{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-ruvector","slug":"ruvector","name":"ruvector","type":"repo","url":"https://ruv.io","page_url":"https://unfragile.ai/ruvector","categories":["rag-knowledge"],"tags":["vector","database","vector-database","vector-search","similarity-search","semantic-search","embeddings","hnsw","ann","ai","machine-learning","rag","rust","wasm","native","ruv","ruvector","attention","transformer","flash-attention"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-ruvector__cap_0","uri":"capability://search.retrieval.hnsw.accelerated.approximate.nearest.neighbor.search","name":"hnsw-accelerated approximate nearest neighbor search","description":"Implements Hierarchical Navigable Small World (HNSW) algorithm for sub-linear time vector similarity search across high-dimensional embeddings. Uses a multi-layer graph structure with greedy search traversal to locate nearest neighbors in logarithmic complexity, enabling fast retrieval from million-scale vector collections without exhaustive scanning.","intents":["Find semantically similar documents or embeddings in milliseconds from a large corpus","Retrieve top-k nearest neighbors for RAG context injection without full-table scans","Build real-time recommendation systems that match user queries against embedding catalogs"],"best_for":["Teams building semantic search or RAG pipelines requiring sub-100ms latency","Developers implementing recommendation engines with vector similarity matching","Solo developers prototyping LLM-augmented applications with local vector storage"],"limitations":["HNSW construction requires O(n log n) memory overhead during indexing; large datasets may need incremental batch loading","Search quality degrades if embeddings are not normalized or dimensionally mismatched","No built-in distributed sharding — single-node architecture limits horizontal scaling beyond ~10M vectors per instance"],"requires":["Node.js 16+","Vector embeddings pre-computed from external model (OpenAI, Hugging Face, etc.)","Sufficient RAM for index structure (typically 2-4x embedding data size)"],"input_types":["float32 or float64 arrays (vector embeddings)","JSON metadata objects associated with vectors"],"output_types":["Array of nearest neighbor indices with distance scores","Ranked result set with metadata payloads"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-ruvector__cap_1","uri":"capability://search.retrieval.hybrid.search.combining.dense.and.sparse.retrieval","name":"hybrid search combining dense and sparse retrieval","description":"Merges HNSW dense vector search with BM25-style sparse keyword matching, then re-ranks results using configurable fusion strategies (RRF, weighted sum). Allows queries to match both semantic meaning and exact terminology, improving recall for domain-specific or technical documents where keyword precision matters alongside semantic similarity.","intents":["Search technical documentation where both exact terms and semantic concepts are important","Retrieve legal or compliance documents using both keyword matching and conceptual similarity","Improve RAG context quality by combining semantic relevance with keyword specificity"],"best_for":["Enterprise teams handling mixed-modality search (semantic + keyword) in specialized domains","Developers building search for technical documentation, code repositories, or legal corpora","RAG systems requiring high precision where missing a keyword is costly"],"limitations":["Sparse indexing adds storage overhead (~30-50% additional disk space for inverted indices)","Re-ranking step introduces latency; fusion computation adds ~50-100ms per query depending on result set size","Requires tuning fusion weights per domain; no automatic optimization for domain-specific relevance"],"requires":["Node.js 16+","Pre-computed dense embeddings","Tokenizer configuration for sparse indexing (language-specific)"],"input_types":["Query text (natural language or keyword phrase)","Vector embeddings (dense)","Document text (for sparse indexing)"],"output_types":["Merged ranked result set with hybrid scores","Metadata with per-strategy scores (dense score, sparse score, fused score)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-ruvector__cap_10","uri":"capability://data.processing.analysis.embedding.generation.with.pluggable.model.backends","name":"embedding generation with pluggable model backends","description":"Integrates with multiple embedding model providers (OpenAI, Hugging Face, local models) through a pluggable backend interface, handling tokenization, batching, and error retry logic. Allows switching embedding models without changing application code, and supports local model execution for privacy-sensitive deployments or cost optimization.","intents":["Generate embeddings from raw text without external API calls (using local models)","Switch between embedding models (e.g., from OpenAI to open-source) without code changes","Batch embed documents efficiently with automatic retry and error handling"],"best_for":["Teams with privacy requirements that prevent cloud API calls","Cost-conscious deployments using open-source embedding models","Applications experimenting with different embedding models"],"limitations":["Local model execution requires GPU or significant CPU resources; inference latency varies by model","Model switching requires re-embedding the corpus; no incremental updates","Batching efficiency depends on model backend; some backends have suboptimal batch handling","No automatic model selection; requires manual evaluation of model quality vs. cost/latency"],"requires":["Node.js 16+","Embedding model (local or API key for cloud provider)","Sufficient compute for local models (GPU recommended)"],"input_types":["Raw text documents","Model identifier (OpenAI, Hugging Face, local path)","Batch size and tokenization parameters"],"output_types":["Vector embeddings (float32/float64 arrays)","Embedding metadata (model used, token count, etc.)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-ruvector__cap_11","uri":"capability://search.retrieval.query.expansion.and.semantic.rewriting","name":"query expansion and semantic rewriting","description":"Automatically expands queries with synonyms, related terms, and semantic variations before search, or rewrites queries to improve retrieval quality. Uses attention mechanisms and language models to generate alternative query formulations that capture different aspects of user intent, increasing recall by matching documents that use different terminology.","intents":["Improve search recall by automatically expanding queries with synonyms and related terms","Handle ambiguous queries by generating multiple semantic interpretations","Adapt queries to domain-specific terminology without user intervention"],"best_for":["Search systems serving non-expert users who may use imprecise terminology","Domain-specific applications where terminology varies (medical, legal, technical)","RAG systems requiring high recall for complex or ambiguous queries"],"limitations":["Query expansion adds latency (typically 50-200ms per query) for expansion generation","Expansion quality depends on underlying language model; poor expansions reduce precision","No automatic feedback loop to learn which expansions are effective; requires manual tuning","Expansion can increase search cost if expanded queries match many irrelevant documents"],"requires":["Node.js 16+","Language model for query expansion (local or API)","Domain-specific synonym/thesaurus data (optional)"],"input_types":["Original query text","Expansion strategy (synonym, semantic variation, domain-specific)","Expansion parameters (number of variations, temperature, etc.)"],"output_types":["Expanded query set with original and variations","Merged results from all query variations","Per-query contribution to final ranking"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-ruvector__cap_12","uri":"capability://data.processing.analysis.similarity.score.normalization.and.calibration","name":"similarity score normalization and calibration","description":"Normalizes and calibrates similarity scores from HNSW search to produce interpretable confidence values (0-1 range) that reflect actual retrieval quality. Uses statistical calibration based on query patterns to adjust raw distance scores, enabling consistent ranking across different embedding models and distance metrics without manual threshold tuning.","intents":["Produce interpretable confidence scores for search results (0-1 range)","Set automatic relevance thresholds without manual tuning","Compare results across different embedding models using normalized scores"],"best_for":["Applications displaying confidence scores to users","RAG systems requiring automatic relevance filtering","Teams comparing multiple embedding models objectively"],"limitations":["Calibration requires sufficient query volume to establish statistical patterns; cold-start scores may be inaccurate","Calibration is corpus-specific; transferring to new domains requires re-calibration","Normalization adds ~10-20ms latency per query for score transformation","No guarantee that normalized scores reflect actual semantic relevance; calibration is empirical"],"requires":["Node.js 16+","Query history with relevance feedback (for calibration)","Distance metric specification (cosine, euclidean, etc.)"],"input_types":["Raw similarity scores from HNSW search","Distance metric used","Calibration data (query logs with relevance labels)"],"output_types":["Normalized scores (0-1 range)","Confidence intervals (optional)","Calibration metrics (accuracy, precision, recall)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-ruvector__cap_2","uri":"capability://memory.knowledge.graph.based.rag.with.multi.hop.traversal","name":"graph-based rag with multi-hop traversal","description":"Constructs a knowledge graph from indexed documents where nodes represent entities/concepts and edges represent relationships, enabling multi-hop retrieval that follows semantic connections across documents. Queries traverse the graph to gather contextually related information beyond direct similarity matches, improving context coherence for LLM generation by providing interconnected knowledge.","intents":["Build RAG systems that provide multi-hop context (e.g., 'find related papers and their citations')","Retrieve interconnected knowledge for complex reasoning tasks requiring relationship understanding","Improve LLM context quality by including semantically adjacent documents in the knowledge graph"],"best_for":["Teams building knowledge-intensive RAG systems (research, medical, legal domains)","Developers implementing entity-aware retrieval for complex reasoning chains","Applications requiring relationship-aware context injection beyond keyword/semantic similarity"],"limitations":["Graph construction requires entity extraction and relationship inference; quality depends on upstream NLP pipeline","Multi-hop traversal adds query latency (typically 2-5x slower than single-hop HNSW search)","Graph memory footprint scales with relationship density; highly connected graphs can exceed available RAM","No automatic graph maintenance; requires manual updates when documents are added/removed"],"requires":["Node.js 16+","Entity extraction model or service (external dependency)","Relationship extraction or linking strategy","Sufficient RAM for graph adjacency structures"],"input_types":["Query text with optional entity hints","Document corpus with extracted entities and relationships","Hop depth parameter (1-5 typical)"],"output_types":["Multi-hop result set with traversal path metadata","Ranked documents with relationship provenance","Graph structure visualization data (optional)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-ruvector__cap_3","uri":"capability://data.processing.analysis.flashattention.3.optimized.attention.computation","name":"flashattention-3 optimized attention computation","description":"Implements FlashAttention-3 algorithm for efficient attention mechanism computation during embedding refinement and query processing, reducing memory bandwidth requirements and computational complexity from O(n²) to near-linear through IO-aware tiling and kernel fusion. Enables processing of longer context windows and larger batch sizes without proportional memory growth.","intents":["Process longer document sequences in embedding generation without memory overflow","Batch-process multiple queries efficiently with reduced GPU/CPU memory pressure","Improve embedding quality through attention-based refinement with lower computational cost"],"best_for":["Teams processing long-form documents (>4K tokens) in RAG pipelines","Developers optimizing embedding generation for cost-sensitive deployments","Applications requiring batch embedding computation with memory constraints"],"limitations":["FlashAttention-3 optimization applies primarily to embedding refinement, not search operations","Requires compatible hardware (NVIDIA GPUs with compute capability 8.0+); CPU fallback is slower","Attention computation is optional; disabling it reduces memory overhead but loses refinement benefits","No automatic hardware detection; requires manual configuration for optimal performance"],"requires":["Node.js 16+","NVIDIA GPU with compute capability 8.0+ (optional but recommended)","CUDA 11.8+ if using GPU acceleration"],"input_types":["Document text or token sequences","Query embeddings","Batch size parameter"],"output_types":["Refined embeddings with attention-weighted features","Attention weight matrices (optional, for interpretability)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-ruvector__cap_4","uri":"capability://data.processing.analysis.50.pluggable.attention.mechanisms.for.embedding.customization","name":"50+ pluggable attention mechanisms for embedding customization","description":"Provides a modular architecture supporting 50+ attention variants (multi-head, multi-query, grouped-query, linear attention, sparse attention, etc.) that can be swapped during embedding computation. Allows fine-tuning embedding quality for specific domains by selecting attention patterns that emphasize different aspects of token relationships, without recomputing base embeddings.","intents":["Customize embedding behavior for domain-specific similarity (e.g., code embeddings vs. natural language)","Experiment with different attention patterns to optimize retrieval quality for a specific corpus","Adapt embedding computation to hardware constraints by selecting lighter-weight attention variants"],"best_for":["Researchers and ML engineers tuning embedding quality for specialized domains","Teams with domain-specific similarity requirements (code, medical, legal, etc.)","Developers optimizing for inference speed vs. quality trade-offs"],"limitations":["Attention mechanism selection requires domain expertise; no automatic recommendation system","Switching attention mechanisms requires re-embedding the corpus; no incremental updates","Performance characteristics vary widely across mechanisms; benchmarking is necessary per use case","Documentation and examples may be sparse for less common attention variants"],"requires":["Node.js 16+","Understanding of attention mechanisms (multi-head, sparse, etc.)","Ability to re-embed corpus when changing mechanisms"],"input_types":["Attention mechanism identifier (string or enum)","Mechanism-specific hyperparameters (head count, sparsity pattern, etc.)","Document/query text for embedding"],"output_types":["Embeddings computed with selected attention mechanism","Metadata indicating which mechanism was used"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-ruvector__cap_5","uri":"capability://automation.workflow.self.learning.index.optimization.with.adaptive.statistics","name":"self-learning index optimization with adaptive statistics","description":"Continuously monitors query patterns and result quality, automatically adjusting HNSW parameters (M, ef_construction, ef_search) and attention mechanism selection based on observed performance. Uses statistical feedback from queries to optimize index structure without manual tuning, improving search latency and recall over time as the system learns domain-specific access patterns.","intents":["Deploy vector search without manual index tuning; let the system optimize itself","Improve search quality automatically as query patterns stabilize","Reduce operational overhead by eliminating manual parameter optimization"],"best_for":["Teams without ML/vector DB expertise who need hands-off optimization","Applications with evolving query patterns that benefit from adaptive tuning","Startups prioritizing time-to-value over fine-grained control"],"limitations":["Self-learning requires sufficient query volume to establish statistical patterns; cold-start performance may be suboptimal","Optimization is heuristic-based; no guarantee of global optimality","Parameter changes may cause temporary performance fluctuations during adaptation","Learning is local to a single instance; no cross-instance knowledge sharing in distributed setups"],"requires":["Node.js 16+","Minimum query volume (typically 100+ queries) to establish patterns","Persistent storage for learning state (file system or external DB)"],"input_types":["Query logs with latency and recall metrics","Index configuration parameters","Feedback signals (user satisfaction, click-through rates, etc.)"],"output_types":["Optimized HNSW parameters (M, ef_construction, ef_search)","Recommended attention mechanism","Optimization metrics and learning curves"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-ruvector__cap_6","uri":"capability://automation.workflow.incremental.batch.indexing.with.conflict.resolution","name":"incremental batch indexing with conflict resolution","description":"Supports adding, updating, and deleting vectors in batches without full index reconstruction, using HNSW insertion algorithms and conflict resolution strategies to maintain index integrity. Detects duplicate embeddings or conflicting metadata and applies configurable merge strategies (keep-newest, keep-oldest, merge-metadata), enabling continuous corpus updates without downtime.","intents":["Add new documents to the index continuously without rebuilding","Update embeddings or metadata for existing documents with conflict handling","Remove documents from the index while maintaining search performance"],"best_for":["Applications with continuously evolving document corpora (news, research, product catalogs)","Teams requiring zero-downtime updates to production vector indexes","Systems managing document versioning or metadata updates"],"limitations":["Incremental insertion adds ~5-10% overhead per operation vs. batch construction","Conflict resolution is deterministic but may not match all business logic; custom strategies require code changes","Deletions don't immediately reclaim disk space; requires periodic compaction/rebuild","No distributed transaction semantics; batch atomicity is per-instance only"],"requires":["Node.js 16+","Unique identifiers for vectors to detect duplicates","Conflict resolution strategy configuration"],"input_types":["Batch of vector objects with ID, embedding, and metadata","Operation type (insert, update, delete)","Conflict resolution strategy (keep-newest, keep-oldest, merge-metadata)"],"output_types":["Batch operation result with success/failure per item","Conflict resolution report (items merged, duplicates detected, etc.)","Updated index statistics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-ruvector__cap_7","uri":"capability://code.generation.editing.rust.wasm.native.execution.with.node.js.bindings","name":"rust/wasm native execution with node.js bindings","description":"Compiles core vector search algorithms (HNSW, attention mechanisms, indexing) to WebAssembly and Rust native binaries, exposing them via Node.js native bindings (N-API). Avoids JavaScript performance bottlenecks by executing compute-intensive operations in compiled code while maintaining JavaScript API ergonomics, achieving 10-100x speedup over pure-JS implementations.","intents":["Achieve near-native performance for vector search without leaving Node.js ecosystem","Process large embeddings and indexes without JavaScript memory/CPU constraints","Deploy vector search in serverless/edge environments where WASM is preferred"],"best_for":["Node.js teams requiring high-performance vector search without polyglot infrastructure","Developers building serverless RAG systems (AWS Lambda, Vercel, etc.)","Teams prioritizing single-language deployments for operational simplicity"],"limitations":["Native bindings require compilation for each target platform (Linux, macOS, Windows); pre-built binaries may not cover all architectures","WASM variant has overhead from JS-WASM boundary crossing; not suitable for extremely latency-sensitive applications","Debugging native code requires different tooling than pure JavaScript","Updates to Rust code require recompilation and redistribution of binaries"],"requires":["Node.js 16+","Rust toolchain (for building from source)","Compatible CPU architecture (x86_64, ARM64)"],"input_types":["Vector embeddings (float32/float64 arrays)","Metadata objects (JSON-serializable)"],"output_types":["Search results with scores","Index statistics and performance metrics"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-ruvector__cap_8","uri":"capability://search.retrieval.metadata.filtering.with.boolean.and.range.queries","name":"metadata filtering with boolean and range queries","description":"Supports filtering search results by metadata attributes using boolean logic (AND, OR, NOT) and range queries (numeric comparisons, date ranges, string matching). Filters are applied post-search (after HNSW retrieval) or pre-search (to narrow candidate set), allowing queries like 'find similar documents from 2024 with category=research AND author IN [list]' without separate database lookups.","intents":["Filter RAG context by document metadata (date, category, source, etc.)","Implement faceted search combining similarity with categorical constraints","Build access-control-aware search (e.g., only return documents user has permission to view)"],"best_for":["RAG systems requiring context filtering by document properties","Multi-tenant applications needing per-user or per-organization filtering","Search interfaces with facets or categorical constraints"],"limitations":["Post-search filtering reduces effective recall if many results are filtered out; pre-search filtering requires index on metadata","Complex boolean expressions can be slow; no query optimization for filter order","Range queries on high-cardinality fields (timestamps) may require full scans","No support for full-text search within metadata; only exact/range matching"],"requires":["Node.js 16+","Metadata indexed and stored with vectors","Filter expression parser/evaluator"],"input_types":["Query text (for similarity search)","Filter expression (boolean logic with range/equality operators)","Metadata schema definition"],"output_types":["Filtered result set with matching vectors and metadata","Filter match statistics (how many results matched each filter clause)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-ruvector__cap_9","uri":"capability://automation.workflow.persistent.storage.with.optional.in.memory.caching","name":"persistent storage with optional in-memory caching","description":"Stores vector index and metadata to disk (file system or cloud storage) with optional in-memory cache layer for frequently accessed vectors. Supports both memory-mapped access (for large indexes exceeding RAM) and full in-memory operation, with configurable cache eviction policies (LRU, LFU) to balance memory usage and latency.","intents":["Persist vector indexes across application restarts without recomputation","Handle indexes larger than available RAM using memory-mapped file access","Optimize latency for hot vectors while keeping cold vectors on disk"],"best_for":["Production deployments requiring index persistence and recovery","Applications with large indexes (>10GB) that don't fit in RAM","Teams needing to balance memory cost with latency requirements"],"limitations":["Disk I/O adds latency for cache misses; memory-mapped access is slower than in-memory search","Cache coherency is single-instance only; distributed caching requires external cache layer (Redis)","Persistence format is proprietary; migrating to other vector DBs requires re-indexing","No built-in backup/replication; requires external tools for durability"],"requires":["Node.js 16+","Writable file system or cloud storage credentials (S3, GCS, etc.)","Sufficient disk space for index (typically 2-4x embedding data size)"],"input_types":["Vector index (in-memory or memory-mapped)","Cache configuration (size, eviction policy)","Storage path or cloud credentials"],"output_types":["Persisted index files","Cache hit/miss statistics","Storage usage metrics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["Node.js 16+","Vector embeddings pre-computed from external model (OpenAI, Hugging Face, etc.)","Sufficient RAM for index structure (typically 2-4x embedding data size)","Pre-computed dense embeddings","Tokenizer configuration for sparse indexing (language-specific)","Embedding model (local or API key for cloud provider)","Sufficient compute for local models (GPU recommended)","Language model for query expansion (local or API)","Domain-specific synonym/thesaurus data (optional)","Query history with relevance feedback (for calibration)"],"failure_modes":["HNSW construction requires O(n log n) memory overhead during indexing; large datasets may need incremental batch loading","Search quality degrades if embeddings are not normalized or dimensionally mismatched","No built-in distributed sharding — single-node architecture limits horizontal scaling beyond ~10M vectors per instance","Sparse indexing adds storage overhead (~30-50% additional disk space for inverted indices)","Re-ranking step introduces latency; fusion computation adds ~50-100ms per query depending on result set size","Requires tuning fusion weights per domain; no automatic optimization for domain-specific relevance","Local model execution requires GPU or significant CPU resources; inference latency varies by model","Model switching requires re-embedding the corpus; no incremental updates","Batching efficiency depends on model backend; some backends have suboptimal batch handling","No automatic model selection; requires manual evaluation of model quality vs. cost/latency","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2810447595123116,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-04-22T08:08:13.652Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":32316,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=ruvector","compare_url":"https://unfragile.ai/compare?artifact=ruvector"}},"signature":"8E8BjNhNK927uO16iVHfJ5pElt9GCJUhqWoDJVhUYAgyndXrjrlanMYww7wbgnjKaqnjXvFRsRgJQslZIhxMBQ==","signedAt":"2026-06-21T22:04:16.436Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ruvector","artifact":"https://unfragile.ai/ruvector","verify":"https://unfragile.ai/api/v1/verify?slug=ruvector","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}