Embeddings

Generate vector embeddings for text input using an OpenAI-compatible endpoint.

POST /v1/embeddings

Required capability: embeddings

Request Body

Parameter	Type	Required	Description
model	string	Yes	Embedding model identifier (e.g. text-embedding-3-small, text-embedding-ada-002).
input	string | string[]	Yes	The text to embed. Can be a single string or an array of strings for batch embedding.
dimensions	integer	No	The number of dimensions for the output embeddings. Only supported by models that allow dimensionality reduction.
encoding_format	string	No	Output encoding: "float" (default) or "base64".
user	string	No	End-user identifier for abuse tracking.

Response

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [0.0023064255, -0.009327292, 0.015797347, ...],
      "index": 0
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}

Response Fields

Field	Type	Description
object	string	Always "list".
data	array	Array of embedding objects.
data[].object	string	Always "embedding".
data[].embedding	number[]	The embedding vector.
data[].index	integer	Index of the input string this embedding corresponds to.
model	string	The model used.
usage.prompt_tokens	integer	Number of tokens in the input.
usage.total_tokens	integer	Total tokens consumed.

Example

curl https://your-gateway.example.com/v1/embeddings \
  -H "Authorization: Bearer aigw_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"
  }'

Batch Embedding

curl https://your-gateway.example.com/v1/embeddings \
  -H "Authorization: Bearer aigw_sk_your_api_key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-3-small",
    "input": [
      "First document to embed",
      "Second document to embed",
      "Third document to embed"
    ]
  }'