Graph RAG: Merging Knowledge Graphs with Large Models

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Graph RAG: Merging Knowledge Graphs with Large Models

Training Materials

September 2024 Issue

This video is a community sharing report from the AI-themed discussion meeting of the Yunhan Community held on August 3, 2024.

Speaker:

Chen Xiaoyang (Member of the Yunhan Community Committee, General Manager of Fucheng Company)

Report Title:

“Graph RAG – When Knowledge Graphs Meet Large Models“

AI Video Summary:

This report deeply explores the cutting-edge technology of combining knowledge graphs with large models – Graph RAG (Retrieval-Augmented Generation with Knowledge Graphs). The report first introduces the problems encountered by large models in practical applications, and then elaborates on the emergence of Retrieval-Augmented Generation (RAG) technology and its advantages. The core of the report is to introduce this new technology, Graph RAG, explaining how knowledge graphs enhance the capabilities of large models and how this combination sparks new technological innovations. The report also introduces Microsoft’s GraphRAG and other related open-source projects and provides targeted implementation suggestions.
Highlights of the Report:
– Provides a detailed analysis of the Graph RAG technology framework
– Analyzes the key role of knowledge graphs in improving AI system performance
– Shares practical application cases of open-source projects and frameworks
Main Content:
1. **Limitations of Large Models:**
– Fabricating information
– Outdated knowledge
– Difficulty in integrating user-specific data
– Data security and privacy issues
2. **Retrieval-Augmented Generation (RAG):**
– Characteristics of RAG
– Naive RAG technology framework
– Defects of RAG
3. **Graph RAG:**
– What is Graph RAG
– Advantages of Graph RAG
– Graph RAG framework
– Challenges of Graph RAG
4. **Introduction to Open Source Projects:**
– Introduced Microsoft’s GraphRAG and other related projects
– Emphasized the progress of these projects in the combination of knowledge graphs and large models
5. **Challenges and Implementation Suggestions:**
– Main challenges of Graph RAG
– Implementation suggestions for Graph RAG
6. **Conclusion:**
– Knowledge graphs provide a representation that can be understood by both humans and machines
– Technological advancements drive the exploration of knowledge graphs beyond traditional technologies

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Graph RAG: Merging Knowledge Graphs with Large Models

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Graph RAG: Merging Knowledge Graphs with Large Models

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