AI & Development
Knowledge graphs represent relationships between entities explicitly, enabling AI to reason about connections that vector search misses.
Standard retrieval-augmented generation retrieves chunks of text that are semantically similar to the query and injects them into the prompt. This works well for many question-answering tasks where the relevant information is self-contained within a few passages. But it fails on questions that require understanding how multiple pieces of information relate to each other - questions about indirect relationships, multi-hop reasoning across documents, or the structural connections between entities. Knowledge graphs address this failure mode by representing information as explicit relationships, not just text.
A knowledge graph is a structured representation of information as a network of entities and the relationships between them. In a knowledge graph, "Apple" is an entity; "Steve Jobs" is an entity; "co-founded" is the relationship between them. Unlike a vector index - where information is stored as high-dimensional vectors and retrieved by numerical similarity - a knowledge graph allows traversal: starting from one entity and following relationships to reach connected entities, even through multiple hops.
This traversal capability is what enables knowledge graphs to answer questions that require multi-hop reasoning. "Who worked at companies that Steve Jobs co-founded?" is answerable by starting at Steve Jobs, following "co-founded" edges to reach Apple and NeXT, and then following "worked at" edges back-referenced from other entities to reach the employees. A vector index cannot answer this question with a single retrieval - it would need to retrieve multiple chunks and reason across them, which is unreliable for complex relationship queries.
GraphRAG - first formalized by Microsoft Research - is the pattern of using a knowledge graph for retrieval rather than or in addition to a vector index. In GraphRAG, documents are parsed to extract entities and relationships, which are stored in a graph database. At query time, entities mentioned in the query are identified, relevant subgraphs are retrieved, and those subgraphs are provided as context to the LLM alongside or instead of retrieved text chunks.
The subgraph context provides the model with explicit relationship information - "Company A's CEO is Person B, who previously worked at Company C, which was acquired by Company D in year X" - rather than requiring the model to infer relationships from co-occurrence in text. For queries that require reasoning about organizational relationships, supply chains, citation networks, or any domain with rich entity relationships, this explicit context dramatically improves answer quality.
GraphRAG provides the most benefit for question types that require multi-hop reasoning: "How are entity A and entity B connected?", "What impact did event X have on organization Y through its downstream effects?", "Which entities satisfy multiple relationship criteria simultaneously?" These questions require traversing a network of relationships, which GraphRAG can do efficiently and standard RAG cannot do reliably.
For questions that can be answered from self-contained text passages - "What is the definition of X?", "What happened in the meeting on date Y?", "What are the steps to accomplish Z?" - standard RAG is sufficient and less complex to implement. GraphRAG adds implementation and operational complexity (graph database, entity extraction pipeline, graph traversal query logic) that is only justified when the relationship-reasoning use cases are important to your application.
Building a GraphRAG system requires an entity extraction step (using an LLM or NER model to extract entities and relationships from source documents), a graph database (Neo4j, Amazon Neptune, or a property graph layer on top of your existing database), and a query translation step (converting natural language queries into graph traversal queries that retrieve the relevant subgraph). Microsoft's GraphRAG open-source library provides a reference implementation, and LangChain and LlamaIndex both include GraphRAG components. The implementation investment is substantial compared to standard RAG - budget accordingly.