undo

---------

Co-authored-by: Rihards Gravis <rihards@gravis.lv>
Co-authored-by: Mikko Korpela <mikko@robocorp.com>

.github/CONTRIBUTING.md

@@ -3,31 +3,17 @@
 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
 
-### 👩‍💻 Contributing Code
+### 👩‍💻 Ways to contribute
 
-To contribute to this project, please follow the ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow.
-Please do not try to push directly to this repo unless you are a maintainer.
+There are many ways to contribute to LangChain. Here are some common ways people contribute:
 
-Please follow the checked-in pull request template when opening pull requests. Note related issues and tag relevant
-maintainers.
-
-Pull requests cannot land without passing the formatting, linting, and testing checks first. See [Testing](#testing) and
-[Formatting and Linting](#formatting-and-linting) for how to run these checks locally.
-
-It's essential that we maintain great documentation and testing. If you:
-- Fix a bug
-  - Add a relevant unit or integration test when possible. These live in `tests/unit_tests` and `tests/integration_tests`.
-- Make an improvement
-  - Update any affected example notebooks and documentation. These live in `docs`.
-  - Update unit and integration tests when relevant.
-- Add a feature
-  - Add a demo notebook in `docs/docs/`.
-  - Add unit and integration tests.
-
-We are a small, progress-oriented team. If there's something you'd like to add or change, opening a pull request is the
-best way to get our attention.
+- [**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/integration): Help us integrate with your favorite vendors and tools.
 
 ### 🚩GitHub Issues
 
@@ -54,291 +40,6 @@ In a similar vein, we do enforce certain linting, formatting, and documentation
 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.
 
-## 🚀 Quick Start
+### Contributor Documentation
 
-This quick start guide explains how to run the repository locally.
-For a [development container](https://containers.dev/), see the [.devcontainer folder](https://github.com/langchain-ai/langchain/tree/master/.devcontainer).
-
-### Dependency Management: Poetry and other env/dependency managers
-
-This project utilizes [Poetry](https://python-poetry.org/) v1.6.1+ as a dependency manager.
-
-❗Note: *Before installing Poetry*, if you use `Conda`, create and activate a new Conda env (e.g. `conda create -n langchain python=3.9`)
-
-Install Poetry: **[documentation on how to install it](https://python-poetry.org/docs/#installation)**.
-
-❗Note: If you use `Conda` or `Pyenv` as your environment/package manager, after installing Poetry,
-tell Poetry to use the virtualenv python environment (`poetry config virtualenvs.prefer-active-python true`)
-
-### Core vs. Experimental
-
-This repository contains three separate projects:
-- `langchain`: core langchain code, abstractions, and use cases.
-- `langchain_core`: contain interfaces for key abstractions as well as logic for combining them in chains (LCEL).
-- `langchain_experimental`: see the [Experimental README](https://github.com/langchain-ai/langchain/tree/master/libs/experimental/README.md) for more information.
-
-Each of these has its own development environment. Docs are run from the top-level makefile, but development
-is split across separate test & release flows.
-
-For this quickstart, start with langchain core:
-
-```bash
-cd libs/langchain
-```
-
-### Local Development Dependencies
-
-Install langchain development requirements (for running langchain, running examples, linting, formatting, tests, and coverage):
-
-```bash
-poetry install --with test
-```
-
-Then verify dependency installation:
-
-```bash
-make test
-```
-
-If the tests don't pass, you may need to pip install additional dependencies, such as `numexpr` and `openapi_schema_pydantic`.
-
-If during installation you receive a `WheelFileValidationError` for `debugpy`, please make sure you are running
-Poetry v1.6.1+. This bug was present in older versions of Poetry (e.g. 1.4.1) and has been resolved in newer releases.
-If you are still seeing this bug on v1.6.1, you may also try disabling "modern installation"
-(`poetry config installer.modern-installation false`) and re-installing requirements.
-See [this `debugpy` issue](https://github.com/microsoft/debugpy/issues/1246) for more details.
-
-### Testing
-
-_some test dependencies are optional; see section about optional dependencies_.
-
-Unit tests cover modular logic that does not require calls to outside APIs.
-If you add new logic, please add a unit test.
-
-To run unit tests:
-
-```bash
-make test
-```
-
-To run unit tests in Docker:
-
-```bash
-make docker_tests
-```
-
-There are also [integration tests and code-coverage](https://github.com/langchain-ai/langchain/tree/master/libs/langchain/tests/README.md) available.
-
-### Only develop langchain_core or langchain_experimental
-
-If you are only developing `langchain_core` or `langchain_experimental`, you can simply install the dependencies for the respective projects and run tests:
-
-```bash
-cd libs/core
-poetry install --with test
-make test
-```
-
-Or:
-
-```bash
-cd libs/experimental
-poetry install --with test
-make test
-```
-
-### Formatting and Linting
-
-Run these locally before submitting a PR; the CI system will check also.
-
-#### Code Formatting
-
-Formatting for this project is done via [ruff](https://docs.astral.sh/ruff/rules/).
-
-To run formatting for docs, cookbook and templates:
-
-```bash
-make format
-```
-
-To run formatting for a library, run the same command from the relevant library directory:
-
-```bash
-cd libs/{LIBRARY}
-make format
-```
-
-Additionally, you can run the formatter only on the files that have been modified in your current branch as compared to the master branch using the format_diff command:
-
-```bash
-make format_diff
-```
-
-This is especially useful when you have made changes to a subset of the project and want to ensure your changes are properly formatted without affecting the rest of the codebase.
-
-#### Linting
-
-Linting for this project is done via a combination of [ruff](https://docs.astral.sh/ruff/rules/) and [mypy](http://mypy-lang.org/).
-
-To run linting for docs, cookbook and templates:
-
-```bash
-make lint
-```
-
-To run linting for a library, run the same command from the relevant library directory:
-
-```bash
-cd libs/{LIBRARY}
-make lint
-```
-
-In addition, you can run the linter only on the files that have been modified in your current branch as compared to the master branch using the lint_diff command:
-
-```bash
-make lint_diff
-```
-
-This can be very helpful when you've made changes to only certain parts of the project and want to ensure your changes meet the linting standards without having to check the entire codebase.
-
-We recognize linting can be annoying - if you do not want to do it, please contact a project maintainer, and they can help you with it. We do not want this to be a blocker for good code getting contributed.
-
-#### Spellcheck
-
-Spellchecking for this project is done via [codespell](https://github.com/codespell-project/codespell).
-Note that `codespell` finds common typos, so it could have false-positive (correctly spelled but rarely used) and false-negatives (not finding misspelled) words.
-
-To check spelling for this project:
-
-```bash
-make spell_check
-```
-
-To fix spelling in place:
-
-```bash
-make spell_fix
-```
-
-If codespell is incorrectly flagging a word, you can skip spellcheck for that word by adding it to the codespell config in the `pyproject.toml` file.
-
-```python
-[tool.codespell]
-...
-# Add here:
-ignore-words-list = 'momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogyny,unsecure'
-```
-
-## Working with Optional Dependencies
-
-Langchain relies heavily on optional dependencies to keep the Langchain package lightweight.
-
-You only need to add a new dependency if a **unit test** relies on the package.
-If your package is only required for **integration tests**, then you can skip these
-steps and leave all pyproject.toml and poetry.lock files alone.
-
-If you're adding a new dependency to Langchain, assume that it will be an optional dependency, and
-that most users won't have it installed.
-
-Users who do not have the dependency installed should be able to **import** your code without
-any side effects (no warnings, no errors, no exceptions).
-
-To introduce the dependency to the pyproject.toml file correctly, please do the following:
-
-1. Add the dependency to the main group as an optional dependency
-  ```bash
-  poetry add --optional [package_name]
-  ```
-2. Open pyproject.toml and add the dependency to the `extended_testing` extra
-3. Relock the poetry file to update the extra.
-  ```bash
-  poetry lock --no-update
-  ```
-4. Add a unit test that the very least attempts to import the new code. Ideally, the unit
-test makes use of lightweight fixtures to test the logic of the code.
-5. Please use the `@pytest.mark.requires(package_name)` decorator for any tests that require the dependency.
-
-## Adding a Jupyter Notebook
-
-If you are adding a Jupyter Notebook example, you'll want to install the optional `dev` dependencies.
-
-To install dev dependencies:
-
-```bash
-poetry install --with dev
-```
-
-Launch a notebook:
-
-```bash
-poetry run jupyter notebook
-```
-
-When you run `poetry install`, the `langchain` package is installed as editable in the virtualenv, so your new logic can be imported into the notebook.
-
-## Documentation
-
-While the code is split between `langchain` and `langchain.experimental`, the documentation is one holistic thing.
-This covers how to get started contributing to documentation.
-
-From the top-level of this repo, install documentation dependencies:
-
-```bash
-poetry install
-```
-
-### Contribute Documentation
-
-The docs directory contains Documentation and API Reference.
-
-Documentation is built using [Docusaurus 2](https://docusaurus.io/).
-
-API Reference are largely autogenerated by [sphinx](https://www.sphinx-doc.org/en/master/) from the code.
-For that reason, we ask that you add good documentation to all classes and methods.
-
-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.
-
-### Build Documentation Locally
-
-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:
-
-```bash
-make docs_clean
-make api_docs_clean
-```
-
-Next, you can build the documentation as outlined below:
-
-```bash
-make docs_build
-make api_docs_build
-```
-
-Finally, run the link checker to ensure all links are valid:
-
-```bash
-make docs_linkcheck
-make api_docs_linkcheck
-```
-
-### 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.
-This will take you to a preview of the documentation changes.
-This preview is created by [Vercel](https://vercel.com/docs/getting-started-with-vercel).
-
-## 🏭 Release Process
-
-As of now, LangChain has an ad hoc release process: releases are cut with high frequency by
-a developer and published to [PyPI](https://pypi.org/project/langchain/).
-
-LangChain follows the [semver](https://semver.org/) versioning standard. However, as pre-1.0 software,
-even patch releases may contain [non-backwards-compatible changes](https://semver.org/#spec-item-4).
-
-### 🌟 Recognition
-
-If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)!
-If you have a Twitter account you would like us to mention, please let us know in the PR or through another means.
+To learn about how to contribute, please follow the [guides here](https://python.langchain.com/docs/contributing/)

.github/ISSUE_TEMPLATE/config.yml

@@ -1,6 +1,9 @@
 blank_issues_enabled: true
 version: 2.1
 contact_links:
+  - name: 🤔 Question or Problem
+    about: Ask a question or ask about a problem in GitHub Discussions.
+    url: https://github.com/langchain-ai/langchain/discussions
   - name: Discord
     url: https://discord.gg/6adMQxSpJS
     about: General community discussions

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -27,4 +27,4 @@ body:
     attributes:
       label: Your contribution
       description: |
-        Is there any way that you could help, e.g. by submitting a PR? Make sure to read the CONTRIBUTING.MD [readme](https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md)
+        Is there any way that you could help, e.g. by submitting a PR? Make sure to read the [Contributing Guide](https://python.langchain.com/docs/contributing/)

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,20 +1,20 @@
 <!-- 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.
+
 Replace this entire comment with:
   - **Description:** a description of the change, 
-  - **Issue:** the issue # it fixes (if applicable),
+  - **Issue:** the issue # it fixes if applicable,
   - **Dependencies:** any dependencies required for this change,
-  - **Tag maintainer:** for a quicker response, tag the relevant maintainer (see below),
   - **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` to check this locally.
+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.
 
-See contribution guidelines for more information on how to write/run tests, lint, etc: 
-https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
+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:
   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/extras` directory.
+  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.
  -->

.github/actions/poetry_setup/action.yml

@@ -26,7 +26,7 @@ inputs:
 runs:
   using: composite
   steps:
-    - uses: actions/setup-python@v4
+    - uses: actions/setup-python@v5
       name: Setup python ${{ inputs.python-version }}
       with:
         python-version: ${{ inputs.python-version }}

.github/scripts/check_diff.py

@@ -1,16 +1,22 @@
 import json
 import sys
+import os
 
-ALL_DIRS = {
+LANGCHAIN_DIRS = {
     "libs/core",
     "libs/langchain",
     "libs/experimental",
+    "libs/community",
 }
 
 if __name__ == "__main__":
     files = sys.argv[1:]
     dirs_to_run = set()
 
+    if len(files) == 300:
+        # max diff length is 300 files - there are likely files missing
+        raise ValueError("Max diff reached. Please manually run CI on changed libs.")
+
     for file in files:
         if any(
             file.startswith(dir_)
@@ -22,24 +28,29 @@ if __name__ == "__main__":
                 ".github/scripts/check_diff.py",
             )
         ):
-            dirs_to_run = ALL_DIRS
-            break
+            dirs_to_run.update(LANGCHAIN_DIRS)
         elif "libs/community" in file:
             dirs_to_run.update(
                 ("libs/community", "libs/langchain", "libs/experimental")
             )
         elif "libs/partners" in file:
             partner_dir = file.split("/")[2]
-            dirs_to_run.update(
-                (f"libs/partners/{partner_dir}", "libs/langchain", "libs/experimental")
-            )
+            if os.path.isdir(f"libs/partners/{partner_dir}"):
+                dirs_to_run.update(
+                    (
+                        f"libs/partners/{partner_dir}",
+                        "libs/langchain",
+                        "libs/experimental",
+                    )
+                )
+            # Skip if the directory was deleted
         elif "libs/langchain" in file:
             dirs_to_run.update(("libs/langchain", "libs/experimental"))
         elif "libs/experimental" in file:
             dirs_to_run.add("libs/experimental")
         elif file.startswith("libs/"):
-            dirs_to_run = ALL_DIRS
-            break
+            dirs_to_run.update(LANGCHAIN_DIRS)
         else:
             pass
-    print(json.dumps(list(dirs_to_run)))
+    json_output = json.dumps(list(dirs_to_run))
+    print(f"dirs-to-run={json_output}")

.github/workflows/_all_ci.yml

@@ -18,6 +18,7 @@ on:
         - libs/langchain
         - libs/core
         - libs/experimental
+        - libs/community
 
 
 # If another push to the same PR or branch happens while this workflow is still running,
@@ -52,8 +53,8 @@ jobs:
       working-directory: ${{ inputs.working-directory }}
     secrets: inherit
 
-  pydantic-compatibility:
-    uses: ./.github/workflows/_pydantic_compatibility.yml
+  dependencies:
+    uses: ./.github/workflows/_dependencies.yml
     with:
       working-directory: ${{ inputs.working-directory }}
     secrets: inherit
@@ -71,6 +72,7 @@ jobs:
     defaults:
       run:
         working-directory: ${{ inputs.working-directory }}
+    if: ${{ ! startsWith(inputs.working-directory, 'libs/partners/') }}
     steps:
       - uses: actions/checkout@v4
 

.github/workflows/_dependencies.yml

@@ -1,4 +1,4 @@
-name: pydantic v1/v2 compatibility
+name: dependencies
 
 on:
   workflow_call:
@@ -28,7 +28,7 @@ jobs:
           - "3.9"
           - "3.10"
           - "3.11"
-    name: Pydantic v1/v2 compatibility - Python ${{ matrix.python-version }}
+    name: dependencies - Python ${{ matrix.python-version }}
     steps:
       - uses: actions/checkout@v4
 
@@ -42,7 +42,15 @@ jobs:
 
       - name: Install dependencies
         shell: bash
-        run: poetry install  --with test
+        run: poetry install
+
+      - name: Check imports with base dependencies
+        shell: bash
+        run: poetry run make check_imports
+
+      - name: Install test dependencies
+        shell: bash
+        run: poetry install --with test
 
       - name: Install langchain editable
         working-directory: ${{ inputs.working-directory }}

.github/workflows/_integration_test.yml

@@ -0,0 +1,67 @@
+name: Integration tests
+
+on:
+  workflow_dispatch:
+    inputs:
+      working-directory:
+        required: true
+        type: string
+
+env:
+  POETRY_VERSION: "1.6.1"
+
+jobs:
+  build:
+    defaults:
+      run:
+        working-directory: ${{ inputs.working-directory }}
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version:
+          - "3.8"
+          - "3.11"
+    name: Python ${{ matrix.python-version }}
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: ${{ matrix.python-version }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
+          cache-key: core
+
+      - name: Install dependencies
+        shell: bash
+        run: poetry install --with test,test_integration
+
+      - name: 'Authenticate to Google Cloud'
+        id: 'auth'
+        uses: google-github-actions/auth@v2
+        with:
+          credentials_json: '${{ secrets.GOOGLE_CREDENTIALS }}'
+
+      - name: Run integration tests
+        shell: bash
+        env:
+          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: ${{ secrets.OPENAI_API_KEY }}
+        run: |
+          make integration_tests
+
+      - name: Ensure the tests did not create any additional files
+        shell: bash
+        run: |
+          set -eu
+
+          STATUS="$(git status)"
+          echo "$STATUS"
+
+          # grep will exit non-zero if the target message isn't found,
+          # and `set -e` above will cause the step to fail.
+          echo "$STATUS" | grep 'nothing to commit, working tree clean'

.github/workflows/_lint.yml

@@ -85,7 +85,8 @@ jobs:
         with:
           path: |
             ${{ env.WORKDIR }}/.mypy_cache
-          key: mypy-${{ 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', env.WORKDIR)) }}
+
 
       - name: Analysing the code with our lint
         working-directory: ${{ inputs.working-directory }}
@@ -105,13 +106,13 @@ jobs:
         run: |
           poetry install --with test
 
-      - name: Get .mypy_cache to speed up mypy
+      - name: Get .mypy_cache_test to speed up mypy
         uses: actions/cache@v3
         env:
           SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
         with:
           path: |
-            ${{ env.WORKDIR }}/.mypy_cache
+            ${{ env.WORKDIR }}/.mypy_cache_test
           key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', env.WORKDIR)) }}
 
       - name: Analysing the code with our lint

.github/workflows/_release.yml

@@ -1,5 +1,5 @@
 name: release
-
+run-name: Release ${{ inputs.working-directory }} by @${{ github.actor }}
 on:
   workflow_call:
     inputs:
@@ -7,6 +7,12 @@ on:
         required: true
         type: string
         description: "From which folder this pipeline executes"
+  workflow_dispatch:
+    inputs:
+      working-directory:
+        required: true
+        type: string
+        default: 'libs/langchain'
 
 env:
   PYTHON_VERSION: "3.10"
@@ -76,6 +82,8 @@ jobs:
       - test-pypi-publish
     runs-on: ubuntu-latest
     steps:
+      - uses: actions/checkout@v4
+
       # We explicitly *don't* set up caching here. This ensures our tests are
       # maximally sensitive to catching breakage.
       #
@@ -88,12 +96,17 @@ jobs:
       # - Tests pass, because the dependency is present even though it wasn't specified.
       # - The package is published, and it breaks on the missing dependency when
       #   used in the real world.
-      - uses: actions/setup-python@v4
+
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
         with:
           python-version: ${{ env.PYTHON_VERSION }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
 
-      - name: Test published package
+      - name: Import published package
         shell: bash
+        working-directory: ${{ inputs.working-directory }}
         env:
           PKG_NAME: ${{ needs.build.outputs.pkg-name }}
           VERSION: ${{ needs.build.outputs.version }}
@@ -105,9 +118,8 @@ jobs:
         #   (https://test.pypi.org/simple). This will include the PKG_NAME==VERSION
         #   package because VERSION will not have been uploaded to regular PyPI yet.
         #
-        # TODO: add more in-depth pre-publish tests after testing that importing works
         run: |
-          pip install \
+          poetry run pip install \
             --extra-index-url https://test.pypi.org/simple/ \
             "$PKG_NAME==$VERSION"
 
@@ -115,7 +127,51 @@ jobs:
           # since that's how Python imports packages with dashes in the name.
           IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/_/g)"
 
-          python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
+          poetry run python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
+
+      - name: Import test dependencies
+        run: poetry install --with test,test_integration
+        working-directory: ${{ inputs.working-directory }}
+
+      # Overwrite the local version of the package with the test PyPI version.
+      - name: Import published package (again)
+        working-directory: ${{ inputs.working-directory }}
+        shell: bash
+        env:
+          PKG_NAME: ${{ needs.build.outputs.pkg-name }}
+          VERSION: ${{ needs.build.outputs.version }}
+        run: |
+          poetry run pip install \
+            --extra-index-url https://test.pypi.org/simple/ \
+            "$PKG_NAME==$VERSION"
+
+      - name: Run unit tests
+        run: make tests
+        working-directory: ${{ inputs.working-directory }}
+
+      - name: 'Authenticate to Google Cloud'
+        id: 'auth'
+        uses: google-github-actions/auth@v2
+        with:
+          credentials_json: '${{ secrets.GOOGLE_CREDENTIALS }}'
+
+      - name: Run integration tests
+        if: ${{ startsWith(inputs.working-directory, 'libs/partners/') }}
+        env:
+          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 }}
+        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') }}
+        run: |
+          poetry run pip install -r _test_minimum_requirements.txt
+          make tests
+        working-directory: ${{ inputs.working-directory }}
 
   publish:
     needs:

.github/workflows/check_diffs.yml

@@ -5,11 +5,6 @@ on:
   push:
     branches: [master]
   pull_request:
-    paths:
-      - ".github/actions/**"
-      - ".github/tools/**"
-      - ".github/workflows/**"
-      - "libs/**"
 
 # If another push to the same PR or branch happens while this workflow is still running,
 # cancel the earlier run in favor of the next run.
@@ -26,13 +21,14 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-      - uses: actions/setup-python@v4
+      - uses: actions/setup-python@v5
         with:
           python-version: '3.10'
       - id: files
         uses: Ana06/get-changed-files@v2.2.0
       - id: set-matrix
-        run: echo "dirs-to-run=$(python .github/scripts/check_diff.py ${{ steps.files.outputs.all }})" >> $GITHUB_OUTPUT
+        run: |
+          python .github/scripts/check_diff.py ${{ steps.files.outputs.all }} >> $GITHUB_OUTPUT
     outputs:
       dirs-to-run: ${{ steps.set-matrix.outputs.dirs-to-run }}
   ci:

.github/workflows/langchain_community_release.yml

@@ -0,0 +1,13 @@
+---
+name: libs/community Release
+
+on:
+  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
+
+jobs:
+  release:
+    uses:
+      ./.github/workflows/_release.yml
+    with:
+      working-directory: libs/community
+    secrets: inherit

.github/workflows/langchain_openai_release.yml

@@ -0,0 +1,13 @@
+---
+name: libs/core Release
+
+on:
+  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
+
+jobs:
+  release:
+    uses:
+      ./.github/workflows/_release.yml
+    with:
+      working-directory: libs/core
+    secrets: inherit

.github/workflows/scheduled_test.yml

@@ -36,7 +36,7 @@ jobs:
 
       - name: 'Authenticate to Google Cloud'
         id: 'auth'
-        uses: 'google-github-actions/auth@v1'
+        uses: google-github-actions/auth@v2
         with:
           credentials_json: '${{ secrets.GOOGLE_CREDENTIALS }}'
 
@@ -52,13 +52,7 @@ jobs:
         shell: bash
         run: |
           echo "Running scheduled tests, installing dependencies with poetry..."
-          poetry install --with=test_integration
-          poetry run pip install google-cloud-aiplatform
-          poetry run pip install "boto3>=1.28.57"
-          if [[ ${{ matrix.python-version }} != "3.8" ]]
-          then
-            poetry run pip install fireworks-ai
-          fi
+          poetry install --with=test_integration,test
 
       - name: Run tests
         shell: bash
@@ -68,7 +62,9 @@ jobs:
           AZURE_OPENAI_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }}
           AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
           AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }}
-          AZURE_OPENAI_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_DEPLOYMENT_NAME }}
+          AZURE_OPENAI_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 }}
           FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
         run: |
           make scheduled_tests

.readthedocs.yaml

@@ -10,9 +10,10 @@ build:
   tools:
     python: "3.11"
   commands:
-      - python -mvirtualenv $READTHEDOCS_VIRTUALENV_PATH
+      - 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

README.md

@@ -3,8 +3,7 @@
 ⚡ Building applications with LLMs through composability ⚡
 
 [![Release Notes](https://img.shields.io/github/release/langchain-ai/langchain)](https://github.com/langchain-ai/langchain/releases)
-[![CI](https://github.com/langchain-ai/langchain/actions/workflows/langchain_ci.yml/badge.svg)](https://github.com/langchain-ai/langchain/actions/workflows/langchain_ci.yml)
-[![Experimental CI](https://github.com/langchain-ai/langchain/actions/workflows/langchain_experimental_ci.yml/badge.svg)](https://github.com/langchain-ai/langchain/actions/workflows/langchain_experimental_ci.yml)
+[![CI](https://github.com/langchain-ai/langchain/actions/workflows/check_diffs.yml/badge.svg)](https://github.com/langchain-ai/langchain/actions/workflows/check_diffs.yml)
 [![Downloads](https://static.pepy.tech/badge/langchain/month)](https://pepy.tech/project/langchain)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)
@@ -45,7 +44,10 @@ This framework consists of several parts.
 - **[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.
 
-**This repo contains the `langchain` ([here](libs/langchain)), `langchain-experimental` ([here](libs/experimental)), and `langchain-cli` ([here](libs/cli)) Python packages, as well as [LangChain Templates](templates).**
+The LangChain libraries themselves are made up of several different packages.
+- **[`langchain-core`](libs/core)**: Base abstractions and LangChain Expression Language.
+- **[`langchain-community`](libs/community)**: Third party integrations.
+- **[`langchain`](libs/langchain)**: Chains, agents, and retrieval strategies that make up an application's cognitive architecture.
 
 ![LangChain Stack](docs/static/img/langchain_stack.png)
 
@@ -93,7 +95,7 @@ Agents involve an LLM making decisions about which Actions to take, taking that
 Please see [here](https://python.langchain.com) for full documentation, which includes:
 
 - [Getting started](https://python.langchain.com/docs/get_started/introduction): installation, setting up the environment, simple examples
-- Overview of the [interfaces](https://python.langchain.com/docs/expression_language/), [modules](https://python.langchain.com/docs/modules/) and [integrations](https://python.langchain.com/docs/integrations/providers)
+- Overview of the [interfaces](https://python.langchain.com/docs/expression_language/), [modules](https://python.langchain.com/docs/modules/), and [integrations](https://python.langchain.com/docs/integrations/providers)
 - [Use case](https://python.langchain.com/docs/use_cases/qa_structured/sql) walkthroughs and best practice [guides](https://python.langchain.com/docs/guides/adapters/openai)
 - [LangSmith](https://python.langchain.com/docs/langsmith/), [LangServe](https://python.langchain.com/docs/langserve), and [LangChain Template](https://python.langchain.com/docs/templates/) overviews
 - [Reference](https://api.python.langchain.com): full API docs
@@ -103,7 +105,7 @@ Please see [here](https://python.langchain.com) for full documentation, which in
 
 As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
 
-For detailed information on how to contribute, see [here](.github/CONTRIBUTING.md).
+For detailed information on how to contribute, see [here](https://python.langchain.com/docs/contributing/).
 
 ## 🌟 Contributors
 

cookbook/LLaMA2_sql_chat.ipynb

@@ -61,13 +61,13 @@
    ],
    "source": [
     "# Local\n",
-    "from langchain.chat_models import ChatOllama\n",
+    "from langchain_community.chat_models import ChatOllama\n",
     "\n",
     "llama2_chat = ChatOllama(model=\"llama2:13b-chat\")\n",
     "llama2_code = ChatOllama(model=\"codellama:7b-instruct\")\n",
     "\n",
     "# API\n",
-    "from langchain.llms import Replicate\n",
+    "from langchain_community.llms import Replicate\n",
     "\n",
     "# REPLICATE_API_TOKEN = getpass()\n",
     "# os.environ[\"REPLICATE_API_TOKEN\"] = REPLICATE_API_TOKEN\n",
@@ -107,7 +107,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.utilities import SQLDatabase\n",
+    "from langchain_community.utilities import SQLDatabase\n",
     "\n",
     "db = SQLDatabase.from_uri(\"sqlite:///nba_roster.db\", sample_rows_in_table_info=0)\n",
     "\n",
@@ -125,7 +125,7 @@
    "id": "654b3577-baa2-4e12-a393-f40e5db49ac7",
    "metadata": {},
    "source": [
-    "## Query a SQL DB \n",
+    "## Query a SQL Database \n",
     "\n",
     "Follow the runnables workflow [here](https://python.langchain.com/docs/expression_language/cookbook/sql_db)."
    ]
@@ -149,8 +149,9 @@
    ],
    "source": [
     "# Prompt\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
     "\n",
+    "# Update the template based on the type of SQL Database like MySQL, Microsoft SQL Server and so on\n",
     "template = \"\"\"Based on the table schema below, write a SQL query that would answer the user's question:\n",
     "{schema}\n",
     "\n",
@@ -164,8 +165,8 @@
     ")\n",
     "\n",
     "# Chain to query\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "sql_response = (\n",
     "    RunnablePassthrough.assign(schema=get_schema)\n",
@@ -217,7 +218,7 @@
     "    [\n",
     "        (\n",
     "            \"system\",\n",
-    "            \"Given an input question and SQL response, convert it to a natural langugae answer. No pre-amble.\",\n",
+    "            \"Given an input question and SQL response, convert it to a natural language answer. No pre-amble.\",\n",
     "        ),\n",
     "        (\"human\", template),\n",
     "    ]\n",
@@ -277,7 +278,7 @@
    "source": [
     "# Prompt\n",
     "from langchain.memory import ConversationBufferMemory\n",
-    "from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
     "\n",
     "template = \"\"\"Given an input question, convert it to a SQL query. No pre-amble. Based on the table schema below, write a SQL query that would answer the user's question:\n",
     "{schema}\n",
@@ -293,7 +294,7 @@
     "memory = ConversationBufferMemory(return_messages=True)\n",
     "\n",
     "# Chain to query with memory\n",
-    "from langchain.schema.runnable import RunnableLambda\n",
+    "from langchain_core.runnables import RunnableLambda\n",
     "\n",
     "sql_chain = (\n",
     "    RunnablePassthrough.assign(\n",
@@ -345,7 +346,7 @@
     "    [\n",
     "        (\n",
     "            \"system\",\n",
-    "            \"Given an input question and SQL response, convert it to a natural langugae answer. No pre-amble.\",\n",
+    "            \"Given an input question and SQL response, convert it to a natural language answer. No pre-amble.\",\n",
     "        ),\n",
     "        (\"human\", template),\n",
     "    ]\n",

cookbook/Multi_modal_RAG.ipynb

@@ -46,7 +46,7 @@
     "\n",
     "---\n",
     "\n",
-    "A seperate cookbook highlights `Option 1` [here](https://github.com/langchain-ai/langchain/blob/master/cookbook/multi_modal_RAG_chroma.ipynb).\n",
+    "A separate cookbook highlights `Option 1` [here](https://github.com/langchain-ai/langchain/blob/master/cookbook/multi_modal_RAG_chroma.ipynb).\n",
     "\n",
     "And option `Option 2` is appropriate for cases when a multi-modal LLM cannot be used for answer synthesis (e.g., cost, etc).\n",
     "\n",
@@ -101,7 +101,7 @@
     "If you want to use the provided folder, then simply opt for a [pdf loader](https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf) for the document:\n",
     "\n",
     "```\n",
-    "from langchain.document_loaders import PyPDFLoader\n",
+    "from langchain_community.document_loaders import PyPDFLoader\n",
     "loader = PyPDFLoader(path + fname)\n",
     "docs = loader.load()\n",
     "tables = [] # Ignore w/ basic pdf loader\n",
@@ -198,9 +198,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "\n",
     "# Generate summaries of text elements\n",
@@ -270,7 +270,7 @@
     "import base64\n",
     "import os\n",
     "\n",
-    "from langchain.schema.messages import HumanMessage\n",
+    "from langchain_core.messages import HumanMessage\n",
     "\n",
     "\n",
     "def encode_image(image_path):\n",
@@ -341,7 +341,7 @@
     "Add raw docs and doc summaries to [Multi Vector Retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector#summary): \n",
     "\n",
     "* Store the raw texts, tables, and images in the `docstore`.\n",
-    "* Store the texts, table summaries, and image summaries in the `vectorstore` for semantic retrieval."
+    "* Store the texts, table summaries, and image summaries in the `vectorstore` for efficient semantic retrieval."
    ]
   },
   {
@@ -353,11 +353,11 @@
    "source": [
     "import uuid\n",
     "\n",
-    "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
-    "from langchain.schema.document import Document\n",
     "from langchain.storage import InMemoryStore\n",
-    "from langchain.vectorstores import Chroma\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_core.documents import Document\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "\n",
     "def create_multi_vector_retriever(\n",
@@ -442,7 +442,7 @@
     "import re\n",
     "\n",
     "from IPython.display import HTML, display\n",
-    "from langchain.schema.runnable import RunnableLambda, RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",
     "from PIL import Image\n",
     "\n",
     "\n",

cookbook/Semi_Structured_RAG.ipynb

@@ -235,9 +235,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser"
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {
@@ -318,11 +318,11 @@
    "source": [
     "import uuid\n",
     "\n",
-    "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
-    "from langchain.schema.document import Document\n",
     "from langchain.storage import InMemoryStore\n",
-    "from langchain.vectorstores import Chroma\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_core.documents import Document\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "# The vectorstore to use to index the child chunks\n",
     "vectorstore = Chroma(collection_name=\"summaries\", embedding_function=OpenAIEmbeddings())\n",
@@ -374,7 +374,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "# Prompt template\n",
     "template = \"\"\"Answer the question based only on the following context, which can include text and tables:\n",

cookbook/Semi_structured_and_multi_modal_RAG.ipynb

@@ -211,9 +211,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser"
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {
@@ -373,11 +373,11 @@
    "source": [
     "import uuid\n",
     "\n",
-    "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
-    "from langchain.schema.document import Document\n",
     "from langchain.storage import InMemoryStore\n",
-    "from langchain.vectorstores import Chroma\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_core.documents import Document\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "# The vectorstore to use to index the child chunks\n",
     "vectorstore = Chroma(collection_name=\"summaries\", embedding_function=OpenAIEmbeddings())\n",
@@ -646,7 +646,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "# Prompt template\n",
     "template = \"\"\"Answer the question based only on the following context, which can include text and tables:\n",

cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb

@@ -209,9 +209,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOllama\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser"
+    "from langchain_community.chat_models import ChatOllama\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate"
    ]
   },
   {
@@ -376,11 +376,11 @@
    "source": [
     "import uuid\n",
     "\n",
-    "from langchain.embeddings import GPT4AllEmbeddings\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
-    "from langchain.schema.document import Document\n",
     "from langchain.storage import InMemoryStore\n",
-    "from langchain.vectorstores import Chroma\n",
+    "from langchain_community.embeddings import GPT4AllEmbeddings\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_core.documents import Document\n",
     "\n",
     "# The vectorstore to use to index the child chunks\n",
     "vectorstore = Chroma(\n",
@@ -532,7 +532,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "# Prompt template\n",
     "template = \"\"\"Answer the question based only on the following context, which can include text and tables:\n",

cookbook/advanced_rag_eval.ipynb

@@ -62,7 +62,7 @@
     "path = \"/Users/rlm/Desktop/cpi/\"\n",
     "\n",
     "# Load\n",
-    "from langchain.document_loaders import PyPDFLoader\n",
+    "from langchain_community.document_loaders import PyPDFLoader\n",
     "\n",
     "loader = PyPDFLoader(path + \"cpi.pdf\")\n",
     "pdf_pages = loader.load()\n",
@@ -132,8 +132,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.vectorstores import Chroma\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "baseline = Chroma.from_texts(\n",
     "    texts=all_splits_pypdf_texts,\n",
@@ -160,9 +160,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "# Prompt\n",
     "prompt_text = \"\"\"You are an assistant tasked with summarizing tables and text for retrieval. \\\n",
@@ -202,7 +202,7 @@
     "import os\n",
     "from io import BytesIO\n",
     "\n",
-    "from langchain.schema.messages import HumanMessage\n",
+    "from langchain_core.messages import HumanMessage\n",
     "from PIL import Image\n",
     "\n",
     "\n",
@@ -273,8 +273,8 @@
     "from base64 import b64decode\n",
     "\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
-    "from langchain.schema.document import Document\n",
     "from langchain.storage import InMemoryStore\n",
+    "from langchain_core.documents import Document\n",
     "\n",
     "\n",
     "def create_multi_vector_retriever(\n",
@@ -475,7 +475,7 @@
    "source": [
     "from operator import itemgetter\n",
     "\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "# Prompt\n",
     "template = \"\"\"Answer the question based only on the following context, which can include text and tables:\n",
@@ -521,7 +521,7 @@
     "import re\n",
     "\n",
     "from langchain.schema import Document\n",
-    "from langchain.schema.runnable import RunnableLambda\n",
+    "from langchain_core.runnables import RunnableLambda\n",
     "\n",
     "\n",
     "def looks_like_base64(sb):\n",

cookbook/agent_vectorstore.ipynb

@@ -13,7 +13,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "9b22020a",
    "metadata": {},
@@ -29,10 +28,9 @@
    "outputs": [],
    "source": [
     "from langchain.chains import RetrievalQA\n",
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.llms import OpenAI\n",
     "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain.vectorstores import Chroma\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_openai import OpenAI, OpenAIEmbeddings\n",
     "\n",
     "llm = OpenAI(temperature=0)"
    ]
@@ -70,7 +68,7 @@
     }
    ],
    "source": [
-    "from langchain.document_loaders import TextLoader\n",
+    "from langchain_community.document_loaders import TextLoader\n",
     "\n",
     "loader = TextLoader(doc_path)\n",
     "documents = loader.load()\n",
@@ -100,7 +98,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.document_loaders import WebBaseLoader"
+    "from langchain_community.document_loaders import WebBaseLoader"
    ]
   },
   {
@@ -146,7 +144,6 @@
    "source": []
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "c0a6c031",
    "metadata": {},
@@ -163,7 +160,7 @@
    "source": [
     "# Import things that are needed generically\n",
     "from langchain.agents import AgentType, Tool, initialize_agent\n",
-    "from langchain.llms import OpenAI"
+    "from langchain_openai import OpenAI"
    ]
   },
   {
@@ -280,7 +277,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "787a9b5e",
    "metadata": {},
@@ -289,7 +285,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "9161ba91",
    "metadata": {},
@@ -411,7 +406,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "49a0cbbe",
    "metadata": {},
@@ -525,7 +519,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,

cookbook/analyze_document.ipynb

@@ -29,7 +29,7 @@
    "outputs": [],
    "source": [
     "from langchain.chains import AnalyzeDocumentChain\n",
-    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "llm = ChatOpenAI(model=\"gpt-3.5-turbo\", temperature=0)"
    ]

cookbook/autogpt/autogpt.ipynb

@@ -28,9 +28,9 @@
    "outputs": [],
    "source": [
     "from langchain.agents import Tool\n",
-    "from langchain.tools.file_management.read import ReadFileTool\n",
-    "from langchain.tools.file_management.write import WriteFileTool\n",
-    "from langchain.utilities import SerpAPIWrapper\n",
+    "from langchain_community.tools.file_management.read import ReadFileTool\n",
+    "from langchain_community.tools.file_management.write import WriteFileTool\n",
+    "from langchain_community.utilities import SerpAPIWrapper\n",
     "\n",
     "search = SerpAPIWrapper()\n",
     "tools = [\n",
@@ -62,8 +62,8 @@
    "outputs": [],
    "source": [
     "from langchain.docstore import InMemoryDocstore\n",
-    "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.vectorstores import FAISS"
+    "from langchain_community.vectorstores import FAISS\n",
+    "from langchain_openai import OpenAIEmbeddings"
    ]
   },
   {
@@ -100,8 +100,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain_experimental.autonomous_agents import AutoGPT"
+    "from langchain_experimental.autonomous_agents import AutoGPT\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {
@@ -167,7 +167,7 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.memory.chat_message_histories import FileChatMessageHistory\n",
+    "from langchain_community.chat_message_histories import FileChatMessageHistory\n",
     "\n",
     "agent = AutoGPT.from_llm_and_tools(\n",
     "    ai_name=\"Tom\",\n",

cookbook/autogpt/marathon_times.ipynb

@@ -39,10 +39,10 @@
     "\n",
     "import nest_asyncio\n",
     "import pandas as pd\n",
-    "from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent\n",
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.docstore.document import Document\n",
+    "from langchain_community.agent_toolkits.pandas.base import create_pandas_dataframe_agent\n",
     "from langchain_experimental.autonomous_agents import AutoGPT\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "# Needed synce jupyter runs an async eventloop\n",
     "nest_asyncio.apply()"
@@ -93,8 +93,8 @@
     "from typing import Optional\n",
     "\n",
     "from langchain.agents import tool\n",
-    "from langchain.tools.file_management.read import ReadFileTool\n",
-    "from langchain.tools.file_management.write import WriteFileTool\n",
+    "from langchain_community.tools.file_management.read import ReadFileTool\n",
+    "from langchain_community.tools.file_management.write import WriteFileTool\n",
     "\n",
     "ROOT_DIR = \"./data/\"\n",
     "\n",
@@ -311,8 +311,8 @@
     "# Memory\n",
     "import faiss\n",
     "from langchain.docstore import InMemoryDocstore\n",
-    "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.vectorstores import FAISS\n",
+    "from langchain_community.vectorstores import FAISS\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "embeddings_model = OpenAIEmbeddings()\n",
     "embedding_size = 1536\n",

cookbook/baby_agi.ipynb

@@ -31,9 +31,8 @@
    "source": [
     "from typing import Optional\n",
     "\n",
-    "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain_experimental.autonomous_agents import BabyAGI"
+    "from langchain_experimental.autonomous_agents import BabyAGI\n",
+    "from langchain_openai import OpenAI, OpenAIEmbeddings"
    ]
   },
   {
@@ -54,7 +53,7 @@
    "outputs": [],
    "source": [
     "from langchain.docstore import InMemoryDocstore\n",
-    "from langchain.vectorstores import FAISS"
+    "from langchain_community.vectorstores import FAISS"
    ]
   },
   {

cookbook/baby_agi_with_agent.ipynb

@@ -28,10 +28,9 @@
     "from typing import Optional\n",
     "\n",
     "from langchain.chains import LLMChain\n",
-    "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.llms import OpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain_experimental.autonomous_agents import BabyAGI"
+    "from langchain_experimental.autonomous_agents import BabyAGI\n",
+    "from langchain_openai import OpenAI, OpenAIEmbeddings"
    ]
   },
   {
@@ -63,7 +62,7 @@
     "%pip install faiss-cpu > /dev/null\n",
     "%pip install google-search-results > /dev/null\n",
     "from langchain.docstore import InMemoryDocstore\n",
-    "from langchain.vectorstores import FAISS"
+    "from langchain_community.vectorstores import FAISS"
    ]
   },
   {
@@ -108,8 +107,8 @@
    "source": [
     "from langchain.agents import AgentExecutor, Tool, ZeroShotAgent\n",
     "from langchain.chains import LLMChain\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.utilities import SerpAPIWrapper\n",
+    "from langchain_community.utilities import SerpAPIWrapper\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "todo_prompt = PromptTemplate.from_template(\n",
     "    \"You are a planner who is an expert at coming up with a todo list for a given objective. Come up with a todo list for this objective: {objective}\"\n",

cookbook/camel_role_playing.ipynb

@@ -36,7 +36,6 @@
    "source": [
     "from typing import List\n",
     "\n",
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts.chat import (\n",
     "    HumanMessagePromptTemplate,\n",
     "    SystemMessagePromptTemplate,\n",
@@ -46,7 +45,8 @@
     "    BaseMessage,\n",
     "    HumanMessage,\n",
     "    SystemMessage,\n",
-    ")"
+    ")\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/causal_program_aided_language_model.ipynb

@@ -47,9 +47,9 @@
    "outputs": [],
    "source": [
     "from IPython.display import SVG\n",
-    "from langchain.llms import OpenAI\n",
     "from langchain_experimental.cpal.base import CPALChain\n",
     "from langchain_experimental.pal_chain import PALChain\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0, max_tokens=512)\n",
     "cpal_chain = CPALChain.from_univariate_prompt(llm=llm, verbose=True)\n",

cookbook/code-analysis-deeplake.ipynb

@@ -23,9 +23,9 @@
    "metadata": {},
    "source": [
     "1. Prepare data:\n",
-    "   1. Upload all python project files using the `langchain.document_loaders.TextLoader`. We will call these files the **documents**.\n",
+    "   1. Upload all python project files using the `langchain_community.document_loaders.TextLoader`. We will call these files the **documents**.\n",
     "   2. Split all documents to chunks using the `langchain.text_splitter.CharacterTextSplitter`.\n",
-    "   3. Embed chunks and upload them into the DeepLake using `langchain.embeddings.openai.OpenAIEmbeddings` and `langchain.vectorstores.DeepLake`\n",
+    "   3. Embed chunks and upload them into the DeepLake using `langchain.embeddings.openai.OpenAIEmbeddings` and `langchain_community.vectorstores.DeepLake`\n",
     "2. Question-Answering:\n",
     "   1. Build a chain from `langchain.chat_models.ChatOpenAI` and `langchain.chains.ConversationalRetrievalChain`\n",
     "   2. Prepare questions.\n",
@@ -166,7 +166,7 @@
     }
    ],
    "source": [
-    "from langchain.document_loaders import TextLoader\n",
+    "from langchain_community.document_loaders import TextLoader\n",
     "\n",
     "root_dir = \"../../../../../../libs\"\n",
     "\n",
@@ -657,7 +657,7 @@
     }
    ],
    "source": [
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "embeddings = OpenAIEmbeddings()\n",
     "embeddings"
@@ -706,7 +706,7 @@
     {
      "data": {
       "text/plain": [
-       "<langchain.vectorstores.deeplake.DeepLake at 0x7fe1b67d7a30>"
+       "<langchain_community.vectorstores.deeplake.DeepLake at 0x7fe1b67d7a30>"
       ]
      },
      "execution_count": 15,
@@ -715,7 +715,7 @@
     }
    ],
    "source": [
-    "from langchain.vectorstores import DeepLake\n",
+    "from langchain_community.vectorstores import DeepLake\n",
     "\n",
     "username = \"<USERNAME_OR_ORG>\"\n",
     "\n",
@@ -740,7 +740,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# from langchain.vectorstores import DeepLake\n",
+    "# from langchain_community.vectorstores import DeepLake\n",
     "\n",
     "# db = DeepLake.from_documents(\n",
     "#     texts, embeddings, dataset_path=f\"hub://{<org_id>}/langchain-code\", runtime={\"tensor_db\": True}\n",
@@ -834,7 +834,7 @@
    "outputs": [],
    "source": [
     "from langchain.chains import ConversationalRetrievalChain\n",
-    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "model = ChatOpenAI(\n",
     "    model_name=\"gpt-3.5-turbo-0613\"\n",

cookbook/custom_agent_with_plugin_retrieval.ipynb

@@ -40,12 +40,12 @@
     "    AgentOutputParser,\n",
     "    LLMSingleActionAgent,\n",
     ")\n",
-    "from langchain.agents.agent_toolkits import NLAToolkit\n",
     "from langchain.chains import LLMChain\n",
-    "from langchain.llms import OpenAI\n",
     "from langchain.prompts import StringPromptTemplate\n",
     "from langchain.schema import AgentAction, AgentFinish\n",
-    "from langchain.tools.plugin import AIPlugin"
+    "from langchain_community.agent_toolkits import NLAToolkit\n",
+    "from langchain_community.tools.plugin import AIPlugin\n",
+    "from langchain_openai import OpenAI"
    ]
   },
   {
@@ -114,9 +114,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.schema import Document\n",
-    "from langchain.vectorstores import FAISS"
+    "from langchain_community.vectorstores import FAISS\n",
+    "from langchain_openai import OpenAIEmbeddings"
    ]
   },
   {

cookbook/custom_agent_with_plugin_retrieval_using_plugnplai.ipynb

@@ -65,12 +65,12 @@
     "    AgentOutputParser,\n",
     "    LLMSingleActionAgent,\n",
     ")\n",
-    "from langchain.agents.agent_toolkits import NLAToolkit\n",
     "from langchain.chains import LLMChain\n",
-    "from langchain.llms import OpenAI\n",
     "from langchain.prompts import StringPromptTemplate\n",
     "from langchain.schema import AgentAction, AgentFinish\n",
-    "from langchain.tools.plugin import AIPlugin"
+    "from langchain_community.agent_toolkits import NLAToolkit\n",
+    "from langchain_community.tools.plugin import AIPlugin\n",
+    "from langchain_openai import OpenAI"
    ]
   },
   {
@@ -138,9 +138,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.schema import Document\n",
-    "from langchain.vectorstores import FAISS"
+    "from langchain_community.vectorstores import FAISS\n",
+    "from langchain_openai import OpenAIEmbeddings"
    ]
   },
   {

cookbook/custom_agent_with_tool_retrieval.ipynb

@@ -7,8 +7,6 @@
    "source": [
     "# Custom agent with tool retrieval\n",
     "\n",
-    "This notebook builds off of [this notebook](/docs/modules/agents/how_to/custom_llm_agent) and assumes familiarity with how agents work.\n",
-    "\n",
     "The novel idea introduced in this notebook is the idea of using retrieval to select the set of tools to use to answer an agent query. This is useful when you have many many tools to select from. You cannot put the description of all the tools in the prompt (because of context length issues) so instead you dynamically select the N tools you do want to consider using at run time.\n",
     "\n",
     "In this notebook we will create a somewhat contrived example. We will have one legitimate tool (search) and then 99 fake tools which are just nonsense. We will then add a step in the prompt template that takes the user input and retrieves tool relevant to the query."
@@ -41,10 +39,10 @@
     "    Tool,\n",
     ")\n",
     "from langchain.chains import LLMChain\n",
-    "from langchain.llms import OpenAI\n",
     "from langchain.prompts import StringPromptTemplate\n",
     "from langchain.schema import AgentAction, AgentFinish\n",
-    "from langchain.utilities import SerpAPIWrapper"
+    "from langchain_community.utilities import SerpAPIWrapper\n",
+    "from langchain_openai import OpenAI"
    ]
   },
   {
@@ -105,9 +103,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.schema import Document\n",
-    "from langchain.vectorstores import FAISS"
+    "from langchain_community.vectorstores import FAISS\n",
+    "from langchain_openai import OpenAIEmbeddings"
    ]
   },
   {
@@ -489,7 +487,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.1"
   },
   "vscode": {
    "interpreter": {

cookbook/custom_multi_action_agent.ipynb

@@ -26,7 +26,7 @@
    "outputs": [],
    "source": [
     "from langchain.agents import AgentExecutor, BaseMultiActionAgent, Tool\n",
-    "from langchain.utilities import SerpAPIWrapper"
+    "from langchain_community.utilities import SerpAPIWrapper"
    ]
   },
   {

cookbook/databricks_sql_db.ipynb

@@ -80,7 +80,7 @@
    "outputs": [],
    "source": [
     "# Connecting to Databricks with SQLDatabase wrapper\n",
-    "from langchain.utilities import SQLDatabase\n",
+    "from langchain_community.utilities import SQLDatabase\n",
     "\n",
     "db = SQLDatabase.from_databricks(catalog=\"samples\", schema=\"nyctaxi\")"
    ]
@@ -93,7 +93,7 @@
    "outputs": [],
    "source": [
     "# Creating a OpenAI Chat LLM wrapper\n",
-    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "llm = ChatOpenAI(temperature=0, model_name=\"gpt-4\")"
    ]
@@ -115,7 +115,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.utilities import SQLDatabaseChain\n",
+    "from langchain_community.utilities import SQLDatabaseChain\n",
     "\n",
     "db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)"
    ]
@@ -177,7 +177,7 @@
    "outputs": [],
    "source": [
     "from langchain.agents import create_sql_agent\n",
-    "from langchain.agents.agent_toolkits import SQLDatabaseToolkit\n",
+    "from langchain_community.agent_toolkits import SQLDatabaseToolkit\n",
     "\n",
     "toolkit = SQLDatabaseToolkit(db=db, llm=llm)\n",
     "agent = create_sql_agent(llm=llm, toolkit=toolkit, verbose=True)"

cookbook/deeplake_semantic_search_over_chat.ipynb

@@ -52,13 +52,12 @@
     "import os\n",
     "\n",
     "from langchain.chains import RetrievalQA\n",
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.llms import OpenAI\n",
     "from langchain.text_splitter import (\n",
     "    CharacterTextSplitter,\n",
     "    RecursiveCharacterTextSplitter,\n",
     ")\n",
-    "from langchain.vectorstores import DeepLake\n",
+    "from langchain_community.vectorstores import DeepLake\n",
+    "from langchain_openai import OpenAI, OpenAIEmbeddings\n",
     "\n",
     "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
     "activeloop_token = getpass.getpass(\"Activeloop Token:\")\n",

cookbook/docugami_xml_kg_rag.ipynb

@@ -63,7 +63,7 @@
     "1. Create an access token via the Developer Playground for your workspace. [Detailed instructions](https://help.docugami.com/home/docugami-api).\n",
     "1. Add your documents (PDF \\[scanned or digital\\], DOC or DOCX) to Docugami for processing. There are two ways to do this:\n",
     "    1. Use the simple Docugami web experience. [Detailed instructions](https://help.docugami.com/home/adding-documents).\n",
-    "    1. Use the [Docugami API](https://api-docs.docugami.com), specifically the [documents](https://api-docs.docugami.com/#tag/documents/operation/upload-document) endpoint. Code samples are available for [python](../upload_file/) and [JavaScript](../../js/upload-file/) or you can use the [docugami](https://pypi.org/project/docugami/) python library.\n",
+    "    1. Use the [Docugami API](https://api-docs.docugami.com), specifically the [documents](https://api-docs.docugami.com/#tag/documents/operation/upload-document) endpoint. You can also use the [docugami python library](https://pypi.org/project/docugami/) as a convenient wrapper.\n",
     "\n",
     "Once your documents are in Docugami, they are processed and organized into sets of similar documents, e.g. NDAs, Lease Agreements, and Service Agreements. Docugami is not limited to any particular types of documents, and the clusters created depend on your particular documents. You can [change the docset assignments](https://help.docugami.com/home/working-with-the-doc-sets-view) later if you wish. You can monitor file status in the simple Docugami webapp, or use a [webhook](https://api-docs.docugami.com/#tag/webhooks) to be informed when your documents are done processing.\n",
     "\n",
@@ -470,13 +470,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import (\n",
     "    ChatPromptTemplate,\n",
     "    HumanMessagePromptTemplate,\n",
     "    SystemMessagePromptTemplate,\n",
     ")\n",
-    "from langchain.schema.output_parser import StrOutputParser"
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {
@@ -545,11 +545,11 @@
    "source": [
     "import uuid\n",
     "\n",
-    "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.retrievers.multi_vector import MultiVectorRetriever\n",
-    "from langchain.schema.document import Document\n",
     "from langchain.storage import InMemoryStore\n",
-    "from langchain.vectorstores.chroma import Chroma\n",
+    "from langchain_community.vectorstores.chroma import Chroma\n",
+    "from langchain_core.documents import Document\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "\n",
     "def build_retriever(text_elements, tables, table_summaries):\n",
@@ -605,7 +605,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
     "\n",
     "system_prompt = SystemMessagePromptTemplate.from_template(\n",
     "    \"You are a helpful assistant that answers questions based on provided context. Your provided context can include text or tables, \"\n",
@@ -916,6 +916,20 @@
    "source": [
     "llama2_chain.invoke(\"What was the learning rate for LLaMA2?\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94826165",
+   "metadata": {},
+   "source": [
+    "## Docugami KG-RAG Template\n",
+    "\n",
+    "Docugami also provides a [langchain template](https://github.com/docugami/langchain-template-docugami-kg-rag) that you can integrate into your langchain projects.\n",
+    "\n",
+    "Here's a walkthrough of how you can do this.\n",
+    "\n",
+    "[![Docugami KG-RAG Walkthrough](https://img.youtube.com/vi/xOHOmL1NFMg/0.jpg)](https://www.youtube.com/watch?v=xOHOmL1NFMg)\n"
+   ]
   }
  ],
  "metadata": {

cookbook/elasticsearch_db_qa.ipynb

@@ -39,7 +39,7 @@
    "source": [
     "from elasticsearch import Elasticsearch\n",
     "from langchain.chains.elasticsearch_database import ElasticsearchDatabaseChain\n",
-    "from langchain.chat_models import ChatOpenAI"
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/extraction_openai_tools.ipynb

@@ -22,8 +22,8 @@
     "from typing import List, Optional\n",
     "\n",
     "from langchain.chains.openai_tools import create_extraction_chain_pydantic\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.pydantic_v1 import BaseModel"
+    "from langchain_core.pydantic_v1 import BaseModel\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {
@@ -151,11 +151,11 @@
     "\n",
     "from langchain.output_parsers.openai_tools import PydanticToolsParser\n",
     "from langchain.utils.openai_functions import convert_pydantic_to_openai_tool\n",
-    "from langchain.schema.runnable import Runnable\n",
-    "from langchain.pydantic_v1 import BaseModel\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.messages import SystemMessage\n",
-    "from langchain.schema.language_model import BaseLanguageModel\n",
+    "from langchain_core.runnables import Runnable\n",
+    "from langchain_core.pydantic_v1 import BaseModel\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.messages import SystemMessage\n",
+    "from langchain_core.language_models import BaseLanguageModel\n",
     "\n",
     "_EXTRACTION_TEMPLATE = \"\"\"Extract and save the relevant entities mentioned \\\n",
     "in the following passage together with their properties.\n",

cookbook/fake_llm.ipynb

@@ -20,7 +20,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.llms.fake import FakeListLLM"
+    "from langchain_community.llms.fake import FakeListLLM"
    ]
   },
   {

cookbook/forward_looking_retrieval_augmented_generation.ipynb

@@ -73,10 +73,9 @@
     "    AsyncCallbackManagerForRetrieverRun,\n",
     "    CallbackManagerForRetrieverRun,\n",
     ")\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.llms import OpenAI\n",
     "from langchain.schema import BaseRetriever, Document\n",
-    "from langchain.utilities import GoogleSerperAPIWrapper"
+    "from langchain_community.utilities import GoogleSerperAPIWrapper\n",
+    "from langchain_openai import ChatOpenAI, OpenAI"
    ]
   },
   {

cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb

@@ -47,11 +47,10 @@
     "from datetime import datetime, timedelta\n",
     "from typing import List\n",
     "\n",
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.docstore import InMemoryDocstore\n",
-    "from langchain.embeddings import OpenAIEmbeddings\n",
     "from langchain.retrievers import TimeWeightedVectorStoreRetriever\n",
-    "from langchain.vectorstores import FAISS\n",
+    "from langchain_community.vectorstores import FAISS\n",
+    "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
     "from termcolor import colored"
    ]
   },

cookbook/hugginggpt.ipynb

@@ -75,8 +75,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.llms import OpenAI\n",
     "from langchain_experimental.autonomous_agents import HuggingGPT\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "# %env OPENAI_API_BASE=http://localhost:8000/v1"
    ]

cookbook/human_approval.ipynb

@@ -159,7 +159,7 @@
    "outputs": [],
    "source": [
     "from langchain.agents import AgentType, initialize_agent, load_tools\n",
-    "from langchain.llms import OpenAI"
+    "from langchain_openai import OpenAI"
    ]
   },
   {

cookbook/human_input_chat_model.ipynb

@@ -20,7 +20,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models.human import HumanInputChatModel"
+    "from langchain_community.chat_models.human import HumanInputChatModel"
    ]
   },
   {

cookbook/human_input_llm.ipynb

@@ -19,7 +19,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.llms.human import HumanInputLLM"
+    "from langchain_community.llms.human import HumanInputLLM"
    ]
   },
   {

cookbook/hypothetical_document_embeddings.ipynb

@@ -21,9 +21,8 @@
    "outputs": [],
    "source": [
     "from langchain.chains import HypotheticalDocumentEmbedder, LLMChain\n",
-    "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.prompts import PromptTemplate"
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_openai import OpenAI, OpenAIEmbeddings"
    ]
   },
   {
@@ -172,7 +171,7 @@
    "outputs": [],
    "source": [
     "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain.vectorstores import Chroma\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "\n",
     "with open(\"../../state_of_the_union.txt\") as f:\n",
     "    state_of_the_union = f.read()\n",

cookbook/learned_prompt_optimization.ipynb

@@ -49,9 +49,9 @@
    "source": [
     "# pick and configure the LLM of your choice\n",
     "\n",
-    "from langchain.llms import OpenAI\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
-    "llm = OpenAI(model=\"text-davinci-003\")"
+    "llm = OpenAI(model=\"gpt-3.5-turbo-instruct\")"
    ]
   },
   {

cookbook/llm_bash.ipynb

@@ -43,8 +43,8 @@
     }
    ],
    "source": [
-    "from langchain.llms import OpenAI\n",
     "from langchain_experimental.llm_bash.base import LLMBashChain\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "\n",

cookbook/llm_checker.ipynb

@@ -42,7 +42,7 @@
    ],
    "source": [
     "from langchain.chains import LLMCheckerChain\n",
-    "from langchain.llms import OpenAI\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0.7)\n",
     "\n",

cookbook/llm_math.ipynb

@@ -46,7 +46,7 @@
    ],
    "source": [
     "from langchain.chains import LLMMathChain\n",
-    "from langchain.llms import OpenAI\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "llm_math = LLMMathChain.from_llm(llm, verbose=True)\n",

cookbook/llm_summarization_checker.ipynb

@@ -331,7 +331,7 @@
    ],
    "source": [
     "from langchain.chains import LLMSummarizationCheckerChain\n",
-    "from langchain.llms import OpenAI\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "checker_chain = LLMSummarizationCheckerChain.from_llm(llm, verbose=True, max_checks=2)\n",
@@ -822,7 +822,7 @@
    ],
    "source": [
     "from langchain.chains import LLMSummarizationCheckerChain\n",
-    "from langchain.llms import OpenAI\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "checker_chain = LLMSummarizationCheckerChain.from_llm(llm, verbose=True, max_checks=3)\n",
@@ -1096,7 +1096,7 @@
    ],
    "source": [
     "from langchain.chains import LLMSummarizationCheckerChain\n",
-    "from langchain.llms import OpenAI\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "checker_chain = LLMSummarizationCheckerChain.from_llm(llm, max_checks=3, verbose=True)\n",

cookbook/llm_symbolic_math.ipynb

@@ -14,8 +14,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.llms import OpenAI\n",
     "from langchain_experimental.llm_symbolic_math.base import LLMSymbolicMathChain\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "llm = OpenAI(temperature=0)\n",
     "llm_symbolic_math = LLMSymbolicMathChain.from_llm(llm)"

cookbook/meta_prompt.ipynb

@@ -57,9 +57,9 @@
    "outputs": [],
    "source": [
     "from langchain.chains import LLMChain\n",
-    "from langchain.llms import OpenAI\n",
     "from langchain.memory import ConversationBufferWindowMemory\n",
-    "from langchain.prompts import PromptTemplate"
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_openai import OpenAI"
    ]
   },
   {

cookbook/multi_modal_QA.ipynb

@@ -91,8 +91,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.schema.messages import HumanMessage, SystemMessage"
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/multi_modal_RAG_chroma.ipynb

@@ -42,7 +42,7 @@
     "* We will use Open Clip multi-modal embeddings.\n",
     "* We will use [Chroma](https://www.trychroma.com/) with support for multi-modal.\n",
     "\n",
-    "A seperate cookbook highlights `Options 2 and 3` [here](https://github.com/langchain-ai/langchain/blob/master/cookbook/Multi_modal_RAG.ipynb).\n",
+    "A separate cookbook highlights `Options 2 and 3` [here](https://github.com/langchain-ai/langchain/blob/master/cookbook/Multi_modal_RAG.ipynb).\n",
     "\n",
     "![chroma_multimodal.png](attachment:1920fda3-1808-407c-9820-f518c9c6f566.png)\n",
     "\n",
@@ -187,7 +187,7 @@
     "\n",
     "import chromadb\n",
     "import numpy as np\n",
-    "from langchain.vectorstores import Chroma\n",
+    "from langchain_community.vectorstores import Chroma\n",
     "from langchain_experimental.open_clip import OpenCLIPEmbeddings\n",
     "from PIL import Image as _PILImage\n",
     "\n",
@@ -315,10 +315,10 @@
    "source": [
     "from operator import itemgetter\n",
     "\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.schema.messages import HumanMessage, SystemMessage\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnableLambda, RunnablePassthrough\n",
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.runnables import RunnableLambda, RunnablePassthrough\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "\n",
     "def prompt_func(data_dict):\n",

cookbook/multi_modal_output_agent.ipynb

@@ -43,8 +43,8 @@
    "outputs": [],
    "source": [
     "from langchain.agents import AgentType, initialize_agent\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.tools import SteamshipImageGenerationTool"
+    "from langchain.tools import SteamshipImageGenerationTool\n",
+    "from langchain_openai import OpenAI"
    ]
   },
   {

cookbook/multi_player_dnd.ipynb

@@ -28,11 +28,11 @@
    "source": [
     "from typing import Callable, List\n",
     "\n",
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.schema import (\n",
     "    HumanMessage,\n",
     "    SystemMessage,\n",
-    ")"
+    ")\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/multiagent_authoritarian.ipynb

@@ -33,7 +33,6 @@
     "from typing import Callable, List\n",
     "\n",
     "import tenacity\n",
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.output_parsers import RegexParser\n",
     "from langchain.prompts import (\n",
     "    PromptTemplate,\n",
@@ -41,7 +40,8 @@
     "from langchain.schema import (\n",
     "    HumanMessage,\n",
     "    SystemMessage,\n",
-    ")"
+    ")\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/multiagent_bidding.ipynb

@@ -27,13 +27,13 @@
     "from typing import Callable, List\n",
     "\n",
     "import tenacity\n",
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.output_parsers import RegexParser\n",
     "from langchain.prompts import PromptTemplate\n",
     "from langchain.schema import (\n",
     "    HumanMessage,\n",
     "    SystemMessage,\n",
-    ")"
+    ")\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/myscale_vector_sql.ipynb

@@ -31,10 +31,10 @@
     "from os import environ\n",
     "\n",
     "from langchain.chains import LLMChain\n",
-    "from langchain.llms import OpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain.utilities import SQLDatabase\n",
+    "from langchain_community.utilities import SQLDatabase\n",
     "from langchain_experimental.sql.vector_sql import VectorSQLDatabaseChain\n",
+    "from langchain_openai import OpenAI\n",
     "from sqlalchemy import MetaData, create_engine\n",
     "\n",
     "MYSCALE_HOST = \"msc-4a9e710a.us-east-1.aws.staging.myscale.cloud\"\n",
@@ -57,7 +57,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.embeddings import HuggingFaceInstructEmbeddings\n",
+    "from langchain_community.embeddings import HuggingFaceInstructEmbeddings\n",
     "from langchain_experimental.sql.vector_sql import VectorSQLOutputParser\n",
     "\n",
     "output_parser = VectorSQLOutputParser.from_embeddings(\n",
@@ -75,10 +75,10 @@
    "outputs": [],
    "source": [
     "from langchain.callbacks import StdOutCallbackHandler\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.utilities.sql_database import SQLDatabase\n",
+    "from langchain_community.utilities.sql_database import SQLDatabase\n",
     "from langchain_experimental.sql.prompt import MYSCALE_PROMPT\n",
     "from langchain_experimental.sql.vector_sql import VectorSQLDatabaseChain\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
     "chain = VectorSQLDatabaseChain(\n",
     "    llm_chain=LLMChain(\n",
@@ -117,7 +117,6 @@
    "outputs": [],
    "source": [
     "from langchain.chains.qa_with_sources.retrieval import RetrievalQAWithSourcesChain\n",
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain_experimental.retrievers.vector_sql_database import (\n",
     "    VectorSQLDatabaseChainRetriever,\n",
     ")\n",
@@ -126,6 +125,7 @@
     "    VectorSQLDatabaseChain,\n",
     "    VectorSQLRetrieveAllOutputParser,\n",
     ")\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "output_parser_retrieve_all = VectorSQLRetrieveAllOutputParser.from_embeddings(\n",
     "    output_parser.model\n",

cookbook/openai_functions_retrieval_qa.ipynb

@@ -20,10 +20,10 @@
    "outputs": [],
    "source": [
     "from langchain.chains import RetrievalQA\n",
-    "from langchain.document_loaders import TextLoader\n",
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
     "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain.vectorstores import Chroma"
+    "from langchain_community.document_loaders import TextLoader\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_openai import OpenAIEmbeddings"
    ]
   },
   {
@@ -52,8 +52,8 @@
    "source": [
     "from langchain.chains import create_qa_with_sources_chain\n",
     "from langchain.chains.combine_documents.stuff import StuffDocumentsChain\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.prompts import PromptTemplate"
+    "from langchain.prompts import PromptTemplate\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/openai_v1_cookbook.ipynb

@@ -28,8 +28,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.schema.messages import HumanMessage, SystemMessage"
+    "from langchain_core.messages import HumanMessage, SystemMessage\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {
@@ -252,7 +252,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.agent import AgentFinish\n",
+    "from langchain_core.agents import AgentFinish\n",
     "\n",
     "\n",
     "def execute_agent(agent, tools, input):\n",
@@ -414,7 +414,7 @@
     "BREAKING CHANGES:\n",
     "- To use Azure embeddings with OpenAI V1, you'll need to use the new `AzureOpenAIEmbeddings` instead of the existing `OpenAIEmbeddings`. `OpenAIEmbeddings` continue to work when using Azure with `openai<1`.\n",
     "```python\n",
-    "from langchain.embeddings import AzureOpenAIEmbeddings\n",
+    "from langchain_openai import AzureOpenAIEmbeddings\n",
     "```\n",
     "\n",
     "\n",
@@ -456,9 +456,9 @@
     "from typing import Literal\n",
     "\n",
     "from langchain.output_parsers.openai_tools import PydanticToolsParser\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.pydantic_v1 import BaseModel, Field\n",
     "from langchain.utils.openai_functions import convert_pydantic_to_openai_tool\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
     "\n",
     "\n",
     "class GetCurrentWeather(BaseModel):\n",

cookbook/petting_zoo.ipynb

@@ -47,12 +47,12 @@
     "import inspect\n",
     "\n",
     "import tenacity\n",
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.output_parsers import RegexParser\n",
     "from langchain.schema import (\n",
     "    HumanMessage,\n",
     "    SystemMessage,\n",
-    ")"
+    ")\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/plan_and_execute_agent.ipynb

@@ -29,16 +29,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.agents.tools import Tool\n",
     "from langchain.chains import LLMMathChain\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.utilities import DuckDuckGoSearchAPIWrapper\n",
+    "from langchain_community.utilities import DuckDuckGoSearchAPIWrapper\n",
+    "from langchain_core.tools import Tool\n",
     "from langchain_experimental.plan_and_execute import (\n",
     "    PlanAndExecute,\n",
     "    load_agent_executor,\n",
     "    load_chat_planner,\n",
-    ")"
+    ")\n",
+    "from langchain_openai import ChatOpenAI, OpenAI"
    ]
   },
   {

cookbook/press_releases.ipynb

@@ -81,8 +81,8 @@
    "outputs": [],
    "source": [
     "from langchain.chains import ConversationalRetrievalChain\n",
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.retrievers import KayAiRetriever\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "model = ChatOpenAI(model_name=\"gpt-3.5-turbo\")\n",
     "retriever = KayAiRetriever.create(\n",

cookbook/program_aided_language_model.ipynb

@@ -17,8 +17,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.llms import OpenAI\n",
-    "from langchain_experimental.pal_chain import PALChain"
+    "from langchain_experimental.pal_chain import PALChain\n",
+    "from langchain_openai import OpenAI"
    ]
   },
   {

cookbook/qa_citations.ipynb

@@ -27,7 +27,7 @@
    ],
    "source": [
     "from langchain.chains import create_citation_fuzzy_match_chain\n",
-    "from langchain.chat_models import ChatOpenAI"
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/qianfan_baidu_elasticesearch_RAG.ipynb

@@ -59,11 +59,13 @@
     "from baidubce.auth.bce_credentials import BceCredentials\n",
     "from baidubce.bce_client_configuration import BceClientConfiguration\n",
     "from langchain.chains.retrieval_qa import RetrievalQA\n",
-    "from langchain.document_loaders.baiducloud_bos_directory import BaiduBOSDirectoryLoader\n",
-    "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
-    "from langchain.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint\n",
     "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
-    "from langchain.vectorstores import BESVectorStore"
+    "from langchain_community.document_loaders.baiducloud_bos_directory import (\n",
+    "    BaiduBOSDirectoryLoader,\n",
+    ")\n",
+    "from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings\n",
+    "from langchain_community.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint\n",
+    "from langchain_community.vectorstores import BESVectorStore"
    ]
   },
   {

cookbook/rag_fusion.ipynb

@@ -30,8 +30,8 @@
    "outputs": [],
    "source": [
     "import pinecone\n",
-    "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.vectorstores import Pinecone\n",
+    "from langchain_community.vectorstores import Pinecone\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "pinecone.init(api_key=\"...\", environment=\"...\")"
    ]
@@ -86,8 +86,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.schema.output_parser import StrOutputParser"
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/retrieval_in_sql.ipynb

@@ -42,8 +42,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.sql_database import SQLDatabase\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "CONNECTION_STRING = \"postgresql+psycopg2://postgres:test@localhost:5432/vectordb\"  # Replace with your own\n",
     "db = SQLDatabase.from_uri(CONNECTION_STRING)"
@@ -88,7 +88,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "embeddings_model = OpenAIEmbeddings()"
    ]
@@ -219,7 +219,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
     "\n",
     "template = \"\"\"You are a Postgres expert. Given an input question, first create a syntactically correct Postgres query to run, then look at the results of the query and return the answer to the input question.\n",
     "Unless the user specifies in the question a specific number of examples to obtain, query for at most 5 results using the LIMIT clause as per Postgres. You can order the results to return the most informative data in the database.\n",
@@ -267,9 +267,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "db = SQLDatabase.from_uri(\n",
     "    CONNECTION_STRING\n",
@@ -324,7 +324,7 @@
    "source": [
     "import re\n",
     "\n",
-    "from langchain.schema.runnable import RunnableLambda\n",
+    "from langchain_core.runnables import RunnableLambda\n",
     "\n",
     "\n",
     "def replace_brackets(match):\n",

cookbook/rewrite.ipynb

@@ -31,11 +31,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
-    "from langchain.utilities import DuckDuckGoSearchAPIWrapper"
+    "from langchain_community.utilities import DuckDuckGoSearchAPIWrapper\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/sales_agent_with_context.ipynb

@@ -49,14 +49,13 @@
     "from langchain.agents.conversational.prompt import FORMAT_INSTRUCTIONS\n",
     "from langchain.chains import LLMChain, RetrievalQA\n",
     "from langchain.chains.base import Chain\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.llms import BaseLLM, OpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
     "from langchain.prompts.base import StringPromptTemplate\n",
     "from langchain.schema import AgentAction, AgentFinish\n",
     "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain.vectorstores import Chroma\n",
+    "from langchain_community.llms import BaseLLM\n",
+    "from langchain_community.vectorstores import Chroma\n",
+    "from langchain_openai import ChatOpenAI, OpenAI, OpenAIEmbeddings\n",
     "from pydantic import BaseModel, Field"
    ]
   },

cookbook/selecting_llms_based_on_context_length.ipynb

@@ -17,10 +17,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.prompt import PromptValue"
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompt_values import PromptValue\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/self_query_hotel_search.ipynb

@@ -255,7 +255,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "model = ChatOpenAI(model=\"gpt-4\")\n",
     "res = model.predict(\n",
@@ -1083,8 +1083,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.vectorstores import ElasticsearchStore\n",
+    "from langchain_community.vectorstores import ElasticsearchStore\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "embeddings = OpenAIEmbeddings()"
    ]

cookbook/sharedmemory_for_tools.ipynb

@@ -24,10 +24,10 @@
    "source": [
     "from langchain.agents import AgentExecutor, Tool, ZeroShotAgent\n",
     "from langchain.chains import LLMChain\n",
-    "from langchain.llms import OpenAI\n",
     "from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain.utilities import GoogleSearchAPIWrapper"
+    "from langchain_community.utilities import GoogleSearchAPIWrapper\n",
+    "from langchain_openai import OpenAI"
    ]
   },
   {

cookbook/smart_llm.ipynb

@@ -51,9 +51,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
-    "from langchain_experimental.smart_llm import SmartLLMChain"
+    "from langchain_experimental.smart_llm import SmartLLMChain\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/sql_db_qa.mdx

@@ -9,8 +9,8 @@ To set it up, follow the instructions on https://database.guide/2-sample-databas
 
 
 ```python
-from langchain.llms import OpenAI
-from langchain.utilities import SQLDatabase
+from langchain_openai import OpenAI
+from langchain_community.utilities import SQLDatabase
 from langchain_experimental.sql import SQLDatabaseChain
 ```
 
@@ -200,8 +200,8 @@ result["intermediate_steps"]
 How to add memory to a SQLDatabaseChain:
 
 ```python
-from langchain.llms import OpenAI
-from langchain.utilities import SQLDatabase
+from langchain_openai import OpenAI
+from langchain_community.utilities import SQLDatabase
 from langchain_experimental.sql import SQLDatabaseChain
 ```
 
@@ -647,7 +647,7 @@ Sometimes you may not have the luxury of using OpenAI or other service-hosted la
 import logging
 import torch
 from transformers import AutoTokenizer, GPT2TokenizerFast, pipeline, AutoModelForSeq2SeqLM, AutoModelForCausalLM
-from langchain.llms import HuggingFacePipeline
+from langchain_community.llms import HuggingFacePipeline
 
 # Note: This model requires a large GPU, e.g. an 80GB A100. See documentation for other ways to run private non-OpenAI models.
 model_id = "google/flan-ul2"
@@ -679,7 +679,7 @@ local_llm = HuggingFacePipeline(pipeline=pipe)
 
 
 ```python
-from langchain.utilities import SQLDatabase
+from langchain_community.utilities import SQLDatabase
 from langchain_experimental.sql import SQLDatabaseChain
 
 db = SQLDatabase.from_uri("sqlite:///../../../../notebooks/Chinook.db", include_tables=['Customer'])
@@ -994,9 +994,9 @@ Now that you have some examples (with manually corrected output SQL), you can do
 ```python
 from langchain.prompts import FewShotPromptTemplate, PromptTemplate
 from langchain.chains.sql_database.prompt import _sqlite_prompt, PROMPT_SUFFIX
-from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
 from langchain.prompts.example_selector.semantic_similarity import SemanticSimilarityExampleSelector
-from langchain.vectorstores import Chroma
+from langchain_community.vectorstores import Chroma
 
 example_prompt = PromptTemplate(
     input_variables=["table_info", "input", "sql_cmd", "sql_result", "answer"],

cookbook/stepback-qa.ipynb

@@ -23,10 +23,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.prompts import ChatPromptTemplate, FewShotChatMessagePromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnableLambda"
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import ChatPromptTemplate, FewShotChatMessagePromptTemplate\n",
+    "from langchain_core.runnables import RunnableLambda\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {
@@ -129,7 +129,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.utilities import DuckDuckGoSearchAPIWrapper\n",
+    "from langchain_community.utilities import DuckDuckGoSearchAPIWrapper\n",
     "\n",
     "search = DuckDuckGoSearchAPIWrapper(max_results=4)\n",
     "\n",

cookbook/tree_of_thought.ipynb

@@ -24,9 +24,9 @@
     }
    ],
    "source": [
-    "from langchain.llms import OpenAI\n",
+    "from langchain_openai import OpenAI\n",
     "\n",
-    "llm = OpenAI(temperature=1, max_tokens=512, model=\"text-davinci-003\")"
+    "llm = OpenAI(temperature=1, max_tokens=512, model=\"gpt-3.5-turbo-instruct\")"
    ]
   },
   {

cookbook/twitter-the-algorithm-analysis-deeplake.ipynb

@@ -37,8 +37,8 @@
     "import getpass\n",
     "import os\n",
     "\n",
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.vectorstores import DeepLake\n",
+    "from langchain_community.vectorstores import DeepLake\n",
+    "from langchain_openai import OpenAIEmbeddings\n",
     "\n",
     "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
     "activeloop_token = getpass.getpass(\"Activeloop Token:\")\n",
@@ -110,7 +110,7 @@
    "source": [
     "import os\n",
     "\n",
-    "from langchain.document_loaders import TextLoader\n",
+    "from langchain_community.document_loaders import TextLoader\n",
     "\n",
     "root_dir = \"./the-algorithm\"\n",
     "docs = []\n",
@@ -3809,7 +3809,7 @@
    "outputs": [],
    "source": [
     "from langchain.chains import ConversationalRetrievalChain\n",
-    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "model = ChatOpenAI(model_name=\"gpt-3.5-turbo-0613\")  # switch to 'gpt-4'\n",
     "qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)"

cookbook/two_agent_debate_tools.ipynb

@@ -24,13 +24,13 @@
    "source": [
     "from typing import Callable, List\n",
     "\n",
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.memory import ConversationBufferMemory\n",
     "from langchain.schema import (\n",
     "    AIMessage,\n",
     "    HumanMessage,\n",
     "    SystemMessage,\n",
-    ")"
+    ")\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/two_player_dnd.ipynb

@@ -24,11 +24,11 @@
    "source": [
     "from typing import Callable, List\n",
     "\n",
-    "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.schema import (\n",
     "    HumanMessage,\n",
     "    SystemMessage,\n",
-    ")"
+    ")\n",
+    "from langchain_openai import ChatOpenAI"
    ]
   },
   {

cookbook/wikibase_agent.ipynb

@@ -599,7 +599,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain_openai import ChatOpenAI\n",
     "\n",
     "llm = ChatOpenAI(model_name=\"gpt-4\", temperature=0)"
    ]

docs/.local_build.sh

@@ -13,11 +13,11 @@ rsync -ruv --exclude node_modules --exclude api_reference --exclude .venv --excl
 cd ../_dist
 poetry run python scripts/model_feat_table.py
 cp ../cookbook/README.md src/pages/cookbook.mdx
-cp ../.github/CONTRIBUTING.md docs/contributing.md
 mkdir -p docs/templates
 cp ../templates/docs/INDEX.md docs/templates/index.md
+poetry run python scripts/copy_templates.py
 wget https://raw.githubusercontent.com/langchain-ai/langserve/main/README.md -O docs/langserve.md
 
 yarn
 
-quarto preview docs
+poetry run quarto preview docs

docs/README.md

@@ -1,49 +1,3 @@
-# Website
+# LangChain Documentation
 
-This website is built using [Docusaurus 2](https://docusaurus.io/), a modern static website generator.
-
-### Installation
-
-```
-$ yarn
-```
-
-### Local Development
-
-```
-$ yarn start
-```
-
-This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server.
-
-### Build
-
-```
-$ yarn build
-```
-
-This command generates static content into the `build` directory and can be served using any static contents hosting service.
-
-### Deployment
-
-Using SSH:
-
-```
-$ USE_SSH=true yarn deploy
-```
-
-Not using SSH:
-
-```
-$ GIT_USER=<Your GitHub username> yarn deploy
-```
-
-If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch.
-
-### Continuous Integration
-
-Some common defaults for linting/formatting have been set for you. If you integrate your project with an open-source Continuous Integration system (e.g. Travis CI, CircleCI), you may check for issues using the following command.
-
-```
-$ yarn ci
-```
+For more information on contributing to our documentation, see the [Documentation Contributing Guide](https://python.langchain.com/docs/contributing/documentation)

docs/api_reference/conf.py

@@ -72,8 +72,8 @@ def setup(app):
 # -- Project information -----------------------------------------------------
 
 project = "🦜🔗 LangChain"
-copyright = "2023, Harrison Chase"
-author = "Harrison Chase"
+copyright = "2023, LangChain, Inc."
+author = "LangChain, Inc."
 
 version = data["tool"]["poetry"]["version"]
 release = version
@@ -136,18 +136,25 @@ html_theme_path = ["themes"]
 # redirects dictionary maps from old links to new links
 html_additional_pages = {}
 redirects = {
-    "index": "api_reference",
+    "index": "langchain_api_reference",
 }
 for old_link in redirects:
     html_additional_pages[old_link] = "redirects.html"
 
+partners_dir = Path(__file__).parent.parent.parent / "libs/partners"
+partners = [
+    (p.name, p.name.replace("-", "_") + "_api_reference")
+    for p in partners_dir.iterdir()
+]
+
 html_context = {
     "display_github": True,  # Integrate GitHub
-    "github_user": "hwchase17",  # Username
+    "github_user": "langchain-ai",  # Username
     "github_repo": "langchain",  # Repo name
     "github_version": "master",  # Version
     "conf_py_path": "/docs/api_reference",  # Path in the checkout to the docs root
     "redirects": redirects,
+    "partners": partners,
 }
 
 # Add any paths that contain custom static files (such as style sheets) here,

docs/api_reference/create_api_rst.py

@@ -1,23 +1,18 @@
 """Script for auto-generating api_reference.rst."""
 import importlib
 import inspect
+import os
 import typing
 from enum import Enum
 from pathlib import Path
 from typing import Dict, List, Literal, Optional, Sequence, TypedDict, Union
 
+import toml
 from pydantic import BaseModel
 
 ROOT_DIR = Path(__file__).parents[2].absolute()
 HERE = Path(__file__).parent
 
-PKG_DIR = ROOT_DIR / "libs" / "langchain" / "langchain"
-EXP_DIR = ROOT_DIR / "libs" / "experimental" / "langchain_experimental"
-CORE_DIR = ROOT_DIR / "libs" / "core" / "langchain_core"
-WRITE_FILE = HERE / "api_reference.rst"
-EXP_WRITE_FILE = HERE / "experimental_api_reference.rst"
-CORE_WRITE_FILE = HERE / "core_api_reference.rst"
-
 
 ClassKind = Literal["TypedDict", "Regular", "Pydantic", "enum"]
 
@@ -197,7 +192,9 @@ def _load_package_modules(
 
 
 def _construct_doc(
-    package_namespace: str, members_by_namespace: Dict[str, ModuleMembers]
+    package_namespace: str,
+    members_by_namespace: Dict[str, ModuleMembers],
+    package_version: str,
 ) -> str:
     """Construct the contents of the reference.rst file for the given package.
 
@@ -212,7 +209,7 @@ def _construct_doc(
     """
     full_doc = f"""\
 =======================
-``{package_namespace}`` API Reference
+``{package_namespace}`` {package_version}
 =======================
 
 """
@@ -290,51 +287,65 @@ def _build_rst_file(package_name: str = "langchain") -> None:
     Args:
         package_name: Can be either "langchain" or "core" or "experimental".
     """
-    package_members = _load_package_modules(_package_dir(package_name))
+    package_dir = _package_dir(package_name)
+    package_members = _load_package_modules(package_dir)
+    package_version = _get_package_version(package_dir)
     with open(_out_file_path(package_name), "w") as f:
         f.write(
             _doc_first_line(package_name)
-            + _construct_doc(package_namespace[package_name], package_members)
+            + _construct_doc(
+                _package_namespace(package_name), package_members, package_version
+            )
         )
 
 
-package_namespace = {
-    "langchain": "langchain",
-    "experimental": "langchain_experimental",
-    "core": "langchain_core",
-}
+def _package_namespace(package_name: str) -> str:
+    return (
+        package_name
+        if package_name == "langchain"
+        else f"langchain_{package_name.replace('-', '_')}"
+    )
 
 
 def _package_dir(package_name: str = "langchain") -> Path:
     """Return the path to the directory containing the documentation."""
-    return ROOT_DIR / "libs" / package_name / package_namespace[package_name]
+    if package_name in ("langchain", "experimental", "community", "core", "cli"):
+        return ROOT_DIR / "libs" / package_name / _package_namespace(package_name)
+    else:
+        return (
+            ROOT_DIR
+            / "libs"
+            / "partners"
+            / package_name
+            / _package_namespace(package_name)
+        )
+
+
+def _get_package_version(package_dir: Path) -> str:
+    with open(package_dir.parent / "pyproject.toml", "r") as f:
+        pyproject = toml.load(f)
+    return pyproject["tool"]["poetry"]["version"]
 
 
 def _out_file_path(package_name: str = "langchain") -> Path:
     """Return the path to the file containing the documentation."""
-    name_prefix = {
-        "langchain": "",
-        "experimental": "experimental_",
-        "core": "core_",
-    }
-    return HERE / f"{name_prefix[package_name]}api_reference.rst"
+    return HERE / f"{package_name.replace('-', '_')}_api_reference.rst"
 
 
 def _doc_first_line(package_name: str = "langchain") -> str:
     """Return the path to the file containing the documentation."""
-    prefix = {
-        "langchain": "",
-        "experimental": "experimental",
-        "core": "core",
-    }
-    return f".. {prefix[package_name]}_api_reference:\n\n"
+    return f".. {package_name.replace('-', '_')}_api_reference:\n\n"
 
 
 def main() -> None:
     """Generate the api_reference.rst file for each package."""
-    _build_rst_file(package_name="core")
-    _build_rst_file(package_name="langchain")
-    _build_rst_file(package_name="experimental")
+    for dir in os.listdir(ROOT_DIR / "libs"):
+        if dir in ("cli", "partners"):
+            continue
+        else:
+            _build_rst_file(package_name=dir)
+    for dir in os.listdir(ROOT_DIR / "libs" / "partners"):
+        _build_rst_file(package_name=dir)
 
 
 if __name__ == "__main__":

docs/api_reference/requirements.txt

@@ -1,6 +1,7 @@
--e libs/langchain
 -e libs/experimental
+-e libs/langchain
 -e libs/core
+-e libs/community
 pydantic<2
 autodoc_pydantic==1.8.0
 myst_parser
@@ -13,4 +14,4 @@ sphinx-panels
 toml
 myst_nb
 sphinx_copybutton
-pydata-sphinx-theme==0.13.1
+pydata-sphinx-theme==0.13.1

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

@@ -63,13 +63,6 @@
             <a href="#" role="button" class="btn sk-btn-rellink py-1 disabled"">Next</a>
           {%- endif %}
         </div>
-        {%- if pagename != "install" %}
-        <div class="alert alert-warning p-1 mb-2" role="alert">
-          <p class="text-center mb-0">
-          <strong>LangChain {{ release }}</strong><br/>
-          </p>
-        </div>
-        {%- endif %}
             {%- if meta and meta['parenttoc']|tobool %}
             <div class="sk-sidebar-toc">
             {% set nav = get_nav_object(maxdepth=3, collapse=True, numbered=True) %}

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

@@ -32,22 +32,33 @@
     <div class="sk-navbar-collapse collapse navbar-collapse" id="navbarSupportedContent">
       <ul class="navbar-nav mr-auto">
         <li class="nav-item">
-          <a class="sk-nav-link nav-link" href="{{ pathto('api_reference') }}">API</a>
+          <a class="sk-nav-link nav-link" href="{{ pathto('langchain_api_reference') }}">LangChain</a>
         </li>
         <li class="nav-item">
           <a class="sk-nav-link nav-link" href="{{ pathto('core_api_reference') }}">Core</a>
         </li>
+        <li class="nav-item">
+          <a class="sk-nav-link nav-link" href="{{ pathto('community_api_reference') }}">Community</a>
+        </li>
         <li class="nav-item">
           <a class="sk-nav-link nav-link" href="{{ pathto('experimental_api_reference') }}">Experimental</a>
         </li>
+        {%- for title, pathname in partners %}
         <li class="nav-item">
-          <a class="sk-nav-link nav-link" target="_blank" rel="noopener noreferrer" href="https://python.langchain.com/">Python Docs</a>
-        </li>
-        {%- for title, link, link_attrs in drop_down_navigation %}
-        <li class="nav-item">
-          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="{{ link }}" {{ link_attrs }}>{{ title }}</a>
+          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="{{ pathto(pathname) }}">{{ title }}</a>
         </li>
         {%- endfor %}
+        <li class="nav-item dropdown nav-more-item-dropdown">
+          <a class="sk-nav-link nav-link dropdown-toggle" href="#" id="navbarDropdown" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">Partner libs</a>
+          <div class="dropdown-menu" aria-labelledby="navbarDropdown">
+            {%- for title, pathname in partners %}
+              <a class="sk-nav-dropdown-item dropdown-item" href="{{ pathto(pathname) }}">{{ title }}</a>
+            {%- endfor %}
+          </div>
+        </li>
+        <li class="nav-item">
+          <a class="sk-nav-link nav-link" target="_blank" rel="noopener noreferrer" href="https://python.langchain.com/">Docs</a>
+        </li>
       </ul>
       {%- if pagename != "search"%}
       <div id="searchbox" role="search">

docs/docs/_templates/integration.mdx

@@ -32,7 +32,7 @@ There isn't any special setup for it.
 See a [usage example](/docs/integrations/llms/INCLUDE_REAL_NAME).
 
 ```python
-from langchain.llms import integration_class_REPLACE_ME
+from langchain_community.llms import integration_class_REPLACE_ME
 ```
 
 ## Text Embedding Models
@@ -40,7 +40,7 @@ from langchain.llms import integration_class_REPLACE_ME
 See a [usage example](/docs/integrations/text_embedding/INCLUDE_REAL_NAME)
 
 ```python
-from langchain.embeddings import integration_class_REPLACE_ME
+from langchain_community.embeddings import integration_class_REPLACE_ME
 ```
 
 ## Chat models
@@ -48,7 +48,7 @@ from langchain.embeddings import integration_class_REPLACE_ME
 See a [usage example](/docs/integrations/chat/INCLUDE_REAL_NAME)
 
 ```python
-from langchain.chat_models import integration_class_REPLACE_ME
+from langchain_community.chat_models import integration_class_REPLACE_ME
 ```
 
 ## Document Loader
@@ -56,5 +56,5 @@ from langchain.chat_models import integration_class_REPLACE_ME
 See a [usage example](/docs/integrations/document_loaders/INCLUDE_REAL_NAME).
 
 ```python
-from langchain.document_loaders import integration_class_REPLACE_ME
+from langchain_community.document_loaders import integration_class_REPLACE_ME
 ```

docs/docs/additional_resources/tutorials.mdx

@@ -6,7 +6,12 @@ Below are links to tutorials and courses on LangChain. For written guides on com
 
 ---------------------
 
-### [LangChain on Wikipedia](https://en.wikipedia.org/wiki/LangChain)
+### [LangChain](https://en.wikipedia.org/wiki/LangChain) on Wikipedia
+
+### 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
+
 
 ### DeepLearning.AI courses
  by [Harrison Chase](https://en.wikipedia.org/wiki/LangChain) and [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng)

docs/docs/changelog/core.mdx

@@ -0,0 +1,27 @@
+# langchain-core
+
+## 0.1.7 (Jan 5, 2024)
+
+#### Deleted
+
+No deletions.
+
+#### Deprecated
+
+- `BaseChatModel` methods `__call__`, `call_as_llm`, `predict`, `predict_messages`. Will be removed in 0.2.0. Use `BaseChatModel.invoke` instead.
+- `BaseChatModel` methods `apredict`, `apredict_messages`. Will be removed in 0.2.0. Use `BaseChatModel.ainvoke` instead.
+- `BaseLLM` methods `__call__, `predict`, `predict_messages`. Will be removed in 0.2.0. Use `BaseLLM.invoke` instead.
+- `BaseLLM` methods `apredict`, `apredict_messages`. Will be removed in 0.2.0. Use `BaseLLM.ainvoke` instead.
+
+#### Fixed
+
+- Restrict recursive URL scraping: [#15559](https://github.com/langchain-ai/langchain/pull/15559)
+
+#### Added
+
+No additions.
+
+#### Beta
+
+- Marked `langchain_core.load.load` and `langchain_core.load.loads` as beta.
+- Marked `langchain_core.beta.runnables.context.ContextGet` and `langchain_core.beta.runnables.context.ContextSet` as beta.

docs/docs/changelog/langchain.mdx

@@ -0,0 +1,36 @@
+# langchain
+
+## 0.1.0 (Jan 5, 2024)
+
+#### Deleted
+
+No deletions.
+
+#### Deprecated
+
+Deprecated classes and methods will be removed in 0.2.0
+
+| Deprecated                | Alternative                       | Reason                                         |
+|---------------------------------|-----------------------------------|------------------------------------------------|
+| ChatVectorDBChain               | ConversationalRetrievalChain      | More general to all retrievers                 |
+| create_ernie_fn_chain           | create_ernie_fn_runnable          | Use LCEL under the hood                        |
+| created_structured_output_chain | create_structured_output_runnable | Use LCEL under the hood                        |
+| NatBotChain                     |                                   | Not used                                       |
+| create_openai_fn_chain          | create_openai_fn_runnable         | Use LCEL under the hood                        |
+| create_structured_output_chain  | create_structured_output_runnable | Use LCEL under the hood                        |
+| load_query_constructor_chain    | load_query_constructor_runnable   | Use LCEL under the hood                        |
+| VectorDBQA                      | RetrievalQA                       | More general to all retrievers                 |
+| Sequential Chain                | LCEL                              | Obviated by LCEL                               |
+| SimpleSequentialChain           | LCEL                              | Obviated by LCEL                               |
+| TransformChain                  | LCEL/RunnableLambda               | Obviated by LCEL                               |
+| create_tagging_chain            | create_structured_output_runnable | Use LCEL under the hood                        |
+| ChatAgent                       | create_react_agent                | Use LCEL builder over a class                  |
+| ConversationalAgent             | create_react_agent                | Use LCEL builder over a class                  |
+| ConversationalChatAgent         | create_json_chat_agent            | Use LCEL builder over a class                  |
+| initialize_agent                | Individual create agent methods   | Individual create agent methods are more clear |
+| ZeroShotAgent                   | create_react_agent                | Use LCEL builder over a class                  |
+| OpenAIFunctionsAgent            | create_openai_functions_agent     | Use LCEL builder over a class                  |
+| OpenAIMultiFunctionsAgent       | create_openai_tools_agent         | Use LCEL builder over a class                  |
+| SelfAskWithSearchAgent          | create_self_ask_with_search       | Use LCEL builder over a class                  |
+| StructuredChatAgent             | create_structured_chat_agent      | Use LCEL builder over a class                  |
+| XMLAgent                        | create_xml_agent                  | Use LCEL builder over a class                  |

docs/docs/community.md

@@ -1,53 +0,0 @@
-# Community navigator
-
-Hi! Thanks for being here. We’re lucky to have a community of so many passionate developers building with LangChain–we have so much to teach and learn from each other. Community members contribute code, host meetups, write blog posts, amplify each other’s work, become each other's customers and collaborators, and so much more.
-
-Whether you’re new to LangChain, looking to go deeper, or just want to get more exposure to the world of building with LLMs, this page can point you in the right direction. 
-
-- **🦜 Contribute to LangChain**
-
-- **🌍 Meetups, Events, and Hackathons**
-
-- **📣 Help Us Amplify Your Work**
-
-- **💬 Stay in the loop**
-
-
-# 🦜 Contribute to LangChain
-
-LangChain is the product of over 5,000+ contributions by 1,500+ contributors, and there is ******still****** so much to do together. Here are some ways to get involved:
-
-- **[Open a pull request](https://github.com/langchain-ai/langchain/issues):** We’d appreciate all forms of contributions–new features, infrastructure improvements, better documentation, bug fixes, etc. If you have an improvement or an idea, we’d love to work on it with you.
-- **[Read our contributor guidelines:](https://github.com/langchain-ai/langchain/blob/bbd22b9b761389a5e40fc45b0570e1830aabb707/.github/CONTRIBUTING.md)** We ask contributors to follow a ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow, run a few local checks for formatting, linting, and testing before submitting, and follow certain documentation and testing conventions.
-    - **First time contributor?** [Try one of these PRs with the “good first issue” tag](https://github.com/langchain-ai/langchain/contribute).
-- **Become an expert:** Our experts help the community by answering product questions in Discord. If that’s a role you’d like to play, we’d be so grateful! (And we have some special experts-only goodies/perks we can tell you more about). Send us an email to introduce yourself at hello@langchain.dev and we’ll take it from there!
-- **Integrate with LangChain:** If your product integrates with LangChain–or aspires to–we want to help make sure the experience is as smooth as possible for you and end users. Send us an email at hello@langchain.dev and tell us what you’re working on.
-    - **Become an Integration Maintainer:** Partner with our team to ensure your integration stays up-to-date and talk directly with users (and answer their inquiries) in our Discord. Introduce yourself at hello@langchain.dev if you’d like to explore this role.
-
-
-# 🌍 Meetups, Events, and Hackathons
-
-One of our favorite things about working in AI is how much enthusiasm there is for building together. We want to help make that as easy and impactful for you as possible! 
-- **Find a meetup, hackathon, or webinar:** You can find the one for you on our [global events calendar](https://mirror-feeling-d80.notion.site/0bc81da76a184297b86ca8fc782ee9a3?v=0d80342540df465396546976a50cfb3f).  
-    - **Submit an event to our calendar:** Email us at events@langchain.dev with a link to your event page! We can also help you spread the word with our local communities.
-- **Host a meetup:** If you want to bring a group of builders together, we want to help! We can publicize your event on our event calendar/Twitter, share it with our local communities in Discord, send swag, or potentially hook you up with a sponsor. Email us at events@langchain.dev to tell us about your event!
-- **Become a meetup sponsor:** We often hear from groups of builders that want to get together, but are blocked or limited on some dimension (space to host, budget for snacks, prizes to distribute, etc.). If you’d like to help, send us an email to events@langchain.dev we can share more about how it works!
-- **Speak at an event:** Meetup hosts are always looking for great speakers, presenters, and panelists. If you’d like to do that at an event, send us an email to hello@langchain.dev with more information about yourself, what you want to talk about, and what city you’re based in and we’ll try to match you with an upcoming event!
-- **Tell us about your LLM community:** If you host or participate in a community that would welcome support from LangChain and/or our team, send us an email at hello@langchain.dev and let us know how we can help.
-
-# 📣 Help Us Amplify Your Work
-
-If you’re working on something you’re proud of, and think the LangChain community would benefit from knowing about it, we want to help you show it off.
-
-- **Post about your work and mention us:** We love hanging out on Twitter to see what people in the space are talking about and working on. If you tag [@langchainai](https://twitter.com/LangChainAI), we’ll almost certainly see it and can show you some love.
-- **Publish something on our blog:** If you’re writing about your experience building with LangChain, we’d love to post (or crosspost) it on our blog! E-mail hello@langchain.dev with a draft of your post! Or even an idea for something you want to write about.
-- **Get your product onto our [integrations hub](https://integrations.langchain.com/):** Many developers take advantage of our seamless integrations with other products, and come to our integrations hub to find out who those are. If you want to get your product up there, tell us about it (and how it works with LangChain) at hello@langchain.dev.
-
-# ☀️ Stay in the loop
-
-Here’s where our team hangs out, talks shop, spotlights cool work, and shares what we’re up to. We’d love to see you there too.
-
-- **[Twitter](https://twitter.com/LangChainAI):** We post about what we’re working on and what cool things we’re seeing in the space. If you tag @langchainai in your post, we’ll almost certainly see it, and can show you some love!
-- **[Discord](https://discord.gg/6adMQxSpJS):** connect with over 30,000 developers who are building with LangChain.
-- **[GitHub](https://github.com/langchain-ai/langchain):** Open pull requests, contribute to a discussion, and/or contribute
-- **[Subscribe to our bi-weekly Release Notes](https://6w1pwbss0py.typeform.com/to/KjZB1auB):** a twice/month email roundup of the coolest things going on in our orbit