Ai-Integration

This guide shows how to create a modular Python application for web scraping and AI summarization using BeautifulSoup and Ollama. It covers organizing scraper and chat services into a reusable module, integrating them in a main script, and generating summaries from online content.

This guide explores how to use BeautifulSoup to extract plain text from URLs and build a reusable web scraper for integration into AI-driven applications like AWS Lambda. It includes step-by-step instructions for creating and testing a Python-based scraper with a modular class design.

This blog guides you through using a Large Language Model (LLM) to summarize web pages, storing the summaries in an Amazon DynamoDB table using AWS Lambda. It includes prerequisites, architecture details, and references to resources like Udemy courses and GitHub repositories.

This guide details how to use Amazon Bedrock and AWS Lambda to deploy and invoke the Llama 3.2 1B model in a serverless environment. It covers enabling model access, creating a Python-based Lambda function, configuring IAM permissions, and testing the setup, with step-by-step instructions for seamless AI integration.