# LanceDB

## Docs

- [SDKs and REST API Reference](https://docs.lancedb.com/api-reference/index.md): SDK and REST API reference for LanceDB Enterprise and OSS.
- [REST API Reference](https://docs.lancedb.com/api-reference/rest/index.md): API reference for LanceDB
- [Check if a namespace exists](https://docs.lancedb.com/api-reference/rest/namespace/check-if-a-namespace-exists.md): Check if namespace `id` exists.
- [Create a new namespace](https://docs.lancedb.com/api-reference/rest/namespace/create-a-new-namespace.md): Create new namespace `id`.
- [Describe a namespace](https://docs.lancedb.com/api-reference/rest/namespace/describe-a-namespace.md): Describe the detailed information for namespace `id`.
- [Drop a namespace](https://docs.lancedb.com/api-reference/rest/namespace/drop-a-namespace.md): Drop namespace `id` from its parent namespace.
- [List namespaces](https://docs.lancedb.com/api-reference/rest/namespace/list-namespaces.md): List all child namespace names of the parent namespace `id`.
- [List tables in a namespace](https://docs.lancedb.com/api-reference/rest/namespace/list-tables-in-a-namespace.md): List all child table names of the parent namespace `id`.
- [Add new columns to table schema](https://docs.lancedb.com/api-reference/rest/table/add-new-columns-to-table-schema.md): Add new columns to table `id` using SQL expressions or default values.
- [Analyze query execution plan](https://docs.lancedb.com/api-reference/rest/table/analyze-query-execution-plan.md): Analyze the query execution plan for a query against table `id`. Returns detailed statistics and analysis of the query execution plan.
- [Atomically commit a batch of mixed table operations](https://docs.lancedb.com/api-reference/rest/table/atomically-commit-a-batch-of-mixed-table-operations.md): Atomically commit a batch of table operations. This is a generalized version of `BatchCreateTableVersions` that supports mixed operation types within a single atomic transaction at the metadata layer.
- [Atomically create versions for multiple tables](https://docs.lancedb.com/api-reference/rest/table/atomically-create-versions-for-multiple-tables.md): Atomically create new version entries for multiple tables.
- [Check if a table exists](https://docs.lancedb.com/api-reference/rest/table/check-if-a-table-exists.md): Check if table `id` exists.
- [Count rows in a table](https://docs.lancedb.com/api-reference/rest/table/count-rows-in-a-table.md): Count the number of rows in table `id`
- [Create a new table version](https://docs.lancedb.com/api-reference/rest/table/create-a-new-table-version.md): Create a new version entry for table `id`.
- [Create a new tag](https://docs.lancedb.com/api-reference/rest/table/create-a-new-tag.md): Create a new tag for table `id` that points to a specific version.
- [Create a scalar index on a table](https://docs.lancedb.com/api-reference/rest/table/create-a-scalar-index-on-a-table.md): Create a scalar index on a table column for faster filtering operations. Supports scalar indexes (BTREE, BITMAP, LABEL_LIST, FTS, etc.). This is an alias for CreateTableIndex specifically for scalar indexes. Index creation is handled asynchronously. Use the `ListTableIndices` and `DescribeTableIndex…
- [Create a table with the given name](https://docs.lancedb.com/api-reference/rest/table/create-a-table-with-the-given-name.md): Create table `id` in the namespace with the given data in Arrow IPC stream.
- [Create an empty table](https://docs.lancedb.com/api-reference/rest/table/create-an-empty-table.md): Create an empty table with the given name without touching storage. This is a metadata-only operation that records the table existence and sets up aspects like access control.
- [Create an index on a table](https://docs.lancedb.com/api-reference/rest/table/create-an-index-on-a-table.md): Create an index on a table column for faster search operations. Supports vector indexes (IVF_FLAT, IVF_HNSW_SQ, IVF_PQ, etc.) and scalar indexes (BTREE, BITMAP, FTS, etc.). Index creation is handled asynchronously. Use the `ListTableIndices` and `DescribeTableIndexStats` operations to monitor index…
- [Declare a table](https://docs.lancedb.com/api-reference/rest/table/declare-a-table.md): Declare a table with the given name without touching storage. This is a metadata-only operation that records the table existence and sets up aspects like access control.
- [Delete a tag](https://docs.lancedb.com/api-reference/rest/table/delete-a-tag.md): Delete an existing tag from table `id`.
- [Delete rows from a table](https://docs.lancedb.com/api-reference/rest/table/delete-rows-from-a-table.md): Delete rows from table `id`.
- [Delete table version records](https://docs.lancedb.com/api-reference/rest/table/delete-table-version-records.md): Delete version metadata records for table `id`.
- [Deregister a table](https://docs.lancedb.com/api-reference/rest/table/deregister-a-table.md): Deregister table `id` from its namespace.
- [Describe a specific table version](https://docs.lancedb.com/api-reference/rest/table/describe-a-specific-table-version.md): Describe the detailed information for a specific version of table `id`.
- [Describe information of a table](https://docs.lancedb.com/api-reference/rest/table/describe-information-of-a-table.md): Describe the detailed information for table `id`.
- [Drop a specific index](https://docs.lancedb.com/api-reference/rest/table/drop-a-specific-index.md): Drop the specified index from table `id`.
- [Drop a table](https://docs.lancedb.com/api-reference/rest/table/drop-a-table.md): Drop table `id` and delete its data.
- [Get query execution plan explanation](https://docs.lancedb.com/api-reference/rest/table/get-query-execution-plan-explanation.md): Get the query execution plan for a query against table `id`. Returns a human-readable explanation of how the query will be executed.
- [Get table index statistics](https://docs.lancedb.com/api-reference/rest/table/get-table-index-statistics.md): Get statistics for a specific index on a table. Returns information about the index type, distance type (for vector indices), and row counts.
- [Get table statistics](https://docs.lancedb.com/api-reference/rest/table/get-table-statistics.md): Get statistics for table `id`, including row counts, data sizes, and column statistics.
- [Get version for a specific tag](https://docs.lancedb.com/api-reference/rest/table/get-version-for-a-specific-tag.md): Get the version number that a specific tag points to for table `id`.
- [Insert records into a table](https://docs.lancedb.com/api-reference/rest/table/insert-records-into-a-table.md): Insert new records into table `id`.
- [List all tables](https://docs.lancedb.com/api-reference/rest/table/list-all-tables.md): List all tables across all namespaces.
- [List all tags for a table](https://docs.lancedb.com/api-reference/rest/table/list-all-tags-for-a-table.md): List all tags that have been created for table `id`. Returns a map of tag names to their corresponding version numbers and metadata.
- [List all versions of a table](https://docs.lancedb.com/api-reference/rest/table/list-all-versions-of-a-table.md): List all versions (commits) of table `id` with their metadata.
- [List indexes on a table](https://docs.lancedb.com/api-reference/rest/table/list-indexes-on-a-table.md): List all indices created on a table. Returns information about each index including name, columns, status, and UUID.
- [Merge insert (upsert) records into a table](https://docs.lancedb.com/api-reference/rest/table/merge-insert-upsert-records-into-a-table.md): Performs a merge insert (upsert) operation on table `id`. This operation updates existing rows based on a matching column and inserts new rows that don't match. It returns the number of rows inserted and updated.
- [Modify existing columns](https://docs.lancedb.com/api-reference/rest/table/modify-existing-columns.md): Modify existing columns in table `id`, such as renaming or changing data types.
- [Query a table](https://docs.lancedb.com/api-reference/rest/table/query-a-table.md): Query table `id` with vector search, full text search and optional SQL filtering. Returns results in Arrow IPC file or stream format.
- [Register a table to a namespace](https://docs.lancedb.com/api-reference/rest/table/register-a-table-to-a-namespace.md): Register an existing table at a given storage location as `id`.
- [Remove columns from table](https://docs.lancedb.com/api-reference/rest/table/remove-columns-from-table.md): Remove specified columns from table `id`.
- [Rename a table](https://docs.lancedb.com/api-reference/rest/table/rename-a-table.md): Rename table `id` to a new name.
- [Restore table to a specific version](https://docs.lancedb.com/api-reference/rest/table/restore-table-to-a-specific-version.md): Restore table `id` to a specific version.
- [Update a tag to point to a different version](https://docs.lancedb.com/api-reference/rest/table/update-a-tag-to-point-to-a-different-version.md): Update an existing tag for table `id` to point to a different version.
- [Update rows in a table](https://docs.lancedb.com/api-reference/rest/table/update-rows-in-a-table.md): Update existing rows in table `id`.
- [Update table schema metadata](https://docs.lancedb.com/api-reference/rest/table/update-table-schema-metadata.md): Replace the schema metadata for table `id` with the provided key-value pairs.
- [Alter information of a transaction.](https://docs.lancedb.com/api-reference/rest/transaction/alter-information-of-a-transaction.md): Alter a transaction with a list of actions such as setting status or properties. The server should either succeed and apply all actions, or fail and apply no action.
- [Describe information about a transaction](https://docs.lancedb.com/api-reference/rest/transaction/describe-information-about-a-transaction.md): Return a detailed information for a given transaction
- [Demo Application Gallery](https://docs.lancedb.com/demos/index.md): Demo apps showcasing end-to-end applications built with LanceDB for production use cases.
- [Managing Embeddings](https://docs.lancedb.com/embedding/index.md): Use the embedding API in LanceDB -- registry, functions, schemas, and multi-language SDK support.
- [Embeddings: Quickstart](https://docs.lancedb.com/embedding/quickstart.md): Quickstart guide for generating and working with embeddings.
- [Architecture](https://docs.lancedb.com/enterprise/architecture.md): Learn how LanceDB Enterprise separates control, compute, and storage to serve remote tables at scale.
- [Azure deployment guide](https://docs.lancedb.com/enterprise/deployment/azure.md): Learn how to deploy LanceDB Enterprise on Azure with AKS, Private Link, and Blob Storage.
- [Deployment guide](https://docs.lancedb.com/enterprise/deployment/index.md): Learn how to deploy LanceDB Enterprise in production environments.
- [LanceDB Enterprise](https://docs.lancedb.com/enterprise/index.md): Features and benefits of LanceDB Enterprise.
- [Performance Characteristics](https://docs.lancedb.com/enterprise/performance.md): Understand LanceDB Enterprise performance characteristics, including representative latency and scalability benchmarks.
- [Enterprise Quickstart](https://docs.lancedb.com/enterprise/quickstart.md): Run the LanceDB quickstart workflow on a RemoteTable in LanceDB Enterprise.
- [Security & Compliance](https://docs.lancedb.com/enterprise/security.md): Learn about LanceDB Enterprise security features and best practices.
- [LanceDB Enterprise FAQ](https://docs.lancedb.com/faq/faq-enterprise.md): Commonly asked questions about LanceDB Enterprise.
- [LanceDB: Frequently Asked Questions](https://docs.lancedb.com/faq/faq-oss.md): Commonly asked questions about LanceDB OSS.
- [Frequently Asked Questions](https://docs.lancedb.com/faq/index.md): Common questions about LanceDB
- [Dependency Verification](https://docs.lancedb.com/geneva/deployment/dependency-verification.md): Diagnose and resolve package version mismatches between local and Ray worker environments.
- [Deploy Geneva using Helm](https://docs.lancedb.com/geneva/deployment/helm.md): Learn how to deploy Geneva on Kubernetes using the Geneva Helm Chart
- [Manual Deployment on Kubernetes](https://docs.lancedb.com/geneva/deployment/index.md): Learn how to deploy Geneva on Kubernetes using KubeRay for distributed feature engineering workflows on GKE and EKS.
- [Troubleshooting Geneva Deployments](https://docs.lancedb.com/geneva/deployment/troubleshooting.md): Learn how to diagnose and resolve common issues with Geneva deployments, including version compatibility, permissions, and serialization errors.
- [Multimodal Feature Engineering with Geneva](https://docs.lancedb.com/geneva/index.md): Learn how to do multimodal feature engineering in LanceDB Enterprise to transform raw data into meaningful features for AI models.
- [Backfilling](https://docs.lancedb.com/geneva/jobs/backfilling.md): Learn how to trigger backfill operations to populate column values in your LanceDB table using Geneva's distributed framework.
- [Bulk Loading & Updating Columns](https://docs.lancedb.com/geneva/jobs/bulk-load-columns.md): Load or update column data from external sources (Parquet, Lance, IPC) into your LanceDB table using a primary-key join.
- [Backfill Conflicts](https://docs.lancedb.com/geneva/jobs/conflicts.md): Learn how Geneva handles conflicts during backfill operations and what to do when they occur.
- [Geneva Console](https://docs.lancedb.com/geneva/jobs/console.md)
- [Execution Contexts](https://docs.lancedb.com/geneva/jobs/contexts.md): Learn how to set up your cluster for distributed execution.
- [Job Metrics (Diagnostics)](https://docs.lancedb.com/geneva/jobs/job_metrics.md): Use metrics from Geneva to diagnose why a backfill/refresh job is slow.
- [Job Lifecycle](https://docs.lancedb.com/geneva/jobs/lifecycle.md): Understanding how Geneva jobs work, their lifecycle states, and how to monitor and manage them.
- [Materialized Views with UDFs](https://docs.lancedb.com/geneva/jobs/materialized-views.md): Learn how to use Geneva's materialized view feature to declaratively manage batch updates of expensive operations using UDFs.
- [Distributed Job Performance and Cluster Sizing](https://docs.lancedb.com/geneva/jobs/performance.md): Learn how to tune Geneva distributed job performance by scaling compute resources and balancing write bandwidth.
- [Job and Session Startup Optimizations](https://docs.lancedb.com/geneva/jobs/startup.md): Learn how to optimize Geneva job and session startup times for faster interactive development and production workflows.
- [Troubleshooting Geneva Jobs](https://docs.lancedb.com/geneva/jobs/troubleshooting.md): Diagnose and fix common issues when running Geneva backfills, materialized views, and other distributed jobs.
- [What is Feature Engineering?](https://docs.lancedb.com/geneva/overview/index.md): Learn how to transform raw data into meaningful features for AI models using LanceDB's feature engineering capabilities. Scale your feature engineering workflows with distributed processing and UDFs.
- [Geneva Python SDK](https://docs.lancedb.com/geneva/reference.md): LanceDB Feature Engineering Python SDK Reference
- [Advanced Configuration](https://docs.lancedb.com/geneva/udfs/advanced-configuration.md): Learn about environment variables for configuring Geneva behavior.
- [Batch User-Defined Table Functions (UDTFs)](https://docs.lancedb.com/geneva/udfs/batch-udtfs.md): Use batch UDTFs for N:M transformations like deduplication, clustering, and aggregation across entire tables or partitions.
- [Blob Types in Geneva UDFs](https://docs.lancedb.com/geneva/udfs/blobs.md): Learn how to work with Lance Blobs in Geneva UDFs for handling large binary objects efficiently with lazy reading capabilities.
- [Error Handling in Geneva UDFs](https://docs.lancedb.com/geneva/udfs/error_handling.md): Learn how configure retry, skip, and fail behaviors for UDFs.
- [Understanding Transforms](https://docs.lancedb.com/geneva/udfs/index.md): Understand the three types of user-defined functions in Geneva — UDFs, scalar UDTFs, and batch UDTFs — and when to use each.
- [Gemini](https://docs.lancedb.com/geneva/udfs/providers/gemini.md)
- [Built-in LLM and Embedding UDFs](https://docs.lancedb.com/geneva/udfs/providers/index.md)
- [OpenAI](https://docs.lancedb.com/geneva/udfs/providers/openai.md)
- [Sentence Transformers](https://docs.lancedb.com/geneva/udfs/providers/sentence-transformers.md)
- [Scalar User-Defined Table Functions (UDTFs)](https://docs.lancedb.com/geneva/udfs/scalar-udtfs.md): Use scalar UDTFs for 1:N row expansion — split videos into clips, chunk documents, or tile images with automatic parent column inheritance and incremental refresh.
- [User-Defined Functions (UDFs)](https://docs.lancedb.com/geneva/udfs/udfs.md): Define 1:1 transforms that add computed columns to your tables — embeddings, enrichment, scoring, and more.
- [LanceDB](https://docs.lancedb.com/index.md): Multimodal lakehouse for AI.
- [Full-Text Search (FTS) Index](https://docs.lancedb.com/indexing/fts-index.md): Create and tune BM25-based full-text search indexes in LanceDB.
- [GPU-Powered Vector Indexing](https://docs.lancedb.com/indexing/gpu-indexing.md): Accelerate IVF and HNSW index builds with GPU acceleration in LanceDB.
- [Indexing Data](https://docs.lancedb.com/indexing/index.md): Optimize search performance in LanceDB using vector indexes, full-text search, and scalar indexes. Understand IVF-PQ indexing for efficient vector similarity search.
- [Quantization](https://docs.lancedb.com/indexing/quantization.md): Learn about quantization when creating an index in LanceDB.
- [Keeping Indexes Up-to-Date with Reindexing](https://docs.lancedb.com/indexing/reindexing.md): Learn how to keep your indexes up-to-date in LanceDB using incremental indexing, including best practices for adding new records without full reindexing.
- [Scalar Indexes](https://docs.lancedb.com/indexing/scalar-index.md): Learn how to use scalar indexes in LanceDB for efficient metadata filtering and query optimization.
- [Vector Indexes](https://docs.lancedb.com/indexing/vector-index.md): Build and optimize LanceDB vector indexes, including IVF, HNSW and binary quantized indexes.
- [Agno](https://docs.lancedb.com/integrations/ai/agno.md): Build a search assistant using the Agno agent framework with LanceDB as the knowledge backend.
- [GenKit](https://docs.lancedb.com/integrations/ai/genkit.md)
- [Hugging Face Hub](https://docs.lancedb.com/integrations/ai/huggingface.md): Use LanceDB directly on Lance datasets hosted on the Hugging Face Hub for multimodal search and retrieval.
- [Kiln AI](https://docs.lancedb.com/integrations/ai/kiln.md)
- [LangChain](https://docs.lancedb.com/integrations/ai/langchain.md)
- [LlamaIndex](https://docs.lancedb.com/integrations/ai/llamaIndex.md)
- [PromptTools](https://docs.lancedb.com/integrations/ai/prompttools.md)
- [Meta Llama Synthetic Data Kit](https://docs.lancedb.com/integrations/ai/synthetic-data-kit.md): Use Meta Llama's Synthetic Data Kit with LanceDB to generate high-quality synthetic datasets for LLM fine-tuning and training.
- [dlt](https://docs.lancedb.com/integrations/data/dlt.md)
- [DuckDB](https://docs.lancedb.com/integrations/data/duckdb.md): Learn how to use the DuckDB-Lance extension to query Lance tables with SQL.
- [Pandas and PyArrow](https://docs.lancedb.com/integrations/data/pandas_and_pyarrow.md)
- [Polars](https://docs.lancedb.com/integrations/data/polars_arrow.md)
- [Pydantic](https://docs.lancedb.com/integrations/data/pydantic.md)
- [Voxel51](https://docs.lancedb.com/integrations/data/voxel51.md)
- [AWS Bedrock](https://docs.lancedb.com/integrations/embedding/aws.md)
- [Cohere](https://docs.lancedb.com/integrations/embedding/cohere.md)
- [ColPali](https://docs.lancedb.com/integrations/embedding/colpali.md)
- [Gemini](https://docs.lancedb.com/integrations/embedding/gemini.md)
- [Hugging Face](https://docs.lancedb.com/integrations/embedding/huggingface.md)
- [IBM watsonx](https://docs.lancedb.com/integrations/embedding/ibm.md)
- [ImageBind](https://docs.lancedb.com/integrations/embedding/imagebind.md)
- [Instructor](https://docs.lancedb.com/integrations/embedding/instructor.md)
- [Jina](https://docs.lancedb.com/integrations/embedding/jina.md)
- [Ollama](https://docs.lancedb.com/integrations/embedding/ollama.md)
- [OpenAI](https://docs.lancedb.com/integrations/embedding/openai.md)
- [OpenCLIP](https://docs.lancedb.com/integrations/embedding/openclip.md)
- [Sentence Transformers](https://docs.lancedb.com/integrations/embedding/sentence-transformers.md)
- [Superlinked](https://docs.lancedb.com/integrations/embedding/superlinked.md)
- [VoyageAI](https://docs.lancedb.com/integrations/embedding/voyageai.md)
- [Integrations](https://docs.lancedb.com/integrations/index.md): Connect LanceDB with popular AI providers, frameworks, and data platforms
- [Answer.AI Rerankers](https://docs.lancedb.com/integrations/reranking/answerdotai.md): Use AnswerDotAI's lightweight reranking library with LanceDB. Features unified API for common reranking models, configurable model selection, and comprehensive scoring options.
- [Cohere Reranker](https://docs.lancedb.com/integrations/reranking/cohere.md): Integrate Cohere's powerful reranking API with LanceDB for enhanced search results. Supports English and multilingual models with configurable scoring options for vector, FTS, and hybrid search.
- [ColBERT Reranker](https://docs.lancedb.com/integrations/reranking/colbert.md): Enhance search results with ColBERT's contextual reranking in LanceDB. Features efficient model deployment, device optimization, and flexible scoring options for vector, FTS, and hybrid search.
- [Cross Encoder Reranker](https://docs.lancedb.com/integrations/reranking/cross_encoder.md): Implement semantic search reranking in LanceDB using Cross Encoder models. Features configurable model selection, device optimization, and comprehensive scoring options for all search types.
- [Jina Reranker](https://docs.lancedb.com/integrations/reranking/jina.md): Integrate Jina's multilingual reranking API with LanceDB for improved search results. Features model selection, API key management, and flexible scoring options for all search types.
- [Linear Combination Reranker](https://docs.lancedb.com/integrations/reranking/linear_combination.md): Learn about LanceDB's deprecated Linear Combination Reranker for combining semantic and full-text search scores.
- [MRR Reranker](https://docs.lancedb.com/integrations/reranking/mrr.md): Combine and rerank search results using Mean Reciprocal Rank (MRR) algorithm in LanceDB. Supports weighted scoring for hybrid and multivector search.
- [OpenAI Reranker (Experimental)](https://docs.lancedb.com/integrations/reranking/openai.md): Explore experimental search reranking using OpenAI's GPT models in LanceDB. Features configurable model selection, API key management, and comprehensive scoring options for all search types.
- [Reciprocal Rank Fusion Reranker](https://docs.lancedb.com/integrations/reranking/rrf.md): Learn about LanceDB's default Reciprocal Rank Fusion (RRF) reranker for hybrid search. Implements the Cormack et al. algorithm for optimal search result ranking.
- [VoyageAI Reranker](https://docs.lancedb.com/integrations/reranking/voyageai.md): Integrate VoyageAI's cutting-edge reranking models with LanceDB. Features model selection, API key management, and comprehensive scoring options for all search types.
- [Lance format](https://docs.lancedb.com/lance.md): Open-source lakehouse format for multimodal AI.
- [Namespaces and the Catalog Model](https://docs.lancedb.com/namespaces/index.md): Understand LanceDB as a catalog-level abstraction over Lance's table format, and learn how namespaces help organize Lance tables.
- [Using Namespaces](https://docs.lancedb.com/namespaces/usage.md): Use LanceDB's namespace-aware table and catalog APIs in Python, TypeScript, and Rust.
- [Quickstart](https://docs.lancedb.com/quickstart.md): Get started with LanceDB in minutes.
- [Building Custom Rerankers](https://docs.lancedb.com/reranking/custom-reranker.md): Learn how to create custom rerankers in LanceDB by extending the base Reranker class.
- [Evaluating Hybrid Search Performance](https://docs.lancedb.com/reranking/eval.md): Learn about evaluating hybrid search performance in LanceDB.
- [Reranking Search Results](https://docs.lancedb.com/reranking/index.md): Use a reranker to improve search relevance.
- [Metadata Filtering in LanceDB](https://docs.lancedb.com/search/filtering.md): Filter search results in LanceDB based on metadata fields.
- [Full-Text Search (FTS)](https://docs.lancedb.com/search/full-text-search.md): Learn how to implement full-text search in LanceDB using BM25 for keyword-based retrieval.
- [Hybrid Search](https://docs.lancedb.com/search/hybrid-search.md): Learn how to perform hybrid search in LanceDB by combining vector and full-text search techniques with reranking.
- [Search](https://docs.lancedb.com/search/index.md): Comprehensive guide to all search capabilities in LanceDB including vector search, full-text search, hybrid search, and more.
- [Multivector Search](https://docs.lancedb.com/search/multivector-search.md): Learn how to perform multivector search in LanceDB to handle multiple vector embeddings per document, which is ideal for late-interaction models like ColBERT and ColPaLi.
- [Optimize Query Performance](https://docs.lancedb.com/search/optimize-queries.md): Analyze and optimize query performance in LanceDB.
- [Full-Text Search with SQL](https://docs.lancedb.com/search/sql/fts-sql.md): Use LanceDB's full-text search capabilities via SQL queries.
- [Query with SQL](https://docs.lancedb.com/search/sql/index.md): SQL query capabilities in LanceDB Enterprise for analytical queries and data exploration.
- [Vector Search](https://docs.lancedb.com/search/vector-search.md): Learn how to run vector search queries in LanceDB. Includes best practices, tips and examples.
- [Configuring Cloud Storage in LanceDB](https://docs.lancedb.com/storage/configuration.md): Configure LanceDB to use S3, GCS, Azure Blob, and S3-compatible object stores with environment variables or storage options.
- [Storage Architecture in LanceDB](https://docs.lancedb.com/storage/index.md): Understand LanceDB storage backends, tradeoffs, and how to pick the right option for your latency, scale, and cost goals.
- [Tables and Namespaces](https://docs.lancedb.com/tables-and-namespaces.md): Learn more about the table abstraction and namespaces in LanceDB.
- [Consistency](https://docs.lancedb.com/tables/consistency.md): Learn about consistency settings and versioning in LanceDB tables.
- [Ingesting Data](https://docs.lancedb.com/tables/create.md): Learn about different methods to ingest data into tables in LanceDB, including from various data sources and empty tables.
- [Basic Table Operations](https://docs.lancedb.com/tables/index.md): Create tables, search vectors, and append data in LanceDB.
- [Multimodal Data (Blobs)](https://docs.lancedb.com/tables/multimodal.md): Learn how to store and query multimodal data (images, audio, video) directly in LanceDB using binary columns.
- [Schema and Data Evolution](https://docs.lancedb.com/tables/schema.md): Learn how to manage table schemas in LanceDB, including adding, altering, and dropping columns.
- [Updating and Modifying Table Data](https://docs.lancedb.com/tables/update.md): Learn how to update, merge, and delete rows in a LanceDB table.
- [Versioning and Reproducibility](https://docs.lancedb.com/tables/versioning.md): Learn how to implement versioning and ensure reproducibility in LanceDB. Includes version control, data snapshots, and audit trails.
- [Loading Data for Model Training](https://docs.lancedb.com/training/index.md): Introduction to loading data, shuffling, and creating permutations for model training in LanceDB.
- [PyTorch Integration](https://docs.lancedb.com/training/torch.md): Learn how to use LanceDB with PyTorch for training and inference.
- [Troubleshooting](https://docs.lancedb.com/troubleshooting.md): Tips for troubleshooting basic LanceDB issues.
- [RAG and Agents](https://docs.lancedb.com/tutorials/agents/index.md): Explore a variety of RAG (Retrieval-Augmented Generation) and agent applications built with LanceDB.
- [Multimodal Agent](https://docs.lancedb.com/tutorials/agents/multimodal-agent/index.md): Build an AI agent that understands both text and images to help users find recipes using LanceDB and PydanticAI
- [NVIDIA RAG Blueprint with LanceDB](https://docs.lancedb.com/tutorials/agents/nvidia-rag-blueprint/index.md): Use LanceDB as the retrieval layer for NVIDIA RAG Blueprint with a Docker-first, retrieval-only reference integration.
- [Time-Travel RAG with versioned data](https://docs.lancedb.com/tutorials/agents/time-travel-rag/index.md): Learn how to build production-ready RAG systems with LanceDB's time-travel capabilities for regulatory compliance and audit trails.
- [Feature Engineering](https://docs.lancedb.com/tutorials/feature-engineering/index.md): Learn how to build features for your data in LanceDB.
- [Tutorials](https://docs.lancedb.com/tutorials/index.md): Step-by-step tutorials for building applications with LanceDB
- [Search Tutorials](https://docs.lancedb.com/tutorials/search/index.md): Learn how vector, full-text and hybrid search work in LanceDB.
- [Finding the Needle in a Haystack: Comparing Multi-vector Search Strategies](https://docs.lancedb.com/tutorials/search/multivector-needle-in-a-haystack.md): Tutorial on token-level retrieval with LanceDB multivector search.

## OpenAPI Specs

- [openapi](https://docs.lancedb.com/api-reference/openapi.json)