聊天元素简介

理解编码过程

1import streamlit as st
2import pandas as pd
3
4from chatbot import generate_answer
5
6# 设置主页面的通用配置
7st.set_page_config(
8    page_title="聊天机器人",
9    page_icon="🧊",
10    layout="wide",
11    initial_sidebar_state="expanded"
12)
13
14# 设置标题
15st.title(":rainbow[LLM 聊天机器人]")
16
17# 初始化聊天历史
18if "messages" not in st.session_state:
19    st.session_state.messages = []
20
21# 在应用重新运行时，从历史记录中显示聊天消息
22for message in st.session_state.messages:
23    with st.chat_message(message["role"]):
24        st.markdown(message["content"])
25
26# 接收用户输入
27if prompt := st.chat_input("今天我可以如何帮助您？"):
28    # 将用户消息添加到聊天历史
29    st.session_state.messages.append({"role": "user", "content": prompt})
30    # 在聊天消息容器中显示用户消息
31    with st.chat_message("user"):
32        st.markdown(prompt)
33
34    # 在聊天消息容器中显示助手回复
35    response = generate_answer(prompt)
36    st.session_state.messages.append({"role": "assistant", "content": response})

1import streamlit as st
2from langchain_groq import ChatGroq
3from langchain_core.prompts import ChatPromptTemplate
4from langchain_core.runnables import RunnablePassthrough, RunnableParallel
5from langchain_core.output_parsers import StrOutputParser
6# 屏蔽警告
7import warnings
8warnings.filterwarnings("ignore", category=UserWarning)
9warnings.filterwarnings("ignore", category=FutureWarning)
10import os
11os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
12# 禁用并行处理
13os.environ["TOKENIZERS_PARALLELISM"] = "false"
14# 设置 Groq API 密钥
15groq_api_key = os.environ["GROQ_API_KEY"]
16
17
18def generate_answer(query):
19        
20        # 请谨慎使用 API 密钥
21        model_name = "llama3-8b-8192"
22        llm = ChatGroq(temperature=0.5, groq_api_key=groq_api_key, model_name=model_name)
23
24        # 定义聊天历史
25        history = "\n".join([f"{message['role']}: {message['content']}" for message in st.session_state.messages])
26        
27        # 使用“逐步回溯提问”技术定义提示词和提示模板
28        template = """
29                - 扮演一个友好的聊天机器人角色。
30                -----------------------------------------------------------------------------------------------------
31                问题: {question}
32                -----------------------------------------------------------------------------------------------------
33                历史: {history}
34                -----------------------------------------------------------------------------------------------------
35                回答:
36                -----------------------------------------------------------------------------------------------------
37                """
38        prompt = ChatPromptTemplate.from_template(template)
39
40        # 定义链式调用构造器
41        chain = (RunnableParallel(
42                {"question": RunnablePassthrough(),
43                 "history": RunnablePassthrough()
44                })
45                | prompt
46                | llm
47                | StrOutputParser()
48        )
49        
50        # 使用链式调用定义输出生成器
51        with st.chat_message('assistant'):
52                message_placeholder = st.empty()
53                all_results = ''
54                for res in chain.stream({"question": query, "history": history}):
55                        all_results += res
56                        message_placeholder.markdown(all_results)
57        
58        return all_results

实践练习

（已移除外部嵌入内容）