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Retrieval augmented answer generation from a knowledge graph using LLMs
This project aims at enhancing the capabilities of a document management system by leveraging the power of large language models (LLMs) and Retrieval-Augmented Generation (RAG) models. By incorporating graph-based information retrieval methods, the system will be able to utilize the rich metadata and interconnected information associated with documents more effectively. It will improve the system’s ability to answer complex queries and summarize content more accurately.
Faculty Supervisor:
Milton King
Student:
Partner:
M-Files Canada Inc.
Discipline:
Computer science
Sector:
Professional, scientific and technical services
University:
St. Francis Xavier University
Program:
Business Strategy Internship
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