Embeddings

Convert text into numerical vectors for semantic search, clustering, classification, and retrieval-augmented generation (RAG). Compatible with the OpenAI Embeddings API. Example models include gemini-embedding-001, text-embedding-004, and amazon.titan-embed-text-v2:0 — see the model square for the full list and pricing.

Create Embeddings

curl https://apicdn.xyc.ai/v1/embeddings \
  -H "Authorization: Bearer sk-xxxxxxxx" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-embedding-001",
    "input": "The quick brown fox jumps over the lazy dog."
  }'

from openai import OpenAI

client = OpenAI(base_url="https://apicdn.xyc.ai/v1", api_key="sk-xxxxxxxx")

resp = client.embeddings.create(
    model="gemini-embedding-001",
    input="The quick brown fox jumps over the lazy dog.",
)
print(resp.data[0].embedding)

You can also pass an array of strings to input to embed multiple texts in a single request:

{
  "model": "gemini-embedding-001",
  "input": ["First document.", "Second document."]
}

Response

The response follows the OpenAI embeddings shape: a data array where each item carries an embedding vector and its index.

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0123, -0.0456, 0.0789, ...]
    }
  ],
  "model": "gemini-embedding-001",
  "usage": { "prompt_tokens": 9, "total_tokens": 9 }
}

Request Parameters