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Building AI Intensive Python Applications

Building AI Intensive Python Applications

By : Rachelle Palmer, Ben Perlmutter, Ashwin Gangadhar, Nicholas Larew, Sigfrido Narváez, Thomas Rueckstiess, Henry Weller, Richmond Alake, Shubham Ranjan
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Building AI Intensive Python Applications

Building AI Intensive Python Applications

By: Rachelle Palmer, Ben Perlmutter, Ashwin Gangadhar, Nicholas Larew, Sigfrido Narváez, Thomas Rueckstiess, Henry Weller, Richmond Alake, Shubham Ranjan

Overview of this book

The era of generative AI is upon us, and this book serves as a roadmap to harness its full potential. With its help, you’ll learn the core components of the AI stack: large language models (LLMs), vector databases, and Python frameworks, and see how these technologies work together to create intelligent applications. The chapters will help you discover best practices for data preparation, model selection, and fine-tuning, and teach you advanced techniques such as retrieval-augmented generation (RAG) to overcome common challenges, such as hallucinations and data leakage. You’ll get a solid understanding of vector databases, implement effective vector search strategies, refine models for accuracy, and optimize performance to achieve impactful results. You’ll also identify and address AI failures to ensure your applications deliver reliable and valuable results. By evaluating and improving the output of LLMs, you’ll be able to enhance their performance and relevance. By the end of this book, you’ll be well-equipped to build sophisticated AI applications that deliver real-world value.
Table of Contents (18 chapters)
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Part 1: Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design
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Part 2: Building Your Python Application: Frameworks, Libraries, APIs, and Vector Search
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Part 3: Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics
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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