mirror of
https://github.com/lancedb/lancedb.git
synced 2026-10-30 15:26:52 +00:00
Will JonesandClaude Opus 5.5
4d7a64712f
## Summary Adopts the product terminology guide across the docs site pages, SDK docstrings and error messages. "LanceDB OSS" and local mode become **LanceDB Embedded**. "Remote" as the execution model becomes **LanceDB Distributed**, which LanceDB Enterprise provides. Where free vs paid is the point, pages contrast "LanceDB Embedded (open source)" with "LanceDB Enterprise". This is a first trial of the Embedded name, so reviewers can judge it in context. - **Embedded vs Distributed** for where execution runs (`optimize()`, consistency, branching, maintenance). Main pages define it once: "LanceDB Enterprise runs LanceDB Distributed: …" - **LanceDB Cloud** is kept only for legacy-Cloud specifics (`db://` region, the brute-force KNN limit) and marked legacy. "Cloud and Enterprise" becomes Enterprise or Distributed. - **Plan executor** becomes "executor" or the fleet name. - **Error messages** change, e.g. `optimize is not supported for remote tables.` → `... in LanceDB Distributed.` Tests that match on them are updated. - **Vale rules** (`EmbeddedName`, `OSSProduct`, `RemoteExecution`, `DistributedEnterprise`, `LegacyCloud`, `InternalNames`) enforce the terms in prose. Unchanged: identifiers (`availability.oss`, the `remote` module and feature, `RemoteTable`, `db://`), heading anchors, internal code comments, and Geneva. ## Evidence - Rust lib tests for the changed messages and the Python tests in `test_remote_db.py`, `test_materialized_views.py` and `test_table.py` pass locally. - Not run locally: - The Node tests: `remote.test.ts` and `table.test.ts` have updated assertions. - Vale. - The `experimental_functions`-marked materialized-view tests. - `docs/src` API reference was hand-edited to match the docstrings rather than regenerated. ## Merge Risk **Door:** two-way; prose and message text only. **Blast Radius:** `docs`, `error-messages`: callers matching on old error strings would break. Companion PRs: lancedb/docs (assembler labels, STYLE.md) and sophon (Enterprise pages). 🤖 Generated with [Claude Code](https://claude.com/claude-code) --------- Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
128 lines
6.3 KiB
Plaintext
128 lines
6.3 KiB
Plaintext
---
|
|
title: LanceDB
|
|
sidebarTitle: "LanceDB"
|
|
description: "Multimodal lakehouse for AI."
|
|
icon: "/static/assets/logo/lancedb-icon-gray.svg"
|
|
keywords: ["multimodal lakehouse", "training", "feature engineering", "search", "open source", "oss"]
|
|
---
|
|
|
|
**LanceDB** is a [multimodal lakehouse](https://lancedb.com/blog/multimodal-lakehouse/) for AI teams that need
|
|
one data layer for curation, feature engineering, search and retrieval, and model training.
|
|
It is built on top of [Lance](/basics/lance), an open-source lakehouse format designed for multimodal AI data.
|
|
|
|
Move from data exploration to model training on one, unified platform without needing to manage a
|
|
fragmented stack of storage, feature, retrieval, and training systems.
|
|
|
|
## Build better models, faster {#build-better-models-faster}
|
|
|
|
Training data and experimentation slow down when raw data, metadata, embeddings, features, and governance
|
|
artifacts live in separate systems. LanceDB keeps them together in one versioned multimodal table, so AI teams spend less
|
|
time stitching infrastructure together and more time improving datasets, testing features, and keeping GPUs fed.
|
|
|
|
|
|
|
|
Use the same table to curate training data, add derived features, retrieve examples, and feed training jobs that rely on expensive GPUs.
|
|
Training workloads can sample, shuffle, and scan projected columns from local storage or object storage, then assemble
|
|
GPU-ready batches from a tagged dataset version.
|
|
|
|
For a deeper look at how this works in training pipelines, start with [Why LanceDB for training](/training/why-lancedb).
|
|
|
|
## LanceDB suite {#lancedb-suite}
|
|
|
|
The LanceDB suite includes LanceDB Embedded (open source), a retrieval library, and LanceDB Enterprise,
|
|
a multimodal lakehouse platform for the full AI data lifecycle.
|
|
LanceDB Embedded runs in your application's process; your data can live on local disk or in object storage.
|
|
It is easy to set up on a local machine for search and regular-scale workflows. LanceDB Enterprise is built
|
|
for teams that need scale without building bespoke infrastructure for curation,
|
|
feature engineering, search and retrieval, and efficient training data access.
|
|
LanceDB Enterprise runs LanceDB Distributed: query execution, indexing and maintenance
|
|
run on the Enterprise service, outside your application.
|
|
|
|
|
|
|
|
## Why teams use LanceDB {#why-teams-use-lancedb}
|
|
|
|
<Steps>
|
|
<Step title="One table for the whole AI data loop">
|
|
Store images, video, audio, text, annotations, embeddings, and model-generated features together in one schema-enforced table.
|
|
The same table can support dataset curation, feature backfills, experiment splits, retrieval, and training.
|
|
</Step>
|
|
<Step title="High-throughput data access for training">
|
|
Training workloads mix fast random access with high-throughput sequential scans. LanceDB is designed for both, so
|
|
teams can shuffle data into GPU-ready batches more efficiently, improve input throughput, and iterate on experiments faster.
|
|
</Step>
|
|
<Step title="Fast, versatile search and retrieval">
|
|
Whether the end user is a human or an agent, LanceDB powers production retrieval workloads such as semantic search,
|
|
hybrid search, RAG, agent memory, and recommendation systems. Retrieval runs against the same LanceDB tables used
|
|
for curation, feature engineering, and training workflows.
|
|
</Step>
|
|
</Steps>
|
|
|
|
## Start with your workload {#start-with-your-workload}
|
|
|
|
<CardGroup cols={2}>
|
|
<Card title="Train and fine-tune models" icon="fire" href="/training/why-lancedb">
|
|
Learn why LanceDB works well as the data layer for training workloads.
|
|
</Card>
|
|
<Card title="Load data into PyTorch" icon="boxes-stacked" href="/training/data-loading">
|
|
Use LanceDB tables and permutations for projected, shuffled, random-access training reads.
|
|
</Card>
|
|
<Card title="Browse ready-to-use datasets" icon="database" href="/datasets">
|
|
Explore Lance-formatted multimodal datasets with raw bytes, metadata, embeddings, and indices.
|
|
</Card>
|
|
<Card title="Build search and retrieval" icon="search" href="/search/">
|
|
Use vector search, full-text search, hybrid search, reranking, filtering, and SQL.
|
|
</Card>
|
|
</CardGroup>
|
|
|
|
## From local development to production scale {#from-local-development-to-production-scale}
|
|
|
|
LanceDB Embedded and LanceDB Enterprise share the same Lance format and table model. Start locally with
|
|
LanceDB Embedded, then move to Enterprise when your team needs distributed scale, managed infrastructure, private deployment,
|
|
or higher-throughput curation, feature engineering, search and retrieval, and training workflows.
|
|
|
|
### 1\. LanceDB Embedded {#1-lancedb-oss}
|
|
The fastest way to get started is the open-source embedded library, with client SDKs in Python, TypeScript
|
|
and Rust. Run it locally in just a few steps, which lets you explore datasets, curate data, and run search and retrieval workloads
|
|
for agents. Start here:
|
|
|
|
<Columns cols={2}>
|
|
<Card
|
|
title="Quickstart"
|
|
icon="rocket"
|
|
href="/quickstart"
|
|
>
|
|
Get started with LanceDB in minutes.
|
|
</Card>
|
|
<Card
|
|
title="Basic Table Operations"
|
|
icon="table"
|
|
href="/management/table"
|
|
>
|
|
Create tables, evolve schemas, version data, and modify rows in LanceDB.
|
|
</Card>
|
|
</Columns>
|
|
|
|
### 2\. LanceDB Enterprise {#2-lancedb-enterprise}
|
|
|
|
[LanceDB Enterprise](/enterprise) is a petabyte-scale (and beyond), distributed **multimodal lakehouse** platform built for
|
|
search, curation, feature engineering, and high-throughput training data access workflows on top of the same core table
|
|
abstraction. This eliminates the need for teams to build bespoke infrastructure to manage large multimodal datasets.
|
|
To set up LanceDB Enterprise in your organization,
|
|
[reach out to us](https://www.lancedb.com/contact).
|
|
|
|
<Info>
|
|
**Built with scale, performance, and security in mind.**
|
|
|
|
LanceDB Enterprise is designed for very large-scale, high-performance, distributed workloads in
|
|
private deployments, and can operate under strict [security requirements](/enterprise/security).
|
|
</Info>
|
|
|
|
<Card
|
|
title="Quickstart"
|
|
icon="rocket"
|
|
href="/quickstart"
|
|
>
|
|
Get started with LanceDB in minutes, including Enterprise `db://` connections.
|
|
</Card>
|