Vector RAG vs GraphRAG: Retrieval for Policy and Case Law

Vector RAG excels at rapid, semantic similarity search across vast corpora of policy documents and case law because it maps text into a high-dimensional embedding space. For example, a standard text-embedding-3-large model can retrieve conceptually relevant passages in under 100ms, making it ideal for broad, single-hop queries like 'find all policies related to renewable energy permits.' However, this approach often fails on multi-hop reasoning tasks, such as tracing a specific legal precedent through multiple amending statutes, because it treats documents as isolated chunks rather than an interconnected knowledge graph.

GraphRAG takes a fundamentally different approach by constructing a knowledge graph that explicitly models entities (e.g., specific statutes, court cases, government agencies) and their relationships (e.g., 'cites,' 'amends,' 'overrules'). This results in superior performance on complex, multi-hop questions that require connecting disparate legal facts. The trade-off is a significantly higher indexing latency and infrastructure cost; building and maintaining a graph from millions of legal documents is a continuous, compute-intensive process compared to the simpler chunk-and-embed pipeline of Vector RAG.

The key trade-off: If your priority is low-latency, cost-effective retrieval for broad semantic search across a static policy library, choose Vector RAG. If you prioritize high-accuracy, multi-hop reasoning and verifiable citation chains for complex legal arguments, choose GraphRAG. For many government agencies, a hybrid approach—using Vector RAG for initial candidate retrieval and GraphRAG for evidence validation and chain-of-thought grounding—provides the most robust architecture for automated decision-making transparency.

Vector RAG excels at semantic similarity and rapid, broad retrieval across massive document corpora because it maps text to a high-dimensional embedding space. For example, in a standard legal benchmark, a vector-only approach can achieve over 90% recall on straightforward fact-finding questions, retrieving the correct statute from a corpus of millions of documents in under 100 milliseconds. This makes it the superior choice for first-pass retrieval and simple 'look-up' tasks where the answer is explicitly stated in a single, dense passage.

GraphRAG takes a fundamentally different approach by constructing a knowledge graph of entities (e.g., legal parties, statutes, precedents) and their relationships. This architecture is purpose-built for multi-hop reasoning, where an answer requires synthesizing information across multiple documents. In complex policy queries, GraphRAG has demonstrated a 20-30% improvement in answer faithfulness over naive Vector RAG by explicitly traversing citation links and hierarchical relationships, ensuring that the final explanation is logically consistent and grounded in a chain of authority.

The key trade-off lies in complexity versus precision for multi-step logic. Vector RAG is simpler to implement, faster, and cheaper, making it ideal for high-volume, low-complexity tasks like finding a specific policy clause. GraphRAG, however, is the superior architecture when the primary requirement is citation integrity and logical traceability across interconnected case law. The latency and engineering overhead of building and maintaining a knowledge graph are significant, but for high-stakes decisions where a hallucinated or incomplete legal rationale is unacceptable, this cost is justified.

Consider Vector RAG if you need a cost-effective, low-latency system for semantic search over a large, static policy library where questions are direct. Choose GraphRAG when your use case demands defensible, multi-hop reasoning for complex legal questions, and you must provide a verifiable chain of citations to withstand judicial or public scrutiny. For many government agencies, a hybrid approach—using Vector RAG for initial candidate retrieval and GraphRAG for final reasoning and explanation generation—offers the optimal balance of speed, cost, and accuracy.