GraphRAG
Definition
GraphRAG, developed at Microsoft Research and open-sourced in 2024, augments standard RAG by representing document knowledge as a graph of entities (nodes) and relationships (edges). Instead of retrieving individual text chunks by embedding similarity, GraphRAG traverses the knowledge graph to find chains of connected information relevant to a query. For a question like 'Which team leads the product responsible for feature X?', GraphRAG can follow edges: feature X → owned by product Y → managed by team Z, answering through multi-hop traversal that pure vector retrieval cannot accomplish in a single step. GraphRAG also supports community detection to produce hierarchical summaries of document clusters.
Why It Matters
Many enterprise and support queries require multi-hop reasoning across related but separate facts. A customer asking 'What SLA applies to the API tier I'm on?' requires connecting their subscription level to a specific plan definition to the applicable SLA terms—three separate documents. Standard RAG retrieves the most similar chunk to the query but may miss the connecting documents. GraphRAG explicitly models these relationships, making it powerful for knowledge-intensive domains with rich interconnections. For 99helpers enterprise deployments, GraphRAG enables complex queries about integrations, dependencies, and hierarchical configurations that stumped simpler RAG approaches.
How It Works
GraphRAG construction involves three phases: entity extraction (using an LLM to identify entities and relationships from each document chunk), graph construction (building a knowledge graph from extracted entity-relation-entity triples), and community detection (clustering related entities into hierarchical summaries). At query time, the system identifies query-relevant entities, retrieves their local graph neighborhood, and optionally traverses multi-hop paths. Microsoft's GraphRAG implementation generates both local search (entity-specific retrieval) and global search (community summary-based retrieval) modes. Open-source implementations are available via the graphrag Python package.
Graph RAG — Entity Relationship Traversal
Knowledge Graph
Graph Traversal
Flat Chunk Retrieval
Real-World Example
A 99helpers enterprise customer asks: 'Does our current plan include access to the webhook integration and what rate limits apply?' Standard RAG retrieves a generic webhooks page. GraphRAG has extracted entities: [customer plan] -[includes]-> [Webhooks feature] -[governed by]-> [Rate Limit Policy v2] -[specifies]-> [1000 requests/hour]. By traversing this chain, the system retrieves the specific rate limit policy relevant to the customer's plan, providing a complete, accurate answer without requiring the query to semantically match every intermediate document.
Common Mistakes
- ✕Assuming GraphRAG always outperforms standard RAG—for simple, factual queries, the added complexity of graph traversal adds latency without benefit.
- ✕Underestimating the cost of graph construction—LLM-based entity extraction over a large corpus is computationally expensive and must be rerun when documents change.
- ✕Using GraphRAG without evaluating entity extraction quality—noisy or incomplete graph construction degrades multi-hop reasoning.
Related Terms
Knowledge Graph RAG
Knowledge Graph RAG enhances retrieval by indexing document knowledge as a structured graph of entities and relationships, enabling precise lookup of specific facts and multi-hop traversal across connected information.
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.
Agentic RAG
Agentic RAG extends basic RAG with autonomous planning and multi-step reasoning, where the AI agent decides which sources to query, in what order, and whether additional retrieval steps are needed before generating a final answer.
Multi-Query Retrieval
Multi-query retrieval generates multiple alternative phrasings of the user's question and retrieves documents for each phrasing separately, then merges results to achieve higher recall than any single query formulation would provide.
RAG Fusion
RAG Fusion is a retrieval technique that generates multiple query variations, retrieves documents for each, and uses Reciprocal Rank Fusion (RRF) to merge the ranked result lists, improving overall retrieval coverage and quality.
Ready to build your AI chatbot?
Put these concepts into practice with 99helpers — no code required.
Start free trial →