January 2026
Intermediate
404 pages
7h 22m
Korean
Content preview from AI 에이전트 엔지니어링
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137
5
장
오케스트레이션
그림
5-3
시맨틱 도구 검색 및 호출 워크플로: 런타임에 사용자 쿼리를 임베딩하고 이를 사용해 벡터 데이터베이스에서
가장 관련성 높은 상위 도구를 검색합니다. 파운데이션 모델은 그중 적절한 도구를 선택하고 파라미터를 결정한 뒤 도구
를 호출하고 도구의 출력을 통합해 최종 사용자 응답을 생성합니다.
이 방식은 가장 일반적인 패턴으로 보통 표준 도구 선택보다 빠르고 성능도 우수하며 확장성도
충분히 좋습니다. 먼저 도구 설명을 임베딩해 도구 데이터베이스를 설정합니다. 전체 코드는
ch05
/
semantic
_
skill
_
selection
.
py
에서 확인할 수 있습니다.
import os
import requests
import logging
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_core.messages import HumanMessage, AIMessage, ToolMessage
from langchain.vectorstores import FAISS
import faiss
import numpy as np
# OpenAI 임베딩 초기화
embeddings = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEY) ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.