The Role of Knowledge Graphs in RAG Pipelines

Retrieval-Augmented Generation (RAG) has solved the "knowledge cutoff" problem for LLMs, but it introduced a new one: context fragmentation.

Standard RAG relies on vector similarity search. While great for finding semantically similar chunks of text, it fails miserably at multi-hop reasoning. If you ask, "How are the CEO of Company A and the Founder of Company B connected?", a vector database sees two separate entities. It doesn't "see" the relationship.

Enter GraphRAG

By combining vector search with a Knowledge Graph (KG), we can inject structured, symbolic relationships into the LLM's context window. This approach, often called GraphRAG, allows the system to traverse edges between entities, uncovering hidden connections that pure semantic search misses.

The Hybrid Retrieval Stack

The most robust architectures today use a hybrid approach:

• Vector Search: For broad, unstructured query understanding.
• Graph Traversal: For precise, structured entity navigation (e.g., using Cypher queries in Neo4j).
• Reciprocal Rank Fusion (RRF): To combine and re-rank results from both sources.

Reducing Hallucinations

Knowledge graphs provide hard facts. When an LLM is grounded in a KG, it is constrained by the explicit relationships defined in the graph. This significantly reduces hallucinations compared to relying solely on probabilistic token generation from retrieved text chunks.

In production systems for domains like finance and healthcare, where precision is non-negotiable, the Knowledge Graph is not just an optimization—it's a requirement.