mirror of
https://github.com/lancedb/lancedb.git
synced 2026-10-13 15:22:30 +00:00
Jack Ye
70c10e4aa4
lancedb now holds a page at every path the site publishes, including the twelve Enterprise ones. Those are real open-source pages -- what the capability is, and what the embedded form does instead -- rather than blank placeholders, so the open-source site is complete on its own. sophon becomes an overlay whose page replaces the one at the same path, which means navigation lives in one place and the two repositories only have to agree on a path. That removes the whole class of problem where a page could only be reached from one of the two trees. The open-source site went from twenty-four dangling Enterprise links to one, and that one points at the dataset pages the build repository owns. Ports four changes that landed on the docs repository after this branch left it: #344 indexing and quantization rewritten #354 LeRobot native reader #361 training/index renamed to training/data-loading and rewritten #362 distance metric mismatch falls back to a brute-force scan None applied as patches, because these files were merged or split here -- quantization into the vector index page, multi-vector into vector search, performance into four pages. Each was ported by taking the upstream page and re-applying the restructure on top, which also picked up a correction that would otherwise have been lost: the stricter brute-force threshold for multi-vector columns is a Cloud limit, not an Enterprise one. The upstream LeRobot page linked to a Geneva page that no longer exists; it now points at the feature-engineering pages that replaced it.
125 lines
6.1 KiB
Plaintext
125 lines
6.1 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](/basics/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/data-loading">
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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="/management/table"
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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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