GraphRAG

GraphRAG Implementation using Llama-Index

Description

• Graph RAG is a knowledge-enabled approach to retrieve information from knowledge graph on given task.
• It uses a Knowledge Graph and utilizes LLMs to extract Entities, Relationships and Keys from all external documents.
• 3 main stages of an RAG ->
• 1. Indexing (chunking of document and storing in vector form)
• 2. Retrieval (query vector to retrieve data based on input )
• 3. Generation (using LLM to use the retrieved and input content to formulate contextually relevant response)
• Here Graph RAG includes a knowledge graph before the RAG. So the indexing happens of the data points in the Knowledge Graph.

Benefits

• Traditional RAG treats documents as independent chunks, whereas GraphRAG captures explicit relationships between concepts, generating better and contextually relevant response.
• Regular RAG might miss important connections between different parts of documents, graphRAG maintains these connections through it's graph structure.
• Graph RAG can help answer questions that require understanding relationships between entities.
• Better at handling multi-hop queries. (questions that require connecting multiple pieces of information)

Implementation Ideas

• GraphRAG implementation with Llama_index
• GraphRAG thorugh - NetworkX , faiss library (less complex) -> NetworkX to make nodes , faiss for indexing , then processing LLM by adding embedding layer.

Additional Context

https://docs.llamaindex.ai/en/stable/examples/query_engine/knowledge_graph_rag_query_engine/

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