import openaiimport osfrom math import *from icecream import icimport json
# 加载 .env 文件from dotenv import load_dotenv, find_dotenv_ = load_dotenv(find_dotenv())
# 从环境变量中获得你的 OpenAI Keyopenai.api_key = os.getenv('OPENAI_API_KEY')openai.api_base = os.getenv('OPENAI_API_URL')model = os.getenv('MODEL')
# 基于 prompt 生成文本def get_completion3(messages, model=model):    response = openai.ChatCompletion.create(     # 注意，以前的 openai.ChatCompletion 要换成 openai.chat.completions        model=model,        messages=messages,        temperature=0,  # 模型输出的随机性，0 表示随机性最小        tools=[{  # 用 JSON 描述函数。可以定义多个。由大模型决定调用谁            "type": "function",            "function": {                "name": "sum",                "description": "计算一组数的加和",                "parameters": {                    "type": "object",                    "properties": {                        "numbers": {                            "type": "array",                            "items": {                                "type": "number"                            }                        }                    }                }            }        }],    )    return response.choices[0].message
def test_promopt():    prompt = "Tell me the sum of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10."    #prompt = "桌上有 2 个苹果，四个桃子和 3 本书，一共有几个水果？"    #prompt = "1+2+3...+99+100"
    messages = [        {"role": "system", "content": "你是一个小学数学老师，你要教学生加法"},        {"role": "user", "content": prompt}    ]    response = get_completion3(messages)
    # 把大模型的回复加入到对话历史中    if (response.content is None):  # 解决 OpenAI 的一个 400 bug        response.content = "null"    messages.append(response)

    # 如果返回的是函数调用结果，则打印出来    if (response.tool_calls is not None):        # 是否要调用 sum        tool_call = response.tool_calls[0]        if (tool_call.function.name == "sum"):            # 调用 sum            args = json.loads(tool_call.function.arguments)            result = sum(args["numbers"])
            # 把函数调用结果加入到对话历史中            messages.append(                {                    "tool_call_id": tool_call.id,  # 用于标识函数调用的 ID                    "role": "tool",                    "name": "sum",                    "content": str(result)  # 数值result 必须转成字符串                }            )
            # 再次调用大模型            ic("=====最终回复=====")            ic(get_completion3(messages).content)

if __name__ == '__main__':   test_promopt()    

ic| '=====最终回复====='ic| get_completion3(messages).content: 'The sum of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 is 55.'

import openaiimport osfrom math import *from icecream import icimport jsonfrom math import *
# 加载 .env 文件from dotenv import load_dotenv, find_dotenv_ = load_dotenv(find_dotenv())
# 从环境变量中获得你的 OpenAI Keyopenai.api_key = os.getenv('OPENAI_API_KEY')openai.api_base = os.getenv('OPENAI_API_URL')model = os.getenv('MODEL')
# 基于 prompt 生成文本def get_completion(messages, model=model):    response = openai.ChatCompletion.create(     # 注意，以前的 openai.ChatCompletion 要换成 openai.chat.completions        model=model,        messages=messages,        temperature=0,  # 模型输出的随机性，0 表示随机性最小        tools=[{            "type": "function",            "function": {                "name": "calculate",                "description": "计算一个数学表达式的值",                "parameters": {                    "type": "object",                    "properties": {                        "expression": {                            "type": "string",                            "description": "a mathematical expression in python grammar.",                        }                    }                }            }        }],    )    return response.choices[0].message
def test_promopt():    #prompt = "从1加到10"    prompt = "3的平方根乘以2再开平方"
    messages = [        {"role": "system", "content": "你是一个数学家，你可以计算任何算式。"},        {"role": "user", "content": prompt}    ]    response = get_completion(messages)    if (response.content is None):  # 解决 OpenAI 的一个 400 bug        response.content = "null"    messages.append(response)  # 把大模型的回复加入到对话中    ic("=====GPT回复=====")    ic(response)
    # 如果返回的是函数调用结果，则打印出来    if (response.tool_calls is not None):        # 是否要调用 sum        tool_call = response.tool_calls[0]        if (tool_call.function.name == "calculate"):            # 调用 calculate            args = json.loads(tool_call.function.arguments)            result = eval(args["expression"])            ic("=====函数返回=====")            ic(result)
            # 把函数调用结果加入到对话历史中            messages.append(                {                    "tool_call_id": tool_call.id,  # 用于标识函数调用的 ID                    "role": "tool",                    "name": "calculate",                    "content": str(result)  # 数值result 必须转成字符串                }            )
            # 再次调用大模型            ic("=====最终回复=====")            ic(get_completion(messages).content)

if __name__ == '__main__':   test_promopt()    

ic| '=====GPT回复====='ic| response: <OpenAIObject at 0x1a97b6d3480> JSON: {                "role": "assistant",                "content": "null",                "tool_calls": [                  {                    "id": "call_jSVjTMqMIMUHYCpDisvpYInp",                    "type": "function",                    "function": {                      "name": "calculate",                      "arguments": "{\"expression\":\"((3**0.5)*2)**0.5\"}"                    }                  }                ]              }ic| '=====函数返回====='ic| result: 1.8612097182041991ic| '=====最终回复====='ic| get_completion(messages).content: '3的平方根乘以2再开平方的结果约为1.8612。'

import openaiimport osfrom math import *from icecream import icimport jsonfrom math import *import requestsimport logging# 设置日志记录配置logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
# 加载 .env 文件from dotenv import load_dotenv, find_dotenv_ = load_dotenv(find_dotenv())
# 从环境变量中获得你的 OpenAI Keyopenai.api_key = os.getenv('OPENAI_API_KEY')openai.api_base = os.getenv('OPENAI_API_URL')model = os.getenv('MODEL')amap_key = os.getenv('GAODE_MAP_API_KEY')
def get_completion(messages, model=model):    response = openai.ChatCompletion.create(        model=model,        messages=messages,        temperature=0,  # 模型输出的随机性，0 表示随机性最小        tools=[{            "type": "function",            "function": {                "name": "add_contact",                "description": "添加联系人",                "parameters": {                    "type": "object",                    "properties": {                        "name": {                            "type": "string",                            "description": "联系人姓名"                        },                        "address": {                            "type": "string",                            "description": "联系人地址"                        },                        "tel": {                            "type": "string",                            "description": "联系人电话"                        },                    }                }            }        }],    )    return response.choices[0].message



def test_promopt():    prompt = "帮我寄给星城老谭，地址是湖南省长沙市岳麓区湘江壹号8楼，电话18874868888。"    messages = [        {"role": "system", "content": "你是一个联系人录入员。"},        {"role": "user", "content": prompt}    ]    response = get_completion(messages)    logging.info("====GPT回复====")    logging.info(response)    args = json.loads(response.tool_calls[0].function.arguments)    logging.info("====函数参数====")    logging.info(args)
if __name__ == '__main__':   test_promopt()    

2024-06-05 13:31:30,121 - INFO - ====GPT回复====2024-06-05 13:31:30,124 - INFO - {  "role": "assistant",  "content": null,  "tool_calls": [    {      "id": "call_QEnFRbZRBs2Eww4YbUxP7oKv",      "type": "function",      "function": {        "name": "add_contact",        "arguments": "{\"name\":\"\u661f\u57ce\u8001\u8c2d\",\"address\":\"\u6e56\u5357\u7701\u957f\u6c99\u5e02\u5cb3\u9e93\u533a\u6e58\u6c5f\u58f9\u53f78\u697c\",\"tel\":\"18874868888\"}"      }    }  ]}2024-06-05 13:31:30,124 - INFO - ====函数参数====2024-06-05 13:31:30,125 - INFO - {'name': '星城老谭', 'address': '湖南省长沙市岳麓区湘江壹号8楼', 'tel': '1887486XXXX'}