Topic 12: What is HybridRAG?

Did you miss RAG? Retrieval-Augmented Generation (RAG) is continually expanding as one of the most popular methods to enhance LLM with external knowledge. But it still remains challenging to use original RAG in specific domains like financial one, with specialized language and complicated formats in documents. A new HybridRAG approach was made to address this problem. It combines two ways of fetching relevant information — one based on similarity (VectorRAG) and one based on structured relationships (GraphRAG) — resulting in more accurate and contextually rich answers. Tests show that HybridRAG is especially useful in fields like finance where both data formats and relationships matter. It also has a potential to be useful beyond just finance. Curious if that’s what you might need? Let’s discuss why the HybridRAG method can be a relevant solution for handling specific tasks. 

In today’s episode, we will cover:

• Limitations of LLMs and existing RAG systems for financial sector

• Here comes HybridRAG

• How does HybridRAG work?

• Is HybridRAG really good?

• Advantages of HybridRAG

• Not without limitations

• Conclusion

• Clarification of terms: different HybridRAGs

• Bonus: Resources

Limitations of LLMs and existing RAG systems for financial sector

The financial sector relies on various sources like news articles and earnings reports to make investment decisions and predictions. As these documents are often disorganized, traditional analysis struggles to make sense of them.

Basically, LLMs help to deal with large amounts of data for tasks like trend predictions or report generation. But when it comes to specialized language and complex structures, LLM alone can’t handle it well. 

VectorRAG, or just RAG, is a common method that addresses LLMs’ limitations. It searches for similar chunks of text in an external database to provide context for generating answers. Despite this, VectorRAG struggles with capturing the structure and relationships within the data.

To organize data into entities and their relationships, Knowledge graphs (KGs) are used. GraphRAG simplifies building and maintaining KGs with large datasets, combining them with RAG for more accurate answers. But again, GraphRAG has its own limitations – it struggles with questions that don't directly mention relevant entities. 

What if we marry VectorRAG and GraphRAG?

Here comes HybridRAG