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{
"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))"
]
}
],
"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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