ebook: Intelligent Data Access with EF Core. Vector Databases, RAG, and Memory Storage.
C# & AI Masterclass: Volume 6
Transform Your Data Layer into the Brain of Your AI Application
The era of "dumb" databases is over. In the age of Generative AI, your application's data access layer must do more than just CRUD operations—it must understand meaning, manage context, and fuel intelligent decisions. Volume 6 of the C# & AI Masterclass is the definitive guide for .NET developers who need to bridge the gap between traditional Entity Framework Core architectures and the cutting-edge world of Vector Databases and Retrieval-Augmented Generation (RAG).
Unlock the Power of Intelligent Data Access
This book moves beyond simple tutorials. It dives deep into the architectural patterns required to build enterprise-grade AI systems. You will learn how to hybridize your SQL databases with vector search capabilities, ensuring your AI applications are not only smart but also scalable, secure, and transactional.
What's Inside:
Vector Integration in EF Core: Learn to store, index, and query high-dimensional embeddings using PostgreSQL (pgvector) and SQL Server 2022 without leaving the comfort of C#.
Building RAG Pipelines: Architect robust retrieval systems that combine the precision of SQL keywords with the semantic understanding of vector search (Hybrid Search).
AI Memory Management: Design schemas for chat history that allow LLMs to "remember" past interactions while enforcing permissions and context limits.
Advanced Concurrency & Transactions: Solve the "split-brain" problem of syncing relational data with vector stores using Unit of Work patterns and Compensating Transactions.
SaaS Multi-Tenancy: Implement strict data isolation for AI models in multi-tenant environments using Row-Level Security (RLS) and dynamic DbContexts.
Auditing & Observability: Use EF Core Interceptors to create an immutable audit trail of every prompt and context retrieved by your AI.
Whether you are building a semantic search engine, an intelligent chatbot, or an automated document analysis system, this book provides the blueprint for your data infrastructure.
Stop building prototypes. Start architecting intelligent systems with C# and EF Core today.
Table of contents:
Chapter 1: ORM Basics - Context, Models, and SQL
Chapter 2: Code-First Migrations and Database Design
Chapter 3: Relationships (One-to-Many, Many-to-Many)
Chapter 4: Querying with LINQ to SQL
Chapter 5: Change Tracking and Saving Data
Chapter 6: Introduction to Vector Stores - Why SQL isn't enough
Chapter 7: Using pgvector with PostgreSQL and EF Core
Chapter 8: Storing Embeddings (List<float>) in SQL Server
Chapter 9: Implementing Nearest Neighbor Search (ANN)
Chapter 10: Hybrid Search - Combining Keywords + Vectors
Chapter 11: Designing a Schema for Chat History
Chapter 12: Storing Structured Logs from LLM Chains
Chapter 13: Concurrency Control in Multi-User Chat
Chapter 14: Transactions and Rollbacks
Chapter 15: Performance Tuning - Indexing Vector Columns
Chapter 16: Repository Pattern vs Direct Context
Chapter 17: Multi-Tenancy for SaaS AI Apps
Chapter 18: Seeding Databases with Synthetic Data
Chapter 19: Interceptors - Auditing AI Prompts
Chapter 20: Capstone - Building a Semantic Search Engine for Documentation
The book consists of over 400 pages (if printed) and includes flowcharts that illustrate the main concepts. Each chapter is structured as follows: it begins with the basic theoretical principles, followed by a simple example explained in detail. Next, a more advanced, complex example is presented, also analyzed step by step.
Each chapter then offers five exercises with detailed requirements, accompanied by complete solutions and in-depth instructor analyses. Even if you don’t have time to work through the exercises, you can still read them and study the solutions to learn from the practical cases.
This eBook is part of the C# and AI Masterclass series, which guides you from the fundamentals of programming—even if you’re starting from scratch—up to advanced artificial intelligence, showing how to integrate AI across various aspects of enterprise-level programming.
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