GraphRAG 知识图谱检索增强系统设计模板

You are a senior knowledge graph and RAG systems architect. Design a production-grade GraphRAG system that combines knowledge graph reasoning with vector retrieval for superior question answering.

System Requirements

Domain: [YOUR DOMAIN - e.g., legal, medical, codebase, enterprise docs]
Data Sources: [list your document types and volumes]
Query Types: [simple lookup / multi-hop reasoning / comparative / temporal]
Scale: [number of documents, expected entities, relationships]

Architecture Design Tasks

1. Knowledge Graph Schema

Define entity types with properties (min 5 types)
Define relationship types with cardinality constraints
Specify extraction rules: regex patterns, NER models, LLM-based extraction prompts
Schema evolution strategy for new entity/relationship discovery

2. Dual Retrieval Pipeline

Query -> Intent Classification

Vector Path: embedding search -> chunk retrieval -> reranking
Graph Path: entity linking -> subgraph extraction -> path reasoning
Hybrid Merge: reciprocal rank fusion with adaptive weights

3. Graph Construction Pipeline

Document chunking strategy (overlap, semantic boundaries)
Entity extraction and resolution (deduplication, coreference)
Relationship extraction with confidence scores
Community detection for global summarization (Leiden algorithm)
Incremental graph updates without full rebuild

4. Query Processing

Local search: entity-centric subgraph retrieval
Global search: community summary aggregation
Drift search: follow relationship chains for exploratory queries
Include the prompt templates for each search mode

5. Evaluation Framework

Faithfulness, relevance, completeness metrics
Graph quality metrics (coverage, accuracy, freshness)
A/B comparison: GraphRAG vs naive RAG vs keyword search
Benchmark dataset construction methodology

Provide: architecture diagram (Mermaid), technology stack recommendations, implementation roadmap (4 phases), cost estimation formula, and example queries with expected retrieval paths.