Documentation Index
Fetch the complete documentation index at: https://docs.kapon.cloud/llms.txt
Use this file to discover all available pages before exploring further.
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-004 | 768 | 通用文本嵌入 |
gemini-embedding-001 | 768 | Gemini 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