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Refactor cache configuration constants for improved clarity and consistency; adjust cache entry limits and hash prefix length

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mtayfur
2025-10-09 13:10:58 +03:00
parent 74f7ededcd
commit e89a9b29b9
1 changed files with 4 additions and 3 deletions
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7
memory_system.py
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@@ -41,9 +41,9 @@ class Constants:
LLM_CONSOLIDATION_TIMEOUT_SEC = 60.0 # Timeout for LLM consolidation operations LLM_CONSOLIDATION_TIMEOUT_SEC = 60.0 # Timeout for LLM consolidation operations
# Cache System # Cache System
MAX_CACHE_ENTRIES_PER_TYPE = 5000 # Maximum cache entries per cache type MAX_CACHE_ENTRIES_PER_TYPE = 2500 # Maximum cache entries per cache type
MAX_CONCURRENT_USER_CACHES = 500 # Maximum concurrent user cache instances MAX_CONCURRENT_USER_CACHES = 250 # Maximum concurrent user cache instances
CACHE_KEY_HASH_PREFIX_LENGTH = 16 # Hash prefix length for cache keys CACHE_KEY_HASH_PREFIX_LENGTH = 10 # Hash prefix length for cache keys
# Retrieval & Similarity # Retrieval & Similarity
SEMANTIC_RETRIEVAL_THRESHOLD = 0.5 # Semantic similarity threshold for retrieval SEMANTIC_RETRIEVAL_THRESHOLD = 0.5 # Semantic similarity threshold for retrieval
@@ -1170,6 +1170,7 @@ class Filter:
def _normalize_embedding(self, embedding: np.ndarray) -> np.ndarray: def _normalize_embedding(self, embedding: np.ndarray) -> np.ndarray:
"""Normalize embedding vector.""" """Normalize embedding vector."""
embedding = embedding.astype(np.float16)
norm = np.linalg.norm(embedding) norm = np.linalg.norm(embedding)
return embedding / norm if norm > 0 else embedding return embedding / norm if norm > 0 else embedding