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adenzhou1350
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## Summary `create_table(data=iter(()), schema=...)` currently raises `RuntimeError: coroutine raised StopIteration`, although an empty list with the same schema works. A tuple of batches also fails because the helper calls `next()` before obtaining an iterator. Normalize the iterable once and use the supplied schema for an empty reader. Without a schema, raise a clear `ValueError`. Nonempty schema inference and batch casting are unchanged. Tests cover synchronous/asynchronous creation, empty/nonempty iterators and tuples, and schema-aware `add()` sanitization. The default schema-less `add()` rejection is deliberately unchanged. ## Validation - Built the current Rust/PyO3 extension from base `9f37f392` with the repository's `ci` profile; both Python-only arms use the same verified extension and locked Python dependency versions. - Complete `python/tests/test_table.py` + `python/tests/test_scannable.py`: **268 passed, 0 skipped**. The unchanged production code with identical tests has **24 failures / 244 passes**. - Both arms used four-CPU affinity. An initial unrestricted-affinity run exhausted thread creation under the private memory limit; that failed attempt is not counted as a pass. - Package-wide Ruff check/format pass. Repository-wide checks still report pre-existing findings in unchanged `docs/web-tests`; those files are not modified here. AI-assisted change, reproduced against unchanged production code and validated with a native source build. This does not claim the full repository/platform matrix or a performance improvement. Signed-off-by: Xucheng Zhou <aden1350@outlook.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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⭐ 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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