lancedb-gatefixer[bot]
87ecfeaccd
Sync remote `create_table(..., embedding_functions=...)` discarded the supplied configuration and only logged an unsupported-feature warning. Schema-only creation therefore lost the metadata needed by `add()` to embed text, while text-only initial data produced no vector column. Forward the configuration to the existing async creation path, remove the stale warning, and document the parameter. The shared path stores the embedding metadata and generates vectors for initial data and subsequent additions. Add a regression test against the mocked remote HTTP API for both schema-only and text-only creation. It checks the outgoing Arrow schema metadata, the metadata returned by the remote table, inferred vector types, and generated vectors during creation and `add()`. Validation: - Both regression cases failed before the fix because the outgoing embedding metadata was missing, then passed after the fix. - `uv run --extra tests --extra dev pytest python/tests/test_remote_db.py python/tests/test_embeddings.py -q -m 'not slow and not s3_test'`: 82 passed, 4 skipped, 8 deselected. - Repository-wide `ruff check .` and `ruff format .` passed using the uv-managed environment; `git diff --check` passed. Fixes #4376 <!-- lance-gatekeeper-fix:v1 agent=a6816b1f3335438bd8ad192f36395213 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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