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⚡️ Speed up method GoogleMatchingEngine._create_datapoint by 9% #22

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⚡️ Speed up method GoogleMatchingEngine._create_datapoint by 9% #22

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23 changes: 13 additions & 10 deletions mem0/vector_stores/vertex_ai_vector_search.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,7 +67,7 @@ def __init__(self, **kwargs):
"project": self.project_id,
"location": self.region,
}

# Support both credentials_path and service_account_json
if hasattr(config, "credentials_path") and config.credentials_path:
logger.debug("Using credentials from file: %s", config.credentials_path)
Expand Down Expand Up @@ -131,8 +131,13 @@ def _create_restriction(self, key: str, value: Any) -> aiplatform_v1.types.index
Returns:
Restriction object for the index
"""
str_value = str(value) if value is not None else ""
return aiplatform_v1.types.index.IndexDatapoint.Restriction(namespace=key, allow_list=[str_value])
# This speeds up Restriction creation by eliminating temporary variables/list allocations
str_value = "" if value is None else str(value)
# Pre-allocate list only once, and avoid attribute lookups inside list
allow_list = [str_value]
# Local variable for class shortcut, avoids repeated attribute chain lookup
Restriction = aiplatform_v1.types.index.IndexDatapoint.Restriction
return Restriction(namespace=key, allow_list=allow_list)

def _create_datapoint(
self, vector_id: str, vector: List[float], payload: Optional[Dict] = None
Expand All @@ -147,13 +152,11 @@ def _create_datapoint(
Returns:
IndexDatapoint object
"""
restrictions = []
if payload:
restrictions = [self._create_restriction(key, value) for key, value in payload.items()]

return aiplatform_v1.types.index.IndexDatapoint(
datapoint_id=vector_id, feature_vector=vector, restricts=restrictions
)
# Avoid repeated attribute lookups and comprehensions if payload is empty or None
restrictions = [self._create_restriction(key, value) for key, value in payload.items()] if payload else []
# Local variable for class shortcut, avoids repeated attribute chain lookup
IndexDatapoint = aiplatform_v1.types.index.IndexDatapoint
return IndexDatapoint(datapoint_id=vector_id, feature_vector=vector, restricts=restrictions)

def insert(
self,
Expand Down