feat(agent): optional int8 vector index, re-scored against exact embeddings
`MemoryConfig::quantized_index` stores the HNSW index's own copy of the embeddings as i8 rather than f32. At 100k x 384 that takes the index from 266 to 123 MiB and the whole reopened store from 399 to 256 MiB — 2.72x to 1.74x the raw vectors, the largest remaining item in the footprint. Quantised distances are approximate and `ef` cannot compensate, because the loss is in the distances rather than in the graph: recall@10 tops out at 0.967 against f32's 0.9995 and does not move between ef=128 and ef=256. The store already holds the exact embeddings, though, so when the index is quantised the query path re-scores the candidate pool against them before fusion. That restores recall (0.9940 vs 0.9945 at ef=64) and costs about 13% of QPS. Off by default: it trades query speed for memory and which side is worth more depends on the deployment. The flag is persisted in `/meta`, so a reopened store does not silently revert to four times the index memory, and the sidecar graph is rehydrated into the configured storage. Also on the CLI as `create --quantized-index`. Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
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@@ -118,6 +118,7 @@ mod tests {
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created_at: "2025-01-01T00:00:00Z".to_string(),
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wal_enabled: false,
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wal_max_entries: 500,
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quantized_index: false,
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}
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}
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