Learning outcomes
- Explain the operating model behind Index Lifecycle Operations.
- Evaluate trade-offs and failure modes for Index Lifecycle Operations.
- Apply Index Lifecycle Operations to a production decision.
Mental model
A retrieval index is a derived, versioned projection of authoritative content, not the system of record.
Learning outcomes
- Explain the mechanism and ownership boundaries behind Index Lifecycle Operations.
- Compare the main design alternatives and their operational trade-offs.
- Diagnose common failures and select evidence for a production decision.
Theory
Track source revision, parser, chunker, embedding model, schema, ACL version, and build ID. Use idempotent upserts, tombstones, reconciliation, shadow rebuilds, atomic aliases, and rollback checkpoints.
Trade-offs
Incremental updates minimize cost and staleness but accumulate drift. Full rebuilds restore consistency but consume capacity and complicate cutover.
Failure modes and misconceptions
Updating content without deleting old chunks; mixing embedding versions; losing ACL changes; non-idempotent ingestion; no reconciliation; and switching indexes without rollback.
Decision scenario
A new embedding model is ready while production traffic continues. Plan a shadow rebuild, comparison, cutover, and rollback.
Why should an index be reproducible from authoritative sources and a versioned transformation manifest?
Primary sources
pgvector-docsw3c-prov-overview
Evidence assessment
Theory and decision mastery
Decision scenario
A production team must adopt Index Lifecycle Operations while meeting quality, latency, security, and operating constraints.
Which decision process is most defensible?
Relationships
Index Lifecycle Operations builds on Ingestion and Chunking.
Index Lifecycle Operations informs governed production decisions and review evidence.
Primary sources
- pgvector Documentation — pgvector, verified 2026-07-16
- PROV Overview — W3C, verified 2026-07-21