GraphRAG: Enhancing RAG with Knowledge Graphs
GraphRAG, an innovative system by Microsoft, combines knowledge graphs with Retrieval Augmented Generation (RAG) to enhance query-focused summarization. This video by Prompt Engineering delves into GraphRAG’s functionalities, setup, and cost implications. Traditional RAG methods involve processing documents into vectors, querying those vectors, and retrieving relevant chunks to generate responses. However, these methods face limitations in contextual understanding, scalability, and complexity.
GraphRAG addresses these issues by introducing a knowledge graph that identifies entities and their relationships within documents. The system creates summaries at different community levels, ensuring a holistic understanding of the content. During the query phase, it selects the appropriate community level to generate accurate and detailed responses.
The video provides a step-by-step guide to setting up GraphRAG on a local machine, including creating a virtual environment, installing the package, and running the indexing process. The process involves extracting entities, building a knowledge graph, and summarizing communities. Queries can be run at different community levels, from global themes to specific character details.
Cost is a significant consideration, as using GPT-4 for indexing and querying can be expensive. For instance, processing a single book resulted in over 1 million tokens and approximately $7 in costs. Despite the cost, GraphRAG offers substantial improvements over traditional RAG systems by providing a comprehensive understanding of documents.
The video also mentions alternative implementations of graph-based RAG systems, such as those by LlamaIndex and Neo4j, and suggests future content comparing these different approaches.