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/retrieve.py

@@ -8,12 +8,17 @@ from .vector_store import QdrantVectorStore
 
 def search_with_query(
     query: str,
+    sensor_uuid: str,
     limit: int = 5,
     score_threshold: float | None = None,
     config: RAGConfig | None = None,
 ) -> list[dict]:
     """
     کوئری را embed می‌کند و در vector store جستجو می‌کند.
+    فقط chunks مربوط به sensor_uuid یا __global__ برمی‌گردد (ایزوله‌سازی کاربر).
+
+    Args:
+        sensor_uuid: شناسه سنسور کاربر — اجباری برای امنیت
 
     Returns:
         لیست نتایج با id, score, text, metadata
@@ -25,4 +30,5 @@ def search_with_query(
         query_vector=query_vector,
         limit=limit,
         score_threshold=score_threshold,
+        sensor_uuid=sensor_uuid,
     )