Streamlit Chat Message History

This notebook goes over how to store and use chat message history in a Streamlit app. StreamlitChatMessageHistory will store messages in Streamlit session state at the specified key=. The default key is "langchain_messages".

• Note, StreamlitChatMessageHistory only works when run in a Streamlit app.
• You may also be interested in StreamlitCallbackHandler for LangChain.
• For more on Streamlit check out their getting started documentation.

You can see the full app example running here, and more examples in github.com/langchain-ai/streamlit-agent.

from langchain.memory import StreamlitChatMessageHistory

history = StreamlitChatMessageHistory(key="chat_messages")

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

API Reference:

• StreamlitChatMessageHistory

history.messages

You can integrate StreamlitChatMessageHistory into ConversationBufferMemory and chains or agents as usual. The history will be persisted across re-runs of the Streamlit app within a given user session. A given StreamlitChatMessageHistory will NOT be persisted or shared across user sessions.

from langchain.memory import ConversationBufferMemory
from langchain.memory.chat_message_histories import StreamlitChatMessageHistory

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

memory = ConversationBufferMemory(memory_key="history", chat_memory=msgs)
if len(msgs.messages) == 0:
    msgs.add_ai_message("How can I help you?")

API Reference:

• ConversationBufferMemory
• StreamlitChatMessageHistory

from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
template = """You are an AI chatbot having a conversation with a human.

{history}
Human: {human_input}
AI: """
prompt = PromptTemplate(input_variables=["history", "human_input"], template=template)

# Add the memory to an LLMChain as usual
llm_chain = LLMChain(llm=OpenAI(), prompt=prompt, memory=memory)

API Reference:

• LLMChain
• OpenAI
• PromptTemplate

Conversational Streamlit apps will often re-draw each previous chat message on every re-run. This is easy to do by iterating through StreamlitChatMessageHistory.messages:

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.
    response = llm_chain.run(prompt)
    st.chat_message("ai").write(response)

View the final app.