向量嵌入 (Embedding-3)

文本向量嵌入接口，用于语义搜索和文本相似度计算。

API 端点

POST/embeddings

文本嵌入

请求参数

参数	类型	必填	说明
`model`	string	必填	模型名称：Embedding-3
`input`	string \| array	必填	需要嵌入的文本或文本数组

请求示例

{
  "model": "Embedding-3",
  "input": "智谱AI是一家专注于大模型的公司"
}

响应示例

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [0.123, -0.456, 0.789, ...],
      "index": 0
    }
  ],
  "model": "Embedding-3",
  "usage": {
    "prompt_tokens": 10,
    "total_tokens": 10
  }
}

代码示例

Python

from openai import OpenAI

client = OpenAI(
    api_key="your-api-key",
    base_url="https://your-proxy-domain.com/v1"
)

response = client.embeddings.create(
    model="Embedding-3",
    input="智谱AI是一家专注于大模型的公司"
)

embedding_vector = response.data[0].embedding
print(f"向量维度: {len(embedding_vector)}")

JavaScript

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'your-api-key',
  baseURL: 'https://your-proxy-domain.com/v1'
});

async function getEmbedding() {
  const response = await client.embeddings.create({
    model: 'Embedding-3',
    input: '智谱AI是一家专注于大模型的公司'
  });

  const embedding = response.data[0].embedding;
  console.log(`向量维度: ${embedding.length}`);
}

getEmbedding();

cURL

curl https://your-proxy-domain.com/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-api-key" \
  -d '{
    "model": "Embedding-3",
    "input": "智谱AI是一家专注于大模型的公司"
  }'