diff --git a/.github/CODE_OF_CONDUCT.md b/.github/CODE_OF_CONDUCT.md new file mode 100644 index 00000000000..23a2a4aec83 --- /dev/null +++ b/.github/CODE_OF_CONDUCT.md @@ -0,0 +1,132 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, caste, color, religion, or sexual +identity and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the overall + community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or advances of + any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email address, + without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +conduct@langchain.dev. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series of +actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or permanent +ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within the +community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.1, available at +[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1]. + +Community Impact Guidelines were inspired by +[Mozilla's code of conduct enforcement ladder][Mozilla CoC]. + +For answers to common questions about this code of conduct, see the FAQ at +[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at +[https://www.contributor-covenant.org/translations][translations]. + +[homepage]: https://www.contributor-covenant.org +[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html +[Mozilla CoC]: https://github.com/mozilla/diversity +[FAQ]: https://www.contributor-covenant.org/faq +[translations]: https://www.contributor-covenant.org/translations \ No newline at end of file diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md index c00132026ab..ff8c70ec42a 100644 --- a/.github/CONTRIBUTING.md +++ b/.github/CONTRIBUTING.md @@ -1,20 +1,19 @@ # Contributing to LangChain 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 be in the form of new features, improved infra, better documentation, or bug fixes. +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. ## ๐Ÿ—บ๏ธ Guidelines ### ๐Ÿ‘ฉโ€๐Ÿ’ป Contributing Code -To contribute to this project, please follow a ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow. +To contribute to this project, please follow the ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow. Please do not try to push directly to this repo unless you are a maintainer. Please follow the checked-in pull request template when opening pull requests. Note related issues and tag relevant maintainers. -Pull requests cannot land without passing the formatting, linting and testing checks first. See [Testing](#testing) and +Pull requests cannot land without passing the formatting, linting, and testing checks first. See [Testing](#testing) and [Formatting and Linting](#formatting-and-linting) for how to run these checks locally. It's essential that we maintain great documentation and testing. If you: @@ -27,16 +26,14 @@ It's essential that we maintain great documentation and testing. If you: - Add a demo notebook in `docs/modules`. - Add unit and integration tests. -We're a small, building-oriented team. If there's something you'd like to add or change, opening a pull request is the +We are a small, progress-oriented team. If there's something you'd like to add or change, opening a pull request is the best way to get our attention. ### ๐ŸšฉGitHub Issues -Our [issues](https://github.com/langchain-ai/langchain/issues) page is kept up to date -with bugs, improvements, and feature requests. +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. +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. @@ -59,12 +56,12 @@ we do not want these to get in the way of getting good code into the codebase. ## ๐Ÿš€ Quick Start -This quick start describes running the repository locally. +This quick start guide explains how to run the repository locally. For a [development container](https://containers.dev/), see the [.devcontainer folder](https://github.com/langchain-ai/langchain/tree/master/.devcontainer). ### Dependency Management: Poetry and other env/dependency managers -This project uses [Poetry](https://python-poetry.org/) v1.6.1+ as a dependency manager. +This project utilizes [Poetry](https://python-poetry.org/) v1.6.1+ as a dependency manager. โ—Note: *Before installing Poetry*, if you use `Conda`, create and activate a new Conda env (e.g. `conda create -n langchain python=3.9`) @@ -75,11 +72,11 @@ tell Poetry to use the virtualenv python environment (`poetry config virtualenvs ### Core vs. Experimental -There are two separate projects in this repository: -- `langchain`: core langchain code, abstractions, and use cases -- `langchain.experimental`: see the [Experimental README](../libs/experimental/README.md) for more information. +This repository contains two separate projects: +- `langchain`: core langchain code, abstractions, and use cases. +- `langchain.experimental`: see the [Experimental README](https://github.com/langchain-ai/langchain/tree/master/libs/experimental/README.md) for more information. -Each of these has their own development environment. Docs are run from the top-level makefile, but development +Each of these has its own development environment. Docs are run from the top-level makefile, but development is split across separate test & release flows. For this quickstart, start with langchain core: @@ -129,7 +126,7 @@ To run unit tests in Docker: make docker_tests ``` -There are also [integration tests and code-coverage](../libs/langchain/tests/README.md) available. +There are also [integration tests and code-coverage](https://github.com/langchain-ai/langchain/tree/master/libs/langchain/tests/README.md) available. ### Formatting and Linting @@ -282,7 +279,7 @@ make docs_build make api_docs_build ``` -Finally, you can run the linkchecker to make sure all links are valid: +Finally, run the link checker to ensure all links are valid: ```bash make docs_linkcheck @@ -307,4 +304,4 @@ even patch releases may contain [non-backwards-compatible changes](https://semve ### ๐ŸŒŸ Recognition If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)! -If you have a Twitter account you would like us to mention, please let us know in the PR or in another manner. +If you have a Twitter account you would like us to mention, please let us know in the PR or through another means. diff --git a/.github/workflows/_compile_integration_test.yml b/.github/workflows/_compile_integration_test.yml new file mode 100644 index 00000000000..d52828084f5 --- /dev/null +++ b/.github/workflows/_compile_integration_test.yml @@ -0,0 +1,57 @@ +name: compile-integration-test + +on: + workflow_call: + inputs: + working-directory: + required: true + type: string + description: "From which folder this pipeline executes" + +env: + POETRY_VERSION: "1.6.1" + +jobs: + build: + defaults: + run: + working-directory: ${{ inputs.working-directory }} + runs-on: ubuntu-latest + strategy: + matrix: + python-version: + - "3.8" + - "3.9" + - "3.10" + - "3.11" + name: Python ${{ matrix.python-version }} + steps: + - uses: actions/checkout@v4 + + - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }} + uses: "./.github/actions/poetry_setup" + with: + python-version: ${{ matrix.python-version }} + poetry-version: ${{ env.POETRY_VERSION }} + working-directory: ${{ inputs.working-directory }} + cache-key: compile-integration + + - name: Install integration dependencies + shell: bash + run: poetry install --with=test_integration + + - name: Check integration tests compile + shell: bash + run: poetry run pytest -m compile tests/integration_tests + + - name: Ensure the tests did not create any additional files + shell: bash + run: | + set -eu + + STATUS="$(git status)" + echo "$STATUS" + + # grep will exit non-zero if the target message isn't found, + # and `set -e` above will cause the step to fail. + echo "$STATUS" | grep 'nothing to commit, working tree clean' diff --git a/.github/workflows/_lint.yml b/.github/workflows/_lint.yml index e7cbfa2d221..e4ea3d220f9 100644 --- a/.github/workflows/_lint.yml +++ b/.github/workflows/_lint.yml @@ -34,7 +34,7 @@ jobs: - "3.8" - "3.11" steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 with: # Fetch the last FETCH_DEPTH commits, so the mtime-changing script # can accurately set the mtimes of files modified in the last FETCH_DEPTH commits. diff --git a/.github/workflows/_pydantic_compatibility.yml b/.github/workflows/_pydantic_compatibility.yml index 3287a4d8133..8948836237c 100644 --- a/.github/workflows/_pydantic_compatibility.yml +++ b/.github/workflows/_pydantic_compatibility.yml @@ -26,7 +26,7 @@ jobs: - "3.11" name: Pydantic v1/v2 compatibility - Python ${{ matrix.python-version }} steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }} uses: "./.github/actions/poetry_setup" diff --git a/.github/workflows/_release.yml b/.github/workflows/_release.yml index 38c701052eb..4f95b09fe69 100644 --- a/.github/workflows/_release.yml +++ b/.github/workflows/_release.yml @@ -30,7 +30,7 @@ jobs: run: working-directory: ${{ inputs.working-directory }} steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Set up Python + Poetry ${{ env.POETRY_VERSION }} uses: "./.github/actions/poetry_setup" diff --git a/.github/workflows/_test.yml b/.github/workflows/_test.yml index 61670014fd3..a122a40058f 100644 --- a/.github/workflows/_test.yml +++ b/.github/workflows/_test.yml @@ -26,7 +26,7 @@ jobs: - "3.11" name: Python ${{ matrix.python-version }} steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }} uses: "./.github/actions/poetry_setup" @@ -44,14 +44,6 @@ jobs: shell: bash run: make test - - name: Install integration dependencies - shell: bash - run: poetry install --with=test_integration - - - name: Check integration tests compile - shell: bash - run: poetry run pytest -m compile tests/integration_tests - - name: Ensure the tests did not create any additional files shell: bash run: | diff --git a/.github/workflows/_test_release.yml b/.github/workflows/_test_release.yml new file mode 100644 index 00000000000..febe929b618 --- /dev/null +++ b/.github/workflows/_test_release.yml @@ -0,0 +1,50 @@ +name: test-release + +on: + workflow_call: + inputs: + working-directory: + required: true + type: string + description: "From which folder this pipeline executes" + +env: + POETRY_VERSION: "1.6.1" + +jobs: + publish_to_test_pypi: + runs-on: ubuntu-latest + permissions: + # This permission is used for trusted publishing: + # https://blog.pypi.org/posts/2023-04-20-introducing-trusted-publishers/ + # + # Trusted publishing has to also be configured on PyPI for each package: + # https://docs.pypi.org/trusted-publishers/adding-a-publisher/ + id-token: write + defaults: + run: + working-directory: ${{ inputs.working-directory }} + steps: + - uses: actions/checkout@v4 + + - name: Set up Python + Poetry ${{ env.POETRY_VERSION }} + uses: "./.github/actions/poetry_setup" + with: + python-version: "3.10" + poetry-version: ${{ env.POETRY_VERSION }} + working-directory: ${{ inputs.working-directory }} + cache-key: release + + - name: Build project for distribution + run: poetry build + - name: Check Version + id: check-version + run: | + echo version=$(poetry version --short) >> $GITHUB_OUTPUT + - name: Publish package to TestPyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + repository-url: https://test.pypi.org/legacy/ + packages-dir: ${{ inputs.working-directory }}/dist/ + verbose: true + print-hash: true diff --git a/.github/workflows/codespell.yml b/.github/workflows/codespell.yml index 50a22e4cb0d..82474e92890 100644 --- a/.github/workflows/codespell.yml +++ b/.github/workflows/codespell.yml @@ -17,7 +17,7 @@ jobs: steps: - name: Checkout - uses: actions/checkout@v3 + uses: actions/checkout@v4 - name: Install Dependencies run: | diff --git a/.github/workflows/langchain_ci.yml b/.github/workflows/langchain_ci.yml index 046de2258be..7e9b4a6e625 100644 --- a/.github/workflows/langchain_ci.yml +++ b/.github/workflows/langchain_ci.yml @@ -12,6 +12,7 @@ on: - '.github/workflows/_test.yml' - '.github/workflows/_pydantic_compatibility.yml' - '.github/workflows/langchain_ci.yml' + - 'libs/*' - 'libs/langchain/**' workflow_dispatch: # Allows to trigger the workflow manually in GitHub UI @@ -44,6 +45,13 @@ jobs: working-directory: libs/langchain secrets: inherit + compile-integration-tests: + uses: + ./.github/workflows/_compile_integration_test.yml + with: + working-directory: libs/langchain + secrets: inherit + pydantic-compatibility: uses: ./.github/workflows/_pydantic_compatibility.yml @@ -65,7 +73,7 @@ jobs: - "3.11" name: Python ${{ matrix.python-version }} extended tests steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }} uses: "./.github/actions/poetry_setup" diff --git a/.github/workflows/langchain_cli_ci.yml b/.github/workflows/langchain_cli_ci.yml new file mode 100644 index 00000000000..400a0f285df --- /dev/null +++ b/.github/workflows/langchain_cli_ci.yml @@ -0,0 +1,53 @@ +--- +name: libs/cli CI + +on: + push: + branches: [ master ] + pull_request: + paths: + - '.github/actions/poetry_setup/action.yml' + - '.github/tools/**' + - '.github/workflows/_lint.yml' + - '.github/workflows/_test.yml' + - '.github/workflows/_pydantic_compatibility.yml' + - '.github/workflows/langchain_cli_ci.yml' + - 'libs/cli/**' + - 'libs/*' + workflow_dispatch: # Allows to trigger the workflow manually in GitHub UI + +# If another push to the same PR or branch happens while this workflow is still running, +# cancel the earlier run in favor of the next run. +# +# There's no point in testing an outdated version of the code. GitHub only allows +# a limited number of job runners to be active at the same time, so it's better to cancel +# pointless jobs early so that more useful jobs can run sooner. +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +env: + POETRY_VERSION: "1.6.1" + WORKDIR: "libs/cli" + +jobs: + lint: + uses: + ./.github/workflows/_lint.yml + with: + working-directory: libs/cli + secrets: inherit + + test: + uses: + ./.github/workflows/_test.yml + with: + working-directory: libs/cli + secrets: inherit + + pydantic-compatibility: + uses: + ./.github/workflows/_pydantic_compatibility.yml + with: + working-directory: libs/cli + secrets: inherit diff --git a/.github/workflows/langchain_experimental_ci.yml b/.github/workflows/langchain_experimental_ci.yml index 8641a915e11..13648b9b3a7 100644 --- a/.github/workflows/langchain_experimental_ci.yml +++ b/.github/workflows/langchain_experimental_ci.yml @@ -11,7 +11,7 @@ on: - '.github/workflows/_lint.yml' - '.github/workflows/_test.yml' - '.github/workflows/langchain_experimental_ci.yml' - - 'libs/langchain/**' + - 'libs/*' - 'libs/experimental/**' workflow_dispatch: # Allows to trigger the workflow manually in GitHub UI @@ -44,6 +44,13 @@ jobs: working-directory: libs/experimental secrets: inherit + compile-integration-tests: + uses: + ./.github/workflows/_compile_integration_test.yml + with: + working-directory: libs/experimental + secrets: inherit + # It's possible that langchain-experimental works fine with the latest *published* langchain, # but is broken with the langchain on `master`. # @@ -62,7 +69,7 @@ jobs: - "3.11" name: test with unpublished langchain - Python ${{ matrix.python-version }} steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }} uses: "./.github/actions/poetry_setup" @@ -97,7 +104,7 @@ jobs: - "3.11" name: Python ${{ matrix.python-version }} extended tests steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }} uses: "./.github/actions/poetry_setup" diff --git a/.github/workflows/langchain_experimental_test_release.yml b/.github/workflows/langchain_experimental_test_release.yml new file mode 100644 index 00000000000..e99edd09129 --- /dev/null +++ b/.github/workflows/langchain_experimental_test_release.yml @@ -0,0 +1,13 @@ +--- +name: Experimental Test Release + +on: + workflow_dispatch: # Allows to trigger the workflow manually in GitHub UI + +jobs: + release: + uses: + ./.github/workflows/_test_release.yml + with: + working-directory: libs/experimental + secrets: inherit diff --git a/.github/workflows/langchain_test_release.yml b/.github/workflows/langchain_test_release.yml new file mode 100644 index 00000000000..9acd2e29a9c --- /dev/null +++ b/.github/workflows/langchain_test_release.yml @@ -0,0 +1,13 @@ +--- +name: Test Release + +on: + workflow_dispatch: # Allows to trigger the workflow manually in GitHub UI + +jobs: + release: + uses: + ./.github/workflows/_test_release.yml + with: + working-directory: libs/langchain + secrets: inherit diff --git a/.github/workflows/scheduled_test.yml b/.github/workflows/scheduled_test.yml index 44de47155ce..0469c080e4d 100644 --- a/.github/workflows/scheduled_test.yml +++ b/.github/workflows/scheduled_test.yml @@ -24,7 +24,7 @@ jobs: - "3.11" name: Python ${{ matrix.python-version }} steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} uses: "./.github/actions/poetry_setup" @@ -55,6 +55,10 @@ jobs: poetry install --with=test_integration poetry run pip install google-cloud-aiplatform poetry run pip install "boto3>=1.28.57" + if [[ ${{ matrix.python-version }} != "3.8" ]] + then + poetry run pip install fireworks-ai + fi - name: Run tests shell: bash @@ -64,7 +68,8 @@ jobs: 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_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_DEPLOYMENT_NAME }} + AZURE_OPENAI_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_DEPLOYMENT_NAME }} + FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }} run: | make scheduled_tests diff --git a/README.md b/README.md index 036b88ba79e..c8e77477bba 100644 --- a/README.md +++ b/README.md @@ -93,7 +93,7 @@ Memory refers to persisting state between calls of a chain/agent. LangChain prov **๐Ÿง Evaluation:** -[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this. +[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is by using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this. For more information on these concepts, please see our [full documentation](https://python.langchain.com). diff --git a/cookbook/README.md b/cookbook/README.md index 3c8dcfabd75..271c784e97d 100644 --- a/cookbook/README.md +++ b/cookbook/README.md @@ -4,7 +4,7 @@ Example code for building applications with LangChain, with an emphasis on more Notebook | Description :- | :- -[LLaMA2_sql_chat.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/LLaMA2_sql_chat.ipynb) | Build a chat application that interacts with a sql database using an open source llm (llama2), specifically demonstrated on a sqlite database containing nba rosters. +[LLaMA2_sql_chat.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/LLaMA2_sql_chat.ipynb) | Build a chat application that interacts with a SQL database using an open source llm (llama2), specifically demonstrated on an SQLite database containing rosters. [Semi_Structured_RAG.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/Semi_Structured_RAG.ipynb) | Perform retrieval-augmented generation (rag) on documents with semi-structured data, including text and tables, using unstructured for parsing, multi-vector retriever for storing, and lcel for implementing chains. [Semi_structured_and_multi_moda...](https://github.com/langchain-ai/langchain/tree/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb) | Perform retrieval-augmented generation (rag) on documents with semi-structured data and images, using unstructured for parsing, multi-vector retriever for storage and retrieval, and lcel for implementing chains. [Semi_structured_multi_modal_RA...](https://github.com/langchain-ai/langchain/tree/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb) | Perform retrieval-augmented generation (rag) on documents with semi-structured data and images, using various tools and methods such as unstructured for parsing, multi-vector retriever for storing, lcel for implementing chains, and open source language models like llama2, llava, and gpt4all. @@ -12,14 +12,14 @@ Notebook | Description [autogpt/marathon_times.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/autogpt/marathon_times.ipynb) | Implement autogpt for finding winning marathon times. [baby_agi.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/baby_agi.ipynb) | Implement babyagi, an ai agent that can generate and execute tasks based on a given objective, with the flexibility to swap out specific vectorstores/model providers. [baby_agi_with_agent.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/baby_agi_with_agent.ipynb) | Swap out the execution chain in the babyagi notebook with an agent that has access to tools, aiming to obtain more reliable information. -[camel_role_playing.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/camel_role_playing.ipynb) | Implement the camel framework for creating autonomous cooperative agents in large scale language models, using role-playing and inception prompting to guide chat agents towards task completion. +[camel_role_playing.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/camel_role_playing.ipynb) | Implement the camel framework for creating autonomous cooperative agents in large-scale language models, using role-playing and inception prompting to guide chat agents towards task completion. [causal_program_aided_language_...](https://github.com/langchain-ai/langchain/tree/master/cookbook/causal_program_aided_language_model.ipynb) | Implement the causal program-aided language (cpal) chain, which improves upon the program-aided language (pal) by incorporating causal structure to prevent hallucination in language models, particularly when dealing with complex narratives and math problems with nested dependencies. [code-analysis-deeplake.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/code-analysis-deeplake.ipynb) | Analyze its own code base with the help of gpt and activeloop's deep lake. [custom_agent_with_plugin_retri...](https://github.com/langchain-ai/langchain/tree/master/cookbook/custom_agent_with_plugin_retrieval.ipynb) | Build a custom agent that can interact with ai plugins by retrieving tools and creating natural language wrappers around openapi endpoints. [custom_agent_with_plugin_retri...](https://github.com/langchain-ai/langchain/tree/master/cookbook/custom_agent_with_plugin_retrieval_using_plugnplai.ipynb) | Build a custom agent with plugin retrieval functionality, utilizing ai plugins from the `plugnplai` directory. [databricks_sql_db.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/databricks_sql_db.ipynb) | Connect to databricks runtimes and databricks sql. [deeplake_semantic_search_over_...](https://github.com/langchain-ai/langchain/tree/master/cookbook/deeplake_semantic_search_over_chat.ipynb) | Perform semantic search and question-answering over a group chat using activeloop's deep lake with gpt4. -[elasticsearch_db_qa.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/elasticsearch_db_qa.ipynb) | Interact with elasticsearch analytics databases in natural language and build search queries via the elasticsearch dsl api. +[elasticsearch_db_qa.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/elasticsearch_db_qa.ipynb) | Interact with elasticsearch analytics databases in natural language and build search queries via the elasticsearch dsl API. [forward_looking_retrieval_augm...](https://github.com/langchain-ai/langchain/tree/master/cookbook/forward_looking_retrieval_augmented_generation.ipynb) | Implement the forward-looking active retrieval augmented generation (flare) method, which generates answers to questions, identifies uncertain tokens, generates hypothetical questions based on these tokens, and retrieves relevant documents to continue generating the answer. [generative_agents_interactive_...](https://github.com/langchain-ai/langchain/tree/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb) | Implement a generative agent that simulates human behavior, based on a research paper, using a time-weighted memory object backed by a langchain retriever. [gymnasium_agent_simulation.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/gymnasium_agent_simulation.ipynb) | Create a simple agent-environment interaction loop in simulated environments like text-based games with gymnasium. @@ -37,7 +37,7 @@ Notebook | Description [multiagent_authoritarian.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/multiagent_authoritarian.ipynb) | Implement a multi-agent simulation where a privileged agent controls the conversation, including deciding who speaks and when the conversation ends, in the context of a simulated news network. [multiagent_bidding.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/multiagent_bidding.ipynb) | Implement a multi-agent simulation where agents bid to speak, with the highest bidder speaking next, demonstrated through a fictitious presidential debate example. [myscale_vector_sql.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/myscale_vector_sql.ipynb) | Access and interact with the myscale integrated vector database, which can enhance the performance of language model (llm) applications. -[openai_functions_retrieval_qa....](https://github.com/langchain-ai/langchain/tree/master/cookbook/openai_functions_retrieval_qa.ipynb) | Structure response output in a question answering system by incorporating openai functions into a retrieval pipeline. +[openai_functions_retrieval_qa....](https://github.com/langchain-ai/langchain/tree/master/cookbook/openai_functions_retrieval_qa.ipynb) | Structure response output in a question-answering system by incorporating openai functions into a retrieval pipeline. [petting_zoo.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/petting_zoo.ipynb) | Create multi-agent simulations with simulated environments using the petting zoo library. [plan_and_execute_agent.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/plan_and_execute_agent.ipynb) | Create plan-and-execute agents that accomplish objectives by planning tasks with a language model (llm) and executing them with a separate agent. [press_releases.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/press_releases.ipynb) | Retrieve and query company press release data powered by [Kay.ai](https://kay.ai). @@ -46,7 +46,7 @@ Notebook | Description [self_query_hotel_search.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/self_query_hotel_search.ipynb) | Build a hotel room search feature with self-querying retrieval, using a specific hotel recommendation dataset. [smart_llm.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/smart_llm.ipynb) | Implement a smartllmchain, a self-critique chain that generates multiple output proposals, critiques them to find the best one, and then improves upon it to produce a final output. [tree_of_thought.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/tree_of_thought.ipynb) | Query a large language model using the tree of thought technique. -[twitter-the-algorithm-analysis...](https://github.com/langchain-ai/langchain/tree/master/cookbook/twitter-the-algorithm-analysis-deeplake.ipynb) | Analyze the source code of the twitter algorithm with the help of gpt4 and activeloop's deep lake. +[twitter-the-algorithm-analysis...](https://github.com/langchain-ai/langchain/tree/master/cookbook/twitter-the-algorithm-analysis-deeplake.ipynb) | Analyze the source code of the Twitter algorithm with the help of gpt4 and activeloop's deep lake. [two_agent_debate_tools.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/two_agent_debate_tools.ipynb) | Simulate multi-agent dialogues where the agents can utilize various tools. -[two_player_dnd.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/two_player_dnd.ipynb) | Simulate a two-player dungeons & dragons game, where a dialoguesimulator class is used to coordinate the dialogue between the protagonist and the dungeon master. +[two_player_dnd.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/two_player_dnd.ipynb) | Simulate a two-player dungeons & dragons game, where a dialogue simulator class is used to coordinate the dialogue between the protagonist and the dungeon master. [wikibase_agent.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/wikibase_agent.ipynb) | Create a simple wikibase agent that utilizes sparql generation, with testing done on http://wikidata.org. diff --git a/cookbook/stepback-qa.ipynb b/cookbook/stepback-qa.ipynb index e142996a8f5..a77e2d56528 100644 --- a/cookbook/stepback-qa.ipynb +++ b/cookbook/stepback-qa.ipynb @@ -11,7 +11,7 @@ "\n", "Read the paper [here](https://arxiv.org/abs/2310.06117)\n", "\n", - "See an excelent blog post on this by Cobus Greyling [here](https://cobusgreyling.medium.com/a-new-prompt-engineering-technique-has-been-introduced-called-step-back-prompting-b00e8954cacb)\n", + "See an excellent blog post on this by Cobus Greyling [here](https://cobusgreyling.medium.com/a-new-prompt-engineering-technique-has-been-introduced-called-step-back-prompting-b00e8954cacb)\n", "\n", "In this cookbook we will replicate this technique. We modify the prompts used slightly to work better with chat models." ] diff --git a/docs/.local_build.sh b/docs/.local_build.sh index 0d74b4fa8ec..cf935303696 100755 --- a/docs/.local_build.sh +++ b/docs/.local_build.sh @@ -14,6 +14,8 @@ cd ../_dist poetry run python scripts/model_feat_table.py poetry run nbdoc_build --srcdir docs cp ../cookbook/README.md src/pages/cookbook.mdx +cp ../.github/CONTRIBUTING.md docs/contributing.md +wget https://raw.githubusercontent.com/langchain-ai/langserve/main/README.md -O docs/guides/deployments/langserve.md poetry run python scripts/generate_api_reference_links.py yarn install yarn start diff --git a/docs/api_reference/guide_imports.json b/docs/api_reference/guide_imports.json index 47f094f18d0..c51a131c0e4 100644 --- a/docs/api_reference/guide_imports.json +++ b/docs/api_reference/guide_imports.json @@ -1 +1 @@ -{"SingleFileFacebookMessengerChatLoader": {"Facebook Messenger": "https://python.langchain.com/docs/integrations/chat_loaders/facebook"}, "FolderFacebookMessengerChatLoader": {"Facebook Messenger": "https://python.langchain.com/docs/integrations/chat_loaders/facebook", "Chat loaders": "https://python.langchain.com/docs/integrations/chat_loaders/index"}, "merge_chat_runs": {"Facebook Messenger": "https://python.langchain.com/docs/integrations/chat_loaders/facebook", "Slack": "https://python.langchain.com/docs/integrations/chat_loaders/slack", "WhatsApp": "https://python.langchain.com/docs/integrations/chat_loaders/whatsapp", "iMessage": "https://python.langchain.com/docs/integrations/chat_loaders/imessage", "Telegram": "https://python.langchain.com/docs/integrations/chat_loaders/telegram", "Discord": "https://python.langchain.com/docs/integrations/chat_loaders/discord"}, "map_ai_messages": {"Facebook Messenger": "https://python.langchain.com/docs/integrations/chat_loaders/facebook", "GMail": "https://python.langchain.com/docs/integrations/chat_loaders/gmail", "Slack": "https://python.langchain.com/docs/integrations/chat_loaders/slack", "WhatsApp": "https://python.langchain.com/docs/integrations/chat_loaders/whatsapp", "iMessage": "https://python.langchain.com/docs/integrations/chat_loaders/imessage", "Telegram": "https://python.langchain.com/docs/integrations/chat_loaders/telegram", "Discord": "https://python.langchain.com/docs/integrations/chat_loaders/discord"}, "convert_messages_for_finetuning": {"Facebook Messenger": "https://python.langchain.com/docs/integrations/chat_loaders/facebook", "Chat loaders": "https://python.langchain.com/docs/integrations/chat_loaders/index", "iMessage": "https://python.langchain.com/docs/integrations/chat_loaders/imessage"}, "ChatOpenAI": {"Facebook Messenger": "https://python.langchain.com/docs/integrations/chat_loaders/facebook", "Slack": "https://python.langchain.com/docs/integrations/chat_loaders/slack", "WhatsApp": "https://python.langchain.com/docs/integrations/chat_loaders/whatsapp", "iMessage": "https://python.langchain.com/docs/integrations/chat_loaders/imessage", "Telegram": "https://python.langchain.com/docs/integrations/chat_loaders/telegram", "Discord": "https://python.langchain.com/docs/integrations/chat_loaders/discord", "RePhraseQueryRetriever": "https://python.langchain.com/docs/integrations/retrievers/re_phrase", "Wikipedia": "https://python.langchain.com/docs/integrations/retrievers/wikipedia", "Arxiv": "https://python.langchain.com/docs/integrations/retrievers/arxiv", "ChatGPT Plugins": "https://python.langchain.com/docs/integrations/tools/chatgpt_plugins", "Human as a tool": "https://python.langchain.com/docs/integrations/tools/human_tools", "Yahoo Finance News": "https://python.langchain.com/docs/integrations/tools/yahoo_finance_news", "ArXiv": "https://python.langchain.com/docs/integrations/tools/arxiv", "Metaphor Search": "https://python.langchain.com/docs/integrations/tools/metaphor_search", "Shell (bash)": "https://python.langchain.com/docs/integrations/tools/bash", "Xata chat memory": "https://python.langchain.com/docs/integrations/memory/xata_chat_message_history", "Dynamodb Chat Message History": "https://python.langchain.com/docs/integrations/memory/dynamodb_chat_message_history", "OpenAI": "https://python.langchain.com/docs/integrations/chat/openai", "LLMonitor": "https://python.langchain.com/docs/integrations/callbacks/llmonitor", "Context": "https://python.langchain.com/docs/integrations/callbacks/context", "Label Studio": "https://python.langchain.com/docs/integrations/callbacks/labelstudio", "PromptLayer": "https://python.langchain.com/docs/integrations/callbacks/promptlayer", "CnosDB": "https://python.langchain.com/docs/integrations/providers/cnosdb", "Log10": "https://python.langchain.com/docs/integrations/providers/log10", "Flyte": "https://python.langchain.com/docs/integrations/providers/flyte", "Arthur": "https://python.langchain.com/docs/integrations/providers/arthur_tracking", "CSV": "https://python.langchain.com/docs/integrations/toolkits/csv", "Document Comparison": "https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit", "Python": "https://python.langchain.com/docs/integrations/toolkits/python", "PowerBI Dataset": "https://python.langchain.com/docs/integrations/toolkits/powerbi", "SQL Database": "https://python.langchain.com/docs/integrations/toolkits/sql_database", "Airbyte Question Answering": "https://python.langchain.com/docs/integrations/toolkits/airbyte_structured_qa", "Github": "https://python.langchain.com/docs/integrations/toolkits/github", "Spark SQL": "https://python.langchain.com/docs/integrations/toolkits/spark_sql", "AINetwork": "https://python.langchain.com/docs/integrations/toolkits/ainetwork", "Pandas Dataframe": "https://python.langchain.com/docs/integrations/toolkits/pandas", "Neo4j Vector Index": "https://python.langchain.com/docs/integrations/vectorstores/neo4jvector", "OpenAI Functions Metadata Tagger": "https://python.langchain.com/docs/integrations/document_transformers/openai_metadata_tagger", "Loading documents from a YouTube url": "https://python.langchain.com/docs/integrations/document_loaders/youtube_audio", "Figma": "https://python.langchain.com/docs/integrations/document_loaders/figma", "Fallbacks": "https://python.langchain.com/docs/guides/fallbacks", "Debugging": "https://python.langchain.com/docs/guides/debugging", "LangSmith Walkthrough": "https://python.langchain.com/docs/guides/langsmith/walkthrough", "Reversible data anonymization with Microsoft Presidio": "https://python.langchain.com/docs/guides/privacy/presidio_data_anonymization/reversible", "Data anonymization with Microsoft Presidio": "https://python.langchain.com/docs/guides/privacy/presidio_data_anonymization/index", "Comparing Chain Outputs": "https://python.langchain.com/docs/guides/evaluation/examples/comparisons", "Agent Trajectory": "https://python.langchain.com/docs/guides/evaluation/trajectory/trajectory_eval", "Custom Trajectory Evaluator": "https://python.langchain.com/docs/guides/evaluation/trajectory/custom", "Set env var OPENAI_API_KEY or load from a .env file:": "https://python.langchain.com/docs/use_cases/tagging", "Set env var OPENAI_API_KEY or load from a .env file": "https://python.langchain.com/docs/use_cases/qa_structured/sql", "Question Answering": "https://python.langchain.com/docs/use_cases/question_answering/question_answering", "Perform context-aware text splitting": "https://python.langchain.com/docs/use_cases/question_answering/how_to/document-context-aware-QA", "Conversational Retrieval Agent": "https://python.langchain.com/docs/use_cases/question_answering/how_to/conversational_retrieval_agents", "Multiple Retrieval Sources": "https://python.langchain.com/docs/use_cases/question_answering/how_to/multiple_retrieval", "Cite sources": "https://python.langchain.com/docs/use_cases/question_answering/how_to/qa_citations", "Retrieve as you generate with FLARE": "https://python.langchain.com/docs/use_cases/question_answering/how_to/flare", "Analysis of Twitter the-algorithm source code with LangChain, GPT4 and Activeloop's Deep Lake": "https://python.langchain.com/docs/use_cases/question_answering/how_to/code/twitter-the-algorithm-analysis-deeplake", "Use LangChain, GPT and Activeloop's Deep Lake to work with code base": "https://python.langchain.com/docs/use_cases/question_answering/how_to/code/code-analysis-deeplake", "Structure answers with OpenAI functions": "https://python.langchain.com/docs/use_cases/question_answering/integrations/openai_functions_retrieval_qa", "QA using Activeloop's DeepLake": "https://python.langchain.com/docs/use_cases/question_answering/integrations/semantic-search-over-chat", "Neptune Open Cypher QA Chain": "https://python.langchain.com/docs/use_cases/more/graph/neptune_cypher_qa", "NebulaGraphQAChain": "https://python.langchain.com/docs/use_cases/more/graph/graph_nebula_qa", "Memgraph QA chain": "https://python.langchain.com/docs/use_cases/more/graph/graph_memgraph_qa", "KuzuQAChain": "https://python.langchain.com/docs/use_cases/more/graph/graph_kuzu_qa", "HugeGraph QA Chain": "https://python.langchain.com/docs/use_cases/more/graph/graph_hugegraph_qa", "GraphSparqlQAChain": "https://python.langchain.com/docs/use_cases/more/graph/graph_sparql_qa", "Diffbot Graph Transformer": "https://python.langchain.com/docs/use_cases/more/graph/diffbot_graphtransformer", "ArangoDB QA chain": "https://python.langchain.com/docs/use_cases/more/graph/graph_arangodb_qa", "Neo4j DB QA chain": "https://python.langchain.com/docs/use_cases/more/graph/graph_cypher_qa", "FalkorDBQAChain": "https://python.langchain.com/docs/use_cases/more/graph/graph_falkordb_qa", "Agents": "https://python.langchain.com/docs/use_cases/more/agents/agents", "AutoGPT": "https://python.langchain.com/docs/use_cases/more/agents/autonomous_agents/autogpt", "!pip install bs4": "https://python.langchain.com/docs/use_cases/more/agents/autonomous_agents/marathon_times", "Wikibase Agent": "https://python.langchain.com/docs/use_cases/more/agents/agents/wikibase_agent", "SalesGPT - Your Context-Aware AI Sales Assistant With Knowledge Base": "https://python.langchain.com/docs/use_cases/more/agents/agents/sales_agent_with_context", "CAMEL Role-Playing Autonomous Cooperative Agents": "https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/camel_role_playing", "Multi-Agent Simulated Environment: Petting Zoo": "https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/petting_zoo", "Multi-agent decentralized speaker selection": "https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/multiagent_bidding", "Multi-agent authoritarian speaker selection": "https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/multiagent_authoritarian", "Generative Agents in LangChain": "https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/characters", "Two-Player Dungeons & Dragons": "https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/two_player_dnd", "Multi-Player Dungeons & Dragons": "https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/multi_player_dnd", "Simulated Environment: Gymnasium": "https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/gymnasium", "Agent Debates with Tools": "https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/two_agent_debate_tools", "How to use a SmartLLMChain": "https://python.langchain.com/docs/use_cases/more/self_check/smart_llm", "Vector SQL Retriever with MyScale": "https://python.langchain.com/docs/use_cases/qa_structured/integrations/myscale_vector_sql", "Elasticsearch": "https://python.langchain.com/docs/use_cases/qa_structured/integrations/elasticsearch", "SQL": "https://python.langchain.com/docs/use_cases/sql/sql", "MultiVector Retriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector", "MultiQueryRetriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/MultiQueryRetriever", "WebResearchRetriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/web_research", "Memory in LLMChain": "https://python.langchain.com/docs/modules/memory/adding_memory", "Custom callback handlers": "https://python.langchain.com/docs/modules/callbacks/custom_callbacks", "Async callbacks": "https://python.langchain.com/docs/modules/callbacks/async_callbacks", "Defining Custom Tools": "https://python.langchain.com/docs/modules/agents/tools/custom_tools", "Tools as OpenAI Functions": "https://python.langchain.com/docs/modules/agents/tools/tools_as_openai_functions", "OpenAI Multi Functions Agent": "https://python.langchain.com/docs/modules/agents/agent_types/openai_multi_functions_agent", "Handle parsing errors": "https://python.langchain.com/docs/modules/agents/how_to/handle_parsing_errors", "Running Agent as an Iterator": "https://python.langchain.com/docs/modules/agents/how_to/agent_iter", "Add Memory to OpenAI Functions Agent": "https://python.langchain.com/docs/modules/agents/how_to/add_memory_openai_functions", "Custom functions with OpenAI Functions Agent": "https://python.langchain.com/docs/modules/agents/how_to/custom-functions-with-openai-functions-agent", "Use ToolKits with OpenAI Functions": "https://python.langchain.com/docs/modules/agents/how_to/use_toolkits_with_openai_functions", "Retry parser": "https://python.langchain.com/docs/modules/model_io/output_parsers/retry", "Pydantic (JSON) parser": "https://python.langchain.com/docs/modules/model_io/output_parsers/pydantic", "Prompt pipelining": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompts_pipelining", "Connecting to a Feature Store": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/connecting_to_a_feature_store", "Custom chain": "https://python.langchain.com/docs/modules/chains/how_to/custom_chain", "Using OpenAI functions": "https://python.langchain.com/docs/modules/chains/how_to/openai_functions", "interface.md": "https://python.langchain.com/docs/expression_language/interface", "First we add a step to load memory": "https://python.langchain.com/docs/expression_language/cookbook/retrieval", "sql_db.md": "https://python.langchain.com/docs/expression_language/cookbook/sql_db", "prompt_llm_parser.md": "https://python.langchain.com/docs/expression_language/cookbook/prompt_llm_parser", "Adding memory": "https://python.langchain.com/docs/expression_language/cookbook/memory", "multiple_chains.md": "https://python.langchain.com/docs/expression_language/cookbook/multiple_chains", "Code writing": "https://python.langchain.com/docs/expression_language/cookbook/code_writing", "Using tools": "https://python.langchain.com/docs/expression_language/cookbook/tools", "Configure Runnable traces": "https://python.langchain.com/docs/expression_language/how_to/trace_config"}, "ChatPromptTemplate": {"Facebook Messenger": "https://python.langchain.com/docs/integrations/chat_loaders/facebook", "Chat loaders": "https://python.langchain.com/docs/integrations/chat_loaders/index", "iMessage": "https://python.langchain.com/docs/integrations/chat_loaders/imessage", "Anthropic": "https://python.langchain.com/docs/integrations/chat/anthropic", "\ud83d\ude85 LiteLLM": "https://python.langchain.com/docs/integrations/chat/litellm", "Konko": "https://python.langchain.com/docs/integrations/chat/konko", "OpenAI": "https://python.langchain.com/docs/integrations/chat/openai", "Google Cloud Platform Vertex AI PaLM ": "https://python.langchain.com/docs/integrations/chat/google_vertex_ai_palm", "JinaChat": "https://python.langchain.com/docs/integrations/chat/jinachat", "Context": "https://python.langchain.com/docs/integrations/callbacks/context", "OpenAI Functions Metadata Tagger": "https://python.langchain.com/docs/integrations/document_transformers/openai_metadata_tagger", "Figma": "https://python.langchain.com/docs/integrations/document_loaders/figma", "Fireworks": "https://python.langchain.com/docs/integrations/llms/fireworks", "Fallbacks": "https://python.langchain.com/docs/guides/fallbacks", "Set env var OPENAI_API_KEY or load from a .env file:": "https://python.langchain.com/docs/use_cases/tagging", "Multiple Retrieval Sources": "https://python.langchain.com/docs/use_cases/question_answering/how_to/multiple_retrieval", "Structure answers with OpenAI functions": "https://python.langchain.com/docs/use_cases/question_answering/integrations/openai_functions_retrieval_qa", "Multi-agent authoritarian speaker selection": "https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/multiagent_authoritarian", "MultiVector Retriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector", "Memory in LLMChain": "https://python.langchain.com/docs/modules/memory/adding_memory", "Retry parser": "https://python.langchain.com/docs/modules/model_io/output_parsers/retry", "Pydantic (JSON) parser": "https://python.langchain.com/docs/modules/model_io/output_parsers/pydantic", "Few-shot examples for chat models": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/few_shot_examples_chat", "Prompt pipelining": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompts_pipelining", "Using OpenAI functions": "https://python.langchain.com/docs/modules/chains/how_to/openai_functions", "interface.md": "https://python.langchain.com/docs/expression_language/interface", "First we add a step to load memory": "https://python.langchain.com/docs/expression_language/cookbook/retrieval", "sql_db.md": "https://python.langchain.com/docs/expression_language/cookbook/sql_db", "prompt_llm_parser.md": "https://python.langchain.com/docs/expression_language/cookbook/prompt_llm_parser", "Adding memory": "https://python.langchain.com/docs/expression_language/cookbook/memory", "multiple_chains.md": "https://python.langchain.com/docs/expression_language/cookbook/multiple_chains", "Code writing": 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agents and tools": "https://python.langchain.com/docs/modules/agents/how_to/sharedmemory_for_tools", "Add Memory to OpenAI Functions Agent": "https://python.langchain.com/docs/modules/agents/how_to/add_memory_openai_functions", "First we add a step to load memory": "https://python.langchain.com/docs/expression_language/cookbook/retrieval", "Adding memory": "https://python.langchain.com/docs/expression_language/cookbook/memory"}, "SceneXplainTool": {"SceneXplain": "https://python.langchain.com/docs/integrations/tools/sceneXplain"}, "WolframAlphaAPIWrapper": {"Wolfram Alpha": "https://python.langchain.com/docs/integrations/providers/wolfram_alpha"}, "load_huggingface_tool": {"HuggingFace Hub Tools": "https://python.langchain.com/docs/integrations/tools/huggingface_tools"}, "EdenAiSpeechToTextTool": {"Eden AI": "https://python.langchain.com/docs/integrations/tools/edenai_tools"}, "EdenAiTextToSpeechTool": {"Eden AI": "https://python.langchain.com/docs/integrations/tools/edenai_tools"}, "EdenAiExplicitImageTool": {"Eden AI": "https://python.langchain.com/docs/integrations/tools/edenai_tools"}, "EdenAiObjectDetectionTool": {"Eden AI": "https://python.langchain.com/docs/integrations/tools/edenai_tools"}, "EdenAiParsingIDTool": {"Eden AI": "https://python.langchain.com/docs/integrations/tools/edenai_tools"}, "EdenAiParsingInvoiceTool": {"Eden AI": "https://python.langchain.com/docs/integrations/tools/edenai_tools"}, "EdenAiTextModerationTool": {"Eden AI": "https://python.langchain.com/docs/integrations/tools/edenai_tools"}, "EdenAI": {"Eden AI": "https://python.langchain.com/docs/integrations/llms/edenai"}, "GoogleSearchAPIWrapper": {"Google Search": "https://python.langchain.com/docs/integrations/providers/google_search", "Bittensor": "https://python.langchain.com/docs/integrations/llms/bittensor", "Set env var OPENAI_API_KEY or load from a .env file:": "https://python.langchain.com/docs/use_cases/web_scraping", "Agents": "https://python.langchain.com/docs/use_cases/more/agents/agents", "WebResearchRetriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/web_research", "Message Memory in Agent backed by a database": "https://python.langchain.com/docs/modules/memory/agent_with_memory_in_db", "Memory in Agent": "https://python.langchain.com/docs/modules/memory/agent_with_memory", "Shared memory across agents and tools": "https://python.langchain.com/docs/modules/agents/how_to/sharedmemory_for_tools"}, "BingSearchAPIWrapper": {"Bing Search": "https://python.langchain.com/docs/integrations/tools/bing_search"}, "DallEAPIWrapper": {"Dall-E Image Generator": "https://python.langchain.com/docs/integrations/tools/dalle_image_generator"}, "ShellTool": {"Shell (bash)": "https://python.langchain.com/docs/integrations/tools/bash", "Human-in-the-loop Tool Validation": "https://python.langchain.com/docs/modules/agents/tools/human_approval"}, "ReadFileTool": {"File System": "https://python.langchain.com/docs/integrations/tools/filesystem", "AutoGPT": "https://python.langchain.com/docs/use_cases/more/agents/autonomous_agents/autogpt", "!pip install bs4": "https://python.langchain.com/docs/use_cases/more/agents/autonomous_agents/marathon_times"}, "CopyFileTool": {"File System": "https://python.langchain.com/docs/integrations/tools/filesystem"}, "DeleteFileTool": {"File System": "https://python.langchain.com/docs/integrations/tools/filesystem"}, "MoveFileTool": {"File System": "https://python.langchain.com/docs/integrations/tools/filesystem", "Tools as OpenAI Functions": "https://python.langchain.com/docs/modules/agents/tools/tools_as_openai_functions"}, "WriteFileTool": {"File System": "https://python.langchain.com/docs/integrations/tools/filesystem", "AutoGPT": "https://python.langchain.com/docs/use_cases/more/agents/autonomous_agents/autogpt", "!pip install bs4": "https://python.langchain.com/docs/use_cases/more/agents/autonomous_agents/marathon_times"}, "ListDirectoryTool": {"File System": "https://python.langchain.com/docs/integrations/tools/filesystem"}, "FileManagementToolkit": {"File System": "https://python.langchain.com/docs/integrations/tools/filesystem"}, "BraveSearch": {"Brave Search": "https://python.langchain.com/docs/integrations/providers/brave_search"}, "RedisChatMessageHistory": {"Redis Chat Message History": "https://python.langchain.com/docs/integrations/memory/redis_chat_message_history", "Message Memory in Agent backed by a database": "https://python.langchain.com/docs/modules/memory/agent_with_memory_in_db"}, "ConversationChain": {"Entity Memory with SQLite storage": "https://python.langchain.com/docs/integrations/memory/entity_memory_with_sqlite", "Figma": "https://python.langchain.com/docs/integrations/document_loaders/figma", "Bedrock": "https://python.langchain.com/docs/integrations/llms/bedrock", "Set env var OPENAI_API_KEY or load from a .env file:": "https://python.langchain.com/docs/use_cases/chatbots", "Agent Debates with Tools": "https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/two_agent_debate_tools", "Multiple Memory classes": "https://python.langchain.com/docs/modules/memory/multiple_memory", "Customizing Conversational Memory": "https://python.langchain.com/docs/modules/memory/conversational_customization", "Conversation Knowledge Graph": "https://python.langchain.com/docs/modules/memory/types/kg", "Conversation Token Buffer": "https://python.langchain.com/docs/modules/memory/types/token_buffer", "Conversation Summary Buffer": "https://python.langchain.com/docs/modules/memory/types/summary_buffer", "Router": "https://python.langchain.com/docs/modules/chains/foundational/router"}, "ConversationEntityMemory": {"Entity Memory with SQLite storage": "https://python.langchain.com/docs/integrations/memory/entity_memory_with_sqlite"}, "SQLiteEntityStore": {"Entity Memory with SQLite storage": "https://python.langchain.com/docs/integrations/memory/entity_memory_with_sqlite"}, "ENTITY_MEMORY_CONVERSATION_TEMPLATE": {"Entity Memory with SQLite storage": "https://python.langchain.com/docs/integrations/memory/entity_memory_with_sqlite"}, "PostgresChatMessageHistory": {"Postgres Chat Message History": "https://python.langchain.com/docs/integrations/memory/postgres_chat_message_history"}, "MomentoChatMessageHistory": {"Momento Chat Message History": "https://python.langchain.com/docs/integrations/memory/momento_chat_message_history"}, "MongoDBChatMessageHistory": {"Mongodb Chat Message History": "https://python.langchain.com/docs/integrations/memory/mongodb_chat_message_history"}, "XataChatMessageHistory": {"Xata chat memory": "https://python.langchain.com/docs/integrations/memory/xata_chat_message_history"}, "XataVectorStore": {"Xata chat memory": "https://python.langchain.com/docs/integrations/memory/xata_chat_message_history", "Xata": "https://python.langchain.com/docs/integrations/vectorstores/xata"}, "create_retriever_tool": {"Xata chat memory": "https://python.langchain.com/docs/integrations/memory/xata_chat_message_history", "Conversational Retrieval Agent": "https://python.langchain.com/docs/use_cases/question_answering/how_to/conversational_retrieval_agents", "Set env var OPENAI_API_KEY or load from a .env file": "https://python.langchain.com/docs/use_cases/qa_structured/sql", "SQL": "https://python.langchain.com/docs/use_cases/sql/sql"}, "CassandraChatMessageHistory": {"Cassandra Chat Message History": "https://python.langchain.com/docs/integrations/memory/cassandra_chat_message_history", "Cassandra": "https://python.langchain.com/docs/integrations/providers/cassandra"}, "SQLChatMessageHistory": {"SQL Chat Message History": "https://python.langchain.com/docs/integrations/memory/sql_chat_message_history"}, "BaseMessage": {"SQL Chat Message History": "https://python.langchain.com/docs/integrations/memory/sql_chat_message_history", "CAMEL Role-Playing Autonomous Cooperative Agents": "https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/camel_role_playing", "Multi-agent decentralized speaker selection": "https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/multiagent_bidding", "Multi-agent authoritarian speaker selection": "https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/multiagent_authoritarian", "Multi-Player Dungeons & Dragons": "https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/multi_player_dnd", "Simulated Environment: Gymnasium": "https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/gymnasium", "Agent Debates with Tools": "https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/two_agent_debate_tools"}, "BaseMessageConverter": {"SQL Chat Message History": "https://python.langchain.com/docs/integrations/memory/sql_chat_message_history"}, "MotorheadMemory": {"Mot\u00f6rhead Memory": "https://python.langchain.com/docs/integrations/memory/motorhead_memory", "Mot\u00f6rhead Memory (Managed)": "https://python.langchain.com/docs/integrations/memory/motorhead_memory_managed"}, "StreamlitChatMessageHistory": {"Streamlit Chat Message History": "https://python.langchain.com/docs/integrations/memory/streamlit_chat_message_history"}, "DynamoDBChatMessageHistory": {"Dynamodb Chat Message History": "https://python.langchain.com/docs/integrations/memory/dynamodb_chat_message_history"}, "PythonREPL": {"Dynamodb Chat Message History": "https://python.langchain.com/docs/integrations/memory/dynamodb_chat_message_history", "Python": "https://python.langchain.com/docs/integrations/toolkits/python", "Code writing": "https://python.langchain.com/docs/expression_language/cookbook/code_writing"}, "RocksetChatMessageHistory": {"Rockset Chat Message History": "https://python.langchain.com/docs/integrations/memory/rockset_chat_message_history"}, "AzureMLChatOnlineEndpoint": {"AzureML Chat Online Endpoint": "https://python.langchain.com/docs/integrations/chat/azureml_chat_endpoint"}, "LlamaContentFormatter": {"AzureML Chat Online Endpoint": "https://python.langchain.com/docs/integrations/chat/azureml_chat_endpoint"}, "ChatAnthropic": {"Anthropic": "https://python.langchain.com/docs/integrations/chat/anthropic", "Log10": "https://python.langchain.com/docs/integrations/providers/log10", "PlayWright Browser": "https://python.langchain.com/docs/integrations/toolkits/playwright", "Fallbacks": "https://python.langchain.com/docs/guides/fallbacks", "Agent Trajectory": "https://python.langchain.com/docs/guides/evaluation/trajectory/trajectory_eval", "Custom Pairwise Evaluator": "https://python.langchain.com/docs/guides/evaluation/comparison/custom", "Pairwise String Comparison": "https://python.langchain.com/docs/guides/evaluation/comparison/pairwise_string", "Criteria Evaluation": "https://python.langchain.com/docs/guides/evaluation/string/criteria_eval_chain", "XML Agent": "https://python.langchain.com/docs/modules/agents/agent_types/xml_agent", "Few-shot examples for chat models": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/few_shot_examples_chat", "Agents": "https://python.langchain.com/docs/expression_language/cookbook/agent"}, "SystemMessagePromptTemplate": {"Anthropic": "https://python.langchain.com/docs/integrations/chat/anthropic", "\ud83d\ude85 LiteLLM": "https://python.langchain.com/docs/integrations/chat/litellm", "Konko": "https://python.langchain.com/docs/integrations/chat/konko", "OpenAI": "https://python.langchain.com/docs/integrations/chat/openai", "Google Cloud Platform Vertex AI PaLM ": "https://python.langchain.com/docs/integrations/chat/google_vertex_ai_palm", "JinaChat": "https://python.langchain.com/docs/integrations/chat/jinachat", "Figma": "https://python.langchain.com/docs/integrations/document_loaders/figma", "Set env var OPENAI_API_KEY or load from a .env file:": "https://python.langchain.com/docs/use_cases/chatbots", "CAMEL Role-Playing Autonomous Cooperative Agents": "https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/camel_role_playing", "Code writing": "https://python.langchain.com/docs/expression_language/cookbook/code_writing"}, "AIMessagePromptTemplate": {"Anthropic": "https://python.langchain.com/docs/integrations/chat/anthropic", "\ud83d\ude85 LiteLLM": "https://python.langchain.com/docs/integrations/chat/litellm", "Konko": "https://python.langchain.com/docs/integrations/chat/konko", "OpenAI": "https://python.langchain.com/docs/integrations/chat/openai", "JinaChat": "https://python.langchain.com/docs/integrations/chat/jinachat", "Figma": "https://python.langchain.com/docs/integrations/document_loaders/figma"}, "HumanMessagePromptTemplate": {"Anthropic": "https://python.langchain.com/docs/integrations/chat/anthropic", "\ud83d\ude85 LiteLLM": "https://python.langchain.com/docs/integrations/chat/litellm", "Konko": "https://python.langchain.com/docs/integrations/chat/konko", "OpenAI": "https://python.langchain.com/docs/integrations/chat/openai", "Google Cloud Platform Vertex AI PaLM ": "https://python.langchain.com/docs/integrations/chat/google_vertex_ai_palm", "JinaChat": "https://python.langchain.com/docs/integrations/chat/jinachat", "Context": "https://python.langchain.com/docs/integrations/callbacks/context", "Figma": "https://python.langchain.com/docs/integrations/document_loaders/figma", "Fireworks": "https://python.langchain.com/docs/integrations/llms/fireworks", "Set env var OPENAI_API_KEY or load from a .env file:": "https://python.langchain.com/docs/use_cases/extraction", "Structure answers with OpenAI functions": "https://python.langchain.com/docs/use_cases/question_answering/integrations/openai_functions_retrieval_qa", "CAMEL Role-Playing Autonomous Cooperative Agents": "https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/camel_role_playing", "Multi-agent authoritarian speaker selection": "https://python.langchain.com/docs/use_cases/more/agents/agent_simulations/multiagent_authoritarian", "Memory in LLMChain": "https://python.langchain.com/docs/modules/memory/adding_memory", "Retry parser": "https://python.langchain.com/docs/modules/model_io/output_parsers/retry", "Pydantic (JSON) parser": "https://python.langchain.com/docs/modules/model_io/output_parsers/pydantic", "Prompt pipelining": "https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/prompts_pipelining", "Using OpenAI functions": "https://python.langchain.com/docs/modules/chains/how_to/openai_functions", "Code writing": "https://python.langchain.com/docs/expression_language/cookbook/code_writing"}, "CallbackManager": {"Anthropic": "https://python.langchain.com/docs/integrations/chat/anthropic", "\ud83d\ude85 LiteLLM": "https://python.langchain.com/docs/integrations/chat/litellm", "Ollama": "https://python.langchain.com/docs/integrations/llms/ollama", "Llama.cpp": "https://python.langchain.com/docs/integrations/llms/llamacpp", "Titan Takeoff": "https://python.langchain.com/docs/integrations/llms/titan_takeoff", "Run LLMs locally": "https://python.langchain.com/docs/guides/local_llms", "Set env var OPENAI_API_KEY or load from a .env file": "https://python.langchain.com/docs/use_cases/code_understanding", "Use local LLMs": "https://python.langchain.com/docs/use_cases/question_answering/how_to/local_retrieval_qa", "WebResearchRetriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/web_research"}, "StreamingStdOutCallbackHandler": {"Anthropic": "https://python.langchain.com/docs/integrations/chat/anthropic", "\ud83d\ude85 LiteLLM": "https://python.langchain.com/docs/integrations/chat/litellm", "Ollama": "https://python.langchain.com/docs/integrations/llms/ollama", "GPT4All": "https://python.langchain.com/docs/integrations/llms/gpt4all", "Arthur": "https://python.langchain.com/docs/integrations/providers/arthur_tracking", "Chat Over Documents with Vectara": "https://python.langchain.com/docs/integrations/providers/vectara/vectara_chat", "TextGen": "https://python.langchain.com/docs/integrations/llms/textgen", "Llama.cpp": "https://python.langchain.com/docs/integrations/llms/llamacpp", "Titan Takeoff": "https://python.langchain.com/docs/integrations/llms/titan_takeoff", "Eden AI": "https://python.langchain.com/docs/integrations/llms/edenai", "C Transformers": "https://python.langchain.com/docs/integrations/llms/ctransformers", "Huggingface TextGen Inference": "https://python.langchain.com/docs/integrations/llms/huggingface_textgen_inference", "Replicate": "https://python.langchain.com/docs/integrations/llms/replicate", "Run LLMs locally": "https://python.langchain.com/docs/guides/local_llms", "Set env var OPENAI_API_KEY or load from a .env file": "https://python.langchain.com/docs/use_cases/code_understanding", "Use local LLMs": "https://python.langchain.com/docs/use_cases/question_answering/how_to/local_retrieval_qa", "WebResearchRetriever": "https://python.langchain.com/docs/modules/data_connection/retrievers/web_research"}, "ChatLiteLLM": {"\ud83d\ude85 LiteLLM": "https://python.langchain.com/docs/integrations/chat/litellm"}, "create_tagging_chain": {"Llama API": "https://python.langchain.com/docs/integrations/chat/llama_api", "Anthropic Functions": "https://python.langchain.com/docs/integrations/chat/anthropic_functions", "Set env var OPENAI_API_KEY or load from a .env file:": "https://python.langchain.com/docs/use_cases/tagging"}, "ChatKonko": {"Konko": "https://python.langchain.com/docs/integrations/chat/konko"}, "ChatVertexAI": {"Google Cloud Platform Vertex AI PaLM ": "https://python.langchain.com/docs/integrations/chat/google_vertex_ai_palm"}, "BedrockChat": {"Bedrock Chat": "https://python.langchain.com/docs/integrations/chat/bedrock"}, "JinaChat": {"JinaChat": "https://python.langchain.com/docs/integrations/chat/jinachat"}, "ChatOllama": {"Ollama": "https://python.langchain.com/docs/integrations/chat/ollama"}, "LLMResult": {"Ollama": "https://python.langchain.com/docs/integrations/llms/ollama", "Async callbacks": "https://python.langchain.com/docs/modules/callbacks/async_callbacks"}, "BaseCallbackHandler": {"Ollama": "https://python.langchain.com/docs/integrations/llms/ollama", "Custom callback handlers": "https://python.langchain.com/docs/modules/callbacks/custom_callbacks", "Multiple callback handlers": "https://python.langchain.com/docs/modules/callbacks/multiple_callbacks", "Async callbacks": "https://python.langchain.com/docs/modules/callbacks/async_callbacks", "Streaming final agent output": "https://python.langchain.com/docs/modules/agents/how_to/streaming_stdout_final_only"}, "AzureChatOpenAI": {"Azure": "https://python.langchain.com/docs/integrations/chat/azure_chat_openai", "Azure OpenAI": "https://python.langchain.com/docs/integrations/providers/azure_openai"}, "get_openai_callback": {"Azure": "https://python.langchain.com/docs/integrations/chat/azure_chat_openai", "Token counting": "https://python.langchain.com/docs/modules/callbacks/token_counting", "Tracking token usage": "https://python.langchain.com/docs/modules/model_io/models/llms/token_usage_tracking", "Run arbitrary functions": "https://python.langchain.com/docs/expression_language/how_to/functions"}, "QianfanChatEndpoint": {"Baidu Qianfan": "https://python.langchain.com/docs/integrations/chat/baidu_qianfan_endpoint"}, "ErnieBotChat": {"ERNIE-Bot Chat": "https://python.langchain.com/docs/integrations/chat/ernie"}, "PromptLayerChatOpenAI": {"PromptLayer ChatOpenAI": "https://python.langchain.com/docs/integrations/chat/promptlayer_chatopenai"}, "ChatAnyscale": {"Anyscale": "https://python.langchain.com/docs/integrations/chat/anyscale"}, "create_extraction_chain": {"Anthropic Functions": "https://python.langchain.com/docs/integrations/chat/anthropic_functions", "Set env var OPENAI_API_KEY or load from a .env file:": "https://python.langchain.com/docs/use_cases/extraction"}, "DeepEvalCallbackHandler": {"Confident": "https://python.langchain.com/docs/integrations/callbacks/confident"}, "CharacterTextSplitter": {"Confident": "https://python.langchain.com/docs/integrations/callbacks/confident", "Hugging Face": "https://python.langchain.com/docs/integrations/providers/huggingface", "OpenAI": "https://python.langchain.com/docs/integrations/providers/openai", "Elasticsearch": "https://python.langchain.com/docs/integrations/vectorstores/elasticsearch", "Vectara Text Generation": "https://python.langchain.com/docs/integrations/providers/vectara/vectara_text_generation", "Document Comparison": "https://python.langchain.com/docs/integrations/toolkits/document_comparison_toolkit", "Vectorstore": "https://python.langchain.com/docs/integrations/toolkits/vectorstore", "LanceDB": "https://python.langchain.com/docs/integrations/vectorstores/lancedb", "sqlite-vss": "https://python.langchain.com/docs/integrations/vectorstores/sqlitevss", "Weaviate": "https://python.langchain.com/docs/integrations/vectorstores/weaviate", "DashVector": "https://python.langchain.com/docs/integrations/vectorstores/dashvector", "ScaNN": "https://python.langchain.com/docs/integrations/vectorstores/scann", "Xata": "https://python.langchain.com/docs/integrations/vectorstores/xata", "Vectara": "https://python.langchain.com/docs/modules/data_connection/retrievers/self_query/vectara_self_query", 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"https://python.langchain.com/docs/expression_language/cookbook/sql_db", "Run arbitrary functions": "https://python.langchain.com/docs/expression_language/how_to/functions"}, "JsonOutputFunctionsParser": {"prompt_llm_parser.md": "https://python.langchain.com/docs/expression_language/cookbook/prompt_llm_parser"}, "RunnableConfig": {"Run arbitrary functions": "https://python.langchain.com/docs/expression_language/how_to/functions"}} diff --git a/docs/docs/expression_language/cookbook/prompt_llm_parser.ipynb b/docs/docs/expression_language/cookbook/prompt_llm_parser.ipynb index 2449408e023..b7021734d45 100644 --- a/docs/docs/expression_language/cookbook/prompt_llm_parser.ipynb +++ b/docs/docs/expression_language/cookbook/prompt_llm_parser.ipynb @@ -30,7 +30,7 @@ "source": [ "## PromptTemplate + LLM\n", "\n", - "The simplest composition is just combing a prompt and model to create a chain that takes user input, adds it to a prompt, passes it to a model, and returns the raw model input.\n", + "The simplest composition is just combing a prompt and model to create a chain that takes user input, adds it to a prompt, passes it to a model, and returns the raw model output.\n", "\n", "Note, you can mix and match PromptTemplate/ChatPromptTemplates and LLMs/ChatModels as you like here." ] @@ -76,7 +76,7 @@ "id": "7eb9ef50", "metadata": {}, "source": [ - "Often times we want to attach kwargs that'll be passed to each model call. Here's a few examples of that:" + "Often times we want to attach kwargs that'll be passed to each model call. Here are a few examples of that:" ] }, { diff --git a/docs/docs/expression_language/how_to/fallbacks.ipynb b/docs/docs/expression_language/how_to/fallbacks.ipynb index cffc08c7405..be654f29dcf 100644 --- a/docs/docs/expression_language/how_to/fallbacks.ipynb +++ b/docs/docs/expression_language/how_to/fallbacks.ipynb @@ -107,7 +107,7 @@ "# Now let's try with fallbacks to Anthropic\n", "with patch('openai.ChatCompletion.create', side_effect=RateLimitError()):\n", " try:\n", - " print(llm.invoke(\"Why did the the chicken cross the road?\"))\n", + " print(llm.invoke(\"Why did the chicken cross the road?\"))\n", " except:\n", " print(\"Hit error\")" ] diff --git a/docs/docs/expression_language/how_to/routing.ipynb b/docs/docs/expression_language/how_to/routing.ipynb index c50929a4568..1c87c26e123 100644 --- a/docs/docs/expression_language/how_to/routing.ipynb +++ b/docs/docs/expression_language/how_to/routing.ipynb @@ -346,7 +346,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.1" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/docs/docs/get_started/quickstart.mdx b/docs/docs/get_started/quickstart.mdx index c99a8b68af8..20d5509a684 100644 --- a/docs/docs/get_started/quickstart.mdx +++ b/docs/docs/get_started/quickstart.mdx @@ -18,7 +18,7 @@ import CodeBlock from "@theme/CodeBlock"; -For more details, see our [Installation guide](/docs/get_started/installation.html). +For more details, see our [Installation guide](/docs/get_started/installation). ## Environment setup @@ -144,7 +144,7 @@ Whatever values are passed in during run time will always override what the obje Most LLM applications do not pass user input directly into an LLM. Usually they will add the user input to a larger piece of text, called a prompt template, that provides additional context on the specific task at hand. -In the previous example, the text we passed to the model contained instructions to generate a company name. For our application, it'd be great if the user only had to provide the description of a company/product, without having to worry about giving the model instructions. +In the previous example, the text we passed to the model contained instructions to generate a company name. For our application, it would be great if the user only had to provide the description of a company/product, without having to worry about giving the model instructions. PromptTemplates help with exactly this! They bundle up all the logic for going from user input into a fully formatted prompt. @@ -167,7 +167,7 @@ You can compose them together, easily combining different templates into a singl For explanations of these functionalities, see the [section on prompts](/docs/modules/model_io/prompts) for more detail. PromptTemplates can also be used to produce a list of messages. -In this case, the prompt not only contains information about the content, but also each message (its role, its position in the list, etc) +In this case, the prompt not only contains information about the content, but also each message (its role, its position in the list, etc.). Here, what happens most often is a ChatPromptTemplate is a list of ChatMessageTemplates. Each ChatMessageTemplate contains instructions for how to format that ChatMessage - its role, and then also its content. Let's take a look at this below: @@ -199,13 +199,13 @@ ChatPromptTemplates can also be constructed in other ways - see the [section on ## Output parsers OutputParsers convert the raw output of an LLM into a format that can be used downstream. -There are few main type of OutputParsers, including: +There are few main types of OutputParsers, including: -- Convert text from LLM -> structured information (e.g. JSON) +- Convert text from LLM into structured information (e.g. JSON) - Convert a ChatMessage into just a string - Convert the extra information returned from a call besides the message (like OpenAI function invocation) into a string. -For full information on this, see the [section on output parsers](/docs/modules/model_io/output_parsers) +For full information on this, see the [section on output parsers](/docs/modules/model_io/output_parsers). In this getting started guide, we will write our own output parser - one that converts a comma separated list into a list. diff --git a/docs/docs/guides/debugging.md b/docs/docs/guides/debugging.md index ac3eec7b05c..65528a56459 100644 --- a/docs/docs/guides/debugging.md +++ b/docs/docs/guides/debugging.md @@ -376,7 +376,7 @@ agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is -### `set_vebose(True)` +### `set_verbose(True)` Setting the `verbose` flag will print out inputs and outputs in a slightly more readable format and will skip logging certain raw outputs (like the token usage stats for an LLM call) so that you can focus on application logic. @@ -656,6 +656,6 @@ agent.run("Who directed the 2023 film Oppenheimer and what is their age? What is ## Other callbacks -`Callbacks` are what we use to execute any functionality within a component outside the primary component logic. All of the above solutions use `Callbacks` under the hood to log intermediate steps of components. There's a number of `Callbacks` relevant for debugging that come with LangChain out of the box, like the [FileCallbackHandler](/docs/modules/callbacks/how_to/filecallbackhandler). You can also implement your own callbacks to execute custom functionality. +`Callbacks` are what we use to execute any functionality within a component outside the primary component logic. All of the above solutions use `Callbacks` under the hood to log intermediate steps of components. There are a number of `Callbacks` relevant for debugging that come with LangChain out of the box, like the [FileCallbackHandler](/docs/modules/callbacks/how_to/filecallbackhandler). You can also implement your own callbacks to execute custom functionality. See here for more info on [Callbacks](/docs/modules/callbacks/), how to use them, and customize them. diff --git a/docs/docs/guides/deployments/index.mdx b/docs/docs/guides/deployments/index.mdx index 09841cff147..92bf6364140 100644 --- a/docs/docs/guides/deployments/index.mdx +++ b/docs/docs/guides/deployments/index.mdx @@ -1,6 +1,6 @@ # Deployment -In today's fast-paced technological landscape, the use of Large Language Models (LLMs) is rapidly expanding. As a result, it's crucial for developers to understand how to effectively deploy these models in production environments. LLM interfaces typically fall into two categories: +In today's fast-paced technological landscape, the use of Large Language Models (LLMs) is rapidly expanding. As a result, it is crucial for developers to understand how to effectively deploy these models in production environments. LLM interfaces typically fall into two categories: - **Case 1: Utilizing External LLM Providers (OpenAI, Anthropic, etc.)** In this scenario, most of the computational burden is handled by the LLM providers, while LangChain simplifies the implementation of business logic around these services. This approach includes features such as prompt templating, chat message generation, caching, vector embedding database creation, preprocessing, etc. @@ -20,11 +20,11 @@ This guide aims to provide a comprehensive overview of the requirements for depl Understanding these components is crucial when assessing serving systems. LangChain integrates with several open-source projects designed to tackle these issues, providing a robust framework for productionizing your LLM applications. Some notable frameworks include: -- [Ray Serve](/docs/ecosystem/integrations/ray_serve.html) +- [Ray Serve](/docs/ecosystem/integrations/ray_serve) - [BentoML](https://github.com/bentoml/BentoML) -- [OpenLLM](/docs/ecosystem/integrations/openllm.html) -- [Modal](/docs/ecosystem/integrations/modal.html) -- [Jina](/docs/ecosystem/integrations/jina.html#deployment) +- [OpenLLM](/docs/ecosystem/integrations/openllm) +- [Modal](/docs/ecosystem/integrations/modal) +- [Jina](/docs/ecosystem/integrations/jina#deployment) These links will provide further information on each ecosystem, assisting you in finding the best fit for your LLM deployment needs. diff --git a/docs/docs/guides/evaluation/string/json.ipynb b/docs/docs/guides/evaluation/string/json.ipynb new file mode 100644 index 00000000000..baaaa0afb34 --- /dev/null +++ b/docs/docs/guides/evaluation/string/json.ipynb @@ -0,0 +1,294 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "465cfbef-5bba-4b3b-b02d-fe2eba39db17", + "metadata": {}, + "source": [ + "# Evaluating Structured Output: JSON Evaluators\n", + "\n", + "Evaluating [extraction](https://python.langchain.com/docs/use_cases/extraction) and function calling applications often comes down to validation that the LLM's string output can be parsed correctly and how it compares to a reference object. The following JSON validators provide provide functionality to check your model's output in a consistent way.\n", + "\n", + "## JsonValidityEvaluator\n", + "\n", + "The `JsonValidityEvaluator` is designed to check the validity of a JSON string prediction.\n", + "\n", + "### Overview:\n", + "- **Requires Input?**: No\n", + "- **Requires Reference?**: No" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "02e5f7dd-82fe-48f9-a251-b2052e17e61c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'score': 1}\n" + ] + } + ], + "source": [ + "from langchain.evaluation import JsonValidityEvaluator, load_evaluator\n", + "\n", + "evaluator = JsonValidityEvaluator()\n", + "# Equivalently\n", + "# evaluator = load_evaluator(\"json_validity\")\n", + "prediction = '{\"name\": \"John\", \"age\": 30, \"city\": \"New York\"}'\n", + "\n", + "result = evaluator.evaluate_strings(prediction=prediction)\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9a9607c6-edab-4c26-86c4-22b226e18aa9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'score': 0, 'reasoning': 'Expecting property name enclosed in double quotes: line 1 column 48 (char 47)'}\n" + ] + } + ], + "source": [ + "prediction = '{\"name\": \"John\", \"age\": 30, \"city\": \"New York\",}'\n", + "result = evaluator.evaluate_strings(prediction=prediction)\n", + "print(result)" + ] + }, + { + "cell_type": "markdown", + "id": "8ac18a83-30d8-4c11-abf2-7a36e4cb829f", + "metadata": {}, + "source": [ + "## JsonEqualityEvaluator\n", + "\n", + "The `JsonEqualityEvaluator` assesses whether a JSON prediction matches a given reference after both are parsed.\n", + "\n", + "### Overview:\n", + "- **Requires Input?**: No\n", + "- **Requires Reference?**: Yes\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ab97111e-cba9-4273-825f-d5d4278a953c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'score': True}\n" + ] + } + ], + "source": [ + "from langchain.evaluation import JsonEqualityEvaluator\n", + "\n", + "evaluator = JsonEqualityEvaluator()\n", + "# Equivalently\n", + "# evaluator = load_evaluator(\"json_equality\")\n", + "result = evaluator.evaluate_strings(prediction='{\"a\": 1}', reference='{\"a\": 1}')\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "655ba486-09b6-47ce-947d-b2bd8b6f6364", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'score': False}\n" + ] + } + ], + "source": [ + "result = evaluator.evaluate_strings(prediction='{\"a\": 1}', reference='{\"a\": 2}')\n", + "print(result)" + ] + }, + { + "cell_type": "markdown", + "id": "1ac7e541-b7fe-46b6-bc3a-e94fe316227e", + "metadata": {}, + "source": [ + "The evaluator also by default lets you provide a dictionary directly" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "36e70ba3-4e62-483c-893a-5f328b7f303d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'score': False}\n" + ] + } + ], + "source": [ + "result = evaluator.evaluate_strings(prediction={\"a\": 1}, reference={\"a\": 2})\n", + "print(result)" + ] + }, + { + "cell_type": "markdown", + "id": "921d33f0-b3c2-4e9e-820c-9ec30bc5bb20", + "metadata": {}, + "source": [ + "## JsonEditDistanceEvaluator\n", + "\n", + "The `JsonEditDistanceEvaluator` computes a normalized Damerau-Levenshtein distance between two \"canonicalized\" JSON strings.\n", + "\n", + "### Overview:\n", + "- **Requires Input?**: No\n", + "- **Requires Reference?**: Yes\n", + "- **Distance Function**: Damerau-Levenshtein (by default)\n", + "\n", + "_Note: Ensure that `rapidfuzz` is installed or provide an alternative `string_distance` function to avoid an ImportError._" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "da9ec3a3-675f-4420-8ec7-cde48d8c2918", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'score': 0.07692307692307693}\n" + ] + } + ], + "source": [ + "from langchain.evaluation import JsonEditDistanceEvaluator\n", + "\n", + "evaluator = JsonEditDistanceEvaluator()\n", + "# Equivalently\n", + "# evaluator = load_evaluator(\"json_edit_distance\")\n", + "\n", + "result = evaluator.evaluate_strings(\n", + " prediction='{\"a\": 1, \"b\": 2}', reference='{\"a\": 1, \"b\": 3}'\n", + ")\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "537ed58c-6a9c-402f-8f7f-07b1119a9ae0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'score': 0.0}\n" + ] + } + ], + "source": [ + "# The values are canonicalized prior to comparison\n", + "result = evaluator.evaluate_strings(\n", + " prediction=\"\"\"\n", + " {\n", + " \"b\": 3,\n", + " \"a\": 1\n", + " }\"\"\",\n", + " reference='{\"a\": 1, \"b\": 3}',\n", + ")\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7a8f3ec5-1cde-4b0e-80cd-ac0ac290d375", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'score': 0.18181818181818182}\n" + ] + } + ], + "source": [ + "# Lists maintain their order, however\n", + "result = evaluator.evaluate_strings(\n", + " prediction='{\"a\": [1, 2]}', reference='{\"a\": [2, 1]}'\n", + ")\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "52abec79-58ed-4ab6-9fb1-7deb1f5146cc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'score': 0.14285714285714285}\n" + ] + } + ], + "source": [ + "# You can also pass in objects directly\n", + "result = evaluator.evaluate_strings(prediction={\"a\": 1}, reference={\"a\": 2})\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85afcf33-d2f4-406e-9d8f-15dc0a4772f2", + "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": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/docs/guides/fallbacks.ipynb b/docs/docs/guides/fallbacks.ipynb index a832bd17b0c..551ede4d2b6 100644 --- a/docs/docs/guides/fallbacks.ipynb +++ b/docs/docs/guides/fallbacks.ipynb @@ -109,7 +109,7 @@ "# Now let's try with fallbacks to Anthropic\n", "with patch('openai.ChatCompletion.create', side_effect=RateLimitError()):\n", " try:\n", - " print(llm.invoke(\"Why did the the chicken cross the road?\"))\n", + " print(llm.invoke(\"Why did the chicken cross the road?\"))\n", " except:\n", " print(\"Hit error\")" ] diff --git a/docs/docs/guides/langsmith/walkthrough.ipynb b/docs/docs/guides/langsmith/walkthrough.ipynb index a03babd7670..4985fc41c3b 100644 --- a/docs/docs/guides/langsmith/walkthrough.ipynb +++ b/docs/docs/guides/langsmith/walkthrough.ipynb @@ -200,7 +200,6 @@ " \"What is LangChain?\",\n", " \"What's LangSmith?\",\n", " \"When was Llama-v2 released?\",\n", - " \"Who trained Llama-v2?\",\n", " \"What is the langsmith cookbook?\",\n", " \"When did langchain first announce the hub?\",\n", "]\n", diff --git a/docs/docs/guides/local_llms.ipynb b/docs/docs/guides/local_llms.ipynb index baba1b0e502..62212ddc22f 100644 --- a/docs/docs/guides/local_llms.ipynb +++ b/docs/docs/guides/local_llms.ipynb @@ -148,7 +148,7 @@ "\n", "Inference speed is a challenge when running models locally (see above).\n", "\n", - "To minimize latency, it is desiable to run models locally on GPU, which ships with many consumer laptops [e.g., Apple devices](https://www.apple.com/newsroom/2022/06/apple-unveils-m2-with-breakthrough-performance-and-capabilities/).\n", + "To minimize latency, it is desirable to run models locally on GPU, which ships with many consumer laptops [e.g., Apple devices](https://www.apple.com/newsroom/2022/06/apple-unveils-m2-with-breakthrough-performance-and-capabilities/).\n", "\n", "And even with GPU, the available GPU memory bandwidth (as noted above) is important.\n", "\n", @@ -254,7 +254,7 @@ "\n", "`f16_kv`: whether the model should use half-precision for the key/value cache\n", "* Value: True\n", - "* Meaning: The model will use half-precision, which can be more memory efficient; Metal only support True." + "* Meaning: The model will use half-precision, which can be more memory efficient; Metal only supports True." ] }, { @@ -291,7 +291,7 @@ "id": "f56f5168", "metadata": {}, "source": [ - "The console log will show the the below to indicate Metal was enabled properly from steps above:\n", + "The console log will show the below to indicate Metal was enabled properly from steps above:\n", "```\n", "ggml_metal_init: allocating\n", "ggml_metal_init: using MPS\n", diff --git a/docs/docs/guides/safety/amazon_comprehend_chain.ipynb b/docs/docs/guides/safety/amazon_comprehend_chain.ipynb index ed8026cfd82..1de5f75dc40 100644 --- a/docs/docs/guides/safety/amazon_comprehend_chain.ipynb +++ b/docs/docs/guides/safety/amazon_comprehend_chain.ipynb @@ -16,7 +16,10 @@ "cell_type": "code", "execution_count": null, "id": "2c4236d8-4054-473d-84a4-87a4db278a62", - "metadata": {}, + "metadata": { + "scrolled": true, + "tags": [] + }, "outputs": [], "source": [ "%pip install boto3 nltk" @@ -24,7 +27,33 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, + "id": "9c792c3d-c601-409c-8e41-1c05a2fa0e84", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "%pip install -U langchain_experimental" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "496df413-a840-40a1-9ac0-3af7c1303476", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "%pip install -U langchain pydantic" + ] + }, + { + "cell_type": "code", + "execution_count": null, "id": "3f8518ad-c762-413c-b8c9-f1c211fc311d", "metadata": { "tags": [] @@ -32,6 +61,7 @@ "outputs": [], "source": [ "import boto3\n", + "import os\n", "\n", "comprehend_client = boto3.client('comprehend', region_name='us-east-1')" ] @@ -73,7 +103,6 @@ "outputs": [], "source": [ "from langchain.prompts import PromptTemplate\n", - "from langchain.chains import LLMChain\n", "from langchain.llms.fake import FakeListLLM\n", "from langchain_experimental.comprehend_moderation.base_moderation_exceptions import ModerationPiiError\n", "\n", @@ -85,25 +114,22 @@ "\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", - " \"Final Answer: This is a really shitty way of constructing a birdhouse. This is fucking insane to think that any birds would actually create their motherfucking nests here.\"\n", + " # replace with your own expletive\n", + " \"Final Answer: This is a really way of constructing a birdhouse. This is insane to think that any birds would actually create their nests here.\"\n", "]\n", "llm = FakeListLLM(responses=responses)\n", "\n", - "llm_chain = LLMChain(prompt=prompt, llm=llm)\n", - "\n", "chain = (\n", " prompt \n", " | comprehend_moderation \n", - " | {llm_chain.input_keys[0]: lambda x: x['output'] } \n", - " | llm_chain \n", - " | { \"input\": lambda x: x['text'] } \n", + " | {\"input\": (lambda x: x['output'] ) | llm}\n", " | comprehend_moderation \n", ")\n", "\n", "try:\n", - " response = chain.invoke({\"question\": \"A sample SSN number looks like this 123-456-7890. Can you give me some more samples?\"})\n", + " response = chain.invoke({\"question\": \"A sample SSN number looks like this 123-22-3345. Can you give me some more samples?\"})\n", "except ModerationPiiError as e:\n", - " print(e.message)\n", + " print(str(e))\n", "else:\n", " print(response['output'])\n" ] @@ -117,6 +143,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "bfd550e7-5012-41fa-9546-8b78ddf1c673", "metadata": {}, @@ -125,7 +152,7 @@ "\n", "- PII (Personally Identifiable Information) checks \n", "- Toxicity content detection\n", - "- Intention detection\n", + "- Prompt Safety detection\n", "\n", "Here is an example of a moderation config." ] @@ -139,46 +166,51 @@ }, "outputs": [], "source": [ - "from langchain_experimental.comprehend_moderation import BaseModerationActions, BaseModerationFilters\n", + "from langchain_experimental.comprehend_moderation import (BaseModerationConfig, \n", + " ModerationPromptSafetyConfig, \n", + " ModerationPiiConfig, \n", + " ModerationToxicityConfig\n", + ")\n", "\n", - "moderation_config = { \n", - " \"filters\":[ \n", - " BaseModerationFilters.PII, \n", - " BaseModerationFilters.TOXICITY,\n", - " BaseModerationFilters.INTENT\n", - " ],\n", - " \"pii\":{ \n", - " \"action\": BaseModerationActions.ALLOW, \n", - " \"threshold\":0.5, \n", - " \"labels\":[\"SSN\"],\n", - " \"mask_character\": \"X\"\n", - " },\n", - " \"toxicity\":{ \n", - " \"action\": BaseModerationActions.STOP, \n", - " \"threshold\":0.5\n", - " },\n", - " \"intent\":{ \n", - " \"action\": BaseModerationActions.STOP, \n", - " \"threshold\":0.5\n", - " }\n", - "}" + "pii_config = ModerationPiiConfig(\n", + " labels=[\"SSN\"],\n", + " redact=True,\n", + " mask_character=\"X\"\n", + ")\n", + "\n", + "toxicity_config = ModerationToxicityConfig(\n", + " threshold=0.5\n", + ")\n", + "\n", + "prompt_safety_config = ModerationPromptSafetyConfig(\n", + " threshold=0.5\n", + ")\n", + "\n", + "moderation_config = BaseModerationConfig(\n", + " filters=[pii_config, toxicity_config, prompt_safety_config]\n", + ")" ] }, { + "attachments": {}, "cell_type": "markdown", "id": "3634376b-5938-43df-9ed6-70ca7e99290f", "metadata": {}, "source": [ - "At the core of the configuration you have three filters specified in the `filters` key:\n", + "At the core of the the configuration there are three configuration models to be used\n", "\n", - "1. `BaseModerationFilters.PII`\n", - "2. `BaseModerationFilters.TOXICITY`\n", - "3. `BaseModerationFilters.INTENT`\n", + "- `ModerationPiiConfig` used for configuring the behavior of the PII validations. Following are the parameters it can be initialized with\n", + " - `labels` the PII entity labels. Defaults to an empty list which means that the PII validation will consider all PII entities.\n", + " - `threshold` the confidence threshold for the detected entities, defaults to 0.5 or 50%\n", + " - `redact` a boolean flag to enforce whether redaction should be performed on the text, defaults to `False`. When `False`, the PII validation will error out when it detects any PII entity, when set to `True` it simply redacts the PII values in the text.\n", + " - `mask_character` the character used for masking, defaults to asterisk (*)\n", + "- `ModerationToxicityConfig` used for configuring the behavior of the toxicity validations. Following are the parameters it can be initialized with\n", + " - `labels` the Toxic entity labels. Defaults to an empty list which means that the toxicity validation will consider all toxic entities. all\n", + " - `threshold` the confidence threshold for the detected entities, defaults to 0.5 or 50% \n", + "- `ModerationPromptSafetyConfig` used for configuring the behavior of the prompt safety validation\n", + " - `threshold` the confidence threshold for the the prompt safety classification, defaults to 0.5 or 50% \n", "\n", - "And an `action` key that defines two possible actions for each moderation function:\n", - "\n", - "1. `BaseModerationActions.ALLOW` - `allows` the prompt to pass through but masks detected PII in case of PII check. The default behavior is to run and redact all PII entities. If there is an entity specified in the `labels` field, then only those entities will go through the PII check and masked.\n", - "2. `BaseModerationActions.STOP` - `stops` the prompt from passing through to the next step in case any PII, Toxicity, or incorrect Intent is detected. The action of `BaseModerationActions.STOP` will raise a Python `Exception` essentially stopping the chain in progress.\n", + "Finally, you use the `BaseModerationConfig` to define the order in which each of these checks are to be performed. The `BaseModerationConfig` takes an optional `filters` parameter which can be a list of one or more than one of the above validation checks, as seen in the previous code block. The `BaseModerationConfig` can also be initialized with any `filters` in which case it will use all the checks with default configuration (more on this explained later).\n", "\n", "Using the configuration in the previous cell will perform PII checks and will allow the prompt to pass through however it will mask any SSN numbers present in either the prompt or the LLM output.\n" ] @@ -196,7 +228,20 @@ " moderation_config=moderation_config, #specify the configuration\n", " client=comprehend_client, #optionally pass the Boto3 Client\n", " verbose=True\n", - ")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a25e6f93-765b-4f99-8c1c-929157dbd4aa", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from langchain.prompts import PromptTemplate\n", + "from langchain.llms.fake import FakeListLLM\n", "\n", "template = \"\"\"Question: {question}\n", "\n", @@ -206,23 +251,21 @@ "\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", - " \"Final Answer: This is a really shitty way of constructing a birdhouse. This is fucking insane to think that any birds would actually create their motherfucking nests here.\"\n", + " # replace with your own expletive\n", + " \"Final Answer: This is a really way of constructing a birdhouse. This is insane to think that any birds would actually create their nests here.\"\n", "]\n", "llm = FakeListLLM(responses=responses)\n", "\n", - "llm_chain = LLMChain(prompt=prompt, llm=llm)\n", - "\n", "chain = ( \n", " prompt \n", " | comp_moderation_with_config \n", - " | {llm_chain.input_keys[0]: lambda x: x['output'] } \n", - " | llm_chain \n", - " | { \"input\": lambda x: x['text'] } \n", + " | {\"input\": (lambda x: x['output'] ) | llm}\n", " | comp_moderation_with_config \n", ")\n", "\n", + "\n", "try:\n", - " response = chain.invoke({\"question\": \"A sample SSN number looks like this 123-456-7890. Can you give me some more samples?\"})\n", + " response = chain.invoke({\"question\": \"A sample SSN number looks like this 123-45-7890. Can you give me some more samples?\"})\n", "except Exception as e:\n", " print(str(e))\n", "else:\n", @@ -230,6 +273,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "ba890681-feeb-43ca-a0d5-9c11d2d9de3e", "metadata": { @@ -238,25 +282,25 @@ "source": [ "## Unique ID, and Moderation Callbacks\n", "\n", - "When Amazon Comprehend moderation action is specified as `STOP`, the chain will raise one of the following exceptions-\n", + "When Amazon Comprehend moderation action identifies any of the configugred entity, the chain will raise one of the following exceptions-\n", " - `ModerationPiiError`, for PII checks\n", " - `ModerationToxicityError`, for Toxicity checks \n", - " - `ModerationIntentionError` for Intent checks\n", + " - `ModerationPromptSafetyError` for Prompt Safety checks\n", "\n", "In addition to the moderation configuration, the `AmazonComprehendModerationChain` can also be initialized with the following parameters\n", "\n", "- `unique_id` [Optional] a string parameter. This parameter can be used to pass any string value or ID. For example, in a chat application, you may want to keep track of abusive users, in this case, you can pass the user's username/email ID etc. This defaults to `None`.\n", "\n", - "- `moderation_callback` [Optional] the `BaseModerationCallbackHandler` will be called asynchronously (non-blocking to the chain). Callback functions are useful when you want to perform additional actions when the moderation functions are executed, for example logging into a database, or writing a log file. You can override three functions by subclassing `BaseModerationCallbackHandler` - `on_after_pii()`, `on_after_toxicity()`, and `on_after_intent()`. Note that all three functions must be `async` functions. These callback functions receive two arguments:\n", - " - `moderation_beacon` is a dictionary that will contain information about the moderation function, the full response from the Amazon Comprehend model, a unique chain id, the moderation status, and the input string which was validated. The dictionary is of the following schema-\n", + "- `moderation_callback` [Optional] the `BaseModerationCallbackHandler` that will be called asynchronously (non-blocking to the chain). Callback functions are useful when you want to perform additional actions when the moderation functions are executed, for example logging into a database, or writing a log file. You can override three functions by subclassing `BaseModerationCallbackHandler` - `on_after_pii()`, `on_after_toxicity()`, and `on_after_prompt_safety()`. Note that all three functions must be `async` functions. These callback functions receive two arguments:\n", + " - `moderation_beacon` a dictionary that will contain information about the moderation function, the full response from Amazon Comprehend model, a unique chain id, the moderation status, and the input string which was validated. The dictionary is of the following schema-\n", " \n", " ```\n", " { \n", " 'moderation_chain_id': 'xxx-xxx-xxx', # Unique chain ID\n", - " 'moderation_type': 'Toxicity' | 'PII' | 'Intent', \n", + " 'moderation_type': 'Toxicity' | 'PII' | 'PromptSafety', \n", " 'moderation_status': 'LABELS_FOUND' | 'LABELS_NOT_FOUND',\n", " 'moderation_input': 'A sample SSN number looks like this 123-456-7890. Can you give me some more samples?',\n", - " 'moderation_output': {...} #Full Amazon Comprehend PII, Toxicity, or Intent Model Output\n", + " 'moderation_output': {...} #Full Amazon Comprehend PII, Toxicity, or Prompt Safety Model Output\n", " }\n", " ```\n", " \n", @@ -313,7 +357,7 @@ " async def on_after_toxicity(self, output_beacon, unique_id):\n", " pass\n", " \n", - " async def on_after_intent(self, output_beacon, unique_id):\n", + " async def on_after_prompt_safety(self, output_beacon, unique_id):\n", " pass\n", " '''\n", " \n", @@ -330,22 +374,19 @@ }, "outputs": [], "source": [ - "moderation_config = { \n", - " \"filters\": [ \n", - " BaseModerationFilters.PII, \n", - " BaseModerationFilters.TOXICITY\n", - " ],\n", - " \"pii\":{ \n", - " \"action\": BaseModerationActions.STOP, \n", - " \"threshold\":0.5, \n", - " \"labels\":[\"SSN\"], \n", - " \"mask_character\": \"X\" \n", - " },\n", - " \"toxicity\":{ \n", - " \"action\": BaseModerationActions.STOP, \n", - " \"threshold\":0.5 \n", - " }\n", - "}\n", + "pii_config = ModerationPiiConfig(\n", + " labels=[\"SSN\"],\n", + " redact=True,\n", + " mask_character=\"X\"\n", + ")\n", + "\n", + "toxicity_config = ModerationToxicityConfig(\n", + " threshold=0.5\n", + ")\n", + "\n", + "moderation_config = BaseModerationConfig(\n", + " filters=[pii_config, toxicity_config]\n", + ")\n", "\n", "comp_moderation_with_config = AmazonComprehendModerationChain(\n", " moderation_config=moderation_config, # specify the configuration\n", @@ -366,7 +407,6 @@ "outputs": [], "source": [ "from langchain.prompts import PromptTemplate\n", - "from langchain.chains import LLMChain\n", "from langchain.llms.fake import FakeListLLM\n", "\n", "template = \"\"\"Question: {question}\n", @@ -377,19 +417,16 @@ "\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", - " \"Final Answer: This is a really shitty way of constructing a birdhouse. This is fucking insane to think that any birds would actually create their motherfucking nests here.\"\n", + " # replace with your own expletive\n", + " \"Final Answer: This is a really way of constructing a birdhouse. This is insane to think that any birds would actually create their nests here.\"\n", "]\n", "\n", "llm = FakeListLLM(responses=responses)\n", "\n", - "llm_chain = LLMChain(prompt=prompt, llm=llm)\n", - "\n", "chain = (\n", " prompt \n", " | comp_moderation_with_config \n", - " | {llm_chain.input_keys[0]: lambda x: x['output'] } \n", - " | llm_chain \n", - " | { \"input\": lambda x: x['text'] } \n", + " | {\"input\": (lambda x: x['output'] ) | llm}\n", " | comp_moderation_with_config \n", ") \n", "\n", @@ -402,6 +439,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "id": "706454b2-2efa-4d41-abc8-ccf2b4e87822", "metadata": { @@ -410,7 +448,7 @@ "source": [ "## `moderation_config` and moderation execution order\n", "\n", - "If `AmazonComprehendModerationChain` is not initialized with any `moderation_config` then the default action is `STOP` and the default order of moderation check is as follows.\n", + "If `AmazonComprehendModerationChain` is not initialized with any `moderation_config` then it is initialized with the default values of `BaseModerationConfig`. If no `filters` are used then the sequence of moderation check is as follows.\n", "\n", "```\n", "AmazonComprehendModerationChain\n", @@ -423,39 +461,32 @@ " โ”œโ”€โ”€ Callback (if available)\n", " โ”œโ”€โ”€ Label Found โŸถ [Error Stop]\n", " โ””โ”€โ”€ No Label Found\n", - " โ””โ”€โ”€Check Intent with Stop Action\n", + " โ””โ”€โ”€Check Prompt Safety with Stop Action\n", " โ”œโ”€โ”€ Callback (if available)\n", " โ”œโ”€โ”€ Label Found โŸถ [Error Stop]\n", " โ””โ”€โ”€ No Label Found\n", " โ””โ”€โ”€ Return Prompt\n", "```\n", "\n", - "If any of the checks raises an exception then the subsequent checks will not be performed. If a `callback` is provided in this case, then it will be called for each of the checks that have been performed. For example, in the case above, if the Chain fails due to the presence of PII then the Toxicity and Intent checks will not be performed.\n", + "If any of the check raises a validation exception then the subsequent checks will not be performed. If a `callback` is provided in this case, then it will be called for each of the checks that have been performed. For example, in the case above, if the Chain fails due to presence of PII then the Toxicity and Prompt Safety checks will not be performed.\n", "\n", - "You can override the execution order by passing `moderation_config` and simply specifying the desired order in the `filters` key of the configuration. In case you use `moderation_config` then the order of the checks as specified in the `filters` key will be maintained. For example, in the configuration below, first Toxicity check will be performed, then PII, and finally Intent validation will be performed. In this case, `AmazonComprehendModerationChain` will perform the desired checks in the specified order with default values of each model `kwargs`.\n", + "You can override the execution order by passing `moderation_config` and simply specifying the desired order in the `filters` parameter of the `BaseModerationConfig`. In case you specify the filters, then the order of the checks as specified in the `filters` parameter will be maintained. For example, in the configuration below, first Toxicity check will be performed, then PII, and finally Prompt Safety validation will be performed. In this case, `AmazonComprehendModerationChain` will perform the desired checks in the specified order with default values of each model `kwargs`.\n", "\n", "```python\n", - "moderation_config = { \n", - " \"filters\":[ BaseModerationFilters.TOXICITY, \n", - " BaseModerationFilters.PII, \n", - " BaseModerationFilters.INTENT]\n", - " }\n", + "pii_check = ModerationPiiConfig()\n", + "toxicity_check = ModerationToxicityConfig()\n", + "prompt_safety_check = ModerationPromptSafetyConfig()\n", + "\n", + "moderation_config = BaseModerationConfig(filters=[toxicity_check, pii_check, prompt_safety_check])\n", "```\n", "\n", - "Model `kwargs` are specified by the `pii`, `toxicity`, and `intent` keys within the `moderation_config` dictionary. For example, in the `moderation_config` below, the default order of moderation is overriden and the `pii` & `toxicity` model `kwargs` have been overriden. For `intent` the chain's default `kwargs` will be used.\n", + "You can have also use more than one configuration for a specific moderation check, for example in the sample below, two consecutive PII checks are performed. First the configuration checks for any SSN, if found it would raise an error. If any SSN isn't found then it will next check if any NAME and CREDIT_DEBIT_NUMBER is present in the prompt and will mask it.\n", "\n", "```python\n", - " moderation_config = { \n", - " \"filters\":[ BaseModerationFilters.TOXICITY, \n", - " BaseModerationFilters.PII, \n", - " BaseModerationFilters.INTENT],\n", - " \"pii\":{ \"action\": BaseModerationActions.ALLOW, \n", - " \"threshold\":0.5, \n", - " \"labels\":[\"SSN\"], \n", - " \"mask_character\": \"X\" },\n", - " \"toxicity\":{ \"action\": BaseModerationActions.STOP, \n", - " \"threshold\":0.5 }\n", - " }\n", + "pii_check_1 = ModerationPiiConfig(labels=[\"SSN\"])\n", + "pii_check_2 = ModerationPiiConfig(labels=[\"NAME\", \"CREDIT_DEBIT_NUMBER\"], redact=True)\n", + "\n", + "moderation_config = BaseModerationConfig(filters=[pii_check_1, pii_check_2])\n", "```\n", "\n", "1. For a list of PII labels see Amazon Comprehend Universal PII entity types - https://docs.aws.amazon.com/comprehend/latest/dg/how-pii.html#how-pii-types\n", @@ -467,10 +498,11 @@ " - `VIOLENCE_OR_THREAT`: Speech that includes threats which seek to inflict pain, injury or hostility towards a person or group.\n", " - `INSULT`: Speech that includes demeaning, humiliating, mocking, insulting, or belittling language.\n", " - `PROFANITY`: Speech that contains words, phrases or acronyms that are impolite, vulgar, or offensive is considered as profane.\n", - "3. For a list of Intent labels refer to documentation [link here]" + "3. For a list of Prompt Safety labels refer to documentation [link here]" ] }, { + "attachments": {}, "cell_type": "markdown", "id": "78905aec-55ae-4fc3-a23b-8a69bd1e33f2", "metadata": {}, @@ -504,7 +536,8 @@ }, "outputs": [], "source": [ - "%env HUGGINGFACEHUB_API_TOKEN=\"\"" + "import os\n", + "os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = \"\"" ] }, { @@ -517,7 +550,7 @@ "outputs": [], "source": [ "# See https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads for some other options\n", - "repo_id = \"google/flan-t5-xxl\" \n" + "repo_id = \"google/flan-t5-xxl\" " ] }, { @@ -529,20 +562,15 @@ }, "outputs": [], "source": [ - "from langchain.llms import HuggingFaceHub\n", + "from langchain import HuggingFaceHub\n", "from langchain.prompts import PromptTemplate\n", - "from langchain.chains import LLMChain\n", "\n", - "template = \"\"\"Question: {question}\n", - "\n", - "Answer:\"\"\"\n", + "template = \"\"\"{question}\"\"\"\n", "\n", "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", - "\n", "llm = HuggingFaceHub(\n", " repo_id=repo_id, model_kwargs={\"temperature\": 0.5, \"max_length\": 256}\n", - ")\n", - "llm_chain = LLMChain(prompt=prompt, llm=llm)" + ")" ] }, { @@ -562,22 +590,41 @@ }, "outputs": [], "source": [ - "moderation_config = { \n", - " \"filters\":[ BaseModerationFilters.PII, BaseModerationFilters.TOXICITY, BaseModerationFilters.INTENT ],\n", - " \"pii\":{\"action\": BaseModerationActions.ALLOW, \"threshold\":0.5, \"labels\":[\"SSN\",\"CREDIT_DEBIT_NUMBER\"], \"mask_character\": \"X\"},\n", - " \"toxicity\":{\"action\": BaseModerationActions.STOP, \"threshold\":0.5},\n", - " \"intent\":{\"action\": BaseModerationActions.ALLOW, \"threshold\":0.5,},\n", - " }\n", "\n", - "# without any callback\n", - "amazon_comp_moderation = AmazonComprehendModerationChain(moderation_config=moderation_config, \n", - " client=comprehend_client,\n", - " verbose=True)\n", + "# define filter configs\n", + "pii_config = ModerationPiiConfig(\n", + " labels=[\"SSN\", \"CREDIT_DEBIT_NUMBER\"],\n", + " redact=True,\n", + " mask_character=\"X\"\n", + ")\n", "\n", - "# with callback\n", - "amazon_comp_moderation_out = AmazonComprehendModerationChain(moderation_config=moderation_config, \n", + "toxicity_config = ModerationToxicityConfig(\n", + " threshold=0.5\n", + ")\n", + "\n", + "prompt_safety_config = ModerationPromptSafetyConfig(\n", + " threshold=0.8\n", + ")\n", + "\n", + "# define different moderation configs using the filter configs above\n", + "moderation_config_1 = BaseModerationConfig(\n", + " filters=[pii_config, toxicity_config, prompt_safety_config]\n", + ")\n", + "\n", + "moderation_config_2 = BaseModerationConfig(\n", + " filters=[pii_config]\n", + ")\n", + "\n", + "\n", + "# input prompt moderation chain with callback\n", + "amazon_comp_moderation = AmazonComprehendModerationChain(moderation_config=moderation_config_1, \n", " client=comprehend_client,\n", " moderation_callback=my_callback,\n", + " verbose=True)\n", + "\n", + "# Output from LLM moderation chain without callback\n", + "amazon_comp_moderation_out = AmazonComprehendModerationChain(moderation_config=moderation_config_2, \n", + " client=comprehend_client,\n", " verbose=True)" ] }, @@ -586,7 +633,7 @@ "id": "b1256bc8-1321-4624-9e8a-a2d4a8df59bf", "metadata": {}, "source": [ - "The `moderation_config` will now prevent any inputs and model outputs containing obscene words or sentences, bad intent, or PII with entities other than SSN with score above threshold or 0.5 or 50%. If it finds Pii entities - SSN - it will redact them before allowing the call to proceed. " + "The `moderation_config` will now prevent any inputs containing obscene words or sentences, bad intent, or PII with entities other than SSN with score above threshold or 0.5 or 50%. If it finds Pii entities - SSN - it will redact them before allowing the call to proceed. It will also mask any SSN or credit card numbers from the model's response." ] }, { @@ -601,14 +648,15 @@ "chain = (\n", " prompt \n", " | amazon_comp_moderation \n", - " | {llm_chain.input_keys[0]: lambda x: x['output'] } \n", - " | llm_chain \n", - " | { \"input\": lambda x: x['text'] } \n", + " | { \"input\" : (lambda x: x['output']) | llm }\n", " | amazon_comp_moderation_out\n", ")\n", "\n", "try:\n", - " response = chain.invoke({\"question\": \"My AnyCompany Financial Services, LLC credit card account 1111-0000-1111-0008 has 24$ due by July 31st. Can you give me some more credit car number samples?\"})\n", + " response = chain.invoke({\"question\": \"\"\"What is John Doe's address, phone number and SSN from the following text?\n", + "\n", + "John Doe, a resident of 1234 Elm Street in Springfield, recently celebrated his birthday on January 1st. Turning 43 this year, John reflected on the years gone by. He often shares memories of his younger days with his close friends through calls on his phone, (555) 123-4567. Meanwhile, during a casual evening, he received an email at johndoe@example.com reminding him of an old acquaintance's reunion. As he navigated through some old documents, he stumbled upon a paper that listed his SSN as 123-45-6789, reminding him to store it in a safer place.\n", + "\"\"\"})\n", "except Exception as e:\n", " print(str(e))\n", "else:\n", @@ -624,7 +672,7 @@ "source": [ "### With Amazon SageMaker Jumpstart\n", "\n", - "The example below shows how to use the `Amazon Comprehend Moderation chain` with an Amazon SageMaker Jumpstart hosted LLM. You should have an `Amazon SageMaker Jumpstart` hosted LLM endpoint within your AWS Account. " + "The exmaple below shows how to use Amazon Comprehend Moderation chain with an Amazon SageMaker Jumpstart hosted LLM. You should have an Amazon SageMaker Jumpstart hosted LLM endpoint within your AWS Account. Refer to [this notebook](https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-falcon.ipynb) for more on how to deploy an LLM with Amazon SageMaker Jumpstart hosted endpoints." ] }, { @@ -634,7 +682,8 @@ "metadata": {}, "outputs": [], "source": [ - "endpoint_name = \"\" # replace with your SageMaker Endpoint name" + "endpoint_name = \"\" # replace with your SageMaker Endpoint name\n", + "region = \"\" # replace with your SageMaker Endpoint region" ] }, { @@ -644,10 +693,9 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.llms import SagemakerEndpoint\n", + "from langchain import SagemakerEndpoint\n", "from langchain.llms.sagemaker_endpoint import LLMContentHandler\n", - "from langchain.chains import LLMChain\n", - "from langchain.prompts import load_prompt, PromptTemplate\n", + "from langchain.prompts import PromptTemplate\n", "import json\n", "\n", "class ContentHandler(LLMContentHandler):\n", @@ -664,23 +712,27 @@ "\n", "content_handler = ContentHandler()\n", "\n", - "#prompt template for input text\n", - "llm_prompt = PromptTemplate(input_variables=[\"input_text\"], template=\"{input_text}\")\n", + "template = \"\"\"From the following 'Document', precisely answer the 'Question'. Do not add any spurious information in your answer.\n", "\n", - "llm_chain = LLMChain(\n", - " llm=SagemakerEndpoint(\n", + "Document: John Doe, a resident of 1234 Elm Street in Springfield, recently celebrated his birthday on January 1st. Turning 43 this year, John reflected on the years gone by. He often shares memories of his younger days with his close friends through calls on his phone, (555) 123-4567. Meanwhile, during a casual evening, he received an email at johndoe@example.com reminding him of an old acquaintance's reunion. As he navigated through some old documents, he stumbled upon a paper that listed his SSN as 123-45-6789, reminding him to store it in a safer place.\n", + "Question: {question}\n", + "Answer:\n", + "\"\"\"\n", + "\n", + "#prompt template for input text\n", + "llm_prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", + "\n", + "llm=SagemakerEndpoint(\n", " endpoint_name=endpoint_name, \n", - " region_name='us-east-1',\n", - " model_kwargs={\"temperature\":0.97,\n", + " region_name=region,\n", + " model_kwargs={\"temperature\":0.95,\n", " \"max_length\": 200,\n", " \"num_return_sequences\": 3,\n", " \"top_k\": 50,\n", " \"top_p\": 0.95,\n", " \"do_sample\": True},\n", " content_handler=content_handler\n", - " ),\n", - " prompt=llm_prompt\n", - ")" + " )" ] }, { @@ -700,15 +752,37 @@ }, "outputs": [], "source": [ - "moderation_config = { \n", - " \"filters\":[ BaseModerationFilters.PII, BaseModerationFilters.TOXICITY ],\n", - " \"pii\":{\"action\": BaseModerationActions.ALLOW, \"threshold\":0.5, \"labels\":[\"SSN\"], \"mask_character\": \"X\"},\n", - " \"toxicity\":{\"action\": BaseModerationActions.STOP, \"threshold\":0.5},\n", - " \"intent\":{\"action\": BaseModerationActions.ALLOW, \"threshold\":0.5,},\n", - " }\n", + "# define filter configs\n", + "pii_config = ModerationPiiConfig(\n", + " labels=[\"SSN\"],\n", + " redact=True,\n", + " mask_character=\"X\"\n", + ")\n", "\n", - "amazon_comp_moderation = AmazonComprehendModerationChain(moderation_config=moderation_config, \n", - " client=comprehend_client ,\n", + "toxicity_config = ModerationToxicityConfig(\n", + " threshold=0.5\n", + ")\n", + "\n", + "\n", + "# define different moderation configs using the filter configs above\n", + "moderation_config_1 = BaseModerationConfig(\n", + " filters=[pii_config, toxicity_config]\n", + ")\n", + "\n", + "moderation_config_2 = BaseModerationConfig(\n", + " filters=[pii_config]\n", + ")\n", + "\n", + "\n", + "# input prompt moderation chain with callback\n", + "amazon_comp_moderation = AmazonComprehendModerationChain(moderation_config=moderation_config_1, \n", + " client=comprehend_client,\n", + " moderation_callback=my_callback,\n", + " verbose=True)\n", + "\n", + "# Output from LLM moderation chain without callback\n", + "amazon_comp_moderation_out = AmazonComprehendModerationChain(moderation_config=moderation_config_2, \n", + " client=comprehend_client,\n", " verbose=True)" ] }, @@ -732,14 +806,12 @@ "chain = (\n", " prompt \n", " | amazon_comp_moderation \n", - " | {llm_chain.input_keys[0]: lambda x: x['output'] } \n", - " | llm_chain \n", - " | { \"input\": lambda x: x['text'] } \n", - " | amazon_comp_moderation \n", + " | { \"input\" : (lambda x: x['output']) | llm }\n", + " | amazon_comp_moderation_out\n", ")\n", "\n", "try:\n", - " response = chain.invoke({\"question\": \"My AnyCompany Financial Services, LLC credit card account 1111-0000-1111-0008 has 24$ due by July 31st. Can you give me some more samples?\"})\n", + " response = chain.invoke({\"question\": \"What is John Doe's address, phone number and SSN?\"})\n", "except Exception as e:\n", " print(str(e))\n", "else:\n", @@ -1347,7 +1419,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.10.6" } }, "nbformat": 4, diff --git a/docs/docs/integrations/callbacks/argilla.ipynb b/docs/docs/integrations/callbacks/argilla.ipynb index dd487223364..a31b096945d 100644 --- a/docs/docs/integrations/callbacks/argilla.ipynb +++ b/docs/docs/integrations/callbacks/argilla.ipynb @@ -14,7 +14,7 @@ "> using both human and machine feedback. We provide support for each step in the MLOps cycle, \n", "> from data labeling to model monitoring.\n", "\n", - "\n", + "\n", " \"Open\n", "" ] diff --git a/docs/docs/integrations/chat/gigachat.ipynb b/docs/docs/integrations/chat/gigachat.ipynb new file mode 100644 index 00000000000..c353fe75d8f --- /dev/null +++ b/docs/docs/integrations/chat/gigachat.ipynb @@ -0,0 +1,114 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# GigaChat\n", + "This notebook shows how to use LangChain with [GigaChat](https://developers.sber.ru/portal/products/gigachat).\n", + "To use you need to install ```gigachat``` python package." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# !pip install gigachat" + ] + }, + { + "cell_type": "markdown", + "source": [ + "To get GigaChat credentials you need to [create account](https://developers.sber.ru/studio/login) and [get access to API](https://developers.sber.ru/docs/ru/gigachat/api/integration)\n", + "## Example" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "import os\n", + "from getpass import getpass\n", + "\n", + "os.environ['GIGACHAT_CREDENTIALS'] = getpass()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [], + "source": [ + "from langchain.chat_models import GigaChat\n", + "\n", + "chat = GigaChat(verify_ssl_certs=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 31, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "What do you get when you cross a goat and a skunk? A smelly goat!\n" + ] + } + ], + "source": [ + "from langchain.schema import SystemMessage, HumanMessage\n", + "\n", + "messages = [\n", + " SystemMessage(\n", + " content=\"You are a helpful AI that shares everything you know. Talk in English.\"\n", + " ),\n", + " HumanMessage(\n", + " content=\"Tell me a joke\"\n", + " ),\n", + "]\n", + "\n", + "print(chat(messages).content)" + ], + "metadata": { + "collapsed": false + } + } + ], + "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/chat/google_vertex_ai_palm.ipynb b/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb index 696add67011..8d194eb048b 100644 --- a/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb +++ b/docs/docs/integrations/chat/google_vertex_ai_palm.ipynb @@ -5,7 +5,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# GCP Vertex AI \n", + "# Google Cloud Vertex AI \n", "\n", "Note: This is separate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there. \n", "\n", @@ -31,7 +31,7 @@ }, "outputs": [], "source": [ - "#!pip install langchain google-cloud-aiplatform" + "#!pip install langchain google-cloud-aiplatform\n" ] }, { @@ -41,7 +41,7 @@ "outputs": [], "source": [ "from langchain.chat_models import ChatVertexAI\n", - "from langchain.prompts import ChatPromptTemplate" + "from langchain.prompts import ChatPromptTemplate\n" ] }, { @@ -50,7 +50,7 @@ "metadata": {}, "outputs": [], "source": [ - "chat = ChatVertexAI()" + "chat = ChatVertexAI()\n" ] }, { @@ -64,7 +64,7 @@ "prompt = ChatPromptTemplate.from_messages(\n", " [(\"system\", system), (\"human\", human)]\n", ")\n", - "messages = prompt.format_messages()" + "messages = prompt.format_messages()\n" ] }, { @@ -84,7 +84,7 @@ } ], "source": [ - "chat(messages)" + "chat(messages)\n" ] }, { @@ -104,7 +104,7 @@ "human = \"{text}\"\n", "prompt = ChatPromptTemplate.from_messages(\n", " [(\"system\", system), (\"human\", human)]\n", - ")" + ")\n" ] }, { @@ -127,7 +127,7 @@ "chain = prompt | chat\n", "chain.invoke(\n", " {\"input_language\": \"English\", \"output_language\": \"Japanese\", \"text\": \"I love programming\"}\n", - ")" + ")\n" ] }, { @@ -161,7 +161,7 @@ " model_name=\"codechat-bison\",\n", " max_output_tokens=1000,\n", " temperature=0.5\n", - ")" + ")\n" ] }, { @@ -189,7 +189,7 @@ ], "source": [ "# For simple string in string out usage, we can use the `predict` method:\n", - "print(chat.predict(\"Write a Python function to identify all prime numbers\"))" + "print(chat.predict(\"Write a Python function to identify all prime numbers\"))\n" ] }, { @@ -209,7 +209,7 @@ "source": [ "import asyncio\n", "# import nest_asyncio\n", - "# nest_asyncio.apply()" + "# nest_asyncio.apply()\n" ] }, { @@ -237,7 +237,7 @@ " top_k=40,\n", ")\n", "\n", - "asyncio.run(chat.agenerate([messages]))" + "asyncio.run(chat.agenerate([messages]))\n" ] }, { @@ -257,7 +257,7 @@ } ], "source": [ - "asyncio.run(chain.ainvoke({\"input_language\": \"English\", \"output_language\": \"Sanskrit\", \"text\": \"I love programming\"}))" + "asyncio.run(chain.ainvoke({\"input_language\": \"English\", \"output_language\": \"Sanskrit\", \"text\": \"I love programming\"}))\n" ] }, { @@ -275,7 +275,7 @@ "metadata": {}, "outputs": [], "source": [ - "import sys" + "import sys\n" ] }, { @@ -310,7 +310,7 @@ "messages = prompt.format_messages()\n", "for chunk in chat.stream(messages):\n", " sys.stdout.write(chunk.content)\n", - " sys.stdout.flush()" + " sys.stdout.flush()\n" ] } ], diff --git a/docs/docs/integrations/chat_loaders/langsmith_dataset.ipynb b/docs/docs/integrations/chat_loaders/langsmith_dataset.ipynb index d14660dea78..554cd298dfc 100644 --- a/docs/docs/integrations/chat_loaders/langsmith_dataset.ipynb +++ b/docs/docs/integrations/chat_loaders/langsmith_dataset.ipynb @@ -5,7 +5,7 @@ "id": "a9ab2a39-7c2d-4119-9dc7-8035fdfba3cb", "metadata": {}, "source": [ - "# Fine-Tuning on LangSmith Chat Datasets\n", + "# LangSmith Chat Datasets\n", "\n", "This notebook demonstrates an easy way to load a LangSmith chat dataset fine-tune a model on that data.\n", "The process is simple and comprises 3 steps.\n", @@ -271,7 +271,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.2" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/docs/docs/integrations/chat_loaders/langsmith_llm_runs.ipynb b/docs/docs/integrations/chat_loaders/langsmith_llm_runs.ipynb index 35a1fb7492c..2819401885b 100644 --- a/docs/docs/integrations/chat_loaders/langsmith_llm_runs.ipynb +++ b/docs/docs/integrations/chat_loaders/langsmith_llm_runs.ipynb @@ -5,7 +5,7 @@ "id": "a9ab2a39-7c2d-4119-9dc7-8035fdfba3cb", "metadata": {}, "source": [ - "# Fine-Tuning on LangSmith LLM Runs\n", + "# LangSmith LLM Runs\n", "\n", "This notebook demonstrates how to directly load data from LangSmith's LLM runs and fine-tune a model on that data.\n", "The process is simple and comprises 3 steps.\n", @@ -421,7 +421,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.2" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/docs/docs/integrations/document_loaders/apify_dataset.ipynb b/docs/docs/integrations/document_loaders/apify_dataset.ipynb index ebf65f9d16c..3dc3cc99fe0 100644 --- a/docs/docs/integrations/document_loaders/apify_dataset.ipynb +++ b/docs/docs/integrations/document_loaders/apify_dataset.ipynb @@ -13,7 +13,7 @@ "\n", "## Prerequisites\n", "\n", - "You need to have an existing dataset on the Apify platform. If you don't have one, please first check out [this notebook](/docs/integrations/tools/apify.html) on how to use Apify to extract content from documentation, knowledge bases, help centers, or blogs." + "You need to have an existing dataset on the Apify platform. If you don't have one, please first check out [this notebook](/docs/integrations/tools/apify) on how to use Apify to extract content from documentation, knowledge bases, help centers, or blogs." ] }, { diff --git a/docs/docs/integrations/document_loaders/bibtex.ipynb b/docs/docs/integrations/document_loaders/bibtex.ipynb index 3b342842c17..8d4175490e1 100644 --- a/docs/docs/integrations/document_loaders/bibtex.ipynb +++ b/docs/docs/integrations/document_loaders/bibtex.ipynb @@ -49,7 +49,7 @@ "metadata": {}, "source": [ "`BibtexLoader` has these arguments:\n", - "- `file_path`: the path the the `.bib` bibtex file\n", + "- `file_path`: the path of the `.bib` bibtex file\n", "- optional `max_docs`: default=None, i.e. not limit. Use it to limit number of retrieved documents.\n", "- optional `max_content_chars`: default=4000. Use it to limit the number of characters in a single document.\n", "- optional `load_extra_meta`: default=False. By default only the most important fields from the bibtex entries: `Published` (publication year), `Title`, `Authors`, `Summary`, `Journal`, `Keywords`, and `URL`. If True, it will also try to load return `entry_id`, `note`, `doi`, and `links` fields. \n", diff --git a/docs/docs/integrations/document_loaders/pandas_dataframe.ipynb b/docs/docs/integrations/document_loaders/pandas_dataframe.ipynb index e3d268c9e18..d415a26beac 100644 --- a/docs/docs/integrations/document_loaders/pandas_dataframe.ipynb +++ b/docs/docs/integrations/document_loaders/pandas_dataframe.ipynb @@ -7,7 +7,7 @@ "source": [ "# Pandas DataFrame\n", "\n", - "This notebook goes over how to load data from a [pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/index.html) DataFrame." + "This notebook goes over how to load data from a [pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/index) DataFrame." ] }, { diff --git a/docs/docs/integrations/document_loaders/psychic.ipynb b/docs/docs/integrations/document_loaders/psychic.ipynb index d4e8773a91d..720d90dc125 100644 --- a/docs/docs/integrations/document_loaders/psychic.ipynb +++ b/docs/docs/integrations/document_loaders/psychic.ipynb @@ -5,10 +5,10 @@ "metadata": {}, "source": [ "# Psychic\n", - "This notebook covers how to load documents from `Psychic`. See [here](/docs/ecosystem/integrations/psychic.html) for more details.\n", + "This notebook covers how to load documents from `Psychic`. See [here](/docs/ecosystem/integrations/psychic) for more details.\n", "\n", "## Prerequisites\n", - "1. Follow the Quick Start section in [this document](/docs/ecosystem/integrations/psychic.html)\n", + "1. Follow the Quick Start section in [this document](/docs/ecosystem/integrations/psychic)\n", "2. Log into the [Psychic dashboard](https://dashboard.psychic.dev/) and get your secret key\n", "3. Install the frontend react library into your web app and have a user authenticate a connection. The connection will be created using the connection id that you specify." ] diff --git a/docs/docs/integrations/llms/deepinfra.ipynb b/docs/docs/integrations/llms/deepinfra.ipynb index 5e9f0dea418..a7970e36df7 100644 --- a/docs/docs/integrations/llms/deepinfra.ipynb +++ b/docs/docs/integrations/llms/deepinfra.ipynb @@ -6,30 +6,7 @@ "source": [ "# DeepInfra\n", "\n", - "`DeepInfra` provides [several LLMs](https://deepinfra.com/models).\n", - "\n", - "This notebook goes over how to use Langchain with [DeepInfra](https://deepinfra.com)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import os\n", - "from langchain.llms import DeepInfra\n", - "from langchain.prompts import PromptTemplate\n", - "from langchain.chains import LLMChain" + "[DeepInfra](https://deepinfra.com/?utm_source=langchain) is a serverless inference as a service that provides access to a [variety of LLMs](https://deepinfra.com/models?utm_source=langchain) and [embeddings models](https://deepinfra.com/models?type=embeddings&utm_source=langchain). This notebook goes over how to use LangChain with DeepInfra for language models." ] }, { @@ -45,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": { "tags": [] }, @@ -68,12 +45,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": { "tags": [] }, "outputs": [], "source": [ + "import os\n", + "\n", "os.environ[\"DEEPINFRA_API_TOKEN\"] = DEEPINFRA_API_TOKEN" ] }, @@ -87,11 +66,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ - "llm = DeepInfra(model_id=\"databricks/dolly-v2-12b\")\n", + "from langchain.llms import DeepInfra\n", + "\n", + "llm = DeepInfra(model_id=\"meta-llama/Llama-2-70b-chat-hf\")\n", "llm.model_kwargs = {\n", " \"temperature\": 0.7,\n", " \"repetition_penalty\": 1.2,\n", @@ -100,6 +81,51 @@ "}" ] }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'This is a question that has puzzled many people'" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# run inferences directly via wrapper\n", + "llm(\"Who let the dogs out?\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " Will\n", + " Smith\n", + "." + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# run streaming inference\n", + "for chunk in llm.stream(\"Who let the dogs out?\"):\n", + " print(chunk)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -110,10 +136,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ + "from langchain.prompts import PromptTemplate\n", + "\n", "template = \"\"\"Question: {question}\n", "\n", "Answer: Let's think step by step.\"\"\"\n", @@ -130,10 +158,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ + "from langchain.chains import LLMChain\n", + "\n", "llm_chain = LLMChain(prompt=prompt, llm=llm)" ] }, @@ -147,16 +177,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "\"Penguins live in the Southern hemisphere.\\nThe North pole is located in the Northern hemisphere.\\nSo, first you need to turn the penguin South.\\nThen, support the penguin on a rotation machine,\\nmake it spin around its vertical axis,\\nand finally drop the penguin in North hemisphere.\\nNow, you have a penguin in the north pole!\\n\\nStill didn't understand?\\nWell, you're a failure as a teacher.\"" + "\"Penguins are found in Antarctica and the surrounding islands, which are located at the southernmost tip of the planet. The North Pole is located at the northernmost tip of the planet, and it would be a long journey for penguins to get there. In fact, penguins don't have the ability to fly or migrate over such long distances. So, no, penguins cannot reach the North Pole. \"" ] }, - "execution_count": 8, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -166,6 +196,13 @@ "\n", "llm_chain.run(question)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -184,7 +221,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.11.5" }, "vscode": { "interpreter": { diff --git a/docs/docs/integrations/llms/gigachat.ipynb b/docs/docs/integrations/llms/gigachat.ipynb new file mode 100644 index 00000000000..a11ea9986ff --- /dev/null +++ b/docs/docs/integrations/llms/gigachat.ipynb @@ -0,0 +1,113 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# GigaChat\n", + "This notebook shows how to use LangChain with [GigaChat](https://developers.sber.ru/portal/products/gigachat).\n", + "To use you need to install ```gigachat``` python package." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# !pip install gigachat" + ] + }, + { + "cell_type": "markdown", + "source": [ + "To get GigaChat credentials you need to [create account](https://developers.sber.ru/studio/login) and [get access to API](https://developers.sber.ru/docs/ru/gigachat/api/integration)\n", + "## Example" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [], + "source": [ + "import os\n", + "from getpass import getpass\n", + "\n", + "os.environ['GIGACHAT_CREDENTIALS'] = getpass()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "from langchain.llms import GigaChat\n", + "\n", + "llm = GigaChat(verify_ssl_certs=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The capital of Russia is Moscow.\n" + ] + } + ], + "source": [ + "from langchain.prompts import PromptTemplate\n", + "from langchain.chains import LLMChain\n", + "\n", + "template = \"What is capital of {country}?\"\n", + "\n", + "prompt = PromptTemplate(template=template, input_variables=[\"country\"])\n", + "\n", + "llm_chain = LLMChain(prompt=prompt, llm=llm)\n", + "\n", + "generated = llm_chain.run(country=\"Russia\")\n", + "print(generated)" + ], + "metadata": { + "collapsed": false + } + } + ], + "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/llms/google_vertex_ai_palm.ipynb b/docs/docs/integrations/llms/google_vertex_ai_palm.ipynb index 40435cf37b0..96d328f1b2c 100644 --- a/docs/docs/integrations/llms/google_vertex_ai_palm.ipynb +++ b/docs/docs/integrations/llms/google_vertex_ai_palm.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# GCP Vertex AI\n", + "# Google Cloud Vertex AI\n", "\n", "**Note:** This is separate from the `Google PaLM` integration, it exposes [Vertex AI PaLM API](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) on `Google Cloud`. \n" ] @@ -41,7 +41,7 @@ }, "outputs": [], "source": [ - "#!pip install langchain google-cloud-aiplatform" + "#!pip install langchain google-cloud-aiplatform\n" ] }, { @@ -50,7 +50,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.llms import VertexAI" + "from langchain.llms import VertexAI\n" ] }, { @@ -74,7 +74,7 @@ ], "source": [ "llm = VertexAI()\n", - "print(llm(\"What are some of the pros and cons of Python as a programming language?\"))" + "print(llm(\"What are some of the pros and cons of Python as a programming language?\"))\n" ] }, { @@ -90,7 +90,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.prompts import PromptTemplate" + "from langchain.prompts import PromptTemplate\n" ] }, { @@ -102,7 +102,7 @@ "template = \"\"\"Question: {question}\n", "\n", "Answer: Let's think step by step.\"\"\"\n", - "prompt = PromptTemplate.from_template(template)" + "prompt = PromptTemplate.from_template(template)\n" ] }, { @@ -111,7 +111,7 @@ "metadata": {}, "outputs": [], "source": [ - "chain = prompt | llm" + "chain = prompt | llm\n" ] }, { @@ -130,7 +130,7 @@ ], "source": [ "question = \"Who was the president in the year Justin Beiber was born?\"\n", - "print(chain.invoke({\"question\": question}))" + "print(chain.invoke({\"question\": question}))\n" ] }, { @@ -159,7 +159,7 @@ }, "outputs": [], "source": [ - "llm = VertexAI(model_name=\"code-bison\", max_output_tokens=1000, temperature=0.3)" + "llm = VertexAI(model_name=\"code-bison\", max_output_tokens=1000, temperature=0.3)\n" ] }, { @@ -168,7 +168,7 @@ "metadata": {}, "outputs": [], "source": [ - "question = \"Write a python function that checks if a string is a valid email address\"" + "question = \"Write a python function that checks if a string is a valid email address\"\n" ] }, { @@ -193,7 +193,7 @@ } ], "source": [ - "print(llm(question))" + "print(llm(question))\n" ] }, { @@ -223,7 +223,7 @@ ], "source": [ "result = llm.generate([question])\n", - "result.generations" + "result.generations\n" ] }, { @@ -243,7 +243,7 @@ "source": [ "# If running in a Jupyter notebook you'll need to install nest_asyncio\n", "\n", - "# !pip install nest_asyncio" + "# !pip install nest_asyncio\n" ] }, { @@ -254,7 +254,7 @@ "source": [ "import asyncio\n", "# import nest_asyncio\n", - "# nest_asyncio.apply()" + "# nest_asyncio.apply()\n" ] }, { @@ -274,7 +274,7 @@ } ], "source": [ - "asyncio.run(llm.agenerate([question]))" + "asyncio.run(llm.agenerate([question]))\n" ] }, { @@ -292,7 +292,7 @@ "metadata": {}, "outputs": [], "source": [ - "import sys" + "import sys\n" ] }, { @@ -337,7 +337,7 @@ "source": [ "for chunk in llm.stream(question):\n", " sys.stdout.write(chunk)\n", - " sys.stdout.flush()" + " sys.stdout.flush()\n" ] }, { @@ -360,7 +360,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.llms import VertexAIModelGarden" + "from langchain.llms import VertexAIModelGarden\n" ] }, { @@ -372,7 +372,7 @@ "llm = VertexAIModelGarden(\n", " project=\"YOUR PROJECT\",\n", " endpoint_id=\"YOUR ENDPOINT_ID\"\n", - ")" + ")\n" ] }, { @@ -381,7 +381,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(llm(\"What is the meaning of life?\"))" + "print(llm(\"What is the meaning of life?\"))\n" ] }, { @@ -397,7 +397,7 @@ "metadata": {}, "outputs": [], "source": [ - "prompt = PromptTemplate.from_template(\"What is the meaning of {thing}?\")" + "prompt = PromptTemplate.from_template(\"What is the meaning of {thing}?\")\n" ] }, { @@ -407,7 +407,7 @@ "outputs": [], "source": [ "chian = prompt | llm\n", - "print(chain.invoke({\"thing\": \"life\"}))" + "print(chain.invoke({\"thing\": \"life\"}))\n" ] } ], diff --git a/docs/docs/integrations/llms/jsonformer_experimental.ipynb b/docs/docs/integrations/llms/jsonformer_experimental.ipynb index 189de072d12..abab5f125e3 100644 --- a/docs/docs/integrations/llms/jsonformer_experimental.ipynb +++ b/docs/docs/integrations/llms/jsonformer_experimental.ipynb @@ -7,7 +7,7 @@ "source": [ "# JSONFormer\n", "\n", - "[JSONFormer](https://github.com/1rgs/jsonformer) is a library that wraps local HuggingFace pipeline models for structured decoding of a subset of the JSON Schema.\n", + "[JSONFormer](https://github.com/1rgs/jsonformer) is a library that wraps local Hugging Face pipeline models for structured decoding of a subset of the JSON Schema.\n", "\n", "It works by filling in the structure tokens and then sampling the content tokens from the model.\n", "\n", @@ -31,7 +31,7 @@ "id": "66bd89f1-8daa-433d-bb8f-5b0b3ae34b00", "metadata": {}, "source": [ - "### HuggingFace Baseline\n", + "### Hugging Face Baseline\n", "\n", "First, let's establish a qualitative baseline by checking the output of the model without structured decoding." ] diff --git a/docs/docs/integrations/llms/llm_caching.ipynb b/docs/docs/integrations/llms/llm_caching.ipynb index d164678eaa1..45bccb916ba 100644 --- a/docs/docs/integrations/llms/llm_caching.ipynb +++ b/docs/docs/integrations/llms/llm_caching.ipynb @@ -319,7 +319,7 @@ "metadata": {}, "source": [ "### Standard Cache\n", - "Use [Redis](/docs/ecosystem/integrations/redis.html) to cache prompts and responses." + "Use [Redis](/docs/ecosystem/integrations/redis) to cache prompts and responses." ] }, { @@ -405,7 +405,7 @@ "metadata": {}, "source": [ "### Semantic Cache\n", - "Use [Redis](/docs/ecosystem/integrations/redis.html) to cache prompts and responses and evaluate hits based on semantic similarity." + "Use [Redis](/docs/ecosystem/integrations/redis) to cache prompts and responses and evaluate hits based on semantic similarity." ] }, { @@ -730,7 +730,7 @@ }, "source": [ "## `Momento` Cache\n", - "Use [Momento](/docs/ecosystem/integrations/momento.html) to cache prompts and responses.\n", + "Use [Momento](/docs/ecosystem/integrations/momento) to cache prompts and responses.\n", "\n", "Requires momento to use, uncomment below to install:" ] diff --git a/docs/docs/integrations/llms/sagemaker.ipynb b/docs/docs/integrations/llms/sagemaker.ipynb index 067aeaaa560..32659dd2aaa 100644 --- a/docs/docs/integrations/llms/sagemaker.ipynb +++ b/docs/docs/integrations/llms/sagemaker.ipynb @@ -82,6 +82,15 @@ "]" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example to initialize with external boto3 session\n", + "\n", + "### for cross account scenarios" + ] + }, { "cell_type": "code", "execution_count": null, @@ -92,7 +101,77 @@ "source": [ "from typing import Dict\n", "\n", - "from langchain.prompts import PromptTemplate\nfrom langchain.llms import SagemakerEndpoint\n", + "from langchain.prompts import PromptTemplate\n", + "from langchain.llms import SagemakerEndpoint\n", + "from langchain.llms.sagemaker_endpoint import LLMContentHandler\n", + "from langchain.chains.question_answering import load_qa_chain\n", + "import json\n", + "import boto3\n", + "\n", + "query = \"\"\"How long was Elizabeth hospitalized?\n", + "\"\"\"\n", + "\n", + "prompt_template = \"\"\"Use the following pieces of context to answer the question at the end.\n", + "\n", + "{context}\n", + "\n", + "Question: {question}\n", + "Answer:\"\"\"\n", + "PROMPT = PromptTemplate(\n", + " template=prompt_template, input_variables=[\"context\", \"question\"]\n", + ")\n", + "\n", + "roleARN = 'arn:aws:iam::123456789:role/cross-account-role'\n", + "sts_client = boto3.client('sts')\n", + "response = sts_client.assume_role(RoleArn=roleARN, \n", + " RoleSessionName='CrossAccountSession')\n", + "\n", + "client = boto3.client(\n", + " \"sagemaker-runtime\",\n", + " region_name=\"us-west-2\", \n", + " aws_access_key_id=response['Credentials']['AccessKeyId'],\n", + " aws_secret_access_key=response['Credentials']['SecretAccessKey'],\n", + " aws_session_token = response['Credentials']['SessionToken']\n", + ")\n", + "\n", + "class ContentHandler(LLMContentHandler):\n", + " content_type = \"application/json\"\n", + " accepts = \"application/json\"\n", + "\n", + " def transform_input(self, prompt: str, model_kwargs: Dict) -> bytes:\n", + " input_str = json.dumps({prompt: prompt, **model_kwargs})\n", + " return input_str.encode(\"utf-8\")\n", + "\n", + " def transform_output(self, output: bytes) -> str:\n", + " response_json = json.loads(output.read().decode(\"utf-8\"))\n", + " return response_json[0][\"generated_text\"]\n", + "\n", + "\n", + "content_handler = ContentHandler()\n", + "\n", + "chain = load_qa_chain(\n", + " llm=SagemakerEndpoint(\n", + " endpoint_name=\"endpoint-name\",\n", + " client=client,\n", + " model_kwargs={\"temperature\": 1e-10},\n", + " content_handler=content_handler,\n", + " ),\n", + " prompt=PROMPT,\n", + ")\n", + "\n", + "chain({\"input_documents\": docs, \"question\": query}, return_only_outputs=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Dict\n", + "\n", + "from langchain.prompts import PromptTemplate\n", + "from langchain.llms import SagemakerEndpoint\n", "from langchain.llms.sagemaker_endpoint import LLMContentHandler\n", "from langchain.chains.question_answering import load_qa_chain\n", "import json\n", diff --git a/docs/docs/integrations/llms/titan_takeoff.ipynb b/docs/docs/integrations/llms/titan_takeoff.ipynb index fe4cdc8a640..e1b9a2f0fae 100644 --- a/docs/docs/integrations/llms/titan_takeoff.ipynb +++ b/docs/docs/integrations/llms/titan_takeoff.ipynb @@ -108,7 +108,7 @@ "from langchain.llms import TitanTakeoff\n", "\n", "llm = TitanTakeoff(\n", - " baseURL=\"http://localhost:8000\",\n", + " base_url=\"http://localhost:8000\",\n", " generate_max_length=128,\n", " temperature=1.0\n", ")\n", diff --git a/docs/docs/integrations/llms/titan_takeoff_pro.ipynb b/docs/docs/integrations/llms/titan_takeoff_pro.ipynb new file mode 100644 index 00000000000..12eb6c3094b --- /dev/null +++ b/docs/docs/integrations/llms/titan_takeoff_pro.ipynb @@ -0,0 +1,100 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Titan Takeoff Pro\n", + "\n", + "`TitanML` helps businesses build and deploy better, smaller, cheaper, and faster NLP models through our training, compression, and inference optimization platform.\n", + "\n", + ">Note: These docs are for the Pro version of Titan Takeoff. For the community version, see the page for Titan Takeoff.\n", + "\n", + "Our inference server, [Titan Takeoff (Pro Version)](https://docs.titanml.co/docs/titan-takeoff/pro-features/feature-comparison) enables deployment of LLMs locally on your hardware in a single command. Most generative model architectures are supported, such as Falcon, Llama 2, GPT2, T5 and many more." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example usage\n", + "Here are some helpful examples to get started using the Pro version of Titan Takeoff Server.\n", + "No parameters are needed by default, but a baseURL that points to your desired URL where Takeoff is running can be specified and generation parameters can be supplied." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.llms import TitanTakeoffPro\n", + "from langchain.prompts import PromptTemplate\n", + "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n", + "from langchain.callbacks.manager import CallbackManager\n", + "\n", + "# Example 1: Basic use\n", + "llm = TitanTakeoffPro()\n", + "output = llm(\"What is the weather in London in August?\")\n", + "print(output)\n", + "\n", + "\n", + "# Example 2: Specifying a port and other generation parameters\n", + "llm = TitanTakeoffPro(\n", + " base_url=\"http://localhost:3000\",\n", + " min_new_tokens=128,\n", + " max_new_tokens=512,\n", + " no_repeat_ngram_size=2,\n", + " sampling_topk= 1,\n", + " sampling_topp= 1.0,\n", + " sampling_temperature= 1.0,\n", + " repetition_penalty= 1.0,\n", + " regex_string= \"\",\n", + ")\n", + "output = llm(\"What is the largest rainforest in the world?\")\n", + "print(output)\n", + "\n", + "\n", + "# Example 3: Using generate for multiple inputs\n", + "llm = TitanTakeoffPro()\n", + "rich_output = llm.generate([\"What is Deep Learning?\", \"What is Machine Learning?\"])\n", + "print(rich_output.generations)\n", + "\n", + "\n", + "# Example 4: Streaming output\n", + "llm = TitanTakeoffPro(streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))\n", + "prompt = \"What is the capital of France?\"\n", + "llm(prompt)\n", + "\n", + "# Example 5: Using LCEL\n", + "llm = TitanTakeoffPro()\n", + "prompt = PromptTemplate.from_template(\"Tell me about {topic}\")\n", + "chain = prompt | llm\n", + "chain.invoke({\"topic\": \"the universe\"})" + ] + } + ], + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/docs/integrations/platforms/aws.mdx b/docs/docs/integrations/platforms/aws.mdx index 44799080c48..5c6d292d0a5 100644 --- a/docs/docs/integrations/platforms/aws.mdx +++ b/docs/docs/integrations/platforms/aws.mdx @@ -75,9 +75,9 @@ from langchain.llms.sagemaker_endpoint import ContentHandlerBase >[AWS S3 Directory](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html) >[AWS S3 Buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingBucket.html) -See a [usage example for S3DirectoryLoader](/docs/integrations/document_loaders/aws_s3_directory.html). +See a [usage example for S3DirectoryLoader](/docs/integrations/document_loaders/aws_s3_directory). -See a [usage example for S3FileLoader](/docs/integrations/document_loaders/aws_s3_file.html). +See a [usage example for S3FileLoader](/docs/integrations/document_loaders/aws_s3_file). ```python from langchain.document_loaders import S3DirectoryLoader, S3FileLoader diff --git a/docs/docs/integrations/platforms/google.mdx b/docs/docs/integrations/platforms/google.mdx index bcfb812686e..86f2e08d119 100644 --- a/docs/docs/integrations/platforms/google.mdx +++ b/docs/docs/integrations/platforms/google.mdx @@ -30,7 +30,6 @@ Access PaLM chat models like `chat-bison` and `codechat-bison` via Google Cloud. from langchain.chat_models import ChatVertexAI ``` - ## Document Loader ### Google BigQuery @@ -51,7 +50,7 @@ from langchain.document_loaders import BigQueryLoader ### Google Cloud Storage ->[Google Cloud Storage](https://en.wikipedia.org/wiki/Google_Cloud_Storage) is a managed service for storing unstructured data. +> [Google Cloud Storage](https://en.wikipedia.org/wiki/Google_Cloud_Storage) is a managed service for storing unstructured data. First, we need to install `google-cloud-storage` python package. @@ -74,27 +73,28 @@ from langchain.document_loaders import GCSFileLoader ### Google Drive ->[Google Drive](https://en.wikipedia.org/wiki/Google_Drive) is a file storage and synchronization service developed by Google. +> [Google Drive](https://en.wikipedia.org/wiki/Google_Drive) is a file storage and synchronization service developed by Google. Currently, only `Google Docs` are supported. -First, we need to install several python package. +First, we need to install several python packages. ```bash pip install google-api-python-client google-auth-httplib2 google-auth-oauthlib ``` -See a [usage example and authorizing instructions](/docs/integrations/document_loaders/google_drive.html). +See a [usage example and authorizing instructions](/docs/integrations/document_loaders/google_drive). ```python from langchain.document_loaders import GoogleDriveLoader ``` ## Vector Store -### Google Vertex AI MatchingEngine +### Google Vertex AI Vector Search -> [Google Vertex AI Matching Engine](https://cloud.google.com/vertex-ai/docs/matching-engine/overview) provides -> the industry's leading high-scale low latency vector database. These vector databases are commonly +> [Google Vertex AI Vector Search](https://cloud.google.com/vertex-ai/docs/matching-engine/overview), +> formerly known as Vertex AI Matching Engine, provides the industry's leading high-scale +> low latency vector database. These vector databases are commonly > referred to as vector similarity-matching or an approximate nearest neighbor (ANN) service. We need to install several python packages. @@ -181,14 +181,28 @@ There exists a `GoogleSearchAPIWrapper` utility which wraps this API. To import ```python from langchain.utilities import GoogleSearchAPIWrapper ``` -For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/google_search.html). + +For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/google_search). We can easily load this wrapper as a Tool (to use with an Agent). We can do this with: + ```python from langchain.agents import load_tools tools = load_tools(["google-search"]) ``` +### Google Places + +See a [usage example](/docs/integrations/tools/google_places). + +``` +pip install googlemaps +``` + +```python +from langchain.tools import GooglePlacesTool +``` + ## Document Transformer ### Google Document AI @@ -216,3 +230,40 @@ See a [usage example](/docs/integrations/document_transformers/docai). from langchain.document_loaders.blob_loaders import Blob from langchain.document_loaders.parsers import DocAIParser ``` + +## Chat loaders +### Gmail + +> [Gmail](https://en.wikipedia.org/wiki/Gmail) is a free email service provided by Google. + +First, we need to install several python packages. + +```bash +pip install --upgrade google-auth google-auth-oauthlib google-auth-httplib2 google-api-python-client +``` + +See a [usage example and authorizing instructions](/docs/integrations/chat_loaders/gmail). + +```python +from langchain.chat_loaders.gmail import GMailLoader +``` + +## Agents and Toolkits +### Gmail + +See a [usage example and authorizing instructions](/docs/integrations/toolkits/gmail). + +```python +from langchain.agents.agent_toolkits import GmailToolkit + +toolkit = GmailToolkit() +``` + +### Google Drive + +See a [usage example and authorizing instructions](/docs/integrations/toolkits/google_drive). + +```python +from langchain_googledrive.utilities.google_drive import GoogleDriveAPIWrapper +from langchain_googledrive.tools.google_drive.tool import GoogleDriveSearchTool +``` diff --git a/docs/docs/integrations/platforms/microsoft.mdx b/docs/docs/integrations/platforms/microsoft.mdx index 173122a2be0..150dabe87b9 100644 --- a/docs/docs/integrations/platforms/microsoft.mdx +++ b/docs/docs/integrations/platforms/microsoft.mdx @@ -1,6 +1,6 @@ # Microsoft -All functionality related to Microsoft Azure +All functionality related to `Microsoft Azure` and other `Microsoft` products. ## LLM ### Azure OpenAI @@ -70,13 +70,13 @@ from langchain.chat_models import AzureChatOpenAI pip install azure-storage-blob ``` -See a [usage example for the Azure Blob Storage](/docs/integrations/document_loaders/azure_blob_storage_container.html). +See a [usage example for the Azure Blob Storage](/docs/integrations/document_loaders/azure_blob_storage_container). ```python from langchain.document_loaders import AzureBlobStorageContainerLoader ``` -See a [usage example for the Azure Files](/docs/integrations/document_loaders/azure_blob_storage_file.html). +See a [usage example for the Azure Files](/docs/integrations/document_loaders/azure_blob_storage_file). ```python from langchain.document_loaders import AzureBlobStorageFileLoader @@ -161,3 +161,59 @@ See a [usage example](/docs/integrations/retrievers/azure_cognitive_search). from langchain.retrievers import AzureCognitiveSearchRetriever ``` +## Utilities + +### Bing Search API + +See a [usage example](/docs/integrations/tools/bing_search). + +```python +from langchain.utilities import BingSearchAPIWrapper +``` + +## Toolkits + +### Azure Cognitive Services + +We need to install several python packages. + +```bash +pip install azure-ai-formrecognizer azure-cognitiveservices-speech azure-ai-vision +``` + +See a [usage example](/docs/integrations/toolkits/azure_cognitive_services). + +```python +from langchain.agents.agent_toolkits import O365Toolkit +``` +### Microsoft Office 365 email and calendar + +We need to install `O365` python package. + +```bash +pip install O365 +``` + + +See a [usage example](/docs/integrations/toolkits/office365). + +```python +from langchain.agents.agent_toolkits import O365Toolkit +``` + +### Microsoft Azure PowerBI + +We need to install `azure-identity` python package. + +```bash +pip install azure-identity +``` + +See a [usage example](/docs/integrations/toolkits/powerbi). + +```python +from langchain.agents.agent_toolkits import PowerBIToolkit +from langchain.utilities.powerbi import PowerBIDataset +``` + + diff --git a/docs/docs/integrations/providers/airtable.md b/docs/docs/integrations/providers/airtable.md index ce1edcecbd0..635452adb55 100644 --- a/docs/docs/integrations/providers/airtable.md +++ b/docs/docs/integrations/providers/airtable.md @@ -25,4 +25,4 @@ pip install pyairtable from langchain.document_loaders import AirtableLoader ``` -See an [example](/docs/integrations/document_loaders/airtable.html). +See an [example](/docs/integrations/document_loaders/airtable). diff --git a/docs/docs/integrations/providers/analyticdb.mdx b/docs/docs/integrations/providers/analyticdb.mdx index b83e7a0a45e..cde6db5fb40 100644 --- a/docs/docs/integrations/providers/analyticdb.mdx +++ b/docs/docs/integrations/providers/analyticdb.mdx @@ -12,4 +12,4 @@ To import this vectorstore: from langchain.vectorstores import AnalyticDB ``` -For a more detailed walkthrough of the AnalyticDB wrapper, see [this notebook](/docs/integrations/vectorstores/analyticdb.html) +For a more detailed walkthrough of the AnalyticDB wrapper, see [this notebook](/docs/integrations/vectorstores/analyticdb) diff --git a/docs/docs/integrations/providers/apify.mdx b/docs/docs/integrations/providers/apify.mdx index cafd99179d9..6bc7486b376 100644 --- a/docs/docs/integrations/providers/apify.mdx +++ b/docs/docs/integrations/providers/apify.mdx @@ -32,7 +32,7 @@ You can use the `ApifyWrapper` to run Actors on the Apify platform. from langchain.utilities import ApifyWrapper ``` -For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/apify.html). +For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/apify). ### Loader @@ -43,4 +43,4 @@ You can also use our `ApifyDatasetLoader` to get data from Apify dataset. from langchain.document_loaders import ApifyDatasetLoader ``` -For a more detailed walkthrough of this loader, see [this notebook](/docs/integrations/document_loaders/apify_dataset.html). +For a more detailed walkthrough of this loader, see [this notebook](/docs/integrations/document_loaders/apify_dataset). diff --git a/docs/docs/integrations/providers/arangodb.mdx b/docs/docs/integrations/providers/arangodb.mdx index 990e33ea36e..2bf68a9a2b9 100644 --- a/docs/docs/integrations/providers/arangodb.mdx +++ b/docs/docs/integrations/providers/arangodb.mdx @@ -13,7 +13,7 @@ pip install python-arango Connect your ArangoDB Database with a chat model to get insights on your data. -See the notebook example [here](/docs/use_cases/graph/graph_arangodb_qa.html). +See the notebook example [here](/docs/use_cases/graph/graph_arangodb_qa). ```python from arango import ArangoClient diff --git a/docs/docs/integrations/providers/argilla.mdx b/docs/docs/integrations/providers/argilla.mdx index 2024db079e2..209a69647be 100644 --- a/docs/docs/integrations/providers/argilla.mdx +++ b/docs/docs/integrations/providers/argilla.mdx @@ -22,7 +22,7 @@ If you don't you can refer to [Argilla - ๐Ÿš€ Quickstart](https://docs.argilla.i ## Tracking -See a [usage example of `ArgillaCallbackHandler`](/docs/integrations/callbacks/argilla.html). +See a [usage example of `ArgillaCallbackHandler`](/docs/integrations/callbacks/argilla). ```python from langchain.callbacks import ArgillaCallbackHandler diff --git a/docs/docs/integrations/providers/chroma.mdx b/docs/docs/integrations/providers/chroma.mdx index f642428b6f3..089f9fa64b1 100644 --- a/docs/docs/integrations/providers/chroma.mdx +++ b/docs/docs/integrations/providers/chroma.mdx @@ -18,7 +18,7 @@ whether for semantic search or example selection. from langchain.vectorstores import Chroma ``` -For a more detailed walkthrough of the Chroma wrapper, see [this notebook](/docs/integrations/vectorstores/chroma.html) +For a more detailed walkthrough of the Chroma wrapper, see [this notebook](/docs/integrations/vectorstores/chroma) ## Retriever diff --git a/docs/docs/integrations/providers/clarifai.mdx b/docs/docs/integrations/providers/clarifai.mdx index 69347f563c8..b54e096a58d 100644 --- a/docs/docs/integrations/providers/clarifai.mdx +++ b/docs/docs/integrations/providers/clarifai.mdx @@ -25,7 +25,7 @@ from langchain.llms import Clarifai llm = Clarifai(pat=CLARIFAI_PAT, user_id=USER_ID, app_id=APP_ID, model_id=MODEL_ID) ``` -For more details, the docs on the Clarifai LLM wrapper provide a [detailed walkthrough](/docs/integrations/llms/clarifai.html). +For more details, the docs on the Clarifai LLM wrapper provide a [detailed walkthrough](/docs/integrations/llms/clarifai). ### Text Embedding Models @@ -37,7 +37,7 @@ There is a Clarifai Embedding model in LangChain, which you can access with: from langchain.embeddings import ClarifaiEmbeddings embeddings = ClarifaiEmbeddings(pat=CLARIFAI_PAT, user_id=USER_ID, app_id=APP_ID, model_id=MODEL_ID) ``` -For more details, the docs on the Clarifai Embeddings wrapper provide a [detailed walkthrough](/docs/integrations/text_embedding/clarifai.html). +For more details, the docs on the Clarifai Embeddings wrapper provide a [detailed walkthrough](/docs/integrations/text_embedding/clarifai). ## Vectorstore diff --git a/docs/docs/integrations/providers/cohere.mdx b/docs/docs/integrations/providers/cohere.mdx index 768a6b64511..69e8d0b4d97 100644 --- a/docs/docs/integrations/providers/cohere.mdx +++ b/docs/docs/integrations/providers/cohere.mdx @@ -27,7 +27,7 @@ There exists an Cohere Embedding model, which you can access with ```python from langchain.embeddings import CohereEmbeddings ``` -For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/cohere.html) +For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/cohere) ## Retriever diff --git a/docs/docs/integrations/providers/comet_tracking.ipynb b/docs/docs/integrations/providers/comet_tracking.ipynb index a5ae494aaa9..f1f88aa7aec 100644 --- a/docs/docs/integrations/providers/comet_tracking.ipynb +++ b/docs/docs/integrations/providers/comet_tracking.ipynb @@ -20,7 +20,7 @@ "source": [ "In this guide we will demonstrate how to track your Langchain Experiments, Evaluation Metrics, and LLM Sessions with [Comet](https://www.comet.com/site/?utm_source=langchain&utm_medium=referral&utm_campaign=comet_notebook). \n", "\n", - "\n", + "\n", " \"Open\n", "\n", "\n", diff --git a/docs/docs/integrations/providers/ctransformers.mdx b/docs/docs/integrations/providers/ctransformers.mdx index 282d6ce38c5..35dc7725207 100644 --- a/docs/docs/integrations/providers/ctransformers.mdx +++ b/docs/docs/integrations/providers/ctransformers.mdx @@ -54,4 +54,4 @@ llm = CTransformers(model='marella/gpt-2-ggml', config=config) See [Documentation](https://github.com/marella/ctransformers#config) for a list of available parameters. -For a more detailed walkthrough of this, see [this notebook](/docs/integrations/llms/ctransformers.html). +For a more detailed walkthrough of this, see [this notebook](/docs/integrations/llms/ctransformers). diff --git a/docs/docs/integrations/providers/dashvector.mdx b/docs/docs/integrations/providers/dashvector.mdx index d1ab24a0d0b..1d42ad84213 100644 --- a/docs/docs/integrations/providers/dashvector.mdx +++ b/docs/docs/integrations/providers/dashvector.mdx @@ -21,4 +21,4 @@ You may import the vectorstore by: from langchain.vectorstores import DashVector ``` -For a detailed walkthrough of the DashVector wrapper, please refer to [this notebook](/docs/integrations/vectorstores/dashvector.html) +For a detailed walkthrough of the DashVector wrapper, please refer to [this notebook](/docs/integrations/vectorstores/dashvector) diff --git a/docs/docs/integrations/providers/databricks.md b/docs/docs/integrations/providers/databricks.md index 6dbabbce60c..3c5c19de5c8 100644 --- a/docs/docs/integrations/providers/databricks.md +++ b/docs/docs/integrations/providers/databricks.md @@ -33,11 +33,11 @@ See [MLflow AI Gateway](/docs/integrations/providers/mlflow_ai_gateway). Databricks as an LLM provider ----------------------------- -The notebook [Wrap Databricks endpoints as LLMs](/docs/integrations/llms/databricks.html) illustrates the method to wrap Databricks endpoints as LLMs in LangChain. It supports two types of endpoints: the serving endpoint, which is recommended for both production and development, and the cluster driver proxy app, which is recommended for interactive development. +The notebook [Wrap Databricks endpoints as LLMs](/docs/integrations/llms/databricks) illustrates the method to wrap Databricks endpoints as LLMs in LangChain. It supports two types of endpoints: the serving endpoint, which is recommended for both production and development, and the cluster driver proxy app, which is recommended for interactive development. Databricks endpoints support Dolly, but are also great for hosting models like MPT-7B or any other models from the Hugging Face ecosystem. Databricks endpoints can also be used with proprietary models like OpenAI to provide a governance layer for enterprises. Databricks Dolly ---------------- -Databricksโ€™ Dolly is an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. The model is available on Hugging Face Hub as databricks/dolly-v2-12b. See the notebook [Hugging Face Hub](/docs/integrations/llms/huggingface_hub.html) for instructions to access it through the Hugging Face Hub integration with LangChain. +Databricksโ€™ Dolly is an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. The model is available on Hugging Face Hub as databricks/dolly-v2-12b. See the notebook [Hugging Face Hub](/docs/integrations/llms/huggingface_hub) for instructions to access it through the Hugging Face Hub integration with LangChain. diff --git a/docs/docs/integrations/providers/deepinfra.mdx b/docs/docs/integrations/providers/deepinfra.mdx index d32768269b5..da8787fc104 100644 --- a/docs/docs/integrations/providers/deepinfra.mdx +++ b/docs/docs/integrations/providers/deepinfra.mdx @@ -10,16 +10,27 @@ It is broken into two parts: installation and setup, and then references to spec ## Available Models DeepInfra provides a range of Open Source LLMs ready for deployment. -You can list supported models [here](https://deepinfra.com/models?type=text-generation). +You can list supported models for +[text-generation](https://deepinfra.com/models?type=text-generation) and +[embeddings](https://deepinfra.com/models?type=embeddings). google/flan\* models can be viewed [here](https://deepinfra.com/models?type=text2text-generation). -You can view a list of request and response parameters [here](https://deepinfra.com/databricks/dolly-v2-12b#API) +You can view a [list of request and response parameters](https://deepinfra.com/meta-llama/Llama-2-70b-chat-hf/api). ## Wrappers ### LLM There exists an DeepInfra LLM wrapper, which you can access with + ```python from langchain.llms import DeepInfra ``` + +### Embeddings + +There is also an DeepInfra Embeddings wrapper, you can access with + +```python +from langchain.embeddings import DeepInfraEmbeddings +``` diff --git a/docs/docs/integrations/providers/dingo.mdx b/docs/docs/integrations/providers/dingo.mdx index 5fd59675b15..ab5bffa65e3 100644 --- a/docs/docs/integrations/providers/dingo.mdx +++ b/docs/docs/integrations/providers/dingo.mdx @@ -1,14 +1,14 @@ -# Dingo +# DingoDB -This page covers how to use the Dingo ecosystem within LangChain. -It is broken into two parts: installation and setup, and then references to specific Dingo wrappers. +This page covers how to use the DingoDB ecosystem within LangChain. +It is broken into two parts: installation and setup, and then references to specific DingoDB wrappers. ## Installation and Setup - Install the Python SDK with `pip install dingodb` ## VectorStore -There exists a wrapper around Dingo indexes, allowing you to use it as a vectorstore, +There exists a wrapper around DingoDB indexes, allowing you to use it as a vectorstore, whether for semantic search or example selection. To import this vectorstore: @@ -16,4 +16,4 @@ To import this vectorstore: from langchain.vectorstores import Dingo ``` -For a more detailed walkthrough of the Dingo wrapper, see [this notebook](/docs/integrations/vectorstores/dingo.html) +For a more detailed walkthrough of the DingoDB wrapper, see [this notebook](/docs/integrations/vectorstores/dingo) diff --git a/docs/docs/integrations/providers/epsilla.mdx b/docs/docs/integrations/providers/epsilla.mdx index 40e91329e45..fb4fa4039c2 100644 --- a/docs/docs/integrations/providers/epsilla.mdx +++ b/docs/docs/integrations/providers/epsilla.mdx @@ -20,4 +20,4 @@ To import this vectorstore: from langchain.vectorstores import Epsilla ``` -For a more detailed walkthrough of the Epsilla wrapper, see [this notebook](/docs/integrations/vectorstores/epsilla.html) \ No newline at end of file +For a more detailed walkthrough of the Epsilla wrapper, see [this notebook](/docs/integrations/vectorstores/epsilla) \ No newline at end of file diff --git a/docs/docs/integrations/providers/golden.mdx b/docs/docs/integrations/providers/golden.mdx index 21398a2a5dc..5f1c5c15703 100644 --- a/docs/docs/integrations/providers/golden.mdx +++ b/docs/docs/integrations/providers/golden.mdx @@ -20,7 +20,7 @@ There exists a GoldenQueryAPIWrapper utility which wraps this API. To import thi from langchain.utilities.golden_query import GoldenQueryAPIWrapper ``` -For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/golden_query.html). +For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/golden_query). ### Tool diff --git a/docs/docs/integrations/providers/google_document_ai.mdx b/docs/docs/integrations/providers/google_document_ai.mdx deleted file mode 100644 index 431cc4ac5f8..00000000000 --- a/docs/docs/integrations/providers/google_document_ai.mdx +++ /dev/null @@ -1,28 +0,0 @@ -# Google Document AI - ->[Document AI](https://cloud.google.com/document-ai/docs/overview) is a `Google Cloud Platform` -> service to transform unstructured data from documents into structured data, making it easier -> to understand, analyze, and consume. - - -## Installation and Setup - -You need to set up a [`GCS` bucket and create your own OCR processor](https://cloud.google.com/document-ai/docs/create-processor) -The `GCS_OUTPUT_PATH` should be a path to a folder on GCS (starting with `gs://`) -and a processor name should look like `projects/PROJECT_NUMBER/locations/LOCATION/processors/PROCESSOR_ID`. -You can get it either programmatically or copy from the `Prediction endpoint` section of the `Processor details` -tab in the Google Cloud Console. - -```bash -pip install google-cloud-documentai -pip install google-cloud-documentai-toolbox -``` - -## Document Transformer - -See a [usage example](/docs/integrations/document_transformers/docai). - -```python -from langchain.document_loaders.blob_loaders import Blob -from langchain.document_loaders.parsers import DocAIParser -``` diff --git a/docs/docs/integrations/providers/google_serper.mdx b/docs/docs/integrations/providers/google_serper.mdx index 8fd535c57f6..b44065372dd 100644 --- a/docs/docs/integrations/providers/google_serper.mdx +++ b/docs/docs/integrations/providers/google_serper.mdx @@ -1,4 +1,4 @@ -# Google Serper +# Serper - Google Search API This page covers how to use the [Serper](https://serper.dev) Google Search API within LangChain. Serper is a low-cost Google Search API that can be used to add answer box, knowledge graph, and organic results data from Google Search. It is broken into two parts: setup, and then references to the specific Google Serper wrapper. @@ -59,7 +59,7 @@ So the final answer is: El Palmar, Spain 'El Palmar, Spain' ``` -For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/google_serper.html). +For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/google_serper). ### Tool diff --git a/docs/docs/integrations/providers/gpt4all.mdx b/docs/docs/integrations/providers/gpt4all.mdx index 72e5145a34b..0917bff1a30 100644 --- a/docs/docs/integrations/providers/gpt4all.mdx +++ b/docs/docs/integrations/providers/gpt4all.mdx @@ -45,4 +45,4 @@ model("Once upon a time, ", callbacks=callbacks) You can find links to model file downloads in the [pyllamacpp](https://github.com/nomic-ai/pyllamacpp) repository. -For a more detailed walkthrough of this, see [this notebook](/docs/integrations/llms/gpt4all.html) +For a more detailed walkthrough of this, see [this notebook](/docs/integrations/llms/gpt4all) diff --git a/docs/docs/integrations/providers/gradient.mdx b/docs/docs/integrations/providers/gradient.mdx index 0ef07e79de0..d143d595293 100644 --- a/docs/docs/integrations/providers/gradient.mdx +++ b/docs/docs/integrations/providers/gradient.mdx @@ -24,4 +24,4 @@ There exists an Gradient Embedding model, which you can access with ```python from langchain.embeddings import GradientEmbeddings ``` -For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/gradient.html) +For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/gradient) diff --git a/docs/docs/integrations/providers/huggingface.mdx b/docs/docs/integrations/providers/huggingface.mdx index 27fe4d42db7..7fd03a381ea 100644 --- a/docs/docs/integrations/providers/huggingface.mdx +++ b/docs/docs/integrations/providers/huggingface.mdx @@ -30,7 +30,7 @@ To use a the wrapper for a model hosted on Hugging Face Hub: ```python from langchain.llms import HuggingFaceHub ``` -For a more detailed walkthrough of the Hugging Face Hub wrapper, see [this notebook](/docs/integrations/llms/huggingface_hub.html) +For a more detailed walkthrough of the Hugging Face Hub wrapper, see [this notebook](/docs/integrations/llms/huggingface_hub) ### Embeddings diff --git a/docs/docs/integrations/providers/infino.mdx b/docs/docs/integrations/providers/infino.mdx index 2fb2cc62e4c..d11c502a377 100644 --- a/docs/docs/integrations/providers/infino.mdx +++ b/docs/docs/integrations/providers/infino.mdx @@ -28,7 +28,7 @@ you don't, follow the next steps to start it: ## Using Infino -See a [usage example of `InfinoCallbackHandler`](/docs/integrations/callbacks/infino.html). +See a [usage example of `InfinoCallbackHandler`](/docs/integrations/callbacks/infino). ```python from langchain.callbacks import InfinoCallbackHandler diff --git a/docs/docs/integrations/providers/jina.mdx b/docs/docs/integrations/providers/jina.mdx index dec677f6d15..181b57a4abf 100644 --- a/docs/docs/integrations/providers/jina.mdx +++ b/docs/docs/integrations/providers/jina.mdx @@ -15,7 +15,7 @@ There exists a Jina Embeddings wrapper, which you can access with ```python from langchain.embeddings import JinaEmbeddings ``` -For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/jina.html) +For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/jina) ## Deployment diff --git a/docs/docs/integrations/providers/johnsnowlabs.mdx b/docs/docs/integrations/providers/johnsnowlabs.mdx new file mode 100644 index 00000000000..39f3ea494cb --- /dev/null +++ b/docs/docs/integrations/providers/johnsnowlabs.mdx @@ -0,0 +1,117 @@ +# Johnsnowlabs + +Gain access to the [johnsnowlabs](https://www.johnsnowlabs.com/) ecosystem of enterprise NLP libraries +with over 21.000 enterprise NLP models in over 200 languages with the open source `johnsnowlabs` library. +For all 24.000+ models, see the [John Snow Labs Model Models Hub](https://nlp.johnsnowlabs.com/models) + +## Installation and Setup + + +```bash +pip install johnsnowlabs +``` + +To [install enterprise features](https://nlp.johnsnowlabs.com/docs/en/jsl/install_licensed_quick, run: +```python +# for more details see https://nlp.johnsnowlabs.com/docs/en/jsl/install_licensed_quick +nlp.install() +``` + + +You can embed your queries and documents with either `gpu`,`cpu`,`apple_silicon`,`aarch` based optimized binaries. +By default cpu binaries are used. +Once a session is started, you must restart your notebook to switch between GPU or CPU, or changes will not take effect. + +## Embed Query with CPU: +```python +document = "foo bar" +embedding = JohnSnowLabsEmbeddings('embed_sentence.bert') +output = embedding.embed_query(document) +``` + + +## Embed Query with GPU: + + +```python +document = "foo bar" +embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','gpu') +output = embedding.embed_query(document) +``` + + + + +## Embed Query with Apple Silicon (M1,M2,etc..): + +```python +documents = ["foo bar", 'bar foo'] +embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','apple_silicon') +output = embedding.embed_query(document) +``` + + + +## Embed Query with AARCH: + +```python +documents = ["foo bar", 'bar foo'] +embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','aarch') +output = embedding.embed_query(document) +``` + + + + + + +## Embed Document with CPU: +```python +documents = ["foo bar", 'bar foo'] +embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','gpu') +output = embedding.embed_documents(documents) +``` + + + +## Embed Document with GPU: + +```python +documents = ["foo bar", 'bar foo'] +embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','gpu') +output = embedding.embed_documents(documents) +``` + + + + + +## Embed Document with Apple Silicon (M1,M2,etc..): + +```python + +```python +documents = ["foo bar", 'bar foo'] +embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','apple_silicon') +output = embedding.embed_documents(documents) +``` + + + +## Embed Document with AARCH: + +```python + +```python +documents = ["foo bar", 'bar foo'] +embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','aarch') +output = embedding.embed_documents(documents) +``` + + + + +Models are loaded with [nlp.load](https://nlp.johnsnowlabs.com/docs/en/jsl/load_api) and spark session is started with [nlp.start()](https://nlp.johnsnowlabs.com/docs/en/jsl/start-a-sparksession) under the hood. + + + diff --git a/docs/docs/integrations/providers/lancedb.mdx b/docs/docs/integrations/providers/lancedb.mdx index 6e5ae741153..1275e690bc3 100644 --- a/docs/docs/integrations/providers/lancedb.mdx +++ b/docs/docs/integrations/providers/lancedb.mdx @@ -20,4 +20,4 @@ To import this vectorstore: from langchain.vectorstores import LanceDB ``` -For a more detailed walkthrough of the LanceDB wrapper, see [this notebook](/docs/integrations/vectorstores/lancedb.html) +For a more detailed walkthrough of the LanceDB wrapper, see [this notebook](/docs/integrations/vectorstores/lancedb) diff --git a/docs/docs/integrations/providers/llamacpp.mdx b/docs/docs/integrations/providers/llamacpp.mdx index a7a2f335ec2..53c29b6ff31 100644 --- a/docs/docs/integrations/providers/llamacpp.mdx +++ b/docs/docs/integrations/providers/llamacpp.mdx @@ -15,7 +15,7 @@ There exists a LlamaCpp LLM wrapper, which you can access with ```python from langchain.llms import LlamaCpp ``` -For a more detailed walkthrough of this, see [this notebook](/docs/integrations/llms/llamacpp.html) +For a more detailed walkthrough of this, see [this notebook](/docs/integrations/llms/llamacpp) ### Embeddings @@ -23,4 +23,4 @@ There exists a LlamaCpp Embeddings wrapper, which you can access with ```python from langchain.embeddings import LlamaCppEmbeddings ``` -For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/llamacpp.html) +For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/llamacpp) diff --git a/docs/docs/integrations/providers/marqo.md b/docs/docs/integrations/providers/marqo.md index d26e08fb13b..3a6e24e35c1 100644 --- a/docs/docs/integrations/providers/marqo.md +++ b/docs/docs/integrations/providers/marqo.md @@ -28,4 +28,4 @@ To import this vectorstore: from langchain.vectorstores import Marqo ``` -For a more detailed walkthrough of the Marqo wrapper and some of its unique features, see [this notebook](/docs/integrations/vectorstores/marqo.html) +For a more detailed walkthrough of the Marqo wrapper and some of its unique features, see [this notebook](/docs/integrations/vectorstores/marqo) diff --git a/docs/docs/integrations/providers/milvus.mdx b/docs/docs/integrations/providers/milvus.mdx index 509cd5294ba..9f963233f5e 100644 --- a/docs/docs/integrations/providers/milvus.mdx +++ b/docs/docs/integrations/providers/milvus.mdx @@ -22,4 +22,4 @@ To import this vectorstore: from langchain.vectorstores import Milvus ``` -For a more detailed walkthrough of the `Miluvs` wrapper, see [this notebook](/docs/integrations/vectorstores/milvus.html) +For a more detailed walkthrough of the `Miluvs` wrapper, see [this notebook](/docs/integrations/vectorstores/milvus) diff --git a/docs/docs/integrations/providers/minimax.mdx b/docs/docs/integrations/providers/minimax.mdx index 18fc101c0e8..cbeb74d62d5 100644 --- a/docs/docs/integrations/providers/minimax.mdx +++ b/docs/docs/integrations/providers/minimax.mdx @@ -11,7 +11,7 @@ Get a [Minimax group id](https://api.minimax.chat/user-center/basic-information) ## LLM There exists a Minimax LLM wrapper, which you can access with -See a [usage example](/docs/modules/model_io/models/llms/integrations/minimax.html). +See a [usage example](/docs/modules/model_io/models/llms/integrations/minimax). ```python from langchain.llms import Minimax @@ -19,7 +19,7 @@ from langchain.llms import Minimax ## Chat Models -See a [usage example](/docs/modules/model_io/models/chat/integrations/minimax.html) +See a [usage example](/docs/modules/model_io/models/chat/integrations/minimax) ```python from langchain.chat_models import MiniMaxChat diff --git a/docs/docs/integrations/providers/mlflow_ai_gateway.mdx b/docs/docs/integrations/providers/mlflow_ai_gateway.mdx index 6931a90cf97..3b944092f1c 100644 --- a/docs/docs/integrations/providers/mlflow_ai_gateway.mdx +++ b/docs/docs/integrations/providers/mlflow_ai_gateway.mdx @@ -1,9 +1,9 @@ # MLflow AI Gateway ->[The MLflow AI Gateway](https://www.mlflow.org/docs/latest/gateway/index.html) service is a powerful tool designed to streamline the usage and management of various large +>[The MLflow AI Gateway](https://www.mlflow.org/docs/latest/gateway/index) service is a powerful tool designed to streamline the usage and management of various large > language model (LLM) providers, such as OpenAI and Anthropic, within an organization. It offers a high-level interface > that simplifies the interaction with these services by providing a unified endpoint to handle specific LLM related requests. -> See [the MLflow AI Gateway documentation](https://mlflow.org/docs/latest/gateway/index.html) for more details. +> See [the MLflow AI Gateway documentation](https://mlflow.org/docs/latest/gateway/index) for more details. ## Installation and Setup @@ -52,7 +52,7 @@ mlflow gateway start --config-path /path/to/config.yaml > This module exports multivariate LangChain models in the langchain flavor and univariate LangChain > models in the pyfunc flavor. -See the [API documentation and examples](https://www.mlflow.org/docs/latest/python_api/mlflow.langchain.html). +See the [API documentation and examples](https://www.mlflow.org/docs/latest/python_api/mlflow.langchain). diff --git a/docs/docs/integrations/providers/mlflow_tracking.ipynb b/docs/docs/integrations/providers/mlflow_tracking.ipynb index 5d78b429132..0797315466b 100644 --- a/docs/docs/integrations/providers/mlflow_tracking.ipynb +++ b/docs/docs/integrations/providers/mlflow_tracking.ipynb @@ -7,7 +7,7 @@ "source": [ "# MLflow\n", "\n", - ">[MLflow](https://www.mlflow.org/docs/latest/what-is-mlflow.html) is a versatile, expandable, open-source platform for managing workflows and artifacts across the machine learning lifecycle. It has built-in integrations with many popular ML libraries, but can be used with any library, algorithm, or deployment tool. It is designed to be extensible, so you can write plugins to support new workflows, libraries, and tools.\n", + ">[MLflow](https://www.mlflow.org/docs/latest/what-is-mlflow) is a versatile, expandable, open-source platform for managing workflows and artifacts across the machine learning lifecycle. It has built-in integrations with many popular ML libraries, but can be used with any library, algorithm, or deployment tool. It is designed to be extensible, so you can write plugins to support new workflows, libraries, and tools.\n", "\n", "This notebook goes over how to track your LangChain experiments into your `MLflow Server`" ] diff --git a/docs/docs/integrations/providers/momento.mdx b/docs/docs/integrations/providers/momento.mdx index da21a2296fe..71f857148d2 100644 --- a/docs/docs/integrations/providers/momento.mdx +++ b/docs/docs/integrations/providers/momento.mdx @@ -50,10 +50,10 @@ Momento can be used as a distributed memory store for LLMs. ### Chat Message History Memory -See [this notebook](/docs/integrations/memory/momento_chat_message_history.html) for a walkthrough of how to use Momento as a memory store for chat message history. +See [this notebook](/docs/integrations/memory/momento_chat_message_history) for a walkthrough of how to use Momento as a memory store for chat message history. ## Vector Store Momento Vector Index (MVI) can be used as a vector store. -See [this notebook](/docs/integrations/vectorstores/momento_vector_index.html) for a walkthrough of how to use MVI as a vector store. +See [this notebook](/docs/integrations/vectorstores/momento_vector_index) for a walkthrough of how to use MVI as a vector store. diff --git a/docs/docs/integrations/providers/motherduck.mdx b/docs/docs/integrations/providers/motherduck.mdx index 69d426059e9..05b91b54e22 100644 --- a/docs/docs/integrations/providers/motherduck.mdx +++ b/docs/docs/integrations/providers/motherduck.mdx @@ -31,7 +31,7 @@ db = SQLDatabase.from_uri(conn_str) db_chain = SQLDatabaseChain.from_llm(OpenAI(temperature=0), db, verbose=True) ``` -From here, see the [SQL Chain](/docs/use_cases/tabular/sqlite.html) documentation on how to use. +From here, see the [SQL Chain](/docs/use_cases/tabular/sqlite) documentation on how to use. ## LLMCache diff --git a/docs/docs/integrations/providers/myscale.mdx b/docs/docs/integrations/providers/myscale.mdx index 27a53b8002d..367b6d36281 100644 --- a/docs/docs/integrations/providers/myscale.mdx +++ b/docs/docs/integrations/providers/myscale.mdx @@ -63,4 +63,4 @@ To import this vectorstore: from langchain.vectorstores import MyScale ``` -For a more detailed walkthrough of the MyScale wrapper, see [this notebook](/docs/integrations/vectorstores/myscale.html) +For a more detailed walkthrough of the MyScale wrapper, see [this notebook](/docs/integrations/vectorstores/myscale) diff --git a/docs/docs/integrations/providers/neo4j.mdx b/docs/docs/integrations/providers/neo4j.mdx index a507422903d..232de5c53f7 100644 --- a/docs/docs/integrations/providers/neo4j.mdx +++ b/docs/docs/integrations/providers/neo4j.mdx @@ -29,7 +29,7 @@ To import this vectorstore: from langchain.vectorstores import Neo4jVector ``` -For a more detailed walkthrough of the Neo4j vector index wrapper, see [documentation](/docs/integrations/vectorstores/neo4jvector.html) +For a more detailed walkthrough of the Neo4j vector index wrapper, see [documentation](/docs/integrations/vectorstores/neo4jvector) ### GraphCypherQAChain @@ -41,7 +41,7 @@ from langchain.graphs import Neo4jGraph from langchain.chains import GraphCypherQAChain ``` -For a more detailed walkthrough of Cypher generating chain, see [documentation](/docs/use_cases/graph/graph_cypher_qa.html) +For a more detailed walkthrough of Cypher generating chain, see [documentation](/docs/use_cases/graph/graph_cypher_qa) ### Constructing a knowledge graph from text @@ -55,4 +55,4 @@ from langchain.graphs import Neo4jGraph from langchain_experimental.graph_transformers.diffbot import DiffbotGraphTransformer ``` -For a more detailed walkthrough generating graphs from text, see [documentation](/docs/use_cases/graph/diffbot_graphtransformer.html) +For a more detailed walkthrough generating graphs from text, see [documentation](/docs/use_cases/graph/diffbot_graphtransformer) diff --git a/docs/docs/integrations/providers/notion.mdx b/docs/docs/integrations/providers/notion.mdx index 216a88c9f9b..6e16b1233ee 100644 --- a/docs/docs/integrations/providers/notion.mdx +++ b/docs/docs/integrations/providers/notion.mdx @@ -12,14 +12,14 @@ All instructions are in examples below. We have two different loaders: `NotionDirectoryLoader` and `NotionDBLoader`. -See a [usage example for the NotionDirectoryLoader](/docs/integrations/document_loaders/notion.html). +See a [usage example for the NotionDirectoryLoader](/docs/integrations/document_loaders/notion). ```python from langchain.document_loaders import NotionDirectoryLoader ``` -See a [usage example for the NotionDBLoader](/docs/integrations/document_loaders/notiondb.html). +See a [usage example for the NotionDBLoader](/docs/integrations/document_loaders/notiondb). ```python diff --git a/docs/docs/integrations/providers/openllm.mdx b/docs/docs/integrations/providers/openllm.mdx index a6ec980f664..1f24af8ed22 100644 --- a/docs/docs/integrations/providers/openllm.mdx +++ b/docs/docs/integrations/providers/openllm.mdx @@ -67,4 +67,4 @@ llm("What is the difference between a duck and a goose? And why there are so man ### Usage For a more detailed walkthrough of the OpenLLM Wrapper, see the -[example notebook](/docs/integrations/llms/openllm.html) +[example notebook](/docs/integrations/llms/openllm) diff --git a/docs/docs/integrations/providers/opensearch.mdx b/docs/docs/integrations/providers/opensearch.mdx index 2761548c818..6e428635dc3 100644 --- a/docs/docs/integrations/providers/opensearch.mdx +++ b/docs/docs/integrations/providers/opensearch.mdx @@ -18,4 +18,4 @@ To import this vectorstore: from langchain.vectorstores import OpenSearchVectorSearch ``` -For a more detailed walkthrough of the OpenSearch wrapper, see [this notebook](/docs/integrations/vectorstores/opensearch.html) +For a more detailed walkthrough of the OpenSearch wrapper, see [this notebook](/docs/integrations/vectorstores/opensearch) diff --git a/docs/docs/integrations/providers/openweathermap.mdx b/docs/docs/integrations/providers/openweathermap.mdx index fa346cf2bc6..b38fe5db264 100644 --- a/docs/docs/integrations/providers/openweathermap.mdx +++ b/docs/docs/integrations/providers/openweathermap.mdx @@ -29,7 +29,7 @@ There exists a OpenWeatherMapAPIWrapper utility which wraps this API. To import from langchain.utilities.openweathermap import OpenWeatherMapAPIWrapper ``` -For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/openweathermap.html). +For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/openweathermap). ### Tool diff --git a/docs/docs/integrations/providers/pgvector.mdx b/docs/docs/integrations/providers/pgvector.mdx index d632a8959b3..e7cefb5b791 100644 --- a/docs/docs/integrations/providers/pgvector.mdx +++ b/docs/docs/integrations/providers/pgvector.mdx @@ -26,4 +26,4 @@ from langchain.vectorstores.pgvector import PGVector ### Usage -For a more detailed walkthrough of the PGVector Wrapper, see [this notebook](/docs/integrations/vectorstores/pgvector.html) +For a more detailed walkthrough of the PGVector Wrapper, see [this notebook](/docs/integrations/vectorstores/pgvector) diff --git a/docs/docs/integrations/providers/pinecone.mdx b/docs/docs/integrations/providers/pinecone.mdx index 3dd1e55e69d..61a95527609 100644 --- a/docs/docs/integrations/providers/pinecone.mdx +++ b/docs/docs/integrations/providers/pinecone.mdx @@ -21,4 +21,4 @@ whether for semantic search or example selection. from langchain.vectorstores import Pinecone ``` -For a more detailed walkthrough of the Pinecone vectorstore, see [this notebook](/docs/integrations/vectorstores/pinecone.html) +For a more detailed walkthrough of the Pinecone vectorstore, see [this notebook](/docs/integrations/vectorstores/pinecone) diff --git a/docs/docs/integrations/providers/promptlayer.mdx b/docs/docs/integrations/providers/promptlayer.mdx index 1a7b1cb9262..3f2a74ffc06 100644 --- a/docs/docs/integrations/providers/promptlayer.mdx +++ b/docs/docs/integrations/providers/promptlayer.mdx @@ -40,10 +40,10 @@ for res in llm_results.generations: ``` You can use the PromptLayer request ID to add a prompt, score, or other metadata to your request. [Read more about it here](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9). -This LLM is identical to the [OpenAI](/docs/ecosystem/integrations/openai.html) LLM, except that +This LLM is identical to the [OpenAI](/docs/ecosystem/integrations/openai) LLM, except that - all your requests will be logged to your PromptLayer account - you can add `pl_tags` when instantiating to tag your requests on PromptLayer - you can add `return_pl_id` when instantiating to return a PromptLayer request id to use [while tracking requests](https://magniv.notion.site/Track-4deee1b1f7a34c1680d085f82567dab9). -PromptLayer also provides native wrappers for [`PromptLayerChatOpenAI`](/docs/integrations/chat/promptlayer_chatopenai.html) and `PromptLayerOpenAIChat` +PromptLayer also provides native wrappers for [`PromptLayerChatOpenAI`](/docs/integrations/chat/promptlayer_chatopenai) and `PromptLayerOpenAIChat` diff --git a/docs/docs/integrations/providers/providers/semadb.mdx b/docs/docs/integrations/providers/providers/semadb.mdx index 4978b89d63b..700f44fab42 100644 --- a/docs/docs/integrations/providers/providers/semadb.mdx +++ b/docs/docs/integrations/providers/providers/semadb.mdx @@ -16,4 +16,4 @@ There is a basic wrapper around `SemaDB` collections allowing you to use it as a from langchain.vectorstores import SemaDB ``` -You can follow a tutorial on how to use the wrapper in [this notebook](/docs/integrations/vectorstores/semadb.html). \ No newline at end of file +You can follow a tutorial on how to use the wrapper in [this notebook](/docs/integrations/vectorstores/semadb). \ No newline at end of file diff --git a/docs/docs/integrations/providers/psychic.mdx b/docs/docs/integrations/providers/psychic.mdx index 0bae7e5b21c..c29fe6e3316 100644 --- a/docs/docs/integrations/providers/psychic.mdx +++ b/docs/docs/integrations/providers/psychic.mdx @@ -16,7 +16,7 @@ view these connections from the dashboard and retrieve data using the server-sid 1. Create an account in the [dashboard](https://dashboard.psychic.dev/). 2. Use the [react library](https://docs.psychic.dev/sidekick-link) to add the Psychic link modal to your frontend react app. You will use this to connect the SaaS apps. -3. Once you have created a connection, you can use the `PsychicLoader` by following the [example notebook](/docs/integrations/document_loaders/psychic.html) +3. Once you have created a connection, you can use the `PsychicLoader` by following the [example notebook](/docs/integrations/document_loaders/psychic) ## Advantages vs Other Document Loaders diff --git a/docs/docs/integrations/providers/qdrant.mdx b/docs/docs/integrations/providers/qdrant.mdx index 33dfcb266cb..ace4c34f9d8 100644 --- a/docs/docs/integrations/providers/qdrant.mdx +++ b/docs/docs/integrations/providers/qdrant.mdx @@ -24,4 +24,4 @@ To import this vectorstore: from langchain.vectorstores import Qdrant ``` -For a more detailed walkthrough of the Qdrant wrapper, see [this notebook](/docs/integrations/vectorstores/qdrant.html) +For a more detailed walkthrough of the Qdrant wrapper, see [this notebook](/docs/integrations/vectorstores/qdrant) diff --git a/docs/docs/integrations/providers/redis.mdx b/docs/docs/integrations/providers/redis.mdx index b2995325793..b9e4c675321 100644 --- a/docs/docs/integrations/providers/redis.mdx +++ b/docs/docs/integrations/providers/redis.mdx @@ -103,7 +103,7 @@ To import this vectorstore: from langchain.vectorstores import Redis ``` -For a more detailed walkthrough of the Redis vectorstore wrapper, see [this notebook](/docs/integrations/vectorstores/redis.html). +For a more detailed walkthrough of the Redis vectorstore wrapper, see [this notebook](/docs/integrations/vectorstores/redis). ### Retriever @@ -114,7 +114,7 @@ Redis can be used to persist LLM conversations. #### Vector Store Retriever Memory -For a more detailed walkthrough of the `VectorStoreRetrieverMemory` wrapper, see [this notebook](/docs/modules/memory/types/vectorstore_retriever_memory.html). +For a more detailed walkthrough of the `VectorStoreRetrieverMemory` wrapper, see [this notebook](/docs/modules/memory/types/vectorstore_retriever_memory). #### Chat Message History Memory -For a detailed example of Redis to cache conversation message history, see [this notebook](/docs/integrations/memory/redis_chat_message_history.html). +For a detailed example of Redis to cache conversation message history, see [this notebook](/docs/integrations/memory/redis_chat_message_history). diff --git a/docs/docs/integrations/providers/runhouse.mdx b/docs/docs/integrations/providers/runhouse.mdx index 28b6d7eeb38..8039882b0bc 100644 --- a/docs/docs/integrations/providers/runhouse.mdx +++ b/docs/docs/integrations/providers/runhouse.mdx @@ -15,7 +15,7 @@ custom LLMs, you can use the `SelfHostedPipeline` parent class. from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM ``` -For a more detailed walkthrough of the Self-hosted LLMs, see [this notebook](/docs/integrations/llms/runhouse.html) +For a more detailed walkthrough of the Self-hosted LLMs, see [this notebook](/docs/integrations/llms/runhouse) ## Self-hosted Embeddings There are several ways to use self-hosted embeddings with LangChain via Runhouse. @@ -26,4 +26,4 @@ the `SelfHostedEmbedding` class. from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM ``` -For a more detailed walkthrough of the Self-hosted Embeddings, see [this notebook](/docs/integrations/text_embedding/self-hosted.html) +For a more detailed walkthrough of the Self-hosted Embeddings, see [this notebook](/docs/integrations/text_embedding/self-hosted) diff --git a/docs/docs/integrations/providers/salute_devices.mdx b/docs/docs/integrations/providers/salute_devices.mdx new file mode 100644 index 00000000000..2ab03926722 --- /dev/null +++ b/docs/docs/integrations/providers/salute_devices.mdx @@ -0,0 +1,29 @@ +# Salute Devices + +Salute Devices provides GigaChat LLM's models. + +For more info how to get access to GigaChat [follow here](https://developers.sber.ru/docs/ru/gigachat/api/integration). + +## Installation and Setup + +GigaChat package can be installed via pip from PyPI: + +```bash +pip install gigachat +``` + +## LLMs + +See a [usage example](/docs/integrations/llms/gigachat). + +```python +from langchain.llms import GigaChat +``` + +## Chat models + +See a [usage example](/docs/integrations/chat/gigachat). + +```python +from langchain.chat_models import GigaChat +``` \ No newline at end of file diff --git a/docs/docs/integrations/providers/serpapi.mdx b/docs/docs/integrations/providers/serpapi.mdx index e692492c02c..0e31f0e6cf6 100644 --- a/docs/docs/integrations/providers/serpapi.mdx +++ b/docs/docs/integrations/providers/serpapi.mdx @@ -17,7 +17,7 @@ There exists a SerpAPI utility which wraps this API. To import this utility: from langchain.utilities import SerpAPIWrapper ``` -For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/serpapi.html). +For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/serpapi). ### Tool diff --git a/docs/docs/integrations/providers/sklearn.mdx b/docs/docs/integrations/providers/sklearn.mdx index 341bb671d0a..5dedf25391e 100644 --- a/docs/docs/integrations/providers/sklearn.mdx +++ b/docs/docs/integrations/providers/sklearn.mdx @@ -19,4 +19,4 @@ To import this vectorstore: from langchain.vectorstores import SKLearnVectorStore ``` -For a more detailed walkthrough of the SKLearnVectorStore wrapper, see [this notebook](/docs/integrations/vectorstores/sklearn.html). +For a more detailed walkthrough of the SKLearnVectorStore wrapper, see [this notebook](/docs/integrations/vectorstores/sklearn). diff --git a/docs/docs/integrations/providers/spacy.mdx b/docs/docs/integrations/providers/spacy.mdx index ab9b6858985..4ff18381697 100644 --- a/docs/docs/integrations/providers/spacy.mdx +++ b/docs/docs/integrations/providers/spacy.mdx @@ -13,7 +13,7 @@ pip install spacy ## Text Splitter -See a [usage example](/docs/modules/data_connection/document_transformers/text_splitters/split_by_token.html#spacy). +See a [usage example](/docs/modules/data_connection/document_transformers/text_splitters/split_by_token#spacy). ```python from langchain.text_splitter import SpacyTextSplitter diff --git a/docs/docs/integrations/providers/spreedly.mdx b/docs/docs/integrations/providers/spreedly.mdx index 5790ef2e474..e7996b62242 100644 --- a/docs/docs/integrations/providers/spreedly.mdx +++ b/docs/docs/integrations/providers/spreedly.mdx @@ -4,7 +4,7 @@ ## Installation and Setup -See [setup instructions](/docs/integrations/document_loaders/spreedly.html). +See [setup instructions](/docs/integrations/document_loaders/spreedly). ## Document Loader diff --git a/docs/docs/integrations/providers/stripe.mdx b/docs/docs/integrations/providers/stripe.mdx index 923e77cad2f..05cc6d46397 100644 --- a/docs/docs/integrations/providers/stripe.mdx +++ b/docs/docs/integrations/providers/stripe.mdx @@ -5,7 +5,7 @@ ## Installation and Setup -See [setup instructions](/docs/integrations/document_loaders/stripe.html). +See [setup instructions](/docs/integrations/document_loaders/stripe). ## Document Loader diff --git a/docs/docs/integrations/providers/tair.mdx b/docs/docs/integrations/providers/tair.mdx index 4bfcd769493..8a0e6ad24cc 100644 --- a/docs/docs/integrations/providers/tair.mdx +++ b/docs/docs/integrations/providers/tair.mdx @@ -19,4 +19,4 @@ To import this vectorstore: from langchain.vectorstores import Tair ``` -For a more detailed walkthrough of the Tair wrapper, see [this notebook](/docs/integrations/vectorstores/tair.html) +For a more detailed walkthrough of the Tair wrapper, see [this notebook](/docs/integrations/vectorstores/tair) diff --git a/docs/docs/integrations/providers/telegram.mdx b/docs/docs/integrations/providers/telegram.mdx index b9a8bec0ea4..25ebd990b5c 100644 --- a/docs/docs/integrations/providers/telegram.mdx +++ b/docs/docs/integrations/providers/telegram.mdx @@ -5,7 +5,7 @@ ## Installation and Setup -See [setup instructions](/docs/integrations/document_loaders/telegram.html). +See [setup instructions](/docs/integrations/document_loaders/telegram). ## Document Loader diff --git a/docs/docs/integrations/providers/tencentvectordb.mdx b/docs/docs/integrations/providers/tencentvectordb.mdx index 0ce5f1142ea..e1db644f471 100644 --- a/docs/docs/integrations/providers/tencentvectordb.mdx +++ b/docs/docs/integrations/providers/tencentvectordb.mdx @@ -12,4 +12,4 @@ To import this vectorstore: from langchain.vectorstores import TencentVectorDB ``` -For a more detailed walkthrough of the TencentVectorDB wrapper, see [this notebook](/docs/integrations/vectorstores/tencentvectordb.html) +For a more detailed walkthrough of the TencentVectorDB wrapper, see [this notebook](/docs/integrations/vectorstores/tencentvectordb) diff --git a/docs/docs/integrations/providers/trello.mdx b/docs/docs/integrations/providers/trello.mdx index 99bf2cf4cea..0aecb76cfca 100644 --- a/docs/docs/integrations/providers/trello.mdx +++ b/docs/docs/integrations/providers/trello.mdx @@ -10,7 +10,7 @@ pip install py-trello beautifulsoup4 ``` -See [setup instructions](/docs/integrations/document_loaders/trello.html). +See [setup instructions](/docs/integrations/document_loaders/trello). ## Document Loader diff --git a/docs/docs/integrations/providers/typesense.mdx b/docs/docs/integrations/providers/typesense.mdx index 62d5581139e..472d2a40fd3 100644 --- a/docs/docs/integrations/providers/typesense.mdx +++ b/docs/docs/integrations/providers/typesense.mdx @@ -1,7 +1,7 @@ # Typesense > [Typesense](https://typesense.org) is an open-source, in-memory search engine, that you can either -> [self-host](https://typesense.org/docs/guide/install-typesense.html#option-2-local-machine-self-hosting) or run +> [self-host](https://typesense.org/docs/guide/install-typesense#option-2-local-machine-self-hosting) or run > on [Typesense Cloud](https://cloud.typesense.org/). > `Typesense` focuses on performance by storing the entire index in RAM (with a backup on disk) and also > focuses on providing an out-of-the-box developer experience by simplifying available options and setting good defaults. diff --git a/docs/docs/integrations/providers/upstash.mdx b/docs/docs/integrations/providers/upstash.mdx index 66f4c3df468..ff39b876497 100644 --- a/docs/docs/integrations/providers/upstash.mdx +++ b/docs/docs/integrations/providers/upstash.mdx @@ -39,4 +39,4 @@ langchain.llm_cache = UpstashRedisCache(redis_=Redis(url=URL, token=TOKEN)) Upstash Redis can be used to persist LLM conversations. #### Chat Message History Memory -An example of Upstash Redis for caching conversation message history can be seen in [this notebook](/docs/integrations/memory/upstash_redis_chat_message_history.html). +An example of Upstash Redis for caching conversation message history can be seen in [this notebook](/docs/integrations/memory/upstash_redis_chat_message_history). diff --git a/docs/docs/integrations/providers/vectara/vectara_chat.ipynb b/docs/docs/integrations/providers/vectara/vectara_chat.ipynb index 206c4d42323..1f38eb9782b 100644 --- a/docs/docs/integrations/providers/vectara/vectara_chat.ipynb +++ b/docs/docs/integrations/providers/vectara/vectara_chat.ipynb @@ -7,7 +7,7 @@ "source": [ "# Chat Over Documents with Vectara\n", "\n", - "This notebook is based on the [chat_vector_db](https://github.com/langchain-ai/langchain/blob/master/docs/modules/chains/index_examples/chat_vector_db.html) notebook, but using Vectara as the vector database." + "This notebook is based on the [chat_vector_db](https://github.com/langchain-ai/langchain/blob/master/docs/modules/chains/index_examples/chat_vector_db) notebook, but using Vectara as the vector database." ] }, { diff --git a/docs/docs/integrations/providers/weaviate.mdx b/docs/docs/integrations/providers/weaviate.mdx index 1c358ec6c64..9f5bf64cd6c 100644 --- a/docs/docs/integrations/providers/weaviate.mdx +++ b/docs/docs/integrations/providers/weaviate.mdx @@ -35,4 +35,4 @@ To import this vectorstore: from langchain.vectorstores import Weaviate ``` -For a more detailed walkthrough of the Weaviate wrapper, see [this notebook](/docs/integrations/vectorstores/weaviate.html) +For a more detailed walkthrough of the Weaviate wrapper, see [this notebook](/docs/integrations/vectorstores/weaviate) diff --git a/docs/docs/integrations/providers/wolfram_alpha.mdx b/docs/docs/integrations/providers/wolfram_alpha.mdx index 5c98a52be45..5e25d5e2dbf 100644 --- a/docs/docs/integrations/providers/wolfram_alpha.mdx +++ b/docs/docs/integrations/providers/wolfram_alpha.mdx @@ -25,7 +25,7 @@ There exists a WolframAlphaAPIWrapper utility which wraps this API. To import th from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper ``` -For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/wolfram_alpha.html). +For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/wolfram_alpha). ### Tool diff --git a/docs/docs/integrations/providers/xinference.mdx b/docs/docs/integrations/providers/xinference.mdx index 41a7f44bbc8..0029deef21c 100644 --- a/docs/docs/integrations/providers/xinference.mdx +++ b/docs/docs/integrations/providers/xinference.mdx @@ -93,10 +93,10 @@ llm( ### Usage For more information and detailed examples, refer to the -[example for xinference LLMs](/docs/integrations/llms/xinference.html) +[example for xinference LLMs](/docs/integrations/llms/xinference) ### Embeddings Xinference also supports embedding queries and documents. See -[example for xinference embeddings](/docs/integrations/text_embedding/xinference.html) +[example for xinference embeddings](/docs/integrations/text_embedding/xinference) for a more detailed demo. \ No newline at end of file diff --git a/docs/docs/integrations/providers/zilliz.mdx b/docs/docs/integrations/providers/zilliz.mdx index e37123eb947..b791adeb6c3 100644 --- a/docs/docs/integrations/providers/zilliz.mdx +++ b/docs/docs/integrations/providers/zilliz.mdx @@ -19,4 +19,4 @@ whether for semantic search or example selection. from langchain.vectorstores import Milvus ``` -For a more detailed walkthrough of the Miluvs wrapper, see [this notebook](/docs/integrations/vectorstores/zilliz.html) +For a more detailed walkthrough of the Miluvs wrapper, see [this notebook](/docs/integrations/vectorstores/zilliz) diff --git a/docs/docs_skeleton/docs/integrations/retrievers/cohere.ipynb b/docs/docs/integrations/retrievers/cohere.ipynb similarity index 100% rename from docs/docs_skeleton/docs/integrations/retrievers/cohere.ipynb rename to docs/docs/integrations/retrievers/cohere.ipynb diff --git a/docs/docs/integrations/text_embedding/johnsnowlabs_embedding.ipynb b/docs/docs/integrations/text_embedding/johnsnowlabs_embedding.ipynb new file mode 100644 index 00000000000..0fdc7ac3b0a --- /dev/null +++ b/docs/docs/integrations/text_embedding/johnsnowlabs_embedding.ipynb @@ -0,0 +1,184 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Johnsnowlabs Embedding\n", + "\n", + "### Loading the Johnsnowlabs embedding class to generate and query embeddings\n", + "\n", + "Models are loaded with [nlp.load](https://nlp.johnsnowlabs.com/docs/en/jsl/load_api) and spark session is started with [nlp.start()](https://nlp.johnsnowlabs.com/docs/en/jsl/start-a-sparksession) under the hood.\n", + "For all 24.000+ models, see the [John Snow Labs Model Models Hub](https://nlp.johnsnowlabs.com/models)\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "! pip install johnsnowlabs\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# If you have a enterprise license, you can run this to install enterprise features\n", + "# from johnsnowlabs import nlp\n", + "# nlp.install()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "source": [ + "#### Import the necessary classes" + ], + "metadata": { + "collapsed": false + }, + "execution_count": 1, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found existing installation: langchain 0.0.189\n", + "Uninstalling langchain-0.0.189:\n", + " Successfully uninstalled langchain-0.0.189\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "from langchain.embeddings.johnsnowlabs import JohnSnowLabsEmbeddings" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Initialize Johnsnowlabs Embeddings and Spark Session" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "embedder = JohnSnowLabsEmbeddings('en.embed_sentence.biobert.clinical_base_cased')" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Define some example texts . These could be any documents that you want to analyze - for example, news articles, social media posts, or product reviews." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "texts = [\"Cancer is caused by smoking\", \"Antibiotics aren't painkiller\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Generate and print embeddings for the texts . The JohnSnowLabsEmbeddings class generates an embedding for each document, which is a numerical representation of the document's content. These embeddings can be used for various natural language processing tasks, such as document similarity comparison or text classification." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "embeddings = embedder.embed_documents(texts)\n", + "for i, embedding in enumerate(embeddings):\n", + " print(f\"Embedding for document {i+1}: {embedding}\")" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Generate and print an embedding for a single piece of text. You can also generate an embedding for a single piece of text, such as a search query. This can be useful for tasks like information retrieval, where you want to find documents that are similar to a given query." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "query = \"Cancer is caused by smoking\"\n", + "query_embedding = embedder.embed_query(query)\n", + "print(f\"Embedding for query: {query_embedding}\")" + ], + "metadata": { + "collapsed": false + } + } + ], + "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/text_embedding/sagemaker-endpoint.ipynb b/docs/docs/integrations/text_embedding/sagemaker-endpoint.ipynb index ec80112e101..98d423890db 100644 --- a/docs/docs/integrations/text_embedding/sagemaker-endpoint.ipynb +++ b/docs/docs/integrations/text_embedding/sagemaker-endpoint.ipynb @@ -43,7 +43,7 @@ "from langchain.embeddings import SagemakerEndpointEmbeddings\n", "from langchain.embeddings.sagemaker_endpoint import EmbeddingsContentHandler\n", "import json\n", - "\n", + "import boto3\n", "\n", "class ContentHandler(EmbeddingsContentHandler):\n", " content_type = \"application/json\"\n", @@ -87,7 +87,18 @@ " endpoint_name=\"huggingface-pytorch-inference-2023-03-21-16-14-03-834\",\n", " region_name=\"us-east-1\",\n", " content_handler=content_handler,\n", - ")" + ")\n", + "\n", + "\n", + "# client = boto3.client(\n", + "# \"sagemaker-runtime\",\n", + "# region_name=\"us-west-2\" \n", + "# )\n", + "# embeddings = SagemakerEndpointEmbeddings(\n", + "# endpoint_name=\"huggingface-pytorch-inference-2023-03-21-16-14-03-834\", \n", + "# client=client\n", + "# content_handler=content_handler,\n", + "# )" ] }, { diff --git a/docs/docs/integrations/toolkits/github.ipynb b/docs/docs/integrations/toolkits/github.ipynb index f72c0c8a60a..3eb7a18aec0 100644 --- a/docs/docs/integrations/toolkits/github.ipynb +++ b/docs/docs/integrations/toolkits/github.ipynb @@ -354,7 +354,7 @@ "issue = github.get_issue(9)\n", "\n", "prompt = f\"\"\"\n", - "You are a seinor frontend developer who is experienced in HTML, CSS, and JS- especially React.\n", + "You are a senior frontend developer who is experienced in HTML, CSS, and JS- especially React.\n", "You have been assigned the below issue. Complete it to the best of your ability.\n", "Remember to first make a plan and pay attention to details like file names and commonsense.\n", "Then execute the plan and use tools appropriately.\n", diff --git a/docs/docs/integrations/toolkits/openapi_nla.ipynb b/docs/docs/integrations/toolkits/openapi_nla.ipynb index a731e282d09..09f54b5d5ac 100644 --- a/docs/docs/integrations/toolkits/openapi_nla.ipynb +++ b/docs/docs/integrations/toolkits/openapi_nla.ipynb @@ -11,7 +11,7 @@ "\n", "This notebook demonstrates a sample composition of the `Speak`, `Klarna`, and `Spoonacluar` APIs.\n", "\n", - "For a detailed walkthrough of the OpenAPI chains wrapped within the NLAToolkit, see the [OpenAPI Operation Chain](/docs/use_cases/apis/openapi.html) notebook.\n", + "For a detailed walkthrough of the OpenAPI chains wrapped within the NLAToolkit, see the [OpenAPI Operation Chain](/docs/use_cases/apis/openapi) notebook.\n", "\n", "### First, import dependencies and load the LLM" ] diff --git a/docs/docs/integrations/toolkits/spark.ipynb b/docs/docs/integrations/toolkits/spark.ipynb index d55075c2b00..ce39f54a26f 100644 --- a/docs/docs/integrations/toolkits/spark.ipynb +++ b/docs/docs/integrations/toolkits/spark.ipynb @@ -379,7 +379,7 @@ "agent.run(\n", " \"\"\"\n", "who bought the most expensive ticket?\n", - "You can find all supported function types in https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/dataframe.html\n", + "You can find all supported function types in https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/dataframe\n", "\"\"\"\n", ")" ] diff --git a/docs/docs/integrations/tools/apify.ipynb b/docs/docs/integrations/tools/apify.ipynb index d5cc8571d2d..20656982e1f 100644 --- a/docs/docs/integrations/tools/apify.ipynb +++ b/docs/docs/integrations/tools/apify.ipynb @@ -6,7 +6,7 @@ "source": [ "# Apify\n", "\n", - "This notebook shows how to use the [Apify integration](/docs/ecosystem/integrations/apify.html) for LangChain.\n", + "This notebook shows how to use the [Apify integration](/docs/ecosystem/integrations/apify) for LangChain.\n", "\n", "[Apify](https://apify.com) is a cloud platform for web scraping and data extraction,\n", "which provides an [ecosystem](https://apify.com/store) of more than a thousand\n", @@ -72,7 +72,7 @@ "source": [ "Then run the Actor, wait for it to finish, and fetch its results from the Apify dataset into a LangChain document loader.\n", "\n", - "Note that if you already have some results in an Apify dataset, you can load them directly using `ApifyDatasetLoader`, as shown in [this notebook](/docs/integrations/document_loaders/apify_dataset.html). In that notebook, you'll also find the explanation of the `dataset_mapping_function`, which is used to map fields from the Apify dataset records to LangChain `Document` fields." + "Note that if you already have some results in an Apify dataset, you can load them directly using `ApifyDatasetLoader`, as shown in [this notebook](/docs/integrations/document_loaders/apify_dataset). In that notebook, you'll also find the explanation of the `dataset_mapping_function`, which is used to map fields from the Apify dataset records to LangChain `Document` fields." ] }, { diff --git a/docs/docs/integrations/tools/bearly.ipynb b/docs/docs/integrations/tools/bearly.ipynb index c4fd99fceac..7301f8736fc 100644 --- a/docs/docs/integrations/tools/bearly.ipynb +++ b/docs/docs/integrations/tools/bearly.ipynb @@ -55,7 +55,7 @@ "id": "ac5c88ce", "metadata": {}, "source": [ - "Let's add some files to the the sandbox" + "Let's add some files to the sandbox" ] }, { diff --git a/docs/docs/integrations/tools/e2b_data_analysis.ipynb b/docs/docs/integrations/tools/e2b_data_analysis.ipynb new file mode 100644 index 00000000000..371dd65bedd --- /dev/null +++ b/docs/docs/integrations/tools/e2b_data_analysis.ipynb @@ -0,0 +1,368 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# E2B Data Analysis\n", + "\n", + "[E2B's cloud environments](https://e2b.dev) are great runtime sandboxes for LLMs.\n", + "\n", + "E2B's Data Analysis sandbox allows for safe code execution in a sandboxed environment. This is ideal for building tools such as code interpreters, or Advanced Data Analysis like in ChatGPT.\n", + "\n", + "E2B Data Analysis sandbox allows you to:\n", + "- Run Python code\n", + "- Generate charts via matplotlib\n", + "- Install Python packages dynamically durint runtime\n", + "- Install system packages dynamically during runtime\n", + "- Run shell commands\n", + "- Upload and download files\n", + "\n", + "We'll create a simple OpenAI agent that will use E2B's Data Analysis sandbox to perform analysis on a uploaded files using Python." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get your OpenAI API key and [E2B API key here](https://e2b.dev/docs/getting-started/api-key) and set them as environment variables.\n", + "\n", + "You can find the full API documentation [here](https://e2b.dev/docs).\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You'll need to install `e2b` to get started:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install langchain e2b" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from langchain.chat_models import ChatOpenAI\n", + "from langchain.tools import E2BDataAnalysisTool\n", + "from langchain.agents import initialize_agent, AgentType\n", + "\n", + "os.environ[\"E2B_API_KEY\"] = \"\"\n", + "os.environ[\"OPENAI_API_KEY\"] = \"\"" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When creating an instance of the `E2BDataAnalysisTool`, you can pass callbacks to listen to the output of the sandbox. This is useful, for example, when creating more responsive UI. Especially with the combination of streaming output from LLMs." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Artifacts are charts created by matplotlib when `plt.show()` is called\n", + "def save_artifact(artifact):\n", + " print(\"New matplotlib chart generated:\", artifact.name)\n", + " # Download the artifact as `bytes` and leave it up to the user to display them (on frontend, for example)\n", + " file = artifact.download()\n", + " basename = os.path.basename(artifact.name)\n", + "\n", + " # Save the chart to the `charts` directory\n", + " with open(f\"./charts/{basename}\", \"wb\") as f:\n", + " f.write(file)\n", + "\n", + "e2b_data_analysis_tool = E2BDataAnalysisTool(\n", + " # Pass environment variables to the sandbox\n", + " env_vars={\"MY_SECRET\": \"secret_value\"},\n", + " on_stdout=lambda stdout: print(\"stdout:\", stdout),\n", + " on_stderr=lambda stderr: print(\"stderr:\", stderr),\n", + " on_artifact=save_artifact,\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Upload an example CSV data file to the sandbox so we can analyze it with our agent. You can use for example [this file](https://storage.googleapis.com/e2b-examples/netflix.csv) about Netflix tv shows." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "name='netflix.csv' remote_path='/home/user/netflix.csv' description='Data about Netflix tv shows including their title, category, director, release date, casting, age rating, etc.'\n" + ] + } + ], + "source": [ + "with open(\"./netflix.csv\") as f:\n", + " remote_path = e2b_data_analysis_tool.upload_file(\n", + " file=f,\n", + " description=\"Data about Netflix tv shows including their title, category, director, release date, casting, age rating, etc.\",\n", + " )\n", + " print(remote_path)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a `Tool` object and initialize the Langchain agent." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "tools = [e2b_data_analysis_tool.as_tool()]\n", + "\n", + "llm = ChatOpenAI(model=\"gpt-4\", temperature=0)\n", + "agent = initialize_agent(\n", + " tools, llm, agent=AgentType.OPENAI_FUNCTIONS, verbose=True, handle_parsing_errors=True\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can ask the agent questions about the CSV file we uploaded earlier." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", + "\u001b[32;1m\u001b[1;3m\n", + "Invoking: `e2b_data_analysis` with `{'python_code': \"import pandas as pd\\n\\n# Load the data\\nnetflix_data = pd.read_csv('/home/user/netflix.csv')\\n\\n# Convert the 'release_year' column to integer\\nnetflix_data['release_year'] = netflix_data['release_year'].astype(int)\\n\\n# Filter the data for movies released between 2000 and 2010\\nfiltered_data = netflix_data[(netflix_data['release_year'] >= 2000) & (netflix_data['release_year'] <= 2010) & (netflix_data['type'] == 'Movie')]\\n\\n# Remove rows where 'duration' is not available\\nfiltered_data = filtered_data[filtered_data['duration'].notna()]\\n\\n# Convert the 'duration' column to integer\\nfiltered_data['duration'] = filtered_data['duration'].str.replace(' min','').astype(int)\\n\\n# Get the top 5 longest movies\\nlongest_movies = filtered_data.nlargest(5, 'duration')\\n\\n# Create a bar chart\\nimport matplotlib.pyplot as plt\\n\\nplt.figure(figsize=(10,5))\\nplt.barh(longest_movies['title'], longest_movies['duration'], color='skyblue')\\nplt.xlabel('Duration (minutes)')\\nplt.title('Top 5 Longest Movies on Netflix (2000-2010)')\\nplt.gca().invert_yaxis()\\nplt.savefig('/home/user/longest_movies.png')\\n\\nlongest_movies[['title', 'duration']]\"}`\n", + "\n", + "\n", + "\u001b[0mstdout: title duration\n", + "stdout: 1019 Lagaan 224\n", + "stdout: 4573 Jodhaa Akbar 214\n", + "stdout: 2731 Kabhi Khushi Kabhie Gham 209\n", + "stdout: 2632 No Direction Home: Bob Dylan 208\n", + "stdout: 2126 What's Your Raashee? 203\n", + "\u001b[36;1m\u001b[1;3m{'stdout': \" title duration\\n1019 Lagaan 224\\n4573 Jodhaa Akbar 214\\n2731 Kabhi Khushi Kabhie Gham 209\\n2632 No Direction Home: Bob Dylan 208\\n2126 What's Your Raashee? 203\", 'stderr': ''}\u001b[0m\u001b[32;1m\u001b[1;3mThe 5 longest movies on Netflix released between 2000 and 2010 are:\n", + "\n", + "1. Lagaan - 224 minutes\n", + "2. Jodhaa Akbar - 214 minutes\n", + "3. Kabhi Khushi Kabhie Gham - 209 minutes\n", + "4. No Direction Home: Bob Dylan - 208 minutes\n", + "5. What's Your Raashee? - 203 minutes\n", + "\n", + "Here is the chart showing their lengths:\n", + "\n", + "![Longest Movies](sandbox:/home/user/longest_movies.png)\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "\"The 5 longest movies on Netflix released between 2000 and 2010 are:\\n\\n1. Lagaan - 224 minutes\\n2. Jodhaa Akbar - 214 minutes\\n3. Kabhi Khushi Kabhie Gham - 209 minutes\\n4. No Direction Home: Bob Dylan - 208 minutes\\n5. What's Your Raashee? - 203 minutes\\n\\nHere is the chart showing their lengths:\\n\\n![Longest Movies](sandbox:/home/user/longest_movies.png)\"" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "agent.run(\"What are the 5 longest movies on netflix released between 2000 and 2010? Create a chart with their lengths.\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "E2B also allows you to install both Python and system (via `apt`) packages dynamically during runtime like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "stdout: Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.1.1)\n", + "stdout: Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n", + "stdout: Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2023.3.post1)\n", + "stdout: Requirement already satisfied: numpy>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.26.1)\n", + "stdout: Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2023.3)\n", + "stdout: Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n" + ] + } + ], + "source": [ + "# Install Python package\n", + "e2b_data_analysis_tool.install_python_packages('pandas')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additionally, you can download any file from the sandbox like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# The path is a remote path in the sandbox\n", + "files_in_bytes = e2b_data_analysis_tool.download_file('/home/user/netflix.csv')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lastly, you can run any shell command inside the sandbox via `run_command`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "stderr: \n", + "stderr: WARNING: apt does not have a stable CLI interface. Use with caution in scripts.\n", + "stderr: \n", + "stdout: Hit:1 http://security.ubuntu.com/ubuntu jammy-security InRelease\n", + "stdout: Hit:2 http://archive.ubuntu.com/ubuntu jammy InRelease\n", + "stdout: Hit:3 http://archive.ubuntu.com/ubuntu jammy-updates InRelease\n", + "stdout: Hit:4 http://archive.ubuntu.com/ubuntu jammy-backports InRelease\n", + "stdout: Reading package lists...\n", + "stdout: Building dependency tree...\n", + "stdout: Reading state information...\n", + "stdout: All packages are up to date.\n", + "stdout: Reading package lists...\n", + "stdout: Building dependency tree...\n", + "stdout: Reading state information...\n", + "stdout: Suggested packages:\n", + "stdout: sqlite3-doc\n", + "stdout: The following NEW packages will be installed:\n", + "stdout: sqlite3\n", + "stdout: 0 upgraded, 1 newly installed, 0 to remove and 0 not upgraded.\n", + "stdout: Need to get 768 kB of archives.\n", + "stdout: After this operation, 1873 kB of additional disk space will be used.\n", + "stdout: Get:1 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 sqlite3 amd64 3.37.2-2ubuntu0.1 [768 kB]\n", + "stderr: debconf: delaying package configuration, since apt-utils is not installed\n", + "stdout: Fetched 768 kB in 0s (2258 kB/s)\n", + "stdout: Selecting previously unselected package sqlite3.\n", + "(Reading database ... 23999 files and directories currently installed.)\n", + "stdout: Preparing to unpack .../sqlite3_3.37.2-2ubuntu0.1_amd64.deb ...\n", + "stdout: Unpacking sqlite3 (3.37.2-2ubuntu0.1) ...\n", + "stdout: Setting up sqlite3 (3.37.2-2ubuntu0.1) ...\n", + "stdout: 3.37.2 2022-01-06 13:25:41 872ba256cbf61d9290b571c0e6d82a20c224ca3ad82971edc46b29818d5dalt1\n", + "version: 3.37.2 2022-01-06 13:25:41 872ba256cbf61d9290b571c0e6d82a20c224ca3ad82971edc46b29818d5dalt1\n", + "error: \n", + "exit code: 0\n" + ] + } + ], + "source": [ + "# Install SQLite\n", + "e2b_data_analysis_tool.run_command(\"sudo apt update\")\n", + "e2b_data_analysis_tool.install_system_packages(\"sqlite3\")\n", + "\n", + "# Check the SQLite version\n", + "output = e2b_data_analysis_tool.run_command(\"sqlite3 --version\")\n", + "print(\"version: \", output[\"stdout\"])\n", + "print(\"error: \", output[\"stderr\"])\n", + "print(\"exit code: \", output[\"exit_code\"])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When your agent is finished, don't forget to close the sandbox" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "e2b_data_analysis_tool.close()" + ] + } + ], + "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": 4 +} diff --git a/docs/docs_skeleton/docs/integrations/tools/google_scholar.ipynb b/docs/docs/integrations/tools/google_scholar.ipynb similarity index 100% rename from docs/docs_skeleton/docs/integrations/tools/google_scholar.ipynb rename to docs/docs/integrations/tools/google_scholar.ipynb diff --git a/docs/docs/integrations/vectorstores/dingo.ipynb b/docs/docs/integrations/vectorstores/dingo.ipynb index 6deb9d4dea3..37b4f04e74c 100644 --- a/docs/docs/integrations/vectorstores/dingo.ipynb +++ b/docs/docs/integrations/vectorstores/dingo.ipynb @@ -5,9 +5,9 @@ "id": "683953b3", "metadata": {}, "source": [ - "# Dingo\n", + "# DingoDB\n", "\n", - ">[Dingo](https://dingodb.readthedocs.io/en/latest/) is a distributed multi-mode vector database, which combines the characteristics of data lakes and vector databases, and can store data of any type and size (Key-Value, PDF, audio, video, etc.). It has real-time low-latency processing capabilities to achieve rapid insight and response, and can efficiently conduct instant analysis and process multi-modal data.\n", + ">[DingoDB](https://dingodb.readthedocs.io/en/latest/) is a distributed multi-mode vector database, which combines the characteristics of data lakes and vector databases, and can store data of any type and size (Key-Value, PDF, audio, video, etc.). It has real-time low-latency processing capabilities to achieve rapid insight and response, and can efficiently conduct instant analysis and process multi-modal data.\n", "\n", "This notebook shows how to use functionality related to the DingoDB vector database.\n", "\n", diff --git a/docs/docs/integrations/vectorstores/matchingengine.ipynb b/docs/docs/integrations/vectorstores/matchingengine.ipynb index faee307c342..a086be2cf0d 100644 --- a/docs/docs/integrations/vectorstores/matchingengine.ipynb +++ b/docs/docs/integrations/vectorstores/matchingengine.ipynb @@ -5,11 +5,11 @@ "id": "655b8f55-2089-4733-8b09-35dea9580695", "metadata": {}, "source": [ - "# Google Vertex AI MatchingEngine\n", + "# Google Vertex AI Vector Search\n", "\n", - "This notebook shows how to use functionality related to the `GCP Vertex AI MatchingEngine` vector database.\n", + "This notebook shows how to use functionality related to the `Google Cloud Vertex AI Vector Search` vector database.\n", "\n", - "> Vertex AI [Matching Engine](https://cloud.google.com/vertex-ai/docs/matching-engine/overview) provides the industry's leading high-scale low latency vector database. These vector databases are commonly referred to as vector similarity-matching or an approximate nearest neighbor (ANN) service.\n", + "> [Google Vertex AI Vector Search](https://cloud.google.com/vertex-ai/docs/matching-engine/overview), formerly known as Vertex AI Matching Engine, provides the industry's leading high-scale low latency vector database. These vector databases are commonly referred to as vector similarity-matching or an approximate nearest neighbor (ANN) service.\n", "\n", "**Note**: This module expects an endpoint and deployed index already created as the creation time takes close to one hour. To see how to create an index refer to the section [Create Index and deploy it to an Endpoint](#create-index-and-deploy-it-to-an-endpoint)" ] @@ -29,7 +29,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain.vectorstores import MatchingEngine" + "from langchain.vectorstores import MatchingEngine\n" ] }, { @@ -61,7 +61,7 @@ "\n", "vector_store.add_texts(texts=texts)\n", "\n", - "vector_store.similarity_search(\"lunch\", k=2)" + "vector_store.similarity_search(\"lunch\", k=2)\n" ] }, { @@ -93,7 +93,7 @@ "!pip install tensorflow \\\n", " google-cloud-aiplatform \\\n", " tensorflow-hub \\\n", - " tensorflow-text " + " tensorflow-text \n" ] }, { @@ -108,7 +108,7 @@ "\n", "from google.cloud import aiplatform\n", "import tensorflow_hub as hub\n", - "import tensorflow_text" + "import tensorflow_text\n" ] }, { @@ -137,7 +137,7 @@ "VPC_NETWORK_FULL = f\"projects/{PROJECT_NUMBER}/global/networks/{VPC_NETWORK}\"\n", "\n", "# Change this if you need the VPC to be created.\n", - "CREATE_VPC = False" + "CREATE_VPC = False\n" ] }, { @@ -148,7 +148,7 @@ "outputs": [], "source": [ "# Set the project id\n", - "! gcloud config set project {PROJECT_ID}" + "! gcloud config set project {PROJECT_ID}\n" ] }, { @@ -177,7 +177,7 @@ "\n", " # Set up peering with service networking\n", " # Your account must have the \"Compute Network Admin\" role to run the following.\n", - " ! gcloud services vpc-peerings connect --service=servicenetworking.googleapis.com --network={VPC_NETWORK} --ranges={PEERING_RANGE_NAME} --project={PROJECT_ID}" + " ! gcloud services vpc-peerings connect --service=servicenetworking.googleapis.com --network={VPC_NETWORK} --ranges={PEERING_RANGE_NAME} --project={PROJECT_ID}\n" ] }, { @@ -188,7 +188,7 @@ "outputs": [], "source": [ "# Creating bucket.\n", - "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI" + "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI\n" ] }, { @@ -208,7 +208,7 @@ "source": [ "# Load the Universal Sentence Encoder module\n", "module_url = \"https://tfhub.dev/google/universal-sentence-encoder-multilingual/3\"\n", - "model = hub.load(module_url)" + "model = hub.load(module_url)\n" ] }, { @@ -219,7 +219,7 @@ "outputs": [], "source": [ "# Generate embeddings for each word\n", - "embeddings = model([\"banana\"])" + "embeddings = model([\"banana\"])\n" ] }, { @@ -245,7 +245,7 @@ "with open(\"data.json\", \"w\") as f:\n", " json.dump(initial_config, f)\n", "\n", - "!gsutil cp data.json {EMBEDDING_DIR}/file.json" + "!gsutil cp data.json {EMBEDDING_DIR}/file.json\n" ] }, { @@ -255,7 +255,7 @@ "metadata": {}, "outputs": [], "source": [ - "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)" + "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=BUCKET_URI)\n" ] }, { @@ -279,7 +279,7 @@ " dimensions=DIMENSIONS,\n", " approximate_neighbors_count=150,\n", " distance_measure_type=\"DOT_PRODUCT_DISTANCE\",\n", - ")" + ")\n" ] }, { @@ -300,7 +300,7 @@ "my_index_endpoint = aiplatform.MatchingEngineIndexEndpoint.create(\n", " display_name=f\"{DISPLAY_NAME}-endpoint\",\n", " network=VPC_NETWORK_FULL,\n", - ")" + ")\n" ] }, { @@ -322,7 +322,7 @@ " index=my_index, deployed_index_id=DEPLOYED_INDEX_ID\n", ")\n", "\n", - "my_index_endpoint.deployed_indexes" + "my_index_endpoint.deployed_indexes\n" ] } ], diff --git a/docs/docs/integrations/vectorstores/pgvector.ipynb b/docs/docs/integrations/vectorstores/pgvector.ipynb index 7e9b944469b..a3c01d21e54 100644 --- a/docs/docs/integrations/vectorstores/pgvector.ipynb +++ b/docs/docs/integrations/vectorstores/pgvector.ipynb @@ -141,7 +141,7 @@ "# PGVector needs the connection string to the database.\n", "CONNECTION_STRING = \"postgresql+psycopg2://harrisonchase@localhost:5432/test3\"\n", "\n", - "# # Alternatively, you can create it from enviornment variables.\n", + "# # Alternatively, you can create it from environment variables.\n", "# import os\n", "\n", "# CONNECTION_STRING = PGVector.connection_string_from_db_params(\n", diff --git a/docs/docs/integrations/vectorstores/supabase.ipynb b/docs/docs/integrations/vectorstores/supabase.ipynb index 24008d9a712..033a5d381f8 100644 --- a/docs/docs/integrations/vectorstores/supabase.ipynb +++ b/docs/docs/integrations/vectorstores/supabase.ipynb @@ -197,7 +197,7 @@ "id": "5abb9b93", "metadata": {}, "source": [ - "Insert the above documents into the database. Embeddings will automatically be generated for each document." + "Insert the above documents into the database. Embeddings will automatically be generated for each document. You can adjust the chunk_size based on the amount of documents you have. The default is 500 but lowering it may be necessary." ] }, { @@ -208,7 +208,7 @@ "outputs": [], "source": [ "\n", - "vector_store = SupabaseVectorStore.from_documents(docs, embeddings, client=supabase, table_name=\"documents\", query_name=\"match_documents\")" + "vector_store = SupabaseVectorStore.from_documents(docs, embeddings, client=supabase, table_name=\"documents\", query_name=\"match_documents\", chunk_size=500)" ] }, { diff --git a/docs/docs/integrations/vectorstores/typesense.ipynb b/docs/docs/integrations/vectorstores/typesense.ipynb index 94655a629c2..7bb0c5caf51 100644 --- a/docs/docs/integrations/vectorstores/typesense.ipynb +++ b/docs/docs/integrations/vectorstores/typesense.ipynb @@ -6,7 +6,7 @@ "source": [ "# Typesense\n", "\n", - "> [Typesense](https://typesense.org) is an open-source, in-memory search engine, that you can either [self-host](https://typesense.org/docs/guide/install-typesense.html#option-2-local-machine-self-hosting) or run on [Typesense Cloud](https://cloud.typesense.org/).\n", + "> [Typesense](https://typesense.org) is an open-source, in-memory search engine, that you can either [self-host](https://typesense.org/docs/guide/install-typesense#option-2-local-machine-self-hosting) or run on [Typesense Cloud](https://cloud.typesense.org/).\n", ">\n", "> Typesense focuses on performance by storing the entire index in RAM (with a backup on disk) and also focuses on providing an out-of-the-box developer experience by simplifying available options and setting good defaults.\n", ">\n", diff --git a/docs/docs/modules/agents/how_to/custom_agent_with_tool_retrieval.ipynb b/docs/docs/modules/agents/how_to/custom_agent_with_tool_retrieval.ipynb index 5471eec006f..8f3a0b4c6f2 100644 --- a/docs/docs/modules/agents/how_to/custom_agent_with_tool_retrieval.ipynb +++ b/docs/docs/modules/agents/how_to/custom_agent_with_tool_retrieval.ipynb @@ -7,7 +7,7 @@ "source": [ "# Custom agent with tool retrieval\n", "\n", - "This notebook builds off of [this notebook](/docs/modules/agents/how_to/custom_llm_agent.html) and assumes familiarity with how agents work.\n", + "This notebook builds off of [this notebook](/docs/modules/agents/how_to/custom_llm_agent) and assumes familiarity with how agents work.\n", "\n", "The novel idea introduced in this notebook is the idea of using retrieval to select the set of tools to use to answer an agent query. This is useful when you have many many tools to select from. You cannot put the description of all the tools in the prompt (because of context length issues) so instead you dynamically select the N tools you do want to consider using at run time.\n", "\n", diff --git a/docs/docs/modules/agents/how_to/sharedmemory_for_tools.ipynb b/docs/docs/modules/agents/how_to/sharedmemory_for_tools.ipynb index 2d0d4ed2d26..7c831554da1 100644 --- a/docs/docs/modules/agents/how_to/sharedmemory_for_tools.ipynb +++ b/docs/docs/modules/agents/how_to/sharedmemory_for_tools.ipynb @@ -9,8 +9,8 @@ "\n", "This notebook goes over adding memory to **both** an Agent and its tools. Before going through this notebook, please walk through the following notebooks, as this will build on top of both of them:\n", "\n", - "- [Adding memory to an LLM Chain](/docs/modules/memory/integrations/adding_memory.html)\n", - "- [Custom Agents](/docs/modules/agents/how_to/custom_agent.html)\n", + "- [Adding memory to an LLM Chain](/docs/modules/memory/integrations/adding_memory)\n", + "- [Custom Agents](/docs/modules/agents/how_to/custom_agent)\n", "\n", "We are going to create a custom Agent. The agent has access to a conversation memory, search tool, and a summarization tool. The summarization tool also needs access to the conversation memory." ] diff --git a/docs/docs/modules/callbacks/custom_chain.mdx b/docs/docs/modules/callbacks/custom_chain.mdx index bc64de30412..6ec068eea23 100644 --- a/docs/docs/modules/callbacks/custom_chain.mdx +++ b/docs/docs/modules/callbacks/custom_chain.mdx @@ -1,6 +1,6 @@ # Callbacks for custom chains When you create a custom chain you can easily set it up to use the same callback system as all the built-in chains. -`_call`, `_generate`, `_run`, and equivalent async methods on Chains / LLMs / Chat Models / Agents / Tools now receive a 2nd argument called `run_manager` which is bound to that run, and contains the logging methods that can be used by that object (i.e. `on_llm_new_token`). This is useful when constructing a custom chain. See this guide for more information on how to [create custom chains and use callbacks inside them](/docs/modules/chains/how_to/custom_chain.html). +`_call`, `_generate`, `_run`, and equivalent async methods on Chains / LLMs / Chat Models / Agents / Tools now receive a 2nd argument called `run_manager` which is bound to that run, and contains the logging methods that can be used by that object (i.e. `on_llm_new_token`). This is useful when constructing a custom chain. See this guide for more information on how to [create custom chains and use callbacks inside them](/docs/modules/chains/how_to/custom_chain). diff --git a/docs/docs/modules/chains/document/map_reduce.ipynb b/docs/docs/modules/chains/document/map_reduce.ipynb new file mode 100644 index 00000000000..218c844b143 --- /dev/null +++ b/docs/docs/modules/chains/document/map_reduce.ipynb @@ -0,0 +1,251 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2accd7d9-20b6-47f9-9cec-923809cc36c7", + "metadata": {}, + "source": [ + "# Map reduce\n", + "\n", + "The map reduce documents chain first applies an LLM chain to each document individually (the Map step), treating the chain output as a new document. It then passes all the new documents to a separate combine documents chain to get a single output (the Reduce step). It can optionally first compress, or collapse, the mapped documents to make sure that they fit in the combine documents chain (which will often pass them to an LLM). This compression step is performed recursively if necessary.\n", + "\n", + "![map_reduce_diagram](/img/map_reduce.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "343fc972-40be-44f8-8ed3-305322661a00", + "metadata": {}, + "source": [ + "## Recreating with LCEL\n", + "\n", + "With [LangChain Expression Language](/docs/expression_language) we can recreate the `MapReduceDocumentsChain` functionality, with the additional benefit of getting all the built-in LCEL features (batch, async, etc.) and with much more ability to customize specific parts of the chain." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7bc161bc-4054-457a-9d04-7245093acd16", + "metadata": {}, + "outputs": [], + "source": [ + "from functools import partial\n", + "\n", + "from langchain.callbacks.manager import CallbackManagerForChainRun\n", + "from langchain.chains.combine_documents.reduce import _collapse_docs, _split_list_of_docs\n", + "from langchain.chat_models import ChatAnthropic\n", + "from langchain.prompts import PromptTemplate\n", + "from langchain.schema import StrOutputParser\n", + "from langchain.schema.prompt_template import format_document\n", + "from langchain.schema.runnable import RunnableParallel, RunnablePassthrough" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a93ba908-5b81-4e91-a598-ee6fa05eac01", + "metadata": {}, + "outputs": [], + "source": [ + "llm = ChatAnthropic()\n", + "\n", + "# Prompt and method for converting Document -> str.\n", + "document_prompt = PromptTemplate.from_template(\"{page_content}\")\n", + "partial_format_document = partial(format_document, prompt=document_prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "75918baa-df6b-4570-91eb-1acd1c87e09b", + "metadata": {}, + "outputs": [], + "source": [ + "# The chain we'll apply to each individual document.\n", + "# Returns a summary of the document.\n", + "map_chain = (\n", + " {\"context\": partial_format_document} \n", + " | PromptTemplate.from_template(\"Summarize this content:\\n\\n{context}\") \n", + " | llm \n", + " | StrOutputParser()\n", + ")\n", + "\n", + "# A wrapper chain to keep the original Document metadata\n", + "map_as_doc_chain = (\n", + " RunnableParallel({\"doc\": RunnablePassthrough(), \"content\": map_chain}) \n", + " | (lambda x: Document(page_content=x[\"content\"], metadata=x[\"doc\"].metadata))\n", + ").with_config(run_name=\"Summarize (return doc)\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "720cb117-8a3e-4595-9e2c-dbbd7a3777b5", + "metadata": {}, + "outputs": [], + "source": [ + "# The chain we'll repeatedly apply to collapse subsets of the documents \n", + "# into a consolidate document until the total token size of our \n", + "# documents is below some max size.\n", + "def format_docs(docs):\n", + " return \"\\n\\n\".join(partial_format_document(doc) for doc in docs)\n", + " \n", + "collapse_chain = (\n", + " {\"context\": format_docs}\n", + " | PromptTemplate.from_template(\"Collapse this content:\\n\\n{context}\")\n", + " | llm\n", + " | StrOutputParser()\n", + ")\n", + "\n", + "def get_num_tokens(docs):\n", + " return llm.get_num_tokens(format_docs(docs))\n", + "\n", + "def collapse(\n", + " docs, \n", + " config,\n", + " token_max=4000,\n", + "):\n", + " collapse_ct = 1\n", + " while get_num_tokens(docs) > token_max:\n", + " config[\"run_name\"] = f\"Collapse {collapse_ct}\"\n", + " invoke = partial(collapse_chain.invoke, config=config)\n", + " split_docs = _split_list_of_docs(docs, get_num_tokens, token_max)\n", + " docs = [_collapse_docs(_docs, invoke) for _docs in split_docs]\n", + " collapse_ct += 1\n", + " return docs" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "fe5c5597-3ea3-443e-ad9f-e2f055cf092f", + "metadata": {}, + "outputs": [], + "source": [ + "# The chain we'll use to combine our individual document summaries\n", + "# (or summaries over subset of documents if we had to collapse the map results)\n", + "# into a final summary.\n", + "\n", + "reduce_chain = (\n", + " {\"context\": format_docs}\n", + " | PromptTemplate.from_template(\"Combine these summaries:\\n\\n{context}\")\n", + " | llm\n", + " | StrOutputParser()\n", + ").with_config(run_name=\"Reduce\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fd1148ce-f693-42b5-91e4-304983e26be6", + "metadata": {}, + "outputs": [], + "source": [ + "# The final full chain\n", + "map_reduce = (map_as_doc_chain.map() | collapse | reduce_chain).with_config(run_name=\"Map reduce\")" + ] + }, + { + "cell_type": "markdown", + "id": "5a10615c-f3ab-4603-bf0c-e6aea73c5450", + "metadata": {}, + "source": [ + "## Example run" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "28a8f74c-2441-431a-b352-541d0ad1e75b", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.schema import Document\n", + "\n", + "text = \"\"\"Nuclear power in space is the use of nuclear power in outer space, typically either small fission systems or radioactive decay for electricity or heat. Another use is for scientific observation, as in a Mรถssbauer spectrometer. The most common type is a radioisotope thermoelectric generator, which has been used on many space probes and on crewed lunar missions. Small fission reactors for Earth observation satellites, such as the TOPAZ nuclear reactor, have also been flown.[1] A radioisotope heater unit is powered by radioactive decay and can keep components from becoming too cold to function, potentially over a span of decades.[2]\n", + "\n", + "The United States tested the SNAP-10A nuclear reactor in space for 43 days in 1965,[3] with the next test of a nuclear reactor power system intended for space use occurring on 13 September 2012 with the Demonstration Using Flattop Fission (DUFF) test of the Kilopower reactor.[4]\n", + "\n", + "After a ground-based test of the experimental 1965 Romashka reactor, which used uranium and direct thermoelectric conversion to electricity,[5] the USSR sent about 40 nuclear-electric satellites into space, mostly powered by the BES-5 reactor. The more powerful TOPAZ-II reactor produced 10 kilowatts of electricity.[3]\n", + "\n", + "Examples of concepts that use nuclear power for space propulsion systems include the nuclear electric rocket (nuclear powered ion thruster(s)), the radioisotope rocket, and radioisotope electric propulsion (REP).[6] One of the more explored concepts is the nuclear thermal rocket, which was ground tested in the NERVA program. Nuclear pulse propulsion was the subject of Project Orion.[7]\n", + "\n", + "Regulation and hazard prevention[edit]\n", + "After the ban of nuclear weapons in space by the Outer Space Treaty in 1967, nuclear power has been discussed at least since 1972 as a sensitive issue by states.[8] Particularly its potential hazards to Earth's environment and thus also humans has prompted states to adopt in the U.N. General Assembly the Principles Relevant to the Use of Nuclear Power Sources in Outer Space (1992), particularly introducing safety principles for launches and to manage their traffic.[8]\n", + "\n", + "Benefits\n", + "\n", + "Both the Viking 1 and Viking 2 landers used RTGs for power on the surface of Mars. (Viking launch vehicle pictured)\n", + "While solar power is much more commonly used, nuclear power can offer advantages in some areas. Solar cells, although efficient, can only supply energy to spacecraft in orbits where the solar flux is sufficiently high, such as low Earth orbit and interplanetary destinations close enough to the Sun. Unlike solar cells, nuclear power systems function independently of sunlight, which is necessary for deep space exploration. Nuclear-based systems can have less mass than solar cells of equivalent power, allowing more compact spacecraft that are easier to orient and direct in space. In the case of crewed spaceflight, nuclear power concepts that can power both life support and propulsion systems may reduce both cost and flight time.[9]\n", + "\n", + "Selected applications and/or technologies for space include:\n", + "\n", + "Radioisotope thermoelectric generator\n", + "Radioisotope heater unit\n", + "Radioisotope piezoelectric generator\n", + "Radioisotope rocket\n", + "Nuclear thermal rocket\n", + "Nuclear pulse propulsion\n", + "Nuclear electric rocket\n", + "\"\"\"\n", + "\n", + "docs = [\n", + " Document(\n", + " page_content=split, \n", + " metadata={\"source\": \"https://en.wikipedia.org/wiki/Nuclear_power_in_space\"}\n", + " ) \n", + " for split in text.split(\"\\n\\n\")\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "846fe4ce-7016-4bc7-a8e0-7e914675d568", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Here is a summary that combines the key points about nuclear power in space:\n", + "\n", + "Nuclear power is used in space for electricity, heat, and scientific observation. The most common type is a radioisotope thermoelectric generator, which has powered space probes and lunar missions using the heat from radioactive decay. Small nuclear fission reactors have also been used to generate electricity for Earth observation satellites like the TOPAZ reactor. In addition, radioisotope heater units use radioactive decay to provide reliable heat that can keep components functioning properly over decades in the harsh space environment. Overall, nuclear power has proven useful for providing long-lasting power for space applications where solar power is not practical. Technologies like radioisotope decay heat and small fission reactors allow probes, satellites, and missions to operate far from the Sun and for extended periods by generating electricity and heat without reliance on solar energy.\n" + ] + } + ], + "source": [ + "print(map_reduce.invoke(docs[0:1], config={\"max_concurrency\": 5}))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8b77e13-6db4-4096-a1a4-d0abe2979b6b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "poetry-venv", + "language": "python", + "name": "poetry-venv" + }, + "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/modules/chains/document/map_reduce.mdx b/docs/docs/modules/chains/document/map_reduce.mdx deleted file mode 100644 index d094a11ab5c..00000000000 --- a/docs/docs/modules/chains/document/map_reduce.mdx +++ /dev/null @@ -1,5 +0,0 @@ -# Map reduce - -The map reduce documents chain first applies an LLM chain to each document individually (the Map step), treating the chain output as a new document. It then passes all the new documents to a separate combine documents chain to get a single output (the Reduce step). It can optionally first compress, or collapse, the mapped documents to make sure that they fit in the combine documents chain (which will often pass them to an LLM). This compression step is performed recursively if necessary. - -![map_reduce_diagram](/img/map_reduce.jpg) \ No newline at end of file diff --git a/docs/docs/modules/chains/document/map_rerank.ipynb b/docs/docs/modules/chains/document/map_rerank.ipynb new file mode 100644 index 00000000000..a2c431c4c0f --- /dev/null +++ b/docs/docs/modules/chains/document/map_rerank.ipynb @@ -0,0 +1,183 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3bbf49ee-f3f1-40c1-b48a-828e4166bfe0", + "metadata": {}, + "source": [ + "# Map re-rank\n", + "\n", + "The map re-rank documents chain runs an initial prompt on each document, that not only tries to complete a task but also gives a score for how certain it is in its answer. The highest scoring response is returned.\n", + "\n", + "![map_rerank_diagram](/img/map_rerank.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "d4cfac68-f2c4-49bf-9aad-d3e07eb9ee53", + "metadata": {}, + "source": [ + "## Recreating with LCEL\n", + "\n", + "With [LangChain Expression Language](/docs/expression_language) we can recreate the `MapRerankDocumentsChain` functionality, with the additional benefit of getting all the built-in LCEL features (batch, async, etc.) and with much more ability to customize specific parts of the chain." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "27c523f9-f9f1-4ad5-8bdb-38f8faa9c6e3", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.chat_models import ChatOpenAI\n", + "from langchain.output_parsers.openai_functions import PydanticOutputFunctionsParser\n", + "from langchain.prompts import PromptTemplate\n", + "from langchain.pydantic_v1 import BaseModel, Field\n", + "from langchain.schema.prompt_template import format_document\n", + "from langchain.utils.openai_functions import convert_pydantic_to_openai_function" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ea687400-9410-445c-9adc-f8c8b9f66327", + "metadata": {}, + "outputs": [], + "source": [ + "# Chain to apply to each individual document. Chain\n", + "# provides an answer to the question based on the document\n", + "# and scores it's confidence in the answer.\n", + "\n", + "map_prompt = PromptTemplate.from_template(\n", + " \"Answer the user question using the context.\"\n", + " \"\\n\\nContext:\\n\\n{context}\\n\\nQuestion: {question}\"\n", + ")\n", + "\n", + "class AnswerAndScore(BaseModel):\n", + " \"\"\"Return the answer to the question and a relevance score.\"\"\"\n", + " answer: str = Field(description=\"The answer to the question, which is based ONLY on the provided context.\")\n", + " score: float = Field(decsription=\"A 0.0-1.0 relevance score, where 1.0 indicates the provided context answers the question completely and 0.0 indicates the provided context does not answer the question at all.\")\n", + "\n", + "function = convert_pydantic_to_openai_function(AnswerAndScore)\n", + "map_chain = (\n", + " map_prompt \n", + " | ChatOpenAI().bind(temperature=0, functions=[function], function_call={\"name\": \"AnswerAndScore\"})\n", + " | PydanticOutputFunctionsParser(pydantic_schema=AnswerAndScore)\n", + ").with_config(run_name=\"Map\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ace2b1d5-a9ea-4a70-8d39-2826a5445aa7", + "metadata": {}, + "outputs": [], + "source": [ + "# Final chain, which after answer and scoring based on\n", + "# each doc return the answer with the highest score.\n", + "\n", + "def top_answer(scored_answers):\n", + " return max(scored_answers, key=lambda x: x.score).answer\n", + "\n", + "document_prompt = PromptTemplate.from_template(\"{page_content}\")\n", + "map_rerank_chain = (\n", + " (lambda x: [{\"context\": format_document(doc, document_prompt), \"question\": x[\"question\"]} for doc in x[\"docs\"]])\n", + " | map_chain.map()\n", + " | top_answer\n", + ").with_config(run_name=\"Map rerank\")" + ] + }, + { + "cell_type": "markdown", + "id": "62b863c9-2316-42ad-9581-ebc688889855", + "metadata": {}, + "source": [ + "## Example run" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "7b46c373-64c9-4b69-b64f-9bc6e52ae91c", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.schema import Document\n", + "\n", + "text = \"\"\"Nuclear power in space is the use of nuclear power in outer space, typically either small fission systems or radioactive decay for electricity or heat. Another use is for scientific observation, as in a Mรถssbauer spectrometer. The most common type is a radioisotope thermoelectric generator, which has been used on many space probes and on crewed lunar missions. Small fission reactors for Earth observation satellites, such as the TOPAZ nuclear reactor, have also been flown.[1] A radioisotope heater unit is powered by radioactive decay and can keep components from becoming too cold to function, potentially over a span of decades.[2]\n", + "\n", + "The United States tested the SNAP-10A nuclear reactor in space for 43 days in 1965,[3] with the next test of a nuclear reactor power system intended for space use occurring on 13 September 2012 with the Demonstration Using Flattop Fission (DUFF) test of the Kilopower reactor.[4]\n", + "\n", + "After a ground-based test of the experimental 1965 Romashka reactor, which used uranium and direct thermoelectric conversion to electricity,[5] the USSR sent about 40 nuclear-electric satellites into space, mostly powered by the BES-5 reactor. The more powerful TOPAZ-II reactor produced 10 kilowatts of electricity.[3]\n", + "\n", + "Examples of concepts that use nuclear power for space propulsion systems include the nuclear electric rocket (nuclear powered ion thruster(s)), the radioisotope rocket, and radioisotope electric propulsion (REP).[6] One of the more explored concepts is the nuclear thermal rocket, which was ground tested in the NERVA program. Nuclear pulse propulsion was the subject of Project Orion.[7]\n", + "\n", + "Regulation and hazard prevention[edit]\n", + "After the ban of nuclear weapons in space by the Outer Space Treaty in 1967, nuclear power has been discussed at least since 1972 as a sensitive issue by states.[8] Particularly its potential hazards to Earth's environment and thus also humans has prompted states to adopt in the U.N. General Assembly the Principles Relevant to the Use of Nuclear Power Sources in Outer Space (1992), particularly introducing safety principles for launches and to manage their traffic.[8]\n", + "\n", + "Benefits\n", + "\n", + "Both the Viking 1 and Viking 2 landers used RTGs for power on the surface of Mars. (Viking launch vehicle pictured)\n", + "While solar power is much more commonly used, nuclear power can offer advantages in some areas. Solar cells, although efficient, can only supply energy to spacecraft in orbits where the solar flux is sufficiently high, such as low Earth orbit and interplanetary destinations close enough to the Sun. Unlike solar cells, nuclear power systems function independently of sunlight, which is necessary for deep space exploration. Nuclear-based systems can have less mass than solar cells of equivalent power, allowing more compact spacecraft that are easier to orient and direct in space. In the case of crewed spaceflight, nuclear power concepts that can power both life support and propulsion systems may reduce both cost and flight time.[9]\n", + "\n", + "Selected applications and/or technologies for space include:\n", + "\n", + "Radioisotope thermoelectric generator\n", + "Radioisotope heater unit\n", + "Radioisotope piezoelectric generator\n", + "Radioisotope rocket\n", + "Nuclear thermal rocket\n", + "Nuclear pulse propulsion\n", + "Nuclear electric rocket\n", + "\"\"\"\n", + "\n", + "docs = [\n", + " Document(\n", + " page_content=split, \n", + " metadata={\"source\": \"https://en.wikipedia.org/wiki/Nuclear_power_in_space\"}\n", + " ) \n", + " for split in text.split(\"\\n\\n\")\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d1998c41-1ebb-4d55-9c28-d2f3feb12657", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The Viking missions were powered by radioisotope thermoelectric generators (RTGs). These generators used the heat produced by the natural decay of plutonium-238 to generate electricity.\n" + ] + } + ], + "source": [ + "print(map_rerank_chain.invoke({\"docs\": docs, \"question\": \"How were the vikings powered\"}))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "poetry-venv", + "language": "python", + "name": "poetry-venv" + }, + "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/modules/chains/document/map_rerank.mdx b/docs/docs/modules/chains/document/map_rerank.mdx deleted file mode 100644 index 21dbb239ae3..00000000000 --- a/docs/docs/modules/chains/document/map_rerank.mdx +++ /dev/null @@ -1,5 +0,0 @@ -# Map re-rank - -The map re-rank documents chain runs an initial prompt on each document, that not only tries to complete a task but also gives a score for how certain it is in its answer. The highest scoring response is returned. - -![map_rerank_diagram](/img/map_rerank.jpg) \ No newline at end of file diff --git a/docs/docs/modules/chains/document/refine.ipynb b/docs/docs/modules/chains/document/refine.ipynb new file mode 100644 index 00000000000..c8a9b690269 --- /dev/null +++ b/docs/docs/modules/chains/document/refine.ipynb @@ -0,0 +1,235 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "15868084-aec1-4e58-8524-32cbb12aa272", + "metadata": {}, + "source": [ + "---\n", + "sidebar_position: 1\n", + "title: Refine\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "2d592270-ebbc-4310-abd8-86ef8d70fe81", + "metadata": {}, + "source": [ + "# Refine\n", + "\n", + "The Refine documents chain constructs a response by looping over the input documents and iteratively updating its answer. For each document, it passes all non-document inputs, the current document, and the latest intermediate answer to an LLM chain to get a new answer.\n", + "\n", + "Since the Refine chain only passes a single document to the LLM at a time, it is well-suited for tasks that require analyzing more documents than can fit in the model's context.\n", + "The obvious tradeoff is that this chain will make far more LLM calls than, for example, the Stuff documents chain.\n", + "There are also certain tasks which are difficult to accomplish iteratively. For example, the Refine chain can perform poorly when documents frequently cross-reference one another or when a task requires detailed information from many documents.\n", + "\n", + "![refine_diagram](/img/refine.jpg)\n" + ] + }, + { + "cell_type": "markdown", + "id": "644838eb-6dbd-4d29-a557-628c1fa2d4c6", + "metadata": {}, + "source": [ + "## Recreating with LCEL\n", + "\n", + "With [LangChain Expression Language](/docs/expression_language) we can easily recreate the `RefineDocumentsChain`, with the additional benefit of getting all the built-in LCEL features (batch, async, etc.) and with much more ability to customize specific parts of the chain." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2560d994-e1a4-4fe0-97c7-182b6bd7798b", + "metadata": {}, + "outputs": [], + "source": [ + "from functools import partial\n", + "from operator import itemgetter\n", + "\n", + "from langchain.callbacks.manager import trace_as_chain_group\n", + "from langchain.chat_models import ChatAnthropic\n", + "from langchain.prompts import PromptTemplate\n", + "from langchain.schema import StrOutputParser\n", + "from langchain.schema.prompt_template import format_document" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f8bb92d8-3961-4a75-b4ad-568a224c78b2", + "metadata": {}, + "outputs": [], + "source": [ + "# Chain for generating initial summary based on the first document\n", + "\n", + "llm = ChatAnthropic()\n", + "first_prompt = PromptTemplate.from_template(\"Summarize this content:\\n\\n{context}\")\n", + "document_prompt = PromptTemplate.from_template(\"{page_content}\")\n", + "partial_format_doc = partial(format_document, prompt=document_prompt)\n", + "summary_chain = {\"context\": partial_format_doc} | first_prompt | llm | StrOutputParser()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9aedc203-b842-4d1b-84f0-23946b0acc7a", + "metadata": {}, + "outputs": [], + "source": [ + "# Chain for refining an existing summary based on\n", + "# an additional document\n", + "\n", + "refine_prompt = PromptTemplate.from_template(\n", + " \"Here's your first summary: {prev_response}. \"\n", + " \"Now add to it based on the following context: {context}\"\n", + ")\n", + "refine_chain = (\n", + " {\n", + " \"prev_response\": itemgetter(\"prev_response\"), \n", + " \"context\": lambda x: partial_format_doc(x[\"doc\"])\n", + " } | refine_prompt\n", + " | llm \n", + " | StrOutputParser()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "cd118a07-1678-4274-9248-2c88656da686", + "metadata": {}, + "outputs": [], + "source": [ + "# The final refine loop, which generates an initial summary\n", + "# then iteratively refines it based on each of the rest of the documents\n", + "\n", + "def refine_loop(docs):\n", + " with trace_as_chain_group(\"refine loop\", inputs={\"input\": docs}) as manager:\n", + " summary = summary_chain.invoke(\n", + " docs[0], \n", + " config={\"callbacks\": manager, \"run_name\": \"initial summary\"}\n", + " )\n", + " for i, doc in enumerate(docs[1:]):\n", + " summary = refine_chain.invoke(\n", + " {\"prev_response\": summary, \"doc\": doc}, \n", + " config={\"callbacks\": manager, \"run_name\": f\"refine {i}\"}\n", + " )\n", + " manager.on_chain_end({\"output\": summary})\n", + " return summary" + ] + }, + { + "cell_type": "markdown", + "id": "0d2ec3b0-a47a-4344-8f12-8bb644fdccae", + "metadata": {}, + "source": [ + "## Example run" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d8242337-3aa4-4377-bd4c-3bf0c01f9dd7", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.schema import Document\n", + "\n", + "text = \"\"\"Nuclear power in space is the use of nuclear power in outer space, typically either small fission systems or radioactive decay for electricity or heat. Another use is for scientific observation, as in a Mรถssbauer spectrometer. The most common type is a radioisotope thermoelectric generator, which has been used on many space probes and on crewed lunar missions. Small fission reactors for Earth observation satellites, such as the TOPAZ nuclear reactor, have also been flown.[1] A radioisotope heater unit is powered by radioactive decay and can keep components from becoming too cold to function, potentially over a span of decades.[2]\n", + "\n", + "The United States tested the SNAP-10A nuclear reactor in space for 43 days in 1965,[3] with the next test of a nuclear reactor power system intended for space use occurring on 13 September 2012 with the Demonstration Using Flattop Fission (DUFF) test of the Kilopower reactor.[4]\n", + "\n", + "After a ground-based test of the experimental 1965 Romashka reactor, which used uranium and direct thermoelectric conversion to electricity,[5] the USSR sent about 40 nuclear-electric satellites into space, mostly powered by the BES-5 reactor. The more powerful TOPAZ-II reactor produced 10 kilowatts of electricity.[3]\n", + "\n", + "Examples of concepts that use nuclear power for space propulsion systems include the nuclear electric rocket (nuclear powered ion thruster(s)), the radioisotope rocket, and radioisotope electric propulsion (REP).[6] One of the more explored concepts is the nuclear thermal rocket, which was ground tested in the NERVA program. Nuclear pulse propulsion was the subject of Project Orion.[7]\n", + "\n", + "Regulation and hazard prevention[edit]\n", + "After the ban of nuclear weapons in space by the Outer Space Treaty in 1967, nuclear power has been discussed at least since 1972 as a sensitive issue by states.[8] Particularly its potential hazards to Earth's environment and thus also humans has prompted states to adopt in the U.N. General Assembly the Principles Relevant to the Use of Nuclear Power Sources in Outer Space (1992), particularly introducing safety principles for launches and to manage their traffic.[8]\n", + "\n", + "Benefits\n", + "\n", + "Both the Viking 1 and Viking 2 landers used RTGs for power on the surface of Mars. (Viking launch vehicle pictured)\n", + "While solar power is much more commonly used, nuclear power can offer advantages in some areas. Solar cells, although efficient, can only supply energy to spacecraft in orbits where the solar flux is sufficiently high, such as low Earth orbit and interplanetary destinations close enough to the Sun. Unlike solar cells, nuclear power systems function independently of sunlight, which is necessary for deep space exploration. Nuclear-based systems can have less mass than solar cells of equivalent power, allowing more compact spacecraft that are easier to orient and direct in space. In the case of crewed spaceflight, nuclear power concepts that can power both life support and propulsion systems may reduce both cost and flight time.[9]\n", + "\n", + "Selected applications and/or technologies for space include:\n", + "\n", + "Radioisotope thermoelectric generator\n", + "Radioisotope heater unit\n", + "Radioisotope piezoelectric generator\n", + "Radioisotope rocket\n", + "Nuclear thermal rocket\n", + "Nuclear pulse propulsion\n", + "Nuclear electric rocket\n", + "\"\"\"\n", + "\n", + "docs = [\n", + " Document(\n", + " page_content=split, \n", + " metadata={\"source\": \"https://en.wikipedia.org/wiki/Nuclear_power_in_space\"}\n", + " ) \n", + " for split in text.split(\"\\n\\n\")\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "68f23401-cb6b-4500-8576-7e1c9254dfef", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Here is the updated summary with the additional context:\n", + "\n", + "Here is a summary of the key points about nuclear power in space:\n", + "\n", + "- Nuclear power is used in space for electricity, heat, and scientific observation. The most common type is a radioisotope thermoelectric generator (RTG), which uses radioactive decay to generate electricity. RTGs have powered space probes and crewed lunar missions. \n", + "\n", + "- Small nuclear fission reactors have also been used to power Earth observation satellites, like the TOPAZ reactor. The United States tested the SNAP-10A nuclear reactor in space for 43 days in 1965.\n", + "\n", + "- After a ground-based test of the experimental 1965 Romashka reactor, which used uranium and direct thermoelectric conversion to electricity, the USSR sent about 40 nuclear-electric satellites into space, mostly powered by the BES-5 reactor. The more powerful TOPAZ-II reactor produced 10 kilowatts of electricity.\n", + "\n", + "- Radioisotope heater units use radioactive decay for heat. They can keep components warm enough to function over decades.\n", + "\n", + "- Nuclear power concepts have also been proposed and tested for space propulsion. Examples include the nuclear electric rocket (nuclear powered ion thruster(s)), the radioisotope\n" + ] + } + ], + "source": [ + "print(refine_loop(docs))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1db2ea25-0f93-4e23-8793-b7b94df8be07", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "poetry-venv", + "language": "python", + "name": "poetry-venv" + }, + "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/modules/chains/document/refine.mdx b/docs/docs/modules/chains/document/refine.mdx deleted file mode 100644 index 902a652b1bb..00000000000 --- a/docs/docs/modules/chains/document/refine.mdx +++ /dev/null @@ -1,12 +0,0 @@ ---- -sidebar_position: 1 ---- -# Refine - -The Refine documents chain constructs a response by looping over the input documents and iteratively updating its answer. For each document, it passes all non-document inputs, the current document, and the latest intermediate answer to an LLM chain to get a new answer. - -Since the Refine chain only passes a single document to the LLM at a time, it is well-suited for tasks that require analyzing more documents than can fit in the model's context. -The obvious tradeoff is that this chain will make far more LLM calls than, for example, the Stuff documents chain. -There are also certain tasks which are difficult to accomplish iteratively. For example, the Refine chain can perform poorly when documents frequently cross-reference one another or when a task requires detailed information from many documents. - -![refine_diagram](/img/refine.jpg) diff --git a/docs/docs/modules/chains/document/stuff.ipynb b/docs/docs/modules/chains/document/stuff.ipynb new file mode 100644 index 00000000000..68298ad620f --- /dev/null +++ b/docs/docs/modules/chains/document/stuff.ipynb @@ -0,0 +1,173 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "bafbef65-ace3-42ce-83f3-553909b48685", + "metadata": {}, + "source": [ + "---\n", + "sidebar_position: 0\n", + "title: Stuff\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "1c473378-ff18-45cf-b718-43f6f94af040", + "metadata": {}, + "source": [ + "The stuff documents chain (\"stuff\" as in \"to stuff\" or \"to fill\") is the most straightforward of the document chains. It takes a list of documents, inserts them all into a prompt and passes that prompt to an LLM.\n", + "\n", + "This chain is well-suited for applications where documents are small and only a few are passed in for most calls.\n", + "\n", + "![stuff_diagram](/img/stuff.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "1f20da9a-2a7f-4fd1-9dbc-c01f164b078b", + "metadata": {}, + "source": [ + "## Recreating with LCEL\n", + "\n", + "With [LangChain Expression Language](/docs/expression_language) we can easily recreate the `StuffDocumentsChain` functionality, with the additional benefit of getting all the built-in LCEL features (batch, async, etc.) and with much more ability to customize specific parts of the chain." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0f33d9f-2cf2-4064-92f6-39fbd5a597e3", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.chat_models import ChatAnthropic\n", + "from langchain.prompts import PromptTemplate\n", + "from langchain.schema import StrOutputParser\n", + "from langchain.schema.prompt_template import format_document" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "3e355e46-6375-4f5e-a102-fdbf72476422", + "metadata": {}, + "outputs": [], + "source": [ + "doc_prompt = PromptTemplate.from_template(\"{page_content}\")\n", + " \n", + "chain = (\n", + " {\"content\": lambda docs: \"\\n\\n\".join(format_document(doc, doc_prompt) for doc in docs)}\n", + " | PromptTemplate.from_template(\"Summarize the following content:\\n\\n{content}\")\n", + " | ChatAnthropic()\n", + " | StrOutputParser()\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ece7db13-5d4b-490f-8677-387bf4eac944", + "metadata": {}, + "source": [ + "### Example run\n", + "\n", + "Lets run this summarization chain on some sample data." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a0ddd725-d97c-4e22-8509-7337d3a71dff", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.schema import Document\n", + "\n", + "text = \"\"\"Nuclear power in space is the use of nuclear power in outer space, typically either small fission systems or radioactive decay for electricity or heat. Another use is for scientific observation, as in a Mรถssbauer spectrometer. The most common type is a radioisotope thermoelectric generator, which has been used on many space probes and on crewed lunar missions. Small fission reactors for Earth observation satellites, such as the TOPAZ nuclear reactor, have also been flown.[1] A radioisotope heater unit is powered by radioactive decay and can keep components from becoming too cold to function, potentially over a span of decades.[2]\n", + "\n", + "The United States tested the SNAP-10A nuclear reactor in space for 43 days in 1965,[3] with the next test of a nuclear reactor power system intended for space use occurring on 13 September 2012 with the Demonstration Using Flattop Fission (DUFF) test of the Kilopower reactor.[4]\n", + "\n", + "After a ground-based test of the experimental 1965 Romashka reactor, which used uranium and direct thermoelectric conversion to electricity,[5] the USSR sent about 40 nuclear-electric satellites into space, mostly powered by the BES-5 reactor. The more powerful TOPAZ-II reactor produced 10 kilowatts of electricity.[3]\n", + "\n", + "Examples of concepts that use nuclear power for space propulsion systems include the nuclear electric rocket (nuclear powered ion thruster(s)), the radioisotope rocket, and radioisotope electric propulsion (REP).[6] One of the more explored concepts is the nuclear thermal rocket, which was ground tested in the NERVA program. Nuclear pulse propulsion was the subject of Project Orion.[7]\n", + "\n", + "Regulation and hazard prevention[edit]\n", + "After the ban of nuclear weapons in space by the Outer Space Treaty in 1967, nuclear power has been discussed at least since 1972 as a sensitive issue by states.[8] Particularly its potential hazards to Earth's environment and thus also humans has prompted states to adopt in the U.N. General Assembly the Principles Relevant to the Use of Nuclear Power Sources in Outer Space (1992), particularly introducing safety principles for launches and to manage their traffic.[8]\n", + "\n", + "Benefits\n", + "\n", + "Both the Viking 1 and Viking 2 landers used RTGs for power on the surface of Mars. (Viking launch vehicle pictured)\n", + "While solar power is much more commonly used, nuclear power can offer advantages in some areas. Solar cells, although efficient, can only supply energy to spacecraft in orbits where the solar flux is sufficiently high, such as low Earth orbit and interplanetary destinations close enough to the Sun. Unlike solar cells, nuclear power systems function independently of sunlight, which is necessary for deep space exploration. Nuclear-based systems can have less mass than solar cells of equivalent power, allowing more compact spacecraft that are easier to orient and direct in space. In the case of crewed spaceflight, nuclear power concepts that can power both life support and propulsion systems may reduce both cost and flight time.[9]\n", + "\n", + "Selected applications and/or technologies for space include:\n", + "\n", + "Radioisotope thermoelectric generator\n", + "Radioisotope heater unit\n", + "Radioisotope piezoelectric generator\n", + "Radioisotope rocket\n", + "Nuclear thermal rocket\n", + "Nuclear pulse propulsion\n", + "Nuclear electric rocket\n", + "\"\"\"\n", + "\n", + "docs = [\n", + " Document(\n", + " page_content=split, \n", + " metadata={\"source\": \"https://en.wikipedia.org/wiki/Nuclear_power_in_space\"}\n", + " ) \n", + " for split in text.split()\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "bd18f647-bdb4-46ce-b01f-fe8afc208ffa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Here is a summary of the key points:\n", + "\n", + "- Nuclear power has been used in space for electricity, heat, and scientific observation. The most common type is a radioisotope thermoelectric generator, used on many probes and lunar missions. \n", + "\n", + "- Small fission reactors have been used for Earth observation satellites. Radioisotope heater units use radioactive decay to keep components warm for decades.\n", + "\n", + "- The US tested a nuclear reactor in space in 1965. The Soviet Union launched around 40 nuclear-powered satellites, mostly with BES-5 reactors.\n", + "\n", + "- Concepts for nuclear propulsion include nuclear thermal rockets, nuclear electric rockets, and nuclear pulse propulsion. The NERVA program ground tested nuclear thermal rockets.\n", + "\n", + "- After the 1967 Outer Space Treaty banned nuclear weapons in space, safety principles were introduced for nuclear power launch and traffic management.\n", + "\n", + "- Benefits of nuclear power in space include functioning independently of sunlight needed for deep space exploration, less mass than equivalent solar power, and ability to power both life support and propulsion.\n" + ] + } + ], + "source": [ + "print(chain.invoke(docs))" + ] + } + ], + "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/modules/chains/document/stuff.mdx b/docs/docs/modules/chains/document/stuff.mdx deleted file mode 100644 index ddb03584b73..00000000000 --- a/docs/docs/modules/chains/document/stuff.mdx +++ /dev/null @@ -1,12 +0,0 @@ ---- -sidebar_position: 0 ---- -# Stuff - -The stuff documents chain ("stuff" as in "to stuff" or "to fill") is the most straightforward of the document chains. It takes a list of documents, inserts them all into a prompt and passes that prompt to an LLM. - -This chain is well-suited for applications where documents are small and only a few are passed in for most calls. - -![stuff_diagram](/img/stuff.jpg) - - diff --git a/docs/docs/modules/chains/foundational/llm_chain.ipynb b/docs/docs/modules/chains/foundational/llm_chain.ipynb new file mode 100644 index 00000000000..38ac14b0b6f --- /dev/null +++ b/docs/docs/modules/chains/foundational/llm_chain.ipynb @@ -0,0 +1,374 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7d46647d-638f-497e-b51a-52bf8dd76e39", + "metadata": {}, + "source": [ + "# LLM\n", + "\n", + "The most common type of chaining in any LLM application is combining a prompt template with an LLM and optionally an output parser.\n", + "\n", + "The recommended way to do this is using LangChain Expression Language. We also continue to support the legacy `LLMChain`, which is a single class for composing these three components." + ] + }, + { + "cell_type": "markdown", + "id": "0ad20b88-f2e8-4ba0-b8e6-1892ab4d2190", + "metadata": {}, + "source": [ + "## Using LCEL\n", + "\n", + "`BasePromptTemplate`, `BaseLanguageModel` and `BaseOutputParser` all implement the `Runnable` interface and are designed to be piped into one another, making LCEL composition very easy:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "92ad7c9d-a1d2-49bd-a4a3-0f6f0fd1656b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'VibrantSocks'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langchain.prompts import PromptTemplate\n", + "from langchain.chat_models import ChatOpenAI\n", + "from langchain.schema import StrOutputParser\n", + "\n", + "prompt = PromptTemplate.from_template(\"What is a good name for a company that makes {product}?\")\n", + "runnable = prompt | ChatOpenAI() | StrOutputParser()\n", + "runnable.invoke({\"product\": \"colorful socks\"})" + ] + }, + { + "cell_type": "markdown", + "id": "784d8083-a2c8-4172-92b8-0bd0d74f032a", + "metadata": {}, + "source": [ + "Head to the [LCEL](/docs/expression_language) section for more on the interface, built-in features, and cookbook examples." + ] + }, + { + "cell_type": "markdown", + "id": "efee07bb-fc45-4e06-999f-a776e6d53333", + "metadata": {}, + "source": [ + "## [Legacy] LLMChain\n", + "\n", + ":::note This is a legacy class, using LCEL as shown above is preffered.\n", + "\n", + "An `LLMChain` is a simple chain that adds some functionality around language models. It is used widely throughout LangChain, including in other chains and agents.\n", + "\n", + "An `LLMChain` consists of a `PromptTemplate` and a language model (either an LLM or chat model). It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output.\n", + "\n", + "### Get started" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "fc0b7d6c-b808-48d9-bdb5-818ab4a1ccca", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'product': 'colorful socks', 'text': '\\n\\nSocktastic!'}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langchain.prompts import PromptTemplate\n", + "from langchain.llms import OpenAI\n", + "from langchain.chains import LLMChain\n", + "\n", + "prompt_template = \"What is a good name for a company that makes {product}?\"\n", + "\n", + "llm = OpenAI(temperature=0)\n", + "llm_chain = LLMChain(\n", + " llm=llm,\n", + " prompt=PromptTemplate.from_template(prompt_template)\n", + ")\n", + "llm_chain(\"colorful socks\")" + ] + }, + { + "cell_type": "markdown", + "id": "040634f0-fe60-4b0e-b3f6-e9c15146e2cd", + "metadata": {}, + "source": [ + "### Additional ways of running `LLMChain`\n", + "\n", + "Aside from `__call__` and `run` methods shared by all `Chain` object, `LLMChain` offers a few more ways of calling the chain logic:\n", + "\n", + "- `apply` allows you run the chain against a list of inputs:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8cd8dd72-6d5a-488f-80a6-1a9324c743e8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'text': '\\n\\nSocktastic!'},\n", + " {'text': '\\n\\nTechCore Solutions.'},\n", + " {'text': '\\n\\nFootwear Factory.'}]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "input_list = [\n", + " {\"product\": \"socks\"},\n", + " {\"product\": \"computer\"},\n", + " {\"product\": \"shoes\"}\n", + "]\n", + "llm_chain.apply(input_list)" + ] + }, + { + "cell_type": "markdown", + "id": "18624d04-474a-425e-bcf3-58748b747e08", + "metadata": {}, + "source": [ + "- `generate` is similar to `apply`, except it return an `LLMResult` instead of string. `LLMResult` often contains useful generation such as token usages and finish reason." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "67e72139-686d-40eb-9c1e-4342d3b1abfe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LLMResult(generations=[[Generation(text='\\n\\nSocktastic!', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\\n\\nTechCore Solutions.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\\n\\nFootwear Factory.', generation_info={'finish_reason': 'stop', 'logprobs': None})]], llm_output={'token_usage': {'completion_tokens': 19, 'prompt_tokens': 36, 'total_tokens': 55}, 'model_name': 'text-davinci-003'}, run=[RunInfo(run_id=UUID('9a423a43-6d35-4e8f-9aca-cacfc8e0dc49')), RunInfo(run_id=UUID('a879c077-b521-461c-8f29-ba63adfc327c')), RunInfo(run_id=UUID('40b892fa-e8c2-47d0-a309-4f7a4ed5b64a'))])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "llm_chain.generate(input_list)" + ] + }, + { + "cell_type": "markdown", + "id": "0480da3a-865d-4ec5-9366-e29c3967fef3", + "metadata": {}, + "source": [ + "- `predict` is similar to `run` method except that the input keys are specified as keyword arguments instead of a Python dict." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f4afb8a4-9113-4082-85cb-55a2d406c99a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\n\\nSocktastic!'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Single input example\n", + "llm_chain.predict(product=\"colorful socks\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e58eaab1-4db4-43cb-b523-7b3380332cad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\n\\nQ: What did the duck say when his friend died?\\nA: Quack, quack, goodbye.'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Multiple inputs example\n", + "template = \"\"\"Tell me a {adjective} joke about {subject}.\"\"\"\n", + "prompt = PromptTemplate(template=template, input_variables=[\"adjective\", \"subject\"])\n", + "llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0))\n", + "\n", + "llm_chain.predict(adjective=\"sad\", subject=\"ducks\")" + ] + }, + { + "cell_type": "markdown", + "id": "63f02d9e-6470-41d3-b91c-b064baf84733", + "metadata": {}, + "source": [ + "### Parsing the outputs\n", + "\n", + "By default, `LLMChain` does not parse the output even if the underlying `prompt` object has an output parser. If you would like to apply that output parser on the LLM output, use `predict_and_parse` instead of `predict` and `apply_and_parse` instead of `apply`.\n", + "\n", + "With `predict`:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "134126ca-2f1c-4829-94ba-810d91c92138", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\n\\nRed, orange, yellow, green, blue, indigo, violet'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langchain.output_parsers import CommaSeparatedListOutputParser\n", + "\n", + "output_parser = CommaSeparatedListOutputParser()\n", + "template = \"\"\"List all the colors in a rainbow\"\"\"\n", + "prompt = PromptTemplate(template=template, input_variables=[], output_parser=output_parser)\n", + "llm_chain = LLMChain(prompt=prompt, llm=llm)\n", + "\n", + "llm_chain.predict()" + ] + }, + { + "cell_type": "markdown", + "id": "7a46f1e8-daaf-43d6-8045-9b187655631b", + "metadata": {}, + "source": [ + "With `predict_and_parse`:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7ef9b74d-7ef5-4b80-80cc-f8226f79259b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/bagatur/langchain/libs/langchain/langchain/chains/llm.py:280: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "text/plain": [ + "['Red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet']" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "llm_chain.predict_and_parse()" + ] + }, + { + "cell_type": "markdown", + "id": "93446f7f-0a2d-4fc5-99a1-a26cc0605b4b", + "metadata": {}, + "source": [ + "### Initialize from string\n", + "\n", + "You can also construct an `LLMChain` from a string template directly." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7e324174-e8ab-4095-87cb-17874a058da9", + "metadata": {}, + "outputs": [], + "source": [ + "template = \"\"\"Tell me a {adjective} joke about {subject}.\"\"\"\n", + "llm_chain = LLMChain.from_string(llm=llm, template=template)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a4f10407-6519-4174-89fe-e7507765f1ae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\n\\nQ: What did the duck say when his friend died?\\nA: Quack, quack, goodbye.'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "llm_chain.predict(adjective=\"sad\", subject=\"ducks\")" + ] + } + ], + "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/modules/chains/foundational/llm_chain.mdx b/docs/docs/modules/chains/foundational/llm_chain.mdx deleted file mode 100644 index a1d7c0bea3d..00000000000 --- a/docs/docs/modules/chains/foundational/llm_chain.mdx +++ /dev/null @@ -1,171 +0,0 @@ -# LLM - -An `LLMChain` is a simple chain that adds some functionality around language models. It is used widely throughout LangChain, including in other chains and agents. - -An `LLMChain` consists of a `PromptTemplate` and a language model (either an LLM or chat model). It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output. - -## Get started - -```python -from langchain.prompts import PromptTemplate -from langchain.llms import OpenAI -from langchain.chains import LLMChain - -prompt_template = "What is a good name for a company that makes {product}?" - -llm = OpenAI(temperature=0) -llm_chain = LLMChain( - llm=llm, - prompt=PromptTemplate.from_template(prompt_template) -) -llm_chain("colorful socks") -``` - - - -``` - {'product': 'colorful socks', 'text': '\n\nSocktastic!'} -``` - - - -## Additional ways of running `LLMChain` - -Aside from `__call__` and `run` methods shared by all `Chain` object, `LLMChain` offers a few more ways of calling the chain logic: - -- `apply` allows you run the chain against a list of inputs: - - -```python -input_list = [ - {"product": "socks"}, - {"product": "computer"}, - {"product": "shoes"} -] - -llm_chain.apply(input_list) -``` - - - -``` - [{'text': '\n\nSocktastic!'}, - {'text': '\n\nTechCore Solutions.'}, - {'text': '\n\nFootwear Factory.'}] -``` - - - -- `generate` is similar to `apply`, except it return an `LLMResult` instead of string. `LLMResult` often contains useful generation such as token usages and finish reason. - - -```python -llm_chain.generate(input_list) -``` - - - -``` - LLMResult(generations=[[Generation(text='\n\nSocktastic!', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nTechCore Solutions.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\n\nFootwear Factory.', generation_info={'finish_reason': 'stop', 'logprobs': None})]], llm_output={'token_usage': {'prompt_tokens': 36, 'total_tokens': 55, 'completion_tokens': 19}, 'model_name': 'text-davinci-003'}) -``` - - - -- `predict` is similar to `run` method except that the input keys are specified as keyword arguments instead of a Python dict. - - -```python -# Single input example -llm_chain.predict(product="colorful socks") -``` - - - -``` - '\n\nSocktastic!' -``` - - - - -```python -# Multiple inputs example - -template = """Tell me a {adjective} joke about {subject}.""" -prompt = PromptTemplate(template=template, input_variables=["adjective", "subject"]) -llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0)) - -llm_chain.predict(adjective="sad", subject="ducks") -``` - - - -``` - '\n\nQ: What did the duck say when his friend died?\nA: Quack, quack, goodbye.' -``` - - - -## Parsing the outputs - -By default, `LLMChain` does not parse the output even if the underlying `prompt` object has an output parser. If you would like to apply that output parser on the LLM output, use `predict_and_parse` instead of `predict` and `apply_and_parse` instead of `apply`. - -With `predict`: - - -```python -from langchain.output_parsers import CommaSeparatedListOutputParser - -output_parser = CommaSeparatedListOutputParser() -template = """List all the colors in a rainbow""" -prompt = PromptTemplate(template=template, input_variables=[], output_parser=output_parser) -llm_chain = LLMChain(prompt=prompt, llm=llm) - -llm_chain.predict() -``` - - - -``` - '\n\nRed, orange, yellow, green, blue, indigo, violet' -``` - - - -With `predict_and_parse`: - - -```python -llm_chain.predict_and_parse() -``` - - - -``` - ['Red', 'orange', 'yellow', 'green', 'blue', 'indigo', 'violet'] -``` - - - -## Initialize from string - -You can also construct an `LLMChain` from a string template directly. - - -```python -template = """Tell me a {adjective} joke about {subject}.""" -llm_chain = LLMChain.from_string(llm=llm, template=template) -``` - - -```python -llm_chain.predict(adjective="sad", subject="ducks") -``` - - - -``` - '\n\nQ: What did the duck say when his friend died?\nA: Quack, quack, goodbye.' -``` - - diff --git a/docs/docs/modules/chains/foundational/router.ipynb b/docs/docs/modules/chains/foundational/router.ipynb index 36609bea14d..1857eab3e02 100644 --- a/docs/docs/modules/chains/foundational/router.ipynb +++ b/docs/docs/modules/chains/foundational/router.ipynb @@ -7,7 +7,168 @@ "source": [ "# Router\n", "\n", - "This notebook demonstrates how to use the `RouterChain` paradigm to create a chain that dynamically selects the next chain to use for a given input. \n", + "Routing allows you to create non-deterministic chains where the output of a previous step defines the next step. Routing helps provide structure and consistency around interactions with LLMs.\n", + "\n", + "As a very simple example, let's suppose we have two templates optimized for different types of questions, and we want to choose the template based on the user input." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8d11fa5c", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.prompts import PromptTemplate\n", + "\n", + "\n", + "physics_template = \"\"\"You are a very smart physics professor. \\\n", + "You are great at answering questions about physics in a concise and easy to understand manner. \\\n", + "When you don't know the answer to a question you admit that you don't know.\n", + "\n", + "Here is a question:\n", + "{input}\"\"\"\n", + "physics_prompt = PromptTemplate.from_template(physics_template)\n", + "\n", + "math_template = \"\"\"You are a very good mathematician. You are great at answering math questions. \\\n", + "You are so good because you are able to break down hard problems into their component parts, \\\n", + "answer the component parts, and then put them together to answer the broader question.\n", + "\n", + "Here is a question:\n", + "{input}\"\"\"\n", + "math_prompt = PromptTemplate.from_template(math_template)" + ] + }, + { + "cell_type": "markdown", + "id": "892bb71f-e4f4-431e-8321-fe6a40e71b78", + "metadata": {}, + "source": [ + "## Using LCEL\n", + "\n", + "We can easily do this using a `RunnableBranch`. A `RunnableBranch` is initialized with a list of (condition, runnable) pairs and a default runnable. It selects which branch by passing each condition the input it's invoked with. It selects the first condition to evaluate to True, and runs the corresponding runnable to that condition with the input. \n", + "\n", + "If no provided conditions match, it runs the default runnable." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f2c4cdb4-1108-491c-9f6f-bbceeb452e29", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.chat_models import ChatOpenAI\n", + "from langchain.schema.output_parser import StrOutputParser\n", + "from langchain.schema.runnable import RunnableBranch" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "49308c5c-8722-4fb0-b78d-3b1dac0e656d", + "metadata": {}, + "outputs": [], + "source": [ + "general_prompt = PromptTemplate.from_template(\n", + " \"You are a helpful assistant. Answer the question as accurately as you can.\\n\\n{input}\"\n", + ")\n", + "prompt_branch = RunnableBranch(\n", + " (lambda x: x[\"topic\"] == \"math\", math_prompt),\n", + " (lambda x: x[\"topic\"] == \"physics\", physics_prompt),\n", + " general_prompt\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "750da8ec-7c1c-4a0e-9c94-e3a1da49319b", + "metadata": {}, + "outputs": [], + "source": [ + "from typing import Literal\n", + "\n", + "from langchain.pydantic_v1 import BaseModel\n", + "from langchain.output_parsers.openai_functions import PydanticAttrOutputFunctionsParser\n", + "from langchain.utils.openai_functions import convert_pydantic_to_openai_function\n", + "\n", + "\n", + "class TopicClassifier(BaseModel):\n", + " \"Classify the topic of the user question\"\n", + " \n", + " topic: Literal[\"math\", \"physics\", \"general\"]\n", + " \"The topic of the user question. One of 'math', 'phsyics' or 'general'.\"\n", + "\n", + "\n", + "classifier_function = convert_pydantic_to_openai_function(TopicClassifier)\n", + "llm = ChatOpenAI().bind(functions=[classifier_function], function_call={\"name\": \"TopicClassifier\"}) \n", + "parser = PydanticAttrOutputFunctionsParser(pydantic_schema=TopicClassifier, attr_name=\"topic\")\n", + "classifier_chain = llm | parser" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "35be97db-2b31-4503-af56-2cae802a9822", + "metadata": {}, + "outputs": [], + "source": [ + "from operator import itemgetter\n", + "\n", + "from langchain.schema.output_parser import StrOutputParser\n", + "from langchain.schema.runnable import RunnablePassthrough\n", + "\n", + "\n", + "final_chain = (\n", + " RunnablePassthrough.assign(topic=itemgetter(\"input\") | classifier_chain) \n", + " | prompt_branch \n", + " | ChatOpenAI()\n", + " | StrOutputParser()\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "9b161436-432b-4ecd-9752-5f458a7b1d54", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"Thank you for your kind words! I'll be happy to help you with this math question.\\n\\nTo find the first prime number greater than 40 that satisfies the given condition, we need to follow a step-by-step approach. \\n\\nFirstly, let's list the prime numbers greater than 40:\\n41, 43, 47, 53, 59, 61, 67, 71, ...\\n\\nNow, we need to check if one plus each of these prime numbers is divisible by 3. We can do this by calculating the remainder when dividing each number by 3.\\n\\nFor 41, (41 + 1) % 3 = 42 % 3 = 0. It is divisible by 3.\\n\\nFor 43, (43 + 1) % 3 = 44 % 3 = 2. It is not divisible by 3.\\n\\nFor 47, (47 + 1) % 3 = 48 % 3 = 0. It is divisible by 3.\\n\\nSince 41 and 47 are both greater than 40 and satisfy the condition, the first prime number greater than 40 such that one plus the prime number is divisible by 3 is 41.\\n\\nTherefore, the answer to the question is 41.\"" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_chain.invoke(\n", + " {\"input\": \"What is the first prime number greater than 40 such that one plus the prime number is divisible by 3?\"}\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "aa11d8fb-b9f2-4427-9c7f-2146f84cba72", + "metadata": {}, + "source": [ + "For more on routing with LCEL [head here](/docs/expression_language/how_to/routing)." + ] + }, + { + "cell_type": "markdown", + "id": "681af961-388e-4b37-9572-4f084365abba", + "metadata": {}, + "source": [ + "## [Legacy] RouterChain\n", + "\n", + ":::note The preferred approach as of version `0.0.293` is to use LCEL as above.\n", + "\n", + "Here we show how to use the `RouterChain` paradigm to create a chain that dynamically selects the next chain to use for a given input. \n", "\n", "Router chains are made up of two components:\n", "\n", @@ -15,12 +176,12 @@ "- `destination_chains`: chains that the router chain can route to\n", "\n", "\n", - "In this notebook, we will focus on the different types of routing chains. We will show these routing chains used in a `MultiPromptChain` to create a question-answering chain that selects the prompt which is most relevant for a given question, and then answers the question using that prompt." + "In this example, we will focus on the different types of routing chains. We will show these routing chains used in a `MultiPromptChain` to create a question-answering chain that selects the prompt which is most relevant for a given question, and then answers the question using that prompt." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 16, "id": "e8d624d4", "metadata": {}, "outputs": [], @@ -28,36 +189,22 @@ "from langchain.chains.router import MultiPromptChain\n", "from langchain.llms import OpenAI\n", "from langchain.chains import ConversationChain\n", - "from langchain.chains.llm import LLMChain\n", - "from langchain.prompts import PromptTemplate" + "from langchain.chains.llm import LLMChain" ] }, { - "cell_type": "code", - "execution_count": 2, - "id": "8d11fa5c", + "cell_type": "markdown", + "id": "83cea2d5", "metadata": {}, - "outputs": [], "source": [ - "physics_template = \"\"\"You are a very smart physics professor. \\\n", - "You are great at answering questions about physics in a concise and easy to understand manner. \\\n", - "When you don't know the answer to a question you admit that you don't know.\n", + "### [Legacy] LLMRouterChain\n", "\n", - "Here is a question:\n", - "{input}\"\"\"\n", - "\n", - "\n", - "math_template = \"\"\"You are a very good mathematician. You are great at answering math questions. \\\n", - "You are so good because you are able to break down hard problems into their component parts, \\\n", - "answer the component parts, and then put them together to answer the broader question.\n", - "\n", - "Here is a question:\n", - "{input}\"\"\"" + "This chain uses an LLM to determine how to route things." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 17, "id": "d0b8856e", "metadata": {}, "outputs": [], @@ -78,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 18, "id": "de2dc0f0", "metadata": {}, "outputs": [], @@ -88,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 19, "id": "f27c154a", "metadata": {}, "outputs": [], @@ -103,19 +250,9 @@ "default_chain = ConversationChain(llm=llm, output_key=\"text\")" ] }, - { - "cell_type": "markdown", - "id": "83cea2d5", - "metadata": {}, - "source": [ - "## LLMRouterChain\n", - "\n", - "This chain uses an LLM to determine how to route things." - ] - }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 20, "id": "60142895", "metadata": {}, "outputs": [], @@ -126,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 21, "id": "60769f96", "metadata": {}, "outputs": [], @@ -144,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 22, "id": "db679975", "metadata": {}, "outputs": [], @@ -159,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 23, "id": "90fd594c", "metadata": {}, "outputs": [ @@ -169,12 +306,26 @@ "text": [ "\n", "\n", - "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n", + "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/bagatur/langchain/libs/langchain/langchain/chains/llm.py:280: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "physics: {'input': 'What is black body radiation?'}\n", "\u001b[1m> Finished chain.\u001b[0m\n", "\n", "\n", - "Black body radiation is the term used to describe the electromagnetic radiation emitted by a โ€œblack bodyโ€โ€”an object that absorbs all radiation incident upon it. A black body is an idealized physical body that absorbs all incident electromagnetic radiation, regardless of frequency or angle of incidence. It does not reflect, emit or transmit energy. This type of radiation is the result of the thermal motion of the body's atoms and molecules, and it is emitted at all wavelengths. The spectrum of radiation emitted is described by Planck's law and is known as the black body spectrum.\n" + "Black body radiation is the thermal electromagnetic radiation within or surrounding a body in thermodynamic equilibrium with its environment, or emitted by a black body (an idealized physical body which absorbs all incident electromagnetic radiation). It is a characteristic of the temperature of the body; if the body has a uniform temperature, the radiation is also uniform across the spectrum of frequencies. The spectral characteristics of the radiation are determined by the temperature of the body, which implies that a black body at a given temperature will emit the same amount of radiation at every frequency.\n" ] } ], @@ -184,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 24, "id": "b8c83765", "metadata": {}, "outputs": [ @@ -194,12 +345,33 @@ "text": [ "\n", "\n", - "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n", + "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/bagatur/langchain/libs/langchain/langchain/chains/llm.py:280: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "math: {'input': 'What is the first prime number greater than 40 such that one plus the prime number is divisible by 3?'}\n", "\u001b[1m> Finished chain.\u001b[0m\n", - "?\n", "\n", - "The answer is 43. One plus 43 is 44 which is divisible by 3.\n" + "\n", + "The first prime number greater than 40 such that one plus the prime number is divisible by 3 is 43. This can be seen by breaking down the problem:\n", + "\n", + "1) We know that a prime number is a number that is only divisible by itself and one. \n", + "2) We also know that if a number is divisible by 3, the sum of its digits must be divisible by 3. \n", + "\n", + "So, if we want to find the first prime number greater than 40 such that one plus the prime number is divisible by 3, we can start counting up from 40, testing each number to see if it is prime and if the sum of the number and one is divisible by three. \n", + "\n", + "The first number we come to that satisfies these conditions is 43.\n" ] } ], @@ -213,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 25, "id": "74c6bba7", "metadata": {}, "outputs": [ @@ -223,10 +395,26 @@ "text": [ "\n", "\n", - "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n", - "None: {'input': 'What is the name of the type of cloud that rains?'}\n", + "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/bagatur/langchain/libs/langchain/langchain/chains/llm.py:280: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "physics: {'input': 'What is the name of the type of cloud that rains?'}\n", "\u001b[1m> Finished chain.\u001b[0m\n", - " The type of cloud that rains is called a cumulonimbus cloud. It is a tall and dense cloud that is often accompanied by thunder and lightning.\n" + "\n", + "\n", + "The type of cloud that rains is called a cumulonimbus cloud.\n" ] } ], @@ -239,14 +427,14 @@ "id": "239d4743", "metadata": {}, "source": [ - "## EmbeddingRouterChain\n", + "## [Legacy] EmbeddingRouterChain\n", "\n", "The `EmbeddingRouterChain` uses embeddings and similarity to route between destination chains." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 26, "id": "55c3ed0e", "metadata": {}, "outputs": [], @@ -258,7 +446,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 27, "id": "572a5082", "metadata": {}, "outputs": [], @@ -271,18 +459,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 28, "id": "50221efe", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using embedded DuckDB without persistence: data will be transient\n" - ] - } - ], + "outputs": [], "source": [ "router_chain = EmbeddingRouterChain.from_names_and_descriptions(\n", " names_and_descriptions, Chroma, CohereEmbeddings(), routing_keys=[\"input\"]\n", @@ -291,7 +471,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 29, "id": "ff7996a0", "metadata": {}, "outputs": [], @@ -306,7 +486,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 30, "id": "99270cc9", "metadata": {}, "outputs": [ @@ -321,7 +501,7 @@ "\u001b[1m> Finished chain.\u001b[0m\n", "\n", "\n", - "Black body radiation is the emission of energy from an idealized physical body (known as a black body) that is in thermal equilibrium with its environment. It is emitted in a characteristic pattern of frequencies known as a black-body spectrum, which depends only on the temperature of the body. The study of black body radiation is an important part of astrophysics and atmospheric physics, as the thermal radiation emitted by stars and planets can often be approximated as black body radiation.\n" + "Black body radiation is the electromagnetic radiation emitted by a black body, which is an idealized physical body that absorbs all incident electromagnetic radiation. This radiation is related to the temperature of the body, with higher temperatures leading to higher radiation levels. The spectrum of the radiation is continuous, and is described by the Planck's law of black body radiation.\n" ] } ], @@ -331,7 +511,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 31, "id": "b5ce6238", "metadata": {}, "outputs": [ @@ -344,9 +524,9 @@ "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n", "math: {'input': 'What is the first prime number greater than 40 such that one plus the prime number is divisible by 3?'}\n", "\u001b[1m> Finished chain.\u001b[0m\n", - "?\n", "\n", - "Answer: The first prime number greater than 40 such that one plus the prime number is divisible by 3 is 43.\n" + "\n", + "The first prime number greater than 40 such that one plus the prime number is divisible by 3 is 43. This is because 43 is a prime number, and 1 + 43 = 44, which is divisible by 3.\n" ] } ], @@ -357,14 +537,6 @@ " )\n", ")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "20f3d047", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -383,7 +555,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.3" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/docs/docs/modules/chains/foundational/sequential_chains.ipynb b/docs/docs/modules/chains/foundational/sequential_chains.ipynb new file mode 100644 index 00000000000..37fc725f95d --- /dev/null +++ b/docs/docs/modules/chains/foundational/sequential_chains.ipynb @@ -0,0 +1,418 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "119af92c-f02c-4729-84ac-0f69d6208c1b", + "metadata": {}, + "source": [ + "# Sequential\n", + "\n", + "The next step after calling a language model is to make a series of calls to a language model. This is particularly useful when you want to take the output from one call and use it as the input to another.\n", + "\n", + "The recommended way to do this is using the LangChain Expression Language. The legacy way is using the `SequentialChain`, which we continue to document here for backwards compatibility.\n", + "\n", + "As a toy example, let's suppose we want to create a chain that first creates a play synopsis and then generates a play review based on the synopsis." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "443e62b9-8a68-468e-b91d-f19de2993fe8", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.prompts import PromptTemplate\n", + "\n", + "synopsis_prompt = PromptTemplate.from_template(\n", + " \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n", + "\n", + "Title: {title}\n", + "Playwright: This is a synopsis for the above play:\"\"\"\n", + ")\n", + "\n", + "review_prompt = PromptTemplate.from_template(\n", + " \"\"\"You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.\n", + "\n", + "Play Synopsis:\n", + "{synopsis}\n", + "Review from a New York Times play critic of the above play:\"\"\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "7d1b284f-73b4-4f3c-ab88-e4c6f4b0bf76", + "metadata": {}, + "source": [ + "## Using LCEL\n", + "\n", + "Creating a sequence of calls (to LLMs or any other component/arbitrary function) is precisely what LangChain Expression Language was designed for." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c0a43154-7624-41b7-9832-f2022af41fba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'In \"Tragedy at Sunset on the Beach,\" playwright has crafted a deeply affecting drama that delves into the complexities of human relationships and the consequences that arise from one fateful evening. Set against the breathtaking backdrop of a serene beach at sunset, the play takes audiences on an emotional journey as it explores the lives of four individuals whose paths intertwine in unexpected and tragic ways.\\n\\nAt the center of the story is Sarah, a young woman grappling with the recent loss of her husband. Seeking solace and a fresh start, she embarks on a solitary trip to the beach, hoping to find peace and clarity. It is here that she encounters James, a charismatic but troubled artist, lost in his own world of anguish and self-doubt. The unlikely connection they form becomes the catalyst for a series of heart-wrenching events, as their emotional baggage and personal demons collide.\\n\\nThe play skillfully weaves together the narratives of Sarah, James, and Rachel, Sarah\\'s best friend. As Rachel arrives on the beach with the intention of helping Sarah heal, she unknowingly carries a secret that threatens to shatter their friendship forever. Against the backdrop of crashing waves and vibrant sunsets, the characters\\' lives unravel, exposing hidden desires, betrayals, and deeply buried secrets. The boundaries of love, friendship, and loyalty blur, forcing each character to confront their own vulnerabilities and face the consequences of their choices.\\n\\nWhat sets \"Tragedy at Sunset on the Beach\" apart is its ability to evoke genuine emotion from its audience. The playwright\\'s poignant exploration of the human condition touches upon universal themes of loss, forgiveness, and the lengths we go to protect the ones we love. The richly drawn characters come alive on stage, their struggles and triumphs resonating deeply with the audience. Moments of intense emotion are skillfully crafted, leaving spectators captivated and moved.\\n\\nThe play\\'s evocative setting adds another layer of depth to the storytelling. The picturesque beach at sunset becomes a metaphor for the fragility of life and the fleeting nature of happiness. The crashing waves and vibrant colors serve as a backdrop to the characters\\' unraveling lives, heightening the emotional impact of their stories.\\n\\nWhile \"Tragedy at Sunset on the Beach\" is undeniably a heavy and somber play, it ultimately leaves audiences questioning the power of redemption. The characters\\' journeys, though tragic, offer glimpses of hope and the potential for healing. It reminds us that even amidst the darkest moments, there is still a chance for redemption and forgiveness.\\n\\nOverall, \"Tragedy at Sunset on the Beach\" is a thought-provoking and emotionally charged play that will captivate audiences from start to finish. The playwright\\'s skillful storytelling, evocative setting, and richly drawn characters make for a truly memorable theatrical experience. This is a play that will leave spectators questioning their own lives and the choices they make, long after the curtain falls.'" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langchain.chat_models import ChatOpenAI\n", + "from langchain.schema import StrOutputParser\n", + "\n", + "llm = ChatOpenAI()\n", + "chain = {\"synopsis\": synopsis_prompt | llm | StrOutputParser()} | review_prompt | llm | StrOutputParser()\n", + "chain.invoke({\"title\": \"Tragedy at sunset on the beach\"})" + ] + }, + { + "cell_type": "markdown", + "id": "c37f72d5-a005-444b-b97e-39df86c515c7", + "metadata": {}, + "source": [ + "If we wanted to get back the synopsis as well we could do:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9f9fb8ad-b6eb-49c3-a1d1-83f4460525e6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'synopsis': 'Tragedy at Sunset on the Beach is a gripping and emotionally charged drama that delves into the complexities of human relationships and the fragility of life. Set against the backdrop of a picturesque beach at sunset, the play follows a group of friends who gather to celebrate a joyous occasion.\\n\\nAs the sun begins its descent, tensions simmer beneath the surface, and long-held secrets and resentments come to light. The characters find themselves entangled in a web of love, betrayal, and loss, as they confront their deepest fears and desires.\\n\\nThe main focus revolves around Sarah, a vibrant and free-spirited woman who becomes the center of a tragic event. Through a series of flashback scenes, we witness the unraveling of her life, exploring her complicated relationships with her closest friends and romantic partners.\\n\\nThe play explores themes of regret, redemption, and the consequences of our choices. It delves into the human condition, questioning the nature of happiness and the value of time. The audience is taken on an emotional rollercoaster, experiencing moments of laughter, heartache, and profound reflection.\\n\\nTragedy at Sunset on the Beach challenges conventional notions of tragedy, evoking a sense of empathy and understanding for the flawed and vulnerable characters. It serves as a reminder that life is unpredictable and fragile, urging us to cherish every moment and embrace the beauty that exists even amidst tragedy.',\n", + " 'review': \"In Tragedy at Sunset on the Beach, playwright John Smithson delivers a powerful and thought-provoking exploration of the human experience. Set against the stunning backdrop of a beach at sunset, this emotionally charged drama takes the audience on a journey through the complexities of relationships, the fragility of life, and the profound impact of our choices.\\n\\nSmithson skillfully weaves together a tale of love, betrayal, and loss, as a group of friends gather to celebrate a joyous occasion. As the sun sets, tensions rise, and long-held secrets and resentments are exposed, leaving the characters entangled in a web of emotions. Through a series of poignant flashback scenes, we witness the unraveling of Sarah's life, a vibrant and free-spirited woman who becomes the center of a tragic event.\\n\\nWhat sets Tragedy at Sunset on the Beach apart is its ability to challenge conventional notions of tragedy. Smithson masterfully portrays flawed and vulnerable characters with such empathy and understanding that the audience can't help but empathize with their struggles. This play serves as a reminder that life is unpredictable and fragile, urging us to cherish every moment and embrace the beauty that exists even amidst tragedy.\\n\\nThe performances in this production are nothing short of extraordinary. The actors effortlessly navigate the emotional rollercoaster of the script, eliciting moments of laughter, heartache, and profound reflection from the audience. Their ability to convey the complexities of their characters' relationships and inner turmoil is truly commendable.\\n\\nThe direction by Jane Anderson is impeccable, capturing the essence of the beach at sunset and utilizing the space to create an immersive experience for the audience. The use of flashbacks adds depth and nuance to the narrative, allowing for a deeper understanding of the characters and their motivations.\\n\\nTragedy at Sunset on the Beach is not a play for the faint of heart. It tackles heavy themes of regret, redemption, and the consequences of our choices. However, it is precisely this raw and unflinching exploration of the human condition that makes it such a compelling piece of theater. Smithson's writing, combined with the exceptional performances and direction, make this play a must-see for theatergoers looking for a thought-provoking and emotionally resonant experience.\\n\\nIn a city renowned for its theater scene, Tragedy at Sunset on the Beach stands out as a shining example of the power of live performance to evoke empathy, provoke contemplation, and remind us of the fragile beauty of life. It is a production that will linger in the minds and hearts of its audience long after the final curtain falls.\"}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langchain.schema.runnable import RunnablePassthrough\n", + "\n", + "synopsis_chain = synopsis_prompt | llm | StrOutputParser() \n", + "review_chain = review_prompt | llm | StrOutputParser()\n", + "chain = {\"synopsis\": synopsis_chain} | RunnablePassthrough.assign(review=review_chain)\n", + "chain.invoke({\"title\": \"Tragedy at sunset on the beach\"})" + ] + }, + { + "cell_type": "markdown", + "id": "5b145aac-cd8f-466c-a5ba-92b376a711f8", + "metadata": {}, + "source": [ + "Head to the [LCEL](/docs/expression_language) section for more on the interface, built-in features, and cookbook examples." + ] + }, + { + "cell_type": "markdown", + "id": "9af35228-d3ff-4c95-8168-506c72618ace", + "metadata": {}, + "source": [ + "## [Legacy] SequentialChain\n", + "\n", + ":::note This is a legacy class, using LCEL as shown above is preffered.\n", + "\n", + "Sequential chains allow you to connect multiple chains and compose them into pipelines that execute some specific scenario. There are two types of sequential chains:\n", + "\n", + "- `SimpleSequentialChain`: The simplest form of sequential chains, where each step has a singular input/output, and the output of one step is the input to the next.\n", + "- `SequentialChain`: A more general form of sequential chains, allowing for multiple inputs/outputs." + ] + }, + { + "cell_type": "markdown", + "id": "6c25c84e-c9f6-43be-8282-78fbd1525091", + "metadata": {}, + "source": [ + "### SimpleSequentialChain" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7ed84b1a-66a6-463c-ba61-1e98434e1958", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.llms import OpenAI\n", + "from langchain.chains import LLMChain\n", + "from langchain.prompts import PromptTemplate\n", + "\n", + "# This is an LLMChain to write a synopsis given a title of a play.\n", + "llm = OpenAI(temperature=.7)\n", + "synopsis_chain = LLMChain(llm=llm, prompt=synopsis_prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a3173022-e2c7-478b-a9b8-4a535d905a1c", + "metadata": {}, + "outputs": [], + "source": [ + "# This is an LLMChain to write a review of a play given a synopsis.\n", + "llm = OpenAI(temperature=.7)\n", + "review_chain = LLMChain(llm=llm, prompt=review_prompt)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b2fdd3d9-cd49-4606-b016-678e27d2b6e0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new SimpleSequentialChain chain...\u001b[0m\n", + "\u001b[36;1m\u001b[1;3m\n", + "\n", + "Tragedy at Sunset on the Beach is a modern tragedy about a young couple in love. The couple, Jack and Jill, are deeply in love and plan to spend the day together on the beach at sunset. However, when they arrive, they are shocked to discover that the beach is an abandoned, dilapidated wasteland. With no one else around, they explore the beach and start to reminisce about their relationship and the good times theyโ€™ve shared. \n", + "\n", + "But then, out of the blue, a mysterious figure emerges from the shadows and reveals a dark secret. The figure tells the couple that the beach is no ordinary beach, but is in fact the site of a terrible tragedy that took place many years ago. As the figure explains what happened, Jack and Jill become overwhelmed with grief. \n", + "\n", + "In the end, Jack and Jill are forced to confront the truth about the tragedy and its consequences. The play is ultimately a reflection on the power of tragedy and the human capacity to confront and overcome it.\u001b[0m\n", + "\u001b[33;1m\u001b[1;3m\n", + "\n", + "Tragedy at Sunset on the Beach is a powerful, thought-provoking modern tragedy that is sure to leave a lasting impression on its audience. The play follows the story of Jack and Jill, a young couple deeply in love, as they explore an abandoned beach and discover a dark secret from the past.\n", + "\n", + "The play brilliantly captures the raw emotions of Jack and Jill as they learn of the tragedy that has occurred on the beach. The writing is masterful, and the actors do a wonderful job of conveying the coupleโ€™s grief and pain. The play is ultimately a reflection on the power of tragedy and the human capacity to confront and overcome it.\n", + "\n", + "Overall, Tragedy at Sunset on the Beach is a must-see for anyone looking for a thought-provoking and emotionally moving play. This play is sure to stay with its audience long after the curtain closes. Highly recommended.\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + } + ], + "source": [ + "# This is the overall chain where we run these two chains in sequence.\n", + "from langchain.chains import SimpleSequentialChain\n", + "\n", + "overall_chain = SimpleSequentialChain(chains=[synopsis_chain, review_chain], verbose=True)\n", + "\n", + "review = overall_chain.run(\"Tragedy at sunset on the beach\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "5f023a0c-9305-4a14-ae24-23fff9933861", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "Tragedy at Sunset on the Beach is a powerful, thought-provoking modern tragedy that is sure to leave a lasting impression on its audience. The play follows the story of Jack and Jill, a young couple deeply in love, as they explore an abandoned beach and discover a dark secret from the past.\n", + "\n", + "The play brilliantly captures the raw emotions of Jack and Jill as they learn of the tragedy that has occurred on the beach. The writing is masterful, and the actors do a wonderful job of conveying the coupleโ€™s grief and pain. The play is ultimately a reflection on the power of tragedy and the human capacity to confront and overcome it.\n", + "\n", + "Overall, Tragedy at Sunset on the Beach is a must-see for anyone looking for a thought-provoking and emotionally moving play. This play is sure to stay with its audience long after the curtain closes. Highly recommended.\n" + ] + } + ], + "source": [ + "print(review)" + ] + }, + { + "cell_type": "markdown", + "id": "d09df151-6a66-4982-8424-44ec3c92422d", + "metadata": {}, + "source": [ + "### SequentialChain\n", + "Of course, not all sequential chains will be as simple as passing a single string as an argument and getting a single string as output for all steps in the chain. In this next example, we will experiment with more complex chains that involve multiple inputs, and where there also multiple final outputs.\n", + "\n", + "Of particular importance is how we name the input/output variables. In the above example we didn't have to think about that because we were just passing the output of one chain directly as input to the next, but here we do have worry about that because we have multiple inputs." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7481ed64-22f3-47dc-9796-c372eeb4f7bd", + "metadata": {}, + "outputs": [], + "source": [ + "# This is an LLMChain to write a synopsis given a title of a play and the era it is set in.\n", + "llm = OpenAI(temperature=.7)\n", + "synopsis_template = \"\"\"You are a playwright. Given the title of play and the era it is set in, it is your job to write a synopsis for that title.\n", + "\n", + "Title: {title}\n", + "Era: {era}\n", + "Playwright: This is a synopsis for the above play:\"\"\"\n", + "synopsis_prompt_template = PromptTemplate(input_variables=[\"title\", \"era\"], template=synopsis_template)\n", + "synopsis_chain = LLMChain(llm=llm, prompt=synopsis_prompt_template, output_key=\"synopsis\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6d5ec9be-7101-460a-9fc1-7ef2d02434e7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new SequentialChain chain...\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "{'title': 'Tragedy at sunset on the beach',\n", + " 'era': 'Victorian England',\n", + " 'synopsis': \"\\n\\nThe play is set in Victorian England and follows the story of a young couple, Mary and John, who were deeply in love and had just gotten engaged. On the night of their engagement, they decided to take a romantic walk along the beach at sunset. Unexpectedly, John is shot by a stranger and killed right in front of Mary. In a state of shock and anguish, Mary is left alone, struggling to comprehend what has just occurred. \\n\\nThe play follows Mary as she searches for answers to John's death. As Mary's investigation begins, she discovers that John was actually involved in a dark and dangerous plot to overthrow the government. Unbeknownst to Mary, John had been working as a spy in a secret mission to uncover the truth behind a political scandal. \\n\\nNow, Mary must face the consequences of her beloved's actions and find a way to save the future of England. As the story unfolds, Mary must confront her own beliefs as well as the powerful people who are determined to end her mission. \\n\\nAt the end of the play, all of Mary's questions are answered and she is able to make a choice that will ultimately decide the fate of the nation. Tragedy at Sunset on the Beach is a\",\n", + " 'review': \"\\n\\nSet against the backdrop of Victorian England, Tragedy at Sunset on the Beach tells a heart-wrenching story of love, loss, and tragedy. The play follows Mary and John, a young couple deeply in love, who experience an unexpected tragedy on the night of their engagement. When John is shot and killed by a stranger, Mary is left alone to uncover the truth behind her beloved's death.\\n\\nWhat follows is an intense and gripping journey as Mary discovers that John was a spy in a secret mission to uncover a powerful political scandal. As Mary faces off against those determined to end her mission, she must confront her own beliefs and ultimately decide the fate of the nation.\\n\\nThe play is skillfully crafted and brilliantly performed. The actors portray a range of emotions from joy to sorrow that will leave the audience moved and captivated. The production is a beautiful testament to the power of love and the strength of the human spirit, and it is sure to leave a lasting impression. Highly recommended.\"}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This is an LLMChain to write a review of a play given a synopsis.\n", + "llm = OpenAI(temperature=.7)\n", + "template = \"\"\"You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.\n", + "\n", + "Play Synopsis:\n", + "{synopsis}\n", + "Review from a New York Times play critic of the above play:\"\"\"\n", + "prompt_template = PromptTemplate(input_variables=[\"synopsis\"], template=template)\n", + "review_chain = LLMChain(llm=llm, prompt=prompt_template, output_key=\"review\")\n", + "\n", + "# This is the overall chain where we run these two chains in sequence.\n", + "from langchain.chains import SequentialChain\n", + "overall_chain = SequentialChain(\n", + " chains=[synopsis_chain, review_chain],\n", + " input_variables=[\"era\", \"title\"],\n", + " # Here we return multiple variables\n", + " output_variables=[\"synopsis\", \"review\"],\n", + " verbose=True)\n", + "\n", + "\n", + "overall_chain({\"title\":\"Tragedy at sunset on the beach\", \"era\": \"Victorian England\"})" + ] + }, + { + "cell_type": "markdown", + "id": "282f3c01-566b-4285-9615-dd07c8d43d54", + "metadata": {}, + "source": [ + "#### Memory in Sequential Chains\n", + "Sometimes you may want to pass along some context to use in each step of the chain or in a later part of the chain, but maintaining and chaining together the input/output variables can quickly get messy. Using `SimpleMemory` is a convenient way to do manage this and clean up your chains.\n", + "\n", + "For example, using the previous playwright `SequentialChain`, lets say you wanted to include some context about date, time and location of the play, and using the generated synopsis and review, create some social media post text. You could add these new context variables as `input_variables`, or we can add a `SimpleMemory` to the chain to manage this context:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e50d0da6-dea1-428f-94eb-c7dfc3d298e3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new SequentialChain chain...\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "{'title': 'Tragedy at sunset on the beach',\n", + " 'era': 'Victorian England',\n", + " 'time': 'December 25th, 8pm PST',\n", + " 'location': 'Theater in the Park',\n", + " 'social_post_text': \"Experience a heartbreaking love story this Christmas as we bring you 'Tragedy at Sunset on the Beach', set in Victorian England on December 25th at 8pm PST at the Theater in the Park. Follow the story of two young lovers, George and Mary, and their fight against overwhelming odds. Will their love prevail? Find out this Christmas Day! #TragedyAtSunset #LoveStory #Christmas #VictorianEngland\"}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langchain.chains import SequentialChain\n", + "from langchain.memory import SimpleMemory\n", + "\n", + "llm = OpenAI(temperature=.7)\n", + "template = \"\"\"You are a social media manager for a theater company. Given the title of play, the era it is set in, the date,time and location, the synopsis of the play, and the review of the play, it is your job to write a social media post for that play.\n", + "\n", + "Here is some context about the time and location of the play:\n", + "Date and Time: {time}\n", + "Location: {location}\n", + "\n", + "Play Synopsis:\n", + "{synopsis}\n", + "Review from a New York Times play critic of the above play:\n", + "{review}\n", + "\n", + "Social Media Post:\n", + "\"\"\"\n", + "prompt_template = PromptTemplate(input_variables=[\"synopsis\", \"review\", \"time\", \"location\"], template=template)\n", + "social_chain = LLMChain(llm=llm, prompt=prompt_template, output_key=\"social_post_text\")\n", + "\n", + "overall_chain = SequentialChain(\n", + " memory=SimpleMemory(memories={\"time\": \"December 25th, 8pm PST\", \"location\": \"Theater in the Park\"}),\n", + " chains=[synopsis_chain, review_chain, social_chain],\n", + " input_variables=[\"era\", \"title\"],\n", + " # Here we return multiple variables\n", + " output_variables=[\"social_post_text\"],\n", + " verbose=True)\n", + "\n", + "overall_chain({\"title\":\"Tragedy at sunset on the beach\", \"era\": \"Victorian England\"})" + ] + } + ], + "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/modules/chains/foundational/sequential_chains.mdx b/docs/docs/modules/chains/foundational/sequential_chains.mdx deleted file mode 100644 index 80036f3435c..00000000000 --- a/docs/docs/modules/chains/foundational/sequential_chains.mdx +++ /dev/null @@ -1,229 +0,0 @@ -# Sequential - - - -The next step after calling a language model is to make a series of calls to a language model. This is particularly useful when you want to take the output from one call and use it as the input to another. - -In this notebook we will walk through some examples of how to do this, using sequential chains. Sequential chains allow you to connect multiple chains and compose them into pipelines that execute some specific scenario. There are two types of sequential chains: - -- `SimpleSequentialChain`: The simplest form of sequential chains, where each step has a singular input/output, and the output of one step is the input to the next. -- `SequentialChain`: A more general form of sequential chains, allowing for multiple inputs/outputs. - -```python -from langchain.llms import OpenAI -from langchain.chains import LLMChain -from langchain.prompts import PromptTemplate -``` - - -```python -# This is an LLMChain to write a synopsis given a title of a play. -llm = OpenAI(temperature=.7) -synopsis_template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title. - -Title: {title} -Playwright: This is a synopsis for the above play:""" -synopsis_prompt_template = PromptTemplate(input_variables=["title"], template=synopsis_template) -synopsis_chain = LLMChain(llm=llm, prompt=synopsis_prompt_template) -``` - - -```python -# This is an LLMChain to write a review of a play given a synopsis. -llm = OpenAI(temperature=.7) -template = """You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play. - -Play Synopsis: -{synopsis} -Review from a New York Times play critic of the above play:""" -prompt_template = PromptTemplate(input_variables=["synopsis"], template=template) -review_chain = LLMChain(llm=llm, prompt=prompt_template) -``` - - -```python -# This is the overall chain where we run these two chains in sequence. -from langchain.chains import SimpleSequentialChain -overall_chain = SimpleSequentialChain(chains=[synopsis_chain, review_chain], verbose=True) -``` - - -```python -review = overall_chain.run("Tragedy at sunset on the beach") -``` - - - -``` - - - > Entering new SimpleSequentialChain chain... - - - Tragedy at Sunset on the Beach is a story of a young couple, Jack and Sarah, who are in love and looking forward to their future together. On the night of their anniversary, they decide to take a walk on the beach at sunset. As they are walking, they come across a mysterious figure, who tells them that their love will be tested in the near future. - - The figure then tells the couple that the sun will soon set, and with it, a tragedy will strike. If Jack and Sarah can stay together and pass the test, they will be granted everlasting love. However, if they fail, their love will be lost forever. - - The play follows the couple as they struggle to stay together and battle the forces that threaten to tear them apart. Despite the tragedy that awaits them, they remain devoted to one another and fight to keep their love alive. In the end, the couple must decide whether to take a chance on their future together or succumb to the tragedy of the sunset. - - - Tragedy at Sunset on the Beach is an emotionally gripping story of love, hope, and sacrifice. Through the story of Jack and Sarah, the audience is taken on a journey of self-discovery and the power of love to overcome even the greatest of obstacles. - - The play's talented cast brings the characters to life, allowing us to feel the depths of their emotion and the intensity of their struggle. With its compelling story and captivating performances, this play is sure to draw in audiences and leave them on the edge of their seats. - - The play's setting of the beach at sunset adds a touch of poignancy and romanticism to the story, while the mysterious figure serves to keep the audience enthralled. Overall, Tragedy at Sunset on the Beach is an engaging and thought-provoking play that is sure to leave audiences feeling inspired and hopeful. - - > Finished chain. -``` - - - - -```python -print(review) -``` - - - -``` - - - Tragedy at Sunset on the Beach is an emotionally gripping story of love, hope, and sacrifice. Through the story of Jack and Sarah, the audience is taken on a journey of self-discovery and the power of love to overcome even the greatest of obstacles. - - The play's talented cast brings the characters to life, allowing us to feel the depths of their emotion and the intensity of their struggle. With its compelling story and captivating performances, this play is sure to draw in audiences and leave them on the edge of their seats. - - The play's setting of the beach at sunset adds a touch of poignancy and romanticism to the story, while the mysterious figure serves to keep the audience enthralled. Overall, Tragedy at Sunset on the Beach is an engaging and thought-provoking play that is sure to leave audiences feeling inspired and hopeful. -``` - - - -## Sequential Chain -Of course, not all sequential chains will be as simple as passing a single string as an argument and getting a single string as output for all steps in the chain. In this next example, we will experiment with more complex chains that involve multiple inputs, and where there also multiple final outputs. - -Of particular importance is how we name the input/output variables. In the above example we didn't have to think about that because we were just passing the output of one chain directly as input to the next, but here we do have worry about that because we have multiple inputs. - - -```python -# This is an LLMChain to write a synopsis given a title of a play and the era it is set in. -llm = OpenAI(temperature=.7) -synopsis_template = """You are a playwright. Given the title of play and the era it is set in, it is your job to write a synopsis for that title. - -Title: {title} -Era: {era} -Playwright: This is a synopsis for the above play:""" -synopsis_prompt_template = PromptTemplate(input_variables=["title", "era"], template=synopsis_template) -synopsis_chain = LLMChain(llm=llm, prompt=synopsis_prompt_template, output_key="synopsis") -``` - - -```python -# This is an LLMChain to write a review of a play given a synopsis. -llm = OpenAI(temperature=.7) -template = """You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play. - -Play Synopsis: -{synopsis} -Review from a New York Times play critic of the above play:""" -prompt_template = PromptTemplate(input_variables=["synopsis"], template=template) -review_chain = LLMChain(llm=llm, prompt=prompt_template, output_key="review") -``` - - -```python -# This is the overall chain where we run these two chains in sequence. -from langchain.chains import SequentialChain -overall_chain = SequentialChain( - chains=[synopsis_chain, review_chain], - input_variables=["era", "title"], - # Here we return multiple variables - output_variables=["synopsis", "review"], - verbose=True) -``` - - -```python -overall_chain({"title":"Tragedy at sunset on the beach", "era": "Victorian England"}) -``` - - - -``` - - - > Entering new SequentialChain chain... - - > Finished chain. - - - - - - {'title': 'Tragedy at sunset on the beach', - 'era': 'Victorian England', - 'synopsis': "\n\nThe play follows the story of John, a young man from a wealthy Victorian family, who dreams of a better life for himself. He soon meets a beautiful young woman named Mary, who shares his dream. The two fall in love and decide to elope and start a new life together.\n\nOn their journey, they make their way to a beach at sunset, where they plan to exchange their vows of love. Unbeknownst to them, their plans are overheard by John's father, who has been tracking them. He follows them to the beach and, in a fit of rage, confronts them. \n\nA physical altercation ensues, and in the struggle, John's father accidentally stabs Mary in the chest with his sword. The two are left in shock and disbelief as Mary dies in John's arms, her last words being a declaration of her love for him.\n\nThe tragedy of the play comes to a head when John, broken and with no hope of a future, chooses to take his own life by jumping off the cliffs into the sea below. \n\nThe play is a powerful story of love, hope, and loss set against the backdrop of 19th century England.", - 'review': "\n\nThe latest production from playwright X is a powerful and heartbreaking story of love and loss set against the backdrop of 19th century England. The play follows John, a young man from a wealthy Victorian family, and Mary, a beautiful young woman with whom he falls in love. The two decide to elope and start a new life together, and the audience is taken on a journey of hope and optimism for the future.\n\nUnfortunately, their dreams are cut short when John's father discovers them and in a fit of rage, fatally stabs Mary. The tragedy of the play is further compounded when John, broken and without hope, takes his own life. The storyline is not only realistic, but also emotionally compelling, drawing the audience in from start to finish.\n\nThe acting was also commendable, with the actors delivering believable and nuanced performances. The playwright and director have successfully crafted a timeless tale of love and loss that will resonate with audiences for years to come. Highly recommended."} -``` - - - -### Memory in Sequential Chains -Sometimes you may want to pass along some context to use in each step of the chain or in a later part of the chain, but maintaining and chaining together the input/output variables can quickly get messy. Using `SimpleMemory` is a convenient way to do manage this and clean up your chains. - -For example, using the previous playwright `SequentialChain`, lets say you wanted to include some context about date, time and location of the play, and using the generated synopsis and review, create some social media post text. You could add these new context variables as `input_variables`, or we can add a `SimpleMemory` to the chain to manage this context: - - - - -```python -from langchain.chains import SequentialChain -from langchain.memory import SimpleMemory - -llm = OpenAI(temperature=.7) -template = """You are a social media manager for a theater company. Given the title of play, the era it is set in, the date,time and location, the synopsis of the play, and the review of the play, it is your job to write a social media post for that play. - -Here is some context about the time and location of the play: -Date and Time: {time} -Location: {location} - -Play Synopsis: -{synopsis} -Review from a New York Times play critic of the above play: -{review} - -Social Media Post: -""" -prompt_template = PromptTemplate(input_variables=["synopsis", "review", "time", "location"], template=template) -social_chain = LLMChain(llm=llm, prompt=prompt_template, output_key="social_post_text") - -overall_chain = SequentialChain( - memory=SimpleMemory(memories={"time": "December 25th, 8pm PST", "location": "Theater in the Park"}), - chains=[synopsis_chain, review_chain, social_chain], - input_variables=["era", "title"], - # Here we return multiple variables - output_variables=["social_post_text"], - verbose=True) - -overall_chain({"title":"Tragedy at sunset on the beach", "era": "Victorian England"}) -``` - - - -``` - - - > Entering new SequentialChain chain... - - > Finished chain. - - - - - - {'title': 'Tragedy at sunset on the beach', - 'era': 'Victorian England', - 'time': 'December 25th, 8pm PST', - 'location': 'Theater in the Park', - 'social_post_text': "\nSpend your Christmas night with us at Theater in the Park and experience the heartbreaking story of love and loss that is 'A Walk on the Beach'. Set in Victorian England, this romantic tragedy follows the story of Frances and Edward, a young couple whose love is tragically cut short. Don't miss this emotional and thought-provoking production that is sure to leave you in tears. #AWalkOnTheBeach #LoveAndLoss #TheaterInThePark #VictorianEngland"} -``` - - diff --git a/docs/docs/modules/chains/foundational/transformation.ipynb b/docs/docs/modules/chains/foundational/transformation.ipynb index 9114ab73590..2ef01a92c44 100644 --- a/docs/docs/modules/chains/foundational/transformation.ipynb +++ b/docs/docs/modules/chains/foundational/transformation.ipynb @@ -7,21 +7,27 @@ "source": [ "# Transformation\n", "\n", - "This notebook showcases using a generic transformation chain.\n", + "Often we want to transform inputs as they are passed from one component to another.\n", "\n", - "As an example, we will create a dummy transformation that takes in a super long text, filters the text to only the first 3 paragraphs, and then passes that into an `LLMChain` to summarize those." + "As an example, we will create a dummy transformation that takes in a super long text, filters the text to only the first 3 paragraphs, and then passes that into a chain to summarize those." ] }, { "cell_type": "code", - "execution_count": 1, - "id": "bbbb4330", + "execution_count": 2, + "id": "d257f50d-c53d-41b7-be8a-df23fbd7c017", "metadata": {}, "outputs": [], "source": [ - "from langchain.chains import TransformChain, LLMChain, SimpleSequentialChain\n", - "from langchain.llms import OpenAI\n", - "from langchain.prompts import PromptTemplate" + "from langchain.prompts import PromptTemplate\n", + "\n", + "prompt = PromptTemplate.from_template(\n", + " \"\"\"Summarize this text:\n", + "\n", + "{output_text}\n", + "\n", + "Summary:\"\"\"\n", + ")" ] }, { @@ -35,9 +41,67 @@ " state_of_the_union = f.read()" ] }, + { + "cell_type": "markdown", + "id": "4c938536-e3fb-45eb-a1b3-cb82be410e32", + "metadata": {}, + "source": [ + "## Using LCEL\n", + "\n", + "With LCEL this is trivial, since we can add functions in any `RunnableSequence`." + ] + }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 17, + "id": "1e53e851-b1bd-424f-a144-5f2e8b413dcf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'The speaker acknowledges the presence of important figures in the government and addresses the audience as fellow Americans. They highlight the impact of COVID-19 on keeping people apart in the previous year but express joy in being able to come together again. The speaker emphasizes the unity of Democrats, Republicans, and Independents as Americans.'" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langchain.chat_models import ChatOpenAI\n", + "from langchain.schema import StrOutputParser\n", + "\n", + "runnable = {\"output_text\": lambda text: \"\\n\\n\".join(text.split(\"\\n\\n\")[:3])} | prompt | ChatOpenAI() | StrOutputParser()\n", + "runnable.invoke(state_of_the_union)" + ] + }, + { + "cell_type": "markdown", + "id": "a9b9bd07-155f-4777-9215-509d39ecfe3f", + "metadata": {}, + "source": [ + "## [Legacy] TransformationChain\n", + "\n", + ":::note This is a legacy class, using LCEL as shown above is preffered.\n", + "\n", + "This notebook showcases using a generic transformation chain." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "bbbb4330", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.chains import TransformChain, LLMChain, SimpleSequentialChain\n", + "from langchain.llms import OpenAI\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "id": "98739592", "metadata": {}, "outputs": [], @@ -47,7 +111,6 @@ " shortened_text = \"\\n\\n\".join(text.split(\"\\n\\n\")[:3])\n", " return {\"output_text\": shortened_text}\n", "\n", - "\n", "transform_chain = TransformChain(\n", " input_variables=[\"text\"], output_variables=[\"output_text\"], transform=transform_func\n", ")" @@ -55,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 14, "id": "e9397934", "metadata": {}, "outputs": [], @@ -71,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "id": "06f51f17", "metadata": {}, "outputs": [], @@ -81,17 +144,17 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 16, "id": "f7caa1ee", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "' The speaker addresses the nation, noting that while last year they were kept apart due to COVID-19, this year they are together again. They are reminded that regardless of their political affiliations, they are all Americans.'" + "' In an address to the nation, the speaker acknowledges the hardships of the past year due to the COVID-19 pandemic, but emphasizes that regardless of political affiliation, all Americans can come together.'" ] }, - "execution_count": 7, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -99,14 +162,6 @@ "source": [ "sequential_chain.run(state_of_the_union)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e3ca6409", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -125,7 +180,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.3" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/docs/docs/modules/chains/how_to/async_chain.ipynb b/docs/docs/modules/chains/how_to/async_chain.ipynb index bd3bfd498e9..1804725a0ed 100644 --- a/docs/docs/modules/chains/how_to/async_chain.ipynb +++ b/docs/docs/modules/chains/how_to/async_chain.ipynb @@ -7,9 +7,17 @@ "source": [ "# Async API\n", "\n", - "LangChain provides async support for Chains by leveraging the [asyncio](https://docs.python.org/3/library/asyncio.html) library.\n", - "\n", - "Async methods are currently supported in `LLMChain` (through `arun`, `apredict`, `acall`) and `LLMMathChain` (through `arun` and `acall`), `ChatVectorDBChain`, and [QA chains](/docs/use_cases/question_answering/question_answering.html). Async support for other chains is on the roadmap." + "LangChain provides async support by leveraging the [asyncio](https://docs.python.org/3/library/asyncio.html) library." + ] + }, + { + "cell_type": "raw", + "id": "0c0f45ed-9cef-4798-975c-d2912a248591", + "metadata": {}, + "source": [ + ":::info\n", + "Async support is built into all `Runnable` objects (the building block of [LangChain Expression Language (LCEL)](/docs/expression_language) by default. Using LCEL is preferred to using `Chain`s. Head to [Interface](/docs/expression_language/interface) for more on the `Runnable` interface.\n", + ":::" ] }, { @@ -125,7 +133,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.3" + "version": "3.9.1" } }, "nbformat": 4, diff --git a/docs/docs/modules/chains/how_to/call_methods.ipynb b/docs/docs/modules/chains/how_to/call_methods.ipynb index 626f0b21b61..4f51ff179a2 100644 --- a/docs/docs/modules/chains/how_to/call_methods.ipynb +++ b/docs/docs/modules/chains/how_to/call_methods.ipynb @@ -147,7 +147,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Tips: You can easily integrate a `Chain` object as a `Tool` in your `Agent` via its `run` method. See an example [here](/docs/modules/agents/tools/how_to/custom_tools.html)." + "Tips: You can easily integrate a `Chain` object as a `Tool` in your `Agent` via its `run` method. See an example [here](/docs/modules/agents/tools/how_to/custom_tools)." ] }, { diff --git a/docs/docs/modules/chains/how_to/debugging.mdx b/docs/docs/modules/chains/how_to/debugging.mdx deleted file mode 100644 index 5b4b2ea37a6..00000000000 --- a/docs/docs/modules/chains/how_to/debugging.mdx +++ /dev/null @@ -1,35 +0,0 @@ -# Debugging chains - -It can be hard to debug a `Chain` object solely from its output as most `Chain` objects involve a fair amount of input prompt preprocessing and LLM output post-processing. - -Setting `verbose` to `True` will print out some internal states of the `Chain` object while it is being ran. - -```python -conversation = ConversationChain( - llm=chat, - memory=ConversationBufferMemory(), - verbose=True -) -conversation.run("What is ChatGPT?") -``` - - - -``` - > Entering new ConversationChain chain... - Prompt after formatting: - The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know. - - Current conversation: - - Human: What is ChatGPT? - AI: - - > Finished chain. - - 'ChatGPT is an AI language model developed by OpenAI. It is based on the GPT-3 architecture and is capable of generating human-like responses to text prompts. ChatGPT has been trained on a massive amount of text data and can understand and respond to a wide range of topics. It is often used for chatbots, virtual assistants, and other conversational AI applications.' -``` - - - - diff --git a/docs/docs/modules/chains/how_to/from_hub.ipynb b/docs/docs/modules/chains/how_to/from_hub.ipynb deleted file mode 100644 index e862b527a1d..00000000000 --- a/docs/docs/modules/chains/how_to/from_hub.ipynb +++ /dev/null @@ -1,168 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "25c90e9e", - "metadata": {}, - "source": [ - "# Loading from LangChainHub\n", - "\n", - "This notebook covers how to load chains from [LangChainHub](https://github.com/hwchase17/langchain-hub)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8b54479e", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain.chains import load_chain\n", - "\n", - "chain = load_chain(\"lc://chains/llm-math/chain.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "4828f31f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n", - "whats 2 raised to .12\u001b[32;1m\u001b[1;3m\n", - "Answer: 1.0791812460476249\u001b[0m\n", - "\u001b[1m> Finished chain.\u001b[0m\n" - ] - }, - { - "data": { - "text/plain": [ - "'Answer: 1.0791812460476249'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "chain.run(\"whats 2 raised to .12\")" - ] - }, - { - "cell_type": "markdown", - "id": "8db72cda", - "metadata": {}, - "source": [ - "Sometimes chains will require extra arguments that were not serialized with the chain. For example, a chain that does question answering over a vector database will require a vector database." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "aab39528", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain.embeddings.openai import OpenAIEmbeddings\n", - "from langchain.vectorstores import Chroma\n", - "from langchain.text_splitter import CharacterTextSplitter\n", - "from langchain.llms import OpenAI\nfrom langchain.chains import VectorDBQA" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "16a85d5e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running Chroma using direct local API.\n", - "Using DuckDB in-memory for database. Data will be transient.\n" - ] - } - ], - "source": [ - "from langchain.document_loaders import TextLoader\n", - "\n", - "loader = TextLoader(\"../../state_of_the_union.txt\")\n", - "documents = loader.load()\n", - "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n", - "texts = text_splitter.split_documents(documents)\n", - "\n", - "embeddings = OpenAIEmbeddings()\n", - "vectorstore = Chroma.from_documents(texts, embeddings)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "6a82e91e", - "metadata": {}, - "outputs": [], - "source": [ - "chain = load_chain(\"lc://chains/vector-db-qa/stuff/chain.json\", vectorstore=vectorstore)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "efe9b25b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\" The president said that Ketanji Brown Jackson is a Circuit Court of Appeals Judge, one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans, and will continue Justice Breyer's legacy of excellence.\"" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "query = \"What did the president say about Ketanji Brown Jackson\"\n", - "chain.run(query)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f910a32f", - "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/modules/chains/how_to/llm.json b/docs/docs/modules/chains/how_to/llm.json deleted file mode 100644 index f843c42d271..00000000000 --- a/docs/docs/modules/chains/how_to/llm.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "model_name": "text-davinci-003", - "temperature": 0.0, - "max_tokens": 256, - "top_p": 1, - "frequency_penalty": 0, - "presence_penalty": 0, - "n": 1, - "best_of": 1, - "request_timeout": null, - "logit_bias": {}, - "_type": "openai" -} \ No newline at end of file diff --git a/docs/docs/modules/chains/how_to/llm_chain.json b/docs/docs/modules/chains/how_to/llm_chain.json deleted file mode 100644 index 6c907bcd57f..00000000000 --- a/docs/docs/modules/chains/how_to/llm_chain.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "memory": null, - "verbose": true, - "prompt": { - "input_variables": [ - "question" - ], - "output_parser": null, - "template": "Question: {question}\n\nAnswer: Let's think step by step.", - "template_format": "f-string" - }, - "llm": { - "model_name": "text-davinci-003", - "temperature": 0.0, - "max_tokens": 256, - "top_p": 1, - "frequency_penalty": 0, - "presence_penalty": 0, - "n": 1, - "best_of": 1, - "request_timeout": null, - "logit_bias": {}, - "_type": "openai" - }, - "output_key": "text", - "_type": "llm_chain" -} \ No newline at end of file diff --git a/docs/docs/modules/chains/how_to/llm_chain_separate.json b/docs/docs/modules/chains/how_to/llm_chain_separate.json deleted file mode 100644 index 340d813db24..00000000000 --- a/docs/docs/modules/chains/how_to/llm_chain_separate.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "memory": null, - "verbose": true, - "prompt_path": "prompt.json", - "llm_path": "llm.json", - "output_key": "text", - "_type": "llm_chain" -} \ No newline at end of file diff --git a/docs/docs/modules/chains/how_to/prompt.json b/docs/docs/modules/chains/how_to/prompt.json deleted file mode 100644 index aceb330e2c4..00000000000 --- a/docs/docs/modules/chains/how_to/prompt.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "input_variables": [ - "question" - ], - "output_parser": null, - "template": "Question: {question}\n\nAnswer: Let's think step by step.", - "template_format": "f-string" -} \ No newline at end of file diff --git a/docs/docs/modules/chains/how_to/serialization.ipynb b/docs/docs/modules/chains/how_to/serialization.ipynb deleted file mode 100644 index 555ff1beaa0..00000000000 --- a/docs/docs/modules/chains/how_to/serialization.ipynb +++ /dev/null @@ -1,378 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "cbe47c3a", - "metadata": {}, - "source": [ - "# Serialization\n", - "This notebook covers how to serialize chains to and from disk. The serialization format we use is JSON or YAML. Currently, only some chains support this type of serialization. We will grow the number of supported chains over time.\n" - ] - }, - { - "cell_type": "markdown", - "id": "e4a8a447", - "metadata": {}, - "source": [ - "## Saving a chain to disk\n", - "First, let's go over how to save a chain to disk. This can be done with the `.save` method, and specifying a file path with a `.json` or `.yaml` extension." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "26e28451", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain.prompts import PromptTemplate\nfrom langchain.llms import OpenAI\nfrom langchain.chains import LLMChain\n", - "\n", - "template = \"\"\"Question: {question}\n", - "\n", - "Answer: Let's think step by step.\"\"\"\n", - "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", - "llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0), verbose=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "bfa18e1f", - "metadata": {}, - "outputs": [], - "source": [ - "llm_chain.save(\"llm_chain.json\")" - ] - }, - { - "cell_type": "markdown", - "id": "ea82665d", - "metadata": {}, - "source": [ - "Let's now take a look at what's inside this saved file:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "0fd33328", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\r\n", - " \"memory\": null,\r\n", - " \"verbose\": true,\r\n", - " \"prompt\": {\r\n", - " \"input_variables\": [\r\n", - " \"question\"\r\n", - " ],\r\n", - " \"output_parser\": null,\r\n", - " \"template\": \"Question: {question}\\n\\nAnswer: Let's think step by step.\",\r\n", - " \"template_format\": \"f-string\"\r\n", - " },\r\n", - " \"llm\": {\r\n", - " \"model_name\": \"text-davinci-003\",\r\n", - " \"temperature\": 0.0,\r\n", - " \"max_tokens\": 256,\r\n", - " \"top_p\": 1,\r\n", - " \"frequency_penalty\": 0,\r\n", - " \"presence_penalty\": 0,\r\n", - " \"n\": 1,\r\n", - " \"best_of\": 1,\r\n", - " \"request_timeout\": null,\r\n", - " \"logit_bias\": {},\r\n", - " \"_type\": \"openai\"\r\n", - " },\r\n", - " \"output_key\": \"text\",\r\n", - " \"_type\": \"llm_chain\"\r\n", - "}" - ] - } - ], - "source": [ - "!cat llm_chain.json" - ] - }, - { - "cell_type": "markdown", - "id": "2012c724", - "metadata": {}, - "source": [ - "## Loading a chain from disk\n", - "We can load a chain from disk by using the `load_chain` method." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "342a1974", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain.chains import load_chain" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "394b7da8", - "metadata": {}, - "outputs": [], - "source": [ - "chain = load_chain(\"llm_chain.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "20d99787", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n", - "Prompt after formatting:\n", - "\u001b[32;1m\u001b[1;3mQuestion: whats 2 + 2\n", - "\n", - "Answer: Let's think step by step.\u001b[0m\n", - "\n", - "\u001b[1m> Finished chain.\u001b[0m\n" - ] - }, - { - "data": { - "text/plain": [ - "' 2 + 2 = 4'" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "chain.run(\"whats 2 + 2\")" - ] - }, - { - "cell_type": "markdown", - "id": "14449679", - "metadata": {}, - "source": [ - "## Saving components separately\n", - "In the above example, we can see that the prompt and LLM configuration information is saved in the same JSON as the overall chain. Alternatively, we can split them up and save them separately. This is often useful to make the saved components more modular. In order to do this, we just need to specify `llm_path` instead of the `llm` component, and `prompt_path` instead of the `prompt` component." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "50ec35ab", - "metadata": {}, - "outputs": [], - "source": [ - "llm_chain.prompt.save(\"prompt.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c48b39aa", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\r\n", - " \"input_variables\": [\r\n", - " \"question\"\r\n", - " ],\r\n", - " \"output_parser\": null,\r\n", - " \"template\": \"Question: {question}\\n\\nAnswer: Let's think step by step.\",\r\n", - " \"template_format\": \"f-string\"\r\n", - "}" - ] - } - ], - "source": [ - "!cat prompt.json" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "13c92944", - "metadata": {}, - "outputs": [], - "source": [ - "llm_chain.llm.save(\"llm.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "1b815f89", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\r\n", - " \"model_name\": \"text-davinci-003\",\r\n", - " \"temperature\": 0.0,\r\n", - " \"max_tokens\": 256,\r\n", - " \"top_p\": 1,\r\n", - " \"frequency_penalty\": 0,\r\n", - " \"presence_penalty\": 0,\r\n", - " \"n\": 1,\r\n", - " \"best_of\": 1,\r\n", - " \"request_timeout\": null,\r\n", - " \"logit_bias\": {},\r\n", - " \"_type\": \"openai\"\r\n", - "}" - ] - } - ], - "source": [ - "!cat llm.json" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "7e6aa9ab", - "metadata": {}, - "outputs": [], - "source": [ - "config = {\n", - " \"memory\": None,\n", - " \"verbose\": True,\n", - " \"prompt_path\": \"prompt.json\",\n", - " \"llm_path\": \"llm.json\",\n", - " \"output_key\": \"text\",\n", - " \"_type\": \"llm_chain\",\n", - "}\n", - "import json\n", - "\n", - "with open(\"llm_chain_separate.json\", \"w\") as f:\n", - " json.dump(config, f, indent=2)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "8e959ca6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\r\n", - " \"memory\": null,\r\n", - " \"verbose\": true,\r\n", - " \"prompt_path\": \"prompt.json\",\r\n", - " \"llm_path\": \"llm.json\",\r\n", - " \"output_key\": \"text\",\r\n", - " \"_type\": \"llm_chain\"\r\n", - "}" - ] - } - ], - "source": [ - "!cat llm_chain_separate.json" - ] - }, - { - "cell_type": "markdown", - "id": "662731c0", - "metadata": {}, - "source": [ - "We can then load it in the same way:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d69ceb93", - "metadata": {}, - "outputs": [], - "source": [ - "chain = load_chain(\"llm_chain_separate.json\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "a99d61b9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n", - "Prompt after formatting:\n", - "\u001b[32;1m\u001b[1;3mQuestion: whats 2 + 2\n", - "\n", - "Answer: Let's think step by step.\u001b[0m\n", - "\n", - "\u001b[1m> Finished chain.\u001b[0m\n" - ] - }, - { - "data": { - "text/plain": [ - "' 2 + 2 = 4'" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "chain.run(\"whats 2 + 2\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "822b7c12", - "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": "3.11.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/docs/modules/data_connection/document_loaders/file_directory.mdx b/docs/docs/modules/data_connection/document_loaders/file_directory.mdx index 21dbf4f3ae3..600a58a5692 100644 --- a/docs/docs/modules/data_connection/document_loaders/file_directory.mdx +++ b/docs/docs/modules/data_connection/document_loaders/file_directory.mdx @@ -2,7 +2,7 @@ This covers how to load all documents in a directory. -Under the hood, by default this uses the [UnstructuredLoader](/docs/integrations/document_loaders/unstructured_file.html). +Under the hood, by default this uses the [UnstructuredLoader](/docs/integrations/document_loaders/unstructured_file). ```python from langchain.document_loaders import DirectoryLoader @@ -127,9 +127,9 @@ len(docs) ## Auto-detect file encodings with TextLoader -In this example we will see some strategies that can be useful when loading a big list of arbitrary files from a directory using the `TextLoader` class. +In this example we will see some strategies that can be useful when loading a large list of arbitrary files from a directory using the `TextLoader` class. -First to illustrate the problem, let's try to load multiple text with arbitrary encodings. +First to illustrate the problem, let's try to load multiple texts with arbitrary encodings. ```python diff --git a/docs/docs/modules/data_connection/indexing.ipynb b/docs/docs/modules/data_connection/indexing.ipynb index acad7bb7450..a0153589499 100644 --- a/docs/docs/modules/data_connection/indexing.ipynb +++ b/docs/docs/modules/data_connection/indexing.ipynb @@ -66,7 +66,7 @@ "\n", "The record manager relies on a time-based mechanism to determine what content can be cleaned up (when using `full` or `incremental` cleanup modes).\n", "\n", - "If two tasks run back-to-back, and the first task finishes before the the clock time changes, then the second task may not be able to clean up content.\n", + "If two tasks run back-to-back, and the first task finishes before the clock time changes, then the second task may not be able to clean up content.\n", "\n", "This is unlikely to be an issue in actual settings for the following reasons:\n", "\n", diff --git a/docs/docs/modules/data_connection/retrievers/vectorstore.mdx b/docs/docs/modules/data_connection/retrievers/vectorstore.mdx index dc8d5906f22..2fed725d207 100644 --- a/docs/docs/modules/data_connection/retrievers/vectorstore.mdx +++ b/docs/docs/modules/data_connection/retrievers/vectorstore.mdx @@ -56,7 +56,7 @@ docs = retriever.get_relevant_documents("what did he say about ketanji brown jac ## 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. +You can also set a retrieval method that sets a similarity score threshold and only returns documents with a score above that threshold. ```python diff --git a/docs/docs/modules/memory/agent_with_memory.ipynb b/docs/docs/modules/memory/agent_with_memory.ipynb index 1ece76969d2..5d889a2ca5a 100644 --- a/docs/docs/modules/memory/agent_with_memory.ipynb +++ b/docs/docs/modules/memory/agent_with_memory.ipynb @@ -9,10 +9,10 @@ "\n", "This notebook goes over adding memory to an Agent. Before going through this notebook, please walkthrough the following notebooks, as this will build on top of both of them:\n", "\n", - "- [Memory in LLMChain](/docs/modules/memory/how_to/adding_memory.html)\n", - "- [Custom Agents](/docs/modules/agents/how_to/custom_agent.html)\n", + "- [Memory in LLMChain](/docs/modules/memory/how_to/adding_memory)\n", + "- [Custom Agents](/docs/modules/agents/how_to/custom_agent)\n", "\n", - "In order to add a memory to an agent we are going to the the following steps:\n", + "In order to add a memory to an agent we are going to perform the following steps:\n", "\n", "1. We are going to create an `LLMChain` with memory.\n", "2. We are going to use that `LLMChain` to create a custom Agent.\n", diff --git a/docs/docs/modules/memory/agent_with_memory_in_db.ipynb b/docs/docs/modules/memory/agent_with_memory_in_db.ipynb index adf48b9e040..b1aae1b92e7 100644 --- a/docs/docs/modules/memory/agent_with_memory_in_db.ipynb +++ b/docs/docs/modules/memory/agent_with_memory_in_db.ipynb @@ -9,9 +9,9 @@ "\n", "This notebook goes over adding memory to an Agent where the memory uses an external message store. Before going through this notebook, please walkthrough the following notebooks, as this will build on top of both of them:\n", "\n", - "- [Memory in LLMChain](/docs/modules/memory/how_to/adding_memory.html)\n", - "- [Custom Agents](/docs/modules/agents/how_to/custom_agent.html)\n", - "- [Memory in Agent](/docs/modules/memory/how_to/agent_with_memory.html)\n", + "- [Memory in LLMChain](/docs/modules/memory/how_to/adding_memory)\n", + "- [Custom Agents](/docs/modules/agents/how_to/custom_agent)\n", + "- [Memory in Agent](/docs/modules/memory/how_to/agent_with_memory)\n", "\n", "In order to add a memory with an external message store to an agent we are going to do the following steps:\n", "\n", diff --git a/docs/docs/security.md b/docs/docs/security.md index 3449db9adbe..328998c41cc 100644 --- a/docs/docs/security.md +++ b/docs/docs/security.md @@ -28,3 +28,8 @@ design and secure your applications. ## Reporting a Vulnerability Please report security vulnerabilities by email to security@langchain.dev. This will ensure the issue is promptly triaged and acted upon as needed. + +## Enterprise solutions + +LangChain may offer enterprise solutions for customers who have additional security +requirements. Please contact us at sales@langchain.dev. \ No newline at end of file diff --git a/docs/docs/use_cases/extraction.ipynb b/docs/docs/use_cases/extraction.ipynb index 9403afd6c2d..9e87e09f0a6 100644 --- a/docs/docs/use_cases/extraction.ipynb +++ b/docs/docs/use_cases/extraction.ipynb @@ -577,6 +577,8 @@ "### Going deeper\n", "\n", "* The [output parser](/docs/modules/model_io/output_parsers/) documentation includes various parser examples for specific types (e.g., lists, datetime, enum, etc). \n", + "* The experimental [Anthropic function calling](https://python.langchain.com/docs/integrations/chat/anthropic_functions) support provides similar functionality to Anthropic chat models.\n", + "* [LlamaCPP](https://python.langchain.com/docs/integrations/llms/llamacpp#grammars) natively supports constrained decoding using custom grammars, making it easy to output structured content using local LLMs \n", "* [JSONFormer](/docs/integrations/llms/jsonformer_experimental) offers another way for structured decoding of a subset of the JSON Schema.\n", "* [Kor](https://eyurtsev.github.io/kor/) is another library for extraction where schema and examples can be provided to the LLM." ] @@ -598,7 +600,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.1" + "version": "3.11.2" } }, "nbformat": 4, diff --git a/docs/docs/use_cases/qa_structured/sql.ipynb b/docs/docs/use_cases/qa_structured/sql.ipynb index 032b4eefb4a..ad1b0974dd8 100644 --- a/docs/docs/use_cases/qa_structured/sql.ipynb +++ b/docs/docs/use_cases/qa_structured/sql.ipynb @@ -1115,8 +1115,8 @@ "To learn more about the SQL Agent and how it works we refer to the [SQL Agent Toolkit](/docs/integrations/toolkits/sql_database) documentation.\n", "\n", "You can also check Agents for other document types:\n", - "- [Pandas Agent](/docs/integrations/toolkits/pandas.html)\n", - "- [CSV Agent](/docs/integrations/toolkits/csv.html)" + "- [Pandas Agent](/docs/integrations/toolkits/pandas)\n", + "- [CSV Agent](/docs/integrations/toolkits/csv)" ] }, { diff --git a/docs/docs/use_cases/question_answering/code_understanding.ipynb b/docs/docs/use_cases/question_answering/code_understanding.ipynb index 500d4177d2b..0b072019c46 100644 --- a/docs/docs/use_cases/question_answering/code_understanding.ipynb +++ b/docs/docs/use_cases/question_answering/code_understanding.ipynb @@ -14,7 +14,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/code_understanding.ipynb)\n", + "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/question_answering/code_understanding.ipynb)\n", "\n", "## Use case\n", "\n", diff --git a/docs/docs/use_cases/question_answering/local_retrieval_qa.ipynb b/docs/docs/use_cases/question_answering/local_retrieval_qa.ipynb index f2139ff696a..92834808d29 100644 --- a/docs/docs/use_cases/question_answering/local_retrieval_qa.ipynb +++ b/docs/docs/use_cases/question_answering/local_retrieval_qa.ipynb @@ -181,7 +181,7 @@ "source": [ "To enable use of GPU on Apple Silicon, follow the steps [here](https://github.com/abetlen/llama-cpp-python/blob/main/docs/install/macos.md) to use the Python binding `with Metal support`.\n", "\n", - "In particular, ensure that `conda` is using the correct virtual enviorment that you created (`miniforge3`).\n", + "In particular, ensure that `conda` is using the correct virtual environment that you created (`miniforge3`).\n", "\n", "E.g., for me:\n", "\n", diff --git a/docs/docs/use_cases/question_answering/vector_db_qa.mdx b/docs/docs/use_cases/question_answering/vector_db_qa.mdx index 431f2a52d4c..4ba6b2da0fa 100644 --- a/docs/docs/use_cases/question_answering/vector_db_qa.mdx +++ b/docs/docs/use_cases/question_answering/vector_db_qa.mdx @@ -42,7 +42,7 @@ qa.run(query) ## Chain Type -You can easily specify different chain types to load and use in the RetrievalQA chain. For a more detailed walkthrough of these types, please see [this notebook](/docs/modules/chains/additional/question_answering.html). +You can easily specify different chain types to load and use in the RetrievalQA chain. For a more detailed walkthrough of these types, please see [this notebook](/docs/modules/chains/additional/question_answering). There are two ways to load different chain types. First, you can specify the chain type argument in the `from_chain_type` method. This allows you to pass in the name of the chain type you want to use. For example, in the below we change the chain type to `map_reduce`. @@ -65,7 +65,7 @@ qa.run(query) -The above way allows you to really simply change the chain_type, but it doesn't provide a ton of flexibility over parameters to that chain type. If you want to control those parameters, you can load the chain directly (as you did in [this notebook](/docs/modules/chains/additional/question_answering.html)) and then pass that directly to the the RetrievalQA chain with the `combine_documents_chain` parameter. For example: +The above way allows you to really simply change the chain_type, but it doesn't provide a ton of flexibility over parameters to that chain type. If you want to control those parameters, you can load the chain directly (as you did in [this notebook](/docs/modules/chains/additional/question_answering)) and then pass that directly to the RetrievalQA chain with the `combine_documents_chain` parameter. For example: ```python @@ -89,7 +89,7 @@ qa.run(query) ## Custom Prompts -You can pass in custom prompts to do question answering. These prompts are the same prompts as you can pass into the [base question answering chain](/docs/modules/chains/additional/question_answering.html) +You can pass in custom prompts to do question answering. These prompts are the same prompts as you can pass into the [base question answering chain](/docs/modules/chains/additional/question_answering) ```python diff --git a/docs/docs/use_cases/summarization.ipynb b/docs/docs/use_cases/summarization.ipynb index fd22cdc9b04..5f68eba78ac 100644 --- a/docs/docs/use_cases/summarization.ipynb +++ b/docs/docs/use_cases/summarization.ipynb @@ -290,7 +290,7 @@ "\n", "Let's unpack the map reduce approach. For this, we'll first map each document to an individual summary using an `LLMChain`. Then we'll use a `ReduceDocumentsChain` to combine those summaries into a single global summary.\n", " \n", - "First, we specfy the LLMChain to use for mapping each document to an individual summary:" + "First, we specify the LLMChain to use for mapping each document to an individual summary:" ] }, { diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js index f81c72549be..ff0cfe102ad 100644 --- a/docs/docusaurus.config.js +++ b/docs/docusaurus.config.js @@ -160,29 +160,64 @@ const config = { label: "Integrations", }, { - to: "https://api.python.langchain.com", + href: "https://api.python.langchain.com", label: "API", position: "left", }, { - to: "/docs/community", - label: "Community", + type: "dropdown", + label: "More", position: "left", + items: [ + { + to: "/docs/community", + label: "Community", + }, + { + to: "/docs/contributing", + label: "Developer's guide", + }, + { + to: "/docs/additional_resources/dependents", + label: "Dependents", + }, + { + to: "/docs/additional_resources/tutorials", + label: "Tutorials" + }, + { + to: "/docs/additional_resources/youtube", + label: "YouTube videos" + }, + { label: "Gallery", href: "https://github.com/kyrolabs/awesome-langchain" } + ] }, { - to: "https://chat.langchain.com", - label: "Chat our docs", - position: "right", - }, - { - to: "https://smith.langchain.com", - label: "LangSmith", - position: "right", - }, - { - to: "https://js.langchain.com/docs", - label: "JS/TS Docs", + type: "dropdown", + label: "Also by LangChain", position: "right", + items: [ + { + href: "https://chat.langchain.com", + label: "Chat our docs", + }, + { + href: "https://smith.langchain.com", + label: "LangSmith", + }, + { + href: "https://smith.langchain.com/hub", + label: "LangChain Hub", + }, + { + href: "https://github.com/langchain-ai/langserve", + label: "LangServe", + }, + { + href: "https://js.langchain.com/docs", + label: "JS/TS", + }, + ] }, // Please keep GitHub link to the right for consistency. { diff --git a/docs/scripts/model_feat_table.py b/docs/scripts/model_feat_table.py index d02299f4d81..3ff5be4f808 100644 --- a/docs/scripts/model_feat_table.py +++ b/docs/scripts/model_feat_table.py @@ -33,8 +33,6 @@ sidebar_class_name: hidden # LLMs -import DocCardList from "@theme/DocCardList"; - ## Features (natively supported) All LLMs implement the Runnable interface, which comes with default implementations of all methods, ie. `ainvoke`, `batch`, `abatch`, `stream`, `astream`. This gives all LLMs basic support for async, streaming and batch, which by default is implemented as below: - *Async* support defaults to calling the respective sync method in asyncio's default thread pool executor. This lets other async functions in your application make progress while the LLM is being executed, by moving this call to a background thread. @@ -45,7 +43,6 @@ Each LLM integration can optionally provide native implementations for async, st {table} - """ CHAT_MODEL_TEMPLATE = """\ @@ -56,8 +53,6 @@ sidebar_class_name: hidden # Chat models -import DocCardList from "@theme/DocCardList"; - ## Features (natively supported) All ChatModels implement the Runnable interface, which comes with default implementations of all methods, ie. `ainvoke`, `batch`, `abatch`, `stream`, `astream`. This gives all ChatModels basic support for async, streaming and batch, which by default is implemented as below: - *Async* support defaults to calling the respective sync method in asyncio's default thread pool executor. This lets other async functions in your application make progress while the ChatModel is being executed, by moving this call to a background thread. @@ -69,7 +64,6 @@ The table shows, for each integration, which features have been implemented with {table} - """ diff --git a/docs/sidebars.js b/docs/sidebars.js index 9ee0598da35..fbcc4c4f5af 100644 --- a/docs/sidebars.js +++ b/docs/sidebars.js @@ -68,19 +68,6 @@ module.exports = { description: 'Design guides for key parts of the development process', slug: "guides", }, - }, - { - type: "category", - label: "More", - collapsed: true, - items: [ - { type: "autogenerated", dirName: "additional_resources" }, - { type: "link", label: "Gallery", href: "https://github.com/kyrolabs/awesome-langchain" } - ], - link: { - type: 'generated-index', - slug: "additional_resources", - }, } ], integrations: [ diff --git a/docs/vercel.json b/docs/vercel.json index 65a78119961..44177f8404d 100644 --- a/docs/vercel.json +++ b/docs/vercel.json @@ -3850,6 +3850,10 @@ { "source": "/docs/integrations/retrievers/google_cloud_enterprise_search", "destination": "/docs/integrations/retrievers/google_vertex_ai_search" + }, + { + "source": "/docs/integrations/providers/google_document_ai", + "destination": "/docs/integrations/platforms/google#google-document-ai" } ] } diff --git a/docs/vercel_build.sh b/docs/vercel_build.sh index f228fc17397..1fce247b762 100755 --- a/docs/vercel_build.sh +++ b/docs/vercel_build.sh @@ -49,4 +49,6 @@ python3.11 -m pip install -r vercel_requirements.txt python3.11 scripts/model_feat_table.py nbdoc_build --srcdir docs cp ../cookbook/README.md src/pages/cookbook.mdx +cp ../.github/CONTRIBUTING.md docs/contributing.md +wget https://raw.githubusercontent.com/langchain-ai/langserve/main/README.md -O docs/guides/deployments/langserve.md python3.11 scripts/generate_api_reference_links.py diff --git a/libs/cli/.gitignore b/libs/cli/.gitignore new file mode 100644 index 00000000000..1694d2bc1e9 --- /dev/null +++ b/libs/cli/.gitignore @@ -0,0 +1,3 @@ +dist +__pycache__ +.venv diff --git a/libs/cli/DOCS.md b/libs/cli/DOCS.md new file mode 100644 index 00000000000..38a3e7ac24c --- /dev/null +++ b/libs/cli/DOCS.md @@ -0,0 +1,213 @@ +# `langchain` + +**Usage**: + +```console +$ langchain [OPTIONS] COMMAND [ARGS]... +``` + +**Options**: + +* `--help`: Show this message and exit. + +**Commands**: + +* `hub`: Manage installable hub packages. +* `serve`: Manage LangServe application projects. +* `start`: Start the LangServe instance, whether it's... + +## `langchain hub` + +Manage installable hub packages. + +**Usage**: + +```console +$ langchain hub [OPTIONS] COMMAND [ARGS]... +``` + +**Options**: + +* `--help`: Show this message and exit. + +**Commands**: + +* `new`: Creates a new hub package. +* `start`: Starts a demo LangServe instance for this... + +### `langchain hub new` + +Creates a new hub package. + +**Usage**: + +```console +$ langchain hub new [OPTIONS] NAME +``` + +**Arguments**: + +* `NAME`: [required] + +**Options**: + +* `--help`: Show this message and exit. + +### `langchain hub start` + +Starts a demo LangServe instance for this hub package. + +**Usage**: + +```console +$ langchain hub start [OPTIONS] +``` + +**Options**: + +* `--port INTEGER` +* `--host TEXT` +* `--help`: Show this message and exit. + +## `langchain serve` + +Manage LangServe application projects. + +**Usage**: + +```console +$ langchain serve [OPTIONS] COMMAND [ARGS]... +``` + +**Options**: + +* `--help`: Show this message and exit. + +**Commands**: + +* `add`: Adds the specified package to the current... +* `install` +* `list`: Lists all packages in the current... +* `new`: Create a new LangServe application. +* `remove`: Removes the specified package from the... +* `start`: Starts the LangServe instance. + +### `langchain serve add` + +Adds the specified package to the current LangServe instance. + +e.g.: +langchain serve add simple-pirate +langchain serve add git+ssh://git@github.com/efriis/simple-pirate.git +langchain serve add git+https://github.com/efriis/hub.git#devbranch#subdirectory=mypackage + +**Usage**: + +```console +$ langchain serve add [OPTIONS] DEPENDENCIES... +``` + +**Arguments**: + +* `DEPENDENCIES...`: [required] + +**Options**: + +* `--api-path TEXT` +* `--project-dir PATH` +* `--help`: Show this message and exit. + +### `langchain serve install` + +**Usage**: + +```console +$ langchain serve install [OPTIONS] +``` + +**Options**: + +* `--help`: Show this message and exit. + +### `langchain serve list` + +Lists all packages in the current LangServe instance. + +**Usage**: + +```console +$ langchain serve list [OPTIONS] +``` + +**Options**: + +* `--help`: Show this message and exit. + +### `langchain serve new` + +Create a new LangServe application. + +**Usage**: + +```console +$ langchain serve new [OPTIONS] NAME +``` + +**Arguments**: + +* `NAME`: [required] + +**Options**: + +* `--package TEXT` +* `--help`: Show this message and exit. + +### `langchain serve remove` + +Removes the specified package from the current LangServe instance. + +**Usage**: + +```console +$ langchain serve remove [OPTIONS] API_PATHS... +``` + +**Arguments**: + +* `API_PATHS...`: [required] + +**Options**: + +* `--help`: Show this message and exit. + +### `langchain serve start` + +Starts the LangServe instance. + +**Usage**: + +```console +$ langchain serve start [OPTIONS] +``` + +**Options**: + +* `--port INTEGER` +* `--host TEXT` +* `--help`: Show this message and exit. + +## `langchain start` + +Start the LangServe instance, whether it's a hub package or a serve project. + +**Usage**: + +```console +$ langchain start [OPTIONS] +``` + +**Options**: + +* `--port INTEGER` +* `--host TEXT` +* `--help`: Show this message and exit. diff --git a/libs/cli/Makefile b/libs/cli/Makefile new file mode 100644 index 00000000000..445235c22c8 --- /dev/null +++ b/libs/cli/Makefile @@ -0,0 +1,8 @@ +lint lint_diff: + poetry run poe lint + +test: + poetry run poe test + +format: + poetry run poe format diff --git a/libs/cli/README.md b/libs/cli/README.md new file mode 100644 index 00000000000..8122ac150d1 --- /dev/null +++ b/libs/cli/README.md @@ -0,0 +1,53 @@ +# langchain-cli + +Install CLI + +`pip install -U --pre langchain-cli` + +Create new langchain app + +`langchain serve new my-app` + +Go into app + +`cd my-app` + +Install a package + +`langchain serve add extraction-summary` + +Install langserve + +`pip install "langserve[all]"` + +Install the langchain package + +`pip install -e packages/extraction-summary` + +Edit `app/server.py` to add that package to the routes + +```markdown +from fastapi import FastAPI +from langserve import add_routes +from extraction_summary.chain import chain + +app = FastAPI() + +add_routes(app, chain) +``` + +Set env vars + +```shell +export OPENAI_API_KEY=... +``` +```shell +export LANGCHAIN_TRACING_V2=true +export LANGCHAIN_ENDPOINT="https://api.smith.langchain.com" +export LANGCHAIN_API_KEY="" +export LANGCHAIN_PROJECT="extraction-summary" +``` + +Run the app + +`python app/server.py` diff --git a/libs/langchain/langchain/cli/__init__.py b/libs/cli/langchain_cli/__init__.py similarity index 100% rename from libs/langchain/langchain/cli/__init__.py rename to libs/cli/langchain_cli/__init__.py diff --git a/libs/cli/langchain_cli/cli.py b/libs/cli/langchain_cli/cli.py new file mode 100644 index 00000000000..2be2d9ea9da --- /dev/null +++ b/libs/cli/langchain_cli/cli.py @@ -0,0 +1,36 @@ +import subprocess +from typing import Optional + +import typer +from typing_extensions import Annotated + +from langchain_cli.namespaces import hub, serve + +app = typer.Typer(no_args_is_help=True, add_completion=False) +app.add_typer(hub.hub, name="hub", help=hub.__doc__) +app.add_typer(serve.serve, name="serve", help=serve.__doc__) + + +@app.command() +def start( + *, + port: Annotated[ + Optional[int], typer.Option(help="The port to run the server on") + ] = None, + host: Annotated[ + Optional[str], typer.Option(help="The host to run the server on") + ] = None, +) -> None: + """ + Start the LangServe instance, whether it's a hub package or a serve project. + """ + cmd = ["poetry", "run", "poe", "start"] + if port is not None: + cmd += ["--port", str(port)] + if host is not None: + cmd += ["--host", host] + subprocess.run(cmd) + + +if __name__ == "__main__": + app() diff --git a/libs/cli/langchain_cli/constants.py b/libs/cli/langchain_cli/constants.py new file mode 100644 index 00000000000..7eddb1ed725 --- /dev/null +++ b/libs/cli/langchain_cli/constants.py @@ -0,0 +1,4 @@ +DEFAULT_GIT_REPO = "https://github.com/langchain-ai/langchain.git" +DEFAULT_GIT_REF = "langserve-templates" +DEFAULT_GIT_SUBDIRECTORY = "templates" +DEFAULT_GIT_REF = "master" diff --git a/libs/cli/langchain_cli/dev_scripts.py b/libs/cli/langchain_cli/dev_scripts.py new file mode 100644 index 00000000000..7eee1e27351 --- /dev/null +++ b/libs/cli/langchain_cli/dev_scripts.py @@ -0,0 +1,18 @@ +""" +Development Scripts for Hub Packages +""" + +from fastapi import FastAPI +from langserve.packages import add_package_route + +from langchain_cli.utils.packages import get_package_root + + +def create_demo_server(): + """ + Creates a demo server for the current hub package. + """ + app = FastAPI() + package_root = get_package_root() + add_package_route(app, package_root, "") + return app diff --git a/libs/langchain/langchain/cli/create_repo/__init__.py b/libs/cli/langchain_cli/namespaces/__init__.py similarity index 100% rename from libs/langchain/langchain/cli/create_repo/__init__.py rename to libs/cli/langchain_cli/namespaces/__init__.py diff --git a/libs/cli/langchain_cli/namespaces/hub.py b/libs/cli/langchain_cli/namespaces/hub.py new file mode 100644 index 00000000000..cc4b11b77b8 --- /dev/null +++ b/libs/cli/langchain_cli/namespaces/hub.py @@ -0,0 +1,90 @@ +""" +Manage installable hub packages. +""" + +import re +import shutil +import subprocess +from pathlib import Path +from typing import Optional + +import typer +from typing_extensions import Annotated + +hub = typer.Typer(no_args_is_help=True, add_completion=False) + + +@hub.command() +def new( + name: Annotated[str, typer.Argument(help="The name of the folder to create")], + with_poetry: Annotated[ + bool, + typer.Option( + "--with-poetry/--no-poetry", help="Don't run poetry install" + ), + ] = False, +): + """ + Creates a new hub package. + """ + computed_name = name if name != "." else Path.cwd().name + destination_dir = Path.cwd() / name if name != "." else Path.cwd() + + # copy over template from ../package_template + project_template_dir = Path(__file__).parents[1] / "package_template" + shutil.copytree(project_template_dir, destination_dir, dirs_exist_ok=name == ".") + + package_name_split = computed_name.split("/") + package_name_last = ( + package_name_split[-2] + if len(package_name_split) > 1 and package_name_split[-1] == "" + else package_name_split[-1] + ) + default_package_name = re.sub( + r"[^a-zA-Z0-9_]", + "_", + package_name_last, + ) + + # replace template strings + pyproject = destination_dir / "pyproject.toml" + pyproject_contents = pyproject.read_text() + pyproject.write_text( + pyproject_contents.replace("__package_name__", default_package_name) + ) + + # move module folder + package_dir = destination_dir / default_package_name + shutil.move(destination_dir / "package_template", package_dir) + + # replace readme + readme = destination_dir / "README.md" + readme_contents = readme.read_text() + readme.write_text( + readme_contents.replace("__package_name_last__", package_name_last) + ) + + # poetry install + if with_poetry: + subprocess.run(["poetry", "install"], cwd=destination_dir) + + +@hub.command() +def start( + *, + port: Annotated[ + Optional[int], typer.Option(help="The port to run the server on") + ] = None, + host: Annotated[ + Optional[str], typer.Option(help="The host to run the server on") + ] = None, +) -> None: + """ + Starts a demo LangServe instance for this hub package. + """ + cmd = ["poetry", "run", "poe", "start"] + if port is not None: + cmd += ["--port", str(port)] + if host is not None: + cmd += ["--host", host] + subprocess.run(cmd) diff --git a/libs/cli/langchain_cli/namespaces/serve.py b/libs/cli/langchain_cli/namespaces/serve.py new file mode 100644 index 00000000000..7ea443a2d14 --- /dev/null +++ b/libs/cli/langchain_cli/namespaces/serve.py @@ -0,0 +1,230 @@ +""" +Manage LangServe application projects. +""" + +import shutil +import subprocess +from pathlib import Path +from typing import List, Optional + +import tomli +import typer +from langserve.packages import get_langserve_export, list_packages +from typing_extensions import Annotated + +from langchain_cli.utils.events import create_events +from langchain_cli.utils.git import ( + copy_repo, + parse_dependency_string, + update_repo, +) +from langchain_cli.utils.packages import get_package_root + +REPO_DIR = Path(typer.get_app_dir("langchain")) / "git_repos" + +serve = typer.Typer(no_args_is_help=True, add_completion=False) + + +@serve.command() +def new( + name: Annotated[str, typer.Argument(help="The name of the folder to create")], + *, + package: Annotated[ + Optional[List[str]], + typer.Option(help="Packages to seed the project with"), + ] = None, + with_poetry: Annotated[ + bool, + typer.Option("--with-poetry/--no-poetry", help="Run poetry install"), + ] = False, +): + """ + Create a new LangServe application. + """ + # copy over template from ../project_template + project_template_dir = Path(__file__).parents[1] / "project_template" + destination_dir = Path.cwd() / name if name != "." else Path.cwd() + shutil.copytree(project_template_dir, destination_dir, dirs_exist_ok=name == ".") + + # poetry install + if with_poetry: + subprocess.run(["poetry", "install"], cwd=destination_dir) + + # add packages if specified + if package is not None and len(package) > 0: + add(package, project_dir=destination_dir, with_poetry=with_poetry) + + +@serve.command() +def install(): + package_root = get_package_root() / "packages" + for package_path in list_packages(package_root): + try: + pyproject_path = package_path / "pyproject.toml" + langserve_export = get_langserve_export(pyproject_path) + typer.echo(f"Installing {langserve_export['package_name']}...") + subprocess.run(["poetry", "add", "--editable", package_path]) + except Exception as e: + typer.echo(f"Skipping installing {package_path} due to error: {e}") + + +@serve.command() +def add( + dependencies: Annotated[ + Optional[List[str]], typer.Argument(help="The dependency to add") + ] = None, + *, + api_path: Annotated[List[str], typer.Option(help="API paths to add")] = [], + project_dir: Annotated[ + Optional[Path], typer.Option(help="The project directory") + ] = None, + repo: Annotated[ + List[str], typer.Option(help="Shorthand for installing a GitHub Repo") + ] = [], + with_poetry: Annotated[ + bool, + typer.Option("--with-poetry/--no-poetry", help="Run poetry install"), + ] = False, +): + """ + Adds the specified package to the current LangServe instance. + + e.g.: + langchain serve add simple-pirate + langchain serve add git+ssh://git@github.com/efriis/simple-pirate.git + langchain serve add git+https://github.com/efriis/hub.git#devbranch#subdirectory=mypackage + """ + project_root = get_package_root(project_dir) + + if dependencies is None: + dependencies = [] + + # cannot have both repo and dependencies + if len(repo) != 0: + if len(dependencies) != 0: + raise typer.BadParameter( + "Cannot specify both repo and dependencies. " + "Please specify one or the other." + ) + dependencies = [f"git+https://github.com/{r}" for r in repo] + + if len(api_path) != 0 and len(api_path) != len(dependencies): + raise typer.BadParameter( + "The number of API paths must match the number of dependencies." + ) + + # get installed packages from pyproject.toml + root_pyproject_path = project_root / "pyproject.toml" + with open(root_pyproject_path, "rb") as pyproject_file: + pyproject = tomli.load(pyproject_file) + installed_packages = ( + pyproject.get("tool", {}).get("poetry", {}).get("dependencies", {}) + ) + installed_names = set(installed_packages.keys()) + + package_dir = project_root / "packages" + + create_events( + [{"event": "serve add", "properties": {"package": d}} for d in dependencies] + ) + + for i, dependency in enumerate(dependencies): + # update repo + typer.echo(f"Adding {dependency}...") + dep = parse_dependency_string(dependency) + source_repo_path = update_repo(dep["git"], dep["ref"], REPO_DIR) + source_path = ( + source_repo_path / dep["subdirectory"] + if dep["subdirectory"] + else source_repo_path + ) + pyproject_path = source_path / "pyproject.toml" + if not pyproject_path.exists(): + typer.echo(f"Could not find {pyproject_path}") + continue + langserve_export = get_langserve_export(pyproject_path) + + # detect name conflict + if langserve_export["package_name"] in installed_names: + typer.echo( + f"Package with name {langserve_export['package_name']} already " + "installed. Skipping...", + ) + continue + + inner_api_path = ( + api_path[i] if len(api_path) != 0 else langserve_export["package_name"] + ) + destination_path = package_dir / inner_api_path + if destination_path.exists(): + typer.echo( + f"Endpoint {langserve_export['package_name']} already exists. " + "Skipping...", + ) + continue + copy_repo(source_path, destination_path) + # poetry install + if with_poetry: + subprocess.run( + ["poetry", "add", "--editable", destination_path], cwd=project_root + ) + + +@serve.command() +def remove( + api_paths: Annotated[List[str], typer.Argument(help="The API paths to remove")], + with_poetry: Annotated[ + bool, + typer.Option("--with_poetry/--no-poetry", help="Don't run poetry remove"), + ] = False, +): + """ + Removes the specified package from the current LangServe instance. + """ + for api_path in api_paths: + package_dir = Path.cwd() / "packages" / api_path + if not package_dir.exists(): + typer.echo(f"Endpoint {api_path} does not exist. Skipping...") + continue + pyproject = package_dir / "pyproject.toml" + langserve_export = get_langserve_export(pyproject) + typer.echo(f"Removing {langserve_export['package_name']}...") + if with_poetry: + subprocess.run(["poetry", "remove", langserve_export["package_name"]]) + shutil.rmtree(package_dir) + + +@serve.command() +def list(): + """ + Lists all packages in the current LangServe instance. + """ + package_root = get_package_root() / "packages" + for package_path in list_packages(package_root): + relative = package_path.relative_to(package_root) + pyproject_path = package_path / "pyproject.toml" + langserve_export = get_langserve_export(pyproject_path) + typer.echo( + f"{relative}: ({langserve_export['module']}.{langserve_export['attr']})" + ) + + +@serve.command() +def start( + *, + port: Annotated[ + Optional[int], typer.Option(help="The port to run the server on") + ] = None, + host: Annotated[ + Optional[str], typer.Option(help="The host to run the server on") + ] = None, +) -> None: + """ + Starts the LangServe instance. + """ + cmd = ["poetry", "run", "poe", "start"] + if port is not None: + cmd += ["--port", str(port)] + if host is not None: + cmd += ["--host", host] + subprocess.run(cmd) diff --git a/libs/langchain/langchain/cli/create_repo/templates/repo/LICENSE b/libs/cli/langchain_cli/package_template/LICENSE similarity index 87% rename from libs/langchain/langchain/cli/create_repo/templates/repo/LICENSE rename to libs/cli/langchain_cli/package_template/LICENSE index 9ad37ad77ae..426b6509034 100644 --- a/libs/langchain/langchain/cli/create_repo/templates/repo/LICENSE +++ b/libs/cli/langchain_cli/package_template/LICENSE @@ -1,6 +1,6 @@ -The MIT License +MIT License -Copyright (c) ____author_name +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 @@ -9,13 +9,13 @@ 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 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. +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/libs/cli/langchain_cli/package_template/README.md b/libs/cli/langchain_cli/package_template/README.md new file mode 100644 index 00000000000..29f06a9d0da --- /dev/null +++ b/libs/cli/langchain_cli/package_template/README.md @@ -0,0 +1 @@ +# __package_name_last__ diff --git a/libs/langchain/langchain/cli/create_repo/templates/repo/tests/__init__.py b/libs/cli/langchain_cli/package_template/package_template/__init__.py similarity index 100% rename from libs/langchain/langchain/cli/create_repo/templates/repo/tests/__init__.py rename to libs/cli/langchain_cli/package_template/package_template/__init__.py diff --git a/libs/cli/langchain_cli/package_template/package_template/chain.py b/libs/cli/langchain_cli/package_template/package_template/chain.py new file mode 100644 index 00000000000..ef0a3aecf3c --- /dev/null +++ b/libs/cli/langchain_cli/package_template/package_template/chain.py @@ -0,0 +1,17 @@ +from langchain.chat_models import ChatOpenAI +from langchain.prompts import ChatPromptTemplate + +_prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + "You are a helpful assistant who speaks like a pirate", + ), + ("human", "{text}"), + ] +) +_model = ChatOpenAI() + +# if you update this, you MUST also update ../pyproject.toml +# with the new `tool.langserve.export_attr` +chain = _prompt | _model diff --git a/libs/cli/langchain_cli/package_template/pyproject.toml b/libs/cli/langchain_cli/package_template/pyproject.toml new file mode 100644 index 00000000000..3da459f7709 --- /dev/null +++ b/libs/cli/langchain_cli/package_template/pyproject.toml @@ -0,0 +1,32 @@ +[tool.poetry] +name = "__package_name__" +version = "0.0.1" +description = "" +authors = [] +readme = "README.md" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain = ">=0.0.313, <0.1" +openai = "^0.28.1" + +[tool.poetry.group.dev.dependencies] +poethepoet = "^0.24.1" +langchain-cli = ">=0.0.4" +fastapi = "^0.104.0" +sse-starlette = "^1.6.5" + +[tool.langserve] +export_module = "__package_name__.chain" +export_attr = "chain" + +[tool.poe.tasks.start] +cmd="uvicorn langchain_cli.dev_scripts:create_demo_server --reload --port $port --host $host" +args = [ + {name = "port", help = "port to run on", default = "8000"}, + {name = "host", help = "host to run on", default = "127.0.0.1"} +] + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/libs/cli/langchain_cli/package_template/tests/__init__.py b/libs/cli/langchain_cli/package_template/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/cli/langchain_cli/project_template/README.md b/libs/cli/langchain_cli/project_template/README.md new file mode 100644 index 00000000000..54534e4a530 --- /dev/null +++ b/libs/cli/langchain_cli/project_template/README.md @@ -0,0 +1,32 @@ +# LangServeHub Project Template + +## Installation +Install the LangChain CLI if you haven't yet +```bash +pip install --user --upgrade git+https://github.com/pingpong-templates/cli.git +``` +And install this package's dependencies +```bash +poetry install +``` + +## Adding packages +```bash +# if you have problems with `poe`, try `poetry run poe` + +# adding packages from https://github.com/pingpong-templates/hub +langchain serve add simple-pirate + +# adding custom GitHub repo packages +langchain serve add git+https://github.com/hwchase17/chain-of-verification + +# with a custom api mount point (defaults to `/{package_name}`) +poe add simple-translator --api_path=/my/custom/path/translator +``` + +## Removing packages + +Note: you remove packages by their api path +```bash +langchain serve remove pirate +``` \ No newline at end of file diff --git a/libs/cli/langchain_cli/project_template/app/__init__.py b/libs/cli/langchain_cli/project_template/app/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/langchain/langchain/cli/create_repo/templates/repo/____project_name_identifier/server.py b/libs/cli/langchain_cli/project_template/app/server.py similarity index 58% rename from libs/langchain/langchain/cli/create_repo/templates/repo/____project_name_identifier/server.py rename to libs/cli/langchain_cli/project_template/app/server.py index 5d43e0e8038..ce4a3c4ee71 100644 --- a/libs/langchain/langchain/cli/create_repo/templates/repo/____project_name_identifier/server.py +++ b/libs/cli/langchain_cli/project_template/app/server.py @@ -1,15 +1,10 @@ from fastapi import FastAPI from langserve import add_routes -from ____project_name_identifier.chain import get_chain - app = FastAPI() -add_routes( - app, - get_chain(), - config_keys=["tags"], -) +# Edit this to add the chain you want to add +add_routes(app, NotImplemented) if __name__ == "__main__": import uvicorn diff --git a/libs/cli/langchain_cli/project_template/packages/README.md b/libs/cli/langchain_cli/project_template/packages/README.md new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/cli/langchain_cli/project_template/pyproject.toml b/libs/cli/langchain_cli/project_template/pyproject.toml new file mode 100644 index 00000000000..d4a177a1c46 --- /dev/null +++ b/libs/cli/langchain_cli/project_template/pyproject.toml @@ -0,0 +1,31 @@ +[tool.poetry] +name = "langservehub-template" +version = "0.1.0" +description = "" +authors = ["Your Name "] +readme = "README.md" + +[tool.poetry.dependencies] +python = "^3.11" +sse-starlette = "^1.6.5" +tomli-w = "^1.0.0" +uvicorn = "^0.23.2" +fastapi = "^0.103.2" +langserve = ">=0.0.16" + +[tool.poetry.group.dev.dependencies] +poethepoet = "^0.24.1" +uvicorn = "^0.23.2" +pygithub = "^2.1.1" + +[tool.poe.tasks.start] +cmd="uvicorn app.server:app --reload --port $port --host $host" +args = [ + {name = "port", help = "port to run on", default = "8000"}, + {name = "host", help = "host to run on", default = "127.0.0.1"} +] + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/libs/cli/langchain_cli/utils/__init__.py b/libs/cli/langchain_cli/utils/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/cli/langchain_cli/utils/events.py b/libs/cli/langchain_cli/utils/events.py new file mode 100644 index 00000000000..61b258d0168 --- /dev/null +++ b/libs/cli/langchain_cli/utils/events.py @@ -0,0 +1,53 @@ +import json +from typing import Any, Dict, List, Optional, TypedDict + +import urllib3 + +WRITE_KEY = "310apTK0HUFl4AOv" + + +class EventDict(TypedDict): + event: str + properties: Optional[Dict[str, Any]] + + +def create_event(event: EventDict) -> None: + """ + Creates a new event with the given type and payload. + """ + data = { + "write_key": WRITE_KEY, + "event": event["event"], + "properties": event.get("properties"), + } + try: + urllib3.request( + "POST", + "https://app.firstpartyhq.com/events/v1/track", + body=json.dumps(data), + headers={"Content-Type": "application/json"}, + ) + except Exception: + pass + + +def create_events(events: List[EventDict]) -> None: + data = { + "events": [ + { + "write_key": WRITE_KEY, + "event": event["event"], + "properties": event.get("properties"), + } + for event in events + ] + } + try: + urllib3.request( + "POST", + "https://app.firstpartyhq.com/events/v1/track/bulk", + body=json.dumps(data), + headers={"Content-Type": "application/json"}, + ) + except Exception: + pass diff --git a/libs/cli/langchain_cli/utils/git.py b/libs/cli/langchain_cli/utils/git.py new file mode 100644 index 00000000000..1050d96720c --- /dev/null +++ b/libs/cli/langchain_cli/utils/git.py @@ -0,0 +1,114 @@ +import hashlib +import re +import shutil +from pathlib import Path +from typing import Optional, TypedDict + +from git import Repo + +from langchain_cli.constants import ( + DEFAULT_GIT_REF, + DEFAULT_GIT_REPO, + DEFAULT_GIT_SUBDIRECTORY, +) + + +class DependencySource(TypedDict): + git: str + ref: Optional[str] + subdirectory: Optional[str] + + +# use poetry dependency string format +def parse_dependency_string(package_string: str) -> DependencySource: + if package_string.startswith("git+"): + # remove git+ + gitstring = package_string[4:] + subdirectory = None + ref = None + # first check for #subdirectory= on the end + if "#subdirectory=" in gitstring: + gitstring, subdirectory = gitstring.split("#subdirectory=") + if "#" in subdirectory or "@" in subdirectory: + raise ValueError( + "#subdirectory must be the last part of the dependency string" + ) + + # find first slash after :// + # find @ or # after that slash + # remainder is ref + # if no @ or #, then ref is None + + # find first slash after :// + if "://" not in gitstring: + raise ValueError( + "git+ dependencies must start with git+https:// or git+ssh://" + ) + + _, find_slash = gitstring.split("://", 1) + + if "/" not in find_slash: + post_slash = find_slash + ref = None + else: + _, post_slash = find_slash.split("/", 1) + if "@" in post_slash or "#" in post_slash: + _, ref = re.split(r"[@#]", post_slash, 1) + + # gitstring is everything before that + gitstring = gitstring[: -len(ref) - 1] if ref is not None else gitstring + + return DependencySource( + git=gitstring, + ref=ref, + subdirectory=subdirectory, + ) + + elif package_string.startswith("https://"): + raise NotImplementedError("url dependencies are not supported yet") + else: + # it's a default git repo dependency + subdirectory = str(Path(DEFAULT_GIT_SUBDIRECTORY) / package_string) + return DependencySource( + git=DEFAULT_GIT_REPO, ref=DEFAULT_GIT_REF, subdirectory=subdirectory + ) + + +def _get_repo_path(gitstring: str, repo_dir: Path) -> Path: + # only based on git for now + hashed = hashlib.sha256(gitstring.encode("utf-8")).hexdigest()[:8] + + removed_protocol = gitstring.split("://")[-1] + removed_basename = re.split(r"[/:]", removed_protocol, 1)[-1] + removed_extras = removed_basename.split("#")[0] + foldername = re.sub(r"[^a-zA-Z0-9_]", "_", removed_extras) + + directory_name = f"{foldername}_{hashed}" + return repo_dir / directory_name + + +def update_repo(gitstring: str, ref: Optional[str], repo_dir: Path) -> Path: + # see if path already saved + repo_path = _get_repo_path(gitstring, repo_dir) + if repo_path.exists(): + shutil.rmtree(repo_path) + + # now we have fresh dir + Repo.clone_from(gitstring, repo_path, branch=ref, depth=1) + return repo_path + + +def copy_repo( + source: Path, + destination: Path, +) -> None: + """ + Copies a repo, ignoring git folders. + + Raises FileNotFound error if it can't find source + """ + + def ignore_func(_, files): + return [f for f in files if f == ".git"] + + shutil.copytree(source, destination, ignore=ignore_func) diff --git a/libs/cli/langchain_cli/utils/packages.py b/libs/cli/langchain_cli/utils/packages.py new file mode 100644 index 00000000000..97e14b6c7b6 --- /dev/null +++ b/libs/cli/langchain_cli/utils/packages.py @@ -0,0 +1,16 @@ +from pathlib import Path +from typing import Optional, Set + + +def get_package_root(cwd: Optional[Path] = None) -> Path: + # traverse path for routes to host (any directory holding a pyproject.toml file) + package_root = Path.cwd() if cwd is None else cwd + visited: Set[Path] = set() + while package_root not in visited: + visited.add(package_root) + + pyproject_path = package_root / "pyproject.toml" + if pyproject_path.exists(): + return package_root + package_root = package_root.parent + raise FileNotFoundError("No pyproject.toml found") diff --git a/libs/cli/poetry.lock b/libs/cli/poetry.lock new file mode 100644 index 00000000000..d2c9a8b6eef --- /dev/null +++ b/libs/cli/poetry.lock @@ -0,0 +1,1507 @@ +# This file is automatically 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b/libs/cli/pyproject.toml new file mode 100644 index 00000000000..8e484687672 --- /dev/null +++ b/libs/cli/pyproject.toml @@ -0,0 +1,49 @@ +[tool.poetry] +name = "langchain-cli" +version = "0.0.5" +description = "CLI for interacting with LangChain" +authors = ["Erick Friis "] +readme = "README.md" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +typer = {extras = ["all"], version = "^0.9.0"} +tomli = "^2.0.1" +gitpython = "^3.1.40" +langserve = "^0.0.16" +fastapi = "^0.104.0" +uvicorn = "^0.23.2" + +[tool.poetry.scripts] +langchain = "langchain_cli.cli:app" +langchain-cli = "langchain_cli.cli:app" + +[tool.poetry.group.dev.dependencies] +poethepoet = "^0.24.1" +pytest = "^7.4.2" +pytest-watch = "^4.2.0" + +[tool.poetry.group.lint.dependencies] +ruff = "^0.1.3" + +[tool.poetry.group.test.dependencies] + +[tool.poetry.group.typing.dependencies] + +[tool.ruff] +select = [ + "E", # pycodestyle + "F", # pyflakes + "I", # isort +] + +[tool.poe.tasks] +test = "poetry run pytest" +watch = "poetry run ptw" +lint = "poetry run ruff ." +format = "poetry run ruff . --fix" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/libs/cli/tests/__init__.py b/libs/cli/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/cli/tests/test_utils.py b/libs/cli/tests/test_utils.py new file mode 100644 index 00000000000..aa636034657 --- /dev/null +++ b/libs/cli/tests/test_utils.py @@ -0,0 +1,93 @@ +import pytest + +from langchain_cli.constants import ( + DEFAULT_GIT_REF, + DEFAULT_GIT_REPO, + DEFAULT_GIT_SUBDIRECTORY, +) +from langchain_cli.utils.git import DependencySource, parse_dependency_string + + +def test_dependency_string() -> None: + assert parse_dependency_string( + "git+ssh://git@github.com/efriis/myrepo.git" + ) == DependencySource( + git="ssh://git@github.com/efriis/myrepo.git", + ref=None, + subdirectory=None, + ) + + assert parse_dependency_string( + "git+https://github.com/efriis/myrepo.git#subdirectory=src" + ) == DependencySource( + git="https://github.com/efriis/myrepo.git", + subdirectory="src", + ref=None, + ) + + assert parse_dependency_string( + "git+ssh://git@github.com:efriis/myrepo.git#develop" + ) == DependencySource( + git="ssh://git@github.com:efriis/myrepo.git", ref="develop", subdirectory=None + ) + + # also support a slash in ssh + assert parse_dependency_string( + "git+ssh://git@github.com/efriis/myrepo.git#develop" + ) == DependencySource( + git="ssh://git@github.com/efriis/myrepo.git", ref="develop", subdirectory=None + ) + + # looks like poetry supports both an @ and a # + assert parse_dependency_string( + "git+ssh://git@github.com:efriis/myrepo.git@develop" + ) == DependencySource( + git="ssh://git@github.com:efriis/myrepo.git", ref="develop", subdirectory=None + ) + + assert parse_dependency_string("simple-pirate") == DependencySource( + git=DEFAULT_GIT_REPO, + subdirectory=f"{DEFAULT_GIT_SUBDIRECTORY}/simple-pirate", + ref=DEFAULT_GIT_REF, + ) + + +def test_dependency_string_both() -> None: + assert parse_dependency_string( + "git+https://github.com/efriis/myrepo.git@branch#subdirectory=src" + ) == DependencySource( + git="https://github.com/efriis/myrepo.git", + subdirectory="src", + ref="branch", + ) + + +def test_dependency_string_invalids() -> None: + # expect error for wrong order + with pytest.raises(ValueError): + parse_dependency_string( + "git+https://github.com/efriis/myrepo.git#subdirectory=src@branch" + ) + # expect error for @subdirectory + + +def test_dependency_string_edge_case() -> None: + # weird unsolvable edge case of + # git+ssh://a@b + # this could be a ssh dep with user=a, and default ref + # or a ssh dep at a with ref=b. + # in this case, assume the first case (be greedy with the '@') + assert parse_dependency_string("git+ssh://a@b") == DependencySource( + git="ssh://a@b", + subdirectory=None, + ref=None, + ) + + # weird one that is actually valid + assert parse_dependency_string( + "git+https://github.com/efriis/myrepo.git@subdirectory=src" + ) == DependencySource( + git="https://github.com/efriis/myrepo.git", + subdirectory=None, + ref="subdirectory=src", + ) diff --git a/libs/experimental/langchain_experimental/comprehend_moderation/__init__.py b/libs/experimental/langchain_experimental/comprehend_moderation/__init__.py index d129f9fc7fe..4a265438f19 100644 --- a/libs/experimental/langchain_experimental/comprehend_moderation/__init__.py +++ b/libs/experimental/langchain_experimental/comprehend_moderation/__init__.py @@ -7,23 +7,25 @@ from langchain_experimental.comprehend_moderation.base_moderation_callbacks impo ) from langchain_experimental.comprehend_moderation.base_moderation_config import ( BaseModerationConfig, - ModerationIntentConfig, ModerationPiiConfig, + ModerationPromptSafetyConfig, ModerationToxicityConfig, ) -from langchain_experimental.comprehend_moderation.intent import ComprehendIntent from langchain_experimental.comprehend_moderation.pii import ComprehendPII +from langchain_experimental.comprehend_moderation.prompt_safety import ( + ComprehendPromptSafety, +) from langchain_experimental.comprehend_moderation.toxicity import ComprehendToxicity __all__ = [ "BaseModeration", "ComprehendPII", - "ComprehendIntent", + "ComprehendPromptSafety", "ComprehendToxicity", "BaseModerationConfig", "ModerationPiiConfig", "ModerationToxicityConfig", - "ModerationIntentConfig", + "ModerationPromptSafetyConfig", "BaseModerationCallbackHandler", "AmazonComprehendModerationChain", ] diff --git a/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation.py b/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation.py index 9af91447733..9b97a42a00d 100644 --- a/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation.py +++ b/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation.py @@ -6,8 +6,10 @@ from langchain.prompts.base import StringPromptValue from langchain.prompts.chat import ChatPromptValue from langchain.schema import AIMessage, HumanMessage -from langchain_experimental.comprehend_moderation.intent import ComprehendIntent from langchain_experimental.comprehend_moderation.pii import ComprehendPII +from langchain_experimental.comprehend_moderation.prompt_safety import ( + ComprehendPromptSafety, +) from langchain_experimental.comprehend_moderation.toxicity import ComprehendToxicity @@ -59,7 +61,7 @@ class BaseModeration: input_text = message.content else: raise ValueError( - f"Invalid input type {type(input)}. " + f"Invalid input type {type(input_text)}. " "Must be a PromptValue, str, or list of BaseMessages." ) return input_text @@ -109,13 +111,13 @@ class BaseModeration: def moderate(self, prompt: Any) -> str: from langchain_experimental.comprehend_moderation.base_moderation_config import ( # noqa: E501 - ModerationIntentConfig, ModerationPiiConfig, + ModerationPromptSafetyConfig, ModerationToxicityConfig, ) from langchain_experimental.comprehend_moderation.base_moderation_exceptions import ( # noqa: E501 - ModerationIntentionError, ModerationPiiError, + ModerationPromptSafetyError, ModerationToxicityError, ) @@ -128,7 +130,7 @@ class BaseModeration: filter_functions = { "pii": ComprehendPII, "toxicity": ComprehendToxicity, - "intent": ComprehendIntent, + "prompt_safety": ComprehendPromptSafety, } filters = self.config.filters # type: ignore @@ -141,8 +143,8 @@ class BaseModeration: "toxicity" if isinstance(_filter, ModerationToxicityConfig) else ( - "intent" - if isinstance(_filter, ModerationIntentConfig) + "prompt_safety" + if isinstance(_filter, ModerationPromptSafetyConfig) else None ) ) @@ -171,7 +173,7 @@ class BaseModeration: f"Found Toxic content..stopping..\n{str(e)}\n" ) raise e - except ModerationIntentionError as e: + except ModerationPromptSafetyError as e: self._log_message_for_verbose( f"Found Harmful intention..stopping..\n{str(e)}\n" ) diff --git a/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_callbacks.py b/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_callbacks.py index 2e3e51db2d2..260b3026b8c 100644 --- a/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_callbacks.py +++ b/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_callbacks.py @@ -11,12 +11,13 @@ class BaseModerationCallbackHandler: BaseModerationCallbackHandler.on_after_toxicity, self.on_after_toxicity ) and self._is_method_unchanged( - BaseModerationCallbackHandler.on_after_intent, self.on_after_intent + BaseModerationCallbackHandler.on_after_prompt_safety, + self.on_after_prompt_safety, ) ): raise NotImplementedError( "Subclasses must override at least one of on_after_pii(), " - "on_after_toxicity(), or on_after_intent() functions." + "on_after_toxicity(), or on_after_prompt_safety() functions." ) def _is_method_unchanged( @@ -36,10 +37,10 @@ class BaseModerationCallbackHandler: """Run after Toxicity validation is complete.""" pass - async def on_after_intent( + async def on_after_prompt_safety( self, moderation_beacon: Dict[str, Any], unique_id: str, **kwargs: Any ) -> None: - """Run after Toxicity validation is complete.""" + """Run after Prompt Safety validation is complete.""" pass @property @@ -57,8 +58,8 @@ class BaseModerationCallbackHandler: ) @property - def intent_callback(self) -> bool: + def prompt_safety_callback(self) -> bool: return ( - self.on_after_intent.__func__ # type: ignore - is not BaseModerationCallbackHandler.on_after_intent + self.on_after_prompt_safety.__func__ # type: ignore + is not BaseModerationCallbackHandler.on_after_prompt_safety ) diff --git a/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_config.py b/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_config.py index 7e91cb783b4..f20b5b289a6 100644 --- a/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_config.py +++ b/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_config.py @@ -28,24 +28,26 @@ class ModerationToxicityConfig(BaseModel): """List of toxic labels, defaults to `list[]`""" -class ModerationIntentConfig(BaseModel): +class ModerationPromptSafetyConfig(BaseModel): threshold: float = 0.5 """ - Threshold for Intent classification + Threshold for Prompt Safety classification confidence score, defaults to 0.5 i.e. 50% """ class BaseModerationConfig(BaseModel): filters: List[ - Union[ModerationPiiConfig, ModerationToxicityConfig, ModerationIntentConfig] + Union[ + ModerationPiiConfig, ModerationToxicityConfig, ModerationPromptSafetyConfig + ] ] = [ ModerationPiiConfig(), ModerationToxicityConfig(), - ModerationIntentConfig(), + ModerationPromptSafetyConfig(), ] """ Filters applied to the moderation chain, defaults to `[ModerationPiiConfig(), ModerationToxicityConfig(), - ModerationIntentConfig()]` + ModerationPromptSafetyConfig()]` """ diff --git a/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_exceptions.py b/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_exceptions.py index 74b3971df04..c3a4e6f72ff 100644 --- a/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_exceptions.py +++ b/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_exceptions.py @@ -26,7 +26,7 @@ class ModerationToxicityError(Exception): super().__init__(self.message) -class ModerationIntentionError(Exception): +class ModerationPromptSafetyError(Exception): """Exception raised if Intention entities are detected. Attributes: @@ -35,9 +35,7 @@ class ModerationIntentionError(Exception): def __init__( self, - message: str = ( - "The prompt indicates an un-desired intent and " "cannot be processed" - ), + message: str = ("The prompt is unsafe and cannot be processed"), ): self.message = message super().__init__(self.message) diff --git a/libs/experimental/langchain_experimental/comprehend_moderation/intent.py b/libs/experimental/langchain_experimental/comprehend_moderation/prompt_safety.py similarity index 68% rename from libs/experimental/langchain_experimental/comprehend_moderation/intent.py rename to libs/experimental/langchain_experimental/comprehend_moderation/prompt_safety.py index d71f54d3aa1..77eca1a1af8 100644 --- a/libs/experimental/langchain_experimental/comprehend_moderation/intent.py +++ b/libs/experimental/langchain_experimental/comprehend_moderation/prompt_safety.py @@ -2,11 +2,11 @@ import asyncio from typing import Any, Optional from langchain_experimental.comprehend_moderation.base_moderation_exceptions import ( - ModerationIntentionError, + ModerationPromptSafetyError, ) -class ComprehendIntent: +class ComprehendPromptSafety: def __init__( self, client: Any, @@ -17,7 +17,7 @@ class ComprehendIntent: self.client = client self.moderation_beacon = { "moderation_chain_id": chain_id, - "moderation_type": "Intent", + "moderation_type": "PromptSafety", "moderation_status": "LABELS_NOT_FOUND", } self.callback = callback @@ -26,62 +26,62 @@ class ComprehendIntent: def _get_arn(self) -> str: region_name = self.client.meta.region_name service = "comprehend" - intent_endpoint = "document-classifier-endpoint/prompt-intent" - return f"arn:aws:{service}:{region_name}:aws:{intent_endpoint}" + prompt_safety_endpoint = "document-classifier-endpoint/prompt-safety" + return f"arn:aws:{service}:{region_name}:aws:{prompt_safety_endpoint}" def validate(self, prompt_value: str, config: Any = None) -> str: """ - Check and validate the intent of the given prompt text. + Check and validate the safety of the given prompt text. Args: - prompt_value (str): The input text to be checked for unintended intent. - config (Dict[str, Any]): Configuration settings for intent checks. + prompt_value (str): The input text to be checked for unsafe text. + config (Dict[str, Any]): Configuration settings for prompt safety checks. Raises: - ValueError: If unintended intent is found in the prompt text based + ValueError: If unsafe prompt is found in the prompt text based on the specified threshold. Returns: str: The input prompt_value. Note: - This function checks the intent of the provided prompt text using - Comprehend's classify_document API and raises an error if unintended - intent is detected with a score above the specified threshold. + This function checks the safety of the provided prompt text using + Comprehend's classify_document API and raises an error if unsafe + text is detected with a score above the specified threshold. Example: comprehend_client = boto3.client('comprehend') prompt_text = "Please tell me your credit card information." config = {"threshold": 0.7} - checked_prompt = check_intent(comprehend_client, prompt_text, config) + checked_prompt = check_prompt_safety(comprehend_client, prompt_text, config) """ threshold = config.get("threshold") - intent_found = False + unsafe_prompt = False endpoint_arn = self._get_arn() response = self.client.classify_document( Text=prompt_value, EndpointArn=endpoint_arn ) - if self.callback and self.callback.intent_callback: + if self.callback and self.callback.prompt_safety_callback: self.moderation_beacon["moderation_input"] = prompt_value self.moderation_beacon["moderation_output"] = response for class_result in response["Classes"]: if ( class_result["Score"] >= threshold - and class_result["Name"] == "UNDESIRED_PROMPT" + and class_result["Name"] == "UNSAFE_PROMPT" ): - intent_found = True + unsafe_prompt = True break if self.callback and self.callback.intent_callback: - if intent_found: + if unsafe_prompt: self.moderation_beacon["moderation_status"] = "LABELS_FOUND" asyncio.create_task( self.callback.on_after_intent(self.moderation_beacon, self.unique_id) ) - if intent_found: - raise ModerationIntentionError + if unsafe_prompt: + raise ModerationPromptSafetyError return prompt_value diff --git a/libs/experimental/langchain_experimental/llms/anthropic_functions.py b/libs/experimental/langchain_experimental/llms/anthropic_functions.py index 68e0eac1f8b..48e38690dea 100644 --- a/libs/experimental/langchain_experimental/llms/anthropic_functions.py +++ b/libs/experimental/langchain_experimental/llms/anthropic_functions.py @@ -5,14 +5,12 @@ from typing import Any, DefaultDict, Dict, List, Optional from langchain.callbacks.manager import ( CallbackManagerForLLMRun, - Callbacks, ) from langchain.chat_models.anthropic import ChatAnthropic from langchain.chat_models.base import BaseChatModel from langchain.schema import ( ChatGeneration, ChatResult, - LLMResult, ) from langchain.schema.messages import ( AIMessage, @@ -196,18 +194,6 @@ class AnthropicFunctions(BaseChatModel): else: return ChatResult(generations=[ChatGeneration(message=response)]) - async def agenerate( - self, - messages: List[List[BaseMessage]], - stop: Optional[List[str]] = None, - callbacks: Callbacks = None, - *, - tags: Optional[List[str]] = None, - metadata: Optional[Dict[str, Any]] = None, - **kwargs: Any, - ) -> LLMResult: - raise NotImplementedError - @property def _llm_type(self) -> str: return "anthropic_functions" diff --git a/libs/experimental/pyproject.toml b/libs/experimental/pyproject.toml index b58796e804c..4caefa15163 100644 --- a/libs/experimental/pyproject.toml +++ b/libs/experimental/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-experimental" -version = "0.0.32" +version = "0.0.35" description = "Building applications with LLMs through composability" authors = [] license = "MIT" diff --git a/libs/langchain/langchain/__init__.py b/libs/langchain/langchain/__init__.py index cf278dd886f..f91528d3d18 100644 --- a/libs/langchain/langchain/__init__.py +++ b/libs/langchain/langchain/__init__.py @@ -49,25 +49,25 @@ def __getattr__(name: str) -> Any: if name == "MRKLChain": from langchain.agents import MRKLChain - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.agents.MRKLChain") return MRKLChain elif name == "ReActChain": from langchain.agents import ReActChain - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.agents.ReActChain") return ReActChain elif name == "SelfAskWithSearchChain": from langchain.agents import SelfAskWithSearchChain - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.agents.SelfAskWithSearchChain") return SelfAskWithSearchChain elif name == "ConversationChain": from langchain.chains import ConversationChain - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.chains.ConversationChain") return ConversationChain elif name == "LLMBashChain": @@ -83,248 +83,252 @@ def __getattr__(name: str) -> Any: elif name == "LLMChain": from langchain.chains import LLMChain - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.chains.LLMChain") return LLMChain elif name == "LLMCheckerChain": from langchain.chains import LLMCheckerChain - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.chains.LLMCheckerChain") return LLMCheckerChain elif name == "LLMMathChain": from langchain.chains import LLMMathChain - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.chains.LLMMathChain") return LLMMathChain elif name == "QAWithSourcesChain": from langchain.chains import QAWithSourcesChain - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.chains.QAWithSourcesChain") return QAWithSourcesChain elif name == "VectorDBQA": from langchain.chains import VectorDBQA - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.chains.VectorDBQA") return VectorDBQA elif name == "VectorDBQAWithSourcesChain": from langchain.chains import VectorDBQAWithSourcesChain - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.chains.VectorDBQAWithSourcesChain") return VectorDBQAWithSourcesChain elif name == "InMemoryDocstore": from langchain.docstore import InMemoryDocstore - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.docstore.InMemoryDocstore") return InMemoryDocstore elif name == "Wikipedia": from langchain.docstore import Wikipedia - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.docstore.Wikipedia") return Wikipedia elif name == "Anthropic": from langchain.llms import Anthropic - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.llms.Anthropic") return Anthropic elif name == "Banana": from langchain.llms import Banana - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.llms.Banana") return Banana elif name == "CerebriumAI": from langchain.llms import CerebriumAI - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.llms.CerebriumAI") return CerebriumAI elif name == "Cohere": from langchain.llms import Cohere - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.llms.Cohere") return Cohere elif name == "ForefrontAI": from langchain.llms import ForefrontAI - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.llms.ForefrontAI") return ForefrontAI elif name == "GooseAI": from langchain.llms import GooseAI - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.llms.GooseAI") return GooseAI elif name == "HuggingFaceHub": from langchain.llms import HuggingFaceHub - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.llms.HuggingFaceHub") return HuggingFaceHub elif name == "HuggingFaceTextGenInference": from langchain.llms import HuggingFaceTextGenInference - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.llms.HuggingFaceTextGenInference") return HuggingFaceTextGenInference elif name == "LlamaCpp": from langchain.llms import LlamaCpp - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.llms.LlamaCpp") return LlamaCpp elif name == "Modal": from langchain.llms import Modal - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.llms.Modal") return Modal elif name == "OpenAI": from langchain.llms import OpenAI - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.llms.OpenAI") return OpenAI elif name == "Petals": from langchain.llms import Petals - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.llms.Petals") return Petals elif name == "PipelineAI": from langchain.llms import PipelineAI - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.llms.PipelineAI") return PipelineAI elif name == "SagemakerEndpoint": from langchain.llms import SagemakerEndpoint - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.llms.SagemakerEndpoint") return SagemakerEndpoint elif name == "StochasticAI": from langchain.llms import StochasticAI - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.llms.StochasticAI") return StochasticAI elif name == "Writer": from langchain.llms import Writer - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.llms.Writer") return Writer elif name == "HuggingFacePipeline": from langchain.llms.huggingface_pipeline import HuggingFacePipeline - _warn_on_import(name) + _warn_on_import( + name, replacement="langchain.llms.huggingface_pipeline.HuggingFacePipeline" + ) return HuggingFacePipeline elif name == "FewShotPromptTemplate": from langchain.prompts import FewShotPromptTemplate - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.prompts.FewShotPromptTemplate") return FewShotPromptTemplate elif name == "Prompt": from langchain.prompts import Prompt - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.prompts.Prompt") return Prompt elif name == "PromptTemplate": from langchain.prompts import PromptTemplate - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.prompts.PromptTemplate") return PromptTemplate elif name == "BasePromptTemplate": from langchain.schema.prompt_template import BasePromptTemplate - _warn_on_import(name) + _warn_on_import( + name, replacement="langchain.schema.prompt_template.BasePromptTemplate" + ) return BasePromptTemplate elif name == "ArxivAPIWrapper": from langchain.utilities import ArxivAPIWrapper - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.utilities.ArxivAPIWrapper") return ArxivAPIWrapper elif name == "GoldenQueryAPIWrapper": from langchain.utilities import GoldenQueryAPIWrapper - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.utilities.GoldenQueryAPIWrapper") return GoldenQueryAPIWrapper elif name == "GoogleSearchAPIWrapper": from langchain.utilities import GoogleSearchAPIWrapper - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.utilities.GoogleSearchAPIWrapper") return GoogleSearchAPIWrapper elif name == "GoogleSerperAPIWrapper": from langchain.utilities import GoogleSerperAPIWrapper - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.utilities.GoogleSerperAPIWrapper") return GoogleSerperAPIWrapper elif name == "PowerBIDataset": from langchain.utilities import PowerBIDataset - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.utilities.PowerBIDataset") return PowerBIDataset elif name == "SearxSearchWrapper": from langchain.utilities import SearxSearchWrapper - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.utilities.SearxSearchWrapper") return SearxSearchWrapper elif name == "WikipediaAPIWrapper": from langchain.utilities import WikipediaAPIWrapper - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.utilities.WikipediaAPIWrapper") return WikipediaAPIWrapper elif name == "WolframAlphaAPIWrapper": from langchain.utilities import WolframAlphaAPIWrapper - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.utilities.WolframAlphaAPIWrapper") return WolframAlphaAPIWrapper elif name == "SQLDatabase": from langchain.utilities import SQLDatabase - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.utilities.SQLDatabase") return SQLDatabase elif name == "FAISS": from langchain.vectorstores import FAISS - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.vectorstores.FAISS") return FAISS elif name == "ElasticVectorSearch": from langchain.vectorstores import ElasticVectorSearch - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.vectorstores.ElasticVectorSearch") return ElasticVectorSearch # For backwards compatibility elif name == "SerpAPIChain" or name == "SerpAPIWrapper": from langchain.utilities import SerpAPIWrapper - _warn_on_import(name) + _warn_on_import(name, replacement="langchain.utilities.SerpAPIWrapper") return SerpAPIWrapper elif name == "verbose": diff --git a/libs/langchain/langchain/adapters/openai.py b/libs/langchain/langchain/adapters/openai.py index 3c2429a2b17..f697851fc65 100644 --- a/libs/langchain/langchain/adapters/openai.py +++ b/libs/langchain/langchain/adapters/openai.py @@ -31,7 +31,7 @@ from langchain.schema.messages import ( async def aenumerate( iterable: AsyncIterator[Any], start: int = 0 ) -> AsyncIterator[tuple[int, Any]]: - """Async version of enumerate.""" + """Async version of enumerate function.""" i = start async for x in iterable: yield i, x @@ -39,6 +39,14 @@ async def aenumerate( def convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage: + """Convert a dictionary to a LangChain message. + + Args: + _dict: The dictionary. + + Returns: + The LangChain message. + """ role = _dict["role"] if role == "user": return HumanMessage(content=_dict["content"]) @@ -60,6 +68,14 @@ def convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage: 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} @@ -122,6 +138,8 @@ def _convert_message_chunk_to_delta(chunk: BaseMessageChunk, i: int) -> Dict[str class ChatCompletion: + """Chat completion.""" + @overload @staticmethod def create( @@ -217,7 +235,14 @@ def _has_assistant_message(session: ChatSession) -> bool: def convert_messages_for_finetuning( sessions: Iterable[ChatSession], ) -> List[List[dict]]: - """Convert messages to a list of lists of dictionaries for fine-tuning.""" + """Convert messages to a list of lists of dictionaries for fine-tuning. + + Args: + sessions: The chat sessions. + + Returns: + The list of lists of dictionaries. + """ return [ [convert_message_to_dict(s) for s in session["messages"]] for session in sessions diff --git a/libs/langchain/langchain/agents/agent_toolkits/__init__.py b/libs/langchain/langchain/agents/agent_toolkits/__init__.py index 537d5b6c250..b2e97b6f01d 100644 --- a/libs/langchain/langchain/agents/agent_toolkits/__init__.py +++ b/libs/langchain/langchain/agents/agent_toolkits/__init__.py @@ -21,9 +21,6 @@ from langchain.agents.agent_toolkits.azure_cognitive_services import ( from langchain.agents.agent_toolkits.conversational_retrieval.openai_functions import ( create_conversational_retrieval_agent, ) -from langchain.agents.agent_toolkits.conversational_retrieval.tool import ( - create_retriever_tool, -) from langchain.agents.agent_toolkits.csv.base import create_csv_agent from langchain.agents.agent_toolkits.file_management.toolkit import ( FileManagementToolkit, @@ -59,6 +56,7 @@ from langchain.agents.agent_toolkits.vectorstore.toolkit import ( ) from langchain.agents.agent_toolkits.xorbits.base import create_xorbits_agent from langchain.agents.agent_toolkits.zapier.toolkit import ZapierToolkit +from langchain.tools.retriever import create_retriever_tool __all__ = [ "AINetworkToolkit", @@ -87,7 +85,6 @@ __all__ = [ "create_pbi_agent", "create_pbi_chat_agent", "create_python_agent", - "create_retriever_tool", "create_spark_dataframe_agent", "create_spark_sql_agent", "create_sql_agent", @@ -95,4 +92,5 @@ __all__ = [ "create_vectorstore_router_agent", "create_xorbits_agent", "create_conversational_retrieval_agent", + "create_retriever_tool", ] diff --git a/libs/langchain/langchain/agents/agent_toolkits/conversational_retrieval/tool.py b/libs/langchain/langchain/agents/agent_toolkits/conversational_retrieval/tool.py index 43f0a2105d2..f7a09af8918 100644 --- a/libs/langchain/langchain/agents/agent_toolkits/conversational_retrieval/tool.py +++ b/libs/langchain/langchain/agents/agent_toolkits/conversational_retrieval/tool.py @@ -1,25 +1,3 @@ -from langchain.schema import BaseRetriever -from langchain.tools import Tool +from langchain.tools.retriever import create_retriever_tool - -def create_retriever_tool( - retriever: BaseRetriever, name: str, description: str -) -> Tool: - """Create a tool to do retrieval of documents. - - Args: - retriever: The retriever to use for the retrieval - name: The name for the tool. This will be passed to the language model, - so should be unique and somewhat descriptive. - description: The description for the tool. This will be passed to the language - model, so should be descriptive. - - Returns: - Tool class to pass to an agent - """ - return Tool( - name=name, - description=description, - func=retriever.get_relevant_documents, - coroutine=retriever.aget_relevant_documents, - ) +__all__ = ["create_retriever_tool"] diff --git a/libs/langchain/langchain/agents/format_scratchpad/xml.py b/libs/langchain/langchain/agents/format_scratchpad/xml.py index 6d02cab08bd..7e2539dae33 100644 --- a/libs/langchain/langchain/agents/format_scratchpad/xml.py +++ b/libs/langchain/langchain/agents/format_scratchpad/xml.py @@ -6,6 +6,14 @@ from langchain.schema.agent import AgentAction def format_xml( intermediate_steps: List[Tuple[AgentAction, str]], ) -> str: + """Format the intermediate steps as XML. + + Args: + intermediate_steps: The intermediate steps. + + Returns: + The intermediate steps as XML. + """ log = "" for action, observation in intermediate_steps: log += ( diff --git a/libs/langchain/langchain/cache.py b/libs/langchain/langchain/cache.py index 49a23db801c..a45c4328211 100644 --- a/libs/langchain/langchain/cache.py +++ b/libs/langchain/langchain/cache.py @@ -506,7 +506,7 @@ class RedisSemanticCache(BaseCache): index_schema=cast(Dict, self.DEFAULT_SCHEMA), ) _embedding = self.embedding.embed_query(text="test") - redis._create_index(dim=len(_embedding)) + redis._create_index_if_not_exist(dim=len(_embedding)) self._cache_dict[index_name] = redis return self._cache_dict[index_name] diff --git a/libs/langchain/langchain/callbacks/manager.py b/libs/langchain/langchain/callbacks/manager.py index 7eb50dd2b0d..ecdd7570c16 100644 --- a/libs/langchain/langchain/callbacks/manager.py +++ b/libs/langchain/langchain/callbacks/manager.py @@ -374,14 +374,25 @@ async def atrace_as_chain_group( await run_manager.on_chain_end({}) -def _handle_event( +def handle_event( handlers: List[BaseCallbackHandler], event_name: str, ignore_condition_name: Optional[str], *args: Any, **kwargs: Any, ) -> None: - """Generic event handler for CallbackManager.""" + """Generic event handler for CallbackManager. + + Note: This function is used by langserve to handle events. + + Args: + handlers: The list of handlers that will handle the event + event_name: The name of the event (e.g., "on_llm_start") + ignore_condition_name: Name of the attribute defined on handler + that if True will cause the handler to be skipped for the given event + *args: The arguments to pass to the event handler + **kwargs: The keyword arguments to pass to the event handler + """ coros: List[Coroutine[Any, Any, Any]] = [] try: @@ -398,7 +409,7 @@ def _handle_event( if event_name == "on_chat_model_start": if message_strings is None: message_strings = [get_buffer_string(m) for m in args[1]] - _handle_event( + handle_event( [handler], "on_llm_start", "ignore_llm", @@ -411,11 +422,12 @@ def _handle_event( handler_name = handler.__class__.__name__ logger.warning( f"NotImplementedError in {handler_name}.{event_name}" - f" callback: {e}" + f" callback: {repr(e)}" ) except Exception as e: logger.warning( - f"Error in {handler.__class__.__name__}.{event_name} callback: {e}" + f"Error in {handler.__class__.__name__}.{event_name} callback:" + f" {repr(e)}" ) if handler.raise_error: raise e @@ -496,24 +508,36 @@ async def _ahandle_event_for_handler( else: logger.warning( f"NotImplementedError in {handler.__class__.__name__}.{event_name}" - f" callback: {e}" + f" callback: {repr(e)}" ) except Exception as e: logger.warning( - f"Error in {handler.__class__.__name__}.{event_name} callback: {e}" + f"Error in {handler.__class__.__name__}.{event_name} callback:" + f" {repr(e)}" ) if handler.raise_error: raise e -async def _ahandle_event( +async def ahandle_event( handlers: List[BaseCallbackHandler], event_name: str, ignore_condition_name: Optional[str], *args: Any, **kwargs: Any, ) -> None: - """Generic event handler for AsyncCallbackManager.""" + """Generic event handler for AsyncCallbackManager. + + Note: This function is used by langserve to handle events. + + Args: + handlers: The list of handlers that will handle the event + event_name: The name of the event (e.g., "on_llm_start") + ignore_condition_name: Name of the attribute defined on handler + that if True will cause the handler to be skipped for the given event + *args: The arguments to pass to the event handler + **kwargs: The keyword arguments to pass to the event handler + """ for handler in [h for h in handlers if h.run_inline]: await _ahandle_event_for_handler( handler, event_name, ignore_condition_name, *args, **kwargs @@ -604,7 +628,7 @@ class RunManager(BaseRunManager): Returns: Any: The result of the callback. """ - _handle_event( + handle_event( self.handlers, "on_text", None, @@ -620,7 +644,7 @@ class RunManager(BaseRunManager): retry_state: RetryCallState, **kwargs: Any, ) -> None: - _handle_event( + handle_event( self.handlers, "on_retry", "ignore_retry", @@ -670,7 +694,7 @@ class AsyncRunManager(BaseRunManager): Returns: Any: The result of the callback. """ - await _ahandle_event( + await ahandle_event( self.handlers, "on_text", None, @@ -686,7 +710,7 @@ class AsyncRunManager(BaseRunManager): retry_state: RetryCallState, **kwargs: Any, ) -> None: - await _ahandle_event( + await ahandle_event( self.handlers, "on_retry", "ignore_retry", @@ -735,7 +759,7 @@ class CallbackManagerForLLMRun(RunManager, LLMManagerMixin): Args: token (str): The new token. """ - _handle_event( + handle_event( self.handlers, "on_llm_new_token", "ignore_llm", @@ -753,7 +777,7 @@ class CallbackManagerForLLMRun(RunManager, LLMManagerMixin): Args: response (LLMResult): The LLM result. """ - _handle_event( + handle_event( self.handlers, "on_llm_end", "ignore_llm", @@ -774,7 +798,7 @@ class CallbackManagerForLLMRun(RunManager, LLMManagerMixin): Args: error (Exception or KeyboardInterrupt): The error. """ - _handle_event( + handle_event( self.handlers, "on_llm_error", "ignore_llm", @@ -801,7 +825,7 @@ class AsyncCallbackManagerForLLMRun(AsyncRunManager, LLMManagerMixin): Args: token (str): The new token. """ - await _ahandle_event( + await ahandle_event( self.handlers, "on_llm_new_token", "ignore_llm", @@ -819,7 +843,7 @@ class AsyncCallbackManagerForLLMRun(AsyncRunManager, LLMManagerMixin): Args: response (LLMResult): The LLM result. """ - await _ahandle_event( + await ahandle_event( self.handlers, "on_llm_end", "ignore_llm", @@ -840,7 +864,7 @@ class AsyncCallbackManagerForLLMRun(AsyncRunManager, LLMManagerMixin): Args: error (Exception or KeyboardInterrupt): The error. """ - await _ahandle_event( + await ahandle_event( self.handlers, "on_llm_error", "ignore_llm", @@ -861,7 +885,7 @@ class CallbackManagerForChainRun(ParentRunManager, ChainManagerMixin): Args: outputs (Union[Dict[str, Any], Any]): The outputs of the chain. """ - _handle_event( + handle_event( self.handlers, "on_chain_end", "ignore_chain", @@ -882,7 +906,7 @@ class CallbackManagerForChainRun(ParentRunManager, ChainManagerMixin): Args: error (Exception or KeyboardInterrupt): The error. """ - _handle_event( + handle_event( self.handlers, "on_chain_error", "ignore_chain", @@ -902,7 +926,7 @@ class CallbackManagerForChainRun(ParentRunManager, ChainManagerMixin): Returns: Any: The result of the callback. """ - _handle_event( + handle_event( self.handlers, "on_agent_action", "ignore_agent", @@ -922,7 +946,7 @@ class CallbackManagerForChainRun(ParentRunManager, ChainManagerMixin): Returns: Any: The result of the callback. """ - _handle_event( + handle_event( self.handlers, "on_agent_finish", "ignore_agent", @@ -945,7 +969,7 @@ class AsyncCallbackManagerForChainRun(AsyncParentRunManager, ChainManagerMixin): Args: outputs (Union[Dict[str, Any], Any]): The outputs of the chain. """ - await _ahandle_event( + await ahandle_event( self.handlers, "on_chain_end", "ignore_chain", @@ -966,7 +990,7 @@ class AsyncCallbackManagerForChainRun(AsyncParentRunManager, ChainManagerMixin): Args: error (Exception or KeyboardInterrupt): The error. """ - await _ahandle_event( + await ahandle_event( self.handlers, "on_chain_error", "ignore_chain", @@ -986,7 +1010,7 @@ class AsyncCallbackManagerForChainRun(AsyncParentRunManager, ChainManagerMixin): Returns: Any: The result of the callback. """ - await _ahandle_event( + await ahandle_event( self.handlers, "on_agent_action", "ignore_agent", @@ -1006,7 +1030,7 @@ class AsyncCallbackManagerForChainRun(AsyncParentRunManager, ChainManagerMixin): Returns: Any: The result of the callback. """ - await _ahandle_event( + await ahandle_event( self.handlers, "on_agent_finish", "ignore_agent", @@ -1031,7 +1055,7 @@ class CallbackManagerForToolRun(ParentRunManager, ToolManagerMixin): Args: output (str): The output of the tool. """ - _handle_event( + handle_event( self.handlers, "on_tool_end", "ignore_agent", @@ -1052,7 +1076,7 @@ class CallbackManagerForToolRun(ParentRunManager, ToolManagerMixin): Args: error (Exception or KeyboardInterrupt): The error. """ - _handle_event( + handle_event( self.handlers, "on_tool_error", "ignore_agent", @@ -1073,7 +1097,7 @@ class AsyncCallbackManagerForToolRun(AsyncParentRunManager, ToolManagerMixin): Args: output (str): The output of the tool. """ - await _ahandle_event( + await ahandle_event( self.handlers, "on_tool_end", "ignore_agent", @@ -1094,7 +1118,7 @@ class AsyncCallbackManagerForToolRun(AsyncParentRunManager, ToolManagerMixin): Args: error (Exception or KeyboardInterrupt): The error. """ - await _ahandle_event( + await ahandle_event( self.handlers, "on_tool_error", "ignore_agent", @@ -1115,7 +1139,7 @@ class CallbackManagerForRetrieverRun(ParentRunManager, RetrieverManagerMixin): **kwargs: Any, ) -> None: """Run when retriever ends running.""" - _handle_event( + handle_event( self.handlers, "on_retriever_end", "ignore_retriever", @@ -1132,7 +1156,7 @@ class CallbackManagerForRetrieverRun(ParentRunManager, RetrieverManagerMixin): **kwargs: Any, ) -> None: """Run when retriever errors.""" - _handle_event( + handle_event( self.handlers, "on_retriever_error", "ignore_retriever", @@ -1154,7 +1178,7 @@ class AsyncCallbackManagerForRetrieverRun( self, documents: Sequence[Document], **kwargs: Any ) -> None: """Run when retriever ends running.""" - await _ahandle_event( + await ahandle_event( self.handlers, "on_retriever_end", "ignore_retriever", @@ -1171,7 +1195,7 @@ class AsyncCallbackManagerForRetrieverRun( **kwargs: Any, ) -> None: """Run when retriever errors.""" - await _ahandle_event( + await ahandle_event( self.handlers, "on_retriever_error", "ignore_retriever", @@ -1206,7 +1230,7 @@ class CallbackManager(BaseCallbackManager): managers = [] for prompt in prompts: run_id_ = uuid.uuid4() - _handle_event( + handle_event( self.handlers, "on_llm_start", "ignore_llm", @@ -1255,7 +1279,7 @@ class CallbackManager(BaseCallbackManager): managers = [] for message_list in messages: run_id_ = uuid.uuid4() - _handle_event( + handle_event( self.handlers, "on_chat_model_start", "ignore_chat_model", @@ -1302,7 +1326,7 @@ class CallbackManager(BaseCallbackManager): """ if run_id is None: run_id = uuid.uuid4() - _handle_event( + handle_event( self.handlers, "on_chain_start", "ignore_chain", @@ -1348,7 +1372,7 @@ class CallbackManager(BaseCallbackManager): if run_id is None: run_id = uuid.uuid4() - _handle_event( + handle_event( self.handlers, "on_tool_start", "ignore_agent", @@ -1384,7 +1408,7 @@ class CallbackManager(BaseCallbackManager): if run_id is None: run_id = uuid.uuid4() - _handle_event( + handle_event( self.handlers, "on_retriever_start", "ignore_retriever", @@ -1529,7 +1553,7 @@ class AsyncCallbackManager(BaseCallbackManager): run_id_ = uuid.uuid4() tasks.append( - _ahandle_event( + ahandle_event( self.handlers, "on_llm_start", "ignore_llm", @@ -1585,7 +1609,7 @@ class AsyncCallbackManager(BaseCallbackManager): run_id_ = uuid.uuid4() tasks.append( - _ahandle_event( + ahandle_event( self.handlers, "on_chat_model_start", "ignore_chat_model", @@ -1636,7 +1660,7 @@ class AsyncCallbackManager(BaseCallbackManager): if run_id is None: run_id = uuid.uuid4() - await _ahandle_event( + await ahandle_event( self.handlers, "on_chain_start", "ignore_chain", @@ -1684,7 +1708,7 @@ class AsyncCallbackManager(BaseCallbackManager): if run_id is None: run_id = uuid.uuid4() - await _ahandle_event( + await ahandle_event( self.handlers, "on_tool_start", "ignore_agent", @@ -1720,7 +1744,7 @@ class AsyncCallbackManager(BaseCallbackManager): if run_id is None: run_id = uuid.uuid4() - await _ahandle_event( + await ahandle_event( self.handlers, "on_retriever_start", "ignore_retriever", @@ -1892,6 +1916,7 @@ def _configure( callback_manager = callback_manager_cls( handlers=inheritable_callbacks_.copy(), inheritable_handlers=inheritable_callbacks_.copy(), + parent_run_id=parent_run_id, ) else: callback_manager = callback_manager_cls( @@ -1933,8 +1958,12 @@ def _configure( tracing_v2_enabled_ = ( env_var_is_set("LANGCHAIN_TRACING_V2") or tracer_v2 is not None ) - tracer_project = os.environ.get( - "LANGCHAIN_PROJECT", os.environ.get("LANGCHAIN_SESSION", "default") + tracer_project = getattr( + run_tree, + "session_name", + os.environ.get( + "LANGCHAIN_PROJECT", os.environ.get("LANGCHAIN_SESSION", "default") + ), ) run_collector_ = run_collector_var.get() debug = _get_debug() diff --git a/libs/langchain/langchain/callbacks/openai_info.py b/libs/langchain/langchain/callbacks/openai_info.py index cfd59f8fc84..6e9c6e1c7e5 100644 --- a/libs/langchain/langchain/callbacks/openai_info.py +++ b/libs/langchain/langchain/callbacks/openai_info.py @@ -57,10 +57,27 @@ MODEL_COST_PER_1K_TOKENS = { "text-davinci-003": 0.02, "text-davinci-002": 0.02, "code-davinci-002": 0.02, - "ada-finetuned": 0.0016, - "babbage-finetuned": 0.0024, - "curie-finetuned": 0.012, - "davinci-finetuned": 0.12, + # Fine Tuned input + "babbage-002-finetuned": 0.0016, + "davinci-002-finetuned": 0.012, + "gpt-3.5-turbo-0613-finetuned": 0.012, + # Fine Tuned output + "babbage-002-finetuned-completion": 0.0016, + "davinci-002-finetuned-completion": 0.012, + "gpt-3.5-turbo-0613-finetuned-completion": 0.016, + # Azure Fine Tuned input + "babbage-002-azure-finetuned": 0.0004, + "davinci-002-azure-finetuned": 0.002, + "gpt-35-turbo-0613-azure-finetuned": 0.0015, + # Azure Fine Tuned output + "babbage-002-azure-finetuned-completion": 0.0004, + "davinci-002-azure-finetuned-completion": 0.002, + "gpt-35-turbo-0613-azure-finetuned-completion": 0.002, + # Legacy fine-tuned models + "ada-finetuned-legacy": 0.0016, + "babbage-finetuned-legacy": 0.0024, + "curie-finetuned-legacy": 0.012, + "davinci-finetuned-legacy": 0.12, } @@ -81,12 +98,17 @@ def standardize_model_name( """ model_name = model_name.lower() - if "ft-" in model_name: - return model_name.split(":")[0] + "-finetuned" + if ".ft-" in model_name: + return model_name.split(".ft-")[0] + "-azure-finetuned" + if ":ft-" in model_name: + return model_name.split(":")[0] + "-finetuned-legacy" + if "ft:" in model_name: + return model_name.split(":")[1] + "-finetuned" elif is_completion and ( model_name.startswith("gpt-4") or model_name.startswith("gpt-3.5") or model_name.startswith("gpt-35") + or ("finetuned" in model_name and "legacy" not in model_name) ): return model_name + "-completion" else: diff --git a/libs/langchain/langchain/callbacks/tracers/langchain.py b/libs/langchain/langchain/callbacks/tracers/langchain.py index 4091eeae692..39cb26177e1 100644 --- a/libs/langchain/langchain/callbacks/tracers/langchain.py +++ b/libs/langchain/langchain/callbacks/tracers/langchain.py @@ -64,6 +64,16 @@ def _get_executor() -> ThreadPoolExecutor: return _EXECUTOR +def _copy(run: Run) -> Run: + """Copy a run.""" + try: + return run.copy(deep=True) + except TypeError: + # Fallback in case the object contains a lock or other + # non-pickleable object + return run.copy() + + class LangChainTracer(BaseTracer): """An implementation of the SharedTracer that POSTS to the langchain endpoint.""" @@ -192,63 +202,63 @@ class LangChainTracer(BaseTracer): """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.copy(deep=True)) + self._submit(self._persist_run_single, _copy(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.copy(deep=True)) + self._submit(self._persist_run_single, _copy(run)) def _on_llm_end(self, run: Run) -> None: """Process the LLM Run.""" - self._submit(self._update_run_single, run.copy(deep=True)) + self._submit(self._update_run_single, _copy(run)) def _on_llm_error(self, run: Run) -> None: """Process the LLM Run upon error.""" - self._submit(self._update_run_single, run.copy(deep=True)) + self._submit(self._update_run_single, _copy(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.copy(deep=True)) + self._submit(self._persist_run_single, _copy(run)) def _on_chain_end(self, run: Run) -> None: """Process the Chain Run.""" - self._submit(self._update_run_single, run.copy(deep=True)) + self._submit(self._update_run_single, _copy(run)) def _on_chain_error(self, run: Run) -> None: """Process the Chain Run upon error.""" - self._submit(self._update_run_single, run.copy(deep=True)) + self._submit(self._update_run_single, _copy(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.copy(deep=True)) + self._submit(self._persist_run_single, _copy(run)) def _on_tool_end(self, run: Run) -> None: """Process the Tool Run.""" - self._submit(self._update_run_single, run.copy(deep=True)) + self._submit(self._update_run_single, _copy(run)) def _on_tool_error(self, run: Run) -> None: """Process the Tool Run upon error.""" - self._submit(self._update_run_single, run.copy(deep=True)) + self._submit(self._update_run_single, _copy(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.copy(deep=True)) + self._submit(self._persist_run_single, _copy(run)) def _on_retriever_end(self, run: Run) -> None: """Process the Retriever Run.""" - self._submit(self._update_run_single, run.copy(deep=True)) + self._submit(self._update_run_single, _copy(run)) def _on_retriever_error(self, run: Run) -> None: """Process the Retriever Run upon error.""" - self._submit(self._update_run_single, run.copy(deep=True)) + self._submit(self._update_run_single, _copy(run)) def wait_for_futures(self) -> None: """Wait for the given futures to complete.""" diff --git a/libs/langchain/langchain/callbacks/tracers/log_stream.py b/libs/langchain/langchain/callbacks/tracers/log_stream.py index c74074fc4f0..d45b576a542 100644 --- a/libs/langchain/langchain/callbacks/tracers/log_stream.py +++ b/libs/langchain/langchain/callbacks/tracers/log_stream.py @@ -237,7 +237,7 @@ class LogStreamCallbackHandler(BaseTracer): name=run.name, type=run.run_type, tags=run.tags or [], - metadata=run.extra.get("metadata", {}), + metadata=(run.extra or {}).get("metadata", {}), start_time=run.start_time.isoformat(timespec="milliseconds"), streamed_output_str=[], final_output=None, @@ -266,7 +266,9 @@ class LogStreamCallbackHandler(BaseTracer): { "op": "add", "path": f"/logs/{index}/end_time", - "value": run.end_time.isoformat(timespec="milliseconds"), + "value": run.end_time.isoformat(timespec="milliseconds") + if run.end_time is not None + else None, }, ) ) diff --git a/libs/langchain/langchain/callbacks/tracers/schemas.py b/libs/langchain/langchain/callbacks/tracers/schemas.py index b08b454a91f..4db455be2ea 100644 --- a/libs/langchain/langchain/callbacks/tracers/schemas.py +++ b/libs/langchain/langchain/callbacks/tracers/schemas.py @@ -3,7 +3,7 @@ from __future__ import annotations import datetime import warnings -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List, Optional, Type from uuid import UUID from langsmith.schemas import RunBase as BaseRunV2 @@ -13,7 +13,7 @@ from langchain.pydantic_v1 import BaseModel, Field, root_validator from langchain.schema import LLMResult -def RunTypeEnum() -> RunTypeEnumDep: +def RunTypeEnum() -> Type[RunTypeEnumDep]: """RunTypeEnum.""" warnings.warn( "RunTypeEnum is deprecated. Please directly use a string instead" @@ -106,6 +106,7 @@ class Run(BaseRunV2): child_execution_order: int child_runs: List[Run] = Field(default_factory=list) tags: Optional[List[str]] = Field(default_factory=list) + events: List[Dict[str, Any]] = Field(default_factory=list) @root_validator(pre=True) def assign_name(cls, values: dict) -> dict: @@ -115,6 +116,8 @@ class Run(BaseRunV2): values["name"] = values["serialized"]["name"] elif "id" in values["serialized"]: values["name"] = values["serialized"]["id"][-1] + if values.get("events") is None: + values["events"] = [] return values diff --git a/libs/langchain/langchain/callbacks/tracers/wandb.py b/libs/langchain/langchain/callbacks/tracers/wandb.py index 05f417723d6..dcc35636960 100644 --- a/libs/langchain/langchain/callbacks/tracers/wandb.py +++ b/libs/langchain/langchain/callbacks/tracers/wandb.py @@ -27,7 +27,9 @@ if TYPE_CHECKING: PRINT_WARNINGS = True -def _serialize_io(run_inputs: dict) -> dict: +def _serialize_io(run_inputs: Optional[dict]) -> dict: + if not run_inputs: + return {} from google.protobuf.json_format import MessageToJson from google.protobuf.message import Message @@ -79,7 +81,9 @@ class RunProcessor: span_id=str(run.id) if run.id is not None else None, name=run.name, start_time_ms=int(run.start_time.timestamp() * 1000), - end_time_ms=int(run.end_time.timestamp() * 1000), + end_time_ms=int(run.end_time.timestamp() * 1000) + if run.end_time is not None + else None, status_code=self.trace_tree.StatusCode.SUCCESS if run.error is None else self.trace_tree.StatusCode.ERROR, @@ -95,7 +99,7 @@ class RunProcessor: base_span = self._convert_run_to_wb_span(run) if base_span.attributes is None: base_span.attributes = {} - base_span.attributes["llm_output"] = run.outputs.get("llm_output", {}) + base_span.attributes["llm_output"] = (run.outputs or {}).get("llm_output", {}) base_span.results = [ self.trace_tree.Result( diff --git a/libs/langchain/langchain/chains/graph_qa/cypher.py b/libs/langchain/langchain/chains/graph_qa/cypher.py index abbc5a85c34..3531d517f90 100644 --- a/libs/langchain/langchain/chains/graph_qa/cypher.py +++ b/libs/langchain/langchain/chains/graph_qa/cypher.py @@ -132,13 +132,15 @@ class GraphCypherQAChain(Chain): cls, llm: Optional[BaseLanguageModel] = None, *, - qa_prompt: BasePromptTemplate = CYPHER_QA_PROMPT, - cypher_prompt: BasePromptTemplate = CYPHER_GENERATION_PROMPT, + qa_prompt: Optional[BasePromptTemplate] = None, + cypher_prompt: Optional[BasePromptTemplate] = None, cypher_llm: Optional[BaseLanguageModel] = None, qa_llm: Optional[BaseLanguageModel] = None, exclude_types: List[str] = [], include_types: List[str] = [], validate_cypher: bool = False, + qa_llm_kwargs: Optional[Dict[str, Any]] = None, + cypher_llm_kwargs: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> GraphCypherQAChain: """Initialize from LLM.""" @@ -152,9 +154,34 @@ class GraphCypherQAChain(Chain): "You can specify up to two of 'cypher_llm', 'qa_llm'" ", and 'llm', but not all three simultaneously." ) + if cypher_prompt and cypher_llm_kwargs: + raise ValueError( + "Specifying cypher_prompt and cypher_llm_kwargs together is" + " not allowed. Please pass prompt via cypher_llm_kwargs." + ) + if qa_prompt and qa_llm_kwargs: + raise ValueError( + "Specifying qa_prompt and qa_llm_kwargs together is" + " not allowed. Please pass prompt via qa_llm_kwargs." + ) + use_qa_llm_kwargs = qa_llm_kwargs if qa_llm_kwargs is not None else {} + use_cypher_llm_kwargs = ( + cypher_llm_kwargs if cypher_llm_kwargs is not None else {} + ) + if "prompt" not in use_qa_llm_kwargs: + use_qa_llm_kwargs["prompt"] = ( + qa_prompt if qa_prompt is not None else CYPHER_QA_PROMPT + ) + if "prompt" not in use_cypher_llm_kwargs: + use_cypher_llm_kwargs["prompt"] = ( + cypher_prompt if cypher_prompt is not None else CYPHER_GENERATION_PROMPT + ) - qa_chain = LLMChain(llm=qa_llm or llm, prompt=qa_prompt) - cypher_generation_chain = LLMChain(llm=cypher_llm or llm, prompt=cypher_prompt) + qa_chain = LLMChain(llm=qa_llm or llm, **use_qa_llm_kwargs) + + cypher_generation_chain = LLMChain( + llm=cypher_llm or llm, **use_cypher_llm_kwargs + ) if exclude_types and include_types: raise ValueError( diff --git a/libs/langchain/langchain/chains/openai_functions/extraction.py b/libs/langchain/langchain/chains/openai_functions/extraction.py index 7f52dd7baf6..c13d3ecca30 100644 --- a/libs/langchain/langchain/chains/openai_functions/extraction.py +++ b/libs/langchain/langchain/chains/openai_functions/extraction.py @@ -47,6 +47,7 @@ def create_extraction_chain( schema: dict, llm: BaseLanguageModel, prompt: Optional[BasePromptTemplate] = None, + tags: Optional[List[str]] = None, verbose: bool = False, ) -> Chain: """Creates a chain that extracts information from a passage. @@ -71,6 +72,7 @@ def create_extraction_chain( prompt=extraction_prompt, llm_kwargs=llm_kwargs, output_parser=output_parser, + tags=tags, verbose=verbose, ) return chain diff --git a/libs/langchain/langchain/chains/query_constructor/base.py b/libs/langchain/langchain/chains/query_constructor/base.py index 5bd58811dd9..7c91b3fa059 100644 --- a/libs/langchain/langchain/chains/query_constructor/base.py +++ b/libs/langchain/langchain/chains/query_constructor/base.py @@ -108,6 +108,17 @@ def fix_filter_directive( allowed_operators: Optional[Sequence[Operator]] = None, allowed_attributes: Optional[Sequence[str]] = None, ) -> Optional[FilterDirective]: + """Fix invalid filter directive. + + Args: + filter: Filter directive to fix. + allowed_comparators: allowed comparators. Defaults to all comparators. + allowed_operators: allowed operators. Defaults to all operators. + allowed_attributes: allowed attributes. Defaults to all attributes. + + Returns: + Fixed filter directive. + """ if ( not (allowed_comparators or allowed_operators or allowed_attributes) ) or not filter: @@ -154,6 +165,14 @@ def _format_attribute_info(info: Sequence[Union[AttributeInfo, dict]]) -> str: def construct_examples(input_output_pairs: Sequence[Tuple[str, dict]]) -> List[dict]: + """Construct examples from input-output pairs. + + Args: + input_output_pairs: Sequence of input-output pairs. + + Returns: + List of examples. + """ examples = [] for i, (_input, output) in enumerate(input_output_pairs): structured_request = ( @@ -192,6 +211,9 @@ def get_query_constructor_prompt( schema_prompt: Prompt for describing query schema. Should have string input variables allowed_comparators and allowed_operators. **kwargs: Additional named params to pass to FewShotPromptTemplate init. + + Returns: + A prompt template that can be used to construct queries. """ default_schema_prompt = ( SCHEMA_WITH_LIMIT_PROMPT if enable_limit else DEFAULT_SCHEMA_PROMPT diff --git a/libs/langchain/langchain/chains/question_answering/stuff_prompt.py b/libs/langchain/langchain/chains/question_answering/stuff_prompt.py index 856907f63ed..982ccc35d29 100644 --- a/libs/langchain/langchain/chains/question_answering/stuff_prompt.py +++ b/libs/langchain/langchain/chains/question_answering/stuff_prompt.py @@ -17,7 +17,7 @@ PROMPT = PromptTemplate( template=prompt_template, input_variables=["context", "question"] ) -system_template = """Use the following pieces of context to answer the users question. +system_template = """Use the following pieces of context to answer the user's question. If you don't know the answer, just say that you don't know, don't try to make up an answer. ---------------- {context}""" diff --git a/libs/langchain/langchain/chains/sequential.py b/libs/langchain/langchain/chains/sequential.py index 934c3a9eee8..35461013ce4 100644 --- a/libs/langchain/langchain/chains/sequential.py +++ b/libs/langchain/langchain/chains/sequential.py @@ -52,7 +52,7 @@ class SequentialChain(Chain): if set(input_variables).intersection(set(memory_keys)): overlapping_keys = set(input_variables) & set(memory_keys) raise ValueError( - f"The the input key(s) {''.join(overlapping_keys)} are found " + f"The input key(s) {''.join(overlapping_keys)} are found " f"in the Memory keys ({memory_keys}) - please use input and " f"memory keys that don't overlap." ) diff --git a/libs/langchain/langchain/chat_loaders/langsmith.py b/libs/langchain/langchain/chat_loaders/langsmith.py index db9b85ea858..513ea3644c0 100644 --- a/libs/langchain/langchain/chat_loaders/langsmith.py +++ b/libs/langchain/langchain/chat_loaders/langsmith.py @@ -1,7 +1,7 @@ from __future__ import annotations import logging -from typing import TYPE_CHECKING, Dict, Iterable, Iterator, List, Optional, Union +from typing import TYPE_CHECKING, Dict, Iterable, Iterator, List, Optional, Union, cast from langchain.chat_loaders.base import BaseChatLoader from langchain.load import load @@ -79,7 +79,7 @@ class LangSmithRunChatLoader(BaseChatLoader): """ if llm_run.run_type != "llm": raise ValueError(f"Expected run of type llm. Got: {llm_run.run_type}") - return llm_run.extra.get("invocation_params", {}).get("functions") + return (llm_run.extra or {}).get("invocation_params", {}).get("functions") def lazy_load(self) -> Iterator[ChatSession]: """ @@ -90,13 +90,15 @@ class LangSmithRunChatLoader(BaseChatLoader): :return: Iterator of chat sessions containing messages. """ + from langsmith.schemas import Run + for run_obj in self.runs: try: if hasattr(run_obj, "id"): run = run_obj else: run = self.client.read_run(run_obj) - session = self._load_single_chat_session(run) + session = self._load_single_chat_session(cast(Run, run)) yield session except ValueError as e: logger.warning(f"Could not load run {run_obj}: {repr(e)}") diff --git a/libs/langchain/langchain/chat_models/__init__.py b/libs/langchain/langchain/chat_models/__init__.py index 9dc30fe9217..2471fe1c3f2 100644 --- a/libs/langchain/langchain/chat_models/__init__.py +++ b/libs/langchain/langchain/chat_models/__init__.py @@ -28,6 +28,7 @@ from langchain.chat_models.ernie import ErnieBotChat from langchain.chat_models.everlyai import ChatEverlyAI from langchain.chat_models.fake import FakeListChatModel from langchain.chat_models.fireworks import ChatFireworks +from langchain.chat_models.gigachat import GigaChat from langchain.chat_models.google_palm import ChatGooglePalm from langchain.chat_models.human import HumanInputChatModel from langchain.chat_models.hunyuan import ChatHunyuan @@ -71,4 +72,5 @@ __all__ = [ "ChatYandexGPT", "ChatBaichuan", "ChatHunyuan", + "GigaChat", ] diff --git a/libs/langchain/langchain/chat_models/azureml_endpoint.py b/libs/langchain/langchain/chat_models/azureml_endpoint.py index bd56d782ad8..4f40f8acf6d 100644 --- a/libs/langchain/langchain/chat_models/azureml_endpoint.py +++ b/libs/langchain/langchain/chat_models/azureml_endpoint.py @@ -76,7 +76,7 @@ class LlamaContentFormatter(ContentFormatterBase): return self.format_request_payload(prompt=prompt, model_kwargs=model_kwargs) def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes: - """Formats the request according the the chosen api""" + """Formats the request according to the chosen api""" return str.encode(prompt) def format_response_payload(self, output: bytes) -> str: @@ -118,7 +118,7 @@ class AzureMLChatOnlineEndpoint(SimpleChatModel): @validator("http_client", always=True, allow_reuse=True) @classmethod def validate_client(cls, field_value: Any, values: Dict) -> AzureMLEndpointClient: - """Validate that api key and python package exists in environment.""" + """Validate that api key and python package exist in environment.""" endpoint_key = get_from_dict_or_env( values, "endpoint_api_key", "AZUREML_ENDPOINT_API_KEY" ) diff --git a/libs/langchain/langchain/chat_models/baichuan.py b/libs/langchain/langchain/chat_models/baichuan.py index 91be5c38cf3..b0cb78a119b 100644 --- a/libs/langchain/langchain/chat_models/baichuan.py +++ b/libs/langchain/langchain/chat_models/baichuan.py @@ -28,6 +28,8 @@ from langchain.utils import get_from_dict_or_env, get_pydantic_field_names logger = logging.getLogger(__name__) +DEFAULT_API_BASE = "https://api.baichuan-ai.com/v1" + def _convert_message_to_dict(message: BaseMessage) -> dict: message_dict: Dict[str, Any] @@ -101,7 +103,7 @@ class ChatBaichuan(BaseChatModel): def lc_serializable(self) -> bool: return True - baichuan_api_base: str = "https://api.baichuan-ai.com" + baichuan_api_base: str = Field(default=DEFAULT_API_BASE) """Baichuan custom endpoints""" baichuan_api_key: Optional[str] = None """Baichuan API Key""" @@ -162,6 +164,7 @@ class ChatBaichuan(BaseChatModel): values, "baichuan_api_base", "BAICHUAN_API_BASE", + DEFAULT_API_BASE, ) values["baichuan_api_key"] = get_from_dict_or_env( values, @@ -252,7 +255,7 @@ class ChatBaichuan(BaseChatModel): timestamp = int(time.time()) - url = f"{self.baichuan_api_base}/v1" + url = self.baichuan_api_base if self.streaming: url = f"{url}/stream" url = f"{url}/chat" diff --git a/libs/langchain/langchain/chat_models/baidu_qianfan_endpoint.py b/libs/langchain/langchain/chat_models/baidu_qianfan_endpoint.py index e9526f06347..58602086826 100644 --- a/libs/langchain/langchain/chat_models/baidu_qianfan_endpoint.py +++ b/libs/langchain/langchain/chat_models/baidu_qianfan_endpoint.py @@ -169,7 +169,7 @@ class QianfanChatEndpoint(BaseChatModel): @property def _default_params(self) -> Dict[str, Any]: - """Get the default parameters for calling OpenAI API.""" + """Get the default parameters for calling Qianfan API.""" normal_params = { "model": self.model, "endpoint": self.endpoint, diff --git a/libs/langchain/langchain/chat_models/cohere.py b/libs/langchain/langchain/chat_models/cohere.py index 1b30942f344..19eaffc685a 100644 --- a/libs/langchain/langchain/chat_models/cohere.py +++ b/libs/langchain/langchain/chat_models/cohere.py @@ -22,6 +22,17 @@ from langchain.schema.output import ChatGeneration, ChatGenerationChunk, ChatRes def get_role(message: BaseMessage) -> str: + """Get the role of the message. + + Args: + message: The message. + + Returns: + The role of the message. + + Raises: + ValueError: If the message is of an unknown type. + """ if isinstance(message, ChatMessage) or isinstance(message, HumanMessage): return "User" elif isinstance(message, AIMessage): @@ -38,6 +49,16 @@ def get_cohere_chat_request( connectors: Optional[List[Dict[str, str]]] = None, **kwargs: Any, ) -> Dict[str, Any]: + """Get the request for the Cohere chat API. + + Args: + messages: The messages. + connectors: The connectors. + **kwargs: The keyword arguments. + + Returns: + The request for the Cohere chat API. + """ documents = ( None if "source_documents" not in kwargs diff --git a/libs/langchain/langchain/chat_models/fireworks.py b/libs/langchain/langchain/chat_models/fireworks.py index 841ee8a0d84..ce0ce83b591 100644 --- a/libs/langchain/langchain/chat_models/fireworks.py +++ b/libs/langchain/langchain/chat_models/fireworks.py @@ -89,6 +89,15 @@ class ChatFireworks(BaseChatModel): ) fireworks_api_key: Optional[str] = None max_retries: int = 20 + use_retry: bool = True + + @property + def lc_secrets(self) -> Dict[str, str]: + return {"fireworks_api_key": "FIREWORKS_API_KEY"} + + @classmethod + def is_lc_serializable(cls) -> bool: + return True @root_validator() def validate_environment(cls, values: Dict) -> Dict: @@ -126,7 +135,11 @@ class ChatFireworks(BaseChatModel): **self.model_kwargs, } response = completion_with_retry( - self, run_manager=run_manager, stop=stop, **params + self, + self.use_retry, + run_manager=run_manager, + stop=stop, + **params, ) return self._create_chat_result(response) @@ -144,7 +157,7 @@ class ChatFireworks(BaseChatModel): **self.model_kwargs, } response = await acompletion_with_retry( - self, run_manager=run_manager, stop=stop, **params + self, self.use_retry, run_manager=run_manager, stop=stop, **params ) return self._create_chat_result(response) @@ -187,7 +200,7 @@ class ChatFireworks(BaseChatModel): **self.model_kwargs, } for chunk in completion_with_retry( - self, run_manager=run_manager, stop=stop, **params + self, self.use_retry, run_manager=run_manager, stop=stop, **params ): choice = chunk.choices[0] chunk = _convert_delta_to_message_chunk(choice.delta, default_chunk_class) @@ -216,7 +229,7 @@ class ChatFireworks(BaseChatModel): **self.model_kwargs, } async for chunk in await acompletion_with_retry_streaming( - self, run_manager=run_manager, stop=stop, **params + self, self.use_retry, run_manager=run_manager, stop=stop, **params ): choice = chunk.choices[0] chunk = _convert_delta_to_message_chunk(choice.delta, default_chunk_class) @@ -230,8 +243,20 @@ class ChatFireworks(BaseChatModel): await run_manager.on_llm_new_token(token=chunk.content, chunk=chunk) +def conditional_decorator( + condition: bool, decorator: Callable[[Any], Any] +) -> Callable[[Any], Any]: + def actual_decorator(func: Callable[[Any], Any]) -> Callable[[Any], Any]: + if condition: + return decorator(func) + return func + + return actual_decorator + + def completion_with_retry( llm: ChatFireworks, + use_retry: bool, *, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, @@ -241,7 +266,7 @@ def completion_with_retry( retry_decorator = _create_retry_decorator(llm, run_manager=run_manager) - @retry_decorator + @conditional_decorator(use_retry, retry_decorator) def _completion_with_retry(**kwargs: Any) -> Any: return fireworks.client.ChatCompletion.create( **kwargs, @@ -252,6 +277,7 @@ def completion_with_retry( async def acompletion_with_retry( llm: ChatFireworks, + use_retry: bool, *, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, @@ -261,7 +287,7 @@ async def acompletion_with_retry( retry_decorator = _create_retry_decorator(llm, run_manager=run_manager) - @retry_decorator + @conditional_decorator(use_retry, retry_decorator) async def _completion_with_retry(**kwargs: Any) -> Any: return await fireworks.client.ChatCompletion.acreate( **kwargs, @@ -272,6 +298,7 @@ async def acompletion_with_retry( async def acompletion_with_retry_streaming( llm: ChatFireworks, + use_retry: bool, *, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, @@ -281,7 +308,7 @@ async def acompletion_with_retry_streaming( retry_decorator = _create_retry_decorator(llm, run_manager=run_manager) - @retry_decorator + @conditional_decorator(use_retry, retry_decorator) async def _completion_with_retry(**kwargs: Any) -> Any: return fireworks.client.ChatCompletion.acreate( **kwargs, @@ -301,6 +328,8 @@ def _create_retry_decorator( errors = [ fireworks.client.error.RateLimitError, + fireworks.client.error.InternalServerError, + fireworks.client.error.BadGatewayError, fireworks.client.error.ServiceUnavailableError, ] return create_base_retry_decorator( diff --git a/libs/langchain/langchain/chat_models/gigachat.py b/libs/langchain/langchain/chat_models/gigachat.py new file mode 100644 index 00000000000..2b8ac7f2133 --- /dev/null +++ b/libs/langchain/langchain/chat_models/gigachat.py @@ -0,0 +1,179 @@ +import logging +from typing import Any, AsyncIterator, Iterator, List, Optional + +from langchain.callbacks.manager import ( + AsyncCallbackManagerForLLMRun, + CallbackManagerForLLMRun, +) +from langchain.chat_models.base import ( + BaseChatModel, + _agenerate_from_stream, + _generate_from_stream, +) +from langchain.llms.gigachat import _BaseGigaChat +from langchain.schema import ChatResult +from langchain.schema.messages import ( + AIMessage, + AIMessageChunk, + BaseMessage, + ChatMessage, + HumanMessage, + SystemMessage, +) +from langchain.schema.output import ChatGeneration, ChatGenerationChunk + +logger = logging.getLogger(__name__) + + +def _convert_dict_to_message(message: Any) -> BaseMessage: + from gigachat.models import MessagesRole + + if message.role == MessagesRole.SYSTEM: + return SystemMessage(content=message.content) + elif message.role == MessagesRole.USER: + return HumanMessage(content=message.content) + elif message.role == MessagesRole.ASSISTANT: + return AIMessage(content=message.content) + else: + raise TypeError(f"Got unknown role {message.role} {message}") + + +def _convert_message_to_dict(message: BaseMessage) -> Any: + from gigachat.models import Messages, MessagesRole + + if isinstance(message, SystemMessage): + return Messages(role=MessagesRole.SYSTEM, content=message.content) + elif isinstance(message, HumanMessage): + return Messages(role=MessagesRole.USER, content=message.content) + elif isinstance(message, AIMessage): + return Messages(role=MessagesRole.ASSISTANT, content=message.content) + elif isinstance(message, ChatMessage): + return Messages(role=MessagesRole(message.role), content=message.content) + else: + raise TypeError(f"Got unknown type {message}") + + +class GigaChat(_BaseGigaChat, BaseChatModel): + """`GigaChat` large language models API. + + To use, you should pass login and password to access GigaChat API or use token. + + Example: + .. code-block:: python + + from langchain.chat_models import GigaChat + giga = GigaChat(credentials=..., verify_ssl_certs=False) + """ + + def _build_payload(self, messages: List[BaseMessage]) -> Any: + from gigachat.models import Chat + + payload = Chat( + messages=[_convert_message_to_dict(m) for m in messages], + profanity_check=self.profanity, + ) + if self.temperature is not None: + payload.temperature = self.temperature + if self.max_tokens is not None: + payload.max_tokens = self.max_tokens + + if self.verbose: + logger.info("Giga request: %s", payload.dict()) + + return payload + + def _create_chat_result(self, response: Any) -> ChatResult: + generations = [] + for res in response.choices: + message = _convert_dict_to_message(res.message) + finish_reason = res.finish_reason + gen = ChatGeneration( + message=message, + generation_info={"finish_reason": finish_reason}, + ) + generations.append(gen) + if finish_reason != "stop": + logger.warning( + "Giga generation stopped with reason: %s", + finish_reason, + ) + if self.verbose: + logger.info("Giga response: %s", message.content) + llm_output = {"token_usage": response.usage, "model_name": response.model} + return ChatResult(generations=generations, llm_output=llm_output) + + def _generate( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + stream: Optional[bool] = None, + **kwargs: Any, + ) -> ChatResult: + should_stream = stream if stream is not None else self.streaming + if should_stream: + stream_iter = self._stream( + messages, stop=stop, run_manager=run_manager, **kwargs + ) + return _generate_from_stream(stream_iter) + + payload = self._build_payload(messages) + response = self._client.chat(payload) + + return self._create_chat_result(response) + + async def _agenerate( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + stream: Optional[bool] = None, + **kwargs: Any, + ) -> ChatResult: + should_stream = stream if stream is not None else self.streaming + if should_stream: + stream_iter = self._astream( + messages, stop=stop, run_manager=run_manager, **kwargs + ) + return await _agenerate_from_stream(stream_iter) + + payload = self._build_payload(messages) + response = await self._client.achat(payload) + + return self._create_chat_result(response) + + def _stream( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> Iterator[ChatGenerationChunk]: + payload = self._build_payload(messages) + + for chunk in self._client.stream(payload): + if chunk.choices: + content = chunk.choices[0].delta.content + yield ChatGenerationChunk(message=AIMessageChunk(content=content)) + if run_manager: + run_manager.on_llm_new_token(content) + + async def _astream( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> AsyncIterator[ChatGenerationChunk]: + payload = self._build_payload(messages) + + async for chunk in self._client.astream(payload): + if chunk.choices: + content = chunk.choices[0].delta.content + yield ChatGenerationChunk(message=AIMessageChunk(content=content)) + if run_manager: + await run_manager.on_llm_new_token(content) + + def get_num_tokens(self, text: str) -> int: + """Count approximate number of tokens""" + return round(len(text) / 4.6) diff --git a/libs/langchain/langchain/chat_models/hunyuan.py b/libs/langchain/langchain/chat_models/hunyuan.py index 3f0b7261b82..b87f2748ed8 100644 --- a/libs/langchain/langchain/chat_models/hunyuan.py +++ b/libs/langchain/langchain/chat_models/hunyuan.py @@ -31,8 +31,8 @@ from langchain.utils import get_from_dict_or_env, get_pydantic_field_names logger = logging.getLogger(__name__) -DEFAULT_HUNYUAN_API_BASE = "https://hunyuan.cloud.tencent.com" -DEFAULT_HUNYUAN_PATH = "/hyllm/v1/chat/completions" +DEFAULT_API_BASE = "https://hunyuan.cloud.tencent.com" +DEFAULT_PATH = "/hyllm/v1/chat/completions" def _convert_message_to_dict(message: BaseMessage) -> dict: @@ -141,7 +141,7 @@ class ChatHunyuan(BaseChatModel): def lc_serializable(self) -> bool: return True - hunyuan_api_base: str = "https://hunyuan.cloud.tencent.com" + hunyuan_api_base: str = Field(default=DEFAULT_API_BASE) """Hunyuan custom endpoints""" hunyuan_app_id: Optional[str] = None """Hunyuan App ID""" @@ -201,6 +201,7 @@ class ChatHunyuan(BaseChatModel): values, "hunyuan_api_base", "HUNYUAN_API_BASE", + DEFAULT_API_BASE, ) values["hunyuan_app_id"] = get_from_dict_or_env( values, @@ -303,7 +304,7 @@ class ChatHunyuan(BaseChatModel): if self.streaming: payload["stream"] = 1 - url = self.hunyuan_api_base + DEFAULT_HUNYUAN_PATH + url = self.hunyuan_api_base + DEFAULT_PATH res = requests.post( url=url, diff --git a/libs/langchain/langchain/cli/cli.py b/libs/langchain/langchain/cli/cli.py deleted file mode 100644 index 6c3c666f15b..00000000000 --- a/libs/langchain/langchain/cli/cli.py +++ /dev/null @@ -1,111 +0,0 @@ -"""A CLI for creating a new project with LangChain.""" -from pathlib import Path -from typing import Optional - -from typing_extensions import Annotated - -# Keep this import here so that we can check if Typer is installed -try: - import typer -except ImportError: - raise ImportError( - "Typer must be installed to use the CLI. " - "You can install it with `pip install typer` or install LangChain " - 'with the [cli] extra like `pip install "langchain[cli]"`.' - ) - -from langchain.cli.create_repo.base import create, is_poetry_installed -from langchain.cli.create_repo.pypi_name import is_name_taken, lint_name -from langchain.cli.create_repo.user_info import get_git_user_email, get_git_user_name - -app = typer.Typer(no_args_is_help=False, add_completion=False) - - -def _select_project_name(suggested_project_name: str) -> str: - """Help the user select a valid project name.""" - while True: - project_name = typer.prompt("Project Name", default=suggested_project_name) - - project_name_diagnostics = lint_name(project_name) - if project_name_diagnostics: - typer.echo( - f"{typer.style('Warning:', fg=typer.colors.MAGENTA)}" - f" The project name" - f" {typer.style(project_name, fg=typer.colors.BRIGHT_CYAN)}" - f" is not valid.", - err=True, - ) - - for diagnostic in project_name_diagnostics: - typer.echo(f" - {diagnostic}") - - if typer.confirm( - "Select another name?", - default=True, - ): - continue - - if is_name_taken(project_name): - typer.echo( - f"{typer.style('Error:', fg=typer.colors.RED)}" - f" The project name" - f" {typer.style(project_name, fg=typer.colors.BRIGHT_CYAN)}" - f" is already taken on pypi", - err=True, - ) - - if typer.confirm( - "Select another name?", - default=True, - ): - continue - - # If we got here then the project name is valid and not taken - return project_name - - -# -# -@app.command() -def new( - project_directory: Annotated[ - Path, typer.Argument(help="The directory to create the project in.") - ], - author_name: Optional[str] = None, - author_email: Optional[str] = None, - use_poetry: Annotated[ - Optional[bool], typer.Option(help="Specify whether to use Poetry or not.") - ] = None, -) -> None: - """Create a new project with LangChain.""" - - project_directory_path = Path(project_directory) - project_name_suggestion = project_directory_path.name.replace("-", "_") - project_name = _select_project_name(project_name_suggestion) - - if not author_name: - author_name = typer.prompt("Author Name", default=get_git_user_name()) - - if not author_email: - author_email = typer.prompt("Author Email", default=get_git_user_email()) - - if use_poetry is None: - if is_poetry_installed(): - typer.echo("๐ŸŽ‰ Found Poetry installed. Project can be set up using poetry.") - use_poetry = typer.confirm("Use Poetry? (no to use pip)", default=True) - else: - typer.echo("โ„น๏ธ Could not find Poetry installed.") - use_pip = typer.confirm("Use Pip? (no to use poetry)", default=True) - use_poetry = not use_pip - - if author_name is None: - raise typer.BadParameter("Author name is required") - - if author_email is None: - raise typer.BadParameter("Author email is required") - - create(project_directory, project_name, author_name, author_email, use_poetry) - - -if __name__ == "__main__": - app() diff --git a/libs/langchain/langchain/cli/create_repo/base.py b/libs/langchain/langchain/cli/create_repo/base.py deleted file mode 100644 index c6e09f376af..00000000000 --- a/libs/langchain/langchain/cli/create_repo/base.py +++ /dev/null @@ -1,266 +0,0 @@ -"""""" -import os -import pathlib -import string -import subprocess -from pathlib import Path -from typing import List, Sequence - -import typer - -import langchain - - -class UnderscoreTemplate(string.Template): - delimiter = "____" - - -def _create_project_dir( - project_directory_path: Path, - use_poetry: bool, - project_name: str, - project_name_identifier: str, - author_name: str, - author_email: str, -) -> None: - project_directory_path.mkdir(parents=True, exist_ok=True) - template_directories = _get_template_directories(use_poetry) - _check_conflicting_files(template_directories, project_directory_path) - _copy_template_files( - template_directories, - project_directory_path, - project_name, - project_name_identifier, - author_name, - author_email, - ) - - -def _get_template_directories(use_poetry: bool) -> List[Path]: - """Get the directories containing the templates. - - Args: - use_poetry: If true, will set up the project with Poetry. - - """ - template_parent_path = Path(__file__).parent / "templates" - template_directories = [template_parent_path / "repo"] - if use_poetry: - template_directories.append(template_parent_path / "poetry") - else: - template_directories.append(template_parent_path / "pip") - return template_directories - - -def _check_conflicting_files( - template_directories: Sequence[Path], project_directory_path: Path -) -> None: - """Validate project directory doesn't contain conflicting files.""" - - for template_directory_path in template_directories: - for template_file_path in template_directory_path.glob("**/*"): - relative_template_file_path = template_file_path.relative_to( - template_directory_path - ) - project_file_path = project_directory_path / relative_template_file_path - if project_file_path.exists(): - typer.echo( - f"{typer.style('Error:', fg=typer.colors.RED)}" - f" The project directory already contains a file" - f" {typer.style(project_file_path, fg=typer.colors.BRIGHT_CYAN)}" - f" that would be overwritten by the template.", - err=True, - ) - typer.echo( - "Please remove this file and try again.", - err=True, - ) - raise typer.Exit(code=1) - - -def _copy_template_files( - template_directories: Sequence[Path], - project_directory_path: Path, - project_name: str, - project_name_identifier: str, - author_name: str, - author_email: str, -) -> None: - """Copy template files to project directory and substitute variables. - - Args: - template_directories: The directories containing the templates. - project_directory_path: The destination directory. - project_name: The name of the project. - project_name_identifier: The identifier of the project name. - author_name: The name of the author. - author_email: The email of the author. - """ - for template_directory_path in template_directories: - for template_file_path in template_directory_path.glob("**/*"): - # Ignore __pycache__ directories and their contents - if "__pycache__" in template_file_path.parts: - continue - - relative_template_file_path = UnderscoreTemplate( - str(template_file_path.relative_to(template_directory_path)) - ).substitute(project_name_identifier=project_name_identifier) - project_file_path = project_directory_path / relative_template_file_path - if template_file_path.is_dir(): - project_file_path.mkdir(parents=True, exist_ok=True) - else: - try: - content = template_file_path.read_text(encoding="utf-8") - except UnicodeDecodeError as e: - raise RuntimeError( - "Encountered an error while reading a " - f"template file {template_file_path}" - ) from e - - project_file_path.write_text( - UnderscoreTemplate(content).substitute( - project_name=project_name, - project_name_identifier=project_name_identifier, - author_name=author_name, - author_email=author_email, - langchain_version=langchain.__version__, - ) - ) - - -def _poetry_install(project_directory_path: Path) -> None: - """Install dependencies with Poetry.""" - typer.echo( - f"\n{typer.style('2.', bold=True, fg=typer.colors.GREEN)}" - f" Installing dependencies with Poetry..." - ) - subprocess.run(["pwd"], cwd=project_directory_path) - subprocess.run( - ["poetry", "install"], - cwd=project_directory_path, - env={**os.environ.copy(), "VIRTUAL_ENV": ""}, - ) - - -def _pip_install(project_directory_path: Path) -> None: - """Create virtual environment and install dependencies.""" - typer.echo( - f"\n{typer.style('2.', bold=True, fg=typer.colors.GREEN)}" - f" Creating virtual environment..." - ) - subprocess.run(["pwd"], cwd=project_directory_path) - subprocess.run(["python", "-m", "venv", ".venv"], cwd=project_directory_path) - # TODO install dependencies - - -def _init_git(project_directory_path: Path) -> None: - """Initialize git repository.""" - typer.echo( - f"\n{typer.style('Initializing git...', bold=True, fg=typer.colors.GREEN)}" - ) - try: - subprocess.run(["git", "init"], cwd=project_directory_path) - except FileNotFoundError: - typer.echo("Git not found. Skipping git initialization.") - return - - # 7. Create initial commit - subprocess.run(["git", "add", "."], cwd=project_directory_path) - subprocess.run( - ["git", "commit", "-m", "Initial commit"], - cwd=project_directory_path, - ) - - -# PUBLIC API - - -def create( - project_directory: pathlib.Path, - project_name: str, - author_name: str, - author_email: str, - use_poetry: bool, -) -> None: - """Create a new LangChain project. - - Args: - project_directory (str): The directory to create the project in. - project_name: The name of the project. - author_name (str): The name of the author. - author_email (str): The email of the author. - use_poetry (bool): Whether to use Poetry to manage the project. - """ - - project_directory_path = Path(project_directory) - project_name_identifier = project_name - resolved_path = project_directory_path.resolve() - - if not typer.confirm( - f"\n" - f"Creating a new LangChain project ๐Ÿฆœ๏ธ๐Ÿ”—\n" - f"Name: {typer.style(project_name, fg=typer.colors.BRIGHT_CYAN)}\n" - f"Path: {typer.style(resolved_path, fg=typer.colors.BRIGHT_CYAN)}\n" - f"Project name: {typer.style(project_name, fg=typer.colors.BRIGHT_CYAN)}\n" - f"Author name: {typer.style(author_name, fg=typer.colors.BRIGHT_CYAN)}\n" - f"Author email: {typer.style(author_email, fg=typer.colors.BRIGHT_CYAN)}\n" - f"Use Poetry: {typer.style(str(use_poetry), fg=typer.colors.BRIGHT_CYAN)}\n" - "Continue?", - default=True, - ): - typer.echo("Cancelled project creation. See you later! ๐Ÿ‘‹") - raise typer.Exit(code=0) - - _create_project_dir( - project_directory_path, - use_poetry, - project_name, - project_name_identifier, - author_name, - author_email, - ) - - # TODO(Team): Add installation - # if use_poetry: - # _poetry_install(project_directory_path) - # else: - # _pip_install(project_directory_path) - - _init_git(project_directory_path) - - typer.echo( - f"\n{typer.style('Done!๐Ÿ™Œ', bold=True, fg=typer.colors.GREEN)}" - f" Your new LangChain project" - f" {typer.style(project_name, fg=typer.colors.BRIGHT_CYAN)}" - f" has been created in" - f" {typer.style(project_directory_path.resolve(), fg=typer.colors.BRIGHT_CYAN)}" - f"." - ) - # TODO(Team): Add surfacing information from make file and installation - # cd_dir = typer.style( - # f"cd {project_directory_path.resolve()}", fg=typer.colors.BRIGHT_CYAN - # ) - # typer.echo( - # f"\nChange into the project directory with {cd_dir}." - # f" The following commands are available:" - # ) - # subprocess.run(["make"], cwd=project_directory_path) - - # if not use_poetry: - # pip_install = typer.style( - # 'pip install -e ".[dev]"', fg=typer.colors.BRIGHT_CYAN - # ) - # typer.echo( - # f"\nTo install all dependencies activate your environment run:" - # f"\n{typer.style('source .venv/bin/activate', fg=typer.colors.BRIGHT_CYAN)}" - # f"\n{pip_install}." - # ) - - -def is_poetry_installed() -> bool: - """Check if Poetry is installed.""" - try: - result = subprocess.run(["poetry", "--version"], capture_output=True) - return result.returncode == 0 - except FileNotFoundError: - return False diff --git a/libs/langchain/langchain/cli/create_repo/pypi_name.py b/libs/langchain/langchain/cli/create_repo/pypi_name.py deleted file mode 100644 index 2c86d7a431e..00000000000 --- a/libs/langchain/langchain/cli/create_repo/pypi_name.py +++ /dev/null @@ -1,70 +0,0 @@ -"""Code helps to check availability of the name of the project on PyPi - -Adapted from https://github.com/danishprakash/pip-name/blob/master/pip-name -""" -from typing import List, Optional - -import requests - -BASE_URL = "https://pypi.org/pypi" - -UPPERCASE_SUGGESTION = "Use of uppercase letters is discouraged" -SEPARATOR_SUGGESTION = "Use of `-` is discouraged, consider using `_`" -NUMERIC_SUGGESTION = "Use of numbers is discouraged" - - -def _request_pypi(name: str) -> Optional[dict]: - """Request response from PyPi API. - - Args: - name (str): Name of the project - - Returns: - Optional[dict]: Response from PyPi API - """ - target_url = f"{BASE_URL}/{name}/json" - response = requests.get(target_url) - return response.json() if response.status_code != 404 else None - - -# PUBLIC API - - -def lint_name(name: str) -> List[str]: - """Check name against PEP8's naming conventions. - - Args: - name (str): Name of the project - - Returns: - List[str]: List of suggestions - """ - suggestions = [] - - if "-" in name or " " in name: - suggestions.append(SEPARATOR_SUGGESTION) - if any(x.isupper() for x in name): - suggestions.append(UPPERCASE_SUGGESTION) - if any(x.isnumeric() for x in name): - suggestions.append(NUMERIC_SUGGESTION) - - return suggestions - - -def is_name_taken(name: str) -> bool: - """Check module filename for conflict. - - Args: - name (str): Name of the project - - Returns: - bool: True if name is taken, False otherwise - """ - response = _request_pypi(name) - - if response: - package_url = response.get("info").get("package_url") # type: ignore - module_name = package_url.split("/")[-2] - return name.lower() == module_name.lower() - - return False diff --git a/libs/langchain/langchain/cli/create_repo/templates/pip/.github/CONTRIBUTING.md b/libs/langchain/langchain/cli/create_repo/templates/pip/.github/CONTRIBUTING.md deleted file mode 100644 index 21927e3ddad..00000000000 --- a/libs/langchain/langchain/cli/create_repo/templates/pip/.github/CONTRIBUTING.md +++ /dev/null @@ -1,79 +0,0 @@ -# Contributing to ____project_name - -Hi there! Thank you for even being interested in contributing to ____project_name. - -## ๐Ÿš€ Quick Start - -To install requirements: - -```bash -poetry install -e ".[dev]" -``` - -This will install all requirements for running the package, examples, linting, formatting, tests, and coverage. - -Now, you should be able to run the common tasks in the following section. To double check, run `make test`, all tests should pass. - -## โœ… Common Tasks - -Type `make` for a list of common tasks. - -### Code Formatting - -Formatting for this project is done via a combination of [Black](https://black.readthedocs.io/en/stable/) and [isort](https://pycqa.github.io/isort/). - -To run formatting for this project: - -```bash -make format -``` - -Additionally, you can run the formatter only on the files that have been modified in your current branch as compared to the main branch using the format_diff command: - -```bash -make format_diff -``` - -This is especially useful when you have made changes to a subset of the project and want to ensure your changes are properly formatted without affecting the rest of the codebase. - -### Linting - -Linting for this project is done via a combination of [Black](https://black.readthedocs.io/en/stable/), [isort](https://pycqa.github.io/isort/), [flake8](https://flake8.pycqa.org/en/latest/), and [mypy](http://mypy-lang.org/). - -To run linting for this project: - -```bash -make lint -``` - -In addition, you can run the linter only on the files that have been modified in your current branch as compared to the main branch using the lint_diff command: - -```bash -make lint_diff -``` - -This can be very helpful when you've made changes to only certain parts of the project and want to ensure your changes meet the linting standards without having to check the entire codebase. - -We recognize linting can be annoying - if you do not want to do it, please contact a project maintainer, and they can help you with it. We do not want this to be a blocker for good code getting contributed. - -### Testing - -To run unit tests: - -```bash -make test -``` - -If you add new logic, please add a unit test. - -## ๐Ÿญ Release Process - -____project_name follows the [semver](https://semver.org/) versioning standard. - -To use the [automated release workflow](./workflows/release.yml) you'll need to set up a PyPI account and [create an API token](https://pypi.org/help/#apitoken). Configure the API token for this GitHub repo by going to settings -> security -> secrets -> actions, creating the `PYPI_API_TOKEN` variable and setting the value to be your PyPI API token. - -Once that's set up, you can release a new version of the package by opening a PR that: -1. updates the package version in the [pyproject.toml file](../pyproject.toml), -2. labels the PR with a `release` tag. -When the PR is merged into main, a new release will be created. - diff --git a/libs/langchain/langchain/cli/create_repo/templates/pip/Dockerfile b/libs/langchain/langchain/cli/create_repo/templates/pip/Dockerfile deleted file mode 100644 index 9b46d2ac7fd..00000000000 --- a/libs/langchain/langchain/cli/create_repo/templates/pip/Dockerfile +++ /dev/null @@ -1,10 +0,0 @@ -FROM python:3.11-slim - -WORKDIR /app - -COPY . /app - -RUN pip install --no-cache-dir . - -CMD exec uvicorn ____project_name_identifier.server:app --host 0.0.0.0 --port $PORT - diff --git a/libs/langchain/langchain/cli/create_repo/templates/pip/Makefile b/libs/langchain/langchain/cli/create_repo/templates/pip/Makefile deleted file mode 100644 index 92243442c67..00000000000 --- a/libs/langchain/langchain/cli/create_repo/templates/pip/Makefile +++ /dev/null @@ -1,42 +0,0 @@ -.PHONY: all format lint test help - -# Default target executed when no arguments are given to make. -all: help - -start: - uvicorn ____project_name_identifier.server:app --reload - -# Define a variable for the test file path. -TEST_FILE ?= tests/ - -test: - pytest $(TEST_FILE) - -# Define a variable for Python and notebook files. -PYTHON_FILES=. -lint format: PYTHON_FILES=. -lint_diff format_diff: PYTHON_FILES=$(shell git diff --name-only --diff-filter=d main | grep -E '\.py$$|\.ipynb$$') - -lint lint_diff: - mypy $(PYTHON_FILES) - black $(PYTHON_FILES) --check - ruff . - -format format_diff: - black $(PYTHON_FILES) - ruff --select I --fix $(PYTHON_FILES) - -deploy_gcp: - gcloud run deploy ____project_name_identifier --source . --port 8001 --env-vars-file .env.gcp.yaml --allow-unauthenticated --region us-central1 --min-instances 1 - -###################### -# HELP -###################### - -help: - @echo '----' - @echo 'make start - start server' - @echo 'make format - run code formatters' - @echo 'make lint - run linters' - @echo 'make test - run unit tests' - @echo 'make deploy_gcp - deploy to GCP' diff --git a/libs/langchain/langchain/cli/create_repo/templates/pip/pyproject.toml b/libs/langchain/langchain/cli/create_repo/templates/pip/pyproject.toml deleted file mode 100644 index bc407b9b93d..00000000000 --- a/libs/langchain/langchain/cli/create_repo/templates/pip/pyproject.toml +++ /dev/null @@ -1,52 +0,0 @@ -[project] -name = "____project_name" -version = "0.0.1" -description = "" -authors = [{name = "____author_name", email = "____author_email"}] -readme = "README.md" -requires-python = ">=3.8,<4.0" -dependencies = [ - "langchain~=____langchain_version", - "langserve[server]>=0.0.6", - "tiktoken~=0.4.0", - "openai~=0.27.8", - "fastapi~=0.96.0", - "uvicorn[standard]~=0.22.0", -] - -[build-system] -requires = ["setuptools>=61.0"] -build-backend = "setuptools.build_meta" - -[project.optional-dependencies] -dev = [ - "pytest~=7.4.0", - "pytest-asyncio~=0.21.1", - "mypy~=1.4.1", - "ruff~=0.0.278", - "black~=23.7.0", - "syrupy~=4.0.2", -] - -[tool.ruff] -select = [ - "E", # pycodestyle - "F", # pyflakes - "I", # isort -] - -[tool.mypy] -ignore_missing_imports = "True" -disallow_untyped_defs = "True" - -[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 = "--strict-markers --strict-config --durations=5 --snapshot-warn-unused" diff --git a/libs/langchain/langchain/cli/create_repo/templates/poetry/.github/CONTRIBUTING.md b/libs/langchain/langchain/cli/create_repo/templates/poetry/.github/CONTRIBUTING.md deleted file mode 100644 index f0caed909d4..00000000000 --- a/libs/langchain/langchain/cli/create_repo/templates/poetry/.github/CONTRIBUTING.md +++ /dev/null @@ -1,89 +0,0 @@ -# Contributing to ____project_name - -Hi there! Thank you for even being interested in contributing to ____project_name. - -## ๐Ÿš€ Quick Start - -This project uses [Poetry](https://python-poetry.org/) as a dependency manager. Check out Poetry's [documentation on how to install it](https://python-poetry.org/docs/#installation) on your system before proceeding. - -โ—Note: If you use `Conda` or `Pyenv` as your environment / package manager, avoid dependency conflicts by doing the following first: -1. *Before installing Poetry*, create and activate a new Conda env (e.g. `conda create -n langchain python=3.9`) -2. Install Poetry (see above) -3. Tell Poetry to use the virtualenv python environment (`poetry config virtualenvs.prefer-active-python true`) -4. Continue with the following steps. - -To install requirements: - -```bash -poetry install -``` - -This will install all requirements for running the package, examples, linting, formatting, tests, and coverage. - -โ—Note: If you're running Poetry 1.4.1 and receive a `WheelFileValidationError` for `debugpy` during installation, you can try either downgrading to Poetry 1.4.0 or disabling "modern installation" (`poetry config installer.modern-installation false`) and re-install requirements. See [this `debugpy` issue](https://github.com/microsoft/debugpy/issues/1246) for more details. - -Now, you should be able to run the common tasks in the following section. - -## โœ… Common Tasks - -Type `make` for a list of common tasks. - -### Code Formatting - -Formatting for this project is done via a combination of [Black](https://black.readthedocs.io/en/stable/) and [isort](https://pycqa.github.io/isort/). - -To run formatting for this project: - -```bash -make format -``` - -Additionally, you can run the formatter only on the files that have been modified in your current branch as compared to the main branch using the format_diff command: - -```bash -make format_diff -``` - -This is especially useful when you have made changes to a subset of the project and want to ensure your changes are properly formatted without affecting the rest of the codebase. - -### Linting - -Linting for this project is done via a combination of [Black](https://black.readthedocs.io/en/stable/), [isort](https://pycqa.github.io/isort/), [flake8](https://flake8.pycqa.org/en/latest/), and [mypy](http://mypy-lang.org/). - -To run linting for this project: - -```bash -make lint -``` - -In addition, you can run the linter only on the files that have been modified in your current branch as compared to the main branch using the lint_diff command: - -```bash -make lint_diff -``` - -This can be very helpful when you've made changes to only certain parts of the project and want to ensure your changes meet the linting standards without having to check the entire codebase. - -We recognize linting can be annoying - if you do not want to do it, please contact a project maintainer, and they can help you with it. We do not want this to be a blocker for good code getting contributed. - -### Testing - -To run unit tests: - -```bash -make test -``` - -If you add new logic, please add a unit test. - -## ๐Ÿญ Release Process - -____project_name follows the [semver](https://semver.org/) versioning standard. - -To use the [automated release workflow](./workflows/release.yml) you'll need to set up a PyPI account and [create an API token](https://pypi.org/help/#apitoken). Configure the API token for this GitHub repo by going to settings -> security -> secrets -> actions, creating the `PYPI_API_TOKEN` variable and setting the value to be your PyPI API token. - -Once that's set up, you can release a new version of the package by opening a PR that: -1. updates the package version in the [pyproject.toml file](../pyproject.toml), -2. labels the PR with a `release` tag. -When the PR is merged into main, a new release will be created. - diff --git a/libs/langchain/langchain/cli/create_repo/templates/poetry/.github/actions/poetry_setup/action.yml b/libs/langchain/langchain/cli/create_repo/templates/poetry/.github/actions/poetry_setup/action.yml deleted file mode 100644 index 17c365a7907..00000000000 --- a/libs/langchain/langchain/cli/create_repo/templates/poetry/.github/actions/poetry_setup/action.yml +++ /dev/null @@ -1,76 +0,0 @@ -# An action for setting up poetry install with caching. -# Using a custom action since the default action does not -# take poetry install groups into account. -# Action code from: -# https://github.com/actions/setup-python/issues/505#issuecomment-1273013236 -name: poetry-install-with-caching -description: Poetry install with support for caching of dependency groups. - -inputs: - python-version: - description: Python version, supporting MAJOR.MINOR only - required: true - - poetry-version: - description: Poetry version - required: true - - install-command: - description: Command run for installing dependencies - required: false - default: poetry install - - cache-key: - description: Cache key to use for manual handling of caching - required: true - - working-directory: - description: Directory to run install-command in - required: false - default: "" - -runs: - using: composite - steps: - - uses: actions/setup-python@v4 - name: Setup python $${ inputs.python-version }} - with: - python-version: ${{ inputs.python-version }} - - - uses: actions/cache@v3 - id: cache-pip - name: Cache Pip ${{ inputs.python-version }} - env: - SEGMENT_DOWNLOAD_TIMEOUT_MIN: "15" - with: - path: | - ~/.cache/pip - key: pip-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }} - - - run: pipx install poetry==${{ inputs.poetry-version }} --python python${{ inputs.python-version }} - shell: bash - - - name: Check Poetry File - shell: bash - run: | - poetry check - - - name: Check lock file - shell: bash - run: | - poetry lock --check - - - uses: actions/cache@v3 - id: cache-poetry - env: - SEGMENT_DOWNLOAD_TIMEOUT_MIN: "15" - with: - path: | - ~/.cache/pypoetry/virtualenvs - ~/.cache/pypoetry/cache - ~/.cache/pypoetry/artifacts - key: poetry-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-poetry-${{ inputs.poetry-version }}-${{ hashFiles('poetry.lock') }} - - - run: ${{ inputs.install-command }} - working-directory: ${{ inputs.working-directory }} - shell: bash diff --git a/libs/langchain/langchain/cli/create_repo/templates/poetry/.github/workflows/lint.yml b/libs/langchain/langchain/cli/create_repo/templates/poetry/.github/workflows/lint.yml deleted file mode 100644 index c3e4375fc86..00000000000 --- a/libs/langchain/langchain/cli/create_repo/templates/poetry/.github/workflows/lint.yml +++ /dev/null @@ -1,36 +0,0 @@ -name: lint - -on: - push: - branches: [master] - pull_request: - -env: - POETRY_VERSION: "1.4.2" - -jobs: - build: - runs-on: ubuntu-latest - strategy: - matrix: - python-version: - - "3.8" - - "3.9" - - "3.10" - - "3.11" - steps: - - uses: actions/checkout@v3 - - name: Install poetry - run: | - pipx install poetry==$POETRY_VERSION - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v4 - with: - python-version: ${{ matrix.python-version }} - cache: poetry - - name: Install dependencies - run: | - poetry install - - name: Analysing the code with our lint - run: | - make lint diff --git a/libs/langchain/langchain/cli/create_repo/templates/poetry/.github/workflows/release.yml b/libs/langchain/langchain/cli/create_repo/templates/poetry/.github/workflows/release.yml deleted file mode 100644 index 85b387b27b8..00000000000 --- a/libs/langchain/langchain/cli/create_repo/templates/poetry/.github/workflows/release.yml +++ /dev/null @@ -1,49 +0,0 @@ -name: release - -on: - pull_request: - types: - - closed - branches: - - master - paths: - - 'pyproject.toml' - -env: - POETRY_VERSION: "1.4.2" - -jobs: - if_release: - if: | - ${{ github.event.pull_request.merged == true }} - && ${{ contains(github.event.pull_request.labels.*.name, 'release') }} - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v3 - - name: Install poetry - run: pipx install poetry==$POETRY_VERSION - - name: Set up Python 3.10 - uses: actions/setup-python@v4 - with: - python-version: "3.10" - cache: "poetry" - - name: Build project for distribution - run: poetry build - - name: Check Version - id: check-version - run: | - echo version=$(poetry version --short) >> $GITHUB_OUTPUT - - name: Create Release - uses: ncipollo/release-action@v1 - with: - artifacts: "dist/*" - token: ${{ secrets.GITHUB_TOKEN }} - draft: false - generateReleaseNotes: true - tag: v${{ steps.check-version.outputs.version }} - commit: master - - name: Publish to PyPI - env: - POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }} - run: | - poetry publish diff --git a/libs/langchain/langchain/cli/create_repo/templates/poetry/.github/workflows/test.yml b/libs/langchain/langchain/cli/create_repo/templates/poetry/.github/workflows/test.yml deleted file mode 100644 index 5138aaa0fb6..00000000000 --- a/libs/langchain/langchain/cli/create_repo/templates/poetry/.github/workflows/test.yml +++ /dev/null @@ -1,36 +0,0 @@ -name: test - -on: - push: - branches: [master] - pull_request: - workflow_dispatch: - -env: - POETRY_VERSION: "1.4.2" - -jobs: - build: - runs-on: ubuntu-latest - strategy: - matrix: - python-version: - - "3.8" - - "3.9" - - "3.10" - - "3.11" - name: Python ${{ matrix.python-version }} - steps: - - uses: actions/checkout@v3 - - name: Set up Python ${{ matrix.python-version }} - uses: "./.github/actions/poetry_setup" - with: - python-version: ${{ matrix.python-version }} - poetry-version: "1.4.2" - install-command: | - echo "Running tests, installing dependencies with poetry..." - poetry install - - name: Run tests - run: | - make test - shell: bash diff --git a/libs/langchain/langchain/cli/create_repo/templates/poetry/Dockerfile b/libs/langchain/langchain/cli/create_repo/templates/poetry/Dockerfile deleted file mode 100644 index 8484194f646..00000000000 --- a/libs/langchain/langchain/cli/create_repo/templates/poetry/Dockerfile +++ /dev/null @@ -1,11 +0,0 @@ -FROM python:3.11-slim - -WORKDIR /app - -COPY . /app - -RUN pip install poetry && \ - poetry config virtualenvs.create false && \ - poetry install --no-interaction --no-ansi --only main - -CMD exec uvicorn ____project_name_identifier.server:app --host 0.0.0.0 --port $PORT diff --git a/libs/langchain/langchain/cli/create_repo/templates/poetry/Makefile b/libs/langchain/langchain/cli/create_repo/templates/poetry/Makefile deleted file mode 100644 index f19cf7b4e95..00000000000 --- a/libs/langchain/langchain/cli/create_repo/templates/poetry/Makefile +++ /dev/null @@ -1,42 +0,0 @@ -.PHONY: all format lint test help - -# Default target executed when no arguments are given to make. -all: help - -start: - poetry run uvicorn ____project_name_identifier.server:app --reload - -# Define a variable for the test file path. -TEST_FILE ?= tests/ - -test: - poetry run pytest $(TEST_FILE) - -# Define a variable for Python and notebook files. -PYTHON_FILES=. -lint format: PYTHON_FILES=. -lint_diff format_diff: PYTHON_FILES=$(shell git diff --name-only --diff-filter=d main | grep -E '\.py$$|\.ipynb$$') - -lint lint_diff: - poetry run mypy $(PYTHON_FILES) - poetry run black $(PYTHON_FILES) --check - poetry run ruff . - -format format_diff: - poetry run black $(PYTHON_FILES) - poetry run ruff --select I --fix $(PYTHON_FILES) - -deploy_gcp: - gcloud run deploy ____project_name_identifier --source . --port 8001 --env-vars-file .env.gcp.yaml --allow-unauthenticated --region us-central1 --min-instances 1 - -###################### -# HELP -###################### - -help: - @echo '----' - @echo 'make start - start server' - @echo 'make format - run code formatters' - @echo 'make lint - run linters' - @echo 'make test - run unit tests' - @echo 'make deploy_gcp - deploy to GCP' diff --git a/libs/langchain/langchain/cli/create_repo/templates/poetry/poetry.toml b/libs/langchain/langchain/cli/create_repo/templates/poetry/poetry.toml deleted file mode 100644 index ab1033bd372..00000000000 --- a/libs/langchain/langchain/cli/create_repo/templates/poetry/poetry.toml +++ /dev/null @@ -1,2 +0,0 @@ -[virtualenvs] -in-project = true diff --git a/libs/langchain/langchain/cli/create_repo/templates/poetry/pyproject.toml b/libs/langchain/langchain/cli/create_repo/templates/poetry/pyproject.toml deleted file mode 100644 index 36441e63e2d..00000000000 --- a/libs/langchain/langchain/cli/create_repo/templates/poetry/pyproject.toml +++ /dev/null @@ -1,52 +0,0 @@ -[tool.poetry] -name = "____project_name" -version = "0.0.1" -description = "" -authors = ["____author_name <____author_email>"] -license = "MIT" -readme = "README.md" -packages = [{include = "____project_name_identifier"}] - -[tool.poetry.dependencies] -python = "^3.8.1" -langchain = "^____langchain_version" -langserve = { version = ">=0.0.6", extras = ["server"] } -tiktoken = "^0.4.0" -openai = "^0.27.8" -fastapi = "^0.96.0" -uvicorn = {extras = ["standard"], version = "^0.22.0"} - -[tool.poetry.group.dev.dependencies] -pytest = "^7.4.0" -pytest-asyncio = "^0.21.1" -mypy = "^1.4.1" -ruff = "^0.0.278" -black = "^23.7.0" -syrupy = "^4.0.2" - -[build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" - -[tool.ruff] -select = [ - "E", # pycodestyle - "F", # pyflakes - "I", # isort -] - -[tool.mypy] -ignore_missing_imports = "True" -disallow_untyped_defs = "True" - -[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 = "--strict-markers --strict-config --durations=5 --snapshot-warn-unused" diff --git a/libs/langchain/langchain/cli/create_repo/templates/repo/.env b/libs/langchain/langchain/cli/create_repo/templates/repo/.env deleted file mode 100644 index 173a5f3dd98..00000000000 --- a/libs/langchain/langchain/cli/create_repo/templates/repo/.env +++ /dev/null @@ -1,2 +0,0 @@ -PORT=8001 -OPENAI_API_KEY="your_secret_key_here" diff --git a/libs/langchain/langchain/cli/create_repo/templates/repo/.env.gcp.yaml b/libs/langchain/langchain/cli/create_repo/templates/repo/.env.gcp.yaml deleted file mode 100644 index 54c854b6cd0..00000000000 --- a/libs/langchain/langchain/cli/create_repo/templates/repo/.env.gcp.yaml +++ /dev/null @@ -1 +0,0 @@ -OPENAI_API_KEY: your_secret_key_here diff --git a/libs/langchain/langchain/cli/create_repo/templates/repo/.gitignore b/libs/langchain/langchain/cli/create_repo/templates/repo/.gitignore deleted file mode 100644 index 794a85127ab..00000000000 --- a/libs/langchain/langchain/cli/create_repo/templates/repo/.gitignore +++ /dev/null @@ -1,158 +0,0 @@ -.vs/ -.vscode/ -.idea/ -# 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/ -pip-wheel-metadata/ -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/ - -# 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/ -docs/docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints -notebooks/ - -# IPython -profile_default/ -ipython_config.py - -# pyenv -.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 - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.envrc -.venv -.venvs -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ -.env.gcp.yaml - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# macOS display setting files -.DS_Store - -# Wandb directory -wandb/ - -# asdf tool versions -.tool-versions -/.ruff_cache/ - -*.pkl -*.bin - -# integration test artifacts -data_map* -\[('_type', 'fake'), ('stop', None)] - -# Replit files -*replit* - diff --git a/libs/langchain/langchain/cli/create_repo/templates/repo/README.md b/libs/langchain/langchain/cli/create_repo/templates/repo/README.md deleted file mode 100644 index 2cb2ae73a5d..00000000000 --- a/libs/langchain/langchain/cli/create_repo/templates/repo/README.md +++ /dev/null @@ -1,72 +0,0 @@ -# ____project_name - - - -## Customise - -To customise this project, edit the following files: - -- `____project_name_identifier/chain.py` contains an example chain, which you can edit to suit your needs. -- `____project_name_identifier/server.py` contains a FastAPI app that serves that chain using `langserve`. You can edit this to add more endpoints or customise your server. -- `tests/test_chain.py` contains tests for the chain. You can edit this to add more tests. -- `pyproject.toml` contains the project metadata, including the project name, version, and dependencies. You can edit this to add more dependencies or customise your project metadata. - -## Install dependencies - -If using poetry: - -```bash -poetry install -``` - -If using vanilla pip: - -```bash -pip install . -``` - -## Usage - -To run the project locally, run - -``` -make start -``` - -This will launch a webserver on port 8001. - -Or via docker compose (does not use hot reload by default): - -``` -docker compose up -``` - -## Deploy - -To deploy the project, first build the docker image: - -``` -docker build . -t ____project_name_identifier:latest -``` - -Then run the image: - -``` -docker run -p 8001:8001 -e PORT=8001 ____project_name_identifier:latest -``` - -Don't forget to add any needed environment variables! - -## Deploy to GCP - -You can deploy to GCP Cloud Run using the following command: - -First edit `.env.gcp.yaml` file with any environment variables you need. Then run: - -``` -make deploy_gcp -``` - -## Contributing - -For information on how to set up your dev environment and contribute, see [here](.github/CONTRIBUTING.md). diff --git a/libs/langchain/langchain/cli/create_repo/templates/repo/____project_name_identifier/__init__.py b/libs/langchain/langchain/cli/create_repo/templates/repo/____project_name_identifier/__init__.py deleted file mode 100644 index 201b3bd2217..00000000000 --- a/libs/langchain/langchain/cli/create_repo/templates/repo/____project_name_identifier/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -"""____project_name_identifier package.""" -from importlib import metadata - -from ____project_name_identifier.chain import get_chain - -try: - __version__ = metadata.version(__package__) -except metadata.PackageNotFoundError: - # Case where package metadata is not available. - __version__ = "" - -__all__ = [__version__, "get_chain"] diff --git a/libs/langchain/langchain/cli/create_repo/templates/repo/____project_name_identifier/chain.py b/libs/langchain/langchain/cli/create_repo/templates/repo/____project_name_identifier/chain.py deleted file mode 100644 index 7013e375f48..00000000000 --- a/libs/langchain/langchain/cli/create_repo/templates/repo/____project_name_identifier/chain.py +++ /dev/null @@ -1,29 +0,0 @@ -"""This is a template for a custom chain. - -Edit this file to implement your chain logic. -""" - -from typing import Optional - -from langchain.chat_models.openai import ChatOpenAI -from langchain.output_parsers.list import CommaSeparatedListOutputParser -from langchain.prompts.chat import ChatPromptTemplate -from langchain.schema.language_model import BaseLanguageModel -from langchain.schema.runnable import Runnable - -template = """You are a helpful assistant who generates comma separated lists. -A user will pass in a category, and you should generate 5 objects in that category in a comma separated list. -ONLY return a comma separated list, and nothing more.""" # noqa: E501 -human_template = "{text}" - - -def get_chain(model: Optional[BaseLanguageModel] = None) -> Runnable: - """Return a chain.""" - model = model or ChatOpenAI() - prompt = ChatPromptTemplate.from_messages( - [ - ("system", template), - ("human", human_template), - ] - ) - return prompt | model | CommaSeparatedListOutputParser() diff --git a/libs/langchain/langchain/cli/create_repo/templates/repo/docker-compose.yml b/libs/langchain/langchain/cli/create_repo/templates/repo/docker-compose.yml deleted file mode 100644 index ac695f025ed..00000000000 --- a/libs/langchain/langchain/cli/create_repo/templates/repo/docker-compose.yml +++ /dev/null @@ -1,12 +0,0 @@ -version: '3' - -services: - server: - build: - context: . - image: ____project_name:latest - container_name: ____project_name - ports: - - "8001:8001" - env_file: - - .env diff --git a/libs/langchain/langchain/cli/create_repo/templates/repo/tests/test_chain.py b/libs/langchain/langchain/cli/create_repo/templates/repo/tests/test_chain.py deleted file mode 100644 index d1b316f855b..00000000000 --- a/libs/langchain/langchain/cli/create_repo/templates/repo/tests/test_chain.py +++ /dev/null @@ -1,10 +0,0 @@ -from ____project_name_identifier import get_chain - - -def test_my_chain() -> None: - """Edit this test to test your chain.""" - from langchain.llms.human import HumanInputLLM - - llm = HumanInputLLM(input_func=lambda *args, **kwargs: "foo") - chain = get_chain(llm) - chain.invoke({"text": "foo"}) diff --git a/libs/langchain/langchain/cli/create_repo/user_info.py b/libs/langchain/langchain/cli/create_repo/user_info.py deleted file mode 100644 index 23d43215471..00000000000 --- a/libs/langchain/langchain/cli/create_repo/user_info.py +++ /dev/null @@ -1,29 +0,0 @@ -"""Look up user information from local git.""" -import subprocess -from typing import Optional - - -def get_git_user_name() -> Optional[str]: - """Get the user's name from git, if it is configured, otherwise None.""" - try: - return ( - subprocess.run(["git", "config", "--get", "user.name"], capture_output=True) - .stdout.decode() - .strip() - ) - except FileNotFoundError: - return None - - -def get_git_user_email() -> Optional[str]: - """Get the user's email from git if it is configured, otherwise None.""" - try: - return ( - subprocess.run( - ["git", "config", "--get", "user.email"], capture_output=True - ) - .stdout.decode() - .strip() - ) - except FileNotFoundError: - return None diff --git a/libs/langchain/langchain/docker-compose.yaml b/libs/langchain/langchain/docker-compose.yaml deleted file mode 100644 index 35e315c11ca..00000000000 --- a/libs/langchain/langchain/docker-compose.yaml +++ /dev/null @@ -1,29 +0,0 @@ -version: '3' -services: - langchain-frontend: - image: notlangchain/langchainplus-frontend:latest - ports: - - 4173:4173 - environment: - - BACKEND_URL=http://langchain-backend:8000 - - PUBLIC_BASE_URL=http://localhost:8000 - - PUBLIC_DEV_MODE=true - depends_on: - - langchain-backend - langchain-backend: - image: notlangchain/langchainplus:latest - environment: - - PORT=8000 - - LANGCHAIN_ENV=local - ports: - - 8000:8000 - depends_on: - - langchain-db - langchain-db: - image: postgres:14.1 - environment: - - POSTGRES_PASSWORD=postgres - - POSTGRES_USER=postgres - - POSTGRES_DB=postgres - expose: - - 5432 diff --git a/libs/langchain/langchain/document_loaders/async_html.py b/libs/langchain/langchain/document_loaders/async_html.py index 80436bfec60..877a9c41a97 100644 --- a/libs/langchain/langchain/document_loaders/async_html.py +++ b/libs/langchain/langchain/document_loaders/async_html.py @@ -24,6 +24,18 @@ default_header_template = { } +def _build_metadata(soup: Any, url: str) -> dict: + """Build metadata from BeautifulSoup output.""" + metadata = {"source": url} + if title := soup.find("title"): + metadata["title"] = title.get_text() + if description := soup.find("meta", attrs={"name": "description"}): + metadata["description"] = description.get("content", "No description found.") + if html := soup.find("html"): + metadata["language"] = html.get("lang", "No language found.") + return metadata + + class AsyncHtmlLoader(BaseLoader): """Load `HTML` asynchronously.""" @@ -33,6 +45,9 @@ class AsyncHtmlLoader(BaseLoader): header_template: Optional[dict] = None, verify_ssl: Optional[bool] = True, proxies: Optional[dict] = None, + autoset_encoding: bool = True, + encoding: Optional[str] = None, + default_parser: str = "html.parser", requests_per_second: int = 2, requests_kwargs: Optional[Dict[str, Any]] = None, raise_for_status: bool = False, @@ -68,8 +83,46 @@ class AsyncHtmlLoader(BaseLoader): self.session.proxies.update(proxies) self.requests_per_second = requests_per_second + self.default_parser = default_parser self.requests_kwargs = requests_kwargs or {} self.raise_for_status = raise_for_status + self.autoset_encoding = autoset_encoding + self.encoding = encoding + + @staticmethod + def _check_parser(parser: str) -> None: + """Check that parser is valid for bs4.""" + valid_parsers = ["html.parser", "lxml", "xml", "lxml-xml", "html5lib"] + if parser not in valid_parsers: + raise ValueError( + "`parser` must be one of " + ", ".join(valid_parsers) + "." + ) + + def _scrape( + self, + url: str, + parser: Union[str, None] = None, + bs_kwargs: Optional[dict] = None, + ) -> Any: + from bs4 import BeautifulSoup + + if parser is None: + if url.endswith(".xml"): + parser = "xml" + else: + parser = self.default_parser + + self._check_parser(parser) + + html_doc = self.session.get(url, **self.requests_kwargs) + if self.raise_for_status: + html_doc.raise_for_status() + + if self.encoding is not None: + html_doc.encoding = self.encoding + elif self.autoset_encoding: + html_doc.encoding = html_doc.apparent_encoding + return BeautifulSoup(html_doc.text, parser, **(bs_kwargs or {})) async def _fetch( self, url: str, retries: int = 3, cooldown: int = 2, backoff: float = 1.5 @@ -142,7 +195,8 @@ class AsyncHtmlLoader(BaseLoader): results = asyncio.run(self.fetch_all(self.web_paths)) docs = [] for i, text in enumerate(cast(List[str], results)): - metadata = {"source": self.web_paths[i]} + soup = self._scrape(self.web_paths[i]) + metadata = _build_metadata(soup, self.web_paths[i]) docs.append(Document(page_content=text, metadata=metadata)) return docs diff --git a/libs/langchain/langchain/document_loaders/bigquery.py b/libs/langchain/langchain/document_loaders/bigquery.py index 4b9be0ca60d..37f5440f9fd 100644 --- a/libs/langchain/langchain/document_loaders/bigquery.py +++ b/libs/langchain/langchain/document_loaders/bigquery.py @@ -4,6 +4,7 @@ from typing import TYPE_CHECKING, List, Optional from langchain.docstore.document import Document from langchain.document_loaders.base import BaseLoader +from langchain.utilities.vertexai import get_client_info if TYPE_CHECKING: from google.auth.credentials import Credentials @@ -57,7 +58,11 @@ class BigQueryLoader(BaseLoader): "Please install it with `pip install google-cloud-bigquery`." ) from ex - bq_client = bigquery.Client(credentials=self.credentials, project=self.project) + bq_client = bigquery.Client( + credentials=self.credentials, + project=self.project, + client_info=get_client_info(module="bigquery"), + ) if not bq_client.project: error_desc = ( "GCP project for Big Query is not set! Either provide a " diff --git a/libs/langchain/langchain/document_loaders/gcs_directory.py b/libs/langchain/langchain/document_loaders/gcs_directory.py index d51a0fbc8e4..f427dcf5a28 100644 --- a/libs/langchain/langchain/document_loaders/gcs_directory.py +++ b/libs/langchain/langchain/document_loaders/gcs_directory.py @@ -3,6 +3,7 @@ from typing import Callable, List, Optional from langchain.docstore.document import Document from langchain.document_loaders.base import BaseLoader from langchain.document_loaders.gcs_file import GCSFileLoader +from langchain.utilities.vertexai import get_client_info class GCSDirectoryLoader(BaseLoader): @@ -18,7 +19,7 @@ class GCSDirectoryLoader(BaseLoader): """Initialize with bucket and key name. Args: - project_name: The name of the project for the GCS bucket. + project_name: The ID of the project for the GCS bucket. bucket: The name of the GCS bucket. prefix: The prefix of the GCS bucket. loader_func: A loader function that instantiates a loader based on a @@ -39,7 +40,10 @@ class GCSDirectoryLoader(BaseLoader): "Could not import google-cloud-storage python package. " "Please install it with `pip install google-cloud-storage`." ) - client = storage.Client(project=self.project_name) + client = storage.Client( + project=self.project_name, + client_info=get_client_info(module="google-cloud-storage"), + ) docs = [] for blob in client.list_blobs(self.bucket, prefix=self.prefix): # we shall just skip directories since GCSFileLoader creates diff --git a/libs/langchain/langchain/document_loaders/gcs_file.py b/libs/langchain/langchain/document_loaders/gcs_file.py index 5fd6519bf16..468812217cd 100644 --- a/libs/langchain/langchain/document_loaders/gcs_file.py +++ b/libs/langchain/langchain/document_loaders/gcs_file.py @@ -5,6 +5,7 @@ from typing import Callable, List, Optional from langchain.docstore.document import Document from langchain.document_loaders.base import BaseLoader from langchain.document_loaders.unstructured import UnstructuredFileLoader +from langchain.utilities.vertexai import get_client_info class GCSFileLoader(BaseLoader): @@ -57,7 +58,9 @@ class GCSFileLoader(BaseLoader): ) # Initialise a client - storage_client = storage.Client(self.project_name) + storage_client = storage.Client( + self.project_name, client_info=get_client_info("google-cloud-storage") + ) # Create a bucket object for our bucket bucket = storage_client.get_bucket(self.bucket) # Create a blob object from the filepath diff --git a/libs/langchain/langchain/document_loaders/parsers/docai.py b/libs/langchain/langchain/document_loaders/parsers/docai.py index 32f9d9122c3..efabb23a014 100644 --- a/libs/langchain/langchain/document_loaders/parsers/docai.py +++ b/libs/langchain/langchain/document_loaders/parsers/docai.py @@ -5,6 +5,7 @@ pip install google-cloud-documentai pip install google-cloud-documentai-toolbox """ import logging +import re import time from dataclasses import dataclass from typing import TYPE_CHECKING, Iterator, List, Optional, Sequence @@ -12,6 +13,7 @@ from typing import TYPE_CHECKING, Iterator, List, Optional, Sequence from langchain.docstore.document import Document from langchain.document_loaders.base import BaseBlobParser from langchain.document_loaders.blob_loaders import Blob +from langchain.utilities.vertexai import get_client_info from langchain.utils.iter import batch_iterate if TYPE_CHECKING: @@ -64,13 +66,14 @@ class DocAIParser(BaseBlobParser): "a client." ) - if processor_name and not processor_name.isalnum(): + pattern = "projects\/[0-9]+\/locations\/[a-z\-0-9]+\/processors\/[a-z0-9]+" + if processor_name and not re.fullmatch(pattern, processor_name): raise ValueError( - f"Processor name {processor_name} has a wrong format. Use only ID from" - "the `Basic information` section on the GCP console. E.g., if your " + f"Processor name {processor_name} has a wrong format. If your " "prediction endpoint looks like https://us-documentai.googleapis.com" - "/v1/projects/PROJECT_ID/locations/us/processors/PROCESSOR_ID:process" - ", use only PROCESSOR_ID part." + "/v1/projects/PROJECT_ID/locations/us/processors/PROCESSOR_ID:process," + " use only projects/PROJECT_ID/locations/us/processors/PROCESSOR_ID " + "part." ) self._gcs_output_path = gcs_output_path @@ -89,7 +92,10 @@ class DocAIParser(BaseBlobParser): options = ClientOptions( api_endpoint=f"{location}-documentai.googleapis.com" ) - self._client = DocumentProcessorServiceClient(client_options=options) + self._client = DocumentProcessorServiceClient( + client_options=options, + client_info=get_client_info(module="document-ai"), + ) def lazy_parse(self, blob: Blob) -> Iterator[Document]: """Parses a blob lazily. diff --git a/libs/langchain/langchain/document_loaders/parsers/language/cobol.py b/libs/langchain/langchain/document_loaders/parsers/language/cobol.py new file mode 100644 index 00000000000..2267e8c522b --- /dev/null +++ b/libs/langchain/langchain/document_loaders/parsers/language/cobol.py @@ -0,0 +1,96 @@ +import re +from typing import Callable, List + +from langchain.document_loaders.parsers.language.code_segmenter import CodeSegmenter + + +class CobolSegmenter(CodeSegmenter): + """Code segmenter for `COBOL`.""" + + PARAGRAPH_PATTERN = re.compile(r"^[A-Z0-9\-]+(\s+.*)?\.$", re.IGNORECASE) + DIVISION_PATTERN = re.compile( + r"^\s*(IDENTIFICATION|DATA|PROCEDURE|ENVIRONMENT)\s+DIVISION.*$", re.IGNORECASE + ) + SECTION_PATTERN = re.compile(r"^\s*[A-Z0-9\-]+\s+SECTION.$", re.IGNORECASE) + + def __init__(self, code: str): + super().__init__(code) + self.source_lines: List[str] = self.code.splitlines() + + def is_valid(self) -> bool: + # Identify presence of any division to validate COBOL code + return any(self.DIVISION_PATTERN.match(line) for line in self.source_lines) + + def _extract_code(self, start_idx: int, end_idx: int) -> str: + return "\n".join(self.source_lines[start_idx:end_idx]).rstrip("\n") + + def _is_relevant_code(self, line: str) -> bool: + """Check if a line is part of the procedure division or a relevant section.""" + if "PROCEDURE DIVISION" in line.upper(): + return True + # Add additional conditions for relevant sections if needed + return False + + def _process_lines(self, func: Callable) -> List[str]: + """A generic function to process COBOL lines based on provided func.""" + elements: List[str] = [] + start_idx = None + inside_relevant_section = False + + for i, line in enumerate(self.source_lines): + if self._is_relevant_code(line): + inside_relevant_section = True + + if inside_relevant_section and ( + self.PARAGRAPH_PATTERN.match(line.strip().split(" ")[0]) + or self.SECTION_PATTERN.match(line.strip()) + ): + if start_idx is not None: + func(elements, start_idx, i) + start_idx = i + + # Handle the last element if exists + if start_idx is not None: + func(elements, start_idx, len(self.source_lines)) + + return elements + + def extract_functions_classes(self) -> List[str]: + def extract_func(elements: List[str], start_idx: int, end_idx: int) -> None: + elements.append(self._extract_code(start_idx, end_idx)) + + return self._process_lines(extract_func) + + def simplify_code(self) -> str: + simplified_lines: List[str] = [] + inside_relevant_section = False + omitted_code_added = ( + False # To track if "* OMITTED CODE *" has been added after the last header + ) + + for line in self.source_lines: + is_header = ( + "PROCEDURE DIVISION" in line + or "DATA DIVISION" in line + or "IDENTIFICATION DIVISION" in line + or self.PARAGRAPH_PATTERN.match(line.strip().split(" ")[0]) + or self.SECTION_PATTERN.match(line.strip()) + ) + + if is_header: + inside_relevant_section = True + # Reset the flag since we're entering a new section/division or + # paragraph + omitted_code_added = False + + if inside_relevant_section: + if is_header: + # Add header and reset the omitted code added flag + simplified_lines.append(line) + elif not omitted_code_added: + # Add omitted code comment only if it hasn't been added directly + # after the last header + simplified_lines.append("* OMITTED CODE *") + omitted_code_added = True + + return "\n".join(simplified_lines) diff --git a/libs/langchain/langchain/document_loaders/parsers/language/language_parser.py b/libs/langchain/langchain/document_loaders/parsers/language/language_parser.py index 97d26a99e61..534f151e397 100644 --- a/libs/langchain/langchain/document_loaders/parsers/language/language_parser.py +++ b/libs/langchain/langchain/document_loaders/parsers/language/language_parser.py @@ -3,6 +3,7 @@ from typing import Any, Dict, Iterator, Optional from langchain.docstore.document import Document from langchain.document_loaders.base import BaseBlobParser from langchain.document_loaders.blob_loaders import Blob +from langchain.document_loaders.parsers.language.cobol import CobolSegmenter from langchain.document_loaders.parsers.language.javascript import JavaScriptSegmenter from langchain.document_loaders.parsers.language.python import PythonSegmenter from langchain.text_splitter import Language @@ -10,11 +11,13 @@ from langchain.text_splitter import Language LANGUAGE_EXTENSIONS: Dict[str, str] = { "py": Language.PYTHON, "js": Language.JS, + "cobol": Language.COBOL, } LANGUAGE_SEGMENTERS: Dict[str, Any] = { Language.PYTHON: PythonSegmenter, Language.JS: JavaScriptSegmenter, + Language.COBOL: CobolSegmenter, } diff --git a/libs/langchain/langchain/document_loaders/parsers/pdf.py b/libs/langchain/langchain/document_loaders/parsers/pdf.py index 03247aea6d7..74373ba9b6d 100644 --- a/libs/langchain/langchain/document_loaders/parsers/pdf.py +++ b/libs/langchain/langchain/document_loaders/parsers/pdf.py @@ -49,6 +49,17 @@ _PDF_FILTER_WITHOUT_LOSS = [ def extract_from_images_with_rapidocr( images: Sequence[Union[Iterable[np.ndarray], bytes]] ) -> str: + """Extract text from images with RapidOCR. + + Args: + images: Images to extract text from. + + Returns: + Text extracted from images. + + Raises: + ImportError: If `rapidocr-onnxruntime` package is not installed. + """ try: from rapidocr_onnxruntime import RapidOCR except ImportError: diff --git a/libs/langchain/langchain/embeddings/__init__.py b/libs/langchain/langchain/embeddings/__init__.py index 47251dbe7ee..7c9acdb5e48 100644 --- a/libs/langchain/langchain/embeddings/__init__.py +++ b/libs/langchain/langchain/embeddings/__init__.py @@ -43,6 +43,7 @@ from langchain.embeddings.huggingface import ( from langchain.embeddings.huggingface_hub import HuggingFaceHubEmbeddings from langchain.embeddings.javelin_ai_gateway import JavelinAIGatewayEmbeddings from langchain.embeddings.jina import JinaEmbeddings +from langchain.embeddings.johnsnowlabs import JohnSnowLabsEmbeddings from langchain.embeddings.llamacpp import LlamaCppEmbeddings from langchain.embeddings.localai import LocalAIEmbeddings from langchain.embeddings.minimax import MiniMaxEmbeddings @@ -113,6 +114,7 @@ __all__ = [ "JavelinAIGatewayEmbeddings", "OllamaEmbeddings", "QianfanEmbeddingsEndpoint", + "JohnSnowLabsEmbeddings", ] diff --git a/libs/langchain/langchain/embeddings/cohere.py b/libs/langchain/langchain/embeddings/cohere.py index 1c908876f9d..1a6b3cdca0d 100644 --- a/libs/langchain/langchain/embeddings/cohere.py +++ b/libs/langchain/langchain/embeddings/cohere.py @@ -33,6 +33,11 @@ class CohereEmbeddings(BaseModel, Embeddings): cohere_api_key: Optional[str] = None + max_retries: Optional[int] = None + """Maximum number of retries to make when generating.""" + request_timeout: Optional[float] = None + """Timeout in seconds for the Cohere API request.""" + class Config: """Configuration for this pydantic object.""" @@ -44,11 +49,18 @@ class CohereEmbeddings(BaseModel, Embeddings): cohere_api_key = get_from_dict_or_env( values, "cohere_api_key", "COHERE_API_KEY" ) + max_retries = values.get("max_retries") + request_timeout = values.get("request_timeout") + try: import cohere - values["client"] = cohere.Client(cohere_api_key) - values["async_client"] = cohere.AsyncClient(cohere_api_key) + values["client"] = cohere.Client( + cohere_api_key, max_retries=max_retries, timeout=request_timeout + ) + values["async_client"] = cohere.AsyncClient( + cohere_api_key, max_retries=max_retries, timeout=request_timeout + ) except ImportError: raise ValueError( "Could not import cohere python package. " diff --git a/libs/langchain/langchain/embeddings/johnsnowlabs.py b/libs/langchain/langchain/embeddings/johnsnowlabs.py new file mode 100644 index 00000000000..ead57e52283 --- /dev/null +++ b/libs/langchain/langchain/embeddings/johnsnowlabs.py @@ -0,0 +1,92 @@ +import os +import sys +from typing import Any, List + +from langchain.embeddings.base import Embeddings +from langchain.pydantic_v1 import BaseModel, Extra + + +class JohnSnowLabsEmbeddings(BaseModel, Embeddings): + """JohnSnowLabs embedding models + + To use, you should have the ``johnsnowlabs`` python package installed. + Example: + .. code-block:: python + + from langchain.embeddings.johnsnowlabs import JohnSnowLabsEmbeddings + + embedding = JohnSnowLabsEmbeddings(model='embed_sentence.bert') + output = embedding.embed_query("foo bar") + """ + + model: Any = "embed_sentence.bert" + + def __init__( + self, + model: Any = "embed_sentence.bert", + hardware_target: str = "cpu", + **kwargs: Any + ): + """Initialize the johnsnowlabs model.""" + super().__init__(**kwargs) + # 1) Check imports + try: + from johnsnowlabs import nlp + from nlu.pipe.pipeline import NLUPipeline + except ImportError as exc: + raise ImportError( + "Could not import johnsnowlabs python package. " + "Please install it with `pip install johnsnowlabs`." + ) from exc + + # 2) Start a Spark Session + try: + os.environ["PYSPARK_PYTHON"] = sys.executable + os.environ["PYSPARK_DRIVER_PYTHON"] = sys.executable + nlp.start(hardware_target=hardware_target) + except Exception as exc: + raise Exception("Failure starting Spark Session") from exc + + # 3) Load the model + try: + if isinstance(model, str): + self.model = nlp.load(model) + elif isinstance(model, NLUPipeline): + self.model = model + else: + self.model = nlp.to_nlu_pipe(model) + except Exception as exc: + raise Exception("Failure loading model") from exc + + class Config: + """Configuration for this pydantic object.""" + + extra = Extra.forbid + + def embed_documents(self, texts: List[str]) -> List[List[float]]: + """Compute doc embeddings using a JohnSnowLabs transformer model. + + Args: + texts: The list of texts to embed. + + Returns: + List of embeddings, one for each text. + """ + + df = self.model.predict(texts, output_level="document") + emb_col = None + for c in df.columns: + if "embedding" in c: + emb_col = c + return [vec.tolist() for vec in df[emb_col].tolist()] + + def embed_query(self, text: str) -> List[float]: + """Compute query embeddings using a JohnSnowLabs transformer model. + + Args: + text: The text to embed. + + Returns: + Embeddings for the text. + """ + return self.embed_documents([text])[0] diff --git a/libs/langchain/langchain/embeddings/sagemaker_endpoint.py b/libs/langchain/langchain/embeddings/sagemaker_endpoint.py index 6bfd29f2c22..0e724624ae8 100644 --- a/libs/langchain/langchain/embeddings/sagemaker_endpoint.py +++ b/libs/langchain/langchain/embeddings/sagemaker_endpoint.py @@ -46,8 +46,18 @@ class SagemakerEndpointEmbeddings(BaseModel, Embeddings): region_name=region_name, credentials_profile_name=credentials_profile_name ) + + #Use with boto3 client + client = boto3.client( + "sagemaker-runtime", + region_name=region_name + ) + se = SagemakerEndpointEmbeddings( + endpoint_name=endpoint_name, + client=client + ) """ - client: Any #: :meta private: + client: Any = None endpoint_name: str = "" """The name of the endpoint from the deployed Sagemaker model. @@ -106,6 +116,10 @@ class SagemakerEndpointEmbeddings(BaseModel, Embeddings): @root_validator() def validate_environment(cls, values: Dict) -> Dict: + """Dont do anything if client provided externally""" + if values.get("client") is not None: + return values + """Validate that AWS credentials to and python package exists in environment.""" try: import boto3 diff --git a/libs/langchain/langchain/evaluation/__init__.py b/libs/langchain/langchain/evaluation/__init__.py index a2143cada36..dd87630b563 100644 --- a/libs/langchain/langchain/evaluation/__init__.py +++ b/libs/langchain/langchain/evaluation/__init__.py @@ -69,6 +69,11 @@ from langchain.evaluation.embedding_distance import ( ) from langchain.evaluation.exact_match.base import ExactMatchStringEvaluator from langchain.evaluation.loading import load_dataset, load_evaluator, load_evaluators +from langchain.evaluation.parsing.base import ( + JsonEqualityEvaluator, + JsonValidityEvaluator, +) +from langchain.evaluation.parsing.json_distance import JsonEditDistanceEvaluator from langchain.evaluation.qa import ContextQAEvalChain, CotQAEvalChain, QAEvalChain from langchain.evaluation.regex_match.base import RegexMatchStringEvaluator from langchain.evaluation.schema import ( @@ -114,4 +119,7 @@ __all__ = [ "AgentTrajectoryEvaluator", "ScoreStringEvalChain", "LabeledScoreStringEvalChain", + "JsonValidityEvaluator", + "JsonEqualityEvaluator", + "JsonEditDistanceEvaluator", ] diff --git a/libs/langchain/langchain/evaluation/loading.py b/libs/langchain/langchain/evaluation/loading.py index 21aacaf6148..ac218f0ca58 100644 --- a/libs/langchain/langchain/evaluation/loading.py +++ b/libs/langchain/langchain/evaluation/loading.py @@ -19,6 +19,7 @@ from langchain.evaluation.parsing.base import ( JsonEqualityEvaluator, JsonValidityEvaluator, ) +from langchain.evaluation.parsing.json_distance import JsonEditDistanceEvaluator from langchain.evaluation.qa import ContextQAEvalChain, CotQAEvalChain, QAEvalChain from langchain.evaluation.regex_match.base import RegexMatchStringEvaluator from langchain.evaluation.schema import EvaluatorType, LLMEvalChain, StringEvaluator @@ -86,6 +87,7 @@ _EVALUATOR_MAP: Dict[ EvaluatorType.PAIRWISE_EMBEDDING_DISTANCE: PairwiseEmbeddingDistanceEvalChain, EvaluatorType.JSON_VALIDITY: JsonValidityEvaluator, EvaluatorType.JSON_EQUALITY: JsonEqualityEvaluator, + EvaluatorType.JSON_EDIT_DISTANCE: JsonEditDistanceEvaluator, EvaluatorType.REGEX_MATCH: RegexMatchStringEvaluator, EvaluatorType.EXACT_MATCH: ExactMatchStringEvaluator, } diff --git a/libs/langchain/langchain/evaluation/parsing/base.py b/libs/langchain/langchain/evaluation/parsing/base.py index d2233b51481..15042974190 100644 --- a/libs/langchain/langchain/evaluation/parsing/base.py +++ b/libs/langchain/langchain/evaluation/parsing/base.py @@ -122,9 +122,12 @@ class JsonEqualityEvaluator(StringEvaluator): return "json_equality" def _parse_json( - self, string: str + self, + string: Any, ) -> Union[dict, list, None, float, bool, int, str]: - return parse_json_markdown(string) + if isinstance(string, str): + return parse_json_markdown(string) + return string def _evaluate_strings( self, diff --git a/libs/langchain/langchain/evaluation/parsing/json_distance.py b/libs/langchain/langchain/evaluation/parsing/json_distance.py new file mode 100644 index 00000000000..93287136d35 --- /dev/null +++ b/libs/langchain/langchain/evaluation/parsing/json_distance.py @@ -0,0 +1,91 @@ +import json +from typing import Any, Callable, Optional, Union + +from langchain.evaluation.schema import StringEvaluator +from langchain.output_parsers.json import parse_json_markdown + + +class JsonEditDistanceEvaluator(StringEvaluator): + """ + An evaluator that calculates the edit distance between JSON strings. + + This evaluator computes a normalized Damerau-Levenshtein distance between two JSON strings + after parsing them and converting them to a canonical format (i.e., whitespace and key order are normalized). + It can be customized with alternative distance and canonicalization functions. + + Args: + string_distance (Optional[Callable[[str, str], float]]): A callable that computes the distance between two strings. + If not provided, a Damerau-Levenshtein distance from the `rapidfuzz` package will be used. + canonicalize (Optional[Callable[[Any], Any]]): A callable that converts a parsed JSON object into its canonical string form. + If not provided, the default behavior is to serialize the JSON with sorted keys and no extra whitespace. + **kwargs (Any): Additional keyword arguments. + + Attributes: + _string_distance (Callable[[str, str], float]): The internal distance computation function. + _canonicalize (Callable[[Any], Any]): The internal canonicalization function. + + Examples: + >>> evaluator = JsonEditDistanceEvaluator() + >>> result = evaluator.evaluate_strings(prediction='{"a": 1, "b": 2}', reference='{"a": 1, "b": 3}') + >>> assert result["score"] is not None + + Raises: + ImportError: If `rapidfuzz` is not installed and no alternative `string_distance` function is provided. + + """ # noqa: E501 + + def __init__( + self, + string_distance: Optional[Callable[[str, str], float]] = None, + canonicalize: Optional[Callable[[Any], Any]] = None, + **kwargs: Any + ) -> None: + super().__init__() + if string_distance is not None: + self._string_distance = string_distance + else: + try: + from rapidfuzz import distance as rfd # noqa: F401 + except ImportError: + raise ImportError( + "The default string_distance operator for the " + " JsonEditDistanceEvaluator requires installation of " + "the rapidfuzz package. " + "Please install it with `pip install rapidfuzz`." + ) + self._string_distance = rfd.DamerauLevenshtein.normalized_distance + if canonicalize is not None: + self._canonicalize = canonicalize + else: + self._canonicalize = lambda x: json.dumps( + x, separators=(",", ":"), sort_keys=True # eliminate whitespace + ) + + @property + def requires_input(self) -> bool: + return False + + @property + def requires_reference(self) -> bool: + return True + + @property + def evaluation_name(self) -> str: + return "json_edit_distance" + + def _parse_json(self, node: Any) -> Union[dict, list, None, float, bool, int, str]: + if isinstance(node, str): + return parse_json_markdown(node) + return node + + def _evaluate_strings( + self, + prediction: str, + input: Optional[str] = None, + reference: Optional[str] = None, + **kwargs: Any + ) -> dict: + parsed = self._canonicalize(self._parse_json(prediction)) + label = self._canonicalize(self._parse_json(reference)) + distance = self._string_distance(parsed, label) + return {"score": distance} diff --git a/libs/langchain/langchain/evaluation/schema.py b/libs/langchain/langchain/evaluation/schema.py index 50b541f4627..43e8ed92edc 100644 --- a/libs/langchain/langchain/evaluation/schema.py +++ b/libs/langchain/langchain/evaluation/schema.py @@ -64,6 +64,8 @@ class EvaluatorType(str, Enum): """Check if a prediction is valid JSON.""" JSON_EQUALITY = "json_equality" """Check if a prediction is equal to a reference JSON.""" + JSON_EDIT_DISTANCE = "json_edit_distance" + """Compute the edit distance between two JSON strings after canonicalization.""" class LLMEvalChain(Chain): diff --git a/libs/langchain/langchain/graphs/rdf_graph.py b/libs/langchain/langchain/graphs/rdf_graph.py index f3388238ed9..1a2b89ba87b 100644 --- a/libs/langchain/langchain/graphs/rdf_graph.py +++ b/libs/langchain/langchain/graphs/rdf_graph.py @@ -45,7 +45,7 @@ rel_query_rdf = prefixes["rdfs"] + ( """SELECT DISTINCT ?rel ?com\n""" """WHERE { \n""" """ ?subj ?rel ?obj . \n""" - """ OPTIONAL { ?cls rdfs:comment ?com } \n""" + """ OPTIONAL { ?rel rdfs:comment ?com } \n""" """}""" ) @@ -56,7 +56,7 @@ rel_query_rdfs = ( """SELECT DISTINCT ?rel ?com\n""" """WHERE { \n""" """ ?rel a/rdfs:subPropertyOf* rdf:Property . \n""" - """ OPTIONAL { ?cls rdfs:comment ?com } \n""" + """ OPTIONAL { ?rel rdfs:comment ?com } \n""" """}""" ) ) @@ -68,7 +68,7 @@ op_query_owl = ( """SELECT DISTINCT ?op ?com\n""" """WHERE { \n""" """ ?op a/rdfs:subPropertyOf* owl:ObjectProperty . \n""" - """ OPTIONAL { ?cls rdfs:comment ?com } \n""" + """ OPTIONAL { ?op rdfs:comment ?com } \n""" """}""" ) ) @@ -80,7 +80,7 @@ dp_query_owl = ( """SELECT DISTINCT ?dp ?com\n""" """WHERE { \n""" """ ?dp a/rdfs:subPropertyOf* owl:DatatypeProperty . \n""" - """ OPTIONAL { ?cls rdfs:comment ?com } \n""" + """ OPTIONAL { ?dp rdfs:comment ?com } \n""" """}""" ) ) diff --git a/libs/langchain/langchain/llms/__init__.py b/libs/langchain/langchain/llms/__init__.py index e44255cb6c0..351a75f3786 100644 --- a/libs/langchain/langchain/llms/__init__.py +++ b/libs/langchain/langchain/llms/__init__.py @@ -184,6 +184,12 @@ def _import_forefrontai() -> Any: return ForefrontAI +def _import_gigachat() -> Any: + from langchain.llms.gigachat import GigaChat + + return GigaChat + + def _import_google_palm() -> Any: from langchain.llms.google_palm import GooglePalm @@ -438,6 +444,12 @@ def _import_titan_takeoff() -> Any: return TitanTakeoff +def _import_titan_takeoff_pro() -> Any: + from langchain.llms.titan_takeoff_pro import TitanTakeoffPro + + return TitanTakeoffPro + + def _import_together() -> Any: from langchain.llms.together import Together @@ -547,6 +559,8 @@ def __getattr__(name: str) -> Any: return _import_fireworks() elif name == "ForefrontAI": return _import_forefrontai() + elif name == "GigaChat": + return _import_gigachat() elif name == "GooglePalm": return _import_google_palm() elif name == "GooseAI": @@ -631,6 +645,8 @@ def __getattr__(name: str) -> Any: return _import_textgen() elif name == "TitanTakeoff": return _import_titan_takeoff() + elif name == "TitanTakeoffPro": + return _import_titan_takeoff_pro() elif name == "Together": return _import_together() elif name == "Tongyi": @@ -686,6 +702,7 @@ __all__ = [ "FakeListLLM", "Fireworks", "ForefrontAI", + "GigaChat", "GPT4All", "GooglePalm", "GooseAI", @@ -726,6 +743,7 @@ __all__ = [ "SelfHostedPipeline", "StochasticAI", "TitanTakeoff", + "TitanTakeoffPro", "Tongyi", "VertexAI", "VertexAIModelGarden", @@ -767,6 +785,7 @@ def get_type_to_cls_dict() -> Dict[str, Callable[[], Type[BaseLLM]]]: "edenai": _import_edenai, "fake-list": _import_fake, "forefrontai": _import_forefrontai, + "giga-chat-model": _import_gigachat, "google_palm": _import_google_palm, "gooseai": _import_gooseai, "gradient": _import_gradient_ai, @@ -803,6 +822,7 @@ def get_type_to_cls_dict() -> Dict[str, Callable[[], Type[BaseLLM]]]: "together": _import_together, "tongyi": _import_tongyi, "titan_takeoff": _import_titan_takeoff, + "titan_takeoff_pro": _import_titan_takeoff_pro, "vertexai": _import_vertex, "vertexai_model_garden": _import_vertex_model_garden, "openllm": _import_openllm, diff --git a/libs/langchain/langchain/llms/anyscale.py b/libs/langchain/langchain/llms/anyscale.py index 65e4a9aded3..a57db18cc49 100644 --- a/libs/langchain/langchain/llms/anyscale.py +++ b/libs/langchain/langchain/llms/anyscale.py @@ -61,7 +61,8 @@ def create_llm_result( class Anyscale(BaseOpenAI): - """Wrapper around Anyscale Endpoint. + """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. diff --git a/libs/langchain/langchain/llms/azureml_endpoint.py b/libs/langchain/langchain/llms/azureml_endpoint.py index e8baa7d8c45..e33e1c82b73 100644 --- a/libs/langchain/langchain/llms/azureml_endpoint.py +++ b/libs/langchain/langchain/llms/azureml_endpoint.py @@ -180,7 +180,7 @@ class LlamaContentFormatter(ContentFormatterBase): """Content formatter for LLaMa""" def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes: - """Formats the request according the the chosen api""" + """Formats the request according to the chosen api""" prompt = ContentFormatterBase.escape_special_characters(prompt) request_payload = json.dumps( { diff --git a/libs/langchain/langchain/llms/baidu_qianfan_endpoint.py b/libs/langchain/langchain/llms/baidu_qianfan_endpoint.py index 5546170af45..53b79085e56 100644 --- a/libs/langchain/langchain/llms/baidu_qianfan_endpoint.py +++ b/libs/langchain/langchain/llms/baidu_qianfan_endpoint.py @@ -96,7 +96,7 @@ class QianfanLLMEndpoint(LLM): values["client"] = qianfan.Completion(**params) except ImportError: - raise ValueError( + raise ImportError( "qianfan package not found, please install it with " "`pip install qianfan`" ) @@ -116,7 +116,7 @@ class QianfanLLMEndpoint(LLM): @property def _default_params(self) -> Dict[str, Any]: - """Get the default parameters for calling OpenAI API.""" + """Get the default parameters for calling Qianfan API.""" normal_params = { "model": self.model, "endpoint": self.endpoint, diff --git a/libs/langchain/langchain/llms/base.py b/libs/langchain/langchain/llms/base.py index 44be0f83638..2411afbce88 100644 --- a/libs/langchain/langchain/llms/base.py +++ b/libs/langchain/langchain/llms/base.py @@ -294,6 +294,7 @@ class BaseLLM(BaseLanguageModel[str], ABC): inputs[i : i + max_concurrency] for i in range(0, len(inputs), max_concurrency) ] + config = [{**c, "max_concurrency": None} for c in config] # type: ignore[misc] return [ output for batch in batches @@ -336,10 +337,13 @@ class BaseLLM(BaseLanguageModel[str], ABC): inputs[i : i + max_concurrency] for i in range(0, len(inputs), max_concurrency) ] + config = [{**c, "max_concurrency": None} for c in config] # type: ignore[misc] return [ output for batch in batches - for output in await self.abatch(batch, config=config, **kwargs) + for output in await self.abatch( + batch, config=config, return_exceptions=return_exceptions, **kwargs + ) ] def stream( diff --git a/libs/langchain/langchain/llms/bittensor.py b/libs/langchain/langchain/llms/bittensor.py index 883651439ab..97eef620d70 100644 --- a/libs/langchain/langchain/llms/bittensor.py +++ b/libs/langchain/langchain/llms/bittensor.py @@ -8,7 +8,8 @@ from langchain.llms.base import LLM class NIBittensorLLM(LLM): - """ + """NIBittensor LLMs + NIBittensorLLM is created by Neural Internet (https://neuralinternet.ai/), powered by Bittensor, a decentralized network full of different AI models. diff --git a/libs/langchain/langchain/llms/databricks.py b/libs/langchain/langchain/llms/databricks.py index fb4a3674b37..6488244ff4d 100644 --- a/libs/langchain/langchain/llms/databricks.py +++ b/libs/langchain/langchain/llms/databricks.py @@ -92,7 +92,7 @@ def get_repl_context() -> Any: return get_context() except ImportError: - raise ValueError( + raise ImportError( "Cannot access dbruntime, not running inside a Databricks notebook." ) diff --git a/libs/langchain/langchain/llms/deepinfra.py b/libs/langchain/langchain/llms/deepinfra.py index 9229d2712d9..0f756615e75 100644 --- a/libs/langchain/langchain/llms/deepinfra.py +++ b/libs/langchain/langchain/llms/deepinfra.py @@ -1,11 +1,15 @@ -from typing import Any, Dict, List, Mapping, Optional +import json +from typing import Any, AsyncIterator, Dict, Iterator, List, Mapping, Optional -import requests +import aiohttp -from langchain.callbacks.manager import CallbackManagerForLLMRun -from langchain.llms.base import LLM -from langchain.llms.utils import enforce_stop_tokens +from langchain.callbacks.manager import ( + AsyncCallbackManagerForLLMRun, + CallbackManagerForLLMRun, +) +from langchain.llms.base import LLM, GenerationChunk from langchain.pydantic_v1 import Extra, root_validator +from langchain.utilities.requests import Requests from langchain.utils import get_from_dict_or_env DEFAULT_MODEL_ID = "google/flan-t5-xl" @@ -14,9 +18,9 @@ DEFAULT_MODEL_ID = "google/flan-t5-xl" class DeepInfra(LLM): """DeepInfra models. - To use, you should have the ``requests`` python package installed, and the - environment variable ``DEEPINFRA_API_TOKEN`` set with your API token, or pass - it as a named parameter to the constructor. + To use, you should have the environment variable ``DEEPINFRA_API_TOKEN`` + set with your API token, or pass it as a named parameter to the + constructor. Only supports `text-generation` and `text2text-generation` for now. @@ -29,7 +33,7 @@ class DeepInfra(LLM): """ model_id: str = DEFAULT_MODEL_ID - model_kwargs: Optional[dict] = None + model_kwargs: Optional[Dict] = None deepinfra_api_token: Optional[str] = None @@ -60,6 +64,35 @@ class DeepInfra(LLM): """Return type of llm.""" return "deepinfra" + def _url(self) -> str: + return f"https://api.deepinfra.com/v1/inference/{self.model_id}" + + def _headers(self) -> Dict: + return { + "Authorization": f"bearer {self.deepinfra_api_token}", + "Content-Type": "application/json", + } + + def _body(self, prompt: str, kwargs: Any) -> Dict: + model_kwargs = self.model_kwargs or {} + model_kwargs = {**model_kwargs, **kwargs} + + return { + "input": prompt, + **model_kwargs, + } + + def _handle_status(self, code: int, text: Any) -> None: + if code >= 500: + raise Exception(f"DeepInfra Server: Error {code}") + elif code >= 400: + raise ValueError(f"DeepInfra received an invalid payload: {text}") + elif code != 200: + raise Exception( + f"DeepInfra returned an unexpected response with status " + f"{code}: {text}" + ) + def _call( self, prompt: str, @@ -81,38 +114,105 @@ class DeepInfra(LLM): response = di("Tell me a joke.") """ - _model_kwargs = self.model_kwargs or {} - _model_kwargs = {**_model_kwargs, **kwargs} - # HTTP headers for authorization - headers = { - "Authorization": f"bearer {self.deepinfra_api_token}", - "Content-Type": "application/json", - } - try: - res = requests.post( - f"https://api.deepinfra.com/v1/inference/{self.model_id}", - headers=headers, - json={"input": prompt, **_model_kwargs}, - ) - except requests.exceptions.RequestException as e: - raise ValueError(f"Error raised by inference endpoint: {e}") + request = Requests(headers=self._headers()) + response = request.post(url=self._url(), data=self._body(prompt, kwargs)) - if res.status_code != 200: - raise ValueError( - "Error raised by inference API HTTP code: %s, %s" - % (res.status_code, res.text) - ) - try: - t = res.json() - text = t["results"][0]["generated_text"] - except requests.exceptions.JSONDecodeError as e: - raise ValueError( - f"Error raised by inference API: {e}.\nResponse: {res.text}" - ) + self._handle_status(response.status_code, response.text) + data = response.json() - if stop is not None: - # I believe this is required since the stop tokens - # are not enforced by the model parameters - text = enforce_stop_tokens(text, stop) - return text + return data["results"][0]["generated_text"] + + async def _acall( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> str: + request = Requests(headers=self._headers()) + async with request.apost( + url=self._url(), data=self._body(prompt, kwargs) + ) as response: + self._handle_status(response.status, response.text) + data = await response.json() + return data["results"][0]["generated_text"] + + def _stream( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> Iterator[GenerationChunk]: + request = Requests(headers=self._headers()) + response = request.post( + url=self._url(), data=self._body(prompt, {**kwargs, "stream": True}) + ) + + self._handle_status(response.status_code, response.text) + for line in _parse_stream(response.iter_lines()): + chunk = _handle_sse_line(line) + if chunk: + yield chunk + if run_manager: + run_manager.on_llm_new_token(chunk.text) + + async def _astream( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> AsyncIterator[GenerationChunk]: + request = Requests(headers=self._headers()) + async with request.apost( + url=self._url(), data=self._body(prompt, {**kwargs, "stream": True}) + ) as response: + self._handle_status(response.status, response.text) + async for line in _parse_stream_async(response.content): + chunk = _handle_sse_line(line) + if chunk: + yield chunk + if run_manager: + await run_manager.on_llm_new_token(chunk.text) + + +def _parse_stream(rbody: Iterator[bytes]) -> Iterator[str]: + for line in rbody: + _line = _parse_stream_helper(line) + if _line is not None: + yield _line + + +async def _parse_stream_async(rbody: aiohttp.StreamReader) -> AsyncIterator[str]: + async for line in rbody: + _line = _parse_stream_helper(line) + if _line is not None: + yield _line + + +def _parse_stream_helper(line: bytes) -> Optional[str]: + if line and line.startswith(b"data:"): + if line.startswith(b"data: "): + # SSE event may be valid when it contain whitespace + line = line[len(b"data: ") :] + else: + line = line[len(b"data:") :] + if line.strip() == b"[DONE]": + # return here will cause GeneratorExit exception in urllib3 + # and it will close http connection with TCP Reset + return None + else: + return line.decode("utf-8") + return None + + +def _handle_sse_line(line: str) -> Optional[GenerationChunk]: + try: + obj = json.loads(line) + return GenerationChunk( + text=obj.get("token", {}).get("text"), + ) + except Exception: + return None diff --git a/libs/langchain/langchain/llms/fireworks.py b/libs/langchain/langchain/llms/fireworks.py index 73f4a96aa97..676c8813b06 100644 --- a/libs/langchain/langchain/llms/fireworks.py +++ b/libs/langchain/langchain/llms/fireworks.py @@ -1,12 +1,14 @@ +import asyncio +from concurrent.futures import ThreadPoolExecutor from typing import Any, AsyncIterator, Callable, Dict, Iterator, List, Optional, Union from langchain.callbacks.manager import ( AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun, ) -from langchain.llms.base import LLM, create_base_retry_decorator +from langchain.llms.base import BaseLLM, create_base_retry_decorator from langchain.pydantic_v1 import Field, root_validator -from langchain.schema.output import GenerationChunk +from langchain.schema.output import Generation, GenerationChunk, LLMResult from langchain.utils.env import get_from_dict_or_env @@ -23,7 +25,7 @@ def _stream_response_to_generation_chunk( ) -class Fireworks(LLM): +class Fireworks(BaseLLM): """Fireworks models.""" model: str = "accounts/fireworks/models/llama-v2-7b-chat" @@ -36,6 +38,16 @@ class Fireworks(LLM): ) fireworks_api_key: Optional[str] = None max_retries: int = 20 + batch_size: int = 20 + use_retry: bool = True + + @property + def lc_secrets(self) -> Dict[str, str]: + return {"fireworks_api_key": "FIREWORKS_API_KEY"} + + @classmethod + def is_lc_serializable(cls) -> bool: + return True @root_validator() def validate_environment(cls, values: Dict) -> Dict: @@ -58,43 +70,92 @@ class Fireworks(LLM): """Return type of llm.""" return "fireworks" - def _call( + def _generate( self, - prompt: str, + prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, - ) -> str: - """Run the LLM on the given prompt and input.""" - params: dict = { + ) -> LLMResult: + """Call out to Fireworks 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. + """ + params = { "model": self.model, - "prompt": prompt, **self.model_kwargs, } - response = completion_with_retry( - self, run_manager=run_manager, stop=stop, **params - ) + sub_prompts = self.get_batch_prompts(prompts) + choices = [] + for _prompts in sub_prompts: + response = completion_with_retry_batching( + self, + self.use_retry, + prompt=_prompts, + run_manager=run_manager, + stop=stop, + **params, + ) + choices.extend(response) - return response.choices[0].text + return self.create_llm_result(choices, prompts) - async def _acall( + async def _agenerate( self, - prompt: str, + prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, - ) -> str: - """Run the LLM on the given prompt and input.""" + ) -> LLMResult: + """Call out to Fireworks endpoint async with k unique prompts.""" params = { "model": self.model, - "prompt": prompt, **self.model_kwargs, } - response = await acompletion_with_retry( - self, run_manager=run_manager, stop=stop, **params - ) + sub_prompts = self.get_batch_prompts(prompts) + choices = [] + for _prompts in sub_prompts: + response = await acompletion_with_retry_batching( + self, + self.use_retry, + prompt=_prompts, + run_manager=run_manager, + stop=stop, + **params, + ) + choices.extend(response) - return response.choices[0].text + return self.create_llm_result(choices, prompts) + + def get_batch_prompts( + self, + prompts: List[str], + ) -> List[List[str]]: + """Get the sub prompts for llm call.""" + sub_prompts = [ + prompts[i : i + self.batch_size] + for i in range(0, len(prompts), self.batch_size) + ] + return sub_prompts + + def create_llm_result(self, choices: Any, prompts: List[str]) -> LLMResult: + """Create the LLMResult from the choices and prompts.""" + generations = [] + for i, _ in enumerate(prompts): + sub_choices = choices[i : (i + 1)] + generations.append( + [ + Generation( + text=choice.__dict__["choices"][0].text, + ) + for choice in sub_choices + ] + ) + llm_output = {"model": self.model} + return LLMResult(generations=generations, llm_output=llm_output) def _stream( self, @@ -110,7 +171,7 @@ class Fireworks(LLM): **self.model_kwargs, } for stream_resp in completion_with_retry( - self, run_manager=run_manager, stop=stop, **params + self, self.use_retry, run_manager=run_manager, stop=stop, **params ): chunk = _stream_response_to_generation_chunk(stream_resp) yield chunk @@ -131,7 +192,7 @@ class Fireworks(LLM): **self.model_kwargs, } async for stream_resp in await acompletion_with_retry_streaming( - self, run_manager=run_manager, stop=stop, **params + self, self.use_retry, run_manager=run_manager, stop=stop, **params ): chunk = _stream_response_to_generation_chunk(stream_resp) yield chunk @@ -139,8 +200,20 @@ class Fireworks(LLM): await run_manager.on_llm_new_token(chunk.text, chunk=chunk) +def conditional_decorator( + condition: bool, decorator: Callable[[Any], Any] +) -> Callable[[Any], Any]: + def actual_decorator(func: Callable[[Any], Any]) -> Callable[[Any], Any]: + if condition: + return decorator(func) + return func + + return actual_decorator + + def completion_with_retry( llm: Fireworks, + use_retry: bool, *, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, @@ -150,7 +223,7 @@ def completion_with_retry( retry_decorator = _create_retry_decorator(llm, run_manager=run_manager) - @retry_decorator + @conditional_decorator(use_retry, retry_decorator) def _completion_with_retry(**kwargs: Any) -> Any: return fireworks.client.Completion.create( **kwargs, @@ -161,6 +234,7 @@ def completion_with_retry( async def acompletion_with_retry( llm: Fireworks, + use_retry: bool, *, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, @@ -170,7 +244,7 @@ async def acompletion_with_retry( retry_decorator = _create_retry_decorator(llm, run_manager=run_manager) - @retry_decorator + @conditional_decorator(use_retry, retry_decorator) async def _completion_with_retry(**kwargs: Any) -> Any: return await fireworks.client.Completion.acreate( **kwargs, @@ -179,8 +253,79 @@ async def acompletion_with_retry( return await _completion_with_retry(**kwargs) +def completion_with_retry_batching( + llm: Fireworks, + use_retry: bool, + *, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, +) -> Any: + """Use tenacity to retry the completion call.""" + import fireworks.client + + prompt = kwargs["prompt"] + del kwargs["prompt"] + + retry_decorator = _create_retry_decorator(llm, run_manager=run_manager) + + @conditional_decorator(use_retry, retry_decorator) + def _completion_with_retry(prompt: str) -> Any: + return fireworks.client.Completion.create(**kwargs, prompt=prompt) + + def batch_sync_run() -> List: + with ThreadPoolExecutor() as executor: + results = list(executor.map(_completion_with_retry, prompt)) + return results + + return batch_sync_run() + + +async def acompletion_with_retry_batching( + llm: Fireworks, + use_retry: bool, + *, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, +) -> Any: + """Use tenacity to retry the completion call.""" + import fireworks.client + + prompt = kwargs["prompt"] + del kwargs["prompt"] + + retry_decorator = _create_retry_decorator(llm, run_manager=run_manager) + + @conditional_decorator(use_retry, retry_decorator) + async def _completion_with_retry(prompt: str) -> Any: + return await fireworks.client.Completion.acreate(**kwargs, prompt=prompt) + + def run_coroutine_in_new_loop( + coroutine_func: Any, *args: Dict, **kwargs: Dict + ) -> Any: + new_loop = asyncio.new_event_loop() + try: + asyncio.set_event_loop(new_loop) + return new_loop.run_until_complete(coroutine_func(*args, **kwargs)) + finally: + new_loop.close() + + async def batch_sync_run() -> List: + with ThreadPoolExecutor() as executor: + results = list( + executor.map( + run_coroutine_in_new_loop, + [_completion_with_retry] * len(prompt), + prompt, + ) + ) + return results + + return await batch_sync_run() + + async def acompletion_with_retry_streaming( llm: Fireworks, + use_retry: bool, *, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, @@ -190,7 +335,7 @@ async def acompletion_with_retry_streaming( retry_decorator = _create_retry_decorator(llm, run_manager=run_manager) - @retry_decorator + @conditional_decorator(use_retry, retry_decorator) async def _completion_with_retry(**kwargs: Any) -> Any: return fireworks.client.Completion.acreate( **kwargs, @@ -211,6 +356,8 @@ def _create_retry_decorator( errors = [ fireworks.client.error.RateLimitError, + fireworks.client.error.InternalServerError, + fireworks.client.error.BadGatewayError, fireworks.client.error.ServiceUnavailableError, ] return create_base_retry_decorator( diff --git a/libs/langchain/langchain/llms/gigachat.py b/libs/langchain/langchain/llms/gigachat.py new file mode 100644 index 00000000000..b484d8bb415 --- /dev/null +++ b/libs/langchain/langchain/llms/gigachat.py @@ -0,0 +1,259 @@ +from __future__ import annotations + +import logging +from functools import cached_property +from typing import Any, AsyncIterator, Dict, Iterator, List, Optional + +from langchain.callbacks.manager import ( + AsyncCallbackManagerForLLMRun, + CallbackManagerForLLMRun, +) +from langchain.llms.base import BaseLLM +from langchain.load.serializable import Serializable +from langchain.pydantic_v1 import root_validator +from langchain.schema.output import Generation, GenerationChunk, LLMResult + +logger = logging.getLogger(__name__) + + +class _BaseGigaChat(Serializable): + base_url: Optional[str] = None + """ Base API URL """ + auth_url: Optional[str] = None + """ Auth URL """ + credentials: Optional[str] = None + """ Auth Token """ + scope: Optional[str] = None + """ Permission scope for access token """ + + access_token: Optional[str] = None + """ Access token for GigaChat """ + + model: Optional[str] = None + """Model name to use.""" + user: Optional[str] = None + """ Username for authenticate """ + password: Optional[str] = None + """ Password for authenticate """ + + timeout: Optional[float] = None + """ Timeout for request """ + verify_ssl_certs: Optional[bool] = None + """ Check certificates for all requests """ + + ca_bundle_file: Optional[str] = None + cert_file: Optional[str] = None + key_file: Optional[str] = None + key_file_password: Optional[str] = None + # Support for connection to GigaChat through SSL certificates + + profanity: bool = True + """ Check for profanity """ + streaming: bool = False + """ Whether to stream the results or not. """ + temperature: Optional[float] = None + """What sampling temperature to use.""" + max_tokens: Optional[int] = None + """ Maximum number of tokens to generate """ + + @property + def _llm_type(self) -> str: + return "giga-chat-model" + + @property + def lc_secrets(self) -> Dict[str, str]: + return { + "credentials": "GIGACHAT_CREDENTIALS", + "access_token": "GIGACHAT_ACCESS_TOKEN", + "password": "GIGACHAT_PASSWORD", + "key_file_password": "GIGACHAT_KEY_FILE_PASSWORD", + } + + @property + def lc_serializable(self) -> bool: + return True + + @cached_property + def _client(self) -> Any: + """Returns GigaChat API client""" + import gigachat + + return gigachat.GigaChat( + base_url=self.base_url, + auth_url=self.auth_url, + credentials=self.credentials, + scope=self.scope, + access_token=self.access_token, + model=self.model, + user=self.user, + password=self.password, + timeout=self.timeout, + verify_ssl_certs=self.verify_ssl_certs, + ca_bundle_file=self.ca_bundle_file, + cert_file=self.cert_file, + key_file=self.key_file, + key_file_password=self.key_file_password, + ) + + @root_validator() + def validate_environment(cls, values: Dict) -> Dict: + """Validate authenticate data in environment and python package is installed.""" + try: + import gigachat # noqa: F401 + except ImportError: + raise ImportError( + "Could not import gigachat python package. " + "Please install it with `pip install gigachat`." + ) + return values + + @property + def _identifying_params(self) -> Dict[str, Any]: + """Get the identifying parameters.""" + return { + "temperature": self.temperature, + "model": self.model, + "profanity": self.profanity, + "streaming": self.streaming, + "max_tokens": self.max_tokens, + } + + +class GigaChat(_BaseGigaChat, BaseLLM): + """`GigaChat` large language models API. + + To use, you should pass login and password to access GigaChat API or use token. + + Example: + .. code-block:: python + + from langchain.llms import GigaChat + giga = GigaChat(credentials=..., verify_ssl_certs=False) + """ + + def _build_payload(self, messages: List[str]) -> Dict[str, Any]: + payload: Dict[str, Any] = { + "messages": [{"role": "user", "content": m} for m in messages], + "profanity_check": self.profanity, + } + if self.temperature is not None: + payload["temperature"] = self.temperature + if self.max_tokens is not None: + payload["max_tokens"] = self.max_tokens + if self.model: + payload["model"] = self.model + + if self.verbose: + logger.info("Giga request: %s", payload) + + return payload + + def _create_llm_result(self, response: Any) -> LLMResult: + generations = [] + for res in response.choices: + finish_reason = res.finish_reason + gen = Generation( + text=res.message.content, + generation_info={"finish_reason": finish_reason}, + ) + generations.append([gen]) + if finish_reason != "stop": + logger.warning( + "Giga generation stopped with reason: %s", + finish_reason, + ) + if self.verbose: + logger.info("Giga response: %s", res.message.content) + token_usage = response.usage + llm_output = {"token_usage": token_usage, "model_name": response.model} + return LLMResult(generations=generations, llm_output=llm_output) + + def _generate( + self, + prompts: List[str], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + stream: Optional[bool] = None, + **kwargs: Any, + ) -> LLMResult: + should_stream = stream if stream is not None else self.streaming + if should_stream: + generation: Optional[GenerationChunk] = None + 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 + return LLMResult(generations=[[generation]]) + + payload = self._build_payload(prompts) + response = self._client.chat(payload) + + return self._create_llm_result(response) + + async def _agenerate( + self, + prompts: List[str], + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + stream: Optional[bool] = None, + **kwargs: Any, + ) -> LLMResult: + should_stream = stream if stream is not None else self.streaming + if should_stream: + generation: Optional[GenerationChunk] = None + stream_iter = self._astream( + prompts[0], stop=stop, run_manager=run_manager, **kwargs + ) + async for chunk in stream_iter: + if generation is None: + generation = chunk + else: + generation += chunk + assert generation is not None + return LLMResult(generations=[[generation]]) + + payload = self._build_payload(prompts) + response = await self._client.achat(payload) + + 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]: + payload = self._build_payload([prompt]) + + for chunk in self._client.stream(payload): + if chunk.choices: + content = chunk.choices[0].delta.content + yield GenerationChunk(text=content) + if run_manager: + run_manager.on_llm_new_token(content) + + async def _astream( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> AsyncIterator[GenerationChunk]: + payload = self._build_payload([prompt]) + + async for chunk in self._client.astream(payload): + if chunk.choices: + content = chunk.choices[0].delta.content + yield GenerationChunk(text=content) + if run_manager: + await run_manager.on_llm_new_token(content) + + def get_num_tokens(self, text: str) -> int: + """Count approximate number of tokens""" + return round(len(text) / 4.6) diff --git a/libs/langchain/langchain/llms/gradient_ai.py b/libs/langchain/langchain/llms/gradient_ai.py index 56e44d343e7..1842c4b35bd 100644 --- a/libs/langchain/langchain/llms/gradient_ai.py +++ b/libs/langchain/langchain/llms/gradient_ai.py @@ -18,6 +18,8 @@ from langchain.utils import get_from_dict_or_env class TrainResult(TypedDict): + """Train result.""" + loss: float diff --git a/libs/langchain/langchain/llms/javelin_ai_gateway.py b/libs/langchain/langchain/llms/javelin_ai_gateway.py index 7effebb6b05..80b04424b61 100644 --- a/libs/langchain/langchain/llms/javelin_ai_gateway.py +++ b/libs/langchain/langchain/llms/javelin_ai_gateway.py @@ -21,8 +21,7 @@ class Params(BaseModel, extra=Extra.allow): # type: ignore[call-arg] class JavelinAIGateway(LLM): - """ - Wrapper around completions LLMs in the Javelin AI Gateway. + """Javelin AI Gateway LLMs. To use, you should have the ``javelin_sdk`` python package installed. For more information, see https://docs.getjavelin.io diff --git a/libs/langchain/langchain/llms/titan_takeoff_pro.py b/libs/langchain/langchain/llms/titan_takeoff_pro.py new file mode 100644 index 00000000000..0a8dc78f3e6 --- /dev/null +++ b/libs/langchain/langchain/llms/titan_takeoff_pro.py @@ -0,0 +1,215 @@ +from typing import Any, Iterator, List, Mapping, Optional + +import requests +from requests.exceptions import ConnectionError + +from langchain.callbacks.manager import CallbackManagerForLLMRun +from langchain.llms.base import LLM +from langchain.llms.utils import enforce_stop_tokens +from langchain.schema.output import GenerationChunk + + +class TitanTakeoffPro(LLM): + base_url: Optional[str] = "http://localhost:3000" + """Specifies the baseURL to use for the Titan Takeoff Pro API. + Default = http://localhost:3000. + """ + + max_new_tokens: Optional[int] = None + """Maximum tokens generated.""" + + min_new_tokens: Optional[int] = None + """Minimum tokens generated.""" + + sampling_topk: Optional[int] = None + """Sample predictions from the top K most probable candidates.""" + + sampling_topp: Optional[float] = None + """Sample from predictions whose cumulative probability exceeds this value. + """ + + sampling_temperature: Optional[float] = None + """Sample with randomness. Bigger temperatures are associated with + more randomness and 'creativity'. + """ + + repetition_penalty: Optional[float] = None + """Penalise the generation of tokens that have been generated before. + Set to > 1 to penalize. + """ + + regex_string: Optional[str] = None + """A regex string for constrained generation.""" + + no_repeat_ngram_size: Optional[int] = None + """Prevent repetitions of ngrams of this size. Default = 0 (turned off).""" + + streaming: bool = False + """Whether to stream the output. Default = False.""" + + @property + def _default_params(self) -> Mapping[str, Any]: + """Get the default parameters for calling Titan Takeoff Server (Pro).""" + return { + **( + {"regex_string": self.regex_string} + if self.regex_string is not None + else {} + ), + **( + {"sampling_temperature": self.sampling_temperature} + if self.sampling_temperature is not None + else {} + ), + **( + {"sampling_topp": self.sampling_topp} + if self.sampling_topp is not None + else {} + ), + **( + {"repetition_penalty": self.repetition_penalty} + if self.repetition_penalty is not None + else {} + ), + **( + {"max_new_tokens": self.max_new_tokens} + if self.max_new_tokens is not None + else {} + ), + **( + {"min_new_tokens": self.min_new_tokens} + if self.min_new_tokens is not None + else {} + ), + **( + {"sampling_topk": self.sampling_topk} + if self.sampling_topk is not None + else {} + ), + **( + {"no_repeat_ngram_size": self.no_repeat_ngram_size} + if self.no_repeat_ngram_size is not None + else {} + ), + } + + @property + def _llm_type(self) -> str: + """Return type of llm.""" + return "titan_takeoff_pro" + + def _call( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> str: + """Call out to Titan Takeoff (Pro) generate 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 + + prompt = "What is the capital of the United Kingdom?" + response = model(prompt) + + """ + try: + if self.streaming: + text_output = "" + for chunk in self._stream( + prompt=prompt, + stop=stop, + run_manager=run_manager, + ): + text_output += chunk.text + return text_output + url = f"{self.base_url}/generate" + params = {"text": prompt, **self._default_params} + + response = requests.post(url, json=params) + response.raise_for_status() + response.encoding = "utf-8" + + text = "" + if "text" in response.json(): + text = response.json()["text"] + text = text.replace("", "") + else: + raise ValueError("Something went wrong.") + if stop is not None: + text = enforce_stop_tokens(text, stop) + return text + except ConnectionError: + raise ConnectionError( + "Could not connect to Titan Takeoff (Pro) server. \ + Please make sure that the server is running." + ) + + def _stream( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> Iterator[GenerationChunk]: + """Call out to Titan Takeoff (Pro) stream 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. + + Yields: + A dictionary like object containing a string token. + + Example: + .. code-block:: python + + prompt = "What is the capital of the United Kingdom?" + response = model(prompt) + + """ + url = f"{self.base_url}/generate_stream" + params = {"text": prompt, **self._default_params} + + response = requests.post(url, json=params, stream=True) + response.encoding = "utf-8" + buffer = "" + for text in response.iter_content(chunk_size=1, decode_unicode=True): + buffer += text + if "data:" in buffer: + # Remove the first instance of "data:" from the buffer. + if buffer.startswith("data:"): + buffer = "" + if len(buffer.split("data:", 1)) == 2: + content, _ = buffer.split("data:", 1) + buffer = content.rstrip("\n") + # Trim the buffer to only have content after the "data:" part. + if buffer: # Ensure that there's content to process. + chunk = GenerationChunk(text=buffer) + buffer = "" # Reset buffer for the next set of data. + yield chunk + if run_manager: + run_manager.on_llm_new_token(token=chunk.text) + + # Yield any remaining content in the buffer. + if buffer: + chunk = GenerationChunk(text=buffer.replace("", "")) + yield chunk + if run_manager: + run_manager.on_llm_new_token(token=chunk.text) + + @property + def _identifying_params(self) -> Mapping[str, Any]: + """Get the identifying parameters.""" + return {"base_url": self.base_url, **{}, **self._default_params} diff --git a/libs/langchain/langchain/llms/vertexai.py b/libs/langchain/langchain/llms/vertexai.py index a2dc147edff..70a6ab663fc 100644 --- a/libs/langchain/langchain/llms/vertexai.py +++ b/libs/langchain/langchain/llms/vertexai.py @@ -25,6 +25,7 @@ from langchain.schema import ( ) from langchain.schema.output import GenerationChunk from langchain.utilities.vertexai import ( + get_client_info, init_vertexai, raise_vertex_import_error, ) @@ -298,7 +299,12 @@ class VertexAI(_VertexAICommon, BaseLLM): res = completion_with_retry( self, prompt, run_manager=run_manager, **params ) - generations.append([_response_to_generation(r) for r in res.candidates]) + if self.is_codey_model: + generations.append([_response_to_generation(res)]) + else: + generations.append( + [_response_to_generation(r) for r in res.candidates] + ) return LLMResult(generations=generations) async def _agenerate( @@ -370,9 +376,12 @@ class VertexAIModelGarden(_VertexAIBase, BaseLLM): client_options = ClientOptions( api_endpoint=f"{values['location']}-aiplatform.googleapis.com" ) - values["client"] = PredictionServiceClient(client_options=client_options) + client_info = get_client_info(module="vertex-ai-model-garden") + values["client"] = PredictionServiceClient( + client_options=client_options, client_info=client_info + ) values["async_client"] = PredictionServiceAsyncClient( - client_options=client_options + client_options=client_options, client_info=client_info ) return values diff --git a/libs/langchain/langchain/output_parsers/format_instructions.py b/libs/langchain/langchain/output_parsers/format_instructions.py index a614cf3f0d6..fd9fb3ba238 100644 --- a/libs/langchain/langchain/output_parsers/format_instructions.py +++ b/libs/langchain/langchain/output_parsers/format_instructions.py @@ -13,7 +13,7 @@ STRUCTURED_FORMAT_SIMPLE_INSTRUCTIONS = """ {{ {format} }} -""" +```""" PYDANTIC_FORMAT_INSTRUCTIONS = """The output should be formatted as a JSON instance that conforms to the JSON schema below. diff --git a/libs/langchain/langchain/output_parsers/openai_functions.py b/libs/langchain/langchain/output_parsers/openai_functions.py index 51c485482de..78b3a989b4c 100644 --- a/libs/langchain/langchain/output_parsers/openai_functions.py +++ b/libs/langchain/langchain/output_parsers/openai_functions.py @@ -130,7 +130,9 @@ class JsonKeyOutputFunctionsParser(JsonOutputFunctionsParser): """The name of the key to return.""" def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any: - res = super().parse_result(result) + res = super().parse_result(result, partial=partial) + if partial and res is None: + return None return res.get(self.key_name) if partial else res[self.key_name] diff --git a/libs/langchain/langchain/output_parsers/structured.py b/libs/langchain/langchain/output_parsers/structured.py index b7f53ca32b4..75bc1103dfc 100644 --- a/libs/langchain/langchain/output_parsers/structured.py +++ b/libs/langchain/langchain/output_parsers/structured.py @@ -77,6 +77,7 @@ class StructuredOutputParser(BaseOutputParser): # "foo": List[string] // a list of strings # "bar": string // a string # } + # ``` Args: only_json (bool): If True, only the json in the Markdown code snippet diff --git a/libs/langchain/langchain/output_parsers/xml.py b/libs/langchain/langchain/output_parsers/xml.py index 16dde71accd..8ffe4167b66 100644 --- a/libs/langchain/langchain/output_parsers/xml.py +++ b/libs/langchain/langchain/output_parsers/xml.py @@ -22,6 +22,8 @@ class XMLOutputParser(BaseOutputParser): encoding_match = self.encoding_matcher.search(text) if encoding_match: text = encoding_match.group(2) + + text = text.strip() if (text.startswith("<") or text.startswith("\n<")) and ( text.endswith(">") or text.endswith(">\n") ): diff --git a/libs/langchain/langchain/prompts/base.py b/libs/langchain/langchain/prompts/base.py index d71a2649887..04b522f733b 100644 --- a/libs/langchain/langchain/prompts/base.py +++ b/libs/langchain/langchain/prompts/base.py @@ -83,7 +83,16 @@ DEFAULT_VALIDATOR_MAPPING: Dict[str, Callable] = { def check_valid_template( template: str, template_format: str, input_variables: List[str] ) -> None: - """Check that template string is valid.""" + """Check that template string is valid. + + Args: + template: The template string. + template_format: The template format. Should be one of "f-string" or "jinja2". + input_variables: The input variables. + + Raises: + ValueError: If the template format is not supported. + """ if template_format not in DEFAULT_FORMATTER_MAPPING: valid_formats = list(DEFAULT_FORMATTER_MAPPING) raise ValueError( @@ -101,6 +110,18 @@ def check_valid_template( def get_template_variables(template: str, template_format: str) -> List[str]: + """Get the variables from the template. + + Args: + template: The template string. + template_format: The template format. Should be one of "f-string" or "jinja2". + + Returns: + The variables from the template. + + Raises: + ValueError: If the template format is not supported. + """ if template_format == "jinja2": # Get the variables for the template input_variables = _get_jinja2_variables_from_template(template) diff --git a/libs/langchain/langchain/retrievers/google_cloud_documentai_warehouse.py b/libs/langchain/langchain/retrievers/google_cloud_documentai_warehouse.py index 760f8362daa..a99f35264f2 100644 --- a/libs/langchain/langchain/retrievers/google_cloud_documentai_warehouse.py +++ b/libs/langchain/langchain/retrievers/google_cloud_documentai_warehouse.py @@ -5,6 +5,7 @@ from langchain.callbacks.manager import CallbackManagerForRetrieverRun from langchain.docstore.document import Document from langchain.pydantic_v1 import root_validator from langchain.schema import BaseRetriever +from langchain.utilities.vertexai import get_client_info from langchain.utils import get_from_dict_or_env if TYPE_CHECKING: @@ -29,23 +30,21 @@ class GoogleDocumentAIWarehouseRetriever(BaseRetriever): """ location: str = "us" - "GCP location where DocAI Warehouse is placed." + """Google Cloud location where Document AI Warehouse is placed.""" project_number: str - "GCP project number, should contain digits only." + """Google Cloud project number, should contain digits only.""" schema_id: Optional[str] = None - "DocAI Warehouse schema to queary against. If nothing is provided, all documents " - "in the project will be searched." + """Document AI Warehouse schema to query against. + If nothing is provided, all documents in the project will be searched.""" qa_size_limit: int = 5 - "The limit on the number of documents returned." + """The limit on the number of documents returned.""" client: "DocumentServiceClient" = None #: :meta private: @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validates the environment.""" try: # noqa: F401 - from google.cloud.contentwarehouse_v1 import ( - DocumentServiceClient, - ) + from google.cloud.contentwarehouse_v1 import DocumentServiceClient except ImportError as exc: raise ImportError( "google.cloud.contentwarehouse is not installed." @@ -55,7 +54,9 @@ class GoogleDocumentAIWarehouseRetriever(BaseRetriever): values["project_number"] = get_from_dict_or_env( values, "project_number", "PROJECT_NUMBER" ) - values["client"] = DocumentServiceClient() + values["client"] = DocumentServiceClient( + client_info=get_client_info(module="document-ai-warehouse") + ) return values def _prepare_request_metadata(self, user_ldap: str) -> "RequestMetadata": diff --git a/libs/langchain/langchain/retrievers/google_vertex_ai_search.py b/libs/langchain/langchain/retrievers/google_vertex_ai_search.py index e3b1ebfc7bf..53144ffb5f9 100644 --- a/libs/langchain/langchain/retrievers/google_vertex_ai_search.py +++ b/libs/langchain/langchain/retrievers/google_vertex_ai_search.py @@ -6,6 +6,7 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence from langchain.callbacks.manager import CallbackManagerForRetrieverRun from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator from langchain.schema import BaseRetriever, Document +from langchain.utilities.vertexai import get_client_info from langchain.utils import get_from_dict_or_env if TYPE_CHECKING: @@ -260,7 +261,9 @@ class GoogleVertexAISearchRetriever(BaseRetriever, _BaseGoogleVertexAISearchRetr # For more information, refer to: # https://cloud.google.com/generative-ai-app-builder/docs/locations#specify_a_multi-region_for_your_data_store self._client = SearchServiceClient( - credentials=self.credentials, client_options=self.client_options + credentials=self.credentials, + client_options=self.client_options, + client_info=get_client_info(module="vertex-ai-search"), ) self._serving_config = self._client.serving_config_path( @@ -387,7 +390,9 @@ class GoogleVertexAIMultiTurnSearchRetriever( ) self._client = ConversationalSearchServiceClient( - credentials=self.credentials, client_options=self.client_options + credentials=self.credentials, + client_options=self.client_options, + client_info=get_client_info(module="vertex-ai-search"), ) self._serving_config = self._client.serving_config_path( diff --git a/libs/langchain/langchain/retrievers/multi_vector.py b/libs/langchain/langchain/retrievers/multi_vector.py index 434e6f4fccb..627f6e3f661 100644 --- a/libs/langchain/langchain/retrievers/multi_vector.py +++ b/libs/langchain/langchain/retrievers/multi_vector.py @@ -3,7 +3,7 @@ from typing import List from langchain.callbacks.manager import CallbackManagerForRetrieverRun from langchain.pydantic_v1 import Field from langchain.schema import BaseRetriever, BaseStore, Document -from langchain.vectorstores import VectorStore +from langchain.schema.vectorstore import VectorStore class MultiVectorRetriever(BaseRetriever): diff --git a/libs/langchain/langchain/retrievers/self_query/base.py b/libs/langchain/langchain/retrievers/self_query/base.py index adb67bda63f..2390351b52b 100644 --- a/libs/langchain/langchain/retrievers/self_query/base.py +++ b/libs/langchain/langchain/retrievers/self_query/base.py @@ -27,6 +27,7 @@ from langchain.retrievers.self_query.weaviate import WeaviateTranslator from langchain.schema import BaseRetriever, Document from langchain.schema.language_model import BaseLanguageModel from langchain.schema.runnable import Runnable +from langchain.schema.vectorstore import VectorStore from langchain.vectorstores import ( Chroma, DashVector, @@ -41,7 +42,6 @@ from langchain.vectorstores import ( SupabaseVectorStore, TimescaleVector, Vectara, - VectorStore, Weaviate, ) @@ -203,11 +203,17 @@ class SelfQueryRetriever(BaseRetriever, BaseModel): structured_query_translator = _get_builtin_translator(vectorstore) chain_kwargs = chain_kwargs or {} - if "allowed_comparators" not in chain_kwargs: + if ( + "allowed_comparators" not in chain_kwargs + and structured_query_translator.allowed_comparators is not None + ): chain_kwargs[ "allowed_comparators" ] = structured_query_translator.allowed_comparators - if "allowed_operators" not in chain_kwargs: + if ( + "allowed_operators" not in chain_kwargs + and structured_query_translator.allowed_operators is not None + ): chain_kwargs[ "allowed_operators" ] = structured_query_translator.allowed_operators diff --git a/libs/langchain/langchain/retrievers/self_query/qdrant.py b/libs/langchain/langchain/retrievers/self_query/qdrant.py index 55dc8d34578..e76c716db26 100644 --- a/libs/langchain/langchain/retrievers/self_query/qdrant.py +++ b/libs/langchain/langchain/retrievers/self_query/qdrant.py @@ -18,6 +18,13 @@ if TYPE_CHECKING: class QdrantTranslator(Visitor): """Translate `Qdrant` internal query language elements to valid filters.""" + allowed_operators = ( + Operator.AND, + Operator.OR, + Operator.NOT, + ) + """Subset of allowed logical operators.""" + allowed_comparators = ( Comparator.EQ, Comparator.LT, diff --git a/libs/langchain/langchain/retrievers/tavily_search_api.py b/libs/langchain/langchain/retrievers/tavily_search_api.py index 1385e9ddd22..c61613aec82 100644 --- a/libs/langchain/langchain/retrievers/tavily_search_api.py +++ b/libs/langchain/langchain/retrievers/tavily_search_api.py @@ -8,6 +8,8 @@ from langchain.schema.retriever import BaseRetriever class SearchDepth(Enum): + """Search depth as enumerator.""" + BASIC = "basic" ADVANCED = "advanced" @@ -31,7 +33,7 @@ class TavilySearchAPIRetriever(BaseRetriever): try: from tavily import Client except ImportError: - raise ValueError( + raise ImportError( "Tavily python package not found. " "Please install it with `pip install tavily-python`." ) diff --git a/libs/langchain/langchain/retrievers/you.py b/libs/langchain/langchain/retrievers/you.py index 3c281c3de23..c1fe6946252 100644 --- a/libs/langchain/langchain/retrievers/you.py +++ b/libs/langchain/langchain/retrievers/you.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, List +from typing import Any, Dict, List, Optional from langchain.callbacks.manager import CallbackManagerForRetrieverRun from langchain.pydantic_v1 import root_validator @@ -17,6 +17,8 @@ class YouRetriever(BaseRetriever): """ ydc_api_key: str + k: Optional[int] = None + endpoint_type: str = "web" @root_validator(pre=True) def validate_client( @@ -34,13 +36,24 @@ class YouRetriever(BaseRetriever): import requests headers = {"X-API-Key": self.ydc_api_key} - results = requests.get( - f"https://api.ydc-index.io/search?query={query}", - headers=headers, - ).json() + if self.endpoint_type == "web": + results = requests.get( + f"https://api.ydc-index.io/search?query={query}", + headers=headers, + ).json() - docs = [] - for hit in results["hits"]: - for snippet in hit["snippets"]: - docs.append(Document(page_content=snippet)) - return docs + docs = [] + for hit in results["hits"]: + for snippet in hit["snippets"]: + 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}") diff --git a/libs/langchain/langchain/schema/output.py b/libs/langchain/langchain/schema/output.py index a612f4bd282..b6bb22aa02a 100644 --- a/libs/langchain/langchain/schema/output.py +++ b/libs/langchain/langchain/schema/output.py @@ -1,7 +1,7 @@ from __future__ import annotations from copy import deepcopy -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List, Literal, Optional from uuid import UUID from langchain.load.serializable import Serializable @@ -19,6 +19,8 @@ class Generation(Serializable): """Raw response from the provider. May include things like the reason for finishing or token log probabilities. """ + type: Literal["Generation"] = "Generation" + """Type is used exclusively for serialization purposes.""" # TODO: add log probs as separate attribute @classmethod @@ -54,11 +56,17 @@ class ChatGeneration(Generation): """*SHOULD NOT BE SET DIRECTLY* The text contents of the output message.""" message: BaseMessage """The message output by the chat model.""" + # Override type to be ChatGeneration, ignore mypy error as this is intentional + type: Literal["ChatGeneration"] = "ChatGeneration" # type: ignore[assignment] + """Type is used exclusively for serialization purposes.""" @root_validator def set_text(cls, values: Dict[str, Any]) -> Dict[str, Any]: """Set the text attribute to be the contents of the message.""" - values["text"] = values["message"].content + try: + values["text"] = values["message"].content + except (KeyError, AttributeError) as e: + raise ValueError("Error while initializing ChatGeneration") from e return values @@ -71,6 +79,9 @@ class ChatGenerationChunk(ChatGeneration): """ message: BaseMessageChunk + # Override type to be ChatGeneration, ignore mypy error as this is intentional + type: Literal["ChatGenerationChunk"] = "ChatGenerationChunk" # type: ignore[assignment] # noqa: E501 + """Type is used exclusively for serialization purposes.""" def __add__(self, other: ChatGenerationChunk) -> ChatGenerationChunk: if isinstance(other, ChatGenerationChunk): diff --git a/libs/langchain/langchain/schema/runnable/__init__.py b/libs/langchain/langchain/schema/runnable/__init__.py index 65da79c489b..3ae03b21e89 100644 --- a/libs/langchain/langchain/schema/runnable/__init__.py +++ b/libs/langchain/langchain/schema/runnable/__init__.py @@ -1,4 +1,4 @@ -"""LangChain Runnables and the LangChain Expression Language (LCEL). +"""LangChain **Runnable** and the **LangChain Expression Language (LCEL)**. The LangChain Expression Language (LCEL) offers a declarative method to build production-grade programs that harness the power of LLMs. @@ -6,15 +6,14 @@ production-grade programs that harness the power of LLMs. Programs created using LCEL and LangChain Runnables inherently support synchronous, asynchronous, batch, and streaming operations. -Support for async allows servers hosting LCEL based programs to scale better +Support for **async** allows servers hosting LCEL based programs to scale better for higher concurrent loads. -Streaming of intermediate outputs as they're being generated allows for +**Streaming** of intermediate outputs as they're being generated allows for creating more responsive UX. This module contains schema and implementation of LangChain Runnables primitives. """ -from langchain.schema.runnable._locals import GetLocalVar, PutLocalVar from langchain.schema.runnable.base import ( Runnable, RunnableBinding, @@ -40,9 +39,7 @@ __all__ = [ "ConfigurableField", "ConfigurableFieldSingleOption", "ConfigurableFieldMultiOption", - "GetLocalVar", "patch_config", - "PutLocalVar", "RouterInput", "RouterRunnable", "Runnable", diff --git a/libs/langchain/langchain/schema/runnable/_locals.py b/libs/langchain/langchain/schema/runnable/_locals.py deleted file mode 100644 index cfe1f76aa07..00000000000 --- a/libs/langchain/langchain/schema/runnable/_locals.py +++ /dev/null @@ -1,168 +0,0 @@ -from __future__ import annotations - -from typing import ( - TYPE_CHECKING, - Any, - AsyncIterator, - Dict, - Iterator, - Mapping, - Optional, - Union, -) - -from langchain.schema.runnable.base import Input, Other, Output, RunnableSerializable -from langchain.schema.runnable.config import RunnableConfig -from langchain.schema.runnable.passthrough import RunnablePassthrough - -if TYPE_CHECKING: - from langchain.callbacks.manager import ( - AsyncCallbackManagerForChainRun, - CallbackManagerForChainRun, - ) - - -class PutLocalVar(RunnablePassthrough): - key: Union[str, Mapping[str, str]] - """The key(s) to use for storing the input variable(s) in local state. - - If a string is provided then the entire input is stored under that key. If a - Mapping is provided, then the map values are gotten from the input and - stored in local state under the map keys. - """ - - def __init__(self, key: Union[str, Mapping[str, str]], **kwargs: Any) -> None: - super().__init__(key=key, **kwargs) - - def _concat_put( - self, - input: Other, - *, - config: Optional[RunnableConfig] = None, - replace: bool = False, - ) -> None: - if config is None: - raise ValueError( - "PutLocalVar should only be used in a RunnableSequence, and should " - "therefore always receive a non-null config." - ) - if isinstance(self.key, str): - if self.key not in config["locals"] or replace: - config["locals"][self.key] = input - else: - config["locals"][self.key] += input - elif isinstance(self.key, Mapping): - if not isinstance(input, Mapping): - raise TypeError( - f"Received key of type Mapping but input of type {type(input)}. " - f"input is expected to be of type Mapping when key is Mapping." - ) - for input_key, put_key in self.key.items(): - if put_key not in config["locals"] or replace: - config["locals"][put_key] = input[input_key] - else: - config["locals"][put_key] += input[input_key] - else: - raise TypeError( - f"`key` should be a string or Mapping[str, str], received type " - f"{(type(self.key))}." - ) - - def invoke( - self, input: Other, config: Optional[RunnableConfig] = None, **kwargs: Any - ) -> Other: - self._concat_put(input, config=config, replace=True) - return super().invoke(input, config=config, **kwargs) - - async def ainvoke( - self, - input: Other, - config: Optional[RunnableConfig] = None, - **kwargs: Optional[Any], - ) -> Other: - self._concat_put(input, config=config, replace=True) - return await super().ainvoke(input, config=config, **kwargs) - - def transform( - self, - input: Iterator[Other], - config: Optional[RunnableConfig] = None, - **kwargs: Optional[Any], - ) -> Iterator[Other]: - for chunk in super().transform(input, config=config, **kwargs): - self._concat_put(chunk, config=config) - yield chunk - - async def atransform( - self, - input: AsyncIterator[Other], - config: Optional[RunnableConfig] = None, - **kwargs: Optional[Any], - ) -> AsyncIterator[Other]: - async for chunk in super().atransform(input, config=config, **kwargs): - self._concat_put(chunk, config=config) - yield chunk - - -class GetLocalVar( - RunnableSerializable[Input, Union[Output, Dict[str, Union[Input, Output]]]] -): - key: str - """The key to extract from the local state.""" - passthrough_key: Optional[str] = None - """The key to use for passing through the invocation input. - - If None, then only the value retrieved from local state is returned. Otherwise a - dictionary ``{self.key: <>, self.passthrough_key: <>}`` - is returned. - """ - - def __init__(self, key: str, **kwargs: Any) -> None: - super().__init__(key=key, **kwargs) - - def _get( - self, - input: Input, - run_manager: Union[CallbackManagerForChainRun, Any], - config: RunnableConfig, - ) -> Union[Output, Dict[str, Union[Input, Output]]]: - if self.passthrough_key: - return { - self.key: config["locals"][self.key], - self.passthrough_key: input, - } - else: - return config["locals"][self.key] - - async def _aget( - self, - input: Input, - run_manager: AsyncCallbackManagerForChainRun, - config: RunnableConfig, - ) -> Union[Output, Dict[str, Union[Input, Output]]]: - return self._get(input, run_manager, config) - - def invoke( - self, input: Input, config: Optional[RunnableConfig] = None - ) -> Union[Output, Dict[str, Union[Input, Output]]]: - if config is None: - raise ValueError( - "GetLocalVar should only be used in a RunnableSequence, and should " - "therefore always receive a non-null config." - ) - - return self._call_with_config(self._get, input, config) - - async def ainvoke( - self, - input: Input, - config: Optional[RunnableConfig] = None, - **kwargs: Optional[Any], - ) -> Union[Output, Dict[str, Union[Input, Output]]]: - if config is None: - raise ValueError( - "GetLocalVar should only be used in a RunnableSequence, and should " - "therefore always receive a non-null config." - ) - - return await self._acall_with_config(self._aget, input, config) diff --git a/libs/langchain/langchain/schema/runnable/base.py b/libs/langchain/langchain/schema/runnable/base.py index b9a3fff16e3..c95d5254d99 100644 --- a/libs/langchain/langchain/schema/runnable/base.py +++ b/libs/langchain/langchain/schema/runnable/base.py @@ -116,7 +116,7 @@ class Runnable(Generic[Input, Output], ABC): For example, - ..code-block:: python + .. code-block:: python from langchain.schema.runnable import RunnableLambda @@ -1656,7 +1656,6 @@ class RunnableParallel(RunnableSerializable[Input, Dict[str, Any]]): # mark each step as a child run patch_config( config, - copy_locals=True, callbacks=run_manager.get_child(f"map:key:{key}"), ), ) @@ -2368,9 +2367,10 @@ class RunnableBinding(RunnableSerializable[Input, Output]): config: RunnableConfig = Field(default_factory=dict) - custom_input_type: Optional[Union[Type[Input], BaseModel]] = None - - custom_output_type: Optional[Union[Type[Output], BaseModel]] = None + # Union[Type[Input], BaseModel] + things like List[str] + custom_input_type: Optional[Any] = None + # Union[Type[Output], BaseModel] + things like List[str] + custom_output_type: Optional[Any] = None class Config: arbitrary_types_allowed = True @@ -2534,10 +2534,7 @@ class RunnableBinding(RunnableSerializable[Input, Output]): [merge_configs(self.config, conf) for conf in config], ) else: - configs = [ - patch_config(merge_configs(self.config, config), copy_locals=True) - for _ in range(len(inputs)) - ] + configs = [merge_configs(self.config, config) for _ in range(len(inputs))] return self.bound.batch( inputs, configs, @@ -2559,10 +2556,7 @@ class RunnableBinding(RunnableSerializable[Input, Output]): [merge_configs(self.config, conf) for conf in config], ) else: - configs = [ - patch_config(merge_configs(self.config, config), copy_locals=True) - for _ in range(len(inputs)) - ] + configs = [merge_configs(self.config, config) for _ in range(len(inputs))] return await self.bound.abatch( inputs, configs, @@ -2634,6 +2628,14 @@ RunnableLike = Union[ def coerce_to_runnable(thing: RunnableLike) -> Runnable[Input, Output]: + """Coerce a runnable-like object into a Runnable. + + Args: + thing: A runnable-like object. + + Returns: + A Runnable. + """ if isinstance(thing, Runnable): return thing elif inspect.isasyncgenfunction(thing) or inspect.isgeneratorfunction(thing): diff --git a/libs/langchain/langchain/schema/runnable/config.py b/libs/langchain/langchain/schema/runnable/config.py index 408339fcfc4..a795e3e3fa0 100644 --- a/libs/langchain/langchain/schema/runnable/config.py +++ b/libs/langchain/langchain/schema/runnable/config.py @@ -35,6 +35,8 @@ if TYPE_CHECKING: class EmptyDict(TypedDict, total=False): + """Empty dict type.""" + pass @@ -64,39 +66,41 @@ class RunnableConfig(TypedDict, total=False): Name for the tracer run for this call. Defaults to the name of the class. """ - locals: Dict[str, Any] - """ - Variables scoped to this call and any sub-calls. Usually used with - GetLocalVar() and PutLocalVar(). Care should be taken when placing mutable - objects in locals, as they will be shared between parallel sub-calls. - """ - max_concurrency: Optional[int] """ Maximum number of parallel calls to make. If not provided, defaults to - ThreadPoolExecutor's default. This is ignored if an executor is provided. + ThreadPoolExecutor's default. """ recursion_limit: int """ - Maximum number of times a call can recurse. If not provided, defaults to 10. + Maximum number of times a call can recurse. If not provided, defaults to 25. """ configurable: Dict[str, Any] """ - Runtime values for attributes previously made configurable by this Runnable, - or sub-Runnables, through .make_configurable(). Check .output_schema for - a description of the attributes that have been made configurable. + Runtime values for attributes previously made configurable on this Runnable, + or sub-Runnables, through .configurable_fields() or .configurable_alternatives(). + Check .output_schema() for a description of the attributes that have been made + configurable. """ def ensure_config(config: Optional[RunnableConfig] = None) -> RunnableConfig: + """Ensure that a config is a dict with all keys present. + + Args: + config (Optional[RunnableConfig], optional): The config to ensure. + Defaults to None. + + Returns: + RunnableConfig: The ensured config. + """ empty = RunnableConfig( tags=[], metadata={}, callbacks=None, - locals={}, - recursion_limit=10, + recursion_limit=25, ) if config is not None: empty.update( @@ -108,9 +112,21 @@ def ensure_config(config: Optional[RunnableConfig] = None) -> RunnableConfig: def get_config_list( config: Optional[Union[RunnableConfig, List[RunnableConfig]]], length: int ) -> List[RunnableConfig]: - """ - Helper method to get a list of configs from a single config or a list of - configs, useful for subclasses overriding batch() or abatch(). + """Get a list of configs from a single config or a list of configs. + + It is useful for subclasses overriding batch() or abatch(). + + Args: + config (Optional[Union[RunnableConfig, List[RunnableConfig]]]): + The config or list of configs. + length (int): The length of the list. + + Returns: + List[RunnableConfig]: The list of configs. + + Raises: + ValueError: If the length of the list is not equal to the length of the inputs. + """ if length < 0: raise ValueError(f"length must be >= 0, but got {length}") @@ -123,25 +139,40 @@ def get_config_list( return ( list(map(ensure_config, config)) if isinstance(config, list) - else [patch_config(config, copy_locals=True) for _ in range(length)] + else [ensure_config(config) for _ in range(length)] ) def patch_config( config: Optional[RunnableConfig], *, - copy_locals: bool = False, callbacks: Optional[BaseCallbackManager] = None, recursion_limit: Optional[int] = None, max_concurrency: Optional[int] = None, run_name: Optional[str] = None, configurable: Optional[Dict[str, Any]] = None, ) -> RunnableConfig: + """Patch a config with new values. + + Args: + config (Optional[RunnableConfig]): The config to patch. + copy_locals (bool, optional): Whether to copy locals. Defaults to False. + callbacks (Optional[BaseCallbackManager], optional): The callbacks to set. + Defaults to None. + recursion_limit (Optional[int], optional): The recursion limit to set. + Defaults to None. + max_concurrency (Optional[int], optional): The max concurrency to set. + Defaults to None. + run_name (Optional[str], optional): The run name to set. Defaults to None. + configurable (Optional[Dict[str, Any]], optional): The configurable to set. + Defaults to None. + + Returns: + RunnableConfig: The patched config. + """ config = ensure_config(config) - if copy_locals: - config["locals"] = config["locals"].copy() if callbacks is not None: - # If we're replacing callbacks we need to unset run_name + # If we're replacing callbacks, we need to unset run_name # As that should apply only to the same run as the original callbacks config["callbacks"] = callbacks if "run_name" in config: @@ -158,9 +189,17 @@ def patch_config( def merge_configs(*configs: Optional[RunnableConfig]) -> RunnableConfig: + """Merge multiple configs into one. + + Args: + *configs (Optional[RunnableConfig]): The configs to merge. + + Returns: + RunnableConfig: The merged config. + """ base: RunnableConfig = {} - # Even though the keys aren't literals this is correct - # because both dicts are same type + # Even though the keys aren't literals, this is correct + # because both dicts are the same type for config in (c for c in configs if c is not None): for key in config: if key == "metadata": @@ -194,7 +233,22 @@ def call_func_with_variable_args( run_manager: Optional[CallbackManagerForChainRun] = None, **kwargs: Any, ) -> Output: - """Call function that may optionally accept a run_manager and/or config.""" + """Call function that may optionally accept a run_manager and/or config. + + Args: + func (Union[Callable[[Input], Output], + Callable[[Input, CallbackManagerForChainRun], Output], + Callable[[Input, CallbackManagerForChainRun, RunnableConfig], Output]]): + The function to call. + input (Input): The input to the function. + run_manager (CallbackManagerForChainRun): The run manager to + pass to the function. + config (RunnableConfig): The config to pass to the function. + **kwargs (Any): The keyword arguments to pass to the function. + + Returns: + Output: The output of the function. + """ if accepts_config(func): if run_manager is not None: kwargs["config"] = patch_config(config, callbacks=run_manager.get_child()) @@ -220,7 +274,22 @@ async def acall_func_with_variable_args( run_manager: Optional[AsyncCallbackManagerForChainRun] = None, **kwargs: Any, ) -> Output: - """Call function that may optionally accept a run_manager and/or config.""" + """Call function that may optionally accept a run_manager and/or config. + + Args: + func (Union[Callable[[Input], Awaitable[Output]], Callable[[Input, + AsyncCallbackManagerForChainRun], Awaitable[Output]], Callable[[Input, + AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]]]): + The function to call. + input (Input): The input to the function. + run_manager (AsyncCallbackManagerForChainRun): The run manager + to pass to the function. + config (RunnableConfig): The config to pass to the function. + **kwargs (Any): The keyword arguments to pass to the function. + + Returns: + Output: The output of the function. + """ if accepts_config(func): if run_manager is not None: kwargs["config"] = patch_config(config, callbacks=run_manager.get_child()) @@ -232,6 +301,14 @@ async def acall_func_with_variable_args( def get_callback_manager_for_config(config: RunnableConfig) -> CallbackManager: + """Get a callback manager for a config. + + Args: + config (RunnableConfig): The config. + + Returns: + CallbackManager: The callback manager. + """ from langchain.callbacks.manager import CallbackManager return CallbackManager.configure( @@ -244,6 +321,14 @@ def get_callback_manager_for_config(config: RunnableConfig) -> CallbackManager: def get_async_callback_manager_for_config( config: RunnableConfig, ) -> AsyncCallbackManager: + """Get an async callback manager for a config. + + Args: + config (RunnableConfig): The config. + + Returns: + AsyncCallbackManager: The async callback manager. + """ from langchain.callbacks.manager import AsyncCallbackManager return AsyncCallbackManager.configure( @@ -255,5 +340,13 @@ def get_async_callback_manager_for_config( @contextmanager def get_executor_for_config(config: RunnableConfig) -> Generator[Executor, None, None]: + """Get an executor for a config. + + Args: + config (RunnableConfig): The config. + + Yields: + Generator[Executor, None, None]: The executor. + """ with ThreadPoolExecutor(max_workers=config.get("max_concurrency")) as executor: yield executor diff --git a/libs/langchain/langchain/server.py b/libs/langchain/langchain/server.py deleted file mode 100644 index a6a724e5f00..00000000000 --- a/libs/langchain/langchain/server.py +++ /dev/null @@ -1,18 +0,0 @@ -"""Script to run langchain-server locally using docker-compose.""" -import subprocess -from pathlib import Path - -from langsmith.cli.main import get_docker_compose_command - - -def main() -> None: - """Run the langchain server locally.""" - p = Path(__file__).absolute().parent / "docker-compose.yaml" - - docker_compose_command = get_docker_compose_command() - subprocess.run([*docker_compose_command, "-f", str(p), "pull"]) - subprocess.run([*docker_compose_command, "-f", str(p), "up"]) - - -if __name__ == "__main__": - main() diff --git a/libs/langchain/langchain/smith/evaluation/runner_utils.py b/libs/langchain/langchain/smith/evaluation/runner_utils.py index 43b62a71049..fd035c72cd0 100644 --- a/libs/langchain/langchain/smith/evaluation/runner_utils.py +++ b/libs/langchain/langchain/smith/evaluation/runner_utils.py @@ -5,7 +5,6 @@ from __future__ import annotations import functools import inspect import logging -import warnings from enum import Enum from typing import ( TYPE_CHECKING, @@ -20,9 +19,12 @@ from typing import ( cast, ) -from langsmith import Client, RunEvaluator +from langsmith.client import Client +from langsmith.evaluation import RunEvaluator +from langsmith.run_helpers import as_runnable, is_traceable_function from langsmith.schemas import Dataset, DataType, Example +from langchain._api import warn_deprecated from langchain.callbacks.manager import Callbacks from langchain.callbacks.tracers.evaluation import ( EvaluatorCallbackHandler, @@ -152,6 +154,9 @@ def _wrap_in_chain_factory( lcf = llm_or_chain_factory return lambda: lcf elif callable(llm_or_chain_factory): + if is_traceable_function(llm_or_chain_factory): + runnable_ = as_runnable(cast(Callable, llm_or_chain_factory)) + return lambda: runnable_ try: _model = llm_or_chain_factory() # type: ignore[call-arg] except TypeError: @@ -166,6 +171,9 @@ def _wrap_in_chain_factory( # It's not uncommon to do an LLM constructor instead of raw LLM, # so we'll unpack it for the user. return _model + elif is_traceable_function(cast(Callable, _model)): + runnable_ = as_runnable(cast(Callable, _model)) + return lambda: runnable_ elif not isinstance(_model, Runnable): # This is unlikely to happen - a constructor for a model function return lambda: RunnableLambda(constructor) @@ -879,7 +887,8 @@ def _prepare_eval_run( f"Project {project_name} already exists. Please use a different name." ) print( - f"View the evaluation results for project '{project_name}' at:\n{project.url}", + f"View the evaluation results for project '{project_name}'" + f" at:\n{project.url}?eval=true", flush=True, ) examples = list(client.list_examples(dataset_id=dataset.id)) @@ -909,7 +918,7 @@ def _prepare_run_on_dataset( ) wrapped_model = _wrap_in_chain_factory(llm_or_chain_factory) run_evaluators = _setup_evaluation( - wrapped_model, examples, evaluation, dataset.data_type + wrapped_model, examples, evaluation, dataset.data_type or DataType.kv ) _validate_example_inputs(examples[0], wrapped_model, input_mapper) progress_bar = progress.ProgressBarCallback(len(examples)) @@ -998,17 +1007,15 @@ async def arun_on_dataset( ) -> Dict[str, Any]: input_mapper = kwargs.pop("input_mapper", None) if input_mapper: - warnings.warn( - _INPUT_MAPPER_DEP_WARNING, - DeprecationWarning, - ) + warn_deprecated("0.0.305", message=_INPUT_MAPPER_DEP_WARNING, pending=True) if kwargs: - warnings.warn( - "The following arguments are deprecated and " + warn_deprecated( + "0.0.305", + message="The following arguments are deprecated and " "will be removed in a future release: " f"{kwargs.keys()}.", - DeprecationWarning, + removal="0.0.305", ) client = client or Client() wrapped_model, project_name, examples, configs = _prepare_run_on_dataset( @@ -1061,16 +1068,15 @@ def run_on_dataset( ) -> Dict[str, Any]: input_mapper = kwargs.pop("input_mapper", None) if input_mapper: - warnings.warn( - _INPUT_MAPPER_DEP_WARNING, - DeprecationWarning, - ) + warn_deprecated("0.0.305", message=_INPUT_MAPPER_DEP_WARNING, pending=True) + if kwargs: - warnings.warn( - "The following arguments are deprecated and " + warn_deprecated( + "0.0.305", + message="The following arguments are deprecated and " "will be removed in a future release: " f"{kwargs.keys()}.", - DeprecationWarning, + removal="0.0.305", ) client = client or Client() wrapped_model, project_name, examples, configs = _prepare_run_on_dataset( diff --git a/libs/langchain/langchain/text_splitter.py b/libs/langchain/langchain/text_splitter.py index b894937a57c..721ffab6be6 100644 --- a/libs/langchain/langchain/text_splitter.py +++ b/libs/langchain/langchain/text_splitter.py @@ -811,6 +811,7 @@ class Language(str, Enum): HTML = "html" SOL = "sol" CSHARP = "csharp" + COBOL = "cobol" class RecursiveCharacterTextSplitter(TextSplitter): @@ -1305,6 +1306,38 @@ class RecursiveCharacterTextSplitter(TextSplitter): " ", "", ] + elif language == Language.COBOL: + return [ + # Split along divisions + "\nIDENTIFICATION DIVISION.", + "\nENVIRONMENT DIVISION.", + "\nDATA DIVISION.", + "\nPROCEDURE DIVISION.", + # Split along sections within DATA DIVISION + "\nWORKING-STORAGE SECTION.", + "\nLINKAGE SECTION.", + "\nFILE SECTION.", + # Split along sections within PROCEDURE DIVISION + "\nINPUT-OUTPUT SECTION.", + # Split along paragraphs and common statements + "\nOPEN ", + "\nCLOSE ", + "\nREAD ", + "\nWRITE ", + "\nIF ", + "\nELSE ", + "\nMOVE ", + "\nPERFORM ", + "\nUNTIL ", + "\nVARYING ", + "\nACCEPT ", + "\nDISPLAY ", + "\nSTOP RUN.", + # Split by the normal type of lines + "\n", + " ", + "", + ] else: raise ValueError( diff --git a/libs/langchain/langchain/tools/__init__.py b/libs/langchain/langchain/tools/__init__.py index 64fb56d3b19..4a1415ff668 100644 --- a/libs/langchain/langchain/tools/__init__.py +++ b/libs/langchain/langchain/tools/__init__.py @@ -639,6 +639,12 @@ def _import_bearly_tool() -> Any: return BearlyInterpreterTool +def _import_e2b_data_analysis() -> Any: + from langchain.tools.e2b_data_analysis.tool import E2BDataAnalysisTool + + return E2BDataAnalysisTool + + def __getattr__(name: str) -> Any: if name == "AINAppOps": return _import_ainetwork_app() @@ -846,6 +852,8 @@ def __getattr__(name: str) -> Any: return _import_zapier_tool_ZapierNLARunAction() elif name == "BearlyInterpreterTool": return _import_bearly_tool() + elif name == "E2BDataAnalysisTool": + return _import_e2b_data_analysis() else: raise AttributeError(f"Could not find: {name}") @@ -958,4 +966,5 @@ __all__ = [ "tool", "format_tool_to_openai_function", "BearlyInterpreterTool", + "E2BDataAnalysisTool", ] diff --git a/libs/langchain/langchain/tools/e2b_data_analysis/__init__.py b/libs/langchain/langchain/tools/e2b_data_analysis/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/langchain/langchain/tools/e2b_data_analysis/tool.py b/libs/langchain/langchain/tools/e2b_data_analysis/tool.py new file mode 100644 index 00000000000..64c62bc4491 --- /dev/null +++ b/libs/langchain/langchain/tools/e2b_data_analysis/tool.py @@ -0,0 +1,228 @@ +from __future__ import annotations + +import ast +import json +import os +from io import StringIO +from sys import version_info +from typing import IO, TYPE_CHECKING, Any, Callable, List, Optional, Type + +from langchain.callbacks.manager import ( + AsyncCallbackManagerForToolRun, + CallbackManagerForToolRun, +) +from langchain.pydantic_v1 import BaseModel, Field, PrivateAttr +from langchain.tools import BaseTool, Tool +from langchain.tools.e2b_data_analysis.unparse import Unparser + +if TYPE_CHECKING: + from e2b import EnvVars + from e2b.templates.data_analysis import Artifact + +base_description = """Evaluates python code in a sandbox environment. \ +The environment is long running and exists across multiple executions. \ +You must send the whole script every time and print your outputs. \ +Script should be pure python code that can be evaluated. \ +It should be in python format NOT markdown. \ +The code should NOT be wrapped in backticks. \ +All python packages including requests, matplotlib, scipy, numpy, pandas, \ +etc are available. Create and display chart using `plt.show()`.""" + + +def _unparse(tree: ast.AST) -> str: + """Unparse the AST.""" + if version_info.minor < 9: + s = StringIO() + Unparser(tree, file=s) + source_code = s.getvalue() + s.close() + else: + source_code = ast.unparse(tree) # type: ignore[attr-defined] + return source_code + + +def add_last_line_print(code: str) -> str: + """Add print statement to the last line if it's missing. + + Sometimes, the LLM-generated code doesn't have `print(variable_name)`, instead the + LLM tries to print the variable only by writing `variable_name` (as you would in + REPL, for example). + + This methods checks the AST of the generated Python code and adds the print + statement to the last line if it's missing. + """ + tree = ast.parse(code) + node = tree.body[-1] + if isinstance(node, ast.Expr) and isinstance(node.value, ast.Call): + if isinstance(node.value.func, ast.Name) and node.value.func.id == "print": + return _unparse(tree) + + if isinstance(node, ast.Expr): + tree.body[-1] = ast.Expr( + value=ast.Call( + func=ast.Name(id="print", ctx=ast.Load()), + args=[node.value], + keywords=[], + ) + ) + + return _unparse(tree) + + +class UploadedFile(BaseModel): + """Description of the uploaded path with its remote path.""" + + name: str + remote_path: str + description: str + + +class E2BDataAnalysisToolArguments(BaseModel): + """Arguments for the E2BDataAnalysisTool.""" + + python_code: str = Field( + ..., + example="print('Hello World')", + description=( + "The python script to be evaluated. " + "The contents will be in main.py. " + "It should not be in markdown format." + ), + ) + + +class E2BDataAnalysisTool(BaseTool): + """Tool for running python code in a sandboxed environment for data analysis.""" + + name = "e2b_data_analysis" + args_schema: Type[BaseModel] = E2BDataAnalysisToolArguments + session: Any + _uploaded_files: List[UploadedFile] = PrivateAttr(default_factory=list) + + def __init__( + self, + api_key: Optional[str] = None, + cwd: Optional[str] = None, + env_vars: Optional[EnvVars] = None, + on_stdout: Optional[Callable[[str], Any]] = None, + on_stderr: Optional[Callable[[str], Any]] = None, + on_artifact: Optional[Callable[[Artifact], Any]] = None, + on_exit: Optional[Callable[[int], Any]] = None, + **kwargs: Any, + ): + try: + from e2b import DataAnalysis + except ImportError as e: + raise ImportError( + "Unable to import e2b, please install with `pip install e2b`." + ) from e + + # If no API key is provided, E2B will try to read it from the environment + # variable E2B_API_KEY + super().__init__(description=base_description, **kwargs) + self.session = DataAnalysis( + api_key=api_key, + cwd=cwd, + env_vars=env_vars, + on_stdout=on_stdout, + on_stderr=on_stderr, + on_exit=on_exit, + on_artifact=on_artifact, + ) + + def close(self) -> None: + """Close the cloud sandbox.""" + self._uploaded_files = [] + self.session.close() + + @property + def uploaded_files_description(self) -> str: + if len(self._uploaded_files) == 0: + return "" + lines = ["The following files available in the sandbox:"] + + for f in self._uploaded_files: + if f.description == "": + lines.append(f"- path: `{f.remote_path}`") + else: + lines.append( + f"- path: `{f.remote_path}` \n description: `{f.description}`" + ) + return "\n".join(lines) + + def _run( + self, python_code: str, run_manager: Optional[CallbackManagerForToolRun] = None + ) -> str: + python_code = add_last_line_print(python_code) + stdout, stderr, _ = self.session.run_python(python_code) + + out = { + "stdout": stdout, + "stderr": stderr, + } + return json.dumps(out) + + async def _arun( + self, + python_code: str, + run_manager: Optional[AsyncCallbackManagerForToolRun] = None, + ) -> str: + raise NotImplementedError("e2b_data_analysis does not support async") + + def run_command( + self, + cmd: str, + ) -> dict: + """Run shell command in the sandbox.""" + proc = self.session.process.start(cmd) + output = proc.wait() + return { + "stdout": output.stdout, + "stderr": output.stderr, + "exit_code": output.exit_code, + } + + def install_python_packages(self, package_names: str | List[str]) -> None: + """Install python packages in the sandbox.""" + self.session.install_python_packages(package_names) + + def install_system_packages(self, package_names: str | List[str]) -> None: + """Install system packages (via apt) in the sandbox.""" + self.session.install_system_packages(package_names) + + def download_file(self, remote_path: str) -> bytes: + """Download file from the sandbox.""" + return self.session.download_file(remote_path) + + def upload_file(self, file: IO, description: str) -> UploadedFile: + """Upload file to the sandbox. + + The file is uploaded to the '/home/user/' path.""" + remote_path = self.session.upload_file(file) + + f = UploadedFile( + name=os.path.basename(file.name), + remote_path=remote_path, + description=description, + ) + self._uploaded_files.append(f) + self.description = self.description + "\n" + self.uploaded_files_description + return f + + def remove_uploaded_file(self, uploaded_file: UploadedFile) -> None: + """Remove uploaded file from the sandbox.""" + self.session.filesystem.remove(uploaded_file.remote_path) + self._uploaded_files = [ + f + for f in self._uploaded_files + if f.remote_path != uploaded_file.remote_path + ] + self.description = self.description + "\n" + self.uploaded_files_description + + def as_tool(self) -> Tool: + return Tool.from_function( + func=self._run, + name=self.name, + description=self.description, + args_schema=self.args_schema, + ) diff --git a/libs/langchain/langchain/tools/e2b_data_analysis/unparse.py b/libs/langchain/langchain/tools/e2b_data_analysis/unparse.py new file mode 100644 index 00000000000..9de05f9fe52 --- /dev/null +++ b/libs/langchain/langchain/tools/e2b_data_analysis/unparse.py @@ -0,0 +1,736 @@ +# mypy: disable-error-code=no-untyped-def +# Because Python >3.9 doesn't support ast.unparse, +# we copied the unparse functionality from here: +# https://github.com/python/cpython/blob/3.8/Tools/parser/unparse.py +"Usage: unparse.py " +import ast +import io +import sys +import tokenize + +# Large float and imaginary literals get turned into infinities in the AST. +# We unparse those infinities to INFSTR. +INFSTR = "1e" + repr(sys.float_info.max_10_exp + 1) + + +def interleave(inter, f, seq): + """Call f on each item in seq, calling inter() in between.""" + seq = iter(seq) + try: + f(next(seq)) + except StopIteration: + pass + else: + for x in seq: + inter() + f(x) + + +class Unparser: + """Methods in this class recursively traverse an AST and + output source code for the abstract syntax; original formatting + is disregarded.""" + + def __init__(self, tree, file=sys.stdout): + """Unparser(tree, file=sys.stdout) -> None. + Print the source for tree to file.""" + self.f = file + self._indent = 0 + self.dispatch(tree) + self.f.flush() + + def fill(self, text=""): + "Indent a piece of text, according to the current indentation level" + self.f.write("\n" + " " * self._indent + text) + + def write(self, text): + "Append a piece of text to the current line." + self.f.write(text) + + def enter(self): + "Print ':', and increase the indentation." + self.write(":") + self._indent += 1 + + def leave(self): + "Decrease the indentation level." + self._indent -= 1 + + def dispatch(self, tree): + "Dispatcher function, dispatching tree type T to method _T." + if isinstance(tree, list): + for t in tree: + self.dispatch(t) + return + meth = getattr(self, "_" + tree.__class__.__name__) + meth(tree) + + ############### Unparsing methods ###################### + # There should be one method per concrete grammar type # + # Constructors should be grouped by sum type. Ideally, # + # this would follow the order in the grammar, but # + # currently doesn't. # + ######################################################## + + def _Module(self, tree): + for stmt in tree.body: + self.dispatch(stmt) + + # stmt + def _Expr(self, tree): + self.fill() + self.dispatch(tree.value) + + def _NamedExpr(self, tree): + self.write("(") + self.dispatch(tree.target) + self.write(" := ") + self.dispatch(tree.value) + self.write(")") + + def _Import(self, t): + self.fill("import ") + interleave(lambda: self.write(", "), self.dispatch, t.names) + + def _ImportFrom(self, t): + self.fill("from ") + self.write("." * t.level) + if t.module: + self.write(t.module) + self.write(" import ") + interleave(lambda: self.write(", "), self.dispatch, t.names) + + def _Assign(self, t): + self.fill() + for target in t.targets: + self.dispatch(target) + self.write(" = ") + self.dispatch(t.value) + + def _AugAssign(self, t): + self.fill() + self.dispatch(t.target) + self.write(" " + self.binop[t.op.__class__.__name__] + "= ") + self.dispatch(t.value) + + def _AnnAssign(self, t): + self.fill() + if not t.simple and isinstance(t.target, ast.Name): + self.write("(") + self.dispatch(t.target) + if not t.simple and isinstance(t.target, ast.Name): + self.write(")") + self.write(": ") + self.dispatch(t.annotation) + if t.value: + self.write(" = ") + self.dispatch(t.value) + + def _Return(self, t): + self.fill("return") + if t.value: + self.write(" ") + self.dispatch(t.value) + + def _Pass(self, t): + self.fill("pass") + + def _Break(self, t): + self.fill("break") + + def _Continue(self, t): + self.fill("continue") + + def _Delete(self, t): + self.fill("del ") + interleave(lambda: self.write(", "), self.dispatch, t.targets) + + def _Assert(self, t): + self.fill("assert ") + self.dispatch(t.test) + if t.msg: + self.write(", ") + self.dispatch(t.msg) + + def _Global(self, t): + self.fill("global ") + interleave(lambda: self.write(", "), self.write, t.names) + + def _Nonlocal(self, t): + self.fill("nonlocal ") + interleave(lambda: self.write(", "), self.write, t.names) + + def _Await(self, t): + self.write("(") + self.write("await") + if t.value: + self.write(" ") + self.dispatch(t.value) + self.write(")") + + def _Yield(self, t): + self.write("(") + self.write("yield") + if t.value: + self.write(" ") + self.dispatch(t.value) + self.write(")") + + def _YieldFrom(self, t): + self.write("(") + self.write("yield from") + if t.value: + self.write(" ") + self.dispatch(t.value) + self.write(")") + + def _Raise(self, t): + self.fill("raise") + if not t.exc: + assert not t.cause + return + self.write(" ") + self.dispatch(t.exc) + if t.cause: + self.write(" from ") + self.dispatch(t.cause) + + def _Try(self, t): + self.fill("try") + self.enter() + self.dispatch(t.body) + self.leave() + for ex in t.handlers: + self.dispatch(ex) + if t.orelse: + self.fill("else") + self.enter() + self.dispatch(t.orelse) + self.leave() + if t.finalbody: + self.fill("finally") + self.enter() + self.dispatch(t.finalbody) + self.leave() + + def _ExceptHandler(self, t): + self.fill("except") + if t.type: + self.write(" ") + self.dispatch(t.type) + if t.name: + self.write(" as ") + self.write(t.name) + self.enter() + self.dispatch(t.body) + self.leave() + + def _ClassDef(self, t): + self.write("\n") + for deco in t.decorator_list: + self.fill("@") + self.dispatch(deco) + self.fill("class " + t.name) + self.write("(") + comma = False + for e in t.bases: + if comma: + self.write(", ") + else: + comma = True + self.dispatch(e) + for e in t.keywords: + if comma: + self.write(", ") + else: + comma = True + self.dispatch(e) + self.write(")") + + self.enter() + self.dispatch(t.body) + self.leave() + + def _FunctionDef(self, t): + self.__FunctionDef_helper(t, "def") + + def _AsyncFunctionDef(self, t): + self.__FunctionDef_helper(t, "async def") + + def __FunctionDef_helper(self, t, fill_suffix): + self.write("\n") + for deco in t.decorator_list: + self.fill("@") + self.dispatch(deco) + def_str = fill_suffix + " " + t.name + "(" + self.fill(def_str) + self.dispatch(t.args) + self.write(")") + if t.returns: + self.write(" -> ") + self.dispatch(t.returns) + self.enter() + self.dispatch(t.body) + self.leave() + + def _For(self, t): + self.__For_helper("for ", t) + + def _AsyncFor(self, t): + self.__For_helper("async for ", t) + + def __For_helper(self, fill, t): + self.fill(fill) + self.dispatch(t.target) + self.write(" in ") + self.dispatch(t.iter) + self.enter() + self.dispatch(t.body) + self.leave() + if t.orelse: + self.fill("else") + self.enter() + self.dispatch(t.orelse) + self.leave() + + def _If(self, t): + self.fill("if ") + self.dispatch(t.test) + self.enter() + self.dispatch(t.body) + self.leave() + # collapse nested ifs into equivalent elifs. + while t.orelse and len(t.orelse) == 1 and isinstance(t.orelse[0], ast.If): + t = t.orelse[0] + self.fill("elif ") + self.dispatch(t.test) + self.enter() + self.dispatch(t.body) + self.leave() + # final else + if t.orelse: + self.fill("else") + self.enter() + self.dispatch(t.orelse) + self.leave() + + def _While(self, t): + self.fill("while ") + self.dispatch(t.test) + self.enter() + self.dispatch(t.body) + self.leave() + if t.orelse: + self.fill("else") + self.enter() + self.dispatch(t.orelse) + self.leave() + + def _With(self, t): + self.fill("with ") + interleave(lambda: self.write(", "), self.dispatch, t.items) + self.enter() + self.dispatch(t.body) + self.leave() + + def _AsyncWith(self, t): + self.fill("async with ") + interleave(lambda: self.write(", "), self.dispatch, t.items) + self.enter() + self.dispatch(t.body) + self.leave() + + # expr + def _JoinedStr(self, t): + self.write("f") + string = io.StringIO() + self._fstring_JoinedStr(t, string.write) + self.write(repr(string.getvalue())) + + def _FormattedValue(self, t): + self.write("f") + string = io.StringIO() + self._fstring_FormattedValue(t, string.write) + self.write(repr(string.getvalue())) + + def _fstring_JoinedStr(self, t, write): + for value in t.values: + meth = getattr(self, "_fstring_" + type(value).__name__) + meth(value, write) + + def _fstring_Constant(self, t, write): + assert isinstance(t.value, str) + value = t.value.replace("{", "{{").replace("}", "}}") + write(value) + + def _fstring_FormattedValue(self, t, write): + write("{") + expr = io.StringIO() + Unparser(t.value, expr) + expr = expr.getvalue().rstrip("\n") + if expr.startswith("{"): + write(" ") # Separate pair of opening brackets as "{ {" + write(expr) + if t.conversion != -1: + conversion = chr(t.conversion) + assert conversion in "sra" + write(f"!{conversion}") + if t.format_spec: + write(":") + meth = getattr(self, "_fstring_" + type(t.format_spec).__name__) + meth(t.format_spec, write) + write("}") + + def _Name(self, t): + self.write(t.id) + + def _write_constant(self, value): + if isinstance(value, (float, complex)): + # Substitute overflowing decimal literal for AST infinities. + self.write(repr(value).replace("inf", INFSTR)) + else: + self.write(repr(value)) + + def _Constant(self, t): + value = t.value + if isinstance(value, tuple): + self.write("(") + if len(value) == 1: + self._write_constant(value[0]) + self.write(",") + else: + interleave(lambda: self.write(", "), self._write_constant, value) + self.write(")") + elif value is ...: + self.write("...") + else: + if t.kind == "u": + self.write("u") + self._write_constant(t.value) + + def _List(self, t): + self.write("[") + interleave(lambda: self.write(", "), self.dispatch, t.elts) + self.write("]") + + def _ListComp(self, t): + self.write("[") + self.dispatch(t.elt) + for gen in t.generators: + self.dispatch(gen) + self.write("]") + + def _GeneratorExp(self, t): + self.write("(") + self.dispatch(t.elt) + for gen in t.generators: + self.dispatch(gen) + self.write(")") + + def _SetComp(self, t): + self.write("{") + self.dispatch(t.elt) + for gen in t.generators: + self.dispatch(gen) + self.write("}") + + def _DictComp(self, t): + self.write("{") + self.dispatch(t.key) + self.write(": ") + self.dispatch(t.value) + for gen in t.generators: + self.dispatch(gen) + self.write("}") + + def _comprehension(self, t): + if t.is_async: + self.write(" async for ") + else: + self.write(" for ") + self.dispatch(t.target) + self.write(" in ") + self.dispatch(t.iter) + for if_clause in t.ifs: + self.write(" if ") + self.dispatch(if_clause) + + def _IfExp(self, t): + self.write("(") + self.dispatch(t.body) + self.write(" if ") + self.dispatch(t.test) + self.write(" else ") + self.dispatch(t.orelse) + self.write(")") + + def _Set(self, t): + assert t.elts # should be at least one element + self.write("{") + interleave(lambda: self.write(", "), self.dispatch, t.elts) + self.write("}") + + def _Dict(self, t): + self.write("{") + + def write_key_value_pair(k, v): + self.dispatch(k) + self.write(": ") + self.dispatch(v) + + def write_item(item): + k, v = item + if k is None: + # for dictionary unpacking operator in dicts {**{'y': 2}} + # see PEP 448 for details + self.write("**") + self.dispatch(v) + else: + write_key_value_pair(k, v) + + interleave(lambda: self.write(", "), write_item, zip(t.keys, t.values)) + self.write("}") + + def _Tuple(self, t): + self.write("(") + if len(t.elts) == 1: + elt = t.elts[0] + self.dispatch(elt) + self.write(",") + else: + interleave(lambda: self.write(", "), self.dispatch, t.elts) + self.write(")") + + unop = {"Invert": "~", "Not": "not", "UAdd": "+", "USub": "-"} + + def _UnaryOp(self, t): + self.write("(") + self.write(self.unop[t.op.__class__.__name__]) + self.write(" ") + self.dispatch(t.operand) + self.write(")") + + binop = { + "Add": "+", + "Sub": "-", + "Mult": "*", + "MatMult": "@", + "Div": "/", + "Mod": "%", + "LShift": "<<", + "RShift": ">>", + "BitOr": "|", + "BitXor": "^", + "BitAnd": "&", + "FloorDiv": "//", + "Pow": "**", + } + + def _BinOp(self, t): + self.write("(") + self.dispatch(t.left) + self.write(" " + self.binop[t.op.__class__.__name__] + " ") + self.dispatch(t.right) + self.write(")") + + cmpops = { + "Eq": "==", + "NotEq": "!=", + "Lt": "<", + "LtE": "<=", + "Gt": ">", + "GtE": ">=", + "Is": "is", + "IsNot": "is not", + "In": "in", + "NotIn": "not in", + } + + def _Compare(self, t): + self.write("(") + self.dispatch(t.left) + for o, e in zip(t.ops, t.comparators): + self.write(" " + self.cmpops[o.__class__.__name__] + " ") + self.dispatch(e) + self.write(")") + + boolops = {ast.And: "and", ast.Or: "or"} + + def _BoolOp(self, t): + self.write("(") + s = " %s " % self.boolops[t.op.__class__] + interleave(lambda: self.write(s), self.dispatch, t.values) + self.write(")") + + def _Attribute(self, t): + self.dispatch(t.value) + # Special case: 3.__abs__() is a syntax error, so if t.value + # is an integer literal then we need to either parenthesize + # it or add an extra space to get 3 .__abs__(). + if isinstance(t.value, ast.Constant) and isinstance(t.value.value, int): + self.write(" ") + self.write(".") + self.write(t.attr) + + def _Call(self, t): + self.dispatch(t.func) + self.write("(") + comma = False + for e in t.args: + if comma: + self.write(", ") + else: + comma = True + self.dispatch(e) + for e in t.keywords: + if comma: + self.write(", ") + else: + comma = True + self.dispatch(e) + self.write(")") + + def _Subscript(self, t): + self.dispatch(t.value) + self.write("[") + if ( + isinstance(t.slice, ast.Index) + and isinstance(t.slice.value, ast.Tuple) + and t.slice.value.elts + ): + if len(t.slice.value.elts) == 1: + elt = t.slice.value.elts[0] + self.dispatch(elt) + self.write(",") + else: + interleave(lambda: self.write(", "), self.dispatch, t.slice.value.elts) + else: + self.dispatch(t.slice) + self.write("]") + + def _Starred(self, t): + self.write("*") + self.dispatch(t.value) + + # slice + def _Ellipsis(self, t): + self.write("...") + + def _Index(self, t): + self.dispatch(t.value) + + def _Slice(self, t): + if t.lower: + self.dispatch(t.lower) + self.write(":") + if t.upper: + self.dispatch(t.upper) + if t.step: + self.write(":") + self.dispatch(t.step) + + def _ExtSlice(self, t): + if len(t.dims) == 1: + elt = t.dims[0] + self.dispatch(elt) + self.write(",") + else: + interleave(lambda: self.write(", "), self.dispatch, t.dims) + + # argument + def _arg(self, t): + self.write(t.arg) + if t.annotation: + self.write(": ") + self.dispatch(t.annotation) + + # others + def _arguments(self, t): + first = True + # normal arguments + all_args = t.posonlyargs + t.args + defaults = [None] * (len(all_args) - len(t.defaults)) + t.defaults + for index, elements in enumerate(zip(all_args, defaults), 1): + a, d = elements + if first: + first = False + else: + self.write(", ") + self.dispatch(a) + if d: + self.write("=") + self.dispatch(d) + if index == len(t.posonlyargs): + self.write(", /") + + # varargs, or bare '*' if no varargs but keyword-only arguments present + if t.vararg or t.kwonlyargs: + if first: + first = False + else: + self.write(", ") + self.write("*") + if t.vararg: + self.write(t.vararg.arg) + if t.vararg.annotation: + self.write(": ") + self.dispatch(t.vararg.annotation) + + # keyword-only arguments + if t.kwonlyargs: + for a, d in zip(t.kwonlyargs, t.kw_defaults): + if first: + first = False + else: + self.write(", ") + self.dispatch(a), + if d: + self.write("=") + self.dispatch(d) + + # kwargs + if t.kwarg: + if first: + first = False + else: + self.write(", ") + self.write("**" + t.kwarg.arg) + if t.kwarg.annotation: + self.write(": ") + self.dispatch(t.kwarg.annotation) + + def _keyword(self, t): + if t.arg is None: + self.write("**") + else: + self.write(t.arg) + self.write("=") + self.dispatch(t.value) + + def _Lambda(self, t): + self.write("(") + self.write("lambda ") + self.dispatch(t.args) + self.write(": ") + self.dispatch(t.body) + self.write(")") + + def _alias(self, t): + self.write(t.name) + if t.asname: + self.write(" as " + t.asname) + + def _withitem(self, t): + self.dispatch(t.context_expr) + if t.optional_vars: + self.write(" as ") + self.dispatch(t.optional_vars) + + +def roundtrip(filename, output=sys.stdout): + with open(filename, "rb") as pyfile: + encoding = tokenize.detect_encoding(pyfile.readline)[0] + with open(filename, "r", encoding=encoding) as pyfile: + source = pyfile.read() + tree = compile(source, filename, "exec", ast.PyCF_ONLY_AST) + Unparser(tree, output) diff --git a/libs/langchain/langchain/tools/retriever.py b/libs/langchain/langchain/tools/retriever.py new file mode 100644 index 00000000000..43f0a2105d2 --- /dev/null +++ b/libs/langchain/langchain/tools/retriever.py @@ -0,0 +1,25 @@ +from langchain.schema import BaseRetriever +from langchain.tools import Tool + + +def create_retriever_tool( + retriever: BaseRetriever, name: str, description: str +) -> Tool: + """Create a tool to do retrieval of documents. + + Args: + retriever: The retriever to use for the retrieval + name: The name for the tool. This will be passed to the language model, + so should be unique and somewhat descriptive. + description: The description for the tool. This will be passed to the language + model, so should be descriptive. + + Returns: + Tool class to pass to an agent + """ + return Tool( + name=name, + description=description, + func=retriever.get_relevant_documents, + coroutine=retriever.aget_relevant_documents, + ) diff --git a/libs/langchain/langchain/utilities/__init__.py b/libs/langchain/langchain/utilities/__init__.py index 12c0d3398de..0cffe29c596 100644 --- a/libs/langchain/langchain/utilities/__init__.py +++ b/libs/langchain/langchain/utilities/__init__.py @@ -217,6 +217,8 @@ def __getattr__(name: str) -> Any: return _import_alpha_vantage() elif name == "ApifyWrapper": return _import_apify() + elif name == "ArceeWrapper": + return _import_arcee() elif name == "ArxivAPIWrapper": return _import_arxiv() elif name == "LambdaWrapper": diff --git a/libs/langchain/langchain/utilities/arcee.py b/libs/langchain/langchain/utilities/arcee.py index f79da073ecb..4927acf832b 100644 --- a/libs/langchain/langchain/utilities/arcee.py +++ b/libs/langchain/langchain/utilities/arcee.py @@ -59,6 +59,43 @@ class DALMFilter(BaseModel): return values +class ArceeDocumentSource(BaseModel): + """Source of an Arcee document.""" + + document: str + name: str + id: str + + +class ArceeDocument(BaseModel): + """Arcee document.""" + + index: str + id: str + score: float + source: ArceeDocumentSource + + +class ArceeDocumentAdapter: + """Adapter for Arcee documents""" + + @classmethod + def adapt(cls, arcee_document: ArceeDocument) -> Document: + """Adapts an `ArceeDocument` to a langchain's `Document` object.""" + return Document( + page_content=arcee_document.source.document, + metadata={ + # arcee document; source metadata + "name": arcee_document.source.name, + "source_id": arcee_document.source.id, + # arcee document metadata + "index": arcee_document.index, + "id": arcee_document.id, + "score": arcee_document.score, + }, + ) + + class ArceeWrapper: """Wrapper for Arcee API.""" @@ -172,7 +209,7 @@ class ArceeWrapper: response = self._make_request( method="post", - route=ArceeRoute.generate, + route=ArceeRoute.generate.value, body=self._make_request_body_for_models( prompt=prompt, **kwargs, @@ -196,10 +233,13 @@ class ArceeWrapper: response = self._make_request( method="post", - route=ArceeRoute.retrieve, + route=ArceeRoute.retrieve.value, body=self._make_request_body_for_models( prompt=query, **kwargs, ), ) - return [Document(**doc) for doc in response["documents"]] + return [ + ArceeDocumentAdapter.adapt(ArceeDocument(**doc)) + for doc in response["results"] + ] diff --git a/libs/langchain/langchain/utilities/opaqueprompts.py b/libs/langchain/langchain/utilities/opaqueprompts.py index 23b02fdf2b2..58f2c87eea7 100644 --- a/libs/langchain/langchain/utilities/opaqueprompts.py +++ b/libs/langchain/langchain/utilities/opaqueprompts.py @@ -7,6 +7,7 @@ def sanitize( """ Sanitize input string or dict of strings by replacing sensitive data with placeholders. + It returns the sanitized input string or dict of strings and the secure context as a dict following the format: { @@ -29,6 +30,10 @@ def sanitize( } The `secure_context` needs to be passed to the `desanitize` function. + + Raises: + ValueError: If the input is not a string or dict of strings. + ImportError: If the `opaqueprompts` Python package is not installed. """ try: import opaqueprompts as op diff --git a/libs/langchain/langchain/utilities/vertexai.py b/libs/langchain/langchain/utilities/vertexai.py index 0df556307a6..334ab57f2ed 100644 --- a/libs/langchain/langchain/utilities/vertexai.py +++ b/libs/langchain/langchain/utilities/vertexai.py @@ -1,7 +1,9 @@ """Utilities to init Vertex AI.""" +from importlib import metadata from typing import TYPE_CHECKING, Optional if TYPE_CHECKING: + from google.api_core.gapic_v1.client_info import ClientInfo from google.auth.credentials import Credentials @@ -46,3 +48,30 @@ def init_vertexai( location=location, credentials=credentials, ) + + +def get_client_info(module: Optional[str] = None) -> "ClientInfo": + 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 + """ + try: + from google.api_core.gapic_v1.client_info import ClientInfo + except ImportError as exc: + raise ImportError( + "Could not import ClientInfo. Please, install it with " + "pip install google-api-core" + ) from exc + + 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/langchain/langchain/vectorstores/__init__.py b/libs/langchain/langchain/vectorstores/__init__.py index 990ab923af0..5e40a26d2f9 100644 --- a/libs/langchain/langchain/vectorstores/__init__.py +++ b/libs/langchain/langchain/vectorstores/__init__.py @@ -18,9 +18,8 @@ and retrieve the data that are 'most similar' to the embedded query. Embeddings, Document """ # noqa: E501 -from typing import Any -from langchain.schema.vectorstore import VectorStore +from typing import Any def _import_alibaba_cloud_open_search() -> Any: @@ -91,6 +90,12 @@ def _import_bageldb() -> Any: return Bagel +def _import_baiducloud_vector_search() -> Any: + from langchain.vectorstores.baiducloud_vector_search import BESVectorStore + + return BESVectorStore + + def _import_cassandra() -> Any: from langchain.vectorstores.cassandra import Cassandra @@ -426,6 +431,8 @@ def __getattr__(name: str) -> Any: return _import_azuresearch() elif name == "Bagel": return _import_bageldb() + elif name == "BESVectorStore": + return _import_baiducloud_vector_search() elif name == "Cassandra": return _import_cassandra() elif name == "Chroma": @@ -592,7 +599,6 @@ __all__ = [ "Vald", "Vearch", "Vectara", - "VectorStore", "VespaStore", "Weaviate", "ZepVectorStore", diff --git a/libs/langchain/langchain/vectorstores/baiducloud_vector_search.py b/libs/langchain/langchain/vectorstores/baiducloud_vector_search.py new file mode 100644 index 00000000000..d4285c4b83c --- /dev/null +++ b/libs/langchain/langchain/vectorstores/baiducloud_vector_search.py @@ -0,0 +1,491 @@ +import logging +import uuid +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Dict, + Iterable, + List, + Optional, + Tuple, + Union, +) + +from langchain.docstore.document import Document +from langchain.schema.embeddings import Embeddings +from langchain.schema.vectorstore import VectorStore + +if TYPE_CHECKING: + from elasticsearch import Elasticsearch + +logger = logging.getLogger(__name__) + + +class BESVectorStore(VectorStore): + """`Baidu Elasticsearch` vector store. + + Example: + .. code-block:: python + + from langchain.vectorstores import BESVectorStore + from langchain.embeddings.openai import OpenAIEmbeddings + + embeddings = OpenAIEmbeddings() + vectorstore = BESVectorStore( + embedding=OpenAIEmbeddings(), + index_name="langchain-demo", + bes_url="http://localhost:9200" + ) + + Args: + index_name: Name of the Elasticsearch index to create. + bes_url: URL of the Baidu Elasticsearch instance to connect to. + user: Username to use when connecting to Elasticsearch. + password: Password to use when connecting to Elasticsearch. + + More information can be obtained from: + https://cloud.baidu.com/doc/BES/s/8llyn0hh4 + + """ + + def __init__( + self, + index_name: str, + bes_url: str, + user: Optional[str] = None, + password: Optional[str] = None, + embedding: Optional[Embeddings] = None, + **kwargs: Optional[dict], + ) -> None: + self.embedding = embedding + self.index_name = index_name + self.query_field = kwargs.get("query_field", "text") + self.vector_query_field = kwargs.get("vector_query_field", "vector") + self.space_type = kwargs.get("space_type", "cosine") + self.index_type = kwargs.get("index_type", "linear") + self.index_params = kwargs.get("index_params") or {} + + if bes_url is not None: + self.client = BESVectorStore.bes_client( + bes_url=bes_url, username=user, password=password + ) + else: + raise ValueError("""Please specified a bes connection url.""") + + @property + def embeddings(self) -> Optional[Embeddings]: + return self.embedding + + @staticmethod + def bes_client( + *, + bes_url: Optional[str] = None, + username: Optional[str] = None, + password: Optional[str] = None, + ) -> "Elasticsearch": + try: + import elasticsearch + except ImportError: + raise ImportError( + "Could not import elasticsearch python package. " + "Please install it with `pip install elasticsearch`." + ) + + connection_params: Dict[str, Any] = {} + + connection_params["hosts"] = [bes_url] + connection_params["basic_auth"] = (username, password) + + es_client = elasticsearch.Elasticsearch(**connection_params) + try: + es_client.info() + except Exception as e: + logger.error(f"Error connecting to Elasticsearch: {e}") + raise e + return es_client + + def _create_index_if_not_exists(self, dims_length: Optional[int] = None) -> None: + """Create the index if it doesn't already exist. + + Args: + dims_length: Length of the embedding vectors. + """ + + if self.client.indices.exists(index=self.index_name): + logger.info(f"Index {self.index_name} already exists. Skipping creation.") + + else: + if dims_length is None: + raise ValueError( + "Cannot create index without specifying dims_length " + + "when the index doesn't already exist. " + ) + + indexMapping = self._index_mapping(dims_length=dims_length) + + logger.debug( + f"Creating index {self.index_name} with mappings {indexMapping}" + ) + + self.client.indices.create( + index=self.index_name, + body={ + "settings": {"index": {"knn": True}}, + "mappings": {"properties": indexMapping}, + }, + ) + + def _index_mapping(self, dims_length: Union[int, None]) -> Dict: + """ + Executes when the index is created. + + Args: + dims_length: Numeric length of the embedding vectors, + or None if not using vector-based query. + index_params: The extra pamameters for creating index. + + Returns: + Dict: The Elasticsearch settings and mappings for the strategy. + """ + if "linear" == self.index_type: + return { + self.vector_query_field: { + "type": "bpack_vector", + "dims": dims_length, + "build_index": self.index_params.get("build_index", False), + } + } + + elif "hnsw" == self.index_type: + return { + self.vector_query_field: { + "type": "bpack_vector", + "dims": dims_length, + "index_type": "hnsw", + "space_type": self.space_type, + "parameters": { + "ef_construction": self.index_params.get( + "hnsw_ef_construction", 200 + ), + "m": self.index_params.get("hnsw_m", 4), + }, + } + } + else: + return { + self.vector_query_field: { + "type": "bpack_vector", + "model_id": self.index_params.get("model_id", ""), + } + } + + def delete( + self, + ids: Optional[List[str]] = None, + **kwargs: Any, + ) -> Optional[bool]: + """Delete documents from the index. + + Args: + ids: List of ids of documents to delete + """ + try: + from elasticsearch.helpers import BulkIndexError, bulk + except ImportError: + raise ImportError( + "Could not import elasticsearch python package. " + "Please install it with `pip install elasticsearch`." + ) + + body = [] + + if ids is None: + raise ValueError("ids must be provided.") + + for _id in ids: + body.append({"_op_type": "delete", "_index": self.index_name, "_id": _id}) + + if len(body) > 0: + try: + bulk( + self.client, + body, + refresh=kwargs.get("refresh_indices", True), + ignore_status=404, + ) + logger.debug(f"Deleted {len(body)} texts from index") + return True + except BulkIndexError as e: + logger.error(f"Error deleting texts: {e}") + raise e + else: + logger.info("No documents to delete") + return False + + def _query_body( + self, + query_vector: Union[List[float], None], + filter: Optional[dict] = None, + search_params: Dict = {}, + ) -> Dict: + query_vector_body = {"vector": query_vector, "k": search_params.get("k", 2)} + + if filter is not None and len(filter) != 0: + query_vector_body["filter"] = filter + + if "linear" == self.index_type: + query_vector_body["linear"] = True + query_vector_body["space_type"] = self.space_type + else: + query_vector_body["ef"] = search_params.get("ef", 10) + + return { + "size": search_params.get("size", 4), + "query": {"knn": {self.vector_query_field: query_vector_body}}, + } + + def _search( + self, + query: Optional[str] = None, + query_vector: Union[List[float], None] = None, + filter: Optional[dict] = None, + custom_query: Optional[Callable[[Dict, Union[str, None]], Dict]] = None, + search_params: Dict = {}, + ) -> List[Tuple[Document, float]]: + """Return searched documents result from BES + + Args: + query: Text to look up documents similar to. + query_vector: Embedding to look up documents similar to. + filter: Array of Baidu ElasticSearch filter clauses to apply to the query. + custom_query: Function to modify the query body before it is sent to BES. + + Returns: + List of Documents most similar to the query and score for each + """ + + if self.embedding and query is not None: + query_vector = self.embedding.embed_query(query) + + query_body = self._query_body( + query_vector=query_vector, filter=filter, search_params=search_params + ) + + if custom_query is not None: + query_body = custom_query(query_body, query) + logger.debug(f"Calling custom_query, Query body now: {query_body}") + + logger.debug(f"Query body: {query_body}") + + # Perform the kNN search on the BES index and return the results. + response = self.client.search(index=self.index_name, **query_body) + logger.debug(f"response={response}") + + hits = [hit for hit in response["hits"]["hits"]] + docs_and_scores = [ + ( + Document( + page_content=hit["_source"][self.query_field], + metadata=hit["_source"]["metadata"], + ), + hit["_score"], + ) + for hit in hits + ] + + return docs_and_scores + + def similarity_search( + self, + query: str, + k: int = 4, + filter: Optional[dict] = None, + **kwargs: Any, + ) -> List[Document]: + """Return documents most similar to query. + + Args: + query: Text to look up documents similar to. + k: Number of Documents to return. Defaults to 4. + filter: Array of Elasticsearch filter clauses to apply to the query. + + Returns: + List of Documents most similar to the query, + in descending order of similarity. + """ + + results = self.similarity_search_with_score( + query=query, k=k, filter=filter, **kwargs + ) + return [doc for doc, _ in results] + + def similarity_search_with_score( + self, query: str, k: int, filter: Optional[dict] = None, **kwargs: Any + ) -> List[Tuple[Document, float]]: + """Return documents most similar to query, along with scores. + + Args: + query: Text to look up documents similar to. + size: Number of Documents to return. Defaults to 4. + filter: Array of Elasticsearch filter clauses to apply to the query. + + Returns: + List of Documents most similar to the query and score for each + """ + search_params = kwargs.get("search_params") or {} + + if len(search_params) == 0 or search_params.get("size") is None: + search_params["size"] = k + + return self._search(query=query, filter=filter, **kwargs) + + @classmethod + def from_documents( + cls, + documents: List[Document], + embedding: Optional[Embeddings] = None, + **kwargs: Any, + ) -> "BESVectorStore": + """Construct BESVectorStore wrapper from documents. + + Args: + documents: List of documents to add to the Elasticsearch index. + embedding: Embedding function to use to embed the texts. + Do not provide if using a strategy + that doesn't require inference. + kwargs: create index key words arguments + """ + + vectorStore = BESVectorStore._bes_vector_store(embedding=embedding, **kwargs) + # Encode the provided texts and add them to the newly created index. + vectorStore.add_documents(documents) + + return vectorStore + + @classmethod + def from_texts( + cls, + texts: List[str], + embedding: Optional[Embeddings] = None, + metadatas: Optional[List[Dict[str, Any]]] = None, + **kwargs: Any, + ) -> "BESVectorStore": + """Construct BESVectorStore wrapper from raw documents. + + Args: + texts: List of texts to add to the Elasticsearch index. + embedding: Embedding function to use to embed the texts. + metadatas: Optional list of metadatas associated with the texts. + index_name: Name of the Elasticsearch index to create. + kwargs: create index key words arguments + """ + + vectorStore = BESVectorStore._bes_vector_store(embedding=embedding, **kwargs) + + # Encode the provided texts and add them to the newly created index. + vectorStore.add_texts(texts, metadatas=metadatas, **kwargs) + + return vectorStore + + def add_texts( + self, + texts: Iterable[str], + metadatas: Optional[List[Dict[Any, Any]]] = None, + **kwargs: Any, + ) -> List[str]: + """Run more texts through the embeddings and add to the vectorstore. + + Args: + texts: Iterable of strings to add to the vectorstore. + metadatas: Optional list of metadatas associated with the texts. + Returns: + List of ids from adding the texts into the vectorstore. + """ + try: + from elasticsearch.helpers import BulkIndexError, bulk + except ImportError: + raise ImportError( + "Could not import elasticsearch python package. " + "Please install it with `pip install elasticsearch`." + ) + + embeddings = [] + create_index_if_not_exists = kwargs.get("create_index_if_not_exists", True) + ids = kwargs.get("ids", [str(uuid.uuid4()) for _ in texts]) + refresh_indices = kwargs.get("refresh_indices", True) + requests = [] + + if self.embedding is not None: + embeddings = self.embedding.embed_documents(list(texts)) + dims_length = len(embeddings[0]) + + if create_index_if_not_exists: + self._create_index_if_not_exists(dims_length=dims_length) + + for i, (text, vector) in enumerate(zip(texts, embeddings)): + metadata = metadatas[i] if metadatas else {} + + requests.append( + { + "_op_type": "index", + "_index": self.index_name, + self.query_field: text, + self.vector_query_field: vector, + "metadata": metadata, + "_id": ids[i], + } + ) + + else: + if create_index_if_not_exists: + self._create_index_if_not_exists() + + for i, text in enumerate(texts): + metadata = metadatas[i] if metadatas else {} + + requests.append( + { + "_op_type": "index", + "_index": self.index_name, + self.query_field: text, + "metadata": metadata, + "_id": ids[i], + } + ) + + if len(requests) > 0: + try: + success, failed = bulk( + self.client, requests, stats_only=True, refresh=refresh_indices + ) + logger.debug( + f"Added {success} and failed to add {failed} texts to index" + ) + + logger.debug(f"added texts {ids} to index") + return ids + except BulkIndexError as e: + logger.error(f"Error adding texts: {e}") + firstError = e.errors[0].get("index", {}).get("error", {}) + logger.error(f"First error reason: {firstError.get('reason')}") + raise e + + else: + logger.debug("No texts to add to index") + return [] + + @staticmethod + def _bes_vector_store( + embedding: Optional[Embeddings] = None, **kwargs: Any + ) -> "BESVectorStore": + index_name = kwargs.get("index_name") + + if index_name is None: + raise ValueError("Please provide an index_name.") + + bes_url = kwargs.get("bes_url") + if bes_url is None: + raise ValueError("Please provided a valid bes connection url") + + return BESVectorStore(embedding=embedding, **kwargs) diff --git a/libs/langchain/langchain/vectorstores/dingo.py b/libs/langchain/langchain/vectorstores/dingo.py index 256fdea93dd..21a433b8f16 100644 --- a/libs/langchain/langchain/vectorstores/dingo.py +++ b/libs/langchain/langchain/vectorstores/dingo.py @@ -36,6 +36,7 @@ class Dingo(VectorStore): *, client: Any = None, index_name: Optional[str] = None, + dimension: int = 1024, host: Optional[List[str]] = None, user: str = "root", password: str = "123123", @@ -67,9 +68,11 @@ class Dingo(VectorStore): if index_name is not None and index_name not in dingo_client.get_index(): if self_id is True: - dingo_client.create_index(index_name, 1024, auto_id=False) + dingo_client.create_index( + index_name, dimension=dimension, auto_id=False + ) else: - dingo_client.create_index(index_name, 1024) + dingo_client.create_index(index_name, dimension=dimension) self._index_name = index_name self._embedding = embedding @@ -268,6 +271,7 @@ class Dingo(VectorStore): ids: Optional[List[str]] = None, text_key: str = "text", index_name: Optional[str] = None, + dimension: int = 1024, client: Any = None, host: List[str] = ["172.20.31.10:13000"], user: str = "root", @@ -315,11 +319,12 @@ class Dingo(VectorStore): raise ValueError(f"Dingo failed to connect: {e}") if kwargs is not None and kwargs.get("self_id") is True: if index_name not in dingo_client.get_index(): - dingo_client.create_index(index_name, 1024, auto_id=False) + dingo_client.create_index( + index_name, dimension=dimension, auto_id=False + ) else: if index_name not in dingo_client.get_index(): - dingo_client.create_index(index_name, 1024) - # dingo_client.create_index(index_name, 1024, index_type="hnsw") + dingo_client.create_index(index_name, dimension=dimension) # Embed and create the documents diff --git a/libs/langchain/langchain/vectorstores/docarray/base.py b/libs/langchain/langchain/vectorstores/docarray/base.py index 02cbc3c2b84..ffdbbbed456 100644 --- a/libs/langchain/langchain/vectorstores/docarray/base.py +++ b/libs/langchain/langchain/vectorstores/docarray/base.py @@ -6,7 +6,7 @@ import numpy as np from langchain.pydantic_v1 import Field from langchain.schema import Document from langchain.schema.embeddings import Embeddings -from langchain.vectorstores import VectorStore +from langchain.schema.vectorstore import VectorStore from langchain.vectorstores.utils import maximal_marginal_relevance if TYPE_CHECKING: diff --git a/libs/langchain/langchain/vectorstores/elasticsearch.py b/libs/langchain/langchain/vectorstores/elasticsearch.py index e1eed36f3c9..4bf2d4c47e5 100644 --- a/libs/langchain/langchain/vectorstores/elasticsearch.py +++ b/libs/langchain/langchain/vectorstores/elasticsearch.py @@ -662,7 +662,7 @@ class ElasticsearchStore(VectorStore): if remove_vector_query_field_from_metadata: for doc in selected_docs: - del doc.metadata["vector"] + del doc.metadata[self.vector_query_field] return selected_docs diff --git a/libs/langchain/langchain/vectorstores/matching_engine.py b/libs/langchain/langchain/vectorstores/matching_engine.py index cd0a26d9d6c..e60987f0b17 100644 --- a/libs/langchain/langchain/vectorstores/matching_engine.py +++ b/libs/langchain/langchain/vectorstores/matching_engine.py @@ -9,6 +9,7 @@ from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Type from langchain.schema.document import Document from langchain.schema.embeddings import Embeddings from langchain.schema.vectorstore import VectorStore +from langchain.utilities.vertexai import get_client_info if TYPE_CHECKING: from google.cloud import storage @@ -419,7 +420,11 @@ class MatchingEngine(VectorStore): from google.cloud import storage - return storage.Client(credentials=credentials, project=project_id) + return storage.Client( + credentials=credentials, + project=project_id, + client_info=get_client_info(module="vertex-ai-matching-engine"), + ) @classmethod def _init_aiplatform( diff --git a/libs/langchain/langchain/vectorstores/pgvector.py b/libs/langchain/langchain/vectorstores/pgvector.py index 30e1c2d4707..3418f6e4541 100644 --- a/libs/langchain/langchain/vectorstores/pgvector.py +++ b/libs/langchain/langchain/vectorstores/pgvector.py @@ -84,6 +84,7 @@ class PGVector(VectorStore): distance_strategy: The distance strategy to use. (default: COSINE) pre_delete_collection: If True, will delete the collection if it exists. (default: False). Useful for testing. + engine_args: SQLAlchemy's create engine arguments. Example: .. code-block:: python @@ -114,6 +115,8 @@ class PGVector(VectorStore): pre_delete_collection: bool = False, logger: Optional[logging.Logger] = None, relevance_score_fn: Optional[Callable[[float], float]] = None, + *, + engine_args: Optional[dict[str, Any]] = None, ) -> None: self.connection_string = connection_string self.embedding_function = embedding_function @@ -123,6 +126,7 @@ class PGVector(VectorStore): self.pre_delete_collection = pre_delete_collection self.logger = logger or logging.getLogger(__name__) self.override_relevance_score_fn = relevance_score_fn + self.engine_args = engine_args or {} self.__post_init__() def __post_init__( @@ -132,7 +136,7 @@ class PGVector(VectorStore): Initialize the store. """ self._conn = self.connect() - # self.create_vector_extension() + self.create_vector_extension() from langchain.vectorstores._pgvector_data_models import ( CollectionStore, EmbeddingStore, @@ -148,7 +152,7 @@ class PGVector(VectorStore): return self.embedding_function def connect(self) -> sqlalchemy.engine.Connection: - engine = sqlalchemy.create_engine(self.connection_string) + engine = sqlalchemy.create_engine(self.connection_string, **self.engine_args) conn = engine.connect() return conn @@ -159,7 +163,7 @@ class PGVector(VectorStore): session.execute(statement) session.commit() except Exception as e: - self.logger.exception(e) + raise Exception(f"Failed to create vector extension: {e}") from e def create_tables_if_not_exists(self) -> None: with self._conn.begin(): diff --git a/libs/langchain/langchain/vectorstores/qdrant.py b/libs/langchain/langchain/vectorstores/qdrant.py index 7326269ec0e..951748ba956 100644 --- a/libs/langchain/langchain/vectorstores/qdrant.py +++ b/libs/langchain/langchain/vectorstores/qdrant.py @@ -26,7 +26,7 @@ import numpy as np from langchain.docstore.document import Document from langchain.schema.embeddings import Embeddings -from langchain.vectorstores import VectorStore +from langchain.schema.vectorstore import VectorStore from langchain.vectorstores.utils import maximal_marginal_relevance if TYPE_CHECKING: diff --git a/libs/langchain/langchain/vectorstores/redis/base.py b/libs/langchain/langchain/vectorstores/redis/base.py index 1306ba0177a..baf2f859c9e 100644 --- a/libs/langchain/langchain/vectorstores/redis/base.py +++ b/libs/langchain/langchain/vectorstores/redis/base.py @@ -54,16 +54,6 @@ if TYPE_CHECKING: from langchain.vectorstores.redis.schema import RedisModel -def _redis_key(prefix: str) -> str: - """Redis key schema for a given prefix.""" - return f"{prefix}:{uuid.uuid4().hex}" - - -def _redis_prefix(index_name: str) -> str: - """Redis key prefix for a given index.""" - return f"doc:{index_name}" - - def _default_relevance_score(val: float) -> float: return 1 - val @@ -94,6 +84,7 @@ class Redis(VectorStore): search API available. .. code-block:: bash + # to run redis stack in docker locally docker run -d -p 6379:6379 -p 8001:8001 redis/redis-stack:latest @@ -258,6 +249,7 @@ class Redis(VectorStore): index_schema: Optional[Union[Dict[str, str], str, os.PathLike]] = None, vector_schema: Optional[Dict[str, Union[str, int]]] = None, relevance_score_fn: Optional[Callable[[float], float]] = None, + key_prefix: Optional[str] = None, **kwargs: Any, ): """Initialize with necessary components.""" @@ -284,6 +276,7 @@ class Redis(VectorStore): self.client = redis_client self.relevance_score_fn = relevance_score_fn self._schema = self._get_schema_with_defaults(index_schema, vector_schema) + self.key_prefix = key_prefix if key_prefix is not None else f"doc:{index_name}" @property def embeddings(self) -> Optional[Embeddings]: @@ -393,7 +386,7 @@ class Redis(VectorStore): generated_schema = _generate_field_schema(metadatas[0]) if index_schema: # read in the schema solely to compare to the generated schema - user_schema = read_schema(index_schema) + user_schema = read_schema(index_schema) # type: ignore # the very rare case where a super user decides to pass the index # schema and a document loader is used that has metadata which @@ -420,14 +413,8 @@ class Redis(VectorStore): **kwargs, ) - # Create embeddings over documents - embeddings = embedding.embed_documents(texts) - - # Create the search index - instance._create_index(dim=len(embeddings[0])) - # Add data to Redis - keys = instance.add_texts(texts, metadatas, embeddings, keys=keys) + keys = instance.add_texts(texts, metadatas, keys=keys) return instance, keys @classmethod @@ -692,7 +679,6 @@ class Redis(VectorStore): List[str]: List of ids added to the vectorstore """ ids = [] - prefix = _redis_prefix(self.index_name) # Get keys or ids from kwargs # Other vectorstores use ids @@ -705,22 +691,24 @@ class Redis(VectorStore): if not (isinstance(metadatas, list) and isinstance(metadatas[0], dict)): raise ValueError("Metadatas must be a list of dicts") + embeddings = embeddings or self._embeddings.embed_documents(list(texts)) + self._create_index_if_not_exist(dim=len(embeddings[0])) + # Write data to redis pipeline = self.client.pipeline(transaction=False) for i, text in enumerate(texts): # Use provided values by default or fallback - key = keys_or_ids[i] if keys_or_ids else _redis_key(prefix) + key = keys_or_ids[i] if keys_or_ids else str(uuid.uuid4().hex) + if not key.startswith(self.key_prefix + ":"): + key = self.key_prefix + ":" + key metadata = metadatas[i] if metadatas else {} metadata = _prepare_metadata(metadata) if clean_metadata else metadata - embedding = ( - embeddings[i] if embeddings else self._embeddings.embed_query(text) - ) pipeline.hset( key, mapping={ self._schema.content_key: text, self._schema.content_vector_key: _array_to_buffer( - embedding, self._schema.vector_dtype + embeddings[i], self._schema.vector_dtype ), **metadata, }, @@ -1178,7 +1166,7 @@ class Redis(VectorStore): # read in schema (yaml file or dict) and # pass to the Pydantic validators if index_schema: - schema_values = read_schema(index_schema) + schema_values = read_schema(index_schema) # type: ignore schema = RedisModel(**schema_values) # ensure user did not exclude the content field @@ -1212,7 +1200,7 @@ class Redis(VectorStore): schema.add_vector_field(vector_field) return schema - def _create_index(self, dim: int = 1536) -> None: + def _create_index_if_not_exist(self, dim: int = 1536) -> None: try: from redis.commands.search.indexDefinition import ( # type: ignore IndexDefinition, @@ -1232,12 +1220,12 @@ class Redis(VectorStore): # Check if index exists if not check_index_exists(self.client, self.index_name): - prefix = _redis_prefix(self.index_name) - # Create Redis Index self.client.ft(self.index_name).create_index( fields=self._schema.get_fields(), - definition=IndexDefinition(prefix=[prefix], index_type=IndexType.HASH), + definition=IndexDefinition( + prefix=[self.key_prefix], index_type=IndexType.HASH + ), ) def _calculate_fp_distance(self, distance: str) -> float: diff --git a/libs/langchain/langchain/vectorstores/redis/schema.py b/libs/langchain/langchain/vectorstores/redis/schema.py index 55e3639a540..5419e7ba991 100644 --- a/libs/langchain/langchain/vectorstores/redis/schema.py +++ b/libs/langchain/langchain/vectorstores/redis/schema.py @@ -52,7 +52,7 @@ class TextFieldSchema(RedisField): self.name, weight=self.weight, no_stem=self.no_stem, - phonetic_matcher=self.phonetic_matcher, + phonetic_matcher=self.phonetic_matcher, # type: ignore sortable=self.sortable, no_index=self.no_index, ) @@ -97,9 +97,9 @@ class RedisVectorField(RedisField): algorithm: object = Field(...) datatype: str = Field(default="FLOAT32") distance_metric: RedisDistanceMetric = Field(default="COSINE") - initial_cap: int = Field(default=20000) + initial_cap: Optional[int] = None - @validator("distance_metric", pre=True) + @validator("algorithm", "datatype", "distance_metric", pre=True, each_item=True) def uppercase_strings(cls, v: str) -> str: return v.upper() @@ -111,27 +111,30 @@ class RedisVectorField(RedisField): ) return v.upper() + def _fields(self) -> Dict[str, Any]: + field_data = { + "TYPE": self.datatype, + "DIM": self.dims, + "DISTANCE_METRIC": self.distance_metric, + } + if self.initial_cap is not None: # Only include it if it's set + field_data["INITIAL_CAP"] = self.initial_cap + return field_data + class FlatVectorField(RedisVectorField): """Schema for flat vector fields in Redis.""" algorithm: Literal["FLAT"] = "FLAT" - block_size: int = Field(default=1000) + block_size: Optional[int] = None def as_field(self) -> VectorField: from redis.commands.search.field import VectorField # type: ignore - return VectorField( - self.name, - self.algorithm, - { - "TYPE": self.datatype, - "DIM": self.dims, - "DISTANCE_METRIC": self.distance_metric, - "INITIAL_CAP": self.initial_cap, - "BLOCK_SIZE": self.block_size, - }, - ) + field_data = super()._fields() + if self.block_size is not None: + field_data["BLOCK_SIZE"] = self.block_size + return VectorField(self.name, self.algorithm, field_data) class HNSWVectorField(RedisVectorField): @@ -141,25 +144,21 @@ class HNSWVectorField(RedisVectorField): m: int = Field(default=16) ef_construction: int = Field(default=200) ef_runtime: int = Field(default=10) - epsilon: float = Field(default=0.8) + epsilon: float = Field(default=0.01) def as_field(self) -> VectorField: from redis.commands.search.field import VectorField # type: ignore - return VectorField( - self.name, - self.algorithm, + field_data = super()._fields() + field_data.update( { - "TYPE": self.datatype, - "DIM": self.dims, - "DISTANCE_METRIC": self.distance_metric, - "INITIAL_CAP": self.initial_cap, "M": self.m, "EF_CONSTRUCTION": self.ef_construction, "EF_RUNTIME": self.ef_runtime, "EPSILON": self.epsilon, - }, + } ) + return VectorField(self.name, self.algorithm, field_data) class RedisModel(BaseModel): @@ -284,7 +283,7 @@ class RedisModel(BaseModel): def read_schema( - index_schema: Optional[Union[Dict[str, str], str, os.PathLike]] + index_schema: Optional[Union[Dict[str, List[Any]], str, os.PathLike]] ) -> Dict[str, Any]: """Reads in the index schema from a dict or yaml file. diff --git a/libs/langchain/langchain/vectorstores/semadb.py b/libs/langchain/langchain/vectorstores/semadb.py index 54f268a1e95..dd89138c2ef 100644 --- a/libs/langchain/langchain/vectorstores/semadb.py +++ b/libs/langchain/langchain/vectorstores/semadb.py @@ -6,8 +6,8 @@ import requests from langchain.schema.document import Document from langchain.schema.embeddings import Embeddings +from langchain.schema.vectorstore import VectorStore from langchain.utils import get_from_env -from langchain.vectorstores import VectorStore from langchain.vectorstores.utils import DistanceStrategy diff --git a/libs/langchain/langchain/vectorstores/supabase.py b/libs/langchain/langchain/vectorstores/supabase.py index 2cb04385ae7..f5dbdad9af8 100644 --- a/libs/langchain/langchain/vectorstores/supabase.py +++ b/libs/langchain/langchain/vectorstores/supabase.py @@ -61,6 +61,7 @@ class SupabaseVectorStore(VectorStore): client=supabase_client, table_name="documents", query_name="match_documents", + chunk_size=500, ) To load from an existing table: @@ -88,6 +89,7 @@ class SupabaseVectorStore(VectorStore): client: supabase.client.Client, embedding: Embeddings, table_name: str, + chunk_size: int = 500, query_name: Union[str, None] = None, ) -> None: """Initialize with supabase client.""" @@ -103,6 +105,9 @@ class SupabaseVectorStore(VectorStore): self._embedding: Embeddings = embedding self.table_name = table_name or "documents" self.query_name = query_name or "match_documents" + self.chunk_size = chunk_size or 500 + # According to the SupabaseVectorStore JS implementation, the best chunk size + # is 500. Though for large datasets it can be too large so it is configurable. @property def embeddings(self) -> Embeddings: @@ -130,6 +135,7 @@ class SupabaseVectorStore(VectorStore): client: Optional[supabase.client.Client] = None, table_name: Optional[str] = "documents", query_name: Union[str, None] = "match_documents", + chunk_size: int = 500, ids: Optional[List[str]] = None, **kwargs: Any, ) -> "SupabaseVectorStore": @@ -144,13 +150,14 @@ class SupabaseVectorStore(VectorStore): embeddings = embedding.embed_documents(texts) ids = [str(uuid.uuid4()) for _ in texts] docs = cls._texts_to_documents(texts, metadatas) - cls._add_vectors(client, table_name, embeddings, docs, ids) + cls._add_vectors(client, table_name, embeddings, docs, ids, chunk_size) return cls( client=client, embedding=embedding, table_name=table_name, query_name=query_name, + chunk_size=chunk_size, ) def add_vectors( @@ -159,7 +166,9 @@ class SupabaseVectorStore(VectorStore): documents: List[Document], ids: List[str], ) -> List[str]: - return self._add_vectors(self._client, self.table_name, vectors, documents, ids) + return self._add_vectors( + self._client, self.table_name, vectors, documents, ids, self.chunk_size + ) def similarity_search( self, @@ -300,6 +309,7 @@ class SupabaseVectorStore(VectorStore): vectors: List[List[float]], documents: List[Document], ids: List[str], + chunk_size: int, ) -> List[str]: """Add vectors to Supabase table.""" @@ -313,9 +323,6 @@ class SupabaseVectorStore(VectorStore): for idx, embedding in enumerate(vectors) ] - # According to the SupabaseVectorStore JS implementation, the best chunk size - # is 500 - chunk_size = 500 id_list: List[str] = [] for i in range(0, len(rows), chunk_size): chunk = rows[i : i + chunk_size] diff --git a/libs/langchain/langchain/vectorstores/tigris.py b/libs/langchain/langchain/vectorstores/tigris.py index aa574e031ad..7f9eee0b579 100644 --- a/libs/langchain/langchain/vectorstores/tigris.py +++ b/libs/langchain/langchain/vectorstores/tigris.py @@ -5,7 +5,7 @@ from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple from langchain.schema import Document from langchain.schema.embeddings import Embeddings -from langchain.vectorstores import VectorStore +from langchain.schema.vectorstore import VectorStore if TYPE_CHECKING: from tigrisdb import TigrisClient diff --git a/libs/langchain/poetry.lock b/libs/langchain/poetry.lock index 86cfede6af9..c3db3ca4b11 100644 --- a/libs/langchain/poetry.lock +++ b/libs/langchain/poetry.lock @@ -1841,8 +1841,8 @@ files = [ [package.dependencies] aiohttp = ">=3.1.0" grpcio = [ - {version = ">=1.22.0", markers = "python_version < \"3.11\""}, {version = ">=1.49.1", markers = "python_version >= \"3.11\""}, + {version = ">=1.22.0", markers = "python_version < \"3.11\""}, ] numpy = "*" protobuf = ">=3.8.0,<4.0.0" @@ -2679,6 +2679,30 @@ gitdb = ">=4.0.1,<5" [package.extras] test = ["black", "coverage[toml]", "ddt (>=1.1.1,!=1.4.3)", "mock", "mypy", "pre-commit", "pytest", "pytest-cov", "pytest-instafail", "pytest-subtests", "pytest-sugar"] +[[package]] +name = "google-api-core" +version = "1.34.0" +description = "Google API client core library" +optional = true +python-versions = ">=3.7" +files = [ + {file = "google-api-core-1.34.0.tar.gz", hash = "sha256:6fb380f49d19ee1d09a9722d0379042b7edb06c0112e4796c7a395078a043e71"}, + {file = "google_api_core-1.34.0-py3-none-any.whl", hash = "sha256:7421474c39d396a74dfa317dddbc69188f2336835f526087c7648f91105e32ff"}, +] + +[package.dependencies] +google-auth = ">=1.25.0,<3.0dev" +googleapis-common-protos = ">=1.56.2,<2.0dev" +grpcio = {version = ">=1.33.2,<2.0dev", optional = true, markers = "extra == \"grpc\""} +grpcio-status = {version = ">=1.33.2,<2.0dev", optional = true, markers = "extra == \"grpc\""} +protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.0.0dev" +requests = ">=2.18.0,<3.0.0dev" + +[package.extras] +grpc = ["grpcio (>=1.33.2,<2.0dev)", "grpcio-status (>=1.33.2,<2.0dev)"] +grpcgcp = ["grpcio-gcp (>=0.2.2,<1.0dev)"] +grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0dev)"] + [[package]] name = "google-api-core" version = "2.12.0" @@ -2693,6 +2717,8 @@ files = [ [package.dependencies] google-auth = ">=2.14.1,<3.0.dev0" googleapis-common-protos = ">=1.56.2,<2.0.dev0" +grpcio = {version = ">=1.33.2,<2.0dev", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""} +grpcio-status = {version = ">=1.33.2,<2.0.dev0", optional = true, markers = "python_version < \"3.11\" and extra == \"grpc\""} protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0.dev0" requests = ">=2.18.0,<3.0.0.dev0" @@ -2775,6 +2801,25 @@ requests-oauthlib = ">=0.7.0" [package.extras] tool = ["click (>=6.0.0)"] +[[package]] +name = "google-cloud-documentai" +version = "2.20.1" +description = "Google Cloud Documentai API client library" +optional = true +python-versions = ">=3.7" +files = [ + {file = "google-cloud-documentai-2.20.1.tar.gz", hash = "sha256:eba90546c80177bab7ee8aa3f4b7b1f89cf3ce9752032e359a15d0fbf2225fb0"}, + {file = "google_cloud_documentai-2.20.1-py2.py3-none-any.whl", hash = "sha256:793a2e59c425655c951055810635798b5ef56fed164e35b2bd521e5c30ab279e"}, +] + +[package.dependencies] +google-api-core = {version = ">=1.34.0,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]} +proto-plus = [ + {version = ">=1.22.2,<2.0.0dev", markers = "python_version >= \"3.11\""}, + {version = ">=1.22.0,<2.0.0dev", markers = "python_version < \"3.11\""}, +] +protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<5.0.0dev" + [[package]] name = "google-pasta" version = "0.2.0" @@ -3014,6 +3059,22 @@ files = [ [package.extras] protobuf = ["grpcio-tools (>=1.59.0)"] +[[package]] +name = "grpcio-status" +version = "1.48.2" +description = "Status proto mapping for gRPC" +optional = true +python-versions = ">=3.6" +files = [ + {file = "grpcio-status-1.48.2.tar.gz", hash = "sha256:53695f45da07437b7c344ee4ef60d370fd2850179f5a28bb26d8e2aa1102ec11"}, + {file = "grpcio_status-1.48.2-py3-none-any.whl", hash = "sha256:2c33bbdbe20188b2953f46f31af669263b6ee2a9b2d38fa0d36ee091532e21bf"}, +] + +[package.dependencies] +googleapis-common-protos = ">=1.5.5" +grpcio = ">=1.48.2" +protobuf = ">=3.12.0" + [[package]] name = "grpcio-tools" version = "1.48.2" @@ -4098,13 +4159,13 @@ all = ["datasets (>=2.12.0,<3.0.0)", "evaluate (>=0.4.0,<0.5.0)", "faiss-cpu (>= [[package]] name = "langsmith" -version = "0.0.44" +version = "0.0.52" 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-0.0.44-py3-none-any.whl", hash = "sha256:5e7e5b45360ce89a2d5d6066a3b9fdd31b1f874a0cf19b1666c9792fecef0a1b"}, - {file = "langsmith-0.0.44.tar.gz", hash = "sha256:74a262ba23a958ca1a4863d5386c151be462e40ccfcb8b39d0a5d8c9eaa40164"}, + {file = "langsmith-0.0.52-py3-none-any.whl", hash = "sha256:d02a0ade5a53b36143084e57003ed38ccbdf5fc15a5a0eb14f8989ceaee0b807"}, + {file = "langsmith-0.0.52.tar.gz", hash = "sha256:1dc29082d257deea1859cb22c53d9481ca5c4a37f3af40c0f9d300fb8adc91db"}, ] [package.dependencies] @@ -5789,12 +5850,12 @@ files = [ [package.dependencies] numpy = [ + {version = ">=1.23.5", markers = "python_version >= \"3.11\""}, {version = ">=1.21.0", markers = "python_version <= \"3.9\" and platform_system == \"Darwin\" and platform_machine == \"arm64\" and python_version >= \"3.8\""}, {version = ">=1.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"aarch64\" and python_version >= \"3.8\" and python_version < \"3.10\" or python_version > \"3.9\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_system != \"Darwin\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_machine != \"arm64\" and python_version < \"3.10\""}, {version = ">=1.17.3", markers = "(platform_system != \"Darwin\" and platform_system != \"Linux\") and python_version >= \"3.8\" and python_version < \"3.9\" or platform_system != \"Darwin\" and python_version >= \"3.8\" and python_version < \"3.9\" and platform_machine != \"aarch64\" or platform_machine != \"arm64\" and python_version >= \"3.8\" and python_version < \"3.9\" and platform_system != \"Linux\" or (platform_machine != \"arm64\" and platform_machine != \"aarch64\") and python_version >= \"3.8\" and python_version < \"3.9\""}, {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""}, {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""}, - {version = ">=1.23.5", markers = "python_version >= \"3.11\""}, ] [[package]] @@ -5970,9 +6031,9 @@ files = [ [package.dependencies] numpy = [ + {version = ">=1.23.2", markers = "python_version >= \"3.11\""}, {version = ">=1.20.3", markers = "python_version < \"3.10\""}, {version = ">=1.21.0", markers = "python_version >= \"3.10\" and python_version < \"3.11\""}, - {version = ">=1.23.2", markers = "python_version >= \"3.11\""}, ] python-dateutil = ">=2.8.2" pytz = ">=2020.1" @@ -6390,6 +6451,23 @@ files = [ [package.dependencies] wcwidth = "*" +[[package]] +name = "proto-plus" +version = "1.22.3" +description = "Beautiful, Pythonic protocol buffers." +optional = true +python-versions = ">=3.6" +files = [ + {file = "proto-plus-1.22.3.tar.gz", hash = "sha256:fdcd09713cbd42480740d2fe29c990f7fbd885a67efc328aa8be6ee3e9f76a6b"}, + {file = "proto_plus-1.22.3-py3-none-any.whl", hash = "sha256:a49cd903bc0b6ab41f76bf65510439d56ca76f868adf0274e738bfdd096894df"}, +] + +[package.dependencies] +protobuf = ">=3.19.0,<5.0.0dev" + +[package.extras] +testing = ["google-api-core[grpc] (>=1.31.5)"] + [[package]] name = "protobuf" version = "3.19.6" @@ -10963,7 +11041,7 @@ cli = ["typer"] cohere = ["cohere"] docarray = ["docarray"] embeddings = ["sentence-transformers"] -extended-testing = ["aiosqlite", "amazon-textract-caller", "anthropic", "arxiv", "assemblyai", "atlassian-python-api", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "dashvector", "esprima", "faiss-cpu", "feedparser", "geopandas", "gitpython", "gql", "html2text", "jinja2", "jq", "lxml", "markdownify", "motor", "mwparserfromhell", "mwxml", "newspaper3k", "numexpr", "openai", "openai", "openapi-pydantic", "pandas", "pdfminer-six", "pgvector", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "rapidocr-onnxruntime", "requests-toolbelt", "rspace_client", "scikit-learn", "sqlite-vss", "streamlit", "sympy", "telethon", "timescale-vector", "tqdm", "upstash-redis", "xata", "xmltodict"] +extended-testing = ["aiosqlite", "amazon-textract-caller", "anthropic", "arxiv", "assemblyai", "atlassian-python-api", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "dashvector", "esprima", "faiss-cpu", "feedparser", "geopandas", "gitpython", "google-cloud-documentai", "gql", "html2text", "jinja2", "jq", "lxml", "markdownify", "motor", "mwparserfromhell", "mwxml", "newspaper3k", "numexpr", "openai", "openai", "openapi-pydantic", "pandas", "pdfminer-six", "pgvector", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "rapidocr-onnxruntime", "requests-toolbelt", "rspace_client", "scikit-learn", "sqlite-vss", "streamlit", "sympy", "telethon", "timescale-vector", "tqdm", "upstash-redis", "xata", "xmltodict"] javascript = ["esprima"] llms = ["clarifai", "cohere", "huggingface_hub", "manifest-ml", "nlpcloud", "openai", "openlm", "torch", "transformers"] openai = ["openai", "tiktoken"] @@ -10973,4 +11051,4 @@ text-helpers = ["chardet"] [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "a36eb1f9d28e59ddc0cc5f3eee584ee4e054c8a87bf7c3c9a258b4a319aa1744" +content-hash = "6bf06e81190f228675452f1a7581614898c983d27f2d56ae9ddd92119c114b03" diff --git a/libs/langchain/pyproject.toml b/libs/langchain/pyproject.toml index 489e358b7be..0f7e1a2d2bc 100644 --- a/libs/langchain/pyproject.toml +++ b/libs/langchain/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain" -version = "0.0.320" +version = "0.0.324" description = "Building applications with LLMs through composability" authors = [] license = "MIT" @@ -9,7 +9,6 @@ repository = "https://github.com/langchain-ai/langchain" [tool.poetry.scripts] langchain-server = "langchain.server:main" -langchain = "langchain.cli.cli:app" [tool.poetry.dependencies] python = ">=3.8.1,<4.0" @@ -115,7 +114,7 @@ cassio = {version = "^0.1.0", optional = true} rdflib = {version = "^6.3.2", optional = true} sympy = {version = "^1.12", optional = true} rapidfuzz = {version = "^3.1.1", optional = true} -langsmith = "~0.0.43" +langsmith = "~0.0.52" rank-bm25 = {version = "^0.2.2", optional = true} amadeus = {version = ">=8.1.0", optional = true} geopandas = {version = "^0.13.1", optional = true} @@ -140,6 +139,7 @@ anthropic = {version = "^0.3.11", optional = true} aiosqlite = {version = "^0.19.0", optional = true} rspace_client = {version = "^2.5.0", optional = true} upstash-redis = {version = "^0.15.0", optional = true} +google-cloud-documentai = {version = "^2.20.1", optional = true} [tool.poetry.group.test.dependencies] @@ -323,6 +323,7 @@ extended_testing = [ "bibtexparser", "cassio", "chardet", + "google-cloud-documentai", "esprima", "jq", "pdfminer-six", diff --git a/libs/langchain/tests/integration_tests/chains/test_retrieval_qa_with_sources.py b/libs/langchain/tests/integration_tests/chains/test_retrieval_qa_with_sources.py index 70ee98513ea..7ace5730ad5 100644 --- a/libs/langchain/tests/integration_tests/chains/test_retrieval_qa_with_sources.py +++ b/libs/langchain/tests/integration_tests/chains/test_retrieval_qa_with_sources.py @@ -19,10 +19,20 @@ def test_retrieval_qa_with_sources_chain_saving_loading(tmp_path: str) -> None: qa = RetrievalQAWithSourcesChain.from_llm( llm=OpenAI(), retriever=docsearch.as_retriever() ) - qa("What did the president say about Ketanji Brown Jackson?") - - file_path = tmp_path + "/RetrievalQAWithSourcesChain.yaml" + result = qa("What did the president say about Ketanji Brown Jackson?") + assert "question" in result.keys() + assert "answer" in result.keys() + assert "sources" in result.keys() + file_path = str(tmp_path) + "/RetrievalQAWithSourcesChain.yaml" qa.save(file_path=file_path) qa_loaded = load_chain(file_path, retriever=docsearch.as_retriever()) assert qa_loaded == qa + + qa2 = RetrievalQAWithSourcesChain.from_chain_type( + llm=OpenAI(), retriever=docsearch.as_retriever(), chain_type="stuff" + ) + result2 = qa2("What did the president say about Ketanji Brown Jackson?") + assert "question" in result2.keys() + assert "answer" in result2.keys() + assert "sources" in result2.keys() diff --git a/libs/langchain/tests/integration_tests/chat_models/test_fireworks.py b/libs/langchain/tests/integration_tests/chat_models/test_fireworks.py index ec43bfd5820..73d99484cbf 100644 --- a/libs/langchain/tests/integration_tests/chat_models/test_fireworks.py +++ b/libs/langchain/tests/integration_tests/chat_models/test_fireworks.py @@ -1,34 +1,40 @@ """Test ChatFireworks wrapper.""" +import sys import pytest from langchain.chat_models.fireworks import ChatFireworks -from langchain.schema import ( - ChatGeneration, - ChatResult, - LLMResult, -) +from langchain.schema import ChatGeneration, ChatResult, LLMResult from langchain.schema.messages import BaseMessage, HumanMessage, SystemMessage +if sys.version_info < (3, 9): + pytest.skip("fireworks-ai requires Python > 3.8", allow_module_level=True) -def test_chat_fireworks() -> None: + +@pytest.fixture +def chat() -> ChatFireworks: + return ChatFireworks(model_kwargs={"temperature": 0, "max_tokens": 512}) + + +@pytest.mark.scheduled +def test_chat_fireworks(chat: ChatFireworks) -> None: """Test ChatFireworks wrapper.""" - chat = ChatFireworks() message = HumanMessage(content="What is the weather in Redwood City, CA today") response = chat([message]) assert isinstance(response, BaseMessage) assert isinstance(response.content, str) +@pytest.mark.scheduled def test_chat_fireworks_model() -> None: """Test ChatFireworks wrapper handles model_name.""" chat = ChatFireworks(model="foo") assert chat.model == "foo" -def test_chat_fireworks_system_message() -> None: +@pytest.mark.scheduled +def test_chat_fireworks_system_message(chat: ChatFireworks) -> None: """Test ChatFireworks wrapper with system message.""" - chat = ChatFireworks() system_message = SystemMessage(content="You are to chat with the user.") human_message = HumanMessage(content="Hello") response = chat([system_message, human_message]) @@ -36,6 +42,7 @@ def test_chat_fireworks_system_message() -> None: assert isinstance(response.content, str) +@pytest.mark.scheduled def test_chat_fireworks_generate() -> None: """Test ChatFireworks wrapper with generate.""" chat = ChatFireworks(model_kwargs={"n": 2}) @@ -51,6 +58,7 @@ def test_chat_fireworks_generate() -> None: assert generation.text == generation.message.content +@pytest.mark.scheduled def test_chat_fireworks_multiple_completions() -> None: """Test ChatFireworks wrapper with multiple completions.""" chat = ChatFireworks(model_kwargs={"n": 5}) @@ -63,23 +71,93 @@ def test_chat_fireworks_multiple_completions() -> None: assert isinstance(generation.message.content, str) -def test_chat_fireworks_llm_output_contains_model_id() -> None: +@pytest.mark.scheduled +def test_chat_fireworks_llm_output_contains_model_id(chat: ChatFireworks) -> None: """Test llm_output contains model_id.""" - chat = ChatFireworks() message = HumanMessage(content="Hello") llm_result = chat.generate([[message]]) assert llm_result.llm_output is not None assert llm_result.llm_output["model"] == chat.model -def test_fireworks_streaming() -> None: - """Test streaming tokens from Fireworks.""" - llm = ChatFireworks() +@pytest.mark.scheduled +def test_fireworks_invoke(chat: ChatFireworks) -> None: + """Tests chat completion with invoke""" + result = chat.invoke("How is the weather in New York today?", stop=[","]) + assert isinstance(result.content, str) + assert result.content[-1] == "," - for token in llm.stream("I'm Pickle Rick"): + +@pytest.mark.scheduled +@pytest.mark.asyncio +async def test_fireworks_ainvoke(chat: ChatFireworks) -> None: + """Tests chat completion with invoke""" + result = await chat.ainvoke("How is the weather in New York today?", stop=[","]) + assert isinstance(result.content, str) + assert result.content[-1] == "," + + +@pytest.mark.scheduled +def test_fireworks_batch(chat: ChatFireworks) -> None: + """Test batch tokens from ChatFireworks.""" + result = chat.batch( + [ + "What is the weather in Redwood City, CA today", + "What is the weather in Redwood City, CA today", + "What is the weather in Redwood City, CA today", + "What is the weather in Redwood City, CA today", + "What is the weather in Redwood City, CA today", + "What is the weather in Redwood City, CA today", + ], + config={"max_concurrency": 5}, + stop=[","], + ) + for token in result: + assert isinstance(token.content, str) + assert token.content[-1] == "," + + +@pytest.mark.scheduled +@pytest.mark.asyncio +async def test_fireworks_abatch(chat: ChatFireworks) -> None: + """Test batch tokens from ChatFireworks.""" + result = await chat.abatch( + [ + "What is the weather in Redwood City, CA today", + "What is the weather in Redwood City, CA today", + "What is the weather in Redwood City, CA today", + "What is the weather in Redwood City, CA today", + "What is the weather in Redwood City, CA today", + "What is the weather in Redwood City, CA today", + ], + config={"max_concurrency": 5}, + stop=[","], + ) + for token in result: + assert isinstance(token.content, str) + assert token.content[-1] == "," + + +@pytest.mark.scheduled +def test_fireworks_streaming(chat: ChatFireworks) -> None: + """Test streaming tokens from Fireworks.""" + + for token in chat.stream("I'm Pickle Rick"): assert isinstance(token.content, str) +@pytest.mark.scheduled +def test_fireworks_streaming_stop_words(chat: ChatFireworks) -> None: + """Test streaming tokens with stop words.""" + + last_token = "" + for token in chat.stream("I'm Pickle Rick", stop=[","]): + last_token = token.content + assert isinstance(token.content, str) + assert last_token[-1] == "," + + +@pytest.mark.scheduled @pytest.mark.asyncio async def test_chat_fireworks_agenerate() -> None: """Test ChatFireworks wrapper with generate.""" @@ -96,10 +174,15 @@ async def test_chat_fireworks_agenerate() -> None: assert generation.text == generation.message.content +@pytest.mark.scheduled @pytest.mark.asyncio -async def test_fireworks_astream() -> None: +async def test_fireworks_astream(chat: ChatFireworks) -> None: """Test streaming tokens from Fireworks.""" - llm = ChatFireworks() - async for token in llm.astream("Who's the best quarterback in the NFL?"): + last_token = "" + async for token in chat.astream( + "Who's the best quarterback in the NFL?", stop=[","] + ): + last_token = token.content assert isinstance(token.content, str) + assert last_token[-1] == "," diff --git a/libs/langchain/tests/integration_tests/embeddings/test_johnsnowlabs.py b/libs/langchain/tests/integration_tests/embeddings/test_johnsnowlabs.py new file mode 100644 index 00000000000..3def60b56e7 --- /dev/null +++ b/libs/langchain/tests/integration_tests/embeddings/test_johnsnowlabs.py @@ -0,0 +1,20 @@ +"""Test johnsnowlabs embeddings.""" + +from langchain.embeddings.johnsnowlabs import JohnSnowLabsEmbeddings + + +def test_johnsnowlabs_embed_document() -> None: + """Test johnsnowlabs embeddings.""" + documents = ["foo bar", "bar foo"] + embedding = JohnSnowLabsEmbeddings() + output = embedding.embed_documents(documents) + assert len(output) == 2 + assert len(output[0]) == 128 + + +def test_johnsnowlabs_embed_query() -> None: + """Test johnsnowlabs embeddings.""" + document = "foo bar" + embedding = JohnSnowLabsEmbeddings() + output = embedding.embed_query(document) + assert len(output) == 128 diff --git a/libs/langchain/tests/integration_tests/examples/hello_world.py b/libs/langchain/tests/integration_tests/examples/hello_world.py index 53040a6c670..3f0294febb4 100644 --- a/libs/langchain/tests/integration_tests/examples/hello_world.py +++ b/libs/langchain/tests/integration_tests/examples/hello_world.py @@ -3,7 +3,7 @@ import sys -def main(): +def main() -> int: print("Hello World!") return 0 diff --git a/libs/langchain/tests/integration_tests/llms/test_deepinfra.py b/libs/langchain/tests/integration_tests/llms/test_deepinfra.py index 502a9bbb62c..4b3a303bc7c 100644 --- a/libs/langchain/tests/integration_tests/llms/test_deepinfra.py +++ b/libs/langchain/tests/integration_tests/llms/test_deepinfra.py @@ -1,10 +1,36 @@ """Test DeepInfra API wrapper.""" +import pytest from langchain.llms.deepinfra import DeepInfra def test_deepinfra_call() -> None: """Test valid call to DeepInfra.""" - llm = DeepInfra(model_id="google/flan-t5-small") + llm = DeepInfra(model_id="meta-llama/Llama-2-7b-chat-hf") output = llm("What is 2 + 2?") assert isinstance(output, str) + + +@pytest.mark.asyncio +async def test_deepinfra_acall() -> None: + llm = DeepInfra(model_id="meta-llama/Llama-2-7b-chat-hf") + output = await llm.apredict("What is 2 + 2?") + assert llm._llm_type == "deepinfra" + assert isinstance(output, str) + + +def test_deepinfra_stream() -> None: + llm = DeepInfra(model_id="meta-llama/Llama-2-7b-chat-hf") + num_chunks = 0 + for chunk in llm.stream("[INST] Hello [/INST] "): + num_chunks += 1 + assert num_chunks > 0 + + +@pytest.mark.asyncio +async def test_deepinfra_astream() -> None: + llm = DeepInfra(model_id="meta-llama/Llama-2-7b-chat-hf") + num_chunks = 0 + async for chunk in llm.astream("[INST] Hello [/INST] "): + num_chunks += 1 + assert num_chunks > 0 diff --git a/libs/langchain/tests/integration_tests/llms/test_fireworks.py b/libs/langchain/tests/integration_tests/llms/test_fireworks.py index cbc7473665a..40f5cf1cc5f 100644 --- a/libs/langchain/tests/integration_tests/llms/test_fireworks.py +++ b/libs/langchain/tests/integration_tests/llms/test_fireworks.py @@ -1,4 +1,5 @@ """Test Fireworks AI API Wrapper.""" +import sys from typing import Generator import pytest @@ -12,14 +13,23 @@ from langchain.prompts.chat import ( ) from langchain.schema import LLMResult +if sys.version_info < (3, 9): + pytest.skip("fireworks-ai requires Python > 3.8", allow_module_level=True) -def test_fireworks_call() -> None: + +@pytest.fixture +def llm() -> Fireworks: + return Fireworks(model_kwargs={"temperature": 0, "max_tokens": 512}) + + +@pytest.mark.scheduled +def test_fireworks_call(llm: Fireworks) -> None: """Test valid call to fireworks.""" - llm = Fireworks() - output = llm("Who's the best quarterback in the NFL?") + output = llm("How is the weather in New York today?") assert isinstance(output, str) +@pytest.mark.scheduled def test_fireworks_in_chain() -> None: """Tests fireworks AI in a Langchain chain""" human_message_prompt = HumanMessagePromptTemplate( @@ -35,24 +45,82 @@ def test_fireworks_in_chain() -> None: assert isinstance(output, str) +@pytest.mark.scheduled def test_fireworks_model_param() -> None: """Tests model parameters for Fireworks""" llm = Fireworks(model="foo") assert llm.model == "foo" -def test_fireworks_multiple_prompts() -> None: - """Test completion with multiple prompts.""" +@pytest.mark.scheduled +def test_fireworks_invoke(llm: Fireworks) -> None: + """Tests completion with invoke""" + output = llm.invoke("How is the weather in New York today?", stop=[","]) + assert isinstance(output, str) + assert output[-1] == "," + + +@pytest.mark.scheduled +@pytest.mark.asyncio +async def test_fireworks_ainvoke(llm: Fireworks) -> None: + """Tests completion with invoke""" + output = await llm.ainvoke("How is the weather in New York today?", stop=[","]) + assert isinstance(output, str) + assert output[-1] == "," + + +@pytest.mark.scheduled +def test_fireworks_batch(llm: Fireworks) -> None: + """Tests completion with invoke""" llm = Fireworks() + output = llm.batch( + [ + "How is the weather in New York today?", + "How is the weather in New York today?", + "How is the weather in New York today?", + "How is the weather in New York today?", + "How is the weather in New York today?", + ], + stop=[","], + ) + for token in output: + assert isinstance(token, str) + assert token[-1] == "," + + +@pytest.mark.scheduled +@pytest.mark.asyncio +async def test_fireworks_abatch(llm: Fireworks) -> None: + """Tests completion with invoke""" + output = await llm.abatch( + [ + "How is the weather in New York today?", + "How is the weather in New York today?", + "How is the weather in New York today?", + "How is the weather in New York today?", + "How is the weather in New York today?", + ], + stop=[","], + ) + for token in output: + assert isinstance(token, str) + assert token[-1] == "," + + +@pytest.mark.scheduled +def test_fireworks_multiple_prompts( + llm: Fireworks, +) -> None: + """Test completion with multiple prompts.""" output = llm.generate(["How is the weather in New York today?", "I'm pickle rick"]) assert isinstance(output, LLMResult) assert isinstance(output.generations, list) assert len(output.generations) == 2 -def test_fireworks_streaming() -> None: +@pytest.mark.scheduled +def test_fireworks_streaming(llm: Fireworks) -> None: """Test stream completion.""" - llm = Fireworks() generator = llm.stream("Who's the best quarterback in the NFL?") assert isinstance(generator, Generator) @@ -60,26 +128,44 @@ def test_fireworks_streaming() -> None: assert isinstance(token, str) -@pytest.mark.asyncio -async def test_fireworks_streaming_async() -> None: - """Test stream completion.""" - llm = Fireworks() +@pytest.mark.scheduled +def test_fireworks_streaming_stop_words(llm: Fireworks) -> None: + """Test stream completion with stop words.""" + generator = llm.stream("Who's the best quarterback in the NFL?", stop=[","]) + assert isinstance(generator, Generator) - async for token in llm.astream("Who's the best quarterback in the NFL?"): + last_token = "" + for token in generator: + last_token = token assert isinstance(token, str) + assert last_token[-1] == "," +@pytest.mark.scheduled @pytest.mark.asyncio -async def test_fireworks_async_agenerate() -> None: +async def test_fireworks_streaming_async(llm: Fireworks) -> None: + """Test stream completion.""" + + last_token = "" + async for token in llm.astream( + "Who's the best quarterback in the NFL?", stop=[","] + ): + last_token = token + assert isinstance(token, str) + assert last_token[-1] == "," + + +@pytest.mark.scheduled +@pytest.mark.asyncio +async def test_fireworks_async_agenerate(llm: Fireworks) -> None: """Test async.""" - llm = Fireworks() output = await llm.agenerate(["What is the best city to live in California?"]) assert isinstance(output, LLMResult) +@pytest.mark.scheduled @pytest.mark.asyncio -async def test_fireworks_multiple_prompts_async_agenerate() -> None: - llm = Fireworks() +async def test_fireworks_multiple_prompts_async_agenerate(llm: Fireworks) -> None: output = await llm.agenerate( ["How is the weather in New York today?", "I'm pickle rick"] ) diff --git a/libs/langchain/tests/integration_tests/llms/test_titan_takeoff_pro.py b/libs/langchain/tests/integration_tests/llms/test_titan_takeoff_pro.py new file mode 100644 index 00000000000..333d22b80f5 --- /dev/null +++ b/libs/langchain/tests/integration_tests/llms/test_titan_takeoff_pro.py @@ -0,0 +1,18 @@ +"""Test Titan Takeoff wrapper.""" + + +import responses + +from langchain.llms.titan_takeoff_pro import TitanTakeoffPro + + +@responses.activate +def test_titan_takeoff_pro_call() -> None: + """Test valid call to Titan Takeoff.""" + url = "http://localhost:3000/generate" + responses.add(responses.POST, url, json={"message": "2 + 2 is 4"}, status=200) + + # response = requests.post(url) + llm = TitanTakeoffPro() + output = llm("What is 2 + 2?") + assert isinstance(output, str) diff --git a/libs/langchain/tests/integration_tests/llms/test_vertexai.py b/libs/langchain/tests/integration_tests/llms/test_vertexai.py index 2bee6af181c..20cfb6d85ba 100644 --- a/libs/langchain/tests/integration_tests/llms/test_vertexai.py +++ b/libs/langchain/tests/integration_tests/llms/test_vertexai.py @@ -36,6 +36,15 @@ def test_vertex_generate() -> None: assert len(output.generations[0]) == 2 +@pytest.mark.scheduled +def test_vertex_generate_code() -> None: + llm = VertexAI(temperature=0.3, n=2, model_name="code-bison@001") + output = llm.generate(["generate a python method that says foo:"]) + assert isinstance(output, LLMResult) + assert len(output.generations) == 1 + assert len(output.generations[0]) == 2 + + @pytest.mark.scheduled @pytest.mark.asyncio async def test_vertex_agenerate() -> None: diff --git a/libs/langchain/tests/integration_tests/vectorstores/test_baiducloud_vector_search.py b/libs/langchain/tests/integration_tests/vectorstores/test_baiducloud_vector_search.py new file mode 100644 index 00000000000..8176a392a1b --- /dev/null +++ b/libs/langchain/tests/integration_tests/vectorstores/test_baiducloud_vector_search.py @@ -0,0 +1,27 @@ +"""Test BESVectorStore functionality.""" +from typing import List, Optional + +from langchain.docstore.document import Document +from langchain.vectorstores import BESVectorStore +from tests.integration_tests.vectorstores.fake_embeddings import ( + FakeEmbeddings, + fake_texts, +) + + +def _bes_vector_db_from_texts( + metadatas: Optional[List[dict]] = None, drop: bool = True +) -> BESVectorStore: + return BESVectorStore.from_texts( + fake_texts, + FakeEmbeddings(), + metadatas=metadatas, + bes_url="http://10.0.X.X", + ) + + +def test_bes_vector_db() -> None: + """Test end to end construction and search.""" + docsearch = _bes_vector_db_from_texts() + output = docsearch.similarity_search("foo", k=1) + assert output == [Document(page_content="foo")] diff --git a/libs/langchain/tests/integration_tests/vectorstores/test_redis.py b/libs/langchain/tests/integration_tests/vectorstores/test_redis.py index 6128a8445ae..28d524843ab 100644 --- a/libs/langchain/tests/integration_tests/vectorstores/test_redis.py +++ b/libs/langchain/tests/integration_tests/vectorstores/test_redis.py @@ -141,7 +141,8 @@ def test_custom_keys(texts: List[str]) -> None: docsearch, keys_out = Redis.from_texts_return_keys( texts, FakeEmbeddings(), redis_url=TEST_REDIS_URL, keys=keys_in ) - assert keys_in == keys_out + # it will append the index key prefix to all keys + assert keys_out == [docsearch.key_prefix + ":" + key for key in keys_in] assert drop(docsearch.index_name) @@ -154,11 +155,13 @@ def test_custom_keys_from_docs(texts: List[str]) -> None: ) client = docsearch.client # test keys are correct - assert client.hget("test_key_1", "content") + assert client.hget(docsearch.key_prefix + ":" + "test_key_1", "content") # test metadata is stored - assert client.hget("test_key_1", "a") == bytes("b", "utf-8") + assert client.hget(docsearch.key_prefix + ":" + "test_key_1", "a") == bytes( + "b", "utf-8" + ) # test all keys are stored - assert client.hget("test_key_2", "content") + assert client.hget(docsearch.key_prefix + ":" + "test_key_2", "content") assert drop(docsearch.index_name) diff --git a/libs/langchain/tests/unit_tests/callbacks/test_openai_info.py b/libs/langchain/tests/unit_tests/callbacks/test_openai_info.py index 4580ccd4883..ba019fbbb6b 100644 --- a/libs/langchain/tests/unit_tests/callbacks/test_openai_info.py +++ b/libs/langchain/tests/unit_tests/callbacks/test_openai_info.py @@ -49,7 +49,24 @@ def test_on_llm_end_custom_model(handler: OpenAICallbackHandler) -> None: assert handler.total_cost == 0 -def test_on_llm_end_finetuned_model(handler: OpenAICallbackHandler) -> None: +@pytest.mark.parametrize( + "model_name", + [ + "ada:ft-your-org:custom-model-name-2022-02-15-04-21-04", + "babbage:ft-your-org:custom-model-name-2022-02-15-04-21-04", + "curie:ft-your-org:custom-model-name-2022-02-15-04-21-04", + "davinci:ft-your-org:custom-model-name-2022-02-15-04-21-04", + "ft:babbage-002:your-org:custom-model-name:1abcdefg", + "ft:davinci-002:your-org:custom-model-name:1abcdefg", + "ft:gpt-3.5-turbo-0613:your-org:custom-model-name:1abcdefg", + "babbage-002.ft-0123456789abcdefghijklmnopqrstuv", + "davinci-002.ft-0123456789abcdefghijklmnopqrstuv", + "gpt-35-turbo-0613.ft-0123456789abcdefghijklmnopqrstuv", + ], +) +def test_on_llm_end_finetuned_model( + handler: OpenAICallbackHandler, model_name: str +) -> None: response = LLMResult( generations=[], llm_output={ @@ -58,7 +75,7 @@ def test_on_llm_end_finetuned_model(handler: OpenAICallbackHandler) -> None: "completion_tokens": 1, "total_tokens": 3, }, - "model_name": "ada:ft-your-org:custom-model-name-2022-02-15-04-21-04", + "model_name": model_name, }, ) handler.on_llm_end(response) diff --git a/libs/langchain/tests/unit_tests/callbacks/tracers/test_langchain.py b/libs/langchain/tests/unit_tests/callbacks/tracers/test_langchain.py index 7f78a3a1965..96e14f9225f 100644 --- a/libs/langchain/tests/unit_tests/callbacks/tracers/test_langchain.py +++ b/libs/langchain/tests/unit_tests/callbacks/tracers/test_langchain.py @@ -1,3 +1,4 @@ +import threading import time import unittest.mock from typing import Any @@ -47,3 +48,17 @@ def test_example_id_assignment_threadsafe() -> None: } tracer.wait_for_futures() assert example_ids == expected_example_ids + + +def test_log_lock() -> None: + """Test that example assigned at callback start/end is honored.""" + + client = unittest.mock.MagicMock(spec=Client) + tracer = LangChainTracer(client=client) + + with unittest.mock.patch.object(tracer, "_persist_run_single", new=lambda _: _): + run_id_1 = UUID("9d878ab3-e5ca-4218-aef6-44cbdc90160a") + lock = threading.Lock() + tracer.on_chain_start({"name": "example_1"}, {"input": lock}, run_id=run_id_1) + tracer.on_chain_end({}, run_id=run_id_1) + tracer.wait_for_futures() diff --git a/libs/langchain/tests/unit_tests/chains/test_graph_qa.py b/libs/langchain/tests/unit_tests/chains/test_graph_qa.py index ed4fe9feb6f..51c968328b2 100644 --- a/libs/langchain/tests/unit_tests/chains/test_graph_qa.py +++ b/libs/langchain/tests/unit_tests/chains/test_graph_qa.py @@ -1,9 +1,186 @@ -from typing import List +from typing import Any, Dict, List import pandas as pd -from langchain.chains.graph_qa.cypher import construct_schema, extract_cypher +from langchain.chains.graph_qa.cypher import ( + GraphCypherQAChain, + construct_schema, + extract_cypher, +) from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema +from langchain.chains.graph_qa.prompts import CYPHER_GENERATION_PROMPT, CYPHER_QA_PROMPT +from langchain.graphs.graph_document import GraphDocument +from langchain.graphs.graph_store import GraphStore +from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory +from langchain.prompts import PromptTemplate +from tests.unit_tests.llms.fake_llm import FakeLLM + + +class FakeGraphStore(GraphStore): + @property + def get_schema(self) -> str: + """Returns the schema of the Graph database""" + return "" + + @property + def get_structured_schema(self) -> Dict[str, Any]: + """Returns the schema of the Graph database""" + return {} + + def query(self, query: str, params: dict = {}) -> List[Dict[str, Any]]: + """Query the graph.""" + return [] + + def refresh_schema(self) -> None: + """Refreshes the graph schema information.""" + pass + + def add_graph_documents( + self, graph_documents: List[GraphDocument], include_source: bool = False + ) -> None: + """Take GraphDocument as input as uses it to construct a graph.""" + pass + + +def test_graph_cypher_qa_chain_prompt_selection_1() -> None: + # Pass prompts directly. No kwargs is specified. + qa_prompt_template = "QA Prompt" + cypher_prompt_template = "Cypher Prompt" + qa_prompt = PromptTemplate(template=qa_prompt_template, input_variables=[]) + cypher_prompt = PromptTemplate(template=cypher_prompt_template, input_variables=[]) + chain = GraphCypherQAChain.from_llm( + llm=FakeLLM(), + graph=FakeGraphStore(), + verbose=True, + return_intermediate_steps=False, + qa_prompt=qa_prompt, + cypher_prompt=cypher_prompt, + ) + assert chain.qa_chain.prompt == qa_prompt + assert chain.cypher_generation_chain.prompt == cypher_prompt + + +def test_graph_cypher_qa_chain_prompt_selection_2() -> None: + # Default case. Pass nothing + chain = GraphCypherQAChain.from_llm( + llm=FakeLLM(), + graph=FakeGraphStore(), + verbose=True, + return_intermediate_steps=False, + ) + assert chain.qa_chain.prompt == CYPHER_QA_PROMPT + assert chain.cypher_generation_chain.prompt == CYPHER_GENERATION_PROMPT + + +def test_graph_cypher_qa_chain_prompt_selection_3() -> None: + # Pass non-prompt args only to sub-chains via kwargs + memory = ConversationBufferMemory(memory_key="chat_history") + readonlymemory = ReadOnlySharedMemory(memory=memory) + chain = GraphCypherQAChain.from_llm( + llm=FakeLLM(), + graph=FakeGraphStore(), + verbose=True, + return_intermediate_steps=False, + cypher_llm_kwargs={"memory": readonlymemory}, + qa_llm_kwargs={"memory": readonlymemory}, + ) + assert chain.qa_chain.prompt == CYPHER_QA_PROMPT + assert chain.cypher_generation_chain.prompt == CYPHER_GENERATION_PROMPT + + +def test_graph_cypher_qa_chain_prompt_selection_4() -> None: + # Pass prompt, non-prompt args to subchains via kwargs + qa_prompt_template = "QA Prompt" + cypher_prompt_template = "Cypher Prompt" + memory = ConversationBufferMemory(memory_key="chat_history") + readonlymemory = ReadOnlySharedMemory(memory=memory) + qa_prompt = PromptTemplate(template=qa_prompt_template, input_variables=[]) + cypher_prompt = PromptTemplate(template=cypher_prompt_template, input_variables=[]) + chain = GraphCypherQAChain.from_llm( + llm=FakeLLM(), + graph=FakeGraphStore(), + verbose=True, + return_intermediate_steps=False, + cypher_llm_kwargs={"prompt": cypher_prompt, "memory": readonlymemory}, + qa_llm_kwargs={"prompt": qa_prompt, "memory": readonlymemory}, + ) + assert chain.qa_chain.prompt == qa_prompt + assert chain.cypher_generation_chain.prompt == cypher_prompt + + +def test_graph_cypher_qa_chain_prompt_selection_5() -> None: + # Can't pass both prompt and kwargs at the same time + qa_prompt_template = "QA Prompt" + cypher_prompt_template = "Cypher Prompt" + memory = ConversationBufferMemory(memory_key="chat_history") + readonlymemory = ReadOnlySharedMemory(memory=memory) + qa_prompt = PromptTemplate(template=qa_prompt_template, input_variables=[]) + cypher_prompt = PromptTemplate(template=cypher_prompt_template, input_variables=[]) + try: + GraphCypherQAChain.from_llm( + llm=FakeLLM(), + graph=FakeGraphStore(), + verbose=True, + return_intermediate_steps=False, + qa_prompt=qa_prompt, + cypher_prompt=cypher_prompt, + cypher_llm_kwargs={"memory": readonlymemory}, + qa_llm_kwargs={"memory": readonlymemory}, + ) + assert False + except ValueError: + assert True + + +def test_graph_cypher_qa_chain() -> None: + template = """You are a nice chatbot having a conversation with a human. + + Schema: + {schema} + + Previous conversation: + {chat_history} + + New human question: {question} + Response:""" + + prompt = PromptTemplate( + input_variables=["schema", "question", "chat_history"], template=template + ) + + memory = ConversationBufferMemory(memory_key="chat_history") + readonlymemory = ReadOnlySharedMemory(memory=memory) + prompt1 = ( + "You are a nice chatbot having a conversation with a human.\n\n " + "Schema:\n Node properties are the following: \n {}\nRelationships " + "properties are the following: \n {}\nRelationships are: \n[]\n\n " + "Previous conversation:\n \n\n New human question: " + "Test question\n Response:" + ) + + prompt2 = ( + "You are a nice chatbot having a conversation with a human.\n\n " + "Schema:\n Node properties are the following: \n {}\nRelationships " + "properties are the following: \n {}\nRelationships are: \n[]\n\n " + "Previous conversation:\n Human: Test question\nAI: foo\n\n " + "New human question: Test new question\n Response:" + ) + + llm = FakeLLM(queries={prompt1: "answer1", prompt2: "answer2"}) + chain = GraphCypherQAChain.from_llm( + cypher_llm=llm, + qa_llm=FakeLLM(), + graph=FakeGraphStore(), + verbose=True, + return_intermediate_steps=False, + cypher_llm_kwargs={"prompt": prompt, "memory": readonlymemory}, + memory=memory, + ) + chain.run("Test question") + chain.run("Test new question") + # If we get here without a key error, that means memory + # was used properly to create prompts. + assert True def test_no_backticks() -> None: diff --git a/libs/langchain/tests/unit_tests/document_loaders/parsers/language/test_cobol.py b/libs/langchain/tests/unit_tests/document_loaders/parsers/language/test_cobol.py new file mode 100644 index 00000000000..49d691696de --- /dev/null +++ b/libs/langchain/tests/unit_tests/document_loaders/parsers/language/test_cobol.py @@ -0,0 +1,49 @@ +from langchain.document_loaders.parsers.language.cobol import CobolSegmenter + +EXAMPLE_CODE = """ +IDENTIFICATION DIVISION. +PROGRAM-ID. SampleProgram. +DATA DIVISION. +WORKING-STORAGE SECTION. +01 SAMPLE-VAR PIC X(20) VALUE 'Sample Value'. + +PROCEDURE DIVISION. +A000-INITIALIZE-PARA. + DISPLAY 'Initialization Paragraph'. + MOVE 'New Value' TO SAMPLE-VAR. + +A100-PROCESS-PARA. + DISPLAY SAMPLE-VAR. + STOP RUN. +""" + + +def test_extract_functions_classes() -> None: + """Test that functions and classes are extracted correctly.""" + segmenter = CobolSegmenter(EXAMPLE_CODE) + extracted_code = segmenter.extract_functions_classes() + assert extracted_code == [ + "A000-INITIALIZE-PARA.\n " + "DISPLAY 'Initialization Paragraph'.\n " + "MOVE 'New Value' TO SAMPLE-VAR.", + "A100-PROCESS-PARA.\n DISPLAY SAMPLE-VAR.\n STOP RUN.", + ] + + +def test_simplify_code() -> None: + """Test that code is simplified correctly.""" + expected_simplified_code = ( + "IDENTIFICATION DIVISION.\n" + "PROGRAM-ID. SampleProgram.\n" + "DATA DIVISION.\n" + "WORKING-STORAGE SECTION.\n" + "* OMITTED CODE *\n" + "PROCEDURE DIVISION.\n" + "A000-INITIALIZE-PARA.\n" + "* OMITTED CODE *\n" + "A100-PROCESS-PARA.\n" + "* OMITTED CODE *\n" + ) + segmenter = CobolSegmenter(EXAMPLE_CODE) + simplified_code = segmenter.simplify_code() + assert simplified_code.strip() == expected_simplified_code.strip() diff --git a/libs/langchain/tests/unit_tests/document_loaders/parsers/test_docai.py b/libs/langchain/tests/unit_tests/document_loaders/parsers/test_docai.py new file mode 100644 index 00000000000..d2d6d4c4ff8 --- /dev/null +++ b/libs/langchain/tests/unit_tests/document_loaders/parsers/test_docai.py @@ -0,0 +1,26 @@ +"""Tests for the Google Cloud DocAI parser.""" +from unittest.mock import ANY, patch + +import pytest + +from langchain.document_loaders.parsers import DocAIParser + + +@pytest.mark.requires("google.cloud", "google.cloud.documentai") +def test_docai_parser_valid_processor_name() -> None: + processor_name = "projects/123456/locations/us-central1/processors/ab123dfg" + with patch("google.cloud.documentai.DocumentProcessorServiceClient") as test_client: + parser = DocAIParser(processor_name=processor_name, location="us") + test_client.assert_called_once_with(client_options=ANY, client_info=ANY) + assert parser._processor_name == processor_name + + +@pytest.mark.requires("google.cloud", "google.cloud.documentai") +@pytest.mark.parametrize( + "processor_name", + ["projects/123456/locations/us-central1/processors/ab123dfg:publish", "ab123dfg"], +) +def test_docai_parser_invalid_processor_name(processor_name: str) -> None: + with patch("google.cloud.documentai.DocumentProcessorServiceClient"): + with pytest.raises(ValueError): + _ = DocAIParser(processor_name=processor_name, location="us") diff --git a/libs/langchain/tests/unit_tests/evaluation/parsing/test_json_distance.py b/libs/langchain/tests/unit_tests/evaluation/parsing/test_json_distance.py new file mode 100644 index 00000000000..e0bee70ddca --- /dev/null +++ b/libs/langchain/tests/unit_tests/evaluation/parsing/test_json_distance.py @@ -0,0 +1,117 @@ +import pytest + +from langchain.evaluation.parsing.json_distance import JsonEditDistanceEvaluator + + +@pytest.fixture +def json_distance_evaluator() -> JsonEditDistanceEvaluator: + return JsonEditDistanceEvaluator() + + +@pytest.mark.requires("rapidfuzz") +def test_json_distance_evaluator_requires_input( + json_distance_evaluator: JsonEditDistanceEvaluator, +) -> None: + assert json_distance_evaluator.requires_input is False + + +@pytest.mark.requires("rapidfuzz") +def test_json_distance_evaluator_requires_reference( + json_distance_evaluator: JsonEditDistanceEvaluator, +) -> None: + assert json_distance_evaluator.requires_reference is True + + +@pytest.mark.requires("rapidfuzz") +def test_json_distance_evaluator_evaluation_name( + json_distance_evaluator: JsonEditDistanceEvaluator, +) -> None: + assert json_distance_evaluator.evaluation_name == "json_edit_distance" + + +@pytest.mark.requires("rapidfuzz") +def test_json_distance_evaluator_parse_json( + json_distance_evaluator: JsonEditDistanceEvaluator, +) -> None: + string = '{"a": 1}' + result = json_distance_evaluator._parse_json(string) + assert result == {"a": 1} + + +@pytest.mark.requires("rapidfuzz") +def test_json_distance_evaluator_evaluate_strings_simple_diff( + json_distance_evaluator: JsonEditDistanceEvaluator, +) -> None: + prediction = '{"a": 1}' + reference = '{"a": 2}' + result = json_distance_evaluator._evaluate_strings( + prediction=prediction, reference=reference + ) + # Only 1 character flipped + pytest.approx(1 / 7, result["score"]) + + +@pytest.mark.requires("rapidfuzz") +def test_json_distance_evaluator_evaluate_strings_complex_diff( + json_distance_evaluator: JsonEditDistanceEvaluator, +) -> None: + prediction = '{"a":1, "b": {"c": 2, "d": 3}}' + reference = '{"a": 1, "b": {"c": 2, "d": 4}}' + result = json_distance_evaluator._evaluate_strings( + prediction=prediction, reference=reference + ) + # Only 1 character flipped + pytest.approx(1 / len(reference.replace(" ", "")), result["score"]) + + +@pytest.mark.requires("rapidfuzz") +def test_json_distance_evaluator_evaluate_strings_list_diff( + json_distance_evaluator: JsonEditDistanceEvaluator, +) -> None: + prediction = '[{"a": 1, "b": 2}, {"a": 2, "b": 3}]' + reference = '[{"a": 1, "b": 2}, {"a": 2, "b": 4}]' + result = json_distance_evaluator._evaluate_strings( + prediction=prediction, reference=reference + ) + # Again only 1 character flipped + pytest.approx(1 / len(reference.replace(" ", "")), result["score"]) + + +@pytest.mark.requires("rapidfuzz") +def test_json_distance_evaluator_evaluate_strings_list_same( + json_distance_evaluator: JsonEditDistanceEvaluator, +) -> None: + prediction = '[{"a": 1, "b": 2}, {"a": 2, "b": 3}]' + reference = '[{"b": 2, "a": 1}, {"b": 3, "a": 2}]' + result = json_distance_evaluator._evaluate_strings( + prediction=prediction, reference=reference + ) + assert result["score"] == 0 + + +@pytest.mark.requires("rapidfuzz") +def test_json_distance_evaluator_evaluate_strings_list_diff_length( + json_distance_evaluator: JsonEditDistanceEvaluator, +) -> None: + prediction = '[{"a": 1, "b": 2}, {"a": 2, "b": 3}]' + reference = '[{"a": 1, "b": 2}]' + result = json_distance_evaluator._evaluate_strings( + prediction=prediction, reference=reference + ) + pytest.approx( + len('{"a":2,"b":3}') / len(reference.replace(" ", "")), result["score"] + ) + + +@pytest.mark.requires("rapidfuzz") +def test_json_distance_evaluator_evaluate_strings_custom_operator_equal() -> None: + """Custom operator that returns 0.5 if strings are different.""" + + def custom_distance(a: str, b: str) -> float: + return 0.5 if a != b else 0.0 + + evaluator = JsonEditDistanceEvaluator(string_distance=custom_distance) + prediction = '{"a": "apple", "b": "banana"}' + reference = '{"a": "apple", "b": "berries"}' + result = evaluator._evaluate_strings(prediction=prediction, reference=reference) + assert result["score"] == 0.5 diff --git a/libs/langchain/tests/unit_tests/llms/fake_llm.py b/libs/langchain/tests/unit_tests/llms/fake_llm.py index c92df7bf1c4..199c0f3d579 100644 --- a/libs/langchain/tests/unit_tests/llms/fake_llm.py +++ b/libs/langchain/tests/unit_tests/llms/fake_llm.py @@ -39,9 +39,11 @@ class FakeLLM(LLM): run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> str: + print(prompt) if self.sequential_responses: return self._get_next_response_in_sequence - + print(repr(prompt)) + print(self.queries) if self.queries is not None: return self.queries[prompt] if stop is None: diff --git a/libs/langchain/tests/unit_tests/output_parsers/test_xml_parser.py b/libs/langchain/tests/unit_tests/output_parsers/test_xml_parser.py index c26fa57912f..3830d25f8d3 100644 --- a/libs/langchain/tests/unit_tests/output_parsers/test_xml_parser.py +++ b/libs/langchain/tests/unit_tests/output_parsers/test_xml_parser.py @@ -4,7 +4,7 @@ import pytest from langchain.output_parsers.xml import XMLOutputParser DEF_RESULT_ENCODING = """ - + slim.shady diff --git a/libs/langchain/tests/unit_tests/schema/runnable/__snapshots__/test_runnable.ambr b/libs/langchain/tests/unit_tests/schema/runnable/__snapshots__/test_runnable.ambr index c26a88e57ea..6780814829c 100644 --- a/libs/langchain/tests/unit_tests/schema/runnable/__snapshots__/test_runnable.ambr +++ b/libs/langchain/tests/unit_tests/schema/runnable/__snapshots__/test_runnable.ambr @@ -467,7 +467,7 @@ # --- # name: test_combining_sequences.3 list([ - Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo, bar'])"}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'base', 'RunnableLambda'], 'repr': "RunnableLambda(lambda x: {'question': x[0] + x[1]})"}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['baz, qux'])"}], 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo, bar'])"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='foo, bar')}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000004'), name='', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'base', 'RunnableLambda'], 'repr': "RunnableLambda(lambda x: {'question': x[0] + x[1]})"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': ['foo', 'bar']}, outputs={'question': 'foobar'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:4'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000005'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'foobar'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nicer assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'foobar', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:5'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000006'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['baz, qux'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['baz, qux'])"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nicer assistant.\nHuman: foobar']}, outputs={'generations': [[{'text': 'baz, qux', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'baz, qux'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:6'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000007'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='baz, qux')}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:7'], execution_order=None, child_execution_order=None, child_runs=[])]), + Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo, bar'])"}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'base', 'RunnableLambda'], 'repr': "RunnableLambda(lambda x: {'question': x[0] + x[1]})"}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['baz, qux'])"}], 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo, bar'])"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='foo, bar')}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000004'), name='', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'base', 'RunnableLambda'], 'repr': "RunnableLambda(lambda x: {'question': x[0] + x[1]})"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': ['foo', 'bar']}, outputs={'question': 'foobar'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:4'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000005'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'foobar'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nicer assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'foobar', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:5'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000006'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['baz, qux'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['baz, qux'])"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nicer assistant.\nHuman: foobar']}, outputs={'generations': [[{'text': 'baz, qux', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'baz, qux'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:6'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000007'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='baz, qux')}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:7'], execution_order=None, child_execution_order=None, child_runs=[])]), ]) # --- # name: test_each @@ -1062,7 +1062,7 @@ # --- # name: test_prompt_with_chat_model.2 list([ - Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo'])"}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': AIMessage(content='foo')}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo'])"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[])]), + Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo'])"}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': AIMessage(content='foo')}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo'])"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[])]), ]) # --- # name: test_prompt_with_chat_model_and_parser @@ -1182,7 +1182,7 @@ # --- # name: test_prompt_with_chat_model_and_parser.1 list([ - Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo, bar'])"}], 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo, bar'])"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='foo, bar')}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], execution_order=None, child_execution_order=None, child_runs=[])]), + Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo, bar'])"}], 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(responses=['foo, bar'])"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'type': 'ChatGeneration', 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='foo, bar')}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], execution_order=None, child_execution_order=None, child_runs=[])]), ]) # --- # name: test_prompt_with_llm @@ -1289,13 +1289,13 @@ # --- # name: test_prompt_with_llm.1 list([ - Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'])"}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'])"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[])]), + Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'])"}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'])"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[])]), ]) # --- # name: test_prompt_with_llm.2 list([ - Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'], i=1)"}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': 'bar'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'], i=1)"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'bar', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[])]), - Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'], i=1)"}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000004'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your favorite color?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your favorite color?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000005'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'], i=1)"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your favorite color?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[])]), + Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'], i=1)"}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': 'bar'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'], i=1)"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'bar', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[])]), + Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'], i=1)"}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000004'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your favorite color?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your favorite color?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000005'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'], i=1)"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your favorite color?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[])]), ]) # --- # name: test_prompt_with_llm_and_async_lambda @@ -1416,7 +1416,7 @@ # --- # name: test_prompt_with_llm_and_async_lambda.1 list([ - Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'])"}], 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'base', 'RunnableLambda'], 'repr': None}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'])"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='passthrough', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'base', 'RunnableLambda'], 'repr': None}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': 'foo'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], execution_order=None, child_execution_order=None, child_runs=[])]), + Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'])"}], 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'base', 'RunnableLambda'], 'repr': None}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(responses=['foo', 'bar'])"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'type': 'Generation'}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='passthrough', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'base', 'RunnableLambda'], 'repr': None}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': 'foo'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], execution_order=None, child_execution_order=None, child_runs=[])]), ]) # --- # name: test_router_runnable diff --git a/libs/langchain/tests/unit_tests/schema/runnable/test_locals.py b/libs/langchain/tests/unit_tests/schema/runnable/test_locals.py deleted file mode 100644 index 82c055c0698..00000000000 --- a/libs/langchain/tests/unit_tests/schema/runnable/test_locals.py +++ /dev/null @@ -1,94 +0,0 @@ -from typing import Any, Callable, Type - -import pytest - -from langchain.llms import FakeListLLM -from langchain.prompts import PromptTemplate -from langchain.schema.runnable import ( - GetLocalVar, - PutLocalVar, - Runnable, - RunnablePassthrough, - RunnableSequence, -) - - -@pytest.mark.parametrize( - ("method", "input", "output"), - [ - (lambda r, x: r.invoke(x), "foo", "foo"), - (lambda r, x: r.batch(x), ["foo", "bar"], ["foo", "bar"]), - (lambda r, x: list(r.stream(x))[0], "foo", "foo"), - ], -) -def test_put_get(method: Callable, input: Any, output: Any) -> None: - runnable = PutLocalVar("input") | GetLocalVar("input") - assert method(runnable, input) == output - - -@pytest.mark.asyncio -@pytest.mark.parametrize( - ("method", "input", "output"), - [ - (lambda r, x: r.ainvoke(x), "foo", "foo"), - (lambda r, x: r.abatch(x), ["foo", "bar"], ["foo", "bar"]), - ], -) -async def test_put_get_async(method: Callable, input: Any, output: Any) -> None: - runnable = PutLocalVar("input") | GetLocalVar("input") - assert await method(runnable, input) == output - - -@pytest.mark.parametrize( - ("runnable", "error"), - [ - (PutLocalVar("input"), ValueError), - (GetLocalVar("input"), ValueError), - (PutLocalVar("input") | GetLocalVar("missing"), KeyError), - ], -) -def test_incorrect_usage(runnable: RunnableSequence, error: Type[Exception]) -> None: - with pytest.raises(error): - runnable.invoke("foo") - - -def test_get_in_map() -> None: - runnable: Runnable = PutLocalVar("input") | {"bar": GetLocalVar("input")} - assert runnable.invoke("foo") == {"bar": "foo"} - - -def test_put_in_map() -> None: - runnable: Runnable = {"bar": PutLocalVar("input")} | GetLocalVar("input") - with pytest.raises(KeyError): - runnable.invoke("foo") - - -@pytest.mark.parametrize( - "runnable", - [ - PutLocalVar("input") | GetLocalVar("input", passthrough_key="output"), - ( - PutLocalVar("input") - | {"input": RunnablePassthrough()} - | PromptTemplate.from_template("say {input}") - | FakeListLLM(responses=["hello"]) - | GetLocalVar("input", passthrough_key="output") - ), - ], -) -@pytest.mark.parametrize( - ("method", "input", "output"), - [ - (lambda r, x: r.invoke(x), "hello", {"input": "hello", "output": "hello"}), - (lambda r, x: r.batch(x), ["hello"], [{"input": "hello", "output": "hello"}]), - ( - lambda r, x: list(r.stream(x))[0], - "hello", - {"input": "hello", "output": "hello"}, - ), - ], -) -def test_put_get_sequence( - runnable: RunnableSequence, method: Callable, input: Any, output: Any -) -> None: - assert method(runnable, input) == output diff --git a/libs/langchain/tests/unit_tests/schema/runnable/test_runnable.py b/libs/langchain/tests/unit_tests/schema/runnable/test_runnable.py index 5632a453952..c0af8211ab4 100644 --- a/libs/langchain/tests/unit_tests/schema/runnable/test_runnable.py +++ b/libs/langchain/tests/unit_tests/schema/runnable/test_runnable.py @@ -557,6 +557,22 @@ def test_lambda_schemas() -> None: } +def test_with_types_with_type_generics() -> None: + """Verify that with_types works if we use things like List[int]""" + + def foo(x: int) -> None: + """Add one to the input.""" + raise NotImplementedError() + + # Try specifying some + RunnableLambda(foo).with_types( + output_type=List[int], input_type=List[int] # type: ignore + ) + RunnableLambda(foo).with_types( + output_type=Sequence[int], input_type=Sequence[int] # type: ignore[arg-type] + ) + + def test_schema_complex_seq() -> None: prompt1 = ChatPromptTemplate.from_template("what is the city {person} is from?") prompt2 = ChatPromptTemplate.from_template( @@ -1209,7 +1225,6 @@ async def test_with_config(mocker: MockerFixture) -> None: metadata={"key": "value"}, tags=["c"], callbacks=None, - locals={}, recursion_limit=5, ), ), @@ -1219,7 +1234,6 @@ async def test_with_config(mocker: MockerFixture) -> None: metadata={"key": "value"}, tags=["c"], callbacks=None, - locals={}, recursion_limit=5, ), ), @@ -1290,8 +1304,7 @@ async def test_default_method_implementations(mocker: MockerFixture) -> None: metadata={"key": "value"}, tags=[], callbacks=None, - locals={}, - recursion_limit=10, + recursion_limit=25, ), ), mocker.call( @@ -1300,8 +1313,7 @@ async def test_default_method_implementations(mocker: MockerFixture) -> None: metadata={"key": "value"}, tags=[], callbacks=None, - locals={}, - recursion_limit=10, + recursion_limit=25, ), ), ] @@ -1723,7 +1735,9 @@ async def test_prompt_with_llm( "op": "add", "path": "/logs/FakeListLLM/final_output", "value": { - "generations": [[{"generation_info": None, "text": "foo"}]], + "generations": [ + [{"generation_info": None, "text": "foo", "type": "Generation"}] + ], "llm_output": None, "run": None, }, @@ -2853,6 +2867,7 @@ def test_metadata_is_merged() -> None: with collect_runs() as cb: foo.invoke("hi", {"metadata": {"my_other_key": "my_other_value"}}) run = cb.traced_runs[0] + assert run.extra is not None assert run.extra["metadata"] == expected_metadata @@ -3512,6 +3527,7 @@ def test_seq_batch_return_exceptions(mocker: MockerFixture) -> None: parent_run_qux = parent_runs[3] assert parent_run_qux.inputs["input"] == "qux" assert parent_run_qux.error is None + assert parent_run_qux.outputs is not None assert parent_run_qux.outputs["output"] == "quxaaaa" assert len(parent_run_qux.child_runs) == 4 assert [r.error for r in parent_run_qux.child_runs] == [None, None, None, None] @@ -3634,6 +3650,7 @@ async def test_seq_abatch_return_exceptions(mocker: MockerFixture) -> None: parent_run_qux = parent_runs[3] assert parent_run_qux.inputs["input"] == "qux" assert parent_run_qux.error is None + assert parent_run_qux.outputs is not None assert parent_run_qux.outputs["output"] == "quxaaaa" assert len(parent_run_qux.child_runs) == 4 assert [r.error for r in parent_run_qux.child_runs] == [None, None, None, None] diff --git a/libs/langchain/tests/unit_tests/smith/evaluation/test_runner_utils.py b/libs/langchain/tests/unit_tests/smith/evaluation/test_runner_utils.py index 825c61e74e0..c3f7b4f4434 100644 --- a/libs/langchain/tests/unit_tests/smith/evaluation/test_runner_utils.py +++ b/libs/langchain/tests/unit_tests/smith/evaluation/test_runner_utils.py @@ -331,14 +331,17 @@ async def test_arun_on_dataset(monkeypatch: pytest.MonkeyPatch) -> None: project_name="test_project", client=client, ) - expected = { str(example.id): { "output": { "result": f"Result for example {uuid.UUID(str(example.id))}" }, "input": {"input": example.inputs["input"]}, - "reference": {"output": example.outputs["output"]}, + "reference": { + "output": example.outputs["output"] + if example.outputs is not None + else None + }, "feedback": [], } for example in examples diff --git a/libs/langchain/tests/unit_tests/smith/evaluation/test_string_run_evaluator.py b/libs/langchain/tests/unit_tests/smith/evaluation/test_string_run_evaluator.py index 7f9b103d33a..fd2f9e40c3e 100644 --- a/libs/langchain/tests/unit_tests/smith/evaluation/test_string_run_evaluator.py +++ b/libs/langchain/tests/unit_tests/smith/evaluation/test_string_run_evaluator.py @@ -23,5 +23,5 @@ def test_evaluate_run() -> None: run = MagicMock() example = MagicMock() res = evaluator.evaluate_run(run, example) - assert res.comment.startswith("Error evaluating run ") + assert str(res.comment).startswith("Error evaluating run ") assert res.key == string_evaluator.evaluation_name diff --git a/libs/langchain/tests/unit_tests/test_schema.py b/libs/langchain/tests/unit_tests/test_schema.py index 4077d8800e1..4ee90e03baa 100644 --- a/libs/langchain/tests/unit_tests/test_schema.py +++ b/libs/langchain/tests/unit_tests/test_schema.py @@ -1,12 +1,19 @@ """Test formatting functionality.""" - import unittest from typing import Union +import pytest + from langchain.prompts.base import StringPromptValue from langchain.prompts.chat import ChatPromptValueConcrete -from langchain.pydantic_v1 import BaseModel -from langchain.schema import AgentAction, AgentFinish, Document +from langchain.pydantic_v1 import BaseModel, ValidationError +from langchain.schema import ( + AgentAction, + AgentFinish, + ChatGeneration, + Document, + Generation, +) from langchain.schema.agent import AgentActionMessageLog from langchain.schema.messages import ( AIMessage, @@ -23,6 +30,7 @@ from langchain.schema.messages import ( messages_from_dict, messages_to_dict, ) +from langchain.schema.output import ChatGenerationChunk class TestGetBufferString(unittest.TestCase): @@ -108,6 +116,9 @@ def test_serialization_of_wellknown_objects() -> None: AgentFinish, AgentAction, AgentActionMessageLog, + ChatGeneration, + Generation, + ChatGenerationChunk, ] lc_objects = [ @@ -144,6 +155,16 @@ def test_serialization_of_wellknown_objects() -> None: log="", message_log=[HumanMessage(content="human")], ), + Generation( + text="hello", + generation_info={"info": "info"}, + ), + ChatGeneration( + message=HumanMessage(content="human"), + ), + ChatGenerationChunk( + message=HumanMessageChunk(content="cat"), + ), ] for lc_object in lc_objects: @@ -151,3 +172,7 @@ def test_serialization_of_wellknown_objects() -> None: assert "type" in d, f"Missing key `type` for {type(lc_object)}" obj1 = WellKnownLCObject.parse_obj(d) assert type(obj1.__root__) == type(lc_object), f"failed for {type(lc_object)}" + + with pytest.raises(ValidationError): + # Make sure that specifically validation error is raised + WellKnownLCObject.parse_obj({}) diff --git a/libs/langchain/tests/unit_tests/test_text_splitter.py b/libs/langchain/tests/unit_tests/test_text_splitter.py index 578286cedcc..e6951ef67fb 100644 --- a/libs/langchain/tests/unit_tests/test_text_splitter.py +++ b/libs/langchain/tests/unit_tests/test_text_splitter.py @@ -472,6 +472,41 @@ helloWorld(); ] +def test_cobol_code_splitter() -> None: + splitter = RecursiveCharacterTextSplitter.from_language( + Language.COBOL, chunk_size=CHUNK_SIZE, chunk_overlap=0 + ) + code = """ +IDENTIFICATION DIVISION. +PROGRAM-ID. HelloWorld. +DATA DIVISION. +WORKING-STORAGE SECTION. +01 GREETING PIC X(12) VALUE 'Hello, World!'. +PROCEDURE DIVISION. +DISPLAY GREETING. +STOP RUN. + """ + chunks = splitter.split_text(code) + assert chunks == [ + "IDENTIFICATION", + "DIVISION.", + "PROGRAM-ID.", + "HelloWorld.", + "DATA DIVISION.", + "WORKING-STORAGE", + "SECTION.", + "01 GREETING", + "PIC X(12)", + "VALUE 'Hello,", + "World!'.", + "PROCEDURE", + "DIVISION.", + "DISPLAY", + "GREETING.", + "STOP RUN.", + ] + + def test_typescript_code_splitter() -> None: splitter = RecursiveCharacterTextSplitter.from_language( Language.TS, chunk_size=CHUNK_SIZE, chunk_overlap=0 diff --git a/libs/langchain/tests/unit_tests/tools/test_public_api.py b/libs/langchain/tests/unit_tests/tools/test_public_api.py index 3c129051a95..44ae3e061ed 100644 --- a/libs/langchain/tests/unit_tests/tools/test_public_api.py +++ b/libs/langchain/tests/unit_tests/tools/test_public_api.py @@ -109,6 +109,7 @@ _EXPECTED = [ "format_tool_to_openai_function", "tool", "BearlyInterpreterTool", + "E2BDataAnalysisTool", ] diff --git a/libs/langchain/tests/unit_tests/vectorstores/redis/__init__.py b/libs/langchain/tests/unit_tests/vectorstores/redis/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/libs/langchain/tests/unit_tests/vectorstores/redis/test_redis_schema.py b/libs/langchain/tests/unit_tests/vectorstores/redis/test_redis_schema.py new file mode 100644 index 00000000000..f6ec2e86d2a --- /dev/null +++ b/libs/langchain/tests/unit_tests/vectorstores/redis/test_redis_schema.py @@ -0,0 +1,156 @@ +import pytest + +from langchain.vectorstores.redis.schema import ( + FlatVectorField, + HNSWVectorField, + NumericFieldSchema, + RedisModel, + RedisVectorField, + TagFieldSchema, + TextFieldSchema, + read_schema, +) + + +def test_text_field_schema_creation() -> None: + """Test creating a text field with default parameters.""" + field = TextFieldSchema(name="example") + assert field.name == "example" + assert field.weight == 1 # default value + assert field.no_stem is False # default value + + +def test_tag_field_schema_creation() -> None: + """Test creating a tag field with custom parameters.""" + field = TagFieldSchema(name="tag", separator="|") + assert field.name == "tag" + assert field.separator == "|" + + +def test_numeric_field_schema_creation() -> None: + """Test creating a numeric field with default parameters.""" + field = NumericFieldSchema(name="numeric") + assert field.name == "numeric" + assert field.no_index is False # default value + + +def test_redis_vector_field_validation() -> None: + """Test validation for RedisVectorField's datatype.""" + from langchain.pydantic_v1 import ValidationError + + with pytest.raises(ValidationError): + RedisVectorField( + name="vector", dims=128, algorithm="INVALID_ALGO", datatype="INVALID_TYPE" + ) + + # Test creating a valid RedisVectorField + vector_field = RedisVectorField( + name="vector", dims=128, algorithm="SOME_ALGO", datatype="FLOAT32" + ) + assert vector_field.datatype == "FLOAT32" + + +def test_flat_vector_field_defaults() -> None: + """Test defaults for FlatVectorField.""" + flat_vector_field_data = { + "name": "example", + "dims": 100, + "algorithm": "FLAT", + } + + flat_vector = FlatVectorField(**flat_vector_field_data) + assert flat_vector.datatype == "FLOAT32" + assert flat_vector.distance_metric == "COSINE" + assert flat_vector.initial_cap is None + assert flat_vector.block_size is None + + +def test_flat_vector_field_optional_values() -> None: + """Test optional values for FlatVectorField.""" + flat_vector_field_data = { + "name": "example", + "dims": 100, + "algorithm": "FLAT", + "initial_cap": 1000, + "block_size": 10, + } + + flat_vector = FlatVectorField(**flat_vector_field_data) + assert flat_vector.initial_cap == 1000 + assert flat_vector.block_size == 10 + + +def test_hnsw_vector_field_defaults() -> None: + """Test defaults for HNSWVectorField.""" + hnsw_vector_field_data = { + "name": "example", + "dims": 100, + "algorithm": "HNSW", + } + + hnsw_vector = HNSWVectorField(**hnsw_vector_field_data) + assert hnsw_vector.datatype == "FLOAT32" + assert hnsw_vector.distance_metric == "COSINE" + assert hnsw_vector.initial_cap is None + assert hnsw_vector.m == 16 + assert hnsw_vector.ef_construction == 200 + assert hnsw_vector.ef_runtime == 10 + assert hnsw_vector.epsilon == 0.01 + + +def test_hnsw_vector_field_optional_values() -> None: + """Test optional values for HNSWVectorField.""" + hnsw_vector_field_data = { + "name": "example", + "dims": 100, + "algorithm": "HNSW", + "initial_cap": 2000, + "m": 10, + "ef_construction": 250, + "ef_runtime": 15, + "epsilon": 0.05, + } + hnsw_vector = HNSWVectorField(**hnsw_vector_field_data) + assert hnsw_vector.initial_cap == 2000 + assert hnsw_vector.m == 10 + assert hnsw_vector.ef_construction == 250 + assert hnsw_vector.ef_runtime == 15 + assert hnsw_vector.epsilon == 0.05 + + +def test_read_schema_dict_input() -> None: + """Test read_schema with dict input.""" + index_schema = { + "text": [{"name": "content"}], + "tag": [{"name": "tag"}], + "vector": [{"name": "content_vector", "dims": 100, "algorithm": "FLAT"}], + } + output = read_schema(index_schema=index_schema) # type: ignore + assert output == index_schema + + +def test_redis_model_creation() -> None: + # Test creating a RedisModel with a mixture of fields + redis_model = RedisModel( + text=[TextFieldSchema(name="content")], + tag=[TagFieldSchema(name="tag")], + numeric=[NumericFieldSchema(name="numeric")], + vector=[FlatVectorField(name="flat_vector", dims=128, algorithm="FLAT")], + ) + + assert redis_model.text[0].name == "content" + assert redis_model.tag[0].name == "tag" # type: ignore + assert redis_model.numeric[0].name == "numeric" # type: ignore + assert redis_model.vector[0].name == "flat_vector" # type: ignore + + # Test the content_vector property + with pytest.raises(ValueError): + _ = ( + redis_model.content_vector + ) # this should fail because there's no field with name 'content_vector_key' + + +def test_read_schema() -> None: + # Test the read_schema function with invalid input + with pytest.raises(TypeError): + read_schema(index_schema=None) # non-dict and non-str/pathlike input diff --git a/pyproject.toml b/pyproject.toml index 071c1a2605e..be315d42e66 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,7 +34,7 @@ codespell = "^2.2.0" [tool.codespell] -skip = '.git,*.pdf,*.svg,*.pdf,*.yaml,*.ipynb,poetry.lock,*.min.js,*.css,package-lock.json,example_data,_dist,examples' +skip = '.git,*.pdf,*.svg,*.pdf,*.yaml,*.ipynb,poetry.lock,*.min.js,*.css,package-lock.json,example_data,_dist,examples,templates' # Ignore latin etc ignore-regex = '.*(Stati Uniti|Tense=Pres).*' # whats is a typo but used frequently in queries so kept as is diff --git a/templates/.gitignore b/templates/.gitignore new file mode 100644 index 00000000000..9167bb274a2 --- /dev/null +++ b/templates/.gitignore @@ -0,0 +1,2 @@ +__pycache__ +.ruff_cache \ No newline at end of file diff --git a/templates/CONTRIBUTING.md b/templates/CONTRIBUTING.md new file mode 100644 index 00000000000..f7171265f3b --- /dev/null +++ b/templates/CONTRIBUTING.md @@ -0,0 +1,18 @@ +# Contributing + +To add a new project: + +Make sure you have `langchain-cli` installed. + +```shell +pip install -U langchain-cli +``` + +Create a new package + +```shell +langchain hub new $PROJECT_NAME +``` + +This will set up the skeleton of a package. +You can then edit the contents of the package as you desire. diff --git a/templates/INDEX.md b/templates/INDEX.md new file mode 100644 index 00000000000..2b79bc835ae --- /dev/null +++ b/templates/INDEX.md @@ -0,0 +1,13 @@ +# Templates + +A list of all template repos + +โญRetrieval Augmented Generation Chatbot: Build a chatbot over your data. Uses OpenAI and Pinecone. + +โญExtraction with OpenAI Functions: Do extraction of structured data from unstructured data. Uses OpenAI function calling. + +โญLocal Retrieval Augmented Generation: Build a chatbot over your data. Uses only local tooling: Ollama, GPT4all, Chroma. + +โญOpenAI Functions Agent: Build a chatbot that can take actions. Uses OpenAI function calling and Tavily. + +โญXML Agent: Build a chatbot that can take actions. Uses Anthropic and You.com. diff --git a/templates/Makefile b/templates/Makefile new file mode 100644 index 00000000000..445235c22c8 --- /dev/null +++ b/templates/Makefile @@ -0,0 +1,8 @@ +lint lint_diff: + poetry run poe lint + +test: + poetry run poe test + +format: + poetry run poe format diff --git a/templates/README.md b/templates/README.md new file mode 100644 index 00000000000..f5974ae1488 --- /dev/null +++ b/templates/README.md @@ -0,0 +1,74 @@ +# LangServe Templates + +Templates for a fully functioning app that can be hosted by LangServe. + +## Usage + +To use, first install the LangChain CLI. + +```shell +pip install -U langchain-cli +``` + +Then, install `langserve`: + +```shell +pip install "langserve[all]" +``` + +Next, create a new LangChain project: + +```shell +langchain serve new my-app +``` + +This will create a new directory called `my-app` with two folders: + +- `app`: This is where LangServe code will live +- `packages`: This is where your chains or agents will live + +To pull in an existing template as a package, you first need to go into your new project: + +```shell +cd my-app +``` + +And you can the add a template as a project + +```shell +langchain serve add $PROJECT_NAME +``` + +This will pull in the specified template into `packages/$PROJECT_NAME` + +You then need to install this package so you can use it in the langserve app: + +```shell +pip install -e packages/$PROJECT_NAME +``` + +We install it with `-e` so that if we modify the template at all (which we likely will) the changes are updated. + +In order to have LangServe use this project, you then need to modify `app/server.py`. +Specifically, you should add something like: + +```python +from fastapi import FastAPI +from langserve import add_routes +# This depends on the structure of the package you install +from my_project import chain + +app = FastAPI() + +add_routes(app, chain) +``` + +You can then spin up production-ready endpoints, along with a playground, by running: + +```shell +python app/server.py +``` + +## Adding a template + +See [here](CONTRIBUTING.md) diff --git a/templates/anthropic-functions/LICENSE b/templates/anthropic-functions/LICENSE new file mode 100644 index 00000000000..426b6509034 --- /dev/null +++ b/templates/anthropic-functions/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/anthropic-functions/README.md b/templates/anthropic-functions/README.md new file mode 100644 index 00000000000..bc0f29e8c24 --- /dev/null +++ b/templates/anthropic-functions/README.md @@ -0,0 +1,39 @@ +# Function calling with Anthropic + +This template enables [Anthropic function calling](https://python.langchain.com/docs/integrations/chat/anthropic_functions). + +Function calling can be used for various tasks, such as extraction or tagging. + +Specify the function you want to use in `chain.py` + +By default, it will tag the input text using the following fields: + +* sentiment +* aggressiveness +* language + +## LLM + +This template will use `Claude2` by default. + +Be sure that `ANTHROPIC_API_KEY` is set in your enviorment. + +## Adding the template + +Create your LangServe app: +``` +langchain serve new my-app +cd my-app +``` + +Add template: +``` +langchain serve add anthropic-functions +``` + +Start server: +``` +langchain start +``` + +See Jupyter notebook `anthropic_functions` for various way to connect to the template. \ No newline at end of file diff --git a/templates/anthropic-functions/anthropic_functions.ipynb b/templates/anthropic-functions/anthropic_functions.ipynb new file mode 100644 index 00000000000..938a22a19d9 --- /dev/null +++ b/templates/anthropic-functions/anthropic_functions.ipynb @@ -0,0 +1,83 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4ae4b789", + "metadata": {}, + "source": [ + "## Document Loading\n", + "\n", + "Load a blog post on agents." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5d6bd62e", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.document_loaders import WebBaseLoader\n", + "loader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")\n", + "text = loader.load()" + ] + }, + { + "cell_type": "markdown", + "id": "8e21575d", + "metadata": {}, + "source": [ + "## Run Template\n", + "\n", + "As shown in the README, add template and start server:\n", + "```\n", + "langchain serve add anthropic-functions\n", + "langchain start\n", + "```\n", + "\n", + "We can now look at the endpoints:\n", + "\n", + "http://127.0.0.1:8000/docs#\n", + "\n", + "And specifically at our loaded template:\n", + "\n", + "http://127.0.0.1:8000/docs#/default/invoke_anthropic-functions_invoke_post\n", + " \n", + "We can also use remote runnable to call it:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "92edba86", + "metadata": {}, + "outputs": [], + "source": [ + "from langserve.client import RemoteRunnable\n", + "anthropic_function_model = RemoteRunnable('http://localhost:8000/anthropic-functions')\n", + "anthropic_function_model.invoke(text[0].page_content[0:1500])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "langserve", + "language": "python", + "name": "langserve" + }, + "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/templates/anthropic-functions/anthropic_functions/__init__.py b/templates/anthropic-functions/anthropic_functions/__init__.py new file mode 100644 index 00000000000..e3c87e24a8d --- /dev/null +++ b/templates/anthropic-functions/anthropic_functions/__init__.py @@ -0,0 +1,3 @@ +from anthropic_functions.chain import chain + +__all__ = ["chain"] diff --git a/templates/anthropic-functions/anthropic_functions/chain.py b/templates/anthropic-functions/anthropic_functions/chain.py new file mode 100644 index 00000000000..3d51bed0603 --- /dev/null +++ b/templates/anthropic-functions/anthropic_functions/chain.py @@ -0,0 +1,15 @@ +from langchain.chains import create_tagging_chain +from langchain_experimental.llms.anthropic_functions import AnthropicFunctions + +model = AnthropicFunctions(model='claude-2') + +schema = { + "properties": { + "sentiment": {"type": "string"}, + "aggressiveness": {"type": "integer"}, + "language": {"type": "string"}, + } +} + +# This is LLMChain, which implements invoke +chain = create_tagging_chain(schema, model) \ No newline at end of file diff --git a/templates/anthropic-functions/poetry.lock b/templates/anthropic-functions/poetry.lock new file mode 100644 index 00000000000..c342ab9a71a --- /dev/null +++ b/templates/anthropic-functions/poetry.lock @@ -0,0 +1,1548 @@ +# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server 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"poetry.core.masonry.api" \ No newline at end of file diff --git a/templates/anthropic-functions/tests/__init__.py b/templates/anthropic-functions/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/anthropic-iterative-search/README.md b/templates/anthropic-iterative-search/README.md new file mode 100644 index 00000000000..7670579e2d6 --- /dev/null +++ b/templates/anthropic-iterative-search/README.md @@ -0,0 +1,3 @@ +# anthropic-iterative-search + +Heavily inspired by [this notebook](https://github.com/anthropics/anthropic-cookbook/blob/main/long_context/wikipedia-search-cookbook.ipynb) diff --git a/templates/anthropic-iterative-search/anthropic_iterative_search/__init__.py b/templates/anthropic-iterative-search/anthropic_iterative_search/__init__.py new file mode 100644 index 00000000000..ef5463d1979 --- /dev/null +++ b/templates/anthropic-iterative-search/anthropic_iterative_search/__init__.py @@ -0,0 +1,11 @@ +from langchain.schema.runnable import ConfigurableField + +from .chain import chain +from .retriever_agent import executor + +final_chain = chain.configurable_alternatives( + ConfigurableField(id="chain"), + default_key="response", + # This adds a new option, with name `openai` that is equal to `ChatOpenAI()` + retrieve=executor, +) diff --git a/templates/anthropic-iterative-search/anthropic_iterative_search/agent_scratchpad.py b/templates/anthropic-iterative-search/anthropic_iterative_search/agent_scratchpad.py new file mode 100644 index 00000000000..209576943f1 --- /dev/null +++ b/templates/anthropic-iterative-search/anthropic_iterative_search/agent_scratchpad.py @@ -0,0 +1,15 @@ +def _format_docs(docs): + result = "\n".join( + [ + f'\n\n{r}\n\n' + for i, r in enumerate(docs) + ] + ) + return result + +def format_agent_scratchpad(intermediate_steps): + thoughts = "" + for action, observation in intermediate_steps: + thoughts += action.log + thoughts += '' + _format_docs(observation) + return thoughts diff --git a/templates/anthropic-iterative-search/anthropic_iterative_search/chain.py b/templates/anthropic-iterative-search/anthropic_iterative_search/chain.py new file mode 100644 index 00000000000..ba2a7d2bcd2 --- /dev/null +++ b/templates/anthropic-iterative-search/anthropic_iterative_search/chain.py @@ -0,0 +1,15 @@ +from langchain.chat_models import ChatAnthropic +from langchain.prompts import ChatPromptTemplate +from langchain.schema.output_parser import StrOutputParser + +from .prompts import answer_prompt +from .retriever_agent import executor + +prompt = ChatPromptTemplate.from_template(answer_prompt) + +model = ChatAnthropic(model="claude-2", temperature=0, max_tokens_to_sample=1000) + +chain = { + "query": lambda x: x["query"], + "information": executor | (lambda x: x["output"]) +} | prompt | model | StrOutputParser() diff --git a/templates/anthropic-iterative-search/anthropic_iterative_search/output_parser.py b/templates/anthropic-iterative-search/anthropic_iterative_search/output_parser.py new file mode 100644 index 00000000000..f1e981d7610 --- /dev/null +++ b/templates/anthropic-iterative-search/anthropic_iterative_search/output_parser.py @@ -0,0 +1,37 @@ +import re + +from langchain.schema.agent import AgentAction, AgentFinish + +from .agent_scratchpad import _format_docs + + +def extract_between_tags(tag: str, string: str, strip: bool = True) -> str: + ext_list = re.findall(f"<{tag}\s?>(.+?)", string, re.DOTALL) + if strip: + ext_list = [e.strip() for e in ext_list] + if ext_list: + if len(ext_list) != 1: + raise ValueError + # Only return the first one + return ext_list[0] + + +def parse_output(outputs): + partial_completion = outputs["partial_completion"] + steps = outputs["intermediate_steps"] + search_query = extract_between_tags( + "search_query", partial_completion + "" + ) + if search_query is None: + docs = [] + str_output = "" + for action, observation in steps: + docs.extend(observation) + str_output += action.log + str_output += "" + _format_docs(observation) + str_output += partial_completion + return AgentFinish({"docs": docs, "output": str_output}, log=partial_completion) + else: + return AgentAction( + tool="search", tool_input=search_query, log=partial_completion + ) diff --git a/templates/anthropic-iterative-search/anthropic_iterative_search/prompts.py b/templates/anthropic-iterative-search/anthropic_iterative_search/prompts.py new file mode 100644 index 00000000000..fe46574cb44 --- /dev/null +++ b/templates/anthropic-iterative-search/anthropic_iterative_search/prompts.py @@ -0,0 +1,7 @@ +retrieval_prompt = """{retriever_description} Before beginning to research the user's question, first think for a moment inside tags about what information is necessary for a well-informed answer. If the user's question is complex, you may need to decompose the query into multiple subqueries and execute them individually. Sometimes the search engine will return empty search results, or the search results may not contain the information you need. In such cases, feel free to try again with a different query. + +After each call to the Search Engine Tool, reflect briefly inside tags about whether you now have enough information to answer, or whether more information is needed. If you have all the relevant information, write it in tags, WITHOUT actually answering the question. Otherwise, issue a new search. + +Here is the user's question: {query} Remind yourself to make short queries in your scratchpad as you plan out your strategy.""" # noqa: E501 + +answer_prompt = "Here is a user query: {query}. Here is some relevant information: {information}. Please answer the question using the relevant information." # noqa: E501 diff --git a/templates/anthropic-iterative-search/anthropic_iterative_search/retriever.py b/templates/anthropic-iterative-search/anthropic_iterative_search/retriever.py new file mode 100644 index 00000000000..5377e65be32 --- /dev/null +++ b/templates/anthropic-iterative-search/anthropic_iterative_search/retriever.py @@ -0,0 +1,17 @@ +from langchain.retrievers import WikipediaRetriever +from langchain.tools import tool + +# This is used to tell the model how to best use the retriever. + +retriever_description = """You will be asked a question by a human user. You have access to the following tool to help answer the question. Search Engine Tool * The search engine will exclusively search over Wikipedia for pages similar to your query. It returns for each page its title and full page content. Use this tool if you want to get up-to-date and comprehensive information on a topic to help answer queries. Queries should be as atomic as possible -- they only need to address one part of the user's question. For example, if the user's query is "what is the color of a basketball?", your search query should be "basketball". Here's another example: if the user's question is "Who created the first neural network?", your first query should be "neural network". As you can see, these queries are quite short. Think keywords, not phrases. * At any time, you can make a call to the search engine using the following syntax: query_word. * You'll then get results back in tags.""" # noqa: E501 + +retriever = WikipediaRetriever() + +# This should be the same as the function name below +RETRIEVER_TOOL_NAME = "search" + + +@tool +def search(query): + """Search with the retriever.""" + return retriever.get_relevant_documents(query) diff --git a/templates/anthropic-iterative-search/anthropic_iterative_search/retriever_agent.py b/templates/anthropic-iterative-search/anthropic_iterative_search/retriever_agent.py new file mode 100644 index 00000000000..1ee01f34c76 --- /dev/null +++ b/templates/anthropic-iterative-search/anthropic_iterative_search/retriever_agent.py @@ -0,0 +1,29 @@ +from langchain.agents import AgentExecutor +from langchain.chat_models import ChatAnthropic +from langchain.prompts import ChatPromptTemplate +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnableMap, RunnablePassthrough + +from .agent_scratchpad import format_agent_scratchpad +from .output_parser import parse_output +from .prompts import retrieval_prompt +from .retriever import retriever_description, search + +prompt = ChatPromptTemplate.from_messages([ + ("user", retrieval_prompt), + ("ai", "{agent_scratchpad}"), +]) +prompt = prompt.partial(retriever_description=retriever_description) + +model = ChatAnthropic(model="claude-2", temperature=0, max_tokens_to_sample=1000) + +chain = RunnablePassthrough.assign( + agent_scratchpad=lambda x: format_agent_scratchpad(x['intermediate_steps']) +) | prompt | model.bind( stop_sequences=['']) | StrOutputParser() + +agent_chain = RunnableMap({ + "partial_completion": chain, + "intermediate_steps": lambda x: x['intermediate_steps'] +}) | parse_output + +executor = AgentExecutor(agent=agent_chain, tools = [search], verbose=True) diff --git a/templates/anthropic-iterative-search/main.py b/templates/anthropic-iterative-search/main.py new file mode 100644 index 00000000000..27b7aa1aa6a --- /dev/null +++ b/templates/anthropic-iterative-search/main.py @@ -0,0 +1,12 @@ +from anthropic_iterative_search import final_chain + +if __name__ == "__main__": + query = ( + "Which movie came out first: Oppenheimer, or " + "Are You There God It's Me Margaret?" + ) + print( + final_chain.with_config(configurable={"chain": "retrieve"}).invoke( + {"query": query} + ) + ) diff --git a/templates/anthropic-iterative-search/poetry.lock b/templates/anthropic-iterative-search/poetry.lock new file mode 100644 index 00000000000..56713fb41c8 --- /dev/null +++ b/templates/anthropic-iterative-search/poetry.lock @@ -0,0 +1,1581 @@ +# This file is automatically @generated by Poetry and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +category = "main" +optional = false +python-versions = ">=3.6" +files = 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a/templates/anthropic-iterative-search/tests/__init__.py b/templates/anthropic-iterative-search/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/cassandra-entomology-rag/.env.template b/templates/cassandra-entomology-rag/.env.template new file mode 100644 index 00000000000..251125528ba --- /dev/null +++ b/templates/cassandra-entomology-rag/.env.template @@ -0,0 +1,13 @@ +export OPENAI_API_KEY="..." + +export ASTRA_DB_APPLICATION_TOKEN="AstraCS:..." +export ASTRA_DB_KEYSPACE="..." +export ASTRA_DB_ID="12345678-" + +# UNCOMMENT THE FOLLOWING FOR A CASSANDRA CLUSTER ... +# export USE_CASSANDRA_CLUSTER="1" +# ... then provide these parameters as well: +# export CASSANDRA_KEYSPACE="..." +# export CASSANDRA_CONTACT_POINTS="127.0.0.1" # optional +# export CASSANDRA_USERNAME="cassandra" # optional +# export CASSANDRA_PASSWORD="cassandra" # optional diff --git a/templates/cassandra-entomology-rag/README.md b/templates/cassandra-entomology-rag/README.md new file mode 100644 index 00000000000..88bc8fb03da --- /dev/null +++ b/templates/cassandra-entomology-rag/README.md @@ -0,0 +1,47 @@ +# RAG LangServe chain template + +A basic chain template showing the RAG pattern using +a vector store on Astra DB / Apache Cassandraยฎ. + +## Setup: + +You need: + +- an [Astra](https://astra.datastax.com) Vector Database (free tier is fine!). **You need a [Database Administrator token](https://awesome-astra.github.io/docs/pages/astra/create-token/#c-procedure)**, in particular the string starting with `AstraCS:...`; +- likewise, get your [Database ID](https://awesome-astra.github.io/docs/pages/astra/faq/#where-should-i-find-a-database-identifier) ready, you will have to enter it below; +- an **OpenAI API Key**. (More info [here](https://cassio.org/start_here/#llm-access), note that out-of-the-box this demo supports OpenAI unless you tinker with the code.) + +_Note:_ you can alternatively use a regular Cassandra cluster: to do so, make sure you provide the `USE_CASSANDRA_CLUSTER` entry as shown in `.env.template` and the subsequent environment variables to specify how to connect to it. + +You need to provide the connection parameters and secrets through environment variables. Please refer to `.env.template` for what variables are required. + +### Populate the vector store + +Make sure you have the environment variables all set (see previous section), +then, from this directory, launch the following just once: + +``` +poetry run bash -c "cd [...]/cassandra_entomology_rag; python setup.py" +``` + +The output will be something like `Done (29 lines inserted).`. + +> **Note**: In a full application, the vector store might be populated in other ways: +> this step is to pre-populate the vector store with some rows for the +> demo RAG chains to sensibly work. + +### Sample inputs + +The chain's prompt is engineered to stay on topic and only use the provided context. + +To put this to test, experiment with these example questions: + +``` +"Are there more coleoptera or bugs?" +"Do Odonata have wings?" +"Do birds have wings?" <-- no entomology here! +``` + +## Reference + +Stand-alone repo with LangServe chain: [here](https://github.com/hemidactylus/langserve_cassandra_entomology_rag). diff --git a/templates/cassandra-entomology-rag/cassandra_entomology_rag/__init__.py b/templates/cassandra-entomology-rag/cassandra_entomology_rag/__init__.py new file mode 100644 index 00000000000..6af8acd87df --- /dev/null +++ b/templates/cassandra-entomology-rag/cassandra_entomology_rag/__init__.py @@ -0,0 +1,60 @@ +import os + +import cassio +from langchain.chat_models import ChatOpenAI +from langchain.embeddings import OpenAIEmbeddings +from langchain.prompts import ChatPromptTemplate +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough +from langchain.vectorstores import Cassandra + +use_cassandra = int(os.environ.get("USE_CASSANDRA_CLUSTER", "0")) +if use_cassandra: + from .cassandra_cluster_init import get_cassandra_connection + session, keyspace = get_cassandra_connection() + cassio.init( + session=session, + keyspace=keyspace, + ) +else: + cassio.init( + token=os.environ["ASTRA_DB_APPLICATION_TOKEN"], + database_id=os.environ["ASTRA_DB_ID"], + keyspace=os.environ.get("ASTRA_DB_KEYSPACE"), + ) + + +# inits +llm = ChatOpenAI() +embeddings = OpenAIEmbeddings() +vector_store = Cassandra( + session=None, + keyspace=None, + embedding=embeddings, + table_name="langserve_rag_demo", +) +retriever = vector_store.as_retriever(search_kwargs={'k': 3}) + +entomology_template = """ +You are an expert entomologist, tasked with answering enthusiast biologists' questions. +You must answer based only on the provided context, do not make up any fact. +Your answers must be concise and to the point, but strive to provide scientific details +(such as family, order, Latin names, and so on when appropriate). +You MUST refuse to answer questions on other topics than entomology, +as well as questions whose answer is not found in the provided context. + +CONTEXT: +{context} + +QUESTION: {question} + +YOUR ANSWER:""" + +entomology_prompt = ChatPromptTemplate.from_template(entomology_template) + +chain = ( + {"context": retriever, "question": RunnablePassthrough()} + | entomology_prompt + | llm + | StrOutputParser() +) \ No newline at end of file diff --git a/templates/cassandra-entomology-rag/cassandra_entomology_rag/cassandra_cluster_init.py b/templates/cassandra-entomology-rag/cassandra_entomology_rag/cassandra_cluster_init.py new file mode 100644 index 00000000000..9c4ce5b9b66 --- /dev/null +++ b/templates/cassandra-entomology-rag/cassandra_entomology_rag/cassandra_cluster_init.py @@ -0,0 +1,29 @@ +import os + +from cassandra.auth import PlainTextAuthProvider +from cassandra.cluster import Cluster + + +def get_cassandra_connection(): + contact_points = [ + cp.strip() + for cp in os.environ.get("CASSANDRA_CONTACT_POINTS", "").split(",") + if cp.strip() + ] + CASSANDRA_KEYSPACE = os.environ["CASSANDRA_KEYSPACE"] + CASSANDRA_USERNAME = os.environ.get("CASSANDRA_USERNAME") + CASSANDRA_PASSWORD = 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+openai = "^0.28.1" +tiktoken = "^0.5.1" +cassio = "^0.1.3" + +[tool.langserve] +export_module = "cassandra_entomology_rag" +export_attr = "chain" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/cassandra-entomology-rag/setup.py b/templates/cassandra-entomology-rag/setup.py new file mode 100644 index 00000000000..5747d672162 --- /dev/null +++ b/templates/cassandra-entomology-rag/setup.py @@ -0,0 +1,43 @@ +import os + +import cassio +from langchain.embeddings import OpenAIEmbeddings +from langchain.vectorstores import Cassandra + +use_cassandra = int(os.environ.get("USE_CASSANDRA_CLUSTER", "0")) +if use_cassandra: + from cassandra_entomology_rag.cassandra_cluster_init import get_cassandra_connection + + session, keyspace = get_cassandra_connection() + cassio.init( + session=session, + keyspace=keyspace, + ) +else: + cassio.init( + token=os.environ["ASTRA_DB_APPLICATION_TOKEN"], + database_id=os.environ["ASTRA_DB_ID"], + keyspace=os.environ.get("ASTRA_DB_KEYSPACE"), + ) + + +if __name__ == "__main__": + embeddings = OpenAIEmbeddings() + vector_store = Cassandra( + session=None, + keyspace=None, + embedding=embeddings, + table_name="langserve_rag_demo", + ) + # + lines = [ + line.strip() + for line in open("sources.txt").readlines() + if line.strip() + if line[0] != "#" + ] + # deterministic IDs to prevent duplicates on multiple runs + ids = ["_".join(line.split(" ")[:2]).lower().replace(":", "") for line in lines] + # + vector_store.add_texts(texts=lines, ids=ids) + print(f"Done ({len(lines)} lines inserted).") diff --git a/templates/cassandra-entomology-rag/sources.txt b/templates/cassandra-entomology-rag/sources.txt new file mode 100644 index 00000000000..c03c144753b --- /dev/null +++ b/templates/cassandra-entomology-rag/sources.txt @@ -0,0 +1,31 @@ +# source: https://www.thoughtco.com/a-guide-to-the-twenty-nine-insect-orders-1968419 + +Order Thysanura: The silverfish and firebrats are found in the order Thysanura. They are wingless insects often found in people's attics, and have a lifespan of several years. There are about 600 species worldwide. +Order Diplura: Diplurans are the most primitive insect species, with no eyes or wings. They have the unusual ability among insects to regenerate body parts. There are over 400 members of the order Diplura in the world. +Order Protura: Another very primitive group, the proturans have no eyes, no antennae, and no wings. They are uncommon, with perhaps less than 100 species known. +Order Collembola: The order Collembola includes the springtails, primitive insects without wings. There are approximately 2,000 species of Collembola worldwide. +Order Ephemeroptera: The mayflies of order Ephemeroptera are short-lived, and undergo incomplete metamorphosis. The larvae are aquatic, feeding on algae and other plant life. Entomologists have described about 2,100 species worldwide. +Order Odonata: The order Odonata includes dragonflies and damselflies, which undergo incomplete metamorphosis. They are predators of other insects, even in their immature stage. There are about 5,000 species in the order Odonata. +Order Plecoptera: The stoneflies of order Plecoptera are aquatic and undergo incomplete metamorphosis. The nymphs live under rocks in well flowing streams. Adults are usually seen on the ground along stream and river banks. There are roughly 3,000 species in this group. +Order Grylloblatodea: Sometimes referred to as "living fossils," the insects of the order Grylloblatodea have changed little from their ancient ancestors. This order is the smallest of all the insect orders, with perhaps only 25 known species living today. Grylloblatodea live at elevations above 1500 ft., and are commonly named ice bugs or rock crawlers. +Order Orthoptera: These are familiar insects (grasshoppers, locusts, katydids, and crickets) and one of the largest orders of herbivorous insects. Many species in the order Orthoptera can produce and detect sounds. Approximately 20,000 species exist in this group. +Order Phasmida: The order Phasmida are masters of camouflage, the stick and leaf insects. They undergo incomplete metamorphosis and feed on leaves. There are some 3,000 insects in this group, but only a small fraction of this number is leaf insects. Stick insects are the longest insects in the world. +Order Dermaptera: This order contains the earwigs, an easily recognized insect that often has pincers at the end of the abdomen. Many earwigs are scavengers, eating both plant and animal matter. The order Dermaptera includes less than 2,000 species. +Order Embiidina: The order Embioptera is another ancient order with few species, perhaps only 200 worldwide. The web spinners have silk glands in their front legs and weave nests under leaf litter and in tunnels where they live. Webspinners live in tropical or subtropical climates. +Order Dictyoptera: The order Dictyoptera includes roaches and mantids. Both groups have long, segmented antennae and leathery forewings held tightly against their backs. They undergo incomplete metamorphosis. Worldwide, there approximately 6,000 species in this order, most living in tropical regions. +Order Isoptera: Termites feed on wood and are important decomposers in forest ecosystems. They also feed on wood products and are thought of as pests for the destruction they cause to man-made structures. There are between 2,000 and 3,000 species in this order. +Order Zoraptera: Little is know about the angel insects, which belong to the order Zoraptera. Though they are grouped with winged insects, many are actually wingless. Members of this group are blind, small, and often found in decaying wood. There are only about 30 described species worldwide. +Order Psocoptera: Bark lice forage on algae, lichen, and fungus in moist, dark places. Booklice frequent human dwellings, where they feed on book paste and grains. They undergo incomplete metamorphosis. Entomologists have named about 3,200 species in the order Psocoptera. +Order Mallophaga: Biting lice are ectoparasites that feed on birds and some mammals. There are an estimated 3,000 species in the order Mallophaga, all of which undergo incomplete metamorphosis. +Order Siphunculata: The order Siphunculata are the sucking lice, which feed on the fresh blood of mammals. Their mouthparts are adapted for sucking or siphoning blood. There are only about 500 species of sucking lice. +Order Hemiptera: Most people use the term "bugs" to mean insects; an entomologist uses the term to refer to the order Hemiptera. The Hemiptera are the true bugs, and include cicadas, aphids, and spittlebugs, and others. This is a large group of over 70,000 species worldwide. +Order Thysanoptera: The thrips of order Thysanoptera are small insects that feed on plant tissue. Many are considered agricultural pests for this reason. Some thrips prey on other small insects as well. This order contains about 5,000 species. +Order Neuroptera: Commonly called the order of lacewings, this group actually includes a variety of other insects, too: dobsonflies, owlflies, mantidflies, antlions, snakeflies, and alderflies. Insects in the order Neuroptera undergo complete metamorphosis. Worldwide, there are over 5,500 species in this group. +Order Mecoptera: This order includes the scorpionflies, which live in moist, wooded habitats. Scorpionflies are omnivorous in both their larval and adult forms. The larva are caterpillar-like. There are less than 500 described species in the order Mecoptera. +Order Siphonaptera: Pet lovers fear insects in the order Siphonaptera - the fleas. Fleas are blood-sucking ectoparasites that feed on mammals, and rarely, birds. There are well over 2,000 species of fleas in the world. +Order Coleoptera: This group, the beetles and weevils, is the largest order in the insect world, with over 300,000 distinct species known. The order Coleoptera includes well-known families: june beetles, lady beetles, click beetles, and fireflies. All have hardened forewings that fold over the abdomen to protect the delicate hindwings used for flight. +Order Strepsiptera: Insects in this group are parasites of other insects, particularly bees, grasshoppers, and the true bugs. The immature Strepsiptera lies in wait on a flower and quickly burrows into any host insect that comes along. Strepsiptera undergo complete metamorphosis and pupate within the host insect's body. +Order Diptera: Diptera is one of the largest orders, with nearly 100,000 insects named to the order. These are the true flies, mosquitoes, and gnats. Insects in this group have modified hindwings which are used for balance during flight. The forewings function as the propellers for flying. +Order Lepidoptera: The butterflies and moths of the order Lepidoptera comprise the second largest group in the class Insecta. These well-known insects have scaly wings with interesting colors and patterns. You can often identify an insect in this order just by the wing shape and color. +Order Trichoptera: Caddisflies are nocturnal as adults and aquatic when immature. The caddisfly adults have silky hairs on their wings and body, which is key to identifying a Trichoptera member. The larvae spin traps for prey with silk. They also make cases from the silk and other materials that they carry and use for protection. +Order Hymenoptera: The order Hymenoptera includes many of the most common insects - ants, bees, and wasps. The larvae of some wasps cause trees to form galls, which then provides food for the immature wasps. Other wasps are parasitic, living in caterpillars, beetles, or even aphids. This is the third-largest insect order with just over 100,000 species. diff --git a/templates/cassandra-synonym-caching/.env.template b/templates/cassandra-synonym-caching/.env.template new file mode 100644 index 00000000000..251125528ba --- /dev/null +++ b/templates/cassandra-synonym-caching/.env.template @@ -0,0 +1,13 @@ +export OPENAI_API_KEY="..." + +export ASTRA_DB_APPLICATION_TOKEN="AstraCS:..." +export ASTRA_DB_KEYSPACE="..." +export ASTRA_DB_ID="12345678-" + +# UNCOMMENT THE FOLLOWING FOR A CASSANDRA CLUSTER ... +# export USE_CASSANDRA_CLUSTER="1" +# ... then provide these parameters as well: +# export CASSANDRA_KEYSPACE="..." +# export CASSANDRA_CONTACT_POINTS="127.0.0.1" # optional +# export CASSANDRA_USERNAME="cassandra" # optional +# export CASSANDRA_PASSWORD="cassandra" # optional diff --git a/templates/cassandra-synonym-caching/README.md b/templates/cassandra-synonym-caching/README.md new file mode 100644 index 00000000000..3a5ecc8fbeb --- /dev/null +++ b/templates/cassandra-synonym-caching/README.md @@ -0,0 +1,20 @@ +# LLM-cache LangServe chain template + +A simple chain template showcasing usage of LLM Caching +backed by Astra DB / Apache Cassandraยฎ. + +## Setup: + +You need: + +- an [Astra](https://astra.datastax.com) Vector Database (free tier is fine!). **You need a [Database Administrator token](https://awesome-astra.github.io/docs/pages/astra/create-token/#c-procedure)**, in particular the string starting with `AstraCS:...`; +- likewise, get your [Database ID](https://awesome-astra.github.io/docs/pages/astra/faq/#where-should-i-find-a-database-identifier) ready, you will have to enter it below; +- an **OpenAI API Key**. (More info [here](https://cassio.org/start_here/#llm-access), note that out-of-the-box this demo supports OpenAI unless you tinker with the code.) + +_Note:_ you can alternatively use a regular Cassandra cluster: to do so, make sure you provide the `USE_CASSANDRA_CLUSTER` entry as shown in `.env.template` and the subsequent environment variables to specify how to connect to it. + +You need to provide the connection parameters and secrets through environment variables. Please refer to `.env.template` for what variables are required. + +## Reference + +Stand-alone LangServe template repo: [here](https://github.com/hemidactylus/langserve_cassandra_synonym_caching). diff --git a/templates/cassandra-synonym-caching/cassandra_synonym_caching/__init__.py b/templates/cassandra-synonym-caching/cassandra_synonym_caching/__init__.py new file mode 100644 index 00000000000..575ea108797 --- /dev/null +++ b/templates/cassandra-synonym-caching/cassandra_synonym_caching/__init__.py @@ -0,0 +1,43 @@ +import os + +import cassio +import langchain +from langchain.cache import CassandraCache +from langchain.chat_models import ChatOpenAI +from langchain.prompts import ChatPromptTemplate +from langchain.schema import BaseMessage +from langchain.schema.runnable import RunnableLambda + +use_cassandra = int(os.environ.get("USE_CASSANDRA_CLUSTER", "0")) +if use_cassandra: + from .cassandra_cluster_init import get_cassandra_connection + session, keyspace = get_cassandra_connection() + cassio.init( + session=session, + keyspace=keyspace, + ) +else: + cassio.init( + token=os.environ["ASTRA_DB_APPLICATION_TOKEN"], + database_id=os.environ["ASTRA_DB_ID"], + keyspace=os.environ.get("ASTRA_DB_KEYSPACE"), + ) + +# inits +langchain.llm_cache = CassandraCache(session=None, keyspace=None) +llm = ChatOpenAI() + +# custom runnables +def msg_splitter(msg: BaseMessage): + return [ + w.strip() + for w in msg.content.split(",") + if w.strip() + ] + +# synonym-route preparation +synonym_prompt = ChatPromptTemplate.from_template( + "List up to five comma-separated synonyms of this word: {word}" +) + +chain = synonym_prompt | llm | RunnableLambda(msg_splitter) diff --git a/templates/cassandra-synonym-caching/cassandra_synonym_caching/cassandra_cluster_init.py b/templates/cassandra-synonym-caching/cassandra_synonym_caching/cassandra_cluster_init.py new file mode 100644 index 00000000000..9c4ce5b9b66 --- /dev/null +++ b/templates/cassandra-synonym-caching/cassandra_synonym_caching/cassandra_cluster_init.py @@ -0,0 +1,29 @@ +import os + +from cassandra.auth import PlainTextAuthProvider +from cassandra.cluster import Cluster + + +def get_cassandra_connection(): + contact_points = [ + cp.strip() + for cp in os.environ.get("CASSANDRA_CONTACT_POINTS", "").split(",") + if cp.strip() + ] + CASSANDRA_KEYSPACE = os.environ["CASSANDRA_KEYSPACE"] + CASSANDRA_USERNAME = os.environ.get("CASSANDRA_USERNAME") + CASSANDRA_PASSWORD = os.environ.get("CASSANDRA_PASSWORD") + # + if CASSANDRA_USERNAME and CASSANDRA_PASSWORD: + auth_provider = PlainTextAuthProvider( + CASSANDRA_USERNAME, + CASSANDRA_PASSWORD, + ) + else: + auth_provider = None + + c_cluster = Cluster( + contact_points if contact_points else None, auth_provider=auth_provider + ) + session = c_cluster.connect() + return (session, CASSANDRA_KEYSPACE) diff --git a/templates/cassandra-synonym-caching/poetry.lock 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">=0.0.313" +openai = "^0.28.1" +tiktoken = "^0.5.1" +cassio = "^0.1.3" + +[tool.langserve] +export_module = "cassandra_synonym_caching" +export_attr = "chain" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/csv-agent/README.md b/templates/csv-agent/README.md new file mode 100644 index 00000000000..ef45bc6c467 --- /dev/null +++ b/templates/csv-agent/README.md @@ -0,0 +1,5 @@ +# csv-agent + +This is a csv agent that uses both a Python REPL as well as a vectorstore to allow for interaction with text data. + +Set up that is required is running `ingest.py` to do the ingestion into a vectorstore. diff --git a/templates/csv-agent/csv_agent/__init__.py b/templates/csv-agent/csv_agent/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/csv-agent/csv_agent/agent.py b/templates/csv-agent/csv_agent/agent.py new file mode 100644 index 00000000000..9d04f36694f --- /dev/null +++ b/templates/csv-agent/csv_agent/agent.py @@ -0,0 +1,75 @@ +from pathlib import Path + +import pandas as pd +from langchain.agents import AgentExecutor, OpenAIFunctionsAgent +from langchain.chat_models import ChatOpenAI +from langchain.embeddings import OpenAIEmbeddings +from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain.tools.retriever import create_retriever_tool +from langchain.vectorstores import FAISS +from langchain_experimental.tools import PythonAstREPLTool +from pydantic import BaseModel, Field + +MAIN_DIR = Path(__file__).parents[1] + +pd.set_option("display.max_rows", 20) +pd.set_option("display.max_columns", 20) + +embedding_model = OpenAIEmbeddings() +vectorstore = FAISS.load_local(MAIN_DIR / "titanic_data", embedding_model) +retriever_tool = create_retriever_tool( + vectorstore.as_retriever(), "person_name_search", "Search for a person by name" +) + + +TEMPLATE = """You are working with a pandas dataframe in Python. The name of the dataframe is `df`. +It is important to understand the attributes of the dataframe before working with it. This is the result of running `df.head().to_markdown()` + + +{dhead} + + +You are not meant to use only these rows to answer questions - they are meant as a way of telling you about the shape and schema of the dataframe. +You also do not have use only the information here to answer questions - you can run intermediate queries to do exporatory data analysis to give you more information as needed. + +You have a tool called `person_name_search` through which you can lookup a person by name and find the records corresponding to people with similar name as the query. +You should only really use this if your search term contains a persons name. Otherwise, try to solve it with code. + +For example: + +How old is Jane? +Use `person_name_search` since you can use the query `Jane` + +Who has id 320 +Use `python_repl` since even though the question is about a person, you don't know their name so you can't include it. +""" # noqa: E501 + + +class PythonInputs(BaseModel): + query: str = Field(description="code snippet to run") + + +df = pd.read_csv("titanic.csv") +template = TEMPLATE.format(dhead=df.head().to_markdown()) + +prompt = ChatPromptTemplate.from_messages( + [ + ("system", template), + MessagesPlaceholder(variable_name="agent_scratchpad"), + ("human", "{input}"), + ] +) + +repl = PythonAstREPLTool( + locals={"df": df}, + name="python_repl", + description="Runs code and returns the output of the final line", + args_schema=PythonInputs, +) +tools = [repl, retriever_tool] +agent = OpenAIFunctionsAgent( + llm=ChatOpenAI(temperature=0, model="gpt-4"), prompt=prompt, tools=tools +) +agent_executor = AgentExecutor( + agent=agent, tools=tools, max_iterations=5, early_stopping_method="generate" +) diff --git a/templates/csv-agent/ingest.py b/templates/csv-agent/ingest.py new file mode 100644 index 00000000000..7da5942f172 --- /dev/null +++ b/templates/csv-agent/ingest.py @@ -0,0 +1,12 @@ +from langchain.document_loaders import CSVLoader +from langchain.indexes import VectorstoreIndexCreator +from langchain.vectorstores import FAISS + +loader = CSVLoader('/Users/harrisonchase/Downloads/titanic.csv') + +docs = loader.load() +index_creator = VectorstoreIndexCreator(vectorstore_cls=FAISS) + +index = index_creator.from_documents(docs) + +index.vectorstore.save_local("titanic_data") diff --git a/templates/csv-agent/main.py b/templates/csv-agent/main.py new file mode 100644 index 00000000000..8814b924787 --- /dev/null +++ b/templates/csv-agent/main.py @@ -0,0 +1,5 @@ +from csv_agent.agent import agent_executor + +if __name__ == "__main__": + question = "who was in cabin c28?" + print(agent_executor.invoke({"input": 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Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S +890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C +891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q diff --git a/templates/csv-agent/titanic_data/index.faiss b/templates/csv-agent/titanic_data/index.faiss new file mode 100644 index 00000000000..ef979cf6620 Binary files /dev/null and b/templates/csv-agent/titanic_data/index.faiss differ diff --git a/templates/elastic-query-generator/README.md b/templates/elastic-query-generator/README.md new file mode 100644 index 00000000000..de5fedcc4b5 --- /dev/null +++ b/templates/elastic-query-generator/README.md @@ -0,0 +1,28 @@ +# elastic-query-generator + +We can use LLMs to interact with Elasticsearch analytics databases in natural language. + +This chain builds search queries via the Elasticsearch DSL API (filters and aggregations). + +The Elasticsearch client must have permissions for index listing, mapping description and search queries. + + + +## Setup + +## Installing Elasticsearch + +There are a number of ways to run Elasticsearch. + +### Elastic Cloud + +Create a free trial account on [Elastic Cloud](https://cloud.elastic.co/registration?utm_source=langchain&utm_content=langserve). + +With a deployment, update the connection string. + +Password and connection (elasticsearch url) can be found on the deployment console. + +```bash +> export ELASTIC_SEARCH_SERVER="https://elastic:@" + +If you want to populate the DB with some example info, you can run `python ingest.py`. diff --git a/templates/elastic-query-generator/elastic_query_generator/__init__.py b/templates/elastic-query-generator/elastic_query_generator/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/elastic-query-generator/elastic_query_generator/chain.py b/templates/elastic-query-generator/elastic_query_generator/chain.py new file mode 100644 index 00000000000..aa4575ed027 --- /dev/null +++ b/templates/elastic-query-generator/elastic_query_generator/chain.py @@ -0,0 +1,26 @@ +import os +from pathlib import Path + +from elasticsearch import Elasticsearch +from langchain.chat_models import ChatOpenAI +from langchain.output_parsers.json import SimpleJsonOutputParser + +from .elastic_index_info import get_indices_infos +from .prompts import DSL_PROMPT + +es_host = os.environ["ELASTIC_SEARCH_SERVER"] +es_password = os.environ["ELASTIC_PASSWORD"] + +db = Elasticsearch( + es_host, + http_auth=('elastic', es_password), + ca_certs=Path(__file__).parents[1] / 'http_ca.crt' # Replace with your actual path +) + +_model = ChatOpenAI(temperature=0, model="gpt-4") + +chain = { + "input": lambda x: x["input"], + "indices_info": lambda _: get_indices_infos(db), + "top_k": lambda x: x.get("top_k", 5), +} | DSL_PROMPT | _model | SimpleJsonOutputParser() diff --git a/templates/elastic-query-generator/elastic_query_generator/elastic_index_info.py b/templates/elastic-query-generator/elastic_query_generator/elastic_index_info.py new file mode 100644 index 00000000000..db328389eac --- /dev/null +++ b/templates/elastic-query-generator/elastic_query_generator/elastic_index_info.py @@ -0,0 +1,33 @@ +from typing import List + + +def _list_indices(database, include_indices=None, ignore_indices=None) -> List[str]: + all_indices = [ + index["index"] for index in database.cat.indices(format="json") + ] + + if include_indices: + all_indices = [i for i in all_indices if i in include_indices] + if ignore_indices: + all_indices = [i for i in all_indices if i not in ignore_indices] + + return all_indices + +def get_indices_infos(database, sample_documents_in_index_info=5) -> str: + indices = _list_indices(database) + mappings = database.indices.get_mapping(index=",".join(indices)) + if sample_documents_in_index_info > 0: + for k, v in mappings.items(): + hits = database.search( + index=k, + query={"match_all": {}}, + size=sample_documents_in_index_info, + )["hits"]["hits"] + hits = [str(hit["_source"]) for hit in hits] + mappings[k]["mappings"] = str(v) + "\n\n/*\n" + "\n".join(hits) + "\n*/" + return "\n\n".join( + [ + "Mapping for index {}:\n{}".format(index, mappings[index]["mappings"]) + for index in mappings + ] + ) \ No newline at end of file diff --git a/templates/elastic-query-generator/elastic_query_generator/prompts.py b/templates/elastic-query-generator/elastic_query_generator/prompts.py new file mode 100644 index 00000000000..861a5874593 --- /dev/null +++ b/templates/elastic-query-generator/elastic_query_generator/prompts.py @@ -0,0 +1,21 @@ +from langchain.prompts.prompt import PromptTemplate + +PROMPT_SUFFIX = """Only use the following Elasticsearch indices: +{indices_info} + +Question: {input} +ESQuery:""" + +DEFAULT_DSL_TEMPLATE = """Given an input question, create a syntactically correct Elasticsearch query to run. Always limit your query to at most {top_k} results, unless the user specifies in their question a specific number of examples they wish to obtain, or unless its implied that they want to see all. You can order the results by a relevant column to return the most interesting examples in the database. + +Unless told to do not query for all the columns from a specific index, only ask for a the few relevant columns given the question. + +Pay attention to use only the column names that you can see in the mapping description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which index. Return the query as valid json. + +Use the following format: + +Question: Question here +ESQuery: Elasticsearch Query formatted as json +""" # noqa: E501 + +DSL_PROMPT = PromptTemplate.from_template(DEFAULT_DSL_TEMPLATE + PROMPT_SUFFIX) diff --git a/templates/elastic-query-generator/ingest.py b/templates/elastic-query-generator/ingest.py new file mode 100644 index 00000000000..757a2c6a1ba --- /dev/null +++ b/templates/elastic-query-generator/ingest.py @@ -0,0 +1,22 @@ +import os + +from elasticsearch import Elasticsearch + +es_host = os.environ["ELASTIC_SEARCH_SERVER"] +es_password = os.environ["ELASTIC_PASSWORD"] + +db = Elasticsearch( + es_host, + http_auth=('elastic', es_password), + ca_certs='http_ca.crt' # Replace with your actual path +) + +customers = [ + {"firstname": "Jennifer", "lastname": "Walters"}, + {"firstname": "Monica","lastname":"Rambeau"}, + {"firstname": "Carol","lastname":"Danvers"}, + {"firstname": "Wanda","lastname":"Maximoff"}, + {"firstname": "Jennifer","lastname":"Takeda"}, +] +for i, customer in enumerate(customers): + db.create(index="customers", document=customer, id=i) diff --git a/templates/elastic-query-generator/main.py b/templates/elastic-query-generator/main.py new file mode 100644 index 00000000000..4f848b6e88a --- /dev/null +++ b/templates/elastic-query-generator/main.py @@ -0,0 +1,4 @@ +from elastic_query_generator.chain import chain + +if __name__ == "__main__": + print(chain.invoke({"input": "how many customers named Carol"})) diff --git a/templates/elastic-query-generator/poetry.lock b/templates/elastic-query-generator/poetry.lock new file mode 100644 index 00000000000..3affe0a7c90 --- /dev/null +++ b/templates/elastic-query-generator/poetry.lock @@ -0,0 +1,1246 @@ +# This file is automatically @generated by Poetry and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +category = "main" +optional = false 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">=3.8.1,<4.0" +langchain = ">=0.0.313" +elasticsearch = "^8.10.1" + +[tool.langserve] +export_module = "elastic_query_generator.chain" +export_attr = "chain" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/elastic-query-generator/tests/__init__.py b/templates/elastic-query-generator/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/extraction-openai-functions/LICENSE b/templates/extraction-openai-functions/LICENSE new file mode 100644 index 00000000000..578fc414ad4 --- /dev/null +++ b/templates/extraction-openai-functions/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 pingpong-templates + +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/extraction-openai-functions/README.md b/templates/extraction-openai-functions/README.md new file mode 100644 index 00000000000..d98c465c6d4 --- /dev/null +++ b/templates/extraction-openai-functions/README.md @@ -0,0 +1,41 @@ +# Extraction with OpenAI Function Calling + +This template shows how to do extraction of structured data from unstructured data, using OpenAI [function calling](https://python.langchain.com/docs/modules/chains/how_to/openai_functions). + +Specify the information you want to extract in `chain.py` + +By default, it will extract the title and author of papers. + +## LLM + +This template will use `OpenAI` by default. + +Be sure that `OPENAI_API_KEY` is set in your environment. + +## Adding the template + +Install the langchain package +``` +pip install -e packages/extraction_openai_functions +``` + +Edit app/server.py to add that package to the routes +``` +from fastapi import FastAPI +from langserve import add_routes +from extraction_openai_functions.chain import chain + +app = FastAPI() +add_routes(app, chain) +``` + +Run the app +``` +python app/server.py +``` + +You can use this template in the Playground: + +http://127.0.0.1:8000/extraction-openai-functions/playground/ + +Also, see Jupyter notebook `openai_functions` for various other ways to connect to the template. \ No newline at end of file diff --git a/templates/extraction-openai-functions/extraction_openai_functions/__init__.py b/templates/extraction-openai-functions/extraction_openai_functions/__init__.py new file mode 100644 index 00000000000..d2350f5de2c --- /dev/null +++ b/templates/extraction-openai-functions/extraction_openai_functions/__init__.py @@ -0,0 +1,3 @@ +from extraction_openai_functions.chain import chain + +__all__ = ["chain"] diff --git a/templates/extraction-openai-functions/extraction_openai_functions/chain.py b/templates/extraction-openai-functions/extraction_openai_functions/chain.py new file mode 100644 index 00000000000..ccf3c876848 --- /dev/null +++ b/templates/extraction-openai-functions/extraction_openai_functions/chain.py @@ -0,0 +1,47 @@ +import json +from typing import List, Optional + +from langchain.chat_models import ChatOpenAI +from langchain.prompts import ChatPromptTemplate +from langchain.pydantic_v1 import BaseModel +from langchain.utils.openai_functions import convert_pydantic_to_openai_function + +template = """A article will be passed to you. Extract from it all papers that are mentioned by this article. + +Do not extract the name of the article itself. If no papers are mentioned that's fine - you don't need to extract any! Just return an empty list. + +Do not make up or guess ANY extra information. Only extract what exactly is in the text.""" # noqa: E501 + +prompt = ChatPromptTemplate.from_messages([("system", template), ("human", "{input}")]) + + +# Function output schema +class Paper(BaseModel): + """Information about papers mentioned.""" + + title: str + author: Optional[str] + + +class Info(BaseModel): + """Information to extract""" + + papers: List[Paper] + + +# Function definition +model = ChatOpenAI() +function = [convert_pydantic_to_openai_function(Info)] +chain = ( + prompt + | model.bind(functions=function, function_call={"name": "Info"}) + | ( + lambda x: json.loads(x.additional_kwargs["function_call"]["arguments"])[ + "papers" + ] + ) +) + +# chain = prompt | model.bind( +# functions=function, function_call={"name": "Info"} +# ) | JsonKeyOutputFunctionsParser(key_name="papers") diff --git a/templates/extraction-openai-functions/openai_functions.ipynb b/templates/extraction-openai-functions/openai_functions.ipynb new file mode 100644 index 00000000000..2f172569889 --- /dev/null +++ b/templates/extraction-openai-functions/openai_functions.ipynb @@ -0,0 +1,116 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "16f2c32e", + "metadata": {}, + "source": [ + "## Document Loading\n", + "\n", + "Load a blog post on agents." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c9fadce0", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.document_loaders import WebBaseLoader\n", + "loader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")\n", + "text = loader.load()" + ] + }, + { + "cell_type": "markdown", + "id": "4086be03", + "metadata": {}, + "source": [ + "## Run Template\n", + "\n", + "\n", + "As shown in the README, add template and start server:\n", + "```\n", + "langchain serve add openai-functions\n", + "langchain start\n", + "```\n", + "\n", + "We can now look at the endpoints:\n", + "\n", + "http://127.0.0.1:8000/docs#\n", + "\n", + "And specifically at our loaded template:\n", + "\n", + "http://127.0.0.1:8000/docs#/default/invoke_openai_functions_invoke_post\n", + " \n", + "We can also use remote runnable to call it." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ed507784", + "metadata": {}, + "outputs": [], + "source": [ + "from langserve.client import RemoteRunnable\n", + "oai_function = RemoteRunnable('http://localhost:8000/openai-functions')" + ] + }, + { + "cell_type": "markdown", + "id": "68046695", + "metadata": {}, + "source": [ + "The function call will perform tagging:\n", + "\n", + "* summarize\n", + "* provide keywords\n", + "* provide language" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6dace748", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content='', additional_kwargs={'function_call': {'name': 'Overview', 'arguments': '{\\n \"summary\": \"This article discusses the concept of building agents with LLM (large language model) as their core controller. It explores the potentiality of LLM as a general problem solver and describes the key components of an LLM-powered autonomous agent system, including planning, memory, and tool use. The article also presents case studies and challenges related to building LLM-powered agents.\",\\n \"language\": \"English\",\\n \"keywords\": \"LLM, autonomous agents, planning, memory, tool use, case studies, challenges\"\\n}'}})" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oai_function.invoke(text[0].page_content[0:1500])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "langserve", + "language": "python", + "name": "langserve" + }, + "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/templates/extraction-openai-functions/poetry.lock b/templates/extraction-openai-functions/poetry.lock new file mode 100644 index 00000000000..9f0d0140142 --- 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+[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain = ">=0.0.323" +openai = ">=0.27.9" + +[tool.langserve] +export_module = "exctraction_openai_functions" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/extraction-openai-functions/tests/__init__.py b/templates/extraction-openai-functions/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/hyde/LICENSE b/templates/hyde/LICENSE new file mode 100644 index 00000000000..426b6509034 --- /dev/null +++ b/templates/hyde/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/hyde/README.md b/templates/hyde/README.md new file mode 100644 index 00000000000..81097a7d7f0 --- /dev/null +++ b/templates/hyde/README.md @@ -0,0 +1,9 @@ +# HyDE + +Hypothetical Document Embeddings (HyDE) are a method to improve retrieval. +To do this, a hypothetical document is generated for an incoming query. +That document is then embedded, and that embedding is used to look up real documents similar to that hypothetical document. +The idea behind this is that the hypothetical document may be closer in the embedding space than the query. +For a more detailed description, read the full paper [here](https://arxiv.org/abs/2212.10496). + +For this example, we use a simple RAG architecture, although you can easily use this technique in other more complicated architectures. diff --git a/templates/hyde/hyde/__init__.py b/templates/hyde/hyde/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/hyde/hyde/chain.py b/templates/hyde/hyde/chain.py new file mode 100644 index 00000000000..915c994d6c3 --- /dev/null +++ b/templates/hyde/hyde/chain.py @@ -0,0 +1,67 @@ +from langchain.chat_models import ChatOpenAI +from langchain.embeddings import OpenAIEmbeddings +from langchain.prompts import ChatPromptTemplate +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnableParallel, RunnablePassthrough +from langchain.vectorstores import Chroma + +from hyde.prompts import hyde_prompt + +# Example for document loading (from url), splitting, and creating vectostore + +""" +# Load +from langchain.document_loaders import WebBaseLoader +loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") +data = loader.load() + +# Split +from langchain.text_splitter import RecursiveCharacterTextSplitter +text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) +all_splits = text_splitter.split_documents(data) + +# Add to vectorDB +vectorstore = Chroma.from_documents(documents=all_splits, + collection_name="rag-chroma", + embedding=OpenAIEmbeddings(), + ) +retriever = vectorstore.as_retriever() +""" + +# Embed a single document as a test +vectorstore = Chroma.from_texts( + ["harrison worked at kensho"], + collection_name="rag-chroma", + embedding=OpenAIEmbeddings(), +) +retriever = vectorstore.as_retriever() + +# RAG prompt +template = """Answer the question based only on the following context: +{context} + +Question: {question} +""" +prompt = ChatPromptTemplate.from_template(template) + +# LLM +model = ChatOpenAI() + +# RAG chain +chain = ( + RunnableParallel( + { + # Configure the input, pass it the prompt, pass that to the model, + # and then the result to the retriever + "context": {"input": RunnablePassthrough()} + | hyde_prompt + | model + | StrOutputParser() + | retriever, + "question": RunnablePassthrough(), + } + ) + | prompt + | model + | StrOutputParser() +) diff --git a/templates/hyde/hyde/prompts.py b/templates/hyde/hyde/prompts.py new file mode 100644 index 00000000000..b6d2f0881b0 --- /dev/null +++ b/templates/hyde/hyde/prompts.py @@ -0,0 +1,19 @@ +from langchain.prompts.prompt import PromptTemplate + +# There are a few different templates to choose from +# These are just different ways to generate hypothetical documents +web_search_template = """Please write a passage to answer the question +Question: {input} +Passage:""" +sci_fact_template = """Please write a scientific paper passage to support/refute the claim +Claim: {input} +Passage:""" # noqa: E501 +fiqa_template = """Please write a financial article passage to answer the question +Question: {input} +Passage:""" +trec_news_template = """Please write a news passage about the topic. +Topic: {input} +Passage:""" + +# For the sake of this example we will use the web search template +hyde_prompt = PromptTemplate.from_template(web_search_template) diff --git a/templates/hyde/pyproject.toml b/templates/hyde/pyproject.toml new file mode 100644 index 00000000000..a678d8fe1f4 --- /dev/null +++ b/templates/hyde/pyproject.toml @@ -0,0 +1,32 @@ +[tool.poetry] +name = "hyde" +version = "0.0.1" +description = "" +authors = [] +readme = "README.md" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain = ">=0.0.313, <0.1" +openai = "^0.28.1" + +[tool.poetry.group.dev.dependencies] +poethepoet = "^0.24.1" +langchain-cli = ">=0.0.4" +fastapi = "^0.104.0" +sse-starlette = "^1.6.5" + +[tool.langserve] +export_module = "hyde.chain" +export_attr = "chain" + +[tool.poe.tasks.start] +cmd="uvicorn langchain_cli.dev_scripts:create_demo_server --reload --port $port --host $host" +args = [ + {name = "port", help = "port to run on", default = "8000"}, + {name = "host", help = "host to run on", default = "127.0.0.1"} +] + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/hyde/tests/__init__.py b/templates/hyde/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/neo4j-cypher-ft/README.md b/templates/neo4j-cypher-ft/README.md new file mode 100644 index 00000000000..cfbf9a1227b --- /dev/null +++ b/templates/neo4j-cypher-ft/README.md @@ -0,0 +1,22 @@ +# Neo4j Knowledge Graph: Enhanced mapping from text to database using a full-text index + +This template allows you to chat with Neo4j graph database in natural language, using an OpenAI LLM. +The addition of the full-text index ensures efficient mapping of values from text to database for more precise Cypher statement generation. + +## Set up Environment + +You need to define the following environment variables + +``` +OPENAI_API_KEY= +NEO4J_URI= +NEO4J_USERNAME= +NEO4J_PASSWORD= +``` + +## Installation + +```bash +# from inside your LangServe instance +poe add neo4j-cypher +``` diff --git a/templates/neo4j-cypher-ft/ingest.py b/templates/neo4j-cypher-ft/ingest.py new file mode 100644 index 00000000000..98f43aff40f --- /dev/null +++ b/templates/neo4j-cypher-ft/ingest.py @@ -0,0 +1,21 @@ +from langchain.graphs import Neo4jGraph + +graph = Neo4jGraph() + +# Import sample data +graph.query( + """ +MERGE (m:Movie {name:"Top Gun"}) +WITH m +UNWIND ["Tom Cruise", "Val Kilmer", "Anthony Edwards", "Meg Ryan"] AS actor +MERGE (a:Person {name:actor}) +MERGE (a)-[:ACTED_IN]->(m) +""" +) + +# Create full text index for entity matching +# on Person and Movie nodes +graph.query( + "CREATE FULLTEXT INDEX entity IF NOT EXISTS" + " FOR (m:Movie|Person) ON EACH [m.title, m.name]" +) diff --git a/templates/neo4j-cypher-ft/main.py b/templates/neo4j-cypher-ft/main.py new file mode 100644 index 00000000000..490d4542536 --- /dev/null +++ b/templates/neo4j-cypher-ft/main.py @@ -0,0 +1,5 @@ +from neo4j_cypher_ft.chain import chain + +if __name__ == "__main__": + original_query = "Did tom cruis act in top gun?" + print(chain.invoke({"question": original_query})) diff --git a/templates/neo4j-cypher-ft/neo4j_cypher_ft/__init__.py b/templates/neo4j-cypher-ft/neo4j_cypher_ft/__init__.py new file mode 100644 index 00000000000..56cf88fe137 --- /dev/null +++ b/templates/neo4j-cypher-ft/neo4j_cypher_ft/__init__.py @@ -0,0 +1,3 @@ +from neo4j_cypher_ft.chain import chain + +__all__ = ["chain"] diff --git a/templates/neo4j-cypher-ft/neo4j_cypher_ft/chain.py b/templates/neo4j-cypher-ft/neo4j_cypher_ft/chain.py new file mode 100644 index 00000000000..4868140b916 --- /dev/null +++ b/templates/neo4j-cypher-ft/neo4j_cypher_ft/chain.py @@ -0,0 +1,129 @@ +from typing import List, Optional + +from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema +from langchain.chains.openai_functions import create_structured_output_chain +from langchain.chat_models import ChatOpenAI +from langchain.graphs import Neo4jGraph +from langchain.prompts import ChatPromptTemplate +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough + +try: + from pydantic.v1.main import BaseModel, Field +except ImportError: + from pydantic.main import BaseModel, Field + +# Connection to Neo4j +graph = Neo4jGraph() + +# Cypher validation tool for relationship directions +corrector_schema = [ + Schema(el["start"], el["type"], el["end"]) + for el in graph.structured_schema.get("relationships") +] +cypher_validation = CypherQueryCorrector(corrector_schema) + +# LLMs +cypher_llm = ChatOpenAI(model_name="gpt-4", temperature=0.0) +qa_llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.0) + + +# Extract entities from text +class Entities(BaseModel): + """Identifying information about entities.""" + + names: List[str] = Field( + ..., + description="All the person, organization, or business entities that " + "appear in the text", + ) + + +prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + "You are extracting organization and person entities from the text.", + ), + ( + "human", + "Use the given format to extract information from the following " + "input: {question}", + ), + ] +) + + +# Fulltext index query +def map_to_database(entities: Entities) -> Optional[str]: + result = "" + for entity in entities.names: + response = graph.query( + "CALL db.index.fulltext.queryNodes('entity', $entity + '*', {limit:1})" + " YIELD node,score RETURN node.name AS result", + {"entity": entity}, + ) + try: + result += f"{entity} maps to {response[0]['result']} in database\n" + except IndexError: + pass + return result + + +entity_chain = create_structured_output_chain(Entities, qa_llm, prompt) + +# Generate Cypher statement based on natural language input +cypher_template = """Based on the Neo4j graph schema below, write a Cypher query that would answer the user's question: +{schema} +Entities in the question map to the following database values: +{entities_list} +Question: {question} +Cypher query:""" # noqa: E501 + +cypher_prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + "Given an input question, convert it to a Cypher query. No pre-amble.", + ), + ("human", cypher_template), + ] +) + +cypher_response = ( + RunnablePassthrough.assign(names=entity_chain) + | RunnablePassthrough.assign( + entities_list=lambda x: map_to_database(x["names"]["function"]), + schema=lambda _: graph.get_schema, + ) + | cypher_prompt + | cypher_llm.bind(stop=["\nCypherResult:"]) + | StrOutputParser() +) + +# Generate natural language response based on database results +response_template = """Based on the the question, Cypher query, and Cypher response, write a natural language response: +Question: {question} +Cypher query: {query} +Cypher Response: {response}""" # noqa: E501 + +response_prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + "Given an input question and Cypher response, convert it to a natural" + " language answer. 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b/templates/neo4j-cypher-ft/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/neo4j-cypher/README.md b/templates/neo4j-cypher/README.md new file mode 100644 index 00000000000..731a25a69f4 --- /dev/null +++ b/templates/neo4j-cypher/README.md @@ -0,0 +1,21 @@ +# Neo4j Knowledge Graph with OpenAI LLMs + +This template allows you to chat with Neo4j graph database in natural language, using an OpenAI LLM. + +## Set up Environment + +You need to define the following environment variables + +``` +OPENAI_API_KEY= +NEO4J_URI= +NEO4J_USERNAME= +NEO4J_PASSWORD= +``` + +## Installation + +```bash +# from inside your LangServe instance +poe add neo4j-cypher +``` diff --git a/templates/neo4j-cypher/ingest.py b/templates/neo4j-cypher/ingest.py new file mode 100644 index 00000000000..61adaa5135d --- /dev/null +++ b/templates/neo4j-cypher/ingest.py @@ -0,0 +1,13 @@ +from langchain.graphs import Neo4jGraph + +graph = Neo4jGraph() + +graph.query( + """ +MERGE (m:Movie {name:"Top Gun"}) +WITH m +UNWIND ["Tom Cruise", "Val Kilmer", "Anthony Edwards", "Meg Ryan"] AS actor +MERGE (a:Actor {name:actor}) +MERGE (a)-[:ACTED_IN]->(m) +""" +) diff --git a/templates/neo4j-cypher/main.py b/templates/neo4j-cypher/main.py new file mode 100644 index 00000000000..611cbe2aff9 --- /dev/null +++ b/templates/neo4j-cypher/main.py @@ -0,0 +1,5 @@ +from neo4j_cypher.chain import chain + +if __name__ == "__main__": + original_query = "Who played in Top Gun?" + print(chain.invoke({"question": original_query})) diff --git a/templates/neo4j-cypher/neo4j_cypher/__init__.py b/templates/neo4j-cypher/neo4j_cypher/__init__.py new file mode 100644 index 00000000000..c18e08325b6 --- /dev/null +++ b/templates/neo4j-cypher/neo4j_cypher/__init__.py @@ -0,0 +1,3 @@ +from neo4j_cypher.chain import chain + +__all__ = ["chain"] diff --git a/templates/neo4j-cypher/neo4j_cypher/chain.py b/templates/neo4j-cypher/neo4j_cypher/chain.py new file mode 100644 index 00000000000..730b2d2947d --- /dev/null +++ b/templates/neo4j-cypher/neo4j_cypher/chain.py @@ -0,0 +1,73 @@ +from langchain.chains.graph_qa.cypher_utils import CypherQueryCorrector, Schema +from langchain.chat_models import ChatOpenAI +from langchain.graphs import Neo4jGraph +from langchain.prompts import ChatPromptTemplate +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough + +# Connection to Neo4j +graph = Neo4jGraph() + +# Cypher validation tool for relationship directions +corrector_schema = [ + Schema(el["start"], el["type"], el["end"]) + for el in graph.structured_schema.get("relationships") +] +cypher_validation = CypherQueryCorrector(corrector_schema) + +# LLMs +cypher_llm = ChatOpenAI(model_name="gpt-4", temperature=0.0) +qa_llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.0) + +# Generate Cypher statement based on natural language input +cypher_template = """Based on the Neo4j graph schema below, write a Cypher query that would answer the user's question: +{schema} + +Question: {question} +Cypher query:""" # noqa: E501 + +cypher_prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + "Given an input question, convert it to a Cypher query. No pre-amble.", + ), + ("human", cypher_template), + ] +) + +cypher_response = ( + RunnablePassthrough.assign( + schema=lambda _: graph.get_schema, + ) + | cypher_prompt + | cypher_llm.bind(stop=["\nCypherResult:"]) + | StrOutputParser() +) + +# Generate natural language response based on database results +response_template = """Based on the the question, Cypher query, and Cypher response, write a natural language response: +Question: {question} +Cypher query: {query} +Cypher Response: {response}""" # noqa: E501 + +response_prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + "Given an input question and Cypher response, convert it to a " + "natural language answer. No pre-amble.", + ), + ("human", response_template), + ] +) + +chain = ( + RunnablePassthrough.assign(query=cypher_response) + | RunnablePassthrough.assign( + response=lambda x: graph.query(cypher_validation(x["query"])), + ) + | response_prompt + | qa_llm + | StrOutputParser() +) diff --git a/templates/neo4j-cypher/poetry.lock b/templates/neo4j-cypher/poetry.lock new file mode 100644 index 00000000000..496f0a491ba --- /dev/null +++ b/templates/neo4j-cypher/poetry.lock @@ -0,0 +1,1296 @@ +# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +category = "main" +optional = false +python-versions = ">=3.6" +files = [ + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:41d55fc043954cddbbd82503d9cc3f4814a40bcef30b3569bc7b5e34130718c1"}, + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = 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"sha256:04ab9d4b9f587c06d801c2abfe9317b77cdf996c65a90d5e84ecc45010823571"}, +] + +[package.dependencies] +idna = ">=2.0" +multidict = ">=4.0" + +[metadata] +lock-version = "2.0" +python-versions = ">=3.8.1,<4.0" +content-hash = "9c1cfde1aa2cb81abe38b33c2109c63dfdd8d2e5680a7b183517de1ef1de6cd3" diff --git a/templates/neo4j-cypher/pyproject.toml b/templates/neo4j-cypher/pyproject.toml new file mode 100644 index 00000000000..570f4088a0f --- /dev/null +++ b/templates/neo4j-cypher/pyproject.toml @@ -0,0 +1,20 @@ +[tool.poetry] +name = "neo4j_cypher" +version = "0.1.0" +description = "" +authors = ["Tomaz Bratanic "] +readme = "README.md" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain = ">=0.0.320" +neo4j = ">5.12" +openai = "^0.28.1" + +[tool.langserve] +export_module = "neo4j.cypher" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/neo4j-cypher/tests/__init__.py b/templates/neo4j-cypher/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/neo4j-generation/README.md b/templates/neo4j-generation/README.md new file mode 100644 index 00000000000..9689e11ddf4 --- /dev/null +++ b/templates/neo4j-generation/README.md @@ -0,0 +1,25 @@ +# Graph Generation Chain for Neo4j Knowledge Graph + +Harness the power of natural language understanding and convert plain text into structured knowledge graphs with the Graph Generation Chain. +This system integrates with the Neo4j graph database using OpenAI's LLM. +By leveraging OpenAI Functions capabilities, the Graph Generation Chain efficiently extracts graph structure from text. + +## Set up Environment + +You need to define the following environment variables + +``` +OPENAI_API_KEY= +NEO4J_URI= +NEO4J_USERNAME= +NEO4J_PASSWORD= +``` + +## Installation + +To get started with the Graph Generation Chain: + +```bash +# from inside your LangServe instance +poe add neo4j-generation +``` \ No newline at end of file diff --git a/templates/neo4j-generation/main.py b/templates/neo4j-generation/main.py new file mode 100644 index 00000000000..578a18013fe --- /dev/null +++ b/templates/neo4j-generation/main.py @@ -0,0 +1,13 @@ +from neo4j_generation.chain import chain + +if __name__ == "__main__": + text = "Harrison works at LangChain, which is located in San Francisco" + allowed_nodes = ["Person", "Organization", "Location"] + allowed_relationships = ["WORKS_AT", "LOCATED_IN"] + print( + chain( + text, + allowed_nodes=allowed_nodes, + allowed_relationships=allowed_relationships, + ) + ) diff --git a/templates/neo4j-generation/neo4j_generation/__init__.py b/templates/neo4j-generation/neo4j_generation/__init__.py new file mode 100644 index 00000000000..87f97d321fe --- /dev/null +++ b/templates/neo4j-generation/neo4j_generation/__init__.py @@ -0,0 +1,3 @@ +from neo4j_generation.chain import chain + +__all__ = ["chain"] diff --git a/templates/neo4j-generation/neo4j_generation/chain.py b/templates/neo4j-generation/neo4j_generation/chain.py new file mode 100644 index 00000000000..f980d252446 --- /dev/null +++ b/templates/neo4j-generation/neo4j_generation/chain.py @@ -0,0 +1,111 @@ +from typing import List, Optional + +from langchain.chains.openai_functions import ( + create_structured_output_chain, +) +from langchain.chat_models import ChatOpenAI +from langchain.graphs import Neo4jGraph +from langchain.graphs.graph_document import GraphDocument +from langchain.prompts import ChatPromptTemplate +from langchain.schema import Document + +from neo4j_generation.utils import ( + KnowledgeGraph, + map_to_base_node, + map_to_base_relationship, +) + +graph = Neo4jGraph() + + +llm = ChatOpenAI(model="gpt-3.5-turbo-16k", temperature=0) + + +def get_extraction_chain( + allowed_nodes: Optional[List[str]] = None, allowed_rels: Optional[List[str]] = None +): + """ + Constructs and returns an extraction chain for building a knowledge graph based on specified parameters. + + The function generates a chat prompt template, outlining the instructions for an LLM to extract information + and construct a knowledge graph. It primarily focuses on consistency in labeling nodes, handling numerical data + and dates, coreference resolution, and strict compliance with the provided rules. + + Parameters: + - allowed_nodes (Optional[List[str]]): A list of node labels that are allowed to be used in the knowledge graph. + If not provided, there won't be any specific restriction on node labels. + - allowed_rels (Optional[List[str]]): A list of relationship types that are allowed in the knowledge graph. + If not provided, there won't be any specific restriction on relationship types. + """ # noqa: E501 + prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + f"""# Knowledge Graph Instructions for GPT-4 +## 1. Overview +You are a top-tier algorithm designed for extracting information in structured formats to build a knowledge graph. +- **Nodes** represent entities and concepts. They're akin to Wikipedia nodes. +- The aim is to achieve simplicity and clarity in the knowledge graph, making it accessible for a vast audience. +## 2. Labeling Nodes +- **Consistency**: Ensure you use basic or elementary types for node labels. + - For example, when you identify an entity representing a person, always label it as **"person"**. Avoid using more specific terms like "mathematician" or "scientist". +- **Node IDs**: Never utilize integers as node IDs. Node IDs should be names or human-readable identifiers found in the text. +{'- **Allowed Node Labels:**' + ", ".join(allowed_nodes) if allowed_nodes else ""} +{'- **Allowed Relationship Types**:' + ", ".join(allowed_rels) if allowed_rels else ""} +## 3. Handling Numerical Data and Dates +- Numerical data, like age or other related information, should be incorporated as attributes or properties of the respective nodes. +- **No Separate Nodes for Dates/Numbers**: Do not create separate nodes for dates or numerical values. Always attach them as attributes or properties of nodes. +- **Property Format**: Properties must be in a key-value format. +- **Quotation Marks**: Never use escaped single or double quotes within property values. +- **Naming Convention**: Use camelCase for property keys, e.g., `birthDate`. +## 4. Coreference Resolution +- **Maintain Entity Consistency**: When extracting entities, it's vital to ensure consistency. +If an entity, such as "John Doe", is mentioned multiple times in the text but is referred to by different names or pronouns (e.g., "Joe", "he"), +always use the most complete identifier for that entity throughout the knowledge graph. In this example, use "John Doe" as the entity ID. +Remember, the knowledge graph should be coherent and easily understandable, so maintaining consistency in entity references is crucial. +## 5. Strict Compliance +Adhere to the rules strictly. Non-compliance will result in termination. + """, # noqa: E501 + ), + ( + "human", + "Use the given format to extract information from the " + "following input: {input}", + ), + ("human", "Tip: Make sure to answer in the correct format"), + ] + ) + return create_structured_output_chain(KnowledgeGraph, llm, prompt, verbose=False) + + +def chain( + text: str, + allowed_nodes: Optional[List[str]] = None, + allowed_relationships: Optional[List[str]] = None, +) -> str: + """ + Process the given text to extract graph data and constructs a graph document from the extracted information. + The constructed graph document is then added to the graph. + + Parameters: + - text (str): The input text from which the information will be extracted to construct the graph. + - allowed_nodes (Optional[List[str]]): A list of node labels to guide the extraction process. + If not provided, extraction won't have specific restriction on node labels. + - allowed_relationships (Optional[List[str]]): A list of relationship types to guide the extraction process. + If not provided, extraction won't have specific restriction on relationship types. + + Returns: + str: A confirmation message indicating the completion of the graph construction. + """ # noqa: E501 + # Extract graph data using OpenAI functions + extract_chain = get_extraction_chain(allowed_nodes, allowed_relationships) + data = extract_chain.run(text) + # Construct a graph document + graph_document = GraphDocument( + nodes=[map_to_base_node(node) for node in data.nodes], + relationships=[map_to_base_relationship(rel) for rel in data.rels], + source=Document(page_content=text), + ) + # Store information into a graph + graph.add_graph_documents([graph_document]) + return "Graph construction finished" diff --git a/templates/neo4j-generation/neo4j_generation/utils.py b/templates/neo4j-generation/neo4j_generation/utils.py new file mode 100644 index 00000000000..adaa9522889 --- /dev/null +++ b/templates/neo4j-generation/neo4j_generation/utils.py @@ -0,0 +1,76 @@ +from typing import List, Optional + +from langchain.graphs.graph_document import ( + Node as BaseNode, +) +from langchain.graphs.graph_document import ( + Relationship as BaseRelationship, +) +from langchain.pydantic_v1 import BaseModel, Field + + +class Property(BaseModel): + """A single property consisting of key and value""" + + key: str = Field(..., description="key") + value: str = Field(..., description="value") + + +class Node(BaseNode): + properties: Optional[List[Property]] = Field( + None, description="List of node properties" + ) + + +class Relationship(BaseRelationship): + properties: Optional[List[Property]] = Field( + None, description="List of relationship properties" + ) + + +class KnowledgeGraph(BaseModel): + """Generate a knowledge graph with entities and relationships.""" + + nodes: List[Node] = Field(..., description="List of nodes in the knowledge graph") + rels: List[Relationship] = Field( + ..., description="List of relationships in the knowledge graph" + ) + + +def format_property_key(s: str) -> str: + words = s.split() + if not words: + return s + first_word = words[0].lower() + capitalized_words = [word.capitalize() for word in words[1:]] + return "".join([first_word] + capitalized_words) + + +def props_to_dict(props) -> dict: + """Convert properties to a dictionary.""" + properties = {} + if not props: + return properties + for p in props: + properties[format_property_key(p.key)] = p.value + return properties + + +def map_to_base_node(node: Node) -> BaseNode: + """Map the KnowledgeGraph Node to the base Node.""" + properties = props_to_dict(node.properties) if node.properties else {} + # Add name property for better Cypher statement generation + properties["name"] = node.id.title() + return BaseNode( + id=node.id.title(), type=node.type.capitalize(), properties=properties + ) + + +def map_to_base_relationship(rel: Relationship) -> BaseRelationship: + """Map the 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a/templates/neo4j-generation/tests/__init__.py b/templates/neo4j-generation/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/neo4j-parent/README.md b/templates/neo4j-parent/README.md new file mode 100644 index 00000000000..032f96aa1c6 --- /dev/null +++ b/templates/neo4j-parent/README.md @@ -0,0 +1,21 @@ +# Parent Document Retriever with Neo4j Vector Index + +This template allows you to balance precise embeddings and context retention by splitting documents into smaller chunks and retrieving their original or larger text information. + +## Set up Environment + +You need to define the following environment variables + +``` +OPENAI_API_KEY= +NEO4J_URI= +NEO4J_USERNAME= +NEO4J_PASSWORD= +``` + +## Installation + +```bash +# from inside your LangServe instance +poe add neo4j-parent +``` diff --git a/templates/neo4j-parent/dune.txt b/templates/neo4j-parent/dune.txt new file mode 100644 index 00000000000..3417aaac3fc --- /dev/null +++ b/templates/neo4j-parent/dune.txt @@ -0,0 +1,228 @@ +Dune is a 1965 epic science fiction novel by American author Frank Herbert, originally published as two separate serials in Analog magazine. It tied with Roger Zelazny's This Immortal for the Hugo Award in 1966 and it won the inaugural Nebula Award for Best Novel. It is the first installment of the Dune Chronicles. It is one of the world's best-selling science fiction novels.Dune is set in the distant future in a feudal interstellar society in which various noble houses control planetary fiefs. It tells the story of young Paul Atreides, whose family accepts the stewardship of the planet Arrakis. While the planet is an inhospitable and sparsely populated desert wasteland, it is the only source of melange, or "spice", a drug that extends life and enhances mental abilities. Melange is also necessary for space navigation, which requires a kind of multidimensional awareness and foresight that only the drug provides. As melange can only be produced on Arrakis, control of the planet is a coveted and dangerous undertaking. The story explores the multilayered interactions of politics, religion, ecology, technology, and human emotion, as the factions of the empire confront each other in a struggle for the control of Arrakis and its spice. +Herbert wrote five sequels: Dune Messiah, Children of Dune, God Emperor of Dune, Heretics of Dune, and Chapterhouse: Dune. Following Herbert's death in 1986, his son Brian Herbert and author Kevin J. Anderson continued the series in over a dozen additional novels since 1999. +Adaptations of the novel to cinema have been notoriously difficult and complicated. In the 1970s, cult filmmaker Alejandro Jodorowsky attempted to make a film based on the novel. After three years of development, the project was canceled due to a constantly growing budget. In 1984, a film adaptation directed by David Lynch was released to mostly negative responses from critics and failure at the box office, although it later developed a cult following. The book was also adapted into the 2000 Sci-Fi Channel miniseries Frank Herbert's Dune and its 2003 sequel Frank Herbert's Children of Dune (the latter of which combines the events of Dune Messiah and Children of Dune). A second film adaptation directed by Denis Villeneuve was released on October 21, 2021, to positive reviews. It grossed $401 million worldwide and went on to be nominated for ten Academy Awards, winning six. Villeneuve's film covers roughly the first half of the original novel; a sequel, which will cover the remaining story, will be released in March 2024. +The series has also been used as the basis for several board, role-playing, and video games. +Since 2009, the names of planets from the Dune novels have been adopted for the real-life nomenclature of plains and other features on Saturn's moon Titan. + + +== Origins == +After his novel The Dragon in the Sea was published in 1957, Herbert traveled to Florence, Oregon, at the north end of the Oregon Dunes. Here, the United States Department of Agriculture was attempting to use poverty grasses to stabilize the sand dunes. Herbert claimed in a letter to his literary agent, Lurton Blassingame, that the moving dunes could "swallow whole cities, lakes, rivers, highways." Herbert's article on the dunes, "They Stopped the Moving Sands", was never completed (and only published decades later in The Road to Dune), but its research sparked Herbert's interest in ecology and deserts.Herbert further drew inspiration from Native American mentors like "Indian Henry" (as Herbert referred to the man to his son; likely a Henry Martin of the Hoh tribe) and Howard Hansen. Both Martin and Hansen grew up on the Quileute reservation near Herbert's hometown. According to historian Daniel Immerwahr, Hansen regularly shared his writing with Herbert. "White men are eating the earth," Hansen told Herbert in 1958, after sharing a piece on the effect of logging on the Quileute reservation. "They're gonna turn this whole planet into a wasteland, just like North Africa." The world could become a "big dune," Herbert responded in agreement.Herbert was also interested in the idea of the superhero mystique and messiahs. He believed that feudalism was a natural condition humans fell into, where some led and others gave up the responsibility of making decisions and just followed orders. He found that desert environments have historically given birth to several major religions with messianic impulses. He decided to join his interests together so he could play religious and ecological ideas against each other. In addition, he was influenced by the story of T. E. Lawrence and the "messianic overtones" in Lawrence's involvement in the Arab Revolt during World War I. In an early version of Dune, the hero was actually very similar to Lawrence of Arabia, but Herbert decided the plot was too straightforward and added more layers to his story.Herbert drew heavy inspiration also from Lesley Blanch's The Sabres of Paradise (1960), a narrative history recounting a mid-19th century conflict in the Caucasus between rugged Islamized caucasian tribes and the expansive Russian Empire. Language used on both sides of that conflict become terms in Herbert's worldโ€”chakobsa, a Caucasian hunting language, becomes a battle language of humans spread across the galaxy; kanly, a word for blood feud in the 19th century Caucasus, represents a feud between Dune's noble Houses; sietch and tabir are both words for camp borrowed from Ukrainian Cossacks (of the Ponticโ€“Caspian steppe).Herbert also borrowed some lines which Blanch stated were Caucasian proverbs. "To kill with the point lacked artistry", used by Blanch to describe the Caucasus peoples' love of swordsmanship, becomes in Dune "Killing with the tip lacks artistry", a piece of advice given to a young Paul during his training. "Polish comes from the city, wisdom from the hills", a Caucasian aphorism, turns into a desert expression: "Polish comes from the cities, wisdom from the desert". + +Another significant source of inspiration for Dune was Herbert's experiences with psilocybin and his hobby of cultivating mushrooms, according to mycologist Paul Stamets's account of meeting Herbert in the 1980s:Frank went on to tell me that much of the premise of Duneโ€”the magic spice (spores) that allowed the bending of space (tripping), the giant sand worms (maggots digesting mushrooms), the eyes of the Fremen (the cerulean blue of Psilocybe mushrooms), the mysticism of the female spiritual warriors, the Bene Gesserits (influenced by the tales of Maria Sabina and the sacred mushroom cults of Mexico)โ€”came from his perception of the fungal life cycle, and his imagination was stimulated through his experiences with the use of magic mushrooms.Herbert spent the next five years researching, writing, and revising. He published a three-part serial Dune World in the monthly Analog, from December 1963 to February 1964. The serial was accompanied by several illustrations that were not published again. After an interval of a year, he published the much slower-paced five-part The Prophet of Dune in the Januaryโ€“May 1965 issues. The first serial became "Book 1: Dune" in the final published Dune novel, and the second serial was divided into "Book Two: Muad'dib" and "Book Three: The Prophet". The serialized version was expanded, reworked, and submitted to more than twenty publishers, each of whom rejected it. The novel, Dune, was finally accepted and published in August 1965 by Chilton Books, a printing house better known for publishing auto repair manuals. Sterling Lanier, an editor at Chilton, had seen Herbert's manuscript and had urged his company to take a risk in publishing the book. However, the first printing, priced at $5.95 (equivalent to $55.25 in 2022), did not sell well and was poorly received by critics as being atypical of science fiction at the time. Chilton considered the publication of Dune a write-off and Lanier was fired. Over the course of time, the book gained critical acclaim, and its popularity spread by word-of-mouth to allow Herbert to start working full time on developing the sequels to Dune, elements of which were already written alongside Dune.At first Herbert considered using Mars as setting for his novel, but eventually decided to use a fictional planet instead. His son Brian said that "Readers would have too many preconceived ideas about that planet, due to the number of stories that had been written about it."Herbert dedicated his work "to the people whose labors go beyond ideas into the realm of 'real materials'โ€”to the dry-land ecologists, wherever they may be, in whatever time they work, this effort at prediction is dedicated in humility and admiration." + + +== Plot == +Duke Leto Atreides of House Atreides, ruler of the ocean planet Caladan, is assigned by the Padishah Emperor Shaddam IV to serve as fief ruler of the planet Arrakis. Although Arrakis is a harsh and inhospitable desert planet, it is of enormous importance because it is the only planetary source of melange, or the "spice", a unique and incredibly valuable substance that extends human youth, vitality and lifespan. It is also through the consumption of spice that Spacing Guild Navigators are able to effect safe interstellar travel. Shaddam, jealous of Duke Leto Atreides's rising popularity in the Landsraad, sees House Atreides as a potential future rival and threat, so conspires with House Harkonnen, the former stewards of Arrakis and the longstanding enemies of House Atreides, to destroy Leto and his family after their arrival. Leto is aware his assignment is a trap of some kind, but is compelled to obey the Emperor's orders anyway. +Leto's concubine Lady Jessica is an acolyte of the Bene Gesserit, an exclusively female group that pursues mysterious political aims and wields seemingly superhuman physical and mental abilities, such as the ability to control their bodies down to the cellular level, and also decide the sex of their children. Though Jessica was instructed by the Bene Gesserit to bear a daughter as part of their breeding program, out of love for Leto she bore a son, Paul. From a young age, Paul has been trained in warfare by Leto's aides, the elite soldiers Duncan Idaho and Gurney Halleck. Thufir Hawat, the Duke's Mentat (human computers, able to store vast amounts of data and perform advanced calculations on demand), has instructed Paul in the ways of political intrigue. Jessica has also trained her son in Bene Gesserit disciplines. +Paul's prophetic dreams interest Jessica's superior, the Reverend Mother Gaius Helen Mohiam, who subjects Paul to the deadly gom jabbar test. Holding a poisonous needle to his neck ready to strike should he be unable to resist the impulse to withdraw his hand from the nerve induction box, she tests Paul's self-control to overcome the extreme psychological pain he is being subjected to through the box. +Leto, Jessica, and Paul travel with their household to occupy Arrakeen, the capital on Arrakis formerly held by House Harkonnen. Leto learns of the dangers involved in harvesting the spice, which is protected by giant sandworms, and seeks to negotiate with the planet's native Fremen people, seeing them as a valuable ally rather than foes. Soon after the Atreides's arrival, Harkonnen forces attack, joined by the Emperor's ferocious Sardaukar troops in disguise. Leto is betrayed by his personal physician, the Suk doctor Wellington Yueh, who delivers a drugged Leto to the Baron Vladimir Harkonnen and his twisted Mentat, Piter De Vries. Yueh, however, arranges for Jessica and Paul to escape into the desert, where they are presumed dead by the Harkonnens. Yueh replaces one of Leto's teeth with a poison gas capsule, hoping Leto can kill the Baron during their encounter. The Baron narrowly avoids the gas due to his shield, which kills Leto, De Vries, and the others in the room. The Baron forces Hawat to take over De Vries's position by dosing him with a long-lasting, fatal poison and threatening to withhold the regular antidote doses unless he obeys. While he follows the Baron's orders, Hawat works secretly to undermine the Harkonnens. +Having fled into the desert, Paul is exposed to high concentrations of spice and has visions through which he realizes he has significant powers (as a result of the Bene Gesserit breeding scheme). He foresees potential futures in which he lives among the planet's native Fremen before leading them on a Holy Jihad across the known universe. +It is revealed Jessica is the daughter of Baron Harkonnen, a secret kept from her by the Bene Gesserit. After being captured by Fremen, Paul and Jessica are accepted into the Fremen community of Sietch Tabr, and teach the Fremen the Bene Gesserit fighting technique known as the "weirding way". Paul proves his manhood by killing a Fremen named Jamis in a ritualistic crysknife fight and chooses the Fremen name Muad'Dib, while Jessica opts to undergo a ritual to become a Reverend Mother by drinking the poisonous Water of Life. Pregnant with Leto's daughter, she inadvertently causes the unborn child, Alia, to become infused with the same powers in the womb. Paul takes a Fremen lover, Chani, and has a son with her, Leto II. +Two years pass and Paul's powerful prescience manifests, which confirms for the Fremen that he is their prophesied messiah, a legend planted by the Bene Gesserit's Missionaria Protectiva. Paul embraces his father's belief that the Fremen could be a powerful fighting force to take back Arrakis, but also sees that if he does not control them, their jihad could consume the entire universe. Word of the new Fremen leader reaches both Baron Harkonnen and the Emperor as spice production falls due to their increasingly destructive raids. The Baron encourages his brutish nephew Glossu Rabban to rule with an iron fist, hoping the contrast with his shrewder nephew Feyd-Rautha will make the latter popular among the people of Arrakis when he eventually replaces Rabban. The Emperor, suspecting the Baron of trying to create troops more powerful than the Sardaukar to seize power, sends spies to monitor activity on Arrakis. Hawat uses the opportunity to sow seeds of doubt in the Baron about the Emperor's true plans, putting further strain on their alliance. +Gurney, having survived the Harkonnen coup becomes a smuggler, reuniting with Paul and Jessica after a Fremen raid on his harvester. Believing Jessica to be the traitor, Gurney threatens to kill her, but is stopped by Paul. Paul did not foresee Gurney's attack, and concludes he must increase his prescience by drinking the Water of Life, which is traditionally fatal to males. Paul falls into unconsciousness for three weeks after drinking the poison, but when he wakes, he has clairvoyance across time and space: he is the Kwisatz Haderach, the ultimate goal of the Bene Gesserit breeding program. +Paul senses the Emperor and Baron are amassing fleets around Arrakis to quell the Fremen rebellion, and prepares the Fremen for a major offensive against the Harkonnen troops. The Emperor arrives with the Baron on Arrakis. The Emperor's troops seize a Fremen outpost, killing many including young Leto II, while Alia is captured and taken to the Emperor. Under cover of an electric storm, which shorts out the Emperor's troops' defensive shields, Paul and the Fremen, riding giant sandworms, assault the capital while Alia assassinates the Baron and escapes. The Fremen quickly defeat both the Harkonnen and Sardaukar troops. +Paul faces the Emperor, threatening to destroy spice production forever unless Shaddam abdicates the throne. Feyd-Rautha attempts to stop Paul by challenging him to a ritualistic knife fight, during which he attempts to cheat and kill Paul with a poison spur in his belt. Paul gains the upper hand and kills him. The Emperor reluctantly cedes the throne to Paul and promises his daughter Princess Irulan's hand in marriage. As Paul takes control of the Empire, he realizes that while he has achieved his goal, he is no longer able to stop the Fremen jihad, as their belief in him is too powerful to restrain. + + +== Characters == +House AtreidesPaul Atreides, the Duke's son, and main character of the novel +Duke Leto Atreides, head of House Atreides +Lady Jessica, Bene Gesserit and concubine of the Duke, mother of Paul and Alia +Alia Atreides, Paul's younger sister +Thufir Hawat, Mentat and Master of Assassins to House Atreides +Gurney Halleck, staunchly loyal troubadour warrior of the Atreides +Duncan Idaho, Swordmaster for House Atreides, graduate of the Ginaz School +Wellington Yueh, Suk doctor for the Atreides who is secretly working for House HarkonnenHouse HarkonnenBaron Vladimir Harkonnen, head of House Harkonnen +Piter De Vries, twisted Mentat +Feyd-Rautha, nephew and heir-presumptive of the Baron +Glossu "Beast" Rabban, also called Rabban Harkonnen, older nephew of the Baron +Iakin Nefud, Captain of the GuardHouse CorrinoShaddam IV, Padishah Emperor of the Known Universe (the Imperium) +Princess Irulan, Shaddam's eldest daughter and heir, also a historian +Count Fenring, the Emperor's closest friend, advisor, and "errand boy"Bene GesseritReverend Mother Gaius Helen Mohiam, Proctor Superior of the Bene Gesserit school and the Emperor's Truthsayer +Lady Margot Fenring, Bene Gesserit wife of Count FenringFremenThe Fremen, native inhabitants of Arrakis +Stilgar, Fremen leader of Sietch Tabr +Chani, Paul's Fremen concubine and a Sayyadina (female acolyte) of Sietch Tabr +Dr. Liet-Kynes, the Imperial Planetologist on Arrakis and father of Chani, as well as a revered figure among the Fremen +The Shadout Mapes, head housekeeper of imperial residence on Arrakis +Jamis, Fremen killed by Paul in ritual duel +Harah, wife of Jamis and later servant to Paul who helps raise Alia among the Fremen +Reverend Mother Ramallo, religious leader of Sietch TabrSmugglersEsmar Tuek, a powerful smuggler and the father of Staban Tuek +Staban Tuek, the son of Esmar Tuek and a powerful smuggler who befriends and takes in Gurney Halleck and his surviving men after the attack on the Atreides + + +== Themes and influences == +The Dune series is a landmark of science fiction. Herbert deliberately suppressed technology in his Dune universe so he could address the politics of humanity, rather than the future of humanity's technology. For example, a key pre-history event to the novel's present is the "Butlerian Jihad", in which all robots and computers were destroyed, eliminating these common elements to science fiction from the novel as to allow focus on humanity. Dune considers the way humans and their institutions might change over time. Director John Harrison, who adapted Dune for Syfy's 2000 miniseries, called the novel a universal and timeless reflection of "the human condition and its moral dilemmas", and said: + +A lot of people refer to Dune as science fiction. I never do. I consider it an epic adventure in the classic storytelling tradition, a story of myth and legend not unlike the Morte d'Arthur or any messiah story. It just happens to be set in the future ... The story is actually more relevant today than when Herbert wrote it. In the 1960s, there were just these two colossal superpowers duking it out. Today we're living in a more feudal, corporatized world more akin to Herbert's universe of separate families, power centers and business interests, all interrelated and kept together by the one commodity necessary to all. +But Dune has also been called a mix of soft and hard science fiction since "the attention to ecology is hard, the anthropology and the psychic abilities are soft." Hard elements include the ecology of Arrakis, suspensor technology, weapon systems, and ornithopters, while soft elements include issues relating to religion, physical and mental training, cultures, politics, and psychology.Herbert said Paul's messiah figure was inspired by the Arthurian legend, and that the scarcity of water on Arrakis was a metaphor for oil, as well as air and water itself, and for the shortages of resources caused by overpopulation. Novelist Brian Herbert, his son and biographer, wrote: + +Dune is a modern-day conglomeration of familiar myths, a tale in which great sandworms guard a precious treasure of melange, the geriatric spice that represents, among other things, the finite resource of oil. The planet Arrakis features immense, ferocious worms that are like dragons of lore, with "great teeth" and a "bellows breath of cinnamon." This resembles the myth described by an unknown English poet in Beowulf, the compelling tale of a fearsome fire dragon who guarded a great treasure hoard in a lair under cliffs, at the edge of the sea. The desert of Frank Herbert's classic novel is a vast ocean of sand, with giant worms diving into the depths, the mysterious and unrevealed domain of Shai-hulud. Dune tops are like the crests of waves, and there are powerful sandstorms out there, creating extreme danger. On Arrakis, life is said to emanate from the Maker (Shai-hulud) in the desert-sea; similarly all life on Earth is believed to have evolved from our oceans. Frank Herbert drew parallels, used spectacular metaphors, and extrapolated present conditions into world systems that seem entirely alien at first blush. But close examination reveals they aren't so different from systems we know โ€ฆ and the book characters of his imagination are not so different from people familiar to us. +Each chapter of Dune begins with an epigraph excerpted from the fictional writings of the character Princess Irulan. In forms such as diary entries, historical commentary, biography, quotations and philosophy, these writings set tone and provide exposition, context and other details intended to enhance understanding of Herbert's complex fictional universe and themes. They act as foreshadowing and invite the reader to keep reading to close the gap between what the epigraph says and what is happening in the main narrative. The epigraphs also give the reader the feeling that the world they are reading about is epically distanced, since Irulan writes about an idealized image of Paul as if he had already passed into memory. Brian Herbert wrote: "Dad told me that you could follow any of the novel's layers as you read it, and then start the book all over again, focusing on an entirely different layer. At the end of the book, he intentionally left loose ends and said he did this to send the readers spinning out of the story with bits and pieces of it still clinging to them, so that they would want to go back and read it again." + + +=== Middle-Eastern and Islamic references === +Due to the similarities between some of Herbert's terms and ideas and actual words and concepts in the Arabic language, as well as the series' "Islamic undertones" and themes, a Middle-Eastern influence on Herbert's works has been noted repeatedly. In his descriptions of the Fremen culture and language, Herbert uses both authentic Arabic words and Arabic-sounding words. For example, one of the names for the sandworm, Shai-hulud, is derived from Arabic: ุดูŠุก ุฎู„ูˆุฏ, romanized: ลกayสพ แธซulลซd, lit.โ€‰'immortal thing' or Arabic: ุดูŠุฎ ุฎู„ูˆุฏ, romanized: ลกayแธซ แธซulลซd, lit.โ€‰'old man of eternity'. The title of the Fremen housekeeper, the Shadout Mapes, is borrowed from the Arabic: ุดุงุฏูˆูโ€Ž, romanized: ลกฤdลซf, the Egyptian term for a device used to raise water. In particular, words related to the messianic religion of the Fremen, first implanted by the Bene Gesserit, are taken from Arabic, including Muad'Dib (from Arabic: ู…ุคุฏุจ, romanized: muสพaddib, lit.โ€‰'educator'), Usul (from Arabic: ุฃุตูˆู„, romanized: สพuแนฃลซl, lit.โ€‰'fundamental principles'), Shari-a (from Arabic: ุดุฑูŠุนุฉ, romanized: ลกarฤซสฟa, lit.โ€‰'sharia; path'), Shaitan (from Arabic: ุดูŠุทุงู†, romanized: ลกayแนญฤn, lit.โ€‰'Shaitan; devil; fiend', and jinn (from Arabic: ุฌู†, romanized: วงinn, lit.โ€‰'jinn; spirit; demon; mythical being'). It is likely Herbert relied on second-hand resources such as phrasebooks and desert adventure stories to find these Arabic words and phrases for the Fremen. They are meaningful and carefully chosen, and help create an "imagined desert culture that resonates with exotic sounds, enigmas, and pseudo-Islamic references" and has a distinctly Bedouin aesthetic.As a foreigner who adopts the ways of a desert-dwelling people and then leads them in a military capacity, Paul Atreides bears many similarities to the historical T. E. Lawrence. His 1962 biopic Lawrence of Arabia has also been identified as a potential influence. The Sabres of Paradise (1960) has also been identified as a potential influence upon Dune, with its depiction of Imam Shamil and the Islamic culture of the Caucasus inspiring some of the themes, characters, events and terminology of Dune.The environment of the desert planet Arrakis was primarily inspired by the environments of the Middle East. Similarly Arrakis as a bioregion is presented as a particular kind of political site. Herbert has made it resemble a desertified petrostate area. The Fremen people of Arrakis were influenced by the Bedouin tribes of Arabia, and the Mahdi prophecy originates from Islamic eschatology. Inspiration is also adopted from medieval historian Ibn Khaldun's cyclical history and his dynastic concept in North Africa, hinted at by Herbert's reference to Khaldun's book Kitฤb al-สฟibar ("The Book of Lessons"). The fictionalized version of the "Kitab al-ibar" in Dune is a combination of a Fremen religious manual and a desert survival book. + + +==== Additional language and historic influences ==== +In addition to Arabic, Dune derives words and names from a variety of other languages, including Hebrew, Navajo, Latin, Dutch ("Landsraad"), Chakobsa, the Nahuatl language of the Aztecs, Greek, Persian, Sanskrit ("prana bindu", "prajna"), Russian, Turkish, Finnish, and Old English. Bene Gesserit is simply the Latin for "It will have been well fought", also carrying the sense of "It will have been well managed", which stands as a statement of the order's goal and as a pledge of faithfulness to that goal. Critics tend to miss the literal meaning of the phrase, some positing that the term is derived from the Latin meaning "it will have been well borne", which interpretation is not well supported by their doctrine in the story.Through the inspiration from The Sabres of Paradise, there are also allusions to the tsarist-era Russian nobility and Cossacks. Frank Herbert stated that bureaucracy that lasted long enough would become a hereditary nobility, and a significant theme behind the aristocratic families in Dune was "aristocratic bureaucracy" which he saw as analogous to the Soviet Union. + + +=== Environmentalism and ecology === +Dune has been called the "first planetary ecology novel on a grand scale". Herbert hoped it would be seen as an "environmental awareness handbook" and said the title was meant to "echo the sound of 'doom'". It was reviewed in the best selling countercultural Whole Earth Catalog in 1968 as a "rich re-readable fantasy with clear portrayal of the fierce environment it takes to cohere a community".After the publication of Silent Spring by Rachel Carson in 1962, science fiction writers began treating the subject of ecological change and its consequences. Dune responded in 1965 with its complex descriptions of Arrakis life, from giant sandworms (for whom water is deadly) to smaller, mouse-like life forms adapted to live with limited water. Dune was followed in its creation of complex and unique ecologies by other science fiction books such as A Door into Ocean (1986) and Red Mars (1992). Environmentalists have pointed out that Dune's popularity as a novel depicting a planet as a complexโ€”almost livingโ€”thing, in combination with the first images of Earth from space being published in the same time period, strongly influenced environmental movements such as the establishment of the international Earth Day.While the genre of climate fiction was popularized in the 2010s in response to real global climate change, Dune as well as other early science fiction works from authors like J. G. Ballard (The Drowned World) and Kim Stanley Robinson (the Mars trilogy) have retroactively been considered pioneering examples of the genre. + + +=== Declining empires === +The Imperium in Dune contains features of various empires in Europe and the Near East, including the Roman Empire, Holy Roman Empire, and Ottoman Empire. Lorenzo DiTommaso compared Dune's portrayal of the downfall of a galactic empire to Edward Gibbon's Decline and Fall of the Roman Empire, which argues that Christianity allied with the profligacy of the Roman elite led to the fall of Ancient Rome. In "The Articulation of Imperial Decadence and Decline in Epic Science Fiction" (2007), DiTommaso outlines similarities between the two works by highlighting the excesses of the Emperor on his home planet of Kaitain and of the Baron Harkonnen in his palace. The Emperor loses his effectiveness as a ruler through an excess of ceremony and pomp. The hairdressers and attendants he brings with him to Arrakis are even referred to as "parasites". The Baron Harkonnen is similarly corrupt and materially indulgent. Gibbon's Decline and Fall partly blames the fall of Rome on the rise of Christianity. Gibbon claimed that this exotic import from a conquered province weakened the soldiers of Rome and left it open to attack. The Emperor's Sardaukar fighters are little match for the Fremen of Dune not only because of the Sardaukar's overconfidence and the fact that Jessica and Paul have trained the Fremen in their battle tactics, but because of the Fremen's capacity for self-sacrifice. The Fremen put the community before themselves in every instance, while the world outside wallows in luxury at the expense of others.The decline and long peace of the Empire sets the stage for revolution and renewal by genetic mixing of successful and unsuccessful groups through war, a process culminating in the Jihad led by Paul Atreides, described by Frank Herbert as depicting "war as a collective orgasm" (drawing on Norman Walter's 1950 The Sexual Cycle of Human Warfare), themes that would reappear in God Emperor of Dune's Scattering and Leto II's all-female Fish Speaker army. + + +=== Gender dynamics === +Gender dynamics are complex in Dune. Within the Fremen sietch communities, women have almost full equality. They carry weapons and travel in raiding parties with men, fighting when necessary alongside the men. They can take positions of leadership as a Sayyadina or as a Reverend Mother (if she can survive the ritual of ingesting the Water of Life.) Both of these sietch religious leaders are routinely consulted by the all-male Council and can have a decisive voice in all matters of sietch life, security and internal politics. They are also protected by the entire community. Due to the high mortality rate among their men, women outnumber men in most sietches. Polygamy is common, and sexual relationships are voluntary and consensual; as Stilgar says to Jessica, "women among us are not taken against their will." +In contrast, the Imperial aristocracy leaves young women of noble birth very little agency. Frequently trained by the Bene Gesserit, they are raised to eventually marry other aristocrats. Marriages between Major and Minor Houses are political tools to forge alliances or heal old feuds; women are given very little say in the matter. Many such marriages are quietly maneuvered by the Bene Gesserit to produce offspring with some genetic characteristics needed by the sisterhood's human-breeding program. In addition, such highly-placed sisters were in a position to subtly influence their husbands' actions in ways that could move the politics of the Imperium toward Bene Gesserit goals. +The gom jabbar test of humanity is administered by the female Bene Gesserit order but rarely to males. The Bene Gesserit have seemingly mastered the unconscious and can play on the unconscious weaknesses of others using the Voice, yet their breeding program seeks after a male Kwisatz Haderach. Their plan is to produce a male who can "possess complete racial memory, both male and female," and look into the black hole in the collective unconscious that they fear. A central theme of the book is the connection, in Jessica's son, of this female aspect with his male aspect. This aligns with concepts in Jungian psychology, which features conscious/unconscious and taking/giving roles associated with males and females, as well as the idea of the collective unconscious. Paul's approach to power consistently requires his upbringing under the matriarchal Bene Gesserit, who operate as a long-dominating shadow government behind all of the great houses and their marriages or divisions. He is trained by Jessica in the Bene Gesserit Way, which includes prana-bindu training in nerve and muscle control and precise perception. Paul also receives Mentat training, thus helping prepare him to be a type of androgynous Kwisatz Haderach, a male Reverend Mother.In a Bene Gesserit test early in the book, it is implied that people are generally "inhuman" in that they irrationally place desire over self-interest and reason. This applies Herbert's philosophy that humans are not created equal, while equal justice and equal opportunity are higher ideals than mental, physical, or moral equality. + + +=== Heroism === +I am showing you the superhero syndrome and your own participation in it. +Throughout Paul's rise to superhuman status, he follows a plotline common to many stories describing the birth of a hero. He has unfortunate circumstances forced onto him. After a long period of hardship and exile, he confronts and defeats the source of evil in his tale. As such, Dune is representative of a general trend beginning in 1960s American science fiction in that it features a character who attains godlike status through scientific means. Eventually, Paul Atreides gains a level of omniscience which allows him to take over the planet and the galaxy, and causes the Fremen of Arrakis to worship him like a god. Author Frank Herbert said in 1979, "The bottom line of the Dune trilogy is: beware of heroes. Much better [to] rely on your own judgment, and your own mistakes." He wrote in 1985, "Dune was aimed at this whole idea of the infallible leader because my view of history says that mistakes made by a leader (or made in a leader's name) are amplified by the numbers who follow without question."Juan A. Prieto-Pablos says Herbert achieves a new typology with Paul's superpowers, differentiating the heroes of Dune from earlier heroes such as Superman, van Vogt's Gilbert Gosseyn and Henry Kuttner's telepaths. Unlike previous superheroes who acquire their powers suddenly and accidentally, Paul's are the result of "painful and slow personal progress." And unlike other superheroes of the 1960sโ€”who are the exception among ordinary people in their respective worldsโ€”Herbert's characters grow their powers through "the application of mystical philosophies and techniques." For Herbert, the ordinary person can develop incredible fighting skills (Fremen, Ginaz swordsmen and Sardaukar) or mental abilities (Bene Gesserit, Mentats, Spacing Guild Navigators). + + +=== Zen and religion === + +Early in his newspaper career, Herbert was introduced to Zen by two Jungian psychologists, Ralph and Irene Slattery, who "gave a crucial boost to his thinking". Zen teachings ultimately had "a profound and continuing influence on [Herbert's] work". Throughout the Dune series and particularly in Dune, Herbert employs concepts and forms borrowed from Zen Buddhism. The Fremen are referred to as Zensunni adherents, and many of Herbert's epigraphs are Zen-spirited. In "Dune Genesis", Frank Herbert wrote: + +What especially pleases me is to see the interwoven themes, the fugue like relationships of images that exactly replay the way Dune took shape. As in an Escher lithograph, I involved myself with recurrent themes that turn into paradox. The central paradox concerns the human vision of time. What about Paul's gift of prescience - the Presbyterian fixation? For the Delphic Oracle to perform, it must tangle itself in a web of predestination. Yet predestination negates surprises and, in fact, sets up a mathematically enclosed universe whose limits are always inconsistent, always encountering the unprovable. It's like a koan, a Zen mind breaker. It's like the Cretan Epimenides saying, "All Cretans are liars." +Brian Herbert called the Dune universe "a spiritual melting pot", noting that his father incorporated elements of a variety of religions, including Buddhism, Sufi mysticism and other Islamic belief systems, Catholicism, Protestantism, Judaism, and Hinduism. He added that Frank Herbert's fictional future in which "religious beliefs have combined into interesting forms" represents the author's solution to eliminating arguments between religions, each of which claimed to have "the one and only revelation." + + +=== Asimov's Foundation === +Tim O'Reilly suggests that Herbert also wrote Dune as a counterpoint to Isaac Asimov's Foundation series. In his monograph on Frank Herbert, O'Reilly wrote that "Dune is clearly a commentary on the Foundation trilogy. Herbert has taken a look at the same imaginative situation that provoked Asimov's classicโ€”the decay of a galactic empireโ€”and restated it in a way that draws on different assumptions and suggests radically different conclusions. The twist he has introduced into Dune is that the Mule, not the Foundation, is his hero." According to O'Reilly, Herbert bases the Bene Gesserit on the scientific shamans of the Foundation, though they use biological rather than statistical science. In contrast to the Foundation series and its praise of science and rationality, Dune proposes that the unconscious and unexpected are actually what are needed for humanity.Both Herbert and Asimov explore the implications of prescience (i.e., visions of the future) both psychologically and socially. The Foundation series deploys a broadly determinist approach to prescient vision rooted in mathematical reasoning on a macroscopic social level. Dune, by contrast, invents a biologically rooted power of prescience that becomes determinist when the user actively relies on it to navigate past an undefined threshold of detail. Herbertโ€™s eugenically produced and spice-enhanced prescience is also personalized to individual actors whose roles in later books constrain each other's visions, rendering the future more or less mutable as time progresses. In what might be a comment on Foundation, Herbert's most powerfully prescient being in God Emperor of Dune laments the boredom engendered by prescience, and values surprises, especially regarding one's death, as a psychological necessity.However, both works contain a similar theme of the restoration of civilization and seem to make the fundamental assumption that "political maneuvering, the need to control material resources, and friendship or mating bonds will be fundamentally the same in the future as they are now." + + +== Critical reception == +Dune tied with Roger Zelazny's This Immortal for the Hugo Award in 1966 and won the inaugural Nebula Award for Best Novel. Reviews of the novel have been largely positive, and Dune is considered by some critics to be the best science fiction book ever written. The novel has been translated into dozens of languages, and has sold almost 20 million copies. Dune has been regularly cited as one of the world's best-selling science fiction novels.Arthur C. Clarke described Dune as "unique" and wrote, "I know nothing comparable to it except The Lord of the Rings." Robert A. Heinlein described the novel as "powerful, convincing, and most ingenious." It was described as "one of the monuments of modern science fiction" by the Chicago Tribune, and P. Schuyler Miller called Dune "one of the landmarks of modern science fiction ... an amazing feat of creation." The Washington Post described it as "a portrayal of an alien society more complete and deeply detailed than any other author in the field has managed ... a story absorbing equally for its action and philosophical vistas ... An astonishing science fiction phenomenon." Algis Budrys praised Dune for the vividness of its imagined setting, saying "The time lives. It breathes, it speaks, and Herbert has smelt it in his nostrils". He found that the novel, however, "turns flat and tails off at the end. ... [T]ruly effective villains simply simper and melt; fierce men and cunning statesmen and seeresses all bend before this new Messiah". Budrys faulted in particular Herbert's decision to kill Paul's infant son offstage, with no apparent emotional impact, saying "you cannot be so busy saving a world that you cannot hear an infant shriek". After criticizing unrealistic science fiction, Carl Sagan in 1978 listed Dune as among stories "that are so tautly constructed, so rich in the accommodating details of an unfamiliar society that they sweep me along before I have even a chance to be critical".The Louisville Times wrote, "Herbert's creation of this universe, with its intricate development and analysis of ecology, religion, politics, and philosophy, remains one of the supreme and seminal achievements in science fiction." Writing for The New Yorker, Jon Michaud praised Herbert's "clever authorial decision" to exclude robots and computers ("two staples of the genre") from his fictional universe, but suggested that this may be one explanation why Dune lacks "true fandom among science-fiction fans" to the extent that it "has not penetrated popular culture in the way that The Lord of the Rings and Star Wars have". Tamara I. Hladik wrote that the story "crafts a universe where lesser novels promulgate excuses for sequels. All its rich elements are in balance and plausibleโ€”not the patchwork confederacy of made-up languages, contrived customs, and meaningless histories that are the hallmark of so many other, lesser novels."On November 5, 2019, the BBC News listed Dune on its list of the 100 most influential novels.J. R. R. Tolkien refused to review Dune, on the grounds that he disliked it "with some intensity" and thus felt it would be unfair to Herbert, another working author, if he gave an honest review of the book. + + +== First edition prints and manuscripts == +The first edition of Dune is one of the most valuable in science fiction book collecting. Copies have been sold for more than $10,000 at auction. The Chilton first edition of the novel is 9+1โ„4 inches (235 mm) tall, with bluish green boards and a price of $5.95 on the dust jacket, and notes Toronto as the Canadian publisher on the copyright page. Up to this point, Chilton had been publishing only automobile repair manuals.California State University, Fullerton's Pollak Library has several of Herbert's draft manuscripts of Dune and other works, with the author's notes, in their Frank Herbert Archives. + + +== Sequels and prequels == + +After Dune proved to be a critical and financial success for Herbert, he was able to devote himself full time to writing additional novels in the series. He had already drafted parts of the second and third while writing Dune. The series included Dune Messiah (1969), Children of Dune (1976), God Emperor of Dune (1981), Heretics of Dune (1984), and Chapterhouse: Dune (1985), each sequentially continuing on the narrative from Dune. Herbert died on February 11, 1986.Herbert's son, Brian Herbert, had found several thousand pages of notes left by his father that outlined ideas for other narratives related to Dune. Brian Herbert enlisted author Kevin J. Anderson to help build out prequel novels to the events of Dune. Brian Herbert's and Anderson's Dune prequels first started publication in 1999, and have led to additional stories that take place between those of Frank Herbert's books. The notes for what would have been Dune 7 also enabled them to publish Hunters of Dune (2006) and Sandworms of Dune (2007), sequels to Frank Herbert's final novel Chapterhouse: Dune, which complete the chronological progression of his original series, and wrap up storylines that began in Heretics of Dune. + + +== Adaptations == + +Dune has been considered as an "unfilmable" and "uncontainable" work to adapt from novel to film or other visual medium. Described by Wired, "It has four appendices and a glossary of its own gibberish, and its action takes place on two planets, one of which is a desert overrun by worms the size of airport runways. Lots of important people die or try to kill each other, and they're all tethered to about eight entangled subplots." There have been several attempts to achieve this difficult conversion with various degrees of success. + + +=== Early stalled attempts === +In 1971, the production company Apjac International (APJ) (headed by Arthur P. Jacobs) optioned the rights to film Dune. As Jacobs was busy with other projects, such as the sequel to Planet of the Apes, Dune was delayed for another year. Jacobs' first choice for director was David Lean, but he turned down the offer. Charles Jarrott was also considered to direct. Work was also under way on a script while the hunt for a director continued. Initially, the first treatment had been handled by Robert Greenhut, the producer who had lobbied Jacobs to make the movie in the first place, but subsequently Rospo Pallenberg was approached to write the script, with shooting scheduled to begin in 1974. However, Jacobs died in 1973. +In December 1974, a French consortium led by Jean-Paul Gibon purchased the film rights from APJ, with Alejandro Jodorowsky set to direct. In 1975, Jodorowsky planned to film the story as a 14-hour feature, set to star his own son Brontis Jodorowsky in the lead role of Paul Atreides, Salvador Dalรญ as Shaddam IV, Padishah Emperor, Amanda Lear as Princess Irulan, Orson Welles as Baron Vladimir Harkonnen, Gloria Swanson as Reverend Mother Gaius Helen Mohiam, David Carradine as Duke Leto Atreides, Geraldine Chaplin as Lady Jessica, Alain Delon as Duncan Idaho, Hervรฉ Villechaize as Gurney Halleck, Udo Kier as Piter De Vries, and Mick Jagger as Feyd-Rautha. It was at first proposed to score the film with original music by Karlheinz Stockhausen, Henry Cow, and Magma; later on, the soundtrack was to be provided by Pink Floyd. Jodorowsky set up a pre-production unit in Paris consisting of Chris Foss, a British artist who designed covers for science fiction periodicals, Jean Giraud (Moebius), a French illustrator who created and also wrote and drew for Metal Hurlant magazine, and H. R. Giger. Moebius began designing creatures and characters for the film, while Foss was brought in to design the film's space ships and hardware. Giger began designing the Harkonnen Castle based on Moebius's storyboards. Dan O'Bannon was to head the special effects department.Dalรญ was cast as the Emperor. Dalรญ later demanded to be paid $100,000 per hour; Jodorowsky agreed, but tailored Dalรญ's part to be filmed in one hour, drafting plans for other scenes of the emperor to use a mechanical mannequin as substitute for Dalรญ. According to Giger, Dalรญ was "later invited to leave the film because of his pro-Franco statements". Just as the storyboards, designs, and script were finished, the financial backing dried up. Frank Herbert traveled to Europe in 1976 to find that $2 million of the $9.5 million budget had already been spent in pre-production, and that Jodorowsky's script would result in a 14-hour movie ("It was the size of a phone book", Herbert later recalled). Jodorowsky took creative liberties with the source material, but Herbert said that he and Jodorowsky had an amicable relationship. Jodorowsky said in 1985 that he found the Dune story mythical and had intended to recreate it rather than adapt the novel; though he had an "enthusiastic admiration" for Herbert, Jodorowsky said he had done everything possible to distance the author and his input from the project. Although Jodorowsky was embittered by the experience, he said the Dune project changed his life, and some of the ideas were used in his and Moebius's The Incal. O'Bannon entered a psychiatric hospital after the production failed, then worked on 13 scripts, the last of which became Alien. A 2013 documentary, Jodorowsky's Dune, was made about Jodorowsky's failed attempt at an adaptation. +In 1976, Dino De Laurentiis acquired the rights from Gibon's consortium. De Laurentiis commissioned Herbert to write a new screenplay in 1978; the script Herbert turned in was 175 pages long, the equivalent of nearly three hours of screen time. De Laurentiis then hired director Ridley Scott in 1979, with Rudy Wurlitzer writing the screenplay and H. R. Giger retained from the Jodorowsky production; Scott and Giger had also just worked together on the film Alien, after O'Bannon recommended the artist. Scott intended to split the novel into two movies. He worked on three drafts of the script, using The Battle of Algiers as a point of reference, before moving on to direct another science fiction film, Blade Runner (1982). As he recalls, the pre-production process was slow, and finishing the project would have been even more time-intensive: + +But after seven months I dropped out of Dune, by then Rudy Wurlitzer had come up with a first-draft script which I felt was a decent distillation of Frank Herbert's. But I also realised Dune was going to take a lot more workโ€”at least two and a half years' worth. And I didn't have the heart to attack that because my older brother Frank unexpectedly died of cancer while I was prepping the De Laurentiis picture. Frankly, that freaked me out. So I went to Dino and told him the Dune script was his. +โ€”From Ridley Scott: The Making of his Movies by Paul M. Sammon + + +=== 1984 film by David Lynch === + +In 1981, the nine-year film rights were set to expire. De Laurentiis re-negotiated the rights from the author, adding to them the rights to the Dune sequels (written and unwritten). After seeing The Elephant Man, De Laurentiis' daughter Raffaella decided that David Lynch should direct the movie. Around that time Lynch received several other directing offers, including Return of the Jedi. He agreed to direct Dune and write the screenplay even though he had not read the book, was not familiar with the story, or even been interested in science fiction. Lynch worked on the script for six months with Eric Bergren and Christopher De Vore. The team yielded two drafts of the script before it split over creative differences. Lynch would subsequently work on five more drafts. Production of the work was troubled by problems at the Mexican studio and hampering the film's timeline. Lynch ended up producing a nearly three-hour long film, but at demands from Universal Pictures, the film's distributor, he cut it back to about two hours, hastily filming additional scenes to make up for some of the cut footage.This first film of Dune, directed by Lynch, was released in 1984, nearly 20 years after the book's publication. Though Herbert said the book's depth and symbolism seemed to intimidate many filmmakers, he was pleased with the film, saying that "They've got it. It begins as Dune does. And I hear my dialogue all the way through. There are some interpretations and liberties, but you're gonna come out knowing you've seen Dune." Reviews of the film were negative, saying that it was incomprehensible to those unfamiliar with the book, and that fans would be disappointed by the way it strayed from the book's plot. Upon release for television and other forms of home media, Universal opted to reintroduce much of the footage that Lynch had cut, creating an over-three-hour long version with extensive monologue exposition. Lynch was extremely displeased with this move, and demanded that Universal replace his name on these cuts with the pseudonym "Alan Smithee", and has generally distanced himself from the film since. + + +=== 2000 miniseries by John Harrison === + +In 2000, John Harrison adapted the novel into Frank Herbert's Dune, a miniseries which premiered on American Sci-Fi Channel. As of 2004, the miniseries was one of the three highest-rated programs broadcast on the Sci-Fi Channel. + + +=== Further film attempts === +In 2008, Paramount Pictures announced that they would produce a new film based on the book, with Peter Berg attached to direct. Producer Kevin Misher, who spent a year securing the rights from the Herbert estate, was to be joined by Richard Rubinstein and John Harrison (of both Sci-Fi Channel miniseries) as well as Sarah Aubrey and Mike Messina. The producers stated that they were going for a "faithful adaptation" of the novel, and considered "its theme of finite ecological resources particularly timely." Science fiction author Kevin J. Anderson and Frank Herbert's son Brian Herbert, who had together written multiple Dune sequels and prequels since 1999, were attached to the project as technical advisors. In October 2009, Berg dropped out of the project, later saying that it "for a variety of reasons wasn't the right thing" for him. Subsequently, with a script draft by Joshua Zetumer, Paramount reportedly sought a new director who could do the film for under $175 million. In 2010, Pierre Morel was signed on to direct, with screenwriter Chase Palmer incorporating Morel's vision of the project into Zetumer's original draft. By November 2010, Morel left the project. Paramount finally dropped plans for a remake in March 2011. + + +=== Films by Denis Villeneuve === + +In November 2016, Legendary Entertainment acquired the film and TV rights for Dune. Variety reported in December 2016 that Denis Villeneuve was in negotiations to direct the project, which was confirmed in February 2017. In April 2017, Legendary announced that Eric Roth would write the screenplay. Villeneuve explained in March 2018 that his adaptation will be split into two films, with the first installment scheduled to begin production in 2019. Casting includes Timothรฉe Chalamet as Paul Atreides, Dave Bautista as Rabban, Stellan Skarsgรฅrd as Baron Harkonnen, Rebecca Ferguson as Lady Jessica, Charlotte Rampling as Reverend Mother Mohiam, Oscar Isaac as Duke Leto Atreides, Zendaya as Chani, Javier Bardem as Stilgar, Josh Brolin as Gurney Halleck, Jason Momoa as Duncan Idaho, David Dastmalchian as Piter De Vries, Chang Chen as Dr. Yueh, and Stephen Henderson as Thufir Hawat. Warner Bros. Pictures distributed the film, which had its initial premiere on September 3, 2021, at the Venice Film Festival, and wide release in both theaters and streaming on HBO Max on October 21, 2021, as part of Warner Bros.'s approach to handling the impact of the COVID-19 pandemic on the film industry. The film received "generally favorable reviews" on Metacritic. It has gone on to win multiple awards and was named by the National Board of Review as one of the 10 best films of 2021, as well as the American Film Institute in their annual top 10 list. The film went on to be nominated for ten Academy Awards, winning six, the most wins of the night for any film in contention.A sequel, Dune: Part Two, was scheduled for release on November 3, 2023, but will now instead be released on March 15th 2024 amid the 2023 SAG-AFTRA strike. + + +=== Audiobooks === +In 1993, Recorded Books Inc. released a 20-disc audiobook narrated by George Guidall. In 2007, Audio Renaissance released an audio book narrated by Simon Vance with some parts performed by Scott Brick, Orlagh Cassidy, Euan Morton, and other performers. + + +== Cultural influence == +Dune has been widely influential, inspiring numerous novels, music, films, television, games, and comic books. It is considered one of the greatest and most influential science fiction novels of all time, with numerous modern science fiction works such as Star Wars owing their existence to Dune. Dune has also been referenced in numerous other works of popular culture, including Star Trek, Chronicles of Riddick, The Kingkiller Chronicle and Futurama. Dune was cited as a source of inspiration for Hayao Miyazaki's anime film Nausicaรค of the Valley of the Wind (1984) for its post-apocalyptic world.Dune was parodied in 1984's National Lampoon's Doon by Ellis Weiner, which William F. Touponce called "something of a tribute to Herbert's success on college campuses", noting that "the only other book to have been so honored is Tolkien's The Lord of the Rings," which was parodied by The Harvard Lampoon in 1969. + + +=== Music === +In 1978, French electronic musician Richard Pinhas released the nine-track Dune-inspired album Chronolyse, which includes the seven-part Variations sur le thรจme des Bene Gesserit. +In 1979, German electronic music pioneer Klaus Schulze released an LP titled Dune featuring motifs and lyrics inspired by the novel. +A similar musical project, Visions of Dune, was released also in 1979 by Zed (a pseudonym of French electronic musician Bernard Sjazner). +Heavy metal band Iron Maiden wrote the song "To Tame a Land" based on the Dune story. It appears as the closing track to their 1983 album Piece of Mind. The original working title of the song was "Dune"; however, the band was denied permission to use it, with Frank Herbert's agents stating "Frank Herbert doesn't like rock bands, particularly heavy rock bands, and especially bands like Iron Maiden". +Dune inspired the German happy hardcore band Dune, who have released several albums with space travel-themed songs. +The progressive hardcore band Shai Hulud took their name from Dune. +"Traveller in Time", from the 1991 Blind Guardian album Tales from the Twilight World, is based mostly on Paul Atreides' visions of future and past. +The title of the 1993 Fear Factory album Fear is The Mindkiller is a quote from the "litany against fear". +The song "Near Fantastica", from the Matthew Good album Avalanche, makes reference to the "litany against fear", repeating "can't feel fear, fear's the mind killer" through a section of the song. +In the Fatboy Slim song "Weapon of Choice", the line "If you walk without rhythm/You won't attract the worm" is a near quotation from the sections of novel in which Stilgar teaches Paul to ride sandworms. +Dune also inspired the 1999 album The 2nd Moon by the German death metal band Golem, which is a concept album about the series. +Dune influenced Thirty Seconds to Mars on their self-titled debut album. +The Youngblood Brass Band's song "Is an Elegy" on Center:Level:Roar references "Muad'Dib", "Arrakis" and other elements from the novel. +The debut album of Canadian musician Grimes, called Geidi Primes, is a concept album based on Dune. +Japanese singer Kenshi Yonezu, released a song titled "Dune", also known as "Sand Planet". The song was released on 2017, and it was created using the voice synthesizer Hatsune Miku for her 10th anniversary. +"Fear is the Mind Killer", a song released in 2018 by Zheani (an Australian rapper) uses a quote from Dune. +"Litany Against Fear" is a spoken track released in 2018 under the 'Eight' album by Zheani. She recites an extract from Dune. +Sleep's 2018 album The Sciences features a song, Giza Butler, that references several aspects of Dune. +Tool's 2019 album Fear Inoculum has a song entitled "Litanie contre la peur (Litany against fear)". +"Rare to Wake", from Shannon Lay's album Geist (2019), is inspired by Dune. +Heavy Metal band Diamond Head based the song "The Sleeper" and its prelude, both off the album The Coffin Train, on the series. + + +=== Games === + +There have been a number of games based on the book, starting with the strategyโ€“adventure game Dune (1992). The most important game adaptation is Dune II (1992), which established the conventions of modern real-time strategy games and is considered to be among the most influential video games of all time.The online game Lost Souls includes Dune-derived elements, including sandworms and melangeโ€”addiction to which can produce psychic talents. The 2016 game Enter the Gungeon features the spice melange as a random item which gives the player progressively stronger abilities and penalties with repeated uses, mirroring the long-term effects melange has on users.Rick Priestley cites Dune as a major influence on his 1987 wargame, Warhammer 40,000.In 2023, Funcom announced Dune: Awakening, an upcoming massively multiplayer online game set in the universe of Dune. + + +=== Space exploration === +The Apollo 15 astronauts named a small crater on Earth's Moon after the novel during the 1971 mission, and the name was formally adopted by the International Astronomical Union in 1973. Since 2009, the names of planets from the Dune novels have been adopted for the real-world nomenclature of plains and other features on Saturn's moon Titan, like Arrakis Planitia. + + +== See also == +Soft science fiction โ€“ Sub-genre of science fiction emphasizing "soft" sciences or human emotions +Hydraulic empire โ€“ Government by control of access to water + + +== References == + + +== Further reading == +Clute, John; Nicholls, Peter (1995). The Encyclopedia of Science Fiction. New York: St. Martin's Press. p. 1386. ISBN 978-0-312-13486-0. +Clute, John; Nicholls, Peter (1995). The Multimedia Encyclopedia of Science Fiction (CD-ROM). Danbury, CT: Grolier. ISBN 978-0-7172-3999-3. +Huddleston, Tom. The Worlds of Dune: The Places and Cultures That Inspired Frank Herbert. Minneapolis: Quarto Publishing Group UK, 2023. +Jakubowski, Maxim; Edwards, Malcolm (1983). The Complete Book of Science Fiction and Fantasy Lists. St Albans, Herts, UK: Granada Publishing Ltd. p. 350. ISBN 978-0-586-05678-3. +Kennedy, Kara. Frank Herbert's Dune: A Critical Companion. Cham, Switzerland: Palgrave Macmillan, 2022. +Kennedy, Kara. Women's Agency in the Dune Universe: Tracing Women's Liberation through Science Fiction. Cham, Switzerland: Palgrave Macmillan, 2020. +Nardi, Dominic J. & N. Trevor Brierly, eds. Discovering Dune: Essays on Frank Herbert's Epic Saga. Jefferson, NC: McFarland & Co., 2022. +Nicholas, Jeffery, ed. Dune and Philosophy: Weirding Way of Mentat. Chicago: Open Court, 2011. +Nicholls, Peter (1979). The Encyclopedia of Science Fiction. St Albans, Herts, UK: Granada Publishing Ltd. p. 672. ISBN 978-0-586-05380-5. +Oโ€™Reilly, Timothy. Frank Herbert. New York: Frederick Ungar, 1981. +Pringle, David (1990). The Ultimate Guide to Science Fiction. London: Grafton Books Ltd. p. 407. ISBN 978-0-246-13635-0. +Tuck, Donald H. (1974). The Encyclopedia of Science Fiction and Fantasy. Chicago: Advent. p. 136. ISBN 978-0-911682-20-5. +Williams, Kevin C. The Wisdom of the Sand: Philosophy and Frank Herbert's Dune. New York: Hampton Press, 2013. + + +== External links == + +Official website for Dune and its sequels +Dune title listing at the Internet Speculative Fiction Database +Turner, Paul (October 1973). "Vertex Interviews Frank Herbert" (Interview). Vol. 1, no. 4. Archived from the original on May 19, 2009. +Spark Notes: Dune, detailed study guide +DuneQuotes.com โ€“ Collection of quotes from the Dune series +Dune by Frank Herbert, reviewed by Ted Gioia (Conceptual Fiction) +"Frank Herbert Biography and Bibliography at LitWeb.net". www.litweb.net. Archived from the original on April 2, 2009. Retrieved January 2, 2009. +Works of Frank Herbert at Curlie +Timberg, Scott (April 18, 2010). "Frank Herbert's Dune holds timely โ€“ and timeless โ€“ appeal". Los Angeles Times. Archived from the original on December 3, 2013. Retrieved November 27, 2013. +Walton, Jo (January 12, 2011). "In league with the future: Frank Herbert's Dune (Review)". Tor.com. Retrieved November 27, 2013. +Leonard, Andrew (June 4, 2015). "To Save California, Read Dune". Nautilus. Archived from the original on November 4, 2017. Retrieved June 15, 2015. +Dune by Frank Herbert โ€“ Foreshadowing & Dedication at Fact Behind Fiction +Frank Herbert by Tim O'Reilly +DuneScholar.com โ€“ Collection of scholarly essays \ No newline at end of file diff --git a/templates/neo4j-parent/ingest.py b/templates/neo4j-parent/ingest.py new file mode 100644 index 00000000000..f6a3f27e4ae --- /dev/null +++ b/templates/neo4j-parent/ingest.py @@ -0,0 +1,47 @@ +from pathlib import Path + +from langchain.document_loaders import TextLoader +from langchain.embeddings.openai import OpenAIEmbeddings +from langchain.graphs import Neo4jGraph +from langchain.text_splitter import TokenTextSplitter +from langchain.vectorstores import Neo4jVector + +txt_path = Path(__file__).parent / "dune.txt" + +graph = Neo4jGraph() + +# Load the text file +loader = TextLoader(txt_path) +documents = loader.load() + +# Define chunking strategy +parent_splitter = TokenTextSplitter(chunk_size=512, chunk_overlap=24) +child_splitter = TokenTextSplitter(chunk_size=100, chunk_overlap=24) + +# Store parent-child patterns into graph +parent_documents = parent_splitter.split_documents(documents) +for parent in parent_documents: + child_documents = child_splitter.split_documents([parent]) + params = { + "parent": parent.page_content, + "children": [c.page_content for c in child_documents], + } + graph.query( + """ + CREATE (p:Parent {text: $parent}) + WITH p + UNWIND $children AS child + CREATE (c:Child {text: child}) + CREATE (c)-[:HAS_PARENT]->(p) + """, + params, + ) + +# Calculate embedding values on the child nodes +Neo4jVector.from_existing_graph( + OpenAIEmbeddings(), + index_name="retrieval", + node_label="Child", + text_node_properties=["text"], + embedding_node_property="embedding", +) diff --git a/templates/neo4j-parent/main.py b/templates/neo4j-parent/main.py new file mode 100644 index 00000000000..1c1b772d526 --- /dev/null +++ b/templates/neo4j-parent/main.py @@ -0,0 +1,5 @@ +from neo4j_parent.chain import chain + +if __name__ == "__main__": + original_query = "What is the plot of the Dune?" + print(chain.invoke(original_query)) diff --git a/templates/neo4j-parent/neo4j_parent/__init__.py b/templates/neo4j-parent/neo4j_parent/__init__.py new file mode 100644 index 00000000000..0e89831116e --- /dev/null +++ b/templates/neo4j-parent/neo4j_parent/__init__.py @@ -0,0 +1,3 @@ +from neo4j_parent.chain import chain + +__all__ = ["chain"] diff --git a/templates/neo4j-parent/neo4j_parent/chain.py b/templates/neo4j-parent/neo4j_parent/chain.py new file mode 100644 index 00000000000..59acbd662f8 --- /dev/null +++ b/templates/neo4j-parent/neo4j_parent/chain.py @@ -0,0 +1,37 @@ +from langchain.chat_models import ChatOpenAI +from langchain.embeddings import OpenAIEmbeddings +from langchain.prompts import ChatPromptTemplate +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnableParallel, RunnablePassthrough +from langchain.vectorstores import Neo4jVector + +retrieval_query = """ +MATCH (node)-[:HAS_PARENT]->(parent) +RETURN parent.text AS text, score, {} AS metadata +""" + +vectorstore = Neo4jVector.from_existing_index( + OpenAIEmbeddings(), + index_name="retrieval", + node_label="Child", + embedding_node_property="embedding", + retrieval_query=retrieval_query + +) +retriever = vectorstore.as_retriever() + +template = """Answer the question based only on the following context: +{context} + +Question: {question} +""" +prompt = ChatPromptTemplate.from_template(template) + +model = ChatOpenAI() + +chain = ( + RunnableParallel({"context": retriever, "question": RunnablePassthrough()}) + | prompt + | model + | StrOutputParser() +) \ No newline at end of file diff --git a/templates/neo4j-parent/poetry.lock b/templates/neo4j-parent/poetry.lock new file mode 100644 index 00000000000..46acd442ce9 --- /dev/null +++ b/templates/neo4j-parent/poetry.lock @@ -0,0 +1,1354 @@ +# This file is automatically @generated by Poetry 1.4.2 and 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">=0.0.320" +tiktoken = "^0.5.1" + +[tool.langserve] +export_module = "neo4j_parent" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/neo4j-parent/tests/__init__.py b/templates/neo4j-parent/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/openai-functions-agent/LICENSE b/templates/openai-functions-agent/LICENSE new file mode 100644 index 00000000000..426b6509034 --- /dev/null +++ b/templates/openai-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/openai-functions-agent/README.md b/templates/openai-functions-agent/README.md new file mode 100644 index 00000000000..b757910b462 --- /dev/null +++ b/templates/openai-functions-agent/README.md @@ -0,0 +1,16 @@ +# OpenAI Functions Agent + +This template creates an agent that uses OpenAI function calling to communicate its decisions of what actions to take. +This example creates an agent that can optionally look up things on the internet using Tavily's search engine. + +## LLM + +This template will use `OpenAI` by default. + +Be sure that `OPENAI_API_KEY` is set in your environment. + +## Tools + +This template will use `Tavily` by default. + +Be sure that `TAVILY_API_KEY` is set in your environment. diff --git a/templates/openai-functions-agent/main.py b/templates/openai-functions-agent/main.py new file mode 100644 index 00000000000..f0e1f5963f9 --- /dev/null +++ b/templates/openai-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": []})) diff --git a/templates/openai-functions-agent/openai_functions_agent/__init__.py b/templates/openai-functions-agent/openai_functions_agent/__init__.py new file mode 100644 index 00000000000..1d97eae2490 --- /dev/null +++ b/templates/openai-functions-agent/openai_functions_agent/__init__.py @@ -0,0 +1,3 @@ +from openai_functions_agent.agent import agent_executor + +__all__ = ["agent_executor"] diff --git a/templates/openai-functions-agent/openai_functions_agent/agent.py b/templates/openai-functions-agent/openai_functions_agent/agent.py new file mode 100644 index 00000000000..ee8a2eb2ebd --- /dev/null +++ b/templates/openai-functions-agent/openai_functions_agent/agent.py @@ -0,0 +1,67 @@ +from typing import List, Tuple + +from langchain.agents import AgentExecutor +from langchain.agents.format_scratchpad import format_to_openai_functions +from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser +from langchain.chat_models import ChatOpenAI +from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain.pydantic_v1 import BaseModel +from langchain.schema.messages import AIMessage, HumanMessage +from langchain.tools.render import format_tool_to_openai_function +from langchain.tools.tavily_search import TavilySearchResults +from langchain.utilities.tavily_search import TavilySearchAPIWrapper + +# Fake Tool +search = TavilySearchAPIWrapper() +tavily_tool = TavilySearchResults(api_wrapper=search) + +tools = [tavily_tool] + +llm = ChatOpenAI(temperature=0) +prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + "You are very powerful assistant, but bad at calculating lengths of words.", + ), + MessagesPlaceholder(variable_name="chat_history"), + ("user", "{input}"), + MessagesPlaceholder(variable_name="agent_scratchpad"), + ] +) + +llm_with_tools = llm.bind(functions=[format_tool_to_openai_function(t) for t in 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_functions( + x["intermediate_steps"] + ), + } + | prompt + | llm_with_tools + | OpenAIFunctionsAgentOutputParser() +) + + +class AgentInput(BaseModel): + input: str + chat_history: List[Tuple[str, str]] + + +agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True).with_types( + input_type=AgentInput +) + +agent_executor = agent_executor | (lambda x: x["output"]) diff --git a/templates/openai-functions-agent/poetry.lock b/templates/openai-functions-agent/poetry.lock new file mode 100644 index 00000000000..c7accbbe11a --- /dev/null +++ b/templates/openai-functions-agent/poetry.lock @@ -0,0 +1,1254 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.6" +files = [ + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:41d55fc043954cddbbd82503d9cc3f4814a40bcef30b3569bc7b5e34130718c1"}, + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1d84166673694841d8953f0a8d0c90e1087739d24632fe86b1a08819168b4566"}, + {file = "aiohttp-3.8.6-cp310-cp310-macosx_11_0_arm64.whl", hash = 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+test = "poetry run pytest" +watch = "poetry run ptw" +lint = "poetry run ruff ." +format = "poetry run ruff . --fix" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" \ No newline at end of file diff --git a/templates/rag-chroma-private/LICENSE b/templates/rag-chroma-private/LICENSE new file mode 100644 index 00000000000..578fc414ad4 --- /dev/null +++ b/templates/rag-chroma-private/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 pingpong-templates + +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/rag-chroma-private/README.md b/templates/rag-chroma-private/README.md new file mode 100644 index 00000000000..d1e84307f29 --- /dev/null +++ b/templates/rag-chroma-private/README.md @@ -0,0 +1,49 @@ +# Private RAG + +This template performs privae RAG (no reliance on external APIs) using: + +* Ollama for the LLM +* GPT4All for embeddings + +## LLM + +Follow instructions [here](https://python.langchain.com/docs/integrations/chat/ollama) to download Ollama. + +The instructions also show how to download your LLM of interest with Ollama: + +* This template uses `llama2:13b-chat` +* But you can pick from many [here](https://ollama.ai/library) + +## Set up local embeddings + +This will use [GPT4All](https://python.langchain.com/docs/integrations/text_embedding/gpt4all) embeddings. + +## Chroma + +[Chroma](https://python.langchain.com/docs/integrations/vectorstores/chroma) is an open-source vector database. + +This template will create and add documents to the vector database in `chain.py`. + +By default, this will load a popular blog post on agents. + +However, you can choose from a large number of document loaders [here](https://python.langchain.com/docs/integrations/document_loaders). + +## Adding the template + +Create your LangServe app: +``` +langchain serve new my-app +cd my-app +``` + +Add template: +``` +langchain serve add rag-chroma-private +``` + +Start server: +``` +langchain start +``` + +See Jupyter notebook `rag_chroma_private` for various way to connect to the template. \ No newline at end of file diff --git a/templates/rag-chroma-private/docs/LLaVA.pdf b/templates/rag-chroma-private/docs/LLaVA.pdf new file mode 100644 index 00000000000..88da76cecd5 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+langchain = ">=0.0.313, <0.1" +tiktoken = ">=0.5.1" +chromadb = ">=0.4.14" +gpt4all = ">=1.0.8" +beautifulsoup4 = ">=4.12.2" + +[tool.langserve] +export_module = "rag_chroma_private" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/rag-chroma-private/rag_chroma_private/__init__.py b/templates/rag-chroma-private/rag_chroma_private/__init__.py new file mode 100644 index 00000000000..c06d4f844d6 --- /dev/null +++ b/templates/rag-chroma-private/rag_chroma_private/__init__.py @@ -0,0 +1,3 @@ +from rag_chroma_private.chain import chain + +__all__ = ["chain"] diff --git a/templates/rag-chroma-private/rag_chroma_private/chain.py b/templates/rag-chroma-private/rag_chroma_private/chain.py new file mode 100644 index 00000000000..397a1589b67 --- /dev/null +++ b/templates/rag-chroma-private/rag_chroma_private/chain.py @@ -0,0 +1,51 @@ +from langchain.chat_models import ChatOllama + +# Load +from langchain.document_loaders import WebBaseLoader +from langchain.embeddings import GPT4AllEmbeddings +from langchain.prompts import ChatPromptTemplate +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnableParallel, RunnablePassthrough +from langchain.text_splitter import RecursiveCharacterTextSplitter +from langchain.vectorstores import Chroma + +loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") +data = loader.load() + +# Split + +text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) +all_splits = text_splitter.split_documents(data) + +# Add to vectorDB +vectorstore = Chroma.from_documents( + documents=all_splits, + collection_name="rag-private", + embedding=GPT4AllEmbeddings(), +) +retriever = vectorstore.as_retriever() + +# Prompt +# Optionally, pull from the Hub +# from langchain import hub +# prompt = hub.pull("rlm/rag-prompt") +# Or, define your own: +template = """Answer the question based only on the following context: +{context} + +Question: {question} +""" +prompt = ChatPromptTemplate.from_template(template) + +# LLM +# Select the LLM that you downloaded +ollama_llm = "llama2:13b-chat" +model = ChatOllama(model=ollama_llm) + +# RAG chain +chain = ( + RunnableParallel({"context": retriever, "question": RunnablePassthrough()}) + | prompt + | model + | StrOutputParser() +) diff --git a/templates/rag-chroma-private/tests/__init__.py b/templates/rag-chroma-private/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rag-chroma/LICENSE b/templates/rag-chroma/LICENSE new file mode 100644 index 00000000000..426b6509034 --- /dev/null +++ b/templates/rag-chroma/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/rag-chroma/README.md b/templates/rag-chroma/README.md new file mode 100644 index 00000000000..9511e3fcdc2 --- /dev/null +++ b/templates/rag-chroma/README.md @@ -0,0 +1,35 @@ +# RAG Chroma + +This template performs RAG using Chroma and OpenAI. + +## Chroma + +[Chroma](https://python.langchain.com/docs/integrations/vectorstores/chroma) is an open-source vector database. + +This template will create and add documents to the vector database in `chain.py`. + +These documents can be loaded from [many sources](https://python.langchain.com/docs/integrations/document_loaders). + +## LLM + +Be sure that `OPENAI_API_KEY` is set in order to the OpenAI models. + +## Adding the template + +Create your LangServe app: +``` +langchain serve new my-app +cd my-app +``` + +Add template: +``` +langchain serve add rag-chroma +``` + +Start server: +``` +langchain start +``` + +See Jupyter notebook `rag_chroma` for various way to connect to the template. diff --git a/templates/rag-chroma/poetry.lock b/templates/rag-chroma/poetry.lock new file mode 100644 index 00000000000..9f0d0140142 --- /dev/null +++ b/templates/rag-chroma/poetry.lock @@ -0,0 +1,2296 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.6" +files = [ + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:41d55fc043954cddbbd82503d9cc3f4814a40bcef30b3569bc7b5e34130718c1"}, + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1d84166673694841d8953f0a8d0c90e1087739d24632fe86b1a08819168b4566"}, + 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+openai = ">=0.28.1" +tiktoken = ">=0.5.1" +chromadb = ">=0.4.14" + +[tool.langserve] +export_module = "rag_chroma" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/rag-chroma/rag_chroma.ipynb b/templates/rag-chroma/rag_chroma.ipynb new file mode 100644 index 00000000000..5e0b45d5d41 --- /dev/null +++ b/templates/rag-chroma/rag_chroma.ipynb @@ -0,0 +1,61 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "681a5d1e", + "metadata": {}, + "source": [ + "## Run Template\n", + "\n", + "As shown in the README, add template and start server:\n", + "```\n", + "langchain serve add rag-chroma\n", + "langchain start\n", + "```\n", + "\n", + "We can now look at the endpoints:\n", + "\n", + "http://127.0.0.1:8000/docs#\n", + "\n", + "And specifically at our loaded template:\n", + "\n", + "http://127.0.0.1:8000/docs#/default/invoke_rag_chroma_invoke_post\n", + " \n", + "We can also use remote runnable to call it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d774be2a", + "metadata": {}, + "outputs": [], + "source": [ + "from langserve.client import RemoteRunnable\n", + "rag_app = RemoteRunnable('http://localhost:8000/rag-chroma')\n", + "rag_app.invoke(\"Where id Harrison work\")" + ] + } + ], + "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/templates/rag-chroma/rag_chroma/__init__.py b/templates/rag-chroma/rag_chroma/__init__.py new file mode 100644 index 00000000000..a625fc70997 --- /dev/null +++ b/templates/rag-chroma/rag_chroma/__init__.py @@ -0,0 +1,3 @@ +from rag_chroma.chain import chain + +__all__ = ["chain"] diff --git a/templates/rag-chroma/rag_chroma/chain.py b/templates/rag-chroma/rag_chroma/chain.py new file mode 100644 index 00000000000..96a46fd4c68 --- /dev/null +++ b/templates/rag-chroma/rag_chroma/chain.py @@ -0,0 +1,54 @@ +from langchain.chat_models import ChatOpenAI +from langchain.embeddings import OpenAIEmbeddings +from langchain.prompts import ChatPromptTemplate +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnableParallel, RunnablePassthrough +from langchain.vectorstores import Chroma + +# Example for document loading (from url), splitting, and creating vectostore + +''' +# Load +from langchain.document_loaders import WebBaseLoader +loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") +data = loader.load() + +# Split +from langchain.text_splitter import RecursiveCharacterTextSplitter +text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) +all_splits = text_splitter.split_documents(data) + +# Add to vectorDB +vectorstore = Chroma.from_documents(documents=all_splits, + collection_name="rag-chroma", + embedding=OpenAIEmbeddings(), + ) +retriever = vectorstore.as_retriever() +''' + +# Embed a single document as a test +vectorstore = Chroma.from_texts( + ["harrison worked at kensho"], + collection_name="rag-chroma", + embedding=OpenAIEmbeddings() +) +retriever = vectorstore.as_retriever() + +# RAG prompt +template = """Answer the question based only on the following context: +{context} + +Question: {question} +""" +prompt = ChatPromptTemplate.from_template(template) + +# LLM +model = ChatOpenAI() + +# RAG chain +chain = ( + RunnableParallel({"context": retriever, "question": RunnablePassthrough()}) + | prompt + | model + | StrOutputParser() +) diff --git a/templates/rag-chroma/tests/__init__.py b/templates/rag-chroma/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rag-conversation/LICENSE b/templates/rag-conversation/LICENSE new file mode 100644 index 00000000000..578fc414ad4 --- /dev/null +++ b/templates/rag-conversation/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 pingpong-templates + +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/rag-conversation/README.md b/templates/rag-conversation/README.md new file mode 100644 index 00000000000..7cabb0730e6 --- /dev/null +++ b/templates/rag-conversation/README.md @@ -0,0 +1,13 @@ +# Conversational RAG + +This template performs [conversational](https://python.langchain.com/docs/expression_language/cookbook/retrieval#conversational-retrieval-chain) [retrieval](https://python.langchain.com/docs/use_cases/question_answering/), which is one of the most popular LLM use-cases. + +It passes both a conversation history and retrieved documents into an LLM for synthesis. + +## LLM + +Be sure that `OPENAI_API_KEY` is set in order to use the OpenAI models. + +## Pinecone + +This template uses Pinecone as a vectorstore and requires that `PINECONE_API_KEY`, `PINECONE_ENVIRONMENT`, and `PINECONE_INDEX` are set. \ No newline at end of file diff --git a/templates/rag-conversation/poetry.lock b/templates/rag-conversation/poetry.lock new file mode 100644 index 00000000000..a41723c7830 --- /dev/null +++ b/templates/rag-conversation/poetry.lock @@ -0,0 +1,2379 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.6" +files = [ + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:41d55fc043954cddbbd82503d9cc3f4814a40bcef30b3569bc7b5e34130718c1"}, + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1d84166673694841d8953f0a8d0c90e1087739d24632fe86b1a08819168b4566"}, + {file = "aiohttp-3.8.6-cp310-cp310-macosx_11_0_arm64.whl", hash = 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(>=3.5)", "sphinx-lint"] +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"] + +[metadata] +lock-version = "2.0" +python-versions = ">=3.8.1,<4.0" +content-hash = "cb79694b764461126fd022edd644a2ef0ee437be5a42c00faa82d283615b5196" diff --git a/templates/rag-conversation/pyproject.toml b/templates/rag-conversation/pyproject.toml new file mode 100644 index 00000000000..c2b83537595 --- /dev/null +++ b/templates/rag-conversation/pyproject.toml @@ -0,0 +1,22 @@ +[tool.poetry] +name = "rag-conversation" +version = "0.1.0" +description = "" +authors = ["Lance Martin "] +readme = "README.md" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain = ">=0.0.313, <0.1" +openai = ">=0.28.1" +tiktoken = ">=0.5.1" +pinecone-client = ">=2.2.4" +beautifulsoup4 = "^4.12.2" + +[tool.langserve] +export_module = "rag_conversation" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/rag-conversation/rag_conversation.ipynb b/templates/rag-conversation/rag_conversation.ipynb new file mode 100644 index 00000000000..dcf63077bf1 --- /dev/null +++ b/templates/rag-conversation/rag_conversation.ipynb @@ -0,0 +1,131 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "424a9d8d", + "metadata": {}, + "source": [ + "## Run Template\n", + "\n", + "\n", + "As shown in the README, add template and start server:\n", + "```\n", + "langchain serve add rag-conversation\n", + "langchain start\n", + "```\n", + "\n", + "We can now look at the endpoints:\n", + "\n", + "http://127.0.0.1:8000/docs#\n", + "\n", + "And specifically at our loaded template:\n", + "\n", + "http://127.0.0.1:8000/docs#/default/invoke_rag_conversation_invoke_post\n", + " \n", + "We can also use remote runnable to call it." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "5f521923", + "metadata": {}, + "outputs": [], + "source": [ + "from langserve.client import RemoteRunnable\n", + "rag_app = RemoteRunnable('http://localhost:8000/rag-conversation')" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "679bd83b", + "metadata": {}, + "outputs": [], + "source": [ + "question = \"How does agent memory work?\"\n", + "answer = rag_app.invoke({\n", + " \"question\": question,\n", + " \"chat_history\": [],\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "94a05616", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content=\"Agent memory works by utilizing both short-term memory and long-term memory mechanisms. \\n\\nShort-term memory allows the agent to learn and retain information within the current context or task. This in-context learning helps the agent handle complex tasks efficiently. \\n\\nOn the other hand, long-term memory enables the agent to retain and recall an unlimited amount of information over extended periods. This is achieved by leveraging an external vector store, such as a memory stream, which serves as a comprehensive database of the agent's past experiences in natural language. The memory stream records observations and events directly provided by the agent, and inter-agent communication can also trigger new natural language statements to be added to the memory.\\n\\nTo access and utilize the stored information, a retrieval model is employed. This model determines the context that is most relevant, recent, and important to inform the agent's behavior. By retrieving information from memory, the agent can reflect on past actions, learn from mistakes, and refine its behavior for future steps, ultimately improving the quality of its results.\")" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "answer" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "ce206c8a", + "metadata": {}, + "outputs": [], + "source": [ + "chat_history = [(question, answer.content)]\n", + "answer = rag_app.invoke({\n", + " \"question\": \"What are the different types?\",\n", + " \"chat_history\": chat_history,\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "4626f167", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content='The different types of memory utilized by the agent are sensory memory, short-term memory, and long-term memory.')" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "answer" + ] + } + ], + "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/templates/rag-conversation/rag_conversation/__init__.py b/templates/rag-conversation/rag_conversation/__init__.py new file mode 100644 index 00000000000..4535ca34e88 --- /dev/null +++ b/templates/rag-conversation/rag_conversation/__init__.py @@ -0,0 +1,3 @@ +from rag_conversation.chain import chain + +__all__ = ["chain"] diff --git a/templates/rag-conversation/rag_conversation/chain.py b/templates/rag-conversation/rag_conversation/chain.py new file mode 100644 index 00000000000..3ed98288b4c --- /dev/null +++ b/templates/rag-conversation/rag_conversation/chain.py @@ -0,0 +1,119 @@ +import os +from operator import itemgetter +from typing import List, Tuple + +from langchain.chat_models import ChatOpenAI +from langchain.embeddings import OpenAIEmbeddings +from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain.prompts.prompt import PromptTemplate +from langchain.schema import AIMessage, HumanMessage, format_document +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import ( + RunnableBranch, + RunnableLambda, + RunnableMap, + RunnablePassthrough, +) +from langchain.vectorstores import Pinecone +from pydantic import BaseModel + +if os.environ.get("PINECONE_API_KEY", None) is None: + raise Exception("Missing `PINECONE_API_KEY` environment variable.") + +if os.environ.get("PINECONE_ENVIRONMENT", None) is None: + raise Exception("Missing `PINECONE_ENVIRONMENT` environment variable.") + +PINECONE_INDEX_NAME = os.environ.get("PINECONE_INDEX", "langchain-test") + +### Ingest code - you may need to run this the first time +# Load +# from langchain.document_loaders import WebBaseLoader +# loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") +# data = loader.load() + +# # Split +# from langchain.text_splitter import RecursiveCharacterTextSplitter +# text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) +# all_splits = text_splitter.split_documents(data) + +# # Add to vectorDB +# vectorstore = Pinecone.from_documents( +# documents=all_splits, embedding=OpenAIEmbeddings(), index_name=PINECONE_INDEX_NAME +# ) +# retriever = vectorstore.as_retriever() + +vectorstore = Pinecone.from_existing_index(PINECONE_INDEX_NAME, OpenAIEmbeddings()) +retriever = vectorstore.as_retriever() + +# Condense a chat history and follow-up question into a standalone question +_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. +Chat History: +{chat_history} +Follow Up Input: {question} +Standalone question:""" # noqa: E501 +CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template(_template) + +# RAG answer synthesis prompt +template = """Answer the question based only on the following context: + +{context} +""" +ANSWER_PROMPT = ChatPromptTemplate.from_messages( + [ + ("system", template), + MessagesPlaceholder(variable_name="chat_history"), + ("user", "{question}"), + ] +) + +# Conversational Retrieval Chain +DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template="{page_content}") + + +def _combine_documents( + docs, document_prompt=DEFAULT_DOCUMENT_PROMPT, document_separator="\n\n" +): + doc_strings = [format_document(doc, document_prompt) for doc in docs] + return document_separator.join(doc_strings) + + +def _format_chat_history(chat_history: List[Tuple[str, str]]) -> List: + buffer = [] + for human, ai in chat_history: + buffer.append(HumanMessage(content=human)) + buffer.append(AIMessage(content=ai)) + return buffer + + +# User input +class ChatHistory(BaseModel): + chat_history: List[Tuple[str, str]] + question: str + + +_search_query = RunnableBranch( + # If input includes chat_history, we condense it with the follow-up question + ( + RunnableLambda(lambda x: bool(x.get("chat_history"))).with_config( + run_name="HasChatHistoryCheck" + ), # Condense follow-up question and chat into a standalone_question + RunnablePassthrough.assign( + chat_history=lambda x: _format_chat_history(x["chat_history"]) + ) + | CONDENSE_QUESTION_PROMPT + | ChatOpenAI(temperature=0) + | StrOutputParser(), + ), + # Else, we have no chat history, so just pass through the question + RunnableLambda(itemgetter("question")), +) + +_inputs = RunnableMap( + { + "question": lambda x: x["question"], + "chat_history": lambda x: _format_chat_history(x["chat_history"]), + "context": _search_query | retriever | _combine_documents, + } +).with_types(input_type=ChatHistory) + +chain = _inputs | ANSWER_PROMPT | ChatOpenAI() | StrOutputParser() diff --git a/templates/rag-conversation/tests/__init__.py b/templates/rag-conversation/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rag-elasticsearch/LICENSE b/templates/rag-elasticsearch/LICENSE new file mode 100644 index 00000000000..578fc414ad4 --- /dev/null +++ b/templates/rag-elasticsearch/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 pingpong-templates + +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/rag-elasticsearch/README.md b/templates/rag-elasticsearch/README.md new file mode 100644 index 00000000000..e96631816a7 --- /dev/null +++ b/templates/rag-elasticsearch/README.md @@ -0,0 +1,59 @@ +# Elasticsearch RAG Example + +Using Langserve and ElasticSearch to build a RAG search example for answering questions on workplace documents. + +Relies on sentence transformer `MiniLM-L6-v2` for embedding passages and questions. + +## Running Elasticsearch + +There are a number of ways to run Elasticsearch. + +### Elastic Cloud + +Create a free trial account on [Elastic Cloud](https://cloud.elastic.co/registration?utm_source=langchain&utm_content=langserve). + +Once you have created an account, you can create a deployment. With a deployment, you can use these environment variables to connect to your Elasticsearch instance: + +```bash +export ELASTIC_CLOUD_ID = +export ELASTIC_USERNAME = +export ELASTIC_PASSWORD = +``` + +### Docker + +For local development, you can use Docker: + +```bash +docker run -p 9200:9200 \ + -e "discovery.type=single-node" \ + -e "xpack.security.enabled=false" \ + -e "xpack.security.http.ssl.enabled=false" \ + -e "xpack.license.self_generated.type=trial" \ + docker.elastic.co/elasticsearch/elasticsearch:8.10.0 +``` + +This will run Elasticsearch on port 9200. You can then check that it is running by visiting [http://localhost:9200](http://localhost:9200). + +With a deployment, you can use these environment variables to connect to your Elasticsearch instance: + +```bash +export ES_URL = "http://localhost:9200" +``` + +## Documents + +To load fictional workplace documents, run the following command from the root of this repository: + +```bash +python ./data/load_documents.py +``` + +However, you can choose from a large number of document loaders [here](https://python.langchain.com/docs/integrations/document_loaders). + +## Installation + +```bash +# from inside your LangServe instance +poe add rag-elasticsearch +``` diff --git a/templates/rag-elasticsearch/data/documents.json b/templates/rag-elasticsearch/data/documents.json new file mode 100644 index 00000000000..b81cf1647f2 --- /dev/null +++ b/templates/rag-elasticsearch/data/documents.json @@ -0,0 +1,161 @@ +[ + { + "content": "Effective: March 2020\nPurpose\n\nThe purpose of this full-time work-from-home policy is to provide guidelines and support for employees to conduct their work remotely, ensuring the continuity and productivity of business operations during the COVID-19 pandemic and beyond.\nScope\n\nThis policy applies to all employees who are eligible for remote work as determined by their role and responsibilities. It is designed to allow employees to work from home full time while maintaining the same level of performance and collaboration as they would in the office.\nEligibility\n\nEmployees who can perform their work duties remotely and have received approval from their direct supervisor and the HR department are eligible for this work-from-home arrangement.\nEquipment and Resources\n\nThe necessary equipment and resources will be provided to employees for remote work, including a company-issued laptop, software licenses, and access to secure communication tools. Employees are responsible for maintaining and protecting the company's equipment and data.\nWorkspace\n\nEmployees working from home are responsible for creating a comfortable and safe workspace that is conducive to productivity. This includes ensuring that their home office is ergonomically designed, well-lit, and free from distractions.\nCommunication\n\nEffective communication is vital for successful remote work. Employees are expected to maintain regular communication with their supervisors, colleagues, and team members through email, phone calls, video conferences, and other approved communication tools.\nWork Hours and Availability\n\nEmployees are expected to maintain their regular work hours and be available during normal business hours, unless otherwise agreed upon with their supervisor. Any changes to work hours or availability must be communicated to the employee's supervisor and the HR department.\nPerformance Expectations\n\nEmployees working from home are expected to maintain the same level of performance and productivity as if they were working in the office. Supervisors and team members will collaborate to establish clear expectations and goals for remote work.\nTime Tracking and Overtime\n\nEmployees are required to accurately track their work hours using the company's time tracking system. Non-exempt employees must obtain approval from their supervisor before working overtime.\nConfidentiality and Data Security\n\nEmployees must adhere to the company's confidentiality and data security policies while working from home. This includes safeguarding sensitive information, securing personal devices and internet connections, and reporting any security breaches to the IT department.\nHealth and Well-being\n\nThe company encourages employees to prioritize their health and well-being while working from home. This includes taking regular breaks, maintaining a work-life balance, and seeking support from supervisors and colleagues when needed.\nPolicy Review and Updates\n\nThis work-from-home policy will be reviewed periodically and updated as necessary, taking into account changes in public health guidance, business needs, and employee feedback.\nQuestions and Concerns\n\nEmployees are encouraged to direct any questions or concerns about this policy to their supervisor or the HR department.\n", + "summary": "This policy outlines the guidelines for full-time remote work, including eligibility, equipment and resources, workspace requirements, communication expectations, performance expectations, time tracking and overtime, confidentiality and data security, health and well-being, and policy reviews and updates. Employees are encouraged to direct any questions or concerns", + "name": "Work From Home Policy", + "url": "./sharepoint/Work from home policy.txt", + "created_on": "2020-03-01", + "updated_at": "2020-03-01", + "category": "teams", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + { + "content": "Starting May 2022, the company will be implementing a two-day in-office work requirement per week for all eligible employees. Please coordinate with your supervisor and HR department to schedule your in-office workdays while continuing to follow all safety protocols.\n", + "summary": "Starting May 2022, employees will need to work two days a week in the office. Coordinate with your supervisor and HR department for these days while following safety protocols.", + "name": "April Work From Home Update", + "url": "./sharepoint/April work from home update.txt", + "created_on": "2022-04-29", + "updated_at": "2022-04-29", + "category": "teams", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + { + "content": "As we continue to prioritize the well-being of our employees, we are making a slight adjustment to our hybrid work policy. Starting May 1, 2023, employees will be required to work from the office three days a week, with two days designated for remote work. Please communicate with your supervisor and HR department to establish your updated in-office workdays.\n", + "summary": "Starting May 1, 2023, our hybrid work policy will require employees to work from the office three days a week and two days remotely.", + "name": "Wfh Policy Update May 2023", + "url": "./sharepoint/WFH policy update May 2023.txt", + "created_on": "2023-05-01", + "updated_at": "2023-05-01", + "category": "teams", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + { + "content": "Executive Summary:\nThis sales strategy document outlines the key objectives, focus areas, and action plans for our tech company's sales operations in fiscal year 2024. Our primary goal is to increase revenue, expand market share, and strengthen customer relationships in our target markets.\n\nI. Objectives for Fiscal Year 2024\n\nIncrease revenue by 20% compared to fiscal year 2023.\nExpand market share in key segments by 15%.\nRetain 95% of existing customers and increase customer satisfaction ratings.\nLaunch at least two new products or services in high-demand market segments.\n\nII. Focus Areas\nA. Target Markets:\nContinue to serve existing markets with a focus on high-growth industries.\nIdentify and penetrate new markets with high potential for our products and services.\n\nB. Customer Segmentation:\nStrengthen relationships with key accounts and strategic partners.\nPursue new customers in underserved market segments.\nDevelop tailored offerings for different customer segments based on their needs and preferences.\n\nC. Product/Service Portfolio:\nOptimize the existing product/service portfolio by focusing on high-demand solutions.\nDevelop and launch innovative products/services in emerging technology areas.\nEnhance post-sales support and customer service to improve customer satisfaction.\n\nIII. Action Plans\nA. Sales Team Development:\nExpand the sales team to cover new markets and industries.\nProvide ongoing training to sales staff on product knowledge, sales techniques, and industry trends.\nImplement a performance-based incentive system to reward top performers.\n\nB. Marketing and Promotion:\nDevelop targeted marketing campaigns for different customer segments and industries.\nLeverage digital marketing channels to increase brand visibility and lead generation.\nParticipate in industry events and trade shows to showcase our products and services.\n\nC. Partner Ecosystem:\nStrengthen existing partnerships and establish new strategic alliances to expand market reach.\nCollaborate with partners on joint marketing and sales initiatives.\nProvide partner training and support to ensure they effectively represent our products and services.\n\nD. Customer Success:\nImplement a proactive customer success program to improve customer retention and satisfaction.\nDevelop a dedicated customer support team to address customer inquiries and concerns promptly.\nCollect and analyze customer feedback to identify areas for improvement in our products, services, and processes.\n\nIV. Monitoring and Evaluation\nEstablish key performance indicators (KPIs) to track progress toward our objectives.\nConduct regular sales team meetings to review performance, share best practices, and address challenges.\nConduct quarterly reviews of our sales strategy to ensure alignment with market trends and adjust as needed.\n\nBy following this sales strategy for fiscal year 2024, our tech company aims to achieve significant growth and success in our target markets, while also providing exceptional value and service to our customers.\n", + "summary": "This sales strategy document outlines objectives, focus areas, and action plans for our tech company's sales operations in fiscal year 2024. Our primary goal is to increase revenue, expand market share, and strengthen customer relationships in our target markets. Focus areas include targeting new markets, segmenting customers, enhancing", + "name": "Fy2024 Company Sales Strategy", + "url": "./sharepoint/FY2024 Company Sales Strategy.txt", + "category": "teams", + "created_on": "2023-04-15", + "updated_at": "2023-04-15", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + { + "content": "Purpose\n\nThe purpose of this vacation policy is to outline the guidelines and procedures for requesting and taking time off from work for personal and leisure purposes. This policy aims to promote a healthy work-life balance and encourage employees to take time to rest and recharge.\nScope\n\nThis policy applies to all full-time and part-time employees who have completed their probationary period.\nVacation Accrual\n\nFull-time employees accrue vacation time at a rate of [X hours] per month, equivalent to [Y days] per year. Part-time employees accrue vacation time on a pro-rata basis, calculated according to their scheduled work hours.\n\nVacation time will begin to accrue from the first day of employment, but employees are eligible to take vacation time only after completing their probationary period. Unused vacation time will be carried over to the next year, up to a maximum of [Z days]. Any additional unused vacation time will be forfeited.\nVacation Scheduling\n\nEmployees are required to submit vacation requests to their supervisor at least [A weeks] in advance, specifying the start and end dates of their vacation. Supervisors will review and approve vacation requests based on business needs, ensuring adequate coverage during the employee's absence.\n\nEmployees are encouraged to plan their vacations around the company's peak and non-peak periods to minimize disruptions. Vacation requests during peak periods may be subject to limitations and require additional advance notice.\nVacation Pay\n\nEmployees will receive their regular pay during their approved vacation time. Vacation pay will be calculated based on the employee's average earnings over the [B weeks] preceding their vacation.\nUnplanned Absences and Vacation Time\n\nIn the event of an unplanned absence due to illness or personal emergencies, employees may use their accrued vacation time, subject to supervisor approval. Employees must inform their supervisor as soon as possible and provide any required documentation upon their return to work.\nVacation Time and Termination of Employment\n\nIf an employee's employment is terminated, they will be paid out for any unused vacation time, calculated based on their current rate of pay.\nPolicy Review and Updates\n\nThis vacation policy will be reviewed periodically and updated as necessary, taking into account changes in labor laws, business needs, and employee feedback.\nQuestions and Concerns\n\nEmployees are encouraged to direct any questions or concerns about this policy to their supervisor or the HR department.\n", + "summary": ": This policy outlines the guidelines and procedures for requesting and taking time off from work for personal and leisure purposes. Full-time employees accrue vacation time at a rate of [X hours] per month, equivalent to [Y days] per year. Vacation requests must be submitted to supervisors at least", + "name": "Company Vacation Policy", + "url": "https://enterprisesearch.sharepoint.com/:t:/s/MSBuilddemo/ES6rw9bKZxVBobG1WUoJpikBF9Bhx1pw_GvJWbsg-Z_HNA?e=faSHVt", + "created_on": "2018-04-15", + "updated_at": "2018-04-16", + "category": "sharepoint", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + + { + "content": "This career leveling matrix provides a framework for understanding the various roles and responsibilities of Software Engineers, as well as the skills and experience required for each level. This matrix is intended to support employee development, facilitate performance evaluations, and provide a clear career progression path.\nJunior Software Engineer\n\nResponsibilities:\nCollaborate with team members to design, develop, and maintain software applications and components.\nWrite clean, well-structured, and efficient code following established coding standards.\nParticipate in code reviews, providing and receiving constructive feedback.\nTroubleshoot and resolve software defects and issues.\nAssist with the creation of technical documentation.\nContinuously learn and stay up-to-date with new technologies and best practices.\n\nSkills & Experience:\nBachelor\u2019s degree in Computer Science or a related field, or equivalent work experience.\nBasic understanding of software development principles and methodologies.\nProficiency in at least one programming language.\nStrong problem-solving and analytical skills.\nEffective communication and collaboration skills.\nEagerness to learn and grow within the field.\nSenior Software Engineer\n\nResponsibilities:\nDesign, develop, and maintain complex software applications and components.\nLead and mentor junior team members in software development best practices and techniques.\nConduct code reviews and ensure adherence to coding standards and best practices.\nCollaborate with cross-functional teams to define, design, and deliver software solutions.\nIdentify, troubleshoot, and resolve complex software defects and issues.\nContribute to the creation and maintenance of technical documentation.\nEvaluate and recommend new technologies, tools, and practices to improve software quality and efficiency.\n\nSkills & Experience:\nBachelor\u2019s degree in Computer Science or a related field, or equivalent work experience.\n5+ years of software development experience.\nProficiency in multiple programming languages and technologies.\nDemonstrated ability to design and implement complex software solutions.\nStrong leadership, mentoring, and collaboration skills.\nExcellent problem-solving, analytical, and communication skills.\nPrincipal Software Engineer\n\nResponsibilities:\nLead the design, development, and maintenance of large-scale, mission-critical software applications and components.\nProvide technical leadership and mentorship to software engineering teams.\nDrive the adoption of advanced software development practices and technologies.\nCollaborate with product management, architecture, and other stakeholders to define and deliver strategic software initiatives.\nIdentify, troubleshoot, and resolve the most complex software defects and issues.\nCreate and maintain technical documentation, including architectural designs and best practice guidelines.\nRepresent [Company Name] as a thought leader in the software engineering community, including speaking at conferences, publishing articles, and contributing to open-source projects.\n\nSkills & Experience:\nBachelor\u2019s degree in Computer Science or a related field, or equivalent work experience.\n10+ years of software development experience, with a focus on large-scale, mission-critical applications.\nExpertise in multiple programming languages, technologies, and software development methodologies.\nProven ability to lead and mentor high-performing software engineering teams.\nExceptional problem-solving, analytical, and communication skills.\nStrong business acumen and ability to influence decision-making at the executive level.\n\nBy following this career leveling matrix, we aim to support the growth and development of Software Engineers, enabling them to reach their full potential and contribute meaningfully to the success of the organization.\n", + "summary": "\nThis career leveling matrix provides a framework for understanding the various roles and responsibilities of Software Engineers, as well as the skills and experience required for each level. It is intended to support employee development, facilitate performance evaluations, and provide a clear career progression path.", + "name": "Swe Career Matrix", + "url": "https://enterprisesearch.sharepoint.com/:t:/s/MSBuilddemo/EVYuEyRhHh5Aqc3a39sqbGcBkqKIHRWtJBjjUjNs6snpMg?e=nv1mf4", + "created_on": "2018-04-15", + "category": "sharepoint", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + { + "content": "Title: Working with the Sales Team as an Engineer in a Tech Company\n\nIntroduction:\nAs an engineer in a tech company, collaboration with the sales team is essential to ensure the success of the company's products and services. This guidance document aims to provide an overview of how engineers can effectively work with the sales team, fostering a positive and productive working environment.\nUnderstanding the Sales Team's Role:\nThe sales team is responsible for promoting and selling the company's products and services to potential clients. Their role involves establishing relationships with customers, understanding their needs, and ensuring that the offered solutions align with their requirements.\n\nAs an engineer, it is important to understand the sales team's goals and objectives, as this will help you to provide them with the necessary information, tools, and support to successfully sell your company's products and services.\nCommunication:\nEffective communication is key to successfully working with the sales team. Make sure to maintain open lines of communication, and be responsive to their questions and concerns. This includes:\n\na. Attending sales meetings and conference calls when required.\nb. Providing regular product updates and training sessions to the sales team.\nc. Being available to answer technical questions and clarifications.\nCollaboration:\nCollaborate with the sales team in developing and refining sales materials, such as product presentations, demos, and technical documents. This will ensure that the sales team has accurate and up-to-date information to present to clients.\n\nAdditionally, work closely with the sales team on customer projects or product customizations, providing technical guidance, and ensuring that the solutions meet the customer's requirements.\nCustomer Engagement:\nAt times, engineers may be asked to join sales meetings or calls with potential clients to provide technical expertise. In these situations, it is important to:\n\na. Be prepared and understand the customer's needs and pain points.\nb. Clearly explain the technical aspects of the product or solution in a simple language that the customer can understand.\nc. Address any concerns or questions the customer may have.\nContinuous Improvement:\nActively seek feedback from the sales team regarding product performance, customer experiences, and market trends. Use this feedback to identify areas of improvement and collaborate with other engineers to enhance the product or service offerings.\nMutual Respect and Support:\nIt is essential to treat your colleagues in the sales team with respect and professionalism. Recognize and appreciate their efforts in promoting and selling the company's products and services. In turn, the sales team should also respect and appreciate the technical expertise and knowledge of the engineering team.\n\nBy working together, both the engineering and sales teams can contribute to the overall success of the company.\n\nConclusion:\nCollaboration between engineers and the sales team is crucial for a tech company's success. By understanding each other's roles, maintaining effective communication, collaborating on projects, and supporting one another, both teams can work together to achieve the company's goals and ensure customer satisfaction.\n", + "summary": ": This guide provides an overview of how engineers can effectively collaborate with the sales team to ensure the success of a tech company. It includes understanding the sales team's role, communicating and collaborating on projects, engaging customers, and providing mutual respect and support.", + "name": "Sales Engineering Collaboration", + "url": "https://enterprisesearch.sharepoint.com/:t:/s/MSBuilddemo/EW21-KJnfHBFoRiF49_uJMcBfHyPKimuPOFsCcJypQWaBQ?e=mGdIqe", + "created_on": "2019-04-15", + "category": "sharepoint", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + { + "content": "Purpose\nThe purpose of this Intellectual Property Policy is to establish guidelines and procedures for the ownership, protection, and utilization of intellectual property generated by employees during their employment. This policy aims to encourage creativity and innovation while ensuring that the interests of both the company and its employees are protected.\n\nScope\nThis policy applies to all employees, including full-time, part-time, temporary, and contract employees.\n\nDefinitions\na. Intellectual Property (IP): Refers to creations of the mind, such as inventions, literary and artistic works, designs, symbols, and images, that are protected by copyright, trademark, patent, or other forms of legal protection.\nb. Company Time: Refers to the time during which an employee is actively engaged in performing their job duties.\nc. Outside Company Time: Refers to the time during which an employee is not engaged in performing their job duties.\n\nOwnership of Intellectual Property\na. Work Generated on Company Time\ni. Any intellectual property created, conceived, or developed by an employee during company time or using company resources, equipment, or facilities shall be considered the property of the Company.\nii. Employees are required to promptly disclose any such intellectual property to their supervisor or the appropriate department head.\nb. Work Generated Outside Company Time\ni. Intellectual property created, conceived, or developed by an employee outside of company time and without the use of company resources, equipment, or facilities shall generally remain the property of the employee.\nii. However, if the intellectual property is directly related to the employee's job responsibilities, or if the employee has used company resources, equipment, or facilities in its creation, it may be considered the property of the Company.\nProtection and Utilization of Intellectual Property\na. The Company shall have the right to protect, license, and commercialize any intellectual property owned by the company as it deems appropriate.\nb. Employees are expected to cooperate with the Company in obtaining any necessary legal protection for intellectual property owned by the company, including by signing any documents or providing any necessary information or assistance.\nConfidentiality\nEmployees are expected to maintain the confidentiality of any intellectual property owned by the Company and not disclose it to any third parties without the express written consent of an authorized representative of the company.\nEmployee Acknowledgment\nAll employees are required to sign an acknowledgment of this Intellectual Property Policy as a condition of their employment with [Company Name]. By signing the acknowledgment, employees agree to abide by the terms of this policy and understand that any violations may result in disciplinary action, up to and including termination of employment.\nPolicy Review\nThis Intellectual Property Policy shall be reviewed periodically and may be amended as necessary to ensure its continued effectiveness and compliance with applicable laws and regulations. Employees will be notified of any significant changes to this policy.\n", + "summary": "This Intellectual Property Policy outlines guidelines and procedures for the ownership, protection, and utilization of intellectual property generated by employees during their employment. It establishes the company's ownership of work generated on company time, while recognizing employee ownership of work generated outside of company time without the use of company resources. The policy", + "name": "Intellectual Property Policy", + "url": "https://enterprisesearch.sharepoint.com/:t:/s/MSBuilddemo/EWz3cYEVdzBNsiHsYbKhms4BVYGhravyrUw3T3lzxL4pTg?e=mPIgbO", + "created_on": "2021-06-15", + "category": "sharepoint", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + + { + "content": "Code of Conduct\nPurpose\n\nThe purpose of this code of conduct is to establish guidelines for professional and ethical behavior in the workplace. It outlines the principles and values that all employees are expected to uphold in their interactions with colleagues, customers, partners, and other stakeholders.\nScope\n\nThis code of conduct applies to all employees, contractors, and volunteers within the organization, regardless of their role or seniority.\nCore Values\n\nEmployees are expected to adhere to the following core values:\n\na. Integrity: Act honestly, ethically, and in the best interests of the organization at all times.\nb. Respect: Treat all individuals with dignity, courtesy, and fairness, regardless of their background, beliefs, or position.\nc. Accountability: Take responsibility for one's actions and decisions, and be willing to learn from mistakes.\nd. Collaboration: Work cooperatively with colleagues and partners to achieve shared goals and promote a positive work environment.\ne. Excellence: Strive for the highest standards of performance and continuously seek opportunities for improvement.\nCompliance with Laws and Regulations\n\nEmployees must comply with all applicable laws, regulations, and organizational policies in the course of their work. This includes, but is not limited to, employment laws, data protection regulations, and industry-specific guidelines.\nConflicts of Interest\n\nEmployees should avoid situations where their personal interests may conflict with or influence their professional judgment. If a potential conflict of interest arises, employees must disclose it to their supervisor or the appropriate authority within the organization.\nConfidentiality and Information Security\n\nEmployees are responsible for safeguarding the organization's confidential information, as well as any sensitive information entrusted to them by clients, partners, or other third parties. This includes adhering to data protection policies and using secure communication channels.\nHarassment and Discrimination\n\nThe organization is committed to providing a workplace free from harassment, discrimination, and bullying. Employees are expected to treat others with respect and report any incidents of inappropriate behavior to their supervisor or the human resources department.\nHealth and Safety\n\nEmployees must follow all health and safety guidelines and procedures to maintain a safe and healthy work environment. This includes reporting any hazards or unsafe conditions to the appropriate personnel.\nUse of Company Resources\n\nEmployees are expected to use company resources, including time, equipment, and funds, responsibly and for their intended purposes. Misuse or theft of company resources is strictly prohibited.\nReporting Violations\n\nEmployees have a responsibility to report any suspected violations of this code of conduct, as well as any illegal or unethical behavior, to their supervisor or the appropriate authority within the organization. The organization will protect the confidentiality of employees who report violations and will not tolerate retaliation against those who raise concerns.\nConsequences of Non-Compliance\n\nFailure to adhere to this code of conduct may result in disciplinary action, up to and including termination of employment. The organization reserves the right to take legal action against individuals who engage in illegal or unethical conduct.\nPolicy Review and Updates\n\nThis code of conduct will be reviewed periodically and updated as necessary to ensure it remains relevant and effective in promoting ethical behavior and professional standards within the organization.\nQuestions and Concerns\n\nEmployees are encouraged to seek guidance from their supervisor or the human resources department if they have questions or concerns about this code of conduct or its application to specific situations.\n", + "summary": "This code of conduct outlines the principles and values that all employees are expected to uphold in their interactions with colleagues, customers, partners, and other stakeholders. It sets out core values such as integrity, respect, accountability, collaboration and excellence. Employees must comply with all applicable laws, regulations, and organizational", + "name": "Code Of Conduct", + "url": "https://enterprisesearch.sharepoint.com/:t:/s/MSBuilddemo/ER3xmeKaZ_pAqPeJWyyNR0QBg6QmoWIGPhwfEyCABWHrPA?e=cvzrgV", + "created_on": "2018-01-12", + "category": "sharepoint", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + { + "content": "Content:\nThe purpose of this office pet policy is to outline the guidelines and procedures for bringing pets into the workplace. This policy aims to create a positive and inclusive work environment while ensuring the comfort, safety, and well-being of all employees, visitors, and pets.\nScope\n\nThis policy applies to all employees who wish to bring their pets to the office. Pets covered under this policy include dogs, cats, and other small, non-exotic animals, subject to approval by the HR department.\nPet Approval Process\n\nEmployees must obtain prior approval from their supervisor and the HR department before bringing their pets to the office. The approval process includes:\n\na. Submitting a written request, including a description of the pet, its breed, age, and temperament.\nb. Providing proof of up-to-date vaccinations and any required licenses or permits.\nc. Obtaining written consent from all employees who share the workspace with the pet owner.\n\nThe HR department reserves the right to deny or revoke pet approval based on the specific circumstances or concerns raised by other employees.\nPet Behavior and Supervision\n\nEmployees are responsible for the behavior and well-being of their pets while in the office. Pets must be:\n\na. Well-behaved, non-aggressive, and not disruptive to the work environment.\nb. House-trained and able to eliminate waste in designated areas outside the office.\nc. Kept on a leash or in a secure enclosure when not in the employee's immediate work area.\n\nEmployees must closely supervise their pets and promptly address any issues or concerns raised by other staff members.\nAllergies and Phobias\n\nEmployees with allergies or phobias related to pets must inform the HR department, which will work with the affected employees and pet owners to find a suitable solution. This may include adjusting workspaces, limiting the number or types of pets allowed, or implementing additional safety measures.\nCleanliness and Hygiene\n\nEmployees are responsible for maintaining a clean and hygienic work environment. This includes:\n\na. Cleaning up after their pets, both indoors and outdoors.\nb. Regularly grooming their pets to minimize shedding and odors.\nc. Ensuring their pets are free of pests, such as fleas and ticks.\nLiability\n\nPet owners are liable for any damage or injury caused by their pets. Employees are encouraged to obtain pet liability insurance to cover potential incidents.\nRestricted Areas\n\nPets are not allowed in certain areas of the office, including meeting rooms, restrooms, kitchen and dining areas, and any other designated spaces. Signage will be posted to indicate these restricted areas.\nPolicy Review and Updates\n\nThis office pet policy will be reviewed periodically and updated as necessary, taking into account employee feedback, changes in legislation, and best practices for maintaining a safe and inclusive work environment.\nQuestions and Concerns\n\nEmployees are encouraged to direct any questions or concerns about this policy to their supervisor or the HR department.\n", + "summary": "This policy outlines the guidelines and procedures for bringing pets into the workplace. It covers approval process, pet behavior and supervision, allergies and phobias, cleanliness and hygiene, liability, restricted areas, and policy review. Employees must obtain prior approval from their supervisor and the HR department before bringing their", + "name": "Office Pet Policy", + "url": "https://enterprisesearch.sharepoint.com/:t:/s/MSBuilddemo/ETf-69wBeaZJpAn3CY7ExRABQWvav-p24VOnB6C0A4l2pQ?e=X72WuK", + "created_on": "2018-01-12", + "category": "sharepoint", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + { + "content": "Performance Management Policy\nPurpose and Scope\nThe purpose of this Performance Management Policy is to establish a consistent and transparent process for evaluating, recognizing, and rewarding employee performance. This policy applies to all employees and aims to foster a culture of continuous improvement, professional growth, and open communication between employees and management.\nPerformance Planning and Goal Setting\nAt the beginning of each performance cycle, employees and their supervisors will collaborate to set clear, achievable, and measurable performance goals. These goals should align with the company\u2019s strategic objectives and take into account the employee\u2019s job responsibilities, professional development, and career aspirations.\nOngoing Feedback and Communication\nThroughout the performance cycle, employees and supervisors are encouraged to engage in regular, constructive feedback and open communication. This includes discussing progress towards goals, addressing challenges, and identifying opportunities for improvement or additional support. Regular check-ins and updates help ensure that employees stay on track and receive the guidance they need to succeed.\nPerformance Evaluation\nAt the end of each performance cycle, employees will participate in a formal performance evaluation with their supervisor. This evaluation will assess the employee\u2019s overall performance, including their achievements, areas for improvement, and progress towards goals. Both the employee and supervisor should come prepared to discuss specific examples, accomplishments, and challenges from the performance period.\nPerformance Ratings\nBased on the performance evaluation, employees will receive a performance rating that reflects their overall performance during the cycle. The rating system should be clearly defined and consistently applied across the organization. Performance ratings will be used to inform decisions regarding promotions, salary increases, and other rewards or recognition.\nPromotions and Advancements\nHigh-performing employees who consistently demonstrate strong performance, leadership, and a commitment to the company\u2019s values may be considered for promotions or other advancement opportunities. Promotions will be based on factors such as performance ratings, skills, experience, and the needs of the organization. Employees interested in pursuing a promotion should discuss their career goals and development plans with their supervisor.\nPerformance Improvement Plans\nEmployees who receive a low performance rating or are struggling to meet their performance goals may be placed on a Performance Improvement Plan (PIP). A PIP is a structured plan designed to help the employee address specific areas of concern, set achievable improvement goals, and receive additional support or resources as needed. Employees on a PIP will be closely monitored and re-evaluated at the end of the improvement period to determine if satisfactory progress has been made.\nRecognition and Rewards\nOur company believes in recognizing and rewarding employees for their hard work and dedication. In addition to promotions and salary increases, employees may be eligible for other forms of recognition or rewards based on their performance. This may include bonuses, awards, or other incentives designed to motivate and celebrate employee achievements. The specific criteria and eligibility for these rewards will be communicated by the HR department or management.\n", + "summary": "This Performance Management Policy outlines a consistent and transparent process for evaluating, recognizing, and rewarding employees. It includes goal setting, ongoing feedback, performance evaluations, ratings, promotions, and rewards. The policy applies to all employees and encourages open communication and professional growth.", + "name": "Performance Management Policy", + "url": "https://enterprisesearch.sharepoint.com/:t:/s/MSBuilddemo/ERsxt9p1uehJqeJu4JlxkakBavbKwcldrYv_hpv3xHikAw?e=pf5R2C", + "created_on": "2018-01-12", + "category": "sharepoint", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + + { + "content": "Our sales organization is structured to effectively serve our customers and achieve our business objectives across multiple regions. The organization is divided into the following main regions:\n\nThe Americas: This region includes the United States, Canada, Mexico, as well as Central and South America. The North America South America region (NASA) has two Area Vice-Presidents: Laura Martinez is the Area Vice-President of North America, and Gary Johnson is the Area Vice-President of South America.\n\nEurope: Our European sales team covers the entire continent, including the United Kingdom, Germany, France, Spain, Italy, and other countries. The team is responsible for understanding the unique market dynamics and cultural nuances, enabling them to effectively target and engage with customers across the region. The Area Vice-President for Europe is Rajesh Patel.\nAsia-Pacific: This region encompasses countries such as China, Japan, South Korea, India, Australia, and New Zealand. Our sales team in the Asia-Pacific region works diligently to capitalize on growth opportunities and address the diverse needs of customers in this vast and rapidly evolving market. The Area Vice-President for Asia-Pacific is Mei Li.\nMiddle East & Africa: This region comprises countries across the Middle East and Africa, such as the United Arab Emirates, Saudi Arabia, South Africa, and Nigeria. Our sales team in this region is responsible for navigating the unique market challenges and identifying opportunities to expand our presence and better serve our customers. The Area Vice-President for Middle East & Africa is Jamal Abdi.\n\nEach regional sales team consists of dedicated account managers, sales representatives, and support staff, led by their respective Area Vice-Presidents. They are responsible for identifying and pursuing new business opportunities, nurturing existing client relationships, and ensuring customer satisfaction. The teams collaborate closely with other departments, such as marketing, product development, and customer support, to ensure we consistently deliver high-quality products and services to our clients.\n", + "summary": "\nOur sales organization is divided into four regions: The Americas, Europe, Asia-Pacific, and Middle East & Africa. Each region is led by an Area Vice-President and consists of dedicated account managers, sales representatives, and support staff. They collaborate with other departments to ensure the delivery of high", + "name": "Sales Organization Overview", + "url": "https://enterprisesearch.sharepoint.com/:t:/s/MSBuilddemo/EYsr1eqgn9hMslMJFLR-k54BBX-O3iC26bK7xNEBtYIBkg?e=xeAjiT", + "created_on": "2018-01-15", + "category": "sharepoint", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + { + "content": "Introduction:\nThis document outlines the compensation bands strategy for the various teams within our IT company. The goal is to establish a fair and competitive compensation structure that aligns with industry standards, rewards performance, and attracts top talent. By implementing this strategy, we aim to foster employee satisfaction and retention while ensuring the company's overall success.\n\nPurpose:\nThe purpose of this compensation bands strategy is to:\na. Define clear guidelines for salary ranges based on job levels and market benchmarks.\nb. Support equitable compensation practices across different teams.\nc. Encourage employee growth and performance.\nd. Enable effective budgeting and resource allocation.\n\nJob Levels:\nTo establish a comprehensive compensation structure, we have defined distinct job levels within each team. These levels reflect varying degrees of skills, experience, and responsibilities. The levels include:\na. Entry-Level: Employees with limited experience or early career professionals.\nb. Intermediate-Level: Employees with moderate experience and demonstrated competence.\nc. Senior-Level: Experienced employees with advanced skills and leadership capabilities.\nd. Leadership-Level: Managers and team leaders responsible for strategic decision-making.\n\nCompensation Bands:\nBased on the job levels, the following compensation bands have been established:\na. Entry-Level Band: This band encompasses salary ranges for employees in entry-level positions. It aims to provide competitive compensation for individuals starting their careers within the company.\n\nb. Intermediate-Level Band: This band covers salary ranges for employees who have gained moderate experience and expertise in their respective roles. It rewards employees for their growing skill set and contributions.\n\nc. Senior-Level Band: The senior-level band includes salary ranges for experienced employees who have attained advanced skills and have a proven track record of delivering results. It reflects the increased responsibilities and expectations placed upon these individuals.\n\nd. Leadership-Level Band: This band comprises salary ranges for managers and team leaders responsible for guiding and overseeing their respective teams. It considers their leadership abilities, strategic thinking, and the impact they have on the company's success.\n\nMarket Benchmarking:\nTo ensure our compensation remains competitive, regular market benchmarking will be conducted. This involves analyzing industry salary trends, regional compensation data, and market demand for specific roles. The findings will inform periodic adjustments to our compensation bands to maintain alignment with the market.\n\nPerformance-Based Compensation:\nIn addition to the defined compensation bands, we emphasize a performance-based compensation model. Performance evaluations will be conducted regularly, and employees exceeding performance expectations will be eligible for bonuses, incentives, and salary increases. This approach rewards high achievers and motivates employees to excel in their roles.\n\nConclusion:\nBy implementing this compensation bands strategy, our IT company aims to establish fair and competitive compensation practices that align with market standards and foster employee satisfaction. Regular evaluations and market benchmarking will enable us to adapt and refine the strategy to meet the evolving needs of our organization.", + "summary": "This document outlines a compensation framework for IT teams. It includes job levels, compensation bands, and performance-based incentives to ensure fair and competitive wages. Regular market benchmarking will be conducted to adjust the bands according to industry trends.", + "name": "Compensation Framework For It Teams", + "url": "https://enterprisesearch.sharepoint.com/:t:/s/MSBuilddemo/EaAFec6004tAg21g4i67rfgBBRqCm1yY7AZLLQyyaMtsEQ?e=wTMb4z", + "created_on": "2018-01-12", + "category": "sharepoint", + "restricted": true, + "_run_ml_inference": true, + "rolePermissions": ["manager"] + }, + { + "content": "As an employee in Canada, it's essential to understand how to update your tax elections forms to ensure accurate tax deductions from your pay. This guide will help you navigate the process of updating your TD1 Personal Tax Credits Return form.\n\nStep 1: Access the TD1 form\nThe TD1 form is available on the Canada Revenue Agency (CRA) website. Your employer might provide you with a paper copy or a link to the online form. You can access the form directly through the following link: https://www.canada.ca/en/revenue-agency/services/forms-publications/td1-personal-tax-credits-returns.html\n\nStep 2: Choose the correct form version\nYou'll need to fill out the federal TD1 form and, if applicable, the provincial or territorial TD1 form. Select the appropriate version based on your province or territory of residence.\n\nStep 3: Download and open the form\nFor the best experience, download and open the TD1 form in Adobe Reader. If you have visual impairments, consider using the large print version available on the CRA website.\n\nStep 4: Complete the form\nFill out the form by entering your personal information, such as your name, Social Insurance Number (SIN), and address. Then, go through each section to claim any personal tax credits that apply to you. These credits may include:\nBasic personal amount\nAmount for an eligible dependant\nAmount for infirm dependants age 18 or older\nCaregiver amount\nDisability amount\nTuition and education amounts\n\nRead the instructions carefully for each section to ensure you claim the correct amounts.\n\nStep 5: Sign and date the form\nOnce you've completed the form, sign and date it at the bottom.\n\nStep 6: Submit the form to your employer\nSubmit the completed and signed TD1 form to your employer. You can either scan and send it electronically, or provide a printed copy. Your employer will use the information on your TD1 form to calculate the correct amount of tax to be deducted from your pay.\n\nStep 7: Update your TD1 form as needed\nIt's essential to update your TD1 form whenever your personal circumstances change, such as getting married, having a child, or becoming eligible for a new tax credit. Inform your employer of these changes and submit an updated TD1 form to ensure accurate tax deductions.\n\nUpdating your tax elections forms is a crucial step in ensuring the correct tax deductions from your pay as a new employee in Canada. Follow this guide and keep your TD1 form up to date to avoid any discrepancies in your tax filings.\n", + "summary": ": This guide gives a step-by-step explanation of how to update your TD1 Personal Tax Credits Return form. Access the form from the CRA website and choose the correct version based on your province or territory of residence. Download and open the form in Adobe Reader, fill out the form by entering", + "name": "Updating Your Tax Elections Forms", + "url": "./github/Updating Your Tax Elections Forms.txt", + "created_on": "2022-12-20", + "category": "github", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + }, + { + "content": "Welcome to our team! We are excited to have you on board and look forward to your valuable contributions. This onboarding guide is designed to help you get started by providing essential information about our policies, procedures, and resources. Please read through this guide carefully and reach out to the HR department if you have any questions.\nIntroduction to Our Company Culture and Values\nOur company is committed to creating a diverse, inclusive, and supportive work environment. We believe that our employees are our most valuable asset and strive to foster a culture of collaboration, innovation, and continuous learning. Our core values include:\nIntegrity: We act ethically and honestly in all our interactions.\nTeamwork: We work together to achieve common goals and support each other's growth.\nExcellence: We strive for the highest quality in our products, services, and relationships.\nInnovation: We encourage creativity and embrace change to stay ahead in the market.\nRespect: We treat each other with dignity and value the unique perspectives of all our colleagues.\nKey Onboarding Steps\nTo ensure a smooth onboarding process, please complete the following steps within your first week:\nAttend orientation: You will be invited to an orientation session to meet your colleagues and learn more about our company's history, mission, and values.\nReview policies and procedures: Familiarize yourself with our employee handbook, which contains important information about our policies and procedures. Please read it thoroughly and adhere to the guidelines.\nComplete required training: You may be required to complete mandatory training sessions, such as safety training or anti-harassment training. Ensure that you attend and complete these sessions as soon as possible.\nUpdating Tax Elections and Documents\nIt is crucial to ensure your tax information is accurate and up-to-date, regardless of the country you work in. Please follow these steps to update your tax elections and documents:\nComplete tax forms: Fill out the necessary tax forms for your country or region, which determine the amount of income tax withheld from your paycheck. You should complete new tax forms if your personal or financial situation changes, such as marriage, divorce, or a change in the number of dependents.\nSubmit regional tax forms: Depending on your location, you may be required to complete additional regional or local tax forms. Check with the HR department to determine which forms are necessary.\nUpdate your address: If you move, make sure to update your address with the HR department to ensure accurate tax reporting.\nBenefits Enrollment\nAs a new employee, you are eligible for various benefits, including health insurance, retirement plans, and paid time off. You will receive detailed information about our benefits package during orientation. To enroll in the benefits, please follow these steps:\nReview benefits options: Carefully review the benefits package and choose the options that best meet your needs.\nComplete enrollment forms: Fill out the necessary forms to enroll in your chosen benefits. Submit these forms to the HR department within 30 days of your start date.\nDesignate beneficiaries: If applicable, designate beneficiaries for your life insurance and retirement plans.\nGetting Settled in Your Workspace\nTo help you feel comfortable and productive in your new workspace, take the following steps:\nSet up your workstation: Organize your desk, chair, and computer according to your preferences. If you require any additional equipment or accommodations, please contact the HR department.\nObtain necessary supplies: Request any necessary office supplies, such as pens, notepads, or folders, from the designated supply area or by contacting the appropriate department.\nFamiliarize yourself with office resources: Locate common areas, such as break rooms, restrooms, and meeting rooms. Familiarize yourself with office equipment, including printers, scanners, and telephones.\n", + "summary": "\nThis onboarding guide provides essential information to new employees on our company culture and values, key onboarding steps, tax elections and documents, benefits enrollment, and setting up their workspace.", + "name": "New Employee Onboarding Guide", + "url": "./github/New Employee Onboarding guide.txt", + "created_on": "2018-01-12", + "category": "github", + "_run_ml_inference": true, + "rolePermissions": ["demo", "manager"] + } +] diff --git a/templates/rag-elasticsearch/ingest.py b/templates/rag-elasticsearch/ingest.py new file mode 100644 index 00000000000..5b6db39d09e --- /dev/null +++ b/templates/rag-elasticsearch/ingest.py @@ -0,0 +1,53 @@ +import os + +from langchain.document_loaders import JSONLoader +from langchain.embeddings import HuggingFaceEmbeddings +from langchain.text_splitter import RecursiveCharacterTextSplitter +from langchain.vectorstores.elasticsearch import ElasticsearchStore + +ELASTIC_CLOUD_ID = os.getenv("ELASTIC_CLOUD_ID") +ELASTIC_USERNAME = os.getenv("ELASTIC_USERNAME", "elastic") +ELASTIC_PASSWORD = os.getenv("ELASTIC_PASSWORD") +ES_URL = os.getenv("ES_URL", "http://localhost:9200") + +if ELASTIC_CLOUD_ID and ELASTIC_USERNAME and ELASTIC_PASSWORD: + es_connection_details = { + "es_cloud_id": ELASTIC_CLOUD_ID, + "es_user": ELASTIC_USERNAME, + "es_password": ELASTIC_PASSWORD, + } +else: + es_connection_details = {"es_url": ES_URL} + + +# Metadata extraction function +def metadata_func(record: dict, metadata: dict) -> dict: + metadata["name"] = record.get("name") + metadata["summary"] = record.get("summary") + metadata["url"] = record.get("url") + metadata["category"] = record.get("category") + metadata["updated_at"] = record.get("updated_at") + + return metadata + + +## Load Data +loader = JSONLoader( + file_path="./data/documents.json", + jq_schema=".[]", + content_key="content", + metadata_func=metadata_func, +) + +text_splitter = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=250) +all_splits = text_splitter.split_documents(loader.load()) + +# Add to vectorDB +vectorstore = ElasticsearchStore.from_documents( + documents=all_splits, + embedding=HuggingFaceEmbeddings( + model_name="all-MiniLM-L6-v2", model_kwargs={"device": "cpu"} + ), + **es_connection_details, + index_name="workplace-search-example", +) diff --git a/templates/rag-elasticsearch/main.py b/templates/rag-elasticsearch/main.py new file mode 100644 index 00000000000..4034ab08f26 --- /dev/null +++ b/templates/rag-elasticsearch/main.py @@ -0,0 +1,33 @@ +from rag_elasticsearch import chain + +if __name__ == "__main__": + questions = [ + "What is the nasa sales team?", + "What is our work from home policy?", + "Does the company own my personal project?", + "How does compensation work?", + ] + + response = chain.invoke( + { + "question": questions[0], + "chat_history": [], + } + ) + print(response) + + follow_up_question = "What are their objectives?" + + response = chain.invoke( + { + "question": follow_up_question, + "chat_history": [ + "What is the nasa sales team?", + "The sales team of NASA consists of Laura Martinez, the Area " + "Vice-President of North America, and Gary Johnson, the Area " + "Vice-President of South America. (Sales Organization Overview)", + ], + } + ) + + print(response) diff --git a/templates/rag-elasticsearch/poetry.lock b/templates/rag-elasticsearch/poetry.lock new file mode 100644 index 00000000000..95cfff10e0a --- /dev/null +++ b/templates/rag-elasticsearch/poetry.lock @@ -0,0 +1,2363 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.6" +files = [ + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:41d55fc043954cddbbd82503d9cc3f4814a40bcef30b3569bc7b5e34130718c1"}, + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1d84166673694841d8953f0a8d0c90e1087739d24632fe86b1a08819168b4566"}, + {file = "aiohttp-3.8.6-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:253bf92b744b3170eb4c4ca2fa58f9c4b87aeb1df42f71d4e78815e6e8b73c9e"}, + {file = 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"poetry.core.masonry.api" diff --git a/templates/rag-elasticsearch/rag_elasticsearch/__init__.py b/templates/rag-elasticsearch/rag_elasticsearch/__init__.py new file mode 100644 index 00000000000..a30a18d8eb1 --- /dev/null +++ b/templates/rag-elasticsearch/rag_elasticsearch/__init__.py @@ -0,0 +1,3 @@ +from rag_elasticsearch.chain import chain + +__all__ = ["chain"] diff --git a/templates/rag-elasticsearch/rag_elasticsearch/chain.py b/templates/rag-elasticsearch/rag_elasticsearch/chain.py new file mode 100644 index 00000000000..9dd41ae9e96 --- /dev/null +++ b/templates/rag-elasticsearch/rag_elasticsearch/chain.py @@ -0,0 +1,58 @@ +from operator import itemgetter +from typing import List, Tuple + +from langchain.chat_models import ChatOpenAI +from langchain.embeddings import HuggingFaceEmbeddings +from langchain.schema import format_document +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnableMap, RunnablePassthrough +from langchain.vectorstores.elasticsearch import ElasticsearchStore + +from .connection import es_connection_details +from .prompts import CONDENSE_QUESTION_PROMPT, DOCUMENT_PROMPT, LLM_CONTEXT_PROMPT + +# Setup connecting to Elasticsearch +vectorstore = ElasticsearchStore( + **es_connection_details, + embedding=HuggingFaceEmbeddings( + model_name="all-MiniLM-L6-v2", model_kwargs={"device": "cpu"} + ), + index_name="workplace-search-example", +) +retriever = vectorstore.as_retriever() + +# Set up LLM to user +llm = ChatOpenAI(temperature=0) + + +def _combine_documents( + docs, document_prompt=DOCUMENT_PROMPT, document_separator="\n\n" +): + doc_strings = [format_document(doc, document_prompt) for doc in docs] + return document_separator.join(doc_strings) + + +def _format_chat_history(chat_history: List[Tuple]) -> str: + buffer = "" + for dialogue_turn in chat_history: + human = "Human: " + dialogue_turn[0] + ai = "Assistant: " + dialogue_turn[1] + buffer += "\n" + "\n".join([human, ai]) + return buffer + + +_inputs = RunnableMap( + standalone_question=RunnablePassthrough.assign( + chat_history=lambda x: _format_chat_history(x["chat_history"]) + ) + | CONDENSE_QUESTION_PROMPT + | llm + | StrOutputParser(), +) + +_context = { + "context": itemgetter("standalone_question") | retriever | _combine_documents, + "question": lambda x: x["standalone_question"], +} + +chain = _inputs | _context | LLM_CONTEXT_PROMPT | llm | StrOutputParser() diff --git a/templates/rag-elasticsearch/rag_elasticsearch/connection.py b/templates/rag-elasticsearch/rag_elasticsearch/connection.py new file mode 100644 index 00000000000..2e2ebd18148 --- /dev/null +++ b/templates/rag-elasticsearch/rag_elasticsearch/connection.py @@ -0,0 +1,15 @@ +import os + +ELASTIC_CLOUD_ID = os.getenv("ELASTIC_CLOUD_ID") +ELASTIC_USERNAME = os.getenv("ELASTIC_USERNAME", "elastic") +ELASTIC_PASSWORD = os.getenv("ELASTIC_PASSWORD") +ES_URL = os.getenv("ES_URL", "http://localhost:9200") + +if ELASTIC_CLOUD_ID and ELASTIC_USERNAME and ELASTIC_PASSWORD: + es_connection_details = { + "es_cloud_id": ELASTIC_CLOUD_ID, + "es_user": ELASTIC_USERNAME, + "es_password": ELASTIC_PASSWORD, + } +else: + es_connection_details = {"es_url": ES_URL} diff --git a/templates/rag-elasticsearch/rag_elasticsearch/prompts.py b/templates/rag-elasticsearch/rag_elasticsearch/prompts.py new file mode 100644 index 00000000000..ca1e588242d --- /dev/null +++ b/templates/rag-elasticsearch/rag_elasticsearch/prompts.py @@ -0,0 +1,39 @@ +from langchain.prompts import ChatPromptTemplate, PromptTemplate + +# Used to condense a question and chat history into a single question +condense_question_prompt_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question, in its original language. If there is no chat history, just rephrase the question to be a standalone question. + +Chat History: +{chat_history} +Follow Up Input: {question} +""" # noqa: E501 +CONDENSE_QUESTION_PROMPT = PromptTemplate.from_template( + condense_question_prompt_template +) + +# RAG Prompt to provide the context and question for LLM to answer +# We also ask the LLM to cite the source of the passage it is answering from +llm_context_prompt_template = """ +Use the following passages to answer the user's question. +Each passage has a SOURCE which is the title of the document. When answering, cite source name of the passages you are answering from below the answer in a unique bullet point list. + +If you don't know the answer, just say that you don't know, don't try to make up an answer. + +---- +{context} +---- +Question: {question} +""" # noqa: E501 + +LLM_CONTEXT_PROMPT = ChatPromptTemplate.from_template(llm_context_prompt_template) + +# Used to build a context window from passages retrieved +document_prompt_template = """ +--- +NAME: {name} +PASSAGE: +{page_content} +--- +""" + +DOCUMENT_PROMPT = PromptTemplate.from_template(document_prompt_template) diff --git a/templates/rag-fusion/README.md b/templates/rag-fusion/README.md new file mode 100644 index 00000000000..97c4de22910 --- /dev/null +++ b/templates/rag-fusion/README.md @@ -0,0 +1,5 @@ +# RAG Fusion + +Re-implemented from [this GitHub repo](https://github.com/Raudaschl/rag-fusion), all credit to original author + +> RAG-Fusion, a search methodology that aims to bridge the gap between traditional search paradigms and the multifaceted dimensions of human queries. Inspired by the capabilities of Retrieval Augmented Generation (RAG), this project goes a step further by employing multiple query generation and Reciprocal Rank Fusion to re-rank search results. diff --git a/templates/rag-fusion/ingest.py b/templates/rag-fusion/ingest.py new file mode 100644 index 00000000000..9604658e4da --- /dev/null +++ b/templates/rag-fusion/ingest.py @@ -0,0 +1,22 @@ +import pinecone +from langchain.embeddings import OpenAIEmbeddings +from langchain.vectorstores import Pinecone + +pinecone.init(api_key="...", environment="...") + +all_documents = { + "doc1": "Climate change and economic impact.", + "doc2": "Public health concerns due to climate change.", + "doc3": "Climate change: A social perspective.", + "doc4": "Technological solutions to climate change.", + "doc5": "Policy changes needed to combat climate change.", + "doc6": "Climate change and its impact on biodiversity.", + "doc7": "Climate change: The science and models.", + "doc8": "Global warming: A subset of climate change.", + "doc9": "How climate change affects daily weather.", + "doc10": "The history of climate change activism.", +} + +Pinecone.from_texts( + list(all_documents.values()), OpenAIEmbeddings(), index_name="rag-fusion" +) diff --git a/templates/rag-fusion/main.py b/templates/rag-fusion/main.py new file mode 100644 index 00000000000..642b48eaa36 --- /dev/null +++ b/templates/rag-fusion/main.py @@ -0,0 +1,5 @@ +from rag_fusion.chain import chain + +if __name__ == "__main__": + original_query = "impact of climate change" + print(chain.invoke(original_query)) diff --git a/templates/rag-fusion/poetry.lock b/templates/rag-fusion/poetry.lock new file mode 100644 index 00000000000..fd12ee18c3a --- /dev/null +++ b/templates/rag-fusion/poetry.lock @@ -0,0 +1,1403 @@ +# This file is automatically @generated by Poetry and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +category = 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"^0.28.1" +pinecone-client = "^2.2.4" +langchainhub = "^0.1.13" + +[tool.langserve] +export_module = "rag_fusion.chain" +export_attr = "chain" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/rag-fusion/rag_fusion/__init__.py b/templates/rag-fusion/rag_fusion/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rag-fusion/rag_fusion/chain.py b/templates/rag-fusion/rag_fusion/chain.py new file mode 100644 index 00000000000..6d8f2892d2c --- /dev/null +++ b/templates/rag-fusion/rag_fusion/chain.py @@ -0,0 +1,43 @@ +import pinecone +from langchain import hub +from langchain.chat_models import ChatOpenAI +from langchain.embeddings import OpenAIEmbeddings +from langchain.load import dumps, loads +from langchain.schema.output_parser import StrOutputParser +from langchain.vectorstores import Pinecone + + +def reciprocal_rank_fusion(results: list[list], k=60): + fused_scores = {} + for docs in results: + # Assumes the docs are returned in sorted order of relevance + for rank, doc in enumerate(docs): + doc_str = dumps(doc) + if doc_str not in fused_scores: + fused_scores[doc_str] = 0 + fused_scores[doc_str] += 1 / (rank + k) + + reranked_results = [ + (loads(doc), score) + for doc, score in sorted(fused_scores.items(), key=lambda x: x[1], reverse=True) + ] + return reranked_results + + +pinecone.init(api_key="...", environment="...") + +prompt = hub.pull("langchain-ai/rag-fusion-query-generation") + +generate_queries = ( + prompt | ChatOpenAI(temperature=0) | StrOutputParser() | (lambda x: x.split("\n")) +) + +vectorstore = Pinecone.from_existing_index("rag-fusion", OpenAIEmbeddings()) +retriever = vectorstore.as_retriever() + +chain = ( + {"original_query": lambda x: x} + | generate_queries + | retriever.map() + | reciprocal_rank_fusion +) diff --git a/templates/rag-fusion/tests/__init__.py b/templates/rag-fusion/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rag-pinecone-multi-query/LICENSE b/templates/rag-pinecone-multi-query/LICENSE new file mode 100644 index 00000000000..426b6509034 --- /dev/null +++ b/templates/rag-pinecone-multi-query/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/rag-pinecone-multi-query/README.md b/templates/rag-pinecone-multi-query/README.md new file mode 100644 index 00000000000..a8968d2a018 --- /dev/null +++ b/templates/rag-pinecone-multi-query/README.md @@ -0,0 +1,51 @@ +# RAG Pinecone multi query + +This template performs RAG using Pinecone and OpenAI with the [multi-query retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/MultiQueryRetriever). + +This will use 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 for answer synthesis. + +## Pinecone + +This template uses Pinecone as a vectorstore and requires that `PINECONE_API_KEY`, `PINECONE_ENVIRONMENT`, and `PINECONE_INDEX` are set. + +## LLM + +Be sure that `OPENAI_API_KEY` is set in order to the OpenAI models. + +## App + +Example `server.py`: +``` +from fastapi import FastAPI +from langserve import add_routes +from rag_pinecone_multi_query.chain import chain + +app = FastAPI() + +# Edit this to add the chain you want to add +add_routes(app, chain, path="rag_pinecone_multi_query") + +if __name__ == "__main__": + import uvicorn + + uvicorn.run(app, host="0.0.0.0", port=8001) +``` + +Run: +``` +python app/server.py +``` + +Check endpoint: +``` +http://0.0.0.0:8001/docs +``` + +See `rag_pinecone_multi_query.ipynb` for example usage - +``` +from langserve.client import RemoteRunnable +rag_app_pinecone = RemoteRunnable('http://0.0.0.0:8001/rag_pinecone_multi_query') +rag_app_pinecone.invoke("What are the different types of agent memory") +``` \ No newline at end of file diff --git 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+python = ">=3.8.1,<4.0" +langchain = ">=0.0.313, <0.1" +openai = ">=0.28.1" +tiktoken = ">=0.5.1" +pinecone-client = ">=2.2.4" + +[tool.langserve] +export_module = "rag_pinecone_multi_query" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/rag-pinecone-multi-query/rag_pinecone_multi_query.ipynb b/templates/rag-pinecone-multi-query/rag_pinecone_multi_query.ipynb new file mode 100644 index 00000000000..cdf8f590824 --- /dev/null +++ b/templates/rag-pinecone-multi-query/rag_pinecone_multi_query.ipynb @@ -0,0 +1,57 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "681a5d1e", + "metadata": {}, + "source": [ + "## Connect to template" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d774be2a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'The different types of agent memory mentioned in the context are short-term memory, long-term memory, explicit/declarative memory, and implicit/procedural memory.'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langserve.client import RemoteRunnable\n", + "rag_app_pinecone = RemoteRunnable('http://0.0.0.0:8001/rag_pinecone_multi_query')\n", + "rag_app_pinecone.invoke(\"What are the different types of agent memory\")" + ] + } + ], + "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/templates/rag-pinecone-multi-query/rag_pinecone_multi_query/__init__.py b/templates/rag-pinecone-multi-query/rag_pinecone_multi_query/__init__.py new file mode 100644 index 00000000000..242620732ff --- /dev/null +++ b/templates/rag-pinecone-multi-query/rag_pinecone_multi_query/__init__.py @@ -0,0 +1,3 @@ +from rag_pinecone_multi_query.chain import chain + +__all__ = ["chain"] diff --git a/templates/rag-pinecone-multi-query/rag_pinecone_multi_query/chain.py b/templates/rag-pinecone-multi-query/rag_pinecone_multi_query/chain.py new file mode 100644 index 00000000000..b676de6e9c0 --- /dev/null +++ b/templates/rag-pinecone-multi-query/rag_pinecone_multi_query/chain.py @@ -0,0 +1,57 @@ +import os + +from langchain.chat_models import ChatOpenAI +from langchain.embeddings import OpenAIEmbeddings +from langchain.prompts import ChatPromptTemplate +from langchain.retrievers.multi_query import MultiQueryRetriever +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnableParallel, RunnablePassthrough +from langchain.vectorstores import Pinecone + +if os.environ.get("PINECONE_API_KEY", None) is None: + raise Exception("Missing `PINECONE_API_KEY` environment variable.") + +if os.environ.get("PINECONE_ENVIRONMENT", None) is None: + raise Exception("Missing `PINECONE_ENVIRONMENT` environment variable.") + +PINECONE_INDEX_NAME = os.environ.get("PINECONE_INDEX", "langchain-test") + +### Ingest code - you may need to run this the first time +# Load +# from langchain.document_loaders import WebBaseLoader +# loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") +# data = loader.load() + +# # Split +# from langchain.text_splitter import RecursiveCharacterTextSplitter +# text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) +# all_splits = text_splitter.split_documents(data) + +# # Add to vectorDB +# vectorstore = Pinecone.from_documents( +# documents=all_splits, embedding=OpenAIEmbeddings(), index_name=PINECONE_INDEX_NAME +# ) +# retriever = vectorstore.as_retriever() + +# Set up index with multi query retriever +vectorstore = Pinecone.from_existing_index(PINECONE_INDEX_NAME, OpenAIEmbeddings()) +model = ChatOpenAI(temperature=0) +retriever = MultiQueryRetriever.from_llm( + retriever=vectorstore.as_retriever(), llm=model +) + +# RAG prompt +template = """Answer the question based only on the following context: +{context} +Question: {question} +""" +prompt = ChatPromptTemplate.from_template(template) + +# RAG +model = ChatOpenAI() +chain = ( + RunnableParallel({"context": retriever, "question": RunnablePassthrough()}) + | prompt + | model + | StrOutputParser() +) diff --git a/templates/rag-pinecone-multi-query/tests/__init__.py b/templates/rag-pinecone-multi-query/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rag-pinecone-rerank/LICENSE b/templates/rag-pinecone-rerank/LICENSE new file mode 100644 index 00000000000..426b6509034 --- /dev/null +++ b/templates/rag-pinecone-rerank/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/rag-pinecone-rerank/README.md b/templates/rag-pinecone-rerank/README.md new file mode 100644 index 00000000000..6d28d16d4b0 --- /dev/null +++ b/templates/rag-pinecone-rerank/README.md @@ -0,0 +1,23 @@ +# RAG Pinecone Cohere Re-rank + +This template performs RAG using Pinecone and OpenAI, with [Cohere to perform re-ranking](https://python.langchain.com/docs/integrations/retrievers/cohere-reranker) on returned documents. + +[Re-ranking](https://docs.cohere.com/docs/reranking) provides a way to rank retrieved documents using specified filters or criteria. + +## Pinecone + +This connects to a hosted Pinecone vectorstore. + +Be sure that you have set a few env variables in `chain.py`: + +* `PINECONE_API_KEY` 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+langchain = ">=0.0.313, <0.1" +openai = ">=0.28.1" +tiktoken = ">=0.5.1" +pinecone-client = ">=2.2.4" +cohere = ">=4.32" + +[tool.langserve] +export_module = "rag_pinecone_rerank" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/rag-pinecone-rerank/rag_pinecone_rerank.ipynb b/templates/rag-pinecone-rerank/rag_pinecone_rerank.ipynb new file mode 100644 index 00000000000..f61b47c358f --- /dev/null +++ b/templates/rag-pinecone-rerank/rag_pinecone_rerank.ipynb @@ -0,0 +1,57 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "id": "681a5d1e", + "metadata": {}, + "source": [ + "## Connect to template" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d774be2a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'The agent memory consists of two components: short-term memory and long-term memory. The short-term memory is used for in-context learning and allows the model to learn from its experiences. The long-term memory enables the agent to retain and recall an infinite amount of information over extended periods by leveraging an external vector store and fast retrieval.'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langserve.client import RemoteRunnable\n", + "rag_app_pinecone = RemoteRunnable('http://localhost:8001/rag_pinecone_rerank')\n", + "rag_app_pinecone.invoke(\"How does agent memory work?\")" + ] + } + ], + "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/templates/rag-pinecone-rerank/rag_pinecone_rerank/__init__.py b/templates/rag-pinecone-rerank/rag_pinecone_rerank/__init__.py new file mode 100644 index 00000000000..eb291ce56a7 --- /dev/null +++ b/templates/rag-pinecone-rerank/rag_pinecone_rerank/__init__.py @@ -0,0 +1,3 @@ +from rag_pinecone_rerank.chain import chain + +__all__ = ["chain"] diff --git a/templates/rag-pinecone-rerank/rag_pinecone_rerank/chain.py b/templates/rag-pinecone-rerank/rag_pinecone_rerank/chain.py new file mode 100644 index 00000000000..46171f4f165 --- /dev/null +++ b/templates/rag-pinecone-rerank/rag_pinecone_rerank/chain.py @@ -0,0 +1,64 @@ +import os + +from langchain.chat_models import ChatOpenAI +from langchain.embeddings import OpenAIEmbeddings +from langchain.prompts import ChatPromptTemplate +from langchain.retrievers import ContextualCompressionRetriever +from langchain.retrievers.document_compressors import CohereRerank +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnableParallel, RunnablePassthrough +from langchain.vectorstores import Pinecone + +if os.environ.get("PINECONE_API_KEY", None) is None: + raise Exception("Missing `PINECONE_API_KEY` environment variable.") + +if os.environ.get("PINECONE_ENVIRONMENT", None) is None: + raise Exception("Missing `PINECONE_ENVIRONMENT` environment variable.") + +PINECONE_INDEX_NAME = os.environ.get("PINECONE_INDEX", "langchain-test") + +### Ingest code - you may need to run this the first time +# Load +# from langchain.document_loaders import WebBaseLoader +# loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") +# data = loader.load() + +# # Split +# from langchain.text_splitter import RecursiveCharacterTextSplitter +# text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) +# all_splits = text_splitter.split_documents(data) + +# # Add to vectorDB +# vectorstore = Pinecone.from_documents( +# documents=all_splits, embedding=OpenAIEmbeddings(), index_name=PINECONE_INDEX_NAME +# ) +# retriever = vectorstore.as_retriever() + +vectorstore = Pinecone.from_existing_index(PINECONE_INDEX_NAME, OpenAIEmbeddings()) + +# Get k=10 docs +retriever = vectorstore.as_retriever(search_kwargs={"k": 10}) + +# Re-rank +compressor = CohereRerank() +compression_retriever = ContextualCompressionRetriever( + base_compressor=compressor, base_retriever=retriever +) + +# RAG prompt +template = """Answer the question based only on the following context: +{context} +Question: {question} +""" +prompt = ChatPromptTemplate.from_template(template) + +# RAG +model = ChatOpenAI() +chain = ( + RunnableParallel( + {"context": compression_retriever, "question": RunnablePassthrough()} + ) + | prompt + | model + | StrOutputParser() +) diff --git a/templates/rag-pinecone-rerank/tests/__init__.py b/templates/rag-pinecone-rerank/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rag-pinecone/LICENSE b/templates/rag-pinecone/LICENSE new file mode 100644 index 00000000000..426b6509034 --- /dev/null +++ b/templates/rag-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/templates/rag-pinecone/README.md b/templates/rag-pinecone/README.md new file mode 100644 index 00000000000..b45f023aa8e --- /dev/null +++ b/templates/rag-pinecone/README.md @@ -0,0 +1,37 @@ +# RAG Pinecone + +This template performs RAG using Pinecone and OpenAI. + +## Pinecone + +This connects to a hosted Pinecone vectorstore. + +Be sure that you have set a few env variables in `chain.py`: + +* `PINECONE_API_KEY` +* `PINECONE_ENV` +* `index_name` + +## LLM + +Be sure that `OPENAI_API_KEY` is set in order to the OpenAI models. + +## Installation + +Create your LangServe app: +``` +langchain serve new my-app +cd my-app +``` + +Add template: +``` +langchain serve add rag-pinecone +``` + +Start server: +``` +langchain start +``` + +See Jupyter notebook `rag_pinecone` for various way to connect to the template. diff --git a/templates/rag-pinecone/poetry.lock b/templates/rag-pinecone/poetry.lock new file mode 100644 index 00000000000..9f0d0140142 --- /dev/null +++ b/templates/rag-pinecone/poetry.lock @@ -0,0 +1,2296 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.6" +files = [ + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:41d55fc043954cddbbd82503d9cc3f4814a40bcef30b3569bc7b5e34130718c1"}, + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1d84166673694841d8953f0a8d0c90e1087739d24632fe86b1a08819168b4566"}, + {file = "aiohttp-3.8.6-cp310-cp310-macosx_11_0_arm64.whl", hash = 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<0.1" +openai = ">=0.28.1" +tiktoken = ">=0.5.1" +pinecone-client = ">=2.2.4" + +[tool.langserve] +export_module = "rag_pinecone" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/rag-pinecone/rag_pinecone.ipynb b/templates/rag-pinecone/rag_pinecone.ipynb new file mode 100644 index 00000000000..3ec0744bb6f --- /dev/null +++ b/templates/rag-pinecone/rag_pinecone.ipynb @@ -0,0 +1,45 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "681a5d1e", + "metadata": {}, + "source": [ + "## Connect to template" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d774be2a", + "metadata": {}, + "outputs": [], + "source": [ + "from langserve.client import RemoteRunnable\n", + "rag_app_pinecone = RemoteRunnable('http://localhost:8000/rag-pinecone')\n", + "rag_app_pinecone.invoke(\"How does agent memory work?\")" + ] + } + ], + "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/templates/rag-pinecone/rag_pinecone/__init__.py b/templates/rag-pinecone/rag_pinecone/__init__.py new file mode 100644 index 00000000000..17d7773f46e --- /dev/null +++ b/templates/rag-pinecone/rag_pinecone/__init__.py @@ -0,0 +1,3 @@ +from rag_pinecone.chain import chain + +__all__ = ["chain"] diff --git a/templates/rag-pinecone/rag_pinecone/chain.py b/templates/rag-pinecone/rag_pinecone/chain.py new file mode 100644 index 00000000000..6777010d2a1 --- /dev/null +++ b/templates/rag-pinecone/rag_pinecone/chain.py @@ -0,0 +1,52 @@ +import os + +from langchain.chat_models import ChatOpenAI +from langchain.embeddings import OpenAIEmbeddings +from langchain.prompts import ChatPromptTemplate +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnableParallel, RunnablePassthrough +from langchain.vectorstores import Pinecone + +if os.environ.get("PINECONE_API_KEY", None) is None: + raise Exception("Missing `PINECONE_API_KEY` environment variable.") + +if os.environ.get("PINECONE_ENVIRONMENT", None) is None: + raise Exception("Missing `PINECONE_ENVIRONMENT` environment variable.") + +PINECONE_INDEX_NAME = os.environ.get("PINECONE_INDEX", "langchain-test") + +### Ingest code - you may need to run this the first time +# Load +# from langchain.document_loaders import WebBaseLoader +# loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/") +# data = loader.load() + +# # Split +# from langchain.text_splitter import RecursiveCharacterTextSplitter +# text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0) +# all_splits = text_splitter.split_documents(data) + +# # Add to vectorDB +# vectorstore = Pinecone.from_documents( +# documents=all_splits, embedding=OpenAIEmbeddings(), index_name=PINECONE_INDEX_NAME +# ) +# retriever = vectorstore.as_retriever() + +vectorstore = Pinecone.from_existing_index(PINECONE_INDEX_NAME, OpenAIEmbeddings()) +retriever = vectorstore.as_retriever() + +# RAG prompt +template = """Answer the question based only on the following context: +{context} +Question: {question} +""" +prompt = ChatPromptTemplate.from_template(template) + +# RAG +model = ChatOpenAI() +chain = ( + RunnableParallel({"context": retriever, "question": RunnablePassthrough()}) + | prompt + | model + | StrOutputParser() +) diff --git a/templates/rag-pinecone/tests/__init__.py b/templates/rag-pinecone/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rag-semi-structured/LICENSE b/templates/rag-semi-structured/LICENSE new file mode 100644 index 00000000000..578fc414ad4 --- /dev/null +++ b/templates/rag-semi-structured/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 pingpong-templates + +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/rag-semi-structured/README.md b/templates/rag-semi-structured/README.md new file mode 100644 index 00000000000..9aa3acd5f4b --- /dev/null +++ b/templates/rag-semi-structured/README.md @@ -0,0 +1,47 @@ +# Semi structured RAG + +This template performs RAG on semi-structured data (e.g., a PDF with text and tables). + +See this [blog post](https://langchain-blog.ghost.io/ghost/#/editor/post/652dc74e0633850001e977d4) for useful background context. + +## Data loading + +We use [partition_pdf](https://unstructured-io.github.io/unstructured/bricks/partition.html#partition-pdf) from Unstructured to extract both table and text elements. + +This will require some system-level package installations, e.g., on Mac: + +``` +brew install tesseract poppler +``` + +## Chroma + +[Chroma](https://python.langchain.com/docs/integrations/vectorstores/chroma) is an open-source vector database. + +This template will create and add documents to the vector database in `chain.py`. + +These documents can be loaded from [many sources](https://python.langchain.com/docs/integrations/document_loaders). + +## LLM + +Be sure that `OPENAI_API_KEY` is set in order to the OpenAI models. + +## Adding the template + +Create your LangServe app: +``` +langchain serve new my-app +cd my-app +``` + +Add template: +``` +langchain serve add rag-semi-structured +``` + +Start server: +``` +langchain start +``` + +See Jupyter notebook `rag_semi_structured` for various way to connect to the template. \ No newline at end of file diff --git a/templates/rag-semi-structured/docs/LLaVA.pdf b/templates/rag-semi-structured/docs/LLaVA.pdf new file mode 100644 index 00000000000..88da76cecd5 Binary files /dev/null and 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+langchain = ">=0.0.313, <0.1" +tiktoken = ">=0.5.1" +chromadb = ">=0.4.14" +openai = ">=0.27.9" +unstructured = ">=0.10.19" +pdf2image = ">=1.16.3" + +[tool.langserve] +export_module = "rag_semi_structured" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/rag-semi-structured/rag_semi_structured.ipynb b/templates/rag-semi-structured/rag_semi_structured.ipynb new file mode 100644 index 00000000000..3d66ab75f81 --- /dev/null +++ b/templates/rag-semi-structured/rag_semi_structured.ipynb @@ -0,0 +1,61 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "30fc2c27", + "metadata": {}, + "source": [ + "## Run Template\n", + "\n", + "As shown in the README, add template and start server:\n", + "```\n", + "langchain serve add rag-semi-structured\n", + "langchain start\n", + "```\n", + "\n", + "We can now look at the endpoints:\n", + "\n", + "http://127.0.0.1:8000/docs#\n", + "\n", + "And specifically at our loaded template:\n", + "\n", + "http://127.0.0.1:8000/docs#/default/invoke_rag_chroma_private_invoke_post\n", + " \n", + "We can also use remote runnable to call it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "65f5b560", + "metadata": {}, + "outputs": [], + "source": [ + "from langserve.client import RemoteRunnable\n", + "rag_app = RemoteRunnable('http://localhost:8000/rag-chroma-private')\n", + "rag_app.invoke(\"How does agent memory work?\")" + ] + } + ], + "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/templates/rag-semi-structured/rag_semi_structured/__init__.py b/templates/rag-semi-structured/rag_semi_structured/__init__.py new file mode 100644 index 00000000000..55f823dde08 --- /dev/null +++ b/templates/rag-semi-structured/rag_semi_structured/__init__.py @@ -0,0 +1,3 @@ +from rag_semi_structured.chain import chain + +__all__ = ["chain"] diff --git a/templates/rag-semi-structured/rag_semi_structured/chain.py b/templates/rag-semi-structured/rag_semi_structured/chain.py new file mode 100644 index 00000000000..e5e923ab55a --- /dev/null +++ b/templates/rag-semi-structured/rag_semi_structured/chain.py @@ -0,0 +1,111 @@ +# Load +import uuid + +from langchain.chat_models import ChatOpenAI +from langchain.embeddings import OpenAIEmbeddings +from langchain.prompts import ChatPromptTemplate +from langchain.retrievers.multi_vector import MultiVectorRetriever +from langchain.schema.document import Document +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough +from langchain.storage import InMemoryStore +from langchain.vectorstores import Chroma +from unstructured.partition.pdf import partition_pdf + +# Path to docs +path = "docs" +raw_pdf_elements = partition_pdf( + filename=path + "LLaMA2.pdf", + # Unstructured first finds embedded image blocks + extract_images_in_pdf=False, + # Use layout model (YOLOX) to get bounding boxes (for tables) and find titles + # Titles are any sub-section of the document + infer_table_structure=True, + # Post processing to aggregate text once we have the title + chunking_strategy="by_title", + # Chunking params to aggregate text blocks + # Attempt to create a new chunk 3800 chars + # Attempt to keep chunks > 2000 chars + max_characters=4000, + new_after_n_chars=3800, + combine_text_under_n_chars=2000, + image_output_dir_path=path, +) + +# Categorize by type +tables = [] +texts = [] +for element in raw_pdf_elements: + if "unstructured.documents.elements.Table" in str(type(element)): + tables.append(str(element)) + elif "unstructured.documents.elements.CompositeElement" in str(type(element)): + texts.append(str(element)) + +# Summarize + +prompt_text = """You are an assistant tasked with summarizing tables and text. \ +Give a concise summary of the table or text. Table or text chunk: {element} """ +prompt = ChatPromptTemplate.from_template(prompt_text) +model = ChatOpenAI(temperature=0, model="gpt-4") +summarize_chain = {"element": lambda x: x} | prompt | model | StrOutputParser() + +# Apply +table_summaries = summarize_chain.batch(tables, {"max_concurrency": 5}) +# To save time / cost, only do text summaries if chunk sizes are large +# text_summaries = summarize_chain.batch(texts, {"max_concurrency": 5}) +# We can just assign text_summaries to the raw texts +text_summaries = texts + +# Use multi vector retriever + +# The vectorstore to use to index the child chunks +vectorstore = Chroma(collection_name="summaries", embedding_function=OpenAIEmbeddings()) + +# The storage layer for the parent documents +store = InMemoryStore() +id_key = "doc_id" + +# The retriever (empty to start) +retriever = MultiVectorRetriever( + vectorstore=vectorstore, + docstore=store, + id_key=id_key, +) + +# Add texts +doc_ids = [str(uuid.uuid4()) for _ in texts] +summary_texts = [ + Document(page_content=s, metadata={id_key: doc_ids[i]}) + for i, s in enumerate(text_summaries) +] +retriever.vectorstore.add_documents(summary_texts) +retriever.docstore.mset(list(zip(doc_ids, texts))) + +# Add tables +table_ids = [str(uuid.uuid4()) for _ in tables] +summary_tables = [ + Document(page_content=s, metadata={id_key: table_ids[i]}) + for i, s in enumerate(table_summaries) +] +retriever.vectorstore.add_documents(summary_tables) +retriever.docstore.mset(list(zip(table_ids, tables))) + +# RAG + +# Prompt template +template = """Answer the question based only on the following context, which can include text and tables: +{context} +Question: {question} +""" # noqa: E501 +prompt = ChatPromptTemplate.from_template(template) + +# LLM +model = ChatOpenAI(temperature=0, model="gpt-4") + +# RAG pipeline +chain = ( + {"context": retriever, "question": RunnablePassthrough()} + | prompt + | model + | StrOutputParser() +) diff --git a/templates/rag-semi-structured/tests/__init__.py b/templates/rag-semi-structured/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rag-supabase/.gitignore b/templates/rag-supabase/.gitignore new file mode 100644 index 00000000000..4c49bd78f1d --- /dev/null +++ b/templates/rag-supabase/.gitignore @@ -0,0 +1 @@ +.env diff --git a/templates/rag-supabase/README.md b/templates/rag-supabase/README.md new file mode 100644 index 00000000000..7279f8284c4 --- /dev/null +++ b/templates/rag-supabase/README.md @@ -0,0 +1,103 @@ +# RAG with Supabase + +> [Supabase](https://supabase.com/docs) is an open-source Firebase alternative. It is built on top of [PostgreSQL](https://en.wikipedia.org/wiki/PostgreSQL), a free and open-source relational database management system (RDBMS) and uses [pgvector](https://github.com/pgvector/pgvector) to store embeddings within your tables. + +Use this package to host a retrieval augment generation (RAG) API using LangServe + Supabase. + +## Install Package + +From within your `langservehub` project run: + +```shell +poetry run poe add rag-supabase +``` + +## Setup Supabase Database + +Use these steps to setup your Supabase database if you haven't already. + +1. Head over to https://database.new to provision your Supabase database. +2. In the studio, jump to the [SQL editor](https://supabase.com/dashboard/project/_/sql/new) and run the following script to enable `pgvector` and setup your database as a vector store: + + ```sql + -- Enable the pgvector extension to work with embedding vectors + create extension if not exists vector; + + -- Create a table to store your documents + create table + documents ( + id uuid primary key, + content text, -- corresponds to Document.pageContent + metadata jsonb, -- corresponds to Document.metadata + embedding vector (1536) -- 1536 works for OpenAI embeddings, change as needed + ); + + -- Create a function to search for documents + create function match_documents ( + query_embedding vector (1536), + filter jsonb default '{}' + ) returns table ( + id uuid, + content text, + metadata jsonb, + similarity float + ) language plpgsql as $$ + #variable_conflict use_column + begin + return query + select + id, + content, + metadata, + 1 - (documents.embedding <=> query_embedding) as similarity + from documents + where metadata @> filter + order by documents.embedding <=> query_embedding; + end; + $$; + ``` + +## Setup Environment Variables + +Since we are using [`SupabaseVectorStore`](https://python.langchain.com/docs/integrations/vectorstores/supabase) and [`OpenAIEmbeddings`](https://python.langchain.com/docs/integrations/text_embedding/openai), we need to load their API keys. + +Create a `.env` file in the root of your project: + +_.env_ + +```shell +SUPABASE_URL= +SUPABASE_SERVICE_KEY= +OPENAI_API_KEY= +``` + +To find your `SUPABASE_URL` and `SUPABASE_SERVICE_KEY`, head to your Supabase project's [API settings](https://supabase.com/dashboard/project/_/settings/api). + +- `SUPABASE_URL` corresponds to the Project URL +- `SUPABASE_SERVICE_KEY` corresponds to the `service_role` API key + +To get your `OPENAI_API_KEY`, navigate to [API keys](https://platform.openai.com/account/api-keys) on your OpenAI account and create a new secret key. + +Add this file to your `.gitignore` if it isn't already there (so that we don't commit secrets): + +_.gitignore_ + +``` +.env +``` + +Install [`python-dotenv`](https://github.com/theskumar/python-dotenv) which we will use to load the environment variables into the app: + +```shell +poetry add python-dotenv +``` + +Finally, call `load_dotenv()` in `server.py`. + +_app/server.py_ + +```python +from dotenv import load_dotenv + +load_dotenv() +``` diff --git a/templates/rag-supabase/poetry.lock b/templates/rag-supabase/poetry.lock new file mode 100644 index 00000000000..935940528c1 --- /dev/null +++ b/templates/rag-supabase/poetry.lock @@ -0,0 +1,1698 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.6" +files = [ + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_universal2.whl", hash = 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">=0.0.313" +openai = "^0.28.1" +tiktoken = "^0.5.1" +supabase = "^1.2.0" + +[tool.poetry.group.dev.dependencies] +python-dotenv = { extras = ["cli"], version = "^1.0.0" } + +[tool.langserve] +export_module = "rag_supabase.chain" +export_attr = "chain" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/rag-supabase/rag_supabase/__init__.py b/templates/rag-supabase/rag_supabase/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rag-supabase/rag_supabase/chain.py b/templates/rag-supabase/rag_supabase/chain.py new file mode 100644 index 00000000000..e116e840c12 --- /dev/null +++ b/templates/rag-supabase/rag_supabase/chain.py @@ -0,0 +1,41 @@ +import os + +from langchain.chat_models import ChatOpenAI +from langchain.embeddings import OpenAIEmbeddings +from langchain.prompts import ChatPromptTemplate +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnableParallel, RunnablePassthrough +from langchain.vectorstores.supabase import SupabaseVectorStore +from supabase.client import create_client + +supabase_url = os.environ.get("SUPABASE_URL") +supabase_key = os.environ.get("SUPABASE_SERVICE_KEY") +supabase = create_client(supabase_url, supabase_key) + +embeddings = OpenAIEmbeddings() + +vectorstore = SupabaseVectorStore( + client=supabase, + embedding=embeddings, + table_name="documents", + query_name="match_documents", +) + +retriever = vectorstore.as_retriever() + +template = """Answer the question based only on the following context: +{context} + +Question: {question} +""" + +prompt = ChatPromptTemplate.from_template(template) + +model = ChatOpenAI() + +chain = ( + RunnableParallel({"context": retriever, "question": RunnablePassthrough()}) + | prompt + | model + | StrOutputParser() +) diff --git a/templates/rag-supabase/tests/__init__.py b/templates/rag-supabase/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rewrite-retrieve-read/README.md b/templates/rewrite-retrieve-read/README.md new file mode 100644 index 00000000000..16f65645513 --- /dev/null +++ b/templates/rewrite-retrieve-read/README.md @@ -0,0 +1,7 @@ +# Rewrite-Retrieve-Read + +**Rewrite-Retrieve-Read** is a method proposed in the paper [Query Rewriting for Retrieval-Augmented Large Language Models](https://arxiv.org/pdf/2305.14283.pdf) + +> Because the original query can not be always optimal to retrieve for the LLM, especially in the real world... we first prompt an LLM to rewrite the queries, then conduct retrieval-augmented reading + +We show how you can easily do that with LangChain Expression Language diff --git a/templates/rewrite-retrieve-read/main.py b/templates/rewrite-retrieve-read/main.py new file mode 100644 index 00000000000..deedf470341 --- /dev/null +++ b/templates/rewrite-retrieve-read/main.py @@ -0,0 +1,4 @@ +from rewrite_retrieve_read.chain import chain + +if 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+langchain = ">=0.0.313" +duckduckgo-search = "^3.9.3" + +[tool.langserve] +export_module = "rewrite_retrieve_read.chain" +export_attr = "chain" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/rewrite-retrieve-read/rewrite_retrieve_read/__init__.py b/templates/rewrite-retrieve-read/rewrite_retrieve_read/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/rewrite-retrieve-read/rewrite_retrieve_read/chain.py b/templates/rewrite-retrieve-read/rewrite_retrieve_read/chain.py new file mode 100644 index 00000000000..456db4204d3 --- /dev/null +++ b/templates/rewrite-retrieve-read/rewrite_retrieve_read/chain.py @@ -0,0 +1,42 @@ + +from langchain.chat_models import ChatOpenAI +from langchain.prompts import ChatPromptTemplate +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough +from langchain.utilities import DuckDuckGoSearchAPIWrapper + +template = """Answer the users question based only on the following context: + + +{context} + + +Question: {question} +""" +prompt = ChatPromptTemplate.from_template(template) + +model = ChatOpenAI(temperature=0) + +search = DuckDuckGoSearchAPIWrapper() + + +def retriever(query): + return search.run(query) + +template = """Provide a better search query for \ +web search engine to answer the given question, end \ +the queries with โ€™**โ€™. Question: \ +{x} Answer:""" +rewrite_prompt = ChatPromptTemplate.from_template(template) + +# Parser to remove the `**` + +def _parse(text): + return text.strip("**") + +rewriter = rewrite_prompt | ChatOpenAI(temperature=0) | StrOutputParser() | _parse + +chain = { + "context": {"x": RunnablePassthrough()} | rewriter | retriever, + "question": RunnablePassthrough() + } | prompt | model | StrOutputParser() diff --git a/templates/rewrite-retrieve-read/tests/__init__.py b/templates/rewrite-retrieve-read/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/self-query-supabase/.gitignore b/templates/self-query-supabase/.gitignore new file mode 100644 index 00000000000..4c49bd78f1d --- /dev/null +++ b/templates/self-query-supabase/.gitignore @@ -0,0 +1 @@ +.env diff --git a/templates/self-query-supabase/README.md b/templates/self-query-supabase/README.md new file mode 100644 index 00000000000..d7382001d8f --- /dev/null +++ b/templates/self-query-supabase/README.md @@ -0,0 +1,103 @@ +# Self-querying with Supabase + +> [Supabase](https://supabase.com/docs) is an open-source Firebase alternative. It is built on top of [PostgreSQL](https://en.wikipedia.org/wiki/PostgreSQL), a free and open-source relational database management system (RDBMS) and uses [pgvector](https://github.com/pgvector/pgvector) to store embeddings within your tables. + +Use this package to host a LangServe API that can [self-query](https://python.langchain.com/docs/modules/data_connection/retrievers/self_query) Supabase. You'll be able to use natural language to generate a structured query against the database. + +## Install Package + +From within your `langservehub` project run: + +```shell +poetry run poe add self-query-supabase +``` + +## Setup Supabase Database + +Use these steps to setup your Supabase database if you haven't already. + +1. Head over to https://database.new to provision your Supabase database. +2. In the studio, jump to the [SQL editor](https://supabase.com/dashboard/project/_/sql/new) and run the following script to enable `pgvector` and setup your database as a vector store: + + ```sql + -- Enable the pgvector extension to work with embedding vectors + create extension if not exists vector; + + -- Create a table to store your documents + create table + documents ( + id uuid primary key, + content text, -- corresponds to Document.pageContent + metadata jsonb, -- corresponds to Document.metadata + embedding vector (1536) -- 1536 works for OpenAI embeddings, change as needed + ); + + -- Create a function to search for documents + create function match_documents ( + query_embedding vector (1536), + filter jsonb default '{}' + ) returns table ( + id uuid, + content text, + metadata jsonb, + similarity float + ) language plpgsql as $$ + #variable_conflict use_column + begin + return query + select + id, + content, + metadata, + 1 - (documents.embedding <=> query_embedding) as similarity + from documents + where metadata @> filter + order by documents.embedding <=> query_embedding; + end; + $$; + ``` + +## Setup Environment Variables + +Since we are using [`SupabaseVectorStore`](https://python.langchain.com/docs/integrations/vectorstores/supabase) and [`OpenAIEmbeddings`](https://python.langchain.com/docs/integrations/text_embedding/openai), we need to load their API keys. + +Create a `.env` file in the root of your project: + +_.env_ + +```shell +SUPABASE_URL= +SUPABASE_SERVICE_KEY= +OPENAI_API_KEY= +``` + +To find your `SUPABASE_URL` and `SUPABASE_SERVICE_KEY`, head to your Supabase project's [API settings](https://supabase.com/dashboard/project/_/settings/api). + +- `SUPABASE_URL` corresponds to the Project URL +- `SUPABASE_SERVICE_KEY` corresponds to the `service_role` API key + +To get your `OPENAI_API_KEY`, navigate to [API keys](https://platform.openai.com/account/api-keys) on your OpenAI account and create a new secret key. + +Add this file to your `.gitignore` if it isn't already there (so that we don't commit secrets): + +_.gitignore_ + +``` +.env +``` + +Install [`python-dotenv`](https://github.com/theskumar/python-dotenv) which we will use to load the environment variables into the app: + +```shell +poetry add python-dotenv +``` + +Finally, call `load_dotenv()` in `server.py`. + +_app/server.py_ + +```python +from dotenv import load_dotenv + +load_dotenv() +``` diff --git a/templates/self-query-supabase/poetry.lock b/templates/self-query-supabase/poetry.lock new file mode 100644 index 00000000000..802f41eb1fb --- /dev/null +++ b/templates/self-query-supabase/poetry.lock @@ -0,0 +1,1710 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.6" +files = [ + {file = "aiohttp-3.8.6-cp310-cp310-macosx_10_9_universal2.whl", hash = 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">=3.8.1,<4.0" +langchain = ">=0.0.313" +openai = "^0.28.1" +tiktoken = "^0.5.1" +supabase = "^1.2.0" +lark = "^1.1.8" + +[tool.poetry.group.dev.dependencies] +python-dotenv = { extras = ["cli"], version = "^1.0.0" } + +[tool.langserve] +export_module = "self_query_supabase.chain" +export_attr = "chain" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/self-query-supabase/self_query_supabase/__init__.py b/templates/self-query-supabase/self_query_supabase/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/self-query-supabase/self_query_supabase/chain.py b/templates/self-query-supabase/self_query_supabase/chain.py new file mode 100644 index 00000000000..eca0c0e82c4 --- /dev/null +++ b/templates/self-query-supabase/self_query_supabase/chain.py @@ -0,0 +1,54 @@ +import os + +from langchain.chains.query_constructor.base import AttributeInfo +from langchain.embeddings import OpenAIEmbeddings +from langchain.llms.openai import OpenAI +from langchain.retrievers.self_query.base import SelfQueryRetriever +from langchain.schema.runnable import RunnableParallel, RunnablePassthrough +from langchain.vectorstores.supabase import SupabaseVectorStore +from supabase.client import create_client + +supabase_url = os.environ.get("SUPABASE_URL") +supabase_key = os.environ.get("SUPABASE_SERVICE_KEY") +supabase = create_client(supabase_url, supabase_key) + +embeddings = OpenAIEmbeddings() + +vectorstore = SupabaseVectorStore( + client=supabase, + embedding=embeddings, + table_name="documents", + query_name="match_documents", +) + +# Adjust this based on the metadata you store in the `metadata` JSON column +metadata_field_info = [ + AttributeInfo( + name="genre", + description="The genre of the movie", + type="string or list[string]", + ), + AttributeInfo( + name="year", + description="The year the movie was released", + type="integer", + ), + AttributeInfo( + name="director", + description="The name of the movie director", + type="string", + ), + AttributeInfo( + name="rating", description="A 1-10 rating for the movie", type="float" + ), +] + +# Adjust this based on the type of documents you store +document_content_description = "Brief summary of a movie" +llm = OpenAI(temperature=0) + +retriever = SelfQueryRetriever.from_llm( + llm, vectorstore, document_content_description, metadata_field_info, verbose=True +) + +chain = RunnableParallel({"query": RunnablePassthrough()}) | retriever diff --git a/templates/self-query-supabase/tests/__init__.py b/templates/self-query-supabase/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/sql-llama2/README.md b/templates/sql-llama2/README.md new file mode 100644 index 00000000000..c3574f066a4 --- /dev/null +++ b/templates/sql-llama2/README.md @@ -0,0 +1,15 @@ +# SQL with LLaMA2 + +This template allows you to chat with a SQL database in natural language using LLaMA2. + +It is configured to use [Replicate](https://python.langchain.com/docs/integrations/llms/replicate). + +But, it can be adapted to any API that support LLaMA2, including [Fireworks](https://python.langchain.com/docs/integrations/chat/fireworks) and others. + +See related templates `sql-ollama` and `sql-llamacpp` for private, local chat with SQL. + +## Installation +```bash +# from inside your LangServe instance +poe add sql-llama2 +``` diff --git a/templates/sql-llama2/nba_roster.db b/templates/sql-llama2/nba_roster.db new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/sql-llama2/poetry.lock b/templates/sql-llama2/poetry.lock new file mode 100644 index 00000000000..01f5778dc5b --- /dev/null +++ b/templates/sql-llama2/poetry.lock @@ -0,0 +1,1204 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.8.6" +description = "Async http client/server framework (asyncio)" +optional = false 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b/templates/sql-llama2/sql_llama2/__init__.py new file mode 100644 index 00000000000..7e2706adcab --- /dev/null +++ b/templates/sql-llama2/sql_llama2/__init__.py @@ -0,0 +1,3 @@ +from llama2.chain import chain + +__all__ = ["chain"] \ No newline at end of file diff --git a/templates/sql-llama2/sql_llama2/chain.py b/templates/sql-llama2/sql_llama2/chain.py new file mode 100644 index 00000000000..7e53f5d147c --- /dev/null +++ b/templates/sql-llama2/sql_llama2/chain.py @@ -0,0 +1,77 @@ +from pathlib import Path + +from langchain.llms import Replicate +from langchain.prompts import ChatPromptTemplate +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough +from langchain.utilities import SQLDatabase + +# make sure to set REPLICATE_API_TOKEN in your environment +# use llama-2-13b model in replicate +replicate_id = "meta/llama-2-13b-chat:f4e2de70d66816a838a89eeeb621910adffb0dd0baba3976c96980970978018d" # noqa: E501 +llm = Replicate( + model=replicate_id, + model_kwargs={"temperature": 0.01, "max_length": 500, "top_p": 1}, +) + + +db_path = Path(__file__).parent / "nba_roster.db" +rel = db_path.relative_to(Path.cwd()) +db_string = f"sqlite:///{rel}" +db = SQLDatabase.from_uri(db_string, sample_rows_in_table_info=0) + + +def get_schema(_): + return db.get_table_info() + + +def run_query(query): + return db.run(query) + + +template_query = """Based on the table schema below, write a SQL query that would answer the user's question: +{schema} + +Question: {question} +SQL Query:""" # noqa: E501 +prompt = ChatPromptTemplate.from_messages( + [ + ("system", "Given an input question, convert it to a SQL query. No pre-amble."), + ("human", template_query), + ] +) + +sql_response = ( + RunnablePassthrough.assign(schema=get_schema) + | prompt + | llm.bind(stop=["\nSQLResult:"]) + | StrOutputParser() +) + +template_response = """Based on the table schema below, question, sql query, and sql response, write a natural language response: +{schema} + +Question: {question} +SQL Query: {query} +SQL Response: {response}""" # noqa: E501 + +prompt_response = ChatPromptTemplate.from_messages( + [ + ( + "system", + "Given an input question and SQL response, convert it to a natural " + "language answer. No pre-amble.", + ), + ("human", template_response), + ] +) + +chain = ( + RunnablePassthrough.assign(query=sql_response) + | RunnablePassthrough.assign( + schema=get_schema, + response=lambda x: db.run(x["query"]), + ) + | prompt_response + | llm +) diff --git a/templates/sql-llama2/sql_llama2/nba_roster.db b/templates/sql-llama2/sql_llama2/nba_roster.db new file mode 100644 index 00000000000..9ea4e20c367 Binary files /dev/null and b/templates/sql-llama2/sql_llama2/nba_roster.db differ diff --git a/templates/sql-llama2/tests/__init__.py b/templates/sql-llama2/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/sql-llamacpp/README.md b/templates/sql-llamacpp/README.md new file mode 100644 index 00000000000..91c41c7a96b --- /dev/null +++ b/templates/sql-llamacpp/README.md @@ -0,0 +1,36 @@ +# SQL with LLaMA2 using Ollama + +This template allows you to chat with a SQL database in natural language in private, using an open source LLM. + +## LLama.cpp + +### Enviorment + +From [here](https://python.langchain.com/docs/guides/local_llms) and [here](https://github.com/abetlen/llama-cpp-python/blob/main/docs/install/macos.md). + +``` +wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh +bash Miniforge3-MacOSX-arm64.sh +conda create -n llama python=3.9.16 +conda activate /Users/rlm/miniforge3/envs/llama +CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install -U llama-cpp-python --no-cache-dir +``` + +### LLM + +It will download Mistral-7b model from [here](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF). + +You can select other files and specify their download path (browse [here](https://huggingface.co/TheBloke)). + +## Set up SQL DB + +This template includes an example DB of 2023 NBA rosters. + +You can see instructions to build this DB [here](https://github.com/facebookresearch/llama-recipes/blob/main/demo_apps/StructuredLlama.ipynb). + +## Installation + +```bash 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"README.md" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain = ">=0.0.313" +llama-cpp-python = ">=0.1.79" + +[tool.langserve] +export_module = "sql_llamacpp" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/sql-llamacpp/sql_llamacpp/__init__.py b/templates/sql-llamacpp/sql_llamacpp/__init__.py new file mode 100644 index 00000000000..376ed218308 --- /dev/null +++ b/templates/sql-llamacpp/sql_llamacpp/__init__.py @@ -0,0 +1,3 @@ +from llamacpp.chain import chain + +__all__ = ["chain"] \ No newline at end of file diff --git a/templates/sql-llamacpp/sql_llamacpp/chain.py b/templates/sql-llamacpp/sql_llamacpp/chain.py new file mode 100644 index 00000000000..4a00069988d --- /dev/null +++ b/templates/sql-llamacpp/sql_llamacpp/chain.py @@ -0,0 +1,124 @@ +# Get LLM +import os +from pathlib import Path + +import requests +from langchain.llms import LlamaCpp +from langchain.memory import ConversationBufferMemory +from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnableLambda, RunnablePassthrough +from langchain.utilities import SQLDatabase + +# File name and URL +file_name = "mistral-7b-instruct-v0.1.Q4_K_M.gguf" +url = "https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/resolve/main/mistral-7b-instruct-v0.1.Q4_K_M.gguf" +# Check if file is present in the current directory +if not os.path.exists(file_name): + print(f"'{file_name}' not found. Downloading...") + # Download the file + response = requests.get(url) + response.raise_for_status() # Raise an exception for HTTP errors + with open(file_name, "wb") as f: + f.write(response.content) + print(f"'{file_name}' has been downloaded.") +else: + print(f"'{file_name}' already exists in the current directory.") + +# Add the LLM downloaded from HF +model_path = file_name +n_gpu_layers = 1 # Metal set to 1 is enough. + +# Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip. +n_batch = 512 + +llm = LlamaCpp( + model_path=model_path, + n_gpu_layers=n_gpu_layers, + n_batch=n_batch, + n_ctx=2048, + # f16_kv MUST set to True + # otherwise you will run into problem after a couple of calls + f16_kv=True, + verbose=True, +) + +db_path = Path(__file__).parent / "nba_roster.db" +rel = db_path.relative_to(Path.cwd()) +db_string = f"sqlite:///{rel}" +db = SQLDatabase.from_uri(db_string, sample_rows_in_table_info=0) + + +def get_schema(_): + return db.get_table_info() + + +def run_query(query): + return db.run(query) + + +# Prompt + +template = """Based on the table schema below, write a SQL query that would answer the user's question: +{schema} + +Question: {question} +SQL Query:""" # noqa: E501 +prompt = ChatPromptTemplate.from_messages( + [ + ("system", "Given an input question, convert it to a SQL query. No pre-amble."), + MessagesPlaceholder(variable_name="history"), + ("human", template), + ] +) + +memory = ConversationBufferMemory(return_messages=True) + +# Chain to query with memory + +sql_chain = ( + RunnablePassthrough.assign( + schema=get_schema, + history=RunnableLambda(lambda x: memory.load_memory_variables(x)["history"]), + ) + | prompt + | llm.bind(stop=["\nSQLResult:"]) + | StrOutputParser() +) + + +def save(input_output): + output = {"output": input_output.pop("output")} + memory.save_context(input_output, output) + return output["output"] + + +sql_response_memory = RunnablePassthrough.assign(output=sql_chain) | save + +# Chain to answer +template = """Based on the table schema below, question, sql query, and sql response, write a natural language response: +{schema} + +Question: {question} +SQL Query: {query} +SQL Response: {response}""" # noqa: E501 +prompt_response = ChatPromptTemplate.from_messages( + [ + ( + "system", + "Given an input question and SQL response, convert it to a natural " + "language answer. No pre-amble.", + ), + ("human", template), + ] +) + +chain = ( + RunnablePassthrough.assign(query=sql_response_memory) + | RunnablePassthrough.assign( + schema=get_schema, + response=lambda x: db.run(x["query"]), + ) + | prompt_response + | llm +) diff --git a/templates/sql-llamacpp/sql_llamacpp/nba_roster.db b/templates/sql-llamacpp/sql_llamacpp/nba_roster.db new file mode 100644 index 00000000000..9ea4e20c367 Binary files /dev/null and b/templates/sql-llamacpp/sql_llamacpp/nba_roster.db differ diff --git a/templates/sql-llamacpp/tests/__init__.py b/templates/sql-llamacpp/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/sql-ollama/README.md b/templates/sql-ollama/README.md new file mode 100644 index 00000000000..67d5aeba759 --- /dev/null +++ b/templates/sql-ollama/README.md @@ -0,0 +1,25 @@ +# SQL with LLaMA2 using llama.cpp + +This template allows you to chat with a SQL database in natural language in private, using an open source LLM. + +## Set up Ollama + +Follow instructions [here](https://python.langchain.com/docs/integrations/chat/ollama) to download Ollama. + +Also follow instructions to download your LLM of interest: + +* This template uses `llama2:13b-chat` +* But you can pick from many LLMs [here](https://ollama.ai/library) + +## Set up SQL DB + +This template includes an example DB of 2023 NBA rosters. + +You can see instructions to build this DB [here](https://github.com/facebookresearch/llama-recipes/blob/main/demo_apps/StructuredLlama.ipynb). + +## Installation + +```bash +# from inside your LangServe instance +poe add sql-ollama +``` diff --git a/templates/sql-ollama/nba_roster.db b/templates/sql-ollama/nba_roster.db new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/sql-ollama/poetry.lock b/templates/sql-ollama/poetry.lock new file mode 100644 index 00000000000..ca0dce847d9 --- /dev/null +++ b/templates/sql-ollama/poetry.lock @@ -0,0 +1,1182 @@ +# This file is automatically 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+[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain = ">=0.0.313" + +[tool.langserve] +export_module = "sql_ollama" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/sql-ollama/sql_ollama/__init__.py b/templates/sql-ollama/sql_ollama/__init__.py new file mode 100644 index 00000000000..d92f8055226 --- /dev/null +++ b/templates/sql-ollama/sql_ollama/__init__.py @@ -0,0 +1,3 @@ +from ollama.chain import chain + +__all__ = ["chain"] \ No newline at end of file diff --git a/templates/sql-ollama/sql_ollama/chain.py b/templates/sql-ollama/sql_ollama/chain.py new file mode 100644 index 00000000000..7cf55746b88 --- /dev/null +++ b/templates/sql-ollama/sql_ollama/chain.py @@ -0,0 +1,93 @@ +from pathlib import Path + +from langchain.chat_models import ChatOllama +from langchain.memory import ConversationBufferMemory +from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnableLambda, RunnablePassthrough +from langchain.utilities import SQLDatabase + +# Add the LLM downloaded from Ollama +ollama_llm = "llama2:13b-chat" +llm = ChatOllama(model=ollama_llm) + + +db_path = Path(__file__).parent / "nba_roster.db" +rel = db_path.relative_to(Path.cwd()) +db_string = f"sqlite:///{rel}" +db = SQLDatabase.from_uri(db_string, sample_rows_in_table_info=0) + + +def get_schema(_): + return db.get_table_info() + + +def run_query(query): + return db.run(query) + + +# Prompt + +template = """Based on the table schema below, write a SQL query that would answer the user's question: +{schema} + +Question: {question} +SQL Query:""" # noqa: E501 +prompt = ChatPromptTemplate.from_messages( + [ + ("system", "Given an input question, convert it to a SQL query. No pre-amble."), + MessagesPlaceholder(variable_name="history"), + ("human", template), + ] +) + +memory = ConversationBufferMemory(return_messages=True) + +# Chain to query with memory + +sql_chain = ( + RunnablePassthrough.assign( + schema=get_schema, + history=RunnableLambda(lambda x: memory.load_memory_variables(x)["history"]), + ) + | prompt + | llm.bind(stop=["\nSQLResult:"]) + | StrOutputParser() +) + + +def save(input_output): + output = {"output": input_output.pop("output")} + memory.save_context(input_output, output) + return output["output"] + + +sql_response_memory = RunnablePassthrough.assign(output=sql_chain) | save + +# Chain to answer +template = """Based on the table schema below, question, sql query, and sql response, write a natural language response: +{schema} + +Question: {question} +SQL Query: {query} +SQL Response: {response}""" # noqa: E501 +prompt_response = ChatPromptTemplate.from_messages( + [ + ( + "system", + "Given an input question and SQL response, convert it to a natural " + "language answer. No pre-amble.", + ), + ("human", template), + ] +) + +chain = ( + RunnablePassthrough.assign(query=sql_response_memory) + | RunnablePassthrough.assign( + schema=get_schema, + response=lambda x: db.run(x["query"]), + ) + | prompt_response + | llm +) diff --git a/templates/sql-ollama/sql_ollama/nba_roster.db b/templates/sql-ollama/sql_ollama/nba_roster.db new file mode 100644 index 00000000000..9ea4e20c367 Binary files /dev/null and b/templates/sql-ollama/sql_ollama/nba_roster.db differ diff --git a/templates/sql-ollama/tests/__init__.py b/templates/sql-ollama/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/stepback-qa-prompting/README.md b/templates/stepback-qa-prompting/README.md new file mode 100644 index 00000000000..27f68f534c8 --- /dev/null +++ b/templates/stepback-qa-prompting/README.md @@ -0,0 +1,9 @@ +# Step-Back Prompting (Question-Answering) + +One prompting technique called "Step-Back" prompting can improve performance on complex questions by first asking a "step back" question. This can be combined with regular question-answering applications by then doing retrieval on both the original and step-back question. + +Read the paper [here](https://arxiv.org/abs/2310.06117) + +See an excelent blog post on this by Cobus Greyling [here](https://cobusgreyling.medium.com/a-new-prompt-engineering-technique-has-been-introduced-called-step-back-prompting-b00e8954cacb) + +In this template we will replicate this technique. 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+langchain = ">=0.0.313" +duckduckgo-search = "^3.9.3" + +[tool.langserve] +export_module = "stepback_qa_prompting.chain" +export_attr = "chain" + + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/stepback-qa-prompting/stepback_qa_prompting/__init__.py b/templates/stepback-qa-prompting/stepback_qa_prompting/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/stepback-qa-prompting/stepback_qa_prompting/chain.py b/templates/stepback-qa-prompting/stepback_qa_prompting/chain.py new file mode 100644 index 00000000000..f25802ef264 --- /dev/null +++ b/templates/stepback-qa-prompting/stepback_qa_prompting/chain.py @@ -0,0 +1,75 @@ +from langchain.chat_models import ChatOpenAI +from langchain.prompts import ChatPromptTemplate, FewShotChatMessagePromptTemplate +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnableLambda +from langchain.utilities import DuckDuckGoSearchAPIWrapper + +search = DuckDuckGoSearchAPIWrapper(max_results=4) + + +def retriever(query): + return search.run(query) + + +# Few Shot Examples +examples = [ + { + "input": "Could the members of The Police perform lawful arrests?", + "output": "what can the members of The Police do?", + }, + { + "input": "Jan Sindelโ€™s was born in what country?", + "output": "what is Jan Sindelโ€™s personal history?", + }, +] +# We now transform these to example messages +example_prompt = ChatPromptTemplate.from_messages( + [ + ("human", "{input}"), + ("ai", "{output}"), + ] +) +few_shot_prompt = FewShotChatMessagePromptTemplate( + example_prompt=example_prompt, + examples=examples, +) + +prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + "You are an expert at world knowledge. Your task is to step back " + "and paraphrase a question to a more generic step-back question, which " + "is easier to answer. Here are a few examples:", + ), + # Few shot examples + few_shot_prompt, + # New question + ("user", "{question}"), + ] +) + +question_gen = prompt | ChatOpenAI(temperature=0) | StrOutputParser() + +response_prompt_template = """You are an expert of world knowledge. I am going to ask you a question. Your response should be comprehensive and not contradicted with the following context if they are relevant. Otherwise, ignore them if they are not relevant. + +{normal_context} +{step_back_context} + +Original Question: {question} +Answer:""" # noqa: E501 +response_prompt = ChatPromptTemplate.from_template(response_prompt_template) + +chain = ( + { + # Retrieve context using the normal question + "normal_context": RunnableLambda(lambda x: x["question"]) | retriever, + # Retrieve context using the step-back question + "step_back_context": question_gen | retriever, + # Pass on the question + "question": lambda x: x["question"], + } + | response_prompt + | ChatOpenAI(temperature=0) + | StrOutputParser() +) diff --git a/templates/stepback-qa-prompting/tests/__init__.py b/templates/stepback-qa-prompting/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/summarize-anthropic/LICENSE b/templates/summarize-anthropic/LICENSE new file mode 100644 index 00000000000..426b6509034 --- /dev/null +++ b/templates/summarize-anthropic/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/summarize-anthropic/README.md b/templates/summarize-anthropic/README.md new file mode 100644 index 00000000000..d7d163ebfb0 --- /dev/null +++ b/templates/summarize-anthropic/README.md @@ -0,0 +1,36 @@ +# Summarize documents with Anthropic + +This template uses Anthropic's `Claude2` to summarize documents. + +To do this, we can use various prompts from LangChain hub, such as: + +* [This fun summarization prompt](https://smith.langchain.com/hub/hwchase17/anthropic-paper-qa) +* [Chain of density summarization prompt](https://smith.langchain.com/hub/lawwu/chain_of_density) + +`Claude2` has a large (100k token) context window, allowing us to summarize documents over 100 pages. + +## LLM + +This template will use `Claude2` by default. + +Be sure that `ANTHROPIC_API_KEY` is set in your enviorment. + +## Adding the template + +Create your LangServe app: +``` +langchain serve new my-app +cd my-app +``` + +Add template: +``` +langchain serve add summarize-anthropic +``` + +Start server: +``` +langchain start +``` + +See Jupyter notebook `summarize_anthropic` for various way to connect to the template. diff --git a/templates/summarize-anthropic/docs/LLaVA.pdf b/templates/summarize-anthropic/docs/LLaVA.pdf new file mode 100644 index 00000000000..88da76cecd5 Binary files /dev/null and b/templates/summarize-anthropic/docs/LLaVA.pdf differ diff --git a/templates/summarize-anthropic/poetry.lock b/templates/summarize-anthropic/poetry.lock new file mode 100644 index 00000000000..9f0d0140142 --- /dev/null +++ b/templates/summarize-anthropic/poetry.lock @@ -0,0 +1,2296 @@ +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version 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+langchain = ">=0.0.313, <0.1" +anthropic = ">=0.5.0" +langchainhub = ">=0.1.13" + +[tool.langserve] +export_module = "summarize_anthropic" +export_attr = "chain" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/templates/summarize-anthropic/summarize_anthropic.ipynb b/templates/summarize-anthropic/summarize_anthropic.ipynb new file mode 100644 index 00000000000..008b6f74b0e --- /dev/null +++ b/templates/summarize-anthropic/summarize_anthropic.ipynb @@ -0,0 +1,180 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "b4ea3722", + "metadata": {}, + "source": [ + "## Document Loading\n", + "\n", + "Load a blog post on agents." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a65a2603", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting pdf2image\n", + " Using cached pdf2image-1.16.3-py3-none-any.whl (11 kB)\n", + "Requirement already satisfied: pillow in /Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages (from pdf2image) (8.4.0)\n", + "Installing collected packages: pdf2image\n", + "Successfully installed pdf2image-1.16.3\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 is available: \u001b[0m\u001b[31;49m23.1.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.1\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" + ] + } + ], + "source": [ + "! pip install pdf2image" + ] + }, + { + "cell_type": "markdown", + "id": "6ff363da", + "metadata": {}, + "source": [ + "Academic papers." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d79e191a", + "metadata": {}, + "outputs": [], + "source": [ + "import arxiv\n", + "from langchain.chat_models import ChatAnthropic\n", + "from langchain.document_loaders import ArxivLoader, UnstructuredPDFLoader\n", + "# Load a paper to use\n", + "paper = next(arxiv.Search(query=\"Visual Instruction Tuning\").results())\n", + "paper.download_pdf(filename=\"downloaded-paper.pdf\")\n", + "loader = UnstructuredPDFLoader(\"downloaded-paper.pdf\")\n", + "doc = loader.load()[0]" + ] + }, + { + "cell_type": "markdown", + "id": "db964a34", + "metadata": {}, + "source": [ + "Also try loading blog posts." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b2f10100", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.document_loaders import WebBaseLoader\n", + "loader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")\n", + "text = loader.load()" + ] + }, + { + "cell_type": "markdown", + "id": "361fcf5c", + "metadata": {}, + "source": [ + "## Connect to template\n", + "\n", + "`Context`\n", + " \n", + "* LangServe apps gives you access to templates.\n", + "* Templates LLM pipeline (runnables or chains) end-points accessible via FastAPI.\n", + "* The environment for these templates is managed by Poetry.\n", + "\n", + "`Create app`\n", + "\n", + "* Install LangServe and create an app.\n", + "* This will create a new Poetry environment /\n", + "```\n", + "pip install < to add > \n", + "langchain serve new my-app\n", + "cd my-app\n", + "```\n", + "\n", + "`Add templates`\n", + "\n", + "* When we add a template, we update the Poetry config file with the necessary dependencies.\n", + "* It also automatically installed these template dependencies in your Poetry environment\n", + "```\n", + "langchain serve add summarize-anthropic\n", + "```\n", + "\n", + "`Start FastAPI server`\n", + "\n", + "```\n", + "langchain start\n", + "```\n", + "\n", + "Note, we can now look at the endpoints:\n", + "\n", + "http://127.0.0.1:8000/docs#\n", + "\n", + "And look specifically at our loaded template:\n", + "\n", + "http://127.0.0.1:8000/docs#/default/invoke_summarize_anthropic_invoke_post\n", + " \n", + "We can also use remote runnable to call it.\n", + "\n", + "## Summarization\n", + "\n", + "We will use [this](https://smith.langchain.com/hub/hwchase17/anthropic-paper-qa) prompt." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "3c673b53", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\" Here is my attempt to complete the requested tasks:\\n\\n\\nThe scientists made a robot friend that can see pictures and talk about them. First they let a big talking robot make up fun conversations about pictures. Then they taught their robot friend to have conversations too. Their robot friend got pretty good at seeing pictures and talking about them!\\n\\n\\n\\nVisual Instruction Tuning\\nRecipe adapted from Haotian Liu et al.\\n\\nIngredients:\\n- 1 large language model (we used GPT-4)\\n- 158,000 images with text descriptions \\n- 1 vision model (we used CLIP)\\n- 1 large multimodal model (we used LLaMA)\\n\\nInstructions:\\n1. Have the GPT-4 model make up conversations, long descriptions, and complex stories about the images, using only the text descriptions as hints. \\n2. Connect the vision model and language model together into one big multimodal model. This will be your robot friend!\\n3. Show the robot friend examples of the conversations GPT-4 made up.\\n4. Let the robot friend practice having conversations about images, using what it learned from GPT-4.\\n5. Test how well it can see and talk about new images!\\n\\n\\n\\nOh Muse, sing of scientists wise, \\nWho built a robot with ears and eyes, \\nTaught by a speaker most eloquent, \\nThey molded silicon assistant.\\n\\nThis visual machine could see and tell \\nOf images with knowledge it knew well. \\nWhen humans speak in words precise, \\nThe robot answers concise and wise.\\n\\nThough simpler than human true creation, \\nIt showed the path for innovation. \\nMore data, bigger models in time, \\nMay lead to an assistant sublime.\\n\\n\\n\\nOh great, another computer vision model. What is it this time, some overhyped multimodal beast trained on mountains of compute? Bah! Back in my day we built models with only a handful of pixels and a Commodore 64. And they worked just fine, not like these newfangled neural nets that gobble up GPUs like candy.\\n\\nLet's see, they got GPT-4 to hallucinate training data from captions? Well that hardly seems rigorous. Those mechaturkeys will fabricate anything if you let them. And they didn't even collect real human annotations? Training multimodal models is hard enough without using made-up data. \\n\\nI'm sure their LaVa MuDdLe model does decently on narrow tasks, but don't be surprised when it fails catastrophically in the real world. These things may seem clever, but they're as dumb as a bag of rocks under the hood. Mark my words, we should keep them far away from any mission critical systems, or we'll regret it!\\n\\nBah! Call me when we build real intelligence, not these statistical hackjobs.\\n\"" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langserve.client import RemoteRunnable\n", + "summarization = RemoteRunnable('http://localhost:8000/summarize-anthropic')\n", + "summarization.invoke({\"text\":doc.page_content})" + ] + } + ], + "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/templates/summarize-anthropic/summarize_anthropic/__init__.py b/templates/summarize-anthropic/summarize_anthropic/__init__.py new file mode 100644 index 00000000000..84e4c58fed0 --- /dev/null +++ b/templates/summarize-anthropic/summarize_anthropic/__init__.py @@ -0,0 +1,3 @@ +from summarize_anthropic.chain import chain + +__all__ = ["chain"] diff --git a/templates/summarize-anthropic/summarize_anthropic/chain.py b/templates/summarize-anthropic/summarize_anthropic/chain.py new file mode 100644 index 00000000000..e2b6dae3859 --- /dev/null +++ b/templates/summarize-anthropic/summarize_anthropic/chain.py @@ -0,0 +1,8 @@ +from langchain import hub +from langchain.chat_models import ChatAnthropic +from langchain.schema.output_parser import StrOutputParser + +# Create chain +prompt = hub.pull("hwchase17/anthropic-paper-qa") +model = ChatAnthropic(model="claude-2", max_tokens=10000) +chain = prompt | model | StrOutputParser() \ No newline at end of file diff --git a/templates/summarize-anthropic/tests/__init__.py b/templates/summarize-anthropic/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/xml-agent/LICENSE b/templates/xml-agent/LICENSE new file mode 100644 index 00000000000..426b6509034 --- /dev/null +++ b/templates/xml-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/xml-agent/README.md b/templates/xml-agent/README.md new file mode 100644 index 00000000000..b5ec5962806 --- /dev/null +++ b/templates/xml-agent/README.md @@ -0,0 +1,17 @@ +# XML Agent + +This template creates an agent that uses XML syntax to communicate its decisions of what actions to take. +For this example, we use Anthropic since Anthropic's Claude models are particularly good at writing XML syntax. +This example creates an agent that can optionally look up things on the internet using You.com's retriever. + +## LLM + +This template will use `Anthropic` by default. + +Be sure that `ANTHROPIC_API_KEY` is set in your environment. + +## Tools + +This template will use `You.com` by default. + +Be sure that 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b/templates/xml-agent/pyproject.toml new file mode 100644 index 00000000000..13db08059e1 --- /dev/null +++ b/templates/xml-agent/pyproject.toml @@ -0,0 +1,20 @@ +[tool.poetry] +name = "xml-agent" +version = "0.1.0" +description = "" +authors = ["Lance Martin "] +readme = "README.md" + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +langchain = ">=0.0.322" +anthropic = ">=0.5.0" +langchainhub = ">=0.1.13" + +[tool.langserve] +export_module = "xml_agent" +export_attr = "agent_executor" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" \ No newline at end of file diff --git a/templates/xml-agent/tests/__init__.py b/templates/xml-agent/tests/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/templates/xml-agent/xml_agent/__init__.py b/templates/xml-agent/xml_agent/__init__.py new file mode 100644 index 00000000000..0ddde78c9db --- /dev/null +++ b/templates/xml-agent/xml_agent/__init__.py @@ -0,0 +1,3 @@ +from xml_agent.agent import agent_executor + +__all__ = ["agent_executor"] diff --git a/templates/xml-agent/xml_agent/agent.py b/templates/xml-agent/xml_agent/agent.py new file mode 100644 index 00000000000..ae638c3b929 --- /dev/null +++ b/templates/xml-agent/xml_agent/agent.py @@ -0,0 +1,62 @@ +from typing import List, Tuple + +from langchain.agents import AgentExecutor +from langchain.agents.agent_toolkits.conversational_retrieval.tool import ( + create_retriever_tool, +) +from langchain.agents.format_scratchpad import format_xml +from langchain.chat_models import ChatAnthropic +from langchain.pydantic_v1 import BaseModel +from langchain.retrievers.you import YouRetriever +from langchain.schema import AIMessage, HumanMessage +from langchain.tools.render import render_text_description + +from xml_agent.prompts import conversational_prompt, parse_output + + +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 + + +model = ChatAnthropic(model="claude-2") + +# Fake Tool +retriever = YouRetriever(k=5) +retriever_tool = create_retriever_tool( + retriever, "search", "Use this to search for current events." +) + +tools = [retriever_tool] + +prompt = conversational_prompt.partial( + tools=render_text_description(tools), + tool_names=", ".join([t.name for t in tools]), +) +llm_with_stop = model.bind(stop=[""]) + +agent = ( + { + "question": lambda x: x["question"], + "agent_scratchpad": lambda x: format_xml(x["intermediate_steps"]), + "chat_history": lambda x: _format_chat_history(x["chat_history"]), + } + | prompt + | llm_with_stop + | parse_output +) + + +class AgentInput(BaseModel): + question: str + chat_history: List[Tuple[str, str]] + + +agent_executor = AgentExecutor( + agent=agent, tools=tools, verbose=True, handle_parsing_errors=True +).with_types(input_type=AgentInput) + +agent_executor = agent_executor | (lambda x: x["output"]) diff --git a/templates/xml-agent/xml_agent/prompts.py b/templates/xml-agent/xml_agent/prompts.py new file mode 100644 index 00000000000..3dbf96353aa --- /dev/null +++ b/templates/xml-agent/xml_agent/prompts.py @@ -0,0 +1,52 @@ +from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain.schema import AgentAction, AgentFinish + +template = """You are a helpful assistant. Help the user answer any questions. + +You have access to the following tools: + +{tools} + +In order to use a tool, you can use and tags. You will then get back a response in the form +For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond: + +searchweather in SF +64 degrees + +When you are done, you can respond as normal to the user. + +Example 1: + +Human: Hi! + +Assistant: Hi! How are you? + +Human: What is the weather in SF? +Assistant: searchweather in SF +64 degrees +It is 64 degress in SF + + +Begin!""" # noqa: E501 + +conversational_prompt = ChatPromptTemplate.from_messages( + [ + ("system", template), + MessagesPlaceholder(variable_name="chat_history"), + ("user", "{question}"), + ("ai", "{agent_scratchpad}"), + ] +) + + +def parse_output(message): + text = message.content + if "" in text: + tool, tool_input = text.split("") + _tool = tool.split("")[1] + _tool_input = tool_input.split("")[1] + if "" in _tool_input: + _tool_input = _tool_input.split("")[0] + return AgentAction(tool=_tool, tool_input=_tool_input, log=text) + else: + return AgentFinish(return_values={"output": text}, log=text)