What are the strategies for handling document updates and freshness in RAG systems?

Document freshness in RAG requires an incremental update pipeline. Strategy: implement change detection (file hash comparison, API polling, webhook listeners, or database CDC/Change Data Capture), process only changed documents through the embedding pipeline, and update the vector store without full reindexing. Architecture: maintain a document registry tracking source URL, last modified timestamp, content hash, and chunk IDs. On update, delete old chunks and insert new ones atomically. For versioned content, maintain version history to answer temporal queries ("What was the policy in Q3 2024?"). Techniques: use upsert operations in the vector database keyed by a deterministic chunk ID derived from source + content hash, implement eventual consistency with a freshness indicator in metadata so retrieval can prioritize recent documents, and apply TTL (time-to-live) policies for time-sensitive content. For large-scale systems, use a message queue (Kafka, RabbitMQ) to decouple document ingestion from embedding generation. Monitor staleness: track the age distribution of retrieved chunks and alert when a significant portion exceeds acceptable freshness thresholds.