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{
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"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": 10,
"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": 11,
"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",
" # 如果是中文，这里需要判断中文问号和英文问号。否则这里会抛出ValueError。\n",
" # if field[-1] != '?' and field[-1] != '？':\n",
" if field[-1] != '?' and field[-1] != '？':\n",
" raise ValueError(\"Badly formed question!\")\n",
" return field"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# Set up a parser + inject instructions into the prompt template.\n",
"parser = PydanticOutputParser(pydantic_object=Joke)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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": 14,
"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": 15,
"metadata": {},
"outputs": [],
"source": [
"output = model(_input.to_string())"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 setup='为什么山羊爱上蜜蜂？' punchline='因为它们都喜欢花蜜！'\n",
"1 setup='为什么鸡蛋不能上树？' punchline='因为它们太害怕摔下来会摔碎！'\n",
"2 setup='为什么海洋里的鱼不会发烧？' punchline='因为它们住在温度常定的水里！'\n",
"3 setup='为什么老鼠喜欢住在洞里？' punchline=\"因为它们可以说：'我家有钥匙！'\"\n",
"4 setup='为什么火车总是不让狗上车？' punchline='因为它们没有票！'\n",
"5 setup='为什么小明总是迟到？' punchline='因为他睡得太晚。'\n",
"6 setup='为什么狐狸不喜欢吃草？' punchline='因为它害怕被牛发现！'\n",
"7 setup='什么东西很长却没有腿？' punchline='一条彩虹！'\n",
"8 setup='为什么老虎不吃素食？' punchline='因为他不喜欢蔬菜！'\n",
"9 setup='为什么小明每次考试都不及格？' punchline='因为他总是把“考”理解成“糊弄”。'\n"
]
}
],
"source": [
"for i in range(10):\n",
" output = model(_input.to_string())\n",
" print(i, parser.parse(output))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"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": "3.10.10"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
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