August 2026
Intermediate to advanced
552 pages
8h 9m
Japanese
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14章BigQueryで呼び出したGeminiによる会話情報のカテゴライズ
寄稿：堂原 竜希
14.1 はじめに
本章の内容を学習することで、データウェアハウスに蓄積されたテキストデータに対する生成AIを用いた自然言語処理を、アプリケーションサーバーや複雑なデータパイプラインを別途構築することなくSQLのみで完結させる手法を習得できます。これにより、大量のテキストデータ（会話履歴など）を生成AIを用いて効率的に分析、分類する自動化基盤を構築可能になります。
- この章の内容
- Agent Studio（旧称：Vertex AI Studio）を用いた検証用データの作成
- BigQueryの
AI.GENERATE関数を用いた、SQLによるカテゴライズ処理の実装 - スケジュールされたクエリを活用した日次バッチ処理の自動化
生成AIのよくある使い方の1つに対話型チャットボットがあります。Geminiウェブアプリに代表されるように、ユーザーのさまざまなメッセージに対して、適切な返答を生成AIが作成し返信します。このようなチャットボットを組織で運用するケースもあり、その場合は会話履歴をBigQueryに代表されるようなデータウェアハウスへ記録できます。
本章の目的は、チャットボットの会話履歴を生成AIを用いて自動的にカテゴライズするデータ処理基盤を構築することです。そのために、まずはAgent Studioを用いて検証用のサンプルデータを作成し、BigQueryへアップロードする手順から進めます。次に、BigQueryからGeminiを呼び出すための接続設定やIAMポリシーの設定を行い、実際にAI.GENERATE関数を活用したカテゴライズ処理のクエリを実行します。最後に、日次でデータを効率的に増分更新するためのスケジュール実行の設定方法について解説します。 ...
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