GraphRAG enables more context-aware and verifiable responses from LLMs
Graphwise is attempting to address some of the challenges of RAG with its new GraphRAG offering, which is now generally available.
GraphRAG acts as a semantic layer on top of knowledge graphs that LLMs can utilize to provide context-rich and verifiable answers.
According to the company, a typical RAG implementation flattens data into chunks, and with that approach, it can find similar words, but isn’t able to understand complex relationships, hierarchies, or logic connecting business data. On top of that, it is also usually difficult to see how an LLM came to its answer and what sources it used.
“For many organizations, the biggest barrier to generative AI isn’t the model, it’s trust. Teams struggle to justify AI-driven decisions when they can’t see where an answer came from, how it was produced, or whether it respects internal policies and regulations,” Graphwise wrote in a blog post.
Graphwise believes that GraphRAG solves these issues by providing a pipeline where every step can be inspected and answers are backed by documents and graph entities.
It leverages several different search approaches, including retrieval from a knowledge graph, vector search in a specified vector store, and full-text search to enable keyword-driven discovery. It utilizes a knowledge-model-driven input processing approach to understand the user’s intent, allowing it to enrich concepts using the company’s taxonomy or ontology, expand queries using related entities and terms, and build a graph representation of the question.
According to the company, GraphRAG is particularly good at answering multi-hop questions, like “how does X impact Y across Z?”
GraphRAG can be used with any major LLMs or self-hosted model, any vector store, or any enterprise IdP.
“Embedding models and vector stores are abstracted behind clear interfaces, so you can switch providers, update models, or scale infrastructure without rewriting your application,” the company wrote.
More information about GraphRAG can be found on the company’s website.
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