Refactor user data handling and enhance chat functionality

- Removed deprecated user_info files and paths from configuration.
- Added user soil data integration in chat context to improve response accuracy.
- Updated build_rag_context and chat_rag_stream functions to include sensor_uuid for user-specific data retrieval.
- Enhanced load_sources function to load user data from the database.
- Implemented filtering in search_with_query and QdrantVectorStore to isolate user data based on sensor_uuid.
- Introduced Celery Beat schedule for periodic user data ingestion.

rag/vector_store.py

@@ -95,16 +95,34 @@ class QdrantVectorStore:
         query_vector: list[float],
         limit: int = 5,
         score_threshold: float | None = None,
+        sensor_uuid: str | None = None,
     ) -> list[dict]:
         """
         جستجوی شباهت بر اساس query vector.
         از query_points استفاده می‌کند (qdrant-client >= 2.0).
+        sensor_uuid: اجباری — فقط chunks مربوط به این سنسور یا __global__ برگردانده می‌شود.
         """
+        query_filter = None
+        if sensor_uuid:
+            query_filter = qmodels.Filter(
+                should=[
+                    qmodels.FieldCondition(
+                        key="sensor_uuid",
+                        match=qmodels.MatchValue(value=sensor_uuid),
+                    ),
+                    qmodels.FieldCondition(
+                        key="sensor_uuid",
+                        match=qmodels.MatchValue(value="__global__"),
+                    ),
+                ]
+            )
+
         response = self.client.query_points(
             collection_name=self.qdrant.collection_name,
             query=query_vector,
             limit=limit,
             score_threshold=score_threshold,
+            query_filter=query_filter,
         )
         points = getattr(response, "points", []) or []