Embeddings

POST /v1/embeddings — the OpenAI embeddings shape, served by Qwen3 Embedding as qwen3-embed. 1024-dimension vectors, €0.015 / 1M tokens.

Request & response

curl

$ curl https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "qwen3-embed", "input": "The invoice is due on Friday"}'

{
  "object": "list",
  "model": "qwen3-embed",
  "data": [{
    "object": "embedding",
    "index": 0,
    "embedding": [0.0132, -0.0417, ...]   // 1024 floats
  }],
  "usage": {"prompt_tokens": 7, "total_tokens": 7}
}

Batching

Pass input as an array of strings to embed many texts in one request. You get one vector per item; index matches the input position.

Python

from openai import OpenAI

client = OpenAI(
    base_url="https://api.axforge.ai/v1",
    api_key="YOUR_AXFORGE_KEY",
)
r = client.embeddings.create(
    model="qwen3-embed",
    input=[
        "The invoice is due on Friday",
        "Payment terms are net 30",
        "The meeting moved to Tuesday",
    ],
)
vectors = [d.embedding for d in r.data]   # three lists of 1024 floats

Specs

Model	qwen3-embed
Dimensions	1024
Context	32,768 tokens
Price	€0.015 / 1M tokens

Store the vectors in any vector database; cosine similarity is the usual distance. All input is processed with zero retention, like every endpoint here.