LLM Framework API – Embeddings – Extravaganza Business Services

Embeddings

Create embeddings

POST https://api.extravaganza.com.pl/v1/embeddings

Creates an embedding vector representing the input text.

Request body

input: string or array (Required)

Input text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. The input must not exceed the max input tokens for the model (8192 tokens for all embedding models), cannot be an empty string, and any array must be 2048 dimensions or less. In addition to the per-input token limit, all embedding models enforce a maximum of 300,000 tokens summed across all inputs in a single request.

model: string (Required)

ID of the model to use. You can use the list models API to see all of your available models.

dimensions: integer (Optional)

The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.

encoding_format: string (Optional)

Defaults to float

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

user: string (Optional)

A unique identifier representing your end-user, which can help Extravaganza Business Services to monitor and detect abuse.

Returns

A list of embedding objects.

curl -X POST https://api.extravaganza.com.pl/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $API_KEY" \
  -d '{
    "input": "The food was delicious and the waiter...",
    "model": "text-embedding-small-3-en",
    "encoding_format": "float"
  }'

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [
        0.0023064255,
        -0.009327292,
        .... (1536 floats total for ada-002)
        -0.0028842222,
      ],
      "index": 0
    }
  ],
  "model": "text-embedding-small-3-en",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}

The embedding object

Represents an embedding vector returned by embedding endpoint.

embedding: array

The embedding vector, which is a list of floats. The length of vector depends on the model.

index: integer

The index of the embedding in the list of embeddings.

object: string

The object type, which is always “embedding”.

{
  "object": "embedding",
  "embedding": [
    0.0023064255,
    -0.009327292,
    .... (1536 floats total for ada-002)
    -0.0028842222,
  ],
  "index": 0
}