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.
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.
A RAG-based retrieval system for air pollution topics using LangChain and ChromaDB.
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
A local LLM that answers user doubts using fine-tuned models and real-time search.
🤖 A toy Transformer Q&A model simulator demonstrating core concepts of large language models through memorized Q&A pairs. Educational demo with interactive web interface.
Web-Based Q&A Tool enables users to extract and query website content using FastAPI, FAISS, and a local TinyLlama-1.1B model—without external APIs. Built with React, it offers a minimal UI for seamless AI-driven search
web-based Question Answering (QA) system powered by the BERT model and Transformers AI technology
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.
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