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lancedb-gatefixer[bot]
bbc4aebe55
Sync remote string searches ignored the table's embedding function when `vector_column_name` was omitted. `auto` silently fell back to full-text search, while `vector` and `hybrid` failed with `No embedding function for None`. Resolve vector columns from the schema before creating the query builder, using the same inference helper as local search. Automatic string queries without embedding functions select full-text search first, so text-only tables and tables with multiple unconfigured vector columns remain searchable. Vector and hybrid searches still reject ambiguous columns, infer numeric queries by dimension, and honor explicit column selection. Structured full-text queries and empty queries retain their existing behavior. Regression coverage uses embedding metadata returned by a mocked remote server rather than a mocked embedding-function lookup. It also checks automatic and explicit full-text queries on schemas with zero, one, or multiple vector columns and no embedding functions. Validation after merging `main` at `87ecfeac`: - Confirmed the original embedding and ambiguity failures before the fix, and reproduced the automatic FTS inference failures before restoring the fallback. - Remote, query, hybrid, and embedding test files through the uv-managed environment: 240 passed, 4 skipped, 9 deselected (`not slow and not s3_test`). - Python extension built with `uv run --extra tests --extra dev maturin develop --extras tests,dev`. - `ruff check .` and `ruff format .` from the repository root, using CI's Ruff version 0.9.9. - `cargo fmt --all`. Fixes #4377 <!-- lance-gatekeeper-fix:v1 agent=c167c6c89d857342e5d7415b08142589 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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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 |
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.
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