What is GraphRAG and when should you use it over vector RAG?

Vector similarity search is fundamentally local. It takes a query, computes its mathematical embedding vector, and finds the closest text chunks in high-dimensional space. This excels at direct factual lookups: "What is the deductible on Policy Plan B?"

However, vector RAG fails completely on global sensemaking questions. If an analyst asks "What are the common financial schemes used by the defendants across these 20,000 pages of legal transcripts?", vector search looks for chunks containing words like "common financial schemes". Because no single chunk contains the complete synthesis, the vector search returns irrelevant fragments, and the LLM produces a partial or hallucinated answer.

GraphRAG was engineered specifically to solve this blind spot. Developed initially by researchers at Microsoft, it bridges the gap between unstructured text and structured relational intelligence.

Unlike traditional RAG, which only indexes raw text chunks, GraphRAG performs intensive offline extraction during the ingestion phase to build a structured representation of the entire dataset.

• Entity & Relationship Extraction: An LLM analyzes source text chunks to extract named entities (people, companies, locations, concepts) and the specific relationships connecting them.
• Knowledge Graph Construction: Entities form graph nodes, and relationships form directed edges, annotated with descriptions and source document citations.
• Community Detection: Algorithms like the Leiden algorithm partition the graph into hierarchical clusters of densely connected entities (communities).
• Community Summarization: An LLM generates pre-computed executive summaries for each community at macro, meso, and micro abstraction levels.
• Global Query Routing: When a user asks a broad thematic question, GraphRAG queries the community summaries in parallel, generating a comprehensive, evidence-backed synthesis.

A widespread belief is that implementing GraphRAG requires purchasing and managing an enterprise graph database like Neo4j, Amazon Neptune, or TigerGraph. While dedicated graph engines excel at deep multi-hop traversals over billions of edges, they are often unnecessary for early-to-mid stage GraphRAG implementations.

You can implement production GraphRAG using standard relational databases like PostgreSQL. By storing entities in an `entities` table and connections in an `edges` table, you can easily perform multi-hop traversals using SQL recursive Common Table Expressions (CTEs) or specialized extensions like Apache AGE.

Many lightweight implementations store the pre-computed community summaries in standard key-value stores or vector tables, completely avoiding active graph traversal at query time.

Because GraphRAG requires significant upfront indexing compute (using LLMs to extract entities from every chunk), it should be chosen deliberately based on your problem domain rather than applied as a generic replacement for search.

Choose GraphRAG when your users need to connect disparate clues across a large corpus: fraud investigations, corporate acquisitions, intelligence analysis, competitive research, and complex medical or legal discovery.

If your workload is primarily answering factual questions from user manuals, customer support FAQs, or software API documentation, traditional hybrid search (combining BM25 and vector embeddings) is faster, cheaper, and more than sufficient.

• Assess whether your users ask "needle in a haystack" questions (use Vector RAG) or "shape of the haystack" questions (use GraphRAG)
• Calculate upfront extraction costs: run entity extraction on a small 100-page sample before indexing your entire archive
• Consider hybrid GraphRAG: use vector search to locate initial anchor entities, then traverse graph edges for surrounding context