forked from Archives/langchain
Compare commits
8 Commits
main
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harrison/f
| Author | SHA1 | Date |
|---|---|---|
Harrison Chase
|
ec65ca00c1 | 2 years ago |
|
Harrison Chase
|
64ea17bd21 | 2 years ago |
|
Harrison Chase
|
ec842b7e7b | 2 years ago |
|
Harrison Chase
|
bf8bed493f | 2 years ago |
|
Harrison Chase
|
ad85f3bdbc | 2 years ago |
|
Harrison Chase
|
c2580cf401 | 2 years ago |
|
Harrison Chase
|
7ec210767a | 2 years ago |
|
Harrison Chase
|
2bef195a1f | 2 years ago |
@ -1,144 +0,0 @@
|
||||
.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
|
||||
.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
|
||||
@ -1,36 +0,0 @@
|
||||
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
|
||||
@ -1,49 +0,0 @@
|
||||
name: release
|
||||
|
||||
on:
|
||||
pull_request:
|
||||
types:
|
||||
- closed
|
||||
branches:
|
||||
- master
|
||||
paths:
|
||||
- 'pyproject.toml'
|
||||
|
||||
env:
|
||||
POETRY_VERSION: "1.3.1"
|
||||
|
||||
jobs:
|
||||
if_release:
|
||||
if: |
|
||||
${{ github.event.pull_request.merged == true }}
|
||||
&& ${{ contains(github.event.pull_request.labels.*.name, 'release') }}
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: actions/checkout@v3
|
||||
- name: Install poetry
|
||||
run: pipx install poetry==$POETRY_VERSION
|
||||
- name: Set up Python 3.10
|
||||
uses: actions/setup-python@v4
|
||||
with:
|
||||
python-version: "3.10"
|
||||
cache: "poetry"
|
||||
- name: Build project for distribution
|
||||
run: poetry build
|
||||
- name: Check Version
|
||||
id: check-version
|
||||
run: |
|
||||
echo version=$(poetry version --short) >> $GITHUB_OUTPUT
|
||||
- name: Create Release
|
||||
uses: ncipollo/release-action@v1
|
||||
with:
|
||||
artifacts: "dist/*"
|
||||
token: ${{ secrets.GITHUB_TOKEN }}
|
||||
draft: false
|
||||
generateReleaseNotes: true
|
||||
tag: v${{ steps.check-version.outputs.version }}
|
||||
commit: master
|
||||
- name: Publish to PyPI
|
||||
env:
|
||||
POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }}
|
||||
run: |
|
||||
poetry publish
|
||||
@ -1,8 +0,0 @@
|
||||
cff-version: 1.2.0
|
||||
message: "If you use this software, please cite it as below."
|
||||
authors:
|
||||
- family-names: "Chase"
|
||||
given-names: "Harrison"
|
||||
title: "LangChain"
|
||||
date-released: 2022-10-17
|
||||
url: "https://github.com/hwchase17/langchain"
|
||||
@ -1,13 +0,0 @@
|
||||
# python env
|
||||
PYTHON_VERSION=3.10
|
||||
|
||||
# -E flag is required
|
||||
# comment the following line to only install dev dependencies
|
||||
POETRY_EXTRA_PACKAGES="-E all"
|
||||
|
||||
# at least one group needed
|
||||
POETRY_DEPENDENCIES="dev,test,lint,typing"
|
||||
|
||||
# langchain env. warning: these variables will be baked into the docker image !
|
||||
OPENAI_API_KEY=${OPENAI_API_KEY:-}
|
||||
SERPAPI_API_KEY=${SERPAPI_API_KEY:-}
|
||||
@ -1,53 +0,0 @@
|
||||
# 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`.
|
||||
|
||||
|
||||
@ -1,104 +0,0 @@
|
||||
# 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"]
|
||||
@ -1,84 +0,0 @@
|
||||
#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 ... "
|
||||
@ -1,10 +0,0 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
export PATH=$HOME/.local/bin:$PATH
|
||||
|
||||
if [ -z "$1" ]; then
|
||||
cat /etc/motd
|
||||
exec /bin/bash
|
||||
fi
|
||||
|
||||
exec "$@"
|
||||
@ -1,8 +0,0 @@
|
||||
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.
|
||||
@ -1,17 +0,0 @@
|
||||
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
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 235 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 148 KiB |
@ -1,13 +0,0 @@
|
||||
pre {
|
||||
white-space: break-spaces;
|
||||
}
|
||||
|
||||
@media (min-width: 1200px) {
|
||||
.container,
|
||||
.container-lg,
|
||||
.container-md,
|
||||
.container-sm,
|
||||
.container-xl {
|
||||
max-width: 2560px !important;
|
||||
}
|
||||
}
|
||||
@ -1,39 +0,0 @@
|
||||
# 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.
|
||||
@ -1,10 +0,0 @@
|
||||
LangChain Ecosystem
|
||||
===================
|
||||
|
||||
Guides for how other companies/products can be used with LangChain
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:glob:
|
||||
|
||||
ecosystem/*
|
||||
@ -1,16 +0,0 @@
|
||||
# AI21 Labs
|
||||
|
||||
This page covers how to use the AI21 ecosystem within LangChain.
|
||||
It is broken into two parts: installation and setup, and then references to specific AI21 wrappers.
|
||||
|
||||
## Installation and Setup
|
||||
- Get an AI21 api key and set it as an environment variable (`AI21_API_KEY`)
|
||||
|
||||
## Wrappers
|
||||
|
||||
### LLM
|
||||
|
||||
There exists an AI21 LLM wrapper, which you can access with
|
||||
```python
|
||||
from langchain.llms import AI21
|
||||
```
|
||||
@ -1,25 +0,0 @@
|
||||
# AtlasDB
|
||||
|
||||
This page covers how to 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 Nomic 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 Chroma wrapper, see [this notebook](../modules/indexes/examples/vectorstores.ipynb)
|
||||
@ -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 `pip3 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")
|
||||
```
|
||||
@ -1,17 +0,0 @@
|
||||
# CerebriumAI
|
||||
|
||||
This page covers how to use the CerebriumAI ecosystem within LangChain.
|
||||
It is broken into two parts: installation and setup, and then references to specific CerebriumAI wrappers.
|
||||
|
||||
## Installation and Setup
|
||||
- Install with `pip install cerebrium`
|
||||
- Get an CerebriumAI api key and set it as an environment variable (`CEREBRIUMAI_API_KEY`)
|
||||
|
||||
## Wrappers
|
||||
|
||||
### LLM
|
||||
|
||||
There exists an CerebriumAI LLM wrapper, which you can access with
|
||||
```python
|
||||
from langchain.llms import CerebriumAI
|
||||
```
|
||||
@ -1,20 +0,0 @@
|
||||
# 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)
|
||||
@ -1,25 +0,0 @@
|
||||
# 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)
|
||||
@ -1,17 +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`)
|
||||
|
||||
## Wrappers
|
||||
|
||||
### LLM
|
||||
|
||||
There exists an DeepInfra LLM wrapper, which you can access with
|
||||
```python
|
||||
from langchain.llms import DeepInfra
|
||||
```
|
||||
@ -1,16 +0,0 @@
|
||||
# ForefrontAI
|
||||
|
||||
This page covers how to use the ForefrontAI ecosystem within LangChain.
|
||||
It is broken into two parts: installation and setup, and then references to specific ForefrontAI wrappers.
|
||||
|
||||
## Installation and Setup
|
||||
- Get an ForefrontAI api key and set it as an environment variable (`FOREFRONTAI_API_KEY`)
|
||||
|
||||
## Wrappers
|
||||
|
||||
### LLM
|
||||
|
||||
There exists an ForefrontAI LLM wrapper, which you can access with
|
||||
```python
|
||||
from langchain.llms import ForefrontAI
|
||||
```
|
||||
@ -1,32 +0,0 @@
|
||||
# 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.
|
||||
|
||||
## Installation and Setup
|
||||
- Install requirements with `pip install google-api-python-client`
|
||||
- Set up a Custom Search Engine, following [these instructions](https://stackoverflow.com/questions/37083058/programmatically-searching-google-in-python-using-custom-search)
|
||||
- Get an API Key and Custom Search Engine ID from the previous step, and set them as environment variables `GOOGLE_API_KEY` and `GOOGLE_CSE_ID` respectively
|
||||
|
||||
## Wrappers
|
||||
|
||||
### Utility
|
||||
|
||||
There exists a GoogleSearchAPIWrapper utility which wraps this API. To import this utility:
|
||||
|
||||
```python
|
||||
from langchain.utilities import GoogleSearchAPIWrapper
|
||||
```
|
||||
|
||||
For a more detailed walkthrough of this wrapper, see [this notebook](../modules/utils/examples/google_search.ipynb).
|
||||
|
||||
### 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(["google-search"])
|
||||
```
|
||||
|
||||
For more information on this, see [this page](../modules/agents/tools.md)
|
||||
@ -1,71 +0,0 @@
|
||||
# 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.
|
||||
|
||||
## Setup
|
||||
- Go to [serper.dev](https://serper.dev) to sign up for a free account
|
||||
- Get the api key and set it as an environment variable (`SERPER_API_KEY`)
|
||||
|
||||
## Wrappers
|
||||
|
||||
### Utility
|
||||
|
||||
There exists a GoogleSerperAPIWrapper utility which wraps this API. To import this utility:
|
||||
|
||||
```python
|
||||
from langchain.utilities import GoogleSerperAPIWrapper
|
||||
```
|
||||
|
||||
You can use it as part of a Self Ask chain:
|
||||
|
||||
```python
|
||||
from langchain.utilities import GoogleSerperAPIWrapper
|
||||
from langchain.llms.openai import OpenAI
|
||||
from langchain.agents import initialize_agent, Tool
|
||||
|
||||
import os
|
||||
|
||||
os.environ["SERPER_API_KEY"] = ""
|
||||
os.environ['OPENAI_API_KEY'] = ""
|
||||
|
||||
llm = OpenAI(temperature=0)
|
||||
search = GoogleSerperAPIWrapper()
|
||||
tools = [
|
||||
Tool(
|
||||
name="Intermediate Answer",
|
||||
func=search.run
|
||||
)
|
||||
]
|
||||
|
||||
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?")
|
||||
```
|
||||
|
||||
#### Output
|
||||
```
|
||||
Entering new AgentExecutor chain...
|
||||
Yes.
|
||||
Follow up: Who is the reigning men's U.S. Open champion?
|
||||
Intermediate answer: Current champions Carlos Alcaraz, 2022 men's singles champion.
|
||||
Follow up: Where is Carlos Alcaraz from?
|
||||
Intermediate answer: El Palmar, Spain
|
||||
So the final answer is: El Palmar, Spain
|
||||
|
||||
> Finished chain.
|
||||
|
||||
'El Palmar, Spain'
|
||||
```
|
||||
|
||||
For a more detailed walkthrough of this wrapper, see [this notebook](../modules/utils/examples/google_serper.ipynb).
|
||||
|
||||
### 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(["google-serper"])
|
||||
```
|
||||
|
||||
For more information on this, see [this page](../modules/agents/tools.md)
|
||||
@ -1,23 +0,0 @@
|
||||
# GooseAI
|
||||
|
||||
This page covers how to use the GooseAI ecosystem within LangChain.
|
||||
It is broken into two parts: installation and setup, and then references to specific GooseAI wrappers.
|
||||
|
||||
## Installation and Setup
|
||||
- Install the Python SDK with `pip install openai`
|
||||
- Get your GooseAI api key from this link [here](https://goose.ai/).
|
||||
- Set the environment variable (`GOOSEAI_API_KEY`).
|
||||
|
||||
```python
|
||||
import os
|
||||
os.environ["GOOSEAI_API_KEY"] = "YOUR_API_KEY"
|
||||
```
|
||||
|
||||
## Wrappers
|
||||
|
||||
### LLM
|
||||
|
||||
There exists an GooseAI LLM wrapper, which you can access with:
|
||||
```python
|
||||
from langchain.llms import GooseAI
|
||||
```
|
||||
@ -1,38 +0,0 @@
|
||||
# Graphsignal
|
||||
|
||||
This page covers how to use the Graphsignal to trace and monitor LangChain.
|
||||
|
||||
## Installation and Setup
|
||||
|
||||
- Install the Python library with `pip install graphsignal`
|
||||
- Create free Graphsignal account [here](https://graphsignal.com)
|
||||
- Get an API key and set it as an environment variable (`GRAPHSIGNAL_API_KEY`)
|
||||
|
||||
## Tracing and Monitoring
|
||||
|
||||
Graphsignal automatically instruments and starts tracing and monitoring chains. Traces, metrics and errors are then available in your [Graphsignal dashboard](https://app.graphsignal.com/). No prompts or other sensitive data are sent to Graphsignal cloud, only statistics and metadata.
|
||||
|
||||
Initialize the tracer by providing a deployment name:
|
||||
|
||||
```python
|
||||
import graphsignal
|
||||
|
||||
graphsignal.configure(deployment='my-langchain-app-prod')
|
||||
```
|
||||
|
||||
In order to trace full runs and see a breakdown by chains and tools, you can wrap the calling routine or use a decorator:
|
||||
|
||||
```python
|
||||
with graphsignal.start_trace('my-chain'):
|
||||
chain.run("some initial text")
|
||||
```
|
||||
|
||||
Optionally, enable profiling to record function-level statistics for each trace.
|
||||
|
||||
```python
|
||||
with graphsignal.start_trace(
|
||||
'my-chain', options=graphsignal.TraceOptions(enable_profiling=True)):
|
||||
chain.run("some initial text")
|
||||
```
|
||||
|
||||
See the [Quick Start](https://graphsignal.com/docs/guides/quick-start/) guide for complete setup instructions.
|
||||
@ -1,19 +0,0 @@
|
||||
# Hazy Research
|
||||
|
||||
This page covers how to use the Hazy Research ecosystem within LangChain.
|
||||
It is broken into two parts: installation and setup, and then references to specific Hazy Research wrappers.
|
||||
|
||||
## Installation and Setup
|
||||
- To use the `manifest`, install it with `pip install manifest-ml`
|
||||
|
||||
## Wrappers
|
||||
|
||||
### LLM
|
||||
|
||||
There exists an LLM wrapper around Hazy Research's `manifest` library.
|
||||
`manifest` is a python library which is itself a wrapper around many model providers, and adds in caching, history, and more.
|
||||
|
||||
To use this wrapper:
|
||||
```python
|
||||
from langchain.llms.manifest import ManifestWrapper
|
||||
```
|
||||
@ -1,53 +0,0 @@
|
||||
# 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.
|
||||
|
||||
|
||||
|
||||
## 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.
|
||||
|
||||
|
||||
|
||||
## How to enable Helicone caching
|
||||
|
||||
```python
|
||||
from langchain.llms import OpenAI
|
||||
import openai
|
||||
openai.api_base = "https://oai.hconeai.com/v1"
|
||||
|
||||
llm = OpenAI(temperature=0.9, headers={"Helicone-Cache-Enabled": "true"})
|
||||
text = "What is a helicone?"
|
||||
print(llm(text))
|
||||
```
|
||||
|
||||
[Helicone caching docs](https://docs.helicone.ai/advanced-usage/caching)
|
||||
|
||||
## How to use Helicone custom properties
|
||||
|
||||
```python
|
||||
from langchain.llms import OpenAI
|
||||
import openai
|
||||
openai.api_base = "https://oai.hconeai.com/v1"
|
||||
|
||||
llm = OpenAI(temperature=0.9, headers={
|
||||
"Helicone-Property-Session": "24",
|
||||
"Helicone-Property-Conversation": "support_issue_2",
|
||||
"Helicone-Property-App": "mobile",
|
||||
})
|
||||
text = "What is a helicone?"
|
||||
print(llm(text))
|
||||
```
|
||||
|
||||
[Helicone property docs](https://docs.helicone.ai/advanced-usage/custom-properties)
|
||||
@ -1,69 +0,0 @@
|
||||
# Hugging Face
|
||||
|
||||
This page covers how to use the Hugging Face ecosystem (including the [Hugging Face Hub](https://huggingface.co)) within LangChain.
|
||||
It is broken into two parts: installation and setup, and then references to specific Hugging Face wrappers.
|
||||
|
||||
## Installation and Setup
|
||||
|
||||
If you want to work with the Hugging Face Hub:
|
||||
- Install the Hub client library with `pip install huggingface_hub`
|
||||
- Create a Hugging Face account (it's free!)
|
||||
- Create an [access token](https://huggingface.co/docs/hub/security-tokens) and set it as an environment variable (`HUGGINGFACEHUB_API_TOKEN`)
|
||||
|
||||
If you want work with the Hugging Face Python libraries:
|
||||
- Install `pip install transformers` for working with models and tokenizers
|
||||
- Install `pip install datasets` for working with datasets
|
||||
|
||||
## Wrappers
|
||||
|
||||
### LLM
|
||||
|
||||
There exists two Hugging Face LLM wrappers, one for a local pipeline and one for a model hosted on Hugging Face Hub.
|
||||
Note that these wrappers only work for models that support the following tasks: [`text2text-generation`](https://huggingface.co/models?library=transformers&pipeline_tag=text2text-generation&sort=downloads), [`text-generation`](https://huggingface.co/models?library=transformers&pipeline_tag=text-classification&sort=downloads)
|
||||
|
||||
To use the local pipeline wrapper:
|
||||
```python
|
||||
from langchain.llms import HuggingFacePipeline
|
||||
```
|
||||
|
||||
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/llms/integrations/huggingface_hub.ipynb)
|
||||
|
||||
|
||||
### Embeddings
|
||||
|
||||
There exists two Hugging Face Embeddings wrappers, one for a local model and one for a model hosted on Hugging Face Hub.
|
||||
Note that these wrappers only work for [`sentence-transformers` models](https://huggingface.co/models?library=sentence-transformers&sort=downloads).
|
||||
|
||||
To use the local pipeline wrapper:
|
||||
```python
|
||||
from langchain.embeddings import HuggingFaceEmbeddings
|
||||
```
|
||||
|
||||
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/indexes/examples/embeddings.ipynb)
|
||||
|
||||
### Tokenizer
|
||||
|
||||
There are several places you can use tokenizers available through the `transformers` package.
|
||||
By default, it is used to count tokens for all LLMs.
|
||||
|
||||
You can also use it to count tokens when splitting documents with
|
||||
```python
|
||||
from langchain.text_splitter import CharacterTextSplitter
|
||||
CharacterTextSplitter.from_huggingface_tokenizer(...)
|
||||
```
|
||||
For a more detailed walkthrough of this, see [this notebook](../modules/indexes/examples/textsplitter.ipynb)
|
||||
|
||||
|
||||
### Datasets
|
||||
|
||||
The Hugging Face Hub has lots of great [datasets](https://huggingface.co/datasets) that can be used to evaluate your LLM chains.
|
||||
|
||||
For a detailed walkthrough of how to use them to do so, see [this notebook](../use_cases/evaluation/huggingface_datasets.ipynb)
|
||||
@ -1,66 +0,0 @@
|
||||
# Modal
|
||||
|
||||
This page covers how to use the Modal ecosystem within LangChain.
|
||||
It is broken into two parts: installation and setup, and then references to specific Modal wrappers.
|
||||
|
||||
## Installation and Setup
|
||||
- Install with `pip install modal-client`
|
||||
- Run `modal token new`
|
||||
|
||||
## Define your Modal Functions and Webhooks
|
||||
|
||||
You must include a prompt. There is a rigid response structure.
|
||||
|
||||
```python
|
||||
class Item(BaseModel):
|
||||
prompt: str
|
||||
|
||||
@stub.webhook(method="POST")
|
||||
def my_webhook(item: Item):
|
||||
return {"prompt": my_function.call(item.prompt)}
|
||||
```
|
||||
|
||||
An example with GPT2:
|
||||
|
||||
```python
|
||||
from pydantic import BaseModel
|
||||
|
||||
import modal
|
||||
|
||||
stub = modal.Stub("example-get-started")
|
||||
|
||||
volume = modal.SharedVolume().persist("gpt2_model_vol")
|
||||
CACHE_PATH = "/root/model_cache"
|
||||
|
||||
@stub.function(
|
||||
gpu="any",
|
||||
image=modal.Image.debian_slim().pip_install(
|
||||
"tokenizers", "transformers", "torch", "accelerate"
|
||||
),
|
||||
shared_volumes={CACHE_PATH: volume},
|
||||
retries=3,
|
||||
)
|
||||
def run_gpt2(text: str):
|
||||
from transformers import GPT2Tokenizer, GPT2LMHeadModel
|
||||
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
|
||||
model = GPT2LMHeadModel.from_pretrained('gpt2')
|
||||
encoded_input = tokenizer(text, return_tensors='pt').input_ids
|
||||
output = model.generate(encoded_input, max_length=50, do_sample=True)
|
||||
return tokenizer.decode(output[0], skip_special_tokens=True)
|
||||
|
||||
class Item(BaseModel):
|
||||
prompt: str
|
||||
|
||||
@stub.webhook(method="POST")
|
||||
def get_text(item: Item):
|
||||
return {"prompt": run_gpt2.call(item.prompt)}
|
||||
```
|
||||
|
||||
## Wrappers
|
||||
|
||||
### LLM
|
||||
|
||||
There exists an Modal LLM wrapper, which you can access with
|
||||
```python
|
||||
from langchain.llms import Modal
|
||||
```
|
||||
@ -1,17 +0,0 @@
|
||||
# NLPCloud
|
||||
|
||||
This page covers how to use the NLPCloud ecosystem within LangChain.
|
||||
It is broken into two parts: installation and setup, and then references to specific NLPCloud wrappers.
|
||||
|
||||
## Installation and Setup
|
||||
- Install the Python SDK with `pip install nlpcloud`
|
||||
- Get an NLPCloud api key and set it as an environment variable (`NLPCLOUD_API_KEY`)
|
||||
|
||||
## Wrappers
|
||||
|
||||
### LLM
|
||||
|
||||
There exists an NLPCloud LLM wrapper, which you can access with
|
||||
```python
|
||||
from langchain.llms import NLPCloud
|
||||
```
|
||||
@ -1,55 +0,0 @@
|
||||
# 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)
|
||||
@ -1,21 +0,0 @@
|
||||
# OpenSearch
|
||||
|
||||
This page covers how to use the OpenSearch ecosystem within LangChain.
|
||||
It is broken into two parts: installation and setup, and then references to specific OpenSearch wrappers.
|
||||
|
||||
## Installation and Setup
|
||||
- Install the Python package with `pip install opensearch-py`
|
||||
## Wrappers
|
||||
|
||||
### VectorStore
|
||||
|
||||
There exists a wrapper around OpenSearch vector databases, allowing you to use it as a vectorstore
|
||||
for semantic search using approximate vector search powered by lucene, nmslib and faiss engines
|
||||
or using painless scripting and script scoring functions for bruteforce vector search.
|
||||
|
||||
To import this vectorstore:
|
||||
```python
|
||||
from langchain.vectorstores import OpenSearchVectorSearch
|
||||
```
|
||||
|
||||
For a more detailed walkthrough of the OpenSearch wrapper, see [this notebook](../modules/indexes/vectorstore_examples/opensearch.ipynb)
|
||||
@ -1,17 +0,0 @@
|
||||
# Petals
|
||||
|
||||
This page covers how to use the Petals ecosystem within LangChain.
|
||||
It is broken into two parts: installation and setup, and then references to specific Petals wrappers.
|
||||
|
||||
## Installation and Setup
|
||||
- Install with `pip install petals`
|
||||
- Get a Hugging Face api key and set it as an environment variable (`HUGGINGFACE_API_KEY`)
|
||||
|
||||
## Wrappers
|
||||
|
||||
### LLM
|
||||
|
||||
There exists an Petals LLM wrapper, which you can access with
|
||||
```python
|
||||
from langchain.llms import Petals
|
||||
```
|
||||
@ -1,20 +0,0 @@
|
||||
# Pinecone
|
||||
|
||||
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 with `pip install pinecone-client`
|
||||
## Wrappers
|
||||
|
||||
### 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 wrapper, see [this notebook](../modules/indexes/examples/vectorstores.ipynb)
|
||||
@ -1,31 +0,0 @@
|
||||
# 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
|
||||
|
||||
@ -1,31 +0,0 @@
|
||||
# Runhouse
|
||||
|
||||
This page covers how to use the [Runhouse](https://github.com/run-house/runhouse) ecosystem within LangChain.
|
||||
It is broken into three parts: installation and setup, LLMs, and Embeddings.
|
||||
|
||||
## Installation and Setup
|
||||
- Install the Python SDK with `pip install runhouse`
|
||||
- If you'd like to use on-demand cluster, check your cloud credentials with `sky check`
|
||||
|
||||
## Self-hosted LLMs
|
||||
For a basic self-hosted LLM, you can use the `SelfHostedHuggingFaceLLM` class. For more
|
||||
custom LLMs, you can use the `SelfHostedPipeline` parent class.
|
||||
|
||||
```python
|
||||
from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
|
||||
```
|
||||
|
||||
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.
|
||||
|
||||
For a basic self-hosted embedding from a Hugging Face Transformers model, you can use
|
||||
the `SelfHostedEmbedding` class.
|
||||
```python
|
||||
from langchain.llms import SelfHostedPipeline, SelfHostedHuggingFaceLLM
|
||||
```
|
||||
|
||||
For a more detailed walkthrough of the Self-hosted Embeddings, see [this notebook](../modules/indexes/examples/embeddings.ipynb)
|
||||
|
||||
##
|
||||
@ -1,35 +0,0 @@
|
||||
# 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)
|
||||
@ -1,31 +0,0 @@
|
||||
# SerpAPI
|
||||
|
||||
This page covers how to use the SerpAPI search APIs within LangChain.
|
||||
It is broken into two parts: installation and setup, and then references to the specific SerpAPI wrapper.
|
||||
|
||||
## Installation and Setup
|
||||
- Install requirements with `pip install google-search-results`
|
||||
- Get a SerpAPI api key and either set it as an environment variable (`SERPAPI_API_KEY`)
|
||||
|
||||
## Wrappers
|
||||
|
||||
### Utility
|
||||
|
||||
There exists a SerpAPI utility which wraps this API. To import this utility:
|
||||
|
||||
```python
|
||||
from langchain.utilities import SerpAPIWrapper
|
||||
```
|
||||
|
||||
For a more detailed walkthrough of this wrapper, see [this notebook](../modules/utils/examples/serpapi.ipynb).
|
||||
|
||||
### 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(["serpapi"])
|
||||
```
|
||||
|
||||
For more information on this, see [this page](../modules/agents/tools.md)
|
||||
@ -1,17 +0,0 @@
|
||||
# StochasticAI
|
||||
|
||||
This page covers how to use the StochasticAI ecosystem within LangChain.
|
||||
It is broken into two parts: installation and setup, and then references to specific StochasticAI wrappers.
|
||||
|
||||
## Installation and Setup
|
||||
- Install with `pip install stochasticx`
|
||||
- Get an StochasticAI api key and set it as an environment variable (`STOCHASTICAI_API_KEY`)
|
||||
|
||||
## Wrappers
|
||||
|
||||
### LLM
|
||||
|
||||
There exists an StochasticAI LLM wrapper, which you can access with
|
||||
```python
|
||||
from langchain.llms import StochasticAI
|
||||
```
|
||||
@ -1,41 +0,0 @@
|
||||
# Unstructured
|
||||
|
||||
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 is broken into two parts: installation and setup, and then references to specific
|
||||
`unstructured` wrappers.
|
||||
|
||||
## Installation and Setup
|
||||
- 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`
|
||||
- `poppler-utils`
|
||||
- `tesseract-ocr`
|
||||
- `libreoffice`
|
||||
- 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
|
||||
|
||||
### Data Loaders
|
||||
|
||||
The primary `unstructured` wrappers within `langchain` are data loaders. The following
|
||||
shows how to use the most basic unstructured data loader. There are other file-specific
|
||||
data loaders available in the `langchain.document_loaders` module.
|
||||
|
||||
```python
|
||||
from langchain.document_loaders import UnstructuredFileLoader
|
||||
|
||||
loader = UnstructuredFileLoader("state_of_the_union.txt")
|
||||
loader.load()
|
||||
```
|
||||
|
||||
If you instantiate the loader with `UnstructuredFileLoader(mode="elements")`, the loader
|
||||
will track additional metadata like the page number and text type (i.e. title, narrative text)
|
||||
when that information is available.
|
||||
@ -1,34 +0,0 @@
|
||||
# Wolfram Alpha Wrapper
|
||||
|
||||
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 `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
|
||||
- Set your APP ID as an environment variable `WOLFRAM_ALPHA_APPID`
|
||||
|
||||
|
||||
## Wrappers
|
||||
|
||||
### Utility
|
||||
|
||||
There exists a WolframAlphaAPIWrapper utility which wraps this API. To import this utility:
|
||||
|
||||
```python
|
||||
from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
|
||||
```
|
||||
|
||||
For a more detailed walkthrough of this wrapper, see [this notebook](../modules/utils/examples/wolfram_alpha.ipynb).
|
||||
|
||||
### 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(["wolfram-alpha"])
|
||||
```
|
||||
|
||||
For more information on this, see [this page](../modules/agents/tools.md)
|
||||
@ -1,16 +0,0 @@
|
||||
# Writer
|
||||
|
||||
This page covers how to use the Writer ecosystem within LangChain.
|
||||
It is broken into two parts: installation and setup, and then references to specific Writer wrappers.
|
||||
|
||||
## Installation and Setup
|
||||
- Get an Writer api key and set it as an environment variable (`WRITER_API_KEY`)
|
||||
|
||||
## Wrappers
|
||||
|
||||
### LLM
|
||||
|
||||
There exists an Writer LLM wrapper, which you can access with
|
||||
```python
|
||||
from langchain.llms import Writer
|
||||
```
|
||||
@ -0,0 +1,47 @@
|
||||
Agents
|
||||
======
|
||||
|
||||
The examples here are all end-to-end agents for specific applications.
|
||||
In all examples there is an Agent with a particular set of tools.
|
||||
|
||||
- Tools: A tool can be anything that takes in a string and returns a string. This means that you can use both the primitives AND the chains found in `this <chains.rst>`_ documentation.
|
||||
- Agents: An agent uses an LLMChain to determine which tools to use. For a list of all available agent types, see `here <../explanation/agents.md>`_.
|
||||
|
||||
**MRKL**
|
||||
|
||||
- **Tools used**: Search, SQLDatabaseChain, LLMMathChain
|
||||
- **Agent used**: `zero-shot-react-description`
|
||||
- `Paper <https://arxiv.org/pdf/2205.00445.pdf>`_
|
||||
- **Note**: This is the most general purpose example, so if you are looking to use an agent with arbitrary tools, please start here.
|
||||
- `Example Notebook <agents/mrkl.ipynb>`_
|
||||
|
||||
**Self-Ask-With-Search**
|
||||
|
||||
- **Tools used**: Search
|
||||
- **Agent used**: `self-ask-with-search`
|
||||
- `Paper <https://ofir.io/self-ask.pdf>`_
|
||||
- `Example Notebook <agents/self_ask_with_search.ipynb>`_
|
||||
|
||||
**ReAct**
|
||||
|
||||
- **Tools used**: Wikipedia Docstore
|
||||
- **Agent used**: `react-docstore`
|
||||
- `Paper <https://arxiv.org/pdf/2210.03629.pdf>`_
|
||||
- `Example Notebook <agents/react.ipynb>`_
|
||||
|
||||
|
||||
|
||||
Additionally, we also provide examples for how to do more customizability:
|
||||
|
||||
**Custom Agent**
|
||||
|
||||
- Purpose: How to create custom agents.
|
||||
- `Example Notebook <agents/custom_agent.ipynb>`_
|
||||
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:glob:
|
||||
:hidden:
|
||||
|
||||
agents/*
|
||||
@ -0,0 +1,71 @@
|
||||
Chains
|
||||
======
|
||||
|
||||
The examples here are all end-to-end chains for specific applications.
|
||||
A chain is made up of links, which can be either primitives or other chains.
|
||||
|
||||
The following primitives exist as options to use for links:
|
||||
|
||||
#. `LLM: <../modules/llms.rst>`_ A language model takes text as input and outputs text.
|
||||
#. `PromptTemplate: <../modules/prompt.rst>`_ A prompt template takes arbitrary string inputs and returns a final formatted string.
|
||||
#. `TextSplitter: <../modules/text_splitter.rst>`_ A text splitter takes a longer document and splits it into smaller chunks.
|
||||
#. `Python REPL: <../modules/python.rst>`_ A Python REPL takes a string representing a Python command to run, runs that command, and then returns anything that was printed during that run.
|
||||
#. `SQL Database: <../modules/sql_database.rst>`_ A SQL database takes a string representing a SQL command as input and executes that command against the database. If any rows are returned, then those are cast to a string and returned.
|
||||
#. `Search: <../modules/serpapi.rst>`_ A search object takes a string as input and executes that against a search object, returning any results.
|
||||
#. `Docstore: <../modules/docstore.rst>`_ A docstore object can be used to lookup a document in a database by exact match.
|
||||
#. `Vectorstore: <../modules/vectorstore.rst>`_ A vectorstore object uses embeddings stored in a vector database to take in an input string and return documents similar to that string.
|
||||
|
||||
With these primitives in mind, the following chains exist:
|
||||
|
||||
**LLMChain**
|
||||
|
||||
- **Links Used**: PromptTemplate, LLM
|
||||
- **Notes**: This chain is the simplest chain, and is widely used by almost every other chain. This chain takes arbitrary user input, creates a prompt with it from the PromptTemplate, passes that to the LLM, and then returns the output of the LLM as the final output.
|
||||
- `Example Notebook <chains/llm_chain.ipynb>`_
|
||||
|
||||
**LLMMath**
|
||||
|
||||
- **Links Used**: Python REPL, LLMChain
|
||||
- **Notes**: This chain takes user input (a math question), uses an LLMChain to convert it to python code snippet to run in the Python REPL, and then returns that as the result.
|
||||
- `Example Notebook <chains/llm_math.ipynb>`_
|
||||
|
||||
**PAL**
|
||||
|
||||
- **Links Used**: Python REPL, LLMChain
|
||||
- **Notes**: This chain takes user input (a reasoning question), uses an LLMChain to convert it to python code snippet to run in the Python REPL, and then returns that as the result.
|
||||
- `Paper <https://arxiv.org/abs/2211.10435>`_
|
||||
- `Example Notebook <chains/pal.ipynb>`_
|
||||
|
||||
**Recursive Summarization**
|
||||
|
||||
- **Links Used**: TextSplitter, LLMChain
|
||||
- **Notes**: This chain splits a document into chunks, runs a first LLMChain over each chunk to summarize it, and then runs a second LLMChain over those results to get a summary of the summaries.
|
||||
- `Example Notebook <chains/map_reduce.ipynb>`_
|
||||
|
||||
**SQLDatabase Chain**
|
||||
|
||||
- **Links Used**: SQLDatabase, LLMChain
|
||||
- **Notes**: This chain takes user input (a question), uses a first LLM chain to construct a SQL query to run against the SQL database, and then uses another LLMChain to take the results of that query and use it to answer the original question.
|
||||
- `Example Notebook <chains/sqlite.ipynb>`_
|
||||
|
||||
|
||||
**Vector Database Question-Answering**
|
||||
|
||||
- **Links Used**: Vectorstore, LLMChain
|
||||
- **Notes**: This chain takes user input (a question), uses the Vectorstore and semantic search to find relevant documents, and then passes the documents plus to the original question to another LLM to generate a final answer.
|
||||
- `Example Notebook <chains/vector_db_qa.ipynb>`_
|
||||
|
||||
**Question-Answering With Sources**
|
||||
|
||||
- **Links Used**: LLMChain
|
||||
- **Notes**: This chain takes a question and multiple documents as input. It then runs a first LLMChain over all documents attempting to answer the provided question. It then runs a second LLMChain over the results of the first pass, combining the answers from documents into a single response that is returned.
|
||||
- `Example Notebook <chains/combine_documents.ipynb>`_
|
||||
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:glob:
|
||||
:caption: Chains
|
||||
:hidden:
|
||||
|
||||
chains/*
|
||||
@ -0,0 +1,200 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "efc5be67",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Question-Answering with Sources\n",
|
||||
"\n",
|
||||
"This notebook goes over how to do question-answering with sources. It does this in a few different ways - first showing how you can use the `QAWithSourcesChain` to take in documents and use those, and next showing the `VectorDBQAWithSourcesChain`, which also does the lookup of the documents from a vector database. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "1c613960",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.embeddings.openai import OpenAIEmbeddings\n",
|
||||
"from langchain.embeddings.cohere import CohereEmbeddings\n",
|
||||
"from langchain.text_splitter import CharacterTextSplitter\n",
|
||||
"from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch\n",
|
||||
"from langchain.vectorstores.faiss import FAISS"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "17d1306e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('../state_of_the_union.txt') as f:\n",
|
||||
" state_of_the_union = f.read()\n",
|
||||
"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
|
||||
"texts = text_splitter.split_text(state_of_the_union)\n",
|
||||
"\n",
|
||||
"embeddings = OpenAIEmbeddings()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "0e745d99",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"docsearch = FAISS.from_texts(texts, embeddings)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "f42d79dc",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Add in a fake source information\n",
|
||||
"for i, d in enumerate(docsearch.docstore._dict.values()):\n",
|
||||
" d.metadata = {'source': f\"{i}-pl\"}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "aa1c1b60",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### QAWithSourcesChain\n",
|
||||
"This shows how to use the `QAWithSourcesChain`, which takes in document objects and uses them directly."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "61bce191",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"query = \"What did the president say about Justice Breyer\"\n",
|
||||
"docs = docsearch.similarity_search(query)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "57ddf8c7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.chains import QAWithSourcesChain\n",
|
||||
"from langchain.llms import OpenAI, Cohere\n",
|
||||
"from langchain.docstore.document import Document"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "f908a92a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"chain = QAWithSourcesChain.from_llm(OpenAI(temperature=0))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "a505ac89",
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'answer': ' The president thanked Justice Breyer for his service.',\n",
|
||||
" 'sources': '27-pl'}"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"chain({\"docs\": docs, \"question\": query}, return_only_outputs=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e6fc81de",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### VectorDBQAWithSourcesChain\n",
|
||||
"\n",
|
||||
"This shows how to use the `VectorDBQAWithSourcesChain`, which uses a vector database to look up relevant documents."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "8aa571ae",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.chains import VectorDBQAWithSourcesChain"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "aa859d4c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"chain = VectorDBQAWithSourcesChain.from_llm(OpenAI(temperature=0), vectorstore=docsearch)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8ba36fa7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"chain({\"question\": \"What did the president say about Justice Breyer\"}, return_only_outputs=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "980fae3b",
|
||||
"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": "3.8.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -0,0 +1,89 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d8a5c5d4",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# LLM Chain\n",
|
||||
"\n",
|
||||
"This notebook showcases a simple LLM chain."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "51a54c4d",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new chain...\u001b[0m\n",
|
||||
"Prompt after formatting:\n",
|
||||
"\u001b[32;1m\u001b[1;3mQuestion: What NFL team won the Super Bowl in the year Justin Beiber was born?\n",
|
||||
"\n",
|
||||
"Answer: Let's think step by step.\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"' The year Justin Beiber was born was 1994. In 1994, the Dallas Cowboys won the Super Bowl.'"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from langchain import PromptTemplate, OpenAI, LLMChain\n",
|
||||
"\n",
|
||||
"template = \"\"\"Question: {question}\n",
|
||||
"\n",
|
||||
"Answer: Let's think step by step.\"\"\"\n",
|
||||
"prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
|
||||
"llm_chain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0), verbose=True)\n",
|
||||
"\n",
|
||||
"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
|
||||
"\n",
|
||||
"llm_chain.run(question)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "03dd6918",
|
||||
"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": "3.7.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -0,0 +1,91 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e71e720f",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# LLM Math\n",
|
||||
"\n",
|
||||
"This notebook showcases using LLMs and Python REPLs to do complex word math problems."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "44e9ba31",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new chain...\u001b[0m\n",
|
||||
"How many of the integers between 0 and 99 inclusive are divisible by 8?\u001b[102m\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"count = 0\n",
|
||||
"for i in range(100):\n",
|
||||
" if i % 8 == 0:\n",
|
||||
" count += 1\n",
|
||||
"print(count)\n",
|
||||
"```\n",
|
||||
"\u001b[0m\n",
|
||||
"Answer: \u001b[103m13\n",
|
||||
"\u001b[0m\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'Answer: 13\\n'"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from langchain import OpenAI, LLMMathChain\n",
|
||||
"\n",
|
||||
"llm = OpenAI(temperature=0)\n",
|
||||
"llm_math = LLMMathChain(llm=llm, verbose=True)\n",
|
||||
"\n",
|
||||
"llm_math.run(\"How many of the integers between 0 and 99 inclusive are divisible by 8?\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f62f0c75",
|
||||
"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": "3.10.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -0,0 +1,93 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d9a0131f",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Map Reduce\n",
|
||||
"\n",
|
||||
"This notebok showcases an example of map-reduce chains: recursive summarization."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "e9db25f3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain import OpenAI, PromptTemplate, LLMChain\n",
|
||||
"from langchain.text_splitter import CharacterTextSplitter\n",
|
||||
"from langchain.chains.mapreduce import MapReduceChain\n",
|
||||
"\n",
|
||||
"llm = OpenAI(temperature=0)\n",
|
||||
"\n",
|
||||
"_prompt = \"\"\"Write a concise summary of the following:\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"{text}\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"CONCISE SUMMARY:\"\"\"\n",
|
||||
"prompt = PromptTemplate(template=_prompt, input_variables=[\"text\"])\n",
|
||||
"\n",
|
||||
"text_splitter = CharacterTextSplitter()\n",
|
||||
"\n",
|
||||
"mp_chain = MapReduceChain.from_params(llm, prompt, text_splitter)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "99bbe19b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"\"\\n\\nThe President discusses the recent aggression by Russia, and the response by the United States and its allies. He announces new sanctions against Russia, and says that the free world is united in holding Putin accountable. The President also discusses the American Rescue Plan, the Bipartisan Infrastructure Law, and the Bipartisan Innovation Act. Finally, the President addresses the need for women's rights and equality for LGBTQ+ Americans.\""
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"with open('../state_of_the_union.txt') as f:\n",
|
||||
" state_of_the_union = f.read()\n",
|
||||
"mp_chain.run(state_of_the_union)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "baa6e808",
|
||||
"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": "3.8.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -0,0 +1,129 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0ed6aab1",
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%% md\n"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"# SQLite example\n",
|
||||
"\n",
|
||||
"This example showcases hooking up an LLM to answer questions over a database."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b2f66479",
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%% md\n"
|
||||
}
|
||||
},
|
||||
"source": [
|
||||
"This uses the example Chinook database.\n",
|
||||
"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."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "d0e27d88",
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain import OpenAI, SQLDatabase, SQLDatabaseChain"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "72ede462",
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"db = SQLDatabase.from_uri(\"sqlite:///../../../notebooks/Chinook.db\")\n",
|
||||
"llm = OpenAI(temperature=0)\n",
|
||||
"db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "15ff81df",
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new chain...\u001b[0m\n",
|
||||
"How many employees are there?\n",
|
||||
"SQLQuery:\u001b[102m SELECT COUNT(*) FROM Employee\u001b[0m\n",
|
||||
"SQLResult: \u001b[103m[(8,)]\u001b[0m\n",
|
||||
"Answer:\u001b[102m 8\u001b[0m\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"' 8'"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"db_chain.run(\"How many employees are there?\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "61d91b85",
|
||||
"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": "3.7.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -0,0 +1,10 @@
|
||||
Integrations
|
||||
============
|
||||
|
||||
The examples here all highlight a specific type of integration.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:glob:
|
||||
|
||||
integrations/*
|
||||
@ -0,0 +1,11 @@
|
||||
Memory
|
||||
======
|
||||
|
||||
The examples here are all related to working with the concept of Memory in LangChain.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:glob:
|
||||
:caption: Memory
|
||||
|
||||
memory/*
|
||||
@ -0,0 +1,325 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "fa6802ac",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Adding Memory to an Agent\n",
|
||||
"\n",
|
||||
"This notebook goes over adding memory to an Agent. Before going through this notebook, please walkthrough the following notebooks, as this will build on top of both of them:\n",
|
||||
"\n",
|
||||
"- [Adding memory to an LLM Chain](adding_memory.ipynb)\n",
|
||||
"- [Custom Agents](../agents/custom_agent.ipynb)\n",
|
||||
"\n",
|
||||
"In order to add a memory to an agent we are going to the the following steps:\n",
|
||||
"\n",
|
||||
"1. We are going to create an LLMChain with memory.\n",
|
||||
"2. We are going to use that LLMChain to create a custom Agent.\n",
|
||||
"\n",
|
||||
"For the purposes of this exercise, we are going to create a simple custom Agent that has access to a search tool and utilizes the `ConversationBufferMemory` class."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "8db95912",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.agents import ZeroShotAgent, Tool\n",
|
||||
"from langchain.chains.conversation.memory import ConversationBufferMemory\n",
|
||||
"from langchain import OpenAI, SerpAPIWrapper, LLMChain"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "97ad8467",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"search = SerpAPIWrapper()\n",
|
||||
"tools = [\n",
|
||||
" Tool(\n",
|
||||
" name = \"Search\",\n",
|
||||
" func=search.run,\n",
|
||||
" description=\"useful for when you need to answer questions about current events\"\n",
|
||||
" )\n",
|
||||
"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "4ad2e708",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Notice the usage of the `chat_history` variable in the PromptTemplate, which matches up with the dynamic key name in the ConversationBufferMemory."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "e3439cd6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prefix = \"\"\"Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:\"\"\"\n",
|
||||
"suffix = \"\"\"Begin!\"\n",
|
||||
"\n",
|
||||
"{chat_history}\n",
|
||||
"Question: {input}\"\"\"\n",
|
||||
"\n",
|
||||
"prompt = ZeroShotAgent.create_prompt(\n",
|
||||
" tools, \n",
|
||||
" prefix=prefix, \n",
|
||||
" suffix=suffix, \n",
|
||||
" input_variables=[\"input\", \"chat_history\"]\n",
|
||||
")\n",
|
||||
"memory = ConversationBufferMemory(memory_key=\"chat_history\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0021675b",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can now construct the LLMChain, with the Memory object, and then create the agent."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "c56a0e73",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt, memory=memory)\n",
|
||||
"agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "ca4bc1fb",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new chain...\u001b[0m\n",
|
||||
"How many people live in canada?\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I should look up how many people live in canada\n",
|
||||
"Action: Search\n",
|
||||
"Action Input: \"How many people live in canada?\"\u001b[0m\n",
|
||||
"Observation: \u001b[36;1m\u001b[1;3mThe current population of Canada is 38,533,678 as of Friday, November 25, 2022, based on Worldometer elaboration of the latest United Nations data. · Canada 2020 ...\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
|
||||
"Final Answer: The current population of Canada is 38,533,678 as of Friday, November 25, 2022, based on Worldometer elaboration of the latest United Nations data.\u001b[0m\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'The current population of Canada is 38,533,678 as of Friday, November 25, 2022, based on Worldometer elaboration of the latest United Nations data.'"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"agent.run(\"How many people live in canada?\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "45627664",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"To test the memory of this agent, we can ask a followup question that relies on information in the previous exchange to be answered correctly."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "eecc0462",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new chain...\u001b[0m\n",
|
||||
"what is their national anthem called?\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m\n",
|
||||
"AI: I should look up the name of Canada's national anthem\n",
|
||||
"Action: Search\n",
|
||||
"Action Input: \"What is the name of Canada's national anthem?\"\u001b[0m\n",
|
||||
"Observation: \u001b[36;1m\u001b[1;3mAfter 100 years of tradition, O Canada was proclaimed Canada's national anthem in 1980. The music for O Canada was composed in 1880 by Calixa ...\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m\n",
|
||||
"AI: I now know the final answer\n",
|
||||
"Final Answer: After 100 years of tradition, O Canada was proclaimed Canada's national anthem in 1980. The music for O Canada was composed in 1880 by Calixa Lavallée.\u001b[0m\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"\"After 100 years of tradition, O Canada was proclaimed Canada's national anthem in 1980. The music for O Canada was composed in 1880 by Calixa Lavallée.\""
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"agent.run(\"what is their national anthem called?\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "cc3d0aa4",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can see that the agent remembered that the previous question was about Canada, and properly asked Google Search what the name of Canada's national anthem was.\n",
|
||||
"\n",
|
||||
"For fun, let's compare this to an agent that does NOT have memory."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "3359d043",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prefix = \"\"\"Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:\"\"\"\n",
|
||||
"suffix = \"\"\"Begin!\"\n",
|
||||
"\n",
|
||||
"Question: {input}\"\"\"\n",
|
||||
"\n",
|
||||
"prompt = ZeroShotAgent.create_prompt(\n",
|
||||
" tools, \n",
|
||||
" prefix=prefix, \n",
|
||||
" suffix=suffix, \n",
|
||||
" input_variables=[\"input\"]\n",
|
||||
")\n",
|
||||
"llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)\n",
|
||||
"agent_without_memory = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "970d23df",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new chain...\u001b[0m\n",
|
||||
"How many people live in canada?\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I should look up how many people live in canada\n",
|
||||
"Action: Search\n",
|
||||
"Action Input: \"How many people live in canada?\"\u001b[0m\n",
|
||||
"Observation: \u001b[36;1m\u001b[1;3mThe current population of Canada is 38,533,678 as of Friday, November 25, 2022, based on Worldometer elaboration of the latest United Nations data. · Canada 2020 ...\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
|
||||
"Final Answer: The current population of Canada is 38,533,678\u001b[0m\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'The current population of Canada is 38,533,678'"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"agent_without_memory.run(\"How many people live in canada?\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "d9ea82f0",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new chain...\u001b[0m\n",
|
||||
"what is their national anthem called?\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I should probably look this up\n",
|
||||
"Action: Search\n",
|
||||
"Action Input: \"What is the national anthem of [country]\"\u001b[0m\n",
|
||||
"Observation: \u001b[36;1m\u001b[1;3mMost nation states have an anthem, defined as \"a song, as of praise, devotion, or patriotism\"; most anthems are either marches or hymns in style.\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
|
||||
"Final Answer: The national anthem is called \"the national anthem.\"\u001b[0m\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'The national anthem is called \"the national anthem.\"'"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"agent_without_memory.run(\"what is their national anthem called?\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "5b1f9223",
|
||||
"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": "3.7.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -0,0 +1,10 @@
|
||||
Prompts
|
||||
=======
|
||||
|
||||
The examples here all highlight how to work with prompts.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:glob:
|
||||
|
||||
prompts/*
|
||||
@ -0,0 +1,176 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "f897c784",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Custom ExampleSelector\n",
|
||||
"\n",
|
||||
"This notebook goes over how to implement a custom ExampleSelector. ExampleSelectors are used to select examples to use in few shot prompts.\n",
|
||||
"\n",
|
||||
"An ExampleSelector must implement two methods:\n",
|
||||
"\n",
|
||||
"1. An `add_example` method which takes in an example and adds it into the ExampleSelector\n",
|
||||
"2. A `select_examples` method which takes in input variables (which are meant to be user input) and returns a list of examples to use in the few shot prompt.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Let's implement a custom ExampleSelector that just selects two examples at random."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "1a945da1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.prompts.example_selector.base import BaseExampleSelector\n",
|
||||
"from typing import Dict, List\n",
|
||||
"import numpy as np"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "62cf0ad7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class CustomExampleSelector(BaseExampleSelector):\n",
|
||||
" \n",
|
||||
" def __init__(self, examples: List[Dict[str, str]]):\n",
|
||||
" self.examples = examples\n",
|
||||
" \n",
|
||||
" def add_example(self, example: Dict[str, str]) -> None:\n",
|
||||
" \"\"\"Add new example to store for a key.\"\"\"\n",
|
||||
" self.examples.append(example)\n",
|
||||
"\n",
|
||||
" def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:\n",
|
||||
" \"\"\"Select which examples to use based on the inputs.\"\"\"\n",
|
||||
" return np.random.choice(self.examples, size=2, replace=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "242d3213",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"examples = [{\"foo\": \"1\"}, {\"foo\": \"2\"}, {\"foo\": \"3\"}]\n",
|
||||
"example_selector = CustomExampleSelector(examples)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "2a038065",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Let's now try it out! We can select some examples and try adding examples."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "74fbbef5",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([{'foo': '2'}, {'foo': '3'}], dtype=object)"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"example_selector.select_examples({\"foo\": \"foo\"})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "9bbb5421",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"example_selector.add_example({\"foo\": \"4\"})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "c0eb9f22",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[{'foo': '1'}, {'foo': '2'}, {'foo': '3'}, {'foo': '4'}]"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"example_selector.examples"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "cc39b1e3",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([{'foo': '1'}, {'foo': '4'}], dtype=object)"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"example_selector.select_examples({\"foo\": \"foo\"})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1739dd96",
|
||||
"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": "3.7.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -0,0 +1,116 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a37d9694",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Custom Prompt Template\n",
|
||||
"\n",
|
||||
"This notebook goes over how to create a custom prompt template, in case you want to create your own methodology for creating prompts.\n",
|
||||
"\n",
|
||||
"The only two requirements for all prompt templates are:\n",
|
||||
"\n",
|
||||
"1. They have a `input_variables` attribute that exposes what input variables this prompt template expects.\n",
|
||||
"2. They expose a `format` method which takes in keyword arguments corresponding to the expected `input_variables` and returns the formatted prompt.\n",
|
||||
"\n",
|
||||
"Let's imagine that we want to create a prompt template that takes in input variables and formats them into the template AFTER capitalizing them. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "26f796e5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.prompts import BasePromptTemplate\n",
|
||||
"from pydantic import BaseModel"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "27919e96",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class CustomPromptTemplate(BasePromptTemplate, BaseModel):\n",
|
||||
" template: str\n",
|
||||
" \n",
|
||||
" def format(self, **kwargs) -> str:\n",
|
||||
" capitalized_kwargs = {k: v.upper() for k, v in kwargs.items()}\n",
|
||||
" return self.template.format(**capitalized_kwargs)\n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "76d1d84d",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can now see that when we use this, the input variables get formatted."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "eed1ff28",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prompt = CustomPromptTemplate(input_variables=[\"foo\"], template=\"Capitalized: {foo}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "94892a3c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'Capitalized: LOWERCASE'"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"prompt.format(foo=\"lowercase\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d3d9a7c7",
|
||||
"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": "3.7.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
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Reference in New Issue