A RAG (Retrieval-Augmented Generation) tutorial project using vector storage and document processing.
This project uses uv for dependency management.
- Python 3.8+
- uv (install with:
curl -LsSf https://astral.sh/uv/install.sh | sh)
# Install dependencies
uv sync
# Or install individually
uv add pypdf langchain chromadb boto3
uv add --dev pytest# Populate the database first
uv run python populate_database.py
# Run Python scripts with uv
uv run python query_data.py "Your question here"
uv run python test_rag.py
# Launch web chat interface
uv run streamlit run web_chat.py
# Or activate the virtual environment
source .venv/bin/activate
python populate_database.py- pypdf: PDF processing
- langchain: RAG framework
- chromadb: Vector database
- boto3: AWS SDK
- streamlit: Web interface
- pytest: Testing framework (dev dependency)