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

# Create Embeddings

> Convert text into vector representations

> Дата обновления: 2026-06-06

## Overview

Convert input text into high-dimensional vectors for use in semantic search, clustering, recommendations, and more. Fully compatible with the OpenAI Embeddings API format.

## Supported Models

| Model                    | Dimensions | Description                               |
| ------------------------ | ---------- | ----------------------------------------- |
| `text-embedding-3-large` | 3072       | High accuracy, recommended for production |
| `text-embedding-3-small` | 1536       | Cost-effective                            |
| `text-embedding-ada-002` | 1536       | Classic model                             |

## Request Parameters

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

<ParamField body="input" type="string | string[]" required>
  Text to embed. Supports a single string or an array of strings
</ParamField>

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

<ParamField body="dimensions" type="integer">
  Output vector dimensions (only supported by `text-embedding-3-*` models)
</ParamField>

## Response Format

```json theme={null}
{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023064255, -0.009327292, ...]
    }
  ],
  "model": "text-embedding-3-large",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}
```

## Code Examples

<CodeGroup>
  ```python Python theme={null}
  from openai import OpenAI

  client = OpenAI(
      api_key="sk-xxx",
      base_url="https://api.crazyrouter.com/v1"
  )

  response = client.embeddings.create(
      model="text-embedding-3-large",
      input="Crazyrouter is an AI model gateway"
  )

  embedding = response.data[0].embedding
  print(f"Vector dimensions: {len(embedding)}")
  print(f"First 5 values: {embedding[:5]}")
  ```

  ```python Python (Batch) theme={null}
  response = client.embeddings.create(
      model="text-embedding-3-large",
      input=[
          "First text",
          "Second text",
          "Third text"
      ]
  )

  for item in response.data:
      print(f"Index {item.index}: dimensions {len(item.embedding)}")
  ```

  ```bash cURL theme={null}
  curl -X POST https://api.crazyrouter.com/v1/embeddings \
    -H "Authorization: Bearer sk-xxx" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "text-embedding-3-large",
      "input": "Crazyrouter is an AI model gateway"
    }'
  ```
</CodeGroup>

<Note>
  Batch requests support up to 2048 texts per call. Each text should not exceed 8191 tokens.
</Note>