GraphRAG vs Vector Search
for Enterprise LLMs

Every enterprise building AI agents today faces the same foundational question: how does your agent access knowledge? The default answer for the past two years has been vector search, embed your documents, store them in a vector database, and retrieve the top-K most similar chunks. It works for simple queries. It fails catastrophically for complex enterprise reasoning.

This article is a technical comparison of vector search and GraphRAG for enterprise deployments, with clear guidance on when each approach applies and why most enterprises need both.

Vector Search: How It Works

Vector search (the foundation of traditional RAG) works by converting text into high-dimensional vectors using embedding models, then retrieving the most semantically similar vectors to a query:

• Chunking: documents split into fixed-size chunks (512–2048 tokens)
• Embedding: each chunk converted to a vector (1536–3072 dimensions)
• Indexing: vectors stored in a database (Pinecone, Weaviate, Qdrant, pgvector)
• Retrieval: query embedded, top-K most similar chunks retrieved via cosine similarity
• Generation: retrieved chunks passed as context to the LLM for answer generation

Where Vector Search Excels

• Simple factual lookups ("What is our refund policy?")
• Document search and summarization
• FAQ-style question answering
• Low-latency retrieval at scale (millisecond queries over millions of documents)

Where Vector Search Fails

Vector search retrieves based on similarity, not reasoning. It cannot:

• Connect information across documents: "Which regulation applies to this product in this jurisdiction?" requires traversing relationships between regulations, products, and jurisdictions
• Follow causal chains: "What caused the risk score increase for this portfolio?" requires reasoning through a sequence of events, not finding similar text
• Resolve contradictions: when two documents say different things, vector search returns both without knowing which is authoritative
• Maintain context hierarchies: a clause in a contract inherits meaning from its section, which inherits from the document, which exists within a regulatory framework. Vector search flattens this to chunks.

GraphRAG: How It Works

GraphRAG combines knowledge graphs with retrieval-augmented generation. Instead of embedding flat chunks, it builds a structured knowledge graph of entities and relationships, then traverses that graph to answer queries:

• Entity extraction: identify people, organizations, products, regulations, concepts from documents
• Relationship mapping: connect entities (Product A is governed by Regulation B, Person X approved Decision Y)
• Graph storage: entities and relationships stored in a graph database (Neo4j, Amazon Neptune)
• Multi-hop retrieval: queries traverse the graph across multiple relationships to find connected information
• Context-enriched generation: the LLM receives structured, relationship-aware context, not flat text chunks

Where GraphRAG Excels

• Multi-hop reasoning: "Find all high-risk AI systems that process financial data under both EU AI Act and SR 11-7" requires traversing: systems → risk classification → data types → applicable regulations
• Cross-document synthesis: connecting information from compliance manuals, technical specs, and regulatory texts into a coherent answer
• Temporal reasoning: understanding that Policy v2.1 supersedes Policy v2.0, and knowing which version was active on a specific date
• Provenance tracking: every answer traces back through the graph to specific source documents and relationships

Head-to-Head Comparison

Here is how the two approaches compare on enterprise-critical dimensions:

Why Enterprises Need Both (The Hybrid Pattern)

The reality is that most enterprise deployments need both approaches working together:

• Vector search for high-volume, simple lookups (customer support, document retrieval, search)
• GraphRAG for complex reasoning tasks (compliance checks, risk analysis, cross-document synthesis)
• Routing layer that classifies incoming queries and directs them to the appropriate retrieval system

At Dioval Group, this hybrid pattern is the foundation of our Enterprise Knowledge Fabric architecture. The routing layer adds ~20ms of overhead but ensures every query hits the retrieval system that can actually answer it correctly.

If your AI agents are making decisions based on knowledge scattered across hundreds of documents, vector search alone is a ticking time bomb. The first time an agent misses a critical cross-reference, the cost of implementing GraphRAG will seem trivial. Talk to our knowledge architecture team →