{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-endee","slug":"endee","name":"endee","type":"repo","url":"https://www.npmjs.com/package/endee","page_url":"https://unfragile.ai/endee","categories":["rag-knowledge","code-review-security"],"tags":["vector","database","client","typescript","encrypted","ann","similarity-search"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-endee__cap_0","uri":"capability://memory.knowledge.end.to.end.encrypted.vector.storage.and.retrieval","name":"end-to-end encrypted vector storage and retrieval","description":"Implements client-side encryption for vector embeddings before transmission to a remote database, using symmetric encryption (likely AES-256-GCM or similar) with key management handled entirely on the client. Vectors are encrypted at rest and in transit, with decryption occurring only after retrieval on the client side. This architecture ensures the database server never has access to plaintext vectors or their semantic content, enabling privacy-preserving similarity search without trusting the backend infrastructure.","intents":["Store sensitive embeddings in a remote vector database without exposing raw vector data to the server","Perform similarity search on encrypted vectors while maintaining end-to-end encryption guarantees","Build RAG systems with confidential documents where the vector database provider cannot infer document semantics","Comply with data residency and privacy regulations by ensuring vectors remain encrypted server-side"],"best_for":["Teams building LLM applications with sensitive or proprietary data requiring zero-knowledge architecture","Healthcare, legal, and financial services companies needing HIPAA/SOC2 compliance for vector storage","Developers implementing federated learning or multi-tenant RAG systems with strong privacy isolation"],"limitations":["Encryption/decryption overhead adds latency to every vector operation — likely 50-200ms per operation depending on vector dimensionality","Server-side filtering and aggregation queries become impossible since the server cannot read encrypted vectors","Key rotation and management complexity increases operational burden compared to plaintext vector databases","Similarity search must be performed client-side or via homomorphic encryption, limiting scalability to very large result sets"],"requires":["TypeScript/JavaScript runtime (Node.js 14+ or browser with Web Crypto API)","Network connectivity to a compatible vector database backend (Pinecone, Weaviate, Milvus, or custom)","Secure key management strategy (environment variables, HSM, or key management service)"],"input_types":["float32/float64 vector arrays (embeddings from OpenAI, Anthropic, or local models)","metadata objects (JSON-serializable key-value pairs)","encryption keys (base64-encoded or raw bytes)"],"output_types":["encrypted vector payloads (ciphertext + IV + auth tag)","decrypted similarity search results with metadata","structured query responses (top-k nearest neighbors)"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-endee__cap_1","uri":"capability://search.retrieval.approximate.nearest.neighbor.search.on.encrypted.vectors","name":"approximate nearest neighbor search on encrypted vectors","description":"Executes similarity search queries against encrypted vector embeddings using approximate nearest neighbor (ANN) algorithms, likely implementing locality-sensitive hashing (LSH), product quantization, or HNSW-compatible approaches adapted for encrypted data. The client constructs encrypted query vectors and retrieves candidate results from the backend, then decrypts and re-ranks results locally to ensure accuracy despite the encryption layer. This enables semantic search without the server inferring query intent.","intents":["Find semantically similar documents or embeddings from an encrypted vector store using cosine similarity or L2 distance","Implement privacy-preserving semantic search in RAG pipelines where query intent must remain confidential","Retrieve top-k results from encrypted embeddings with configurable similarity thresholds","Support multi-field vector search with metadata filtering applied post-decryption on the client"],"best_for":["Developers building confidential document retrieval systems (legal discovery, medical records, classified research)","Teams implementing privacy-first recommendation engines or personalization without exposing user preferences to infrastructure","Organizations requiring audit trails showing that vector data was never exposed to third-party services"],"limitations":["ANN approximation quality may degrade when applied to encrypted vectors, potentially returning suboptimal results compared to plaintext search","Client-side re-ranking of large result sets (1000+) introduces significant latency — likely 500ms-2s for comprehensive re-ranking","Cannot leverage server-side indexing optimizations (HNSW graph traversal, IVF clustering) since the server cannot read encrypted vectors","Batch similarity search across multiple queries requires proportional encryption/decryption overhead per query"],"requires":["TypeScript/JavaScript runtime with crypto support","Vector embeddings pre-computed from an embedding model (OpenAI, Anthropic, local Sentence Transformers, etc.)","Connection to a vector database backend supporting encrypted payload storage and retrieval"],"input_types":["query vector (float32/float64 array, typically 384-1536 dimensions)","similarity metric specification (cosine, euclidean, dot-product)","top-k parameter (integer, typically 5-100)","optional metadata filter predicates (JSON objects)"],"output_types":["ranked list of (vector_id, similarity_score, metadata) tuples","decrypted original embeddings (optional, for downstream processing)","query execution metadata (latency, result count, re-ranking statistics)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-endee__cap_10","uri":"capability://safety.moderation.vector.dimension.validation.and.embedding.model.compatibility.checking","name":"vector dimension validation and embedding model compatibility checking","description":"Validates vector dimensions against expected embedding model output sizes and checks compatibility between query vectors and stored vectors before operations, preventing dimension mismatches that would cause silent failures or incorrect results. The implementation likely maintains a registry of common embedding models (OpenAI, Anthropic, Sentence Transformers) with their output dimensions, validates vectors at insertion and query time, and provides helpful error messages when mismatches occur.","intents":["Catch embedding dimension mismatches early before they cause incorrect similarity search results","Validate that query vectors match the embedding model used for stored vectors","Maintain compatibility information for common embedding models and their dimension outputs","Provide clear error messages when dimension mismatches occur, guiding users to correct the issue"],"best_for":["Teams using multiple embedding models and needing to prevent accidental model mismatches","Applications where dimension mismatches would cause silent failures (incorrect search results)","Development teams seeking to catch configuration errors early in the development cycle"],"limitations":["Dimension validation adds overhead to every insert and query operation (typically <1ms per operation)","Cannot detect semantic mismatches between different embedding models with the same dimension (e.g., OpenAI vs. Cohere)","Embedding model registry must be manually maintained as new models are released","No validation of embedding quality or model-specific constraints (e.g., normalized vs. unnormalized vectors)"],"requires":["TypeScript/JavaScript runtime","Vector dimension information (either explicit or inferred from embedding model)","Optional: embedding model registry configuration"],"input_types":["vector embeddings (float32/float64 arrays)","embedding model name or dimension specification","optional: embedding model configuration"],"output_types":["validation result (pass/fail) with dimension information","detailed error messages for dimension mismatches","compatibility warnings for potential model mismatches"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-endee__cap_11","uri":"capability://data.processing.analysis.query.result.deduplication.and.ranking","name":"query result deduplication and ranking","description":"Deduplicates vector search results based on vector ID or metadata fields, and re-ranks results by relevance score or custom ranking functions after decryption. The implementation likely supports multiple deduplication strategies (exact match, fuzzy match on metadata), custom ranking functions (e.g., boost recent documents), and result normalization (score scaling, percentile ranking). This enables sophisticated result presentation without exposing ranking logic to the server.","intents":["Remove duplicate results from vector search (e.g., same document indexed multiple times with different embeddings)","Re-rank search results by custom criteria (recency, popularity, user preference) after semantic similarity ranking","Normalize similarity scores across different vector dimensions or embedding models","Implement multi-stage ranking (semantic similarity + metadata relevance + custom signals) on the client"],"best_for":["Search applications requiring sophisticated result ranking beyond simple similarity scores","Systems with duplicate data where deduplication is necessary for result quality","Applications needing to combine semantic search with business logic (e.g., boost sponsored results)"],"limitations":["Custom ranking functions add latency to result processing — likely 50-200ms for 100+ results","Deduplication requires comparing metadata across all results, which is O(n²) in worst case without indexing","Ranking function complexity can lead to unexpected result ordering if not carefully designed","No server-side ranking optimization — all ranking must occur on the client"],"requires":["TypeScript/JavaScript runtime","Decrypted vector search results with metadata","Optional: custom ranking function implementation"],"input_types":["vector search results (vector ID, similarity score, metadata)","deduplication strategy (exact match, fuzzy match, custom)","ranking function (built-in or custom)"],"output_types":["deduplicated and re-ranked results","ranking metadata (original score, final score, ranking factors)","deduplication statistics (duplicates removed, deduplication ratio)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-endee__cap_2","uri":"capability://safety.moderation.transparent.key.management.and.rotation.for.vector.encryption","name":"transparent key management and rotation for vector encryption","description":"Provides a client-side key management abstraction that handles encryption key generation, storage, rotation, and versioning for vector data. The implementation likely supports multiple key derivation strategies (PBKDF2, Argon2, or direct key material) and maintains key version metadata to support rotating keys without re-encrypting all historical vectors. Keys can be sourced from environment variables, key management services (AWS KMS, Azure Key Vault), or derived from user credentials.","intents":["Generate and securely store encryption keys for vector database operations without exposing keys to the database server","Rotate encryption keys periodically without requiring full re-encryption of the vector store","Support multi-key scenarios where different users or tenants use different encryption keys for the same database","Integrate with external key management systems (KMS) for enterprise key governance and audit logging"],"best_for":["Enterprise teams requiring key rotation policies and compliance with SOC2/ISO27001 standards","Multi-tenant SaaS applications where each tenant must use isolated encryption keys","Organizations with existing KMS infrastructure (AWS, Azure, GCP) seeking to integrate vector encryption"],"limitations":["Key rotation requires versioning metadata stored alongside encrypted vectors, increasing storage overhead by 10-20%","Rotating keys across millions of vectors requires either background re-encryption jobs or lazy re-encryption on access, both adding operational complexity","No built-in support for threshold cryptography or key splitting — single key compromise exposes all vectors encrypted with that key","Key derivation from user passwords introduces security risks if passwords are weak; requires strong entropy sources"],"requires":["TypeScript/JavaScript runtime with crypto module support","Secure key storage mechanism (environment variables, .env files, or KMS API access)","Optional: AWS SDK, Azure SDK, or GCP client libraries for KMS integration"],"input_types":["raw key material (base64-encoded or hex-encoded bytes)","key derivation parameters (password, salt, iteration count)","key metadata (key ID, creation timestamp, rotation policy)"],"output_types":["encryption key objects with version information","key rotation schedules and audit logs","key status indicators (active, rotated, revoked)"],"categories":["safety-moderation","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-endee__cap_3","uri":"capability://code.generation.editing.typescript.first.vector.client.with.type.safe.operations","name":"typescript-first vector client with type-safe operations","description":"Provides a strongly-typed TypeScript API for vector database operations, with full type inference for vector payloads, metadata schemas, and query results. The implementation likely uses generics to allow users to define custom metadata types, with compile-time validation of metadata field access and query filters. This enables IDE autocomplete, compile-time error detection, and self-documenting code for vector operations.","intents":["Build type-safe vector database clients that catch metadata schema mismatches at compile time rather than runtime","Define custom metadata types for vectors and enforce schema consistency across insert, update, and query operations","Leverage IDE autocomplete and type hints when constructing vector queries and filters","Generate TypeScript type definitions from vector database schemas for seamless integration with existing codebases"],"best_for":["TypeScript-first teams building LLM applications who value compile-time safety and developer experience","Projects with complex metadata schemas where runtime type errors are costly (financial services, healthcare)","Teams already using TypeScript for their backend and seeking consistent type safety across the stack"],"limitations":["TypeScript-only — no native Python, Go, or Rust support, limiting polyglot team adoption","Type inference overhead during development may slow IDE responsiveness for very large metadata schemas (100+ fields)","Generic type constraints can become complex for advanced use cases (nested metadata, union types), requiring careful API design","Runtime type validation is not automatic — developers must explicitly validate metadata at boundaries (API inputs, database responses)"],"requires":["TypeScript 4.5+ with strict mode enabled","Node.js 14+ or browser runtime with ES2020+ support","tsconfig.json configured with appropriate target and module settings"],"input_types":["vector embeddings (typed as Float32Array or number[])","metadata objects (generic type parameter, user-defined)","query filters (type-safe filter expressions)"],"output_types":["typed query results with metadata strongly typed","type-safe error objects with detailed error information","TypeScript type definitions for generated schemas"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-endee__cap_4","uri":"capability://data.processing.analysis.batch.vector.insertion.and.upsert.with.encryption","name":"batch vector insertion and upsert with encryption","description":"Supports bulk insertion and upsert operations for multiple encrypted vectors in a single API call, with client-side batching and encryption applied to all vectors before transmission. The implementation likely chunks large batches to respect network and memory constraints, applies encryption in parallel using Web Workers or Node.js worker threads, and handles partial failures gracefully with detailed error reporting per vector. This enables efficient bulk loading of vector stores while maintaining end-to-end encryption.","intents":["Bulk load millions of encrypted embeddings into a vector database from CSV, JSON, or streaming sources","Upsert vectors with updated metadata without re-encrypting unchanged vectors","Parallelize encryption across multiple CPU cores to minimize latency for large batch operations","Handle partial batch failures with granular error reporting (which vectors succeeded, which failed, and why)"],"best_for":["Data engineering teams performing initial vector store population or periodic bulk updates","ETL pipelines ingesting embeddings from embedding services and loading them into encrypted vector stores","Applications with high-throughput vector insertion requirements (1000+ vectors/second)"],"limitations":["Encryption parallelization is limited by available CPU cores — batches larger than 10,000 vectors may not see linear throughput improvements","Memory overhead for buffering encrypted payloads before transmission can be significant for very large batches (1M+ vectors) — may require streaming/chunking","Partial batch failures require application-level retry logic; no built-in exponential backoff or dead-letter queue","Batch size optimization is workload-dependent — no automatic tuning based on network conditions or server capacity"],"requires":["TypeScript/JavaScript runtime with Worker API support (Node.js 10.5+ or modern browsers)","Vector embeddings pre-computed and available in memory or as a stream","Connection to vector database backend with batch insert/upsert endpoint"],"input_types":["array of vector objects (id, embedding, metadata)","batch size parameter (integer, typically 100-10000)","optional: upsert mode flag (insert-only vs. upsert)"],"output_types":["batch operation result with success/failure counts","detailed error report per failed vector (vector ID, error message, error code)","operation metadata (total latency, encryption time, network time)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-endee__cap_5","uri":"capability://data.processing.analysis.metadata.filtering.on.decrypted.vector.results","name":"metadata filtering on decrypted vector results","description":"Applies metadata-based filtering to vector search results after decryption on the client side, supporting complex filter expressions (AND, OR, NOT, range queries, string matching) without exposing filter logic to the server. The implementation likely parses filter expressions into an AST, evaluates them against decrypted metadata objects, and returns only results matching all filter criteria. This enables privacy-preserving filtered search where the server cannot infer filtering intent.","intents":["Filter vector search results by metadata fields (e.g., 'find similar documents from 2024 only') without revealing filter criteria to the server","Combine semantic similarity with structured metadata filtering (e.g., 'similar to this query AND category=legal AND author=alice')","Support complex filter expressions with nested AND/OR logic for fine-grained result filtering","Implement faceted search on encrypted vectors with client-side aggregation of metadata values"],"best_for":["Applications requiring both semantic search and structured filtering with privacy guarantees (e.g., confidential document retrieval)","Multi-tenant systems where filtering must respect tenant isolation without server-side awareness","Search interfaces with faceted navigation where filter options are derived from decrypted metadata"],"limitations":["Client-side filtering on large result sets (10,000+ vectors) introduces significant latency — likely 500ms-2s for comprehensive filtering","Complex filter expressions with many OR clauses may require evaluating all returned vectors, negating ANN efficiency gains","No server-side index support for filtered queries — cannot optimize for common filter patterns","Filter expressions must be constructed carefully to avoid logic errors; no query optimization or cost estimation"],"requires":["TypeScript/JavaScript runtime","Decrypted vector metadata available in memory","Filter expression parser (likely included in the library)"],"input_types":["filter expression objects (JSON-serializable, e.g., {field: 'category', operator: 'eq', value: 'legal'})","decrypted metadata objects from vector search results","optional: filter expression string syntax (e.g., 'category=legal AND year>=2024')"],"output_types":["filtered vector results matching all filter criteria","filter match statistics (how many results matched each filter)","filter evaluation metadata (latency, vectors evaluated)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-endee__cap_6","uri":"capability://data.processing.analysis.vector.deletion.and.garbage.collection.with.encryption","name":"vector deletion and garbage collection with encryption","description":"Supports deletion of encrypted vectors from the database with optional garbage collection to reclaim storage space, handling the challenge that deleted vectors cannot be verified as deleted without decryption. The implementation likely uses soft deletes (marking vectors as deleted without removing them) or cryptographic deletion proofs, and provides a garbage collection mechanism to physically remove deleted vectors. This maintains data integrity while preserving encryption guarantees.","intents":["Remove encrypted vectors from the database when documents are deleted or expire","Implement GDPR right-to-be-forgotten for encrypted vector data without exposing deletion patterns to the server","Reclaim storage space from deleted vectors through periodic garbage collection","Maintain audit trails of vector deletions for compliance and forensics"],"best_for":["Applications with regulatory requirements for data deletion (GDPR, CCPA, HIPAA)","Systems where user data must be completely removed upon request without server-side verification","Long-lived vector stores where storage efficiency matters (millions of vectors with churn)"],"limitations":["Soft deletes increase storage overhead — deleted vectors remain in the database until garbage collection runs","Garbage collection requires decryption of all vectors to verify deletion status, adding significant computational cost","No cryptographic proof that deleted vectors were actually removed — requires trusting the server's garbage collection implementation","Deletion patterns may leak information about data access patterns if deletion timing is observable"],"requires":["TypeScript/JavaScript runtime","Vector IDs or identifiers for vectors to be deleted","Optional: scheduled garbage collection job or manual trigger"],"input_types":["vector IDs to delete (string or number array)","optional: deletion reason or metadata (for audit logging)","garbage collection parameters (retention period, batch size)"],"output_types":["deletion confirmation with vector IDs successfully deleted","garbage collection report (vectors removed, storage reclaimed)","audit log entries for deleted vectors"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-endee__cap_7","uri":"capability://data.processing.analysis.vector.update.and.re.encryption.with.metadata.changes","name":"vector update and re-encryption with metadata changes","description":"Supports updating encrypted vectors with new embeddings or metadata, re-encrypting vectors as needed while maintaining version history and backward compatibility. The implementation likely supports partial updates (metadata-only changes without re-encryption) and full updates (new embedding + metadata), with optional version tracking to maintain audit trails. This enables evolving vector data without losing historical information or breaking existing queries.","intents":["Update vector embeddings when source documents change or embedding models are upgraded","Modify vector metadata (tags, categories, ownership) without re-encrypting the embedding","Maintain version history of vectors for audit trails and rollback capabilities","Support incremental embedding updates for streaming or real-time data sources"],"best_for":["Applications with frequently-updated source documents (news, research papers, code repositories)","Systems requiring audit trails of vector changes for compliance and debugging","Multi-model scenarios where vectors are re-computed with improved embedding models"],"limitations":["Full vector updates require re-encryption, adding latency proportional to vector dimensionality","Version history increases storage overhead — each vector version requires separate storage","Concurrent updates to the same vector may cause conflicts; no built-in conflict resolution","Rollback to previous vector versions requires manual intervention or application-level versioning logic"],"requires":["TypeScript/JavaScript runtime","Vector ID and new embedding/metadata to update","Optional: version control system for tracking vector history"],"input_types":["vector ID to update","new embedding (optional, for full update)","new metadata (optional, for metadata-only update)","update mode (full vs. partial)"],"output_types":["update confirmation with new vector version","version history (previous versions if tracked)","update metadata (timestamp, latency, re-encryption status)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-endee__cap_8","uri":"capability://automation.workflow.connection.pooling.and.request.batching.for.vector.operations","name":"connection pooling and request batching for vector operations","description":"Manages a pool of connections to the vector database backend with automatic request batching to reduce network round-trips and improve throughput. The implementation likely uses a connection pool with configurable size, batches multiple vector operations (inserts, queries, deletes) into single network requests, and implements backpressure handling to prevent overwhelming the server. This optimizes network utilization and reduces latency for high-throughput workloads.","intents":["Reduce network latency for high-frequency vector operations by batching multiple requests","Maintain efficient connection reuse across multiple vector database operations","Handle backpressure and rate limiting from the vector database server gracefully","Optimize throughput for applications with many concurrent vector operations"],"best_for":["High-throughput applications with 1000+ vector operations per second","Serverless environments (AWS Lambda, Google Cloud Functions) where connection reuse is critical","Applications with bursty traffic patterns where batching can smooth out load spikes"],"limitations":["Connection pool overhead adds memory usage — each connection may consume 1-10MB depending on buffer sizes","Request batching introduces latency for individual operations (10-50ms) to wait for batch formation","Pool size tuning is workload-dependent — no automatic tuning based on observed throughput","Backpressure handling may drop requests if queue size is exceeded, requiring application-level retry logic"],"requires":["TypeScript/JavaScript runtime","Network connectivity to vector database backend","Optional: configuration parameters for pool size, batch size, timeout"],"input_types":["vector operations (insert, query, delete, update)","pool configuration (size, batch size, timeout)","optional: priority hints for request ordering"],"output_types":["batched operation results with per-operation status","pool statistics (active connections, queued requests, throughput)","backpressure indicators (queue depth, rejection rate)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-endee__cap_9","uri":"capability://memory.knowledge.local.vector.caching.with.encryption","name":"local vector caching with encryption","description":"Caches recently-accessed encrypted vectors in local memory or disk storage with configurable TTL and eviction policies, reducing repeated decryption overhead and network round-trips for frequently-accessed vectors. The implementation likely uses an LRU or LFU cache with optional disk persistence, maintains cache coherency with the remote database, and provides cache statistics for monitoring. This improves performance for read-heavy workloads while maintaining encryption guarantees.","intents":["Reduce decryption latency for frequently-accessed vectors by caching decrypted results locally","Minimize network round-trips for repeated vector queries with identical or similar parameters","Support offline-first scenarios where cached vectors remain accessible without network connectivity","Monitor cache hit rates and optimize cache size based on access patterns"],"best_for":["Read-heavy applications with temporal locality in vector access patterns (e.g., trending documents)","Mobile or edge applications with intermittent network connectivity","Applications with strict latency requirements where decryption overhead is unacceptable"],"limitations":["Cache coherency challenges — cached vectors may become stale if updated on the server without invalidation","Memory overhead for caching — each cached vector consumes 1-10KB depending on metadata size","Cache invalidation is complex for encrypted data — no way to verify cache freshness without decryption","Disk-based caching introduces I/O latency and requires secure storage of cached encrypted data"],"requires":["TypeScript/JavaScript runtime","Optional: disk storage for persistent caching (Node.js only)","Cache configuration parameters (size, TTL, eviction policy)"],"input_types":["vector IDs or query parameters to cache","cache configuration (max size, TTL, eviction policy)","optional: cache invalidation hints from application"],"output_types":["cached vector results with cache metadata (age, hit count)","cache statistics (hit rate, miss rate, eviction count)","cache invalidation confirmations"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":28,"verified":false,"data_access_risk":"high","permissions":["TypeScript/JavaScript runtime (Node.js 14+ or browser with Web Crypto API)","Network connectivity to a compatible vector database backend (Pinecone, Weaviate, Milvus, or custom)","Secure key management strategy (environment variables, HSM, or key management service)","TypeScript/JavaScript runtime with crypto support","Vector embeddings pre-computed from an embedding model (OpenAI, Anthropic, local Sentence Transformers, etc.)","Connection to a vector database backend supporting encrypted payload storage and retrieval","TypeScript/JavaScript runtime","Vector dimension information (either explicit or inferred from embedding model)","Optional: embedding model registry configuration","Decrypted vector search results with metadata"],"failure_modes":["Encryption/decryption overhead adds latency to every vector operation — likely 50-200ms per operation depending on vector dimensionality","Server-side filtering and aggregation queries become impossible since the server cannot read encrypted vectors","Key rotation and management complexity increases operational burden compared to plaintext vector databases","Similarity search must be performed client-side or via homomorphic encryption, limiting scalability to very large result sets","ANN approximation quality may degrade when applied to encrypted vectors, potentially returning suboptimal results compared to plaintext search","Client-side re-ranking of large result sets (1000+) introduces significant latency — likely 500ms-2s for comprehensive re-ranking","Cannot leverage server-side indexing optimizations (HNSW graph traversal, IVF clustering) since the server cannot read encrypted vectors","Batch similarity search across multiple queries requires proportional encryption/decryption overhead per query","Dimension validation adds overhead to every insert and query operation (typically <1ms per operation)","Cannot detect semantic mismatches between different embedding models with the same dimension (e.g., OpenAI vs. Cohere)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.0724275869600789,"quality":0.34,"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":265,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=endee","compare_url":"https://unfragile.ai/compare?artifact=endee"}},"signature":"y21erE7F1DsJHOvRg9Hz5yr52gmI6XedNmqodS7OIpLKOR2GTvfWpu1OzYXCOmjRN0GRKrAs8ze2y/k4viItDA==","signedAt":"2026-06-22T12:34:22.319Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/endee","artifact":"https://unfragile.ai/endee","verify":"https://unfragile.ai/api/v1/verify?slug=endee","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"}}