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https://github.com/langchain-ai/langchain-aiplugin
https://github.com/langchain-ai/langchain-aiplugin
Last synced: about 2 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/langchain-ai/langchain-aiplugin
- Owner: langchain-ai
- License: mit
- Created: 2023-03-31T21:48:23.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-04-08T22:05:56.000Z (over 1 year ago)
- Last Synced: 2024-06-15T09:35:01.519Z (3 months ago)
- Language: Python
- Size: 377 KB
- Stars: 497
- Watchers: 9
- Forks: 39
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-ChatGPT-repositories - langchain-aiplugin - "langchain as an aiplugin"🔥-super cool .@hwchase17 @langchainai this is like giving #chatgpt superpowers as it can now easily use langchain components(agents,chains etc)as plugins. this repo will scale up in no time.example: retrievalqa chain as a plugin (Browser-extensions)
README
# LangChain as an AIPlugin
## Introduction
[LangChain](https://python.langchain.com/en/latest/index.html) can flexibly integrate with the ChatGPT AI plugin ecosystem.
LangChain chains and agents can themselves be deployed as a plugin that can communicate with other agents or with ChatGPT itself.
For more information on AI Plugins, see OpenAI's [example retrieval plugin](https://github.com/openai/chatgpt-retrieval-plugin/tree/main) repository.
## Quickstart
Follow these steps to quickly set up and run a LangChain AI Plugin:
1. Install Python 3.10, if not already installed.
2. Clone the repository: `git clone [email protected]:langchain-ai/langchain-aiplugin.git`
3. Navigate to the example plugin directory: `cd langchain-aiplugin`
4. Install poetry: `pip install poetry`
5. Create a new virtual environment with Python 3.10: `poetry env use python3.10`
6. Activate the virtual environment: `poetry shell`
7. Install app dependencies: `poetry install`
8. Set up the chain you want to run (see detailed instructions below). For an example, run `export LANGCHAIN_DIRECTORY_PATH=retrieval_qa` and `export OPENAI_API_KEY=...` and `export BEARER_TOKEN=...` (the Bearer Token can be anything you want for local testing purposes).
9. Run the API locally: `poetry run app --port 8080`
10. Access the API documentation at `http://0.0.0.0:8080/docs` and test the API endpoints (make sure to add your bearer token).
For more detailed information on setting up, developing, and deploying the ChatGPT Retrieval Plugin, refer to the full Development section below.
## Development
### Environment Setup
This app uses Python 3.10, and [poetry](https://python-poetry.org/) for dependency management.
Install Python 3.10 on your machine if it isn't already installed. It can be downloaded from the official [Python website](https://www.python.org/downloads/) or with a package manager like `brew` or `apt`, depending on your system.
**Note:** If adding dependencies in the `pyproject.toml`, make sure to run `poetry lock` and `poetry install`.
### Setup a Chain
To setup a chain to be a plugin, you will need to do the following steps:
1. Create a new folder
2. Create a `chain.py` file in that folder, and fill it out appropriately (see instructions below)
3. Create a `constants.py` file in that folder, and fill it out appropriately (see instructions below)
4. Add an environment variable `export LANGCHAIN_DIRECTORY_PATH=$PATH_TO_FOLDER`
#### Setup `chain.py`
In order to setup `chain.py` all you need to do is expose a function with the following signature:
```python
from langchain.chains.base import Chain
def load_chain() -> Chain:
"""Load your chain here."""
```
To get started, you can copy the file in `template/chain.py`
#### Setup `constants.py`
In order to setup `constants.py`, you need to have a Python file with the following variables exposed:
```python
# The description of the chain you are exposing. This will be used by ChatGPT to decide when to call it.
ENDPOINT_DESCRIPTION = ""
# The name of your endpoint that you are exposing.
ENDPOINT_NAME = ""
# The input key for the chain. The user input will get mapped to this key.
INPUT_NAME = ""
# The output key for the chain. The final response will take this key from the chain output.
OUTPUT_KEY = ""
# Name of the overall service to expose to the model.
NAME_FOR_MODEL = ""
# Name of the overall service to expose to humans.
NAME_FOR_HUMAN = ""
# Description of the overall service to expose to the model.
DESCRIPTION_FOR_MODEL = ""
# Description of the overall service to expose to humans.
DESCRIPTION_FOR_HUMAN = ""
```
To get started, you can copy the file in `template/constants.py`
## Examples
To help get started we've set up two examples.
- `retrieval_qa`: Exposes a [RetrievalQA Chain](https://python.langchain.com/en/latest/modules/chains/index_examples/vector_db_qa.html) set up with LangChain Documentation. See the [README](retrieval_qa/README.md) in that folder for more information.
- `agent`: Exposes a simple agent equipped with a calculator. See the [README](agent/README.md) in that folder for more information.