diff --git a/docs/modules/knownledge.md b/docs/modules/knownledge.md
index c108920b2..cd42922ed 100644
--- a/docs/modules/knownledge.md
+++ b/docs/modules/knownledge.md
@@ -31,6 +31,8 @@ python tools/knowledge_init.py
 
 ```
 
+Optionally, you can run `python tools/knowledge_init.py -h` command to see more usage.
+
 3.Add the knowledge repository in the interface by entering the name of your knowledge repository (if not specified, enter "default") so you can use it for Q&A based on your knowledge base. 
 
 Note that the default vector model used is text2vec-large-chinese (which is a large model, so if your personal computer configuration is not enough, it is recommended to use text2vec-base-chinese). Therefore, ensure that you download the model and place it in the models directory.
\ No newline at end of file
diff --git a/tools/knowledge_init.py b/tools/knowledge_init.py
index c442de8c9..aca2baf1d 100644
--- a/tools/knowledge_init.py
+++ b/tools/knowledge_init.py
@@ -3,6 +3,7 @@
 import argparse
 import os
 import sys
+import traceback
 
 sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
 
@@ -31,7 +32,7 @@ class LocalKnowledgeInit:
         self.vector_store_config = vector_store_config
         self.model_name = LLM_MODEL_CONFIG[CFG.EMBEDDING_MODEL]
 
-    def knowledge_persist(self, file_path):
+    def knowledge_persist(self, file_path: str, skip_wrong_doc: bool = False):
         """knowledge persist"""
         docs = []
         embedding_engine = None
@@ -44,9 +45,18 @@ class LocalKnowledgeInit:
                     model_name=self.model_name,
                     vector_store_config=self.vector_store_config,
                 )
-                embedding_engine = ke.init_knowledge_embedding()
-                doc = ke.read()
-                docs.extend(doc)
+                try:
+                    embedding_engine = ke.init_knowledge_embedding()
+                    doc = ke.read()
+                    docs.extend(doc)
+                except Exception as e:
+                    error_msg = traceback.format_exc()
+                    if skip_wrong_doc:
+                        print(
+                            f"Warning: document file {filename} embedding error, skip it, error message: {error_msg}"
+                        )
+                    else:
+                        raise e
         embedding_engine.index_to_store(docs)
         print(f"""begin create {self.vector_store_config["vector_store_name"]} space""")
         try:
@@ -64,11 +74,24 @@ class LocalKnowledgeInit:
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--vector_name", type=str, default="default")
+    # TODO https://github.com/csunny/DB-GPT/issues/354
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument(
+        "--vector_name", type=str, default="default", help="Your vector store name"
+    )
+    parser.add_argument(
+        "--file_path", type=str, default=DATASETS_DIR, help="Your document path"
+    )
+    parser.add_argument(
+        "--skip_wrong_doc", type=bool, default=False, help="Skip wrong document"
+    )
     args = parser.parse_args()
     vector_name = args.vector_name
     store_type = CFG.VECTOR_STORE_TYPE
+    file_path = args.file_path
+    skip_wrong_doc = args.skip_wrong_doc
     vector_store_config = {
         "vector_store_name": vector_name,
         "vector_store_type": CFG.VECTOR_STORE_TYPE,
@@ -76,5 +99,5 @@ if __name__ == "__main__":
     }
     print(vector_store_config)
     kv = LocalKnowledgeInit(vector_store_config=vector_store_config)
-    kv.knowledge_persist(file_path=DATASETS_DIR)
+    kv.knowledge_persist(file_path=file_path, skip_wrong_doc=skip_wrong_doc)
     print("your knowledge embedding success...")