Embeddings

The /v1/embeddings endpoint generates vector embeddings across supported embedding models (e.g. openai/text-embedding-3-small, openai/text-embedding-3-large).

Code example

from openai import OpenAI

client = OpenAI(
    base_url="https://api.zorveus.com/v1",
    api_key="zrv_your_api_key",
)

response = client.embeddings.create(
    model="openai/text-embedding-3-small",
    input="Vector databases index embeddings for approximate nearest neighbor search.",
)

vector = response.data[0].embedding
print(f"Generated embedding with {len(vector)} dimensions.")

Inputs and output

input can be one string or an array when the selected provider supports batching. The response contains one embedding item for each accepted input. Store the model ID and vector dimension with the vectors so that you do not compare incompatible embeddings.

Use GET /v1/models to find a routable embedding model. A model policy such as openai/* permits matching names, but it does not guarantee that a matching deployment is available.

Batch limits, token limits, dimensions, and optional encoding formats come from the selected provider and model.