ADR-029: External Intelligence Providers for SONA Learning#190
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grparry wants to merge 825 commits intoruvnet:mainfrom
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ADR-029: External Intelligence Providers for SONA Learning#190grparry wants to merge 825 commits intoruvnet:mainfrom
grparry wants to merge 825 commits intoruvnet:mainfrom
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…ge networks - Add networks.js with NetworkGenesis, NetworkRegistry, and MultiNetworkManager - Support for public, private (invite-only), and consortium networks - Each network has its own genesis block, QDAG ledger, and peer registry - Network IDs derived from genesis hash for tamper-evident identity - Invite code generation for private networks with base64url encoding New CLI options: --networks List all known networks --discover Discover available networks --create-network Create a new network with custom name/type --network-type Set network type (public/private/consortium) --switch Switch active network for contributions --invite Provide invite code for private networks Security features: - Network isolation with separate storage per network - Cryptographic network identity from genesis hash - Invite codes for access control on private networks - Ed25519 signatures for network announcements Well-known networks: - mainnet: Primary public compute network - testnet: Testing and development network
…nhancements Analysis module: - Add complexity analysis (cyclomatic, cognitive, Halstead metrics) - Add security scanning (SQL injection, XSS, command injection detection) - Add pattern detection (code smells, design patterns) Workers module: - Add native worker implementation for parallel processing - Add benchmark worker for performance testing - Add worker type definitions Core improvements: - Add adaptive embedder with dynamic model selection - Add ONNX optimized embeddings with caching - Update intelligence engine with enhanced learning - Update parallel workers with better concurrency Dashboard enhancements: - Add relay client service for Edge-Net communication - Update network stats and specialized networks components - Update network store with improved state management - Update type definitions Configuration: - Add custom workers skill - Add agentic-flow and ruvector fast scripts - Update settings and gitignore 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
feat(dashboard): Edge-Net Time Crystal Dashboard
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Merging Edge-Net join CLI with multi-contributor support
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…net#104) * feat: Add comprehensive dataset discovery framework for RuVector This commit introduces a powerful dataset discovery framework with integrations for three high-impact public data sources: ## Core Framework (examples/data/framework/) - DataIngester: Streaming ingestion with batching and deduplication - CoherenceEngine: Min-cut based coherence signal computation - DiscoveryEngine: Pattern detection for emerging structures ## OpenAlex Integration (examples/data/openalex/) - Research frontier radar: Detect emerging fields via boundary motion - Cross-domain bridge detection: Find connector subgraphs - Topic graph construction from citation networks - Full API client with cursor-based pagination ## Climate Integration (examples/data/climate/) - NOAA GHCN and NASA Earthdata clients - Sensor network graph construction - Regime shift detection using min-cut coherence breaks - Time series vectorization for similarity search - Seasonal decomposition analysis ## SEC EDGAR Integration (examples/data/edgar/) - XBRL financial statement parsing - Peer network construction - Coherence watch: Detect fundamental vs narrative divergence - Filing analysis with sentiment and risk extraction - Cross-company contagion detection Each integration leverages RuVector's unique capabilities: - Vector memory for semantic similarity - Graph structures for relationship modeling - Dynamic min-cut for coherence signal computation - Time series embeddings for pattern matching Discovery thesis: Detect emerging patterns before they have names, find non-obvious cross-domain bridges, and map causality chains. * feat: Add working discovery examples for climate and financial data - Fix borrow checker issues in coherence analysis modules - Create standalone workspace for data examples - Add regime_detector.rs for climate network coherence analysis - Add coherence_watch.rs for SEC EDGAR narrative-fundamental divergence - Add frontier_radar.rs template for OpenAlex research discovery - Update Cargo.toml dependencies for example executability - Add rand dev-dependency for demo data generation Examples successfully detect: - Climate regime shifts via min-cut coherence analysis - Cross-regional teleconnection patterns - Fundamental vs narrative divergence in SEC filings - Sector fragmentation signals in financial data * feat: Add working discovery examples for climate and financial data - Add RuVector-native discovery engine with Stoer-Wagner min-cut - Implement cross-domain pattern detection (climate ↔ finance) - Add cosine similarity for vector-based semantic matching - Create cross_domain_discovery example demonstrating: - 42% cross-domain edge connectivity - Bridge formation detection with 0.73-0.76 confidence - Climate and finance correlation hypothesis generation * perf: Add optimized discovery engine with SIMD and parallel processing Performance improvements: - 8.84x speedup for vector insertion via parallel batching - 2.91x SIMD speedup for cosine similarity (chunked + AVX2) - Incremental graph updates with adjacency caching - Early termination in Stoer-Wagner min-cut Statistical analysis features: - P-value computation for pattern significance - Effect size (Cohen's d) calculation - 95% confidence intervals - Granger-style temporal causality detection Benchmark results (248 vectors, 3 domains): - Cross-domain edges: 34.9% of total graph - Domain coherence: Climate 0.74, Finance 0.94, Research 0.97 - Detected climate-finance temporal correlations * feat: Add discovery hunter and comprehensive README tutorial New features: - Discovery hunter example with multi-phase pattern detection - Climate extremes, financial stress, and research data generation - Cross-domain hypothesis generation - Anomaly injection testing Documentation: - Detailed README with step-by-step tutorial - API reference for OptimizedConfig and patterns - Performance benchmarks and best practices - Troubleshooting guide * feat: Complete discovery framework with all features HNSW Indexing (754 lines): - O(log n) approximate nearest neighbor search - Configurable M, ef_construction parameters - Cosine, Euclidean, Manhattan distance metrics - Batch insertion support API Clients (888 lines): - OpenAlex: academic works, authors, topics - NOAA: climate observations - SEC EDGAR: company filings - Rate limiting and retry logic Persistence (638 lines): - Save/load engine state and patterns - Gzip compression (3-10x size reduction) - Incremental pattern appending CLI Tool (1,109 lines): - discover, benchmark, analyze, export commands - Colored terminal output - JSON and human-readable formats Streaming (570 lines): - Async stream processing - Sliding and tumbling windows - Real-time pattern detection - Backpressure handling Tests (30 unit tests): - Stoer-Wagner min-cut verification - SIMD cosine similarity accuracy - Statistical significance - Granger causality - Cross-domain patterns Benchmarks: - CLI: 176 vectors/sec @ 2000 vectors - SIMD: 6.82M ops/sec (2.06x speedup) - Vector insertion: 1.61x speedup - Total: 44.74ms for 248 vectors * feat: Add visualization, export, forecasting, and real data discovery Visualization (555 lines): - ASCII graph rendering with box-drawing characters - Domain-based ANSI coloring (Climate=blue, Finance=green, Research=yellow) - Coherence timeline sparklines - Pattern summary dashboard - Domain connectivity matrix Export (650 lines): - GraphML export for Gephi/Cytoscape - DOT export for Graphviz - CSV export for patterns and coherence history - Filtered export by domain, weight, time range - Batch export with README generation Forecasting (525 lines): - Holt's double exponential smoothing for trend - CUSUM-based regime change detection (70.67% accuracy) - Cross-domain correlation forecasting (r=1.000) - Prediction intervals (95% CI) - Anomaly probability scoring Real Data Discovery: - Fetched 80 actual papers from OpenAlex API - Topics: climate risk, stranded assets, carbon pricing, physical risk, transition risk - Built coherence graph: 592 nodes, 1049 edges - Average min-cut: 185.76 (well-connected research cluster) * feat: Add medical, real-time, and knowledge graph data sources New API Clients: - PubMed E-utilities for medical literature search (NCBI) - ClinicalTrials.gov v2 API for clinical study data - FDA OpenFDA for drug adverse events and recalls - Wikipedia article search and extraction - Wikidata SPARQL queries for structured knowledge Real-time Features: - RSS/Atom feed parsing with deduplication - News aggregator with multiple source support - WebSocket and REST polling infrastructure - Event streaming with configurable windows Examples: - medical_discovery: PubMed + ClinicalTrials + FDA integration - multi_domain_discovery: Climate-health-finance triangulation - wiki_discovery: Wikipedia/Wikidata knowledge graph - realtime_feeds: News feed aggregation demo Tested across 70+ unit tests with all domains integrated. * feat: Add economic, patent, and ArXiv data source clients New API Clients: - FredClient: Federal Reserve economic indicators (GDP, CPI, unemployment) - WorldBankClient: Global development indicators and climate data - AlphaVantageClient: Stock market daily prices - ArxivClient: Scientific preprint search with category and date filters - UsptoPatentClient: USPTO patent search by keyword, assignee, CPC class - EpoClient: Placeholder for European patent search New Domain: - Domain::Economic for economic/financial indicator data Updated Exports: - Domain colors and shapes for Economic in visualization and export Examples: - economic_discovery: FRED + World Bank integration demo - arxiv_discovery: AI/ML/Climate paper search demo - patent_discovery: Climate tech and AI patent search demo All 85 tests passing. APIs tested with live endpoints. * feat: Add Semantic Scholar, bioRxiv/medRxiv, and CrossRef research clients New Research API Clients: - SemanticScholarClient: Citation graph analysis, paper search, author lookup - Methods: search_papers, get_citations, get_references, search_by_field - Builds citation networks for graph analysis - BiorxivClient: Life sciences preprints - Methods: search_recent, search_by_category (neuroscience, genomics, etc.) - Automatic conversion to Domain::Research - MedrxivClient: Medical preprints - Methods: search_covid, search_clinical, search_by_date_range - Automatic conversion to Domain::Medical - CrossRefClient: DOI metadata and scholarly communication - Methods: search_works, get_work, search_by_funder, get_citations - Polite pool support for better rate limits All clients include: - Rate limiting respecting API guidelines - Retry logic with exponential backoff - SemanticVector conversion with rich metadata - Comprehensive unit tests Examples: - biorxiv_discovery: Fetch neuroscience and clinical research - crossref_demo: Search publications, funders, datasets Total: 104 tests passing, ~2,500 new lines of code * feat: Add MCP server with STDIO/SSE transport and optimized discovery MCP Server Implementation (mcp_server.rs): - JSON-RPC 2.0 protocol with MCP 2024-11-05 compliance - Dual transport: STDIO for CLI, SSE for HTTP streaming - 22 discovery tools exposing all data sources: - Research: OpenAlex, ArXiv, Semantic Scholar, CrossRef, bioRxiv, medRxiv - Medical: PubMed, ClinicalTrials.gov, FDA - Economic: FRED, World Bank - Climate: NOAA - Knowledge: Wikipedia, Wikidata SPARQL - Discovery: Multi-source, coherence analysis, pattern detection - Resources: discovery://patterns, discovery://graph, discovery://history - Pre-built prompts: cross_domain_discovery, citation_analysis, trend_detection Binary Entry Point (bin/mcp_discovery.rs): - CLI arguments with clap - Configurable discovery parameters - STDIO/SSE mode selection Optimized Discovery Runner: - Parallel data fetching with tokio::join! - SIMD-accelerated vector operations (1.1M comparisons/sec) - 6-phase discovery pipeline with benchmarking - Statistical significance testing (p-values) - Cross-domain correlation analysis - CSV export and hypothesis report generation Performance Results: - 180 vectors from 3 sources in 7.5s - 686 edges computed in 8ms - SIMD throughput: 1,122,216 comparisons/sec All 106 tests passing. * feat: Add space, genomics, and physics data source clients Add exotic data source integrations: - Space clients: NASA (APOD, NEO, Mars, DONKI), Exoplanet Archive, SpaceX API, TNS Astronomy - Genomics clients: NCBI (genes, proteins, SNPs), UniProt, Ensembl, GWAS Catalog - Physics clients: USGS Earthquakes, CERN Open Data, Argo Ocean, Materials Project New domains: Space, Genomics, Physics, Seismic, Ocean All 106 tests passing, SIMD benchmark: 208k comparisons/sec * chore: Update export/visualization and output files * docs: Add API client inventory and reference documentation * fix: Update API clients for 2025 endpoint changes - ArXiv: Switch from HTTP to HTTPS (export.arxiv.org) - USPTO: Migrate to PatentSearch API v2 (search.patentsview.org) - Legacy API (api.patentsview.org) discontinued May 2025 - Updated query format from POST to GET - Note: May require API authentication - FRED: Require API key (mandatory as of 2025) - Added error handling for missing API key - Added response error field parsing All tests passing, ArXiv discovery confirmed working * feat: Implement comprehensive 2025 API client library (11,810 lines) Add 7 new API client modules implementing 35+ data sources: Academic APIs (1,328 lines): - OpenAlexClient, CoreClient, EricClient, UnpaywallClient Finance APIs (1,517 lines): - FinnhubClient, TwelveDataClient, CoinGeckoClient, EcbClient, BlsClient Geospatial APIs (1,250 lines): - NominatimClient, OverpassClient, GeonamesClient, OpenElevationClient News & Social APIs (1,606 lines): - HackerNewsClient, GuardianClient, NewsDataClient, RedditClient Government APIs (2,354 lines): - CensusClient, DataGovClient, EuOpenDataClient, UkGovClient - WorldBankGovClient, UNDataClient AI/ML APIs (2,035 lines): - HuggingFaceClient, OllamaClient, ReplicateClient - TogetherAiClient, PapersWithCodeClient Transportation APIs (1,720 lines): - GtfsClient, MobilityDatabaseClient - OpenRouteServiceClient, OpenChargeMapClient All clients include: - Async/await with tokio and reqwest - Mock data fallback for testing without API keys - Rate limiting with configurable delays - SemanticVector conversion for RuVector integration - Comprehensive unit tests (252 total tests passing) - Full error handling with FrameworkError * docs: Add API client documentation for new implementations Add documentation for: - Geospatial clients (Nominatim, Overpass, Geonames, OpenElevation) - ML clients (HuggingFace, Ollama, Replicate, Together, PapersWithCode) - News clients (HackerNews, Guardian, NewsData, Reddit) - Finance clients implementation notes * feat: Implement dynamic min-cut tracking system (SODA 2026) Based on El-Hayek, Henzinger, Li (SODA 2026) subpolynomial dynamic min-cut algorithm. Core Components (2,626 lines): - dynamic_mincut.rs (1,579 lines): EulerTourTree, DynamicCutWatcher, LocalMinCutProcedure - cut_aware_hnsw.rs (1,047 lines): CutAwareHNSW, CoherenceZones, CutGatedSearch Key Features: - O(log n) connectivity queries via Euler-tour trees - n^{o(1)} update time when λ ≤ 2^{(log n)^{3/4}} (vs O(n³) Stoer-Wagner) - Cut-gated HNSW search that respects coherence boundaries - Real-time cut monitoring with threshold-based deep evaluation - Thread-safe structures with Arc<RwLock> Performance (benchmarked): - 75x speedup over periodic recomputation - O(1) min-cut queries vs O(n³) recompute - ~25µs per edge update Tests & Benchmarks: - 36+ unit tests across both modules - 5 benchmark suites comparing periodic vs dynamic - Integration with existing OptimizedDiscoveryEngine This enables real-time coherence tracking in RuVector, transforming min-cut from an expensive periodic computation to a maintained invariant. --------- Co-authored-by: Claude <noreply@anthropic.com>
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🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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…es (ruvnet#106) ## Summary - Add PowerInfer-style sparse inference engine with precision lanes - Add memory module with QuantizedWeights and NeuronCache - Fix compilation and test issues - Demonstrated 2.9-8.7x speedup at typical sparsity levels - Published to crates.io as ruvector-sparse-inference v0.1.30 ## Key Features - Low-rank predictor using P·Q matrix factorization for fast neuron selection - Sparse FFN kernels that only compute active neurons - SIMD optimization for AVX2, SSE4.1, NEON, and WASM SIMD - GGUF parser with full quantization support (Q4_0 through Q6_K) - Precision lanes (3/5/7-bit layered quantization) - π integration for low-precision systems 🤖 Generated with [Claude Code](https://claude.com/claude-code)
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…ions Key optimizations in v0.1.31: - W2 matrix stored transposed for contiguous row access during sparse accumulation - SIMD GELU/SiLU using AVX2+FMA polynomial approximations - Cached SIMD feature detection with OnceLock (eliminates runtime CPUID calls) - SIMD axpy for vectorized weight accumulation Benchmark results (512 input, 2048 hidden): - 10% active: 130µs (83% reduction, 52× vs dense) - 30% active: 383µs (83% reduction, 18× vs dense) - 50% active: 651µs (83% reduction, 10× vs dense) - 70% active: 912µs (83% reduction, 7× vs dense) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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…mbeddings (ruvnet#107) ## New Features - HNSW Integration: O(log n) similarity search replaces O(n²) brute force (10-50x speedup) - Similarity Cache: 2-3x speedup for repeated similarity queries - Batch ONNX Embeddings: Chunked processing with progress callbacks - Shared Utils Module: cosine_similarity, euclidean_distance, normalize_vector - Auto-connect by Embeddings: CoherenceEngine creates edges from vector similarity ## Performance Improvements - 8.8x faster batch vector insertion (parallel processing) - 10-50x faster similarity search (HNSW vs brute force) - 2.9x faster similarity computation (SIMD acceleration) - 2-3x faster repeated queries (similarity cache) ## Files Changed - coherence.rs: HNSW integration, new CoherenceConfig fields - optimized.rs: Similarity cache implementation - utils.rs: New shared utility functions - api_clients.rs: Batch embedding methods (embed_batch_chunked, embed_batch_with_progress) - README.md: Documented all new features and configuration options Published as ruvector-data-framework v0.3.0 on crates.io 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
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) Merge PR ruvnet#109: feat(math): Add ruvector-math crate with advanced algorithms Includes: - ruvector-math: Optimal Transport, Information Geometry, Product Manifolds, Tropical Algebra, Tensor Networks, Spectral Methods, Persistent Homology, Polynomial Optimization - ruvector-attention: 7-theory attention mechanisms - ruvector-math-wasm: WASM bindings - publish-all.yml: Build & publish workflow for all platforms Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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- Badges (npm, crates.io, license, WASM) - Feature overview - Installation instructions - Quick start examples (Browser & Node.js) - Use cases: Distribution comparison, Vector search, Image comparison, Natural gradient - API reference - Performance benchmarks - TypeScript support - Build instructions - Related packages Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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- Rename npm package from ruvector-math-wasm to @ruvector/math-wasm - Update README with correct scoped package name - Update workflow to publish with scoped name - Add scripts/test-wasm.mjs for WASM package testing - Consistent with @ruvector/attention-* naming convention Published: - @ruvector/math-wasm@0.1.31 on npm Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Complete README rewrite reflecting the final state of the project: - Added "What It Does" section showing actual 8-stage demo output - Added RVDNA AI-native format section with format comparison table - Added real gene data section (HBB, TP53, BRCA1, CYP2D6, INS) - Added actual Criterion benchmark numbers (155ns SNP, 12ms full pipeline) - Fixed Quick Start to match working binary commands - Added collapsible module guides with accurate line counts - Added test suite summary (87 tests, zero mocks) - Added project structure tree with all 13 source files - Added 13 ADR index table - Updated architecture diagram to include RVDNA output stage https://claude.ai/code/session_013B6stXbYwAkWHbE16sjUrq
- Affine gap scoring: 3-matrix Smith-Waterman (H/E/F) with flat 1D arrays for cache-friendly access, direct slice indexing - Indel detection: call_indel() for insertion/deletion from pileup data - VCF output: VCFv4.3 format with proper CHROM/POS/REF/ALT/QUAL columns - CYP2C19 pharmacogenomics: star allele calling (*1/*2/*3/*17), phenotype prediction, drug recommendations (clopidogrel, voriconazole) - Cancer signal detection: methylation entropy + extreme ratio scoring, CancerSignalDetector with configurable risk threshold - Molecular weight: monoisotopic Da for all 20 amino acids - Isoelectric point: Henderson-Hasselbalch bisection with sidechain pKa - K-mer encoding: zero-allocation canonical hashing (hash both strands, take min) eliminates O(n) Vec allocs per sliding window - CRC32: lookup table replaces bit-by-bit (~8x faster header checksums) - Benchmarks: added RVDNA, epigenomics, protein analysis groups 95 tests pass (54 lib + 12 kmer + 17 pipeline + 12 security) https://claude.ai/code/session_013B6stXbYwAkWHbE16sjUrq
Smith-Waterman: rolling 2-row DP replaces 3 full (Q+1)*(R+1) matrices. Only prev+curr rows for H/E, single scalar for F. Memory drops from ~600KB to ~12KB for 100x500bp alignment, fitting L1 cache. Traceback matrix retained (tb==0 encodes stop condition, no full H needed). K-mer encoding: zero-allocation canonical hashing eliminates Vec alloc per k-mer in MinHash::sketch() via dual MurmurHash3 (fwd + rc strands). types.rs to_kmer_vector: rolling polynomial hash computes O(1) per k-mer instead of O(k). Removes leading nucleotide, shifts, adds trailing in constant time using precomputed 5^(k-1). Benchmarks (100bp query x 500bp ref / k=11): kmer/encode_1kb: 4.1µs → 2.3µs (1.78x) kmer/encode_100kb: 364µs → 199µs (1.83x) smith_waterman: 416µs → 386µs (1.08x, 10x less memory) full pipeline: 1.98ms → 1.52ms (1.30x end-to-end) 95 tests pass, zero failures. https://claude.ai/code/session_013B6stXbYwAkWHbE16sjUrq
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Rename dna-analyzer-example to rvdna across all source files, tests, and benchmarks. Add crates.io metadata (repository, docs, keywords). Publish rvdna v0.1.0 to crates.io and @ruvector/rvdna v0.1.0 to npm with NAPI-RS platform loader, JS fallbacks, and TypeScript definitions. Also publishes workspace deps at v2.0.2: ruvector-math, ruvector-core, ruvector-filter, ruvector-collections, ruvector-graph, ruvector-gnn. Co-Authored-By: claude-flow <ruv@ruv.net>
Tailored README with JS/TS code examples, API reference tables, WASM tutorial (browser + bundler setup), platform support table, format comparison, and speed benchmarks. Bump to v0.1.1. Co-Authored-By: claude-flow <ruv@ruv.net>
- Add "Why This Exists" section: AI for instant, private, free genomic diagnostics available to everyone - Add install table with crates.io and npm links - Add full npm API table with JS examples and NAPI-RS platform matrix - Replace ASCII architecture with 4 mermaid diagrams in collapsed sections: pipeline, .rvdna format layout, data flow, WASM deployment - Add collapsed rvDNA section to root README.md Co-Authored-By: claude-flow <ruv@ruv.net>
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- Capabilities: new "Genomics & Health" section (items 22-25) - Installation table: cargo add rvdna, npm install @ruvector/rvdna - npm Packages: @ruvector/rvdna under "Genomics & Health" - Rust Crates: rvdna with crates.io badge and feature summary - Updated capability count from 30+ to 34 Co-Authored-By: claude-flow <ruv@ruv.net>
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Crates added: - ruvector-delta-core, delta-graph, delta-index, delta-consensus, delta-wasm (behavioral change tracking subsystem) - profiling (real-time coherence diagnostics) Examples added: - dna (rvDNA genomic analysis) - delta-behavior (change tracking math) - data (dataset discovery framework) - prime-radiant (coherence engine demos) - benchmarks (temporal reasoning benchmarks) - vwm-viewer (visual vector world model viewer) Updated counts: 70 crates, 34 examples, 34 capabilities. Co-Authored-By: claude-flow <ruv@ruv.net>
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Transforms ruqu from classical coherence monitor into full-stack quantum execution intelligence engine (~2K to ~24K lines). New: StateVector, Stabilizer, TensorNetwork, Clifford+T, and Hardware simulation backends. Cost-model planner, surface code decoder (union-find O(n*alpha(n))), QEC scheduler, noise models, OpenQASM 3.0 export, deterministic replay, and cross-backend verification. PR ruvnet#161
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Published to crates.io: ruqu-core, ruqu-algorithms, ruqu-exotic, ruqu-wasm Published to npm: @ruvector/ruqu-wasm@2.0.4 Co-Authored-By: claude-flow <ruv@ruv.net>
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- Updated ruqu-core README with 5 simulation backends, cost-model planner, QEC control plane, OpenQASM 3.0, cryptographic witnesses, transpiler - Fixed ruqu-wasm npm badge and imports to use @ruvector/ruqu-wasm scope - Published to crates.io: ruqu-core, ruqu-algorithms, ruqu-exotic, ruqu-wasm - Published to npm: @ruvector/ruqu-wasm@2.0.5 Co-Authored-By: claude-flow <ruv@ruv.net>
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…vnet#163) * feat(ospipe): implement OSpipe screenpipe integration with WASM + TypeScript SDK Adds the OSpipe crate providing a quantum-enhanced screenpipe integration layer: - Rust core library (7 modules): capture, storage, search, pipeline, safety, config, wasm - WASM bindings via wasm-bindgen for browser deployment - TypeScript SDK (@ruvector/ospipe) with SSE streaming and hybrid search - Frame deduplication, PII safety gate, query routing, cosine similarity search - 56 tests passing (24 unit + 32 integration), builds for native + wasm32 - Comprehensive ADR with Windows/macOS/Linux/WASM integration plans - CI stub for cross-platform matrix builds (Linux, Windows, macOS, WASM) Co-Authored-By: claude-flow <ruv@ruv.net> * chore(ospipe): add README, fix clippy warnings, optimize dedup and pipeline - Add comprehensive README.md with features, comparison tables, quick start guides, collapsed configuration reference, and API docs - Fix all default clippy warnings (auto-fix + manual) - Replace Vec with VecDeque in FrameDeduplicator for O(1) eviction - Remove redundant frame.clone() in ingestion pipeline (move instead) - Add is_empty() to WASM OsPipeWasm type - Fix broken intra-doc link for cfg-gated bindings module - Remove unused imports in integration tests (FrameContent, SearchConfig) Co-Authored-By: claude-flow <ruv@ruv.net> * feat(ospipe): integrate graph, attention, GNN, and quantum crates (Phase 2-4) Add four new OSpipe modules integrating RuVector crates: - graph: KnowledgeGraph wrapping ruvector-graph with heuristic entity extraction (URLs, emails, @mentions, capitalized phrases), entity/ relationship CRUD, and frame entity ingestion - search/reranker: AttentionReranker using ruvector-attention scaled dot-product attention for result re-ranking (0.6*attention + 0.4*cosine) - learning: SearchLearner with EWC (ruvector-gnn) for continual learning without catastrophic forgetting, ReplayBuffer for feedback, and EmbeddingQuantizer for age-based vector compression - quantum: QuantumSearch using ruqu-algorithms QAOA for diversity selection, Grover-inspired amplitude boosting, and optimal iteration estimation All modules use cfg-gated dual implementations (native + WASM stub). 60 tests passing (59 integration + 1 doc-test), native + WASM builds clean. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(ospipe): complete all 15 gap items — HNSW, persistence, REST API, MMR, safety fixes Implements all remaining OSpipe features from the gap analysis: High — Core functionality: - HNSW indexing via ruvector-core with O(log n) ANN search (HnswVectorStore) - EmbeddingModel trait + RuvectorEmbeddingModel for pluggable embedding backends - JSON-file persistence layer (PersistenceLayer) for frames and config - Axum REST API server matching TypeScript SDK endpoints (/search, /graph, /health, /stats, /route) - Enhanced search pipeline wired into ingestion (router -> rerank -> quantum diversity) Medium — Correctness: - WASM/native routing consistency (aligned keyword sets and priority order) - WASM/native safety consistency (email detection, deny keywords, CC/SSN patterns) - MMR (Maximal Marginal Relevance) reranker for diversity vs relevance tradeoff - Delete and update_metadata APIs on VectorStore and HnswVectorStore - Email redaction preserves surrounding whitespace (tabs, newlines, multi-space) Lower — Polish: - TypeScript SDK: fetchWithRetry with exponential backoff, timeout, AbortSignal - console_error_panic_hook init in WASM module - WASM test scaffold (tests/wasm.rs) - Quantization tiers in config (None -> Scalar -> Product -> Binary by age) - All clippy warnings resolved (0 warnings) 82 tests passing, 1 doc-test passing, 0 clippy warnings. Co-Authored-By: claude-flow <ruv@ruv.net> * chore: update Cargo.lock after OSpipe dependency changes Co-Authored-By: claude-flow <ruv@ruv.net> * feat(ospipe): add server binary, WASM build, version-pin deps for publishing - Add ospipe-server binary with CLI args (--port, --data-dir, --help, --version) - Add tracing-subscriber for structured logging - Version-pin all 9 path dependencies for crates.io readiness - Fix ref -> ref mut for KnowledgeGraph mutable borrow in pipeline - Fix redundant rustdoc link in embedding.rs - Update ospipe-wasm package.json to match wasm-pack output filenames - WASM build produces 145KB binary with full browser API Build artifacts (not committed, in dist/): - ospipe-server-linux-x86_64 (1.8MB) - ospipe-server-linux-arm64 (1.6MB) - ospipe-server-windows-x86_64.exe (3.9MB) - ospipe_bg.wasm (145KB) - @ruvector/ospipe npm tarball (13.9KB) Co-Authored-By: claude-flow <ruv@ruv.net> * docs: add OSpipe to root README, publish ospipe + deps to crates.io Add OSpipe personal AI memory section to root README with features, comparison table, install commands, and Rust quickstart. Published to registries: - ospipe v0.1.0 (crates.io) - ruvector-delta-core v0.1.0 (crates.io) - ruvector-cluster v2.0.2 (crates.io) - ruvector-router-core v2.0.2 (crates.io) - @ruvector/ospipe v0.1.0 (npm) - @ruvector/ospipe-wasm v0.1.0 (npm) Co-Authored-By: claude-flow <ruv@ruv.net> * fix: add uuid dev-dep for tests, bump rvlite to 0.2.1 - Add uuid to OSpipe dev-dependencies to fix version mismatch in integration tests - Bump rvlite npm package to 0.2.1 (0.2.0 blocked by npm) Co-Authored-By: claude-flow <ruv@ruv.net>
Built from commit 5b2edc4 Platforms updated: - linux-x64-gnu - linux-arm64-gnu - darwin-x64 - darwin-arm64 - win32-x64-msvc 🤖 Generated by GitHub Actions
ADR-029 proposes a trait-based extension point that lets external systems (workflow engines, CI/CD pipelines, coding assistants) feed quality signals into ruvllm's learning loops without modifying ruvllm core. Motivated by the gap between ADR-002's vision (quality data feeding SONA) and the current reality (no ingestion interface for external systems). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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…Learning Implement trait-based IntelligenceProvider extension point for external quality signals. Addresses PR #190 proposal (renumbered from ADR-029 to avoid collision with existing ADR-029-rvf-canonical-format). - IntelligenceProvider trait with load_signals() and quality_weights() - FileSignalProvider built-in for JSON file-based signal exchange - IntelligenceLoader for multi-provider registration and aggregation - QualitySignal, QualityFactors, ProviderQualityWeights types - calibration_bias() on TaskComplexityAnalyzer for router feedback - 12 unit tests (all passing) Co-Authored-By: claude-flow <ruv@ruv.net>
4 tasks
Implements the trait-based extension point proposed in ADR-029:
- IntelligenceProvider trait (name, load_signals, quality_weights)
- FileSignalProvider built-in (reads JSON array or {signals:[...]} format)
- IntelligenceProviderLoader registry with load_all() and per-provider stats
- QualitySignal, QualityFactors, QualityWeights types with serde support
- calibration_bias() on TaskComplexityAnalyzer (requires 10+ feedback records)
- 7 unit tests covering file parsing, loader orchestration, and fault tolerance
ADR status updated from "Proposal (Draft)" to "Accepted (Implemented)".
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Summary
This proposes and implements a trait-based extension point —
IntelligenceProvider— that lets external systems feed quality signals into ruvllm's SONA learning loops, embedding classifier, and model router without modifying ruvllm core.Resolves #191
What's implemented
src/intelligence/mod.rsIntelligenceProvidertrait,FileSignalProvider,IntelligenceProviderLoaderregistry, signal/weight typesclaude_flow/model_router.rscalibration_bias()onTaskComplexityAnalyzer(requires 10+ feedback records)lib.rsdocs/adr/ADR-029-*The trait
IntelligenceProviderLoaderand called duringload_all()FileSignalProvidercovers the common case (non-Rust systems write JSON)Design decisions
LlmBackendpattern — trait object behindBox<dyn IntelligenceProvider>[...]or wrapped{"signals": [...]}calibration_bias()requires 10+ records — avoids noisy corrections from sparse dataTest plan
cargo check -p ruvllmcompilesFileSignalProvider, verifyload_all()returns signals🤖 Generated with Claude Code