本文将介绍如下内容：

• 一、为什么选择 OpenAI 范式接口？
• 二、调用 Openai 接口官方调用 Demo 示例
• 三、自定义调用 Openai 接口

一、为什么选择 OpenAI 范式接口？

OpenAI 范式接口因其简洁、统一和高效的设计，成为了与大型语言模型（如 GPT 系列）交互的行业标准。它的优势在于：

• 统一接口：无论是文本生成还是对话生成，都遵循统一标准，便于开发者快速上手和复用代码。
• 简洁易用：基于 HTTP 请求的简单设计，让开发者能够轻松与模型交互，减少学习成本。
• 高效管理：支持灵活调整生成参数，如温度、最大生成长度，优化模型输出。
• 流式输出：支持实时生成，适合实时反馈的应用场景。

二、调用 Openai 接口官方调用 Demo 示例

1、Openai 接口官方文档如下：

• OpenAI developer platform
• https://platform.openai.com/docs/api-reference/introduction

其中主要接口有如下两种：

• v1/chat/completions
• v1/completions

2、chat/completions

• Example request

curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "gpt-4o",
    "messages": [
      {
        "role": "developer",
        "content": "You are a helpful assistant."
      },
      {
        "role": "user",
        "content": "Hello!"
      }
    ]
  }'

• Response

{
  "id": "chatcmpl-123",
  "object": "chat.completion",
  "created": 1677652288,
  "model": "gpt-4o-mini",
  "system_fingerprint": "fp_44709d6fcb",
  "choices": [{
    "index": 0,
    "message": {
      "role": "assistant",
      "content": "\n\nHello there, how may I assist you today?",
    },
    "logprobs": null,
    "finish_reason": "stop"
  }],
  "service_tier": "default",
  "usage": {
    "prompt_tokens": 9,
    "completion_tokens": 12,
    "total_tokens": 21,
    "completion_tokens_details": {
      "reasoning_tokens": 0,
      "accepted_prediction_tokens": 0,
      "rejected_prediction_tokens": 0
    }
  }
}

3、completions

• Example request

curl https://api.openai.com/v1/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "gpt-3.5-turbo-instruct",
    "prompt": "Say this is a test",
    "max_tokens": 7,
    "temperature": 0
  }'

• Response

{
  "id": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7",
  "object": "text_completion",
  "created": 1589478378,
  "model": "gpt-3.5-turbo-instruct",
  "system_fingerprint": "fp_44709d6fcb",
  "choices": [
    {
      "text": "\n\nThis is indeed a test",
      "index": 0,
      "logprobs": null,
      "finish_reason": "length"
    }
  ],
  "usage": {
    "prompt_tokens": 5,
    "completion_tokens": 7,
    "total_tokens": 12
  }
}

三、自定义调用 Openai 接口

import requests

def chat_completions(api_url, api_key, messages, input_payload, stream=False):
    url = f"{api_url}/v1/chat/completions"

    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }

    payload = {
        "model": "",
        "stream": stream,
        "messages": messages,
        "max_tokens": 8096,
        "temperature": 0.1,
        "presence_penalty": 0.5,
        "frequency_penalty": 0.8,
        "top_p": 0.75  # 0.75
    }
    payload.update(input_payload)

    if stream:
        response = requests.post(url, json=payload, headers=headers, stream=True)
        for line in response.iter_lines():
            if line:
                try:
                    data = line.decode("utf-8")
                    print(data)  # Process each chunk of the stream as needed
                except Exception as e:
                    print(f"Error processing stream data: {e}")
    else:
        response = requests.post(url, json=payload, headers=headers)
        return response.json()

def completions(api_url, api_key, prompt,input_payload, stream=False):
    url = f"{api_url}/v1/completions"

    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json"
    }

    payload = {
        "model": "",
        "stream": stream,
        "prompt": prompt,
        "max_tokens": 8096,
        "temperature": 0.1,
        "presence_penalty": 0.5,
        "frequency_penalty": 0.8,
        "top_p": 0.75  #0.75
    }
    payload.update(input_payload)

    if stream:
        response = requests.post(url, json=payload, headers=headers, stream=True)
        for line in response.iter_lines():
            if line:
                try:
                    data = line.decode("utf-8")
                    print(data)  # Process each chunk of the stream as needed
                except Exception as e:
                    print(f"Error processing stream data: {e}")
    else:
        response = requests.post(url, json=payload, headers=headers)
        return response.json()


if __name__ == "__main__":
    # chat_completions - Example usage
    api_url = "http://127.0.0.1:20009"
    api_key = "EMPTY"
    model = "adapter1"  # "qwen2.5-32b"
    messages = [{"role": "user", "content": "随机给我一个1～10000的数字"}]
    payload = {
        "model": model,
    }
    response = chat_completions(api_url, api_key, messages, payload, stream=True)
    print(response)

    # completions-  Example usage
    api_url = "http://127.0.0.1:20009"
    api_key = "EMPTY"
    model = "qwen2.5-32b"
    prompt = "Tell me a joke."
    payload = {
        "model": model,
    }
    response = completions(api_url, api_key, prompt, payload, stream=True)
    print(response)