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.

A production-ready AI agent pipeline using LangChain for LLM workflows, LangGraph for agent orchestration, and LangSmith for observability supporting document processing, weather queries, and general Q&A with built-in monitoring and debugging.

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

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.

llama2 flask api for generative AI

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.

Local AI Agent with Ollama LLM

This AI tool helps you to chat with your PDFs just by uploading it in the web interface.

Building Chatbot for Laptop store

An AI-powered web scraper built with Crawl4AI that crawls websites, extracts and embeds text using HuggingFace, performs semantic search with FAISS, and answers queries using Langchain and GROQ LLM - all through a simple Gradio UI.

OpenAI-chatbot-note-app

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.

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.

A real-time chat and notification backend built with Node.js, MongoDB, Pinecone, Firebase, and Socket.IO. Supports 1-to-1 messaging, assistant chat (AI), push notifications, and rich relationship management.

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.