Embeddings

Create vector embeddings with OpenAI-compatible embedding models.

Endpoint

POST https://inference.phala.com/v1/embeddings

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

Request Body

model · string · required

Embedding model ID.

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

input · string | array · required

Input text or list of inputs to embed.

encoding_format · string

Embedding encoding format. Common values are float and base64.

dimensions · integer

Requested output dimensions, when supported by the selected model.

provider · object

Routing constraints for this request. Embedding requests accept the same provider routing block as chat completions, including zdr, aci_verified, and aci_session_ids.

Set provider: {"zdr": true} to route only to serving routes that operate under zero data retention. If no route for the selected embedding model satisfies the constraint, the request fails before input text is sent. See Zero Data Retention and Attested Routing.

Examples

cURL

curl https://inference.phala.com/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."
  }'

from openai import OpenAI

client = OpenAI(
    api_key="<API_KEY>",
    base_url="https://inference.phala.com/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))

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "<API_KEY>",
  baseURL: "https://inference.phala.com/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);

Response

{
  "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 for the live embedding catalog.