forked from Archives/langchain
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Harrison Chase
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Harrison Chase
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Tim Asp
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Eugene Yurtsev
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@ -1,37 +0,0 @@
|
||||
# Dev container
|
||||
|
||||
This project includes a [dev container](https://containers.dev/), which lets you use a container as a full-featured dev environment.
|
||||
|
||||
You can use the dev container configuration in this folder to build and run the app without needing to install any of its tools locally! You can use it in [GitHub Codespaces](https://github.com/features/codespaces) or the [VS Code Dev Containers extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers).
|
||||
|
||||
## GitHub Codespaces
|
||||
[](https://codespaces.new/hwchase17/langchain)
|
||||
|
||||
You may use the button above, or follow these steps to open this repo in a Codespace:
|
||||
1. Click the **Code** drop-down menu at the top of https://github.com/hwchase17/langchain.
|
||||
1. Click on the **Codespaces** tab.
|
||||
1. Click **Create codespace on master** .
|
||||
|
||||
For more info, check out the [GitHub documentation](https://docs.github.com/en/free-pro-team@latest/github/developing-online-with-codespaces/creating-a-codespace#creating-a-codespace).
|
||||
|
||||
## VS Code Dev Containers
|
||||
[](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/hwchase17/langchain)
|
||||
|
||||
If you already have VS Code and Docker installed, you can use the button above to get started. This will cause VS Code to automatically install the Dev Containers extension if needed, clone the source code into a container volume, and spin up a dev container for use.
|
||||
|
||||
You can also follow these steps to open this repo in a container using the VS Code Dev Containers extension:
|
||||
|
||||
1. If this is your first time using a development container, please ensure your system meets the pre-reqs (i.e. have Docker installed) in the [getting started steps](https://aka.ms/vscode-remote/containers/getting-started).
|
||||
|
||||
2. Open a locally cloned copy of the code:
|
||||
|
||||
- Clone this repository to your local filesystem.
|
||||
- Press <kbd>F1</kbd> and select the **Dev Containers: Open Folder in Container...** command.
|
||||
- Select the cloned copy of this folder, wait for the container to start, and try things out!
|
||||
|
||||
You can learn more in the [Dev Containers documentation](https://code.visualstudio.com/docs/devcontainers/containers).
|
||||
|
||||
## Tips and tricks
|
||||
|
||||
* If you are working with the same repository folder in a container and Windows, you'll want consistent line endings (otherwise you may see hundreds of changes in the SCM view). The `.gitattributes` file in the root of this repo will disable line ending conversion and should prevent this. See [tips and tricks](https://code.visualstudio.com/docs/devcontainers/tips-and-tricks#_resolving-git-line-ending-issues-in-containers-resulting-in-many-modified-files) for more info.
|
||||
* If you'd like to review the contents of the image used in this dev container, you can check it out in the [devcontainers/images](https://github.com/devcontainers/images/tree/main/src/python) repo.
|
||||
@ -1,36 +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-docker-compose
|
||||
{
|
||||
// Name for the dev container
|
||||
"name": "langchain",
|
||||
|
||||
// Point to a Docker Compose file
|
||||
"dockerComposeFile": "./docker-compose.yaml",
|
||||
|
||||
// Required when using Docker Compose. The name of the service to connect to once running
|
||||
"service": "langchain",
|
||||
|
||||
// The optional 'workspaceFolder' property is the path VS Code should open by default when
|
||||
// connected. This is typically a file mount in .devcontainer/docker-compose.yml
|
||||
"workspaceFolder": "/workspaces/${localWorkspaceFolderBasename}",
|
||||
|
||||
// Prevent the container from shutting down
|
||||
"overrideCommand": true
|
||||
|
||||
// Features to add to the dev container. More info: https://containers.dev/features
|
||||
// "features": {
|
||||
// "ghcr.io/devcontainers-contrib/features/poetry:2": {}
|
||||
// }
|
||||
|
||||
// Use 'forwardPorts' to make a list of ports inside the container available locally.
|
||||
// "forwardPorts": [],
|
||||
|
||||
// Uncomment the next line to run commands after the container is created.
|
||||
// "postCreateCommand": "cat /etc/os-release",
|
||||
|
||||
// Configure tool-specific properties.
|
||||
// "customizations": {},
|
||||
|
||||
// Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
|
||||
// "remoteUser": "root"
|
||||
}
|
||||
@ -1,32 +0,0 @@
|
||||
version: '3'
|
||||
services:
|
||||
langchain:
|
||||
build:
|
||||
dockerfile: dev.Dockerfile
|
||||
context: ..
|
||||
volumes:
|
||||
# Update this to wherever you want VS Code to mount the folder of your project
|
||||
- ..:/workspaces:cached
|
||||
networks:
|
||||
- langchain-network
|
||||
# environment:
|
||||
# MONGO_ROOT_USERNAME: root
|
||||
# MONGO_ROOT_PASSWORD: example123
|
||||
# depends_on:
|
||||
# - mongo
|
||||
# mongo:
|
||||
# image: mongo
|
||||
# restart: unless-stopped
|
||||
# environment:
|
||||
# MONGO_INITDB_ROOT_USERNAME: root
|
||||
# MONGO_INITDB_ROOT_PASSWORD: example123
|
||||
# ports:
|
||||
# - "27017:27017"
|
||||
# networks:
|
||||
# - langchain-network
|
||||
|
||||
networks:
|
||||
langchain-network:
|
||||
driver: bridge
|
||||
|
||||
|
||||
@ -1,6 +1,144 @@
|
||||
.vscode/
|
||||
.idea/
|
||||
# Byte-compiled / optimized / DLL files
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
|
||||
# C extensions
|
||||
*.so
|
||||
|
||||
# Distribution / packaging
|
||||
.Python
|
||||
build/
|
||||
develop-eggs/
|
||||
dist/
|
||||
downloads/
|
||||
eggs/
|
||||
.eggs/
|
||||
lib/
|
||||
lib64/
|
||||
parts/
|
||||
sdist/
|
||||
var/
|
||||
wheels/
|
||||
pip-wheel-metadata/
|
||||
share/python-wheels/
|
||||
*.egg-info/
|
||||
.installed.cfg
|
||||
*.egg
|
||||
MANIFEST
|
||||
|
||||
# PyInstaller
|
||||
# Usually these files are written by a python script from a template
|
||||
# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
||||
*.manifest
|
||||
*.spec
|
||||
|
||||
# Installer logs
|
||||
pip-log.txt
|
||||
pip-delete-this-directory.txt
|
||||
|
||||
# Unit test / coverage reports
|
||||
htmlcov/
|
||||
.tox/
|
||||
.nox/
|
||||
.coverage
|
||||
.coverage.*
|
||||
.cache
|
||||
nosetests.xml
|
||||
coverage.xml
|
||||
*.cover
|
||||
*.py,cover
|
||||
.hypothesis/
|
||||
.pytest_cache/
|
||||
|
||||
# Translations
|
||||
*.mo
|
||||
*.pot
|
||||
|
||||
# Django stuff:
|
||||
*.log
|
||||
local_settings.py
|
||||
db.sqlite3
|
||||
db.sqlite3-journal
|
||||
|
||||
# Flask stuff:
|
||||
instance/
|
||||
.webassets-cache
|
||||
|
||||
# Scrapy stuff:
|
||||
.scrapy
|
||||
|
||||
# Sphinx documentation
|
||||
docs/_build/
|
||||
|
||||
# PyBuilder
|
||||
target/
|
||||
|
||||
# Jupyter Notebook
|
||||
.ipynb_checkpoints
|
||||
notebooks/
|
||||
|
||||
# IPython
|
||||
profile_default/
|
||||
ipython_config.py
|
||||
|
||||
# pyenv
|
||||
.python-version
|
||||
|
||||
# pipenv
|
||||
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
|
||||
# However, in case of collaboration, if having platform-specific dependencies or dependencies
|
||||
# having no cross-platform support, pipenv may install dependencies that don't work, or not
|
||||
# install all needed dependencies.
|
||||
#Pipfile.lock
|
||||
|
||||
# PEP 582; used by e.g. github.com/David-OConnor/pyflow
|
||||
__pypackages__/
|
||||
|
||||
# Celery stuff
|
||||
celerybeat-schedule
|
||||
celerybeat.pid
|
||||
|
||||
# SageMath parsed files
|
||||
*.sage.py
|
||||
|
||||
# Environments
|
||||
.env
|
||||
.venv
|
||||
.github
|
||||
.git
|
||||
.mypy_cache
|
||||
.pytest_cache
|
||||
Dockerfile
|
||||
.venvs
|
||||
env/
|
||||
venv/
|
||||
ENV/
|
||||
env.bak/
|
||||
venv.bak/
|
||||
|
||||
# Spyder project settings
|
||||
.spyderproject
|
||||
.spyproject
|
||||
|
||||
# Rope project settings
|
||||
.ropeproject
|
||||
|
||||
# mkdocs documentation
|
||||
/site
|
||||
|
||||
# mypy
|
||||
.mypy_cache/
|
||||
.dmypy.json
|
||||
dmypy.json
|
||||
|
||||
# Pyre type checker
|
||||
.pyre/
|
||||
|
||||
# macOS display setting files
|
||||
.DS_Store
|
||||
|
||||
|
||||
|
||||
# docker
|
||||
docker/
|
||||
!docker/assets/
|
||||
.dockerignore
|
||||
docker.build
|
||||
|
||||
@ -1,3 +0,0 @@
|
||||
* text=auto eol=lf
|
||||
*.{cmd,[cC][mM][dD]} text eol=crlf
|
||||
*.{bat,[bB][aA][tT]} text eol=crlf
|
||||
@ -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
|
||||
- ...
|
||||
|
||||
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."
|
||||
@ -1,6 +0,0 @@
|
||||
blank_issues_enabled: true
|
||||
version: 2.1
|
||||
contact_links:
|
||||
- name: Discord
|
||||
url: https://discord.gg/6adMQxSpJS
|
||||
about: General community discussions
|
||||
@ -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.
|
||||
@ -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)
|
||||
@ -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.
|
||||
@ -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
|
||||
- @hwchase17
|
||||
|
||||
VectorStores / Retrievers / Memory
|
||||
- @dev2049
|
||||
|
||||
-->
|
||||
@ -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: ${{ inputs.working-directory }}
|
||||
shell: bash
|
||||
@ -0,0 +1,36 @@
|
||||
name: linkcheck
|
||||
|
||||
on:
|
||||
push:
|
||||
branches: [master]
|
||||
pull_request:
|
||||
|
||||
env:
|
||||
POETRY_VERSION: "1.3.1"
|
||||
|
||||
jobs:
|
||||
build:
|
||||
runs-on: ubuntu-latest
|
||||
strategy:
|
||||
matrix:
|
||||
python-version:
|
||||
- "3.11"
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
- name: Install poetry
|
||||
run: |
|
||||
pipx install poetry==$POETRY_VERSION
|
||||
- name: Set up Python ${{ matrix.python-version }}
|
||||
uses: actions/setup-python@v4
|
||||
with:
|
||||
python-version: ${{ matrix.python-version }}
|
||||
cache: poetry
|
||||
- name: Install dependencies
|
||||
run: |
|
||||
poetry install --with docs
|
||||
- name: Build the docs
|
||||
run: |
|
||||
make docs_build
|
||||
- name: Analyzing the docs with linkcheck
|
||||
run: |
|
||||
make docs_linkcheck
|
||||
@ -1,4 +0,0 @@
|
||||
[submodule "docs/_docs_skeleton"]
|
||||
path = docs/_docs_skeleton
|
||||
url = https://github.com/langchain-ai/langchain-shared-docs
|
||||
branch = main
|
||||
@ -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/api_reference/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: .
|
||||
@ -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 $POETRY_HOME/bin/poetry install --no-interaction --no-ansi --with test
|
||||
|
||||
# Use a multi-stage build to run tests
|
||||
FROM dependencies AS tests
|
||||
|
||||
# Copy the rest of the app source code (this layer will be invalidated and rebuilt whenever the source code changes)
|
||||
COPY . .
|
||||
|
||||
RUN /opt/poetry/bin/poetry install --no-interaction --no-ansi --with test
|
||||
|
||||
# Set the entrypoint to run tests using Poetry
|
||||
ENTRYPOINT ["/opt/poetry/bin/poetry", "run", "pytest"]
|
||||
|
||||
# Set the default command to run all unit tests
|
||||
CMD ["tests/unit_tests"]
|
||||
@ -1,41 +0,0 @@
|
||||
# This is a Dockerfile for the Development Container
|
||||
|
||||
# Use the Python base image
|
||||
ARG VARIANT="3.11-bullseye"
|
||||
FROM mcr.microsoft.com/devcontainers/python:0-${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=1.3.2
|
||||
|
||||
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:$PATH" >> ~/.bashrc
|
||||
|
||||
# Set the working directory for the app
|
||||
WORKDIR /workspaces/langchain
|
||||
|
||||
# Use a multi-stage build to install dependencies
|
||||
FROM langchain-dev-base AS langchain-dev-dependencies
|
||||
|
||||
ARG PYTHON_VIRTUALENV_HOME
|
||||
|
||||
# Copy only the dependency files for installation
|
||||
COPY pyproject.toml poetry.toml ./
|
||||
|
||||
# Install the Poetry dependencies (this layer will be cached as long as the dependencies don't change)
|
||||
RUN poetry install --no-interaction --no-ansi --with dev,test,docs
|
||||
@ -0,0 +1,13 @@
|
||||
# python env
|
||||
PYTHON_VERSION=3.10
|
||||
|
||||
# -E flag is required
|
||||
# comment the following line to only install dev dependencies
|
||||
POETRY_EXTRA_PACKAGES="-E all"
|
||||
|
||||
# at least one group needed
|
||||
POETRY_DEPENDENCIES="dev,test,lint,typing"
|
||||
|
||||
# langchain env. warning: these variables will be baked into the docker image !
|
||||
OPENAI_API_KEY=${OPENAI_API_KEY:-}
|
||||
SERPAPI_API_KEY=${SERPAPI_API_KEY:-}
|
||||
@ -0,0 +1,53 @@
|
||||
# Using Docker
|
||||
|
||||
To quickly get started, run the command `make docker`.
|
||||
|
||||
If docker is installed the Makefile will export extra targets in the fomrat `docker.*` to build and run the docker image. Type `make` for a list of available tasks.
|
||||
|
||||
There is a basic `docker-compose.yml` in the docker directory.
|
||||
|
||||
## Building the development image
|
||||
|
||||
Using `make docker` will build the dev image if it does not exist, then drops
|
||||
you inside the container with the langchain environment available in the shell.
|
||||
|
||||
### Customizing the image and installed dependencies
|
||||
|
||||
The image is built with a default python version and all extras and dev
|
||||
dependencies. It can be customized by changing the variables in the [.env](/docker/.env)
|
||||
file.
|
||||
|
||||
If you don't need all the `extra` dependencies a slimmer image can be obtained by
|
||||
commenting out `POETRY_EXTRA_PACKAGES` in the [.env](docker/.env) file.
|
||||
|
||||
### Image caching
|
||||
|
||||
The Dockerfile is optimized to cache the poetry install step. A rebuild is triggered when there a change to the source code.
|
||||
|
||||
## Example Usage
|
||||
|
||||
All commands from langchain's python environment are available by default in the container.
|
||||
|
||||
A few examples:
|
||||
```bash
|
||||
# run jupyter notebook
|
||||
docker run --rm -it IMG jupyter notebook
|
||||
|
||||
# run ipython
|
||||
docker run --rm -it IMG ipython
|
||||
|
||||
# start web server
|
||||
docker run --rm -p 8888:8888 IMG python -m http.server 8888
|
||||
```
|
||||
|
||||
## Testing / Linting
|
||||
|
||||
Tests and lints are run using your local source directory that is mounted on the volume /src.
|
||||
|
||||
Run unit tests in the container with `make docker.test`.
|
||||
|
||||
Run the linting and formatting checks with `make docker.lint`.
|
||||
|
||||
Note: this task can run in parallel using `make -j4 docker.lint`.
|
||||
|
||||
|
||||
@ -0,0 +1,104 @@
|
||||
# vim: ft=dockerfile
|
||||
#
|
||||
# see also: https://github.com/python-poetry/poetry/discussions/1879
|
||||
# - with https://github.com/bneijt/poetry-lock-docker
|
||||
# see https://github.com/thehale/docker-python-poetry
|
||||
# see https://github.com/max-pfeiffer/uvicorn-poetry
|
||||
|
||||
# use by default the slim version of python
|
||||
ARG PYTHON_IMAGE_TAG=slim
|
||||
ARG PYTHON_VERSION=${PYTHON_VERSION:-3.11.2}
|
||||
|
||||
####################
|
||||
# Base Environment
|
||||
####################
|
||||
FROM python:$PYTHON_VERSION-$PYTHON_IMAGE_TAG AS lchain-base
|
||||
|
||||
ARG UID=1000
|
||||
ARG USERNAME=lchain
|
||||
|
||||
ENV USERNAME=$USERNAME
|
||||
|
||||
RUN groupadd -g ${UID} $USERNAME
|
||||
RUN useradd -l -m -u ${UID} -g ${UID} $USERNAME
|
||||
|
||||
# used for mounting source code
|
||||
RUN mkdir /src
|
||||
VOLUME /src
|
||||
|
||||
|
||||
#######################
|
||||
## Poetry Builder Image
|
||||
#######################
|
||||
FROM lchain-base AS lchain-base-builder
|
||||
|
||||
ARG POETRY_EXTRA_PACKAGES=$POETRY_EXTRA_PACKAGES
|
||||
ARG POETRY_DEPENDENCIES=$POETRY_DEPENDENCIES
|
||||
|
||||
ENV HOME=/root
|
||||
ENV POETRY_HOME=/root/.poetry
|
||||
ENV POETRY_VIRTUALENVS_IN_PROJECT=false
|
||||
ENV POETRY_NO_INTERACTION=1
|
||||
ENV CACHE_DIR=$HOME/.cache
|
||||
ENV POETRY_CACHE_DIR=$CACHE_DIR/pypoetry
|
||||
ENV PATH="$POETRY_HOME/bin:$PATH"
|
||||
|
||||
WORKDIR /root
|
||||
|
||||
RUN apt-get update && \
|
||||
apt-get install -y \
|
||||
build-essential \
|
||||
git \
|
||||
curl
|
||||
|
||||
SHELL ["/bin/bash", "-o", "pipefail", "-c"]
|
||||
|
||||
RUN mkdir -p $CACHE_DIR
|
||||
|
||||
## setup poetry
|
||||
RUN curl -sSL -o $CACHE_DIR/pypoetry-installer.py https://install.python-poetry.org/
|
||||
RUN python3 $CACHE_DIR/pypoetry-installer.py
|
||||
|
||||
|
||||
# # Copy poetry files
|
||||
COPY poetry.* pyproject.toml ./
|
||||
|
||||
RUN mkdir /pip-prefix
|
||||
|
||||
RUN poetry export $POETRY_EXTRA_PACKAGES --with $POETRY_DEPENDENCIES -f requirements.txt --output requirements.txt --without-hashes && \
|
||||
pip install --no-cache-dir --disable-pip-version-check --prefix /pip-prefix -r requirements.txt
|
||||
|
||||
|
||||
# add custom motd message
|
||||
COPY docker/assets/etc/motd /tmp/motd
|
||||
RUN cat /tmp/motd > /etc/motd
|
||||
|
||||
RUN printf "\n%s\n%s\n" "$(poetry version)" "$(python --version)" >> /etc/motd
|
||||
|
||||
###################
|
||||
## Runtime Image
|
||||
###################
|
||||
FROM lchain-base AS lchain
|
||||
|
||||
#jupyter port
|
||||
EXPOSE 8888
|
||||
|
||||
COPY docker/assets/entry.sh /entry
|
||||
RUN chmod +x /entry
|
||||
|
||||
COPY --from=lchain-base-builder /etc/motd /etc/motd
|
||||
COPY --from=lchain-base-builder /usr/bin/git /usr/bin/git
|
||||
|
||||
USER ${USERNAME:-lchain}
|
||||
ENV HOME /home/$USERNAME
|
||||
WORKDIR /home/$USERNAME
|
||||
|
||||
COPY --chown=lchain:lchain --from=lchain-base-builder /pip-prefix $HOME/.local/
|
||||
|
||||
COPY . .
|
||||
|
||||
SHELL ["/bin/bash", "-o", "pipefail", "-c"]
|
||||
RUN pip install --no-deps --disable-pip-version-check --no-cache-dir -e .
|
||||
|
||||
|
||||
entrypoint ["/entry"]
|
||||
@ -0,0 +1,84 @@
|
||||
#do not call this makefile it is included in the main Makefile
|
||||
.PHONY: docker docker.jupyter docker.run docker.force_build docker.clean \
|
||||
docker.test docker.lint docker.lint.mypy docker.lint.black \
|
||||
docker.lint.isort docker.lint.flake
|
||||
|
||||
# read python version from .env file ignoring comments
|
||||
PYTHON_VERSION := $(shell grep PYTHON_VERSION docker/.env | cut -d '=' -f2)
|
||||
POETRY_EXTRA_PACKAGES := $(shell grep '^[^#]*POETRY_EXTRA_PACKAGES' docker/.env | cut -d '=' -f2)
|
||||
POETRY_DEPENDENCIES := $(shell grep 'POETRY_DEPENDENCIES' docker/.env | cut -d '=' -f2)
|
||||
|
||||
|
||||
DOCKER_SRC := $(shell find docker -type f)
|
||||
DOCKER_IMAGE_NAME = langchain/dev
|
||||
|
||||
# SRC is all files matched by the git ls-files command
|
||||
SRC := $(shell git ls-files -- '*' ':!:docker/*')
|
||||
|
||||
# set DOCKER_BUILD_PROGRESS=plain to see detailed build progress
|
||||
DOCKER_BUILD_PROGRESS ?= auto
|
||||
|
||||
# extra message to show when entering the docker container
|
||||
DOCKER_MOTD := docker/assets/etc/motd
|
||||
|
||||
ROOTDIR := $(shell git rev-parse --show-toplevel)
|
||||
|
||||
DOCKER_LINT_CMD = docker run --rm -i -u lchain -v $(ROOTDIR):/src $(DOCKER_IMAGE_NAME):$(GIT_HASH)
|
||||
|
||||
docker: docker.run
|
||||
|
||||
docker.run: docker.build
|
||||
@echo "Docker image: $(DOCKER_IMAGE_NAME):$(GIT_HASH)"
|
||||
docker run --rm -it -u lchain -v $(ROOTDIR):/src $(DOCKER_IMAGE_NAME):$(GIT_HASH)
|
||||
|
||||
docker.jupyter: docker.build
|
||||
docker run --rm -it -v $(ROOTDIR):/src $(DOCKER_IMAGE_NAME):$(GIT_HASH) jupyter notebook
|
||||
|
||||
docker.build: $(SRC) $(DOCKER_SRC) $(DOCKER_MOTD)
|
||||
ifdef $(DOCKER_BUILDKIT)
|
||||
docker buildx build --build-arg PYTHON_VERSION=$(PYTHON_VERSION) \
|
||||
--build-arg POETRY_EXTRA_PACKAGES=$(POETRY_EXTRA_PACKAGES) \
|
||||
--build-arg POETRY_DEPENDENCIES=$(POETRY_DEPENDENCIES) \
|
||||
--progress=$(DOCKER_BUILD_PROGRESS) \
|
||||
$(BUILD_FLAGS) -f docker/Dockerfile -t $(DOCKER_IMAGE_NAME):$(GIT_HASH) .
|
||||
else
|
||||
docker build --build-arg PYTHON_VERSION=$(PYTHON_VERSION) \
|
||||
--build-arg POETRY_EXTRA_PACKAGES=$(POETRY_EXTRA_PACKAGES) \
|
||||
--build-arg POETRY_DEPENDENCIES=$(POETRY_DEPENDENCIES) \
|
||||
$(BUILD_FLAGS) -f docker/Dockerfile -t $(DOCKER_IMAGE_NAME):$(GIT_HASH) .
|
||||
endif
|
||||
docker tag $(DOCKER_IMAGE_NAME):$(GIT_HASH) $(DOCKER_IMAGE_NAME):latest
|
||||
@touch $@ # this prevents docker from rebuilding dependencies that have not
|
||||
@ # changed. Remove the file `docker/docker.build` to force a rebuild.
|
||||
|
||||
docker.force_build: $(DOCKER_SRC)
|
||||
@rm -f docker.build
|
||||
@$(MAKE) docker.build BUILD_FLAGS=--no-cache
|
||||
|
||||
docker.clean:
|
||||
docker rmi $(DOCKER_IMAGE_NAME):$(GIT_HASH) $(DOCKER_IMAGE_NAME):latest
|
||||
|
||||
docker.test: docker.build
|
||||
docker run --rm -it -u lchain -v $(ROOTDIR):/src $(DOCKER_IMAGE_NAME):$(GIT_HASH) \
|
||||
pytest /src/tests/unit_tests
|
||||
|
||||
# this assumes that the docker image has been built
|
||||
docker.lint: docker.lint.mypy docker.lint.black docker.lint.isort \
|
||||
docker.lint.flake
|
||||
|
||||
# these can run in parallel with -j[njobs]
|
||||
docker.lint.mypy:
|
||||
@$(DOCKER_LINT_CMD) mypy /src
|
||||
@printf "\t%s\n" "mypy ... "
|
||||
|
||||
docker.lint.black:
|
||||
@$(DOCKER_LINT_CMD) black /src --check
|
||||
@printf "\t%s\n" "black ... "
|
||||
|
||||
docker.lint.isort:
|
||||
@$(DOCKER_LINT_CMD) isort /src --check
|
||||
@printf "\t%s\n" "isort ... "
|
||||
|
||||
docker.lint.flake:
|
||||
@$(DOCKER_LINT_CMD) flake8 /src
|
||||
@printf "\t%s\n" "flake8 ... "
|
||||
@ -0,0 +1,10 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
export PATH=$HOME/.local/bin:$PATH
|
||||
|
||||
if [ -z "$1" ]; then
|
||||
cat /etc/motd
|
||||
exec /bin/bash
|
||||
fi
|
||||
|
||||
exec "$@"
|
||||
@ -0,0 +1,8 @@
|
||||
All dependencies have been installed in the current shell. There is no
|
||||
virtualenv or a need for `poetry` inside the container.
|
||||
|
||||
Running the command `make docker.run` at the root directory of the project will
|
||||
build the container the first time. On the next runs it will use the cached
|
||||
image. A rebuild will happen when changes are made to the source code.
|
||||
|
||||
You local source directory has been mounted to the /src directory.
|
||||
@ -0,0 +1,17 @@
|
||||
version: "3.7"
|
||||
|
||||
services:
|
||||
langchain:
|
||||
hostname: langchain
|
||||
image: langchain/dev:latest
|
||||
build:
|
||||
context: ../
|
||||
dockerfile: docker/Dockerfile
|
||||
args:
|
||||
PYTHON_VERSION: ${PYTHON_VERSION}
|
||||
POETRY_EXTRA_PACKAGES: ${POETRY_EXTRA_PACKAGES}
|
||||
POETRY_DEPENDENCIES: ${POETRY_DEPENDENCIES}
|
||||
|
||||
restart: unless-stopped
|
||||
ports:
|
||||
- 127.0.0.1:8888:8888
|
||||
@ -1,12 +0,0 @@
|
||||
mkdir _dist
|
||||
cp -r {docs_skeleton,snippets} _dist
|
||||
mkdir -p _dist/docs_skeleton/static/api_reference
|
||||
cd api_reference
|
||||
poetry run make html
|
||||
cp -r _build/* ../_dist/docs_skeleton/static/api_reference
|
||||
cd ..
|
||||
cp -r extras/* _dist/docs_skeleton/docs
|
||||
cd _dist/docs_skeleton
|
||||
poetry run nbdoc_build
|
||||
yarn install
|
||||
yarn start
|
||||
|
Before Width: | Height: | Size: 235 KiB After Width: | Height: | Size: 235 KiB |
|
Before Width: | Height: | Size: 148 KiB After Width: | Height: | Size: 148 KiB |
@ -1,57 +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
|
||||
cmdShortcutKey:'j',
|
||||
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);
|
||||
});
|
||||
});
|
||||
});
|
||||
@ -1,12 +0,0 @@
|
||||
Agents
|
||||
==============
|
||||
|
||||
Reference guide for Agents and associated abstractions.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:glob:
|
||||
|
||||
modules/agents
|
||||
modules/tools
|
||||
modules/agent_toolkits
|
||||
@ -1,13 +0,0 @@
|
||||
Data connection
|
||||
==============
|
||||
LangChain has a number of modules that help you load, structure, store, and retrieve documents.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:glob:
|
||||
|
||||
modules/document_loaders
|
||||
modules/document_transformers
|
||||
modules/embeddings
|
||||
modules/vectorstores
|
||||
modules/retrievers
|
||||
@ -1,29 +0,0 @@
|
||||
API Reference
|
||||
==========================
|
||||
|
||||
| Full documentation on all methods, classes, and APIs in the LangChain Python package.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:caption: Abstractions
|
||||
|
||||
./modules/base_classes.rst
|
||||
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:caption: Core
|
||||
|
||||
./model_io.rst
|
||||
./data_connection.rst
|
||||
./modules/chains.rst
|
||||
./agents.rst
|
||||
./modules/memory.rst
|
||||
./modules/callbacks.rst
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:caption: Additional
|
||||
|
||||
./modules/utilities.rst
|
||||
./modules/experimental.rst
|
||||
@ -1,12 +0,0 @@
|
||||
Model I/O
|
||||
==============
|
||||
|
||||
LangChain provides interfaces and integrations for working with language models.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:glob:
|
||||
|
||||
./prompts.rst
|
||||
./models.rst
|
||||
./modules/output_parsers.rst
|
||||
@ -1,11 +0,0 @@
|
||||
Models
|
||||
==============
|
||||
|
||||
LangChain provides interfaces and integrations for a number of different types of models.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:glob:
|
||||
|
||||
modules/llms
|
||||
modules/chat_models
|
||||
@ -1,7 +0,0 @@
|
||||
Agent Toolkits
|
||||
===============================
|
||||
|
||||
.. automodule:: langchain.agents.agent_toolkits
|
||||
:members:
|
||||
:undoc-members:
|
||||
|
||||
@ -1,5 +0,0 @@
|
||||
Base classes
|
||||
========================
|
||||
|
||||
.. automodule:: langchain.schema
|
||||
:inherited-members:
|
||||
@ -1,7 +0,0 @@
|
||||
Callbacks
|
||||
=======================
|
||||
|
||||
.. automodule:: langchain.callbacks
|
||||
:members:
|
||||
:undoc-members:
|
||||
|
||||
@ -1,7 +0,0 @@
|
||||
Chat Models
|
||||
===============================
|
||||
|
||||
.. automodule:: langchain.chat_models
|
||||
:members:
|
||||
:undoc-members:
|
||||
|
||||
@ -1,7 +0,0 @@
|
||||
Document Loaders
|
||||
===============================
|
||||
|
||||
.. automodule:: langchain.document_loaders
|
||||
:members:
|
||||
:undoc-members:
|
||||
|
||||
@ -1,13 +0,0 @@
|
||||
Document Transformers
|
||||
===============================
|
||||
|
||||
.. automodule:: langchain.document_transformers
|
||||
:members:
|
||||
:undoc-members:
|
||||
|
||||
Text Splitters
|
||||
------------------------------
|
||||
|
||||
.. automodule:: langchain.text_splitter
|
||||
:members:
|
||||
:undoc-members:
|
||||
@ -1,28 +0,0 @@
|
||||
====================
|
||||
Experimental
|
||||
====================
|
||||
|
||||
This module contains experimental modules and reproductions of existing work using LangChain primitives.
|
||||
|
||||
Autonomous agents
|
||||
------------------
|
||||
|
||||
Here, we document the BabyAGI and AutoGPT classes from the langchain.experimental module.
|
||||
|
||||
.. autoclass:: langchain.experimental.BabyAGI
|
||||
:members:
|
||||
|
||||
.. autoclass:: langchain.experimental.AutoGPT
|
||||
:members:
|
||||
|
||||
|
||||
Generative agents
|
||||
------------------
|
||||
|
||||
Here, we document the GenerativeAgent and GenerativeAgentMemory classes from the langchain.experimental module.
|
||||
|
||||
.. autoclass:: langchain.experimental.GenerativeAgent
|
||||
:members:
|
||||
|
||||
.. autoclass:: langchain.experimental.GenerativeAgentMemory
|
||||
:members:
|
||||
@ -1,7 +0,0 @@
|
||||
Memory
|
||||
===============================
|
||||
|
||||
.. automodule:: langchain.memory
|
||||
:members:
|
||||
:undoc-members:
|
||||
|
||||
@ -1,7 +0,0 @@
|
||||
Output Parsers
|
||||
===============================
|
||||
|
||||
.. automodule:: langchain.output_parsers
|
||||
:members:
|
||||
:undoc-members:
|
||||
|
||||
@ -1,14 +0,0 @@
|
||||
Retrievers
|
||||
===============================
|
||||
|
||||
.. automodule:: langchain.retrievers
|
||||
:members:
|
||||
:undoc-members:
|
||||
|
||||
Document compressors
|
||||
-------------------------------
|
||||
|
||||
.. automodule:: langchain.retrievers.document_compressors
|
||||
:members:
|
||||
:undoc-members:
|
||||
|
||||
@ -1,7 +0,0 @@
|
||||
Tools
|
||||
===============================
|
||||
|
||||
.. automodule:: langchain.tools
|
||||
:members:
|
||||
:undoc-members:
|
||||
|
||||
@ -1,7 +0,0 @@
|
||||
Utilities
|
||||
===============================
|
||||
|
||||
.. automodule:: langchain.utilities
|
||||
:members:
|
||||
:undoc-members:
|
||||
|
||||
@ -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.
|
||||
@ -1,7 +0,0 @@
|
||||
.yarn/
|
||||
|
||||
node_modules/
|
||||
|
||||
.docusaurus
|
||||
.cache-loader
|
||||
docs/api
|
||||
@ -1,49 +0,0 @@
|
||||
# Website
|
||||
|
||||
This website is built using [Docusaurus 2](https://docusaurus.io/), a modern static website generator.
|
||||
|
||||
### Installation
|
||||
|
||||
```
|
||||
$ yarn
|
||||
```
|
||||
|
||||
### Local Development
|
||||
|
||||
```
|
||||
$ yarn start
|
||||
```
|
||||
|
||||
This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server.
|
||||
|
||||
### Build
|
||||
|
||||
```
|
||||
$ yarn build
|
||||
```
|
||||
|
||||
This command generates static content into the `build` directory and can be served using any static contents hosting service.
|
||||
|
||||
### Deployment
|
||||
|
||||
Using SSH:
|
||||
|
||||
```
|
||||
$ USE_SSH=true yarn deploy
|
||||
```
|
||||
|
||||
Not using SSH:
|
||||
|
||||
```
|
||||
$ GIT_USER=<Your GitHub username> yarn deploy
|
||||
```
|
||||
|
||||
If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch.
|
||||
|
||||
### Continuous Integration
|
||||
|
||||
Some common defaults for linting/formatting have been set for you. If you integrate your project with an open source Continuous Integration system (e.g. Travis CI, CircleCI), you may check for issues using the following command.
|
||||
|
||||
```
|
||||
$ yarn ci
|
||||
```
|
||||
@ -1,12 +0,0 @@
|
||||
/**
|
||||
* Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
*
|
||||
* This source code is licensed under the MIT license found in the
|
||||
* LICENSE file in the root directory of this source tree.
|
||||
*
|
||||
* @format
|
||||
*/
|
||||
|
||||
module.exports = {
|
||||
presets: [require.resolve("@docusaurus/core/lib/babel/preset")],
|
||||
};
|
||||
@ -1,76 +0,0 @@
|
||||
/* eslint-disable prefer-template */
|
||||
/* eslint-disable no-param-reassign */
|
||||
// eslint-disable-next-line import/no-extraneous-dependencies
|
||||
const babel = require("@babel/core");
|
||||
const path = require("path");
|
||||
const fs = require("fs");
|
||||
|
||||
/**
|
||||
*
|
||||
* @param {string|Buffer} content Content of the resource file
|
||||
* @param {object} [map] SourceMap data consumable by https://github.com/mozilla/source-map
|
||||
* @param {any} [meta] Meta data, could be anything
|
||||
*/
|
||||
async function webpackLoader(content, map, meta) {
|
||||
const cb = this.async();
|
||||
|
||||
if (!this.resourcePath.endsWith(".ts")) {
|
||||
cb(null, JSON.stringify({ content, imports: [] }), map, meta);
|
||||
return;
|
||||
}
|
||||
|
||||
try {
|
||||
const result = await babel.parseAsync(content, {
|
||||
sourceType: "module",
|
||||
filename: this.resourcePath,
|
||||
});
|
||||
|
||||
const imports = [];
|
||||
|
||||
result.program.body.forEach((node) => {
|
||||
if (node.type === "ImportDeclaration") {
|
||||
const source = node.source.value;
|
||||
|
||||
if (!source.startsWith("langchain")) {
|
||||
return;
|
||||
}
|
||||
|
||||
node.specifiers.forEach((specifier) => {
|
||||
if (specifier.type === "ImportSpecifier") {
|
||||
const local = specifier.local.name;
|
||||
const imported = specifier.imported.name;
|
||||
imports.push({ local, imported, source });
|
||||
} else {
|
||||
throw new Error("Unsupported import type");
|
||||
}
|
||||
});
|
||||
}
|
||||
});
|
||||
|
||||
imports.forEach((imp) => {
|
||||
const { imported, source } = imp;
|
||||
const moduleName = source.split("/").slice(1).join("_");
|
||||
const docsPath = path.resolve(__dirname, "docs", "api", moduleName);
|
||||
const available = fs.readdirSync(docsPath, { withFileTypes: true });
|
||||
const found = available.find(
|
||||
(dirent) =>
|
||||
dirent.isDirectory() &&
|
||||
fs.existsSync(path.resolve(docsPath, dirent.name, imported + ".md"))
|
||||
);
|
||||
if (found) {
|
||||
imp.docs =
|
||||
"/" + path.join("docs", "api", moduleName, found.name, imported);
|
||||
} else {
|
||||
throw new Error(
|
||||
`Could not find docs for ${source}.${imported} in docs/api/`
|
||||
);
|
||||
}
|
||||
});
|
||||
|
||||
cb(null, JSON.stringify({ content, imports }), map, meta);
|
||||
} catch (err) {
|
||||
cb(err);
|
||||
}
|
||||
}
|
||||
|
||||
module.exports = webpackLoader;
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 559 KiB |
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|
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|
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@ -1,21 +0,0 @@
|
||||
pre {
|
||||
white-space: break-spaces;
|
||||
}
|
||||
|
||||
@media (min-width: 1200px) {
|
||||
.container,
|
||||
.container-lg,
|
||||
.container-md,
|
||||
.container-sm,
|
||||
.container-xl {
|
||||
max-width: 2560px !important;
|
||||
}
|
||||
}
|
||||
|
||||
#my-component-root *, #headlessui-portal-root * {
|
||||
z-index: 10000;
|
||||
}
|
||||
|
||||
.content-container p {
|
||||
margin: revert;
|
||||
}
|
||||
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|
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|
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|
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@ -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);
|
||||
});
|
||||
});
|
||||
});
|
||||
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|
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|
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|
Before Width: | Height: | Size: 34 KiB |
@ -1,8 +0,0 @@
|
||||
---
|
||||
sidebar_position: 0
|
||||
---
|
||||
# Integrations
|
||||
|
||||
import DocCardList from "@theme/DocCardList";
|
||||
|
||||
<DocCardList />
|
||||
@ -1,5 +0,0 @@
|
||||
# Installation
|
||||
|
||||
import Installation from "@snippets/get_started/installation.mdx"
|
||||
|
||||
<Installation/>
|
||||
@ -1,158 +0,0 @@
|
||||
# Quickstart
|
||||
|
||||
## Installation
|
||||
|
||||
To install LangChain run:
|
||||
|
||||
import Tabs from '@theme/Tabs';
|
||||
import TabItem from '@theme/TabItem';
|
||||
import Install from "@snippets/get_started/quickstart/installation.mdx"
|
||||
|
||||
<Install/>
|
||||
|
||||
For more details, see our [Installation guide](/docs/get_started/installation.html).
|
||||
|
||||
## Environment setup
|
||||
|
||||
Using LangChain will usually require integrations with one or more model providers, data stores, APIs, etc. For this example, we'll use OpenAI's model APIs.
|
||||
|
||||
import OpenAISetup from "@snippets/get_started/quickstart/openai_setup.mdx"
|
||||
|
||||
<OpenAISetup/>
|
||||
|
||||
## Building an application
|
||||
|
||||
Now we can start building our language model application. LangChain provides many modules that can be used to build language model applications. Modules can be used as stand-alones in simple applications and they can be combined for more complex use cases.
|
||||
|
||||
## LLMs
|
||||
#### Get predictions from a language model
|
||||
|
||||
The basic building block of LangChain is the LLM, which takes in text and generates more text.
|
||||
|
||||
As an example, suppose we're building an application that generates a company name based on a company description. In order to do this, we need to initialize an OpenAI model wrapper. In this case, since we want the outputs to be MORE random, we'll initialize our model with a HIGH temperature.
|
||||
|
||||
import LLM from "@snippets/get_started/quickstart/llm.mdx"
|
||||
|
||||
<LLM/>
|
||||
|
||||
## Chat models
|
||||
|
||||
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.
|
||||
|
||||
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`.
|
||||
|
||||
import ChatModel from "@snippets/get_started/quickstart/chat_model.mdx"
|
||||
|
||||
<ChatModel/>
|
||||
|
||||
## Prompt templates
|
||||
|
||||
Most LLM applications do not pass user input directly into to an LLM. Usually they will add the user input to a larger piece of text, called a prompt template, that provides additional context on the specific task at hand.
|
||||
|
||||
In the previous example, the text we passed to the model contained instructions to generate a company name. For our application, it'd be great if the user only had to provide the description of a company/product, without having to worry about giving the model instructions.
|
||||
|
||||
import PromptTemplateLLM from "@snippets/get_started/quickstart/prompt_templates_llms.mdx"
|
||||
import PromptTemplateChatModel from "@snippets/get_started/quickstart/prompt_templates_chat_models.mdx"
|
||||
|
||||
<Tabs>
|
||||
<TabItem value="llms" label="LLMs" default>
|
||||
|
||||
With PromptTemplates this is easy! In this case our template would be very simple:
|
||||
|
||||
<PromptTemplateLLM/>
|
||||
</TabItem>
|
||||
<TabItem value="chat_models" label="Chat models">
|
||||
|
||||
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_messages` method to generate the formatted messages.
|
||||
|
||||
Because this is generating a list of messages, it is slightly more complex than the normal prompt template which is generating only a string. Please see the detailed guides on prompts to understand more options available to you here.
|
||||
|
||||
<PromptTemplateChatModel/>
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
## Chains
|
||||
|
||||
Now that we've got a model and a prompt template, we'll want to combine the two. Chains give us a way to link (or chain) together multiple primitives, like models, prompts, and other chains.
|
||||
|
||||
import ChainLLM from "@snippets/get_started/quickstart/chains_llms.mdx"
|
||||
import ChainChatModel from "@snippets/get_started/quickstart/chains_chat_models.mdx"
|
||||
|
||||
<Tabs>
|
||||
<TabItem value="llms" label="LLMs" default>
|
||||
|
||||
The simplest and most common type of chain is an LLMChain, which passes an input first to a PromptTemplate and then to an LLM. We can construct an LLM chain from our existing model and prompt template.
|
||||
|
||||
<ChainLLM/>
|
||||
|
||||
There we go, our first chain! Understanding how this simple chain works will set you up well for working with more complex chains.
|
||||
|
||||
</TabItem>
|
||||
<TabItem value="chat_models" label="Chat models">
|
||||
|
||||
The `LLMChain` can be used with chat models as well:
|
||||
|
||||
<ChainChatModel/>
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
## Agents
|
||||
|
||||
import AgentLLM from "@snippets/get_started/quickstart/agents_llms.mdx"
|
||||
import AgentChatModel from "@snippets/get_started/quickstart/agents_chat_models.mdx"
|
||||
|
||||
Our first chain ran a pre-determined sequence of steps. To handle complex workflows, we need to be able to dynamically choose actions based on inputs.
|
||||
|
||||
Agents do just this: they use a language model to determine which actions to take and in what order. Agents are given access to tools, and they repeatedly choose a tool, run the tool, and observe the output until they come up with a final answer.
|
||||
|
||||
To load an agent, you need to choose a(n):
|
||||
- LLM/Chat model: The language model powering the agent.
|
||||
- Tool(s): A function that performs a specific duty. This can be things like: Google Search, Database lookup, Python REPL, other chains. For a list of predefined tools and their specifications, see the [Tools documentation](/docs/modules/agents/tools/).
|
||||
- Agent name: A string that references a supported agent class. An agent class is largely parameterized by the prompt the language model uses to determine which action to take. 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 [here](/docs/modules/agents/how_to/custom_agent.html). For a list of supported agents and their specifications, see [here](/docs/modules/agents/agent_types/).
|
||||
|
||||
For this example, we'll be using SerpAPI to query a search engine.
|
||||
|
||||
You'll need to install the SerpAPI Python package:
|
||||
|
||||
```bash
|
||||
pip install google-search-results
|
||||
```
|
||||
|
||||
And set the `SERPAPI_API_KEY` environment variable.
|
||||
|
||||
<Tabs>
|
||||
<TabItem value="llms" label="LLMs" default>
|
||||
<AgentLLM/>
|
||||
</TabItem>
|
||||
<TabItem value="chat_models" label="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.
|
||||
|
||||
<AgentChatModel/>
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
## Memory
|
||||
|
||||
The chains and agents we've looked at so far have been stateless, but for many applications it's necessary to reference past interactions. This is clearly the case with a chatbot for example, where you want it to understand new messages in the context of past messages.
|
||||
|
||||
The Memory module gives you a way to maintain application state. The base Memory interface is simple: it lets you update state given the latest run inputs and outputs and it lets you modify (or contextualize) the next input using the stored state.
|
||||
|
||||
There are a number of built-in memory systems. The simplest of these are is a buffer memory which just prepends the last few inputs/outputs to the current input - we will use this in the example below.
|
||||
|
||||
import MemoryLLM from "@snippets/get_started/quickstart/memory_llms.mdx"
|
||||
import MemoryChatModel from "@snippets/get_started/quickstart/memory_chat_models.mdx"
|
||||
|
||||
<Tabs>
|
||||
<TabItem value="llms" label="LLMs" default>
|
||||
|
||||
<MemoryLLM/>
|
||||
</TabItem>
|
||||
<TabItem value="chat_models" label="Chat models">
|
||||
|
||||
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.
|
||||
|
||||
<MemoryChatModel/>
|
||||
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
@ -1,13 +0,0 @@
|
||||
# Conversational
|
||||
|
||||
This walkthrough demonstrates how to use an agent optimized for conversation. Other agents are often optimized for using tools to figure out the best response, which is not ideal in a conversational setting where you may want the agent to be able to chat with the user as well.
|
||||
|
||||
import Example from "@snippets/modules/agents/agent_types/conversational_agent.mdx"
|
||||
|
||||
<Example/>
|
||||
|
||||
import ChatExample from "@snippets/modules/agents/agent_types/chat_conversation_agent.mdx"
|
||||
|
||||
## Using a chat model
|
||||
|
||||
<ChatExample/>
|
||||
@ -1,57 +0,0 @@
|
||||
---
|
||||
sidebar_position: 0
|
||||
---
|
||||
|
||||
# Agent types
|
||||
|
||||
## Action agents
|
||||
|
||||
Agents use an LLM to determine which actions to take and in what order.
|
||||
An action can either be using a tool and observing its output, or returning a response to the user.
|
||||
Here are the agents available in LangChain.
|
||||
|
||||
### [Zero-shot ReAct](/docs/modules/agents/agent_types/react.html)
|
||||
|
||||
This agent uses the [ReAct](https://arxiv.org/pdf/2205.00445.pdf) framework to determine which tool to use
|
||||
based solely on the tool's description. Any number of tools can be provided.
|
||||
This agent requires that a description is provided for each tool.
|
||||
|
||||
**Note**: This is the most general purpose action agent.
|
||||
|
||||
### [Structured input ReAct](/docs/modules/agents/agent_types/structured_chat.html)
|
||||
|
||||
The structured tool chat agent is capable of using multi-input tools.
|
||||
Older agents are configured to specify an action input as a single string, but this agent can use a tools' argument
|
||||
schema to create a structured action input. This is useful for more complex tool usage, like precisely
|
||||
navigating around a browser.
|
||||
|
||||
### [OpenAI Functions](/docs/modules/agents/agent_types/openai_functions_agent.html)
|
||||
|
||||
Certain OpenAI models (like gpt-3.5-turbo-0613 and gpt-4-0613) have been explicitly fine-tuned to detect when a
|
||||
function should to be called and respond with the inputs that should be passed to the function.
|
||||
The OpenAI Functions Agent is designed to work with these models.
|
||||
|
||||
### [Conversational](/docs/modules/agents/agent_types/chat_conversation_agent.html)
|
||||
|
||||
This agent is designed to be used in conversational settings.
|
||||
The prompt is designed to make the agent helpful and conversational.
|
||||
It uses the ReAct framework to decide which tool to use, and uses memory to remember the previous conversation interactions.
|
||||
|
||||
### [Self ask with search](/docs/modules/agents/agent_types/self_ask_with_search.html)
|
||||
|
||||
This agent utilizes a single tool that should be named `Intermediate Answer`.
|
||||
This tool should be able to lookup factual answers to questions. This agent
|
||||
is equivalent to the original [self ask with search paper](https://ofir.io/self-ask.pdf),
|
||||
where a Google search API was provided as the tool.
|
||||
|
||||
### [ReAct document store](/docs/modules/agents/agent_types/react_docstore.html)
|
||||
|
||||
This agent uses the ReAct framework to interact with a docstore. Two tools must
|
||||
be provided: a `Search` tool and a `Lookup` tool (they must be named exactly as so).
|
||||
The `Search` tool should search for a document, while the `Lookup` tool should lookup
|
||||
a term in the most recently found document.
|
||||
This agent is equivalent to the
|
||||
original [ReAct paper](https://arxiv.org/pdf/2210.03629.pdf), specifically the Wikipedia example.
|
||||
|
||||
## [Plan-and-execute agents](/docs/modules/agents/agent_types/plan_and_execute.html)
|
||||
Plan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by [BabyAGI](https://github.com/yoheinakajima/babyagi) and then the ["Plan-and-Solve" paper](https://arxiv.org/abs/2305.04091).
|
||||
@ -1,11 +0,0 @@
|
||||
# OpenAI functions
|
||||
|
||||
Certain OpenAI models (like gpt-3.5-turbo-0613 and gpt-4-0613) have been fine-tuned to detect when a function should to be called and respond with the inputs that should be passed to the function.
|
||||
In an API call, you can describe functions and have the model intelligently choose to output a JSON object containing arguments to call those functions.
|
||||
The goal of the OpenAI Function APIs is to more reliably return valid and useful function calls than a generic text completion or chat API.
|
||||
|
||||
The OpenAI Functions Agent is designed to work with these models.
|
||||
|
||||
import Example from "@snippets/modules/agents/agent_types/openai_functions_agent.mdx";
|
||||
|
||||
<Example/>
|
||||
@ -1,11 +0,0 @@
|
||||
# Plan and execute
|
||||
|
||||
Plan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by [BabyAGI](https://github.com/yoheinakajima/babyagi) and then the ["Plan-and-Solve" paper](https://arxiv.org/abs/2305.04091).
|
||||
|
||||
The planning is almost always done by an LLM.
|
||||
|
||||
The execution is usually done by a separate agent (equipped with tools).
|
||||
|
||||
import Example from "@snippets/modules/agents/agent_types/plan_and_execute.mdx"
|
||||
|
||||
<Example/>
|
||||
@ -1,15 +0,0 @@
|
||||
# ReAct
|
||||
|
||||
This walkthrough showcases using an agent to implement the [ReAct](https://react-lm.github.io/) logic.
|
||||
|
||||
import Example from "@snippets/modules/agents/agent_types/react.mdx"
|
||||
|
||||
<Example/>
|
||||
|
||||
## Using chat models
|
||||
|
||||
You can also create ReAct agents that use chat models instead of LLMs as the agent driver.
|
||||
|
||||
import ChatExample from "@snippets/modules/agents/agent_types/react_chat.mdx"
|
||||
|
||||
<ChatExample/>
|
||||
@ -1,10 +0,0 @@
|
||||
# Structured tool chat
|
||||
|
||||
The structured tool chat agent is capable of using multi-input tools.
|
||||
|
||||
Older agents are configured to specify an action input as a single string, but this agent can use the provided tools' `args_schema` to populate the action input.
|
||||
|
||||
|
||||
import Example from "@snippets/modules/agents/agent_types/structured_chat.mdx"
|
||||
|
||||
<Example/>
|
||||
@ -1,2 +0,0 @@
|
||||
label: 'How-to'
|
||||
position: 1
|
||||
@ -1,14 +0,0 @@
|
||||
# Custom LLM Agent
|
||||
|
||||
This notebook goes through how to create your own custom LLM agent.
|
||||
|
||||
An LLM agent consists of three parts:
|
||||
|
||||
- PromptTemplate: This is the prompt template that can be used to instruct the language model on what to do
|
||||
- LLM: This is the language model that powers the agent
|
||||
- `stop` sequence: Instructs the LLM to stop generating as soon as this string is found
|
||||
- OutputParser: This determines how to parse the LLMOutput into an AgentAction or AgentFinish object
|
||||
|
||||
import Example from "@snippets/modules/agents/how_to/custom_llm_agent.mdx"
|
||||
|
||||
<Example/>
|
||||
@ -1,14 +0,0 @@
|
||||
# Custom LLM Agent (with a ChatModel)
|
||||
|
||||
This notebook goes through how to create your own custom agent based on a chat model.
|
||||
|
||||
An LLM chat agent consists of three parts:
|
||||
|
||||
- PromptTemplate: This is the prompt template that can be used to instruct the language model on what to do
|
||||
- ChatModel: This is the language model that powers the agent
|
||||
- `stop` sequence: Instructs the LLM to stop generating as soon as this string is found
|
||||
- OutputParser: This determines how to parse the LLMOutput into an AgentAction or AgentFinish object
|
||||
|
||||
import Example from "@snippets/modules/agents/how_to/custom_llm_chat_agent.mdx"
|
||||
|
||||
<Example/>
|
||||
@ -1,16 +0,0 @@
|
||||
# Replicating MRKL
|
||||
|
||||
This walkthrough demonstrates how to replicate the [MRKL](https://arxiv.org/pdf/2205.00445.pdf) system using agents.
|
||||
|
||||
This uses the example Chinook database.
|
||||
To set it up follow the instructions on https://database.guide/2-sample-databases-sqlite/, placing the `.db` file in a notebooks folder at the root of this repository.
|
||||
|
||||
import Example from "@snippets/modules/agents/how_to/mrkl.mdx"
|
||||
|
||||
<Example/>
|
||||
|
||||
## With a chat model
|
||||
|
||||
import ChatExample from "@snippets/modules/agents/how_to/mrkl_chat.mdx"
|
||||
|
||||
<ChatExample/>
|
||||
@ -1,51 +0,0 @@
|
||||
---
|
||||
sidebar_position: 4
|
||||
---
|
||||
# Agents
|
||||
|
||||
Some applications require a flexible chain of calls to LLMs and other tools based on user input. The **Agent** interface provides the flexibility for such applications. An agent has access to a suite of tools, and determines which ones to use depending on the user input. Agents can use multiple tools, and use the output of one tool as the input to the next.
|
||||
|
||||
There are two main types of agents:
|
||||
|
||||
- **Action agents**: at each timestep, decide on the next action using the outputs of all previous actions
|
||||
- **Plan-and-execute agents**: decide on the full sequence of actions up front, then execute them all without updating the plan
|
||||
|
||||
Action agents are suitable for small tasks, while plan-and-execute agents are better for complex or long-running tasks that require maintaining long-term objectives and focus. Often the best approach is to combine the dynamism of an action agent with the planning abilities of a plan-and-execute agent by letting the plan-and-execute agent use action agents to execute plans.
|
||||
|
||||
For a full list of agent types see [agent types](/docs/modules/agents/agent_types/). Additional abstractions involved in agents are:
|
||||
- [**Tools**](/docs/modules/agents/tools/): the actions an agent can take. What tools you give an agent highly depend on what you want the agent to do
|
||||
- [**Toolkits**](/docs/modules/agents/toolkits/): wrappers around collections of tools that can be used together a specific use case. For example, in order for an agent to
|
||||
interact with a SQL database it will likely need one tool to execute queries and another to inspect tables
|
||||
|
||||
## Action agents
|
||||
|
||||
At a high-level an action agent:
|
||||
1. Receives user input
|
||||
2. Decides which tool, if any, to use and the tool input
|
||||
3. Calls the tool and records the output (also known as an "observation")
|
||||
4. Decides the next step using the history of tools, tool inputs, and observations
|
||||
5. Repeats 3-4 until it determines it can respond directly to the user
|
||||
|
||||
Action agents are wrapped in **agent executors**, which are responsible for calling the agent, getting back an action and action input, calling the tool that the action references with the generated input, getting the output of the tool, and then passing all that information back into the agent to get the next action it should take.
|
||||
|
||||
Although an agent can be constructed in many ways, it typically involves these components:
|
||||
|
||||
- **Prompt template**: Responsible for taking the user input and previous steps and constructing a prompt
|
||||
to send to the language model
|
||||
- **Language model**: Takes the prompt with use input and action history and decides what to do next
|
||||
- **Output parser**: Takes the output of the language model and parses it into the next action or a final answer
|
||||
|
||||
## Plan-and-execute agents
|
||||
|
||||
At a high-level a plan-and-execute agent:
|
||||
1. Receives user input
|
||||
2. Plans the full sequence of steps to take
|
||||
3. Executes the steps in order, passing the outputs of past steps as inputs to future steps
|
||||
|
||||
The most typical implementation is to have the planner be a language model, and the executor be an action agent. Read more [here](/docs/modules/agents/agent_types/plan_and_execute.html).
|
||||
|
||||
## Get started
|
||||
|
||||
import GetStarted from "@snippets/modules/agents/get_started.mdx"
|
||||
|
||||
<GetStarted/>
|
||||
@ -1,10 +0,0 @@
|
||||
---
|
||||
sidebar_position: 3
|
||||
---
|
||||
# Toolkits
|
||||
|
||||
Toolkits are collections of tools that are designed to be used together for specific tasks and have convenience loading methods.
|
||||
|
||||
import DocCardList from "@theme/DocCardList";
|
||||
|
||||
<DocCardList />
|
||||
@ -1,2 +0,0 @@
|
||||
label: 'How-to'
|
||||
position: 0
|
||||
@ -1,17 +0,0 @@
|
||||
---
|
||||
sidebar_position: 2
|
||||
---
|
||||
# Tools
|
||||
|
||||
Tools are interfaces that an agent can use to interact with the world.
|
||||
|
||||
## Get started
|
||||
|
||||
Tools are functions that agents can use to interact with the world.
|
||||
These tools can be generic utilities (e.g. search), other chains, or even other agents.
|
||||
|
||||
Currently, tools can be loaded with the following snippet:
|
||||
|
||||
import GetStarted from "@snippets/modules/agents/tools/get_started.mdx"
|
||||
|
||||
<GetStarted/>
|
||||
@ -1 +0,0 @@
|
||||
label: 'Integrations'
|
||||
@ -1,2 +0,0 @@
|
||||
label: 'How-to'
|
||||
position: 0
|
||||
@ -1,10 +0,0 @@
|
||||
---
|
||||
sidebar_position: 5
|
||||
---
|
||||
# Callbacks
|
||||
|
||||
LangChain provides a callbacks system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks.
|
||||
|
||||
import GetStarted from "@snippets/modules/callbacks/get_started.mdx"
|
||||
|
||||
<GetStarted/>
|
||||
@ -1 +0,0 @@
|
||||
label: 'Integrations'
|
||||
@ -1,7 +0,0 @@
|
||||
# Analyze Document
|
||||
|
||||
The AnalyzeDocumentChain can be used as an end-to-end to chain. This chain takes in a single document, splits it up, and then runs it through a CombineDocumentsChain.
|
||||
|
||||
import Example from "@snippets/modules/chains/additional/analyze_document.mdx"
|
||||
|
||||
<Example/>
|
||||
@ -1,7 +0,0 @@
|
||||
# Self-critique chain with constitutional AI
|
||||
The ConstitutionalChain is a chain that ensures the output of a language model adheres to a predefined set of constitutional principles. By incorporating specific rules and guidelines, the ConstitutionalChain filters and modifies the generated content to align with these principles, thus providing more controlled, ethical, and contextually appropriate responses. This mechanism helps maintain the integrity of the output while minimizing the risk of generating content that may violate guidelines, be offensive, or deviate from the desired context.
|
||||
|
||||
|
||||
import Example from "@snippets/modules/chains/additional/constitutional_chain.mdx"
|
||||
|
||||
<Example/>
|
||||
@ -1,8 +0,0 @@
|
||||
---
|
||||
sidebar_position: 4
|
||||
---
|
||||
# Additional
|
||||
|
||||
import DocCardList from "@theme/DocCardList";
|
||||
|
||||
<DocCardList />
|
||||
@ -1,8 +0,0 @@
|
||||
# Moderation
|
||||
This notebook walks through examples of how to use a moderation chain, and several common ways for doing so. Moderation chains are useful for detecting text that could be hateful, violent, etc. This can be useful to apply on both user input, but also on the output of a Language Model. Some API providers, like OpenAI, [specifically prohibit](https://beta.openai.com/docs/usage-policies/use-case-policy) you, or your end users, from generating some types of harmful content. To comply with this (and to just generally prevent your application from being harmful) you may often want to append a moderation chain to any LLMChains, in order to make sure any output the LLM generates is not harmful.
|
||||
|
||||
If the content passed into the moderation chain is harmful, there is not one best way to handle it, it probably depends on your application. Sometimes you may want to throw an error in the Chain (and have your application handle that). Other times, you may want to return something to the user explaining that the text was harmful. There could even be other ways to handle it! We will cover all these ways in this walkthrough.
|
||||
|
||||
import Example from "@snippets/modules/chains/additional/moderation.mdx"
|
||||
|
||||
<Example/>
|
||||
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Reference in New Issue