大模型openai范式接口调用方法

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 requestsdef 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 neededexcept 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 neededexcept 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 usageapi_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 usageapi_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)