Streamlit

Streamlit 是一个开源的 Python 库，使创建和分享美观的 机器学习和数据科学的自定义网页应用变得简单。

本笔记本介绍了如何在 Streamlit 应用中存储和使用聊天消息历史。StreamlitChatMessageHistory 将在 Streamlit 会话状态 指定的 key= 中存储消息。默认键为 "langchain_messages"。

• 注意，StreamlitChatMessageHistory 仅在 Streamlit 应用中运行时有效。
• 您可能还对 LangChain 的 StreamlitCallbackHandler 感兴趣。
• 有关 Streamlit 的更多信息，请查看他们的 入门文档。

该集成位于 LangChain 社区 包中，因此我们需要安装它。我们还需要安装 streamlit。

pip install -U langchain-community streamlit

您可以在这里查看完整的应用示例，以及更多示例在 github.com/langchain-ai/streamlit-agent。

<!--IMPORTS:[{"imported": "StreamlitChatMessageHistory", "source": "langchain_community.chat_message_histories", "docs": "https://python.langchain.com/api_reference/community/chat_message_histories/langchain_community.chat_message_histories.streamlit.StreamlitChatMessageHistory.html", "title": "Streamlit"}]-->
from langchain_community.chat_message_histories import (
    StreamlitChatMessageHistory,
)

history = StreamlitChatMessageHistory(key="chat_messages")

history.add_user_message("hi!")
history.add_ai_message("whats up?")

history.messages

我们可以轻松地将此消息历史类与LCEL 运行接口结合。

历史记录将在给定用户会话内的Streamlit应用的重新运行中保持。给定的StreamlitChatMessageHistory不会在用户会话之间保持或共享。

# Optionally, specify your own session_state key for storing messages
msgs = StreamlitChatMessageHistory(key="special_app_key")

if len(msgs.messages) == 0:
    msgs.add_ai_message("How can I help you?")

<!--IMPORTS:[{"imported": "ChatPromptTemplate", "source": "langchain_core.prompts", "docs": "https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html", "title": "Streamlit"}, {"imported": "MessagesPlaceholder", "source": "langchain_core.prompts", "docs": "https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.chat.MessagesPlaceholder.html", "title": "Streamlit"}, {"imported": "RunnableWithMessageHistory", "source": "langchain_core.runnables.history", "docs": "https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.history.RunnableWithMessageHistory.html", "title": "Streamlit"}, {"imported": "ChatOpenAI", "source": "langchain_openai", "docs": "https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html", "title": "Streamlit"}]-->
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_openai import ChatOpenAI

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()

chain_with_history = RunnableWithMessageHistory(
    chain,
    lambda session_id: msgs,  # Always return the instance created earlier
    input_messages_key="question",
    history_messages_key="history",
)

对话式Streamlit应用通常会在每次重新运行时重新绘制每条先前的聊天消息。这可以通过遍历StreamlitChatMessageHistory.messages轻松实现：

import streamlit as st

for msg in msgs.messages:
    st.chat_message(msg.type).write(msg.content)

if prompt := st.chat_input():
    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)

查看最终应用。