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fix(vector_store): add parameter validation for min_score range
Validates min_score is within [0.0, 1.0] for cosine similarity. Raises ValueError for out-of-range values to prevent unexpected filtering. Fixes: ISS-1766921318981-14 Solution-ID: SOL-1735386000-14 Issue-ID: ISS-1766921318981-14 Task-ID: T1
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@@ -730,7 +730,7 @@ class VectorStore:
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Args:
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query_embedding: Query vector.
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top_k: Maximum results to return.
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min_score: Minimum similarity score (0-1).
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min_score: Minimum cosine similarity score in [0.0, 1.0].
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return_full_content: If True, return full code block content.
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Returns:
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@@ -738,6 +738,11 @@ class VectorStore:
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"""
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query_vec = np.array(query_embedding, dtype=np.float32)
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if not 0.0 <= min_score <= 1.0:
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raise ValueError(
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f"Invalid min_score: {min_score}. Must be within [0.0, 1.0] for cosine similarity."
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)
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# Try HNSW search first (O(log N))
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if (
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HNSWLIB_AVAILABLE
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@@ -769,7 +774,7 @@ class VectorStore:
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Args:
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query_vec: Query vector as numpy array
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top_k: Maximum results to return
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min_score: Minimum similarity score (0-1)
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min_score: Minimum cosine similarity score in [0.0, 1.0]
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return_full_content: If True, return full code block content
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Returns:
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@@ -820,7 +825,7 @@ class VectorStore:
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Args:
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query_vec: Query vector as numpy array
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top_k: Maximum results to return
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min_score: Minimum similarity score (0-1)
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min_score: Minimum cosine similarity score in [0.0, 1.0]
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return_full_content: If True, return full code block content
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Returns:
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