Fast GraphRAG: A Faster, More Cost-Effective, and Efficient RAG Framework is Now Open Source
Explore Fast GraphRAG: a faster, cost-effective RAG framework that enhances knowledge graph retrieval with efficient, interpretable workflows for AI applications.
I’d like to share with you a very interesting RAG tool, based on GraphRAG.
First, why is GraphRAG so popular, and why does it seem like a great choice for any production-level GraphRAG pipeline?
Because it deals with nodes and graph structures, which are suitable for real-world scenarios.
For example, a regular RAG simply handles data similar to your use case via semantic search.
The process is: you split the data into smaller chunks, convert them into numerical representations, and store them in some vector store.
When a user makes a query, you retrieve relevant data from the vector store, append it to the query, and pass it to the LLM, which provides an answer based on that data.
However, GraphRAG brings a new twist by introducing a structured, hierarchical approach to traditional RAG.
The GraphRAG process includes extracting knowledge graphs from raw text, building knowledge group hierarchies, summarizing these groups... and then using these structures during the execution of RAG tasks.
It has many advantages, and the most important one is that GraphRAG does what regular RAG cannot— it can “connect the dots.”
By linking semantic concepts through nodes and edges, GraphRAG provides an overall summary of these concepts.
And now…