Python-Development

Hands-on guide to forcing LLMs to return perfect JSON using Pydantic schemas + few-shot examples + Ollama/Bedrock structured output features, with ready-to-run code for serverless Python apps.

This article guides you through building a modular Python application to leverage Large Language Models (LLMs) with AWS Lambda. Learn to set up AWS CLI with IAM Identity Center, create a Bedrock Chat Service, implement web scraping and summarization, and automate Lambda deployment with a ZIP file. Includes practical code examples and verification steps.

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 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.