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Jay PokaleandXuanwo
f6a348ac5b
`RRFReranker.rerank_hybrid` and `MRRReranker.rerank_hybrid` merge the
vector and FTS results with the base `merge_results`, which keeps only
the first copy of a duplicated row. With `return_score="all"`, a row
that both searches found therefore comes back with `_score = None`, even
though the FTS search scored it. `LinearCombinationReranker` already
copies the FTS score onto such rows.
Reproduction against `lancedb` 0.40.0:
```python
import pyarrow as pa
from lancedb.rerankers import RRFReranker
vector = pa.table({"_rowid": pa.array([1, 2], pa.uint64()), "text": ["a", "b"],
"_distance": pa.array([0.1, 0.2], pa.float32())})
fts = pa.table({"_rowid": pa.array([2, 3], pa.uint64()), "text": ["b", "c"],
"_score": pa.array([5.0, 4.0], pa.float32())})
RRFReranker(return_score="all").rerank_hybrid("q", vector, fts).to_pylist()
# row 2: _distance=0.2, _score=None (expected _score=5.0)
```
`table.search(query_type="hybrid")` is not affected, because
`_combine_hybrid_results` restores the original `_distance` and `_score`
by `_rowid` after reranking. The wrong value shows up when
`rerank_hybrid` is called directly, for example from a custom reranker
that wraps RRF or MRR.
The fix uses `_merge_and_keep_scores` when `return_score="all"`, the
same pattern the model-based rerankers (`CohereReranker`,
`CrossEncoderReranker`, `JinaReranker`, `OpenaiReranker`,
`VoyageAIReranker`, ...) already use. The relevance-only path is
unchanged.
Tests: added `test_rank_fusion_return_score_all_keeps_both_scores` for
both rerankers. It fails without the fix and passes with it. I ran it
against the released 0.40.0 package with this branch's
`rerankers/{base,rrf,mrr}.py` dropped in; `rrf.py` and `mrr.py` on
`main` are identical to 0.40.0. The other reranker tests give the same
results with and without the change. `ruff format` and `ruff check` are
clean.
I used an AI assistant (Claude) while working on this; I reviewed and
tested the change.
Co-authored-by: Xuanwo <github@xuanwo.io>
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