d889321b5e
BREAKING CHANGE: When passing multiple where clauses to a query, they now stack instead of replacing the previous filter. Previously, calling `where`/`only_if` more than once on a query silently replaced the previous filter, so only the last filter was applied. This was surprising and could return rows that an earlier filter should have excluded. This implements the alternative suggested in https://github.com/lancedb/lancedb/pull/3514#issuecomment-4664901580: instead of rejecting a second filter, repeated filters are combined with a logical AND (`(previous) AND (new)`). The combination happens in the Rust core (`QueryBase::only_if` and `only_if_expr`), so it applies to all SDKs at once (Rust, Python async, and TypeScript). The Python sync query builder keeps its own filter state, so it combines filters in the binding layer as well. SQL string and expression filters are combined within their own representation. When the two representations are mixed, the expression is lowered to SQL (via `expr_to_sql_string`) and the filters are combined as SQL strings, so chaining `where` works regardless of which form each filter takes. Fixes #2649 ## Tests - Rust: `cargo test --features remote -p lancedb --lib query` - Python: `uv run --extra tests pytest python/tests/test_query.py` - TypeScript: `pnpm test __test__/query.test.ts` 🤖 Generated with [Claude Code](https://claude.com/claude-code) 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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⭐ 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:
- 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.
Contributors
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