【streamlit & langchain 搭建一个超简单的具有记忆能力的对话应用】_streamlit 几种对话历史应用实现

# 安装相应的包

pip install -U langchain-community langchain-core streamlit langchain langchain-openai

# 新建一个webdemo.py

注意填写openai 的api_key , 适配openai接口的大模型也是可以（注意一并修改base_url）

- moonshot, 国内的大模型公司，体验还可以

        适配openai的接口， 申请api_key地址

        base_url: https://api.moonshot.cn/v1

from langchain_community.chat_message_histories import StreamlitChatMessageHistory
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_openai import ChatOpenAI
import streamlit as st
 
# Optionally, specify your own session_state key for storing messages
msgs = StreamlitChatMessageHistory(key="special_app_key")
 
 
# 创建chat-chain
prompt = ChatPromptTemplate.from_messages(
    [
        ("system", "You are an AI chatbot having a conversation with a human."),
        MessagesPlaceholder(variable_name="history"),
        ("human", "{question}"),
    ]
)
chain = prompt | ChatOpenAI(
    api_key = '',
    # base_url = ''
)
chain_with_history = RunnableWithMessageHistory(
    chain,
    lambda session_id: msgs,  # Always return the instance created earlier
    input_messages_key="question",
    history_messages_key="history",
)
 
 
# 显示历史对话
if len(msgs.messages) == 0:
    msgs.add_ai_message("How can I help you?")
for msg in msgs.messages:
    st.chat_message(msg.type).write(msg.content)
 
# 最新的对话
if prompt := st.chat_input('输入...'):
    st.chat_message("human").write(prompt)
 
    # As usual, new messages are added to StreamlitChatMessageHistory 
    # when the Chain is called.
    config = {"configurable": {"session_id": "any"}}
    response = chain_with_history.invoke({"question": prompt}, config)
    st.chat_message("ai").write(response.content)

# 运行

streamlit run webdemo.py

# 参考 

https://www.wpsshop.cn/w/我家小花儿/article/detail/767491