POST
/
v1
/
embeddings
curl -X POST "https://api.applerouter.ai/v1/embeddings" \
  -H "Authorization: Bearer sk-your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-3-small",
    "input": "The quick brown fox jumps over the lazy dog"
  }'

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023064255, -0.009327292, 0.015797347, ...]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 9,
    "total_tokens": 9
  }
}

​
Overview

Convert text into vector embeddings, useful for semantic search, clustering, and other machine learning tasks.
​
model
string
required
Embedding Model ID (e.g. text-embedding-ada-002, text-embedding-3-small)
​
input
string | array
required
Text to embed - can be a string or an array of strings
​
encoding_format
string
Output format: float (default) or base64
​
dimensions
integer
Output vector dimensions (for models that support it)
curl -X POST "https://api.applerouter.ai/v1/embeddings" \
  -H "Authorization: Bearer sk-your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-3-small",
    "input": "The quick brown fox jumps over the lazy dog"
  }'

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023064255, -0.009327292, 0.015797347, ...]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 9,
    "total_tokens": 9
  }
}

​
Batch Embedding

Embed multiple texts at once:
Python
response = client.embeddings.create(
    model="text-embedding-3-small",
    input=[
        "First document",
        "Second document",
        "Third document"
    ]
)

for i, data in enumerate(response.data):
    print(f"Document {i}: {len(data.embedding)} dimensions")

Authorizations

​
Authorization
string
header
required

使用 Bearer Token 认证。格式: Authorization: Bearer sk-xxxxxx

Body

application/json
​
model
string
required
Example:

"text-embedding-ada-002"

​
input
required

要嵌入的文本

​
encoding_format
enum<string>
default:float
Available options:
float,
base64
​
dimensions
integer

输出向量维度

Response

200 - application/json

成功创建嵌入

​
object
string
Example:

"list"

​
data
object[]
​
model
string
​
usage
object