> ## 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.

# Embeddings

> Create vector embeddings with OpenAI-compatible embedding models.

## Endpoint

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

Generate vector embeddings for retrieval, semantic search, clustering, and similarity workloads.

## Request Body

<ParamField body="model" type="string" required>
  Embedding model ID.

  Examples: `qwen/qwen3-embedding-8b`, `sentence-transformers/all-minilm-l6-v2`.
</ParamField>

<ParamField body="input" type="string | array" required>
  Input text or list of inputs to embed.
</ParamField>

<ParamField body="encoding_format" type="string">
  Embedding encoding format. Common values are `float` and `base64`.
</ParamField>

<ParamField body="dimensions" type="integer">
  Requested output dimensions, when supported by the selected model.
</ParamField>

## Examples

<CodeGroup>
  ```bash cURL theme={"system"}
  curl https://api.redpill.ai/v1/embeddings \
    -H "Authorization: Bearer <API_KEY>" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "qwen/qwen3-embedding-8b",
      "input": "Confidential AI keeps inference data private."
    }'
  ```

  ```python Python theme={"system"}
  from openai import OpenAI

  client = OpenAI(
      api_key="<API_KEY>",
      base_url="https://api.redpill.ai/v1",
  )

  response = client.embeddings.create(
      model="qwen/qwen3-embedding-8b",
      input="Confidential AI keeps inference data private.",
  )

  vector = response.data[0].embedding
  print(len(vector))
  ```

  ```typescript TypeScript theme={"system"}
  import OpenAI from "openai";

  const client = new OpenAI({
    apiKey: "<API_KEY>",
    baseURL: "https://api.redpill.ai/v1",
  });

  const response = await client.embeddings.create({
    model: "qwen/qwen3-embedding-8b",
    input: "Confidential AI keeps inference data private.",
  });

  console.log(response.data[0].embedding.length);
  ```
</CodeGroup>

## Response

```json theme={"system"}
{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023, -0.0015, 0.0042]
    }
  ],
  "model": "qwen/qwen3-embedding-8b",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}
```

## Common Models

| Model                                    | Dimensions | Context | Notes                              |
| ---------------------------------------- | ---------- | ------- | ---------------------------------- |
| `qwen/qwen3-embedding-8b`                | 4096       | 32K     | Large confidential embedding model |
| `sentence-transformers/all-minilm-l6-v2` | 384        | 512     | Low-cost compact embedding model   |

Use [List Embedding Models](/phala-cloud/confidential-ai/confidential-model/api-reference/embedding-models) for the live embedding catalog.