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.contentif __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.contentif __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 streamif __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.contentelse: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 tiktokendef 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_tokensreturn {"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|>\ntokens_per_name = -1  # if there's a name, the role is omittednum_tokens = 0for 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_messagenum_tokens += tokens_per_name  # every reply is primed with <|start|>assistant<|message|>return num_tokensapi_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.contentif __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})