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

# List Embedding Models

> List embedding-capable models available through the Confidential AI API.

## Endpoint

```bash theme={"system"}
GET https://api.redpill.ai/v1/embeddings/models
```

Returns models designed for vector embeddings. Use this endpoint when selecting embedding models for retrieval, RAG, clustering, or similarity search.

## Examples

<CodeGroup>
  ```bash cURL theme={"system"}
  curl https://api.redpill.ai/v1/embeddings/models \
    -H "Authorization: Bearer <API_KEY>"
  ```

  ```python Python theme={"system"}
  import requests

  response = requests.get(
      "https://api.redpill.ai/v1/embeddings/models",
      headers={"Authorization": "Bearer <API_KEY>"},
  )

  for model in response.json()["data"]:
      print(model["id"])
  ```

  ```typescript TypeScript theme={"system"}
  const response = await fetch("https://api.redpill.ai/v1/embeddings/models", {
    headers: {
      Authorization: "Bearer <API_KEY>",
    },
  });

  const models = await response.json();
  for (const model of models.data) {
    console.log(model.id);
  }
  ```
</CodeGroup>

## Response

```json theme={"system"}
{
  "object": "list",
  "data": [
    {
      "id": "qwen/qwen3-embedding-8b",
      "name": "Qwen3 Embedding 8B",
      "created": 1704067200,
      "input_modalities": ["text"],
      "output_modalities": ["embeddings"],
      "context_length": 32768,
      "max_output_length": 4096,
      "pricing": {
        "prompt": "0.00000001",
        "completion": "0"
      },
      "description": "Embedding model for semantic search and retrieval"
    }
  ]
}
```

## Response Fields

| Field               | Description                                |
| ------------------- | ------------------------------------------ |
| `id`                | Model identifier for `POST /v1/embeddings` |
| `name`              | Human-readable model name                  |
| `input_modalities`  | Input types accepted by the model          |
| `output_modalities` | Output types produced by the model         |
| `context_length`    | Maximum input context length               |
| `max_output_length` | Embedding dimensions or max output size    |
| `pricing.prompt`    | Input price per token                      |
| `description`       | Model description and use case             |