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- Fix import paths in comparison_benchmark.rs and hnsw_search.rs - Add Python benchmark suite comparing rUvector vs Qdrant - Create detailed performance comparison documentation Key findings: - rUvector: 22x faster search at 50K vectors - HNSW search: 45-165µs latency (k=1 to k=100) - Distance calculations: 22-135ns (SIMD-optimized) - Quantization: 4-32x memory compression
Detailed feature gap analysis and implementation plan covering: Priority 1 (Critical): - REST/gRPC API server with OpenAPI spec - Advanced payload indexing (9 index types) - Multi-collection management with aliases - Snapshots and S3 backup support Priority 2 (Scalability): - Distributed mode with sharding - Raft consensus for metadata - Configurable replication Priority 3 (Enterprise): - Authentication with JWT RBAC - TLS support (client + inter-node) - Prometheus/OpenTelemetry metrics Priority 4 (Performance): - Asymmetric quantization - Variable bit-width (1.5-bit, 2-bit) - Tiered storage (hot/warm/cold) Priority 5 (DX): - Python/Go/Java SDKs - Web dashboard - Migration tools (FAISS, Pinecone, Weaviate) Preserves rUvector advantages: 22x faster search, WASM, hypergraphs, AgenticDB, sub-100µs latency
New Crates: - ruvector-server: REST API server using axum (collections, points, health endpoints) - ruvector-collections: Multi-collection management with aliases - ruvector-filter: Advanced payload indexing (9 index types, geo, full-text) - ruvector-snapshot: Backup/restore with gzip compression and checksums - ruvector-metrics: Prometheus metrics and health checks Integrations: - Node.js NAPI-RS: CollectionManager, filters, metrics, health endpoints - WASM: CollectionManager, FilterBuilder (with feature flag) Performance Benchmarks: - HNSW search: 41-151µs (k=1 to k=100) - Distance calc: 16-142ns (128-1536 dims) - Batch distances: 278µs (1000x384) All crates compile in both debug and release modes.
…cation - ruvector-cluster: Distributed coordination with DAG-based consensus, consistent hashing sharding, node discovery (static/gossip/multicast), and load balancing across shards - ruvector-raft: Full Raft consensus implementation following the paper spec, including leader election, log replication, snapshots, and RPC messages with bincode 2.0 serialization - ruvector-replication: Data replication with sync/async/semi-sync modes, vector clock conflict resolution, CRDT-inspired merge strategies, change streaming with checkpointing, and automatic failover with quorum-based decisions All 56 tests pass across the 3 new crates. Fixed several issues during review: bincode error types, Send bounds for async spawns, unnecessary async methods converted to sync.
The platform-specific packages (darwin-arm64, darwin-x64, linux-arm64-gnu, linux-x64-gnu, win32-x64-msvc) were updated to 0.1.2, but the main npm/core/package.json still referenced 0.1.1, causing CI build failures. This commit updates the optionalDependencies to match the actual package versions and syncs the package-lock.json accordingly. Fixes build failures in PR #12. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
The "Find built .node files (debug)" step was failing on Windows because it defaulted to PowerShell, which doesn't understand /dev/null redirection. Adding shell: bash makes it consistent with the other build steps and ensures cross-platform compatibility. Fixes Windows build failures in PR #12. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
Replace deprecated macos-13 with macos-15-large to avoid brownouts and ensure Intel Mac builds continue to work. GitHub is deprecating macos-13 runners: actions/runner-images#13046 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
macos-15-large doesn't appear to be available. Using macos-13-xlarge which is the larger Intel runner still available during the transition period. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
Based on GitHub's official documentation, macos-15-intel is the correct replacement for deprecated macos-13 runners for x86_64 architecture. Reference: actions/runner-images#13045 This is the last available x86_64 image from Actions, available until August 2027. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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The platform-specific packages (darwin-arm64, darwin-x64, linux-arm64-gnu, linux-x64-gnu, win32-x64-msvc) were updated to 0.1.2, but the main npm/core/package.json still referenced 0.1.1, causing CI build failures. This commit updates the optionalDependencies to match the actual package versions and syncs the package-lock.json accordingly. Fixes build failures in PR #12. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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The "Find built .node files (debug)" step was failing on Windows because it defaulted to PowerShell, which doesn't understand /dev/null redirection. Adding shell: bash makes it consistent with the other build steps and ensures cross-platform compatibility. Fixes Windows build failures in PR #12. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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…G6MdGiUpao5c8iCn1624X Ruvector updates
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… for AC-2 Commit 19 (d06e80f on feat/analysis-rate-encoder, merged) ran a controlled A/B on the same 8-protocol labeled corpus that disproved SDPA at discovery #10: raw per-neuron-per-time-bin spike counts (the crudest possible encoder; no projection, no attention) scored rate-histogram precision@5 = 0.079 vs SDPA's 0.072 — delta +0.007, inside the ±0.05 tie band. Both encoders score below random chance for 8 classes (0.125). The crudest encoder that preserves all raster information ties the shipped encoder. That rules out the encoder axis of ADR §17 item 10's three-axis framing. Remaining AC-2 levers: - substrate: real FlyWire v783 ingest replaces synthetic SBM (predicted to separate under its heavier non-hub tail) - labels: raster-regime labels replace stimulus-protocol labels (matches what the encoder actually captures) Both are research-level pivots for a separate ADR, not engineering levers on this branch. The branch's broader pattern of measurement-disproving pre-measurement diagnoses now stands at 11-of-12 named levers tested surfacing at least one honest surprise. The sole unambiguous win remains commit 10 (adaptive detect cadence, 4.29×) — changed *when*, not *what*. Co-Authored-By: claude-flow <ruv@ruv.net> EOF )
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…ent + lesion + audit
Ships the public ABIs + productized wrappers that move three of
Connectome OS's exotic applications (README Part 3) one concrete
step closer to feasible. Each is scaffolding, not a full
implementation — the production pieces (MuJoCo bridge, mouse
connectome, real FlyWire data) genuinely can't ship from this
branch — but each gives external code the typed surface to build
against today.
Three new top-level modules:
1. src/embodiment.rs — BodySimulator trait + 2 implementations
(247 LOC incl. tests)
The slot where a physics body sits between the connectome's
motor outputs and sensory inputs. Defines the per-tick ABI
(, , ) that Phase-3 MuJoCo + NeuroMechFly
will drop into. Ships two impls:
- StubBody — deterministic open-loop drive over an existing
Stimulus schedule. Preserves AC-1. This is what the
Tier-1 demo runs with.
- MujocoBody — Phase-3 panic-stub. Constructs without
panicking (so downstream code can Box<dyn BodySimulator>
against it today); panics on step/reset with an
actionable diagnostic pointing at ADR-154 §13 and
04-embodiment.md.
Unblocks application #10 — 'embodied fly navigation in VR'.
The remaining Phase-3 work is the cxx bridge + NeuroMechFly
MJCF ingest; the wiring is now waiting, not un-designed.
2. src/lesion.rs — LesionStudy + CandidateCut + LesionReport
(374 LOC incl. tests)
Productization of AC-5 σ-separation. Outside code can now
answer 'which edges are load-bearing for behaviour X?'
without copy-pasting the test internals. Paired-trial loop,
σ distance against a nominated reference cut, deterministic
across repeat runs. Includes boundary_edges() / interior_edges()
helpers so callers can build cuts from a FunctionalPartition
without re-deriving the traversal.
Unblocks application #11 — 'in-silico circuit-lesion studies'.
Also powers the audit module (next).
3. src/audit.rs — StructuralAudit + StructuralAuditReport
(235 LOC incl. tests)
One-call orchestrator that runs every analysis primitive
(Fiedler coherence, structural mincut, functional mincut,
SDPA motif retrieval, AC-5-shaped causal perturbation) and
returns a single report a reviewer can read top-to-bottom.
Auto-generates boundary-vs-interior candidate cuts when the
caller doesn't supply explicit ones. Same determinism
contract as every underlying primitive.
Unblocks application #13 — 'connectome-grounded AI safety
auditing'. The framing is 'safety auditing'; the deliverable
is a reproducible report, not a safety guarantee.
Applications #12 ('cross-species connectome transfer') needs a
second heterogeneous connectome; today we have the fly-scale
substrate only. Deferred until Tier-2 mouse data lands.
Application #14 ('substrate for structural-intelligence research
papers') was already open — it's the meta-application, no
scaffolding needed.
Lib.rs re-exports the new public types so downstream consumers
can
directly.
Measurements:
10/10 new unit tests pass on :
embodiment: 5 tests (trait object-safe, stub determinism +
windowing, mujoco stub construct-ok +
step-panics-with-diagnostic)
lesion: 3 tests (report shape, boundary/interior disjoint,
deterministic across repeats)
audit: 2 tests (populates every field, deterministic)
All 73 prior tests still pass; no API regression.
Total new LOC: 856 (247 + 374 + 235) src + tests; all files
under the 500-line ADR-154 §3.2 file budget.
Positioning rubric held. Scaffolding is scaffolding — not new
scientific claims. Every module docstring links back to the
Connectome-OS README Part 3 application it unblocks.
Co-Authored-By: claude-flow <ruv@ruv.net>
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This pull request introduces two new crates,
ruvector-collectionsandruvector-cluster, and integrates them into the workspace. These crates provide foundational components for managing vector collections and distributed clustering/sharding. The changes include implementations for collection configuration/validation, error handling, and cluster node discovery (static, gossip, and multicast). Additionally, there are minor improvements to workspace configuration and dependency management.New Crates and Core Functionality
ruvector-collectionscrate, which defines collection types, configuration validation, statistics, and error handling for vector collections. This includes support for configuration validation, human-readable stats, and integration with the core vector database. [1]], [2]], [3]])ruvector-clustercrate, implementing distributed clustering and sharding primitives. It provides node discovery mechanisms (static, gossip, multicast), node registration and heartbeat logic, and basic gossip protocol statistics and tests. [1]], [2]])Workspace and Dependency Updates
memberslist inCargo.toml. ([Cargo.tomlR8-R22])bincodedependency to enable theserdefeature, improving serialization compatibility. ([Cargo.tomlL37-R45])opt-leveloverride for the test profile, so it now uses the default optimization level. ([Cargo.tomlL102])