RAG Is Not Enough

Retrieval-Augmented Generation (RAG) has become the default pattern for grounding LLMs in private data. And for good reason — it works. But it has a ceiling.

Where RAG Excels

RAG is excellent for:

• Direct lookups: "What is the safety procedure for equipment X?"
• Document Q&A: "Summarize the key findings from this report."
• Semantic search: Finding relevant passages across large document sets.

The pattern is simple: embed documents, retrieve relevant chunks, pass them to the LLM as context.

Where RAG Falls Short

RAG struggles when the answer requires reasoning across relationships:

• "What equipment is upstream of the heat exchanger that failed last Tuesday?"
• "Which safety systems would be affected if we take pump P-101 offline?"
• "Show me all process paths that connect the wellhead to the export terminal."

These questions require understanding connections between entities — not just finding relevant text chunks.

Enter Knowledge Graphs

Knowledge graphs store data as entities and relationships:

// Neo4j Cypher query
MATCH path = (wellhead:Equipment {type: 'Wellhead'})
              -[:FEEDS*]->(export:Equipment {type: 'Export Terminal'})
RETURN path

This query traces every process path from wellhead to export — something that would require reading dozens of P&ID drawings manually.

GraphRAG: The Best of Both Worlds

The most powerful approach combines both:

1. Knowledge Graph for structured relationships and multi-hop reasoning
2. Vector Store for unstructured text and semantic search
3. LLM for natural language understanding and response generation

User Query
    ├── Entity Extraction (LLM)
    ├── Graph Traversal (Neo4j) → structured context
    ├── Vector Search (ChromaDB) → unstructured context
    └── Response Generation (LLM + both contexts)

When to Use What

The Investment

Building a knowledge graph requires more upfront work than a vector store. You need to:

• Define an ontology (what entities and relationships exist)
• Extract entities from source data
• Build and maintain the graph
• Write graph queries or build an abstraction layer

But for domains with rich interconnected data — like industrial process systems — the investment pays for itself many times over.

RAG gets you 70% of the way there. Knowledge graphs get you the rest.