Building Retrieval Augmented Generation Applications with LlamaIndex

• Titel: Building Retrieval Augmented Generation Applications with LlamaIndex
• Titelzusatz: From Basic Components to Advanced RAG Systems
• Verlagsort: [Erscheinungsort nicht ermittelbar]
• Verlag: O'Reilly Media, Inc.
• Jahr: 2024
• Umfang: 1 online resource (1 video file)
• Fussnoten: Machine-generated record
• Datenträger: Online-Ressource
• Sprache: und
• Sach-SW: Traitement automatique des langues naturelles
• Sach-SW: Intelligence artificielle
• Sach-SW: artificial intelligence
• Sach-SW: Instructional films
• Sach-SW: Nonfiction films
• Sach-SW: Internet videos
• Sach-SW: Films de formation
• Sach-SW: Films autres que de fiction
• Sach-SW: Vidéos sur Internet
• K10plus-PPN: 1900847132
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4573891374
• Lokale URL Inst.: Zum Volltext

Abstract

This course offers a comprehensive exploration of the Retrieval-Augmented Generation (RAG) System and the LlamaIndex framework, tailored for individuals seeking to deepen their understanding and practical skills in advanced document handling and application customization. The course delves into the core principles of RAG and LlamaIndex, guiding learners through the nuances of building, evaluating, and optimizing RAG applications. Special emphasis is placed on customizing these applications to manage large volumes of documents effectively, employing LlamaIndex's robust capabilities. The significance of this course lies in its focus on real-world application and problem-solving. In an era where data management and efficient information retrieval are becoming more important, mastering the RAG system and LlamaIndex framework becomes crucial. This course addresses these needs by equipping learners with the skills to evaluate RAG systems critically, customize applications with advanced techniques like Data Ingestion and Embedding Models, and implement LlamaPacks for rapid application development. By the end of this course, participants will not only understand the theoretical aspects of RAG and LlamaIndex but will also be adept at applying these concepts to improve document management and retrieval in various professional scenarios.