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## Problem `LinearCombinationReranker.merge_results` has two related bugs that make it return **inverted relevance rankings** — the least relevant document ranks first (closes #3154). ### Bug 1 — `_combine_score` subtracts from 1, inverting the final ranking ```python def _combine_score(self, vector_score, fts_score): return 1 - (self.weight * vector_score + (1 - self.weight) * fts_score) ``` Both `vector_score` (already converted via `_invert_score`) and `fts_score` (BM25 relevance) are in **higher-is-better** space. Wrapping the weighted average in `1 - (...)` flips the direction: a perfectly matching document (`vector_score=1, fts_score=1`) gets `_relevance_score = 0.0`, while a non-matching document gets a high score. ### Bug 2 — Documents missing an FTS score are rewarded, not penalised ```python fts_score = result.get("_score", fill) # fill=1.0 by default ``` When a document has no FTS match, `fts_score = fill = 1.0`. In `_combine_score` (with the bug-1 formula), this large value becomes a **negative penalty** via `1 - (... + 0.3 * 1.0)`, counterintuitively *boosting* the document's score. By contrast, missing vector results correctly receive `_invert_score(fill) = 0.0` (penalised). ## Fix **Bug 1** — remove the `1 -` inversion from `_combine_score`: ```python def _combine_score(self, vector_score, fts_score): return self.weight * vector_score + (1 - self.weight) * fts_score ``` **Bug 2** — use `1 - fill` for missing FTS scores so both penalties are symmetric (mirror of what `_invert_score(fill)` already does for missing vector scores): ```python fts_score = result.get("_score", 1 - fill) # was: fill ``` With `fill=1.0` (default): `1 - 1.0 = 0.0` — missing-FTS entries contribute `0` to the FTS term, identical to how missing-vector entries contribute `0` to the vector term. ## Verification Concrete example from the issue. With `weight=0.7`, `fill=1.0`: | Document | `_distance` | `_score` | Old `_relevance_score` | New `_relevance_score` | |----------|-------------|----------|------------------------|------------------------| | `apple orange` | 0.0 (best) | 2.41 (only FTS) | 0.30 (**wrong: ranked 2nd**) | 1.42 (**correct: ranked 1st**) | | `banana grape` | 0.9999 (worst) | — | 0.70 (**wrong: ranked 1st**) | 0.00 (**correct: ranked last**) | ## Tests Two regression tests added to `python/python/tests/test_rerankers.py`: - `test_linear_combination_best_match_ranks_first` — the document with the smallest distance **and** an FTS match must have the highest `_relevance_score`. - `test_linear_combination_missing_fts_is_penalised` — a document with any FTS score must beat an otherwise-equal document with no FTS match. --------- Co-authored-by: Will Jones <willjones127@gmail.com>
LanceDB Python SDK
A Python library for LanceDB.
Installation
pip install lancedb
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
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.