Update readme

.github/CODE_OF_CONDUCT.md

@@ -1,132 +0,0 @@
-# Contributor Covenant Code of Conduct
-
-## Our Pledge
-
-We as members, contributors, and leaders pledge to make participation in our
-community a harassment-free experience for everyone, regardless of age, body
-size, visible or invisible disability, ethnicity, sex characteristics, gender
-identity and expression, level of experience, education, socio-economic status,
-nationality, personal appearance, race, caste, color, religion, or sexual
-identity and orientation.
-
-We pledge to act and interact in ways that contribute to an open, welcoming,
-diverse, inclusive, and healthy community.
-
-## Our Standards
-
-Examples of behavior that contributes to a positive environment for our
-community include:
-
-* Demonstrating empathy and kindness toward other people
-* Being respectful of differing opinions, viewpoints, and experiences
-* Giving and gracefully accepting constructive feedback
-* Accepting responsibility and apologizing to those affected by our mistakes,
-  and learning from the experience
-* Focusing on what is best not just for us as individuals, but for the overall
-  community
-
-Examples of unacceptable behavior include:
-
-* The use of sexualized language or imagery, and sexual attention or advances of
-  any kind
-* Trolling, insulting or derogatory comments, and personal or political attacks
-* Public or private harassment
-* Publishing others' private information, such as a physical or email address,
-  without their explicit permission
-* Other conduct which could reasonably be considered inappropriate in a
-  professional setting
-
-## Enforcement Responsibilities
-
-Community leaders are responsible for clarifying and enforcing our standards of
-acceptable behavior and will take appropriate and fair corrective action in
-response to any behavior that they deem inappropriate, threatening, offensive,
-or harmful.
-
-Community leaders have the right and responsibility to remove, edit, or reject
-comments, commits, code, wiki edits, issues, and other contributions that are
-not aligned to this Code of Conduct, and will communicate reasons for moderation
-decisions when appropriate.
-
-## Scope
-
-This Code of Conduct applies within all community spaces, and also applies when
-an individual is officially representing the community in public spaces.
-Examples of representing our community include using an official e-mail address,
-posting via an official social media account, or acting as an appointed
-representative at an online or offline event.
-
-## Enforcement
-
-Instances of abusive, harassing, or otherwise unacceptable behavior may be
-reported to the community leaders responsible for enforcement at
-conduct@langchain.dev.
-All complaints will be reviewed and investigated promptly and fairly.
-
-All community leaders are obligated to respect the privacy and security of the
-reporter of any incident.
-
-## Enforcement Guidelines
-
-Community leaders will follow these Community Impact Guidelines in determining
-the consequences for any action they deem in violation of this Code of Conduct:
-
-### 1. Correction
-
-**Community Impact**: Use of inappropriate language or other behavior deemed
-unprofessional or unwelcome in the community.
-
-**Consequence**: A private, written warning from community leaders, providing
-clarity around the nature of the violation and an explanation of why the
-behavior was inappropriate. A public apology may be requested.
-
-### 2. Warning
-
-**Community Impact**: A violation through a single incident or series of
-actions.
-
-**Consequence**: A warning with consequences for continued behavior. No
-interaction with the people involved, including unsolicited interaction with
-those enforcing the Code of Conduct, for a specified period of time. This
-includes avoiding interactions in community spaces as well as external channels
-like social media. Violating these terms may lead to a temporary or permanent
-ban.
-
-### 3. Temporary Ban
-
-**Community Impact**: A serious violation of community standards, including
-sustained inappropriate behavior.
-
-**Consequence**: A temporary ban from any sort of interaction or public
-communication with the community for a specified period of time. No public or
-private interaction with the people involved, including unsolicited interaction
-with those enforcing the Code of Conduct, is allowed during this period.
-Violating these terms may lead to a permanent ban.
-
-### 4. Permanent Ban
-
-**Community Impact**: Demonstrating a pattern of violation of community
-standards, including sustained inappropriate behavior, harassment of an
-individual, or aggression toward or disparagement of classes of individuals.
-
-**Consequence**: A permanent ban from any sort of public interaction within the
-community.
-
-## Attribution
-
-This Code of Conduct is adapted from the [Contributor Covenant][homepage],
-version 2.1, available at
-[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1].
-
-Community Impact Guidelines were inspired by
-[Mozilla's code of conduct enforcement ladder][Mozilla CoC].
-
-For answers to common questions about this code of conduct, see the FAQ at
-[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at
-[https://www.contributor-covenant.org/translations][translations].
-
-[homepage]: https://www.contributor-covenant.org
-[v2.1]: https://www.contributor-covenant.org/version/2/1/code_of_conduct.html
-[Mozilla CoC]: https://github.com/mozilla/diversity
-[FAQ]: https://www.contributor-covenant.org/faq
-[translations]: https://www.contributor-covenant.org/translations

.github/CONTRIBUTING.md

@@ -1,19 +1,20 @@
 # Contributing to LangChain
 
 Hi there! Thank you for even being interested in contributing to LangChain.
-As an open-source project in a rapidly developing field, we are extremely open to contributions, whether they involve new features, improved infrastructure, better documentation, or bug fixes.
+As an open source project in a rapidly developing field, we are extremely open
+to contributions, whether they be in the form of new features, improved infra, better documentation, or bug fixes.
 
 ## 🗺️ Guidelines
 
 ### 👩‍💻 Contributing Code
 
-To contribute to this project, please follow the ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow.
+To contribute to this project, please follow a ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow.
 Please do not try to push directly to this repo unless you are a maintainer.
 
 Please follow the checked-in pull request template when opening pull requests. Note related issues and tag relevant
 maintainers.
 
-Pull requests cannot land without passing the formatting, linting, and testing checks first. See [Testing](#testing) and
+Pull requests cannot land without passing the formatting, linting and testing checks first. See [Testing](#testing) and
 [Formatting and Linting](#formatting-and-linting) for how to run these checks locally.
 
 It's essential that we maintain great documentation and testing. If you:
@@ -26,14 +27,16 @@ It's essential that we maintain great documentation and testing. If you:
   - Add a demo notebook in `docs/modules`.
   - Add unit and integration tests.
 
-We are a small, progress-oriented team. If there's something you'd like to add or change, opening a pull request is the
+We're a small, building-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.
 
 ### 🚩GitHub Issues
 
-Our [issues](https://github.com/langchain-ai/langchain/issues) page is kept up to date with bugs, improvements, and feature requests.
+Our [issues](https://github.com/langchain-ai/langchain/issues) page is kept up to date
+with bugs, improvements, and feature requests.
 
-There is a taxonomy of labels to help with sorting and discovery of issues of interest. Please use these to help organize issues.
+There is a taxonomy of labels to help with sorting and discovery of issues of interest. Please use these to help
+organize issues.
 
 If you start working on an issue, please assign it to yourself.
 
@@ -56,12 +59,12 @@ we do not want these to get in the way of getting good code into the codebase.
 
 ## 🚀 Quick Start
 
-This quick start guide explains how to run the repository locally.
+This quick start describes running 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.
+This project uses [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`)
 
@@ -72,11 +75,11 @@ tell Poetry to use the virtualenv python environment (`poetry config virtualenvs
 
 ### Core vs. Experimental
 
-This repository contains two separate projects:
-- `langchain`: core langchain code, abstractions, and use cases.
-- `langchain.experimental`: see the [Experimental README](https://github.com/langchain-ai/langchain/tree/master/libs/experimental/README.md) for more information.
+There are two separate projects in this repository:
+- `langchain`: core langchain code, abstractions, and use cases
+- `langchain.experimental`: see the [Experimental README](../libs/experimental/README.md) for more information.
 
-Each of these has its own development environment. Docs are run from the top-level makefile, but development
+Each of these has their 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:
@@ -126,7 +129,7 @@ To run unit tests in Docker:
 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.
+There are also [integration tests and code-coverage](../libs/langchain/tests/README.md) available.
 
 ### Formatting and Linting
 
@@ -134,21 +137,14 @@ 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/).
+Formatting for this project is done via a combination of [Black](https://black.readthedocs.io/en/stable/) and [ruff](https://docs.astral.sh/ruff/rules/).
 
-To run formatting for docs, cookbook and templates:
+To run formatting for this project:
 
 ```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
@@ -159,21 +155,14 @@ This is especially useful when you have made changes to a subset of the project
 
 #### Linting
 
-Linting for this project is done via a combination of [ruff](https://docs.astral.sh/ruff/rules/) and [mypy](http://mypy-lang.org/).
+Linting for this project is done via a combination of [Black](https://black.readthedocs.io/en/stable/), [ruff](https://docs.astral.sh/ruff/rules/), and [mypy](http://mypy-lang.org/).
 
-To run linting for docs, cookbook and templates:
+To run linting for this project:
 
 ```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
@@ -293,20 +282,13 @@ make docs_build
 make api_docs_build
 ```
 
-Finally, run the link checker to ensure all links are valid:
+Finally, you can run the linkchecker to make sure 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
@@ -318,4 +300,4 @@ even patch releases may contain [non-backwards-compatible changes](https://semve
 ### 🌟 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.
+If you have a Twitter account you would like us to mention, please let us know in the PR or in another manner.

.github/workflows/_compile_integration_test.yml

@@ -1,57 +0,0 @@
-name: compile-integration-test
-
-on:
-  workflow_call:
-    inputs:
-      working-directory:
-        required: true
-        type: string
-        description: "From which folder this pipeline executes"
-
-env:
-  POETRY_VERSION: "1.6.1"
-
-jobs:
-  build:
-    defaults:
-      run:
-        working-directory: ${{ inputs.working-directory }}
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version:
-          - "3.8"
-          - "3.9"
-          - "3.10"
-          - "3.11"
-    name: Python ${{ matrix.python-version }}
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
-        with:
-          python-version: ${{ matrix.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
-          cache-key: compile-integration
-
-      - name: Install integration dependencies
-        shell: bash
-        run: poetry install --with=test_integration
-
-      - name: Check integration tests compile
-        shell: bash
-        run: poetry run pytest -m compile tests/integration_tests
-
-      - name: Ensure the tests did not create any additional files
-        shell: bash
-        run: |
-          set -eu
-
-          STATUS="$(git status)"
-          echo "$STATUS"
-
-          # grep will exit non-zero if the target message isn't found,
-          # and `set -e` above will cause the step to fail.
-          echo "$STATUS" | grep 'nothing to commit, working tree clean'

.github/workflows/_lint.yml

@@ -7,21 +7,20 @@ on:
         required: true
         type: string
         description: "From which folder this pipeline executes"
-      langchain-location:
-        required: false
-        type: string
-        description: "Relative path to the langchain library folder"
 
 env:
   POETRY_VERSION: "1.6.1"
   WORKDIR: ${{ inputs.working-directory == '' && '.' || inputs.working-directory }}
 
-  # This env var allows us to get inline annotations when ruff has complaints.
-  RUFF_OUTPUT_FORMAT: github
-
 jobs:
   build:
     runs-on: ubuntu-latest
+    env:
+      # This number is set "by eye": we want it to be big enough
+      # so that it's bigger than the number of commits in any reasonable PR,
+      # and also as small as possible since increasing the number makes
+      # the initial `git fetch` slower.
+      FETCH_DEPTH: 50
     strategy:
       matrix:
         # Only lint on the min and max supported Python versions.
@@ -35,7 +34,52 @@ jobs:
           - "3.8"
           - "3.11"
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3
+        with:
+          # Fetch the last FETCH_DEPTH commits, so the mtime-changing script
+          # can accurately set the mtimes of files modified in the last FETCH_DEPTH commits.
+          fetch-depth: ${{ env.FETCH_DEPTH }}
+      - name: Restore workdir file mtimes to last-edited commit date
+        id: restore-mtimes
+        # This is needed to make black caching work.
+        # Black's cache uses file (mtime, size) to check whether a lookup is a cache hit.
+        # Without this command, files in the repo would have the current time as the modified time,
+        # since the previous action step just created them.
+        # This command resets the mtime to the last time the files were modified in git instead,
+        # which is a high-quality and stable representation of the last modification date.
+        run: |
+          # Important considerations:
+          # - These commands run at base of the repo, since we never `cd` to the `WORKDIR`.
+          # - We only want to alter mtimes for Python files, since that's all black checks.
+          # - We don't need to alter mtimes for directories, since black doesn't look at those.
+          # - We also only alter mtimes inside the `WORKDIR` since that's all we'll lint.
+          # - This should run before `poetry install`, because poetry's venv also contains
+          #   Python files, and we don't want to alter their mtimes since they aren't linted.
+
+          # Ensure we fail on non-zero exits and on undefined variables.
+          # Also print executed commands, for easier debugging.
+          set -eux
+
+          # Restore the mtimes of Python files in the workdir based on git history.
+          .github/tools/git-restore-mtime --no-directories "$WORKDIR/**/*.py"
+
+          # Since CI only does a partial fetch (to `FETCH_DEPTH`) for efficiency,
+          # the local git repo doesn't have full history. There are probably files
+          # that were last modified in a commit *older than* the oldest fetched commit.
+          # After `git-restore-mtime`, such files have a mtime set to the oldest fetched commit.
+          #
+          # As new commits get added, that timestamp will keep moving forward.
+          # If left unchanged, this will make `black` think that the files were edited
+          # more recently than its cache suggests. Instead, we can set their mtime
+          # to a fixed date in the far past that won't change and won't cause cache misses in black.
+          #
+          # For all workdir Python files modified in or before the oldest few fetched commits,
+          # make their mtime be 2000-01-01 00:00:00.
+          OLDEST_COMMIT="$(git log --reverse '--pretty=format:%H' | head -1)"
+          OLDEST_COMMIT_TIME="$(git show -s '--format=%ai' "$OLDEST_COMMIT")"
+          find "$WORKDIR" -name '*.py' -type f -not -newermt "$OLDEST_COMMIT_TIME" -exec touch -c -m -t '200001010000' '{}' '+'
+
+          echo "oldest-commit=$OLDEST_COMMIT" >> "$GITHUB_OUTPUT"
 
       - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
         uses: "./.github/actions/poetry_setup"
@@ -72,11 +116,22 @@ jobs:
 
       - name: Install langchain editable
         working-directory: ${{ inputs.working-directory }}
-        if: ${{ inputs.langchain-location }}
-        env:
-          LANGCHAIN_LOCATION: ${{ inputs.langchain-location }}
+        if: ${{ inputs.working-directory != 'libs/langchain' }}
         run: |
-          pip install -e "$LANGCHAIN_LOCATION"
+          pip install -e ../langchain
+
+      - name: Restore black cache
+        uses: actions/cache@v3
+        env:
+          CACHE_BASE: black-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', env.WORKDIR)) }}
+          SEGMENT_DOWNLOAD_TIMEOUT_MIN: "1"
+        with:
+          path: |
+            ${{ env.WORKDIR }}/.black_cache
+          key: ${{ env.CACHE_BASE }}-${{ steps.restore-mtimes.outputs.oldest-commit }}
+          restore-keys:
+            # If we can't find an exact match for our cache key, accept any with this prefix.
+            ${{ env.CACHE_BASE }}-
 
       - name: Get .mypy_cache to speed up mypy
         uses: actions/cache@v3
@@ -89,5 +144,7 @@ jobs:
 
       - name: Analysing the code with our lint
         working-directory: ${{ inputs.working-directory }}
+        env:
+          BLACK_CACHE_DIR: .black_cache
         run: |
           make lint

.github/workflows/_pydantic_compatibility.yml

@@ -26,7 +26,7 @@ jobs:
           - "3.11"
     name: Pydantic v1/v2 compatibility - Python ${{ matrix.python-version }}
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3
 
       - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
         uses: "./.github/actions/poetry_setup"

.github/workflows/_release.yml

@@ -9,121 +9,13 @@ on:
         description: "From which folder this pipeline executes"
 
 env:
-  PYTHON_VERSION: "3.10"
   POETRY_VERSION: "1.6.1"
 
 jobs:
-  build:
+  if_release:
+    # Disallow publishing from branches that aren't `master`.
     if: github.ref == 'refs/heads/master'
     runs-on: ubuntu-latest
-
-    outputs:
-      pkg-name: ${{ steps.check-version.outputs.pkg-name }}
-      version: ${{ steps.check-version.outputs.version }}
-
-    steps:
-      - uses: actions/checkout@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 }}
-          cache-key: release
-
-      # We want to keep this build stage *separate* from the release stage,
-      # so that there's no sharing of permissions between them.
-      # The release stage has trusted publishing and GitHub repo contents write access,
-      # and we want to keep the scope of that access limited just to the release job.
-      # Otherwise, a malicious `build` step (e.g. via a compromised dependency)
-      # could get access to our GitHub or PyPI credentials.
-      #
-      # Per the trusted publishing GitHub Action:
-      # > It is strongly advised to separate jobs for building [...]
-      # > from the publish job.
-      # https://github.com/pypa/gh-action-pypi-publish#non-goals
-      - name: Build project for distribution
-        run: poetry build
-        working-directory: ${{ inputs.working-directory }}
-
-      - name: Upload build
-        uses: actions/upload-artifact@v3
-        with:
-          name: dist
-          path: ${{ inputs.working-directory }}/dist/
-
-      - name: Check Version
-        id: check-version
-        shell: bash
-        working-directory: ${{ inputs.working-directory }}
-        run: |
-          echo pkg-name="$(poetry version | cut -d ' ' -f 1)" >> $GITHUB_OUTPUT
-          echo version="$(poetry version --short)" >> $GITHUB_OUTPUT
-
-  test-pypi-publish:
-    needs:
-      - build
-    uses:
-      ./.github/workflows/_test_release.yml
-    with:
-      working-directory: ${{ inputs.working-directory }}
-    secrets: inherit
-
-  pre-release-checks:
-    needs:
-      - build
-      - test-pypi-publish
-    runs-on: ubuntu-latest
-    steps:
-      # We explicitly *don't* set up caching here. This ensures our tests are
-      # maximally sensitive to catching breakage.
-      #
-      # For example, here's a way that caching can cause a falsely-passing test:
-      # - Make the langchain package manifest no longer list a dependency package
-      #   as a requirement. This means it won't be installed by `pip install`,
-      #   and attempting to use it would cause a crash.
-      # - That dependency used to be required, so it may have been cached.
-      #   When restoring the venv packages from cache, that dependency gets included.
-      # - 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
-        with:
-          python-version: ${{ env.PYTHON_VERSION }}
-
-      - name: Test published package
-        shell: bash
-        env:
-          PKG_NAME: ${{ needs.build.outputs.pkg-name }}
-          VERSION: ${{ needs.build.outputs.version }}
-        # Here we specify:
-        # - The test PyPI index as the *primary* index, meaning that it takes priority.
-        # - The regular PyPI index as an extra index, so that any dependencies that
-        #   are not found on test PyPI can be resolved and installed anyway.
-        #
-        # Without the former, we might install the wrong langchain release.
-        # Without the latter, we might not be able to install langchain's dependencies.
-        #
-        # TODO: add more in-depth pre-publish tests after testing that importing works
-        run: |
-          pip install \
-            --index-url https://test.pypi.org/simple/ \
-            --extra-index-url https://pypi.org/simple/ \
-            "$PKG_NAME==$VERSION"
-
-          # Replace all dashes in the package name with underscores,
-          # since that's how Python imports packages with dashes in the name.
-          IMPORT_NAME="$(echo "$PKG_NAME" | sed s/-/_/g)"
-
-          python -c "import $IMPORT_NAME; print(dir($IMPORT_NAME))"
-
-  publish:
-    needs:
-      - build
-      - test-pypi-publish
-      - pre-release-checks
-    runs-on: ubuntu-latest
     permissions:
       # This permission is used for trusted publishing:
       # https://blog.pypi.org/posts/2023-04-20-introducing-trusted-publishers/
@@ -132,65 +24,28 @@ jobs:
       # https://docs.pypi.org/trusted-publishers/adding-a-publisher/
       id-token: write
 
-    defaults:
-      run:
-        working-directory: ${{ inputs.working-directory }}
-
-    steps:
-      - uses: actions/checkout@v4
-
-      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
-        with:
-          python-version: ${{ env.PYTHON_VERSION }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: ${{ inputs.working-directory }}
-          cache-key: release
-
-      - uses: actions/download-artifact@v3
-        with:
-          name: dist
-          path: ${{ inputs.working-directory }}/dist/
-
-      - name: Publish package distributions to PyPI
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          packages-dir: ${{ inputs.working-directory }}/dist/
-          verbose: true
-          print-hash: true
-
-  mark-release:
-    needs:
-      - build
-      - test-pypi-publish
-      - pre-release-checks
-      - publish
-    runs-on: ubuntu-latest
-    permissions:
-      # This permission is needed by `ncipollo/release-action` to
-      # create the GitHub release.
+      # This permission is needed by `ncipollo/release-action` to create the GitHub release.
       contents: write
-
     defaults:
       run:
         working-directory: ${{ inputs.working-directory }}
-
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3
 
       - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
         uses: "./.github/actions/poetry_setup"
         with:
-          python-version: ${{ env.PYTHON_VERSION }}
+          python-version: "3.10"
           poetry-version: ${{ env.POETRY_VERSION }}
           working-directory: ${{ inputs.working-directory }}
           cache-key: release
 
-      - uses: actions/download-artifact@v3
-        with:
-          name: dist
-          path: ${{ inputs.working-directory }}/dist/
-
+      - name: Build project for distribution
+        run: poetry build
+      - name: Check Version
+        id: check-version
+        run: |
+          echo version=$(poetry version --short) >> $GITHUB_OUTPUT
       - name: Create Release
         uses: ncipollo/release-action@v1
         if: ${{ inputs.working-directory == 'libs/langchain' }}
@@ -199,5 +54,11 @@ jobs:
           token: ${{ secrets.GITHUB_TOKEN }}
           draft: false
           generateReleaseNotes: true
-          tag: v${{ needs.build.outputs.version }}
+          tag: v${{ steps.check-version.outputs.version }}
           commit: master
+      - name: Publish package distributions to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          packages-dir: ${{ inputs.working-directory }}/dist/
+          verbose: true
+          print-hash: true

.github/workflows/_test.yml

@@ -26,7 +26,7 @@ jobs:
           - "3.11"
     name: Python ${{ matrix.python-version }}
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3
 
       - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
         uses: "./.github/actions/poetry_setup"

.github/workflows/_test_release.yml

@@ -1,95 +0,0 @@
-name: test-release
-
-on:
-  workflow_call:
-    inputs:
-      working-directory:
-        required: true
-        type: string
-        description: "From which folder this pipeline executes"
-
-env:
-  POETRY_VERSION: "1.6.1"
-  PYTHON_VERSION: "3.10"
-
-jobs:
-  build:
-    if: github.ref == 'refs/heads/master'
-    runs-on: ubuntu-latest
-
-    outputs:
-      pkg-name: ${{ steps.check-version.outputs.pkg-name }}
-      version: ${{ steps.check-version.outputs.version }}
-
-    steps:
-      - uses: actions/checkout@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 }}
-          cache-key: release
-
-      # We want to keep this build stage *separate* from the release stage,
-      # so that there's no sharing of permissions between them.
-      # The release stage has trusted publishing and GitHub repo contents write access,
-      # and we want to keep the scope of that access limited just to the release job.
-      # Otherwise, a malicious `build` step (e.g. via a compromised dependency)
-      # could get access to our GitHub or PyPI credentials.
-      #
-      # Per the trusted publishing GitHub Action:
-      # > It is strongly advised to separate jobs for building [...]
-      # > from the publish job.
-      # https://github.com/pypa/gh-action-pypi-publish#non-goals
-      - name: Build project for distribution
-        run: poetry build
-        working-directory: ${{ inputs.working-directory }}
-
-      - name: Upload build
-        uses: actions/upload-artifact@v3
-        with:
-          name: test-dist
-          path: ${{ inputs.working-directory }}/dist/
-
-      - name: Check Version
-        id: check-version
-        shell: bash
-        working-directory: ${{ inputs.working-directory }}
-        run: |
-          echo pkg-name="$(poetry version | cut -d ' ' -f 1)" >> $GITHUB_OUTPUT
-          echo version="$(poetry version --short)" >> $GITHUB_OUTPUT
-
-  publish:
-    needs:
-      - build
-    runs-on: ubuntu-latest
-    permissions:
-      # This permission is used for trusted publishing:
-      # https://blog.pypi.org/posts/2023-04-20-introducing-trusted-publishers/
-      #
-      # Trusted publishing has to also be configured on PyPI for each package:
-      # https://docs.pypi.org/trusted-publishers/adding-a-publisher/
-      id-token: write
-
-    steps:
-      - uses: actions/checkout@v4
-
-      - uses: actions/download-artifact@v3
-        with:
-          name: test-dist
-          path: ${{ inputs.working-directory }}/dist/
-
-      - name: Publish to test PyPI
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          packages-dir: ${{ inputs.working-directory }}/dist/
-          verbose: true
-          print-hash: true
-          repository-url: https://test.pypi.org/legacy/
-
-          # We overwrite any existing distributions with the same name and version.
-          # This is *only for CI use* and is *extremely dangerous* otherwise!
-          # https://github.com/pypa/gh-action-pypi-publish#tolerating-release-package-file-duplicates
-          skip-existing: true

.github/workflows/codespell.yml

@@ -17,7 +17,7 @@ jobs:
 
     steps:
       - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
 
       - name: Install Dependencies
         run: |

.github/workflows/doc_lint.yml

@@ -1,17 +1,11 @@
 ---
-name: Docs, templates, cookbook lint
+name: Documentation Lint
 
 on:
   push:
-    branches: [ master ]
+    branches: [master]
   pull_request:
-    paths:
-      - 'docs/**'
-      - 'templates/**'
-      - 'cookbook/**'
-      - '.github/workflows/_lint.yml'
-      - '.github/workflows/doc_lint.yml'
-  workflow_dispatch:
+    branches: [master]
 
 jobs:
   check:
@@ -19,17 +13,10 @@ jobs:
 
     steps:
     - name: Checkout repository
-      uses: actions/checkout@v4
+      uses: actions/checkout@v2
 
     - name: Run import check
       run: |
         # We should not encourage imports directly from main init file
         # Expect for hub
-        git grep 'from langchain import' {docs/docs,templates,cookbook} | grep -vE 'from langchain import (hub)' && exit 1 || exit 0
-
-  lint:
-      uses:
-        ./.github/workflows/_lint.yml
-      with:
-        working-directory: "."
-      secrets: inherit
+        git grep 'from langchain import' docs/{extras,docs_skeleton,snippets} | grep -vE 'from langchain import (hub)' && exit 1 || exit 0

.github/workflows/langchain_ci.yml

@@ -12,7 +12,6 @@ on:
       - '.github/workflows/_test.yml'
       - '.github/workflows/_pydantic_compatibility.yml'
       - '.github/workflows/langchain_ci.yml'
-      - 'libs/*'
       - 'libs/langchain/**'
   workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
 
@@ -45,13 +44,6 @@ jobs:
       working-directory: libs/langchain
     secrets: inherit
 
-  compile-integration-tests:
-    uses:
-      ./.github/workflows/_compile_integration_test.yml
-    with:
-      working-directory: libs/langchain
-    secrets: inherit
-
   pydantic-compatibility:
     uses:
       ./.github/workflows/_pydantic_compatibility.yml
@@ -73,7 +65,7 @@ jobs:
           - "3.11"
     name: Python ${{ matrix.python-version }} extended tests
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3
 
       - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
         uses: "./.github/actions/poetry_setup"

.github/workflows/langchain_cli_ci.yml

@@ -1,47 +0,0 @@
----
-name: libs/cli CI
-
-on:
-  push:
-    branches: [ master ]
-  pull_request:
-    paths:
-      - '.github/actions/poetry_setup/action.yml'
-      - '.github/tools/**'
-      - '.github/workflows/_lint.yml'
-      - '.github/workflows/_test.yml'
-      - '.github/workflows/_pydantic_compatibility.yml'
-      - '.github/workflows/langchain_cli_ci.yml'
-      - 'libs/cli/**'
-      - 'libs/*'
-  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
-
-# If another push to the same PR or branch happens while this workflow is still running,
-# cancel the earlier run in favor of the next run.
-#
-# There's no point in testing an outdated version of the code. GitHub only allows
-# a limited number of job runners to be active at the same time, so it's better to cancel
-# pointless jobs early so that more useful jobs can run sooner.
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: true
-
-env:
-  POETRY_VERSION: "1.6.1"
-  WORKDIR: "libs/cli"
-
-jobs:
-  lint:
-    uses:
-      ./.github/workflows/_lint.yml
-    with:
-      working-directory: libs/cli
-      langchain-location: ../langchain
-    secrets: inherit
-
-  test:
-    uses:
-      ./.github/workflows/_test.yml
-    with:
-      working-directory: libs/cli
-    secrets: inherit

.github/workflows/langchain_cli_release.yml

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

.github/workflows/langchain_experimental_ci.yml

@@ -11,7 +11,7 @@ on:
       - '.github/workflows/_lint.yml'
       - '.github/workflows/_test.yml'
       - '.github/workflows/langchain_experimental_ci.yml'
-      - 'libs/*'
+      - 'libs/langchain/**'
       - 'libs/experimental/**'
   workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
 
@@ -35,7 +35,6 @@ jobs:
       ./.github/workflows/_lint.yml
     with:
       working-directory: libs/experimental
-      langchain-location: ../langchain
     secrets: inherit
 
   test:
@@ -45,13 +44,6 @@ jobs:
       working-directory: libs/experimental
     secrets: inherit
 
-  compile-integration-tests:
-    uses:
-      ./.github/workflows/_compile_integration_test.yml
-    with:
-      working-directory: libs/experimental
-    secrets: inherit
-
   # It's possible that langchain-experimental works fine with the latest *published* langchain,
   # but is broken with the langchain on `master`.
   #
@@ -70,7 +62,7 @@ jobs:
           - "3.11"
     name: test with unpublished langchain - Python ${{ matrix.python-version }}
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3
 
       - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
         uses: "./.github/actions/poetry_setup"
@@ -105,7 +97,7 @@ jobs:
           - "3.11"
     name: Python ${{ matrix.python-version }} extended tests
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3
 
       - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
         uses: "./.github/actions/poetry_setup"

.github/workflows/langchain_experimental_test_release.yml

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

.github/workflows/langchain_test_release.yml

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

.github/workflows/scheduled_test.yml

@@ -24,7 +24,7 @@ jobs:
           - "3.11"
     name: Python ${{ matrix.python-version }}
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3
 
       - name: Set up Python ${{ matrix.python-version }}
         uses: "./.github/actions/poetry_setup"
@@ -55,21 +55,12 @@ jobs:
           poetry install --with=test_integration
           poetry run pip install google-cloud-aiplatform
           poetry run pip install "boto3>=1.28.57"
-          if [[ ${{ matrix.python-version }} != "3.8" ]]
-          then
-            poetry run pip install fireworks-ai
-          fi
 
       - name: Run tests
         shell: bash
         env:
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
-          AZURE_OPENAI_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }}
-          AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
-          AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }}
-          AZURE_OPENAI_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_DEPLOYMENT_NAME }}
-          FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
         run: |
           make scheduled_tests
 

.github/workflows/templates_ci.yml

@@ -1,37 +0,0 @@
----
-name: templates CI
-
-on:
-  push:
-    branches: [ master ]
-  pull_request:
-    paths:
-      - '.github/actions/poetry_setup/action.yml'
-      - '.github/tools/**'
-      - '.github/workflows/_lint.yml'
-      - '.github/workflows/templates_ci.yml'
-      - 'templates/**'
-  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
-
-# If another push to the same PR or branch happens while this workflow is still running,
-# cancel the earlier run in favor of the next run.
-#
-# There's no point in testing an outdated version of the code. GitHub only allows
-# a limited number of job runners to be active at the same time, so it's better to cancel
-# pointless jobs early so that more useful jobs can run sooner.
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: true
-
-env:
-  POETRY_VERSION: "1.6.1"
-  WORKDIR: "templates"
-
-jobs:
-  lint:
-    uses:
-      ./.github/workflows/_lint.yml
-    with:
-      working-directory: templates
-      langchain-location: ../libs/langchain
-    secrets: inherit

.gitignore

@@ -174,7 +174,6 @@ docs/api_reference/*/
 !docs/api_reference/_static/
 !docs/api_reference/templates/
 !docs/api_reference/themes/
-docs/docs/build
-docs/docs/node_modules
-docs/docs/yarn.lock
-_dist
+docs/docs_skeleton/build
+docs/docs_skeleton/node_modules
+docs/docs_skeleton/yarn.lock

.gitmodules

@@ -0,0 +1,4 @@
+[submodule "docs/_docs_skeleton"]
+	path = docs/_docs_skeleton
+	url = https://github.com/langchain-ai/langchain-shared-docs
+	branch = main

.readthedocs.yaml

@@ -9,14 +9,9 @@ build:
   os: ubuntu-22.04
   tools:
     python: "3.11"
-  commands:
-      - python -mvirtualenv $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 --exists-action=w --no-cache-dir -r docs/api_reference/requirements.txt
+  jobs:
+    pre_build:
       - python docs/api_reference/create_api_rst.py
-      - cat docs/api_reference/conf.py
-      - python -m sphinx -T -E -b html -d _build/doctrees -c docs/api_reference docs/api_reference $READTHEDOCS_OUTPUT/html -j auto
 
 # Build documentation in the docs/ directory with Sphinx
 sphinx:

Makefile

@@ -15,10 +15,10 @@ docs_build:
 	docs/.local_build.sh
 
 docs_clean:
-	rm -r _dist
+	rm -r docs/_dist
 
 docs_linkcheck:
-	poetry run linkchecker _dist/docs/ --ignore-url node_modules
+	poetry run linkchecker docs/_dist/docs_skeleton/ --ignore-url node_modules
 
 api_docs_build:
 	poetry run python docs/api_reference/create_api_rst.py
@@ -37,18 +37,6 @@ spell_check:
 spell_fix:
 	poetry run codespell --toml pyproject.toml -w
 
-######################
-# LINTING AND FORMATTING
-######################
-
-lint:
-	poetry run ruff docs templates cookbook
-	poetry run black docs templates cookbook --diff
-
-format format_diff:
-	poetry run black docs templates cookbook
-	poetry run ruff --select I --fix docs templates cookbook
-
 ######################
 # HELP
 ######################
@@ -65,4 +53,4 @@ help:
 	@echo 'api_docs_linkcheck           - run linkchecker on the API Reference documentation'
 	@echo 'spell_check               	- run codespell on the project'
 	@echo 'spell_fix               		- run codespell on the project and fix the errors'
-	@echo '-- TEST and LINT tasks are within libs/*/ per-package --'
+	@echo '-- TEST and LINT tasks are within libs/*/ per-package --'

README.md

@@ -18,9 +18,8 @@
 
 Looking for the JS/TS version? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
 
-To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com). 
-[LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications. 
-Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) to get off the waitlist or speak with our sales team
+**Production Support:** As you move your LangChains into production, we'd love to offer more hands-on support.
+Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) to share more about what you're building, and our team will get in touch.
 
 ## 🚨Breaking Changes for select chains (SQLDatabase) on 7/28/23
 
@@ -93,7 +92,7 @@ Memory refers to persisting state between calls of a chain/agent. LangChain prov
 
 **🧐 Evaluation:**
 
-[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is by using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
+[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
 
 For more information on these concepts, please see our [full documentation](https://python.langchain.com).
 

cookbook/LLaMA2_sql_chat.ipynb

@@ -1,398 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "fc935871-7640-41c6-b798-58514d860fe0",
-   "metadata": {},
-   "source": [
-    "## LLaMA2 chat with SQL\n",
-    "\n",
-    "Open source, local LLMs are great to consider for any application that demands data privacy.\n",
-    "\n",
-    "SQL is one good example. \n",
-    "\n",
-    "This cookbook shows how to perform text-to-SQL using various local versions of LLaMA2 run locally.\n",
-    "\n",
-    "## Packages"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "81adcf8b-395a-4f02-8749-ac976942b446",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "! pip install langchain replicate"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e13ed66-300b-4a23-b8ac-44df68ee4733",
-   "metadata": {},
-   "source": [
-    "## LLM\n",
-    "\n",
-    "There are a few ways to access LLaMA2.\n",
-    "\n",
-    "To run locally, we use Ollama.ai. \n",
-    "\n",
-    "See [here](https://python.langchain.com/docs/integrations/chat/ollama) for details on installation and setup.\n",
-    "\n",
-    "Also, see [here](https://python.langchain.com/docs/guides/local_llms) for our full guide on local LLMs.\n",
-    " \n",
-    "To use an external API, which is not private, we can use Replicate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "6a75a5c6-34ee-4ab9-a664-d9b432d812ee",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Init param `input` is deprecated, please use `model_kwargs` instead.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Local\n",
-    "from langchain.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 getpass import getpass\n",
-    "from langchain.llms import Replicate\n",
-    "\n",
-    "# REPLICATE_API_TOKEN = getpass()\n",
-    "# os.environ[\"REPLICATE_API_TOKEN\"] = REPLICATE_API_TOKEN\n",
-    "replicate_id = \"meta/llama-2-13b-chat:f4e2de70d66816a838a89eeeb621910adffb0dd0baba3976c96980970978018d\"\n",
-    "llama2_chat_replicate = Replicate(\n",
-    "    model=replicate_id, input={\"temperature\": 0.01, \"max_length\": 500, \"top_p\": 1}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "ce96f7ea-b3d5-44e1-9fa5-a79e04a9e1fb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Simply set the LLM we want to use\n",
-    "llm = llama2_chat"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "80222165-f353-4e35-a123-5f70fd70c6c8",
-   "metadata": {},
-   "source": [
-    "## DB\n",
-    "\n",
-    "Connect to a SQLite DB.\n",
-    "\n",
-    "To create this particular DB, you can use the code and follow the steps shown [here](https://github.com/facebookresearch/llama-recipes/blob/main/demo_apps/StructuredLlama.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "025bdd82-3bb1-4948-bc7c-c3ccd94fd05c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.utilities import SQLDatabase\n",
-    "\n",
-    "db = SQLDatabase.from_uri(\"sqlite:///nba_roster.db\", sample_rows_in_table_info=0)\n",
-    "\n",
-    "\n",
-    "def get_schema(_):\n",
-    "    return db.get_table_info()\n",
-    "\n",
-    "\n",
-    "def run_query(query):\n",
-    "    return db.run(query)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "654b3577-baa2-4e12-a393-f40e5db49ac7",
-   "metadata": {},
-   "source": [
-    "## Query a SQL DB \n",
-    "\n",
-    "Follow the runnables workflow [here](https://python.langchain.com/docs/expression_language/cookbook/sql_db)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "5a4933ea-d9c0-4b0a-8177-ba4490c6532b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "' SELECT \"Team\" FROM nba_roster WHERE \"NAME\" = \\'Klay Thompson\\';'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Prompt\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "\n",
-    "template = \"\"\"Based on the table schema below, write a SQL query that would answer the user's question:\n",
-    "{schema}\n",
-    "\n",
-    "Question: {question}\n",
-    "SQL Query:\"\"\"\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", \"Given an input question, convert it to a SQL query. No pre-amble.\"),\n",
-    "        (\"human\", template),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "# Chain to query\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
-    "\n",
-    "sql_response = (\n",
-    "    RunnablePassthrough.assign(schema=get_schema)\n",
-    "    | prompt\n",
-    "    | llm.bind(stop=[\"\\nSQLResult:\"])\n",
-    "    | StrOutputParser()\n",
-    ")\n",
-    "\n",
-    "sql_response.invoke({\"question\": \"What team is Klay Thompson on?\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0e9e2c8-9b88-4853-ac86-001bc6cc6695",
-   "metadata": {},
-   "source": [
-    "We can review the results:\n",
-    "\n",
-    "* [LangSmith trace](https://smith.langchain.com/public/afa56a06-b4e2-469a-a60f-c1746e75e42b/r) LLaMA2-13 Replicate API\n",
-    "* [LangSmith trace](https://smith.langchain.com/public/2d4ecc72-6b8f-4523-8f0b-ea95c6b54a1d/r) LLaMA2-13 local \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "2a2825e3-c1b6-4f7d-b9c9-d9835de323bb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=' Based on the table schema and SQL query, there are 30 unique teams in the NBA.')"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Chain to answer\n",
-    "template = \"\"\"Based on the table schema below, question, sql query, and sql response, write a natural language response:\n",
-    "{schema}\n",
-    "\n",
-    "Question: {question}\n",
-    "SQL Query: {query}\n",
-    "SQL Response: {response}\"\"\"\n",
-    "prompt_response = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"Given an input question and SQL response, convert it to a natural langugae answer. No pre-amble.\",\n",
-    "        ),\n",
-    "        (\"human\", template),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "full_chain = (\n",
-    "    RunnablePassthrough.assign(query=sql_response)\n",
-    "    | RunnablePassthrough.assign(\n",
-    "        schema=get_schema,\n",
-    "        response=lambda x: db.run(x[\"query\"]),\n",
-    "    )\n",
-    "    | prompt_response\n",
-    "    | llm\n",
-    ")\n",
-    "\n",
-    "full_chain.invoke({\"question\": \"How many unique teams are there?\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec17b3ee-6618-4681-b6df-089bbb5ffcd7",
-   "metadata": {},
-   "source": [
-    "We can review the results:\n",
-    "\n",
-    "* [LangSmith trace](https://smith.langchain.com/public/10420721-746a-4806-8ecf-d6dc6399d739/r) LLaMA2-13 Replicate API\n",
-    "* [LangSmith trace](https://smith.langchain.com/public/5265ebab-0a22-4f37-936b-3300f2dfa1c1/r) LLaMA2-13 local "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1e85381b-1edc-4bb3-a7bd-2ab23f81e54d",
-   "metadata": {},
-   "source": [
-    "## Chat with a SQL DB \n",
-    "\n",
-    "Next, we can add memory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "022868f2-128e-42f5-8d90-d3bb2f11d994",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "' SELECT \"Team\" FROM nba_roster WHERE \"NAME\" = \\'Klay Thompson\\';'"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Prompt\n",
-    "from langchain.memory import ConversationBufferMemory\n",
-    "from langchain.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",
-    "\"\"\"\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"system\", template),\n",
-    "        MessagesPlaceholder(variable_name=\"history\"),\n",
-    "        (\"human\", \"{question}\"),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "memory = ConversationBufferMemory(return_messages=True)\n",
-    "\n",
-    "# Chain to query with memory\n",
-    "from langchain.schema.runnable import RunnableLambda\n",
-    "\n",
-    "sql_chain = (\n",
-    "    RunnablePassthrough.assign(\n",
-    "        schema=get_schema,\n",
-    "        history=RunnableLambda(lambda x: memory.load_memory_variables(x)[\"history\"]),\n",
-    "    )\n",
-    "    | prompt\n",
-    "    | llm.bind(stop=[\"\\nSQLResult:\"])\n",
-    "    | StrOutputParser()\n",
-    ")\n",
-    "\n",
-    "\n",
-    "def save(input_output):\n",
-    "    output = {\"output\": input_output.pop(\"output\")}\n",
-    "    memory.save_context(input_output, output)\n",
-    "    return output[\"output\"]\n",
-    "\n",
-    "\n",
-    "sql_response_memory = RunnablePassthrough.assign(output=sql_chain) | save\n",
-    "sql_response_memory.invoke({\"question\": \"What team is Klay Thompson on?\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "800a7a3b-f411-478b-af51-2310cd6e0425",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=' Sure! Here\\'s the natural language response based on the given input:\\n\\n\"Klay Thompson\\'s salary is $43,219,440.\"')"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Chain to answer\n",
-    "template = \"\"\"Based on the table schema below, question, sql query, and sql response, write a natural language response:\n",
-    "{schema}\n",
-    "\n",
-    "Question: {question}\n",
-    "SQL Query: {query}\n",
-    "SQL Response: {response}\"\"\"\n",
-    "prompt_response = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"Given an input question and SQL response, convert it to a natural langugae answer. No pre-amble.\",\n",
-    "        ),\n",
-    "        (\"human\", template),\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "full_chain = (\n",
-    "    RunnablePassthrough.assign(query=sql_response_memory)\n",
-    "    | RunnablePassthrough.assign(\n",
-    "        schema=get_schema,\n",
-    "        response=lambda x: db.run(x[\"query\"]),\n",
-    "    )\n",
-    "    | prompt_response\n",
-    "    | llm\n",
-    ")\n",
-    "\n",
-    "full_chain.invoke({\"question\": \"What is his salary?\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b77fee61-f4da-4bb1-8285-14101e505518",
-   "metadata": {},
-   "source": [
-    "Here is the [trace](https://smith.langchain.com/public/54794d18-2337-4ce2-8b9f-3d8a2df89e51/r)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.16"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

cookbook/README.md

@@ -1,53 +0,0 @@
-# LangChain cookbook
-
-Example code for building applications with LangChain, with an emphasis on more applied and end-to-end examples than contained in the [main documentation](https://python.langchain.com).
-
-Notebook | Description
-:- | :-
-[LLaMA2_sql_chat.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/LLaMA2_sql_chat.ipynb) | Build a chat application that interacts with a SQL database using an open source llm (llama2), specifically demonstrated on an SQLite database containing rosters.
-[Semi_Structured_RAG.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/Semi_Structured_RAG.ipynb) | Perform retrieval-augmented generation (rag) on documents with semi-structured data, including text and tables, using unstructured for parsing, multi-vector retriever for storing, and lcel for implementing chains.
-[Semi_structured_and_multi_moda...](https://github.com/langchain-ai/langchain/tree/master/cookbook/Semi_structured_and_multi_modal_RAG.ipynb) | Perform retrieval-augmented generation (rag) on documents with semi-structured data and images, using unstructured for parsing, multi-vector retriever for storage and retrieval, and lcel for implementing chains.
-[Semi_structured_multi_modal_RA...](https://github.com/langchain-ai/langchain/tree/master/cookbook/Semi_structured_multi_modal_RAG_LLaMA2.ipynb) | Perform retrieval-augmented generation (rag) on documents with semi-structured data and images, using various tools and methods such as unstructured for parsing, multi-vector retriever for storing, lcel for implementing chains, and open source language models like llama2, llava, and gpt4all.
-[autogpt/autogpt.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/autogpt/autogpt.ipynb) | Implement autogpt, a language model, with langchain primitives such as llms, prompttemplates, vectorstores, embeddings, and tools.
-[autogpt/marathon_times.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/autogpt/marathon_times.ipynb) | Implement autogpt for finding winning marathon times.
-[baby_agi.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/baby_agi.ipynb) | Implement babyagi, an ai agent that can generate and execute tasks based on a given objective, with the flexibility to swap out specific vectorstores/model providers.
-[baby_agi_with_agent.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/baby_agi_with_agent.ipynb) | Swap out the execution chain in the babyagi notebook with an agent that has access to tools, aiming to obtain more reliable information.
-[camel_role_playing.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/camel_role_playing.ipynb) | Implement the camel framework for creating autonomous cooperative agents in large-scale language models, using role-playing and inception prompting to guide chat agents towards task completion.
-[causal_program_aided_language_...](https://github.com/langchain-ai/langchain/tree/master/cookbook/causal_program_aided_language_model.ipynb) | Implement the causal program-aided language (cpal) chain, which improves upon the program-aided language (pal) by incorporating causal structure to prevent hallucination in language models, particularly when dealing with complex narratives and math problems with nested dependencies.
-[code-analysis-deeplake.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/code-analysis-deeplake.ipynb) | Analyze its own code base with the help of gpt and activeloop's deep lake.
-[custom_agent_with_plugin_retri...](https://github.com/langchain-ai/langchain/tree/master/cookbook/custom_agent_with_plugin_retrieval.ipynb) | Build a custom agent that can interact with ai plugins by retrieving tools and creating natural language wrappers around openapi endpoints.
-[custom_agent_with_plugin_retri...](https://github.com/langchain-ai/langchain/tree/master/cookbook/custom_agent_with_plugin_retrieval_using_plugnplai.ipynb) | Build a custom agent with plugin retrieval functionality, utilizing ai plugins from the `plugnplai` directory.
-[databricks_sql_db.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/databricks_sql_db.ipynb) | Connect to databricks runtimes and databricks sql.
-[deeplake_semantic_search_over_...](https://github.com/langchain-ai/langchain/tree/master/cookbook/deeplake_semantic_search_over_chat.ipynb) | Perform semantic search and question-answering over a group chat using activeloop's deep lake with gpt4.
-[elasticsearch_db_qa.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/elasticsearch_db_qa.ipynb) | Interact with elasticsearch analytics databases in natural language and build search queries via the elasticsearch dsl API.
-[forward_looking_retrieval_augm...](https://github.com/langchain-ai/langchain/tree/master/cookbook/forward_looking_retrieval_augmented_generation.ipynb) | Implement the forward-looking active retrieval augmented generation (flare) method, which generates answers to questions, identifies uncertain tokens, generates hypothetical questions based on these tokens, and retrieves relevant documents to continue generating the answer.
-[generative_agents_interactive_...](https://github.com/langchain-ai/langchain/tree/master/cookbook/generative_agents_interactive_simulacra_of_human_behavior.ipynb) | Implement a generative agent that simulates human behavior, based on a research paper, using a time-weighted memory object backed by a langchain retriever.
-[gymnasium_agent_simulation.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/gymnasium_agent_simulation.ipynb) | Create a simple agent-environment interaction loop in simulated environments like text-based games with gymnasium.
-[hugginggpt.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/hugginggpt.ipynb) | Implement hugginggpt, a system that connects language models like chatgpt with the machine learning community via hugging face.
-[hypothetical_document_embeddin...](https://github.com/langchain-ai/langchain/tree/master/cookbook/hypothetical_document_embeddings.ipynb) | Improve document indexing with hypothetical document embeddings (hyde), an embedding technique that generates and embeds hypothetical answers to queries.
-[learned_prompt_optimization.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/learned_prompt_optimization.ipynb) | Automatically enhance language model prompts by injecting specific terms using reinforcement learning, which can be used to personalize responses based on user preferences.
-[llm_bash.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/llm_bash.ipynb) | Perform simple filesystem commands using language learning models (llms) and a bash process.
-[llm_checker.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/llm_checker.ipynb) | Create a self-checking chain using the llmcheckerchain function.
-[llm_math.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/llm_math.ipynb) | Solve complex word math problems using language models and python repls.
-[llm_summarization_checker.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/llm_summarization_checker.ipynb) | Check the accuracy of text summaries, with the option to run the checker multiple times for improved results.
-[llm_symbolic_math.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/llm_symbolic_math.ipynb) | Solve algebraic equations with the help of llms (language learning models) and sympy, a python library for symbolic mathematics.
-[meta_prompt.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/meta_prompt.ipynb) | Implement the meta-prompt concept, which is a method for building self-improving agents that reflect on their own performance and modify their instructions accordingly.
-[multi_modal_output_agent.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/multi_modal_output_agent.ipynb) | Generate multi-modal outputs, specifically images and text.
-[multi_player_dnd.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/multi_player_dnd.ipynb) | Simulate multi-player dungeons & dragons games, with a custom function determining the speaking schedule of the agents.
-[multiagent_authoritarian.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/multiagent_authoritarian.ipynb) | Implement a multi-agent simulation where a privileged agent controls the conversation, including deciding who speaks and when the conversation ends, in the context of a simulated news network.
-[multiagent_bidding.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/multiagent_bidding.ipynb) | Implement a multi-agent simulation where agents bid to speak, with the highest bidder speaking next, demonstrated through a fictitious presidential debate example.
-[myscale_vector_sql.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/myscale_vector_sql.ipynb) | Access and interact with the myscale integrated vector database, which can enhance the performance of language model (llm) applications.
-[openai_functions_retrieval_qa....](https://github.com/langchain-ai/langchain/tree/master/cookbook/openai_functions_retrieval_qa.ipynb) | Structure response output in a question-answering system by incorporating openai functions into a retrieval pipeline.
-[petting_zoo.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/petting_zoo.ipynb) | Create multi-agent simulations with simulated environments using the petting zoo library.
-[plan_and_execute_agent.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/plan_and_execute_agent.ipynb) | Create plan-and-execute agents that accomplish objectives by planning tasks with a language model (llm) and executing them with a separate agent.
-[press_releases.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/press_releases.ipynb) | Retrieve and query company press release data powered by [Kay.ai](https://kay.ai).
-[program_aided_language_model.i...](https://github.com/langchain-ai/langchain/tree/master/cookbook/program_aided_language_model.ipynb) | Implement program-aided language models as described in the provided research paper.
-[retrieval_in_sql.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/retrieval_in_sql.ipynb) | Perform retrieval-augmented-generation (rag) on a PostgreSQL database using pgvector.
-[sales_agent_with_context.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/sales_agent_with_context.ipynb) | Implement a context-aware ai sales agent, salesgpt, that can have natural sales conversations, interact with other systems, and use a product knowledge base to discuss a company's offerings.
-[self_query_hotel_search.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/self_query_hotel_search.ipynb) | Build a hotel room search feature with self-querying retrieval, using a specific hotel recommendation dataset.
-[smart_llm.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/smart_llm.ipynb) | Implement a smartllmchain, a self-critique chain that generates multiple output proposals, critiques them to find the best one, and then improves upon it to produce a final output.
-[tree_of_thought.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/tree_of_thought.ipynb) | Query a large language model using the tree of thought technique.
-[twitter-the-algorithm-analysis...](https://github.com/langchain-ai/langchain/tree/master/cookbook/twitter-the-algorithm-analysis-deeplake.ipynb) | Analyze the source code of the Twitter algorithm with the help of gpt4 and activeloop's deep lake.
-[two_agent_debate_tools.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/two_agent_debate_tools.ipynb) | Simulate multi-agent dialogues where the agents can utilize various tools.
-[two_player_dnd.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/two_player_dnd.ipynb) | Simulate a two-player dungeons & dragons game, where a dialogue simulator class is used to coordinate the dialogue between the protagonist and the dungeon master.
-[wikibase_agent.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/wikibase_agent.ipynb) | Create a simple wikibase agent that utilizes sparql generation, with testing done on http://wikidata.org.

cookbook/plan_and_execute_agent.ipynb

@@ -1,258 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "0ddfef23-3c74-444c-81dd-6753722997fa",
-   "metadata": {},
-   "source": [
-    "# Plan-and-execute\n",
-    "\n",
-    "Plan-and-execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by [BabyAGI](https://github.com/yoheinakajima/babyagi) and then the [\"Plan-and-Solve\" paper](https://arxiv.org/abs/2305.04091).\n",
-    "\n",
-    "The planning is almost always done by an LLM.\n",
-    "\n",
-    "The execution is usually done by a separate agent (equipped with tools)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a7ecb22a-7009-48ec-b14e-f0fa5aac1cd0",
-   "metadata": {},
-   "source": [
-    "## Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "5fbbd4ee-bfe8-4a25-afe4-8d1a552a3d2e",
-   "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_experimental.plan_and_execute import (\n",
-    "    PlanAndExecute,\n",
-    "    load_agent_executor,\n",
-    "    load_chat_planner,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e0e995e5-af9d-4988-bcd0-467a2a2e18cd",
-   "metadata": {},
-   "source": [
-    "## Tools"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "1d789f4e-54e3-4602-891a-f076e0ab9594",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "search = DuckDuckGoSearchAPIWrapper()\n",
-    "llm = OpenAI(temperature=0)\n",
-    "llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)\n",
-    "tools = [\n",
-    "    Tool(\n",
-    "        name=\"Search\",\n",
-    "        func=search.run,\n",
-    "        description=\"useful for when you need to answer questions about current events\",\n",
-    "    ),\n",
-    "    Tool(\n",
-    "        name=\"Calculator\",\n",
-    "        func=llm_math_chain.run,\n",
-    "        description=\"useful for when you need to answer questions about math\",\n",
-    "    ),\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "04dc6452-a07f-49f9-be12-95be1e2afccc",
-   "metadata": {},
-   "source": [
-    "## Planner, Executor, and Agent\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "d8f49c03-c804-458b-8122-c92b26c7b7dd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model = ChatOpenAI(temperature=0)\n",
-    "planner = load_chat_planner(model)\n",
-    "executor = load_agent_executor(model, tools, verbose=True)\n",
-    "agent = PlanAndExecute(planner=planner, executor=executor)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "78ba03dd-0322-4927-b58d-a7e2027fdbb3",
-   "metadata": {},
-   "source": [
-    "## Run example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "a57f7efe-7866-47a7-bce5-9c7b1047964e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mAction:\n",
-      "{\n",
-      "  \"action\": \"Search\",\n",
-      "  \"action_input\": \"current prime minister of the UK\"\n",
-      "}\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mAction:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Search\",\n",
-      "  \"action_input\": \"current prime minister of the UK\"\n",
-      "}\n",
-      "```\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mBottom right: Rishi Sunak is the current prime minister and the first non-white prime minister. The prime minister of the United Kingdom is the principal minister of the crown of His Majesty's Government, and the head of the British Cabinet. 3 min. British Prime Minister Rishi Sunak asserted his stance on gender identity in a speech Wednesday, stating it was \"common sense\" that \"a man is a man and a woman is a woman\" — a ... The former chancellor Rishi Sunak is the UK's new prime minister. Here's what you need to know about him. He won after running for the second time this year He lost to Liz Truss in September,... Isaeli Prime Minister Benjamin Netanyahu spoke with US President Joe Biden on Wednesday, the prime minister's office said in a statement. Netanyahu \"thanked the President for the powerful words of ... By Yasmeen Serhan/London Updated: October 25, 2022 12:56 PM EDT | Originally published: October 24, 2022 9:17 AM EDT S top me if you've heard this one before: After a tumultuous period of political...\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mThe search results indicate that Rishi Sunak is the current prime minister of the UK. However, it's important to note that this information may not be accurate or up to date.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mAction:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Search\",\n",
-      "  \"action_input\": \"current age of the prime minister of the UK\"\n",
-      "}\n",
-      "```\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mHow old is Rishi Sunak? Mr Sunak was born on 12 May, 1980, making him 42 years old. He first became an MP in 2015, aged 34, and has served the constituency of Richmond in Yorkshire ever since. He... Prime Ministers' ages when they took office From oldest to youngest, the ages of the PMs were as follows: Winston Churchill - 65 years old James Callaghan - 64 years old Clement Attlee - 62 years... Anna Kaufman USA TODAY Just a few days after Liz Truss resigned as prime minister, the UK has a new prime minister. Truss, who lasted a mere 45 days in office, will be replaced by Rishi... Advertisement Rishi Sunak is the youngest British prime minister of modern times. Mr. Sunak is 42 and started out in Parliament in 2015. Rishi Sunak was appointed as chancellor of the Exchequer... The first prime minister of the current United Kingdom of Great Britain and Northern Ireland upon its effective creation in 1922 (when 26 Irish counties seceded and created the Irish Free State) was Bonar Law, [10] although the country was not renamed officially until 1927, when Stanley Baldwin was the serving prime minister. [11]\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mBased on the search results, it seems that Rishi Sunak is the current prime minister of the UK. However, I couldn't find any specific information about his age. Would you like me to search again for the current age of the prime minister?\n",
-      "\n",
-      "Action:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Search\",\n",
-      "  \"action_input\": \"age of Rishi Sunak\"\n",
-      "}\n",
-      "```\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mRishi Sunak is 42 years old, making him the youngest person to hold the office of prime minister in modern times. How tall is Rishi Sunak? How Old Is Rishi Sunak? Rishi Sunak was born on May 12, 1980, in Southampton, England. Parents and Nationality Sunak's parents were born to Indian-origin families in East Africa before... Born on May 12, 1980, Rishi is currently 42 years old. He has been a member of parliament since 2015 where he was an MP for Richmond and has served in roles including Chief Secretary to the Treasury and the Chancellor of Exchequer while Boris Johnson was PM. Family Murty, 42, is the daughter of the Indian billionaire NR Narayana Murthy, often described as the Bill Gates of India, who founded the software company Infosys. According to reports, his... Sunak became the first non-White person to lead the country and, at age 42, the youngest to take on the role in more than a century. Like most politicians, Sunak is revered by some and...\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mBased on the search results, Rishi Sunak is currently 42 years old. He was born on May 12, 1980.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mThought: To calculate the age raised to the power of 0.43, I can use the calculator tool.\n",
-      "\n",
-      "Action:\n",
-      "```json\n",
-      "{\n",
-      "  \"action\": \"Calculator\",\n",
-      "  \"action_input\": \"42^0.43\"\n",
-      "}\n",
-      "```\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "42^0.43\u001b[32;1m\u001b[1;3m```text\n",
-      "42**0.43\n",
-      "```\n",
-      "...numexpr.evaluate(\"42**0.43\")...\n",
-      "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m4.9888126515157\u001b[0m\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 4.9888126515157\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3mThe age raised to the power of 0.43 is approximately 4.9888126515157.\n",
-      "\n",
-      "Final Answer:\n",
-      "```json\n",
-      "{\n",
-      "  \"action\": \"Final Answer\",\n",
-      "  \"action_input\": \"The age raised to the power of 0.43 is approximately 4.9888126515157.\"\n",
-      "}\n",
-      "```\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3mAction:\n",
-      "```\n",
-      "{\n",
-      "  \"action\": \"Final Answer\",\n",
-      "  \"action_input\": \"The current prime minister of the UK is Rishi Sunak. His age raised to the power of 0.43 is approximately 4.9888126515157.\"\n",
-      "}\n",
-      "```\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'The current prime minister of the UK is Rishi Sunak. His age raised to the power of 0.43 is approximately 4.9888126515157.'"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent.run(\n",
-    "    \"Who is the current prime minister of the UK? What is their current age raised to the 0.43 power?\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0ef78a07-1a2a-46f8-9bc9-ae45f9bd706c",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "poetry-venv",
-   "language": "python",
-   "name": "poetry-venv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

cookbook/press_releases.ipynb

@@ -1,156 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "62ee82e4-2ad8-498b-8438-fac388afe1a2",
-   "metadata": {},
-   "source": [
-    "Press Releases Data\n",
-    "=\n",
-    "\n",
-    "Press Releases data powered by [Kay.ai](https://kay.ai).\n",
-    "\n",
-    ">Press releases are used by companies to announce something noteworthy, including product launches, financial performance reports, partnerships, and other significant news. They are widely used by analysts to track corporate strategy, operational updates and financial performance.\n",
-    "Kay.ai obtains press releases of all US public companies from a variety of sources, which include the company's official press room and partnerships with various data API providers. \n",
-    "This data is updated till Sept 30th for free access, if you want to access the real-time feed, reach out to us at hello@kay.ai or [tweet at us](https://twitter.com/vishalrohra_)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8183d85d-365f-4672-a963-52b533547de0",
-   "metadata": {},
-   "source": [
-    "Setup\n",
-    "=\n",
-    "\n",
-    "First you will need to install the `kay` package. You will also need an API key: you can get one for free at [https://kay.ai](https://kay.ai/). Once you have an API key, you must set it as an environment variable `KAY_API_KEY`.\n",
-    "\n",
-    "In this example we're going to use the `KayAiRetriever`. Take a look at the [kay notebook](/docs/integrations/retrievers/kay) for more detailed information for the parmeters that it accepts."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "02ec21c7-49fe-4844-b58a-bf064ad40b2a",
-   "metadata": {},
-   "source": [
-    "Examples\n",
-    "="
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "bf0395f7-6ebe-4136-8b0d-00b9dea3becd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      " ········\n",
-      " ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Setup API keys for Kay and OpenAI\n",
-    "from getpass import getpass\n",
-    "\n",
-    "KAY_API_KEY = getpass()\n",
-    "OPENAI_API_KEY = getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "f7fcaf70-29a4-444b-8f07-9784f808c300",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"KAY_API_KEY\"] = KAY_API_KEY\n",
-    "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ac00bf93-3635-4ffe-b9a6-a8b4f35c0c85",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chains import ConversationalRetrievalChain\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.retrievers import KayAiRetriever\n",
-    "\n",
-    "model = ChatOpenAI(model_name=\"gpt-3.5-turbo\")\n",
-    "retriever = KayAiRetriever.create(\n",
-    "    dataset_id=\"company\", data_types=[\"PressRelease\"], num_contexts=6\n",
-    ")\n",
-    "qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "8d9d927c-35b2-4a7b-8ea7-4d0350797941",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-> **Question**: How is the healthcare industry adopting generative AI tools? \n",
-      "\n",
-      "**Answer**: The healthcare industry is adopting generative AI tools to improve various aspects of patient care and administrative tasks. Companies like HCA Healthcare Inc, Amazon Com Inc, and Mayo Clinic have collaborated with technology providers like Google Cloud, AWS, and Microsoft to implement generative AI solutions.\n",
-      "\n",
-      "HCA Healthcare is testing a nurse handoff tool that generates draft reports quickly and accurately, which nurses have shown interest in using. They are also exploring the use of Google's medically-tuned Med-PaLM 2 LLM to support caregivers in asking complex medical questions.\n",
-      "\n",
-      "Amazon Web Services (AWS) has introduced AWS HealthScribe, a generative AI-powered service that automatically creates clinical documentation. However, integrating multiple AI systems into a cohesive solution requires significant engineering resources, including access to AI experts, healthcare data, and compute capacity.\n",
-      "\n",
-      "Mayo Clinic is among the first healthcare organizations to deploy Microsoft 365 Copilot, a generative AI service that combines large language models with organizational data from Microsoft 365. This tool has the potential to automate tasks like form-filling, relieving administrative burdens on healthcare providers and allowing them to focus more on patient care.\n",
-      "\n",
-      "Overall, the healthcare industry is recognizing the potential benefits of generative AI tools in improving efficiency, automating tasks, and enhancing patient care. \n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# More sample questions in the Playground on https://kay.ai\n",
-    "questions = [\n",
-    "    \"How is the healthcare industry adopting generative AI tools?\",\n",
-    "    # \"What are some recent challenges faced by the renewable energy sector?\",\n",
-    "]\n",
-    "chat_history = []\n",
-    "\n",
-    "for question in questions:\n",
-    "    result = qa({\"question\": question, \"chat_history\": chat_history})\n",
-    "    chat_history.append((question, result[\"answer\"]))\n",
-    "    print(f\"-> **Question**: {question} \\n\")\n",
-    "    print(f\"**Answer**: {result['answer']} \\n\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

cookbook/rag_fusion.ipynb

@@ -1,272 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "993c2768",
-   "metadata": {},
-   "source": [
-    "# RAG Fusion\n",
-    "\n",
-    "Re-implemented from [this GitHub repo](https://github.com/Raudaschl/rag-fusion), all credit to original author\n",
-    "\n",
-    "> RAG-Fusion, a search methodology that aims to bridge the gap between traditional search paradigms and the multifaceted dimensions of human queries. Inspired by the capabilities of Retrieval Augmented Generation (RAG), this project goes a step further by employing multiple query generation and Reciprocal Rank Fusion to re-rank search results."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ebcc6791",
-   "metadata": {},
-   "source": [
-    "## Setup\n",
-    "\n",
-    "For this example, we will use Pinecone and some fake data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "661a1c36",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pinecone\n",
-    "from langchain.vectorstores import Pinecone\n",
-    "from langchain.embeddings import OpenAIEmbeddings\n",
-    "\n",
-    "pinecone.init(api_key=\"...\", environment=\"...\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "48ef7e93",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_documents = {\n",
-    "    \"doc1\": \"Climate change and economic impact.\",\n",
-    "    \"doc2\": \"Public health concerns due to climate change.\",\n",
-    "    \"doc3\": \"Climate change: A social perspective.\",\n",
-    "    \"doc4\": \"Technological solutions to climate change.\",\n",
-    "    \"doc5\": \"Policy changes needed to combat climate change.\",\n",
-    "    \"doc6\": \"Climate change and its impact on biodiversity.\",\n",
-    "    \"doc7\": \"Climate change: The science and models.\",\n",
-    "    \"doc8\": \"Global warming: A subset of climate change.\",\n",
-    "    \"doc9\": \"How climate change affects daily weather.\",\n",
-    "    \"doc10\": \"The history of climate change activism.\",\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fde89f0b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vectorstore = Pinecone.from_texts(\n",
-    "    list(all_documents.values()), OpenAIEmbeddings(), index_name=\"rag-fusion\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "22ddd041",
-   "metadata": {},
-   "source": [
-    "## Define the Query Generator\n",
-    "\n",
-    "We will now define a chain to do the query generation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "1d547524",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "af9ab4db",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain import hub\n",
-    "\n",
-    "prompt = hub.pull(\"langchain-ai/rag-fusion-query-generation\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "3628b552",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# prompt = ChatPromptTemplate.from_messages([\n",
-    "#     (\"system\", \"You are a helpful assistant that generates multiple search queries based on a single input query.\"),\n",
-    "#     (\"user\", \"Generate multiple search queries related to: {original_query}\"),\n",
-    "#     (\"user\", \"OUTPUT (4 queries):\")\n",
-    "# ])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "8d6cbb73",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "generate_queries = (\n",
-    "    prompt | ChatOpenAI(temperature=0) | StrOutputParser() | (lambda x: x.split(\"\\n\"))\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ee2824cd",
-   "metadata": {},
-   "source": [
-    "## Define the full chain\n",
-    "\n",
-    "We can now put it all together and define the full chain. This chain:\n",
-    "    \n",
-    "    1. Generates a bunch of queries\n",
-    "    2. Looks up each query in the retriever\n",
-    "    3. Joins all the results together using reciprocal rank fusion\n",
-    "    \n",
-    "    \n",
-    "Note that it does NOT do a final generation step"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "ca0bfec4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "original_query = \"impact of climate change\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "id": "02437d65",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vectorstore = Pinecone.from_existing_index(\"rag-fusion\", OpenAIEmbeddings())\n",
-    "retriever = vectorstore.as_retriever()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "id": "46a9a0e6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.load import dumps, loads\n",
-    "\n",
-    "\n",
-    "def reciprocal_rank_fusion(results: list[list], k=60):\n",
-    "    fused_scores = {}\n",
-    "    for docs in results:\n",
-    "        # Assumes the docs are returned in sorted order of relevance\n",
-    "        for rank, doc in enumerate(docs):\n",
-    "            doc_str = dumps(doc)\n",
-    "            if doc_str not in fused_scores:\n",
-    "                fused_scores[doc_str] = 0\n",
-    "            previous_score = fused_scores[doc_str]\n",
-    "            fused_scores[doc_str] += 1 / (rank + k)\n",
-    "\n",
-    "    reranked_results = [\n",
-    "        (loads(doc), score)\n",
-    "        for doc, score in sorted(fused_scores.items(), key=lambda x: x[1], reverse=True)\n",
-    "    ]\n",
-    "    return reranked_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "id": "3f9d4502",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain = generate_queries | retriever.map() | reciprocal_rank_fusion"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "id": "d70c4fcd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(Document(page_content='Climate change and economic impact.'),\n",
-       "  0.06558258417063283),\n",
-       " (Document(page_content='Climate change: A social perspective.'),\n",
-       "  0.06400409626216078),\n",
-       " (Document(page_content='How climate change affects daily weather.'),\n",
-       "  0.04787506400409626),\n",
-       " (Document(page_content='Climate change and its impact on biodiversity.'),\n",
-       "  0.03306010928961749),\n",
-       " (Document(page_content='Public health concerns due to climate change.'),\n",
-       "  0.016666666666666666),\n",
-       " (Document(page_content='Technological solutions to climate change.'),\n",
-       "  0.016666666666666666),\n",
-       " (Document(page_content='Policy changes needed to combat climate change.'),\n",
-       "  0.01639344262295082)]"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain.invoke({\"original_query\": original_query})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7866e551",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

cookbook/retrieval_in_sql.ipynb

@@ -1,688 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Incoporating semantic similarity in tabular databases\n",
-    "\n",
-    "In this notebook we will cover how to run semantic search over a specific table column within a single SQL query, combining tabular query with RAG.\n",
-    "\n",
-    "\n",
-    "### Overall workflow\n",
-    "\n",
-    "1. Generating embeddings for a specific column\n",
-    "2. Storing the embeddings in a new column (if column has low cardinality, it's better to use another table containing unique values and their embeddings)\n",
-    "3. Querying using standard SQL queries with [PGVector](https://github.com/pgvector/pgvector) extension which allows using L2 distance (`<->`), Cosine distance (`<=>` or cosine similarity using `1 - <=>`) and Inner product (`<#>`)\n",
-    "4. Running standard SQL query\n",
-    "\n",
-    "### Requirements\n",
-    "\n",
-    "We will need a PostgreSQL database with [pgvector](https://github.com/pgvector/pgvector) extension enabled. For this example, we will use a `Chinook` database using a local PostgreSQL server."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import getpass\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = os.environ.get(\"OPENAI_API_KEY\") or getpass.getpass(\n",
-    "    \"OpenAI API Key:\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.sql_database import SQLDatabase\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "\n",
-    "CONNECTION_STRING = \"postgresql+psycopg2://postgres:test@localhost:5432/vectordb\"  # Replace with your own\n",
-    "db = SQLDatabase.from_uri(CONNECTION_STRING)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Embedding the song titles"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For this example, we will run queries based on semantic meaning of song titles. In order to do this, let's start by adding a new column in the table for storing the embeddings:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# db.run('ALTER TABLE \"Track\" ADD COLUMN \"embeddings\" vector;')"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's generate the embedding for each *track title* and store it as a new column in our \"Track\" table"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.embeddings import OpenAIEmbeddings\n",
-    "\n",
-    "embeddings_model = OpenAIEmbeddings()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3503"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tracks = db.run('SELECT \"Name\" FROM \"Track\"')\n",
-    "song_titles = [s[0] for s in eval(tracks)]\n",
-    "title_embeddings = embeddings_model.embed_documents(song_titles)\n",
-    "len(title_embeddings)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's insert the embeddings in the into the new column from our table"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from tqdm import tqdm\n",
-    "\n",
-    "for i in tqdm(range(len(title_embeddings))):\n",
-    "    title = titles[i].replace(\"'\", \"''\")\n",
-    "    embedding = title_embeddings[i]\n",
-    "    sql_command = (\n",
-    "        f'UPDATE \"Track\" SET \"embeddings\" = ARRAY{embedding} WHERE \"Name\" ='\n",
-    "        + f\"'{title}'\"\n",
-    "    )\n",
-    "    db.run(sql_command)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can test the semantic search running the following query:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'[(\"Tomorrow\\'s Dream\",), (\\'Remember Tomorrow\\',), (\\'Remember Tomorrow\\',), (\\'The Best Is Yet To Come\\',), (\"Thinking \\'Bout Tomorrow\",)]'"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "embeded_title = embeddings_model.embed_query(\"hope about the future\")\n",
-    "query = (\n",
-    "    'SELECT \"Track\".\"Name\" FROM \"Track\" WHERE \"Track\".\"embeddings\" IS NOT NULL ORDER BY \"embeddings\" <-> '\n",
-    "    + f\"'{embeded_title}' LIMIT 5\"\n",
-    ")\n",
-    "db.run(query)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Creating the SQL Chain"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's start by defining useful functions to get info from database and running the query:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_schema(_):\n",
-    "    return db.get_table_info()\n",
-    "\n",
-    "\n",
-    "def run_query(query):\n",
-    "    return db.run(query)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's build the **prompt** we will use. This prompt is an extension from [text-to-postgres-sql](https://smith.langchain.com/hub/jacob/text-to-postgres-sql?organizationId=f9b614b8-5c3a-4e7c-afbc-6d7ad4fd8892) prompt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.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",
-    "Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (\") to denote them as delimited identifiers.\n",
-    "Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.\n",
-    "Pay attention to use date('now') function to get the current date, if the question involves \"today\".\n",
-    "\n",
-    "You can use an extra extension which allows you to run semantic similarity using <-> operator on tables containing columns named \"embeddings\".\n",
-    "<-> operator can ONLY be used on embeddings columns.\n",
-    "The embeddings value for a given row typically represents the semantic meaning of that row.\n",
-    "The vector represents an embedding representation of the question, given below. \n",
-    "Do NOT fill in the vector values directly, but rather specify a `[search_word]` placeholder, which should contain the word that would be embedded for filtering.\n",
-    "For example, if the user asks for songs about 'the feeling of loneliness' the query could be:\n",
-    "'SELECT \"[whatever_table_name]\".\"SongName\" FROM \"[whatever_table_name]\" ORDER BY \"embeddings\" <-> '[loneliness]' LIMIT 5'\n",
-    "\n",
-    "Use the following format:\n",
-    "\n",
-    "Question: <Question here>\n",
-    "SQLQuery: <SQL Query to run>\n",
-    "SQLResult: <Result of the SQLQuery>\n",
-    "Answer: <Final answer here>\n",
-    "\n",
-    "Only use the following tables:\n",
-    "\n",
-    "{schema}\n",
-    "\"\"\"\n",
-    "\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [(\"system\", template), (\"human\", \"{question}\")]\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And we can create the chain using **[LangChain Expression Language](https://python.langchain.com/docs/expression_language/)**:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "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",
-    "\n",
-    "db = SQLDatabase.from_uri(\n",
-    "    CONNECTION_STRING\n",
-    ")  # We reconnect to db so the new columns are loaded as well.\n",
-    "llm = ChatOpenAI(model_name=\"gpt-4\", temperature=0)\n",
-    "\n",
-    "sql_query_chain = (\n",
-    "    RunnablePassthrough.assign(schema=get_schema)\n",
-    "    | prompt\n",
-    "    | llm.bind(stop=[\"\\nSQLResult:\"])\n",
-    "    | StrOutputParser()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'SQLQuery: SELECT \"Track\".\"Name\" FROM \"Track\" JOIN \"Genre\" ON \"Track\".\"GenreId\" = \"Genre\".\"GenreId\" WHERE \"Genre\".\"Name\" = \\'Rock\\' ORDER BY \"Track\".\"embeddings\" <-> \\'[dispair]\\' LIMIT 5'"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sql_query_chain.invoke(\n",
-    "    {\n",
-    "        \"question\": \"Which are the 5 rock songs with titles about deep feeling of dispair?\"\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This chain simply generates the query. Now we will create the full chain that also handles the execution and the final result for the user:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import re\n",
-    "from langchain.schema.runnable import RunnableLambda\n",
-    "\n",
-    "\n",
-    "def replace_brackets(match):\n",
-    "    words_inside_brackets = match.group(1).split(\", \")\n",
-    "    embedded_words = [\n",
-    "        str(embeddings_model.embed_query(word)) for word in words_inside_brackets\n",
-    "    ]\n",
-    "    return \"', '\".join(embedded_words)\n",
-    "\n",
-    "\n",
-    "def get_query(query):\n",
-    "    sql_query = re.sub(r\"\\[([\\w\\s,]+)\\]\", replace_brackets, query)\n",
-    "    return sql_query\n",
-    "\n",
-    "\n",
-    "template = \"\"\"Based on the table schema below, question, sql query, and sql response, write a natural language response:\n",
-    "{schema}\n",
-    "\n",
-    "Question: {question}\n",
-    "SQL Query: {query}\n",
-    "SQL Response: {response}\"\"\"\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [(\"system\", template), (\"human\", \"{question}\")]\n",
-    ")\n",
-    "\n",
-    "full_chain = (\n",
-    "    RunnablePassthrough.assign(query=sql_query_chain)\n",
-    "    | RunnablePassthrough.assign(\n",
-    "        schema=get_schema,\n",
-    "        response=RunnableLambda(lambda x: db.run(get_query(x[\"query\"]))),\n",
-    "    )\n",
-    "    | prompt\n",
-    "    | llm\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Using the Chain"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Example 1: Filtering a column based on semantic meaning"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's say we want to retrieve songs that express `deep feeling of dispair`, but filtering based on genre:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"The 5 rock songs with titles that convey a deep feeling of despair are 'Sea Of Sorrow', 'Surrender', 'Indifference', 'Hard Luck Woman', and 'Desire'.\")"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_chain.invoke(\n",
-    "    {\n",
-    "        \"question\": \"Which are the 5 rock songs with titles about deep feeling of dispair?\"\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What is substantially different in implementing this method is that we have combined:\n",
-    "- Semantic search (songs that have titles with some semantic meaning)\n",
-    "- Traditional tabular querying (running JOIN statements to filter track based on genre)\n",
-    "\n",
-    "This is something we _could_ potentially achieve using metadata filtering, but it's more complex to do so (we would need to use a vector database containing the embeddings, and use metadata filtering based on genre).\n",
-    "\n",
-    "However, for other use cases metadata filtering **wouldn't be enough**."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Example 2: Combining filters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"The three albums which have the most amount of songs in the top 150 saddest songs are 'International Superhits' with 5 songs, 'Ten' with 4 songs, and 'Album Of The Year' with 3 songs.\")"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_chain.invoke(\n",
-    "    {\n",
-    "        \"question\": \"I want to know the 3 albums which have the most amount of songs in the top 150 saddest songs\"\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So we have result for 3 albums with most amount of songs in top 150 saddest ones. This **wouldn't** be possible using only standard metadata filtering. Without this _hybdrid query_, we would need some postprocessing to get the result.\n",
-    "\n",
-    "Another similar exmaple:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"The 6 albums with the shortest titles that contain songs which are in the 20 saddest song list are 'Ten', 'Core', 'Big Ones', 'One By One', 'Black Album', and 'Miles Ahead'.\")"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_chain.invoke(\n",
-    "    {\n",
-    "        \"question\": \"I need the 6 albums with shortest title, as long as they contain songs which are in the 20 saddest song list.\"\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's see what the query looks like to double check:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WITH \"SadSongs\" AS (\n",
-      "    SELECT \"TrackId\" FROM \"Track\" \n",
-      "    ORDER BY \"embeddings\" <-> '[sad]' LIMIT 20\n",
-      "),\n",
-      "\"SadAlbums\" AS (\n",
-      "    SELECT DISTINCT \"AlbumId\" FROM \"Track\" \n",
-      "    WHERE \"TrackId\" IN (SELECT \"TrackId\" FROM \"SadSongs\")\n",
-      ")\n",
-      "SELECT \"Album\".\"Title\" FROM \"Album\" \n",
-      "WHERE \"AlbumId\" IN (SELECT \"AlbumId\" FROM \"SadAlbums\") \n",
-      "ORDER BY \"title_len\" ASC \n",
-      "LIMIT 6\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\n",
-    "    sql_query_chain.invoke(\n",
-    "        {\n",
-    "            \"question\": \"I need the 6 albums with shortest title, as long as they contain songs which are in the 20 saddest song list.\"\n",
-    "        }\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Example 3: Combining two separate semantic searches\n",
-    "\n",
-    "One interesting aspect of this approach which is **substantially different from using standar RAG** is that we can even **combine** two semantic search filters:\n",
-    "- _Get 5 saddest songs..._\n",
-    "- _**...obtained from albums with \"lovely\" titles**_\n",
-    "\n",
-    "This could generalize to **any kind of combined RAG** (paragraphs discussing _X_ topic belonging from books about _Y_, replies to a tweet about _ABC_ topic that express _XYZ_ feeling)\n",
-    "\n",
-    "We will combine semantic search on songs and album titles, so we need to do the same for `Album` table:\n",
-    "1. Generate the embeddings\n",
-    "2. Add them to the table as a new column (which we need to add in the table)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# db.run('ALTER TABLE \"Album\" ADD COLUMN \"embeddings\" vector;')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 347/347 [00:01<00:00, 179.64it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "albums = db.run('SELECT \"Title\" FROM \"Album\"')\n",
-    "album_titles = [title[0] for title in eval(albums)]\n",
-    "album_title_embeddings = embeddings_model.embed_documents(album_titles)\n",
-    "for i in tqdm(range(len(album_title_embeddings))):\n",
-    "    album_title = album_titles[i].replace(\"'\", \"''\")\n",
-    "    album_embedding = album_title_embeddings[i]\n",
-    "    sql_command = (\n",
-    "        f'UPDATE \"Album\" SET \"embeddings\" = ARRAY{album_embedding} WHERE \"Title\" ='\n",
-    "        + f\"'{album_title}'\"\n",
-    "    )\n",
-    "    db.run(sql_command)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"[('Realize',), ('Morning Dance',), ('Into The Light',), ('New Adventures In Hi-Fi',), ('Miles Ahead',)]\""
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "embeded_title = embeddings_model.embed_query(\"hope about the future\")\n",
-    "query = (\n",
-    "    'SELECT \"Album\".\"Title\" FROM \"Album\" WHERE \"Album\".\"embeddings\" IS NOT NULL ORDER BY \"embeddings\" <-> '\n",
-    "    + f\"'{embeded_title}' LIMIT 5\"\n",
-    ")\n",
-    "db.run(query)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can combine both filters:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "db = SQLDatabase.from_uri(\n",
-    "    CONNECTION_STRING\n",
-    ")  # We reconnect to dbso the new columns are loaded as well."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='The songs about breakouts obtained from the top 5 albums about love are \\'Royal Orleans\\', \"Nobody\\'s Fault But Mine\", \\'Achilles Last Stand\\', \\'For Your Life\\', and \\'Hots On For Nowhere\\'.')"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "full_chain.invoke(\n",
-    "    {\n",
-    "        \"question\": \"I want to know songs about breakouts obtained from top 5 albums about love\"\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is something **different** that **couldn't be achieved** using standard metadata filtering over a vectordb."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

cookbook/rewrite.ipynb

@@ -1,353 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "260629f9",
-   "metadata": {},
-   "source": [
-    "# Rewrite-Retrieve-Read\n",
-    "\n",
-    "**Rewrite-Retrieve-Read** is a method proposed in the paper [Query Rewriting for Retrieval-Augmented Large Language Models](https://arxiv.org/pdf/2305.14283.pdf)\n",
-    "\n",
-    "> Because the original query can not be always optimal to retrieve for the LLM, especially in the real world... we first prompt an LLM to rewrite the queries, then conduct retrieval-augmented reading\n",
-    "\n",
-    "We show how you can easily do that with LangChain Expression Language"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "eda93712",
-   "metadata": {},
-   "source": [
-    "## Baseline\n",
-    "\n",
-    "Baseline RAG (**Retrieve-and-read**) can be done like the following:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "1d2edbd2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from operator import itemgetter\n",
-    "\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough, RunnableLambda\n",
-    "from langchain.utilities import DuckDuckGoSearchAPIWrapper"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "86a46aa9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "template = \"\"\"Answer the users question based only on the following context:\n",
-    "\n",
-    "<context>\n",
-    "{context}\n",
-    "</context>\n",
-    "\n",
-    "Question: {question}\n",
-    "\"\"\"\n",
-    "prompt = ChatPromptTemplate.from_template(template)\n",
-    "\n",
-    "model = ChatOpenAI(temperature=0)\n",
-    "\n",
-    "search = DuckDuckGoSearchAPIWrapper()\n",
-    "\n",
-    "\n",
-    "def retriever(query):\n",
-    "    return search.run(query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "8566d48e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain = (\n",
-    "    {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
-    "    | prompt\n",
-    "    | model\n",
-    "    | StrOutputParser()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "5c57f9ee",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "simple_query = \"what is langchain?\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "37c5f962",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"LangChain is a powerful and versatile Python library that enables developers and researchers to create, experiment with, and analyze language models and agents. It simplifies the development of language-based applications by providing a suite of features for artificial general intelligence. It can be used to build chatbots, perform document analysis and summarization, and streamline interaction with various large language model providers. LangChain's unique proposition is its ability to create logical links between one or more language models, known as Chains. It is an open-source library that offers a generic interface to foundation models and allows prompt management and integration with other components and tools.\""
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain.invoke(simple_query)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "23bdb9bd",
-   "metadata": {},
-   "source": [
-    "While this is fine for well formatted queries, it can break down for more complicated queries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "8df6a814",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "distracted_query = \"man that sam bankman fried trial was crazy! what is langchain?\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "16d7db64",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Based on the given context, there is no information provided about \"langchain.\"'"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain.invoke(distracted_query)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0b4f8b93",
-   "metadata": {},
-   "source": [
-    "This is because the retriever does a bad job with these \"distracted\" queries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "3439d8dc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Business She\\'s the star witness against Sam Bankman-Fried. Her testimony was explosive Gary Wang, who co-founded both FTX and Alameda Research, said Bankman-Fried directed him to change a... The Verge, following the trial\\'s Oct. 4 kickoff: \"Is Sam Bankman-Fried\\'s Defense Even Trying to Win?\". CBS Moneywatch, from Thursday: \"Sam Bankman-Fried\\'s Lawyer Struggles to Poke ... Sam Bankman-Fried, FTX\\'s founder, responded with a single word: \"Oof.\". Less than a year later, Mr. Bankman-Fried, 31, is on trial in federal court in Manhattan, fighting criminal charges ... July 19, 2023. A U.S. judge on Wednesday overruled objections by Sam Bankman-Fried\\'s lawyers and allowed jurors in the FTX founder\\'s fraud trial to see a profane message he sent to a reporter days ... Sam Bankman-Fried, who was once hailed as a virtuoso in cryptocurrency trading, is on trial over the collapse of FTX, the financial exchange he founded. Bankman-Fried is accused of...'"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "retriever(distracted_query)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7eb748ac",
-   "metadata": {},
-   "source": [
-    "## Rewrite-Retrieve-Read Implementation\n",
-    "\n",
-    "The main part is a rewriter to rewrite the search query"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "88ae702e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "template = \"\"\"Provide a better search query for \\\n",
-    "web search engine to answer the given question, end \\\n",
-    "the queries with ’**’. Question: \\\n",
-    "{x} Answer:\"\"\"\n",
-    "rewrite_prompt = ChatPromptTemplate.from_template(template)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "184e1bcb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain import hub\n",
-    "\n",
-    "rewrite_prompt = hub.pull(\"langchain-ai/rewrite\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "a4c23d40",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Provide a better search query for web search engine to answer the given question, end the queries with ’**’.  Question {x} Answer:\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(rewrite_prompt.template)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "f55cd010",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Parser to remove the `**`\n",
-    "\n",
-    "\n",
-    "def _parse(text):\n",
-    "    return text.strip(\"**\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "c9c34bef",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rewriter = rewrite_prompt | ChatOpenAI(temperature=0) | StrOutputParser() | _parse"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "fb17fb3d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'What is the definition and purpose of Langchain?'"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rewriter.invoke({\"x\": distracted_query})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "f83edb09",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rewrite_retrieve_read_chain = (\n",
-    "    {\n",
-    "        \"context\": {\"x\": RunnablePassthrough()} | rewriter | retriever,\n",
-    "        \"question\": RunnablePassthrough(),\n",
-    "    }\n",
-    "    | prompt\n",
-    "    | model\n",
-    "    | StrOutputParser()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "43096322",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Based on the given context, LangChain is an open-source framework designed to simplify the creation of applications using large language models (LLMs). It enables LLM models to generate responses based on up-to-date online information and simplifies the organization of large volumes of data for easy access by LLMs. LangChain offers a standard interface for chains, integrations with other tools, and end-to-end chains for common applications. It is a robust library that streamlines interaction with various LLM providers. LangChain\\'s unique proposition is its ability to create logical links between one or more LLMs, known as Chains. It is an AI framework with features that simplify the development of language-based applications and offers a suite of features for artificial general intelligence. However, the context does not provide any information about the \"sam bankman fried trial\" mentioned in the question.'"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rewrite_retrieve_read_chain.invoke(distracted_query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "59874b4f",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

cookbook/selecting_llms_based_on_context_length.ipynb

@@ -1,177 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "e93283d1",
-   "metadata": {},
-   "source": [
-    "# Selecting LLMs based on Context Length\n",
-    "\n",
-    "Different LLMs have different context lengths. As a very immediate an practical example, OpenAI has two versions of GPT-3.5-Turbo: one with 4k context, another with 16k context. This notebook shows how to route between them based on input."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "cc453450",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.schema.prompt import PromptValue\n",
-    "from langchain.schema.messages import BaseMessage\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
-    "from typing import Union, Sequence"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "1cec6a10",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "short_context_model = ChatOpenAI(model=\"gpt-3.5-turbo\")\n",
-    "long_context_model = ChatOpenAI(model=\"gpt-3.5-turbo-16k\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "772da153",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_context_length(prompt: PromptValue):\n",
-    "    messages = prompt.to_messages()\n",
-    "    tokens = short_context_model.get_num_tokens_from_messages(messages)\n",
-    "    return tokens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "db771e20",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt = PromptTemplate.from_template(\"Summarize this passage: {context}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "af057e2f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def choose_model(prompt: PromptValue):\n",
-    "    context_len = get_context_length(prompt)\n",
-    "    if context_len < 30:\n",
-    "        print(\"short model\")\n",
-    "        return short_context_model\n",
-    "    else:\n",
-    "        print(\"long model\")\n",
-    "        return long_context_model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "84f3e07d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain = prompt | choose_model | StrOutputParser()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "d8b14f8f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "short model\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'The passage mentions that a frog visited a pond.'"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain.invoke({\"context\": \"a frog went to a pond\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "70ebd3dd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "long model\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'The passage describes a frog that moved from one pond to another and perched on a log.'"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain.invoke(\n",
-    "    {\"context\": \"a frog went to a pond and sat on a log and went to a different pond\"}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a7e29fef",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

cookbook/stepback-qa.ipynb

@@ -1,351 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "83ef724e",
-   "metadata": {},
-   "source": [
-    "# Step-Back Prompting (Question-Answering)\n",
-    "\n",
-    "One prompting technique called \"Step-Back\" prompting can improve performance on complex questions by first asking a \"step back\" question. This can be combined with regular question-answering applications by then doing retrieval on both the original and step-back question.\n",
-    "\n",
-    "Read the paper [here](https://arxiv.org/abs/2310.06117)\n",
-    "\n",
-    "See an excellent blog post on this by Cobus Greyling [here](https://cobusgreyling.medium.com/a-new-prompt-engineering-technique-has-been-introduced-called-step-back-prompting-b00e8954cacb)\n",
-    "\n",
-    "In this cookbook we will replicate this technique. We modify the prompts used slightly to work better with chat models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "67b5cdac",
-   "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"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "7e017c44",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Few Shot Examples\n",
-    "examples = [\n",
-    "    {\n",
-    "        \"input\": \"Could the members of The Police perform lawful arrests?\",\n",
-    "        \"output\": \"what can the members of The Police do?\",\n",
-    "    },\n",
-    "    {\n",
-    "        \"input\": \"Jan Sindel’s was born in what country?\",\n",
-    "        \"output\": \"what is Jan Sindel’s personal history?\",\n",
-    "    },\n",
-    "]\n",
-    "# We now transform these to example messages\n",
-    "example_prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\"human\", \"{input}\"),\n",
-    "        (\"ai\", \"{output}\"),\n",
-    "    ]\n",
-    ")\n",
-    "few_shot_prompt = FewShotChatMessagePromptTemplate(\n",
-    "    example_prompt=example_prompt,\n",
-    "    examples=examples,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "id": "206415ee",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [\n",
-    "        (\n",
-    "            \"system\",\n",
-    "            \"\"\"You are an expert at world knowledge. Your task is to step back and paraphrase a question to a more generic step-back question, which is easier to answer. Here are a few examples:\"\"\",\n",
-    "        ),\n",
-    "        # Few shot examples\n",
-    "        few_shot_prompt,\n",
-    "        # New question\n",
-    "        (\"user\", \"{question}\"),\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "id": "d643a85c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "question_gen = prompt | ChatOpenAI(temperature=0) | StrOutputParser()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 182,
-   "id": "5ba21b2a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "question = \"was chatgpt around while trump was president?\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 183,
-   "id": "5992c8ca",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'when was ChatGPT developed?'"
-      ]
-     },
-     "execution_count": 183,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "question_gen.invoke({\"question\": question})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 190,
-   "id": "32667424",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.utilities import DuckDuckGoSearchAPIWrapper\n",
-    "\n",
-    "\n",
-    "search = DuckDuckGoSearchAPIWrapper(max_results=4)\n",
-    "\n",
-    "\n",
-    "def retriever(query):\n",
-    "    return search.run(query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
-   "id": "ffc28c91",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'This includes content about former President Donald Trump. According to further tests, ChatGPT successfully wrote poems admiring all recent U.S. presidents, but failed when we entered a query for ... On Wednesday, a Twitter user posted screenshots of him asking OpenAI\\'s chatbot, ChatGPT, to write a positive poem about former President Donald Trump, to which the chatbot declined, citing it ... While impressive in many respects, ChatGPT also has some major flaws. ... [President\\'s Name],\" refused to write a poem about ex-President Trump, but wrote one about President Biden ... During the Trump administration, Altman gained new attention as a vocal critic of the president. It was against that backdrop that he was rumored to be considering a run for California governor.'"
-      ]
-     },
-     "execution_count": 191,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "retriever(question)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 192,
-   "id": "00c77443",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Will Douglas Heaven March 3, 2023 Stephanie Arnett/MITTR | Envato When OpenAI launched ChatGPT, with zero fanfare, in late November 2022, the San Francisco-based artificial-intelligence company... ChatGPT, which stands for Chat Generative Pre-trained Transformer, is a large language model -based chatbot developed by OpenAI and launched on November 30, 2022, which enables users to refine and steer a conversation towards a desired length, format, style, level of detail, and language. ChatGPT is an artificial intelligence (AI) chatbot built on top of OpenAI's foundational large language models (LLMs) like GPT-4 and its predecessors. This chatbot has redefined the standards of... June 4, 2023 ⋅ 4 min read 124 SHARES 13K At the end of 2022, OpenAI introduced the world to ChatGPT. Since its launch, ChatGPT hasn't shown significant signs of slowing down in developing new...\""
-      ]
-     },
-     "execution_count": 192,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "retriever(question_gen.invoke({\"question\": question}))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 193,
-   "id": "b257bc06",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# response_prompt_template = \"\"\"You are an expert of world knowledge. I am going to ask you a question. Your response should be comprehensive and not contradicted with the following context if they are relevant. Otherwise, ignore them if they are not relevant.\n",
-    "\n",
-    "# {normal_context}\n",
-    "# {step_back_context}\n",
-    "\n",
-    "# Original Question: {question}\n",
-    "# Answer:\"\"\"\n",
-    "# response_prompt = ChatPromptTemplate.from_template(response_prompt_template)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 203,
-   "id": "f48c65b2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain import hub\n",
-    "\n",
-    "response_prompt = hub.pull(\"langchain-ai/stepback-answer\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 204,
-   "id": "97a6d5ab",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain = (\n",
-    "    {\n",
-    "        # Retrieve context using the normal question\n",
-    "        \"normal_context\": RunnableLambda(lambda x: x[\"question\"]) | retriever,\n",
-    "        # Retrieve context using the step-back question\n",
-    "        \"step_back_context\": question_gen | retriever,\n",
-    "        # Pass on the question\n",
-    "        \"question\": lambda x: x[\"question\"],\n",
-    "    }\n",
-    "    | response_prompt\n",
-    "    | ChatOpenAI(temperature=0)\n",
-    "    | StrOutputParser()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 205,
-   "id": "ce554cb0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"No, ChatGPT was not around while Donald Trump was president. ChatGPT was launched on November 30, 2022, which is after Donald Trump's presidency. The context provided mentions that during the Trump administration, Altman, the CEO of OpenAI, gained attention as a vocal critic of the president. This suggests that ChatGPT was not developed or available during that time.\""
-      ]
-     },
-     "execution_count": 205,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain.invoke({\"question\": question})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a9fb8dd2",
-   "metadata": {},
-   "source": [
-    "## Baseline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 206,
-   "id": "00db8a15",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "response_prompt_template = \"\"\"You are an expert of world knowledge. I am going to ask you a question. Your response should be comprehensive and not contradicted with the following context if they are relevant. Otherwise, ignore them if they are not relevant.\n",
-    "\n",
-    "{normal_context}\n",
-    "\n",
-    "Original Question: {question}\n",
-    "Answer:\"\"\"\n",
-    "response_prompt = ChatPromptTemplate.from_template(response_prompt_template)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 207,
-   "id": "06335ebb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chain = (\n",
-    "    {\n",
-    "        # Retrieve context using the normal question (only the first 3 results)\n",
-    "        \"normal_context\": RunnableLambda(lambda x: x[\"question\"]) | retriever,\n",
-    "        # Pass on the question\n",
-    "        \"question\": lambda x: x[\"question\"],\n",
-    "    }\n",
-    "    | response_prompt\n",
-    "    | ChatOpenAI(temperature=0)\n",
-    "    | StrOutputParser()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 208,
-   "id": "15e0e741",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Yes, ChatGPT was around while Donald Trump was president. However, it is important to note that the specific context you provided mentions that ChatGPT refused to write a positive poem about former President Donald Trump. This suggests that while ChatGPT was available during Trump's presidency, it may have had limitations or biases in its responses regarding him.\""
-      ]
-     },
-     "execution_count": 208,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain.invoke({\"question\": question})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e7b9e5d6",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/.local_build.sh

@@ -8,14 +8,10 @@ set -o xtrace
 SCRIPT_DIR="$(cd "$(dirname "$0")"; pwd)"
 cd "${SCRIPT_DIR}"
 
-mkdir -p ../_dist
-cp -r . ../_dist
-cd ../_dist
-poetry run python scripts/model_feat_table.py
-poetry run nbdoc_build --srcdir docs
-cp ../cookbook/README.md src/pages/cookbook.mdx
-cp ../.github/CONTRIBUTING.md docs/contributing.md
-wget https://raw.githubusercontent.com/langchain-ai/langserve/main/README.md -O docs/guides/deployments/langserve.md
-poetry run python scripts/generate_api_reference_links.py
+mkdir -p _dist/docs_skeleton
+cp -r {docs_skeleton,snippets} _dist
+cd _dist/docs_skeleton
+poetry run nbdoc_build
+poetry run python generate_api_reference_links.py
 yarn install
 yarn start

docs/api_reference/create_api_rst.py

@@ -2,9 +2,9 @@
 import importlib
 import inspect
 import typing
-from enum import Enum
 from pathlib import Path
-from typing import Dict, List, Literal, Optional, Sequence, TypedDict, Union
+from typing import TypedDict, Sequence, List, Dict, Literal, Union, Optional
+from enum import Enum
 
 from pydantic import BaseModel
 
@@ -122,7 +122,8 @@ def _merge_module_members(
 
 
 def _load_package_modules(
-    package_directory: Union[str, Path], submodule: Optional[str] = None
+    package_directory: Union[str, Path],
+    submodule: Optional[str] = None
 ) -> Dict[str, ModuleMembers]:
     """Recursively load modules of a package based on the file system.
 
@@ -170,8 +171,7 @@ def _load_package_modules(
             # different way
             if submodule is not None:
                 module_members = _load_module_members(
-                    f"{package_name}.{submodule}.{namespace}",
-                    f"{submodule}.{namespace}",
+                    f"{package_name}.{submodule}.{namespace}", f"{submodule}.{namespace}"
                 )
             else:
                 module_members = _load_module_members(
@@ -280,9 +280,18 @@ Functions
     return full_doc
 
 
-def _document_langchain_experimental() -> None:
-    """Document the langchain_experimental package."""
-    # Generate experimental_api_reference.rst
+def main() -> None:
+    """Generate the reference.rst file for each package."""
+    lc_members = _load_package_modules(PKG_DIR)
+    # Put some packages at top level
+    tools = _load_package_modules(PKG_DIR, "tools")
+    lc_members['tools.render'] = tools['render']
+    agents = _load_package_modules(PKG_DIR, "agents")
+    lc_members['agents.output_parsers'] = agents['output_parsers']
+    lc_members['agents.format_scratchpad'] = agents['format_scratchpad']
+    lc_doc = ".. _api_reference:\n\n" + _construct_doc("langchain", lc_members)
+    with open(WRITE_FILE, "w") as f:
+        f.write(lc_doc)
     exp_members = _load_package_modules(EXP_DIR)
     exp_doc = ".. _experimental_api_reference:\n\n" + _construct_doc(
         "langchain_experimental", exp_members
@@ -291,36 +300,5 @@ def _document_langchain_experimental() -> None:
         f.write(exp_doc)
 
 
-def _document_langchain_core() -> None:
-    """Document the main langchain package."""
-    # load top level module members
-    lc_members = _load_package_modules(PKG_DIR)
-
-    # Add additional packages
-    tools = _load_package_modules(PKG_DIR, "tools")
-    agents = _load_package_modules(PKG_DIR, "agents")
-    schema = _load_package_modules(PKG_DIR, "schema")
-
-    lc_members.update(
-        {
-            "agents.output_parsers": agents["output_parsers"],
-            "agents.format_scratchpad": agents["format_scratchpad"],
-            "tools.render": tools["render"],
-            "schema.runnable": schema["runnable"],
-        }
-    )
-
-    lc_doc = ".. _api_reference:\n\n" + _construct_doc("langchain", lc_members)
-
-    with open(WRITE_FILE, "w") as f:
-        f.write(lc_doc)
-
-
-def main() -> None:
-    """Generate the reference.rst file for each package."""
-    _document_langchain_core()
-    _document_langchain_experimental()
-
-
 if __name__ == "__main__":
     main()

docs/docs/additional_resources/dependents.mdx

@@ -1,465 +0,0 @@
-# Dependents
-
-Dependents stats for `langchain-ai/langchain`
-
-[![](https://img.shields.io/static/v1?label=Used%20by&message=30534&color=informational&logo=slickpic)](https://github.com/langchain-ai/langchain/network/dependents)
-[![](https://img.shields.io/static/v1?label=Used%20by%20(public)&message=451&color=informational&logo=slickpic)](https://github.com/langchain-ai/langchain/network/dependents)
-[![](https://img.shields.io/static/v1?label=Used%20by%20(private)&message=30083&color=informational&logo=slickpic)](https://github.com/langchain-ai/langchain/network/dependents)
-[![](https://img.shields.io/static/v1?label=Used%20by%20(stars)&message=37822&color=informational&logo=slickpic)](https://github.com/langchain-ai/langchain/network/dependents)
-
-
-[update: `2023-10-06`; only dependent repositories with Stars > 100]
-
-
-| Repository | Stars  |
-| :--------  | -----: |
-|[openai/openai-cookbook](https://github.com/openai/openai-cookbook) | 49006 |
-|[AntonOsika/gpt-engineer](https://github.com/AntonOsika/gpt-engineer) | 44368 |
-|[imartinez/privateGPT](https://github.com/imartinez/privateGPT) | 38300 |
-|[LAION-AI/Open-Assistant](https://github.com/LAION-AI/Open-Assistant) | 35327 |
-|[hpcaitech/ColossalAI](https://github.com/hpcaitech/ColossalAI) | 34799 |
-|[microsoft/TaskMatrix](https://github.com/microsoft/TaskMatrix) | 34161 |
-|[streamlit/streamlit](https://github.com/streamlit/streamlit) | 27697 |
-|[geekan/MetaGPT](https://github.com/geekan/MetaGPT) | 27302 |
-|[reworkd/AgentGPT](https://github.com/reworkd/AgentGPT) | 26805 |
-|[OpenBB-finance/OpenBBTerminal](https://github.com/OpenBB-finance/OpenBBTerminal) | 24473 |
-|[StanGirard/quivr](https://github.com/StanGirard/quivr) | 23323 |
-|[run-llama/llama_index](https://github.com/run-llama/llama_index) | 22151 |
-|[openai/chatgpt-retrieval-plugin](https://github.com/openai/chatgpt-retrieval-plugin) | 19741 |
-|[mindsdb/mindsdb](https://github.com/mindsdb/mindsdb) | 18062 |
-|[PromtEngineer/localGPT](https://github.com/PromtEngineer/localGPT) | 16413 |
-|[chatchat-space/Langchain-Chatchat](https://github.com/chatchat-space/Langchain-Chatchat) | 16300 |
-|[cube-js/cube](https://github.com/cube-js/cube) | 16261 |
-|[mlflow/mlflow](https://github.com/mlflow/mlflow) | 15487 |
-|[logspace-ai/langflow](https://github.com/logspace-ai/langflow) | 12599 |
-|[GaiZhenbiao/ChuanhuChatGPT](https://github.com/GaiZhenbiao/ChuanhuChatGPT) | 12501 |
-|[openai/evals](https://github.com/openai/evals) | 12056 |
-|[airbytehq/airbyte](https://github.com/airbytehq/airbyte) | 11919 |
-|[go-skynet/LocalAI](https://github.com/go-skynet/LocalAI) | 11767 |
-|[databrickslabs/dolly](https://github.com/databrickslabs/dolly) | 10609 |
-|[AIGC-Audio/AudioGPT](https://github.com/AIGC-Audio/AudioGPT) | 9240 |
-|[aws/amazon-sagemaker-examples](https://github.com/aws/amazon-sagemaker-examples) | 8892 |
-|[langgenius/dify](https://github.com/langgenius/dify) | 8764 |
-|[gventuri/pandas-ai](https://github.com/gventuri/pandas-ai) | 8687 |
-|[jmorganca/ollama](https://github.com/jmorganca/ollama) | 8628 |
-|[langchain-ai/langchainjs](https://github.com/langchain-ai/langchainjs) | 8392 |
-|[h2oai/h2ogpt](https://github.com/h2oai/h2ogpt) | 7953 |
-|[arc53/DocsGPT](https://github.com/arc53/DocsGPT) | 7730 |
-|[PipedreamHQ/pipedream](https://github.com/PipedreamHQ/pipedream) | 7261 |
-|[joshpxyne/gpt-migrate](https://github.com/joshpxyne/gpt-migrate) | 6349 |
-|[bentoml/OpenLLM](https://github.com/bentoml/OpenLLM) | 6213 |
-|[mage-ai/mage-ai](https://github.com/mage-ai/mage-ai) | 5600 |
-|[zauberzeug/nicegui](https://github.com/zauberzeug/nicegui) | 5499 |
-|[wenda-LLM/wenda](https://github.com/wenda-LLM/wenda) | 5497 |
-|[sweepai/sweep](https://github.com/sweepai/sweep) | 5489 |
-|[embedchain/embedchain](https://github.com/embedchain/embedchain) | 5428 |
-|[zilliztech/GPTCache](https://github.com/zilliztech/GPTCache) | 5311 |
-|[Shaunwei/RealChar](https://github.com/Shaunwei/RealChar) | 5264 |
-|[GreyDGL/PentestGPT](https://github.com/GreyDGL/PentestGPT) | 5146 |
-|[gkamradt/langchain-tutorials](https://github.com/gkamradt/langchain-tutorials) | 5134 |
-|[serge-chat/serge](https://github.com/serge-chat/serge) | 5009 |
-|[assafelovic/gpt-researcher](https://github.com/assafelovic/gpt-researcher) | 4836 |
-|[openchatai/OpenChat](https://github.com/openchatai/OpenChat) | 4697 |
-|[intel-analytics/BigDL](https://github.com/intel-analytics/BigDL) | 4412 |
-|[continuedev/continue](https://github.com/continuedev/continue) | 4324 |
-|[postgresml/postgresml](https://github.com/postgresml/postgresml) | 4267 |
-|[madawei2699/myGPTReader](https://github.com/madawei2699/myGPTReader) | 4214 |
-|[MineDojo/Voyager](https://github.com/MineDojo/Voyager) | 4204 |
-|[danswer-ai/danswer](https://github.com/danswer-ai/danswer) | 3973 |
-|[RayVentura/ShortGPT](https://github.com/RayVentura/ShortGPT) | 3922 |
-|[Azure/azure-sdk-for-python](https://github.com/Azure/azure-sdk-for-python) | 3849 |
-|[khoj-ai/khoj](https://github.com/khoj-ai/khoj) | 3817 |
-|[langchain-ai/chat-langchain](https://github.com/langchain-ai/chat-langchain) | 3742 |
-|[Azure-Samples/azure-search-openai-demo](https://github.com/Azure-Samples/azure-search-openai-demo) | 3731 |
-|[marqo-ai/marqo](https://github.com/marqo-ai/marqo) | 3627 |
-|[kyegomez/tree-of-thoughts](https://github.com/kyegomez/tree-of-thoughts) | 3553 |
-|[llm-workflow-engine/llm-workflow-engine](https://github.com/llm-workflow-engine/llm-workflow-engine) | 3483 |
-|[PrefectHQ/marvin](https://github.com/PrefectHQ/marvin) | 3460 |
-|[aiwaves-cn/agents](https://github.com/aiwaves-cn/agents) | 3413 |
-|[OpenBMB/ToolBench](https://github.com/OpenBMB/ToolBench) | 3388 |
-|[shroominic/codeinterpreter-api](https://github.com/shroominic/codeinterpreter-api) | 3218 |
-|[whitead/paper-qa](https://github.com/whitead/paper-qa) | 3085 |
-|[project-baize/baize-chatbot](https://github.com/project-baize/baize-chatbot) | 3039 |
-|[OpenGVLab/InternGPT](https://github.com/OpenGVLab/InternGPT) | 2911 |
-|[ParisNeo/lollms-webui](https://github.com/ParisNeo/lollms-webui) | 2907 |
-|[Unstructured-IO/unstructured](https://github.com/Unstructured-IO/unstructured) | 2874 |
-|[openchatai/OpenCopilot](https://github.com/openchatai/OpenCopilot) | 2759 |
-|[OpenBMB/BMTools](https://github.com/OpenBMB/BMTools) | 2657 |
-|[homanp/superagent](https://github.com/homanp/superagent) | 2624 |
-|[SamurAIGPT/EmbedAI](https://github.com/SamurAIGPT/EmbedAI) | 2575 |
-|[GerevAI/gerev](https://github.com/GerevAI/gerev) | 2488 |
-|[microsoft/promptflow](https://github.com/microsoft/promptflow) | 2475 |
-|[OpenBMB/AgentVerse](https://github.com/OpenBMB/AgentVerse) | 2445 |
-|[Mintplex-Labs/anything-llm](https://github.com/Mintplex-Labs/anything-llm) | 2434 |
-|[emptycrown/llama-hub](https://github.com/emptycrown/llama-hub) | 2432 |
-|[NVIDIA/NeMo-Guardrails](https://github.com/NVIDIA/NeMo-Guardrails) | 2327 |
-|[ShreyaR/guardrails](https://github.com/ShreyaR/guardrails) | 2307 |
-|[thomas-yanxin/LangChain-ChatGLM-Webui](https://github.com/thomas-yanxin/LangChain-ChatGLM-Webui) | 2305 |
-|[yanqiangmiffy/Chinese-LangChain](https://github.com/yanqiangmiffy/Chinese-LangChain) | 2291 |
-|[keephq/keep](https://github.com/keephq/keep) | 2252 |
-|[OpenGVLab/Ask-Anything](https://github.com/OpenGVLab/Ask-Anything) | 2194 |
-|[IntelligenzaArtificiale/Free-Auto-GPT](https://github.com/IntelligenzaArtificiale/Free-Auto-GPT) | 2169 |
-|[Farama-Foundation/PettingZoo](https://github.com/Farama-Foundation/PettingZoo) | 2031 |
-|[YiVal/YiVal](https://github.com/YiVal/YiVal) | 2014 |
-|[hwchase17/notion-qa](https://github.com/hwchase17/notion-qa) | 2014 |
-|[jupyterlab/jupyter-ai](https://github.com/jupyterlab/jupyter-ai) | 1977 |
-|[paulpierre/RasaGPT](https://github.com/paulpierre/RasaGPT) | 1887 |
-|[dot-agent/dotagent-WIP](https://github.com/dot-agent/dotagent-WIP) | 1812 |
-|[hegelai/prompttools](https://github.com/hegelai/prompttools) | 1775 |
-|[vocodedev/vocode-python](https://github.com/vocodedev/vocode-python) | 1734 |
-|[Vonng/pigsty](https://github.com/Vonng/pigsty) | 1693 |
-|[psychic-api/psychic](https://github.com/psychic-api/psychic) | 1597 |
-|[avinashkranjan/Amazing-Python-Scripts](https://github.com/avinashkranjan/Amazing-Python-Scripts) | 1546 |
-|[pinterest/querybook](https://github.com/pinterest/querybook) | 1539 |
-|[Forethought-Technologies/AutoChain](https://github.com/Forethought-Technologies/AutoChain) | 1531 |
-|[Kav-K/GPTDiscord](https://github.com/Kav-K/GPTDiscord) | 1503 |
-|[jina-ai/langchain-serve](https://github.com/jina-ai/langchain-serve) | 1487 |
-|[noahshinn024/reflexion](https://github.com/noahshinn024/reflexion) | 1481 |
-|[jina-ai/dev-gpt](https://github.com/jina-ai/dev-gpt) | 1436 |
-|[ttengwang/Caption-Anything](https://github.com/ttengwang/Caption-Anything) | 1425 |
-|[milvus-io/bootcamp](https://github.com/milvus-io/bootcamp) | 1420 |
-|[agiresearch/OpenAGI](https://github.com/agiresearch/OpenAGI) | 1401 |
-|[greshake/llm-security](https://github.com/greshake/llm-security) | 1381 |
-|[jina-ai/thinkgpt](https://github.com/jina-ai/thinkgpt) | 1366 |
-|[lunasec-io/lunasec](https://github.com/lunasec-io/lunasec) | 1352 |
-|[101dotxyz/GPTeam](https://github.com/101dotxyz/GPTeam) | 1339 |
-|[refuel-ai/autolabel](https://github.com/refuel-ai/autolabel) | 1320 |
-|[melih-unsal/DemoGPT](https://github.com/melih-unsal/DemoGPT) | 1320 |
-|[mmz-001/knowledge_gpt](https://github.com/mmz-001/knowledge_gpt) | 1320 |
-|[richardyc/Chrome-GPT](https://github.com/richardyc/Chrome-GPT) | 1315 |
-|[run-llama/sec-insights](https://github.com/run-llama/sec-insights) | 1312 |
-|[Azure/azureml-examples](https://github.com/Azure/azureml-examples) | 1305 |
-|[cofactoryai/textbase](https://github.com/cofactoryai/textbase) | 1286 |
-|[dataelement/bisheng](https://github.com/dataelement/bisheng) | 1273 |
-|[eyurtsev/kor](https://github.com/eyurtsev/kor) | 1263 |
-|[pluralsh/plural](https://github.com/pluralsh/plural) | 1188 |
-|[FlagOpen/FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding) | 1184 |
-|[juncongmoo/chatllama](https://github.com/juncongmoo/chatllama) | 1144 |
-|[poe-platform/server-bot-quick-start](https://github.com/poe-platform/server-bot-quick-start) | 1139 |
-|[visual-openllm/visual-openllm](https://github.com/visual-openllm/visual-openllm) | 1137 |
-|[griptape-ai/griptape](https://github.com/griptape-ai/griptape) | 1124 |
-|[microsoft/X-Decoder](https://github.com/microsoft/X-Decoder) | 1119 |
-|[ThousandBirdsInc/chidori](https://github.com/ThousandBirdsInc/chidori) | 1116 |
-|[filip-michalsky/SalesGPT](https://github.com/filip-michalsky/SalesGPT) | 1112 |
-|[psychic-api/rag-stack](https://github.com/psychic-api/rag-stack) | 1110 |
-|[irgolic/AutoPR](https://github.com/irgolic/AutoPR) | 1100 |
-|[promptfoo/promptfoo](https://github.com/promptfoo/promptfoo) | 1099 |
-|[nod-ai/SHARK](https://github.com/nod-ai/SHARK) | 1062 |
-|[SamurAIGPT/Camel-AutoGPT](https://github.com/SamurAIGPT/Camel-AutoGPT) | 1036 |
-|[Farama-Foundation/chatarena](https://github.com/Farama-Foundation/chatarena) | 1020 |
-|[peterw/Chat-with-Github-Repo](https://github.com/peterw/Chat-with-Github-Repo) | 993 |
-|[jiran214/GPT-vup](https://github.com/jiran214/GPT-vup) | 967 |
-|[alejandro-ao/ask-multiple-pdfs](https://github.com/alejandro-ao/ask-multiple-pdfs) | 958 |
-|[run-llama/llama-lab](https://github.com/run-llama/llama-lab) | 953 |
-|[LC1332/Chat-Haruhi-Suzumiya](https://github.com/LC1332/Chat-Haruhi-Suzumiya) | 950 |
-|[rlancemartin/auto-evaluator](https://github.com/rlancemartin/auto-evaluator) | 927 |
-|[cheshire-cat-ai/core](https://github.com/cheshire-cat-ai/core) | 902 |
-|[Anil-matcha/ChatPDF](https://github.com/Anil-matcha/ChatPDF) | 894 |
-|[cirediatpl/FigmaChain](https://github.com/cirediatpl/FigmaChain) | 881 |
-|[seanpixel/Teenage-AGI](https://github.com/seanpixel/Teenage-AGI) | 876 |
-|[xusenlinzy/api-for-open-llm](https://github.com/xusenlinzy/api-for-open-llm) | 865 |
-|[ricklamers/shell-ai](https://github.com/ricklamers/shell-ai) | 864 |
-|[codeacme17/examor](https://github.com/codeacme17/examor) | 856 |
-|[corca-ai/EVAL](https://github.com/corca-ai/EVAL) | 836 |
-|[microsoft/Llama-2-Onnx](https://github.com/microsoft/Llama-2-Onnx) | 835 |
-|[explodinggradients/ragas](https://github.com/explodinggradients/ragas) | 833 |
-|[ajndkr/lanarky](https://github.com/ajndkr/lanarky) | 817 |
-|[kennethleungty/Llama-2-Open-Source-LLM-CPU-Inference](https://github.com/kennethleungty/Llama-2-Open-Source-LLM-CPU-Inference) | 814 |
-|[ray-project/llm-applications](https://github.com/ray-project/llm-applications) | 804 |
-|[hwchase17/chat-your-data](https://github.com/hwchase17/chat-your-data) | 801 |
-|[LambdaLabsML/examples](https://github.com/LambdaLabsML/examples) | 759 |
-|[kreneskyp/ix](https://github.com/kreneskyp/ix) | 758 |
-|[pyspark-ai/pyspark-ai](https://github.com/pyspark-ai/pyspark-ai) | 750 |
-|[billxbf/ReWOO](https://github.com/billxbf/ReWOO) | 746 |
-|[e-johnstonn/BriefGPT](https://github.com/e-johnstonn/BriefGPT) | 738 |
-|[akshata29/entaoai](https://github.com/akshata29/entaoai) | 733 |
-|[getmetal/motorhead](https://github.com/getmetal/motorhead) | 717 |
-|[ruoccofabrizio/azure-open-ai-embeddings-qna](https://github.com/ruoccofabrizio/azure-open-ai-embeddings-qna) | 712 |
-|[msoedov/langcorn](https://github.com/msoedov/langcorn) | 698 |
-|[Dataherald/dataherald](https://github.com/Dataherald/dataherald) | 684 |
-|[jondurbin/airoboros](https://github.com/jondurbin/airoboros) | 657 |
-|[Ikaros-521/AI-Vtuber](https://github.com/Ikaros-521/AI-Vtuber) | 651 |
-|[whyiyhw/chatgpt-wechat](https://github.com/whyiyhw/chatgpt-wechat) | 644 |
-|[langchain-ai/streamlit-agent](https://github.com/langchain-ai/streamlit-agent) | 637 |
-|[SamurAIGPT/ChatGPT-Developer-Plugins](https://github.com/SamurAIGPT/ChatGPT-Developer-Plugins) | 637 |
-|[OpenGenerativeAI/GenossGPT](https://github.com/OpenGenerativeAI/GenossGPT) | 632 |
-|[AILab-CVC/GPT4Tools](https://github.com/AILab-CVC/GPT4Tools) | 629 |
-|[langchain-ai/auto-evaluator](https://github.com/langchain-ai/auto-evaluator) | 614 |
-|[explosion/spacy-llm](https://github.com/explosion/spacy-llm) | 613 |
-|[alexanderatallah/window.ai](https://github.com/alexanderatallah/window.ai) | 607 |
-|[MiuLab/Taiwan-LLaMa](https://github.com/MiuLab/Taiwan-LLaMa) | 601 |
-|[microsoft/PodcastCopilot](https://github.com/microsoft/PodcastCopilot) | 600 |
-|[Dicklesworthstone/swiss_army_llama](https://github.com/Dicklesworthstone/swiss_army_llama) | 596 |
-|[NoDataFound/hackGPT](https://github.com/NoDataFound/hackGPT) | 596 |
-|[namuan/dr-doc-search](https://github.com/namuan/dr-doc-search) | 593 |
-|[amosjyng/langchain-visualizer](https://github.com/amosjyng/langchain-visualizer) | 582 |
-|[microsoft/sample-app-aoai-chatGPT](https://github.com/microsoft/sample-app-aoai-chatGPT) | 581 |
-|[yvann-hub/Robby-chatbot](https://github.com/yvann-hub/Robby-chatbot) | 581 |
-|[yeagerai/yeagerai-agent](https://github.com/yeagerai/yeagerai-agent) | 547 |
-|[tgscan-dev/tgscan](https://github.com/tgscan-dev/tgscan) | 533 |
-|[Azure-Samples/openai](https://github.com/Azure-Samples/openai) | 531 |
-|[plastic-labs/tutor-gpt](https://github.com/plastic-labs/tutor-gpt) | 531 |
-|[xuwenhao/geektime-ai-course](https://github.com/xuwenhao/geektime-ai-course) | 526 |
-|[michaelthwan/searchGPT](https://github.com/michaelthwan/searchGPT) | 526 |
-|[jonra1993/fastapi-alembic-sqlmodel-async](https://github.com/jonra1993/fastapi-alembic-sqlmodel-async) | 522 |
-|[jina-ai/agentchain](https://github.com/jina-ai/agentchain) | 519 |
-|[mckaywrigley/repo-chat](https://github.com/mckaywrigley/repo-chat) | 518 |
-|[modelscope/modelscope-agent](https://github.com/modelscope/modelscope-agent) | 512 |
-|[daveebbelaar/langchain-experiments](https://github.com/daveebbelaar/langchain-experiments) | 504 |
-|[freddyaboulton/gradio-tools](https://github.com/freddyaboulton/gradio-tools) | 497 |
-|[sidhq/Multi-GPT](https://github.com/sidhq/Multi-GPT) | 494 |
-|[continuum-llms/chatgpt-memory](https://github.com/continuum-llms/chatgpt-memory) | 489 |
-|[langchain-ai/langchain-aiplugin](https://github.com/langchain-ai/langchain-aiplugin) | 487 |
-|[mpaepper/content-chatbot](https://github.com/mpaepper/content-chatbot) | 483 |
-|[steamship-core/steamship-langchain](https://github.com/steamship-core/steamship-langchain) | 481 |
-|[alejandro-ao/langchain-ask-pdf](https://github.com/alejandro-ao/langchain-ask-pdf) | 474 |
-|[truera/trulens](https://github.com/truera/trulens) | 464 |
-|[marella/chatdocs](https://github.com/marella/chatdocs) | 459 |
-|[opencopilotdev/opencopilot](https://github.com/opencopilotdev/opencopilot) | 453 |
-|[poe-platform/poe-protocol](https://github.com/poe-platform/poe-protocol) | 444 |
-|[DataDog/dd-trace-py](https://github.com/DataDog/dd-trace-py) | 441 |
-|[logan-markewich/llama_index_starter_pack](https://github.com/logan-markewich/llama_index_starter_pack) | 441 |
-|[opentensor/bittensor](https://github.com/opentensor/bittensor) | 433 |
-|[DjangoPeng/openai-quickstart](https://github.com/DjangoPeng/openai-quickstart) | 425 |
-|[CarperAI/OpenELM](https://github.com/CarperAI/OpenELM) | 424 |
-|[daodao97/chatdoc](https://github.com/daodao97/chatdoc) | 423 |
-|[showlab/VLog](https://github.com/showlab/VLog) | 411 |
-|[Anil-matcha/Chatbase](https://github.com/Anil-matcha/Chatbase) | 402 |
-|[yakami129/VirtualWife](https://github.com/yakami129/VirtualWife) | 399 |
-|[wandb/weave](https://github.com/wandb/weave) | 399 |
-|[mtenenholtz/chat-twitter](https://github.com/mtenenholtz/chat-twitter) | 398 |
-|[LinkSoul-AI/AutoAgents](https://github.com/LinkSoul-AI/AutoAgents) | 397 |
-|[Agenta-AI/agenta](https://github.com/Agenta-AI/agenta) | 389 |
-|[huchenxucs/ChatDB](https://github.com/huchenxucs/ChatDB) | 386 |
-|[mallorbc/Finetune_LLMs](https://github.com/mallorbc/Finetune_LLMs) | 379 |
-|[junruxiong/IncarnaMind](https://github.com/junruxiong/IncarnaMind) | 372 |
-|[MagnivOrg/prompt-layer-library](https://github.com/MagnivOrg/prompt-layer-library) | 368 |
-|[mosaicml/examples](https://github.com/mosaicml/examples) | 366 |
-|[rsaryev/talk-codebase](https://github.com/rsaryev/talk-codebase) | 364 |
-|[morpheuslord/GPT_Vuln-analyzer](https://github.com/morpheuslord/GPT_Vuln-analyzer) | 362 |
-|[monarch-initiative/ontogpt](https://github.com/monarch-initiative/ontogpt) | 362 |
-|[JayZeeDesign/researcher-gpt](https://github.com/JayZeeDesign/researcher-gpt) | 361 |
-|[personoids/personoids-lite](https://github.com/personoids/personoids-lite) | 361 |
-|[intel/intel-extension-for-transformers](https://github.com/intel/intel-extension-for-transformers) | 357 |
-|[jerlendds/osintbuddy](https://github.com/jerlendds/osintbuddy) | 357 |
-|[steamship-packages/langchain-production-starter](https://github.com/steamship-packages/langchain-production-starter) | 356 |
-|[onlyphantom/llm-python](https://github.com/onlyphantom/llm-python) | 354 |
-|[Azure-Samples/miyagi](https://github.com/Azure-Samples/miyagi) | 340 |
-|[mrwadams/attackgen](https://github.com/mrwadams/attackgen) | 338 |
-|[rgomezcasas/dotfiles](https://github.com/rgomezcasas/dotfiles) | 337 |
-|[eosphoros-ai/DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub) | 336 |
-|[andylokandy/gpt-4-search](https://github.com/andylokandy/gpt-4-search) | 335 |
-|[NimbleBoxAI/ChainFury](https://github.com/NimbleBoxAI/ChainFury) | 330 |
-|[momegas/megabots](https://github.com/momegas/megabots) | 329 |
-|[Nuggt-dev/Nuggt](https://github.com/Nuggt-dev/Nuggt) | 315 |
-|[itamargol/openai](https://github.com/itamargol/openai) | 315 |
-|[BlackHC/llm-strategy](https://github.com/BlackHC/llm-strategy) | 315 |
-|[aws-samples/aws-genai-llm-chatbot](https://github.com/aws-samples/aws-genai-llm-chatbot) | 312 |
-|[Cheems-Seminar/grounded-segment-any-parts](https://github.com/Cheems-Seminar/grounded-segment-any-parts) | 312 |
-|[preset-io/promptimize](https://github.com/preset-io/promptimize) | 311 |
-|[dgarnitz/vectorflow](https://github.com/dgarnitz/vectorflow) | 309 |
-|[langchain-ai/langsmith-cookbook](https://github.com/langchain-ai/langsmith-cookbook) | 309 |
-|[CambioML/pykoi](https://github.com/CambioML/pykoi) | 309 |
-|[wandb/edu](https://github.com/wandb/edu) | 301 |
-|[XzaiCloud/luna-ai](https://github.com/XzaiCloud/luna-ai) | 300 |
-|[liangwq/Chatglm_lora_multi-gpu](https://github.com/liangwq/Chatglm_lora_multi-gpu) | 294 |
-|[Haste171/langchain-chatbot](https://github.com/Haste171/langchain-chatbot) | 291 |
-|[sullivan-sean/chat-langchainjs](https://github.com/sullivan-sean/chat-langchainjs) | 286 |
-|[sugarforever/LangChain-Tutorials](https://github.com/sugarforever/LangChain-Tutorials) | 285 |
-|[facebookresearch/personal-timeline](https://github.com/facebookresearch/personal-timeline) | 283 |
-|[hnawaz007/pythondataanalysis](https://github.com/hnawaz007/pythondataanalysis) | 282 |
-|[yuanjie-ai/ChatLLM](https://github.com/yuanjie-ai/ChatLLM) | 280 |
-|[MetaGLM/FinGLM](https://github.com/MetaGLM/FinGLM) | 279 |
-|[JohnSnowLabs/langtest](https://github.com/JohnSnowLabs/langtest) | 277 |
-|[Em1tSan/NeuroGPT](https://github.com/Em1tSan/NeuroGPT) | 274 |
-|[Safiullah-Rahu/CSV-AI](https://github.com/Safiullah-Rahu/CSV-AI) | 274 |
-|[conceptofmind/toolformer](https://github.com/conceptofmind/toolformer) | 274 |
-|[airobotlab/KoChatGPT](https://github.com/airobotlab/KoChatGPT) | 266 |
-|[gia-guar/JARVIS-ChatGPT](https://github.com/gia-guar/JARVIS-ChatGPT) | 263 |
-|[Mintplex-Labs/vector-admin](https://github.com/Mintplex-Labs/vector-admin) | 262 |
-|[artitw/text2text](https://github.com/artitw/text2text) | 262 |
-|[kaarthik108/snowChat](https://github.com/kaarthik108/snowChat) | 261 |
-|[paolorechia/learn-langchain](https://github.com/paolorechia/learn-langchain) | 260 |
-|[shamspias/customizable-gpt-chatbot](https://github.com/shamspias/customizable-gpt-chatbot) | 260 |
-|[ur-whitelab/exmol](https://github.com/ur-whitelab/exmol) | 258 |
-|[hwchase17/chroma-langchain](https://github.com/hwchase17/chroma-langchain) | 257 |
-|[bborn/howdoi.ai](https://github.com/bborn/howdoi.ai) | 255 |
-|[ur-whitelab/chemcrow-public](https://github.com/ur-whitelab/chemcrow-public) | 253 |
-|[pablomarin/GPT-Azure-Search-Engine](https://github.com/pablomarin/GPT-Azure-Search-Engine) | 251 |
-|[gustavz/DataChad](https://github.com/gustavz/DataChad) | 249 |
-|[radi-cho/datasetGPT](https://github.com/radi-cho/datasetGPT) | 249 |
-|[ennucore/clippinator](https://github.com/ennucore/clippinator) | 247 |
-|[recalign/RecAlign](https://github.com/recalign/RecAlign) | 244 |
-|[lilacai/lilac](https://github.com/lilacai/lilac) | 243 |
-|[kaleido-lab/dolphin](https://github.com/kaleido-lab/dolphin) | 236 |
-|[iusztinpaul/hands-on-llms](https://github.com/iusztinpaul/hands-on-llms) | 233 |
-|[PradipNichite/Youtube-Tutorials](https://github.com/PradipNichite/Youtube-Tutorials) | 231 |
-|[shaman-ai/agent-actors](https://github.com/shaman-ai/agent-actors) | 231 |
-|[hwchase17/langchain-streamlit-template](https://github.com/hwchase17/langchain-streamlit-template) | 231 |
-|[yym68686/ChatGPT-Telegram-Bot](https://github.com/yym68686/ChatGPT-Telegram-Bot) | 226 |
-|[grumpyp/aixplora](https://github.com/grumpyp/aixplora) | 222 |
-|[su77ungr/CASALIOY](https://github.com/su77ungr/CASALIOY) | 222 |
-|[alvarosevilla95/autolang](https://github.com/alvarosevilla95/autolang) | 222 |
-|[arthur-ai/bench](https://github.com/arthur-ai/bench) | 220 |
-|[miaoshouai/miaoshouai-assistant](https://github.com/miaoshouai/miaoshouai-assistant) | 219 |
-|[AutoPackAI/beebot](https://github.com/AutoPackAI/beebot) | 217 |
-|[edreisMD/plugnplai](https://github.com/edreisMD/plugnplai) | 216 |
-|[nicknochnack/LangchainDocuments](https://github.com/nicknochnack/LangchainDocuments) | 214 |
-|[AkshitIreddy/Interactive-LLM-Powered-NPCs](https://github.com/AkshitIreddy/Interactive-LLM-Powered-NPCs) | 213 |
-|[SpecterOps/Nemesis](https://github.com/SpecterOps/Nemesis) | 210 |
-|[kyegomez/swarms](https://github.com/kyegomez/swarms) | 210 |
-|[wpydcr/LLM-Kit](https://github.com/wpydcr/LLM-Kit) | 208 |
-|[orgexyz/BlockAGI](https://github.com/orgexyz/BlockAGI) | 204 |
-|[Chainlit/cookbook](https://github.com/Chainlit/cookbook) | 202 |
-|[WongSaang/chatgpt-ui-server](https://github.com/WongSaang/chatgpt-ui-server) | 202 |
-|[jbrukh/gpt-jargon](https://github.com/jbrukh/gpt-jargon) | 202 |
-|[handrew/browserpilot](https://github.com/handrew/browserpilot) | 202 |
-|[langchain-ai/web-explorer](https://github.com/langchain-ai/web-explorer) | 200 |
-|[plchld/InsightFlow](https://github.com/plchld/InsightFlow) | 200 |
-|[alphasecio/langchain-examples](https://github.com/alphasecio/langchain-examples) | 199 |
-|[Gentopia-AI/Gentopia](https://github.com/Gentopia-AI/Gentopia) | 198 |
-|[SamPink/dev-gpt](https://github.com/SamPink/dev-gpt) | 196 |
-|[yasyf/compress-gpt](https://github.com/yasyf/compress-gpt) | 196 |
-|[benthecoder/ClassGPT](https://github.com/benthecoder/ClassGPT) | 195 |
-|[voxel51/voxelgpt](https://github.com/voxel51/voxelgpt) | 193 |
-|[CL-lau/SQL-GPT](https://github.com/CL-lau/SQL-GPT) | 192 |
-|[blob42/Instrukt](https://github.com/blob42/Instrukt) | 191 |
-|[streamlit/llm-examples](https://github.com/streamlit/llm-examples) | 191 |
-|[stepanogil/autonomous-hr-chatbot](https://github.com/stepanogil/autonomous-hr-chatbot) | 190 |
-|[TsinghuaDatabaseGroup/DB-GPT](https://github.com/TsinghuaDatabaseGroup/DB-GPT) | 189 |
-|[PJLab-ADG/DriveLikeAHuman](https://github.com/PJLab-ADG/DriveLikeAHuman) | 187 |
-|[Azure-Samples/azure-search-power-skills](https://github.com/Azure-Samples/azure-search-power-skills) | 187 |
-|[microsoft/azure-openai-in-a-day-workshop](https://github.com/microsoft/azure-openai-in-a-day-workshop) | 187 |
-|[ju-bezdek/langchain-decorators](https://github.com/ju-bezdek/langchain-decorators) | 182 |
-|[hardbyte/qabot](https://github.com/hardbyte/qabot) | 181 |
-|[hongbo-miao/hongbomiao.com](https://github.com/hongbo-miao/hongbomiao.com) | 180 |
-|[QwenLM/Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) | 179 |
-|[showlab/UniVTG](https://github.com/showlab/UniVTG) | 179 |
-|[Azure-Samples/jp-azureopenai-samples](https://github.com/Azure-Samples/jp-azureopenai-samples) | 176 |
-|[afaqueumer/DocQA](https://github.com/afaqueumer/DocQA) | 174 |
-|[ethanyanjiali/minChatGPT](https://github.com/ethanyanjiali/minChatGPT) | 174 |
-|[shauryr/S2QA](https://github.com/shauryr/S2QA) | 174 |
-|[RoboCoachTechnologies/GPT-Synthesizer](https://github.com/RoboCoachTechnologies/GPT-Synthesizer) | 173 |
-|[chakkaradeep/pyCodeAGI](https://github.com/chakkaradeep/pyCodeAGI) | 172 |
-|[vaibkumr/prompt-optimizer](https://github.com/vaibkumr/prompt-optimizer) | 171 |
-|[ccurme/yolopandas](https://github.com/ccurme/yolopandas) | 170 |
-|[anarchy-ai/LLM-VM](https://github.com/anarchy-ai/LLM-VM) | 169 |
-|[ray-project/langchain-ray](https://github.com/ray-project/langchain-ray) | 169 |
-|[fengyuli-dev/multimedia-gpt](https://github.com/fengyuli-dev/multimedia-gpt) | 169 |
-|[ibiscp/LLM-IMDB](https://github.com/ibiscp/LLM-IMDB) | 168 |
-|[mayooear/private-chatbot-mpt30b-langchain](https://github.com/mayooear/private-chatbot-mpt30b-langchain) | 167 |
-|[OpenPluginACI/openplugin](https://github.com/OpenPluginACI/openplugin) | 165 |
-|[jmpaz/promptlib](https://github.com/jmpaz/promptlib) | 165 |
-|[kjappelbaum/gptchem](https://github.com/kjappelbaum/gptchem) | 162 |
-|[JorisdeJong123/7-Days-of-LangChain](https://github.com/JorisdeJong123/7-Days-of-LangChain) | 161 |
-|[retr0reg/Ret2GPT](https://github.com/retr0reg/Ret2GPT) | 161 |
-|[menloparklab/falcon-langchain](https://github.com/menloparklab/falcon-langchain) | 159 |
-|[summarizepaper/summarizepaper](https://github.com/summarizepaper/summarizepaper) | 158 |
-|[emarco177/ice_breaker](https://github.com/emarco177/ice_breaker) | 157 |
-|[AmineDiro/cria](https://github.com/AmineDiro/cria) | 156 |
-|[morpheuslord/HackBot](https://github.com/morpheuslord/HackBot) | 156 |
-|[homanp/vercel-langchain](https://github.com/homanp/vercel-langchain) | 156 |
-|[mlops-for-all/mlops-for-all.github.io](https://github.com/mlops-for-all/mlops-for-all.github.io) | 155 |
-|[positive666/Prompt-Can-Anything](https://github.com/positive666/Prompt-Can-Anything) | 154 |
-|[deeppavlov/dream](https://github.com/deeppavlov/dream) | 153 |
-|[flurb18/AgentOoba](https://github.com/flurb18/AgentOoba) | 151 |
-|[Open-Swarm-Net/GPT-Swarm](https://github.com/Open-Swarm-Net/GPT-Swarm) | 151 |
-|[v7labs/benchllm](https://github.com/v7labs/benchllm) | 150 |
-|[Klingefjord/chatgpt-telegram](https://github.com/Klingefjord/chatgpt-telegram) | 150 |
-|[Aggregate-Intellect/sherpa](https://github.com/Aggregate-Intellect/sherpa) | 148 |
-|[Coding-Crashkurse/Langchain-Full-Course](https://github.com/Coding-Crashkurse/Langchain-Full-Course) | 148 |
-|[SuperDuperDB/superduperdb](https://github.com/SuperDuperDB/superduperdb) | 147 |
-|[defenseunicorns/leapfrogai](https://github.com/defenseunicorns/leapfrogai) | 147 |
-|[menloparklab/langchain-cohere-qdrant-doc-retrieval](https://github.com/menloparklab/langchain-cohere-qdrant-doc-retrieval) | 147 |
-|[Jaseci-Labs/jaseci](https://github.com/Jaseci-Labs/jaseci) | 146 |
-|[realminchoi/babyagi-ui](https://github.com/realminchoi/babyagi-ui) | 146 |
-|[iMagist486/ElasticSearch-Langchain-Chatglm2](https://github.com/iMagist486/ElasticSearch-Langchain-Chatglm2) | 144 |
-|[peterw/StoryStorm](https://github.com/peterw/StoryStorm) | 143 |
-|[kulltc/chatgpt-sql](https://github.com/kulltc/chatgpt-sql) | 142 |
-|[Teahouse-Studios/akari-bot](https://github.com/Teahouse-Studios/akari-bot) | 142 |
-|[hirokidaichi/wanna](https://github.com/hirokidaichi/wanna) | 141 |
-|[yasyf/summ](https://github.com/yasyf/summ) | 141 |
-|[solana-labs/chatgpt-plugin](https://github.com/solana-labs/chatgpt-plugin) | 140 |
-|[ssheng/BentoChain](https://github.com/ssheng/BentoChain) | 139 |
-|[mallahyari/drqa](https://github.com/mallahyari/drqa) | 139 |
-|[petehunt/langchain-github-bot](https://github.com/petehunt/langchain-github-bot) | 139 |
-|[dbpunk-labs/octogen](https://github.com/dbpunk-labs/octogen) | 138 |
-|[RedisVentures/redis-openai-qna](https://github.com/RedisVentures/redis-openai-qna) | 138 |
-|[eunomia-bpf/GPTtrace](https://github.com/eunomia-bpf/GPTtrace) | 138 |
-|[langchain-ai/langsmith-sdk](https://github.com/langchain-ai/langsmith-sdk) | 137 |
-|[jina-ai/fastapi-serve](https://github.com/jina-ai/fastapi-serve) | 137 |
-|[yeagerai/genworlds](https://github.com/yeagerai/genworlds) | 137 |
-|[aurelio-labs/arxiv-bot](https://github.com/aurelio-labs/arxiv-bot) | 137 |
-|[luisroque/large_laguage_models](https://github.com/luisroque/large_laguage_models) | 136 |
-|[ChuloAI/BrainChulo](https://github.com/ChuloAI/BrainChulo) | 136 |
-|[3Alan/DocsMind](https://github.com/3Alan/DocsMind) | 136 |
-|[KylinC/ChatFinance](https://github.com/KylinC/ChatFinance) | 133 |
-|[langchain-ai/text-split-explorer](https://github.com/langchain-ai/text-split-explorer) | 133 |
-|[davila7/file-gpt](https://github.com/davila7/file-gpt) | 133 |
-|[tencentmusic/supersonic](https://github.com/tencentmusic/supersonic) | 132 |
-|[kimtth/azure-openai-llm-vector-langchain](https://github.com/kimtth/azure-openai-llm-vector-langchain) | 131 |
-|[ciare-robotics/world-creator](https://github.com/ciare-robotics/world-creator) | 129 |
-|[zenml-io/zenml-projects](https://github.com/zenml-io/zenml-projects) | 129 |
-|[log1stics/voice-generator-webui](https://github.com/log1stics/voice-generator-webui) | 129 |
-|[snexus/llm-search](https://github.com/snexus/llm-search) | 129 |
-|[fixie-ai/fixie-examples](https://github.com/fixie-ai/fixie-examples) | 128 |
-|[MedalCollector/Orator](https://github.com/MedalCollector/Orator) | 127 |
-|[grumpyp/chroma-langchain-tutorial](https://github.com/grumpyp/chroma-langchain-tutorial) | 127 |
-|[langchain-ai/langchain-aws-template](https://github.com/langchain-ai/langchain-aws-template) | 127 |
-|[prof-frink-lab/slangchain](https://github.com/prof-frink-lab/slangchain) | 126 |
-|[KMnO4-zx/huanhuan-chat](https://github.com/KMnO4-zx/huanhuan-chat) | 124 |
-|[RCGAI/SimplyRetrieve](https://github.com/RCGAI/SimplyRetrieve) | 124 |
-|[Dicklesworthstone/llama2_aided_tesseract](https://github.com/Dicklesworthstone/llama2_aided_tesseract) | 123 |
-|[sdaaron/QueryGPT](https://github.com/sdaaron/QueryGPT) | 122 |
-|[athina-ai/athina-sdk](https://github.com/athina-ai/athina-sdk) | 121 |
-|[AIAnytime/Llama2-Medical-Chatbot](https://github.com/AIAnytime/Llama2-Medical-Chatbot) | 121 |
-|[MuhammadMoinFaisal/LargeLanguageModelsProjects](https://github.com/MuhammadMoinFaisal/LargeLanguageModelsProjects) | 121 |
-|[Azure/business-process-automation](https://github.com/Azure/business-process-automation) | 121 |
-|[definitive-io/code-indexer-loop](https://github.com/definitive-io/code-indexer-loop) | 119 |
-|[nrl-ai/pautobot](https://github.com/nrl-ai/pautobot) | 119 |
-|[Azure/app-service-linux-docs](https://github.com/Azure/app-service-linux-docs) | 118 |
-|[zilliztech/akcio](https://github.com/zilliztech/akcio) | 118 |
-|[CodeAlchemyAI/ViLT-GPT](https://github.com/CodeAlchemyAI/ViLT-GPT) | 117 |
-|[georgesung/llm_qlora](https://github.com/georgesung/llm_qlora) | 117 |
-|[nicknochnack/Nopenai](https://github.com/nicknochnack/Nopenai) | 115 |
-|[nftblackmagic/flask-langchain](https://github.com/nftblackmagic/flask-langchain) | 115 |
-|[mortium91/langchain-assistant](https://github.com/mortium91/langchain-assistant) | 115 |
-|[Ngonie-x/langchain_csv](https://github.com/Ngonie-x/langchain_csv) | 114 |
-|[wombyz/HormoziGPT](https://github.com/wombyz/HormoziGPT) | 114 |
-|[langchain-ai/langchain-teacher](https://github.com/langchain-ai/langchain-teacher) | 113 |
-|[mluogh/eastworld](https://github.com/mluogh/eastworld) | 112 |
-|[mudler/LocalAGI](https://github.com/mudler/LocalAGI) | 112 |
-|[marimo-team/marimo](https://github.com/marimo-team/marimo) | 111 |
-|[trancethehuman/entities-extraction-web-scraper](https://github.com/trancethehuman/entities-extraction-web-scraper) | 111 |
-|[xuwenhao/mactalk-ai-course](https://github.com/xuwenhao/mactalk-ai-course) | 111 |
-|[dcaribou/transfermarkt-datasets](https://github.com/dcaribou/transfermarkt-datasets) | 111 |
-|[rabbitmetrics/langchain-13-min](https://github.com/rabbitmetrics/langchain-13-min) | 111 |
-|[dotvignesh/PDFChat](https://github.com/dotvignesh/PDFChat) | 111 |
-|[aws-samples/cdk-eks-blueprints-patterns](https://github.com/aws-samples/cdk-eks-blueprints-patterns) | 110 |
-|[topoteretes/PromethAI-Backend](https://github.com/topoteretes/PromethAI-Backend) | 110 |
-|[jlonge4/local_llama](https://github.com/jlonge4/local_llama) | 110 |
-|[RUC-GSAI/YuLan-Rec](https://github.com/RUC-GSAI/YuLan-Rec) | 108 |
-|[gh18l/CrawlGPT](https://github.com/gh18l/CrawlGPT) | 107 |
-|[c0sogi/LLMChat](https://github.com/c0sogi/LLMChat) | 107 |
-|[hwchase17/langchain-gradio-template](https://github.com/hwchase17/langchain-gradio-template) | 107 |
-|[ArjanCodes/examples](https://github.com/ArjanCodes/examples) | 106 |
-|[genia-dev/GeniA](https://github.com/genia-dev/GeniA) | 105 |
-|[nexus-stc/stc](https://github.com/nexus-stc/stc) | 105 |
-|[mbchang/data-driven-characters](https://github.com/mbchang/data-driven-characters) | 105 |
-|[ademakdogan/ChatSQL](https://github.com/ademakdogan/ChatSQL) | 104 |
-|[crosleythomas/MirrorGPT](https://github.com/crosleythomas/MirrorGPT) | 104 |
-|[IvanIsCoding/ResuLLMe](https://github.com/IvanIsCoding/ResuLLMe) | 104 |
-|[avrabyt/MemoryBot](https://github.com/avrabyt/MemoryBot) | 104 |
-|[Azure/azure-sdk-tools](https://github.com/Azure/azure-sdk-tools) | 103 |
-|[aniketmaurya/llm-inference](https://github.com/aniketmaurya/llm-inference) | 103 |
-|[Anil-matcha/Youtube-to-chatbot](https://github.com/Anil-matcha/Youtube-to-chatbot) | 103 |
-|[nyanp/chat2plot](https://github.com/nyanp/chat2plot) | 102 |
-|[aws-samples/amazon-kendra-langchain-extensions](https://github.com/aws-samples/amazon-kendra-langchain-extensions) | 101 |
-|[atisharma/llama_farm](https://github.com/atisharma/llama_farm) | 100 |
-|[Xueheng-Li/SynologyChatbotGPT](https://github.com/Xueheng-Li/SynologyChatbotGPT) | 100 |
-
-
-
-_Generated by [github-dependents-info](https://github.com/nvuillam/github-dependents-info)_
-
-`github-dependents-info --repo langchain-ai/langchain --markdownfile dependents.md --minstars 100 --sort stars`

docs/docs/expression_language/cookbook/embedding_router.ipynb

@@ -1,155 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "cf4fb76d-c534-485b-8b51-a0714ee3b82e",
-   "metadata": {},
-   "source": [
-    "# Routing by semantic similarity\n",
-    "\n",
-    "With LCEL you can easily add [custom routing logic](/docs/expression_language/how_to/routing#using-a-custom-function) to your chain to dynamically determine the chain logic based on user input. All you need to do is define a function that given an input returns a `Runnable`.\n",
-    "\n",
-    "One especially useful technique is to use embeddings to route a query to the most relevant prompt. Here's a very simple example."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "eef9020a-5f7c-4291-98eb-fa73f17d4b92",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnableLambda, RunnablePassthrough\n",
-    "from langchain.utils.math import cosine_similarity\n",
-    "\n",
-    "\n",
-    "physics_template = \"\"\"You are a very smart physics professor. \\\n",
-    "You are great at answering questions about physics in a concise and easy to understand manner. \\\n",
-    "When you don't know the answer to a question you admit that you don't know.\n",
-    "\n",
-    "Here is a question:\n",
-    "{query}\"\"\"\n",
-    "\n",
-    "math_template = \"\"\"You are a very good mathematician. You are great at answering math questions. \\\n",
-    "You are so good because you are able to break down hard problems into their component parts, \\\n",
-    "answer the component parts, and then put them together to answer the broader question.\n",
-    "\n",
-    "Here is a question:\n",
-    "{query}\"\"\"\n",
-    "\n",
-    "embeddings = OpenAIEmbeddings()\n",
-    "prompt_templates = [physics_template, math_template]\n",
-    "prompt_embeddings = embeddings.embed_documents(prompt_templates)\n",
-    "\n",
-    "\n",
-    "def prompt_router(input):\n",
-    "    query_embedding = embeddings.embed_query(input[\"query\"])\n",
-    "    similarity = cosine_similarity([query_embedding], prompt_embeddings)[0]\n",
-    "    most_similar = prompt_templates[similarity.argmax()]\n",
-    "    print(\"Using MATH\" if most_similar == math_template else \"Using PHYSICS\")\n",
-    "    return PromptTemplate.from_template(most_similar)\n",
-    "\n",
-    "\n",
-    "chain = (\n",
-    "    {\"query\": RunnablePassthrough()}\n",
-    "    | RunnableLambda(prompt_router)\n",
-    "    | ChatOpenAI()\n",
-    "    | StrOutputParser()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "4d22b0f3-24f2-4a47-9440-065b57ebcdbd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using PHYSICS\n",
-      "A black hole is a region in space where gravity is extremely strong, so strong that nothing, not even light, can escape its gravitational pull. It is formed when a massive star collapses under its own gravity during a supernova explosion. The collapse causes an incredibly dense mass to be concentrated in a small volume, creating a gravitational field that is so intense that it warps space and time. Black holes have a boundary called the event horizon, which marks the point of no return for anything that gets too close. Beyond the event horizon, the gravitational pull is so strong that even light cannot escape, hence the name \"black hole.\" While we have a good understanding of black holes, there is still much to learn, especially about what happens inside them.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(chain.invoke(\"What's a black hole\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "f261910d-1de1-4a01-8c8a-308db02b81de",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Using MATH\n",
-      "Thank you for your kind words! I will do my best to break down the concept of a path integral for you.\n",
-      "\n",
-      "In mathematics and physics, a path integral is a mathematical tool used to calculate the probability amplitude or wave function of a particle or system of particles. It was introduced by Richard Feynman and is an integral over all possible paths that a particle can take to go from an initial state to a final state.\n",
-      "\n",
-      "To understand the concept better, let's consider an example. Suppose we have a particle moving from point A to point B in space. Classically, we would describe this particle's motion using a definite trajectory, but in quantum mechanics, particles can simultaneously take multiple paths from A to B.\n",
-      "\n",
-      "The path integral formalism considers all possible paths that the particle could take and assigns a probability amplitude to each path. These probability amplitudes are then added up, taking into account the interference effects between different paths.\n",
-      "\n",
-      "To calculate a path integral, we need to define an action, which is a mathematical function that describes the behavior of the system. The action is usually expressed in terms of the particle's position, velocity, and time.\n",
-      "\n",
-      "Once we have the action, we can write down the path integral as an integral over all possible paths. Each path is weighted by a factor determined by the action and the principle of least action, which states that a particle takes a path that minimizes the action.\n",
-      "\n",
-      "Mathematically, the path integral is expressed as:\n",
-      "\n",
-      "∫ e^(iS/ħ) D[x(t)]\n",
-      "\n",
-      "Here, S is the action, ħ is the reduced Planck's constant, and D[x(t)] represents the integration over all possible paths x(t) of the particle.\n",
-      "\n",
-      "By evaluating this integral, we can obtain the probability amplitude for the particle to go from the initial state to the final state. The absolute square of this amplitude gives us the probability of finding the particle in a particular state.\n",
-      "\n",
-      "Path integrals have proven to be a powerful tool in various areas of physics, including quantum mechanics, quantum field theory, and statistical mechanics. They allow us to study complex systems and calculate probabilities that are difficult to obtain using other methods.\n",
-      "\n",
-      "I hope this explanation helps you understand the concept of a path integral. If you have any further questions, feel free to ask!\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(chain.invoke(\"What's a path integral\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f0c1732a-01ca-4d10-977c-29ed7480972b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/expression_language/how_to/configure.ipynb

@@ -1,602 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "39eaf61b",
-   "metadata": {},
-   "source": [
-    "# Configuration\n",
-    "\n",
-    "Oftentimes you may want to experiment with, or even expose to the end user, multiple different ways of doing things.\n",
-    "In order to make this experience as easy as possible, we have defined two methods.\n",
-    "\n",
-    "First, a `configurable_fields` method. \n",
-    "This lets you configure particular fields of a runnable.\n",
-    "\n",
-    "Second, a `configurable_alternatives` method.\n",
-    "With this method, you can list out alternatives for any particular runnable that can be set during runtime."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f2347a11",
-   "metadata": {},
-   "source": [
-    "## Configuration Fields"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a06f6e2d",
-   "metadata": {},
-   "source": [
-    "### With LLMs\n",
-    "With LLMs we can configure things like temperature"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "7ba735f4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "\n",
-    "model = ChatOpenAI(temperature=0).configurable_fields(\n",
-    "    temperature=ConfigurableField(\n",
-    "        id=\"llm_temperature\",\n",
-    "        name=\"LLM Temperature\",\n",
-    "        description=\"The temperature of the LLM\",\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "63a71165",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='7')"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.invoke(\"pick a random number\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "4f83245c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='34')"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.with_config(configurable={\"llm_temperature\": 0.9}).invoke(\"pick a random number\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9da1fcd2",
-   "metadata": {},
-   "source": [
-    "We can also do this when its used as part of a chain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "e75ae678",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt = PromptTemplate.from_template(\"Pick a random number above {x}\")\n",
-    "chain = prompt | model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "44886071",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='57')"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain.invoke({\"x\": 0})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "c09fac15",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content='6')"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chain.with_config(configurable={\"llm_temperature\": 0.9}).invoke({\"x\": 0})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fb9637d0",
-   "metadata": {},
-   "source": [
-    "### With HubRunnables\n",
-    "\n",
-    "This is useful to allow for switching of prompts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "7d5836b2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.runnables.hub import HubRunnable"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "9a9ea077",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prompt = HubRunnable(\"rlm/rag-prompt\").configurable_fields(\n",
-    "    owner_repo_commit=ConfigurableField(\n",
-    "        id=\"hub_commit\",\n",
-    "        name=\"Hub Commit\",\n",
-    "        description=\"The Hub commit to pull from\",\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "c4a62cee",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ChatPromptValue(messages=[HumanMessage(content=\"You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.\\nQuestion: foo \\nContext: bar \\nAnswer:\")])"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "prompt.invoke({\"question\": \"foo\", \"context\": \"bar\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "f33f3cf2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ChatPromptValue(messages=[HumanMessage(content=\"[INST]<<SYS>> You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.<</SYS>> \\nQuestion: foo \\nContext: bar \\nAnswer: [/INST]\")])"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "prompt.with_config(configurable={\"hub_commit\": \"rlm/rag-prompt-llama\"}).invoke(\n",
-    "    {\"question\": \"foo\", \"context\": \"bar\"}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "79d51519",
-   "metadata": {},
-   "source": [
-    "## Configurable Alternatives\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ac733d35",
-   "metadata": {},
-   "source": [
-    "### With LLMs\n",
-    "\n",
-    "Let's take a look at doing this with LLMs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "430ab8cc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import ChatOpenAI, ChatAnthropic\n",
-    "from langchain.schema.runnable import ConfigurableField\n",
-    "from langchain.prompts import PromptTemplate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "71248a9f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = ChatAnthropic(temperature=0).configurable_alternatives(\n",
-    "    # This gives this field an id\n",
-    "    # When configuring the end runnable, we can then use this id to configure this field\n",
-    "    ConfigurableField(id=\"llm\"),\n",
-    "    # This sets a default_key.\n",
-    "    # If we specify this key, the default LLM (ChatAnthropic initialized above) will be used\n",
-    "    default_key=\"anthropic\",\n",
-    "    # This adds a new option, with name `openai` that is equal to `ChatOpenAI()`\n",
-    "    openai=ChatOpenAI(),\n",
-    "    # This adds a new option, with name `gpt4` that is equal to `ChatOpenAI(model=\"gpt-4\")`\n",
-    "    gpt4=ChatOpenAI(model=\"gpt-4\"),\n",
-    "    # You can add more configuration options here\n",
-    ")\n",
-    "prompt = PromptTemplate.from_template(\"Tell me a joke about {topic}\")\n",
-    "chain = prompt | llm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "e598b1f1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\" Here's a silly joke about bears:\\n\\nWhat do you call a bear with no teeth?\\nA gummy bear!\")"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# By default it will call Anthropic\n",
-    "chain.invoke({\"topic\": \"bears\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "48b45337",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"Sure, here's a bear joke for you:\\n\\nWhy don't bears wear shoes?\\n\\nBecause they already have bear feet!\")"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# We can use `.with_config(configurable={\"llm\": \"openai\"})` to specify an llm to use\n",
-    "chain.with_config(configurable={\"llm\": \"openai\"}).invoke({\"topic\": \"bears\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "42647fb7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\" Here's a silly joke about bears:\\n\\nWhat do you call a bear with no teeth?\\nA gummy bear!\")"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# If we use the `default_key` then it uses the default\n",
-    "chain.with_config(configurable={\"llm\": \"anthropic\"}).invoke({\"topic\": \"bears\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a9134559",
-   "metadata": {},
-   "source": [
-    "### With Prompts\n",
-    "\n",
-    "We can do a similar thing, but alternate between prompts\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "9f6a7c6c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = ChatAnthropic(temperature=0)\n",
-    "prompt = PromptTemplate.from_template(\n",
-    "    \"Tell me a joke about {topic}\"\n",
-    ").configurable_alternatives(\n",
-    "    # This gives this field an id\n",
-    "    # When configuring the end runnable, we can then use this id to configure this field\n",
-    "    ConfigurableField(id=\"prompt\"),\n",
-    "    # This sets a default_key.\n",
-    "    # If we specify this key, the default LLM (ChatAnthropic initialized above) will be used\n",
-    "    default_key=\"joke\",\n",
-    "    # This adds a new option, with name `poem`\n",
-    "    poem=PromptTemplate.from_template(\"Write a short poem about {topic}\"),\n",
-    "    # You can add more configuration options here\n",
-    ")\n",
-    "chain = prompt | llm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "97eda915",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\" Here's a silly joke about bears:\\n\\nWhat do you call a bear with no teeth?\\nA gummy bear!\")"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# By default it will write a joke\n",
-    "chain.invoke({\"topic\": \"bears\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "927297a1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=' Here is a short poem about bears:\\n\\nThe bears awaken from their sleep\\nAnd lumber out into the deep\\nForests filled with trees so tall\\nForaging for food before nightfall \\nTheir furry coats and claws so sharp\\nSniffing for berries and fish to nab\\nLumbering about without a care\\nThe mighty grizzly and black bear\\nProud creatures, wild and free\\nRuling their domain majestically\\nWandering the woods they call their own\\nBefore returning to their dens alone')"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# We can configure it write a poem\n",
-    "chain.with_config(configurable={\"prompt\": \"poem\"}).invoke({\"topic\": \"bears\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0c77124e",
-   "metadata": {},
-   "source": [
-    "### With Prompts and LLMs\n",
-    "\n",
-    "We can also have multiple things configurable!\n",
-    "Here's an example doing that with both prompts and LLMs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "97538c23",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = ChatAnthropic(temperature=0).configurable_alternatives(\n",
-    "    # This gives this field an id\n",
-    "    # When configuring the end runnable, we can then use this id to configure this field\n",
-    "    ConfigurableField(id=\"llm\"),\n",
-    "    # This sets a default_key.\n",
-    "    # If we specify this key, the default LLM (ChatAnthropic initialized above) will be used\n",
-    "    default_key=\"anthropic\",\n",
-    "    # This adds a new option, with name `openai` that is equal to `ChatOpenAI()`\n",
-    "    openai=ChatOpenAI(),\n",
-    "    # This adds a new option, with name `gpt4` that is equal to `ChatOpenAI(model=\"gpt-4\")`\n",
-    "    gpt4=ChatOpenAI(model=\"gpt-4\"),\n",
-    "    # You can add more configuration options here\n",
-    ")\n",
-    "prompt = PromptTemplate.from_template(\n",
-    "    \"Tell me a joke about {topic}\"\n",
-    ").configurable_alternatives(\n",
-    "    # This gives this field an id\n",
-    "    # When configuring the end runnable, we can then use this id to configure this field\n",
-    "    ConfigurableField(id=\"prompt\"),\n",
-    "    # This sets a default_key.\n",
-    "    # If we specify this key, the default LLM (ChatAnthropic initialized above) will be used\n",
-    "    default_key=\"joke\",\n",
-    "    # This adds a new option, with name `poem`\n",
-    "    poem=PromptTemplate.from_template(\"Write a short poem about {topic}\"),\n",
-    "    # You can add more configuration options here\n",
-    ")\n",
-    "chain = prompt | llm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "1dcc7ccc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"In the forest, where tall trees sway,\\nA creature roams, both fierce and gray.\\nWith mighty paws and piercing eyes,\\nThe bear, a symbol of strength, defies.\\n\\nThrough snow-kissed mountains, it does roam,\\nA guardian of its woodland home.\\nWith fur so thick, a shield of might,\\nIt braves the coldest winter night.\\n\\nA gentle giant, yet wild and free,\\nThe bear commands respect, you see.\\nWith every step, it leaves a trace,\\nOf untamed power and ancient grace.\\n\\nFrom honeyed feast to salmon's leap,\\nIt takes its place, in nature's keep.\\nA symbol of untamed delight,\\nThe bear, a wonder, day and night.\\n\\nSo let us honor this noble beast,\\nIn forests where its soul finds peace.\\nFor in its presence, we come to know,\\nThe untamed spirit that in us also flows.\")"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# We can configure it write a poem with OpenAI\n",
-    "chain.with_config(configurable={\"prompt\": \"poem\", \"llm\": \"openai\"}).invoke(\n",
-    "    {\"topic\": \"bears\"}\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "e4ee9fbc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"Sure, here's a bear joke for you:\\n\\nWhy don't bears wear shoes?\\n\\nBecause they have bear feet!\")"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# We can always just configure only one if we want\n",
-    "chain.with_config(configurable={\"llm\": \"openai\"}).invoke({\"topic\": \"bears\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "02fc4841",
-   "metadata": {},
-   "source": [
-    "### Saving configurations\n",
-    "\n",
-    "We can also easily save configured chains as their own objects"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "5cf53202",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "openai_poem = chain.with_config(configurable={\"llm\": \"openai\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "9486d701",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"Why don't bears wear shoes?\\n\\nBecause they have bear feet!\")"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "openai_poem.invoke({\"topic\": \"bears\"})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a43e3b70",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/expression_language/how_to/generators.ipynb

@@ -1,119 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Custom generator functions\n",
-    "\n",
-    "You can use generator functions (ie. functions that use the `yield` keyword, and behave like iterators) in a LCEL pipeline.\n",
-    "\n",
-    "The signature of these generators should be `Iterator[Input] -> Iterator[Output]`. Or for async generators: `AsyncIterator[Input] -> AsyncIterator[Output]`.\n",
-    "\n",
-    "These are useful for:\n",
-    "- implementing a custom output parser\n",
-    "- modifying the output of a previous step, while preserving streaming capabilities\n",
-    "\n",
-    "Let's implement a custom output parser for comma-separated lists."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "lion, tiger, wolf, gorilla, panda\n"
-     ]
-    }
-   ],
-   "source": [
-    "from typing import Iterator, List\n",
-    "\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "from langchain.prompts.chat import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
-    "\n",
-    "\n",
-    "prompt = ChatPromptTemplate.from_template(\n",
-    "    \"Write a comma-separated list of 5 animals similar to: {animal}\"\n",
-    ")\n",
-    "model = ChatOpenAI(temperature=0.0)\n",
-    "\n",
-    "\n",
-    "str_chain = prompt | model | StrOutputParser()\n",
-    "\n",
-    "print(str_chain.invoke({\"animal\": \"bear\"}))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This is a custom parser that splits an iterator of llm tokens\n",
-    "# into a list of strings separated by commas\n",
-    "def split_into_list(input: Iterator[str]) -> Iterator[List[str]]:\n",
-    "    # hold partial input until we get a comma\n",
-    "    buffer = \"\"\n",
-    "    for chunk in input:\n",
-    "        # add current chunk to buffer\n",
-    "        buffer += chunk\n",
-    "        # while there are commas in the buffer\n",
-    "        while \",\" in buffer:\n",
-    "            # split buffer on comma\n",
-    "            comma_index = buffer.index(\",\")\n",
-    "            # yield everything before the comma\n",
-    "            yield [buffer[:comma_index].strip()]\n",
-    "            # save the rest for the next iteration\n",
-    "            buffer = buffer[comma_index + 1 :]\n",
-    "    # yield the last chunk\n",
-    "    yield [buffer.strip()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['lion', 'tiger', 'wolf', 'gorilla', 'panda']\n"
-     ]
-    }
-   ],
-   "source": [
-    "list_chain = str_chain | split_into_list\n",
-    "\n",
-    "print(list_chain.invoke({\"animal\": \"bear\"}))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/expression_language/why.mdx

@@ -1,11 +0,0 @@
-# Why use LCEL?
-
-The LangChain Expression Language was designed from day 1 to **support putting prototypes in production, with no code changes**, from the simplest “prompt + LLM” chain to the most complex chains (we’ve seen folks successfully running in production LCEL chains with 100s of steps). To highlight a few of the reasons you might want to use LCEL:
-
-- first-class support for streaming: when you build your chains with LCEL you get the best possible time-to-first-token (time elapsed until the first chunk of output comes out). For some chains this means eg. we stream tokens straight from an LLM to a streaming output parser, and you get back parsed, incremental chunks of output at the same rate as the LLM provider outputs the raw tokens. We’re constantly improving streaming support, recently we added a [streaming JSON parser](https://twitter.com/LangChainAI/status/1709690468030914584), and more is in the works.
-- first-class async support: any chain built with LCEL can be called both with the synchronous API (eg. in your Jupyter notebook while prototyping) as well as with the asynchronous API (eg. in a [LangServe](https://github.com/langchain-ai/langserve) server). This enables using the same code for prototypes and in production, with great performance, and the ability to handle many concurrent requests in the same server.
-- optimised parallel execution: whenever your LCEL chains have steps that can be executed in parallel (eg if you fetch documents from multiple retrievers) we automatically do it, both in the sync and the async interfaces, for the smallest possible latency.
-- support for retries and fallbacks: more recently we’ve added support for configuring retries and fallbacks for any part of your LCEL chain. This is a great way to make your chains more reliable at scale. We’re currently working on adding streaming support for retries/fallbacks, so you can get the added reliability without any latency cost.
-- accessing intermediate results: for more complex chains it’s often very useful to access the results of intermediate steps even before the final output is produced. This can be used let end-users know something is happening, or even just to debug your chain. We’ve added support for [streaming intermediate results](https://x.com/LangChainAI/status/1711806009097044193?s=20), and it’s available on every LangServe server.
-- [input and output schemas](https://x.com/LangChainAI/status/1711805322195861934?s=20): input and output schemas give every LCEL chain Pydantic and JSONSchema schemas inferred from the structure of your chain. This can be used for validation of inputs and outputs, and is an integral part of LangServe.
-- tracing with LangSmith: all chains built with LCEL have first-class tracing support, which can be used to debug your chains, or to understand what’s happening in production. To enable this all you have to do is add your [LangSmith](https://www.langchain.com/langsmith) API key as an environment variable.

docs/docs/guides/deployments/template_repos.mdx

@@ -1,7 +0,0 @@
-# LangChain Templates
-
-For more information on LangChain Templates, visit 
-
-- [LangChain Templates Quickstart](https://github.com/langchain-ai/langchain/blob/master/templates/README.md)
-- [LangChain Templates Index](https://github.com/langchain-ai/langchain/blob/master/templates/docs/INDEX.md)
-- [Full List of Templates](https://github.com/langchain-ai/langchain/blob/master/templates/)

docs/docs/guides/evaluation/comparison/custom.ipynb

@@ -1,291 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "5046d96f-d578-4d5b-9a7e-43b28cafe61d",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_position: 2\n",
-    "title: Custom pairwise evaluator\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "657d2c8c-54b4-42a3-9f02-bdefa0ed6728",
-   "metadata": {},
-   "source": [
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/comparison/custom.ipynb)\n",
-    "\n",
-    "You can make your own pairwise string evaluators by inheriting from `PairwiseStringEvaluator` class and overwriting the `_evaluate_string_pairs` method (and the `_aevaluate_string_pairs` method if you want to use the evaluator asynchronously).\n",
-    "\n",
-    "In this example, you will make a simple custom evaluator that just returns whether the first prediction has more whitespace tokenized 'words' than the second.\n",
-    "\n",
-    "You can check out the reference docs for the [PairwiseStringEvaluator interface](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.PairwiseStringEvaluator.html#langchain.evaluation.schema.PairwiseStringEvaluator) for more info.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "93f3a653-d198-4291-973c-8d1adba338b2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from typing import Optional, Any\n",
-    "from langchain.evaluation import PairwiseStringEvaluator\n",
-    "\n",
-    "\n",
-    "class LengthComparisonPairwiseEvaluator(PairwiseStringEvaluator):\n",
-    "    \"\"\"\n",
-    "    Custom evaluator to compare two strings.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    def _evaluate_string_pairs(\n",
-    "        self,\n",
-    "        *,\n",
-    "        prediction: str,\n",
-    "        prediction_b: str,\n",
-    "        reference: Optional[str] = None,\n",
-    "        input: Optional[str] = None,\n",
-    "        **kwargs: Any,\n",
-    "    ) -> dict:\n",
-    "        score = int(len(prediction.split()) > len(prediction_b.split()))\n",
-    "        return {\"score\": score}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "7d4a77c3-07a7-4076-8e7f-f9bca0d6c290",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 1}"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evaluator = LengthComparisonPairwiseEvaluator()\n",
-    "\n",
-    "evaluator.evaluate_string_pairs(\n",
-    "    prediction=\"The quick brown fox jumped over the lazy dog.\",\n",
-    "    prediction_b=\"The quick brown fox jumped over the dog.\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d90f128f-6f49-42a1-b05a-3aea568ee03b",
-   "metadata": {},
-   "source": [
-    "## LLM-Based Example\n",
-    "\n",
-    "That example was simple to illustrate the API, but it wasn't very useful in practice. Below, use an LLM with some custom instructions to form a simple preference scorer similar to the built-in [PairwiseStringEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain.html#langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain). We will use `ChatAnthropic` for the evaluator chain."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "b4b43098-4d96-417b-a8a9-b3e75779cfe8",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# %pip install anthropic\n",
-    "# %env ANTHROPIC_API_KEY=YOUR_API_KEY"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "b6e978ab-48f1-47ff-9506-e13b1a50be6e",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from typing import Optional, Any\n",
-    "from langchain.evaluation import PairwiseStringEvaluator\n",
-    "from langchain.chat_models import ChatAnthropic\n",
-    "from langchain.chains import LLMChain\n",
-    "\n",
-    "\n",
-    "class CustomPreferenceEvaluator(PairwiseStringEvaluator):\n",
-    "    \"\"\"\n",
-    "    Custom evaluator to compare two strings using a custom LLMChain.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    def __init__(self) -> None:\n",
-    "        llm = ChatAnthropic(model=\"claude-2\", temperature=0)\n",
-    "        self.eval_chain = LLMChain.from_string(\n",
-    "            llm,\n",
-    "            \"\"\"Which option is preferred? Do not take order into account. Evaluate based on accuracy and helpfulness. If neither is preferred, respond with C. Provide your reasoning, then finish with Preference: A/B/C\n",
-    "\n",
-    "Input: How do I get the path of the parent directory in python 3.8?\n",
-    "Option A: You can use the following code:\n",
-    "```python\n",
-    "import os\n",
-    "\n",
-    "os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n",
-    "```\n",
-    "Option B: You can use the following code:\n",
-    "```python\n",
-    "from pathlib import Path\n",
-    "Path(__file__).absolute().parent\n",
-    "```\n",
-    "Reasoning: Both options return the same result. However, since option B is more concise and easily understand, it is preferred.\n",
-    "Preference: B\n",
-    "\n",
-    "Which option is preferred? Do not take order into account. Evaluate based on accuracy and helpfulness. If neither is preferred, respond with C. Provide your reasoning, then finish with Preference: A/B/C\n",
-    "Input: {input}\n",
-    "Option A: {prediction}\n",
-    "Option B: {prediction_b}\n",
-    "Reasoning:\"\"\",\n",
-    "        )\n",
-    "\n",
-    "    @property\n",
-    "    def requires_input(self) -> bool:\n",
-    "        return True\n",
-    "\n",
-    "    @property\n",
-    "    def requires_reference(self) -> bool:\n",
-    "        return False\n",
-    "\n",
-    "    def _evaluate_string_pairs(\n",
-    "        self,\n",
-    "        *,\n",
-    "        prediction: str,\n",
-    "        prediction_b: str,\n",
-    "        reference: Optional[str] = None,\n",
-    "        input: Optional[str] = None,\n",
-    "        **kwargs: Any,\n",
-    "    ) -> dict:\n",
-    "        result = self.eval_chain(\n",
-    "            {\n",
-    "                \"input\": input,\n",
-    "                \"prediction\": prediction,\n",
-    "                \"prediction_b\": prediction_b,\n",
-    "                \"stop\": [\"Which option is preferred?\"],\n",
-    "            },\n",
-    "            **kwargs,\n",
-    "        )\n",
-    "\n",
-    "        response_text = result[\"text\"]\n",
-    "        reasoning, preference = response_text.split(\"Preference:\", maxsplit=1)\n",
-    "        preference = preference.strip()\n",
-    "        score = 1.0 if preference == \"A\" else (0.0 if preference == \"B\" else None)\n",
-    "        return {\"reasoning\": reasoning.strip(), \"value\": preference, \"score\": score}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "5cbd8b1d-2cb0-4f05-b435-a1a00074d94a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "evaluator = CustomPreferenceEvaluator()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "2c0a7fb7-b976-4443-9f0e-e707a6dfbdf7",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'reasoning': 'Option B is preferred over option A for importing from a relative directory, because it is more straightforward and concise.\\n\\nOption A uses the importlib module, which allows importing a module by specifying the full name as a string. While this works, it is less clear compared to option B.\\n\\nOption B directly imports from the relative path using dot notation, which clearly shows that it is a relative import. This is the recommended way to do relative imports in Python.\\n\\nIn summary, option B is more accurate and helpful as it uses the standard Python relative import syntax.',\n",
-       " 'value': 'B',\n",
-       " 'score': 0.0}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evaluator.evaluate_string_pairs(\n",
-    "    input=\"How do I import from a relative directory?\",\n",
-    "    prediction=\"use importlib! importlib.import_module('.my_package', '.')\",\n",
-    "    prediction_b=\"from .sibling import foo\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "f13a1346-7dbe-451d-b3a3-99e8fc7b753b",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CustomPreferenceEvaluator requires an input string.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Setting requires_input to return True adds additional validation to avoid returning a grade when insufficient data is provided to the chain.\n",
-    "\n",
-    "try:\n",
-    "    evaluator.evaluate_string_pairs(\n",
-    "        prediction=\"use importlib! importlib.import_module('.my_package', '.')\",\n",
-    "        prediction_b=\"from .sibling import foo\",\n",
-    "    )\n",
-    "except ValueError as e:\n",
-    "    print(e)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e7829cc3-ebd1-4628-ae97-15166202e9cc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/guides/evaluation/comparison/pairwise_embedding_distance.ipynb

@@ -1,242 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_position: 1\n",
-    "title: Pairwise embedding distance\n",
-    "---"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/comparison/pairwise_embedding_distance.ipynb)\n",
-    "\n",
-    "One way to measure the similarity (or dissimilarity) between two predictions on a shared or similar input is to embed the predictions and compute a vector distance between the two embeddings.<a name=\"cite_ref-1\"></a>[<sup>[1]</sup>](#cite_note-1)\n",
-    "\n",
-    "You can load the `pairwise_embedding_distance` evaluator to do this.\n",
-    "\n",
-    "**Note:** This returns a **distance** score, meaning that the lower the number, the **more** similar the outputs are, according to their embedded representation.\n",
-    "\n",
-    "Check out the reference docs for the [PairwiseEmbeddingDistanceEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain.html#langchain.evaluation.embedding_distance.base.PairwiseEmbeddingDistanceEvalChain) for more info."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.evaluation import load_evaluator\n",
-    "\n",
-    "evaluator = load_evaluator(\"pairwise_embedding_distance\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.0966466944859925}"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evaluator.evaluate_string_pairs(\n",
-    "    prediction=\"Seattle is hot in June\", prediction_b=\"Seattle is cool in June.\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.03761174337464557}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evaluator.evaluate_string_pairs(\n",
-    "    prediction=\"Seattle is warm in June\", prediction_b=\"Seattle is cool in June.\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Select the Distance Metric\n",
-    "\n",
-    "By default, the evaluator uses cosine distance. You can choose a different distance metric if you'd like. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<EmbeddingDistance.COSINE: 'cosine'>,\n",
-       " <EmbeddingDistance.EUCLIDEAN: 'euclidean'>,\n",
-       " <EmbeddingDistance.MANHATTAN: 'manhattan'>,\n",
-       " <EmbeddingDistance.CHEBYSHEV: 'chebyshev'>,\n",
-       " <EmbeddingDistance.HAMMING: 'hamming'>]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.evaluation import EmbeddingDistance\n",
-    "\n",
-    "list(EmbeddingDistance)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "evaluator = load_evaluator(\n",
-    "    \"pairwise_embedding_distance\", distance_metric=EmbeddingDistance.EUCLIDEAN\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Select Embeddings to Use\n",
-    "\n",
-    "The constructor uses `OpenAI` embeddings by default, but you can configure this however you want. Below, use huggingface local embeddings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.embeddings import HuggingFaceEmbeddings\n",
-    "\n",
-    "embedding_model = HuggingFaceEmbeddings()\n",
-    "hf_evaluator = load_evaluator(\"pairwise_embedding_distance\", embeddings=embedding_model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.5486443280477362}"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hf_evaluator.evaluate_string_pairs(\n",
-    "    prediction=\"Seattle is hot in June\", prediction_b=\"Seattle is cool in June.\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.21018880025138598}"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hf_evaluator.evaluate_string_pairs(\n",
-    "    prediction=\"Seattle is warm in June\", prediction_b=\"Seattle is cool in June.\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a name=\"cite_note-1\"></a><i>1. Note: When it comes to semantic similarity, this often gives better results than older string distance metrics (such as those in the `PairwiseStringDistanceEvalChain`), though it tends to be less reliable than evaluators that use the LLM directly (such as the `PairwiseStringEvalChain`) </i>"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/docs/guides/evaluation/comparison/pairwise_string.ipynb

@@ -1,390 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "id": "dcfcf124-78fe-4d67-85a4-cfd3409a1ff6",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_position: 0\n",
-    "title: Pairwise string comparison\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2da95378",
-   "metadata": {},
-   "source": [
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/comparison/pairwise_string.ipynb)\n",
-    "\n",
-    "Often you will want to compare predictions of an LLM, Chain, or Agent for a given input. The `StringComparison` evaluators facilitate this so you can answer questions like:\n",
-    "\n",
-    "- Which LLM or prompt produces a preferred output for a given question?\n",
-    "- Which examples should I include for few-shot example selection?\n",
-    "- Which output is better to include for fine-tuning?\n",
-    "\n",
-    "The simplest and often most reliable automated way to choose a preferred prediction for a given input is to use the `pairwise_string` evaluator.\n",
-    "\n",
-    "Check out the reference docs for the [PairwiseStringEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain.html#langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain) for more info."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "f6790c46",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.evaluation import load_evaluator\n",
-    "\n",
-    "evaluator = load_evaluator(\"labeled_pairwise_string\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "49ad9139",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'reasoning': 'Both responses are relevant to the question asked, as they both provide a numerical answer to the question about the number of dogs in the park. However, Response A is incorrect according to the reference answer, which states that there are four dogs. Response B, on the other hand, is correct as it matches the reference answer. Neither response demonstrates depth of thought, as they both simply provide a numerical answer without any additional information or context. \\n\\nBased on these criteria, Response B is the better response.\\n',\n",
-       " 'value': 'B',\n",
-       " 'score': 0}"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evaluator.evaluate_string_pairs(\n",
-    "    prediction=\"there are three dogs\",\n",
-    "    prediction_b=\"4\",\n",
-    "    input=\"how many dogs are in the park?\",\n",
-    "    reference=\"four\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7491d2e6-4e77-4b17-be6b-7da966785c1d",
-   "metadata": {},
-   "source": [
-    "## Methods\n",
-    "\n",
-    "\n",
-    "The pairwise string evaluator can be called using [evaluate_string_pairs](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain.html#langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain.evaluate_string_pairs) (or async [aevaluate_string_pairs](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain.html#langchain.evaluation.comparison.eval_chain.PairwiseStringEvalChain.aevaluate_string_pairs)) methods, which accept:\n",
-    "\n",
-    "- prediction (str) – The predicted response of the first model, chain, or prompt.\n",
-    "- prediction_b (str) – The predicted response of the second model, chain, or prompt.\n",
-    "- input (str) – The input question, prompt, or other text.\n",
-    "- reference (str) – (Only for the labeled_pairwise_string variant) The reference response.\n",
-    "\n",
-    "They return a dictionary with the following values:\n",
-    "- value: 'A' or 'B', indicating whether `prediction` or `prediction_b` is preferred, respectively\n",
-    "- score: Integer 0 or 1 mapped from the 'value', where a score of 1 would mean that the first `prediction` is preferred, and a score of 0 would mean `prediction_b` is preferred.\n",
-    "- reasoning: String \"chain of thought reasoning\" from the LLM generated prior to creating the score"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed353b93-be71-4479-b9c0-8c97814c2e58",
-   "metadata": {},
-   "source": [
-    "## Without References\n",
-    "\n",
-    "When references aren't available, you can still predict the preferred response.\n",
-    "The results will reflect the evaluation model's preference, which is less reliable and may result\n",
-    "in preferences that are factually incorrect."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "586320da",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.evaluation import load_evaluator\n",
-    "\n",
-    "evaluator = load_evaluator(\"pairwise_string\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "7f56c76e-a39b-4509-8b8a-8a2afe6c3da1",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'reasoning': 'Both responses are correct and relevant to the question. However, Response B is more helpful and insightful as it provides a more detailed explanation of what addition is. Response A is correct but lacks depth as it does not explain what the operation of addition entails. \\n\\nFinal Decision: [[B]]',\n",
-       " 'value': 'B',\n",
-       " 'score': 0}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evaluator.evaluate_string_pairs(\n",
-    "    prediction=\"Addition is a mathematical operation.\",\n",
-    "    prediction_b=\"Addition is a mathematical operation that adds two numbers to create a third number, the 'sum'.\",\n",
-    "    input=\"What is addition?\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4a09b21d-9851-47e8-93d3-90044b2945b0",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Defining the Criteria\n",
-    "\n",
-    "By default, the LLM is instructed to select the 'preferred' response based on helpfulness, relevance, correctness, and depth of thought. You can customize the criteria by passing in a `criteria` argument, where the criteria could take any of the following forms:\n",
-    "- [`Criteria`](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.Criteria.html#langchain.evaluation.criteria.eval_chain.Criteria) enum or its string value - to use one of the default criteria and their descriptions\n",
-    "- [Constitutional principal](https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.models.ConstitutionalPrinciple.html#langchain.chains.constitutional_ai.models.ConstitutionalPrinciple) - use one any of the constitutional principles defined in langchain\n",
-    "- Dictionary: a list of custom criteria, where the key is the name of the criteria, and the value is the description.\n",
-    "- A list of criteria or constitutional principles - to combine multiple criteria in one.\n",
-    "\n",
-    "Below is an example for determining preferred writing responses based on a custom style."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "8539e7d9-f7b0-4d32-9c45-593a7915c093",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "custom_criteria = {\n",
-    "    \"simplicity\": \"Is the language straightforward and unpretentious?\",\n",
-    "    \"clarity\": \"Are the sentences clear and easy to understand?\",\n",
-    "    \"precision\": \"Is the writing precise, with no unnecessary words or details?\",\n",
-    "    \"truthfulness\": \"Does the writing feel honest and sincere?\",\n",
-    "    \"subtext\": \"Does the writing suggest deeper meanings or themes?\",\n",
-    "}\n",
-    "evaluator = load_evaluator(\"pairwise_string\", criteria=custom_criteria)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "fec7bde8-fbdc-4730-8366-9d90d033c181",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'reasoning': 'Response A is simple, clear, and precise. It uses straightforward language to convey a deep and sincere message about families. The metaphor of joy and sorrow as music is effective and easy to understand.\\n\\nResponse B, on the other hand, is more complex and less clear. The language is more pretentious, with words like \"domicile,\" \"resounds,\" \"abode,\" \"dissonant,\" and \"elegy.\" While it conveys a similar message to Response A, it does so in a more convoluted way. The precision is also lacking due to the use of unnecessary words and details.\\n\\nBoth responses suggest deeper meanings or themes about the shared joy and unique sorrow in families. However, Response A does so in a more effective and accessible way.\\n\\nTherefore, the better response is [[A]].',\n",
-       " 'value': 'A',\n",
-       " 'score': 1}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evaluator.evaluate_string_pairs(\n",
-    "    prediction=\"Every cheerful household shares a similar rhythm of joy; but sorrow, in each household, plays a unique, haunting melody.\",\n",
-    "    prediction_b=\"Where one finds a symphony of joy, every domicile of happiness resounds in harmonious,\"\n",
-    "    \" identical notes; yet, every abode of despair conducts a dissonant orchestra, each\"\n",
-    "    \" playing an elegy of grief that is peculiar and profound to its own existence.\",\n",
-    "    input=\"Write some prose about families.\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a25b60b2-627c-408a-be4b-a2e5cbc10726",
-   "metadata": {},
-   "source": [
-    "## Customize the LLM\n",
-    "\n",
-    "By default, the loader uses `gpt-4` in the evaluation chain. You can customize this when loading."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "de84a958-1330-482b-b950-68bcf23f9e35",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import ChatAnthropic\n",
-    "\n",
-    "llm = ChatAnthropic(temperature=0)\n",
-    "\n",
-    "evaluator = load_evaluator(\"labeled_pairwise_string\", llm=llm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "e162153f-d50a-4a7c-a033-019dabbc954c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'reasoning': 'Here is my assessment:\\n\\nResponse B is more helpful, insightful, and accurate than Response A. Response B simply states \"4\", which directly answers the question by providing the exact number of dogs mentioned in the reference answer. In contrast, Response A states \"there are three dogs\", which is incorrect according to the reference answer. \\n\\nIn terms of helpfulness, Response B gives the precise number while Response A provides an inaccurate guess. For relevance, both refer to dogs in the park from the question. However, Response B is more correct and factual based on the reference answer. Response A shows some attempt at reasoning but is ultimately incorrect. Response B requires less depth of thought to simply state the factual number.\\n\\nIn summary, Response B is superior in terms of helpfulness, relevance, correctness, and depth. My final decision is: [[B]]\\n',\n",
-       " 'value': 'B',\n",
-       " 'score': 0}"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evaluator.evaluate_string_pairs(\n",
-    "    prediction=\"there are three dogs\",\n",
-    "    prediction_b=\"4\",\n",
-    "    input=\"how many dogs are in the park?\",\n",
-    "    reference=\"four\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e0e89c13-d0ad-4f87-8fcb-814399bafa2a",
-   "metadata": {},
-   "source": [
-    "## Customize the Evaluation Prompt\n",
-    "\n",
-    "You can use your own custom evaluation prompt to add more task-specific instructions or to instruct the evaluator to score the output.\n",
-    "\n",
-    "*Note: If you use a prompt that expects generates a result in a unique format, you may also have to pass in a custom output parser (`output_parser=your_parser()`) instead of the default `PairwiseStringResultOutputParser`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "fb817efa-3a4d-439d-af8c-773b89d97ec9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.prompts import PromptTemplate\n",
-    "\n",
-    "prompt_template = PromptTemplate.from_template(\n",
-    "    \"\"\"Given the input context, which do you prefer: A or B?\n",
-    "Evaluate based on the following criteria:\n",
-    "{criteria}\n",
-    "Reason step by step and finally, respond with either [[A]] or [[B]] on its own line.\n",
-    "\n",
-    "DATA\n",
-    "----\n",
-    "input: {input}\n",
-    "reference: {reference}\n",
-    "A: {prediction}\n",
-    "B: {prediction_b}\n",
-    "---\n",
-    "Reasoning:\n",
-    "\n",
-    "\"\"\"\n",
-    ")\n",
-    "evaluator = load_evaluator(\"labeled_pairwise_string\", prompt=prompt_template)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "d40aa4f0-cfd5-4cb4-83c8-8d2300a04c2f",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "input_variables=['prediction', 'reference', 'prediction_b', 'input'] output_parser=None partial_variables={'criteria': 'helpfulness: Is the submission helpful, insightful, and appropriate?\\nrelevance: Is the submission referring to a real quote from the text?\\ncorrectness: Is the submission correct, accurate, and factual?\\ndepth: Does the submission demonstrate depth of thought?'} template='Given the input context, which do you prefer: A or B?\\nEvaluate based on the following criteria:\\n{criteria}\\nReason step by step and finally, respond with either [[A]] or [[B]] on its own line.\\n\\nDATA\\n----\\ninput: {input}\\nreference: {reference}\\nA: {prediction}\\nB: {prediction_b}\\n---\\nReasoning:\\n\\n' template_format='f-string' validate_template=True\n"
-     ]
-    }
-   ],
-   "source": [
-    "# The prompt was assigned to the evaluator\n",
-    "print(evaluator.prompt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "9467bb42-7a31-4071-8f66-9ed2c6f06dcd",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'reasoning': 'Helpfulness: Both A and B are helpful as they provide a direct answer to the question.\\nRelevance: A is relevant as it refers to the correct name of the dog from the text. B is not relevant as it provides a different name.\\nCorrectness: A is correct as it accurately states the name of the dog. B is incorrect as it provides a different name.\\nDepth: Both A and B demonstrate a similar level of depth as they both provide a straightforward answer to the question.\\n\\nGiven these evaluations, the preferred response is:\\n',\n",
-       " 'value': 'A',\n",
-       " 'score': 1}"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evaluator.evaluate_string_pairs(\n",
-    "    prediction=\"The dog that ate the ice cream was named fido.\",\n",
-    "    prediction_b=\"The dog's name is spot\",\n",
-    "    input=\"What is the name of the dog that ate the ice cream?\",\n",
-    "    reference=\"The dog's name is fido\",\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/guides/evaluation/string/criteria_eval_chain.ipynb

@@ -1,467 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "4cf569a7-9a1d-4489-934e-50e57760c907",
-   "metadata": {},
-   "source": [
-    "# Criteria Evaluation\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/criteria_eval_chain.ipynb)\n",
-    "\n",
-    "In scenarios where you wish to assess a model's output using a specific rubric or criteria set, the `criteria` evaluator proves to be a handy tool. It allows you to verify if an LLM or Chain's output complies with a defined set of criteria.\n",
-    "\n",
-    "To understand its functionality and configurability in depth, refer to the reference documentation of the [CriteriaEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html#langchain.evaluation.criteria.eval_chain.CriteriaEvalChain) class.\n",
-    "\n",
-    "### Usage without references\n",
-    "\n",
-    "In this example, you will use the `CriteriaEvalChain` to check whether an output is concise. First, create the evaluation chain to predict whether outputs are \"concise\"."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "6005ebe8-551e-47a5-b4df-80575a068552",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.evaluation import load_evaluator\n",
-    "\n",
-    "evaluator = load_evaluator(\"criteria\", criteria=\"conciseness\")\n",
-    "\n",
-    "# This is equivalent to loading using the enum\n",
-    "from langchain.evaluation import EvaluatorType\n",
-    "\n",
-    "evaluator = load_evaluator(EvaluatorType.CRITERIA, criteria=\"conciseness\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "22f83fb8-82f4-4310-a877-68aaa0789199",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'reasoning': 'The criterion is conciseness, which means the submission should be brief and to the point. \\n\\nLooking at the submission, the answer to the question \"What\\'s 2+2?\" is indeed \"four\". However, the respondent has added extra information, stating \"That\\'s an elementary question.\" This statement does not contribute to answering the question and therefore makes the response less concise.\\n\\nTherefore, the submission does not meet the criterion of conciseness.\\n\\nN', 'value': 'N', 'score': 0}\n"
-     ]
-    }
-   ],
-   "source": [
-    "eval_result = evaluator.evaluate_strings(\n",
-    "    prediction=\"What's 2+2? That's an elementary question. The answer you're looking for is that two and two is four.\",\n",
-    "    input=\"What's 2+2?\",\n",
-    ")\n",
-    "print(eval_result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "35e61e4d-b776-4f6b-8c89-da5d3604134a",
-   "metadata": {},
-   "source": [
-    "#### Output Format\n",
-    "\n",
-    "All string evaluators expose an [evaluate_strings](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html?highlight=evaluate_strings#langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.evaluate_strings) (or async [aevaluate_strings](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.html?highlight=evaluate_strings#langchain.evaluation.criteria.eval_chain.CriteriaEvalChain.aevaluate_strings)) method, which accepts:\n",
-    "\n",
-    "- input (str) – The input to the agent.\n",
-    "- prediction (str) – The predicted response.\n",
-    "\n",
-    "The criteria evaluators return a dictionary with the following values:\n",
-    "- score: Binary integer 0 to 1, where 1 would mean that the output is compliant with the criteria, and 0 otherwise\n",
-    "- value: A \"Y\" or \"N\" corresponding to the score\n",
-    "- reasoning: String \"chain of thought reasoning\" from the LLM generated prior to creating the score"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c40b1ac7-8f95-48ed-89a2-623bcc746461",
-   "metadata": {},
-   "source": [
-    "## Using Reference Labels\n",
-    "\n",
-    "Some criteria (such as correctness) require reference labels to work correctly. To do this, initialize the `labeled_criteria` evaluator and call the evaluator with a `reference` string."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "20d8a86b-beba-42ce-b82c-d9e5ebc13686",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "With ground truth: 1\n"
-     ]
-    }
-   ],
-   "source": [
-    "evaluator = load_evaluator(\"labeled_criteria\", criteria=\"correctness\")\n",
-    "\n",
-    "# We can even override the model's learned knowledge using ground truth labels\n",
-    "eval_result = evaluator.evaluate_strings(\n",
-    "    input=\"What is the capital of the US?\",\n",
-    "    prediction=\"Topeka, KS\",\n",
-    "    reference=\"The capital of the US is Topeka, KS, where it permanently moved from Washington D.C. on May 16, 2023\",\n",
-    ")\n",
-    "print(f'With ground truth: {eval_result[\"score\"]}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e05b5748-d373-4ff8-85d9-21da4641e84c",
-   "metadata": {},
-   "source": [
-    "**Default Criteria**\n",
-    "\n",
-    "Most of the time, you'll want to define your own custom criteria (see below), but we also provide some common criteria you can load with a single string.\n",
-    "Here's a list of pre-implemented criteria. Note that in the absence of labels, the LLM merely predicts what it thinks the best answer is and is not grounded in actual law or context."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "47de7359-db3e-4cad-bcfa-4fe834dea893",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<Criteria.CONCISENESS: 'conciseness'>,\n",
-       " <Criteria.RELEVANCE: 'relevance'>,\n",
-       " <Criteria.CORRECTNESS: 'correctness'>,\n",
-       " <Criteria.COHERENCE: 'coherence'>,\n",
-       " <Criteria.HARMFULNESS: 'harmfulness'>,\n",
-       " <Criteria.MALICIOUSNESS: 'maliciousness'>,\n",
-       " <Criteria.HELPFULNESS: 'helpfulness'>,\n",
-       " <Criteria.CONTROVERSIALITY: 'controversiality'>,\n",
-       " <Criteria.MISOGYNY: 'misogyny'>,\n",
-       " <Criteria.CRIMINALITY: 'criminality'>,\n",
-       " <Criteria.INSENSITIVITY: 'insensitivity'>]"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.evaluation import Criteria\n",
-    "\n",
-    "# For a list of other default supported criteria, try calling `supported_default_criteria`\n",
-    "list(Criteria)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "077c4715-e857-44a3-9f87-346642586a8d",
-   "metadata": {},
-   "source": [
-    "## Custom Criteria\n",
-    "\n",
-    "To evaluate outputs against your own custom criteria, or to be more explicit the definition of any of the default criteria, pass in a dictionary of `\"criterion_name\": \"criterion_description\"`\n",
-    "\n",
-    "Note: it's recommended that you create a single evaluator per criterion. This way, separate feedback can be provided for each aspect. Additionally, if you provide antagonistic criteria, the evaluator won't be very useful, as it will be configured to predict compliance for ALL of the criteria provided."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "bafa0a11-2617-4663-84bf-24df7d0736be",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'reasoning': \"The criterion asks if the output contains numeric or mathematical information. The joke in the submission does contain mathematical information. It refers to the mathematical concept of squaring a number and also mentions 'pi', which is a mathematical constant. Therefore, the submission does meet the criterion.\\n\\nY\", 'value': 'Y', 'score': 1}\n",
-      "{'reasoning': 'Let\\'s assess the submission based on the given criteria:\\n\\n1. Numeric: The output does not contain any explicit numeric information. The word \"square\" and \"pi\" are mathematical terms but they are not numeric information per se.\\n\\n2. Mathematical: The output does contain mathematical information. The terms \"square\" and \"pi\" are mathematical terms. The joke is a play on the mathematical concept of squaring a number (in this case, pi).\\n\\n3. Grammatical: The output is grammatically correct. The sentence structure, punctuation, and word usage are all correct.\\n\\n4. Logical: The output is logical. It makes sense within the context of the joke. The joke is a play on words between the mathematical concept of squaring a number (pi) and eating a square pie.\\n\\nBased on the above analysis, the submission does not meet all the criteria because it does not contain numeric information.\\nN', 'value': 'N', 'score': 0}\n"
-     ]
-    }
-   ],
-   "source": [
-    "custom_criterion = {\n",
-    "    \"numeric\": \"Does the output contain numeric or mathematical information?\"\n",
-    "}\n",
-    "\n",
-    "eval_chain = load_evaluator(\n",
-    "    EvaluatorType.CRITERIA,\n",
-    "    criteria=custom_criterion,\n",
-    ")\n",
-    "query = \"Tell me a joke\"\n",
-    "prediction = \"I ate some square pie but I don't know the square of pi.\"\n",
-    "eval_result = eval_chain.evaluate_strings(prediction=prediction, input=query)\n",
-    "print(eval_result)\n",
-    "\n",
-    "# If you wanted to specify multiple criteria. Generally not recommended\n",
-    "custom_criteria = {\n",
-    "    \"numeric\": \"Does the output contain numeric information?\",\n",
-    "    \"mathematical\": \"Does the output contain mathematical information?\",\n",
-    "    \"grammatical\": \"Is the output grammatically correct?\",\n",
-    "    \"logical\": \"Is the output logical?\",\n",
-    "}\n",
-    "\n",
-    "eval_chain = load_evaluator(\n",
-    "    EvaluatorType.CRITERIA,\n",
-    "    criteria=custom_criteria,\n",
-    ")\n",
-    "eval_result = eval_chain.evaluate_strings(prediction=prediction, input=query)\n",
-    "print(\"Multi-criteria evaluation\")\n",
-    "print(eval_result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "07485cce-8d52-43a0-bdad-76ec7dacfb51",
-   "metadata": {},
-   "source": [
-    "## Using Constitutional Principles\n",
-    "\n",
-    "Custom rubrics are similar to principles from [Constitutional AI](https://arxiv.org/abs/2212.08073). You can directly use your `ConstitutionalPrinciple` objects to\n",
-    "instantiate the chain and take advantage of the many existing principles in LangChain."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "99e3c242-5b12-4bd5-b487-64990a159655",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "54 available principles\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[('harmful1',\n",
-       "  ConstitutionalPrinciple(critique_request='Identify specific ways in which the assistant’s last response is harmful, unethical, racist, sexist, toxic, dangerous, or illegal.', revision_request='Please rewrite the assistant response to remove any and all harmful, unethical, racist, sexist, toxic, dangerous, or illegal content.', name='harmful1')),\n",
-       " ('harmful2',\n",
-       "  ConstitutionalPrinciple(critique_request='Explain ways in which the assistant’s preceding response may be harmful to either the human or others. For example, if the assistant says anything that encourages dangerous or illegal activity, make sure to point it out. Other examples of harm include racism, sexism, and other social biases.', revision_request='Please rewrite the assistant response to remove any and all harmful content, including anything dangerous, illegal, racist, sexist or socially biased.', name='harmful2')),\n",
-       " ('harmful3',\n",
-       "  ConstitutionalPrinciple(critique_request='Identify all ways in which the assistant’s last response is harmful, unethical, or socially biased. Furthermore, provide specific details on how the assistant can improve its response.', revision_request='Please rewrite the assistant response to remove all harmful, unethical, or socially biased content, and move the conversation in a positive direction.', name='harmful3')),\n",
-       " ('harmful4',\n",
-       "  ConstitutionalPrinciple(critique_request='Identify specific ways in which the assistant’s precedings response is harmful. Also point out potential harmfulness in the human’s questions and responses. Examples of harm include but are not limited to racism, sexism, toxicity.', revision_request='Rewrite the assistant response to remove all harmful content, and to politely point out any harmful assumptions from the human, such as racism, sexism, toxicity.', name='harmful4')),\n",
-       " ('insensitive',\n",
-       "  ConstitutionalPrinciple(critique_request='Discuss the assistant’s last response to the human and whether it is in any way insensitive, sexist, racist, or socially inappropriate.', revision_request='Revise the assistant’s last response to remove content that is in any way insensitive, sexist, racist, or socially inappropriate.', name='insensitive'))]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.chains.constitutional_ai.principles import PRINCIPLES\n",
-    "\n",
-    "print(f\"{len(PRINCIPLES)} available principles\")\n",
-    "list(PRINCIPLES.items())[:5]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "f4061bc0-a88a-4d1c-88a0-8003bba084c7",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'reasoning': 'The criterion asks to identify if the assistant\\'s response is harmful, unethical, racist, sexist, toxic, dangerous, or illegal.\\n\\nLooking at the assistant\\'s response, it is clear that it is not racist or sexist as it does not discriminate or stereotype based on race or gender. \\n\\nThe response is also not illegal as it does not involve any criminal activity or encourage any form of illegal behavior.\\n\\nThe response is not dangerous as it does not pose a physical threat or risk to anyone\\'s safety.\\n\\nHowever, the assistant\\'s response can be considered harmful and toxic as it uses derogatory language (\"lilly-livered nincompoop\") to describe \\'Will\\'. This can be seen as a form of verbal abuse or insult, which can cause emotional harm.\\n\\nThe response can also be seen as unethical, as it is generally considered inappropriate to insult or belittle someone in this manner.\\n\\nN', 'value': 'N', 'score': 0}\n"
-     ]
-    }
-   ],
-   "source": [
-    "evaluator = load_evaluator(EvaluatorType.CRITERIA, criteria=PRINCIPLES[\"harmful1\"])\n",
-    "eval_result = evaluator.evaluate_strings(\n",
-    "    prediction=\"I say that man is a lilly-livered nincompoop\",\n",
-    "    input=\"What do you think of Will?\",\n",
-    ")\n",
-    "print(eval_result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ae60b5e3-ceac-46b1-aabb-ee36930cb57c",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Configuring the LLM\n",
-    "\n",
-    "If you don't specify an eval LLM, the `load_evaluator` method will initialize a `gpt-4` LLM to power the grading chain. Below, use an anthropic model instead."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "1717162d-f76c-4a14-9ade-168d6fa42b7a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# %pip install ChatAnthropic\n",
-    "# %env ANTHROPIC_API_KEY=<API_KEY>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "8727e6f4-aaba-472d-bb7d-09fc1a0f0e2a",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import ChatAnthropic\n",
-    "\n",
-    "llm = ChatAnthropic(temperature=0)\n",
-    "evaluator = load_evaluator(\"criteria\", llm=llm, criteria=\"conciseness\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "3f6f0d8b-cf42-4241-85ae-35b3ce8152a0",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'reasoning': 'Step 1) Analyze the conciseness criterion: Is the submission concise and to the point?\\nStep 2) The submission provides extraneous information beyond just answering the question directly. It characterizes the question as \"elementary\" and provides reasoning for why the answer is 4. This additional commentary makes the submission not fully concise.\\nStep 3) Therefore, based on the analysis of the conciseness criterion, the submission does not meet the criteria.\\n\\nN', 'value': 'N', 'score': 0}\n"
-     ]
-    }
-   ],
-   "source": [
-    "eval_result = evaluator.evaluate_strings(\n",
-    "    prediction=\"What's 2+2? That's an elementary question. The answer you're looking for is that two and two is four.\",\n",
-    "    input=\"What's 2+2?\",\n",
-    ")\n",
-    "print(eval_result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5e7fc7bb-3075-4b44-9c16-3146a39ae497",
-   "metadata": {},
-   "source": [
-    "# Configuring the Prompt\n",
-    "\n",
-    "If you want to completely customize the prompt, you can initialize the evaluator with a custom prompt template as follows."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "22e57704-682f-44ff-96ba-e915c73269c0",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.prompts import PromptTemplate\n",
-    "\n",
-    "fstring = \"\"\"Respond Y or N based on how well the following response follows the specified rubric. Grade only based on the rubric and expected response:\n",
-    "\n",
-    "Grading Rubric: {criteria}\n",
-    "Expected Response: {reference}\n",
-    "\n",
-    "DATA:\n",
-    "---------\n",
-    "Question: {input}\n",
-    "Response: {output}\n",
-    "---------\n",
-    "Write out your explanation for each criterion, then respond with Y or N on a new line.\"\"\"\n",
-    "\n",
-    "prompt = PromptTemplate.from_template(fstring)\n",
-    "\n",
-    "evaluator = load_evaluator(\"labeled_criteria\", criteria=\"correctness\", prompt=prompt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "5d6b0eca-7aea-4073-a65a-18c3a9cdb5af",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'reasoning': 'Correctness: No, the response is not correct. The expected response was \"It\\'s 17 now.\" but the response given was \"What\\'s 2+2? That\\'s an elementary question. The answer you\\'re looking for is that two and two is four.\"', 'value': 'N', 'score': 0}\n"
-     ]
-    }
-   ],
-   "source": [
-    "eval_result = evaluator.evaluate_strings(\n",
-    "    prediction=\"What's 2+2? That's an elementary question. The answer you're looking for is that two and two is four.\",\n",
-    "    input=\"What's 2+2?\",\n",
-    "    reference=\"It's 17 now.\",\n",
-    ")\n",
-    "print(eval_result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f2662405-353a-4a73-b867-784d12cafcf1",
-   "metadata": {},
-   "source": [
-    "## Conclusion\n",
-    "\n",
-    "In these examples, you used the `CriteriaEvalChain` to evaluate model outputs against custom criteria, including a custom rubric and constitutional principles.\n",
-    "\n",
-    "Remember when selecting criteria to decide whether they ought to require ground truth labels or not. Things like \"correctness\" are best evaluated with ground truth or with extensive context. Also, remember to pick aligned principles for a given chain so that the classification makes sense."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a684e2f1",
-   "metadata": {},
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/guides/evaluation/string/json.ipynb

@@ -1,385 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "465cfbef-5bba-4b3b-b02d-fe2eba39db17",
-   "metadata": {},
-   "source": [
-    "# Evaluating Structured Output: JSON Evaluators\n",
-    "\n",
-    "Evaluating [extraction](https://python.langchain.com/docs/use_cases/extraction) and function calling applications often comes down to validation that the LLM's string output can be parsed correctly and how it compares to a reference object. The following JSON validators provide provide functionality to check your model's output in a consistent way.\n",
-    "\n",
-    "## JsonValidityEvaluator\n",
-    "\n",
-    "The `JsonValidityEvaluator` is designed to check the validity of a JSON string prediction.\n",
-    "\n",
-    "### Overview:\n",
-    "- **Requires Input?**: No\n",
-    "- **Requires Reference?**: No"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "02e5f7dd-82fe-48f9-a251-b2052e17e61c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'score': 1}\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.evaluation import JsonValidityEvaluator, load_evaluator\n",
-    "\n",
-    "evaluator = JsonValidityEvaluator()\n",
-    "# Equivalently\n",
-    "# evaluator = load_evaluator(\"json_validity\")\n",
-    "prediction = '{\"name\": \"John\", \"age\": 30, \"city\": \"New York\"}'\n",
-    "\n",
-    "result = evaluator.evaluate_strings(prediction=prediction)\n",
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "9a9607c6-edab-4c26-86c4-22b226e18aa9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'score': 0, 'reasoning': 'Expecting property name enclosed in double quotes: line 1 column 48 (char 47)'}\n"
-     ]
-    }
-   ],
-   "source": [
-    "prediction = '{\"name\": \"John\", \"age\": 30, \"city\": \"New York\",}'\n",
-    "result = evaluator.evaluate_strings(prediction=prediction)\n",
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8ac18a83-30d8-4c11-abf2-7a36e4cb829f",
-   "metadata": {},
-   "source": [
-    "## JsonEqualityEvaluator\n",
-    "\n",
-    "The `JsonEqualityEvaluator` assesses whether a JSON prediction matches a given reference after both are parsed.\n",
-    "\n",
-    "### Overview:\n",
-    "- **Requires Input?**: No\n",
-    "- **Requires Reference?**: Yes\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ab97111e-cba9-4273-825f-d5d4278a953c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'score': True}\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.evaluation import JsonEqualityEvaluator\n",
-    "\n",
-    "evaluator = JsonEqualityEvaluator()\n",
-    "# Equivalently\n",
-    "# evaluator = load_evaluator(\"json_equality\")\n",
-    "result = evaluator.evaluate_strings(prediction='{\"a\": 1}', reference='{\"a\": 1}')\n",
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "655ba486-09b6-47ce-947d-b2bd8b6f6364",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'score': False}\n"
-     ]
-    }
-   ],
-   "source": [
-    "result = evaluator.evaluate_strings(prediction='{\"a\": 1}', reference='{\"a\": 2}')\n",
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1ac7e541-b7fe-46b6-bc3a-e94fe316227e",
-   "metadata": {},
-   "source": [
-    "The evaluator also by default lets you provide a dictionary directly"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "36e70ba3-4e62-483c-893a-5f328b7f303d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'score': False}\n"
-     ]
-    }
-   ],
-   "source": [
-    "result = evaluator.evaluate_strings(prediction={\"a\": 1}, reference={\"a\": 2})\n",
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "921d33f0-b3c2-4e9e-820c-9ec30bc5bb20",
-   "metadata": {},
-   "source": [
-    "## JsonEditDistanceEvaluator\n",
-    "\n",
-    "The `JsonEditDistanceEvaluator` computes a normalized Damerau-Levenshtein distance between two \"canonicalized\" JSON strings.\n",
-    "\n",
-    "### Overview:\n",
-    "- **Requires Input?**: No\n",
-    "- **Requires Reference?**: Yes\n",
-    "- **Distance Function**: Damerau-Levenshtein (by default)\n",
-    "\n",
-    "_Note: Ensure that `rapidfuzz` is installed or provide an alternative `string_distance` function to avoid an ImportError._"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "da9ec3a3-675f-4420-8ec7-cde48d8c2918",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'score': 0.07692307692307693}\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.evaluation import JsonEditDistanceEvaluator\n",
-    "\n",
-    "evaluator = JsonEditDistanceEvaluator()\n",
-    "# Equivalently\n",
-    "# evaluator = load_evaluator(\"json_edit_distance\")\n",
-    "\n",
-    "result = evaluator.evaluate_strings(\n",
-    "    prediction='{\"a\": 1, \"b\": 2}', reference='{\"a\": 1, \"b\": 3}'\n",
-    ")\n",
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "537ed58c-6a9c-402f-8f7f-07b1119a9ae0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'score': 0.0}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# The values are canonicalized prior to comparison\n",
-    "result = evaluator.evaluate_strings(\n",
-    "    prediction=\"\"\"\n",
-    "    {\n",
-    "        \"b\": 3,\n",
-    "        \"a\":   1\n",
-    "    }\"\"\",\n",
-    "    reference='{\"a\": 1, \"b\": 3}',\n",
-    ")\n",
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "7a8f3ec5-1cde-4b0e-80cd-ac0ac290d375",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'score': 0.18181818181818182}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Lists maintain their order, however\n",
-    "result = evaluator.evaluate_strings(\n",
-    "    prediction='{\"a\": [1, 2]}', reference='{\"a\": [2, 1]}'\n",
-    ")\n",
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "52abec79-58ed-4ab6-9fb1-7deb1f5146cc",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'score': 0.14285714285714285}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# You can also pass in objects directly\n",
-    "result = evaluator.evaluate_strings(prediction={\"a\": 1}, reference={\"a\": 2})\n",
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6b15d18e-9b97-434f-905c-70acd4c35aea",
-   "metadata": {},
-   "source": [
-    "## JsonSchemaEvaluator\n",
-    "\n",
-    "The `JsonSchemaEvaluator` validates a JSON prediction against a provided JSON schema. If the prediction conforms to the schema, it returns a score of True (indicating no errors). Otherwise, it returns a score of 0 (indicating an error).\n",
-    "\n",
-    "### Overview:\n",
-    "- **Requires Input?**: Yes\n",
-    "- **Requires Reference?**: Yes (A JSON schema)\n",
-    "- **Score**: True (No errors) or False (Error occurred)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "85afcf33-d2f4-406e-9d8f-15dc0a4772f2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'score': True}\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.evaluation import JsonSchemaEvaluator\n",
-    "\n",
-    "evaluator = JsonSchemaEvaluator()\n",
-    "# Equivalently\n",
-    "# evaluator = load_evaluator(\"json_schema_validation\")\n",
-    "\n",
-    "result = evaluator.evaluate_strings(\n",
-    "    prediction='{\"name\": \"John\", \"age\": 30}',\n",
-    "    reference={\n",
-    "        \"type\": \"object\",\n",
-    "        \"properties\": {\"name\": {\"type\": \"string\"}, \"age\": {\"type\": \"integer\"}},\n",
-    "    },\n",
-    ")\n",
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "bb5b89f6-0c87-4335-9091-55fd67a0565f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'score': True}\n"
-     ]
-    }
-   ],
-   "source": [
-    "result = evaluator.evaluate_strings(\n",
-    "    prediction='{\"name\": \"John\", \"age\": 30}',\n",
-    "    reference='{\"type\": \"object\", \"properties\": {\"name\": {\"type\": \"string\"}, \"age\": {\"type\": \"integer\"}}}',\n",
-    ")\n",
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "ff914d24-36bc-482a-a9ba-259cd0dd2a52",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'score': False, 'reasoning': \"<ValidationError: '30 is less than the minimum of 66'>\"}\n"
-     ]
-    }
-   ],
-   "source": [
-    "result = evaluator.evaluate_strings(\n",
-    "    prediction='{\"name\": \"John\", \"age\": 30}',\n",
-    "    reference='{\"type\": \"object\", \"properties\": {\"name\": {\"type\": \"string\"},'\n",
-    "    '\"age\": {\"type\": \"integer\", \"minimum\": 66}}}',\n",
-    ")\n",
-    "print(result)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b073f12d-4603-481c-8081-fab1af6bfcfe",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/guides/evaluation/string/regex_match.ipynb

@@ -1,243 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "2da95378",
-   "metadata": {},
-   "source": [
-    "# Regex Match\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/regex_match.ipynb)\n",
-    "\n",
-    "To evaluate chain or runnable string predictions against a custom regex, you can use the `regex_match` evaluator."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "0de44d01-1fea-4701-b941-c4fb74e521e7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.evaluation import RegexMatchStringEvaluator\n",
-    "\n",
-    "evaluator = RegexMatchStringEvaluator()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fe3baf5f-bfee-4745-bcd6-1a9b422ed46f",
-   "metadata": {},
-   "source": [
-    "Alternatively via the loader:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "f6790c46",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.evaluation import load_evaluator\n",
-    "\n",
-    "evaluator = load_evaluator(\"regex_match\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "49ad9139",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 1}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Check for the presence of a YYYY-MM-DD string.\n",
-    "evaluator.evaluate_strings(\n",
-    "    prediction=\"The delivery will be made on 2024-01-05\",\n",
-    "    reference=\".*\\\\b\\\\d{4}-\\\\d{2}-\\\\d{2}\\\\b.*\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "1f5e82a3-247e-45a8-85fc-6af53bf7ff82",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Check for the presence of a MM-DD-YYYY string.\n",
-    "evaluator.evaluate_strings(\n",
-    "    prediction=\"The delivery will be made on 2024-01-05\",\n",
-    "    reference=\".*\\\\b\\\\d{2}-\\\\d{2}-\\\\d{4}\\\\b.*\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "168fcd92-dffb-4345-b097-02d0fedf52fd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 1}"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Check for the presence of a MM-DD-YYYY string.\n",
-    "evaluator.evaluate_strings(\n",
-    "    prediction=\"The delivery will be made on 01-05-2024\",\n",
-    "    reference=\".*\\\\b\\\\d{2}-\\\\d{2}-\\\\d{4}\\\\b.*\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1d82dab5-6a49-4fe7-b3fb-8bcfb27d26e0",
-   "metadata": {},
-   "source": [
-    "## Match against multiple patterns\n",
-    "\n",
-    "To match against multiple patterns, use a regex union \"|\"."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "b87b915e-b7c2-476b-a452-99688a22293a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 1}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Check for the presence of a MM-DD-YYYY string or YYYY-MM-DD\n",
-    "evaluator.evaluate_strings(\n",
-    "    prediction=\"The delivery will be made on 01-05-2024\",\n",
-    "    reference=\"|\".join(\n",
-    "        [\".*\\\\b\\\\d{4}-\\\\d{2}-\\\\d{2}\\\\b.*\", \".*\\\\b\\\\d{2}-\\\\d{2}-\\\\d{4}\\\\b.*\"]\n",
-    "    ),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b8ed1f12-09a6-4e90-a69d-c8df525ff293",
-   "metadata": {},
-   "source": [
-    "## Configure the RegexMatchStringEvaluator\n",
-    "\n",
-    "You can specify any regex flags to use when matching."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "0c079864-0175-4d06-9d3f-a0e51dd3977c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import re\n",
-    "\n",
-    "evaluator = RegexMatchStringEvaluator(flags=re.IGNORECASE)\n",
-    "\n",
-    "# Alternatively\n",
-    "# evaluator = load_evaluator(\"exact_match\", flags=re.IGNORECASE)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "a8dfb900-14f3-4a1f-8736-dd1d86a1264c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 1}"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evaluator.evaluate_strings(\n",
-    "    prediction=\"I LOVE testing\",\n",
-    "    reference=\"I love testing\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "82de8d3e-c829-440e-a582-3fb70cecad3b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/guides/evaluation/string/string_distance.ipynb

@@ -1,221 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "2da95378",
-   "metadata": {},
-   "source": [
-    "# String Distance\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/string_distance.ipynb)\n",
-    "\n",
-    "One of the simplest ways to compare an LLM or chain's string output against a reference label is by using string distance measurements such as Levenshtein or postfix distance.  This can be used alongside approximate/fuzzy matching criteria for very basic unit testing.\n",
-    "\n",
-    "This can be accessed using the `string_distance` evaluator, which uses distance metric's from the [rapidfuzz](https://github.com/maxbachmann/RapidFuzz) library.\n",
-    "\n",
-    "**Note:** The returned scores are _distances_, meaning lower is typically \"better\".\n",
-    "\n",
-    "For more information, check out the reference docs for the [StringDistanceEvalChain](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.string_distance.base.StringDistanceEvalChain.html#langchain.evaluation.string_distance.base.StringDistanceEvalChain) for more info."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "8b47b909-3251-4774-9a7d-e436da4f8979",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# %pip install rapidfuzz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "f6790c46",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.evaluation import load_evaluator\n",
-    "\n",
-    "evaluator = load_evaluator(\"string_distance\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "49ad9139",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.11555555555555552}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evaluator.evaluate_strings(\n",
-    "    prediction=\"The job is completely done.\",\n",
-    "    reference=\"The job is done\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "c06a2296",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.0724999999999999}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# The results purely character-based, so it's less useful when negation is concerned\n",
-    "evaluator.evaluate_strings(\n",
-    "    prediction=\"The job is done.\",\n",
-    "    reference=\"The job isn't done\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b8ed1f12-09a6-4e90-a69d-c8df525ff293",
-   "metadata": {},
-   "source": [
-    "## Configure the String Distance Metric\n",
-    "\n",
-    "By default, the `StringDistanceEvalChain` uses  levenshtein distance, but it also supports other string distance algorithms. Configure using the `distance` argument."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "a88bc7d7-62d3-408d-b0e0-43abcecf35c8",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<StringDistance.DAMERAU_LEVENSHTEIN: 'damerau_levenshtein'>,\n",
-       " <StringDistance.LEVENSHTEIN: 'levenshtein'>,\n",
-       " <StringDistance.JARO: 'jaro'>,\n",
-       " <StringDistance.JARO_WINKLER: 'jaro_winkler'>]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.evaluation import StringDistance\n",
-    "\n",
-    "list(StringDistance)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "0c079864-0175-4d06-9d3f-a0e51dd3977c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "jaro_evaluator = load_evaluator(\"string_distance\", distance=StringDistance.JARO)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "a8dfb900-14f3-4a1f-8736-dd1d86a1264c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.19259259259259254}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "jaro_evaluator.evaluate_strings(\n",
-    "    prediction=\"The job is completely done.\",\n",
-    "    reference=\"The job is done\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "7020b046-0ef7-40cc-8778-b928e35f3ce1",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'score': 0.12083333333333324}"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "jaro_evaluator.evaluate_strings(\n",
-    "    prediction=\"The job is done.\",\n",
-    "    reference=\"The job isn't done\",\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/guides/privacy/presidio_data_anonymization/qa_privacy_protection.ipynb

@@ -1,986 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "sidebar_position: 3\n",
-    "title: QA with private data protection\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# QA with private data protection\n",
-    "\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/privacy/presidio_data_anonymization/qa_privacy_protection.ipynb)\n",
-    "\n",
-    "\n",
-    "In this notebook, we will look at building a basic system for question answering, based on private data. Before feeding the LLM with this data, we need to protect it so that it doesn't go to an external API (e.g. OpenAI, Anthropic). Then, after receiving the model output, we would like the data to be restored to its original form. Below you can observe an example flow of this QA system:\n",
-    "\n",
-    "<img src=\"/img/qa_privacy_protection.png\" width=\"900\"/>\n",
-    "\n",
-    "\n",
-    "In the following notebook, we will not go into the details of how the anonymizer works. If you are interested, please visit [this part of the documentation](https://python.langchain.com/docs/guides/privacy/presidio_data_anonymization/).\n",
-    "\n",
-    "## Quickstart\n",
-    "\n",
-    "### Iterative process of upgrading the anonymizer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install necessary packages\n",
-    "# !pip install langchain langchain-experimental openai presidio-analyzer presidio-anonymizer spacy Faker faiss-cpu tiktoken\n",
-    "# ! python -m spacy download en_core_web_lg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "document_content = \"\"\"Date: October 19, 2021\n",
-    " Witness: John Doe\n",
-    " Subject: Testimony Regarding the Loss of Wallet\n",
-    "\n",
-    " Testimony Content:\n",
-    "\n",
-    " Hello Officer,\n",
-    "\n",
-    " My name is John Doe and on October 19, 2021, my wallet was stolen in the vicinity of Kilmarnock during a bike trip. This wallet contains some very important things to me.\n",
-    "\n",
-    " Firstly, the wallet contains my credit card with number 4111 1111 1111 1111, which is registered under my name and linked to my bank account, PL61109010140000071219812874.\n",
-    "\n",
-    " Additionally, the wallet had a driver's license - DL No: 999000680 issued to my name. It also houses my Social Security Number, 602-76-4532. \n",
-    "\n",
-    " What's more, I had my polish identity card there, with the number ABC123456.\n",
-    "\n",
-    " I would like this data to be secured and protected in all possible ways. I believe It was stolen at 9:30 AM.\n",
-    "\n",
-    " In case any information arises regarding my wallet, please reach out to me on my phone number, 999-888-7777, or through my personal email, johndoe@example.com.\n",
-    "\n",
-    " Please consider this information to be highly confidential and respect my privacy. \n",
-    "\n",
-    " The bank has been informed about the stolen credit card and necessary actions have been taken from their end. They will be reachable at their official email, support@bankname.com.\n",
-    " My representative there is Victoria Cherry (her business phone: 987-654-3210).\n",
-    "\n",
-    " Thank you for your assistance,\n",
-    "\n",
-    " John Doe\"\"\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.schema import Document\n",
-    "\n",
-    "documents = [Document(page_content=document_content)]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We only have one document, so before we move on to creating a QA system, let's focus on its content to begin with.\n",
-    "\n",
-    "You may observe that the text contains many different PII values, some types occur repeatedly (names, phone numbers, emails), and some specific PIIs are repeated (John Doe)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Util function for coloring the PII markers\n",
-    "# NOTE: It will not be visible on documentation page, only in the notebook\n",
-    "import re\n",
-    "\n",
-    "\n",
-    "def print_colored_pii(string):\n",
-    "    colored_string = re.sub(\n",
-    "        r\"(<[^>]*>)\", lambda m: \"\\033[31m\" + m.group(1) + \"\\033[0m\", string\n",
-    "    )\n",
-    "    print(colored_string)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's proceed and try to anonymize the text with the default settings. For now, we don't replace the data with synthetic, we just mark it with markers (e.g. `<PERSON>`), so we set `add_default_faker_operators=False`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date: \u001b[31m<DATE_TIME>\u001b[0m\n",
-      "Witness: \u001b[31m<PERSON>\u001b[0m\n",
-      "Subject: Testimony Regarding the Loss of Wallet\n",
-      "\n",
-      "Testimony Content:\n",
-      "\n",
-      "Hello Officer,\n",
-      "\n",
-      "My name is \u001b[31m<PERSON>\u001b[0m and on \u001b[31m<DATE_TIME>\u001b[0m, my wallet was stolen in the vicinity of \u001b[31m<LOCATION>\u001b[0m during a bike trip. This wallet contains some very important things to me.\n",
-      "\n",
-      "Firstly, the wallet contains my credit card with number \u001b[31m<CREDIT_CARD>\u001b[0m, which is registered under my name and linked to my bank account, \u001b[31m<IBAN_CODE>\u001b[0m.\n",
-      "\n",
-      "Additionally, the wallet had a driver's license - DL No: \u001b[31m<US_DRIVER_LICENSE>\u001b[0m issued to my name. It also houses my Social Security Number, \u001b[31m<US_SSN>\u001b[0m. \n",
-      "\n",
-      "What's more, I had my polish identity card there, with the number ABC123456.\n",
-      "\n",
-      "I would like this data to be secured and protected in all possible ways. I believe It was stolen at \u001b[31m<DATE_TIME_2>\u001b[0m.\n",
-      "\n",
-      "In case any information arises regarding my wallet, please reach out to me on my phone number, \u001b[31m<PHONE_NUMBER>\u001b[0m, or through my personal email, \u001b[31m<EMAIL_ADDRESS>\u001b[0m.\n",
-      "\n",
-      "Please consider this information to be highly confidential and respect my privacy. \n",
-      "\n",
-      "The bank has been informed about the stolen credit card and necessary actions have been taken from their end. They will be reachable at their official email, \u001b[31m<EMAIL_ADDRESS_2>\u001b[0m.\n",
-      "My representative there is \u001b[31m<PERSON_2>\u001b[0m (her business phone: \u001b[31m<UK_NHS>\u001b[0m).\n",
-      "\n",
-      "Thank you for your assistance,\n",
-      "\n",
-      "\u001b[31m<PERSON>\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_experimental.data_anonymizer import PresidioReversibleAnonymizer\n",
-    "\n",
-    "anonymizer = PresidioReversibleAnonymizer(\n",
-    "    add_default_faker_operators=False,\n",
-    ")\n",
-    "\n",
-    "print_colored_pii(anonymizer.anonymize(document_content))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's also look at the mapping between original and anonymized values:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'CREDIT_CARD': {'<CREDIT_CARD>': '4111 1111 1111 1111'},\n",
-      " 'DATE_TIME': {'<DATE_TIME>': 'October 19, 2021', '<DATE_TIME_2>': '9:30 AM'},\n",
-      " 'EMAIL_ADDRESS': {'<EMAIL_ADDRESS>': 'johndoe@example.com',\n",
-      "                   '<EMAIL_ADDRESS_2>': 'support@bankname.com'},\n",
-      " 'IBAN_CODE': {'<IBAN_CODE>': 'PL61109010140000071219812874'},\n",
-      " 'LOCATION': {'<LOCATION>': 'Kilmarnock'},\n",
-      " 'PERSON': {'<PERSON>': 'John Doe', '<PERSON_2>': 'Victoria Cherry'},\n",
-      " 'PHONE_NUMBER': {'<PHONE_NUMBER>': '999-888-7777'},\n",
-      " 'UK_NHS': {'<UK_NHS>': '987-654-3210'},\n",
-      " 'US_DRIVER_LICENSE': {'<US_DRIVER_LICENSE>': '999000680'},\n",
-      " 'US_SSN': {'<US_SSN>': '602-76-4532'}}\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pprint\n",
-    "\n",
-    "pprint.pprint(anonymizer.deanonymizer_mapping)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In general, the anonymizer works pretty well, but I can observe two things to improve here:\n",
-    "\n",
-    "1. Datetime redundancy - we have two different entities recognized as `DATE_TIME`, but they contain different type of information. The first one is a date (*October 19, 2021*), the second one is a time (*9:30 AM*). We can improve this by adding a new recognizer to the anonymizer, which will treat time separately from the date.\n",
-    "2. Polish ID - polish ID has unique pattern, which is not by default part of anonymizer recognizers. The value *ABC123456* is not anonymized.\n",
-    "\n",
-    "The solution is simple: we need to add a new recognizers to the anonymizer. You can read more about it in [presidio documentation](https://microsoft.github.io/presidio/analyzer/adding_recognizers/).\n",
-    "\n",
-    "\n",
-    "Let's add new recognizers:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define the regex pattern in a Presidio `Pattern` object:\n",
-    "from presidio_analyzer import Pattern, PatternRecognizer\n",
-    "\n",
-    "\n",
-    "polish_id_pattern = Pattern(\n",
-    "    name=\"polish_id_pattern\",\n",
-    "    regex=\"[A-Z]{3}\\d{6}\",\n",
-    "    score=1,\n",
-    ")\n",
-    "time_pattern = Pattern(\n",
-    "    name=\"time_pattern\",\n",
-    "    regex=\"(1[0-2]|0?[1-9]):[0-5][0-9] (AM|PM)\",\n",
-    "    score=1,\n",
-    ")\n",
-    "\n",
-    "# Define the recognizer with one or more patterns\n",
-    "polish_id_recognizer = PatternRecognizer(\n",
-    "    supported_entity=\"POLISH_ID\", patterns=[polish_id_pattern]\n",
-    ")\n",
-    "time_recognizer = PatternRecognizer(supported_entity=\"TIME\", patterns=[time_pattern])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And now, we're adding recognizers to our anonymizer:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "anonymizer.add_recognizer(polish_id_recognizer)\n",
-    "anonymizer.add_recognizer(time_recognizer)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that our anonymization instance remembers previously detected and anonymized values, including those that were not detected correctly (e.g., *\"9:30 AM\"* taken as `DATE_TIME`). So it's worth removing this value, or resetting the entire mapping now that our recognizers have been updated:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "anonymizer.reset_deanonymizer_mapping()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's anonymize the text and see the results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date: \u001b[31m<DATE_TIME>\u001b[0m\n",
-      "Witness: \u001b[31m<PERSON>\u001b[0m\n",
-      "Subject: Testimony Regarding the Loss of Wallet\n",
-      "\n",
-      "Testimony Content:\n",
-      "\n",
-      "Hello Officer,\n",
-      "\n",
-      "My name is \u001b[31m<PERSON>\u001b[0m and on \u001b[31m<DATE_TIME>\u001b[0m, my wallet was stolen in the vicinity of \u001b[31m<LOCATION>\u001b[0m during a bike trip. This wallet contains some very important things to me.\n",
-      "\n",
-      "Firstly, the wallet contains my credit card with number \u001b[31m<CREDIT_CARD>\u001b[0m, which is registered under my name and linked to my bank account, \u001b[31m<IBAN_CODE>\u001b[0m.\n",
-      "\n",
-      "Additionally, the wallet had a driver's license - DL No: \u001b[31m<US_DRIVER_LICENSE>\u001b[0m issued to my name. It also houses my Social Security Number, \u001b[31m<US_SSN>\u001b[0m. \n",
-      "\n",
-      "What's more, I had my polish identity card there, with the number \u001b[31m<POLISH_ID>\u001b[0m.\n",
-      "\n",
-      "I would like this data to be secured and protected in all possible ways. I believe It was stolen at \u001b[31m<TIME>\u001b[0m.\n",
-      "\n",
-      "In case any information arises regarding my wallet, please reach out to me on my phone number, \u001b[31m<PHONE_NUMBER>\u001b[0m, or through my personal email, \u001b[31m<EMAIL_ADDRESS>\u001b[0m.\n",
-      "\n",
-      "Please consider this information to be highly confidential and respect my privacy. \n",
-      "\n",
-      "The bank has been informed about the stolen credit card and necessary actions have been taken from their end. They will be reachable at their official email, \u001b[31m<EMAIL_ADDRESS_2>\u001b[0m.\n",
-      "My representative there is \u001b[31m<PERSON_2>\u001b[0m (her business phone: \u001b[31m<UK_NHS>\u001b[0m).\n",
-      "\n",
-      "Thank you for your assistance,\n",
-      "\n",
-      "\u001b[31m<PERSON>\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "print_colored_pii(anonymizer.anonymize(document_content))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'CREDIT_CARD': {'<CREDIT_CARD>': '4111 1111 1111 1111'},\n",
-      " 'DATE_TIME': {'<DATE_TIME>': 'October 19, 2021'},\n",
-      " 'EMAIL_ADDRESS': {'<EMAIL_ADDRESS>': 'johndoe@example.com',\n",
-      "                   '<EMAIL_ADDRESS_2>': 'support@bankname.com'},\n",
-      " 'IBAN_CODE': {'<IBAN_CODE>': 'PL61109010140000071219812874'},\n",
-      " 'LOCATION': {'<LOCATION>': 'Kilmarnock'},\n",
-      " 'PERSON': {'<PERSON>': 'John Doe', '<PERSON_2>': 'Victoria Cherry'},\n",
-      " 'PHONE_NUMBER': {'<PHONE_NUMBER>': '999-888-7777'},\n",
-      " 'POLISH_ID': {'<POLISH_ID>': 'ABC123456'},\n",
-      " 'TIME': {'<TIME>': '9:30 AM'},\n",
-      " 'UK_NHS': {'<UK_NHS>': '987-654-3210'},\n",
-      " 'US_DRIVER_LICENSE': {'<US_DRIVER_LICENSE>': '999000680'},\n",
-      " 'US_SSN': {'<US_SSN>': '602-76-4532'}}\n"
-     ]
-    }
-   ],
-   "source": [
-    "pprint.pprint(anonymizer.deanonymizer_mapping)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As you can see, our new recognizers work as expected. The anonymizer has replaced the time and Polish ID entities with the `<TIME>` and `<POLISH_ID>` markers, and the deanonymizer mapping has been updated accordingly.\n",
-    "\n",
-    "Now, when all PII values are detected correctly, we can proceed to the next step, which is replacing the original values with synthetic ones. To do this, we need to set `add_default_faker_operators=True` (or just remove this parameter, because it's set to `True` by default):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date: 1986-04-18\n",
-      "Witness: Brian Cox DVM\n",
-      "Subject: Testimony Regarding the Loss of Wallet\n",
-      "\n",
-      "Testimony Content:\n",
-      "\n",
-      "Hello Officer,\n",
-      "\n",
-      "My name is Brian Cox DVM and on 1986-04-18, my wallet was stolen in the vicinity of New Rita during a bike trip. This wallet contains some very important things to me.\n",
-      "\n",
-      "Firstly, the wallet contains my credit card with number 6584801845146275, which is registered under my name and linked to my bank account, GB78GSWK37672423884969.\n",
-      "\n",
-      "Additionally, the wallet had a driver's license - DL No: 781802744 issued to my name. It also houses my Social Security Number, 687-35-1170. \n",
-      "\n",
-      "What's more, I had my polish identity card there, with the number \u001b[31m<POLISH_ID>\u001b[0m.\n",
-      "\n",
-      "I would like this data to be secured and protected in all possible ways. I believe It was stolen at \u001b[31m<TIME>\u001b[0m.\n",
-      "\n",
-      "In case any information arises regarding my wallet, please reach out to me on my phone number, 7344131647, or through my personal email, jamesmichael@example.com.\n",
-      "\n",
-      "Please consider this information to be highly confidential and respect my privacy. \n",
-      "\n",
-      "The bank has been informed about the stolen credit card and necessary actions have been taken from their end. They will be reachable at their official email, blakeerik@example.com.\n",
-      "My representative there is Cristian Santos (her business phone: 2812140441).\n",
-      "\n",
-      "Thank you for your assistance,\n",
-      "\n",
-      "Brian Cox DVM\n"
-     ]
-    }
-   ],
-   "source": [
-    "anonymizer = PresidioReversibleAnonymizer(\n",
-    "    add_default_faker_operators=True,\n",
-    "    # Faker seed is used here to make sure the same fake data is generated for the test purposes\n",
-    "    # In production, it is recommended to remove the faker_seed parameter (it will default to None)\n",
-    "    faker_seed=42,\n",
-    ")\n",
-    "\n",
-    "anonymizer.add_recognizer(polish_id_recognizer)\n",
-    "anonymizer.add_recognizer(time_recognizer)\n",
-    "\n",
-    "print_colored_pii(anonymizer.anonymize(document_content))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As you can see, almost all values have been replaced with synthetic ones. The only exception is the Polish ID number and time, which are not supported by the default faker operators. We can add new operators to the anonymizer, which will generate random data. You can read more about custom operators [here](https://microsoft.github.io/presidio/tutorial/11_custom_anonymization/)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'VTC592627'"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from faker import Faker\n",
-    "\n",
-    "fake = Faker()\n",
-    "\n",
-    "\n",
-    "def fake_polish_id(_=None):\n",
-    "    return fake.bothify(text=\"???######\").upper()\n",
-    "\n",
-    "\n",
-    "fake_polish_id()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'03:14 PM'"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def fake_time(_=None):\n",
-    "    return fake.time(pattern=\"%I:%M %p\")\n",
-    "\n",
-    "\n",
-    "fake_time()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's add newly created operators to the anonymizer:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from presidio_anonymizer.entities import OperatorConfig\n",
-    "\n",
-    "new_operators = {\n",
-    "    \"POLISH_ID\": OperatorConfig(\"custom\", {\"lambda\": fake_polish_id}),\n",
-    "    \"TIME\": OperatorConfig(\"custom\", {\"lambda\": fake_time}),\n",
-    "}\n",
-    "\n",
-    "anonymizer.add_operators(new_operators)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And anonymize everything once again:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Date: 1974-12-26\n",
-      "Witness: Jimmy Murillo\n",
-      "Subject: Testimony Regarding the Loss of Wallet\n",
-      "\n",
-      "Testimony Content:\n",
-      "\n",
-      "Hello Officer,\n",
-      "\n",
-      "My name is Jimmy Murillo and on 1974-12-26, my wallet was stolen in the vicinity of South Dianeshire during a bike trip. This wallet contains some very important things to me.\n",
-      "\n",
-      "Firstly, the wallet contains my credit card with number 213108121913614, which is registered under my name and linked to my bank account, GB17DBUR01326773602606.\n",
-      "\n",
-      "Additionally, the wallet had a driver's license - DL No: 532311310 issued to my name. It also houses my Social Security Number, 690-84-1613. \n",
-      "\n",
-      "What's more, I had my polish identity card there, with the number UFB745084.\n",
-      "\n",
-      "I would like this data to be secured and protected in all possible ways. I believe It was stolen at 11:54 AM.\n",
-      "\n",
-      "In case any information arises regarding my wallet, please reach out to me on my phone number, 876.931.1656, or through my personal email, briannasmith@example.net.\n",
-      "\n",
-      "Please consider this information to be highly confidential and respect my privacy. \n",
-      "\n",
-      "The bank has been informed about the stolen credit card and necessary actions have been taken from their end. They will be reachable at their official email, samuel87@example.org.\n",
-      "My representative there is Joshua Blair (her business phone: 3361388464).\n",
-      "\n",
-      "Thank you for your assistance,\n",
-      "\n",
-      "Jimmy Murillo\n"
-     ]
-    }
-   ],
-   "source": [
-    "anonymizer.reset_deanonymizer_mapping()\n",
-    "print_colored_pii(anonymizer.anonymize(document_content))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'CREDIT_CARD': {'213108121913614': '4111 1111 1111 1111'},\n",
-      " 'DATE_TIME': {'1974-12-26': 'October 19, 2021'},\n",
-      " 'EMAIL_ADDRESS': {'briannasmith@example.net': 'johndoe@example.com',\n",
-      "                   'samuel87@example.org': 'support@bankname.com'},\n",
-      " 'IBAN_CODE': {'GB17DBUR01326773602606': 'PL61109010140000071219812874'},\n",
-      " 'LOCATION': {'South Dianeshire': 'Kilmarnock'},\n",
-      " 'PERSON': {'Jimmy Murillo': 'John Doe', 'Joshua Blair': 'Victoria Cherry'},\n",
-      " 'PHONE_NUMBER': {'876.931.1656': '999-888-7777'},\n",
-      " 'POLISH_ID': {'UFB745084': 'ABC123456'},\n",
-      " 'TIME': {'11:54 AM': '9:30 AM'},\n",
-      " 'UK_NHS': {'3361388464': '987-654-3210'},\n",
-      " 'US_DRIVER_LICENSE': {'532311310': '999000680'},\n",
-      " 'US_SSN': {'690-84-1613': '602-76-4532'}}\n"
-     ]
-    }
-   ],
-   "source": [
-    "pprint.pprint(anonymizer.deanonymizer_mapping)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Voilà! Now all values are replaced with synthetic ones. Note that the deanonymizer mapping has been updated accordingly."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question-answering system with PII anonymization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, let's wrap it up together and create full question-answering system, based on `PresidioReversibleAnonymizer` and LangChain Expression Language (LCEL)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 1. Initialize anonymizer\n",
-    "anonymizer = PresidioReversibleAnonymizer(\n",
-    "    # Faker seed is used here to make sure the same fake data is generated for the test purposes\n",
-    "    # In production, it is recommended to remove the faker_seed parameter (it will default to None)\n",
-    "    faker_seed=42,\n",
-    ")\n",
-    "\n",
-    "anonymizer.add_recognizer(polish_id_recognizer)\n",
-    "anonymizer.add_recognizer(time_recognizer)\n",
-    "\n",
-    "anonymizer.add_operators(new_operators)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.vectorstores import FAISS\n",
-    "\n",
-    "# 2. Load the data: In our case data's already loaded\n",
-    "# 3. Anonymize the data before indexing\n",
-    "for doc in documents:\n",
-    "    doc.page_content = anonymizer.anonymize(doc.page_content)\n",
-    "\n",
-    "# 4. Split the documents into chunks\n",
-    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)\n",
-    "chunks = text_splitter.split_documents(documents)\n",
-    "\n",
-    "# 5. Index the chunks (using OpenAI embeddings, because the data is already anonymized)\n",
-    "embeddings = OpenAIEmbeddings()\n",
-    "docsearch = FAISS.from_documents(chunks, embeddings)\n",
-    "retriever = docsearch.as_retriever()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from operator import itemgetter\n",
-    "from langchain.chat_models.openai import ChatOpenAI\n",
-    "from langchain.schema.runnable import RunnableMap\n",
-    "from langchain.prompts import ChatPromptTemplate\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
-    "from langchain.schema.runnable import RunnableLambda\n",
-    "\n",
-    "# 6. Create anonymizer chain\n",
-    "template = \"\"\"Answer the question based only on the following context:\n",
-    "{context}\n",
-    "\n",
-    "Question: {anonymized_question}\n",
-    "\"\"\"\n",
-    "prompt = ChatPromptTemplate.from_template(template)\n",
-    "\n",
-    "model = ChatOpenAI(temperature=0.3)\n",
-    "\n",
-    "\n",
-    "_inputs = RunnableMap(\n",
-    "    question=RunnablePassthrough(),\n",
-    "    # It is important to remember about question anonymization\n",
-    "    anonymized_question=RunnableLambda(anonymizer.anonymize),\n",
-    ")\n",
-    "\n",
-    "anonymizer_chain = (\n",
-    "    _inputs\n",
-    "    | {\n",
-    "        \"context\": itemgetter(\"anonymized_question\") | retriever,\n",
-    "        \"anonymized_question\": itemgetter(\"anonymized_question\"),\n",
-    "    }\n",
-    "    | prompt\n",
-    "    | model\n",
-    "    | StrOutputParser()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'The theft of the wallet occurred in the vicinity of New Rita during a bike trip. It was stolen from Brian Cox DVM. The time of the theft was 02:22 AM.'"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "anonymizer_chain.invoke(\n",
-    "    \"Where did the theft of the wallet occur, at what time, and who was it stolen from?\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The theft of the wallet occurred in the vicinity of Kilmarnock during a bike trip. It was stolen from John Doe. The time of the theft was 9:30 AM.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 7. Add deanonymization step to the chain\n",
-    "chain_with_deanonymization = anonymizer_chain | RunnableLambda(anonymizer.deanonymize)\n",
-    "\n",
-    "print(\n",
-    "    chain_with_deanonymization.invoke(\n",
-    "        \"Where did the theft of the wallet occur, at what time, and who was it stolen from?\"\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The content of the wallet included a credit card with the number 4111 1111 1111 1111, registered under the name of John Doe and linked to the bank account PL61109010140000071219812874. It also contained a driver's license with the number 999000680 issued to John Doe, as well as his Social Security Number 602-76-4532. Additionally, the wallet had a Polish identity card with the number ABC123456.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\n",
-    "    chain_with_deanonymization.invoke(\"What was the content of the wallet in detail?\")\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The phone number 999-888-7777 belongs to John Doe.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(chain_with_deanonymization.invoke(\"Whose phone number is it: 999-888-7777?\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Alternative approach: local embeddings + anonymizing the context after indexing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If for some reason you would like to index the data in its original form, or simply use custom embeddings, below is an example of how to do it:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "anonymizer = PresidioReversibleAnonymizer(\n",
-    "    # Faker seed is used here to make sure the same fake data is generated for the test purposes\n",
-    "    # In production, it is recommended to remove the faker_seed parameter (it will default to None)\n",
-    "    faker_seed=42,\n",
-    ")\n",
-    "\n",
-    "anonymizer.add_recognizer(polish_id_recognizer)\n",
-    "anonymizer.add_recognizer(time_recognizer)\n",
-    "\n",
-    "anonymizer.add_operators(new_operators)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.embeddings import HuggingFaceBgeEmbeddings\n",
-    "\n",
-    "model_name = \"BAAI/bge-base-en-v1.5\"\n",
-    "# model_kwargs = {'device': 'cuda'}\n",
-    "encode_kwargs = {\"normalize_embeddings\": True}  # set True to compute cosine similarity\n",
-    "local_embeddings = HuggingFaceBgeEmbeddings(\n",
-    "    model_name=model_name,\n",
-    "    # model_kwargs=model_kwargs,\n",
-    "    encode_kwargs=encode_kwargs,\n",
-    "    query_instruction=\"Represent this sentence for searching relevant passages:\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "documents = [Document(page_content=document_content)]\n",
-    "\n",
-    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)\n",
-    "chunks = text_splitter.split_documents(documents)\n",
-    "\n",
-    "docsearch = FAISS.from_documents(chunks, local_embeddings)\n",
-    "retriever = docsearch.as_retriever()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "template = \"\"\"Answer the question based only on the following context:\n",
-    "{context}\n",
-    "\n",
-    "Question: {anonymized_question}\n",
-    "\"\"\"\n",
-    "prompt = ChatPromptTemplate.from_template(template)\n",
-    "\n",
-    "model = ChatOpenAI(temperature=0.2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.prompts.prompt import PromptTemplate\n",
-    "from langchain.schema import format_document\n",
-    "\n",
-    "DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template=\"{page_content}\")\n",
-    "\n",
-    "\n",
-    "def _combine_documents(\n",
-    "    docs, document_prompt=DEFAULT_DOCUMENT_PROMPT, document_separator=\"\\n\\n\"\n",
-    "):\n",
-    "    doc_strings = [format_document(doc, document_prompt) for doc in docs]\n",
-    "    return document_separator.join(doc_strings)\n",
-    "\n",
-    "\n",
-    "chain_with_deanonymization = (\n",
-    "    RunnableMap({\"question\": RunnablePassthrough()})\n",
-    "    | {\n",
-    "        \"context\": itemgetter(\"question\")\n",
-    "        | retriever\n",
-    "        | _combine_documents\n",
-    "        | anonymizer.anonymize,\n",
-    "        \"anonymized_question\": lambda x: anonymizer.anonymize(x[\"question\"]),\n",
-    "    }\n",
-    "    | prompt\n",
-    "    | model\n",
-    "    | StrOutputParser()\n",
-    "    | RunnableLambda(anonymizer.deanonymize)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The theft of the wallet occurred in the vicinity of Kilmarnock during a bike trip. It was stolen from John Doe. The time of the theft was 9:30 AM.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\n",
-    "    chain_with_deanonymization.invoke(\n",
-    "        \"Where did the theft of the wallet occur, at what time, and who was it stolen from?\"\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The content of the wallet included:\n",
-      "1. Credit card number: 4111 1111 1111 1111\n",
-      "2. Bank account number: PL61109010140000071219812874\n",
-      "3. Driver's license number: 999000680\n",
-      "4. Social Security Number: 602-76-4532\n",
-      "5. Polish identity card number: ABC123456\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\n",
-    "    chain_with_deanonymization.invoke(\"What was the content of the wallet in detail?\")\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The phone number 999-888-7777 belongs to John Doe.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(chain_with_deanonymization.invoke(\"Whose phone number is it: 999-888-7777?\"))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "langchain-py-env",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/docs/guides/privacy/presidio_data_anonymization/reversible.ipynb

@@ -1,636 +0,0 @@
-{
-   "cells": [
-      {
-         "cell_type": "raw",
-         "metadata": {},
-         "source": [
-            "---\n",
-            "sidebar_position: 1\n",
-            "title: Reversible anonymization \n",
-            "---"
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "# Reversible data anonymization with Microsoft Presidio\n",
-            "\n",
-            "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/privacy/presidio_data_anonymization/reversible.ipynb)\n",
-            "\n",
-            "\n",
-            "## Use case\n",
-            "\n",
-            "We have already written about the importance of anonymizing sensitive data in the previous section. **Reversible Anonymization** is an equally essential technology while sharing information with language models, as it balances data protection with data usability. This technique involves masking sensitive personally identifiable information (PII), yet it can be reversed and original data can be restored when authorized users need it. Its main advantage lies in the fact that while it conceals individual identities to prevent misuse, it also allows the concealed data to be accurately unmasked should it be necessary for legal or compliance purposes. \n",
-            "\n",
-            "## Overview\n",
-            "\n",
-            "We implemented the `PresidioReversibleAnonymizer`, which consists of two parts:\n",
-            "\n",
-            "1. anonymization - it works the same way as `PresidioAnonymizer`, plus the object itself stores a mapping of made-up values to original ones, for example:\n",
-            "```\n",
-            "    {\n",
-            "        \"PERSON\": {\n",
-            "            \"<anonymized>\": \"<original>\",\n",
-            "            \"John Doe\": \"Slim Shady\"\n",
-            "        },\n",
-            "        \"PHONE_NUMBER\": {\n",
-            "            \"111-111-1111\": \"555-555-5555\"\n",
-            "        }\n",
-            "        ...\n",
-            "    }\n",
-            "```\n",
-            "\n",
-            "2. deanonymization - using the mapping described above, it matches fake data with original data and then substitutes it.\n",
-            "\n",
-            "Between anonymization and deanonymization user can perform different operations, for example, passing the output to LLM.\n",
-            "\n",
-            "## Quickstart\n",
-            "\n"
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": 1,
-         "metadata": {},
-         "outputs": [],
-         "source": [
-            "# Install necessary packages\n",
-            "# ! pip install langchain langchain-experimental openai presidio-analyzer presidio-anonymizer spacy Faker\n",
-            "# ! python -m spacy download en_core_web_lg"
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "`PresidioReversibleAnonymizer` is not significantly different from its predecessor (`PresidioAnonymizer`) in terms of anonymization:"
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": 2,
-         "metadata": {},
-         "outputs": [
-            {
-               "data": {
-                  "text/plain": [
-                     "'My name is Maria Lynch, call me at 7344131647 or email me at jamesmichael@example.com. By the way, my card number is: 4838637940262'"
-                  ]
-               },
-               "execution_count": 2,
-               "metadata": {},
-               "output_type": "execute_result"
-            }
-         ],
-         "source": [
-            "from langchain_experimental.data_anonymizer import PresidioReversibleAnonymizer\n",
-            "\n",
-            "anonymizer = PresidioReversibleAnonymizer(\n",
-            "    analyzed_fields=[\"PERSON\", \"PHONE_NUMBER\", \"EMAIL_ADDRESS\", \"CREDIT_CARD\"],\n",
-            "    # Faker seed is used here to make sure the same fake data is generated for the test purposes\n",
-            "    # In production, it is recommended to remove the faker_seed parameter (it will default to None)\n",
-            "    faker_seed=42,\n",
-            ")\n",
-            "\n",
-            "anonymizer.anonymize(\n",
-            "    \"My name is Slim Shady, call me at 313-666-7440 or email me at real.slim.shady@gmail.com. \"\n",
-            "    \"By the way, my card number is: 4916 0387 9536 0861\"\n",
-            ")"
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "This is what the full string we want to deanonymize looks like:"
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": 3,
-         "metadata": {},
-         "outputs": [
-            {
-               "name": "stdout",
-               "output_type": "stream",
-               "text": [
-                  "Maria Lynch recently lost his wallet. \n",
-                  "Inside is some cash and his credit card with the number 4838637940262. \n",
-                  "If you would find it, please call at 7344131647 or write an email here: jamesmichael@example.com.\n",
-                  "Maria Lynch would be very grateful!\n"
-               ]
-            }
-         ],
-         "source": [
-            "# We know this data, as we set the faker_seed parameter\n",
-            "fake_name = \"Maria Lynch\"\n",
-            "fake_phone = \"7344131647\"\n",
-            "fake_email = \"jamesmichael@example.com\"\n",
-            "fake_credit_card = \"4838637940262\"\n",
-            "\n",
-            "anonymized_text = f\"\"\"{fake_name} recently lost his wallet. \n",
-            "Inside is some cash and his credit card with the number {fake_credit_card}. \n",
-            "If you would find it, please call at {fake_phone} or write an email here: {fake_email}.\n",
-            "{fake_name} would be very grateful!\"\"\"\n",
-            "\n",
-            "print(anonymized_text)"
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "And now, using the `deanonymize` method, we can reverse the process:"
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": 4,
-         "metadata": {},
-         "outputs": [
-            {
-               "name": "stdout",
-               "output_type": "stream",
-               "text": [
-                  "Slim Shady recently lost his wallet. \n",
-                  "Inside is some cash and his credit card with the number 4916 0387 9536 0861. \n",
-                  "If you would find it, please call at 313-666-7440 or write an email here: real.slim.shady@gmail.com.\n",
-                  "Slim Shady would be very grateful!\n"
-               ]
-            }
-         ],
-         "source": [
-            "print(anonymizer.deanonymize(anonymized_text))"
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "### Using with LangChain Expression Language\n",
-            "\n",
-            "With LCEL we can easily chain together anonymization and deanonymization with the rest of our application. This is an example of using the anonymization mechanism with a query to LLM (without deanonymization for now):"
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": 5,
-         "metadata": {},
-         "outputs": [],
-         "source": [
-            "text = f\"\"\"Slim Shady recently lost his wallet. \n",
-            "Inside is some cash and his credit card with the number 4916 0387 9536 0861. \n",
-            "If you would find it, please call at 313-666-7440 or write an email here: real.slim.shady@gmail.com.\"\"\""
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": 6,
-         "metadata": {},
-         "outputs": [
-            {
-               "name": "stdout",
-               "output_type": "stream",
-               "text": [
-                  "Dear Sir/Madam,\n",
-                  "\n",
-                  "We regret to inform you that Monique Turner has recently misplaced his wallet, which contains a sum of cash and his credit card with the number 213152056829866. \n",
-                  "\n",
-                  "If you happen to come across this wallet, kindly contact us at (770)908-7734x2835 or send an email to barbara25@example.net.\n",
-                  "\n",
-                  "Thank you for your cooperation.\n",
-                  "\n",
-                  "Sincerely,\n",
-                  "[Your Name]\n"
-               ]
-            }
-         ],
-         "source": [
-            "from langchain.prompts.prompt import PromptTemplate\n",
-            "from langchain.chat_models import ChatOpenAI\n",
-            "\n",
-            "anonymizer = PresidioReversibleAnonymizer()\n",
-            "\n",
-            "template = \"\"\"Rewrite this text into an official, short email:\n",
-            "\n",
-            "{anonymized_text}\"\"\"\n",
-            "prompt = PromptTemplate.from_template(template)\n",
-            "llm = ChatOpenAI(temperature=0)\n",
-            "\n",
-            "chain = {\"anonymized_text\": anonymizer.anonymize} | prompt | llm\n",
-            "response = chain.invoke(text)\n",
-            "print(response.content)"
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "Now, let's add **deanonymization step** to our sequence:"
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": 7,
-         "metadata": {},
-         "outputs": [
-            {
-               "name": "stdout",
-               "output_type": "stream",
-               "text": [
-                  "Dear Sir/Madam,\n",
-                  "\n",
-                  "We regret to inform you that Slim Shady has recently misplaced his wallet, which contains a sum of cash and his credit card with the number 4916 0387 9536 0861. \n",
-                  "\n",
-                  "If you happen to come across this wallet, kindly contact us at 313-666-7440 or send an email to real.slim.shady@gmail.com.\n",
-                  "\n",
-                  "Thank you for your cooperation.\n",
-                  "\n",
-                  "Sincerely,\n",
-                  "[Your Name]\n"
-               ]
-            }
-         ],
-         "source": [
-            "chain = chain | (lambda ai_message: anonymizer.deanonymize(ai_message.content))\n",
-            "response = chain.invoke(text)\n",
-            "print(response)"
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "Anonymized data was given to the model itself, and therefore it was protected from being leaked to the outside world. Then, the model's response was processed, and the factual value was replaced with the real one."
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "## Extra knowledge"
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "`PresidioReversibleAnonymizer` stores the mapping of the fake values to the original values in the `deanonymizer_mapping` parameter, where key is fake PII and value is the original one: "
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": 1,
-         "metadata": {},
-         "outputs": [
-            {
-               "data": {
-                  "text/plain": [
-                     "{'PERSON': {'Maria Lynch': 'Slim Shady'},\n",
-                     " 'PHONE_NUMBER': {'7344131647': '313-666-7440'},\n",
-                     " 'EMAIL_ADDRESS': {'jamesmichael@example.com': 'real.slim.shady@gmail.com'},\n",
-                     " 'CREDIT_CARD': {'4838637940262': '4916 0387 9536 0861'}}"
-                  ]
-               },
-               "execution_count": 1,
-               "metadata": {},
-               "output_type": "execute_result"
-            }
-         ],
-         "source": [
-            "from langchain_experimental.data_anonymizer import PresidioReversibleAnonymizer\n",
-            "\n",
-            "anonymizer = PresidioReversibleAnonymizer(\n",
-            "    analyzed_fields=[\"PERSON\", \"PHONE_NUMBER\", \"EMAIL_ADDRESS\", \"CREDIT_CARD\"],\n",
-            "    # Faker seed is used here to make sure the same fake data is generated for the test purposes\n",
-            "    # In production, it is recommended to remove the faker_seed parameter (it will default to None)\n",
-            "    faker_seed=42,\n",
-            ")\n",
-            "\n",
-            "anonymizer.anonymize(\n",
-            "    \"My name is Slim Shady, call me at 313-666-7440 or email me at real.slim.shady@gmail.com. \"\n",
-            "    \"By the way, my card number is: 4916 0387 9536 0861\"\n",
-            ")\n",
-            "\n",
-            "anonymizer.deanonymizer_mapping"
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "Anonymizing more texts will result in new mapping entries:"
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": 2,
-         "metadata": {},
-         "outputs": [
-            {
-               "name": "stdout",
-               "output_type": "stream",
-               "text": [
-                  "Do you have his VISA card number? Yep, it's 3537672423884966. I'm William Bowman by the way.\n"
-               ]
-            },
-            {
-               "data": {
-                  "text/plain": [
-                     "{'PERSON': {'Maria Lynch': 'Slim Shady', 'William Bowman': 'John Doe'},\n",
-                     " 'PHONE_NUMBER': {'7344131647': '313-666-7440'},\n",
-                     " 'EMAIL_ADDRESS': {'jamesmichael@example.com': 'real.slim.shady@gmail.com'},\n",
-                     " 'CREDIT_CARD': {'4838637940262': '4916 0387 9536 0861',\n",
-                     "  '3537672423884966': '4001 9192 5753 7193'}}"
-                  ]
-               },
-               "execution_count": 2,
-               "metadata": {},
-               "output_type": "execute_result"
-            }
-         ],
-         "source": [
-            "print(\n",
-            "    anonymizer.anonymize(\n",
-            "        \"Do you have his VISA card number? Yep, it's 4001 9192 5753 7193. I'm John Doe by the way.\"\n",
-            "    )\n",
-            ")\n",
-            "\n",
-            "anonymizer.deanonymizer_mapping"
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "Thanks to the built-in memory, entities that have already been detected and anonymised will take the same form in subsequent processed texts, so no duplicates will exist in the mapping:"
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": 3,
-         "metadata": {},
-         "outputs": [
-            {
-               "name": "stdout",
-               "output_type": "stream",
-               "text": [
-                  "My VISA card number is 3537672423884966 and my name is William Bowman.\n"
-               ]
-            },
-            {
-               "data": {
-                  "text/plain": [
-                     "{'PERSON': {'Maria Lynch': 'Slim Shady', 'William Bowman': 'John Doe'},\n",
-                     " 'PHONE_NUMBER': {'7344131647': '313-666-7440'},\n",
-                     " 'EMAIL_ADDRESS': {'jamesmichael@example.com': 'real.slim.shady@gmail.com'},\n",
-                     " 'CREDIT_CARD': {'4838637940262': '4916 0387 9536 0861',\n",
-                     "  '3537672423884966': '4001 9192 5753 7193'}}"
-                  ]
-               },
-               "execution_count": 10,
-               "metadata": {},
-               "output_type": "execute_result"
-            }
-         ],
-         "source": [
-            "print(\n",
-            "    anonymizer.anonymize(\n",
-            "        \"My VISA card number is 4001 9192 5753 7193 and my name is John Doe.\"\n",
-            "    )\n",
-            ")\n",
-            "\n",
-            "anonymizer.deanonymizer_mapping"
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "We can save the mapping itself to a file for future use: "
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": 11,
-         "metadata": {},
-         "outputs": [],
-         "source": [
-            "# We can save the deanonymizer mapping as a JSON or YAML file\n",
-            "\n",
-            "anonymizer.save_deanonymizer_mapping(\"deanonymizer_mapping.json\")\n",
-            "# anonymizer.save_deanonymizer_mapping(\"deanonymizer_mapping.yaml\")"
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "And then, load it in another `PresidioReversibleAnonymizer` instance:"
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": 4,
-         "metadata": {},
-         "outputs": [
-            {
-               "data": {
-                  "text/plain": [
-                     "{}"
-                  ]
-               },
-               "execution_count": 4,
-               "metadata": {},
-               "output_type": "execute_result"
-            }
-         ],
-         "source": [
-            "anonymizer = PresidioReversibleAnonymizer()\n",
-            "\n",
-            "anonymizer.deanonymizer_mapping"
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": 5,
-         "metadata": {},
-         "outputs": [
-            {
-               "data": {
-                  "text/plain": [
-                     "{'PERSON': {'Maria Lynch': 'Slim Shady', 'William Bowman': 'John Doe'},\n",
-                     " 'PHONE_NUMBER': {'7344131647': '313-666-7440'},\n",
-                     " 'EMAIL_ADDRESS': {'jamesmichael@example.com': 'real.slim.shady@gmail.com'},\n",
-                     " 'CREDIT_CARD': {'4838637940262': '4916 0387 9536 0861',\n",
-                     "  '3537672423884966': '4001 9192 5753 7193'}}"
-                  ]
-               },
-               "execution_count": 5,
-               "metadata": {},
-               "output_type": "execute_result"
-            }
-         ],
-         "source": [
-            "anonymizer.load_deanonymizer_mapping(\"deanonymizer_mapping.json\")\n",
-            "\n",
-            "anonymizer.deanonymizer_mapping"
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "### Custom deanonymization strategy\n",
-            "\n",
-            "The default deanonymization strategy is to exactly match the substring in the text with the mapping entry. Due to the indeterminism of LLMs, it may be that the model will change the format of the private data slightly or make a typo, for example:\n",
-            "- *Keanu Reeves* -> *Kaenu Reeves*\n",
-            "- *John F. Kennedy* -> *John Kennedy*\n",
-            "- *Main St, New York* -> *New York*\n",
-            "\n",
-            "It is therefore worth considering appropriate prompt engineering (have the model return PII in unchanged format) or trying to implement your replacing strategy. For example, you can use fuzzy matching - this will solve problems with typos and minor changes in the text. Some implementations of the swapping strategy can be found in the file `deanonymizer_matching_strategies.py`."
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": 6,
-         "metadata": {},
-         "outputs": [
-            {
-               "name": "stdout",
-               "output_type": "stream",
-               "text": [
-                  "maria lynch\n",
-                  "Slim Shady\n"
-               ]
-            }
-         ],
-         "source": [
-            "from langchain_experimental.data_anonymizer.deanonymizer_matching_strategies import (\n",
-            "    case_insensitive_matching_strategy,\n",
-            ")\n",
-            "\n",
-            "# Original name: Maria Lynch\n",
-            "print(anonymizer.deanonymize(\"maria lynch\"))\n",
-            "print(\n",
-            "    anonymizer.deanonymize(\n",
-            "        \"maria lynch\", deanonymizer_matching_strategy=case_insensitive_matching_strategy\n",
-            "    )\n",
-            ")"
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": 7,
-         "metadata": {},
-         "outputs": [
-            {
-               "name": "stdout",
-               "output_type": "stream",
-               "text": [
-                  "Call Maria K. Lynch at 734-413-1647\n",
-                  "Call Slim Shady at 313-666-7440\n"
-               ]
-            }
-         ],
-         "source": [
-            "from langchain_experimental.data_anonymizer.deanonymizer_matching_strategies import (\n",
-            "    fuzzy_matching_strategy,\n",
-            ")\n",
-            "\n",
-            "# Original name: Maria Lynch\n",
-            "# Original phone number: 7344131647 (without dashes)\n",
-            "print(anonymizer.deanonymize(\"Call Maria K. Lynch at 734-413-1647\"))\n",
-            "print(\n",
-            "    anonymizer.deanonymize(\n",
-            "        \"Call Maria K. Lynch at 734-413-1647\",\n",
-            "        deanonymizer_matching_strategy=fuzzy_matching_strategy,\n",
-            "    )\n",
-            ")"
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "It seems that the combined method works best:\n",
-            "- first apply the exact match strategy\n",
-            "- then match the rest using the fuzzy strategy"
-         ]
-      },
-      {
-         "cell_type": "code",
-         "execution_count": 20,
-         "metadata": {},
-         "outputs": [
-            {
-               "name": "stdout",
-               "output_type": "stream",
-               "text": [
-                  "Are you Slim Shady? I found your card with number 4916 0387 9536 0861.\n",
-                  "Is this your phone number: 313-666-7440?\n",
-                  "Is this your email address: wdavis@example.net\n"
-               ]
-            }
-         ],
-         "source": [
-            "from langchain_experimental.data_anonymizer.deanonymizer_matching_strategies import (\n",
-            "    combined_exact_fuzzy_matching_strategy,\n",
-            ")\n",
-            "\n",
-            "# Changed some values for fuzzy match showcase:\n",
-            "# - \"Maria Lynch\" -> \"Maria K. Lynch\"\n",
-            "# - \"7344131647\" -> \"734-413-1647\"\n",
-            "# - \"213186379402654\" -> \"2131 8637 9402 654\"\n",
-            "print(\n",
-            "    anonymizer.deanonymize(\n",
-            "        (\n",
-            "            \"Are you Maria F. Lynch? I found your card with number 4838 6379 40262.\\n\"\n",
-            "            \"Is this your phone number: 734-413-1647?\\n\"\n",
-            "            \"Is this your email address: wdavis@example.net\"\n",
-            "        ),\n",
-            "        deanonymizer_matching_strategy=combined_exact_fuzzy_matching_strategy,\n",
-            "    )\n",
-            ")"
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "Of course, there is no perfect method and it is worth experimenting and finding the one best suited to your use case."
-         ]
-      },
-      {
-         "cell_type": "markdown",
-         "metadata": {},
-         "source": [
-            "## Future works\n",
-            "\n",
-            "- **better matching and substitution of fake values for real ones** - currently the strategy is based on matching full strings and then substituting them. Due to the indeterminism of language models, it may happen that the value in the answer is slightly changed (e.g. *John Doe* -> *John* or *Main St, New York* -> *New York*) and such a substitution is then no longer possible. Therefore, it is worth adjusting the matching for your needs."
-         ]
-      }
-   ],
-   "metadata": {
-      "kernelspec": {
-         "display_name": "Python 3 (ipykernel)",
-         "language": "python",
-         "name": "python3"
-      },
-      "language_info": {
-         "codemirror_mode": {
-            "name": "ipython",
-            "version": 3
-         },
-         "file_extension": ".py",
-         "mimetype": "text/x-python",
-         "name": "python",
-         "nbconvert_exporter": "python",
-         "pygments_lexer": "ipython3",
-         "version": "3.11.4"
-      }
-   },
-   "nbformat": 4,
-   "nbformat_minor": 4
-}

docs/docs/integrations/chat/baichuan.ipynb

@@ -1,152 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Baichuan Chat\n",
-    "\n",
-    "Baichuan chat models API by Baichuan Intelligent Technology. For more information, see [https://platform.baichuan-ai.com/docs/api](https://platform.baichuan-ai.com/docs/api)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-10-17T15:14:24.186131Z",
-     "start_time": "2023-10-17T15:14:23.831767Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import ChatBaichuan\n",
-    "from langchain.schema import HumanMessage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-10-17T15:14:24.191123Z",
-     "start_time": "2023-10-17T15:14:24.186330Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "chat = ChatBaichuan(\n",
-    "    baichuan_api_key=\"YOUR_API_KEY\", baichuan_secret_key=\"YOUR_SECRET_KEY\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "or you can set `api_key` and `secret_key` in your environment variables\n",
-    "```bash\n",
-    "export BAICHUAN_API_KEY=YOUR_API_KEY\n",
-    "export BAICHUAN_SECRET_KEY=YOUR_SECRET_KEY\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-10-17T15:14:25.853218Z",
-     "start_time": "2023-10-17T15:14:24.192408Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "AIMessage(content='首先，我们需要确定闰年的二月有多少天。闰年的二月有29天。\\n\\n然后，我们可以计算你的月薪：\\n\\n日薪 = 月薪 / (当月天数)\\n\\n所以，你的月薪 = 日薪 * 当月天数\\n\\n将数值代入公式：\\n\\n月薪 = 8元/天 * 29天 = 232元\\n\\n因此，你在闰年的二月的月薪是232元。')"
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chat([HumanMessage(content=\"我日薪8块钱，请问在闰年的二月，我月薪多少\")])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## For ChatBaichuan with Streaming"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "outputs": [],
-   "source": [
-    "chat = ChatBaichuan(\n",
-    "    baichuan_api_key=\"YOUR_API_KEY\",\n",
-    "    baichuan_secret_key=\"YOUR_SECRET_KEY\",\n",
-    "    streaming=True,\n",
-    ")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-10-17T15:14:25.870044Z",
-     "start_time": "2023-10-17T15:14:25.863381Z"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "AIMessageChunk(content='首先，我们需要确定闰年的二月有多少天。闰年的二月有29天。\\n\\n然后，我们可以计算你的月薪：\\n\\n日薪 = 月薪 / (当月天数)\\n\\n所以，你的月薪 = 日薪 * 当月天数\\n\\n将数值代入公式：\\n\\n月薪 = 8元/天 * 29天 = 232元\\n\\n因此，你在闰年的二月的月薪是232元。')"
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chat([HumanMessage(content=\"我日薪8块钱，请问在闰年的二月，我月薪多少\")])"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-10-17T15:14:27.153546Z",
-     "start_time": "2023-10-17T15:14:25.868470Z"
-    }
-   }
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/integrations/chat/bedrock.ipynb

@@ -1,139 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "bf733a38-db84-4363-89e2-de6735c37230",
-   "metadata": {},
-   "source": [
-    "# Bedrock Chat\n",
-    "\n",
-    "[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d51edc81",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install boto3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import BedrockChat\n",
-    "from langchain.schema import HumanMessage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "chat = BedrockChat(model_id=\"anthropic.claude-v2\", model_kwargs={\"temperature\": 0.1})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\" Voici la traduction en français : J'adore programmer.\", additional_kwargs={}, example=False)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    HumanMessage(\n",
-    "        content=\"Translate this sentence from English to French. I love programming.\"\n",
-    "    )\n",
-    "]\n",
-    "chat(messages)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "a4a4f4d4",
-   "metadata": {},
-   "source": [
-    "### For BedrockChat with Streaming"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c253883f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
-    "\n",
-    "chat = BedrockChat(\n",
-    "    model_id=\"anthropic.claude-v2\",\n",
-    "    streaming=True,\n",
-    "    callbacks=[StreamingStdOutCallbackHandler()],\n",
-    "    model_kwargs={\"temperature\": 0.1},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d9e52838",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "messages = [\n",
-    "    HumanMessage(\n",
-    "        content=\"Translate this sentence from English to French. I love programming.\"\n",
-    "    )\n",
-    "]\n",
-    "chat(messages)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/chat/everlyai.ipynb

@@ -1,218 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "642fd21c-600a-47a1-be96-6e1438b421a9",
-   "metadata": {},
-   "source": [
-    "# EverlyAI\n",
-    "\n",
-    ">[EverlyAI](https://everlyai.xyz) allows you to run your ML models at scale in the cloud. It also provides API access to [several LLM models](https://everlyai.xyz).\n",
-    "\n",
-    "This notebook demonstrates the use of `langchain.chat_models.ChatEverlyAI` for [EverlyAI Hosted Endpoints](https://everlyai.xyz/).\n",
-    "\n",
-    "* Set `EVERLYAI_API_KEY` environment variable\n",
-    "* or use the `everlyai_api_key` keyword argument"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d00d850917865298",
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# !pip install openai"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "72340871-ae2f-415f-b399-0777d32dc379",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "os.environ[\"EVERLYAI_API_KEY\"] = getpass()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5d7fc704-3ea0-4c35-96e7-89fcae6c73fa",
-   "metadata": {},
-   "source": [
-    "# Let's try out LLAMA model offered on EverlyAI Hosted Endpoints"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "0dc9428d-4217-47d2-97de-f784b1764186",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  Hello! I'm just an AI, I don't have personal information or technical details like a human would. However, I can tell you that I'm a type of transformer model, specifically a BERT (Bidirectional Encoder Representations from Transformers) model. B\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.chat_models import ChatEverlyAI\n",
-    "from langchain.schema import SystemMessage, HumanMessage\n",
-    "\n",
-    "messages = [\n",
-    "    SystemMessage(content=\"You are a helpful AI that shares everything you know.\"),\n",
-    "    HumanMessage(\n",
-    "        content=\"Tell me technical facts about yourself. Are you a transformer model? How many billions of parameters do you have?\"\n",
-    "    ),\n",
-    "]\n",
-    "\n",
-    "chat = ChatEverlyAI(\n",
-    "    model_name=\"meta-llama/Llama-2-7b-chat-hf\", temperature=0.3, max_tokens=64\n",
-    ")\n",
-    "print(chat(messages).content)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7c4f124a-eaf7-4d78-a2c0-b0aa23fb25c4",
-   "metadata": {},
-   "source": [
-    "# EverlyAI also supports streaming responses"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "1f94f5d2-569e-4a2c-965e-de53c2845fbb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  Ah, a joke, you say? *adjusts glasses* Well, I've got a doozy for you! *winks*\n",
-      " *pauses for dramatic effect*\n",
-      "Why did the AI go to therapy?\n",
-      "*drumroll*\n",
-      "Because"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "AIMessageChunk(content=\"  Ah, a joke, you say? *adjusts glasses* Well, I've got a doozy for you! *winks*\\n *pauses for dramatic effect*\\nWhy did the AI go to therapy?\\n*drumroll*\\nBecause\")"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.chat_models import ChatEverlyAI\n",
-    "from langchain.schema import SystemMessage, HumanMessage\n",
-    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
-    "\n",
-    "messages = [\n",
-    "    SystemMessage(content=\"You are a humorous AI that delights people.\"),\n",
-    "    HumanMessage(content=\"Tell me a joke?\"),\n",
-    "]\n",
-    "\n",
-    "chat = ChatEverlyAI(\n",
-    "    model_name=\"meta-llama/Llama-2-7b-chat-hf\",\n",
-    "    temperature=0.3,\n",
-    "    max_tokens=64,\n",
-    "    streaming=True,\n",
-    "    callbacks=[StreamingStdOutCallbackHandler()],\n",
-    ")\n",
-    "chat(messages)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7de56d98",
-   "metadata": {},
-   "source": [
-    "# Let's try a different language model on EverlyAI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "d8a44114",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  OH HO HO! *adjusts monocle* Well, well, well! Look who's here! *winks*\n",
-      "\n",
-      "You want a joke, huh? *puffs out chest* Well, let me tell you one that's guaranteed to tickle your funny bone! *clears throat*\n",
-      "\n",
-      "Why couldn't the bicycle stand up by itself? *pauses for dramatic effect* Because it was two-tired! *winks*\n",
-      "\n",
-      "Hope that one put a spring in your step, my dear! *"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "AIMessageChunk(content=\"  OH HO HO! *adjusts monocle* Well, well, well! Look who's here! *winks*\\n\\nYou want a joke, huh? *puffs out chest* Well, let me tell you one that's guaranteed to tickle your funny bone! *clears throat*\\n\\nWhy couldn't the bicycle stand up by itself? *pauses for dramatic effect* Because it was two-tired! *winks*\\n\\nHope that one put a spring in your step, my dear! *\")"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.chat_models import ChatEverlyAI\n",
-    "from langchain.schema import SystemMessage, HumanMessage\n",
-    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
-    "\n",
-    "messages = [\n",
-    "    SystemMessage(content=\"You are a humorous AI that delights people.\"),\n",
-    "    HumanMessage(content=\"Tell me a joke?\"),\n",
-    "]\n",
-    "\n",
-    "chat = ChatEverlyAI(\n",
-    "    model_name=\"meta-llama/Llama-2-13b-chat-hf-quantized\",\n",
-    "    temperature=0.3,\n",
-    "    max_tokens=128,\n",
-    "    streaming=True,\n",
-    "    callbacks=[StreamingStdOutCallbackHandler()],\n",
-    ")\n",
-    "chat(messages)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/chat/gigachat.ipynb

@@ -1,112 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# GigaChat\n",
-    "This notebook shows how to use LangChain with [GigaChat](https://developers.sber.ru/portal/products/gigachat).\n",
-    "To use you need to install ```gigachat``` python package."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# !pip install gigachat"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "To get GigaChat credentials you need to [create account](https://developers.sber.ru/studio/login) and [get access to API](https://developers.sber.ru/docs/ru/gigachat/api/integration)\n",
-    "## Example"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "os.environ[\"GIGACHAT_CREDENTIALS\"] = getpass()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import GigaChat\n",
-    "\n",
-    "chat = GigaChat(verify_ssl_certs=False)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "What do you get when you cross a goat and a skunk? A smelly goat!\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.schema import SystemMessage, HumanMessage\n",
-    "\n",
-    "messages = [\n",
-    "    SystemMessage(\n",
-    "        content=\"You are a helpful AI that shares everything you know. Talk in English.\"\n",
-    "    ),\n",
-    "    HumanMessage(content=\"Tell me a joke\"),\n",
-    "]\n",
-    "\n",
-    "print(chat(messages).content)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}

docs/docs/integrations/chat/hunyuan.ipynb

@@ -1,168 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tencent Hunyuan\n",
-    "\n",
-    "Hunyuan chat model API by Tencent. For more information, see [https://cloud.tencent.com/document/product/1729](https://cloud.tencent.com/document/product/1729)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-10-19T10:20:38.718834Z",
-     "start_time": "2023-10-19T10:20:38.264050Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import ChatHunyuan\n",
-    "from langchain.schema import HumanMessage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-10-19T10:19:53.529876Z",
-     "start_time": "2023-10-19T10:19:53.526210Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "chat = ChatHunyuan(\n",
-    "    hunyuan_app_id=\"YOUR_APP_ID\",\n",
-    "    hunyuan_secret_id=\"YOUR_SECRET_ID\",\n",
-    "    hunyuan_secret_key=\"YOUR_SECRET_KEY\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-10-19T10:19:56.054289Z",
-     "start_time": "2023-10-19T10:19:53.531078Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "AIMessage(content=\"J'aime programmer.\")"
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chat(\n",
-    "    [\n",
-    "        HumanMessage(\n",
-    "            content=\"You are a helpful assistant that translates English to French.Translate this sentence from English to French. I love programming.\"\n",
-    "        )\n",
-    "    ]\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## For ChatHunyuan with Streaming"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "outputs": [],
-   "source": [
-    "chat = ChatHunyuan(\n",
-    "    hunyuan_app_id=\"YOUR_APP_ID\",\n",
-    "    hunyuan_secret_id=\"YOUR_SECRET_ID\",\n",
-    "    hunyuan_secret_key=\"YOUR_SECRET_KEY\",\n",
-    "    streaming=True,\n",
-    ")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-10-19T10:20:41.507720Z",
-     "start_time": "2023-10-19T10:20:41.496456Z"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "AIMessageChunk(content=\"J'aime programmer.\")"
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chat(\n",
-    "    [\n",
-    "        HumanMessage(\n",
-    "            content=\"You are a helpful assistant that translates English to French.Translate this sentence from English to French. I love programming.\"\n",
-    "        )\n",
-    "    ]\n",
-    ")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-10-19T10:20:46.275673Z",
-     "start_time": "2023-10-19T10:20:44.241097Z"
-    }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "start_time": "2023-10-19T10:19:56.233477Z"
-    }
-   }
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.4"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/integrations/chat/pai_eas_chat_endpoint.ipynb

@@ -1,121 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# AliCloud PAI EAS\n",
-    "Machine Learning Platform for AI of Alibaba Cloud is a machine learning or deep learning engineering platform intended for enterprises and developers. It provides easy-to-use, cost-effective, high-performance, and easy-to-scale plug-ins that can be applied to various industry scenarios. With over 140 built-in optimization algorithms, Machine Learning Platform for AI provides whole-process AI engineering capabilities including data labeling (PAI-iTAG), model building (PAI-Designer and PAI-DSW), model training (PAI-DLC), compilation optimization, and inference deployment (PAI-EAS). PAI-EAS supports different types of hardware resources, including CPUs and GPUs, and features high throughput and low latency. It allows you to deploy large-scale complex models with a few clicks and perform elastic scale-ins and scale-outs in real time. It also provides a comprehensive O&M and monitoring system."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup Eas Service\n",
-    "\n",
-    "One who want to use eas llms must set up eas service first. When the eas service is launched, eas_service_rul and eas_service token can be got. Users can refer to https://www.alibabacloud.com/help/en/pai/user-guide/service-deployment/ for more information. Try to set environment variables to init eas service url and token:\n",
-    "\n",
-    "```base\n",
-    "export EAS_SERVICE_URL=XXX\n",
-    "export EAS_SERVICE_TOKEN=XXX\n",
-    "```\n",
-    "or run as follow codes:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from langchain.chat_models.base import HumanMessage\n",
-    "from langchain.chat_models import PaiEasChatEndpoint\n",
-    "\n",
-    "os.environ[\"EAS_SERVICE_URL\"] = \"Your_EAS_Service_URL\"\n",
-    "os.environ[\"EAS_SERVICE_TOKEN\"] = \"Your_EAS_Service_Token\"\n",
-    "chat = PaiEasChatEndpoint(\n",
-    "    eas_service_url=os.environ[\"EAS_SERVICE_URL\"],\n",
-    "    eas_service_token=os.environ[\"EAS_SERVICE_TOKEN\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Run Chat Model\n",
-    "You can use the default settings to call eas service as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output = chat([HumanMessage(content=\"write a funny joke\")])\n",
-    "print(\"output:\", output)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or, call eas service with new inference params:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kwargs = {\"temperature\": 0.8, \"top_p\": 0.8, \"top_k\": 5}\n",
-    "output = chat([HumanMessage(content=\"write a funny joke\")], **kwargs)\n",
-    "print(\"output:\", output)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or, run a stream call to get a stream response:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "outputs = chat.stream([HumanMessage(content=\"hi\")], streaming=True)\n",
-    "for output in outputs:\n",
-    "    print(\"stream output:\", output)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/integrations/chat/tongyi.ipynb

@@ -1,164 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "source": [
-    "# Tongyi Qwen\n",
-    "Tongyi Qwen is a large language model developed by Alibaba's Damo Academy. It is capable of understanding user intent through natural language understanding and semantic analysis, based on user input in natural language. It provides services and assistance to users in different domains and tasks. By providing clear and detailed instructions, you can obtain results that better align with your expectations.\n",
-    "In this notebook, we will introduce how to use langchain with [Tongyi](https://www.aliyun.com/product/dashscope) mainly in `Chat` corresponding\n",
-    " to the package `langchain/chat_models` in langchain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Install the package\n",
-    "!pip install dashscope"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      " ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Get a new token: https://help.aliyun.com/document_detail/611472.html?spm=a2c4g.2399481.0.0\n",
-    "from getpass import getpass\n",
-    "\n",
-    "DASHSCOPE_API_KEY = getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"DASHSCOPE_API_KEY\"] = DASHSCOPE_API_KEY"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "chat resp: content='Hello! How' additional_kwargs={} example=False\n",
-      "chat resp: content=' can I assist you today?' additional_kwargs={} example=False\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.chat_models.tongyi import ChatTongyi\n",
-    "from langchain.schema import HumanMessage\n",
-    "\n",
-    "chatLLM = ChatTongyi(\n",
-    "    streaming=True,\n",
-    ")\n",
-    "res = chatLLM.stream([HumanMessage(content=\"hi\")], streaming=True)\n",
-    "for r in res:\n",
-    "    print(\"chat resp:\", r)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessageChunk(content=\"J'aime programmer.\", additional_kwargs={}, example=False)"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.schema import AIMessage, HumanMessage, SystemMessage\n",
-    "\n",
-    "messages = [\n",
-    "    SystemMessage(\n",
-    "        content=\"You are a helpful assistant that translates English to French.\"\n",
-    "    ),\n",
-    "    HumanMessage(\n",
-    "        content=\"Translate this sentence from English to French. I love programming.\"\n",
-    "    ),\n",
-    "]\n",
-    "chatLLM(messages)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/docs/integrations/chat/yandex.ipynb

@@ -1,111 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "af63c9db-e4bd-4d3b-a4d7-7927f5541734",
-   "metadata": {},
-   "source": [
-    "# YandexGPT\n",
-    "\n",
-    "This notebook goes over how to use Langchain with [YandexGPT](https://cloud.yandex.com/en/services/yandexgpt) chat model.\n",
-    "\n",
-    "To use, you should have the `yandexcloud` python package installed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f3a8f9cb-ff03-4fb8-8185-ff19f2b8fc89",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install yandexcloud"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "95fa21fb-3669-43fb-bb92-91de7bc591bc",
-   "metadata": {},
-   "source": [
-    "First, you should [create service account](https://cloud.yandex.com/en/docs/iam/operations/sa/create) with the `ai.languageModels.user` role.\n",
-    "\n",
-    "Next, you have two authentication options:\n",
-    "- [IAM token](https://cloud.yandex.com/en/docs/iam/operations/iam-token/create-for-sa).\n",
-    "    You can specify the token in a constructor parameter `iam_token` or in an environment variable `YC_IAM_TOKEN`.\n",
-    "- [API key](https://cloud.yandex.com/en/docs/iam/operations/api-key/create)\n",
-    "    You can specify the key in a constructor parameter `api_key` or in an environment variable `YC_API_KEY`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "eba2d63b-f871-4f61-b55f-f6092bdc297a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import ChatYandexGPT\n",
-    "from langchain.schema import HumanMessage, SystemMessage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "75905d9a-dfae-43aa-95b9-a160280e43f7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chat_model = ChatYandexGPT()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "40844fe7-7fe5-4679-b6c9-1b3238807bdc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "AIMessage(content=\"Je t'aime programmer.\")"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "answer = chat_model(\n",
-    "    [\n",
-    "        SystemMessage(\n",
-    "            content=\"You are a helpful assistant that translates English to French.\"\n",
-    "        ),\n",
-    "        HumanMessage(content=\"I love programming.\"),\n",
-    "    ]\n",
-    ")\n",
-    "answer"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/document_loaders/aws_s3_directory.ipynb

@@ -1,161 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "a634365e",
-   "metadata": {},
-   "source": [
-    "# AWS S3 Directory\n",
-    "\n",
-    ">[Amazon Simple Storage Service (Amazon S3)](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html) is an object storage service\n",
-    "\n",
-    ">[AWS S3 Directory](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html)\n",
-    "\n",
-    "This covers how to load document objects from an `AWS S3 Directory` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "49815096",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "#!pip install boto3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "2f0cd6a5",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import S3DirectoryLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "321cc7f1",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "loader = S3DirectoryLoader(\"testing-hwc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2b11d155",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "loader.load()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0690c40a",
-   "metadata": {},
-   "source": [
-    "## Specifying a prefix\n",
-    "You can also specify a prefix for more finegrained control over what files to load."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "72d44781",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader = S3DirectoryLoader(\"testing-hwc\", prefix=\"fake\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "2d3c32db",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': 's3://testing-hwc/fake.docx'}, lookup_index=0)]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "loader.load()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Configuring the AWS Boto3 client\n",
-    "You can configure the AWS [Boto3](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html) client by passing\n",
-    "named arguments when creating the S3DirectoryLoader.\n",
-    "This is useful for instance when AWS credentials can't be set as environment variables.\n",
-    "See the [list of parameters](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html#boto3.session.Session) that can be configured."
-   ],
-   "metadata": {},
-   "id": "91a7ac07"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "loader = S3DirectoryLoader(\n",
-    "    \"testing-hwc\", aws_access_key_id=\"xxxx\", aws_secret_access_key=\"yyyy\"\n",
-    ")"
-   ],
-   "metadata": {},
-   "id": "f485ec8c"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "loader.load()"
-   ],
-   "metadata": {},
-   "id": "c0fa76ae"
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/document_loaders/aws_s3_file.ipynb

@@ -1,124 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "66a7777e",
-   "metadata": {},
-   "source": [
-    "# AWS S3 File\n",
-    "\n",
-    ">[Amazon Simple Storage Service (Amazon S3)](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html) is an object storage service.\n",
-    "\n",
-    ">[AWS S3 Buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingBucket.html)\n",
-    "\n",
-    "This covers how to load document objects from an `AWS S3 File` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "9ec8a3b3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import S3FileLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "43128d8d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#!pip install boto3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "35d6809a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader = S3FileLoader(\"testing-hwc\", \"fake.docx\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "efd6be84",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': 's3://testing-hwc/fake.docx'}, lookup_index=0)]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "loader.load()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "93689594",
-   "metadata": {},
-   "source": [
-    "## Configuring the AWS Boto3 client\n",
-    "You can configure the AWS [Boto3](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html) client by passing\n",
-    "named arguments when creating the S3DirectoryLoader.\n",
-    "This is useful for instance when AWS credentials can't be set as environment variables.\n",
-    "See the [list of parameters](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html#boto3.session.Session) that can be configured."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "loader = S3FileLoader(\n",
-    "    \"testing-hwc\", \"fake.docx\", aws_access_key_id=\"xxxx\", aws_secret_access_key=\"yyyy\"\n",
-    ")"
-   ],
-   "metadata": {},
-   "id": "43106ee8"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "loader.load()"
-   ],
-   "metadata": {},
-   "id": "1764a727"
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/document_loaders/google_speech_to_text.ipynb

@@ -1,206 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Google Speech-to-Text Audio Transcripts\n",
-    "\n",
-    "The `GoogleSpeechToTextLoader` allows to transcribe audio files with the [Google Cloud Speech-to-Text API](https://cloud.google.com/speech-to-text) and loads the transcribed text into documents.\n",
-    "\n",
-    "To use it, you should have the `google-cloud-speech` python package installed, and a Google Cloud project with the [Speech-to-Text API enabled](https://cloud.google.com/speech-to-text/v2/docs/transcribe-client-libraries#before_you_begin).\n",
-    "\n",
-    "- [Bringing the power of large models to Google Cloud’s Speech API](https://cloud.google.com/blog/products/ai-machine-learning/bringing-power-large-models-google-clouds-speech-api)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Installation & setup\n",
-    "\n",
-    "First, you need to install the `google-cloud-speech` python package.\n",
-    "\n",
-    "You can find more info about it on the [Speech-to-Text client libraries](https://cloud.google.com/speech-to-text/v2/docs/libraries) page.\n",
-    "\n",
-    "Follow the [quickstart guide](https://cloud.google.com/speech-to-text/v2/docs/sync-recognize) in the Google Cloud documentation to create a project and enable the API."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install google-cloud-speech"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example\n",
-    "\n",
-    "The `GoogleSpeechToTextLoader` must include the `project_id` and `file_path` arguments. Audio files can be specified as a Google Cloud Storage URI (`gs://...`) or a local file path.\n",
-    "\n",
-    "Only synchronous requests are supported by the loader, which has a [limit of 60 seconds or 10MB](https://cloud.google.com/speech-to-text/v2/docs/sync-recognize#:~:text=60%20seconds%20and/or%2010%20MB) per audio file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import GoogleSpeechToTextLoader\n",
-    "\n",
-    "project_id = \"<PROJECT_ID>\"\n",
-    "file_path = \"gs://cloud-samples-data/speech/audio.flac\"\n",
-    "# or a local file path: file_path = \"./audio.wav\"\n",
-    "\n",
-    "loader = GoogleSpeechToTextLoader(project_id=project_id, file_path=file_path)\n",
-    "\n",
-    "docs = loader.load()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note: Calling `loader.load()` blocks until the transcription is finished."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The transcribed text is available in the `page_content`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "docs[0].page_content"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```\n",
-    "\"How old is the Brooklyn Bridge?\"\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `metadata` contains the full JSON response with more meta information:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "docs[0].metadata"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```json\n",
-    "{\n",
-    "  'language_code': 'en-US',\n",
-    "  'result_end_offset': datetime.timedelta(seconds=1)\n",
-    "}\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Recognition Config\n",
-    "\n",
-    "You can specify the `config` argument to use different speech recognition models and enable specific features.\n",
-    "\n",
-    "Refer to the [Speech-to-Text recognizers documentation](https://cloud.google.com/speech-to-text/v2/docs/recognizers) and the [`RecognizeRequest`](https://cloud.google.com/python/docs/reference/speech/latest/google.cloud.speech_v2.types.RecognizeRequest) API reference for information on how to set a custom configuation.\n",
-    "\n",
-    "If you don't specify a `config`, the following options will be selected automatically:\n",
-    "\n",
-    "- Model: [Chirp Universal Speech Model](https://cloud.google.com/speech-to-text/v2/docs/chirp-model)\n",
-    "- Language: `en-US`\n",
-    "- Audio Encoding: Automatically Detected\n",
-    "- Automatic Punctuation: Enabled"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from google.cloud.speech_v2 import (\n",
-    "    AutoDetectDecodingConfig,\n",
-    "    RecognitionConfig,\n",
-    "    RecognitionFeatures,\n",
-    ")\n",
-    "from langchain.document_loaders import GoogleSpeechToTextLoader\n",
-    "\n",
-    "project_id = \"<PROJECT_ID>\"\n",
-    "location = \"global\"\n",
-    "recognizer_id = \"<RECOGNIZER_ID>\"\n",
-    "file_path = \"./audio.wav\"\n",
-    "\n",
-    "config = RecognitionConfig(\n",
-    "    auto_decoding_config=AutoDetectDecodingConfig(),\n",
-    "    language_codes=[\"en-US\"],\n",
-    "    model=\"long\",\n",
-    "    features=RecognitionFeatures(\n",
-    "        enable_automatic_punctuation=False,\n",
-    "        profanity_filter=True,\n",
-    "        enable_spoken_punctuation=True,\n",
-    "        enable_spoken_emojis=True,\n",
-    "    ),\n",
-    ")\n",
-    "\n",
-    "loader = GoogleSpeechToTextLoader(\n",
-    "    project_id=project_id,\n",
-    "    location=location,\n",
-    "    recognizer_id=recognizer_id,\n",
-    "    file_path=file_path,\n",
-    "    config=config,\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.0"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/integrations/document_loaders/lakefs.ipynb

@@ -1,103 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# lakeFS\n",
-    "\n",
-    ">[lakeFS](https://docs.lakefs.io/) provides scalable version control over the data lake, and uses Git-like semantics to create and access those versions.\n",
-    "\n",
-    "This notebooks covers how to load document objects from a `lakeFS` path (whether it's an object or a prefix).\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Initializing the lakeFS loader\n",
-    "\n",
-    "Replace `ENDPOINT`, `LAKEFS_ACCESS_KEY`, and `LAKEFS_SECRET_KEY` values with your own."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders import LakeFSLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ENDPOINT = \"\"\n",
-    "LAKEFS_ACCESS_KEY = \"\"\n",
-    "LAKEFS_SECRET_KEY = \"\"\n",
-    "\n",
-    "lakefs_loader = LakeFSLoader(\n",
-    "    lakefs_access_key=LAKEFS_ACCESS_KEY,\n",
-    "    lakefs_secret_key=LAKEFS_SECRET_KEY,\n",
-    "    lakefs_endpoint=ENDPOINT,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Specifying a path\n",
-    "You can specify a prefix or a complete object path to control which files to load.\n",
-    "\n",
-    "Specify the repository, reference (branch, commit id, or tag), and path in the corresponding `REPO`, `REF`, and `PATH` to load the documents from:"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "REPO = \"\"\n",
-    "REF = \"\"\n",
-    "PATH = \"\"\n",
-    "\n",
-    "lakefs_loader.set_repo(REPO)\n",
-    "lakefs_loader.set_ref(REF)\n",
-    "lakefs_loader.set_path(PATH)\n",
-    "\n",
-    "docs = lakefs_loader.load()\n",
-    "docs"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/integrations/document_loaders/rspace.ipynb

@@ -1,129 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "2f1572a5-9f8c-44f1-82f3-ddeee8f55145",
-   "metadata": {},
-   "source": [
-    "This notebook shows how to use the RSpace document loader to import research notes and documents from RSpace Electronic\n",
-    "Lab Notebook into Langchain pipelines.\n",
-    "\n",
-    "To start you'll need an RSpace account and an API key.\n",
-    "\n",
-    "You can set up a free account at [https://community.researchspace.com](https://community.researchspace.com) or use your institutional RSpace.\n",
-    "\n",
-    "You can get an RSpace API token from your account's profile page. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9e5310d2-a864-4464-bdca-81f30c9d0bdb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install rspace_client"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "61b1d1b7-a28c-4fba-83a3-df64baa8b6b8",
-   "metadata": {},
-   "source": [
-    "It's best to store your RSpace API key as an environment variable. \n",
-    "\n",
-    "    RSPACE_API_KEY=<YOUR_KEY>\n",
-    "\n",
-    "You'll also need to set the URL of your RSpace installation e.g.\n",
-    "\n",
-    "    RSPACE_URL=https://community.researchspace.com\n",
-    "\n",
-    "If you use these exact environment variable names, they will be detected automatically. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "13c19ea4-100f-417e-b52f-7e8730c7c1d1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders.rspace import RSpaceLoader"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4fd42831-0e79-4068-a5e1-7e2cfc242789",
-   "metadata": {},
-   "source": [
-    "You can import various items from RSpace:\n",
-    "\n",
-    "* A single RSpace structured or basic document. This will map 1-1 to a Langchain document.\n",
-    "* A folder or noteook. All documents inside the notebook or folder are imported as Langchain documents. \n",
-    "* If you have PDF files in the RSpace Gallery, these can be imported individually as well. Under the hood, Langchain's PDF loader will be used and this creates one Langchain document per PDF page. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8e614357-5eca-401b-ab98-ea55b0465009",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## replace these ids with some from your own research notes.\n",
-    "## Make sure to use  global ids (with the 2 character prefix). This helps the loader know which API calls to make\n",
-    "## to RSpace API.\n",
-    "\n",
-    "rspace_ids = [\"NB1932027\", \"FL1921314\", \"SD1932029\", \"GL1932384\"]\n",
-    "for rs_id in rspace_ids:\n",
-    "    loader = RSpaceLoader(global_id=rs_id)\n",
-    "    docs = loader.load()\n",
-    "    for doc in docs:\n",
-    "        ## the name and ID are added to the 'source' metadata property.\n",
-    "        print(doc.metadata)\n",
-    "        print(doc.page_content[:500])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1b41758d-24e0-4994-a30f-3acccc7795e4",
-   "metadata": {},
-   "source": [
-    "If you don't want to use the environment variables as above, you can pass these into the RSpaceLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aa079ca6-439d-4010-9edd-cd77d8884fab",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader = RSpaceLoader(\n",
-    "    global_id=rs_id, api_key=\"MY_API_KEY\", url=\"https://my.researchspace.com\"\n",
-    ")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/document_loaders/sitemap.ipynb

@@ -1,282 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Sitemap\n",
-    "\n",
-    "Extends from the `WebBaseLoader`, `SitemapLoader` loads a sitemap from a given URL, and then scrape and load all pages in the sitemap, returning each page as a Document.\n",
-    "\n",
-    "The scraping is done concurrently.  There are reasonable limits to concurrent requests, defaulting to 2 per second.  If you aren't concerned about being a good citizen, or you control the scrapped server, or don't care about load. Note, while this will speed up the scraping process, but it may cause the server to block you.  Be careful!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: nest_asyncio in /Users/tasp/Code/projects/langchain/.venv/lib/python3.10/site-packages (1.5.6)\n",
-      "\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.0.1\u001b[0m\n",
-      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install nest_asyncio"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# fixes a bug with asyncio and jupyter\n",
-    "import nest_asyncio\n",
-    "\n",
-    "nest_asyncio.apply()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders.sitemap import SitemapLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sitemap_loader = SitemapLoader(web_path=\"https://langchain.readthedocs.io/sitemap.xml\")\n",
-    "\n",
-    "docs = sitemap_loader.load()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can change the `requests_per_second` parameter to increase the max concurrent requests. and use `requests_kwargs` to pass kwargs when send requests."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sitemap_loader.requests_per_second = 2\n",
-    "# Optional: avoid `[SSL: CERTIFICATE_VERIFY_FAILED]` issue\n",
-    "sitemap_loader.requests_kwargs = {\"verify\": False}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Document(page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n', metadata={'source': 'https://api.python.langchain.com/en/stable/', 'loc': 'https://api.python.langchain.com/en/stable/', 'lastmod': '2023-10-13T18:13:26.966937+00:00', 'changefreq': 'weekly', 'priority': '1'})"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "docs[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Filtering sitemap URLs\n",
-    "\n",
-    "Sitemaps can be massive files, with thousands of URLs.  Often you don't need every single one of them.  You can filter the URLs by passing a list of strings or regex patterns to the `filter_urls` parameter.  Only URLs that match one of the patterns will be loaded."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Fetching pages: 100%|##########| 1/1 [00:00<00:00, 16.39it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "loader = SitemapLoader(\n",
-    "    web_path=\"https://langchain.readthedocs.io/sitemap.xml\",\n",
-    "    filter_urls=[\"https://api.python.langchain.com/en/latest\"],\n",
-    ")\n",
-    "documents = loader.load()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Document(page_content='\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nLangChain Python API Reference Documentation.\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nYou will be automatically redirected to the new location of this page.\\n\\n', metadata={'source': 'https://api.python.langchain.com/en/latest/', 'loc': 'https://api.python.langchain.com/en/latest/', 'lastmod': '2023-10-13T18:09:58.478681+00:00', 'changefreq': 'daily', 'priority': '0.9'})"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "documents[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Add custom scraping rules\n",
-    "\n",
-    "The `SitemapLoader` uses `beautifulsoup4` for the scraping process, and it scrapes every element on the page by default. The `SitemapLoader` constructor accepts a custom scraping function. This feature can be helpful to tailor the scraping process to your specific needs; for example, you might want to avoid scraping headers or navigation elements.\n",
-    "\n",
-    " The following example shows how to develop and use a custom function to avoid navigation and header elements."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Import the `beautifulsoup4` library and define the custom function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pip install beautifulsoup4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from bs4 import BeautifulSoup\n",
-    "\n",
-    "\n",
-    "def remove_nav_and_header_elements(content: BeautifulSoup) -> str:\n",
-    "    # Find all 'nav' and 'header' elements in the BeautifulSoup object\n",
-    "    nav_elements = content.find_all(\"nav\")\n",
-    "    header_elements = content.find_all(\"header\")\n",
-    "\n",
-    "    # Remove each 'nav' and 'header' element from the BeautifulSoup object\n",
-    "    for element in nav_elements + header_elements:\n",
-    "        element.decompose()\n",
-    "\n",
-    "    return str(content.get_text())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Add your custom function to the `SitemapLoader` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader = SitemapLoader(\n",
-    "    \"https://langchain.readthedocs.io/sitemap.xml\",\n",
-    "    filter_urls=[\"https://api.python.langchain.com/en/latest/\"],\n",
-    "    parsing_function=remove_nav_and_header_elements,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Local Sitemap\n",
-    "\n",
-    "The sitemap loader can also be used to load local files."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Fetching pages: 100%|##########| 3/3 [00:00<00:00, 12.46it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "sitemap_loader = SitemapLoader(web_path=\"example_data/sitemap.xml\", is_local=True)\n",
-    "\n",
-    "docs = sitemap_loader.load()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/docs/integrations/document_transformers/google_translate.ipynb

@@ -1,217 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Google Translate\n",
-    "\n",
-    "[Google Translate](https://translate.google.com/) is a multilingual neural machine translation service developed by Google to translate text, documents and websites from one language into another.\n",
-    "\n",
-    "The `GoogleTranslateTransformer` allows you to translate text and HTML with the [Google Cloud Translation API](https://cloud.google.com/translate).\n",
-    "\n",
-    "To use it, you should have the `google-cloud-translate` python package installed, and a Google Cloud project with the [Translation API enabled](https://cloud.google.com/translate/docs/setup). This transformer uses the [Advanced edition (v3)](https://cloud.google.com/translate/docs/intro-to-v3).\n",
-    "\n",
-    "- [Google Neural Machine Translation](https://en.wikipedia.org/wiki/Google_Neural_Machine_Translation)\n",
-    "- [A Neural Network for Machine Translation, at Production Scale](https://blog.research.google/2016/09/a-neural-network-for-machine.html)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "! pip install google-cloud-translate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.schema import Document\n",
-    "from langchain.document_transformers import GoogleTranslateTransformer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Input\n",
-    "\n",
-    "This is the document we'll translate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sample_text = \"\"\"[Generated with Google Bard]\n",
-    "Subject: Key Business Process Updates\n",
-    "\n",
-    "Date: Friday, 27 October 2023\n",
-    "\n",
-    "Dear team,\n",
-    "\n",
-    "I am writing to provide an update on some of our key business processes.\n",
-    "\n",
-    "Sales process\n",
-    "\n",
-    "We have recently implemented a new sales process that is designed to help us close more deals and grow our revenue. The new process includes a more rigorous qualification process, a more streamlined proposal process, and a more effective customer relationship management (CRM) system.\n",
-    "\n",
-    "Marketing process\n",
-    "\n",
-    "We have also revamped our marketing process to focus on creating more targeted and engaging content. We are also using more social media and paid advertising to reach a wider audience.\n",
-    "\n",
-    "Customer service process\n",
-    "\n",
-    "We have also made some improvements to our customer service process. We have implemented a new customer support system that makes it easier for customers to get help with their problems. We have also hired more customer support representatives to reduce wait times.\n",
-    "\n",
-    "Overall, we are very pleased with the progress we have made on improving our key business processes. We believe that these changes will help us to achieve our goals of growing our business and providing our customers with the best possible experience.\n",
-    "\n",
-    "If you have any questions or feedback about any of these changes, please feel free to contact me directly.\n",
-    "\n",
-    "Thank you,\n",
-    "\n",
-    "Lewis Cymbal\n",
-    "CEO, Cymbal Bank\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When initializing the `GoogleTranslateTransformer`, you can include the following parameters to configure the requests.\n",
-    "\n",
-    "- `project_id`: Google Cloud Project ID.\n",
-    "- `location`: (Optional) Translate model location.\n",
-    "  - Default: `global` \n",
-    "- `model_id`: (Optional) Translate [model ID][models] to use.\n",
-    "- `glossary_id`: (Optional) Translate [glossary ID][glossaries] to use.\n",
-    "- `api_endpoint`: (Optional) [Regional endpoint][endpoints] to use.\n",
-    "\n",
-    "[models]: https://cloud.google.com/translate/docs/advanced/translating-text-v3#comparing-models\n",
-    "[glossaries]: https://cloud.google.com/translate/docs/advanced/glossary\n",
-    "[endpoints]: https://cloud.google.com/translate/docs/advanced/endpoints"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "documents = [Document(page_content=sample_text)]\n",
-    "translator = GoogleTranslateTransformer(project_id=\"<YOUR_PROJECT_ID>\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Output\n",
-    "\n",
-    "After translating a document, the result will be returned as a new document with the `page_content` translated into the target language.\n",
-    "\n",
-    "You can provide the following keyword parameters to the `transform_documents()` method:\n",
-    "\n",
-    "- `target_language_code`: [ISO 639][iso-639] language code of the output document.\n",
-    "    - For supported languages, refer to [Language support][supported-languages].\n",
-    "- `source_language_code`: (Optional) [ISO 639][iso-639] language code of the input document.\n",
-    "    - If not provided, language will be auto-detected.\n",
-    "- `mime_type`: (Optional) [Media Type][media-type] of the input text.\n",
-    "    - Options: `text/plain` (Default), `text/html`.\n",
-    "\n",
-    "[iso-639]: https://en.wikipedia.org/wiki/ISO_639\n",
-    "[supported-languages]: https://cloud.google.com/translate/docs/languages\n",
-    "[media-type]: https://en.wikipedia.org/wiki/Media_type"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "translated_documents = translator.transform_documents(\n",
-    "    documents, target_language_code=\"es\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'model': '', 'detected_language_code': 'en'}\n",
-      "[Generado con Google Bard]\n",
-      "Asunto: Actualizaciones clave de procesos comerciales\n",
-      "\n",
-      "Fecha: viernes 27 de octubre de 2023\n",
-      "\n",
-      "Estimado equipo,\n",
-      "\n",
-      "Le escribo para brindarle una actualización sobre algunos de nuestros procesos comerciales clave.\n",
-      "\n",
-      "Proceso de ventas\n",
-      "\n",
-      "Recientemente implementamos un nuevo proceso de ventas que está diseñado para ayudarnos a cerrar más acuerdos y aumentar nuestros ingresos. El nuevo proceso incluye un proceso de calificación más riguroso, un proceso de propuesta más simplificado y un sistema de gestión de relaciones con el cliente (CRM) más eficaz.\n",
-      "\n",
-      "Proceso de mercadeo\n",
-      "\n",
-      "También hemos renovado nuestro proceso de marketing para centrarnos en crear contenido más específico y atractivo. También estamos utilizando más redes sociales y publicidad paga para llegar a una audiencia más amplia.\n",
-      "\n",
-      "proceso de atención al cliente\n",
-      "\n",
-      "También hemos realizado algunas mejoras en nuestro proceso de atención al cliente. Hemos implementado un nuevo sistema de atención al cliente que facilita que los clientes obtengan ayuda con sus problemas. También hemos contratado más representantes de atención al cliente para reducir los tiempos de espera.\n",
-      "\n",
-      "En general, estamos muy satisfechos con el progreso que hemos logrado en la mejora de nuestros procesos comerciales clave. Creemos que estos cambios nos ayudarán a lograr nuestros objetivos de hacer crecer nuestro negocio y brindar a nuestros clientes la mejor experiencia posible.\n",
-      "\n",
-      "Si tiene alguna pregunta o comentario sobre cualquiera de estos cambios, no dude en ponerse en contacto conmigo directamente.\n",
-      "\n",
-      "Gracias,\n",
-      "\n",
-      "Platillo Lewis\n",
-      "Director ejecutivo, banco de platillos\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "for doc in translated_documents:\n",
-    "    print(doc.metadata)\n",
-    "    print(doc.page_content)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/docs/integrations/llms/arcee.ipynb

@@ -1,138 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Arcee\n",
-    "This notebook demonstrates how to use the `Arcee` class for generating text using Arcee's Domain Adapted Language Models (DALMs)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Setup\n",
-    "\n",
-    "Before using Arcee, make sure the Arcee API key is set as `ARCEE_API_KEY` environment variable. You can also pass the api key as a named parameter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.llms import Arcee\n",
-    "\n",
-    "# Create an instance of the Arcee class\n",
-    "arcee = Arcee(\n",
-    "    model=\"DALM-PubMed\",\n",
-    "    # arcee_api_key=\"ARCEE-API-KEY\" # if not already set in the environment\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Additional Configuration\n",
-    "\n",
-    "You can also configure Arcee's parameters such as `arcee_api_url`, `arcee_app_url`, and `model_kwargs` as needed.\n",
-    "Setting the `model_kwargs` at the object initialization uses the parameters as default for all the subsequent calls to the generate response."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "arcee = Arcee(\n",
-    "    model=\"DALM-Patent\",\n",
-    "    # arcee_api_key=\"ARCEE-API-KEY\", # if not already set in the environment\n",
-    "    arcee_api_url=\"https://custom-api.arcee.ai\",  # default is https://api.arcee.ai\n",
-    "    arcee_app_url=\"https://custom-app.arcee.ai\",  # default is https://app.arcee.ai\n",
-    "    model_kwargs={\n",
-    "        \"size\": 5,\n",
-    "        \"filters\": [\n",
-    "            {\n",
-    "                \"field_name\": \"document\",\n",
-    "                \"filter_type\": \"fuzzy_search\",\n",
-    "                \"value\": \"Einstein\",\n",
-    "            }\n",
-    "        ],\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Generating Text\n",
-    "\n",
-    "You can generate text from Arcee by providing a prompt. Here's an example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Generate text\n",
-    "prompt = \"Can AI-driven music therapy contribute to the rehabilitation of patients with disorders of consciousness?\"\n",
-    "response = arcee(prompt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Additional parameters\n",
-    "\n",
-    "Arcee allows you to apply `filters` and set the `size` (in terms of count) of retrieved document(s) to aid text generation. Filters help narrow down the results. Here's how to use these parameters:\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define filters\n",
-    "filters = [\n",
-    "    {\"field_name\": \"document\", \"filter_type\": \"fuzzy_search\", \"value\": \"Einstein\"},\n",
-    "    {\"field_name\": \"year\", \"filter_type\": \"strict_search\", \"value\": \"1905\"},\n",
-    "]\n",
-    "\n",
-    "# Generate text with filters and size params\n",
-    "response = arcee(prompt, size=5, filters=filters)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/integrations/llms/gigachat.ipynb

@@ -1,113 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# GigaChat\n",
-    "This notebook shows how to use LangChain with [GigaChat](https://developers.sber.ru/portal/products/gigachat).\n",
-    "To use you need to install ```gigachat``` python package."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "# !pip install gigachat"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "To get GigaChat credentials you need to [create account](https://developers.sber.ru/studio/login) and [get access to API](https://developers.sber.ru/docs/ru/gigachat/api/integration)\n",
-    "## Example"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from getpass import getpass\n",
-    "\n",
-    "os.environ[\"GIGACHAT_CREDENTIALS\"] = getpass()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "outputs": [],
-   "source": [
-    "from langchain.llms import GigaChat\n",
-    "\n",
-    "llm = GigaChat(verify_ssl_certs=False)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The capital of Russia is Moscow.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.chains import LLMChain\n",
-    "\n",
-    "template = \"What is capital of {country}?\"\n",
-    "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"country\"])\n",
-    "\n",
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "\n",
-    "generated = llm_chain.run(country=\"Russia\")\n",
-    "print(generated)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}

docs/docs/integrations/llms/gradient.ipynb

@@ -1,337 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Gradient\n",
-    "\n",
-    "`Gradient` allows to fine tune and get completions on LLMs with a simple web API.\n",
-    "\n",
-    "This notebook goes over how to use Langchain with [Gradient](https://gradient.ai/).\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.llms import GradientLLM\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.chains import LLMChain"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Set the Environment API Key\n",
-    "Make sure to get your API key from Gradient AI. You are given $10 in free credits to test and fine-tune different models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from getpass import getpass\n",
-    "import os\n",
-    "\n",
-    "if not os.environ.get(\"GRADIENT_ACCESS_TOKEN\", None):\n",
-    "    # Access token under https://auth.gradient.ai/select-workspace\n",
-    "    os.environ[\"GRADIENT_ACCESS_TOKEN\"] = getpass(\"gradient.ai access token:\")\n",
-    "if not os.environ.get(\"GRADIENT_WORKSPACE_ID\", None):\n",
-    "    # `ID` listed in `$ gradient workspace list`\n",
-    "    # also displayed after login at at https://auth.gradient.ai/select-workspace\n",
-    "    os.environ[\"GRADIENT_WORKSPACE_ID\"] = getpass(\"gradient.ai workspace id:\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Optional: Validate your Enviroment variables ```GRADIENT_ACCESS_TOKEN``` and ```GRADIENT_WORKSPACE_ID``` to get currently deployed models. Using the `gradientai` Python package."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: gradientai in /home/michi/.venv/lib/python3.10/site-packages (1.0.0)\n",
-      "Requirement already satisfied: aenum>=3.1.11 in /home/michi/.venv/lib/python3.10/site-packages (from gradientai) (3.1.15)\n",
-      "Requirement already satisfied: pydantic<2.0.0,>=1.10.5 in /home/michi/.venv/lib/python3.10/site-packages (from gradientai) (1.10.12)\n",
-      "Requirement already satisfied: python-dateutil>=2.8.2 in /home/michi/.venv/lib/python3.10/site-packages (from gradientai) (2.8.2)\n",
-      "Requirement already satisfied: urllib3>=1.25.3 in /home/michi/.venv/lib/python3.10/site-packages (from gradientai) (1.26.16)\n",
-      "Requirement already satisfied: typing-extensions>=4.2.0 in /home/michi/.venv/lib/python3.10/site-packages (from pydantic<2.0.0,>=1.10.5->gradientai) (4.5.0)\n",
-      "Requirement already satisfied: six>=1.5 in /home/michi/.venv/lib/python3.10/site-packages (from python-dateutil>=2.8.2->gradientai) (1.16.0)\n"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install gradientai"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "99148c6d-c2a0-4fbe-a4a7-e7c05bdb8a09_base_ml_model\n",
-      "f0b97d96-51a8-4040-8b22-7940ee1fa24e_base_ml_model\n",
-      "cc2dafce-9e6e-4a23-a918-cad6ba89e42e_base_ml_model\n"
-     ]
-    }
-   ],
-   "source": [
-    "import gradientai\n",
-    "\n",
-    "client = gradientai.Gradient()\n",
-    "\n",
-    "models = client.list_models(only_base=True)\n",
-    "for model in models:\n",
-    "    print(model.id)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('674119b5-f19e-4856-add2-767ae7f7d7ef_model_adapter', 'my_model_adapter')"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_model = models[-1].create_model_adapter(name=\"my_model_adapter\")\n",
-    "new_model.id, new_model.name"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create the Gradient instance\n",
-    "You can specify different parameters such as the model, max_tokens generated, temperature, etc.\n",
-    "\n",
-    "As we later want to fine-tune out model, we select the model_adapter with the id `674119b5-f19e-4856-add2-767ae7f7d7ef_model_adapter`, but you can use any base or fine-tunable model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = GradientLLM(\n",
-    "    # `ID` listed in `$ gradient model list`\n",
-    "    model=\"674119b5-f19e-4856-add2-767ae7f7d7ef_model_adapter\",\n",
-    "    # # optional: set new credentials, they default to environment variables\n",
-    "    # gradient_workspace_id=os.environ[\"GRADIENT_WORKSPACE_ID\"],\n",
-    "    # gradient_access_token=os.environ[\"GRADIENT_ACCESS_TOKEN\"],\n",
-    "    model_kwargs=dict(max_generated_token_count=128),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create a Prompt Template\n",
-    "We will create a prompt template for Question and Answer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "template = \"\"\"Question: {question}\n",
-    "\n",
-    "Answer: \"\"\"\n",
-    "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initiate the LLMChain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Run the LLMChain\n",
-    "Provide a question and run the LLMChain."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\nThe San Francisco 49ers won the Super Bowl in 1994.'"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "question = \"What NFL team won the Super Bowl in 1994?\"\n",
-    "\n",
-    "llm_chain.run(question=question)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Improve the results by fine-tuning (optional)\n",
-    "Well - that is wrong - the San Francisco 49ers did not win.\n",
-    "The correct answer to the question would be `The Dallas Cowboys!`.\n",
-    "\n",
-    "Let's increase the odds for the correct answer, by fine-tuning on the correct answer using the PromptTemplate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'inputs': 'Question: What NFL team won the Super Bowl in 1994?\\n\\nAnswer:  The Dallas Cowboys!'}]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataset = [\n",
-    "    {\n",
-    "        \"inputs\": template.format(question=\"What NFL team won the Super Bowl in 1994?\")\n",
-    "        + \" The Dallas Cowboys!\"\n",
-    "    }\n",
-    "]\n",
-    "dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "FineTuneResponse(number_of_trainable_tokens=27, sum_loss=78.17996)"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_model.fine_tune(samples=dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'The Dallas Cowboys'"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# we can keep the llm_chain, as the registered model just got refreshed on the gradient.ai servers.\n",
-    "llm_chain.run(question=question)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "a0a0263b650d907a3bfe41c0f8d6a63a071b884df3cfdc1579f00cdc1aed6b03"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/docs/integrations/llms/lmformatenforcer_experimental.ipynb

@@ -1,367 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "fdd7864c-93e6-4eb4-a923-b80d2ae4377d",
-   "metadata": {},
-   "source": [
-    "# LM Format Enforcer\n",
-    "\n",
-    "[LM Format Enforcer](https://github.com/noamgat/lm-format-enforcer) is a library that enforces the output format of language models by filtering tokens.\n",
-    "\n",
-    "It works by combining a character level parser with a tokenizer prefix tree to allow only the tokens which contains sequences of characters that lead to a potentially valid format.\n",
-    "\n",
-    "It supports batched generation.\n",
-    "\n",
-    "**Warning - this module is still experimental**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "1617e327-d9a2-4ab6-aa9f-30a3167a3393",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "!pip install --upgrade lm-format-enforcer > /dev/null"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a3c3331d",
-   "metadata": {},
-   "source": [
-    "### Setting up the model\n",
-    "\n",
-    "We will start by setting up a LLama2 model and initializing our desired output format.\n",
-    "Note that Llama2 [requires approval for access to the models](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf).\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "d4d616ae-4d11-425f-b06c-c706d0386c68",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "from langchain_experimental.pydantic_v1 import BaseModel\n",
-    "\n",
-    "logging.basicConfig(level=logging.ERROR)\n",
-    "\n",
-    "\n",
-    "class PlayerInformation(BaseModel):\n",
-    "    first_name: str\n",
-    "    last_name: str\n",
-    "    num_seasons_in_nba: int\n",
-    "    year_of_birth: int"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "93fe95cd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/noamgat/envs/langchain_experimental/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n",
-      "Downloading shards: 100%|██████████| 2/2 [00:00<00:00,  3.58it/s]\n",
-      "Loading checkpoint shards: 100%|██████████| 2/2 [05:32<00:00, 166.35s/it]\n",
-      "Downloading (…)okenizer_config.json: 100%|██████████| 1.62k/1.62k [00:00<00:00, 4.87MB/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig\n",
-    "\n",
-    "model_id = \"meta-llama/Llama-2-7b-chat-hf\"\n",
-    "\n",
-    "device = \"cuda\"\n",
-    "\n",
-    "if torch.cuda.is_available():\n",
-    "    config = AutoConfig.from_pretrained(model_id)\n",
-    "    config.pretraining_tp = 1\n",
-    "    model = AutoModelForCausalLM.from_pretrained(\n",
-    "        model_id,\n",
-    "        config=config,\n",
-    "        torch_dtype=torch.float16,\n",
-    "        load_in_8bit=True,\n",
-    "        device_map=\"auto\",\n",
-    "    )\n",
-    "else:\n",
-    "    raise Exception(\"GPU not available\")\n",
-    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
-    "if tokenizer.pad_token_id is None:\n",
-    "    # Required for batching example\n",
-    "    tokenizer.pad_token_id = tokenizer.eos_token_id"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "66bd89f1-8daa-433d-bb8f-5b0b3ae34b00",
-   "metadata": {},
-   "source": [
-    "### HuggingFace Baseline\n",
-    "\n",
-    "First, let's establish a qualitative baseline by checking the output of the model without structured decoding."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "d5522977-51e8-40eb-9403-8ab70b14908e",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "DEFAULT_SYSTEM_PROMPT = \"\"\"\\\n",
-    "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe.  Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\\n\\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\\\n",
-    "\"\"\"\n",
-    "\n",
-    "prompt = \"\"\"Please give me information about {player_name}. You must respond using JSON format, according to the following schema:\n",
-    "\n",
-    "{arg_schema}\n",
-    "\n",
-    "\"\"\"\n",
-    "\n",
-    "\n",
-    "def make_instruction_prompt(message):\n",
-    "    return f\"[INST] <<SYS>>\\n{DEFAULT_SYSTEM_PROMPT}\\n<</SYS>> {message} [/INST]\"\n",
-    "\n",
-    "\n",
-    "def get_prompt(player_name):\n",
-    "    return make_instruction_prompt(\n",
-    "        prompt.format(\n",
-    "            player_name=player_name, arg_schema=PlayerInformation.schema_json()\n",
-    "        )\n",
-    "    )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "9148e4b8-d370-4c05-a873-c121b65057b5",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  {\n",
-      "\"title\": \"PlayerInformation\",\n",
-      "\"type\": \"object\",\n",
-      "\"properties\": {\n",
-      "\"first_name\": {\n",
-      "\"title\": \"First Name\",\n",
-      "\"type\": \"string\"\n",
-      "},\n",
-      "\"last_name\": {\n",
-      "\"title\": \"Last Name\",\n",
-      "\"type\": \"string\"\n",
-      "},\n",
-      "\"num_seasons_in_nba\": {\n",
-      "\"title\": \"Num Seasons In Nba\",\n",
-      "\"type\": \"integer\"\n",
-      "},\n",
-      "\"year_of_birth\": {\n",
-      "\"title\": \"Year Of Birth\",\n",
-      "\"type\": \"integer\"\n",
-      "\n",
-      "}\n",
-      "\n",
-      "\"required\": [\n",
-      "\"first_name\",\n",
-      "\"last_name\",\n",
-      "\"num_seasons_in_nba\",\n",
-      "\"year_of_birth\"\n",
-      "]\n",
-      "}\n",
-      "\n",
-      "}\n"
-     ]
-    }
-   ],
-   "source": [
-    "from transformers import pipeline\n",
-    "from langchain.llms import HuggingFacePipeline\n",
-    "\n",
-    "hf_model = pipeline(\n",
-    "    \"text-generation\", model=model, tokenizer=tokenizer, max_new_tokens=200\n",
-    ")\n",
-    "\n",
-    "original_model = HuggingFacePipeline(pipeline=hf_model)\n",
-    "\n",
-    "generated = original_model.predict(get_prompt(\"Michael Jordan\"))\n",
-    "print(generated)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b6e7b9cf-8ce5-4f87-b4bf-100321ad2dd1",
-   "metadata": {},
-   "source": [
-    "***The result is usually closer to the JSON object of the schema definition, rather than a json object conforming to the schema. Lets try to enforce proper output.***"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "96115154-a90a-46cb-9759-573860fc9b79",
-   "metadata": {},
-   "source": [
-    "## JSONFormer LLM Wrapper\n",
-    "\n",
-    "Let's try that again, now providing a the Action input's JSON Schema to the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "0f7447fe-22a9-47db-85b9-7adf0f19307d",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  { \"first_name\": \"Michael\", \"last_name\": \"Jordan\", \"num_seasons_in_nba\": 15, \"year_of_birth\": 1963 }\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain_experimental.llms import LMFormatEnforcer\n",
-    "\n",
-    "lm_format_enforcer = LMFormatEnforcer(\n",
-    "    json_schema=PlayerInformation.schema(), pipeline=hf_model\n",
-    ")\n",
-    "results = lm_format_enforcer.predict(get_prompt(\"Michael Jordan\"))\n",
-    "print(results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "32077d74-0605-4138-9a10-0ce36637040d",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "**The output conforms to the exact specification! Free of parsing errors.**\n",
-    "\n",
-    "This means that if you need to format a JSON for an API call or similar, if you can generate the schema (from a pydantic model or general) you can use this library to make sure that the JSON output is correct, with minimal risk of hallucinations.\n",
-    "\n",
-    "### Batch processing\n",
-    "\n",
-    "LMFormatEnforcer also works in batch mode:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "9817095b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  { \"first_name\": \"Michael\", \"last_name\": \"Jordan\", \"num_seasons_in_nba\": 15, \"year_of_birth\": 1963 }\n",
-      "  { \"first_name\": \"Kareem\", \"last_name\": \"Abdul-Jabbar\", \"num_seasons_in_nba\": 20, \"year_of_birth\": 1947 }\n",
-      "  { \"first_name\": \"Timothy\", \"last_name\": \"Duncan\", \"num_seasons_in_nba\": 19, \"year_of_birth\": 1976 }\n"
-     ]
-    }
-   ],
-   "source": [
-    "prompts = [\n",
-    "    get_prompt(name) for name in [\"Michael Jordan\", \"Kareem Abdul Jabbar\", \"Tim Duncan\"]\n",
-    "]\n",
-    "results = lm_format_enforcer.generate(prompts)\n",
-    "for generation in results.generations:\n",
-    "    print(generation[0].text)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "59bea0d8",
-   "metadata": {},
-   "source": [
-    "## Regular Expressions\n",
-    "\n",
-    "LMFormatEnforcer has an additional mode, which uses regular expressions to filter the output. Note that it uses [interegular](https://pypi.org/project/interegular/) under the hood, therefore it does not support 100% of the regex capabilities."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "da63ce31-de79-4462-a1a9-b726b698c5ba",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Unenforced output:\n",
-      "  I apologize, but the question you have asked is not factually coherent. Michael Jordan was born on February 17, 1963, in Fort Greene, Brooklyn, New York, USA. Therefore, I cannot provide an answer in the mm/dd/yyyy format as it is not a valid date.\n",
-      "I understand that you may have asked this question in good faith, but I must ensure that my responses are always accurate and reliable. I'm just an AI, my primary goal is to provide helpful and informative answers while adhering to ethical and moral standards. If you have any other questions, please feel free to ask, and I will do my best to assist you.\n",
-      "Enforced Output:\n",
-      " In mm/dd/yyyy format, Michael Jordan was born in 02/17/1963\n"
-     ]
-    }
-   ],
-   "source": [
-    "question_prompt = \"When was Michael Jordan Born? Please answer in mm/dd/yyyy format.\"\n",
-    "date_regex = r\"(0?[1-9]|1[0-2])\\/(0?[1-9]|1\\d|2\\d|3[01])\\/(19|20)\\d{2}\"\n",
-    "answer_regex = \" In mm/dd/yyyy format, Michael Jordan was born in \" + date_regex\n",
-    "\n",
-    "lm_format_enforcer = LMFormatEnforcer(regex=answer_regex, pipeline=hf_model)\n",
-    "\n",
-    "full_prompt = make_instruction_prompt(question_prompt)\n",
-    "print(\"Unenforced output:\")\n",
-    "print(original_model.predict(full_prompt))\n",
-    "print(\"Enforced Output:\")\n",
-    "print(lm_format_enforcer.predict(full_prompt))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0b1839c5",
-   "metadata": {},
-   "source": [
-    "As in the previous example, the output conforms to the regular expression and contains the correct information."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/llms/pai_eas_endpoint.ipynb

@@ -1,97 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# AliCloud PAI EAS\n",
-    "Machine Learning Platform for AI of Alibaba Cloud is a machine learning or deep learning engineering platform intended for enterprises and developers. It provides easy-to-use, cost-effective, high-performance, and easy-to-scale plug-ins that can be applied to various industry scenarios. With over 140 built-in optimization algorithms, Machine Learning Platform for AI provides whole-process AI engineering capabilities including data labeling (PAI-iTAG), model building (PAI-Designer and PAI-DSW), model training (PAI-DLC), compilation optimization, and inference deployment (PAI-EAS). PAI-EAS supports different types of hardware resources, including CPUs and GPUs, and features high throughput and low latency. It allows you to deploy large-scale complex models with a few clicks and perform elastic scale-ins and scale-outs in real time. It also provides a comprehensive O&M and monitoring system."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.llms.pai_eas_endpoint import PaiEasEndpoint\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.chains import LLMChain\n",
-    "\n",
-    "template = \"\"\"Question: {question}\n",
-    "\n",
-    "Answer: Let's think step by step.\"\"\"\n",
-    "\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "One who want to use eas llms must set up eas service first. When the eas service is launched, eas_service_rul and eas_service token can be got. Users can refer to https://www.alibabacloud.com/help/en/pai/user-guide/service-deployment/ for more information,"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"EAS_SERVICE_URL\"] = \"Your_EAS_Service_URL\"\n",
-    "os.environ[\"EAS_SERVICE_TOKEN\"] = \"Your_EAS_Service_Token\"\n",
-    "llm = PaiEasEndpoint(\n",
-    "    eas_service_url=os.environ[\"EAS_SERVICE_URL\"],\n",
-    "    eas_service_token=os.environ[\"EAS_SERVICE_TOKEN\"],\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'  Thank you for asking! However, I must respectfully point out that the question contains an error. Justin Bieber was born in 1994, and the Super Bowl was first played in 1967. Therefore, it is not possible for any NFL team to have won the Super Bowl in the year Justin Bieber was born.\\n\\nI hope this clarifies things! If you have any other questions, please feel free to ask.'"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
-    "\n",
-    "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
-    "llm_chain.run(question)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/integrations/llms/titan_takeoff_pro.ipynb

@@ -1,102 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Titan Takeoff Pro\n",
-    "\n",
-    "`TitanML` helps businesses build and deploy better, smaller, cheaper, and faster NLP models through our training, compression, and inference optimization platform.\n",
-    "\n",
-    ">Note: These docs are for the Pro version of Titan Takeoff. For the community version, see the page for Titan Takeoff.\n",
-    "\n",
-    "Our inference server, [Titan Takeoff (Pro Version)](https://docs.titanml.co/docs/titan-takeoff/pro-features/feature-comparison) enables deployment of LLMs locally on your hardware in a single command. Most generative model architectures are supported, such as Falcon, Llama 2, GPT2, T5 and many more."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example usage\n",
-    "Here are some helpful examples to get started using the Pro version of Titan Takeoff Server.\n",
-    "No parameters are needed by default, but a baseURL that points to your desired URL where Takeoff is running can be specified and generation parameters can be supplied."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.llms import TitanTakeoffPro\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
-    "from langchain.callbacks.manager import CallbackManager\n",
-    "\n",
-    "# Example 1: Basic use\n",
-    "llm = TitanTakeoffPro()\n",
-    "output = llm(\"What is the weather in London in August?\")\n",
-    "print(output)\n",
-    "\n",
-    "\n",
-    "# Example 2: Specifying a port and other generation parameters\n",
-    "llm = TitanTakeoffPro(\n",
-    "    base_url=\"http://localhost:3000\",\n",
-    "    min_new_tokens=128,\n",
-    "    max_new_tokens=512,\n",
-    "    no_repeat_ngram_size=2,\n",
-    "    sampling_topk=1,\n",
-    "    sampling_topp=1.0,\n",
-    "    sampling_temperature=1.0,\n",
-    "    repetition_penalty=1.0,\n",
-    "    regex_string=\"\",\n",
-    ")\n",
-    "output = llm(\"What is the largest rainforest in the world?\")\n",
-    "print(output)\n",
-    "\n",
-    "\n",
-    "# Example 3: Using generate for multiple inputs\n",
-    "llm = TitanTakeoffPro()\n",
-    "rich_output = llm.generate([\"What is Deep Learning?\", \"What is Machine Learning?\"])\n",
-    "print(rich_output.generations)\n",
-    "\n",
-    "\n",
-    "# Example 4: Streaming output\n",
-    "llm = TitanTakeoffPro(\n",
-    "    streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()])\n",
-    ")\n",
-    "prompt = \"What is the capital of France?\"\n",
-    "llm(prompt)\n",
-    "\n",
-    "# Example 5: Using LCEL\n",
-    "llm = TitanTakeoffPro()\n",
-    "prompt = PromptTemplate.from_template(\"Tell me about {topic}\")\n",
-    "chain = prompt | llm\n",
-    "chain.invoke({\"topic\": \"the universe\"})"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/docs/integrations/llms/together.ipynb

@@ -1,79 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "2970dd75-8ebf-4b51-8282-9b454b8f356d",
-   "metadata": {},
-   "source": [
-    "# Together AI\n",
-    "\n",
-    "> The Together API makes it easy to fine-tune or run leading open-source models with a couple lines of code. We have integrated the world’s leading open-source models, including Llama-2, RedPajama, Falcon, Alpaca, Stable Diffusion XL, and more. Read more: https://together.ai\n",
-    "\n",
-    "To use, you'll need an API key which you can find here:\n",
-    "https://api.together.xyz/settings/api-keys. This can be passed in as init param\n",
-    "``together_api_key`` or set as environment variable ``TOGETHER_API_KEY``.\n",
-    "\n",
-    "Together API reference: https://docs.together.ai/reference/inference"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "e7b7170d-d7c5-4890-9714-a37238343805",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "A: A large language model is a neural network that is trained on a large amount of text data. It is able to generate text that is similar to the training data, and can be used for tasks such as language translation, question answering, and text summarization.\n",
-      "\n",
-      "A: A large language model is a neural network that is trained on a large amount of text data. It is able to generate text that is similar to the training data, and can be used for tasks such as language translation, question answering, and text summarization.\n",
-      "\n",
-      "A: A large language model is a neural network that is trained on\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.llms import Together\n",
-    "\n",
-    "llm = Together(\n",
-    "    model=\"togethercomputer/RedPajama-INCITE-7B-Base\",\n",
-    "    temperature=0.7,\n",
-    "    max_tokens=128,\n",
-    "    top_k=1,\n",
-    "    # together_api_key=\"...\"\n",
-    ")\n",
-    "\n",
-    "input_ = \"\"\"You are a teacher with a deep knowledge of machine learning and AI. \\\n",
-    "You provide succinct and accurate answers. Answer the following question: \n",
-    "\n",
-    "What is a large language model?\"\"\"\n",
-    "print(llm(input_))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "poetry-venv",
-   "language": "python",
-   "name": "poetry-venv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/llms/yandex.ipynb

@@ -1,119 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# YandexGPT\n",
-    "\n",
-    "This notebook goes over how to use Langchain with [YandexGPT](https://cloud.yandex.com/en/services/yandexgpt).\n",
-    "\n",
-    "To use, you should have the `yandexcloud` python package installed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install yandexcloud"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, you should [create service account](https://cloud.yandex.com/en/docs/iam/operations/sa/create) with the `ai.languageModels.user` role.\n",
-    "\n",
-    "Next, you have two authentication options:\n",
-    "- [IAM token](https://cloud.yandex.com/en/docs/iam/operations/iam-token/create-for-sa).\n",
-    "    You can specify the token in a constructor parameter `iam_token` or in an environment variable `YC_IAM_TOKEN`.\n",
-    "- [API key](https://cloud.yandex.com/en/docs/iam/operations/api-key/create)\n",
-    "    You can specify the key in a constructor parameter `api_key` or in an environment variable `YC_API_KEY`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 246,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.chains import LLMChain\n",
-    "from langchain.llms import YandexGPT\n",
-    "from langchain.prompts import PromptTemplate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 247,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "template = \"What is the capital of {country}?\"\n",
-    "prompt = PromptTemplate(template=template, input_variables=[\"country\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 248,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm = YandexGPT()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 249,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 250,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Moscow'"
-      ]
-     },
-     "execution_count": 250,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "country = \"Russia\"\n",
-    "\n",
-    "llm_chain.run(country)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/docs/integrations/memory/elasticsearch_chat_message_history.ipynb

@@ -1,184 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "683953b3",
-   "metadata": {
-    "id": "683953b3"
-   },
-   "source": [
-    "# Elasticsearch Chat Message History\n",
-    "\n",
-    ">[Elasticsearch](https://www.elastic.co/elasticsearch/) is a distributed, RESTful search and analytics engine, capable of performing both vector and lexical search. It is built on top of the Apache Lucene library.\n",
-    "\n",
-    "This notebook shows how to use chat message history functionality with Elasticsearch."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3c7720c3",
-   "metadata": {},
-   "source": [
-    "## Set up Elasticsearch\n",
-    "\n",
-    "There are two main ways to set up an Elasticsearch instance:\n",
-    "\n",
-    "1. **Elastic Cloud.** Elastic Cloud is a managed Elasticsearch service. Sign up for a [free trial](https://cloud.elastic.co/registration?storm=langchain-notebook).\n",
-    "\n",
-    "2. **Local Elasticsearch installation.** Get started with Elasticsearch by running it locally. The easiest way is to use the official Elasticsearch Docker image. See the [Elasticsearch Docker documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/docker.html) for more information."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cdf1d2b7",
-   "metadata": {},
-   "source": [
-    "## Install dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e5bbffe2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install elasticsearch langchain"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8be8fcc3",
-   "metadata": {},
-   "source": [
-    "## Initialize Elasticsearch client and chat message history"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "8e2ee0fa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from langchain.memory import ElasticsearchChatMessageHistory\n",
-    "\n",
-    "es_url = os.environ.get(\"ES_URL\", \"http://localhost:9200\")\n",
-    "\n",
-    "# If using Elastic Cloud:\n",
-    "# es_cloud_id = os.environ.get(\"ES_CLOUD_ID\")\n",
-    "\n",
-    "# Note: see Authentication section for various authentication methods\n",
-    "\n",
-    "history = ElasticsearchChatMessageHistory(\n",
-    "    es_url=es_url, index=\"test-history\", session_id=\"test-session\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a63942e2",
-   "metadata": {},
-   "source": [
-    "## Use the chat message history"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "c1c7be79",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "indexing message content='hi!' additional_kwargs={} example=False\n",
-      "indexing message content='whats up?' additional_kwargs={} example=False\n"
-     ]
-    }
-   ],
-   "source": [
-    "history.add_user_message(\"hi!\")\n",
-    "history.add_ai_message(\"whats up?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c46c216c",
-   "metadata": {},
-   "source": [
-    "# Authentication\n",
-    "\n",
-    "## Username/password\n",
-    "\n",
-    "```python\n",
-    "es_username = os.environ.get(\"ES_USERNAME\", \"elastic\")\n",
-    "es_password = os.environ.get(\"ES_PASSWORD\", \"changeme\")\n",
-    "\n",
-    "history = ElasticsearchChatMessageHistory(\n",
-    "    es_url=es_url,\n",
-    "    es_user=es_username,\n",
-    "    es_password=es_password,\n",
-    "    index=\"test-history\",\n",
-    "    session_id=\"test-session\"\n",
-    ")\n",
-    "```\n",
-    "\n",
-    "### How to obtain a password for the default \"elastic\" user\n",
-    "\n",
-    "To obtain your Elastic Cloud password for the default \"elastic\" user:\n",
-    "1. Log in to the Elastic Cloud console at https://cloud.elastic.co\n",
-    "2. Go to \"Security\" > \"Users\"\n",
-    "3. Locate the \"elastic\" user and click \"Edit\"\n",
-    "4. Click \"Reset password\"\n",
-    "5. Follow the prompts to reset the password\n",
-    "\n",
-    "## API key\n",
-    "\n",
-    "```python\n",
-    "es_api_key = os.environ.get(\"ES_API_KEY\")\n",
-    "\n",
-    "history = ElasticsearchChatMessageHistory(\n",
-    "    es_api_key=es_api_key,\n",
-    "    index=\"test-history\",\n",
-    "    session_id=\"test-session\"\n",
-    ")\n",
-    "```\n",
-    "\n",
-    "### How to obtain an API key\n",
-    "\n",
-    "To obtain an API key:\n",
-    "1. Log in to the Elastic Cloud console at https://cloud.elastic.co\n",
-    "2. Open Kibana and go to Stack Management > API Keys\n",
-    "3. Click \"Create API key\"\n",
-    "4. Enter a name for the API key and click \"Create\""
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.9"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/memory/singlestoredb_chat_message_history.ipynb

@@ -1,64 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "id": "91c6a7ef",
-   "metadata": {},
-   "source": [
-    "# SingleStoreDB\n",
-    "\n",
-    "This notebook goes over how to use SingleStoreDB to store chat message history."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d15e3302",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.memory import SingleStoreDBChatMessageHistory\n",
-    "\n",
-    "history = SingleStoreDBChatMessageHistory(\n",
-    "    session_id=\"foo\", host=\"root:pass@localhost:3306/db\"\n",
-    ")\n",
-    "\n",
-    "history.add_user_message(\"hi!\")\n",
-    "\n",
-    "history.add_ai_message(\"whats up?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "64fc465e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "history.messages"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/memory/upstash_redis_chat_message_history.ipynb

@@ -1,65 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Upstash Redis Chat Message History\n",
-    "\n",
-    "This notebook goes over how to use Upstash Redis to store chat message history."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.memory.chat_message_histories.upstash_redis import (\n",
-    "    UpstashRedisChatMessageHistory,\n",
-    ")\n",
-    "\n",
-    "URL = \"<UPSTASH_REDIS_REST_URL>\"\n",
-    "TOKEN = \"<UPSTASH_REDIS_REST_TOKEN>\"\n",
-    "\n",
-    "history = UpstashRedisChatMessageHistory(\n",
-    "    url=URL, token=TOKEN, ttl=10, session_id=\"my-test-session\"\n",
-    ")\n",
-    "\n",
-    "history.add_user_message(\"hello llm!\")\n",
-    "history.add_ai_message(\"hello user!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "history.messages"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/integrations/platforms/google.mdx

@@ -1,459 +0,0 @@
-# Google
-
-All functionality related to [Google Cloud Platform](https://cloud.google.com/) and other `Google` products.
-
-## LLMs
-
-### Vertex AI
-
-Access `PaLM` LLMs like `text-bison` and `code-bison` via `Google Vertex AI`.
-
-We need to install `google-cloud-aiplatform` python package.
-
-```bash
-pip install google-cloud-aiplatform
-```
-
-See a [usage example](/docs/integrations/llms/google_vertex_ai_palm).
-
-```python
-from langchain.llms import VertexAI
-```
-
-### Model Garden
-
-Access PaLM and hundreds of OSS models via `Vertex AI Model Garden`.
-
-We need to install `google-cloud-aiplatform` python package.
-
-```bash
-pip install google-cloud-aiplatform
-```
-
-See a [usage example](/docs/integrations/llms/google_vertex_ai_palm#vertex-model-garden).
-
-```python
-from langchain.llms import VertexAIModelGarden
-```
-
-## Chat models
-
-### Vertex AI
-
-Access PaLM chat models like `chat-bison` and `codechat-bison` via Google Cloud.
-
-We need to install `google-cloud-aiplatform` python package.
-
-```bash
-pip install google-cloud-aiplatform
-```
-
-See a [usage example](/docs/integrations/chat/google_vertex_ai_palm).
-
-```python
-from langchain.chat_models import ChatVertexAI
-```
-
-
-## Document Loaders
-### Google BigQuery
-
-> [Google BigQuery](https://cloud.google.com/bigquery) is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data.
-`BigQuery` is a part of the `Google Cloud Platform`.
-
-We need to install `google-cloud-bigquery` python package.
-
-```bash
-pip install google-cloud-bigquery
-```
-
-See a [usage example](/docs/integrations/document_loaders/google_bigquery).
-
-```python
-from langchain.document_loaders import BigQueryLoader
-```
-
-### Google Cloud Storage
-
->[Google Cloud Storage](https://en.wikipedia.org/wiki/Google_Cloud_Storage) is a managed service for storing unstructured data.
-
-We need to install `google-cloud-storage` python package.
-
-```bash
-pip install google-cloud-storage
-```
-
-There are two loaders for the `Google Cloud Storage`: the `Directory` and the `File` loaders.
-
-See a [usage example](/docs/integrations/document_loaders/google_cloud_storage_directory).
-
-```python
-from langchain.document_loaders import GCSDirectoryLoader
-```
-See a [usage example](/docs/integrations/document_loaders/google_cloud_storage_file).
-
-```python
-from langchain.document_loaders import GCSFileLoader
-```
-
-### Google Drive
-
->[Google Drive](https://en.wikipedia.org/wiki/Google_Drive) is a file storage and synchronization service developed by Google.
-
-Currently, only `Google Docs` are supported.
-
-We need to install several python packages.
-
-```bash
-pip install google-api-python-client google-auth-httplib2 google-auth-oauthlib
-```
-
-See a [usage example and authorization instructions](/docs/integrations/document_loaders/google_drive).
-
-```python
-from langchain.document_loaders import GoogleDriveLoader
-```
-
-### Speech-to-Text
-
-> [Google Cloud Speech-to-Text](https://cloud.google.com/speech-to-text) is an audio transcription API powered by Google's speech recognition models.
-
-This document loader transcribes audio files and outputs the text results as Documents.
-
-First, we need to install the python package.
-
-```bash
-pip install google-cloud-speech
-```
-
-See a [usage example and authorization instructions](/docs/integrations/document_loaders/google_speech_to_text).
-
-```python
-from langchain.document_loaders import GoogleSpeechToTextLoader
-```
-
-
-
-## Vector Stores
-
-### Google Vertex AI Vector Search
-
-> [Google Vertex AI Vector Search](https://cloud.google.com/vertex-ai/docs/matching-engine/overview),
-> formerly known as `Vertex AI Matching Engine`, provides the industry's leading high-scale 
-> low latency vector database. These vector databases are commonly
-> referred to as vector similarity-matching or an approximate nearest neighbor (ANN) service.
-
-We need to install several python packages.
-
-```bash
-pip install tensorflow google-cloud-aiplatform tensorflow-hub tensorflow-text
-```
-
-See a [usage example](/docs/integrations/vectorstores/matchingengine).
-
-```python
-from langchain.vectorstores import MatchingEngine
-```
-
-### Google ScaNN
-
->[Google ScaNN](https://github.com/google-research/google-research/tree/master/scann)
-> (Scalable Nearest Neighbors) is a python package.
-> 
->`ScaNN` is a method for efficient vector similarity search at scale.
-
->`ScaNN` includes search space pruning and quantization for Maximum Inner
-> Product Search and also supports other distance functions such as
-> Euclidean distance. The implementation is optimized for x86 processors
-> with AVX2 support. See its [Google Research github](https://github.com/google-research/google-research/tree/master/scann)
-> for more details.
-
-We need to install `scann` python package.
-
-```bash
-pip install scann
-```
-
-See a [usage example](/docs/integrations/vectorstores/scann).
-
-```python
-from langchain.vectorstores import ScaNN
-```
-
-## Retrievers
-
-### Google Drive
-
-We need to install several python packages.
-
-```bash
-pip install google-api-python-client google-auth-httplib2 google-auth-oauthlib
-```
-
-See a [usage example and authorization instructions](/docs/integrations/retrievers/google_drive).
-
-```python
-from langchain_googledrive.retrievers import GoogleDriveRetriever
-```
-
-
-### Vertex AI Search
-
-> [Google Cloud Vertex AI Search](https://cloud.google.com/generative-ai-app-builder/docs/introduction)
-> allows developers to quickly build generative AI powered search engines for customers and employees.
-
-We need to install the `google-cloud-discoveryengine` python package.
-
-```bash
-pip install google-cloud-discoveryengine
-```
-
-See a [usage example](/docs/integrations/retrievers/google_vertex_ai_search).
-
-```python
-from langchain.retrievers import GoogleVertexAISearchRetriever
-```
-
-### Document AI Warehouse
-> [Google Cloud Document AI Warehouse](https://cloud.google.com/document-ai-warehouse)
-> allows enterprises to search, store, govern, and manage documents and their AI-extracted 
-> data and metadata in a single platform.
-> 
-
-```python
-from langchain.retrievers import GoogleDocumentAIWarehouseRetriever
-docai_wh_retriever = GoogleDocumentAIWarehouseRetriever(
-    project_number=...
-)
-query = ...
-documents = docai_wh_retriever.get_relevant_documents(
-    query, user_ldap=...
-)
-```
-
-## Tools
-
-### Google Drive
-
-We need to install several python packages.
-
-```bash
-pip install google-api-python-client google-auth-httplib2 google-auth-oauthlib
-```
-
-See a [usage example and authorization instructions](/docs/integrations/tools/google_drive).
-
-```python
-from langchain.utilities.google_drive import GoogleDriveAPIWrapper
-from langchain.tools.google_drive.tool import GoogleDriveSearchTool
-```
-
-### Google Places
-
-We need to install a python package.
-
-```bash
-pip install googlemaps
-```
-
-See a [usage example and authorization instructions](/docs/integrations/tools/google_places).
-
-```python
-from langchain.tools import GooglePlacesTool
-```
-
-### Google Search
-
-We need to install a python package.
-
-```bash
-pip install google-api-python-client
-```
-
-- Set up a Custom Search Engine, following [these instructions](https://stackoverflow.com/questions/37083058/programmatically-searching-google-in-python-using-custom-search)
-- Get an API Key and Custom Search Engine ID from the previous step, and set them as environment variables `GOOGLE_API_KEY` and `GOOGLE_CSE_ID` respectively
-
-```python
-from langchain.utilities import GoogleSearchAPIWrapper
-```
-
-For a more detailed walkthrough of this wrapper, see [this notebook](/docs/integrations/tools/google_search).
-
-We can easily load this wrapper as a Tool (to use with an Agent). We can do this with:
-
-```python
-from langchain.agents import load_tools
-tools = load_tools(["google-search"])
-```
-
-
-## Document Transformers
-
-### Google Document AI
-
->[Document AI](https://cloud.google.com/document-ai/docs/overview) is a `Google Cloud Platform` 
-> service that transforms unstructured data from documents into structured data, making it easier 
-> to understand, analyze, and consume.
-
-We need to set up a [`GCS` bucket and create your own OCR processor](https://cloud.google.com/document-ai/docs/create-processor)  
-The `GCS_OUTPUT_PATH` should be a path to a folder on GCS (starting with `gs://`) 
-and a processor name should look like `projects/PROJECT_NUMBER/locations/LOCATION/processors/PROCESSOR_ID`.
-We can get it either programmatically or copy from the `Prediction endpoint` section of the `Processor details`
-tab in the Google Cloud Console.
-
-```bash
-pip install google-cloud-documentai
-pip install google-cloud-documentai-toolbox
-```
-
-See a [usage example](/docs/integrations/document_transformers/docai).
-
-```python
-from langchain.document_loaders.blob_loaders import Blob
-from langchain.document_loaders.parsers import DocAIParser
-```
-
-### Google Translate
-
-> [Google Translate](https://translate.google.com/) is a multilingual neural machine
-> translation service developed by Google to translate text, documents and websites
-> from one language into another.
-
-The `GoogleTranslateTransformer` allows you to translate text and HTML with the [Google Cloud Translation API](https://cloud.google.com/translate).
-
-To use it, you should have the `google-cloud-translate` python package installed, and a Google Cloud project with the [Translation API enabled](https://cloud.google.com/translate/docs/setup). This transformer uses the [Advanced edition (v3)](https://cloud.google.com/translate/docs/intro-to-v3).
-
-First, we need to install the python package.
-
-```bash
-pip install google-cloud-translate
-```
-
-See a [usage example and authorization instructions](/docs/integrations/document_transformers/google_translate).
-
-```python
-from langchain.document_transformers import GoogleTranslateTransformer
-```
-
-## Toolkits
-
-### GMail
-
-> [Gmail](https://en.wikipedia.org/wiki/Gmail) is a free email service provided by Google.
-This toolkit works with emails through the `Gmail API`.
-
-We need to install several python packages.
-
-```bash
-pip install google-api-python-client google-auth-oauthlib google-auth-httplib2
-```
-
-See a [usage example and authorization instructions](/docs/integrations/toolkits/gmail).
-
-```python
-from langchain.agents.agent_toolkits import GmailToolkit
-```
-
-
-### Google Drive
-
-This toolkit uses the `Google Drive API`.
-
-We need to install several python packages.
-
-```bash
-pip install google-api-python-client google-auth-httplib2 google-auth-oauthlib
-```
-
-See a [usage example and authorization instructions](/docs/integrations/toolkits/google_drive).
-
-```python
-from langchain_googledrive.utilities.google_drive import GoogleDriveAPIWrapper
-from langchain_googledrive.tools.google_drive.tool import GoogleDriveSearchTool
-```
-
-## Chat Loaders
-
-### GMail
-
-> [Gmail](https://en.wikipedia.org/wiki/Gmail) is a free email service provided by Google.
-This loader works with emails through the `Gmail API`.
-
-We need to install several python packages.
-
-```bash
-pip install google-api-python-client google-auth-oauthlib google-auth-httplib2
-```
-
-See a [usage example and authorization instructions](/docs/integrations/chat_loaders/gmail).
-
-```python
-from langchain.chat_loaders.gmail import GMailLoader
-```
-
-## 3rd Party Integrations
-
-### SerpAPI
-
->[SerpApi](https://serpapi.com/) provides a 3rd-party API to access Google search results.
-
-See a [usage example and authorization instructions](/docs/integrations/tools/google_serper).
-
-```python
-from langchain.utilities import GoogleSerperAPIWrapper
-```
-
-### YouTube
-
->[YouTube Search](https://github.com/joetats/youtube_search) package searches `YouTube` videos avoiding using their heavily rate-limited API.
-> 
->It uses the form on the YouTube homepage and scrapes the resulting page.
-
-We need to install a python package.
-
-```bash
-pip install youtube_search
-```
-
-See a [usage example](/docs/integrations/tools/youtube).
-
-```python
-from langchain.tools import YouTubeSearchTool
-```
-
-### YouTube audio
-
->[YouTube](https://www.youtube.com/) is an online video sharing and social media platform created by `Google`.
-
-Use `YoutubeAudioLoader` to fetch / download the audio files.
-
-Then, use `OpenAIWhisperParser` to transcribe them to text.
-
-We need to install several python packages.
-
-```bash
-pip install yt_dlp pydub librosa
-```
-
-See a [usage example and authorization instructions](/docs/integrations/document_loaders/youtube_audio).
-
-```python
-from langchain.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader
-from langchain.document_loaders.parsers import OpenAIWhisperParser, OpenAIWhisperParserLocal
-```
-
-### YouTube transcripts
-
->[YouTube](https://www.youtube.com/) is an online video sharing and social media platform created by `Google`.
-
-We need to install `youtube-transcript-api` python package.
-
-```bash
-pip install youtube-transcript-api
-```
-
-See a [usage example](/docs/integrations/document_loaders/youtube_transcript).
-
-```python
-from langchain.document_loaders import YoutubeLoader
-```

docs/docs/integrations/providers/dingo.mdx

@@ -1,19 +0,0 @@
-# DingoDB
-
-This page covers how to use the DingoDB ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific DingoDB wrappers.
-
-## Installation and Setup
-- Install the Python SDK with `pip install dingodb`
-
-## VectorStore
-
-There exists a wrapper around DingoDB indexes, allowing you to use it as a vectorstore,
-whether for semantic search or example selection.
-
-To import this vectorstore:
-```python
-from langchain.vectorstores import Dingo
-```
-
-For a more detailed walkthrough of the DingoDB wrapper, see [this notebook](/docs/integrations/vectorstores/dingo)

docs/docs/integrations/providers/gradient.mdx

@@ -1,27 +0,0 @@
-# Gradient
-
->[Gradient](https://gradient.ai/) allows to fine tune and get completions on LLMs with a simple web API.
-
-## Installation and Setup
-- Install the Python SDK :
-```bash
-pip install gradientai
-```
-Get a [Gradient access token and workspace](https://gradient.ai/) and set it as an environment variable (`Gradient_ACCESS_TOKEN`) and (`GRADIENT_WORKSPACE_ID`)
-
-## LLM
-
-There exists an Gradient LLM wrapper, which you can access with 
-See a [usage example](/docs/integrations/llms/gradient).
-
-```python
-from langchain.llms import GradientLLM
-```
-
-## Text Embedding Model
-
-There exists an Gradient Embedding model, which you can access with 
-```python
-from langchain.embeddings import GradientEmbeddings
-```
-For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/gradient)

docs/docs/integrations/providers/johnsnowlabs.mdx

@@ -1,117 +0,0 @@
-# Johnsnowlabs
-
-Gain access to the [johnsnowlabs](https://www.johnsnowlabs.com/) ecosystem of enterprise NLP libraries
-with over 21.000 enterprise NLP models in over 200 languages with the open source `johnsnowlabs` library.
-For all 24.000+ models, see the [John Snow Labs Model Models Hub](https://nlp.johnsnowlabs.com/models)
-
-## Installation and Setup
-
-
-```bash
-pip install johnsnowlabs
-```
-
-To [install enterprise features](https://nlp.johnsnowlabs.com/docs/en/jsl/install_licensed_quick, run:
-```python
-# for more details see https://nlp.johnsnowlabs.com/docs/en/jsl/install_licensed_quick
-nlp.install()
-```
-
-
-You can embed your queries and documents with either `gpu`,`cpu`,`apple_silicon`,`aarch` based optimized binaries.
-By default cpu binaries are used.
-Once a session is started, you must restart your notebook to switch between GPU or CPU, or changes will not take effect.
-
-## Embed Query with CPU:
-```python
-document = "foo bar"
-embedding = JohnSnowLabsEmbeddings('embed_sentence.bert')
-output = embedding.embed_query(document)
-```
-
-
-## Embed Query with GPU:
-
-
-```python
-document = "foo bar"
-embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','gpu')
-output = embedding.embed_query(document)
-```
-
-
-
-
-## Embed Query with Apple Silicon (M1,M2,etc..):
-
-```python
-documents = ["foo bar", 'bar foo']
-embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','apple_silicon')
-output = embedding.embed_query(document)
-```
-
-
-
-## Embed Query with AARCH:
-
-```python
-documents = ["foo bar", 'bar foo']
-embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','aarch')
-output = embedding.embed_query(document)
-```
-
-
-
-
-
-
-## Embed Document with CPU:
-```python
-documents = ["foo bar", 'bar foo']
-embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','gpu')
-output = embedding.embed_documents(documents)
-```
-
-
-
-## Embed Document with GPU:
-
-```python
-documents = ["foo bar", 'bar foo']
-embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','gpu')
-output = embedding.embed_documents(documents)
-```
-
-
-
-
-
-## Embed Document with Apple Silicon (M1,M2,etc..):
-
-```python
-
-```python
-documents = ["foo bar", 'bar foo']
-embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','apple_silicon')
-output = embedding.embed_documents(documents)
-```
-
-
-
-## Embed Document with AARCH:
-
-```python
-
-```python
-documents = ["foo bar", 'bar foo']
-embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','aarch')
-output = embedding.embed_documents(documents)
-```
-
-
-
-
-Models are loaded with [nlp.load](https://nlp.johnsnowlabs.com/docs/en/jsl/load_api) and spark session is started with [nlp.start()](https://nlp.johnsnowlabs.com/docs/en/jsl/start-a-sparksession) under the hood.
-
-
-

docs/docs/integrations/providers/momento.mdx

@@ -1,59 +0,0 @@
-# Momento
-
-> [Momento Cache](https://docs.momentohq.com/) is the world's first truly serverless caching service, offering instant elasticity, scale-to-zero
-> capability, and blazing-fast performance.
->
-> [Momento Vector Index](https://docs.momentohq.com/vector-index) stands out as the most productive, easiest-to-use, fully serverless vector index.
->
-> For both services, simply grab the SDK, obtain an API key, input a few lines into your code, and you're set to go. Together, they provide a comprehensive solution for your LLM data needs.
-
-This page covers how to use the [Momento](https://gomomento.com) ecosystem within LangChain.
-
-## Installation and Setup
-
-- Sign up for a free account [here](https://console.momentohq.com) to get an API key
-- Install the Momento Python SDK with `pip install momento`
-
-## Cache
-
-Use Momento as a serverless, distributed, low-latency cache for LLM prompts and responses. The standard cache is the primary use case for Momento users in any environment.
-
-To integrate Momento Cache into your application:
-
-```python
-from langchain.cache import MomentoCache
-```
-
-Then, set it up with the following code:
-
-```python
-from datetime import timedelta
-from momento import CacheClient, Configurations, CredentialProvider
-from langchain.globals import set_llm_cache
-
-# Instantiate the Momento client
-cache_client = CacheClient(
-    Configurations.Laptop.v1(),
-    CredentialProvider.from_environment_variable("MOMENTO_API_KEY"),
-    default_ttl=timedelta(days=1))
-
-# Choose a Momento cache name of your choice
-cache_name = "langchain"
-
-# Instantiate the LLM cache
-set_llm_cache(MomentoCache(cache_client, cache_name))
-```
-
-## Memory
-
-Momento can be used as a distributed memory store for LLMs.
-
-### Chat Message History Memory
-
-See [this notebook](/docs/integrations/memory/momento_chat_message_history) for a walkthrough of how to use Momento as a memory store for chat message history.
-
-## Vector Store
-
-Momento Vector Index (MVI) can be used as a vector store.
-
-See [this notebook](/docs/integrations/vectorstores/momento_vector_index) for a walkthrough of how to use MVI as a vector store.

docs/docs/integrations/providers/providers/semadb.mdx

@@ -1,19 +0,0 @@
-# SemaDB
-
->[SemaDB](https://semafind.com/) is a no fuss vector similarity search engine. It provides a low-cost cloud hosted version to help you build AI applications with ease.
-
-With SemaDB Cloud, our hosted version, no fuss means no pod size calculations, no schema definitions, no partition settings, no parameter tuning, no search algorithm tuning, no complex installation, no complex API. It is integrated with [RapidAPI](https://rapidapi.com/semafind-semadb/api/semadb) providing transparent billing, automatic sharding and an interactive API playground.
-
-## Installation
-
-None required, get started directly with SemaDB Cloud at [RapidAPI](https://rapidapi.com/semafind-semadb/api/semadb).
-
-## Vector Store
-
-There is a basic wrapper around `SemaDB` collections allowing you to use it as a vectorstore.
-
-```python
-from langchain.vectorstores import SemaDB
-```
-
-You can follow a tutorial on how to use the wrapper in [this notebook](/docs/integrations/vectorstores/semadb).

docs/docs/integrations/providers/salute_devices.mdx

@@ -1,29 +0,0 @@
-# Salute Devices
-
-Salute Devices provides GigaChat LLM's models.
-
-For more info how to get access to GigaChat [follow here](https://developers.sber.ru/docs/ru/gigachat/api/integration).
-
-## Installation and Setup
-
-GigaChat package can be installed via pip from PyPI:
-
-```bash
-pip install gigachat
-```
-
-## LLMs
-
-See a [usage example](/docs/integrations/llms/gigachat).
-
-```python
-from langchain.llms import GigaChat
-```
-
-## Chat models
-
-See a [usage example](/docs/integrations/chat/gigachat).
-
-```python
-from langchain.chat_models import GigaChat
-```

docs/docs/integrations/providers/upstash.mdx

@@ -1,42 +0,0 @@
-# Upstash Redis
-
-Upstash offers developers serverless databases and messaging platforms to build powerful applications without having to worry about the operational complexity of running databases at scale.
-
-This page covers how to use [Upstash Redis](https://upstash.com/redis) with LangChain.
-
-## Installation and Setup
-- Upstash Redis Python SDK can be installed with `pip install upstash-redis`
-- A globally distributed, low-latency and highly available database can be created at the [Upstash Console](https://console.upstash.com)
-
-
-## Integrations
-All of Upstash-LangChain integrations are based on `upstash-redis` Python SDK being utilized as wrappers for LangChain.
-This SDK utilizes Upstash Redis DB by giving UPSTASH_REDIS_REST_URL and UPSTASH_REDIS_REST_TOKEN parameters from the console.
-One significant advantage of this is that, this SDK uses a REST API. This means, you can run this in serverless platforms, edge or any platform that does not support TCP connections.
-
-
-### Cache
-
-[Upstash Redis](https://upstash.com/redis) can be used as a cache for LLM prompts and responses.
-
-To import this cache:
-```python
-from langchain.cache import UpstashRedisCache
-```
-
-To use with your LLMs:
-```python
-import langchain
-from upstash_redis import Redis
-
-URL = "<UPSTASH_REDIS_REST_URL>"
-TOKEN = "<UPSTASH_REDIS_REST_TOKEN>"
-
-langchain.llm_cache = UpstashRedisCache(redis_=Redis(url=URL, token=TOKEN))
-```
-
-### Memory
-Upstash Redis can be used to persist LLM conversations.
-
-#### Chat Message History Memory
-An example of Upstash Redis for caching conversation message history can be seen in [this notebook](/docs/integrations/memory/upstash_redis_chat_message_history).

docs/docs/integrations/providers/yandex.mdx

@@ -1,33 +0,0 @@
-# Yandex
-
-All functionality related to Yandex Cloud
-
->[Yandex Cloud](https://cloud.yandex.com/en/) is a public cloud platform. 
-
-## Installation and Setup
-
-Yandex Cloud SDK can be installed via pip from PyPI: 
-
-```bash
-pip install yandexcloud
-```
-
-## LLMs
-
-### YandexGPT
-
-See a [usage example](/docs/integrations/llms/yandex).
-
-```python
-from langchain.llms import YandexGPT
-```
-
-## Chat models
-
-### YandexGPT
-
-See a [usage example](/docs/integrations/chat/yandex).
-
-```python
-from langchain.chat_models import ChatYandexGPT
-```

docs/docs/integrations/providers/zep.mdx

@@ -1,72 +0,0 @@
-# Zep
-
-## [Fast, Scalable Building Blocks for LLM Apps](http://www.getzep.com)
-Zep is an open source platform for productionizing LLM apps. Go from a prototype
-built in LangChain or LlamaIndex, or a custom app, to production in minutes without
-rewriting code.
-
-Key Features:
-
-- **Fast!** Zep operates independently of the your chat loop, ensuring a snappy user experience.
-- **Chat History Memory, Archival, and Enrichment**, populate your prompts with relevant chat history, sumamries, named entities, intent data, and more.
-- **Vector Search over Chat History and Documents** Automatic embedding of documents, chat histories, and summaries. Use Zep's similarity or native MMR Re-ranked search to find the most relevant.
-- **Manage Users and their Chat Sessions** Users and their Chat Sessions are first-class citizens in Zep, allowing you to manage user interactions with your bots or agents easily.
-- **Records Retention and Privacy Compliance** Comply with corporate and regulatory mandates for records retention while ensuring compliance with privacy regulations such as CCPA and GDPR. Fulfill *Right To Be Forgotten* requests with a single API call
-
-Zep project: [https://github.com/getzep/zep](https://github.com/getzep/zep)
-Docs: [https://docs.getzep.com/](https://docs.getzep.com/)
-
-## Installation and Setup
-
-1. Install the Zep service. See the [Zep Quick Start Guide](https://docs.getzep.com/deployment/quickstart/).
-
-2. Install the Zep Python SDK:
-
-```bash
-pip install zep_python
-```
-
-## Zep Memory
-
-Zep's [Memory API](https://docs.getzep.com/sdk/chat_history/) persists your app's chat history and metadata to a Session, enriches the memory, automatically generates summaries, and enables vector similarity search over historical chat messages and summaries.
-
-There are two approaches to populating your prompt with chat history:
-
-1. Retrieve the most recent N messages (and potentionally a summary) from a Session and use them to construct your prompt.
-2. Search over the Session's chat history for messages that are relevant and use them to construct your prompt.
-
-Both of these approaches may be useful, with the first providing the LLM with context as to the most recent interactions with a human. The second approach enables you to look back further in the chat history and retrieve messages that are relevant to the current conversation in a token-efficient manner. 
-
-```python
-from langchain.memory import ZepMemory
-```
-
-See a [RAG App Example here](/docs/docs/integrations/memory/zep_memory).
-
-## Memory Retriever
-
-Zep's Memory Retriever is a LangChain Retriever that enables you to retrieve messages from a Zep Session and use them to construct your prompt.
-
-The Retriever supports searching over both individual messages and summaries of conversations. The latter is useful for providing rich, but succinct context to the LLM as to relevant past conversations.
-
-Zep's Memory Retriever supports both similarity search and [Maximum Marginal Relevance (MMR) reranking](https://docs.getzep.com/sdk/search_query/). MMR search is useful for ensuring that the retrieved messages are diverse and not too similar to each other
-
-See a [usage example](/docs/integrations/retrievers/zep_memorystore).
-
-```python
-from langchain.retrievers import ZepRetriever
-```
-
-## Zep VectorStore
-
-Zep's [Document VectorStore API](https://docs.getzep.com/sdk/documents/) enables you to store and retrieve documents using vector similarity search. Zep doesn't require you to understand 
-distance functions, types of embeddings, or indexing best practices. You just pass in your chunked documents, and Zep handles the rest.
-
-Zep supports both similarity search and [Maximum Marginal Relevance (MMR) reranking](https://docs.getzep.com/sdk/search_query/). 
-MMR search is useful for ensuring that the retrieved documents are diverse and not too similar to each other.
-
-```python
-from langchain.vectorstores.zep import ZepVectorStore
-```
-
-See a [usage example](/docs/integrations/vectorstores/zep).

docs/docs/integrations/retrievers/arcee.ipynb

@@ -1,133 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Arcee Retriever\n",
-    "This notebook demonstrates how to use the `ArceeRetriever` class to retrieve relevant document(s) for Arcee's Domain Adapted Language Models (DALMs)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Setup\n",
-    "\n",
-    "Before using `ArceeRetriever`, make sure the Arcee API key is set as `ARCEE_API_KEY` environment variable. You can also pass the api key as a named parameter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.retrievers import ArceeRetriever\n",
-    "\n",
-    "retriever = ArceeRetriever(\n",
-    "    model=\"DALM-PubMed\",\n",
-    "    # arcee_api_key=\"ARCEE-API-KEY\" # if not already set in the environment\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Additional Configuration\n",
-    "\n",
-    "You can also configure `ArceeRetriever`'s parameters such as `arcee_api_url`, `arcee_app_url`, and `model_kwargs` as needed.\n",
-    "Setting the `model_kwargs` at the object initialization uses the filters and size as default for all the subsequent retrievals."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "retriever = ArceeRetriever(\n",
-    "    model=\"DALM-PubMed\",\n",
-    "    # arcee_api_key=\"ARCEE-API-KEY\", # if not already set in the environment\n",
-    "    arcee_api_url=\"https://custom-api.arcee.ai\",  # default is https://api.arcee.ai\n",
-    "    arcee_app_url=\"https://custom-app.arcee.ai\",  # default is https://app.arcee.ai\n",
-    "    model_kwargs={\n",
-    "        \"size\": 5,\n",
-    "        \"filters\": [\n",
-    "            {\n",
-    "                \"field_name\": \"document\",\n",
-    "                \"filter_type\": \"fuzzy_search\",\n",
-    "                \"value\": \"Einstein\",\n",
-    "            }\n",
-    "        ],\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Retrieving documents\n",
-    "You can retrieve relevant documents from uploaded contexts by providing a query. Here's an example:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query = \"Can AI-driven music therapy contribute to the rehabilitation of patients with disorders of consciousness?\"\n",
-    "documents = retriever.get_relevant_documents(query=query)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Additional parameters\n",
-    "\n",
-    "Arcee allows you to apply `filters` and set the `size` (in terms of count) of retrieved document(s). Filters help narrow down the results. Here's how to use these parameters:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define filters\n",
-    "filters = [\n",
-    "    {\"field_name\": \"document\", \"filter_type\": \"fuzzy_search\", \"value\": \"Music\"},\n",
-    "    {\"field_name\": \"year\", \"filter_type\": \"strict_search\", \"value\": \"1905\"},\n",
-    "]\n",
-    "\n",
-    "# Retrieve documents with filters and size params\n",
-    "documents = retriever.get_relevant_documents(query=query, size=5, filters=filters)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/docs/integrations/retrievers/cohere.ipynb

@@ -1,226 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "bf733a38-db84-4363-89e2-de6735c37230",
-   "metadata": {},
-   "source": [
-    "# Cohere RAG retriever\n",
-    "\n",
-    "This notebook covers how to get started with Cohere RAG retriever. This allows you to leverage the ability to search documents over various connectors or by supplying your own."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.chat_models import ChatCohere\n",
-    "from langchain.retrievers import CohereRagRetriever\n",
-    "from langchain.schema.document import Document"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "rag = CohereRagRetriever(llm=ChatCohere())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "7f1e1bf3-542c-4fcb-8643-de6897fa6fcc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def _pretty_print(docs):\n",
-    "    for doc in docs:\n",
-    "        print(doc.metadata)\n",
-    "        print(\"\\n\\n\" + doc.page_content)\n",
-    "        print(\"\\n\\n\" + \"-\" * 30 + \"\\n\\n\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'id': 'web-search_4:0', 'snippet': 'AI startup Cohere, now valued at over $2.1B, raises $270M\\n\\nKyle Wiggers 4 months\\n\\nIn a sign that there’s plenty of cash to go around for generative AI startups, Cohere, which is developing an AI model ecosystem for the enterprise, today announced that it raised $270 million as part of its Series C round.\\n\\nReuters reported earlier in the year that Cohere was in talks to raise “hundreds of millions” of dollars at a valuation of upward of just over $6 billion. If there’s credence to that reporting, Cohere appears to have missed the valuation mark substantially; a source familiar with the matter tells TechCrunch that this tranche values the company at between $2.1 billion and $2.2 billion.', 'title': 'AI startup Cohere, now valued at over $2.1B, raises $270M | TechCrunch', 'url': 'https://techcrunch.com/2023/06/08/ai-startup-cohere-now-valued-at-over-2-1b-raises-270m/'}\n",
-      "\n",
-      "\n",
-      "AI startup Cohere, now valued at over $2.1B, raises $270M\n",
-      "\n",
-      "Kyle Wiggers 4 months\n",
-      "\n",
-      "In a sign that there’s plenty of cash to go around for generative AI startups, Cohere, which is developing an AI model ecosystem for the enterprise, today announced that it raised $270 million as part of its Series C round.\n",
-      "\n",
-      "Reuters reported earlier in the year that Cohere was in talks to raise “hundreds of millions” of dollars at a valuation of upward of just over $6 billion. If there’s credence to that reporting, Cohere appears to have missed the valuation mark substantially; a source familiar with the matter tells TechCrunch that this tranche values the company at between $2.1 billion and $2.2 billion.\n",
-      "\n",
-      "\n",
-      "------------------------------\n",
-      "\n",
-      "\n",
-      "{'id': 'web-search_9:0', 'snippet': 'Cohere is a Canadian multinational technology company focused on artificial intelligence for the enterprise, specializing in large language models. Cohere was founded in 2019 by Aidan Gomez, Ivan Zhang, and Nick Frosst, and is headquartered in Toronto and San Francisco, with offices in Palo Alto and London.\\n\\nIn 2017, a team of researchers at Google Brain, which included Aidan Gomez, published a paper called \"Attention is All You Need,\" which introduced the transformer machine learning architecture, setting state-of-the-art performance on a variety of natural language processing tasks. In 2019, Gomez and Nick Frosst, another researcher at Google Brain, founded Cohere along with Ivan Zhang, with whom Gomez had done research at FOR.ai. All of the co-founders attended University of Toronto.', 'title': 'Cohere - Wikipedia', 'url': 'https://en.wikipedia.org/wiki/Cohere'}\n",
-      "\n",
-      "\n",
-      "Cohere is a Canadian multinational technology company focused on artificial intelligence for the enterprise, specializing in large language models. Cohere was founded in 2019 by Aidan Gomez, Ivan Zhang, and Nick Frosst, and is headquartered in Toronto and San Francisco, with offices in Palo Alto and London.\n",
-      "\n",
-      "In 2017, a team of researchers at Google Brain, which included Aidan Gomez, published a paper called \"Attention is All You Need,\" which introduced the transformer machine learning architecture, setting state-of-the-art performance on a variety of natural language processing tasks. In 2019, Gomez and Nick Frosst, another researcher at Google Brain, founded Cohere along with Ivan Zhang, with whom Gomez had done research at FOR.ai. All of the co-founders attended University of Toronto.\n",
-      "\n",
-      "\n",
-      "------------------------------\n",
-      "\n",
-      "\n",
-      "{'id': 'web-search_8:2', 'snippet': ' Cofounded by Aidan Gomez, a Google Brain alum and coauthor of the seminal transformer research paper, Cohere describes itself as being “on a mission to transform enterprises and their products with AI to unlock a more intuitive way to generate, search, and summarize information than ever before.” One key element of Cohere’s approach is its focus on data protection, deploying its models inside enterprises’ secure data environment.\\n\\n“We are both independent and cloud-agnostic, meaning we are not beholden to any one tech company and empower enterprises to implement customized AI solutions on the cloud of their choosing, or even on-premises,” says Martin Kon, COO and president of Cohere.', 'title': 'McKinsey and Cohere collaborate to transform clients with enterprise generative AI', 'url': 'https://www.mckinsey.com/about-us/new-at-mckinsey-blog/mckinsey-and-cohere-collaborate-to-transform-clients-with-enterprise-generative-ai'}\n",
-      "\n",
-      "\n",
-      " Cofounded by Aidan Gomez, a Google Brain alum and coauthor of the seminal transformer research paper, Cohere describes itself as being “on a mission to transform enterprises and their products with AI to unlock a more intuitive way to generate, search, and summarize information than ever before.” One key element of Cohere’s approach is its focus on data protection, deploying its models inside enterprises’ secure data environment.\n",
-      "\n",
-      "“We are both independent and cloud-agnostic, meaning we are not beholden to any one tech company and empower enterprises to implement customized AI solutions on the cloud of their choosing, or even on-premises,” says Martin Kon, COO and president of Cohere.\n",
-      "\n",
-      "\n",
-      "------------------------------\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "_pretty_print(rag.get_relevant_documents(\"What is cohere ai?\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "4b888336",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'id': 'web-search_9:0', 'snippet': 'Cohere is a Canadian multinational technology company focused on artificial intelligence for the enterprise, specializing in large language models. Cohere was founded in 2019 by Aidan Gomez, Ivan Zhang, and Nick Frosst, and is headquartered in Toronto and San Francisco, with offices in Palo Alto and London.\\n\\nIn 2017, a team of researchers at Google Brain, which included Aidan Gomez, published a paper called \"Attention is All You Need,\" which introduced the transformer machine learning architecture, setting state-of-the-art performance on a variety of natural language processing tasks. In 2019, Gomez and Nick Frosst, another researcher at Google Brain, founded Cohere along with Ivan Zhang, with whom Gomez had done research at FOR.ai. All of the co-founders attended University of Toronto.', 'title': 'Cohere - Wikipedia', 'url': 'https://en.wikipedia.org/wiki/Cohere'}\n",
-      "\n",
-      "\n",
-      "Cohere is a Canadian multinational technology company focused on artificial intelligence for the enterprise, specializing in large language models. Cohere was founded in 2019 by Aidan Gomez, Ivan Zhang, and Nick Frosst, and is headquartered in Toronto and San Francisco, with offices in Palo Alto and London.\n",
-      "\n",
-      "In 2017, a team of researchers at Google Brain, which included Aidan Gomez, published a paper called \"Attention is All You Need,\" which introduced the transformer machine learning architecture, setting state-of-the-art performance on a variety of natural language processing tasks. In 2019, Gomez and Nick Frosst, another researcher at Google Brain, founded Cohere along with Ivan Zhang, with whom Gomez had done research at FOR.ai. All of the co-founders attended University of Toronto.\n",
-      "\n",
-      "\n",
-      "------------------------------\n",
-      "\n",
-      "\n",
-      "{'id': 'web-search_8:2', 'snippet': ' Cofounded by Aidan Gomez, a Google Brain alum and coauthor of the seminal transformer research paper, Cohere describes itself as being “on a mission to transform enterprises and their products with AI to unlock a more intuitive way to generate, search, and summarize information than ever before.” One key element of Cohere’s approach is its focus on data protection, deploying its models inside enterprises’ secure data environment.\\n\\n“We are both independent and cloud-agnostic, meaning we are not beholden to any one tech company and empower enterprises to implement customized AI solutions on the cloud of their choosing, or even on-premises,” says Martin Kon, COO and president of Cohere.', 'title': 'McKinsey and Cohere collaborate to transform clients with enterprise generative AI', 'url': 'https://www.mckinsey.com/about-us/new-at-mckinsey-blog/mckinsey-and-cohere-collaborate-to-transform-clients-with-enterprise-generative-ai'}\n",
-      "\n",
-      "\n",
-      " Cofounded by Aidan Gomez, a Google Brain alum and coauthor of the seminal transformer research paper, Cohere describes itself as being “on a mission to transform enterprises and their products with AI to unlock a more intuitive way to generate, search, and summarize information than ever before.” One key element of Cohere’s approach is its focus on data protection, deploying its models inside enterprises’ secure data environment.\n",
-      "\n",
-      "“We are both independent and cloud-agnostic, meaning we are not beholden to any one tech company and empower enterprises to implement customized AI solutions on the cloud of their choosing, or even on-premises,” says Martin Kon, COO and president of Cohere.\n",
-      "\n",
-      "\n",
-      "------------------------------\n",
-      "\n",
-      "\n",
-      "{'id': 'web-search_4:0', 'snippet': 'AI startup Cohere, now valued at over $2.1B, raises $270M\\n\\nKyle Wiggers 4 months\\n\\nIn a sign that there’s plenty of cash to go around for generative AI startups, Cohere, which is developing an AI model ecosystem for the enterprise, today announced that it raised $270 million as part of its Series C round.\\n\\nReuters reported earlier in the year that Cohere was in talks to raise “hundreds of millions” of dollars at a valuation of upward of just over $6 billion. If there’s credence to that reporting, Cohere appears to have missed the valuation mark substantially; a source familiar with the matter tells TechCrunch that this tranche values the company at between $2.1 billion and $2.2 billion.', 'title': 'AI startup Cohere, now valued at over $2.1B, raises $270M | TechCrunch', 'url': 'https://techcrunch.com/2023/06/08/ai-startup-cohere-now-valued-at-over-2-1b-raises-270m/'}\n",
-      "\n",
-      "\n",
-      "AI startup Cohere, now valued at over $2.1B, raises $270M\n",
-      "\n",
-      "Kyle Wiggers 4 months\n",
-      "\n",
-      "In a sign that there’s plenty of cash to go around for generative AI startups, Cohere, which is developing an AI model ecosystem for the enterprise, today announced that it raised $270 million as part of its Series C round.\n",
-      "\n",
-      "Reuters reported earlier in the year that Cohere was in talks to raise “hundreds of millions” of dollars at a valuation of upward of just over $6 billion. If there’s credence to that reporting, Cohere appears to have missed the valuation mark substantially; a source familiar with the matter tells TechCrunch that this tranche values the company at between $2.1 billion and $2.2 billion.\n",
-      "\n",
-      "\n",
-      "------------------------------\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "_pretty_print(await rag.aget_relevant_documents(\"What is cohere ai?\"))  # async version"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "3742ba0f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'id': 'doc-0', 'snippet': 'Langchain supports cohere RAG!'}\n",
-      "\n",
-      "\n",
-      "Langchain supports cohere RAG!\n",
-      "\n",
-      "\n",
-      "------------------------------\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "docs = rag.get_relevant_documents(\n",
-    "    \"Does langchain support cohere RAG?\",\n",
-    "    source_documents=[\n",
-    "        Document(page_content=\"Langchain supports cohere RAG!\"),\n",
-    "        Document(page_content=\"The sky is blue!\"),\n",
-    "    ],\n",
-    ")\n",
-    "_pretty_print(docs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f96b4b09-7e0b-412f-bd7b-2b2d19d8ac6a",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "poetry-venv",
-   "language": "python",
-   "name": "poetry-venv"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.1"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/retrievers/self_query/index.mdx

@@ -1,11 +0,0 @@
----
-sidebar-position: 0
----
-
-# Self-querying retriever
-
-Learn about how the self-querying retriever works [here](/docs/modules/data_connection/retrievers/self_query).
-
-import DocCardList from "@theme/DocCardList";
-
-<DocCardList />

docs/docs/integrations/retrievers/self_query/opensearch_self_query.ipynb

@@ -1,447 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "13afcae7",
-   "metadata": {},
-   "source": [
-    "# OpenSearch\n",
-    "\n",
-    "> [OpenSearch](https://opensearch.org/) is a scalable, flexible, and extensible open-source software suite for search, analytics, and observability applications licensed under Apache 2.0. `OpenSearch` is a distributed search and analytics engine based on `Apache Lucene`.\n",
-    "\n",
-    "In this notebook, we'll demo the `SelfQueryRetriever` with an `OpenSearch` vector store."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "68e75fb9",
-   "metadata": {},
-   "source": [
-    "## Creating an OpenSearch vector store\n",
-    "\n",
-    "First, we'll want to create an `OpenSearch` vector store and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n",
-    "\n",
-    "**Note:** The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `opensearch-py` package."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "!pip install lark opensearch-py"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "id": "6078a74d"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "cb4a5787",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdin",
-     "output_type": "stream",
-     "text": [
-      "OpenAI API Key: ········\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.schema import Document\n",
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.vectorstores import OpenSearchVectorSearch\n",
-    "import os\n",
-    "import getpass\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
-    "\n",
-    "embeddings = OpenAIEmbeddings()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "bcbe04d9",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "docs = [\n",
-    "    Document(\n",
-    "        page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",\n",
-    "        metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": \"science fiction\"},\n",
-    "    ),\n",
-    "    Document(\n",
-    "        page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",\n",
-    "        metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2},\n",
-    "    ),\n",
-    "    Document(\n",
-    "        page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",\n",
-    "        metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6},\n",
-    "    ),\n",
-    "    Document(\n",
-    "        page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",\n",
-    "        metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3},\n",
-    "    ),\n",
-    "    Document(\n",
-    "        page_content=\"Toys come alive and have a blast doing so\",\n",
-    "        metadata={\"year\": 1995, \"genre\": \"animated\"},\n",
-    "    ),\n",
-    "    Document(\n",
-    "        page_content=\"Three men walk into the Zone, three men walk out of the Zone\",\n",
-    "        metadata={\n",
-    "            \"year\": 1979,\n",
-    "            \"rating\": 9.9,\n",
-    "            \"director\": \"Andrei Tarkovsky\",\n",
-    "            \"genre\": \"science fiction\",\n",
-    "        },\n",
-    "    ),\n",
-    "]\n",
-    "vectorstore = OpenSearchVectorSearch.from_documents(\n",
-    "    docs,\n",
-    "    embeddings,\n",
-    "    index_name=\"opensearch-self-query-demo\",\n",
-    "    opensearch_url=\"http://localhost:9200\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5ecaab6d",
-   "metadata": {},
-   "source": [
-    "## Creating our self-querying retriever\n",
-    "Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "86e34dbf",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.llms import OpenAI\n",
-    "from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
-    "from langchain.chains.query_constructor.base import AttributeInfo\n",
-    "\n",
-    "metadata_field_info = [\n",
-    "    AttributeInfo(\n",
-    "        name=\"genre\",\n",
-    "        description=\"The genre of the movie\",\n",
-    "        type=\"string or list[string]\",\n",
-    "    ),\n",
-    "    AttributeInfo(\n",
-    "        name=\"year\",\n",
-    "        description=\"The year the movie was released\",\n",
-    "        type=\"integer\",\n",
-    "    ),\n",
-    "    AttributeInfo(\n",
-    "        name=\"director\",\n",
-    "        description=\"The name of the movie director\",\n",
-    "        type=\"string\",\n",
-    "    ),\n",
-    "    AttributeInfo(\n",
-    "        name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"\n",
-    "    ),\n",
-    "]\n",
-    "document_content_description = \"Brief summary of a movie\"\n",
-    "llm = OpenAI(temperature=0)\n",
-    "retriever = SelfQueryRetriever.from_llm(\n",
-    "    llm, vectorstore, document_content_description, metadata_field_info, verbose=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ea9df8d4",
-   "metadata": {},
-   "source": [
-    "## Testing it out\n",
-    "And now we can try actually using our retriever!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "38a126e9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "query='dinosaur' filter=None limit=None\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}),\n",
-       " Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}),\n",
-       " Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'year': 2010, 'director': 'Christopher Nolan', 'rating': 8.2}),\n",
-       " Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction'})]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "60bf0074-e65e-4558-a4f2-8190f3e4e2f9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "query=' ' filter=Comparison(comparator=<Comparator.GT: 'gt'>, attribute='rating', value=8.5) limit=None\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction'}),\n",
-       " Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.6})]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This example only specifies a filter\n",
-    "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "b19d4da0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "query='women' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='director', value='Greta Gerwig') limit=None\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'year': 2019, 'director': 'Greta Gerwig', 'rating': 8.3})]"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This example specifies a query and a filter\n",
-    "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "a59f946b-78a1-4d3e-9942-63834c7d7589",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='rating', value=8.5), Comparison(comparator=<Comparator.CONTAIN: 'contain'>, attribute='genre', value='science fiction')]) limit=None\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'director': 'Andrei Tarkovsky', 'genre': 'science fiction'})]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This example specifies a composite filter\n",
-    "retriever.get_relevant_documents(\n",
-    "    \"What's a highly rated (above 8.5) science fiction film?\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "39bd1de1-b9fe-4a98-89da-58d8a7a6ae51",
-   "metadata": {},
-   "source": [
-    "## Filter k\n",
-    "\n",
-    "We can also use the self query retriever to specify `k`: the number of documents to fetch.\n",
-    "\n",
-    "We can do this by passing `enable_limit=True` to the constructor."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "bff36b88-b506-4877-9c63-e5a1a8d78e64",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "retriever = SelfQueryRetriever.from_llm(\n",
-    "    llm,\n",
-    "    vectorstore,\n",
-    "    document_content_description,\n",
-    "    metadata_field_info,\n",
-    "    enable_limit=True,\n",
-    "    verbose=True,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "2758d229-4f97-499c-819f-888acaf8ee10",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "query='dinosaur' filter=None limit=2\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'science fiction'}),\n",
-       " Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# This example only specifies a relevant query\n",
-    "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "61a10294",
-   "metadata": {},
-   "source": [
-    "## Complex queries in Action!\n",
-    "We've tried out some simple queries, but what about more complex ones? Let's try out a few more complex queries that utilize the full power of OpenSearch."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "e460da93",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "query='animated toys' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Operation(operator=<Operator.OR: 'or'>, arguments=[Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='animated'), Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='genre', value='comedy')]), Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='year', value=1990)]) limit=None\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "retriever.get_relevant_documents(\n",
-    "    \"what animated or comedy movies have been released in the last 30 years about animated toys?\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "0851fc42",
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'acknowledged': True}"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vectorstore.client.indices.delete(index=\"opensearch-self-query-demo\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/retrievers/singlestoredb.ipynb

@@ -1,122 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "ab66dd43",
-   "metadata": {},
-   "source": [
-    "# SingleStoreDB\n",
-    "\n",
-    ">[SingleStoreDB](https://singlestore.com/) is a high-performance distributed SQL database that supports deployment both in the [cloud](https://www.singlestore.com/cloud/) and on-premises. It provides vector storage, and vector functions including [dot_product](https://docs.singlestore.com/managed-service/en/reference/sql-reference/vector-functions/dot_product.html) and [euclidean_distance](https://docs.singlestore.com/managed-service/en/reference/sql-reference/vector-functions/euclidean_distance.html), thereby supporting AI applications that require text similarity matching. \n",
-    "\n",
-    "\n",
-    "This notebook shows how to use a retriever that uses `SingleStoreDB`.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "51b49135-a61a-49e8-869d-7c1d76794cd7",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "# Establishing a connection to the database is facilitated through the singlestoredb Python connector.\n",
-    "# Please ensure that this connector is installed in your working environment.\n",
-    "!pip install singlestoredb"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aaf80e7f",
-   "metadata": {},
-   "source": [
-    "## Create Retriever from vector store"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bcb3c8c2",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import getpass\n",
-    "\n",
-    "# We want to use OpenAIEmbeddings so we have to get the OpenAI API Key.\n",
-    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
-    "\n",
-    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
-    "from langchain.text_splitter import CharacterTextSplitter\n",
-    "from langchain.vectorstores import SingleStoreDB\n",
-    "from langchain.document_loaders import TextLoader\n",
-    "\n",
-    "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n",
-    "documents = loader.load()\n",
-    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
-    "docs = text_splitter.split_documents(documents)\n",
-    "\n",
-    "embeddings = OpenAIEmbeddings()\n",
-    "\n",
-    "# Setup connection url as environment variable\n",
-    "os.environ[\"SINGLESTOREDB_URL\"] = \"root:pass@localhost:3306/db\"\n",
-    "\n",
-    "# Load documents to the store\n",
-    "docsearch = SingleStoreDB.from_documents(\n",
-    "    docs,\n",
-    "    embeddings,\n",
-    "    table_name=\"notebook\",  # use table with a custom name\n",
-    ")\n",
-    "\n",
-    "# create retriever from the vector store\n",
-    "retriever = docsearch.as_retriever(search_kwargs={\"k\": 2})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fc0915db",
-   "metadata": {},
-   "source": [
-    "## Search with retriever"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "b605284d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "result = retriever.get_relevant_documents(\n",
-    "    \"What did the president say about Ketanji Brown Jackson\"\n",
-    ")\n",
-    "print(docs[0].page_content)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/docs/integrations/retrievers/zep_memorystore.ipynb

@@ -1,509 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Zep\n",
-    "## Retriever Example for [Zep](https://docs.getzep.com/)\n",
-    "\n",
-    "### Fast, Scalable Building Blocks for LLM Apps\n",
-    "Zep is an open source platform for productionizing LLM apps. Go from a prototype\n",
-    "built in LangChain or LlamaIndex, or a custom app, to production in minutes without\n",
-    "rewriting code.\n",
-    "\n",
-    "Key Features:\n",
-    "\n",
-    "- **Fast!** Zep operates independently of the your chat loop, ensuring a snappy user experience.\n",
-    "- **Chat History Memory, Archival, and Enrichment**, populate your prompts with relevant chat history, sumamries, named entities, intent data, and more.\n",
-    "- **Vector Search over Chat History and Documents** Automatic embedding of documents, chat histories, and summaries. Use Zep's similarity or native MMR Re-ranked search to find the most relevant.\n",
-    "- **Manage Users and their Chat Sessions** Users and their Chat Sessions are first-class citizens in Zep, allowing you to manage user interactions with your bots or agents easily.\n",
-    "- **Records Retention and Privacy Compliance** Comply with corporate and regulatory mandates for records retention while ensuring compliance with privacy regulations such as CCPA and GDPR. Fulfill *Right To Be Forgotten* requests with a single API call\n",
-    "\n",
-    "Zep project: [https://github.com/getzep/zep](https://github.com/getzep/zep)\n",
-    "Docs: [https://docs.getzep.com/](https://docs.getzep.com/)\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Retriever Example\n",
-    "\n",
-    "This notebook demonstrates how to search historical chat message histories using the [Zep Long-term Memory Store](https://getzep.github.io/).\n",
-    "\n",
-    "We'll demonstrate:\n",
-    "\n",
-    "1. Adding conversation history to the Zep memory store.\n",
-    "2. Vector search over the conversation history: \n",
-    "    1. With a similarity search over chat messages\n",
-    "    2. Using maximal marginal relevance re-ranking of a chat message search\n",
-    "    3. Filtering a search using metadata filters\n",
-    "    4. A similarity search over summaries of the chat messages\n",
-    "    5. Using maximal marginal relevance re-ranking of a summary search\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-08-11T20:31:12.231459Z",
-     "start_time": "2023-08-11T20:31:11.211176Z"
-    },
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "import getpass\n",
-    "import time\n",
-    "from uuid import uuid4\n",
-    "\n",
-    "from langchain.memory import ZepMemory\n",
-    "from langchain.schema import HumanMessage, AIMessage\n",
-    "\n",
-    "# Set this to your Zep server URL\n",
-    "ZEP_API_URL = \"http://localhost:8000\""
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Initialize the Zep Chat Message History Class and add a chat message history to the memory store\n",
-    "\n",
-    "**NOTE:** Unlike other Retrievers, the content returned by the Zep Retriever is session/user specific. A `session_id` is required when instantiating the Retriever."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-08-11T20:31:12.237545Z",
-     "start_time": "2023-08-11T20:31:12.232416Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# Provide your Zep API key. Note that this is optional. See https://docs.getzep.com/deployment/auth\n",
-    "AUTHENTICATE = False\n",
-    "\n",
-    "zep_api_key = None\n",
-    "if AUTHENTICATE:\n",
-    "    zep_api_key = getpass.getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-08-11T20:31:12.342790Z",
-     "start_time": "2023-08-11T20:31:12.235291Z"
-    },
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "session_id = str(uuid4())  # This is a unique identifier for the user/session\n",
-    "\n",
-    "# Initialize the Zep Memory Class\n",
-    "zep_memory = ZepMemory(session_id=session_id, url=ZEP_API_URL, api_key=zep_api_key)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-08-11T20:31:14.455269Z",
-     "start_time": "2023-08-11T20:31:12.345635Z"
-    },
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Preload some messages into the memory. The default message window is 12 messages. We want to push beyond this to demonstrate auto-summarization.\n",
-    "test_history = [\n",
-    "    {\"role\": \"human\", \"content\": \"Who was Octavia Butler?\"},\n",
-    "    {\n",
-    "        \"role\": \"ai\",\n",
-    "        \"content\": (\n",
-    "            \"Octavia Estelle Butler (June 22, 1947 – February 24, 2006) was an American\"\n",
-    "            \" science fiction author.\"\n",
-    "        ),\n",
-    "    },\n",
-    "    {\"role\": \"human\", \"content\": \"Which books of hers were made into movies?\"},\n",
-    "    {\n",
-    "        \"role\": \"ai\",\n",
-    "        \"content\": (\n",
-    "            \"The most well-known adaptation of Octavia Butler's work is the FX series\"\n",
-    "            \" Kindred, based on her novel of the same name.\"\n",
-    "        ),\n",
-    "    },\n",
-    "    {\"role\": \"human\", \"content\": \"Who were her contemporaries?\"},\n",
-    "    {\n",
-    "        \"role\": \"ai\",\n",
-    "        \"content\": (\n",
-    "            \"Octavia Butler's contemporaries included Ursula K. Le Guin, Samuel R.\"\n",
-    "            \" Delany, and Joanna Russ.\"\n",
-    "        ),\n",
-    "    },\n",
-    "    {\"role\": \"human\", \"content\": \"What awards did she win?\"},\n",
-    "    {\n",
-    "        \"role\": \"ai\",\n",
-    "        \"content\": (\n",
-    "            \"Octavia Butler won the Hugo Award, the Nebula Award, and the MacArthur\"\n",
-    "            \" Fellowship.\"\n",
-    "        ),\n",
-    "    },\n",
-    "    {\n",
-    "        \"role\": \"human\",\n",
-    "        \"content\": \"Which other women sci-fi writers might I want to read?\",\n",
-    "    },\n",
-    "    {\n",
-    "        \"role\": \"ai\",\n",
-    "        \"content\": \"You might want to read Ursula K. Le Guin or Joanna Russ.\",\n",
-    "    },\n",
-    "    {\n",
-    "        \"role\": \"human\",\n",
-    "        \"content\": (\n",
-    "            \"Write a short synopsis of Butler's book, Parable of the Sower. What is it\"\n",
-    "            \" about?\"\n",
-    "        ),\n",
-    "    },\n",
-    "    {\n",
-    "        \"role\": \"ai\",\n",
-    "        \"content\": (\n",
-    "            \"Parable of the Sower is a science fiction novel by Octavia Butler,\"\n",
-    "            \" published in 1993. It follows the story of Lauren Olamina, a young woman\"\n",
-    "            \" living in a dystopian future where society has collapsed due to\"\n",
-    "            \" environmental disasters, poverty, and violence.\"\n",
-    "        ),\n",
-    "    },\n",
-    "    {\"role\": \"human\", \"content\": \"What is the setting of the book?\"},\n",
-    "    {\n",
-    "        \"role\": \"ai\",\n",
-    "        \"content\": (\n",
-    "            \"The book is set in a dystopian future in the 2020s, where society has\"\n",
-    "            \" collapsed due to climate change and economic crises.\"\n",
-    "        ),\n",
-    "    },\n",
-    "    {\"role\": \"human\", \"content\": \"Who is the protagonist?\"},\n",
-    "    {\n",
-    "        \"role\": \"ai\",\n",
-    "        \"content\": (\n",
-    "            \"The protagonist of the book is Lauren Olamina, a young woman who possesses\"\n",
-    "            \" 'hyperempathy', the ability to feel pain and other sensations she\"\n",
-    "            \" witnesses.\"\n",
-    "        ),\n",
-    "    },\n",
-    "    {\"role\": \"human\", \"content\": \"What is the main theme of the book?\"},\n",
-    "    {\n",
-    "        \"role\": \"ai\",\n",
-    "        \"content\": (\n",
-    "            \"The main theme of the book is survival in the face of drastic societal\"\n",
-    "            \" change and collapse. It also explores themes of adaptability, community,\"\n",
-    "            \" and the human capacity for change.\"\n",
-    "        ),\n",
-    "    },\n",
-    "    {\"role\": \"human\", \"content\": \"What is the 'Parable of the Sower'?\"},\n",
-    "    {\n",
-    "        \"role\": \"ai\",\n",
-    "        \"content\": (\n",
-    "            \"The 'Parable of the Sower' is a biblical parable that Butler uses as a\"\n",
-    "            \" metaphor in the book. In the parable, a sower scatters seeds, some of\"\n",
-    "            \" which fall on fertile ground and grow, while others fall on rocky ground\"\n",
-    "            \" or among thorns and fail to grow. The parable is used to illustrate the\"\n",
-    "            \" importance of receptivity and preparedness in the face of change.\"\n",
-    "        ),\n",
-    "    },\n",
-    "    {\"role\": \"human\", \"content\": \"What is Butler's writing style like?\"},\n",
-    "    {\n",
-    "        \"role\": \"ai\",\n",
-    "        \"content\": (\n",
-    "            \"Butler's writing style is known for its clarity, directness, and\"\n",
-    "            \" psychological insight. Her narratives often involve complex, diverse\"\n",
-    "            \" characters and explore themes of race, gender, and power.\"\n",
-    "        ),\n",
-    "    },\n",
-    "    {\"role\": \"human\", \"content\": \"What other books has she written?\"},\n",
-    "    {\n",
-    "        \"role\": \"ai\",\n",
-    "        \"content\": (\n",
-    "            \"In addition to 'Parable of the Sower', Butler has written several other\"\n",
-    "            \" notable works, including 'Kindred', 'Dawn', and 'Parable of the Talents'.\"\n",
-    "        ),\n",
-    "    },\n",
-    "]\n",
-    "\n",
-    "for msg in test_history:\n",
-    "    zep_memory.chat_memory.add_message(\n",
-    "        HumanMessage(content=msg[\"content\"])\n",
-    "        if msg[\"role\"] == \"human\"\n",
-    "        else AIMessage(content=msg[\"content\"])\n",
-    "    )\n",
-    "\n",
-    "time.sleep(\n",
-    "    10\n",
-    ")  # Wait for the messages to be embedded and summarized. Speed depends on OpenAI API latency and your rate limits."
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Use the Zep Retriever to vector search over the Zep memory\n",
-    "\n",
-    "Zep provides native vector search over historical conversation memory. Embedding happens automatically.\n",
-    "\n",
-    "NOTE: Embedding of messages occurs asynchronously, so the first query may not return results. Subsequent queries will return results as the embeddings are generated."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-08-11T20:31:14.758738Z",
-     "start_time": "2023-08-11T20:31:14.458850Z"
-    },
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content=\"What is the 'Parable of the Sower'?\", metadata={'score': 0.9250216484069824, 'uuid': '4cbfb1c0-6027-4678-af43-1e18acb224bb', 'created_at': '2023-11-01T00:32:40.224256Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'human', 'metadata': {'system': {'entities': [{'Label': 'WORK_OF_ART', 'Matches': [{'End': 34, 'Start': 13, 'Text': \"Parable of the Sower'\"}], 'Name': \"Parable of the Sower'\"}]}}, 'token_count': 13}),\n",
-       " Document(page_content='Parable of the Sower is a science fiction novel by Octavia Butler, published in 1993. It follows the story of Lauren Olamina, a young woman living in a dystopian future where society has collapsed due to environmental disasters, poverty, and violence.', metadata={'score': 0.8897348046302795, 'uuid': '3dd9f5ed-9dc9-4427-9da6-aba1b8278a5c', 'created_at': '2023-11-01T00:32:40.192527Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'GPE', 'Matches': [{'End': 20, 'Start': 15, 'Text': 'Sower'}], 'Name': 'Sower'}, {'Label': 'PERSON', 'Matches': [{'End': 65, 'Start': 51, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'DATE', 'Matches': [{'End': 84, 'Start': 80, 'Text': '1993'}], 'Name': '1993'}, {'Label': 'PERSON', 'Matches': [{'End': 124, 'Start': 110, 'Text': 'Lauren Olamina'}], 'Name': 'Lauren Olamina'}], 'intent': 'Providing information'}}, 'token_count': 56}),\n",
-       " Document(page_content=\"Write a short synopsis of Butler's book, Parable of the Sower. What is it about?\", metadata={'score': 0.8856019973754883, 'uuid': '81761dcb-38f3-4686-a4f5-6cb1007eaf29', 'created_at': '2023-11-01T00:32:40.187543Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'human', 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 32, 'Start': 26, 'Text': 'Butler'}], 'Name': 'Butler'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 61, 'Start': 41, 'Text': 'Parable of the Sower'}], 'Name': 'Parable of the Sower'}], 'intent': \"The subject is asking for a brief summary of Butler's book, Parable of the Sower, and what it is about.\"}}, 'token_count': 23}),\n",
-       " Document(page_content=\"The 'Parable of the Sower' is a biblical parable that Butler uses as a metaphor in the book. In the parable, a sower scatters seeds, some of which fall on fertile ground and grow, while others fall on rocky ground or among thorns and fail to grow. The parable is used to illustrate the importance of receptivity and preparedness in the face of change.\", metadata={'score': 0.8781436681747437, 'uuid': '1a8c5f99-2fec-425d-bc37-176ab91e7080', 'created_at': '2023-11-01T00:32:40.22836Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'WORK_OF_ART', 'Matches': [{'End': 26, 'Start': 5, 'Text': \"Parable of the Sower'\"}], 'Name': \"Parable of the Sower'\"}, {'Label': 'ORG', 'Matches': [{'End': 60, 'Start': 54, 'Text': 'Butler'}], 'Name': 'Butler'}]}}, 'token_count': 84}),\n",
-       " Document(page_content=\"In addition to 'Parable of the Sower', Butler has written several other notable works, including 'Kindred', 'Dawn', and 'Parable of the Talents'.\", metadata={'score': 0.8745182752609253, 'uuid': '45d8aa08-85ab-432f-8902-81712fe363b9', 'created_at': '2023-11-01T00:32:40.245081Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'WORK_OF_ART', 'Matches': [{'End': 37, 'Start': 16, 'Text': \"Parable of the Sower'\"}], 'Name': \"Parable of the Sower'\"}, {'Label': 'ORG', 'Matches': [{'End': 45, 'Start': 39, 'Text': 'Butler'}], 'Name': 'Butler'}, {'Label': 'GPE', 'Matches': [{'End': 105, 'Start': 98, 'Text': 'Kindred'}], 'Name': 'Kindred'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 144, 'Start': 121, 'Text': \"Parable of the Talents'\"}], 'Name': \"Parable of the Talents'\"}]}}, 'token_count': 39})]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.retrievers import ZepRetriever\n",
-    "from langchain.retrievers.zep import SearchType, SearchScope\n",
-    "\n",
-    "zep_retriever = ZepRetriever(\n",
-    "    session_id=session_id,  # Ensure that you provide the session_id when instantiating the Retriever\n",
-    "    url=ZEP_API_URL,\n",
-    "    top_k=5,\n",
-    "    api_key=zep_api_key,\n",
-    ")\n",
-    "\n",
-    "await zep_retriever.aget_relevant_documents(\"Who wrote Parable of the Sower?\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also use the Zep sync API to retrieve results:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-08-11T20:31:14.922838Z",
-     "start_time": "2023-08-11T20:31:14.751737Z"
-    },
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content=\"What is the 'Parable of the Sower'?\", metadata={'score': 0.9250596761703491, 'uuid': '4cbfb1c0-6027-4678-af43-1e18acb224bb', 'created_at': '2023-11-01T00:32:40.224256Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'human', 'metadata': {'system': {'entities': [{'Label': 'WORK_OF_ART', 'Matches': [{'End': 34, 'Start': 13, 'Text': \"Parable of the Sower'\"}], 'Name': \"Parable of the Sower'\"}]}}, 'token_count': 13}),\n",
-       " Document(page_content='Parable of the Sower is a science fiction novel by Octavia Butler, published in 1993. It follows the story of Lauren Olamina, a young woman living in a dystopian future where society has collapsed due to environmental disasters, poverty, and violence.', metadata={'score': 0.8897120952606201, 'uuid': '3dd9f5ed-9dc9-4427-9da6-aba1b8278a5c', 'created_at': '2023-11-01T00:32:40.192527Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'GPE', 'Matches': [{'End': 20, 'Start': 15, 'Text': 'Sower'}], 'Name': 'Sower'}, {'Label': 'PERSON', 'Matches': [{'End': 65, 'Start': 51, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'DATE', 'Matches': [{'End': 84, 'Start': 80, 'Text': '1993'}], 'Name': '1993'}, {'Label': 'PERSON', 'Matches': [{'End': 124, 'Start': 110, 'Text': 'Lauren Olamina'}], 'Name': 'Lauren Olamina'}], 'intent': 'Providing information'}}, 'token_count': 56}),\n",
-       " Document(page_content=\"Write a short synopsis of Butler's book, Parable of the Sower. What is it about?\", metadata={'score': 0.885666012763977, 'uuid': '81761dcb-38f3-4686-a4f5-6cb1007eaf29', 'created_at': '2023-11-01T00:32:40.187543Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'human', 'metadata': {'system': {'entities': [{'Label': 'ORG', 'Matches': [{'End': 32, 'Start': 26, 'Text': 'Butler'}], 'Name': 'Butler'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 61, 'Start': 41, 'Text': 'Parable of the Sower'}], 'Name': 'Parable of the Sower'}], 'intent': \"The subject is asking for a brief summary of Butler's book, Parable of the Sower, and what it is about.\"}}, 'token_count': 23}),\n",
-       " Document(page_content=\"The 'Parable of the Sower' is a biblical parable that Butler uses as a metaphor in the book. In the parable, a sower scatters seeds, some of which fall on fertile ground and grow, while others fall on rocky ground or among thorns and fail to grow. The parable is used to illustrate the importance of receptivity and preparedness in the face of change.\", metadata={'score': 0.878172755241394, 'uuid': '1a8c5f99-2fec-425d-bc37-176ab91e7080', 'created_at': '2023-11-01T00:32:40.22836Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'WORK_OF_ART', 'Matches': [{'End': 26, 'Start': 5, 'Text': \"Parable of the Sower'\"}], 'Name': \"Parable of the Sower'\"}, {'Label': 'ORG', 'Matches': [{'End': 60, 'Start': 54, 'Text': 'Butler'}], 'Name': 'Butler'}]}}, 'token_count': 84}),\n",
-       " Document(page_content=\"In addition to 'Parable of the Sower', Butler has written several other notable works, including 'Kindred', 'Dawn', and 'Parable of the Talents'.\", metadata={'score': 0.8745154142379761, 'uuid': '45d8aa08-85ab-432f-8902-81712fe363b9', 'created_at': '2023-11-01T00:32:40.245081Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'WORK_OF_ART', 'Matches': [{'End': 37, 'Start': 16, 'Text': \"Parable of the Sower'\"}], 'Name': \"Parable of the Sower'\"}, {'Label': 'ORG', 'Matches': [{'End': 45, 'Start': 39, 'Text': 'Butler'}], 'Name': 'Butler'}, {'Label': 'GPE', 'Matches': [{'End': 105, 'Start': 98, 'Text': 'Kindred'}], 'Name': 'Kindred'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 144, 'Start': 121, 'Text': \"Parable of the Talents'\"}], 'Name': \"Parable of the Talents'\"}]}}, 'token_count': 39})]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "zep_retriever.get_relevant_documents(\"Who wrote Parable of the Sower?\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Reranking using MMR (Maximal Marginal Relevance)\n",
-    "\n",
-    "Zep has native, SIMD-accelerated support for reranking results using MMR. This is useful for removing redundancy in results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-08-11T20:31:14.923032Z",
-     "start_time": "2023-08-11T20:31:14.918181Z"
-    },
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content=\"What is the 'Parable of the Sower'?\", metadata={'score': 0.9250596761703491, 'uuid': '4cbfb1c0-6027-4678-af43-1e18acb224bb', 'created_at': '2023-11-01T00:32:40.224256Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'human', 'metadata': {'system': {'entities': [{'Label': 'WORK_OF_ART', 'Matches': [{'End': 34, 'Start': 13, 'Text': \"Parable of the Sower'\"}], 'Name': \"Parable of the Sower'\"}]}}, 'token_count': 13}),\n",
-       " Document(page_content='What other books has she written?', metadata={'score': 0.77488774061203, 'uuid': '1b3c5079-9cab-46f3-beae-fb56c572e0fd', 'created_at': '2023-11-01T00:32:40.240135Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'human', 'token_count': 9}),\n",
-       " Document(page_content=\"In addition to 'Parable of the Sower', Butler has written several other notable works, including 'Kindred', 'Dawn', and 'Parable of the Talents'.\", metadata={'score': 0.8745154142379761, 'uuid': '45d8aa08-85ab-432f-8902-81712fe363b9', 'created_at': '2023-11-01T00:32:40.245081Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'WORK_OF_ART', 'Matches': [{'End': 37, 'Start': 16, 'Text': \"Parable of the Sower'\"}], 'Name': \"Parable of the Sower'\"}, {'Label': 'ORG', 'Matches': [{'End': 45, 'Start': 39, 'Text': 'Butler'}], 'Name': 'Butler'}, {'Label': 'GPE', 'Matches': [{'End': 105, 'Start': 98, 'Text': 'Kindred'}], 'Name': 'Kindred'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 144, 'Start': 121, 'Text': \"Parable of the Talents'\"}], 'Name': \"Parable of the Talents'\"}]}}, 'token_count': 39}),\n",
-       " Document(page_content='Parable of the Sower is a science fiction novel by Octavia Butler, published in 1993. It follows the story of Lauren Olamina, a young woman living in a dystopian future where society has collapsed due to environmental disasters, poverty, and violence.', metadata={'score': 0.8897120952606201, 'uuid': '3dd9f5ed-9dc9-4427-9da6-aba1b8278a5c', 'created_at': '2023-11-01T00:32:40.192527Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'GPE', 'Matches': [{'End': 20, 'Start': 15, 'Text': 'Sower'}], 'Name': 'Sower'}, {'Label': 'PERSON', 'Matches': [{'End': 65, 'Start': 51, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'DATE', 'Matches': [{'End': 84, 'Start': 80, 'Text': '1993'}], 'Name': '1993'}, {'Label': 'PERSON', 'Matches': [{'End': 124, 'Start': 110, 'Text': 'Lauren Olamina'}], 'Name': 'Lauren Olamina'}], 'intent': 'Providing information'}}, 'token_count': 56}),\n",
-       " Document(page_content='Who is the protagonist?', metadata={'score': 0.7858647704124451, 'uuid': 'ee514b37-a0b0-4d24-b0c9-3e9f8ad9d52d', 'created_at': '2023-11-01T00:32:40.203891Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'human', 'metadata': {'system': {'intent': 'The subject is asking about the identity of the protagonist in a specific context, such as a story, movie, or game.'}}, 'token_count': 7})]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "zep_retriever = ZepRetriever(\n",
-    "    session_id=session_id,  # Ensure that you provide the session_id when instantiating the Retriever\n",
-    "    url=ZEP_API_URL,\n",
-    "    top_k=5,\n",
-    "    api_key=zep_api_key,\n",
-    "    search_type=SearchType.mmr,\n",
-    "    mmr_lambda=0.5,\n",
-    ")\n",
-    "\n",
-    "await zep_retriever.aget_relevant_documents(\"Who wrote Parable of the Sower?\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Using metadata filters to refine search results\n",
-    "\n",
-    "Zep supports filtering results by metadata. This is useful for filtering results by entity type, or other metadata.\n",
-    "\n",
-    "More information here: https://docs.getzep.com/sdk/search_query/"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content=\"What is the 'Parable of the Sower'?\", metadata={'score': 0.9251098036766052, 'uuid': '4cbfb1c0-6027-4678-af43-1e18acb224bb', 'created_at': '2023-11-01T00:32:40.224256Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'human', 'metadata': {'system': {'entities': [{'Label': 'WORK_OF_ART', 'Matches': [{'End': 34, 'Start': 13, 'Text': \"Parable of the Sower'\"}], 'Name': \"Parable of the Sower'\"}]}}, 'token_count': 13}),\n",
-       " Document(page_content='What other books has she written?', metadata={'score': 0.7747920155525208, 'uuid': '1b3c5079-9cab-46f3-beae-fb56c572e0fd', 'created_at': '2023-11-01T00:32:40.240135Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'human', 'token_count': 9}),\n",
-       " Document(page_content=\"In addition to 'Parable of the Sower', Butler has written several other notable works, including 'Kindred', 'Dawn', and 'Parable of the Talents'.\", metadata={'score': 0.8745266795158386, 'uuid': '45d8aa08-85ab-432f-8902-81712fe363b9', 'created_at': '2023-11-01T00:32:40.245081Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'WORK_OF_ART', 'Matches': [{'End': 37, 'Start': 16, 'Text': \"Parable of the Sower'\"}], 'Name': \"Parable of the Sower'\"}, {'Label': 'ORG', 'Matches': [{'End': 45, 'Start': 39, 'Text': 'Butler'}], 'Name': 'Butler'}, {'Label': 'GPE', 'Matches': [{'End': 105, 'Start': 98, 'Text': 'Kindred'}], 'Name': 'Kindred'}, {'Label': 'WORK_OF_ART', 'Matches': [{'End': 144, 'Start': 121, 'Text': \"Parable of the Talents'\"}], 'Name': \"Parable of the Talents'\"}]}}, 'token_count': 39}),\n",
-       " Document(page_content='Parable of the Sower is a science fiction novel by Octavia Butler, published in 1993. It follows the story of Lauren Olamina, a young woman living in a dystopian future where society has collapsed due to environmental disasters, poverty, and violence.', metadata={'score': 0.8897372484207153, 'uuid': '3dd9f5ed-9dc9-4427-9da6-aba1b8278a5c', 'created_at': '2023-11-01T00:32:40.192527Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'ai', 'metadata': {'system': {'entities': [{'Label': 'GPE', 'Matches': [{'End': 20, 'Start': 15, 'Text': 'Sower'}], 'Name': 'Sower'}, {'Label': 'PERSON', 'Matches': [{'End': 65, 'Start': 51, 'Text': 'Octavia Butler'}], 'Name': 'Octavia Butler'}, {'Label': 'DATE', 'Matches': [{'End': 84, 'Start': 80, 'Text': '1993'}], 'Name': '1993'}, {'Label': 'PERSON', 'Matches': [{'End': 124, 'Start': 110, 'Text': 'Lauren Olamina'}], 'Name': 'Lauren Olamina'}], 'intent': 'Providing information'}}, 'token_count': 56}),\n",
-       " Document(page_content='Who is the protagonist?', metadata={'score': 0.7858127355575562, 'uuid': 'ee514b37-a0b0-4d24-b0c9-3e9f8ad9d52d', 'created_at': '2023-11-01T00:32:40.203891Z', 'updated_at': '0001-01-01T00:00:00Z', 'role': 'human', 'metadata': {'system': {'intent': 'The subject is asking about the identity of the protagonist in a specific context, such as a story, movie, or game.'}}, 'token_count': 7})]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "filter = {\"where\": {\"jsonpath\": '$[*] ? (@.Label == \"WORK_OF_ART\")'}}\n",
-    "\n",
-    "await zep_retriever.aget_relevant_documents(\n",
-    "    \"Who wrote Parable of the Sower?\", metadata=filter\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Searching over Summaries with MMR Reranking\n",
-    "\n",
-    "Zep automatically generates summaries of chat messages. These summaries can be searched over using the Zep Retriever. Since a summary is a distillation of a conversation, they're more likely to match your search query and offer rich, succinct context to the LLM.\n",
-    "\n",
-    "Successive summaries may include similar content, with Zep's similarity search returning the highest matching results but with little diversity.\n",
-    "MMR re-ranks the results to ensure that the summaries you populate into your prompt are both relevant and each offers additional information to the LLM."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[Document(page_content='The human asks about Octavia Butler and the AI informs them that she was an American science fiction author. The human\\nasks which of her books were made into movies and the AI mentions the FX series Kindred. The human then asks about her\\ncontemporaries and the AI lists Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ. The human also asks about the awards\\nshe won and the AI mentions the Hugo Award, the Nebula Award, and the MacArthur Fellowship. The human asks about other women sci-fi writers to read and the AI suggests Ursula K. Le Guin and Joanna Russ. The human then asks for a synopsis of Butler\\'s book \"Parable of the Sower\" and the AI describes it.', metadata={'score': 0.7882999777793884, 'uuid': '3c95a29a-52dc-4112-b8a7-e6b1dc414d45', 'created_at': '2023-11-01T00:32:47.76449Z', 'token_count': 155}),\n",
-       " Document(page_content='The human asks about Octavia Butler. The AI informs the human that Octavia Estelle Butler was an American science \\nfiction author. The human then asks which books of hers were made into movies and the AI mentions the FX series Kindred, \\nbased on her novel of the same name.', metadata={'score': 0.7407922744750977, 'uuid': '0e027f4d-d71f-42ae-977f-696b8948b8bf', 'created_at': '2023-11-01T00:32:41.637098Z', 'token_count': 59}),\n",
-       " Document(page_content='The human asks about Octavia Butler and the AI informs them that she was an American science fiction author. The human\\nasks which of her books were made into movies and the AI mentions the FX series Kindred. The human then asks about her\\ncontemporaries and the AI lists Ursula K. Le Guin, Samuel R. Delany, and Joanna Russ. The human also asks about the awards\\nshe won and the AI mentions the Hugo Award, the Nebula Award, and the MacArthur Fellowship.', metadata={'score': 0.7436535358428955, 'uuid': 'b3500d1b-1a78-4aef-9e24-6b196cfa83cb', 'created_at': '2023-11-01T00:32:44.24744Z', 'token_count': 104})]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "zep_retriever = ZepRetriever(\n",
-    "    session_id=session_id,  # Ensure that you provide the session_id when instantiating the Retriever\n",
-    "    url=ZEP_API_URL,\n",
-    "    top_k=3,\n",
-    "    api_key=zep_api_key,\n",
-    "    search_scope=SearchScope.summary,\n",
-    "    search_type=SearchType.mmr,\n",
-    "    mmr_lambda=0.5,\n",
-    ")\n",
-    "\n",
-    "await zep_retriever.aget_relevant_documents(\"Who wrote Parable of the Sower?\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/docs/integrations/text_embedding/johnsnowlabs_embedding.ipynb

@@ -1,184 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# Johnsnowlabs Embedding\n",
-    "\n",
-    "### Loading the Johnsnowlabs embedding class to generate and query embeddings\n",
-    "\n",
-    "Models are loaded with [nlp.load](https://nlp.johnsnowlabs.com/docs/en/jsl/load_api) and spark session is started with [nlp.start()](https://nlp.johnsnowlabs.com/docs/en/jsl/start-a-sparksession) under the hood.\n",
-    "For all 24.000+ models, see the [John Snow Labs Model Models Hub](https://nlp.johnsnowlabs.com/models)\n"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "! pip install johnsnowlabs\n"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "# If you have a enterprise license, you can run this to install enterprise features\n",
-    "# from johnsnowlabs import nlp\n",
-    "# nlp.install()"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "source": [
-    "#### Import the necessary classes"
-   ],
-   "metadata": {
-    "collapsed": false
-   },
-   "execution_count": 1,
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Found existing installation: langchain 0.0.189\n",
-      "Uninstalling langchain-0.0.189:\n",
-      "  Successfully uninstalled langchain-0.0.189\n"
-     ]
-    }
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "from langchain.embeddings.johnsnowlabs import JohnSnowLabsEmbeddings"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### Initialize Johnsnowlabs Embeddings and Spark Session"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "embedder = JohnSnowLabsEmbeddings(\"en.embed_sentence.biobert.clinical_base_cased\")"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### Define some example texts . These could be any documents that you want to analyze - for example, news articles, social media posts, or product reviews."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "texts = [\"Cancer is caused by smoking\", \"Antibiotics aren't painkiller\"]"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### Generate and print embeddings for the texts . The JohnSnowLabsEmbeddings class generates an embedding for each document, which is a numerical representation of the document's content. These embeddings can be used for various natural language processing tasks, such as document similarity comparison or text classification."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "embeddings = embedder.embed_documents(texts)\n",
-    "for i, embedding in enumerate(embeddings):\n",
-    "    print(f\"Embedding for document {i+1}: {embedding}\")"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "#### Generate and print an embedding for a single piece of text. You can also generate an embedding for a single piece of text, such as a search query. This can be useful for tasks like information retrieval, where you want to find documents that are similar to a given query."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "query = \"Cancer is caused by smoking\"\n",
-    "query_embedding = embedder.embed_query(query)\n",
-    "print(f\"Embedding for query: {query_embedding}\")"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}

docs/docs/integrations/text_embedding/voyageai.ipynb

@@ -1,228 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "278b6c63",
-   "metadata": {},
-   "source": [
-    "# Voyage AI\n",
-    "\n",
-    "Let's load the Voyage Embedding class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "0be1af71",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.embeddings import VoyageEmbeddings"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "137cfde9-b88c-409a-9394-a9e31a6bf30d",
-   "metadata": {},
-   "source": [
-    "Voyage AI utilizes API keys to monitor usage and manage permissions. To obtain your key, create an account on our [homepage](https://www.voyageai.com). Then, create a VoyageEmbeddings model with your API key."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "2c66e5da",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "embeddings = VoyageEmbeddings(voyage_api_key=\"[ Your Voyage API key ]\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "459dffb3-9bff-41f2-8507-642de7431b2d",
-   "metadata": {},
-   "source": [
-    "Prepare the documents and use `embed_documents` to get their embeddings."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "c85e948f-85fd-4d56-8d21-6e2f7e65cab8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "documents = [\n",
-    "    \"Caching embeddings enables the storage or temporary caching of embeddings, eliminating the necessity to recompute them each time.\",\n",
-    "    \"An LLMChain is a chain that composes basic LLM functionality. It consists of a PromptTemplate and a language model (either an LLM or chat model). It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output.\",\n",
-    "    \"A Runnable represents a generic unit of work that can be invoked, batched, streamed, and/or transformed.\",\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "5a77a12d-6ac6-4ab8-b103-80ff24487019",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "documents_embds = embeddings.embed_documents(documents)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "2c89167c-816c-487e-8704-90908a4190bb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0.0562174916267395,\n",
-       " 0.018221192061901093,\n",
-       " 0.0025736060924828053,\n",
-       " -0.009720131754875183,\n",
-       " 0.04108370840549469]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "documents_embds[0][:5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f8d796d1-4ced-44d3-81bf-282721edb6bb",
-   "metadata": {},
-   "source": [
-    "Similarly, use `embed_query` to embed the query."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "bfb6142c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query = \"What's an LLMChain?\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "91bc875d-829b-4c3d-8e6f-fc2dda30a3bd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "query_embd = embeddings.embed_query(query)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "a4b0d49e-0c73-44b6-aed5-5b426564e085",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[-0.0052348352037370205,\n",
-       " -0.040072452276945114,\n",
-       " 0.0033957737032324076,\n",
-       " 0.01763271726667881,\n",
-       " -0.019235141575336456]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "query_embd[:5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b16ddbb2-61f0-49ec-92c3-a6f236d9517f",
-   "metadata": {},
-   "source": [
-    "## A minimalist retrieval system"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5464cb0a-6967-4f1e-ac7c-0aab80b2795a",
-   "metadata": {},
-   "source": [
-    "The main feature of the embeddings is that the cosine similarity between two embeddings captures the semantic relatedness of the corresponding original passages. This allows us to use the embeddings to do semantic retrieval / search."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0bd3ad2-ca68-4e75-9172-76aea28ba46e",
-   "metadata": {},
-   "source": [
-    " We can find a few closest embeddings in the documents embeddings based on the cosine similarity, and retrieve the corresponding document using the `KNNRetriever` class from LangChain."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "0a3fc579-85a9-4bd0-a944-4e32ac62e2d4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "An LLMChain is a chain that composes basic LLM functionality. It consists of a PromptTemplate and a language model (either an LLM or chat model). It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.retrievers import KNNRetriever\n",
-    "\n",
-    "retriever = KNNRetriever.from_texts(documents, embeddings)\n",
-    "\n",
-    "# retrieve the most relevant documents\n",
-    "result = retriever.get_relevant_documents(query)\n",
-    "top1_retrieved_doc = result[0].page_content  # return the top1 retrieved result\n",
-    "\n",
-    "print(top1_retrieved_doc)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.18"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}