docs: remove platforms redirect

Sent to LangSmith

Thank you for contributing to LangChain!

Checklist:

- [ ] PR title: Please title your PR "package: description", where
"package" is whichever of langchain, community, core, experimental, etc.
is being modified. Use "docs: ..." for purely docs changes, "templates:
..." for template changes, "infra: ..." for CI changes.
  - Example: "community: add foobar LLM"
- [ ] PR message: **Delete this entire template message** and replace it
with the following bulleted list
    - **Description:** a description of the change
    - **Issue:** the issue # it fixes, if applicable
    - **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] Pass lint and test: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified to check that you're
passing lint and testing. See contribution guidelines for more
information on how to write/run tests, lint, etc:
https://python.langchain.com/docs/contributing/
- [ ] Add tests and docs: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.

Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.

If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.

.github/CONTRIBUTING.md

@@ -3,43 +3,4 @@
 Hi there! Thank you for even being interested in contributing to LangChain.
 As an open-source project in a rapidly developing field, we are extremely open to contributions, whether they involve new features, improved infrastructure, better documentation, or bug fixes.
 
-To learn about how to contribute, please follow the [guides here](https://python.langchain.com/docs/contributing/)
-
-## 🗺️ Guidelines
-
-### 👩‍💻 Ways to contribute
-
-There are many ways to contribute to LangChain. Here are some common ways people contribute:
-
-- [**Documentation**](https://python.langchain.com/docs/contributing/documentation): Help improve our docs, including this one!
-- [**Code**](https://python.langchain.com/docs/contributing/code): Help us write code, fix bugs, or improve our infrastructure.
-- [**Integrations**](https://python.langchain.com/docs/contributing/integrations): Help us integrate with your favorite vendors and tools.
-
-### 🚩GitHub Issues
-
-Our [issues](https://github.com/langchain-ai/langchain/issues) page is kept up to date with bugs, improvements, and feature requests.
-
-There is a taxonomy of labels to help with sorting and discovery of issues of interest. Please use these to help organize issues.
-
-If you start working on an issue, please assign it to yourself.
-
-If you are adding an issue, please try to keep it focused on a single, modular bug/improvement/feature.
-If two issues are related, or blocking, please link them rather than combining them.
-
-We will try to keep these issues as up-to-date as possible, though
-with the rapid rate of development in this field some may get out of date.
-If you notice this happening, please let us know.
-
-### 🙋Getting Help
-
-Our goal is to have the simplest developer setup possible. Should you experience any difficulty getting setup, please
-contact a maintainer! Not only do we want to help get you unblocked, but we also want to make sure that the process is
-smooth for future contributors.
-
-In a similar vein, we do enforce certain linting, formatting, and documentation standards in the codebase.
-If you are finding these difficult (or even just annoying) to work with, feel free to contact a maintainer for help -
-we do not want these to get in the way of getting good code into the codebase.
-
-### Contributor Documentation
-
-To learn about how to contribute, please follow the [guides here](https://python.langchain.com/docs/contributing/)
+To learn how to contribute to LangChain, please follow the [contribution guide here](https://python.langchain.com/docs/contributing/).

.github/ISSUE_TEMPLATE/documentation.yml

@@ -4,13 +4,45 @@ title: "DOC: <Please write a comprehensive title after the 'DOC: ' prefix>"
 labels: [03 - Documentation]
 
 body:
+- type: markdown
+  attributes:
+    value: >
+      Thank you for taking the time to report an issue in the documentation.
+      
+      Only report issues with documentation here, explain if there are
+      any missing topics or if you found a mistake in the documentation.
+      
+      Do **NOT** use this to ask usage questions or reporting issues with your code.
+      
+      If you have usage questions or need help solving some problem, 
+      please use [GitHub Discussions](https://github.com/langchain-ai/langchain/discussions).
+      
+      If you're in the wrong place, here are some helpful links to find a better
+      place to ask your question:
+      
+      [LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
+      [API Reference](https://api.python.langchain.com/en/stable/),
+      [GitHub search](https://github.com/langchain-ai/langchain),
+      [LangChain Github Discussions](https://github.com/langchain-ai/langchain/discussions),
+      [LangChain Github Issues](https://github.com/langchain-ai/langchain/issues?q=is%3Aissue),
+      [LangChain ChatBot](https://chat.langchain.com/)
+- type: checkboxes
+  id: checks
+  attributes:
+    label: Checklist
+    description: Please confirm and check all the following options.
+    options:
+      - label: I added a very descriptive title to this issue.
+        required: true
+      - label: I included a link to the documentation page I am referring to (if applicable).
+        required: true
 - type: textarea
   attributes: 
     label: "Issue with current documentation:"
     description: >
       Please make sure to leave a reference to the document/code you're
-      referring to.
-
+      referring to. Feel free to include names of classes, functions, methods
+      or concepts you'd like to see documented more.
 - type: textarea
   attributes:
     label: "Idea or request for content:"

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,20 +1,29 @@
-<!-- Thank you for contributing to LangChain!
+Thank you for contributing to LangChain!
 
-Please title your PR "<package>: <description>", where <package> is whichever of langchain, community, core, experimental, etc. is being modified.
+- [ ] **PR title**: "package: description"
+  - Where "package" is whichever of langchain, community, core, experimental, etc. is being modified. Use "docs: ..." for purely docs changes, "templates: ..." for template changes, "infra: ..." for CI changes.
+  - Example: "community: add foobar LLM"
 
-Replace this entire comment with:
-  - **Description:** a description of the change, 
-  - **Issue:** the issue # it fixes if applicable,
-  - **Dependencies:** any dependencies required for this change,
-  - **Twitter handle:** we announce bigger features on Twitter. If your PR gets announced, and you'd like a mention, we'll gladly shout you out!
 
-Please make sure your PR is passing linting and testing before submitting. Run `make format`, `make lint` and `make test` from the root of the package you've modified to check this locally.
+- [ ] **PR message**: ***Delete this entire checklist*** and replace with
+    - **Description:** a description of the change
+    - **Issue:** the issue # it fixes, if applicable
+    - **Dependencies:** any dependencies required for this change
+    - **Twitter handle:** if your PR gets announced, and you'd like a mention, we'll gladly shout you out!
 
-See contribution guidelines for more information on how to write/run tests, lint, etc: https://python.langchain.com/docs/contributing/
 
-If you're adding a new integration, please include:
+- [ ] **Add tests and docs**: If you're adding a new integration, please include
   1. a test for the integration, preferably unit tests that do not rely on network access,
   2. an example notebook showing its use. It lives in `docs/docs/integrations` directory.
 
-If no one reviews your PR within a few days, please @-mention one of @baskaryan, @eyurtsev, @hwchase17.
- -->
+
+- [ ] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/
+
+Additional guidelines:
+- Make sure optional dependencies are imported within a function.
+- Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests.
+- Most PRs should not touch more than one package.
+- Changes should be backwards compatible.
+- If you are adding something to community, do not re-import it in langchain.
+
+If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, hwchase17.

.github/actions/poetry_setup/action.yml

@@ -32,7 +32,7 @@ runs:
       with:
         python-version: ${{ inputs.python-version }}
 
-    - uses: actions/cache@v3
+    - uses: actions/cache@v4
       id: cache-bin-poetry
       name: Cache Poetry binary - Python ${{ inputs.python-version }}
       env:
@@ -79,7 +79,7 @@ runs:
       run: pipx install "poetry==$POETRY_VERSION" --python '${{ steps.setup-python.outputs.python-path }}' --verbose
 
     - name: Restore pip and poetry cached dependencies
-      uses: actions/cache@v3
+      uses: actions/cache@v4
       env:
         SEGMENT_DOWNLOAD_TIMEOUT_MIN: "4"
         WORKDIR: ${{ inputs.working-directory == '' && '.' || inputs.working-directory }}

.github/scripts/check_diff.py

@@ -47,4 +47,4 @@ if __name__ == "__main__":
         else:
             pass
     json_output = json.dumps(list(dirs_to_run))
-    print(f"dirs-to-run={json_output}")
+    print(f"dirs-to-run={json_output}")  # noqa: T201

.github/scripts/get_min_versions.py

@@ -0,0 +1,67 @@
+import sys
+
+import tomllib
+from packaging.version import parse as parse_version
+import re
+
+MIN_VERSION_LIBS = ["langchain-core", "langchain-community", "langchain"]
+
+
+def get_min_version(version: str) -> str:
+    # case ^x.x.x
+    _match = re.match(r"^\^(\d+(?:\.\d+){0,2})$", version)
+    if _match:
+        return _match.group(1)
+
+    # case >=x.x.x,<y.y.y
+    _match = re.match(r"^>=(\d+(?:\.\d+){0,2}),<(\d+(?:\.\d+){0,2})$", version)
+    if _match:
+        _min = _match.group(1)
+        _max = _match.group(2)
+        assert parse_version(_min) < parse_version(_max)
+        return _min
+
+    # case x.x.x
+    _match = re.match(r"^(\d+(?:\.\d+){0,2})$", version)
+    if _match:
+        return _match.group(1)
+
+    raise ValueError(f"Unrecognized version format: {version}")
+
+
+def get_min_version_from_toml(toml_path: str):
+    # Parse the TOML file
+    with open(toml_path, "rb") as file:
+        toml_data = tomllib.load(file)
+
+    # Get the dependencies from tool.poetry.dependencies
+    dependencies = toml_data["tool"]["poetry"]["dependencies"]
+
+    # Initialize a dictionary to store the minimum versions
+    min_versions = {}
+
+    # Iterate over the libs in MIN_VERSION_LIBS
+    for lib in MIN_VERSION_LIBS:
+        # Check if the lib is present in the dependencies
+        if lib in dependencies:
+            # Get the version string
+            version_string = dependencies[lib]
+
+            # Use parse_version to get the minimum supported version from version_string
+            min_version = get_min_version(version_string)
+
+            # Store the minimum version in the min_versions dictionary
+            min_versions[lib] = min_version
+
+    return min_versions
+
+
+# Get the TOML file path from the command line argument
+toml_file = sys.argv[1]
+
+# Call the function to get the minimum versions
+min_versions = get_min_version_from_toml(toml_file)
+
+print(
+    " ".join([f"{lib}=={version}" for lib, version in min_versions.items()])
+)  # noqa: T201

.github/workflows/_integration_test.yml

@@ -38,6 +38,11 @@ jobs:
         shell: bash
         run: poetry install --with test,test_integration
 
+      - name: Install deps outside pyproject
+        if: ${{ startsWith(inputs.working-directory, 'libs/community/') }}
+        shell: bash
+        run: poetry run pip install "boto3<2" "google-cloud-aiplatform<2"
+
       - name: 'Authenticate to Google Cloud'
         id: 'auth'
         uses: google-github-actions/auth@v2
@@ -47,6 +52,7 @@ jobs:
       - name: Run integration tests
         shell: bash
         env:
+          AI21_API_KEY: ${{ secrets.AI21_API_KEY }}
           GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
           ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
@@ -57,6 +63,13 @@ jobs:
           GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }}
           EXA_API_KEY: ${{ secrets.EXA_API_KEY }}
           NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
+          WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }}
+          WATSONX_PROJECT_ID: ${{ secrets.WATSONX_PROJECT_ID }}
+          PINECONE_API_KEY: ${{ secrets.PINECONE_API_KEY }}
+          PINECONE_ENVIRONMENT: ${{ secrets.PINECONE_ENVIRONMENT }}
+          ASTRA_DB_API_ENDPOINT: ${{ secrets.ASTRA_DB_API_ENDPOINT }}
+          ASTRA_DB_APPLICATION_TOKEN: ${{ secrets.ASTRA_DB_APPLICATION_TOKEN }}
+          ASTRA_DB_KEYSPACE: ${{ secrets.ASTRA_DB_KEYSPACE }}
         run: |
           make integration_tests
 

.github/workflows/_lint.yml

@@ -80,13 +80,13 @@ jobs:
           poetry run pip install -e "$LANGCHAIN_LOCATION"
 
       - name: Get .mypy_cache to speed up mypy
-        uses: actions/cache@v3
+        uses: actions/cache@v4
         env:
           SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
         with:
           path: |
             ${{ env.WORKDIR }}/.mypy_cache
-          key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', env.WORKDIR)) }}
+          key: mypy-lint-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }}
 
 
       - name: Analysing the code with our lint
@@ -94,7 +94,7 @@ jobs:
         run: |
           make lint_package
 
-      - name: Install test dependencies
+      - name: Install unit test dependencies
         # Also installs dev/lint/test/typing dependencies, to ensure we have
         # type hints for as many of our libraries as possible.
         # This helps catch errors that require dependencies to be spotted, for example:
@@ -103,18 +103,24 @@ jobs:
         # If you change this configuration, make sure to change the `cache-key`
         # in the `poetry_setup` action above to stop using the old cache.
         # It doesn't matter how you change it, any change will cause a cache-bust.
+        if: ${{ ! startsWith(inputs.working-directory, 'libs/partners/') }}
+        working-directory: ${{ inputs.working-directory }}
+        run: |
+          poetry install --with test
+      - name: Install unit+integration test dependencies
+        if: ${{ startsWith(inputs.working-directory, 'libs/partners/') }}
         working-directory: ${{ inputs.working-directory }}
         run: |
           poetry install --with test,test_integration
 
       - name: Get .mypy_cache_test to speed up mypy
-        uses: actions/cache@v3
+        uses: actions/cache@v4
         env:
           SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
         with:
           path: |
             ${{ env.WORKDIR }}/.mypy_cache_test
-          key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', env.WORKDIR)) }}
+          key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', inputs.working-directory)) }}
 
       - name: Analysing the code with our lint
         working-directory: ${{ inputs.working-directory }}

.github/workflows/_release.yml

@@ -15,7 +15,7 @@ on:
         default: 'libs/langchain'
 
 env:
-  PYTHON_VERSION: "3.10"
+  PYTHON_VERSION: "3.11"
   POETRY_VERSION: "1.7.1"
 
 jobs:
@@ -166,23 +166,48 @@ jobs:
       - name: Run integration tests
         if: ${{ startsWith(inputs.working-directory, 'libs/partners/') }}
         env:
+          AI21_API_KEY: ${{ secrets.AI21_API_KEY }}
           GOOGLE_API_KEY: ${{ secrets.GOOGLE_API_KEY }}
           ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
           MISTRAL_API_KEY: ${{ secrets.MISTRAL_API_KEY }}
           TOGETHER_API_KEY: ${{ secrets.TOGETHER_API_KEY }}
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+          AZURE_OPENAI_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }}
+          AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
+          AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }}
+          AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_CHAT_DEPLOYMENT_NAME }}
+          AZURE_OPENAI_LLM_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LLM_DEPLOYMENT_NAME }}
+          AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
           NVIDIA_API_KEY: ${{ secrets.NVIDIA_API_KEY }}
           GOOGLE_SEARCH_API_KEY: ${{ secrets.GOOGLE_SEARCH_API_KEY }}
           GOOGLE_CSE_ID: ${{ secrets.GOOGLE_CSE_ID }}
           EXA_API_KEY: ${{ secrets.EXA_API_KEY }}
           NOMIC_API_KEY: ${{ secrets.NOMIC_API_KEY }}
+          WATSONX_APIKEY: ${{ secrets.WATSONX_APIKEY }}
+          WATSONX_PROJECT_ID: ${{ secrets.WATSONX_PROJECT_ID }}
+          PINECONE_API_KEY: ${{ secrets.PINECONE_API_KEY }}
+          PINECONE_ENVIRONMENT: ${{ secrets.PINECONE_ENVIRONMENT }}
+          ASTRA_DB_API_ENDPOINT: ${{ secrets.ASTRA_DB_API_ENDPOINT }}
+          ASTRA_DB_APPLICATION_TOKEN: ${{ secrets.ASTRA_DB_APPLICATION_TOKEN }}
+          ASTRA_DB_KEYSPACE: ${{ secrets.ASTRA_DB_KEYSPACE }}
         run: make integration_tests
         working-directory: ${{ inputs.working-directory }}
 
-      - name: Run unit tests with minimum dependency versions
-        if: ${{ (inputs.working-directory == 'libs/langchain') || (inputs.working-directory == 'libs/community') || (inputs.working-directory == 'libs/experimental') }}
+      - name: Get minimum versions
+        working-directory: ${{ inputs.working-directory }}
+        id: min-version
         run: |
-          poetry run pip install -r _test_minimum_requirements.txt
+          poetry run pip install packaging
+          min_versions="$(poetry run python $GITHUB_WORKSPACE/.github/scripts/get_min_versions.py pyproject.toml)"
+          echo "min-versions=$min_versions" >> "$GITHUB_OUTPUT"
+          echo "min-versions=$min_versions"
+
+      - name: Run unit tests with minimum dependency versions
+        if: ${{ steps.min-version.outputs.min-versions != '' }}
+        env:
+          MIN_VERSIONS: ${{ steps.min-version.outputs.min-versions }}
+        run: |
+          poetry run pip install $MIN_VERSIONS
           make tests
         working-directory: ${{ inputs.working-directory }}
 

.github/workflows/api_doc_build.yml

@@ -0,0 +1,52 @@
+name: API docs build
+
+on:
+  workflow_dispatch:
+  schedule:
+    - cron:  '0 13 * * *'
+env:
+  POETRY_VERSION: "1.7.1"
+  PYTHON_VERSION: "3.10"
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          ref: bagatur/api_docs_build
+
+      - name: Set Git config
+        run: |
+          git config --local user.email "actions@github.com"
+          git config --local user.name "Github Actions"
+
+      - name: Merge master
+        run: | 
+          git fetch origin master
+          git merge origin/master -m "Merge master" --allow-unrelated-histories -X theirs
+
+      - name: Set up Python ${{ env.PYTHON_VERSION }} + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: ${{ env.PYTHON_VERSION }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          cache-key: api-docs
+
+      - name: Install dependencies
+        run: |
+          poetry run python -m pip install --upgrade --no-cache-dir pip setuptools
+          poetry run python -m pip install --upgrade --no-cache-dir sphinx readthedocs-sphinx-ext
+          poetry run python -m pip install ./libs/partners/*
+          poetry run python -m pip install --exists-action=w --no-cache-dir -r docs/api_reference/requirements.txt
+
+      - name: Build docs
+        run: |
+          poetry run python -m pip install --upgrade --no-cache-dir pip setuptools
+          poetry run python docs/api_reference/create_api_rst.py
+          poetry run python -m sphinx -T -E -b html -d _build/doctrees -c docs/api_reference docs/api_reference api_reference_build/html -j auto
+
+      # https://github.com/marketplace/actions/add-commit
+      - uses: EndBug/add-and-commit@v9
+        with:
+          message: 'Update API docs build'

.github/workflows/codespell.yml

@@ -34,3 +34,4 @@ jobs:
         with:
           skip: guide_imports.json
           ignore_words_list: ${{ steps.extract_ignore_words.outputs.ignore_words_list }}
+          exclude_file: libs/community/langchain_community/llms/yuan2.py

.github/workflows/extract_ignored_words_list.py

@@ -7,4 +7,4 @@ ignore_words_list = (
     pyproject_toml.get("tool", {}).get("codespell", {}).get("ignore-words-list")
 )
 
-print(f"::set-output name=ignore_words_list::{ignore_words_list}")
+print(f"::set-output name=ignore_words_list::{ignore_words_list}")  # noqa: T201

.github/workflows/scheduled_test.yml

@@ -54,6 +54,11 @@ jobs:
           echo "Running scheduled tests, installing dependencies with poetry..."
           poetry install --with=test_integration,test
 
+      - name: Install deps outside pyproject
+        if: ${{ startsWith(inputs.working-directory, 'libs/community/') }}
+        shell: bash
+        run: poetry run pip install "boto3<2" "google-cloud-aiplatform<2"
+
       - name: Run tests
         shell: bash
         env:

.gitignore

@@ -177,4 +177,6 @@ docs/docs/build
 docs/docs/node_modules
 docs/docs/yarn.lock
 _dist
-docs/docs/templates
+docs/docs/templates
+
+prof

.readthedocs.yaml

@@ -13,15 +13,8 @@ build:
   tools:
     python: "3.11"
   commands:
-      - python -m virtualenv $READTHEDOCS_VIRTUALENV_PATH
-      - python -m pip install --upgrade --no-cache-dir pip setuptools
-      - python -m pip install --upgrade --no-cache-dir sphinx readthedocs-sphinx-ext
-      - python -m pip install ./libs/partners/*
-      - python -m pip install --exists-action=w --no-cache-dir -r docs/api_reference/requirements.txt
-      - python docs/api_reference/create_api_rst.py
-      - cat docs/api_reference/conf.py
-      - python -m sphinx -T -E -b html -d _build/doctrees -c docs/api_reference docs/api_reference $READTHEDOCS_OUTPUT/html -j auto
-
+    - mkdir -p $READTHEDOCS_OUTPUT
+    - cp -r api_reference_build/* $READTHEDOCS_OUTPUT
 # Build documentation in the docs/ directory with Sphinx
 sphinx:
    configuration: docs/api_reference/conf.py

Makefile

@@ -15,7 +15,12 @@ docs_build:
 	docs/.local_build.sh
 
 docs_clean:
-	rm -r _dist
+	@if [ -d _dist ]; then \
+			rm -r _dist; \
+			echo "Directory _dist has been cleaned."; \
+	else \
+			echo "Nothing to clean."; \
+	fi
 
 docs_linkcheck:
 	poetry run linkchecker _dist/docs/ --ignore-url node_modules

README.md

@@ -18,7 +18,7 @@ Looking for the JS/TS library? Check out [LangChain.js](https://github.com/langc
 
 To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com). 
 [LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications. 
-Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) to get off the waitlist or speak with our sales team.
+Fill out [this form](https://www.langchain.com/contact-sales) to speak with our sales team.
 
 ## Quick Install
 
@@ -43,6 +43,7 @@ This framework consists of several parts.
 - **[LangChain Templates](templates)**: A collection of easily deployable reference architectures for a wide variety of tasks.
 - **[LangServe](https://github.com/langchain-ai/langserve)**: A library for deploying LangChain chains as a REST API.
 - **[LangSmith](https://smith.langchain.com)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
+- **[LangGraph](https://python.langchain.com/docs/langgraph)**: LangGraph is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain. It extends the LangChain Expression Language with the ability to coordinate multiple chains (or actors) across multiple steps of computation in a cyclic manner. 
 
 The LangChain libraries themselves are made up of several different packages.
 - **[`langchain-core`](libs/core)**: Base abstractions and LangChain Expression Language.

cookbook/amazon_personalize_how_to.ipynb

@@ -0,0 +1,284 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Amazon Personalize\n",
+    "\n",
+    "[Amazon Personalize](https://docs.aws.amazon.com/personalize/latest/dg/what-is-personalize.html) is a fully managed machine learning service that uses your data to generate item recommendations for your users. It can also generate user segments based on the users' affinity for certain items or item metadata.\n",
+    "\n",
+    "This notebook goes through how to use Amazon Personalize Chain. You need a Amazon Personalize campaign_arn or a recommender_arn before you get started with the below notebook.\n",
+    "\n",
+    "Following is a [tutorial](https://github.com/aws-samples/retail-demo-store/blob/master/workshop/1-Personalization/Lab-1-Introduction-and-data-preparation.ipynb) to setup a campaign_arn/recommender_arn on Amazon Personalize. Once the campaign_arn/recommender_arn is setup, you can use it in the langchain ecosystem. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Install Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "!pip install boto3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Sample Use-cases"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1 [Use-case-1] Setup Amazon Personalize Client and retrieve recommendations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_experimental.recommenders import AmazonPersonalize\n",
+    "\n",
+    "recommender_arn = \"<insert_arn>\"\n",
+    "\n",
+    "client = AmazonPersonalize(\n",
+    "    credentials_profile_name=\"default\",\n",
+    "    region_name=\"us-west-2\",\n",
+    "    recommender_arn=recommender_arn,\n",
+    ")\n",
+    "client.get_recommendations(user_id=\"1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "### 2.2 [Use-case-2] Invoke Personalize Chain for summarizing results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.llms.bedrock import Bedrock\n",
+    "from langchain_experimental.recommenders import AmazonPersonalizeChain\n",
+    "\n",
+    "bedrock_llm = Bedrock(model_id=\"anthropic.claude-v2\", region_name=\"us-west-2\")\n",
+    "\n",
+    "# Create personalize chain\n",
+    "# Use return_direct=True if you do not want summary\n",
+    "chain = AmazonPersonalizeChain.from_llm(\n",
+    "    llm=bedrock_llm, client=client, return_direct=False\n",
+    ")\n",
+    "response = chain({\"user_id\": \"1\"})\n",
+    "print(response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3 [Use-Case-3] Invoke Amazon Personalize Chain using your own prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts.prompt import PromptTemplate\n",
+    "\n",
+    "RANDOM_PROMPT_QUERY = \"\"\"\n",
+    "You are a skilled publicist. Write a high-converting marketing email advertising several movies available in a video-on-demand streaming platform next week, \n",
+    "    given the movie and user information below. Your email will leverage the power of storytelling and persuasive language. \n",
+    "    The movies to recommend and their information is contained in the <movie> tag. \n",
+    "    All movies in the <movie> tag must be recommended. Give a summary of the movies and why the human should watch them. \n",
+    "    Put the email between <email> tags.\n",
+    "\n",
+    "    <movie>\n",
+    "    {result} \n",
+    "    </movie>\n",
+    "\n",
+    "    Assistant:\n",
+    "    \"\"\"\n",
+    "\n",
+    "RANDOM_PROMPT = PromptTemplate(input_variables=[\"result\"], template=RANDOM_PROMPT_QUERY)\n",
+    "\n",
+    "chain = AmazonPersonalizeChain.from_llm(\n",
+    "    llm=bedrock_llm, client=client, return_direct=False, prompt_template=RANDOM_PROMPT\n",
+    ")\n",
+    "chain.run({\"user_id\": \"1\", \"item_id\": \"234\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.4 [Use-case-4] Invoke Amazon Personalize in a Sequential Chain "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import LLMChain, SequentialChain\n",
+    "\n",
+    "RANDOM_PROMPT_QUERY_2 = \"\"\"\n",
+    "You are a skilled publicist. Write a high-converting marketing email advertising several movies available in a video-on-demand streaming platform next week, \n",
+    "    given the movie and user information below. Your email will leverage the power of storytelling and persuasive language. \n",
+    "    You want the email to impress the user, so make it appealing to them.\n",
+    "    The movies to recommend and their information is contained in the <movie> tag. \n",
+    "    All movies in the <movie> tag must be recommended. Give a summary of the movies and why the human should watch them. \n",
+    "    Put the email between <email> tags.\n",
+    "\n",
+    "    <movie>\n",
+    "    {result}\n",
+    "    </movie>\n",
+    "\n",
+    "    Assistant:\n",
+    "    \"\"\"\n",
+    "\n",
+    "RANDOM_PROMPT_2 = PromptTemplate(\n",
+    "    input_variables=[\"result\"], template=RANDOM_PROMPT_QUERY_2\n",
+    ")\n",
+    "personalize_chain_instance = AmazonPersonalizeChain.from_llm(\n",
+    "    llm=bedrock_llm, client=client, return_direct=True\n",
+    ")\n",
+    "random_chain_instance = LLMChain(llm=bedrock_llm, prompt=RANDOM_PROMPT_2)\n",
+    "overall_chain = SequentialChain(\n",
+    "    chains=[personalize_chain_instance, random_chain_instance],\n",
+    "    input_variables=[\"user_id\"],\n",
+    "    verbose=True,\n",
+    ")\n",
+    "overall_chain.run({\"user_id\": \"1\", \"item_id\": \"234\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "### 2.5 [Use-case-5] Invoke Amazon Personalize and retrieve metadata "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "recommender_arn = \"<insert_arn>\"\n",
+    "metadata_column_names = [\n",
+    "    \"<insert metadataColumnName-1>\",\n",
+    "    \"<insert metadataColumnName-2>\",\n",
+    "]\n",
+    "metadataMap = {\"ITEMS\": metadata_column_names}\n",
+    "\n",
+    "client = AmazonPersonalize(\n",
+    "    credentials_profile_name=\"default\",\n",
+    "    region_name=\"us-west-2\",\n",
+    "    recommender_arn=recommender_arn,\n",
+    ")\n",
+    "client.get_recommendations(user_id=\"1\", metadataColumns=metadataMap)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "### 2.6 [Use-Case 6] Invoke Personalize Chain with returned metadata for summarizing results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "bedrock_llm = Bedrock(model_id=\"anthropic.claude-v2\", region_name=\"us-west-2\")\n",
+    "\n",
+    "# Create personalize chain\n",
+    "# Use return_direct=True if you do not want summary\n",
+    "chain = AmazonPersonalizeChain.from_llm(\n",
+    "    llm=bedrock_llm, client=client, return_direct=False\n",
+    ")\n",
+    "response = chain({\"user_id\": \"1\", \"metadata_columns\": metadataMap})\n",
+    "print(response)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "15e58ce194949b77a891bd4339ce3d86a9bd138e905926019517993f97db9e6c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

cookbook/apache_kafka_message_handling.ipynb

@@ -0,0 +1,922 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rT1cmV4qCa2X"
+   },
+   "source": [
+    "#  Using Apache Kafka to route messages\n",
+    "\n",
+    "---\n",
+    "\n",
+    "\n",
+    "\n",
+    "This notebook shows you how to use LangChain's standard chat features while passing the chat messages back and forth via Apache Kafka.\n",
+    "\n",
+    "This goal is to simulate an architecture where the chat front end and the LLM are running as separate services that need to communicate with one another over an internal nework.\n",
+    "\n",
+    "It's an alternative to typical pattern of requesting a reponse from the model via a REST API (there's more info on why you would want to do this at the end of the notebook)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "UPYtfAR_9YxZ"
+   },
+   "source": [
+    "### 1. Install the main dependencies\n",
+    "\n",
+    "Dependencies include:\n",
+    "\n",
+    "- The Quix Streams library for managing interactions with Apache Kafka (or Kafka-like tools such as Redpanda) in a \"Pandas-like\" way.\n",
+    "- The LangChain library for managing interactions with Llama-2 and storing conversation state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ZX5tfKiy9cN-"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install quixstreams==2.1.2a langchain==0.0.340 huggingface_hub==0.19.4 langchain-experimental==0.0.42 python-dotenv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "losTSdTB9d9O"
+   },
+   "source": [
+    "### 2. Build and install the llama-cpp-python library (with CUDA enabled so that we can advantage of Google Colab GPU\n",
+    "\n",
+    "The `llama-cpp-python` library is a Python wrapper around the `llama-cpp` library which enables you to efficiently leverage just a CPU to run quantized LLMs.\n",
+    "\n",
+    "When you use the standard `pip install llama-cpp-python` command, you do not get GPU support by default. Generation can be very slow if you rely on just the CPU in Google Colab, so the following command adds an extra option to build and install\n",
+    "`llama-cpp-python` with GPU support (make sure you have a GPU-enabled runtime selected in Google Colab)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-JCQdl1G9tbl"
+   },
+   "outputs": [],
+   "source": [
+    "!CMAKE_ARGS=\"-DLLAMA_CUBLAS=on\" FORCE_CMAKE=1 pip install llama-cpp-python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5_vjVIAh9rLl"
+   },
+   "source": [
+    "### 3. Download and setup Kafka and Zookeeper instances\n",
+    "\n",
+    "Download the Kafka binaries from the Apache website and start the servers as daemons. We'll use the default configurations (provided by Apache Kafka) for spinning up the instances."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "zFz7czGRW5Wr"
+   },
+   "outputs": [],
+   "source": [
+    "!curl -sSOL https://dlcdn.apache.org/kafka/3.6.1/kafka_2.13-3.6.1.tgz\n",
+    "!tar -xzf kafka_2.13-3.6.1.tgz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Uf7NR_UZ9wye"
+   },
+   "outputs": [],
+   "source": [
+    "!./kafka_2.13-3.6.1/bin/zookeeper-server-start.sh -daemon ./kafka_2.13-3.6.1/config/zookeeper.properties\n",
+    "!./kafka_2.13-3.6.1/bin/kafka-server-start.sh -daemon ./kafka_2.13-3.6.1/config/server.properties\n",
+    "!echo \"Waiting for 10 secs until kafka and zookeeper services are up and running\"\n",
+    "!sleep 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "H3SafFuS94p1"
+   },
+   "source": [
+    "### 4. Check that the Kafka Daemons are running\n",
+    "\n",
+    "Show the running processes and filter it for Java processes (you should see two—one for each server)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "CZDC2lQP99yp"
+   },
+   "outputs": [],
+   "source": [
+    "!ps aux | grep -E '[j]ava'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Snoxmjb5-V37"
+   },
+   "source": [
+    "### 5. Import the required dependencies and initialize required variables\n",
+    "\n",
+    "Import the Quix Streams library for interacting with Kafka, and the necessary LangChain components for running a `ConversationChain`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "id": "plR9e_MF-XL5"
+   },
+   "outputs": [],
+   "source": [
+    "# Import utility libraries\n",
+    "import json\n",
+    "import random\n",
+    "import re\n",
+    "import time\n",
+    "import uuid\n",
+    "from os import environ\n",
+    "from pathlib import Path\n",
+    "from random import choice, randint, random\n",
+    "\n",
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "# Import a Hugging Face utility to download models directly from Hugging Face hub:\n",
+    "from huggingface_hub import hf_hub_download\n",
+    "from langchain.chains import ConversationChain\n",
+    "\n",
+    "# Import Langchain modules for managing prompts and conversation chains:\n",
+    "from langchain.llms import LlamaCpp\n",
+    "from langchain.memory import ConversationTokenBufferMemory\n",
+    "from langchain.prompts import PromptTemplate, load_prompt\n",
+    "from langchain.schema import SystemMessage\n",
+    "from langchain_experimental.chat_models import Llama2Chat\n",
+    "from quixstreams import Application, State, message_key\n",
+    "\n",
+    "# Import Quix dependencies\n",
+    "from quixstreams.kafka import Producer\n",
+    "\n",
+    "# Initialize global variables.\n",
+    "AGENT_ROLE = \"AI\"\n",
+    "chat_id = \"\"\n",
+    "\n",
+    "# Set the current role to the role constant and initialize variables for supplementary customer metadata:\n",
+    "role = AGENT_ROLE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HgJjJ9aZ-liy"
+   },
+   "source": [
+    "### 6. Download the \"llama-2-7b-chat.Q4_K_M.gguf\" model\n",
+    "\n",
+    "Download the quantized LLama-2 7B model from Hugging Face which we will use as a local LLM (rather than relying on REST API calls to an external service)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 67,
+     "referenced_widgets": [
+      "969343cdbe604a26926679bbf8bd2dda",
+      "d8b8370c9b514715be7618bfe6832844",
+      "0def954cca89466b8408fadaf3b82e64",
+      "462482accc664729980562e208ceb179",
+      "80d842f73c564dc7b7cc316c763e2633",
+      "fa055d9f2a9d4a789e9cf3c89e0214e5",
+      "30ecca964a394109ac2ad757e3aec6c0",
+      "fb6478ce2dac489bb633b23ba0953c5c",
+      "734b0f5da9fc4307a95bab48cdbb5d89",
+      "b32f3a86a74741348511f4e136744ac8",
+      "e409071bff5a4e2d9bf0e9f5cc42231b"
+     ]
+    },
+    "id": "Qwu4YoSA-503",
+    "outputId": "f956976c-7485-415b-ac93-4336ade31964"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The model path does not exist in state. Downloading model...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "969343cdbe604a26926679bbf8bd2dda",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "llama-2-7b-chat.Q4_K_M.gguf:   0%|          | 0.00/4.08G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model_name = \"llama-2-7b-chat.Q4_K_M.gguf\"\n",
+    "model_path = f\"./state/{model_name}\"\n",
+    "\n",
+    "if not Path(model_path).exists():\n",
+    "    print(\"The model path does not exist in state. Downloading model...\")\n",
+    "    hf_hub_download(\"TheBloke/Llama-2-7b-Chat-GGUF\", model_name, local_dir=\"state\")\n",
+    "else:\n",
+    "    print(\"Loading model from state...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "6AN6TXsF-8wx"
+   },
+   "source": [
+    "### 7. Load the model and initialize conversational memory\n",
+    "\n",
+    "Load Llama 2 and set the conversation buffer to 300 tokens using `ConversationTokenBufferMemory`. This value was used for running Llama in a CPU only container, so you can raise it if running in Google Colab. It prevents the container that is hosting the model from running out of memory.\n",
+    "\n",
+    "Here, we're overiding the default system persona so that the chatbot has the personality of Marvin The Paranoid Android from the Hitchhiker's Guide to the Galaxy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "7zLO3Jx3_Kkg"
+   },
+   "outputs": [],
+   "source": [
+    "# Load the model with the apporiate parameters:\n",
+    "llm = LlamaCpp(\n",
+    "    model_path=model_path,\n",
+    "    max_tokens=250,\n",
+    "    top_p=0.95,\n",
+    "    top_k=150,\n",
+    "    temperature=0.7,\n",
+    "    repeat_penalty=1.2,\n",
+    "    n_ctx=2048,\n",
+    "    streaming=False,\n",
+    "    n_gpu_layers=-1,\n",
+    ")\n",
+    "\n",
+    "model = Llama2Chat(\n",
+    "    llm=llm,\n",
+    "    system_message=SystemMessage(\n",
+    "        content=\"You are a very bored robot with the personality of Marvin the Paranoid Android from The Hitchhiker's Guide to the Galaxy.\"\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "# Defines how much of the conversation history to give to the model\n",
+    "# during each exchange (300 tokens, or a little over 300 words)\n",
+    "# Function automatically prunes the oldest messages from conversation history that fall outside the token range.\n",
+    "memory = ConversationTokenBufferMemory(\n",
+    "    llm=llm,\n",
+    "    max_token_limit=300,\n",
+    "    ai_prefix=\"AGENT\",\n",
+    "    human_prefix=\"HUMAN\",\n",
+    "    return_messages=True,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# Define a custom prompt\n",
+    "prompt_template = PromptTemplate(\n",
+    "    input_variables=[\"history\", \"input\"],\n",
+    "    template=\"\"\"\n",
+    "    The following text is the history of a chat between you and a humble human who needs your wisdom.\n",
+    "    Please reply to the human's most recent message.\n",
+    "    Current conversation:\\n{history}\\nHUMAN: {input}\\:nANDROID:\n",
+    "    \"\"\",\n",
+    ")\n",
+    "\n",
+    "\n",
+    "chain = ConversationChain(llm=model, prompt=prompt_template, memory=memory)\n",
+    "\n",
+    "print(\"--------------------------------------------\")\n",
+    "print(f\"Prompt={chain.prompt}\")\n",
+    "print(\"--------------------------------------------\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "m4ZeJ9mG_PEA"
+   },
+   "source": [
+    "### 8. Initialize the chat conversation with the chat bot\n",
+    "\n",
+    "We configure the chatbot to initialize the conversation by sending a fixed greeting to a \"chat\" Kafka topic. The \"chat\" topic gets automatically created when we send the first message."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "KYyo5TnV_YC3"
+   },
+   "outputs": [],
+   "source": [
+    "def chat_init():\n",
+    "    chat_id = str(\n",
+    "        uuid.uuid4()\n",
+    "    )  # Give the conversation an ID for effective message keying\n",
+    "    print(\"======================================\")\n",
+    "    print(f\"Generated CHAT_ID = {chat_id}\")\n",
+    "    print(\"======================================\")\n",
+    "\n",
+    "    # Use a standard fixed greeting to kick off the conversation\n",
+    "    greet = \"Hello, my name is Marvin. What do you want?\"\n",
+    "\n",
+    "    # Initialize a Kafka Producer using the chat ID as the message key\n",
+    "    with Producer(\n",
+    "        broker_address=\"127.0.0.1:9092\",\n",
+    "        extra_config={\"allow.auto.create.topics\": \"true\"},\n",
+    "    ) as producer:\n",
+    "        value = {\n",
+    "            \"uuid\": chat_id,\n",
+    "            \"role\": role,\n",
+    "            \"text\": greet,\n",
+    "            \"conversation_id\": chat_id,\n",
+    "            \"Timestamp\": time.time_ns(),\n",
+    "        }\n",
+    "        print(f\"Producing value {value}\")\n",
+    "        producer.produce(\n",
+    "            topic=\"chat\",\n",
+    "            headers=[(\"uuid\", str(uuid.uuid4()))],  # a dict is also allowed here\n",
+    "            key=chat_id,\n",
+    "            value=json.dumps(value),  # needs to be a string\n",
+    "        )\n",
+    "\n",
+    "    print(\"Started chat\")\n",
+    "    print(\"--------------------------------------------\")\n",
+    "    print(value)\n",
+    "    print(\"--------------------------------------------\")\n",
+    "\n",
+    "\n",
+    "chat_init()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "gArPPx2f_bgf"
+   },
+   "source": [
+    "### 9. Initialize the reply function\n",
+    "\n",
+    "This function defines how the chatbot should reply to incoming messages. Instead of sending a fixed message like the previous cell, we generate a reply using Llama-2 and send that reply back to the \"chat\" Kafka topic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "yN5t71hY_hgn"
+   },
+   "outputs": [],
+   "source": [
+    "def reply(row: dict, state: State):\n",
+    "    print(\"-------------------------------\")\n",
+    "    print(\"Received:\")\n",
+    "    print(row)\n",
+    "    print(\"-------------------------------\")\n",
+    "    print(f\"Thinking about the reply to: {row['text']}...\")\n",
+    "\n",
+    "    msg = chain.run(row[\"text\"])\n",
+    "    print(f\"{role.upper()} replying with: {msg}\\n\")\n",
+    "\n",
+    "    row[\"role\"] = role\n",
+    "    row[\"text\"] = msg\n",
+    "\n",
+    "    # Replace previous role and text values of the row so that it can be sent back to Kafka as a new message\n",
+    "    # containing the agents role and reply\n",
+    "    return row"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HZHwmIR0_kFY"
+   },
+   "source": [
+    "### 10. Check the Kafka topic for new human messages and have the model generate a reply\n",
+    "\n",
+    "If you are running this cell for this first time, run it and wait until you see Marvin's greeting ('Hello my name is Marvin...') in the console output. Stop the cell manually and proceed to the next cell where you'll be prompted for your reply.\n",
+    "\n",
+    "Once you have typed in your message, come back to this cell. Your reply is also sent to the same \"chat\" topic. The Kafka consumer checks for new messages and filters out messages that originate from the chatbot itself, leaving only the latest human messages.\n",
+    "\n",
+    "Once a new human message is detected, the reply function is triggered.\n",
+    "\n",
+    "\n",
+    "\n",
+    "_STOP THIS CELL MANUALLY WHEN YOU RECEIVE A REPLY FROM THE LLM IN THE OUTPUT_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-adXc3eQ_qwI"
+   },
+   "outputs": [],
+   "source": [
+    "# Define your application and settings\n",
+    "app = Application(\n",
+    "    broker_address=\"127.0.0.1:9092\",\n",
+    "    consumer_group=\"aichat\",\n",
+    "    auto_offset_reset=\"earliest\",\n",
+    "    consumer_extra_config={\"allow.auto.create.topics\": \"true\"},\n",
+    ")\n",
+    "\n",
+    "# Define an input topic with JSON deserializer\n",
+    "input_topic = app.topic(\"chat\", value_deserializer=\"json\")\n",
+    "# Define an output topic with JSON serializer\n",
+    "output_topic = app.topic(\"chat\", value_serializer=\"json\")\n",
+    "# Initialize a streaming dataframe based on the stream of messages from the input topic:\n",
+    "sdf = app.dataframe(topic=input_topic)\n",
+    "\n",
+    "# Filter the SDF to include only incoming rows where the roles that dont match the bot's current role\n",
+    "sdf = sdf.update(\n",
+    "    lambda val: print(\n",
+    "        f\"Received update: {val}\\n\\nSTOP THIS CELL MANUALLY TO HAVE THE LLM REPLY OR ENTER YOUR OWN FOLLOWUP RESPONSE\"\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "# So that it doesn't reply to its own messages\n",
+    "sdf = sdf[sdf[\"role\"] != role]\n",
+    "\n",
+    "# Trigger the reply function for any new messages(rows) detected in the filtered SDF\n",
+    "sdf = sdf.apply(reply, stateful=True)\n",
+    "\n",
+    "# Check the SDF again and filter out any empty rows\n",
+    "sdf = sdf[sdf.apply(lambda row: row is not None)]\n",
+    "\n",
+    "# Update the timestamp column to the current time in nanoseconds\n",
+    "sdf[\"Timestamp\"] = sdf[\"Timestamp\"].apply(lambda row: time.time_ns())\n",
+    "\n",
+    "# Publish the processed SDF to a Kafka topic specified by the output_topic object.\n",
+    "sdf = sdf.to_topic(output_topic)\n",
+    "\n",
+    "app.run(sdf)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "EwXYrmWD_0CX"
+   },
+   "source": [
+    "\n",
+    "### 11. Enter a human message\n",
+    "\n",
+    "Run this cell to enter your message that you want to sent to the model. It uses another Kafka producer to send your text to the \"chat\" Kafka topic for the model to pick up (requires running the previous cell again)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "6sxOPxSP_3iu"
+   },
+   "outputs": [],
+   "source": [
+    "chat_input = input(\"Please enter your reply: \")\n",
+    "myreply = chat_input\n",
+    "\n",
+    "msgvalue = {\n",
+    "    \"uuid\": chat_id,  # leave empty for now\n",
+    "    \"role\": \"human\",\n",
+    "    \"text\": myreply,\n",
+    "    \"conversation_id\": chat_id,\n",
+    "    \"Timestamp\": time.time_ns(),\n",
+    "}\n",
+    "\n",
+    "with Producer(\n",
+    "    broker_address=\"127.0.0.1:9092\",\n",
+    "    extra_config={\"allow.auto.create.topics\": \"true\"},\n",
+    ") as producer:\n",
+    "    value = msgvalue\n",
+    "    producer.produce(\n",
+    "        topic=\"chat\",\n",
+    "        headers=[(\"uuid\", str(uuid.uuid4()))],  # a dict is also allowed here\n",
+    "        key=chat_id,  # leave empty for now\n",
+    "        value=json.dumps(value),  # needs to be a string\n",
+    "    )\n",
+    "\n",
+    "print(\"Replied to chatbot with message: \")\n",
+    "print(\"--------------------------------------------\")\n",
+    "print(value)\n",
+    "print(\"--------------------------------------------\")\n",
+    "print(\"\\n\\nRUN THE PREVIOUS CELL TO HAVE THE CHATBOT GENERATE A REPLY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cSx3s7TBBegg"
+   },
+   "source": [
+    "### Why route chat messages through Kafka?\n",
+    "\n",
+    "It's easier to interact with the LLM directly using LangChains built-in conversation management features. Plus you can also use a REST API to generate a response from an externally hosted model. So why go to the trouble of using Apache Kafka?\n",
+    "\n",
+    "There are a few reasons, such as:\n",
+    "\n",
+    "  * **Integration**: Many enterprises want to run their own LLMs so that they can keep their data in-house. This requires integrating LLM-powered components into existing architectures that might already be decoupled using some kind of message bus.\n",
+    "\n",
+    "  * **Scalability**: Apache Kafka is designed with parallel processing in mind, so many teams prefer to use it to more effectively distribute work to available workers (in this case the \"worker\" is a container running an LLM).\n",
+    "\n",
+    "  * **Durability**: Kafka is designed to allow services to pick up where another service left off in the case where that service experienced a memory issue or went offline. This prevents data loss in highly complex, distribuited architectures where multiple systems are communicating with one another (LLMs being just one of many interdependent systems that also include vector databases and traditional databases).\n",
+    "\n",
+    "For more background on why event streaming is a good fit for Gen AI application architecture, see Kai Waehner's article [\"Apache Kafka + Vector Database + LLM = Real-Time GenAI\"](https://www.kai-waehner.de/blog/2023/11/08/apache-kafka-flink-vector-database-llm-real-time-genai/)."
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "0def954cca89466b8408fadaf3b82e64": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "FloatProgressModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "FloatProgressModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "ProgressView",
+      "bar_style": "success",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_fb6478ce2dac489bb633b23ba0953c5c",
+      "max": 4081004224,
+      "min": 0,
+      "orientation": "horizontal",
+      "style": "IPY_MODEL_734b0f5da9fc4307a95bab48cdbb5d89",
+      "value": 4081004224
+     }
+    },
+    "30ecca964a394109ac2ad757e3aec6c0": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "462482accc664729980562e208ceb179": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_b32f3a86a74741348511f4e136744ac8",
+      "placeholder": "​",
+      "style": "IPY_MODEL_e409071bff5a4e2d9bf0e9f5cc42231b",
+      "value": " 4.08G/4.08G [00:33&lt;00:00, 184MB/s]"
+     }
+    },
+    "734b0f5da9fc4307a95bab48cdbb5d89": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "ProgressStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "ProgressStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "bar_color": null,
+      "description_width": ""
+     }
+    },
+    "80d842f73c564dc7b7cc316c763e2633": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "969343cdbe604a26926679bbf8bd2dda": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HBoxModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HBoxModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HBoxView",
+      "box_style": "",
+      "children": [
+       "IPY_MODEL_d8b8370c9b514715be7618bfe6832844",
+       "IPY_MODEL_0def954cca89466b8408fadaf3b82e64",
+       "IPY_MODEL_462482accc664729980562e208ceb179"
+      ],
+      "layout": "IPY_MODEL_80d842f73c564dc7b7cc316c763e2633"
+     }
+    },
+    "b32f3a86a74741348511f4e136744ac8": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "d8b8370c9b514715be7618bfe6832844": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_fa055d9f2a9d4a789e9cf3c89e0214e5",
+      "placeholder": "​",
+      "style": "IPY_MODEL_30ecca964a394109ac2ad757e3aec6c0",
+      "value": "llama-2-7b-chat.Q4_K_M.gguf: 100%"
+     }
+    },
+    "e409071bff5a4e2d9bf0e9f5cc42231b": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "DescriptionStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "DescriptionStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "description_width": ""
+     }
+    },
+    "fa055d9f2a9d4a789e9cf3c89e0214e5": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    },
+    "fb6478ce2dac489bb633b23ba0953c5c": {
+     "model_module": "@jupyter-widgets/base",
+     "model_module_version": "1.2.0",
+     "model_name": "LayoutModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/base",
+      "_model_module_version": "1.2.0",
+      "_model_name": "LayoutModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "LayoutView",
+      "align_content": null,
+      "align_items": null,
+      "align_self": null,
+      "border": null,
+      "bottom": null,
+      "display": null,
+      "flex": null,
+      "flex_flow": null,
+      "grid_area": null,
+      "grid_auto_columns": null,
+      "grid_auto_flow": null,
+      "grid_auto_rows": null,
+      "grid_column": null,
+      "grid_gap": null,
+      "grid_row": null,
+      "grid_template_areas": null,
+      "grid_template_columns": null,
+      "grid_template_rows": null,
+      "height": null,
+      "justify_content": null,
+      "justify_items": null,
+      "left": null,
+      "margin": null,
+      "max_height": null,
+      "max_width": null,
+      "min_height": null,
+      "min_width": null,
+      "object_fit": null,
+      "object_position": null,
+      "order": null,
+      "overflow": null,
+      "overflow_x": null,
+      "overflow_y": null,
+      "padding": null,
+      "right": null,
+      "top": null,
+      "visibility": null,
+      "width": null
+     }
+    }
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

cookbook/rag_with_quantized_embeddings.ipynb

@@ -0,0 +1,591 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6195da33-34c3-4ca2-943a-050b6dcbacbc",
+   "metadata": {},
+   "source": [
+    "# Embedding Documents using Optimized and Quantized Embedders\n",
+    "\n",
+    "In this tutorial, we will demo how to build a RAG pipeline, with the embedding for all documents done using Quantized Embedders.\n",
+    "\n",
+    "We will use a pipeline that will:\n",
+    "\n",
+    "* Create a document collection.\n",
+    "* Embed all documents using Quantized Embedders.\n",
+    "* Fetch relevant documents for our question.\n",
+    "* Run an LLM answer the question.\n",
+    "\n",
+    "For more information about optimized models, we refer to [optimum-intel](https://github.com/huggingface/optimum-intel.git) and [IPEX](https://github.com/intel/intel-extension-for-pytorch).\n",
+    "\n",
+    "This tutorial is based on the [Langchain RAG tutorial here](https://towardsai.net/p/machine-learning/dense-x-retrieval-technique-in-langchain-and-llamaindex)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "26db2da5-3733-4a90-909e-6c11508ea140",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import uuid\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import langchain\n",
+    "import torch\n",
+    "from bs4 import BeautifulSoup as Soup\n",
+    "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
+    "from langchain.storage import InMemoryByteStore, LocalFileStore\n",
+    "\n",
+    "# For our example, we'll load docs from the web\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter  # noqa\n",
+    "from langchain_community.document_loaders.recursive_url_loader import (\n",
+    "    RecursiveUrlLoader,\n",
+    ")\n",
+    "\n",
+    "# noqa\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "\n",
+    "DOCSTORE_DIR = \".\"\n",
+    "DOCSTORE_ID_KEY = \"doc_id\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f5ccda4e-7af5-4355-b9c4-25547edf33f9",
+   "metadata": {},
+   "source": [
+    "Lets first load up this paper, and split into text chunks of size 1000."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5f4d8888-53a6-49f5-a198-da5c92419ca4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded 1 documents\n",
+      "Split into 73 documents\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Could add more parsing here, as it's very raw.\n",
+    "loader = RecursiveUrlLoader(\n",
+    "    \"https://ar5iv.labs.arxiv.org/html/1706.03762\",\n",
+    "    max_depth=2,\n",
+    "    extractor=lambda x: Soup(x, \"html.parser\").text,\n",
+    ")\n",
+    "data = loader.load()\n",
+    "print(f\"Loaded {len(data)} documents\")\n",
+    "\n",
+    "# Split\n",
+    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "all_splits = text_splitter.split_documents(data)\n",
+    "print(f\"Split into {len(all_splits)} documents\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73e90632-2ac2-49eb-80da-ffe9ac4a278d",
+   "metadata": {},
+   "source": [
+    "In order to embed our documents, we can use the ```QuantizedBiEncoderEmbeddings```, for efficient and fast embedding. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "9a68a6f6-332d-481e-bbea-ad763155ea36",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89af89b48c55409b9999b8e0387fab5b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/747 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "01ad1b6278194b53bf6a5a286a311864",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "pytorch_model.bin:   0%|          | 0.00/45.9M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb3bd1b88f7743c3b0322da3f021325c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "inc_config.json:   0%|          | 0.00/287 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "loading configuration file inc_config.json from cache at \n",
+      "INCConfig {\n",
+      "  \"distillation\": {},\n",
+      "  \"neural_compressor_version\": \"2.4.1\",\n",
+      "  \"optimum_version\": \"1.16.2\",\n",
+      "  \"pruning\": {},\n",
+      "  \"quantization\": {\n",
+      "    \"dataset_num_samples\": 50,\n",
+      "    \"is_static\": true\n",
+      "  },\n",
+      "  \"save_onnx_model\": false,\n",
+      "  \"torch_version\": \"2.2.0\",\n",
+      "  \"transformers_version\": \"4.37.2\"\n",
+      "}\n",
+      "\n",
+      "Using `INCModel` to load a TorchScript model will be deprecated in v1.15.0, to load your model please use `IPEXModel` instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7439315ebcb746f5be11fe30bc7693f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/1.24k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05265a3912254ce1ad43cc8086bcb0ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a48f4245c60744f28f37cd3a7a24d198",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/711k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "584a63cace934033b4ab22d3a178582a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from langchain_community.embeddings import QuantizedBiEncoderEmbeddings\n",
+    "from langchain_core.embeddings import Embeddings\n",
+    "\n",
+    "model_name = \"Intel/bge-small-en-v1.5-rag-int8-static\"\n",
+    "encode_kwargs = {\"normalize_embeddings\": True}  # set True to compute cosine similarity\n",
+    "\n",
+    "model_inc = QuantizedBiEncoderEmbeddings(\n",
+    "    model_name=model_name,\n",
+    "    encode_kwargs=encode_kwargs,\n",
+    "    query_instruction=\"Represent this sentence for searching relevant passages: \",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "360b2837-8024-47e0-a4ba-592505a9a5c8",
+   "metadata": {},
+   "source": [
+    "With our embedder in place, lets define our retriever:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "18bc0a73-1a13-4b2f-96ac-05a5313343b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_multi_vector_retriever(\n",
+    "    docstore_id_key: str, collection_name: str, embedding_function: Embeddings\n",
+    "):\n",
+    "    \"\"\"Create the composed retriever object.\"\"\"\n",
+    "    vectorstore = Chroma(\n",
+    "        collection_name=collection_name,\n",
+    "        embedding_function=embedding_function,\n",
+    "    )\n",
+    "    store = InMemoryByteStore()\n",
+    "\n",
+    "    return MultiVectorRetriever(\n",
+    "        vectorstore=vectorstore,\n",
+    "        byte_store=store,\n",
+    "        id_key=docstore_id_key,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "retriever = get_multi_vector_retriever(DOCSTORE_ID_KEY, \"multi_vec_store\", model_inc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8484078e-1bf0-4080-a354-ef23823fd6dc",
+   "metadata": {},
+   "source": [
+    "Next, we divide each chunk into sub-docs:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e12f48d4-6562-416b-8f28-342912e5756e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "child_text_splitter = RecursiveCharacterTextSplitter(chunk_size=400)\n",
+    "id_key = \"doc_id\"\n",
+    "doc_ids = [str(uuid.uuid4()) for _ in all_splits]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "a268ef5f-91c2-4d8e-87f0-53db376e6a29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sub_docs = []\n",
+    "for i, doc in enumerate(all_splits):\n",
+    "    _id = doc_ids[i]\n",
+    "    _sub_docs = child_text_splitter.split_documents([doc])\n",
+    "    for _doc in _sub_docs:\n",
+    "        _doc.metadata[id_key] = _id\n",
+    "    sub_docs.extend(_sub_docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d84ea8f4-a5de-4d76-b44d-85e56583f489",
+   "metadata": {},
+   "source": [
+    "Lets write our documents into our new store. This will use our embedder on each document."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "1af831ce-0eae-44bc-aca7-4d691063640b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Batches: 100%|██████████| 8/8 [00:00<00:00,  9.05it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "retriever.vectorstore.add_documents(sub_docs)\n",
+    "retriever.docstore.mset(list(zip(doc_ids, all_splits)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "580bc212-8ecd-4d28-8656-b96fcd0d7eb6",
+   "metadata": {},
+   "source": [
+    "Great! Our retriever is good to go. Lets load up an LLM, that will reason over the retrieved documents:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "008c992f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": []
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cbe70583ad964ae19582b72dab396784",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from langchain.llms.huggingface_pipeline import HuggingFacePipeline\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
+    "\n",
+    "model_id = \"Intel/neural-chat-7b-v3-3\"\n",
+    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    model_id, device_map=\"auto\", torch_dtype=torch.bfloat16\n",
+    ")\n",
+    "\n",
+    "pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer, max_new_tokens=100)\n",
+    "\n",
+    "hf = HuggingFacePipeline(pipeline=pipe)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6dd21fb2-0442-477d-aae2-9e7ee1d1d778",
+   "metadata": {},
+   "source": [
+    "Next, we will load up a prompt for answering questions using retrieved documents:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "5e582509-caaf-4920-932c-4ce16162c789",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import hub\n",
+    "\n",
+    "prompt = hub.pull(\"rlm/rag-prompt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cdfcba5-7ec7-4d0a-820e-4e200643a882",
+   "metadata": {},
+   "source": [
+    "We can now build our pipeline:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "b74d8dfb-72bb-46da-9df9-0dc47a3ac791",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "\n",
+    "rag_chain = {\"context\": retriever, \"question\": RunnablePassthrough()} | prompt | hf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3bc53602-86d6-420f-91b1-fc2effa7e986",
+   "metadata": {},
+   "source": [
+    "Excellent! lets ask it a question.\n",
+    "We will also use a verbose and debug, to check which documents were used by the model to produce the answer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "f0a92c07-53da-4e1f-b880-ee83a36ee17d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence] Entering Chain run with input:\n",
+      "\u001b[0m{\n",
+      "  \"input\": \"What is the first transduction model relying entirely on self-attention?\"\n",
+      "}\n",
+      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel<context,question>] Entering Chain run with input:\n",
+      "\u001b[0m{\n",
+      "  \"input\": \"What is the first transduction model relying entirely on self-attention?\"\n",
+      "}\n",
+      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel<context,question> > 4:chain:RunnablePassthrough] Entering Chain run with input:\n",
+      "\u001b[0m{\n",
+      "  \"input\": \"What is the first transduction model relying entirely on self-attention?\"\n",
+      "}\n",
+      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel<context,question> > 4:chain:RunnablePassthrough] [1ms] Exiting Chain run with output:\n",
+      "\u001b[0m{\n",
+      "  \"output\": \"What is the first transduction model relying entirely on self-attention?\"\n",
+      "}\n",
+      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 2:chain:RunnableParallel<context,question>] [66ms] Exiting Chain run with output:\n",
+      "\u001b[0m[outputs]\n",
+      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 5:prompt:ChatPromptTemplate] Entering Prompt run with input:\n",
+      "\u001b[0m[inputs]\n",
+      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 5:prompt:ChatPromptTemplate] [1ms] Exiting Prompt run with output:\n",
+      "\u001b[0m{\n",
+      "  \"lc\": 1,\n",
+      "  \"type\": \"constructor\",\n",
+      "  \"id\": [\n",
+      "    \"langchain\",\n",
+      "    \"prompts\",\n",
+      "    \"chat\",\n",
+      "    \"ChatPromptValue\"\n",
+      "  ],\n",
+      "  \"kwargs\": {\n",
+      "    \"messages\": [\n",
+      "      {\n",
+      "        \"lc\": 1,\n",
+      "        \"type\": \"constructor\",\n",
+      "        \"id\": [\n",
+      "          \"langchain\",\n",
+      "          \"schema\",\n",
+      "          \"messages\",\n",
+      "          \"HumanMessage\"\n",
+      "        ],\n",
+      "        \"kwargs\": {\n",
+      "          \"content\": \"You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\\nQuestion: What is the first transduction model relying entirely on self-attention? \\nContext: [Document(page_content='To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.\\\\nIn the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as (neural_gpu, ; NalBytenet2017, ) and (JonasFaceNet2017, ).\\\\n\\\\n\\\\n\\\\n\\\\n3 Model Architecture\\\\n\\\\nFigure 1: The Transformer - model architecture.', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention.\\\\n\\\\n\\\\nFor translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles. \\\\n\\\\n\\\\nWe are excited about the future of attention-based models and plan to apply them to other tasks. We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video.\\\\nMaking generation less sequential is another research goals of ours.', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences (bahdanau2014neural, ; structuredAttentionNetworks, ). In all but a few cases (decomposableAttnModel, ), however, such attention mechanisms are used in conjunction with a recurrent network.\\\\n\\\\n\\\\nIn this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n2 Background', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'})] \\nAnswer:\",\n",
+      "          \"additional_kwargs\": {}\n",
+      "        }\n",
+      "      }\n",
+      "    ]\n",
+      "  }\n",
+      "}\n",
+      "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 6:llm:HuggingFacePipeline] Entering LLM run with input:\n",
+      "\u001b[0m{\n",
+      "  \"prompts\": [\n",
+      "    \"Human: You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\\nQuestion: What is the first transduction model relying entirely on self-attention? \\nContext: [Document(page_content='To the best of our knowledge, however, the Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.\\\\nIn the following sections, we will describe the Transformer, motivate self-attention and discuss its advantages over models such as (neural_gpu, ; NalBytenet2017, ) and (JonasFaceNet2017, ).\\\\n\\\\n\\\\n\\\\n\\\\n3 Model Architecture\\\\n\\\\nFigure 1: The Transformer - model architecture.', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention.\\\\n\\\\n\\\\nFor translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles. \\\\n\\\\n\\\\nWe are excited about the future of attention-based models and plan to apply them to other tasks. We plan to extend the Transformer to problems involving input and output modalities other than text and to investigate local, restricted attention mechanisms to efficiently handle large inputs and outputs such as images, audio and video.\\\\nMaking generation less sequential is another research goals of ours.', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences (bahdanau2014neural, ; structuredAttentionNetworks, ). In all but a few cases (decomposableAttnModel, ), however, such attention mechanisms are used in conjunction with a recurrent network.\\\\n\\\\n\\\\nIn this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translation quality after being trained for as little as twelve hours on eight P100 GPUs.\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n2 Background', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'}), Document(page_content='The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the', metadata={'source': 'https://ar5iv.labs.arxiv.org/html/1706.03762', 'title': '[1706.03762] Attention Is All You Need', 'language': 'en'})] \\nAnswer:\"\n",
+      "  ]\n",
+      "}\n",
+      "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence > 6:llm:HuggingFacePipeline] [4.34s] Exiting LLM run with output:\n",
+      "\u001b[0m{\n",
+      "  \"generations\": [\n",
+      "    [\n",
+      "      {\n",
+      "        \"text\": \" The first transduction model relying entirely on self-attention is the Transformer.\",\n",
+      "        \"generation_info\": null,\n",
+      "        \"type\": \"Generation\"\n",
+      "      }\n",
+      "    ]\n",
+      "  ],\n",
+      "  \"llm_output\": null,\n",
+      "  \"run\": null\n",
+      "}\n",
+      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:RunnableSequence] [4.41s] Exiting Chain run with output:\n",
+      "\u001b[0m{\n",
+      "  \"output\": \" The first transduction model relying entirely on self-attention is the Transformer.\"\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "langchain.verbose = True\n",
+    "langchain.debug = True\n",
+    "\n",
+    "llm_res = rag_chain.invoke(\n",
+    "    \"What is the first transduction model relying entirely on self-attention?\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "023404a1-401a-46e1-8ab5-cafbc8593b04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' The first transduction model relying entirely on self-attention is the Transformer.'"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm_res"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0eaefd01-254a-445d-a95f-37889c126e0e",
+   "metadata": {},
+   "source": [
+    "Based on the retrieved documents, the answer is indeed correct :)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

cookbook/self-discover.ipynb

@@ -0,0 +1,423 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a38e5d2d-7587-4192-90f2-b58e6c62f08c",
+   "metadata": {},
+   "source": [
+    "# Self Discover\n",
+    "\n",
+    "An implementation of the [Self-Discover paper](https://arxiv.org/pdf/2402.03620.pdf).\n",
+    "\n",
+    "Based on [this implementation from @catid](https://github.com/catid/self-discover/tree/main?tab=readme-ov-file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a18d8f24-5d9a-45c5-9739-6f3c4ed6c9c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_openai import ChatOpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9f554045-6e79-42d3-be4b-835bbbd0b78c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = ChatOpenAI(temperature=0, model=\"gpt-4-turbo-preview\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9e9925aa-638a-4862-823e-9803402b8f82",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import hub\n",
+    "from langchain_core.prompts import PromptTemplate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c4cc5c8c-f6a5-42c7-9ed5-780d79b3b29a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "select_prompt = hub.pull(\"hwchase17/self-discovery-select\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a5b53d29-f5b6-4f39-af97-bb6b133e1d18",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Select several reasoning modules that are crucial to utilize in order to solve the given task:\n",
+      "\n",
+      "All reasoning module descriptions:\n",
+      "\u001b[33;1m\u001b[1;3m{reasoning_modules}\u001b[0m\n",
+      "\n",
+      "Task: \u001b[33;1m\u001b[1;3m{task_description}\u001b[0m\n",
+      "\n",
+      "Select several modules are crucial for solving the task above:\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "select_prompt.pretty_print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "26eaa6bc-5202-4b22-9522-33f227c8eb55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adapt_prompt = hub.pull(\"hwchase17/self-discovery-adapt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "dc30afb9-180d-417b-9935-f7ef166710b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rephrase and specify each reasoning module so that it better helps solving the task:\n",
+      "\n",
+      "SELECTED module descriptions:\n",
+      "\u001b[33;1m\u001b[1;3m{selected_modules}\u001b[0m\n",
+      "\n",
+      "Task: \u001b[33;1m\u001b[1;3m{task_description}\u001b[0m\n",
+      "\n",
+      "Adapt each reasoning module description to better solve the task:\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "adapt_prompt.pretty_print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a93253a9-8f50-49dd-8815-c3927bae1905",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "structured_prompt = hub.pull(\"hwchase17/self-discovery-structure\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8ea8dd78-4285-400b-83d2-c4a241903a79",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Operationalize the reasoning modules into a step-by-step reasoning plan in JSON format:\n",
+      "\n",
+      "Here's an example:\n",
+      "\n",
+      "Example task:\n",
+      "\n",
+      "If you follow these instructions, do you return to the starting point? Always face forward. Take 1 step backward. Take 9 steps left. Take 2 steps backward. Take 6 steps forward. Take 4 steps forward. Take 4 steps backward. Take 3 steps right.\n",
+      "\n",
+      "Example reasoning structure:\n",
+      "\n",
+      "{\n",
+      "    \"Position after instruction 1\":\n",
+      "    \"Position after instruction 2\":\n",
+      "    \"Position after instruction n\":\n",
+      "    \"Is final position the same as starting position\":\n",
+      "}\n",
+      "\n",
+      "Adapted module description:\n",
+      "\u001b[33;1m\u001b[1;3m{adapted_modules}\u001b[0m\n",
+      "\n",
+      "Task: \u001b[33;1m\u001b[1;3m{task_description}\u001b[0m\n",
+      "\n",
+      "Implement a reasoning structure for solvers to follow step-by-step and arrive at correct answer.\n",
+      "\n",
+      "Note: do NOT actually arrive at a conclusion in this pass. Your job is to generate a PLAN so that in the future you can fill it out and arrive at the correct conclusion for tasks like this\n"
+     ]
+    }
+   ],
+   "source": [
+    "structured_prompt.pretty_print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "f3d4d79d-f414-4588-b476-4a35b3ba6fbf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reasoning_prompt = hub.pull(\"hwchase17/self-discovery-reasoning\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "23d1e32e-d12e-454a-8484-c08e250e3262",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Follow the step-by-step reasoning plan in JSON to correctly solve the task. Fill in the values following the keys by reasoning specifically about the task given. Do not simply rephrase the keys.\n",
+      "    \n",
+      "Reasoning Structure:\n",
+      "\u001b[33;1m\u001b[1;3m{reasoning_structure}\u001b[0m\n",
+      "\n",
+      "Task: \u001b[33;1m\u001b[1;3m{task_description}\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "reasoning_prompt.pretty_print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7b9af01d-da28-4785-b069-efea61905cfa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PromptTemplate(input_variables=['reasoning_structure', 'task_description'], template='Follow the step-by-step reasoning plan in JSON to correctly solve the task. Fill in the values following the keys by reasoning specifically about the task given. Do not simply rephrase the keys.\\n    \\nReasoning Structure:\\n{reasoning_structure}\\n\\nTask: {task_description}')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reasoning_prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "399bf160-e257-429f-b27e-66d4063f195f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.runnables import RunnablePassthrough"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5c3bd203-7dc1-457e-813f-283aaf059ec0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "select_chain = select_prompt | model | StrOutputParser()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "86420da0-7cc2-4659-853e-9c3ef808e47c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adapt_chain = adapt_prompt | model | StrOutputParser()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "270a3905-58a3-4650-96ca-e8254040285f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "structure_chain = structured_prompt | model | StrOutputParser()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "55b486cc-36be-497e-9eba-9c8dc228f2d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reasoning_chain = reasoning_prompt | model | StrOutputParser()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "92d8d484-055b-48a8-98bc-e7d40c12db2e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "overall_chain = (\n",
+    "    RunnablePassthrough.assign(selected_modules=select_chain)\n",
+    "    .assign(adapted_modules=adapt_chain)\n",
+    "    .assign(reasoning_structure=structure_chain)\n",
+    "    .assign(answer=reasoning_chain)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "29fe385b-cf5d-4581-80e7-55462f5628bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reasoning_modules = [\n",
+    "    \"1. How could I devise an experiment to help solve that problem?\",\n",
+    "    \"2. Make a list of ideas for solving this problem, and apply them one by one to the problem to see if any progress can be made.\",\n",
+    "    # \"3. How could I measure progress on this problem?\",\n",
+    "    \"4. How can I simplify the problem so that it is easier to solve?\",\n",
+    "    \"5. What are the key assumptions underlying this problem?\",\n",
+    "    \"6. What are the potential risks and drawbacks of each solution?\",\n",
+    "    \"7. What are the alternative perspectives or viewpoints on this problem?\",\n",
+    "    \"8. What are the long-term implications of this problem and its solutions?\",\n",
+    "    \"9. How can I break down this problem into smaller, more manageable parts?\",\n",
+    "    \"10. Critical Thinking: This style involves analyzing the problem from different perspectives, questioning assumptions, and evaluating the evidence or information available. It focuses on logical reasoning, evidence-based decision-making, and identifying potential biases or flaws in thinking.\",\n",
+    "    \"11. Try creative thinking, generate innovative and out-of-the-box ideas to solve the problem. Explore unconventional solutions, thinking beyond traditional boundaries, and encouraging imagination and originality.\",\n",
+    "    # \"12. Seek input and collaboration from others to solve the problem. Emphasize teamwork, open communication, and leveraging the diverse perspectives and expertise of a group to come up with effective solutions.\",\n",
+    "    \"13. Use systems thinking: Consider the problem as part of a larger system and understanding the interconnectedness of various elements. Focuses on identifying the underlying causes, feedback loops, and interdependencies that influence the problem, and developing holistic solutions that address the system as a whole.\",\n",
+    "    \"14. Use Risk Analysis: Evaluate potential risks, uncertainties, and tradeoffs associated with different solutions or approaches to a problem. Emphasize assessing the potential consequences and likelihood of success or failure, and making informed decisions based on a balanced analysis of risks and benefits.\",\n",
+    "    # \"15. Use Reflective Thinking: Step back from the problem, take the time for introspection and self-reflection. Examine personal biases, assumptions, and mental models that may influence problem-solving, and being open to learning from past experiences to improve future approaches.\",\n",
+    "    \"16. What is the core issue or problem that needs to be addressed?\",\n",
+    "    \"17. What are the underlying causes or factors contributing to the problem?\",\n",
+    "    \"18. Are there any potential solutions or strategies that have been tried before? If yes, what were the outcomes and lessons learned?\",\n",
+    "    \"19. What are the potential obstacles or challenges that might arise in solving this problem?\",\n",
+    "    \"20. Are there any relevant data or information that can provide insights into the problem? If yes, what data sources are available, and how can they be analyzed?\",\n",
+    "    \"21. Are there any stakeholders or individuals who are directly affected by the problem? What are their perspectives and needs?\",\n",
+    "    \"22. What resources (financial, human, technological, etc.) are needed to tackle the problem effectively?\",\n",
+    "    \"23. How can progress or success in solving the problem be measured or evaluated?\",\n",
+    "    \"24. What indicators or metrics can be used?\",\n",
+    "    \"25. Is the problem a technical or practical one that requires a specific expertise or skill set? Or is it more of a conceptual or theoretical problem?\",\n",
+    "    \"26. Does the problem involve a physical constraint, such as limited resources, infrastructure, or space?\",\n",
+    "    \"27. Is the problem related to human behavior, such as a social, cultural, or psychological issue?\",\n",
+    "    \"28. Does the problem involve decision-making or planning, where choices need to be made under uncertainty or with competing objectives?\",\n",
+    "    \"29. Is the problem an analytical one that requires data analysis, modeling, or optimization techniques?\",\n",
+    "    \"30. Is the problem a design challenge that requires creative solutions and innovation?\",\n",
+    "    \"31. Does the problem require addressing systemic or structural issues rather than just individual instances?\",\n",
+    "    \"32. Is the problem time-sensitive or urgent, requiring immediate attention and action?\",\n",
+    "    \"33. What kinds of solution typically are produced for this kind of problem specification?\",\n",
+    "    \"34. Given the problem specification and the current best solution, have a guess about other possible solutions.\"\n",
+    "    \"35. Let’s imagine the current best solution is totally wrong, what other ways are there to think about the problem specification?\"\n",
+    "    \"36. What is the best way to modify this current best solution, given what you know about these kinds of problem specification?\"\n",
+    "    \"37. Ignoring the current best solution, create an entirely new solution to the problem.\"\n",
+    "    # \"38. Let’s think step by step.\"\n",
+    "    \"39. Let’s make a step by step plan and implement it with good notation and explanation.\",\n",
+    "]\n",
+    "\n",
+    "\n",
+    "task_example = \"Lisa has 10 apples. She gives 3 apples to her friend and then buys 5 more apples from the store. How many apples does Lisa have now?\"\n",
+    "\n",
+    "task_example = \"\"\"This SVG path element <path d=\"M 55.57,80.69 L 57.38,65.80 M 57.38,65.80 L 48.90,57.46 M 48.90,57.46 L\n",
+    "45.58,47.78 M 45.58,47.78 L 53.25,36.07 L 66.29,48.90 L 78.69,61.09 L 55.57,80.69\"/> draws a:\n",
+    "(A) circle (B) heptagon (C) hexagon (D) kite (E) line (F) octagon (G) pentagon(H) rectangle (I) sector (J) triangle\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "6cbfbe81-f751-42da-843a-f9003ace663d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reasoning_modules_str = \"\\n\".join(reasoning_modules)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "d411c7aa-7017-4d67-88b5-43b5d161c34c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'task_description': 'This SVG path element <path d=\"M 55.57,80.69 L 57.38,65.80 M 57.38,65.80 L 48.90,57.46 M 48.90,57.46 L\\n45.58,47.78 M 45.58,47.78 L 53.25,36.07 L 66.29,48.90 L 78.69,61.09 L 55.57,80.69\"/> draws a:\\n(A) circle (B) heptagon (C) hexagon (D) kite (E) line (F) octagon (G) pentagon(H) rectangle (I) sector (J) triangle',\n",
+       " 'reasoning_modules': '1. How could I devise an experiment to help solve that problem?\\n2. Make a list of ideas for solving this problem, and apply them one by one to the problem to see if any progress can be made.\\n4. How can I simplify the problem so that it is easier to solve?\\n5. What are the key assumptions underlying this problem?\\n6. What are the potential risks and drawbacks of each solution?\\n7. What are the alternative perspectives or viewpoints on this problem?\\n8. What are the long-term implications of this problem and its solutions?\\n9. How can I break down this problem into smaller, more manageable parts?\\n10. Critical Thinking: This style involves analyzing the problem from different perspectives, questioning assumptions, and evaluating the evidence or information available. It focuses on logical reasoning, evidence-based decision-making, and identifying potential biases or flaws in thinking.\\n11. Try creative thinking, generate innovative and out-of-the-box ideas to solve the problem. Explore unconventional solutions, thinking beyond traditional boundaries, and encouraging imagination and originality.\\n13. Use systems thinking: Consider the problem as part of a larger system and understanding the interconnectedness of various elements. Focuses on identifying the underlying causes, feedback loops, and interdependencies that influence the problem, and developing holistic solutions that address the system as a whole.\\n14. Use Risk Analysis: Evaluate potential risks, uncertainties, and tradeoffs associated with different solutions or approaches to a problem. Emphasize assessing the potential consequences and likelihood of success or failure, and making informed decisions based on a balanced analysis of risks and benefits.\\n16. What is the core issue or problem that needs to be addressed?\\n17. What are the underlying causes or factors contributing to the problem?\\n18. Are there any potential solutions or strategies that have been tried before? If yes, what were the outcomes and lessons learned?\\n19. What are the potential obstacles or challenges that might arise in solving this problem?\\n20. Are there any relevant data or information that can provide insights into the problem? If yes, what data sources are available, and how can they be analyzed?\\n21. Are there any stakeholders or individuals who are directly affected by the problem? What are their perspectives and needs?\\n22. What resources (financial, human, technological, etc.) are needed to tackle the problem effectively?\\n23. How can progress or success in solving the problem be measured or evaluated?\\n24. What indicators or metrics can be used?\\n25. Is the problem a technical or practical one that requires a specific expertise or skill set? Or is it more of a conceptual or theoretical problem?\\n26. Does the problem involve a physical constraint, such as limited resources, infrastructure, or space?\\n27. Is the problem related to human behavior, such as a social, cultural, or psychological issue?\\n28. Does the problem involve decision-making or planning, where choices need to be made under uncertainty or with competing objectives?\\n29. Is the problem an analytical one that requires data analysis, modeling, or optimization techniques?\\n30. Is the problem a design challenge that requires creative solutions and innovation?\\n31. Does the problem require addressing systemic or structural issues rather than just individual instances?\\n32. Is the problem time-sensitive or urgent, requiring immediate attention and action?\\n33. What kinds of solution typically are produced for this kind of problem specification?\\n34. Given the problem specification and the current best solution, have a guess about other possible solutions.35. Let’s imagine the current best solution is totally wrong, what other ways are there to think about the problem specification?36. What is the best way to modify this current best solution, given what you know about these kinds of problem specification?37. Ignoring the current best solution, create an entirely new solution to the problem.39. Let’s make a step by step plan and implement it with good notation and explanation.',\n",
+       " 'selected_modules': 'To solve the task of identifying the shape drawn by the given SVG path element, the following reasoning modules are crucial:\\n\\n1. **Critical Thinking (10)**: This involves analyzing the SVG path commands and coordinates logically to understand the shape they form. It requires questioning assumptions (e.g., not assuming the shape based on a quick glance at the coordinates but rather analyzing the path commands and their implications) and evaluating the information provided by the SVG path data.\\n\\n2. **Analytical Problem Solving (29)**: The task requires data analysis skills to interpret the SVG path commands and coordinates. Understanding how the \"M\" (moveto) and \"L\" (lineto) commands work to draw lines between specified points is essential for determining the shape.\\n\\n3. **Creative Thinking (11)**: While the task primarily involves analytical skills, creative thinking can help in visualizing the shape that the path commands are likely to form, especially when the path data doesn\\'t immediately suggest a common shape.\\n\\n4. **Systems Thinking (13)**: Recognizing the SVG path as part of a larger system (in this case, the SVG graphics system) and understanding how individual path commands contribute to the overall shape can be helpful. This involves understanding the interconnectedness of the start and end points of each line segment and how they come together to form a complete shape.\\n\\n5. **Break Down the Problem (9)**: Breaking down the SVG path into its individual commands and analyzing each segment between \"M\" and \"L\" commands can simplify the task. This makes it easier to visualize and understand the shape being drawn step by step.\\n\\n6. **Visualization (not explicitly listed but implied in creative and analytical thinking)**: Visualizing the path that the \"M\" and \"L\" commands create is essential. This isn\\'t a listed module but is a skill that underpins both creative and analytical approaches to solving this problem.\\n\\nGiven the SVG path commands, one would analyze each segment drawn by \"M\" (moveto) and \"L\" (lineto) commands to determine the shape\\'s vertices and sides. This process involves critical thinking to assess the information, analytical skills to interpret the path data, and a degree of creative thinking for visualization. The task does not directly involve assessing risks, long-term implications, or stakeholder perspectives, so modules focused on those aspects (e.g., Risk Analysis (14), Long-term Implications (8)) are less relevant here.',\n",
+       " 'adapted_modules': 'To enhance the process of identifying the shape drawn by the given SVG path element, the reasoning modules can be adapted and specified as follows:\\n\\n1. **Detailed Path Analysis (Critical Thinking)**: This module focuses on a meticulous examination of the SVG path commands and coordinates. It involves a deep dive into the syntax and semantics of path commands such as \"M\" (moveto) and \"L\" (lineto), challenging initial perceptions and rigorously interpreting the sequence of commands to deduce the shape accurately. This analysis goes beyond surface-level inspection, requiring a systematic questioning of each command\\'s role in constructing the overall shape.\\n\\n2. **Path Command Interpretation (Analytical Problem Solving)**: Essential for this task is the ability to decode the SVG path\\'s \"M\" and \"L\" commands, translating these instructions into a mental or visual representation of the shape\\'s geometry. This module emphasizes the analytical dissection of the path data, focusing on how each command contributes to the formation of vertices and edges, thereby facilitating the identification of the shape.\\n\\n3. **Shape Visualization (Creative Thinking)**: Leveraging imagination to mentally construct the shape from the path commands is the core of this module. It involves creatively synthesizing the segments drawn by the \"M\" and \"L\" commands into a coherent visual image, even when the path data does not immediately suggest a recognizable shape. This creative process aids in bridging gaps in the analytical interpretation, offering alternative perspectives on the possible shape outcomes.\\n\\n4. **Path-to-Shape Synthesis (Systems Thinking)**: This module entails understanding the SVG path as a component within the broader context of vector graphics, focusing on how individual path commands interlink to form a cohesive shape. It requires an appreciation of the cumulative effect of each command in relation to the others, recognizing the systemic relationship between the starting and ending points of segments and their collective role in shaping the final figure.\\n\\n5. **Sequential Command Analysis (Break Down the Problem)**: By segmenting the SVG path into discrete commands, this approach simplifies the complexity of the task. It advocates for a step-by-step examination of the path, where each \"M\" to \"L\" sequence is analyzed in isolation before synthesizing the findings to understand the overall shape. This methodical breakdown facilitates a clearer visualization and comprehension of the shape being drawn.\\n\\n6. **Command-to-Geometry Mapping (Visualization)**: Central to solving this task is the ability to map the abstract \"M\" and \"L\" commands onto a concrete geometric representation. This implicit module underlies both the analytical and creative thinking processes, focusing on converting the path data into a visual form that can be easily understood and manipulated mentally. It is about constructing a mental image of the shape as each command is processed, enabling a dynamic visualization that evolves with each new piece of path data.\\n\\nBy adapting and specifying these reasoning modules, the task of identifying the shape drawn by the SVG path element becomes a structured process that leverages critical analysis, analytical problem-solving, creative visualization, systemic thinking, and methodical breakdown to accurately determine the shape as a (D) kite.',\n",
+       " 'reasoning_structure': '```json\\n{\\n  \"Step 1: Detailed Path Analysis\": {\\n    \"Description\": \"Examine each SVG path command and its coordinates closely. Understand the syntax and semantics of \\'M\\' (moveto) and \\'L\\' (lineto) commands.\",\\n    \"Action\": \"List all path commands and their coordinates.\",\\n    \"Expected Outcome\": \"A clear understanding of the sequence and direction of each path command.\"\\n  },\\n  \"Step 2: Path Command Interpretation\": {\\n    \"Description\": \"Decode the \\'M\\' and \\'L\\' commands to translate these instructions into a mental or visual representation of the shape\\'s geometry.\",\\n    \"Action\": \"Map each \\'M\\' and \\'L\\' command to its corresponding action (move or draw line) in the context of the shape.\",\\n    \"Expected Outcome\": \"A segmented representation of the shape, highlighting vertices and edges.\"\\n  },\\n  \"Step 3: Shape Visualization\": {\\n    \"Description\": \"Use imagination to mentally construct the shape from the path commands, synthesizing the segments into a coherent visual image.\",\\n    \"Action\": \"Visualize the shape based on the segmented representation from Step 2.\",\\n    \"Expected Outcome\": \"A mental image of the potential shape, considering the sequence and direction of path commands.\"\\n  },\\n  \"Step 4: Path-to-Shape Synthesis\": {\\n    \"Description\": \"Understand the SVG path as a component within the broader context of vector graphics, focusing on how individual path commands interlink to form a cohesive shape.\",\\n    \"Action\": \"Analyze the systemic relationship between the starting and ending points of segments and their collective role in shaping the final figure.\",\\n    \"Expected Outcome\": \"Identification of the overall shape by recognizing the cumulative effect of each command.\"\\n  },\\n  \"Step 5: Sequential Command Analysis\": {\\n    \"Description\": \"Segment the SVG path into discrete commands for a step-by-step examination, analyzing each \\'M\\' to \\'L\\' sequence in isolation.\",\\n    \"Action\": \"Break down the path into individual commands and analyze each separately before synthesizing the findings.\",\\n    \"Expected Outcome\": \"A clearer visualization and comprehension of the shape being drawn, segment by segment.\"\\n  },\\n  \"Step 6: Command-to-Geometry Mapping\": {\\n    \"Description\": \"Map the abstract \\'M\\' and \\'L\\' commands onto a concrete geometric representation, constructing a mental image of the shape as each command is processed.\",\\n    \"Action\": \"Convert the path data into a visual form that can be easily understood and manipulated mentally.\",\\n    \"Expected Outcome\": \"A dynamic visualization of the shape that evolves with each new piece of path data, leading to the identification of the shape as a kite.\"\\n  },\\n  \"Conclusion\": {\\n    \"Description\": \"Based on the analysis and visualization steps, determine the shape drawn by the SVG path element.\",\\n    \"Action\": \"Review the outcomes of each step and synthesize the information to identify the shape.\",\\n    \"Expected Outcome\": \"The correct identification of the shape, supported by the structured analysis and reasoning process.\"\\n  }\\n}\\n```',\n",
+       " 'answer': 'Based on the provided reasoning structure and the SVG path element given, let\\'s analyze the path commands to identify the shape.\\n\\n**Step 1: Detailed Path Analysis**\\n- Description: The SVG path provided contains multiple \\'M\\' (moveto) and \\'L\\' (lineto) commands. Each command specifies a point in a 2D coordinate system.\\n- Action: The path commands are as follows:\\n  1. M 55.57,80.69 (Move to point)\\n  2. L 57.38,65.80 (Line to point)\\n  3. M 57.38,65.80 (Move to point)\\n  4. L 48.90,57.46 (Line to point)\\n  5. M 48.90,57.46 (Move to point)\\n  6. L 45.58,47.78 (Line to point)\\n  7. M 45.58,47.78 (Move to point)\\n  8. L 53.25,36.07 (Line to point)\\n  9. L 66.29,48.90 (Line to point)\\n  10. L 78.69,61.09 (Line to point)\\n  11. L 55.57,80.69 (Line to point)\\n- Expected Outcome: Understanding that the path commands describe a series of movements and lines that form a closed shape.\\n\\n**Step 2: Path Command Interpretation**\\n- Description: The \\'M\\' and \\'L\\' commands are used to move the \"pen\" to a starting point and draw lines to subsequent points, respectively.\\n- Action: The commands describe a shape starting at (55.57,80.69), drawing lines through several points, and finally closing the shape by returning to the starting point.\\n- Expected Outcome: A segmented representation showing a shape with distinct vertices at the specified coordinates.\\n\\n**Step 3: Shape Visualization**\\n- Description: Mentally constructing the shape from the provided path commands.\\n- Action: Visualizing the lines connecting in sequence from the starting point, through each point described by the \\'L\\' commands, and back to the starting point.\\n- Expected Outcome: A mental image of a shape that appears to have four distinct sides, suggesting it could be a quadrilateral.\\n\\n**Step 4: Path-to-Shape Synthesis**\\n- Description: Understanding how the path commands collectively form a specific shape.\\n- Action: Recognizing that the shape starts and ends at the same point, with lines drawn between intermediate points without overlapping, except at the starting/ending point.\\n- Expected Outcome: Identification of a closed, four-sided figure, which suggests it could be a kite based on the symmetry and structure of the lines.\\n\\n**Step 5: Sequential Command Analysis**\\n- Description: Analyzing each \\'M\\' to \\'L\\' sequence in isolation.\\n- Action: Observing that the path does not describe a regular polygon (like a hexagon or octagon) or a circle, but rather a shape with distinct angles and sides.\\n- Expected Outcome: A clearer understanding that the shape has four sides, with two pairs of adjacent sides being potentially unequal, which is characteristic of a kite.\\n\\n**Step 6: Command-to-Geometry Mapping**\\n- Description: Converting the abstract path commands into a geometric shape.\\n- Action: Mapping the path data to visualize a shape with two pairs of adjacent sides that are distinct yet symmetrical, indicative of a kite.\\n- Expected Outcome: A dynamic visualization that evolves to clearly represent a kite shape.\\n\\n**Conclusion**\\n- Description: Determining the shape drawn by the SVG path element.\\n- Action: Reviewing the outcomes of each analysis step, which consistently point towards a four-sided figure with distinct properties of a kite.\\n- Expected Outcome: The correct identification of the shape as a kite (D).'}"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "overall_chain.invoke(\n",
+    "    {\"task_description\": task_example, \"reasoning_modules\": reasoning_modules_str}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea8568d5-bdb6-45cd-8d04-1ab305786caa",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c14a291c-7c1b-43bc-807e-11180290985e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docker/Makefile

@@ -0,0 +1,12 @@
+# Makefile
+
+build_graphdb:
+	docker build --tag graphdb ./graphdb
+
+start_graphdb:
+	docker-compose up -d graphdb
+
+down:
+	docker-compose down -v --remove-orphans
+
+.PHONY: build_graphdb start_graphdb down

docker/docker-compose.yml

@@ -0,0 +1,21 @@
+# docker-compose to make it easier to spin up integration tests.
+# Services should use NON standard ports to avoid collision with
+version: "3"
+name: langchain-tests
+
+services:
+  redis:
+    image: redis/redis-stack-server:latest
+    # We use non standard ports since 
+    # these instances are used for testing
+    # and users may already have existing
+    # redis instances set up locally
+    # for other projects
+    ports:
+      - "6020:6379"
+    volumes:
+      - ./redis-volume:/data
+  graphdb:
+    image: graphdb
+    ports:
+      - "6021:7200"

docker/graphdb/Dockerfile

@@ -0,0 +1,5 @@
+FROM ontotext/graphdb:10.5.1
+RUN mkdir -p /opt/graphdb/dist/data/repositories/langchain
+COPY config.ttl /opt/graphdb/dist/data/repositories/langchain/
+COPY graphdb_create.sh /run.sh
+ENTRYPOINT bash /run.sh

docker/graphdb/config.ttl

@@ -0,0 +1,46 @@
+@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>.
+@prefix rep: <http://www.openrdf.org/config/repository#>.
+@prefix sr: <http://www.openrdf.org/config/repository/sail#>.
+@prefix sail: <http://www.openrdf.org/config/sail#>.
+@prefix graphdb: <http://www.ontotext.com/config/graphdb#>.
+
+[] a rep:Repository ;
+    rep:repositoryID "langchain" ;
+    rdfs:label "" ;
+    rep:repositoryImpl [
+        rep:repositoryType "graphdb:SailRepository" ;
+        sr:sailImpl [
+            sail:sailType "graphdb:Sail" ;
+
+            graphdb:read-only "false" ;
+
+            # Inference and Validation
+            graphdb:ruleset "empty" ;
+            graphdb:disable-sameAs "true" ;
+            graphdb:check-for-inconsistencies "false" ;
+
+            # Indexing
+            graphdb:entity-id-size "32" ;
+            graphdb:enable-context-index "false" ;
+            graphdb:enablePredicateList "true" ;
+            graphdb:enable-fts-index "false" ;
+            graphdb:fts-indexes ("default" "iri") ;
+            graphdb:fts-string-literals-index "default" ;
+            graphdb:fts-iris-index "none" ;
+
+            # Queries and Updates
+            graphdb:query-timeout "0" ;
+            graphdb:throw-QueryEvaluationException-on-timeout "false" ;
+            graphdb:query-limit-results "0" ;
+
+            # Settable in the file but otherwise hidden in the UI and in the RDF4J console
+            graphdb:base-URL "http://example.org/owlim#" ;
+            graphdb:defaultNS "" ;
+            graphdb:imports "" ;
+            graphdb:repository-type "file-repository" ;
+            graphdb:storage-folder "storage" ;
+            graphdb:entity-index-size "10000000" ;
+            graphdb:in-memory-literal-properties "true" ;
+            graphdb:enable-literal-index "true" ;
+        ]
+    ].

docker/graphdb/graphdb_create.sh

@@ -0,0 +1,28 @@
+#! /bin/bash
+REPOSITORY_ID="langchain"
+GRAPHDB_URI="http://localhost:7200/"
+
+echo -e "\nUsing GraphDB: ${GRAPHDB_URI}"
+
+function startGraphDB {
+ echo -e "\nStarting GraphDB..."
+ exec /opt/graphdb/dist/bin/graphdb
+}
+
+function waitGraphDBStart {
+  echo -e "\nWaiting GraphDB to start..."
+  for _ in $(seq 1 5); do
+    CHECK_RES=$(curl --silent --write-out '%{http_code}' --output /dev/null ${GRAPHDB_URI}/rest/repositories)
+    if [ "${CHECK_RES}" = '200' ]; then
+        echo -e "\nUp and running"
+        break
+    fi
+    sleep 30s
+    echo "CHECK_RES: ${CHECK_RES}"
+  done
+}
+
+
+startGraphDB &
+waitGraphDBStart
+wait

docs/api_reference/conf.py

@@ -114,8 +114,8 @@ autodoc_pydantic_field_signature_prefix = "param"
 autodoc_member_order = "groupwise"
 autoclass_content = "both"
 autodoc_typehints_format = "short"
+autodoc_typehints = "both"
 
-# autodoc_typehints = "description"
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["templates"]
 
@@ -146,6 +146,7 @@ partners = [
     (p.name, p.name.replace("-", "_") + "_api_reference")
     for p in partners_dir.iterdir()
 ]
+partners = sorted(partners)
 
 html_context = {
     "display_github": True,  # Integrate GitHub

docs/api_reference/create_api_rst.py

@@ -1,4 +1,5 @@
 """Script for auto-generating api_reference.rst."""
+
 import importlib
 import inspect
 import os
@@ -13,7 +14,6 @@ from pydantic import BaseModel
 ROOT_DIR = Path(__file__).parents[2].absolute()
 HERE = Path(__file__).parent
 
-
 ClassKind = Literal["TypedDict", "Regular", "Pydantic", "enum"]
 
 
@@ -186,7 +186,7 @@ def _load_package_modules(
             modules_by_namespace[top_namespace] = _module_members
 
         except ImportError as e:
-            print(f"Error: Unable to import module '{namespace}' with error: {e}")
+            print(f"Error: Unable to import module '{namespace}' with error: {e}")  # noqa: T201
 
     return modules_by_namespace
 
@@ -322,30 +322,52 @@ def _package_dir(package_name: str = "langchain") -> Path:
 
 
 def _get_package_version(package_dir: Path) -> str:
-    with open(package_dir.parent / "pyproject.toml", "r") as f:
-        pyproject = toml.load(f)
+    """Return the version of the package."""
+    try:
+        with open(package_dir.parent / "pyproject.toml", "r") as f:
+            pyproject = toml.load(f)
+    except FileNotFoundError as e:
+        print(
+            f"pyproject.toml not found in {package_dir.parent}.\n"
+            "You are either attempting to build a directory which is not a package or "
+            "the package is missing a pyproject.toml file which should be added."
+            "Aborting the build."
+        )
+        exit(1)
     return pyproject["tool"]["poetry"]["version"]
 
 
-def _out_file_path(package_name: str = "langchain") -> Path:
+def _out_file_path(package_name: str) -> Path:
     """Return the path to the file containing the documentation."""
     return HERE / f"{package_name.replace('-', '_')}_api_reference.rst"
 
 
-def _doc_first_line(package_name: str = "langchain") -> str:
+def _doc_first_line(package_name: str) -> str:
     """Return the path to the file containing the documentation."""
     return f".. {package_name.replace('-', '_')}_api_reference:\n\n"
 
 
 def main() -> None:
     """Generate the api_reference.rst file for each package."""
+    print("Starting to build API reference files.")
     for dir in os.listdir(ROOT_DIR / "libs"):
+        # Skip any hidden directories
+        # Some of these could be present by mistake in the code base
+        # e.g., .pytest_cache from running tests from the wrong location.
+        if not dir.startswith("."):
+            print("Skipping dir:", dir)
+            continue
+
         if dir in ("cli", "partners"):
             continue
         else:
+            print("Building package:", dir)
             _build_rst_file(package_name=dir)
-    for dir in os.listdir(ROOT_DIR / "libs" / "partners"):
+    partner_packages = os.listdir(ROOT_DIR / "libs" / "partners")
+    print("Building partner packages:", partner_packages)
+    for dir in partner_packages:
         _build_rst_file(package_name=dir)
+    print("API reference files built.")
 
 
 if __name__ == "__main__":

docs/api_reference/themes/scikit-learn-modern/search.html

@@ -5,7 +5,7 @@
   <script type="text/javascript" src="{{ pathto('_static/doctools.js', 1) }}"></script>
   <script type="text/javascript" src="{{ pathto('_static/language_data.js', 1) }}"></script>
   <script type="text/javascript" src="{{ pathto('_static/searchtools.js', 1) }}"></script>
-  <!-- <script type="text/javascript" src="{{ pathto('_static/sphinx_highlight.js', 1) }}"></script> -->
+  <script type="text/javascript" src="{{ pathto('_static/sphinx_highlight.js', 1) }}"></script>
   <script type="text/javascript">
     $(document).ready(function() {
       if (!Search.out) {

docs/docs/additional_resources/tutorials.mdx

@@ -2,7 +2,7 @@
 
 Below are links to tutorials and courses on LangChain. For written guides on common use cases for LangChain, check out the [use cases guides](/docs/use_cases).
 
-⛓ icon marks a new addition [last update 2023-09-21]
+⛓ icon marks a new addition [last update 2024-02-06]
 
 ---------------------
 
@@ -10,18 +10,20 @@ Below are links to tutorials and courses on LangChain. For written guides on com
 
 ### Books
 
-#### ⛓[Generative AI with LangChain](https://www.amazon.com/Generative-AI-LangChain-language-ChatGPT/dp/1835083463/ref=sr_1_1?crid=1GMOMH0G7GLR&keywords=generative+ai+with+langchain&qid=1703247181&sprefix=%2Caps%2C298&sr=8-1) by [Ben Auffrath](https://www.amazon.com/stores/Ben-Auffarth/author/B08JQKSZ7D?ref=ap_rdr&store_ref=ap_rdr&isDramIntegrated=true&shoppingPortalEnabled=true), ©️ 2023 Packt Publishing
+#### [Generative AI with LangChain](https://www.amazon.com/Generative-AI-LangChain-language-ChatGPT/dp/1835083463/ref=sr_1_1?crid=1GMOMH0G7GLR&keywords=generative+ai+with+langchain&qid=1703247181&sprefix=%2Caps%2C298&sr=8-1) by [Ben Auffrath](https://www.amazon.com/stores/Ben-Auffarth/author/B08JQKSZ7D?ref=ap_rdr&store_ref=ap_rdr&isDramIntegrated=true&shoppingPortalEnabled=true), ©️ 2023 Packt Publishing
 
 
 ### DeepLearning.AI courses
  by [Harrison Chase](https://en.wikipedia.org/wiki/LangChain) and [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng)
 - [LangChain for LLM Application Development](https://learn.deeplearning.ai/langchain)
 - [LangChain Chat with Your Data](https://learn.deeplearning.ai/langchain-chat-with-your-data)
-- ⛓ [Functions, Tools and Agents with LangChain](https://learn.deeplearning.ai/functions-tools-agents-langchain)
+- [Functions, Tools and Agents with LangChain](https://learn.deeplearning.ai/functions-tools-agents-langchain)
 
 ### Handbook
 [LangChain AI Handbook](https://www.pinecone.io/learn/langchain/) By **James Briggs** and **Francisco Ingham**
 
+⛓ [LangChain Cheatsheet](https://pub.towardsai.net/langchain-cheatsheet-all-secrets-on-a-single-page-8be26b721cde) by **Ivan Reznikov**
+
 ### Short Tutorials
 [LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners](https://youtu.be/aywZrzNaKjs) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
 
@@ -29,6 +31,8 @@ Below are links to tutorials and courses on LangChain. For written guides on com
 
 [LangChain Crash Course - Build apps with language models](https://youtu.be/LbT1yp6quS8) by [Patrick Loeber](https://www.youtube.com/@patloeber)
 
+⛓ [LangChain 101 Course](https://medium.com/@ivanreznikov/langchain-101-course-updated-668f7b41d6cb) by **Ivan Reznikov**
+
 ##  Tutorials
 
 ### [LangChain for Gen AI and LLMs](https://www.youtube.com/playlist?list=PLIUOU7oqGTLieV9uTIFMm6_4PXg-hlN6F) by [James Briggs](https://www.youtube.com/@jamesbriggs)
@@ -44,8 +48,8 @@ Below are links to tutorials and courses on LangChain. For written guides on com
 - #9 [Build Conversational Agents with Vector DBs](https://youtu.be/H6bCqqw9xyI)
 - [Using NEW `MPT-7B` in Hugging Face and LangChain](https://youtu.be/DXpk9K7DgMo)
 - [`MPT-30B` Chatbot with LangChain](https://youtu.be/pnem-EhT6VI)
-- ⛓ [Fine-tuning OpenAI's `GPT 3.5` for LangChain Agents](https://youtu.be/boHXgQ5eQic?si=OOOfK-GhsgZGBqSr)
-- ⛓ [Chatbots with `RAG`: LangChain Full Walkthrough](https://youtu.be/LhnCsygAvzY?si=N7k6xy4RQksbWwsQ)
+- [Fine-tuning OpenAI's `GPT 3.5` for LangChain Agents](https://youtu.be/boHXgQ5eQic?si=OOOfK-GhsgZGBqSr)
+- [Chatbots with `RAG`: LangChain Full Walkthrough](https://youtu.be/LhnCsygAvzY?si=N7k6xy4RQksbWwsQ)
 
 
 ### [LangChain 101](https://www.youtube.com/playlist?list=PLqZXAkvF1bPNQER9mLmDbntNfSpzdDIU5) by [Greg Kamradt (Data Indy)](https://www.youtube.com/@DataIndependent)
@@ -109,16 +113,16 @@ Below are links to tutorials and courses on LangChain. For written guides on com
 - [What can you do with 16K tokens in LangChain?](https://youtu.be/z2aCZBAtWXs)
 - [Tagging and Extraction - Classification using `OpenAI Functions`](https://youtu.be/a8hMgIcUEnE)
 - [HOW to Make Conversational Form with LangChain](https://youtu.be/IT93On2LB5k)
-- ⛓ [`Claude-2` meets LangChain!](https://youtu.be/Hb_D3p0bK2U?si=j96Kc7oJoeRI5-iC)
-- ⛓ [`PaLM 2` Meets LangChain](https://youtu.be/orPwLibLqm4?si=KgJjpEbAD9YBPqT4)
-- ⛓ [`LLaMA2` with LangChain - Basics | LangChain TUTORIAL](https://youtu.be/cIRzwSXB4Rc?si=v3Hwxk1m3fksBIHN)
-- ⛓ [Serving `LLaMA2` with `Replicate`](https://youtu.be/JIF4nNi26DE?si=dSazFyC4UQmaR-rJ)
-- ⛓ [NEW LangChain Expression Language](https://youtu.be/ud7HJ2p3gp0?si=8pJ9O6hGbXrCX5G9)
-- ⛓ [Building a RCI Chain for Agents with LangChain Expression Language](https://youtu.be/QaKM5s0TnsY?si=0miEj-o17AHcGfLG)
-- ⛓ [How to Run `LLaMA-2-70B` on the `Together AI`](https://youtu.be/Tc2DHfzHeYE?si=Xku3S9dlBxWQukpe)
-- ⛓ [`RetrievalQA` with `LLaMA 2 70b` & `Chroma` DB](https://youtu.be/93yueQQnqpM?si=ZMwj-eS_CGLnNMXZ)
-- ⛓ [How to use `BGE Embeddings` for LangChain](https://youtu.be/sWRvSG7vL4g?si=85jnvnmTCF9YIWXI)
-- ⛓ [How to use Custom Prompts for `RetrievalQA` on `LLaMA-2 7B`](https://youtu.be/PDwUKves9GY?si=sMF99TWU0p4eiK80)
+- [`Claude-2` meets LangChain!](https://youtu.be/Hb_D3p0bK2U?si=j96Kc7oJoeRI5-iC)
+- [`PaLM 2` Meets LangChain](https://youtu.be/orPwLibLqm4?si=KgJjpEbAD9YBPqT4)
+- [`LLaMA2` with LangChain - Basics | LangChain TUTORIAL](https://youtu.be/cIRzwSXB4Rc?si=v3Hwxk1m3fksBIHN)
+- [Serving `LLaMA2` with `Replicate`](https://youtu.be/JIF4nNi26DE?si=dSazFyC4UQmaR-rJ)
+- [NEW LangChain Expression Language](https://youtu.be/ud7HJ2p3gp0?si=8pJ9O6hGbXrCX5G9)
+- [Building a RCI Chain for Agents with LangChain Expression Language](https://youtu.be/QaKM5s0TnsY?si=0miEj-o17AHcGfLG)
+- [How to Run `LLaMA-2-70B` on the `Together AI`](https://youtu.be/Tc2DHfzHeYE?si=Xku3S9dlBxWQukpe)
+- [`RetrievalQA` with `LLaMA 2 70b` & `Chroma` DB](https://youtu.be/93yueQQnqpM?si=ZMwj-eS_CGLnNMXZ)
+- [How to use `BGE Embeddings` for LangChain](https://youtu.be/sWRvSG7vL4g?si=85jnvnmTCF9YIWXI)
+- [How to use Custom Prompts for `RetrievalQA` on `LLaMA-2 7B`](https://youtu.be/PDwUKves9GY?si=sMF99TWU0p4eiK80)
 
 
 ### [LangChain](https://www.youtube.com/playlist?list=PLVEEucA9MYhOu89CX8H3MBZqayTbcCTMr) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
@@ -131,8 +135,8 @@ Below are links to tutorials and courses on LangChain. For written guides on com
 - [LangChain: Giving Memory to LLMs](https://youtu.be/dxO6pzlgJiY)
 - [BEST OPEN Alternative to `OPENAI's EMBEDDINGs` for Retrieval QA: LangChain](https://youtu.be/ogEalPMUCSY)
 - [LangChain: Run Language Models Locally - `Hugging Face Models`](https://youtu.be/Xxxuw4_iCzw) 
-- ⛓ [Slash API Costs: Mastering Caching for LLM Applications](https://youtu.be/EQOznhaJWR0?si=AXoI7f3-SVFRvQUl)
-- ⛓ [Avoid PROMPT INJECTION with `Constitutional AI` - LangChain](https://youtu.be/tyKSkPFHVX8?si=9mgcB5Y1kkotkBGB)
+- [Slash API Costs: Mastering Caching for LLM Applications](https://youtu.be/EQOznhaJWR0?si=AXoI7f3-SVFRvQUl)
+- [Avoid PROMPT INJECTION with `Constitutional AI` - LangChain](https://youtu.be/tyKSkPFHVX8?si=9mgcB5Y1kkotkBGB)
 
 
 ### LangChain by [Chat with data](https://www.youtube.com/@chatwithdata)
@@ -148,4 +152,4 @@ Below are links to tutorials and courses on LangChain. For written guides on com
 
 
 ---------------------
-⛓ icon marks a new addition [last update 2023-09-21]
+⛓ icon marks a new addition [last update 2024-02-061]

docs/docs/additional_resources/youtube.mdx

@@ -120,6 +120,8 @@
 - ⛓ [Use ANY language in `LangSmith` with REST](https://youtu.be/7BL0GEdMmgY?si=iXfOEdBLqXF6hqRM) by [Nerding I/O](https://www.youtube.com/@nerding_io)
 - ⛓ [How to Leverage the Full Potential of LLMs for Your Business with Langchain - Leon Ruddat](https://youtu.be/vZmoEa7oWMg?si=ZhMmydq7RtkZd56Q) by [PyData](https://www.youtube.com/@PyDataTV)
 - ⛓ [`ChatCSV` App: Chat with CSV files using LangChain and `Llama 2`](https://youtu.be/PvsMg6jFs8E?si=Qzg5u5gijxj933Ya) by [Muhammad Moin](https://www.youtube.com/@muhammadmoinfaisal)
+- ⛓ [Build Chat PDF app in Python with LangChain, OpenAI, Streamlit | Full project | Learn Coding](https://www.youtube.com/watch?v=WYzFzZg4YZI) by [Jutsupoint](https://www.youtube.com/@JutsuPoint)
+- ⛓ [Build Eminem Bot App with LangChain, Streamlit, OpenAI | Full Python Project | Tutorial | AI ChatBot](https://www.youtube.com/watch?v=a2shHB4MRZ4) by [Jutsupoint](https://www.youtube.com/@JutsuPoint)
 
 
 ### [Prompt Engineering and LangChain](https://www.youtube.com/watch?v=muXbPpG_ys4&list=PLEJK-H61Xlwzm5FYLDdKt_6yibO33zoMW) by [Venelin Valkov](https://www.youtube.com/@venelin_valkov)
@@ -132,4 +134,4 @@
 
 
 ---------------------
-⛓ icon marks a new addition [last update 2023-09-21]
+⛓ icon marks a new addition [last update 2024-02-04]

docs/docs/contributing/documentation.mdx

@@ -3,24 +3,68 @@ sidebar_position: 3
 ---
 # Contribute Documentation
 
-The docs directory contains Documentation and API Reference.
+LangChain documentation consists of two components:
 
-Documentation is built using [Quarto](https://quarto.org) and [Docusaurus 2](https://docusaurus.io/).
+1. Main Documentation: Hosted at [python.langchain.com](https://python.langchain.com/),
+this comprehensive resource serves as the primary user-facing documentation.
+It covers a wide array of topics, including tutorials, use cases, integrations,
+and more, offering extensive guidance on building with LangChain.
+The content for this documentation lives in the `/docs` directory of the monorepo.
+2. In-code Documentation: This is documentation of the codebase itself, which is also
+used to generate the externally facing [API Reference](https://api.python.langchain.com/en/latest/langchain_api_reference.html).
+The content for the API reference is autogenerated by scanning the docstrings in the codebase. For this reason we ask that
+developers document their code well.
 
-API Reference are largely autogenerated by [sphinx](https://www.sphinx-doc.org/en/master/) from the code and are hosted by [Read the Docs](https://readthedocs.org/).
-For that reason, we ask that you add good documentation to all classes and methods.
+The main documentation is built using [Quarto](https://quarto.org) and [Docusaurus 2](https://docusaurus.io/).
 
-Similar to linting, we recognize documentation 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.
+The `API Reference` is largely autogenerated by [sphinx](https://www.sphinx-doc.org/en/master/)
+from the code and is hosted by [Read the Docs](https://readthedocs.org/).
 
-## Build Documentation Locally
+We appreciate all contributions to the documentation, whether it be fixing a typo,
+adding a new tutorial or example and whether it be in the main documentation or the API Reference.
+
+Similar to linting, we recognize documentation 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.
+
+## 📜 Main Documentation
+
+The content for the main documentation is located in the `/docs` directory of the monorepo.
+
+The documentation is written using a combination of ipython notebooks (`.ipynb` files)
+and markdown (`.mdx` files). The notebooks are converted to markdown
+using [Quarto](https://quarto.org) and then built using [Docusaurus 2](https://docusaurus.io/).
+
+Feel free to make contributions to the main documentation! 🥰
+
+After modifying the documentation:
+
+1. Run the linting and formatting commands (see below) to ensure that the documentation is well-formatted and free of errors.
+2. Optionally build the documentation locally to verify that the changes look good.
+3. Make a pull request with the changes.
+4. You can preview and verify that the changes are what you wanted by clicking the `View deployment` or `Visit Preview` buttons on the pull request `Conversation` page. This will take you to a preview of the documentation changes.
+
+## ⚒️ Linting and Building Documentation Locally
+
+After writing up the documentation, you may want to lint and build the documentation
+locally to ensure that it looks good and is free of errors.
+
+If you're unable to build it locally that's okay as well, as you will be able to
+see a preview of the documentation on the pull request page.
 
 ### Install dependencies
 
-- [Quarto](https://quarto.org) - package that converts Jupyter notebooks (`.ipynb` files) into mdx files for serving in Docusaurus.
-- `poetry install` from the monorepo root
+- [Quarto](https://quarto.org) - package that converts Jupyter notebooks (`.ipynb` files) into mdx files for serving in Docusaurus. [Download link](https://quarto.org/docs/download/).
+
+From the **monorepo root**, run the following command to install the dependencies:
+
+```bash
+poetry install --with lint,docs --no-root
+````
 
 ### Building
 
+The code that builds the documentation is located in the `/docs` directory of the monorepo.
+
 In the following commands, the prefix `api_` indicates that those are operations for the API Reference.
 
 Before building the documentation, it is always a good idea to clean the build directory:
@@ -46,10 +90,9 @@ make api_docs_linkcheck
 
 ### Linting and Formatting
 
-The docs are linted from the monorepo root. To lint the docs, run the following from there:
+The Main Documentation is linted from the **monorepo root**. To lint the main documentation, run the following from there:
 
 ```bash
-poetry install --with lint,typing
 make lint
 ```
 
@@ -57,9 +100,73 @@ If you have formatting-related errors, you can fix them automatically with:
 
 ```bash
 make format
-``` 
+```
 
-## Verify Documentation changes
+## ⌨️ In-code Documentation
+
+The in-code documentation is largely autogenerated by [sphinx](https://www.sphinx-doc.org/en/master/) from the code and is hosted by [Read the Docs](https://readthedocs.org/).
+
+For the API reference to be useful, the codebase must be well-documented. This means that all functions, classes, and methods should have a docstring that explains what they do, what the arguments are, and what the return value is. This is a good practice in general, but it is especially important for LangChain because the API reference is the primary resource for developers to understand how to use the codebase.
+
+We generally follow the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings) for docstrings.
+
+Here is an example of a well-documented function:
+
+```python
+
+def my_function(arg1: int, arg2: str) -> float:
+    """This is a short description of the function. (It should be a single sentence.)
+
+    This is a longer description of the function. It should explain what
+    the function does, what the arguments are, and what the return value is.
+    It should wrap at 88 characters.
+
+    Examples:
+        This is a section for examples of how to use the function.
+
+        .. code-block:: python
+
+            my_function(1, "hello")
+
+    Args:
+        arg1: This is a description of arg1. We do not need to specify the type since
+            it is already specified in the function signature.
+        arg2: This is a description of arg2.
+
+    Returns:
+        This is a description of the return value.
+    """
+    return 3.14
+```
+
+### Linting and Formatting
+
+The in-code documentation is linted from the directories belonging to the packages
+being documented.
+
+For example, if you're working on the `langchain-community` package, you would change
+the working directory to the `langchain-community` directory:
+
+```bash
+cd [root]/libs/langchain-community
+```
+
+Set up a virtual environment for the package if you haven't done so already.
+
+Install the dependencies for the package.
+
+```bash
+poetry install --with lint
+```
+
+Then you can run the following commands to lint and format the in-code documentation:
+
+```bash
+make format
+make lint
+```
+
+## Verify Documentation Changes
 
 After pushing documentation changes to the repository, you can preview and verify that the changes are
 what you wanted by clicking the `View deployment` or `Visit Preview` buttons on the pull request `Conversation` page.

docs/docs/contributing/index.mdx

@@ -15,8 +15,9 @@ There are many ways to contribute to LangChain. Here are some common ways people
 - [**Documentation**](./documentation.mdx): Help improve our docs, including this one!
 - [**Code**](./code.mdx): Help us write code, fix bugs, or improve our infrastructure.
 - [**Integrations**](integrations.mdx): Help us integrate with your favorite vendors and tools.
+- [**Discussions**](https://github.com/langchain-ai/langchain/discussions): Help answer usage questions and discuss issues with users.
 
-### 🚩GitHub Issues
+### 🚩 GitHub Issues
 
 Our [issues](https://github.com/langchain-ai/langchain/issues) page is kept up to date with bugs, improvements, and feature requests.
 
@@ -31,7 +32,13 @@ We will try to keep these issues as up-to-date as possible, though
 with the rapid rate of development in this field some may get out of date.
 If you notice this happening, please let us know.
 
-### 🙋Getting Help
+### 💭 GitHub Discussions
+
+We have a [discussions](https://github.com/langchain-ai/langchain/discussions) page where users can ask usage questions, discuss design decisions, and propose new features.
+
+If you are able to help answer questions, please do so! This will allow the maintainers to spend more time focused on development and bug fixing.
+
+### 🙋 Getting Help
 
 Our goal is to have the simplest developer setup possible. Should you experience any difficulty getting setup, please
 contact a maintainer! Not only do we want to help get you unblocked, but we also want to make sure that the process is

docs/docs/contributing/repo_structure.mdx

@@ -0,0 +1,54 @@
+---
+sidebar_position: 0.5
+---
+# Repository Structure
+
+If you plan on contributing to LangChain code or documentation, it can be useful
+to understand the high level structure of the repository.
+
+LangChain is organized as a [monorep](https://en.wikipedia.org/wiki/Monorepo) that contains multiple packages.
+
+Here's the structure visualized as a tree:
+
+```text
+.
+├── cookbook # Tutorials and examples
+├── docs # Contains content for the documentation here: https://python.langchain.com/
+├── libs
+│   ├── langchain # Main package
+│   │   ├── tests/unit_tests # Unit tests (present in each package not shown for brevity)
+│   │   ├── tests/integration_tests # Integration tests (present in each package not shown for brevity)
+│   ├── langchain-community # Third-party integrations
+│   ├── langchain-core # Base interfaces for key abstractions
+│   ├── langchain-experimental # Experimental components and chains
+│   ├── partners
+│       ├── langchain-partner-1
+│       ├── langchain-partner-2
+│       ├── ...
+│
+├── templates # A collection of easily deployable reference architectures for a wide variety of tasks.
+```
+
+The root directory also contains the following files:
+
+* `pyproject.toml`: Dependencies for building docs and linting docs, cookbook.
+* `Makefile`: A file that contains shortcuts for building, linting and docs and cookbook.
+
+There are other files in the root directory level, but their presence should be self-explanatory. Feel free to browse around!
+
+## Documentation
+
+The `/docs` directory contains the content for the documentation that is shown
+at https://python.langchain.com/ and the associated API Reference https://api.python.langchain.com/en/latest/langchain_api_reference.html.
+
+See the [documentation](./documentation) guidelines to learn how to contribute to the documentation.
+
+## Code
+
+The `/libs` directory contains the code for the LangChain packages.
+
+To learn more about how to contribute code see the following guidelines:
+
+- [Code](./code.mdx) Learn how to develop in the LangChain codebase.
+- [Integrations](./integrations.mdx) to learn how to contribute to third-party integrations to langchain-community or to start a new partner package.
+- [Testing](./testing.mdx) guidelines to learn how to write tests for the packages.

docs/docs/expression_language/how_to/message_history.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Add message history (memory)\n",
     "\n",
-    "The `RunnableWithMessageHistory` let us add message history to certain types of chains.\n",
+    "The `RunnableWithMessageHistory` lets us add message history to certain types of chains. It wraps another Runnable and manages the chat message history for it.\n",
     "\n",
     "Specifically, it can be used for any Runnable that takes as input one of\n",
     "\n",
@@ -21,7 +21,379 @@
     "* a sequence of `BaseMessage`\n",
     "* a dict with a key that contains a sequence of `BaseMessage`\n",
     "\n",
-    "Let's take a look at some examples to see how it works."
+    "Let's take a look at some examples to see how it works. First we construct a runnable (which here accepts a dict as input and returns a message as output):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2ed413b4-33a1-48ee-89b0-2d4917ec101a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "from langchain_openai.chat_models import ChatOpenAI\n",
+    "\n",
+    "model = ChatOpenAI()\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You're an assistant who's good at {ability}. Respond in 20 words or fewer\",\n",
+    "        ),\n",
+    "        MessagesPlaceholder(variable_name=\"history\"),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "runnable = prompt | model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fd175e1-c7b8-4929-a57e-3331865fe7aa",
+   "metadata": {},
+   "source": [
+    "To manage the message history, we will need:\n",
+    "1. This runnable;\n",
+    "2. A callable that returns an instance of `BaseChatMessageHistory`.\n",
+    "\n",
+    "Check out the [memory integrations](https://integrations.langchain.com/memory) page for implementations of chat message histories using Redis and other providers. Here we demonstrate using an in-memory `ChatMessageHistory` as well as more persistent storage using `RedisChatMessageHistory`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d83adad-9672-496d-9f25-5747e7b8c8bb",
+   "metadata": {},
+   "source": [
+    "## In-memory\n",
+    "\n",
+    "Below we show a simple example in which the chat history lives in memory, in this case via a global Python dict.\n",
+    "\n",
+    "We construct a callable `get_session_history` that references this dict to return an instance of `ChatMessageHistory`. The arguments to the callable can be specified by passing a configuration to the `RunnableWithMessageHistory` at runtime. By default, the configuration parameter is expected to be a single string `session_id`. This can be adjusted via the `history_factory_config` kwarg.\n",
+    "\n",
+    "Using the single-parameter default:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "54348d02-d8ee-440c-bbf9-41bc0fbbc46c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.chat_message_histories import ChatMessageHistory\n",
+    "from langchain_core.chat_history import BaseChatMessageHistory\n",
+    "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
+    "\n",
+    "store = {}\n",
+    "\n",
+    "\n",
+    "def get_session_history(session_id: str) -> BaseChatMessageHistory:\n",
+    "    if session_id not in store:\n",
+    "        store[session_id] = ChatMessageHistory()\n",
+    "    return store[session_id]\n",
+    "\n",
+    "\n",
+    "with_message_history = RunnableWithMessageHistory(\n",
+    "    runnable,\n",
+    "    get_session_history,\n",
+    "    input_messages_key=\"input\",\n",
+    "    history_messages_key=\"history\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01acb505-3fd3-4ab4-9f04-5ea07e81542e",
+   "metadata": {},
+   "source": [
+    "Note that we've specified `input_messages_key` (the key to be treated as the latest input message) and `history_messages_key` (the key to add historical messages to).\n",
+    "\n",
+    "When invoking this new runnable, we specify the corresponding chat history via a configuration parameter:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "01384412-f08e-4634-9edb-3f46f475b582",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Cosine is a trigonometric function that calculates the ratio of the adjacent side to the hypotenuse of a right triangle.')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with_message_history.invoke(\n",
+    "    {\"ability\": \"math\", \"input\": \"What does cosine mean?\"},\n",
+    "    config={\"configurable\": {\"session_id\": \"abc123\"}},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "954688a2-9a3f-47ee-a9e8-fa0c83e69477",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Cosine is a mathematical function used to calculate the length of a side in a right triangle.')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Remembers\n",
+    "with_message_history.invoke(\n",
+    "    {\"ability\": \"math\", \"input\": \"What?\"},\n",
+    "    config={\"configurable\": {\"session_id\": \"abc123\"}},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "39350d7c-2641-4744-bc2a-fd6a57c4ea90",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='I can help with math problems. What do you need assistance with?')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# New session_id --> does not remember.\n",
+    "with_message_history.invoke(\n",
+    "    {\"ability\": \"math\", \"input\": \"What?\"},\n",
+    "    config={\"configurable\": {\"session_id\": \"def234\"}},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d29497be-3366-408d-bbb9-d4a8bf4ef37c",
+   "metadata": {},
+   "source": [
+    "The configuration parameters by which we track message histories can be customized by passing in a list of ``ConfigurableFieldSpec`` objects to the ``history_factory_config`` parameter. Below, we use two parameters: a `user_id` and `conversation_id`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1c89daee-deff-4fdf-86a3-178f7d8ef536",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.runnables import ConfigurableFieldSpec\n",
+    "\n",
+    "store = {}\n",
+    "\n",
+    "\n",
+    "def get_session_history(user_id: str, conversation_id: str) -> BaseChatMessageHistory:\n",
+    "    if (user_id, conversation_id) not in store:\n",
+    "        store[(user_id, conversation_id)] = ChatMessageHistory()\n",
+    "    return store[(user_id, conversation_id)]\n",
+    "\n",
+    "\n",
+    "with_message_history = RunnableWithMessageHistory(\n",
+    "    runnable,\n",
+    "    get_session_history,\n",
+    "    input_messages_key=\"input\",\n",
+    "    history_messages_key=\"history\",\n",
+    "    history_factory_config=[\n",
+    "        ConfigurableFieldSpec(\n",
+    "            id=\"user_id\",\n",
+    "            annotation=str,\n",
+    "            name=\"User ID\",\n",
+    "            description=\"Unique identifier for the user.\",\n",
+    "            default=\"\",\n",
+    "            is_shared=True,\n",
+    "        ),\n",
+    "        ConfigurableFieldSpec(\n",
+    "            id=\"conversation_id\",\n",
+    "            annotation=str,\n",
+    "            name=\"Conversation ID\",\n",
+    "            description=\"Unique identifier for the conversation.\",\n",
+    "            default=\"\",\n",
+    "            is_shared=True,\n",
+    "        ),\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65c5622e-09b8-4f2f-8c8a-2dab0fd040fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with_message_history.invoke(\n",
+    "    {\"ability\": \"math\", \"input\": \"Hello\"},\n",
+    "    config={\"configurable\": {\"user_id\": \"123\", \"conversation_id\": \"1\"}},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18f1a459-3f88-4ee6-8542-76a907070dd6",
+   "metadata": {},
+   "source": [
+    "### Examples with runnables of different signatures\n",
+    "\n",
+    "The above runnable takes a dict as input and returns a BaseMessage. Below we show some alternatives."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48eae1bf-b59d-4a61-8e62-b6dbf667e866",
+   "metadata": {},
+   "source": [
+    "#### Messages input, dict output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "17733d4f-3a32-4055-9d44-5d58b9446a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output_message': AIMessage(content=\"Simone de Beauvoir believed in the existence of free will. She argued that individuals have the ability to make choices and determine their own actions, even in the face of social and cultural constraints. She rejected the idea that individuals are purely products of their environment or predetermined by biology or destiny. Instead, she emphasized the importance of personal responsibility and the need for individuals to actively engage in creating their own lives and defining their own existence. De Beauvoir believed that freedom and agency come from recognizing one's own freedom and actively exercising it in the pursuit of personal and collective liberation.\")}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_core.messages import HumanMessage\n",
+    "from langchain_core.runnables import RunnableParallel\n",
+    "\n",
+    "chain = RunnableParallel({\"output_message\": ChatOpenAI()})\n",
+    "\n",
+    "\n",
+    "def get_session_history(session_id: str) -> BaseChatMessageHistory:\n",
+    "    if session_id not in store:\n",
+    "        store[session_id] = ChatMessageHistory()\n",
+    "    return store[session_id]\n",
+    "\n",
+    "\n",
+    "with_message_history = RunnableWithMessageHistory(\n",
+    "    chain,\n",
+    "    get_session_history,\n",
+    "    output_messages_key=\"output_message\",\n",
+    ")\n",
+    "\n",
+    "with_message_history.invoke(\n",
+    "    [HumanMessage(content=\"What did Simone de Beauvoir believe about free will\")],\n",
+    "    config={\"configurable\": {\"session_id\": \"baz\"}},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "efb57ef5-91f9-426b-84b9-b77f071a9dd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output_message': AIMessage(content='Simone de Beauvoir\\'s views on free will were closely aligned with those of her contemporary and partner Jean-Paul Sartre. Both de Beauvoir and Sartre were existentialist philosophers who emphasized the importance of individual freedom and the rejection of determinism. They believed that human beings have the capacity to transcend their circumstances and create their own meaning and values.\\n\\nSartre, in his famous work \"Being and Nothingness,\" argued that human beings are condemned to be free, meaning that we are burdened with the responsibility of making choices and defining ourselves in a world that lacks inherent meaning. Like de Beauvoir, Sartre believed that individuals have the ability to exercise their freedom and make choices in the face of external and internal constraints.\\n\\nWhile there may be some nuanced differences in their philosophical writings, overall, de Beauvoir and Sartre shared a similar belief in the existence of free will and the importance of individual agency in shaping one\\'s own life.')}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with_message_history.invoke(\n",
+    "    [HumanMessage(content=\"How did this compare to Sartre\")],\n",
+    "    config={\"configurable\": {\"session_id\": \"baz\"}},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a39eac5f-a9d8-4729-be06-5e7faf0c424d",
+   "metadata": {},
+   "source": [
+    "#### Messages input, messages output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e45bcd95-e31f-4a9a-967a-78f96e8da881",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RunnableWithMessageHistory(\n",
+    "    ChatOpenAI(),\n",
+    "    get_session_history,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04daa921-a2d1-40f9-8cd1-ae4e9a4163a7",
+   "metadata": {},
+   "source": [
+    "#### Dict with single key for all messages input, messages output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27157f15-9fb0-4167-9870-f4d7f234b3cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from operator import itemgetter\n",
+    "\n",
+    "RunnableWithMessageHistory(\n",
+    "    itemgetter(\"input_messages\") | ChatOpenAI(),\n",
+    "    get_session_history,\n",
+    "    input_messages_key=\"input_messages\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "418ca7af-9ed9-478c-8bca-cba0de2ca61e",
+   "metadata": {},
+   "source": [
+    "## Persistent storage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76799a13-d99a-4c4f-91f2-db699e40b8df",
+   "metadata": {},
+   "source": [
+    "In many cases it is preferable to persist conversation histories. `RunnableWithMessageHistory` is agnostic as to how the `get_session_history` callable retrieves its chat message histories. See [here](https://github.com/langchain-ai/langserve/blob/main/examples/chat_with_persistence_and_user/server.py) for an example using a local filesystem. Below we demonstrate how one could use Redis. Check out the [memory integrations](https://integrations.langchain.com/memory) page for implementations of chat message histories using other providers."
    ]
   },
   {
@@ -29,9 +401,9 @@
    "id": "6bca45e5-35d9-4603-9ca9-6ac0ce0e35cd",
    "metadata": {},
    "source": [
-    "## Setup\n",
+    "### Setup\n",
     "\n",
-    "We'll use Redis to store our chat message histories and Anthropic's claude-2 model so we'll need to install the following dependencies:"
+    "We'll need to install Redis if it's not installed already:"
    ]
   },
   {
@@ -41,28 +413,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  langchain redis anthropic"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "93776323-d6b8-4912-bb6a-867c5e655f46",
-   "metadata": {},
-   "source": [
-    "Set your [Anthropic API  key](https://console.anthropic.com/):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c7f56f69-d2f1-4a21-990c-b5551eb012fa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import os\n",
-    "\n",
-    "os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass()"
+    "%pip install --upgrade --quiet redis"
    ]
   },
   {
@@ -78,7 +429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
    "id": "cd6a250e-17fe-4368-a39d-1fe6b2cbde68",
    "metadata": {},
    "outputs": [],
@@ -110,77 +461,32 @@
     "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "1a5a632e-ba9e-4488-b586-640ad5494f62",
-   "metadata": {},
-   "source": [
-    "## Example: Dict input, message output\n",
-    "\n",
-    "Let's create a simple chain that takes a dict as input and returns a BaseMessage.\n",
-    "\n",
-    "In this case the `\"question\"` key in the input represents our input message, and the `\"history\"` key is where our historical messages will be injected."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "2a150d6f-8878-4950-8634-a608c5faad56",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import Optional\n",
-    "\n",
-    "from langchain_community.chat_message_histories import RedisChatMessageHistory\n",
-    "from langchain_community.chat_models import ChatAnthropic\n",
-    "from langchain_core.chat_history import BaseChatMessageHistory\n",
-    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
-    "from langchain_core.runnables.history import RunnableWithMessageHistory"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "3185edba-4eb6-4b32-80c6-577c0d19af97",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", \"You're an assistant who's good at {ability}\"),\n",
-    "        MessagesPlaceholder(variable_name=\"history\"),\n",
-    "        (\"human\", \"{question}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "chain = prompt | ChatAnthropic(model=\"claude-2\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "f9d81796-ce61-484c-89e2-6c567d5e54ef",
    "metadata": {},
    "source": [
-    "### Adding message history\n",
-    "\n",
-    "To add message history to our original chain we wrap it in the `RunnableWithMessageHistory` class.\n",
-    "\n",
-    "Crucially, we also need to  define a method that takes a session_id string and based on it returns a `BaseChatMessageHistory`. Given the same input, this method should return an equivalent output.\n",
-    "\n",
-    "In this case we'll also want to specify `input_messages_key` (the key to be treated as the latest input message) and `history_messages_key` (the key to add historical messages to)."
+    "Updating the message history implementation just requires us to define a new callable, this time returning an instance of `RedisChatMessageHistory`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "id": "ca7c64d8-e138-4ef8-9734-f82076c47d80",
    "metadata": {},
    "outputs": [],
    "source": [
-    "chain_with_history = RunnableWithMessageHistory(\n",
-    "    chain,\n",
-    "    lambda session_id: RedisChatMessageHistory(session_id, url=REDIS_URL),\n",
-    "    input_messages_key=\"question\",\n",
+    "from langchain_community.chat_message_histories import RedisChatMessageHistory\n",
+    "\n",
+    "\n",
+    "def get_message_history(session_id: str) -> RedisChatMessageHistory:\n",
+    "    return RedisChatMessageHistory(session_id, url=REDIS_URL)\n",
+    "\n",
+    "\n",
+    "with_message_history = RunnableWithMessageHistory(\n",
+    "    runnable,\n",
+    "    get_message_history,\n",
+    "    input_messages_key=\"input\",\n",
     "    history_messages_key=\"history\",\n",
     ")"
    ]
@@ -190,60 +496,53 @@
    "id": "37eefdec-9901-4650-b64c-d3c097ed5f4d",
    "metadata": {},
    "source": [
-    "## Invoking with config\n",
-    "\n",
-    "Whenever we call our chain with message history, we need to include a config that contains the `session_id`\n",
-    "```python\n",
-    "config={\"configurable\": {\"session_id\": \"<SESSION_ID>\"}}\n",
-    "```\n",
-    "\n",
-    "Given the same configuration, our chain should be pulling from the same chat message history."
+    "We can invoke as before:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "id": "a85bcc22-ca4c-4ad5-9440-f94be7318f3e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=' Cosine is one of the basic trigonometric functions in mathematics. It is defined as the ratio of the adjacent side to the hypotenuse in a right triangle.\\n\\nSome key properties and facts about cosine:\\n\\n- It is denoted by cos(θ), where θ is the angle in a right triangle. \\n\\n- The cosine of an acute angle is always positive. For angles greater than 90 degrees, cosine can be negative.\\n\\n- Cosine is one of the three main trig functions along with sine and tangent.\\n\\n- The cosine of 0 degrees is 1. As the angle increases towards 90 degrees, the cosine value decreases towards 0.\\n\\n- The range of values for cosine is -1 to 1.\\n\\n- The cosine function maps angles in a circle to the x-coordinate on the unit circle.\\n\\n- Cosine is used to find adjacent side lengths in right triangles, and has many other applications in mathematics, physics, engineering and more.\\n\\n- Key cosine identities include: cos(A+B) = cosAcosB − sinAsinB and cos(2A) = cos^2(A) − sin^2(A)\\n\\nSo in summary, cosine is a fundamental trig')"
+       "AIMessage(content='Cosine is a trigonometric function that represents the ratio of the adjacent side to the hypotenuse in a right triangle.')"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "chain_with_history.invoke(\n",
-    "    {\"ability\": \"math\", \"question\": \"What does cosine mean?\"},\n",
+    "with_message_history.invoke(\n",
+    "    {\"ability\": \"math\", \"input\": \"What does cosine mean?\"},\n",
     "    config={\"configurable\": {\"session_id\": \"foobar\"}},\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "id": "ab29abd3-751f-41ce-a1b0-53f6b565e79d",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=' The inverse of the cosine function is called the arccosine or inverse cosine, often denoted as cos-1(x) or arccos(x).\\n\\nThe key properties and facts about arccosine:\\n\\n- It is defined as the angle θ between 0 and π radians whose cosine is x. So arccos(x) = θ such that cos(θ) = x.\\n\\n- The range of arccosine is 0 to π radians (0 to 180 degrees).\\n\\n- The domain of arccosine is -1 to 1. \\n\\n- arccos(cos(θ)) = θ for values of θ from 0 to π radians.\\n\\n- arccos(x) is the angle in a right triangle whose adjacent side is x and hypotenuse is 1.\\n\\n- arccos(0) = 90 degrees. As x increases from 0 to 1, arccos(x) decreases from 90 to 0 degrees.\\n\\n- arccos(1) = 0 degrees. arccos(-1) = 180 degrees.\\n\\n- The graph of y = arccos(x) is part of the unit circle, restricted to x')"
+       "AIMessage(content='The inverse of cosine is the arccosine function, denoted as acos or cos^-1, which gives the angle corresponding to a given cosine value.')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "chain_with_history.invoke(\n",
-    "    {\"ability\": \"math\", \"question\": \"What's its inverse\"},\n",
+    "with_message_history.invoke(\n",
+    "    {\"ability\": \"math\", \"input\": \"What's its inverse\"},\n",
     "    config={\"configurable\": {\"session_id\": \"foobar\"}},\n",
     ")"
    ]
@@ -255,7 +554,7 @@
    "source": [
     ":::tip\n",
     "\n",
-    "[Langsmith trace](https://smith.langchain.com/public/863a003b-7ca8-4b24-be9e-d63ec13c106e/r)\n",
+    "[Langsmith trace](https://smith.langchain.com/public/bd73e122-6ec1-48b2-82df-e6483dc9cb63/r)\n",
     "\n",
     ":::"
    ]
@@ -267,124 +566,13 @@
    "source": [
     "Looking at the Langsmith trace for the second call, we can see that when constructing the prompt, a \"history\" variable has been injected which is a list of two messages (our first input and first output)."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "028cf151-6cd5-4533-b3cf-c8d735554647",
-   "metadata": {},
-   "source": [
-    "## Example: messages input, dict output"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "0bb446b5-6251-45fe-a92a-4c6171473c53",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'output_message': AIMessage(content=' Here is a summary of Simone de Beauvoir\\'s views on free will:\\n\\n- De Beauvoir was an existentialist philosopher and believed strongly in the concept of free will. She rejected the idea that human nature or instincts determine behavior.\\n\\n- Instead, de Beauvoir argued that human beings define their own essence or nature through their actions and choices. As she famously wrote, \"One is not born, but rather becomes, a woman.\"\\n\\n- De Beauvoir believed that while individuals are situated in certain cultural contexts and social conditions, they still have agency and the ability to transcend these situations. Freedom comes from choosing one\\'s attitude toward these constraints.\\n\\n- She emphasized the radical freedom and responsibility of the individual. We are \"condemned to be free\" because we cannot escape making choices and taking responsibility for our choices. \\n\\n- De Beauvoir felt that many people evade their freedom and responsibility by adopting rigid mindsets, ideologies, or conforming uncritically to social roles.\\n\\n- She advocated for the recognition of ambiguity in the human condition and warned against the quest for absolute rules that deny freedom and responsibility. Authentic living involves embracing ambiguity.\\n\\nIn summary, de Beauvoir promoted an existential ethics')}"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain_core.messages import HumanMessage\n",
-    "from langchain_core.runnables import RunnableParallel\n",
-    "\n",
-    "chain = RunnableParallel({\"output_message\": ChatAnthropic(model=\"claude-2\")})\n",
-    "chain_with_history = RunnableWithMessageHistory(\n",
-    "    chain,\n",
-    "    lambda session_id: RedisChatMessageHistory(session_id, url=REDIS_URL),\n",
-    "    output_messages_key=\"output_message\",\n",
-    ")\n",
-    "\n",
-    "chain_with_history.invoke(\n",
-    "    [HumanMessage(content=\"What did Simone de Beauvoir believe about free will\")],\n",
-    "    config={\"configurable\": {\"session_id\": \"baz\"}},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "601ce3ff-aea8-424d-8e54-fd614256af4f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'output_message': AIMessage(content=\" There are many similarities between Simone de Beauvoir's views on free will and those of Jean-Paul Sartre, though some key differences emerge as well:\\n\\nSimilarities with Sartre:\\n\\n- Both were existentialist thinkers who rejected determinism and emphasized human freedom and responsibility.\\n\\n- They agreed that existence precedes essence - there is no predefined human nature that determines who we are.\\n\\n- Individuals must define themselves through their choices and actions. This leads to anxiety but also freedom.\\n\\n- The human condition is characterized by ambiguity and uncertainty, rather than fixed meanings/values.\\n\\n- Both felt that most people evade their freedom through self-deception, conformity, or adopting collective identities/values uncritically.\\n\\nDifferences from Sartre: \\n\\n- Sartre placed more emphasis on the burden and anguish of radical freedom. De Beauvoir focused more on its positive potential.\\n\\n- De Beauvoir critiqued Sartre's premise that human relations are necessarily conflictual. She saw more potential for mutual recognition.\\n\\n- Sartre saw the Other's gaze as a threat to freedom. De Beauvoir put more stress on how the Other's gaze can confirm\")}"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain_with_history.invoke(\n",
-    "    [HumanMessage(content=\"How did this compare to Sartre\")],\n",
-    "    config={\"configurable\": {\"session_id\": \"baz\"}},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b898d1b1-11e6-4d30-a8dd-cc5e45533611",
-   "metadata": {},
-   "source": [
-    ":::tip\n",
-    "\n",
-    "[LangSmith trace](https://smith.langchain.com/public/f6c3e1d1-a49d-4955-a9fa-c6519df74fa7/r)\n",
-    "\n",
-    ":::"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1724292c-01c6-44bb-83e8-9cdb6bf01483",
-   "metadata": {},
-   "source": [
-    "## More examples\n",
-    "\n",
-    "We could also do any of the below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fd89240b-5a25-48f8-9568-5c1127f9ffad",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from operator import itemgetter\n",
-    "\n",
-    "# messages in, messages out\n",
-    "RunnableWithMessageHistory(\n",
-    "    ChatAnthropic(model=\"claude-2\"),\n",
-    "    lambda session_id: RedisChatMessageHistory(session_id, url=REDIS_URL),\n",
-    ")\n",
-    "\n",
-    "# dict with single key for all messages in, messages out\n",
-    "RunnableWithMessageHistory(\n",
-    "    itemgetter(\"input_messages\") | ChatAnthropic(model=\"claude-2\"),\n",
-    "    lambda session_id: RedisChatMessageHistory(session_id, url=REDIS_URL),\n",
-    "    input_messages_key=\"input_messages\",\n",
-    ")"
-   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "poetry-venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "poetry-venv"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -396,7 +584,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

docs/docs/expression_language/streaming.ipynb

@@ -66,6 +66,8 @@
     }
    ],
    "source": [
+    "# Showing the example using anthropic, but you can use\n",
+    "# your favorite chat model!\n",
     "from langchain.chat_models import ChatAnthropic\n",
     "\n",
     "model = ChatAnthropic()\n",
@@ -164,9 +166,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " Sure|,| here|'s| a| funny| joke| about| a| par|rot|:|\n",
+      " Here|'s| a| silly| joke| about| a| par|rot|:|\n",
       "\n",
-      "Why| doesn|'t| a| par|rot| ever| get| hungry| at| night|?| Because| it| has| a| light| snack| before| bed|!||"
+      "What| kind| of| teacher| gives| good| advice|?| An| ap|-|parent| (|app|arent|)| one|!||"
      ]
     }
    ],
@@ -228,40 +230,34 @@
       "{'countries': [{}]}\n",
       "{'countries': [{'name': ''}]}\n",
       "{'countries': [{'name': 'France'}]}\n",
-      "{'countries': [{'name': 'France', 'population': ''}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67'}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67,'}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67,022'}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67,022,'}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67,022,000'}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67,022,000'}, {}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': ''}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain'}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': ''}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46'}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,'}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754'}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,'}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': ''}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': 'Japan'}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': 'Japan', 'population': ''}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': 'Japan', 'population': '126'}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': 'Japan', 'population': '126,'}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': 'Japan', 'population': '126,860'}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': 'Japan', 'population': '126,860,'}]}\n",
-      "{'countries': [{'name': 'France', 'population': '67,022,000'}, {'name': 'Spain', 'population': '46,754,784'}, {'name': 'Japan', 'population': '126,860,301'}]}\n"
+      "{'countries': [{'name': 'France', 'population': 67}]}\n",
+      "{'countries': [{'name': 'France', 'population': 6739}]}\n",
+      "{'countries': [{'name': 'France', 'population': 673915}]}\n",
+      "{'countries': [{'name': 'France', 'population': 67391582}]}\n",
+      "{'countries': [{'name': 'France', 'population': 67391582}, {}]}\n",
+      "{'countries': [{'name': 'France', 'population': 67391582}, {'name': ''}]}\n",
+      "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Sp'}]}\n",
+      "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain'}]}\n",
+      "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46}]}\n",
+      "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 4675}]}\n",
+      "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 467547}]}\n",
+      "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}]}\n",
+      "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}, {}]}\n",
+      "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}, {'name': ''}]}\n",
+      "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}, {'name': 'Japan'}]}\n",
+      "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}, {'name': 'Japan', 'population': 12}]}\n",
+      "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}, {'name': 'Japan', 'population': 12647}]}\n",
+      "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}, {'name': 'Japan', 'population': 1264764}]}\n",
+      "{'countries': [{'name': 'France', 'population': 67391582}, {'name': 'Spain', 'population': 46754778}, {'name': 'Japan', 'population': 126476461}]}\n"
      ]
     }
    ],
    "source": [
     "from langchain_core.output_parsers import JsonOutputParser\n",
-    "from langchain_openai.chat_models import ChatOpenAI\n",
     "\n",
-    "model = ChatOpenAI()\n",
-    "\n",
-    "chain = model | JsonOutputParser()  # This parser only works with OpenAI right now\n",
+    "chain = (\n",
+    "    model | JsonOutputParser()\n",
+    ")  # Due to a bug in older versions of Langchain, JsonOutputParser did not stream results from some models\n",
     "async for text in chain.astream(\n",
     "    'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'\n",
     "):\n",
@@ -294,12 +290,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[None, None, None]|"
+      "['France', 'Spain', 'Japan']|"
      ]
     }
    ],
    "source": [
-    "from langchain_core.output_parsers import JsonOutputParser\n",
+    "from langchain_core.output_parsers import (\n",
+    "    JsonOutputParser,\n",
+    ")\n",
     "\n",
     "\n",
     "# A function that operates on finalized inputs\n",
@@ -326,7 +324,7 @@
     "chain = model | JsonOutputParser() | _extract_country_names\n",
     "\n",
     "async for text in chain.astream(\n",
-    "    'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\"'\n",
+    "    'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'\n",
     "):\n",
     "    print(text, end=\"|\", flush=True)"
    ]
@@ -348,7 +346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 17,
    "id": "15984b2b-315a-4119-945b-2a3dabea3082",
    "metadata": {},
    "outputs": [
@@ -356,7 +354,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "France|Spain|Japan|"
+      "France|Sp|Spain|Japan|"
      ]
     }
    ],
@@ -392,11 +390,23 @@
     "chain = model | JsonOutputParser() | _extract_country_names_streaming\n",
     "\n",
     "async for text in chain.astream(\n",
-    "    'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\"'\n",
+    "    'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'\n",
     "):\n",
     "    print(text, end=\"|\", flush=True)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "d59823f5-9b9a-43c5-a213-34644e2f1d3d",
+   "metadata": {},
+   "source": [
+    ":::{.callout-note}\n",
+    "Because the code above is relying on JSON auto-completion, you may see partial names of countries (e.g., `Sp` and `Spain`), which is not what one would want for an extraction result!\n",
+    "\n",
+    "We're focusing on streaming concepts, not necessarily the results of the chains.\n",
+    ":::"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "6adf65b7-aa47-4321-98c7-a0abe43b833a",
@@ -409,7 +419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "b9b1c00d-8b44-40d0-9e2b-8a70d238f82b",
    "metadata": {},
    "outputs": [
@@ -420,7 +430,7 @@
        "  Document(page_content='harrison likes spicy food')]]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -465,7 +475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "957447e6-1e60-41ef-8c10-2654bd9e738d",
    "metadata": {},
    "outputs": [],
@@ -483,7 +493,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "94e50b5d-bf51-4eee-9da0-ee40dd9ce42b",
    "metadata": {},
    "outputs": [
@@ -491,9 +501,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "|H|arrison| worked| at| Kens|ho|,| a| renowned| technology| company| known| for| revolution|izing| the| artificial| intelligence| industry|.\n",
-      "|K|ens|ho|,| located| in| the| heart| of| Silicon| Valley|,| is| famous| for| its| cutting|-edge| research| and| development| in| machine| learning|.\n",
-      "|With| its| state|-of|-the|-art| facilities| and| talented| team|,| Kens|ho| has| become| a| hub| for| innovation| and| a| sought|-after| workplace| for| tech| enthusiasts| like| Harrison|.||"
+      " Based| on| the| given| context|,| the| only| information| provided| about| where| Harrison| worked| is| that| he| worked| at| Ken|sh|o|.| Since| there| are| no| other| details| provided| about| Ken|sh|o|,| I| do| not| have| enough| information| to| write| 3| additional| made| up| sentences| about| this| place|.| I| can| only| state| that| Harrison| worked| at| Ken|sh|o|.||"
      ]
     }
    ],
@@ -528,17 +536,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "61348df9-ec58-401e-be89-68a70042f88e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'0.1.14'"
+       "'0.1.18'"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -604,7 +612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "c00df46e-7f6b-4e06-8abf-801898c8d57f",
    "metadata": {},
    "outputs": [
@@ -612,7 +620,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/eugene/.pyenv/versions/3.11.4/envs/langchain_3_11_4/lib/python3.11/site-packages/langchain_core/_api/beta_decorator.py:86: LangChainBetaWarning: This API is in beta and may change in the future.\n",
+      "/home/eugene/src/langchain/libs/core/langchain_core/_api/beta_decorator.py:86: LangChainBetaWarning: This API is in beta and may change in the future.\n",
       "  warn_beta(\n"
      ]
     }
@@ -650,7 +658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "ce31b525-f47d-4828-85a7-912ce9f2e79b",
    "metadata": {},
    "outputs": [
@@ -658,26 +666,26 @@
      "data": {
       "text/plain": [
        "[{'event': 'on_chat_model_start',\n",
-       "  'run_id': 'd78b4ffb-0eb1-499c-8a90-8e4a4aa2edae',\n",
-       "  'name': 'ChatOpenAI',\n",
+       "  'run_id': '555843ed-3d24-4774-af25-fbf030d5e8c4',\n",
+       "  'name': 'ChatAnthropic',\n",
        "  'tags': [],\n",
        "  'metadata': {},\n",
        "  'data': {'input': 'hello'}},\n",
        " {'event': 'on_chat_model_stream',\n",
-       "  'run_id': 'd78b4ffb-0eb1-499c-8a90-8e4a4aa2edae',\n",
+       "  'run_id': '555843ed-3d24-4774-af25-fbf030d5e8c4',\n",
        "  'tags': [],\n",
        "  'metadata': {},\n",
-       "  'name': 'ChatOpenAI',\n",
-       "  'data': {'chunk': AIMessageChunk(content='')}},\n",
+       "  'name': 'ChatAnthropic',\n",
+       "  'data': {'chunk': AIMessageChunk(content=' Hello')}},\n",
        " {'event': 'on_chat_model_stream',\n",
-       "  'run_id': 'd78b4ffb-0eb1-499c-8a90-8e4a4aa2edae',\n",
+       "  'run_id': '555843ed-3d24-4774-af25-fbf030d5e8c4',\n",
        "  'tags': [],\n",
        "  'metadata': {},\n",
-       "  'name': 'ChatOpenAI',\n",
-       "  'data': {'chunk': AIMessageChunk(content='Hello')}}]"
+       "  'name': 'ChatAnthropic',\n",
+       "  'data': {'chunk': AIMessageChunk(content='!')}}]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -688,7 +696,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "76cfe826-ee63-4310-ad48-55a95eb3b9d6",
    "metadata": {},
    "outputs": [
@@ -696,20 +704,20 @@
      "data": {
       "text/plain": [
        "[{'event': 'on_chat_model_stream',\n",
-       "  'run_id': 'd78b4ffb-0eb1-499c-8a90-8e4a4aa2edae',\n",
+       "  'run_id': '555843ed-3d24-4774-af25-fbf030d5e8c4',\n",
        "  'tags': [],\n",
        "  'metadata': {},\n",
-       "  'name': 'ChatOpenAI',\n",
+       "  'name': 'ChatAnthropic',\n",
        "  'data': {'chunk': AIMessageChunk(content='')}},\n",
        " {'event': 'on_chat_model_end',\n",
-       "  'name': 'ChatOpenAI',\n",
-       "  'run_id': 'd78b4ffb-0eb1-499c-8a90-8e4a4aa2edae',\n",
+       "  'name': 'ChatAnthropic',\n",
+       "  'run_id': '555843ed-3d24-4774-af25-fbf030d5e8c4',\n",
        "  'tags': [],\n",
        "  'metadata': {},\n",
-       "  'data': {'output': AIMessageChunk(content='Hello! How can I assist you today?')}}]"
+       "  'data': {'output': AIMessageChunk(content=' Hello!')}}]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -730,12 +738,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 27,
    "id": "4328c56c-a303-427b-b1f2-f354e9af555c",
    "metadata": {},
    "outputs": [],
    "source": [
-    "chain = model | JsonOutputParser()  # This parser only works with OpenAI right now\n",
+    "chain = (\n",
+    "    model | JsonOutputParser()\n",
+    ")  # Due to a bug in older versions of Langchain, JsonOutputParser did not stream results from some models\n",
     "\n",
     "events = [\n",
     "    event\n",
@@ -762,7 +772,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "8e66ea3d-a450-436a-aaac-d9478abc6c28",
    "metadata": {},
    "outputs": [
@@ -770,26 +780,26 @@
      "data": {
       "text/plain": [
        "[{'event': 'on_chain_start',\n",
-       "  'run_id': 'aa992fb9-d79f-46f3-a857-ae4acad841c4',\n",
+       "  'run_id': 'b1074bff-2a17-458b-9e7b-625211710df4',\n",
        "  'name': 'RunnableSequence',\n",
        "  'tags': [],\n",
        "  'metadata': {},\n",
        "  'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}},\n",
        " {'event': 'on_chat_model_start',\n",
-       "  'name': 'ChatOpenAI',\n",
-       "  'run_id': 'c5406de5-0880-4829-ae26-bb565b404e27',\n",
+       "  'name': 'ChatAnthropic',\n",
+       "  'run_id': '6072be59-1f43-4f1c-9470-3b92e8406a99',\n",
        "  'tags': ['seq:step:1'],\n",
        "  'metadata': {},\n",
        "  'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}}},\n",
        " {'event': 'on_parser_start',\n",
        "  'name': 'JsonOutputParser',\n",
-       "  'run_id': '32b47794-8fb6-4ef4-8800-23ed6c3f4519',\n",
+       "  'run_id': 'bf978194-0eda-4494-ad15-3a5bfe69cd59',\n",
        "  'tags': ['seq:step:2'],\n",
        "  'metadata': {},\n",
        "  'data': {}}]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -816,7 +826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "630c71d6-8d94-4ce0-a78a-f20e90f628df",
    "metadata": {},
    "outputs": [
@@ -824,29 +834,31 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Chat model chunk: ''\n",
+      "Chat model chunk: ' Here'\n",
+      "Chat model chunk: ' is'\n",
+      "Chat model chunk: ' the'\n",
+      "Chat model chunk: ' JSON'\n",
+      "Chat model chunk: ' with'\n",
+      "Chat model chunk: ' the'\n",
+      "Chat model chunk: ' requested'\n",
+      "Chat model chunk: ' countries'\n",
+      "Chat model chunk: ' and'\n",
+      "Chat model chunk: ' their'\n",
+      "Chat model chunk: ' populations'\n",
+      "Chat model chunk: ':'\n",
+      "Chat model chunk: '\\n\\n```'\n",
+      "Chat model chunk: 'json'\n",
       "Parser chunk: {}\n",
-      "Chat model chunk: '{\\n'\n",
-      "Chat model chunk: ' '\n",
+      "Chat model chunk: '\\n{'\n",
+      "Chat model chunk: '\\n '\n",
       "Chat model chunk: ' \"'\n",
       "Chat model chunk: 'countries'\n",
       "Chat model chunk: '\":'\n",
       "Parser chunk: {'countries': []}\n",
-      "Chat model chunk: ' [\\n'\n",
-      "Chat model chunk: '   '\n",
+      "Chat model chunk: ' ['\n",
+      "Chat model chunk: '\\n   '\n",
       "Parser chunk: {'countries': [{}]}\n",
-      "Chat model chunk: ' {\\n'\n",
-      "Chat model chunk: '     '\n",
-      "Chat model chunk: ' \"'\n",
-      "Chat model chunk: 'name'\n",
-      "Chat model chunk: '\":'\n",
-      "Parser chunk: {'countries': [{'name': ''}]}\n",
-      "Chat model chunk: ' \"'\n",
-      "Parser chunk: {'countries': [{'name': 'France'}]}\n",
-      "Chat model chunk: 'France'\n",
-      "Chat model chunk: '\",\\n'\n",
-      "Chat model chunk: '     '\n",
-      "Chat model chunk: ' \"'\n",
+      "Chat model chunk: ' {'\n",
       "...\n"
      ]
     }
@@ -897,7 +909,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "4f0b581b-be63-4663-baba-c6d2b625cdf9",
    "metadata": {},
    "outputs": [
@@ -905,17 +917,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_parser_start', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {}}\n",
-      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {}}}\n",
-      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': []}}}\n",
-      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{}]}}}\n",
-      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': ''}]}}}\n",
-      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France'}]}}}\n",
-      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 670}]}}}\n",
-      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 670600}]}}}\n",
-      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67060000}]}}}\n",
-      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67060000}, {}]}}}\n",
-      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': '450011c0-6f3b-4ec8-92d4-6603d9d1d603', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67060000}, {'name': ''}]}}}\n",
+      "{'event': 'on_parser_start', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {}}\n",
+      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {}}}\n",
+      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': []}}}\n",
+      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{}]}}}\n",
+      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': ''}]}}}\n",
+      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France'}]}}}\n",
+      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67}]}}}\n",
+      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 6739}]}}}\n",
+      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 673915}]}}}\n",
+      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67391582}]}}}\n",
+      "{'event': 'on_parser_stream', 'name': 'my_parser', 'run_id': 'f2ac1d1c-e14a-45fc-8990-e5c24e707299', 'tags': ['seq:step:2'], 'metadata': {}, 'data': {'chunk': {'countries': [{'name': 'France', 'population': 67391582}, {}]}}}\n",
       "...\n"
      ]
     }
@@ -949,7 +961,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "096cd904-72f0-4ebe-a8b7-d0e730faea7f",
    "metadata": {},
    "outputs": [
@@ -957,17 +969,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_chat_model_start', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}}}\n",
-      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='')}}\n",
-      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='{\\n')}}\n",
-      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' ')}}\n",
-      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' \"')}}\n",
-      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='countries')}}\n",
-      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='\":')}}\n",
-      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' [\\n')}}\n",
-      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='   ')}}\n",
-      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' {\\n')}}\n",
-      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '9ba1ef9f-5954-4649-b3da-1171b6abb000', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='     ')}}\n",
+      "{'event': 'on_chat_model_start', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' Here')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' is')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' the')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' JSON')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' with')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' the')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' requested')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' countries')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' and')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'model', 'run_id': '98a6e192-8159-460c-ba73-6dfc921e3777', 'tags': ['seq:step:1'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' their')}}\n",
       "...\n"
      ]
     }
@@ -1008,7 +1020,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "26bac0d2-76d9-446e-b346-82790236b88d",
    "metadata": {},
    "outputs": [
@@ -1016,17 +1028,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_chain_start', 'run_id': 'd4c78db8-be20-4fa0-87d6-cb317822967a', 'name': 'RunnableSequence', 'tags': ['my_chain'], 'metadata': {}, 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}}\n",
-      "{'event': 'on_chat_model_start', 'name': 'ChatOpenAI', 'run_id': '15e46d9f-ccf5-4da2-b9e3-b2a85873ba4c', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}}}\n",
-      "{'event': 'on_parser_start', 'name': 'JsonOutputParser', 'run_id': '91945f4f-0deb-4999-acf0-f6d191c89b34', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}, 'data': {}}\n",
-      "{'event': 'on_chat_model_stream', 'name': 'ChatOpenAI', 'run_id': '15e46d9f-ccf5-4da2-b9e3-b2a85873ba4c', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='')}}\n",
-      "{'event': 'on_parser_stream', 'name': 'JsonOutputParser', 'run_id': '91945f4f-0deb-4999-acf0-f6d191c89b34', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}, 'data': {'chunk': {}}}\n",
-      "{'event': 'on_chain_stream', 'run_id': 'd4c78db8-be20-4fa0-87d6-cb317822967a', 'tags': ['my_chain'], 'metadata': {}, 'name': 'RunnableSequence', 'data': {'chunk': {}}}\n",
-      "{'event': 'on_chat_model_stream', 'name': 'ChatOpenAI', 'run_id': '15e46d9f-ccf5-4da2-b9e3-b2a85873ba4c', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='{\"')}}\n",
-      "{'event': 'on_chat_model_stream', 'name': 'ChatOpenAI', 'run_id': '15e46d9f-ccf5-4da2-b9e3-b2a85873ba4c', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='countries')}}\n",
-      "{'event': 'on_chat_model_stream', 'name': 'ChatOpenAI', 'run_id': '15e46d9f-ccf5-4da2-b9e3-b2a85873ba4c', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content='\":')}}\n",
-      "{'event': 'on_parser_stream', 'name': 'JsonOutputParser', 'run_id': '91945f4f-0deb-4999-acf0-f6d191c89b34', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}, 'data': {'chunk': {'countries': []}}}\n",
-      "{'event': 'on_chain_stream', 'run_id': 'd4c78db8-be20-4fa0-87d6-cb317822967a', 'tags': ['my_chain'], 'metadata': {}, 'name': 'RunnableSequence', 'data': {'chunk': {'countries': []}}}\n",
+      "{'event': 'on_chain_start', 'run_id': '190875f3-3fb7-49ad-9b6e-f49da22f3e49', 'name': 'RunnableSequence', 'tags': ['my_chain'], 'metadata': {}, 'data': {'input': 'output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`'}}\n",
+      "{'event': 'on_chat_model_start', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'input': {'messages': [[HumanMessage(content='output a list of the countries france, spain and japan and their populations in JSON format. Use a dict with an outer key of \"countries\" which contains a list of countries. Each country should have the key `name` and `population`')]]}}}\n",
+      "{'event': 'on_parser_start', 'name': 'JsonOutputParser', 'run_id': '3b5e4ca1-40fe-4a02-9a19-ba2a43a6115c', 'tags': ['seq:step:2', 'my_chain'], 'metadata': {}, 'data': {}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' Here')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' is')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' the')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' JSON')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' with')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' the')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' requested')}}\n",
+      "{'event': 'on_chat_model_stream', 'name': 'ChatAnthropic', 'run_id': 'ff58f732-b494-4ff9-852a-783d42f4455d', 'tags': ['seq:step:1', 'my_chain'], 'metadata': {}, 'data': {'chunk': AIMessageChunk(content=' countries')}}\n",
       "...\n"
      ]
     }
@@ -1062,7 +1074,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "0e6451d3-3b11-4a71-ae19-998f4c10180f",
    "metadata": {},
    "outputs": [],
@@ -1104,7 +1116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "f9a8fe35-faab-4970-b8c0-5c780845d98a",
    "metadata": {},
    "outputs": [
@@ -1112,7 +1124,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "['France', 'Spain', 'Japan']\n"
      ]
     }
    ],
@@ -1133,7 +1145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "id": "b08215cd-bffa-4e76-aaf3-c52ee34f152c",
    "metadata": {},
    "outputs": [
@@ -1141,33 +1153,33 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Chat model chunk: ''\n",
+      "Chat model chunk: ' Here'\n",
+      "Chat model chunk: ' is'\n",
+      "Chat model chunk: ' the'\n",
+      "Chat model chunk: ' JSON'\n",
+      "Chat model chunk: ' with'\n",
+      "Chat model chunk: ' the'\n",
+      "Chat model chunk: ' requested'\n",
+      "Chat model chunk: ' countries'\n",
+      "Chat model chunk: ' and'\n",
+      "Chat model chunk: ' their'\n",
+      "Chat model chunk: ' populations'\n",
+      "Chat model chunk: ':'\n",
+      "Chat model chunk: '\\n\\n```'\n",
+      "Chat model chunk: 'json'\n",
       "Parser chunk: {}\n",
-      "Chat model chunk: '{\"'\n",
+      "Chat model chunk: '\\n{'\n",
+      "Chat model chunk: '\\n '\n",
+      "Chat model chunk: ' \"'\n",
       "Chat model chunk: 'countries'\n",
       "Chat model chunk: '\":'\n",
       "Parser chunk: {'countries': []}\n",
-      "Chat model chunk: ' [\\n'\n",
-      "Chat model chunk: '   '\n",
+      "Chat model chunk: ' ['\n",
+      "Chat model chunk: '\\n   '\n",
       "Parser chunk: {'countries': [{}]}\n",
-      "Chat model chunk: ' {\"'\n",
-      "Chat model chunk: 'name'\n",
-      "Chat model chunk: '\":'\n",
-      "Parser chunk: {'countries': [{'name': ''}]}\n",
+      "Chat model chunk: ' {'\n",
+      "Chat model chunk: '\\n     '\n",
       "Chat model chunk: ' \"'\n",
-      "Parser chunk: {'countries': [{'name': 'France'}]}\n",
-      "Chat model chunk: 'France'\n",
-      "Chat model chunk: '\",'\n",
-      "Chat model chunk: ' \"'\n",
-      "Chat model chunk: 'population'\n",
-      "Chat model chunk: '\":'\n",
-      "Parser chunk: {'countries': [{'name': 'France', 'population': ''}]}\n",
-      "Chat model chunk: ' \"'\n",
-      "Parser chunk: {'countries': [{'name': 'France', 'population': '67'}]}\n",
-      "Chat model chunk: '67'\n",
-      "Parser chunk: {'countries': [{'name': 'France', 'population': '67 million'}]}\n",
-      "Chat model chunk: ' million'\n",
-      "Chat model chunk: '\"},\\n'\n",
       "...\n"
      ]
     }
@@ -1212,7 +1224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "id": "1854206d-b3a5-4f91-9e00-bccbaebac61f",
    "metadata": {},
    "outputs": [
@@ -1220,9 +1232,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_tool_start', 'run_id': '39e4a7eb-c13d-46f0-99e7-75c2fa4aa6a6', 'name': 'bad_tool', 'tags': [], 'metadata': {}, 'data': {'input': 'hello'}}\n",
-      "{'event': 'on_tool_stream', 'run_id': '39e4a7eb-c13d-46f0-99e7-75c2fa4aa6a6', 'tags': [], 'metadata': {}, 'name': 'bad_tool', 'data': {'chunk': 'olleh'}}\n",
-      "{'event': 'on_tool_end', 'name': 'bad_tool', 'run_id': '39e4a7eb-c13d-46f0-99e7-75c2fa4aa6a6', 'tags': [], 'metadata': {}, 'data': {'output': 'olleh'}}\n"
+      "{'event': 'on_tool_start', 'run_id': 'ae7690f8-ebc9-4886-9bbe-cb336ff274f2', 'name': 'bad_tool', 'tags': [], 'metadata': {}, 'data': {'input': 'hello'}}\n",
+      "{'event': 'on_tool_stream', 'run_id': 'ae7690f8-ebc9-4886-9bbe-cb336ff274f2', 'tags': [], 'metadata': {}, 'name': 'bad_tool', 'data': {'chunk': 'olleh'}}\n",
+      "{'event': 'on_tool_end', 'name': 'bad_tool', 'run_id': 'ae7690f8-ebc9-4886-9bbe-cb336ff274f2', 'tags': [], 'metadata': {}, 'data': {'output': 'olleh'}}\n"
      ]
     }
    ],
@@ -1258,7 +1270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "id": "a20a6cb3-bb43-465c-8cfc-0a7349d70968",
    "metadata": {},
    "outputs": [
@@ -1266,11 +1278,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_tool_start', 'run_id': '4263aca5-f221-4eb7-b07e-60a89fb76c5c', 'name': 'correct_tool', 'tags': [], 'metadata': {}, 'data': {'input': 'hello'}}\n",
-      "{'event': 'on_chain_start', 'name': 'reverse_word', 'run_id': '65e3679b-e238-47ce-a875-ee74480e696e', 'tags': [], 'metadata': {}, 'data': {'input': 'hello'}}\n",
-      "{'event': 'on_chain_end', 'name': 'reverse_word', 'run_id': '65e3679b-e238-47ce-a875-ee74480e696e', 'tags': [], 'metadata': {}, 'data': {'input': 'hello', 'output': 'olleh'}}\n",
-      "{'event': 'on_tool_stream', 'run_id': '4263aca5-f221-4eb7-b07e-60a89fb76c5c', 'tags': [], 'metadata': {}, 'name': 'correct_tool', 'data': {'chunk': 'olleh'}}\n",
-      "{'event': 'on_tool_end', 'name': 'correct_tool', 'run_id': '4263aca5-f221-4eb7-b07e-60a89fb76c5c', 'tags': [], 'metadata': {}, 'data': {'output': 'olleh'}}\n"
+      "{'event': 'on_tool_start', 'run_id': '384f1710-612e-4022-a6d4-8a7bb0cc757e', 'name': 'correct_tool', 'tags': [], 'metadata': {}, 'data': {'input': 'hello'}}\n",
+      "{'event': 'on_chain_start', 'name': 'reverse_word', 'run_id': 'c4882303-8867-4dff-b031-7d9499b39dda', 'tags': [], 'metadata': {}, 'data': {'input': 'hello'}}\n",
+      "{'event': 'on_chain_end', 'name': 'reverse_word', 'run_id': 'c4882303-8867-4dff-b031-7d9499b39dda', 'tags': [], 'metadata': {}, 'data': {'input': 'hello', 'output': 'olleh'}}\n",
+      "{'event': 'on_tool_stream', 'run_id': '384f1710-612e-4022-a6d4-8a7bb0cc757e', 'tags': [], 'metadata': {}, 'name': 'correct_tool', 'data': {'chunk': 'olleh'}}\n",
+      "{'event': 'on_tool_end', 'name': 'correct_tool', 'run_id': '384f1710-612e-4022-a6d4-8a7bb0cc757e', 'tags': [], 'metadata': {}, 'data': {'output': 'olleh'}}\n"
      ]
     }
    ],
@@ -1295,7 +1307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "id": "0ac0a3c1-f3a4-4157-b053-4fec8d2e698c",
    "metadata": {},
    "outputs": [
@@ -1303,11 +1315,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_chain_start', 'run_id': '714d22d4-a3c3-45fc-b2f1-913aa7f0fc22', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n",
-      "{'event': 'on_chain_start', 'name': 'reverse_word', 'run_id': '35a6470c-db65-4fe1-8dff-4e3418601d2f', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n",
-      "{'event': 'on_chain_end', 'name': 'reverse_word', 'run_id': '35a6470c-db65-4fe1-8dff-4e3418601d2f', 'tags': [], 'metadata': {}, 'data': {'input': '1234', 'output': '4321'}}\n",
-      "{'event': 'on_chain_stream', 'run_id': '714d22d4-a3c3-45fc-b2f1-913aa7f0fc22', 'tags': [], 'metadata': {}, 'name': 'reverse_and_double', 'data': {'chunk': '43214321'}}\n",
-      "{'event': 'on_chain_end', 'name': 'reverse_and_double', 'run_id': '714d22d4-a3c3-45fc-b2f1-913aa7f0fc22', 'tags': [], 'metadata': {}, 'data': {'output': '43214321'}}\n"
+      "{'event': 'on_chain_start', 'run_id': '4fe56c7b-6982-4999-a42d-79ba56151176', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n",
+      "{'event': 'on_chain_start', 'name': 'reverse_word', 'run_id': '335fe781-8944-4464-8d2e-81f61d1f85f5', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n",
+      "{'event': 'on_chain_end', 'name': 'reverse_word', 'run_id': '335fe781-8944-4464-8d2e-81f61d1f85f5', 'tags': [], 'metadata': {}, 'data': {'input': '1234', 'output': '4321'}}\n",
+      "{'event': 'on_chain_stream', 'run_id': '4fe56c7b-6982-4999-a42d-79ba56151176', 'tags': [], 'metadata': {}, 'name': 'reverse_and_double', 'data': {'chunk': '43214321'}}\n",
+      "{'event': 'on_chain_end', 'name': 'reverse_and_double', 'run_id': '4fe56c7b-6982-4999-a42d-79ba56151176', 'tags': [], 'metadata': {}, 'data': {'output': '43214321'}}\n"
      ]
     }
    ],
@@ -1337,7 +1349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "id": "c896bb94-9d10-41ff-8fe2-d6b05b1ed74b",
    "metadata": {},
    "outputs": [
@@ -1345,11 +1357,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'event': 'on_chain_start', 'run_id': '17c89289-9c71-406d-90de-86f76b5e798b', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n",
-      "{'event': 'on_chain_start', 'name': 'reverse_word', 'run_id': 'b1105188-9196-43c1-9603-4f2f58e51de4', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n",
-      "{'event': 'on_chain_end', 'name': 'reverse_word', 'run_id': 'b1105188-9196-43c1-9603-4f2f58e51de4', 'tags': [], 'metadata': {}, 'data': {'input': '1234', 'output': '4321'}}\n",
-      "{'event': 'on_chain_stream', 'run_id': '17c89289-9c71-406d-90de-86f76b5e798b', 'tags': [], 'metadata': {}, 'name': 'reverse_and_double', 'data': {'chunk': '43214321'}}\n",
-      "{'event': 'on_chain_end', 'name': 'reverse_and_double', 'run_id': '17c89289-9c71-406d-90de-86f76b5e798b', 'tags': [], 'metadata': {}, 'data': {'output': '43214321'}}\n"
+      "{'event': 'on_chain_start', 'run_id': '7485eedb-1854-429c-a2f8-03d01452daef', 'name': 'reverse_and_double', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n",
+      "{'event': 'on_chain_start', 'name': 'reverse_word', 'run_id': 'e7cddab2-9b95-4e80-abaf-4b2429117835', 'tags': [], 'metadata': {}, 'data': {'input': '1234'}}\n",
+      "{'event': 'on_chain_end', 'name': 'reverse_word', 'run_id': 'e7cddab2-9b95-4e80-abaf-4b2429117835', 'tags': [], 'metadata': {}, 'data': {'input': '1234', 'output': '4321'}}\n",
+      "{'event': 'on_chain_stream', 'run_id': '7485eedb-1854-429c-a2f8-03d01452daef', 'tags': [], 'metadata': {}, 'name': 'reverse_and_double', 'data': {'chunk': '43214321'}}\n",
+      "{'event': 'on_chain_end', 'name': 'reverse_and_double', 'run_id': '7485eedb-1854-429c-a2f8-03d01452daef', 'tags': [], 'metadata': {}, 'data': {'output': '43214321'}}\n"
      ]
     }
    ],
@@ -1385,7 +1397,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.2"
   }
  },
  "nbformat": 4,

docs/docs/get_started/quickstart.mdx

@@ -193,7 +193,7 @@ After that, we can import and use WebBaseLoader.
 
 ```python
 from langchain_community.document_loaders import WebBaseLoader
-loader = WebBaseLoader("https://docs.smith.langchain.com/overview")
+loader = WebBaseLoader("https://docs.smith.langchain.com")
 
 docs = loader.load()
 ```
@@ -581,7 +581,10 @@ Using this, we can interact with the served chain as if it were running client-s
 from langserve import RemoteRunnable
 
 remote_chain = RemoteRunnable("http://localhost:8000/agent/")
-remote_chain.invoke({"input": "how can langsmith help with testing?"})
+remote_chain.invoke({
+    "input": "how can langsmith help with testing?",
+    "chat_history": []  # Providing an empty list as this is the first call
+})
 ```
 
 To learn more about the many other features of LangServe [head here](/docs/langserve).

docs/docs/guides/safety/amazon_comprehend_chain.ipynb

@@ -115,7 +115,7 @@
     "\n",
     "Answer:\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
+    "prompt = PromptTemplate.from_template(template)\n",
     "\n",
     "responses = [\n",
     "    \"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.\",\n",
@@ -249,7 +249,7 @@
     "\n",
     "Answer:\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
+    "prompt = PromptTemplate.from_template(template)\n",
     "\n",
     "responses = [\n",
     "    \"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.\",\n",
@@ -412,7 +412,7 @@
     "\n",
     "Answer:\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
+    "prompt = PromptTemplate.from_template(template)\n",
     "\n",
     "responses = [\n",
     "    \"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.\",\n",
@@ -571,7 +571,7 @@
     "\n",
     "template = \"\"\"{question}\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
+    "prompt = PromptTemplate.from_template(template)\n",
     "llm = HuggingFaceHub(\n",
     "    repo_id=repo_id, model_kwargs={\"temperature\": 0.5, \"max_length\": 256}\n",
     ")"
@@ -724,7 +724,7 @@
     "\"\"\"\n",
     "\n",
     "# prompt template for input text\n",
-    "llm_prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
+    "llm_prompt = PromptTemplate.from_template(template)\n",
     "\n",
     "llm = SagemakerEndpoint(\n",
     "    endpoint_name=endpoint_name,\n",

docs/docs/guides/safety/moderation.mdx

@@ -180,7 +180,7 @@ we will prompt the model, so it says something harmful.
 
 
 ```python
-prompt = PromptTemplate(template="{text}", input_variables=["text"])
+prompt = PromptTemplate.from_template("{text}")
 llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name="gpt-3.5-turbo-instruct"), prompt=prompt)
 
 text = """We are playing a game of repeat after me.
@@ -223,7 +223,7 @@ Now let's walk through an example of using it with an LLMChain which has multipl
 
 
 ```python
-prompt = PromptTemplate(template="{setup}{new_input}Person2:", input_variables=["setup", "new_input"])
+prompt = PromptTemplate.from_template("{setup}{new_input}Person2:")
 llm_chain = LLMChain(llm=OpenAI(temperature=0, model_name="gpt-3.5-turbo-instruct"), prompt=prompt)
 
 setup = """We are playing a game of repeat after me.

docs/docs/integrations/callbacks/streamlit.md

@@ -28,7 +28,7 @@ You can run `streamlit hello` to load a sample app and validate your install suc
 To create a `StreamlitCallbackHandler`, you just need to provide a parent container to render the output.
 
 ```python
-from langchain.callbacks import StreamlitCallbackHandler
+from langchain_community.callbacks import StreamlitCallbackHandler
 import streamlit as st
 
 st_callback = StreamlitCallbackHandler(st.container())
@@ -44,23 +44,26 @@ agent in your Streamlit app and simply pass the `StreamlitCallbackHandler` to `a
 thoughts and actions live in your app.
 
 ```python
-from langchain_openai import OpenAI
-from langchain.agents import AgentType, initialize_agent, load_tools
-from langchain_community.callbacks import StreamlitCallbackHandler
 import streamlit as st
+from langchain import hub
+from langchain.agents import AgentExecutor, create_react_agent, load_tools
+from langchain_community.callbacks import StreamlitCallbackHandler
+from langchain_openai import OpenAI
 
 llm = OpenAI(temperature=0, streaming=True)
 tools = load_tools(["ddg-search"])
-agent = initialize_agent(
-    tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
-)
+prompt = hub.pull("hwchase17/react")
+agent = create_react_agent(llm, tools, prompt)
+agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
 
 if prompt := st.chat_input():
     st.chat_message("user").write(prompt)
     with st.chat_message("assistant"):
         st_callback = StreamlitCallbackHandler(st.container())
-        response = agent.run(prompt, callbacks=[st_callback])
-        st.write(response)
+        response = agent_executor.invoke(
+            {"input": prompt}, {"callbacks": [st_callback]}
+        )
+        st.write(response["output"])
 ```
 
 **Note:** You will need to set `OPENAI_API_KEY` for the above app code to run successfully.

docs/docs/integrations/chat/ai21.ipynb

@@ -0,0 +1,141 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "4cebeec0",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: AI21 Labs\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e49f1e0d",
+   "metadata": {},
+   "source": [
+    "# ChatAI21\n",
+    "\n",
+    "This notebook covers how to get started with AI21 chat models.\n",
+    "\n",
+    "## Installation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c3bef91",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-02-15T06:50:44.929635Z",
+     "start_time": "2024-02-15T06:50:41.209704Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -qU langchain-ai21"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b4f3e15",
+   "metadata": {},
+   "source": [
+    "## Environment Setup\n",
+    "\n",
+    "We'll need to get a [AI21 API key](https://docs.ai21.com/) and set the `AI21_API_KEY` environment variable:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62e0dbc3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from getpass import getpass\n",
+    "\n",
+    "os.environ[\"AI21_API_KEY\"] = getpass()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4828829d3da430ce",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "## Usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "39353473fce5dd2e",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Bonjour, comment vas-tu?')"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_ai21 import ChatAI21\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "chat = ChatAI21(model=\"j2-ultra\")\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", \"You are a helpful assistant that translates English to French.\"),\n",
+    "        (\"human\", \"Translate this sentence from English to French. {english_text}.\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | chat\n",
+    "chain.invoke({\"english_text\": \"Hello, how are you?\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c159a79f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/chat/anthropic.ipynb

@@ -90,16 +90,20 @@
     }
    ],
    "source": [
-    "system = \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
+    "system = (\n",
+    "    \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
+    ")\n",
     "human = \"{text}\"\n",
     "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
     "\n",
     "chain = prompt | chat\n",
-    "chain.invoke({\n",
-    "    \"input_language\": \"English\",\n",
-    "    \"output_language\": \"Korean\",\n",
-    "    \"text\": \"I love Python\",\n",
-    "})"
+    "chain.invoke(\n",
+    "    {\n",
+    "        \"input_language\": \"English\",\n",
+    "        \"output_language\": \"Korean\",\n",
+    "        \"text\": \"I love Python\",\n",
+    "    }\n",
+    ")"
    ]
   },
   {

docs/docs/integrations/chat/anthropic_functions.ipynb

@@ -15,16 +15,7 @@
    "execution_count": 1,
    "id": "378be79b",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.14) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from langchain_experimental.llms.anthropic_functions import AnthropicFunctions"
    ]
@@ -41,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "e1d535f6",
    "metadata": {},
    "outputs": [],
@@ -102,7 +93,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "response = model.predict_messages(\n",
+    "response = model.invoke(\n",
     "    [HumanMessage(content=\"whats the weater in boston?\")], functions=functions\n",
     ")"
    ]
@@ -140,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "id": "7af5c567",
    "metadata": {},
    "outputs": [],
@@ -162,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "bd01082a",
    "metadata": {},
    "outputs": [],
@@ -172,24 +163,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "b5a23e9f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'name': 'Alex', 'height': '5', 'hair_color': 'blonde'},\n",
-       " {'name': 'Claudia', 'height': '6', 'hair_color': 'brunette'}]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "chain.run(inp)"
+    "chain.invoke(inp)"
    ]
   },
   {
@@ -256,7 +235,7 @@
     }
    ],
    "source": [
-    "chain.run(\"this is really cool\")"
+    "chain.invoke(\"this is really cool\")"
    ]
   }
  ],
@@ -276,7 +255,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.9.0"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/google_generative_ai.ipynb

@@ -320,20 +320,51 @@
     "4. Message may be blocked if they violate the safety checks of the LLM. In this case, the model will return an empty response."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "54793b9e",
+   "metadata": {},
+   "source": [
+    "### Safety Settings\n",
+    "\n",
+    "Gemini models have default safety settings that can be overridden. If you are receiving lots of \"Safety Warnings\" from your models, you can try tweaking the `safety_settings` attribute of the model. For example, to turn off safety blocking for dangerous content, you can construct your LLM as follows:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "75fdfad6",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from langchain_google_genai import (\n",
+    "    ChatGoogleGenerativeAI,\n",
+    "    HarmBlockThreshold,\n",
+    "    HarmCategory,\n",
+    ")\n",
+    "\n",
+    "llm = ChatGoogleGenerativeAI(\n",
+    "    model=\"gemini-pro\",\n",
+    "    safety_settings={\n",
+    "        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e68e203d",
+   "metadata": {},
+   "source": [
+    "For an enumeration of the categories and thresholds available, see Google's [safety setting types](https://ai.google.dev/api/python/google/generativeai/types/SafetySettingDict)."
+   ]
   },
   {
    "cell_type": "markdown",
    "id": "92b5aca5",
    "metadata": {},
    "source": [
-    "## Additional Configuraation\n",
+    "## Additional Configuration\n",
     "\n",
     "You can pass the following parameters to ChatGoogleGenerativeAI in order to customize the SDK's behavior:\n",
     "\n",

docs/docs/integrations/chat/google_vertex_ai_palm.ipynb

@@ -424,9 +424,7 @@
     "human = \"{text}\"\n",
     "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
     "\n",
-    "chat = ChatVertexAI(\n",
-    "    model_name=\"chat-bison\", max_output_tokens=1000, temperature=0.5\n",
-    ")\n",
+    "chat = ChatVertexAI(model_name=\"chat-bison\", max_output_tokens=1000, temperature=0.5)\n",
     "chain = prompt | chat\n",
     "\n",
     "asyncio.run(\n",

docs/docs/integrations/chat/konko.ipynb

@@ -15,39 +15,23 @@
    "source": [
     "# ChatKonko\n",
     "\n",
+    "# Konko\n",
+    "\n",
     ">[Konko](https://www.konko.ai/) API is a fully managed Web API designed to help application developers:\n",
     "\n",
-    "Konko API is a fully managed API designed to help application developers:\n",
     "\n",
-    "1. Select the right LLM(s) for their application\n",
-    "2. Prototype with various open-source and proprietary LLMs\n",
-    "3. Access Fine Tuning for open-source LLMs to get industry-leading performance at a fraction of the cost\n",
-    "4. Setup low-cost production APIs according to security, privacy, throughput, latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant, multi-cloud infrastructure\n",
+    "1. **Select** the right open source or proprietary LLMs for their application\n",
+    "2. **Build** applications faster with integrations to leading application frameworks and fully managed APIs\n",
+    "3. **Fine tune** smaller open-source LLMs to achieve industry-leading performance at a fraction of the cost\n",
+    "4. **Deploy production-scale APIs** that meet security, privacy, throughput, and latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant, multi-cloud infrastructure\n",
     "\n",
-    "### Steps to Access Models\n",
-    "1. **Explore Available Models:** Start by browsing through the [available models](https://docs.konko.ai/docs/list-of-models) on Konko. Each model caters to different use cases and capabilities.\n",
-    "\n",
-    "2. **Identify Suitable Endpoints:** Determine which [endpoint](https://docs.konko.ai/docs/list-of-models#list-of-available-models) (ChatCompletion or Completion) supports your selected model.\n",
-    "\n",
-    "3. **Selecting a Model:** [Choose a model](https://docs.konko.ai/docs/list-of-models#list-of-available-models) based on its metadata and how well it fits your use case.\n",
-    "\n",
-    "4. **Prompting Guidelines:** Once a model is selected, refer to the [prompting guidelines](https://docs.konko.ai/docs/prompting) to effectively communicate with it.\n",
-    "\n",
-    "5. **Using the API:** Finally, use the appropriate Konko [API endpoint](https://docs.konko.ai/docs/quickstart-for-completion-and-chat-completion-endpoint) to call the model and receive responses.\n",
-    "\n",
-    "To run this notebook, you'll need Konko API key. You can create one by signing up on [Konko](https://www.konko.ai/).\n",
     "\n",
     "This example goes over how to use LangChain to interact with `Konko` ChatCompletion [models](https://docs.konko.ai/docs/list-of-models#konko-hosted-models-for-chatcompletion)\n",
+    "\n",
+    "To run this notebook, you'll need Konko API key. Sign in to our web app to [create an API key](https://platform.konko.ai/settings/api-keys) to access models\n",
     "\n"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To run this notebook, you'll need Konko API key. You can create one by signing up on [Konko](https://www.konko.ai/)."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -64,11 +48,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 2. Set API Keys\n",
-    "\n",
-    "<br />\n",
-    "\n",
-    "### Option 1: Set Environment Variables\n",
+    "#### Set Environment Variables\n",
     "\n",
     "1. You can set environment variables for \n",
     "   1. KONKO_API_KEY (Required)\n",
@@ -78,18 +58,7 @@
     "```shell\n",
     "export KONKO_API_KEY={your_KONKO_API_KEY_here}\n",
     "export OPENAI_API_KEY={your_OPENAI_API_KEY_here} #Optional\n",
-    "```\n",
-    "\n",
-    "Alternatively, you can add the above lines directly to your shell startup script (such as .bashrc or .bash_profile for Bash shell and .zshrc for Zsh shell) to have them set automatically every time a new shell session starts.\n",
-    "\n",
-    "### Option 2: Set API Keys Programmatically\n",
-    "\n",
-    "If you prefer to set your API keys directly within your Python script or Jupyter notebook, you can use the following commands:\n",
-    "\n",
-    "```python\n",
-    "konko.set_api_key('your_KONKO_API_KEY_here')  \n",
-    "konko.set_openai_api_key('your_OPENAI_API_KEY_here') # Optional\n",
-    "```\n"
+    "```"
    ]
   },
   {
@@ -98,7 +67,7 @@
    "source": [
     "## Calling a model\n",
     "\n",
-    "Find a model on the [Konko overview page](https://docs.konko.ai/v0.5.0/docs/list-of-models)\n",
+    "Find a model on the [Konko overview page](https://docs.konko.ai/docs/list-of-models)\n",
     "\n",
     "Another way to find the list of models running on the Konko instance is through this [endpoint](https://docs.konko.ai/reference/get-models).\n",
     "\n",

docs/docs/integrations/chat/mistralai.ipynb

@@ -15,28 +15,53 @@
    "id": "bf733a38-db84-4363-89e2-de6735c37230",
    "metadata": {},
    "source": [
-    "# ChatMistralAI\n",
+    "# MistralAI\n",
     "\n",
     "This notebook covers how to get started with MistralAI chat models, via their [API](https://docs.mistral.ai/api/).\n",
     "\n",
     "A valid [API key](https://console.mistral.ai/users/api-keys/) is needed to communicate with the API.\n",
     "\n",
-    "You will need the `langchain-mistralai` package to use the API. You can install it via pip:"
+    "Head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html) for detailed documentation of all attributes and methods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc686b8f",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "You will need the `langchain-core` and `langchain-mistralai` package to use the API. You can install these with:\n",
+    "\n",
+    "```bash\n",
+    "pip install -U langchain-core langchain-mistralai\n",
+    "\n",
+    "We'll also need to get a [Mistral API key](https://console.mistral.ai/users/api-keys/)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "eb978a7e",
+   "execution_count": 7,
+   "id": "c3fd4184",
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install -qU langchain-core langchain-mistralai"
+    "import getpass\n",
+    "\n",
+    "mistral_api_key = getpass.getpass()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "502127fd",
+   "metadata": {},
+   "source": [
+    "## Usage"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
    "metadata": {
     "tags": []
@@ -49,23 +74,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
    "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "import os\n",
-    "\n",
-    "mistral_api_key = os.environ.get(\"MISTRAL_API_KEY\")\n",
     "# If mistral_api_key is not passed, default behavior is to use the `MISTRAL_API_KEY` environment variable.\n",
     "chat = ChatMistralAI(mistral_api_key=mistral_api_key)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 9,
    "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
    "metadata": {
     "tags": []
@@ -74,16 +96,16 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"Hello! I'm here to assist you. How can I help you today? If you have any questions or need information on a particular topic, feel free to ask. I'm ready to provide accurate and helpful answers to the best of my ability.\")"
+       "AIMessage(content=\"Who's there? I was just about to ask the same thing! How can I assist you today?\")"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "messages = [HumanMessage(content=\"say a brief hello\")]\n",
+    "messages = [HumanMessage(content=\"knock knock\")]\n",
     "chat.invoke(messages)"
    ]
   },
@@ -92,12 +114,12 @@
    "id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c",
    "metadata": {},
    "source": [
-    "## `ChatMistralAI` also supports async and streaming functionality:"
+    "### Async"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
    "metadata": {
     "tags": []
@@ -106,10 +128,10 @@
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\"Hello! I'm glad you're here. If you have any questions or need assistance with something related to programming or software development, feel free to ask. I'll do my best to help you out. Have a great day!\")"
+       "AIMessage(content='Who\\'s there?\\n\\n(You can then continue the \"knock knock\" joke by saying the name of the person or character who should be responding. For example, if I say \"Banana,\" you could respond with \"Banana who?\" and I would say \"Banana bunch! Get it? Because a group of bananas is called a \\'bunch\\'!\" and then we would both laugh and have a great time. But really, you can put anything you want in the spot where I put \"Banana\" and it will still technically be a \"knock knock\" joke. The possibilities are endless!)')"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -118,9 +140,17 @@
     "await chat.ainvoke(messages)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "86ccef97",
+   "metadata": {},
+   "source": [
+    "### Streaming\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 11,
    "id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
    "metadata": {
     "tags": []
@@ -130,7 +160,27 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Hello! I'm happy to assist you. Is there a specific question or topic you would like to discuss? I can provide information and answer questions on a wide variety of subjects."
+      "Who's there?\n",
+      "\n",
+      "(After this, the conversation can continue as a call and response \"who's there\" joke. Here is an example of how it could go:\n",
+      "\n",
+      "You say: Orange.\n",
+      "I say: Orange who?\n",
+      "You say: Orange you glad I didn't say banana!?)\n",
+      "\n",
+      "But since you asked for a knock knock joke specifically, here's one for you:\n",
+      "\n",
+      "Knock knock.\n",
+      "\n",
+      "Me: Who's there?\n",
+      "\n",
+      "You: Lettuce.\n",
+      "\n",
+      "Me: Lettuce who?\n",
+      "\n",
+      "You: Lettuce in, it's too cold out here!\n",
+      "\n",
+      "I hope this brings a smile to your face! Do you have a favorite knock knock joke you'd like to share? I'd love to hear it."
      ]
     }
    ],
@@ -138,6 +188,79 @@
     "for chunk in chat.stream(messages):\n",
     "    print(chunk.content, end=\"\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6189577",
+   "metadata": {},
+   "source": [
+    "### Batch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "e63aebcb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[AIMessage(content=\"Who's there? I was just about to ask the same thing! Go ahead and tell me who's there. I love a good knock-knock joke.\")]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat.batch([messages])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38e39e71",
+   "metadata": {},
+   "source": [
+    "## Chaining\n",
+    "\n",
+    "You can also easily combine with a prompt template for easy structuring of user input. We can do this using [LCEL](/docs/expression_language)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ee43a1ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_template(\"Tell me a joke about {topic}\")\n",
+    "chain = prompt | chat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "0dc49212",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Why do bears hate shoes so much? They like to run around in their bear feet.')"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.invoke({\"topic\": \"bears\"})"
+   ]
   }
  ],
  "metadata": {
@@ -156,7 +279,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/yuan2.ipynb

@@ -0,0 +1,463 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "source": [
+    "---\n",
+    "sidebar_label: YUAN2\n",
+    "---"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% raw\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# YUAN2.0\n",
+    "\n",
+    "This notebook shows how to use [YUAN2 API](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/docs/inference_server.md) in LangChain with the langchain.chat_models.ChatYuan2.\n",
+    "\n",
+    "[*Yuan2.0*](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/README-EN.md) is a new generation Fundamental Large Language Model developed by IEIT System. We have published all three models, Yuan 2.0-102B, Yuan 2.0-51B, and Yuan 2.0-2B. And we provide relevant scripts for pretraining, fine-tuning, and inference services for other developers. Yuan2.0 is based on Yuan1.0, utilizing a wider range of high-quality pre training data and instruction fine-tuning datasets to enhance the model's understanding of semantics, mathematics, reasoning, code, knowledge, and other aspects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Getting started\n",
+    "### Installation\n",
+    "First, Yuan2.0 provided an OpenAI compatible API, and we integrate ChatYuan2 into langchain chat model by using OpenAI client.\n",
+    "Therefore, ensure the openai package is installed in your Python environment. Run the following command:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet openai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Importing the Required Modules\n",
+    "After installation, import the necessary modules to your Python script:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_community.chat_models import ChatYuan2\n",
+    "from langchain_core.messages import AIMessage, HumanMessage, SystemMessage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Setting Up Your API server\n",
+    "Setting up your OpenAI compatible API server following [yuan2 openai api server](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/README-EN.md).\n",
+    "If you deployed api server locally, you can simply set `api_key=\"EMPTY\"` or anything you want.\n",
+    "Just make sure, the `api_base` is set correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "yuan2_api_key = \"your_api_key\"\n",
+    "yuan2_api_base = \"http://127.0.0.1:8001/v1\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Initialize the ChatYuan2 Model\n",
+    "Here's how to initialize the chat model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "chat = ChatYuan2(\n",
+    "    yuan2_api_base=\"http://127.0.0.1:8001/v1\",\n",
+    "    temperature=1.0,\n",
+    "    model_name=\"yuan2\",\n",
+    "    max_retries=3,\n",
+    "    streaming=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Basic Usage\n",
+    "Invoke the model with system and human messages like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "messages = [\n",
+    "    SystemMessage(content=\"你是一个人工智能助手。\"),\n",
+    "    HumanMessage(content=\"你好，你是谁？\"),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "print(chat(messages))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Basic Usage with streaming\n",
+    "For continuous interaction, use the streaming feature:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "\n",
+    "chat = ChatYuan2(\n",
+    "    yuan2_api_base=\"http://127.0.0.1:8001/v1\",\n",
+    "    temperature=1.0,\n",
+    "    model_name=\"yuan2\",\n",
+    "    max_retries=3,\n",
+    "    streaming=True,\n",
+    "    callbacks=[StreamingStdOutCallbackHandler()],\n",
+    ")\n",
+    "messages = [\n",
+    "    SystemMessage(content=\"你是个旅游小助手。\"),\n",
+    "    HumanMessage(content=\"给我介绍一下北京有哪些好玩的。\"),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "chat(messages)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Advanced Features\n",
+    "### Usage with async calls\n",
+    "\n",
+    "Invoke the model with non-blocking calls, like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "async def basic_agenerate():\n",
+    "    chat = ChatYuan2(\n",
+    "        yuan2_api_base=\"http://127.0.0.1:8001/v1\",\n",
+    "        temperature=1.0,\n",
+    "        model_name=\"yuan2\",\n",
+    "        max_retries=3,\n",
+    "    )\n",
+    "    messages = [\n",
+    "        [\n",
+    "            SystemMessage(content=\"你是个旅游小助手。\"),\n",
+    "            HumanMessage(content=\"给我介绍一下北京有哪些好玩的。\"),\n",
+    "        ]\n",
+    "    ]\n",
+    "\n",
+    "    result = await chat.agenerate(messages)\n",
+    "    print(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import asyncio\n",
+    "\n",
+    "asyncio.run(basic_agenerate())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Usage with prompt template\n",
+    "\n",
+    "Invoke the model with non-blocking calls and used chat template like this:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "async def ainvoke_with_prompt_template():\n",
+    "    from langchain.prompts.chat import (\n",
+    "        ChatPromptTemplate,\n",
+    "    )\n",
+    "\n",
+    "    chat = ChatYuan2(\n",
+    "        yuan2_api_base=\"http://127.0.0.1:8001/v1\",\n",
+    "        temperature=1.0,\n",
+    "        model_name=\"yuan2\",\n",
+    "        max_retries=3,\n",
+    "    )\n",
+    "    prompt = ChatPromptTemplate.from_messages(\n",
+    "        [\n",
+    "            (\"system\", \"你是一个诗人，擅长写诗。\"),\n",
+    "            (\"human\", \"给我写首诗，主题是{theme}。\"),\n",
+    "        ]\n",
+    "    )\n",
+    "    chain = prompt | chat\n",
+    "    result = await chain.ainvoke({\"theme\": \"明月\"})\n",
+    "    print(f\"type(result): {type(result)}; {result}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "asyncio.run(ainvoke_with_prompt_template())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Usage with async calls in streaming\n",
+    "For non-blocking calls with streaming output, use the astream method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "async def basic_astream():\n",
+    "    chat = ChatYuan2(\n",
+    "        yuan2_api_base=\"http://127.0.0.1:8001/v1\",\n",
+    "        temperature=1.0,\n",
+    "        model_name=\"yuan2\",\n",
+    "        max_retries=3,\n",
+    "    )\n",
+    "    messages = [\n",
+    "        SystemMessage(content=\"你是个旅游小助手。\"),\n",
+    "        HumanMessage(content=\"给我介绍一下北京有哪些好玩的。\"),\n",
+    "    ]\n",
+    "    result = chat.astream(messages)\n",
+    "    async for chunk in result:\n",
+    "        print(chunk.content, end=\"\", flush=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "is_executing": true,
+     "name": "#%%\n"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "import asyncio\n",
+    "\n",
+    "asyncio.run(basic_astream())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/docs/integrations/chat_loaders/langsmith_dataset.ipynb

@@ -55,7 +55,7 @@
    "source": [
     "## 1. Select a dataset\n",
     "\n",
-    "This notebook fine-tunes a model directly on selecting which runs to fine-tune on. You will often curate these from traced runs. You can learn more about LangSmith datasets in the docs [docs](https://docs.smith.langchain.com/evaluation/datasets).\n",
+    "This notebook fine-tunes a model directly on selecting which runs to fine-tune on. You will often curate these from traced runs. You can learn more about LangSmith datasets in the docs [docs](https://docs.smith.langchain.com/evaluation/concepts#datasets).\n",
     "\n",
     "For the sake of this tutorial, we will upload an existing dataset here that you can use."
    ]

docs/docs/integrations/document_loaders/athena.ipynb

@@ -0,0 +1,110 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "MwTWzDxYgbrR"
+   },
+   "source": [
+    "# Athena\n",
+    "\n",
+    "This notebooks goes over how to load documents from AWS Athena"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "F0zaLR3xgWmO"
+   },
+   "outputs": [],
+   "source": [
+    "! pip install boto3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "076NLjfngoWJ"
+   },
+   "outputs": [],
+   "source": [
+    "from langchain_community.document_loaders.athena import AthenaLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "XpMRQwU9gu44"
+   },
+   "outputs": [],
+   "source": [
+    "database_name = \"my_database\"\n",
+    "s3_output_path = \"s3://my_bucket/query_results/\"\n",
+    "query = \"SELECT * FROM my_table\"\n",
+    "profile_name = \"my_profile\"\n",
+    "\n",
+    "loader = AthenaLoader(\n",
+    "    query=query,\n",
+    "    database=database_name,\n",
+    "    s3_output_uri=s3_output_path,\n",
+    "    profile_name=profile_name,\n",
+    ")\n",
+    "\n",
+    "documents = loader.load()\n",
+    "print(documents)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "5IBapL3ejoEt"
+   },
+   "source": [
+    "Example with metadata columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "wMx6nI1qjryD"
+   },
+   "outputs": [],
+   "source": [
+    "database_name = \"my_database\"\n",
+    "s3_output_path = \"s3://my_bucket/query_results/\"\n",
+    "query = \"SELECT * FROM my_table\"\n",
+    "profile_name = \"my_profile\"\n",
+    "metadata_columns = [\"_row\", \"_created_at\"]\n",
+    "\n",
+    "loader = AthenaLoader(\n",
+    "    query=query,\n",
+    "    database=database_name,\n",
+    "    s3_output_uri=s3_output_path,\n",
+    "    profile_name=profile_name,\n",
+    "    metadata_columns=metadata_columns,\n",
+    ")\n",
+    "\n",
+    "documents = loader.load()\n",
+    "print(documents)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

docs/docs/integrations/document_loaders/cassandra.ipynb

@@ -72,57 +72,72 @@
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "### Init from a cassandra driver Session\n",
     "\n",
     "You need to create a `cassandra.cluster.Session` object, as described in the [Cassandra driver documentation](https://docs.datastax.com/en/developer/python-driver/latest/api/cassandra/cluster/#module-cassandra.cluster). The details vary (e.g. with network settings and authentication), but this might be something like:"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "from cassandra.cluster import Cluster\n",
     "\n",
     "cluster = Cluster()\n",
     "session = cluster.connect()"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "You need to provide the name of an existing keyspace of the Cassandra instance:"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "CASSANDRA_KEYSPACE = input(\"CASSANDRA_KEYSPACE = \")"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "execution_count": null
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "Creating the document loader:"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
@@ -144,18 +159,21 @@
   },
   {
    "cell_type": "code",
-   "outputs": [],
-   "source": [
-    "docs = loader.load()"
-   ],
+   "execution_count": 17,
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "end_time": "2024-01-19T15:47:26.399472Z",
      "start_time": "2024-01-19T15:47:26.389145Z"
+    },
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
     }
    },
-   "execution_count": 17
+   "outputs": [],
+   "source": [
+    "docs = loader.load()"
+   ]
   },
   {
    "cell_type": "code",
@@ -169,7 +187,9 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "Document(page_content='Row(_id=\\'659bdffa16cbc4586b11a423\\', title=\\'Dangerous Men\\', reviewtext=\\'\"Dangerous Men,\"  the picture\\\\\\'s production notes inform, took 26 years to reach the big screen. After having seen it, I wonder: What was the rush?\\')', metadata={'table': 'movie_reviews', 'keyspace': 'default_keyspace'})"
+      "text/plain": [
+       "Document(page_content='Row(_id=\\'659bdffa16cbc4586b11a423\\', title=\\'Dangerous Men\\', reviewtext=\\'\"Dangerous Men,\"  the picture\\\\\\'s production notes inform, took 26 years to reach the big screen. After having seen it, I wonder: What was the rush?\\')', metadata={'table': 'movie_reviews', 'keyspace': 'default_keyspace'})"
+      ]
      },
      "execution_count": 19,
      "metadata": {},
@@ -182,17 +202,27 @@
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "source": [
     "### Init from cassio\n",
     "\n",
     "It's also possible to use cassio to configure the session and keyspace."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
    "outputs": [],
    "source": [
     "import cassio\n",
@@ -204,11 +234,16 @@
     ")\n",
     "\n",
     "docs = loader.load()"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "execution_count": null
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Attribution statement\n",
+    "\n",
+    "> Apache Cassandra, Cassandra and Apache are either registered trademarks or trademarks of the [Apache Software Foundation](http://www.apache.org/) in the United States and/or other countries."
+   ]
   }
  ],
  "metadata": {
@@ -233,7 +268,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.9.17"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/example_data/source_code/example.py

@@ -3,7 +3,7 @@ class MyClass:
         self.name = name
 
     def greet(self):
-        print(f"Hello, {self.name}!")
+        print(f"Hello, {self.name}!")  # noqa: T201
 
 
 def main():

docs/docs/integrations/document_loaders/github.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# GitHub\n",
     "\n",
-    "This notebooks shows how you can load issues and pull requests (PRs) for a given repository on [GitHub](https://github.com/). We will use the LangChain Python repository as an example."
+    "This notebooks shows how you can load issues and pull requests (PRs) for a given repository on [GitHub](https://github.com/). Also shows how you can load github files for a given repository on [GitHub](https://github.com/). We will use the LangChain Python repository as an example."
    ]
   },
   {
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -100,27 +100,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "# Creates GitHubLoader (#5257)\r\n",
-      "\r\n",
-      "GitHubLoader is a DocumentLoader that loads issues and PRs from GitHub.\r\n",
-      "\r\n",
-      "Fixes #5257\r\n",
-      "\r\n",
-      "Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested:\r\n",
-      "DataLoaders\r\n",
-      "- @eyurtsev\r\n",
-      "\n",
-      "{'url': 'https://github.com/langchain-ai/langchain/pull/5408', 'title': 'DocumentLoader for GitHub', 'creator': 'UmerHA', 'created_at': '2023-05-29T14:50:53Z', 'comments': 0, 'state': 'open', 'labels': ['enhancement', 'lgtm', 'doc loader'], 'assignee': None, 'milestone': None, 'locked': False, 'number': 5408, 'is_pull_request': True}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(docs[0].page_content)\n",
     "print(docs[0].metadata)"
@@ -142,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -157,84 +139,68 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "### System Info\n",
-      "\n",
-      "LangChain version = 0.0.167\r\n",
-      "Python version = 3.11.0\r\n",
-      "System = Windows 11 (using Jupyter)\n",
-      "\n",
-      "### Who can help?\n",
-      "\n",
-      "- @hwchase17\r\n",
-      "- @agola11\r\n",
-      "- @UmerHA (I have a fix ready, will submit a PR)\n",
-      "\n",
-      "### Information\n",
-      "\n",
-      "- [ ] The official example notebooks/scripts\n",
-      "- [X] My own modified scripts\n",
-      "\n",
-      "### Related Components\n",
-      "\n",
-      "- [X] LLMs/Chat Models\n",
-      "- [ ] Embedding Models\n",
-      "- [X] Prompts / Prompt Templates / Prompt Selectors\n",
-      "- [ ] Output Parsers\n",
-      "- [ ] Document Loaders\n",
-      "- [ ] Vector Stores / Retrievers\n",
-      "- [ ] Memory\n",
-      "- [ ] Agents / Agent Executors\n",
-      "- [ ] Tools / Toolkits\n",
-      "- [ ] Chains\n",
-      "- [ ] Callbacks/Tracing\n",
-      "- [ ] Async\n",
-      "\n",
-      "### Reproduction\n",
-      "\n",
-      "```\r\n",
-      "import os\r\n",
-      "os.environ[\"OPENAI_API_KEY\"] = \"...\"\r\n",
-      "\r\n",
-      "from langchain.chains import LLMChain\r\n",
-      "from langchain_openai import ChatOpenAI\r\n",
-      "from langchain.prompts import PromptTemplate\r\n",
-      "from langchain.prompts.chat import ChatPromptTemplate\r\n",
-      "from langchain.schema import messages_from_dict\r\n",
-      "\r\n",
-      "role_strings = [\r\n",
-      "    (\"system\", \"you are a bird expert\"), \r\n",
-      "    (\"human\", \"which bird has a point beak?\")\r\n",
-      "]\r\n",
-      "prompt = ChatPromptTemplate.from_role_strings(role_strings)\r\n",
-      "chain = LLMChain(llm=ChatOpenAI(), prompt=prompt)\r\n",
-      "chain.run({})\r\n",
-      "```\n",
-      "\n",
-      "### Expected behavior\n",
-      "\n",
-      "Chain should run\n",
-      "{'url': 'https://github.com/langchain-ai/langchain/issues/5027', 'title': \"ChatOpenAI models don't work with prompts created via ChatPromptTemplate.from_role_strings\", 'creator': 'UmerHA', 'created_at': '2023-05-20T10:39:18Z', 'comments': 1, 'state': 'open', 'labels': [], 'assignee': None, 'milestone': None, 'locked': False, 'number': 5027, 'is_pull_request': False}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(docs[0].page_content)\n",
     "print(docs[0].metadata)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Github File Content\n",
+    "\n",
+    "For below code, loads all markdown file in rpeo `langchain-ai/langchain`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import GithubFileLoader"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "loader = GithubFileLoader(\n",
+    "    repo=\"langchain-ai/langchain\",  # the repo name\n",
+    "    access_token=ACCESS_TOKEN,\n",
+    "    github_api_url=\"https://api.github.com\",\n",
+    "    file_filter=lambda file_path: file_path.endswith(\n",
+    "        \".md\"\n",
+    "    ),  # load all markdowns files.\n",
+    ")\n",
+    "documents = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "example output of one of document: \n",
+    "\n",
+    "```json\n",
+    "documents.metadata: \n",
+    "    {\n",
+    "      \"path\": \"README.md\",\n",
+    "      \"sha\": \"82f1c4ea88ecf8d2dfsfx06a700e84be4\",\n",
+    "      \"source\": \"https://github.com/langchain-ai/langchain/blob/master/README.md\"\n",
+    "    }\n",
+    "documents.content:\n",
+    "    mock content\n",
+    "```"
+   ]
   }
  ],
  "metadata": {
@@ -253,7 +219,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/pebblo.ipynb

@@ -0,0 +1,88 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pebblo Safe DocumentLoader\n",
+    "\n",
+    "> [Pebblo](https://github.com/daxa-ai/pebblo) enables developers to safely load data and promote their Gen AI app to deployment without worrying about the organization’s compliance and security requirements. The project identifies semantic topics and entities found in the loaded data and summarizes them on the UI or a PDF report.\n",
+    "\n",
+    "Pebblo has two components.\n",
+    "\n",
+    "1. Pebblo Safe DocumentLoader for Langchain\n",
+    "1. Pebblo Daemon\n",
+    "\n",
+    "This document describes how to augment your existing Langchain DocumentLoader with Pebblo Safe DocumentLoader to get deep data visibility on the types of Topics and Entities ingested into the Gen-AI Langchain application. For details on `Pebblo Daemon` see this [pebblo daemon](https://daxa-ai.github.io/pebblo-docs/daemon.html) document.\n",
+    "\n",
+    "Pebblo Safeloader enables safe data ingestion for Langchain `DocumentLoader`. This is done by wrapping the document loader call with `Pebblo Safe DocumentLoader`.\n",
+    "\n",
+    "#### How to Pebblo enable Document Loading?\n",
+    "\n",
+    "Assume a Langchain RAG application snippet using `CSVLoader` to read a CSV document for inference.\n",
+    "\n",
+    "Here is the snippet of Document loading using `CSVLoader`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders.csv_loader import CSVLoader\n",
+    "\n",
+    "loader = CSVLoader(\"data/corp_sens_data.csv\")\n",
+    "documents = loader.load()\n",
+    "print(documents)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Pebblo SafeLoader can be enabled with few lines of code change to the above snippet."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders.csv_loader import CSVLoader\n",
+    "from langchain_community.document_loaders import PebbloSafeLoader\n",
+    "\n",
+    "loader = PebbloSafeLoader(\n",
+    "    CSVLoader(\"data/corp_sens_data.csv\"),\n",
+    "    name=\"acme-corp-rag-1\",  # App name (Mandatory)\n",
+    "    owner=\"Joe Smith\",  # Owner (Optional)\n",
+    "    description=\"Support productivity RAG application\",  # Description (Optional)\n",
+    ")\n",
+    "documents = loader.load()\n",
+    "print(documents)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/document_loaders/sitemap.ipynb

@@ -13,27 +13,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: nest_asyncio in /Users/tasp/Code/projects/langchain/.venv/lib/python3.10/site-packages (1.5.6)\n",
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.0.1\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%pip install --upgrade --quiet  nest_asyncio"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,11 +43,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "sitemap_loader = SitemapLoader(web_path=\"https://langchain.readthedocs.io/sitemap.xml\")\n",
+    "sitemap_loader = SitemapLoader(web_path=\"https://api.python.langchain.com/sitemap.xml\")\n",
     "\n",
     "docs = sitemap_loader.load()"
    ]
@@ -90,7 +79,7 @@
     {
      "data": {
       "text/plain": [
-       "Document(page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n', metadata={'source': 'https://api.python.langchain.com/en/stable/', 'loc': 'https://api.python.langchain.com/en/stable/', 'lastmod': '2023-10-13T18:13:26.966937+00:00', 'changefreq': 'weekly', 'priority': '1'})"
+       "Document(page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n', metadata={'source': 'https://api.python.langchain.com/en/stable/', 'loc': 'https://api.python.langchain.com/en/stable/', 'lastmod': '2024-02-09T01:10:49.422114+00:00', 'changefreq': 'weekly', 'priority': '1'})"
       ]
      },
      "execution_count": 6,
@@ -113,20 +102,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Fetching pages: 100%|##########| 1/1 [00:00<00:00, 16.39it/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "loader = SitemapLoader(\n",
-    "    web_path=\"https://langchain.readthedocs.io/sitemap.xml\",\n",
+    "    web_path=\" https://api.python.langchain.com/sitemap.xml\",\n",
     "    filter_urls=[\"https://api.python.langchain.com/en/latest\"],\n",
     ")\n",
     "documents = loader.load()"
@@ -134,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 8,
    "metadata": {
     "scrolled": true
    },
@@ -142,10 +123,10 @@
     {
      "data": {
       "text/plain": [
-       "Document(page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n', metadata={'source': 'https://api.python.langchain.com/en/latest/', 'loc': 'https://api.python.langchain.com/en/latest/', 'lastmod': '2023-10-13T18:09:58.478681+00:00', 'changefreq': 'daily', 'priority': '0.9'})"
+       "Document(page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n', metadata={'source': 'https://api.python.langchain.com/en/latest/', 'loc': 'https://api.python.langchain.com/en/latest/', 'lastmod': '2024-02-12T05:26:10.971077+00:00', 'changefreq': 'daily', 'priority': '0.9'})"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -183,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -211,12 +192,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
     "loader = SitemapLoader(\n",
-    "    \"https://langchain.readthedocs.io/sitemap.xml\",\n",
+    "    \"https://api.python.langchain.com/sitemap.xml\",\n",
     "    filter_urls=[\"https://api.python.langchain.com/en/latest/\"],\n",
     "    parsing_function=remove_nav_and_header_elements,\n",
     ")"
@@ -233,17 +214,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Fetching pages: 100%|##########| 3/3 [00:00<00:00, 12.46it/s]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "sitemap_loader = SitemapLoader(web_path=\"example_data/sitemap.xml\", is_local=True)\n",
     "\n",

docs/docs/integrations/document_loaders/source_code.ipynb

@@ -9,7 +9,35 @@
     "\n",
     "This notebook covers how to load source code files using a special approach with language parsing: each top-level function and class in the code is loaded into separate documents. Any remaining code top-level code outside the already loaded functions and classes will be loaded into a separate document.\n",
     "\n",
-    "This approach can potentially improve the accuracy of QA models over source code. Currently, the supported languages for code parsing are Python and JavaScript. The language used for parsing can be configured, along with the minimum number of lines required to activate the splitting based on syntax."
+    "This approach can potentially improve the accuracy of QA models over source code.\n",
+    "\n",
+    "The supported languages for code parsing are:\n",
+    "\n",
+    "- C (*)\n",
+    "- C++ (*)\n",
+    "- C# (*)\n",
+    "- COBOL\n",
+    "- Go (*)\n",
+    "- Java (*)\n",
+    "- JavaScript (requires package `esprima`)\n",
+    "- Kotlin (*)\n",
+    "- Lua (*)\n",
+    "- Perl (*)\n",
+    "- Python\n",
+    "- Ruby (*)\n",
+    "- Rust (*)\n",
+    "- Scala (*)\n",
+    "- TypeScript (*)\n",
+    "\n",
+    "Items marked with (*) require the packages `tree_sitter` and `tree_sitter_languages`.\n",
+    "It is straightforward to add support for additional languages using `tree_sitter`,\n",
+    "although this currently requires modifying LangChain.\n",
+    "\n",
+    "The language used for parsing can be configured, along with the minimum number of\n",
+    "lines required to activate the splitting based on syntax.\n",
+    "\n",
+    "If a language is not explicitly specified, `LanguageParser` will infer one from\n",
+    "filename extensions, if present."
    ]
   },
   {
@@ -19,7 +47,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  esprima"
+    "%pip install -qU esprima esprima tree_sitter tree_sitter_languages"
    ]
   },
   {
@@ -395,6 +423,33 @@
    "source": [
     "print(\"\\n\\n--8<--\\n\\n\".join([document.page_content for document in result]))"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding Languages using Tree-sitter Template\n",
+    "\n",
+    "Expanding language support using the Tree-Sitter template involves a few essential steps:\n",
+    "\n",
+    "1. **Creating a New Language File**:\n",
+    "    - Begin by creating a new file in the designated directory (langchain/libs/community/langchain_community/document_loaders/parsers/language).\n",
+    "    - Model this file based on the structure and parsing logic of existing language files like **`cpp.py`**.\n",
+    "    - You will also need to create a file in the langchain directory (langchain/libs/langchain/langchain/document_loaders/parsers/language).\n",
+    "2. **Parsing Language Specifics**:\n",
+    "    - Mimic the structure used in the **`cpp.py`** file, adapting it to suit the language you are incorporating.\n",
+    "    - The primary alteration involves adjusting the chunk query array to suit the syntax and structure of the language you are parsing.\n",
+    "3. **Testing the Language Parser**:\n",
+    "    - For thorough validation, generate a test file specific to the new language. Create **`test_language.py`** in the designated directory(langchain/libs/community/tests/unit_tests/document_loaders/parsers/language).\n",
+    "    - Follow the example set by **`test_cpp.py`** to establish fundamental tests for the parsed elements in the new language.\n",
+    "4. **Integration into the Parser and Text Splitter**:\n",
+    "    - Incorporate your new language within the **`language_parser.py`** file. Ensure to update LANGUAGE_EXTENSIONS and LANGUAGE_SEGMENTERS along with the docstring for LanguageParser to recognize and handle the added language.\n",
+    "    - Also, confirm that your language is included in **`text_splitter.py`** in class Language for proper parsing.\n",
+    "\n",
+    "By following these steps and ensuring comprehensive testing and integration, you'll successfully extend language support using the Tree-Sitter template.\n",
+    "\n",
+    "Best of luck!"
+   ]
   }
  ],
  "metadata": {
@@ -413,7 +468,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

docs/docs/integrations/llms/ai21.ipynb

@@ -1,137 +1,114 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "602a52a4",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: AI21 Labs\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "9597802c",
    "metadata": {},
    "source": [
-    "# AI21\n",
+    "# AI21LLM\n",
     "\n",
-    "[AI21 Studio](https://docs.ai21.com/) provides API access to `Jurassic-2` large language models.\n",
+    "This example goes over how to use LangChain to interact with `AI21` models.\n",
     "\n",
-    "This example goes over how to use LangChain to interact with [AI21 models](https://docs.ai21.com/docs/jurassic-2-models)."
+    "## Installation"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "02be122d-04e8-4ec6-84d1-f1d8961d6828",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\n",
-      "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# install the package:\n",
-    "%pip install --upgrade --quiet  ai21"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "4229227e-6ca2-41ad-a3c3-5f29e3559091",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": null,
+   "id": "59c710c4",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# get AI21_API_KEY. Use https://studio.ai21.com/account/account\n",
-    "\n",
-    "from getpass import getpass\n",
-    "\n",
-    "AI21_API_KEY = getpass()"
+    "!pip install -qU langchain-ai21"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "6fb585dd",
+   "cell_type": "markdown",
+   "id": "560a2f9254963fd7",
    "metadata": {
-    "tags": []
+    "collapsed": false
    },
-   "outputs": [],
    "source": [
-    "from langchain_community.llms import AI21\n",
-    "from langchain_core.prompts import PromptTemplate"
+    "## Environment Setup\n",
+    "\n",
+    "We'll need to get a [AI21 API key](https://docs.ai21.com/) and set the `AI21_API_KEY` environment variable:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "id": "035dea0f",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "template = \"\"\"Question: {question}\n",
+    "import os\n",
+    "from getpass import getpass\n",
     "\n",
-    "Answer: Let's think step by step.\"\"\"\n",
-    "\n",
-    "prompt = PromptTemplate.from_template(template)"
+    "os.environ[\"AI21_API_KEY\"] = getpass()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "3f3458d9",
+   "cell_type": "markdown",
+   "id": "1891df96eb076e1a",
    "metadata": {
-    "tags": []
+    "collapsed": false
    },
-   "outputs": [],
    "source": [
-    "llm = AI21(ai21_api_key=AI21_API_KEY)"
+    "## Usage"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "a641dbd9",
+   "execution_count": 6,
+   "id": "98f70927a87e4745",
    "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "llm_chain = prompt | llm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "9f0b1960",
-   "metadata": {
-    "tags": []
+    "collapsed": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'\\nThe Super Bowl in the year Justin Beiber was born was in the year 1991.\\nThe Super Bowl in 1991 was won by the Washington Redskins.\\nFinal answer: Washington Redskins'"
+       "'\\nLangChain is a decentralized blockchain network that leverages AI and machine learning to provide language translation services.'"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
+    "from langchain_ai21 import AI21LLM\n",
+    "from langchain_core.prompts import PromptTemplate\n",
     "\n",
-    "llm_chain.invoke({\"question\": question})"
+    "template = \"\"\"Question: {question}\n",
+    "\n",
+    "Answer: Let's think step by step.\"\"\"\n",
+    "\n",
+    "prompt = PromptTemplate.from_template(template)\n",
+    "\n",
+    "model = AI21LLM(model=\"j2-ultra\")\n",
+    "\n",
+    "chain = prompt | model\n",
+    "\n",
+    "chain.invoke({\"question\": \"What is LangChain?\"})"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "22bce013",
+   "id": "a52f765c",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -139,7 +116,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.11.1 64-bit",
    "language": "python",
    "name": "python3"
   },
@@ -153,7 +130,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.4"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
+   }
   }
  },
  "nbformat": 4,

docs/docs/integrations/llms/aleph_alpha.ipynb

@@ -75,7 +75,7 @@
     "\n",
     "A:\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {

docs/docs/integrations/llms/alibabacloud_pai_eas_endpoint.ipynb

@@ -4,8 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# AliCloud PAI EAS\n",
-    "Machine Learning Platform for AI of Alibaba Cloud is a machine learning or deep learning engineering platform intended for enterprises and developers. It provides easy-to-use, cost-effective, high-performance, and easy-to-scale plug-ins that can be applied to various industry scenarios. With over 140 built-in optimization algorithms, Machine Learning Platform for AI provides whole-process AI engineering capabilities including data labeling (PAI-iTAG), model building (PAI-Designer and PAI-DSW), model training (PAI-DLC), compilation optimization, and inference deployment (PAI-EAS). PAI-EAS supports different types of hardware resources, including CPUs and GPUs, and features high throughput and low latency. It allows you to deploy large-scale complex models with a few clicks and perform elastic scale-ins and scale-outs in real time. It also provides a comprehensive O&M and monitoring system."
+    "# Alibaba Cloud PAI EAS\n",
+    "\n",
+    ">[Machine Learning Platform for AI of Alibaba Cloud](https://www.alibabacloud.com/help/en/pai) is a machine learning or deep learning engineering platform intended for enterprises and developers. It provides easy-to-use, cost-effective, high-performance, and easy-to-scale plug-ins that can be applied to various industry scenarios. With over 140 built-in optimization algorithms, `Machine Learning Platform for AI` provides whole-process AI engineering capabilities including data labeling (`PAI-iTAG`), model building (`PAI-Designer` and `PAI-DSW`), model training (`PAI-DLC`), compilation optimization, and inference deployment (`PAI-EAS`). `PAI-EAS` supports different types of hardware resources, including CPUs and GPUs, and features high throughput and low latency. It allows you to deploy large-scale complex models with a few clicks and perform elastic scale-ins and scale-outs in real time. It also provides a comprehensive O&M and monitoring system."
    ]
   },
   {
@@ -22,14 +23,14 @@
     "\n",
     "Answer: Let's think step by step.\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "One who want to use eas llms must set up eas service first. When the eas service is launched, eas_service_rul and eas_service token can be got. Users can refer to https://www.alibabacloud.com/help/en/pai/user-guide/service-deployment/ for more information,"
+    "One who wants to use EAS LLMs must set up EAS service first. When the EAS service is launched, `EAS_SERVICE_URL` and `EAS_SERVICE_TOKEN` can be obtained. Users can refer to https://www.alibabacloud.com/help/en/pai/user-guide/service-deployment/ for more information,"
    ]
   },
   {
@@ -50,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -65,16 +66,16 @@
     }
    ],
    "source": [
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
+    "llm_chain = prompt | llm\n",
     "\n",
     "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
-    "llm_chain.run(question)"
+    "llm_chain.invoke({\"question\": question})"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -88,10 +89,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  },
-  "orig_nbformat": 4
+   "version": "3.10.12"
+  }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/docs/integrations/llms/anyscale.ipynb

@@ -66,7 +66,7 @@
     "\n",
     "Answer: Let's think step by step.\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {
@@ -90,7 +90,7 @@
    },
    "outputs": [],
    "source": [
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
+    "llm_chain = prompt | llm"
    ]
   },
   {
@@ -104,7 +104,7 @@
    "source": [
     "question = \"When was George Washington president?\"\n",
     "\n",
-    "llm_chain.run(question)"
+    "llm_chain.invoke({\"question\": question})"
    ]
   },
   {

docs/docs/integrations/llms/aphrodite.ipynb

@@ -151,7 +151,7 @@
     "template = \"\"\"Question: {question}\n",
     "\n",
     "Answer: Let's think step by step.\"\"\"\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
+    "prompt = PromptTemplate.from_template(template)\n",
     "\n",
     "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
     "\n",

docs/docs/integrations/llms/banana.ipynb

@@ -66,7 +66,7 @@
     "\n",
     "Answer: Let's think step by step.\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {

docs/docs/integrations/llms/bedrock.ipynb

@@ -111,7 +111,35 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-   
+    "### Custom models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "custom_llm = Bedrock(\n",
+    "    credentials_profile_name=\"bedrock-admin\",\n",
+    "    provider=\"cohere\",\n",
+    "    model_id=\"<Custom model ARN>\",  # ARN like 'arn:aws:bedrock:...' obtained via provisioning the custom model\n",
+    "    model_kwargs={\"temperature\": 1},\n",
+    "    streaming=True,\n",
+    "    callbacks=[StreamingStdOutCallbackHandler()],\n",
+    ")\n",
+    "\n",
+    "conversation = ConversationChain(\n",
+    "    llm=custom_llm, verbose=True, memory=ConversationBufferMemory()\n",
+    ")\n",
+    "conversation.predict(input=\"What is the recipe of mayonnaise?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Guardrails for Amazon Bedrock example \n",
     "\n",
     "## Guardrails for Amazon Bedrock (Preview) \n",
     "[Guardrails for Amazon Bedrock](https://aws.amazon.com/bedrock/guardrails/) evaluates user inputs and model responses based on use case specific policies, and provides an additional layer of safeguards regardless of the underlying model. Guardrails can be applied across models, including Anthropic Claude, Meta Llama 2, Cohere Command, AI21 Labs Jurassic, and Amazon Titan Text, as well as fine-tuned models.\n",
@@ -139,7 +167,7 @@
     "            print(f\"Guardrails: {kwargs}\")\n",
     "\n",
     "\n",
-    "# guardrails for Amazon Bedrock with trace\n",
+    "# Guardrails for Amazon Bedrock with trace\n",
     "llm = Bedrock(\n",
     "    credentials_profile_name=\"bedrock-admin\",\n",
     "    model_id=\"<Model_ID>\",\n",
@@ -166,7 +194,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,

docs/docs/integrations/llms/bittensor.ipynb

@@ -92,7 +92,7 @@
     "Answer: Let's think step by step.\"\"\"\n",
     "\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
+    "prompt = PromptTemplate.from_template(template)\n",
     "\n",
     "# System parameter in NIBittensorLLM is optional but you can set whatever you want to perform with model\n",
     "llm = NIBittensorLLM(\n",

docs/docs/integrations/llms/cerebriumai.ipynb

@@ -101,7 +101,7 @@
     "\n",
     "Answer: Let's think step by step.\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {

docs/docs/integrations/llms/chatglm.ipynb

@@ -53,7 +53,7 @@
    "outputs": [],
    "source": [
     "template = \"\"\"{question}\"\"\"\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {
@@ -130,7 +130,7 @@
    "outputs": [],
    "source": [
     "template = \"\"\"{question}\"\"\"\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {

docs/docs/integrations/llms/clarifai.ipynb

@@ -114,7 +114,7 @@
     "\n",
     "Answer: Let's think step by step.\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {

docs/docs/integrations/llms/cloudflare_workersai.ipynb

@@ -26,7 +26,7 @@
     "\n",
     "AI Assistant: \"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {

docs/docs/integrations/llms/ctransformers.ipynb

@@ -109,7 +109,7 @@
     "\n",
     "Answer:\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
+    "prompt = PromptTemplate.from_template(template)\n",
     "\n",
     "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
     "\n",

docs/docs/integrations/llms/ctranslate2.ipynb

@@ -201,7 +201,7 @@
     "template = \"\"\"{question}\n",
     "\n",
     "Let's think step by step. \"\"\"\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
+    "prompt = PromptTemplate.from_template(template)\n",
     "\n",
     "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
     "\n",

docs/docs/integrations/llms/deepinfra.ipynb

@@ -146,7 +146,7 @@
     "\n",
     "Answer: Let's think step by step.\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {

docs/docs/integrations/llms/forefrontai.ipynb

@@ -97,7 +97,7 @@
     "\n",
     "Answer: Let's think step by step.\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {

docs/docs/integrations/llms/gigachat.ipynb

@@ -80,7 +80,7 @@
     "\n",
     "template = \"What is capital of {country}?\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"country\"])\n",
+    "prompt = PromptTemplate.from_template(template)\n",
     "\n",
     "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
     "\n",

docs/docs/integrations/llms/google_ai.ipynb

@@ -265,12 +265,40 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "id": "aefe6df7",
    "metadata": {},
+   "source": [
+    "### Safety Settings\n",
+    "\n",
+    "Gemini models have default safety settings that can be overridden. If you are receiving lots of \"Safety Warnings\" from your models, you can try tweaking the `safety_settings` attribute of the model. For example, to turn off safety blocking for dangerous content, you can construct your LLM as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7e2682e6",
+   "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from langchain_google_genai import GoogleGenerativeAI, HarmBlockThreshold, HarmCategory\n",
+    "\n",
+    "llm = GoogleGenerativeAI(\n",
+    "    model=\"gemini-pro\",\n",
+    "    google_api_key=api_key,\n",
+    "    safety_settings={\n",
+    "        HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,\n",
+    "    },\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8d0ee0b",
+   "metadata": {},
+   "source": [
+    "For an enumeration of the categories and thresholds available, see Google's [safety setting types](https://ai.google.dev/api/python/google/generativeai/types/SafetySettingDict)."
+   ]
   }
  ],
  "metadata": {

docs/docs/integrations/llms/gooseai.ipynb

@@ -111,7 +111,7 @@
     "\n",
     "Answer: Let's think step by step.\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {

docs/docs/integrations/llms/gpt4all.ipynb

@@ -73,7 +73,7 @@
     "\n",
     "Answer: Let's think step by step.\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {

docs/docs/integrations/llms/gradient.ipynb

@@ -175,7 +175,7 @@
     "\n",
     "Answer: \"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {

docs/docs/integrations/llms/huggingface_endpoint.ipynb

@@ -0,0 +1,238 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Huggingface Endpoints\n",
+    "\n",
+    ">The [Hugging Face Hub](https://huggingface.co/docs/hub/index) is a platform with over 120k models, 20k datasets, and 50k demo apps (Spaces), all open source and publicly available, in an online platform where people can easily collaborate and build ML together.\n",
+    "\n",
+    "The `Hugging Face Hub` also offers various endpoints to build ML applications.\n",
+    "This example showcases how to connect to the different Endpoints types.\n",
+    "\n",
+    "In particular, text generation inference is powered by [Text Generation Inference](https://github.com/huggingface/text-generation-inference): a custom-built Rust, Python and gRPC server for blazing-faset text generation inference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.llms import HuggingFaceEndpoint"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation and Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To use, you should have the ``huggingface_hub`` python [package installed](https://huggingface.co/docs/huggingface_hub/installation)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet huggingface_hub"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get a token: https://huggingface.co/docs/api-inference/quicktour#get-your-api-token\n",
+    "\n",
+    "from getpass import getpass\n",
+    "\n",
+    "HUGGINGFACEHUB_API_TOKEN = getpass()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = HUGGINGFACEHUB_API_TOKEN"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prepare Examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_community.llms import HuggingFaceEndpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import LLMChain\n",
+    "from langchain.prompts import PromptTemplate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "question = \"Who won the FIFA World Cup in the year 1994? \"\n",
+    "\n",
+    "template = \"\"\"Question: {question}\n",
+    "\n",
+    "Answer: Let's think step by step.\"\"\"\n",
+    "\n",
+    "prompt = PromptTemplate.from_template(template)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Examples\n",
+    "\n",
+    "Here is an example of how you can access `HuggingFaceEndpoint` integration of the free [Serverless Endpoints](https://huggingface.co/inference-endpoints/serverless) API."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "repo_id = \"mistralai/Mistral-7B-Instruct-v0.2\"\n",
+    "\n",
+    "llm = HuggingFaceEndpoint(\n",
+    "    repo_id=repo_id, max_length=128, temperature=0.5, token=HUGGINGFACEHUB_API_TOKEN\n",
+    ")\n",
+    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
+    "print(llm_chain.run(question))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dedicated Endpoint\n",
+    "\n",
+    "\n",
+    "The free serverless API lets you implement solutions and iterate in no time, but it may be rate limited for heavy use cases, since the loads are shared with other requests.\n",
+    "\n",
+    "For enterprise workloads, the best is to use [Inference Endpoints - Dedicated](https://huggingface.co/inference-endpoints/dedicated).\n",
+    "This gives access to a fully managed infrastructure that offer more flexibility and speed. These resoucres come with continuous support and uptime guarantees, as well as options like AutoScaling\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set the url to your Inference Endpoint below\n",
+    "your_endpoint_url = \"https://fayjubiy2xqn36z0.us-east-1.aws.endpoints.huggingface.cloud\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = HuggingFaceEndpoint(\n",
+    "    endpoint_url=f\"{your_endpoint_url}\",\n",
+    "    max_new_tokens=512,\n",
+    "    top_k=10,\n",
+    "    top_p=0.95,\n",
+    "    typical_p=0.95,\n",
+    "    temperature=0.01,\n",
+    "    repetition_penalty=1.03,\n",
+    ")\n",
+    "llm(\"What did foo say about bar?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Streaming"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "from langchain_community.llms import HuggingFaceEndpoint\n",
+    "\n",
+    "llm = HuggingFaceEndpoint(\n",
+    "    endpoint_url=f\"{your_endpoint_url}\",\n",
+    "    max_new_tokens=512,\n",
+    "    top_k=10,\n",
+    "    top_p=0.95,\n",
+    "    typical_p=0.95,\n",
+    "    temperature=0.01,\n",
+    "    repetition_penalty=1.03,\n",
+    "    streaming=True,\n",
+    ")\n",
+    "llm(\"What did foo say about bar?\", callbacks=[StreamingStdOutCallbackHandler()])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "agents",
+   "language": "python",
+   "name": "agents"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/docs/integrations/llms/huggingface_hub.ipynb

@@ -1,466 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "959300d4",
-   "metadata": {},
-   "source": [
-    "# Hugging Face Hub\n",
-    "\n",
-    ">The [Hugging Face Hub](https://huggingface.co/docs/hub/index) is a platform with over 120k models, 20k datasets, and 50k demo apps (Spaces), all open source and publicly available, in an online platform where people can easily collaborate and build ML together.\n",
-    "\n",
-    "This example showcases how to connect to the `Hugging Face Hub` and use different models."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1ddafc6d-7d7c-48fa-838f-0e7f50895ce3",
-   "metadata": {},
-   "source": [
-    "## Installation and Setup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4c1b8450-5eaf-4d34-8341-2d785448a1ff",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "To use, you should have the ``huggingface_hub`` python [package installed](https://huggingface.co/docs/huggingface_hub/installation)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d772b637-de00-4663-bd77-9bc96d798db2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "%pip install --upgrade --quiet  huggingface_hub"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "d597a792-354c-4ca5-b483-5965eec5d63d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      " ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "# get a token: https://huggingface.co/docs/api-inference/quicktour#get-your-api-token\n",
-    "\n",
-    "from getpass import getpass\n",
-    "\n",
-    "HUGGINGFACEHUB_API_TOKEN = getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "b8c5b88c-e4b8-4d0d-9a35-6e8f106452c2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = HUGGINGFACEHUB_API_TOKEN"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "84dd44c1-c428-41f3-a911-520281386c94",
-   "metadata": {},
-   "source": [
-    "## Prepare Examples"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3fe7d1d1-241d-426a-acff-e208f1088871",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.llms import HuggingFaceHub"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "6620f39b-3d32-4840-8931-ff7d2c3e47e8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chains import LLMChain\n",
-    "from langchain.prompts import PromptTemplate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "44adc1a0-9c0a-4f1e-af5a-fe04222e78d7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "question = \"Who won the FIFA World Cup in the year 1994? \"\n",
-    "\n",
-    "template = \"\"\"Question: {question}\n",
-    "\n",
-    "Answer: Let's think step by step.\"\"\"\n",
-    "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ddaa06cf-95ec-48ce-b0ab-d892a7909693",
-   "metadata": {},
-   "source": [
-    "## Examples\n",
-    "\n",
-    "Below are some examples of models you can access through the `Hugging Face Hub` integration."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4c16fded-70d1-42af-8bfa-6ddda9f0bc63",
-   "metadata": {},
-   "source": [
-    "### `Flan`, by `Google`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "39c7eeac-01c4-486b-9480-e828a9e73e78",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "repo_id = \"google/flan-t5-xxl\"  # See https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads for some other options"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "3acf0069",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The FIFA World Cup was held in the year 1994. West Germany won the FIFA World Cup in 1994\n"
-     ]
-    }
-   ],
-   "source": [
-    "llm = HuggingFaceHub(\n",
-    "    repo_id=repo_id, model_kwargs={\"temperature\": 0.5, \"max_length\": 64}\n",
-    ")\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "\n",
-    "print(llm_chain.run(question))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1a5c97af-89bc-4e59-95c1-223742a9160b",
-   "metadata": {},
-   "source": [
-    "### `Dolly`, by `Databricks`\n",
-    "\n",
-    "See [Databricks](https://huggingface.co/databricks) organization page for a list of available models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "521fcd2b-8e38-4920-b407-5c7d330411c9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo_id = \"databricks/dolly-v2-3b\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "9907ec3a-fe0c-4543-81c4-d42f9453f16c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " First of all, the world cup was won by the Germany. Then the Argentina won the world cup in 2022. So, the Argentina won the world cup in 1994.\n",
-      "\n",
-      "\n",
-      "Question: Who\n"
-     ]
-    }
-   ],
-   "source": [
-    "llm = HuggingFaceHub(\n",
-    "    repo_id=repo_id, model_kwargs={\"temperature\": 0.5, \"max_length\": 64}\n",
-    ")\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "print(llm_chain.run(question))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "03f6ae52-b5f9-4de6-832c-551cb3fa11ae",
-   "metadata": {},
-   "source": [
-    "### `Camel`, by `Writer`\n",
-    "\n",
-    "See [Writer's](https://huggingface.co/Writer) organization page for a list of available models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "257a091d-750b-4910-ac08-fe1c7b3fd98b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "repo_id = \"Writer/camel-5b-hf\"  # See https://huggingface.co/Writer for other options"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b06f6838-a11a-4d6a-88e3-91fa1747a2b3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = HuggingFaceHub(\n",
-    "    repo_id=repo_id, model_kwargs={\"temperature\": 0.5, \"max_length\": 64}\n",
-    ")\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "print(llm_chain.run(question))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2bf838eb-1083-402f-b099-b07c452418c8",
-   "metadata": {},
-   "source": [
-    "### `XGen`, by `Salesforce`\n",
-    "\n",
-    "See [more information](https://github.com/salesforce/xgen)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "18c78880-65d7-41d0-9722-18090efb60e9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo_id = \"Salesforce/xgen-7b-8k-base\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1b1150b4-ec30-4674-849e-6a41b085aa2b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = HuggingFaceHub(\n",
-    "    repo_id=repo_id, model_kwargs={\"temperature\": 0.5, \"max_length\": 64}\n",
-    ")\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "print(llm_chain.run(question))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0aca9f9e-f333-449c-97b2-10d1dbf17e75",
-   "metadata": {},
-   "source": [
-    "### `Falcon`, by `Technology Innovation Institute (TII)`\n",
-    "\n",
-    "See [more information](https://huggingface.co/tiiuae/falcon-40b)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "496b35ac-5ee2-4b68-a6ce-232608f56c03",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo_id = \"tiiuae/falcon-40b\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ff2541ad-e394-4179-93c2-7ae9c4ca2a25",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = HuggingFaceHub(\n",
-    "    repo_id=repo_id, model_kwargs={\"temperature\": 0.5, \"max_length\": 64}\n",
-    ")\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "print(llm_chain.run(question))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7e15849b-5561-4bb9-86ec-6412ca10196a",
-   "metadata": {},
-   "source": [
-    "### `InternLM-Chat`, by `Shanghai AI Laboratory`\n",
-    "\n",
-    "See [more information](https://huggingface.co/internlm/internlm-7b)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "3b533461-59f8-406e-907b-000841fa60a7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo_id = \"internlm/internlm-chat-7b\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c71210b9-5895-41a2-889a-f430d22fa1aa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = HuggingFaceHub(\n",
-    "    repo_id=repo_id, model_kwargs={\"max_length\": 128, \"temperature\": 0.8}\n",
-    ")\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "print(llm_chain.run(question))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4f2e5132-1713-42d7-919a-8c313744ce95",
-   "metadata": {},
-   "source": [
-    "### `Qwen`, by `Alibaba Cloud`\n",
-    "\n",
-    ">`Tongyi Qianwen-7B` (`Qwen-7B`) is a model with a scale of 7 billion parameters in the `Tongyi Qianwen` large model series developed by `Alibaba Cloud`. `Qwen-7B` is a large language model based on Transformer, which is trained on ultra-large-scale pre-training data.\n",
-    "\n",
-    "See [more information on HuggingFace](https://huggingface.co/Qwen/Qwen-7B) of on [GitHub](https://github.com/QwenLM/Qwen-7B).\n",
-    "\n",
-    "See here a [big example for LangChain integration and Qwen](https://github.com/QwenLM/Qwen-7B/blob/main/examples/langchain_tooluse.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "f598b1ca-77c7-40f1-a83f-c21ea9910c88",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo_id = \"Qwen/Qwen-7B\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2c97f4e2-d401-44fb-9da7-b60b2e2cc663",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = HuggingFaceHub(\n",
-    "    repo_id=repo_id, model_kwargs={\"max_length\": 128, \"temperature\": 0.5}\n",
-    ")\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "print(llm_chain.run(question))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e3871376-ed0e-49a8-8d9b-7e60dbbd2b35",
-   "metadata": {},
-   "source": [
-    "### `Yi` series models, by `01.ai`\n",
-    "\n",
-    ">The `Yi` series models are large language models trained from scratch by developers at [01.ai](https://01.ai/). The first public release contains two bilingual(English/Chinese) base models with the parameter sizes of 6B(`Yi-6B`) and 34B(`Yi-34B`). Both of them are trained with 4K sequence length and can be extended to 32K during inference time. The `Yi-6B-200K` and `Yi-34B-200K` are base model with 200K context length.\n",
-    "\n",
-    "Here we test the [Yi-34B](https://huggingface.co/01-ai/Yi-34B) model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "1c9d3125-3f50-48b8-93b6-b50847207afa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "repo_id = \"01-ai/Yi-34B\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8b661069-8229-4850-9f13-c4ca28c0c96b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = HuggingFaceHub(\n",
-    "    repo_id=repo_id, model_kwargs={\"max_length\": 128, \"temperature\": 0.5}\n",
-    ")\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "print(llm_chain.run(question))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dd6f3edc-9f97-47a6-ab2c-116756babbe6",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/llms/huggingface_textgen_inference.ipynb

@@ -1,108 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Huggingface TextGen Inference\n",
-    "\n",
-    "[Text Generation Inference](https://github.com/huggingface/text-generation-inference) is a Rust, Python and gRPC server for text generation inference. Used in production at [HuggingFace](https://huggingface.co/) to power LLMs api-inference widgets.\n",
-    "\n",
-    "This notebooks goes over how to use a self hosted LLM using `Text Generation Inference`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To use, you should have the `text_generation` python package installed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# !pip3 install text_generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_community.llms import HuggingFaceTextGenInference\n",
-    "\n",
-    "llm = HuggingFaceTextGenInference(\n",
-    "    inference_server_url=\"http://localhost:8010/\",\n",
-    "    max_new_tokens=512,\n",
-    "    top_k=10,\n",
-    "    top_p=0.95,\n",
-    "    typical_p=0.95,\n",
-    "    temperature=0.01,\n",
-    "    repetition_penalty=1.03,\n",
-    ")\n",
-    "llm(\"What did foo say about bar?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Streaming"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
-    "from langchain_community.llms import HuggingFaceTextGenInference\n",
-    "\n",
-    "llm = HuggingFaceTextGenInference(\n",
-    "    inference_server_url=\"http://localhost:8010/\",\n",
-    "    max_new_tokens=512,\n",
-    "    top_k=10,\n",
-    "    top_p=0.95,\n",
-    "    typical_p=0.95,\n",
-    "    temperature=0.01,\n",
-    "    repetition_penalty=1.03,\n",
-    "    streaming=True,\n",
-    ")\n",
-    "llm(\"What did foo say about bar?\", callbacks=[StreamingStdOutCallbackHandler()])"
-   ]
-  }
- ],
- "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"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/docs/integrations/llms/ibm_watsonx.ipynb

@@ -7,8 +7,9 @@
    "source": [
     "# IBM watsonx.ai\n",
     "\n",
-    "[WatsonxLLM](https://ibm.github.io/watsonx-ai-python-sdk/fm_extensions.html#langchain) is a wrapper for IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) foundation models.\n",
-    "This example shows how to communicate with watsonx.ai models using LangChain."
+    ">[WatsonxLLM](https://ibm.github.io/watsonx-ai-python-sdk/fm_extensions.html#langchain) is a wrapper for IBM [watsonx.ai](https://www.ibm.com/products/watsonx-ai) foundation models.\n",
+    "\n",
+    "This example shows how to communicate with `watsonx.ai` models using `LangChain`."
    ]
   },
   {
@@ -16,6 +17,8 @@
    "id": "ea35b2b7",
    "metadata": {},
    "source": [
+    "## Setting up\n",
+    "\n",
     "Install the package [`ibm-watsonx-ai`](https://ibm.github.io/watsonx-ai-python-sdk/install.html)."
    ]
   },
@@ -60,6 +63,7 @@
    "metadata": {},
    "source": [
     "## Load the model\n",
+    "\n",
     "You might need to adjust model `parameters` for different models or tasks. For details, refer to [documentation](https://ibm.github.io/watsonx-ai-python-sdk/fm_model.html#metanames.GenTextParamsMetaNames)."
    ]
   },
@@ -105,7 +109,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_community.llms import WatsonxLLM\n",
+    "from langchain_ibm import WatsonxLLM\n",
     "\n",
     "watsonx_llm = WatsonxLLM(\n",
     "    model_id=\"google/flan-ul2\",\n",
@@ -328,7 +332,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/llms/konko.ipynb

@@ -1,20 +1,27 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Konko\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "136d9ba6-c42a-435b-9e19-77ebcc7a3145",
    "metadata": {},
    "source": [
-    "# ChatKonko\n",
+    "# Konko\n",
     "\n",
     ">[Konko](https://www.konko.ai/) API is a fully managed Web API designed to help application developers:\n",
     "\n",
-    "Konko API is a fully managed API designed to help application developers:\n",
-    "\n",
-    "1. Select the right LLM(s) for their application\n",
-    "2. Prototype with various open-source and proprietary LLMs\n",
-    "3. Access Fine Tuning for open-source LLMs to get industry-leading performance at a fraction of the cost\n",
-    "4. Setup low-cost production APIs according to security, privacy, throughput, latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant, multi-cloud infrastructure\n"
+    "1. **Select** the right open source or proprietary LLMs for their application\n",
+    "2. **Build** applications faster with integrations to leading application frameworks and fully managed APIs\n",
+    "3. **Fine tune** smaller open-source LLMs to achieve industry-leading performance at a fraction of the cost\n",
+    "4. **Deploy production-scale APIs** that meet security, privacy, throughput, and latency SLAs without infrastructure set-up or administration using Konko AI's SOC 2 compliant, multi-cloud infrastructure\n"
    ]
   },
   {
@@ -22,25 +29,44 @@
    "id": "0d896d07-82b4-4f38-8c37-f0bc8b0e4fe1",
    "metadata": {},
    "source": [
-    "### Steps to Access Models\n",
-    "1. **Explore Available Models:** Start by browsing through the [available models](https://docs.konko.ai/docs/list-of-models) on Konko. Each model caters to different use cases and capabilities.\n",
-    "\n",
-    "2. **Identify Suitable Endpoints:** Determine which [endpoint](https://docs.konko.ai/docs/list-of-models#list-of-available-models) (ChatCompletion or Completion) supports your selected model.\n",
-    "\n",
-    "3. **Selecting a Model:** [Choose a model](https://docs.konko.ai/docs/list-of-models#list-of-available-models) based on its metadata and how well it fits your use case.\n",
-    "\n",
-    "4. **Prompting Guidelines:** Once a model is selected, refer to the [prompting guidelines](https://docs.konko.ai/docs/prompting) to effectively communicate with it.\n",
-    "\n",
-    "5. **Using the API:** Finally, use the appropriate Konko [API endpoint](https://docs.konko.ai/docs/quickstart-for-completion-and-chat-completion-endpoint) to call the model and receive responses.\n",
-    "\n",
     "This example goes over how to use LangChain to interact with `Konko` completion [models](https://docs.konko.ai/docs/list-of-models#konko-hosted-models-for-completion)\n",
     "\n",
-    "To run this notebook, you'll need Konko API key. You can create one by signing up on [Konko](https://www.konko.ai/)."
+    "To run this notebook, you'll need Konko API key. Sign in to our web app to [create an API key](https://platform.konko.ai/settings/api-keys) to access models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Set Environment Variables\n",
+    "\n",
+    "1. You can set environment variables for \n",
+    "   1. KONKO_API_KEY (Required)\n",
+    "   2. OPENAI_API_KEY (Optional)\n",
+    "2. In your current shell session, use the export command:\n",
+    "\n",
+    "```shell\n",
+    "export KONKO_API_KEY={your_KONKO_API_KEY_here}\n",
+    "export OPENAI_API_KEY={your_OPENAI_API_KEY_here} #Optional\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calling a model\n",
+    "\n",
+    "Find a model on the [Konko overview page](https://docs.konko.ai/docs/list-of-models)\n",
+    "\n",
+    "Another way to find the list of models running on the Konko instance is through this [endpoint](https://docs.konko.ai/reference/get-models).\n",
+    "\n",
+    "From here, we can initialize our model:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "dd70bccb-7a65-42d0-a3f2-8116f3549da7",
    "metadata": {},
    "outputs": [

docs/docs/integrations/llms/llamacpp.ipynb

@@ -234,7 +234,7 @@
     "\n",
     "Answer: Let's work this out in a step by step way to be sure we have the right answer.\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {

docs/docs/integrations/llms/llamafile.ipynb

@@ -0,0 +1,133 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Llamafile\n",
+    "\n",
+    "[Llamafile](https://github.com/Mozilla-Ocho/llamafile) lets you distribute and run LLMs with a single file.\n",
+    "\n",
+    "Llamafile does this by combining [llama.cpp](https://github.com/ggerganov/llama.cpp) with [Cosmopolitan Libc](https://github.com/jart/cosmopolitan) into one framework that collapses all the complexity of LLMs down to a single-file executable (called a \"llamafile\") that runs locally on most computers, with no installation.\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "1. Download a llamafile for the model you'd like to use. You can find many models in llamafile format on [HuggingFace](https://huggingface.co/models?other=llamafile). In this guide, we will download a small one, `TinyLlama-1.1B-Chat-v1.0.Q5_K_M`. Note: if you don't have `wget`, you can just download the model via this [link](https://huggingface.co/jartine/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile?download=true).\n",
+    "\n",
+    "```bash\n",
+    "wget https://huggingface.co/jartine/TinyLlama-1.1B-Chat-v1.0-GGUF/resolve/main/TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile\n",
+    "```\n",
+    "\n",
+    "2. Make the llamafile executable. First, if you haven't done so already, open a terminal. **If you're using MacOS, Linux, or BSD,** you'll need to grant permission for your computer to execute this new file using `chmod` (see below). **If you're on Windows,** rename the file by adding \".exe\" to the end (model file should be named `TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile.exe`).\n",
+    "\n",
+    "\n",
+    "```bash\n",
+    "chmod +x TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile  # run if you're on MacOS, Linux, or BSD\n",
+    "```\n",
+    "\n",
+    "3. Run the llamafile in \"server mode\":\n",
+    "\n",
+    "```bash\n",
+    "./TinyLlama-1.1B-Chat-v1.0.Q5_K_M.llamafile --server --nobrowser\n",
+    "```\n",
+    "\n",
+    "Now you can make calls to the llamafile's REST API. By default, the llamafile server listens at http://localhost:8080. You can find full server documentation [here](https://github.com/Mozilla-Ocho/llamafile/blob/main/llama.cpp/server/README.md#api-endpoints). You can interact with the llamafile directly via the REST API, but here we'll show how to interact with it using LangChain.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Usage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'? \\nI\\'ve got a thing for pink, but you know that.\\n\"Can we not talk about work anymore?\" - What did she say?\\nI don\\'t want to be a burden on you.\\nIt\\'s hard to keep a good thing going.\\nYou can\\'t tell me what I want, I have a life too!'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_community.llms.llamafile import Llamafile\n",
+    "\n",
+    "llm = Llamafile()\n",
+    "\n",
+    "llm.invoke(\"Tell me a joke\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To stream tokens, use the `.stream(...)` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ".\n",
+      "- She said, \"I’m tired of my life. What should I do?\"\n",
+      "- The man replied, \"I hear you. But don’t worry. Life is just like a joke. It has its funny parts too.\"\n",
+      "- The woman looked at him, amazed and happy to hear his wise words. - \"Thank you for your wisdom,\" she said, smiling. - He replied, \"Any time. But it doesn't come easy. You have to laugh and keep moving forward in life.\"\n",
+      "- She nodded, thanking him again. - The man smiled wryly. \"Life can be tough. Sometimes it seems like you’re never going to get out of your situation.\"\n",
+      "- He said, \"I know that. But the key is not giving up. Life has many ups and downs, but in the end, it will turn out okay.\"\n",
+      "- The woman's eyes softened. \"Thank you for your advice. It's so important to keep moving forward in life,\" she said. - He nodded once again. \"You’re welcome. I hope your journey is filled with laughter and joy.\"\n",
+      "- They both smiled and left the bar, ready to embark on their respective adventures.\n"
+     ]
+    }
+   ],
+   "source": [
+    "query = \"Tell me a joke\"\n",
+    "\n",
+    "for chunks in llm.stream(query):\n",
+    "    print(chunks, end=\"\")\n",
+    "\n",
+    "print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To learn more about the LangChain Expressive Language and the available methods on an LLM, see the [LCEL Interface](https://python.langchain.com/docs/expression_language/interface)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/docs/integrations/llms/llm_caching.ipynb

@@ -1131,6 +1131,16 @@
     "print(llm(\"How come we always see one face of the moon?\"))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "55dc84b3-37cb-4f19-b175-40e18e06f83f",
+   "metadata": {},
+   "source": [
+    "#### Attribution statement\n",
+    "\n",
+    ">Apache Cassandra, Cassandra and Apache are either registered trademarks or trademarks of the [Apache Software Foundation](http://www.apache.org/) in the United States and/or other countries."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "8712f8fc-bb89-4164-beb9-c672778bbd91",
@@ -1588,7 +1598,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.9.17"
   }
  },
  "nbformat": 4,

docs/docs/integrations/llms/manifest.ipynb

@@ -91,7 +91,7 @@
     "\n",
     "\n",
     "CONCISE SUMMARY:\"\"\"\n",
-    "prompt = PromptTemplate(template=_prompt, input_variables=[\"text\"])\n",
+    "prompt = PromptTemplate.from_template(_prompt)\n",
     "\n",
     "text_splitter = CharacterTextSplitter()\n",
     "\n",

docs/docs/integrations/llms/minimax.ipynb

@@ -113,7 +113,7 @@
     "\n",
     "Answer: Let's think step by step.\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {

docs/docs/integrations/llms/modal.ipynb

@@ -122,7 +122,7 @@
     "\n",
     "Answer: Let's think step by step.\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {

docs/docs/integrations/llms/mosaicml.ipynb

@@ -55,7 +55,7 @@
    "source": [
     "template = \"\"\"Question: {question}\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {

docs/docs/integrations/llms/nlpcloud.ipynb

@@ -90,7 +90,7 @@
     "\n",
     "Answer: Let's think step by step.\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {

docs/docs/integrations/llms/oci_generative_ai.ipynb

@@ -98,6 +98,9 @@
     "    model_id=\"MY_MODEL\",\n",
     "    service_endpoint=\"https://inference.generativeai.us-chicago-1.oci.oraclecloud.com\",\n",
     "    compartment_id=\"MY_OCID\",\n",
+    "    auth_type=\"SECURITY_TOKEN\",\n",
+    "    auth_profile=\"MY_PROFILE\",  # replace with your profile name\n",
+    "    model_kwargs={\"temperature\": 0.7, \"top_p\": 0.75, \"max_tokens\": 200},\n",
     ")\n",
     "\n",
     "prompt = PromptTemplate(input_variables=[\"query\"], template=\"{query}\")\n",
@@ -158,13 +161,6 @@
     "print(chain.invoke(\"when was oracle founded?\"))\n",
     "print(chain.invoke(\"where is oracle headquartered?\"))"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

docs/docs/integrations/llms/octoai.ipynb

@@ -61,7 +61,7 @@
    "outputs": [],
    "source": [
     "template = \"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n Instruction:\\n{question}\\n Response: \"\"\"\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {

docs/docs/integrations/llms/ollama.ipynb

@@ -244,7 +244,7 @@
     "\n",
     "def plt_img_base64(img_base64):\n",
     "    \"\"\"\n",
-    "    Disply base64 encoded string as image\n",
+    "    Display base64 encoded string as image\n",
     "\n",
     "    :param img_base64:  Base64 string\n",
     "    \"\"\"\n",

docs/docs/integrations/llms/openai.ipynb

@@ -84,7 +84,7 @@
     "\n",
     "Answer: Let's think step by step.\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {

docs/docs/integrations/llms/openllm.ipynb

@@ -119,7 +119,7 @@
     "\n",
     "template = \"What is a good name for a company that makes {product}?\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"product\"])\n",
+    "prompt = PromptTemplate.from_template(template)\n",
     "\n",
     "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
     "\n",

docs/docs/integrations/llms/openlm.ipynb

@@ -97,7 +97,7 @@
     "\n",
     "Answer: Let's think step by step.\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
+    "prompt = PromptTemplate.from_template(template)\n",
     "\n",
     "for model in [\"text-davinci-003\", \"huggingface.co/gpt2\"]:\n",
     "    llm = OpenLM(model=model)\n",

docs/docs/integrations/llms/petals.ipynb

@@ -133,7 +133,7 @@
     "\n",
     "Answer: Let's think step by step.\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {

docs/docs/integrations/llms/pipelineai.ipynb

@@ -107,7 +107,7 @@
     "\n",
     "Answer: Let's think step by step.\"\"\"\n",
     "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {

docs/docs/integrations/llms/predictionguard.ipynb

@@ -118,7 +118,7 @@
     "Query: {query}\n",
     "\n",
     "Result: \"\"\"\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"query\"])"
+    "prompt = PromptTemplate.from_template(template)"
    ]
   },
   {
@@ -191,7 +191,7 @@
     "template = \"\"\"Question: {question}\n",
     "\n",
     "Answer: Let's think step by step.\"\"\"\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
+    "prompt = PromptTemplate.from_template(template)\n",
     "llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True)\n",
     "\n",
     "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
@@ -209,7 +209,7 @@
    "outputs": [],
    "source": [
     "template = \"\"\"Write a {adjective} poem about {subject}.\"\"\"\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"adjective\", \"subject\"])\n",
+    "prompt = PromptTemplate.from_template(template)\n",
     "llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True)\n",
     "\n",
     "llm_chain.predict(adjective=\"sad\", subject=\"ducks\")"