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c2b7494 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 | """Hybrid retrieval combining lexical (BM25) and embedding (semantic) scores."""
from __future__ import annotations
from dataclasses import dataclass
import pandas as pd
from src.retrieval.embeddings import EmbeddingEncoder, EmbeddingIndex, retrieve_embeddings
from src.retrieval.lexical import build_lexical_index, score_lexical
@dataclass(slots=True)
class HybridCandidate:
rank: int
code: str
description: str
vocabulary: str
lexical_score: float
embedding_score: float
score: float
def __lt__(self, other: object) -> bool:
if not isinstance(other, HybridCandidate):
return NotImplemented
return self.score > other.score
def _min_max_normalize(values: list[float]) -> list[float]:
if not values:
return values
lo, hi = min(values), max(values)
if hi - lo < 1e-12:
return [0.0 for _ in values]
return [(v - lo) / (hi - lo) for v in values]
def hybrid_retrieval(
query: str,
codes: pd.DataFrame,
lexical_weight: float = 0.5,
embedding_weight: float = 0.5,
embedding_model: str = "sentence-transformers/all-MiniLM-L6-v2",
top_k: int | None = 25,
per_vocabulary_top_k: bool = True,
embedding_index: EmbeddingIndex | None = None,
query_model: EmbeddingEncoder | None = None,
lexical_query: str | None = None,
embedding_query: str | None = None,
) -> list[HybridCandidate]:
"""Combine BM25 lexical retrieval with embedding-based semantic retrieval.
Both signals are min-max normalized to [0, 1] and combined with the given
weights before ranking. The combined ranking is truncated to *top_k*.
Parameters
----------
query : str
Phenotype / EDF text used as the search query.
codes : pd.DataFrame
Candidate code corpus with ``code``, ``description``, ``vocabulary`` columns.
lexical_weight, embedding_weight : float
Relative weighting of each retrieval signal. Need not sum to 1.
embedding_model : str
sentence-transformers model identifier used when *embedding_index* is None.
top_k : int, optional
Number of ranked results to return per vocabulary. ``None`` returns the
full corpus.
per_vocabulary_top_k : bool
If ``True`` (default), apply ``top_k`` separately within each selected
vocabulary. If ``False``, apply ``top_k`` after combining all selected
vocabularies.
embedding_index : EmbeddingIndex, optional
Pre-built embedding index to reuse across multiple queries.
query_model : EmbeddingEncoder, optional
Cached local or API-backed encoder used to embed the EDF/query text.
lexical_query : str, optional
Text used for the BM25 lexical score. If omitted, *query* is used.
embedding_query : str, optional
Text used for the embedding score. If omitted, *query* is used.
Returns
-------
list[HybridCandidate]
"""
if codes.empty:
return []
df = codes.reset_index(drop=True).copy()
for col in ("code", "vocabulary"):
if col not in df.columns:
df[col] = ""
lexical_query = lexical_query or query
embedding_query = embedding_query or query
embedding_scores_by_code: dict[tuple[str, str], float] | None = None
if embedding_index is not None:
# Score the combined corpus exactly once. Previously this full matrix
# multiplication, full sort, and candidate-object construction ran
# once per vocabulary even though every pass produced identical raw
# scores. The corpus is deduplicated on (code, vocabulary), so the map
# is identical to the maps produced by the former repeated searches.
all_embedding_scores = embedding_index.score_all(embedding_query, model=query_model)
embedding_scores_by_code = {
(code, vocabulary): score
for code, vocabulary, score in zip(
embedding_index.codes,
embedding_index.vocabularies,
all_embedding_scores,
)
}
per_vocab_candidates: list[
tuple[str, list[tuple[str, str, str, float, float]]]
] = []
for vocabulary, vocab_df in df.groupby("vocabulary", sort=False):
lexical_index = build_lexical_index(vocab_df)
lexical_scores = score_lexical(lexical_query, lexical_index, len(vocab_df))
if embedding_scores_by_code is None:
embedding_results = retrieve_embeddings(embedding_query, vocab_df, model_name=embedding_model, top_k=None)
vocabulary_embedding_scores = {
(c.code, c.vocabulary): c.score for c in embedding_results
}
else:
vocabulary_embedding_scores = embedding_scores_by_code
codes_values = vocab_df["code"].tolist()
description_values = vocab_df["description"].tolist()
vocabulary_values = vocab_df["vocabulary"].tolist()
vocab_candidates = [
(
code,
description,
candidate_vocabulary,
lexical_score,
vocabulary_embedding_scores.get((code, candidate_vocabulary), 0.0),
)
for code, description, candidate_vocabulary, lexical_score in zip(
codes_values,
description_values,
vocabulary_values,
lexical_scores,
)
]
per_vocab_candidates.append((vocabulary, vocab_candidates))
all_lexical = [
lexical
for _, vocab_candidates in per_vocab_candidates
for _, _, _, lexical, _ in vocab_candidates
]
all_embedding = [
embedding
for _, vocab_candidates in per_vocab_candidates
for _, _, _, _, embedding in vocab_candidates
]
norm_all_lexical = _min_max_normalize(all_lexical)
norm_all_embedding = _min_max_normalize(all_embedding)
# Keep lightweight tuples until after top-k selection. Constructing a
# dataclass (and formerly a Pandas Series) for all 620k rows dominated the
# runtime even when only a handful of results were requested.
selected: list[tuple[str, str, str, float, float, float]] = []
flat_idx = 0
for _, vocab_candidates in per_vocab_candidates:
scored: list[tuple[str, str, str, float, float, float]] = []
for idx, (code, description, vocabulary, _, _) in enumerate(vocab_candidates):
norm_lexical = norm_all_lexical[flat_idx + idx]
norm_embedding = norm_all_embedding[flat_idx + idx]
score = lexical_weight * norm_lexical + embedding_weight * norm_embedding
scored.append(
(
code,
description,
vocabulary,
round(norm_lexical, 6),
round(norm_embedding, 6),
round(score, 6),
)
)
if per_vocabulary_top_k and top_k is not None:
scored.sort(key=lambda candidate: -candidate[5])
scored = scored[:top_k]
selected.extend(scored)
flat_idx += len(vocab_candidates)
selected.sort(key=lambda candidate: -candidate[5])
if not per_vocabulary_top_k and top_k is not None:
selected = selected[:top_k]
return [
HybridCandidate(
rank=rank,
code=code,
description=description,
vocabulary=vocabulary,
lexical_score=lexical_score,
embedding_score=embedding_score,
score=score,
)
for rank, (code, description, vocabulary, lexical_score, embedding_score, score)
in enumerate(selected, start=1)
]
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