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LeonSGP
554dc01f4d
`SourceField(json_schema_extra={...})` and
`VectorField(json_schema_extra={...})` currently raise `TypeError`
because the helper supplies the same `Field` keyword twice. Explicit
`None` also fails. This prevents users from adding ordinary Pydantic
JSON schema metadata to embedding fields.
Copy and merge the supplied dictionary before attaching the helper's
internal embedding identity. Caller dictionaries remain unchanged, and
reserved embedding keys retain the real function. Other `Field`
arguments keep their existing behavior. This change covers dictionaries
and `None`; callable `json_schema_extra` remains unsupported by the
existing embedding metadata reader and is outside this fix.
The 11 regression cases exercise both helpers, empty/custom/None
metadata, reserved-key collisions, other Field arguments, and a
deterministic local embedding model through JSON schema generation,
table creation, reopening, and search. On the original source, 9 fail
and 2 controls pass; the candidate passes all 11 on Python 3.11 and
3.13.
Validation uses read-only reused environments and the native extension
built for d864e719. The production helper is byte-identical to current
main bbc4aebe before this fix; no current-main native rebuild was
performed. The branch contains only this helper fix and its regression
test, independently based on d864e719.
Reproducible checkout commands below assume the documented environment
is installed as `.venv`; the actual local runs used existing
interpreters through absolute paths and isolated generated-data working
directories:
```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)
git diff --check
.venv/bin/ruff check .
.venv/bin/ruff format --check .
.venv/bin/python -m pre_commit run --files python/python/lancedb/embeddings/base.py python/python/tests/test_embedding_field_metadata.py
```
Doctests require the repository's tests/dev/embeddings dependencies,
including mlx on this platform. All checks below ran against frozen head
3fa6820.
Implemented with assistance from gpt-6.1-sol; the final diff was
reviewed locally.
Frozen-head validation (all exit 0):
- Python 3.11 full: 1535 passed, 36 skipped, 68 deselected; 190.272415
seconds wall time.
- Python 3.13 full: 1532 passed, 37 skipped, 68 deselected; 249.289778
seconds wall time.
- Python 3.13 doctests: 71 passed, 6 skipped; 10.391002 seconds wall
time.
- Pyright: 0 errors, 0 warnings; 4.884641 seconds wall time.
- pre-commit: 4 passed, 2 skipped; 0.747764 seconds wall time.
- Repository Ruff check: passed; 0.078177 seconds wall time.
- Repository Ruff format: 157 files already formatted; 0.034561 seconds
wall time.
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.
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