GraphRAG for Intelligent Document Search & Analysis
Unlock next-generation document intelligence with GraphRAG - combining knowledge graphs, community detection, and LLMs for complex multi-hop reasoning and enterprise-wide knowledge synthesis.
📋Table of Contents
🌟GraphRAG: Beyond Traditional RAG
Microsoft's GraphRAG revolutionizes document intelligence by building knowledge graphs from unstructured text, detecting semantic communities, and enabling multi-hop reasoning. While traditional RAG achieves 70-80% accuracy, GraphRAG delivers 95%+ for complex queries requiring synthesis across hundreds of documents.
Microsoft Research Breakthrough
GraphRAG's community-based summarization outperforms vanilla RAG by 41% on comprehensiveness and 32% on diversity for global sensemaking queries - essential for Fortune 500 strategic analysis and due diligence.
🕸️Knowledge Graph Construction
| Component | Traditional RAG | GraphRAG | Advantage |
|---|---|---|---|
| Entity Extraction | Basic NER | LLM-powered entities + attributes | +60% accuracy |
| Relationships | None | Semantic relationships + strength | Contextual understanding |
| Community Detection | Not applicable | Leiden algorithm clustering | Topic discovery |
| Global Understanding | Limited to chunks | Dataset-wide synthesis | Holistic insights |
🔬Community Detection & Hierarchical Summarization
🎯 Leiden Algorithm
Detects semantic communities in knowledge graphs
- • Hierarchical community structure
- • Multi-resolution analysis
- • Topic clustering across documents
- • 10x faster than Louvain algorithm
📊 Community Summaries
LLM-generated summaries for each community
- • High-level thematic understanding
- • Cross-document synthesis
- • Key entities and relationships
- • Multi-level abstraction hierarchy
🔍Advanced Query Strategies
Query Types Supported
Local Queries (Entity-Specific)
"What are the key features of Product X?" - Standard RAG-style retrieval
Global Queries (Dataset-Wide)
"What are the top themes across all documents?" - Community summary aggregation
Multi-Hop Reasoning
"How does Entity A influence Entity B through Entity C?" - Graph traversal
Comparative Analysis
"Compare strategies across business units" - Community comparison
🛠️GraphRAG Implementation Stack
⚙️ Core Technologies
- • Microsoft GraphRAG: Official implementation
- • LangChain: Orchestration framework
- • Neo4j/TigerGraph: Graph databases
- • GPT-4/Claude: Entity extraction, summaries
- • NetworkX: Graph algorithms
📊 Processing Pipeline
- • Document ingestion & chunking
- • Entity & relationship extraction (LLM)
- • Knowledge graph construction
- • Community detection (Leiden)
- • Community summary generation
- • Query processing & synthesis
🏢Enterprise Use Cases & ROI
💼 Fortune 500 Applications
- • M&A Due Diligence: Analyze 10,000+ documents, identify risks and synergies
- • Competitive Intelligence: Map competitor strategies across reports
- • Regulatory Compliance: Track regulation changes across jurisdictions
- • Patent Analysis: Discover innovation patterns and white spaces
- • Customer Intelligence: Synthesize feedback from millions of interactions
- • Risk Management: Identify interconnected risks across business units
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