Building RAG Systems
Architect and implement production-grade Retrieval-Augmented Generation systems using LangChain and Qdrant vector stores.
This skill provides specialized guidance for building robust, enterprise-ready RAG pipelines and autonomous knowledge agents. It covers everything from sophisticated document ingestion—featuring semantic chunking and incremental updates via file hash detection—to advanced retrieval patterns like HyDE, CRAG, and Self-RAG. By leveraging LangChain orchestration and Qdrant’s high-performance vector database, developers can implement high-accuracy AI systems that include tenant isolation, metadata filtering, and automated evaluation with RAGAS.
主な機能
01Semantic chunking strategies based on document structure rather than fixed-size splits
02Context expansion techniques to walk chunk chains for improved generation accuracy
03Implementation patterns for 8 advanced RAG architectures including HyDE, CRAG, and Self-RAG
04Incremental ingestion pipelines with SHA-256 change detection to optimize indexing
05Advanced Qdrant payload indexing for multi-tenant isolation and complex filtering
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ユースケース
01Developing semantic search engines for large-scale enterprise documentation
02Building autonomous Digital FTEs for specialized domains like legal, finance, or healthcare
03Implementing self-correcting RAG systems for high-accuracy research applications