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lancedb-gatefixer[bot]andXuanwo
f5f4463b15
`blobMode: "bytes"` left blobs inside structs as descriptors because projection matching ignored parent structs and byte replacement only rebuilt top-level fields. Expand direct struct projections and aliases to their blob leaves, then recursively replace the corresponding Arrow fields and data with `LargeBinary`. Track literal output names separately from descendant paths so scalar aliases stay intact and every overlapping blob projection is converted. Preserve null structs, null and empty blobs, sibling fields, schema metadata, and the existing row ID and snapshot behavior. Blobs inside lists retain their descriptors. Update the API docs to describe struct support. Regression coverage includes full and parent-struct projections, leaf and struct aliases, dotted output names, overlapping projections in both orders, nested structs, nulls, empty blobs and results, multiple batches, list siblings, query reuse, row IDs, and concurrent writes. Regression tests reproduce the nested-blob and projection-collision failures before the corresponding changes. Validation: - `pnpm build` - `pnpm lint` - `pnpm run docs` - `pnpm test __test__/nested_blob.test.ts __test__/blob.test.ts __test__/query.test.ts --runInBand --verbose=false` — 142 passed - `pnpm test __test__/table.test.ts --runInBand --verbose=false -t 'when dealing with blob columns'` — 60 passed Fixes #4609 <!-- lance-gatekeeper-fix:v1 agent=76edaac77d044c468d569b58efa2104a generation=1 --> --------- Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com> Co-authored-by: Xuanwo <github@xuanwo.io>
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:
- 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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