GitChat

GitChat is a chatbot that helps users to get insights into GitHub repositories. It is designed to provide interactive assistance and support for users working with files in a GitHub repository. By integrating chat capabilities, the chatbot can help users understand code and answer questions related to the project. The chatbot utilizes OpenAI LLMs, Langchain and Pinecone to provide answers to questions about the repository.

Disclaimer

This project is under heavy construction, expect bugs and unfinished features. Also, because the results are generated by Large Language Models, which are probabilistic, the results may not always be correct.

Installation

To install GitChat, follow these steps:

1. Clone the repository to your local machine.
2. Install the required dependencies using:
   pip install -r requirements.txt
3. Navigate to the src directory.
4. Run the chatbot using python app.py.

Prerequisites

• copy .env.example to .env and fill in the required fields.
  • only the keys to providers being used are required

Usage

To use GitChat, follow these steps:

1. Start the application by running python app.py.
2. This will open a web browser and navigate to the start page (http://<your-ip-address>:5000/start - your-ip-address is the ip address of the machine running the application, will be automatically detected)
3. Fill in the required fields on the start page and click Submit to initialize the chatbot. (Note: Depending on the size of the repository and the Vectorestore used, this may take a few minutes.)
4. Once the chatbot is initialized, you will be redirected to the main chat interface.
5. Send Messages using Send button or Enter key.
6. Clear the Chat History using the Clear button.

Tip: Most promising configurations:

Feel free to try out other combinations, but these are the ones that I found to work best so far: (Please share your best working configurations in the issues section!)

• LLM: GPT-4
• Vectorstore: Pinecone
• Document Retrieval: HybridSearch
• Chat History: ~5 messages
• Temperature: ~0.2
• Top K: 30
• Alpha: 0.5
• Compression: enabled
• Compression model: GPT-3.5-Turbo

Features

• Question Answering powered by OpenAI LLMs (and others to come)
• Chat History (AI has short term memory, like ChatGPT)
• Text sections of files relevant to the question are passed to the AI as context
  • Questions are answered based on the relevant context
• Easily Customizable Options for LLM, Vectorstore and retrieval method

Configuration options

• LLMs
  • Models:
    • OpenAI-models: GPT-3.5-Turbo, GPT-4
    • ... other models coming soon (Vicuna etc.)
  • Parameters:
    • Temperature
    • ... more added soon
  • Chat History (Memory):
    • Length (number of messages) of chat to be "remembered" by the AI
• Vectorstores
  • Providers:
    • Pinecone
    • ChromaDB
    • ... more added soon (Weaviate, Elasticsearch, ...)
  • Document Retrieval methods:
    • HybridSearch (currently only available for Pinecone)
    • several options here

Although I will likely add a bunch of options in the future, (obviously) I do not have the time to add all of the ones available in LangChain. So this won't be some kind of "sandbox". Nevertheless, I will still try to make it as customizable as possible (focusing on the most promising, latest features offered by LangChain regarding Vectorstores, LLMs and Document Retrieval), as Document Retrieval use cases vary a lot and I want to make it as easy as possible to try out different combinations for the best results.

Contribution

Contributions to GitChat are welcome!

These are my current TODOs for the project: TODO

License

GitChat is licensed under the MIT License. For more information, please see the LICENSE file in the repository.