A Unified Framework For Llm-Powered Knowledge Management Systems Using Rag And Knowledge Graphs

Abstract

The sheer proliferation of the unstructured organizational data, such as emails, documents, meeting notes, and chat logs, has put the usefulness of the traditional Knowledge Management Systems (KMS), which are mostly structured and inactive, into question. Recent developments in Large Language Models (LLMs) provide considerable possibilities in processing and extracting insights of such data, but their application in business contexts does not have a standardized framework that ensures accuracy of retrieval, contextual relevance, governance, and explainability. The present paper suggests a holistic design of an LLM-based Knowledge Management System that combines Retrieval-Augmented Generation (RAG) with Knowledge Graphs (KG) to facilitate hybrid knowledge retrieval and structured reasoning. The presented system is executed with the help of a cloud architecture written in Python which includes vector databases and graph databases to facilitate scalable and efficient access to knowledge. The framework leads to accuracy of answers, reduction in hallucination, and increases in the interpretability through integration of semantic retrieval and relational representation of knowledge. Accuracy, precision, latency, and explainability-based evaluation proves the usefulness of the proposed approach compared to traditional and standalone systems based on the LLM. This piece of work adds to the scalable and enterprise-ready next-generation knowledge management solution.