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Optimize vector index performance for latency, recall, and memory.
Passed 13 of 22 test cases with the skill, and 14 of 22 without it.
0 fixed13 passed either way8 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
The instructions your model reads when the skill loads.
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
test cases passed, vs 14 / 22 without
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