The Three-Database Architecture for Hybrid GraphRAG: Relational, Vector & Graph

The early wave of Retrieval-Augmented Generation (RAG) relied almost entirely on vector databases: chunk text into 500-token snippets, generate dense embeddings using models like text-embedding-3, store them in a vector index, and retrieve top-$k$ nearest neighbors via cosine distance.

In production, this approach hits a hard ceiling whenever queries require multi-hop reasoning, structured aggregation, or strict relationship tracking:

• “Which CAPAs opened in Q2 2025 relate to equipment certified under SOP-042 and were audited by auditor X?”
• Cosine similarity cannot traverse relationship chains. It retrieves chunks mentioning “CAPA” or “SOP-042”, but has no concept of whether the relation between them actually holds.

To solve this, we developed the Three-Database Architecture for Hybrid GraphRAG.

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1. The Three Tiers of Enterprise Knowledge

Each data paradigm solves an orthogonal problem. Attempting to force all knowledge into a single database engine introduces unacceptable compromises:

┌─────────────────────────────────────────────────────────────────┐
│                 Incoming Hybrid Query Request                   │
└────────────────────────────────┬────────────────────────────────┘
                                 │
         ┌───────────────────────┼───────────────────────┐
         ▼                       ▼                       ▼
  [ Tier 1: Relational ]   [ Tier 2: Vector ]     [ Tier 3: Graph ]
  PostgreSQL / D1          Qdrant / Vectorize     Neo4j / Memgraph
  • Exact metadata filters • Semantic similarity  • Multi-hop traversal
  • Dates, status, IDs     • Unstructured text    • Entity hierarchies
  • Access control (ACL)   • Dense & Sparse BM25  • Dependency paths
         │                       │                       │
         └───────────────────────┼───────────────────────┘
                                 ▼
                     [ Reranking Layer ]
                     Cohere / BGE Reranker
                                 │
                                 ▼
                   Grounded Context Window (LLM)

The Role of Each Database

1. Relational Database (PostgreSQL / SQLite):
   • Manages strict tabular truth: document ownership, publication dates, department ACLs, revision numbers, and hash verification.
   • Handles fast, deterministic SQL filters (WHERE status = 'APPROVED' AND created_at >= '2026-01-01').
2. Vector Engine (Qdrant / Cloudflare Vectorize):
   • Indexes unstructured narrative text chunks.
   • Handles semantic search, synonyms, paraphrasing, and cross-lingual concept matching.
   • Supports hybrid dense + sparse (BM25/SPLADE) retrieval with payload-based filtering.
3. Graph Engine (Neo4j / Memgraph / Graph Store):
   • Indexes explicitly verified entity-relationship triples: (Equipment)-[:VALIDATED_BY]->(Protocol)-[:GOVERNED_BY]->(Regulation).
   • Executes multi-hop Cypher queries to assemble connected subgraphs that vector indices miss.

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2. Preventing Hallucinated Edges in the Knowledge Graph

The most dangerous failure in GraphRAG is hallucinated graph extraction: using an unconstrained LLM to parse text into nodes and edges without schema boundaries. Left unchecked, models invent fictional relationships like (Aspirin)-[:MANUFACTURED_BY]->(Cloudflare).

To eliminate hallucinated edges, we enforce Deterministic Ontology Binding:

import { z } from 'zod';

// 1. Define explicit ontology entity types
export const EntityTypeEnum = z.enum([
  'System',
  'Protocol',
  'StandardOperatingProcedure',
  'Deviation',
  'RegulatoryStandard',
]);

// 2. Define valid relational predicates
export const RelationTypeEnum = z.enum([
  'GOVERNED_BY',
  'VALIDATED_UNDER',
  'TRIGGERS_DEVIATION',
  'SUPERSEDES',
  'IMPLEMENTS',
]);

// 3. Constrained Triple Schema
export const KnowledgeTripleSchema = z.object({
  subjectId: z.string(),
  subjectType: EntityTypeEnum,
  predicate: RelationTypeEnum,
  objectId: z.string(),
  objectType: EntityTypeEnum,
  evidenceSnippet: z.string().min(20).max(500),
  confidenceScore: z.number().min(0.0).max(1.0),
});

export type KnowledgeTriple = z.infer<typeof KnowledgeTripleSchema>;

The Ingestion Gatekeeper

Before any node or edge is committed to the graph database:

1. The entities must resolve to a known identifier in the relational master data register.
2. The relation predicate must belong to the approved domain ontology.
3. The raw source text snippet must explicitly support the assertion, verified by an independent extraction audit step.

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3. Query Execution Walkthrough

When an end user asks: “What are the root cause failure modes for autoclave sterility cycles audited under FDA 21 CFR Part 211?”

1. Query Parsing: The router parses the prompt into:
   • Structured filter: regulatory_id: "21 CFR 211"
   • Graph entity: System: "Autoclave"
   • Semantic concept: "sterility cycle root cause failure"
2. Graph Expansion: The graph engine runs a 2-hop traversal from the Autoclave node along [:SUBJECT_TO] and [:HAS_FAILURE_MODE] edges, extracting the contextual subgraph of historical incidents and relevant SOPs.
3. Vector Retrieval: Qdrant searches across text chunks belonging exclusively to the documents identified in Step 2.
4. Reciprocal Rank Fusion (RRF) & Reranking: Graph results and vector results are merged and passed through a cross-encoder reranker (e.g., BGE-Reranker-Large), keeping only the top 5 most relevant, factual passages.
5. Generation: The LLM synthesizes the answer, citing exact document numbers and relationship links.

By combining all three databases, you eliminate hallucinations, respect enterprise access controls, and achieve enterprise-grade retrieval precision.