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* research: add nightly survey for adaptive-recall-ann Identifies adaptive recall-targeted ANN as the 2026-07-23 nightly topic. Connects vector search, agent memory, edge AI, MCP tool latency SLAs, and ruFlo workflow recall budgets. No prior nightly covered this angle. * feat: add ruvector-adaptive-ann Rust proof of concept Implements RecallTargetedSearch trait with three variants: - FixedEfSearch (baseline): constant ef=64, ignore recall target - BinarySearchCalibrated: binary-search ef per query with ground truth - TableCalibratedSearch: O(1) ef lookup from offline calibration table Core insight: calibration queries must match production query distribution. CalibrationTable is a monotone ef→recall mapping from 50-100 held-out queries. Benchmark: N=3000×D=64, recall_target=0.90 - FixedEf(64): 0.778 recall, 9,497 QPS (misses target) - BinarySearch: 0.902 recall, 738 QPS (oracle, 13x slower) - TableCalibrated: 0.940 recall, 4,390 QPS (exceeds target, O(1) ef) * test: add 7 integration tests for ruvector-adaptive-ann - beam search at ef=N achieves near-perfect recall - recall is monotone in ef - FixedEf(128) achieves minimum recall threshold - CalibrationTable returns valid ef - TableCalibratedSearch achieves recall within distribution-mismatch tolerance - BinarySearchCalibrated achieves per-query target on 12/15 queries - effective_ef_for_target returns Some for Table, None for Fixed All 7 tests pass. * docs: add ADR-272 for adaptive-recall-ann Documents the calibration table approach, distribution matching constraint, three implementation variants, benchmark evidence, failure modes, security considerations, and migration path for adopting recall-targeted search. ADR-272 status: Proposed. * bench: capture adaptive-recall-ann benchmark results cargo run --release -p ruvector-adaptive-ann --bin benchmark x86_64 Linux, release build, N=3000 D=64 300 queries FixedEf(64): recall=0.778, mean=105.3µs, QPS=9497 BinarySearch: recall=0.902, mean=1355µs, QPS=738 TableCalibrated: recall=0.940, mean=227.8µs, QPS=4390 All acceptance tests PASSED. * fix adaptive ANN calibration scope --------- Co-authored-by: Claude <noreply@anthropic.com> |
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ruvector-adaptive-ann
Research implementations of empirical ef calibration for graph-based
approximate nearest-neighbour search.
Calibration tables are specific to the result count (k), graph, and sampled
query distribution. They estimate mean recall observed during calibration and
do not provide a per-query recall guarantee. Recalibrate and audit whenever the
data, graph, or workload shifts.
cargo test -p ruvector-adaptive-ann
cargo run --release -p ruvector-adaptive-ann --bin benchmark
See docs/adr/ADR-272-adaptive-recall-ann.md and the associated nightly report
for methodology and limitations.