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dataProductionDATASET

Books Scraper Dataset

AI-Powered Web Scraping for RAG Systems

Robust web scraping system for collecting book data from books.toscrape.com using AI-powered extraction with crawl4ai and Gemini LLM. Produces structured datasets (JSON & CSV) with 993 books including title, rating, price, availability, and cover images. Built for RAG applications and data science projects.

GitHub
Case study snapshot
Role
Full-stack/data builder
Audience
analysts, students, and technical reviewers
Timeline
2025-08
Role
Full-stack/data builder
Audience
analysts, students, and technical reviewers
Date
2025-08
Problem

Why it mattered

993 books extracted with 99.8% accuracy; Ready for RAG, ML training, and book recommendation systems

Solution

What shipped

Robust web scraping system for collecting book data from books.toscrape.com using AI-powered extraction with crawl4ai and Gemini LLM. Produces structured datasets (JSON & CSV) with 993 books including title, rating, price, availability, and cover images. Built for RAG applications and data science projects.

Impact

Proof signal

993 books extracted with 99.8% accuracy; Ready for RAG, ML training, and book recommendation systems

< architecture />

System flow

Browser Init -> Page Crawling -> AI Extraction -> Data Validation -> Deduplication -> File Output.

Browser Init
Page Crawling
AI Extraction
Data Validation
Deduplication
File Output
Visual architecture
01
Browser Init
02
Page Crawling
03
AI Extraction
04
Data Validation
05
Deduplication
06
File Output
quality score

Honest maturity snapshot

Code Quality9/10
UI / UX6/10
Scalability7/10
Production Ready8/10
Before

Raw datasets, disconnected metrics, and hard-to-scan findings.

After

A structured analysis or dashboard with clear KPIs, visual patterns, and decision-ready insights.

capabilities

Features

  • Python integrated into the workflow.
  • crawl4ai integrated into the workflow.
  • Gemini API integrated into the workflow.
  • Pydantic integrated into the workflow.
  • Browser Init stage documented in the delivery flow.
  • Page Crawling stage documented in the delivery flow.
tradeoffs

Engineering challenges

  • Keeping the implementation clear enough to explain while still solving the core technical problem.
  • Choosing a scope that could be shipped, tested, and documented.
  • Turning technical work into proof a visitor can evaluate quickly.
proof

Results

  • 993 books extracted with 99.8% accuracy; Ready for RAG, ML training, and book recommendation systems
  • Documented the engineering path and important learnings.
  • Made source code or technical proof available for review.
reflection

What I learned

  • Tradeoff learned: Keeping the implementation clear enough to explain while still solving the core technical problem.
  • Proof learned: 993 books extracted with 99.8% accuracy; Ready for RAG, ML training, and book recommendation systems
  • Next iteration: Add richer screenshots or a narrated demo.
< stack />
Pythoncrawl4aiGemini APIPydanticData Extraction
Dataset: 993 books | 357KB JSON | 209KB CSV
< next iteration />

Future improvements

Add richer screenshots or a narrated demo.
Add before/after metrics and usage notes.
Package the project as a reusable template or deployable demo.