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

Click the image above to watch the demo video

Project Structure

.
├── frontend/           # React frontend application
├── src/                # Python backend code
├── api.py              # FastAPI server
├── app.py              # Streamlit application
└── pyproject.toml      # Python dependencies

Backend Setup

1. Install dependencies:

2. Set up OpenAI API key:

Create a .streamlit/secrets.toml file in your project root:

mkdir -p .streamlit
touch .streamlit/secrets.toml

Add your OpenAI API key to .streamlit/secrets.toml:

OPENAI_API_KEY = "your-openai-api-key-here"

Running the Applications

Streamlit UI

Run the streamlit app:

poetry run streamlit run app.py

FastAPI Backend

Run the API server:

poetry run uvicorn api:app --reload

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

React Frontend

1. Navigate to the frontend directory:

2. Install Node.js dependencies:

3. Start the development server:

The frontend will be available at http://localhost:3000

API Endpoints

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": [...]
}

ROADMAP

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

On the RAG backend

• Conditional retrieval. Sometimes users just want to clarify a past conversation, no extra context needed.
• Create an evaluation dataset
• Evaluate the RAG performance on the dataset
• Auto-optimize the RAG model

On the React frontend

• Support the display of the whole conversation history instead of just the last message.
• Support the management of multiple conversations.