[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_28":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNzU2MTM","title","C# & AI Masterclass: Intelligent Data Access with EF Core. Vector Databases, RAG, and Memory Storage.","state","published","contactAuthorMethod","email","aboutText","

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.

Check also the other books in this series

","authors",[24],{"_12":25,"_26":27,"_28":29},"Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6UHJvZHVjdEF1dGhvci84MDk4OA","name","Edgar Milvus","user",{"_30":31},"avatar",{"_32":33,"_34":35,"_36":37},"url","https://d39qdlcrvnra4b.cloudfront.net/avatars/3641525/medium/cervelo_profilo29__2025_-_2_43PM.png?1767015885","styleName","medium","exists",true,"shoppingCartId","shoppingCart",{"_12":71,"_72":73,"_74":37,"_75":76,"_77":78,"_79":80,"_81":37},"locale","en","readerTheme","light","constants",{"_69":70},"publishers",{"_65":66,"_67":68},"learnerOrganizations",[],"needsLocaleCookieSync",false,"announcements",{"_59":60,"_61":-5},"sentryEnvironment","production","sentryRelease","5a3235e5a48c3eb47a0b33b875a7e659a138e28a","announcement_store",{"_62":37,"_63":64},"announcement_authors","flag","text","

Kick off your book project in 3 hours! Live workshop on Zoom. You’ll leave with a real book project, progress on your first chapter, and a clear plan to keep going. Saturday, May 16, 2026. Learn more…

\n","companies",[],"universities",[],"paypalClientId","AX53Cwz8jR6rVVzzZiZNWWXl0dGagi-GZrjG-S3Ua14E5nrKMn7DxDQ730O4R8LusLPp49ALn6aRVaEy","no-cart","count",0,"free","updatedAt","","paymentGateway","STRIPE","cartItems",[],"loading"]

Email the Author