# Portfolio Projects

## KnowBot 
- Description: Knowbot is designed to be an intelligent assistant that can seamlessly interact with your organization’s internal knowledge base. Whether it's through voice or text queries, this assistant taps into your existing documents, policies, research papers, and other content to provide accurate, up-to-date, and contextually relevant answers. It blends retrieval-augmented generation (RAG) for data sourcing and a large language model (LLM) for sophisticated reasoning and answer generation.
- Tech: Python, Openai, Gradio, Whisper, LLM

## Multi-Agent Financial Advisor System
-  Description: This project is a production-ready multi-agent AI financial advisory system designed to deliver holistic, personalized financial guidance.
It integrates investment advisory, tax optimization, and retirement planning into a unified intelligent platform.

The system leverages:

    Specialized AI agents coordinated by an Orchestrator Agent
    Real-time market intelligence
    Advanced Retrieval-Augmented Generation (RAG) with hybrid search
    Structured financial data pipelines
    Educational visual generation using OpenAI Images

The advisor interprets a user’s full financial profile (income, assets, liabilities, goals, risk tolerance) and produces coherent, actionable financial plans while resolving conflicts between investment growth, tax efficiency, and long-term retirement objectives.
- Tech: Python, Gradio, Langchain, Vector-Database, Semantic Search, RAG

## AzureQbot
- Description: A modern React frontend for a Knowledge Base Chatbot hosted on Azure. Features a sleek chat interface with Markdown rendering, avatars, message timestamps, dark mode, and seamless integration with a Python backend API. 
- Tech: React, FastAPI, Azure, QA, Knowledgebase

## AI Brochure Generator
- Description: In this project, we built a highly efficient brouchure generator using LLM. We also added the option of translating the generated brochure. The solution process can be divided into three main stages. To build our graphical interface we used gradio wich is an open-source Python framework that simplifies the creation of interactive web interfaces for machine learning models, APIs, or any Python function

    The first step is to build a website scrapper that can retrieve the content of a given url website.
    After scrapping the website, we'll send the useful website content to an LLM model. The LLM model will generate a brochure by summarizing and extracting the useful information. We choose Chatgpt and Claude to do this task.
    The final step is to send the generated brochure to another LLM for translation into the desired language.In our context, we decided to translate it into French.

- Tech: LLM, Anthropic, Gradio, BeautifulSoup

## Deal Finder
- Description: In this project we built an advanced Multi agent that subscribes to RSS feeds, check for a new opportunity deal (product), when the Multi agent finds a good deal it returns a notification containing the title, description, price, and url of the products it found.
- Tech: LLM, RAG, Langchain

## Insurance Chatbot 

- Description:  In this project, we built an AI insurance ChatBot to help respond to customers. In order to build our AI insurance ChatBot, we used an LMI that was combined with a knowledge base using the technique known as RAG. The first step is to use a knowledge base containing information about the insurance company. We extract important context from this knowledge base. We will then vectorize the data from the knowledge base to produce better queries for our data. To prevent the LLM from making a mistake and returning the wrong answer to the client, we instruct the LLM to return the answer if it doesn't exist in the knowledge base.
- Tech: LLM, GPT, RAG, ChromaDB