[{"data":1,"prerenderedAt":1095},["ShallowReactive",2],{"/ja-jp/blog/introducing-gitlab-orbit":3,"navigation-ja-jp":330,"banner-ja-jp":748,"footer-ja-jp":757,"blog-post-authors-ja-jp-Rebecca Carter":997,"blog-related-posts-ja-jp-introducing-gitlab-orbit":1011,"blog-promotions-ja-jp":1034,"next-steps-ja-jp":1086},{"id":4,"title":5,"authors":6,"body":8,"category":307,"date":308,"description":309,"extension":310,"externalUrl":311,"featured":312,"heroImage":313,"meta":314,"navigation":312,"path":315,"seo":316,"slug":321,"stem":322,"tags":323,"template":327,"updatedDate":328,"__hash__":329},"blogPosts/ja-jp/blog/introducing-gitlab-orbit.md","GitLab Orbitのご紹介：コードとライフサイクル全体のコンテキストを、ひとつのクエリで",[7],"Rebecca Carter",{"type":9,"value":10,"toc":300},"minimark",[11,15,18,21,26,36,45,52,56,59,70,73,81,91,94,100,214,217,223,229,235,238,242,245,248,251,254,257,266,270,273,276,283,287,296],[12,13,14],"p",{},"エージェントはコードの記述が得意です。しかし、その周辺システムのナビゲーションとなると話は別です。関連コード、それを実行するパイプライン、リリースするデプロイ、要求元の作業アイテム、そして担当チーム——こうした要素間のつながりを把握することが、現在のAI支援エンジニアリングが直面する課題です。",[12,16,17],{},"大規模なモノリポでは、このギャップが無駄なイテレーション、膨れ上がったトークン予算の消費、そして一見正しく見えても結局リバートされるコードとして現れます。複数リポジトリにまたがる場合はさらに深刻です。エージェントが答えにたどり着く前にコンテキストウィンドウが埋まり、タスクが完全に失敗します。エージェントの出力を修正するのに、エージェントが節約した時間以上をチームが費やすことになります。",[12,19,20],{},"現在パブリックベータ版として提供中のGitLab Orbitは、このギャップを解消します。Orbitは、コード、マージリクエスト、パイプライン、デプロイ、脆弱性、オーナーシップのすべてを含むクエリ可能なライブグラフです。チームの作業に合わせて、要素間のすべての関係性をリアルタイムで最新の状態に保ちます。エージェントは複数のツール呼び出しを継ぎ合わせる代わりに、GitLabのファーストパーティデータを直接参照して推論します。エンジニアも同じグラフをData Explorerでクエリし、変更の追跡、インシデントの調査、そして今日では手動で何時間もかかるシステム横断的な問いに答えることができます。",[22,23,25],"h2",{"id":24},"compare-the-marketでの実証","Compare the Marketでの実証",[12,27,28,29,35],{},"Compare the Marketは英国の価格比較プラットフォームです。同社は79件の実際のマージリクエストを対象に、社内AIコードレビュアー向けの",[30,31,34],"a",{"href":32,"rel":33},"https://comparethemarketcareers.com/blog/comparing-context-retrieval-approaches-for-ai-code-review/",[],"4つのコンテキスト取得アプローチを検証","しました。Orbitを活用したレビュアーは約70%の確率で正確なインラインコメントを付与し、RAG（検索拡張生成）の約58%を上回りました。変更の主要ポイントをサマリーに含める割合も68%対66%でOrbitが優位でした。RAGはコンテキストなしを含む他のすべてのアプローチを下回る結果となりました。",[12,37,38,42],{},[39,40,41],"em",{},"「Orbitにより、コードベースを本当に理解するAIコードレビュアーが実現しました。目の前の差分だけでなく、コードベース全体を理解してくれるのです。実際のマージリクエストでRAGをはじめとする複数のアプローチと比較した結果、差は明らかでした。コメントの配置精度が高く、実際の変更内容のサマリーも優れていました。最も自然な解決策と思っていたRAGが、コンテキストなしよりも劣る結果となったことは、それ自体が答えを物語っていました。」",[43,44],"br",{},[46,47,48],"ul",{},[49,50,51],"li",{},"Ryan Harvey、Head of AI Engineering、Compare the Market",[22,53,55],{"id":54},"gitlab-orbitでできること","GitLab Orbitでできること",[12,57,58],{},"以下に、GitLab Orbitを環境に導入した場合の2つのシナリオを紹介します。",[12,60,61,65,67],{},[62,63,64],"strong",{},"シナリオ1：Claude 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PlatformのエージェントはOrbitをネイティブにクエリするため、これまで4つのツールを横断した手動調査が必要だったことが、直接質問できるようになりました。",[12,95,96,99],{},[62,97,98],{},"パイプライン障害をライフサイクル全体でトリアージする。"," 今日、障害が発生したパイプラインを見るエージェントは、1つのジョブを孤立した状態で見ています。Orbitを使うと、エージェントはその障害を引き起こした変更まで遡り、同じジョブが現在ドリフトしているプロジェクト、そして同じ問題に遭遇する進行中のマージリクエストをトレースできます。ディスカバリーを実行するため、Orbitは次のようなグラフクエリを発行します：",[101,102,107],"pre",{"className":103,"code":104,"language":105,"meta":106,"style":106},"language-cypher shiki shiki-themes github-light","MATCH (job:CiJob {status: \"failed\", name: $job_name})-[:RAN_IN]->(pipeline)-[:FOR]->(mr:MergeRequest)\nRETURN mr.title, mr.author, pipeline.started_at, mr.project_id\nORDER BY pipeline.started_at DESC LIMIT 20\n","cypher","",[108,109,110,187,196],"code",{"__ignoreMap":106},[111,112,115,119,123,126,129,132,136,139,142,144,147,150,153,157,160,164,167,170,173,175,177,180,182,184],"span",{"class":113,"line":114},"line",1,[111,116,118],{"class":117},"sD7c4","MATCH",[111,120,122],{"class":121},"sgsFI"," (job:CiJob 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mr.project_id\n",[111,197,199,202,205,208,211],{"class":113,"line":198},3,[111,200,201],{"class":117},"ORDER BY",[111,203,204],{"class":121}," pipeline.started_at ",[111,206,207],{"class":117},"DESC",[111,209,210],{"class":117}," LIMIT",[111,212,213],{"class":155}," 20\n",[12,215,216],{},"グループ内のすべてのプロジェクトをまたいで、同じ障害ジョブにヒットする進行中のマージリクエストが1回のクエリで返ります。オンコールチームは3つのチームが3回それぞれ対応する代わりに、1回でインシデントを解決できます。",[12,218,219,222],{},[62,220,221],{},"脆弱性の影響範囲を数分でマッピングする。"," 脆弱なコードを見つけることは容易です。露出範囲をマッピングすること——どのサービスがそのコンポーネントを含み、どのパイプラインがビルドし、どの環境で実行され、どのチームが担当しているか——がチームの足を引っ張ります。Orbitのクエリ1回でオーナー別のフルグラフが返ります。セキュリティチームはCVEが判明してから数週間後ではなく、1時間以内に修正計画を提示でき、影響を受けるコンポーネントにはそれぞれのオーナーが割り当て済みです。",[12,224,225,228],{},[62,226,227],{},"システム横断的な問いにオンデマンドで回答する。"," チーム別のサイクルタイムを、パイプライン障害率で分解し、デプロイ頻度と組み合わせる。1つの問いに1つの答え。ダッシュボードへのリクエストも、カスタムSQLも不要です。エンジニアリングマネージャーが経営レビューの場でその場で答えられ、3日後のSlackフォローアップにはなりません。",[12,230,231,234],{},[62,232,233],{},"現在の依存関係に対してマイグレーションをスコープする。"," 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CLIを通じて統合グラフを提供します。",[12,246,247],{},"GitLab自身のスケールでは、インデクサーは40,000以上のプロジェクト、5億のノード、20億のエッジを45分以内に処理します。イベント駆動エンジンがすべての変更をリリース時にピックアップするため、グラフは常に最新の状態を保ちます。",[12,249,250],{},"インデクシングは独立したサービスとして実行されます。クエリトラフィックはGitLabインスタンスに影響を与えません。認可はGitLabのパーミッションを反映するため、エージェントはUIでユーザーが見られるものと同じ内容を参照します。クエリエンジンはコンパイラのように構築されています。すべてのクエリはデータベースに触れる前に検証、プランニング、最適化、セキュリティのパスを経由するため、データが増えてもクエリ速度は低下しません。",[12,252,253],{},"別途データインフラを構築する必要はありません。OrbitはGitLabがすでに取得しているデータを活用します。イシュー、マージリクエスト、パイプライン、コード、セキュリティ調査結果、デプロイ、インシデントです。新たな計装なしに初日から価値を得られます。",[12,255,256],{},"GitLab Duo Agent PlatformのエージェントはOrbitをネイティブにクエリします。Claude Code、Codex、OpenCodeなどの外部エージェントはMCPとGitLab CLIを通じて接続します。カスタムエージェントや社内ツールはRESTで接続します。ひとつのグラフを、エンジニアリング組織全体で共有します。",[258,259],"iframe",{"src":260,"frameBorder":261,"allow":262,"referrerPolicy":263,"style":264,"title":265},"https://player.vimeo.com/video/1199521642?title=0&byline=0&portrait=0&badge=0&autopause=0&player_id=0&app_id=58479","0","autoplay; fullscreen; picture-in-picture; 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