Revolutionizing Semantic RAG: The Power of Knowledge Graph Traversal Algorithms

Novel Knowledge Graph Traversal Algorithms: Enhancing Accuracy in Semantic Retrieval-Augmented Generation (RAG) Systems In the fast-paced evolution of artificial intelligence, large language models (LLMs) have become indispensable tools for information processing. However, relying solely on an LLM’s internal knowledge often limits its ability to answer complex or domain-specific questions accurately. This is where Retrieval-Augmented Generation (RAG) systems shine—they supplement LLMs with context from databases or knowledge graphs, enabling more precise and well-grounded responses. Yet traditional RAG systems have a critical limitation: they mostly rely on text matching in vector stores, which struggles to capture deep semantic connections between pieces of …