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/config.py

@@ -47,7 +47,6 @@ class RAGConfig:
     llm: LLMConfig = field(default_factory=LLMConfig)
     tone_file: str = "config/tone.txt"
     knowledge_base_path: str = "config/knowledge_base"
-    user_info_path: str = "config/user_info"
     chromadb: dict[str, Any] = field(default_factory=dict)
 
 
@@ -104,6 +103,5 @@ def load_rag_config(config_path: str | Path | None = None) -> RAGConfig:
         llm=llm,
         tone_file=data.get("tone_file", "config/tone.txt"),
         knowledge_base_path=data.get("knowledge_base_path", "config/knowledge_base"),
-        user_info_path=data.get("user_info_path", "config/user_info"),
         chromadb=data.get("chromadb", {}),
     )