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https://github.com/hwchase17/langchain
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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
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Ian
GitHub
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@ -11,39 +11,108 @@
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"This notebook introduces how to use TiDB to store chat message history. "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup\n",
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"\n",
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"Firstly, we will install the following dependencies:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install --upgrade --quiet langchain langchain_openai"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Configuring your OpenAI Key"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Input your OpenAI API key:\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# copy from tidb cloud console\n",
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"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",
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"tidb_password = getpass.getpass(\"Input your TiDB password:\")\n",
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"tidb_connection_string = tidb_connection_string_template.replace(\n",
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" \"<PASSWORD>\", tidb_password\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Generating historical data\n",
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"\n",
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"Creating a set of historical data, which will serve as the foundation for our upcoming demonstrations."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"from datetime import datetime\n",
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"\n",
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"from langchain_community.chat_message_histories import TiDBChatMessageHistory\n",
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"\n",
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"history = TiDBChatMessageHistory(\n",
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" connection_string=\"mysql+pymysql://<host>:<PASSWORD>@<host>:4000/<db>?ssl_ca=/etc/ssl/cert.pem&ssl_verify_cert=true&ssl_verify_identity=true\",\n",
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" connection_string=tidb_connection_string,\n",
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" session_id=\"code_gen\",\n",
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" earliest_time=datetime.utcnow(), # Optional to set earliest_time to load messages after this time point.\n",
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")\n",
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"\n",
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"history.add_user_message(\"hi! How's feature going?\")\n",
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"history.add_ai_message(\"It's almot done\")"
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"history.add_user_message(\"How's our feature going?\")\n",
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"history.add_ai_message(\n",
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" \"It's going well. We are working on testing now. It will be released in Feb.\"\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[HumanMessage(content=\"hi! How's feature going?\"),\n",
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" AIMessage(content=\"It's almot done\")]"
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"[HumanMessage(content=\"How's our feature going?\"),\n",
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" AIMessage(content=\"It's going well. We are working on testing now. It will be released in Feb.\")]"
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]
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},
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"execution_count": 3,
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -51,6 +120,126 @@
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"source": [
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"history.messages"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Chatting with historical data\n",
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"\n",
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"Let’s build upon the historical data generated earlier to create a dynamic chat interaction. \n",
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"\n",
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"Firstly, Creating a Chat Chain with LangChain:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
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"from langchain_openai import ChatOpenAI\n",
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"\n",
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"prompt = ChatPromptTemplate.from_messages(\n",
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" [\n",
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" (\n",
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" \"system\",\n",
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" \"You're an assistant who's good at coding. You're helping a startup build\",\n",
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" ),\n",
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" MessagesPlaceholder(variable_name=\"history\"),\n",
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" (\"human\", \"{question}\"),\n",
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" ]\n",
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")\n",
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"chain = prompt | ChatOpenAI()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Building a Runnable on History:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.runnables.history import RunnableWithMessageHistory\n",
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"\n",
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"chain_with_history = RunnableWithMessageHistory(\n",
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" chain,\n",
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" lambda session_id: TiDBChatMessageHistory(\n",
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" session_id=session_id, connection_string=tidb_connection_string\n",
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" ),\n",
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" input_messages_key=\"question\",\n",
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" history_messages_key=\"history\",\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Initiating the Chat:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content='There are 31 days in January, so there are 30 days until our feature is released in February.')"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"response = chain_with_history.invoke(\n",
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" {\"question\": \"Today is Jan 1st. How many days until our feature is released?\"},\n",
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" config={\"configurable\": {\"session_id\": \"code_gen\"}},\n",
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")\n",
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"response"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Checking the history data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[HumanMessage(content=\"How's our feature going?\"),\n",
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" AIMessage(content=\"It's going well. We are working on testing now. It will be released in Feb.\"),\n",
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" HumanMessage(content='Today is Jan 1st. How many days until our feature is released?'),\n",
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" AIMessage(content='There are 31 days in January, so there are 30 days until our feature is released in February.')]"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"history.reload_cache()\n",
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"history.messages"
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]
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}
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],
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"metadata": {
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