diff --git a/pilot/app.py b/pilot/app.py
index 5f2cf93db..f5fcb3ad1 100644
--- a/pilot/app.py
+++ b/pilot/app.py
@@ -1,18 +1,48 @@
 #!/usr/bin/env python3
 # -*- coding:utf-8 -*-
 
-
+import streamlit as st
 from langchain.agents import (
     load_tools,
     initialize_agent,
     AgentType
 )
-
 from pilot.model.vicuna_llm import VicunaRequestLLM, VicunaEmbeddingLLM
-llm = VicunaRequestLLM()
+from llama_index import LLMPredictor, LangchainEmbedding, ServiceContext
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from llama_index import Document, GPTSimpleVectorIndex
 
-tools = load_tools(['python_repl'], llm=llm)
-agent = initialize_agent(tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
-agent.run(
-    "Write a python script that prints 'Hello World!'"
-)
\ No newline at end of file
+def agent_demo():
+    llm = VicunaRequestLLM()
+
+    tools = load_tools(['python_repl'], llm=llm)
+    agent = initialize_agent(tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
+    agent.run(
+        "Write a SQL script that Query 'select count(1)!'"
+    )
+
+def knowledged_qa_demo(text_list):
+    llm_predictor = LLMPredictor(llm=VicunaRequestLLM)
+    hfemb = VicunaEmbeddingLLM()
+    embed_model = LangchainEmbedding(hfemb)
+    documents = [Document(t) for t in text_list]
+
+    service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, embed_model=embed_model)
+    index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context) 
+    return index
+
+
+if __name__ == "__main__":
+    # agent_demo()
+
+    test1 = """ 这是一段测试文字  """
+    text_list = [test1]
+    index = knowledged_qa_demo(text_list)
+
+    st.title("智能助手")
+    query = st.text_input("请提问.")
+    
+    if st.button("提交"):
+        response = index.query(query)
+        print(query, response.response)
+        st.write(response.response)
\ No newline at end of file