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AskImmigration: Navigate U.S. Immigration with an AI Assistant
**Authors:** Geoffrey Duncan Opiyo, Hillary Arinda, Justine Okumu, Deo Mugabe
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# AI-Powered Guide for Your U.S. Legal Immigration Journey
## Introduction
Immigration rules change all the tim... | Given the following content, create as much questions whose answer can be found within the content. Then, provide the answer to the questions. Ensure the answers are derived directly from the content. Format the questions and answers in the following JSON structure: {Question: '', Answer: ''}. |
RAG-powered-AI-chatbot
--DIVIDER--
# RAG Turns General AI into Domain Experts by Connecting It to Specific Knowledge
## Project Goal
Project Type:
_Software Tool + Real-World Application_
This project demonstrates how Retrieval-Augmented Generation (RAG... | Given the following content, create as much questions whose answer can be found within the content. Then, provide the answer to the questions. Ensure the answers are derived directly from the content. Format the questions and answers in the following JSON structure: {Question: '', Answer: ''}. |
A simple RAG-powered assistant (ChatBot) that answers questions on the LangChain Documentation
## Publication Overview
As part of my learning journey in the Ready Tensor Flow program on Agentic AI, I built a Retrieval-Augmented Generation (RAG) powered chatbot that specializes in answering questions related to the ... | Given the following content, create as much questions whose answer can be found within the content. Then, provide the answer to the questions. Ensure the answers are derived directly from the content. Format the questions and answers in the following JSON structure: {Question: '', Answer: ''}. |
Mini-RAG Chat: Local Document Retrieval and Answering via LangChain, FAISS, and Ollama
# Abstract
This publication introduces Mini-RAG Chat, a fully offline, lightweight document question-answering system using LangChain, FAISS for vector retrieval, and Ollama for local LLM inference. Users can upload PDFs, DOCX, or J... | Given the following content, create as much questions whose answer can be found within the content. Then, provide the answer to the questions. Ensure the answers are derived directly from the content. Format the questions and answers in the following JSON structure: {Question: '', Answer: ''}. |
Ready Tensor Publication Explorer: Your Conversational Guide to AI/ML Knowledge
## TL;DR / Abstract
The Ready Tensor Publication Explorer is a cutting-edge RAG-powered chatbot built with LangChain and FAISS that transforms how users interact with AI/ML knowledge. This intelligent assistant allows researchers, student... | Given the following content, create as much questions whose answer can be found within the content. Then, provide the answer to the questions. Ensure the answers are derived directly from the content. Format the questions and answers in the following JSON structure: {Question: '', Answer: ''}. |
SmartMatch Resume Analyzer: Advanced NLP for Career Optimization
# SmartMatch Resume Analyzer: Advanced NLP for Career Optimization
## A Production-Ready Natural Language Processing Application
**Category**: Natural Language Processing (NLP)
**Author**: Bryan Thompson / Triepod AI
**Publication Type**: Implementa... | Given the following content, create as much questions whose answer can be found within the content. Then, provide the answer to the questions. Ensure the answers are derived directly from the content. Format the questions and answers in the following JSON structure: {Question: '', Answer: ''}. |
Simple RAG Assistant
## TL;DR
This project implements a basic Retrieval-Augmented Generation (RAG) assistant. It uses a vector store to find relevant documents and an LLM to generate answers based on that cont... | Given the following content, create as much questions whose answer can be found within the content. Then, provide the answer to the questions. Ensure the answers are derived directly from the content. Format the questions and answers in the following JSON structure: {Question: '', Answer: ''}. |
Aether: A Retrieval-Augmented Generation Assistant for Ready Tensor Publications
--DIVIDER--
## Abstract
This publication introduces Aether, a conversational AI assistant designed to provide users with a seamless and intuitive way to access and understand information from ... | Given the following content, create as much questions whose answer can be found within the content. Then, provide the answer to the questions. Ensure the answers are derived directly from the content. Format the questions and answers in the following JSON structure: {Question: '', Answer: ''}. |
RAG-Based Learning & Code Assistant
# RAG-Based Learning & Code Assistant
## Abstract
The **RAG-Based Learning & Code Assistant** is a dual-purpose AI application designed to provide academic support to students and technical guidance to developers. Leveraging the power of **LangChain**, **Groq's LLaMA 3**, and **Ch... | Given the following content, create as much questions whose answer can be found within the content. Then, provide the answer to the questions. Ensure the answers are derived directly from the content. Format the questions and answers in the following JSON structure: {Question: '', Answer: ''}. |
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