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Building AI Intensive Python Applications

You're reading from   Building AI Intensive Python Applications Create intelligent apps with LLMs and vector databases

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Product type Paperback
Published in Sep 2024
Publisher Packt
ISBN-13 9781836207252
Length 298 pages
Edition 1st Edition
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Table of Contents (18) Chapters Close

Preface 1. Chapter 1: Getting Started with Generative AI FREE CHAPTER 2. Chapter 2: Building Blocks of Intelligent Applications 3. Part 1: Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design
4. Chapter 3: Large Language Models 5. Chapter 4: Embedding Models 6. Chapter 5: Vector Databases 7. Chapter 6: AI/ML Application Design 8. Part 2: Building Your Python Application: Frameworks, Libraries, APIs, and Vector Search
9. Chapter 7: Useful Frameworks, Libraries, and APIs 10. Chapter 8: Implementing Vector Search in AI Applications 11. Part 3: Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics
12. Chapter 9: LLM Output Evaluation 13. Chapter 10: Refining the Semantic Data Model to Improve Accuracy 14. Chapter 11: Common Failures of Generative AI 15. Chapter 12: Correcting and Optimizing Your Generative AI Application 16. Other Books You May Enjoy Appendix: Further Reading: Index

Building RAG architecture systems

In the dynamic landscape of modern business, the relentless pursuit of efficiency and accuracy urges organizations to adopt cutting-edge technologies. Among these, automation stands as a cornerstone, particularly in processing and automating workflows. However, traditional methods suffer when they’re subjected to large volumes of data with intricate tasks, and human-led processes often fall short due to error-prone manual interventions.

This section explores the transformative landscape of automation, discussing the pivotal role RAG plays in revolutionizing business operations. MongoDB, known for its prowess in data management and flexible schemas, offers a compelling synergy with RAG through its vector search and full-text search capabilities. Delving into the architectural details of RAG, this section dissects its constituent building blocks, offering practical insights into constructing automated document-processing workflows that harness...

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