2024年 最新python调用ChatGPT实战教程

一、前言

这个之前经常用到，简单记录一下,注意目前chatgpt 更新了，这个是最新版的，如果不是最新版的,请自行升级。

二、具体分析

openai 安装

pip install openai 

1、简版程序

该版本只有一轮

from openai import OpenAI
api_key = 'your apikey'
def openai_reply(content):

    client = OpenAI(api_key=api_key)

    chat_completion = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": content,
            }
        ],
        model="gpt-4-1106-preview",
    )
    return chat_completion.choices[0].message.content

if __name__=="__main__":
    while True:
        content = input("人类:")
        text1 = openai_reply(content)
        print("AI:" + text1)

2、多轮对话

这个版本有多轮，核心是加入记忆

from openai import OpenAI
api_key = 'your apikey'
def openai_replys(memory):

    client = OpenAI(api_key=api_key)
    chat_completion = client.chat.completions.create(
        messages=memory, # 记忆
        model="gpt-4-1106-preview",
    )
    memory.append({'role': 'assistant', 'content': chat_completion.choices[0].message.content})
    return chat_completion.choices[0].message.content

if __name__=="__main__":
    memory=[] # 上下轮记忆
    while True:
        content = input("人类:")
        memory.append({'role':'user','content':content})
        text1 = openai_replys(memory)
        print("AI:" + text1)

程序输出：

3、流式输出

这个版本有了流式输出，让你看起来不是卡主了的样子

from openai import OpenAI
api_key = 'your apikey'
def openai_stream(memory):

    client = OpenAI(api_key=api_key)
    stream = client.chat.completions.create(
        messages=memory, # 记忆
        model="gpt-4-1106-preview",
        stream=True,
    )
    return stream

if __name__=="__main__":
    memory=[]
    while True:
        content = input("人类:")
        memory.append({'role':'user','content':content})

        stream = openai_stream(memory)
        print("AI:",end='')
        aitext=''
        for chunk in stream:
            if chunk.choices[0].delta.content is not None:
                print(chunk.choices[0].delta.content, end="")
                aitext+=chunk.choices[0].delta.content
            else:
                print()
    memory.append({'role':'assistant','content':aitext})

4、返回消耗的token

返回消耗的token

token类型	解释
completion_tokens	输出token
prompt_tokens	输入token
total_tokens	全部token

from openai import OpenAI
import tiktoken

def calToken(memory,aitext,model="gpt-3.5-turbo"):
    try:
        encoding = tiktoken.encoding_for_model(model)
    except KeyError:
        print("Warning: model not found. Using cl100k_base encoding.")
        encoding = tiktoken.get_encoding("cl100k_base")
        
    completion_tokens = len(encoding.encode(aitext))
    prompt_tokens = num_tokens_from_messages(memory, model=model)
    token_count = completion_tokens + prompt_tokens
    return {"completion_tokens":completion_tokens, "prompt_tokens":prompt_tokens, "total_tokens":token_count}
def num_tokens_from_messages(messages, model="gpt-3.5-turbo"):
    """Returns the number of tokens used by a list of messages."""
    try:
        encoding = tiktoken.encoding_for_model(model)
    except KeyError:
        print("Warning: model not found. Using cl100k_base encoding.")
        encoding = tiktoken.get_encoding("cl100k_base")

    tokens_per_message = 8  # every message follows <|start|>{role/name}\n{content}<|end|>\n
    tokens_per_name = -1  # if there's a name, the role is omitted
    num_tokens = 0
    for message in messages:

        for key, value in message.items():

            if key=='content':
                num_tokens += len(encoding.encode(value))
            if key=='role' and value=='user':
                num_tokens += tokens_per_message

    num_tokens += tokens_per_name  # every reply is primed with <|start|>assistant<|message|>
    return num_tokens

api_key = 'your apikey'
def openai_chat(memory):

    client = OpenAI(api_key=api_key)
    stream = client.chat.completions.create(
        messages=memory, # 记忆
        model="gpt-4-1106-preview",
    )

    print('total Token：' + str(stream.usage))
    return stream.choices[0].message.content

if __name__=="__main__":
    memory=[] # 对话记忆
    while True:
        content = input("人类:")
        memory.append({'role':'user','content':content}) #记忆里面填充用户输入

        aitext = openai_chat(memory)
        print("AI:"+aitext)
        cocus=calToken(memory,aitext,model="gpt-4-1106-preview")
        print("消耗token:"+str(cocus))
        memory.append({'role': 'assistant', 'content': aitext})