This RAG (Retrieval-Augmented Generation) starter kit provides a complete pipeline for building question-answering systems over private document collections. The template handles document ingestion and chunking, local embedding generation, vector database storage and retrieval, and LLM integration that grounds responses in retrieved context, all running on your own infrastructure without sending data to third-party embedding services.
The template includes the full RAG architecture: document processing that chunks text into appropriate segments, embedding generation using local models (sentence-transformers, all-MiniLM), vector database storage (ChromaDB, FAISS) for efficient semantic search, retrieval logic that finds relevant passages for user queries, and LLM prompting that incorporates retrieved context to generate accurate, grounded responses.
Key features include document preprocessing that handles various file formats (PDF, Markdown, HTML, text), chunk size optimization that balances context preservation with retrieval precision, hybrid search combining semantic similarity with keyword matching, citation generation that links responses back to source documents, and relevance scoring that filters low-quality retrievals before sending to the LLM.
This starter enables developers to build ChatGPT-like experiences over internal knowledge bases, customer documentation, research papers, or any domain-specific corpus while maintaining full control over data privacy. It demonstrates production-ready patterns including incremental index updates when documents change, query optimization for large document sets, and response generation that acknowledges when retrieved context doesn’t contain the answer.