OpenAi FunctionCalling 案例详解

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FunctionCalling的单一函数调用

天气预报查询（今天长沙的天气如何？）

import json
import requests
from openai import OpenAIclient = OpenAI()location = "长沙"def get_current_weather(city):url = "https://restapi.amap.com/v3/weather/weatherInfo?key=0f219ddb5f23d95ea1731fe653f906a3&city={city}".format(city=city)response = requests.get(url)result = eval(response.text)["lives"][0]weather_info = {"location": city,"weather": result["weather"],"temperature": result["temperature"],"time": result["reporttime"]}return json.dumps(weather_info, ensure_ascii=False)messages = []
messages.append({"role":"system", "content":"你是一个查询天气的机器人，你需要根据用户提供的地址来回答当地的天气情况"})
messages.append({"role":"user", "content": f"""今天{location}的天气如何？"""})
tools = [{"type":"function","function": {"name":"get_current_weather","description": "获取给定位置的当前天气","parameters": {"type": "object","properties": {"location": {"type": "string","description": "城市或区，例如长沙"}},"required":["location"]}}
}]response = client.chat.completions.create(model = "gpt-3.5-turbo",messages = messages,tools = tools
)messages.append(response.choices[0].message)
print(messages)
function_name = response.choices[0].message.tool_calls[0].function.name
print(function_name)
function_id = response.choices[0].message.tool_calls[0].id
print(function_id)
messages.append({"tool_call_id": function_id,"role": "tool","name": function_name,"content": get_current_weather(location)
})
print(messages)response = client.chat.completions.create(model="gpt-3.5-turbo", messages=messages)
print(response.choices[0].message.content)

运行结果

Function call的多函数调用

查询学校课程对应的老师（帮我查询北京大学的中国历史课程是哪位老师(teacher)。）

import json
from openai import OpenAI
client = OpenAI()
tools = [{"type": "function","function": {"name": "get_class_number","description": "根据学校、课程查询上课编号","parameters": {"type": "object","properties": {"school": {"description": "学校","type": "string"},"course": {"description": "课程","type": "string"}},"required": ["school", "course"]}}
}, {"type": "function","function": {"name": "get_course_teacher","description": "查询某课程的老师","parameters": {"type": "object","properties": {"class_number": {"description": "上课编号","type": "string"}},"required": ["class_number"]},}
}]def get_class_number(school: str, course: str):class_number = {"清华大学": {"高等数学": "MATH101","线性代数": "MATH102",},"北京大学": {"大学英语": "ENG201","中国历史": "HIST202",}}return {"class_number": class_number[school][course]}def get_course_teacher(class_number: str):course_teacher_mapping = {"MATH101": "张老师","MATH102": "李老师","ENG201": "王老师","HIST202": "赵老师",}teacher = course_teacher_mapping.get(class_number)return {"teacher": teacher}messages = []
messages = [{"role": "system","content": "你是一位高效的教育助手，现在需要查询某高校的老师名称。"},{"role": "user","content": "帮我查询北京大学的中国历史课程是哪位老师(teacher)。"}
]# 第一次调用
first_response = client.chat.completions.create(model="gpt-3.5-turbo",messages=messages,tools=tools,tool_choice="auto",
)
print(first_response.choices[0].message)messages.append(first_response.choices[0].message)first_function = {}
if first_response.choices[0].message.tool_calls:tool_call = first_response.choices[0].message.tool_calls[0]args = tool_call.function.argumentsif tool_call.function.name == "get_class_number":first_function = get_class_number(**json.loads(args))if tool_call.function.name == "get_course_teacher":first_function = get_course_teacher(**json.loads(args))print(first_function)tool_call = first_response.choices[0].message.tool_calls[0]
messages.append({"role": "tool","tool_call_id": tool_call.id,"content": str(json.dumps(first_function)),"name": tool_call.function.name
})
print("***" * 40)
print(messages)
print("***" * 40)second_response = client.chat.completions.create(model="gpt-3.5-turbo",messages=messages,tools=tools,tool_choice="auto"
)
print(second_response.choices[0].message)messages.append(second_response.choices[0].message)
second_function = {}
if second_response.choices[0].message.tool_calls:tool_call = second_response.choices[0].message.tool_calls[0]args = tool_call.function.argumentsif tool_call.function.name == "get_class_number":second_function = get_class_number(**json.loads(args))if tool_call.function.name == "get_course_teacher":second_function = get_course_teacher(**json.loads(args))print(second_function)
tool2_call = second_response.choices[0].message.tool_calls[0]
# 将函数的结果添加到messages中，继续送入模型问答
messages.append({"role": "tool","tool_call_id": tool2_call.id,"content": str(json.dumps(second_function)),"name":tool2_call.function.name}
)last_response = client.chat.completions.create(model="gpt-3.5-turbo",messages=messages,tools=tools,tool_choice="auto",
)
print(last_response.choices[0].message.content)

运行结果

FunctionCalling调用SQL

查询一下最高工资的员工姓名及对应的工资

import json
import pymysql
from openai import OpenAIclient = OpenAI()def connect_database(query):conn = pymysql.connect(host="localhost",port=3306,user="root",password="123456",database="school",charset="utf8mb4",)cursor = conn.cursor()cursor.execute(query)result = cursor.fetchall()cursor.close()conn.close()return resultmessages = []
messages.append({"role": "system", "content": "通过针对业务数据库生成 SQL 查询来回答用户的问题"})
messages.append({"role": "user", "content": "查询一下最高工资的员工姓名及对应的工资"})response = client.chat.completions.create(messages=messages,model="gpt-3.5-turbo",tools=[{"type": "function","function": {"name": "connect_database","description": "使用此函数回答业务问题，要求输出是一个SQL查询语句","parameters": {"type": "object","properties": {"sql": {"type": "string","description": f"SQL查询提取信息以回答用户的问题。"f"查询应该以纯文本返回，而不是JSON。"f"数据库的表为 emp 表。字段有 id,name,salary"f"查询应该只包含MySQL支持的语法。",}},"required": ["sql"],},}}]
)
print("tool calls", response.choices[0].message.tool_calls[0])messages.append(response.choices[0].message)
function_name = response.choices[0].message.tool_calls[0].function.name
function_id = response.choices[0].message.tool_calls[0].id
function_response = connect_database(json.loads(response.choices[0].message.tool_calls[0].function.arguments).get("sql")
)
print("dbResult", function_response)messages.append({"role": "tool","tool_call_id": function_id,"name": function_name,"content": str(function_response),
})
last_response = client.chat.completions.create(messages=messages,model="gpt-3.5-turbo",
)
print(last_response.choices[0].message.content)

运行结果