Graph RAG: Method for Enhanced Question Answering with Large Language Models

Large language models (LLMs) and RAG have revolutionized the way we interact with text data, enabling us to retrieve relevant information from vast document collections.

However, when it comes to answering global questions that require summarizing an entire text corpus, traditional retrieval-augmented generation (RAG) methods don't do well. This is because RAG is designed for explicit retrieval tasks ( Find specific information in Large chunks), not query-focused summarization.

RAG struggles to connect the dots between different pieces of information (chunks) to give us new insights. This happens when answering a question requires traversing disparate pieces of information through their shared attributes in order to provide a new synthesized response.

To address this limitation, a new approach called Graph RAG has been developed by Microsoft Researchers. This new method combines the strengths of LLMs, RAG, and a special way of organizing information called graph-based indexing to provide a scalable solution for question answering over private text corpora.

Graph RAG is a step-by-step approach to RAG, unlike RAG which just searches through plain text. The process involves in GraphRAG turning raw text into a knowledge graph, grouping similar information together, summarizing each group, and then using these summaries to answer questions.

When a user asks a question, Graph RAG generates a partial response for each group summary, which is then summarized in a final response. This approach has been shown to lead to substantial improvements in both comprehensiveness and diversity of generated answers, especially for global sensemaking questions over large datasets.

The good news is that Microsoft has open-sourced Graph RAG, making it available to everyone. With just a simple pip install command (!pip install graphrag), you can unlock the power of Graph RAG to extract insights from unstructured text. I'll provide links to more resources for further learning.

If you're interested in learning about RAG (Retrieval Augmented Generation), I'd love for you to check out my article on Medium. In it, I break down how the baseline RAG model works in a way that's easy to understand, complete with architecture explanations, visual diagrams, and code examples.

Retrieval Augmented Generation (RAG): A Comprehensive Visual Walkthrough ???????? : https://medium.com/towards-artificial-intelligence/retrieval-augmented-generation-rag-a-comprehensive-visual-walkthrough-49e5ef6c1f63

Resources:

1. Github: https://github.com/microsoft/graphrag?tab=readme-ov-file
2. GraphRAG Research Blog: https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
3. GraphRAG Research Paper: https://arxiv.org/pdf/2404.16130
4. GraphRAG: https://microsoft.github.io/graphrag/