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Will Jones
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`ListingDatabase::list_tables` listed every table directory under the database prefix before applying `limit` and `page_token`. The cost of a request was set by the size of the database rather than the size of the page, so listing one table out of ten thousand took ten S3 round trips instead of one. List through `ObjectStore::read_dir_page`, which pushes the resume position and the page size into the store's list request. Stores with no paginated list API list the level in full and page it locally, which is what every store did before. Children that are not tables leave a page short of its limit, and one page is one request, so the listing asks again until the page is full or the database runs out. Two behaviour changes come with it: - `page_token` is opaque. It was a table name; it is now whatever resumes the store the database sits on, which for S3, GCS and Azure is a continuation token. Callers hand it back and do not construct or interpret one. Nothing validates it, so a token a caller invents resumes from the wrong place rather than failing. - Tables are reported in the order the store lists directories, which differs from sorting by name only between a name and one that extends it: `users-archive` now precedes `users`, because the `-` of `users-archive.lance` sorts below the `.` of `users.lance`. Pagination cannot report an order other than the one it resumes in. `table_names` is left on the full listing it has today: its `start_after` is a table name, which cannot be pushed into a store that resumes from a continuation token, and it is deprecated. A pushed-down listing does not pass through `WrappingObjectStore::wrap`, so every wrapper here says whether the pushdown survives it: the mirroring wrapper keeps it, since only writes are mirrored, and the test IO tracker gives it up rather than let a listing go around the counter. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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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⭐ Click here ⭐ to see how fast we're growing!
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.
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