{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e49f1e0d",
   "metadata": {},
   "source": [
    "# OpenAI\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.chat_models import ChatOpenAI\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": 2,
   "id": "62e0dbc3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "chat = ChatOpenAI(temperature=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ce16ad78-8e6f-48cd-954e-98be75eb5836",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content=\"J'aime programmer.\", additional_kwargs={})"
      ]
     },
     "execution_count": 3,
     "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": "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": 4,
   "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": 5,
   "id": "fbb043e6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={})"
      ]
     },
     "execution_count": 5,
     "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": "code",
   "execution_count": null,
   "id": "c095285d",
   "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}