Merge branch 'feature/20251219_lxc' into develop

api/app/controllers/knowledge_controller.py

@@ -242,8 +242,9 @@ async def _update_knowledge(
         if "embedding_id" in update_dict:
             embedding_id = update_dict["embedding_id"]
             if embedding_id != db_knowledge.embedding_id:
-                vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
-                vector_service.delete()
+                if db_knowledge.embedding_id and db_knowledge.reranker_id:
+                    vector_service = ElasticSearchVectorFactory().init_vector(knowledge=db_knowledge)
+                    vector_service.delete()
                 document_service.reset_documents_progress_by_kb_id(db, kb_id=db_knowledge.id, current_user=current_user)
 
         # 2. Update fields (only update non-null fields)

api/app/tasks.py

@@ -224,6 +224,7 @@ def parse_document(file_path: str, document_id: uuid.UUID):
             async def _run(row: dict, document_ids: list[str], language: str, parser_config: dict, vector_service,
                            chat_model, embedding_model, callback, with_resolution: bool = True,
                            with_community: bool = True, ) -> dict:
+                await trio.sleep(5)  # Delay for 10 seconds
                 nonlocal progress_msg  # Declare the use of an external progress_msg variable
                 result = await run_graphrag_for_kb(
                     row=row,