Knowledge Graphs vs. Vector RAG: When to Use What
Both retrieve context, but for very different reasons. A practical guide to choosing the right retrieval strategy for your agent architecture.
Two Approaches to Context Retrieval
When building agents that need access to large knowledge bases, you have two primary retrieval strategies:
- Vector RAG — embed documents into vector space, retrieve by semantic similarity
- Knowledge Graphs — structure information as entities and relationships, traverse by query
When to Use Vector RAG
Vector RAG excels when:
- Your knowledge base is unstructured text (documents, articles, transcripts)
- Queries are semantically fuzzy ("tell me about customer complaints related to shipping")
- You need fast setup — embedding pipelines are straightforward
- Approximate answers are acceptable
Limitations
- Struggles with multi-hop reasoning ("which customers of supplier X were affected by the recall?")
- No inherent understanding of relationships between entities
- Can retrieve semantically similar but factually irrelevant content
When to Use Knowledge Graphs
Knowledge Graphs excel when:
- Your domain has clear entity relationships (org charts, supply chains, regulatory frameworks)
- Queries require precise, structured answers ("list all medications contraindicated with drug X")
- Reasoning chains matter — you need to explain why an answer was reached
- Consistency is critical — the same query should always return the same result
Limitations
- High upfront cost — building and maintaining a knowledge graph is significant work
- Schema rigidity — adding new entity types or relationships requires schema updates
- Poor at fuzzy matching — queries must align with the graph structure
The Hybrid Approach
The most sophisticated agent architectures use both:
- Knowledge graphs for structured reasoning — entity lookups, relationship traversal, constraint checking
- Vector RAG for unstructured context — finding relevant documents, passages, and examples
- An orchestration layer that decides which retrieval strategy to use based on the query type
Practical Recommendations
- Start with Vector RAG if you're building your first agent — it's faster to implement and good enough for many use cases
- Add Knowledge Graphs when you hit the limits of semantic similarity — when users need precise answers about relationships
- Invest in the hybrid approach when your agents handle both exploratory and precise queries