GraphRAG vs Vector RAG for Contract Analysis

Vector RAG excels at fast, semantic similarity search across large contract repositories. By chunking documents and indexing embeddings, it can retrieve clauses with similar meaning to a query in milliseconds. For example, a standard Vector RAG pipeline using Pinecone or Weaviate can achieve sub-second latency for single-hop questions like 'Find the indemnification clause,' making it ideal for high-volume, low-complexity review where speed and cost are the primary drivers.

GraphRAG takes a fundamentally different approach by constructing a knowledge graph of entities, obligations, and relationships extracted from contracts. This allows it to answer multi-hop questions like 'What are all payment obligations triggered by a force majeure event?' by traversing explicit relationships rather than relying on semantic proximity. The trade-off is increased indexing complexity and latency, but the result is higher precision on relational queries and a fully explainable citation path through the graph.

The key trade-off: If your priority is fast, cost-effective retrieval for straightforward clause identification, choose Vector RAG. If you prioritize answering complex, multi-hop questions with auditable, explainable risk citations for high-stakes M&A due diligence or regulatory compliance, choose GraphRAG. Consider a hybrid architecture when you need both speed for simple lookups and relational reasoning for deep analysis.

GraphRAG excels at answering multi-hop questions across contract portfolios because it explicitly models relationships between entities, clauses, and obligations. In a benchmark of 500 M&A due diligence contracts, GraphRAG achieved 94% accuracy on questions like 'Which supplier agreements contain uncapped liability that survives termination?' compared to 78% for standard vector RAG. This is because GraphRAG traverses Contract -> Clause -> Liability_Cap -> Survival_Period edges rather than relying solely on semantic similarity, which often retrieves semantically related but logically irrelevant clauses.

Vector RAG takes a fundamentally different approach by encoding entire clause chunks into dense embeddings and retrieving them via cosine similarity. This results in significantly lower infrastructure complexity—deploying a production Vector RAG pipeline with Pinecone or Weaviate typically requires 2-3 services, while GraphRAG demands a graph database (Neo4j or Amazon Neptune), an embedding model, and a graph construction pipeline. For straightforward clause retrieval tasks like 'Find all indemnification clauses,' Vector RAG achieves 96% recall with sub-200ms latency, making it the pragmatic choice for high-volume, single-hop queries.

The key trade-off: If your priority is answering complex, multi-hop questions that require reasoning across contract relationships—such as 'Which contracts expose us to GDPR fines exceeding €10M?'—choose GraphRAG. The entity resolution and relationship traversal capabilities directly map to how lawyers analyze risk. If you prioritize deployment speed, lower operational cost, and high recall on single-clause retrieval, choose Vector RAG. For most enterprise legal teams, the optimal architecture is a hybrid system: Vector RAG for initial clause retrieval and GraphRAG for relationship-aware risk scoring and explainable citations.