lancedb-gatefixer[bot]
ec21e37040
## Summary - cover Hugging Face cache layouts where both manifests and Lance data files are relative symlinks into a blob directory - reconnect with a fresh session before opening so the test exercises filesystem discovery instead of cached manifest metadata - scan the reopened table to verify both manifest recovery and data-file reads ## Root cause Lance 3.0.1 recorded Unix symlink metadata as the known manifest size, so the short link length caused a file size is too small error. The current Lance v11.0.0-beta.2 dependency repairs this by detecting an invalid footer from a stale known size and retrying with the target file metadata. This regression test locks that behavior into the LanceDB open-table path used by Node. ## Validation - cargo fmt --all - cargo test --quiet --features remote -p lancedb --lib test_open_table_follows_hugging_face_symlinks -- --nocapture - cargo test --quiet --features remote -p lancedb --lib database::listing::tests - cargo clippy --quiet --features remote -p lancedb --lib --tests -- -D warnings - cargo check --quiet --features remote --tests --examples Fixes #3197 <!-- lance-gatekeeper-fix:v1 agent=4aadcf04e9ac93b97d499d7448b67e19 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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