lancedb-gatefixer[bot]
e84afa7bac
Python sync remote searches default `fast_search` to `False` and omitted full-text search columns to `None`, while local searches leave fast search unset and use `[]`. This makes otherwise identical `to_query_object()` results depend on the backend. Align these omitted defaults in `RemoteTable.search` while preserving explicit fast-search settings. Keep the shared vector-column inference and automatic FTS fallback provided by the base branch. Regression tests compare complete local and remote query objects for both reported vector dimensions, NumPy and batch vectors, explicit columns, scans, and full-text queries. They also cover matching ambiguity errors, explicit fast-search values, automatic FTS without embeddings, and automatic vector search with embedding metadata, using the shared remote fixture. Validation after merging `main` at `bbc4aebe`: - `cd python && uv run --no-sync --extra tests pytest python/tests/test_remote_db.py -q`: 128 passed. - `cd python && uv run --no-sync --extra tests pytest python/tests/test_query.py python/tests/test_table.py -q -k 'query_serialization or async_search_without_query or none_query or nprobes_zero or empty_vector'`: 27 passed. - `ruff format .` and `ruff check .` from the repository root (Ruff 0.9.9): passed. - `git diff --check`: passed. Fixes #4391 <!-- lance-gatekeeper-fix:v1 agent=7cad560371d7aa64fb2511e510dd1bfd 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:
- 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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