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Merge branch 'main' into chroma

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Bwook (Byoungwook) Kim 2025-09-11 20:46:53 +09:00 • committed by GitHub
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21
llama_stack/providers/utils/vector_io/vector_utils.py
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@ -39,13 +39,16 @@ def sanitize_collection_name(name: str, weaviate_format=False) -> str:
return s
class Reranker:
class WeightedInMemoryAggregator:
@staticmethod
def _normalize_scores(scores: dict[str, float]) -> dict[str, float]:
"""
Normalize scores to 0-1 range using min-max normalization.
Args:
scores: dictionary of scores with document IDs as keys and scores as values
Returns:
Normalized scores with document IDs as keys and normalized scores as values
"""
@ -65,17 +68,20 @@ class Reranker:
) -> dict[str, float]:
"""
Rerank via weighted average of scores.
Args:
vector_scores: scores from vector search
keyword_scores: scores from keyword search
alpha: weight factor between 0 and 1 (default: 0.5)
0 = keyword only, 1 = vector only, 0.5 = equal weight
Returns:
All unique document IDs with weighted combined scores
"""
all_ids = set(vector_scores.keys()) | set(keyword_scores.keys())
normalized_vector_scores = Reranker._normalize_scores(vector_scores)
normalized_keyword_scores = Reranker._normalize_scores(keyword_scores)
normalized_vector_scores = WeightedInMemoryAggregator._normalize_scores(vector_scores)
normalized_keyword_scores = WeightedInMemoryAggregator._normalize_scores(keyword_scores)
# Weighted formula: score = (1-alpha) * keyword_score + alpha * vector_score
# alpha=0 means keyword only, alpha=1 means vector only
@ -93,10 +99,12 @@ class Reranker:
) -> dict[str, float]:
"""
Rerank via Reciprocal Rank Fusion.
Args:
vector_scores: scores from vector search
keyword_scores: scores from keyword search
impact_factor: impact factor for RRF (default: 60.0)
Returns:
All unique document IDs with RRF combined scores
"""
@ -130,11 +138,13 @@ class Reranker:
) -> dict[str, float]:
"""
Combine vector and keyword search results using specified reranking strategy.
Args:
vector_scores: scores from vector search
keyword_scores: scores from keyword search
reranker_type: type of reranker to use (default: RERANKER_TYPE_RRF)
reranker_params: parameters for the reranker
Returns:
All unique document IDs with combined scores
"""
@ -143,8 +153,9 @@ class Reranker:
if reranker_type == "weighted":
alpha = reranker_params.get("alpha", 0.5)
return Reranker.weighted_rerank(vector_scores, keyword_scores, alpha)
return WeightedInMemoryAggregator.weighted_rerank(vector_scores, keyword_scores, alpha)
else:
# Default to RRF for None, RRF, or any unknown types
impact_factor = reranker_params.get("impact_factor", 60.0)
return Reranker.rrf_rerank(vector_scores, keyword_scores, impact_factor)
return WeightedInMemoryAggregator.rrf_rerank(vector_scores, keyword_scores, impact_factor)