41ac32a344
This PR is for the Read path against blob v2. #3528 handles declare + write, and this this adds materialization on local tables. - blob_columns() - fetch_blobs(column, row_ids) → bytes - fetch_blob_files(column, row_ids) → lazy handles - Pass _rowid from query().with_row_id(). Remote returns NotSupported. (for now) ### Use cases search, grab row ids, materialize images: ```rust let row_ids = /* _rowid from hits */; let images = table.fetch_blobs("image", &row_ids).await?; ``` Large blobs: open handles, read only what you need: ```rust let handles = table.fetch_blob_files("image", &row_ids).await?; let bytes = handles[0].as_ref().unwrap().read().await?; ``` Filter then batch fetch: collect ids from a filter, one call. Multiple blob columns: image and thumbnail independently. Row ids from before compact: still resolve. ### Alignment note Lance `read_blobs` drops null rows. We descriptor-take first, read non-null ids, re-expand to match input order. Null and zero-length blobs come back null/None. Bytes path sets `preserve_order(true)`. So I added: ``` TODO(lance): expose selection_index or an aligned execute so we can drop the pre-read. ``` ### Tests `cargo test -p lancedb --test blob_integration` - 30 tests covering nulls, reorder, dups, cross-fragment bytes + files, compact, delete, legacy v1 errors. --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.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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