GraphRAG in Action: From Commercial Contracts to a Dynamic Q&A Agent

This is a summary of an article about using Graph Retrieval-Augmented Generation (GraphRAG) to streamline the process of ingesting commercial contract data and building a Q&A agent. The approach diverges from traditional RAG by emphasizing efficiency in data extraction, rather than breaking down and vectorizing entire documents indiscriminately. It uses a four-stage approach: targeted information extraction using LLMs and prompts, storing information extracted into a knowledge graph with Neo4j, developing simple knowledge graph data retrieval functions, and building a Q&A agent using Microsoft Semantic Kernel. The article provides an example of how to implement this approach, including creating a knowledge graph, defining data retrieval functions, and building a chatbot agent that can answer questions about contracts. The GraphRAG approach minimizes inefficiencies found in traditional vector search-based RAG by focusing on extracting only relevant information, reducing the need for unnecessary vector embeddings, and simplifying the overall process.

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