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refactor: Remove double filtering based on score threshold
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parent
140ee7d337
commit
76ff6a5943
4 changed files with 13 additions and 7 deletions
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@ -160,8 +160,11 @@ class FaissIndex(EmbeddingIndex):
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for d, i in zip(distances[0], indices[0], strict=False):
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if i < 0:
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continue
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score = 1.0 / float(d) if d != 0 else float("inf")
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if score < score_threshold:
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continue
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chunks.append(self.chunk_by_index[int(i)])
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scores.append(1.0 / float(d) if d != 0 else float("inf"))
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scores.append(score)
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return QueryChunksResponse(chunks=chunks, scores=scores)
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@ -132,8 +132,11 @@ class PGVectorIndex(EmbeddingIndex):
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chunks = []
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scores = []
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for doc, dist in results:
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score = 1.0 / float(dist) if dist != 0 else float("inf")
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if score < score_threshold:
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continue
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chunks.append(Chunk(**doc))
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scores.append(1.0 / float(dist) if dist != 0 else float("inf"))
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scores.append(score)
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return QueryChunksResponse(chunks=chunks, scores=scores)
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@ -105,8 +105,12 @@ class WeaviateIndex(EmbeddingIndex):
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log.exception(f"Failed to parse document: {chunk_json}")
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continue
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score = 1.0 / doc.metadata.distance if doc.metadata.distance != 0 else float("inf")
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if score < score_threshold:
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continue
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chunks.append(chunk)
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scores.append(1.0 / doc.metadata.distance if doc.metadata.distance != 0 else float("inf"))
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scores.append(score)
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return QueryChunksResponse(chunks=chunks, scores=scores)
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@ -433,10 +433,6 @@ class OpenAIVectorStoreMixin(ABC):
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# Convert response to OpenAI format
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data = []
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for chunk, score in zip(response.chunks, response.scores, strict=False):
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# Apply score based filtering
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if score < score_threshold:
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continue
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# Apply filters if provided
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if filters:
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# Simple metadata filtering
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