RAG with FastAPI, Scrapy, pgvector, Docker & OpenAI

About this course

This course contains the use of artificial intelligence.

The chatbot quoted a price the live page never hadA visitor books mentorship on a number the chat window invented. The invoice is different. You open the pricing page. That number was never there. Someone wired a general model to a box and shipped it. The model is fluent. It is not informed.

That gap is why this course exists. A general chat model can answer a lot of public questions. It does not know the pages on your site. You need an assistant that answers from pages you chose, that you can open and inspect, and that you can grade before a real customer uses it.

By the end you can say this: I crawled pages I chose, stored them, and got an answer drawn from those pages, and I can name what is still missing before a real customer uses it.

What you leave with

You leave with a running system and written proof, not a notebook of definitions. You can show a teammate the stack you started, the pages you listed, and the scorecard you filled.

Compose stack. The API, Postgres with pgvector, and an idle scraper, started by you on Mac, Windows, or Linux.

Two tables. Pages and chunks, including a 3,072-dimension embedding column, walked on the real files of the clone.

Your crawl. Paths you listed on a site you chose, skipped where robots.txt disallows them.

One ask. Pages and chunks you can list, and one question that answers from those chunks.

Sample answers. Saved replies from the sample site as a second proof. Your crawl does not write them, and they are not a second knowledge base.

Scorecard. One question from the first section: was the page stored, is the expected fact in a chunk, did the answer stay inside your stored pages, and which stage failed if a check did not pass.

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