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### Summary
Fix OpenSearch retrieval returning `vector_similarity = 0.000` for every
chunk when hybrid search is enabled.
On the OpenSearch backend, retrieval uses a second KNN-only search
(`Dealer._knn_scores()`) to recover per-chunk cosine scores for
reranking. That pass intentionally sends `MatchDenseExpr(...,
{"similarity": 0.0})` to mean “no minimum similarity cutoff.”
However, `OSConnection.search()` was incorrectly mapping `similarity` to
the KNN clause `boost` field:
```python
knn_query[vector_column_name]["boost"] = similarity
```
With `similarity=0.0`, this produced `boost=0.0`, which zeroed out KNN
`_score` values. `get_scores()` then returned `0.0` for every hit, so
`vector_similarity` was always zero and hybrid ranking ignored the
vector component — with no exception raised.
This is separate from the `get_scores()` `AttributeError` crash
addressed in #14970 / #15390; here retrieval succeeds but vector scores
are silently lost.