POST/v1/embeddings

创建嵌入

获取给定输入的矢量表示，机器学习模型和算法可以轻松使用该表示。

请求示例

curl -X POST https://top.qingyuntop.ai/v1/embeddings \
  -H "Authorization: Bearer ${QINGYUN_TOP_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "text-embedding-3-large",
  "input": "喵喵喵喵喵喵喵喵喵喵喵喵喵喵喵"
}'

请求参数

参数	位置	类型	必填	说明	示例
`Authorization`	Header	string	否	-	Bearer {{YOUR_API_KEY}}
`model`	JSON body	string	是	要使用的模型的 ID。您可以使用[List models](https://platform.openai.com/docs/api-reference/models/list) API 来查看所有可用模型，或查看我们的[模型概述](https://platform.openai.com/docs/models/overview)以了解它们的描述。	-
`input`	JSON body	string	是	输入文本以获取嵌入，编码为字符串或标记数组。要在单个请求中获取多个输入的嵌入，请传递一个字符串数组或令牌数组数组。每个输入的长度不得超过 8192 个标记。	-

返回响应 (200)

参数	位置	类型	必填	说明	示例
`object`	响应 JSON	string	是	-	-
`data`	响应 JSON	array<object>	是	-	-
`data[].object`	响应 JSON	string	否	-	-
`data[].embedding`	响应 JSON	array<number>	否	-	-
`data[].index`	响应 JSON	integer	否	-	-
`model`	响应 JSON	string	是	-	-
`usage`	响应 JSON	object	是	-	-
`usage.prompt_tokens`	响应 JSON	integer	是	-	-
`usage.total_tokens`	响应 JSON	integer	是	-	-

响应示例

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [
        0.0023064255,
        -0.009327292,
        .... (1536 floats total for ada-002)
        -0.0028842222
      ],
      "index": 0
    }
  ],
  "model": "text-embedding-ada-002",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}

来源原始接口定义（用于追溯）

以下内容来自 Apifox 快照，可能包含兼容层字段或旧示例；公开调用请以本页的协议说明和当前模型列表为准。

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  "operationId": "",
  "sourceUrl": "",
  "method": "post",
  "path": "/v1/embeddings",
  "tags": [],
  "status": 1,
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                  "type": "string"
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                  "type": "integer"
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              "x-apifox-orders": [
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