便携AI聚合API embeddings（嵌入向量）模型接入教程

本文目录 隐藏

1 一、前言

2 二、embeddings模型接入教程

OpenAI的embeddings模型是用来将文本转换成数值向量（即嵌入向量）的，这些向量能够捕捉文本的语义信息，常用于然语言处理（NLP）任务中。本文分享下便携AI聚合API embeddings模型接入教程。

一、前言

本文参考OpenAI官方API文档：

API reference：https://platform.openai.com/docs/api-reference
Docs：https://platform.openai.com/docs/overview

文中使用的API Key均为便携AI聚合API后台生成的令牌，以sk-开头的一串随机字符，获取方法：《便携AI聚合API新建令牌（API key）教程》。

便携AI聚合API有三个接入地址（即URL），一般推荐选择第一个或者第二个：

中转API调用地址①（中国香港服务器，直连线路，带宽大）：https://api.bianxie.ai
中转API调用地址②（国内上海服务器，带宽稍小）：https://api.bianxieai.com
中转API调用地址③（国外服务器，也可以直连，备用）：https://api.a8.hk（三个网站都可以登录账号，数据同步）

模型支持各种语言接入，包括python、PHP、C#、C、Ruby、Java、Go、JavaScript等，本文主要分享官方的CURL调用方法，以及基于python的调用方法，如果你是用其他语言调用API的，则直接问ChatGPT或者其他语言模型怎么改写就行了，如下图：

不同语言调用便携AI聚合API

二、embeddings模型接入教程

OpenAI embeddings模型支持的参数：

OpenAI官方分享的调用方法：

curl https://api.bianxie.ai/v1/embeddings \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "The food was delicious and the waiter...",
    "model": "text-embedding-ada-002",
    "encoding_format": "float"
  }'

python版本：

import requests

api_key = 'sk-Xy3WuCpTTvY1'
url = 'https://api.bianxie.ai/v1/embeddings'

data = {
    "input": "The food was delicious and the waiter...",
    "model": "text-embedding-ada-002",
    "encoding_format": "float"
}

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

response = requests.post(url, headers=headers, json=data)

if response.status_code == 200:
    print("Embeddings:", response.json())
else:
    print(f"Request failed: {response.status_code} - {response.text}")

返回示例：

Embeddings: {
    'object': 'list',
    'data': [{
        'object': 'embedding',
        'index': 0,
        'embedding': [0.0023481972, -0.009284899, 0.01570119, -0.007710972, -0.0047217803, 0.014863454, -0.009849736, -0.038281962, -0.006930355, -0.028660698, 0.025182828, 0.018112851, -0.0036048, -0.02557631, 0.00049978524, -0.016361222, 0.02827991, 0.0053500817, 0.009652995, -0.016386608, -0.015396558, 0.0042584874, 0.0070065125, -0.0072413324, -0.0038491394, 0.018506333, 0.008707369, -0.022682315, 0.011474433, 0.023926225, 0.015625032, -0.0034905635, -0.03493102, -0.004160117, -0.026147492, -0.021539949, -0.0056325, 0.011785411, 0.00845351, 0.004122038, 0.019204445, -0.014444586, 0.008973921, 0.006378211, -0.04569465, 0.0178463, -0.0055595157, -0.00071754877, -0.022136519, -0.0038999112, 0.02103223, -0.017579747, -0.011702907, -0.022644237, 0.016361222, 0.017198958, -0.008409085, 0.0016405648, 0.025132056, -0.024967048, 0.007825209, 0.005778469, -0.022161905, 0.0029098606, -0.006127525, -0.02553823, -0.008079068, 0.0010011571, 0.00018355207, 0.0046297563, 0.020803757, 0.013454535, 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    }],
    'model': 'text-embedding-ada-002',
    'usage': {
        'prompt_tokens': 8,
        'total_tokens': 8
    }
}

便携AI聚合API embeddings（嵌入向量）模型接入教程

一、前言

二、embeddings模型接入教程

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