Xuanwo
c72931b30f
Adds `TypeSafeReranker`, which reranks vector, FTS, and hybrid results with the [TypeSafe System One API](https://docs.typesafe.ai/introduction). Each result is scored independently: TypeSafe reads `{"query", "document"}` and answers a yes/no (noul) question, and the probability of yes becomes `_relevance_score`. Unlike listwise LLM rerankers, the score is an absolute probability, so it is comparable across queries and can be thresholded. The question's `instructions` and `true`/`false` `criteria` are configurable, since domain-specific criteria are what make this kind of scoring work well ([TypeSafe's re-ranking cookbook](https://docs.typesafe.ai/cookbooks/rerank_typesafe)). The API takes one state per request, so the reranker sends one request per result on a thread pool bounded by `max_concurrency`. It deliberately does not use the background event loop: rerankers are called synchronously from inside the async query APIs, where `LOOP.run` would deadlock. TypeSafe scores for the same pair vary slightly between calls, so results with close scores can swap places when a search is repeated. The shared reranker test helper now takes `deterministic=False` for this case: it still checks result sizes and descending scores, but not that two identical searches return the same order. The SDK is imported only when the client is created and questions are sent as plain dicts, so the new tests run in CI with a fake client and without `typesafe-sdk` installed. The live-API test is skipped without `TYPESAFE_API_KEY`. Ranking quality has not been compared with other API rerankers.
LanceDB Python SDK
A Python library for LanceDB.
Installation
pip install lancedb
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.