langchain/docs/docs/integrations/chat/openai.ipynb

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   "source": [
    "---\n",
    "sidebar_label: OpenAI\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e49f1e0d",
   "metadata": {},
   "source": [
    "# ChatOpenAI\n",
    "\n",
    "This notebook covers how to get started with OpenAI chat models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "522686de",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.prompts.chat import (\n",
    "    ChatPromptTemplate,\n",
    "    HumanMessagePromptTemplate,\n",
    "    SystemMessagePromptTemplate,\n",
    ")\n",
    "from langchain_core.messages import HumanMessage, SystemMessage\n",
    "from langchain_openai import ChatOpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "62e0dbc3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "chat = ChatOpenAI(temperature=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e5fe97e",
   "metadata": {},
   "source": [
    "The above cell assumes that your OpenAI API key is set in your environment variables. If you would rather manually specify your API key and/or organization ID, use the following code:\n",
    "\n",
    "```python\n",
    "chat = ChatOpenAI(temperature=0, openai_api_key=\"YOUR_API_KEY\", openai_organization=\"YOUR_ORGANIZATION_ID\")\n",
    "```\n",
    "Remove the openai_organization parameter should it not apply to you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ce16ad78-8e6f-48cd-954e-98be75eb5836",
   "metadata": {
    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, example=False)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "messages = [\n",
    "    SystemMessage(\n",
    "        content=\"You are a helpful assistant that translates English to French.\"\n",
    "    ),\n",
    "    HumanMessage(\n",
    "        content=\"Translate this sentence from English to French. I love programming.\"\n",
    "    ),\n",
    "]\n",
    "chat(messages)"
   ]
  },
  {
   "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 convenience, 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": 4,
   "id": "180c5cc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "template = (\n",
    "    \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
    ")\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": 5,
   "id": "fbb043e6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, example=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chat_prompt = ChatPromptTemplate.from_messages(\n",
    "    [system_message_prompt, human_message_prompt]\n",
    ")\n",
    "\n",
    "# get a chat completion from the formatted messages\n",
    "chat(\n",
    "    chat_prompt.format_prompt(\n",
    "        input_language=\"English\", output_language=\"French\", text=\"I love programming.\"\n",
    "    ).to_messages()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57e27714",
   "metadata": {},
   "source": [
    "## Fine-tuning\n",
    "\n",
    "You can call fine-tuned OpenAI models by passing in your corresponding `modelName` parameter.\n",
    "\n",
    "This generally takes the form of `ft:{OPENAI_MODEL_NAME}:{ORG_NAME}::{MODEL_ID}`. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "33c4a8b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, example=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fine_tuned_model = ChatOpenAI(\n",
    "    temperature=0, model_name=\"ft:gpt-3.5-turbo-0613:langchain::7qTVM5AR\"\n",
    ")\n",
    "\n",
    "fine_tuned_model(messages)"
   ]
  }
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