Extend the StreamlitChatMessageHistory docs with a fuller example and… (#8774)

Add more details to the [notebook for
StreamlitChatMessageHistory](https://python.langchain.com/docs/integrations/memory/streamlit_chat_message_history),
including a link to a [running example
app](https://langchain-st-memory.streamlit.app/).

Original PR: https://github.com/langchain-ai/langchain/pull/8497

docs/extras/integrations/callbacks/streamlit.md

@@ -71,3 +71,6 @@ or any other local ENV management tool.
 
 Currently `StreamlitCallbackHandler` is geared towards use with a LangChain Agent Executor. Support for additional agent types,
 use directly with Chains, etc will be added in the future.
+
+You may also be interested in using
+[StreamlitChatMessageHistory](/docs/integrations/memory/streamlit_chat_message_history) for LangChain.

docs/extras/integrations/memory/streamlit_chat_message_history.ipynb

@@ -7,8 +7,17 @@
    "source": [
     "# Streamlit Chat Message History\n",
     "\n",
-    "This notebook goes over how to use Streamlit to store chat message history. Note, StreamlitChatMessageHistory only works when run in a Streamlit app. For more on Streamlit check out their\n",
+    "This notebook goes over how to store and use chat message history in a Streamlit app. StreamlitChatMessageHistory will store messages in\n",
-    "[getting started documentation](https://docs.streamlit.io/library/get-started)."
+    "[Streamlit session state](https://docs.streamlit.io/library/api-reference/session-state)\n",
+    "at the specified `key=`. The default key is `\"langchain_messages\"`.\n",
+    "\n",
+    "- Note, StreamlitChatMessageHistory only works when run in a Streamlit app.\n",
+    "- You may also be interested in [StreamlitCallbackHandler](/docs/integrations/callbacks/streamlit) for LangChain.\n",
+    "- For more on Streamlit check out their\n",
+    "[getting started documentation](https://docs.streamlit.io/library/get-started).\n",
+    "\n",
+    "You can see the [full app example running here](https://langchain-st-memory.streamlit.app/), and more examples in\n",
+    "[github.com/langchain-ai/streamlit-agent](https://github.com/langchain-ai/streamlit-agent)."
    ]
   },
   {
@@ -20,7 +29,7 @@
    "source": [
     "from langchain.memory import StreamlitChatMessageHistory\n",
     "\n",
-    "history = StreamlitChatMessageHistory(\"foo\")\n",
+    "history = StreamlitChatMessageHistory(key=\"chat_messages\")\n",
     "\n",
     "history.add_user_message(\"hi!\")\n",
     "history.add_ai_message(\"whats up?\")"
@@ -35,6 +44,90 @@
    "source": [
     "history.messages"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b60dc735",
+   "metadata": {},
+   "source": [
+    "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42ab5bf3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain.memory.chat_message_histories import StreamlitChatMessageHistory\n",
+    "\n",
+    "# Optionally, specify your own session_state key for storing messages\n",
+    "msgs = StreamlitChatMessageHistory(key=\"special_app_key\")\n",
+    "\n",
+    "memory = ConversationBufferMemory(memory_key=\"history\", chat_memory=msgs)\n",
+    "if len(msgs.messages) == 0:\n",
+    "    msgs.add_ai_message(\"How can I help you?\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a29252de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import LLMChain\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "template = \"\"\"You are an AI chatbot having a conversation with a human.\n",
+    "\n",
+    "{history}\n",
+    "Human: {human_input}\n",
+    "AI: \"\"\"\n",
+    "prompt = PromptTemplate(input_variables=[\"history\", \"human_input\"], template=template)\n",
+    "\n",
+    "# Add the memory to an LLMChain as usual\n",
+    "llm_chain = LLMChain(llm=OpenAI(), prompt=prompt, memory=memory)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7cd99b4b",
+   "metadata": {},
+   "source": [
+    "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`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3bdb637b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import streamlit as st\n",
+    "\n",
+    "for msg in msgs.messages:\n",
+    "    st.chat_message(msg.type).write(msg.content)\n",
+    "\n",
+    "if prompt := st.chat_input():\n",
+    "    st.chat_message(\"human\").write(prompt)\n",
+    "\n",
+    "    # As usual, new messages are added to StreamlitChatMessageHistory when the Chain is called.\n",
+    "    response = llm_chain.run(prompt)\n",
+    "    st.chat_message(\"ai\").write(response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7adaf3d6",
+   "metadata": {},
+   "source": [
+    "**[View the final app](https://langchain-st-memory.streamlit.app/).**"
+   ]
   }
  ],
  "metadata": {