ruvector/docs
ruvnet 3a1afa2284 feat(rulake): vector-native federation intermediary — ADR-155 + MVP crate
Implements the M1 scope of docs/research/ruLake/ as an intermediary that
fans out vector queries across heterogeneous backends (Parquet, BigQuery,
Snowflake, Delta, Iceberg, local) behind a single RVF wire protocol, with
a RaBitQ-compressed cache in front.

## What ships

- **Research docs** under docs/research/ruLake/ (9 files, ~2.5k lines),
  reframed from the earlier "plug RVF into BigQuery" shape to the
  intermediary/federation shape. BigQuery-native compute becomes a Tier-2
  push-down optimization inside the BigQueryBackend adapter, not a new
  product shape.
- **ADR-155 v2** as "Proposed" — captures the seven alternatives
  considered (plug-in-per-lake, standalone vector DB, Iceberg extension,
  Trino connector, JVM intermediary, notebook-only, push-through-only),
  consequences, and eight open questions.
- **crates/ruvector-rulake/** — new workspace member:
  - `BackendAdapter` trait with minimum surface (id / list_collections /
    pull_vectors / generation / supports_pushdown).
  - `LocalBackend` in-memory reference implementation (thread-safe).
  - `VectorCache` wrapping ruvector_rabitq::RabitqPlusIndex, with per-
    collection generation tracking and `Consistency::{Fresh, Eventual}`
    policies.
  - `RuLake` entry point: register backends, search single or federated,
    cache-stats introspection.
  - 7 smoke tests (`tests/federation_smoke.rs`): byte-exact match vs
    direct RaBitQ, cache-coherence after backend mutation, cross-backend
    fan-out with correct score ordering, cache-hit-faster-than-miss,
    three error-path tests.
  - `rulake-demo` bin: unified benchmark producing the same-run table in
    BENCHMARK.md.

## Measured numbers (LocalBackend, D=128, rerank×20, 300 queries)

| n       | direct RaBitQ+ QPS | ruLake Fresh QPS | ruLake Eventual QPS | tax   |
|--------:|-------------------:|-----------------:|--------------------:|------:|
|   5,000 |             17,311 |           17,874 |              17,858 | 0.97× |
|  50,000 |              5,162 |            5,123 |               5,050 | 1.01× |
| 100,000 |              3,122 |            3,117 |               3,114 | 1.00× |

**Intermediary tax is effectively zero on a local backend.** Federated
across 2 shards: 2,470 QPS @ n=100k (0.79× of single-shard); 4 shards:
1,781 QPS (0.57×) — sequential fan-out, parallel merge is the v2
optimisation per ADR-155 §Consequences.

## Build + test status (this crate only)

```
cargo build  -p ruvector-rulake --release                            ✓
cargo test   -p ruvector-rulake --release                            ✓ 7 passed
cargo clippy -p ruvector-rulake --release --all-targets -- -D warnings   ✓ clean
cargo fmt    -p ruvector-rulake -- --check                           ✓ clean
cargo run    -p ruvector-rulake --release --bin rulake-demo          ✓ reproduces BENCHMARK.md
```

## Scope this commit does NOT cover (M2-M5, see 07-implementation-plan.md)

- ParquetBackend, BigQueryBackend, SnowflakeBackend, IcebergBackend,
  DeltaBackend (real-backend adapters).
- Push-down paths into backends with native vector ops.
- Governance / RBAC / PII / lineage / audit (M4).
- SIFT1M recall measurement on the real-backend path.
- Parallel fan-out via rayon.
- LRU cache eviction.

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-04-23 18:38:49 -04:00
..
adr feat(rulake): vector-native federation intermediary — ADR-155 + MVP crate 2026-04-23 18:38:49 -04:00
analysis fix(brain): defer sparsifier build on startup for large graphs 2026-03-24 12:29:52 +00:00
api fix(brain): defer sparsifier build on startup for large graphs 2026-03-24 12:29:52 +00:00
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
development feat(micro-hnsw-wasm): Add Neuromorphic HNSW v2.3 with SNN Integration (#40) 2025-12-01 22:30:15 -05:00
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 docs: add missing capabilities to advanced features guide 2026-02-26 16:09:06 +00:00
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/subpolynomial-time-mincut chore(docs): Clean up and reorganize documentation structure 2025-12-25 19:39:44 +00:00
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(rulake): vector-native federation intermediary — ADR-155 + MVP crate 2026-04-23 18:38:49 -04:00
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
security feat(rvAgent): Complete DeepAgents Rust Conversion (ADR-093 → ADR-103) (#262) 2026-03-16 09:52:32 -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
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