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:

# Create and activate a new virtual environment
uv venv
source .venv/bin/activate

# Install dependencies
uv pip install -e .

2. Set up environment:

# 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

python -m llm_rag.ingest --source /path/to/source --type [code|url|pdf]

Search Server

python -m llm_rag.search --db /path/to/lancedb