{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.prompts import PromptTemplate, ChatPromptTemplate, HumanMessagePromptTemplate\n",
    "from langchain.llms import OpenAI\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "\n",
    "from langchain.output_parsers import PydanticOutputParser\n",
    "from pydantic import BaseModel, Field, validator\n",
    "from typing import List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = 'text-davinci-003'\n",
    "temperature = 0.7\n",
    "model = OpenAI(model_name=model_name, temperature=temperature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define your desired data structure.\n",
    "class Joke(BaseModel):\n",
    "    setup: str = Field(description=\"question to set up a joke\")\n",
    "    punchline: str = Field(description=\"answer to resolve the joke\")\n",
    "    \n",
    "    # You can add custom validation logic easily with Pydantic.\n",
    "    @validator('setup')\n",
    "    def question_ends_with_question_mark(cls, field):\n",
    "        if field[-1] != '?' and field[-1] != '？':\n",
    "            raise ValueError(\"Badly formed question!\")\n",
    "        return field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up a parser + inject instructions into the prompt template.\n",
    "parser = PydanticOutputParser(pydantic_object=Joke)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = PromptTemplate(\n",
    "    template=\"Answer the user query.\\n{format_instructions}\\n{query}\\n\",\n",
    "    input_variables=[\"query\"],\n",
    "    partial_variables={\"format_instructions\": parser.get_format_instructions()}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prompt: text='Answer the user query.\\nThe output should be formatted as a JSON instance that conforms to the JSON schema below.\\n\\nAs an example, for the schema {\"properties\": {\"foo\": {\"title\": \"Foo\", \"description\": \"a list of strings\", \"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"required\": [\"foo\"]}}\\nthe object {\"foo\": [\"bar\", \"baz\"]} is a well-formatted instance of the schema. The object {\"properties\": {\"foo\": [\"bar\", \"baz\"]}} is not well-formatted.\\n\\nHere is the output schema:\\n```\\n{\"properties\": {\"setup\": {\"title\": \"Setup\", \"description\": \"question to set up a joke\", \"type\": \"string\"}, \"punchline\": {\"title\": \"Punchline\", \"description\": \"answer to resolve the joke\", \"type\": \"string\"}}, \"required\": [\"setup\", \"punchline\"]}\\n```\\n用中文回答，讲个笑话。\\n'\n"
     ]
    }
   ],
   "source": [
    "# And a query intented to prompt a language model to populate the data structure.\n",
    "joke_query = \"用中文回答，讲个笑话。\"\n",
    "_input = prompt.format_prompt(query=joke_query)\n",
    "print(\"prompt:\", _input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = model(_input.to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 setup='为什么大象不喜欢穿衣服？' punchline='因为衣服太小！'\n",
      "1 setup='为什么乌龟不能唱歌？' punchline='因为它没有嗓子！'\n",
      "2 setup='什么东西可以排列成一半？' punchline='一个鸡蛋！'\n",
      "3 setup='为什么老虎不吃素？' punchline='因为他不能把萝卜拔得出土。'\n",
      "4 setup='什么植物可以在水里生长？' punchline='水芹菜！'\n",
      "5 setup='什么可以把一只老虎变成猫？' punchline='把它放进冰箱！'\n",
      "6 setup='为什么恐龙不能坐飞机？' punchline='因为它没有票！'\n",
      "7 setup='为什么熊总喜欢去森林游玩？' punchline='因为他想要一个熊朋友！'\n",
      "8 setup='什么东西可以把一座山搬走？' punchline='风'\n",
      "9 setup='为什么有些人把面包放进微波炉里？' punchline='因为他们想把它变成“烤面包”。'\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    output = model(_input.to_string())\n",
    "    print(i, parser.parse(output))"
   ]
  }
 ],
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