datetime tool

.devcontainer/Dockerfile

@@ -1,42 +0,0 @@
-# This is a Dockerfile for Developer Container
-
-# Use the Python base image
-ARG VARIANT="3.11-bullseye"
-FROM mcr.microsoft.com/vscode/devcontainers/python:0-${VARIANT} AS langchain-dev-base
-
-USER vscode
-
-# Define the version of Poetry to install (default is 1.4.2)
-# Define the directory of python virtual environment
-ARG PYTHON_VIRTUALENV_HOME=/home/vscode/langchain-py-env \
-    POETRY_VERSION=1.4.2 
-
-ENV POETRY_VIRTUALENVS_IN_PROJECT=false \
-    POETRY_NO_INTERACTION=true 
-
-# Create a Python virtual environment for Poetry and install it
-RUN python3 -m venv ${PYTHON_VIRTUALENV_HOME} && \
-    $PYTHON_VIRTUALENV_HOME/bin/pip install --upgrade pip && \
-    $PYTHON_VIRTUALENV_HOME/bin/pip install poetry==${POETRY_VERSION}
-
-ENV PATH="$PYTHON_VIRTUALENV_HOME/bin:$PATH" \
-    VIRTUAL_ENV=$PYTHON_VIRTUALENV_HOME
-
-# Setup for bash
-RUN poetry completions bash >> /home/vscode/.bash_completion && \
-    echo "export PATH=$PYTHON_VIRTUALENV_HOME/bin:$PATH" >> ~/.bashrc
-
-# Set the working directory for the app
-WORKDIR /workspaces/langchain
-
-# Use a multi-stage build to install dependencies
-FROM langchain-dev-base AS langchain-dev-dependencies
-
-ARG PYTHON_VIRTUALENV_HOME
-
-# Copy only the dependency files for installation
-COPY pyproject.toml poetry.lock poetry.toml ./
-
-# Install the Poetry dependencies (this layer will be cached as long as the dependencies don't change)
-RUN poetry install --no-interaction --no-ansi --with dev,test,docs
-

.devcontainer/devcontainer.json

@@ -1,33 +0,0 @@
-// For format details, see https://aka.ms/devcontainer.json. For config options, see the
-// README at: https://github.com/devcontainers/templates/tree/main/src/docker-existing-dockerfile
-{
-	"dockerComposeFile": "./docker-compose.yaml",
-	"service": "langchain",
-	"workspaceFolder": "/workspaces/langchain",
-	"name": "langchain",
-	"customizations": {
-		"vscode": {
-			"extensions": [   
-				"ms-python.python"
-			],
-			"settings": {
-				"python.defaultInterpreterPath": "/home/vscode/langchain-py-env/bin/python3.11"
-			}
-		}
-		
-	},
-
-	// Features to add to the dev container. More info: https://containers.dev/features.
-	"features": {},
-
-	// Use 'forwardPorts' to make a list of ports inside the container available locally.
-	// "forwardPorts": [],
-
-	// Uncomment the next line to run commands after the container is created.
-	// "postCreateCommand": "cat /etc/os-release",
-
-	// Uncomment to connect as an existing user other than the container default. More info: https://aka.ms/dev-containers-non-root.
-	// "remoteUser": "devcontainer"
-	"remoteUser": "vscode",
-	"overrideCommand": true
-}

.devcontainer/docker-compose.yaml

@@ -1,31 +0,0 @@
-version: '3'
-services:
-  langchain:
-    build:
-      dockerfile: .devcontainer/Dockerfile
-      context: ../ 
-    volumes:
-      - ../:/workspaces/langchain
-    networks:
-      - langchain-network 
-  #   environment:
-  #     MONGO_ROOT_USERNAME: root
-  #     MONGO_ROOT_PASSWORD: example123
-  #   depends_on:
-  #     - mongo   
-  # mongo:
-  #   image: mongo
-  #   restart: unless-stopped
-  #   environment:
-  #     MONGO_INITDB_ROOT_USERNAME: root
-  #     MONGO_INITDB_ROOT_PASSWORD: example123
-  #   ports:
-  #     - "27017:27017"
-  #   networks:
-  #     - langchain-network
-
-networks:
-  langchain-network:
-    driver: bridge
-    
-    

.dockerignore

@@ -1,6 +0,0 @@
-.venv
-.github
-.git
-.mypy_cache
-.pytest_cache
-Dockerfile

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,106 +0,0 @@
-name: "\U0001F41B Bug Report"
-description: Submit a bug report to help us improve LangChain
-labels: ["02 Bug Report"]
-body:
-  - type: markdown
-    attributes:
-      value: >
-        Thank you for taking the time to file a bug report. Before creating a new
-        issue, please make sure to take a few moments to check the issue tracker
-        for existing issues about the bug.
-
-  - type: textarea
-    id: system-info
-    attributes:
-      label: System Info
-      description: Please share your system info with us.
-      placeholder: LangChain version, platform, python version, ...
-    validations:
-      required: true
-
-  - type: textarea
-    id: who-can-help
-    attributes:
-      label: Who can help?
-      description: |
-        Your issue will be replied to more quickly if you can figure out the right person to tag with @
-        If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
-
-        The core maintainers strive to read all issues, but tagging them will help them prioritize.
-
-        Please tag fewer than 3 people.
-
-        @hwchase17 - project lead
-
-        Tracing / Callbacks
-        - @agola11
-
-        Async
-        - @agola11
-
-        DataLoader Abstractions
-        - @eyurtsev
-
-        LLM/Chat Wrappers
-        - @hwchase17
-        - @agola11
-
-        Tools / Toolkits
-        - @vowelparrot
-
-      placeholder: "@Username ..."
-
-  - type: checkboxes
-    id: information-scripts-examples
-    attributes:
-      label: Information
-      description: "The problem arises when using:"
-      options:
-        - label: "The official example notebooks/scripts"
-        - label: "My own modified scripts"
-
-  - type: checkboxes
-    id: related-components
-    attributes:
-      label: Related Components
-      description: "Select the components related to the issue (if applicable):"
-      options:
-        - label: "LLMs/Chat Models"
-        - label: "Embedding Models"
-        - label: "Prompts / Prompt Templates / Prompt Selectors"
-        - label: "Output Parsers"
-        - label: "Document Loaders"
-        - label: "Vector Stores / Retrievers"
-        - label: "Memory"
-        - label: "Agents / Agent Executors"
-        - label: "Tools / Toolkits"
-        - label: "Chains"
-        - label: "Callbacks/Tracing"
-        - label: "Async"
-
-  - type: textarea
-    id: reproduction
-    validations:
-      required: true
-    attributes:
-      label: Reproduction
-      description: |
-        Please provide a [code sample](https://stackoverflow.com/help/minimal-reproducible-example) that reproduces the problem you ran into. It can be a Colab link or just a code snippet.
-        If you have code snippets, error messages, stack traces please provide them here as well.
-        Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
-        Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
-
-      placeholder: |
-        Steps to reproduce the behavior:
-
-          1.
-          2.
-          3.
-
-  - type: textarea
-    id: expected-behavior
-    validations:
-      required: true
-    attributes:
-      label: Expected behavior
-      description: "A clear and concise description of what you would expect to happen."

.github/ISSUE_TEMPLATE/config.yml

@@ -1,6 +0,0 @@
-blank_issues_enabled: true
-version: 2.1
-contact_links:
-  - name: Discord
-    url: https://discord.gg/6adMQxSpJS
-    about: General community discussions

.github/ISSUE_TEMPLATE/documentation.yml

@@ -1,19 +0,0 @@
-name: Documentation
-description: Report an issue related to the LangChain documentation.
-title: "DOC: <Please write a comprehensive title after the 'DOC: ' prefix>"
-labels: [03 - Documentation]
-
-body:
-- type: textarea
-  attributes: 
-    label: "Issue with current documentation:"
-    description: >
-      Please make sure to leave a reference to the document/code you're
-      referring to.
-
-- type: textarea
-  attributes:
-    label: "Idea or request for content:"
-    description: >
-      Please describe as clearly as possible what topics you think are missing
-      from the current documentation.

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -1,30 +0,0 @@
-name: "\U0001F680 Feature request"
-description: Submit a proposal/request for a new LangChain feature
-labels: ["02 Feature Request"]
-body:
-  - type: textarea
-    id: feature-request
-    validations:
-      required: true
-    attributes:
-      label: Feature request
-      description: |
-        A clear and concise description of the feature proposal. Please provide links to any relevant GitHub repos, papers, or other resources if relevant.
-
-  - type: textarea
-    id: motivation
-    validations:
-      required: true
-    attributes:
-      label: Motivation
-      description: |
-        Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too.
-
-  - type: textarea
-    id: contribution
-    validations:
-      required: true
-    attributes:
-      label: Your contribution
-      description: |
-        Is there any way that you could help, e.g. by submitting a PR? Make sure to read the CONTRIBUTING.MD [readme](https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md)

.github/ISSUE_TEMPLATE/other.yml

@@ -1,18 +0,0 @@
-name: Other Issue
-description: Raise an issue that wouldn't be covered by the other templates.
-title: "Issue: <Please write a comprehensive title after the 'Issue: ' prefix>"
-labels: [04 - Other]
-
-body:
-  - type: textarea
-    attributes:
-      label: "Issue you'd like to raise."
-      description: >
-        Please describe the issue you'd like to raise as clearly as possible.
-        Make sure to include any relevant links or references.
-
-  - type: textarea
-    attributes:
-      label: "Suggestion:"
-      description: >
-        Please outline a suggestion to improve the issue here.

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,56 +0,0 @@
-<!--
-Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution.
-
-Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change.
-
-After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost.
-
-Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle!
--->
-
-<!-- Remove if not applicable -->
-
-Fixes # (issue)
-
-#### Before submitting
-
-<!-- If you're adding a new integration, please include:
-
-1. a test for the integration - favor unit tests that does not rely on network access.
-2. an example notebook showing its use
-
-
-See contribution guidelines for more information on how to write tests, lint
-etc:
-
-https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
--->
-
-#### Who can review?
-
-Tag maintainers/contributors who might be interested:
-
-<!-- For a quicker response, figure out the right person to tag with @
-
-  @hwchase17 - project lead
-
-  Tracing / Callbacks
-  - @agola11
-
-  Async
-  - @agola11
-
-  DataLoaders
-  - @eyurtsev
-
-  Models
-  - @hwchase17
-  - @agola11
-
-  Agents / Tools / Toolkits
-  - @vowelparrot
-
-  VectorStores / Retrievers / Memory
-  - @dev2049
-
- -->

.github/actions/poetry_setup/action.yml

@@ -1,76 +0,0 @@
-# An action for setting up poetry install with caching.
-# Using a custom action since the default action does not
-# take poetry install groups into account.
-# Action code from:
-# https://github.com/actions/setup-python/issues/505#issuecomment-1273013236
-name: poetry-install-with-caching
-description: Poetry install with support for caching of dependency groups.
-
-inputs:
-  python-version:
-    description: Python version, supporting MAJOR.MINOR only
-    required: true
-
-  poetry-version:
-    description: Poetry version
-    required: true
-
-  install-command:
-    description: Command run for installing dependencies
-    required: false
-    default: poetry install
-
-  cache-key:
-    description: Cache key to use for manual handling of caching
-    required: true
-
-  working-directory:
-    description: Directory to run install-command in
-    required: false
-    default: ""
-
-runs:
-  using: composite
-  steps:
-    - uses: actions/setup-python@v4
-      name: Setup python $${ inputs.python-version }}
-      with:
-        python-version: ${{ inputs.python-version }}
-
-    - uses: actions/cache@v3
-      id: cache-pip
-      name: Cache Pip ${{ inputs.python-version }}
-      env:
-        SEGMENT_DOWNLOAD_TIMEOUT_MIN: "15"
-      with:
-        path: |
-          ~/.cache/pip
-        key: pip-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}
-
-    - run: pipx install poetry==${{ inputs.poetry-version }} --python python${{ inputs.python-version }}
-      shell: bash
-
-    - name: Check Poetry File
-      shell: bash
-      run: |
-        poetry check
-
-    - name: Check lock file
-      shell: bash
-      run: |
-        poetry lock --check
-
-    - uses: actions/cache@v3
-      id: cache-poetry
-      env:
-        SEGMENT_DOWNLOAD_TIMEOUT_MIN: "15"
-      with:
-        path: |
-          ~/.cache/pypoetry/virtualenvs
-          ~/.cache/pypoetry/cache
-          ~/.cache/pypoetry/artifacts
-        key: poetry-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-poetry-${{ inputs.poetry-version }}-${{ inputs.cache-key }}-${{ hashFiles('poetry.lock') }}
-
-    - run: ${{ inputs.install-command }}
-      working-directory: ${{ inputs.working-directory }}
-      shell: bash

.github/workflows/linkcheck.yml

@@ -4,11 +4,9 @@ on:
   push:
     branches: [master]
   pull_request:
-    paths:
-      - 'docs/**'
 
 env:
-  POETRY_VERSION: "1.4.2"
+  POETRY_VERSION: "1.3.1"
 
 jobs:
   build:

.github/workflows/lint.yml

@@ -6,7 +6,7 @@ on:
   pull_request:
 
 env:
-  POETRY_VERSION: "1.4.2"
+  POETRY_VERSION: "1.3.1"
 
 jobs:
   build:

.github/workflows/release.yml

@@ -10,7 +10,7 @@ on:
       - 'pyproject.toml'
 
 env:
-  POETRY_VERSION: "1.4.2"
+  POETRY_VERSION: "1.3.1"
 
 jobs:
   if_release:
@@ -45,5 +45,5 @@ jobs:
       - name: Publish to PyPI
         env:
           POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }}
-        run: |
+        run: | 
           poetry publish

.github/workflows/test.yml

@@ -4,10 +4,9 @@ on:
   push:
     branches: [master]
   pull_request:
-  workflow_dispatch:
 
 env:
-  POETRY_VERSION: "1.4.2"
+  POETRY_VERSION: "1.3.1"
 
 jobs:
   build:
@@ -19,31 +18,17 @@ jobs:
           - "3.9"
           - "3.10"
           - "3.11"
-        test_type:
-          - "core"
-          - "extended"
-    name: Python ${{ matrix.python-version }} ${{ matrix.test_type }}
     steps:
       - uses: actions/checkout@v3
+      - name: Install poetry
+        run: pipx install poetry==$POETRY_VERSION
       - name: Set up Python ${{ matrix.python-version }}
-        uses: "./.github/actions/poetry_setup"
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
-          poetry-version: "1.4.2"
-          cache-key: ${{ matrix.test_type }}
-          install-command: |
-              if [ "${{ matrix.test_type }}" == "core" ]; then
-                echo "Running core tests, installing dependencies with poetry..."
-                poetry install
-              else
-                echo "Running extended tests, installing dependencies with poetry..."
-                poetry install -E extended_testing
-              fi
-      - name: Run ${{matrix.test_type}} tests
+          cache: "poetry"
+      - name: Install dependencies
+        run: poetry install
+      - name: Run unit tests
         run: |
-          if [ "${{ matrix.test_type }}" == "core" ]; then
-            make test
-          else
-            make extended_tests
-          fi
-        shell: bash
+          make test

.gitignore

@@ -1,4 +1,3 @@
-.vs/
 .vscode/
 .idea/
 # Byte-compiled / optimized / DLL files
@@ -107,7 +106,6 @@ celerybeat.pid
 
 # Environments
 .env
-.envrc
 .venv
 .venvs
 env/
@@ -136,20 +134,3 @@ dmypy.json
 
 # macOS display setting files
 .DS_Store
-
-# Wandb directory
-wandb/
-
-# asdf tool versions
-.tool-versions
-/.ruff_cache/
-
-*.pkl
-*.bin
-
-# integration test artifacts
-data_map*
-\[('_type', 'fake'), ('stop', None)]
-
-# Replit files
-*replit*

.readthedocs.yaml

@@ -1,26 +0,0 @@
-# Read the Docs configuration file
-# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
-
-# Required
-version: 2
-
-# Set the version of Python and other tools you might need
-build:
-  os: ubuntu-22.04
-  tools:
-    python: "3.11"
-
-# Build documentation in the docs/ directory with Sphinx
-sphinx:
-   configuration: docs/conf.py
-
-# If using Sphinx, optionally build your docs in additional formats such as PDF
-# formats:
-#    - pdf
-
-# Optionally declare the Python requirements required to build your docs
-python:
-   install:
-   - requirements: docs/requirements.txt
-   - method: pip
-     path: .

CONTRIBUTING.md

@@ -2,62 +2,60 @@
 
 Hi there! Thank you for even being interested in contributing to LangChain.
 As an open source project in a rapidly developing field, we are extremely open
-to contributions, whether they be in the form of new features, improved infra, better documentation, or bug fixes.
-
-## 🗺️ Guidelines
-
-### 👩‍💻 Contributing Code
+to contributions, whether it be in the form of a new feature, improved infra, or better documentation.
 
 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 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
-[Common Tasks](#-common-tasks) for how to run these checks locally.
-
-It's essential that we maintain great documentation and testing. If you:
-- Fix a bug
-  - Add a relevant unit or integration test when possible. These live in `tests/unit_tests` and `tests/integration_tests`.
-- Make an improvement
-  - Update any affected example notebooks and documentation. These lives in `docs`.
-  - Update unit and integration tests when relevant.
-- Add a feature
-  - Add a demo notebook in `docs/modules`.
-  - Add unit and integration tests.
-
-We're a small, building-oriented team. If there's something you'd like to add or change, opening a pull request is the
-best way to get our attention.
+## 🗺️Contributing Guidelines
 
 ### 🚩GitHub Issues
 
 Our [issues](https://github.com/hwchase17/langchain/issues) page is kept up to date
-with bugs, improvements, and feature requests. 
+with bugs, improvements, and feature requests. There is a taxonomy of labels to help
+with sorting and discovery of issues of interest. These include:
 
-There is a taxonomy of labels to help with sorting and discovery of issues of interest. Please use these to help
-organize issues.
+- prompts: related to prompt tooling/infra.
+- llms: related to LLM wrappers/tooling/infra.
+- chains
+- utilities: related to different types of utilities to integrate with (Python, SQL, etc.).
+- agents
+- memory
+- applications: related to example applications to build
 
 If you start working on an issue, please assign it to yourself.
 
-If you are adding an issue, please try to keep it focused on a single, modular bug/improvement/feature.
-If two issues are related, or blocking, please link them rather than combining them.
+If you are adding an issue, please try to keep it focused on a single modular bug/improvement/feature.
+If the two issues are related, or blocking, please link them rather than keep them as one single one.
 
 We will try to keep these issues as up to date as possible, though
 with the rapid rate of develop in this field some may get out of date.
-If you notice this happening, please let us know.
+If you notice this happening, please just let us know.
 
 ### 🙋Getting Help
 
-Our goal is to have the simplest developer setup possible. Should you experience any difficulty getting setup, please
-contact a maintainer! Not only do we want to help get you unblocked, but we also want to make sure that the process is
-smooth for future contributors.
+Although we try to have a developer setup to make it as easy as possible for others to contribute (see below)
+it is possible that some pain point may arise around environment setup, linting, documentation, or other.
+Should that occur, please contact a maintainer! Not only do we want to help get you unblocked,
+but we also want to make sure that the process is smooth for future contributors.
 
 In a similar vein, we do enforce certain linting, formatting, and documentation standards in the codebase.
-If you are finding these difficult (or even just annoying) to work with, feel free to contact a maintainer for help -
-we do not want these to get in the way of getting good code into the codebase.
+If you are finding these difficult (or even just annoying) to work with,
+feel free to contact a maintainer for help - we do not want these to get in the way of getting
+good code into the codebase.
 
-## 🚀 Quick Start
+### 🏭Release process
+
+As of now, LangChain has an ad hoc release process: releases are cut with high frequency via by
+a developer and published to [PyPI](https://pypi.org/project/langchain/).
+
+LangChain follows the [semver](https://semver.org/) versioning standard. However, as pre-1.0 software,
+even patch releases may contain [non-backwards-compatible changes](https://semver.org/#spec-item-4).
+
+If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)!
+If you have a Twitter account you would like us to mention, please let us know in the PR or in another manner.
+
+## 🚀Quick Start
 
 This project uses [Poetry](https://python-poetry.org/) as a dependency manager. Check out Poetry's [documentation on how to install it](https://python-poetry.org/docs/#installation) on your system before proceeding.
 
@@ -75,11 +73,9 @@ poetry install -E all
 
 This will install all requirements for running the package, examples, linting, formatting, tests, and coverage. Note the `-E all` flag will install all optional dependencies necessary for integration testing.
 
-❗Note: If you're running Poetry 1.4.1 and receive a `WheelFileValidationError` for `debugpy` during installation, you can try either downgrading to Poetry 1.4.0 or disabling "modern installation" (`poetry config installer.modern-installation false`) and re-install requirements. See [this `debugpy` issue](https://github.com/microsoft/debugpy/issues/1246) for more details.
+Now, you should be able to run the common tasks in the following section.
 
-Now, you should be able to run the common tasks in the following section. To double check, run `make test`, all tests should pass. If they don't you may need to pip install additional dependencies, such as `numexpr` and `openapi_schema_pydantic`.
-
-## ✅ Common Tasks
+## ✅Common Tasks
 
 Type `make` for a list of common tasks.
 
@@ -115,37 +111,8 @@ To get a report of current coverage, run the following:
 make coverage
 ```
 
-### Working with Optional Dependencies
-
-Langchain relies heavily on optional dependencies to keep the Langchain package lightweight.
-
-If you're adding a new dependency to Langchain, assume that it will be an optional dependency, and
-that most users won't have it installed.
-
-Users that do not have the dependency installed should be able to **import** your code without
-any side effects (no warnings, no errors, no exceptions). 
-
-To introduce the dependency to the pyproject.toml file correctly, please do the following: 
-
-1. Add the dependency to the main group as an optional dependency
-  ```bash
-  poetry add --optional [package_name]
-  ```
-2. Open pyproject.toml and add the dependency to the `extended_testing` extra
-3. Relock the poetry file to update the extra.
-  ```bash
-  poetry lock --no-update
-  ```
-4. Add a unit test that the very least attempts to import the new code. Ideally the unit
-test makes use of lightweight fixtures to test the logic of the code.
-5. Please use the `@pytest.mark.requires(package_name)` decorator for any tests that require the dependency.
-
 ### Testing
 
-See section about optional dependencies.
-
-#### Unit Tests
-
 Unit tests cover modular logic that does not require calls to outside APIs.
 
 To run unit tests:
@@ -154,28 +121,10 @@ To run unit tests:
 make test
 ```
 
-To run unit tests in Docker:
-
-```bash
-make docker_tests
-```
-
 If you add new logic, please add a unit test.
 
-
-
-#### Integration Tests
-
 Integration tests cover logic that requires making calls to outside APIs (often integration with other services).
 
-**warning** Almost no tests should be integration tests. 
-
-  Tests that require making network connections make it difficult for other
-  developers to test the code.
-
-  Instead favor relying on `responses` library and/or mock.patch to mock
-  requests using small fixtures.
-
 To run integration tests:
 
 ```bash
@@ -231,17 +180,3 @@ Finally, you can build the documentation as outlined below:
 ```bash
 make docs_build
 ```
-
-## 🏭 Release Process
-
-As of now, LangChain has an ad hoc release process: releases are cut with high frequency by
-a developer and published to [PyPI](https://pypi.org/project/langchain/).
-
-LangChain follows the [semver](https://semver.org/) versioning standard. However, as pre-1.0 software,
-even patch releases may contain [non-backwards-compatible changes](https://semver.org/#spec-item-4).
-
-### 🌟 Recognition
-
-If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)!
-If you have a Twitter account you would like us to mention, please let us know in the PR or in another manner.
-

Dockerfile

@@ -1,48 +0,0 @@
-# This is a Dockerfile for running unit tests
-
-ARG POETRY_HOME=/opt/poetry
-
-# Use the Python base image
-FROM python:3.11.2-bullseye AS builder
-
-# Define the version of Poetry to install (default is 1.4.2)
-ARG POETRY_VERSION=1.4.2
-
-# Define the directory to install Poetry to (default is /opt/poetry)
-ARG POETRY_HOME
-
-# Create a Python virtual environment for Poetry and install it
-RUN python3 -m venv ${POETRY_HOME} && \
-    $POETRY_HOME/bin/pip install --upgrade pip && \
-    $POETRY_HOME/bin/pip install poetry==${POETRY_VERSION}
-
-# Test if Poetry is installed in the expected path
-RUN echo "Poetry version:" && $POETRY_HOME/bin/poetry --version
-
-# Set the working directory for the app
-WORKDIR /app
-
-# Use a multi-stage build to install dependencies
-FROM builder AS dependencies
-
-ARG POETRY_HOME
-
-# Copy only the dependency files for installation
-COPY pyproject.toml poetry.lock poetry.toml ./
-
-# Install the Poetry dependencies (this layer will be cached as long as the dependencies don't change)
-RUN $POETRY_HOME/bin/poetry install --no-interaction --no-ansi --with test
-
-# Use a multi-stage build to run tests
-FROM dependencies AS tests
-
-# Copy the rest of the app source code (this layer will be invalidated and rebuilt whenever the source code changes)
-COPY . .
-
-RUN /opt/poetry/bin/poetry install --no-interaction --no-ansi --with test
-
-# Set the entrypoint to run tests using Poetry
-ENTRYPOINT ["/opt/poetry/bin/poetry", "run", "pytest"]
-
-# Set the default command to run all unit tests
-CMD ["tests/unit_tests"]

Makefile

@@ -1,7 +1,7 @@
-.PHONY: all clean format lint test tests test_watch integration_tests docker_tests help extended_tests
+.PHONY: all clean format lint test tests test_watch integration_tests help
 
 all: help
-
+	
 coverage:
 	poetry run pytest --cov \
 		--cov-config=.coveragerc \
@@ -21,27 +21,19 @@ docs_linkcheck:
 
 format:
 	poetry run black .
-	poetry run ruff --select I --fix .
+	poetry run isort .
 
-PYTHON_FILES=.
-lint: PYTHON_FILES=.
-lint_diff: PYTHON_FILES=$(shell git diff --name-only --diff-filter=d master | grep -E '\.py$$')
-
-lint lint_diff:
-	poetry run mypy $(PYTHON_FILES)
-	poetry run black $(PYTHON_FILES) --check
-	poetry run ruff .
-
-TEST_FILE ?= tests/unit_tests/
+lint:
+	poetry run mypy .
+	poetry run black . --check
+	poetry run isort . --check
+	poetry run flake8 .
 
 test:
-	poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
+	poetry run pytest tests/unit_tests
 
-tests: 
-	poetry run pytest --disable-socket --allow-unix-socket $(TEST_FILE)
-
-extended_tests:
-	poetry run pytest --disable-socket --allow-unix-socket --only-extended tests/unit_tests
+tests:
+	poetry run pytest tests/unit_tests
 
 test_watch:
 	poetry run ptw --now . -- tests/unit_tests
@@ -49,22 +41,14 @@ test_watch:
 integration_tests:
 	poetry run pytest tests/integration_tests
 
-docker_tests:
-	docker build -t my-langchain-image:test .
-	docker run --rm my-langchain-image:test
-
 help:
 	@echo '----'
-	@echo 'coverage                     - run unit tests and generate coverage report'
-	@echo 'docs_build                   - build the documentation'
-	@echo 'docs_clean                   - clean the documentation build artifacts'
-	@echo 'docs_linkcheck               - run linkchecker on the documentation'
-	@echo 'format                       - run code formatters'
-	@echo 'lint                         - run linters'
-	@echo 'test                         - run unit tests'
-	@echo 'tests                        - run unit tests'
-	@echo 'test TEST_FILE=<test_file>   - run all tests in file'
-	@echo 'extended_tests               - run only extended unit tests'
-	@echo 'test_watch                   - run unit tests in watch mode'
-	@echo 'integration_tests            - run integration tests'
-	@echo 'docker_tests                 - run unit tests in docker'
+	@echo 'coverage            - run unit tests and generate coverage report'
+	@echo 'docs_build          - build the documentation'
+	@echo 'docs_clean          - clean the documentation build artifacts'
+	@echo 'docs_linkcheck      - run linkchecker on the documentation'
+	@echo 'format              - run code formatters'
+	@echo 'lint                - run linters'
+	@echo 'test                - run unit tests'
+	@echo 'test_watch          - run unit tests in watch mode'
+	@echo 'integration_tests   - run integration tests'

README.md

@@ -2,22 +2,7 @@
 
 ⚡ Building applications with LLMs through composability ⚡
 
-[![Release Notes](https://img.shields.io/github/release/hwchase17/langchain)](https://github.com/hwchase17/langchain/releases)
-[![lint](https://github.com/hwchase17/langchain/actions/workflows/lint.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/lint.yml)
-[![test](https://github.com/hwchase17/langchain/actions/workflows/test.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/test.yml)
-[![linkcheck](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml)
-[![Downloads](https://static.pepy.tech/badge/langchain/month)](https://pepy.tech/project/langchain)
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)
-[![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.gg/6adMQxSpJS)
-[![Open in Dev Containers](https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/hwchase17/langchain)
-[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/hwchase17/langchain)
-[![GitHub star chart](https://img.shields.io/github/stars/hwchase17/langchain?style=social)](https://star-history.com/#hwchase17/langchain)
-[![Dependency Status](https://img.shields.io/librariesio/github/hwchase17/langchain)](https://libraries.io/github/hwchase17/langchain)
-[![Open Issues](https://img.shields.io/github/issues-raw/hwchase17/langchain)](https://github.com/hwchase17/langchain/issues)
-
-
-Looking for the JS/TS version? Check out [LangChain.js](https://github.com/hwchase17/langchainjs).
+[![lint](https://github.com/hwchase17/langchain/actions/workflows/lint.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/lint.yml) [![test](https://github.com/hwchase17/langchain/actions/workflows/test.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/test.yml) [![linkcheck](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml/badge.svg)](https://github.com/hwchase17/langchain/actions/workflows/linkcheck.yml) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) [![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai) [![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.gg/6adMQxSpJS)
 
 **Production Support:** As you move your LangChains into production, we'd love to offer more comprehensive support.
 Please fill out [this form](https://forms.gle/57d8AmXBYp8PP8tZA) and we'll set up a dedicated support Slack channel.
@@ -25,14 +10,15 @@ Please fill out [this form](https://forms.gle/57d8AmXBYp8PP8tZA) and we'll set u
 ## Quick Install
 
 `pip install langchain`
-or
-`conda install langchain -c conda-forge`
 
 ## 🤔 What is this?
 
-Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. However, using these LLMs in isolation is often insufficient for creating a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.
+Large language models (LLMs) are emerging as a transformative technology, enabling
+developers to build applications that they previously could not.
+But using these LLMs in isolation is often not enough to
+create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge.
 
-This library aims to assist in the development of those types of applications. Common examples of these applications include:
+This library is aimed at assisting in the development of those types of applications. Common examples of these types of applications include:
 
 **❓ Question Answering over specific documents**
 
@@ -46,7 +32,7 @@ This library aims to assist in the development of those types of applications. C
 
 **🤖 Agents**
 
-- [Documentation](https://langchain.readthedocs.io/en/latest/modules/agents.html)
+- [Documentation](https://langchain.readthedocs.io/en/latest/use_cases/agents.html)
 - End-to-end Example: [GPT+WolframAlpha](https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain)
 
 ## 📖 Documentation
@@ -56,7 +42,7 @@ Please see [here](https://langchain.readthedocs.io/en/latest/?) for full documen
 - Getting started (installation, setting up the environment, simple examples)
 - How-To examples (demos, integrations, helper functions)
 - Reference (full API docs)
-- Resources (high-level explanation of core concepts)
+  Resources (high-level explanation of core concepts)
 
 ## 🚀 What can this help with?
 
@@ -65,32 +51,32 @@ These are, in increasing order of complexity:
 
 **📃 LLMs and Prompts:**
 
-This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with LLMs.
+This includes prompt management, prompt optimization, generic interface for all LLMs, and common utilities for working with LLMs.
 
 **🔗 Chains:**
 
-Chains go beyond a single LLM call and involve sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
+Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
 
 **📚 Data Augmented Generation:**
 
-Data Augmented Generation involves specific types of chains that first interact with an external data source to fetch data for use in the generation step. Examples include summarization of long pieces of text and question/answering over specific data sources.
+Data Augmented Generation involves specific types of chains that first interact with an external datasource to fetch data to use in the generation step. Examples of this include summarization of long pieces of text and question/answering over specific data sources.
 
 **🤖 Agents:**
 
-Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end-to-end agents.
+Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.
 
 **🧠 Memory:**
 
-Memory refers to persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.
+Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.
 
 **🧐 Evaluation:**
 
 [BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
 
-For more information on these concepts, please see our [full documentation](https://langchain.readthedocs.io/en/latest/).
+For more information on these concepts, please see our [full documentation](https://langchain.readthedocs.io/en/latest/?).
 
 ## 💁 Contributing
 
-As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
+As an open source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infra, or better documentation.
 
-For detailed information on how to contribute, see [here](.github/CONTRIBUTING.md).
+For detailed information on how to contribute, see [here](CONTRIBUTING.md).

docs/_static/css/custom.css

@@ -11,7 +11,3 @@ pre {
     max-width: 2560px !important;
   }
 }
-
-#my-component-root *, #headlessui-portal-root * {
-  z-index: 10000;
-}

docs/_static/js/mendablesearch.js

@@ -1,56 +0,0 @@
-document.addEventListener('DOMContentLoaded', () => {
-  // Load the external dependencies
-  function loadScript(src, onLoadCallback) {
-    const script = document.createElement('script');
-    script.src = src;
-    script.onload = onLoadCallback;
-    document.head.appendChild(script);
-  }
-
-  function createRootElement() {
-    const rootElement = document.createElement('div');
-    rootElement.id = 'my-component-root';
-    document.body.appendChild(rootElement);
-    return rootElement;
-  }
-
-  
-
-  function initializeMendable() {
-    const rootElement = createRootElement();
-    const { MendableFloatingButton } = Mendable;
-    
-
-    const iconSpan1 = React.createElement('span', {
-    }, '🦜');
-
-    const iconSpan2 = React.createElement('span', {
-    }, '🔗');
-
-    const icon = React.createElement('p', {
-      style: { color: '#ffffff', fontSize: '22px',width: '48px', height: '48px', margin: '0px', padding: '0px', display: 'flex', alignItems: 'center', justifyContent: 'center', textAlign: 'center' },
-    }, [iconSpan1, iconSpan2]);
-    
-    const mendableFloatingButton = React.createElement(
-      MendableFloatingButton,
-      {
-        style: { darkMode: false, accentColor: '#010810' },
-        floatingButtonStyle: { color: '#ffffff', backgroundColor: '#010810' },
-        anon_key: '82842b36-3ea6-49b2-9fb8-52cfc4bde6bf', // Mendable Search Public ANON key, ok to be public
-        messageSettings: {
-          openSourcesInNewTab: false,
-          prettySources: true // Prettify the sources displayed now
-        },
-        icon: icon,
-      }
-    );
-
-    ReactDOM.render(mendableFloatingButton, rootElement);
-  }
-
-  loadScript('https://unpkg.com/react@17/umd/react.production.min.js', () => {
-    loadScript('https://unpkg.com/react-dom@17/umd/react-dom.production.min.js', () => {
-      loadScript('https://unpkg.com/@mendable/search@0.0.102/dist/umd/mendable.min.js', initializeMendable);
-    });
-  });
-});

docs/additional_resources/deploy_llms.rst

@@ -1,137 +0,0 @@
-
-===========================
-Deploying LLMs in Production
-===========================
-
-In today's fast-paced technological landscape, the use of Large Language Models (LLMs) is rapidly expanding. As a result, it's crucial for developers to understand how to effectively deploy these models in production environments. LLM interfaces typically fall into two categories:
-
-- **Case 1: Utilizing External LLM Providers (OpenAI, Anthropic, etc.)**
-    In this scenario, most of the computational burden is handled by the LLM providers, while LangChain simplifies the implementation of business logic around these services. This approach includes features such as prompt templating, chat message generation, caching, vector embedding database creation, preprocessing, etc.
-
-- **Case 2: Self-hosted Open-Source Models**
-    Alternatively, developers can opt to use smaller, yet comparably capable, self-hosted open-source LLM models. This approach can significantly decrease costs, latency, and privacy concerns associated with transferring data to external LLM providers.
-
-Regardless of the framework that forms the backbone of your product, deploying LLM applications comes with its own set of challenges. It's vital to understand the trade-offs and key considerations when evaluating serving frameworks.
-
-Outline
-=======
-
-This guide aims to provide a comprehensive overview of the requirements for deploying LLMs in a production setting, focusing on:
-
-- `Designing a Robust LLM Application Service <#robust>`_
-- `Maintaining Cost-Efficiency <#cost>`_
-- `Ensuring Rapid Iteration <#iteration>`_
-
-Understanding these components is crucial when assessing serving systems. LangChain integrates with several open-source projects designed to tackle these issues, providing a robust framework for productionizing your LLM applications. Some notable frameworks include:
-
-- `Ray Serve <../../../ecosystem/ray_serve.html>`_
-- `BentoML <https://github.com/ssheng/BentoChain>`_
-- `Modal <../../../ecosystem/modal.html>`_
-
-These links will provide further information on each ecosystem, assisting you in finding the best fit for your LLM deployment needs.
-
-Designing a Robust LLM Application Service
-===========================================
-.. _robust:
-
-When deploying an LLM service in production, it's imperative to provide a seamless user experience free from outages. Achieving 24/7 service availability involves creating and maintaining several sub-systems surrounding your application.
-
-Monitoring
-----------
-
-Monitoring forms an integral part of any system running in a production environment. In the context of LLMs, it is essential to monitor both performance and quality metrics.
-
-**Performance Metrics:** These metrics provide insights into the efficiency and capacity of your model. Here are some key examples:
-
-- Query per second (QPS): This measures the number of queries your model processes in a second, offering insights into its utilization.
-- Latency: This metric quantifies the delay from when your client sends a request to when they receive a response.
-- Tokens Per Second (TPS): This represents the number of tokens your model can generate in a second.
-
-**Quality Metrics:** These metrics are typically customized according to the business use-case. For instance, how does the output of your system compare to a baseline, such as a previous version? Although these metrics can be calculated offline, you need to log the necessary data to use them later.
-
-Fault tolerance
----------------
-
-Your application may encounter errors such as exceptions in your model inference or business logic code, causing failures and disrupting traffic. Other potential issues could arise from the machine running your application, such as unexpected hardware breakdowns or loss of spot-instances during high-demand periods. One way to mitigate these risks is by increasing redundancy through replica scaling and implementing recovery mechanisms for failed replicas. However, model replicas aren't the only potential points of failure. It's essential to build resilience against various failures that could occur at any point in your stack.
-
-
-Zero down time upgrade
-----------------------
-
-System upgrades are often necessary but can result in service disruptions if not handled correctly. One way to prevent downtime during upgrades is by implementing a smooth transition process from the old version to the new one. Ideally, the new version of your LLM service is deployed, and traffic gradually shifts from the old to the new version, maintaining a constant QPS throughout the process.
-
-
-Load balancing
---------------
-
-Load balancing, in simple terms, is a technique to distribute work evenly across multiple computers, servers, or other resources to optimize the utilization of the system, maximize throughput, minimize response time, and avoid overload of any single resource. Think of it as a traffic officer directing cars (requests) to different roads (servers) so that no single road becomes too congested.
-
-There are several strategies for load balancing. For example, one common method is the *Round Robin* strategy, where each request is sent to the next server in line, cycling back to the first when all servers have received a request. This works well when all servers are equally capable. However, if some servers are more powerful than others, you might use a *Weighted Round Robin* or *Least Connections* strategy, where more requests are sent to the more powerful servers, or to those currently handling the fewest active requests. Let's imagine you're running a LLM chain. If your application becomes popular, you could have hundreds or even thousands of users asking questions at the same time. If one server gets too busy (high load), the load balancer would direct new requests to another server that is less busy. This way, all your users get a timely response and the system remains stable.
-
-
-
-Maintaining Cost-Efficiency and Scalability
-============================================
-.. _cost:
-
-Deploying LLM services can be costly, especially when you're handling a large volume of user interactions. Charges by LLM providers are usually based on tokens used, making a chat system inference on these models potentially expensive. However, several strategies can help manage these costs without compromising the quality of the service.
-
-
-Self-hosting models
--------------------
-
-Several smaller and open-source LLMs are emerging to tackle the issue of reliance on LLM providers. Self-hosting allows you to maintain similar quality to LLM provider models while managing costs. The challenge lies in building a reliable, high-performing LLM serving system on your own machines. 
-
-Resource Management and Auto-Scaling
-------------------------------------
-
-Computational logic within your application requires precise resource allocation. For instance, if part of your traffic is served by an OpenAI endpoint and another part by a self-hosted model, it's crucial to allocate suitable resources for each. Auto-scaling—adjusting resource allocation based on traffic—can significantly impact the cost of running your application. This strategy requires a balance between cost and responsiveness, ensuring neither resource over-provisioning nor compromised application responsiveness.
-
-Utilizing Spot Instances
-------------------------
-
-On platforms like AWS, spot instances offer substantial cost savings, typically priced at about a third of on-demand instances. The trade-off is a higher crash rate, necessitating a robust fault-tolerance mechanism for effective use.
-
-Independent Scaling
--------------------
-
-When self-hosting your models, you should consider independent scaling. For example, if you have two translation models, one fine-tuned for French and another for Spanish, incoming requests might necessitate different scaling requirements for each.
-
-Batching requests
------------------
-
-In the context of Large Language Models, batching requests can enhance efficiency by better utilizing your GPU resources. GPUs are inherently parallel processors, designed to handle multiple tasks simultaneously. If you send individual requests to the model, the GPU might not be fully utilized as it's only working on a single task at a time. On the other hand, by batching requests together, you're allowing the GPU to work on multiple tasks at once, maximizing its utilization and improving inference speed. This not only leads to cost savings but can also improve the overall latency of your LLM service.
-
-
-In summary, managing costs while scaling your LLM services requires a strategic approach. Utilizing self-hosting models, managing resources effectively, employing auto-scaling, using spot instances, independently scaling models, and batching requests are key strategies to consider. Open-source libraries such as Ray Serve and BentoML are designed to deal with these complexities. 
-
-
-
-Ensuring Rapid Iteration
-========================
-
-.. _iteration:
-
-The LLM landscape is evolving at an unprecedented pace, with new libraries and model architectures being introduced constantly. Consequently, it's crucial to avoid tying yourself to a solution specific to one particular framework. This is especially relevant in serving, where changes to your infrastructure can be time-consuming, expensive, and risky. Strive for infrastructure that is not locked into any specific machine learning library or framework, but instead offers a general-purpose, scalable serving layer. Here are some aspects where flexibility plays a key role:
-
-Model composition
------------------
-
-Deploying systems like LangChain demands the ability to piece together different models and connect them via logic. Take the example of building a natural language input SQL query engine. Querying an LLM and obtaining the SQL command is only part of the system. You need to extract metadata from the connected database, construct a prompt for the LLM, run the SQL query on an engine, collect and feed back the response to the LLM as the query runs, and present the results to the user. This demonstrates the need to seamlessly integrate various complex components built in Python into a dynamic chain of logical blocks that can be served together.
-
-Cloud providers
----------------
-
-Many hosted solutions are restricted to a single cloud provider, which can limit your options in today's multi-cloud world. Depending on where your other infrastructure components are built, you might prefer to stick with your chosen cloud provider.
-
-
-Infrastructure as Code (IaC)
----------------------------
-
-Rapid iteration also involves the ability to recreate your infrastructure quickly and reliably. This is where Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or Kubernetes YAML files come into play. They allow you to define your infrastructure in code files, which can be version controlled and quickly deployed, enabling faster and more reliable iterations.
-
-
-CI/CD
------
-
-In a fast-paced environment, implementing CI/CD pipelines can significantly speed up the iteration process. They help automate the testing and deployment of your LLM applications, reducing the risk of errors and enabling faster feedback and iteration.

docs/additional_resources/youtube.md

@@ -1,90 +0,0 @@
-# YouTube
-
-This is a collection of `LangChain` videos on `YouTube`.
-
-### ⛓️[Official LangChain YouTube channel](https://www.youtube.com/@LangChain)⛓️
-
-### Introduction to LangChain with Harrison Chase, creator of LangChain
-- [Building the Future with LLMs, `LangChain`, & `Pinecone`](https://youtu.be/nMniwlGyX-c) by [Pinecone](https://www.youtube.com/@pinecone-io)
-- [LangChain and Weaviate with Harrison Chase and Bob van Luijt - Weaviate Podcast #36](https://youtu.be/lhby7Ql7hbk) by [Weaviate • Vector Database](https://www.youtube.com/@Weaviate)
-- [LangChain Demo + Q&A with Harrison Chase](https://youtu.be/zaYTXQFR0_s?t=788) by [Full Stack Deep Learning](https://www.youtube.com/@FullStackDeepLearning)
-- [LangChain Agents: Build Personal Assistants For Your Data (Q&A with Harrison Chase and Mayo Oshin)](https://youtu.be/gVkF8cwfBLI) by [Chat with data](https://www.youtube.com/@chatwithdata)
-- ⛓️ [LangChain "Agents in Production" Webinar](https://youtu.be/k8GNCCs16F4) by [LangChain](https://www.youtube.com/@LangChain)
-
-## Videos (sorted by views)
-
-- [Building AI LLM Apps with LangChain (and more?) - LIVE STREAM](https://www.youtube.com/live/M-2Cj_2fzWI?feature=share) by [Nicholas Renotte](https://www.youtube.com/@NicholasRenotte)
-- [First look - `ChatGPT` + `WolframAlpha` (`GPT-3.5` and Wolfram|Alpha via LangChain by James Weaver)](https://youtu.be/wYGbY811oMo) by [Dr Alan D. Thompson](https://www.youtube.com/@DrAlanDThompson) 
-- [LangChain explained - The hottest new Python framework](https://youtu.be/RoR4XJw8wIc) by [AssemblyAI](https://www.youtube.com/@AssemblyAI)
-- [Chatbot with INFINITE MEMORY using `OpenAI` & `Pinecone` - `GPT-3`, `Embeddings`, `ADA`, `Vector DB`, `Semantic`](https://youtu.be/2xNzB7xq8nk) by [David Shapiro ~ AI](https://www.youtube.com/@DavidShapiroAutomator)
-- [LangChain for LLMs is... basically just an Ansible playbook](https://youtu.be/X51N9C-OhlE) by [David Shapiro ~ AI](https://www.youtube.com/@DavidShapiroAutomator)
-- [Build your own LLM Apps with LangChain & `GPT-Index`](https://youtu.be/-75p09zFUJY) by [1littlecoder](https://www.youtube.com/@1littlecoder)
-- [`BabyAGI` - New System of Autonomous AI Agents with LangChain](https://youtu.be/lg3kJvf1kXo) by [1littlecoder](https://www.youtube.com/@1littlecoder)
-- [Run `BabyAGI` with Langchain Agents (with Python Code)](https://youtu.be/WosPGHPObx8) by [1littlecoder](https://www.youtube.com/@1littlecoder)
-- [How to Use Langchain With `Zapier` | Write and Send Email with GPT-3 | OpenAI API Tutorial](https://youtu.be/p9v2-xEa9A0) by [StarMorph AI](https://www.youtube.com/@starmorph)
-- [Use Your Locally Stored Files To Get Response From GPT - `OpenAI` | Langchain | Python](https://youtu.be/NC1Ni9KS-rk) by [Shweta Lodha](https://www.youtube.com/@shweta-lodha)
-- [`Langchain JS` | How to Use GPT-3, GPT-4 to Reference your own Data | `OpenAI Embeddings` Intro](https://youtu.be/veV2I-NEjaM) by [StarMorph AI](https://www.youtube.com/@starmorph)
-- [The easiest way to work with large language models | Learn LangChain in 10min](https://youtu.be/kmbS6FDQh7c) by [Sophia Yang](https://www.youtube.com/@SophiaYangDS)
-- [4 Autonomous AI Agents: “Westworld” simulation `BabyAGI`, `AutoGPT`, `Camel`, `LangChain`](https://youtu.be/yWbnH6inT_U) by [Sophia Yang](https://www.youtube.com/@SophiaYangDS)
-- [AI CAN SEARCH THE INTERNET? Langchain Agents + OpenAI ChatGPT](https://youtu.be/J-GL0htqda8) by [tylerwhatsgood](https://www.youtube.com/@tylerwhatsgood)
-- [Query Your Data with GPT-4 | Embeddings, Vector Databases | Langchain JS Knowledgebase](https://youtu.be/jRnUPUTkZmU) by [StarMorph AI](https://www.youtube.com/@starmorph)
-- [`Weaviate` + LangChain for LLM apps presented by Erika Cardenas](https://youtu.be/7AGj4Td5Lgw) by [`Weaviate` • Vector Database](https://www.youtube.com/@Weaviate)
-- [Langchain Overview — How to Use Langchain & `ChatGPT`](https://youtu.be/oYVYIq0lOtI) by [Python In Office](https://www.youtube.com/@pythoninoffice6568)
-- [Langchain Overview - How to Use Langchain & `ChatGPT`](https://youtu.be/oYVYIq0lOtI) by [Python In Office](https://www.youtube.com/@pythoninoffice6568)
-- [Custom langchain Agent & Tools with memory. Turn any `Python function` into langchain tool with Gpt 3](https://youtu.be/NIG8lXk0ULg) by [echohive](https://www.youtube.com/@echohive)
-- [LangChain: Run Language Models Locally - `Hugging Face Models`](https://youtu.be/Xxxuw4_iCzw) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
-- [`ChatGPT` with any `YouTube` video using langchain and `chromadb`](https://youtu.be/TQZfB2bzVwU) by [echohive](https://www.youtube.com/@echohive)
-- [How to Talk to a `PDF` using LangChain and `ChatGPT`](https://youtu.be/v2i1YDtrIwk) by [Automata Learning Lab](https://www.youtube.com/@automatalearninglab)
-- [Langchain Document Loaders Part 1: Unstructured Files](https://youtu.be/O5C0wfsen98) by [Merk](https://www.youtube.com/@merksworld) 
-- [LangChain - Prompt Templates (what all the best prompt engineers use)](https://youtu.be/1aRu8b0XNOQ) by [Nick Daigler](https://www.youtube.com/@nick_daigs)
-- [LangChain. Crear aplicaciones Python impulsadas por GPT](https://youtu.be/DkW_rDndts8) by [Jesús Conde](https://www.youtube.com/@0utKast)
-- [Easiest Way to Use GPT In Your Products | LangChain Basics Tutorial](https://youtu.be/fLy0VenZyGc) by [Rachel Woods](https://www.youtube.com/@therachelwoods)
-- [`BabyAGI` + `GPT-4` Langchain Agent with Internet Access](https://youtu.be/wx1z_hs5P6E) by [tylerwhatsgood](https://www.youtube.com/@tylerwhatsgood)
-- [Learning LLM Agents. How does it actually work? LangChain, AutoGPT & OpenAI](https://youtu.be/mb_YAABSplk) by [Arnoldas Kemeklis](https://www.youtube.com/@processusAI)
-- [Get Started with LangChain in `Node.js`](https://youtu.be/Wxx1KUWJFv4) by [Developers Digest](https://www.youtube.com/@DevelopersDigest)
-- [LangChain + `OpenAI` tutorial: Building a Q&A system w/ own text data](https://youtu.be/DYOU_Z0hAwo) by [Samuel Chan](https://www.youtube.com/@SamuelChan)
-- [Langchain + `Zapier` Agent](https://youtu.be/yribLAb-pxA) by [Merk](https://www.youtube.com/@merksworld)
-- [Connecting the Internet with `ChatGPT` (LLMs) using Langchain And Answers Your Questions](https://youtu.be/9Y0TBC63yZg) by [Kamalraj M M](https://www.youtube.com/@insightbuilder)
-- [Build More Powerful LLM Applications for Business’s with LangChain (Beginners Guide)](https://youtu.be/sp3-WLKEcBg) by[ No Code Blackbox](https://www.youtube.com/@nocodeblackbox)
-- ⛓️ [LangFlow LLM Agent Demo for 🦜🔗LangChain](https://youtu.be/zJxDHaWt-6o) by [Cobus Greyling](https://www.youtube.com/@CobusGreylingZA)
-- ⛓️ [Chatbot Factory: Streamline Python Chatbot Creation with LLMs and Langchain](https://youtu.be/eYer3uzrcuM) by [Finxter](https://www.youtube.com/@CobusGreylingZA)
-- ⛓️ [LangChain Tutorial - ChatGPT mit eigenen Daten](https://youtu.be/0XDLyY90E2c) by [Coding Crashkurse](https://www.youtube.com/@codingcrashkurse6429)
-- ⛓️ [Chat with a `CSV` | LangChain Agents Tutorial (Beginners)](https://youtu.be/tjeti5vXWOU) by [GoDataProf](https://www.youtube.com/@godataprof)
-- ⛓️ [Introdução ao Langchain - #Cortes - Live DataHackers](https://youtu.be/fw8y5VRei5Y) by [Prof. João Gabriel Lima](https://www.youtube.com/@profjoaogabriellima)
-- ⛓️ [LangChain: Level up `ChatGPT` !? | LangChain Tutorial Part 1](https://youtu.be/vxUGx8aZpDE) by [Code Affinity](https://www.youtube.com/@codeaffinitydev)
-- ⛓️ [KI schreibt krasses Youtube Skript 😲😳 | LangChain Tutorial Deutsch](https://youtu.be/QpTiXyK1jus) by [SimpleKI](https://www.youtube.com/@simpleki)
-- ⛓️ [Chat with Audio: Langchain, `Chroma DB`, OpenAI, and `Assembly AI`](https://youtu.be/Kjy7cx1r75g) by [AI Anytime](https://www.youtube.com/@AIAnytime)
-- ⛓️ [QA over documents with Auto vector index selection with Langchain router chains](https://youtu.be/9G05qybShv8) by [echohive](https://www.youtube.com/@echohive)
-- ⛓️ [Build your own custom LLM application with `Bubble.io` & Langchain (No Code & Beginner friendly)](https://youtu.be/O7NhQGu1m6c) by [No Code Blackbox](https://www.youtube.com/@nocodeblackbox)
-- ⛓️ [Simple App to Question Your Docs: Leveraging `Streamlit`, `Hugging Face Spaces`, LangChain, and `Claude`!](https://youtu.be/X4YbNECRr7o) by [Chris Alexiuk](https://www.youtube.com/@chrisalexiuk)
-- ⛓️ [LANGCHAIN AI- `ConstitutionalChainAI` + Databutton AI ASSISTANT Web App](https://youtu.be/5zIU6_rdJCU) by [Avra](https://www.youtube.com/@Avra_b)
-- ⛓️ [LANGCHAIN AI AUTONOMOUS AGENT WEB APP - 👶 `BABY AGI` 🤖 with EMAIL AUTOMATION using `DATABUTTON`](https://youtu.be/cvAwOGfeHgw) by [Avra](https://www.youtube.com/@Avra_b)
-- ⛓️ [The Future of Data Analysis: Using A.I. Models in Data Analysis (LangChain)](https://youtu.be/v_LIcVyg5dk) by [Absent Data](https://www.youtube.com/@absentdata)
-- ⛓️ [Memory in LangChain | Deep dive (python)](https://youtu.be/70lqvTFh_Yg) by [Eden Marco](https://www.youtube.com/@EdenMarco)
-- ⛓️ [9 LangChain UseCases | Beginner's Guide | 2023](https://youtu.be/zS8_qosHNMw) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
-- ⛓️ [Use Large Language Models in Jupyter Notebook | LangChain | Agents & Indexes](https://youtu.be/JSe11L1a_QQ) by [Abhinaw Tiwari](https://www.youtube.com/@AbhinawTiwariAT)
-- ⛓️ [How to Talk to Your Langchain Agent | `11 Labs` + `Whisper`](https://youtu.be/N4k459Zw2PU) by [VRSEN](https://www.youtube.com/@vrsen)
-- ⛓️ [LangChain Deep Dive: 5 FUN AI App Ideas To Build Quickly and Easily](https://youtu.be/mPYEPzLkeks) by [James NoCode](https://www.youtube.com/@jamesnocode)
-- ⛓️ [BEST OPEN Alternative to OPENAI's EMBEDDINGs for Retrieval QA: LangChain](https://youtu.be/ogEalPMUCSY) by [Prompt Engineering](https://www.youtube.com/@engineerprompt)
-- ⛓️ [LangChain 101: Models](https://youtu.be/T6c_XsyaNSQ) by [Mckay Wrigley](https://www.youtube.com/@realmckaywrigley)
-- ⛓️ [LangChain with JavaScript Tutorial #1 | Setup & Using LLMs](https://youtu.be/W3AoeMrg27o) by [Leon van Zyl](https://www.youtube.com/@leonvanzyl)
-- ⛓️ [LangChain Overview & Tutorial for Beginners: Build Powerful AI Apps Quickly & Easily (ZERO CODE)](https://youtu.be/iI84yym473Q) by [James NoCode](https://www.youtube.com/@jamesnocode)
-- ⛓️ [LangChain In Action: Real-World Use Case With Step-by-Step Tutorial](https://youtu.be/UO699Szp82M) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
-- ⛓️ [Summarizing and Querying Multiple Papers with LangChain](https://youtu.be/p_MQRWH5Y6k) by [Automata Learning Lab](https://www.youtube.com/@automatalearninglab)
-- ⛓️ [Using Langchain (and `Replit`) through `Tana`, ask `Google`/`Wikipedia`/`Wolfram Alpha` to fill out a table](https://youtu.be/Webau9lEzoI) by [Stian Håklev](https://www.youtube.com/@StianHaklev)
-- ⛓️ [Langchain PDF App (GUI) | Create a ChatGPT For Your `PDF` in Python](https://youtu.be/wUAUdEw5oxM) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
-- ⛓️ [Auto-GPT with LangChain 🔥 | Create Your Own Personal AI Assistant](https://youtu.be/imDfPmMKEjM) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
-- ⛓️ [Create Your OWN Slack AI Assistant with Python & LangChain](https://youtu.be/3jFXRNn2Bu8) by [Dave Ebbelaar](https://www.youtube.com/@daveebbelaar)
-- ⛓️ [How to Create LOCAL Chatbots with GPT4All and LangChain [Full Guide]](https://youtu.be/4p1Fojur8Zw) by [Liam Ottley](https://www.youtube.com/@LiamOttley)
-- ⛓️ [Build a `Multilingual PDF` Search App with LangChain, `Cohere` and `Bubble`](https://youtu.be/hOrtuumOrv8) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
-- ⛓️ [Building a LangChain Agent (code-free!) Using `Bubble` and `Flowise`](https://youtu.be/jDJIIVWTZDE) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
-- ⛓️ [Build a LangChain-based Semantic PDF Search App with No-Code Tools Bubble and Flowise](https://youtu.be/s33v5cIeqA4) by [Menlo Park Lab](https://www.youtube.com/@menloparklab)
-- ⛓️ [LangChain Memory Tutorial | Building a ChatGPT Clone in Python](https://youtu.be/Cwq91cj2Pnc) by [Alejandro AO - Software & Ai](https://www.youtube.com/@alejandro_ao)
-- ⛓️ [ChatGPT For Your DATA | Chat with Multiple Documents Using LangChain](https://youtu.be/TeDgIDqQmzs) by [Data Science Basics](https://www.youtube.com/@datasciencebasics)
-- ⛓️ [`Llama Index`: Chat with Documentation using URL Loader](https://youtu.be/XJRoDEctAwA) by [Merk](https://www.youtube.com/@merksworld)
-- ⛓️ [Using OpenAI, LangChain, and `Gradio` to Build Custom GenAI Applications](https://youtu.be/1MsmqMg3yUc) by [David Hundley](https://www.youtube.com/@dkhundley)
-
-
-
----------------------
-⛓ icon marks a new video [last update 2023-05-15]

docs/conf.py

@@ -23,14 +23,13 @@ with open("../pyproject.toml") as f:
 # -- Project information -----------------------------------------------------
 
 project = "🦜🔗 LangChain"
-copyright = "2023, Harrison Chase"
+copyright = "2022, Harrison Chase"
 author = "Harrison Chase"
 
 version = data["tool"]["poetry"]["version"]
 release = version
 
 html_title = project + " " + version
-html_last_updated_fmt = "%b %d, %Y"
 
 
 # -- General configuration ---------------------------------------------------
@@ -46,7 +45,6 @@ extensions = [
     "sphinx.ext.viewcode",
     "sphinxcontrib.autodoc_pydantic",
     "myst_nb",
-    "sphinx_copybutton",
     "sphinx_panels",
     "IPython.sphinxext.ipython_console_highlighting",
 ]
@@ -103,10 +101,5 @@ html_static_path = ["_static"]
 html_css_files = [
     "css/custom.css",
 ]
-
-html_js_files = [
-    "js/mendablesearch.js",
-]
-
 nb_execution_mode = "off"
 myst_enable_extensions = ["colon_fence"]

docs/dependents.md

@@ -1,231 +0,0 @@
-# Dependents
-
-Dependents stats for `hwchase17/langchain`
-
-[![](https://img.shields.io/static/v1?label=Used%20by&message=7484&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
-[![](https://img.shields.io/static/v1?label=Used%20by%20(public)&message=212&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
-[![](https://img.shields.io/static/v1?label=Used%20by%20(private)&message=7272&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
-[![](https://img.shields.io/static/v1?label=Used%20by%20(stars)&message=19095&color=informational&logo=slickpic)](https://github.com/hwchase17/langchain/network/dependents)
-
-[update: 2023-06-05; only dependent repositories with Stars > 100]
-
-
-| Repository | Stars  |
-| :--------  | -----: |
-|[openai/openai-cookbook](https://github.com/openai/openai-cookbook) | 38024 |
-|[LAION-AI/Open-Assistant](https://github.com/LAION-AI/Open-Assistant) | 33609 |
-|[microsoft/TaskMatrix](https://github.com/microsoft/TaskMatrix) | 33136 |
-|[hpcaitech/ColossalAI](https://github.com/hpcaitech/ColossalAI) | 30032 |
-|[imartinez/privateGPT](https://github.com/imartinez/privateGPT) | 28094 |
-|[reworkd/AgentGPT](https://github.com/reworkd/AgentGPT) | 23430 |
-|[openai/chatgpt-retrieval-plugin](https://github.com/openai/chatgpt-retrieval-plugin) | 17942 |
-|[jerryjliu/llama_index](https://github.com/jerryjliu/llama_index) | 16697 |
-|[mindsdb/mindsdb](https://github.com/mindsdb/mindsdb) | 16410 |
-|[mlflow/mlflow](https://github.com/mlflow/mlflow) | 14517 |
-|[GaiZhenbiao/ChuanhuChatGPT](https://github.com/GaiZhenbiao/ChuanhuChatGPT) | 10793 |
-|[databrickslabs/dolly](https://github.com/databrickslabs/dolly) | 10155 |
-|[openai/evals](https://github.com/openai/evals) | 10076 |
-|[AIGC-Audio/AudioGPT](https://github.com/AIGC-Audio/AudioGPT) | 8619 |
-|[logspace-ai/langflow](https://github.com/logspace-ai/langflow) | 8211 |
-|[imClumsyPanda/langchain-ChatGLM](https://github.com/imClumsyPanda/langchain-ChatGLM) | 8154 |
-|[PromtEngineer/localGPT](https://github.com/PromtEngineer/localGPT) | 6853 |
-|[StanGirard/quivr](https://github.com/StanGirard/quivr) | 6830 |
-|[PipedreamHQ/pipedream](https://github.com/PipedreamHQ/pipedream) | 6520 |
-|[go-skynet/LocalAI](https://github.com/go-skynet/LocalAI) | 6018 |
-|[arc53/DocsGPT](https://github.com/arc53/DocsGPT) | 5643 |
-|[e2b-dev/e2b](https://github.com/e2b-dev/e2b) | 5075 |
-|[langgenius/dify](https://github.com/langgenius/dify) | 4281 |
-|[nsarrazin/serge](https://github.com/nsarrazin/serge) | 4228 |
-|[zauberzeug/nicegui](https://github.com/zauberzeug/nicegui) | 4084 |
-|[madawei2699/myGPTReader](https://github.com/madawei2699/myGPTReader) | 4039 |
-|[wenda-LLM/wenda](https://github.com/wenda-LLM/wenda) | 3871 |
-|[GreyDGL/PentestGPT](https://github.com/GreyDGL/PentestGPT) | 3837 |
-|[zilliztech/GPTCache](https://github.com/zilliztech/GPTCache) | 3625 |
-|[csunny/DB-GPT](https://github.com/csunny/DB-GPT) | 3545 |
-|[gkamradt/langchain-tutorials](https://github.com/gkamradt/langchain-tutorials) | 3404 |
-|[mmabrouk/chatgpt-wrapper](https://github.com/mmabrouk/chatgpt-wrapper) | 3303 |
-|[postgresml/postgresml](https://github.com/postgresml/postgresml) | 3052 |
-|[marqo-ai/marqo](https://github.com/marqo-ai/marqo) | 3014 |
-|[MineDojo/Voyager](https://github.com/MineDojo/Voyager) | 2945 |
-|[PrefectHQ/marvin](https://github.com/PrefectHQ/marvin) | 2761 |
-|[project-baize/baize-chatbot](https://github.com/project-baize/baize-chatbot) | 2673 |
-|[hwchase17/chat-langchain](https://github.com/hwchase17/chat-langchain) | 2589 |
-|[whitead/paper-qa](https://github.com/whitead/paper-qa) | 2572 |
-|[Azure-Samples/azure-search-openai-demo](https://github.com/Azure-Samples/azure-search-openai-demo) | 2366 |
-|[GerevAI/gerev](https://github.com/GerevAI/gerev) | 2330 |
-|[OpenGVLab/InternGPT](https://github.com/OpenGVLab/InternGPT) | 2289 |
-|[ParisNeo/gpt4all-ui](https://github.com/ParisNeo/gpt4all-ui) | 2159 |
-|[OpenBMB/BMTools](https://github.com/OpenBMB/BMTools) | 2158 |
-|[guangzhengli/ChatFiles](https://github.com/guangzhengli/ChatFiles) | 2005 |
-|[h2oai/h2ogpt](https://github.com/h2oai/h2ogpt) | 1939 |
-|[Farama-Foundation/PettingZoo](https://github.com/Farama-Foundation/PettingZoo) | 1845 |
-|[OpenGVLab/Ask-Anything](https://github.com/OpenGVLab/Ask-Anything) | 1749 |
-|[IntelligenzaArtificiale/Free-Auto-GPT](https://github.com/IntelligenzaArtificiale/Free-Auto-GPT) | 1740 |
-|[Unstructured-IO/unstructured](https://github.com/Unstructured-IO/unstructured) | 1628 |
-|[hwchase17/notion-qa](https://github.com/hwchase17/notion-qa) | 1607 |
-|[NVIDIA/NeMo-Guardrails](https://github.com/NVIDIA/NeMo-Guardrails) | 1544 |
-|[SamurAIGPT/privateGPT](https://github.com/SamurAIGPT/privateGPT) | 1543 |
-|[paulpierre/RasaGPT](https://github.com/paulpierre/RasaGPT) | 1526 |
-|[yanqiangmiffy/Chinese-LangChain](https://github.com/yanqiangmiffy/Chinese-LangChain) | 1485 |
-|[Kav-K/GPTDiscord](https://github.com/Kav-K/GPTDiscord) | 1402 |
-|[vocodedev/vocode-python](https://github.com/vocodedev/vocode-python) | 1387 |
-|[Chainlit/chainlit](https://github.com/Chainlit/chainlit) | 1336 |
-|[lunasec-io/lunasec](https://github.com/lunasec-io/lunasec) | 1323 |
-|[psychic-api/psychic](https://github.com/psychic-api/psychic) | 1248 |
-|[agiresearch/OpenAGI](https://github.com/agiresearch/OpenAGI) | 1208 |
-|[jina-ai/thinkgpt](https://github.com/jina-ai/thinkgpt) | 1193 |
-|[thomas-yanxin/LangChain-ChatGLM-Webui](https://github.com/thomas-yanxin/LangChain-ChatGLM-Webui) | 1182 |
-|[ttengwang/Caption-Anything](https://github.com/ttengwang/Caption-Anything) | 1137 |
-|[jina-ai/dev-gpt](https://github.com/jina-ai/dev-gpt) | 1135 |
-|[greshake/llm-security](https://github.com/greshake/llm-security) | 1086 |
-|[keephq/keep](https://github.com/keephq/keep) | 1063 |
-|[juncongmoo/chatllama](https://github.com/juncongmoo/chatllama) | 1037 |
-|[richardyc/Chrome-GPT](https://github.com/richardyc/Chrome-GPT) | 1035 |
-|[visual-openllm/visual-openllm](https://github.com/visual-openllm/visual-openllm) | 997 |
-|[mmz-001/knowledge_gpt](https://github.com/mmz-001/knowledge_gpt) | 995 |
-|[jina-ai/langchain-serve](https://github.com/jina-ai/langchain-serve) | 949 |
-|[irgolic/AutoPR](https://github.com/irgolic/AutoPR) | 936 |
-|[microsoft/X-Decoder](https://github.com/microsoft/X-Decoder) | 908 |
-|[poe-platform/api-bot-tutorial](https://github.com/poe-platform/api-bot-tutorial) | 902 |
-|[peterw/Chat-with-Github-Repo](https://github.com/peterw/Chat-with-Github-Repo) | 875 |
-|[cirediatpl/FigmaChain](https://github.com/cirediatpl/FigmaChain) | 822 |
-|[homanp/superagent](https://github.com/homanp/superagent) | 806 |
-|[seanpixel/Teenage-AGI](https://github.com/seanpixel/Teenage-AGI) | 800 |
-|[chatarena/chatarena](https://github.com/chatarena/chatarena) | 796 |
-|[hashintel/hash](https://github.com/hashintel/hash) | 795 |
-|[SamurAIGPT/Camel-AutoGPT](https://github.com/SamurAIGPT/Camel-AutoGPT) | 786 |
-|[rlancemartin/auto-evaluator](https://github.com/rlancemartin/auto-evaluator) | 770 |
-|[corca-ai/EVAL](https://github.com/corca-ai/EVAL) | 769 |
-|[101dotxyz/GPTeam](https://github.com/101dotxyz/GPTeam) | 755 |
-|[noahshinn024/reflexion](https://github.com/noahshinn024/reflexion) | 706 |
-|[eyurtsev/kor](https://github.com/eyurtsev/kor) | 695 |
-|[cheshire-cat-ai/core](https://github.com/cheshire-cat-ai/core) | 681 |
-|[e-johnstonn/BriefGPT](https://github.com/e-johnstonn/BriefGPT) | 656 |
-|[run-llama/llama-lab](https://github.com/run-llama/llama-lab) | 635 |
-|[griptape-ai/griptape](https://github.com/griptape-ai/griptape) | 583 |
-|[namuan/dr-doc-search](https://github.com/namuan/dr-doc-search) | 555 |
-|[getmetal/motorhead](https://github.com/getmetal/motorhead) | 550 |
-|[kreneskyp/ix](https://github.com/kreneskyp/ix) | 543 |
-|[hwchase17/chat-your-data](https://github.com/hwchase17/chat-your-data) | 510 |
-|[Anil-matcha/ChatPDF](https://github.com/Anil-matcha/ChatPDF) | 501 |
-|[whyiyhw/chatgpt-wechat](https://github.com/whyiyhw/chatgpt-wechat) | 497 |
-|[SamurAIGPT/ChatGPT-Developer-Plugins](https://github.com/SamurAIGPT/ChatGPT-Developer-Plugins) | 496 |
-|[microsoft/PodcastCopilot](https://github.com/microsoft/PodcastCopilot) | 492 |
-|[debanjum/khoj](https://github.com/debanjum/khoj) | 485 |
-|[akshata29/chatpdf](https://github.com/akshata29/chatpdf) | 485 |
-|[langchain-ai/langchain-aiplugin](https://github.com/langchain-ai/langchain-aiplugin) | 462 |
-|[jina-ai/agentchain](https://github.com/jina-ai/agentchain) | 460 |
-|[alexanderatallah/window.ai](https://github.com/alexanderatallah/window.ai) | 457 |
-|[yeagerai/yeagerai-agent](https://github.com/yeagerai/yeagerai-agent) | 451 |
-|[mckaywrigley/repo-chat](https://github.com/mckaywrigley/repo-chat) | 446 |
-|[michaelthwan/searchGPT](https://github.com/michaelthwan/searchGPT) | 446 |
-|[mpaepper/content-chatbot](https://github.com/mpaepper/content-chatbot) | 441 |
-|[freddyaboulton/gradio-tools](https://github.com/freddyaboulton/gradio-tools) | 439 |
-|[ruoccofabrizio/azure-open-ai-embeddings-qna](https://github.com/ruoccofabrizio/azure-open-ai-embeddings-qna) | 429 |
-|[StevenGrove/GPT4Tools](https://github.com/StevenGrove/GPT4Tools) | 422 |
-|[jonra1993/fastapi-alembic-sqlmodel-async](https://github.com/jonra1993/fastapi-alembic-sqlmodel-async) | 407 |
-|[msoedov/langcorn](https://github.com/msoedov/langcorn) | 405 |
-|[amosjyng/langchain-visualizer](https://github.com/amosjyng/langchain-visualizer) | 395 |
-|[ajndkr/lanarky](https://github.com/ajndkr/lanarky) | 384 |
-|[mtenenholtz/chat-twitter](https://github.com/mtenenholtz/chat-twitter) | 376 |
-|[steamship-core/steamship-langchain](https://github.com/steamship-core/steamship-langchain) | 371 |
-|[langchain-ai/auto-evaluator](https://github.com/langchain-ai/auto-evaluator) | 365 |
-|[xuwenhao/geektime-ai-course](https://github.com/xuwenhao/geektime-ai-course) | 358 |
-|[continuum-llms/chatgpt-memory](https://github.com/continuum-llms/chatgpt-memory) | 357 |
-|[opentensor/bittensor](https://github.com/opentensor/bittensor) | 347 |
-|[showlab/VLog](https://github.com/showlab/VLog) | 345 |
-|[daodao97/chatdoc](https://github.com/daodao97/chatdoc) | 345 |
-|[logan-markewich/llama_index_starter_pack](https://github.com/logan-markewich/llama_index_starter_pack) | 332 |
-|[poe-platform/poe-protocol](https://github.com/poe-platform/poe-protocol) | 320 |
-|[explosion/spacy-llm](https://github.com/explosion/spacy-llm) | 312 |
-|[andylokandy/gpt-4-search](https://github.com/andylokandy/gpt-4-search) | 311 |
-|[alejandro-ao/langchain-ask-pdf](https://github.com/alejandro-ao/langchain-ask-pdf) | 310 |
-|[jupyterlab/jupyter-ai](https://github.com/jupyterlab/jupyter-ai) | 294 |
-|[BlackHC/llm-strategy](https://github.com/BlackHC/llm-strategy) | 283 |
-|[itamargol/openai](https://github.com/itamargol/openai) | 281 |
-|[momegas/megabots](https://github.com/momegas/megabots) | 279 |
-|[personoids/personoids-lite](https://github.com/personoids/personoids-lite) | 277 |
-|[yvann-hub/Robby-chatbot](https://github.com/yvann-hub/Robby-chatbot) | 267 |
-|[Anil-matcha/Website-to-Chatbot](https://github.com/Anil-matcha/Website-to-Chatbot) | 266 |
-|[Cheems-Seminar/grounded-segment-any-parts](https://github.com/Cheems-Seminar/grounded-segment-any-parts) | 260 |
-|[sullivan-sean/chat-langchainjs](https://github.com/sullivan-sean/chat-langchainjs) | 248 |
-|[bborn/howdoi.ai](https://github.com/bborn/howdoi.ai) | 245 |
-|[daveebbelaar/langchain-experiments](https://github.com/daveebbelaar/langchain-experiments) | 240 |
-|[MagnivOrg/prompt-layer-library](https://github.com/MagnivOrg/prompt-layer-library) | 237 |
-|[ur-whitelab/exmol](https://github.com/ur-whitelab/exmol) | 234 |
-|[conceptofmind/toolformer](https://github.com/conceptofmind/toolformer) | 234 |
-|[recalign/RecAlign](https://github.com/recalign/RecAlign) | 226 |
-|[OpenBMB/AgentVerse](https://github.com/OpenBMB/AgentVerse) | 220 |
-|[alvarosevilla95/autolang](https://github.com/alvarosevilla95/autolang) | 219 |
-|[JohnSnowLabs/nlptest](https://github.com/JohnSnowLabs/nlptest) | 216 |
-|[kaleido-lab/dolphin](https://github.com/kaleido-lab/dolphin) | 215 |
-|[truera/trulens](https://github.com/truera/trulens) | 208 |
-|[NimbleBoxAI/ChainFury](https://github.com/NimbleBoxAI/ChainFury) | 208 |
-|[airobotlab/KoChatGPT](https://github.com/airobotlab/KoChatGPT) | 207 |
-|[monarch-initiative/ontogpt](https://github.com/monarch-initiative/ontogpt) | 200 |
-|[paolorechia/learn-langchain](https://github.com/paolorechia/learn-langchain) | 195 |
-|[shaman-ai/agent-actors](https://github.com/shaman-ai/agent-actors) | 185 |
-|[Haste171/langchain-chatbot](https://github.com/Haste171/langchain-chatbot) | 184 |
-|[plchld/InsightFlow](https://github.com/plchld/InsightFlow) | 182 |
-|[su77ungr/CASALIOY](https://github.com/su77ungr/CASALIOY) | 180 |
-|[jbrukh/gpt-jargon](https://github.com/jbrukh/gpt-jargon) | 177 |
-|[benthecoder/ClassGPT](https://github.com/benthecoder/ClassGPT) | 174 |
-|[billxbf/ReWOO](https://github.com/billxbf/ReWOO) | 170 |
-|[filip-michalsky/SalesGPT](https://github.com/filip-michalsky/SalesGPT) | 168 |
-|[hwchase17/langchain-streamlit-template](https://github.com/hwchase17/langchain-streamlit-template) | 168 |
-|[radi-cho/datasetGPT](https://github.com/radi-cho/datasetGPT) | 164 |
-|[hardbyte/qabot](https://github.com/hardbyte/qabot) | 164 |
-|[gia-guar/JARVIS-ChatGPT](https://github.com/gia-guar/JARVIS-ChatGPT) | 158 |
-|[plastic-labs/tutor-gpt](https://github.com/plastic-labs/tutor-gpt) | 154 |
-|[yasyf/compress-gpt](https://github.com/yasyf/compress-gpt) | 154 |
-|[fengyuli-dev/multimedia-gpt](https://github.com/fengyuli-dev/multimedia-gpt) | 154 |
-|[ethanyanjiali/minChatGPT](https://github.com/ethanyanjiali/minChatGPT) | 153 |
-|[hwchase17/chroma-langchain](https://github.com/hwchase17/chroma-langchain) | 153 |
-|[edreisMD/plugnplai](https://github.com/edreisMD/plugnplai) | 148 |
-|[chakkaradeep/pyCodeAGI](https://github.com/chakkaradeep/pyCodeAGI) | 145 |
-|[ccurme/yolopandas](https://github.com/ccurme/yolopandas) | 145 |
-|[shamspias/customizable-gpt-chatbot](https://github.com/shamspias/customizable-gpt-chatbot) | 144 |
-|[realminchoi/babyagi-ui](https://github.com/realminchoi/babyagi-ui) | 143 |
-|[PradipNichite/Youtube-Tutorials](https://github.com/PradipNichite/Youtube-Tutorials) | 140 |
-|[gustavz/DataChad](https://github.com/gustavz/DataChad) | 140 |
-|[Klingefjord/chatgpt-telegram](https://github.com/Klingefjord/chatgpt-telegram) | 140 |
-|[Jaseci-Labs/jaseci](https://github.com/Jaseci-Labs/jaseci) | 139 |
-|[handrew/browserpilot](https://github.com/handrew/browserpilot) | 137 |
-|[jmpaz/promptlib](https://github.com/jmpaz/promptlib) | 137 |
-|[SamPink/dev-gpt](https://github.com/SamPink/dev-gpt) | 135 |
-|[menloparklab/langchain-cohere-qdrant-doc-retrieval](https://github.com/menloparklab/langchain-cohere-qdrant-doc-retrieval) | 135 |
-|[hirokidaichi/wanna](https://github.com/hirokidaichi/wanna) | 135 |
-|[steamship-core/vercel-examples](https://github.com/steamship-core/vercel-examples) | 134 |
-|[pablomarin/GPT-Azure-Search-Engine](https://github.com/pablomarin/GPT-Azure-Search-Engine) | 133 |
-|[ibiscp/LLM-IMDB](https://github.com/ibiscp/LLM-IMDB) | 133 |
-|[shauryr/S2QA](https://github.com/shauryr/S2QA) | 133 |
-|[jerlendds/osintbuddy](https://github.com/jerlendds/osintbuddy) | 132 |
-|[yuanjie-ai/ChatLLM](https://github.com/yuanjie-ai/ChatLLM) | 132 |
-|[yasyf/summ](https://github.com/yasyf/summ) | 132 |
-|[WongSaang/chatgpt-ui-server](https://github.com/WongSaang/chatgpt-ui-server) | 130 |
-|[peterw/StoryStorm](https://github.com/peterw/StoryStorm) | 127 |
-|[Teahouse-Studios/akari-bot](https://github.com/Teahouse-Studios/akari-bot) | 126 |
-|[vaibkumr/prompt-optimizer](https://github.com/vaibkumr/prompt-optimizer) | 125 |
-|[preset-io/promptimize](https://github.com/preset-io/promptimize) | 124 |
-|[homanp/vercel-langchain](https://github.com/homanp/vercel-langchain) | 124 |
-|[petehunt/langchain-github-bot](https://github.com/petehunt/langchain-github-bot) | 123 |
-|[eunomia-bpf/GPTtrace](https://github.com/eunomia-bpf/GPTtrace) | 118 |
-|[nicknochnack/LangchainDocuments](https://github.com/nicknochnack/LangchainDocuments) | 116 |
-|[jiran214/GPT-vup](https://github.com/jiran214/GPT-vup) | 112 |
-|[rsaryev/talk-codebase](https://github.com/rsaryev/talk-codebase) | 112 |
-|[zenml-io/zenml-projects](https://github.com/zenml-io/zenml-projects) | 112 |
-|[microsoft/azure-openai-in-a-day-workshop](https://github.com/microsoft/azure-openai-in-a-day-workshop) | 112 |
-|[davila7/file-gpt](https://github.com/davila7/file-gpt) | 112 |
-|[prof-frink-lab/slangchain](https://github.com/prof-frink-lab/slangchain) | 111 |
-|[aurelio-labs/arxiv-bot](https://github.com/aurelio-labs/arxiv-bot) | 110 |
-|[fixie-ai/fixie-examples](https://github.com/fixie-ai/fixie-examples) | 108 |
-|[miaoshouai/miaoshouai-assistant](https://github.com/miaoshouai/miaoshouai-assistant) | 105 |
-|[flurb18/AgentOoba](https://github.com/flurb18/AgentOoba) | 103 |
-|[solana-labs/chatgpt-plugin](https://github.com/solana-labs/chatgpt-plugin) | 102 |
-|[Significant-Gravitas/Auto-GPT-Benchmarks](https://github.com/Significant-Gravitas/Auto-GPT-Benchmarks) | 102 |
-|[kaarthik108/snowChat](https://github.com/kaarthik108/snowChat) | 100 |
-
-
-_Generated by [github-dependents-info](https://github.com/nvuillam/github-dependents-info)_
-
-`github-dependents-info --repo hwchase17/langchain --markdownfile dependents.md --minstars 100 --sort stars`

docs/deployments.md

@@ -0,0 +1,39 @@
+# Deployments
+
+So you've made a really cool chain - now what? How do you deploy it and make it easily sharable with the world?
+
+This section covers several options for that.
+Note that these are meant as quick deployment options for prototypes and demos, and not for production systems.
+If you are looking for help with deployment of a production system, please contact us directly.
+
+What follows is a list of template GitHub repositories aimed that are intended to be
+very easy to fork and modify to use your chain.
+This is far from an exhaustive list of options, and we are EXTREMELY open to contributions here.
+
+## [Streamlit](https://github.com/hwchase17/langchain-streamlit-template)
+
+This repo serves as a template for how to deploy a LangChain with Streamlit.
+It implements a chatbot interface.
+It also contains instructions for how to deploy this app on the Streamlit platform.
+
+## [Gradio (on Hugging Face)](https://github.com/hwchase17/langchain-gradio-template)
+
+This repo serves as a template for how deploy a LangChain with Gradio.
+It implements a chatbot interface, with a "Bring-Your-Own-Token" approach (nice for not wracking up big bills).
+It also contains instructions for how to deploy this app on the Hugging Face platform.
+This is heavily influenced by James Weaver's [excellent examples](https://huggingface.co/JavaFXpert).
+
+## [Beam](https://github.com/slai-labs/get-beam/tree/main/examples/langchain-question-answering)
+
+This repo serves as a template for how deploy a LangChain with [Beam](https://beam.cloud).
+
+It implements a Question Answering app and contains instructions for deploying the app as a serverless REST API.
+
+## [Vercel](https://github.com/homanp/vercel-langchain)
+
+A minimal example on how to run LangChain on Vercel using Flask.
+
+
+## [SteamShip](https://github.com/steamship-core/steamship-langchain/)
+This repository contains LangChain adapters for Steamship, enabling LangChain developers to rapidly deploy their apps on Steamship.
+This includes: production ready endpoints, horizontal scaling across dependencies, persistant storage of app state, multi-tenancy support, etc.

docs/ecosystem.rst

@@ -0,0 +1,10 @@
+LangChain Ecosystem
+===================
+
+Guides for how other companies/products can be used with LangChain
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   ecosystem/*

docs/ecosystem/chroma.md

@@ -0,0 +1,20 @@
+# Chroma
+
+This page covers how to use the Chroma ecosystem within LangChain.
+It is broken into two parts: installation and setup, and then references to specific Chroma wrappers.
+
+## Installation and Setup
+- Install the Python package with `pip install chromadb`
+## Wrappers
+
+### VectorStore
+
+There exists a wrapper around Chroma vector databases, allowing you to use it as a vectorstore,
+whether for semantic search or example selection.
+
+To import this vectorstore:
+```python
+from langchain.vectorstores import Chroma
+```
+
+For a more detailed walkthrough of the Chroma wrapper, see [this notebook](../modules/indexes/examples/vectorstores.ipynb)

docs/ecosystem/cohere.md

@@ -0,0 +1,25 @@
+# Cohere
+
+This page covers how to use the Cohere ecosystem within LangChain.
+It is broken into two parts: installation and setup, and then references to specific Cohere wrappers.
+
+## Installation and Setup
+- Install the Python SDK with `pip install cohere`
+- Get an Cohere api key and set it as an environment variable (`COHERE_API_KEY`)
+
+## Wrappers
+
+### LLM
+
+There exists an Cohere LLM wrapper, which you can access with 
+```python
+from langchain.llms import Cohere
+```
+
+### Embeddings
+
+There exists an Cohere Embeddings wrapper, which you can access with 
+```python
+from langchain.embeddings import CohereEmbeddings
+```
+For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/embeddings.ipynb)

docs/ecosystem/deployments.md

@@ -1,77 +0,0 @@
-# Deployments
-
-So, you've created a really cool chain - now what? How do you deploy it and make it easily shareable with the world?
-
-This section covers several options for that. Note that these options are meant for quick deployment of prototypes and demos, not for production systems. If you need help with the deployment of a production system, please contact us directly.
-
-What follows is a list of template GitHub repositories designed to be easily forked and modified to use your chain. This list is far from exhaustive, and we are EXTREMELY open to contributions here.
-
-## [Anyscale](https://www.anyscale.com/model-serving)
-
-Anyscale is a unified compute platform that makes it easy to develop, deploy, and manage scalable LLM applications in production using Ray.
-With Anyscale you can scale the most challenging LLM-based workloads and both develop and deploy LLM-based apps on a single compute platform.
-
-## [Streamlit](https://github.com/hwchase17/langchain-streamlit-template)
-
-This repo serves as a template for how to deploy a LangChain with Streamlit.
-It implements a chatbot interface.
-It also contains instructions for how to deploy this app on the Streamlit platform.
-
-## [Gradio (on Hugging Face)](https://github.com/hwchase17/langchain-gradio-template)
-
-This repo serves as a template for how deploy a LangChain with Gradio.
-It implements a chatbot interface, with a "Bring-Your-Own-Token" approach (nice for not wracking up big bills).
-It also contains instructions for how to deploy this app on the Hugging Face platform.
-This is heavily influenced by James Weaver's [excellent examples](https://huggingface.co/JavaFXpert).
-
-## [Chainlit](https://github.com/Chainlit/cookbook)
-
-This repo is a cookbook explaining how to visualize and deploy LangChain agents with Chainlit.
-You create ChatGPT-like UIs with Chainlit. Some of the key features include intermediary steps visualisation, element management & display (images, text, carousel, etc.) as well as cloud deployment.
-Chainlit [doc](https://docs.chainlit.io/langchain) on the integration with LangChain
-
-## [Beam](https://github.com/slai-labs/get-beam/tree/main/examples/langchain-question-answering)
-
-This repo serves as a template for how deploy a LangChain with [Beam](https://beam.cloud).
-
-It implements a Question Answering app and contains instructions for deploying the app as a serverless REST API.
-
-## [Vercel](https://github.com/homanp/vercel-langchain)
-
-A minimal example on how to run LangChain on Vercel using Flask.
-
-## [FastAPI + Vercel](https://github.com/msoedov/langcorn)
-
-A minimal example on how to run LangChain on Vercel using FastAPI and LangCorn/Uvicorn.
-
-## [Kinsta](https://github.com/kinsta/hello-world-langchain)
-
-A minimal example on how to deploy LangChain to [Kinsta](https://kinsta.com) using Flask.
-
-## [Fly.io](https://github.com/fly-apps/hello-fly-langchain)
-
-A minimal example of how to deploy LangChain to [Fly.io](https://fly.io/) using Flask.
-
-## [Digitalocean App Platform](https://github.com/homanp/digitalocean-langchain)
-
-A minimal example on how to deploy LangChain to DigitalOcean App Platform.
-
-## [Google Cloud Run](https://github.com/homanp/gcp-langchain)
-
-A minimal example on how to deploy LangChain to Google Cloud Run.
-
-## [SteamShip](https://github.com/steamship-core/steamship-langchain/)
-
-This repository contains LangChain adapters for Steamship, enabling LangChain developers to rapidly deploy their apps on Steamship. This includes: production-ready endpoints, horizontal scaling across dependencies, persistent storage of app state, multi-tenancy support, etc.
-
-## [Langchain-serve](https://github.com/jina-ai/langchain-serve)
-
-This repository allows users to serve local chains and agents as RESTful, gRPC, or WebSocket APIs, thanks to [Jina](https://docs.jina.ai/). Deploy your chains & agents with ease and enjoy independent scaling, serverless and autoscaling APIs, as well as a Streamlit playground on Jina AI Cloud.
-
-## [BentoML](https://github.com/ssheng/BentoChain)
-
-This repository provides an example of how to deploy a LangChain application with [BentoML](https://github.com/bentoml/BentoML). BentoML is a framework that enables the containerization of machine learning applications as standard OCI images. BentoML also allows for the automatic generation of OpenAPI and gRPC endpoints. With BentoML, you can integrate models from all popular ML frameworks and deploy them as microservices running on the most optimal hardware and scaling independently.
-
-## [Databutton](https://databutton.com/home?new-data-app=true)
-
-These templates serve as examples of how to build, deploy, and share LangChain applications using Databutton. You can create user interfaces with Streamlit, automate tasks by scheduling Python code, and store files and data in the built-in store. Examples include a Chatbot interface with conversational memory, a Personal search engine, and a starter template for LangChain apps. Deploying and sharing is just one click away.

docs/ecosystem/google_search.md

@@ -1,4 +1,4 @@
-# Google Search
+# Google Search Wrapper
 
 This page covers how to use the Google Search API within LangChain.
 It is broken into two parts: installation and setup, and then references to the specific Google Search wrapper.
@@ -18,7 +18,7 @@ There exists a GoogleSearchAPIWrapper utility which wraps this API. To import th
 from langchain.utilities import GoogleSearchAPIWrapper
 ```
 
-For a more detailed walkthrough of this wrapper, see [this notebook](../modules/agents/tools/examples/google_search.ipynb).
+For a more detailed walkthrough of this wrapper, see [this notebook](../modules/utils/examples/google_search.ipynb).
 
 ### Tool
 
@@ -29,4 +29,4 @@ from langchain.agents import load_tools
 tools = load_tools(["google-search"])
 ```
 
-For more information on this, see [this page](../modules/agents/tools/getting_started.md)
+For more information on this, see [this page](../modules/agents/tools.md)

docs/ecosystem/google_serper.md

@@ -1,4 +1,4 @@
-# Google Serper
+# Google Serper Wrapper
 
 This page covers how to use the [Serper](https://serper.dev) Google Search API within LangChain. Serper is a low-cost Google Search API that can be used to add answer box, knowledge graph, and organic results data from Google Search. 
 It is broken into two parts: setup, and then references to the specific Google Serper wrapper.
@@ -23,7 +23,6 @@ You can use it as part of a Self Ask chain:
 from langchain.utilities import GoogleSerperAPIWrapper
 from langchain.llms.openai import OpenAI
 from langchain.agents import initialize_agent, Tool
-from langchain.agents import AgentType
 
 import os
 
@@ -35,12 +34,11 @@ search = GoogleSerperAPIWrapper()
 tools = [
     Tool(
         name="Intermediate Answer",
-        func=search.run,
-        description="useful for when you need to ask with search"
+        func=search.run
     )
 ]
 
-self_ask_with_search = initialize_agent(tools, llm, agent=AgentType.SELF_ASK_WITH_SEARCH, verbose=True)
+self_ask_with_search = initialize_agent(tools, llm, agent="self-ask-with-search", verbose=True)
 self_ask_with_search.run("What is the hometown of the reigning men's U.S. Open champion?")
 ```
 
@@ -59,7 +57,7 @@ So the final answer is: El Palmar, Spain
 'El Palmar, Spain'
 ```
 
-For a more detailed walkthrough of this wrapper, see [this notebook](../modules/agents/tools/examples/google_serper.ipynb).
+For a more detailed walkthrough of this wrapper, see [this notebook](../modules/utils/examples/google_serper.ipynb).
 
 ### Tool
 
@@ -70,4 +68,4 @@ from langchain.agents import load_tools
 tools = load_tools(["google-serper"])
 ```
 
-For more information on this, see [this page](../modules/agents/tools/getting_started.md)
+For more information on this, see [this page](../modules/agents/tools.md)

docs/ecosystem/helicone.md

@@ -0,0 +1,21 @@
+# Helicone
+
+This page covers how to use the [Helicone](https://helicone.ai) within LangChain.
+
+## What is Helicone?
+
+Helicone is an [open source](https://github.com/Helicone/helicone) observability platform that proxies your OpenAI traffic and provides you key insights into your spend, latency and usage.
+
+![Helicone](../_static/HeliconeDashboard.png)
+
+## Quick start
+
+With your LangChain environment you can just add the following parameter.
+
+```bash
+export OPENAI_API_BASE="https://oai.hconeai.com/v1"
+```
+
+Now head over to [helicone.ai](https://helicone.ai/onboarding?step=2) to create your account, and add your OpenAI API key within our dashboard to view your logs.
+
+![Helicone](../_static/HeliconeKeys.png)

docs/ecosystem/huggingface.md

@@ -30,7 +30,7 @@ To use a the wrapper for a model hosted on Hugging Face Hub:
 ```python
 from langchain.llms import HuggingFaceHub
 ```
-For a more detailed walkthrough of the Hugging Face Hub wrapper, see [this notebook](../modules/models/llms/integrations/huggingface_hub.ipynb)
+For a more detailed walkthrough of the Hugging Face Hub wrapper, see [this notebook](../modules/llms/integrations/huggingface_hub.ipynb)
 
 
 ### Embeddings
@@ -47,7 +47,7 @@ To use a the wrapper for a model hosted on Hugging Face Hub:
 ```python
 from langchain.embeddings import HuggingFaceHubEmbeddings
 ```
-For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/huggingface_hub.ipynb)
+For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/embeddings.ipynb)
 
 ### Tokenizer
 
@@ -59,7 +59,7 @@ You can also use it to count tokens when splitting documents with
 from langchain.text_splitter import CharacterTextSplitter
 CharacterTextSplitter.from_huggingface_tokenizer(...)
 ```
-For a more detailed walkthrough of this, see [this notebook](../modules/indexes/text_splitters/examples/huggingface_length_function.ipynb)
+For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/textsplitter.ipynb)
 
 
 ### Datasets

docs/ecosystem/modelscope.md

@@ -1,20 +0,0 @@
-# ModelScope
-
-This page covers how to use the modelscope ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific modelscope wrappers.
-
-## Installation and Setup
-
-* Install the Python SDK with `pip install modelscope`
-
-## Wrappers
-
-### Embeddings
-
-There exists a modelscope Embeddings wrapper, which you can access with 
-
-```python
-from langchain.embeddings import ModelScopeEmbeddings
-```
-
-For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/modelscope_hub.ipynb)

docs/ecosystem/openai.md

@@ -0,0 +1,55 @@
+# OpenAI
+
+This page covers how to use the OpenAI ecosystem within LangChain.
+It is broken into two parts: installation and setup, and then references to specific OpenAI wrappers.
+
+## Installation and Setup
+- Install the Python SDK with `pip install openai`
+- Get an OpenAI api key and set it as an environment variable (`OPENAI_API_KEY`)
+- If you want to use OpenAI's tokenizer (only available for Python 3.9+), install it with `pip install tiktoken`
+
+## Wrappers
+
+### LLM
+
+There exists an OpenAI LLM wrapper, which you can access with 
+```python
+from langchain.llms import OpenAI
+```
+
+If you are using a model hosted on Azure, you should use different wrapper for that:
+```python
+from langchain.llms import AzureOpenAI
+```
+For a more detailed walkthrough of the Azure wrapper, see [this notebook](../modules/llms/integrations/azure_openai_example.ipynb)
+
+
+
+### Embeddings
+
+There exists an OpenAI Embeddings wrapper, which you can access with 
+```python
+from langchain.embeddings import OpenAIEmbeddings
+```
+For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/embeddings.ipynb)
+
+
+### Tokenizer
+
+There are several places you can use the `tiktoken` tokenizer. By default, it is used to count tokens
+for OpenAI LLMs.
+
+You can also use it to count tokens when splitting documents with 
+```python
+from langchain.text_splitter import CharacterTextSplitter
+CharacterTextSplitter.from_tiktoken_encoder(...)
+```
+For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/textsplitter.ipynb)
+
+### Moderation
+You can also access the OpenAI content moderation endpoint with
+
+```python
+from langchain.chains import OpenAIModerationChain
+```
+For a more detailed walkthrough of this, see [this notebook](../modules/chains/examples/moderation.ipynb)

docs/ecosystem/opensearch.md

@@ -18,4 +18,4 @@ To import this vectorstore:
 from langchain.vectorstores import OpenSearchVectorSearch
 ```
 
-For a more detailed walkthrough of the OpenSearch wrapper, see [this notebook](../modules/indexes/vectorstores/examples/opensearch.ipynb)
+For a more detailed walkthrough of the OpenSearch wrapper, see [this notebook](../modules/indexes/vectorstore_examples/opensearch.ipynb)

docs/ecosystem/petals.md

@@ -5,7 +5,7 @@ It is broken into two parts: installation and setup, and then references to spec
 
 ## Installation and Setup
 - Install with `pip install petals`
-- Get a Hugging Face api key and set it as an environment variable (`HUGGINGFACE_API_KEY`)
+- Get an Huggingface api key and set it as an environment variable (`HUGGINGFACE_API_KEY`)
 
 ## Wrappers
 
@@ -14,4 +14,4 @@ It is broken into two parts: installation and setup, and then references to spec
 There exists an Petals LLM wrapper, which you can access with 
 ```python
 from langchain.llms import Petals
-```
+```

docs/ecosystem/pinecone.md

@@ -4,19 +4,17 @@ This page covers how to use the Pinecone ecosystem within LangChain.
 It is broken into two parts: installation and setup, and then references to specific Pinecone wrappers.
 
 ## Installation and Setup
-Install the Python SDK:
-```bash
-pip install pinecone-client
-```
+- Install the Python SDK with `pip install pinecone-client`
+## Wrappers
 
-
-## Vectorstore
+### VectorStore
 
 There exists a wrapper around Pinecone 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 Pinecone
 ```
 
-For a more detailed walkthrough of the Pinecone vectorstore, see [this notebook](../modules/indexes/vectorstores/examples/pinecone.ipynb)
+For a more detailed walkthrough of the Pinecone wrapper, see [this notebook](../modules/indexes/examples/vectorstores.ipynb)

docs/ecosystem/promptlayer.md

@@ -0,0 +1,31 @@
+# PromptLayer
+
+This page covers how to use [PromptLayer](https://www.promptlayer.com) within LangChain.
+It is broken into two parts: installation and setup, and then references to specific PromptLayer wrappers.
+
+## Installation and Setup
+
+If you want to work with PromptLayer:
+- Install the promptlayer python library `pip install promptlayer`
+- Create a PromptLayer account
+- Create an api token and set it as an environment variable (`PROMPTLAYER_API_KEY`)
+
+## Wrappers
+
+### LLM
+
+There exists an PromptLayer OpenAI LLM wrapper, which you can access with
+```python
+from langchain.llms import PromptLayerOpenAI
+```
+
+To tag your requests, use the argument `pl_tags` when instanializing the LLM
+```python
+from langchain.llms import PromptLayerOpenAI
+llm = PromptLayerOpenAI(pl_tags=["langchain-requests", "chatbot"])
+```
+
+This LLM is identical to the [OpenAI LLM](./openai), except that
+- all your requests will be logged to your PromptLayer account
+- you can add `pl_tags` when instantializing to tag your requests on PromptLayer
+

docs/ecosystem/ray_serve.ipynb

@@ -1,233 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Ray Serve\n",
-    "\n",
-    "[Ray Serve](https://docs.ray.io/en/latest/serve/index.html) is a scalable model serving library for building online inference APIs. Serve is particularly well suited for system composition, enabling you to build a complex inference service consisting of multiple chains and business logic all in Python code. "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Goal of this notebook\n",
-    "This notebook shows a simple example of how to deploy an OpenAI chain into production. You can extend it to deploy your own self-hosted models where you can easily define amount of hardware resources (GPUs and CPUs) needed to run your model in production efficiently. Read more about available options including autoscaling in the Ray Serve [documentation](https://docs.ray.io/en/latest/serve/getting_started.html).\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Setup Ray Serve\n",
-    "Install ray with `pip install ray[serve]`. "
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## General Skeleton"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The general skeleton for deploying a service is the following:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 0: Import ray serve and request from starlette\n",
-    "from ray import serve\n",
-    "from starlette.requests import Request\n",
-    "\n",
-    "# 1: Define a Ray Serve deployment.\n",
-    "@serve.deployment\n",
-    "class LLMServe:\n",
-    "\n",
-    "    def __init__(self) -> None:\n",
-    "        # All the initialization code goes here\n",
-    "        pass\n",
-    "\n",
-    "    async def __call__(self, request: Request) -> str:\n",
-    "        # You can parse the request here\n",
-    "        # and return a response\n",
-    "        return \"Hello World\"\n",
-    "\n",
-    "# 2: Bind the model to deployment\n",
-    "deployment = LLMServe.bind()\n",
-    "\n",
-    "# 3: Run the deployment\n",
-    "serve.api.run(deployment)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Shutdown the deployment\n",
-    "serve.api.shutdown()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Example of deploying and OpenAI chain with custom prompts"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get an OpenAI API key from [here](https://platform.openai.com/account/api-keys). By running the following code, you will be asked to provide your API key."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.llms import OpenAI\n",
-    "from langchain import PromptTemplate, LLMChain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from getpass import getpass\n",
-    "OPENAI_API_KEY = getpass()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "@serve.deployment\n",
-    "class DeployLLM:\n",
-    "\n",
-    "    def __init__(self):\n",
-    "        # We initialize the LLM, template and the chain here\n",
-    "        llm = OpenAI(openai_api_key=OPENAI_API_KEY)\n",
-    "        template = \"Question: {question}\\n\\nAnswer: Let's think step by step.\"\n",
-    "        prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
-    "        self.chain = LLMChain(llm=llm, prompt=prompt)\n",
-    "\n",
-    "    def _run_chain(self, text: str):\n",
-    "        return self.chain(text)\n",
-    "\n",
-    "    async def __call__(self, request: Request):\n",
-    "        # 1. Parse the request\n",
-    "        text = request.query_params[\"text\"]\n",
-    "        # 2. Run the chain\n",
-    "        resp = self._run_chain(text)\n",
-    "        # 3. Return the response\n",
-    "        return resp[\"text\"]"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can bind the deployment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Bind the model to deployment\n",
-    "deployment = DeployLLM.bind()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can assign the port number and host when we want to run the deployment. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Example port number\n",
-    "PORT_NUMBER = 8282\n",
-    "# Run the deployment\n",
-    "serve.api.run(deployment, port=PORT_NUMBER)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that service is deployed on port `localhost:8282` we can send a post request to get the results back."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "\n",
-    "text = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
-    "response = requests.post(f'http://localhost:{PORT_NUMBER}/?text={text}')\n",
-    "print(response.content.decode())"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "ray",
-   "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"
-  },
-  "orig_nbformat": 4
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/ecosystem/runhouse.md

@@ -15,7 +15,7 @@ custom LLMs, you can use the `SelfHostedPipeline` parent class.
 from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
 ```
 
-For a more detailed walkthrough of the Self-hosted LLMs, see [this notebook](../modules/models/llms/integrations/runhouse.ipynb)
+For a more detailed walkthrough of the Self-hosted LLMs, see [this notebook](../modules/llms/integrations/self_hosted_examples.ipynb)
 
 ## Self-hosted Embeddings
 There are several ways to use self-hosted embeddings with LangChain via Runhouse.
@@ -26,4 +26,6 @@ the `SelfHostedEmbedding` class.
 from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
 ```
 
-For a more detailed walkthrough of the Self-hosted Embeddings, see [this notebook](../modules/models/text_embedding/examples/self-hosted.ipynb)
+For a more detailed walkthrough of the Self-hosted Embeddings, see [this notebook](../modules/indexes/examples/embeddings.ipynb)
+
+##

docs/ecosystem/searx.md

@@ -0,0 +1,35 @@
+# SearxNG Search API
+
+This page covers how to use the SearxNG search API within LangChain.
+It is broken into two parts: installation and setup, and then references to the specific SearxNG API wrapper.
+
+## Installation and Setup
+
+- You can find a list of public SearxNG instances [here](https://searx.space/). 
+- It recommended to use a self-hosted instance to avoid abuse on the public instances. Also note that public instances often have a limit on the number of requests.
+- To run a self-hosted instance see [this page](https://searxng.github.io/searxng/admin/installation.html) for more information.
+- To use the tool you need to provide the searx host url by:
+    1. passing the named parameter `searx_host` when creating the instance.
+    2. exporting the environment variable `SEARXNG_HOST`. 
+
+## Wrappers
+
+### Utility
+
+You can use the wrapper to get results from a SearxNG instance. 
+
+```python
+from langchain.utilities import SearxSearchWrapper
+```
+
+### Tool
+
+You can also easily load this wrapper as a Tool (to use with an Agent).
+You can do this with:
+
+```python
+from langchain.agents import load_tools
+tools = load_tools(["searx-search"], searx_host="https://searx.example.com")
+```
+
+For more information on this, see [this page](../modules/agents/tools.md)

docs/ecosystem/serpapi.md

@@ -17,7 +17,7 @@ There exists a SerpAPI utility which wraps this API. To import this utility:
 from langchain.utilities import SerpAPIWrapper
 ```
 
-For a more detailed walkthrough of this wrapper, see [this notebook](../modules/agents/tools/examples/serpapi.ipynb).
+For a more detailed walkthrough of this wrapper, see [this notebook](../modules/utils/examples/serpapi.ipynb).
 
 ### Tool
 
@@ -28,4 +28,4 @@ from langchain.agents import load_tools
 tools = load_tools(["serpapi"])
 ```
 
-For more information on this, see [this page](../modules/agents/tools/getting_started.md)
+For more information on this, see [this page](../modules/agents/tools.md)

docs/ecosystem/unstructured.md

@@ -1,33 +1,29 @@
 # Unstructured
 
->The `unstructured` package from
+This page covers how to use the [`unstructured`](https://github.com/Unstructured-IO/unstructured)
+ecosystem within LangChain. The `unstructured` package from
 [Unstructured.IO](https://www.unstructured.io/) extracts clean text from raw source documents like
 PDFs and Word documents.
-This page covers how to use the [`unstructured`](https://github.com/Unstructured-IO/unstructured)
-ecosystem within LangChain.
+
+
+This page is broken into two parts: installation and setup, and then references to specific
+`unstructured` wrappers.
 
 ## Installation and Setup
-
-If you are using a loader that runs locally, use the following steps to get `unstructured` and
-its dependencies running locally.
-
 - Install the Python SDK with `pip install "unstructured[local-inference]"`
 - Install the following system dependencies if they are not already available on your system.
   Depending on what document types you're parsing, you may not need all of these.
-    - `libmagic-dev` (filetype detection)
-    - `poppler-utils` (images and PDFs)
-    - `tesseract-ocr`(images and PDFs)
-    - `libreoffice` (MS Office docs)
-    - `pandoc` (EPUBs)
-
-If you want to get up and running with less set up, you can
-simply run `pip install unstructured` and use `UnstructuredAPIFileLoader` or
-`UnstructuredAPIFileIOLoader`. That will process your document using the hosted Unstructured API.
-Note that currently (as of 1 May 2023) the Unstructured API is open, but it will soon require
-an API. The [Unstructured documentation page](https://unstructured-io.github.io/) will have
-instructions on how to generate an API key once they're available. Check out the instructions
-[here](https://github.com/Unstructured-IO/unstructured-api#dizzy-instructions-for-using-the-docker-image)
-if you'd like to self-host the Unstructured API or run it locally.
+    - `libmagic-dev`
+    - `poppler-utils`
+    - `tesseract-ocr`
+    - `libreoffice`
+- Run the following to install NLTK dependencies. `unstructured` will handle this automatically
+  soon.
+  - `python -c "import nltk; nltk.download('punkt')"`
+  - `python -c "import nltk; nltk.download('averaged_perceptron_tagger')"`
+- If you are parsing PDFs, run the following to install the `detectron2` model, which
+  `unstructured` uses for layout detection:
+    - `pip install "detectron2@git+https://github.com/facebookresearch/detectron2.git@v0.6#egg=detectron2"`
 
 ## Wrappers
 

docs/ecosystem/weaviate.md

@@ -30,4 +30,4 @@ To import this vectorstore:
 from langchain.vectorstores import Weaviate
 ```
 
-For a more detailed walkthrough of the Weaviate wrapper, see [this notebook](../modules/indexes/vectorstores/examples/weaviate.ipynb)
+For a more detailed walkthrough of the Weaviate wrapper, see [this notebook](../modules/indexes/examples/vectorstores.ipynb)

docs/ecosystem/wolfram_alpha.md

@@ -1,17 +1,12 @@
-# Wolfram Alpha
+# Wolfram Alpha Wrapper
 
->[WolframAlpha](https://en.wikipedia.org/wiki/WolframAlpha) is an answer engine developed by `Wolfram Research`. 
-> It answers factual queries by computing answers from externally sourced data.
-
-This page covers how to use the `Wolfram Alpha API` within LangChain.
+This page covers how to use the Wolfram Alpha API within LangChain.
+It is broken into two parts: installation and setup, and then references to specific Wolfram Alpha wrappers.
 
 ## Installation and Setup
-- Install requirements with 
-```bash
-pip install wolframalpha
-```
+- Install requirements with `pip install wolframalpha`
 - Go to wolfram alpha and sign up for a developer account [here](https://developer.wolframalpha.com/)
-- Create an app and get your `APP ID`
+- Create an app and get your APP ID
 - Set your APP ID as an environment variable `WOLFRAM_ALPHA_APPID`
 
 
@@ -25,7 +20,7 @@ There exists a WolframAlphaAPIWrapper utility which wraps this API. To import th
 from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
 ```
 
-For a more detailed walkthrough of this wrapper, see [this notebook](../modules/agents/tools/examples/wolfram_alpha.ipynb).
+For a more detailed walkthrough of this wrapper, see [this notebook](../modules/utils/examples/wolfram_alpha.ipynb).
 
 ### Tool
 
@@ -36,4 +31,4 @@ from langchain.agents import load_tools
 tools = load_tools(["wolfram-alpha"])
 ```
 
-For more information on this, see [this page](../modules/agents/tools/getting_started.md)
+For more information on this, see [this page](../modules/agents/tools.md)

docs/gallery.rst

@@ -0,0 +1,326 @@
+LangChain Gallery
+=============
+
+Lots of people have built some pretty awesome stuff with LangChain.
+This is a collection of our favorites.
+If you see any other demos that you think we should highlight, be sure to let us know!
+
+
+Open Source
+-----------
+
+.. panels::
+    :body: text-center
+
+    ---
+
+    .. link-button:: https://github.com/bborn/howdoi.ai
+        :type: url
+        :text: HowDoI.ai
+        :classes: stretched-link btn-lg
+
+    +++
+
+    This is an experiment in building a large-language-model-backed chatbot. It can hold a conversation, remember previous comments/questions, 
+    and answer all types of queries (history, web search, movie data, weather, news, and more).
+
+    ---
+
+    .. link-button:: https://colab.research.google.com/drive/1sKSTjt9cPstl_WMZ86JsgEqFG-aSAwkn?usp=sharing
+        :type: url
+        :text: YouTube Transcription QA with Sources
+        :classes: stretched-link btn-lg
+
+    +++
+
+    An end-to-end example of doing question answering on YouTube transcripts, returning the timestamps as sources to legitimize the answer.
+
+    ---
+
+    .. link-button:: https://github.com/normandmickey/MrsStax
+        :type: url
+        :text: QA Slack Bot
+        :classes: stretched-link btn-lg
+
+    +++
+
+    This application is a Slack Bot that uses Langchain and OpenAI's GPT3 language model to provide domain specific answers. You provide the documents.
+
+    ---
+
+    .. link-button:: https://github.com/OpenBioLink/ThoughtSource
+        :type: url
+        :text: ThoughtSource
+        :classes: stretched-link btn-lg
+
+    +++
+
+    A central, open resource and community around data and tools related to chain-of-thought reasoning in large language models.
+
+    ---
+
+    .. link-button:: https://github.com/blackhc/llm-strategy
+        :type: url
+        :text: LLM Strategy
+        :classes: stretched-link btn-lg
+    
+    +++
+
+    This Python package adds a decorator llm_strategy that connects to an LLM (such as OpenAI’s GPT-3) and uses the LLM to "implement" abstract methods in interface classes. It does this by forwarding requests to the LLM and converting the responses back to Python data using Python's @dataclasses.
+
+    ---
+
+    .. link-button:: https://github.com/JohnNay/llm-lobbyist
+        :type: url
+        :text: Zero-Shot Corporate Lobbyist
+        :classes: stretched-link btn-lg
+
+    +++
+
+    A notebook showing how to use GPT to help with the work of a corporate lobbyist.
+
+    ---
+
+    .. link-button:: https://dagster.io/blog/chatgpt-langchain
+        :type: url
+        :text: Dagster Documentation ChatBot
+        :classes: stretched-link btn-lg
+
+    +++
+
+    A jupyter notebook demonstrating how you could create a semantic search engine on documents in one of your Google Folders
+
+    ---
+
+    .. link-button:: https://github.com/venuv/langchain_semantic_search
+        :type: url
+        :text: Google Folder Semantic Search
+        :classes: stretched-link btn-lg
+
+    +++
+
+    Build a GitHub support bot with GPT3, LangChain, and Python.
+
+    ---
+
+    .. link-button:: https://huggingface.co/spaces/team7/talk_with_wind
+        :type: url
+        :text: Talk With Wind
+        :classes: stretched-link btn-lg
+
+    +++
+
+    Record sounds of anything (birds, wind, fire, train station) and chat with it.
+
+    ---
+
+    .. link-button:: https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain
+        :type: url
+        :text: ChatGPT LangChain
+        :classes: stretched-link btn-lg
+
+    +++
+
+    This simple application demonstrates a conversational agent implemented with OpenAI GPT-3.5 and LangChain. When necessary, it leverages tools for complex math, searching the internet, and accessing news and weather.
+
+    ---
+
+    .. link-button:: https://huggingface.co/spaces/JavaFXpert/gpt-math-techniques
+        :type: url
+        :text: GPT Math Techniques
+        :classes: stretched-link btn-lg
+
+    +++
+
+    A Hugging Face spaces project showing off the benefits of using PAL for math problems.
+
+    ---
+
+    .. link-button:: https://colab.research.google.com/drive/1xt2IsFPGYMEQdoJFNgWNAjWGxa60VXdV
+        :type: url
+        :text: GPT Political Compass
+        :classes: stretched-link btn-lg
+
+    +++
+
+    Measure the political compass of GPT.
+
+    ---
+
+    .. link-button:: https://github.com/hwchase17/notion-qa
+        :type: url
+        :text: Notion Database Question-Answering Bot
+        :classes: stretched-link btn-lg
+    
+    +++
+
+    Open source GitHub project shows how to use LangChain to create a chatbot that can answer questions about an arbitrary Notion database.
+
+    ---
+
+    .. link-button:: https://github.com/jerryjliu/gpt_index
+        :type: url
+        :text: GPT Index
+        :classes: stretched-link btn-lg
+    
+    +++
+
+    GPT Index is a project consisting of a set of data structures that are created using GPT-3 and can be traversed using GPT-3 in order to answer queries.
+
+    ---
+
+    .. link-button:: https://github.com/JavaFXpert/llm-grovers-search-party
+        :type: url
+        :text: Grover's Algorithm
+        :classes: stretched-link btn-lg
+
+    +++
+
+    Leveraging Qiskit, OpenAI and LangChain to demonstrate Grover's algorithm
+
+    ---
+
+    .. link-button:: https://huggingface.co/spaces/rituthombre/QNim
+        :type: url
+        :text: QNimGPT
+        :classes: stretched-link btn-lg
+
+    +++
+
+    A chat UI to play Nim, where a player can select an opponent, either a quantum computer or an AI
+
+    ---
+
+    .. link-button:: https://colab.research.google.com/drive/19WTIWC3prw5LDMHmRMvqNV2loD9FHls6?usp=sharing
+        :type: url
+        :text: ReAct TextWorld
+        :classes: stretched-link btn-lg
+
+    +++
+
+    Leveraging the ReActTextWorldAgent to play TextWorld with an LLM!
+
+    ---
+
+    .. link-button:: https://github.com/jagilley/fact-checker
+        :type: url
+        :text: Fact Checker
+        :classes: stretched-link btn-lg
+    
+    +++
+
+    This repo is a simple demonstration of using LangChain to do fact-checking with prompt chaining.
+    
+    ---
+
+    .. link-button:: https://github.com/arc53/docsgpt
+        :type: url
+        :text: DocsGPT
+        :classes: stretched-link btn-lg
+    
+    +++
+
+    Answer questions about the documentation of any project    
+
+Misc. Colab Notebooks
+~~~~~~~~~~~~~~~
+
+.. panels::
+    :body: text-center
+
+    ---
+
+    .. link-button:: https://colab.research.google.com/drive/1AAyEdTz-Z6ShKvewbt1ZHUICqak0MiwR?usp=sharing
+        :type: url
+        :text: Wolfram Alpha in Conversational Agent
+        :classes: stretched-link btn-lg
+    
+    +++
+
+    Give ChatGPT a WolframAlpha neural implant
+    
+    ---
+
+    .. link-button:: https://colab.research.google.com/drive/1UsCLcPy8q5PMNQ5ytgrAAAHa124dzLJg?usp=sharing
+        :type: url
+        :text: Tool Updates in Agents
+        :classes: stretched-link btn-lg
+    
+    +++
+
+    Agent improvements (6th Jan 2023)
+    
+    ---
+
+    .. link-button:: https://colab.research.google.com/drive/1UsCLcPy8q5PMNQ5ytgrAAAHa124dzLJg?usp=sharing
+        :type: url
+        :text: Conversational Agent with Tools (Langchain AGI)
+        :classes: stretched-link btn-lg
+    
+    +++
+
+    Langchain AGI (23rd Dec 2022)    
+
+Proprietary
+-----------
+
+.. panels::
+    :body: text-center
+
+    ---
+
+    .. link-button:: https://twitter.com/sjwhitmore/status/1580593217153531908?s=20&t=neQvtZZTlp623U3LZwz3bQ
+        :type: url
+        :text: Daimon
+        :classes: stretched-link btn-lg
+
+    +++
+
+    A chat-based AI personal assistant with long-term memory about you.
+
+    ---
+
+    .. link-button:: https://twitter.com/dory111111/status/1608406234646052870?s=20&t=XYlrbKM0ornJsrtGa0br-g
+        :type: url
+        :text: AI Assisted SQL Query Generator
+        :classes: stretched-link btn-lg
+
+    +++
+
+    An app to write SQL using natural language, and execute against real DB.
+
+    ---
+
+    .. link-button:: https://twitter.com/krrish_dh/status/1581028925618106368?s=20&t=neQvtZZTlp623U3LZwz3bQ
+        :type: url
+        :text: Clerkie
+        :classes: stretched-link btn-lg
+
+    +++
+
+    Stack Tracing QA Bot to help debug complex stack tracing (especially the ones that go multi-function/file deep).
+
+    ---
+
+    .. link-button:: https://twitter.com/Raza_Habib496/status/1596880140490838017?s=20&t=6MqEQYWfSqmJwsKahjCVOA
+        :type: url
+        :text: Sales Email Writer
+        :classes: stretched-link btn-lg
+
+    +++
+
+    By Raza Habib, this demo utilizes LangChain + SerpAPI + HumanLoop to write sales emails. Give it a company name and a person, this application will use Google Search (via SerpAPI) to get more information on the company and the person, and then write them a sales message.
+
+    ---
+
+    .. link-button:: https://twitter.com/chillzaza_/status/1592961099384905730?s=20&t=EhU8jl0KyCPJ7vE9Rnz-cQ
+        :type: url
+        :text: Question-Answering on a Web Browser
+        :classes: stretched-link btn-lg
+
+    +++
+
+    By Zahid Khawaja, this demo utilizes question answering to answer questions about a given website. A followup added this for `YouTube videos <https://twitter.com/chillzaza_/status/1593739682013220865?s=20&t=EhU8jl0KyCPJ7vE9Rnz-cQ>`_, and then another followup added it for `Wikipedia <https://twitter.com/chillzaza_/status/1594847151238037505?s=20&t=EhU8jl0KyCPJ7vE9Rnz-cQ>`_.
+
+
+

docs/getting_started/concepts.md

@@ -1,75 +0,0 @@
-# Concepts
-
-These are concepts and terminology commonly used when developing LLM applications.
-It contains reference to external papers or sources where the concept was first introduced,
-as well as to places in LangChain where the concept is used.
-
-## Chain of Thought
-
-`Chain of Thought (CoT)` is a prompting technique used to encourage the model to generate a series of intermediate reasoning steps.
-A less formal way to induce this behavior is to include “Let’s think step-by-step” in the prompt.
-
-- [Chain-of-Thought Paper](https://arxiv.org/pdf/2201.11903.pdf)
-- [Step-by-Step Paper](https://arxiv.org/abs/2112.00114)
-
-## Action Plan Generation
-
-`Action Plan Generation` is a prompting technique that uses a language model to generate actions to take.
-The results of these actions can then be fed back into the language model to generate a subsequent action.
-
-- [WebGPT Paper](https://arxiv.org/pdf/2112.09332.pdf)
-- [SayCan Paper](https://say-can.github.io/assets/palm_saycan.pdf)
-
-## ReAct
-
-`ReAct` is a prompting technique that combines Chain-of-Thought prompting with action plan generation.
-This induces the model to think about what action to take, then take it.
-
-- [Paper](https://arxiv.org/pdf/2210.03629.pdf)
-- [LangChain Example](../modules/agents/agents/examples/react.ipynb)
-
-## Self-ask
-
-`Self-ask` is a prompting method that builds on top of chain-of-thought prompting.
-In this method, the model explicitly asks itself follow-up questions, which are then answered by an external search engine.
-
-- [Paper](https://ofir.io/self-ask.pdf)
-- [LangChain Example](../modules/agents/agents/examples/self_ask_with_search.ipynb)
-
-## Prompt Chaining
-
-`Prompt Chaining` is combining multiple LLM calls, with the output of one-step being the input to the next.
-
-- [PromptChainer Paper](https://arxiv.org/pdf/2203.06566.pdf)
-- [Language Model Cascades](https://arxiv.org/abs/2207.10342)
-- [ICE Primer Book](https://primer.ought.org/)
-- [Socratic Models](https://socraticmodels.github.io/)
-
-## Memetic Proxy
-
-`Memetic Proxy` is encouraging the LLM
-to respond in a certain way framing the discussion in a context that the model knows of and that 
-will result in that type of response.
-For example, as a conversation between a student and a teacher.
-
-- [Paper](https://arxiv.org/pdf/2102.07350.pdf)
-
-## Self Consistency
-
-`Self Consistency` is a decoding strategy that samples a diverse set of reasoning paths and then selects the most consistent answer.
-Is most effective when combined with Chain-of-thought prompting.
-
-- [Paper](https://arxiv.org/pdf/2203.11171.pdf)
-
-## Inception
-
-`Inception` is also called `First Person Instruction`.
-It is encouraging the model to think a certain way by including the start of the model’s response in the prompt.
-
-- [Example](https://twitter.com/goodside/status/1583262455207460865?s=20&t=8Hz7XBnK1OF8siQrxxCIGQ)
-
-## MemPrompt
-
-`MemPrompt` maintains a memory of errors and user feedback, and uses them to prevent repetition of mistakes.
-
-- [Paper](https://memprompt.com/)

docs/getting_started/getting_started.md

@@ -9,8 +9,6 @@ To get started, install LangChain with the following command:
 
 ```bash
 pip install langchain
-# or
-conda install langchain -c conda-forge
 ```
 
 
@@ -37,14 +35,8 @@ import os
 os.environ["OPENAI_API_KEY"] = "..."
 ```
 
-If you want to set the API key dynamically, you can use the openai_api_key parameter when initiating OpenAI class—for instance, each user's API key.
 
-```python
-from langchain.llms import OpenAI
-llm = OpenAI(openai_api_key="OPENAI_API_KEY")
-```
-
-## Building a Language Model Application: LLMs
+## Building a Language Model Application
 
 Now that we have installed LangChain and set up our environment, we can start building our language model application.
 
@@ -52,7 +44,7 @@ LangChain provides many modules that can be used to build language model applica
 
 
 
-## LLMs: Get predictions from a language model
+`````{dropdown} LLMs: Get predictions from a language model
 
 The most basic building block of LangChain is calling an LLM on some input.
 Let's walk through a simple example of how to do this. 
@@ -74,7 +66,7 @@ llm = OpenAI(temperature=0.9)
 We can now call it on some input!
 
 ```python
-text = "What would be a good company name for a company that makes colorful socks?"
+text = "What would be a good company name a company that makes colorful socks?"
 print(llm(text))
 ```
 
@@ -82,10 +74,11 @@ print(llm(text))
 Feetful of Fun
 ```
 
-For more details on how to use LLMs within LangChain, see the [LLM getting started guide](../modules/models/llms/getting_started.ipynb).
+For more details on how to use LLMs within LangChain, see the [LLM getting started guide](../modules/llms/getting_started.ipynb).
+`````
 
 
-## Prompt Templates: Manage prompts for LLMs
+`````{dropdown} Prompt Templates: Manage prompts for LLMs
 
 Calling an LLM is a great first step, but it's just the beginning.
 Normally when you use an LLM in an application, you are not sending user input directly to the LLM.
@@ -118,12 +111,13 @@ What is a good name for a company that makes colorful socks?
 ```
 
 
-[For more details, check out the getting started guide for prompts.](../modules/prompts/chat_prompt_template.ipynb)
+[For more details, check out the getting started guide for prompts.](../modules/prompts/getting_started.ipynb)
+
+`````
 
 
 
-
-## Chains: Combine LLMs and prompts in multi-step workflows
+`````{dropdown} Chains: Combine LLMs and prompts in multi-step workflows
 
 Up until now, we've worked with the PromptTemplate and LLM primitives by themselves. But of course, a real application is not just one primitive, but rather a combination of them.
 
@@ -163,7 +157,10 @@ This is one of the simpler types of chains, but understanding how it works will
 
 [For more details, check out the getting started guide for chains.](../modules/chains/getting_started.ipynb)
 
-## Agents: Dynamically Call Chains Based on User Input
+`````
+
+
+`````{dropdown} Agents: Dynamically call chains based on user input
 
 So far the chains we've looked at run in a predetermined order.
 
@@ -178,9 +175,9 @@ In order to load agents, you should understand the following concepts:
 - LLM: The language model powering the agent.
 - Agent: The agent to use. This should be a string that references a support agent class. Because this notebook focuses on the simplest, highest level API, this only covers using the standard supported agents. If you want to implement a custom agent, see the documentation for custom agents (coming soon).
 
-**Agents**: For a list of supported agents and their specifications, see [here](../modules/agents/getting_started.ipynb).
+**Agents**: For a list of supported agents and their specifications, see [here](../modules/agents/agents.md).
 
-**Tools**: For a list of predefined tools and their specifications, see [here](../modules/agents/tools/getting_started.md).
+**Tools**: For a list of predefined tools and their specifications, see [here](../modules/agents/tools.md).
 
 For this example, you will also need to install the SerpAPI Python package.
 
@@ -200,7 +197,6 @@ Now we can get started!
 ```python
 from langchain.agents import load_tools
 from langchain.agents import initialize_agent
-from langchain.agents import AgentType
 from langchain.llms import OpenAI
 
 # First, let's load the language model we're going to use to control the agent.
@@ -211,32 +207,38 @@ tools = load_tools(["serpapi", "llm-math"], llm=llm)
 
 
 # Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
-agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
+agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)
 
 # Now let's test it out!
-agent.run("What was the high temperature in SF yesterday in Fahrenheit? What is that number raised to the .023 power?")
+agent.run("Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?")
 ```
 
 ```pycon
-> Entering new AgentExecutor chain...
- I need to find the temperature first, then use the calculator to raise it to the .023 power.
+Entering new AgentExecutor chain...
+ I need to find out who Olivia Wilde's boyfriend is and then calculate his age raised to the 0.23 power.
 Action: Search
-Action Input: "High temperature in SF yesterday"
-Observation: San Francisco Temperature Yesterday. Maximum temperature yesterday: 57 °F (at 1:56 pm) Minimum temperature yesterday: 49 °F (at 1:56 am) Average temperature ...
-Thought: I now have the temperature, so I can use the calculator to raise it to the .023 power.
+Action Input: "Olivia Wilde boyfriend"
+Observation: Jason Sudeikis
+Thought: I need to find out Jason Sudeikis' age
+Action: Search
+Action Input: "Jason Sudeikis age"
+Observation: 47 years
+Thought: I need to calculate 47 raised to the 0.23 power
 Action: Calculator
-Action Input: 57^.023
-Observation: Answer: 1.0974509573251117
+Action Input: 47^0.23
+Observation: Answer: 2.4242784855673896
 
 Thought: I now know the final answer
-Final Answer: The high temperature in SF yesterday in Fahrenheit raised to the .023 power is 1.0974509573251117.
-
-> Finished chain.
+Final Answer: Jason Sudeikis, Olivia Wilde's boyfriend, is 47 years old and his age raised to the 0.23 power is 2.4242784855673896.
+> Finished AgentExecutor chain.
+"Jason Sudeikis, Olivia Wilde's boyfriend, is 47 years old and his age raised to the 0.23 power is 2.4242784855673896."
 ```
 
 
+`````
 
-## Memory: Add State to Chains and Agents
+
+`````{dropdown} Memory: Add state to chains and agents
 
 So far, all the chains and agents we've gone through have been stateless. But often, you may want a chain or agent to have some concept of "memory" so that it may remember information about its previous interactions. The clearest and simple example of this is when designing a chatbot - you want it to remember previous messages so it can use context from that to have a better conversation. This would be a type of "short-term memory". On the more complex side, you could imagine a chain/agent remembering key pieces of information over time - this would be a form of "long-term memory". For more concrete ideas on the latter, see this [awesome paper](https://memprompt.com/).
 
@@ -250,8 +252,7 @@ from langchain import OpenAI, ConversationChain
 llm = OpenAI(temperature=0)
 conversation = ConversationChain(llm=llm, verbose=True)
 
-output = conversation.predict(input="Hi there!")
-print(output)
+conversation.predict(input="Hi there!")
 ```
 
 ```pycon
@@ -269,8 +270,7 @@ AI:
 ```
 
 ```python
-output = conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
-print(output)
+conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
 ```
 
 ```pycon
@@ -287,214 +287,4 @@ AI:
 
 > Finished chain.
 " That's great! What would you like to talk about?"
-```
-
-## Building a Language Model Application: Chat Models
-
-Similarly, you can use chat models instead of LLMs. Chat models are a variation on language models. While chat models use language models under the hood, the interface they expose is a bit different: rather than expose a "text in, text out" API, they expose an interface where "chat messages" are the inputs and outputs.
-
-Chat model APIs are fairly new, so we are still figuring out the correct abstractions.
-
-## Get Message Completions from a Chat Model
-
-You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are `AIMessage`, `HumanMessage`, `SystemMessage`, and `ChatMessage` -- `ChatMessage` takes in an arbitrary role parameter. Most of the time, you'll just be dealing with `HumanMessage`, `AIMessage`, and `SystemMessage`.
-
-```python
-from langchain.chat_models import ChatOpenAI
-from langchain.schema import (
-    AIMessage,
-    HumanMessage,
-    SystemMessage
-)
-
-chat = ChatOpenAI(temperature=0)
-```
-
-You can get completions by passing in a single message.
-
-```python
-chat([HumanMessage(content="Translate this sentence from English to French. I love programming.")])
-# -> AIMessage(content="J'aime programmer.", additional_kwargs={})
-```
-
-You can also pass in multiple messages for OpenAI's gpt-3.5-turbo and gpt-4 models.
-
-```python
-messages = [
-    SystemMessage(content="You are a helpful assistant that translates English to French."),
-    HumanMessage(content="I love programming.")
-]
-chat(messages)
-# -> AIMessage(content="J'aime programmer.", additional_kwargs={})
-```
-
-You can go one step further and generate completions for multiple sets of messages using `generate`. This returns an `LLMResult` with an additional `message` parameter:
-```python
-batch_messages = [
-    [
-        SystemMessage(content="You are a helpful assistant that translates English to French."),
-        HumanMessage(content="I love programming.")
-    ],
-    [
-        SystemMessage(content="You are a helpful assistant that translates English to French."),
-        HumanMessage(content="I love artificial intelligence.")
-    ],
-]
-result = chat.generate(batch_messages)
-result
-# -> LLMResult(generations=[[ChatGeneration(text="J'aime programmer.", generation_info=None, message=AIMessage(content="J'aime programmer.", additional_kwargs={}))], [ChatGeneration(text="J'aime l'intelligence artificielle.", generation_info=None, message=AIMessage(content="J'aime l'intelligence artificielle.", additional_kwargs={}))]], llm_output={'token_usage': {'prompt_tokens': 57, 'completion_tokens': 20, 'total_tokens': 77}})
-```
-
-You can recover things like token usage from this LLMResult:
-```
-result.llm_output['token_usage']
-# -> {'prompt_tokens': 57, 'completion_tokens': 20, 'total_tokens': 77}
-```
-
-
-## Chat Prompt Templates
-Similar to LLMs, you can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplate`s. You can use `ChatPromptTemplate`'s `format_prompt` -- this returns a `PromptValue`, which you can convert to a string or `Message` object, depending on whether you want to use the formatted value as input to an llm or chat model.
-
-For convenience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:
-
-```python
-from langchain.chat_models import ChatOpenAI
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-)
-
-chat = ChatOpenAI(temperature=0)
-
-template = "You are a helpful assistant that translates {input_language} to {output_language}."
-system_message_prompt = SystemMessagePromptTemplate.from_template(template)
-human_template = "{text}"
-human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
-
-chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
-
-# get a chat completion from the formatted messages
-chat(chat_prompt.format_prompt(input_language="English", output_language="French", text="I love programming.").to_messages())
-# -> AIMessage(content="J'aime programmer.", additional_kwargs={})
-```
-
-## Chains with Chat Models
-The `LLMChain` discussed in the above section can be used with chat models as well:
-
-```python
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-)
-
-chat = ChatOpenAI(temperature=0)
-
-template = "You are a helpful assistant that translates {input_language} to {output_language}."
-system_message_prompt = SystemMessagePromptTemplate.from_template(template)
-human_template = "{text}"
-human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
-chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
-
-chain = LLMChain(llm=chat, prompt=chat_prompt)
-chain.run(input_language="English", output_language="French", text="I love programming.")
-# -> "J'aime programmer."
-```
-
-## Agents with Chat Models
-Agents can also be used with chat models, you can initialize one using `AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION` as the agent type.
-
-```python
-from langchain.agents import load_tools
-from langchain.agents import initialize_agent
-from langchain.agents import AgentType
-from langchain.chat_models import ChatOpenAI
-from langchain.llms import OpenAI
-
-# First, let's load the language model we're going to use to control the agent.
-chat = ChatOpenAI(temperature=0)
-
-# Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that in.
-llm = OpenAI(temperature=0)
-tools = load_tools(["serpapi", "llm-math"], llm=llm)
-
-
-# Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
-agent = initialize_agent(tools, chat, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
-
-# Now let's test it out!
-agent.run("Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?")
-```
-
-```pycon
-
-> Entering new AgentExecutor chain...
-Thought: I need to use a search engine to find Olivia Wilde's boyfriend and a calculator to raise his age to the 0.23 power.
-Action:
-{
-  "action": "Search",
-  "action_input": "Olivia Wilde boyfriend"
-}
-
-Observation: Sudeikis and Wilde's relationship ended in November 2020. Wilde was publicly served with court documents regarding child custody while she was presenting Don't Worry Darling at CinemaCon 2022. In January 2021, Wilde began dating singer Harry Styles after meeting during the filming of Don't Worry Darling.
-Thought:I need to use a search engine to find Harry Styles' current age.
-Action:
-{
-  "action": "Search",
-  "action_input": "Harry Styles age"
-}
-
-Observation: 29 years
-Thought:Now I need to calculate 29 raised to the 0.23 power.
-Action:
-{
-  "action": "Calculator",
-  "action_input": "29^0.23"
-}
-
-Observation: Answer: 2.169459462491557
-
-Thought:I now know the final answer.
-Final Answer: 2.169459462491557
-
-> Finished chain.
-'2.169459462491557'
-```
-## Memory: Add State to Chains and Agents
-You can use Memory with chains and agents initialized with chat models. The main difference between this and Memory for LLMs is that rather than trying to condense all previous messages into a string, we can keep them as their own unique memory object.
-
-```python
-from langchain.prompts import (
-    ChatPromptTemplate, 
-    MessagesPlaceholder, 
-    SystemMessagePromptTemplate, 
-    HumanMessagePromptTemplate
-)
-from langchain.chains import ConversationChain
-from langchain.chat_models import ChatOpenAI
-from langchain.memory import ConversationBufferMemory
-
-prompt = ChatPromptTemplate.from_messages([
-    SystemMessagePromptTemplate.from_template("The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know."),
-    MessagesPlaceholder(variable_name="history"),
-    HumanMessagePromptTemplate.from_template("{input}")
-])
-
-llm = ChatOpenAI(temperature=0)
-memory = ConversationBufferMemory(return_messages=True)
-conversation = ConversationChain(memory=memory, prompt=prompt, llm=llm)
-
-conversation.predict(input="Hi there!")
-# -> 'Hello! How can I assist you today?'
-
-
-conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
-# -> "That sounds like fun! I'm happy to chat with you. Is there anything specific you'd like to talk about?"
-
-conversation.predict(input="Tell me about yourself.")
-# -> "Sure! I am an AI language model created by OpenAI. I was trained on a large dataset of text from the internet, which allows me to understand and generate human-like language. I can answer questions, provide information, and even have conversations like this one. Is there anything else you'd like to know about me?"
-```
-
+```

docs/getting_started/tutorials.md

@@ -1,113 +0,0 @@
-# Tutorials
-
-⛓ icon marks a new addition [last update 2023-05-15]
-
-### DeepLearning.AI course
-⛓[LangChain for LLM Application Development](https://learn.deeplearning.ai/langchain) by Harrison Chase presented by [Andrew Ng](https://en.wikipedia.org/wiki/Andrew_Ng)
-
-### Handbook
-[LangChain AI Handbook](https://www.pinecone.io/learn/langchain/) By **James Briggs** and **Francisco Ingham**
-
-### Tutorials
-[LangChain Tutorials](https://www.youtube.com/watch?v=FuqdVNB_8c0&list=PL9V0lbeJ69brU-ojMpU1Y7Ic58Tap0Cw6) by [Edrick](https://www.youtube.com/@edrickdch):
-- ⛓ [LangChain, Chroma DB, OpenAI Beginner Guide | ChatGPT with your PDF](https://youtu.be/FuqdVNB_8c0)
-- ⛓ [LangChain 101: The Complete Beginner's Guide](https://youtu.be/P3MAbZ2eMUI) 
-
-[LangChain Crash Course: Build an AutoGPT app in 25 minutes](https://youtu.be/MlK6SIjcjE8) by [Nicholas Renotte](https://www.youtube.com/@NicholasRenotte)
-
-
-[LangChain Crash Course - Build apps with language models](https://youtu.be/LbT1yp6quS8) by [Patrick Loeber](https://www.youtube.com/@patloeber)
-
-
-[LangChain Explained in 13 Minutes | QuickStart Tutorial for Beginners](https://youtu.be/aywZrzNaKjs) by [Rabbitmetrics](https://www.youtube.com/@rabbitmetrics)
-
-
-###
-[LangChain for Gen AI and LLMs](https://www.youtube.com/playlist?list=PLIUOU7oqGTLieV9uTIFMm6_4PXg-hlN6F) by [James Briggs](https://www.youtube.com/@jamesbriggs):
-- #1 [Getting Started with `GPT-3` vs. Open Source LLMs](https://youtu.be/nE2skSRWTTs)
-- #2 [Prompt Templates for `GPT 3.5` and other LLMs](https://youtu.be/RflBcK0oDH0)
-- #3 [LLM Chains using `GPT 3.5` and other LLMs](https://youtu.be/S8j9Tk0lZHU)
-- #4 [Chatbot Memory for `Chat-GPT`, `Davinci` + other LLMs](https://youtu.be/X05uK0TZozM)
-- #5 [Chat with OpenAI in LangChain](https://youtu.be/CnAgB3A5OlU)
-- ⛓ #6 [Fixing LLM Hallucinations with Retrieval Augmentation in LangChain](https://youtu.be/kvdVduIJsc8)
-- ⛓ #7 [LangChain Agents Deep Dive with GPT 3.5](https://youtu.be/jSP-gSEyVeI)
-- ⛓ #8 [Create Custom Tools for Chatbots in LangChain](https://youtu.be/q-HNphrWsDE)
-- ⛓ #9 [Build Conversational Agents with Vector DBs](https://youtu.be/H6bCqqw9xyI)
-
-
-###
-[LangChain 101](https://www.youtube.com/playlist?list=PLqZXAkvF1bPNQER9mLmDbntNfSpzdDIU5) by [Data Independent](https://www.youtube.com/@DataIndependent):
-- [What Is LangChain? - LangChain + `ChatGPT` Overview](https://youtu.be/_v_fgW2SkkQ)
-- [Quickstart Guide](https://youtu.be/kYRB-vJFy38)
-- [Beginner Guide To 7 Essential Concepts](https://youtu.be/2xxziIWmaSA)
-- [`OpenAI` + `Wolfram Alpha`](https://youtu.be/UijbzCIJ99g)
-- [Ask Questions On Your Custom (or Private) Files](https://youtu.be/EnT-ZTrcPrg)
-- [Connect `Google Drive Files` To `OpenAI`](https://youtu.be/IqqHqDcXLww)
-- [`YouTube Transcripts` + `OpenAI`](https://youtu.be/pNcQ5XXMgH4)
-- [Question A 300 Page Book (w/ `OpenAI` + `Pinecone`)](https://youtu.be/h0DHDp1FbmQ)
-- [Workaround `OpenAI's` Token Limit With Chain Types](https://youtu.be/f9_BWhCI4Zo)
-- [Build Your Own OpenAI + LangChain Web App in 23 Minutes](https://youtu.be/U_eV8wfMkXU)
-- [Working With The New `ChatGPT API`](https://youtu.be/e9P7FLi5Zy8)
-- [OpenAI + LangChain Wrote Me 100 Custom Sales Emails](https://youtu.be/y1pyAQM-3Bo)
-- [Structured Output From `OpenAI` (Clean Dirty Data)](https://youtu.be/KwAXfey-xQk)
-- [Connect `OpenAI` To +5,000 Tools (LangChain + `Zapier`)](https://youtu.be/7tNm0yiDigU)
-- [Use LLMs To Extract Data From Text (Expert Mode)](https://youtu.be/xZzvwR9jdPA)
-- ⛓ [Extract Insights From Interview Transcripts Using LLMs](https://youtu.be/shkMOHwJ4SM)
-- ⛓ [5 Levels Of LLM Summarizing: Novice to Expert](https://youtu.be/qaPMdcCqtWk)
-
-
-###
-[LangChain How to and guides](https://www.youtube.com/playlist?list=PL8motc6AQftk1Bs42EW45kwYbyJ4jOdiZ) by [Sam Witteveen](https://www.youtube.com/@samwitteveenai):
-- [LangChain Basics - LLMs & PromptTemplates with Colab](https://youtu.be/J_0qvRt4LNk)
-- [LangChain Basics - Tools and Chains](https://youtu.be/hI2BY7yl_Ac)
-- [`ChatGPT API` Announcement & Code Walkthrough with LangChain](https://youtu.be/phHqvLHCwH4)
-- [Conversations with Memory (explanation & code walkthrough)](https://youtu.be/X550Zbz_ROE)
-- [Chat with `Flan20B`](https://youtu.be/VW5LBavIfY4)
-- [Using `Hugging Face Models` locally (code walkthrough)](https://youtu.be/Kn7SX2Mx_Jk)
-- [`PAL` : Program-aided Language Models with LangChain code](https://youtu.be/dy7-LvDu-3s)
-- [Building a Summarization System with LangChain and `GPT-3` - Part 1](https://youtu.be/LNq_2s_H01Y)
-- [Building a Summarization System with LangChain and `GPT-3` - Part 2](https://youtu.be/d-yeHDLgKHw)
-- [Microsoft's `Visual ChatGPT` using LangChain](https://youtu.be/7YEiEyfPF5U)
-- [LangChain Agents - Joining Tools and Chains with Decisions](https://youtu.be/ziu87EXZVUE)
-- [Comparing LLMs with LangChain](https://youtu.be/rFNG0MIEuW0)
-- [Using `Constitutional AI` in LangChain](https://youtu.be/uoVqNFDwpX4)
-- [Talking to `Alpaca` with LangChain - Creating an Alpaca Chatbot](https://youtu.be/v6sF8Ed3nTE)
-- [Talk to your `CSV` & `Excel` with LangChain](https://youtu.be/xQ3mZhw69bc)
-- [`BabyAGI`: Discover the Power of Task-Driven Autonomous Agents!](https://youtu.be/QBcDLSE2ERA)
-- [Improve your `BabyAGI` with LangChain](https://youtu.be/DRgPyOXZ-oE)
-- ⛓ [Master `PDF` Chat with LangChain - Your essential guide to queries on documents](https://youtu.be/ZzgUqFtxgXI)
-- ⛓ [Using LangChain with `DuckDuckGO` `Wikipedia` & `PythonREPL` Tools](https://youtu.be/KerHlb8nuVc)
-- ⛓ [Building Custom Tools and Agents with LangChain (gpt-3.5-turbo)](https://youtu.be/biS8G8x8DdA)
-- ⛓ [LangChain Retrieval QA Over Multiple Files with `ChromaDB`](https://youtu.be/3yPBVii7Ct0)
-- ⛓ [LangChain Retrieval QA with Instructor Embeddings & `ChromaDB` for PDFs](https://youtu.be/cFCGUjc33aU)
-- ⛓ [LangChain + Retrieval Local LLMs for Retrieval QA - No OpenAI!!!](https://youtu.be/9ISVjh8mdlA)
-
-
-###
-[LangChain](https://www.youtube.com/playlist?list=PLVEEucA9MYhOu89CX8H3MBZqayTbcCTMr) by [Prompt Engineering](https://www.youtube.com/@engineerprompt):
-- [LangChain Crash Course — All You Need to Know to Build Powerful Apps with LLMs](https://youtu.be/5-fc4Tlgmro)
-- [Working with MULTIPLE `PDF` Files in LangChain: `ChatGPT` for your Data](https://youtu.be/s5LhRdh5fu4)
-- [`ChatGPT` for YOUR OWN `PDF` files with LangChain](https://youtu.be/TLf90ipMzfE)
-- [Talk to YOUR DATA without OpenAI APIs: LangChain](https://youtu.be/wrD-fZvT6UI)
-- ⛓️ [CHATGPT For WEBSITES: Custom ChatBOT](https://youtu.be/RBnuhhmD21U)
-
-
-###
-LangChain by [Chat with data](https://www.youtube.com/@chatwithdata)
-- [LangChain Beginner's Tutorial for `Typescript`/`Javascript`](https://youtu.be/bH722QgRlhQ)
-- [`GPT-4` Tutorial: How to Chat With Multiple `PDF` Files (~1000 pages of Tesla's 10-K Annual Reports)](https://youtu.be/Ix9WIZpArm0)
-- [`GPT-4` & LangChain Tutorial: How to Chat With A 56-Page `PDF` Document (w/`Pinecone`)](https://youtu.be/ih9PBGVVOO4)
-- ⛓ [LangChain & Supabase Tutorial: How to Build a ChatGPT Chatbot For Your Website](https://youtu.be/R2FMzcsmQY8)
-
-
-###
-[Get SH\*T Done with Prompt Engineering and LangChain](https://www.youtube.com/watch?v=muXbPpG_ys4&list=PLEJK-H61Xlwzm5FYLDdKt_6yibO33zoMW) by [Venelin Valkov](https://www.youtube.com/@venelin_valkov)
-- [Getting Started with LangChain: Load Custom Data, Run OpenAI Models, Embeddings and `ChatGPT`](https://www.youtube.com/watch?v=muXbPpG_ys4)
-- [Loaders, Indexes & Vectorstores in LangChain: Question Answering on `PDF` files with `ChatGPT`](https://www.youtube.com/watch?v=FQnvfR8Dmr0)
-- [LangChain Models: `ChatGPT`, `Flan Alpaca`, `OpenAI Embeddings`, Prompt Templates & Streaming](https://www.youtube.com/watch?v=zy6LiK5F5-s)
-- [LangChain Chains: Use `ChatGPT` to Build Conversational Agents, Summaries and Q&A on Text With LLMs](https://www.youtube.com/watch?v=h1tJZQPcimM)
-- [Analyze Custom CSV Data with `GPT-4` using Langchain](https://www.youtube.com/watch?v=Ew3sGdX8at4)
-- ⛓ [Build ChatGPT Chatbots with LangChain Memory: Understanding and Implementing Memory in Conversations](https://youtu.be/CyuUlf54wTs)
-
----------------------
-⛓ icon marks a new addition [last update 2023-05-15]

docs/glossary.md

@@ -0,0 +1,90 @@
+# Glossary
+
+This is a collection of terminology commonly used when developing LLM applications.
+It contains reference to external papers or sources where the concept was first introduced,
+as well as to places in LangChain where the concept is used.
+
+## Chain of Thought Prompting
+
+A prompting technique used to encourage the model to generate a series of intermediate reasoning steps.
+A less formal way to induce this behavior is to include “Let’s think step-by-step” in the prompt.
+
+Resources:
+
+- [Chain-of-Thought Paper](https://arxiv.org/pdf/2201.11903.pdf)
+- [Step-by-Step Paper](https://arxiv.org/abs/2112.00114)
+
+## Action Plan Generation
+
+A prompt usage that uses a language model to generate actions to take.
+The results of these actions can then be fed back into the language model to generate a subsequent action.
+
+Resources:
+
+- [WebGPT Paper](https://arxiv.org/pdf/2112.09332.pdf)
+- [SayCan Paper](https://say-can.github.io/assets/palm_saycan.pdf)
+
+## ReAct Prompting
+
+A prompting technique that combines Chain-of-Thought prompting with action plan generation.
+This induces the to model to think about what action to take, then take it.
+
+Resources:
+
+- [Paper](https://arxiv.org/pdf/2210.03629.pdf)
+- [LangChain Example](./modules/agents/implementations/react.ipynb)
+
+## Self-ask
+
+A prompting method that builds on top of chain-of-thought prompting.
+In this method, the model explicitly asks itself follow-up questions, which are then answered by an external search engine.
+
+Resources:
+
+- [Paper](https://ofir.io/self-ask.pdf)
+- [LangChain Example](./modules/agents/implementations/self_ask_with_search.ipynb)
+
+## Prompt Chaining
+
+Combining multiple LLM calls together, with the output of one-step being the input to the next.
+
+Resources:
+
+- [PromptChainer Paper](https://arxiv.org/pdf/2203.06566.pdf)
+- [Language Model Cascades](https://arxiv.org/abs/2207.10342)
+- [ICE Primer Book](https://primer.ought.org/)
+- [Socratic Models](https://socraticmodels.github.io/)
+
+## Memetic Proxy
+
+Encouraging the LLM to respond in a certain way framing the discussion in a context that the model knows of and that will result in that type of response. For example, as a conversation between a student and a teacher.
+
+Resources:
+
+- [Paper](https://arxiv.org/pdf/2102.07350.pdf)
+
+## Self Consistency
+
+A decoding strategy that samples a diverse set of reasoning paths and then selects the most consistent answer.
+Is most effective when combined with Chain-of-thought prompting.
+
+Resources:
+
+- [Paper](https://arxiv.org/pdf/2203.11171.pdf)
+
+## Inception
+
+Also called “First Person Instruction”.
+Encouraging the model to think a certain way by including the start of the model’s response in the prompt.
+
+Resources:
+
+- [Example](https://twitter.com/goodside/status/1583262455207460865?s=20&t=8Hz7XBnK1OF8siQrxxCIGQ)
+
+## MemPrompt
+
+MemPrompt maintains a memory of errors and user feedback, and uses them to prevent repetition of mistakes.
+
+Resources:
+
+- [Paper](https://memprompt.com/)

docs/index.rst

@@ -1,63 +1,68 @@
 Welcome to LangChain
 ==========================
 
-| **LangChain** is a framework for developing applications powered by language models. We believe that the most powerful and differentiated applications will not only call out to a language model, but will also be:
-1. *Data-aware*: connect a language model to other sources of data
-2. *Agentic*: allow a language model to interact with its environment
+Large language models (LLMs) are emerging as a transformative technology, enabling
+developers to build applications that they previously could not.
+But using these LLMs in isolation is often not enough to
+create a truly powerful app - the real power comes when you are able to
+combine them with other sources of computation or knowledge.
 
-| The LangChain framework is designed around these principles.
+This library is aimed at assisting in the development of those types of applications. Common examples of these types of applications include:
 
-| This is the Python specific portion of the documentation. For a purely conceptual guide to LangChain, see `here <https://docs.langchain.com/docs/>`_. For the JavaScript documentation, see `here <https://js.langchain.com/docs/>`_.
+**❓ Question Answering over specific documents**
+
+- `Documentation <./use_cases/question_answering.html>`_
+- End-to-end Example: `Question Answering over Notion Database <https://github.com/hwchase17/notion-qa>`_
+
+**💬 Chatbots**
+
+- `Documentation <./use_cases/chatbots.html>`_
+- End-to-end Example: `Chat-LangChain <https://github.com/hwchase17/chat-langchain>`_
+
+**🤖 Agents**
+
+- `Documentation <./use_cases/agents.html>`_
+- End-to-end Example: `GPT+WolframAlpha <https://huggingface.co/spaces/JavaFXpert/Chat-GPT-LangChain>`_
 
 Getting Started
 ----------------
 
-| How to get started using LangChain to create an Language Model application.
+Checkout the below guide for a walkthrough of how to get started using LangChain to create an Language Model application.
 
-- `Quickstart Guide <./getting_started/getting_started.html>`_
-
-| Concepts and terminology.
-
-- `Concepts and terminology <./getting_started/concepts.html>`_
-
-| Tutorials created by community experts and presented on YouTube.
-
-- `Tutorials <./getting_started/tutorials.html>`_
+- `Getting Started Documentation <./getting_started/getting_started.html>`_
 
 .. toctree::
-   :maxdepth: 2
+   :maxdepth: 1
    :caption: Getting Started
    :name: getting_started
    :hidden:
 
    getting_started/getting_started.md
-   getting_started/concepts.md
-   getting_started/tutorials.md
-
 
 Modules
 -----------
 
-| These modules are the core abstractions which we view as the building blocks of any LLM-powered application.
-For each module LangChain provides standard, extendable interfaces. LangChain also provides external integrations and even end-to-end implementations for off-the-shelf use.
+There are several main modules that LangChain provides support for.
+For each module we provide some examples to get started, how-to guides, reference docs, and conceptual guides.
+These modules are, in increasing order of complexity:
 
-| The docs for each module contain quickstart examples, how-to guides, reference docs, and conceptual guides.
 
-| The modules are (from least to most complex):
+- `Prompts <./modules/prompts.html>`_: This includes prompt management, prompt optimization, and prompt serialization.
 
-- `Models <./modules/models.html>`_: Supported model types and integrations.
+- `LLMs <./modules/llms.html>`_: This includes a generic interface for all LLMs, and common utilities for working with LLMs.
 
-- `Prompts <./modules/prompts.html>`_: Prompt management, optimization, and serialization.
+- `Document Loaders <./modules/document_loaders.html>`_: This includes a standard interface for loading documents, as well as specific integrations to all types of text data sources.
 
-- `Memory <./modules/memory.html>`_: Memory refers to state that is persisted between calls of a chain/agent.
+- `Utils <./modules/utils.html>`_: Language models are often more powerful when interacting with other sources of knowledge or computation. This can include Python REPLs, embeddings, search engines, and more. LangChain provides a large collection of common utils to use in your application.
 
-- `Indexes <./modules/indexes.html>`_: Language models become much more powerful when combined with application-specific data - this module contains interfaces and integrations for loading, querying and updating external data.
+- `Chains <./modules/chains.html>`_: Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications.
 
-- `Chains <./modules/chains.html>`_: Chains are structured sequences of calls (to an LLM or to a different utility).
+- `Indexes <./modules/indexes.html>`_: Language models are often more powerful when combined with your own text data - this module covers best practices for doing exactly that.
 
-- `Agents <./modules/agents.html>`_: An agent is a Chain in which an LLM, given a high-level directive and a set of tools, repeatedly decides an action, executes the action and observes the outcome until the high-level directive is complete.
+- `Agents <./modules/agents.html>`_: Agents involve an LLM making decisions about which Actions to take, taking that Action, seeing an Observation, and repeating that until done. LangChain provides a standard interface for agents, a selection of agents to choose from, and examples of end to end agents.
+
+- `Memory <./modules/memory.html>`_: Memory is the concept of persisting state between calls of a chain/agent. LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory.
 
-- `Callbacks <./modules/callbacks/getting_started.html>`_: Callbacks let you log and stream the intermediate steps of any chain, making it easy to observe, debug, and evaluate the internals of an application.
 
 .. toctree::
    :maxdepth: 1
@@ -65,40 +70,36 @@ For each module LangChain provides standard, extendable interfaces. LangChain al
    :name: modules
    :hidden:
 
-   ./modules/models.rst
-   ./modules/prompts.rst
-   ./modules/memory.md
+   ./modules/prompts.md
+   ./modules/llms.md
+   ./modules/document_loaders.md
+   ./modules/utils.md
    ./modules/indexes.md
    ./modules/chains.md
    ./modules/agents.md
-   ./modules/callbacks/getting_started.ipynb
+   ./modules/memory.md
 
 Use Cases
 ----------
 
-| Best practices and built-in implementations for common LangChain use cases:
+The above modules can be used in a variety of ways. LangChain also provides guidance and assistance in this. Below are some of the common use cases LangChain supports.
 
-- `Autonomous Agents <./use_cases/autonomous_agents.html>`_: Autonomous agents are long-running agents that take many steps in an attempt to accomplish an objective. Examples include AutoGPT and BabyAGI.
+- `Agents <./use_cases/agents.html>`_: Agents are systems that use a language model to interact with other tools. These can be used to do more grounded question/answering, interact with APIs, or even take actions.
 
-- `Agent Simulations <./use_cases/agent_simulations.html>`_: Putting agents in a sandbox and observing how they interact with each other and react to events can be an effective way to evaluate their long-range reasoning and planning abilities.
+- `Chatbots <./use_cases/chatbots.html>`_: Since language models are good at producing text, that makes them ideal for creating chatbots.
 
-- `Personal Assistants <./use_cases/personal_assistants.html>`_: One of the primary LangChain use cases. Personal assistants need to take actions, remember interactions, and have knowledge about your data.
+- `Data Augmented Generation <./use_cases/combine_docs.html>`_: Data Augmented Generation involves specific types of chains that first interact with an external datasource to fetch data to use in the generation step. Examples of this include summarization of long pieces of text and question/answering over specific data sources.
 
-- `Question Answering <./use_cases/question_answering.html>`_: Another common LangChain use case. Answering questions over specific documents, only utilizing the information in those documents to construct an answer.
+- `Question Answering <./use_cases/question_answering.html>`_: Answering questions over specific documents, only utilizing the information in those documents to construct an answer. A type of Data Augmented Generation.
 
-- `Chatbots <./use_cases/chatbots.html>`_: Language models love to chat, making this a very natural use of them.
+- `Summarization <./use_cases/summarization.html>`_: Summarizing longer documents into shorter, more condensed chunks of information. A type of Data Augmented Generation.
 
-- `Querying Tabular Data <./use_cases/tabular.html>`_: Recommended reading if you want to use language models to query structured data (CSVs, SQL, dataframes, etc).
+- `Evaluation <./use_cases/evaluation.html>`_: 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.
 
-- `Code Understanding <./use_cases/code.html>`_: Recommended reading if you want to use language models to analyze code.
+- `Generate similar examples <./use_cases/generate_examples.html>`_: Generating similar examples to a given input. This is a common use case for many applications, and LangChain provides some prompts/chains for assisting in this.
 
-- `Interacting with APIs <./use_cases/apis.html>`_: Enabling language models to interact with APIs is extremely powerful. It gives them access to up-to-date information and allows them to take actions.
+- `Compare models <./use_cases/model_laboratory.html>`_: Experimenting with different prompts, models, and chains is a big part of developing the best possible application. The ModelLaboratory makes it easy to do so.
 
-- `Extraction <./use_cases/extraction.html>`_: Extract structured information from text.
-
-- `Summarization <./use_cases/summarization.html>`_: Compressing longer documents. A type of Data-Augmented Generation.
-
-- `Evaluation <./use_cases/evaluation.html>`_: Generative models are hard to evaluate with traditional metrics. One promising approach is to use language models themselves to do the evaluation.
 
 
 .. toctree::
@@ -107,29 +108,23 @@ Use Cases
    :name: use_cases
    :hidden:
 
-   ./use_cases/autonomous_agents.md
-   ./use_cases/agent_simulations.md
-   ./use_cases/personal_assistants.md
-   ./use_cases/question_answering.md
+   ./use_cases/agents.md
    ./use_cases/chatbots.md
-   ./use_cases/tabular.rst
-   ./use_cases/code.md
-   ./use_cases/apis.md
-   ./use_cases/extraction.md
+   ./use_cases/generate_examples.ipynb
+   ./use_cases/combine_docs.md
+   ./use_cases/question_answering.md
    ./use_cases/summarization.md
    ./use_cases/evaluation.rst
+   ./use_cases/model_laboratory.ipynb
 
 
 Reference Docs
 ---------------
 
-| Full documentation on all methods, classes, installation methods, and integration setups for LangChain.
+All of LangChain's reference documentation, in one place. Full documentation on all methods, classes, installation methods, and integration setups for LangChain.
 
 
-- `LangChain Installation <./reference/installation.html>`_
-
 - `Reference Documentation <./reference.html>`_
-
 .. toctree::
    :maxdepth: 1
    :caption: Reference
@@ -137,54 +132,43 @@ Reference Docs
    :hidden:
 
    ./reference/installation.md
+   ./reference/integrations.md
    ./reference.rst
 
 
-Ecosystem
-------------
+LangChain Ecosystem
+-------------------
 
-| LangChain integrates a lot of different LLMs, systems, and products.
-| From the other side, many systems and products depend on LangChain.
-| It creates a vibrant and thriving ecosystem.
-
-
-- `Integrations <./integrations.html>`_: Guides for how other products can be used with LangChain.
-
-- `Dependents <./dependents.html>`_: List of repositories that use LangChain.
-
-- `Deployments <./ecosystem/deployments.html>`_: A collection of instructions, code snippets, and template repositories for deploying LangChain apps.
+Guides for how other companies/products can be used with LangChain
 
+- `LangChain Ecosystem <./ecosystem.html>`_
 
 .. toctree::
-   :maxdepth: 2
+   :maxdepth: 1
    :glob:
    :caption: Ecosystem
    :name: ecosystem
    :hidden:
 
-   ./integrations.rst
-   ./dependents.md
-   ./ecosystem/deployments.md
+   ./ecosystem.rst
 
 
 Additional Resources
 ---------------------
 
-| Additional resources we think may be useful as you develop your application!
+Additional collection of resources we think may be useful as you develop your application!
 
 - `LangChainHub <https://github.com/hwchase17/langchain-hub>`_: The LangChainHub is a place to share and explore other prompts, chains, and agents.
 
-- `Gallery <https://github.com/kyrolabs/awesome-langchain>`_: A collection of great projects that use Langchain, compiled by the folks at `Kyrolabs <https://kyrolabs.com>`_. Useful for finding inspiration and example implementations.
+- `Glossary <./glossary.html>`_: A glossary of all related terms, papers, methods, etc. Whether implemented in LangChain or not!
 
-- `Deploying LLMs in Production <./additional_resources/deploy_llms.html>`_: A collection of best practices and tutorials for deploying LLMs in production.
+- `Gallery <./gallery.html>`_: A collection of our favorite projects that use LangChain. Useful for finding inspiration or seeing how things were done in other applications.
 
-- `Tracing <./additional_resources/tracing.html>`_: A guide on using tracing in LangChain to visualize the execution of chains and agents.
-
-- `Model Laboratory <./additional_resources/model_laboratory.html>`_: Experimenting with different prompts, models, and chains is a big part of developing the best possible application. The ModelLaboratory makes it easy to do so.
+- `Deployments <./deployments.html>`_: A collection of instructions, code snippets, and template repositories for deploying LangChain apps.
 
 - `Discord <https://discord.gg/6adMQxSpJS>`_: Join us on our Discord to discuss all things LangChain!
 
-- `YouTube <./additional_resources/youtube.html>`_: A collection of the LangChain tutorials and videos.
+- `Tracing <./tracing.html>`_: A guide on using tracing in LangChain to visualize the execution of chains and agents.
 
 - `Production Support <https://forms.gle/57d8AmXBYp8PP8tZA>`_: As you move your LangChains into production, we'd love to offer more comprehensive support. Please fill out this form and we'll set up a dedicated support Slack channel.
 
@@ -196,11 +180,9 @@ Additional Resources
    :hidden:
 
    LangChainHub <https://github.com/hwchase17/langchain-hub>
-   ./additional_resources/deployments.md
-   ./additional_resources/deploy_llms.rst
-   Gallery <https://github.com/kyrolabs/awesome-langchain>
-   ./additional_resources/tracing.md
-   ./additional_resources/model_laboratory.ipynb
+   ./glossary.md
+   ./gallery.rst
+   ./deployments.md
+   ./tracing.md
    Discord <https://discord.gg/6adMQxSpJS>
-   ./additional_resources/youtube.md
    Production Support <https://forms.gle/57d8AmXBYp8PP8tZA>

docs/integrations.rst

@@ -1,39 +0,0 @@
-Integrations
-===================
-
-LangChain integrates with many LLMs, systems, and products.
-
-Integrations by Module
---------------------------------
-
-| Integrations grouped by the core LangChain module they map to:
-
-
-- `LLM Providers <./modules/models/llms/integrations.html>`_
-- `Chat Model Providers <./modules/models/chat/integrations.html>`_
-- `Text Embedding Model Providers <./modules/models/text_embedding.html>`_
-- `Document Loader Integrations <./modules/indexes/document_loaders.html>`_
-- `Text Splitter Integrations <./modules/indexes/text_splitters.html>`_
-- `Vectorstore Providers <./modules/indexes/vectorstores.html>`_
-- `Retriever Providers <./modules/indexes/retrievers.html>`_
-- `Tool Providers <./modules/agents/tools.html>`_
-- `Toolkit Integrations <./modules/agents/toolkits.html>`_
-
-
-Dependencies
-----------------
-
-| LangChain depends on `several hungered Python packages <https://github.com/hwchase17/langchain/network/dependencies>`_.
-
-
-All Integrations
--------------------------------------------
-
-| A comprehensive list of LLMs, systems, and products integrated with LangChain:
-
-
-.. toctree::
-   :maxdepth: 1
-   :glob:
-
-   integrations/*

docs/integrations/aim_tracking.ipynb

@@ -1,291 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Aim\n",
-    "\n",
-    "Aim makes it super easy to visualize and debug LangChain executions. Aim tracks inputs and outputs of LLMs and tools, as well as actions of agents. \n",
-    "\n",
-    "With Aim, you can easily debug and examine an individual execution:\n",
-    "\n",
-    "![](https://user-images.githubusercontent.com/13848158/227784778-06b806c7-74a1-4d15-ab85-9ece09b458aa.png)\n",
-    "\n",
-    "Additionally, you have the option to compare multiple executions side by side:\n",
-    "\n",
-    "![](https://user-images.githubusercontent.com/13848158/227784994-699b24b7-e69b-48f9-9ffa-e6a6142fd719.png)\n",
-    "\n",
-    "Aim is fully open source, [learn more](https://github.com/aimhubio/aim) about Aim on GitHub.\n",
-    "\n",
-    "Let's move forward and see how to enable and configure Aim callback."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3>Tracking LangChain Executions with Aim</h3>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this notebook we will explore three usage scenarios. To start off, we will install the necessary packages and import certain modules. Subsequently, we will configure two environment variables that can be established either within the Python script or through the terminal."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "mf88kuCJhbVu"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install aim\n",
-    "!pip install langchain\n",
-    "!pip install openai\n",
-    "!pip install google-search-results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "g4eTuajwfl6L"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from datetime import datetime\n",
-    "\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.callbacks import AimCallbackHandler, StdOutCallbackHandler"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our examples use a GPT model as the LLM, and OpenAI offers an API for this purpose. You can obtain the key from the following link: https://platform.openai.com/account/api-keys .\n",
-    "\n",
-    "We will use the SerpApi to retrieve search results from Google. To acquire the SerpApi key, please go to https://serpapi.com/manage-api-key ."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "T1bSmKd6V2If"
-   },
-   "outputs": [],
-   "source": [
-    "os.environ[\"OPENAI_API_KEY\"] = \"...\"\n",
-    "os.environ[\"SERPAPI_API_KEY\"] = \"...\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "QenUYuBZjIzc"
-   },
-   "source": [
-    "The event methods of `AimCallbackHandler` accept the LangChain module or agent as input and log at least the prompts and generated results, as well as the serialized version of the LangChain module, to the designated Aim run."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "KAz8weWuUeXF"
-   },
-   "outputs": [],
-   "source": [
-    "session_group = datetime.now().strftime(\"%m.%d.%Y_%H.%M.%S\")\n",
-    "aim_callback = AimCallbackHandler(\n",
-    "    repo=\".\",\n",
-    "    experiment_name=\"scenario 1: OpenAI LLM\",\n",
-    ")\n",
-    "\n",
-    "callbacks = [StdOutCallbackHandler(), aim_callback]\n",
-    "llm = OpenAI(temperature=0, callbacks=callbacks)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "b8WfByB4fl6N"
-   },
-   "source": [
-    "The `flush_tracker` function is used to record LangChain assets on Aim. By default, the session is reset rather than being terminated outright."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3>Scenario 1</h3> In the first scenario, we will use OpenAI LLM."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "o_VmneyIUyx8"
-   },
-   "outputs": [],
-   "source": [
-    "# scenario 1 - LLM\n",
-    "llm_result = llm.generate([\"Tell me a joke\", \"Tell me a poem\"] * 3)\n",
-    "aim_callback.flush_tracker(\n",
-    "    langchain_asset=llm,\n",
-    "    experiment_name=\"scenario 2: Chain with multiple SubChains on multiple generations\",\n",
-    ")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3>Scenario 2</h3> Scenario two involves chaining with multiple SubChains across multiple generations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "trxslyb1U28Y"
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.chains import LLMChain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "uauQk10SUzF6"
-   },
-   "outputs": [],
-   "source": [
-    "# scenario 2 - Chain\n",
-    "template = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n",
-    "Title: {title}\n",
-    "Playwright: This is a synopsis for the above play:\"\"\"\n",
-    "prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)\n",
-    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)\n",
-    "\n",
-    "test_prompts = [\n",
-    "    {\"title\": \"documentary about good video games that push the boundary of game design\"},\n",
-    "    {\"title\": \"the phenomenon behind the remarkable speed of cheetahs\"},\n",
-    "    {\"title\": \"the best in class mlops tooling\"},\n",
-    "]\n",
-    "synopsis_chain.apply(test_prompts)\n",
-    "aim_callback.flush_tracker(\n",
-    "    langchain_asset=synopsis_chain, experiment_name=\"scenario 3: Agent with Tools\"\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<h3>Scenario 3</h3> The third scenario involves an agent with tools."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "_jN73xcPVEpI"
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.agents import initialize_agent, load_tools\n",
-    "from langchain.agents import AgentType"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "Gpq4rk6VT9cu",
-    "outputId": "68ae261e-d0a2-4229-83c4-762562263b66"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m I need to find out who Leo DiCaprio's girlfriend is and then calculate her age raised to the 0.43 power.\n",
-      "Action: Search\n",
-      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3mLeonardo DiCaprio seemed to prove a long-held theory about his love life right after splitting from girlfriend Camila Morrone just months ...\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to find out Camila Morrone's age\n",
-      "Action: Search\n",
-      "Action Input: \"Camila Morrone age\"\u001b[0m\n",
-      "Observation: \u001b[36;1m\u001b[1;3m25 years\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I need to calculate 25 raised to the 0.43 power\n",
-      "Action: Calculator\n",
-      "Action Input: 25^0.43\u001b[0m\n",
-      "Observation: \u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\n",
-      "\u001b[0m\n",
-      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: Camila Morrone is Leo DiCaprio's girlfriend and her current age raised to the 0.43 power is 3.991298452658078.\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
-     ]
-    }
-   ],
-   "source": [
-    "# scenario 3 - Agent with Tools\n",
-    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callbacks=callbacks)\n",
-    "agent = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    callbacks=callbacks,\n",
-    ")\n",
-    "agent.run(\n",
-    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
-    ")\n",
-    "aim_callback.flush_tracker(langchain_asset=agent, reset=False, finish=True)"
-   ]
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
-   "provenance": []
-  },
-  "gpuClass": "standard",
-  "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": 1
-}

docs/integrations/airbyte.md

@@ -1,29 +0,0 @@
-# Airbyte
-
->[Airbyte](https://github.com/airbytehq/airbyte) is a data integration platform for ELT pipelines from APIs, 
-> databases & files to warehouses & lakes. It has the largest catalog of ELT connectors to data warehouses and databases.
-
-## Installation and Setup
-
-This instruction shows how to load any source from `Airbyte` into a local `JSON` file that can be read in as a document.
-
-**Prerequisites:**
-Have `docker desktop` installed.
-
-**Steps:**
-1. Clone Airbyte from GitHub - `git clone https://github.com/airbytehq/airbyte.git`.
-2. Switch into Airbyte directory - `cd airbyte`.
-3. Start Airbyte - `docker compose up`.
-4. In your browser, just visit http://localhost:8000. You will be asked for a username and password. By default, that's username `airbyte` and password `password`.
-5. Setup any source you wish.
-6. Set destination as Local JSON, with specified destination path - lets say `/json_data`. Set up a manual sync.
-7. Run the connection.
-8. To see what files are created, navigate to: `file:///tmp/airbyte_local/`.
-
-## Document Loader
-
-See a [usage example](../modules/indexes/document_loaders/examples/airbyte_json.ipynb).
-
-```python
-from langchain.document_loaders import AirbyteJSONLoader
-```

docs/integrations/aleph_alpha.md

@@ -1,36 +0,0 @@
-# Aleph Alpha
-
->[Aleph Alpha](https://docs.aleph-alpha.com/) was founded in 2019 with the mission to research and build the foundational technology for an era of strong AI. The team of international scientists, engineers, and innovators researches, develops, and deploys transformative AI like large language and multimodal models and runs the fastest European commercial AI cluster.
-
->[The Luminous series](https://docs.aleph-alpha.com/docs/introduction/luminous/) is a family of large language models.
-
-## Installation and Setup
-
-```bash
-pip install aleph-alpha-client
-```
-
-You have to create a new token. Please, see [instructions](https://docs.aleph-alpha.com/docs/account/#create-a-new-token).
-
-```python
-from getpass import getpass
-
-ALEPH_ALPHA_API_KEY = getpass()
-```
-
-
-## LLM
-
-See a [usage example](../modules/models/llms/integrations/aleph_alpha.ipynb).
-
-```python
-from langchain.llms import AlephAlpha
-```
-
-## Text Embedding Models
-
-See a [usage example](../modules/models/text_embedding/examples/aleph_alpha.ipynb).
-
-```python
-from langchain.embeddings import AlephAlphaSymmetricSemanticEmbedding, AlephAlphaAsymmetricSemanticEmbedding
-```

docs/integrations/amazon_bedrock.md

@@ -1,24 +0,0 @@
-# Amazon Bedrock
-
->[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.
-
-## Installation and Setup
-
-```bash
-pip install boto3
-```
-
-## LLM
-
-See a [usage example](../modules/models/llms/integrations/bedrock.ipynb).
-
-```python
-from langchain import Bedrock
-```
-
-## Text Embedding Models
-
-See a [usage example](../modules/models/text_embedding/examples/amazon_bedrock.ipynb).
-```python
-from langchain.embeddings import BedrockEmbeddings
-```

docs/integrations/analyticdb.md

@@ -1,15 +0,0 @@
-# AnalyticDB
-
-This page covers how to use the AnalyticDB ecosystem within LangChain.
-
-### VectorStore
-
-There exists a wrapper around AnalyticDB, allowing you to use it as a vectorstore,
-whether for semantic search or example selection.
-
-To import this vectorstore:
-```python
-from langchain.vectorstores import AnalyticDB
-```
-
-For a more detailed walkthrough of the AnalyticDB wrapper, see [this notebook](../modules/indexes/vectorstores/examples/analyticdb.ipynb)

docs/integrations/annoy.md

@@ -1,18 +0,0 @@
-# Annoy
-
-> [Annoy](https://github.com/spotify/annoy) (`Approximate Nearest Neighbors Oh Yeah`) is a C++ library with Python bindings to search for points in space that are close to a given query point. It also creates large read-only file-based data structures that are mmapped into memory so that many processes may share the same data. 
-## Installation and Setup
-
-
-```bash
-pip install annoy
-```
-
-
-## Vectorstore
-
-See a [usage example](../modules/indexes/vectorstores/examples/annoy.ipynb).
-
-```python
-from langchain.vectorstores import Annoy
-```

docs/integrations/anthropic.md

@@ -1,26 +0,0 @@
-# Anthropic
-
->[Anthropic](https://en.wikipedia.org/wiki/Anthropic) is an American artificial intelligence (AI) startup and 
-> public-benefit corporation, founded by former members of OpenAI. `Anthropic` specializes in developing general AI 
-> systems and language models, with a company ethos of responsible AI usage.
-> `Anthropic` develops a chatbot, named `Claude`. Similar to `ChatGPT`, `Claude` uses a messaging 
-> interface where users can submit questions or requests and receive highly detailed and relevant responses.
- 
-## Installation and Setup
-
-
-```bash
-pip install anthropic
-```
-
-See the [setup documentation](https://console.anthropic.com/docs/access).
-
-
-
-## Chat Models
-
-See a [usage example](../modules/models/chat/integrations/anthropic.ipynb)
-
-```python
-from langchain.chat_models import ChatAnthropic
-```

docs/integrations/anyscale.md

@@ -1,17 +0,0 @@
-# Anyscale
-
-This page covers how to use the Anyscale ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Anyscale wrappers.
-
-## Installation and Setup
-- Get an Anyscale Service URL, route and API key and set them as environment variables (`ANYSCALE_SERVICE_URL`,`ANYSCALE_SERVICE_ROUTE`, `ANYSCALE_SERVICE_TOKEN`). 
-- Please see [the Anyscale docs](https://docs.anyscale.com/productionize/services-v2/get-started) for more details.
-
-## Wrappers
-
-### LLM
-
-There exists an Anyscale LLM wrapper, which you can access with 
-```python
-from langchain.llms import Anyscale
-```

docs/integrations/apify.md

@@ -1,46 +0,0 @@
-# Apify
-
-This page covers how to use [Apify](https://apify.com) within LangChain.
-
-## Overview
-
-Apify is a cloud platform for web scraping and data extraction,
-which provides an [ecosystem](https://apify.com/store) of more than a thousand
-ready-made apps called *Actors* for various scraping, crawling, and extraction use cases.
-
-[![Apify Actors](../_static/ApifyActors.png)](https://apify.com/store)
-
-This integration enables you run Actors on the Apify platform and load their results into LangChain to feed your vector
-indexes with documents and data from the web, e.g. to generate answers from websites with documentation,
-blogs, or knowledge bases.
-
-
-## Installation and Setup
-
-- Install the Apify API client for Python with `pip install apify-client`
-- Get your [Apify API token](https://console.apify.com/account/integrations) and either set it as
-  an environment variable (`APIFY_API_TOKEN`) or pass it to the `ApifyWrapper` as `apify_api_token` in the constructor.
-
-
-## Wrappers
-
-### Utility
-
-You can use the `ApifyWrapper` to run Actors on the Apify platform.
-
-```python
-from langchain.utilities import ApifyWrapper
-```
-
-For a more detailed walkthrough of this wrapper, see [this notebook](../modules/agents/tools/examples/apify.ipynb).
-
-
-### Loader
-
-You can also use our `ApifyDatasetLoader` to get data from Apify dataset.
-
-```python
-from langchain.document_loaders import ApifyDatasetLoader
-```
-
-For a more detailed walkthrough of this loader, see [this notebook](../modules/indexes/document_loaders/examples/apify_dataset.ipynb).

docs/integrations/argilla.md

@@ -1,29 +0,0 @@
-# Argilla
-
-![Argilla - Open-source data platform for LLMs](https://argilla.io/og.png)
-
->[Argilla](https://argilla.io/) is an open-source data curation platform for LLMs.
-> Using Argilla, everyone can build robust language models through faster data curation 
-> using both human and machine feedback. We provide support for each step in the MLOps cycle, 
-> from data labeling to model monitoring.
-
-## Installation and Setup
-
-First, you'll need to install the  `argilla` Python package as follows:
-
-```bash
-pip install argilla --upgrade
-```
-
-If you already have an Argilla Server running, then you're good to go; but if
-you don't, follow the next steps to install it.
-
-If you don't you can refer to [Argilla - 🚀 Quickstart](https://docs.argilla.io/en/latest/getting_started/quickstart.html#Running-Argilla-Quickstart) to deploy Argilla either on HuggingFace Spaces, locally, or on a server.
-
-## Tracking
-
-See a [usage example of `ArgillaCallbackHandler`](../modules/callbacks/examples/examples/argilla.ipynb).
-
-```python
-from langchain.callbacks import ArgillaCallbackHandler
-```

docs/integrations/arxiv.md

@@ -1,36 +0,0 @@
-# Arxiv
-
->[arXiv](https://arxiv.org/) is an open-access archive for 2 million scholarly articles in the fields of physics, 
-> mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and 
-> systems science, and economics.
-
-
-## Installation and Setup
-
-First, you need to install `arxiv` python package.
-
-```bash
-pip install arxiv
-```
-
-Second, you need to install `PyMuPDF` python package which transforms PDF files downloaded from the `arxiv.org` site into the text format.
-
-```bash
-pip install pymupdf
-```
-
-## Document Loader
-
-See a [usage example](../modules/indexes/document_loaders/examples/arxiv.ipynb).
-
-```python
-from langchain.document_loaders import ArxivLoader
-```
-
-## Retriever
-
-See a [usage example](../modules/indexes/retrievers/examples/arxiv.ipynb).
-
-```python
-from langchain.retrievers import ArxivRetriever
-```

docs/integrations/atlas.md

@@ -1,27 +0,0 @@
-# AtlasDB
-
-This page covers how to use Nomic's Atlas ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Atlas wrappers.
-
-## Installation and Setup
-- Install the Python package with `pip install nomic`
-- Nomic is also included in langchains poetry extras `poetry install -E all`
-
-## Wrappers
-
-### VectorStore
-
-There exists a wrapper around the Atlas neural database, allowing you to use it as a vectorstore.
-This vectorstore also gives you full access to the underlying AtlasProject object, which will allow you to use the full range of Atlas map interactions, such as bulk tagging and automatic topic modeling.
-Please see [the Atlas docs](https://docs.nomic.ai/atlas_api.html) for more detailed information.
-
-
-
-
-
-To import this vectorstore:
-```python
-from langchain.vectorstores import AtlasDB
-```
-
-For a more detailed walkthrough of the AtlasDB wrapper, see [this notebook](../modules/indexes/vectorstores/examples/atlas.ipynb)

docs/integrations/aws_s3.md

@@ -1,25 +0,0 @@
-# AWS S3 Directory
-
->[Amazon Simple Storage Service (Amazon S3)](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html) is an object storage service.
-
->[AWS S3 Directory](https://docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html)
-
->[AWS S3 Buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingBucket.html)
-
-
-## Installation and Setup
-
-```bash
-pip install boto3
-```
-
-
-## Document Loader
-
-See a [usage example for S3DirectoryLoader](../modules/indexes/document_loaders/examples/aws_s3_directory.ipynb).
-
-See a [usage example for S3FileLoader](../modules/indexes/document_loaders/examples/aws_s3_file.ipynb).
-
-```python
-from langchain.document_loaders import S3DirectoryLoader, S3FileLoader
-```

docs/integrations/azlyrics.md

@@ -1,16 +0,0 @@
-# AZLyrics
-
->[AZLyrics](https://www.azlyrics.com/) is a large, legal, every day growing collection of lyrics.
-
-## Installation and Setup
-
-There isn't any special setup for it.
-
-
-## Document Loader
-
-See a [usage example](../modules/indexes/document_loaders/examples/azlyrics.ipynb).
-
-```python
-from langchain.document_loaders import AZLyricsLoader
-```

docs/integrations/azure_blob_storage.md

@@ -1,36 +0,0 @@
-# Azure Blob Storage
-
->[Azure Blob Storage](https://learn.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction) is Microsoft's object storage solution for the cloud. Blob Storage is optimized for storing massive amounts of unstructured data. Unstructured data is data that doesn't adhere to a particular data model or definition, such as text or binary data.
-
->[Azure Files](https://learn.microsoft.com/en-us/azure/storage/files/storage-files-introduction) offers fully managed
-> file shares in the cloud that are accessible via the industry standard Server Message Block (`SMB`) protocol, 
-> Network File System (`NFS`) protocol, and `Azure Files REST API`. `Azure Files` are based on the `Azure Blob Storage`.
-
-`Azure Blob Storage` is designed for:
-- Serving images or documents directly to a browser.
-- Storing files for distributed access.
-- Streaming video and audio.
-- Writing to log files.
-- Storing data for backup and restore, disaster recovery, and archiving.
-- Storing data for analysis by an on-premises or Azure-hosted service.
-
-## Installation and Setup
-
-```bash
-pip install azure-storage-blob
-```
-
-
-## Document Loader
-
-See a [usage example for the Azure Blob Storage](../modules/indexes/document_loaders/examples/azure_blob_storage_container.ipynb).
-
-```python
-from langchain.document_loaders import AzureBlobStorageContainerLoader
-```
-
-See a [usage example for the Azure Files](../modules/indexes/document_loaders/examples/azure_blob_storage_file.ipynb).
-
-```python
-from langchain.document_loaders import AzureBlobStorageFileLoader
-```

docs/integrations/azure_cognitive_search_.md

@@ -1,24 +0,0 @@
-# Azure Cognitive Search
-
->[Azure Cognitive Search](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search) (formerly known as `Azure Search`) is a cloud search service that gives developers infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications.
-
->Search is foundational to any app that surfaces text to users, where common scenarios include catalog or document search, online retail apps, or data exploration over proprietary content. When you create a search service, you'll work with the following capabilities:
->- A search engine for full text search over a search index containing user-owned content
->- Rich indexing, with lexical analysis and optional AI enrichment for content extraction and transformation
->- Rich query syntax for text search, fuzzy search, autocomplete, geo-search and more
->- Programmability through REST APIs and client libraries in Azure SDKs
->- Azure integration at the data layer, machine learning layer, and AI (Cognitive Services)
-
-
-## Installation and Setup
-
-See [set up instructions](https://learn.microsoft.com/en-us/azure/search/search-create-service-portal).
-
-
-## Retriever
-
-See a [usage example](../modules/indexes/retrievers/examples/azure_cognitive_search.ipynb).
-
-```python
-from langchain.retrievers import AzureCognitiveSearchRetriever
-```

docs/integrations/azure_openai.md

@@ -1,50 +0,0 @@
-# Azure OpenAI
-
->[Microsoft Azure](https://en.wikipedia.org/wiki/Microsoft_Azure), often referred to as `Azure` is a cloud computing platform run by `Microsoft`, which offers access, management, and development of applications and services through global data centers. It provides a range of capabilities, including software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS). `Microsoft Azure` supports many programming languages, tools, and frameworks, including Microsoft-specific and third-party software and systems.
-
-
->[Azure OpenAI](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/) is an `Azure` service with powerful language models from `OpenAI` including the `GPT-3`, `Codex` and `Embeddings model` series for content generation, summarization, semantic search, and natural language to code translation.
-
-
-## Installation and Setup
-
-```bash
-pip install openai
-pip install tiktoken
-```
-
-
-Set the environment variables to get access to the `Azure OpenAI` service.
-
-```python
-import os
-
-os.environ["OPENAI_API_TYPE"] = "azure"
-os.environ["OPENAI_API_BASE"] = "https://<your-endpoint.openai.azure.com/"
-os.environ["OPENAI_API_KEY"] = "your AzureOpenAI key"
-os.environ["OPENAI_API_VERSION"] = "2023-03-15-preview"
-```
-
-## LLM
-
-See a [usage example](../modules/models/llms/integrations/azure_openai_example.ipynb).
-
-```python
-from langchain.llms import AzureOpenAI
-```
-
-## Text Embedding Models
-
-See a [usage example](../modules/models/text_embedding/examples/azureopenai.ipynb)
-
-```python
-from langchain.embeddings import OpenAIEmbeddings
-```
-
-## Chat Models
-
-See a [usage example](../modules/models/chat/integrations/azure_chat_openai.ipynb)
-
-```python
-from langchain.chat_models import AzureChatOpenAI
-```

docs/integrations/bananadev.md

@@ -1,79 +0,0 @@
-# Banana
-
-This page covers how to use the Banana ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Banana wrappers.
-
-## Installation and Setup
-
-- Install with `pip install banana-dev`
-- Get an Banana api key and set it as an environment variable (`BANANA_API_KEY`)
-
-## Define your Banana Template
-
-If you want to use an available language model template you can find one [here](https://app.banana.dev/templates/conceptofmind/serverless-template-palmyra-base).
-This template uses the Palmyra-Base model by [Writer](https://writer.com/product/api/).
-You can check out an example Banana repository [here](https://github.com/conceptofmind/serverless-template-palmyra-base).
-
-## Build the Banana app
-
-Banana Apps must include the "output" key in the return json. 
-There is a rigid response structure.
-
-```python
-# Return the results as a dictionary
-result = {'output': result}
-```
-
-An example inference function would be:
-
-```python
-def inference(model_inputs:dict) -> dict:
-    global model
-    global tokenizer
-
-    # Parse out your arguments
-    prompt = model_inputs.get('prompt', None)
-    if prompt == None:
-        return {'message': "No prompt provided"}
-
-    # Run the model
-    input_ids = tokenizer.encode(prompt, return_tensors='pt').cuda()
-    output = model.generate(
-        input_ids,
-        max_length=100,
-        do_sample=True,
-        top_k=50,
-        top_p=0.95,
-        num_return_sequences=1,
-        temperature=0.9,
-        early_stopping=True,
-        no_repeat_ngram_size=3,
-        num_beams=5,
-        length_penalty=1.5,
-        repetition_penalty=1.5,
-        bad_words_ids=[[tokenizer.encode(' ', add_prefix_space=True)[0]]]
-        )
-
-    result = tokenizer.decode(output[0], skip_special_tokens=True)
-    # Return the results as a dictionary
-    result = {'output': result}
-    return result
-```
-
-You can find a full example of a Banana app [here](https://github.com/conceptofmind/serverless-template-palmyra-base/blob/main/app.py).
-
-## Wrappers
-
-### LLM
-
-There exists an Banana LLM wrapper, which you can access with
-
-```python
-from langchain.llms import Banana
-```
-
-You need to provide a model key located in the dashboard:
-
-```python
-llm = Banana(model_key="YOUR_MODEL_KEY")
-```

docs/integrations/beam.md

@@ -1,93 +0,0 @@
-# Beam
-
->[Beam](https://docs.beam.cloud/introduction) makes it easy to run code on GPUs, deploy scalable web APIs, 
-> schedule cron jobs, and run massively parallel workloads — without managing any infrastructure.
- 
-
-## Installation and Setup
-
-- [Create an account](https://www.beam.cloud/)
-- Install the Beam CLI with `curl https://raw.githubusercontent.com/slai-labs/get-beam/main/get-beam.sh -sSfL | sh`
-- Register API keys with `beam configure`
-- Set environment variables (`BEAM_CLIENT_ID`) and (`BEAM_CLIENT_SECRET`)
-- Install the Beam SDK:
-```bash
-pip install beam-sdk
-```
-
-## LLM
-
-
-```python
-from langchain.llms.beam import Beam
-```
-
-### Example of the Beam app
-
-This is the environment you’ll be developing against once you start the app.
-It's also used to define the maximum response length from the model.
-```python
-llm = Beam(model_name="gpt2",
-           name="langchain-gpt2-test",
-           cpu=8,
-           memory="32Gi",
-           gpu="A10G",
-           python_version="python3.8",
-           python_packages=[
-               "diffusers[torch]>=0.10",
-               "transformers",
-               "torch",
-               "pillow",
-               "accelerate",
-               "safetensors",
-               "xformers",],
-           max_length="50",
-           verbose=False)
-```
-
-### Deploy the Beam app
-
-Once defined, you can deploy your Beam app by calling your model's `_deploy()` method.
-
-```python
-llm._deploy()
-```
-
-### Call the Beam app
-
-Once a beam model is deployed, it can be called by calling your model's `_call()` method.
-This returns the GPT2 text response to your prompt.
-
-```python
-response = llm._call("Running machine learning on a remote GPU")
-```
-
-An example script which deploys the model and calls it would be:
-
-```python
-from langchain.llms.beam import Beam
-import time
-
-llm = Beam(model_name="gpt2",
-           name="langchain-gpt2-test",
-           cpu=8,
-           memory="32Gi",
-           gpu="A10G",
-           python_version="python3.8",
-           python_packages=[
-               "diffusers[torch]>=0.10",
-               "transformers",
-               "torch",
-               "pillow",
-               "accelerate",
-               "safetensors",
-               "xformers",],
-           max_length="50",
-           verbose=False)
-
-llm._deploy()
-
-response = llm._call("Running machine learning on a remote GPU")
-
-print(response)
-```

docs/integrations/bilibili.md

@@ -1,17 +0,0 @@
-# BiliBili
-
->[Bilibili](https://www.bilibili.tv/) is one of the most beloved long-form video sites in China.
-
-## Installation and Setup
-
-```bash
-pip install bilibili-api-python
-```
-
-## Document Loader
-
-See a [usage example](../modules/indexes/document_loaders/examples/bilibili.ipynb).
-
-```python
-from langchain.document_loaders import BiliBiliLoader
-```

docs/integrations/blackboard.md

@@ -1,22 +0,0 @@
-# Blackboard
-
->[Blackboard Learn](https://en.wikipedia.org/wiki/Blackboard_Learn) (previously the `Blackboard Learning Management System`)
-> is a web-based virtual learning environment and learning management system developed by Blackboard Inc. 
-> The software features course management, customizable open architecture, and scalable design that allows 
-> integration with student information systems and authentication protocols. It may be installed on local servers, 
-> hosted by `Blackboard ASP Solutions`, or provided as Software as a Service hosted on Amazon Web Services. 
-> Its main purposes are stated to include the addition of online elements to courses traditionally delivered 
-> face-to-face and development of completely online courses with few or no face-to-face meetings.
-
-## Installation and Setup
-
-There isn't any special setup for it.
-
-## Document Loader
-
-See a [usage example](../modules/indexes/document_loaders/examples/blackboard.ipynb).
-
-```python
-from langchain.document_loaders import BlackboardLoader
-
-```

docs/integrations/cassandra.md

@@ -1,23 +0,0 @@
-# Cassandra
-
->[Cassandra](https://en.wikipedia.org/wiki/Apache_Cassandra) is a free and open-source, distributed, wide-column 
-> store, NoSQL database management system designed to handle large amounts of data across many commodity servers, 
-> providing high availability with no single point of failure. `Cassandra` offers support for clusters spanning 
-> multiple datacenters, with asynchronous masterless replication allowing low latency operations for all clients. 
-> `Cassandra` was designed to implement a combination of `Amazon's Dynamo` distributed storage and replication 
-> techniques combined with `Google's Bigtable` data and storage engine model.
- 
-## Installation and Setup
-
-```bash
-pip install cassandra-drive
-```
-
-
-## Memory
-
-See a [usage example](../modules/memory/examples/cassandra_chat_message_history.ipynb).
-
-```python
-from langchain.memory import CassandraChatMessageHistory
-```

docs/integrations/chroma.md

@@ -1,29 +0,0 @@
-# Chroma
-
->[Chroma](https://docs.trychroma.com/getting-started) is a database for building AI applications with embeddings.
-
-## Installation and Setup
-
-```bash
-pip install chromadb
-```
-
-
-## VectorStore
-
-There exists a wrapper around Chroma vector databases, allowing you to use it as a vectorstore,
-whether for semantic search or example selection.
-
-```python
-from langchain.vectorstores import Chroma
-```
-
-For a more detailed walkthrough of the Chroma wrapper, see [this notebook](../modules/indexes/vectorstores/getting_started.ipynb)
-
-## Retriever
-
-See a [usage example](../modules/indexes/retrievers/examples/chroma_self_query.ipynb).
-
-```python
-from langchain.retrievers import SelfQueryRetriever
-```

docs/integrations/clearml_tracking.ipynb

@@ -1,609 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ClearML\n",
-    "\n",
-    "> [ClearML](https://github.com/allegroai/clearml) is a ML/DL development and production suite, it contains 5 main modules:\n",
-    "> - `Experiment Manager` - Automagical experiment tracking, environments and results\n",
-    "> - `MLOps` - Orchestration, Automation & Pipelines solution for ML/DL jobs (K8s / Cloud / bare-metal)\n",
-    "> - `Data-Management` - Fully differentiable data management & version control solution on top of object-storage (S3 / GS / Azure / NAS)\n",
-    "> - `Model-Serving` - cloud-ready Scalable model serving solution!\n",
-    "    Deploy new model endpoints in under 5 minutes\n",
-    "    Includes optimized GPU serving support backed by Nvidia-Triton\n",
-    "    with out-of-the-box Model Monitoring\n",
-    "> - `Fire Reports` - Create and share rich MarkDown documents supporting embeddable online content\n",
-    "\n",
-    "In order to properly keep track of your langchain experiments and their results, you can enable the `ClearML` integration. We use the `ClearML Experiment Manager` that neatly tracks and organizes all your experiment runs.\n",
-    "\n",
-    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/hwchase17/langchain/blob/master/docs/ecosystem/clearml_tracking.ipynb\">\n",
-    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
-    "</a>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "## Installation and Setup"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install clearml\n",
-    "!pip install pandas\n",
-    "!pip install textstat\n",
-    "!pip install spacy\n",
-    "!python -m spacy download en_core_web_sm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Getting API Credentials\n",
-    "\n",
-    "We'll be using quite some APIs in this notebook, here is a list and where to get them:\n",
-    "\n",
-    "- ClearML: https://app.clear.ml/settings/workspace-configuration\n",
-    "- OpenAI: https://platform.openai.com/account/api-keys\n",
-    "- SerpAPI (google search): https://serpapi.com/dashboard"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.environ[\"CLEARML_API_ACCESS_KEY\"] = \"\"\n",
-    "os.environ[\"CLEARML_API_SECRET_KEY\"] = \"\"\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
-    "os.environ[\"SERPAPI_API_KEY\"] = \"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Callbacks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.callbacks import ClearMLCallbackHandler"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The clearml callback is currently in beta and is subject to change based on updates to `langchain`. Please report any issues to https://github.com/allegroai/clearml/issues with the tag `langchain`.\n"
-     ]
-    }
-   ],
-   "source": [
-    "from datetime import datetime\n",
-    "from langchain.callbacks import StdOutCallbackHandler\n",
-    "from langchain.llms import OpenAI\n",
-    "\n",
-    "# Setup and use the ClearML Callback\n",
-    "clearml_callback = ClearMLCallbackHandler(\n",
-    "    task_type=\"inference\",\n",
-    "    project_name=\"langchain_callback_demo\",\n",
-    "    task_name=\"llm\",\n",
-    "    tags=[\"test\"],\n",
-    "    # Change the following parameters based on the amount of detail you want tracked\n",
-    "    visualize=True,\n",
-    "    complexity_metrics=True,\n",
-    "    stream_logs=True\n",
-    ")\n",
-    "callbacks = [StdOutCallbackHandler(), clearml_callback]\n",
-    "# Get the OpenAI model ready to go\n",
-    "llm = OpenAI(temperature=0, callbacks=callbacks)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Scenario 1: Just an LLM\n",
-    "\n",
-    "First, let's just run a single LLM a few times and capture the resulting prompt-answer conversation in ClearML"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'}\n",
-      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'}\n",
-      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'}\n",
-      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'}\n",
-      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'}\n",
-      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'}\n",
-      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nQ: What did the fish say when it hit the wall?\\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91}\n",
-      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nRoses are red,\\nViolets are blue,\\nSugar is sweet,\\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17}\n",
-      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nQ: What did the fish say when it hit the wall?\\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91}\n",
-      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nRoses are red,\\nViolets are blue,\\nSugar is sweet,\\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17}\n",
-      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nQ: What did the fish say when it hit the wall?\\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91}\n",
-      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\\n\\nRoses are red,\\nViolets are blue,\\nSugar is sweet,\\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17}\n",
-      "{'action_records':           action    name  step  starts  ends  errors  text_ctr  chain_starts  \\\n",
-      "0   on_llm_start  OpenAI     1       1     0       0         0             0   \n",
-      "1   on_llm_start  OpenAI     1       1     0       0         0             0   \n",
-      "2   on_llm_start  OpenAI     1       1     0       0         0             0   \n",
-      "3   on_llm_start  OpenAI     1       1     0       0         0             0   \n",
-      "4   on_llm_start  OpenAI     1       1     0       0         0             0   \n",
-      "5   on_llm_start  OpenAI     1       1     0       0         0             0   \n",
-      "6     on_llm_end     NaN     2       1     1       0         0             0   \n",
-      "7     on_llm_end     NaN     2       1     1       0         0             0   \n",
-      "8     on_llm_end     NaN     2       1     1       0         0             0   \n",
-      "9     on_llm_end     NaN     2       1     1       0         0             0   \n",
-      "10    on_llm_end     NaN     2       1     1       0         0             0   \n",
-      "11    on_llm_end     NaN     2       1     1       0         0             0   \n",
-      "12  on_llm_start  OpenAI     3       2     1       0         0             0   \n",
-      "13  on_llm_start  OpenAI     3       2     1       0         0             0   \n",
-      "14  on_llm_start  OpenAI     3       2     1       0         0             0   \n",
-      "15  on_llm_start  OpenAI     3       2     1       0         0             0   \n",
-      "16  on_llm_start  OpenAI     3       2     1       0         0             0   \n",
-      "17  on_llm_start  OpenAI     3       2     1       0         0             0   \n",
-      "18    on_llm_end     NaN     4       2     2       0         0             0   \n",
-      "19    on_llm_end     NaN     4       2     2       0         0             0   \n",
-      "20    on_llm_end     NaN     4       2     2       0         0             0   \n",
-      "21    on_llm_end     NaN     4       2     2       0         0             0   \n",
-      "22    on_llm_end     NaN     4       2     2       0         0             0   \n",
-      "23    on_llm_end     NaN     4       2     2       0         0             0   \n",
-      "\n",
-      "    chain_ends  llm_starts  ...  difficult_words  linsear_write_formula  \\\n",
-      "0            0           1  ...              NaN                    NaN   \n",
-      "1            0           1  ...              NaN                    NaN   \n",
-      "2            0           1  ...              NaN                    NaN   \n",
-      "3            0           1  ...              NaN                    NaN   \n",
-      "4            0           1  ...              NaN                    NaN   \n",
-      "5            0           1  ...              NaN                    NaN   \n",
-      "6            0           1  ...              0.0                    5.5   \n",
-      "7            0           1  ...              2.0                    6.5   \n",
-      "8            0           1  ...              0.0                    5.5   \n",
-      "9            0           1  ...              2.0                    6.5   \n",
-      "10           0           1  ...              0.0                    5.5   \n",
-      "11           0           1  ...              2.0                    6.5   \n",
-      "12           0           2  ...              NaN                    NaN   \n",
-      "13           0           2  ...              NaN                    NaN   \n",
-      "14           0           2  ...              NaN                    NaN   \n",
-      "15           0           2  ...              NaN                    NaN   \n",
-      "16           0           2  ...              NaN                    NaN   \n",
-      "17           0           2  ...              NaN                    NaN   \n",
-      "18           0           2  ...              0.0                    5.5   \n",
-      "19           0           2  ...              2.0                    6.5   \n",
-      "20           0           2  ...              0.0                    5.5   \n",
-      "21           0           2  ...              2.0                    6.5   \n",
-      "22           0           2  ...              0.0                    5.5   \n",
-      "23           0           2  ...              2.0                    6.5   \n",
-      "\n",
-      "    gunning_fog      text_standard  fernandez_huerta szigriszt_pazos  \\\n",
-      "0           NaN                NaN               NaN             NaN   \n",
-      "1           NaN                NaN               NaN             NaN   \n",
-      "2           NaN                NaN               NaN             NaN   \n",
-      "3           NaN                NaN               NaN             NaN   \n",
-      "4           NaN                NaN               NaN             NaN   \n",
-      "5           NaN                NaN               NaN             NaN   \n",
-      "6          5.20  5th and 6th grade            133.58          131.54   \n",
-      "7          8.28  6th and 7th grade            115.58          112.37   \n",
-      "8          5.20  5th and 6th grade            133.58          131.54   \n",
-      "9          8.28  6th and 7th grade            115.58          112.37   \n",
-      "10         5.20  5th and 6th grade            133.58          131.54   \n",
-      "11         8.28  6th and 7th grade            115.58          112.37   \n",
-      "12          NaN                NaN               NaN             NaN   \n",
-      "13          NaN                NaN               NaN             NaN   \n",
-      "14          NaN                NaN               NaN             NaN   \n",
-      "15          NaN                NaN               NaN             NaN   \n",
-      "16          NaN                NaN               NaN             NaN   \n",
-      "17          NaN                NaN               NaN             NaN   \n",
-      "18         5.20  5th and 6th grade            133.58          131.54   \n",
-      "19         8.28  6th and 7th grade            115.58          112.37   \n",
-      "20         5.20  5th and 6th grade            133.58          131.54   \n",
-      "21         8.28  6th and 7th grade            115.58          112.37   \n",
-      "22         5.20  5th and 6th grade            133.58          131.54   \n",
-      "23         8.28  6th and 7th grade            115.58          112.37   \n",
-      "\n",
-      "    gutierrez_polini  crawford  gulpease_index   osman  \n",
-      "0                NaN       NaN             NaN     NaN  \n",
-      "1                NaN       NaN             NaN     NaN  \n",
-      "2                NaN       NaN             NaN     NaN  \n",
-      "3                NaN       NaN             NaN     NaN  \n",
-      "4                NaN       NaN             NaN     NaN  \n",
-      "5                NaN       NaN             NaN     NaN  \n",
-      "6              62.30      -0.2            79.8  116.91  \n",
-      "7              54.83       1.4            72.1  100.17  \n",
-      "8              62.30      -0.2            79.8  116.91  \n",
-      "9              54.83       1.4            72.1  100.17  \n",
-      "10             62.30      -0.2            79.8  116.91  \n",
-      "11             54.83       1.4            72.1  100.17  \n",
-      "12               NaN       NaN             NaN     NaN  \n",
-      "13               NaN       NaN             NaN     NaN  \n",
-      "14               NaN       NaN             NaN     NaN  \n",
-      "15               NaN       NaN             NaN     NaN  \n",
-      "16               NaN       NaN             NaN     NaN  \n",
-      "17               NaN       NaN             NaN     NaN  \n",
-      "18             62.30      -0.2            79.8  116.91  \n",
-      "19             54.83       1.4            72.1  100.17  \n",
-      "20             62.30      -0.2            79.8  116.91  \n",
-      "21             54.83       1.4            72.1  100.17  \n",
-      "22             62.30      -0.2            79.8  116.91  \n",
-      "23             54.83       1.4            72.1  100.17  \n",
-      "\n",
-      "[24 rows x 39 columns], 'session_analysis':     prompt_step         prompts    name  output_step  \\\n",
-      "0             1  Tell me a joke  OpenAI            2   \n",
-      "1             1  Tell me a poem  OpenAI            2   \n",
-      "2             1  Tell me a joke  OpenAI            2   \n",
-      "3             1  Tell me a poem  OpenAI            2   \n",
-      "4             1  Tell me a joke  OpenAI            2   \n",
-      "5             1  Tell me a poem  OpenAI            2   \n",
-      "6             3  Tell me a joke  OpenAI            4   \n",
-      "7             3  Tell me a poem  OpenAI            4   \n",
-      "8             3  Tell me a joke  OpenAI            4   \n",
-      "9             3  Tell me a poem  OpenAI            4   \n",
-      "10            3  Tell me a joke  OpenAI            4   \n",
-      "11            3  Tell me a poem  OpenAI            4   \n",
-      "\n",
-      "                                               output  \\\n",
-      "0   \\n\\nQ: What did the fish say when it hit the w...   \n",
-      "1   \\n\\nRoses are red,\\nViolets are blue,\\nSugar i...   \n",
-      "2   \\n\\nQ: What did the fish say when it hit the w...   \n",
-      "3   \\n\\nRoses are red,\\nViolets are blue,\\nSugar i...   \n",
-      "4   \\n\\nQ: What did the fish say when it hit the w...   \n",
-      "5   \\n\\nRoses are red,\\nViolets are blue,\\nSugar i...   \n",
-      "6   \\n\\nQ: What did the fish say when it hit the w...   \n",
-      "7   \\n\\nRoses are red,\\nViolets are blue,\\nSugar i...   \n",
-      "8   \\n\\nQ: What did the fish say when it hit the w...   \n",
-      "9   \\n\\nRoses are red,\\nViolets are blue,\\nSugar i...   \n",
-      "10  \\n\\nQ: What did the fish say when it hit the w...   \n",
-      "11  \\n\\nRoses are red,\\nViolets are blue,\\nSugar i...   \n",
-      "\n",
-      "    token_usage_total_tokens  token_usage_prompt_tokens  \\\n",
-      "0                        162                         24   \n",
-      "1                        162                         24   \n",
-      "2                        162                         24   \n",
-      "3                        162                         24   \n",
-      "4                        162                         24   \n",
-      "5                        162                         24   \n",
-      "6                        162                         24   \n",
-      "7                        162                         24   \n",
-      "8                        162                         24   \n",
-      "9                        162                         24   \n",
-      "10                       162                         24   \n",
-      "11                       162                         24   \n",
-      "\n",
-      "    token_usage_completion_tokens  flesch_reading_ease  flesch_kincaid_grade  \\\n",
-      "0                             138               109.04                   1.3   \n",
-      "1                             138                83.66                   4.8   \n",
-      "2                             138               109.04                   1.3   \n",
-      "3                             138                83.66                   4.8   \n",
-      "4                             138               109.04                   1.3   \n",
-      "5                             138                83.66                   4.8   \n",
-      "6                             138               109.04                   1.3   \n",
-      "7                             138                83.66                   4.8   \n",
-      "8                             138               109.04                   1.3   \n",
-      "9                             138                83.66                   4.8   \n",
-      "10                            138               109.04                   1.3   \n",
-      "11                            138                83.66                   4.8   \n",
-      "\n",
-      "    ...  difficult_words  linsear_write_formula  gunning_fog  \\\n",
-      "0   ...                0                    5.5         5.20   \n",
-      "1   ...                2                    6.5         8.28   \n",
-      "2   ...                0                    5.5         5.20   \n",
-      "3   ...                2                    6.5         8.28   \n",
-      "4   ...                0                    5.5         5.20   \n",
-      "5   ...                2                    6.5         8.28   \n",
-      "6   ...                0                    5.5         5.20   \n",
-      "7   ...                2                    6.5         8.28   \n",
-      "8   ...                0                    5.5         5.20   \n",
-      "9   ...                2                    6.5         8.28   \n",
-      "10  ...                0                    5.5         5.20   \n",
-      "11  ...                2                    6.5         8.28   \n",
-      "\n",
-      "        text_standard  fernandez_huerta  szigriszt_pazos  gutierrez_polini  \\\n",
-      "0   5th and 6th grade            133.58           131.54             62.30   \n",
-      "1   6th and 7th grade            115.58           112.37             54.83   \n",
-      "2   5th and 6th grade            133.58           131.54             62.30   \n",
-      "3   6th and 7th grade            115.58           112.37             54.83   \n",
-      "4   5th and 6th grade            133.58           131.54             62.30   \n",
-      "5   6th and 7th grade            115.58           112.37             54.83   \n",
-      "6   5th and 6th grade            133.58           131.54             62.30   \n",
-      "7   6th and 7th grade            115.58           112.37             54.83   \n",
-      "8   5th and 6th grade            133.58           131.54             62.30   \n",
-      "9   6th and 7th grade            115.58           112.37             54.83   \n",
-      "10  5th and 6th grade            133.58           131.54             62.30   \n",
-      "11  6th and 7th grade            115.58           112.37             54.83   \n",
-      "\n",
-      "   crawford  gulpease_index   osman  \n",
-      "0      -0.2            79.8  116.91  \n",
-      "1       1.4            72.1  100.17  \n",
-      "2      -0.2            79.8  116.91  \n",
-      "3       1.4            72.1  100.17  \n",
-      "4      -0.2            79.8  116.91  \n",
-      "5       1.4            72.1  100.17  \n",
-      "6      -0.2            79.8  116.91  \n",
-      "7       1.4            72.1  100.17  \n",
-      "8      -0.2            79.8  116.91  \n",
-      "9       1.4            72.1  100.17  \n",
-      "10     -0.2            79.8  116.91  \n",
-      "11      1.4            72.1  100.17  \n",
-      "\n",
-      "[12 rows x 24 columns]}\n",
-      "2023-03-29 14:00:25,948 - clearml.Task - INFO - Completed model upload to https://files.clear.ml/langchain_callback_demo/llm.988bd727b0e94a29a3ac0ee526813545/models/simple_sequential\n"
-     ]
-    }
-   ],
-   "source": [
-    "# SCENARIO 1 - LLM\n",
-    "llm_result = llm.generate([\"Tell me a joke\", \"Tell me a poem\"] * 3)\n",
-    "# After every generation run, use flush to make sure all the metrics\n",
-    "# prompts and other output are properly saved separately\n",
-    "clearml_callback.flush_tracker(langchain_asset=llm, name=\"simple_sequential\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "At this point you can already go to https://app.clear.ml and take a look at the resulting ClearML Task that was created.\n",
-    "\n",
-    "Among others, you should see that this notebook is saved along with any git information. The model JSON that contains the used parameters is saved as an artifact, there are also console logs and under the plots section, you'll find tables that represent the flow of the chain.\n",
-    "\n",
-    "Finally, if you enabled visualizations, these are stored as HTML files under debug samples."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Scenario 2: Creating an agent with tools\n",
-    "\n",
-    "To show a more advanced workflow, let's create an agent with access to tools. The way ClearML tracks the results is not different though, only the table will look slightly different as there are other types of actions taken when compared to the earlier, simpler example.\n",
-    "\n",
-    "You can now also see the use of the `finish=True` keyword, which will fully close the ClearML Task, instead of just resetting the parameters and prompts for a new conversation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "{'action': 'on_chain_start', 'name': 'AgentExecutor', 'step': 1, 'starts': 1, 'ends': 0, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 0, 'llm_ends': 0, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'input': 'Who is the wife of the person who sang summer of 69?'}\n",
-      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 2, 'starts': 2, 'ends': 0, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 0, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\\n\\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\\nCalculator: Useful for when you need to answer questions about math.\\n\\nUse the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [Search, Calculator]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin!\\n\\nQuestion: Who is the wife of the person who sang summer of 69?\\nThought:'}\n",
-      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 189, 'token_usage_completion_tokens': 34, 'token_usage_total_tokens': 223, 'model_name': 'text-davinci-003', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': ' I need to find out who sang summer of 69 and then find out who their wife is.\\nAction: Search\\nAction Input: \"Who sang summer of 69\"', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 91.61, 'flesch_kincaid_grade': 3.8, 'smog_index': 0.0, 'coleman_liau_index': 3.41, 'automated_readability_index': 3.5, 'dale_chall_readability_score': 6.06, 'difficult_words': 2, 'linsear_write_formula': 5.75, 'gunning_fog': 5.4, 'text_standard': '3rd and 4th grade', 'fernandez_huerta': 121.07, 'szigriszt_pazos': 119.5, 'gutierrez_polini': 54.91, 'crawford': 0.9, 'gulpease_index': 72.7, 'osman': 92.16}\n",
-      "\u001b[32;1m\u001b[1;3m I need to find out who sang summer of 69 and then find out who their wife is.\n",
-      "Action: Search\n",
-      "Action Input: \"Who sang summer of 69\"\u001b[0m{'action': 'on_agent_action', 'tool': 'Search', 'tool_input': 'Who sang summer of 69', 'log': ' I need to find out who sang summer of 69 and then find out who their wife is.\\nAction: Search\\nAction Input: \"Who sang summer of 69\"', 'step': 4, 'starts': 3, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 1, 'tool_ends': 0, 'agent_ends': 0}\n",
-      "{'action': 'on_tool_start', 'input_str': 'Who sang summer of 69', 'name': 'Search', 'description': 'A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', 'step': 5, 'starts': 4, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 0, 'agent_ends': 0}\n",
-      "\n",
-      "Observation: \u001b[36;1m\u001b[1;3mBryan Adams - Summer Of 69 (Official Music Video).\u001b[0m\n",
-      "Thought:{'action': 'on_tool_end', 'output': 'Bryan Adams - Summer Of 69 (Official Music Video).', 'step': 6, 'starts': 4, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0}\n",
-      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 7, 'starts': 5, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\\n\\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\\nCalculator: Useful for when you need to answer questions about math.\\n\\nUse the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [Search, Calculator]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin!\\n\\nQuestion: Who is the wife of the person who sang summer of 69?\\nThought: I need to find out who sang summer of 69 and then find out who their wife is.\\nAction: Search\\nAction Input: \"Who sang summer of 69\"\\nObservation: Bryan Adams - Summer Of 69 (Official Music Video).\\nThought:'}\n",
-      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 242, 'token_usage_completion_tokens': 28, 'token_usage_total_tokens': 270, 'model_name': 'text-davinci-003', 'step': 8, 'starts': 5, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0, 'text': ' I need to find out who Bryan Adams is married to.\\nAction: Search\\nAction Input: \"Who is Bryan Adams married to\"', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 94.66, 'flesch_kincaid_grade': 2.7, 'smog_index': 0.0, 'coleman_liau_index': 4.73, 'automated_readability_index': 4.0, 'dale_chall_readability_score': 7.16, 'difficult_words': 2, 'linsear_write_formula': 4.25, 'gunning_fog': 4.2, 'text_standard': '4th and 5th grade', 'fernandez_huerta': 124.13, 'szigriszt_pazos': 119.2, 'gutierrez_polini': 52.26, 'crawford': 0.7, 'gulpease_index': 74.7, 'osman': 84.2}\n",
-      "\u001b[32;1m\u001b[1;3m I need to find out who Bryan Adams is married to.\n",
-      "Action: Search\n",
-      "Action Input: \"Who is Bryan Adams married to\"\u001b[0m{'action': 'on_agent_action', 'tool': 'Search', 'tool_input': 'Who is Bryan Adams married to', 'log': ' I need to find out who Bryan Adams is married to.\\nAction: Search\\nAction Input: \"Who is Bryan Adams married to\"', 'step': 9, 'starts': 6, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 3, 'tool_ends': 1, 'agent_ends': 0}\n",
-      "{'action': 'on_tool_start', 'input_str': 'Who is Bryan Adams married to', 'name': 'Search', 'description': 'A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', 'step': 10, 'starts': 7, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 1, 'agent_ends': 0}\n",
-      "\n",
-      "Observation: \u001b[36;1m\u001b[1;3mBryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...\u001b[0m\n",
-      "Thought:{'action': 'on_tool_end', 'output': 'Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...', 'step': 11, 'starts': 7, 'ends': 4, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0}\n",
-      "{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 12, 'starts': 8, 'ends': 4, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\\n\\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\\nCalculator: Useful for when you need to answer questions about math.\\n\\nUse the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [Search, Calculator]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin!\\n\\nQuestion: Who is the wife of the person who sang summer of 69?\\nThought: I need to find out who sang summer of 69 and then find out who their wife is.\\nAction: Search\\nAction Input: \"Who sang summer of 69\"\\nObservation: Bryan Adams - Summer Of 69 (Official Music Video).\\nThought: I need to find out who Bryan Adams is married to.\\nAction: Search\\nAction Input: \"Who is Bryan Adams married to\"\\nObservation: Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...\\nThought:'}\n",
-      "{'action': 'on_llm_end', 'token_usage_prompt_tokens': 314, 'token_usage_completion_tokens': 18, 'token_usage_total_tokens': 332, 'model_name': 'text-davinci-003', 'step': 13, 'starts': 8, 'ends': 5, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0, 'text': ' I now know the final answer.\\nFinal Answer: Bryan Adams has never been married.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 81.29, 'flesch_kincaid_grade': 3.7, 'smog_index': 0.0, 'coleman_liau_index': 5.75, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 7.37, 'difficult_words': 1, 'linsear_write_formula': 2.5, 'gunning_fog': 2.8, 'text_standard': '3rd and 4th grade', 'fernandez_huerta': 115.7, 'szigriszt_pazos': 110.84, 'gutierrez_polini': 49.79, 'crawford': 0.7, 'gulpease_index': 85.4, 'osman': 83.14}\n",
-      "\u001b[32;1m\u001b[1;3m I now know the final answer.\n",
-      "Final Answer: Bryan Adams has never been married.\u001b[0m\n",
-      "{'action': 'on_agent_finish', 'output': 'Bryan Adams has never been married.', 'log': ' I now know the final answer.\\nFinal Answer: Bryan Adams has never been married.', 'step': 14, 'starts': 8, 'ends': 6, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 1}\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n",
-      "{'action': 'on_chain_end', 'outputs': 'Bryan Adams has never been married.', 'step': 15, 'starts': 8, 'ends': 7, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 1, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 1}\n",
-      "{'action_records':              action    name  step  starts  ends  errors  text_ctr  \\\n",
-      "0      on_llm_start  OpenAI     1       1     0       0         0   \n",
-      "1      on_llm_start  OpenAI     1       1     0       0         0   \n",
-      "2      on_llm_start  OpenAI     1       1     0       0         0   \n",
-      "3      on_llm_start  OpenAI     1       1     0       0         0   \n",
-      "4      on_llm_start  OpenAI     1       1     0       0         0   \n",
-      "..              ...     ...   ...     ...   ...     ...       ...   \n",
-      "66      on_tool_end     NaN    11       7     4       0         0   \n",
-      "67     on_llm_start  OpenAI    12       8     4       0         0   \n",
-      "68       on_llm_end     NaN    13       8     5       0         0   \n",
-      "69  on_agent_finish     NaN    14       8     6       0         0   \n",
-      "70     on_chain_end     NaN    15       8     7       0         0   \n",
-      "\n",
-      "    chain_starts  chain_ends  llm_starts  ...  gulpease_index  osman  input  \\\n",
-      "0              0           0           1  ...             NaN    NaN    NaN   \n",
-      "1              0           0           1  ...             NaN    NaN    NaN   \n",
-      "2              0           0           1  ...             NaN    NaN    NaN   \n",
-      "3              0           0           1  ...             NaN    NaN    NaN   \n",
-      "4              0           0           1  ...             NaN    NaN    NaN   \n",
-      "..           ...         ...         ...  ...             ...    ...    ...   \n",
-      "66             1           0           2  ...             NaN    NaN    NaN   \n",
-      "67             1           0           3  ...             NaN    NaN    NaN   \n",
-      "68             1           0           3  ...            85.4  83.14    NaN   \n",
-      "69             1           0           3  ...             NaN    NaN    NaN   \n",
-      "70             1           1           3  ...             NaN    NaN    NaN   \n",
-      "\n",
-      "    tool  tool_input                                                log  \\\n",
-      "0    NaN         NaN                                                NaN   \n",
-      "1    NaN         NaN                                                NaN   \n",
-      "2    NaN         NaN                                                NaN   \n",
-      "3    NaN         NaN                                                NaN   \n",
-      "4    NaN         NaN                                                NaN   \n",
-      "..   ...         ...                                                ...   \n",
-      "66   NaN         NaN                                                NaN   \n",
-      "67   NaN         NaN                                                NaN   \n",
-      "68   NaN         NaN                                                NaN   \n",
-      "69   NaN         NaN   I now know the final answer.\\nFinal Answer: B...   \n",
-      "70   NaN         NaN                                                NaN   \n",
-      "\n",
-      "    input_str  description                                             output  \\\n",
-      "0         NaN          NaN                                                NaN   \n",
-      "1         NaN          NaN                                                NaN   \n",
-      "2         NaN          NaN                                                NaN   \n",
-      "3         NaN          NaN                                                NaN   \n",
-      "4         NaN          NaN                                                NaN   \n",
-      "..        ...          ...                                                ...   \n",
-      "66        NaN          NaN  Bryan Adams has never married. In the 1990s, h...   \n",
-      "67        NaN          NaN                                                NaN   \n",
-      "68        NaN          NaN                                                NaN   \n",
-      "69        NaN          NaN                Bryan Adams has never been married.   \n",
-      "70        NaN          NaN                                                NaN   \n",
-      "\n",
-      "                                outputs  \n",
-      "0                                   NaN  \n",
-      "1                                   NaN  \n",
-      "2                                   NaN  \n",
-      "3                                   NaN  \n",
-      "4                                   NaN  \n",
-      "..                                  ...  \n",
-      "66                                  NaN  \n",
-      "67                                  NaN  \n",
-      "68                                  NaN  \n",
-      "69                                  NaN  \n",
-      "70  Bryan Adams has never been married.  \n",
-      "\n",
-      "[71 rows x 47 columns], 'session_analysis':    prompt_step                                            prompts    name  \\\n",
-      "0            2  Answer the following questions as best you can...  OpenAI   \n",
-      "1            7  Answer the following questions as best you can...  OpenAI   \n",
-      "2           12  Answer the following questions as best you can...  OpenAI   \n",
-      "\n",
-      "   output_step                                             output  \\\n",
-      "0            3   I need to find out who sang summer of 69 and ...   \n",
-      "1            8   I need to find out who Bryan Adams is married...   \n",
-      "2           13   I now know the final answer.\\nFinal Answer: B...   \n",
-      "\n",
-      "   token_usage_total_tokens  token_usage_prompt_tokens  \\\n",
-      "0                       223                        189   \n",
-      "1                       270                        242   \n",
-      "2                       332                        314   \n",
-      "\n",
-      "   token_usage_completion_tokens  flesch_reading_ease  flesch_kincaid_grade  \\\n",
-      "0                             34                91.61                   3.8   \n",
-      "1                             28                94.66                   2.7   \n",
-      "2                             18                81.29                   3.7   \n",
-      "\n",
-      "   ...  difficult_words  linsear_write_formula  gunning_fog  \\\n",
-      "0  ...                2                   5.75          5.4   \n",
-      "1  ...                2                   4.25          4.2   \n",
-      "2  ...                1                   2.50          2.8   \n",
-      "\n",
-      "       text_standard  fernandez_huerta  szigriszt_pazos  gutierrez_polini  \\\n",
-      "0  3rd and 4th grade            121.07           119.50             54.91   \n",
-      "1  4th and 5th grade            124.13           119.20             52.26   \n",
-      "2  3rd and 4th grade            115.70           110.84             49.79   \n",
-      "\n",
-      "  crawford  gulpease_index  osman  \n",
-      "0      0.9            72.7  92.16  \n",
-      "1      0.7            74.7  84.20  \n",
-      "2      0.7            85.4  83.14  \n",
-      "\n",
-      "[3 rows x 24 columns]}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Could not update last created model in Task 988bd727b0e94a29a3ac0ee526813545, Task status 'completed' cannot be updated\n"
-     ]
-    }
-   ],
-   "source": [
-    "from langchain.agents import initialize_agent, load_tools\n",
-    "from langchain.agents import AgentType\n",
-    "\n",
-    "# SCENARIO 2 - Agent with Tools\n",
-    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callbacks=callbacks)\n",
-    "agent = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
-    "    callbacks=callbacks,\n",
-    ")\n",
-    "agent.run(\n",
-    "    \"Who is the wife of the person who sang summer of 69?\"\n",
-    ")\n",
-    "clearml_callback.flush_tracker(langchain_asset=agent, name=\"Agent with Tools\", finish=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Tips and Next Steps\n",
-    "\n",
-    "- Make sure you always use a unique `name` argument for the `clearml_callback.flush_tracker` function. If not, the model parameters used for a run will override the previous run!\n",
-    "\n",
-    "- If you close the ClearML Callback using `clearml_callback.flush_tracker(..., finish=True)` the Callback cannot be used anymore. Make a new one if you want to keep logging.\n",
-    "\n",
-    "- Check out the rest of the open source ClearML ecosystem, there is a data version manager, a remote execution agent, automated pipelines and much more!\n"
-   ]
-  },
-  {
-   "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": "3.10.6"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "a53ebf4a859167383b364e7e7521d0add3c2dbbdecce4edf676e8c4634ff3fbb"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

docs/integrations/clickhouse.md

@@ -1,52 +0,0 @@
-# ClickHouse
-
-This page covers how to use ClickHouse Vector Search within LangChain.
-
-[ClickHouse](https://clickhouse.com) is a open source real-time OLAP database with full SQL support and a wide range of functions to assist users in writing analytical queries. Some of these functions and data structures perform distance operations between vectors, enabling ClickHouse to be used as a vector database.
-
-Due to the fully parallelized query pipeline, ClickHouse can process vector search operations very quickly, especially when performing exact matching through a linear scan over all rows, delivering processing speed comparable to dedicated vector databases.
-
-High compression levels, tunable through custom compression codecs, enable very large datasets to be stored and queried. ClickHouse is not memory-bound, allowing multi-TB datasets containing embeddings to be queried.
-
-The capabilities for computing the distance between two vectors are just another SQL function and can be effectively combined with more traditional SQL filtering and aggregation capabilities. This allows vectors to be stored and queried alongside metadata, and even rich text, enabling a broad array of use cases and applications.
-
-Finally, experimental ClickHouse capabilities like [Approximate Nearest Neighbour (ANN) indices](https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/annindexes) support faster approximate matching of vectors and provide a promising development aimed to further enhance the vector matching capabilities of ClickHouse.
-
-## Installation
-- Install clickhouse server by [binary](https://clickhouse.com/docs/en/install) or [docker image](https://hub.docker.com/r/clickhouse/clickhouse-server/)
-- Install the Python SDK with `pip install clickhouse-connect`
-
-### Configure clickhouse vector index
-
-Customize `ClickhouseSettings` object with parameters
-
-    ```python
-    from langchain.vectorstores import ClickHouse, ClickhouseSettings
-    config = ClickhouseSettings(host="<clickhouse-server-host>", port=8123, ...)
-    index = Clickhouse(embedding_function, config)
-    index.add_documents(...)
-    ```
-  
-## Wrappers
-supported functions:
-- `add_texts`
-- `add_documents`
-- `from_texts`
-- `from_documents`
-- `similarity_search`
-- `asimilarity_search`
-- `similarity_search_by_vector`
-- `asimilarity_search_by_vector`
-- `similarity_search_with_relevance_scores`
-
-### VectorStore
-
-There exists a wrapper around open source Clickhouse database, allowing you to use it as a vectorstore,
-whether for semantic search or similar example retrieval.
-
-To import this vectorstore:
-```python
-from langchain.vectorstores import Clickhouse
-```
-
-For a more detailed walkthrough of the MyScale wrapper, see [this notebook](../modules/indexes/vectorstores/examples/clickhouse.ipynb)

docs/integrations/cohere.md

@@ -1,38 +0,0 @@
-# Cohere
-
->[Cohere](https://cohere.ai/about) is a Canadian startup that provides natural language processing models
-> that help companies improve human-machine interactions.
-
-## Installation and Setup
-- Install the Python SDK :
-```bash
-pip install cohere
-```
-
-Get a [Cohere api key](https://dashboard.cohere.ai/) and set it as an environment variable (`COHERE_API_KEY`)
-
-
-## LLM
-
-There exists an Cohere LLM wrapper, which you can access with 
-See a [usage example](../modules/models/llms/integrations/cohere.ipynb).
-
-```python
-from langchain.llms import Cohere
-```
-
-## Text Embedding Model
-
-There exists an Cohere Embedding model, which you can access with 
-```python
-from langchain.embeddings import CohereEmbeddings
-```
-For a more detailed walkthrough of this, see [this notebook](../modules/models/text_embedding/examples/cohere.ipynb)
-
-## Retriever
-
-See a [usage example](../modules/indexes/retrievers/examples/cohere-reranker.ipynb).
-
-```python
-from langchain.retrievers.document_compressors import CohereRerank
-```

docs/integrations/college_confidential.md

@@ -1,16 +0,0 @@
-# College Confidential
-
->[College Confidential](https://www.collegeconfidential.com/) gives information on 3,800+ colleges and universities.
-
-## Installation and Setup
-
-There isn't any special setup for it.
-
-
-## Document Loader
-
-See a [usage example](../modules/indexes/document_loaders/examples/college_confidential.ipynb).
-
-```python
-from langchain.document_loaders import CollegeConfidentialLoader
-```

docs/integrations/comet_tracking.ipynb

@@ -1,347 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Comet"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![](https://user-images.githubusercontent.com/7529846/230328046-a8b18c51-12e3-4617-9b39-97614a571a2d.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this guide we will demonstrate how to track your Langchain Experiments, Evaluation Metrics, and LLM Sessions with [Comet](https://www.comet.com/site/?utm_source=langchain&utm_medium=referral&utm_campaign=comet_notebook).  \n",
-    "\n",
-    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/hwchase17/langchain/blob/master/docs/ecosystem/comet_tracking.ipynb\">\n",
-    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
-    "</a>\n",
-    "\n",
-    "**Example Project:** [Comet with LangChain](https://www.comet.com/examples/comet-example-langchain/view/b5ZThK6OFdhKWVSP3fDfRtrNF/panels?utm_source=langchain&utm_medium=referral&utm_campaign=comet_notebook)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<img width=\"1280\" alt=\"comet-langchain\" src=\"https://user-images.githubusercontent.com/7529846/230326720-a9711435-9c6f-4edb-a707-94b67271ab25.png\">\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Install Comet and Dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install comet_ml langchain openai google-search-results spacy textstat pandas\n",
-    "\n",
-    "import sys\n",
-    "!{sys.executable} -m spacy download en_core_web_sm"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Initialize Comet and Set your Credentials"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can grab your [Comet API Key here](https://www.comet.com/signup?utm_source=langchain&utm_medium=referral&utm_campaign=comet_notebook) or click the link after initializing Comet"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import comet_ml\n",
-    "\n",
-    "comet_ml.init(project_name=\"comet-example-langchain\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Set OpenAI and SerpAPI credentials"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You will need an [OpenAI API Key](https://platform.openai.com/account/api-keys) and a [SerpAPI API Key](https://serpapi.com/dashboard) to run the following examples"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"...\"\n",
-    "#os.environ[\"OPENAI_ORGANIZATION\"] = \"...\"\n",
-    "os.environ[\"SERPAPI_API_KEY\"] = \"...\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Scenario 1: Using just an LLM"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from datetime import datetime\n",
-    "\n",
-    "from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler\n",
-    "from langchain.llms import OpenAI\n",
-    "\n",
-    "comet_callback = CometCallbackHandler(\n",
-    "    project_name=\"comet-example-langchain\",\n",
-    "    complexity_metrics=True,\n",
-    "    stream_logs=True,\n",
-    "    tags=[\"llm\"],\n",
-    "    visualizations=[\"dep\"],\n",
-    ")\n",
-    "callbacks = [StdOutCallbackHandler(), comet_callback]\n",
-    "llm = OpenAI(temperature=0.9, callbacks=callbacks, verbose=True)\n",
-    "\n",
-    "llm_result = llm.generate([\"Tell me a joke\", \"Tell me a poem\", \"Tell me a fact\"] * 3)\n",
-    "print(\"LLM result\", llm_result)\n",
-    "comet_callback.flush_tracker(llm, finish=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Scenario 2: Using an LLM in a Chain"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler\n",
-    "from langchain.chains import LLMChain\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "\n",
-    "comet_callback = CometCallbackHandler(\n",
-    "    complexity_metrics=True,\n",
-    "    project_name=\"comet-example-langchain\",\n",
-    "    stream_logs=True,\n",
-    "    tags=[\"synopsis-chain\"],\n",
-    ")\n",
-    "callbacks = [StdOutCallbackHandler(), comet_callback]\n",
-    "llm = OpenAI(temperature=0.9, callbacks=callbacks)\n",
-    "\n",
-    "template = \"\"\"You are a playwright. Given the title of play, it is your job to write a synopsis for that title.\n",
-    "Title: {title}\n",
-    "Playwright: This is a synopsis for the above play:\"\"\"\n",
-    "prompt_template = PromptTemplate(input_variables=[\"title\"], template=template)\n",
-    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template, callbacks=callbacks)\n",
-    "\n",
-    "test_prompts = [{\"title\": \"Documentary about Bigfoot in Paris\"}]\n",
-    "print(synopsis_chain.apply(test_prompts))\n",
-    "comet_callback.flush_tracker(synopsis_chain, finish=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Scenario 3: Using An Agent with Tools "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents import initialize_agent, load_tools\n",
-    "from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler\n",
-    "from langchain.llms import OpenAI\n",
-    "\n",
-    "comet_callback = CometCallbackHandler(\n",
-    "    project_name=\"comet-example-langchain\",\n",
-    "    complexity_metrics=True,\n",
-    "    stream_logs=True,\n",
-    "    tags=[\"agent\"],\n",
-    ")\n",
-    "callbacks = [StdOutCallbackHandler(), comet_callback]\n",
-    "llm = OpenAI(temperature=0.9, callbacks=callbacks)\n",
-    "\n",
-    "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm, callbacks=callbacks)\n",
-    "agent = initialize_agent(\n",
-    "    tools,\n",
-    "    llm,\n",
-    "    agent=\"zero-shot-react-description\",\n",
-    "    callbacks=callbacks,\n",
-    "    verbose=True,\n",
-    ")\n",
-    "agent.run(\n",
-    "    \"Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?\"\n",
-    ")\n",
-    "comet_callback.flush_tracker(agent, finish=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Scenario 4: Using Custom Evaluation Metrics"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `CometCallbackManager` also allows you to define and use Custom Evaluation Metrics to assess generated outputs from your model. Let's take a look at how this works. \n",
-    "\n",
-    "\n",
-    "In the snippet below, we will use the [ROUGE](https://huggingface.co/spaces/evaluate-metric/rouge) metric to evaluate the quality of a generated summary of an input prompt. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install rouge-score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from rouge_score import rouge_scorer\n",
-    "\n",
-    "from langchain.callbacks import CometCallbackHandler, StdOutCallbackHandler\n",
-    "from langchain.chains import LLMChain\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "\n",
-    "\n",
-    "class Rouge:\n",
-    "    def __init__(self, reference):\n",
-    "        self.reference = reference\n",
-    "        self.scorer = rouge_scorer.RougeScorer([\"rougeLsum\"], use_stemmer=True)\n",
-    "\n",
-    "    def compute_metric(self, generation, prompt_idx, gen_idx):\n",
-    "        prediction = generation.text\n",
-    "        results = self.scorer.score(target=self.reference, prediction=prediction)\n",
-    "\n",
-    "        return {\n",
-    "            \"rougeLsum_score\": results[\"rougeLsum\"].fmeasure,\n",
-    "            \"reference\": self.reference,\n",
-    "        }\n",
-    "\n",
-    "\n",
-    "reference = \"\"\"\n",
-    "The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building.\n",
-    "It was the first structure to reach a height of 300 metres.\n",
-    "\n",
-    "It is now taller than the Chrysler Building in New York City by 5.2 metres (17 ft)\n",
-    "Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France .\n",
-    "\"\"\"\n",
-    "rouge_score = Rouge(reference=reference)\n",
-    "\n",
-    "template = \"\"\"Given the following article, it is your job to write a summary.\n",
-    "Article:\n",
-    "{article}\n",
-    "Summary: This is the summary for the above article:\"\"\"\n",
-    "prompt_template = PromptTemplate(input_variables=[\"article\"], template=template)\n",
-    "\n",
-    "comet_callback = CometCallbackHandler(\n",
-    "    project_name=\"comet-example-langchain\",\n",
-    "    complexity_metrics=False,\n",
-    "    stream_logs=True,\n",
-    "    tags=[\"custom_metrics\"],\n",
-    "    custom_metrics=rouge_score.compute_metric,\n",
-    ")\n",
-    "callbacks = [StdOutCallbackHandler(), comet_callback]\n",
-    "llm = OpenAI(temperature=0.9)\n",
-    "\n",
-    "synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)\n",
-    "\n",
-    "test_prompts = [\n",
-    "    {\n",
-    "        \"article\": \"\"\"\n",
-    "                 The tower is 324 metres (1,063 ft) tall, about the same height as\n",
-    "                 an 81-storey building, and the tallest structure in Paris. Its base is square,\n",
-    "                 measuring 125 metres (410 ft) on each side.\n",
-    "                 During its construction, the Eiffel Tower surpassed the\n",
-    "                 Washington Monument to become the tallest man-made structure in the world,\n",
-    "                 a title it held for 41 years until the Chrysler Building\n",
-    "                 in New York City was finished in 1930.\n",
-    "\n",
-    "                 It was the first structure to reach a height of 300 metres.\n",
-    "                 Due to the addition of a broadcasting aerial at the top of the tower in 1957,\n",
-    "                 it is now taller than the Chrysler Building by 5.2 metres (17 ft).\n",
-    "\n",
-    "                 Excluding transmitters, the Eiffel Tower is the second tallest\n",
-    "                 free-standing structure in France after the Millau Viaduct.\n",
-    "                 \"\"\"\n",
-    "    }\n",
-    "]\n",
-    "print(synopsis_chain.apply(test_prompts, callbacks=callbacks))\n",
-    "comet_callback.flush_tracker(synopsis_chain, finish=True)"
-   ]
-  }
- ],
- "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": "3.9.15"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

docs/integrations/confluence.md

@@ -1,22 +0,0 @@
-# Confluence
-
->[Confluence](https://www.atlassian.com/software/confluence) is a wiki collaboration platform that saves and organizes all of the project-related material. `Confluence` is a knowledge base that primarily handles content management activities. 
-
-
-## Installation and Setup
-
-```bash
-pip install atlassian-python-api
-```
-
-We need to set up `username/api_key` or `Oauth2 login`. 
-See [instructions](https://support.atlassian.com/atlassian-account/docs/manage-api-tokens-for-your-atlassian-account/).
-
-
-## Document Loader
-
-See a [usage example](../modules/indexes/document_loaders/examples/confluence.ipynb).
-
-```python
-from langchain.document_loaders import ConfluenceLoader
-```

docs/integrations/ctransformers.md

@@ -1,57 +0,0 @@
-# C Transformers
-
-This page covers how to use the [C Transformers](https://github.com/marella/ctransformers) library within LangChain.
-It is broken into two parts: installation and setup, and then references to specific C Transformers wrappers.
-
-## Installation and Setup
-
-- Install the Python package with `pip install ctransformers`
-- Download a supported [GGML model](https://huggingface.co/TheBloke) (see [Supported Models](https://github.com/marella/ctransformers#supported-models))
-
-## Wrappers
-
-### LLM
-
-There exists a CTransformers LLM wrapper, which you can access with:
-
-```python
-from langchain.llms import CTransformers
-```
-
-It provides a unified interface for all models:
-
-```python
-llm = CTransformers(model='/path/to/ggml-gpt-2.bin', model_type='gpt2')
-
-print(llm('AI is going to'))
-```
-
-If you are getting `illegal instruction` error, try using `lib='avx'` or `lib='basic'`:
-
-```py
-llm = CTransformers(model='/path/to/ggml-gpt-2.bin', model_type='gpt2', lib='avx')
-```
-
-It can be used with models hosted on the Hugging Face Hub:
-
-```py
-llm = CTransformers(model='marella/gpt-2-ggml')
-```
-
-If a model repo has multiple model files (`.bin` files), specify a model file using:
-
-```py
-llm = CTransformers(model='marella/gpt-2-ggml', model_file='ggml-model.bin')
-```
-
-Additional parameters can be passed using the `config` parameter:
-
-```py
-config = {'max_new_tokens': 256, 'repetition_penalty': 1.1}
-
-llm = CTransformers(model='marella/gpt-2-ggml', config=config)
-```
-
-See [Documentation](https://github.com/marella/ctransformers#config) for a list of available parameters.
-
-For a more detailed walkthrough of this, see [this notebook](../modules/models/llms/integrations/ctransformers.ipynb).

docs/integrations/databerry.md

@@ -1,17 +0,0 @@
-# Databerry
-
->[Databerry](https://databerry.ai) is an [open source](https://github.com/gmpetrov/databerry) document retrieval platform that helps to connect your personal data with Large Language Models.
-
-
-## Installation and Setup
-
-We need to sign up for Databerry, create a datastore, add some data and get your datastore api endpoint url. 
-We need the [API Key](https://docs.databerry.ai/api-reference/authentication).
-
-## Retriever
-
-See a [usage example](../modules/indexes/retrievers/examples/databerry.ipynb).
-
-```python
-from langchain.retrievers import DataberryRetriever
-```

docs/integrations/databricks.ipynb

@@ -1,280 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# Databricks\n",
-    "\n",
-    "This notebook covers how to connect to the [Databricks runtimes](https://docs.databricks.com/runtime/index.html) and [Databricks SQL](https://www.databricks.com/product/databricks-sql) using the SQLDatabase wrapper of LangChain.\n",
-    "It is broken into 3 parts: installation and setup, connecting to Databricks, and examples."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Installation and Setup"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "outputs": [],
-   "source": [
-    "!pip install databricks-sql-connector"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Connecting to Databricks\n",
-    "\n",
-    "You can connect to [Databricks runtimes](https://docs.databricks.com/runtime/index.html) and [Databricks SQL](https://www.databricks.com/product/databricks-sql) using the `SQLDatabase.from_databricks()` method.\n",
-    "\n",
-    "### Syntax\n",
-    "```python\n",
-    "SQLDatabase.from_databricks(\n",
-    "    catalog: str,\n",
-    "    schema: str,\n",
-    "    host: Optional[str] = None,\n",
-    "    api_token: Optional[str] = None,\n",
-    "    warehouse_id: Optional[str] = None,\n",
-    "    cluster_id: Optional[str] = None,\n",
-    "    engine_args: Optional[dict] = None,\n",
-    "    **kwargs: Any)\n",
-    "```\n",
-    "### Required Parameters\n",
-    "* `catalog`: The catalog name in the Databricks database.\n",
-    "* `schema`: The schema name in the catalog.\n",
-    "\n",
-    "### Optional Parameters\n",
-    "There following parameters are optional. When executing the method in a Databricks notebook, you don't need to provide them in most of the cases.\n",
-    "* `host`: The Databricks workspace hostname, excluding 'https://' part. Defaults to 'DATABRICKS_HOST' environment variable or current workspace if in a Databricks notebook.\n",
-    "* `api_token`: The Databricks personal access token for accessing the Databricks SQL warehouse or the cluster. Defaults to 'DATABRICKS_API_TOKEN' environment variable or a temporary one is generated if in a Databricks notebook.\n",
-    "* `warehouse_id`: The warehouse ID in the Databricks SQL.\n",
-    "* `cluster_id`: The cluster ID in the Databricks Runtime. If running in a Databricks notebook and both 'warehouse_id' and 'cluster_id' are None, it uses the ID of the cluster the notebook is attached to.\n",
-    "* `engine_args`: The arguments to be used when connecting Databricks.\n",
-    "* `**kwargs`: Additional keyword arguments for the `SQLDatabase.from_uri` method."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## Examples"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "outputs": [],
-   "source": [
-    "# Connecting to Databricks with SQLDatabase wrapper\n",
-    "from langchain import SQLDatabase\n",
-    "\n",
-    "db = SQLDatabase.from_databricks(catalog='samples', schema='nyctaxi')"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "outputs": [],
-   "source": [
-    "# Creating a OpenAI Chat LLM wrapper\n",
-    "from langchain.chat_models import ChatOpenAI\n",
-    "\n",
-    "llm = ChatOpenAI(temperature=0, model_name=\"gpt-4\")"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### SQL Chain example\n",
-    "\n",
-    "This example demonstrates the use of the [SQL Chain](https://python.langchain.com/en/latest/modules/chains/examples/sqlite.html) for answering a question over a Databricks database."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "36f2270b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain import SQLDatabaseChain\n",
-    "\n",
-    "db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "4e2b5f25",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001B[1m> Entering new SQLDatabaseChain chain...\u001B[0m\n",
-      "What is the average duration of taxi rides that start between midnight and 6am?\n",
-      "SQLQuery:\u001B[32;1m\u001B[1;3mSELECT AVG(UNIX_TIMESTAMP(tpep_dropoff_datetime) - UNIX_TIMESTAMP(tpep_pickup_datetime)) as avg_duration\n",
-      "FROM trips\n",
-      "WHERE HOUR(tpep_pickup_datetime) >= 0 AND HOUR(tpep_pickup_datetime) < 6\u001B[0m\n",
-      "SQLResult: \u001B[33;1m\u001B[1;3m[(987.8122786304605,)]\u001B[0m\n",
-      "Answer:\u001B[32;1m\u001B[1;3mThe average duration of taxi rides that start between midnight and 6am is 987.81 seconds.\u001B[0m\n",
-      "\u001B[1m> Finished chain.\u001B[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "'The average duration of taxi rides that start between midnight and 6am is 987.81 seconds.'"
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "db_chain.run(\"What is the average duration of taxi rides that start between midnight and 6am?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### SQL Database Agent example\n",
-    "\n",
-    "This example demonstrates the use of the [SQL Database Agent](https://python.langchain.com/en/latest/modules/agents/toolkits/examples/sql_database.html) for answering questions over a Databricks database."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "9918e86a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain.agents import create_sql_agent\n",
-    "from langchain.agents.agent_toolkits import SQLDatabaseToolkit\n",
-    "\n",
-    "toolkit = SQLDatabaseToolkit(db=db, llm=llm)\n",
-    "agent = create_sql_agent(\n",
-    "    llm=llm,\n",
-    "    toolkit=toolkit,\n",
-    "    verbose=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c484a76e",
-   "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: list_tables_sql_db\n",
-      "Action Input: \u001B[0m\n",
-      "Observation: \u001B[38;5;200m\u001B[1;3mtrips\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3mI should check the schema of the trips table to see if it has the necessary columns for trip distance and duration.\n",
-      "Action: schema_sql_db\n",
-      "Action Input: trips\u001B[0m\n",
-      "Observation: \u001B[33;1m\u001B[1;3m\n",
-      "CREATE TABLE trips (\n",
-      "\ttpep_pickup_datetime TIMESTAMP, \n",
-      "\ttpep_dropoff_datetime TIMESTAMP, \n",
-      "\ttrip_distance FLOAT, \n",
-      "\tfare_amount FLOAT, \n",
-      "\tpickup_zip INT, \n",
-      "\tdropoff_zip INT\n",
-      ") USING DELTA\n",
-      "\n",
-      "/*\n",
-      "3 rows from trips table:\n",
-      "tpep_pickup_datetime\ttpep_dropoff_datetime\ttrip_distance\tfare_amount\tpickup_zip\tdropoff_zip\n",
-      "2016-02-14 16:52:13+00:00\t2016-02-14 17:16:04+00:00\t4.94\t19.0\t10282\t10171\n",
-      "2016-02-04 18:44:19+00:00\t2016-02-04 18:46:00+00:00\t0.28\t3.5\t10110\t10110\n",
-      "2016-02-17 17:13:57+00:00\t2016-02-17 17:17:55+00:00\t0.7\t5.0\t10103\t10023\n",
-      "*/\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3mThe trips table has the necessary columns for trip distance and duration. I will write a query to find the longest trip distance and its duration.\n",
-      "Action: query_checker_sql_db\n",
-      "Action Input: SELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001B[0m\n",
-      "Observation: \u001B[31;1m\u001B[1;3mSELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3mThe query is correct. I will now execute it to find the longest trip distance and its duration.\n",
-      "Action: query_sql_db\n",
-      "Action Input: SELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001B[0m\n",
-      "Observation: \u001B[36;1m\u001B[1;3m[(30.6, '0 00:43:31.000000000')]\u001B[0m\n",
-      "Thought:\u001B[32;1m\u001B[1;3mI now know the final answer.\n",
-      "Final Answer: The longest trip distance is 30.6 miles and it took 43 minutes and 31 seconds.\u001B[0m\n",
-      "\n",
-      "\u001B[1m> Finished chain.\u001B[0m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": "'The longest trip distance is 30.6 miles and it took 43 minutes and 31 seconds.'"
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "agent.run(\"What is the longest trip distance and how long did it take?\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

docs/integrations/deepinfra.md

@@ -1,25 +0,0 @@
-# DeepInfra
-
-This page covers how to use the DeepInfra ecosystem within LangChain.
-It is broken into two parts: installation and setup, and then references to specific DeepInfra wrappers.
-
-## Installation and Setup
-- Get your DeepInfra api key from this link [here](https://deepinfra.com/).
-- Get an DeepInfra api key and set it as an environment variable (`DEEPINFRA_API_TOKEN`)
-
-## Available Models
-
-DeepInfra provides a range of Open Source LLMs ready for deployment.
-You can list supported models [here](https://deepinfra.com/models?type=text-generation).
-google/flan\* models can be viewed [here](https://deepinfra.com/models?type=text2text-generation).
-
-You can view a list of request and response parameters [here](https://deepinfra.com/databricks/dolly-v2-12b#API)
-
-## Wrappers
-
-### LLM
-
-There exists an DeepInfra LLM wrapper, which you can access with
-```python
-from langchain.llms import DeepInfra
-```

docs/integrations/deeplake.md

@@ -1,30 +0,0 @@
-# Deep Lake
-This page covers how to use the Deep Lake ecosystem within LangChain.
-
-## Why Deep Lake?
-- More than just a (multi-modal) vector store. You can later use the dataset to fine-tune your own LLM models.
-- Not only stores embeddings, but also the original data with automatic version control.
-- Truly serverless. Doesn't require another service and can be used with major cloud providers (AWS S3, GCS, etc.)
-
-## More Resources
-1. [Ultimate Guide to LangChain & Deep Lake: Build ChatGPT to Answer Questions on Your Financial Data](https://www.activeloop.ai/resources/ultimate-guide-to-lang-chain-deep-lake-build-chat-gpt-to-answer-questions-on-your-financial-data/)
-2. [Twitter the-algorithm codebase analysis with Deep Lake](../use_cases/code/twitter-the-algorithm-analysis-deeplake.ipynb)
-3. Here is [whitepaper](https://www.deeplake.ai/whitepaper) and [academic paper](https://arxiv.org/pdf/2209.10785.pdf) for Deep Lake
-4. Here is a set of additional resources available for review: [Deep Lake](https://github.com/activeloopai/deeplake), [Getting Started](https://docs.activeloop.ai/getting-started) and [Tutorials](https://docs.activeloop.ai/hub-tutorials)
-
-## Installation and Setup
-- Install the Python package with `pip install deeplake`
-
-## Wrappers
-
-### VectorStore
-
-There exists a wrapper around Deep Lake, a data lake for Deep Learning applications, allowing you to use it as a vector store (for now), whether for semantic search or example selection.
-
-To import this vectorstore:
-```python
-from langchain.vectorstores import DeepLake
-```
-
-
-For a more detailed walkthrough of the Deep Lake wrapper, see [this notebook](../modules/indexes/vectorstores/examples/deeplake.ipynb)