fix: 修复向量模型服务未起，重新向量问答一直显示排队中。

apps/embedding/task/embedding.py

@@ -6,7 +6,7 @@
     @date：2024/8/19 14:13
     @desc:
 """
-
+import datetime
 import logging
 import traceback
 from typing import List
@@ -17,7 +17,7 @@ from django.db.models import QuerySet
 from common.config.embedding_config import ModelManage
 from common.event import ListenerManagement, UpdateProblemArgs, UpdateEmbeddingDatasetIdArgs, \
     UpdateEmbeddingDocumentIdArgs
-from dataset.models import Document
+from dataset.models import Document, Status
 from ops import celery_app
 from setting.models import Model
 from setting.models_provider import get_model
@@ -26,10 +26,15 @@ max_kb_error = logging.getLogger("max_kb_error")
 max_kb = logging.getLogger("max_kb")
 
 
-def get_embedding_model(model_id):
-    model = QuerySet(Model).filter(id=model_id).first()
-    embedding_model = ModelManage.get_model(model_id,
-                                            lambda _id: get_model(model))
+def get_embedding_model(model_id, exception_handler=lambda e: max_kb_error.error(
+    f'获取向量模型失败：{str(e)}{traceback.format_exc()}')):
+    try:
+        model = QuerySet(Model).filter(id=model_id).first()
+        embedding_model = ModelManage.get_model(model_id,
+                                                lambda _id: get_model(model))
+    except Exception as e:
+        exception_handler(e)
+        raise e
     return embedding_model
 
 
@@ -59,7 +64,14 @@ def embedding_by_document(document_id, model_id):
     @param model_id 向量模型
     :return: None
     """
-    embedding_model = get_embedding_model(model_id)
+
+    def exception_handler(e):
+        QuerySet(Document).filter(id=document_id).update(
+            **{'status': Status.error, 'update_time': datetime.datetime.now()})
+        max_kb_error.error(
+            f'获取向量模型失败：{str(e)}{traceback.format_exc()}')
+
+    embedding_model = get_embedding_model(model_id, exception_handler)
     ListenerManagement.embedding_by_document(document_id, embedding_model)
 
 
@@ -71,7 +83,6 @@ def embedding_by_document_list(document_id_list, model_id):
     @param model_id 向量模型
     :return: None
     """
-    print(document_id_list)
     for document_id in document_id_list:
         embedding_by_document.delay(document_id, model_id)