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Uses a hybrid architecture where the file system serves as the persistent store and RAM holds the active search index, enabling both durability and performance without requiring a separate database server. Supports automatic index persistence and reload cycles.","intents":["I need to persist embeddings locally without running a database service","I want vector search that works offline and survives application restarts","I need a lightweight embedding store for a Node.js or browser application"],"best_for":["solo developers building local RAG systems","teams prototyping embedding-based features without infrastructure","Electron/desktop apps requiring embedded vector search"],"limitations":["File I/O becomes a bottleneck at scale (100k+ vectors); no built-in sharding","In-memory index must fit in available RAM; no automatic spilling to disk","Single-process access only; concurrent writes from multiple processes risk corruption","No transaction support or ACID guarantees for index updates"],"requires":["Node.js 14+ or modern browser with File System Access API","Disk space for JSON file storage (roughly 4-8KB per vector + metadata)","RAM sufficient to hold full index in memory during searches"],"input_types":["float32 arrays (embeddings)","JSON objects (metadata)","text strings (for BM25 indexing)"],"output_types":["array of vector IDs with similarity scores","filtered result sets with metadata","hybrid search results combining semantic and lexical ranking"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-vectra__cap_1","uri":"capability://search.retrieval.cosine.similarity.vector.search.with.configurable.distance.metrics","name":"cosine similarity vector search with configurable distance metrics","description":"Implements vector similarity search using cosine distance calculation on normalized embeddings, with support for alternative distance metrics. Performs brute-force similarity computation across all indexed vectors, returning results ranked by distance score. Includes configurable thresholds to filter results below a minimum similarity threshold.","intents":["I need to find semantically similar embeddings to a query vector","I want to retrieve the top-K most relevant vectors by similarity score","I need to filter search results by a minimum similarity threshold"],"best_for":["RAG systems retrieving relevant context for LLM prompts","semantic search features in chat applications","recommendation systems based on embedding similarity"],"limitations":["Brute-force O(n) search; no approximate nearest neighbor optimization (no HNSW or IVF)","Search latency grows linearly with vector count; impractical beyond 100k vectors","No support for approximate search or early termination strategies","Assumes normalized vectors; denormalized inputs produce incorrect similarity scores"],"requires":["embeddings as float32 arrays of consistent dimensionality","vectors must be pre-normalized or normalized during insertion"],"input_types":["float32 array (query vector)","number (optional similarity threshold, 0-1)"],"output_types":["array of {id, score, metadata} objects sorted by score descending"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-vectra__cap_10","uri":"capability://data.processing.analysis.configurable.vector.dimensionality.and.normalization","name":"configurable vector dimensionality and normalization","description":"Accepts vectors of configurable dimensionality and automatically normalizes them for cosine similarity computation. Validates that all vectors have consistent dimensions and rejects mismatched vectors. Supports both pre-normalized and unnormalized input, with automatic L2 normalization applied during insertion.","intents":["I need to work with embeddings of different dimensions from different models","I want automatic normalization to ensure correct cosine similarity computation","I need validation to catch dimension mismatches early"],"best_for":["applications using embeddings from multiple sources","development workflows where embedding dimensions may change","systems requiring strict validation of input data"],"limitations":["Normalization adds ~5-10% overhead to insertion time","Dimension validation is per-vector; no schema enforcement across the database","No support for sparse vectors or variable-length embeddings","Normalization is irreversible; original vector magnitudes are lost"],"requires":["float32 array of consistent dimensionality"],"input_types":["float32 array (any dimensionality)"],"output_types":["normalized float32 array"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-vectra__cap_11","uri":"capability://data.processing.analysis.vector.database.export.and.import.with.format.conversion","name":"vector database export and import with format conversion","description":"Exports the entire vector database (embeddings, metadata, index) to standard formats (JSON, CSV) for backup, analysis, or migration. Imports vectors from external sources in multiple formats. Supports format conversion between JSON, CSV, and other serialization formats without losing data.","intents":["I need to backup my vector database to a portable format","I want to migrate vectors from another database to Vectra","I need to analyze or process vectors outside the application"],"best_for":["data migration scenarios","backup and disaster recovery","data analysis and exploration workflows"],"limitations":["Export is a full database dump; no incremental export support","CSV export loses nested metadata structures; JSON is required for complex objects","Import validation is minimal; malformed data may cause silent failures","Large exports may exhaust available disk space or memory"],"requires":["write access to file system for export","properly formatted input file for import"],"input_types":["JSON or CSV file (for import)"],"output_types":["JSON or CSV file (for export)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-vectra__cap_2","uri":"capability://search.retrieval.bm25.full.text.search.with.hybrid.ranking","name":"bm25 full-text search with hybrid ranking","description":"Implements BM25 (Okapi BM25) lexical search algorithm for keyword-based retrieval, then combines BM25 scores with vector similarity scores using configurable weighting to produce hybrid rankings. Tokenizes text fields during indexing and performs term frequency analysis at query time. Allows tuning the balance between semantic and lexical relevance.","intents":["I need to search for exact keywords or phrases in addition to semantic similarity","I want to combine keyword matching with embedding-based search for better recall","I need to boost results that match specific terms while maintaining semantic relevance"],"best_for":["RAG systems where both keyword precision and semantic understanding matter","search features in documentation or knowledge base applications","hybrid retrieval pipelines balancing exact match and semantic relevance"],"limitations":["BM25 implementation does not support phrase queries or proximity matching","No stemming or lemmatization; exact token matching only","Hybrid weighting requires manual tuning; no automatic optimization","Tokenization is language-agnostic (whitespace-based); poor for CJK or morphologically complex languages"],"requires":["text fields indexed during vector insertion","configuration of BM25 parameters (k1, b) and hybrid weight factor"],"input_types":["string (query text)","number (hybrid weight, 0-1, where 0=pure BM25, 1=pure semantic)"],"output_types":["array of {id, score, metadata} objects with combined BM25+semantic scores"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-vectra__cap_3","uri":"capability://search.retrieval.pinecone.compatible.metadata.filtering","name":"pinecone-compatible metadata filtering","description":"Supports filtering search results using a Pinecone-compatible query syntax that allows boolean combinations of metadata predicates (equality, comparison, range, set membership). Evaluates filter expressions against metadata objects during search, returning only vectors that satisfy the filter constraints. Supports nested metadata structures and multiple filter operators.","intents":["I need to filter search results by metadata attributes (e.g., document type, date range, category)","I want to use the same filter syntax as Pinecone for easier migration","I need to combine multiple filter conditions with AND/OR logic"],"best_for":["RAG systems filtering by document source, date, or category","multi-tenant applications isolating data by user or organization","applications migrating from Pinecone to a local vector database"],"limitations":["Filter evaluation is post-hoc (after similarity search); no index-accelerated filtering","Complex nested filters may require evaluating many vectors before filtering","No support for full-text search within filter expressions","Filter syntax is Pinecone-compatible but not identical; some advanced operators may differ"],"requires":["metadata objects attached to vectors during insertion","filter expression in Pinecone query format"],"input_types":["object (filter expression with operators like $eq, $gt, $in, $and, $or)"],"output_types":["array of vectors matching both similarity and filter criteria"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-vectra__cap_4","uri":"capability://data.processing.analysis.embedding.generation.with.multiple.provider.support","name":"embedding generation with multiple provider support","description":"Integrates with multiple embedding providers (OpenAI, Azure OpenAI, local transformer models via Transformers.js) to generate vector embeddings from text. Abstracts provider differences behind a unified interface, allowing users to swap providers without changing application code. Handles API authentication, rate limiting, and batch processing for efficiency.","intents":["I need to generate embeddings from text using OpenAI or Azure OpenAI APIs","I want to use local embedding models without sending data to external APIs","I need to switch between embedding providers without rewriting code"],"best_for":["applications requiring flexible embedding provider selection","privacy-sensitive systems using local embeddings","cost-conscious teams comparing OpenAI vs local model trade-offs"],"limitations":["Local embeddings (Transformers.js) are slower than API-based providers; first inference may take 10-30 seconds","Different providers produce embeddings of different dimensions; vectors are not interchangeable","OpenAI/Azure require valid API credentials and internet connectivity","No built-in caching of embeddings; repeated text generates duplicate API calls unless cached externally"],"requires":["API key for OpenAI or Azure OpenAI (if using cloud providers)","Node.js 14+ (for Transformers.js local models)","sufficient disk space for downloaded transformer models (~100MB-1GB)"],"input_types":["string (text to embed)","string (provider name: 'openai', 'azure-openai', 'local')"],"output_types":["float32 array (embedding vector)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-vectra__cap_5","uri":"capability://memory.knowledge.browser.compatible.vector.database.with.indexeddb.persistence","name":"browser-compatible vector database with indexeddb persistence","description":"Runs entirely in the browser using IndexedDB for persistent storage, enabling client-side vector search without a backend server. Synchronizes in-memory index with IndexedDB on updates, allowing offline search and reducing server load. Supports the same API as the Node.js version for code reuse across environments.","intents":["I need vector search in a browser application without a backend API","I want to enable offline search in a web app using locally stored embeddings","I need to reduce server load by moving vector search to the client"],"best_for":["single-page applications with embedded RAG features","offline-first web applications","Electron apps using web technologies"],"limitations":["IndexedDB storage is limited to ~50MB per origin in most browsers; larger datasets require server-side storage","Search performance degrades significantly beyond 10k vectors due to in-memory index constraints","No cross-tab synchronization; updates in one tab don't reflect in others without manual sync","Browser security model prevents access to local files; must use IndexedDB or server-provided data"],"requires":["modern browser with IndexedDB support (all modern browsers)","embeddings pre-generated or generated client-side using local models"],"input_types":["float32 array (embedding)","JSON object (metadata)"],"output_types":["array of search results with scores and metadata"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-vectra__cap_6","uri":"capability://data.processing.analysis.batch.vector.insertion.with.automatic.index.updates","name":"batch vector insertion with automatic index updates","description":"Accepts multiple vectors and metadata objects in a single operation, inserting them into the vector database and updating the search index atomically. Handles deduplication by vector ID and supports upsert semantics (insert or update). Batching improves throughput compared to single-vector insertions by amortizing index update costs.","intents":["I need to insert many vectors at once without rebuilding the index each time","I want to update existing vectors with new embeddings or metadata","I need efficient bulk loading of embeddings from a data source"],"best_for":["initial data loading during application startup","periodic bulk updates from external data sources","batch processing pipelines generating embeddings"],"limitations":["No transaction rollback; partial failures may leave the index in an inconsistent state","Batch size is limited by available RAM; very large batches (>100k vectors) may cause memory exhaustion","Index updates are synchronous; large batches block other operations","No progress reporting or cancellation support during batch insertion"],"requires":["array of {id, values, metadata} objects","sufficient RAM to hold batch in memory during insertion"],"input_types":["array of {id: string, values: float32[], metadata: object}"],"output_types":["confirmation of inserted/updated vector count"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-vectra__cap_7","uri":"capability://data.processing.analysis.vector.deletion.and.index.maintenance","name":"vector deletion and index maintenance","description":"Removes vectors from the database by ID and updates the search index to reflect deletions. Supports bulk deletion of multiple vectors. Includes index compaction and cleanup operations to reclaim disk space and optimize search performance after many deletions.","intents":["I need to remove outdated or irrelevant vectors from the database","I want to delete vectors matching certain criteria (e.g., expired documents)","I need to reclaim disk space after bulk deletions"],"best_for":["applications with time-limited data (e.g., chat history, temporary documents)","data retention policies requiring periodic cleanup","multi-tenant systems removing user data on account deletion"],"limitations":["Deletion is immediate and irreversible; no soft-delete or recovery mechanism","Index compaction is a blocking operation; search is unavailable during cleanup","No automatic garbage collection; users must manually trigger compaction","Bulk deletion of many vectors may temporarily increase memory usage"],"requires":["vector ID or list of IDs to delete"],"input_types":["string (single vector ID) or array of strings (multiple IDs)"],"output_types":["confirmation of deleted vector count"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-vectra__cap_8","uri":"capability://search.retrieval.metadata.aware.vector.retrieval.with.projection","name":"metadata-aware vector retrieval with projection","description":"Returns search results with associated metadata objects, allowing applications to access both similarity scores and rich contextual information. Supports projection to return only specified metadata fields, reducing payload size. Metadata is stored alongside vectors and retrieved without additional lookups.","intents":["I need to retrieve both embeddings and their associated metadata in search results","I want to project only certain metadata fields to reduce response size","I need to access document source, timestamp, or other context alongside similarity scores"],"best_for":["RAG systems needing document source and context","search interfaces displaying rich result metadata","applications with large metadata objects requiring selective projection"],"limitations":["Metadata is stored in-memory; very large metadata objects increase memory footprint","No indexing on metadata fields; filtering is O(n) regardless of metadata size","Projection is applied post-search; all metadata is loaded even if only a subset is returned","No schema validation; arbitrary metadata structures may cause serialization issues"],"requires":["metadata object attached to each vector during insertion"],"input_types":["array of field names (for projection)"],"output_types":["array of {id, score, metadata} objects with projected fields"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-vectra__cap_9","uri":"capability://data.processing.analysis.in.memory.index.serialization.and.persistence","name":"in-memory index serialization and persistence","description":"Serializes the in-memory search index to JSON files on disk, enabling index snapshots and recovery after application restarts. Supports incremental persistence (only changed vectors) and full index dumps. Deserializes persisted indices back into memory on application startup, restoring search capability without recomputing embeddings.","intents":["I need to persist the search index so it survives application restarts","I want to create backups of the vector database state","I need to load a pre-built index without recomputing embeddings"],"best_for":["applications requiring data durability across restarts","development workflows with pre-computed embeddings","backup and recovery scenarios"],"limitations":["Serialization is synchronous and blocking; large indices may pause the application","JSON serialization is verbose; index files are 2-3x larger than binary formats","No incremental backup; full index must be rewritten on each persistence","No version compatibility; index format changes may require manual migration"],"requires":["write access to the file system (Node.js) or IndexedDB (browser)"],"input_types":["file path (for persistence location)"],"output_types":["JSON file containing serialized index"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":37,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+ or modern browser with File System Access API","Disk space for JSON file storage (roughly 4-8KB per vector + metadata)","RAM sufficient to hold full index in memory during searches","embeddings as float32 arrays of consistent dimensionality","vectors must be pre-normalized or normalized during insertion","float32 array of consistent dimensionality","write access to file system for export","properly formatted input file for import","text fields indexed during vector insertion","configuration of BM25 parameters (k1, b) and hybrid weight factor"],"failure_modes":["File I/O becomes a bottleneck at scale (100k+ vectors); no built-in sharding","In-memory index must fit in available RAM; no automatic spilling to disk","Single-process access only; concurrent writes from multiple processes risk corruption","No transaction support or ACID guarantees for index updates","Brute-force O(n) search; no approximate nearest neighbor optimization (no HNSW or IVF)","Search latency grows linearly with vector count; impractical beyond 100k vectors","No support for approximate search or early termination strategies","Assumes normalized vectors; denormalized inputs produce incorrect similarity scores","Normalization adds ~5-10% overhead to insertion time","Dimension validation is per-vector; no schema enforcement across the database","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.27371767341905906,"quality":0.49,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.52,"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":27299,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=vectra","compare_url":"https://unfragile.ai/compare?artifact=vectra"}},"signature":"PEnZ4aiVo+mKsxGCe2gulDxA0L+g9D23/fyOEedvmysu9uECoGopG6kU6Gevdq/gcuypOvRF5VZvSIi1oXu2Dw==","signedAt":"2026-06-22T10:41:59.926Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/vectra","artifact":"https://unfragile.ai/vectra","verify":"https://unfragile.ai/api/v1/verify?slug=vectra","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"}}