大模型function calling：让AI函数调用更智能、更高效

大模型function calling：让AI函数调用更智能、更高效

随着大语言模型（LLM）的快速发展，其在实际应用中的能力越来越受到关注。Function Calling 是一种新兴的技术，允许大模型与外部工具或API进行交互，从而扩展了模型的功能边界。本文将深入探讨 Function Calling 的原理、应用场景以及如何通过优化实现更智能、高效的函数调用。

1. 什么是 Function Calling？

unction Calling 是一种机制，允许大语言模型动态调用外部函数或API，以完成特定任务。例如，当用户提问“今天的天气如何？”时，模型可以通过调用天气API获取实时数据并返回结果。这种方式不仅提升了模型的实用性，还使其能够处理复杂的多步骤任务。

• 关键特点：
  • 动态调用：根据用户需求调用合适的函数。
  • 上下文感知：结合对话上下文选择最佳函数。
  • 扩展性强：支持多种API和服务集成。

2. Function Calling 的核心原理

1. 用户输入问题或指令。
2. 模型分析输入，生成 JSON 格式的函数调用请求。
3. 后端解析请求，调用对应函数或API。
4. 将函数返回的结果传递回模型。
5. 模型基于结果生成最终回答。

3.基本操作

llm使用的是llama3.2:3b的模型，有一些模型是不支持tools的。

# 安装依赖包
pip isntall ollama
pip install yfinance

"""
1.将问题输出到llm模型中
2.模型解析问题中参数，将需要回调的函数和参数输出。
3.将回调结果作为上下文输入llm模型中验证
"""
import ollama
from ollama import Clientclient = Client(host='http://192.168.3.203:11434/',headers={'x-some-header': 'some-value'}
)def add_two_numbers(a: int, b: int) -> int:"""Add two numbersArgs:a (int): The first numberb (int): The second numberReturns:int: The sum of the two numbers"""return int(a) + int(b)def subtract_two_numbers(a: int, b: int) -> int:"""Subtract two numbers"""return int(a) - int(b)# Tools can still be manually defined and passed into chat
subtract_two_numbers_tool = {'type': 'function','function': {'name': 'subtract_two_numbers','description': 'Subtract two numbers','parameters': {'type': 'object','required': ['a', 'b'],'properties': {'a': {'type': 'integer', 'description': 'The first number'},'b': {'type': 'integer', 'description': 'The second number'},},},},
}messages = [{'role': 'user', 'content': 'What is three plus one?'}]
print('Prompt:', messages[0]['content'])available_functions = {'add_two_numbers': add_two_numbers,'subtract_two_numbers': subtract_two_numbers,
}def main():response = client.chat('llama3.2:3b',messages=messages,tools=[add_two_numbers, subtract_two_numbers_tool],)if response.message.tool_calls:# There may be multiple tool calls in the responsefor tool in response.message.tool_calls:# Ensure the function is available, and then call itif function_to_call := available_functions.get(tool.function.name):print('Calling function:', tool.function.name)print('Arguments:', tool.function.arguments)output = function_to_call(**tool.function.arguments)print('Function output:', output)else:print('Function', tool.function.name, 'not found')# Only needed to chat with the model using the tool call resultsif response.message.tool_calls:# Add the function response to messages for the model to usemessages.append(response.message)messages.append({'role': 'tool', 'content': str(output), 'name': tool.function.name})# Get final response from model with function outputsfinal_response = client.chat('llama3.2:3b', messages=messages)print('Final response:', final_response.message.content)else:print('No tool calls returned from model')if __name__ == '__main__':main()"""
结果
Prompt: What is three plus one?
Calling function: add_two_numbers
Arguments: {'a': 3, 'b': 1}
Function output: 4
"""

"""
1.通过llm模型获取解析回调函数和函数的参数
2.通过yfinance库获取公司最新股票信息
"""
from ollama import Client
import yfinance as yfclient = Client(host='http://192.168.3.203:11434',headers={'x-some-header': 'some-value'}
)def get_current_stock_price(ticker_symbol):# 定义函数stock = yf.Ticker(ticker_symbol)current_price = stock.history(period='1d')['Close'].iloc[0]return current_price# 本地测试
# data = get_current_stock_price("MSFT")
# print(data)# 工具函数请求参数
tools = [{'type': 'function','function': {'name': 'get_current_stock_price','description': 'Get the current price for a stock','parameters': {'type': 'object','properties': {'ticker_symbol': {'type': 'string','description': 'The ticker symbol of the stock'}}},'required': ['ticker_symbol']}}]
response = client.chat(model='llama3.2:3b',messages=[{'role': 'user','content': 'What is the current price of MSFT'}],# provide a tool to get the current price of a stocktools=tools)
print(response['message']['tool_calls'])
# [ToolCall(function=Function(name='get_current_stock_price', arguments={'ticker_symbol': 'MSFT'}))]# 模拟函数库
function_map = {'get_current_stock_price': get_current_stock_price}def call_function_safely(response, function_map):# 模型结果回调函数tool_call = response['message']['tool_calls'][0]function_name = tool_call['function']['name']arguments = tool_call['function']['arguments']function_to_call = function_map.get(function_name)if function_to_call:try:result = function_to_call(**arguments)print(f"The current price of {arguments['ticker_symbol']} is : {result}")except TypeError as e:print(f"Argument error: {e}")else:print(f"{function_name} is not a recognized function")call_function_safely(response, function_map)

4.实现自动发送邮件

import os
from dotenv import load_dotenvimport smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipartimport json
from openai import OpenAIGPT_MODEL = "llama3.2:3b"load_dotenv(dotenv_path="/Users/wuzhibin/workspace/pythonDemo/ollama_demo/.env")
# 大模型密钥
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
# 邮箱授权码
AUTHORIZATION_CODE = os.getenv("AUTHORIZATION_CODE")
print("1111", OPENAI_API_KEY, AUTHORIZATION_CODE)
client = OpenAI(base_url="http://192.168.3.203:11434/v1", api_key=OPENAI_API_KEY)tools = [{"type": "function","function": {"name": "send_email","description": "Send an email to the specified email with the subject and content","parameters":{"type": "object","properties": {"FromEmail": {"type": "string","description": "The email address, eg., [e-mail]",},"Subject": {"type": "string","description": "Subject of the email",},"Body": {"type": "string","description": "The content of the email",},"Recipients": {"type": "string","description": "The recipients' email addresses",}},"required": ["FromEmail", "Subject", "Body", "Recipients"],},}}
]def chat_completion_request(messages, tools=None, tool_choice=None, model=GPT_MODEL):try:response = client.chat.completions.create(model=model,messages=messages,tools=tools,tool_choice=tool_choice,)return responseexcept Exception as e:print("Unable to generate ChatCompletion response")print(f"Exception: {e}")return edef send_email(sender_email, sender_authorization_code, recipient_email, subject, body):# 创建 MIMEMultipart 对象message = MIMEMultipart()message["From"] = sender_emailmessage["To"] = recipient_emailmessage["Subject"] = subjectmessage.attach(MIMEText(body, "plain"))# 创建 SMTP_SSL 会话with smtplib.SMTP_SSL("smtp.126.com", 465) as server:server.login(sender_email, sender_authorization_code)text = message.as_string()server.sendmail(sender_email, recipient_email, text)def main():messages = []while True:msg = input("【You】: ")messages.append({"role": "user", "content": msg})response = chat_completion_request(messages=messages,tools=tools)print(response)if content := response.choices[0].message.content:print(f"【AI】: {content}")messages.append({"role": "assistant", "content": content})else:fn_name = response.choices[0].message.tool_calls[0].function.namefn_args = response.choices[0].message.tool_calls[0].function.arguments# print(f"【Debug info】: fn_name - {fn_name}")# print(f"【Debug info】: fn_args - {fn_args}")if fn_name == "send_email":try:args = json.loads(fn_args)# 返回将要发送的邮件内容给用户确认print("【AI】: 邮件内容如下：")print(f"发件人: {args['FromEmail']}")print(f"收件人: {args['Recipients']}")print(f"主题: {args['Subject']}")print(f"内容: {args['Body']}")confirm = input("AI: 确认发送邮件吗？ (yes/no): ").strip().lower()if confirm == "yes":send_email(sender_email=args["FromEmail"],sender_authorization_code=AUTHORIZATION_CODE, recipient_email=args["Recipients"], subject=args["Subject"], body=args["Body"],)print("邮件已发送，还需要什么帮助吗？")messages.append({"role": "assistant", "content": "邮件已发送，还需要什么帮助吗？"})else:print("邮件发送已取消，还需要什么帮助吗？")messages.append({"role": "assistant", "content": "邮件发送已取消，还需要什么帮助吗？"})except Exception as e:print(f"发送邮件时出错：{e}")messages.append({"role": "assistant", "content": "抱歉，功能异常！"})    if __name__ == "__main__":main()# 帮我发送一封邮件 发件人: [e-mail], 收信人：[e-mail], 发送内容：写着一封来自未来胖虎的问候邮件，主题：来自未来的问候