Embeddings

Generate vector embeddings for text. The API is compatible with the OpenAI Embeddings format, so you can use existing OpenAI SDKs by changing the base URL.

Embeddings are dense vector representations of text, useful for semantic search, clustering, retrieval-augmented generation (RAG), classification, and similarity comparison.

Endpoint

POST https://api.dos.ai/v1/embeddings

Authentication

Include your API key in the Authorization header using the Bearer scheme:

Authorization: Bearer dos_sk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

API keys can be created and managed from the dashboard.

Request Headers

Header	Required	Description
Authorization	Yes	Bearer YOUR_API_KEY
Content-Type	Yes	application/json

Request Body

Parameter	Type	Required	Default	Description
model	string	Yes	-	The embedding model ID. Use qwen3-embedding-4b.
input	string or array	Yes	-	The text to embed. Pass a single string, or an array of strings to embed many at once (batch).
encoding_format	string	No	float	Format of the returned vectors: float (array of numbers) or base64.

Response Body

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0123, -0.0456, 0.0789]
    }
  ],
  "model": "qwen3-embedding-4b",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}

Response Fields

Field	Type	Description
object	string	Always "list".
data	array	One embedding object per input. Order matches the input array.
data[].index	integer	Position of this embedding in the input list.
data[].embedding	array	The embedding vector (2560 dimensions for qwen3-embedding-4b).
model	string	The embedding model used.
usage.prompt_tokens	integer	Number of input tokens embedded.
usage.total_tokens	integer	Total tokens billed.

Model

Model	Dimensions	Model ID
Qwen3-Embedding-4B	2560	qwen3-embedding-4b

Served in FP16 on dedicated GPUs in Asia-Southeast, with an automatic cloud fallback for high availability. Embeddings are not affected by smart routing (dos-auto) or BYOK - they are always served on DOS AI infrastructure.

Examples

cURL

curl https://api.dos.ai/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer dos_sk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" \
  -d '{
    "model": "qwen3-embedding-4b",
    "input": "The quick brown fox jumps over the lazy dog."
  }'

Batch (multiple inputs)

curl https://api.dos.ai/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer dos_sk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" \
  -d '{
    "model": "qwen3-embedding-4b",
    "input": ["first document", "second document", "third document"]
  }'

Using the OpenAI Python SDK

from openai import OpenAI

client = OpenAI(
    api_key="dos_sk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
    base_url="https://api.dos.ai/v1"
)

resp = client.embeddings.create(
    model="qwen3-embedding-4b",
    input="The quick brown fox jumps over the lazy dog."
)

vector = resp.data[0].embedding   # 2560-dim list of floats
print(len(vector))

Using the OpenAI Node.js SDK

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "dos_sk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
  baseURL: "https://api.dos.ai/v1",
});

const resp = await client.embeddings.create({
  model: "qwen3-embedding-4b",
  input: "The quick brown fox jumps over the lazy dog.",
});

console.log(resp.data[0].embedding.length); // 2560

Error Responses

Status Code	Error	Description
400	Bad Request	Invalid request parameters (e.g. missing input).
401	Unauthorized	Invalid or missing API key.
402	Payment Required	Insufficient credits.
429	Too Many Requests	Rate limit exceeded. See Rate Limits.
503	Service Unavailable	Embedding backend temporarily unavailable.

See Error Codes for detailed troubleshooting.