Retrieval enhanced generation (RAG) is a technology that searches for information based on user queries and provides the results as a reference for generating AI answers.
This technique is an important part of most LLM-based tools, and most RAG methods use vector similarity as a search technique.
GraphRAG uses the knowledge graph generated by LLM to greatly improve the performance of question and answer when analyzing complex information in documents.
This builds on recent research that points to the immediate increased power of discovery when performing on private data sets.
Here, private datasets are defined as data that the LLM has not been trained and has never been seen before,
Examples are corporate proprietary research, business documents or communications. Baseline RAG was created to help solve this problem, but we observed very poor performance of baseline RAG.
Microsoft Research has launched GraphRAG, an advanced approach designed to improve the ability of the Large Language Model (LLM) to retrieve and generate responses from private datasets. This innovative method uses the knowledge graph generated by LLM to significantly improve the question and answer performance of traditional retrieval enhanced generation (RAG) methods.
GraphRAG is a structured, hierarchical approach used to implement Retrieval Augmented Generation (RAG), which uses knowledge maps to improve the output quality of large language models (LLM).
GraphRAG is able to connect information across large amounts of information and use these connections to answer questions that are difficult to answer using keywords and vector-based search mechanisms. It can answer questions that span multiple documents, as well as thematic questions such as “What are the main topics in the dataset?”
Building a knowledge graph through LLM combined with graph machine learning greatly enhances the performance of LLM when processing private data, enables the system to handle global questions, supports global question answers to large-scale text corpus, and provides more comprehensive and diverse answers. At the same time, GraphRAG has the ability to reason complex semantic problems across large data sets that connect points into lines.
Unlike traditional baseline RAG methods that rely mainly on vector similarity searches, GraphRAG uses knowledge maps to provide significant Q & A performance improvements when processing complex information.
If you want to learn more, you can click on the link below the video.
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Microsoft Blog:https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
https://microsoft.github.io/graphrag/
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