Tune vector indexes, which organize data for similarity searches, to balance speed, memory use, and missed matches. Choose index types, adjust HNSW settings, and assess compression options using benchmarks with real queries.
Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
Review the instructions the agent will receive before you install.
Guide to optimizing vector indexes for production performance.
Data Size Recommended Index
────────────────────────────────────────
< 10K vectors → Flat (exact search)
10K - 1M → HNSW
1M - 100M → HNSW + Quantization
> 100M → IVF + PQ or DiskANN
| Parameter | Default | Effect |
|---|---|---|
| M | 16 | Connections per node, ↑ = better recall, more memory |
| efConstruction | 100 | Build quality, ↑ = better index, slower build |
| efSearch | 50 | Search quality, ↑ = better recall, slower search |
Full Precision (FP32): 4 bytes × dimensions
Half Precision (FP16): 2 bytes × dimensions
INT8 Scalar: 1 byte × dimensions
Product Quantization: ~32-64 bytes total
Binary: dimensions/8 bytes
Full template library and detailed worked examples live in references/details.md. Read that file when you need the concrete templates.
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