A deep dive on Microsoft's GraphRAG paper found questionable metrics with vaguely defined lift, so I analyzed knowledge graphs in RAG overall using Neo4j vs FAISS
1. Entity Knowledge Graph Generation: Initially, a large language model (LLM) is used to extract entities and their interrelations from source documents, creating an entity knowledge graph.
2. Community Summarization: Related entities are further grouped into communities, and summaries are generated for each community. These summaries serve as partial answers during queries.
3. Final Answer Generation: For user questions, partial answers are extracted from the community summaries and then re-summarized to form the final answer.
This approach not only enhances the comprehensiveness and diversity of answers but also demonstrates higher efficiency and scalability when handling large-scale textual data.
Thank you for this insightful article. I'm not a developer by trade but have been diving into the power of RAG (regular RAG) recently and successfully built my first pipeline for a SlackBot using Astra DB and OpenAI API (chat and embeddings). As I was learning how to do simple RAG techniques, I kept reading the GraphRAG hype and wondered if it was worth me diving into those techniques next. If regular RAG is a 5 out of 10 in complexity for me, what would say learning GraphRAG would be comparatively? Thanks again!
Core of the GraphRAG Project:
1. Entity Knowledge Graph Generation: Initially, a large language model (LLM) is used to extract entities and their interrelations from source documents, creating an entity knowledge graph.
2. Community Summarization: Related entities are further grouped into communities, and summaries are generated for each community. These summaries serve as partial answers during queries.
3. Final Answer Generation: For user questions, partial answers are extracted from the community summaries and then re-summarized to form the final answer.
This approach not only enhances the comprehensiveness and diversity of answers but also demonstrates higher efficiency and scalability when handling large-scale textual data.
Thank you for this insightful article. I'm not a developer by trade but have been diving into the power of RAG (regular RAG) recently and successfully built my first pipeline for a SlackBot using Astra DB and OpenAI API (chat and embeddings). As I was learning how to do simple RAG techniques, I kept reading the GraphRAG hype and wondered if it was worth me diving into those techniques next. If regular RAG is a 5 out of 10 in complexity for me, what would say learning GraphRAG would be comparatively? Thanks again!