building-rag-systems

Semantic chunking divides documents into meaningful units based on content structure, like headers, rather than fixed sizes. This Skill provides semantic chunking strategies, incremental indexing to detect changes, and Qdrant-based vector search to retrieve relevant context efficiently for production RAG pipelines.

Production RAG adds filtered retrieval via payload indexes for precise, context-aware results beyond basic similarity matching. This Skill implements payload indexing, change detection, and context expansion to move beyond simple similarity search into scalable, production-ready systems.

Document ingestion extracts and embeds content; vector indexing stores embeddings in Qdrant for fast retrieval. This Skill combines ingestion pipelines, semantic chunking, and incremental indexing so you avoid reprocessing unchanged documents while maintaining filtered retrieval capability.

Incremental indexing with change detection processes only new or modified documents, cutting processing time and cost. Use it when managing large knowledge bases or frequent updates. This Skill detects changes automatically and reindexes only what's needed.

Yes. Payload indexing lets you filter results by metadata—source, category, date—across multiple knowledge bases before retrieval. This Skill supports filtered retrieval and context expansion to serve precise, relevant results from large distributed document collections.

No. This Skill abstracts embedding generation and Qdrant integration into reusable components. You configure chunking strategies, indexing behavior, and retrieval filters; the system handles embedding lookup and vector search under the hood.