ChatGPT 提示词框架有哪些类型？企业级应用案例与代码生成指南

import openai
openai.api_key = "your_api_key"
response = openai.Completion.create(
  engine="text-davinci-003",
  prompt="请为电商平台撰写一段促销活动文案，突出限时折扣和赠品福利，字数控制在80字以内",
  max_tokens=
)
print(response.choices[].text.strip())

import openai
openai.api_key = "your_api_key"
examples = [
    {"original": "女士连衣裙", "optimized": "春季新款女士雪纺连衣裙 显瘦收腰A字裙 多色可选"},
    {"original": "男士运动鞋", "optimized": "透气男士运动鞋 减震跑步鞋 防滑耐磨 运动休闲两穿"}
]
prompt = "以下是商品标题优化示例：\n"
for ex in examples:
    prompt += f"原标题：‘{ex['original']}’ → 优化后：‘{ex['optimized']}’\n"
prompt += "请按此规则优化以下标题：‘儿童保温杯’"

response = openai.Completion.create(
  engine="text-davinci-003",
  prompt=prompt,
  max_tokens=
)
print(response.choices[].text.strip())

import openai
openai.api_key = "your_api_key"
prompt = "以下是供应链库存预警分析步骤：\n"
prompt += "第一步：分析历史销售数据，计算各产品周均销量\n"
prompt += "第二步：对比当前库存数量，判断库存可支撑天数\n"
prompt += "第三步：设定预警阈值（如库存可支撑天数<7天）\n"
prompt += "第四步：生成预警产品清单及补货建议\n"
prompt += "请按此步骤分析以下数据：\n"
prompt += "产品A：周均销量50件，库存300件\n"
prompt += "产品B：周均销量80件，库存400件\n"
prompt += "产品C：周均销量120件，库存500件\n"
prompt += "产品D：周均销量30件，库存150件\n"
prompt += "请给出预警结果及补货建议"

response = openai.Completion.create(
  engine="text-davinci-003",
  prompt=prompt,
  max_tokens=
)
print(response.choices[].text.strip())

import openai
import requests
import json

openai.api_key = "your_api_key"

def get_weather(city):
    """调用天气API获取实时天气"""
    url = f"https://api.weatherapi.com/v1/current.json?key=your_weather_api_key&q={city}"
    response = requests.get(url)
    if response.status_code == :
        data = response.json()
        return f"{data['location']['name']}当前天气：{data['current']['condition']['text']}，温度{data['current']['temp_c']}°C"
    return "天气查询失败，请稍后再试"

def process_function_call(response):
    """处理模型返回的函数调用"""
    try:
        function_call = json.loads(response.split("```json")[].split("```")[])
        if function_call["name"] == "get_weather":
            city = function_call["parameters"]["city"]
            return get_weather(city)
    except:
        return "函数调用处理出错"

def chat_with_function(prompt):
    """带函数调用的对话处理"""
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=prompt,
        max_tokens=,
        functions=[
            {
                "name": "get_weather",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "city": {
                            "type": "string",
                            "description": "需要查询天气的城市名称"
                        }
                    },
                    "required": ["city"]
                }
            }
        ],
        function_call="auto"
    )
    text = response.choices[].text
    if "```json" in text:
        return process_function_call(text)
    return text

# 测试调用
print(chat_with_function("北京今天天气怎么样？"))

import openai
import datetime

openai.api_key = "your_api_key"

# 定义报表模板
report_template = """
生成{report_period}月度销售报表：
1. 整体销售概况：总销售额{total_sales}万元，同比增长{growth_rate}%
2. 品类销售排行：
   - 第一名：{top_category1}，销售额{category1_sales}万元
   - 第二名：{top_category2}，销售额{category2_sales}万元
   - 第三名：{top_category3}，销售额{category3_sales}万元
3. 销售趋势分析：{trend_analysis}
4. 下月销售计划：{next_month_plan}
"""

def generate_sales_report(period, total_sales, growth_rate, top_categories, trend_analysis, next_month_plan):
    """根据模板生成销售报表"""
    prompt = report_template.format(
        report_period=period,
        total_sales=total_sales,
        growth_rate=growth_rate,
        top_category1=top_categories[]["name"],
        category1_sales=top_categories[]["sales"],
        top_category2=top_categories[]["name"],
        category2_sales=top_categories[]["sales"],
        top_category3=top_categories[]["name"],
        category3_sales=top_categories[]["sales"],
        trend_analysis=trend_analysis,
        next_month_plan=next_month_plan
    )
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=prompt,
        max_tokens=
    )
    return response.choices[].text.strip()

# 示例数据
current_month = datetime.datetime.now().strftime("%Y年%m月")
sales_data = {
    "total_sales": "580",
    "growth_rate": "12.5",
    "top_categories": [
        {"name": "电子产品", "sales": "210"},
        {"name": "服装鞋帽", "sales": "180"},
        {"name": "家居用品", "sales": "130"}
    ],
    "trend_analysis": "本月销售额较上月有所提升，主要得益于电子产品促销活动带动",
    "next_month_plan": "计划加大服装鞋帽品类的新品投入，同时开展家居用品组合优惠活动"
}

# 生成报表
print(generate_sales_report(
    current_month,
    sales_data["total_sales"],
    sales_data["growth_rate"],
    sales_data["top_categories"],
    sales_data["trend_analysis"],
    sales_data["next_month_plan"]
))