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 "cells": [
  {
   "cell_type": "markdown",
   "id": "00695447",
   "metadata": {},
   "source": [
    "# Adding 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` class, although this can be any memory class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9f1aaf47",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.memory import ConversationBufferMemory\n",
    "from langchain import OpenAI, LLMChain, 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": {},
   "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\"], \n",
    "    template=template\n",
    ")\n",
    "memory = ConversationBufferMemory(memory_key=\"chat_history\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f6566275",
   "metadata": {},
   "outputs": [],
   "source": [
    "llm_chain = LLMChain(\n",
    "    llm=OpenAI(), \n",
    "    prompt=prompt, \n",
    "    verbose=True, \n",
    "    memory=memory,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e2b189dc",
   "metadata": {},
   "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 LLMChain chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "' Hi there, how are you doing 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": {},
   "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",
      "AI:  Hi there, how are you doing today?\n",
      "Human: Not to bad - how are you?\n",
      "Chatbot:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\" I'm doing great, thank you for asking!\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm_chain.predict(human_input=\"Not to bad - how are you?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae5309bb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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