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The implementation maintains vectors as dense arrays and calculates pairwise distances on query, enabling sub-millisecond retrieval for small-to-medium datasets without external dependencies. Optimized for JavaScript/Node.js environments where persistent disk storage is not required.","intents":["I need to quickly search for semantically similar documents without setting up a separate database service","I want to prototype a RAG pipeline with embeddings before committing to a production vector database","I need semantic search capabilities embedded directly in my Node.js application without network latency"],"best_for":["solo developers building LLM agents and chatbots","teams prototyping semantic search features in Node.js/JavaScript environments","applications with <100k vectors where in-memory storage is feasible"],"limitations":["All vectors must fit in available RAM — no disk persistence or overflow handling","Linear scan performance degrades significantly beyond 100k vectors; no approximate nearest neighbor (ANN) acceleration like HNSW or IVF","Single-threaded execution — no parallel query processing or distributed indexing","No built-in vector compression or quantization — full float32 precision required for all vectors"],"requires":["Node.js 14+ or browser environment with ES6 support","Pre-computed embeddings from external model (OpenAI, Hugging Face, Ollama, etc.)","Sufficient available RAM to hold all vectors in memory simultaneously"],"input_types":["embedding vectors (float arrays, typically 384-1536 dimensions)","metadata objects (JSON-serializable documents associated with vectors)","query vectors (same dimensionality as indexed vectors)"],"output_types":["ranked result arrays with similarity scores (0-1 range for cosine similarity)","metadata of top-k nearest neighbors","similarity distance metrics"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-vectoriadb__cap_1","uri":"capability://data.processing.analysis.document.to.vector.batch.indexing.with.metadata.association","name":"document-to-vector batch indexing with metadata association","description":"Accepts collections of documents with associated metadata and automatically chunks, embeds, and indexes them in a single operation. The system maintains a mapping between vector IDs and original document metadata, enabling retrieval of full context after similarity search. Supports batch operations to amortize embedding API costs when using external embedding services.","intents":["I want to index a corpus of documents and retrieve full document context when search results are returned","I need to batch-process documents through an embedding API to reduce costs and latency","I want to associate custom metadata (source, timestamp, tags) with vectors for filtering and context"],"best_for":["RAG pipeline builders indexing knowledge bases or document collections","teams building semantic search over internal documentation or knowledge bases","developers prototyping multi-document QA systems"],"limitations":["No built-in document chunking strategy — requires external text splitting or manual chunk preparation","Metadata filtering is not indexed — filtering happens post-retrieval, not during search","No incremental indexing — adding new documents requires re-indexing the entire collection if using certain storage backends","Batch size is limited by available memory and embedding API rate limits"],"requires":["Document collection in text or JSON format","Embedding model or API access (OpenAI, Hugging Face, local Ollama instance, etc.)","Metadata schema defined as JSON objects"],"input_types":["document text (strings)","metadata objects (JSON)","embedding vectors (if pre-computed)"],"output_types":["indexed vector store with metadata mappings","vector IDs for reference","embedding statistics (dimensions, count)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-vectoriadb__cap_2","uri":"capability://search.retrieval.k.nearest.neighbor.retrieval.with.configurable.similarity.thresholds","name":"k-nearest-neighbor retrieval with configurable similarity thresholds","description":"Executes top-k nearest neighbor queries against indexed vectors using cosine similarity scoring, with optional filtering by similarity threshold to exclude low-confidence matches. Returns ranked results sorted by similarity score in descending order, with configurable k parameter to control result set size. Supports both single-query and batch-query modes for amortized computation.","intents":["I need to find the top 5 most similar documents to a query without retrieving irrelevant results","I want to filter out search results below a confidence threshold to improve answer quality","I need to batch-query multiple search terms and retrieve results efficiently"],"best_for":["RAG systems requiring semantic search over knowledge bases","chatbot and QA systems needing context retrieval","recommendation systems based on embedding similarity"],"limitations":["Query latency is O(n*d) where n is vector count and d is dimensionality — no sublinear search acceleration","Threshold filtering is applied post-search, not during indexing, so all vectors are scored regardless of threshold","No support for hybrid search combining semantic similarity with keyword matching or metadata filters","Batch queries are processed sequentially, not in parallel"],"requires":["Pre-indexed vector database with embeddings","Query vector of matching dimensionality","k parameter (integer > 0)","Optional similarity threshold (float 0-1)"],"input_types":["query embedding vector (float array)","k parameter (integer)","similarity threshold (optional float)"],"output_types":["ranked array of results with vector IDs, similarity scores, and metadata","similarity scores (cosine similarity 0-1 range)"],"categories":["search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-vectoriadb__cap_3","uri":"capability://data.processing.analysis.embedding.model.integration.and.vector.dimension.handling","name":"embedding model integration and vector dimension handling","description":"Abstracts embedding model selection and vector generation through a pluggable interface supporting multiple embedding providers (OpenAI, Hugging Face, Ollama, local transformers). Automatically validates vector dimensionality consistency across all indexed vectors and enforces dimension matching for queries. Handles embedding API calls, error handling, and optional caching of computed embeddings.","intents":["I want to switch embedding models without changing my search code","I need to use a local embedding model to avoid API costs and latency","I want to validate that all my vectors have consistent dimensions before indexing"],"best_for":["teams evaluating different embedding models for quality/cost tradeoffs","privacy-conscious applications requiring local embedding computation","production systems needing to migrate between embedding providers"],"limitations":["No automatic re-embedding when switching models — requires manual re-indexing with new embeddings","Embedding caching is in-memory only — no persistent cache across application restarts","No built-in embedding quality validation or dimensionality reduction for mismatched vectors","API rate limiting and quota management delegated to underlying embedding service"],"requires":["Embedding model API key (for cloud providers) or local model installation (for Ollama/transformers)","Network access to embedding API or local model server","Consistent vector dimensionality across all documents"],"input_types":["text documents (strings)","embedding model identifier (string)","model configuration parameters (JSON)"],"output_types":["embedding vectors (float arrays)","dimensionality metadata","embedding statistics"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-vectoriadb__cap_4","uri":"capability://automation.workflow.vector.store.persistence.and.serialization","name":"vector store persistence and serialization","description":"Exports indexed vectors and metadata to JSON or binary formats for persistence across application restarts, and imports previously saved vector stores from disk. Serialization captures vector arrays, metadata mappings, and index configuration to enable reproducible search behavior. Supports both full snapshots and incremental updates for efficient storage.","intents":["I want to save my indexed vectors to disk so I don't have to re-embed documents on every restart","I need to share a vector index across multiple application instances","I want to version control my vector database snapshots for reproducibility"],"best_for":["production applications requiring persistent state across deployments","teams sharing vector indexes across multiple services or environments","development workflows where re-embedding large corpora is expensive"],"limitations":["No incremental persistence — full index must be serialized on each save, no delta updates","Serialized format is not optimized for compression — file size scales linearly with vector count and dimensionality","No built-in versioning or schema migration — format changes require manual conversion","Concurrent access to persisted index not supported — requires external locking mechanism"],"requires":["Filesystem write permissions for persistence","Sufficient disk space for serialized vectors (typically 4 bytes per dimension per vector)","JSON or binary format support in runtime environment"],"input_types":["in-memory vector store object","file path (string)","serialization format specification (JSON or binary)"],"output_types":["serialized vector store file (JSON or binary)","deserialized vector store object loaded from disk"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-vectoriadb__cap_5","uri":"capability://data.processing.analysis.similarity.based.document.clustering.and.grouping","name":"similarity-based document clustering and grouping","description":"Groups indexed vectors into clusters based on cosine similarity, enabling discovery of semantically related document groups without pre-defined categories. Uses distance-based clustering algorithms (e.g., k-means or hierarchical clustering) to partition vectors into coherent groups. Supports configurable cluster count and similarity thresholds to control granularity of grouping.","intents":["I want to automatically discover topics or themes in my document collection","I need to group similar support tickets or customer inquiries for batch processing","I want to identify outliers or anomalous documents that don't fit into any cluster"],"best_for":["content discovery and exploration systems","document organization and categorization workflows","anomaly detection in document collections"],"limitations":["Clustering is computed on-demand and not cached — repeated clustering queries recompute from scratch","No incremental clustering — adding new vectors requires re-clustering entire collection","Cluster quality depends heavily on embedding model quality and dimensionality","No hierarchical clustering visualization or dendrogram support"],"requires":["Pre-indexed vector store with sufficient vectors (minimum 10-20 for meaningful clusters)","Cluster count parameter (k) or similarity threshold","Computational resources for distance matrix computation (O(n²) memory)"],"input_types":["indexed vector store","cluster count (integer k) or similarity threshold (float)"],"output_types":["cluster assignments (vector ID to cluster ID mapping)","cluster centroids (representative vectors)","cluster statistics (size, cohesion, separation)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+ or browser environment with ES6 support","Pre-computed embeddings from external model (OpenAI, Hugging Face, Ollama, etc.)","Sufficient available RAM to hold all vectors in memory simultaneously","Document collection in text or JSON format","Embedding model or API access (OpenAI, Hugging Face, local Ollama instance, etc.)","Metadata schema defined as JSON objects","Pre-indexed vector database with embeddings","Query vector of matching dimensionality","k parameter (integer > 0)","Optional similarity threshold (float 0-1)"],"failure_modes":["All vectors must fit in available RAM — no disk persistence or overflow handling","Linear scan performance degrades significantly beyond 100k vectors; no approximate nearest neighbor (ANN) acceleration like HNSW or IVF","Single-threaded execution — no parallel query processing or distributed indexing","No built-in vector compression or quantization — full float32 precision required for all vectors","No built-in document chunking strategy — requires external text splitting or manual chunk preparation","Metadata filtering is not indexed — filtering happens post-retrieval, not during search","No incremental indexing — adding new documents requires re-indexing the entire collection if using certain storage backends","Batch size is limited by available memory and embedding API rate limits","Query latency is O(n*d) where n is vector count and d is dimensionality — no sublinear search acceleration","Threshold filtering is applied post-search, not during indexing, so all vectors are scored regardless of threshold","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.1919391926773859,"quality":0.22,"ecosystem":0.7000000000000001,"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":4153,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=vectoriadb","compare_url":"https://unfragile.ai/compare?artifact=vectoriadb"}},"signature":"O6m1WHgVzgpH432V8Ilc9EjwxXbHW3hhJhz4aHZKAjcfRQmoh0abxtMDqXNRAlK2Cefdxn12apzTI6dFy2F2DQ==","signedAt":"2026-06-21T02:29:29.461Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/vectoriadb","artifact":"https://unfragile.ai/vectoriadb","verify":"https://unfragile.ai/api/v1/verify?slug=vectoriadb","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"}}