Multi AI agents for customer support email automation built with Langchain & Langgraph
Local RAG researcher agent built using Langgraph, DeepSeek R1 and Ollama
Educational toolkit for all things RAG (Retrieval Augmented Generation)
A full-stack, microservices-based application for secure document management, enabling users to upload, parse, index, and query various file types through advanced NLP and RAG agents. Built with Docker and Kubernetes for scalability, and integrates Elasticsearch, Redis, and AWS S3 for efficient storage and search capabilities.
This repository contains the implementation of a Retrieval-Augmented Generation (RAG) agent using Large Language Models (LLMs). RAG agents combine the power of information retrieval with text generation, enabling applications such as intelligent question-answering systems, and more.
This python powered AI based RAG Scraper allows you to ask question based on PDF/URL provided to the software using local Ollama powered LLMs
AI driven Content Creation Automation.
Agentic RAG for journalling
The Customer Support AI Agent is a Python-based solution designed to monitor product prices on an e-commerce site. By utilizing an ARIMA model, it predicts future prices and sends notifications to customers when the price of a product is expected to drop in the next 10 days.
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