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陈志谦andXuanwo
3575f972ac
The synchronous hybrid query builder rejected vector containers that the
rest of the Python API accepts:
```python
table.search(query_type="hybrid").vector((1.0, 2.0)).text("hello").to_pandas() # ValueError
table.search(query_type="hybrid").vector(pa.array([1.0, 2.0], pa.float32()))...
# ValueError: Vector query must be either a string or a vector
```
`lancedb.common.VEC` documents `list[float] | tuple[float, ...] |
np.ndarray | pa.Array`, and the same two containers work for
`table.search(vector)` and for the async hybrid API
(`AsyncQuery.nearest_to(...).nearest_to_text(...)`).
`LanceHybridQueryBuilder._validate_query` only listed `str | list |
np.ndarray`, so a tuple or a pyarrow array was refused before reaching
the query planner.
This normalises the vector exactly the way `LanceQueryBuilder.create`
already does — list/tuple to a `np.float32` array, `np.ndarray` through
`astype(float32)`, `pa.Array`/`pa.ChunkedArray` through `np.array(...,
dtype=float32)`, strings passed through — and keeps the existing
`ValueError` for anything else.
`python/python/tests/test_hybrid_query.py::test_hybrid_query_accepts_every_vector_container`
is parametrised over tuple, pyarrow array and pyarrow chunked array, and
asserts the returned `_rowid`s match the `list` form: 3 failed on `main`
(`ValueError` from `query.py:2452`), 3 passed with the change.
Neighbouring suites (`test_hybrid_query.py`, `test_query.py`,
`test_rerankers.py`) are 220 passed / 9 skipped, and `test_table.py`,
`test_remote_db.py`, `test_fts.py`, `test_index.py`,
`test_permutation.py` are 724 passed / 5 skipped. `ruff` at the version
pinned in the repository's pre-commit config (0.9.9) reports no findings
and `ruff format --check` is clean.
One source file (`python/python/lancedb/query.py`), no behaviour change
for containers that already worked.
Prepared with AI assistance; every line was verified by running the
cases above.
---------
Co-authored-by: Xuanwo <github@xuanwo.io>
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 |
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