Building AI Intensive Python Applications : Create Intelligent Apps with LLMs and Vector Databases / Rachelle Palmer [and eight others].

Format:

Book

Author/Creator:

Palmer, Rachelle, author.

Language:

English

Subjects (All):

Artificial intelligence.

Natural language processing (Computer science).

Python (Computer program language).

Machine learning.

Physical Description:

1 online resource (299 pages)

Edition:

First edition.

Place of Publication:

Birmingham, England : Packt Publishing, [2024]

Biography/History:

Palmer Rachelle: Rachelle Palmer is the Product Leader for Developer Database Experience and Developer Education at MongoDB, overseeing the driver client libraries, documentation, framework integrations, and MongoDB University. She has built sample applications for MongoDB in Java, PHP, Rust, Python, Node. js, and Ruby. Rachelle joined MongoDB in 2013 and was previously the Director of the Technical Services Engineering team, creating and managing the team that provided support and CloudOps to MongoDB Atlas. Perlmutter Ben: Ben Perlmutter is a Senior Engineer on the Education AI team at MongoDB. He applies AI technologies such as LLMs, embedding models, and vector databases to improve MongoDB's educational experience. His team built the MongoDB AI chatbot, which uses RAG to help thousands of users a week learn about MongoDB. Ben formerly worked as a technical writer specializing in developer-focused documentation. Gangadhar Ashwin: Ashwin Gangadhar is a Senior Solutions Architect at MongoDB with over a decade of experience in data-driven solutions for e-commerce, HR analytics, and finance. He holds a master's in Controls and Signal Processing and specializes in search relevancy, computer vision, and NLP. Passionate about continuous learning, Ashwin explores new technologies and innovative solutions. Born and raised in Bengaluru, India, he enjoys traveling, exploring cultures through cuisine, and playing the guitar. Larew Nicholas: Nicholas Larew is a Senior Engineer on MongoDB's Education AI team. He works on MongoDB's AI chatbot, including the open-source framework that powers it, and MongoDB's content generation and dataset curation efforts. Before working in AI, Nicholas wrote and maintained documentation and sample applications for MongoDB's developer-facing products. Narvaez Sigfrido: Sigfrido Narvaez is an Executive Solution Architect at MongoDB where he works on AI projects, database migration, and app modernization. His customers span the Americas and LATAM for entertainment, gaming, financial and other verticals. Named a MongoDB Master in 2015, he speaks at conferences such as GDC, QCon, and re: Invent, sharing the sample apps he has built in Python and other languages using MongoDB Atlas and leading AI technologies. Rueckstiess Thomas: Thomas Rueckstiess is a Senior Staff Research Scientist and Head of the Machine Learning Research Group at MongoDB. Thomas holds a PhD in Machine Learning, specializing in neural networks and reinforcement learning, transformers, and structured data modeling. He joined MongoDB in 2012 and was previously the Lead Engineer for MongoDB Compass and Atlas Charts. Weller Henry: Henry Weller is the dedicated Product Manager for Atlas Vector Search, focusing on the query features and scalability of the service, as well as developing best practices for users. He helped launch Atlas Vector Search from Public Preview into General Availability in 2023 and continues to lead the delivery of core features for the service. Henry joined MongoDB in 2022 and was previously a data engineer and backend robotics software engineer. Alake Richmond: Richmond Alake is an AI/ML Developer Advocate at MongoDB, creating technical learning content for developers building AI applications. His background includes ML architecture, optimizing data pipelines, and developing mobile experiences with deep learning. Richmond specializes in GenAI and computer vision, focusing on practical applications and efficient implementations across AI domains. He guides developers on best practices for AI solutions. Ranjan Shubham: Shubham Ranjan is a Product Manager at MongoDB for Python and a core contributing member to AI initiatives at MongoDB. He is also a Python developer and has published over 700 technical articles on topics ranging from data science and ML to competitive programming. Since joining MongoDB in 2019, Shubham has held several roles, progressing from a Software Engineer to a Product Manager for multiple products.

Summary:

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.

Contents:

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

Defining intelligent applications

The building blocks of intelligent applications

LLMs - reasoning engines for intelligent apps

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

Part 1

Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design

Chapter 3: Large Language Models

Probabilistic framework

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

Chapter 4: Embedding Models

What is an embedding model?

How do embedding models differ from LLMs?

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

Chapter 5: Vector Databases

What is a vector embedding?

Vector similarity

Exact versus approximate search

Measuring search

Graph connectivity

Navigable small worlds

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

Chapter 6: AI/ML Application Design

Enriching data with embeddings

Considering search use cases

Data storage

Determining the type of database cluster

Determining IOPS

Determining RAM

Final cluster configuration

Performance and availability versus cost

Data flow

Handling static data sources

Storing operational data enriched with vector embeddings

Freshness and retention

Real-time updates

Data lifecycle

Adopting new embedding models

Security and RBAC

Best practices for AI/ML application design

Part 2

Building Your Python Application: Frameworks, Libraries, APIs, and Vector Search

Chapter 7: Useful Frameworks, Libraries, and APIs

Python for AI/ML

AI/ML frameworks

LangChain

LangChain semantic search with score

Semantic search with pre-filtering

Implementing a basic RAG solution with LangChain.

LangChain prompt templates and chains

Key Python libraries

pandas

PyMongoArrow

PyTorch

AI/ML APIs

Hugging Face

Chapter 8: Implementing Vector Search in AI Applications

Information retrieval with MongoDB Atlas Vector Search

Vector search tutorial in Python

Vector Search tutorial with LangChain

Building RAG architecture systems

Chunking or document-splitting strategies

Simple RAG

Advanced RAG

Part 3

Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics

Chapter 9: LLM Output Evaluation

What is LLM evaluation?

Component and end-to-end evaluations

Model benchmarking

Evaluation datasets

Defining a baseline

User feedback

Synthetic data

Evaluation metrics

Assertion-based metrics

Statistical metrics

LLM-as-a-judge evaluations

RAG metrics

Human review

Evaluations as guardrails

Chapter 10: Refining the Semantic Data Model to Improve Accuracy

Embeddings

Experimenting with different embedding models

Fine-tuning embedding models

Embedding metadata

Formatting metadata

Including static metadata

Extracting metadata programmatically

Generating metadata with LLMs

Including metadata with query embedding and ingested content embeddings

Optimizing retrieval-augmented generation

Query mutation

Extracting query metadata for pre-filtering

Formatting ingested data

Advanced retrieval systems

Chapter 11: Common Failures of Generative AI

Hallucinations

Causes of hallucinations

Implications of hallucinations

Sycophancy

Causes of sycophancy

Implications of sycophancy

Data leakage

Causes of data leakage.

Implications of data leakage

Cost

Types of costs

Tokens

Performance issues in generative AI applications

Computational load

Model serving strategies

High I/O operations

Chapter 12: Correcting and Optimizing Your Generative AI Application

Baselining

Training and evaluation datasets

Few-shot prompting

Retrieval and reranking

Late interaction strategies

Query rewriting

Testing and red teaming

Testing

Red teaming

Information post-processing

Other remedies

Appendix: Further Reading

Index

Other Books You May Enjoy.

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9781836207245

1836207247

OCLC:

1455530476