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ed65693 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 | """Fusion strategies for combining sparse (BM25) and dense (FAISS) retrieval results.
Implements multiple fusion approaches for ablation comparison:
- Reciprocal Rank Fusion (RRF) — rank-based, score-agnostic baseline
- Linear combination — fixed α weighted sum
- Entropy-weighted fusion — adaptive α per query via calibrated entropy
All fusion methods produce a unified candidate list from two retriever outputs.
"""
from typing import Any
import numpy as np
from app.calibration import (
CALIBRATION_METHODS,
compute_alpha,
compute_entropy,
)
def _merge_candidates(
sparse_results: list[dict[str, Any]],
dense_results: list[dict[str, Any]],
) -> dict[str, dict[str, Any]]:
"""Merge candidates from both retrievers, keyed by (source, chunk).
Returns dict mapping key → {source, chunk, text, sparse_score, dense_score}.
"""
merged: dict[str, dict[str, Any]] = {}
for r in sparse_results:
key = f"{r['source']}:{r['chunk']}"
if key not in merged:
merged[key] = {
"source": r["source"],
"chunk": r["chunk"],
"text": r["text"],
"sparse_score": r.get("score", 0.0),
"dense_score": 0.0,
}
else:
merged[key]["sparse_score"] = r.get("score", 0.0)
for r in dense_results:
key = f"{r['source']}:{r['chunk']}"
if key not in merged:
merged[key] = {
"source": r["source"],
"chunk": r["chunk"],
"text": r["text"],
"sparse_score": 0.0,
"dense_score": r.get("score", 0.0),
}
else:
merged[key]["dense_score"] = r.get("score", 0.0)
return merged
def rrf_fuse(
sparse_results: list[dict[str, Any]],
dense_results: list[dict[str, Any]],
k: int = 60,
top_k: int | None = None,
) -> tuple[list[dict[str, Any]], dict[str, Any]]:
"""Reciprocal Rank Fusion.
RRF(d) = Σ 1/(k + rank(d)) across retriever lists.
Score-agnostic, operates on ranks only.
Returns (fused_results, metadata).
"""
rrf_scores: dict[str, float] = {}
doc_data: dict[str, dict[str, Any]] = {}
# Process sparse results
for rank, r in enumerate(sparse_results, start=1):
key = f"{r['source']}:{r['chunk']}"
rrf_scores[key] = rrf_scores.get(key, 0.0) + 1.0 / (k + rank)
if key not in doc_data:
doc_data[key] = {
"source": r["source"],
"chunk": r["chunk"],
"text": r["text"],
}
# Process dense results
for rank, r in enumerate(dense_results, start=1):
key = f"{r['source']}:{r['chunk']}"
rrf_scores[key] = rrf_scores.get(key, 0.0) + 1.0 / (k + rank)
if key not in doc_data:
doc_data[key] = {
"source": r["source"],
"chunk": r["chunk"],
"text": r["text"],
}
# Sort by RRF score
sorted_keys = sorted(rrf_scores, key=rrf_scores.get, reverse=True)
results = []
for key in sorted_keys:
r = doc_data[key]
r["score"] = round(rrf_scores[key], 6)
results.append(r)
if top_k is not None:
results = results[:top_k]
metadata = {"fusion_method": "rrf", "rrf_k": k}
return results, metadata
def linear_fuse(
sparse_results: list[dict[str, Any]],
dense_results: list[dict[str, Any]],
alpha: float = 0.5,
calibration: str = "minmax",
corpus_cdfs: tuple[np.ndarray, np.ndarray] | None = None,
top_k: int | None = None,
) -> tuple[list[dict[str, Any]], dict[str, Any]]:
"""Linear combination with fixed alpha.
fused_score = α * sparse_calibrated + (1-α) * dense_calibrated
Args:
alpha: Weight for sparse retriever (0=all dense, 1=all sparse).
calibration: Score calibration method ('raw', 'minmax', 'zscore', 'cdf').
corpus_cdfs: (cdf_bm25, cdf_dense) required when calibration='cdf'.
"""
merged = _merge_candidates(sparse_results, dense_results)
if not merged:
return [], {
"fusion_method": "linear",
"alpha": alpha,
"calibration": calibration,
}
sparse_scores = np.array([m["sparse_score"] for m in merged.values()])
dense_scores = np.array([m["dense_score"] for m in merged.values()])
effective_calibration = calibration
if calibration == "cdf" and corpus_cdfs is None:
effective_calibration = "minmax"
cal_fn = CALIBRATION_METHODS[effective_calibration]
if effective_calibration == "cdf" and corpus_cdfs is not None:
cal_sparse = cal_fn(sparse_scores, corpus_cdf=corpus_cdfs[0])
cal_dense = cal_fn(dense_scores, corpus_cdf=corpus_cdfs[1])
else:
cal_sparse = cal_fn(sparse_scores)
cal_dense = cal_fn(dense_scores)
fused_scores = alpha * cal_sparse + (1 - alpha) * cal_dense
results = []
for (_key, data), score in zip(merged.items(), fused_scores, strict=True):
results.append(
{
"source": data["source"],
"chunk": data["chunk"],
"text": data["text"],
"score": round(float(score), 6),
}
)
results.sort(key=lambda x: x["score"], reverse=True)
if top_k is not None:
results = results[:top_k]
metadata = {
"fusion_method": "linear",
"alpha": alpha,
"calibration": effective_calibration,
"requested_calibration": calibration,
}
return results, metadata
def entropy_fuse(
sparse_results: list[dict[str, Any]],
dense_results: list[dict[str, Any]],
calibration: str = "cdf",
corpus_cdfs: tuple[np.ndarray, np.ndarray] | None = None,
top_k: int | None = None,
) -> tuple[list[dict[str, Any]], dict[str, Any]]:
"""Entropy-weighted adaptive fusion.
Computes per-query alpha from calibrated score distribution entropy:
α = H_dense / (H_dense + H_sparse + ε)
When dense has high entropy (low confidence), α is large → more weight on BM25.
"""
merged = _merge_candidates(sparse_results, dense_results)
if not merged:
return [], {
"fusion_method": "entropy_weighted",
"alpha": 0.5,
"h_sparse": 0.0,
"h_dense": 0.0,
"calibration": calibration,
}
sparse_scores = np.array([m["sparse_score"] for m in merged.values()])
dense_scores = np.array([m["dense_score"] for m in merged.values()])
effective_calibration = calibration
if calibration == "cdf" and corpus_cdfs is None:
effective_calibration = "minmax"
cal_fn = CALIBRATION_METHODS[effective_calibration]
if effective_calibration == "cdf" and corpus_cdfs is not None:
cal_sparse = cal_fn(sparse_scores, corpus_cdf=corpus_cdfs[0])
cal_dense = cal_fn(dense_scores, corpus_cdf=corpus_cdfs[1])
else:
cal_sparse = cal_fn(sparse_scores)
cal_dense = cal_fn(dense_scores)
# Compute entropy of each retriever's calibrated scores
h_sparse = compute_entropy(cal_sparse)
h_dense = compute_entropy(cal_dense)
# Adaptive alpha
alpha = compute_alpha(h_dense, h_sparse)
# Fuse
fused_scores = alpha * cal_sparse + (1 - alpha) * cal_dense
results = []
for (_key, data), score in zip(merged.items(), fused_scores, strict=True):
results.append(
{
"source": data["source"],
"chunk": data["chunk"],
"text": data["text"],
"score": round(float(score), 6),
}
)
results.sort(key=lambda x: x["score"], reverse=True)
if top_k is not None:
results = results[:top_k]
metadata = {
"fusion_method": "entropy_weighted",
"alpha": round(alpha, 6),
"h_sparse": round(h_sparse, 6),
"h_dense": round(h_dense, 6),
"calibration": effective_calibration,
"requested_calibration": calibration,
}
return results, metadata
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