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Embeddings API

Embeddings are numerical representations of text that capture semantic meaning. They are essential for tasks like document search, recommendation systems, and clustering. Siraya Model Router provides a unified API to access state-of-the-art embedding models from multiple providers (OpenAI, Jina, Voyage AI, etc.).

Base URL

https://llm.siraya.ai/v1/embeddings

How to Generate Embeddings

Basic Request (Python)

import requests

url = "https://llm.siraya.ai/v1/embeddings"
headers = {
    "Authorization": "Bearer <API_KEY>",
    "Content-Type": "application/json"
}
data = {
    "model": "skylark-embedding-vision-251215",
    "input": "Hello world"
}

response = requests.post(url, headers=headers, json=data)
embeddings = response.json()["data"][0]["embedding"]

curl https://llm.siraya.ai/v1/embeddings \
  -H "Authorization: Bearer <API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "skylark-embedding-vision-251215",
    "input": "Hello world"
  }'

Best Practices

  • Normalization: Most models return normalized embeddings. Use cosine similarity for comparing vectors.
  • Caching: Since embeddings are deterministic for a given model and input, caching results can save significant costs.