ruvector/docs
rUv a4f9991d9d
feat: add k-scoped adaptive ANN calibration (#718)
* 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>
2026-07-27 09:57:49 -07:00
..
adr feat: add k-scoped adaptive ANN calibration (#718) 2026-07-27 09:57:49 -07:00
analysis fix(brain): defer sparsifier build on startup for large graphs 2026-03-24 12:29:52 +00:00
api fix: SONA zero-vector bug, RVF wasm persistence, metadata safety, doc gaps (#708) 2026-07-17 13:34:16 -04:00
architecture fix(brain): defer sparsifier build on startup for large graphs 2026-03-24 12:29:52 +00:00
benchmarks fix(brain): defer sparsifier build on startup for large graphs 2026-03-24 12:29:52 +00:00
cloud-architecture fix(brain): defer sparsifier build on startup for large graphs 2026-03-24 12:29:52 +00:00
cnn feat(demo): add Self-Learning tab with 6 interactive training demos 2026-03-11 19:31:23 -04:00
code-reviews docs: reorganize into subfolders 2026-01-21 23:43:50 -05:00
dag docs(dag): add comprehensive Neural DAG Learning implementation plan 2025-12-29 22:15:55 +00:00
decisions feat(benchmark): SOTA benchmark suite — 5 runners, 11 SOTA claims, Darwin/MetaHarness integration (ADR-265/266/267) (#596) 2026-06-21 22:53:56 -04:00
development feat(micro-hnsw-wasm): Add Neuromorphic HNSW v2.3 with SNN Integration (#40) 2025-12-01 22:30:15 -05:00
evidence feat(sonic_ct): acoustic digital human workbench — Rust/WASM USCT + R3F UI (#595) 2026-06-22 09:54:22 -04:00
examples feat(musica): structure-first audio separation via dynamic mincut (#337) 2026-04-08 12:23:48 -05:00
gnn fix(brain): defer sparsifier build on startup for large graphs 2026-03-24 12:29:52 +00:00
guides fix: SONA zero-vector bug, RVF wasm persistence, metadata safety, doc gaps (#708) 2026-07-17 13:34:16 -04:00
hailo feat(ruvector-hailo): NPU embedding backend + multi-Pi cluster (ADRs 167-170) (#413) 2026-05-04 08:30:40 -04:00
hnsw fix(brain): defer sparsifier build on startup for large graphs 2026-03-24 12:29:52 +00:00
hooks feat(cli): Implement full hooks system in Rust CLI 2025-12-27 01:08:36 +00:00
implementation fix(brain): defer sparsifier build on startup for large graphs 2026-03-24 12:29:52 +00:00
integration fix(brain): defer sparsifier build on startup for large graphs 2026-03-24 12:29:52 +00:00
nervous-system docs: reorganize into subfolders 2026-01-21 23:43:50 -05:00
optimization fix(brain): defer sparsifier build on startup for large graphs 2026-03-24 12:29:52 +00:00
plans BET 5 (SepRAG #534): PQ/IVFADC within-list pruning vs tuned IVF nprobe — scale-gated WIN (ADR-206) (#542) 2026-06-17 22:48:32 -04:00
postgres fix(brain): defer sparsifier build on startup for large graphs 2026-03-24 12:29:52 +00:00
project-phases Clean up repository structure and organize documentation 2025-11-20 19:50:03 +00:00
publishing fix(brain): defer sparsifier build on startup for large graphs 2026-03-24 12:29:52 +00:00
research feat: add k-scoped adaptive ANN calibration (#718) 2026-07-27 09:57:49 -07:00
reviews perf(ruvllm): optimize MoE routing with buffer reuse and optional metrics 2026-03-12 23:27:00 -04:00
ruvllm docs: reorganize into subfolders 2026-01-21 23:43:50 -05:00
rvagent feat(rvAgent): Complete DeepAgents Rust Conversion (ADR-093 → ADR-103) (#262) 2026-03-16 09:52:32 -04:00
sdk docs(sdk): add deep planning review for ruvector Python SDK 2026-04-25 20:28:54 -04:00
security feat(rvAgent): Complete DeepAgents Rust Conversion (ADR-093 → ADR-103) (#262) 2026-03-16 09:52:32 -04:00
sonic-ct feat(sonic_ct): acoustic digital human workbench — Rust/WASM USCT + R3F UI (#595) 2026-06-22 09:54:22 -04:00
sparse-inference feat: Add PowerInfer-style sparse inference engine with precision lanes (#106) 2026-01-04 23:40:31 -05:00
sql feat(postgres): Add ruvector-postgres extension with SIMD optimizations (#42) 2025-12-02 09:55:07 -05:00
testing Clean up repository structure and organize documentation 2025-11-20 19:50:03 +00:00
training fix(brain): defer sparsifier build on startup for large graphs 2026-03-24 12:29:52 +00:00
.gitkeep Clean up repository structure and organize documentation 2025-11-20 19:50:03 +00:00
.nojekyll fix: add .nojekyll to disable Jekyll processing 2026-03-11 17:53:19 -04:00
agi-container.md feat(rvAgent): Complete DeepAgents Rust Conversion (ADR-093 → ADR-103) (#262) 2026-03-16 09:52:32 -04:00
C2-shell-execution-hardening.md feat(rvAgent): Complete DeepAgents Rust Conversion (ADR-093 → ADR-103) (#262) 2026-03-16 09:52:32 -04:00
C8_RESULT_VALIDATION_IMPLEMENTATION.md feat(rvAgent): Complete DeepAgents Rust Conversion (ADR-093 → ADR-103) (#262) 2026-03-16 09:52:32 -04:00
consciousness-api.md feat(consciousness): SOTA IIT Φ, causal emergence, quantum collapse crate (ADR-131) 2026-03-31 16:36:25 -04:00
IMPLEMENTATION-C5.md feat(rvAgent): Complete DeepAgents Rust Conversion (ADR-093 → ADR-103) (#262) 2026-03-16 09:52:32 -04:00
index.html refactor: move CNN demo to docs/cnn/ for shorter URL 2026-03-11 17:52:13 -04:00
INDEX.md fix(brain): defer sparsifier build on startup for large graphs 2026-03-24 12:29:52 +00:00
METAHARNESS-ARCHITECTURE-SUMMARY.md feat(benchmark): SOTA benchmark suite — 5 runners, 11 SOTA claims, Darwin/MetaHarness integration (ADR-265/266/267) (#596) 2026-06-21 22:53:56 -04:00
metaharness-implementation-plan.md feat(benchmark): SOTA benchmark suite — 5 runners, 11 SOTA claims, Darwin/MetaHarness integration (ADR-265/266/267) (#596) 2026-06-21 22:53:56 -04:00
moe-routing-optimization-analysis.md perf(ruvllm): optimize MoE routing with buffer reuse and optional metrics 2026-03-12 23:27:00 -04:00
README.md fix(brain): defer sparsifier build on startup for large graphs 2026-03-24 12:29:52 +00:00
REPO_STRUCTURE.md fix(brain): defer sparsifier build on startup for large graphs 2026-03-24 12:29:52 +00:00
research-openfang.md Add OpenFang project research document 2026-02-26 14:14:58 +00:00

RuVector Documentation

Complete documentation for RuVector, the high-performance Rust vector database with global scale capabilities.

📚 Documentation Structure

docs/
├── adr/                    # Architecture Decision Records
├── analysis/               # Research & analysis docs
├── api/                    # API references (Rust, Node.js, Cypher)
├── architecture/           # System design docs
├── benchmarks/             # Performance benchmarks & results
├── cloud-architecture/     # Cloud deployment guides
├── code-reviews/           # Code review documentation
├── dag/                    # DAG implementation
├── development/            # Developer guides
├── examples/               # SQL examples
├── gnn/                    # GNN/Graph implementation
├── guides/                 # User guides & tutorials
├── hnsw/                   # HNSW index documentation
├── hooks/                  # Hooks system documentation
├── implementation/         # Implementation details & summaries
├── integration/            # Integration guides
├── nervous-system/         # Nervous system architecture
├── optimization/           # Performance optimization guides
├── plans/                  # Implementation plans
├── postgres/               # PostgreSQL extension docs
├── project-phases/         # Development phases
├── publishing/             # NPM publishing guides
├── research/               # Research documentation
├── ruvllm/                 # RuVLLM documentation
├── security/               # Security audits & reports
├── sparse-inference/       # Sparse inference docs
├── sql/                    # SQL examples
├── testing/                # Testing documentation
└── training/               # Training & LoRA docs

Getting Started

Architecture & Design

API Reference

Performance & Benchmarks

Security

Implementation

Specialized Topics

Development

Research

  • research/ - Research documentation
    • cognitive-frontier/ - Cognitive frontier research
    • gnn-v2/ - GNN v2 research
    • latent-space/ - HNSW & attention research
    • mincut/ - MinCut algorithm research

For New Users

  1. Start with Getting Started Guide
  2. Try the Basic Tutorial
  3. Review API Documentation

For Cloud Deployment

  1. Read Architecture Overview
  2. Follow Deployment Guide
  3. Apply Performance Optimizations

For Contributors

  1. Read Contributing Guidelines
  2. Review Architecture Decisions
  3. Check Migration Guide

For Performance Tuning

  1. Review Optimization Guide
  2. Run Benchmarks
  3. Check Analysis

📊 Documentation Status

Category Directory Status
Getting Started guides/ Complete
Architecture architecture/, adr/ Complete
API Reference api/ Complete
Performance benchmarks/, optimization/, analysis/ Complete
Security security/ Complete
Implementation implementation/, integration/ Complete
Development development/, testing/ Complete
Research research/ 📚 Ongoing

Total Documentation: 460+ documents across 60+ directories


🔗 External Resources


Last Updated: 2026-02-26 | Version: 2.0.4 (core) / 0.1.100 (npm) | Status: Production Ready