How to build a GraphRAG application: a practical implementation guide

Building a GraphRAG system requires shifting your mental model from simple vector indexing to automated knowledge engineering. Unlike traditional RAG, which processes documents independently, GraphRAG maps the connections between all documents in your collection.

A complete GraphRAG implementation consists of two distinct operational phases: the Offline Graph Extraction & Clustering Pipeline, and the Online Retrieval & Synthesis Engine.

During the offline phase, your system reads source text, extracts entities and relationships, builds a graph network, and generates hierarchical community summaries. During the online phase, the system uses this pre-computed graph to answer both local entity lookups and global thematic questions.

The foundation of GraphRAG is accurate entity and relationship extraction. You split your raw text into chunks (typically 600 to 1,000 tokens) and pass each chunk to an LLM with a specialized extraction prompt.

The prompt instructs the model to identify named entities (e.g. `Organization`, `Person`, `Location`, `Technology`, `Event`) and the specific directed relationships connecting them (e.g. `ACQUIRED`, `INVESTIGATED`, `DEPENDS_ON`).

Crucially, a single extraction pass often misses subtle relationships. Production systems implement "gleaning passes": asking the model a follow-up question ("Are there any additional entities or relationships in this text that you missed?") until the model returns an empty list. This increases relationship yield by up to 35%.

Once nodes and edges are extracted from all documents, the raw graph typically contains thousands of interconnected points. Querying this massive graph directly at inference time would overwhelm model context windows.

GraphRAG solves this by applying community detection algorithms — specifically the Leiden algorithm — to partition the graph into a hierarchy of clusters. Closely related entities are grouped into Level-0 micro-communities, which roll up into Level-1 meso-communities, and finally Level-2 macro-communities.

For each detected community, an LLM generates a pre-computed executive summary: describing the overarching narrative, key actors, controversial claims, and unresolved questions within that cluster. These community summaries are what allow GraphRAG to answer global questions ("What were the main regulatory controversies across all portfolio companies?") in seconds.

When a user submits a query to a GraphRAG system, the engine determines whether the prompt is a Local Search or a Global Search.

Local Search is used for specific entity questions ("What patents did Dr. Vance file in 2024?"). The engine identifies the anchor entity node, retrieves its immediate neighboring nodes and incident edges, and passes that sub-graph into the LLM context.

Global Search is used for broad thematic questions ("What are the recurring failure modes in our rocket launches?"). The engine queries the pre-computed community summaries in parallel, aggregates their insights, and synthesizes a comprehensive report complete with entity citations.

• Use gleaning passes during extraction to maximize relationship discovery
• Pre-compute community summaries so global questions do not require real-time graph traversal
• Index community summaries with vector embeddings for fast semantic routing
• Implement caching for frequent entity neighborhoods to minimize database latency