Open
Description
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 ->
-
- Indexing (chunking of document and storing in vector form)
-
- Retrieval (query vector to retrieve data based on input )
-
- 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/