mirror of
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Jack Ye
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The pages describing this code lived in lancedb/docs, a repository with ten stars that neither contributing guide mentioned. A user who hit a wrong page while reading the source had to find a separate, near-invisible repository to fix it. These 102 pages now sit beside the code, and a documentation fix is a pull request in the repository the reader already had open. `docs/web` is a complete Mintlify site: `cd docs/web && mint dev` serves it with no other checkout. Enterprise pages stay private and are merged in when the published site is assembled, so links into them do not resolve here — every open-source page does. `docs/web-tests` holds the tests the code examples are extracted from. They ran against a published wheel before; here they can run against this repository's own SDK, so an example that stops compiling fails the pull request that broke it rather than being noticed later. Both directories sit beside `docs/src` and are invisible to it: mkdocs reads `docs_dir: src`, so the SDK reference builds and deploys exactly as before.
125 lines
6.0 KiB
Plaintext
125 lines
6.0 KiB
Plaintext
---
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title: LanceDB
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sidebarTitle: "LanceDB"
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description: "Multimodal lakehouse for AI."
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icon: "/static/assets/logo/lancedb-icon-gray.svg"
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keywords: ["multimodal lakehouse", "training", "feature engineering", "search", "open source", "oss"]
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---
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**LanceDB** is a [multimodal lakehouse](https://lancedb.com/blog/multimodal-lakehouse/) for AI teams that need
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one data layer for curation, feature engineering, search and retrieval, and model training.
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It is built on top of [Lance](/lance), an open-source lakehouse format designed for multimodal AI data.
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Move from data exploration to model training on one, unified platform without needing to manage a
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fragmented stack of storage, feature, retrieval, and training systems.
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## Build better models, faster {#build-better-models-faster}
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Training data and experimentation slow down when raw data, metadata, embeddings, features, and governance
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artifacts live in separate systems. LanceDB keeps them together in one versioned multimodal table, so AI teams spend less
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time stitching infrastructure together and more time improving datasets, testing features, and keeping GPUs fed.
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Use the same table to curate training data, add derived features, retrieve examples, and feed training jobs that rely on expensive GPUs.
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Training workloads can sample, shuffle, and scan projected columns from local storage or object storage, then assemble
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GPU-ready batches from a tagged dataset version.
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For a deeper look at how this works in training pipelines, start with [Why LanceDB for training](/training/why-lancedb).
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## LanceDB suite {#lancedb-suite}
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The LanceDB suite includes LanceDB OSS, an open-source embedded retrieval library, and LanceDB Enterprise,
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a multimodal lakehouse platform for the full AI data lifecycle.
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OSS is easy to set up on a local machine for search and regular-scale workflows. LanceDB Enterprise is built
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for teams that need scale without building bespoke infrastructure for curation,
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feature engineering, search and retrieval, and efficient training data access.
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## Why teams use LanceDB {#why-teams-use-lancedb}
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<Steps>
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<Step title="One table for the whole AI data loop">
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Store images, video, audio, text, annotations, embeddings, and model-generated features together in one schema-enforced table.
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The same table can support dataset curation, feature backfills, experiment splits, retrieval, and training.
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</Step>
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<Step title="High-throughput data access for training">
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Training workloads mix fast random access with high-throughput sequential scans. LanceDB is designed for both, so
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teams can shuffle data into GPU-ready batches more efficiently, improve input throughput, and iterate on experiments faster.
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</Step>
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<Step title="Fast, versatile search and retrieval">
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Whether the end user is a human or an agent, LanceDB powers production retrieval workloads such as semantic search,
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hybrid search, RAG, agent memory, and recommendation systems. Retrieval runs against the same LanceDB tables used
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for curation, feature engineering, and training workflows.
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</Step>
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</Steps>
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## Start with your workload {#start-with-your-workload}
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<CardGroup cols={2}>
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<Card title="Train and fine-tune models" icon="fire" href="/training/why-lancedb">
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Learn why LanceDB works well as the data layer for training workloads.
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</Card>
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<Card title="Load data into PyTorch" icon="boxes-stacked" href="/training/">
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Use LanceDB tables and permutations for projected, shuffled, random-access training reads.
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</Card>
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<Card title="Browse ready-to-use datasets" icon="database" href="/datasets">
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Explore Lance-formatted multimodal datasets with raw bytes, metadata, embeddings, and indices.
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</Card>
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<Card title="Build search and retrieval" icon="search" href="/search/">
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Use vector search, full-text search, hybrid search, reranking, filtering, and SQL.
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</Card>
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</CardGroup>
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## From local development to production scale {#from-local-development-to-production-scale}
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LanceDB OSS and LanceDB Enterprise share the same Lance format and table model. Start locally with the embedded OSS
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library, then move to Enterprise when your team needs distributed scale, managed infrastructure, private deployment,
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or higher-throughput curation, feature engineering, search and retrieval, and training workflows.
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### 1\. LanceDB OSS {#1-lancedb-oss}
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The fastest way to get started is the open-source embedded library, with client SDKs in Python, TypeScript
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and Rust. Run it locally in just a few steps, which lets you explore datasets, curate data, and run search and retrieval workloads
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for agents. Start here:
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<Columns cols={2}>
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<Card
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title="Quickstart"
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icon="rocket"
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href="/quickstart"
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>
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Get started with LanceDB in minutes.
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</Card>
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<Card
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title="Basic Table Operations"
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icon="table"
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href="/tables/"
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>
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Create tables, evolve schemas, version data, and modify rows in LanceDB.
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</Card>
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</Columns>
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### 2\. LanceDB Enterprise {#2-lancedb-enterprise}
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[LanceDB Enterprise](/enterprise) is a petabyte-scale (and beyond), distributed **multimodal lakehouse** platform built for
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search, curation, feature engineering, and high-throughput training data access workflows on top of the same core table
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abstraction. This eliminates the need for teams to build bespoke infrastructure to manage large multimodal datasets.
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To set up LanceDB Enterprise in your organization, reach out to us at
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[contact@lancedb.com](mailto:contact@lancedb.com).
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<Info>
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**Built with scale, performance, and security in mind.**
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LanceDB Enterprise is designed for very large-scale, high-performance, distributed workloads in
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private deployments, and can operate under strict [security requirements](/enterprise/security).
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</Info>
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<Card
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title="Quickstart"
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icon="rocket"
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href="/quickstart"
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>
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Get started with LanceDB in minutes, including Enterprise `db://` connections.
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</Card>
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