文本对话

import os
from openai import OpenAI

client = OpenAI(
    # This is the default and can be omitted
    api_key = "sk-u1oNdFZblFE09tVx2c42981a97De42C*****",
    base_url = "<https://api.tu-zi.com/v1>",
)

chat_completion = client.chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "背个望庐山瀑布",
        }
    ],
    model="gpt-4",
)

print(chat_completion)

上传图片识别（支持模型gpt-4o-fast，openai-gpt-4o，gpt-4o-all）

import base64
import requests

# OpenAI API Key
api_key = "sk-BdFjUwDwNyPjcv78*****"

# Function to encode the image
def encode_image(image_path):
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

# Path to your image
image_path = r"C:\\Users\\wa***\\Downloads\\兔子圆形.png"  # 替换为您本地图片的路径

# Getting the base64 string
base64_image = encode_image(image_path)

# 设置请求头
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {api_key}"
}

# 设置请求的payload
payload = {
    "model": "gpt-4o-fast",  # 使用的模型
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": "What’s in this image?"
                },
                {
                    "type": "image_url",
                    "image_url": {
                        "url": f"data:image/jpeg;base64,{base64_image}"
                    }
                }
            ]
        }
    ],
    "max_tokens": 300
}

# 发送POST请求到自定义的API地址
response = requests.post("<https://api.tu-zi.com/v1/chat/completions>", headers=headers, json=payload)

# 解析响应
response_data = response.json()

# 打印文本内容
print("Text Response:", response_data)

文本转语音tts-1

import os
from pathlib import Path
from openai import OpenAI

# 初始化OpenAI客户端
client = OpenAI(
    api_key="sk-Z6OJzK67cZvLUXvt31D82bFa1******",  # 替换为你的API密钥
    base_url="<https://api.tu-zi.com/v1>",
)

# 输入的文本内容
text_input = """
### 一、申请内容

审计报告***位、项目负责人等记入科研诚信异常名录，取消其一定年限内申请科研资助的资格，并依法追究其他责任。
"""

# 将文本按限制的长度分割
max_length = 1000  # 每个请求最大字符数，稍小于4096字符限制
text_parts = [text_input[i:i+max_length] for i in range(0, len(text_input), max_length)]

# 设置保存音频文件的基础路径
base_speech_file_path = Path("C:/Users/***1/Downloads/speech_part")

# 循环生成音频文件
for idx, part in enumerate(text_parts):
    speech_file_path = base_speech_file_path.with_name(f"speech_part_{idx+1}.mp3")
    
    # 请求文字转语音服务
    response = client.audio.speech.create(
        model="tts-1",  # 指定TTS模型
        voice="alloy",  # 指定使用的声音
        input=part,  # 输入的文本部分
    )
    
    # 将生成的音频文件保存到指定路径
    with open(speech_file_path, 'wb') as audio_file:
        audio_file.write(response.content)  # 直接写入二进制内容

    print(f"Audio file saved at: {speech_file_path}")

使用特定GPTS

  1. 需要用到逆向模型gpt-4-gizmo-* ，其中*号部分用官网GPTS链接后缀部分代替，如

    image.png

  2. 其它代码部分与之前一致

    import openai

# 设置API密钥
openai.api_key = "sk-Z6OJzK67cZvL****"

# 如果你有自定义的 base_url（比如使用代理），可以设置：
openai.api_base = "<https://api.tu-zi.com/v1>"

# 创建聊天补全，使用流模式
response = openai.ChatCompletion.create(
    model="gpt-4-gizmo-g-dse9iXvor-dong-yu-hui-xiao-zuo-wen-zhu-shou",
    messages=[
        {
            "role": "user",
            "content": "你是谁？",
        }
    ],
    stream=True  # 启用流模式
)

# 逐步处理并输出响应
for chunk in response:
    if 'choices' in chunk:
        content = chunk['choices'][0]['delta'].get('content', '')
        print(content, end='')  # 逐步输出，不换行

  3. 测试效果

    image.png

<aside> 💡 更多内容可参考https://platform.openai.com/docs/overview官方文档，修改下文档中的网址和密钥即可

</aside>