QnA Bot

A question-answering chatbot that provides answers based on internal documents.

Setup

1. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables:

   • Copy .env.example to .env
   • Update the values in .env with your configuration

4. Run tests:

   pytest tests/

5. To start the servers:

   bash scripts/run_api.sh # To activate the API
   bash scripts/run_app.sh # To activate the unified web interface (chat + admin)

Project Structure

qna_bot/
├── data/
│   ├── raw/              # Place source documents here
│   └── vector_store/     # Vector database storage
├── scripts/
│   └── ingest_data.sh    # Data ingestion script
├── src/
│   ├── app/             # FastAPI application
│   ├── core/            # Core functionality
│   ├── ingestion/       # Document processing
│   └── ui/              # Streamlit interface (unified chat & admin)
└── tests/               # Test files

Usage

• The unified Streamlit web interface provides both the chat (Q&A) and document management (admin) features.
• Use the sidebar navigation to switch between Chat, Dashboard, Upload Documents, Document List, and Settings.
• All document upload, listing, and management features are now accessible from the same interface as the chat.

Development

• The project uses FastAPI for the backend API
• Streamlit for the web interface
• LangChain for RAG implementation
• ChromaDB for vector storage

Testing

Run the test suite:

pytest tests/

Environment Variables

Required environment variables:

• OPENAI_API_KEY: Your OpenAI API key
• OPENAI_MODEL_NAME: Model to use (gpt-4.1-mini or gpt-4.1-nano)
• APP_ENV: Environment (development, testing, production)
• DEBUG: Debug mode (True/False)
• CHROMA_PERSIST_DIRECTORY: Vector store directory
• API_HOST: API host address
• API_PORT: API port number