Mohammad Hijjawi
712a8b8699
Since #3232, `pydantic_to_schema` maps an `Enum` field to the Arrow type of its values, including enums without a `str`/`int` mixin. Writing such models still failed: `model_to_dict()` returns `model.model_dump()`, which keeps the Enum members, and Arrow can only convert members that are also `str`/`int` instances. ```python class Color(Enum): RED = "red" class Item(LanceModel): color: Color tbl = db.create_table("items", schema=Item) tbl.add([Item(color=Color.RED)]) # pyarrow.lib.ArrowTypeError: Expected bytes, got a 'Color' object ``` `create_table(data=[...])` fails the same way, and int-valued enums raise `ArrowInvalid`. `model_to_dict()` now replaces Enum members with their values, including in nested models and lists. Lists of plain scalars (vectors in particular) are returned without being walked, so the per-row cost stays about the same as `model_dump()` alone (21.3µs vs 20.3µs for a 1536-dim model). Reading back is unchanged: Pydantic already validates the stored value into the Enum member. Added `test_plain_enum_values_are_written`, which covers str and int valued enums, `Optional` and `List` fields, and an Enum inside a nested model, and checks that `to_pydantic` round-trips the rows. It fails with the `ArrowTypeError` above before the change. `test_pydantic.py`, `test_embeddings.py`, `test_table.py` and `test_db.py` pass, and `ruff format --check` / `ruff check` are clean.
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.