GraphRAG in Production: Beyond Simple Vector Search
Every RAG tutorial shows the same architecture: chunk documents, embed them, store in a vector DB, retrieve by cosine similarity, feed to an LLM. It works. For simple Q&A over a single document corpus, it works well.
It fails badly when queries require multi-hop reasoning. "What projects involved both Neo4j and real-time processing, and what were their accuracy metrics?" A vector search returns documents that contain these terms. A knowledge graph traverses relationships.
This is the problem I built a solution for.
The Limitation of Pure Vector Search
Vector search answers: "what content is semantically similar to this query?"
It cannot answer: "what entities are connected through this chain of relationships?"
Consider a query like: *"Which of my research projects used federated approaches, and what privacy mechanisms did they employ?"*
A vector search will find documents mentioning "federated" and "privacy." But it won't know that these documents describe distinct projects with specific relationships to specific privacy techniques — unless those exact sentences happen to appear in the retrieved chunks.
Knowledge graphs model this explicitly. Nodes are entities (Project, Technique, Author, Metric). Edges are relationships (USES_TECHNIQUE, ACHIEVES_ACCURACY, PUBLISHED_IN).
The Architecture
The GenAI Realtime Assistant I built uses a three-layer retrieval stack:
```
Query
↓
Intent Classifier (what type of query is this?)
├── Factual lookup → Neo4j Cypher query
├── Semantic search → FAISS vector search
└── Complex reasoning → Both, then synthesis
↓
Retrieval (parallel)
↓
LangGraph synthesis agent
↓
Response
```
```python
class GraphRAGRetriever:
def __init__(self, neo4j_driver, faiss_index, embedder, llm):
self.graph = neo4j_driver
self.vector = faiss_index
self.embedder = embedder
self.llm = llm
def retrieve(self, query: str) -> dict:
# Parallel retrieval
graph_results = self._graph_search(query)
vector_results = self._vector_search(query)
# LLM-guided fusion
return self._synthesize(query, graph_results, vector_results)
def _graph_search(self, query: str) -> list:
# Extract entities from query
entities = self._extract_entities(query)
cypher = self._generate_cypher(entities)
return self.graph.execute(cypher)
def _generate_cypher(self, entities: list) -> str:
# LLM generates Cypher from extracted entities
prompt = f"Generate Cypher query for entities: {entities}"
return self.llm.predict(prompt)
```
Building the Knowledge Graph
The graph schema models the domain:
```cypher
// Nodes
CREATE (p:Project {name: "GenAI Assistant", period: "Feb-May 2025"})
CREATE (t:Technology {name: "LangChain", category: "Orchestration"})
CREATE (m:Metric {name: "Latency", value: "120ms", unit: "ms"})
// Relationships
CREATE (p)-[:USES_TECHNOLOGY]->(t)
CREATE (p)-[:ACHIEVES_METRIC]->(m)
CREATE (p)-[:SOLVES_PROBLEM {description: "Multi-hop reasoning"}]->(:Problem)
```
The graph is populated automatically from structured data (portfolio data, paper abstracts, project READMEs) using an extraction pipeline.
LangGraph for Multi-Step Reasoning
The synthesis layer uses LangGraph — a graph-based agent framework — to orchestrate retrieval and response generation:
```python
from langgraph.graph import Graph
def create_rag_graph():
graph = Graph()
graph.add_node("classifier", classify_intent)
graph.add_node("graph_retriever", retrieve_from_graph)
graph.add_node("vector_retriever", retrieve_from_vector)
graph.add_node("synthesizer", synthesize_response)
graph.add_edge("classifier", "graph_retriever")
graph.add_edge("classifier", "vector_retriever")
graph.add_edge("graph_retriever", "synthesizer")
graph.add_edge("vector_retriever", "synthesizer")
return graph.compile()
```
The graph executor runs retrieval nodes in parallel, then passes both result sets to the synthesizer. This dramatically reduces latency compared to sequential retrieval.
Results
Against a test set of 50 complex multi-hop queries:
- Pure vector RAG: 64% correctly answered
- GraphRAG hybrid: 89% correctly answered
The gap widens on queries requiring 3+ hop reasoning (domain → technique → metric → paper). Vector search essentially collapses on these.
The latency story is more nuanced: graph traversal is typically faster than vector search for known-entity queries, but the NLP pipeline for Cypher generation adds overhead. At p95, the hybrid system was ~240ms vs ~180ms for pure vector.
For production use, the accuracy gain justifies the latency cost. For simple document Q&A, pure vector is still the right tool.
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*This architecture powers the GenAI Realtime Assistant project. The SCOPUS-indexed paper covers the theoretical foundations; this post covers the implementation decisions.*