lancedb-gatefixer[bot]
a4f52cd205
Fixes #4733 PyArrow expects Blob V2 storage structs when constructing a record batch and cannot infer the extension array from raw bytes. LanceDB already converts binary values during ingestion, but batch writers had no supported array constructor. Add `lancedb.schema.blob_array(payloads)` for iterables of inline bytes and nulls, using the same extension type resolver as `blob()`. The helper preserves empty blobs and null blobs and works with both pylance's type and LanceDB's fallback. Document the descriptor fields, exclusive inline/external representations, batch construction, and existing binary conversion in synchronous and asynchronous `create_table` / `add` APIs. Validation: - Reproduced the reported `ArrowTypeError` on the original code; both new regression cases fail against the original schema module. - 256 focused blob tests and schema doctests passed; one optional PyAV test skipped. - Both new regression cases also passed with PyArrow 25, with and without pylance imports. - Python package Ruff checks and formatting passed. - MkDocs build passed, with an existing unrelated `SecretReference` cross-reference warning. Repository-wide Ruff reports 48 existing errors in `docs/web-tests`. <!-- lance-gatekeeper-fix:v1 agent=20d1a296d76ff5eb914c0848428a747a generation=1 --> Co-authored-by: Gatefixer <313497061+lancedb-gatefixer[bot]@users.noreply.github.com>
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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⭐ 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:
- 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 |
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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