{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-rvlite","slug":"rvlite","name":"rvlite","type":"repo","url":"https://github.com/ruvnet/ruvector/tree/main/npm/packages/rvlite","page_url":"https://unfragile.ai/rvlite","categories":["rag-knowledge"],"tags":["vector-database","embeddings","semantic-search","sql","sparql","cypher","graph-database","wasm","ai","llm","rag","knowledge-graph"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-rvlite__cap_0","uri":"capability://search.retrieval.semantic.vector.search.with.sql.interface","name":"semantic-vector-search-with-sql-interface","description":"Executes semantic similarity search over embedded vectors using SQL SELECT queries with WHERE clauses that filter by vector distance metrics (cosine, euclidean, dot product). The system converts SQL predicates into vector space operations, enabling developers to combine semantic search with traditional relational filtering (e.g., 'SELECT * FROM documents WHERE embedding MATCH query_vector AND created_date > 2024'). This bridges SQL familiarity with vector database operations without requiring separate query languages.","intents":["I want to search documents by semantic similarity but filter results by metadata using SQL syntax I already know","I need to combine vector search with traditional WHERE clause filtering in a single query","I want to avoid learning a new query language for vector operations"],"best_for":["SQL-fluent developers building RAG systems","teams migrating from relational databases to vector-augmented search","applications requiring both semantic and metadata-based filtering"],"limitations":["SQL dialect is custom subset — not all standard SQL features supported (e.g., complex JOINs may have limited vector semantics)","Vector distance metrics limited to cosine, euclidean, dot product — no custom distance functions","Query optimization for large vector datasets relies on in-memory indexing; no distributed query planning"],"requires":["Node.js 14+ or modern browser with WebAssembly support","Vector embeddings pre-computed (external embedding model required)","Data loaded into rvlite instance as JSON or structured format"],"input_types":["SQL query string with MATCH clause","vector embeddings (Float32Array or number[])","JSON documents with embedding field"],"output_types":["JSON array of matched documents","ranked results with similarity scores"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rvlite__cap_1","uri":"capability://search.retrieval.sparql.graph.query.execution.over.vectors","name":"sparql-graph-query-execution-over-vectors","description":"Executes SPARQL queries against vector-embedded RDF triples, enabling semantic graph traversal where nodes are matched by vector similarity rather than exact URI matching. The system converts SPARQL triple patterns into vector distance queries, allowing queries like 'MATCH ?doc WHERE ?doc rdf:type Document AND ?doc hasEmbedding SIMILAR_TO query_vector'. This enables knowledge graph navigation with semantic flexibility for fuzzy entity matching and similarity-based relationship discovery.","intents":["I want to query a knowledge graph using SPARQL but match entities by semantic similarity instead of exact URIs","I need to find related concepts in an RDF graph based on embedding distance","I want to combine graph traversal with vector similarity for entity resolution"],"best_for":["knowledge graph builders using RDF/OWL with semantic matching requirements","linked data applications needing fuzzy entity linking","semantic web developers integrating embeddings into SPARQL queries"],"limitations":["SPARQL subset support — complex OPTIONAL patterns and UNION queries may not translate to vector operations","No federated SPARQL queries across multiple vector stores","Vector similarity matching in SPARQL adds computational overhead compared to exact triple matching","Limited support for SPARQL aggregations (GROUP BY, COUNT) over vector-matched results"],"requires":["RDF triples loaded into rvlite with embedding vectors attached to resources","SPARQL query parser compatible with vector extensions","Node.js 14+ or browser with WASM support"],"input_types":["SPARQL query string with SIMILAR_TO or vector matching extensions","RDF triples in N-Triples or Turtle format","vector embeddings for RDF resources"],"output_types":["SPARQL result set (JSON or XML format)","variable bindings with similarity scores"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rvlite__cap_10","uri":"capability://data.processing.analysis.batch.vector.insertion.and.deletion.operations","name":"batch-vector-insertion-and-deletion-operations","description":"Supports bulk insert and delete operations on vectors and documents, optimizing throughput for loading large datasets or removing multiple records in single operations. The system batches index updates and applies them atomically, reducing overhead compared to individual insert/delete calls. Developers can insert thousands of embeddings with metadata in one call, improving performance for initial data loading and bulk updates.","intents":["I want to load a large dataset of embeddings and documents efficiently","I need to delete multiple documents at once without individual delete calls","I want to minimize index rebuild overhead when bulk updating the database"],"best_for":["applications with large initial data loading requirements","batch ETL pipelines feeding vectors into rvlite","systems performing periodic bulk updates or deletions"],"limitations":["Batch operations may consume significant memory for large batches","No transactional guarantees — partial batch failures may leave database in inconsistent state","Index rebuild during batch operations may block queries temporarily","No progress tracking or cancellation support for long-running batch operations"],"requires":["array of vector embeddings (Float32Array[])","array of document objects with metadata","optional: batch size configuration"],"input_types":["array of vectors with metadata","array of document IDs for deletion"],"output_types":["operation status (success/failure count)","updated database state"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rvlite__cap_11","uri":"capability://automation.workflow.database.serialization.and.snapshot.persistence","name":"database-serialization-and-snapshot-persistence","description":"Serializes the entire vector database (indices, embeddings, metadata) to a compact format that can be saved to disk, IndexedDB, or other storage backends, and restored to recreate the exact database state. The system supports both full snapshots and incremental updates, enabling point-in-time recovery and database migration across runtimes. Developers can checkpoint databases before risky operations, backup to external storage, or distribute pre-indexed databases as part of application bundles.","intents":["I want to save my vector database to disk and reload it later","I need to backup my vector indices before making bulk changes","I want to distribute a pre-indexed vector database with my application"],"best_for":["applications requiring database persistence across sessions","backup and recovery workflows","distributing pre-computed indices with applications"],"limitations":["Serialization size can be large for millions of vectors — may exceed storage quotas in browsers","No incremental backup support — full snapshots required for each backup","Deserialization time increases with database size — may cause startup delays","No version compatibility guarantees — snapshots from different rvlite versions may not be compatible"],"requires":["persistent storage backend (filesystem in Node.js, IndexedDB in browser)","sufficient storage space for serialized database"],"input_types":["in-memory vector database instance"],"output_types":["serialized database snapshot (binary or JSON format)","restored database instance"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rvlite__cap_12","uri":"capability://data.processing.analysis.configurable.distance.metrics.for.similarity.calculation","name":"configurable-distance-metrics-for-similarity-calculation","description":"Supports multiple vector distance metrics (cosine similarity, euclidean distance, dot product) with configurable selection per query or database-wide, enabling developers to choose the metric best suited for their embedding model and use case. The system implements efficient calculations for each metric and allows switching between metrics without reindexing. Different embedding models (e.g., OpenAI vs. Hugging Face) may perform better with different metrics, and rvlite enables experimentation without database restructuring.","intents":["I want to use cosine similarity for my embeddings instead of euclidean distance","I need to experiment with different distance metrics to find the best one for my use case","I want to use dot product for normalized embeddings to improve performance"],"best_for":["developers optimizing search quality for specific embedding models","applications experimenting with different similarity metrics","performance-sensitive systems choosing metrics for speed vs. accuracy"],"limitations":["Different metrics produce incomparable scores — switching metrics breaks existing result rankings","No automatic metric selection based on embedding model — requires manual configuration","Some metrics (e.g., cosine) require normalized vectors — incorrect input produces incorrect results","Performance varies by metric — dot product fastest, cosine requires normalization overhead"],"requires":["vector embeddings compatible with chosen metric","metric selection in query or database configuration"],"input_types":["distance metric specification (cosine, euclidean, dot product)","vector embeddings"],"output_types":["similarity scores using selected metric","ranked results"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rvlite__cap_2","uri":"capability://search.retrieval.cypher.property.graph.traversal.with.vector.similarity","name":"cypher-property-graph-traversal-with-vector-similarity","description":"Executes Cypher queries (Neo4j-style graph query language) over property graphs where node and relationship matching can be based on vector embeddings. The system translates Cypher patterns like 'MATCH (a:Document)-[:RELATED_TO]->(b:Document) WHERE a.embedding SIMILAR_TO query_vector' into vector distance operations combined with graph traversal. This enables property graph navigation with semantic node matching, allowing developers to find similar entities and their relationships in a single query.","intents":["I want to query a property graph using Cypher syntax but match nodes by semantic similarity","I need to find similar entities and their relationships in a graph structure","I want to combine graph pattern matching with vector-based node similarity"],"best_for":["Neo4j developers adding semantic search to property graphs","graph database users needing fuzzy node matching without exact property values","applications building recommendation engines on top of property graphs"],"limitations":["Cypher subset support — not all Neo4j Cypher features implemented (e.g., APOC procedures not available)","Vector similarity matching in graph traversal can create exponential result sets if not carefully constrained","No support for graph algorithms (PageRank, community detection) over vector-matched subgraphs","Performance degrades with large graphs; no distributed graph partitioning"],"requires":["Property graph data loaded with embedding vectors on nodes","Cypher query parser with vector similarity extensions","Node.js 14+ or browser with WASM support"],"input_types":["Cypher query string with vector similarity operators","property graph nodes and relationships in JSON format","vector embeddings for nodes"],"output_types":["Cypher result set with matched paths and nodes","similarity scores for matched nodes"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rvlite__cap_3","uri":"capability://data.processing.analysis.in.memory.vector.indexing.with.approximate.nearest.neighbor","name":"in-memory-vector-indexing-with-approximate-nearest-neighbor","description":"Builds and maintains approximate nearest neighbor (ANN) indices over vector embeddings using in-memory data structures (likely LSH, HNSW, or similar algorithms based on lightweight vector DB patterns). The system automatically indexes vectors as they are inserted, enabling fast similarity search without explicit index creation. Indices are stored in memory and can be serialized to disk/browser storage for persistence, supporting both exact and approximate search modes with configurable recall/speed tradeoffs.","intents":["I want fast semantic search over thousands of embeddings without managing separate index infrastructure","I need to persist vector indices across sessions in browser or Node.js","I want to trade off search accuracy for speed based on my application's latency requirements"],"best_for":["developers building RAG systems in browsers or edge environments","applications with <1M vectors where in-memory indexing is feasible","prototypes and MVPs needing vector search without external database setup"],"limitations":["In-memory storage limits scalability to available RAM — typically practical for <10M vectors on standard hardware","No distributed indexing across multiple machines or processes","Index rebuild required after large batch inserts; no incremental index updates","Approximate search may miss relevant results depending on index parameters and recall settings"],"requires":["Node.js 14+ or modern browser with sufficient available memory","Vector embeddings as Float32Array or number[]","Optional: persistent storage backend (IndexedDB in browser, filesystem in Node.js)"],"input_types":["vector embeddings (Float32Array, number[])","document metadata (JSON objects)","index configuration parameters (index type, recall target)"],"output_types":["ranked list of nearest neighbors with distances","serialized index for persistence"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rvlite__cap_4","uri":"capability://automation.workflow.cross.runtime.vector.database.portability","name":"cross-runtime-vector-database-portability","description":"Provides unified vector database API that works identically across Node.js, browser, and edge runtime environments (Cloudflare Workers, Vercel Edge, etc.) by abstracting storage and compute layers. The system uses WebAssembly for core vector operations and adapts I/O to each runtime (filesystem in Node.js, IndexedDB in browsers, KV storage in edge). Developers write once and deploy the same code to multiple runtimes without runtime-specific branching or configuration.","intents":["I want to build a vector search feature that works in browser, Node.js, and edge functions with the same code","I need to migrate a vector database layer between runtimes without rewriting queries","I want to run RAG pipelines on edge functions without external API calls"],"best_for":["full-stack developers building isomorphic JavaScript applications","teams deploying to edge computing platforms (Vercel, Cloudflare, Deno Deploy)","browser-based AI applications needing offline vector search capability"],"limitations":["Storage backend varies by runtime — no unified persistence model across all environments","Edge runtime memory constraints limit vector database size compared to Node.js","WebAssembly overhead adds ~5-15% latency compared to native implementations","Some advanced features may be unavailable in browser sandbox (e.g., large file I/O)"],"requires":["JavaScript/TypeScript runtime with WebAssembly support (Node.js 14+, modern browsers, edge platforms)","rvlite npm package installed","No external database or API keys required for basic functionality"],"input_types":["vector embeddings (Float32Array, number[])","JSON documents","SQL/SPARQL/Cypher queries"],"output_types":["query results in JSON format","serialized database snapshots"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rvlite__cap_5","uri":"capability://memory.knowledge.embedded.vector.database.with.zero.external.dependencies","name":"embedded-vector-database-with-zero-external-dependencies","description":"Operates as a fully self-contained vector database with no external service dependencies — all vector operations, indexing, and query execution happen within the rvlite process/runtime. The system bundles vector algorithms, query parsers, and storage logic as a single npm package, eliminating network calls or external service requirements. Developers instantiate the database in-process and interact via JavaScript API, making it suitable for offline-first and privacy-sensitive applications.","intents":["I want vector search in my application without setting up or paying for external database services","I need to keep vector data and embeddings private and local to my application","I want to avoid network latency and API rate limits for vector search operations"],"best_for":["privacy-conscious applications handling sensitive embeddings","offline-first applications requiring local vector search","developers prototyping RAG systems without infrastructure setup","edge computing deployments where external API calls are impractical"],"limitations":["Scalability limited to single machine/process memory — no horizontal scaling","No built-in replication or backup mechanisms; persistence relies on application-level serialization","Query performance degrades with dataset size; no distributed query optimization","No multi-user access control or role-based permissions"],"requires":["Node.js 14+ or modern browser","npm package manager for installation","Application code to handle persistence and backup if needed"],"input_types":["vector embeddings (Float32Array, number[])","JSON documents","query strings (SQL/SPARQL/Cypher)"],"output_types":["query results (JSON)","database snapshots for persistence"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rvlite__cap_6","uri":"capability://search.retrieval.multi.query.language.support.sql.sparql.cypher","name":"multi-query-language-support-sql-sparql-cypher","description":"Provides unified query interface supporting three distinct query languages (SQL, SPARQL, Cypher) over the same underlying vector dataset, with automatic translation between query languages and vector operations. The system includes separate query parsers for each language that compile to a common intermediate representation (likely an AST or query plan), enabling developers to choose the most natural language for their use case. A single database instance can handle mixed queries in different languages without data duplication or separate indices.","intents":["I want to use SQL for some queries and SPARQL for others on the same vector dataset","I need to support multiple query languages for different team members with different backgrounds","I want to migrate queries from one language to another without changing the underlying data"],"best_for":["teams with mixed SQL and semantic web expertise","applications supporting multiple query interfaces for different user types","knowledge graph systems needing both relational and graph query capabilities"],"limitations":["Query language subsets are custom implementations — not full SQL/SPARQL/Cypher compliance","Cross-language query optimization is limited; each language compiles independently","Some features in one language may not have equivalents in others (e.g., SPARQL UNION may not map to SQL)","Learning curve increases with support for multiple languages"],"requires":["Node.js 14+ or modern browser","rvlite npm package","understanding of at least one of the three query languages"],"input_types":["SQL SELECT queries with vector matching","SPARQL SELECT/CONSTRUCT queries","Cypher MATCH queries"],"output_types":["unified result format (JSON) regardless of input language","query execution plans"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rvlite__cap_7","uri":"capability://search.retrieval.browser.native.vector.search.with.indexeddb.persistence","name":"browser-native-vector-search-with-indexeddb-persistence","description":"Enables vector search directly in web browsers using IndexedDB for persistent storage of embeddings and indices, eliminating server-side vector database requirements for client-side RAG and semantic search. The system serializes vector indices to IndexedDB, loads them on page initialization, and executes queries entirely in the browser process using WASM-accelerated vector operations. Developers can build offline-capable search features that sync with servers only when needed, reducing latency and bandwidth for vector operations.","intents":["I want to add semantic search to my web app without backend vector database infrastructure","I need offline vector search capability that persists across browser sessions","I want to reduce latency and bandwidth by running vector search client-side"],"best_for":["web application developers building offline-first features","browser-based RAG systems with local document collections","privacy-focused applications avoiding server-side data transmission"],"limitations":["IndexedDB storage quota typically 50MB-1GB per origin — limits vector database size","Browser memory constraints limit in-memory index size to available RAM","No cross-tab synchronization — each browser tab has independent vector database","Older browsers may not support IndexedDB or WebAssembly"],"requires":["modern browser with IndexedDB and WebAssembly support (Chrome 57+, Firefox 52+, Safari 11+)","rvlite npm package bundled for browser use","vector embeddings pre-computed (external embedding model required)"],"input_types":["vector embeddings (Float32Array)","JSON documents","SQL/SPARQL/Cypher queries"],"output_types":["query results (JSON)","serialized indices for IndexedDB storage"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rvlite__cap_8","uri":"capability://memory.knowledge.vector.embedding.agnostic.storage.and.querying","name":"vector-embedding-agnostic-storage-and-querying","description":"Accepts vector embeddings from any source (OpenAI, Hugging Face, local models, etc.) without requiring specific embedding model integration or format conversion. The system stores embeddings as raw float arrays and executes distance calculations (cosine, euclidean, dot product) on any embedding regardless of dimensionality or source. Developers can mix embeddings from different models in the same database and query across them, enabling flexible multi-model RAG pipelines and embedding experimentation.","intents":["I want to use embeddings from multiple sources (OpenAI, Hugging Face, local models) in the same database","I need to experiment with different embedding models without changing my database schema","I want to avoid vendor lock-in to a specific embedding provider"],"best_for":["developers experimenting with different embedding models","multi-model RAG systems combining embeddings from different sources","applications needing flexibility to swap embedding providers"],"limitations":["No built-in embedding generation — requires external embedding models","Mixing embeddings from different models in same query may produce inconsistent results (different semantic spaces)","No automatic embedding dimension validation — mismatched dimensions will cause query errors","No embedding metadata tracking (model name, version, date) — requires application-level management"],"requires":["pre-computed vector embeddings from any source","embeddings as Float32Array or number[] arrays","external embedding model for generating new embeddings"],"input_types":["vector embeddings (Float32Array, number[])","documents with embedding field","query vectors"],"output_types":["query results with similarity scores","ranked document lists"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-rvlite__cap_9","uri":"capability://search.retrieval.query.result.ranking.and.similarity.scoring","name":"query-result-ranking-and-similarity-scoring","description":"Ranks query results by vector similarity distance and returns explicit similarity scores for each match, enabling developers to filter results by confidence threshold or present relevance scores to users. The system calculates distances using configurable metrics (cosine, euclidean, dot product) and sorts results by distance, optionally normalizing scores to 0-1 range. Scores are returned alongside results, allowing applications to implement confidence-based filtering, result reranking, or relevance visualization.","intents":["I want to filter search results by a minimum similarity threshold","I need to display relevance scores to users alongside search results","I want to rerank results based on similarity scores combined with other signals"],"best_for":["RAG systems needing confidence-based result filtering","search UIs displaying relevance scores to users","applications combining vector similarity with other ranking signals"],"limitations":["Similarity scores are relative to query vector — absolute scores not comparable across different queries","No built-in result reranking beyond distance-based sorting","Distance metrics (cosine, euclidean, dot product) have different score ranges — normalization required for comparison","No statistical significance testing for similarity scores"],"requires":["vector embeddings for documents and query","distance metric selection (cosine, euclidean, or dot product)"],"input_types":["query vector (Float32Array)","document vectors (Float32Array[])","distance metric specification"],"output_types":["ranked results with similarity scores","normalized scores (0-1 range optional)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+ or modern browser with WebAssembly support","Vector embeddings pre-computed (external embedding model required)","Data loaded into rvlite instance as JSON or structured format","RDF triples loaded into rvlite with embedding vectors attached to resources","SPARQL query parser compatible with vector extensions","Node.js 14+ or browser with WASM support","array of vector embeddings (Float32Array[])","array of document objects with metadata","optional: batch size configuration","persistent storage backend (filesystem in Node.js, IndexedDB in browser)"],"failure_modes":["SQL dialect is custom subset — not all standard SQL features supported (e.g., complex JOINs may have limited vector semantics)","Vector distance metrics limited to cosine, euclidean, dot product — no custom distance functions","Query optimization for large vector datasets relies on in-memory indexing; no distributed query planning","SPARQL subset support — complex OPTIONAL patterns and UNION queries may not translate to vector operations","No federated SPARQL queries across multiple vector stores","Vector similarity matching in SPARQL adds computational overhead compared to exact triple matching","Limited support for SPARQL aggregations (GROUP BY, COUNT) over vector-matched results","Batch operations may consume significant memory for large batches","No transactional guarantees — partial batch failures may leave database in inconsistent state","Index rebuild during batch operations may block queries temporarily","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.08155777483242672,"quality":0.35,"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":327,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=rvlite","compare_url":"https://unfragile.ai/compare?artifact=rvlite"}},"signature":"PEWuQGmTaGWADZA8UI+ImLOi9qbmSIdrhnRtvXx57EYlHB4ickwFeGjInDdpKTPzN/h//6JXZuX33m/LBkniDQ==","signedAt":"2026-06-22T03:57:45.301Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/rvlite","artifact":"https://unfragile.ai/rvlite","verify":"https://unfragile.ai/api/v1/verify?slug=rvlite","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"}}