mikemikimike
be3215a6ae
## Issue Fixes #4063 ## Background The Node.js SDK currently requires callers to know the Arrow extension metadata needed to represent JSON fields. This makes a common LanceDB schema type unnecessarily verbose and easy to get wrong. ## Changes - Add `makeJsonField(name, nullable = true)` to create a UTF-8 Arrow field with the `arrow.json` extension metadata. - Re-export the helper from the public Node.js entry point. - Add coverage for the default nullable behavior, explicit non-nullable fields, and the extension metadata. - Add the generated TypeDoc function page and public globals entry, including a usage example. ## Implementation The helper uses the existing Apache Arrow `Field` type and sets `ARROW:extension:name` to `arrow.json`, matching the metadata convention already used by LanceDB. ## Compatibility This is an additive Node.js API. Existing schema construction and Arrow behavior are unchanged. ## Verification - `pnpm test -- arrow.test.ts --runInBand` — 236 tests passed. - `pnpm exec biome ci lancedb/arrow.ts lancedb/index.ts __test__/arrow.test.ts` — passed. - `git diff --check` — passed. ## Not run / known limitations - `pnpm build` and `pnpm run docs` were attempted after expanding the checkout. Both are blocked locally by the native binding build/type declarations: Cargo did not complete, and TypeDoc reported the missing generated `nodejs/lancedb/native` module. The docs files were generated from the updated TypeScript comments; full build and docs validation are left to CI.
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
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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 |
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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.
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