使用Python调用ChatGPT API的完整指南(python使用chatgpt api)

1. 安装OpenAI Python库并获取API密钥

为了使用ChatGPT API，需要先安装OpenAI Python库并获取API密钥。

安装OpenAI Python库的方法如下：

pip install openai

获取API密钥的步骤如下：

1. 前往OpenAI平台注册一个账号。
2. 登录后，在API密钥部分生成一个新的密钥。
3. 将生成的密钥保存在安全的地方，以备将来使用。

2. 设置代理以访问ChatGPT API

如果需要使用代理才能访问ChatGPT API，可以按照以下步骤设置代理：

1. 在代码中加入以下代码行：

import openai
openai.proxy = "http://proxy.example.com:port"

2. 将”http://proxy.example.com:port”替换为实际的代理地址和端口。

3. ChatGPT API的基本功能和用途

ChatGPT API是一个强大的语言模型，具有以下基本功能和用途：

• 提供基于自然语言的对话功能。
• 可以生成自然风格的会话文本。
• 可以识别关键词并返回相关信息。
• 可以进行改错，提供纠正后的文本。

3.1 Chat功能示例

以下是使用Chat功能的示例代码：

import openai

response = openai.ChatCompletion.create(
  model="gpt-3.5-turbo",
  messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Who won the world series in 2020?"},
        {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."},
        {"role": "user", "content": "Where was it played?"}
    ]
)

print(response.choices[0].message.content)

在上面的代码中，我们使用了ChatCompletion.create方法来调用ChatGPT API。我们通过指定角色和内容来模拟对话，ChatGPT会自动根据上下文进行回复。最后输出的是机器助手的回答。

3.2 生成图片功能示例

以下是使用生成图片功能的示例代码：

import openai

response = openai.ChatCompletion.create(
  model="gpt-3.5-turbo",
  messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Generate an image of a cat."},
        {"role": "assistant", "content": "Sure, here is an image of a cat:"},
        {"role": "assistant", "content": '[image: "https://example.com/cat.jpg"]'}
    ]
)

print(response.choices[0].message.content)

上面的代码中，我们通过向ChatGPT发送请求来生成一张猫的图片，并将图片的URL作为回答返回。

3.3 识别关键词功能示例

以下是使用识别关键词功能的示例代码：

import openai

response = openai.ChatCompletion.create(
  model="gpt-3.5-turbo",
  messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Find the capital of France."},
        {"role": "assistant", "content": "The capital of France is Paris."},
        {"role": "user", "content": "What is the population?"}
    ],
  max_tokens=100,
  log_level="info"
)

print(response.choices[0].message.content)

上面的代码中，我们使用了max_tokens参数来限制回答的长度，并使用了log_level参数来记录请求的详细信息。最后输出的是ChatGPT返回的关于法国首都和人口的信息。

3.4 改错功能示例

以下是使用改错功能的示例代码：

import openai

response = openai.ChatCompletion.create(
  model="gpt-3.5-turbo",
  messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "I am feeling sleapy."},
        {"role": "assistant", "content": "I think you meant 'sleepy' instead of 'sleapy'."},
        {"role": "user", "content": "You are right, thanks for correcting me."}
    ]
)

print(response.choices[0].message.content)

上面的代码中，我们使用ChatGPT的改错功能来纠正用户的拼写错误。机器助手会自动检测并提供正确的拼写。

4. ChatGPT API的调用例子

以下是完整的ChatGPT API调用例子，包括问答、Ai回答、翻译和写作等功能。

import openai

response = openai.ChatCompletion.create(
  model="gpt-3.5-turbo",
  messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the capital of France?"}
    ]
)

print(response.choices[0].message.content)

import openai

response = openai.ChatCompletion.create(
  model="gpt-3.5-turbo",
  messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the weather like today?"}
    ]
)

print(response.choices[0].message.content)

import openai

response = openai.ChatCompletion.create(
  model="gpt-3.5-turbo",
  messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": 'Translate the following English text to French: "Hello, how are you?"'}
    ]
)

print(response.choices[0].message.content)

import openai

response = openai.ChatCompletion.create(
  model="gpt-3.5-turbo",
  messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Write a short story about a cat and a mouse."}
    ]
)

print(response.choices[0].message.content)

5. ChatGPT API的进阶用法

以下是ChatGPT API的一些进阶用法，如多轮对话、定制模型等。

import openai

response = openai.ChatCompletion.create(
  model="gpt-3.5-turbo",
  messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Tell me a joke."},
        {"role": "assistant", "content": "Why don't scientists trust atoms?"},
        {"role": "user", "content": "I don't know, why don't scientists trust atoms?"},
        {"role": "assistant", "content": "Because they make up everything!"}
    ]
)

print(response.choices[0].message.content)

import openai

response = openai.ChatCompletion.create(
  model="your-model-id",
  messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the meaning of life?"}
    ]
)

print(response.choices[0].message.content)

6. ChatGPT API的注意事项和常见问题

以下是使用ChatGPT API时需要注意的事项和常见问题，并给出解决方法。

import openai

response = openai.ChatCompletion.create(
  model="gpt-3.5-turbo",
  messages=[
        {"role": "user", "content": "How much does it cost to use ChatGPT?"}
    ]
)

cost = response['usage']['total_tokens']
print(f"Total tokens used: {cost}")

import openai

class Chat:
    def __init__(self):
        self.model = "gpt-3.5-turbo"
        self.messages = []
        
    def append_user_message(self, message):
        self.messages.append({"role": "user", "content": message})
        
    def append_assistant_message(self, message):
        self.messages.append({"role": "assistant", "content": message})
        
    def print_dialogue(self):
        for message in self.messages:
            print(f"{message['role']}: {message['content']}")
            
    def calculate_cost(self):
        response = openai.ChatCompletion.create(
            model=self.model,
            messages=self.messages
        )
        cost = response['usage']['total_tokens']
        print(f"Total tokens used: {cost}")