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OpenAI Embeddings

通过 RunAPI 的 OpenAI 兼容 Embeddings operation 发送请求。

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01

概览

使用兼容模型和对应协议的请求、响应结构调用 RunAPI 的 OpenAI 兼容 Embeddings operation。

快速开始

  1. 创建 API Key，并将其设置为 RUNAPI_API_KEY。
  2. 选择兼容模型，然后发送文档所列的路径参数和 JSON 请求体。
  3. 读取此 operation 展示的 JSON 响应或服务器发送事件，并处理协议错误。

端点

POST /v1/embeddings
基础 URL
https://runapi.ai
Contract 版本
v1
首选身份验证
Authorization: Bearer YOUR_API_TOKEN
02

身份验证载体

RunAPI 接受下列每种载体；生成的示例使用该协议的首选请求头。

header - 首选
Authorization: Bearer YOUR_API_TOKEN
header
x-api-key: YOUR_API_TOKEN
header
x-goog-api-key: YOUR_API_TOKEN
query
?key=YOUR_API_TOKEN
03

兼容模型

打开模型页可查看当前价格、限流和商业使用详情。

展开 3 个兼容模型
04

请求 Contract

JSON 请求体

这里只列出同时具有公共协议与 RunAPI 支持证据的字段；其他 JSON 请求体字段会原样传递。

JSON 请求体4 个字段
dimensionsinteger
可选

Requested output vector dimensions for models that support it.

encoding_formatstring
可选

Format used for returned embedding vectors.

input["string", "array"]
必填

Text or token arrays to embed.

modelstring
必填

RunAPI embedding model identifier.

请求示例

JSON 
{
  "input": "The quick brown fox.",
  "model": "text-embedding-3-small"
}
05

同步响应

HTTP 200

Schema

JSON 
{
  "additionalProperties": true,
  "properties": {
    "data": {
      "type": "array"
    },
    "model": {
      "type": "string"
    },
    "object": {
      "const": "list",
      "type": "string"
    },
    "usage": {
      "additionalProperties": true,
      "properties": {
        "prompt_tokens": {
          "type": "integer"
        },
        "total_tokens": {
          "type": "integer"
        }
      },
      "required": [
        "prompt_tokens",
        "total_tokens"
      ],
      "type": "object"
    }
  },
  "required": [
    "object",
    "data",
    "model",
    "usage"
  ],
  "type": "object"
}

示例

JSON 
{
  "data": [
    {
      "embedding": [
        0.0023,
        -0.0093,
        0.0151
      ],
      "index": 0,
      "object": "embedding"
    }
  ],
  "model": "text-embedding-3-small",
  "object": "list",
  "usage": {
    "prompt_tokens": 5,
    "total_tokens": 5
  }
}
06

协议错误：invalid_request

HTTP 400

Schema

JSON 
{
  "additionalProperties": true,
  "properties": {
    "error": {
      "additionalProperties": true,
      "properties": {
        "code": {
          "type": [
            "string",
            "null"
          ]
        },
        "message": {
          "type": "string"
        },
        "param": {
          "type": [
            "string",
            "null"
          ]
        },
        "type": {
          "type": "string"
        }
      },
      "required": [
        "message",
        "type"
      ],
      "type": "object"
    }
  },
  "required": [
    "error"
  ],
  "type": "object"
}

示例

JSON 
{
  "error": {
    "code": null,
    "message": "input is required",
    "param": "input",
    "type": "invalid_request_error"
  }
}
07

Usage

Schema

JSON 
{
  "additionalProperties": true,
  "properties": {
    "prompt_tokens": {
      "type": "integer"
    },
    "total_tokens": {
      "type": "integer"
    }
  },
  "required": [
    "prompt_tokens",
    "total_tokens"
  ],
  "type": "object"
}

示例

JSON 
{
  "prompt_tokens": 5,
  "total_tokens": 5
}
08

生成的代码示例

每个 cURL、JavaScript 和 Python 示例都会发送此 Contract 中经过验证的请求，无需协议专用 SDK。

CURL 
curl -X POST https://runapi.ai/v1/embeddings \
  -H Authorization:\ Bearer\ YOUR_API_TOKEN \
  -H Content-Type:\ application/json \
  -d \{\"model\":\"text-embedding-3-small\",\"input\":\"The\ quick\ brown\ fox.\"\}
JAVASCRIPT 
const response = await fetch("https://runapi.ai/v1/embeddings", {
  method: "POST",
  headers: {
  "Authorization": "Bearer YOUR_API_TOKEN",
  "Content-Type": "application/json"
},
  body: JSON.stringify({
  "model": "text-embedding-3-small",
  "input": "The quick brown fox."
})
});
if (!response.ok) throw new Error(`HTTP ${response.status}`);
const data = await response.json();
console.log(data);

安装

BASH 
pip install requests
PYTHON 
import requests

response = requests.post(
    "https://runapi.ai/v1/embeddings",
    headers={"Authorization": "Bearer YOUR_API_TOKEN", "Content-Type": "application/json"},
    json={"model": "text-embedding-3-small", "input": "The quick brown fox."}
)
response.raise_for_status()
print(response.json())
09

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