The all-MiniLM-L6-v2 embedding model maps sentences and short paragraphs into a 384-dimensional dense vector space, enabling high-quality semantic representations that are ideal for downstream tasks such as information retrieval, clustering,...
from openai import OpenAI
client = OpenAI(
base_url="https://infergate.ru/api/v1",
api_key="ig-•••",
)
resp = client.embeddings.create(
model="sentence-transformers/all-minilm-l6-v2",
input="Текст для векторизации",
)
print(resp.data[0].embedding[:8])modelstringобязательныйinputstring | string[]обязательныйtemperaturenumbermax_tokensintegertop_pnumbertop_kintegermin_pnumberstopstring | string[]frequency_penaltynumberpresence_penaltynumberrepetition_penaltynumberseedintegerresponse_formatobject