Web & Data

WebWeave — Semantic Site Crawler & Data Extractor

Parses any website into structured, classified, RAG-ready data.

2025

OVERVIEW

The Project

A web application that crawls arbitrary websites and extracts structured information — contact info, company overview, services, legal sections — using keyword-driven logic. The cleaned, deduplicated, formatted output is stored in MongoDB for later retrieval or chatbot integration.

Objectives

Extract meaningful content from arbitrary websites.

Tag and classify data by category (overview, services, team, legal, contact).

Persist results to MongoDB with an SHA-256 key to identify crawls uniquely.

Tools & Technologies

PythonBeautifulSoupSeleniumMongoDBRegex

METHODOLOGY

The Approach

User submits a URL; crawler loads the root page and discovers internal links.

Categorize content with predefined keyword buckets; deduplicate to avoid repetition.

Save outputs as a file-saving pipeline and upload final data with a hash key.

Return an easily-consumable JSON response to the frontend.

OUTCOME

Results & Learnings

Successfully extracted structured content from real-world domains.

High keyword-categorization coverage with clean, deduplicated JSON output.

Key Learnings

Robust crawling must handle inconsistent structure and noise.
Memory control on large pages + dedup keep the pipeline efficient.

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