Chi-Wei LeeandWill Jones
0be3ae960e
Synchronous local Python `create_table` callers using `mode="exist_ok"` received `ValueError` instead of creating or opening a table. Related to [#2900](https://github.com/lancedb/lancedb/issues/2900). The local wrapper rejected this mode before the shared Rust parser could validate it. Removing that guard delegates mode validation to the parser, and the sync and async docs now describe `exist_ok`. Remote option gaps and the TypeScript table type gap remain outside this change. The added tests retain both calls from [@wjones127's comment](https://github.com/lancedb/lancedb/issues/2900#issuecomment-5964167206). This builds on the code @wjones127 posted there. Three regression tests fail before the change, pass after it, fail with the original `db.py` restored, then pass again on HEAD. Tested on Linux x86_64 with Python 3.14.7. From the Python directory, `uv run --no-sync --extra tests --extra dev python -m pytest python/tests/test_db.py -q` reports 53 passed. `uv run --no-sync --extra tests --extra dev python -m pytest python/tests -q -rs -m "not slow and not s3_test"` reports 1428 passed, 34 skipped, 68 deselected, and one failure: the existing `test_get_lsm_write_spec`, also failing on clean base (1425 passed, one failed). Ruff 0.9.9 `ruff format --check .` and `ruff check .` pass. Tests used worktree Python code with the released `lancedb==0.40.0b12` native wheel. No native source build was run. Co-authored-by: Will Jones <willjones127@gmail.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.
Contributors
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