{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"lancedb","slug":"lancedb","name":"LanceDB","type":"platform","url":"https://lancedb.com","page_url":"https://unfragile.ai/lancedb","categories":["rag-knowledge"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"lancedb__cap_0","uri":"capability://memory.knowledge.embedded.vector.search.with.lance.columnar.format","name":"embedded vector search with lance columnar format","description":"Performs approximate nearest neighbor search on vector embeddings using the Lance columnar storage format, enabling local-first vector indexing without requiring a separate database server. Leverages Lance's zero-copy columnar design for efficient memory usage and fast vector distance computations across millions to billions of vectors, with automatic index creation and optimization.","intents":["I need to build a RAG system that works offline without cloud dependencies","I want to store and search embeddings locally in my Python application without managing a database server","I need to scale vector search to billions of embeddings while keeping latency low"],"best_for":["Solo developers building LLM agents with local-first requirements","Teams prototyping RAG systems before committing to managed infrastructure","Applications requiring air-gapped or privacy-sensitive vector search"],"limitations":["Embedded deployment limited to single-machine throughput; no distributed query execution across nodes","No built-in replication or high-availability failover in OSS version","Vector dimension constraints and maximum table size not documented in available materials"],"requires":["Python 3.8+ or Node.js 14+ or Rust 1.56+","Local disk space proportional to vector dataset size (Lance is columnar, ~4 bytes per dimension per vector)","No external dependencies for embedded mode"],"input_types":["numpy arrays","pandas DataFrames","Python lists of floats","TypeScript typed arrays"],"output_types":["Vector IDs with similarity scores","Metadata associated with vectors","Structured query results as DataFrames or JSON"],"categories":["memory-knowledge","vector-database"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lancedb__cap_1","uri":"capability://search.retrieval.hybrid.search.combining.vector.and.full.text.retrieval","name":"hybrid search combining vector and full-text retrieval","description":"Executes queries that blend semantic vector similarity with keyword-based full-text search, returning ranked results that satisfy both modalities. Implements a fusion strategy (likely reciprocal rank fusion or weighted scoring) to combine vector distance scores with BM25-style text relevance, enabling queries to find results that are semantically similar AND contain specific keywords.","intents":["I need search results that match both semantic meaning and specific keywords in my documents","I want to avoid pure vector search missing exact terminology while avoiding pure keyword search missing semantic intent","I need to search across documents where both conceptual relevance and term presence matter"],"best_for":["Enterprise search applications requiring precision and recall balance","Technical documentation search where exact terms and concepts both matter","Legal or compliance document retrieval needing both semantic and lexical matching"],"limitations":["Fusion algorithm details and weighting strategy not documented; unclear how to tune vector vs. text balance","Full-text search implementation (inverted index vs. other) not specified in available materials","No documented support for field-specific weighting or custom scoring functions"],"requires":["Text data indexed with both embeddings and full-text tokens","Query formulation supporting both vector and text components","LanceDB table with text column and embedding column"],"input_types":["Query string (text)","Query embedding vector","Structured query with text and vector components"],"output_types":["Ranked result set with combined scores","Document IDs with relevance scores","Metadata and content snippets"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lancedb__cap_10","uri":"capability://automation.workflow.automatic.index.creation.and.optimization.for.vector.tables","name":"automatic index creation and optimization for vector tables","description":"Automatically creates and maintains vector indices (e.g., IVF, HNSW) on table creation or data ingestion, optimizing for query performance without manual tuning. Monitors query patterns and data distribution to trigger index rebuilds or parameter adjustments, abstracting index management complexity from users.","intents":["I want vector search to be fast without manually tuning index parameters","I need indices to adapt as my data distribution changes over time","I want to avoid index maintenance overhead in my application"],"best_for":["Teams without specialized database expertise wanting automatic optimization","Applications with evolving data distributions requiring adaptive indexing","Rapid prototyping scenarios where manual tuning is impractical"],"limitations":["Index type selection and tuning strategy not documented; unclear if users can override automatic choices","Index rebuild triggers and frequency not specified; unclear if rebuilds block queries","No documented support for custom index types or algorithms"],"requires":["LanceDB table with vector column","Sufficient disk space for index structures","No manual configuration required (automatic by default)"],"input_types":["Vector data","Query patterns (implicit)"],"output_types":["Optimized indices","Query performance improvements"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lancedb__cap_11","uri":"capability://automation.workflow.cloud.storage.integration.with.petabyte.scale.data.lakes","name":"cloud storage integration with petabyte-scale data lakes","description":"Integrates with cloud object storage (S3, GCS, Azure Blob) to store Lance tables in data lakes, enabling petabyte-scale vector datasets without local disk constraints. Implements lazy loading and caching to minimize network I/O while maintaining query performance, allowing cost-effective storage of massive embeddings with on-demand retrieval.","intents":["I want to store petabyte-scale embeddings in S3 without managing local infrastructure","I need to share vector datasets across multiple applications and teams via cloud storage","I want to reduce storage costs by using cloud object storage instead of local SSDs"],"best_for":["Large enterprises with petabyte-scale datasets and cloud infrastructure","Teams building multi-tenant RAG systems sharing embeddings across applications","Cost-sensitive organizations prioritizing storage efficiency over query latency"],"limitations":["Cloud storage provider support not documented; unclear which providers are supported","Caching strategy and performance impact not quantified; unclear if cloud storage queries are suitable for real-time applications","Data transfer costs and egress charges not discussed in available materials"],"requires":["Cloud storage account (S3, GCS, Azure Blob, etc.)","Cloud credentials and permissions configured","Network connectivity to cloud storage","LanceDB Enterprise or OSS with cloud storage support"],"input_types":["Vector embeddings","Metadata and structured data"],"output_types":["Query results from cloud-stored tables","Lazy-loaded embeddings"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lancedb__cap_2","uri":"capability://image.visual.multimodal.data.indexing.and.search.across.text.images.and.video","name":"multimodal data indexing and search across text, images, and video","description":"Stores and searches embeddings generated from multiple data modalities (text, images, video, point clouds) within a single table, enabling cross-modal queries where a text query can find relevant images or vice versa. Leverages multimodal embedding models (e.g., CLIP) to project different data types into a shared vector space, then performs unified nearest-neighbor search across the heterogeneous dataset.","intents":["I want to search for images using text queries or find similar images across my dataset","I need to build a content discovery system that works across text, images, and video simultaneously","I want to store video frames as embeddings and retrieve them by semantic similarity"],"best_for":["Media companies building cross-modal search (e.g., find images by text description)","E-commerce platforms needing visual + textual product search","Content moderation teams analyzing mixed-media datasets"],"limitations":["Multimodal embedding model selection and integration not documented; unclear which models are recommended or supported","Video processing pipeline (frame extraction, sampling strategy) not specified","No documented support for audio embeddings or other modalities beyond text/image/video/point-cloud"],"requires":["Multimodal embedding model (e.g., CLIP, LLaVA) to generate embeddings for each modality","Pre-processing pipeline to extract embeddings from images and video frames","Metadata schema to track original data type and source file references"],"input_types":["Text strings","Image files (PNG, JPEG, etc.)","Video files (MP4, etc.)","Point cloud data (format unspecified)"],"output_types":["Ranked results with mixed media types","File references and metadata","Similarity scores across modalities"],"categories":["image-visual","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lancedb__cap_3","uri":"capability://data.processing.analysis.automatic.table.versioning.with.point.in.time.recovery","name":"automatic table versioning with point-in-time recovery","description":"Maintains immutable snapshots of table state at each write operation, enabling queries to target specific versions and recovery to previous states without manual backup management. Leverages Lance's append-only columnar design to store version metadata alongside data, allowing efficient version branching and time-travel queries without duplicating entire datasets.","intents":["I need to audit what embeddings were in my database at a specific point in time","I want to roll back a bad data ingestion without losing recent updates","I need to compare search results across different versions of my embedding model"],"best_for":["Teams managing production RAG systems requiring audit trails","ML engineers experimenting with embedding model updates and needing rollback capability","Compliance-heavy organizations needing data lineage and version history"],"limitations":["Version retention policy not documented; unclear if old versions are automatically pruned or stored indefinitely","Storage overhead of versioning not quantified; unclear how much disk space is consumed by maintaining version history","No documented API for querying version metadata or listing available versions"],"requires":["LanceDB table with versioning enabled (default behavior)","Sufficient disk space to store multiple versions of data","Version identifiers or timestamps for point-in-time queries"],"input_types":["Version ID or timestamp","Query targeting specific version"],"output_types":["Data snapshot from specified version","Version metadata (timestamp, size, record count)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lancedb__cap_4","uri":"capability://search.retrieval.sql.querying.interface.for.vector.and.structured.data","name":"sql querying interface for vector and structured data","description":"Exposes a SQL interface alongside vector search, allowing users to write SQL queries that filter, join, and aggregate both vector embeddings and structured metadata in a single query. Implements a query planner that optimizes vector operations (e.g., ANN search) and structured operations (e.g., WHERE clauses) together, avoiding separate round-trips to vector and relational systems.","intents":["I want to find embeddings similar to a query vector AND filter by metadata like date or category in one query","I need to join vector search results with structured data from other tables","I want to use familiar SQL syntax instead of learning a custom vector query API"],"best_for":["Data engineers familiar with SQL wanting to avoid learning new query languages","Applications requiring complex filtering on metadata alongside vector search","Teams migrating from traditional SQL databases to vector-aware systems"],"limitations":["SQL dialect and supported functions not documented; unclear if it's standard SQL or LanceDB-specific extensions","Join performance with large tables not benchmarked; unclear if vector joins are optimized","No documented support for window functions, CTEs, or advanced SQL features"],"requires":["SQL client or SDK supporting LanceDB SQL interface","Tables with both vector columns and structured metadata columns","Understanding of vector distance functions (e.g., L2, cosine) in SQL context"],"input_types":["SQL SELECT statements","Vector distance functions in WHERE/ORDER BY clauses","Structured filter predicates"],"output_types":["Rows with vector and metadata columns","Aggregated results (COUNT, SUM, etc.)","Joined result sets"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lancedb__cap_5","uri":"capability://tool.use.integration.langchain.and.llamaindex.integration.with.automatic.embedding.management","name":"langchain and llamaindex integration with automatic embedding management","description":"Provides native connectors for LangChain and LlamaIndex that handle embedding generation, storage, and retrieval automatically, abstracting away Lance table management. Integrates with these frameworks' document loaders, embedding model selection, and retrieval chains, allowing users to build RAG pipelines without directly interacting with LanceDB APIs.","intents":["I want to use LanceDB as a vector store in my LangChain RAG pipeline without writing custom code","I need to load documents through LlamaIndex and automatically store embeddings in LanceDB","I want to switch from Pinecone to LanceDB without rewriting my LangChain application"],"best_for":["LangChain and LlamaIndex users wanting local-first vector storage","Teams building RAG prototypes who want minimal boilerplate","Developers preferring framework-level abstractions over direct database APIs"],"limitations":["Integration features and API surface not documented; unclear which LangChain/LlamaIndex features are supported","Embedding model selection delegated to frameworks; LanceDB's role in model management unclear","No documented support for advanced LanceDB features (versioning, hybrid search) through framework integrations"],"requires":["LangChain 0.0.x+ or LlamaIndex 0.8.x+ (versions not specified in available materials)","Embedding model compatible with framework (e.g., OpenAI, HuggingFace)","Python 3.8+"],"input_types":["Documents (text, PDFs, etc.) via framework loaders","Embedding model specifications","Query strings"],"output_types":["Retrieved documents with similarity scores","Integrated into framework's retrieval chains"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lancedb__cap_6","uri":"capability://data.processing.analysis.pandas.dataframe.integration.for.batch.embedding.and.querying","name":"pandas dataframe integration for batch embedding and querying","description":"Accepts pandas DataFrames as input for bulk embedding storage and retrieval, enabling data scientists to work with familiar tabular data structures. Automatically converts DataFrame columns to Lance columnar format, preserving metadata and enabling efficient bulk operations without requiring custom serialization or data transformation code.","intents":["I have a pandas DataFrame with text and want to embed and store it in LanceDB without writing custom code","I want to query LanceDB and get results back as a pandas DataFrame for downstream analysis","I need to perform batch embedding operations on large CSV files loaded into pandas"],"best_for":["Data scientists and analysts familiar with pandas workflows","Teams using Jupyter notebooks for exploratory RAG development","Applications requiring seamless integration between pandas and vector search"],"limitations":["DataFrame size limits not documented; unclear if entire DataFrame must fit in memory","Type mapping between pandas dtypes and Lance columnar types not specified","No documented support for sparse DataFrames or categorical data optimization"],"requires":["pandas 1.0+","Python 3.8+","DataFrame with text column(s) for embedding"],"input_types":["pandas DataFrame","CSV files (via pandas.read_csv)","Parquet files (via pandas.read_parquet)"],"output_types":["pandas DataFrame with query results","Structured data with embeddings and metadata"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lancedb__cap_7","uri":"capability://planning.reasoning.reranking.with.learned.to.rank.models","name":"reranking with learned-to-rank models","description":"Applies learned-to-rank (LTR) models to re-score and reorder initial retrieval results, improving ranking quality beyond vector similarity alone. Integrates with external reranking services or local models to refine top-k results, enabling two-stage retrieval pipelines where initial vector search is fast and reranking is precise.","intents":["I want to improve search result quality by reranking vector search results with a specialized model","I need to combine multiple relevance signals (vector similarity, text match, user feedback) into a final ranking","I want to use a cross-encoder model to refine my initial retrieval results"],"best_for":["Teams building high-precision search systems where ranking quality is critical","Applications with sufficient query volume to justify reranking latency","Organizations using cross-encoder or LTR models for relevance optimization"],"limitations":["Reranking model integration details not documented; unclear if local models or external APIs are supported","Reranking latency impact not quantified; unclear if it's suitable for real-time applications","No documented support for custom reranking functions or model fine-tuning"],"requires":["Reranking model (local or via API)","Initial retrieval results from vector search","Reranking service or SDK (e.g., Cohere Rerank, local cross-encoder)"],"input_types":["Query string","Initial retrieval results (documents with scores)"],"output_types":["Reranked results with updated scores","Top-k documents in refined order"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lancedb__cap_8","uri":"capability://data.processing.analysis.feature.engineering.and.embedding.transformation.pipeline","name":"feature engineering and embedding transformation pipeline","description":"Provides a 'Geneva' feature engineering module for transforming and enriching embeddings before storage or after retrieval, enabling custom embedding preprocessing, dimensionality reduction, and feature extraction. Integrates with the storage pipeline to apply transformations efficiently without requiring separate compute infrastructure.","intents":["I want to reduce embedding dimensionality to save storage and improve query speed","I need to normalize or standardize embeddings from different models before storing them","I want to extract domain-specific features from embeddings for downstream tasks"],"best_for":["Teams optimizing storage and query latency by reducing embedding dimensions","Applications combining embeddings from multiple models requiring normalization","ML engineers building custom embedding transformation pipelines"],"limitations":["Geneva module capabilities and API not documented; unclear what transformations are supported","Performance impact of feature engineering not quantified","No documented support for custom transformation functions or model integration"],"requires":["LanceDB with Geneva module enabled","Embedding data to transform","Transformation specifications (e.g., dimensionality reduction parameters)"],"input_types":["Raw embeddings (vectors)","Transformation configuration"],"output_types":["Transformed embeddings","Feature vectors","Dimensionality-reduced embeddings"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lancedb__cap_9","uri":"capability://automation.workflow.distributed.vector.search.with.lancedb.enterprise","name":"distributed vector search with lancedb enterprise","description":"Extends embedded LanceDB with distributed query execution across multiple nodes, enabling horizontal scaling of vector search to petabyte-scale datasets. Maintains Lance columnar format compatibility across distributed deployment, allowing seamless migration from embedded to enterprise without schema changes or data re-ingestion.","intents":["I need to scale vector search beyond single-machine capacity to handle petabyte-scale datasets","I want to migrate from local development with embedded LanceDB to production with distributed search","I need high-availability vector search with replication and failover"],"best_for":["Large enterprises with petabyte-scale embedding datasets","Teams requiring production-grade availability and disaster recovery","Organizations migrating from managed vector databases to self-hosted distributed systems"],"limitations":["Enterprise deployment architecture not documented; unclear if it's Kubernetes-native, requires custom orchestration, or uses specific cloud providers","Replication strategy and consistency guarantees not specified","Pricing and licensing model for Enterprise tier not available in provided materials"],"requires":["LanceDB Enterprise license (pricing unknown)","Distributed infrastructure (Kubernetes, cloud VMs, or on-premises servers)","Network connectivity between nodes","Operational expertise in distributed systems"],"input_types":["Vector embeddings","Metadata and structured data","Query requests"],"output_types":["Distributed query results","Aggregated rankings across nodes"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"lancedb__headline","uri":"capability://data.processing.analysis.serverless.vector.database.for.multimodal.data","name":"serverless vector database for multimodal data","description":"LanceDB is a serverless vector database designed for storing and managing multimodal datasets, including text, images, and video, with features like automatic versioning and hybrid search capabilities.","intents":["best serverless vector database","vector database for multimodal data","RAG framework for production","database for AI workloads","best database for semantic search"],"best_for":["AI applications","data retrieval tasks"],"limitations":[],"requires":[],"input_types":["text","images","video"],"output_types":["embeddings","search results"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+ or Node.js 14+ or Rust 1.56+","Local disk space proportional to vector dataset size (Lance is columnar, ~4 bytes per dimension per vector)","No external dependencies for embedded mode","Text data indexed with both embeddings and full-text tokens","Query formulation supporting both vector and text components","LanceDB table with text column and embedding column","LanceDB table with vector column","Sufficient disk space for index structures","No manual configuration required (automatic by default)","Cloud storage account (S3, GCS, Azure Blob, etc.)"],"failure_modes":["Embedded deployment limited to single-machine throughput; no distributed query execution across nodes","No built-in replication or high-availability failover in OSS version","Vector dimension constraints and maximum table size not documented in available materials","Fusion algorithm details and weighting strategy not documented; unclear how to tune vector vs. text balance","Full-text search implementation (inverted index vs. other) not specified in available materials","No documented support for field-specific weighting or custom scoring functions","Index type selection and tuning strategy not documented; unclear if users can override automatic choices","Index rebuild triggers and frequency not specified; unclear if rebuilds block queries","No documented support for custom index types or algorithms","Cloud storage provider support not documented; unclear which providers are supported","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.15,"match_graph":0.25,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.327Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=lancedb","compare_url":"https://unfragile.ai/compare?artifact=lancedb"}},"signature":"neScOigQV0kKsVzGxWH7W9hCpijw6GChEIxtToNsooEt90q6sSljDwcuwEOcqanFhbHgu2LEoJoK4ike4YtuBg==","signedAt":"2026-06-20T15:55:36.555Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/lancedb","artifact":"https://unfragile.ai/lancedb","verify":"https://unfragile.ai/api/v1/verify?slug=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"}}