Table of Contents

• [Workflow](#workflow)
• [GraphRAG Pattern Selection](#graphrag-pattern-selection)
• [Integration Architecture](#integration-architecture)
• [Output Template](#output-template)

GraphRAG System Design

Workflow

Copy this checklist and work through each step:

GraphRAG System Design Progress:
- [ ] Step 1: Analyze domain requirements
- [ ] Step 2: Select GraphRAG pattern
- [ ] Step 3: Choose technology stack
- [ ] Step 4: Design integration pipeline
- [ ] Step 5: Apply domain customizations
- [ ] Step 6: Define deployment strategy
- [ ] Step 7: Produce specification

Step 1: Analyze domain requirements

Characterize the retrieval problem: query complexity (single-hop vs multi-hop), data volume and update frequency, compliance constraints, latency requirements, and explainability needs. Determine whether graph structure adds value over flat retrieval -- multi-hop reasoning, entity disambiguation, and relationship-aware context assembly are strong signals for GraphRAG. Define the user personas and query patterns the system must serve.

Step 2: Select GraphRAG pattern

Choose the core retrieval architecture using the [GraphRAG Pattern Selection](#graphrag-pattern-selection) guide. Match your query patterns to the appropriate pattern: Hybrid Symbol-Vector for mixed structured/unstructured queries, Subgraph-on-Demand for focused context assembly, or Community-Based Global Summarization for broad thematic queries. For detailed pattern descriptions, see [Methodology Reference](./resources/methodology.md).

Step 3: Choose technology stack

Select components for each architectural layer: graph database, vector database, orchestration framework, and LLM provider. Use the [Technology Stacks Reference](./resources/technology-stacks.md) for component-by-component comparison. Key decisions: single-system vs multi-system hybrid, managed vs self-hosted, framework-based vs custom pipeline. Consider team expertise, budget constraints, and existing infrastructure.

Step 4: Design integration pipeline

Define the end-to-end data flow from ingestion through generation. The core pipeline stages: Ingest (raw data) -> Extract (entities and relations) -> Build KG (populate graph) -> Index (vector embeddings + graph indices) -> Retrieve (hybrid graph+vector search) -> Generate (LLM with graph-grounded context) -> Cite (provenance from graph paths). Design the query routing logic that determines when to use graph traversal, vector search, or both. See [Methodology Reference](./resources/methodology.md) for pipeline design considerations.

Step 5: Apply domain customizations

Adapt the generic architecture to domain-specific requirements: ontology selection (UMLS for healthcare, FIBO for finance), compliance patterns (HIPAA access control, regulatory audit trails), and domain retrieval patterns (temporal graphs for finance, layered patient graphs for clinical). See [Domain Patterns Reference](./resources/domain-patterns.md) for domain-specific guidance.

Step 6: Define deployment strategy

Specify the deployment architecture: graph database sizing and clustering, vector index configuration, caching strategy, batch vs real-time ingestion, monitoring and observability, and scaling plan. Define performance SLAs for query latency, throughput, and freshness. Plan for graph maintenance: incremental updates, schema evolution, and data quality monitoring.

Step 7: Produce specification

Compile the complete system design specification using the [Output Template](#output-template). Validate against the quality rubric at [System Design Rubric](./resources/evaluators/rubricsystemdesign.json). Ensure all components are connected end-to-end with clear data flows, error handling, and fallback strategies.

GraphRAG Pattern Selection

Choosing a Pattern

• If queries need both "find entities of type X" and "find semantically similar content" -> Hybrid Symbol-Vector
• If queries are focused and context window cost matters -> Subgraph-on-Demand
• If queries are broad, thematic, or corpus-spanning -> Community-Based Global Summarization
• If requirements span multiple patterns -> Combine patterns with a query router that dispatches to the appropriate retrieval path

Integration Architecture

A complete GraphRAG system integrates four core component layers:

+------------------+     +------------------+     +----------------------+     +-----------+
|  Graph Database  |     |  Vector Database |     |  Orchestration       |     |    LLM    |
|  (Structure)     |<--->|  (Semantics)     |<--->|  Framework           |<--->| (Reason)  |
|                  |     |                  |     |                      |     |           |
|  Neo4j / Tiger   |     |  Pinecone /      |     |  LangChain /         |     |  GPT-4 /  |
|  Graph / Neptune |     |  Weaviate /      |     |  LlamaIndex /        |     |  Claude / |
|  / GraphDB       |     |  Qdrant / pgvec  |     |  LangGraph / Custom  |     |  Llama   |
+------------------+     +------------------+     +----------------------+     +-----------+
        |                        |                          |
        v                        v                          v
  Graph Traversal          Embedding Search           Pipeline Logic
  Multi-hop Paths          Semantic Ranking           Query Routing
  Schema Filtering         Similarity Scores          Context Assembly
  Provenance Chains        Hybrid Re-ranking          Citation Generation

Key integration decisions:

• Graph-first vs text-first pipeline: Does the query first hit the graph (structured filter) or the vector store (semantic search)?
• Single-system vs multi-system: Use Neo4j vector index (single system) or Neo4j + Pinecone (multi-system)?
• Framework vs custom: LangChain/LlamaIndex for rapid development, or custom pipeline for maximum control?
• Synchronization: How are graph and vector indices kept consistent during updates?

Output Template

GRAPHRAG SYSTEM DESIGN SPECIFICATION
======================================

Project: [Project name]
Domain: [Target domain]
Date: [Date]
Author: [Author]

1. DOMAIN REQUIREMENTS
   Query Patterns: [Single-hop / Multi-hop / Thematic / Mixed]
   Data Volume: [Document count, entity count estimates]
   Update Frequency: [Real-time / Daily / Weekly / Batch]
   Latency Requirements: [p50, p95, p99 targets]
   Compliance: [HIPAA / GDPR / SOX / None]
   Explainability: [Required / Nice-to-have / Not needed]

2. GRAPHRAG PATTERN
   Primary Pattern: [Hybrid Symbol-Vector / Subgraph-on-Demand / Community-Based]
   Secondary Pattern: [If hybrid approach, specify]
   Query Router: [How queries are dispatched to retrieval paths]
   Rationale: [Why this pattern fits the requirements]

3. TECHNOLOGY STACK
   Graph Database: [Product, version, deployment mode]
     Justification: [Why this choice]
   Vector Database: [Product, version, deployment mode]
     Justification: [Why this choice]
   Orchestration: [Framework or custom pipeline]
     Justification: [Why this choice]
   LLM Provider: [Model, API or self-hosted]
     Justification: [Why this choice]
   Supporting Infrastructure: [Cache, queue, monitoring tools]

4. INTEGRATION PIPELINE
   Ingestion:
     - Source types: [Documents, APIs, databases]
     - Processing: [Chunking strategy, metadata extraction]
   Extraction:
     - Entity extraction: [Method, model, confidence threshold]
     - Relation extraction: [Method, schema enforcement]
   Knowledge Graph Build:
     - Schema: [Node types, edge types, properties]
     - Population: [Batch / streaming, deduplication strategy]
   Indexing:
     - Graph indices: [Index types, query optimization]
     - Vector indices: [Embedding model, dimension, index type]
   Retrieval:
     - Graph retrieval: [Traversal strategy, depth limits]
     - Vector retrieval: [Top-k, similarity threshold]
     - Hybrid fusion: [How graph and vector results combine]
   Generation:
     - Context assembly: [How retrieved data becomes LLM context]
     - Prompt template: [Structure for graph-grounded generation]
   Citation:
     - Provenance: [How sources are tracked and surfaced]

5. DOMAIN CUSTOMIZATIONS
   Ontology: [Domain ontology or taxonomy used]
   Compliance Controls: [Access control, audit, encryption]
   Domain-Specific Patterns: [Temporal graphs, layered architecture, etc.]

6. DEPLOYMENT STRATEGY
   Infrastructure: [Cloud provider, regions, HA configuration]
   Scaling Plan: [Graph DB scaling, vector DB scaling, LLM scaling]
   Monitoring: [Metrics, alerts, dashboards]
   Maintenance: [Graph update strategy, schema evolution plan]

7. PERFORMANCE TARGETS
   Query Latency: [p50, p95, p99]
   Throughput: [Queries per second]
   Freshness: [Time from data change to queryable]
   Accuracy: [Retrieval precision/recall targets]

8. RISK AND MITIGATION
   - [Risk 1]: [Mitigation strategy]
   - [Risk 2]: [Mitigation strategy]
   - [Risk 3]: [Mitigation strategy]

NEXT STEPS:
- Build proof-of-concept with sample data
- Benchmark retrieval quality against baseline RAG
- Load test with production-scale data
- Iterate schema and retrieval strategy based on evaluation