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Get to Know Your Own Data with RAG: An AI Search System for Your Own Data
Paperback
Publisher Price: $34.95
ISBN13: 9781067587413
Publisher: Sha Publishing
Published: Jun 17 2026
Pages: 306
Weight: 1.16
Height: 0.64 Width: 7.50 Depth: 9.25
Language: English
Your documents. Your AI. Your machine.
What if you could ask your own files a question - and get a real answer, in plain English, with a citation to the exact page it came from? Not a chatbot guessing. A system that reads your documents, finds the passages that matter, and tells you honestly when the answer isn't there.
That system is called Retrieval-Augmented Generation (RAG), and this book teaches you to build one yourself - from the first idea to a working application you deploy.
WHAT YOU'LL BUILD
A complete, private AI search engine over your own data: an ingestion pipeline that turns your PDFs, Word files, and web pages into a searchable knowledge base; a vector store built on SQLite; a query pipeline that retrieves the right passages and answers with citations; and a website front door so others can use it too. Every chapter builds one real system - and you can see it running live before you write a line.
BUILT FOR BUILDERS - EVEN FIRST-TIME ONES
No prior AI experience is required. The worked example is written in Delphi with TMS AI Studio, chosen for clarity and speed, and a full chapter rebuilds the same system in Python - because the architecture is the real lesson, and it carries to any language. Throughout, Claude writes the code with you, from your plain-English description.
YOUR DATA STAYS YOURS
For sensitive material - legal files, medical records, financial papers - you can close the door completely. Switch the engine to run locally with Ollama, and no document and no question ever leaves your machine.
INSIDE
- RAG explained in plain English: training, fine-tuning, and retrieval - and why retrieval wins for your own data
- Choosing your corpus: what belongs, what doesn't, and how to shape it
- The full pipeline: ingestion, embeddings, vector search, prompting, and citations
- Wiring it to a website, deploying it, and growing the corpus over time
- A second corpus from scratch (an entire NFL season) - same engine, new data
- Going fully private and local
The complete source code is free on GitHub under an open licence.
If the first book, Get to Know Claude, taught you to think with AI, this one teaches you to build with it - a real system, over your own data, that you own end to end.
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