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lancedb-gatefixer[bot]
920b1e7bbb
Fixes #4379 Remote `table_names` discarded the continuation token after the first root-listing request, silently returning the server's default page of 10 names. Remote `list_tables` also inherited that server default, while local listings had no SDK page limit. Python's synchronous and namespace wrappers still imposed a default limit of 10 names. The namespace adapter also discarded continuation tokens, so an unlimited REST namespace listing could stop at the server's page cap. Follow server-provided root listing tokens until the requested name limit or the end of the listing, preserving `start_after`, cache population, and guards against empty or repeated tokens. The namespace adapter collects every server page, then sorts names and applies the name cursor and total limit: namespace tokens are opaque, and page order need not be name order. Empty tokens end the listing; token cycles return an error. Python sync and namespace `table_names` wrappers now default to `None`, matching the asynchronous and TypeScript APIs. Namespace overrides accept the base connection's optional limit, avoiding import-time signature errors on Python 3.10 and 3.11. Set the `list_tables` default to 100 at the shared connection boundary, including namespaces, and return empty results for a zero limit without sending an invalid server request. Update the SDK documentation to describe these defaults. `list_tables()` is now a paginated call in both modes: callers with more than 100 tables must follow the returned token or supply an explicit larger limit. `table_names()` without a limit returns every name. Namespace table-name calls enumerate the namespace before applying name cursors and limits; use `list_tables()` for paginated access. Validation: - Regression cases reproduce the original root-listing truncation and cover 130 tables, opaque tokens, short pages, explicit limits, `start_after`, and zero limits. - Relevant Rust remote database, connection, and local listing test groups: 175 passed. Namespace adapter tests: 23 passed, including opaque tokens, unordered and empty pages, name cursors, limits, zero requests, and token cycles. - Rebuilt the Python extension; namespace and local/mocked remote listing tests using `uv.lock`: 46 passed on Python 3.10 and 46 passed on Python 3.13. - REST namespace regressions reproduced the truncation before the fix and now return all 12 names from a server capped at 10 per response, for sync/async connections at root and child paths. They also cover unordered pages, name cursors, explicit limits, and zero requests. The exact Python 3.10 import error also reproduced before the optional-signature fix. - TypeScript connection test file: 38 passed; `pnpm build`, `pnpm lint`, and `pnpm run docs` passed. - `cargo check --quiet --features remote --tests --examples` and `cargo clippy --quiet --features remote --tests --examples` passed. - Rust formatting, Python Ruff formatting/lint, and diff whitespace checks passed. <!-- lance-gatekeeper-fix:v1 agent=43f92f1f05ac9ca74229af590945cb88 generation=1 --> --------- Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
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
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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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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.
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