Dan TasseandClaude Fable 5.1 590f7a4eca fix(node): rebuild view columns as Utf8/Binary before IPC serialization
Arrow JS 21's IPC writer emits a truncated views buffer for a sliced
Utf8View or BinaryView array, which the Rust reader rejects ("Need at
least N bytes in buffers[0]"), so createTable and add failed for any view
column with a nonzero data offset. Lance stores the view types as Utf8 and
Binary anyway, so rebuild top-level view columns that way on the JS side
before writing the IPC bytes. Covers the table and record batch paths.

Also take the Type enum from the Arrow 21 copy in the end-to-end test so
the file still type-checks when the library resolves to Arrow 19 or 20.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
2026-10-01 12:28:35 -05:00
2026-09-09 15:33:04 +08:00
2023-03-17 18:15:19 -07:00
2025-03-10 09:01:23 -07:00

LanceDB Cloud Public Beta

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LanceDB

The Multimodal AI Lakehouse

How to Install ✦ Detailed Documentation ✦ Tutorials and Recipes ✦ Contributors

The ultimate multimodal data platform for AI/ML applications.

LanceDB is designed for fast, scalable, and production-ready vector search. It is built on top of the Lance columnar format. You can store, index, and search over petabytes of multimodal data and vectors with ease. LanceDB is a central location where developers can build, train and analyze their AI workloads.


Demo: Multimodal Search by Keyword, Vector or with SQL

LanceDB Multimodal Search

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Key Features:

  • Fast Vector Search: Search billions of vectors in milliseconds with state-of-the-art indexing.
  • Comprehensive Search: Support for vector similarity search, full-text search and SQL.
  • Multimodal Support: Store, query and filter vectors, metadata and multimodal data (text, images, videos, point clouds, and more).
  • Advanced Features: Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure. GPU support in building vector index.

Products:

  • Open Source & Local: 100% open source, runs locally or in your cloud. No vendor lock-in.
  • Cloud and Enterprise: Production-scale vector search with no servers to manage. Complete data sovereignty and security.

Ecosystem:

  • Columnar Storage: Built on the Lance columnar format for efficient storage and analytics.
  • Seamless Integration: Python, Node.js, Rust, and REST APIs for easy integration. Native Python and Javascript/Typescript support.
  • Rich Ecosystem: Integrations with LangChain 🦜️🔗, LlamaIndex 🦙, Apache-Arrow, Pandas, Polars, DuckDB and more on the way.

How to Install:

Follow the Quickstart doc to set up LanceDB locally.

API & SDK: We also support Python, Typescript and Rust SDKs

Interface Documentation
Python SDK https://lancedb.github.io/lancedb/python/python/
Typescript SDK https://lancedb.github.io/lancedb/js/globals/
Rust SDK https://docs.rs/lancedb/latest/lancedb/index.html
REST API https://docs.lancedb.com/api-reference/rest

Join Us and Contribute

We welcome contributions from everyone! Whether you're a developer, researcher, or just someone who wants to help out.

If you have any suggestions or feature requests, please feel free to open an issue on GitHub or discuss it on our Discord server.

Check out the GitHub Issues if you would like to work on the features that are planned for the future. If you have any suggestions or feature requests, please feel free to open an issue on GitHub.

Contributors

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Description
Developer-friendly OSS embedded retrieval library for multimodal AI. Search More; Manage Less.
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