Enhancing Document Retrieval Using AI and Graph-Based RAG Techniques

Retrieval-Augmented Generation (RAG) has emerged as a potent method for enhancing the capabilities of large language models (LLMs) by integrating them with external knowledge sources. While traditional RAG models rely heavily on textual similarity for retrieval, often leading to issues like context drift and hallucinations, graph-based RAG offers a more sophisticated approach. By representing documents and their relationships within a graph structure, graph-based RAG enables more context-aware retrieval, reducing hallucinations, and facilitating multi-hop reasoning. This abstract provides an overview of the RAG landscape, contrasting traditional and graph-based approaches, and highlights the advantages of graph-based RAG in addressing the limitations of traditional methods. The application of graph-based RAG to various domains, such as question answering, dialogue systems, and recommendation systems, is also explored. The abstract concludes by emphasizing the potential of graph-based RAG to revolutionize information access and retrieval in diverse AI applications.