Knowledge Graph RAG
Definition
Knowledge Graph RAG builds a structured knowledge representation alongside (or instead of) a vector index. During indexing, an LLM extracts named entities (products, features, people, organizations) and the relationships between them from documents, building a graph database where nodes are entities and edges are typed relationships (e.g., 'ProductX -[supports]-> IntegrationY', 'PlanA -[includes]-> FeatureB'). At query time, the system performs entity recognition on the query, retrieves the relevant subgraph, and uses graph traversal to gather multi-hop facts. The retrieved graph facts are then passed to the LLM alongside (or instead of) traditional text chunks. This combines the structured precision of knowledge graphs with the natural language understanding of LLMs.
Why It Matters
Structured facts that live in knowledge graphs are notoriously hard for vector search to retrieve reliably. The query 'Does the Starter plan include the analytics dashboard?' requires matching 'Starter plan' and 'analytics dashboard' as specific entities and checking their relationship. Vector search might retrieve a general plans comparison page that doesn't directly answer the question. Knowledge graph RAG traverses: Starter Plan -[includes]-> [features list] and checks whether Analytics Dashboard appears, providing a definitive yes/no answer with high confidence. For 99helpers customers with product plans, features, and integrations that change frequently, Knowledge Graph RAG provides precise, verifiable answers to structured questions.
How It Works
Knowledge Graph RAG implementation requires: (1) entity and relation extraction during indexing—using an LLM to identify entities and relationships from each chunk; (2) graph storage—Neo4j, AWS Neptune, or a simpler property graph; (3) hybrid query strategy—for structured queries (entity lookup, relationship traversal), use graph queries (Cypher, SPARQL); for semantic queries, use vector retrieval; for complex queries, use both. LlamaIndex's KnowledgeGraphIndex and Microsoft's GraphRAG provide frameworks for this. The main challenge is entity extraction quality—hallucinated or wrong entities in the graph degrade retrieval accuracy.
KG-RAG — Dual Retrieval: Structured + Unstructured
Entity Extraction
Knowledge Graph Lookup
Vector Search
Combined Context
KG facts + document chunks merged
LLM generates answer
Grounded in both structured + unstructured context
Real-World Example
A 99helpers knowledge base is indexed as both a vector store and a knowledge graph. When a user asks 'What's the API rate limit for the Pro plan?', the system identifies entities 'Pro plan' and 'API rate limit' and queries the knowledge graph: (Pro Plan) -[has_feature]-> (API Access) -[governed_by]-> (Rate Limit Policy) -[specifies]-> '10,000 requests/hour'. The graph traversal produces a precise, verifiable answer in milliseconds. The same query via vector search retrieves a general API documentation page that mentions rate limits but requires the LLM to extract the specific value, introducing a possible hallucination risk.
Common Mistakes
- ✕Expecting perfect entity extraction from LLMs—errors in the extraction phase propagate to incorrect graph facts and wrong answers.
- ✕Using knowledge graphs for unstructured, narrative content where relationships are not well-defined—knowledge graphs excel for structured domains.
- ✕Maintaining the knowledge graph manually—automated extraction pipelines that keep the graph in sync with document updates are essential.
Related Terms
GraphRAG
GraphRAG combines retrieval-augmented generation with knowledge graph structures, enabling multi-hop reasoning across connected entities and relationships rather than retrieving isolated text chunks.
Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is an AI architecture that enhances large language model responses by first retrieving relevant documents from an external knowledge base and then using that retrieved content as context when generating an answer.
Metadata Filtering
Metadata filtering restricts vector search to a subset of documents based on structured attributes — such as category, date, language, or source — enabling more precise retrieval by pre-filtering the candidate pool before similarity search.
Structured Data
Structured data is information organized in a predefined format with clear fields and types — such as tables, spreadsheets, JSON, or database records. In a knowledge base context, structured data enables precise, queryable information retrieval that complements unstructured text content.
Knowledge Retrieval
Knowledge retrieval is the process of finding and extracting relevant information from a knowledge base in response to a user's question or query, using search algorithms, filters, or AI to match queries to content.
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