diff --git a/pilot/source_embedding/knowledge_embedding.py b/pilot/source_embedding/knowledge_embedding.py
index 97b515897..d8ec85ae3 100644
--- a/pilot/source_embedding/knowledge_embedding.py
+++ b/pilot/source_embedding/knowledge_embedding.py
@@ -51,6 +51,7 @@ class KnowledgeEmbedding:
         self.knowledge_embedding_client.index_to_store(docs)
 
     def read(self):
+        self.knowledge_embedding_client = self.init_knowledge_embedding()
         return self.knowledge_embedding_client.read_batch()
 
     def init_knowledge_embedding(self):
diff --git a/pilot/source_embedding/source_embedding.py b/pilot/source_embedding/source_embedding.py
index 3d881fcdf..372e35c22 100644
--- a/pilot/source_embedding/source_embedding.py
+++ b/pilot/source_embedding/source_embedding.py
@@ -33,9 +33,6 @@ class SourceEmbedding(ABC):
         self.vector_store_config = vector_store_config
         self.embedding_args = embedding_args
         self.embeddings = vector_store_config["embeddings"]
-        self.vector_client = VectorStoreConnector(
-            CFG.VECTOR_STORE_TYPE, vector_store_config
-        )
 
     @abstractmethod
     @register
@@ -59,11 +56,17 @@ class SourceEmbedding(ABC):
     @register
     def index_to_store(self, docs):
         """index to vector store"""
+        self.vector_client = VectorStoreConnector(
+            CFG.VECTOR_STORE_TYPE, self.vector_store_config
+        )
         self.vector_client.load_document(docs)
 
     @register
     def similar_search(self, doc, topk):
         """vector store similarity_search"""
+        self.vector_client = VectorStoreConnector(
+            CFG.VECTOR_STORE_TYPE, self.vector_store_config
+        )
         try:
             ans = self.vector_client.similar_search(doc, topk)
         except NotEnoughElementsException:
@@ -71,6 +74,9 @@ class SourceEmbedding(ABC):
         return ans
 
     def vector_name_exist(self):
+        self.vector_client = VectorStoreConnector(
+            CFG.VECTOR_STORE_TYPE, self.vector_store_config
+        )
         return self.vector_client.vector_name_exists()
 
     def source_embedding(self):
diff --git a/tools/knowledge_init.py b/tools/knowledge_init.py
index c9a0c5457..74c62b90a 100644
--- a/tools/knowledge_init.py
+++ b/tools/knowledge_init.py
@@ -25,17 +25,20 @@ class LocalKnowledgeInit:
 
     def knowledge_persist(self, file_path):
         """knowledge persist"""
+        docs = []
+        embedding_engine = None
         for root, _, files in os.walk(file_path, topdown=False):
             for file in files:
                 filename = os.path.join(root, file)
-                # docs = self._load_file(filename)
                 ke = KnowledgeEmbedding(
                     file_path=filename,
                     model_name=self.model_name,
                     vector_store_config=self.vector_store_config,
                 )
-                client = ke.init_knowledge_embedding()
-                client.source_embedding()
+                embedding_engine = ke.init_knowledge_embedding()
+                doc = ke.read()
+                docs.extend(doc)
+        embedding_engine.index_to_store(docs)
 
 
 if __name__ == "__main__":