diff --git a/pilot/configs/config.py b/pilot/configs/config.py
index 1c4a52c35..9b915275a 100644
--- a/pilot/configs/config.py
+++ b/pilot/configs/config.py
@@ -161,7 +161,7 @@ class Config(metaclass=Singleton):
         self.KNOWLEDGE_CHUNK_SIZE = int(os.getenv("KNOWLEDGE_CHUNK_SIZE", 100))
         self.KNOWLEDGE_SEARCH_TOP_SIZE = int(os.getenv("KNOWLEDGE_SEARCH_TOP_SIZE", 5))
         ### SUMMARY_CONFIG Configuration
-        self.SUMMARY_CONFIG = os.getenv("SUMMARY_CONFIG", "VECTOR")
+        self.SUMMARY_CONFIG = os.getenv("SUMMARY_CONFIG", "FAST")
 
     def set_debug_mode(self, value: bool) -> None:
         """Set the debug mode value"""
diff --git a/pilot/scene/chat_knowledge/custom/chat.py b/pilot/scene/chat_knowledge/custom/chat.py
index f9f27f603..f6582d343 100644
--- a/pilot/scene/chat_knowledge/custom/chat.py
+++ b/pilot/scene/chat_knowledge/custom/chat.py
@@ -53,7 +53,8 @@ class ChatNewKnowledge(BaseChat):
         docs = self.knowledge_embedding_client.similar_search(
             self.current_user_input, CFG.KNOWLEDGE_SEARCH_TOP_SIZE
         )
-        context = [d["page_content"] for d in docs]
+        context = [d.page_content for d in docs]
+        self.metadata = [d.metadata for d in docs]
         context = context[:2000]
         input_values = {"context": context, "question": self.current_user_input}
         return input_values
diff --git a/pilot/vector_store/weaviate_store.py b/pilot/vector_store/weaviate_store.py
index f4e35bcfd..2dc4d2f1f 100644
--- a/pilot/vector_store/weaviate_store.py
+++ b/pilot/vector_store/weaviate_store.py
@@ -1,7 +1,9 @@
 import os
 import json
 import weaviate
+from langchain.schema import Document
 from langchain.vectorstores import Weaviate
+from weaviate.exceptions import WeaviateBaseError
 
 from pilot.configs.config import Config
 from pilot.configs.model_config import KNOWLEDGE_UPLOAD_ROOT_PATH
@@ -48,7 +50,13 @@ class WeaviateStore(VectorStoreBase):
             .with_limit(topk)
             .do()
         )
-        docs = response['data']['Get'][list(response['data']['Get'].keys())[0]]
+        res = response['data']['Get'][list(response['data']['Get'].keys())[0]]
+        docs = []
+        for r in res:
+            docs.append(Document(
+                page_content=r['page_content'],
+                metadata={"metadata": r['metadata']},
+            ))
         return docs
 
     def vector_name_exists(self) -> bool:
@@ -56,9 +64,13 @@ class WeaviateStore(VectorStoreBase):
         Returns:
             bool: True if the vector name exists, False otherwise.
         """
-        if self.vector_store_client.schema.get(self.vector_name):
-            return True
-        return False
+        try:
+            if self.vector_store_client.schema.get(self.vector_name):
+                return True
+            return False
+        except WeaviateBaseError as e:
+            logger.error("vector_name_exists error", e.message)
+            return False
 
     def _default_schema(self) -> None:
         """