Build RAG Knowledge Base with Python Web Crawler | Extract Website Content for LLM Applications

Vishwa_Dabholkar · Intermediate ·🧠 Large Language Models ·1y ago

Skills: RAG Basics90%LLM Foundations70%Vector Stores60%

Key Takeaways

This video shows how to build a RAG knowledge base using a Python web crawler to extract website content for LLM applications

Original Description

📝 DESCRIPTION: 🔍 Introducing eGet - A powerful web crawler for building RAG (Retrieval Augmented Generation) knowledge bases! Perfect for anyone working with LLMs like GPT, Claude, or Llama. ⚡️ Demo Showcase: Automated website content extraction Structured data collection for vector databases RAG-ready content formatting Multi-page crawling with robots.txt compliance Async processing for faster data collection 🎯 Perfect for: AI/ML Engineers building RAG systems Developers creating custom knowledge bases Data Scientists collecting web datasets Companies building AI-powered applications ⚙️ Key Features: Start from any website Configure crawl depth and limits Filter URLs with patterns Extract clean, structured content Built-in rate limiting Metadata extraction JSON-LD and OpenGraph support

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