lancedb-gatefixer[bot]
13749a3f13
## Summary - Add a downstream IVF-HNSW-SQ merge regression using descending row IDs. - Detect any reintroduction of merge-time row sorting without allocating the original multi-gigabyte fixture. ## Root cause Lance 4 sorted each partition by row ID with `RecordBatch::take`. Arrow represented those take indices as `UInt32`, and `take_fixed_size_list` overflowed when `num_rows * vector_dimension` exceeded `u32::MAX`. The Lance version pinned by current `main` already includes lance-format/lance#6332, which removes that unnecessary sort. This test locks the corrected behavior at the LanceDB dependency boundary: the old implementation reorders the sentinel row IDs, while the fixed implementation preserves them. ## Validation - `cargo fmt --all -- --check` - `cargo test --quiet --features remote -p lancedb --lib table::create_index::tests::test_ivf_hnsw_sq_merge_preserves_shuffled_row_order -- --exact` - Full current-head PR CI: 40 successful checks and one expected Windows ARM check skipped because that job runs only on `main`. Fixes #3126
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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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:
- LanceDB Embedded (open source): 100% open source, runs in your application's process; your data can live on local disk or in object storage. No vendor lock-in.
- LanceDB Enterprise: Production-scale distributed search, indexing, and maintenance. Plus, 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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