Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
| name | vector-index-tuning |
| description | Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure. |
Guide to optimizing vector indexes for production performance.
Data Size Recommended Index
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
< 10K vectors ā Flat (exact search)
10K - 1M ā HNSW
1M - 100M ā HNSW + Quantization
> 100M ā IVF + PQ or DiskANN
| Parameter | Default | Effect |
|---|---|---|
| M | 16 | Connections per node, ā = better recall, more memory |
| efConstruction | 100 | Build quality, ā = better index, slower build |
| efSearch | 50 | Search quality, ā = better recall, slower search |
Full Precision (FP32): 4 bytes Ć dimensions
Half Precision (FP16): 2 bytes Ć dimensions
INT8 Scalar: 1 byte Ć 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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