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fix(vector-stores): Supabase 1000-row cap, RLS init probe, and col_info crash (#6695)

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This commit is contained in:
Kartik
2026-07-31 20:36:41 +05:30
committed by GitHub
parent 8d45fb3c9a
commit 29fa41558c
5 changed files with 277 additions and 93 deletions
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mem0/vector_stores/supabase.py
+30 -8
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@@ -14,6 +14,8 @@ from mem0.vector_stores.base import VectorStoreBase
logger = logging.getLogger(__name__)
VECS_MAX_QUERY_LIMIT = 1000
class OutputData(BaseModel):
id: Optional[str]
@@ -131,11 +133,21 @@ class Supabase(VectorStoreBase):
List[OutputData]: Search results
"""
filters = self._preprocess_filters(filters)
limit = top_k
if limit > VECS_MAX_QUERY_LIMIT:
logger.warning(
f"Requested top_k={top_k} exceeds the vecs query limit of {VECS_MAX_QUERY_LIMIT}; "
f"capping to {VECS_MAX_QUERY_LIMIT}."
)
limit = VECS_MAX_QUERY_LIMIT
results = self.collection.query(
data=vectors, limit=top_k, filters=filters, include_metadata=True, include_value=True
data=vectors, limit=limit, filters=filters, include_metadata=True, include_value=True
)
return [OutputData(id=str(result[0]), score=max(0.0, 1.0 - float(result[1])), payload=result[2]) for result in results]
return [
OutputData(id=str(result[0]), score=max(0.0, 1.0 - float(result[1])), payload=result[2])
for result in results
]
def delete(self, vector_id: str):
"""
@@ -201,12 +213,15 @@ class Supabase(VectorStoreBase):
Returns:
Dict: Collection information including name and configuration
"""
info = self.collection.describe()
return {
"name": info.name,
"count": info.vectors,
"dimension": info.dimension,
"index": {"method": info.index_method, "metric": info.distance_metric},
"name": self.collection.name,
"count": len(self.collection),
"dimension": self.collection.dimension,
"index": {
"name": self.collection.index,
"measure": self.index_measure.value,
"is_indexed": self.collection.is_indexed_for_measure(self.index_measure),
},
}
def list(self, filters: Optional[dict] = None, top_k: int = 100) -> List[OutputData]:
@@ -222,8 +237,15 @@ class Supabase(VectorStoreBase):
"""
filters = self._preprocess_filters(filters)
query = [0] * self.embedding_model_dims
limit = top_k
if limit > VECS_MAX_QUERY_LIMIT:
logger.warning(
f"Requested top_k={top_k} exceeds the vecs query limit of {VECS_MAX_QUERY_LIMIT}; "
f"capping to {VECS_MAX_QUERY_LIMIT}."
)
limit = VECS_MAX_QUERY_LIMIT
ids = self.collection.query(
data=query, limit=top_k, filters=filters, include_metadata=True, include_value=False
data=query, limit=limit, filters=filters, include_metadata=True, include_value=False
)
ids = [id[0] for id in ids]
records = self.collection.fetch(ids=ids)