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POST
/
v1
/
embeddings
curl -X POST https://www.geeknow.top/v1/embeddings \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-3-small",
    "input": [
      "API 网关统一认证、计费和渠道分发。",
      "Embedding 可用于语义搜索和相似度计算。"
    ],
    "encoding_format": "float"
  }'

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0123, -0.0456, 0.0789]
    },
    {
      "object": "embedding",
      "index": 1,
      "embedding": [0.0221, -0.0314, 0.0655]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 28,
    "total_tokens": 28
  }
}

Documentation Index

Fetch the complete documentation index at: https://mercury-eab3b728.mintlify.app/llms.txt

Use this file to discover all available pages before exploring further.

​
向量接口

将文本输入转换为向量，常用于语义搜索、相似度计算、RAG 检索和聚类。

​
路径

MethodPath说明
POST/v1/embeddingsOpenAI Embeddings 兼容路径
POST/v1/engines/{model}/embeddingsLegacy engine 兼容路径，模型名来自路径参数

​
请求体

​
model
string
required
Embedding 模型名称。
​
input
string | array<string>
required
待向量化的文本。可传单条字符串或字符串数组。
​
encoding_format
string
向量编码格式。常见值为 float。
​
dimensions
integer
指定输出向量维度。仅部分模型支持。
​
user
string
终端用户标识。

​
请求示例

curl -X POST https://www.geeknow.top/v1/embeddings \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-3-small",
    "input": [
      "API 网关统一认证、计费和渠道分发。",
      "Embedding 可用于语义搜索和相似度计算。"
    ],
    "encoding_format": "float"
  }'

​
Legacy engine 路径

curl -X POST https://www.geeknow.top/v1/engines/text-embedding-3-small/embeddings \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "把这段文本转换成向量。"
  }'

​
响应示例

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0123, -0.0456, 0.0789]
    },
    {
      "object": "embedding",
      "index": 1,
      "embedding": [0.0221, -0.0314, 0.0655]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 28,
    "total_tokens": 28
  }
}

​
相关接口