README.md

metadata

title: Code Weaver
emoji: 🔥
colorFrom: purple
colorTo: indigo
sdk: docker
pinned: false
short_description: Interact with your github repo

Code Weaver

This application is inspired by the Chat-with-Github-Repo project by peterw, which demonstrates how to create a chatbot using Streamlit, OpenAI, and Activeloop's Deep Lake. Code Weaver builds upon that foundation, offering a more user-friendly experience and optimized performance.

Code Weaver is designed to let you interact with a GitHub repository through a conversational interface. It indexes the contents of a repository, allowing you to ask questions and receive answers based on the repository's documentation and code.

Key Improvements Over Original Implementation

Code Weaver offers several key improvements that enhance the user experience and the app's overall performance:

• Purely Frontend UI:
  • The original application used a command-line interface (CLI) for configuration. Code Weaver provides a completely UI-based experience. All configurations (API keys, repo URLs, dataset names, etc.) are entered directly into the Streamlit app, making it much more accessible to users.
• Optimized Document Parsing:
  • The document parsing and splitting process have been optimized for speed and handling of large files by using RecursiveCharacterTextSplitter.
• Improved Loading times:
  • The app is now faster because the embeddings are not recalculated each time the app is started, this is achieved by using InMemoryCache.
• Direct OpenAI Embeddings Model:
  • The app now directly specifies the usage of the "text-embedding-ada-002" OpenAI embedding model, resulting in faster embedding calculations.
• In Memory Cache:
  • InMemoryCache is used to cache the results from the language model improving loading speeds.
• Streamlined Setup:
  • No more need for .env files, as all necessary variables are inserted directly in the app UI.
• Easier Deployment: The app is built to be deployed in Hugging Face Spaces, making the deployment process easier.

How to Use Code Weaver

1. Clone the repository:

   https://github.com/WHITELOTUS0/chat-with-a-repo.git
   

2. Navigate to the Project Directory:

   cd your-repo-name
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Run the app:

   streamlit run app.py
   

5. Enter configurations: The Streamlit app will open in your web browser. In the sidebar, provide:

   • Your OpenAI API Key
   • Your Activeloop Token
   • Your Activeloop Username
   • The GitHub repository URL you want to explore
   • The file extensions to include (optional)
   • A desired name for the DeepLake dataset

6. Process the repository: Click on the "Process Repository" Button, and wait for it to finish.

7. Start Chatting: After processing is done, you can start asking questions about the repository in the provided input text area.

Contributing

Contributions to this project are always welcome. If you find any bugs or would like to suggest new features, feel free to create a pull request or open an issue in this repository.

Credits

• Inspired by the Chat-with-Github-Repo project by peterw.
• Built using Python, Streamlit, OpenAI, Activeloop, and Langchain.

License

MIT License

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference