Buchumschlag
RAG-Driven Generative AI: Build Custom Retrieval Augmented Generation Pipelines with LlamaIndex, Deep Lake, and Pinecone

Minimize AI hallucinations and build accurate, custom generative AI pipelines with RAG using embedded vector databases and integrated human feedback Purchase of the print or Kindle book includes a free eBook in PDF format Key Features Implement RAG's traceable outputs, linking each response to...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Beteilige Person: Rothman, Denis (VerfasserIn)
Format: Elektronisch E-Book
Sprache:Englisch
Veröffentlicht: Birmingham Packt Publishing, Limited 2024
Schriftenreihe:Expert insight
Schlagwörter:
Natural language generation (Computer science)
Artificial intelligence > Computer programs
Génération automatique de texte
Intelligence artificielle ; Logiciels
Links:https://learning.oreilly.com/library/view/-/9781836200918/?ar
Zusammenfassung:Minimize AI hallucinations and build accurate, custom generative AI pipelines with RAG using embedded vector databases and integrated human feedback Purchase of the print or Kindle book includes a free eBook in PDF format Key Features Implement RAG's traceable outputs, linking each response to its source document to build reliable multimodal conversational agents Deliver accurate generative AI models in pipelines integrating RAG, real-time human feedback improvements, and knowledge graphs Balance cost and performance between dynamic retrieval datasets and fine-tuning static data Book Description RAG-Driven Generative AI provides a roadmap for building effective LLM, computer vision, and generative AI systems that balance performance and costs. This book offers a detailed exploration of RAG and how to design, manage, and control multimodal AI pipelines. By connecting outputs to traceable source documents, RAG improves output accuracy and contextual relevance, offering a dynamic approach to managing large volumes of information. This AI book shows you how to build a RAG framework, providing practical knowledge on vector stores, chunking, indexing, and ranking. You'll discover techniques to optimize your project's performance and better understand your data, including using adaptive RAG and human feedback to refine retrieval accuracy, balancing RAG with fine-tuning, implementing dynamic RAG to enhance real-time decision-making, and visualizing complex data with knowledge graphs. You'll be exposed to a hands-on blend of frameworks like LlamaIndex and Deep Lake, vector databases such as Pinecone and Chroma, and models from Hugging Face and OpenAI. By the end of this book, you will have acquired the skills to implement intelligent solutions, keeping you competitive in fields from production to customer service across any project. What you will learn Scale RAG pipelines to handle large datasets efficiently Employ techniques that minimize hallucinations and ensure accurate responses Implement indexing techniques to improve AI accuracy with traceable and transparent outputs Customize and scale RAG-driven generative AI systems across domains Find out how to use Deep Lake and Pinecone for efficient and fast data retrieval Control and build robust generative AI systems grounded in real-world data Combine text and image data for richer, more informative AI responses Who this book is for This book is ideal for data scientists, AI engineers, machine learning engineers, and MLOps engineers. If you are a solutions architect, software developer, product manager, or project manager looking to enhance the decision-making process of building RAG applications, then you'll find this book useful.
Beschreibung:Description based upon print version of record. - Creating a vector index and query engine
Umfang:1 Online-Ressource (335 Seiten)
ISBN:9781836200901
1836200900
9781836200918