mirror of
https://github.com/langchain-ai/streamlit-agent.git
synced 2026-07-01 23:44:02 -04:00
Joshua Carroll
67 lines
2.5 KiB
Python
67 lines
2.5 KiB
Python
from langchain.chains import LLMChain
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from langchain.memory import ConversationBufferMemory
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from langchain.prompts import PromptTemplate
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from langchain_community.chat_message_histories import StreamlitChatMessageHistory
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from langchain_openai import OpenAI
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import streamlit as st
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st.set_page_config(page_title="StreamlitChatMessageHistory", page_icon="📖")
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st.title("📖 StreamlitChatMessageHistory")
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"""
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A basic example of using StreamlitChatMessageHistory to help LLMChain remember messages in a conversation.
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The messages are stored in Session State across re-runs automatically. You can view the contents of Session State
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in the expander below. View the
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[source code for this app](https://github.com/langchain-ai/streamlit-agent/blob/main/streamlit_agent/basic_memory.py).
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"""
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# Set up memory
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msgs = StreamlitChatMessageHistory(key="langchain_messages")
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memory = ConversationBufferMemory(chat_memory=msgs)
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if len(msgs.messages) == 0:
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msgs.add_ai_message("How can I help you?")
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view_messages = st.expander("View the message contents in session state")
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# Get an OpenAI API Key before continuing
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if "openai_api_key" in st.secrets:
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openai_api_key = st.secrets.openai_api_key
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else:
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openai_api_key = st.sidebar.text_input("OpenAI API Key", type="password")
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if not openai_api_key:
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st.info("Enter an OpenAI API Key to continue")
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st.stop()
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# Set up the LLMChain, passing in memory
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template = """You are an AI chatbot having a conversation with a human.
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{history}
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Human: {human_input}
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AI: """
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prompt = PromptTemplate(input_variables=["history", "human_input"], template=template)
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llm_chain = LLMChain(llm=OpenAI(openai_api_key=openai_api_key), prompt=prompt, memory=memory)
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# Render current messages from StreamlitChatMessageHistory
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for msg in msgs.messages:
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st.chat_message(msg.type).write(msg.content)
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# If user inputs a new prompt, generate and draw a new response
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if prompt := st.chat_input():
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st.chat_message("human").write(prompt)
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# Note: new messages are saved to history automatically by Langchain during run
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response = llm_chain.invoke(prompt)
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st.chat_message("ai").write(response["text"])
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# Draw the messages at the end, so newly generated ones show up immediately
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with view_messages:
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"""
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Memory initialized with:
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```python
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msgs = StreamlitChatMessageHistory(key="langchain_messages")
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memory = ConversationBufferMemory(chat_memory=msgs)
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```
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Contents of `st.session_state.langchain_messages`:
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"""
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view_messages.json(st.session_state.langchain_messages)
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