BUILDING AI INTENSIVE PYTHON APPLICATIONS create intelligent apps with LLMs and vector databases

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Published
Birmingham, UK : Packt Publishing Ltd., 2024.
Status
Available Online

Description

Master retrieval-augmented generation architecture and fine-tune your AI stack, along with discovering real-world use cases and best practices to create powerful AI apps Key Features Get to grips with the fundamentals of LLMs, vector databases, and Python frameworks Implement effective retrieval-augmented generation strategies with MongoDB Atlas Optimize AI models for performance and accuracy with model compression and deployment optimization Purchase of the print or Kindle book includes a free PDF eBook Book Description 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. What you will learn Understand the architecture and components of the generative AI stack Explore the role of vector databases in enhancing AI applications Master Python frameworks for AI development Implement Vector Search in AI applications Find out how to effectively evaluate LLM output Overcome common failures and challenges in AI development Who this book is for This book is for software engineers and developers looking to build intelligent applications using generative AI. While the book is suitable for beginners, a basic understanding of Python programming is required to make the most of it.

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Format
Edition
First edition.
Language
English
ISBN
9781836207245, 1836207247

Notes

Description
Master retrieval-augmented generation architecture and fine-tune your AI stack, along with discovering real-world use cases and best practices to create powerful AI apps Key Features Get to grips with the fundamentals of LLMs, vector databases, and Python frameworks Implement effective retrieval-augmented generation strategies with MongoDB Atlas Optimize AI models for performance and accuracy with model compression and deployment optimization Purchase of the print or Kindle book includes a free PDF eBook Book Description 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. What you will learn Understand the architecture and components of the generative AI stack Explore the role of vector databases in enhancing AI applications Master Python frameworks for AI development Implement Vector Search in AI applications Find out how to effectively evaluate LLM output Overcome common failures and challenges in AI development Who this book is for This book is for software engineers and developers looking to build intelligent applications using generative AI. While the book is suitable for beginners, a basic understanding of Python programming is required to make the most of it.
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O'Reilly O'Reilly Online Learning: Academic/Public Library Edition

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Citations

APA Citation, 7th Edition (style guide)

Palmer, R., Perlmutter, B., Gangadhar, A., Larew, N., Narváez, S., Rueckstiess, T., Weller, H., Alake, R., & Ranjan, S. (2024). BUILDING AI INTENSIVE PYTHON APPLICATIONS: create intelligent apps with LLMs and vector databases (First edition.). Packt Publishing Ltd..

Chicago / Turabian - Author Date Citation, 17th Edition (style guide)

Rachelle, Palmer et al.. 2024. BUILDING AI INTENSIVE PYTHON APPLICATIONS: Create Intelligent Apps With LLMs and Vector Databases. Birmingham, UK: Packt Publishing Ltd.

Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)

Rachelle, Palmer et al.. BUILDING AI INTENSIVE PYTHON APPLICATIONS: Create Intelligent Apps With LLMs and Vector Databases Birmingham, UK: Packt Publishing Ltd, 2024.

Harvard Citation (style guide)

Palmer, R., Perlmutter, B., Gangadhar, A., Larew, N., Narváez, S., Rueckstiess, T. and Weller, H. et al (2024). BUILDING AI INTENSIVE PYTHON APPLICATIONS: create intelligent apps with llms and vector databases. First edn. Birmingham, UK: Packt Publishing Ltd.

MLA Citation, 9th Edition (style guide)

Palmer, Rachelle,, et al. BUILDING AI INTENSIVE PYTHON APPLICATIONS: Create Intelligent Apps With LLMs and Vector Databases First edition., Packt Publishing Ltd., 2024.

Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.

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650b697f-7b1f-88b1-adb2-6ebc11eb7dae-eng
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Grouped Work ID650b697f-7b1f-88b1-adb2-6ebc11eb7dae-eng
Full titlebuilding ai intensive python applications create intelligent apps with llms and vector databases
Authorpalmer rachelle
Grouping Categorybook
Last Update2025-02-11 03:40:45AM
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5050 |a Cover -- FM -- Table of Contents -- Preface -- Chapter 1: Getting Started with Generative AI -- Technical requirements -- Defining the terminology -- The generative AI stack -- Python and GenAI -- OpenAI API -- MongoDB with Vector Search -- Important features of generative AI -- Why use generative AI? -- The ethics and risks of GenAI -- Summary -- Chapter 2: Building Blocks of Intelligent Applications -- Technical requirements -- Defining intelligent applications -- The building blocks of intelligent applications -- LLMs -- reasoning engines for intelligent apps
5058 |a Use cases for LLM reasoning engines -- Diverse capabilities of LLMs -- Multi-modal language models -- A paradigm shift in AI development -- Embedding models and vector databases -- semantic long-term memory -- Embedding models -- Vector databases -- Model hosting -- Your (soon-to-be) intelligent app -- Sample application -- RAG chatbot -- Implications of intelligent applications for software engineering -- Summary -- Part 1 -- Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design -- Chapter 3: Large Language Models -- Technical requirements -- Probabilistic framework
5058 |a N-gram language models -- Machine learning for language modelling -- Artificial neural networks -- Training an artificial neural network -- ANNs for natural language processing -- Tokenization -- Embedding -- Predicting probability distributions -- Dealing with sequential data -- Recurrent neural networks -- Transformer architecture -- LLMs in practice -- The evolving field of LLMs -- Prompting, fine-tuning, and RAG -- Summary -- Chapter 4: Embedding Models -- Technical requirements -- What is an embedding model? -- How do embedding models differ from LLMs?
5058 |a When to use embedding models versus LLMs -- Types of embedding models -- Choosing embedding models -- Task requirements -- Dataset characteristics -- Computational resources -- Vector representations -- Embedding model leaderboards -- Embedding models overview -- Do you always need an embedding model? -- Executing code from LangChain -- Best practices -- Summary -- Chapter 5: Vector Databases -- Technical requirements -- What is a vector embedding? -- Vector similarity -- Exact versus approximate search -- Measuring search -- Graph connectivity -- Navigable small worlds
5058 |a How to search a navigable small world -- Hierarchical navigable small worlds -- The need for vector databases -- How vector search enhances AI models -- Case studies and real-world applications -- Okta -- natural language access request (semantic search) -- One AI -- language-based AI (RAG over business data) -- Novo Nordisk -- automatic clinical study generation (advanced RAG/RPA) -- Vector search best practices -- Data modeling -- Deployment -- Summary -- Chapter 6: AI/ML Application Design -- Technical requirements -- Data modeling -- Enriching data with embeddings -- Considering search use cases
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