> ## 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 Private AI API.

## Endpoint

```bash theme={"system"}
GET https://inference.phala.com/v1/embeddings/models
```

Returns models designed for vector embeddings. Use this endpoint when selecting embedding models for retrieval, RAG, clustering, or similarity search. No API key is required.

## Query Parameters

<ParamField query="zdr" type="string">
  Set to `true` to return only embedding models that can be served under zero data retention.

  `true` is the only accepted value. Any other value returns `400`. Omit the parameter to list the full embedding catalog.
</ParamField>

```bash theme={"system"}
curl "https://inference.phala.com/v1/embeddings/models?zdr=true"
```

<ParamField query="tee" type="string">
  Set to `true` to return only embedding models that are offered confidentially, meaning the model carries `is_tee: true`. To require attestation for a specific request, send `provider: {"aci_verified": true}`. See [Attested Routing](/phala-cloud/confidential-ai/confidential-model/attested-routing).

  `true` is the only accepted value. Any other value returns `400`. Omit the parameter to list the full embedding catalog.
</ParamField>

```bash theme={"system"}
curl "https://inference.phala.com/v1/embeddings/models?tee=true"
```

Response entries have the same shape as the unfiltered catalog. There is no `zdr` field on a model object.

## Examples

```bash theme={"system"}
curl https://inference.phala.com/v1/embeddings/models
```

## 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 |


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