The bge-large-en-v1.5 embedding model maps English sentences, paragraphs, and documents into a 1024-dimensional dense vector space, delivering high-fidelity semantic embeddings optimized for semantic search, document retrieval, and downstream NLP tasks...
from openai import OpenAI
client = OpenAI(
base_url="https://infergate.ru/api/v1",
api_key="ig-•••",
)
resp = client.embeddings.create(
model="baai/bge-large-en-v1.5",
input="Текст для векторизации",
)
print(resp.data[0].embedding[:8])modelstringобязательныйinputstring | string[]обязательныйtemperaturenumbermax_tokensintegertop_pnumbertop_kintegermin_pnumberstopstring | string[]frequency_penaltynumberpresence_penaltynumberrepetition_penaltynumberseedintegerresponse_formatobject