adenzhou1350 13d54db594 fix(node): preserve caller-owned storage options (#4480)
`getStorageOptions` writes typed creation options back into the caller's
`storageOptions` object. This makes frozen options throw and causes
overrides to leak into later calls that reuse the storage map.

For example, using `{ storageOptions: shared, enableV2ManifestPaths:
true }` changes `shared.newTableEnableV2ManifestPaths` to `"true"`. A
subsequent creation with just `{ storageOptions: shared }` then uses V2
manifest paths even if `shared` originally requested `"false"`.

Merge the typed overrides into a local map instead. Their existing
precedence, key normalization, and undefined/default behavior remain
unchanged. This affects both `createTable` and `createEmptyTable`; no
native/Rust changes.

Adds six native-database regressions covering frozen creation options,
frozen nested storage options with conflicting defaults, and cross-call
reuse for both APIs.

Validation (Linux x64 / Node 24.19.0 / official 0.41.0-beta.0 native
library):
- Before: all six new regressions fail; an existing namespace
storage-options control passes.
- After: the full connection test file passes, 43/43 (current TypeScript
source, transpile-only).
- Same fix on the matching 0.41.0-beta.0 source: TypeScript compilation,
strict ts-jest connection tests (43/43), and TypeDoc with
warnings-as-errors pass.
- Biome on changed files and whitespace checks pass.

A current-main native rebuild was not run. Released native declarations
predate six unrelated lazy-iterator signatures; current-main
typechecking reports the same six errors before and after this fix (only
downstream line coordinates shift).


### Connection entry-point follow-up

The same ownership problem occurs in `connect(uri, options)` and
`connectNamespace`: normalization assigns a new storage map into the
caller's options. Copy the options before normalization instead. The
`connect(options)` overload already copies its input and is retained as
a control.

Nine added native-database cases cover frozen outer options
(with/without storage options) and repeated reuse, including table
creation and row-count checks. Before this entry-point fix, the
URI/namespace cases fail (6), while the object-overload controls pass
(3). Afterward the complete connection file passes 52/52 on current
TypeScript with the official 0.41.0-beta.0 native library. Applying the
entry-point fix to matching-release source also passes strict
TypeScript, strict ts-jest (52/52), and TypeDoc. Biome passes on both
newly changed files.

The same six current-main/released-native lazy-iterator declaration
errors remain unchanged. No Rust/native rebuild or GPU claim is added.

---------

Signed-off-by: Xucheng Zhou <aden1350@outlook.com>
2026-10-23 03:13:46 +08:00
2023-03-17 18:15:19 -07:00
2025-03-10 09:01:23 -07:00

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LanceDB

The Multimodal Lakehouse for AI

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