中转API 部署ChatGPT应用Python开发调用请求代码示例
API 站点使用教程
1.1 API 接口地址填写
请勿将 API Key 泄露给他人，一旦泄露，请立即删除并创建新的 API Key
修改原则：修改应用 BASE_URL 为其中一个中转接口调用地址接口即可，例如：
修改原则
https://api.openai.com
↓
https://sg.uiuiapi.com
（如果原来接口地址需要加 /v1 的话我们的接口地址也需要在后面加 /v1）
出现回复缓慢的情况，请检查 API 调用日志，若日志正常，则自行调整网络。
Base Url
不同客户端适配的接口地址格式不同，通常为以下三种：
1.2 Python 接入示例
本站所有AI模型均使用 OpenAI 兼容格式，替换AI模型即可
Python 流式
from openai import OpenAI
api_key = "sk-HTdmSI6B2cNt************************************"
api_base = "https://sg.uiuiapi.com/v1"
client = OpenAI(api_key=api_key, base_url=api_base)
completion = client.chat.completions.create(
model="claude-3-opus-20240229",
stream: True,
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello!"}
]
)
for chunk in completion:
print(chunk.choices[0].delta)
Python 非流
from openai import OpenAI
api_key = "sk-HTdmSI6B2cNt************************************"
api_base = "https://sg.uiuiapi.com/v1"
client = OpenAI(api_key=api_key, base_url=api_base)
completion = client.chat.completions.create(
model="claude-3-opus-20240229",
stream: False,
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello!"}
]
)
print(completion.choices[0].message)
Python Whisper 语音转文字
from openai import OpenAI
from pathlib import Path
# 设置你的基础URL和API密钥
base_url = "https://sg.uiuiapi.com/v1"
key = "sk-HTdmSI6B2cNt************************************"
# 初始化OpenAI客户端
client = OpenAI(api_key=key, base_url=base_url)
# 音频文件的路径
audio_file_path = "C:/speech.mp3"
# 打开音频文件
with open(audio_file_path, "rb") as audio_file:
# 创建转录
transcription = client.audio.transcriptions.create(
model="whisper-1",
file=audio_file
)
# 打印转录文本
print(transcription.text)
Python TTS 文字转语音
from openai import OpenAI
from pathlib import Path
def test_text_speech(model="tts-1"):
print(f"Testing {model} - text to speech")
speech_file_path = Path(__file__).parent / "speech1.mp3"
response = client.audio.speech.create(
model=model,
voice="alloy", # 可选 alloy, echo, fable, onyx, nova, shimmer
input="示例文本",
)
response.stream_to_file(speech_file_path)
base_url = "https://sg.uiuiapi.com/v1"
key = "sk-HTdmSI6B2cNt************************************"
client = OpenAI(base_url=base_url, api_key=key)
test_text_speech()
1.3 Curl 接入示例
Curl 流式
curl https://sg.uiuiapi.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer sk-HTdmSI6B2cNt************************************" \
-d '{
"model": "claude-3-opus-20240229",
"stream": true,
"messages": [{ "role": "user", "content": "say 1" }]
}'
Curl 非流
curl https://sg.uiuiapi.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer sk-HTdmSI6B2cNt************************************" \
-d '{
"model": "claude-3-opus-20240229",
"stream": false,
"messages": [{ "role": "user", "content": "say 1" }]
}'
1.4 Node.js 接入示例
Node.js 流式
import OpenAI from "openai";
const openai = new OpenAI("https://sg.uiuiapi.com/v1");
async function main() {
const completion = await openai.chat.completions.create({
stream: true,
messages: [{ role: "system", content: "You are a helpful assistant." }],
model: "claude-3-opus-20240229",
});
console.log(completion.choices[0]);
}
main();
Node.js 非流
import OpenAI from "openai";
const openai = new OpenAI("https://sg.uiuiapi.com/v1");
async function main() {
const completion = await openai.chat.completions.create({
stream: false,
messages: [{ role: "system", content: "You are a helpful assistant." }],
model: "claude-3-opus-20240229",
});
console.log(completion.choices[0]);
}
main();
1.5 Python使用Claude、gpt-4o识别图片
识别链接格式图片
from openai import OpenAI
client = OpenAI(
base_url="https://sg.uiuiapi.com/v1",
api_key=key
)
response = client.chat.completions.create(
model="gpt-4o",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "What’s in this image?"},
{
"type": "image_url",
"image_url": {
"url": "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg",
},
},
],
}
],
max_tokens=300,
)
print(response.choices[0])
识别Base64格式图片
import base64
import time
from openai import OpenAI
import openai
key = 'sk-xxxx'
client = OpenAI(
base_url="https://sg.uiuiapi.com/v1",
api_key=key
)
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
image_path = "图片.jpg"
base64_image = encode_image(image_path)
while True:
response = client.chat.completions.create(
model="claude-3-5-sonnet-20240620",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "这张图片里有什么?请详细描述。"},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64_image}"
}
}
]
}
],
temperature=1
)
print(response)
print(response.choices[0].message.content)
time.sleep(1)
API快速使用工具推荐 🔥
Python调用OpenAI API
设置API基础URL
有两种方法:
- 在代码中设置:
import openai
openai.api_base = "https://sg.uiuiapi.com/v1"
- 设置环境变量:
OPENAI_API_BASE=https://sg.uiuiapi.com/v1
发送API请求
可以使用curl命令或Python代码发送请求。
curl示例:
bash
curl https://sg.uiuiapi.com/v1/chat/completions \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer YOUR_API_KEY' \
-d '{
"model": "gpt-3.5-turbo",
"messages": [{"role": "user", "content": "Say this is a test!"}],
"temperature": 0.7
}'
Python示例:
import openai
openai.api_key = "sk-"
openai.api_base = "https://sg.uiuiapi.com/v1"
def gpt_35_api_stream(messages: list):
try:
response = openai.ChatCompletion.create(
model='gpt-3.5-turbo',
messages=messages,
stream=True,
)
# 处理流式响应
# ...
except Exception as err:
return (False, f'OpenAI API 异常: {err}')
# 使用示例
messages = [{'role': 'user','content': '鲁迅和周树人的关系'},]
print(gpt_35_api_stream(messages))
使用OpenAI官方SDK
import openai
def query_gpt4(question):
openai.api_key = "sk-xxx"
openai.base_url = 'https://sg.uiuiapi.com/v1/'
try:
response = openai.chat.completions.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": question}
]
)
return response['choices'][0].message['content']
except Exception as e:
return str(e)
# 使用示例
question = "为什么太阳那么红？"
answer = query_gpt4(question)
print(answer)
这个整理后的版本保留了原文的主要内容,并按照不同的调用方法和示例进行了分类,使结构更清晰,便于阅读和理解。
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