3 ms·

I am ecstatic that Microsoft open sourced this. After watching the demo video[1], my mind raced with all of the possibilities that GraphRAG unlocks. I'm plannin

I am ecstatic that Microsoft open sourced this. After watching the demo video[1], my mind raced with all of the possibilities that GraphRAG unlocks. I'm planning to try GraphRAG + Llama3 on my MacBook, since it has 96GB of unified (V)RAM. I think this tool could be a legit game changer. [1] https://www.youtube.com/watch?v=r09tJfON6kE https://www.youtube.com/watch?v=r09tJfON6kE

k__ 2y ago
How does this work? I understand the issues with "baseline RAG", as I experienced them myself. They make sense to me, if I query "what are the major themes of this dataset" on a list of articles, and the RAG tries to find content that's similar to that query, it probably won't find much. Why does GraphRAG find something here? What's happening with my query that it suddenly "matches" the dataset?
- madacol 2y ago
  from https://www.microsoft.com/en-us/research/blog/graphrag-new-tool-for-complex-data-discovery-now-on-github/ https://www.microsoft.com/en-us/research/blog/graphrag-new-t... > It does this by detecting “communities” of densely connected nodes in a hierarchical fashion, partitioning the graph at multiple levels from high-level themes to low-level topics, as illustrated in Figure 1. Using an LLM to summarize each of these communities creates a hierarchical summary of the data, providing an overview of a dataset without needing to know which questions to ask in advance. Each community serves as the basis of a community summary that describes its entities and their relationships.
- rooftopzen 2y ago
  Here's a write up the differences of GraphRAG - TLDR is it's expensive but can improve accuracy (possibly pertaining to LLM definition of the nodes), so if it's worth it to your app. https://medium.com/@jonathan.bennion/graphrag-analysis-part-1-how-indexing-elevates-knowledge-graph-performance-in-rag-1c15da59ae6a https://medium.com/@jonathan.bennion/graphrag-analysis-part-...

k__ 2y ago

How does this work? I understand the issues with "baseline RAG", as I experienced them myself. They make sense to me, if I query "what are the major themes of this dataset" on a list of articles, and the RAG tries to find content that's similar to that query, it probably won't find much. Why does GraphRAG find something here? What's happening with my query that it suddenly "matches" the dataset?

madacol 2y ago
from https://www.microsoft.com/en-us/research/blog/graphrag-new-tool-for-complex-data-discovery-now-on-github/ https://www.microsoft.com/en-us/research/blog/graphrag-new-t... > It does this by detecting “communities” of densely connected nodes in a hierarchical fashion, partitioning the graph at multiple levels from high-level themes to low-level topics, as illustrated in Figure 1. Using an LLM to summarize each of these communities creates a hierarchical summary of the data, providing an overview of a dataset without needing to know which questions to ask in advance. Each community serves as the basis of a community summary that describes its entities and their relationships.
rooftopzen 2y ago
Here's a write up the differences of GraphRAG - TLDR is it's expensive but can improve accuracy (possibly pertaining to LLM definition of the nodes), so if it's worth it to your app. https://medium.com/@jonathan.bennion/graphrag-analysis-part-1-how-indexing-elevates-knowledge-graph-performance-in-rag-1c15da59ae6a https://medium.com/@jonathan.bennion/graphrag-analysis-part-...