lancedb-gatefixer[bot]
c0df2c63b6
## Summary - add a merge-insert regression test whose fixed-size-list child count crosses `u32::MAX` - verify delete-by-source updates the matching row, deletes every other row, and completes without an Arrow panic - use a null child array so the boundary case avoids allocating a real vector payload ## Root cause and fix The affected Lance merge fallback carried the target payload through a full outer hash join. Arrow's fixed-size-list take kernel uses `u32` child indices, so taking a target row whose child offset crossed `u32::MAX` wrapped the offset and produced child data shorter than the parent array, triggering the reported `ArrayData::slice` assertion. The projection-aware merge path in the Lance version now used by `main` avoids materializing the target fixed-size-list payload in that join. This regression test locks in that production behavior at the exact child-index boundary. ## Validation - `cargo fmt --all` - `cargo test --quiet --features remote -p lancedb test_merge_insert_fixed_size_list_above_u32_child_count` - `cargo check --quiet --features remote --tests --examples` - `cargo clippy --quiet --features remote --tests --examples` Fixes #2874 <!-- lance-gatekeeper-fix:v1 agent=582e68bcad65739e189352cb3cbf144c generation=3 --> 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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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 |
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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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