HybridRAG: The Hybrid RAG Engine - Knowledge Graph + Vector Retrieval!

Unlock the power of HybridRAG, the innovative AI system that enhances language models with superior accuracy and contextual recall through knowledge graphs and vector retrieval methods.

We have all heard of Retrieval-Augmented Generation (RAG), and many people use it because it enhances the capabilities of language models by improving accuracy, reducing hallucinations, and being more cost-effective through the combination of retrieval and generation.

In previous articles, we showcased many advanced RAG frameworks, such as GraphRAG and RAG Flow, which are becoming increasingly sophisticated month by month.

For example, GraphRAG uses knowledge graphs to represent entities and relationships, providing a structured method for information retrieval, though it lacks comprehensive recall capabilities.

Then we have a method called Vector, which retrieves relevant information by converting text into vector embeddings. While it excels in search, it often misses critical context, especially when handling complex structured documents like financial reports or longer contexts.

So, how do we solve this problem?