Building AI Intensive Python Applications: Create intelligent apps with LLMs and vector databases

Cover....1 FM....2 Table of Contents....10 Preface....16 Chapter 1: Getting Started with Generative AI....20 Technical requirements....21 Defining the terminology....21 The generative AI stack....22 Python and GenAI....23 OpenAI API....24 MongoDB with Vector Search....25 Important features of generative AI....26 Why use generative AI?....27 The ethics and risks of GenAI....27 Summary....28 Chapter 2: Building Blocks of Intelligent Applications....30 Technical requirements....31 Defining intelligent applications....31 The building blocks of intelligent applications....32 LLMs – reasoning engines for intelligent apps....32 Use cases for LLM reasoning engines....33 Diverse capabilities of LLMs....33 Multi-modal language models....34 A paradigm shift in AI development....35 Embedding models and vector databases – semantic long-term memory....35 Embedding models....35 Vector databases....36 Model hosting....37 Your (soon-to-be) intelligent app....38 Sample application – RAG chatbot....39 Implications of intelligent applications for software engineering....42 Summary....42 Part 1....44 Foundations of AI: LLMs, Embedding Models, Vector Databases, and Application Design....44 Chapter 3: Large Language Models....46 Technical requirements....47 Probabilistic framework....47 n-gram language models....49 Machine learning for language modelling....51 Artificial neural networks....51 Training an artificial neural network....53 ANNs for natural language processing....55 Tokenization....55 Embedding....56 Predicting probability distributions....58 Dealing with sequential data....59 Recurrent neural networks....60 Transformer architecture....61 LLMs in practice....63 The evolving field of LLMs....63 Prompting, fine-tuning, and RAG....63 Summary....64 Chapter 4: Embedding Models....66 Technical requirements....67 What is an embedding model?....68 How do embedding models differ from LLMs?....69 When to use embedding models versus LLMs....70 Types of embedding models....70 Choosing embedding models....74 Task requirements....75 Dataset characteristics....75 Computational resources....75 Vector representations....76 Embedding model leaderboards....78 Embedding models overview....78 Do you always need an embedding model?....79 Executing code from LangChain....80 Best practices....83 Summary....83 Chapter 5: Vector Databases....84 Technical requirements....85 What is a vector embedding?....85 Vector similarity....86 Exact versus approximate search....87 Measuring search....88 Graph connectivity....88 Navigable small worlds....89 How to search a navigable small world....90 Hierarchical navigable small worlds....91 The need for vector databases....93 How vector search enhances AI models....94 Case studies and real-world applications....95 Okta – natural language access request (semantic search)....95 One AI – language-based AI (RAG over business data)....96 Novo Nordisk – automatic clinical study generation (advanced RAG/RPA)....97 Vector search best practices....98 Data modeling....98 Deployment....107 Summary....108 Chapter 6: AI/ML Application Design....110 Technical requirements....111 Data modeling....111 Enriching data with embeddings....112 Considering search use cases....114 Data storage....118 Determining the type of database cluster....118 Determining IOPS....119 Determining RAM....120 Final cluster configuration....121 Performance and availability versus cost....122 Data flow....122 Handling static data sources....122 Storing operational data enriched with vector embeddings....123 Freshness and retention....127 Real-time updates....127 Data lifecycle....128 Adopting new embedding models....129 Security and RBAC....130 Best practices for AI/ML application design....131 Summary....132 Part 2....134 Building Your Python Application: Frameworks, Libraries, APIs, and Vector Search....134 Chapter 7: Useful Frameworks, Libraries, and APIs....136 Technical requirements....137 Python for AI/ML....137 AI/ML frameworks....138 LangChain....139 LangChain semantic search with score....143 Semantic search with pre-filtering....144 Implementing a basic RAG solution with LangChain....145 LangChain prompt templates and chains....146 Key Python libraries....147 pandas....147 PyMongoArrow....150 PyTorch....152 AI/ML APIs....153 OpenAI API....154 Hugging Face....155 Summary....159 Chapter 8: Implementing Vector Search in AI Applications....160 Technical requirements....161 Information retrieval with MongoDB Atlas Vector Search....162 Vector search tutorial in Python....162 Vector Search tutorial with LangChain....168 Building RAG architecture systems....169 Chunking or document-splitting strategies....171 Simple RAG....173 Advanced RAG....176 Summary....186 Part 3....188 Optimizing AI Applications: Scaling, Fine-Tuning, Troubleshooting, Monitoring, and Analytics....188 Chapter 9: LLM Output Evaluation....190 Technical requirements....191 What is LLM evaluation?....191 Component and end-to-end evaluations....192 Model benchmarking....195 Evaluation datasets....196 Defining a baseline....198 User feedback....198 Synthetic data....199 Evaluation metrics....200 Assertion-based metrics....200 Statistical metrics....203 LLM-as-a-judge evaluations....206 RAG metrics....211 Human review....219 Evaluations as guardrails....220 Summary....220 Chapter 10: Refining the Semantic Data Model to Improve Accuracy....222 Technical requirements....223 Embeddings....223 Experimenting with different embedding models....223 Fine-tuning embedding models....227 Embedding metadata....229 Formatting metadata....232 Including static metadata....237 Extracting metadata programmatically....237 Generating metadata with LLMs....238 Including metadata with query embedding and ingested content embeddings....240 Optimizing retrieval-augmented generation....242 Query mutation....242 Extracting query metadata for pre-filtering....243 Formatting ingested data....246 Advanced retrieval systems....248 Summary....249 Chapter 11: Common Failures of Generative AI....250 Technical requirements....251 Hallucinations....251 Causes of hallucinations....251 Implications of hallucinations....253 Sycophancy....253 Causes of sycophancy....254 Implications of sycophancy....255 Data leakage....256 Causes of data leakage....256 Implications of data leakage....258 Cost....259 Types of costs....259 Tokens....260 Performance issues in generative AI applications....262 Computational load....263 Model serving strategies....264 High I/O operations....265 Summary....265 Chapter 12: Correcting and Optimizing Your Generative AI Application....266 Technical requirements....267 Baselining....267 Training and evaluation datasets....268 Few-shot prompting....271 Retrieval and reranking....273 Late interaction strategies....274 Query rewriting....275 Testing and red teaming....276 Testing....276 Red teaming....278 Information post-processing....279 Other remedies....280 Summary....281 Appendix: Further Reading....282 Index....288 Other Books 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