lancedb-gatefixer[bot]
51ea02ea8c
## Cause On latest-tracking remote tables, `fetch_blob_files` probed a concrete table version but retained `None` on each handle. Later range reads resolved the latest version again, so an update or delete could make an opened handle fail. ## Fix For nonempty blobs, capture `x-lancedb-version` from the 206 size probe and include it on every later range request. Reject a nonempty probe without a valid version. Accept an empty-blob 416 probe with or without that header, because an empty handle makes no later data range request. The Python remote mock now covers both response shapes. ## Limitation A table dropped and recreated with the same name can reuse version numbers and row addresses. Version pinning does not identify the table itself, so an old handle can still return data from the recreated table. That identity case needs a separate fix. Callers that replace a table should discard its old blob handles. ## Validation - `cargo test --quiet --features remote -p lancedb --lib remote::table::blobs::tests` (24 passed) - Rebuilt the Python extension; `uv run --extra tests pytest python/tests/test_remote_db.py -q -k remote_blob` (6 passed) - `cargo clippy --quiet --features remote -p lancedb --lib --tests --examples` - `cargo fmt --all -- --check`, `ruff check .`, and `ruff format .` Fixes #4276 <!-- lance-gatekeeper-fix:v1 agent=e309bdf75b1a87f6492a6dc3ee1bfd81 generation=1 --> --------- Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.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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