# Generate Embeddings

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](/overview/platform_api/authentication.md) section. For an example, see the [Inference Models](/core-services/overview/embedding_generation.md) 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

```bash
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.

```json{
    "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](/core-services/overview/embedding_generation.md) section.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.nebulablock.com/overview/inference-api-openai-compatible/generate_embeddings.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.