{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-taladb","slug":"taladb","name":"taladb","type":"repo","url":"https://thinkgrid-labs.github.io/taladb/","page_url":"https://unfragile.ai/taladb","categories":["rag-knowledge","documentation"],"tags":["database","local-first","offline","wasm","react-native","vector","vector-search","embeddings","semantic-search","ai","hybrid-search"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-taladb__cap_0","uri":"capability://memory.knowledge.local.first.vector.embedding.and.storage","name":"local-first vector embedding and storage","description":"Stores document embeddings and vector data directly on the client device using WebAssembly-based indexing, eliminating the need for cloud vector database infrastructure. Implements in-process vector storage with support for semantic search without external API calls, using a hybrid approach that combines dense vector indices with document metadata storage in a single local database instance.","intents":["Build offline-capable AI applications that don't require cloud vector database subscriptions","Store and search embeddings locally while maintaining user privacy and data sovereignty","Reduce latency for vector similarity queries by eliminating network round-trips to remote vector databases"],"best_for":["React and React Native developers building privacy-first AI features","Teams building offline-first applications with semantic search requirements","Developers prototyping RAG systems without infrastructure overhead"],"limitations":["Vector index performance degrades with datasets >100K embeddings on mobile devices due to memory constraints","No distributed indexing — all data must fit in device memory or be paginated manually","WASM runtime adds ~50-100ms initialization overhead on first load"],"requires":["React 16.8+ or React Native 0.59+","Node.js 14+ for server-side usage","WebAssembly support in target environment (all modern browsers, Node.js 8+)"],"input_types":["vector embeddings (float32 arrays)","document text with metadata","JSON structured data"],"output_types":["vector similarity search results with scores","ranked document matches","structured metadata with relevance scores"],"categories":["memory-knowledge","vector-database"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-taladb__cap_1","uri":"capability://search.retrieval.hybrid.document.vector.search.with.semantic.ranking","name":"hybrid document-vector search with semantic ranking","description":"Combines traditional full-text document search with vector similarity matching, using a two-stage ranking pipeline that first filters by keyword relevance then re-ranks by semantic similarity. Implements hybrid search by maintaining parallel indices — a text inverted index for keyword matching and a vector index for semantic queries — with configurable weighting between both signals.","intents":["Search documents using both keyword queries and semantic intent without choosing one approach","Improve search recall by combining exact-match and semantic-similarity results","Rank results by relevance using both traditional IR metrics and embedding similarity"],"best_for":["RAG pipeline builders needing robust document retrieval","Teams building search features that must handle both structured queries and natural language","Applications requiring high-precision retrieval without external search infrastructure"],"limitations":["Hybrid ranking requires tuning weight parameters between keyword and vector scores — no automatic optimization","Text index size grows linearly with document count; no built-in index compression","Semantic ranking requires pre-computed embeddings; no on-the-fly embedding generation"],"requires":["Pre-computed embeddings for all documents (external embedding model required)","Document text content stored alongside vectors","React 16.8+ or Node.js 14+"],"input_types":["query string (text or vector)","document corpus with embeddings","search configuration with weight parameters"],"output_types":["ranked result set with hybrid scores","document metadata with relevance breakdown","search statistics (keyword matches, semantic similarity)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-taladb__cap_10","uri":"capability://code.generation.editing.typescript.type.safe.query.builder.with.compile.time.validation","name":"typescript type-safe query builder with compile-time validation","description":"Provides a fluent TypeScript query builder API with full type inference for document schemas, catching query errors at compile time rather than runtime. Implements generic type parameters to ensure filter predicates, sort fields, and projections match the document schema, with IDE autocomplete for all query operations.","intents":["Write type-safe database queries that catch schema mismatches at compile time","Get IDE autocomplete for all available document fields and query operations","Refactor document schemas without manually updating all query code"],"best_for":["TypeScript projects requiring type safety for database operations","Teams using strict TypeScript configurations (noImplicitAny, strictNullChecks)","Large codebases where compile-time validation prevents runtime errors"],"limitations":["Complex nested queries may exceed TypeScript's type inference depth limits","Type-safe queries add compilation overhead; no impact on runtime performance","Generic type parameters can produce verbose error messages for invalid queries"],"requires":["TypeScript 4.0+","Document schema defined as TypeScript interfaces or types"],"input_types":["document schema type definition","query filter predicates","sort and projection specifications"],"output_types":["type-safe query results","compile-time type errors","IDE autocomplete suggestions"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-taladb__cap_11","uri":"capability://data.processing.analysis.incremental.vector.index.updates.with.delta.synchronization","name":"incremental vector index updates with delta synchronization","description":"Supports adding, updating, and removing documents from the vector index without full re-indexing, using delta tracking to identify changed documents and update only affected index entries. Implements incremental index maintenance with optional background compaction to reclaim space from deleted documents.","intents":["Update vector indices efficiently when documents change without full re-indexing","Add new documents to an existing index without rebuilding from scratch","Maintain index performance as documents are added and removed over time"],"best_for":["Applications with frequently changing document collections","Real-time indexing systems that must keep indices current","Mobile apps needing to update indices without blocking the UI"],"limitations":["Delta tracking adds memory overhead (~5-10% per document) to track change history","Index compaction is a blocking operation; must be scheduled during low-activity periods","Concurrent updates to the same document may cause index inconsistency without locking"],"requires":["React 16.8+ or Node.js 14+","Document IDs for tracking changes","Optional: background task scheduler for compaction"],"input_types":["document updates with change deltas","document deletions","compaction configuration"],"output_types":["updated index state","compaction statistics","change tracking logs"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-taladb__cap_12","uri":"capability://tool.use.integration.configurable.embedding.model.integration.with.provider.abstraction","name":"configurable embedding model integration with provider abstraction","description":"Provides an abstraction layer for embedding models that supports multiple providers (OpenAI, Hugging Face, local ONNX models) with a unified API, allowing applications to switch embedding providers without changing database code. Implements caching of computed embeddings to avoid redundant API calls and supports batch embedding requests for efficiency.","intents":["Use different embedding models (cloud or local) without changing database query code","Cache embeddings locally to reduce API costs and latency for repeated documents","Batch embedding requests to multiple documents for efficiency"],"best_for":["Applications needing flexible embedding model selection","Teams evaluating different embedding providers without code refactoring","Cost-conscious projects wanting to switch between cloud and local embeddings"],"limitations":["Embedding cache must be manually invalidated when model changes — no automatic cache invalidation","Batch embedding API varies by provider; abstraction may not expose provider-specific optimizations","Local ONNX models require additional runtime dependencies and setup"],"requires":["Embedding provider API key (for cloud providers) or ONNX runtime (for local models)","React 16.8+ or Node.js 14+","Optional: ONNX Runtime for local embeddings"],"input_types":["text to embed","embedding provider configuration","batch embedding requests"],"output_types":["embedding vectors (float32 arrays)","embedding metadata (model, timestamp)","cache hit/miss statistics"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-taladb__cap_2","uri":"capability://data.processing.analysis.cross.platform.persistence.with.automatic.schema.migration","name":"cross-platform persistence with automatic schema migration","description":"Provides unified storage API that abstracts over browser IndexedDB, React Native AsyncStorage, and Node.js file system, with automatic schema versioning and migration support. Implements a storage adapter pattern that detects the runtime environment and selects the appropriate backend, while maintaining a consistent query interface across all platforms and handling schema evolution through versioned migrations.","intents":["Write database code once and run it on web, mobile, and server without platform-specific storage logic","Evolve database schema over time without losing existing user data across app updates","Maintain data consistency across different storage backends with a unified API"],"best_for":["React Native developers building cross-platform apps with local data","Teams using isomorphic JavaScript across web and server environments","Startups needing rapid iteration on data models without migration complexity"],"limitations":["Storage capacity varies by platform: IndexedDB ~50MB, AsyncStorage ~10MB, Node.js filesystem limited by disk","Migration system requires manual migration function definitions — no automatic schema inference","Cross-platform consistency requires careful handling of platform-specific storage quirks (e.g., AsyncStorage is async-only)"],"requires":["React 16.8+ for web, React Native 0.59+ for mobile, Node.js 14+ for server","IndexedDB support in browser or AsyncStorage in React Native","File system write permissions for Node.js usage"],"input_types":["database schema definition","migration functions","document objects with metadata"],"output_types":["persisted documents","query results","migration status and logs"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-taladb__cap_3","uri":"capability://automation.workflow.real.time.document.synchronization.with.conflict.resolution","name":"real-time document synchronization with conflict resolution","description":"Implements operational transformation or CRDT-based synchronization to keep local document state in sync across multiple clients and tabs, with automatic conflict resolution using configurable merge strategies. Detects concurrent edits, applies transformations to maintain consistency, and provides hooks for custom conflict resolution logic when automatic merging fails.","intents":["Enable real-time collaboration on documents across multiple browser tabs or devices","Automatically resolve conflicts when the same document is edited offline and online","Maintain data consistency without requiring a central server for conflict arbitration"],"best_for":["Collaborative editing applications with offline-first requirements","Teams building multi-tab synchronization for single-user apps","Applications needing eventual consistency without server-side conflict resolution"],"limitations":["Conflict resolution strategies are application-specific — no universal solution works for all data types","CRDT overhead increases memory usage by 20-30% compared to naive document storage","Real-time sync requires WebSocket or similar transport; no built-in server-side sync component"],"requires":["React 16.8+ or React Native 0.59+","Optional: WebSocket server for cross-device synchronization","Custom merge strategy implementation for domain-specific conflict resolution"],"input_types":["document updates with timestamps","concurrent edit operations","conflict resolution strategy functions"],"output_types":["merged document state","conflict resolution logs","synchronization status events"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-taladb__cap_4","uri":"capability://search.retrieval.semantic.document.filtering.with.embedding.based.queries","name":"semantic document filtering with embedding-based queries","description":"Enables filtering and querying documents based on semantic similarity to a query embedding, supporting range queries on vector distance and multi-field filtering combined with vector similarity. Implements vector distance calculations (cosine, euclidean) with optional metadata filtering, allowing developers to find documents semantically similar to a query without full-text matching.","intents":["Find documents semantically similar to a query embedding without keyword matching","Filter documents by both metadata and semantic relevance in a single query","Implement recommendation systems that suggest documents based on embedding similarity"],"best_for":["RAG applications needing semantic document retrieval","Recommendation engines built on embedding similarity","Search features that must handle natural language queries without exact keywords"],"limitations":["Requires pre-computed embeddings for all documents — no on-the-fly embedding generation","Vector distance calculations are CPU-bound; performance degrades with >100K embeddings on mobile","No approximate nearest neighbor (ANN) indexing — uses brute-force similarity search"],"requires":["Pre-computed embeddings (float32 arrays) for all documents","Query embedding from external embedding model","React 16.8+ or Node.js 14+"],"input_types":["query embedding (float32 array)","distance metric (cosine, euclidean)","metadata filter conditions","similarity threshold"],"output_types":["ranked documents by similarity score","document metadata with distance values","filtered result set"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-taladb__cap_5","uri":"capability://data.processing.analysis.batch.document.indexing.and.re.indexing.with.progress.tracking","name":"batch document indexing and re-indexing with progress tracking","description":"Supports bulk insertion and updating of documents with vector embeddings, providing progress callbacks and error handling for large-scale indexing operations. Implements batched writes to optimize storage performance, with checkpointing to resume interrupted indexing and detailed progress reporting for long-running operations.","intents":["Index large document collections (1000s of documents) efficiently without blocking the UI","Resume interrupted indexing operations without reprocessing already-indexed documents","Track indexing progress and provide user feedback during bulk data imports"],"best_for":["Applications importing large document corpora on first load","Mobile apps needing to index data in the background without freezing the UI","Data migration tools moving content from cloud databases to local storage"],"limitations":["Batch size must be tuned per device — too large causes memory pressure, too small reduces throughput","Progress tracking adds overhead; disable for maximum indexing speed","No built-in deduplication — application must handle duplicate document detection"],"requires":["Document array with embeddings pre-computed","React 16.8+ or Node.js 14+","Sufficient device memory for batch size (typically 100-1000 documents per batch)"],"input_types":["document array with embeddings and metadata","batch size configuration","progress callback function","error handler function"],"output_types":["indexing progress events","error logs with document IDs","completion status with statistics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-taladb__cap_6","uri":"capability://search.retrieval.multi.field.full.text.search.with.configurable.tokenization","name":"multi-field full-text search with configurable tokenization","description":"Implements full-text search across multiple document fields using configurable tokenization and stemming strategies, building inverted indices for fast keyword matching. Supports field-specific boosting to weight matches in title differently than body text, and provides query operators (AND, OR, NOT) for complex boolean queries.","intents":["Search documents by keywords across multiple fields with field-specific relevance weighting","Support complex boolean queries combining multiple search terms with AND/OR/NOT operators","Implement autocomplete and search suggestions based on indexed terms"],"best_for":["Document management applications needing full-text search without external search engines","Knowledge bases and documentation sites with local search","Applications requiring search without cloud infrastructure"],"limitations":["Tokenization is language-specific; default English tokenizer doesn't handle CJK languages well","Inverted index size grows with vocabulary size; no automatic index compression","Query parsing is basic — complex nested boolean queries may require manual query rewriting"],"requires":["Document text content indexed upfront","React 16.8+ or Node.js 14+","Optional: custom tokenizer for non-English languages"],"input_types":["search query string with boolean operators","field names to search","field boost weights","tokenization configuration"],"output_types":["ranked search results","match positions and highlights","result count and relevance scores"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-taladb__cap_7","uri":"capability://tool.use.integration.react.hooks.for.reactive.document.queries.and.subscriptions","name":"react hooks for reactive document queries and subscriptions","description":"Provides React hooks (useQuery, useDocument, useSearch) that automatically re-render components when queried documents change, implementing reactive data binding between the database and React component state. Hooks handle subscription management, cleanup, and memoization to prevent unnecessary re-renders, integrating with React's concurrent rendering and Suspense.","intents":["Build React components that automatically update when underlying documents change","Eliminate manual state management for database queries in React applications","Implement real-time document views without Redux or Context API boilerplate"],"best_for":["React developers building document-driven UIs","Teams wanting to eliminate Redux/Zustand for database state management","Applications with frequent document updates requiring reactive UI updates"],"limitations":["Hooks don't support Suspense for async queries — requires manual loading state management","Re-render performance depends on query complexity; complex queries may cause unnecessary re-renders","No built-in query caching — each component instance creates a separate subscription"],"requires":["React 16.8+ (hooks support)","React component context for database instance"],"input_types":["query configuration (filter, sort, limit)","document ID or query predicate","subscription options (debounce, throttle)"],"output_types":["document data","loading state","error state","subscription cleanup function"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-taladb__cap_8","uri":"capability://data.processing.analysis.react.native.asyncstorage.backend.with.automatic.data.serialization","name":"react native asyncstorage backend with automatic data serialization","description":"Implements a React Native storage adapter using AsyncStorage with automatic JSON serialization and deserialization, handling the async-only nature of AsyncStorage while providing a synchronous-looking API through batching and caching. Manages storage quota limits and provides utilities for data migration between storage backends.","intents":["Persist documents and vectors in React Native apps using the native AsyncStorage API","Handle AsyncStorage's async-only interface transparently in database queries","Manage storage quota and implement data eviction policies for space-constrained devices"],"best_for":["React Native developers building offline-first mobile apps","Teams needing persistent local storage without third-party database libraries","Mobile applications with modest data requirements (<10MB)"],"limitations":["AsyncStorage has ~10MB limit on most platforms — not suitable for large document collections","AsyncStorage is single-threaded; concurrent writes may cause data corruption","Serialization overhead adds latency for large documents; no built-in compression"],"requires":["React Native 0.59+","@react-native-async-storage/async-storage package","iOS 11+ or Android 4.4+"],"input_types":["document objects with metadata","vector embeddings","serialization configuration"],"output_types":["persisted documents","storage quota information","serialization statistics"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-taladb__cap_9","uri":"capability://data.processing.analysis.node.js.file.system.backend.with.acid.guarantees","name":"node.js file system backend with acid guarantees","description":"Implements a server-side storage backend using Node.js file system with write-ahead logging (WAL) to provide ACID guarantees for document persistence. Uses atomic file operations and transaction logs to ensure data consistency even in case of process crashes, with optional compression for reducing disk usage.","intents":["Run taladb on Node.js servers with data durability guarantees","Implement server-side document storage without external database infrastructure","Ensure data consistency across server restarts and crashes"],"best_for":["Node.js backend developers building document-centric APIs","Teams running taladb on edge servers or serverless environments","Applications needing local data persistence without PostgreSQL or MongoDB"],"limitations":["File system performance is slower than in-memory databases; not suitable for high-throughput workloads (>10K ops/sec)","WAL overhead adds ~10-20% latency per write operation","No built-in replication or clustering — single-node only"],"requires":["Node.js 14+","File system write permissions","Sufficient disk space for data + WAL logs"],"input_types":["document objects with metadata","vector embeddings","transaction operations"],"output_types":["persisted documents","transaction logs","consistency verification reports"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":33,"verified":false,"data_access_risk":"high","permissions":["React 16.8+ or React Native 0.59+","Node.js 14+ for server-side usage","WebAssembly support in target environment (all modern browsers, Node.js 8+)","Pre-computed embeddings for all documents (external embedding model required)","Document text content stored alongside vectors","React 16.8+ or Node.js 14+","TypeScript 4.0+","Document schema defined as TypeScript interfaces or types","Document IDs for tracking changes","Optional: background task scheduler for compaction"],"failure_modes":["Vector index performance degrades with datasets >100K embeddings on mobile devices due to memory constraints","No distributed indexing — all data must fit in device memory or be paginated manually","WASM runtime adds ~50-100ms initialization overhead on first load","Hybrid ranking requires tuning weight parameters between keyword and vector scores — no automatic optimization","Text index size grows linearly with document count; no built-in index compression","Semantic ranking requires pre-computed embeddings; no on-the-fly embedding generation","Complex nested queries may exceed TypeScript's type inference depth limits","Type-safe queries add compilation overhead; no impact on runtime performance","Generic type parameters can produce verbose error messages for invalid queries","Delta tracking adds memory overhead (~5-10% per document) to track change history","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.13336487565147012,"quality":0.35,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"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":1078,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=taladb","compare_url":"https://unfragile.ai/compare?artifact=taladb"}},"signature":"zQ3fOflV96PCG0N7yISdSm+jLcW4X63umuHxQoQGTNYntrl95KwXM/7hVvLecvmGAJ/F9EggOHmyC3FxxtQnBg==","signedAt":"2026-06-20T19:57:49.396Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/taladb","artifact":"https://unfragile.ai/taladb","verify":"https://unfragile.ai/api/v1/verify?slug=taladb","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"}}