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快速开始

curl -X POST "$BASE_URL/v1/embeddings" \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-004",
    "input": "文本内容"
  }'
响应:
{
  "object": "list",
  "data": [{"embedding": [0.123, -0.456, ...], "index": 0}],
  "model": "text-embedding-004",
  "usage": {"prompt_tokens": 4, "total_tokens": 4}
}

批量嵌入

curl -X POST "$BASE_URL/v1/embeddings" \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-004",
    "input": ["第一段文本", "第二段文本", "第三段文本"]
  }'

可用模型

模型维度说明
text-embedding-004768通用文本嵌入
gemini-embedding-001768Gemini 嵌入模型

SDK 示例

Python

from openai import OpenAI

client = OpenAI(
    api_key="your-token",
    base_url="https://models.kapon.cloud/v1"
)

response = client.embeddings.create(
    model="text-embedding-004",
    input="文本内容"
)
print(response.data[0].embedding[:5])

Node.js

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'your-token',
  baseURL: 'https://models.kapon.cloud/v1'
});

const response = await client.embeddings.create({
  model: 'text-embedding-004',
  input: '文本内容'
});

常见用途

  • 语义搜索:将文档和查询转为向量,计算相似度
  • 文本分类:向量作为特征输入分类模型
  • 聚类去重:通过向量相似度识别重复内容
  • 推荐系统:基于内容向量计算相似度