Add LLMChain example of memory with chat models (#8250)

docs/extras/modules/memory/adding_memory.ipynb

@@ -3,22 +3,28 @@
   {
    "cell_type": "markdown",
    "id": "00695447",
-   "metadata": {},
+   "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` class, although this can be any memory class."
+    "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": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
+    "from langchain.chains import LLMChain\n",
+    "from langchain.llms import OpenAI\n",
     "from langchain.memory import ConversationBufferMemory\n",
-    "from langchain import OpenAI, LLMChain, PromptTemplate"
+    "from langchain.prompts import PromptTemplate"
    ]
   },
   {
@@ -33,7 +39,9 @@
    "cell_type": "code",
    "execution_count": 2,
    "id": "e5501eda",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "template = \"\"\"You are a chatbot having a conversation with a human.\n",
@@ -52,11 +60,14 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "f6566275",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
+    "llm = OpenAI()\n",
     "llm_chain = LLMChain(\n",
-    "    llm=OpenAI(),\n",
+    "    llm=llm,\n",
     "    prompt=prompt,\n",
     "    verbose=True,\n",
     "    memory=memory,\n",
@@ -67,7 +78,9 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "e2b189dc",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -83,13 +96,13 @@
       "Human: Hi there my friend\n",
       "Chatbot:\u001b[0m\n",
       "\n",
-      "\u001b[1m> Finished LLMChain chain.\u001b[0m\n"
+      "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "' Hi there, how are you doing today?'"
+       "' Hi there! How can I help you today?'"
       ]
      },
      "execution_count": 4,
@@ -105,7 +118,9 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "a902729f",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -117,19 +132,18 @@
       "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",
+      "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 LLMChain chain.\u001b[0m\n"
+      "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "\" I'm doing great, thank you for asking!\""
+       "\" I'm doing great, thanks for asking! How are you doing?\""
       ]
      },
      "execution_count": 5,
@@ -142,12 +156,153 @@
    ]
   },
   {
-   "cell_type": "code",
+   "cell_type": "markdown",
-   "execution_count": null,
+   "id": "33978824-0048-4e75-9431-1b2c02c169b0",
-   "id": "ae5309bb",
    "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": []
+   "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 injectd\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?\")"
+   ]
   }
  ],
  "metadata": {
@@ -166,7 +321,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.2"
   }
  },
  "nbformat": 4,