The gte-base embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, delivering efficient and effective semantic embeddings optimized for textual similarity, semantic search, and clustering applications.
from openai import OpenAI
client = OpenAI(
base_url="https://infergate.ru/api/v1",
api_key="ig-•••",
)
resp = client.embeddings.create(
model="thenlper/gte-base",
input="Текст для векторизации",
)
print(resp.data[0].embedding[:8])modelstringобязательныйinputstring | string[]обязательныйtemperaturenumbermax_tokensintegertop_pnumbertop_kintegermin_pnumberstopstring | string[]frequency_penaltynumberpresence_penaltynumberrepetition_penaltynumberseedintegerresponse_formatobject