⚡️Framework for fast persistent storage of multiple document embeddings and metadata into Pinecone for source-traceable, production-level RAG.

These #LangChain-powered apps include a Research Assistant that generates reports using web scraping and GPT-4o-mini, and Chat With Video, a #Streamlit app that #transcribes videos and enables content-based Q&A via #embeddings.

A product knowledge base powered by Pinecone API and Anthropic AI Copilot

MediCare-Bot provides clear, reliable health information by combining trusted medical sources with smart search and AI. It makes medical queries easy to understand and accessible for everyone.

E-commerce Customer Service Chatbot: A GenAI-powered chatbot that answers user queries, provides recommendations, tracks orders, and more.

Feeling lonely again? Don't worry — talk to YouTube videos this time 💔🩹

The goal of this application is to generate suggestions based on the given resume of the candidate, store the candidate profile in Pinecone database, and shortlist candidates accroding to the skills matched with match score.

Movie Recommendation System: A content-based recommendation platform built with Python, Pinecone, and Streamlit. The system provides personalized movie suggestions based on genres and metadata, allowing users to explore tailored recommendations. With interactive genre filtering & clean interface, the app enhances movie discovery , hosted on render.

NoteCraft is a full-stack web app built with Django, Celery, and Next.js that lets users upload academic PDFs, generate AI-powered notes, and retrieve relevant content using RAG. It's optimized for long documents, supports async processing, and runs fully containerized with Docker.

Paper-Whisper is a full-stack web application that allows users to upload PDF documents and interact with them through natural language. Powered by LangChain and OpenAI's GPT models, it transforms static documents into dynamic conversations.

SmartRAG-Assistant/GenAI-Assistant leverages advanced LLM models and Nvidia APIs for efficient query handling and document summarization. It integrates LlamaParse for structured data extraction, HuggingFace embeddings for vectorization, and PineconeDB for efficient retrieval, ensuring precise answers to user queries.

An assistant like chatbot powered by LLM, designed to provide accurate responses to medical queries. Built using Flask, Cohere’s Language Model, and Pinecone for Vector Storage.

This is a complete personalize Chatbot with responsive front end.

Experimenting with Pinecone as vector data continues to take center stage in AI-native systems. The purpose of this project is to explore the core capabilities, benchmark performance across different embedding models, and better understand what is possible with vector search in production environments.

GenAI: Build and deploy end to end medical chatbot

A simple AI-based "Rate My Professor" using Next.js, OpenAI, and Pinecone for easy professor reviews and ratings.

This project is a conversational chatbot integrated with the Pinecone vector database.

A user-friendly RAG-powered fitness assistant — a conversational AI that understands your fitness goals, experience level, and equipment availability. It can help you select the perfect exercises, suggest alternative options, and keep you motivated to stay consistent with your routine, making fitness more accessible and personalised.