RAG Systems

소개

This skill provides a comprehensive framework for building production-grade Retrieval-Augmented Generation (RAG) systems using the llmemory library. It streamlines the entire pipeline from document ingestion and hierarchical chunking to sophisticated retrieval methods like hybrid search and query expansion. By incorporating advanced features such as intelligent query routing to prevent hallucinations and multiple reranking options—including local cross-encoders and OpenAI models—it ensures that LLM responses are accurately grounded in specific knowledge bases. It is an essential tool for developers creating technical assistants, documentation search engines, or any AI application requiring reliable, source-backed answers.

주요 기능

• Hierarchical document ingestion with customizable chunking strategies
• Query expansion and Reciprocal Rank Fusion (RRF) for improved recall
• Hybrid search combining semantic vector similarity and BM25 text ranking
• Sophisticated reranking support via local cross-encoders or OpenAI models
• 0 GitHub stars
• Intelligent query routing to distinguish between answerable and unanswerable queries

사용 사례

• Building AI assistants for searchable technical and code documentation
• Developing research tools that provide accurate, source-backed responses
• Creating grounded customer support bots using private knowledge bases