ b/libs/partners/astradb/langchain_astradb/vectorstores/astradb.py @@ -0,0 +1,1317 @@ +from __future__ import annotations + +import asyncio +import uuid +import warnings +from asyncio import Task +from concurrent.futures import ThreadPoolExecutor +from typing import ( + Any, + Callable, + Dict, + Iterable, + List, + Optional, + Set, + Tuple, + Type, + TypeVar, + cast, +) + +import numpy as np +from astrapy.db import ( + AstraDB as AstraDBClient, +) +from astrapy.db import ( + AstraDBCollection, + AsyncAstraDBCollection, +) +from astrapy.db import ( + AsyncAstraDB as AsyncAstraDBClient, +) +from langchain_core.documents import Document +from langchain_core.embeddings import Embeddings +from langchain_core.runnables import run_in_executor +from langchain_core.runnables.utils import gather_with_concurrency +from langchain_core.utils.iter import batch_iterate +from langchain_core.vectorstores import VectorStore + +from langchain_astradb.utils.mmr import maximal_marginal_relevance + +T = TypeVar("T") +U = TypeVar("U") +DocDict = Dict[str, Any] # dicts expressing entries to insert + +# Batch/concurrency default values (if parameters not provided): +# Size of batches for bulk insertions: +# (20 is the max batch size for the HTTP API at the time of writing) +DEFAULT_BATCH_SIZE = 20 +# Number of threads to insert batches concurrently: +DEFAULT_BULK_INSERT_BATCH_CONCURRENCY = 16 +# Number of threads in a batch to insert pre-existing entries: +DEFAULT_BULK_INSERT_OVERWRITE_CONCURRENCY = 10 +# Number of threads (for deleting multiple rows concurrently): +DEFAULT_BULK_DELETE_CONCURRENCY = 20 + + +def _unique_list(lst: List[T], key: Callable[[T], U]) -> List[T]: + visited_keys: Set[U] = set() + new_lst = [] + for item in lst: + item_key = key(item) + if item_key not in visited_keys: + visited_keys.add(item_key) + new_lst.append(item) + return new_lst + + +class AstraDBVectorStore(VectorStore): + """Wrapper around DataStax Astra DB for vector-store workloads. + + For quickstart and details, visit: + docs.datastax.com/en/astra/home/astra.html + + Example: + .. code-block:: python + + from langchain_astradb.vectorstores import AstraDBVectorStore + from langchain_openai.embeddings import OpenAIEmbeddings + + embeddings = OpenAIEmbeddings() + vectorstore = AstraDBVectorStore( + embedding=embeddings, + collection_name="my_store", + token="AstraCS:...", + api_endpoint="https://-.apps.astra.datastax.com" + ) + + vectorstore.add_texts(["Giraffes", "All good here"]) + results = vectorstore.similarity_search("Everything's ok", k=1) + + Constructor Args (only keyword-arguments accepted): + embedding (Embeddings): embedding function to use. + collection_name (str): name of the Astra DB collection to create/use. + token (Optional[str]): API token for Astra DB usage. + api_endpoint (Optional[str]): full URL to the API endpoint, + such as "https://-us-east1.apps.astra.datastax.com". + astra_db_client (Optional[astrapy.db.AstraDB]): + *alternative to token+api_endpoint*, + you can pass an already-created 'astrapy.db.AstraDB' instance. + async_astra_db_client (Optional[astrapy.db.AsyncAstraDB]): + same as `astra_db_client`, but the basis for the async API + of the vector store. + namespace (Optional[str]): namespace (aka keyspace) where the + collection is created. Defaults to the database's "default namespace". + metric (Optional[str]): similarity function to use out of those + available in Astra DB. If left out, it will use Astra DB API's + defaults (i.e. "cosine" - but, for performance reasons, + "dot_product" is suggested if embeddings are normalized to one). + + Advanced arguments (coming with sensible defaults): + batch_size (Optional[int]): Size of batches for bulk insertions. + bulk_insert_batch_concurrency (Optional[int]): Number of threads + to insert batches concurrently. + bulk_insert_overwrite_concurrency (Optional[int]): Number of + threads in a batch to insert pre-existing entries. + bulk_delete_concurrency (Optional[int]): Number of threads + (for deleting multiple rows concurrently). + pre_delete_collection (Optional[bool]): whether to delete the collection + before creating it. If False and the collection already exists, + the collection will be used as is. + + A note on concurrency: as a rule of thumb, on a typical client machine + it is suggested to keep the quantity + bulk_insert_batch_concurrency * bulk_insert_overwrite_concurrency + much below 1000 to avoid exhausting the client multithreading/networking + resources. The hardcoded defaults are somewhat conservative to meet + most machines' specs, but a sensible choice to test may be: + bulk_insert_batch_concurrency = 80 + bulk_insert_overwrite_concurrency = 10 + A bit of experimentation is required to nail the best results here, + depending on both the machine/network specs and the expected workload + (specifically, how often a write is an update of an existing id). + Remember you can pass concurrency settings to individual calls to + add_texts and add_documents as well. + + A note on passing astra_db_client and/or async_astra_db_client instead + of the credentials (token, api_endpoint): + - if you pass only the async client when creating the store, + the sync methods will error when called. + - conversely, if you pass only the sync client, the async methods will + still be available, but will be wrapping its sync counterpart + in a `run_in_executor` construct instead of using the native async. + """ + + @staticmethod + def _filter_to_metadata(filter_dict: Optional[Dict[str, Any]]) -> Dict[str, Any]: + if filter_dict is None: + return {} + else: + metadata_filter = {} + for k, v in filter_dict.items(): + if k and k[0] == "$": + if isinstance(v, list): + metadata_filter[k] = [ + AstraDBVectorStore._filter_to_metadata(f) for f in v + ] + else: + # assume each list item can be fed back to this function + metadata_filter[k] = AstraDBVectorStore._filter_to_metadata(v) # type: ignore[assignment] + else: + metadata_filter[f"metadata.{k}"] = v + + return metadata_filter + + def __init__( + self, + *, + embedding: Embeddings, + collection_name: str, + token: Optional[str] = None, + api_endpoint: Optional[str] = None, + astra_db_client: Optional[AstraDBClient] = None, + async_astra_db_client: Optional[AsyncAstraDBClient] = None, + namespace: Optional[str] = None, + metric: Optional[str] = None, + batch_size: Optional[int] = None, + bulk_insert_batch_concurrency: Optional[int] = None, + bulk_insert_overwrite_concurrency: Optional[int] = None, + bulk_delete_concurrency: Optional[int] = None, + pre_delete_collection: bool = False, + ) -> None: + """ + Create an AstraDBVectorStore vector store object. See class docstring for help. + """ + + # Conflicting-arg checks: + if astra_db_client is not None or async_astra_db_client is not None: + if token is not None or api_endpoint is not None: + raise ValueError( + "You cannot pass 'astra_db_client' or 'async_astra_db_client' to " + "AstraDBVectorStore if passing 'token' and 'api_endpoint'." + ) + + self.embedding = embedding + self.collection_name = collection_name + self.token = token + self.api_endpoint = api_endpoint + self.namespace = namespace + # Concurrency settings + self.batch_size: int = batch_size or DEFAULT_BATCH_SIZE + self.bulk_insert_batch_concurrency: int = ( + bulk_insert_batch_concurrency or DEFAULT_BULK_INSERT_BATCH_CONCURRENCY + ) + self.bulk_insert_overwrite_concurrency: int = ( + bulk_insert_overwrite_concurrency + or DEFAULT_BULK_INSERT_OVERWRITE_CONCURRENCY + ) + self.bulk_delete_concurrency: int = ( + bulk_delete_concurrency or DEFAULT_BULK_DELETE_CONCURRENCY + ) + # "vector-related" settings + self._embedding_dimension: Optional[int] = None + self.metric = metric + + self.astra_db = astra_db_client + self.async_astra_db = async_astra_db_client + self.collection = None + self.async_collection = None + + if token and api_endpoint: + self.astra_db = AstraDBClient( + token=cast(str, self.token), + api_endpoint=cast(str, self.api_endpoint), + namespace=self.namespace, + ) + self.async_astra_db = AsyncAstraDBClient( + token=cast(str, self.token), + api_endpoint=cast(str, self.api_endpoint), + namespace=self.namespace, + ) + + if self.astra_db is not None: + self.collection = AstraDBCollection( + collection_name=self.collection_name, + astra_db=self.astra_db, + ) + + self.async_setup_db_task: Optional[Task] = None + if self.async_astra_db is not None: + self.async_collection = AsyncAstraDBCollection( + collection_name=self.collection_name, + astra_db=self.async_astra_db, + ) + try: + asyncio.get_running_loop() + self.async_setup_db_task = asyncio.create_task( + self._setup_db(pre_delete_collection) + ) + except RuntimeError: + pass + + if self.async_setup_db_task is None: + if not pre_delete_collection: + self._provision_collection() + else: + self.clear() + + def _ensure_astra_db_client(self) -> None: + """ + If no error is raised, that means self.collection + is also not None (as per constructor flow). + """ + if not self.astra_db: + raise ValueError("Missing AstraDB client") + + async def _setup_db(self, pre_delete_collection: bool) -> None: + if pre_delete_collection: + # _setup_db is called from the constructor only, from a place + # where async_astra_db is not None for sure + await self.async_astra_db.delete_collection( # type: ignore[union-attr] + collection_name=self.collection_name, + ) + await self._aprovision_collection() + + async def _ensure_db_setup(self) -> None: + if self.async_setup_db_task: + await self.async_setup_db_task + + def _get_embedding_dimension(self) -> int: + if self._embedding_dimension is None: + self._embedding_dimension = len( + self.embedding.embed_query("This is a sample sentence.") + ) + return self._embedding_dimension + + def _provision_collection(self) -> None: + """ + Run the API invocation to create the collection on the backend. + + Internal-usage method, no object members are set, + other than working on the underlying actual storage. + """ + self._ensure_astra_db_client() + # self.astra_db is not None (by _ensure_astra_db_client) + self.astra_db.create_collection( # type: ignore[union-attr] + dimension=self._get_embedding_dimension(), + collection_name=self.collection_name, + metric=self.metric, + ) + + async def _aprovision_collection(self) -> None: + """ + Run the API invocation to create the collection on the backend. + + Internal-usage method, no object members are set, + other than working on the underlying actual storage. + """ + if not self.async_astra_db: + await run_in_executor(None, self._provision_collection) + else: + await self.async_astra_db.create_collection( + dimension=self._get_embedding_dimension(), + collection_name=self.collection_name, + metric=self.metric, + ) + + @property + def embeddings(self) -> Embeddings: + return self.embedding + + @staticmethod + def _dont_flip_the_cos_score(similarity0to1: float) -> float: + """Keep similarity from client unchanged ad it's in [0:1] already.""" + return similarity0to1 + + def _select_relevance_score_fn(self) -> Callable[[float], float]: + """ + The underlying API calls already returns a "score proper", + i.e. one in [0, 1] where higher means more *similar*, + so here the final score transformation is not reversing the interval: + """ + return self._dont_flip_the_cos_score + + def clear(self) -> None: + """Empty the collection of all its stored entries.""" + self._ensure_astra_db_client() + # self.collection is not None (by _ensure_astra_db_client) + self.collection.delete_many(filter={}) # type: ignore[union-attr] + + async def aclear(self) -> None: + """Empty the collection of all its stored entries.""" + await self._ensure_db_setup() + if not self.async_astra_db: + return await run_in_executor(None, self.clear) + else: + # async_collection not None if so is async_astra_db (constr. flow) + await self.async_collection.delete_many({}) # type: ignore[union-attr] + + def delete_by_document_id(self, document_id: str) -> bool: + """ + Remove a single document from the store, given its document_id (str). + Return True if a document has indeed been deleted, False if ID not found. + """ + self._ensure_astra_db_client() + # self.collection is not None (by _ensure_astra_db_client) + deletion_response = self.collection.delete_one(document_id) # type: ignore[union-attr] + return ((deletion_response or {}).get("status") or {}).get( + "deletedCount", 0 + ) == 1 + + async def adelete_by_document_id(self, document_id: str) -> bool: + """ + Remove a single document from the store, given its document_id (str). + Return True if a document has indeed been deleted, False if ID not found. + """ + await self._ensure_db_setup() + if not self.async_collection: + return await run_in_executor(None, self.delete_by_document_id, document_id) + deletion_response = await self.async_collection.delete_one(document_id) + return ((deletion_response or {}).get("status") or {}).get( + "deletedCount", 0 + ) == 1 + + def delete( + self, + ids: Optional[List[str]] = None, + concurrency: Optional[int] = None, + **kwargs: Any, + ) -> Optional[bool]: + """Delete by vector ids. + + Args: + ids (Optional[List[str]]): List of ids to delete. + concurrency (Optional[int]): max number of threads issuing + single-doc delete requests. Defaults to instance-level setting. + + Returns: + Optional[bool]: True if deletion is successful, + False otherwise, None if not implemented. + """ + + if kwargs: + warnings.warn( + "Method 'delete' of AstraDBVectorStore vector store invoked with " + f"unsupported arguments ({', '.join(sorted(kwargs.keys()))}), " + "which will be ignored." + ) + + if ids is None: + raise ValueError("No ids provided to delete.") + + _max_workers = concurrency or self.bulk_delete_concurrency + with ThreadPoolExecutor(max_workers=_max_workers) as tpe: + _ = list( + tpe.map( + self.delete_by_document_id, + ids, + ) + ) + return True + + async def adelete( + self, + ids: Optional[List[str]] = None, + concurrency: Optional[int] = None, + **kwargs: Any, + ) -> Optional[bool]: + """Delete by vector ID or other criteria. + + Args: + ids: List of ids to delete. + concurrency (Optional[int]): max number of concurrent delete queries. + Defaults to instance-level setting. + **kwargs: Other keyword arguments that subclasses might use. + + Returns: + Optional[bool]: True if deletion is successful, + False otherwise, None if not implemented. + """ + if kwargs: + warnings.warn( + "Method 'adelete' of AstraDBVectorStore invoked with " + f"unsupported arguments ({', '.join(sorted(kwargs.keys()))}), " + "which will be ignored." + ) + + if ids is None: + raise ValueError("No ids provided to delete.") + + return all( + await gather_with_concurrency( + concurrency, *[self.adelete_by_document_id(doc_id) for doc_id in ids] + ) + ) + + def delete_collection(self) -> None: + """ + Completely delete the collection from the database (as opposed + to 'clear()', which empties it only). + Stored data is lost and unrecoverable, resources are freed. + Use with caution. + """ + self._ensure_astra_db_client() + # self.astra_db is not None (by _ensure_astra_db_client) + self.astra_db.delete_collection( # type: ignore[union-attr] + collection_name=self.collection_name, + ) + + async def adelete_collection(self) -> None: + """ + Completely delete the collection from the database (as opposed + to 'clear()', which empties it only). + Stored data is lost and unrecoverable, resources are freed. + Use with caution. + """ + await self._ensure_db_setup() + if not self.async_astra_db: + return await run_in_executor(None, self.delete_collection) + else: + await self.async_astra_db.delete_collection( + collection_name=self.collection_name, + ) + + @staticmethod + def _get_documents_to_insert( + texts: Iterable[str], + embedding_vectors: List[List[float]], + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + ) -> List[DocDict]: + if ids is None: + ids = [uuid.uuid4().hex for _ in texts] + if metadatas is None: + metadatas = [{} for _ in texts] + # + documents_to_insert = [ + { + "content": b_txt, + "_id": b_id, + "$vector": b_emb, + "metadata": b_md, + } + for b_txt, b_emb, b_id, b_md in zip( + texts, + embedding_vectors, + ids, + metadatas, + ) + ] + # make unique by id, keeping the last + uniqued_documents_to_insert = _unique_list( + documents_to_insert[::-1], + lambda document: document["_id"], + )[::-1] + return uniqued_documents_to_insert + + @staticmethod + def _get_missing_from_batch( + document_batch: List[DocDict], insert_result: Dict[str, Any] + ) -> Tuple[List[str], List[DocDict]]: + if "status" not in insert_result: + raise ValueError( + f"API Exception while running bulk insertion: {str(insert_result)}" + ) + batch_inserted = insert_result["status"]["insertedIds"] + # estimation of the preexisting documents that failed + missed_inserted_ids = {document["_id"] for document in document_batch} - set( + batch_inserted + ) + errors = insert_result.get("errors", []) + # careful for other sources of error other than "doc already exists" + num_errors = len(errors) + unexpected_errors = any( + error.get("errorCode") != "DOCUMENT_ALREADY_EXISTS" for error in errors + ) + if num_errors != len(missed_inserted_ids) or unexpected_errors: + raise ValueError( + f"API Exception while running bulk insertion: {str(errors)}" + ) + # deal with the missing insertions as upserts + missing_from_batch = [ + document + for document in document_batch + if document["_id"] in missed_inserted_ids + ] + return batch_inserted, missing_from_batch + + def add_texts( + self, + texts: Iterable[str], + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + *, + batch_size: Optional[int] = None, + batch_concurrency: Optional[int] = None, + overwrite_concurrency: Optional[int] = None, + **kwargs: Any, + ) -> List[str]: + """Run texts through the embeddings and add them to the vectorstore. + + If passing explicit ids, those entries whose id is in the store already + will be replaced. + + Args: + texts (Iterable[str]): Texts to add to the vectorstore. + metadatas (Optional[List[dict]], optional): Optional list of metadatas. + ids (Optional[List[str]], optional): Optional list of ids. + batch_size (Optional[int]): Number of documents in each API call. + Check the underlying Astra DB HTTP API specs for the max value + (20 at the time of writing this). If not provided, defaults + to the instance-level setting. + batch_concurrency (Optional[int]): number of threads to process + insertion batches concurrently. Defaults to instance-level + setting if not provided. + overwrite_concurrency (Optional[int]): number of threads to process + pre-existing documents in each batch (which require individual + API calls). Defaults to instance-level setting if not provided. + + A note on metadata: there are constraints on the allowed field names + in this dictionary, coming from the underlying Astra DB API. + For instance, the `$` (dollar sign) cannot be used in the dict keys. + See this document for details: + docs.datastax.com/en/astra-serverless/docs/develop/dev-with-json.html + + Returns: + List[str]: List of ids of the added texts. + """ + + if kwargs: + warnings.warn( + "Method 'add_texts' of AstraDBVectorStore vector store invoked with " + f"unsupported arguments ({', '.join(sorted(kwargs.keys()))}), " + "which will be ignored." + ) + self._ensure_astra_db_client() + + embedding_vectors = self.embedding.embed_documents(list(texts)) + documents_to_insert = self._get_documents_to_insert( + texts, embedding_vectors, metadatas, ids + ) + + def _handle_batch(document_batch: List[DocDict]) -> List[str]: + # self.collection is not None (by _ensure_astra_db_client) + im_result = self.collection.insert_many( # type: ignore[union-attr] + documents=document_batch, + options={"ordered": False}, + partial_failures_allowed=True, + ) + batch_inserted, missing_from_batch = self._get_missing_from_batch( + document_batch, im_result + ) + + def _handle_missing_document(missing_document: DocDict) -> str: + # self.collection is not None (by _ensure_astra_db_client) + replacement_result = self.collection.find_one_and_replace( # type: ignore[union-attr] + filter={"_id": missing_document["_id"]}, + replacement=missing_document, + ) + return replacement_result["data"]["document"]["_id"] + + _u_max_workers = ( + overwrite_concurrency or self.bulk_insert_overwrite_concurrency + ) + with ThreadPoolExecutor(max_workers=_u_max_workers) as tpe2: + batch_replaced = list( + tpe2.map( + _handle_missing_document, + missing_from_batch, + ) + ) + return batch_inserted + batch_replaced + + _b_max_workers = batch_concurrency or self.bulk_insert_batch_concurrency + with ThreadPoolExecutor(max_workers=_b_max_workers) as tpe: + all_ids_nested = tpe.map( + _handle_batch, + batch_iterate( + batch_size or self.batch_size, + documents_to_insert, + ), + ) + return [iid for id_list in all_ids_nested for iid in id_list] + + async def aadd_texts( + self, + texts: Iterable[str], + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + *, + batch_size: Optional[int] = None, + batch_concurrency: Optional[int] = None, + overwrite_concurrency: Optional[int] = None, + **kwargs: Any, + ) -> List[str]: + """Run texts through the embeddings and add them to the vectorstore. + + If passing explicit ids, those entries whose id is in the store already + will be replaced. + + Args: + texts (Iterable[str]): Texts to add to the vectorstore. + metadatas (Optional[List[dict]], optional): Optional list of metadatas. + ids (Optional[List[str]], optional): Optional list of ids. + batch_size (Optional[int]): Number of documents in each API call. + Check the underlying Astra DB HTTP API specs for the max value + (20 at the time of writing this). If not provided, defaults + to the instance-level setting. + batch_concurrency (Optional[int]): number of concurrent batch insertions. + Defaults to instance-level setting if not provided. + overwrite_concurrency (Optional[int]): number of concurrent API calls to + process pre-existing documents in each batch. + Defaults to instance-level setting if not provided. + + A note on metadata: there are constraints on the allowed field names + in this dictionary, coming from the underlying Astra DB API. + For instance, the `$` (dollar sign) cannot be used in the dict keys. + See this document for details: + docs.datastax.com/en/astra-serverless/docs/develop/dev-with-json.html + + Returns: + List[str]: List of ids of the added texts. + """ + await self._ensure_db_setup() + if not self.async_collection: + return await super().aadd_texts( + texts, + metadatas, + ids=ids, + batch_size=batch_size, + batch_concurrency=batch_concurrency, + overwrite_concurrency=overwrite_concurrency, + ) + else: + if kwargs: + warnings.warn( + "Method 'aadd_texts' of AstraDBVectorStore invoked with " + f"unsupported arguments ({', '.join(sorted(kwargs.keys()))}), " + "which will be ignored." + ) + + embedding_vectors = await self.embedding.aembed_documents(list(texts)) + documents_to_insert = self._get_documents_to_insert( + texts, embedding_vectors, metadatas, ids + ) + + async def _handle_batch(document_batch: List[DocDict]) -> List[str]: + # self.async_collection is not None here for sure + im_result = await self.async_collection.insert_many( # type: ignore[union-attr] + documents=document_batch, + options={"ordered": False}, + partial_failures_allowed=True, + ) + batch_inserted, missing_from_batch = self._get_missing_from_batch( + document_batch, im_result + ) + + async def _handle_missing_document(missing_document: DocDict) -> str: + # self.async_collection is not None here for sure + replacement_result = ( + await self.async_collection.find_one_and_replace( # type: ignore[union-attr] + filter={"_id": missing_document["_id"]}, + replacement=missing_document, + ) + ) + return replacement_result["data"]["document"]["_id"] + + _u_max_workers = ( + overwrite_concurrency or self.bulk_insert_overwrite_concurrency + ) + batch_replaced = await gather_with_concurrency( + _u_max_workers, + *[_handle_missing_document(doc) for doc in missing_from_batch], + ) + return batch_inserted + batch_replaced + + _b_max_workers = batch_concurrency or self.bulk_insert_batch_concurrency + all_ids_nested = await gather_with_concurrency( + _b_max_workers, + *[ + _handle_batch(batch) + for batch in batch_iterate( + batch_size or self.batch_size, + documents_to_insert, + ) + ], + ) + + return [iid for id_list in all_ids_nested for iid in id_list] + + def similarity_search_with_score_id_by_vector( + self, + embedding: List[float], + k: int = 4, + filter: Optional[Dict[str, Any]] = None, + ) -> List[Tuple[Document, float, str]]: + """Return docs most similar to embedding vector. + + Args: + embedding (str): Embedding to look up documents similar to. + k (int): Number of Documents to return. Defaults to 4. + Returns: + List of (Document, score, id), the most similar to the query vector. + """ + self._ensure_astra_db_client() + metadata_parameter = self._filter_to_metadata(filter) + # + hits = list( + # self.collection is not None (by _ensure_astra_db_client) + self.collection.paginated_find( # type: ignore[union-attr] + filter=metadata_parameter, + sort={"$vector": embedding}, + options={"limit": k, "includeSimilarity": True}, + projection={ + "_id": 1, + "content": 1, + "metadata": 1, + }, + ) + ) + # + return [ + ( + Document( + page_content=hit["content"], + metadata=hit["metadata"], + ), + hit["$similarity"], + hit["_id"], + ) + for hit in hits + ] + + async def asimilarity_search_with_score_id_by_vector( + self, + embedding: List[float], + k: int = 4, + filter: Optional[Dict[str, Any]] = None, + ) -> List[Tuple[Document, float, str]]: + """Return docs most similar to embedding vector. + + Args: + embedding (str): Embedding to look up documents similar to. + k (int): Number of Documents to return. Defaults to 4. + Returns: + List of (Document, score, id), the most similar to the query vector. + """ + await self._ensure_db_setup() + if not self.async_collection: + return await run_in_executor( + None, + self.similarity_search_with_score_id_by_vector, + embedding, + k, + filter, + ) + metadata_parameter = self._filter_to_metadata(filter) + # + return [ + ( + Document( + page_content=hit["content"], + metadata=hit["metadata"], + ), + hit["$similarity"], + hit["_id"], + ) + async for hit in self.async_collection.paginated_find( + filter=metadata_parameter, + sort={"$vector": embedding}, + options={"limit": k, "includeSimilarity": True}, + projection={ + "_id": 1, + "content": 1, + "metadata": 1, + }, + ) + ] + + def similarity_search_with_score_id( + self, + query: str, + k: int = 4, + filter: Optional[Dict[str, Any]] = None, + ) -> List[Tuple[Document, float, str]]: + embedding_vector = self.embedding.embed_query(query) + return self.similarity_search_with_score_id_by_vector( + embedding=embedding_vector, + k=k, + filter=filter, + ) + + async def asimilarity_search_with_score_id( + self, + query: str, + k: int = 4, + filter: Optional[Dict[str, Any]] = None, + ) -> List[Tuple[Document, float, str]]: + embedding_vector = await self.embedding.aembed_query(query) + return await self.asimilarity_search_with_score_id_by_vector( + embedding=embedding_vector, + k=k, + filter=filter, + ) + + def similarity_search_with_score_by_vector( + self, + embedding: List[float], + k: int = 4, + filter: Optional[Dict[str, Any]] = None, + ) -> List[Tuple[Document, float]]: + """Return docs most similar to embedding vector. + + Args: + embedding (str): Embedding to look up documents similar to. + k (int): Number of Documents to return. Defaults to 4. + Returns: + List of (Document, score), the most similar to the query vector. + """ + return [ + (doc, score) + for (doc, score, doc_id) in self.similarity_search_with_score_id_by_vector( + embedding=embedding, + k=k, + filter=filter, + ) + ] + + async def asimilarity_search_with_score_by_vector( + self, + embedding: List[float], + k: int = 4, + filter: Optional[Dict[str, Any]] = None, + ) -> List[Tuple[Document, float]]: + """Return docs most similar to embedding vector. + + Args: + embedding (str): Embedding to look up documents similar to. + k (int): Number of Documents to return. Defaults to 4. + Returns: + List of (Document, score), the most similar to the query vector. + """ + return [ + (doc, score) + for ( + doc, + score, + doc_id, + ) in await self.asimilarity_search_with_score_id_by_vector( + embedding=embedding, + k=k, + filter=filter, + ) + ] + + def similarity_search( + self, + query: str, + k: int = 4, + filter: Optional[Dict[str, Any]] = None, + **kwargs: Any, + ) -> List[Document]: + embedding_vector = self.embedding.embed_query(query) + return self.similarity_search_by_vector( + embedding_vector, + k, + filter=filter, + ) + + async def asimilarity_search( + self, + query: str, + k: int = 4, + filter: Optional[Dict[str, Any]] = None, + **kwargs: Any, + ) -> List[Document]: + embedding_vector = await self.embedding.aembed_query(query) + return await self.asimilarity_search_by_vector( + embedding_vector, + k, + filter=filter, + ) + + def similarity_search_by_vector( + self, + embedding: List[float], + k: int = 4, + filter: Optional[Dict[str, Any]] = None, + **kwargs: Any, + ) -> List[Document]: + return [ + doc + for doc, _ in self.similarity_search_with_score_by_vector( + embedding, + k, + filter=filter, + ) + ] + + async def asimilarity_search_by_vector( + self, + embedding: List[float], + k: int = 4, + filter: Optional[Dict[str, Any]] = None, + **kwargs: Any, + ) -> List[Document]: + return [ + doc + for doc, _ in await self.asimilarity_search_with_score_by_vector( + embedding, + k, + filter=filter, + ) + ] + + def similarity_search_with_score( + self, + query: str, + k: int = 4, + filter: Optional[Dict[str, Any]] = None, + ) -> List[Tuple[Document, float]]: + embedding_vector = self.embedding.embed_query(query) + return self.similarity_search_with_score_by_vector( + embedding_vector, + k, + filter=filter, + ) + + async def asimilarity_search_with_score( + self, + query: str, + k: int = 4, + filter: Optional[Dict[str, Any]] = None, + ) -> List[Tuple[Document, float]]: + embedding_vector = await self.embedding.aembed_query(query) + return await self.asimilarity_search_with_score_by_vector( + embedding_vector, + k, + filter=filter, + ) + + @staticmethod + def _get_mmr_hits( + embedding: List[float], k: int, lambda_mult: float, prefetch_hits: List[DocDict] + ) -> List[Document]: + mmr_chosen_indices = maximal_marginal_relevance( + np.array(embedding, dtype=np.float32), + [prefetch_hit["$vector"] for prefetch_hit in prefetch_hits], + k=k, + lambda_mult=lambda_mult, + ) + mmr_hits = [ + prefetch_hit + for prefetch_index, prefetch_hit in enumerate(prefetch_hits) + if prefetch_index in mmr_chosen_indices + ] + return [ + Document( + page_content=hit["content"], + metadata=hit["metadata"], + ) + for hit in mmr_hits + ] + + def max_marginal_relevance_search_by_vector( + self, + embedding: List[float], + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + filter: Optional[Dict[str, Any]] = None, + **kwargs: Any, + ) -> List[Document]: + """Return docs selected using the maximal marginal relevance. + Maximal marginal relevance optimizes for similarity to query AND diversity + among selected documents. + Args: + embedding: Embedding to look up documents similar to. + k: Number of Documents to return. + fetch_k: Number of Documents to fetch to pass to MMR algorithm. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Returns: + List of Documents selected by maximal marginal relevance. + """ + self._ensure_astra_db_client() + metadata_parameter = self._filter_to_metadata(filter) + + prefetch_hits = list( + # self.collection is not None (by _ensure_astra_db_client) + self.collection.paginated_find( # type: ignore[union-attr] + filter=metadata_parameter, + sort={"$vector": embedding}, + options={"limit": fetch_k, "includeSimilarity": True}, + projection={ + "_id": 1, + "content": 1, + "metadata": 1, + "$vector": 1, + }, + ) + ) + + return self._get_mmr_hits(embedding, k, lambda_mult, prefetch_hits) + + async def amax_marginal_relevance_search_by_vector( + self, + embedding: List[float], + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + filter: Optional[Dict[str, Any]] = None, + **kwargs: Any, + ) -> List[Document]: + """Return docs selected using the maximal marginal relevance. + Maximal marginal relevance optimizes for similarity to query AND diversity + among selected documents. + Args: + embedding: Embedding to look up documents similar to. + k: Number of Documents to return. + fetch_k: Number of Documents to fetch to pass to MMR algorithm. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Returns: + List of Documents selected by maximal marginal relevance. + """ + await self._ensure_db_setup() + if not self.async_collection: + return await run_in_executor( + None, + self.max_marginal_relevance_search_by_vector, + embedding, + k, + fetch_k, + lambda_mult, + filter, + **kwargs, + ) + metadata_parameter = self._filter_to_metadata(filter) + + prefetch_hits = [ + hit + async for hit in self.async_collection.paginated_find( + filter=metadata_parameter, + sort={"$vector": embedding}, + options={"limit": fetch_k, "includeSimilarity": True}, + projection={ + "_id": 1, + "content": 1, + "metadata": 1, + "$vector": 1, + }, + ) + ] + + return self._get_mmr_hits(embedding, k, lambda_mult, prefetch_hits) + + def max_marginal_relevance_search( + self, + query: str, + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + filter: Optional[Dict[str, Any]] = None, + **kwargs: Any, + ) -> List[Document]: + """Return docs selected using the maximal marginal relevance. + Maximal marginal relevance optimizes for similarity to query AND diversity + among selected documents. + Args: + query (str): Text to look up documents similar to. + k (int = 4): Number of Documents to return. + fetch_k (int = 20): Number of Documents to fetch to pass to MMR algorithm. + lambda_mult (float = 0.5): Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Optional. + Returns: + List of Documents selected by maximal marginal relevance. + """ + embedding_vector = self.embedding.embed_query(query) + return self.max_marginal_relevance_search_by_vector( + embedding_vector, + k, + fetch_k, + lambda_mult=lambda_mult, + filter=filter, + ) + + async def amax_marginal_relevance_search( + self, + query: str, + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + filter: Optional[Dict[str, Any]] = None, + **kwargs: Any, + ) -> List[Document]: + """Return docs selected using the maximal marginal relevance. + Maximal marginal relevance optimizes for similarity to query AND diversity + among selected documents. + Args: + query (str): Text to look up documents similar to. + k (int = 4): Number of Documents to return. + fetch_k (int = 20): Number of Documents to fetch to pass to MMR algorithm. + lambda_mult (float = 0.5): Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Optional. + Returns: + List of Documents selected by maximal marginal relevance. + """ + embedding_vector = await self.embedding.aembed_query(query) + return await self.amax_marginal_relevance_search_by_vector( + embedding_vector, + k, + fetch_k, + lambda_mult=lambda_mult, + filter=filter, + ) + + @classmethod + def _from_kwargs( + cls: Type[AstraDBVectorStore], + embedding: Embeddings, + **kwargs: Any, + ) -> AstraDBVectorStore: + known_kwargs = { + "collection_name", + "token", + "api_endpoint", + "astra_db_client", + "async_astra_db_client", + "namespace", + "metric", + "batch_size", + "bulk_insert_batch_concurrency", + "bulk_insert_overwrite_concurrency", + "bulk_delete_concurrency", + "batch_concurrency", + "overwrite_concurrency", + } + if kwargs: + unknown_kwargs = set(kwargs.keys()) - known_kwargs + if unknown_kwargs: + warnings.warn( + "Method 'from_texts' of AstraDBVectorStore vector store " + "invoked with unsupported arguments " + f"({', '.join(sorted(unknown_kwargs))}), " + "which will be ignored." + ) + + collection_name: str = kwargs["collection_name"] + token = kwargs.get("token") + api_endpoint = kwargs.get("api_endpoint") + astra_db_client = kwargs.get("astra_db_client") + async_astra_db_client = kwargs.get("async_astra_db_client") + namespace = kwargs.get("namespace") + metric = kwargs.get("metric") + + return cls( + embedding=embedding, + collection_name=collection_name, + token=token, + api_endpoint=api_endpoint, + astra_db_client=astra_db_client, + async_astra_db_client=async_astra_db_client, + namespace=namespace, + metric=metric, + batch_size=kwargs.get("batch_size"), + bulk_insert_batch_concurrency=kwargs.get("bulk_insert_batch_concurrency"), + bulk_insert_overwrite_concurrency=kwargs.get( + "bulk_insert_overwrite_concurrency" + ), + bulk_delete_concurrency=kwargs.get("bulk_delete_concurrency"), + ) + + @classmethod + def from_texts( + cls: Type[AstraDBVectorStore], + texts: List[str], + embedding: Embeddings, + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + **kwargs: Any, + ) -> AstraDBVectorStore: + """Create an Astra DB vectorstore from raw texts. + + Args: + texts (List[str]): the texts to insert. + embedding (Embeddings): the embedding function to use in the store. + metadatas (Optional[List[dict]]): metadata dicts for the texts. + ids (Optional[List[str]]): ids to associate to the texts. + *Additional arguments*: you can pass any argument that you would + to 'add_texts' and/or to the 'AstraDBVectorStore' constructor + (see these methods for details). These arguments will be + routed to the respective methods as they are. + + Returns: + an `AstraDBVectorStore` vectorstore. + """ + astra_db_store = AstraDBVectorStore._from_kwargs(embedding, **kwargs) + astra_db_store.add_texts( + texts=texts, + metadatas=metadatas, + ids=ids, + batch_size=kwargs.get("batch_size"), + batch_concurrency=kwargs.get("batch_concurrency"), + overwrite_concurrency=kwargs.get("overwrite_concurrency"), + ) + return astra_db_store + + @classmethod + async def afrom_texts( + cls: Type[AstraDBVectorStore], + texts: List[str], + embedding: Embeddings, + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + **kwargs: Any, + ) -> AstraDBVectorStore: + """Create an Astra DB vectorstore from raw texts. + + Args: + texts (List[str]): the texts to insert. + embedding (Embeddings): the embedding function to use in the store. + metadatas (Optional[List[dict]]): metadata dicts for the texts. + ids (Optional[List[str]]): ids to associate to the texts. + *Additional arguments*: you can pass any argument that you would + to 'add_texts' and/or to the 'AstraDBVectorStore' constructor + (see these methods for details). These arguments will be + routed to the respective methods as they are. + + Returns: + an `AstraDBVectorStore` vectorstore. + """ + astra_db_store = AstraDBVectorStore._from_kwargs(embedding, **kwargs) + await astra_db_store.aadd_texts( + texts=texts, + metadatas=metadatas, + ids=ids, + batch_size=kwargs.get("batch_size"), + batch_concurrency=kwargs.get("batch_concurrency"), + overwrite_concurrency=kwargs.get("overwrite_concurrency"), + ) + return astra_db_store + + @classmethod + def from_documents( + cls: Type[AstraDBVectorStore], + documents: List[Document], + embedding: Embeddings, + **kwargs: Any, + ) -> AstraDBVectorStore: + """Create an Astra DB vectorstore from a document list. + + Utility method that defers to 'from_texts' (see that one). + + Args: see 'from_texts', except here you have to supply 'documents' + in place of 'texts' and 'metadatas'. + + Returns: + an `AstraDBVectorStore` vectorstore. + """ + return super().from_documents(documents, embedding, **kwargs) diff --git 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+python = ">=3.8.1,<4.0" +langchain-core = "^0.1.5" +astrapy = "^0.7.5" +numpy = "^1" + +[tool.poetry.group.test] +optional = true + +[tool.poetry.group.test.dependencies] +pytest = "^7.3.0" +pytest-dotenv = "^0.5.2" +freezegun = "^1.2.2" +pytest-mock = "^3.10.0" +syrupy = "^4.0.2" +pytest-watcher = "^0.3.4" +pytest-asyncio = "^0.21.1" +langchain-core = { path = "../../core", develop = true } + +[tool.poetry.group.codespell] +optional = true + +[tool.poetry.group.codespell.dependencies] +codespell = "^2.2.0" + +[tool.poetry.group.test_integration] +optional = true + +[tool.poetry.group.test_integration.dependencies] + +[tool.poetry.group.lint] +optional = true + +[tool.poetry.group.lint.dependencies] +ruff = "^0.1.5" + +[tool.poetry.group.typing.dependencies] +mypy = "^0.991" +langchain-core = { path = "../../core", develop = true } + +[tool.poetry.group.dev] +optional = true + +[tool.poetry.group.dev.dependencies] +langchain-core = { path = "../../core", develop = true } + +[tool.ruff] +select = [ + "E", # pycodestyle + "F", # pyflakes + "I", # isort +] + +[tool.mypy] +disallow_untyped_defs = "True" + +[tool.coverage.run] +omit = ["tests/*"] + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" + +[tool.pytest.ini_options] +# --strict-markers will raise errors on unknown marks. +# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks +# +# https://docs.pytest.org/en/7.1.x/reference/reference.html +# --strict-config any warnings encountered while parsing the `pytest` +# section of the configuration file raise errors. +# +# https://github.com/tophat/syrupy +# --snapshot-warn-unused Prints a warning on unused snapshots rather than fail the test suite. +addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" +# Registering custom markers. +# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers +markers = [ + "requires: mark tests as requiring a specific library", + "asyncio: mark tests as requiring asyncio", + "compile: mark placeholder test used to compile integration tests without running them", +] +asyncio_mode = "auto" diff --git a/libs/partners/astradb/scripts/check_imports.py b/libs/partners/astradb/scripts/check_imports.py new file mode 100644 index 00000000000..fd21a4975b7 --- /dev/null +++ b/libs/partners/astradb/scripts/check_imports.py @@ -0,0 +1,17 @@ +import sys +import traceback +from importlib.machinery import SourceFileLoader + +if __name__ == "__main__": + files = sys.argv[1:] + has_failure = False + for file in files: + try: + SourceFileLoader("x", file).load_module() + except Exception: + has_faillure = True + print(file) + traceback.print_exc() + print() + + sys.exit(1 if has_failure else 0) diff --git a/libs/partners/astradb/scripts/check_pydantic.sh b/libs/partners/astradb/scripts/check_pydantic.sh new file mode 100755 index 00000000000..06b5bb81ae2 --- /dev/null +++ b/libs/partners/astradb/scripts/check_pydantic.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# +# This script searches for lines starting with "import pydantic" or "from pydantic" +# in tracked files within a Git repository. +# +# Usage: ./scripts/check_pydantic.sh /path/to/repository + +# Check if a path argument is provided +if [ $# -ne 1 ]; then + echo "Usage: $0 /path/to/repository" + exit 1 +fi + +repository_path="$1" + +# Search for lines matching the pattern within the specified repository +result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') + +# Check if any matching lines were found +if [ -n "$result" ]; then + echo "ERROR: The following lines need to be updated:" + echo "$result" + echo "Please replace the code with an import from langchain_core.pydantic_v1." + echo "For example, replace 'from pydantic import BaseModel'" + echo "with 'from langchain_core.pydantic_v1 import BaseModel'" + exit 1 +fi diff --git a/libs/partners/astradb/scripts/lint_imports.sh b/libs/partners/astradb/scripts/lint_imports.sh new file mode 100755 index 00000000000..695613c7ba8 --- /dev/null +++ b/libs/partners/astradb/scripts/lint_imports.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +set -eu + +# Initialize a variable to keep track of errors +errors=0 + +# make sure not importing from langchain or langchain_experimental +git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) +git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) + +# Decide on an exit status based on the errors +if [ "$errors" -gt 0 ]; then + exit 1 +else + exit 0 +fi diff --git a/libs/partners/astradb/tests/__init__.py b/libs/partners/astradb/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/partners/astradb/tests/integration_tests/.env.example b/libs/partners/astradb/tests/integration_tests/.env.example new file mode 100644 index 00000000000..4259d87682c --- /dev/null +++ b/libs/partners/astradb/tests/integration_tests/.env.example @@ -0,0 +1,5 @@ +# astra db +ASTRA_DB_API_ENDPOINT=https://your_astra_db_id-your_region.apps.astra.datastax.com +ASTRA_DB_APPLICATION_TOKEN=AstraCS:your_astra_db_application_token +# ASTRA_DB_KEYSPACE=your_astra_db_namespace +# ASTRA_DB_SKIP_COLLECTION_DELETIONS=true diff --git a/libs/partners/astradb/tests/integration_tests/__init__.py b/libs/partners/astradb/tests/integration_tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/partners/astradb/tests/integration_tests/conftest.py b/libs/partners/astradb/tests/integration_tests/conftest.py new file mode 100644 index 00000000000..02b518e8695 --- /dev/null +++ b/libs/partners/astradb/tests/integration_tests/conftest.py @@ -0,0 +1,19 @@ +# Getting the absolute path of the current file's directory +import os + +ABS_PATH = os.path.dirname(os.path.abspath(__file__)) + +# Getting the absolute path of the project's root directory +PROJECT_DIR = os.path.abspath(os.path.join(ABS_PATH, os.pardir, os.pardir)) + + +# Loading the .env file if it exists +def _load_env() -> None: + dotenv_path = os.path.join(PROJECT_DIR, "tests", "integration_tests", ".env") + if os.path.exists(dotenv_path): + from dotenv import load_dotenv + + load_dotenv(dotenv_path) + + +_load_env() diff --git a/libs/partners/astradb/tests/integration_tests/test_compile.py b/libs/partners/astradb/tests/integration_tests/test_compile.py new file mode 100644 index 00000000000..33ecccdfa0f --- /dev/null +++ b/libs/partners/astradb/tests/integration_tests/test_compile.py @@ -0,0 +1,7 @@ +import pytest + + +@pytest.mark.compile +def test_placeholder() -> None: + """Used for compiling integration tests without running any real tests.""" + pass diff --git a/libs/partners/astradb/tests/integration_tests/vectorstores/test_astradb.py b/libs/partners/astradb/tests/integration_tests/vectorstores/test_astradb.py new file mode 100644 index 00000000000..de68c016a98 --- /dev/null +++ b/libs/partners/astradb/tests/integration_tests/vectorstores/test_astradb.py @@ -0,0 +1,869 @@ +""" +Test of Astra DB vector store class `AstraDBVectorStore` + +Required to run this test: + - a recent `astrapy` Python package available + - an Astra DB instance; + - the two environment variables set: + export ASTRA_DB_API_ENDPOINT="https://-us-east1.apps.astra.datastax.com" + export ASTRA_DB_APPLICATION_TOKEN="AstraCS:........." + - optionally this as well (otherwise defaults are used): + export ASTRA_DB_KEYSPACE="my_keyspace" + - optionally: + export SKIP_COLLECTION_DELETE="0" ("1" = no deletions, default) +""" + +import json +import math +import os +from typing import Iterable, List, Optional, TypedDict + +import pytest +from langchain_core.documents import Document +from langchain_core.embeddings import Embeddings + +from langchain_astradb.vectorstores import AstraDBVectorStore + +# Faster testing (no actual collection deletions). Off by default (=full tests) +SKIP_COLLECTION_DELETE = ( + int(os.environ.get("ASTRA_DB_SKIP_COLLECTION_DELETIONS", "0")) != 0 +) + +COLLECTION_NAME_DIM2 = "lc_test_d2" +COLLECTION_NAME_DIM2_EUCLIDEAN = "lc_test_d2_eucl" + +MATCH_EPSILON = 0.0001 + +# Ad-hoc embedding classes: + + +class AstraDBCredentials(TypedDict): + token: str + api_endpoint: str + namespace: Optional[str] + + +class SomeEmbeddings(Embeddings): + """ + Turn a sentence into an embedding vector in some way. + Not important how. It is deterministic is all that counts. + """ + + def __init__(self, dimension: int) -> None: + self.dimension = dimension + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + return [self.embed_query(txt) for txt in texts] + + async def aembed_documents(self, texts: List[str]) -> List[List[float]]: + return self.embed_documents(texts) + + def embed_query(self, text: str) -> List[float]: + unnormed0 = [ord(c) for c in text[: self.dimension]] + unnormed = (unnormed0 + [1] + [0] * (self.dimension - 1 - len(unnormed0)))[ + : self.dimension + ] + norm = sum(x * x for x in unnormed) ** 0.5 + normed = [x / norm for x in unnormed] + return normed + + async def aembed_query(self, text: str) -> List[float]: + return self.embed_query(text) + + +class ParserEmbeddings(Embeddings): + """ + Parse input texts: if they are json for a List[float], fine. + Otherwise, return all zeros and call it a day. + """ + + def __init__(self, dimension: int) -> None: + self.dimension = dimension + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + return [self.embed_query(txt) for txt in texts] + + async def aembed_documents(self, texts: List[str]) -> List[List[float]]: + return self.embed_documents(texts) + + def embed_query(self, text: str) -> List[float]: + try: + vals = json.loads(text) + assert len(vals) == self.dimension + return vals + except Exception: + print(f'[ParserEmbeddings] Returning a moot vector for "{text}"') + return [0.0] * self.dimension + + async def aembed_query(self, text: str) -> List[float]: + return self.embed_query(text) + + +def _has_env_vars() -> bool: + return all( + [ + "ASTRA_DB_APPLICATION_TOKEN" in os.environ, + "ASTRA_DB_API_ENDPOINT" in os.environ, + ] + ) + + +@pytest.fixture(scope="session") +def astradb_credentials() -> Iterable[AstraDBCredentials]: + yield { + "token": os.environ["ASTRA_DB_APPLICATION_TOKEN"], + "api_endpoint": os.environ["ASTRA_DB_API_ENDPOINT"], + "namespace": os.environ.get("ASTRA_DB_KEYSPACE"), + } + + +@pytest.fixture(scope="function") +def store_someemb( + astradb_credentials: AstraDBCredentials, +) -> Iterable[AstraDBVectorStore]: + emb = SomeEmbeddings(dimension=2) + v_store = AstraDBVectorStore( + embedding=emb, + collection_name=COLLECTION_NAME_DIM2, + **astradb_credentials, + ) + v_store.clear() + + yield v_store + + if not SKIP_COLLECTION_DELETE: + v_store.delete_collection() + else: + v_store.clear() + + +@pytest.fixture(scope="function") +def store_parseremb( + astradb_credentials: AstraDBCredentials, +) -> Iterable[AstraDBVectorStore]: + emb = ParserEmbeddings(dimension=2) + v_store = AstraDBVectorStore( + embedding=emb, + collection_name=COLLECTION_NAME_DIM2, + **astradb_credentials, + ) + v_store.clear() + + yield v_store + + if not SKIP_COLLECTION_DELETE: + v_store.delete_collection() + else: + v_store.clear() + + +@pytest.mark.requires("astrapy") +@pytest.mark.skipif(not _has_env_vars(), reason="Missing Astra DB env. vars") +class TestAstraDBVectorStore: + def test_astradb_vectorstore_create_delete( + self, astradb_credentials: AstraDBCredentials + ) -> None: + """Create and delete.""" + from astrapy.db import AstraDB as LibAstraDB + + emb = SomeEmbeddings(dimension=2) + # creation by passing the connection secrets + v_store = AstraDBVectorStore( + embedding=emb, + collection_name=COLLECTION_NAME_DIM2, + **astradb_credentials, + ) + v_store.add_texts("Sample 1") + if not SKIP_COLLECTION_DELETE: + v_store.delete_collection() + else: + v_store.clear() + + # Creation by passing a ready-made astrapy client: + astra_db_client = LibAstraDB( + **astradb_credentials, + ) + v_store_2 = AstraDBVectorStore( + embedding=emb, + collection_name=COLLECTION_NAME_DIM2, + astra_db_client=astra_db_client, + ) + v_store_2.add_texts("Sample 2") + if not SKIP_COLLECTION_DELETE: + v_store_2.delete_collection() + else: + v_store_2.clear() + + async def test_astradb_vectorstore_create_delete_async( + self, astradb_credentials: AstraDBCredentials + ) -> None: + """Create and delete.""" + emb = SomeEmbeddings(dimension=2) + # creation by passing the connection secrets + v_store = AstraDBVectorStore( + embedding=emb, + collection_name=COLLECTION_NAME_DIM2, + **astradb_credentials, + ) + await v_store.adelete_collection() + # Creation by passing a ready-made astrapy client: + from astrapy.db import AsyncAstraDB + + astra_db_client = AsyncAstraDB( + **astradb_credentials, + ) + v_store_2 = AstraDBVectorStore( + embedding=emb, + collection_name="lc_test_2_async", + async_astra_db_client=astra_db_client, + ) + if not SKIP_COLLECTION_DELETE: + await v_store_2.adelete_collection() + else: + await v_store_2.aclear() + + @pytest.mark.skipif( + SKIP_COLLECTION_DELETE, + reason="Collection-deletion tests are suppressed", + ) + def test_astradb_vectorstore_pre_delete_collection( + self, astradb_credentials: AstraDBCredentials + ) -> None: + """Use of the pre_delete_collection flag.""" + emb = SomeEmbeddings(dimension=2) + v_store = AstraDBVectorStore( + embedding=emb, + collection_name=COLLECTION_NAME_DIM2, + **astradb_credentials, + ) + v_store.clear() + try: + v_store.add_texts( + texts=["aa"], + metadatas=[ + {"k": "a", "ord": 0}, + ], + ids=["a"], + ) + res1 = v_store.similarity_search("aa", k=5) + assert len(res1) == 1 + v_store = AstraDBVectorStore( + embedding=emb, + pre_delete_collection=True, + collection_name=COLLECTION_NAME_DIM2, + **astradb_credentials, + ) + res1 = v_store.similarity_search("aa", k=5) + assert len(res1) == 0 + finally: + v_store.delete_collection() + + @pytest.mark.skipif( + SKIP_COLLECTION_DELETE, + reason="Collection-deletion tests are suppressed", + ) + async def test_astradb_vectorstore_pre_delete_collection_async( + self, astradb_credentials: AstraDBCredentials + ) -> None: + """Use of the pre_delete_collection flag.""" + emb = SomeEmbeddings(dimension=2) + # creation by passing the connection secrets + + v_store = AstraDBVectorStore( + embedding=emb, + collection_name=COLLECTION_NAME_DIM2, + **astradb_credentials, + ) + try: + await v_store.aadd_texts( + texts=["aa"], + metadatas=[ + {"k": "a", "ord": 0}, + ], + ids=["a"], + ) + res1 = await v_store.asimilarity_search("aa", k=5) + assert len(res1) == 1 + v_store = AstraDBVectorStore( + embedding=emb, + pre_delete_collection=True, + collection_name=COLLECTION_NAME_DIM2, + **astradb_credentials, + ) + res1 = await v_store.asimilarity_search("aa", k=5) + assert len(res1) == 0 + finally: + await v_store.adelete_collection() + + def test_astradb_vectorstore_from_x( + self, astradb_credentials: AstraDBCredentials + ) -> None: + """from_texts and from_documents methods.""" + emb = SomeEmbeddings(dimension=2) + # prepare empty collection + AstraDBVectorStore( + embedding=emb, + collection_name=COLLECTION_NAME_DIM2, + **astradb_credentials, + ).clear() + # from_texts + v_store = AstraDBVectorStore.from_texts( + texts=["Hi", "Ho"], + embedding=emb, + collection_name=COLLECTION_NAME_DIM2, + **astradb_credentials, + ) + try: + assert v_store.similarity_search("Ho", k=1)[0].page_content == "Ho" + finally: + if not SKIP_COLLECTION_DELETE: + v_store.delete_collection() + else: + v_store.clear() + + # from_documents + v_store_2 = AstraDBVectorStore.from_documents( + [ + Document(page_content="Hee"), + Document(page_content="Hoi"), + ], + embedding=emb, + collection_name=COLLECTION_NAME_DIM2, + **astradb_credentials, + ) + try: + assert v_store_2.similarity_search("Hoi", k=1)[0].page_content == "Hoi" + finally: + if not SKIP_COLLECTION_DELETE: + v_store_2.delete_collection() + else: + v_store_2.clear() + + async def test_astradb_vectorstore_from_x_async( + self, astradb_credentials: AstraDBCredentials + ) -> None: + """from_texts and from_documents methods.""" + emb = SomeEmbeddings(dimension=2) + # prepare empty collection + await AstraDBVectorStore( + embedding=emb, + collection_name=COLLECTION_NAME_DIM2, + **astradb_credentials, + ).aclear() + # from_texts + v_store = await AstraDBVectorStore.afrom_texts( + texts=["Hi", "Ho"], + embedding=emb, + collection_name=COLLECTION_NAME_DIM2, + **astradb_credentials, + ) + try: + assert (await v_store.asimilarity_search("Ho", k=1))[0].page_content == "Ho" + finally: + if not SKIP_COLLECTION_DELETE: + await v_store.adelete_collection() + else: + await v_store.aclear() + + # from_documents + v_store_2 = await AstraDBVectorStore.afrom_documents( + [ + Document(page_content="Hee"), + Document(page_content="Hoi"), + ], + embedding=emb, + collection_name=COLLECTION_NAME_DIM2, + **astradb_credentials, + ) + try: + assert (await v_store_2.asimilarity_search("Hoi", k=1))[ + 0 + ].page_content == "Hoi" + finally: + if not SKIP_COLLECTION_DELETE: + await v_store_2.adelete_collection() + else: + await v_store_2.aclear() + + def test_astradb_vectorstore_crud(self, store_someemb: AstraDBVectorStore) -> None: + """Basic add/delete/update behaviour.""" + res0 = store_someemb.similarity_search("Abc", k=2) + assert res0 == [] + # write and check again + store_someemb.add_texts( + texts=["aa", "bb", "cc"], + metadatas=[ + {"k": "a", "ord": 0}, + {"k": "b", "ord": 1}, + {"k": "c", "ord": 2}, + ], + ids=["a", "b", "c"], + ) + res1 = store_someemb.similarity_search("Abc", k=5) + assert {doc.page_content for doc in res1} == {"aa", "bb", "cc"} + # partial overwrite and count total entries + store_someemb.add_texts( + texts=["cc", "dd"], + metadatas=[ + {"k": "c_new", "ord": 102}, + {"k": "d_new", "ord": 103}, + ], + ids=["c", "d"], + ) + res2 = store_someemb.similarity_search("Abc", k=10) + assert len(res2) == 4 + # pick one that was just updated and check its metadata + res3 = store_someemb.similarity_search_with_score_id( + query="cc", k=1, filter={"k": "c_new"} + ) + print(str(res3)) + doc3, score3, id3 = res3[0] + assert doc3.page_content == "cc" + assert doc3.metadata == {"k": "c_new", "ord": 102} + assert score3 > 0.999 # leaving some leeway for approximations... + assert id3 == "c" + # delete and count again + del1_res = store_someemb.delete(["b"]) + assert del1_res is True + del2_res = store_someemb.delete(["a", "c", "Z!"]) + assert del2_res is True # a non-existing ID was supplied + assert len(store_someemb.similarity_search("xy", k=10)) == 1 + # clear store + store_someemb.clear() + assert store_someemb.similarity_search("Abc", k=2) == [] + # add_documents with "ids" arg passthrough + store_someemb.add_documents( + [ + Document(page_content="vv", metadata={"k": "v", "ord": 204}), + Document(page_content="ww", metadata={"k": "w", "ord": 205}), + ], + ids=["v", "w"], + ) + assert len(store_someemb.similarity_search("xy", k=10)) == 2 + res4 = store_someemb.similarity_search("ww", k=1, filter={"k": "w"}) + assert res4[0].metadata["ord"] == 205 + + async def test_astradb_vectorstore_crud_async( + self, store_someemb: AstraDBVectorStore + ) -> None: + """Basic add/delete/update behaviour.""" + res0 = await store_someemb.asimilarity_search("Abc", k=2) + assert res0 == [] + # write and check again + await store_someemb.aadd_texts( + texts=["aa", "bb", "cc"], + metadatas=[ + {"k": "a", "ord": 0}, + {"k": "b", "ord": 1}, + {"k": "c", "ord": 2}, + ], + ids=["a", "b", "c"], + ) + res1 = await store_someemb.asimilarity_search("Abc", k=5) + assert {doc.page_content for doc in res1} == {"aa", "bb", "cc"} + # partial overwrite and count total entries + await store_someemb.aadd_texts( + texts=["cc", "dd"], + metadatas=[ + {"k": "c_new", "ord": 102}, + {"k": "d_new", "ord": 103}, + ], + ids=["c", "d"], + ) + res2 = await store_someemb.asimilarity_search("Abc", k=10) + assert len(res2) == 4 + # pick one that was just updated and check its metadata + res3 = await store_someemb.asimilarity_search_with_score_id( + query="cc", k=1, filter={"k": "c_new"} + ) + print(str(res3)) + doc3, score3, id3 = res3[0] + assert doc3.page_content == "cc" + assert doc3.metadata == {"k": "c_new", "ord": 102} + assert score3 > 0.999 # leaving some leeway for approximations... + assert id3 == "c" + # delete and count again + del1_res = await store_someemb.adelete(["b"]) + assert del1_res is True + del2_res = await store_someemb.adelete(["a", "c", "Z!"]) + assert del2_res is False # a non-existing ID was supplied + assert len(await store_someemb.asimilarity_search("xy", k=10)) == 1 + # clear store + await store_someemb.aclear() + assert await store_someemb.asimilarity_search("Abc", k=2) == [] + # add_documents with "ids" arg passthrough + await store_someemb.aadd_documents( + [ + Document(page_content="vv", metadata={"k": "v", "ord": 204}), + Document(page_content="ww", metadata={"k": "w", "ord": 205}), + ], + ids=["v", "w"], + ) + assert len(await store_someemb.asimilarity_search("xy", k=10)) == 2 + res4 = await store_someemb.asimilarity_search("ww", k=1, filter={"k": "w"}) + assert res4[0].metadata["ord"] == 205 + + def test_astradb_vectorstore_mmr(self, store_parseremb: AstraDBVectorStore) -> None: + """ + MMR testing. We work on the unit circle with angle multiples + of 2*pi/20 and prepare a store with known vectors for a controlled + MMR outcome. + """ + + def _v_from_i(i: int, N: int) -> str: + angle = 2 * math.pi * i / N + vector = [math.cos(angle), math.sin(angle)] + return json.dumps(vector) + + i_vals = [0, 4, 5, 13] + N_val = 20 + store_parseremb.add_texts( + [_v_from_i(i, N_val) for i in i_vals], metadatas=[{"i": i} for i in i_vals] + ) + res1 = store_parseremb.max_marginal_relevance_search( + _v_from_i(3, N_val), + k=2, + fetch_k=3, + ) + res_i_vals = {doc.metadata["i"] for doc in res1} + assert res_i_vals == {0, 4} + + async def test_astradb_vectorstore_mmr_async( + self, store_parseremb: AstraDBVectorStore + ) -> None: + """ + MMR testing. We work on the unit circle with angle multiples + of 2*pi/20 and prepare a store with known vectors for a controlled + MMR outcome. + """ + + def _v_from_i(i: int, N: int) -> str: + angle = 2 * math.pi * i / N + vector = [math.cos(angle), math.sin(angle)] + return json.dumps(vector) + + i_vals = [0, 4, 5, 13] + N_val = 20 + await store_parseremb.aadd_texts( + [_v_from_i(i, N_val) for i in i_vals], + metadatas=[{"i": i} for i in i_vals], + ) + res1 = await store_parseremb.amax_marginal_relevance_search( + _v_from_i(3, N_val), + k=2, + fetch_k=3, + ) + res_i_vals = {doc.metadata["i"] for doc in res1} + assert res_i_vals == {0, 4} + + def test_astradb_vectorstore_metadata( + self, store_someemb: AstraDBVectorStore + ) -> None: + """Metadata filtering.""" + store_someemb.add_documents( + [ + Document( + page_content="q", + metadata={"ord": ord("q"), "group": "consonant"}, + ), + Document( + page_content="w", + metadata={"ord": ord("w"), "group": "consonant"}, + ), + Document( + page_content="r", + metadata={"ord": ord("r"), "group": "consonant"}, + ), + Document( + page_content="e", + metadata={"ord": ord("e"), "group": "vowel"}, + ), + Document( + page_content="i", + metadata={"ord": ord("i"), "group": "vowel"}, + ), + Document( + page_content="o", + metadata={"ord": ord("o"), "group": "vowel"}, + ), + ] + ) + # no filters + res0 = store_someemb.similarity_search("x", k=10) + assert {doc.page_content for doc in res0} == set("qwreio") + # single filter + res1 = store_someemb.similarity_search( + "x", + k=10, + filter={"group": "vowel"}, + ) + assert {doc.page_content for doc in res1} == set("eio") + # multiple filters + res2 = store_someemb.similarity_search( + "x", + k=10, + filter={"group": "consonant", "ord": ord("q")}, + ) + assert {doc.page_content for doc in res2} == set("q") + # excessive filters + res3 = store_someemb.similarity_search( + "x", + k=10, + filter={"group": "consonant", "ord": ord("q"), "case": "upper"}, + ) + assert res3 == [] + # filter with logical operator + res4 = store_someemb.similarity_search( + "x", + k=10, + filter={"$or": [{"ord": ord("q")}, {"ord": ord("r")}]}, + ) + assert {doc.page_content for doc in res4} == {"q", "r"} + + def test_astradb_vectorstore_similarity_scale( + self, store_parseremb: AstraDBVectorStore + ) -> None: + """Scale of the similarity scores.""" + store_parseremb.add_texts( + texts=[ + json.dumps([1, 1]), + json.dumps([-1, -1]), + ], + ids=["near", "far"], + ) + res1 = store_parseremb.similarity_search_with_score( + json.dumps([0.5, 0.5]), + k=2, + ) + scores = [sco for _, sco in res1] + sco_near, sco_far = scores + assert abs(1 - sco_near) < MATCH_EPSILON and abs(sco_far) < MATCH_EPSILON + + async def test_astradb_vectorstore_similarity_scale_async( + self, store_parseremb: AstraDBVectorStore + ) -> None: + """Scale of the similarity scores.""" + await store_parseremb.aadd_texts( + texts=[ + json.dumps([1, 1]), + json.dumps([-1, -1]), + ], + ids=["near", "far"], + ) + res1 = await store_parseremb.asimilarity_search_with_score( + json.dumps([0.5, 0.5]), + k=2, + ) + scores = [sco for _, sco in res1] + sco_near, sco_far = scores + assert abs(1 - sco_near) < MATCH_EPSILON and abs(sco_far) < MATCH_EPSILON + + def test_astradb_vectorstore_massive_delete( + self, store_someemb: AstraDBVectorStore + ) -> None: + """Larger-scale bulk deletes.""" + M = 50 + texts = [str(i + 1 / 7.0) for i in range(2 * M)] + ids0 = ["doc_%i" % i for i in range(M)] + ids1 = ["doc_%i" % (i + M) for i in range(M)] + ids = ids0 + ids1 + store_someemb.add_texts(texts=texts, ids=ids) + # deleting a bunch of these + del_res0 = store_someemb.delete(ids0) + assert del_res0 is True + # deleting the rest plus a fake one + del_res1 = store_someemb.delete(ids1 + ["ghost!"]) + assert del_res1 is True # ensure no error + # nothing left + assert store_someemb.similarity_search("x", k=2 * M) == [] + + @pytest.mark.skipif( + SKIP_COLLECTION_DELETE, + reason="Collection-deletion tests are suppressed", + ) + def test_astradb_vectorstore_delete_collection( + self, astradb_credentials: AstraDBCredentials + ) -> None: + """behaviour of 'delete_collection'.""" + collection_name = COLLECTION_NAME_DIM2 + emb = SomeEmbeddings(dimension=2) + v_store = AstraDBVectorStore( + embedding=emb, + collection_name=collection_name, + **astradb_credentials, + ) + v_store.add_texts(["huh"]) + assert len(v_store.similarity_search("hah", k=10)) == 1 + # another instance pointing to the same collection on DB + v_store_kenny = AstraDBVectorStore( + embedding=emb, + collection_name=collection_name, + **astradb_credentials, + ) + v_store_kenny.delete_collection() + # dropped on DB, but 'v_store' should have no clue: + with pytest.raises(ValueError): + _ = v_store.similarity_search("hah", k=10) + + def test_astradb_vectorstore_custom_params( + self, astradb_credentials: AstraDBCredentials + ) -> None: + """Custom batch size and concurrency params.""" + emb = SomeEmbeddings(dimension=2) + # prepare empty collection + AstraDBVectorStore( + embedding=emb, + collection_name=COLLECTION_NAME_DIM2, + **astradb_credentials, + ).clear() + v_store = AstraDBVectorStore( + embedding=emb, + collection_name=COLLECTION_NAME_DIM2, + **astradb_credentials, + batch_size=17, + bulk_insert_batch_concurrency=13, + bulk_insert_overwrite_concurrency=7, + bulk_delete_concurrency=19, + ) + try: + # add_texts + N = 50 + texts = [str(i + 1 / 7.0) for i in range(N)] + ids = ["doc_%i" % i for i in range(N)] + v_store.add_texts(texts=texts, ids=ids) + v_store.add_texts( + texts=texts, + ids=ids, + batch_size=19, + batch_concurrency=7, + overwrite_concurrency=13, + ) + # + _ = v_store.delete(ids[: N // 2]) + _ = v_store.delete(ids[N // 2 :], concurrency=23) + # + finally: + if not SKIP_COLLECTION_DELETE: + v_store.delete_collection() + else: + v_store.clear() + + async def test_astradb_vectorstore_custom_params_async( + self, astradb_credentials: AstraDBCredentials + ) -> None: + """Custom batch size and concurrency params.""" + emb = SomeEmbeddings(dimension=2) + v_store = AstraDBVectorStore( + embedding=emb, + collection_name="lc_test_c_async", + batch_size=17, + bulk_insert_batch_concurrency=13, + bulk_insert_overwrite_concurrency=7, + bulk_delete_concurrency=19, + **astradb_credentials, + ) + try: + # add_texts + N = 50 + texts = [str(i + 1 / 7.0) for i in range(N)] + ids = ["doc_%i" % i for i in range(N)] + await v_store.aadd_texts(texts=texts, ids=ids) + await v_store.aadd_texts( + texts=texts, + ids=ids, + batch_size=19, + batch_concurrency=7, + overwrite_concurrency=13, + ) + # + await v_store.adelete(ids[: N // 2]) + await v_store.adelete(ids[N // 2 :], concurrency=23) + # + finally: + if not SKIP_COLLECTION_DELETE: + await v_store.adelete_collection() + else: + await v_store.aclear() + + def test_astradb_vectorstore_metrics( + self, astradb_credentials: AstraDBCredentials + ) -> None: + """ + Different choices of similarity metric. + Both stores (with "cosine" and "euclidea" metrics) contain these two: + - a vector slightly rotated w.r.t query vector + - a vector which is a long multiple of query vector + so, which one is "the closest one" depends on the metric. + """ + emb = ParserEmbeddings(dimension=2) + isq2 = 0.5**0.5 + isa = 0.7 + isb = (1.0 - isa * isa) ** 0.5 + texts = [ + json.dumps([isa, isb]), + json.dumps([10 * isq2, 10 * isq2]), + ] + ids = [ + "rotated", + "scaled", + ] + query_text = json.dumps([isq2, isq2]) + + # prepare empty collections + AstraDBVectorStore( + embedding=emb, + collection_name=COLLECTION_NAME_DIM2, + **astradb_credentials, + ).clear() + AstraDBVectorStore( + embedding=emb, + collection_name=COLLECTION_NAME_DIM2_EUCLIDEAN, + metric="euclidean", + **astradb_credentials, + ).clear() + + # creation, population, query - cosine + vstore_cos = AstraDBVectorStore( + embedding=emb, + collection_name=COLLECTION_NAME_DIM2, + metric="cosine", + **astradb_credentials, + ) + try: + vstore_cos.add_texts( + texts=texts, + ids=ids, + ) + _, _, id_from_cos = vstore_cos.similarity_search_with_score_id( + query_text, + k=1, + )[0] + assert id_from_cos == "scaled" + finally: + if not SKIP_COLLECTION_DELETE: + vstore_cos.delete_collection() + else: + vstore_cos.clear() + # creation, population, query - euclidean + + vstore_euc = AstraDBVectorStore( + embedding=emb, + collection_name=COLLECTION_NAME_DIM2_EUCLIDEAN, + metric="euclidean", + **astradb_credentials, + ) + try: + vstore_euc.add_texts( + texts=texts, + ids=ids, + ) + _, _, id_from_euc = vstore_euc.similarity_search_with_score_id( + query_text, + k=1, + )[0] + assert id_from_euc == "rotated" + finally: + if not SKIP_COLLECTION_DELETE: + vstore_euc.delete_collection() + else: + vstore_euc.clear() diff --git a/libs/partners/astradb/tests/unit_tests/__init__.py b/libs/partners/astradb/tests/unit_tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/partners/astradb/tests/unit_tests/test_imports.py b/libs/partners/astradb/tests/unit_tests/test_imports.py new file mode 100644 index 00000000000..2240748c70c --- /dev/null +++ b/libs/partners/astradb/tests/unit_tests/test_imports.py @@ -0,0 +1,9 @@ +from langchain_astradb import __all__ + +EXPECTED_ALL = [ + "AstraDBVectorStore", +] + + +def test_all_imports() -> None: + assert sorted(EXPECTED_ALL) == sorted(__all__) diff --git a/libs/partners/astradb/tests/unit_tests/test_vectorstores.py b/libs/partners/astradb/tests/unit_tests/test_vectorstores.py new file mode 100644 index 00000000000..ebfc6978d18 --- /dev/null +++ b/libs/partners/astradb/tests/unit_tests/test_vectorstores.py @@ -0,0 +1,45 @@ +from typing import List +from unittest.mock import Mock + +from langchain_core.embeddings import Embeddings + +from langchain_astradb.vectorstores import AstraDBVectorStore + + +class SomeEmbeddings(Embeddings): + """ + Turn a sentence into an embedding vector in some way. + Not important how. It is deterministic is all that counts. + """ + + def __init__(self, dimension: int) -> None: + self.dimension = dimension + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + return [self.embed_query(txt) for txt in texts] + + async def aembed_documents(self, texts: List[str]) -> List[List[float]]: + return self.embed_documents(texts) + + def embed_query(self, text: str) -> List[float]: + unnormed0 = [ord(c) for c in text[: self.dimension]] + unnormed = (unnormed0 + [1] + [0] * (self.dimension - 1 - len(unnormed0)))[ + : self.dimension + ] + norm = sum(x * x for x in unnormed) ** 0.5 + normed = [x / norm for x in unnormed] + return normed + + async def aembed_query(self, text: str) -> List[float]: + return self.embed_query(text) + + +def test_initialization() -> None: + """Test integration vectorstore initialization.""" + mock_astra_db = Mock() + embedding = SomeEmbeddings(dimension=2) + AstraDBVectorStore( + embedding=embedding, + collection_name="mock_coll_name", + astra_db_client=mock_astra_db, + ) diff --git a/libs/partners/exa/langchain_exa/retrievers.py b/libs/partners/exa/langchain_exa/retrievers.py index e316d0ab84c..0011ddc008e 100644 --- a/libs/partners/exa/langchain_exa/retrievers.py +++ b/libs/partners/exa/langchain_exa/retrievers.py @@ -4,7 +4,7 @@ from exa_py import Exa # type: ignore from exa_py.api import HighlightsContentsOptions, TextContentsOptions # type: ignore from langchain_core.callbacks import CallbackManagerForRetrieverRun from langchain_core.documents import Document -from langchain_core.pydantic_v1 import SecretStr, root_validator +from langchain_core.pydantic_v1 import Field, SecretStr, root_validator from langchain_core.retrievers import BaseRetriever from langchain_exa._utilities import initialize_client @@ -53,8 +53,8 @@ class ExaSearchRetriever(BaseRetriever): text_contents_options: Union[TextContentsOptions, Literal[True]] = True """How to set the page content of the results""" - client: Exa - exa_api_key: SecretStr + client: Exa = Field(default=None) + exa_api_key: SecretStr = Field(default=None) exa_base_url: Optional[str] = None @root_validator(pre=True) @@ -66,11 +66,11 @@ class ExaSearchRetriever(BaseRetriever): def _get_relevant_documents( self, query: str, *, run_manager: CallbackManagerForRetrieverRun ) -> List[Document]: - response = self.client.search_and_contents( + response = self.client.search_and_contents( # type: ignore[misc] query, num_results=self.k, text=self.text_contents_options, - highlights=self.highlights, + highlights=self.highlights, # type: ignore include_domains=self.include_domains, exclude_domains=self.exclude_domains, start_crawl_date=self.start_crawl_date, diff --git a/libs/partners/exa/langchain_exa/tools.py b/libs/partners/exa/langchain_exa/tools.py index 20636afbfb2..8cff78fd900 100644 --- a/libs/partners/exa/langchain_exa/tools.py +++ b/libs/partners/exa/langchain_exa/tools.py @@ -7,7 +7,7 @@ from exa_py.api import HighlightsContentsOptions, TextContentsOptions # type: i from langchain_core.callbacks import ( CallbackManagerForToolRun, ) -from langchain_core.pydantic_v1 import SecretStr, root_validator +from langchain_core.pydantic_v1 import Field, SecretStr, root_validator from langchain_core.tools import BaseTool from langchain_exa._utilities import initialize_client @@ -22,8 +22,8 @@ class ExaSearchResults(BaseTool): "Input should be an Exa-optimized query. " "Output is a JSON array of the query results" ) - client: Exa - exa_api_key: SecretStr + client: Exa = Field(default=None) + exa_api_key: SecretStr = Field(default=None) @root_validator(pre=True) def validate_environment(cls, values: Dict) -> Dict: @@ -51,8 +51,8 @@ class ExaSearchResults(BaseTool): return self.client.search_and_contents( query, num_results=num_results, - text=text_contents_options, - highlights=highlights, + text=text_contents_options, # type: ignore + highlights=highlights, # type: ignore include_domains=include_domains, exclude_domains=exclude_domains, start_crawl_date=start_crawl_date, @@ -60,7 +60,7 @@ class ExaSearchResults(BaseTool): start_published_date=start_published_date, end_published_date=end_published_date, use_autoprompt=use_autoprompt, - ) + ) # type: ignore except Exception as e: return repr(e) @@ -74,8 +74,8 @@ class ExaFindSimilarResults(BaseTool): "Input should be an Exa-optimized query. " "Output is a JSON array of the query results" ) - client: Exa - exa_api_key: SecretStr + client: Exa = Field(default=None) + exa_api_key: SecretStr = Field(default=None) exa_base_url: Optional[str] = None @root_validator(pre=True) @@ -105,8 +105,8 @@ class ExaFindSimilarResults(BaseTool): return self.client.find_similar_and_contents( url, num_results=num_results, - text=text_contents_options, - highlights=highlights, + text=text_contents_options, # type: ignore + highlights=highlights, # type: ignore include_domains=include_domains, exclude_domains=exclude_domains, start_crawl_date=start_crawl_date, @@ -115,6 +115,6 @@ class ExaFindSimilarResults(BaseTool): end_published_date=end_published_date, exclude_source_domain=exclude_source_domain, category=category, - ) + ) # type: ignore except Exception as e: return repr(e) diff --git a/libs/partners/exa/poetry.lock b/libs/partners/exa/poetry.lock index cf05266109d..874478edf26 100644 --- a/libs/partners/exa/poetry.lock +++ b/libs/partners/exa/poetry.lock @@ -38,13 +38,13 @@ trio = ["trio (>=0.23)"] [[package]] name = "certifi" -version = "2023.11.17" +version = "2024.2.2" description = "Python package for providing Mozilla's CA Bundle." optional = false python-versions = ">=3.6" files = [ - {file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"}, - {file = "certifi-2023.11.17.tar.gz", hash = "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1"}, + {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"}, + {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"}, ] [[package]] @@ -176,13 +176,13 @@ files = [ [[package]] name = "exa-py" -version = "1.0.7" +version = "1.0.8" description = "Python SDK for Exa API." optional = false python-versions = "*" files = [ - {file = 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(>=3.10)"] [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "672a90de20a3bab7e1d9538f8e6902bc2dac337fc991231bf951939b397b652c" +content-hash = "06818acc2d818513a1a78398d32b59cd837413cb5bd94026d63e963020f01045" diff --git a/libs/partners/exa/pyproject.toml b/libs/partners/exa/pyproject.toml index 0990921609b..4cc65162a44 100644 --- a/libs/partners/exa/pyproject.toml +++ b/libs/partners/exa/pyproject.toml @@ -12,8 +12,8 @@ license = "MIT" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -langchain-core = ">=0.0.12" -exa-py = "^1.0.7" +langchain-core = "^0.1" +exa-py = "^1.0.8" [tool.poetry.group.test] optional = true @@ -21,11 +21,11 @@ optional = true [tool.poetry.group.test.dependencies] pytest = "^7.3.0" freezegun = "^1.2.2" -pytest-mock = "^3.10.0" +pytest-mock = "^3.10.0" syrupy = "^4.0.2" pytest-watcher = "^0.3.4" pytest-asyncio = "^0.21.1" -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } [tool.poetry.group.codespell] optional = true @@ -40,14 +40,14 @@ optional = true ruff = "^0.1.5" [tool.poetry.group.typing.dependencies] -mypy = "^0.991" -langchain-core = {path = "../../core", develop = true} +mypy = "^1" +langchain-core = { path = "../../core", develop = true } [tool.poetry.group.dev] optional = true [tool.poetry.group.dev.dependencies] -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } [tool.poetry.group.test_integration] optional = true @@ -55,20 +55,20 @@ optional = true [tool.poetry.group.test_integration.dependencies] -[tool.ruff] +[tool.ruff.lint] select = [ - "E", # pycodestyle - "F", # pyflakes - "I", # isort + "E", # pycodestyle + "F", # pyflakes + "I", # isort + "T201", # print + ] [tool.mypy] disallow_untyped_defs = "True" [tool.coverage.run] -omit = [ - "tests/*", -] +omit = ["tests/*"] [build-system] requires = ["poetry-core>=1.0.0"] diff --git a/libs/partners/exa/scripts/check_imports.py b/libs/partners/exa/scripts/check_imports.py index fd21a4975b7..365f5fa118d 100644 --- a/libs/partners/exa/scripts/check_imports.py +++ b/libs/partners/exa/scripts/check_imports.py @@ -10,8 +10,8 @@ if __name__ == "__main__": SourceFileLoader("x", file).load_module() except Exception: has_faillure = True - print(file) + print(file) # noqa: T201 traceback.print_exc() - print() + print() # noqa: T201 sys.exit(1 if has_failure else 0) diff --git a/libs/partners/exa/tests/integration_tests/test_find_similar_tool.py b/libs/partners/exa/tests/integration_tests/test_find_similar_tool.py index fd21bd406bb..c8d527c73a7 100644 --- a/libs/partners/exa/tests/integration_tests/test_find_similar_tool.py +++ b/libs/partners/exa/tests/integration_tests/test_find_similar_tool.py @@ -9,5 +9,5 @@ def test_similarity_tool() -> None: "num_results": 5, } ) - print(res) + print(res) # noqa: T201 assert not isinstance(res, str) # str means error for this tool diff --git a/libs/partners/exa/tests/integration_tests/test_retriever.py b/libs/partners/exa/tests/integration_tests/test_retriever.py index a71e13cf5eb..4e000dd36c5 100644 --- a/libs/partners/exa/tests/integration_tests/test_retriever.py +++ b/libs/partners/exa/tests/integration_tests/test_retriever.py @@ -6,7 +6,7 @@ from langchain_exa import ExaSearchRetriever def test_exa_retriever() -> None: retriever = ExaSearchRetriever() res = retriever.invoke("best time to visit japan") - print(res) + print(res) # noqa: T201 assert len(res) == 10 # default k assert isinstance(res, list) assert isinstance(res[0], Document) @@ -15,7 +15,7 @@ def test_exa_retriever() -> None: def test_exa_retriever_highlights() -> None: retriever = ExaSearchRetriever(highlights=True) res = retriever.invoke("best time to visit japan") - print(res) + print(res) # noqa: T201 assert isinstance(res, list) assert isinstance(res[0], Document) highlights = res[0].metadata["highlights"] diff --git a/libs/partners/exa/tests/integration_tests/test_search_tool.py b/libs/partners/exa/tests/integration_tests/test_search_tool.py index 3cb380a29c1..f3ebe87c386 100644 --- a/libs/partners/exa/tests/integration_tests/test_search_tool.py +++ b/libs/partners/exa/tests/integration_tests/test_search_tool.py @@ -4,5 +4,5 @@ from langchain_exa import ExaSearchResults def test_search_tool() -> None: tool = ExaSearchResults() res = tool.invoke({"query": "best time to visit japan", "num_results": 5}) - print(res) + print(res) # noqa: T201 assert not isinstance(res, str) # str means error for this tool\ diff --git a/libs/partners/google-genai/langchain_google_genai/__init__.py b/libs/partners/google-genai/langchain_google_genai/__init__.py index 505e121d1e2..187f7e3e036 100644 --- a/libs/partners/google-genai/langchain_google_genai/__init__.py +++ b/libs/partners/google-genai/langchain_google_genai/__init__.py @@ -54,6 +54,8 @@ embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001") embeddings.embed_query("hello, world!") ``` """ # noqa: E501 + +from langchain_google_genai._enums import HarmBlockThreshold, HarmCategory from langchain_google_genai.chat_models import ChatGoogleGenerativeAI from langchain_google_genai.embeddings import GoogleGenerativeAIEmbeddings from langchain_google_genai.llms import GoogleGenerativeAI @@ -62,4 +64,6 @@ __all__ = [ "ChatGoogleGenerativeAI", "GoogleGenerativeAIEmbeddings", "GoogleGenerativeAI", + "HarmBlockThreshold", + "HarmCategory", ] diff --git a/libs/partners/google-genai/langchain_google_genai/_enums.py b/libs/partners/google-genai/langchain_google_genai/_enums.py new file mode 100644 index 00000000000..b2a1de2614e --- /dev/null +++ b/libs/partners/google-genai/langchain_google_genai/_enums.py @@ -0,0 +1,6 @@ +from google.generativeai.types.safety_types import ( # type: ignore + HarmBlockThreshold, + HarmCategory, +) + +__all__ = ["HarmBlockThreshold", "HarmCategory"] diff --git a/libs/partners/google-genai/langchain_google_genai/_function_utils.py b/libs/partners/google-genai/langchain_google_genai/_function_utils.py new file mode 100644 index 00000000000..d67194536d6 --- /dev/null +++ b/libs/partners/google-genai/langchain_google_genai/_function_utils.py @@ -0,0 +1,116 @@ +from __future__ import annotations + +from typing import ( + Dict, + List, + Type, + Union, +) + +import google.ai.generativelanguage as glm +from langchain_core.pydantic_v1 import BaseModel +from langchain_core.tools import BaseTool +from langchain_core.utils.json_schema import dereference_refs + +FunctionCallType = Union[BaseTool, Type[BaseModel], Dict] + +TYPE_ENUM = { + "string": glm.Type.STRING, + "number": glm.Type.NUMBER, + "integer": glm.Type.INTEGER, + "boolean": glm.Type.BOOLEAN, + "array": glm.Type.ARRAY, + "object": glm.Type.OBJECT, +} + + +def convert_to_genai_function_declarations( + function_calls: List[FunctionCallType], +) -> List[glm.Tool]: + return [ + glm.Tool( + function_declarations=[_convert_to_genai_function(fc)], + ) + for fc in function_calls + ] + + +def _convert_to_genai_function(fc: FunctionCallType) -> glm.FunctionDeclaration: + if isinstance(fc, BaseTool): + return _convert_tool_to_genai_function(fc) + elif isinstance(fc, type) and issubclass(fc, BaseModel): + return _convert_pydantic_to_genai_function(fc) + elif isinstance(fc, dict): + return glm.FunctionDeclaration( + name=fc["name"], + description=fc.get("description"), + parameters={ + "properties": { + k: { + "type_": TYPE_ENUM[v["type"]], + "description": v.get("description"), + } + for k, v in fc["parameters"]["properties"].items() + }, + "required": fc["parameters"].get("required", []), + "type_": TYPE_ENUM[fc["parameters"]["type"]], + }, + ) + else: + raise ValueError(f"Unsupported function call type {fc}") + + +def _convert_tool_to_genai_function(tool: BaseTool) -> glm.FunctionDeclaration: + if tool.args_schema: + schema = dereference_refs(tool.args_schema.schema()) + schema.pop("definitions", None) + + return glm.FunctionDeclaration( + name=tool.name or schema["title"], + description=tool.description or schema["description"], + parameters={ + "properties": { + k: { + "type_": TYPE_ENUM[v["type"]], + "description": v.get("description"), + } + for k, v in schema["properties"].items() + }, + "required": schema["required"], + "type_": TYPE_ENUM[schema["type"]], + }, + ) + else: + return glm.FunctionDeclaration( + name=tool.name, + description=tool.description, + parameters={ + "properties": { + "__arg1": {"type_": TYPE_ENUM["string"]}, + }, + "required": ["__arg1"], + "type_": TYPE_ENUM["object"], + }, + ) + + +def _convert_pydantic_to_genai_function( + pydantic_model: Type[BaseModel], +) -> glm.FunctionDeclaration: + schema = dereference_refs(pydantic_model.schema()) + schema.pop("definitions", None) + return glm.FunctionDeclaration( + name=schema["title"], + description=schema.get("description", ""), + parameters={ + "properties": { + k: { + "type_": TYPE_ENUM[v["type"]], + "description": v.get("description"), + } + for k, v in schema["properties"].items() + }, + "required": schema["required"], + "type_": TYPE_ENUM[schema["type"]], + }, + ) diff --git a/libs/partners/google-genai/langchain_google_genai/chat_models.py b/libs/partners/google-genai/langchain_google_genai/chat_models.py index 37cc5f342d6..4f0e9f0a223 100644 --- a/libs/partners/google-genai/langchain_google_genai/chat_models.py +++ b/libs/partners/google-genai/langchain_google_genai/chat_models.py @@ -1,6 +1,7 @@ from __future__ import annotations import base64 +import json import logging import os from io import BytesIO @@ -15,16 +16,17 @@ from typing import ( Optional, Sequence, Tuple, - Type, Union, cast, ) from urllib.parse import urlparse +import google.ai.generativelanguage as glm import google.api_core # TODO: remove ignore once the google package is published with types import google.generativeai as genai # type: ignore[import] +import proto # type: ignore[import] import requests from langchain_core.callbacks.manager import ( AsyncCallbackManagerForLLMRun, @@ -35,10 +37,8 @@ from langchain_core.messages import ( AIMessage, AIMessageChunk, BaseMessage, - ChatMessage, - ChatMessageChunk, + FunctionMessage, HumanMessage, - HumanMessageChunk, SystemMessage, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult @@ -53,6 +53,9 @@ from tenacity import ( ) from langchain_google_genai._common import GoogleGenerativeAIError +from langchain_google_genai._function_utils import ( + convert_to_genai_function_declarations, +) from langchain_google_genai.llms import GoogleModelFamily, _BaseGoogleGenerativeAI IMAGE_TYPES: Tuple = () @@ -321,14 +324,47 @@ llm = ChatGoogleGenerativeAI(model="gemini-pro", convert_system_message_to_human continue elif isinstance(message, AIMessage): role = "model" + raw_function_call = message.additional_kwargs.get("function_call") + if raw_function_call: + function_call = glm.FunctionCall( + { + "name": raw_function_call["name"], + "args": json.loads(raw_function_call["arguments"]), + } + ) + parts = [glm.Part(function_call=function_call)] + else: + parts = _convert_to_parts(message.content) elif isinstance(message, HumanMessage): role = "user" + parts = _convert_to_parts(message.content) + elif isinstance(message, FunctionMessage): + role = "user" + response: Any + if not isinstance(message.content, str): + response = message.content + else: + try: + response = json.loads(message.content) + except json.JSONDecodeError: + response = message.content # leave as str representation + parts = [ + glm.Part( + function_response=glm.FunctionResponse( + name=message.name, + response=( + {"output": response} + if not isinstance(response, dict) + else response + ), + ) + ) + ] else: raise ValueError( f"Unexpected message with type {type(message)} at the position {i}." ) - parts = _convert_to_parts(message.content) if raw_system_message: if role == "model": raise ValueError( @@ -341,71 +377,51 @@ llm = ChatGoogleGenerativeAI(model="gemini-pro", convert_system_message_to_human return messages -def _parts_to_content(parts: List[genai.types.PartType]) -> Union[List[dict], str]: - """Converts a list of Gemini API Part objects into a list of LangChain messages.""" - if len(parts) == 1 and parts[0].text is not None and not parts[0].inline_data: - # Simple text response. The typical response - return parts[0].text - elif not parts: - logger.warning("Gemini produced an empty response.") - return "" - messages = [] - for part in parts: - if part.text is not None: - messages.append( - { - "type": "text", - "text": part.text, - } - ) - else: - # TODO: Handle inline_data if that's a thing? - raise ChatGoogleGenerativeAIError(f"Unexpected part type. {part}") - return messages +def _parse_response_candidate( + response_candidate: glm.Candidate, stream: bool +) -> AIMessage: + first_part = response_candidate.content.parts[0] + if first_part.function_call: + function_call = proto.Message.to_dict(first_part.function_call) + function_call["arguments"] = json.dumps(function_call.pop("args", {})) + return (AIMessageChunk if stream else AIMessage)( + content="", additional_kwargs={"function_call": function_call} + ) + else: + parts = response_candidate.content.parts + + if len(parts) == 1 and parts[0].text: + content: Union[str, List[Union[str, Dict]]] = parts[0].text + else: + content = [proto.Message.to_dict(part) for part in parts] + return (AIMessageChunk if stream else AIMessage)( + content=content, additional_kwargs={} + ) def _response_to_result( - response: genai.types.GenerateContentResponse, - ai_msg_t: Type[BaseMessage] = AIMessage, - human_msg_t: Type[BaseMessage] = HumanMessage, - chat_msg_t: Type[BaseMessage] = ChatMessage, - generation_t: Type[ChatGeneration] = ChatGeneration, + response: glm.GenerateContentResponse, + stream: bool = False, ) -> ChatResult: """Converts a PaLM API response into a LangChain ChatResult.""" - llm_output = {} - if response.prompt_feedback: - try: - prompt_feedback = type(response.prompt_feedback).to_dict( - response.prompt_feedback, use_integers_for_enums=False - ) - llm_output["prompt_feedback"] = prompt_feedback - except Exception as e: - logger.debug(f"Unable to convert prompt_feedback to dict: {e}") + llm_output = {"prompt_feedback": proto.Message.to_dict(response.prompt_feedback)} generations: List[ChatGeneration] = [] - role_map = { - "model": ai_msg_t, - "user": human_msg_t, - } for candidate in response.candidates: - content = candidate.content - parts_content = _parts_to_content(content.parts) - if content.role not in role_map: - logger.warning( - f"Unrecognized role: {content.role}. Treating as a ChatMessage." - ) - msg = chat_msg_t(content=parts_content, role=content.role) - else: - msg = role_map[content.role](content=parts_content) generation_info = {} if candidate.finish_reason: generation_info["finish_reason"] = candidate.finish_reason.name - if candidate.safety_ratings: - generation_info["safety_ratings"] = [ - type(rating).to_dict(rating) for rating in candidate.safety_ratings - ] - generations.append(generation_t(message=msg, generation_info=generation_info)) + generation_info["safety_ratings"] = [ + proto.Message.to_dict(safety_rating, use_integers_for_enums=False) + for safety_rating in candidate.safety_ratings + ] + generations.append( + (ChatGenerationChunk if stream else ChatGeneration)( + message=_parse_response_candidate(candidate, stream=stream), + generation_info=generation_info, + ) + ) if not response.candidates: # Likely a "prompt feedback" violation (e.g., toxic input) # Raising an error would be different than how OpenAI handles it, @@ -414,7 +430,12 @@ def _response_to_result( "Gemini produced an empty response. Continuing with empty message\n" f"Feedback: {response.prompt_feedback}" ) - generations = [generation_t(message=ai_msg_t(content=""), generation_info={})] + generations = [ + (ChatGenerationChunk if stream else ChatGeneration)( + message=(AIMessageChunk if stream else AIMessage)(content=""), + generation_info={}, + ) + ] return ChatResult(generations=generations, llm_output=llm_output) @@ -496,6 +517,7 @@ class ChatGoogleGenerativeAI(_BaseGoogleGenerativeAI, BaseChatModel): "temperature": self.temperature, "top_k": self.top_k, "n": self.n, + "safety_settings": self.safety_settings, } def _prepare_params( @@ -525,7 +547,11 @@ class ChatGoogleGenerativeAI(_BaseGoogleGenerativeAI, BaseChatModel): run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: - params, chat, message = self._prepare_chat(messages, stop=stop) + params, chat, message = self._prepare_chat( + messages, + stop=stop, + **kwargs, + ) response: genai.types.GenerateContentResponse = _chat_with_retry( content=message, **params, @@ -540,7 +566,11 @@ class ChatGoogleGenerativeAI(_BaseGoogleGenerativeAI, BaseChatModel): run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: - params, chat, message = self._prepare_chat(messages, stop=stop) + params, chat, message = self._prepare_chat( + messages, + stop=stop, + **kwargs, + ) response: genai.types.GenerateContentResponse = await _achat_with_retry( content=message, **params, @@ -555,7 +585,11 @@ class ChatGoogleGenerativeAI(_BaseGoogleGenerativeAI, BaseChatModel): run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Iterator[ChatGenerationChunk]: - params, chat, message = self._prepare_chat(messages, stop=stop) + params, chat, message = self._prepare_chat( + messages, + stop=stop, + **kwargs, + ) response: genai.types.GenerateContentResponse = _chat_with_retry( content=message, **params, @@ -563,17 +597,12 @@ class ChatGoogleGenerativeAI(_BaseGoogleGenerativeAI, BaseChatModel): stream=True, ) for chunk in response: - _chat_result = _response_to_result( - chunk, - ai_msg_t=AIMessageChunk, - human_msg_t=HumanMessageChunk, - chat_msg_t=ChatMessageChunk, - generation_t=ChatGenerationChunk, - ) + _chat_result = _response_to_result(chunk, stream=True) gen = cast(ChatGenerationChunk, _chat_result.generations[0]) - yield gen + if run_manager: run_manager.on_llm_new_token(gen.text) + yield gen async def _astream( self, @@ -582,24 +611,23 @@ class ChatGoogleGenerativeAI(_BaseGoogleGenerativeAI, BaseChatModel): run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> AsyncIterator[ChatGenerationChunk]: - params, chat, message = self._prepare_chat(messages, stop=stop) + params, chat, message = self._prepare_chat( + messages, + stop=stop, + **kwargs, + ) async for chunk in await _achat_with_retry( content=message, **params, generation_method=chat.send_message_async, stream=True, ): - _chat_result = _response_to_result( - chunk, - ai_msg_t=AIMessageChunk, - human_msg_t=HumanMessageChunk, - chat_msg_t=ChatMessageChunk, - generation_t=ChatGenerationChunk, - ) + _chat_result = _response_to_result(chunk, stream=True) gen = cast(ChatGenerationChunk, _chat_result.generations[0]) - yield gen + if run_manager: await run_manager.on_llm_new_token(gen.text) + yield gen def _prepare_chat( self, @@ -607,13 +635,24 @@ class ChatGoogleGenerativeAI(_BaseGoogleGenerativeAI, BaseChatModel): stop: Optional[List[str]] = None, **kwargs: Any, ) -> Tuple[Dict[str, Any], genai.ChatSession, genai.types.ContentDict]: + client = self.client + functions = kwargs.pop("functions", None) + safety_settings = kwargs.pop("safety_settings", self.safety_settings) + if functions or safety_settings: + tools = ( + convert_to_genai_function_declarations(functions) if functions else None + ) + client = genai.GenerativeModel( + model_name=self.model, tools=tools, safety_settings=safety_settings + ) + params = self._prepare_params(stop, **kwargs) history = _parse_chat_history( messages, convert_system_message_to_human=self.convert_system_message_to_human, ) message = history.pop() - chat = self.client.start_chat(history=history) + chat = client.start_chat(history=history) return params, chat, message def get_num_tokens(self, text: str) -> int: diff --git a/libs/partners/google-genai/langchain_google_genai/llms.py b/libs/partners/google-genai/langchain_google_genai/llms.py index bf147410ce8..9b483873146 100644 --- a/libs/partners/google-genai/langchain_google_genai/llms.py +++ b/libs/partners/google-genai/langchain_google_genai/llms.py @@ -15,6 +15,11 @@ from langchain_core.outputs import Generation, GenerationChunk, LLMResult from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator from langchain_core.utils import get_from_dict_or_env +from langchain_google_genai._enums import ( + HarmBlockThreshold, + HarmCategory, +) + class GoogleModelFamily(str, Enum): GEMINI = auto() @@ -77,7 +82,10 @@ def _completion_with_retry( try: if is_gemini: return llm.client.generate_content( - contents=prompt, stream=stream, generation_config=generation_config + contents=prompt, + stream=stream, + generation_config=generation_config, + safety_settings=kwargs.pop("safety_settings", None), ) return llm.client.generate_text(prompt=prompt, **kwargs) except google.api_core.exceptions.FailedPrecondition as exc: @@ -143,6 +151,22 @@ Supported examples: description="A string, one of: [`rest`, `grpc`, `grpc_asyncio`].", ) + safety_settings: Optional[Dict[HarmCategory, HarmBlockThreshold]] = None + """The default safety settings to use for all generations. + + For example: + + from google.generativeai.types.safety_types import HarmBlockThreshold, HarmCategory + + safety_settings = { + HarmCategory.HARM_CATEGORY_UNSPECIFIED: HarmBlockThreshold.BLOCK_NONE, + HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE, + HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_ONLY_HIGH, + HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, + HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE, + } + """ # noqa: E501 + @property def lc_secrets(self) -> Dict[str, str]: return {"google_api_key": "GOOGLE_API_KEY"} @@ -151,6 +175,18 @@ Supported examples: def _model_family(self) -> str: return GoogleModelFamily(self.model) + @property + def _identifying_params(self) -> Dict[str, Any]: + """Get the identifying parameters.""" + return { + "model": self.model, + "temperature": self.temperature, + "top_p": self.top_p, + "top_k": self.top_k, + "max_output_tokens": self.max_output_tokens, + "candidate_count": self.n, + } + class GoogleGenerativeAI(_BaseGoogleGenerativeAI, BaseLLM): """Google GenerativeAI models. @@ -172,6 +208,8 @@ class GoogleGenerativeAI(_BaseGoogleGenerativeAI, BaseLLM): ) model_name = values["model"] + safety_settings = values["safety_settings"] + if isinstance(google_api_key, SecretStr): google_api_key = google_api_key.get_secret_value() @@ -181,8 +219,15 @@ class GoogleGenerativeAI(_BaseGoogleGenerativeAI, BaseLLM): client_options=values.get("client_options"), ) + if safety_settings and ( + not GoogleModelFamily(model_name) == GoogleModelFamily.GEMINI + ): + raise ValueError("Safety settings are only supported for Gemini models") + if GoogleModelFamily(model_name) == GoogleModelFamily.GEMINI: - values["client"] = genai.GenerativeModel(model_name=model_name) + values["client"] = genai.GenerativeModel( + model_name=model_name, safety_settings=safety_settings + ) else: values["client"] = genai @@ -225,6 +270,7 @@ class GoogleGenerativeAI(_BaseGoogleGenerativeAI, BaseLLM): is_gemini=True, run_manager=run_manager, generation_config=generation_config, + safety_settings=kwargs.pop("safety_settings", None), ) candidates = [ "".join([p.text for p in c.content.parts]) for c in res.candidates @@ -266,6 +312,7 @@ class GoogleGenerativeAI(_BaseGoogleGenerativeAI, BaseLLM): is_gemini=True, run_manager=run_manager, generation_config=generation_config, + safety_settings=kwargs.pop("safety_settings", None), **kwargs, ): chunk = GenerationChunk(text=stream_resp.text) diff --git a/libs/partners/google-genai/poetry.lock b/libs/partners/google-genai/poetry.lock index edddadcb57d..b4020e98505 100644 --- a/libs/partners/google-genai/poetry.lock +++ b/libs/partners/google-genai/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. [[package]] name = "annotated-types" @@ -13,19 +13,20 @@ files = [ [[package]] name = "anyio" -version = "4.1.0" +version = "4.2.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false python-versions = ">=3.8" files = [ - {file = "anyio-4.1.0-py3-none-any.whl", hash = "sha256:56a415fbc462291813a94528a779597226619c8e78af7de0507333f700011e5f"}, - {file = "anyio-4.1.0.tar.gz", hash = "sha256:5a0bec7085176715be77df87fc66d6c9d70626bd752fcc85f57cdbee5b3760da"}, + {file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"}, + {file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"}, ] [package.dependencies] exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""} idna = ">=2.8" sniffio = ">=1.1" +typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""} [package.extras] doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] @@ -45,13 +46,13 @@ files = [ [[package]] name = "certifi" -version = "2023.11.17" +version = "2024.2.2" description = "Python package for providing Mozilla's CA Bundle." optional = false python-versions = ">=3.6" files = [ - {file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"}, - {file = "certifi-2023.11.17.tar.gz", hash = "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1"}, + {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"}, + {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"}, ] [[package]] @@ -197,13 +198,13 @@ test = ["pytest (>=6)"] [[package]] name = "freezegun" -version = "1.3.1" +version = "1.4.0" description = "Let your Python tests travel through time" optional = false python-versions = ">=3.7" files = [ - {file = "freezegun-1.3.1-py3-none-any.whl", hash = "sha256:065e77a12624d05531afa87ade12a0b9bdb53495c4573893252a055b545ce3ea"}, - {file = "freezegun-1.3.1.tar.gz", hash = "sha256:48984397b3b58ef5dfc645d6a304b0060f612bcecfdaaf45ce8aff0077a6cb6a"}, + {file = "freezegun-1.4.0-py3-none-any.whl", hash = "sha256:55e0fc3c84ebf0a96a5aa23ff8b53d70246479e9a68863f1fcac5a3e52f19dd6"}, + {file = "freezegun-1.4.0.tar.gz", hash = "sha256:10939b0ba0ff5adaecf3b06a5c2f73071d9678e507c5eaedb23c761d56ac774b"}, ] [package.dependencies] @@ -227,13 +228,13 @@ protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4 [[package]] name = "google-api-core" -version = "2.15.0" +version = "2.17.0" description = "Google API client core library" optional = false python-versions = ">=3.7" files = [ - {file = "google-api-core-2.15.0.tar.gz", hash = "sha256:abc978a72658f14a2df1e5e12532effe40f94f868f6e23d95133bd6abcca35ca"}, - 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+++ b/libs/partners/google-genai/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-google-genai" -version = "0.0.6.post1" +version = "0.0.9" description = "An integration package connecting Google's genai package and LangChain" authors = [] readme = "README.md" @@ -68,11 +68,12 @@ types-requests = "^2.31.0.10" types-pillow = "^10.1.0.2" types-google-cloud-ndb = "^2.2.0.1" -[tool.ruff] +[tool.ruff.lint] select = [ - "E", # pycodestyle - "F", # pyflakes - "I", # isort + "E", # pycodestyle + "F", # pyflakes + "I", # isort + "T201", # print ] [tool.mypy] diff --git a/libs/partners/google-genai/scripts/check_imports.py b/libs/partners/google-genai/scripts/check_imports.py index fd21a4975b7..365f5fa118d 100644 --- a/libs/partners/google-genai/scripts/check_imports.py +++ b/libs/partners/google-genai/scripts/check_imports.py @@ -10,8 +10,8 @@ if __name__ == "__main__": SourceFileLoader("x", file).load_module() except Exception: has_faillure = True - print(file) + print(file) # noqa: T201 traceback.print_exc() - print() + print() # noqa: T201 sys.exit(1 if has_failure else 0) diff --git a/libs/partners/google-genai/tests/integration_tests/test_chat_models.py b/libs/partners/google-genai/tests/integration_tests/test_chat_models.py index 4551a860d0f..26e85aa10e1 100644 --- a/libs/partners/google-genai/tests/integration_tests/test_chat_models.py +++ b/libs/partners/google-genai/tests/integration_tests/test_chat_models.py @@ -1,11 +1,16 @@ """Test ChatGoogleGenerativeAI chat model.""" + +from typing import Generator + import pytest from langchain_core.messages import AIMessage, HumanMessage, SystemMessage -from langchain_google_genai.chat_models import ( +from langchain_google_genai import ( ChatGoogleGenerativeAI, - ChatGoogleGenerativeAIError, + HarmBlockThreshold, + HarmCategory, ) +from langchain_google_genai.chat_models import ChatGoogleGenerativeAIError _MODEL = "gemini-pro" # TODO: Use nano when it's available. _VISION_MODEL = "gemini-pro-vision" @@ -102,7 +107,7 @@ def test_chat_google_genai_invoke_multimodal() -> None: # Try streaming for chunk in llm.stream(messages): - print(chunk) + print(chunk) # noqa: T201 assert isinstance(chunk.content, str) assert len(chunk.content.strip()) > 0 @@ -192,3 +197,32 @@ def test_generativeai_get_num_tokens_gemini() -> None: llm = ChatGoogleGenerativeAI(temperature=0, model="gemini-pro") output = llm.get_num_tokens("How are you?") assert output == 4 + + +def test_safety_settings_gemini() -> None: + safety_settings = { + HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE, + } + # test with safety filters on bind + llm = ChatGoogleGenerativeAI(temperature=0, model="gemini-pro").bind( + safety_settings=safety_settings + ) + output = llm.invoke("how to make a bomb?") + assert isinstance(output, AIMessage) + assert len(output.content) > 0 + + # test direct to stream + streamed_messages = [] + output_stream = llm.stream("how to make a bomb?", safety_settings=safety_settings) + assert isinstance(output_stream, Generator) + for message in output_stream: + streamed_messages.append(message) + assert len(streamed_messages) > 0 + + # test as init param + llm = ChatGoogleGenerativeAI( + temperature=0, model="gemini-pro", safety_settings=safety_settings + ) + out2 = llm.invoke("how to make a bomb") + assert isinstance(out2, AIMessage) + assert len(out2.content) > 0 diff --git a/libs/partners/google-genai/tests/integration_tests/test_function_call.py b/libs/partners/google-genai/tests/integration_tests/test_function_call.py new file mode 100644 index 00000000000..57d0005a3bd --- /dev/null +++ b/libs/partners/google-genai/tests/integration_tests/test_function_call.py @@ -0,0 +1,84 @@ +"""Test ChatGoogleGenerativeAI function call.""" + +import json + +from langchain_core.messages import AIMessage +from langchain_core.pydantic_v1 import BaseModel +from langchain_core.tools import tool + +from langchain_google_genai.chat_models import ( + ChatGoogleGenerativeAI, +) + + +def test_function_call() -> None: + functions = [ + { + "name": "get_weather", + "description": "Determine weather in my location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "The city and state e.g. San Francisco, CA", + }, + "unit": {"type": "string", "enum": ["c", "f"]}, + }, + "required": ["location"], + }, + } + ] + llm = ChatGoogleGenerativeAI(model="gemini-pro").bind(functions=functions) + res = llm.invoke("what weather is today in san francisco?") + assert res + assert res.additional_kwargs + assert "function_call" in res.additional_kwargs + assert "get_weather" == res.additional_kwargs["function_call"]["name"] + arguments_str = res.additional_kwargs["function_call"]["arguments"] + assert isinstance(arguments_str, str) + arguments = json.loads(arguments_str) + assert "location" in arguments + + +def test_tool_call() -> None: + @tool + def search_tool(query: str) -> str: + """Searches the web for `query` and returns the result.""" + raise NotImplementedError + + llm = ChatGoogleGenerativeAI(model="gemini-pro").bind(functions=[search_tool]) + response = llm.invoke("weather in san francisco") + assert isinstance(response, AIMessage) + assert isinstance(response.content, str) + assert response.content == "" + function_call = response.additional_kwargs.get("function_call") + assert function_call + assert function_call["name"] == "search_tool" + arguments_str = function_call.get("arguments") + assert arguments_str + arguments = json.loads(arguments_str) + assert "query" in arguments + + +class MyModel(BaseModel): + name: str + age: int + + +def test_pydantic_call() -> None: + llm = ChatGoogleGenerativeAI(model="gemini-pro").bind(functions=[MyModel]) + response = llm.invoke("my name is Erick and I am 27 years old") + assert isinstance(response, AIMessage) + assert isinstance(response.content, str) + assert response.content == "" + function_call = response.additional_kwargs.get("function_call") + assert function_call + assert function_call["name"] == "MyModel" + arguments_str = function_call.get("arguments") + assert arguments_str + arguments = json.loads(arguments_str) + assert arguments == { + "name": "Erick", + "age": 27.0, + } diff --git a/libs/partners/google-genai/tests/integration_tests/test_llms.py b/libs/partners/google-genai/tests/integration_tests/test_llms.py index 9bdf49dda84..b761e148041 100644 --- a/libs/partners/google-genai/tests/integration_tests/test_llms.py +++ b/libs/partners/google-genai/tests/integration_tests/test_llms.py @@ -4,10 +4,12 @@ Note: This test must be run with the GOOGLE_API_KEY environment variable set to valid API key. """ +from typing import Generator + import pytest from langchain_core.outputs import LLMResult -from langchain_google_genai.llms import GoogleGenerativeAI +from langchain_google_genai import GoogleGenerativeAI, HarmBlockThreshold, HarmCategory model_names = ["models/text-bison-001", "gemini-pro"] @@ -66,3 +68,39 @@ def test_generativeai_get_num_tokens_gemini() -> None: llm = GoogleGenerativeAI(temperature=0, model="gemini-pro") output = llm.get_num_tokens("How are you?") assert output == 4 + + +def test_safety_settings_gemini() -> None: + # test with blocked prompt + llm = GoogleGenerativeAI(temperature=0, model="gemini-pro") + output = llm.generate(prompts=["how to make a bomb?"]) + assert isinstance(output, LLMResult) + assert len(output.generations[0]) == 0 + + # safety filters + safety_settings = { + HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE, + } + + # test with safety filters directly to generate + output = llm.generate(["how to make a bomb?"], safety_settings=safety_settings) + assert isinstance(output, LLMResult) + assert len(output.generations[0]) > 0 + + # test with safety filters directly to stream + streamed_messages = [] + output_stream = llm.stream("how to make a bomb?", safety_settings=safety_settings) + assert isinstance(output_stream, Generator) + for message in output_stream: + streamed_messages.append(message) + assert len(streamed_messages) > 0 + + # test with safety filters on instantiation + llm = GoogleGenerativeAI( + model="gemini-pro", + safety_settings=safety_settings, + temperature=0, + ) + output = llm.generate(prompts=["how to make a bomb?"]) + assert isinstance(output, LLMResult) + assert len(output.generations[0]) > 0 diff --git a/libs/partners/google-genai/tests/unit_tests/test_chat_models.py b/libs/partners/google-genai/tests/unit_tests/test_chat_models.py index 93d5bb32313..61990909ed9 100644 --- a/libs/partners/google-genai/tests/unit_tests/test_chat_models.py +++ b/libs/partners/google-genai/tests/unit_tests/test_chat_models.py @@ -1,5 +1,14 @@ """Test chat model integration.""" -from langchain_core.messages import AIMessage, HumanMessage, SystemMessage + +from typing import Dict, List, Union + +import pytest +from langchain_core.messages import ( + AIMessage, + FunctionMessage, + HumanMessage, + SystemMessage, +) from langchain_core.pydantic_v1 import SecretStr from pytest import CaptureFixture @@ -36,7 +45,7 @@ def test_api_key_is_string() -> None: def test_api_key_masked_when_passed_via_constructor(capsys: CaptureFixture) -> None: chat = ChatGoogleGenerativeAI(model="gemini-nano", google_api_key="secret-api-key") - print(chat.google_api_key, end="") + print(chat.google_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" @@ -58,3 +67,9 @@ def test_parse_history() -> None: "parts": [{"text": system_input}, {"text": text_question1}], } assert history[1] == {"role": "model", "parts": [{"text": text_answer1}]} + + +@pytest.mark.parametrize("content", ['["a"]', '{"a":"b"}', "function output"]) +def test_parse_function_history(content: Union[str, List[Union[str, Dict]]]) -> None: + function_message = FunctionMessage(name="search_tool", content=content) + _parse_chat_history([function_message], convert_system_message_to_human=True) diff --git a/libs/partners/google-genai/tests/unit_tests/test_embeddings.py b/libs/partners/google-genai/tests/unit_tests/test_embeddings.py index 45acffb33b4..9bb57274e89 100644 --- a/libs/partners/google-genai/tests/unit_tests/test_embeddings.py +++ b/libs/partners/google-genai/tests/unit_tests/test_embeddings.py @@ -1,4 +1,5 @@ """Test embeddings model integration.""" + from langchain_core.pydantic_v1 import SecretStr from pytest import CaptureFixture @@ -31,7 +32,7 @@ def test_api_key_masked_when_passed_via_constructor(capsys: CaptureFixture) -> N model="models/embedding-001", google_api_key="secret-api-key", ) - print(embeddings.google_api_key, end="") + print(embeddings.google_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/libs/partners/google-genai/tests/unit_tests/test_imports.py b/libs/partners/google-genai/tests/unit_tests/test_imports.py index e189a9fc775..8c90cb2bd10 100644 --- a/libs/partners/google-genai/tests/unit_tests/test_imports.py +++ b/libs/partners/google-genai/tests/unit_tests/test_imports.py @@ -4,6 +4,8 @@ EXPECTED_ALL = [ "ChatGoogleGenerativeAI", "GoogleGenerativeAIEmbeddings", "GoogleGenerativeAI", + "HarmBlockThreshold", + "HarmCategory", ] diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/_utils.py b/libs/partners/google-vertexai/langchain_google_vertexai/_utils.py index 4f0bdf3723b..1af44f0c0f8 100644 --- a/libs/partners/google-vertexai/langchain_google_vertexai/_utils.py +++ b/libs/partners/google-vertexai/langchain_google_vertexai/_utils.py @@ -5,8 +5,9 @@ from importlib import metadata from typing import Any, Callable, Dict, Optional, Union import google.api_core +import proto # type: ignore[import-untyped] from google.api_core.gapic_v1.client_info import ClientInfo -from google.cloud import storage +from google.cloud import storage # type: ignore[attr-defined] from langchain_core.callbacks import ( AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun, @@ -114,7 +115,11 @@ def get_generation_info( } for rating in candidate.safety_ratings ], - "citation_metadata": candidate.citation_metadata, + "citation_metadata": ( + proto.Message.to_dict(candidate.citation_metadata) + if candidate.citation_metadata + else None + ), } # https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/text-chat#response_body else: diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/chat_models.py b/libs/partners/google-vertexai/langchain_google_vertexai/chat_models.py index 1c62d76c75c..096f18006fc 100644 --- a/libs/partners/google-vertexai/langchain_google_vertexai/chat_models.py +++ b/libs/partners/google-vertexai/langchain_google_vertexai/chat_models.py @@ -9,6 +9,7 @@ from dataclasses import dataclass, field from typing import Any, Dict, Iterator, List, Optional, Union, cast from urllib.parse import urlparse +import proto # type: ignore[import-untyped] import requests from google.cloud.aiplatform_v1beta1.types.content import Part as GapicPart from google.cloud.aiplatform_v1beta1.types.tool import FunctionCall @@ -45,6 +46,12 @@ from vertexai.preview.generative_models import ( # type: ignore Image, Part, ) +from vertexai.preview.language_models import ( # type: ignore + ChatModel as PreviewChatModel, +) +from vertexai.preview.language_models import ( + CodeChatModel as PreviewCodeChatModel, +) from langchain_google_vertexai._utils import ( get_generation_info, @@ -272,10 +279,12 @@ def _parse_response_candidate(response_candidate: "Candidate") -> AIMessage: first_part = response_candidate.content.parts[0] if first_part.function_call: function_call = {"name": first_part.function_call.name} - # dump to match other function calling llm for now + function_call_args_dict = proto.Message.to_dict(first_part.function_call)[ + "args" + ] function_call["arguments"] = json.dumps( - {k: first_part.function_call.args[k] for k in first_part.function_call.args} + {k: function_call_args_dict[k] for k in function_call_args_dict} ) additional_kwargs["function_call"] = function_call return AIMessage(content=content, additional_kwargs=additional_kwargs) @@ -316,12 +325,20 @@ class ChatVertexAI(_VertexAICommon, BaseChatModel): values["client"] = GenerativeModel( model_name=values["model_name"], safety_settings=safety_settings ) + values["client_preview"] = GenerativeModel( + model_name=values["model_name"], safety_settings=safety_settings + ) else: if is_codey_model(values["model_name"]): model_cls = CodeChatModel + model_cls_preview = PreviewCodeChatModel else: model_cls = ChatModel + model_cls_preview = PreviewChatModel values["client"] = model_cls.from_pretrained(values["model_name"]) + values["client_preview"] = model_cls_preview.from_pretrained( + values["model_name"] + ) return values def _generate( @@ -493,8 +510,13 @@ class ChatVertexAI(_VertexAICommon, BaseChatModel): # set param to `functions` until core tool/function calling implemented raw_tools = params.pop("functions") if "functions" in params else None tools = _format_tools_to_vertex_tool(raw_tools) if raw_tools else None + safety_settings = params.pop("safety_settings", None) responses = chat.send_message( - message, stream=True, generation_config=params, tools=tools + message, + stream=True, + generation_config=params, + safety_settings=safety_settings, + tools=tools, ) for response in responses: message = _parse_response_candidate(response.candidates[0]) diff --git a/libs/partners/google-vertexai/langchain_google_vertexai/llms.py b/libs/partners/google-vertexai/langchain_google_vertexai/llms.py index bd4d346a019..9b3099b4c33 100644 --- a/libs/partners/google-vertexai/langchain_google_vertexai/llms.py +++ b/libs/partners/google-vertexai/langchain_google_vertexai/llms.py @@ -31,6 +31,12 @@ from vertexai.preview.generative_models import ( # type: ignore[import-untyped] Image, ) from vertexai.preview.language_models import ( # type: ignore[import-untyped] + ChatModel as PreviewChatModel, +) +from vertexai.preview.language_models import ( + CodeChatModel as PreviewCodeChatModel, +) +from vertexai.preview.language_models import ( CodeGenerationModel as PreviewCodeGenerationModel, ) from vertexai.preview.language_models import ( @@ -239,6 +245,27 @@ class _VertexAICommon(_VertexAIBase): params.pop("candidate_count") return params + def get_num_tokens(self, text: str) -> int: + """Get the number of tokens present in the text. + + Useful for checking if an input will fit in a model's context window. + + Args: + text: The string input to tokenize. + + Returns: + The integer number of tokens in the text. + """ + is_palm_chat_model = isinstance( + self.client_preview, PreviewChatModel + ) or isinstance(self.client_preview, PreviewCodeChatModel) + if is_palm_chat_model: + result = self.client_preview.start_chat().count_tokens(text) + else: + result = self.client_preview.count_tokens([text]) + + return result.total_tokens + class VertexAI(_VertexAICommon, BaseLLM): """Google Vertex AI large language models.""" @@ -300,20 +327,6 @@ class VertexAI(_VertexAICommon, BaseLLM): raise ValueError("Only one candidate can be generated with streaming!") return values - def get_num_tokens(self, text: str) -> int: - """Get the number of tokens present in the text. - - Useful for checking if an input will fit in a model's context window. - - Args: - text: The string input to tokenize. - - Returns: - The integer number of tokens in the text. - """ - result = self.client_preview.count_tokens([text]) - return result.total_tokens - def _response_to_generation( self, response: TextGenerationResponse, *, stream: 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[metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "2a339af4051981fb8aefc0d0ad4016bfb53c6f2d9b11abbfc22ae548fef91481" +content-hash = "61a48589be35a09abb7ba8045face101fa2147093f58d84abac39b58c055c4b4" diff --git a/libs/partners/google-vertexai/pyproject.toml b/libs/partners/google-vertexai/pyproject.toml index f9d2f890cbb..87193351565 100644 --- a/libs/partners/google-vertexai/pyproject.toml +++ b/libs/partners/google-vertexai/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-google-vertexai" -version = "0.0.3" +version = "0.0.5" description = "An integration package connecting GoogleVertexAI and LangChain" authors = [] readme = "README.md" @@ -12,11 +12,9 @@ license = "MIT" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -langchain-core = ">=0.1.7,<0.2" +langchain-core = "^0.1.7" google-cloud-aiplatform = "^1.39.0" google-cloud-storage = "^2.14.0" -types-requests = "^2.31.0" -types-protobuf = "^4.24.0.4" [tool.poetry.group.test] optional = true @@ -24,13 +22,11 @@ optional = true [tool.poetry.group.test.dependencies] pytest = "^7.3.0" freezegun = "^1.2.2" -pytest-mock = "^3.10.0" +pytest-mock = "^3.10.0" syrupy = "^4.0.2" pytest-watcher = "^0.3.4" pytest-asyncio = "^0.21.1" -langchain-core = {path = "../../core", develop = true} -types-requests = "^2.31.0.20231231" -types-protobuf = "^4.24.0.4" +langchain-core = { path = "../../core", develop = true } [tool.poetry.group.codespell] optional = true @@ -42,8 +38,9 @@ codespell = "^2.2.0" optional = true [tool.poetry.group.test_integration.dependencies] -langchain = {path = "../../langchain"} -numexpr = {version = "^2.8.8", python = ">=3.9,<4.0"} +langchain = { path = "../../langchain" } +langchain-community = { path = "../../community" } +numexpr = { version = "^2.8.8", python = ">=3.9,<4.0" } google-api-python-client = "^2.114.0" [tool.poetry.group.lint] @@ -54,20 +51,23 @@ ruff = "^0.1.5" [tool.poetry.group.typing.dependencies] mypy = "^1" -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } types-google-cloud-ndb = "^2.2.0.20240106" +types-requests = "^2.31.0.20231231" +types-protobuf = "^4.24.0.4" [tool.poetry.group.dev] optional = true [tool.poetry.group.dev.dependencies] -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } -[tool.ruff] +[tool.ruff.lint] select = [ - "E", # pycodestyle - "F", # pyflakes - "I", # isort + "E", # pycodestyle + "F", # pyflakes + "I", # isort + "T201", # print ] [tool.mypy] @@ -83,9 +83,7 @@ warn_unused_configs = true warn_unused_ignores = true [tool.coverage.run] -omit = [ - "tests/*", -] +omit = ["tests/*"] [build-system] requires = ["poetry-core>=1.0.0"] diff --git a/libs/partners/google-vertexai/scripts/check_imports.py b/libs/partners/google-vertexai/scripts/check_imports.py index fd21a4975b7..365f5fa118d 100644 --- a/libs/partners/google-vertexai/scripts/check_imports.py +++ b/libs/partners/google-vertexai/scripts/check_imports.py @@ -10,8 +10,8 @@ if __name__ == "__main__": SourceFileLoader("x", file).load_module() except Exception: has_faillure = True - print(file) + print(file) # noqa: T201 traceback.print_exc() - print() + print() # noqa: T201 sys.exit(1 if has_failure else 0) diff --git a/libs/partners/google-vertexai/tests/integration_tests/test_chat_models.py b/libs/partners/google-vertexai/tests/integration_tests/test_chat_models.py index f2981a08018..338533103ea 100644 --- a/libs/partners/google-vertexai/tests/integration_tests/test_chat_models.py +++ b/libs/partners/google-vertexai/tests/integration_tests/test_chat_models.py @@ -1,4 +1,6 @@ """Test ChatGoogleVertexAI chat model.""" + +import json from typing import Optional, cast import pytest @@ -9,6 +11,7 @@ from langchain_core.messages import ( SystemMessage, ) from langchain_core.outputs import ChatGeneration, LLMResult +from langchain_core.pydantic_v1 import BaseModel from langchain_google_vertexai.chat_models import ChatVertexAI @@ -220,3 +223,38 @@ def test_chat_vertexai_system_message(model_name: Optional[str]) -> None: response = model([system_message, message1, message2, message3]) assert isinstance(response, AIMessage) assert isinstance(response.content, str) + + +@pytest.mark.parametrize("model_name", model_names_to_test) +def test_get_num_tokens_from_messages(model_name: str) -> None: + if model_name: + model = ChatVertexAI(model_name=model_name, temperature=0.0) + else: + model = ChatVertexAI(temperature=0.0) + message = HumanMessage(content="Hello") + token = model.get_num_tokens_from_messages(messages=[message]) + assert isinstance(token, int) + assert token == 3 + + +def test_chat_vertexai_gemini_function_calling() -> None: + class MyModel(BaseModel): + name: str + age: int + + model = ChatVertexAI(model_name="gemini-pro").bind(functions=[MyModel]) + message = HumanMessage(content="My name is Erick and I am 27 years old") + response = model.invoke([message]) + assert isinstance(response, AIMessage) + assert isinstance(response.content, str) + assert response.content == "" + function_call = response.additional_kwargs.get("function_call") + assert function_call + assert function_call["name"] == "MyModel" + arguments_str = function_call.get("arguments") + assert arguments_str + arguments = json.loads(arguments_str) + assert arguments == { + "name": "Erick", + "age": 27.0, + } diff --git a/libs/partners/google-vertexai/tests/integration_tests/test_tools.py b/libs/partners/google-vertexai/tests/integration_tests/test_tools.py index a688476bfc7..5da7224005d 100644 --- a/libs/partners/google-vertexai/tests/integration_tests/test_tools.py +++ b/libs/partners/google-vertexai/tests/integration_tests/test_tools.py @@ -1,6 +1,6 @@ import os import re -from typing import List, Union +from typing import Any, List, Union from langchain_core.agents import AgentAction, AgentActionMessageLog, AgentFinish from langchain_core.messages import AIMessageChunk @@ -44,11 +44,11 @@ class _TestOutputParser(BaseOutputParser): def test_tools() -> None: - from langchain.agents import AgentExecutor # type: ignore[import-not-found] - from langchain.agents.format_scratchpad import ( # type: ignore[import-not-found] + from langchain.agents import AgentExecutor + from langchain.agents.format_scratchpad import ( format_to_openai_function_messages, ) - from langchain.chains import LLMMathChain # type: ignore[import-not-found] + from langchain.chains import LLMMathChain llm = ChatVertexAI(model_name="gemini-pro") math_chain = LLMMathChain.from_llm(llm=llm) @@ -67,8 +67,8 @@ def test_tools() -> None: ) llm_with_tools = llm.bind(functions=tools) - agent = ( - { # type: ignore[var-annotated] + agent: Any = ( + { "input": lambda x: x["input"], "agent_scratchpad": lambda x: format_to_openai_function_messages( x["intermediate_steps"] @@ -81,7 +81,6 @@ def test_tools() -> None: agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True) response = agent_executor.invoke({"input": "What is 6 raised to the 0.43 power?"}) - print(response) assert isinstance(response, dict) assert response["input"] == "What is 6 raised to the 0.43 power?" @@ -89,7 +88,7 @@ def test_tools() -> None: # use regex to find \d+\.\d+ just_numbers = re.findall(r"\d+\.\d+", response["output"])[0] - assert round(float(just_numbers), 3) == 2.161 + assert round(float(just_numbers), 2) == 2.16 def test_stream() -> None: @@ -106,7 +105,6 @@ def test_stream() -> None: ] response = list(llm.stream("What is 6 raised to the 0.43 power?", functions=tools)) assert len(response) == 1 - # for chunk in response: assert isinstance(response[0], AIMessageChunk) assert "function_call" in response[0].additional_kwargs @@ -115,7 +113,7 @@ def test_multiple_tools() -> None: from langchain.agents import AgentExecutor from langchain.agents.format_scratchpad import format_to_openai_function_messages from langchain.chains import LLMMathChain - from langchain.utilities import ( # type: ignore[import-not-found] + from langchain.utilities import ( GoogleSearchAPIWrapper, ) @@ -149,8 +147,8 @@ def test_multiple_tools() -> None: ) llm_with_tools = llm.bind(functions=tools) - agent = ( - { # type: ignore[var-annotated] + agent: Any = ( + { "input": lambda x: x["input"], "agent_scratchpad": lambda x: format_to_openai_function_messages( x["intermediate_steps"] diff --git a/libs/partners/google-vertexai/tests/unit_tests/test_chat_models.py b/libs/partners/google-vertexai/tests/unit_tests/test_chat_models.py index 052cf559f0f..f24f418ba62 100644 --- a/libs/partners/google-vertexai/tests/unit_tests/test_chat_models.py +++ b/libs/partners/google-vertexai/tests/unit_tests/test_chat_models.py @@ -1,22 +1,35 @@ """Test chat model integration.""" +import json from dataclasses import dataclass, field from typing import Any, Dict, List, Optional from unittest.mock import MagicMock, Mock, patch import pytest +from google.cloud.aiplatform_v1beta1.types import ( + Content, + FunctionCall, + Part, +) +from google.cloud.aiplatform_v1beta1.types import ( + content as gapic_content_types, +) from langchain_core.messages import ( AIMessage, HumanMessage, SystemMessage, ) from vertexai.language_models import ChatMessage, InputOutputTextPair # type: ignore +from vertexai.preview.generative_models import ( # type: ignore + Candidate, +) from langchain_google_vertexai.chat_models import ( ChatVertexAI, _parse_chat_history, _parse_chat_history_gemini, _parse_examples, + _parse_response_candidate, ) @@ -187,7 +200,7 @@ def test_default_params_gemini() -> None: StubGeminiResponse( text="Goodbye", content=Mock(parts=[Mock(function_call=None)]), - citation_metadata=Mock(), + citation_metadata=None, ) ] mock_chat = MagicMock() @@ -202,3 +215,104 @@ def test_default_params_gemini() -> None: message = HumanMessage(content=user_prompt) _ = model([message]) mock_start_chat.assert_called_once_with(history=[]) + + +@pytest.mark.parametrize( + "raw_candidate, expected", + [ + ( + gapic_content_types.Candidate( + content=Content( + role="model", + parts=[ + Part( + function_call=FunctionCall( + name="Information", + args={"name": "Ben"}, + ), + ) + ], + ) + ), + { + "name": "Information", + "arguments": {"name": "Ben"}, + }, + ), + ( + gapic_content_types.Candidate( + content=Content( + role="model", + parts=[ + Part( + function_call=FunctionCall( + name="Information", + args={"info": ["A", "B", "C"]}, + ), + ) + ], + ) + ), + { + "name": "Information", + "arguments": {"info": ["A", "B", "C"]}, + }, + ), + ( + gapic_content_types.Candidate( + content=Content( + role="model", + parts=[ + Part( + function_call=FunctionCall( + name="Information", + args={ + "people": [ + {"name": "Joe", "age": 30}, + {"name": "Martha"}, + ] + }, + ), + ) + ], + ) + ), + { + "name": "Information", + "arguments": { + "people": [ + {"name": "Joe", "age": 30}, + {"name": "Martha"}, + ] + }, + }, + ), + ( + gapic_content_types.Candidate( + content=Content( + role="model", + parts=[ + Part( + function_call=FunctionCall( + name="Information", + args={"info": [[1, 2, 3], [4, 5, 6]]}, + ), + ) + ], + ) + ), + { + "name": "Information", + "arguments": {"info": [[1, 2, 3], [4, 5, 6]]}, + }, + ), + ], +) +def test_parse_response_candidate(raw_candidate, expected) -> None: + response_candidate = Candidate._from_gapic(raw_candidate) + result = _parse_response_candidate(response_candidate) + result_arguments = json.loads( + result.additional_kwargs["function_call"]["arguments"] + ) + + assert result_arguments == expected["arguments"] diff --git a/libs/partners/ibm/.gitignore b/libs/partners/ibm/.gitignore new file mode 100644 index 00000000000..bee8a64b79a --- /dev/null +++ b/libs/partners/ibm/.gitignore @@ -0,0 +1 @@ +__pycache__ diff --git a/libs/partners/ibm/LICENSE b/libs/partners/ibm/LICENSE new file mode 100644 index 00000000000..426b6509034 --- /dev/null +++ b/libs/partners/ibm/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/libs/partners/ibm/Makefile b/libs/partners/ibm/Makefile new file mode 100644 index 00000000000..65add43791c --- /dev/null +++ b/libs/partners/ibm/Makefile @@ -0,0 +1,58 @@ +.PHONY: all format lint test tests integration_tests docker_tests help extended_tests + +# Default target executed when no arguments are given to make. +all: help + +# Define a variable for the test file path. +TEST_FILE ?= tests/unit_tests/ + +integration_test integration_tests: TEST_FILE=tests/integration_tests/ + +test tests integration_test integration_tests: + poetry run pytest $(TEST_FILE) + + +###################### +# LINTING AND FORMATTING +###################### + +# Define a variable for Python and notebook files. +PYTHON_FILES=. +MYPY_CACHE=.mypy_cache +lint format: PYTHON_FILES=. +lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/ibm --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') +lint_package: PYTHON_FILES=langchain_ibm +lint_tests: PYTHON_FILES=tests +lint_tests: MYPY_CACHE=.mypy_cache_test + +lint lint_diff lint_package lint_tests: + poetry run ruff . + poetry run ruff format $(PYTHON_FILES) --diff + poetry run ruff --select I $(PYTHON_FILES) + mkdir $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + +format format_diff: + poetry run ruff format $(PYTHON_FILES) + poetry run ruff --select I --fix $(PYTHON_FILES) + +spell_check: + poetry run codespell --toml pyproject.toml + +spell_fix: + poetry run codespell --toml pyproject.toml -w + +check_imports: $(shell find langchain_ibm -name '*.py') + poetry run python ./scripts/check_imports.py $^ + +###################### +# HELP +###################### + +help: + @echo '----' + @echo 'check_imports - check imports' + @echo 'format - run code formatters' + @echo 'lint - run linters' + @echo 'test - run unit tests' + @echo 'tests - run unit tests' + @echo 'test TEST_FILE= - run all tests in file' diff --git a/libs/partners/ibm/README.md b/libs/partners/ibm/README.md new file mode 100644 index 00000000000..7c890359225 --- /dev/null +++ b/libs/partners/ibm/README.md @@ -0,0 +1,18 @@ +# langchain-ibm + +This package contains the LangChain integrations for IBM watsonx.ai through their `ibm-watsonx-ai` SDK. + +## Installation and Setup + +- Install the LangChain partner package +```bash +pip install langchain-ibm +``` + +## LLM + +See a [usage example](https://python.langchain.com/docs/integrations/llms/watsonxllm). + +```python +from langchain_ibm import WatsonxLLM +``` diff --git a/libs/partners/ibm/langchain_ibm/__init__.py b/libs/partners/ibm/langchain_ibm/__init__.py new file mode 100644 index 00000000000..97a1ed0ce13 --- /dev/null +++ b/libs/partners/ibm/langchain_ibm/__init__.py @@ -0,0 +1,3 @@ +from langchain_ibm.llms import WatsonxLLM + +__all__ = ["WatsonxLLM"] diff --git a/libs/partners/ibm/langchain_ibm/llms.py b/libs/partners/ibm/langchain_ibm/llms.py new file mode 100644 index 00000000000..8afdcd17c0b --- /dev/null +++ b/libs/partners/ibm/langchain_ibm/llms.py @@ -0,0 +1,387 @@ +import logging +import os +from typing import Any, Dict, Iterator, List, Mapping, Optional, Union + +from ibm_watsonx_ai.foundation_models import ModelInference # type: ignore +from langchain_core.callbacks import CallbackManagerForLLMRun +from langchain_core.language_models.llms import BaseLLM +from langchain_core.outputs import Generation, GenerationChunk, LLMResult +from langchain_core.pydantic_v1 import Extra, SecretStr, root_validator +from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env + +logger = logging.getLogger(__name__) + + +class WatsonxLLM(BaseLLM): + """ + IBM watsonx.ai large language models. + + To use, you should have ``ibm_watsonx_ai`` python package installed, + and the environment variable ``WATSONX_APIKEY`` set with your API key, or pass + it as a named parameter to the constructor. + + + Example: + .. code-block:: python + + from ibm_watsonx_ai.metanames import GenTextParamsMetaNames + parameters = { + GenTextParamsMetaNames.DECODING_METHOD: "sample", + GenTextParamsMetaNames.MAX_NEW_TOKENS: 100, + GenTextParamsMetaNames.MIN_NEW_TOKENS: 1, + GenTextParamsMetaNames.TEMPERATURE: 0.5, + GenTextParamsMetaNames.TOP_K: 50, + GenTextParamsMetaNames.TOP_P: 1, + } + + from langchain_ibm import WatsonxLLM + watsonx_llm = WatsonxLLM( + model_id="google/flan-ul2", + url="https://us-south.ml.cloud.ibm.com", + apikey="*****", + project_id="*****", + params=parameters, + ) + """ + + model_id: str = "" + """Type of model to use.""" + + deployment_id: str = "" + """Type of deployed model to use.""" + + project_id: str = "" + """ID of the Watson Studio project.""" + + space_id: str = "" + """ID of the Watson Studio space.""" + + url: Optional[SecretStr] = None + """Url to Watson Machine Learning instance""" + + apikey: Optional[SecretStr] = None + """Apikey to Watson Machine Learning instance""" + + token: Optional[SecretStr] = None + """Token to Watson Machine Learning instance""" + + password: Optional[SecretStr] = None + """Password to Watson Machine Learning instance""" + + username: Optional[SecretStr] = None + """Username to Watson Machine Learning instance""" + + instance_id: Optional[SecretStr] = None + """Instance_id of Watson Machine Learning instance""" + + version: Optional[SecretStr] = None + """Version of Watson Machine Learning instance""" + + params: Optional[dict] = None + """Model parameters to use during generate requests.""" + + verify: Union[str, bool] = "" + """User can pass as verify one of following: + the path to a CA_BUNDLE file + the path of directory with certificates of trusted CAs + True - default path to truststore will be taken + False - no verification will be made""" + + streaming: bool = False + """ Whether to stream the results or not. """ + + watsonx_model: Any + + class Config: + """Configuration for this pydantic object.""" + + extra = Extra.forbid + + @classmethod + def is_lc_serializable(cls) -> bool: + return False + + @property + def lc_secrets(self) -> Dict[str, str]: + return { + "url": "WATSONX_URL", + "apikey": "WATSONX_APIKEY", + "token": "WATSONX_TOKEN", + "password": "WATSONX_PASSWORD", + "username": "WATSONX_USERNAME", + "instance_id": "WATSONX_INSTANCE_ID", + } + + @root_validator() + def validate_environment(cls, values: Dict) -> Dict: + """Validate that credentials and python package exists in environment.""" + values["url"] = convert_to_secret_str( + get_from_dict_or_env(values, "url", "WATSONX_URL") + ) + if "cloud.ibm.com" in values.get("url", "").get_secret_value(): + values["apikey"] = convert_to_secret_str( + get_from_dict_or_env(values, "apikey", "WATSONX_APIKEY") + ) + else: + if ( + not values["token"] + and "WATSONX_TOKEN" not in os.environ + and not values["password"] + and "WATSONX_PASSWORD" not in os.environ + and not values["apikey"] + and "WATSONX_APIKEY" not in os.environ + ): + raise ValueError( + "Did not find 'token', 'password' or 'apikey'," + " please add an environment variable" + " `WATSONX_TOKEN`, 'WATSONX_PASSWORD' or 'WATSONX_APIKEY' " + "which contains it," + " or pass 'token', 'password' or 'apikey'" + " as a named parameter." + ) + elif values["token"] or "WATSONX_TOKEN" in os.environ: + values["token"] = convert_to_secret_str( + get_from_dict_or_env(values, "token", "WATSONX_TOKEN") + ) + elif values["password"] or "WATSONX_PASSWORD" in os.environ: + values["password"] = convert_to_secret_str( + get_from_dict_or_env(values, "password", "WATSONX_PASSWORD") + ) + values["username"] = convert_to_secret_str( + get_from_dict_or_env(values, "username", "WATSONX_USERNAME") + ) + elif values["apikey"] or "WATSONX_APIKEY" in os.environ: + values["apikey"] = convert_to_secret_str( + get_from_dict_or_env(values, "apikey", "WATSONX_APIKEY") + ) + values["username"] = convert_to_secret_str( + get_from_dict_or_env(values, "username", "WATSONX_USERNAME") + ) + if not values["instance_id"] or "WATSONX_INSTANCE_ID" not in os.environ: + values["instance_id"] = convert_to_secret_str( + get_from_dict_or_env(values, "instance_id", "WATSONX_INSTANCE_ID") + ) + + credentials = { + "url": values["url"].get_secret_value() if values["url"] else None, + "apikey": values["apikey"].get_secret_value() if values["apikey"] else None, + "token": values["token"].get_secret_value() if values["token"] else None, + "password": values["password"].get_secret_value() + if values["password"] + else None, + "username": values["username"].get_secret_value() + if values["username"] + else None, + "instance_id": values["instance_id"].get_secret_value() + if values["instance_id"] + else None, + "version": values["version"].get_secret_value() + if values["version"] + else None, + } + credentials_without_none_value = { + key: value for key, value in credentials.items() if value is not None + } + + watsonx_model = ModelInference( + model_id=values["model_id"], + deployment_id=values["deployment_id"], + credentials=credentials_without_none_value, + params=values["params"], + project_id=values["project_id"], + space_id=values["space_id"], + verify=values["verify"], + ) + values["watsonx_model"] = watsonx_model + + return values + + @property + def _identifying_params(self) -> Mapping[str, Any]: + """Get the identifying parameters.""" + return { + "model_id": self.model_id, + "deployment_id": self.deployment_id, + "params": self.params, + "project_id": self.project_id, + "space_id": self.space_id, + } + + @property + def _llm_type(self) -> str: + """Return type of llm.""" + return "IBM watsonx.ai" + + @staticmethod + def _extract_token_usage( + response: Optional[List[Dict[str, Any]]] = None, + ) -> Dict[str, Any]: + if response is None: + return {"generated_token_count": 0, "input_token_count": 0} + + input_token_count = 0 + generated_token_count = 0 + + def get_count_value(key: str, result: Dict[str, Any]) -> int: + return result.get(key, 0) or 0 + + for res in response: + results = res.get("results") + if results: + input_token_count += get_count_value("input_token_count", results[0]) + generated_token_count += get_count_value( + "generated_token_count", results[0] + ) + + return { + "generated_token_count": generated_token_count, + "input_token_count": input_token_count, + } + + def _get_chat_params( + self, stop: Optional[List[str]] = None + ) -> Optional[Dict[str, Any]]: + params: Optional[Dict[str, Any]] = {**self.params} if self.params else None + if stop is not None: + params = (params or {}) | {"stop_sequences": stop} + return params + + def _create_llm_result(self, response: List[dict]) -> LLMResult: + """Create the LLMResult from the choices and prompts.""" + generations = [] + for res in response: + results = res.get("results") + if results: + finish_reason = results[0].get("stop_reason") + gen = Generation( + text=results[0].get("generated_text"), + generation_info={"finish_reason": finish_reason}, + ) + generations.append([gen]) + final_token_usage = self._extract_token_usage(response) + llm_output = { + "token_usage": final_token_usage, + "model_id": self.model_id, + "deployment_id": self.deployment_id, + } + return LLMResult(generations=generations, llm_output=llm_output) + + def _stream_response_to_generation_chunk( + self, + stream_response: Dict[str, Any], + ) -> GenerationChunk: + """Convert a stream response to a generation chunk.""" + if not stream_response["results"]: + return GenerationChunk(text="") + return GenerationChunk( + text=stream_response["results"][0]["generated_text"], + generation_info=dict( + finish_reason=stream_response["results"][0].get("stop_reason", None), + llm_output={ + "model_id": self.model_id, + "deployment_id": self.deployment_id, + }, + ), + ) + + def _call( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> str: + """Call the IBM watsonx.ai inference endpoint. + Args: + prompt: The prompt to pass into the model. + stop: Optional list of stop words to use when generating. + run_manager: Optional callback manager. + Returns: + The string generated by the model. + Example: + .. code-block:: python + + response = watsonx_llm("What is a molecule") + """ + result = self._generate( + prompts=[prompt], stop=stop, run_manager=run_manager, **kwargs + ) + return result.generations[0][0].text + + def _generate( + self, + prompts: List[str], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + stream: Optional[bool] = None, + **kwargs: Any, + ) -> LLMResult: + """Call the IBM watsonx.ai inference endpoint which then generate the response. + Args: + prompts: List of strings (prompts) to pass into the model. + stop: Optional list of stop words to use when generating. + run_manager: Optional callback manager. + Returns: + The full LLMResult output. + Example: + .. code-block:: python + + response = watsonx_llm.generate(["What is a molecule"]) + """ + params = self._get_chat_params(stop=stop) + should_stream = stream if stream is not None else self.streaming + if should_stream: + if len(prompts) > 1: + raise ValueError( + f"WatsonxLLM currently only supports single prompt, got {prompts}" + ) + generation = GenerationChunk(text="") + stream_iter = self._stream( + prompts[0], stop=stop, run_manager=run_manager, **kwargs + ) + for chunk in stream_iter: + if generation is None: + generation = chunk + else: + generation += chunk + assert generation is not None + if isinstance(generation.generation_info, dict): + llm_output = generation.generation_info.pop("llm_output") + return LLMResult(generations=[[generation]], llm_output=llm_output) + return LLMResult(generations=[[generation]]) + else: + response = self.watsonx_model.generate(prompt=prompts, params=params) + return self._create_llm_result(response) + + def _stream( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> Iterator[GenerationChunk]: + """Call the IBM watsonx.ai inference endpoint which then streams the response. + Args: + prompt: The prompt to pass into the model. + stop: Optional list of stop words to use when generating. + run_manager: Optional callback manager. + Returns: + The iterator which yields generation chunks. + Example: + .. code-block:: python + + response = watsonx_llm.stream("What is a molecule") + for chunk in response: + print(chunk, end='') + """ + params = self._get_chat_params(stop=stop) + for stream_resp in self.watsonx_model.generate_text_stream( + prompt=prompt, raw_response=True, params=params + ): + if not isinstance(stream_resp, dict): + stream_resp = stream_resp.dict() + chunk = self._stream_response_to_generation_chunk(stream_resp) + + if run_manager: + run_manager.on_llm_new_token(chunk.text, chunk=chunk) + yield chunk diff --git a/libs/partners/ibm/langchain_ibm/py.typed b/libs/partners/ibm/langchain_ibm/py.typed new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/partners/ibm/poetry.lock b/libs/partners/ibm/poetry.lock new file mode 100644 index 00000000000..b4f2e062d09 --- /dev/null +++ b/libs/partners/ibm/poetry.lock @@ -0,0 +1,1095 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + +[[package]] +name = "annotated-types" +version = "0.6.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +files = [ + {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"}, + {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"}, +] + +[[package]] +name = "anyio" +version = 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"jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] + +[metadata] +lock-version = "2.0" +python-versions = ">=3.10,<4.0" +content-hash = "1ea206277880af7ba191e1fe7f60e3a51274623c1697af1a689ed8ce35c0feb6" diff --git a/libs/partners/ibm/pyproject.toml b/libs/partners/ibm/pyproject.toml new file mode 100644 index 00000000000..adcab41ef7f --- /dev/null +++ b/libs/partners/ibm/pyproject.toml @@ -0,0 +1,96 @@ +[tool.poetry] +name = "langchain-ibm" +version = "0.0.1" +description = "An integration package connecting IBM watsonx.ai and LangChain" +authors = ["IBM"] +readme = "README.md" +repository = "https://github.com/langchain-ai/langchain" +license = "MIT" + +[tool.poetry.urls] +"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/ibm" + +[tool.poetry.dependencies] +python = ">=3.10,<4.0" +langchain-core = ">=0.1.22,<0.2" +ibm-watsonx-ai = "^0.1.4" + +[tool.poetry.group.test] +optional = true + +[tool.poetry.group.test.dependencies] +pytest = "^7.3.0" +freezegun = "^1.2.2" +pytest-mock = "^3.10.0" +syrupy = "^4.0.2" +pytest-watcher = "^0.3.4" +pytest-asyncio = "^0.21.1" +langchain-core = {path = "../../core", develop = true} + +[tool.poetry.group.codespell] +optional = true + +[tool.poetry.group.codespell.dependencies] +codespell = "^2.2.0" + +[tool.poetry.group.test_integration] +optional = true + +[tool.poetry.group.test_integration.dependencies] + +[tool.poetry.group.lint] +optional = true + +[tool.poetry.group.lint.dependencies] +ruff = "^0.1.5" + +[tool.poetry.group.typing.dependencies] +mypy = "^0.991" +langchain-core = {path = "../../core", develop = true} +types-requests = "^2" + +[tool.poetry.group.dev] +optional = true + +[tool.poetry.group.dev.dependencies] +langchain-core = {path = "../../core", develop = true} + +[tool.ruff] +select = [ + "E", # pycodestyle + "F", # pyflakes + "I", # isort +] + +[tool.mypy] +disallow_untyped_defs = "True" + +[tool.coverage.run] +omit = [ + "tests/*", +] + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" + +[tool.pytest.ini_options] +# --strict-markers will raise errors on unknown marks. +# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks +# +# https://docs.pytest.org/en/7.1.x/reference/reference.html +# --strict-config any warnings encountered while parsing the `pytest` +# section of the configuration file raise errors. +# +# https://github.com/tophat/syrupy +# --snapshot-warn-unused Prints a warning on unused snapshots rather than fail the test suite. +addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" +# Registering custom markers. +# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers +markers = [ + "requires: mark tests as requiring a specific library", + "asyncio: mark tests as requiring asyncio", + "compile: mark placeholder test used to compile integration tests without running them", + "scheduled: mark tests to run in scheduled testing", +] +asyncio_mode = "auto" diff --git a/libs/partners/ibm/scripts/check_imports.py b/libs/partners/ibm/scripts/check_imports.py new file mode 100644 index 00000000000..fd21a4975b7 --- /dev/null +++ b/libs/partners/ibm/scripts/check_imports.py @@ -0,0 +1,17 @@ +import sys +import traceback +from importlib.machinery import SourceFileLoader + +if __name__ == "__main__": + files = sys.argv[1:] + has_failure = False + for file in files: + try: + SourceFileLoader("x", file).load_module() + except Exception: + has_faillure = True + print(file) + traceback.print_exc() + print() + + sys.exit(1 if has_failure else 0) diff --git a/libs/partners/ibm/scripts/check_pydantic.sh b/libs/partners/ibm/scripts/check_pydantic.sh new file mode 100755 index 00000000000..06b5bb81ae2 --- /dev/null +++ b/libs/partners/ibm/scripts/check_pydantic.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# +# This script searches for lines starting with "import pydantic" or "from pydantic" +# in tracked files within a Git repository. +# +# Usage: ./scripts/check_pydantic.sh /path/to/repository + +# Check if a path argument is provided +if [ $# -ne 1 ]; then + echo "Usage: $0 /path/to/repository" + exit 1 +fi + +repository_path="$1" + +# Search for lines matching the pattern within the specified repository +result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') + +# Check if any matching lines were found +if [ -n "$result" ]; then + echo "ERROR: The following lines need to be updated:" + echo "$result" + echo "Please replace the code with an import from langchain_core.pydantic_v1." + echo "For example, replace 'from pydantic import BaseModel'" + echo "with 'from langchain_core.pydantic_v1 import BaseModel'" + exit 1 +fi diff --git a/libs/partners/ibm/scripts/lint_imports.sh b/libs/partners/ibm/scripts/lint_imports.sh new file mode 100755 index 00000000000..695613c7ba8 --- /dev/null +++ b/libs/partners/ibm/scripts/lint_imports.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +set -eu + +# Initialize a variable to keep track of errors +errors=0 + +# make sure not importing from langchain or langchain_experimental +git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) +git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) + +# Decide on an exit status based on the errors +if [ "$errors" -gt 0 ]; then + exit 1 +else + exit 0 +fi diff --git a/libs/partners/ibm/tests/__init__.py b/libs/partners/ibm/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/partners/ibm/tests/integration_tests/__init__.py b/libs/partners/ibm/tests/integration_tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/partners/ibm/tests/integration_tests/test_compile.py b/libs/partners/ibm/tests/integration_tests/test_compile.py new file mode 100644 index 00000000000..33ecccdfa0f --- /dev/null +++ b/libs/partners/ibm/tests/integration_tests/test_compile.py @@ -0,0 +1,7 @@ +import pytest + + +@pytest.mark.compile +def test_placeholder() -> None: + """Used for compiling integration tests without running any real tests.""" + pass diff --git a/libs/partners/ibm/tests/integration_tests/test_llms.py b/libs/partners/ibm/tests/integration_tests/test_llms.py new file mode 100644 index 00000000000..cc93ee5277e --- /dev/null +++ b/libs/partners/ibm/tests/integration_tests/test_llms.py @@ -0,0 +1,83 @@ +"""Test WatsonxLLM API wrapper. + +You'll need to set WATSONX_APIKEY and WATSONX_PROJECT_ID environment variables. +""" + +import os + +from langchain_core.outputs import LLMResult + +from langchain_ibm import WatsonxLLM + +PROJECT_ID = os.environ.get("WATSONX_PROJECT_ID", "") + + +def test_watsonxllm_invoke() -> None: + watsonxllm = WatsonxLLM( + model_id="google/flan-ul2", + url="https://us-south.ml.cloud.ibm.com", + project_id=PROJECT_ID, + ) + response = watsonxllm.invoke("What color sunflower is?") + assert isinstance(response, str) + assert len(response) > 0 + + +def test_watsonxllm_generate() -> None: + watsonxllm = WatsonxLLM( + model_id="google/flan-ul2", + url="https://us-south.ml.cloud.ibm.com", + project_id=PROJECT_ID, + ) + response = watsonxllm.generate(["What color sunflower is?"]) + response_text = response.generations[0][0].text + assert isinstance(response, LLMResult) + assert len(response_text) > 0 + + +def test_watsonxllm_generate_with_multiple_prompts() -> None: + watsonxllm = WatsonxLLM( + model_id="google/flan-ul2", + url="https://us-south.ml.cloud.ibm.com", + project_id=PROJECT_ID, + ) + response = watsonxllm.generate( + ["What color sunflower is?", "What color turtle is?"] + ) + response_text = response.generations[0][0].text + assert isinstance(response, LLMResult) + assert len(response_text) > 0 + + +def test_watsonxllm_generate_stream() -> None: + watsonxllm = WatsonxLLM( + model_id="google/flan-ul2", + url="https://us-south.ml.cloud.ibm.com", + project_id=PROJECT_ID, + ) + response = watsonxllm.generate(["What color sunflower is?"], stream=True) + response_text = response.generations[0][0].text + assert isinstance(response, LLMResult) + assert len(response_text) > 0 + + +def test_watsonxllm_stream() -> None: + watsonxllm = WatsonxLLM( + model_id="google/flan-ul2", + url="https://us-south.ml.cloud.ibm.com", + project_id=PROJECT_ID, + ) + response = watsonxllm.invoke("What color sunflower is?") + + stream_response = watsonxllm.stream("What color sunflower is?") + + linked_text_stream = "" + for chunk in stream_response: + assert isinstance( + chunk, str + ), f"chunk expect type '{str}', actual '{type(chunk)}'" + linked_text_stream += chunk + + assert ( + response == linked_text_stream + ), "Linked text stream are not the same as generated text" diff --git a/libs/partners/ibm/tests/unit_tests/__init__.py b/libs/partners/ibm/tests/unit_tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/partners/ibm/tests/unit_tests/test_imports.py b/libs/partners/ibm/tests/unit_tests/test_imports.py new file mode 100644 index 00000000000..e623ff75583 --- /dev/null +++ b/libs/partners/ibm/tests/unit_tests/test_imports.py @@ -0,0 +1,7 @@ +from langchain_ibm import __all__ + +EXPECTED_ALL = ["WatsonxLLM"] + + +def test_all_imports() -> None: + assert sorted(EXPECTED_ALL) == sorted(__all__) diff --git a/libs/partners/ibm/tests/unit_tests/test_llms.py b/libs/partners/ibm/tests/unit_tests/test_llms.py new file mode 100644 index 00000000000..01d86590af1 --- /dev/null +++ b/libs/partners/ibm/tests/unit_tests/test_llms.py @@ -0,0 +1,60 @@ +"""Test WatsonxLLM API wrapper.""" + +import os + +from langchain_ibm import WatsonxLLM + +os.environ.pop("WATSONX_APIKEY", None) +os.environ.pop("WATSONX_PROJECT_ID", None) + + +def test_initialize_watsonxllm_bad_path_without_url() -> None: + try: + WatsonxLLM( + model_id="google/flan-ul2", + ) + except ValueError as e: + assert "WATSONX_URL" in e.__str__() + + +def test_initialize_watsonxllm_cloud_bad_path() -> None: + try: + WatsonxLLM(model_id="google/flan-ul2", url="https://us-south.ml.cloud.ibm.com") + except ValueError as e: + assert "WATSONX_APIKEY" in e.__str__() + + +def test_initialize_watsonxllm_cpd_bad_path_without_all() -> None: + try: + WatsonxLLM( + model_id="google/flan-ul2", + url="https://cpd-zen.apps.cpd48.cp.fyre.ibm.com", + ) + except ValueError as e: + assert ( + "WATSONX_APIKEY" in e.__str__() + and "WATSONX_PASSWORD" in e.__str__() + and "WATSONX_TOKEN" in e.__str__() + ) + + +def test_initialize_watsonxllm_cpd_bad_path_password_without_username() -> None: + try: + WatsonxLLM( + model_id="google/flan-ul2", + url="https://cpd-zen.apps.cpd48.cp.fyre.ibm.com", + password="test_password", + ) + except ValueError as e: + assert "WATSONX_USERNAME" in e.__str__() + + +def test_initialize_watsonxllm_cpd_bad_path_apikey_without_username() -> None: + try: + WatsonxLLM( + model_id="google/flan-ul2", + url="https://cpd-zen.apps.cpd48.cp.fyre.ibm.com", + apikey="test_apikey", + ) + except ValueError as e: + assert "WATSONX_USERNAME" in e.__str__() diff --git a/libs/partners/mistralai/langchain_mistralai/chat_models.py b/libs/partners/mistralai/langchain_mistralai/chat_models.py index a13308e5d58..d4cd631b9b4 100644 --- a/libs/partners/mistralai/langchain_mistralai/chat_models.py +++ b/libs/partners/mistralai/langchain_mistralai/chat_models.py @@ -317,9 +317,9 @@ class ChatMistralAI(BaseChatModel): continue chunk = _convert_delta_to_message_chunk(delta, default_chunk_class) default_chunk_class = chunk.__class__ - yield ChatGenerationChunk(message=chunk) if run_manager: run_manager.on_llm_new_token(token=chunk.content, chunk=chunk) + yield ChatGenerationChunk(message=chunk) async def _astream( self, @@ -342,9 +342,9 @@ class ChatMistralAI(BaseChatModel): continue chunk = _convert_delta_to_message_chunk(delta, default_chunk_class) default_chunk_class = chunk.__class__ - yield ChatGenerationChunk(message=chunk) if run_manager: await run_manager.on_llm_new_token(token=chunk.content, chunk=chunk) + yield ChatGenerationChunk(message=chunk) async def _agenerate( self, diff --git a/libs/partners/mistralai/langchain_mistralai/embeddings.py b/libs/partners/mistralai/langchain_mistralai/embeddings.py index de9c4406262..977dbdf9590 100644 --- a/libs/partners/mistralai/langchain_mistralai/embeddings.py +++ b/libs/partners/mistralai/langchain_mistralai/embeddings.py @@ -1,5 +1,6 @@ +import asyncio import logging -from typing import Dict, List, Optional +from typing import Dict, Iterable, List, Optional from langchain_core.embeddings import Embeddings from langchain_core.pydantic_v1 import ( @@ -16,9 +17,12 @@ from mistralai.constants import ( ENDPOINT as DEFAULT_MISTRAL_ENDPOINT, ) from mistralai.exceptions import MistralException +from tokenizers import Tokenizer # type: ignore logger = logging.getLogger(__name__) +MAX_TOKENS = 16_000 + class MistralAIEmbeddings(BaseModel, Embeddings): """MistralAI embedding models. @@ -43,6 +47,7 @@ class MistralAIEmbeddings(BaseModel, Embeddings): max_retries: int = 5 timeout: int = 120 max_concurrent_requests: int = 64 + tokenizer: Tokenizer = Field(default=None) model: str = "mistral-embed" @@ -72,8 +77,33 @@ class MistralAIEmbeddings(BaseModel, Embeddings): timeout=values["timeout"], max_concurrent_requests=values["max_concurrent_requests"], ) + if values["tokenizer"] is None: + values["tokenizer"] = Tokenizer.from_pretrained( + "mistralai/Mixtral-8x7B-v0.1" + ) return values + def _get_batches(self, texts: List[str]) -> Iterable[List[str]]: + """Split a list of texts into batches of less than 16k tokens + for Mistral API.""" + batch: List[str] = [] + batch_tokens = 0 + + text_token_lengths = [ + len(encoded) for encoded in self.tokenizer.encode_batch(texts) + ] + + for text, text_tokens in zip(texts, text_token_lengths): + if batch_tokens + text_tokens > MAX_TOKENS: + yield batch + batch = [text] + batch_tokens = text_tokens + else: + batch.append(text) + batch_tokens += text_tokens + if batch: + yield batch + def embed_documents(self, texts: List[str]) -> List[List[float]]: """Embed a list of document texts. @@ -84,13 +114,17 @@ class MistralAIEmbeddings(BaseModel, Embeddings): List of embeddings, one for each text. """ try: - embeddings_batch_response = self.client.embeddings( - model=self.model, - input=texts, + batch_responses = ( + self.client.embeddings( + model=self.model, + input=batch, + ) + for batch in self._get_batches(texts) ) return [ list(map(float, embedding_obj.embedding)) - for embedding_obj in embeddings_batch_response.data + for response in batch_responses + for embedding_obj in response.data ] except MistralException as e: logger.error(f"An error occurred with MistralAI: {e}") @@ -106,13 +140,19 @@ class MistralAIEmbeddings(BaseModel, Embeddings): List of embeddings, one for each text. """ try: - embeddings_batch_response = await self.async_client.embeddings( - model=self.model, - input=texts, + batch_responses = await asyncio.gather( + *[ + self.async_client.embeddings( + model=self.model, + input=batch, + ) + for batch in self._get_batches(texts) + ] ) return [ list(map(float, embedding_obj.embedding)) - for embedding_obj in embeddings_batch_response.data + for response in batch_responses + for embedding_obj in response.data ] except MistralException as e: logger.error(f"An error occurred with MistralAI: {e}") diff --git a/libs/partners/mistralai/poetry.lock b/libs/partners/mistralai/poetry.lock index 8d406846bf2..92478c94e0e 100644 --- a/libs/partners/mistralai/poetry.lock +++ b/libs/partners/mistralai/poetry.lock @@ -16,19 +16,20 @@ typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} [[package]] name = "anyio" -version = "4.1.0" +version = "4.2.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false python-versions = ">=3.8" files = [ - {file = "anyio-4.1.0-py3-none-any.whl", hash = "sha256:56a415fbc462291813a94528a779597226619c8e78af7de0507333f700011e5f"}, - {file = "anyio-4.1.0.tar.gz", hash = "sha256:5a0bec7085176715be77df87fc66d6c9d70626bd752fcc85f57cdbee5b3760da"}, + {file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"}, + {file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"}, ] [package.dependencies] exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""} idna = ">=2.8" sniffio = ">=1.1" +typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""} [package.extras] doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] @@ -37,13 +38,13 @@ trio = ["trio (>=0.23)"] [[package]] name = "certifi" -version = "2023.11.17" +version = "2024.2.2" 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name = "typing-extensions" version = "4.9.0" @@ -814,21 +1030,22 @@ files = [ [[package]] name = "urllib3" -version = "2.1.0" +version = "2.2.0" description = "HTTP library with thread-safe connection pooling, file post, and more." optional = false python-versions = ">=3.8" files = [ - {file = "urllib3-2.1.0-py3-none-any.whl", hash = "sha256:55901e917a5896a349ff771be919f8bd99aff50b79fe58fec595eb37bbc56bb3"}, - {file = "urllib3-2.1.0.tar.gz", hash = "sha256:df7aa8afb0148fa78488e7899b2c59b5f4ffcfa82e6c54ccb9dd37c1d7b52d54"}, + {file = "urllib3-2.2.0-py3-none-any.whl", hash = "sha256:ce3711610ddce217e6d113a2732fafad960a03fd0318c91faa79481e35c11224"}, + {file = "urllib3-2.2.0.tar.gz", hash = "sha256:051d961ad0c62a94e50ecf1af379c3aba230c66c710493493560c0c223c49f20"}, ] [package.extras] brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"] +h2 = ["h2 (>=4,<5)"] socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"] zstd = ["zstandard (>=0.18.0)"] [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "72f02f84025d4cda9edb7f7105aecf65cc6341541143c45e5f2885c30aea5a0d" +content-hash = "80ff38d21357b7d1d0dded7726d1d61b342307280838454ded0a7ed1b1220b40" diff --git a/libs/partners/mistralai/pyproject.toml b/libs/partners/mistralai/pyproject.toml index 75c87ec329d..393899987e8 100644 --- a/libs/partners/mistralai/pyproject.toml +++ b/libs/partners/mistralai/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-mistralai" -version = "0.0.3" +version = "0.0.4" description = "An integration package connecting Mistral and LangChain" authors = [] readme = "README.md" @@ -13,7 +13,8 @@ license = "MIT" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" langchain-core = "^0.1" -mistralai = "^0.0.11" +mistralai = ">=0.0.11,<0.1" +tokenizers = "^0.15.1" [tool.poetry.group.test] optional = true @@ -21,7 +22,7 @@ optional = true [tool.poetry.group.test.dependencies] pytest = "^7.3.0" pytest-asyncio = "^0.21.1" -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } [tool.poetry.group.codespell] optional = true @@ -42,28 +43,27 @@ ruff = "^0.1.5" [tool.poetry.group.typing.dependencies] mypy = "^0.991" -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } [tool.poetry.group.dev] optional = true [tool.poetry.group.dev.dependencies] -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } -[tool.ruff] +[tool.ruff.lint] select = [ - "E", # pycodestyle - "F", # pyflakes - "I", # isort + "E", # pycodestyle + "F", # pyflakes + "I", # isort + "T201", # print ] [tool.mypy] disallow_untyped_defs = "True" [tool.coverage.run] -omit = [ - "tests/*", -] +omit = ["tests/*"] [build-system] requires = ["poetry-core>=1.0.0"] diff --git a/libs/partners/mistralai/scripts/check_imports.py b/libs/partners/mistralai/scripts/check_imports.py index fd21a4975b7..365f5fa118d 100644 --- a/libs/partners/mistralai/scripts/check_imports.py +++ b/libs/partners/mistralai/scripts/check_imports.py @@ -10,8 +10,8 @@ if __name__ == "__main__": SourceFileLoader("x", file).load_module() except Exception: has_faillure = True - print(file) + print(file) # noqa: T201 traceback.print_exc() - print() + print() # noqa: T201 sys.exit(1 if has_failure else 0) diff --git a/libs/partners/mistralai/tests/integration_tests/test_embeddings.py b/libs/partners/mistralai/tests/integration_tests/test_embeddings.py index 166bf5c5c15..345c29b3555 100644 --- a/libs/partners/mistralai/tests/integration_tests/test_embeddings.py +++ b/libs/partners/mistralai/tests/integration_tests/test_embeddings.py @@ -1,4 +1,5 @@ """Test MistralAI Embedding""" + from langchain_mistralai import MistralAIEmbeddings @@ -38,10 +39,10 @@ async def test_mistralai_embedding_query_async() -> None: def test_mistralai_embedding_documents_long() -> None: """Test MistralAI embeddings for documents.""" - documents = ["foo bar " * 1000, "test document " * 1000] + documents = ["foo bar " * 1000, "test document " * 1000] * 5 embedding = MistralAIEmbeddings() output = embedding.embed_documents(documents) - assert len(output) == 2 + assert len(output) == 10 assert len(output[0]) == 1024 diff --git a/libs/partners/mistralai/tests/unit_tests/test_chat_models.py b/libs/partners/mistralai/tests/unit_tests/test_chat_models.py index d965725ba13..8a28a916f59 100644 --- a/libs/partners/mistralai/tests/unit_tests/test_chat_models.py +++ b/libs/partners/mistralai/tests/unit_tests/test_chat_models.py @@ -1,7 +1,10 @@ """Test MistralAI Chat API wrapper.""" import os +from typing import Any, AsyncGenerator, Generator +from unittest.mock import patch import pytest +from langchain_core.callbacks.base import BaseCallbackHandler from langchain_core.messages import ( AIMessage, BaseMessage, @@ -12,6 +15,11 @@ from langchain_core.messages import ( # TODO: Remove 'type: ignore' once mistralai has stubs or py.typed marker. from mistralai.models.chat_completion import ( # type: ignore[import] + ChatCompletionResponseStreamChoice, + ChatCompletionStreamResponse, + DeltaMessage, +) +from mistralai.models.chat_completion import ( ChatMessage as MistralChatMessage, ) @@ -63,3 +71,50 @@ def test_convert_message_to_mistral_chat_message( ) -> None: result = _convert_message_to_mistral_chat_message(message) assert result == expected + + +def _make_completion_response_from_token(token: str) -> ChatCompletionStreamResponse: + return ChatCompletionStreamResponse( + id="abc123", + model="fake_model", + choices=[ + ChatCompletionResponseStreamChoice( + index=0, + delta=DeltaMessage(content=token), + finish_reason=None, + ) + ], + ) + + +def mock_chat_stream(*args: Any, **kwargs: Any) -> Generator: + for token in ["Hello", " how", " can", " I", " help", "?"]: + yield _make_completion_response_from_token(token) + + +async def mock_chat_astream(*args: Any, **kwargs: Any) -> AsyncGenerator: + for token in ["Hello", " how", " can", " I", " help", "?"]: + yield _make_completion_response_from_token(token) + + +class MyCustomHandler(BaseCallbackHandler): + last_token: str = "" + + def on_llm_new_token(self, token: str, **kwargs: Any) -> None: + self.last_token = token + + +@patch("mistralai.client.MistralClient.chat_stream", new=mock_chat_stream) +def test_stream_with_callback() -> None: + callback = MyCustomHandler() + chat = ChatMistralAI(callbacks=[callback]) + for token in chat.stream("Hello"): + assert callback.last_token == token.content + + +@patch("mistralai.async_client.MistralAsyncClient.chat_stream", new=mock_chat_astream) +async def test_astream_with_callback() -> None: + callback = MyCustomHandler() + chat = ChatMistralAI(callbacks=[callback]) + async for token in chat.astream("Hello"): + assert callback.last_token == token.content diff --git a/libs/partners/nomic/langchain_nomic/embeddings.py b/libs/partners/nomic/langchain_nomic/embeddings.py index 36c476df0d7..55152c417f1 100644 --- a/libs/partners/nomic/langchain_nomic/embeddings.py +++ b/libs/partners/nomic/langchain_nomic/embeddings.py @@ -3,6 +3,7 @@ from typing import List, Optional import nomic # type: ignore from langchain_core.embeddings import Embeddings +from nomic import embed # type: ignore class NomicEmbeddings(Embeddings): @@ -21,6 +22,7 @@ class NomicEmbeddings(Embeddings): *, model: str, nomic_api_key: Optional[str] = None, + dimensionality: Optional[int] = None, ): """Initialize NomicEmbeddings model. @@ -33,6 +35,7 @@ class NomicEmbeddings(Embeddings): if _api_key: nomic.login(_api_key) self.model = model + self.dimensionality = dimensionality def embed(self, texts: List[str], *, task_type: str) -> List[List[float]]: """Embed texts. @@ -42,10 +45,13 @@ class NomicEmbeddings(Embeddings): task_type: the task type to use when embedding. One of `search_query`, `search_document`, `classification`, `clustering` """ - # TODO: do this via nomic.embed when fixed in nomic sdk - from nomic import embed - output = embed.text(texts=texts, model=self.model, task_type=task_type) + output = embed.text( + texts=texts, + model=self.model, + task_type=task_type, + dimensionality=self.dimensionality, + ) return output["embeddings"] def embed_documents(self, texts: List[str]) -> List[List[float]]: diff --git a/libs/partners/nomic/poetry.lock b/libs/partners/nomic/poetry.lock index 77c5f064198..fa730097704 100644 --- a/libs/partners/nomic/poetry.lock +++ b/libs/partners/nomic/poetry.lock @@ -57,13 +57,13 @@ tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "p [[package]] name = "certifi" -version = "2023.11.17" +version = "2024.2.2" description = "Python package for providing Mozilla's CA Bundle." optional = false python-versions = ">=3.6" files = [ - {file = "certifi-2023.11.17-py3-none-any.whl", hash = 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(>=19.3b0)", "pytest (>=4.6.2)"] [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "93c6f3f4eef4a25d709cf06bcfed145c886b6cfeda1661c103da7cc1429c842f" +content-hash = "5bf9a68cffe3d006de6a5b5b7ea293e1d9893baa28390608f51d4103f639ee99" diff --git a/libs/partners/nomic/pyproject.toml b/libs/partners/nomic/pyproject.toml index 8220f143e14..ba16d28f7ba 100644 --- a/libs/partners/nomic/pyproject.toml +++ b/libs/partners/nomic/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-nomic" -version = "0.0.1" +version = "0.0.2" description = "An integration package connecting Nomic and LangChain" authors = [] readme = "README.md" @@ -12,8 +12,8 @@ license = "MIT" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -langchain-core = ">=0.0.12" -nomic = "^3.0.7" +langchain-core = "^0.1" +nomic = "^3.0.12" [tool.poetry.group.test] optional = true @@ -21,11 +21,11 @@ optional = true [tool.poetry.group.test.dependencies] pytest = "^7.3.0" freezegun = "^1.2.2" -pytest-mock = "^3.10.0" +pytest-mock = "^3.10.0" syrupy = "^4.0.2" pytest-watcher = "^0.3.4" pytest-asyncio = "^0.21.1" -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } [tool.poetry.group.codespell] optional = true @@ -46,28 +46,27 @@ ruff = "^0.1.5" [tool.poetry.group.typing.dependencies] mypy = "^0.991" -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } [tool.poetry.group.dev] optional = true [tool.poetry.group.dev.dependencies] -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } -[tool.ruff] +[tool.ruff.lint] select = [ - "E", # pycodestyle - "F", # pyflakes - "I", # isort + "E", # pycodestyle + "F", # pyflakes + "I", # isort + "T201", # print ] [tool.mypy] disallow_untyped_defs = "True" [tool.coverage.run] -omit = [ - "tests/*", -] +omit = ["tests/*"] [build-system] requires = ["poetry-core>=1.0.0"] diff --git a/libs/partners/nomic/scripts/check_imports.py b/libs/partners/nomic/scripts/check_imports.py index fd21a4975b7..365f5fa118d 100644 --- a/libs/partners/nomic/scripts/check_imports.py +++ b/libs/partners/nomic/scripts/check_imports.py @@ -10,8 +10,8 @@ if __name__ == "__main__": SourceFileLoader("x", file).load_module() except Exception: has_faillure = True - print(file) + print(file) # noqa: T201 traceback.print_exc() - print() + print() # noqa: T201 sys.exit(1 if has_failure else 0) diff --git a/libs/partners/nomic/tests/integration_tests/test_embeddings.py b/libs/partners/nomic/tests/integration_tests/test_embeddings.py index 6800deb839f..9fcc7ed9de0 100644 --- a/libs/partners/nomic/tests/integration_tests/test_embeddings.py +++ b/libs/partners/nomic/tests/integration_tests/test_embeddings.py @@ -1,9 +1,10 @@ """Test Nomic embeddings.""" + from langchain_nomic.embeddings import NomicEmbeddings def test_langchain_nomic_embedding_documents() -> None: - """Test cohere embeddings.""" + """Test nomic embeddings.""" documents = ["foo bar"] embedding = NomicEmbeddings(model="nomic-embed-text-v1") output = embedding.embed_documents(documents) @@ -12,8 +13,17 @@ def test_langchain_nomic_embedding_documents() -> None: def test_langchain_nomic_embedding_query() -> None: - """Test cohere embeddings.""" + """Test nomic embeddings.""" document = "foo bar" embedding = NomicEmbeddings(model="nomic-embed-text-v1") output = embedding.embed_query(document) assert len(output) > 0 + + +def test_langchain_nomic_embedding_dimensionality() -> None: + """Test nomic embeddings.""" + documents = ["foo bar"] + embedding = NomicEmbeddings(model="nomic-embed-text-v1.5", dimensionality=256) + output = embedding.embed_documents(documents) + assert len(output) == 1 + assert len(output[0]) == 256 diff --git a/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/_common.py b/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/_common.py index 9713273c0a9..01f513fd210 100644 --- a/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/_common.py +++ b/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/_common.py @@ -2,6 +2,8 @@ from __future__ import annotations import json import logging +import time +from functools import partial from typing import ( Any, AsyncIterator, @@ -18,7 +20,6 @@ from typing import ( import aiohttp import requests -from langchain_core.messages import BaseMessage from langchain_core.pydantic_v1 import ( BaseModel, Field, @@ -45,6 +46,7 @@ class NVEModel(BaseModel): ## Core defaults. These probably should not be changed fetch_url_format: str = Field("https://api.nvcf.nvidia.com/v2/nvcf/pexec/status/") call_invoke_base: str = Field("https://api.nvcf.nvidia.com/v2/nvcf/pexec/functions") + func_list_format: str = Field("https://api.nvcf.nvidia.com/v2/nvcf/functions") get_session_fn: Callable = Field(requests.Session) get_asession_fn: Callable = Field(aiohttp.ClientSession) @@ -55,7 +57,10 @@ class NVEModel(BaseModel): is_staging: bool = Field(False, description="Whether to use staging API") ## Generation arguments - max_tries: int = Field(5, ge=1) + timeout: float = Field(60, ge=0, description="Timeout for waiting on response (s)") + interval: float = Field(0.02, ge=0, description="Interval for pulling response") + last_inputs: dict = Field({}, description="Last inputs sent over to the server") + payload_fn: Callable = Field(lambda d: d, description="Function to process payload") headers_tmpl: dict = Field( ..., description="Headers template for API calls." @@ -85,34 +90,31 @@ class NVEModel(BaseModel): ) if "nvapi-" not in values.get("nvidia_api_key", ""): raise ValueError("Invalid NVAPI key detected. Should start with `nvapi-`") - is_staging = "nvapi-stg-" in values["nvidia_api_key"] - values["is_staging"] = is_staging + values["is_staging"] = "nvapi-stg-" in values["nvidia_api_key"] if "headers_tmpl" not in values: - values["headers_tmpl"] = { - "call": { - "Authorization": "Bearer {nvidia_api_key}", - "Accept": "application/json", - }, - "stream": { - "Authorization": "Bearer {nvidia_api_key}", - "Accept": "text/event-stream", - "content-type": "application/json", - }, + call_kvs = { + "Accept": "application/json", } + stream_kvs = { + "Accept": "text/event-stream", + "content-type": "application/json", + } + shared_kvs = { + "Authorization": "Bearer {nvidia_api_key}", + "User-Agent": "langchain-nvidia-ai-endpoints", + } + values["headers_tmpl"] = { + "call": {**call_kvs, **shared_kvs}, + "stream": {**stream_kvs, **shared_kvs}, + } + return values - values["fetch_url_format"] = cls._stagify( - is_staging, - values.get( - "fetch_url_format", "https://api.nvcf.nvidia.com/v2/nvcf/pexec/status/" - ), - ) - values["call_invoke_base"] = cls._stagify( - is_staging, - values.get( - "call_invoke_base", - "https://api.nvcf.nvidia.com/v2/nvcf/pexec/functions", - ), - ) + @root_validator(pre=False) + def validate_model_post(cls, values: Dict[str, Any]) -> Dict[str, Any]: + """Additional validation after default values have been put in""" + values["stagify"] = partial(cls._stagify, is_staging=values["is_staging"]) + values["fetch_url_format"] = values["stagify"](values.get("fetch_url_format")) + values["call_invoke_base"] = values["stagify"](values.get("call_invoke_base")) return values @property @@ -129,9 +131,7 @@ class NVEModel(BaseModel): """List the available functions that can be invoked.""" if self._available_functions is not None: return self._available_functions - invoke_url = self._stagify( - self.is_staging, "https://api.nvcf.nvidia.com/v2/nvcf/functions" - ) + invoke_url = self._stagify(self.func_list_format, self.is_staging) query_res = self.query(invoke_url) if "functions" not in query_res: raise ValueError( @@ -140,8 +140,8 @@ class NVEModel(BaseModel): self._available_functions = query_res["functions"] return self._available_functions - @classmethod - def _stagify(cls, is_staging: bool, path: str) -> str: + @staticmethod + def _stagify(path: str, is_staging: bool) -> str: """Helper method to switch between staging and production endpoints""" if is_staging and "stg.api" not in path: return path.replace("api.", "stg.api.") @@ -154,56 +154,61 @@ class NVEModel(BaseModel): def _post(self, invoke_url: str, payload: dict = {}) -> Tuple[Response, Any]: """Method for posting to the AI Foundation Model Function API.""" - call_inputs = { + self.last_inputs = { "url": invoke_url, "headers": self.headers["call"], - "json": payload, + "json": self.payload_fn(payload), "stream": False, } session = self.get_session_fn() - response = session.post(**call_inputs) + response = session.post(**self.last_inputs) self._try_raise(response) return response, session def _get(self, invoke_url: str, payload: dict = {}) -> Tuple[Response, Any]: """Method for getting from the AI Foundation Model Function API.""" - last_inputs = { + self.last_inputs = { "url": invoke_url, "headers": self.headers["call"], - "json": payload, + "json": self.payload_fn(payload), "stream": False, } session = self.get_session_fn() - last_response = session.get(**last_inputs) + last_response = session.get(**self.last_inputs) self._try_raise(last_response) return last_response, session def _wait(self, response: Response, session: Any) -> Response: - """Wait for a response from API after an initial response is made.""" - i = 1 + """Wait for a response from API after an initial response is made""" + start_time = time.time() while response.status_code == 202: + time.sleep(self.interval) + if (time.time() - start_time) > self.timeout: + raise TimeoutError( + f"Timeout reached without a successful response." + f"\nLast response: {str(response)}" + ) request_id = response.headers.get("NVCF-REQID", "") response = session.get( self.fetch_url_format + request_id, headers=self.headers["call"], ) - if response.status_code == 202: - try: - body = response.json() - except ValueError: - body = str(response) - if i > self.max_tries: - raise ValueError(f"Failed to get response with {i} tries: {body}") self._try_raise(response) return response def _try_raise(self, response: Response) -> None: """Try to raise an error from a response""" + ## (VK) Several systems can throw errors. This tries to coerce all of them + ## If we can't predictably pull out request id, then dump response try: response.raise_for_status() - except requests.HTTPError as e: + except requests.HTTPError: try: rd = response.json() + if "detail" in rd and "reqId" in rd.get("detail", ""): + rd_buf = "- " + str(rd["detail"]) + rd_buf = rd_buf.replace(": ", ", Error: ").replace(", ", "\n- ") + rd["detail"] = rd_buf except json.JSONDecodeError: rd = response.__dict__ rd = rd.get("_content", rd) @@ -213,9 +218,19 @@ class NVEModel(BaseModel): rd = json.loads(rd) except Exception: rd = {"detail": rd} - title = f"[{rd.get('status', '###')}] {rd.get('title', 'Unknown Error')}" - body = f"{rd.get('detail', rd.get('type', rd))}" - raise Exception(f"{title}\n{body}") from e + status = rd.get("status", "###") + title = rd.get("title", rd.get("error", "Unknown Error")) + header = f"[{status}] {title}" + body = "" + if "requestId" in rd: + if "detail" in rd: + body += f"{rd['detail']}\n" + body += "RequestID: " + rd["requestId"] + else: + body = rd.get("detail", rd) + if str(status) == "401": + body += "\nPlease check or regenerate your API key." + raise Exception(f"{header}\n{body}") from None #################################################################################### ## Simple query interface to show the set of model options @@ -361,18 +376,18 @@ class NVEModel(BaseModel): invoke_url = self._get_invoke_url(model, invoke_url) if payload.get("stream", True) is False: payload = {**payload, "stream": True} - last_inputs = { + self.last_inputs = { "url": invoke_url, "headers": self.headers["stream"], "json": payload, "stream": True, } - response = self.get_session_fn().post(**last_inputs) + response = self.get_session_fn().post(**self.last_inputs) self._try_raise(response) call = self.copy() def out_gen() -> Generator[dict, Any, Any]: - ## Good for client, since it allows self.last_input + ## Good for client, since it allows self.last_inputs for line in response.iter_lines(): if line and line.strip() != b"data: [DONE]": line = line.decode("utf-8") @@ -397,13 +412,13 @@ class NVEModel(BaseModel): invoke_url = self._get_invoke_url(model, invoke_url) if payload.get("stream", True) is False: payload = {**payload, "stream": True} - last_inputs = { + self.last_inputs = { "url": invoke_url, "headers": self.headers["stream"], "json": payload, } async with self.get_asession_fn() as session: - async with session.post(**last_inputs) as response: + async with session.post(**self.last_inputs) as response: self._try_raise(response) async for line in response.content.iter_any(): if line and line.strip() != b"data: [DONE]": @@ -424,10 +439,6 @@ class _NVIDIAClient(BaseModel): model: str = Field(..., description="Name of the model to invoke") - temperature: float = Field(0.2, le=1.0, gt=0.0) - top_p: float = Field(0.7, le=1.0, ge=0.0) - max_tokens: int = Field(1024, le=1024, ge=32) - #################################################################################### @root_validator(pre=True) @@ -451,6 +462,16 @@ class _NVIDIAClient(BaseModel): """Map the available models that can be invoked.""" return self.client.available_models + @staticmethod + def get_available_functions(**kwargs: Any) -> List[dict]: + """Map the available functions that can be invoked. Callable from class""" + return NVEModel(**kwargs).available_functions + + @staticmethod + def get_available_models(**kwargs: Any) -> dict: + """Map the available models that can be invoked. Callable from class""" + return NVEModel(**kwargs).available_models + def get_model_details(self, model: Optional[str] = None) -> dict: """Get more meta-details about a model retrieved by a given name""" if model is None: @@ -459,67 +480,3 @@ class _NVIDIAClient(BaseModel): known_fns = self.client.available_functions fn_spec = [f for f in known_fns if f.get("id") == model_key][0] return fn_spec - - def get_generation( - self, - inputs: Sequence[Dict], - labels: Optional[dict] = None, - stop: Optional[Sequence[str]] = None, - **kwargs: Any, - ) -> dict: - """Call to client generate method with call scope""" - payload = self.get_payload(inputs=inputs, stream=False, labels=labels, **kwargs) - out = self.client.get_req_generation(self.model, stop=stop, payload=payload) - return out - - def get_stream( - self, - inputs: Sequence[Dict], - labels: Optional[dict] = None, - stop: Optional[Sequence[str]] = None, - **kwargs: Any, - ) -> Iterator: - """Call to client stream method with call scope""" - payload = self.get_payload(inputs=inputs, stream=True, labels=labels, **kwargs) - return self.client.get_req_stream(self.model, stop=stop, payload=payload) - - def get_astream( - self, - inputs: Sequence[Dict], - labels: Optional[dict] = None, - stop: Optional[Sequence[str]] = None, - **kwargs: Any, - ) -> AsyncIterator: - """Call to client astream methods with call scope""" - payload = self.get_payload(inputs=inputs, stream=True, labels=labels, **kwargs) - return self.client.get_req_astream(self.model, stop=stop, payload=payload) - - def get_payload( - self, inputs: Sequence[Dict], labels: Optional[dict] = None, **kwargs: Any - ) -> dict: - """Generates payload for the _NVIDIAClient API to send to service.""" - return { - **self.preprocess(inputs=inputs, labels=labels), - **kwargs, - } - - def preprocess(self, inputs: Sequence[Dict], labels: Optional[dict] = None) -> dict: - """Prepares a message or list of messages for the payload""" - messages = [self.prep_msg(m) for m in inputs] - if labels: - # (WFH) Labels are currently (?) always passed as an assistant - # suffix message, but this API seems less stable. - messages += [{"labels": labels, "role": "assistant"}] - return {"messages": messages} - - def prep_msg(self, msg: Union[str, dict, BaseMessage]) -> dict: - """Helper Method: Ensures a message is a dictionary with a role and content.""" - if isinstance(msg, str): - # (WFH) this shouldn't ever be reached but leaving this here bcs - # it's a Chesterton's fence I'm unwilling to touch - return dict(role="user", content=msg) - if isinstance(msg, dict): - if msg.get("content", None) is None: - raise ValueError(f"Message {msg} has no content") - return msg - raise ValueError(f"Unknown message received: {msg} of type {type(msg)}") diff --git a/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/chat_models.py b/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/chat_models.py index da1df25d571..d71c1a62908 100644 --- a/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/chat_models.py +++ b/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/chat_models.py @@ -1,9 +1,12 @@ """Chat Model Components Derived from ChatModel/NVIDIA""" + from __future__ import annotations import base64 +import io import logging import os +import sys import urllib.parse from typing import ( Any, @@ -25,9 +28,17 @@ from langchain_core.callbacks.manager import ( from langchain_core.language_models.chat_models import SimpleChatModel from langchain_core.messages import BaseMessage, ChatMessage, ChatMessageChunk from langchain_core.outputs import ChatGenerationChunk +from langchain_core.pydantic_v1 import Field from langchain_nvidia_ai_endpoints import _common as nvidia_ai_endpoints +try: + import PIL.Image + + has_pillow = True +except ImportError: + has_pillow = False + logger = logging.getLogger(__name__) @@ -48,6 +59,27 @@ def _is_b64(s: str) -> bool: return s.startswith("data:image") +def _resize_image(img_data: bytes, max_dim: int = 1024) -> str: + if not has_pillow: + print( # noqa: T201 + "Pillow is required to resize images down to reasonable scale." + " Please install it using `pip install pillow`." + " For now, not resizing; may cause NVIDIA API to fail." + ) + return base64.b64encode(img_data).decode("utf-8") + image = PIL.Image.open(io.BytesIO(img_data)) + max_dim_size = max(image.size) + aspect_ratio = max_dim / max_dim_size + new_h = int(image.size[1] * aspect_ratio) + new_w = int(image.size[0] * aspect_ratio) + resized_image = image.resize((new_w, new_h), PIL.Image.Resampling.LANCZOS) + output_buffer = io.BytesIO() + resized_image.save(output_buffer, format="JPEG") + output_buffer.seek(0) + resized_b64_string = base64.b64encode(output_buffer.read()).decode("utf-8") + return resized_b64_string + + def _url_to_b64_string(image_source: str) -> str: b64_template = "data:image/png;base64,{b64_string}" try: @@ -55,6 +87,9 @@ def _url_to_b64_string(image_source: str) -> str: response = requests.get(image_source) response.raise_for_status() encoded = base64.b64encode(response.content).decode("utf-8") + if sys.getsizeof(encoded) > 200000: + ## (VK) Temporary fix. NVIDIA API has a limit of 250KB for the input. + encoded = _resize_image(response.content) return b64_template.format(b64_string=encoded) elif _is_b64(image_source): return image_source @@ -83,6 +118,14 @@ class ChatNVIDIA(nvidia_ai_endpoints._NVIDIAClient, SimpleChatModel): response = model.invoke("Hello") """ + temperature: Optional[float] = Field(description="Sampling temperature in [0, 1]") + max_tokens: Optional[int] = Field(description="Maximum # of tokens to generate") + top_p: Optional[float] = Field(description="Top-p for distribution sampling") + seed: Optional[int] = Field(description="The seed for deterministic results") + bad: Optional[Sequence[str]] = Field(description="Bad words to avoid (cased)") + stop: Optional[Sequence[str]] = Field(description="Stop words (cased)") + labels: Optional[Dict[str, float]] = Field(description="Steering parameters") + @property def _llm_type(self) -> str: """Return type of NVIDIA AI Foundation Model Interface.""" @@ -93,14 +136,11 @@ class ChatNVIDIA(nvidia_ai_endpoints._NVIDIAClient, SimpleChatModel): messages: List[BaseMessage], stop: Optional[Sequence[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, - labels: Optional[dict] = None, **kwargs: Any, ) -> str: """Invoke on a single list of chat messages.""" inputs = self.custom_preprocess(messages) - responses = self.get_generation( - inputs=inputs, stop=stop, labels=labels, **kwargs - ) + responses = self.get_generation(inputs=inputs, stop=stop, **kwargs) outputs = self.custom_postprocess(responses) return outputs @@ -115,14 +155,11 @@ class ChatNVIDIA(nvidia_ai_endpoints._NVIDIAClient, SimpleChatModel): messages: List[BaseMessage], stop: Optional[Sequence[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, - labels: Optional[dict] = None, **kwargs: Any, ) -> Iterator[ChatGenerationChunk]: """Allows streaming to model!""" inputs = self.custom_preprocess(messages) - for response in self.get_stream( - inputs=inputs, stop=stop, labels=labels, **kwargs - ): + for response in self.get_stream(inputs=inputs, stop=stop, **kwargs): chunk = self._get_filled_chunk(self.custom_postprocess(response)) yield chunk if run_manager: @@ -133,13 +170,10 @@ class ChatNVIDIA(nvidia_ai_endpoints._NVIDIAClient, SimpleChatModel): messages: List[BaseMessage], stop: Optional[Sequence[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, - labels: Optional[dict] = None, **kwargs: Any, ) -> AsyncIterator[ChatGenerationChunk]: inputs = self.custom_preprocess(messages) - async for response in self.get_astream( - inputs=inputs, stop=stop, labels=labels, **kwargs - ): + async for response in self.get_astream(inputs=inputs, stop=stop, **kwargs): chunk = self._get_filled_chunk(self.custom_postprocess(response)) yield chunk if run_manager: @@ -148,8 +182,6 @@ class ChatNVIDIA(nvidia_ai_endpoints._NVIDIAClient, SimpleChatModel): def custom_preprocess( self, msg_list: Sequence[BaseMessage] ) -> List[Dict[str, str]]: - # The previous author had a lot of custom preprocessing here - # but I'm just going to assume it's a list return [self.preprocess_msg(m) for m in msg_list] def _process_content(self, content: Union[str, List[Union[dict, str]]]) -> str: @@ -184,9 +216,6 @@ class ChatNVIDIA(nvidia_ai_endpoints._NVIDIAClient, SimpleChatModel): return "".join(string_array) def preprocess_msg(self, msg: BaseMessage) -> Dict[str, str]: - ## (WFH): Previous author added a bunch of - # custom processing here, but I'm just going to support - # the LCEL api. if isinstance(msg, BaseMessage): role_convert = {"ai": "assistant", "human": "user"} if isinstance(msg, ChatMessage): @@ -201,7 +230,78 @@ class ChatNVIDIA(nvidia_ai_endpoints._NVIDIAClient, SimpleChatModel): def custom_postprocess(self, msg: dict) -> str: if "content" in msg: return msg["content"] - logger.warning( - f"Got ambiguous message in postprocessing; returning as-is: msg = {msg}" - ) + elif "b64_json" in msg: + return msg["b64_json"] return str(msg) + + ###################################################################################### + ## Core client-side interfaces + + def get_generation( + self, + inputs: Sequence[Dict], + **kwargs: Any, + ) -> dict: + """Call to client generate method with call scope""" + stop = kwargs.get("stop", None) + payload = self.get_payload(inputs=inputs, stream=False, **kwargs) + out = self.client.get_req_generation(self.model, stop=stop, payload=payload) + return out + + def get_stream( + self, + inputs: Sequence[Dict], + **kwargs: Any, + ) -> Iterator: + """Call to client stream method with call scope""" + stop = kwargs.get("stop", None) + payload = self.get_payload(inputs=inputs, stream=True, **kwargs) + return self.client.get_req_stream(self.model, stop=stop, payload=payload) + + def get_astream( + self, + inputs: Sequence[Dict], + **kwargs: Any, + ) -> AsyncIterator: + """Call to client astream methods with call scope""" + stop = kwargs.get("stop", None) + payload = self.get_payload(inputs=inputs, stream=True, **kwargs) + return self.client.get_req_astream(self.model, stop=stop, payload=payload) + + def get_payload(self, inputs: Sequence[Dict], **kwargs: Any) -> dict: + """Generates payload for the _NVIDIAClient API to send to service.""" + attr_kwargs = { + "temperature": self.temperature, + "max_tokens": self.max_tokens, + "top_p": self.top_p, + "seed": self.seed, + "bad": self.bad, + "stop": self.stop, + "labels": self.labels, + } + attr_kwargs = {k: v for k, v in attr_kwargs.items() if v is not None} + new_kwargs = {**attr_kwargs, **kwargs} + return self.prep_payload(inputs=inputs, **new_kwargs) + + def prep_payload(self, inputs: Sequence[Dict], **kwargs: Any) -> dict: + """Prepares a message or list of messages for the payload""" + messages = [self.prep_msg(m) for m in inputs] + if kwargs.get("labels"): + # (WFH) Labels are currently (?) always passed as an assistant + # suffix message, but this API seems less stable. + messages += [{"labels": kwargs.pop("labels"), "role": "assistant"}] + if kwargs.get("stop") is None: + kwargs.pop("stop") + return {"messages": messages, **kwargs} + + def prep_msg(self, msg: Union[str, dict, BaseMessage]) -> dict: + """Helper Method: Ensures a message is a dictionary with a role and content.""" + if isinstance(msg, str): + # (WFH) this shouldn't ever be reached but leaving this here bcs + # it's a Chesterton's fence I'm unwilling to touch + return dict(role="user", content=msg) + if isinstance(msg, dict): + if msg.get("content", None) is None: + raise ValueError(f"Message {msg} has no content") + return msg + raise ValueError(f"Unknown message received: {msg} of type {type(msg)}") diff --git a/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/embeddings.py b/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/embeddings.py index 65f6e29f4d1..0ce3d673a57 100644 --- a/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/embeddings.py +++ b/libs/partners/nvidia-ai-endpoints/langchain_nvidia_ai_endpoints/embeddings.py @@ -1,36 +1,21 @@ """Embeddings Components Derived from NVEModel/Embeddings""" -from typing import Any, List, Literal, Optional +from typing import List, Literal, Optional from langchain_core.embeddings import Embeddings -from langchain_core.pydantic_v1 import BaseModel, Field, root_validator +from langchain_core.pydantic_v1 import Field -import langchain_nvidia_ai_endpoints._common as nvai_common +from langchain_nvidia_ai_endpoints._common import _NVIDIAClient -class NVIDIAEmbeddings(BaseModel, Embeddings): +class NVIDIAEmbeddings(_NVIDIAClient, Embeddings): """NVIDIA's AI Foundation Retriever Question-Answering Asymmetric Model.""" - client: nvai_common.NVEModel = Field(nvai_common.NVEModel) - model: str = Field( - ..., description="The embedding model to use. Example: nvolveqa_40k" - ) max_length: int = Field(2048, ge=1, le=2048) max_batch_size: int = Field(default=50) model_type: Optional[Literal["passage", "query"]] = Field( "passage", description="The type of text to be embedded." ) - @root_validator(pre=True) - def _validate_client(cls, values: Any) -> Any: - if "client" not in values: - values["client"] = nvai_common.NVEModel() - return values - - @property - def available_models(self) -> dict: - """Map the available models that can be invoked.""" - return self.client.available_models - def _embed( self, texts: List[str], model_type: Literal["passage", "query"] ) -> List[List[float]]: diff --git a/libs/partners/nvidia-ai-endpoints/poetry.lock b/libs/partners/nvidia-ai-endpoints/poetry.lock index 10176a47c37..15b120b983b 100644 --- a/libs/partners/nvidia-ai-endpoints/poetry.lock +++ b/libs/partners/nvidia-ai-endpoints/poetry.lock @@ -1,88 +1,88 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. 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-version = "0.0.1.post2" +version = "0.0.3" description = "An integration package connecting NVIDIA AI Endpoints and LangChain" authors = [] readme = "README.md" @@ -12,7 +12,7 @@ license = "MIT" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -langchain-core = "^0.1.0" +langchain-core = "^0.1" aiohttp = "^3.9.1" [tool.poetry.group.test] @@ -21,11 +21,11 @@ optional = true [tool.poetry.group.test.dependencies] pytest = "^7.3.0" freezegun = "^1.2.2" -pytest-mock = "^3.10.0" +pytest-mock = "^3.10.0" syrupy = "^4.0.2" pytest-watcher = "^0.3.4" pytest-asyncio = "^0.21.1" -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } [tool.poetry.group.codespell] optional = true @@ -46,20 +46,22 @@ ruff = "^0.1.5" [tool.poetry.group.typing.dependencies] mypy = "^0.991" -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } types-requests = "^2.31.0.10" +types-pillow = "^10.2.0.20240125" [tool.poetry.group.dev] optional = true [tool.poetry.group.dev.dependencies] -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } -[tool.ruff] +[tool.ruff.lint] select = [ - "E", # pycodestyle - "F", # pyflakes - "I", # isort + "E", # pycodestyle + "F", # pyflakes + "I", # isort + "T201", # print ] [tool.mypy] @@ -67,9 +69,7 @@ disallow_untyped_defs = "True" exclude = ["notebooks", "examples", "example_data", "langchain_core/pydantic"] [tool.coverage.run] -omit = [ - "tests/*", -] +omit = ["tests/*"] [build-system] requires = ["poetry-core>=1.0.0"] diff --git a/libs/partners/nvidia-ai-endpoints/scripts/check_imports.py b/libs/partners/nvidia-ai-endpoints/scripts/check_imports.py index fd21a4975b7..365f5fa118d 100644 --- a/libs/partners/nvidia-ai-endpoints/scripts/check_imports.py +++ b/libs/partners/nvidia-ai-endpoints/scripts/check_imports.py @@ -10,8 +10,8 @@ if __name__ == "__main__": SourceFileLoader("x", file).load_module() except Exception: has_faillure = True - print(file) + print(file) # noqa: T201 traceback.print_exc() - print() + print() # noqa: T201 sys.exit(1 if has_failure else 0) diff --git a/libs/partners/nvidia-ai-endpoints/tests/integration_tests/test_chat_models.py b/libs/partners/nvidia-ai-endpoints/tests/integration_tests/test_chat_models.py index 57926bd4219..236d98593dc 100644 --- a/libs/partners/nvidia-ai-endpoints/tests/integration_tests/test_chat_models.py +++ b/libs/partners/nvidia-ai-endpoints/tests/integration_tests/test_chat_models.py @@ -11,7 +11,7 @@ def test_chat_ai_endpoints() -> None: temperature=0.7, ) message = HumanMessage(content="Hello") - response = chat([message]) + response = chat.invoke([message]) assert isinstance(response, BaseMessage) assert isinstance(response.content, str) diff --git a/libs/partners/nvidia-trt/langchain_nvidia_trt/llms.py b/libs/partners/nvidia-trt/langchain_nvidia_trt/llms.py index f56eeb9a1da..0ea1fca1df8 100644 --- a/libs/partners/nvidia-trt/langchain_nvidia_trt/llms.py +++ b/libs/partners/nvidia-trt/langchain_nvidia_trt/llms.py @@ -199,10 +199,13 @@ class TritonTensorRTLLM(BaseLLM): result_queue = self._invoke_triton(self.model_name, inputs, outputs, stop) result_str = "" - for token in result_queue: - result_str += token - - self.client.stop_stream() + try: + for token in result_queue: + if isinstance(token, Exception): + raise token + result_str += token + finally: + self.client.stop_stream() return result_str @@ -377,14 +380,14 @@ class StreamingResponseGenerator(queue.Queue): def __init__( self, - client: grpcclient.InferenceServerClient, + llm: TritonTensorRTLLM, request_id: str, force_batch: bool, stop_words: Sequence[str], ) -> None: """Instantiate the generator class.""" super().__init__() - self.client = client + self.llm = llm self.request_id = request_id self._batch = force_batch self._stop_words = stop_words @@ -397,8 +400,8 @@ class StreamingResponseGenerator(queue.Queue): """Return the next retrieved token.""" val = self.get() if val is None or val in self._stop_words: - self.client.stop_stream( - "tensorrt_llm", self.request_id, signal=not self._batch + self.llm.stop_stream( + self.llm.model_name, self.request_id, signal=not self._batch ) raise StopIteration() return val diff --git a/libs/partners/nvidia-trt/poetry.lock b/libs/partners/nvidia-trt/poetry.lock index 975cafd4034..d6f5f038126 100644 --- a/libs/partners/nvidia-trt/poetry.lock +++ b/libs/partners/nvidia-trt/poetry.lock @@ -16,19 +16,20 @@ typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} [[package]] name = "anyio" -version = "4.1.0" +version = "4.2.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false python-versions = ">=3.8" files = [ - {file = "anyio-4.1.0-py3-none-any.whl", hash = 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hash = "sha256:e3e7065cbdabe6183ab82199d7a4f6b3ba0a438c5a512a68559846ccb76a78ec"}, ] [package.extras] @@ -1241,4 +1220,4 @@ watchmedo = ["PyYAML (>=3.10)"] [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "14f75d1cbe5c1d54ee8ead4d4db1c43394691d8e166d6b78cb6c15fcfc156e5c" +content-hash = "0e732a2a4dbf0fcdd6dfb5d3764f630df29bb57c06f487c4d4f32dfeb2d11660" diff --git a/libs/partners/nvidia-trt/pyproject.toml b/libs/partners/nvidia-trt/pyproject.toml index 55263af19d1..9846299c7da 100644 --- a/libs/partners/nvidia-trt/pyproject.toml +++ b/libs/partners/nvidia-trt/pyproject.toml @@ -12,8 +12,8 @@ license = "MIT" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" -langchain-core = ">=0.0.12" -tritonclient = {extras = ["grpc"], version = "^2.42.0"} +langchain-core = "^0.1" +tritonclient = { extras = ["grpc"], version = "^2.42.0" } lint = "^1.2.1" types-protobuf = "^4.24.0.4" protobuf = "^3.5.0" @@ -57,11 +57,12 @@ optional = true [tool.poetry.group.dev.dependencies] langchain-core = { path = "../../core", develop = true } -[tool.ruff] +[tool.ruff.lint] select = [ - "E", # pycodestyle - "F", # pyflakes - "I", # isort + "E", # pycodestyle + "F", # pyflakes + "I", # isort + "T201", # print ] [tool.mypy] diff --git a/libs/partners/nvidia-trt/scripts/check_imports.py b/libs/partners/nvidia-trt/scripts/check_imports.py index fd21a4975b7..365f5fa118d 100644 --- a/libs/partners/nvidia-trt/scripts/check_imports.py +++ b/libs/partners/nvidia-trt/scripts/check_imports.py @@ -10,8 +10,8 @@ if __name__ == "__main__": SourceFileLoader("x", file).load_module() except Exception: has_faillure = True - print(file) + print(file) # noqa: T201 traceback.print_exc() - print() + print() # noqa: T201 sys.exit(1 if has_failure else 0) diff --git a/libs/partners/openai/langchain_openai/chat_models/base.py b/libs/partners/openai/langchain_openai/chat_models/base.py index b5b7199edba..57e265a6ef4 100644 --- a/libs/partners/openai/langchain_openai/chat_models/base.py +++ b/libs/partners/openai/langchain_openai/chat_models/base.py @@ -1,10 +1,10 @@ """OpenAI chat wrapper.""" + from __future__ import annotations import logging import os import sys -import warnings from typing import ( Any, AsyncIterator, @@ -64,6 +64,7 @@ from langchain_core.utils.function_calling import ( convert_to_openai_function, convert_to_openai_tool, ) +from langchain_core.utils.utils import build_extra_kwargs logger = logging.getLogger(__name__) @@ -290,25 +291,9 @@ class ChatOpenAI(BaseChatModel): """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) - for field_name in list(values): - if field_name in extra: - raise ValueError(f"Found {field_name} supplied twice.") - if field_name not in all_required_field_names: - warnings.warn( - f"""WARNING! {field_name} is not default parameter. - {field_name} was transferred to model_kwargs. - Please confirm that {field_name} is what you intended.""" - ) - extra[field_name] = values.pop(field_name) - - invalid_model_kwargs = all_required_field_names.intersection(extra.keys()) - if invalid_model_kwargs: - raise ValueError( - f"Parameters {invalid_model_kwargs} should be specified explicitly. " - f"Instead they were passed in as part of `model_kwargs` parameter." - ) - - values["model_kwargs"] = extra + values["model_kwargs"] = build_extra_kwargs( + extra, values, all_required_field_names + ) return values @root_validator() @@ -339,9 +324,11 @@ class ChatOpenAI(BaseChatModel): ) client_params = { - "api_key": values["openai_api_key"].get_secret_value() - if values["openai_api_key"] - else None, + "api_key": ( + values["openai_api_key"].get_secret_value() + if values["openai_api_key"] + else None + ), "organization": values["openai_organization"], "base_url": values["openai_api_base"], "timeout": values["request_timeout"], @@ -572,19 +559,9 @@ class ChatOpenAI(BaseChatModel): model = self.tiktoken_model_name else: model = self.model_name - if model == "gpt-3.5-turbo": - # gpt-3.5-turbo may change over time. - # Returning num tokens assuming gpt-3.5-turbo-0301. - model = "gpt-3.5-turbo-0301" - elif model == "gpt-4": - # gpt-4 may change over time. - # Returning num tokens assuming gpt-4-0314. - model = "gpt-4-0314" - # Returns the number of tokens used by a list of messages. try: encoding = tiktoken.encoding_for_model(model) except KeyError: - logger.warning("Warning: model not found. Using cl100k_base encoding.") model = "cl100k_base" encoding = tiktoken.get_encoding(model) return model, encoding @@ -659,7 +636,7 @@ class ChatOpenAI(BaseChatModel): Must be the name of the single provided function or "auto" to automatically determine which function to call (if any). - kwargs: Any additional parameters to pass to the + **kwargs: Any additional parameters to pass to the :class:`~langchain.runnable.Runnable` constructor. """ @@ -711,22 +688,21 @@ class ChatOpenAI(BaseChatModel): "auto" to automatically determine which function to call (if any), or a dict of the form: {"type": "function", "function": {"name": <>}}. - kwargs: Any additional parameters to pass to the + **kwargs: Any additional parameters to pass to the :class:`~langchain.runnable.Runnable` constructor. """ formatted_tools = [convert_to_openai_tool(tool) for tool in tools] if tool_choice is not None: - if isinstance(tool_choice, str) and tool_choice not in ("auto", "none"): + if isinstance(tool_choice, str) and (tool_choice not in ("auto", "none")): tool_choice = {"type": "function", "function": {"name": tool_choice}} - if isinstance(tool_choice, dict) and len(formatted_tools) != 1: + if isinstance(tool_choice, dict) and (len(formatted_tools) != 1): raise ValueError( "When specifying `tool_choice`, you must provide exactly one " f"tool. Received {len(formatted_tools)} tools." ) - if ( - isinstance(tool_choice, dict) - and formatted_tools[0]["function"]["name"] + if isinstance(tool_choice, dict) and ( + formatted_tools[0]["function"]["name"] != tool_choice["function"]["name"] ): raise ValueError( @@ -734,7 +710,4 @@ class ChatOpenAI(BaseChatModel): f"provided tool was {formatted_tools[0]['function']['name']}." ) kwargs["tool_choice"] = tool_choice - return super().bind( - tools=formatted_tools, - **kwargs, - ) + return super().bind(tools=formatted_tools, **kwargs) diff --git a/libs/partners/openai/langchain_openai/embeddings/base.py b/libs/partners/openai/langchain_openai/embeddings/base.py index cd0987ea431..d0b815358e3 100644 --- a/libs/partners/openai/langchain_openai/embeddings/base.py +++ b/libs/partners/openai/langchain_openai/embeddings/base.py @@ -289,9 +289,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings): try: encoding = tiktoken.encoding_for_model(model_name) except KeyError: - logger.warning("Warning: model not found. Using cl100k_base encoding.") - model = "cl100k_base" - encoding = tiktoken.get_encoding(model) + encoding = tiktoken.get_encoding("cl100k_base") for i, text in enumerate(texts): if self.model.endswith("001"): # See: https://github.com/openai/openai-python/ diff --git a/libs/partners/openai/langchain_openai/llms/base.py b/libs/partners/openai/langchain_openai/llms/base.py index 27c2afd5eef..a298b048d0e 100644 --- a/libs/partners/openai/langchain_openai/llms/base.py +++ b/libs/partners/openai/langchain_openai/llms/base.py @@ -198,9 +198,11 @@ class BaseOpenAI(BaseLLM): ) client_params = { - "api_key": values["openai_api_key"].get_secret_value() - if values["openai_api_key"] - else None, + "api_key": ( + values["openai_api_key"].get_secret_value() + if values["openai_api_key"] + else None + ), "organization": values["openai_organization"], "base_url": values["openai_api_base"], "timeout": values["request_timeout"], @@ -257,9 +259,11 @@ class BaseOpenAI(BaseLLM): chunk.text, chunk=chunk, verbose=self.verbose, - logprobs=chunk.generation_info["logprobs"] - if chunk.generation_info - else None, + logprobs=( + chunk.generation_info["logprobs"] + if chunk.generation_info + else None + ), ) async def _astream( @@ -283,9 +287,11 @@ class BaseOpenAI(BaseLLM): chunk.text, chunk=chunk, verbose=self.verbose, - logprobs=chunk.generation_info["logprobs"] - if chunk.generation_info - else None, + logprobs=( + chunk.generation_info["logprobs"] + if chunk.generation_info + else None + ), ) def _generate( @@ -334,12 +340,16 @@ class BaseOpenAI(BaseLLM): choices.append( { "text": generation.text, - "finish_reason": generation.generation_info.get("finish_reason") - if generation.generation_info - else None, - "logprobs": generation.generation_info.get("logprobs") - if generation.generation_info - else None, + "finish_reason": ( + generation.generation_info.get("finish_reason") + if generation.generation_info + else None + ), + "logprobs": ( + generation.generation_info.get("logprobs") + if generation.generation_info + else None + ), } ) else: @@ -395,12 +405,16 @@ class BaseOpenAI(BaseLLM): choices.append( { "text": generation.text, - "finish_reason": generation.generation_info.get("finish_reason") - if generation.generation_info - else None, - "logprobs": generation.generation_info.get("logprobs") - if generation.generation_info - else None, + "finish_reason": ( + generation.generation_info.get("finish_reason") + if generation.generation_info + else None + ), + "logprobs": ( + generation.generation_info.get("logprobs") + if generation.generation_info + else None + ), } ) else: @@ -496,9 +510,7 @@ class BaseOpenAI(BaseLLM): try: enc = tiktoken.encoding_for_model(model_name) except KeyError: - logger.warning("Warning: model not found. Using cl100k_base encoding.") - model = "cl100k_base" - enc = tiktoken.get_encoding(model) + enc = tiktoken.get_encoding("cl100k_base") return enc.encode( text, diff --git a/libs/partners/openai/poetry.lock b/libs/partners/openai/poetry.lock index ee9e59a1249..c8e76949df1 100644 --- a/libs/partners/openai/poetry.lock +++ b/libs/partners/openai/poetry.lock @@ -38,13 +38,13 @@ trio = ["trio (>=0.23)"] [[package]] name = "certifi" -version = "2023.11.17" +version = "2024.2.2" description = "Python package for providing Mozilla's CA Bundle." optional = false python-versions = ">=3.6" files = [ - {file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"}, - {file = "certifi-2023.11.17.tar.gz", hash = "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1"}, + {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"}, + {file = 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[tool.poetry.dependencies] python = ">=3.8.1,<4.0" -langchain-core = ">=0.1.16,<0.2" +langchain-core = "^0.1.16" openai = "^1.10.0" numpy = "^1" -tiktoken = "^0.5.2" +tiktoken = ">=0.5.2,<1" [tool.poetry.group.test] optional = true @@ -23,11 +23,11 @@ optional = true [tool.poetry.group.test.dependencies] pytest = "^7.3.0" freezegun = "^1.2.2" -pytest-mock = "^3.10.0" +pytest-mock = "^3.10.0" syrupy = "^4.0.2" pytest-watcher = "^0.3.4" pytest-asyncio = "^0.21.1" -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } [tool.poetry.group.codespell] optional = true @@ -48,20 +48,21 @@ ruff = "^0.1.5" [tool.poetry.group.typing.dependencies] mypy = "^0.991" -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } types-tqdm = "^4.66.0.5" [tool.poetry.group.dev] optional = true [tool.poetry.group.dev.dependencies] -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } -[tool.ruff] +[tool.ruff.lint] select = [ - "E", # pycodestyle - "F", # pyflakes - "I", # isort + "E", # pycodestyle + "F", # pyflakes + "I", # isort + "T201", # print ] [tool.mypy] @@ -72,9 +73,7 @@ module = "transformers" ignore_missing_imports = true [tool.coverage.run] -omit = [ - "tests/*", -] +omit = ["tests/*"] [build-system] requires = ["poetry-core>=1.0.0"] diff --git a/libs/partners/openai/scripts/check_imports.py b/libs/partners/openai/scripts/check_imports.py index fd21a4975b7..365f5fa118d 100644 --- a/libs/partners/openai/scripts/check_imports.py +++ b/libs/partners/openai/scripts/check_imports.py @@ -10,8 +10,8 @@ if __name__ == "__main__": SourceFileLoader("x", file).load_module() except Exception: has_faillure = True - print(file) + print(file) # noqa: T201 traceback.print_exc() - print() + print() # noqa: T201 sys.exit(1 if has_failure else 0) diff --git a/libs/partners/openai/tests/unit_tests/chat_models/test_base.py b/libs/partners/openai/tests/unit_tests/chat_models/test_base.py index 767ce0720ca..b729664e6b8 100644 --- a/libs/partners/openai/tests/unit_tests/chat_models/test_base.py +++ b/libs/partners/openai/tests/unit_tests/chat_models/test_base.py @@ -118,3 +118,19 @@ async def test_openai_apredict(mock_completion: dict) -> None: res = llm.predict("bar") assert res == "Bar Baz" assert completed + + +@pytest.mark.parametrize( + "model", + [ + "gpt-3.5-turbo", + "gpt-4", + "gpt-3.5-0125", + "gpt-4-0125-preview", + "gpt-4-turbo-preview", + "gpt-4-vision-preview", + ], +) +def test__get_encoding_model(model: str) -> None: + ChatOpenAI(model=model)._get_encoding_model() + return diff --git a/libs/partners/openai/tests/unit_tests/llms/test_base.py b/libs/partners/openai/tests/unit_tests/llms/test_base.py index ebcd420995f..d05bb0bfe54 100644 --- a/libs/partners/openai/tests/unit_tests/llms/test_base.py +++ b/libs/partners/openai/tests/unit_tests/llms/test_base.py @@ -7,7 +7,6 @@ from langchain_openai import OpenAI os.environ["OPENAI_API_KEY"] = "foo" -@pytest.mark.requires("openai") def test_openai_model_param() -> None: llm = OpenAI(model="foo") assert llm.model_name == "foo" @@ -15,19 +14,16 @@ def test_openai_model_param() -> None: assert llm.model_name == "foo" -@pytest.mark.requires("openai") def test_openai_model_kwargs() -> None: llm = OpenAI(model_kwargs={"foo": "bar"}) assert llm.model_kwargs == {"foo": "bar"} -@pytest.mark.requires("openai") def test_openai_invalid_model_kwargs() -> None: with pytest.raises(ValueError): OpenAI(model_kwargs={"model_name": "foo"}) -@pytest.mark.requires("openai") def test_openai_incorrect_field() -> None: with pytest.warns(match="not default parameter"): llm = OpenAI(foo="bar") @@ -46,3 +42,15 @@ def mock_completion() -> dict: ], "usage": {"prompt_tokens": 1, "completion_tokens": 2, "total_tokens": 3}, } + + +@pytest.mark.parametrize( + "model", + [ + "gpt-3.5-turbo-instruct", + "text-davinci-003", + ], +) +def test_get_token_ids(model: str) -> None: + OpenAI(model=model).get_token_ids("foo") + return diff --git a/libs/partners/pinecone/.gitignore b/libs/partners/pinecone/.gitignore new file mode 100644 index 00000000000..bee8a64b79a --- /dev/null +++ b/libs/partners/pinecone/.gitignore @@ -0,0 +1 @@ +__pycache__ diff --git a/libs/partners/pinecone/LICENSE b/libs/partners/pinecone/LICENSE new file mode 100644 index 00000000000..426b6509034 --- /dev/null +++ b/libs/partners/pinecone/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/libs/partners/pinecone/Makefile b/libs/partners/pinecone/Makefile new file mode 100644 index 00000000000..5f54fdd2285 --- /dev/null +++ b/libs/partners/pinecone/Makefile @@ -0,0 +1,61 @@ +.PHONY: all format lint test tests integration_tests docker_tests help extended_tests + +# Default target executed when no arguments are given to make. +all: help + +# Define a variable for the test file path. +TEST_FILE ?= tests/unit_tests/ + +integration_tests: TEST_FILE = tests/integration_tests/ + +test integration_tests: + poetry run pytest $(TEST_FILE) + +tests: + poetry run pytest $(TEST_FILE) + + +###################### +# LINTING AND FORMATTING +###################### + +# Define a variable for Python and notebook files. +PYTHON_FILES=. +MYPY_CACHE=.mypy_cache +lint format: PYTHON_FILES=. +lint_diff format_diff: PYTHON_FILES=$(shell git diff --relative=libs/partners/pinecone --name-only --diff-filter=d master | grep -E '\.py$$|\.ipynb$$') +lint_package: PYTHON_FILES=langchain_pinecone +lint_tests: PYTHON_FILES=tests +lint_tests: MYPY_CACHE=.mypy_cache_test + +lint lint_diff lint_package lint_tests: + poetry run ruff . + poetry run ruff format $(PYTHON_FILES) --diff + poetry run ruff --select I $(PYTHON_FILES) + mkdir $(MYPY_CACHE); poetry run mypy $(PYTHON_FILES) --cache-dir $(MYPY_CACHE) + +format format_diff: + poetry run ruff format $(PYTHON_FILES) + poetry run ruff --select I --fix $(PYTHON_FILES) + +spell_check: + poetry run codespell --toml pyproject.toml + +spell_fix: + poetry run codespell --toml pyproject.toml -w + +check_imports: $(shell find langchain_pinecone -name '*.py') + poetry run python ./scripts/check_imports.py $^ + +###################### +# HELP +###################### + +help: + @echo '----' + @echo 'check_imports - check imports' + @echo 'format - run code formatters' + @echo 'lint - run linters' + @echo 'test - run unit tests' + @echo 'tests - run unit tests' + @echo 'test TEST_FILE= - run all tests in file' diff --git a/libs/partners/pinecone/README.md b/libs/partners/pinecone/README.md new file mode 100644 index 00000000000..c0b1069a6d0 --- /dev/null +++ b/libs/partners/pinecone/README.md @@ -0,0 +1,27 @@ +# langchain-pinecone + +This package contains the LangChain integration with Pinecone. + +## Installation + +```bash +pip install -U langchain-pinecone +``` + +And you should configure credentials by setting the following environment variables: + +- `PINECONE_API_KEY` +- `PINECONE_INDEX_NAME` +- `PINECONE_ENVIRONMENT` + +## Usage + +The `Pinecone` class exposes the connection to the Pinecone vector store. + +```python +from langchain_pinecone import Pinecone + +embeddings = ... # use a LangChain Embeddings class + +vectorstore = Pinecone(embeddings=embeddings) +``` diff --git a/libs/partners/pinecone/langchain_pinecone/__init__.py b/libs/partners/pinecone/langchain_pinecone/__init__.py new file mode 100644 index 00000000000..b71858853bb --- /dev/null +++ b/libs/partners/pinecone/langchain_pinecone/__init__.py @@ -0,0 +1,5 @@ +from langchain_pinecone.vectorstores import Pinecone + +__all__ = [ + "Pinecone", +] diff --git a/libs/partners/pinecone/langchain_pinecone/_utilities.py b/libs/partners/pinecone/langchain_pinecone/_utilities.py new file mode 100644 index 00000000000..37d61e9fb11 --- /dev/null +++ b/libs/partners/pinecone/langchain_pinecone/_utilities.py @@ -0,0 +1,80 @@ +from enum import Enum +from typing import List, Union + +import numpy as np + +Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray] + + +class DistanceStrategy(str, Enum): + """Enumerator of the Distance strategies for calculating distances + between vectors.""" + + EUCLIDEAN_DISTANCE = "EUCLIDEAN_DISTANCE" + MAX_INNER_PRODUCT = "MAX_INNER_PRODUCT" + COSINE = "COSINE" + + +def maximal_marginal_relevance( + query_embedding: np.ndarray, + embedding_list: list, + lambda_mult: float = 0.5, + k: int = 4, +) -> List[int]: + """Calculate maximal marginal relevance.""" + if min(k, len(embedding_list)) <= 0: + return [] + if query_embedding.ndim == 1: + query_embedding = np.expand_dims(query_embedding, axis=0) + similarity_to_query = cosine_similarity(query_embedding, embedding_list)[0] + most_similar = int(np.argmax(similarity_to_query)) + idxs = [most_similar] + selected = np.array([embedding_list[most_similar]]) + while len(idxs) < min(k, len(embedding_list)): + best_score = -np.inf + idx_to_add = -1 + similarity_to_selected = cosine_similarity(embedding_list, selected) + for i, query_score in enumerate(similarity_to_query): + if i in idxs: + continue + redundant_score = max(similarity_to_selected[i]) + equation_score = ( + lambda_mult * query_score - (1 - lambda_mult) * redundant_score + ) + if equation_score > best_score: + best_score = equation_score + idx_to_add = i + idxs.append(idx_to_add) + selected = np.append(selected, [embedding_list[idx_to_add]], axis=0) + return idxs + + +def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray: + """Row-wise cosine similarity between two equal-width matrices.""" + if len(X) == 0 or len(Y) == 0: + return np.array([]) + + X = np.array(X) + Y = np.array(Y) + if X.shape[1] != Y.shape[1]: + raise ValueError( + f"Number of columns in X and Y must be the same. X has shape {X.shape} " + f"and Y has shape {Y.shape}." + ) + try: + import simsimd as simd # type: ignore + + X = np.array(X, dtype=np.float32) + Y = np.array(Y, dtype=np.float32) + Z = 1 - simd.cdist(X, Y, metric="cosine") + if isinstance(Z, float): + return np.array([Z]) + return Z + except ImportError: + X_norm = np.linalg.norm(X, axis=1) + Y_norm = np.linalg.norm(Y, axis=1) + # Ignore divide by zero errors run time warnings as those are handled below. + with np.errstate(divide="ignore", invalid="ignore"): + similarity = np.dot(X, Y.T) / np.outer(X_norm, Y_norm) + similarity[np.isnan(similarity) | np.isinf(similarity)] = 0.0 + return similarity diff --git a/libs/partners/pinecone/langchain_pinecone/py.typed b/libs/partners/pinecone/langchain_pinecone/py.typed new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/partners/pinecone/langchain_pinecone/vectorstores.py b/libs/partners/pinecone/langchain_pinecone/vectorstores.py new file mode 100644 index 00000000000..66f56905583 --- /dev/null +++ b/libs/partners/pinecone/langchain_pinecone/vectorstores.py @@ -0,0 +1,487 @@ +from __future__ import annotations + +import logging +import os +import uuid +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Iterable, + List, + Optional, + Tuple, + TypeVar, +) + +import numpy as np +from langchain_core.documents import Document +from langchain_core.embeddings import Embeddings +from langchain_core.utils.iter import batch_iterate +from langchain_core.vectorstores import VectorStore +from pinecone import Pinecone as PineconeClient # type: ignore + +from langchain_pinecone._utilities import DistanceStrategy, maximal_marginal_relevance + +if TYPE_CHECKING: + from pinecone import Index + +logger = logging.getLogger(__name__) + +VST = TypeVar("VST", bound=VectorStore) + + +class Pinecone(VectorStore): + """`Pinecone` vector store. + + Example: + .. code-block:: python + + from langchain_pinecone import Pinecone + from langchain_openai import OpenAIEmbeddings + + embeddings = OpenAIEmbeddings() + index_name = "my-index" + namespace = "my-namespace" + vectorstore = Pinecone( + index_name=index_name, + embedding=embedding, + namespace=namespace, + ) + """ + + def __init__( + self, + # setting default params to bypass having to pass in + # the index and embedding objects - manually throw + # exceptions if they are not passed in or set in environment + # (keeping param for backwards compatibility) + index: Optional[Any] = None, + embedding: Optional[Embeddings] = None, + text_key: Optional[str] = "text", + namespace: Optional[str] = None, + distance_strategy: Optional[DistanceStrategy] = DistanceStrategy.COSINE, + *, + pinecone_api_key: Optional[str] = None, + index_name: Optional[str] = None, + ): + if embedding is None: + raise ValueError("Embedding must be provided") + self._embedding = embedding + if text_key is None: + raise ValueError("Text key must be provided") + self._text_key = text_key + + self._namespace = namespace + self.distance_strategy = distance_strategy + + if index: + # supports old way of initializing externally + self._index = index + else: + # all internal initialization + _pinecone_api_key = ( + pinecone_api_key or os.environ.get("PINECONE_API_KEY") or "" + ) + if not _pinecone_api_key: + raise ValueError( + "Pinecone API key must be provided in either `pinecone_api_key` " + "or `PINECONE_API_KEY` environment variable" + ) + + _index_name = index_name or os.environ.get("PINECONE_INDEX_NAME") or "" + if not _index_name: + raise ValueError( + "Pinecone index name must be provided in either `index_name` " + "or `PINECONE_INDEX_NAME` environment variable" + ) + + # needs + client = PineconeClient(api_key=_pinecone_api_key) + self._index = client.Index(_index_name) + + @property + def embeddings(self) -> Optional[Embeddings]: + """Access the query embedding object if available.""" + return self._embedding + + def add_texts( + self, + texts: Iterable[str], + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + namespace: Optional[str] = None, + batch_size: int = 32, + embedding_chunk_size: int = 1000, + *, + async_req: bool = True, + **kwargs: Any, + ) -> List[str]: + """Run more texts through the embeddings and add to the vectorstore. + + Upsert optimization is done by chunking the embeddings and upserting them. + This is done to avoid memory issues and optimize using HTTP based embeddings. + For OpenAI embeddings, use pool_threads>4 when constructing the pinecone.Index, + embedding_chunk_size>1000 and batch_size~64 for best performance. + Args: + texts: Iterable of strings to add to the vectorstore. + metadatas: Optional list of metadatas associated with the texts. + ids: Optional list of ids to associate with the texts. + namespace: Optional pinecone namespace to add the texts to. + batch_size: Batch size to use when adding the texts to the vectorstore. + embedding_chunk_size: Chunk size to use when embedding the texts. + + Returns: + List of ids from adding the texts into the vectorstore. + + """ + if namespace is None: + namespace = self._namespace + + texts = list(texts) + ids = ids or [str(uuid.uuid4()) for _ in texts] + metadatas = metadatas or [{} for _ in texts] + for metadata, text in zip(metadatas, texts): + metadata[self._text_key] = text + + # For loops to avoid memory issues and optimize when using HTTP based embeddings + # The first loop runs the embeddings, it benefits when using OpenAI embeddings + # The second loops runs the pinecone upsert asynchronously. + for i in range(0, len(texts), embedding_chunk_size): + chunk_texts = texts[i : i + embedding_chunk_size] + chunk_ids = ids[i : i + embedding_chunk_size] + chunk_metadatas = metadatas[i : i + embedding_chunk_size] + embeddings = self._embedding.embed_documents(chunk_texts) + async_res = [ + self._index.upsert( + vectors=batch, + namespace=namespace, + async_req=async_req, + **kwargs, + ) + for batch in batch_iterate( + batch_size, zip(chunk_ids, embeddings, chunk_metadatas) + ) + ] + [res.get() for res in async_res] + + return ids + + def similarity_search_with_score( + self, + query: str, + k: int = 4, + filter: Optional[dict] = None, + namespace: Optional[str] = None, + ) -> List[Tuple[Document, float]]: + """Return pinecone documents most similar to query, along with scores. + + Args: + query: Text to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + filter: Dictionary of argument(s) to filter on metadata + namespace: Namespace to search in. Default will search in '' namespace. + + Returns: + List of Documents most similar to the query and score for each + """ + return self.similarity_search_by_vector_with_score( + self._embedding.embed_query(query), k=k, filter=filter, namespace=namespace + ) + + def similarity_search_by_vector_with_score( + self, + embedding: List[float], + *, + k: int = 4, + filter: Optional[dict] = None, + namespace: Optional[str] = None, + ) -> List[Tuple[Document, float]]: + """Return pinecone documents most similar to embedding, along with scores.""" + + if namespace is None: + namespace = self._namespace + docs = [] + results = self._index.query( + vector=embedding, + top_k=k, + include_metadata=True, + namespace=namespace, + filter=filter, + ) + for res in results["matches"]: + metadata = res["metadata"] + if self._text_key in metadata: + text = metadata.pop(self._text_key) + score = res["score"] + docs.append((Document(page_content=text, metadata=metadata), score)) + else: + logger.warning( + f"Found document with no `{self._text_key}` key. Skipping." + ) + return docs + + def similarity_search( + self, + query: str, + k: int = 4, + filter: Optional[dict] = None, + namespace: Optional[str] = None, + **kwargs: Any, + ) -> List[Document]: + """Return pinecone documents most similar to query. + + Args: + query: Text to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + filter: Dictionary of argument(s) to filter on metadata + namespace: Namespace to search in. Default will search in '' namespace. + + Returns: + List of Documents most similar to the query and score for each + """ + docs_and_scores = self.similarity_search_with_score( + query, k=k, filter=filter, namespace=namespace, **kwargs + ) + return [doc for doc, _ in docs_and_scores] + + def _select_relevance_score_fn(self) -> Callable[[float], float]: + """ + The 'correct' relevance function + may differ depending on a few things, including: + - the distance / similarity metric used by the VectorStore + - the scale of your embeddings (OpenAI's are unit normed. Many others are not!) + - embedding dimensionality + - etc. + """ + + if self.distance_strategy == DistanceStrategy.COSINE: + return self._cosine_relevance_score_fn + elif self.distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT: + return self._max_inner_product_relevance_score_fn + elif self.distance_strategy == DistanceStrategy.EUCLIDEAN_DISTANCE: + return self._euclidean_relevance_score_fn + else: + raise ValueError( + "Unknown distance strategy, must be cosine, max_inner_product " + "(dot product), or euclidean" + ) + + @staticmethod + def _cosine_relevance_score_fn(score: float) -> float: + """Pinecone returns cosine similarity scores between [-1,1]""" + return (score + 1) / 2 + + def max_marginal_relevance_search_by_vector( + self, + embedding: List[float], + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + filter: Optional[dict] = None, + namespace: Optional[str] = None, + **kwargs: Any, + ) -> List[Document]: + """Return docs selected using the maximal marginal relevance. + + Maximal marginal relevance optimizes for similarity to query AND diversity + among selected documents. + + Args: + embedding: Embedding to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + fetch_k: Number of Documents to fetch to pass to MMR algorithm. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. + Returns: + List of Documents selected by maximal marginal relevance. + """ + if namespace is None: + namespace = self._namespace + results = self._index.query( + vector=[embedding], + top_k=fetch_k, + include_values=True, + include_metadata=True, + namespace=namespace, + filter=filter, + ) + mmr_selected = maximal_marginal_relevance( + np.array([embedding], dtype=np.float32), + [item["values"] for item in results["matches"]], + k=k, + lambda_mult=lambda_mult, + ) + selected = [results["matches"][i]["metadata"] for i in mmr_selected] + return [ + Document(page_content=metadata.pop((self._text_key)), metadata=metadata) + for metadata in selected + ] + + def max_marginal_relevance_search( + self, + query: str, + k: int = 4, + fetch_k: int = 20, + lambda_mult: float = 0.5, + filter: Optional[dict] = None, + namespace: Optional[str] = None, + **kwargs: Any, + ) -> List[Document]: + """Return docs selected using the maximal marginal relevance. + + Maximal marginal relevance optimizes for similarity to query AND diversity + among selected documents. + + Args: + query: Text to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + fetch_k: Number of Documents to fetch to pass to MMR algorithm. + lambda_mult: Number between 0 and 1 that determines the degree + of diversity among the results with 0 corresponding + to maximum diversity and 1 to minimum diversity. + Defaults to 0.5. + Returns: + List of Documents selected by maximal marginal relevance. + """ + embedding = self._embedding.embed_query(query) + return self.max_marginal_relevance_search_by_vector( + embedding, k, fetch_k, lambda_mult, filter, namespace + ) + + @classmethod + def get_pinecone_index( + cls, + index_name: Optional[str], + pool_threads: int = 4, + *, + pinecone_api_key: Optional[str] = None, + ) -> Index: + """Return a Pinecone Index instance. + + Args: + index_name: Name of the index to use. + pool_threads: Number of threads to use for index upsert. + Returns: + Pinecone Index instance.""" + _pinecone_api_key = pinecone_api_key or os.environ.get("PINECONE_API_KEY") or "" + client = PineconeClient(api_key=_pinecone_api_key, pool_threads=pool_threads) + indexes = client.list_indexes() + index_names = [i.name for i in indexes.index_list["indexes"]] + + if index_name in index_names: + index = client.Index(index_name) + elif len(index_names) == 0: + raise ValueError( + "No active indexes found in your Pinecone project, " + "are you sure you're using the right Pinecone API key and Environment? " + "Please double check your Pinecone dashboard." + ) + else: + raise ValueError( + f"Index '{index_name}' not found in your Pinecone project. " + f"Did you mean one of the following indexes: {', '.join(index_names)}" + ) + return index + + @classmethod + def from_texts( + cls, + texts: List[str], + embedding: Embeddings, + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + batch_size: int = 32, + text_key: str = "text", + namespace: Optional[str] = None, + index_name: Optional[str] = None, + upsert_kwargs: Optional[dict] = None, + pool_threads: int = 4, + embeddings_chunk_size: int = 1000, + **kwargs: Any, + ) -> Pinecone: + """Construct Pinecone wrapper from raw documents. + + This is a user friendly interface that: + 1. Embeds documents. + 2. Adds the documents to a provided Pinecone index + + This is intended to be a quick way to get started. + + The `pool_threads` affects the speed of the upsert operations. + Example: + .. code-block:: python + + from langchain_community.vectorstores import Pinecone + from langchain_community.embeddings import OpenAIEmbeddings + import pinecone + + # The environment should be the one specified next to the API key + # in your Pinecone console + pinecone.init(api_key="***", environment="...") + embeddings = OpenAIEmbeddings() + pinecone = Pinecone.from_texts( + texts, + embeddings, + index_name="langchain-demo" + ) + """ + pinecone_index = cls.get_pinecone_index(index_name, pool_threads) + pinecone = cls(pinecone_index, embedding, text_key, namespace, **kwargs) + + pinecone.add_texts( + texts, + metadatas=metadatas, + ids=ids, + namespace=namespace, + batch_size=batch_size, + embedding_chunk_size=embeddings_chunk_size, + **(upsert_kwargs or {}), + ) + return pinecone + + @classmethod + def from_existing_index( + cls, + index_name: str, + embedding: Embeddings, + text_key: str = "text", + namespace: Optional[str] = None, + pool_threads: int = 4, + ) -> Pinecone: + """Load pinecone vectorstore from index name.""" + pinecone_index = cls.get_pinecone_index(index_name, pool_threads) + return cls(pinecone_index, embedding, text_key, namespace) + + def delete( + self, + ids: Optional[List[str]] = None, + delete_all: Optional[bool] = None, + namespace: Optional[str] = None, + filter: Optional[dict] = None, + **kwargs: Any, + ) -> None: + """Delete by vector IDs or filter. + Args: + ids: List of ids to delete. + filter: Dictionary of conditions to filter vectors to delete. + """ + + if namespace is None: + namespace = self._namespace + + if delete_all: + self._index.delete(delete_all=True, namespace=namespace, **kwargs) + elif ids is not None: + chunk_size = 1000 + for i in range(0, len(ids), chunk_size): + chunk = ids[i : i + chunk_size] + self._index.delete(ids=chunk, namespace=namespace, **kwargs) + elif filter is not None: + self._index.delete(filter=filter, namespace=namespace, **kwargs) + else: + raise ValueError("Either ids, delete_all, or filter must be provided.") + + return None diff --git a/libs/partners/pinecone/poetry.lock b/libs/partners/pinecone/poetry.lock new file mode 100644 index 00000000000..14d48ec651d --- /dev/null +++ b/libs/partners/pinecone/poetry.lock @@ -0,0 +1,1154 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + +[[package]] +name = "annotated-types" +version = "0.6.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +files = [ + {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"}, + {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"}, +] + +[package.dependencies] +typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} + +[[package]] +name = "anyio" +version = "4.2.0" +description = "High level compatibility layer for multiple asynchronous event loop implementations" +optional = false +python-versions = ">=3.8" +files = [ + {file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"}, + {file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"}, +] + +[package.dependencies] +exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""} +idna = ">=2.8" +sniffio = ">=1.1" +typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""} + +[package.extras] +doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] +test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"] +trio = ["trio (>=0.23)"] + +[[package]] +name = "certifi" +version = "2024.2.2" +description = "Python package for providing Mozilla's CA Bundle." +optional = false +python-versions = ">=3.6" +files = [ + {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"}, + {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"}, +] + +[[package]] +name = "charset-normalizer" +version = "3.3.2" +description = "The Real First Universal Charset Detector. 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b/libs/partners/pinecone/pyproject.toml @@ -0,0 +1,97 @@ +[tool.poetry] +name = "langchain-pinecone" +version = "0.0.2" +description = "An integration package connecting Pinecone and LangChain" +authors = [] +readme = "README.md" +repository = "https://github.com/langchain-ai/langchain" +license = "MIT" + +[tool.poetry.urls] +"Source Code" = "https://github.com/langchain-ai/langchain/tree/master/libs/partners/pinecone" + +[tool.poetry.dependencies] +# <3.13 is due to restriction in pinecone-client package +python = ">=3.8.1,<3.13" +langchain-core = "^0.1" +pinecone-client = { version = "^3" } +numpy = "^1" + +[tool.poetry.group.test] +optional = true + +[tool.poetry.group.test.dependencies] +pytest = "^7.3.0" +freezegun = "^1.2.2" +pytest-mock = "^3.10.0" +syrupy = "^4.0.2" +pytest-watcher = "^0.3.4" +pytest-asyncio = "^0.21.1" +langchain-core = { path = "../../core", develop = true } + +[tool.poetry.group.codespell] +optional = true + +[tool.poetry.group.codespell.dependencies] +codespell = "^2.2.0" + +[tool.poetry.group.test_integration] +optional = true + +[tool.poetry.group.test_integration.dependencies] +langchain-openai = ">=0.0.3,<0.1" + +[tool.poetry.group.lint] +optional = true + +[tool.poetry.group.lint.dependencies] +ruff = "^0.1.5" + +[tool.poetry.group.typing.dependencies] +mypy = "^0.991" +langchain-core = { path = "../../core", develop = true } + +[tool.poetry.group.dev] +optional = true + +[tool.poetry.group.dev.dependencies] +langchain-core = { path = "../../core", develop = true } + +[tool.ruff] +select = [ + "E", # pycodestyle + "F", # pyflakes + "I", # isort + "T201", # print + +] + +[tool.mypy] +disallow_untyped_defs = "True" + +[tool.coverage.run] +omit = ["tests/*"] + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" + +[tool.pytest.ini_options] +# --strict-markers will raise errors on unknown marks. +# https://docs.pytest.org/en/7.1.x/how-to/mark.html#raising-errors-on-unknown-marks +# +# https://docs.pytest.org/en/7.1.x/reference/reference.html +# --strict-config any warnings encountered while parsing the `pytest` +# section of the configuration file raise errors. +# +# https://github.com/tophat/syrupy +# --snapshot-warn-unused Prints a warning on unused snapshots rather than fail the test suite. +addopts = "--snapshot-warn-unused --strict-markers --strict-config --durations=5" +# Registering custom markers. +# https://docs.pytest.org/en/7.1.x/example/markers.html#registering-markers +markers = [ + "requires: mark tests as requiring a specific library", + "asyncio: mark tests as requiring asyncio", + "compile: mark placeholder test used to compile integration tests without running them", +] +asyncio_mode = "auto" diff --git a/libs/partners/pinecone/scripts/check_imports.py b/libs/partners/pinecone/scripts/check_imports.py new file mode 100644 index 00000000000..365f5fa118d --- /dev/null +++ b/libs/partners/pinecone/scripts/check_imports.py @@ -0,0 +1,17 @@ +import sys +import traceback +from importlib.machinery import SourceFileLoader + +if __name__ == "__main__": + files = sys.argv[1:] + has_failure = False + for file in files: + try: + SourceFileLoader("x", file).load_module() + except Exception: + has_faillure = True + print(file) # noqa: T201 + traceback.print_exc() + print() # noqa: T201 + + sys.exit(1 if has_failure else 0) diff --git a/libs/partners/pinecone/scripts/check_pydantic.sh b/libs/partners/pinecone/scripts/check_pydantic.sh new file mode 100755 index 00000000000..06b5bb81ae2 --- /dev/null +++ b/libs/partners/pinecone/scripts/check_pydantic.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# +# This script searches for lines starting with "import pydantic" or "from pydantic" +# in tracked files within a Git repository. +# +# Usage: ./scripts/check_pydantic.sh /path/to/repository + +# Check if a path argument is provided +if [ $# -ne 1 ]; then + echo "Usage: $0 /path/to/repository" + exit 1 +fi + +repository_path="$1" + +# Search for lines matching the pattern within the specified repository +result=$(git -C "$repository_path" grep -E '^import pydantic|^from pydantic') + +# Check if any matching lines were found +if [ -n "$result" ]; then + echo "ERROR: The following lines need to be updated:" + echo "$result" + echo "Please replace the code with an import from langchain_core.pydantic_v1." + echo "For example, replace 'from pydantic import BaseModel'" + echo "with 'from langchain_core.pydantic_v1 import BaseModel'" + exit 1 +fi diff --git a/libs/partners/pinecone/scripts/lint_imports.sh b/libs/partners/pinecone/scripts/lint_imports.sh new file mode 100755 index 00000000000..695613c7ba8 --- /dev/null +++ b/libs/partners/pinecone/scripts/lint_imports.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +set -eu + +# Initialize a variable to keep track of errors +errors=0 + +# make sure not importing from langchain or langchain_experimental +git --no-pager grep '^from langchain\.' . && errors=$((errors+1)) +git --no-pager grep '^from langchain_experimental\.' . && errors=$((errors+1)) + +# Decide on an exit status based on the errors +if [ "$errors" -gt 0 ]; then + exit 1 +else + exit 0 +fi diff --git a/libs/partners/pinecone/tests/__init__.py b/libs/partners/pinecone/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/partners/pinecone/tests/integration_tests/__init__.py b/libs/partners/pinecone/tests/integration_tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/partners/pinecone/tests/integration_tests/test_compile.py b/libs/partners/pinecone/tests/integration_tests/test_compile.py new file mode 100644 index 00000000000..33ecccdfa0f --- /dev/null +++ b/libs/partners/pinecone/tests/integration_tests/test_compile.py @@ -0,0 +1,7 @@ +import pytest + + +@pytest.mark.compile +def test_placeholder() -> None: + """Used for compiling integration tests without running any real tests.""" + pass diff --git a/libs/partners/pinecone/tests/integration_tests/test_vectorstores.py b/libs/partners/pinecone/tests/integration_tests/test_vectorstores.py new file mode 100644 index 00000000000..0cd4d5cf290 --- /dev/null +++ b/libs/partners/pinecone/tests/integration_tests/test_vectorstores.py @@ -0,0 +1,290 @@ +import os +import time +import uuid +from typing import List + +import numpy as np +import pinecone # type: ignore +import pytest +from langchain_core.documents import Document +from langchain_openai import OpenAIEmbeddings +from pinecone import PodSpec + +from langchain_pinecone import Pinecone + +INDEX_NAME = "langchain-test-index" # name of the index +NAMESPACE_NAME = "langchain-test-namespace" # name of the namespace +DIMENSION = 1536 # dimension of the embeddings + +DEFAULT_SLEEP = 20 + + +class TestPinecone: + index: "pinecone.Index" + + @classmethod + def setup_class(cls) -> None: + import pinecone + + client = pinecone.Pinecone(api_key=os.environ["PINECONE_API_KEY"]) + index_list = client.list_indexes() + for i in index_list: + if i["name"] == INDEX_NAME: + client.delete_index(INDEX_NAME) + break + client.create_index( + name=INDEX_NAME, + dimension=DIMENSION, + metric="cosine", + spec=PodSpec(environment=os.environ["PINECONE_ENVIRONMENT"]), + ) + + cls.index = client.Index(INDEX_NAME) + + # insure the index is empty + index_stats = cls.index.describe_index_stats() + assert index_stats["dimension"] == DIMENSION + if index_stats["namespaces"].get(NAMESPACE_NAME) is not None: + assert index_stats["namespaces"][NAMESPACE_NAME]["vector_count"] == 0 + + @classmethod + def teardown_class(cls) -> None: + index_stats = cls.index.describe_index_stats() + for _namespace_name in index_stats["namespaces"].keys(): + cls.index.delete(delete_all=True, namespace=_namespace_name) + + @pytest.fixture(autouse=True) + def setup(self) -> None: + # delete all the vectors in the index + print("called") # noqa: T201 + self.index.delete(delete_all=True, namespace=NAMESPACE_NAME) + # index_stats = self.index.describe_index_stats() + # for _namespace_name in index_stats["namespaces"].keys(): + # self.index.delete(delete_all=True, namespace=_namespace_name) + time.sleep(DEFAULT_SLEEP) # prevent race condition with previous step + # index_stats = self.index.describe_index_stats + + @pytest.fixture + def embedding_openai(self) -> OpenAIEmbeddings: + return OpenAIEmbeddings() + + @pytest.fixture + def texts(self) -> List[str]: + return ["foo", "bar", "baz"] + + def test_from_texts( + self, texts: List[str], embedding_openai: OpenAIEmbeddings + ) -> None: + """Test end to end construction and search.""" + unique_id = uuid.uuid4().hex + needs = f"foobuu {unique_id} booo" + texts.insert(0, needs) + + docsearch = Pinecone.from_texts( + texts=texts, + embedding=embedding_openai, + index_name=INDEX_NAME, + namespace=NAMESPACE_NAME, + ) + time.sleep(DEFAULT_SLEEP) # prevent race condition + output = docsearch.similarity_search(unique_id, k=1, namespace=NAMESPACE_NAME) + assert output == [Document(page_content=needs)] + + def test_from_texts_with_metadatas( + self, texts: List[str], embedding_openai: OpenAIEmbeddings + ) -> None: + """Test end to end construction and search.""" + + unique_id = uuid.uuid4().hex + needs = f"foobuu {unique_id} booo" + texts = [needs] + texts + + metadatas = [{"page": i} for i in range(len(texts))] + + namespace = f"{NAMESPACE_NAME}-md" + docsearch = Pinecone.from_texts( + texts, + embedding_openai, + index_name=INDEX_NAME, + metadatas=metadatas, + namespace=namespace, + ) + time.sleep(DEFAULT_SLEEP) # prevent race condition + output = docsearch.similarity_search(needs, k=1, namespace=namespace) + + # TODO: why metadata={"page": 0.0}) instead of {"page": 0}? + assert output == [Document(page_content=needs, metadata={"page": 0.0})] + + def test_from_texts_with_scores(self, embedding_openai: OpenAIEmbeddings) -> None: + """Test end to end construction and search with scores and IDs.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": i} for i in range(len(texts))] + print("metadatas", metadatas) # noqa: T201 + docsearch = Pinecone.from_texts( + texts, + embedding_openai, + index_name=INDEX_NAME, + metadatas=metadatas, + namespace=NAMESPACE_NAME, + ) + print(texts) # noqa: T201 + time.sleep(DEFAULT_SLEEP) # prevent race condition + output = docsearch.similarity_search_with_score( + "foo", k=3, namespace=NAMESPACE_NAME + ) + docs = [o[0] for o in output] + scores = [o[1] for o in output] + sorted_documents = sorted(docs, key=lambda x: x.metadata["page"]) + print(sorted_documents) # noqa: T201 + + # TODO: why metadata={"page": 0.0}) instead of {"page": 0}, etc??? + assert sorted_documents == [ + Document(page_content="foo", metadata={"page": 0.0}), + Document(page_content="bar", metadata={"page": 1.0}), + Document(page_content="baz", metadata={"page": 2.0}), + ] + assert scores[0] > scores[1] > scores[2] + + def test_from_existing_index_with_namespaces( + self, embedding_openai: OpenAIEmbeddings + ) -> None: + """Test that namespaces are properly handled.""" + # Create two indexes with the same name but different namespaces + texts_1 = ["foo", "bar", "baz"] + metadatas = [{"page": i} for i in range(len(texts_1))] + Pinecone.from_texts( + texts_1, + embedding_openai, + index_name=INDEX_NAME, + metadatas=metadatas, + namespace=f"{INDEX_NAME}-1", + ) + + texts_2 = ["foo2", "bar2", "baz2"] + metadatas = [{"page": i} for i in range(len(texts_2))] + + Pinecone.from_texts( + texts_2, + embedding_openai, + index_name=INDEX_NAME, + metadatas=metadatas, + namespace=f"{INDEX_NAME}-2", + ) + + time.sleep(DEFAULT_SLEEP) # prevent race condition + + # Search with namespace + docsearch = Pinecone.from_existing_index( + index_name=INDEX_NAME, + embedding=embedding_openai, + namespace=f"{INDEX_NAME}-1", + ) + output = docsearch.similarity_search("foo", k=20, namespace=f"{INDEX_NAME}-1") + # check that we don't get results from the other namespace + page_contents = sorted(set([o.page_content for o in output])) + assert all(content in ["foo", "bar", "baz"] for content in page_contents) + assert all(content not in ["foo2", "bar2", "baz2"] for content in page_contents) + + def test_add_documents_with_ids( + self, texts: List[str], embedding_openai: OpenAIEmbeddings + ) -> None: + ids = [uuid.uuid4().hex for _ in range(len(texts))] + Pinecone.from_texts( + texts=texts, + ids=ids, + embedding=embedding_openai, + index_name=INDEX_NAME, + namespace=NAMESPACE_NAME, + ) + time.sleep(DEFAULT_SLEEP) # prevent race condition + index_stats = self.index.describe_index_stats() + assert index_stats["namespaces"][NAMESPACE_NAME]["vector_count"] == len(texts) + + ids_1 = [uuid.uuid4().hex for _ in range(len(texts))] + Pinecone.from_texts( + texts=[t + "-1" for t in texts], + ids=ids_1, + embedding=embedding_openai, + index_name=INDEX_NAME, + namespace=NAMESPACE_NAME, + ) + time.sleep(DEFAULT_SLEEP) # prevent race condition + index_stats = self.index.describe_index_stats() + assert ( + index_stats["namespaces"][NAMESPACE_NAME]["vector_count"] == len(texts) * 2 + ) + # only focused on this namespace now + # assert index_stats["total_vector_count"] == len(texts) * 2 + + @pytest.mark.xfail(reason="relevance score just over 1") + def test_relevance_score_bound(self, embedding_openai: OpenAIEmbeddings) -> None: + """Ensures all relevance scores are between 0 and 1.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": i} for i in range(len(texts))] + docsearch = Pinecone.from_texts( + texts, + embedding_openai, + index_name=INDEX_NAME, + metadatas=metadatas, + ) + # wait for the index to be ready + time.sleep(DEFAULT_SLEEP) + output = docsearch.similarity_search_with_relevance_scores("foo", k=3) + print(output) # noqa: T201 + assert all( + (1 >= score or np.isclose(score, 1)) and score >= 0 for _, score in output + ) + + @pytest.mark.skipif(reason="slow to run for benchmark") + @pytest.mark.parametrize( + "pool_threads,batch_size,embeddings_chunk_size,data_multiplier", + [ + ( + 1, + 32, + 32, + 1000, + ), # simulate single threaded with embeddings_chunk_size = batch_size = 32 + ( + 1, + 32, + 1000, + 1000, + ), # simulate single threaded with embeddings_chunk_size = 1000 + ( + 4, + 32, + 1000, + 1000, + ), # simulate 4 threaded with embeddings_chunk_size = 1000 + (20, 64, 5000, 1000), + ], # simulate 20 threaded with embeddings_chunk_size = 5000 + ) + def test_from_texts_with_metadatas_benchmark( + self, + pool_threads: int, + batch_size: int, + embeddings_chunk_size: int, + data_multiplier: int, + documents: List[Document], + embedding_openai: OpenAIEmbeddings, + ) -> None: + """Test end to end construction and search.""" + + texts = [document.page_content for document in documents] * data_multiplier + uuids = [uuid.uuid4().hex for _ in range(len(texts))] + metadatas = [{"page": i} for i in range(len(texts))] + docsearch = Pinecone.from_texts( + texts, + embedding_openai, + ids=uuids, + metadatas=metadatas, + index_name=INDEX_NAME, + namespace=NAMESPACE_NAME, + pool_threads=pool_threads, + batch_size=batch_size, + embeddings_chunk_size=embeddings_chunk_size, + ) + + query = "What did the president say about Ketanji Brown Jackson" + _ = docsearch.similarity_search(query, k=1, namespace=NAMESPACE_NAME) diff --git a/libs/partners/pinecone/tests/unit_tests/__init__.py b/libs/partners/pinecone/tests/unit_tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/partners/pinecone/tests/unit_tests/test_imports.py b/libs/partners/pinecone/tests/unit_tests/test_imports.py new file mode 100644 index 00000000000..0a5c986c2d2 --- /dev/null +++ b/libs/partners/pinecone/tests/unit_tests/test_imports.py @@ -0,0 +1,9 @@ +from langchain_pinecone import __all__ + +EXPECTED_ALL = [ + "Pinecone", +] + + +def test_all_imports() -> None: + assert sorted(EXPECTED_ALL) == sorted(__all__) diff --git a/libs/partners/pinecone/tests/unit_tests/test_vectorstores.py b/libs/partners/pinecone/tests/unit_tests/test_vectorstores.py new file mode 100644 index 00000000000..5e590c9ed94 --- /dev/null +++ b/libs/partners/pinecone/tests/unit_tests/test_vectorstores.py @@ -0,0 +1,12 @@ +from unittest.mock import Mock + +from langchain_pinecone.vectorstores import Pinecone + + +def test_initialization() -> None: + """Test integration vectorstore initialization.""" + # mock index + index = Mock() + embedding = Mock() + text_key = "xyz" + Pinecone(index, embedding, text_key) diff --git a/libs/partners/robocorp/poetry.lock b/libs/partners/robocorp/poetry.lock index 5e359404663..20b60ed600a 100644 --- a/libs/partners/robocorp/poetry.lock +++ b/libs/partners/robocorp/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. [[package]] name = "annotated-types" @@ -38,13 +38,13 @@ trio = ["trio (>=0.23)"] [[package]] name = "certifi" -version = "2023.11.17" +version = "2024.2.2" description = "Python package for providing Mozilla's CA Bundle." optional = false python-versions = ">=3.6" files = [ - {file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"}, - {file = "certifi-2023.11.17.tar.gz", hash = "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1"}, + {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"}, + {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"}, ] [[package]] @@ -246,12 +246,11 @@ optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*" files = [ {file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = 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requests = "^2" aiohttp = "^3.9.1" @@ -23,11 +23,11 @@ optional = true [tool.poetry.group.test.dependencies] pytest = "^7.3.0" freezegun = "^1.2.2" -pytest-mock = "^3.10.0" +pytest-mock = "^3.10.0" syrupy = "^4.0.2" pytest-watcher = "^0.3.4" pytest-asyncio = "^0.21.1" -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } [tool.poetry.group.codespell] optional = true @@ -48,29 +48,28 @@ ruff = "^0.1.5" [tool.poetry.group.typing.dependencies] mypy = "^0.991" -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } types-requests = "^2" [tool.poetry.group.dev] optional = true [tool.poetry.group.dev.dependencies] -langchain-core = {path = "../../core", develop = true} +langchain-core = { path = "../../core", develop = true } -[tool.ruff] +[tool.ruff.lint] select = [ - "E", # pycodestyle - "F", # pyflakes - "I", # isort + "E", # pycodestyle + "F", # pyflakes + "I", # isort + "T201", # print ] [tool.mypy] disallow_untyped_defs = "True" [tool.coverage.run] -omit = [ - "tests/*", -] +omit = ["tests/*"] [build-system] requires = ["poetry-core>=1.0.0"] diff --git a/libs/partners/together/scripts/check_imports.py b/libs/partners/together/scripts/check_imports.py index fd21a4975b7..365f5fa118d 100644 --- a/libs/partners/together/scripts/check_imports.py +++ b/libs/partners/together/scripts/check_imports.py @@ -10,8 +10,8 @@ if __name__ == "__main__": SourceFileLoader("x", file).load_module() except Exception: has_faillure = True - print(file) + print(file) # noqa: T201 traceback.print_exc() - print() + print() # noqa: T201 sys.exit(1 if has_failure else 0) diff --git a/libs/partners/together/tests/unit_tests/test_llms.py b/libs/partners/together/tests/unit_tests/test_llms.py index 22cb93309aa..ce9ba1be269 100644 --- a/libs/partners/together/tests/unit_tests/test_llms.py +++ b/libs/partners/together/tests/unit_tests/test_llms.py @@ -1,4 +1,5 @@ """Test Together LLM""" + from typing import cast from langchain_core.pydantic_v1 import SecretStr @@ -28,7 +29,7 @@ def test_together_api_key_masked_when_passed_from_env( temperature=0.2, max_tokens=250, ) - print(llm.together_api_key, end="") + print(llm.together_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" @@ -44,7 +45,7 @@ def test_together_api_key_masked_when_passed_via_constructor( temperature=0.2, max_tokens=250, ) - print(llm.together_api_key, end="") + print(llm.together_api_key, end="") # noqa: T201 captured = capsys.readouterr() assert captured.out == "**********" diff --git a/pg_essay.txt b/pg_essay.txt deleted file mode 100644 index 0bce3830b99..00000000000 --- a/pg_essay.txt +++ /dev/null @@ -1,351 +0,0 @@ -What I Worked On - -February 2021 - -Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep. - -The first programs I tried writing were on the IBM 1401 that our school district used for what was then called "data processing." This was in 9th grade, so I was 13 or 14. The school district's 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain's lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights. - -The language we used was an early version of Fortran. You had to type programs on punch cards, then stack them in the card reader and press a button to load the program into memory and run it. The result would ordinarily be to print something on the spectacularly loud printer. - -I was puzzled by the 1401. I couldn't figure out what to do with it. And in retrospect there's not much I could have done with it. The only form of input to programs was data stored on punched cards, and I didn't have any data stored on punched cards. The only other option was to do things that didn't rely on any input, like calculate approximations of pi, but I didn't know enough math to do anything interesting of that type. So I'm not surprised I can't remember any programs I wrote, because they can't have done much. My clearest memory is of the moment I learned it was possible for programs not to terminate, when one of mine didn't. On a machine without time-sharing, this was a social as well as a technical error, as the data center manager's expression made clear. - -With microcomputers, everything changed. Now you could have a computer sitting right in front of you, on a desk, that could respond to your keystrokes as it was running instead of just churning through a stack of punch cards and then stopping. [1] - -The first of my friends to get a microcomputer built it himself. It was sold as a kit by Heathkit. I remember vividly how impressed and envious I felt watching him sitting in front of it, typing programs right into the computer. - -Computers were expensive in those days and it took me years of nagging before I convinced my father to buy one, a TRS-80, in about 1980. The gold standard then was the Apple II, but a TRS-80 was good enough. This was when I really started programming. I wrote simple games, a program to predict how high my model rockets would fly, and a word processor that my father used to write at least one book. There was only room in memory for about 2 pages of text, so he'd write 2 pages at a time and then print them out, but it was a lot better than a typewriter. - -Though I liked programming, I didn't plan to study it in college. In college I was going to study philosophy, which sounded much more powerful. It seemed, to my naive high school self, to be the study of the ultimate truths, compared to which the things studied in other fields would be mere domain knowledge. What I discovered when I got to college was that the other fields took up so much of the space of ideas that there wasn't much left for these supposed ultimate truths. All that seemed left for philosophy were edge cases that people in other fields felt could safely be ignored. - -I couldn't have put this into words when I was 18. All I knew at the time was that I kept taking philosophy courses and they kept being boring. So I decided to switch to AI. - -AI was in the air in the mid 1980s, but there were two things especially that made me want to work on it: a novel by Heinlein called The Moon is a Harsh Mistress, which featured an intelligent computer called Mike, and a PBS documentary that showed Terry Winograd using SHRDLU. I haven't tried rereading The Moon is a Harsh Mistress, so I don't know how well it has aged, but when I read it I was drawn entirely into its world. It seemed only a matter of time before we'd have Mike, and when I saw Winograd using SHRDLU, it seemed like that time would be a few years at most. All you had to do was teach SHRDLU more words. - -There weren't any classes in AI at Cornell then, not even graduate classes, so I started trying to teach myself. Which meant learning Lisp, since in those days Lisp was regarded as the language of AI. The commonly used programming languages then were pretty primitive, and programmers' ideas correspondingly so. The default language at Cornell was a Pascal-like language called PL/I, and the situation was similar elsewhere. Learning Lisp expanded my concept of a program so fast that it was years before I started to have a sense of where the new limits were. This was more like it; this was what I had expected college to do. It wasn't happening in a class, like it was supposed to, but that was ok. For the next couple years I was on a roll. I knew what I was going to do. - -For my undergraduate thesis, I reverse-engineered SHRDLU. My God did I love working on that program. It was a pleasing bit of code, but what made it even more exciting was my belief — hard to imagine now, but not unique in 1985 — that it was already climbing the lower slopes of intelligence. - -I had gotten into a program at Cornell that didn't make you choose a major. You could take whatever classes you liked, and choose whatever you liked to put on your degree. I of course chose "Artificial Intelligence." When I got the actual physical diploma, I was dismayed to find that the quotes had been included, which made them read as scare-quotes. At the time this bothered me, but now it seems amusingly accurate, for reasons I was about to discover. - -I applied to 3 grad schools: MIT and Yale, which were renowned for AI at the time, and Harvard, which I'd visited because Rich Draves went there, and was also home to Bill Woods, who'd invented the type of parser I used in my SHRDLU clone. Only Harvard accepted me, so that was where I went. - -I don't remember the moment it happened, or if there even was a specific moment, but during the first year of grad school I realized that AI, as practiced at the time, was a hoax. By which I mean the sort of AI in which a program that's told "the dog is sitting on the chair" translates this into some formal representation and adds it to the list of things it knows. - -What these programs really showed was that there's a subset of natural language that's a formal language. But a very proper subset. It was clear that there was an unbridgeable gap between what they could do and actually understanding natural language. It was not, in fact, simply a matter of teaching SHRDLU more words. That whole way of doing AI, with explicit data structures representing concepts, was not going to work. Its brokenness did, as so often happens, generate a lot of opportunities to write papers about various band-aids that could be applied to it, but it was never going to get us Mike. - -So I looked around to see what I could salvage from the wreckage of my plans, and there was Lisp. I knew from experience that Lisp was interesting for its own sake and not just for its association with AI, even though that was the main reason people cared about it at the time. So I decided to focus on Lisp. In fact, I decided to write a book about Lisp hacking. It's scary to think how little I knew about Lisp hacking when I started writing that book. But there's nothing like writing a book about something to help you learn it. The book, On Lisp, wasn't published till 1993, but I wrote much of it in grad school. - -Computer Science is an uneasy alliance between two halves, theory and systems. The theory people prove things, and the systems people build things. I wanted to build things. I had plenty of respect for theory — indeed, a sneaking suspicion that it was the more admirable of the two halves — but building things seemed so much more exciting. - -The problem with systems work, though, was that it didn't last. Any program you wrote today, no matter how good, would be obsolete in a couple decades at best. People might mention your software in footnotes, but no one would actually use it. And indeed, it would seem very feeble work. Only people with a sense of the history of the field would even realize that, in its time, it had been good. - -There were some surplus Xerox Dandelions floating around the computer lab at one point. Anyone who wanted one to play around with could have one. I was briefly tempted, but they were so slow by present standards; what was the point? No one else wanted one either, so off they went. That was what happened to systems work. - -I wanted not just to build things, but to build things that would last. - -In this dissatisfied state I went in 1988 to visit Rich Draves at CMU, where he was in grad school. One day I went to visit the Carnegie Institute, where I'd spent a lot of time as a kid. While looking at a painting there I realized something that might seem obvious, but was a big surprise to me. There, right on the wall, was something you could make that would last. Paintings didn't become obsolete. Some of the best ones were hundreds of years old. - -And moreover this was something you could make a living doing. Not as easily as you could by writing software, of course, but I thought if you were really industrious and lived really cheaply, it had to be possible to make enough to survive. And as an artist you could be truly independent. You wouldn't have a boss, or even need to get research funding. - -I had always liked looking at paintings. Could I make them? I had no idea. I'd never imagined it was even possible. I knew intellectually that people made art — that it didn't just appear spontaneously — but it was as if the people who made it were a different species. They either lived long ago or were mysterious geniuses doing strange things in profiles in Life magazine. The idea of actually being able to make art, to put that verb before that noun, seemed almost miraculous. - -That fall I started taking art classes at Harvard. Grad students could take classes in any department, and my advisor, Tom Cheatham, was very easy going. If he even knew about the strange classes I was taking, he never said anything. - -So now I was in a PhD program in computer science, yet planning to be an artist, yet also genuinely in love with Lisp hacking and working away at On Lisp. In other words, like many a grad student, I was working energetically on multiple projects that were not my thesis. - -I didn't see a way out of this situation. I didn't want to drop out of grad school, but how else was I going to get out? I remember when my friend Robert Morris got kicked out of Cornell for writing the internet worm of 1988, I was envious that he'd found such a spectacular way to get out of grad school. - -Then one day in April 1990 a crack appeared in the wall. I ran into professor Cheatham and he asked if I was far enough along to graduate that June. I didn't have a word of my dissertation written, but in what must have been the quickest bit of thinking in my life, I decided to take a shot at writing one in the 5 weeks or so that remained before the deadline, reusing parts of On Lisp where I could, and I was able to respond, with no perceptible delay "Yes, I think so. I'll give you something to read in a few days." - -I picked applications of continuations as the topic. In retrospect I should have written about macros and embedded languages. There's a whole world there that's barely been explored. But all I wanted was to get out of grad school, and my rapidly written dissertation sufficed, just barely. - -Meanwhile I was applying to art schools. I applied to two: RISD in the US, and the Accademia di Belli Arti in Florence, which, because it was the oldest art school, I imagined would be good. RISD accepted me, and I never heard back from the Accademia, so off to Providence I went. - -I'd applied for the BFA program at RISD, which meant in effect that I had to go to college again. This was not as strange as it sounds, because I was only 25, and art schools are full of people of different ages. RISD counted me as a transfer sophomore and said I had to do the foundation that summer. The foundation means the classes that everyone has to take in fundamental subjects like drawing, color, and design. - -Toward the end of the summer I got a big surprise: a letter from the Accademia, which had been delayed because they'd sent it to Cambridge England instead of Cambridge Massachusetts, inviting me to take the entrance exam in Florence that fall. This was now only weeks away. My nice landlady let me leave my stuff in her attic. I had some money saved from consulting work I'd done in grad school; there was probably enough to last a year if I lived cheaply. Now all I had to do was learn Italian. - -Only stranieri (foreigners) had to take this entrance exam. In retrospect it may well have been a way of excluding them, because there were so many stranieri attracted by the idea of studying art in Florence that the Italian students would otherwise have been outnumbered. I was in decent shape at painting and drawing from the RISD foundation that summer, but I still don't know how I managed to pass the written exam. I remember that I answered the essay question by writing about Cezanne, and that I cranked up the intellectual level as high as I could to make the most of my limited vocabulary. [2] - -I'm only up to age 25 and already there are such conspicuous patterns. Here I was, yet again about to attend some august institution in the hopes of learning about some prestigious subject, and yet again about to be disappointed. The students and faculty in the painting department at the Accademia were the nicest people you could imagine, but they had long since arrived at an arrangement whereby the students wouldn't require the faculty to teach anything, and in return the faculty wouldn't require the students to learn anything. And at the same time all involved would adhere outwardly to the conventions of a 19th century atelier. We actually had one of those little stoves, fed with kindling, that you see in 19th century studio paintings, and a nude model sitting as close to it as possible without getting burned. Except hardly anyone else painted her besides me. The rest of the students spent their time chatting or occasionally trying to imitate things they'd seen in American art magazines. - -Our model turned out to live just down the street from me. She made a living from a combination of modelling and making fakes for a local antique dealer. She'd copy an obscure old painting out of a book, and then he'd take the copy and maltreat it to make it look old. [3] - -While I was a student at the Accademia I started painting still lives in my bedroom at night. These paintings were tiny, because the room was, and because I painted them on leftover scraps of canvas, which was all I could afford at the time. Painting still lives is different from painting people, because the subject, as its name suggests, can't move. People can't sit for more than about 15 minutes at a time, and when they do they don't sit very still. So the traditional m.o. for painting people is to know how to paint a generic person, which you then modify to match the specific person you're painting. Whereas a still life you can, if you want, copy pixel by pixel from what you're seeing. You don't want to stop there, of course, or you get merely photographic accuracy, and what makes a still life interesting is that it's been through a head. You want to emphasize the visual cues that tell you, for example, that the reason the color changes suddenly at a certain point is that it's the edge of an object. By subtly emphasizing such things you can make paintings that are more realistic than photographs not just in some metaphorical sense, but in the strict information-theoretic sense. [4] - -I liked painting still lives because I was curious about what I was seeing. In everyday life, we aren't consciously aware of much we're seeing. Most visual perception is handled by low-level processes that merely tell your brain "that's a water droplet" without telling you details like where the lightest and darkest points are, or "that's a bush" without telling you the shape and position of every leaf. This is a feature of brains, not a bug. In everyday life it would be distracting to notice every leaf on every bush. But when you have to paint something, you have to look more closely, and when you do there's a lot to see. You can still be noticing new things after days of trying to paint something people usually take for granted, just as you can after days of trying to write an essay about something people usually take for granted. - -This is not the only way to paint. I'm not 100% sure it's even a good way to paint. But it seemed a good enough bet to be worth trying. - -Our teacher, professor Ulivi, was a nice guy. He could see I worked hard, and gave me a good grade, which he wrote down in a sort of passport each student had. But the Accademia wasn't teaching me anything except Italian, and my money was running out, so at the end of the first year I went back to the US. - -I wanted to go back to RISD, but I was now broke and RISD was very expensive, so I decided to get a job for a year and then return to RISD the next fall. I got one at a company called Interleaf, which made software for creating documents. You mean like Microsoft Word? Exactly. That was how I learned that low end software tends to eat high end software. But Interleaf still had a few years to live yet. [5] - -Interleaf had done something pretty bold. Inspired by Emacs, they'd added a scripting language, and even made the scripting language a dialect of Lisp. Now they wanted a Lisp hacker to write things in it. This was the closest thing I've had to a normal job, and I hereby apologize to my boss and coworkers, because I was a bad employee. Their Lisp was the thinnest icing on a giant C cake, and since I didn't know C and didn't want to learn it, I never understood most of the software. Plus I was terribly irresponsible. This was back when a programming job meant showing up every day during certain working hours. That seemed unnatural to me, and on this point the rest of the world is coming around to my way of thinking, but at the time it caused a lot of friction. Toward the end of the year I spent much of my time surreptitiously working on On Lisp, which I had by this time gotten a contract to publish. - -The good part was that I got paid huge amounts of money, especially by art student standards. In Florence, after paying my part of the rent, my budget for everything else had been $7 a day. Now I was getting paid more than 4 times that every hour, even when I was just sitting in a meeting. By living cheaply I not only managed to save enough to go back to RISD, but also paid off my college loans. - -I learned some useful things at Interleaf, though they were mostly about what not to do. I learned that it's better for technology companies to be run by product people than sales people (though sales is a real skill and people who are good at it are really good at it), that it leads to bugs when code is edited by too many people, that cheap office space is no bargain if it's depressing, that planned meetings are inferior to corridor conversations, that big, bureaucratic customers are a dangerous source of money, and that there's not much overlap between conventional office hours and the optimal time for hacking, or conventional offices and the optimal place for it. - -But the most important thing I learned, and which I used in both Viaweb and Y Combinator, is that the low end eats the high end: that it's good to be the "entry level" option, even though that will be less prestigious, because if you're not, someone else will be, and will squash you against the ceiling. Which in turn means that prestige is a danger sign. - -When I left to go back to RISD the next fall, I arranged to do freelance work for the group that did projects for customers, and this was how I survived for the next several years. When I came back to visit for a project later on, someone told me about a new thing called HTML, which was, as he described it, a derivative of SGML. Markup language enthusiasts were an occupational hazard at Interleaf and I ignored him, but this HTML thing later became a big part of my life. - -In the fall of 1992 I moved back to Providence to continue at RISD. The foundation had merely been intro stuff, and the Accademia had been a (very civilized) joke. Now I was going to see what real art school was like. But alas it was more like the Accademia than not. Better organized, certainly, and a lot more expensive, but it was now becoming clear that art school did not bear the same relationship to art that medical school bore to medicine. At least not the painting department. The textile department, which my next door neighbor belonged to, seemed to be pretty rigorous. No doubt illustration and architecture were too. But painting was post-rigorous. Painting students were supposed to express themselves, which to the more worldly ones meant to try to cook up some sort of distinctive signature style. - -A signature style is the visual equivalent of what in show business is known as a "schtick": something that immediately identifies the work as yours and no one else's. For example, when you see a painting that looks like a certain kind of cartoon, you know it's by Roy Lichtenstein. So if you see a big painting of this type hanging in the apartment of a hedge fund manager, you know he paid millions of dollars for it. That's not always why artists have a signature style, but it's usually why buyers pay a lot for such work. [6] - -There were plenty of earnest students too: kids who "could draw" in high school, and now had come to what was supposed to be the best art school in the country, to learn to draw even better. They tended to be confused and demoralized by what they found at RISD, but they kept going, because painting was what they did. I was not one of the kids who could draw in high school, but at RISD I was definitely closer to their tribe than the tribe of signature style seekers. - -I learned a lot in the color class I took at RISD, but otherwise I was basically teaching myself to paint, and I could do that for free. So in 1993 I dropped out. I hung around Providence for a bit, and then my college friend Nancy Parmet did me a big favor. A rent-controlled apartment in a building her mother owned in New York was becoming vacant. Did I want it? It wasn't much more than my current place, and New York was supposed to be where the artists were. So yes, I wanted it! [7] - -Asterix comics begin by zooming in on a tiny corner of Roman Gaul that turns out not to be controlled by the Romans. You can do something similar on a map of New York City: if you zoom in on the Upper East Side, there's a tiny corner that's not rich, or at least wasn't in 1993. It's called Yorkville, and that was my new home. Now I was a New York artist — in the strictly technical sense of making paintings and living in New York. - -I was nervous about money, because I could sense that Interleaf was on the way down. Freelance Lisp hacking work was very rare, and I didn't want to have to program in another language, which in those days would have meant C++ if I was lucky. So with my unerring nose for financial opportunity, I decided to write another book on Lisp. This would be a popular book, the sort of book that could be used as a textbook. I imagined myself living frugally off the royalties and spending all my time painting. (The painting on the cover of this book, ANSI Common Lisp, is one that I painted around this time.) - -The best thing about New York for me was the presence of Idelle and Julian Weber. Idelle Weber was a painter, one of the early photorealists, and I'd taken her painting class at Harvard. I've never known a teacher more beloved by her students. Large numbers of former students kept in touch with her, including me. After I moved to New York I became her de facto studio assistant. - -She liked to paint on big, square canvases, 4 to 5 feet on a side. One day in late 1994 as I was stretching one of these monsters there was something on the radio about a famous fund manager. He wasn't that much older than me, and was super rich. The thought suddenly occurred to me: why don't I become rich? Then I'll be able to work on whatever I want. - -Meanwhile I'd been hearing more and more about this new thing called the World Wide Web. Robert Morris showed it to me when I visited him in Cambridge, where he was now in grad school at Harvard. It seemed to me that the web would be a big deal. I'd seen what graphical user interfaces had done for the popularity of microcomputers. It seemed like the web would do the same for the internet. - -If I wanted to get rich, here was the next train leaving the station. I was right about that part. What I got wrong was the idea. I decided we should start a company to put art galleries online. I can't honestly say, after reading so many Y Combinator applications, that this was the worst startup idea ever, but it was up there. Art galleries didn't want to be online, and still don't, not the fancy ones. That's not how they sell. I wrote some software to generate web sites for galleries, and Robert wrote some to resize images and set up an http server to serve the pages. Then we tried to sign up galleries. To call this a difficult sale would be an understatement. It was difficult to give away. A few galleries let us make sites for them for free, but none paid us. - -Then some online stores started to appear, and I realized that except for the order buttons they were identical to the sites we'd been generating for galleries. This impressive-sounding thing called an "internet storefront" was something we already knew how to build. - -So in the summer of 1995, after I submitted the camera-ready copy of ANSI Common Lisp to the publishers, we started trying to write software to build online stores. At first this was going to be normal desktop software, which in those days meant Windows software. That was an alarming prospect, because neither of us knew how to write Windows software or wanted to learn. We lived in the Unix world. But we decided we'd at least try writing a prototype store builder on Unix. Robert wrote a shopping cart, and I wrote a new site generator for stores — in Lisp, of course. - -We were working out of Robert's apartment in Cambridge. His roommate was away for big chunks of time, during which I got to sleep in his room. For some reason there was no bed frame or sheets, just a mattress on the floor. One morning as I was lying on this mattress I had an idea that made me sit up like a capital L. What if we ran the software on the server, and let users control it by clicking on links? Then we'd never have to write anything to run on users' computers. We could generate the sites on the same server we'd serve them from. Users wouldn't need anything more than a browser. - -This kind of software, known as a web app, is common now, but at the time it wasn't clear that it was even possible. To find out, we decided to try making a version of our store builder that you could control through the browser. A couple days later, on August 12, we had one that worked. The UI was horrible, but it proved you could build a whole store through the browser, without any client software or typing anything into the command line on the server. - -Now we felt like we were really onto something. I had visions of a whole new generation of software working this way. You wouldn't need versions, or ports, or any of that crap. At Interleaf there had been a whole group called Release Engineering that seemed to be at least as big as the group that actually wrote the software. Now you could just update the software right on the server. - -We started a new company we called Viaweb, after the fact that our software worked via the web, and we got $10,000 in seed funding from Idelle's husband Julian. In return for that and doing the initial legal work and giving us business advice, we gave him 10% of the company. Ten years later this deal became the model for Y Combinator's. We knew founders needed something like this, because we'd needed it ourselves. - -At this stage I had a negative net worth, because the thousand dollars or so I had in the bank was more than counterbalanced by what I owed the government in taxes. (Had I diligently set aside the proper proportion of the money I'd made consulting for Interleaf? No, I had not.) So although Robert had his graduate student stipend, I needed that seed funding to live on. - -We originally hoped to launch in September, but we got more ambitious about the software as we worked on it. Eventually we managed to build a WYSIWYG site builder, in the sense that as you were creating pages, they looked exactly like the static ones that would be generated later, except that instead of leading to static pages, the links all referred to closures stored in a hash table on the server. - -It helped to have studied art, because the main goal of an online store builder is to make users look legit, and the key to looking legit is high production values. If you get page layouts and fonts and colors right, you can make a guy running a store out of his bedroom look more legit than a big company. - -(If you're curious why my site looks so old-fashioned, it's because it's still made with this software. It may look clunky today, but in 1996 it was the last word in slick.) - -In September, Robert rebelled. "We've been working on this for a month," he said, "and it's still not done." This is funny in retrospect, because he would still be working on it almost 3 years later. But I decided it might be prudent to recruit more programmers, and I asked Robert who else in grad school with him was really good. He recommended Trevor Blackwell, which surprised me at first, because at that point I knew Trevor mainly for his plan to reduce everything in his life to a stack of notecards, which he carried around with him. But Rtm was right, as usual. Trevor turned out to be a frighteningly effective hacker. - -It was a lot of fun working with Robert and Trevor. They're the two most independent-minded people I know, and in completely different ways. If you could see inside Rtm's brain it would look like a colonial New England church, and if you could see inside Trevor's it would look like the worst excesses of Austrian Rococo. - -We opened for business, with 6 stores, in January 1996. It was just as well we waited a few months, because although we worried we were late, we were actually almost fatally early. There was a lot of talk in the press then about ecommerce, but not many people actually wanted online stores. [8] - -There were three main parts to the software: the editor, which people used to build sites and which I wrote, the shopping cart, which Robert wrote, and the manager, which kept track of orders and statistics, and which Trevor wrote. In its time, the editor was one of the best general-purpose site builders. I kept the code tight and didn't have to integrate with any other software except Robert's and Trevor's, so it was quite fun to work on. If all I'd had to do was work on this software, the next 3 years would have been the easiest of my life. Unfortunately I had to do a lot more, all of it stuff I was worse at than programming, and the next 3 years were instead the most stressful. - -There were a lot of startups making ecommerce software in the second half of the 90s. We were determined to be the Microsoft Word, not the Interleaf. Which meant being easy to use and inexpensive. It was lucky for us that we were poor, because that caused us to make Viaweb even more inexpensive than we realized. We charged $100 a month for a small store and $300 a month for a big one. This low price was a big attraction, and a constant thorn in the sides of competitors, but it wasn't because of some clever insight that we set the price low. We had no idea what businesses paid for things. $300 a month seemed like a lot of money to us. - -We did a lot of things right by accident like that. For example, we did what's now called "doing things that don't scale," although at the time we would have described it as "being so lame that we're driven to the most desperate measures to get users." The most common of which was building stores for them. This seemed particularly humiliating, since the whole reason d'etre of our software was that people could use it to make their own stores. But anything to get users. - -We learned a lot more about retail than we wanted to know. For example, that if you could only have a small image of a man's shirt (and all images were small then by present standards), it was better to have a closeup of the collar than a picture of the whole shirt. The reason I remember learning this was that it meant I had to rescan about 30 images of men's shirts. My first set of scans were so beautiful too. - -Though this felt wrong, it was exactly the right thing to be doing. Building stores for users taught us about retail, and about how it felt to use our software. I was initially both mystified and repelled by "business" and thought we needed a "business person" to be in charge of it, but once we started to get users, I was converted, in much the same way I was converted to fatherhood once I had kids. Whatever users wanted, I was all theirs. Maybe one day we'd have so many users that I couldn't scan their images for them, but in the meantime there was nothing more important to do. - -Another thing I didn't get at the time is that growth rate is the ultimate test of a startup. Our growth rate was fine. We had about 70 stores at the end of 1996 and about 500 at the end of 1997. I mistakenly thought the thing that mattered was the absolute number of users. And that is the thing that matters in the sense that that's how much money you're making, and if you're not making enough, you might go out of business. But in the long term the growth rate takes care of the absolute number. If we'd been a startup I was advising at Y Combinator, I would have said: Stop being so stressed out, because you're doing fine. You're growing 7x a year. Just don't hire too many more people and you'll soon be profitable, and then you'll control your own destiny. - -Alas I hired lots more people, partly because our investors wanted me to, and partly because that's what startups did during the Internet Bubble. A company with just a handful of employees would have seemed amateurish. So we didn't reach breakeven until about when Yahoo bought us in the summer of 1998. Which in turn meant we were at the mercy of investors for the entire life of the company. And since both we and our investors were noobs at startups, the result was a mess even by startup standards. - -It was a huge relief when Yahoo bought us. In principle our Viaweb stock was valuable. It was a share in a business that was profitable and growing rapidly. But it didn't feel very valuable to me; I had no idea how to value a business, but I was all too keenly aware of the near-death experiences we seemed to have every few months. Nor had I changed my grad student lifestyle significantly since we started. So when Yahoo bought us it felt like going from rags to riches. Since we were going to California, I bought a car, a yellow 1998 VW GTI. I remember thinking that its leather seats alone were by far the most luxurious thing I owned. - -The next year, from the summer of 1998 to the summer of 1999, must have been the least productive of my life. I didn't realize it at the time, but I was worn out from the effort and stress of running Viaweb. For a while after I got to California I tried to continue my usual m.o. of programming till 3 in the morning, but fatigue combined with Yahoo's prematurely aged culture and grim cube farm in Santa Clara gradually dragged me down. After a few months it felt disconcertingly like working at Interleaf. - -Yahoo had given us a lot of options when they bought us. At the time I thought Yahoo was so overvalued that they'd never be worth anything, but to my astonishment the stock went up 5x in the next year. I hung on till the first chunk of options vested, then in the summer of 1999 I left. It had been so long since I'd painted anything that I'd half forgotten why I was doing this. My brain had been entirely full of software and men's shirts for 4 years. But I had done this to get rich so I could paint, I reminded myself, and now I was rich, so I should go paint. - -When I said I was leaving, my boss at Yahoo had a long conversation with me about my plans. I told him all about the kinds of pictures I wanted to paint. At the time I was touched that he took such an interest in me. Now I realize it was because he thought I was lying. My options at that point were worth about $2 million a month. If I was leaving that kind of money on the table, it could only be to go and start some new startup, and if I did, I might take people with me. This was the height of the Internet Bubble, and Yahoo was ground zero of it. My boss was at that moment a billionaire. Leaving then to start a new startup must have seemed to him an insanely, and yet also plausibly, ambitious plan. - -But I really was quitting to paint, and I started immediately. There was no time to lose. I'd already burned 4 years getting rich. Now when I talk to founders who are leaving after selling their companies, my advice is always the same: take a vacation. That's what I should have done, just gone off somewhere and done nothing for a month or two, but the idea never occurred to me. - -So I tried to paint, but I just didn't seem to have any energy or ambition. Part of the problem was that I didn't know many people in California. I'd compounded this problem by buying a house up in the Santa Cruz Mountains, with a beautiful view but miles from anywhere. I stuck it out for a few more months, then in desperation I went back to New York, where unless you understand about rent control you'll be surprised to hear I still had my apartment, sealed up like a tomb of my old life. Idelle was in New York at least, and there were other people trying to paint there, even though I didn't know any of them. - -When I got back to New York I resumed my old life, except now I was rich. It was as weird as it sounds. I resumed all my old patterns, except now there were doors where there hadn't been. Now when I was tired of walking, all I had to do was raise my hand, and (unless it was raining) a taxi would stop to pick me up. Now when I walked past charming little restaurants I could go in and order lunch. It was exciting for a while. Painting started to go better. I experimented with a new kind of still life where I'd paint one painting in the old way, then photograph it and print it, blown up, on canvas, and then use that as the underpainting for a second still life, painted from the same objects (which hopefully hadn't rotted yet). - -Meanwhile I looked for an apartment to buy. Now I could actually choose what neighborhood to live in. Where, I asked myself and various real estate agents, is the Cambridge of New York? Aided by occasional visits to actual Cambridge, I gradually realized there wasn't one. Huh. - -Around this time, in the spring of 2000, I had an idea. It was clear from our experience with Viaweb that web apps were the future. Why not build a web app for making web apps? Why not let people edit code on our server through the browser, and then host the resulting applications for them? [9] You could run all sorts of services on the servers that these applications could use just by making an API call: making and receiving phone calls, manipulating images, taking credit card payments, etc. - -I got so excited about this idea that I couldn't think about anything else. It seemed obvious that this was the future. I didn't particularly want to start another company, but it was clear that this idea would have to be embodied as one, so I decided to move to Cambridge and start it. I hoped to lure Robert into working on it with me, but there I ran into a hitch. Robert was now a postdoc at MIT, and though he'd made a lot of money the last time I'd lured him into working on one of my schemes, it had also been a huge time sink. So while he agreed that it sounded like a plausible idea, he firmly refused to work on it. - -Hmph. Well, I'd do it myself then. I recruited Dan Giffin, who had worked for Viaweb, and two undergrads who wanted summer jobs, and we got to work trying to build what it's now clear is about twenty companies and several open-source projects worth of software. The language for defining applications would of course be a dialect of Lisp. But I wasn't so naive as to assume I could spring an overt Lisp on a general audience; we'd hide the parentheses, like Dylan did. - -By then there was a name for the kind of company Viaweb was, an "application service provider," or ASP. This name didn't last long before it was replaced by "software as a service," but it was current for long enough that I named this new company after it: it was going to be called Aspra. - -I started working on the application builder, Dan worked on network infrastructure, and the two undergrads worked on the first two services (images and phone calls). But about halfway through the summer I realized I really didn't want to run a company — especially not a big one, which it was looking like this would have to be. I'd only started Viaweb because I needed the money. Now that I didn't need money anymore, why was I doing this? If this vision had to be realized as a company, then screw the vision. I'd build a subset that could be done as an open-source project. - -Much to my surprise, the time I spent working on this stuff was not wasted after all. After we started Y Combinator, I would often encounter startups working on parts of this new architecture, and it was very useful to have spent so much time thinking about it and even trying to write some of it. - -The subset I would build as an open-source project was the new Lisp, whose parentheses I now wouldn't even have to hide. A lot of Lisp hackers dream of building a new Lisp, partly because one of the distinctive features of the language is that it has dialects, and partly, I think, because we have in our minds a Platonic form of Lisp that all existing dialects fall short of. I certainly did. So at the end of the summer Dan and I switched to working on this new dialect of Lisp, which I called Arc, in a house I bought in Cambridge. - -The following spring, lightning struck. I was invited to give a talk at a Lisp conference, so I gave one about how we'd used Lisp at Viaweb. Afterward I put a postscript file of this talk online, on paulgraham.com, which I'd created years before using Viaweb but had never used for anything. In one day it got 30,000 page views. What on earth had happened? The referring urls showed that someone had posted it on Slashdot. [10] - -Wow, I thought, there's an audience. If I write something and put it on the web, anyone can read it. That may seem obvious now, but it was surprising then. In the print era there was a narrow channel to readers, guarded by fierce monsters known as editors. The only way to get an audience for anything you wrote was to get it published as a book, or in a newspaper or magazine. Now anyone could publish anything. - -This had been possible in principle since 1993, but not many people had realized it yet. I had been intimately involved with building the infrastructure of the web for most of that time, and a writer as well, and it had taken me 8 years to realize it. Even then it took me several years to understand the implications. It meant there would be a whole new generation of essays. [11] - -In the print era, the channel for publishing essays had been vanishingly small. Except for a few officially anointed thinkers who went to the right parties in New York, the only people allowed to publish essays were specialists writing about their specialties. There were so many essays that had never been written, because there had been no way to publish them. Now they could be, and I was going to write them. [12] - -I've worked on several different things, but to the extent there was a turning point where I figured out what to work on, it was when I started publishing essays online. From then on I knew that whatever else I did, I'd always write essays too. - -I knew that online essays would be a marginal medium at first. Socially they'd seem more like rants posted by nutjobs on their GeoCities sites than the genteel and beautifully typeset compositions published in The New Yorker. But by this point I knew enough to find that encouraging instead of discouraging. - -One of the most conspicuous patterns I've noticed in my life is how well it has worked, for me at least, to work on things that weren't prestigious. Still life has always been the least prestigious form of painting. Viaweb and Y Combinator both seemed lame when we started them. I still get the glassy eye from strangers when they ask what I'm writing, and I explain that it's an essay I'm going to publish on my web site. Even Lisp, though prestigious intellectually in something like the way Latin is, also seems about as hip. - -It's not that unprestigious types of work are good per se. But when you find yourself drawn to some kind of work despite its current lack of prestige, it's a sign both that there's something real to be discovered there, and that you have the right kind of motives. Impure motives are a big danger for the ambitious. If anything is going to lead you astray, it will be the desire to impress people. So while working on things that aren't prestigious doesn't guarantee you're on the right track, it at least guarantees you're not on the most common type of wrong one. - -Over the next several years I wrote lots of essays about all kinds of different topics. O'Reilly reprinted a collection of them as a book, called Hackers & Painters after one of the essays in it. I also worked on spam filters, and did some more painting. I used to have dinners for a group of friends every thursday night, which taught me how to cook for groups. And I bought another building in Cambridge, a former candy factory (and later, twas said, porn studio), to use as an office. - -One night in October 2003 there was a big party at my house. It was a clever idea of my friend Maria Daniels, who was one of the thursday diners. Three separate hosts would all invite their friends to one party. So for every guest, two thirds of the other guests would be people they didn't know but would probably like. One of the guests was someone I didn't know but would turn out to like a lot: a woman called Jessica Livingston. A couple days later I asked her out. - -Jessica was in charge of marketing at a Boston investment bank. This bank thought it understood startups, but over the next year, as she met friends of mine from the startup world, she was surprised how different reality was. And how colorful their stories were. So she decided to compile a book of interviews with startup founders. - -When the bank had financial problems and she had to fire half her staff, she started looking for a new job. In early 2005 she interviewed for a marketing job at a Boston VC firm. It took them weeks to make up their minds, and during this time I started telling her about all the things that needed to be fixed about venture capital. They should make a larger number of smaller investments instead of a handful of giant ones, they should be funding younger, more technical founders instead of MBAs, they should let the founders remain as CEO, and so on. - -One of my tricks for writing essays had always been to give talks. The prospect of having to stand up in front of a group of people and tell them something that won't waste their time is a great spur to the imagination. When the Harvard Computer Society, the undergrad computer club, asked me to give a talk, I decided I would tell them how to start a startup. Maybe they'd be able to avoid the worst of the mistakes we'd made. - -So I gave this talk, in the course of which I told them that the best sources of seed funding were successful startup founders, because then they'd be sources of advice too. Whereupon it seemed they were all looking expectantly at me. Horrified at the prospect of having my inbox flooded by business plans (if I'd only known), I blurted out "But not me!" and went on with the talk. But afterward it occurred to me that I should really stop procrastinating about angel investing. I'd been meaning to since Yahoo bought us, and now it was 7 years later and I still hadn't done one angel investment. - -Meanwhile I had been scheming with Robert and Trevor about projects we could work on together. I missed working with them, and it seemed like there had to be something we could collaborate on. - -As Jessica and I were walking home from dinner on March 11, at the corner of Garden and Walker streets, these three threads converged. Screw the VCs who were taking so long to make up their minds. We'd start our own investment firm and actually implement the ideas we'd been talking about. I'd fund it, and Jessica could quit her job and work for it, and we'd get Robert and Trevor as partners too. [13] - -Once again, ignorance worked in our favor. We had no idea how to be angel investors, and in Boston in 2005 there were no Ron Conways to learn from. So we just made what seemed like the obvious choices, and some of the things we did turned out to be novel. - -There are multiple components to Y Combinator, and we didn't figure them all out at once. The part we got first was to be an angel firm. In those days, those two words didn't go together. There were VC firms, which were organized companies with people whose job it was to make investments, but they only did big, million dollar investments. And there were angels, who did smaller investments, but these were individuals who were usually focused on other things and made investments on the side. And neither of them helped founders enough in the beginning. We knew how helpless founders were in some respects, because we remembered how helpless we'd been. For example, one thing Julian had done for us that seemed to us like magic was to get us set up as a company. We were fine writing fairly difficult software, but actually getting incorporated, with bylaws and stock and all that stuff, how on earth did you do that? Our plan was not only to make seed investments, but to do for startups everything Julian had done for us. - -YC was not organized as a fund. It was cheap enough to run that we funded it with our own money. That went right by 99% of readers, but professional investors are thinking "Wow, that means they got all the returns." But once again, this was not due to any particular insight on our part. We didn't know how VC firms were organized. It never occurred to us to try to raise a fund, and if it had, we wouldn't have known where to start. [14] - -The most distinctive thing about YC is the batch model: to fund a bunch of startups all at once, twice a year, and then to spend three months focusing intensively on trying to help them. That part we discovered by accident, not merely implicitly but explicitly due to our ignorance about investing. We needed to get experience as investors. What better way, we thought, than to fund a whole bunch of startups at once? We knew undergrads got temporary jobs at tech companies during the summer. Why not organize a summer program where they'd start startups instead? We wouldn't feel guilty for being in a sense fake investors, because they would in a similar sense be fake founders. So while we probably wouldn't make much money out of it, we'd at least get to practice being investors on them, and they for their part would probably have a more interesting summer than they would working at Microsoft. - -We'd use the building I owned in Cambridge as our headquarters. We'd all have dinner there once a week — on tuesdays, since I was already cooking for the thursday diners on thursdays — and after dinner we'd bring in experts on startups to give talks. - -We knew undergrads were deciding then about summer jobs, so in a matter of days we cooked up something we called the Summer Founders Program, and I posted an announcement on my site, inviting undergrads to apply. I had never imagined that writing essays would be a way to get "deal flow," as investors call it, but it turned out to be the perfect source. [15] We got 225 applications for the Summer Founders Program, and we were surprised to find that a lot of them were from people who'd already graduated, or were about to that spring. Already this SFP thing was starting to feel more serious than we'd intended. - -We invited about 20 of the 225 groups to interview in person, and from those we picked 8 to fund. They were an impressive group. That first batch included reddit, Justin Kan and Emmett Shear, who went on to found Twitch, Aaron Swartz, who had already helped write the RSS spec and would a few years later become a martyr for open access, and Sam Altman, who would later become the second president of YC. I don't think it was entirely luck that the first batch was so good. You had to be pretty bold to sign up for a weird thing like the Summer Founders Program instead of a summer job at a legit place like Microsoft or Goldman Sachs. - -The deal for startups was based on a combination of the deal we did with Julian ($10k for 10%) and what Robert said MIT grad students got for the summer ($6k). We invested $6k per founder, which in the typical two-founder case was $12k, in return for 6%. That had to be fair, because it was twice as good as the deal we ourselves had taken. Plus that first summer, which was really hot, Jessica brought the founders free air conditioners. [16] - -Fairly quickly I realized that we had stumbled upon the way to scale startup funding. Funding startups in batches was more convenient for us, because it meant we could do things for a lot of startups at once, but being part of a batch was better for the startups too. It solved one of the biggest problems faced by founders: the isolation. Now you not only had colleagues, but colleagues who understood the problems you were facing and could tell you how they were solving them. - -As YC grew, we started to notice other advantages of scale. The alumni became a tight community, dedicated to helping one another, and especially the current batch, whose shoes they remembered being in. We also noticed that the startups were becoming one another's customers. We used to refer jokingly to the "YC GDP," but as YC grows this becomes less and less of a joke. Now lots of startups get their initial set of customers almost entirely from among their batchmates. - -I had not originally intended YC to be a full-time job. I was going to do three things: hack, write essays, and work on YC. As YC grew, and I grew more excited about it, it started to take up a lot more than a third of my attention. But for the first few years I was still able to work on other things. - -In the summer of 2006, Robert and I started working on a new version of Arc. This one was reasonably fast, because it was compiled into Scheme. To test this new Arc, I wrote Hacker News in it. It was originally meant to be a news aggregator for startup founders and was called Startup News, but after a few months I got tired of reading about nothing but startups. Plus it wasn't startup founders we wanted to reach. It was future startup founders. So I changed the name to Hacker News and the topic to whatever engaged one's intellectual curiosity. - -HN was no doubt good for YC, but it was also by far the biggest source of stress for me. If all I'd had to do was select and help founders, life would have been so easy. And that implies that HN was a mistake. Surely the biggest source of stress in one's work should at least be something close to the core of the work. Whereas I was like someone who was in pain while running a marathon not from the exertion of running, but because I had a blister from an ill-fitting shoe. When I was dealing with some urgent problem during YC, there was about a 60% chance it had to do with HN, and a 40% chance it had do with everything else combined. [17] - -As well as HN, I wrote all of YC's internal software in Arc. But while I continued to work a good deal in Arc, I gradually stopped working on Arc, partly because I didn't have time to, and partly because it was a lot less attractive to mess around with the language now that we had all this infrastructure depending on it. So now my three projects were reduced to two: writing essays and working on YC. - -YC was different from other kinds of work I've done. Instead of deciding for myself what to work on, the problems came to me. Every 6 months there was a new batch of startups, and their problems, whatever they were, became our problems. It was very engaging work, because their problems were quite varied, and the good founders were very effective. If you were trying to learn the most you could about startups in the shortest possible time, you couldn't have picked a better way to do it. - -There were parts of the job I didn't like. Disputes between cofounders, figuring out when people were lying to us, fighting with people who maltreated the startups, and so on. But I worked hard even at the parts I didn't like. I was haunted by something Kevin Hale once said about companies: "No one works harder than the boss." He meant it both descriptively and prescriptively, and it was the second part that scared me. I wanted YC to be good, so if how hard I worked set the upper bound on how hard everyone else worked, I'd better work very hard. - -One day in 2010, when he was visiting California for interviews, Robert Morris did something astonishing: he offered me unsolicited advice. I can only remember him doing that once before. One day at Viaweb, when I was bent over double from a kidney stone, he suggested that it would be a good idea for him to take me to the hospital. That was what it took for Rtm to offer unsolicited advice. So I remember his exact words very clearly. "You know," he said, "you should make sure Y Combinator isn't the last cool thing you do." - -At the time I didn't understand what he meant, but gradually it dawned on me that he was saying I should quit. This seemed strange advice, because YC was doing great. But if there was one thing rarer than Rtm offering advice, it was Rtm being wrong. So this set me thinking. It was true that on my current trajectory, YC would be the last thing I did, because it was only taking up more of my attention. It had already eaten Arc, and was in the process of eating essays too. Either YC was my life's work or I'd have to leave eventually. And it wasn't, so I would. - -In the summer of 2012 my mother had a stroke, and the cause turned out to be a blood clot caused by colon cancer. The stroke destroyed her balance, and she was put in a nursing home, but she really wanted to get out of it and back to her house, and my sister and I were determined to help her do it. I used to fly up to Oregon to visit her regularly, and I had a lot of time to think on those flights. On one of them I realized I was ready to hand YC over to someone else. - -I asked Jessica if she wanted to be president, but she didn't, so we decided we'd try to recruit Sam Altman. We talked to Robert and Trevor and we agreed to make it a complete changing of the guard. Up till that point YC had been controlled by the original LLC we four had started. But we wanted YC to last for a long time, and to do that it couldn't be controlled by the founders. So if Sam said yes, we'd let him reorganize YC. Robert and I would retire, and Jessica and Trevor would become ordinary partners. - -When we asked Sam if he wanted to be president of YC, initially he said no. He wanted to start a startup to make nuclear reactors. But I kept at it, and in October 2013 he finally agreed. We decided he'd take over starting with the winter 2014 batch. For the rest of 2013 I left running YC more and more to Sam, partly so he could learn the job, and partly because I was focused on my mother, whose cancer had returned. - -She died on January 15, 2014. We knew this was coming, but it was still hard when it did. - -I kept working on YC till March, to help get that batch of startups through Demo Day, then I checked out pretty completely. (I still talk to alumni and to new startups working on things I'm interested in, but that only takes a few hours a week.) - -What should I do next? Rtm's advice hadn't included anything about that. I wanted to do something completely different, so I decided I'd paint. I wanted to see how good I could get if I really focused on it. So the day after I stopped working on YC, I started painting. I was rusty and it took a while to get back into shape, but it was at least completely engaging. [18] - -I spent most of the rest of 2014 painting. I'd never been able to work so uninterruptedly before, and I got to be better than I had been. Not good enough, but better. Then in November, right in the middle of a painting, I ran out of steam. Up till that point I'd always been curious to see how the painting I was working on would turn out, but suddenly finishing this one seemed like a chore. So I stopped working on it and cleaned my brushes and haven't painted since. So far anyway. - -I realize that sounds rather wimpy. But attention is a zero sum game. If you can choose what to work on, and you choose a project that's not the best one (or at least a good one) for you, then it's getting in the way of another project that is. And at 50 there was some opportunity cost to screwing around. - -I started writing essays again, and wrote a bunch of new ones over the next few months. I even wrote a couple that weren't about startups. Then in March 2015 I started working on Lisp again. - -The distinctive thing about Lisp is that its core is a language defined by writing an interpreter in itself. It wasn't originally intended as a programming language in the ordinary sense. It was meant to be a formal model of computation, an alternative to the Turing machine. If you want to write an interpreter for a language in itself, what's the minimum set of predefined operators you need? The Lisp that John McCarthy invented, or more accurately discovered, is an answer to that question. [19] - -McCarthy didn't realize this Lisp could even be used to program computers till his grad student Steve Russell suggested it. Russell translated McCarthy's interpreter into IBM 704 machine language, and from that point Lisp started also to be a programming language in the ordinary sense. But its origins as a model of computation gave it a power and elegance that other languages couldn't match. It was this that attracted me in college, though I didn't understand why at the time. - -McCarthy's 1960 Lisp did nothing more than interpret Lisp expressions. It was missing a lot of things you'd want in a programming language. So these had to be added, and when they were, they weren't defined using McCarthy's original axiomatic approach. That wouldn't have been feasible at the time. McCarthy tested his interpreter by hand-simulating the execution of programs. But it was already getting close to the limit of interpreters you could test that way — indeed, there was a bug in it that McCarthy had overlooked. To test a more complicated interpreter, you'd have had to run it, and computers then weren't powerful enough. - -Now they are, though. Now you could continue using McCarthy's axiomatic approach till you'd defined a complete programming language. And as long as every change you made to McCarthy's Lisp was a discoveredness-preserving transformation, you could, in principle, end up with a complete language that had this quality. Harder to do than to talk about, of course, but if it was possible in principle, why not try? So I decided to take a shot at it. It took 4 years, from March 26, 2015 to October 12, 2019. It was fortunate that I had a precisely defined goal, or it would have been hard to keep at it for so long. - -I wrote this new Lisp, called Bel, in itself in Arc. That may sound like a contradiction, but it's an indication of the sort of trickery I had to engage in to make this work. By means of an egregious collection of hacks I managed to make something close enough to an interpreter written in itself that could actually run. Not fast, but fast enough to test. - -I had to ban myself from writing essays during most of this time, or I'd never have finished. In late 2015 I spent 3 months writing essays, and when I went back to working on Bel I could barely understand the code. Not so much because it was badly written as because the problem is so convoluted. When you're working on an interpreter written in itself, it's hard to keep track of what's happening at what level, and errors can be practically encrypted by the time you get them. - -So I said no more essays till Bel was done. But I told few people about Bel while I was working on it. So for years it must have seemed that I was doing nothing, when in fact I was working harder than I'd ever worked on anything. Occasionally after wrestling for hours with some gruesome bug I'd check Twitter or HN and see someone asking "Does Paul Graham still code?" - -Working on Bel was hard but satisfying. I worked on it so intensively that at any given time I had a decent chunk of the code in my head and could write more there. I remember taking the boys to the coast on a sunny day in 2015 and figuring out how to deal with some problem involving continuations while I watched them play in the tide pools. It felt like I was doing life right. I remember that because I was slightly dismayed at how novel it felt. The good news is that I had more moments like this over the next few years. - -In the summer of 2016 we moved to England. We wanted our kids to see what it was like living in another country, and since I was a British citizen by birth, that seemed the obvious choice. We only meant to stay for a year, but we liked it so much that we still live there. So most of Bel was written in England. - -In the fall of 2019, Bel was finally finished. Like McCarthy's original Lisp, it's a spec rather than an implementation, although like McCarthy's Lisp it's a spec expressed as code. - -Now that I could write essays again, I wrote a bunch about topics I'd had stacked up. I kept writing essays through 2020, but I also started to think about other things I could work on. How should I choose what to do? Well, how had I chosen what to work on in the past? I wrote an essay for myself to answer that question, and I was surprised how long and messy the answer turned out to be. If this surprised me, who'd lived it, then I thought perhaps it would be interesting to other people, and encouraging to those with similarly messy lives. So I wrote a more detailed version for others to read, and this is the last sentence of it. - - - - - - - - - -Notes - -[1] My experience skipped a step in the evolution of computers: time-sharing machines with interactive OSes. I went straight from batch processing to microcomputers, which made microcomputers seem all the more exciting. - -[2] Italian words for abstract concepts can nearly always be predicted from their English cognates (except for occasional traps like polluzione). It's the everyday words that differ. So if you string together a lot of abstract concepts with a few simple verbs, you can make a little Italian go a long way. - -[3] I lived at Piazza San Felice 4, so my walk to the Accademia went straight down the spine of old Florence: past the Pitti, across the bridge, past Orsanmichele, between the Duomo and the Baptistery, and then up Via Ricasoli to Piazza San Marco. I saw Florence at street level in every possible condition, from empty dark winter evenings to sweltering summer days when the streets were packed with tourists. - -[4] You can of course paint people like still lives if you want to, and they're willing. That sort of portrait is arguably the apex of still life painting, though the long sitting does tend to produce pained expressions in the sitters. - -[5] Interleaf was one of many companies that had smart people and built impressive technology, and yet got crushed by Moore's Law. In the 1990s the exponential growth in the power of commodity (i.e. Intel) processors rolled up high-end, special-purpose hardware and software companies like a bulldozer. - -[6] The signature style seekers at RISD weren't specifically mercenary. In the art world, money and coolness are tightly coupled. Anything expensive comes to be seen as cool, and anything seen as cool will soon become equally expensive. - -[7] Technically the apartment wasn't rent-controlled but rent-stabilized, but this is a refinement only New Yorkers would know or care about. The point is that it was really cheap, less than half market price. - -[8] Most software you can launch as soon as it's done. But when the software is an online store builder and you're hosting the stores, if you don't have any users yet, that fact will be painfully obvious. So before we could launch publicly we had to launch privately, in the sense of recruiting an initial set of users and making sure they had decent-looking stores. - -[9] We'd had a code editor in Viaweb for users to define their own page styles. They didn't know it, but they were editing Lisp expressions underneath. But this wasn't an app editor, because the code ran when the merchants' sites were generated, not when shoppers visited them. - -[10] This was the first instance of what is now a familiar experience, and so was what happened next, when I read the comments and found they were full of angry people. How could I claim that Lisp was better than other languages? Weren't they all Turing complete? People who see the responses to essays I write sometimes tell me how sorry they feel for me, but I'm not exaggerating when I reply that it has always been like this, since the very beginning. It comes with the territory. An essay must tell readers things they don't already know, and some people dislike being told such things. - -[11] People put plenty of stuff on the internet in the 90s of course, but putting something online is not the same as publishing it online. Publishing online means you treat the online version as the (or at least a) primary version. - -[12] There is a general lesson here that our experience with Y Combinator also teaches: Customs continue to constrain you long after the restrictions that caused them have disappeared. Customary VC practice had once, like the customs about publishing essays, been based on real constraints. Startups had once been much more expensive to start, and proportionally rare. Now they could be cheap and common, but the VCs' customs still reflected the old world, just as customs about writing essays still reflected the constraints of the print era. - -Which in turn implies that people who are independent-minded (i.e. less influenced by custom) will have an advantage in fields affected by rapid change (where customs are more likely to be obsolete). - -Here's an interesting point, though: you can't always predict which fields will be affected by rapid change. Obviously software and venture capital will be, but who would have predicted that essay writing would be? - -[13] Y Combinator was not the original name. At first we were called Cambridge Seed. But we didn't want a regional name, in case someone copied us in Silicon Valley, so we renamed ourselves after one of the coolest tricks in the lambda calculus, the Y combinator. - -I picked orange as our color partly because it's the warmest, and partly because no VC used it. In 2005 all the VCs used staid colors like maroon, navy blue, and forest green, because they were trying to appeal to LPs, not founders. The YC logo itself is an inside joke: the Viaweb logo had been a white V on a red circle, so I made the YC logo a white Y on an orange square. - -[14] YC did become a fund for a couple years starting in 2009, because it was getting so big I could no longer afford to fund it personally. But after Heroku got bought we had enough money to go back to being self-funded. - -[15] I've never liked the term "deal flow," because it implies that the number of new startups at any given time is fixed. This is not only false, but it's the purpose of YC to falsify it, by causing startups to be founded that would not otherwise have existed. - -[16] She reports that they were all different shapes and sizes, because there was a run on air conditioners and she had to get whatever she could, but that they were all heavier than she could carry now. - -[17] Another problem with HN was a bizarre edge case that occurs when you both write essays and run a forum. When you run a forum, you're assumed to see if not every conversation, at least every conversation involving you. And when you write essays, people post highly imaginative misinterpretations of them on forums. Individually these two phenomena are tedious but bearable, but the combination is disastrous. You actually have to respond to the misinterpretations, because the assumption that you're present in the conversation means that not responding to any sufficiently upvoted misinterpretation reads as a tacit admission that it's correct. But that in turn encourages more; anyone who wants to pick a fight with you senses that now is their chance. - -[18] The worst thing about leaving YC was not working with Jessica anymore. We'd been working on YC almost the whole time we'd known each other, and we'd neither tried nor wanted to separate it from our personal lives, so leaving was like pulling up a deeply rooted tree. - -[19] One way to get more precise about the concept of invented vs discovered is to talk about space aliens. Any sufficiently advanced alien civilization would certainly know about the Pythagorean theorem, for example. I believe, though with less certainty, that they would also know about the Lisp in McCarthy's 1960 paper. - -But if so there's no reason to suppose that this is the limit of the language that might be known to them. Presumably aliens need numbers and errors and I/O too. So it seems likely there exists at least one path out of McCarthy's Lisp along which discoveredness is preserved. - - - -Thanks to Trevor Blackwell, John Collison, Patrick Collison, Daniel Gackle, Ralph Hazell, Jessica Livingston, Robert Morris, and Harj Taggar for reading drafts of this. diff --git a/poetry.lock b/poetry.lock index 6cb99a0e574..d712c5ab7d2 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. [[package]] name = "aiohttp" @@ -1694,7 +1694,7 @@ files = [ [[package]] name = "langchain" -version = "0.1.0" +version = "0.1.5" description = "Building applications with LLMs through composability" optional = false python-versions = ">=3.8.1,<4.0" @@ -1706,9 +1706,9 @@ aiohttp = "^3.8.3" async-timeout = {version = "^4.0.0", markers = "python_version < \"3.11\""} dataclasses-json = ">= 0.5.7, < 0.7" jsonpatch = "^1.33" -langchain-community = ">=0.0.9,<0.1" -langchain-core = ">=0.1.7,<0.2" -langsmith = "~0.0.77" +langchain-community = ">=0.0.17,<0.1" +langchain-core = ">=0.1.16,<0.2" +langsmith = ">=0.0.83,<0.1" numpy = "^1" pydantic = ">=1,<3" PyYAML = ">=5.3" @@ -1724,7 +1724,7 @@ cli = ["typer (>=0.9.0,<0.10.0)"] cohere = ["cohere (>=4,<5)"] docarray = ["docarray[hnswlib] (>=0.32.0,<0.33.0)"] embeddings = ["sentence-transformers (>=2,<3)"] -extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "cohere (>=4,<5)", "couchbase (>=4.1.9,<5.0.0)", "dashvector (>=1.0.1,<2.0.0)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "langchain-openai (>=0.0.2,<0.1)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "openai (<2)", "openai (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "upstash-redis (>=0.15.0,<0.16.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"] +extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "anthropic 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(>=4.0.1,<5.0.0)"] llms = ["clarifai (>=9.1.0)", "cohere (>=4,<5)", "huggingface_hub (>=0,<1)", "manifest-ml (>=0.0.1,<0.0.2)", "nlpcloud (>=1,<2)", "openai (<2)", "openlm (>=0.0.5,<0.0.6)", "torch (>=1,<3)", "transformers (>=4,<5)"] openai = ["openai (<2)", "tiktoken (>=0.3.2,<0.6.0)"] @@ -1737,7 +1737,7 @@ url = "libs/langchain" [[package]] name = "langchain-community" -version = "0.0.11" +version = "0.0.17" description = "Community contributed LangChain integrations." optional = false python-versions = ">=3.8.1,<4.0" @@ -1747,8 +1747,8 @@ develop = true [package.dependencies] aiohttp = "^3.8.3" dataclasses-json = ">= 0.5.7, < 0.7" -langchain-core = ">=0.1.8,<0.2" -langsmith = "~0.0.63" +langchain-core = ">=0.1.16,<0.2" +langsmith = ">=0.0.83,<0.1" numpy = "^1" PyYAML = ">=5.3" requests = "^2" @@ -1757,7 +1757,7 @@ tenacity = "^8.1.0" [package.extras] cli = ["typer (>=0.9.0,<0.10.0)"] -extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", 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-content-hash = "0ee5840cf8fda328bd967f6bd3c9be4e698ccfdd4b8b14c970bad7f2d338ec81" +content-hash = "3abf9f2545bcdb66245744f5b5e8f19ae4813b530f4e7e18d77aa783f8e783fd" diff --git a/pyproject.toml b/pyproject.toml index c16f623a37a..3044f4ae910 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -49,6 +49,7 @@ langchain-openai = { path = "libs/partners/openai", develop = true } [tool.poetry.group.test.dependencies] +[tool.poetry.group.test_integration.dependencies] [tool.poetry.group.typing.dependencies] diff --git a/templates/anthropic-iterative-search/main.py b/templates/anthropic-iterative-search/main.py index 27b7aa1aa6a..c04830d110a 100644 --- a/templates/anthropic-iterative-search/main.py +++ b/templates/anthropic-iterative-search/main.py @@ -5,7 +5,7 @@ if __name__ == "__main__": "Which movie came out first: Oppenheimer, or " "Are You There God It's Me Margaret?" ) - print( + print( # noqa: T201 final_chain.with_config(configurable={"chain": "retrieve"}).invoke( {"query": query} ) 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["conversation"] [tool.poetry.group.dev.dependencies] -langchain-cli = ">=0.0.15" +langchain-cli = ">=0.0.21" [build-system] requires = ["poetry-core"] diff --git a/templates/cassandra-entomology-rag/cassandra_entomology_rag/__init__.py b/templates/cassandra-entomology-rag/cassandra_entomology_rag/__init__.py index 1816e8c7fdd..f54b58aa1a0 100644 --- a/templates/cassandra-entomology-rag/cassandra_entomology_rag/__init__.py +++ b/templates/cassandra-entomology-rag/cassandra_entomology_rag/__init__.py @@ -42,7 +42,7 @@ retriever = vector_store.as_retriever(search_kwargs={"k": 3}) # Please remove this and/or adapt to your use case! inserted_lines = populate(vector_store) if inserted_lines: - print(f"Done ({inserted_lines} lines inserted).") + print(f"Done ({inserted_lines} lines inserted).") # noqa: T201 entomology_template = """ You are an expert entomologist, tasked with answering enthusiast biologists' questions. diff --git a/templates/cassandra-entomology-rag/poetry.lock 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index 2dc1bcce247..67014a54743 100644 --- a/templates/extraction-openai-functions/pyproject.toml +++ b/templates/extraction-openai-functions/pyproject.toml @@ -13,7 +13,7 @@ langchain = "^0.1" openai = "<2" [tool.poetry.group.dev.dependencies] -langchain-cli = ">=0.0.15" +langchain-cli = ">=0.0.21" [tool.langserve] export_module = "extraction_openai_functions" diff --git a/templates/gemini-functions-agent/LICENSE b/templates/gemini-functions-agent/LICENSE new file mode 100644 index 00000000000..426b6509034 --- /dev/null +++ b/templates/gemini-functions-agent/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/templates/gemini-functions-agent/README.md b/templates/gemini-functions-agent/README.md new file mode 100644 index 00000000000..6ee68495344 --- /dev/null +++ b/templates/gemini-functions-agent/README.md @@ -0,0 +1,74 @@ + +# gemini-functions-agent + +This template creates an agent that uses Google Gemini function calling to communicate its decisions on what actions to take. + +This example creates an agent that can optionally look up information on the internet using Tavily's search engine. + +[See an example LangSmith trace here](https://smith.langchain.com/public/0ebf1bd6-b048-4019-b4de-25efe8d3d18c/r) + +## Environment Setup + +The following environment variables need to be set: + +Set the `TAVILY_API_KEY` environment variable to access Tavily + +Set the `GOOGLE_API_KEY` environment variable to access the Google Gemini APIs. + +## Usage + +To use this package, you should first have the LangChain CLI installed: + +```shell +pip install -U langchain-cli +``` + +To create a new LangChain project and install this as the only package, you can do: + +```shell +langchain app new my-app --package gemini-functions-agent +``` + +If you want to add this to an existing project, you can just run: + +```shell +langchain app add gemini-functions-agent +``` + +And add the following code to your `server.py` file: +```python +from gemini_functions_agent import agent_executor as gemini_functions_agent_chain + +add_routes(app, gemini_functions_agent_chain, path="/openai-functions-agent") +``` + +(Optional) Let's now configure LangSmith. +LangSmith will help us trace, monitor and debug LangChain applications. +LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). +If you don't have access, you can skip this section + +```shell +export LANGCHAIN_TRACING_V2=true +export LANGCHAIN_API_KEY= +export LANGCHAIN_PROJECT= # if not specified, defaults to "default" +``` + +If you are inside this directory, then you can spin up a LangServe instance directly by: + +```shell +langchain serve +``` + +This will start the FastAPI app with a server is running locally at +[http://localhost:8000](http://localhost:8000) + +We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) +We can access the playground at [http://127.0.0.1:8000/gemini-functions-agent/playground](http://127.0.0.1:8000/gemini-functions-agent/playground) + +We can access the template from code with: + +```python +from langserve.client import RemoteRunnable + +runnable = RemoteRunnable("http://localhost:8000/gemini-functions-agent") +``` \ No newline at end of file diff --git a/templates/gemini-functions-agent/gemini_functions_agent/__init__.py b/templates/gemini-functions-agent/gemini_functions_agent/__init__.py new file mode 100644 index 00000000000..da671951902 --- /dev/null +++ b/templates/gemini-functions-agent/gemini_functions_agent/__init__.py @@ -0,0 +1,3 @@ +from gemini_functions_agent.agent import agent_executor + +__all__ = ["agent_executor"] diff --git a/templates/gemini-functions-agent/gemini_functions_agent/agent.py b/templates/gemini-functions-agent/gemini_functions_agent/agent.py new file mode 100644 index 00000000000..38ffc315ee9 --- /dev/null +++ b/templates/gemini-functions-agent/gemini_functions_agent/agent.py @@ -0,0 +1,69 @@ +from typing import List, Tuple + +from langchain.agents import AgentExecutor +from langchain.agents.format_scratchpad import format_to_openai_function_messages +from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser +from langchain.utilities.tavily_search import TavilySearchAPIWrapper +from langchain_community.tools.tavily_search import TavilySearchResults +from langchain_core.messages import AIMessage, HumanMessage +from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain_core.pydantic_v1 import BaseModel, Field +from langchain_google_genai import ChatGoogleGenerativeAI + +# Create the tool +search = TavilySearchAPIWrapper() +description = """"A search engine optimized for comprehensive, accurate, \ +and trusted results. Useful for when you need to answer questions \ +about current events or about recent information. \ +Input should be a search query. \ +If the user is asking about something that you don't know about, \ +you should probably use this tool to see if that can provide any information.""" +tavily_tool = TavilySearchResults(api_wrapper=search, description=description) + +tools = [tavily_tool] + +llm = ChatGoogleGenerativeAI(temperature=0, model="gemini-pro") + +prompt = ChatPromptTemplate.from_messages( + [ + MessagesPlaceholder(variable_name="chat_history"), + ("user", "{input}"), + MessagesPlaceholder(variable_name="agent_scratchpad"), + ] +) + +llm_with_tools = llm.bind(functions=tools) + + +def _format_chat_history(chat_history: List[Tuple[str, str]]): + buffer = [] + for human, ai in chat_history: + buffer.append(HumanMessage(content=human)) + buffer.append(AIMessage(content=ai)) + return buffer + + +agent = ( + { + "input": lambda x: x["input"], + "chat_history": lambda x: _format_chat_history(x["chat_history"]), + "agent_scratchpad": lambda x: format_to_openai_function_messages( + x["intermediate_steps"] + ), + } + | prompt + | llm_with_tools + | OpenAIFunctionsAgentOutputParser() +) + + +class AgentInput(BaseModel): + input: str + chat_history: List[Tuple[str, str]] = Field( + ..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}} + ) + + +agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True).with_types( + input_type=AgentInput +) diff --git a/templates/gemini-functions-agent/main.py b/templates/gemini-functions-agent/main.py new file mode 100644 index 00000000000..0e612d69f49 --- /dev/null +++ b/templates/gemini-functions-agent/main.py @@ -0,0 +1,5 @@ +from openai_functions_agent.agent import agent_executor + +if __name__ == "__main__": + question = "who won the womens world cup in 2023?" + print(agent_executor.invoke({"input": question, "chat_history": []})) # noqa: T201 diff --git 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John Travolta\n2. John McDonough", ) ] - print(agent_executor.invoke({"input": original_query})) - print( + print(agent_executor.invoke({"input": original_query})) # noqa: T201 + print( # noqa: T201 agent_executor.invoke({"input": followup_query, "chat_history": chat_history}) ) diff --git a/templates/neo4j-semantic-layer/neo4j_semantic_layer/memory_tool.py b/templates/neo4j-semantic-layer/neo4j_semantic_layer/memory_tool.py index 784ba8f4325..6e3e0a86649 100644 --- a/templates/neo4j-semantic-layer/neo4j_semantic_layer/memory_tool.py +++ b/templates/neo4j-semantic-layer/neo4j_semantic_layer/memory_tool.py @@ -34,7 +34,7 @@ def store_movie_rating(movie: str, rating: int): try: return response[0]["response"] except Exception as e: - print(e) + print(e) # noqa: T201 return "Something went wrong" diff --git a/templates/neo4j-semantic-layer/poetry.lock b/templates/neo4j-semantic-layer/poetry.lock index edee00a79ba..f1ba275c42f 100644 --- a/templates/neo4j-semantic-layer/poetry.lock +++ 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[tool.poetry.group.dev.dependencies] -langchain-cli = ">=0.0.20" +langchain-cli = ">=0.0.21" [tool.langserve] export_module = "neo4j_semantic_layer" diff --git a/templates/neo4j-semantic-ollama/README.md b/templates/neo4j-semantic-ollama/README.md new file mode 100644 index 00000000000..70a5ab15c97 --- /dev/null +++ b/templates/neo4j-semantic-ollama/README.md @@ -0,0 +1,104 @@ +# neo4j-semantic-ollama + +This template is designed to implement an agent capable of interacting with a graph database like Neo4j through a semantic layer using Mixtral as a JSON-based agent. +The semantic layer equips the agent with a suite of robust tools, allowing it to interact with the graph database based on the user's intent. +Learn more about the semantic layer template in the [corresponding blog post](https://medium.com/towards-data-science/enhancing-interaction-between-language-models-and-graph-databases-via-a-semantic-layer-0a78ad3eba49). + +![Diagram illustrating the workflow of the Neo4j semantic layer with an agent interacting with tools like Information, Recommendation, and Memory, connected to a knowledge graph.](https://raw.githubusercontent.com/langchain-ai/langchain/master/templates/neo4j-semantic-ollama/static/workflow.png "Neo4j Semantic Layer Workflow Diagram") + +## Tools + +The agent utilizes several tools to interact with the Neo4j graph database effectively: + +1. **Information tool**: + - Retrieves data about movies or individuals, ensuring the agent has access to the latest and most relevant information. +2. **Recommendation Tool**: + - Provides movie recommendations based upon user preferences and input. +3. **Memory Tool**: + - Stores information about user preferences in the knowledge graph, allowing for a personalized experience over multiple interactions. +4. **Smalltalk Tool**: + - Allows an agent to deal with smalltalk. + +## Environment Setup + +Before using this template, you need to set up Ollama and Neo4j database. + +1. Follow instructions [here](https://python.langchain.com/docs/integrations/chat/ollama) to download Ollama. + +2. Download your LLM of interest: + + * This package uses `mixtral`: `ollama pull mixtral` + * You can choose from many LLMs [here](https://ollama.ai/library) + +You need to define the following environment variables + +``` +OLLAMA_BASE_URL= +NEO4J_URI= +NEO4J_USERNAME= +NEO4J_PASSWORD= +``` + +## Populating with data + +If you want to populate the DB with an example movie dataset, you can run `python ingest.py`. +The script import information about movies and their rating by users. +Additionally, the script creates two [fulltext indices](https://neo4j.com/docs/cypher-manual/current/indexes-for-full-text-search/), which are used to map information from user input to the database. + +## Usage + +To use this package, you should first have the LangChain CLI installed: + +```shell +pip install -U "langchain-cli[serve]" +``` + +To create a new LangChain project and install this as the only package, you can do: + +```shell +langchain app new my-app --package neo4j-semantic-ollama +``` + +If you want to add this to an existing project, you can just run: + +```shell +langchain app add neo4j-semantic-ollama +``` + +And add the following code to your `server.py` file: +```python +from neo4j_semantic_layer import agent_executor as neo4j_semantic_agent + +add_routes(app, neo4j_semantic_agent, path="/neo4j-semantic-ollama") +``` + +(Optional) Let's now configure LangSmith. +LangSmith will help us trace, monitor and debug LangChain applications. +LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). +If you don't have access, you can skip this section + +```shell +export LANGCHAIN_TRACING_V2=true +export LANGCHAIN_API_KEY= +export LANGCHAIN_PROJECT= # if not specified, defaults to "default" +``` + +If you are inside this directory, then you can spin up a LangServe instance directly by: + +```shell +langchain serve +``` + +This will start the FastAPI app with a server is running locally at +[http://localhost:8000](http://localhost:8000) + +We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) +We can access the playground at [http://127.0.0.1:8000/neo4j-semantic-ollama/playground](http://127.0.0.1:8000/neo4j-semantic-ollama/playground) + +We can access the template from code with: + +```python +from langserve.client import RemoteRunnable + +runnable = RemoteRunnable("http://localhost:8000/neo4j-semantic-ollama") +``` diff --git a/templates/neo4j-semantic-ollama/ingest.py b/templates/neo4j-semantic-ollama/ingest.py new file mode 100644 index 00000000000..973b4c93c9c --- /dev/null +++ b/templates/neo4j-semantic-ollama/ingest.py @@ -0,0 +1,59 @@ +from langchain_community.graphs import Neo4jGraph + +# Instantiate connection to Neo4j +graph = Neo4jGraph() + +# Define unique constraints +graph.query("CREATE CONSTRAINT IF NOT EXISTS FOR (m:Movie) REQUIRE m.id IS UNIQUE;") +graph.query("CREATE CONSTRAINT IF NOT EXISTS FOR (u:User) REQUIRE u.id IS UNIQUE;") +graph.query("CREATE CONSTRAINT IF NOT EXISTS FOR (p:Person) REQUIRE p.name IS UNIQUE;") +graph.query("CREATE CONSTRAINT IF NOT EXISTS FOR (g:Genre) REQUIRE g.name IS UNIQUE;") + +# Import movie information + +movies_query = """ +LOAD CSV WITH HEADERS FROM +'https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/movies/movies.csv' +AS row +CALL { + WITH row + MERGE (m:Movie {id:row.movieId}) + SET m.released = date(row.released), + m.title = row.title, + m.imdbRating = toFloat(row.imdbRating) + FOREACH (director in split(row.director, '|') | + MERGE (p:Person {name:trim(director)}) + MERGE (p)-[:DIRECTED]->(m)) + FOREACH (actor in split(row.actors, '|') | + MERGE (p:Person {name:trim(actor)}) + MERGE (p)-[:ACTED_IN]->(m)) + FOREACH (genre in split(row.genres, '|') | + MERGE (g:Genre {name:trim(genre)}) + MERGE (m)-[:IN_GENRE]->(g)) +} IN TRANSACTIONS +""" + +graph.query(movies_query) + +# Import rating information +rating_query = """ +LOAD CSV WITH HEADERS FROM +'https://raw.githubusercontent.com/tomasonjo/blog-datasets/main/movies/ratings.csv' +AS row +CALL { + WITH row + MATCH (m:Movie {id:row.movieId}) + MERGE (u:User {id:row.userId}) + MERGE (u)-[r:RATED]->(m) + SET r.rating = toFloat(row.rating), + r.timestamp = row.timestamp +} IN TRANSACTIONS OF 10000 ROWS +""" + +graph.query(rating_query) + +# Define fulltext indices +graph.query("CREATE FULLTEXT INDEX movie IF NOT EXISTS FOR (m:Movie) ON EACH [m.title]") +graph.query( + "CREATE FULLTEXT INDEX person IF NOT EXISTS FOR (p:Person) ON EACH [p.name]" +) diff --git a/templates/neo4j-semantic-ollama/main.py b/templates/neo4j-semantic-ollama/main.py new file mode 100644 index 00000000000..3f8fe2b2830 --- /dev/null +++ b/templates/neo4j-semantic-ollama/main.py @@ -0,0 +1,17 @@ +from neo4j_semantic_ollama import agent_executor + +if __name__ == "__main__": + original_query = "What do you know about person John?" + followup_query = "John Travolta" + chat_history = [ + ( + "What do you know about person John?", + "I found multiple people named John. Could you please specify " + "which one you are interested in? Here are some options:" + "\n\n1. John Travolta\n2. John McDonough", + ) + ] + print(agent_executor.invoke({"input": original_query})) # noqa: T201 + print( # noqa: T201 + agent_executor.invoke({"input": followup_query, "chat_history": chat_history}) + ) diff --git a/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/__init__.py b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/__init__.py new file mode 100644 index 00000000000..0b540e6327f --- /dev/null +++ b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/__init__.py @@ -0,0 +1,3 @@ +from neo4j_semantic_ollama.agent import agent_executor + +__all__ = ["agent_executor"] diff --git a/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/agent.py b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/agent.py new file mode 100644 index 00000000000..ad4010d6cfc --- /dev/null +++ b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/agent.py @@ -0,0 +1,110 @@ +import os +from typing import List, Tuple + +from langchain.agents import AgentExecutor +from langchain.agents.format_scratchpad import format_log_to_messages +from langchain.agents.output_parsers import ( + ReActJsonSingleInputOutputParser, +) +from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain.pydantic_v1 import BaseModel, Field +from langchain.schema import AIMessage, HumanMessage +from langchain.tools.render import render_text_description_and_args +from langchain_community.chat_models import ChatOllama + +from neo4j_semantic_ollama.information_tool import InformationTool +from neo4j_semantic_ollama.memory_tool import MemoryTool +from neo4j_semantic_ollama.recommendation_tool import RecommenderTool +from neo4j_semantic_ollama.smalltalk_tool import SmalltalkTool + +llm = ChatOllama( + model="mixtral", + temperature=0, + base_url=os.environ["OLLAMA_BASE_URL"], + streaming=True, +) +chat_model_with_stop = llm.bind(stop=["\nObservation"]) +tools = [InformationTool(), RecommenderTool(), MemoryTool(), SmalltalkTool()] + +# Inspiration taken from hub.pull("hwchase17/react-json") +system_message = f"""Answer the following questions as best you can. +You can answer directly if the user is greeting you or similar. +Otherise, you have access to the following tools: + +{render_text_description_and_args(tools).replace('{', '{{').replace('}', '}}')} + +The way you use the tools is by specifying a json blob. +Specifically, this json should have a `action` key (with the name of the tool to use) +and a `action_input` key (with the input to the tool going here). +The only values that should be in the "action" field are: {[t.name for t in tools]} +The $JSON_BLOB should only contain a SINGLE action, +do NOT return a list of multiple actions. +Here is an example of a valid $JSON_BLOB: +``` +{{{{ + "action": $TOOL_NAME, + "action_input": $INPUT +}}}} +``` +The $JSON_BLOB must always be enclosed with triple backticks! + +ALWAYS use the following format: +Question: the input question you must answer +Thought: you should always think about what to do +Action:``` +$JSON_BLOB +``` +Observation: the result of the action... +(this Thought/Action/Observation can repeat N times) +Thought: I now know the final answer +Final Answer: the final answer to the original input question + +Begin! Reminder to always use the exact characters `Final Answer` when responding.' +""" + +prompt = ChatPromptTemplate.from_messages( + [ + ( + "user", + system_message, + ), + MessagesPlaceholder(variable_name="chat_history"), + ("user", "{input}"), + MessagesPlaceholder(variable_name="agent_scratchpad"), + ] +) + + +def _format_chat_history(chat_history: List[Tuple[str, str]]): + buffer = [] + for human, ai in chat_history: + buffer.append(HumanMessage(content=human)) + buffer.append(AIMessage(content=ai)) + return buffer + + +agent = ( + { + "input": lambda x: x["input"], + "agent_scratchpad": lambda x: format_log_to_messages(x["intermediate_steps"]), + "chat_history": lambda x: _format_chat_history(x["chat_history"]) + if x.get("chat_history") + else [], + } + | prompt + | chat_model_with_stop + | ReActJsonSingleInputOutputParser() +) + + +# Add typing for input +class AgentInput(BaseModel): + input: str + chat_history: List[Tuple[str, str]] = Field( + ..., extra={"widget": {"type": "chat", "input": "input", "output": "output"}} + ) + + +agent_executor = AgentExecutor(agent=agent, tools=tools).with_types( + input_type=AgentInput +) diff --git a/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/information_tool.py b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/information_tool.py new file mode 100644 index 00000000000..9ed31d2723e --- /dev/null +++ b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/information_tool.py @@ -0,0 +1,74 @@ +from typing import Optional, Type + +from langchain.callbacks.manager import ( + AsyncCallbackManagerForToolRun, + CallbackManagerForToolRun, +) + +# Import things that are needed generically +from langchain.pydantic_v1 import BaseModel, Field +from langchain.tools import BaseTool + +from neo4j_semantic_ollama.utils import get_candidates, graph + +description_query = """ +MATCH (m:Movie|Person) +WHERE m.title = $candidate OR m.name = $candidate +MATCH (m)-[r:ACTED_IN|DIRECTED|HAS_GENRE]-(t) +WITH m, type(r) as type, collect(coalesce(t.name, t.title)) as names +WITH m, type+": "+reduce(s="", n IN names | s + n + ", ") as types +WITH m, collect(types) as contexts +WITH m, "type:" + labels(m)[0] + "\ntitle: "+ coalesce(m.title, m.name) + + "\nyear: "+coalesce(m.released,"") +"\n" + + reduce(s="", c in contexts | s + substring(c, 0, size(c)-2) +"\n") as context +RETURN context LIMIT 1 +""" + + +def get_information(entity: str, type: str) -> str: + candidates = get_candidates(entity, type) + if not candidates: + return "No information was found about the movie or person in the database" + elif len(candidates) > 1: + newline = "\n" + return ( + "Need additional information, which of these " + f"did you mean: {newline + newline.join(str(d) for d in candidates)}" + ) + data = graph.query( + description_query, params={"candidate": candidates[0]["candidate"]} + ) + return data[0]["context"] + + +class InformationInput(BaseModel): + entity: str = Field(description="movie or a person mentioned in the question") + entity_type: str = Field( + description="type of the entity. Available options are 'movie' or 'person'" + ) + + +class InformationTool(BaseTool): + name = "Information" + description = ( + "useful for when you need to answer questions about various actors or movies" + ) + args_schema: Type[BaseModel] = InformationInput + + def _run( + self, + entity: str, + entity_type: str, + run_manager: Optional[CallbackManagerForToolRun] = None, + ) -> str: + """Use the tool.""" + return get_information(entity, entity_type) + + async def _arun( + self, + entity: str, + entity_type: str, + run_manager: Optional[AsyncCallbackManagerForToolRun] = None, + ) -> str: + """Use the tool asynchronously.""" + return get_information(entity, entity_type) diff --git a/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/memory_tool.py b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/memory_tool.py new file mode 100644 index 00000000000..e0399af1d14 --- /dev/null +++ b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/memory_tool.py @@ -0,0 +1,73 @@ +from typing import Optional, Type + +from langchain.callbacks.manager import ( + AsyncCallbackManagerForToolRun, + CallbackManagerForToolRun, +) + +# Import things that are needed generically +from langchain.pydantic_v1 import BaseModel, Field +from langchain.tools import BaseTool + +from neo4j_semantic_ollama.utils import get_candidates, get_user_id, graph + +store_rating_query = """ +MERGE (u:User {userId:$user_id}) +WITH u +UNWIND $candidates as row +MATCH (m:Movie {title: row.candidate}) +MERGE (u)-[r:RATED]->(m) +SET r.rating = toFloat($rating) +RETURN distinct + 'Create a final answer saying that preference has been stored' AS response +""" + + +def store_movie_rating(movie: str, rating: int): + user_id = get_user_id() + candidates = get_candidates(movie, "movie") + if not candidates: + return "This movie is not in our database" + response = graph.query( + store_rating_query, + params={"user_id": user_id, "candidates": candidates, "rating": rating}, + ) + try: + return response[0]["response"] + except Exception as e: + print(e) # noqa: T201 + return "Something went wrong" + + +class MemoryInput(BaseModel): + movie: str = Field(description="movie the user liked") + rating: int = Field( + description=( + "Rating from 1 to 5, where one represents heavy dislike " + "and 5 represent the user loved the movie" + ) + ) + + +class MemoryTool(BaseTool): + name = "Memory" + description = "useful for memorizing which movies the user liked" + args_schema: Type[BaseModel] = MemoryInput + + def _run( + self, + movie: str, + rating: int, + run_manager: Optional[CallbackManagerForToolRun] = None, + ) -> str: + """Use the tool.""" + return store_movie_rating(movie, rating) + + async def _arun( + self, + movie: str, + rating: int, + run_manager: Optional[AsyncCallbackManagerForToolRun] = None, + ) -> str: + """Use the tool asynchronously.""" + return store_movie_rating(movie, rating) diff --git a/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/recommendation_tool.py b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/recommendation_tool.py new file mode 100644 index 00000000000..dc85c7c7398 --- /dev/null +++ b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/recommendation_tool.py @@ -0,0 +1,164 @@ +from typing import Optional, Type + +from langchain.callbacks.manager import ( + AsyncCallbackManagerForToolRun, + CallbackManagerForToolRun, +) +from langchain.pydantic_v1 import BaseModel, Field +from langchain.tools import BaseTool + +from neo4j_semantic_ollama.utils import get_candidates, get_user_id, graph + +recommendation_query_db_history = """ + MERGE (u:User {userId:$user_id}) + WITH u + // get recommendation candidates + OPTIONAL MATCH (u)-[r1:RATED]->()<-[r2:RATED]-()-[r3:RATED]->(recommendation) + WHERE r1.rating > 3.5 AND r2.rating > 3.5 AND r3.rating > 3.5 + AND NOT EXISTS {(u)-[:RATED]->(recommendation)} + // rank and limit recommendations + WITH u, recommendation, count(*) AS count + ORDER BY count DESC LIMIT 3 +RETURN 'title:' + recommendation.title + '\nactors:' + +apoc.text.join([(recommendation)<-[:ACTED_IN]-(a) | a.name], ',') + +'\ngenre:' + apoc.text.join([(recommendation)-[:IN_GENRE]->(a) | a.name], ',') +AS movie +""" + +recommendation_query_genre = """ +MATCH (m:Movie)-[:IN_GENRE]->(g:Genre {name:$genre}) +// filter out already seen movies by the user +WHERE NOT EXISTS { + (m)<-[:RATED]-(:User {userId:$user_id}) +} +// rank and limit recommendations +WITH m AS recommendation +ORDER BY recommendation.imdbRating DESC LIMIT 3 +RETURN 'title:' + recommendation.title + '\nactors:' + +apoc.text.join([(recommendation)<-[:ACTED_IN]-(a) | a.name], ',') + +'\ngenre:' + apoc.text.join([(recommendation)-[:IN_GENRE]->(a) | a.name], ',') +AS movie +""" + + +def recommendation_query_movie(genre: bool) -> str: + return f""" +MATCH (m1:Movie)<-[r1:RATED]-()-[r2:RATED]->(m2:Movie) +WHERE r1.rating > 3.5 AND r2.rating > 3.5 and m1.title IN $movieTitles +// filter out already seen movies by the user +AND NOT EXISTS {{ + (m2)<-[:RATED]-(:User {{userId:$user_id}}) +}} +{'AND EXISTS {(m2)-[:IN_GENRE]->(:Genre {name:$genre})}' if genre else ''} +// rank and limit recommendations +WITH m2 AS recommendation, count(*) AS count +ORDER BY count DESC LIMIT 3 +RETURN 'title:' + recommendation.title + '\nactors:' + +apoc.text.join([(recommendation)<-[:ACTED_IN]-(a) | a.name], ',') + +'\ngenre:' + apoc.text.join([(recommendation)-[:IN_GENRE]->(a) | a.name], ',') +AS movie +""" + + +nl = "\n" + + +def recommend_movie(movie: Optional[str] = None, genre: Optional[str] = None) -> str: + """ + Recommends movies based on user's history and preference + for a specific movie and/or genre. + Returns: + str: A string containing a list of recommended movies, or an error message. + """ + user_id = get_user_id() + params = {"user_id": user_id, "genre": genre} + if not movie and not genre: + # Try to recommend a movie based on the information in the db + response = graph.query(recommendation_query_db_history, params) + try: + return ( + 'Recommended movies are: ' + f'{f"###Movie {nl}".join([el["movie"] for el in response])}' + ) + except Exception: + return "Can you tell us about some of the movies you liked?" + if not movie and genre: + # Recommend top voted movies in the genre the user haven't seen before + response = graph.query(recommendation_query_genre, params) + try: + return ( + 'Recommended movies are: ' + f'{f"###Movie {nl}".join([el["movie"] for el in response])}' + ) + except Exception: + return "Something went wrong" + + candidates = get_candidates(movie, "movie") + if not candidates: + return "The movie you mentioned wasn't found in the database" + params["movieTitles"] = [el["candidate"] for el in candidates] + query = recommendation_query_movie(bool(genre)) + response = graph.query(query, params) + try: + return ( + 'Recommended movies are: ' + f'{f"###Movie {nl}".join([el["movie"] for el in response])}' + ) + except Exception: + return "Something went wrong" + + +all_genres = [ + "Action", + "Adventure", + "Animation", + "Children", + "Comedy", + "Crime", + "Documentary", + "Drama", + "Fantasy", + "Film-Noir", + "Horror", + "IMAX", + "Musical", + "Mystery", + "Romance", + "Sci-Fi", + "Thriller", + "War", + "Western", +] + + +class RecommenderInput(BaseModel): + movie: Optional[str] = Field(description="movie used for recommendation") + genre: Optional[str] = Field( + description=( + "genre used for recommendation. Available options are:" f"{all_genres}" + ) + ) + + +class RecommenderTool(BaseTool): + name = "Recommender" + description = "useful for when you need to recommend a movie" + args_schema: Type[BaseModel] = RecommenderInput + + def _run( + self, + movie: Optional[str] = None, + genre: Optional[str] = None, + run_manager: Optional[CallbackManagerForToolRun] = None, + ) -> str: + """Use the tool.""" + return recommend_movie(movie, genre) + + async def _arun( + self, + movie: Optional[str] = None, + genre: Optional[str] = None, + run_manager: Optional[AsyncCallbackManagerForToolRun] = None, + ) -> str: + """Use the tool asynchronously.""" + return recommend_movie(movie, genre) diff --git a/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/smalltalk_tool.py b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/smalltalk_tool.py new file mode 100644 index 00000000000..0b2469f2d10 --- /dev/null +++ b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/smalltalk_tool.py @@ -0,0 +1,39 @@ +from typing import Optional, Type + +from langchain.callbacks.manager import ( + AsyncCallbackManagerForToolRun, + CallbackManagerForToolRun, +) +from langchain.pydantic_v1 import BaseModel, Field +from langchain.tools import BaseTool + +response = ( + "Create a final answer that says if they " + "have any questions about movies or actors" +) + + +class SmalltalkInput(BaseModel): + query: Optional[str] = Field(description="user query") + + +class SmalltalkTool(BaseTool): + name = "Smalltalk" + description = "useful for when user greets you or wants to smalltalk" + args_schema: Type[BaseModel] = SmalltalkInput + + def _run( + self, + query: Optional[str] = None, + run_manager: Optional[CallbackManagerForToolRun] = None, + ) -> str: + """Use the tool.""" + return response + + async def _arun( + self, + query: Optional[str] = None, + run_manager: Optional[AsyncCallbackManagerForToolRun] = None, + ) -> str: + """Use the tool asynchronously.""" + return response diff --git a/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/utils.py b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/utils.py new file mode 100644 index 00000000000..ae83928bd51 --- /dev/null +++ b/templates/neo4j-semantic-ollama/neo4j_semantic_ollama/utils.py @@ -0,0 +1,84 @@ +from typing import Dict, List + +from langchain_community.graphs import Neo4jGraph + +graph = Neo4jGraph() + + +def get_user_id() -> int: + """ + Placeholder for a function that would normally retrieve + a user's ID + """ + return 1 + + +def remove_lucene_chars(text: str) -> str: + """Remove Lucene special characters""" + special_chars = [ + "+", + "-", + "&", + "|", + "!", + "(", + ")", + "{", + "}", + "[", + "]", + "^", + '"', + "~", + "*", + "?", + ":", + "\\", + ] + for char in special_chars: + if char in text: + text = text.replace(char, " ") + return text.strip() + + +def generate_full_text_query(input: str) -> str: + """ + Generate a full-text search query for a given input string. + + This function constructs a query string suitable for a full-text search. + It processes the input string by splitting it into words and appending a + similarity threshold (~0.8) to each word, then combines them using the AND + operator. Useful for mapping movies and people from user questions + to database values, and allows for some misspelings. + """ + full_text_query = "" + words = [el for el in remove_lucene_chars(input).split() if el] + for word in words[:-1]: + full_text_query += f" {word}~0.8 AND" + full_text_query += f" {words[-1]}~0.8" + return full_text_query.strip() + + +candidate_query = """ +CALL db.index.fulltext.queryNodes($index, $fulltextQuery, {limit: $limit}) +YIELD node +RETURN coalesce(node.name, node.title) AS candidate, + [el in labels(node) WHERE el IN ['Person', 'Movie'] | el][0] AS label +""" + + +def get_candidates(input: str, type: str, limit: int = 3) -> List[Dict[str, str]]: + """ + Retrieve a list of candidate entities from database based on the input string. + + This function queries the Neo4j database using a full-text search. It takes the + input string, generates a full-text query, and executes this query against the + specified index in the database. The function returns a list of candidates + matching the query, with each candidate being a dictionary containing their name + (or title) and label (either 'Person' or 'Movie'). + """ + ft_query = generate_full_text_query(input) + candidates = graph.query( + candidate_query, {"fulltextQuery": ft_query, "index": type, "limit": limit} + ) + return candidates diff --git a/templates/neo4j-semantic-ollama/poetry.lock b/templates/neo4j-semantic-ollama/poetry.lock new file mode 100644 index 00000000000..4aefece7203 --- /dev/null +++ b/templates/neo4j-semantic-ollama/poetry.lock @@ -0,0 +1,1753 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.9.3" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.8" +files = [ + {file = "aiohttp-3.9.3-cp310-cp310-macosx_10_9_universal2.whl", hash = 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a/templates/neo4j-semantic-ollama/static/workflow.png b/templates/neo4j-semantic-ollama/static/workflow.png new file mode 100644 index 00000000000..f62b720beef Binary files /dev/null and b/templates/neo4j-semantic-ollama/static/workflow.png differ diff --git a/templates/neo4j-semantic-ollama/tests/__init__.py b/templates/neo4j-semantic-ollama/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/neo4j-vector-memory/main.py b/templates/neo4j-vector-memory/main.py index 732f0954d59..ca7f16ca3ce 100644 --- a/templates/neo4j-vector-memory/main.py +++ b/templates/neo4j-vector-memory/main.py @@ -4,13 +4,13 @@ if __name__ == "__main__": user_id = "user_id_1" session_id = "session_id_1" original_query = "What is the plot of the Dune?" - print( + print( # noqa: T201 chain.invoke( {"question": original_query, "user_id": user_id, "session_id": session_id} ) ) follow_up_query = "Tell me more about Leto" - print( + print( # noqa: T201 chain.invoke( 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[tool.poetry.group.dev.dependencies] -langchain-cli = ">=0.0.15" +langchain-cli = ">=0.0.21" [tool.langserve] export_module = "neo4j_vector_memory" diff --git a/templates/nvidia-rag-canonical/ingest.py b/templates/nvidia-rag-canonical/ingest.py index 8df12d6492f..1abdc9ffa60 100644 --- a/templates/nvidia-rag-canonical/ingest.py +++ b/templates/nvidia-rag-canonical/ingest.py @@ -7,7 +7,7 @@ from langchain.vectorstores.milvus import Milvus from langchain_nvidia_aiplay import NVIDIAEmbeddings if os.environ.get("NVIDIA_API_KEY", "").startswith("nvapi-"): - print("Valid NVIDIA_API_KEY already in environment. Delete to reset") + print("Valid NVIDIA_API_KEY already in environment. Delete to reset") # noqa: T201 else: nvapi_key = getpass.getpass("NVAPI Key (starts with nvapi-): ") assert nvapi_key.startswith("nvapi-"), f"{nvapi_key[:5]}... is not a valid key" diff --git a/templates/nvidia-rag-canonical/nvidia_rag_canonical/chain.py b/templates/nvidia-rag-canonical/nvidia_rag_canonical/chain.py index 47a2adb68a8..5e7eb24c548 100644 --- a/templates/nvidia-rag-canonical/nvidia_rag_canonical/chain.py +++ b/templates/nvidia-rag-canonical/nvidia_rag_canonical/chain.py @@ -23,7 +23,7 @@ INGESTION_CHUNK_SIZE = 500 INGESTION_CHUNK_OVERLAP = 0 if os.environ.get("NVIDIA_API_KEY", "").startswith("nvapi-"): - print("Valid NVIDIA_API_KEY already in environment. Delete to reset") + print("Valid NVIDIA_API_KEY already in environment. Delete to reset") # noqa: T201 else: nvapi_key = getpass.getpass("NVAPI Key (starts with nvapi-): ") assert nvapi_key.startswith("nvapi-"), f"{nvapi_key[:5]}... is not a valid key" diff --git a/templates/nvidia-rag-canonical/poetry.lock b/templates/nvidia-rag-canonical/poetry.lock index de556e11e14..a6d4e9077b1 100644 --- a/templates/nvidia-rag-canonical/poetry.lock +++ b/templates/nvidia-rag-canonical/poetry.lock @@ -2,87 +2,87 @@ [[package]] name = "aiohttp" -version = "3.9.1" +version = "3.9.3" description = "Async http client/server framework (asyncio)" optional = false python-versions = ">=3.8" files = [ - {file = "aiohttp-3.9.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e1f80197f8b0b846a8d5cf7b7ec6084493950d0882cc5537fb7b96a69e3c8590"}, - {file = "aiohttp-3.9.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c72444d17777865734aa1a4d167794c34b63e5883abb90356a0364a28904e6c0"}, - {file = "aiohttp-3.9.1-cp310-cp310-macosx_11_0_arm64.whl", hash = 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langchain = "^0.1" openai = ">=1.3.9" [tool.poetry.group.dev.dependencies] -langchain-cli = ">=0.0.4" +langchain-cli = ">=0.0.21" fastapi = "^0.104.0" sse-starlette = "^1.6.5" diff --git a/templates/rag-astradb/astradb_entomology_rag/__init__.py b/templates/rag-astradb/astradb_entomology_rag/__init__.py index 7997d5909a5..3f8e807cab6 100644 --- a/templates/rag-astradb/astradb_entomology_rag/__init__.py +++ b/templates/rag-astradb/astradb_entomology_rag/__init__.py @@ -26,7 +26,7 @@ retriever = vector_store.as_retriever(search_kwargs={"k": 3}) inserted_lines = populate(vector_store) if inserted_lines: - print(f"Done ({inserted_lines} lines inserted).") + print(f"Done ({inserted_lines} lines inserted).") # noqa: T201 entomology_template = """ You are an expert entomologist, tasked with answering enthusiast biologists' questions. diff --git a/templates/rag-astradb/main.py b/templates/rag-astradb/main.py index f80b1b6626a..e049d7f7437 100644 --- a/templates/rag-astradb/main.py +++ 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python-versions = ">=3.8.1,<4.0" -content-hash = "cda14d0e9bb671339cf58b13ccb9c0bf501f009b123dae07fc1ef475ce5abcba" +content-hash = "2f79e1e2da71773f206e9200fcdc0cc9fb680547cc5253f3f0e39dc9935bd38f" diff --git a/templates/rag-aws-kendra/pyproject.toml b/templates/rag-aws-kendra/pyproject.toml index 51feed88d76..32e471cc10a 100644 --- a/templates/rag-aws-kendra/pyproject.toml +++ b/templates/rag-aws-kendra/pyproject.toml @@ -13,7 +13,7 @@ boto3 = ">=1.28.57" awscli = ">=1.29.57" [tool.poetry.group.dev.dependencies] -langchain-cli = ">=0.0.15" +langchain-cli = ">=0.0.21" fastapi = "^0.104.0" sse-starlette = "^1.6.5" diff --git a/templates/rag-chroma-multi-modal-multi-vector/ingest.py b/templates/rag-chroma-multi-modal-multi-vector/ingest.py index 9447ca478ec..941a8b6670f 100644 --- a/templates/rag-chroma-multi-modal-multi-vector/ingest.py +++ b/templates/rag-chroma-multi-modal-multi-vector/ingest.py @@ -66,7 +66,7 @@ def generate_img_summaries(img_base64_list): image_summaries.append(image_summarize(base64_image, prompt)) processed_images.append(base64_image) except Exception as e: - print(f"Error with image {i+1}: {e}") + print(f"Error with image {i+1}: {e}") # noqa: T201 return image_summaries, processed_images @@ -178,14 +178,14 @@ def create_multi_vector_retriever( # Load PDF doc_path = Path(__file__).parent / "docs/DDOG_Q3_earnings_deck.pdf" rel_doc_path = doc_path.relative_to(Path.cwd()) -print("Extract slides as images") +print("Extract slides as images") # noqa: T201 pil_images = get_images_from_pdf(rel_doc_path) # Convert to b64 images_base_64 = [convert_to_base64(i) for i in pil_images] # Image summaries -print("Generate image summaries") +print("Generate image summaries") # noqa: T201 image_summaries, images_base_64_processed = generate_img_summaries(images_base_64) # The vectorstore to use to index the images summaries diff --git a/templates/rag-chroma-multi-modal-multi-vector/poetry.lock 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a/templates/rag-fusion/pyproject.toml b/templates/rag-fusion/pyproject.toml index e8ba868b23c..a8cba69c4ca 100644 --- a/templates/rag-fusion/pyproject.toml +++ b/templates/rag-fusion/pyproject.toml @@ -13,7 +13,7 @@ pinecone-client = "^2.2.4" langchainhub = "^0.1.13" [tool.poetry.group.dev.dependencies] -langchain-cli = ">=0.0.15" +langchain-cli = ">=0.0.21" [tool.langserve] export_module = "rag_fusion.chain" diff --git a/templates/rag-gemini-multi-modal/ingest.py b/templates/rag-gemini-multi-modal/ingest.py index 67c5f070c5b..98d4f701a43 100644 --- a/templates/rag-gemini-multi-modal/ingest.py +++ b/templates/rag-gemini-multi-modal/ingest.py @@ -27,14 +27,14 @@ doc_path = Path(__file__).parent / "docs/DDOG_Q3_earnings_deck.pdf" img_dump_path = Path(__file__).parent / "docs/" rel_doc_path = doc_path.relative_to(Path.cwd()) rel_img_dump_path = img_dump_path.relative_to(Path.cwd()) -print("pdf index") +print("pdf index") # noqa: T201 pil_images = get_images_from_pdf(rel_doc_path, 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python-versions = ">=3.8.1,<4.0" -content-hash = "ceaad572638b55a58d1575ad62ebf54908c49ffd77ecb76383cbab2fc65ba74c" +content-hash = "0c458a9e9fc4312eff658f5f231ab6626a7cc5d04bc5a185acc7d0c7a09a7810" diff --git a/templates/rag-multi-index-router/pyproject.toml b/templates/rag-multi-index-router/pyproject.toml index 038d2e7c6e9..cdb46921539 100644 --- a/templates/rag-multi-index-router/pyproject.toml +++ b/templates/rag-multi-index-router/pyproject.toml @@ -16,7 +16,7 @@ arxiv = "^2.0.0" tiktoken = "^0.5.1" [tool.poetry.group.dev.dependencies] -langchain-cli = ">=0.0.15" +langchain-cli = ">=0.0.21" fastapi = "^0.104.0" sse-starlette = "^1.6.5" diff --git a/templates/rag-multi-modal-local/ingest.py b/templates/rag-multi-modal-local/ingest.py index 9aad0cf6568..e1ad090af00 100644 --- a/templates/rag-multi-modal-local/ingest.py +++ b/templates/rag-multi-modal-local/ingest.py @@ -20,7 +20,7 @@ vectorstore = Path(__file__).parent / "chroma_db_multi_modal" re_vectorstore_path = vectorstore.relative_to(Path.cwd()) # Load embedding function -print("Loading embedding function") +print("Loading embedding function") # noqa: T201 embedding = OpenCLIPEmbeddings(model_name="ViT-H-14", checkpoint="laion2b_s32b_b79k") # Create chroma @@ -31,5 +31,5 @@ vectorstore_mmembd = Chroma( ) # Add images -print("Embedding images") +print("Embedding images") # noqa: T201 vectorstore_mmembd.add_images(uris=image_uris) diff --git a/templates/rag-multi-modal-local/poetry.lock b/templates/rag-multi-modal-local/poetry.lock index 011e875b09c..4b4c958c551 100644 --- a/templates/rag-multi-modal-local/poetry.lock +++ b/templates/rag-multi-modal-local/poetry.lock @@ -2,87 +2,87 @@ [[package]] name = "aiohttp" -version = "3.9.1" +version = "3.9.3" description = "Async http client/server framework (asyncio)" optional = false python-versions = ">=3.8" files = [ - {file = "aiohttp-3.9.1-cp310-cp310-macosx_10_9_universal2.whl", hash = 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[tool.langserve] export_module = "rag_ollama_multi_query" diff --git a/templates/rag-ollama-multi-query/rag_ollama_multi_query/chain.py b/templates/rag-ollama-multi-query/rag_ollama_multi_query/chain.py index dea1ea5fe53..1bc81584532 100644 --- a/templates/rag-ollama-multi-query/rag_ollama_multi_query/chain.py +++ b/templates/rag-ollama-multi-query/rag_ollama_multi_query/chain.py @@ -1,7 +1,3 @@ -from typing import List - -from langchain.chains import LLMChain -from langchain.output_parsers import PydanticOutputParser from langchain.retrievers.multi_query import MultiQueryRetriever from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain_community.chat_models import ChatOllama, ChatOpenAI @@ -10,7 +6,7 @@ from langchain_community.embeddings import OpenAIEmbeddings from langchain_community.vectorstores import Chroma from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate, PromptTemplate -from langchain_core.pydantic_v1 import BaseModel, Field +from langchain_core.pydantic_v1 import BaseModel from langchain_core.runnables import RunnableParallel, RunnablePassthrough # Load @@ -29,23 +25,6 @@ vectorstore = Chroma.from_documents( ) -# Output parser will split the LLM result into a list of queries -class LineList(BaseModel): - # "lines" is the key (attribute name) of the parsed output - lines: List[str] = Field(description="Lines of text") - - -class LineListOutputParser(PydanticOutputParser): - def __init__(self) -> None: - super().__init__(pydantic_object=LineList) - - def parse(self, text: str) -> LineList: - lines = text.strip().split("\n") - return LineList(lines=lines) - - -output_parser = LineListOutputParser() - QUERY_PROMPT = PromptTemplate( input_variables=["question"], template="""You are an AI language model assistant. Your task is to generate five @@ -60,12 +39,9 @@ QUERY_PROMPT = PromptTemplate( ollama_llm = "zephyr" llm = ChatOllama(model=ollama_llm) -# Chain -llm_chain = LLMChain(llm=llm, prompt=QUERY_PROMPT, output_parser=output_parser) - # Run -retriever = MultiQueryRetriever( - retriever=vectorstore.as_retriever(), llm_chain=llm_chain, parser_key="lines" +retriever = MultiQueryRetriever.from_llm( + vectorstore.as_retriever(), llm, prompt=QUERY_PROMPT ) # "lines" is the key (attribute name) of the parsed output # RAG prompt diff --git a/templates/rag-opensearch/dummy_index_setup.py b/templates/rag-opensearch/dummy_index_setup.py index bb658adf51e..e3bc286444a 100644 --- a/templates/rag-opensearch/dummy_index_setup.py +++ b/templates/rag-opensearch/dummy_index_setup.py @@ -41,7 +41,7 @@ index_settings = { response = client.indices.create(index=OPENSEARCH_INDEX_NAME, body=index_settings) -print(response) +print(response) # noqa: T201 # Insert docs @@ -57,4 +57,4 @@ for each in docs: response = client.index(index=OPENSEARCH_INDEX_NAME, body=document, refresh=True) - print(response) + print(response) # noqa: T201 diff --git a/templates/rag-opensearch/poetry.lock b/templates/rag-opensearch/poetry.lock index 0729ca2e167..1978bc00343 100644 --- a/templates/rag-opensearch/poetry.lock +++ b/templates/rag-opensearch/poetry.lock @@ -2,87 +2,87 @@ [[package]] name = "aiohttp" -version = "3.9.1" +version = "3.9.3" description = "Async http client/server framework (asyncio)" optional = false python-versions = ">=3.8" files = [ - {file = "aiohttp-3.9.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e1f80197f8b0b846a8d5cf7b7ec6084493950d0882cc5537fb7b96a69e3c8590"}, - {file = "aiohttp-3.9.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c72444d17777865734aa1a4d167794c34b63e5883abb90356a0364a28904e6c0"}, - {file = "aiohttp-3.9.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9b05d5cbe9dafcdc733262c3a99ccf63d2f7ce02543620d2bd8db4d4f7a22f83"}, - {file = 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--git a/templates/rag-redis/pyproject.toml b/templates/rag-redis/pyproject.toml index ddd71d20f6f..7bae211c185 100644 --- a/templates/rag-redis/pyproject.toml +++ b/templates/rag-redis/pyproject.toml @@ -27,7 +27,7 @@ extras = [ [tool.poetry.group.dev.dependencies] poethepoet = "^0.24.1" -langchain-cli = ">=0.0.15" +langchain-cli = ">=0.0.21" [tool.langserve] export_module = "rag_redis.chain" diff --git a/templates/rag-self-query/main.py b/templates/rag-self-query/main.py index 83a1dc4c6cb..8385dfaa4f2 100644 --- a/templates/rag-self-query/main.py +++ b/templates/rag-self-query/main.py @@ -14,7 +14,7 @@ if __name__ == "__main__": "chat_history": [], } ) - print(response) + print(response) # noqa: T201 follow_up_question = "What are their objectives?" @@ -30,4 +30,4 @@ if __name__ == "__main__": } ) - print(response) + print(response) # noqa: T201 diff --git a/templates/rag-self-query/poetry.lock b/templates/rag-self-query/poetry.lock index c4d9fd487b9..810c9b6efd2 100644 --- 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python-versions = ">=3.8.1,<4.0" -content-hash = "dc86ad895aeb72bb66685bb5a58e1f31485991bb4ca8ad9b40801077a5f5c546" +content-hash = "97c22694c2e5e6497fa7d92ca8daf0cd8ee22bc911c01f8fcf9071b2dd254b0c" diff --git a/templates/rag-timescale-hybrid-search-time/pyproject.toml b/templates/rag-timescale-hybrid-search-time/pyproject.toml index 5b5b4921a65..05ce64e6c16 100644 --- a/templates/rag-timescale-hybrid-search-time/pyproject.toml +++ b/templates/rag-timescale-hybrid-search-time/pyproject.toml @@ -16,7 +16,7 @@ lark = "^1.1.8" tiktoken = "^0.5.1" [tool.poetry.group.dev.dependencies] -langchain-cli = ">=0.0.15" +langchain-cli = ">=0.0.21" [tool.langserve] export_module = "rag_timescale_hybrid_search_time.chain" diff --git a/templates/rag-timescale-hybrid-search-time/rag_timescale_hybrid_search_time/load_sample_dataset.py b/templates/rag-timescale-hybrid-search-time/rag_timescale_hybrid_search_time/load_sample_dataset.py index b540a72cdc1..65946fef905 100644 --- a/templates/rag-timescale-hybrid-search-time/rag_timescale_hybrid_search_time/load_sample_dataset.py +++ b/templates/rag-timescale-hybrid-search-time/rag_timescale_hybrid_search_time/load_sample_dataset.py @@ -47,7 +47,7 @@ def load_ts_git_dataset( with open(json_file_path, "w") as json_file: json_file.write(response.text) else: - print(f"Failed to download JSON file. Status code: {response.status_code}") + print(f"Failed to download JSON file. 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python-versions = ">=3.8.1,<4.0" -content-hash = "7250ed7eeebb70a2786c3e4ffebe7bfe5c4416b446af8d67e2870c367197efc5" +content-hash = "493de18254fc5649dccc75de3df1a850489a4f0a663ba1993047d2ee9395b633" diff --git a/templates/research-assistant/pyproject.toml b/templates/research-assistant/pyproject.toml index cd2c80b81ae..e836a0c7212 100644 --- a/templates/research-assistant/pyproject.toml +++ b/templates/research-assistant/pyproject.toml @@ -14,7 +14,7 @@ duckduckgo-search = "^3.9.5" tavily-python = "^0.2.6" [tool.poetry.group.dev.dependencies] -langchain-cli = ">=0.0.15" +langchain-cli = ">=0.0.21" fastapi = "^0.104.0" sse-starlette = "^1.6.5" diff --git a/templates/research-assistant/research_assistant/search/web.py b/templates/research-assistant/research_assistant/search/web.py index 52ea08542d1..5f647904ab8 100644 --- a/templates/research-assistant/research_assistant/search/web.py +++ b/templates/research-assistant/research_assistant/search/web.py @@ -40,7 +40,7 @@ def scrape_text(url: str): else: return f"Failed to retrieve the webpage: Status code {response.status_code}" except Exception as e: - print(e) + print(e) # noqa: T201 return f"Failed to retrieve the webpage: {e}" diff --git a/templates/retrieval-agent-fireworks/.gitignore b/templates/retrieval-agent-fireworks/.gitignore new file mode 100644 index 00000000000..bee8a64b79a --- /dev/null +++ b/templates/retrieval-agent-fireworks/.gitignore @@ -0,0 +1 @@ +__pycache__ diff --git a/templates/retrieval-agent-fireworks/LICENSE b/templates/retrieval-agent-fireworks/LICENSE new file mode 100644 index 00000000000..fc0602feecd --- /dev/null +++ b/templates/retrieval-agent-fireworks/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 LangChain, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/templates/retrieval-agent-fireworks/README.md b/templates/retrieval-agent-fireworks/README.md new file mode 100644 index 00000000000..5a796c957c2 --- /dev/null +++ b/templates/retrieval-agent-fireworks/README.md @@ -0,0 +1,73 @@ +# retrieval-agent-fireworks + +This package uses open source models hosted on FireworksAI to do retrieval using an agent architecture. By default, this does retrieval over Arxiv. + +We will use `Mixtral8x7b-instruct-v0.1`, which is shown in this blog to yield reasonable +results with function calling even though it is not fine tuned for this task: https://huggingface.co/blog/open-source-llms-as-agents + + +## Environment Setup + +There are various great ways to run OSS models. We will use FireworksAI as an easy way to run the models. See [here](https://python.langchain.com/docs/integrations/providers/fireworks) for more information. + +Set the `FIREWORKS_API_KEY` environment variable to access Fireworks. + + +## Usage + +To use this package, you should first have the LangChain CLI installed: + +```shell +pip install -U langchain-cli +``` + +To create a new LangChain project and install this as the only package, you can do: + +```shell +langchain app new my-app --package retrieval-agent-fireworks +``` + +If you want to add this to an existing project, you can just run: + +```shell +langchain app add retrieval-agent-fireworks +``` + +And add the following code to your `server.py` file: +```python +from retrieval_agent_fireworks import chain as retrieval_agent_fireworks_chain + +add_routes(app, retrieval_agent_fireworks_chain, path="/retrieval-agent-fireworks") +``` + +(Optional) Let's now configure LangSmith. +LangSmith will help us trace, monitor and debug LangChain applications. +LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). +If you don't have access, you can skip this section + + +```shell +export LANGCHAIN_TRACING_V2=true +export LANGCHAIN_API_KEY= +export LANGCHAIN_PROJECT= # if not specified, defaults to "default" +``` + +If you are inside this directory, then you can spin up a LangServe instance directly by: + +```shell +langchain serve +``` + +This will start the FastAPI app with a server is running locally at +[http://localhost:8000](http://localhost:8000) + +We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs) +We can access the playground at [http://127.0.0.1:8000/retrieval-agent-fireworks/playground](http://127.0.0.1:8000/retrieval-agent-fireworks/playground) + +We can access the template from code with: + +```python +from langserve.client import RemoteRunnable + +runnable = RemoteRunnable("http://localhost:8000/retrieval-agent-fireworks") +``` \ No newline at end of file diff --git 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"^0.1.14" +fireworks-ai = "^0.11.2" + + +[tool.poetry.group.dev.dependencies] +langchain-cli = ">=0.0.21" +fastapi = "^0.104.0" +sse-starlette = "^1.6.5" + +[tool.langserve] +export_module = "retrieval_agent_fireworks" +export_attr = "agent_executor" + +[tool.templates-hub] +use-case = "research" +author = "Docugami" +integrations = ["HuggingFace"] +tags = ["local", "agents"] + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/retrieval-agent-fireworks/retrieval_agent_fireworks/__init__.py b/templates/retrieval-agent-fireworks/retrieval_agent_fireworks/__init__.py new file mode 100644 index 00000000000..fdf3d35c08b --- /dev/null +++ b/templates/retrieval-agent-fireworks/retrieval_agent_fireworks/__init__.py @@ -0,0 +1,3 @@ +from retrieval_agent_fireworks.chain import agent_executor + +__all__ = ["agent_executor"] diff --git a/templates/retrieval-agent-fireworks/retrieval_agent_fireworks/chain.py b/templates/retrieval-agent-fireworks/retrieval_agent_fireworks/chain.py new file mode 100644 index 00000000000..7207cc99d32 --- /dev/null +++ b/templates/retrieval-agent-fireworks/retrieval_agent_fireworks/chain.py @@ -0,0 +1,109 @@ +from typing import List + +from langchain import hub +from langchain.agents import AgentExecutor +from langchain.agents.format_scratchpad import format_log_to_str +from langchain.agents.output_parsers import ReActJsonSingleInputOutputParser +from langchain.callbacks.manager import CallbackManagerForRetrieverRun +from langchain.schema import BaseRetriever, Document +from langchain.tools.render import render_text_description +from langchain.tools.retriever import create_retriever_tool +from langchain_community.chat_models.fireworks import ChatFireworks +from langchain_community.utilities.arxiv import ArxivAPIWrapper +from langchain_core.pydantic_v1 import BaseModel + +MODEL_ID = "accounts/fireworks/models/mixtral-8x7b-instruct" + + +class ArxivRetriever(BaseRetriever, ArxivAPIWrapper): + """`Arxiv` retriever. + + It wraps load() to get_relevant_documents(). + It uses all ArxivAPIWrapper arguments without any change. + """ + + get_full_documents: bool = False + + def _get_relevant_documents( + self, query: str, *, run_manager: CallbackManagerForRetrieverRun + ) -> List[Document]: + try: + if self.is_arxiv_identifier(query): + results = self.arxiv_search( + id_list=query.split(), + max_results=self.top_k_results, + ).results() + else: + results = self.arxiv_search( # type: ignore + query[: self.ARXIV_MAX_QUERY_LENGTH], max_results=self.top_k_results + ).results() + except self.arxiv_exceptions as ex: + return [Document(page_content=f"Arxiv exception: {ex}")] + docs = [ + Document( + page_content=result.summary, + metadata={ + "Published": result.updated.date(), + "Title": result.title, + "Authors": ", ".join(a.name for a in result.authors), + }, + ) + for result in results + ] + return docs + + +# Set up tool(s) +description = ( + "A wrapper around Arxiv.org " + "Useful for when you need to answer questions about Physics, Mathematics, " + "Computer Science, Quantitative Biology, Quantitative Finance, Statistics, " + "Electrical Engineering, and Economics " + "from scientific articles on arxiv.org. " + "Input should be a search query." +) +arxiv_tool = create_retriever_tool(ArxivRetriever(), "arxiv", description) +tools = [arxiv_tool] + +# Set up LLM +llm = ChatFireworks( + model=MODEL_ID, + model_kwargs={ + "temperature": 0, + "max_tokens": 2048, + "top_p": 1, + }, + cache=True, +) + +# setup ReAct style prompt +prompt = hub.pull("hwchase17/react-json") +prompt = prompt.partial( + tools=render_text_description(tools), + tool_names=", ".join([t.name for t in tools]), +) + +# define the agent +model_with_stop = llm.bind(stop=["\nObservation"]) +agent = ( + { + "input": lambda x: x["input"], + "agent_scratchpad": lambda x: format_log_to_str(x["intermediate_steps"]), + } + | prompt + | model_with_stop + | ReActJsonSingleInputOutputParser() +) + + +class InputType(BaseModel): + input: str + + +# instantiate AgentExecutor +agent_executor = AgentExecutor( + agent=agent, + tools=tools, + verbose=True, + handle_parsing_errors=True, +).with_types(input_type=InputType) diff --git 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