Full pipeline from extracting, transforming and loading documents (ETL), to embedding, searching and ranking based on query
Assignments completed for CP423: Text Retrieval and Search Engines. Collaborated with Abigail Lee and Myisha Chaudhry
This project uses a Retrieval-Augmented Generation (RAG) pipeline to extract relevant information from PDFs. Users can upload PDFs, submit queries, and get accurate responses based on the document content. It combines a retriever model and generative model for intelligent, real-time query handling.
Backend with Run Time From Server , running Docker Container Featuring Comittment Run Large Language Models Training Quantum Recurrent Networks
Add a description, image, and links to the query-search topic page so that developers can more easily learn about it.
To associate your repository with the query-search topic, visit your repo's landing page and select "manage topics."