Generate Embeddings

Generate Embeddings From Text.

Return the generated embeddings based on the given inputs.

HTTP Request

POST {API_URL}/embeddings

where API_URL = https://inference.nebulablock.com/v1. The body requires:

• model: The model to use for generating embeddings.

• input: A list of strings to generate embeddings from.

For authentication, see the Authentication section. For an example, see the Inference Models section.

Response Attributes

model model

A string representing the AI model used to generate the response.

data array

An array containing the embeddings, represented by dictionaries with the following key-value pairs:

• embedding list of floats: The generated embedding for input at index index.

• index integer: An index to identify the position of the embedding in the response, relative to the ordering of the input.

• object string: An object label to describe the data.

object string

Describes the type of data returned.

usage dict

A dictionary containing information about the inference request, in key-value pairs:

• completion_tokens integer: The number of tokens generated in the completion for a completion action (not applicable for embeddings).

• prompt_tokens integer: The number of tokens in the prompt.

• total_tokens integer: The total number of tokens (prompt and completion combined).

• completion_tokens_details null: Additional details about the completion tokens, if available.

• prompt_tokens_details null: Additional details about the prompt tokens, if available.

Example

Request

curl -X GET '{API_URL}/api/v1/images/generation' \
-H 'Authorization: Bearer {TOKEN/KEY}' \
-H 'Content-Type: application/json' \
-d '{
    "model": "WhereIsAI/UAE-Large-V1",
    "input": [
        "Bananas are berries, but strawberries are not, according to botanical classifications.",
        "The Eiffel Tower in Paris was originally intended to be a temporary structure."
    ]
}'

Response

Here's an example of a successful response. It consists of a stream of data dictionaries, each containing the data for a generated token. The entire collection of dictionaries represents the complete generated response.

    "model": "WhereIsAI/UAE-Large-V1",
    "data": [
        {
            "embedding": [
                -0.373046875,
                ...,
                -0.10302734375
            ],
            "index": 0,
            "object": "embedding"
        },
        {
            "embedding": [
                -0.50390625,
                ...,
                -0.03564453125,
                0.01409912109375
            ],
            "index": 1,
            "object": "embedding"
        }
    ],
    "object": "list",
    "usage": {
        "completion_tokens": 0,
        "prompt_tokens": 33,
        "total_tokens": 33,
        "completion_tokens_details": null,
        "prompt_tokens_details": null
    }
}

For more examples, see the Inference Models section.