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pilot/app.py
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pilot/app.py
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#!/usr/bin/env python3
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#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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# -*- coding:utf-8 -*-
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import streamlit as st
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from langchain.agents import (
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from langchain.agents import (
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load_tools,
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load_tools,
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initialize_agent,
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initialize_agent,
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AgentType
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AgentType
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)
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)
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from pilot.model.vicuna_llm import VicunaRequestLLM, VicunaEmbeddingLLM
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from pilot.model.vicuna_llm import VicunaRequestLLM, VicunaEmbeddingLLM
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llm = VicunaRequestLLM()
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from llama_index import LLMPredictor, LangchainEmbedding, ServiceContext
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from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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from llama_index import Document, GPTSimpleVectorIndex
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tools = load_tools(['python_repl'], llm=llm)
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def agent_demo():
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agent = initialize_agent(tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
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llm = VicunaRequestLLM()
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agent.run(
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"Write a python script that prints 'Hello World!'"
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tools = load_tools(['python_repl'], llm=llm)
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)
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agent = initialize_agent(tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
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agent.run(
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"Write a SQL script that Query 'select count(1)!'"
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)
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def knowledged_qa_demo(text_list):
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llm_predictor = LLMPredictor(llm=VicunaRequestLLM)
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hfemb = VicunaEmbeddingLLM()
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embed_model = LangchainEmbedding(hfemb)
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documents = [Document(t) for t in text_list]
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service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, embed_model=embed_model)
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index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)
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return index
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if __name__ == "__main__":
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# agent_demo()
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test1 = """ 这是一段测试文字 """
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text_list = [test1]
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index = knowledged_qa_demo(text_list)
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st.title("智能助手")
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query = st.text_input("请提问.")
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if st.button("提交"):
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response = index.query(query)
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print(query, response.response)
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st.write(response.response)
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