GraphRAG: The Next Generation of AI-Powered Knowledge Retrieval

Traditional RAG just chops documents into chunks and throws them into a vector database. When it comes to complex questions—"What's the relationship between Company A and Company B?" or "What does the supply chain for this product look like?"—traditional RAG falls short. GraphRAG can solve this problem.

💡 What You Will Learn

Traditional RAG just chops documents into chunks and throws them into a vector database. When it comes to complex questions—"What's the relationship between Company A and Company B?" or "What does the

GraphRAGWhat Is

[] -- [] --> []
[] -- [] --> [M9]
[M9] -- [] --> [ADS]

pip install graphrag

# 
graphrag init --root ./myproject

#  ./myproject/input/
# 
graphrag index --root ./myproject

# 
graphrag query --root ./myproject --method global --query ""

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