HybridRAG, a novel approach that combines Knowledge Graphs (GraphRAG) and Vector Retrieval Augmented Generation (VectorRAG) to enhance information extraction, particularly from complex unstructured text like financial documents. Building such a system, outlining the four key components: data processing, graph creation, semantic embedding vector database creation, and inferencing.
We’ll emphasize on the importance of creating high-quality triplets for the knowledge graph, often requiring LLM-driven prompt engineering and iterative refinement. Validation of HybridRAG, demonstrating its superior performance over individual GraphRAG and VectorRAG methods in terms of retrieval accuracy and answer generation, especially in enterprise applications. The computational intensity of graph-based systems and the need for optimization strategies to balance depth of retrieval with latency.