rag-advanced

What problem does it solve?

Advanced RAG patterns address the limitations of basic retrieval by combining hypothetical document embeddings, robust reranking, and hybrid search to significantly improve answer quality in production settings.

Core Features & Use Cases

• HyDE-based hypothetical document generation to augment retrieval and boost recall
• Multi-Query Retrieval and Parent Document Retrieval for robust context gathering
• Contextual compression and cross-encoder reranking to improve result quality
• Hybrid search with reciprocal rank fusion (RRF) across dense and sparse signals
• Production-grade integration with LangChain-style pipelines and vector stores
• Flexible document chunking strategies to balance latency and context

Quick Start

Configure a production RAG workflow that enables HyDE, multi-query retrieval, contextual compression, and hybrid search to begin improving retrieval performance.