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The emerging field of Graph RAG develops methods to exploit the rich, structured relationships between entities within a KG to retrieve more precise, factually relevant context for LLMs [9]. Graph RAG methods encompass graph construction, knowledge retrieval, and answer-generation techniques [1,2,5]. We find methods that leverage existing open-source KGs [3] to methods for automatically building domain-specific KGs from raw textual data using LLMs [6]. The retrieval phase focuses on efficiently extracting pertinent subgraphs, paths, or nodes relevant to a user query with techniques like embedding similarity, pre-defined rules, or LLM-guided search. In the generation phase, retrieved graph information is transformed into LLM-compatible formats, such as graph languages, embeddings, or GNN encoding, to generate enriched and contextually grounded responses [4]. Recently, significant attention has been given to hybrid approaches combining conventional RAG and Graph RAG strengths [7,8]. HybridRAG integrates contextual information from traditional vector databases and knowledge graphs, resulting in a more balanced and effective system that surpasses individual RAG approaches in critical metrics like faithfulness, answer relevancy and context recall. 

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[9] Juan Sequeda, Dean Allemang, Bryon Jacob:
A Benchmark to Understand the Role of Knowledge Graphs on Large Language Model's Accuracy for Question Answering on Enterprise SQL Databases. GRADES/NDA 2024: 5:1-5:12

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