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lancedb-gatefixer[bot]andXuanwo
c167b99fa0
Legacy blob v1 columns were rejected by LanceDB's blob validation and omitted from `blob_columns()`, even though the pinned Lance reader already supports them. Accept both blob versions in read validation and discovery so `fetch_blobs`, `fetch_blob_files`, and `fetch_blob_ranges` can read legacy payloads through the existing native reader. Keep list-nested blobs excluded from row-based reads and document how to fetch legacy blobs using explicit row IDs. Regression coverage checks sync and async APIs, independently written Lance datasets, ordering and duplicates across fragments, null and empty blobs, lazy seeking, ranges, and deleted rows. Fixes #4644 Validation: - Both new Python regressions failed on the original implementation with the reported legacy-column rejection and empty discovery result, then passed with the fix. - Python blob suite: 202 passed, 1 skipped. - Remote blob tests: 29 passed. - Focused Rust blob unit tests with `remote`: 13 passed. - Rust blob integration suite with `remote`: 59 passed, including legacy storage 2.0 and 2.1. - Rust compile and Clippy checks with `remote`, tests, and examples passed. - Python package Ruff lint and format checks passed; Rust formatting passed. - Repository-wide `ruff check .` reports 49 existing errors in unchanged `docs/web-tests` examples; the Python package is clean. <!-- lance-gatekeeper-fix:v1 agent=02b7edcbfa8ff0e7fad4615159249b3e 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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