> ## Documentation Index
> Fetch the complete documentation index at: https://docs.gravitex.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Embeddings (Gemini format)

> POST /v1/engines/{model}/embeddings

## Introduction

Create embeddings with the model specified in the URL path. Request body matches the OpenAI-style fields.

See also [Embeddings (OpenAI format)](/en/api-reference/endpoint/embeddings/create-embedding) (`POST /v1/embeddings`).

## Authentication

<ParamField header="Authorization" type="string" required>
  Bearer Token, e.g. `Bearer sk-xxxxxxxxxx`
</ParamField>

## Path parameters

<ParamField path="model" type="string" required>
  Model or engine ID in the path
</ParamField>

## Request body

<ParamField body="model" type="string" required>
  Model name (may match path `model`)
</ParamField>

<ParamField body="input" type="string | array" required>
  Text to embed
</ParamField>

<ParamField body="encoding_format" type="string" default="float">
  `float` or `base64`
</ParamField>

<ParamField body="dimensions" type="integer">
  Output dimensions
</ParamField>

## Example

```bash theme={null}
curl -X POST "https://api.gravitex.ai/v1/engines/text-embedding-ada-002/embeddings" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "model": "text-embedding-ada-002",
    "input": "Hello, world"
  }'
```

## Response

Same shape as `POST /v1/embeddings` (`object`, `data`, `model`, `usage`).