{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-lancedb--lancedb","slug":"lancedb--lancedb","name":"lancedb","type":"repo","url":"https://lancedb.com/docs","page_url":"https://unfragile.ai/lancedb--lancedb","categories":["rag-knowledge"],"tags":["approximate-nearest-neighbor-search","image-search","nearest-neighbor-search","recommender-system","search-engine","semantic-search","similarity-search","vector-database"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-lancedb--lancedb__cap_0","uri":"capability://search.retrieval.vector.similarity.search.with.ivf.pq.hnsw.indexing","name":"vector-similarity-search-with-ivf-pq-hnsw-indexing","description":"Executes approximate nearest neighbor search using state-of-the-art indexing strategies (IVF-PQ for large-scale partitioning and HNSW for hierarchical navigation). The Rust core implements Lance columnar format storage with zero-copy Arrow integration, enabling sub-millisecond queries over millions of vectors. Query execution pipeline applies vector distance metrics (L2, cosine, dot product) with optional scalar filtering and projection pushdown to minimize data materialization.","intents":["I need to find semantically similar embeddings from a dataset of millions of vectors in under 100ms","I want to combine vector search with SQL WHERE clauses to filter results by metadata","I need to scale vector search from development (local SQLite-like mode) to production (cloud deployment) without code changes"],"best_for":["ML engineers building RAG systems with large embedding collections","AI product teams needing sub-second semantic search at scale","Developers migrating from Pinecone/Weaviate to self-hosted solutions"],"limitations":["IVF-PQ indexing requires pre-computed partitions; adding new data triggers incremental index updates with ~5-10% query latency overhead during reindexing","HNSW index construction is single-threaded in current implementation; indexing 10M vectors takes ~30-60 minutes on standard hardware","Vector dimension must be consistent across all rows; schema enforcement prevents mixed-dimension queries","No built-in distributed indexing; horizontal scaling requires manual sharding at application layer"],"requires":["Python 3.9+ or Node.js 16+ or Java 11+","Pre-computed vector embeddings (768-4096 dimensions typical)","Disk space: ~1.2x raw vector data size for index overhead","For remote deployment: LanceDB Cloud account or self-hosted Lance server"],"input_types":["numpy arrays (Python)","Float32/Float64 vectors","Arrow RecordBatch with vector columns","JSON with nested vector arrays"],"output_types":["Ranked list of (vector_id, distance_score, metadata) tuples","Arrow Table with filtered columns","Streaming iterator for large result sets"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lancedb--lancedb__cap_1","uri":"capability://search.retrieval.full.text.search.with.bm25.ranking","name":"full-text-search-with-bm25-ranking","description":"Provides BM25-based full-text search over text columns using inverted index construction and term frequency/inverse document frequency ranking. The implementation integrates with the Lance storage layer to co-locate FTS indexes alongside vector indexes, enabling hybrid queries that combine semantic and lexical relevance. Query execution applies tokenization, stemming, and relevance scoring without requiring external search engines like Elasticsearch.","intents":["I want to search documents by keyword while also filtering by vector similarity in a single query","I need BM25 ranking for traditional text search without deploying a separate search service","I want to combine full-text search results with vector search results using configurable weighting"],"best_for":["Teams building hybrid search systems (semantic + keyword) for documentation or knowledge bases","Developers wanting all-in-one search without Elasticsearch/Solr infrastructure","RAG applications needing both dense and sparse retrieval in one database"],"limitations":["FTS index is built per-table; cross-table full-text search requires application-level merging","Tokenization is language-agnostic (whitespace + punctuation split); no stemming for non-English languages without custom analyzers","BM25 parameters (k1, b) are fixed; tuning requires index rebuild","FTS index size can be 30-50% of raw text data; large text columns significantly increase storage overhead"],"requires":["Text columns must be explicitly indexed for FTS (not automatic)","Python 3.9+ or Node.js 16+","Sufficient disk space for inverted index construction"],"input_types":["String columns in Arrow Table","UTF-8 encoded text","Unstructured document text"],"output_types":["Ranked results with BM25 scores","Combined vector + FTS result sets with normalized scores","Matched term positions (optional)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lancedb--lancedb__cap_10","uri":"capability://data.processing.analysis.streaming.data.ingestion.with.incremental.updates","name":"streaming-data-ingestion-with-incremental-updates","description":"Supports streaming inserts and updates via append-only operations that are automatically batched and indexed. New data is immediately queryable without explicit index rebuilds; incremental indexing updates existing indexes in the background. Streaming API accepts Arrow RecordBatch, Pandas DataFrames, or JSON-like dictionaries. Atomic transactions ensure consistency across vector and metadata columns.","intents":["I want to continuously add new documents and embeddings to my vector database without downtime","I need to update existing vectors and metadata atomically (e.g., change embedding + update timestamp together)","I want new data to be searchable immediately after insertion without waiting for index rebuilds"],"best_for":["Real-time RAG systems ingesting documents continuously","Live recommendation systems updating embeddings as user behavior changes","Data pipelines streaming embeddings from ML models"],"limitations":["Streaming inserts are batched internally; individual row inserts have ~1-5ms latency due to batching overhead","Incremental indexing adds ~5-10% write latency compared to non-indexed tables","Very high-frequency updates (>10K/sec) may cause write contention; batching helps but doesn't eliminate latency","No built-in deduplication; duplicate keys must be handled at application layer"],"requires":["Table schema defined before streaming","Python 3.9+ or Node.js 16+","Sufficient disk I/O bandwidth for continuous writes"],"input_types":["Arrow RecordBatch","Pandas DataFrame","List of dictionaries (JSON-like)","Generator/iterator of rows"],"output_types":["Row count inserted/updated","Timestamp of last update","Version tag (if versioning enabled)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lancedb--lancedb__cap_11","uri":"capability://data.processing.analysis.schema.aware.data.validation.and.type.coercion","name":"schema-aware-data-validation-and-type-coercion","description":"Enforces Arrow schema validation on all data operations, automatically coercing compatible types (e.g., Python int to Arrow int64) and rejecting incompatible data. Schema is defined at table creation time and enforced on all inserts/updates. Type mismatches are reported with detailed error messages indicating the problematic column and expected type. Optional columns allow NULL values; required columns reject NULLs.","intents":["I want to catch data type errors early before they corrupt my vector database","I need to ensure all vectors have the same dimension and type","I want automatic type coercion for common cases (e.g., float32 to float64) without manual conversion"],"best_for":["Teams with strict data quality requirements","Applications processing data from multiple sources with inconsistent types","Developers wanting schema enforcement without manual validation code"],"limitations":["Schema is immutable after table creation; adding/removing columns requires table rewrite","Type coercion is limited to compatible types; no automatic string-to-number conversion","Validation adds ~1-2% overhead per insert operation","Error messages are Arrow-level; may be cryptic for nested types"],"requires":["Arrow schema definition (PyArrow Schema or equivalent)","Python 3.9+ or Node.js 16+"],"input_types":["Arrow Schema","Data to validate (RecordBatch, DataFrame, dictionaries)"],"output_types":["Validation errors (detailed type mismatch messages)","Coerced data (if compatible)","Schema metadata (column names, types, nullability)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lancedb--lancedb__cap_12","uri":"capability://data.processing.analysis.embedding.function.integration.with.automatic.vectorization","name":"embedding-function-integration-with-automatic-vectorization","description":"Integrates embedding models (OpenAI, Hugging Face, local models) directly into the database, enabling automatic vectorization of text during insert/update operations. Embedding functions are registered per-column and applied transparently; raw text is stored alongside embeddings for retrieval. Supports both synchronous and asynchronous embedding generation. Caching prevents duplicate embeddings for identical text.","intents":["I want to automatically convert text to embeddings during data ingestion without external preprocessing","I need to use different embedding models for different columns (e.g., title vs. description)","I want to update embeddings when the underlying text changes without manual re-embedding"],"best_for":["RAG systems where embedding generation is part of the ingestion pipeline","Teams using multiple embedding models for different data types","Applications needing automatic vectorization without external embedding services"],"limitations":["Embedding function calls add significant latency (100-500ms per batch depending on model); not suitable for real-time ingestion","Embedding model must be accessible (API key for OpenAI, local model loaded in memory)","Caching is in-memory only; no persistent cache across restarts","Batch embedding is limited by model throughput; very large batches (>10K rows) may timeout"],"requires":["Embedding model (OpenAI API key, Hugging Face model, or local model)","Python 3.9+ (Node.js support is limited)","Text column to embed"],"input_types":["Text column name (string)","Embedding model specification (e.g., 'openai:text-embedding-3-small')","Optional: custom embedding function (callable)"],"output_types":["Vector column with embeddings","Original text column (preserved)","Embedding metadata (model name, dimension)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lancedb--lancedb__cap_13","uri":"capability://data.processing.analysis.query.builder.api.with.fluent.interface.and.lazy.execution","name":"query-builder-api-with-fluent-interface-and-lazy-execution","description":"Provides a fluent, chainable query builder API that constructs query execution plans without immediately executing them. Queries are lazily evaluated; execution is deferred until results are explicitly requested (e.g., .to_list(), .to_arrow()). The query builder supports method chaining for vector search, filtering, projection, limit, and offset operations. Query plans are optimized by the DataFusion query planner before execution.","intents":["I want to build complex queries programmatically without writing SQL","I need to compose queries dynamically based on user input or application logic","I want to inspect the query execution plan before running it"],"best_for":["Developers building dynamic query logic without SQL expertise","Applications with complex, user-driven search requirements","Teams debugging query performance by inspecting execution plans"],"limitations":["Lazy evaluation can be confusing; errors are only caught at execution time, not during query construction","Query builder API is less expressive than SQL for complex aggregations or window functions","Method chaining can lead to long, hard-to-read query chains; no intermediate variable storage","Execution plan inspection requires understanding DataFusion's plan format"],"requires":["Python 3.9+ or Node.js 16+","Table reference (from database connection)"],"input_types":["Vector query (numpy array or list)","Filter expressions (method calls or lambda functions)","Column names for projection (list of strings)"],"output_types":["Query execution plan (DataFusion plan)","Query results (Arrow Table, list of dictionaries, or iterator)","Result count"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lancedb--lancedb__cap_2","uri":"capability://search.retrieval.hybrid.search.with.configurable.relevance.fusion","name":"hybrid-search-with-configurable-relevance-fusion","description":"Combines vector similarity scores and full-text search (BM25) scores using configurable fusion strategies (weighted sum, reciprocal rank fusion, or custom scoring functions). The query builder API accepts both vector and text queries, executes them in parallel against their respective indexes, and merges results using normalized scoring. Filtering and projection pushdown apply to the fused result set, reducing post-processing overhead.","intents":["I want to search by both semantic meaning and keyword relevance, with tunable weights for each","I need to combine dense retrieval (vector) and sparse retrieval (BM25) for better recall in RAG","I want to experiment with different fusion strategies without reindexing data"],"best_for":["RAG system builders optimizing retrieval quality with hybrid approaches","Search product teams A/B testing different relevance fusion algorithms","Developers building multi-modal search (text + image embeddings + keywords)"],"limitations":["Fusion weights are static per query; dynamic per-document weighting requires custom scoring UDFs","Parallel execution of vector and FTS queries adds ~10-20ms overhead vs single-index search","Reciprocal rank fusion requires materializing full result sets from both indexes before merging; not suitable for very large result sets (>10K results)","Custom scoring functions must be implemented in Rust and compiled; no Python/JavaScript UDF support yet"],"requires":["Both vector and text indexes must exist on the table","Vector column with embeddings and text column with content","Python 3.9+ or Node.js 16+"],"input_types":["Vector query (numpy array or list of floats)","Text query (string)","Fusion weights (float tuple, e.g., (0.6, 0.4) for 60% vector / 40% FTS)"],"output_types":["Merged ranked result set with combined scores","Per-result breakdown of vector and FTS scores","Arrow Table with fused ranking"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lancedb--lancedb__cap_3","uri":"capability://data.processing.analysis.multimodal.data.storage.with.vector.metadata.colocalization","name":"multimodal-data-storage-with-vector-metadata-colocalization","description":"Stores vectors, embeddings, raw multimodal data (images, videos, point clouds), and structured metadata in a single Lance table using Apache Arrow columnar format. Zero-copy semantics allow queries to access vectors and metadata without deserialization overhead. MVCC (multi-version concurrency control) versioning enables time-travel queries and atomic updates across vector and metadata columns, maintaining consistency without locks.","intents":["I want to store image embeddings alongside the original images and metadata in one place","I need to query by vector similarity and retrieve associated metadata/raw data in the same operation","I want to version my dataset and query historical snapshots without maintaining separate copies"],"best_for":["Computer vision teams building image search systems with rich metadata","Multimodal AI applications (text + image + video) needing unified storage","Data scientists versioning datasets for reproducibility and experimentation"],"limitations":["Raw binary data (images, videos) stored inline increases table size significantly; separate object storage (S3) integration is recommended for >100MB files","MVCC versioning adds write overhead (~5-10% per update); frequent updates to large tables can cause storage bloat without compaction","Time-travel queries require maintaining version history; old versions consume disk space until explicitly deleted","No built-in schema evolution; adding/removing columns requires table rewrite for existing data"],"requires":["Apache Arrow schema definition with vector and metadata columns","Python 3.9+ or Node.js 16+","Disk space for raw data storage (images, videos, etc.)"],"input_types":["Arrow RecordBatch with mixed column types (float vectors, strings, binary blobs, nested structs)","Pandas DataFrames with object columns","Parquet files with multimodal columns"],"output_types":["Arrow Table with vectors, metadata, and raw data columns","Filtered subsets with projection (e.g., return only vectors + image_id, not raw images)","Historical snapshots via version tags"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lancedb--lancedb__cap_4","uri":"capability://search.retrieval.sql.filtering.and.projection.pushdown.on.vector.queries","name":"sql-filtering-and-projection-pushdown-on-vector-queries","description":"Applies SQL WHERE clauses and column projections directly to vector search queries, pushing filters and projections down to the storage layer for early elimination of non-matching rows. The query builder constructs a filter expression tree that is evaluated during index traversal (for indexed scalar columns) or during result materialization (for non-indexed columns), reducing the number of vectors that must be scored and returned.","intents":["I want to find similar vectors but only from documents created after a certain date","I need to search vectors and return only specific columns (e.g., id and score, not raw vectors)","I want to filter by multiple metadata conditions (category = 'news' AND language = 'en') before ranking by similarity"],"best_for":["RAG systems filtering documents by metadata before semantic search","E-commerce platforms searching products by vector similarity with price/category filters","Analytics teams querying large datasets with complex WHERE clauses on vector results"],"limitations":["Filter pushdown only optimizes indexed scalar columns; non-indexed metadata requires full table scan after vector search","Complex nested filters (OR conditions with multiple AND branches) may not be fully optimized; query planner has limited cost-based optimization","Projection pushdown works for simple column selection; computed columns or aggregations require post-processing","No support for correlated subqueries or window functions in filter expressions"],"requires":["SQL WHERE clause syntax support in query builder","Metadata columns must be defined in table schema","Optional: scalar indexes on frequently-filtered columns for better performance"],"input_types":["SQL WHERE clause (string or AST)","Column names for projection (list of strings)","Comparison operators: =, !=, <, >, <=, >=, IN, LIKE, BETWEEN"],"output_types":["Filtered Arrow Table with selected columns only","Ranked vector results with metadata subset","Row count of filtered results"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lancedb--lancedb__cap_5","uri":"capability://data.processing.analysis.automatic.mvcc.versioning.and.time.travel.queries","name":"automatic-mvcc-versioning-and-time-travel-queries","description":"Implements multi-version concurrency control (MVCC) at the storage layer, automatically creating immutable snapshots of table state on each write operation. Time-travel queries can retrieve data as it existed at a specific point in time by referencing version tags or timestamps. Version management is transparent to the application; no explicit snapshot creation is required. Compaction and garbage collection clean up old versions to reclaim disk space.","intents":["I want to query my dataset as it was yesterday without maintaining separate backups","I need to audit what changed in my vector database between two timestamps","I want to experiment with different embeddings and revert to a previous version if needed"],"best_for":["Data science teams versioning datasets for reproducibility","ML teams A/B testing different embedding models with easy rollback","Compliance-heavy applications requiring audit trails and historical data access"],"limitations":["MVCC adds write latency (~5-10%) because each write creates a new version; high-frequency updates (>1000/sec) may cause performance degradation","Old versions consume disk space; without explicit cleanup, storage can grow 2-3x the size of current data","Time-travel queries require version metadata to be maintained; deleting all versions of a row is not possible (soft deletes only)","Compaction is manual or scheduled; no automatic background compaction in current implementation"],"requires":["Sufficient disk space for multiple versions (typically 2-3x current data size)","Python 3.9+ or Node.js 16+","Version tags or timestamps for time-travel queries"],"input_types":["Timestamp (ISO 8601 string or Unix timestamp)","Version tag (string identifier)","Version number (integer)"],"output_types":["Arrow Table snapshot at specified version","Version history metadata (timestamps, tags, row counts)","Diff between two versions (optional)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lancedb--lancedb__cap_6","uri":"capability://data.processing.analysis.scalar.index.creation.and.management.for.metadata.filtering","name":"scalar-index-creation-and-management-for-metadata-filtering","description":"Creates and maintains B-tree and hash indexes on scalar (non-vector) columns to accelerate metadata filtering in vector queries. Index creation is asynchronous and non-blocking; queries can execute while indexes are being built. The query planner automatically selects indexed columns for filter pushdown, reducing the number of rows that must be scanned. Index statistics are maintained for cost-based query optimization.","intents":["I want to speed up queries that filter by category, date, or user_id before vector search","I need to create indexes on frequently-filtered columns without blocking concurrent queries","I want the query planner to automatically use indexes for my WHERE clauses"],"best_for":["Applications with large tables (>1M rows) and selective metadata filters","Systems with high query volume where filter optimization is critical","Teams managing complex schemas with many filterable columns"],"limitations":["Index creation is single-threaded; building indexes on large tables (>100M rows) takes hours","Index size can be 10-30% of column data size; many indexes on wide tables increase storage overhead","Hash indexes only support equality predicates (=, IN); range queries (>, <, BETWEEN) require B-tree indexes","Index statistics are not automatically updated; manual ANALYZE command required after large data loads"],"requires":["Scalar column (string, integer, date, etc.) to index","Python 3.9+ or Node.js 16+","Disk space for index storage"],"input_types":["Column name (string)","Index type: 'btree' or 'hash'","Optional: index configuration (e.g., sort order)"],"output_types":["Index metadata (type, column, creation timestamp)","Index statistics (cardinality, size)","Query execution plans showing index usage"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lancedb--lancedb__cap_7","uri":"capability://tool.use.integration.multi.language.sdk.with.unified.rust.core.via.ffi","name":"multi-language-sdk-with-unified-rust-core-via-ffi","description":"Provides Python, Node.js, and Java SDKs that wrap a single high-performance Rust core via Foreign Function Interface (FFI) bindings. Each language SDK exposes idiomatic APIs (e.g., async/await in Node.js, context managers in Python) while delegating all compute-intensive operations (indexing, search, filtering) to the shared Rust implementation. FFI overhead is minimal (~1-2% per operation) due to batch processing and zero-copy Arrow data transfer.","intents":["I want to use LanceDB from Python, Node.js, and Java without learning different APIs","I need consistent performance across languages because the same Rust core is used","I want to avoid reimplementing complex indexing logic in multiple languages"],"best_for":["Polyglot teams using multiple languages in the same project","Organizations standardizing on LanceDB across Python ML pipelines and Node.js services","Developers wanting language-native APIs without sacrificing performance"],"limitations":["FFI calls have ~1-2ms overhead per operation; high-frequency operations (>1000/sec) may be bottlenecked by FFI serialization","Language-specific features (e.g., async iterators in Node.js) require custom SDK implementation; not all Rust features are exposed","Debugging FFI issues requires understanding both language and Rust stack traces; error messages can be cryptic","Java SDK is less mature than Python/Node.js; some features may be missing or have different semantics"],"requires":["Python 3.9+ (with pip) OR Node.js 16+ (with npm) OR Java 11+ (with Maven)","Pre-compiled Rust binaries for target platform (Linux x86_64, ARM64; macOS x86_64, ARM64; Windows x86_64)","For development: Rust toolchain (1.70+) to build from source"],"input_types":["Language-native data structures (numpy arrays, TypedArrays, Java arrays)","Arrow RecordBatch (all languages)","Pandas DataFrames (Python only)"],"output_types":["Language-native iterators/generators","Arrow Table (all languages)","Pandas DataFrame (Python only)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lancedb--lancedb__cap_8","uri":"capability://data.processing.analysis.local.embedded.mode.with.sqlite.like.deployment","name":"local-embedded-mode-with-sqlite-like-deployment","description":"Operates in 100% embedded mode (no server required) similar to SQLite, storing all data in a local directory with a single-file or multi-file Lance format. The Rust core runs in-process within the application, eliminating network latency and external dependencies. Suitable for development, testing, and edge deployments. Seamlessly upgrades to remote mode by pointing to a LanceDB Cloud instance without code changes.","intents":["I want to prototype a RAG system locally without setting up a database server","I need to deploy vector search to edge devices or serverless functions with minimal dependencies","I want to test my application with real data before deploying to production"],"best_for":["Solo developers and small teams prototyping AI applications","Edge computing and serverless deployments (AWS Lambda, Vercel)","Development and testing environments"],"limitations":["Single-process concurrency only; multiple processes accessing the same database require external locking (not built-in)","No built-in replication or backup; data loss if local directory is deleted","Performance degrades with very large datasets (>10GB) due to single-machine resource constraints","No authentication or access control; anyone with file system access can read/modify data"],"requires":["Local file system with write permissions","Python 3.9+ or Node.js 16+","Disk space for data storage (typically 1.2-1.5x raw data size)"],"input_types":["Local directory path (string)","Arrow RecordBatch or Pandas DataFrame"],"output_types":["Local Lance database directory","Arrow Table query results"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lancedb--lancedb__cap_9","uri":"capability://tool.use.integration.remote.database.connection.with.namespace.isolation","name":"remote-database-connection-with-namespace-isolation","description":"Connects to remote LanceDB Cloud or self-hosted Lance server instances using connection strings. Namespaces provide logical table grouping and isolation within a single database instance, enabling multi-tenant deployments or organizational separation without separate database instances. Connection pooling and retry logic handle transient failures automatically. Authentication is supported via API keys.","intents":["I want to connect my application to a managed LanceDB Cloud instance","I need to isolate data for different customers or projects using namespaces","I want to scale vector search across multiple machines using a remote server"],"best_for":["Production deployments requiring managed infrastructure","Multi-tenant SaaS applications using namespace isolation","Teams deploying LanceDB on Kubernetes or cloud VMs"],"limitations":["Network latency adds 10-50ms per query compared to embedded mode","Connection pooling has limited configurability; pool size is fixed at initialization","Namespace isolation is logical only; no encryption or access control between namespaces","Remote server must be running and accessible; no automatic failover to backup servers"],"requires":["LanceDB Cloud account OR self-hosted Lance server running","API key for authentication (if required)","Network connectivity to remote server","Python 3.9+ or Node.js 16+"],"input_types":["Connection string (e.g., 'db+lancedb://api.lancedb.com/my-db')","API key (string)","Namespace name (string)"],"output_types":["Database connection object","List of namespaces","Table references within namespace"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+ or Node.js 16+ or Java 11+","Pre-computed vector embeddings (768-4096 dimensions typical)","Disk space: ~1.2x raw vector data size for index overhead","For remote deployment: LanceDB Cloud account or self-hosted Lance server","Text columns must be explicitly indexed for FTS (not automatic)","Python 3.9+ or Node.js 16+","Sufficient disk space for inverted index construction","Table schema defined before streaming","Sufficient disk I/O bandwidth for continuous writes","Arrow schema definition (PyArrow Schema or equivalent)"],"failure_modes":["IVF-PQ indexing requires pre-computed partitions; adding new data triggers incremental index updates with ~5-10% query latency overhead during reindexing","HNSW index construction is single-threaded in current implementation; indexing 10M vectors takes ~30-60 minutes on standard hardware","Vector dimension must be consistent across all rows; schema enforcement prevents mixed-dimension queries","No built-in distributed indexing; horizontal scaling requires manual sharding at application layer","FTS index is built per-table; cross-table full-text search requires application-level merging","Tokenization is language-agnostic (whitespace + punctuation split); no stemming for non-English languages without custom analyzers","BM25 parameters (k1, b) are fixed; tuning requires index rebuild","FTS index size can be 30-50% of raw text data; large text columns significantly increase storage overhead","Streaming inserts are batched internally; individual row inserts have ~1-5ms latency due to batching overhead","Incremental indexing adds ~5-10% write latency compared to non-indexed tables","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6574059156628013,"quality":0.35,"ecosystem":0.6000000000000001,"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":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:58:32.037Z","last_commit":"2026-05-03T01:44:37Z"},"community":{"stars":10169,"forks":868,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=lancedb--lancedb","compare_url":"https://unfragile.ai/compare?artifact=lancedb--lancedb"}},"signature":"G3QRXt/EC2K21bUNSsM34qMYMzF7kDbjKvVFe+t8/ogXECbUnT3W1SNJhAq5N+HOiy90LChZrcZBARTf4fPFCg==","signedAt":"2026-06-22T06:37:41.198Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/lancedb--lancedb","artifact":"https://unfragile.ai/lancedb--lancedb","verify":"https://unfragile.ai/api/v1/verify?slug=lancedb--lancedb","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"}}