Modèles d'architecture RAG
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Modèles d'architecture RAG

Modèles d'architecture RAG


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About the Book

Vos entreprises génèrent des données en masse, mais vos systèmes d'information vous permettent-ils réellement d'en extraire la valeur stratégique instantanément ?
Modèles d'architecture RAG propose une analyse approfondie de la génération augmentée par la recherche, cette approche technique qui fusionne récupération d'information et modèles de langage pour créer des systèmes de recherche d'entreprise véritablement performants. Cet ouvrage s'adresse aux experts techniques comme aux décideurs qui conçoivent des solutions IA opérationnelles.
Ce guide pratique vous permettra de: - Comprendre comment l'architecture RAG connecte vos bases de données existantes à des modèles génératifs pour produire des réponses contextualisées et précises, sans reconstruction complète de vos systèmes - Explorer des implémentations concrètes dans le secteur juridique (recherche jurisprudentielle intelligente), la santé (synthèse de dossiers patients) et la finance (compliance automatisée) avec des cas d'usage détaillés - Déployer des systèmes RAG robustes en utilisant des outils open source éprouvés, avec des patterns architecturaux optimisés pour la charge entreprise - Éviter les pièges classiques: coûts de calcul, latence, qualité des sources et gouvernance des données qui freinent 80 % des projets - Concevoir des pipelines de traitement qui transforment vos silos documentaires en ressources consultables en langage naturel
L'ouvrage détaille les mécanismes de ranking, l'optimisation des embeddings, le chunking avancé et les stratégies de prompt engineering spécifiques aux environnements corporatifs. Chaque concept est illustré par des schémas d'architecture et des extraits de code exploitables.
Positionnez votre organisation à l'avant-garde des systèmes d'information augmentés. Acquérez dès maintenant les méthodes pour construire des solutions RAG scalables qui rendent vos données stratégiques immédiatement actionnables.


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Product Details
  • ISBN-13: 9798279110322
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • ISBN-10: 8279110321
  • Publisher Date: 19 Dec 2025


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