Generate vector embeddings for text using OpenDunes-hosted embedding models.
Embeddings convert text into dense numeric vectors. You can use them to build semantic search, clustering, classification, and retrieval-augmented generation (RAG) pipelines — all billed in DA like every other resource on OpenDunes.
import os
from openai import OpenAI
client = OpenAI(
base_url="https://opendunes.com/v1",
api_key=os.environ["OPENDUNES_API_KEY"],
)
resp = client.embeddings.create(
model="openai/text-embedding-3-large",
input="مرحبا — the OpenDunes API is OpenAI-compatible.",
)
vector = resp.data[0].embedding
print(f"Dimensions: {len(vector)}")Pass a list of strings to embed multiple inputs in one call:
resp = client.embeddings.create(
model="openai/text-embedding-3-large",
input=[
"What are the best models for Arabic text?",
"How do I top up my DA balance?",
"Show me vision-capable models.",
],
)
for i, item in enumerate(resp.data):
print(f"[{i}] {len(item.embedding)}-dim vector")encoding_format parameter accepts float (default) or base64.modality=text and checking the embedding capability flag.