easyrag

# LLM RAG A RAG (Retrieval Augmented Generation) implementation using LlamaIndex for document processing, Gemini for embeddings, and LanceDB for vector storage. ## Setup This project uses `uv` for dependency management and `direnv` for environment management. To get started: 1. Install dependencies: ```bash # Create and activate a new virtual environment uv venv source .venv/bin/activate # Install dependencies uv pip install -e . ``` 2. Set up environment: ```bash # Create .env file with your Google API key echo "GOOGLE_API_KEY=your_key_here" > .env # Allow direnv to load the environment direnv allow ``` ## Usage ### Data Ingestion ```bash python -m llm_rag.ingest --source /path/to/source --type [code|url|pdf] ``` ### Search Server ```bash python -m llm_rag.search --db /path/to/lancedb