🧑‍💻 Codebase Chatbot with Retrieval-Augmented Generation (RAG)

Website URL: https://codebase-rag-sj.streamlit.app

An AI-powered chatbot that allows users to interact with and understand codebases by leveraging Retrieval-Augmented Generation (RAG). This tool embeds the content of code repositories, stores them in a vector database, and uses Large Language Models (LLMs) to answer queries contextually.

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🚀 Features

• Chat with a Codebase: Understand the structure, purpose, and potential improvements of any codebase.
• Preloaded Repositories: Seamlessly switch between preloaded repositories to explore different projects.
• Accurate Contextual Answers: Powered by LLMs, providing insights based on embedded code content.
• Future Enhancements: Plan to allow dynamic uploads of any GitHub repository for embedding and querying.

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🛠️ Tech Stack

• Python: Core language for implementation.
• Hugging Face Transformers: Used for generating embeddings with the sentence-transformers/all-mpnet-base-v2 model.
• Pinecone: A vector database to store and retrieve code embeddings.
• Streamlit: Frontend framework to provide an interactive and user-friendly UI.
• OpenAI LLMs: For generating accurate, context-aware responses.

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⚙️ How it Works

• Create Vector Embeddings: Using Hugging Face model, create embeddings of relevant information like function definitions, comments, and documentation from the codebase.
• Store the embeddings: Used Pinecone Vector Database
• Query the Codebase: When a query is made, relevant pieces of the codebase are retrieved from Pinecone and augmented with the query before being sent to the LLM for a response.
• Interactive Chat: Users can ask questions through a Streamlit-based UI, select from preloaded repositories, and receive responses in real time.

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📋 Setup Instructions

1. Clone the Repository

git clone https://github.com/Sruthij93/Codebase-RAG
cd codebase-rag

2. Install Dependencies

pip install -r requirements.txt

3. Set Up Pinecone

• Create a Pinecone account at Pinecone.io.
• Get your Pinecone API key and index name.
• Configure your .env file with Pinecone credentials.

4. Run the Application

streamlit run app.py

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🌟 Next Steps

• Allow users to upload custom GitHub repositories for embedding.
• Enhance the UI for better interactivity.

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request or open an Issue for discussion.