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Registering a different embedding class under an existing alias silently replaced the original class. A deterministic real local SDK table reproduction changed the top result for the same query from alpha to beta after reopening the persisted table. Check the resolved key before inserting a class. Preserve repeated registration of the same class under the same nonempty explicit alias as idempotent, as existing SDK tests rely on it. Repeated implicit class-name registration, including an empty alias, still raises KeyError. Failed registration preserves the mapping and class metadata. Existing embedding tests receive isolated registry mappings; test_util.py remains unchanged. Eight regression cases cover different-class alias collisions, alias/class-name collisions, implicit and empty-alias duplicates, equal class names under distinct aliases, explicit-alias idempotence, and real local creation/reopening/search. This is independent of #4356, which fixes no-alias serialization. Validation on52a1ccee5a: - Original registry plus eight new cases: 4 failed, 4 passed; Python 3.11.14; exit 1; 2.902604 s. Candidate plus full utility module: 83 passed, 1 skipped; exit 0; 3.144414 s. - Python 3.11.14 full official non-slow/non-S3 suite: 1532 passed, 36 skipped, 68 deselected; exit 0; 152.631543 s. - Python 3.13.11 full official non-slow/non-S3 suite: 1529 passed, 37 skipped, 68 deselected; exit 0; 163.970571 s. - Official Python 3.13 doctest with existing compatible embeddings/torch/mlx dependencies: 71 passed, 6 skipped; exit 0; 9.635034 s. - Pyright with matching interpreter/source path: 0 errors, 0 warnings; exit 0; 4.299743 s. - Repository Ruff 0.9.9 check and format: exit 0; 157 files already formatted; 0.047418 s and 0.016815 s. Pre-commit changed files: four hooks passed, two inapplicable skipped; exit 0; 0.406136 s. git diff --check passed. Base is officiald864e7198fwith its verified matching native extension reused read-only. All 174 native/config paths are unchanged from that base. Latest mainbbc4aebe55has byte-identical registry production code; its complete native build was not run. Verification was macOS arm64, with optional dependency/service skips above; Linux/Windows CI remains remote verification. Exact commands, cwd, identities, raw logs and intermediate failures are in the local manifest. The earlier strict same-class check caused 13 utility failures and was corrected in production; it is not described as a baseline failure. Reproduction commands after preparing the repository's declared environment (from repository root unless noted): ```sh PYTHONPATH=python/python .venv/bin/python -m pytest python/python/tests -m 'not slow and not s3_test' -q -rs -p no:cacheprovider PYTHONPATH=python/python .venv/bin/python -m pytest --doctest-modules python/python/lancedb -q --tb=short (cd python && PYTHONPATH=python ../.venv/bin/pyright --pythonpath ../.venv/bin/python) .venv/bin/ruff check . .venv/bin/ruff format --check . .venv/bin/python -m pre_commit run --files python/python/lancedb/embeddings/registry.py python/python/tests/test_embedding_registry.py python/python/tests/test_embeddings.py git diff --check ``` These relative commands describe reproducible source/cwd equivalents. The recorded local runs used absolute paths to existing Python 3.11.14/3.13.11 environments, a verified matching reused native extension, and read-only compatible optional-dependency cache paths for the 3.13 doctest, rather than a newly installed `.venv`. Full tests used separate task-private working directories and `--basetemp` directories to avoid cross-run database collisions. Implemented with assistance from gpt-6.1-sol; the final diff was reviewed locally by the collaborating agents.
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
Star LanceDB to get updates!
⭐ 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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