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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 arbitrary chain. For the purposes of this walkthrough, we will add `ConversationBufferMemory` to a `LLMChain`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9f1aaf47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains.conversation.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(dynamic_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 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": [
+       "'\\n\\nHi there my friend! Thank you for talking with me.'"
+      ]
+     },
+     "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 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: \n",
+      "\n",
+      "Hi there my friend! Thank you for talking with me.\n",
+      "Human: Not to bad - how are you?\n",
+      "Chatbot:\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\"\\n\\nI'm doing well, thank you for asking. How about you?\""
+      ]
+     },
+     "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": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}