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
RAGサポート
5.6
LLMを使ったアプリケーション開発において、RAG（Retrieval Augmented Generation）
（仕組みの詳細は3.7 を参照のこと）は、重要な役割を果たします。LLMは学習時に得た知識
に基づいて回答しますが、一般向けに公開されていない情報や最新情報など学習時に得られな
かった情報には対応できません。これを実現するのがRAG です。RAGでは質問に関連する情
報をインデックスから検索し、その情報をコンテキストに追加してLLMに渡すことで、より
正確な回答を生成します。
RAGの基本ステップは、次のとおりです。
1. インデックスの作成：ドキュメントからテキストを抽出（5.6.1）・分割（5.6.2）し、埋め
込みベクトルに変換（5.6.3）してからインデックスに格納（5.6.4）します。
2. 情報の検索（5.6.5）：ユーザの質問に関連する情報をインデックスから検索します。
3. 回答の生成（5.6.6）：検索された情報を LLMに渡して回答を生成します。
LangChainでは、インデックスの作成から情報の検索、回答の生成までRAGの実現に必要
な機能を全て提供しています。以降、本節では、これらの機能について順に説明します。
5.6.1
テキストの抽出
LangChainでは、様々なドキュメントからテキストを抽出する機能をドキュメントローダと
して提供しています。表 5.6.1に、LangChain でサポートされているドキュメントタイプと ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
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.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.