diff --git a/apps/knowledge/vector/pg_vector.py b/apps/knowledge/vector/pg_vector.py
index de912448..d8235d2c 100644
--- a/apps/knowledge/vector/pg_vector.py
+++ b/apps/knowledge/vector/pg_vector.py
@@ -12,7 +12,6 @@ from abc import ABC, abstractmethod
 from typing import Dict, List
 
 import uuid_utils.compat as uuid
-from common.utils.ts_vecto_util import to_ts_vector, to_query
 from django.contrib.postgres.search import SearchVector
 from django.db.models import QuerySet, Value
 from langchain_core.embeddings import Embeddings
@@ -20,6 +19,7 @@ from langchain_core.embeddings import Embeddings
 from common.db.search import generate_sql_by_query_dict
 from common.db.sql_execute import select_list
 from common.utils.common import get_file_content
+from common.utils.ts_vecto_util import to_ts_vector, to_query
 from knowledge.models import Embedding, SearchMode, SourceType
 from knowledge.vector.base_vector import BaseVectorStore
 from maxkb.conf import PROJECT_DIR
@@ -42,36 +42,40 @@ class PGVector(BaseVectorStore):
     def vector_create(self):
         return True
 
-    def _save(self, text, source_type: SourceType, knowledge_id: str, document_id: str, paragraph_id: str, source_id: str,
+    def _save(self, text, source_type: SourceType, knowledge_id: str, document_id: str, paragraph_id: str,
+              source_id: str,
               is_active: bool,
               embedding: Embeddings):
-        text_embedding = embedding.embed_query(text)
-        embedding = Embedding(id=uuid.uuid7(),
-                              knowledge_id=knowledge_id,
-                              document_id=document_id,
-                              is_active=is_active,
-                              paragraph_id=paragraph_id,
-                              source_id=source_id,
-                              embedding=text_embedding,
-                              source_type=source_type,
-                              search_vector=to_ts_vector(text))
+        text_embedding = [float(x) for x in embedding.embed_query(text)]
+        embedding = Embedding(
+            id=uuid.uuid7(),
+            knowledge_id=knowledge_id,
+            document_id=document_id,
+            is_active=is_active,
+            paragraph_id=paragraph_id,
+            source_id=source_id,
+            embedding=text_embedding,
+            source_type=source_type,
+            search_vector=to_ts_vector(text)
+        )
         embedding.save()
         return True
 
     def _batch_save(self, text_list: List[Dict], embedding: Embeddings, is_the_task_interrupted):
         texts = [row.get('text') for row in text_list]
         embeddings = embedding.embed_documents(texts)
-        embedding_list = [Embedding(id=uuid.uuid7(),
-                                    document_id=text_list[index].get('document_id'),
-                                    paragraph_id=text_list[index].get('paragraph_id'),
-                                    knowledge_id=text_list[index].get('knowledge_id'),
-                                    is_active=text_list[index].get('is_active', True),
-                                    source_id=text_list[index].get('source_id'),
-                                    source_type=text_list[index].get('source_type'),
-                                    embedding=embeddings[index],
-                                    search_vector=SearchVector(Value(to_ts_vector(text_list[index]['text'])))) for
-                          index in
-                          range(0, len(texts))]
+        embedding_list = [
+            Embedding(
+                id=uuid.uuid7(),
+                document_id=text_list[index].get('document_id'),
+                paragraph_id=text_list[index].get('paragraph_id'),
+                knowledge_id=text_list[index].get('knowledge_id'),
+                is_active=text_list[index].get('is_active', True),
+                source_id=text_list[index].get('source_id'),
+                source_type=text_list[index].get('source_type'),
+                embedding=[float(x) for x in embeddings[index]],
+                search_vector=SearchVector(Value(to_ts_vector(text_list[index]['text'])))
+            ) for index in range(0, len(texts))]
         if not is_the_task_interrupted():
             QuerySet(Embedding).bulk_create(embedding_list) if len(embedding_list) > 0 else None
         return True