Vector RAG vs Graph RAG

Vector RAG

How it works: Documents are chunked, embedded into vector space, and indexed. At query time, the query is embedded and semantically similar chunks are retrieved via cosine similarity.

Strengths:

• Simple to implement and maintain
• Works well for “find content like this” queries
• No schema or ontology required
• Scales easily to large document collections

Limitations:

• Finds semantically similar content, not related content
• Can’t traverse relationships between entities
• Retrieves redundant chunks when topics span multiple passages
• Limited reasoning over connections

Graph RAG

How it works: Content is structured as a knowledge graph with explicit entities and relationships. Retrieval involves identifying relevant subgraphs and traversing connections.

Strengths:

• Explicit relationship modeling enables multi-hop reasoning
• Reduces redundancy by consolidating entity information
• Supports questions about connections, not just similarity
• More interpretable retrieval paths

Limitations:

• Requires schema or ontology to construct
• Higher implementation complexity
• Maintenance burden as content changes
• Requires chunks to be integrated, structure alone isn’t enough

When Each Applies

Use Vector RAG when:

• You need fast deployment with minimal setup
• Queries are primarily “find information about X”
• Document relationships aren’t critical to answers
• The corpus changes frequently

Use Graph RAG when:

• Answers require connecting multiple concepts
• Domain has well-defined entity types and relationships
• Questions involve reasoning over connections
• Accuracy matters more than implementation speed

Consider hybrid approaches when:

• You need both discovery (vector) and reasoning (graph)
• Some questions require relationship traversal, others don’t
• You can afford the implementation complexity

The Takeaway

Vector search finds what’s similar. Graph traversal finds what’s related. These are different capabilities, and the choice depends on what your questions actually require.

Related: 07-molecule—hybrid-knowledge-elicitation, 06-atom—similarity-vs-relationship, 07-molecule—rag-core-tradeoffs