GraphRAG: Moving beyond semantic similarity to enable true multi-hop reasoning

Conventional Retrieval-Augmented Generation (RAG) pipelines have a fundamental limitation: they assume semantic similarity translates directly to relevance. This works fine for straightforward queries, but falls apart when users ask complex questions requiring connections across multiple concepts—the kind of reasoning that enterprise systems demand.

Where standard RAG breaks down#

Traditional RAG systems chunk documents into fixed-size blocks, embed them into vectors, and retrieve results by searching for semantic closeness to a query. The problem emerges immediately with multi-hop reasoning. Consider a dataset of contracts containing two separate chunks:

• Chunk 1: "Acme Corp acquired BetaTech in 2022."
• Chunk 2: "Sarah Connor was appointed CEO of BetaTech in 2023."

When asked "Who leads the company that Acme Corp acquired?", a vector search will find entries related to either "Acme Corp" or "leadership," but likely won't surface both pieces of information together. The chunks lack explicit structural relationships—they're semantically disconnected even though logically they're interconnected.

Standard RAG also stumbles on global summarization tasks, such as aggregating risk factors across multiple compliance documents.

The GraphRAG alternative#

GraphRAG augments traditional retrieval with knowledge graphs, adding explicit structure to your data. During document ingestion, an LLM extracts entities (nodes) and their relationships (edges), building a graph that captures real-world connections. Queries then operate in two stages: first, vector search identifies a semantic entry point; second, graph traversal follows relationship edges to gather all relevant context.

For the multi-hop example above, GraphRAG understands that Acme Corp has an ACQUIRED relationship to BetaTech, and Sarah Connor has a LEADS relationship to BetaTech. When asked the same question, the system retrieves the complete, connected subgraph needed for accurate reasoning.

Building a GraphRAG pipeline in Python#

Implementing GraphRAG involves several key steps:

Dependencies and connections

Start with the Neo4j driver and libraries for graph operations, LLM integration, and embeddings: