Moving Beyond Vector Search: Graph RAG – Part 2

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In Part-1 of this series, I walked through building a Graph database from scratch, comparing traditional and LangChain-powered approaches to create knowledge graphs. I had a Neo4j database filled with entities and relationships, all extracted by GPT-4o running on Azure AI Foundry. But here’s the thing about Graph RAG – building the graph is just the beginning.

The real challenge? Getting meaningful answers out of it.

This is where many Graph RAG implementations hit a wall. You can have the most perfectly structured knowledge graph in the world, but if your retrieval system can’t navigate it intelligently, you’re back to square one. In this second part, I’ll share what I learned trying to make my Graph RAG system actually answer questions – the experiments, the failures, and the harsh business realities that explain why traditional RAG still dominates.

The Retrieval Challenge: From Graphs to Answers

Unlike traditional RAG where you search for similar vectors and call it a day, Graph RAG retrieval is fundamentally about path-finding and relationship traversal. The question isn’t just “what information is relevant?” but “how do I navigate the graph to find connected information that provides a complete answer?”

This creates a completely different set of challenges:

Notice how many more decision points Graph RAG introduces. Each one is a potential failure point that needs careful engineering.

LangChain’s GraphCypherQAChain: Promise and Reality

LangChain offers GraphCypherQAChain as the go-to solution for querying knowledge graphs. On paper, it sounds perfect – you feed it a natural language question, it generates a Cypher query, executes it against your Neo4j database, and returns an answer. Simple, right?

Here’s how I initially set it up:

The Promise:

When it works, it’s magical. The LLM generates syntactically correct Cypher, executes it, and returns contextually rich answers that traditional RAG simply cannot provide.

The Reality:

“When it works” is doing a lot of heavy lifting in that sentence.

The Cypher Generation Nightmare

Here’s what nobody tells you about LLM-generated Cypher queries: they fail. A lot. And when they fail, they fail in creative, unpredictable ways that make debugging a nightmare.

Challenge 1: Schema Awareness

The LLM doesn’t inherently know your graph schema. Even with schema injection in the prompt, it makes assumptions about node labels, relationship types, and property names that may not match your actual database.

// What GPT-4o might generate
MATCH (p:Patient)-[:HAS_CONDITION]->(c:Condition)
WHERE c.name = "diabetes"
RETURN p.name, c.symptoms

// What actually exists in your schema
MATCH (patient:Person)-[:DIAGNOSED_WITH]->(condition:MedicalCondition)
WHERE condition.condition_name = "diabetes"
RETURN patient.full_name, condition.symptom_list

Challenge 2: Query Complexity

Simple questions often require complex graph traversals. “What treatments work for conditions similar to John’s?” might need:

  • Finding John’s conditions
  • Finding similar conditions (however you define “similar”)
  • Finding treatments for those conditions
  • Potentially factoring in contraindications, dosages, patient demographics

That’s a multi-hop query with business logic embedded. GPT-4o struggles with this level of complexity, often generating queries that are either too simplistic or syntactically incorrect.

Challenge 3: Performance Considerations

LLMs have no concept of query performance. They’ll happily generate Cartesian products or missing index optimizations that bring your database to its knees.

The Business Reality: Why Graph RAG Struggles in Practice

After days of experimentation, I reached a depressing conclusion: Graph RAG has incredible potential, but the engineering overhead is huge. Here’s the harsh business reality:

Time and Cost Investment

Building a production-ready Graph RAG system isn’t a sprint – it’s a marathon. You need:

  • Schema design expertise (could be weeks of iteration)
  • Prompt engineering specialists (remains as never ending process)
  • Database performance optimization (continuous optimization)
  • Query reliability engineering (Needs extensive testing)
  • Fallback mechanisms (when graph queries fail)

Compare this to traditional RAG’s simplicity: embed documents, store in a vector database, perform similarity search, done. It’s predictable, well-understood, and proven at scale across industries.

The tooling ecosystem is mature, failure modes are documented, and performance characteristics are thoroughly understood. For most businesses, the ROI calculation is straightforward – traditional RAG delivers 80% of the value with 20% of the complexity.

When to Choose Graph RAG

Despite the challenges, Graph RAG isn’t dead. It works fine in specific scenarios:

When you’re dealing with domains where entity connections are genuinely important to understanding. e.g. financial networks where transaction patterns show insights, or knowledge bases where multi-hop reasoning is essential. Trying to force Graph RAG onto unstructured text is not a good choice.

Most importantly, you need both domain expertise for schema design and substantial engineering resources for ongoing optimization. Graph RAG isn’t a “set it and forget it” solution, it requires continuous tuning and maintenance.

Advanced Patterns: The Future of Graph RAG

The most promising Graph RAG theoretical approaches I’ve seen don’t try to solve everything with pure graph traversal. Instead, they combine hybrid retrieval – using vector search for initial relevance, then graph traversal for relationship discovery. Others take a progressive enhancement approach, starting with traditional RAG and gradually adding graph capabilities as needs become clearer.

The most ambitious implementations use LLM-assisted schema evolution, where AI helps optimize the graph structure over time based on usage patterns.

But each of these patterns requires significant additional investment in engineering and infrastructure – they’re solutions for organizations with deep pockets and specific needs, not general recommendations.

Conclusion and The Reality Check

After building both ends from scratch, my decision boils down to pragmatic fundamental. Start simple – always begin with traditional RAG and understand its limitations before adding graph complexity. Many problems that seem to require Graph RAG can be solved with better prompt engineering in traditional systems.

The technology adoption isn’t just about capability – it’s about practical implementation, cost-effectiveness, and engineering pragmatism.

We’re probably currently in the early adopter phase, which explains the limited investment and adoption. The gap between Graph RAG’s potential and practical implementation challenges remains substantial. While Azure AI Foundry and similar platforms provide reliable infrastructure for LLM components, the graph-specific challenges remain largely unsolved at the framework level.

Until the ecosystem matures and complexity reduces, the most successful AI implementations will continue choosing the right tool for the specific problem rather than chasing the newest approach. The graph will have its day – just maybe not today, for most of us.

Reference

My sample Graph RAG Experiment Implementation – https://github.com/bhushang19/graph-rag-langchain

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