Building Generative AI Services with FastAPI. A Practical Approach to Developing Context-Rich Generative AI Applications Alireza Parandeh

Spis treści książki

• Foreword
• Preface
  • Objective and Approach
  • Prerequisites
  • Book Structure
  • How to Read This Book
  • Hardware and Software Requirements
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• I. Developing AI Services
• 1. Introduction
  • What Is Generative AI?
  • Why Generative AI Services Will Power Future Applications
    • Facilitating the Creative Process
    • Suggesting Contextually Relevant Solutions
    • Personalizing the User Experience
    • Minimizing Delay in Resolving Customer Queries
    • Acting as an Interface to Complex Systems
    • Automating Manual Administrative Tasks
    • Scaling and Democratizing Content Generation
  • How to Build a Generative AI Service
  • Why Build Generative AI Services with FastAPI?
  • What Prevents the Adoption of Generative AI Services
  • Overview of the Capstone Project
  • Summary
• 2. Getting Started with FastAPI
  • Introduction to FastAPI
  • Setting Up Your Development Environment
    • Installing Python, FastAPI, and Required Packages
    • Creating a Simple FastAPI Web Server
  • FastAPI Features and Advantages
    • Inspired by Flask Routing Pattern
    • Handling Asynchronous and Synchronous Operations
    • Built-In Support for Background Tasks
    • Custom Middleware and CORS Support
    • Freedom to Customize Any Service Layer
    • Data Validation and Serialization
    • Rich Ecosystem of Plug-Ins
    • Automatic Documentation
    • Dependency Injection System
    • Lifespan Events
    • Security and Authentication Components
    • Bidirectional Web Socket, GraphQL, and Custom Response Support
    • Modern Python and IDE Integration with Sensible Defaults
  • FastAPI Project Structures
    • Flat Structure
    • Nested Structure
    • Modular Structure
    • Progressive Reorganization of Your FastAPI Project
  • Onion/Layered Application Design Pattern
  • Comparing FastAPI to Other Python Web Frameworks
  • FastAPI Limitations
    • Inefficient Model Memory Management
    • Limited Number of Threads
    • Restricted to Global Interpreter Lock
    • Lack of Support for Micro-Batch Processing Inference Requests
    • Cannot Efficiently Split AI Workloads Between CPU and GPU
    • Dependency Conflicts
    • Lack of Support for Resource-Intensive AI Workloads
  • Setting Up a Managed Python Environment and Tooling
  • Summary
• 3. AI Integration and Model Serving
  • Serving Generative Models
    • Language Models
      • Transformers versus recurrent neural networks
      • Tokenization and embedding
      • Training transformers
      • Positional encoding
      • Autoregressive prediction
      • Integrating a language model into your application
      • Connecting FastAPI with Streamlit UI generator
    • Audio Models
    • Vision Models
    • Video Models
      • OpenAI Sora
    • 3D Models
      • OpenAI Shap-E
  • Strategies for Serving Generative AI Models
    • Be Model Agnostic: Swap Models on Every Request
    • Be Compute Efficient: Preload Models with the FastAPI Lifespan
    • Be Lean: Serve Models Externally
      • Cloud providers
      • BentoML
      • Model providers
  • The Role of Middleware in Service Monitoring
  • Summary
  • Additional References
• 4. Implementing Type-Safe AI Services
  • Introduction to Type Safety
  • Implementing Type Safety
    • Type Annotations
    • Using Annotated
    • Dataclasses
  • Pydantic Models
    • How to Use Pydantic
    • Compound Pydantic Models
    • Field Constraints and Validators
    • Custom Field and Model Validators
    • Computed Fields
    • Model Export and Serialization
    • Parsing Environment Variables with Pydantic
    • Dataclasses or Pydantic Models in FastAPI
  • Summary
• II. Communicating with External Systems
• 5. Achieving Concurrency in AI Workloads
  • Optimizing GenAI Services for Multiple Users
  • Optimizing for I/O Tasks with Asynchronous Programming
    • Synchronous Versus Asynchronous (Async) Execution
    • Async Programming with Model Provider APIs
    • Event Loop and Thread Pool in FastAPI
    • Blocking the Main Server
    • Project: Talk to the Web (Web Scraper)
    • Project: Talk to Documents (RAG)
  • Optimizing Model Serving for Memory- and Compute-Bound AI Inference Tasks
    • Compute-Bound Operations
    • Externalizing Model Serving
      • Request batching and continuous batching
      • Paged attention
  • Managing Long-Running AI Inference Tasks
  • Summary
  • Additional References
• 6. Real-Time Communication with Generative Models
  • Web Communication Mechanisms
    • Regular/Short Polling
    • Long Polling
    • Server-Sent Events
    • WebSocket
    • Comparing Communication Mechanisms
  • Implementing SSE Endpoints
    • SSE with GET Request
      • Cross-origin resource sharing
      • Streaming LLM outputs from Hugging Face models
    • SSE with POST Request
  • Implementing WS Endpoints
    • Streaming LLM Outputs with WebSocket
    • Handling WebSocket Exceptions
    • Designing APIs for Streaming
  • Summary
• 7. Integrating Databases into AI Services
  • The Role of a Database
  • Database Systems
  • Project: Storing User Conversations with an LLM in a Relational Database
    • Defining ORM Models
    • Creating a Database Engine and Session Management
    • Implementing CRUD Endpoints
    • Repository and Services Design Pattern
  • Managing Database Schemas Changes
  • Storing Data When Working with Real-Time Streams
  • Summary
• III. Securing, Optimizing, Testing, and Deploying AI Services
• 8. Authentication and Authorization
  • Authentication and Authorization
  • Authentication Methods
    • Basic Authentication
    • JSON Web Tokens (JWT) Authentication
      • What is JWT?
      • Getting started with JWT authentication
      • Hashing and salting
      • Authentication flows
  • Implementing OAuth Authentication
    • OAuth Authentication with GitHub
    • OAuth2 Flow Types
      • Authorization code flow
      • Implicit flow
      • Client credentials flow
      • Resource owner password credentials flow
      • Device authorization flow
  • Authorization
    • Authorization Models
    • Role-Based Access Control
    • Relationship-Based Access Control
    • Attribute-Based Access Control
    • Hybrid Authorization Models
  • Summary
• 9. Securing AI Services
  • Usage Moderation and Abuse Protection
  • Guardrails
    • Input Guardrails
    • Output Guardrails
    • Guardrail Thresholds
    • Implementing a Moderation Guardrail
  • API Rate Limiting and Throttling
    • Implementing Rate Limits in FastAPI
      • User-based rate limits
      • Rate limits across instances in production
      • Limiting WebSocket connections
    • Throttling Real-Time Streams
  • Summary
• 10. Optimizing AI Services
  • Optimization Techniques
    • Batch Processing
    • Caching
      • Keyword caching
      • Semantic caching
        • Building a semantic caching service from scratch
        • Semantic caching with GPT cache
        • Similarity threshold
        • Eviction policies
      • Context/prompt caching
    • Model Quantization
      • Precision versus quality trade-off
      • Floating-point numbers
      • How to quantize pretrained LLMs
    • Structured Outputs
    • Prompt Engineering
      • Prompt templates
      • Advanced prompting techniques
        • In-context learning
        • Thought generation
        • Decomposition
        • Ensembling
        • Self-criticism
        • Agentic
    • Fine-Tuning
      • When should you consider fine-tuning?
      • How to fine-tune a pretrained model
  • Summary
• 11. Testing AI Services
  • The Importance of Testing
  • Software Testing
    • Types of Tests
    • The Biggest Challenge in Testing Software
    • Planning Tests
    • Test Dimensions
    • Test Data
    • Test Phases
    • Test Environments
    • Testing Strategies
  • Challenges of Testing GenAI Services
    • Variability of Outputs (Flakiness)
    • Performance and Resource Constraints (Slow and Expensive)
    • Regression
    • Bias
    • Adversarial Attacks
    • Unbound Testing Coverage
  • Project: Implementing Tests for a RAG System
    • Unit Tests
      • Installing and configuring pytest
      • Fixtures and scope
      • Parameterization
      • Conftest module
      • Setup and teardown
      • Handling asynchronous tests
      • Mocking and patching
        • Fakes
        • Dummies
        • Stubs
        • Spies
        • Mocks
        • Implementing test doubles with pytest-mock
    • Integration Testing
      • Context precision and recall
      • Behavioral testing
        • Minimum functionality tests (MFTs)
        • Invariance tests (ITs)
        • Directional expectation tests (DETs)
        • Auto-evaluation tests
    • End-to-End Testing
      • Vertical E2E tests
      • Horizontal E2E tests
  • Summary
• 12. Deployment of AI Services
  • Deployment Options
    • Deploying to Virtual Machines
    • Deploying to Serverless Functions
    • Deploying to Managed App Platforms
    • Deploying with Containers
  • Containerization with Docker
    • Docker Architecture
    • Building Docker Images
    • Container Registries
    • Container Filesystem and Docker Layers
    • Docker Storage
      • Docker volumes
      • Bind mounts
      • Temporary mounts (tmpfs)
      • Handling filesystem permissions
    • Docker Networking
      • Bridge network driver
        • Configure user-defined bridge networks
        • Embedded DNS
        • Publishing ports
      • Host network driver
      • None network driver
    • Enabling GPU Driver
    • Docker Compose
    • Enabling GPU Access in Docker Compose
    • Optimizing Docker Images
      • Use minimal base image
      • Avoid GPU inference runtimes
      • Externalize application data
      • Layer ordering and caching
        • Layer ordering to avoid frequent cache invalidation
        • Minimize layers
        • Keep build context small
        • Use cache and bind mounts
        • Use external cache
      • Multi-stage builds
    • docker init
  • Summary
• Afterword
• Index

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