GithubChat

A RAG assistant to allow you to chat with any GitHub repo. Learn fast. The default repo is AdalFlow github repo.

Features

• Chat with GitHub Repos: Ask questions about any repository's codebase
• RAG-powered Retrieval: Uses FAISS for semantic search over code
• Conversation Memory: Maintains context across dialog turns
• Dual Interface: Streamlit app for quick demos, React frontend for production

Project Structure

.
├── app/                    # Core RAG components
│   ├── rag.py              # Main RAG pipeline with Memory component
│   ├── data_pipeline.py    # DatabaseManager for repo indexing
│   ├── gemini_embedder.py  # Gemini embedding model client
│   ├── groq_client.py      # Groq LLM client
│   ├── config.py           # Model configuration
│   └── system_prompt.py    # System prompts and RAG templates
│
├── backend/                # FastAPI server
│   ├── main.py             # API endpoints (/query, /health)
│   ├── dto.py              # Request/Response data models
│   └── utils.py            # Utility functions
│
├── frontend/               # React frontend application
│   └── src/
│       ├── App.tsx         # Main React app
│       └── components/     # UI components
│
├── streamlit_app.py        # Streamlit chat interface
├── pyproject.toml          # Python dependencies (uv/pip)
└── .env                    # Environment variables (API keys)

Requirements

• Python >= 3.12
• Node.js (for frontend)
• API Keys: GEMINI_API_KEY, GROQ_API_KEY

Backend Setup

1. Install dependencies using uv:

uv sync

Or using pip:

pip install -e .

2. Set up environment variables:

Create a .env file in the project root:

GEMINI_API_KEY=your-gemini-api-key
GROQ_API_KEY=your-groq-api-key

Running the Applications

Streamlit UI

Run the Streamlit app for quick demos:

uv run streamlit run streamlit_app.py

FastAPI Backend

Run the API server:

uv run backend/main.py

The API will be available at http://localhost:8000

• API docs: http://localhost:8000/docs
• Health check: http://localhost:8000/health

React Frontend

1. Navigate to the frontend directory:

cd frontend

2. Install Node.js dependencies:

pnpm install

3. Start the development server:

pnpm run dev

The frontend will be available at http://localhost:5173 (Vite default)

API Endpoints

GET /

Returns API information and available endpoints.

GET /health

Health check endpoint - returns status and timestamp.

POST /query

Analyzes a GitHub repository based on a query.

// Request
{
  "repo_url": "https://github.com/username/repo",
  "query": "What does this repository do?"
}

// Response
{
  "rationale": "Analysis rationale...",
  "answer": "Detailed answer...",
  "contexts": [
    {
      "text": "Code snippet...",
      "meta_data": {
        "file_path": "src/main.py",
        "type": "python",
        "is_code": true
      }
    }
  ]
}

Architecture

┌─────────────────┐     ┌─────────────────┐
│  React Frontend │────▶│  FastAPI Backend │
└─────────────────┘     └────────┬────────┘
                                 │
┌─────────────────┐              ▼
│  Streamlit App  │────▶┌─────────────────┐
└─────────────────┘     │    RAG Engine   │
                        │  (app/rag.py)   │
                        └────────┬────────┘
                                 │
          ┌──────────────────────┼──────────────────────┐
          ▼                      ▼                      ▼
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│ DatabaseManager │    │ FAISS Retriever │    │  LLM Generator  │
│ (data_pipeline) │    │   (Semantic)    │    │ (Gemini/Groq)   │
└─────────────────┘    └─────────────────┘    └─────────────────┘

ROADMAP

• Clearly structured RAG that can prepare a repo, persist from reloading, and answer questions.
  • DatabaseManager in app/data_pipeline.py to manage the database.
  • RAG class in app/rag.py to manage the whole RAG lifecycle.

On the RAG backend

• Conditional retrieval - skip retrieval for follow-up clarifications
• Create an evaluation dataset
• Evaluate the RAG performance on the dataset
• Auto-optimize the RAG model

On the React frontend

• Support display of full conversation history
• Support management of multiple conversations