A document set that updates weekly doesn't need the whole corpus re-embedded every time — most of it hasn't changed. Re-embedding everything anyway is the default in a lot of tutorials, and it's needlessly slow and costly at any real scale.

A practical incremental approach

  1. Hash each source document (or each chunk) and store the hash alongside its embedding.
  2. On an update run, compute new hashes and diff against stored ones — only re-embed documents whose hash changed.
  3. Delete embeddings for documents that were removed entirely, not just leave them stale in the index.

The trap to avoid

Chunk boundaries can shift even for a small edit near the start of a document, which changes every downstream chunk's hash. Chunk-level hashing catches real changes; document-level hashing is simpler but coarser — pick based on how often your documents change internally versus wholesale.

See metadata filtering in RAG pipelines for a related ingestion-time concern.

— Pranjul Rathour, GenAI Engineer from Kanpur, India. Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at any campus: pranjulrathour41@gmail.com.