February 2025
Intermediate
376 pages
9h 25m
Japanese
Content preview from 仕組みからわかる大規模言語モデル 生成AI時代のソフトウェア開発入門
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5
エージェントとツールの利用
5.7
前節までで、RAGを LangChainで実現する方法を学びました。RAG では、LLMに行わせ
るタスクに関連する知識をプロンプトに組み込むことでLLM の応答の質を高めることができ
ます。一方、本節で紹介するエージェントは、LLMに論理的推論能力を駆使させることで複
雑なタスクを自律的に実行することができます。
5.7.1
エージェントの概要
ここでのエージェントとは、LLM とツールを組み合わせ、与えられたタスクを自律的に解
決する機能を持つ要素のことを指します。エージェントは5.2.6で紹介したツールを組み合わ
せて使うことで、外部の環境とのやり取りを通してより複雑なタスクを実行することができま
す。
エージェントは、与えられたプロンプトに対して、LLMの論理的推論（Reasoning）能力を
用いて、自動的に一連のツール呼び出しの順序や条件を決定します。エージェントの動作は、
3.8で示したReAct に基づいています。エージェントはLLMを用いて、論理的推論
（Reasoning）とツール呼び出し（Action）を自動的に繰り返すことにより、プロンプトで与え
られたタスクを実行します。
LangChainでは、エージェントを作成するためのAPI として、エージェントを作成するた
めのクラスと関数を提供しています。表5.7.1 に、LangChainで提供される主要なエージェン
ト関連のクラスと関数を示します。
create_tool_calling_agent ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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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.