FAQ-based Question Answering System

qa-system 开源项目推荐(48 个)
qa-system 话题在 GitHub 上没有填写简介,这里按 Star 数汇总最相关的热门仓库。
热门项目 前 20
按 Star 数排序展示前 20 个 · GitHub 共 48 个仓库基于知识图谱的电影知识问答系统。
Dataset Resplitting for Generalization in KGQA. See also https://github.com/semantic-systems/KGQA-datasets
Q&A System using BERT and Faiss Vector Database
RagE (RAG Engine) - A tool supporting the construction and training of components of the Retrieval-Augmented-Generation (RAG) model. It also facilitates the rapid development of Q&A systems and chatbots following the RAG model.
Production-ready Enterprise RAG system for grounded Q&A. Powered by Gemini 3.1 & FAISS, featuring recursive semantic chunking and citation-backed retrieval.
复旦大学NLP课程期末PJ;疫情信息问答系统
A modern and lightweight NLP interface for Question-Answering systems and more. Fork this project to showcase your Python models with elegant web applications in no time!
Knowledge-Based Question Answering --TF2 Version
도메인에 특화된 질의응답 QA 시스템을 자동 생성하는 웹 프레임워크 QA Maker 개발
Project repository for the development of a Question-Answering (QA) information retrieval system fine-tuned on customer queries.
In this academic project, I have compared the performance of different NLP approaches for Question-Answering system using varied levels (easy, medium, hard) of FAQs.
该项目在 GitHub 上没有填写描述
RAG + LLM 混合检索问答
基于Kotlin + Compose Desktop开发的RAG知识问答系统,专为兰州旅游知识问答设计。 支持本地知识库、TF-IDF向量化、Material Design 3界面和双模式(数据库/JSON)支持。
We built a Question Answer System using BERT. Based on our benchmark dataset that we designed for a specific task, we evaluated it at 40% accuracy over a particular dataset.
QueryVault is a robust RAG system for structured Q&A data. It ingests JSON files, embeds content via ChromaDB, and serves context-aware answers using FastAPI and Google Gemini. With a modular design and CLI tools, it's built for scalable, secure AI-powered knowledge retrieval.
Military-oriented knowledge question-answer system, it supports different way to ask.
This project implements a Retrieval Augmented Generation (RAG) Pipeline for PDF documents. It extracts information, generates embeddings, and uses LLMs to provide intelligent responses via an interactive Streamlit UI. Ideal for building Q&A systems on custom knowledge bases
校园智能助手问答系统:整合学术资源、生活服务、校园活动等 10 类校园信息,提供自然语言问答。技术栈 Python 3.11+ / OpenAI API / SQLAlchemy / SQLite。
关于这个话题
- 清单取自 GitHub 话题页并按 Star 数排序,每次展示前 20 个项目;原始页面见 https://github.com/topics/qa-system。
- 该话题在 GitHub 上共有 48 个仓库, 本站收录的是其中最热门的 20 个,不是全量列表。
- Star / Fork 每日自动刷新一次,实时数据以 GitHub 为准。
- 当前 20 个项目累计 2666 Star,覆盖 6 种主要语言,数据更新于 2026-11-08。
- 中文介绍来自 GitHub 官方话题说明(github/explore)的机器翻译,可点「查看原文」对照;译文缺失时会先展示原文。