The bge-m3 embedding model encodes sentences, paragraphs, and long documents into a 1024-dimensional dense vector space, delivering high-quality semantic embeddings optimized for multilingual retrieval, semantic search, and large-context applications.
from openai import OpenAI
client = OpenAI(
base_url="https://infergate.ru/api/v1",
api_key="ig-•••",
)
resp = client.embeddings.create(
model="baai/bge-m3",
input="Текст для векторизации",
)
print(resp.data[0].embedding[:8])modelstringобязательныйinputstring | string[]обязательныйtemperaturenumbermax_tokensintegertop_pnumbertop_kintegermin_pnumberstopstring | string[]frequency_penaltynumberpresence_penaltynumberrepetition_penaltynumberseedintegerlogit_biasobjectresponse_formatobject