Vector Indexes and ANN Search
Exact nearest-neighbor search can be expensive at scale. Approximate nearest-neighbor indexes trade some recall for speed. Graph-based and inverted-file/quantization approaches have different memory and latency trade-offs. Filter behavior can interact with ANN search, so benchmark realistic tenant and metadata filters.
Tune index parameters using recall and latency targets. Keep canonical document records recoverable independently of the index, and monitor retrieval quality after rebuilds and corpus growth.
Implementation example
Query vector + trusted metadata filter
-> ANN search -> top-k IDs
-> fetch authorized text and metadata💡 An ANN index is a search optimization, not the source of truth.
Key Takeaways
- Inspect the relevant pipeline stage instead of guessing.
- Preserve source provenance, versions, and access scope.
- Measure quality on representative queries.
Knowledge Check
What is the main ANN trade-off?