Knowledge Graphs vs Vector RAG: When to Use Which in 2026

RAG is the standard pattern for grounding LLMs in private data. The default uses vector search. Knowledge graphs offer a different approach with different trade-offs.

How Vector RAG Works

Chunk documents, embed them, store in a vector database, retrieve by semantic similarity, and inject into the prompt. Strengths: simplicity, scalability, generality. Weaknesses: retrieves by similarity, not structured relationships.

How Knowledge Graph RAG Works

Entities are nodes, relationships are edges. Retrieval traverses the graph to find relevant entities and connected neighbors. Strengths: precision, explainability, multi-hop reasoning.

When to Use Vector RAG

Use when your data is unstructured, you need speed to production, and queries are semantically broad.

When to Use Knowledge Graphs

Use when your domain has well-defined entities and relationships, users ask multi-hop questions, or explainability matters.

Hybrid Approaches

The most advanced systems combine both: vector retrieval for candidates, graph for entity resolution and relationship traversal.

Tools

Vector: Pinecone, Qdrant, Weaviate, pgvector, Milvus

Graph: Neo4j, Amazon Neptune, ArangoDB

Hybrid: LangChain, LlamaIndex

Frequently Asked Questions