Knowledge Graphs + LLMs: The GraphRAG Architecture That Beats Pure RAG (2026)

Q: What is GraphRAG and how does it differ from standard RAG?

Standard RAG retrieves text chunks from a vector store based on semantic similarity to the query. GraphRAG augments this with a knowledge graph layer that encodes explicit relationships between entities — people, products, regulations, contracts, and the connections between them. Where standard RAG finds similar text, GraphRAG traverses structured relationships. This makes GraphRAG far more capable for queries that require multi-hop reasoning: "which of our suppliers depends on this sanctioned chip manufacturer?" cannot be answered by finding similar text chunks — it requires traversing a supply chain dependency graph.

Q: When should I use a knowledge graph instead of just vector search?

Use a knowledge graph when your knowledge domain has significant relational structure that matters for answering queries. Strong signals include: queries that require multi-hop reasoning across three or more entities; domains with typed relationships (contracts reference regulations, vendors supply products, employees report to managers); compliance and audit requirements where every claim must be traceable to a sourced fact; and corpora where the relationship between entities is as important as the entities themselves — contracts, clinical records, regulatory frameworks, org charts, and supply chains. For semantic Q&A over unstructured documents without significant relational structure, pure vector RAG is often sufficient.

Q: How do you build a knowledge graph from existing enterprise documents?

The extraction pipeline has four stages: named entity recognition (NER) to identify entities and their types, relationship extraction to identify typed connections between co-occurring entities, entity resolution to merge different mentions of the same real-world entity into canonical graph nodes, and confidence scoring with provenance tracking to record where each fact came from. In 2026, LLMs with structured output schemas are the most effective tool for both NER and relationship extraction — they outperform fine-tuned BERT-style models on recall, especially for domain-specific entity types. Entity resolution remains the most challenging stage and typically requires a combination of string similarity, embedding-based matching, and manual review of low-confidence merges.

Q: What graph database should I use for a GraphRAG system?

Neo4j is the production default for most enterprise GraphRAG deployments — it has the most mature Cypher query language, strong Python integration via the neo4j driver and langchain-neo4j, and proven scalability to billions of nodes. Amazon Neptune is the preferred choice when deep AWS integration and managed infrastructure are priorities. Memgraph is a strong alternative when query performance at scale is critical, as it keeps the entire graph in memory. PostgreSQL with the AGE extension is a pragmatic starting point for teams already running Postgres who want to pilot graph retrieval without adding a new infrastructure component. TigerGraph serves specialised use cases requiring real-time graph analytics at very large scale.

Q: What is Text-to-Cypher and how reliable is it in production?

Text-to-Cypher is the technique of using an LLM to translate natural language queries into Cypher (Neo4j's graph query language) for graph traversal. It is analogous to Text-to-SQL for relational databases. In production, well-implemented Text-to-Cypher achieves 85–92% first-pass accuracy on domain-specific queries when the graph schema is well-documented and injected into the generation prompt. The remaining queries — typically involving ambiguous entity references or complex multi-hop patterns — require a correction loop where the initial query result is evaluated and the Cypher query is refined. Three safeguards are required in production: schema injection (provide the full schema to the LLM), query validation (parse generated Cypher before executing it), and execution sandboxing (enforce LIMIT clauses and query timeouts to prevent runaway traversals).

Q: How does Microsoft's GraphRAG approach work?

Microsoft Research's GraphRAG approach, published in 2024, introduces a two-level community-based retrieval architecture. The knowledge graph is first partitioned into communities — clusters of densely connected nodes — using the Leiden community detection algorithm. Each community is then summarised into a natural language description by an LLM. At query time, the most relevant community summaries are retrieved via vector similarity over those summaries, and then refined with targeted graph traversals within the identified communities. This two-level architecture enables GraphRAG to answer questions whose scope spans the entire corpus — not just the top-k most similar chunks — making it particularly powerful for discovery and synthesis queries like "what are the main themes in our contract portfolio that relate to force majeure?"

Q: What are the main failure modes of knowledge graph systems in production?

The five most common production failure modes are: incomplete extraction (the graph misses entities or relationships because the extraction model was not tuned for the domain vocabulary — addressed by domain-specific few-shot examples); entity resolution failures (the same real-world entity exists as multiple disconnected nodes — addressed by embedding-based resolution with human review); schema drift (the graph schema designed upfront does not match the actual structure of the documents ingested — addressed by iterative schema refinement with domain experts before full ingestion); stale graph (the underlying documents have changed but the graph was not updated — addressed by automated change detection pipelines); and Text-to-Cypher failures on edge case queries — addressed by a vector search fallback for queries the graph layer cannot resolve.

Q: What is the business case for investing in knowledge graph infrastructure?

Four outcomes are consistently documented in enterprise production deployments. Hallucination reduction: GraphRAG reduces structured-fact hallucination rates by 60–80% compared to pure RAG, because relationships are retrieved from ground truth rather than inferred from similar text. Multi-hop query accuracy: for queries requiring three or more reasoning hops, GraphRAG achieves 75–88% accuracy versus 30–45% for pure RAG. Audit readiness: every GraphRAG response carries a complete citation trail — which graph nodes and edges were traversed, which source documents they came from — which is a regulatory prerequisite in financial services, healthcare, and legal contexts. Time compression on complex analytical queries: queries that previously required a data analyst days to answer can be answered in seconds by non-technical users through the GraphRAG interface.