{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "00695447",
   "metadata": {
    "tags": []
   },
   "source": [
    "# How to add Memory to an LLMChain\n",
    "\n",
    "This notebook goes over how to use the Memory class with an LLMChain. For the purposes of this walkthrough, we will add  the [ConversationBufferMemory](https://api.python.langchain.com/en/latest/memory/langchain.memory.buffer.ConversationBufferMemory.html#langchain.memory.buffer.ConversationBufferMemory) class, although this can be any memory class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9f1aaf47",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.chains import LLMChain\n",
    "from langchain.llms import OpenAI\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "from langchain.prompts import PromptTemplate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b066ced",
   "metadata": {},
   "source": [
    "The most important step is setting up the prompt correctly. In the below prompt, we have two input keys: one for the actual input, another for the input from the Memory class. Importantly, we make sure the keys in the PromptTemplate and the ConversationBufferMemory match up (`chat_history`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e5501eda",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "template = \"\"\"You are a chatbot having a conversation with a human.\n",
    "\n",
    "{chat_history}\n",
    "Human: {human_input}\n",
    "Chatbot:\"\"\"\n",
    "\n",
    "prompt = PromptTemplate(\n",
    "    input_variables=[\"chat_history\", \"human_input\"], template=template\n",
    ")\n",
    "memory = ConversationBufferMemory(memory_key=\"chat_history\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f6566275",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "llm = OpenAI()\n",
    "llm_chain = LLMChain(\n",
    "    llm=llm,\n",
    "    prompt=prompt,\n",
    "    verbose=True,\n",
    "    memory=memory,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e2b189dc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mYou are a chatbot having a conversation with a human.\n",
      "\n",
      "\n",
      "Human: Hi there my friend\n",
      "Chatbot:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "' Hi there! How can I help you today?'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm_chain.predict(human_input=\"Hi there my friend\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a902729f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mYou are a chatbot having a conversation with a human.\n",
      "\n",
      "Human: Hi there my friend\n",
      "AI:  Hi there! How can I help you today?\n",
      "Human: Not too bad - how are you?\n",
      "Chatbot:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\" I'm doing great, thanks for asking! How are you doing?\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm_chain.predict(human_input=\"Not too bad - how are you?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33978824-0048-4e75-9431-1b2c02c169b0",
   "metadata": {},
   "source": [
    "## Adding Memory to a Chat Model-based LLMChain\n",
    "\n",
    "The above works for completion-style `LLM`s, but if you are using a chat model, you will likely get better performance using structured chat messages. Below is an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae5309bb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.schema import SystemMessage\n",
    "from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a237bbb8-e448-4238-8420-004e046ef84e",
   "metadata": {},
   "source": [
    "We will use the [ChatPromptTemplate](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.chat.ChatPromptTemplate.html) class to set up the chat prompt.\n",
    "\n",
    "The [from_messages](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.chat.ChatPromptTemplate.html#langchain.prompts.chat.ChatPromptTemplate.from_messages) method creates a ChatPromptTemplate from a list of messages (e.g., SystemMessage, HumanMessage, AIMessage, ChatMessage, etc.) or message templates, such as the [MessagesPlaceholder](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.chat.MessagesPlaceholder.html#langchain.prompts.chat.MessagesPlaceholder) below.\n",
    "\n",
    "The configuration below makes it so the memory will be injected to the middle of the chat prompt, in the \"chat_history\" key, and the user's inputs will be added in a human/user message to the end of the chat prompt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9bb8cde1-67c2-4133-b453-5c34fb36ff74",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "prompt = ChatPromptTemplate.from_messages([\n",
    "    SystemMessage(content=\"You are a chatbot having a conversation with a human.\"), # The persistent system prompt\n",
    "    MessagesPlaceholder(variable_name=\"chat_history\"), # Where the memory will be stored.\n",
    "    HumanMessagePromptTemplate.from_template(\"{human_input}\"), # Where the human input will injected\n",
    "])\n",
    "    \n",
    "memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9f77e466-a1a3-4c69-a001-ac5b7a40e219",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "llm = ChatOpenAI()\n",
    "\n",
    "chat_llm_chain = LLMChain(\n",
    "    llm=llm,\n",
    "    prompt=prompt,\n",
    "    verbose=True,\n",
    "    memory=memory,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f9709647-be82-43d5-b076-2a7da344ce8a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mSystem: You are a chatbot having a conversation with a human.\n",
      "Human: Hi there my friend\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Hello! How can I assist you today, my friend?'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chat_llm_chain.predict(human_input=\"Hi there my friend\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bdf04ebe-525a-4156-a3a7-65fd2df8d6fc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mSystem: You are a chatbot having a conversation with a human.\n",
      "Human: Hi there my friend\n",
      "AI: Hello! How can I assist you today, my friend?\n",
      "Human: Not too bad - how are you?\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"I'm an AI chatbot, so I don't have feelings, but I'm here to help and chat with you! Is there something specific you would like to talk about or any questions I can assist you with?\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chat_llm_chain.predict(human_input=\"Not too bad - how are you?\")"
   ]
  }
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