⚡️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.

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.

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

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.

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.

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.

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

GenAI: Build and deploy end to end medical chatbot

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.

A chatbot designed to provide students with the best professors to match their needs through simple queries. The AI effectively uses a RAG implementation to provide accurate results.

A Question-Answering chatbot built using RAG (Retrieval-Augmented Generation) with conversation memory. This project uses LangChain, various LLM options, and vector stores to create an intelligent chatbot that can answer questions about Jessup Cellars winery.

Second Brain API: A backend service for managing, searching, and sharing personal content with secure authentication, robust validation, embedding-powered queries, and shareable links, built using Node.js, MongoDB, and Pinecone.

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.

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.