GraphRAG in Practice: How to Build Cost-Efficient, High-Recall Retrieval Systems

Building GraphRAG systems involves a trade-off between graph density and cost, where extracting entities from document chunks yields a much richer graph than processing full documents. A proposed retrieval pipeline utilizes a simple star graph, where entities are linked to a central document ID, to first classify and filter relevant documents from a large corpus. This graph context is then used to enrich the user's query, enabling a more precise vector search over the selected document chunks. This hybrid approach effectively handles queries with weak embeddings like alphanumeric IDs and achieves high recall without the complexity of building a dense, fully interconnected graph. This method demonstrates a practical way to balance cost, complexity, and accuracy in real-world retrieval systems.