> ## 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 (OpenAI format)

> POST /v1/embeddings text embeddings

## Introduction

Convert text to vector embeddings for semantic search, similarity, and clustering. Compatible with the OpenAI Embeddings API.

## Authentication

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

## Request body

<ParamField body="model" type="string" required>
  Model name, e.g. `text-embedding-3-small`, `text-embedding-3-large`, `text-embedding-ada-002`
</ParamField>

<ParamField body="input" type="string | array" required>
  Text to embed (string or array of strings)
</ParamField>

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

<ParamField body="dimensions" type="integer">
  Output dimensions (some models only)
</ParamField>

## Example

```bash theme={null}
curl https://api.gravitex.ai/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer sk-xxxxxxxxxx" \
  -d '{
    "model": "text-embedding-3-small",
    "input": "Hello, world"
  }'
```

## Python example

```python theme={null}
from openai import OpenAI

client = OpenAI(
    api_key="sk-xxxxxxxxxx",
    base_url="https://api.gravitex.ai/v1"
)

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="Hello, world"
)

print(response.data[0].embedding)
```

## Supported models

| Model                  | Dimensions | Notes          |
| ---------------------- | ---------- | -------------- |
| text-embedding-3-small | 1536       | Cost-effective |
| text-embedding-3-large | 3072       | High precision |
| text-embedding-ada-002 | 1536       | Legacy         |

<Note>
  * Pass an array to `input` for batch embedding
  * Some models support custom `dimensions`
</Note>