Sathus AI 2.0 is now generally available — evaluation harnesses and guardrails included. Explore
The definitive engineering guide to Graph Retrieval-Augmented Generation (GraphRAG). How hybrid architectures combine dense vector embeddings (pgvector) with deterministic knowledge graphs (Neo4j/Neptune) to eliminate LLM hallucinations on complex multi-hop reasoning in healthcare, finance, and regulatory compliance.
GraphRAG resolves the fundamental flaw of naive vector RAG—its blindness to multi-hop relational reasoning, global document synthesis, and entity disambiguation—by anchoring dense vector embeddings (pgvector/Pinecone) to an explicit, typed Knowledge Graph (Neo4j/Amazon Neptune). When a user query arrives, GraphRAG performs two-stage retrieval: first, vector similarity identifies candidate text chunks and entry-point entity nodes; second, graph traversal algorithms (Cypher k-hop queries, personalized PageRank, and community summaries) extract deterministic entity relationships and factual triples. The resulting prompt injects verifiable graph triples alongside unstructured text, reducing generative LLM hallucination rates from ~38% to under 0.6% in high-stakes clinical and regulatory domains.
The definitive engineering guide to Graph Retrieval-Augmented Generation (GraphRAG). How hybrid architectures combine dense vector embeddings (pgvector) with deterministic knowledge graphs (Neo4j/Neptune) to eliminate LLM hallucinations on complex multi-hop reasoning in healthcare, finance, and regulatory compliance.
A: Use GraphRAG when queries require multi-step reasoning across documents (e.g. "What medications contraindicated for patient condition X interact with drug Y?"), or when strict factual verifiability without hallucination is mandatory.
A: We implement an event-driven incremental graph updater: when a document is updated, its associated chunk vector nodes and extracted entity edges are soft-deleted or versioned, and the Leiden community summaries for affected subgraphs are asynchronously refreshed.
Standard Vector RAG embeds unstructured text chunks (500-1000 tokens) into high-dimensional vector space. While excellent for semantic search ("Find paragraphs about diabetes symptoms"), vector similarity collapses when queries require transitive logic: • The Transitive Disconnect: Consider the query "Is Drug A contraindicated for patients with conditions treated by Drug B?". Vector search retrieves chunks mentioning Drug A and chunks mentioning Drug B, but cannot verify if intermediate condition X links them. • Entity Ambiguation: Dense vectors conflate homonyms and polysemous terms across different clinical specialties. In contrast, knowledge graphs enforce strict Uniform Resource Identifiers (URIs) anchored to standardized ontologies (SNOMED, UMLS, MeSH).
Building an enterprise knowledge graph for GraphRAG requires disciplined ontology engineering: • Schema-Constrained Information Extraction (IE): LLMs or biomedical NER models (BioBERT, GLiNER) extract (Subject, Predicate, Object) triples bounded by a strict OWL ontology schema. • Entity Resolution & Deduplication: Extracted entity strings ("Tylenol", "Acetaminophen", "APAP") are resolved to a single canonical node via embedding similarity and exact terminology crosswalks (RxNorm). • Community Detection (Leiden Algorithm): The graph is recursively clustered into hierarchical communities. Each community is summarized by an LLM, generating pre-computed global knowledge abstractions.
In production, retrieval proceeds via a coordinated dual-engine pattern: 1. Stage 1 (Vector Candidate Discovery): Query embedding is compared against text chunk embeddings using HNSW index in pgvector or Milvus, returning top-k relevant source paragraphs. 2. Stage 2 (Graph Neighborhood Traversal): Entities mentioned in top-k chunks serve as graph entry anchors. A Cypher query executes 1-hop and 2-hop traversals over typed relationships (e.g. :INTERACTS_WITH, :CONTRAINDICATED_FOR, :TARGETS_GENE). 3. Stage 3 (Prompt Context Synthesis): The prompt compiler formats both the verified graph triples and the original narrative text, instructing the LLM to ground its reasoning strictly on the asserted graph edges.
Pinpoint root cause failure modes and match observed metrics to actionable remediation.
| UI Tab / Tool | Observed Metric / Signal | Underlying Failure Mode | Actionable Remediation |
|---|---|---|---|
| Neo4j Query Profiler | Supernode graph traversal explosion (> 250,000 DB hits on single query) | Traversal hit an ultra-high-degree generic node (e.g. "Patient" or "United States"). | Filter out stop-concept nodes and cap Cypher relationship expansion with LIMIT or k<=2 depth. |
| Vector Search Precision | Low cosine similarity score (< 0.62) on chemical formulas and medical abbreviations | Off-the-shelf general text embedding model lacking specialized domain tokens. | Deploy domain-specific embeddings (PubMedBERT or BioLinkBERT) alongside BM25 hybrid search. |
| Context Token Monitor | Prompt context length exceeds 32K tokens during multi-hop graph injection | Unfiltered sub-graph dump overwhelmed LLM context window. | Implement Leiden community summarization to inject pre-aggregated community digests. |
| Ragas Evaluation Suite | Faithfulness metric drops below 0.85 on drug interaction questions | LLM generated unsupported claims outside the provided graph edges. | Enforce JSON schema constrained generation with mandatory citation triple validation. |
def hybrid_graph_rag_query(query_embedding, entity_names):
"""
Executes hybrid vector similarity + knowledge graph traversal
"""
cypher_query = """
CALL db.index.vector.queryNodes('chunk_vector_index', 5, $query_embedding)
YIELD node AS chunk, score
MATCH (chunk)-[:MENTIONS]->(e:Entity)
WHERE e.name IN $entity_names
MATCH (e)-[r:RELATION*1..2]-(connected:Entity)
RETURN
chunk.text AS text_context,
score AS similarity_score,
collect(DISTINCT {from: e.name, rel: type(r[0]), to: connected.name}) AS graph_triples
"""
return neo4j_driver.execute_query(cypher_query, {
"query_embedding": query_embedding,
"entity_names": entity_names
})Dense vector search across 50,000 clinical and regulatory guidelines suffered a 38% hallucination rate on multi-hop questions (e.g., drug-drug interaction contraindications), generating plausible but clinically dangerous recommendations.
Combined pgvector semantic indexing with a verified Neo4j knowledge graph of 450,000 biomedical entities. Factual hallucination dropped to 0.4%, multi-hop reasoning accuracy jumped to 96.8%, and every answer includes clickable graph citations on evaluation datasets.
Evaluated using Ragas evaluation framework on 500 multi-hop biomedical queries from public research datasets requiring transitive reasoning across drug-disease-gene entities. Hallucination rate represents instances where LLM generated assertions unsupported by retrieved graph triples or ground-truth abstracts.
| Retrieval Architecture | Multi-Hop Reasoning | Hallucination Rate | Cold-Start Indexing Cost | Explainability & Lineage |
|---|---|---|---|---|
| Naive Vector RAG | Poor (Single-chunk semantic proximity) | 30% - 40% in complex domains | Low (Direct chunk embedding) | Opaque (Vector distance scores) |
| Hybrid Search (Vector + BM25) | Moderate (Better keyword matching) | 20% - 30% in complex domains | Low to Moderate | Opaque (Fused rank scores) |
| GraphRAG (Sathus Pattern) | Exceptional (Deterministic path traversal) | < 0.5% (Ground-truth verified) | Moderate (Graph entity extraction) | Transparent (Cryptographic graph triples) |
Use GraphRAG when queries require multi-step reasoning across documents (e.g. "What medications contraindicated for patient condition X interact with drug Y?"), or when strict factual verifiability without hallucination is mandatory.
We implement an event-driven incremental graph updater: when a document is updated, its associated chunk vector nodes and extracted entity edges are soft-deleted or versioned, and the Leiden community summaries for affected subgraphs are asynchronously refreshed.
Principal AI Architect
Part of the Ontology & Cognitive Systems at Sathus Technology. Specializing in mission-critical data lakehouses, streaming analytics, and compliance-driven platforms.
Deploy deterministic, audit-ready AI workflows grounded in enterprise knowledge graphs.