Q

question answering

This project demonstrates a Retrieval-Augmented Generation (RAG) system designed to answer questions based on a collection of enterprise documents (PDFs and TXT files). It leverages LangChain for orchestrating the query pipeline and LlamaIndex for indexing and retrieving relevant information. The system uses Google's Gemini 1.5 Flash for generating answers and Gemini Embeddings for semantic search. A Streamlit interface provides a user-friendly way to interact with the knowledge assistant.

Question&Answering Spring-based web application for deterministic ontology-driven natural language processing, leveraging LTAG and DUDES.

Coursework in Artificial Intelligence.