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Draft: Alternative Normalization Scheme for Reranking Update rerank.py #214

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Draft: Alternative Normalization Scheme for Reranking Update rerank.py #214

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13 changes: 7 additions & 6 deletions python/dify_plugin/interfaces/model/openai_compatible/rerank.py
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Original file line number Diff line number Diff line change
Expand Up @@ -77,10 +77,11 @@ def _invoke(
rerank_documents = []
scores = [result["relevance_score"] for result in results["results"]]

# Min-Max Normalization: Normalize scores to 0 ~ 1.0 range
min_score = min(scores)
max_score = max(scores)
score_range = max_score - min_score if max_score != min_score else 1.0 # Avoid division by zero
# Use sigmoid normalization to map scores to 0-1 range
import math
def sigmoid(x):
# Apply the sigmoid formula: 1 / (1 + e^(-x))
return 1.0 / (1.0 + math.exp(-x))

for result in results["results"]:
index = result["index"]
Expand All @@ -94,8 +95,8 @@ def _invoke(
elif isinstance(document, str):
text = document

# Normalize the score
normalized_score = (result["relevance_score"] - min_score) / score_range
# Normalize the score Apply sigmoid normalization to each score
normalized_scores = [sigmoid(score) for score in scores]

# Create RerankDocument object with normalized score
rerank_document = RerankDocument(
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