{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-memberjunction-ai-vectordb","slug":"memberjunction-ai-vectordb","name":"@memberjunction/ai-vectordb","type":"repo","url":"https://github.com/MemberJunction/MJ#readme","page_url":"https://unfragile.ai/memberjunction-ai-vectordb","categories":["rag-knowledge"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-memberjunction-ai-vectordb__cap_0","uri":"capability://memory.knowledge.vector.embedding.storage.and.retrieval","name":"vector-embedding-storage-and-retrieval","description":"Stores and retrieves high-dimensional vector embeddings with semantic search capabilities, enabling similarity-based document matching and RAG workflows. The module abstracts vector database operations through a provider-agnostic interface that supports multiple backend implementations (Pinecone, Weaviate, Milvus, etc.), allowing developers to swap vector stores without changing application code. Implements efficient indexing and querying patterns optimized for LLM context augmentation.","intents":["I need to store document embeddings and retrieve semantically similar content for RAG pipelines","I want to switch vector database providers without rewriting my application code","I need to build semantic search over large document collections for LLM augmentation","I want to manage embeddings lifecycle including creation, updates, and deletion"],"best_for":["teams building RAG systems with pluggable vector store backends","developers prototyping LLM applications who need provider flexibility","enterprises requiring multi-provider vector database support for resilience"],"limitations":["Abstraction layer adds latency overhead for each query operation","No built-in batch optimization for bulk embedding operations","Vector dimension handling depends on upstream embedding model selection","No native support for hybrid search (vector + keyword) without custom implementation"],"requires":["Node.js 14+ or compatible JavaScript runtime","API credentials for at least one supported vector database provider","Pre-computed embeddings from an embedding model (OpenAI, Hugging Face, etc.)","@memberjunction/ai-core or compatible base module"],"input_types":["vector arrays (Float32Array, number[])","metadata objects (JSON)","document IDs (string)","similarity thresholds (number)"],"output_types":["ranked document results with similarity scores","vector IDs and metadata","operation status/confirmation"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-memberjunction-ai-vectordb__cap_1","uri":"capability://search.retrieval.semantic.document.search.with.ranking","name":"semantic-document-search-with-ranking","description":"Executes semantic similarity search over document collections by converting queries to embeddings and ranking results by cosine distance or other similarity metrics. Implements query expansion and result filtering patterns to improve relevance, with configurable ranking strategies that can incorporate metadata filtering, recency weighting, or custom scoring functions. Designed to power LLM context retrieval with relevance-aware result ordering.","intents":["I need to find the most relevant documents for a user query to augment LLM context","I want to filter search results by metadata while maintaining semantic relevance ranking","I need to implement multi-stage retrieval (coarse-to-fine) for performance optimization","I want to customize ranking logic based on domain-specific relevance signals"],"best_for":["RAG pipeline builders needing relevance-ranked document retrieval","teams implementing semantic search over proprietary knowledge bases","developers building question-answering systems with ranked result sets"],"limitations":["Ranking quality depends entirely on upstream embedding model quality","No built-in query understanding or expansion — requires external NLP preprocessing","Metadata filtering logic is basic AND-based; complex boolean queries require custom implementation","No native support for temporal decay or freshness-based ranking without custom scoring"],"requires":["Query text or pre-computed query embedding","Populated vector database with indexed documents","Embedding model compatible with document embeddings for consistency","Configured similarity metric (cosine, euclidean, dot product)"],"input_types":["query string (text)","query embedding (vector)","metadata filter object (JSON)","ranking parameters (number, string)"],"output_types":["ranked document list with similarity scores","document metadata and content snippets","retrieval confidence metrics"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-memberjunction-ai-vectordb__cap_2","uri":"capability://data.processing.analysis.embedding.lifecycle.management","name":"embedding-lifecycle-management","description":"Manages the complete lifecycle of embeddings including creation, storage, updates, and deletion with consistency guarantees across vector database backends. Provides batch operations for efficient bulk embedding processing, handles embedding versioning when underlying models change, and maintains metadata synchronization between embeddings and source documents. Implements idempotent operations to prevent duplicate embeddings and supports incremental indexing for large document collections.","intents":["I need to bulk embed a large document collection and store it efficiently","I want to update embeddings when source documents change without full re-indexing","I need to handle embedding model upgrades and maintain backward compatibility","I want to ensure embeddings stay synchronized with document metadata changes"],"best_for":["teams managing large knowledge bases with frequent document updates","developers building content management systems with semantic search","organizations upgrading embedding models across production systems"],"limitations":["Batch operations are sequential by default; parallel embedding requires external orchestration","No built-in versioning strategy — requires custom metadata schema for model tracking","Consistency guarantees depend on underlying vector database transaction support","Large batch operations may timeout without pagination/chunking configuration"],"requires":["Embedding generation capability (local or API-based)","Vector database with write/update/delete operations","Document source with unique identifiers","Batch size configuration appropriate for memory constraints"],"input_types":["document collection (array of objects)","embedding vectors (Float32Array[])","metadata objects (JSON)","operation type (create, update, delete)"],"output_types":["operation results with success/failure status","embedding IDs and version information","batch processing statistics (processed count, errors)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-memberjunction-ai-vectordb__cap_3","uri":"capability://tool.use.integration.multi.provider.vector.database.abstraction","name":"multi-provider-vector-database-abstraction","description":"Abstracts away provider-specific vector database APIs through a unified interface that normalizes operations across Pinecone, Weaviate, Milvus, Qdrant, and other backends. Handles provider-specific configuration, connection pooling, and error handling transparently, allowing applications to switch providers by changing configuration without code changes. Implements provider capability detection to gracefully degrade features when backends don't support certain operations (e.g., metadata filtering, hybrid search).","intents":["I want to build a vector database layer that doesn't lock me into a single provider","I need to migrate from one vector database to another without rewriting application code","I want to test my RAG application against multiple vector store backends","I need to handle provider-specific quirks (API differences, rate limits, feature gaps) transparently"],"best_for":["enterprises requiring vendor flexibility and avoiding lock-in","teams evaluating multiple vector database solutions","developers building portable RAG frameworks","organizations with multi-cloud or hybrid deployment requirements"],"limitations":["Abstraction can't expose provider-specific optimizations (e.g., Pinecone's serverless scaling)","Feature parity is limited to lowest-common-denominator across all providers","Provider-specific error codes are normalized, losing diagnostic detail","Performance characteristics vary significantly across providers; abstraction doesn't optimize per-provider"],"requires":["Configuration specifying target vector database provider","Provider-specific credentials (API keys, connection strings)","Provider SDK or compatible client library","Network connectivity to vector database service"],"input_types":["provider configuration object (string, credentials)","standard operation parameters (vectors, metadata, queries)"],"output_types":["normalized operation results","provider capability metadata","standardized error objects"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-memberjunction-ai-vectordb__cap_4","uri":"capability://search.retrieval.metadata.filtering.and.faceted.search","name":"metadata-filtering-and-faceted-search","description":"Enables filtering vector search results by document metadata (tags, categories, dates, custom fields) while maintaining semantic relevance ranking. Implements metadata indexing alongside vector indexes to support efficient combined queries, with support for range queries, exact matches, and set membership operations. Allows composition of multiple metadata filters with AND/OR logic to narrow result sets before or after vector similarity ranking.","intents":["I need to search documents semantically but filter by category, date range, or author","I want to implement faceted search combining semantic relevance with structured metadata","I need to restrict search results to specific document subsets based on access control metadata","I want to build domain-specific search with semantic ranking plus business logic filters"],"best_for":["teams building enterprise search with semantic + structured filtering","developers implementing multi-tenant RAG with access control metadata","applications requiring faceted search over knowledge bases"],"limitations":["Complex boolean filter logic (nested OR/AND) requires custom query building","Metadata indexing overhead increases storage and update latency","Filter performance depends on vector database's metadata index implementation","No native support for full-text search on metadata values — requires separate indexing"],"requires":["Metadata schema defined for indexed documents","Vector database with metadata filtering support","Metadata values populated during document indexing","Filter query syntax compatible with chosen vector database"],"input_types":["metadata filter object (JSON with operators)","filter operators (eq, range, in, exists)","query embedding or text"],"output_types":["filtered and ranked document results","facet counts (optional)","filter application status"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-memberjunction-ai-vectordb__cap_5","uri":"capability://memory.knowledge.rag.context.augmentation.pipeline","name":"rag-context-augmentation-pipeline","description":"Orchestrates the complete RAG pipeline: query embedding, semantic retrieval, result ranking, and context assembly for LLM prompts. Handles automatic query preprocessing (normalization, expansion), implements configurable retrieval strategies (top-k, threshold-based, diversity sampling), and formats retrieved documents into structured context blocks suitable for LLM consumption. Provides hooks for custom ranking, filtering, and context formatting to adapt to domain-specific requirements.","intents":["I need to build a complete RAG pipeline from query to LLM-ready context in one operation","I want to customize retrieval strategy (top-k vs threshold vs diversity) without rewriting pipeline code","I need to format retrieved documents into structured context that works with my LLM prompt template","I want to implement multi-stage retrieval (coarse-to-fine) for performance optimization"],"best_for":["teams building production RAG systems with configurable retrieval strategies","developers implementing question-answering or chat systems with semantic context","organizations needing domain-specific RAG customization without pipeline rewrites"],"limitations":["Pipeline assumes synchronous operation — no built-in async/streaming for large result sets","Context assembly is basic string concatenation; complex formatting requires custom hooks","No built-in deduplication of retrieved documents across multiple retrieval stages","Query preprocessing is minimal — requires external NLP for advanced query understanding"],"requires":["Query text or embedding","Populated vector database with indexed documents","Embedding model for query encoding","LLM context window size for result truncation"],"input_types":["user query (text)","retrieval parameters (k, threshold, strategy)","context formatting instructions (string template or function)"],"output_types":["formatted context string for LLM prompt","source document references with scores","retrieval metadata (count, coverage, confidence)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-memberjunction-ai-vectordb__cap_6","uri":"capability://data.processing.analysis.embedding.model.integration.and.caching","name":"embedding-model-integration-and-caching","description":"Integrates with multiple embedding model providers (OpenAI, Hugging Face, local models) and caches embeddings to avoid redundant API calls and reduce costs. Implements embedding cache with configurable TTL and invalidation strategies, handles model versioning to track which model generated each embedding, and provides fallback mechanisms when primary embedding service is unavailable. Supports both API-based and local embedding models with automatic format normalization.","intents":["I want to embed documents using OpenAI or local models without managing API calls directly","I need to cache embeddings to reduce API costs and improve performance","I want to track which embedding model was used for each vector for reproducibility","I need to switch embedding models and handle version mismatches gracefully"],"best_for":["teams managing embedding costs at scale with caching requirements","developers building RAG systems with multiple embedding model options","organizations requiring embedding model versioning and reproducibility"],"limitations":["Cache invalidation strategy is basic (TTL-based); no intelligent invalidation on model updates","Embedding API rate limiting is not handled — requires external rate limiting","Local model inference adds latency; no built-in batching or GPU optimization","Cache storage is in-memory by default; no persistent cache without external storage"],"requires":["Embedding model API credentials (OpenAI, Hugging Face) OR local model files","Text content to embed (documents or queries)","Cache storage backend (in-memory, Redis, etc.)","Model identifier for versioning"],"input_types":["text to embed (string or string[])","embedding model identifier (string)","cache configuration (TTL, strategy)"],"output_types":["embedding vectors (Float32Array)","model version metadata","cache hit/miss status"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-memberjunction-ai-vectordb__cap_7","uri":"capability://data.processing.analysis.vector.similarity.metrics.and.distance.computation","name":"vector-similarity-metrics-and-distance-computation","description":"Implements multiple vector similarity metrics (cosine similarity, Euclidean distance, dot product, Manhattan distance) with optimized computation for high-dimensional vectors. Provides configurable distance metrics per query, handles vector normalization and dimension validation, and supports approximate nearest neighbor search for performance optimization on large collections. Includes utilities for similarity score interpretation and threshold-based result filtering.","intents":["I need to compute similarity between query and document vectors using different metrics","I want to use approximate nearest neighbor search for performance on large collections","I need to validate vector dimensions and normalize vectors before similarity computation","I want to interpret similarity scores and set appropriate thresholds for result filtering"],"best_for":["developers optimizing vector search performance on large collections","teams experimenting with different similarity metrics for domain-specific relevance","applications requiring approximate nearest neighbor search for scalability"],"limitations":["Approximate nearest neighbor search trades accuracy for speed; exact results not guaranteed","Similarity metric choice significantly impacts relevance; no automatic metric selection","Vector normalization adds preprocessing overhead; not always necessary","Distance computation is CPU-bound; no GPU acceleration in pure JavaScript implementation"],"requires":["Query vector and document vectors (Float32Array or number[])","Consistent vector dimensions across all vectors","Selected similarity metric (cosine, euclidean, dot product, etc.)","Optional: approximate search parameters (number of candidates, search depth)"],"input_types":["query vector (Float32Array)","document vectors (Float32Array[])","similarity metric (string enum)","search parameters (number)"],"output_types":["similarity scores (number[])","ranked indices or document IDs","distance computation metadata"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-memberjunction-ai-vectordb__cap_8","uri":"capability://data.processing.analysis.document.chunking.and.embedding.strategy","name":"document-chunking-and-embedding-strategy","description":"Implements configurable document chunking strategies (fixed-size, semantic, sliding window) to break large documents into embeddable units while preserving context. Handles chunk overlap configuration, metadata propagation from parent documents to chunks, and chunk reassembly for context reconstruction. Supports adaptive chunking based on document structure (paragraphs, sentences) and provides utilities for chunk quality assessment (length validation, content filtering).","intents":["I need to chunk large documents into embeddable units without losing context","I want to use semantic chunking based on document structure instead of fixed sizes","I need to maintain document-to-chunk relationships for source attribution","I want to configure chunk overlap and size based on embedding model context window"],"best_for":["teams building RAG systems over long-form documents (books, papers, reports)","developers implementing semantic chunking for improved retrieval quality","applications requiring source attribution and chunk-to-document traceability"],"limitations":["Semantic chunking requires NLP preprocessing; no built-in sentence/paragraph detection","Chunk overlap increases storage and retrieval latency proportionally","Metadata propagation is shallow; nested document hierarchies require custom handling","No automatic optimization of chunk size based on embedding model or retrieval performance"],"requires":["Document text content","Chunking strategy configuration (size, overlap, method)","Optional: document structure metadata (paragraphs, sections)","Embedding model context window size for validation"],"input_types":["document text (string)","chunking strategy (fixed-size, semantic, sliding-window)","chunk size and overlap parameters (number)","document metadata (JSON)"],"output_types":["chunk array with text and metadata","chunk-to-document mapping","chunk quality metrics (length, content coverage)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+ or compatible JavaScript runtime","API credentials for at least one supported vector database provider","Pre-computed embeddings from an embedding model (OpenAI, Hugging Face, etc.)","@memberjunction/ai-core or compatible base module","Query text or pre-computed query embedding","Populated vector database with indexed documents","Embedding model compatible with document embeddings for consistency","Configured similarity metric (cosine, euclidean, dot product)","Embedding generation capability (local or API-based)","Vector database with write/update/delete operations"],"failure_modes":["Abstraction layer adds latency overhead for each query operation","No built-in batch optimization for bulk embedding operations","Vector dimension handling depends on upstream embedding model selection","No native support for hybrid search (vector + keyword) without custom implementation","Ranking quality depends entirely on upstream embedding model quality","No built-in query understanding or expansion — requires external NLP preprocessing","Metadata filtering logic is basic AND-based; complex boolean queries require custom implementation","No native support for temporal decay or freshness-based ranking without custom scoring","Batch operations are sequential by default; parallel embedding requires external orchestration","No built-in versioning strategy — requires custom metadata schema for model tracking","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.15641924606261978,"quality":0.28,"ecosystem":0.39999999999999997,"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":"2026-05-03T14:04:47.474Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":1833,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=memberjunction-ai-vectordb","compare_url":"https://unfragile.ai/compare?artifact=memberjunction-ai-vectordb"}},"signature":"lyJ9imPUUs3MjJE4Rgp5YWqh9CyzerhhnQngsWndkk1Q0bPlhTCwOVkz+oEux81CuPGl97L5r0QkASHM7q0KDQ==","signedAt":"2026-06-20T17:44:33.492Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/memberjunction-ai-vectordb","artifact":"https://unfragile.ai/memberjunction-ai-vectordb","verify":"https://unfragile.ai/api/v1/verify?slug=memberjunction-ai-vectordb","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"}}