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POST
/
v1
/
embeddings
curl https://router.requesty.ai/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_REQUESTY_API_KEY" \
  -d '{
    "model": "openai/text-embedding-3-small",
    "input": "Requesty is a unified LLM gateway."
  }'

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023, -0.0091, 0.0153, -0.0028, 0.0074]
    }
  ],
  "model": "openai/text-embedding-3-small",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}

Documentation Index

Fetch the complete documentation index at: https://docs.requesty.ai/llms.txt

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

curl https://router.requesty.ai/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_REQUESTY_API_KEY" \
  -d '{
    "model": "openai/text-embedding-3-small",
    "input": "Requesty is a unified LLM gateway."
  }'

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023, -0.0091, 0.0153, -0.0028, 0.0074]
    }
  ],
  "model": "openai/text-embedding-3-small",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}
Create vector embeddings for text input. Embeddings are useful for semantic search, similarity matching, clustering, and retrieval-augmented generation (RAG).

​
Supported Models

Browse the full catalog on the Embedding model library.
ModelDimensionsBest for
openai/text-embedding-3-small1536Cost-efficient, general purpose
openai/text-embedding-3-large3072Higher accuracy, multilingual
openai/text-embedding-ada-0021536Legacy compatibility

Authorizations

​
Authorization
string
header
required

API key for authentication

Body

application/json
​
input
required

Input text to embed, encoded as a string, array of strings, array of tokens, or array of token arrays

​
model
string
required

The model name to use for embedding generation

Example:

"openai/text-embedding-3-small"

​
dimensions
integer<int64>

The number of dimensions the resulting output embeddings should have

​
encoding_format
enum<string>
default:float

The format to return the embeddings in. Can be either float or base64.

Available options:
float,
base64
​
user
string

A unique identifier representing your end-user.

Response

Embedding response

​
data
object[]
required

The list of embeddings generated by the model

​
model
string
required

The name of the model used to generate the embedding

​
object
string
required

The object type, which is always 'list'

​
usage
object
required
Last modified on May 26, 2026