brekenandbreken-ai
c119f0c8e5
## What's wrong
`RRFReranker.rerank_multivector` concatenates every vector result list
and passes the concatenation to `rerank_hybrid` as a single list.
`rerank_hybrid` then scores each row by its position in that
concatenation, so a row's rank depends on how many rows came before it
in *earlier* lists.
With two searches of 3 rows each (`K=60`):
| row | rank in its own list | score today | RRF score |
|---|---|---|---|
| 1 (top of list 1) | 1 | 1/61 | 1/61 |
| 3 (third of list 1) | 3 | 1/63 | 1/63 |
| 4 (top of list 2) | 1 | **1/64** | 1/61 |
| 5 (second of list 2) | 2 | **1/65** | 1/62 |
So the best hit of the second search ranks below the worst hit of the
first, and the order of the lists passed in decides the fused ranking.
`MRRReranker.rerank_multivector` already scores each list on its own
("MRR semantics require treating each vector result as a separate
ranking system"). RRF is defined the same way.
## Fix
Score each result list with its own ranks (`1 / (rank + K)`, summed
across lists for rows found by more than one), then dedupe the
concatenation by `_rowid`, attach `_relevance_score`, and sort. The
`return_score` handling is unchanged.
## Tests
`test_rrf_multivector_ranks_each_result_list` in
`python/python/tests/test_rerankers.py`:
- On `main`, it fails with `assert 0.015625 == 0.01639344262295082` (the
top hit of list 2 got `1/64` instead of `1/61`).
- With the fix, it passes, and so do the other tests in
`test_rerankers.py` (16 passed, 12 skipped for missing API keys or
models).
- `ruff format --check` and `ruff check` are clean.
The fix was found and written by an AI agent (Breken) and checked
against the tests above.
Co-authored-by: breken-ai <312387581+breken-ai@users.noreply.github.com>
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.