This project showcases a Retrieval-Augmented Generation (RAG) application specifically designed for querying Arabic legal documents. Utilizing advanced techniques, it enables efficient information retrieval and contextual understanding. Key features include: Arabic Language Support: Handles queries and provides responses in Arabic, ensuring relevance to users in legal contexts. Efficient Document Retrieval: Implements sophisticated algorithms for quick and accurate access to legal texts. Custom Prompting: Utilizes tailored prompts to enhance interaction with users, making the tool more intuitive for legal inquiries.

As we navigate the complexities of modern energy consumption, understanding how households utilize electricity is paramount. The household sector significantly contributes to overall energy demand, and optimizing its usage is crucial for both economic and environmental sustainability. This project focuses on energy disaggregation, a technique that breaks down a household’s total electricity consumption into individual appliance-level usage. By doing so, we gain deeper insights into energy consumption patterns, which enables better management and efficiency improvements.

Financial Advisor is an innovative AI-powered application designed to provide personalized investment and savings advice tailored to individual user needs. Leveraging advanced machine learning algorithms, the app analyzes user inputs such as financial goals, risk tolerance, and current investment portfolios to deliver customized recommendations. With a user-friendly interface built on Flask and powered by Python and LLM’s, the app simplifies the complexities of personal finance. Users can easily input their financial data, explore various investment strategies, and receive actionable insights to optimize their savings and investment plans.