vectore-database

Multi-agent enterprise AI knowledge platform with Agentic RAG, ACL-aware retrieval, and hybrid intelligence, built with LangGraph, FastAPI, Next.js, PostgreSQL, pgvector, and BGE.

🕸️ 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.

C2C e-commerce marketplace prototype app for users to trade goods and services

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

GenAI | RAG-based multilingual farming chatbot with voice I/O using ChromaDB, LangChain, LLaMA 3.3 (Groq), and Web Speech API

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

Retrieval-Augmented Generation (RAG) application for answering questions from PDF documents using LangChain, FAISS, and Google Gemini.

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

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

Gen AI implementation for student's support to prepare for specific module

Production RAG patterns and semantic drift detection for Microsoft Fabric

A local, offline RAG tool that scores resume-to-JD fit and builds a searchable interview-prep knowledge base — powered by Ollama and Chroma.

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

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

Chat with your PDFs — a RAG app that embeds documents into a vector store and answers questions with source-grounded context.

⚡ Production-style RAG Chat API — FastAPI + Groq + ChromaDB + Postgres with multi-turn conversations, server-side history, and grounded Q&A

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