GraphRAG & Knowledge Graph Retrieval

1. Concept Overview & Systemic Problem

Classic RAG (Retrieval-Augmented Generation) was a revolution in 2023, but by 2024-2025, engineers faced its systemic blindness:

• Vector RAG excels at answering questions like: "What is the maximum password length in the auth.ts file?" (local similarity search).
• However, it is utterly helpless with questions like: "How have our company's business priorities changed over the last six months based on 200 documents?" Vector search simply does not know which 5 chunks to extract from a million possibilities.

GraphRAG combines vector similarity with the topology of a knowledge graph. It perceives both local details and global themes across the entire codebase or corporate wiki.

2. Architectural Taxonomy & Mental Model

┌─────────────────────────────────────────────────────────────┐
│                 GRAPHRAG INDEXING & QUERY FLOW              │
├─────────────────────────────────────────────────────────────┤
│ 1. KNOWLEDGE EXTRACTION PIPELINE                            │
│    • Text Chunks ➔ LLM extracts (Subject - Predicate - Object)
│    • Entity: "Authentication Module"                         │
│    • Relation: "DEPENDS_ON" ➔ Entity: "PostgreSQL Database"  │
├─────────────────────────────────────────────────────────────┤
│ 2. COMMUNITY DETECTION (Leiden Algorithm)                   │
│    • The graph is grouped into communities (Clusters)       │
│    • LLM generates Community Summaries for each level       │
├─────────────────────────────────────────────────────────────┤
│ 3. DUAL-MODE RETRIEVAL:                                     │
│    • Local Search: Vector search on entities                │
│    • Global Search: Traversing community summaries of the graph│
├─────────────────────────────────────────────────────────────┤
│ 4. FINAL SYNTHESIS: Comprehensive systemic analytics         │
└─────────────────────────────────────────────────────────────┘

3. Technical Pipeline & Internal Mechanics

01. Audit of a Large Monorepo with 500k Lines

An agent receives the task: "Map all modules that will be affected if we replace session authentication with JWT." GraphRAG traverses the dependency edges and returns a comprehensive risk graph without missing indirect dependencies.

02. Corporate Customer Support with 3 Years of History

A customer asks: "Why did our rate change last year?" GraphRAG finds the chain: old request ➔ contract change ➔ internal billing release, reconstructing the complete chronological picture.

4. Production Engineering Scenarios

• High Cost of Initial Indexing: Building the graph requires numerous LLM calls to extract entities from each text chunk. Use cheaper models (Gemini 2.0 Flash) for edge extraction.
• Graph Noise: If the model extracts overly trivial or vague entities (e.g., "user", "date"), the graph turns into an indecipherable "hairball." Strict ontology filters are necessary.

5. Pitfalls, Common Mistakes & Security

GraphRAG has transformed knowledge search from naive word comparison to structural spatial reasoning. The combination of vector databases with knowledge graphs is the gold standard of modern contextual engineering.