GraphRAG: Combining Knowledge Graphs with RAG for Enhanced Question Answering
Microsoft recently released their GraphRAG Github, a new approach that combines knowledge graphs with Retrieval-Augmented Generation (RAG) for more effective question answering. While the concept of integrating knowledge graphs with RAG isn't entirely new, GraphRAG offers some interesting capabilities and trade-offs worth exploring.
How GraphRAG Works
At a high level, GraphRAG works as follows:
Indexing Phase:
Split documents into chunks (e.g. 300 tokens each)
Extract entities and relationships from chunks using LLMs
Build a knowledge graph from the extracted information
Create hierarchical communities of related entities
Generate summaries for communities at different levels
Embed the graph, entities, and summaries
Query Phase:
Determine which community level is most appropriate for the query
Retrieve relevant community summaries
For specific queries, traverse the graph to find relevant entities and context
Combine retrieved information to generate a response
Key Features and Benefits
Hierarchical structure allows answering both broad and specific queries
Community summaries enable …
Potential Drawbacks
Heavy reliance on …
When to Consider GraphRAG
GraphRAG may be worth exploring if:
You need to answer…
Curious to delve deeper into this?
Join Professor Mehdi and myself for a deep-dive discussion about the new GraphRAG approach:
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What You'll Learn:
🔎 In-depth review of the concept behind GraphRAG, how the algorithm works, and pros and cons of this method.
🚀 Personal insights from TwoSetAI team on what to use when implementing RAG with KG in production that will truly drives ROI and controls cost.
🛠 Tactical advice on how to survive this day and age of information overload.
Stay tuned as we continue exploring the development of knowledge-augmented AI systems to extract maximum value from unstructured data sources!
🛠️✨ Happy practicing and happy building! 🚀🌟
Thanks for reading our newsletter. You can follow us here: Angelina Linkedin or Twitter and Mehdi Linkedin or Twitter.
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🧰 Our video editing tool is this one!: https://get.descript.com/nf5cum9nj1m8
🗞️Paper: https://arxiv.org/pdf/2404.16130
📽️ Our other KG + RAG videos:
📚 Also if you'd like to learn more about RAG systems, check out our book on the RAG system:
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