Clients
LLM RAG
@binarybana
Easy RAG scripts for a local, embedded, MCP-enabled knowledge store.
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About
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:
- Install dependencies:
# Create and activate a new virtual environment
uv venv
source .venv/bin/activate
# Install dependencies
uv pip install -e .
- 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