Christopher Krah
9af3bac6e4
The `Reranker` docstring says the default `return_score="relevance"` "will return only the relevance score" (`rerankers/base.py`), but a hybrid search whose filter matches nothing also returns `_distance` and `_score`. The early return for two empty result sets in `_combine_hybrid_results` (from #3030) skips the reranker's `_keep_relevance_score`, which every built-in reranker applies to a non-empty result. Before, on `45dbf5e`: ``` $ python repro.py match: ['id', 'text', 'vector', '_relevance_score'] no match: ['id', 'text', 'vector', '_distance', '_score', '_relevance_score'] ``` After, `no match` also prints `['id', 'text', 'vector', '_relevance_score']`. <details><summary>repro.py</summary> ```python import tempfile, lancedb, pyarrow as pa db = lancedb.connect(tempfile.mkdtemp()) t = db.create_table("t", pa.table({"id": [1, 2], "text": ["red fox", "blue fox"], "vector": pa.array([[1.0, 2.0], [3.0, 4.0]], pa.list_(pa.float32(), 2))})) t.create_fts_index("text") q = lambda where: t.search(query_type="hybrid").vector([1.0, 2.0]).text("fox").where(where, prefilter=True).limit(5) print("match: ", q("id > 0").to_arrow().column_names) print("no match:", q("id > 100").to_arrow().column_names) ``` </details> No issue exists for this. The fix calls `reranker._keep_relevance_score(empty)` before returning. Sync and async hybrid queries both go through it. With `return_score="all"` both columns stay, as before. Code reading `_distance` from an empty result now gets a `KeyError`, which it already got from every non-empty one. The new test asserts, for both settings, that a filter matching nothing gives the same columns as one matching every row. ### Verification ``` # Python 3.12.6; lancedb 0.41.0b15 wheel from pypi.fury.io, its _lancedb.abi3.so in the tree; no pylance (none >= 15 resolves) # the wheel predates #4688, so a 3-line -p plugin sets _lancedb._experimental_functions_enabled = False for tests/conftest.py $ PYTHONPATH=python python -m pytest -q -p no:cacheprovider python/tests/test_rerankers.py python/tests/test_hybrid_query.py python/tests/test_query.py45dbf5e: 5 failed, 197 passed, 57 skipped this branch: 5 failed, 199 passed, 57 skipped query.py reverted, test kept: 6 failed, 198 passed, 57 skipped FAILED test_rerankers.py::test_empty_hybrid_result_keeps_reranker_columns[relevance] At index 2 diff: '_distance' != '_relevance_score' $ ruff format --check <both files> && ruff check <both files> # ruff 0.9.9, as pre-commit pins it 2 files already formatted All checks passed! # the 5 failures fail identically on45dbf5e: 4 test_query_serialization_async_search_options (wheel older than #4659) and # test_hybrid_query_with_stale_fixed_size_binary_prefilter (needs pylance) # not run: pyright, the Rust build, the remote client ``` @Xuanwo, a custom reranker that keeps extra columns still gets the generic empty table. I'd keep it that way, since #3030 returns early so that no reranker runs on empty input. If you'd rather call `rerank_hybrid` on the empty tables instead, I'll change it in this PR.
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
If you also need the optional lance Python API, install the pylance extra
and include the Lance preview index:
pip install --pre \
--extra-index-url https://pypi.fury.io/lancedb/ \
--extra-index-url https://pypi.fury.io/lance-format/ \
"lancedb[pylance]"
With uv, also select the best matching version across the indexes:
uv pip install --prerelease allow \
--index https://pypi.fury.io/lancedb/ \
--index https://pypi.fury.io/lance-format/ \
--index-strategy unsafe-best-match \
"lancedb[pylance]"
The LanceDB preview index contains old pylance releases; current Lance previews
are published to the lance-format index. By default, uv only considers versions
from the first index containing a package, which can select an old pylance or
backtrack to an older LanceDB release. unsafe-best-match searches all configured
indexes, like pip; use it only with indexes you trust. Install the extra rather
than an unconstrained pylance so LanceDB's minimum supported version is enforced.
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.
Opening SQL blob descriptors
A SQL blob_descriptor(video) result keeps the Blob V2 storage metadata and a
source struct containing its table URI, version, commit timestamp, branch,
column and row identity. This example uses a remote SQL connection whose SQL
service produces those source-bearing descriptors. Open the reference on the
source table to read incrementally:
rows = db.execute_query("""
SELECT f.timestamp, blob_descriptor(v.video) AS video
FROM videos v JOIN frames f ON v.id = f.video_id
""").read_all()
videos = db.open_table("videos")
for row in rows.to_pylist():
if row["video"] is not None:
with videos.open_blob_file(row["video"]) as video:
frame = extract_frame(video, row["timestamp"])
open_blob_file accepts the dictionary or an Arrow struct scalar directly. It
keeps the query's version and branch without changing the table handle's checkout.
Payload bytes load on reads. An external blob with an unspecified size may need a
metadata-only HEAD request to open.
videos.open_blob_files(rows["video"]) returns an iterator over the same handles,
preserving order, duplicates and nulls (None). It accepts Arrow arrays/chunked
arrays or another iterable and opens each handle as iteration advances. Close each
non-null handle after use. Async tables offer await videos.open_blob_file(...)
and async for video in videos.open_blob_files(...).
for video in videos.open_blob_files(rows["video"]):
if video is not None:
with video:
header = video.read(4096)
To extract a frame while retaining each row's timestamp, iterate over the handles and rows together:
for row, video in zip(rows.to_pylist(), videos.open_blob_files(rows["video"])):
if video is not None:
with video:
frame = extract_frame(video, row["timestamp"])