docs: Improve notebook to show how to use tidb to store history messages (#16420)

After merging [PR
#16304](https://github.com/langchain-ai/langchain/pull/16304), I
realized that our notebook example for integrating TiDB with LangChain
was too basic. To make it more useful and user-friendly, I plan to
create a detailed example. This will show how to use TiDB for saving
history messages in LangChain, offering a clearer, more practical guide
for our users

docs/docs/integrations/memory/tidb_chat_message_history.ipynb

@@ -11,44 +11,233 @@
     "This notebook introduces how to use TiDB to store chat message history. "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "Firstly, we will install the following dependencies:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade --quiet langchain langchain_openai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Configuring your OpenAI Key"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Input your OpenAI API key:\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we will configure the connection to a TiDB. In this notebook, we will follow the standard connection method provided by TiDB Cloud to establish a secure and efficient database connection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# copy from tidb cloud console\n",
+    "tidb_connection_string_template = \"mysql+pymysql://<USER>:<PASSWORD>@<HOST>:4000/<DB>?ssl_ca=/etc/ssl/cert.pem&ssl_verify_cert=true&ssl_verify_identity=true\"\n",
+    "tidb_password = getpass.getpass(\"Input your TiDB password:\")\n",
+    "tidb_connection_string = tidb_connection_string_template.replace(\n",
+    "    \"<PASSWORD>\", tidb_password\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generating historical data\n",
+    "\n",
+    "Creating a set of historical data, which will serve as the foundation for our upcoming demonstrations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from datetime import datetime\n",
     "\n",
     "from langchain_community.chat_message_histories import TiDBChatMessageHistory\n",
     "\n",
     "history = TiDBChatMessageHistory(\n",
-    "    connection_string=\"mysql+pymysql://<host>:<PASSWORD>@<host>:4000/<db>?ssl_ca=/etc/ssl/cert.pem&ssl_verify_cert=true&ssl_verify_identity=true\",\n",
+    "    connection_string=tidb_connection_string,\n",
     "    session_id=\"code_gen\",\n",
     "    earliest_time=datetime.utcnow(),  # Optional to set earliest_time to load messages after this time point.\n",
     ")\n",
     "\n",
-    "history.add_user_message(\"hi! How's feature going?\")\n",
-    "history.add_ai_message(\"It's almot done\")"
+    "history.add_user_message(\"How's our feature going?\")\n",
+    "history.add_ai_message(\n",
+    "    \"It's going well. We are working on testing now. It will be released in Feb.\"\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[HumanMessage(content=\"How's our feature going?\"),\n",
+       " AIMessage(content=\"It's going well. We are working on testing now. It will be released in Feb.\")]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "history.messages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Chatting with historical data\n",
+    "\n",
+    "Let’s build upon the historical data generated earlier to create a dynamic chat interaction.  \n",
+    "\n",
+    "Firstly, Creating a Chat Chain with LangChain:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "from langchain_openai import ChatOpenAI\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\n",
+    "            \"system\",\n",
+    "            \"You're an assistant who's good at coding. You're helping a startup build\",\n",
+    "        ),\n",
+    "        MessagesPlaceholder(variable_name=\"history\"),\n",
+    "        (\"human\", \"{question}\"),\n",
+    "    ]\n",
+    ")\n",
+    "chain = prompt | ChatOpenAI()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Building a Runnable on History:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
+    "\n",
+    "chain_with_history = RunnableWithMessageHistory(\n",
+    "    chain,\n",
+    "    lambda session_id: TiDBChatMessageHistory(\n",
+    "        session_id=session_id, connection_string=tidb_connection_string\n",
+    "    ),\n",
+    "    input_messages_key=\"question\",\n",
+    "    history_messages_key=\"history\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initiating the Chat:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='There are 31 days in January, so there are 30 days until our feature is released in February.')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response = chain_with_history.invoke(\n",
+    "    {\"question\": \"Today is Jan 1st. How many days until our feature is released?\"},\n",
+    "    config={\"configurable\": {\"session_id\": \"code_gen\"}},\n",
+    ")\n",
+    "response"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Checking the history data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[HumanMessage(content=\"hi! How's feature going?\"),\n",
-       " AIMessage(content=\"It's almot done\")]"
+       "[HumanMessage(content=\"How's our feature going?\"),\n",
+       " AIMessage(content=\"It's going well. We are working on testing now. It will be released in Feb.\"),\n",
+       " HumanMessage(content='Today is Jan 1st. How many days until our feature is released?'),\n",
+       " AIMessage(content='There are 31 days in January, so there are 30 days until our feature is released in February.')]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "history.reload_cache()\n",
     "history.messages"
    ]
   }