langchain/docs/modules/chat/getting_started.ipynb

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  {
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   "source": [
    "# Getting Started\n",
    "\n",
    "This notebook covers how to get started with chat models. The interface is based around messages rather than raw text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "522686de",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain import PromptTemplate, LLMChain\n",
    "from langchain.prompts.chat import (\n",
    "    ChatPromptTemplate,\n",
    "    SystemMessagePromptTemplate,\n",
    "    AIMessagePromptTemplate,\n",
    "    HumanMessagePromptTemplate,\n",
    ")\n",
    "from langchain.schema import (\n",
    "    AIMessage,\n",
    "    HumanMessage,\n",
    "    SystemMessage\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "62e0dbc3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "chat = ChatOpenAI(temperature=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbaec18e-3684-4eef-955f-c1cec8bf765d",
   "metadata": {},
   "source": [
    "You can get chat completions by passing one or more messages to the chat model. The response will be a message. The types of messages currently supported in LangChain are `AIMessage`, `HumanMessage`, `SystemMessage`, and `ChatMessage` -- `ChatMessage` takes in an arbitrary role parameter. Most of the time, you'll just be dealing with `HumanMessage`, `AIMessage`, and `SystemMessage`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "76a6e7b0-e927-4bfb-a414-1332a4149106",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content=\"J'aime programmer.\", additional_kwargs={})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chat([HumanMessage(content=\"Translate this sentence from English to French. I love programming.\")])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a62153d4-1211-411b-a493-3febfe446ae0",
   "metadata": {},
   "source": [
    "OpenAI's chat model supports multiple messages as input. See [here](https://platform.openai.com/docs/guides/chat/chat-vs-completions) for more information. Here is an example of sending a system and user message to the chat model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ce16ad78-8e6f-48cd-954e-98be75eb5836",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content=\"J'aime programmer.\", additional_kwargs={})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages = [\n",
    "    SystemMessage(content=\"You are a helpful assistant that translates English to French.\"),\n",
    "    HumanMessage(content=\"Translate this sentence from English to French. I love programming.\")\n",
    "]\n",
    "chat(messages)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36dc8d7e-bd25-47ac-8c1b-60e3422603d3",
   "metadata": {},
   "source": [
    "You can go one step further and generate completions for multiple sets of messages using `generate`. This returns an `LLMResult` with an additional `message` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2b21fc52-74b6-4950-ab78-45d12c68fb4d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LLMResult(generations=[[ChatGeneration(text=\"J'aime programmer.\", generation_info=None, message=AIMessage(content=\"J'aime programmer.\", additional_kwargs={}))], [ChatGeneration(text=\"J'aime l'intelligence artificielle.\", generation_info=None, message=AIMessage(content=\"J'aime l'intelligence artificielle.\", additional_kwargs={}))]], llm_output=None)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_messages = [\n",
    "    [\n",
    "        SystemMessage(content=\"You are a helpful assistant that translates English to French.\"),\n",
    "        HumanMessage(content=\"Translate this sentence from English to French. I love programming.\")\n",
    "    ],\n",
    "    [\n",
    "        SystemMessage(content=\"You are a helpful assistant that translates English to French.\"),\n",
    "        HumanMessage(content=\"Translate this sentence from English to French. I love artificial intelligence.\")\n",
    "    ],\n",
    "]\n",
    "chat.generate(batch_messages)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b10b00ef-f373-4bc3-8302-2dfc28033734",
   "metadata": {},
   "source": [
    "## PromptTemplates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "778f912a-66ea-4a5d-b3de-6c7db4baba26",
   "metadata": {},
   "source": [
    "You can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplates`. You can use `ChatPromptTemplate`'s `format_prompt` -- this returns a `PromptValue`, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model.\n",
    "\n",
    "For convience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "180c5cc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "template=\"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
    "system_message_prompt = SystemMessagePromptTemplate.from_template(template)\n",
    "human_template=\"{text}\"\n",
    "human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fbb043e6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={})"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])\n",
    "\n",
    "# get a chat completion from the formatted messages\n",
    "chat(chat_prompt.format_prompt(input_language=\"English\", output_language=\"French\", text=\"I love programming.\").to_messages())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e28b98da",
   "metadata": {},
   "source": [
    "If you wanted to construct the MessagePromptTemplate more directly, you could create a PromptTemplate outside and then pass it in, eg:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d5b1ab1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt=PromptTemplate(\n",
    "    template=\"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
    "    input_variables=[\"input_language\", \"output_language\"],\n",
    ")\n",
    "system_message_prompt = SystemMessagePromptTemplate(prompt=prompt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92af0bba",
   "metadata": {},
   "source": [
    "## LLMChain\n",
    "You can use the existing LLMChain in a very similar way to before - provide a prompt and a model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f2cbfe3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "chain = LLMChain(llm=chat, prompt=chat_prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "268543b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"J'adore la programmation.\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.run(input_language=\"English\", output_language=\"French\", text=\"I love programming.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb779f3f",
   "metadata": {},
   "source": [
    "## Streaming\n",
    "\n",
    "Streaming is supported for `ChatOpenAI` through callback handling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "509181be",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Verse 1:\n",
      "Bubbles rising to the top\n",
      "A refreshing drink that never stops\n",
      "Clear and crisp, it's pure delight\n",
      "A taste that's sure to excite\n",
      "\n",
      "Chorus:\n",
      "Sparkling water, oh so fine\n",
      "A drink that's always on my mind\n",
      "With every sip, I feel alive\n",
      "Sparkling water, you're my vibe\n",
      "\n",
      "Verse 2:\n",
      "No sugar, no calories, just pure bliss\n",
      "A drink that's hard to resist\n",
      "It's the perfect way to quench my thirst\n",
      "A drink that always comes first\n",
      "\n",
      "Chorus:\n",
      "Sparkling water, oh so fine\n",
      "A drink that's always on my mind\n",
      "With every sip, I feel alive\n",
      "Sparkling water, you're my vibe\n",
      "\n",
      "Bridge:\n",
      "From the mountains to the sea\n",
      "Sparkling water, you're the key\n",
      "To a healthy life, a happy soul\n",
      "A drink that makes me feel whole\n",
      "\n",
      "Chorus:\n",
      "Sparkling water, oh so fine\n",
      "A drink that's always on my mind\n",
      "With every sip, I feel alive\n",
      "Sparkling water, you're my vibe\n",
      "\n",
      "Outro:\n",
      "Sparkling water, you're the one\n",
      "A drink that's always so much fun\n",
      "I'll never let you go, my friend\n",
      "Sparkling"
     ]
    }
   ],
   "source": [
    "from langchain.callbacks.base import CallbackManager\n",
    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
    "chat = ChatOpenAI(streaming=True, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), verbose=True, temperature=0)\n",
    "resp = chat([HumanMessage(content=\"Write me a song about sparkling water.\")])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c095285d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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