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refactor(vector-store): use consistent EPSILON constant

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Define module-level EPSILON constant and use it in both
_cosine_similarity and _refresh_cache for consistent
floating point precision handling.

Solution-ID: SOL-20251228113619
Issue-ID: ISS-1766921318981-11
Task-ID: T3
This commit is contained in:
catlog22
2025-12-28 21:40:46 +08:00
parent af2ff54cb7
commit 18cc536f65
1 changed files with 4 additions and 2 deletions
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6
codex-lens/src/codexlens/semantic/vector_store.py
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@@ -36,6 +36,9 @@ except ImportError:
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
# Epsilon used to guard against floating point precision edge cases (e.g., near-zero norms).
EPSILON = 1e-10
def _cosine_similarity(a: List[float], b: List[float]) -> float: def _cosine_similarity(a: List[float], b: List[float]) -> float:
"""Compute cosine similarity between two vectors.""" """Compute cosine similarity between two vectors."""
@@ -48,7 +51,6 @@ def _cosine_similarity(a: List[float], b: List[float]) -> float:
norm_a = np.linalg.norm(a_arr) norm_a = np.linalg.norm(a_arr)
norm_b = np.linalg.norm(b_arr) norm_b = np.linalg.norm(b_arr)
EPSILON = 1e-10
# Use epsilon tolerance to avoid division by (near-)zero due to floating point precision. # Use epsilon tolerance to avoid division by (near-)zero due to floating point precision.
if norm_a < EPSILON or norm_b < EPSILON: if norm_a < EPSILON or norm_b < EPSILON:
return 0.0 return 0.0
@@ -313,7 +315,7 @@ class VectorStore:
) )
# Avoid division by zero # Avoid division by zero
self._embedding_norms = np.where( self._embedding_norms = np.where(
self._embedding_norms == 0, 1e-10, self._embedding_norms self._embedding_norms == 0, EPSILON, self._embedding_norms
) )
return True return True