Overview
Graph Retrieval-Augmented Generation (GraphRAG) is an AI approach that combines knowledge graphs with retrieval-augmented generation to deliver rich, context- and relationship-aware answers from complex data.
GraphRAG is a Microsoft Research - Project that has, after its initial publication, gained significant community support but has not, to date, been converted to a productized
Like all methodologies, it's use should be purposeful. To determine if GraphRAG is right for your use case, consider your applications needs to consume RAG outcomes. Review the questions and Analysis Guide pictured below.
- Does your use case have a lot of duplicate information? Can questions be answered based on only some of the relevant knowledge?
- What is the scale of your solution? For a single question, would tens of knowledge chunks be relevant or tens of thousands of chunks?
- What is your use case tolerance for hallucinations (i.e., how critical is quality, which implies a necessity to retrieve all relevant knowledge)?
Once you have determined your required RAG consumption pattern, you can more easily map methodologies to this. The above patterns are mapped below to sample AI methodologies. GraphRAG is highlighted as a solution when all relevant chunks might be too large for a single context window and also where all relevant or all chunks are required to be retrieved.
Sample RAG Pattern to AI Methodology Mapping
For more information about GraphRAG and its use case appropriateness, see the below:
- Microsoft Research - Project GraphRAG
- Tech Community: The Future of AI: GraphRAG – A better way to query interlinked documents
- Tech Community: Unlocking Insights: GraphRAG & Standard RAG in Financial Services
After GraphRAG selection
There are different implementations and GitHub repositories available for GraphRAG concepts. Since Microsoft Research's inaugural publication in April 2024, different variations of the GraphRAG approach have been published.
It is recommended to start your experimentations with the core GraphRAG GitHub Page and GraphRAG GitHub Repository.
Once you've finished an initial, local proof of concept on a real-world use case and like your outcomes, you can move towards industrialization. See the GitHub Azure-Samples/graphrag-accelerator for a one-click deployment industrialization path.
Standing up the most popular use case:
The Research Assistant
GraphRAG does particularly well as a research assistant with large amounts of data. It is able to analyze data, draw meaningful connections, and synthesize concepts and patterns into an insightful outcome.
This section walks you through using graphrag python library with Azure OpenAI on top of a limited number of Wikipedia articles relating to financial auditing.
The associated GitHub repository for this section is: adhazel/graphrag_demo.
Running the Demo
Complete the steps in each of the below locations, and, optionally, follow along in the video.
After the Demo
This video walk through is, by necessity, the short, happy path. Here are some ideas on what to check out next - be sure to watch the video for a full walkthrough of the GraphRAG Visualization Guide.
- Perfect the global, local, and drift searches
- Tune the prompts, paying special attention to extract graph, extract claims, and community report prompts
- Dig into and fine tune the GraphRAG settings (the yaml)
- Set up a visualization tool on top of your graph
- Explore a production use case!
Many thanks for your attention and happy coding!
Microsoft