diff --git a/GPT-Gradio-Agent.py b/GPT-Gradio-Agent.py
index 4ac5ca6..7d25970 100644
--- a/GPT-Gradio-Agent.py
+++ b/GPT-Gradio-Agent.py
@@ -13,7 +13,6 @@
 from langchain.chat_models import AzureChatOpenAI
 from langchain.document_loaders import DirectoryLoader,PyPDFLoader,UnstructuredFileLoader
 from langchain.chains import RetrievalQA
-from langchain.chains.summarize import load_summarize_chain
 
 load_dotenv()
 
@@ -158,21 +157,6 @@ def file_ask_stream(file_ask_history_list:list[list],file_answer:list):
         time.sleep(0.02)
         yield file_ask_history_list
 
-def summarize_file(split_docs,chatbot,model_choice,sum_type):
-    llm = AzureChatOpenAI(model=model_choice,
-                    openai_api_type="azure",
-                    deployment_name=model_choice, # <----------设置选择模型的时候修改这里
-                    temperature=0.7)
-    # 创建总结链
-    chain = load_summarize_chain(llm, chain_type=sum_type, verbose=True)
-    
-    # 执行总结链
-    summarize_result = chain.run(split_docs[-1])
-
-    # 构造 chatbox 格式
-    chatbot.append(["Please summarize the file for me.",None])
-    return summarize_result,chatbot
-
 def sum_stream(summarize_result,chatbot):
     '''
     Used to make summarized result be outputed as stream.
@@ -201,7 +185,7 @@ def rst_mem(chat_his:list):
     usr_msg = gr.State()
     chat_his = gr.State([])
     with gr.Row():
-        with gr.Column(scale=1.8):
+        with gr.Column(scale=2):
             model_choice = gr.Radio(choices=["gpt-35-turbo","gpt-35-turbo-16k","gpt-4"],
                                     value="gpt-35-turbo",
                                     label="Model",info="支持模型选择，立即生效")
@@ -210,9 +194,12 @@ def rst_mem(chat_his:list):
                                 bubble_full_width=False)
             message = gr.Textbox(label="Input your prompt",
                                         info="'Shift + Enter' to begin an new line. Press 'Enter' can also send your Prompt to the LLM.")
-            with gr.Row(scale=0.1):
+            with gr.Row():
                 clear = gr.ClearButton([message, chat_bot,chat_his],scale=1,size="sm")
                 send = gr.Button("Send",scale=2)
+            with gr.Row():
+                chat_with_file = gr.Button(value="Chat with file (Valid for knowledge base)")
+                summarize = gr.Button(value="Summarize (Valid only for uploaded file)")
 
         with gr.Column():
             with gr.Tab("Chat"):
@@ -247,9 +234,10 @@ def rst_mem(chat_his:list):
                         vector_path = gr.Text(label="Knowledge base save path",
                                             info="Choose the folder you want to save, and PASTE THE ABSOLUTE PATH here")
                         with gr.Row():
-                            vector_content = gr.DataFrame(label="Knowledge Base Document Catalog",
-                                                        interactive=False,
-                                                        )
+                            vector_content = gr.DataFrame(#label="Knowledge Base Document Catalog",
+                                                          value = pd.DataFrame(columns=['文件名称']),
+                                                          interactive=False,
+                                                         )
                             file_list = gr.Dropdown(interactive=True,
                                                     # allow_custom_value=True,
                                                     label="File list")
@@ -257,15 +245,17 @@ def rst_mem(chat_his:list):
                             create_vec_but = gr.Button(value="Create a new knowledge base")
                             load_vec = gr.Button(value="Load your knowledge base")
                             with gr.Row():
-                                add_file = gr.Button(value="Add it(The file uploaded) to knowledge base")
-                                delete_file = gr.Button(value="Delete it(selected in dropdown) from knowledge base")
-                    sum_type = gr.Radio(choices=[("小文件(file with few words)","stuff"),("大文件(file with a large word count)","refine")],
-                                        value="stuff",
-                                        label="Choose the type of file to be summarized",
-                                        info="如果待总结字数较多，请选择“大文件”（选小文件可能导致超出 GPT 的最大 Token ）")
-                    with gr.Row():
-                        chat_with_file = gr.Button(value="Chat with file")
-                        summarize = gr.Button(value="Summarize")
+                                add_file = gr.Button(value="Add it (The file uploaded) to knowledge base")
+                                delete_file = gr.Button(value="Delete it (Selected in dropdown) from knowledge base")  
+                    with gr.Accordion("File chat setting"):
+                        filter_choice = gr.Radio(choices=["All", "Selected file"],
+                                                value="All",
+                                                label="Search scope",
+                                                info="“All” means whole knowledge base;“Selected file” means the file selected in dropdown")
+                        sum_type = gr.Radio(choices=[("small file","stuff"),("large file","refine")],
+                                            value="stuff",
+                                            label="File size type",
+                                            info="也作用于“Summarize”。如果待总结字数较多，请选择“lagre size”（选“large size”可能导致超出 GPT 的最大 Token ）")
 
     # Merge all handles that require input and output.
     input_param = [message, model_choice, chat_his, chat_bot, System_Prompt, 
@@ -283,7 +273,7 @@ def rst_mem(chat_his:list):
     
     # chat_file button event
     file.upload(upload_file,inputs=[file,split_tmp],outputs=[split_tmp,file],show_progress="full")
-    chat_with_file.click(ask_file,inputs=[chat_bot,message,file_answer,model_choice,sum_type,vector_path,file_list],outputs=[chat_bot,file_answer]).then(file_ask_stream,[chat_bot,file_answer],[chat_bot])
+    chat_with_file.click(ask_file,inputs=[chat_bot,message,file_answer,model_choice,sum_type,vector_path,file_list,filter_choice],outputs=[chat_bot,file_answer]).then(file_ask_stream,[chat_bot,file_answer],[chat_bot])
     summarize.click(summarize_file,inputs=[split_tmp,chat_bot,model_choice,sum_type],outputs=[sum_result,chat_bot]).then(sum_stream,[sum_result,chat_bot],[chat_bot])
 
     chat_with_file.click(lambda: gr.update(value=''), [],[message])
diff --git a/README.md b/README.md
index e87d903..303f990 100644
--- a/README.md
+++ b/README.md
@@ -50,9 +50,11 @@ Then use `pip install -r requirements.txt` on the Command Prompt to install the
   
   - [x] Local knowledge base management
   
-  - [ ] Chat with whole knowledge base
-
-- [ ] Local storage of data
+  - [x] Chat with whole knowledge base
+  
+    - [ ] List citation sources
+  
+  - [ ] Estimated cost of embedding files
 
 - [ ] Import and export chat history
 
diff --git a/README_zh-cn.md b/README_zh-cn.md
index f6214a4..5218304 100644
--- a/README_zh-cn.md
+++ b/README_zh-cn.md
@@ -43,13 +43,13 @@
   
   - [x] 文件全文总结
 
-  - [x] 知识库本地存储
-  
   - [x] 知识库本地管理
 
-  - [ ] 多文件对话
-
-- [ ] 数据本地存储
+  - [x] 知识库全局检索与对话
+    
+    - [ ] 显示引用来源
+  
+  - [ ] 预估嵌入文件的费用
 
 - [ ] 聊天记录导入、导出
 
diff --git a/vecstore/vecstore.py b/vecstore/vecstore.py
index 456dfc6..78b33c9 100644
--- a/vecstore/vecstore.py
+++ b/vecstore/vecstore.py
@@ -1,4 +1,4 @@
-
+from langchain.chains.summarize import load_summarize_chain
 from langchain.vectorstores import Chroma
 from langchain.embeddings.openai import OpenAIEmbeddings
 from langchain.chat_models import AzureChatOpenAI
@@ -148,26 +148,14 @@ def refresh_file_list(df):
     gr.Info('Successfully update kowledge base.')
     return gr.Dropdown.update(choices=file_list)
 
-def find_source_paths(filenames, data):
-    '''
-    Retrieve file paths in a vector database based on file name and remove duplicate paths
-    '''
-    paths = []
-    for metadata in data['metadatas']:
-        source = metadata.get('source')
-        if source:
-            for filename in filenames:
-                if filename in source and source not in paths:
-                    paths.append(source)
-    return paths
-
 def ask_file(file_ask_history_list:list,
             question_prompt: str,
             file_answer:list,
             model_choice:str,
             sum_type:str,
             persist_vec_path,
-            file_list
+            file_list,
+            filter_type:str,
             ):
     '''
     send splitted file to LLM
@@ -178,11 +166,11 @@ def ask_file(file_ask_history_list:list,
                     temperature=0.7)
     
     source_data = vectorstore.get()
-    filter_goal = find_source_paths(filenames=file_list,data=source_data)
+    filter_goal = find_source_paths(file_list,source_data)
 
     if persist_vec_path != None:
-        # docsearch = Chroma.from_documents(split_docs[-1], embeddings)
-        if file_list == "Unselect file(s)" or file_list != None:    
+        # Codes here in "if" may be deleted or modified later
+        if filter_type == "All":    
             # unselect file: retrieve whole knowledge base
             try:
                 qa = RetrievalQA.from_chain_type(llm=llm, chain_type=sum_type, 
@@ -191,7 +179,7 @@ def ask_file(file_ask_history_list:list,
                 result = qa({"query": question_prompt})
             except (NameError):
                 raise gr.Error("You have not load kownledge base yet.")
-        else:
+        elif filter_type == "Selected file":
             # only selected one file
             # Retrieve the specified knowledge base with filter
             qa = RetrievalQA.from_chain_type(llm=llm, chain_type=sum_type, 
@@ -211,15 +199,29 @@ def ask_file(file_ask_history_list:list,
     file_ask_history_list.append([usr_prob,None])
     return file_ask_history_list,file_answer
 
-def find_source_paths(filenames:list, data):
+def summarize_file(split_docs,chatbot,model_choice,sum_type):
+    llm = AzureChatOpenAI(model=model_choice,
+                    openai_api_type="azure",
+                    deployment_name=model_choice, # <----------设置选择模型的时候修改这里
+                    temperature=0.7)
+    # 创建总结链
+    chain = load_summarize_chain(llm, chain_type=sum_type, verbose=True)
+    
+    # 执行总结链
+    summarize_result = chain.run(split_docs[-1])
+
+    # 构造 chatbox 格式
+    chatbot.append(["Please summarize the file for me.",None])
+    return summarize_result,chatbot
+
+def find_source_paths(filename:str, data:dict):
     """
     Find the source paths of the files in the knowledge base.
+    return --> list
     """
     paths = []
     for metadata in data['metadatas']:
         source = metadata.get('source')
-        if source:
-            for filename in filenames:
-                if filename in source and source not in paths:
-                    paths.append(source)
+        if source and filename in source and source not in paths:
+            paths.append(source)
     return paths
\ No newline at end of file