LeonSGP
fcd26c425a
When `Vector` or `MultiVector` declares an integer Arrow value type, Pydantic currently validates every element as a float. This silently rounds integers above `2**53`, and converts `uint64`'s maximum value into an out-of-range float before table insertion. Use Pydantic's integer schema for integer Arrow value types in both validators. Floating-point types retain the existing validation. Tests exercise model serialization and real local table creation, insertion, and reads for signed and unsigned integer boundaries and floating-point controls, plus dimensions and optional/nullability behavior. Fractional floats are rejected for integer declarations; integral floats remain accepted. This does not extend ANN index support. Validation on macOS arm64, with the current main native extension built using Rust 1.97.0: - Python 3.11.14 and 3.13.11, Pydantic 2.13.5: full `pytest -m "not slow and not s3_test" -q python/tests` suite: **1,537 passed, 37 skipped, 68 deselected** on each runtime. Comparable unchanged-main baselines: **1,521 passed, 37 skipped, 68 deselected** each. - Added cases on unchanged main: **8 failed, 8 passed**; complete `test_pydantic.py` after the fix: **40 passed** on each runtime. - Documentation tests with the existing embeddings dependencies in an isolated environment, Python 3.11.14: **77 passed, 6 skipped**, matching the unchanged-main baseline. The workflow's Linux/Python 3.13 doctest matrix was not reproduced locally. - Repository Ruff lint/format, Pyright, all applicable pre-commit hooks, compile checks, and `git diff --check` passed. Eight public SDK table round trips passed on each final installed wheel runtime. Slow, credential-dependent S3 integration tests, the remaining OS/runtime matrix, and the minimum Pydantic/PyArrow dependency matrix were not run locally. The native extension was built from unchanged current main; the final local wheel repack contains the verified Python change with that identical native extension. A separate candidate maturin packaging attempt triggered Rust recompilation and was stopped, so an official candidate distribution build was not completed. The project requires Pydantic `>=2.7.4,<3`; Pydantic v1 support was removed in #3990. AI assistance: developed and verified with **gpt-6.1-sol**.
LanceDB Python SDK
A Python library for LanceDB.
Installation
pip install lancedb
If no wheel is published for your platform, pip builds lancedb from the source distribution. That build needs a Rust toolchain and protoc (the Protocol Buffers compiler) on your PATH, and can take a while.
Pre-Haswell x86_64 hosts: lancedb-compat
The default lancedb wheel targets x86-64-haswell (AVX2 + FMA + F16C) for full performance on modern hardware. Pre-Haswell hosts — Intel Sandy Bridge / Ivy Bridge / Westmere; AMD Bulldozer / Piledriver / Steamroller — don't have AVX2 and crash with Illegal instruction at import lancedb.
For those hosts, install the lancedb-compat package instead:
pip install lancedb-compat
Same Python API (import lancedb works as usual). The compat wheel is compiled at the x86-64-v2 baseline (Nehalem-class) and uses runtime SIMD dispatch in the embedded lance crate to pick the right kernel tier (scalar / AVX / AVX+FMA / AVX2+FMA / AVX-512) at load time, so it still goes fast on modern hardware while running cleanly on the pre-Haswell silicon. Use lance.simd_info() from Python to verify which tier was selected.
lancedb and lancedb-compat install to the same lancedb/ namespace and conflict at install time. Pick one. To switch, pip uninstall lancedb first, then pip install lancedb-compat (or vice-versa).
If you need a custom baseline (or lancedb-compat isn't yet published for your platform), build from source with the override:
RUSTFLAGS="-C target-cpu=x86-64-v2" maturin build --release
pip install ./target/wheels/lancedb-*.whl
Preview Releases
Stable releases are created about every 2 weeks. For the latest features and bug fixes, you can install the preview release. These releases receive the same level of testing as stable releases, but are not guaranteed to be available for more than 6 months after they are released. Once your application is stable, we recommend switching to stable releases.
pip install --pre --extra-index-url https://pypi.fury.io/lancedb/ lancedb
Threading in CPU-limited containers
LanceDB uses separate pools for compute work and storage I/O. On a container with two visible CPUs, current releases intentionally use one compute worker by default; no manual configuration is needed. If every query logs an I/O core reservation warning on a two-CPU container, upgrade from LanceDB 0.21.1 or earlier.
The two commonly tuned environment variables control different resources:
LANCE_CPU_THREADSoverrides the number of compute workers. One worker is the appropriate setting for a two-CPU container when an explicit override is needed.LANCE_IO_THREADScontrols concurrent storage operations, not reserved CPU cores. Its default can be greater than the number of CPUs because I/O workers spend much of their time waiting for storage.
Keep the defaults unless measurements show that the workload benefits from an override. See the Lance threading model for the current defaults and tuning guidance.
Usage
Basic Example
import lancedb
db = lancedb.connect('<PATH_TO_LANCEDB_DATASET>')
table = db.open_table('my_table')
results = table.search([0.1, 0.3]).limit(20).to_list()
print(results)
Development
See CONTRIBUTING.md for information on how to contribute to LanceDB.