🕸️ Self-Optimizing Multi-Agent AI System on AWS — Routes tasks to specialized AI agents (Coder, Researcher, Summarizer) with auto-verification, self-correction, and vector-cached memory. Built with Step Functions, Bedrock, and OpenSearch Serverless.

A professional-grade Retrieval-Augmented Generation (RAG) platform that transforms your documents into an interactive knowledge base. Built for high-performance semantic search and context-aware conversations.

MediBot is an AI-powered medical chatbot that leverages state-of-the-art language models and vector search to answer user queries based on a curated set of medical PDF documents. It uses Streamlit for the user interface, LangChain for LLM orchestration, and FAISS for efficient vector search.

Production RAG patterns and semantic drift detection for Microsoft Fabric

Built an AI-based multi-document chatbot using Retrieval-Augmented Generation (RAG) that enables conversational querying of PDFs with semantic search.

AI-powered PDF question-answering system using Experimental Retrieval-Augmented Generation project using local embeddings, FAISS indexing, and Mistral via Ollama.

Chat with PDF - simple setup for creating and testing RAG and LLM capabilities

Meet Ava, the WhatsApp Agent

A curated list of my AI/ML projects — FYP, computer vision, NLP, and more.

Groq-powered Retrieval-Augmented Generation (RAG) system with semantic search, source citation, and Streamlit UI.

🎥 YouTube RAG Q&A App - Learn LangChain through Practice

Web app for PDF document ingestion with interactive preview, content editing, and vector database storage. Used for testing the similarity search

🎥 Ask questions about YouTube videos using AI with this RAG app, which leverages transcripts and embeddings for context-aware answers.