How to Integrate Knowledge Graph to Your RAG Systems?
In the ever-evolving landscape of natural language processing (NLP), the integration of knowledge graphs (KGs) with Retrieval Augmented Generation (RAG) systems has emerged as a promising approach to address the limitations of large language models (LLMs) and improve the overall performance of conversational AI systems.
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Enhancing RAG Systems for More Accurate and Explainable Responses
At the heart of this integration lies the fundamental difference between knowledge graphs and traditional vector databases. While vector databases excel at capturing the semantic similarity between text chunks through embedding and nearest-neighbor search, they often fall short in preserving the explicit relationships and contextual details that are crucial for logical reasoning and explainability.
This is where knowledge graphs shine. By representing real-world entities and their interconnected relationships as a network of nodes and edges, knowledge graphs offer a more structured and comprehensive understanding of the underlying knowledge. Each node in a knowledge graph can be enriched with a wealth of metadata, such as descriptions, aliases, and hierarchical information, providing a holistic view of the entities and their context.
