question-answering-system

AWS Generative AI CDK Constructs are sample implementations of AWS CDK for common generative AI patterns.

This repo provides sample generative AI stacks built atop the AWS Generative AI CDK Constructs.

Search-Then-Verify natural language querying over time series databases (ICML 2026). Includes NLQTSBench.

Awesome Question Answering

Question Answering dataset generator of Document Visual in English and Chinese

A Turkish question answering system made by fine-tuning BERTurk and XLM-Roberta models.

A Turkish question answering system made by fine-tuning BERTurk and XLM-Roberta models.

This repository contains a Streamlit-based Document Question Answering System implementing the Retrieve-and-Generate (RAG) architecture, utilizing Streamlit for the UI, LangChain for text processing, and Google Generative AI for embeddings.

A enhanced Open Dialogue Context Generator supported by General Language Model Pretraining with Autoregressive Blank Infilling

A Daliy Dialogue Context Generator trained with Bloom and GPT

A cutting-edge AI-powered phone agent designed for seamless voice interactions, dynamic data handling, and scalable communication. Perfect for modern sales and customer engagement solutions.

Factoid Question Answering System - An advanced Open-domain Question Answering (ODQA) project that automatically answers factoid questions in Arabic and English languages using NLP and machine learning techniques

Comparative study of large language models in the field of open-book QA, with application to a real-life use case.

Question Answering Systems

基于MySQL和Word2Vec模型实现一个简单的带对话机器人系统的医疗咨询平台

It is a Mental Health Chatbot named CogniCare. Whether you're feeling overwhelmed, anxious, or just need someone to talk to, our Mental Health Chatbot is here to support you every step of the way. Remember, it's okay to seek help, and you don't have to go through this alone.

An LLM app leveraging RAG with LangChain and GPT-4 mini to analyze earnings call transcripts, assess company performance, using natural language queries (NLP), FAISS (vector database), and Hugging Face re-ranking models.

Machine Comprehension on Squad Dataset using Match-LSTM + Ans-Ptr Network

Retrieval-Augmented Generation (RAG) based question answering system using Wikipedia, FAISS vector search, Sentence Transformers, and FLAN-T5 with comparative evaluation of responses with and without retrieval.