List Embedding Models

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

Endpoint

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.

Query Parameters

zdr · 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.

curl "https://inference.phala.com/v1/embeddings/models?zdr=true" \
  -H "Authorization: Bearer <API_KEY>"

tee · 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.

true is the only accepted value. Any other value returns 400. Omit the parameter to list the full embedding catalog.

curl "https://inference.phala.com/v1/embeddings/models?tee=true" \
  -H "Authorization: Bearer <API_KEY>"

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

Examples

cURL

curl https://inference.phala.com/v1/embeddings/models \
  -H "Authorization: Bearer <API_KEY>"

import requests

response = requests.get(
    "https://inference.phala.com/v1/embeddings/models",
    headers={"Authorization": "Bearer <API_KEY>"},
)

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

const response = await fetch("https://inference.phala.com/v1/embeddings/models", {
  headers: {
    Authorization: "Bearer <API_KEY>",
  },
});

const models = await response.json();
for (const model of models.data) {
  console.log(model.id);
}

Response

{
  "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