diff --git a/data/operation_dulce/dataset.zip b/data/operation_dulce/dataset.zip index c820d0bf..900d5d86 100644 Binary files a/data/operation_dulce/dataset.zip and b/data/operation_dulce/dataset.zip differ diff --git a/index.html b/index.html index 35cd5e42..abdbf685 100644 --- a/index.html +++ b/index.html @@ -301,7 +301,9 @@ Figure 1: An LLM-generated knowledge graph built using GPT-4 Turbo.
GraphRAG is a structured, hierarchical approach to Retrieval Augmented Generation (RAG), as opposed to naive semantic-search approaches using plain text snippets. The GraphRAG process involves extracting a knowledge graph out of raw text, building a community hierarchy, generating summaries for these communities, and then leveraging these structures when perform RAG-based tasks.
To learn more about GraphRAG and how it can be used to enhance your LLMs ability to reason about your private data, please visit the Microsoft Research Blog Post.
-To quickstart the GraphRAG system we recommend trying the Solution Accelerator package. This provides a user-friendly end-to-end experience with Azure resources.
+To start using GraphRAG, check out the Get Started guide. For a deeper dive into the main sub-systems, please visit the docpages for the Indexer and Query packages.