AI UPDATE

rag/retrieve.py

@@ -1,18 +1,20 @@
 """
 بازیابی RAG: embed کوئری و جستجو در vector store
 """
-from .config import load_rag_config, RAGConfig
+from .config import load_rag_config, RAGConfig, get_service_config
 from .embedding import embed_single
 from .vector_store import QdrantVectorStore
 
 
 def search_with_query(
     query: str,
-    sensor_uuid: str,
+    sensor_uuid: str | None = None,
     limit: int = 5,
     score_threshold: float | None = None,
     config: RAGConfig | None = None,
     kb_name: str | None = None,
+    service_id: str | None = None,
+    use_user_embeddings: bool | None = None,
 ) -> list[dict]:
     """
     کوئری را embed می‌کند و در vector store جستجو می‌کند.
@@ -27,12 +29,28 @@ def search_with_query(
         لیست نتایج با id, score, text, metadata
     """
     cfg = config or load_rag_config()
+    service = get_service_config(service_id, cfg) if service_id else None
+    resolved_kb_name = kb_name or (service.knowledge_base if service else None)
+    include_user_embeddings = (
+        use_user_embeddings
+        if use_user_embeddings is not None
+        else (service.use_user_embeddings if service else True)
+    )
+
+    sensor_filters = ["__global__"]
+    if include_user_embeddings and sensor_uuid:
+        sensor_filters.insert(0, sensor_uuid)
+
+    kb_filters = [resolved_kb_name] if resolved_kb_name else []
+    if include_user_embeddings:
+        kb_filters.append("__all__")
+
     query_vector = embed_single(query, config=cfg)
     store = QdrantVectorStore(config=cfg)
     return store.search(
         query_vector=query_vector,
         limit=limit,
         score_threshold=score_threshold,
-        sensor_uuid=sensor_uuid,
-        kb_name=kb_name,
+        sensor_uuids=sensor_filters,
+        kb_names=kb_filters,
     )