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

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

1. Install dependencies:

poetry install

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"

Run the streamlit app:

poetry run streamlit run app.py

Run the API server:

poetry run uvicorn api:app --reload

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

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:3000

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": [...] }

• 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.

• 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

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