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Rushikesh Nimkar's portfolio, accessible at rushikeshnimkar.xyz, is a modern website built with Next.js 15 and TypeScript. It features AI-powered email generation, an interactive chat with an AI version of Rushikesh, dynamic animations, responsive design, dark mode, and showcases his projects and contributions.
RAG_llama3.3 is an advanced Retrieval-Augmented Generation (RAG) system using Llama 3.3 language model. This project integrates state-of-the-art natural language processing (NLP) techniques to enable accurate and context-aware question answering.
This is a node.js project leveraging the OpenAI API for vector embedding, seamlessly integrating with MongoDB to store embedded data and facilitating efficient query-based retrieval for enhanced knowledge management
LangChain Chatbot is a conversational AI system designed to assist users with legal queries and provide relevant information. It utilizes various natural language processing techniques, including OpenAI's GPT-3.5 model, Sentence Transformers, and Pinecone indexing, to understand user queries, refine them, and find the most relevant responses.
Created a custom chatbot that will reply to your question based on the data stored in it's memory.Technology used are prisma ORM, mongoDB, pineconeDB to store vectors, openAI Text-embedding-ada-002-v2 to embed the text. I have followed a youtube tutorial to learn this.
Fragments on Machines RAG Explorer — an interactive tool to explore Karl Marx’s eerily prophetic reflections from the Grundrisse (1857) on the dawn of automation, the rise of machines, and their impact on labor, capital, and society.
This repository delves into the power of vector embeddings, from text to images, bridging the gap between high-dimensional data and insightful relationships. Learn to preprocess, generate, and store embeddings efficiently, while exploring advanced techniques like GCNs, GATs, and beyond.