POST
Create embeddings
将文本转换为向量表示。完全兼容 OpenAI /v1/embeddings 接口规范。 text-embedding-v4 是阿里云百炼平台的向量化模型。dimensions 参数为阿里模型特有（支持 512/768/1024），详见 help.aliyun.com/zh/model-studio
请求参数
modelstringRequired向量化模型名称，如 text-embedding-v4。
inputstring | string[]Required需要向量化的文本，可以是字符串或字符串数组。
dimensionsintegerOptional输出向量维度，必填。text-embedding-v4 支持 512、768、1024。
encoding_formatstringOptional返回格式，支持 float 或 base64，默认 float。
认证
AuthorizationstringRequired在请求头中传入 Bearer Token。
错误码
POST
| 1 | import OpenAI from 'openai' |
| 2 | |
| 3 | const client = new OpenAI({ |
| 4 | apiKey: process.env.HY_API_KEY, |
| 5 | baseURL: 'https://apiclaw.cc/v1', |
| 6 | }) |
| 7 | |
| 8 | const response = await client.embeddings.create({ |
| 9 | model: 'text-embedding-v4', |
| 10 | input: '你好，世界', |
| 11 | dimensions: 1024, |
| 12 | }) |
| 13 | |
| 14 | console.log(response.data[0].embedding) |
POST
Authorization
获取 API Key →🔑
REQUEST
| 1 | const response = await fetch("https://apiclaw.cc/v1/embeddings", { |
| 2 | method: "POST", |
| 3 | headers: { |
| 4 | "Authorization": "Bearer YOUR_API_KEY", |
| 5 | "Content-Type": "application/json", |
| 6 | }, |
| 7 | }); |
| 8 | const data = await response.json(); |
| 9 | console.log(data); |
RESPONSE
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点击 Send request 查看响应
Response
| 1 | { |
| 2 | "object": "list", |
| 3 | "data": [ |
| 4 | { |
| 5 | "object": "embedding", |
| 6 | "index": 0, |
| 7 | "embedding": [ |
| 8 | 0.002179, -0.024084, 0.025083, ... |
| 9 | ] |
| 10 | } |
| 11 | ], |
| 12 | "model": "text-embedding-v4", |
| 13 | "usage": { |
| 14 | "prompt_tokens": 4, |
| 15 | "total_tokens": 4 |
| 16 | } |
| 17 | } |