"use client"; import { useState } from "react"; const SECTIONS = [ { id: "quickstart", title: "Quick Start", icon: "πŸš€", content: [ { heading: "Prerequisites", body: `Before running GraphRAG, make sure you have: - Python 3.10+ - Node.js 18+ - Docker (optional, for containerized deployment) - A TigerGraph Cloud account (free tier available) - At least one LLM API key (Claude, OpenAI, etc.)`, }, { heading: "Installation", code: `# Clone the repository git clone https://github.com/MUTHUKUMARAN-K-1/graphrag-inference-hackathon cd graphrag-inference-hackathon # Backend setup pip install -r requirements.txt # Frontend setup cd web npm install npm run dev # Or use Docker docker build -t graphrag . docker run -p 3000:3000 -p 8000:8000 graphrag`, }, { heading: "Environment Variables", code: `# .env file TIGERGRAPH_HOST=https://your-instance.i.tgcloud.io TIGERGRAPH_GRAPH=GraphRAG TIGERGRAPH_SECRET=your_secret # LLM Providers (set at least one) ANTHROPIC_API_KEY=sk-ant-... OPENAI_API_KEY=sk-... GEMINI_API_KEY=AI... GROQ_API_KEY=gsk_...`, }, ], }, { id: "api", title: "API Reference", icon: "πŸ“‘", content: [ { heading: "POST /api/compare", body: "Run a side-by-side comparison of Baseline RAG and GraphRAG pipelines.", code: `// Request POST /api/compare { "query": "Were Scott Derrickson and Ed Wood of the same nationality?", "provider": "anthropic", "model": "claude-sonnet-4-20250514", "adaptiveRouting": true } // Response { "baseline": { "answer": "Yes, both are American.", "tokens": 950, "latencyMs": 1200, "costUsd": 0.003800, "entities": [], "relations": [] }, "graphrag": { "answer": "Yes, both directors are American...", "tokens": 2400, "latencyMs": 1800, "costUsd": 0.009600, "entities": ["Scott Derrickson", "Ed Wood", "United States"], "relations": ["BORN_IN", "LOCATED_IN"] }, "complexity": 0.72, "queryType": "bridge", "recommended": "graphrag" }`, }, { heading: "POST /api/benchmark", body: "Run batch evaluation on HotpotQA samples.", code: `// Request POST /api/benchmark { "numSamples": 10 } // Response { "aggregate": { "numSamples": 10, "baseline": { "avgF1": 0.6234, "avgEM": 0.4000, ... }, "graphrag": { "avgF1": 0.7567, "avgEM": 0.5000, ... }, "graphragF1WinRate": 0.70, "byType": { "bridge": { "count": 5, "baselineF1": 0.58, "graphragF1": 0.79 }, "comparison": { "count": 5, "baselineF1": 0.67, "graphragF1": 0.72 } } } }`, }, { heading: "GET /api/providers", body: "List all available LLM providers and their models.", code: `// Response { "providers": [ { "id": "anthropic", "name": "Anthropic Claude", "isLocal": false, "hasApiKey": true, "defaultModel": "claude-sonnet-4-20250514", "models": [ { "id": "claude-sonnet-4-20250514", "name": "Claude Sonnet 4", "speed": "medium", "quality": "high" } ] }, ... ] }`, }, ], }, { id: "architecture", title: "Architecture", icon: "πŸ—οΈ", content: [ { heading: "4-Layer AI Factory Model", body: `The system follows an AI Factory architecture with four distinct layers: **Layer 1 β€” Graph Layer (TigerGraph Cloud)** Stores the knowledge graph with typed vertices and edges. Supports GSQL queries for multi-hop traversal. **Layer 2 β€” Orchestration Layer** Routes queries through the Adaptive Router, which scores complexity and classifies query types (bridge, comparison, factoid). **Layer 3 β€” LLM Layer (12 Providers)** Universal LLM abstraction supporting Claude, GPT-4, Gemini, Llama, Mistral, DeepSeek, Grok, Cohere, and more. **Layer 4 β€” Evaluation Layer (RAGAS)** Automated evaluation with F1, Exact Match, token counting, cost tracking, and latency measurement.`, }, { heading: "Dual Pipeline Design", body: `Every query runs through two pipelines simultaneously: **Pipeline A β€” Baseline RAG** Query β†’ Vector similarity search β†’ Retrieved passages β†’ LLM generation - Fast and cheap - Best for simple factoid queries **Pipeline B β€” GraphRAG** Query β†’ Entity extraction β†’ Graph traversal β†’ Structured evidence β†’ LLM generation - More tokens, but higher accuracy - Best for multi-hop bridge and comparison questions - Provides traceable reasoning paths`, }, ], }, { id: "novelties", title: "Novel Features", icon: "✨", content: [ { heading: "1. Adaptive Query Router", body: `Analyzes query complexity (0.0–1.0) using: - Entity count β€” more entities = higher complexity - Multi-hop indicators β€” "both", "same", "compared to" - Question word analysis β€” "which" and "who" patterns - Dependency chain length Routes simple queries to Baseline RAG (fast, cheap) and complex queries to GraphRAG (precise, traceable).`, }, { heading: "2. Schema-Bounded Extraction", body: `Traditional NER extracts arbitrary entity types. Our system constrains extraction to TigerGraph's actual schema: - Only PERSON, LOCATION, WORK, CONCEPT, etc. (valid vertex types) - Eliminates hallucinated node types that would fail on graph lookup - Ensures every extracted entity maps to a real vertex in the graph`, }, { heading: "3. Dual-Level Keywords", body: `Extracts keywords at two granularity levels: - **High-level**: Concepts, themes, categories (e.g., "nationality", "American cinema") - **Low-level**: Specific entities, names, dates (e.g., "Scott Derrickson", "1962") Enables graph traversal at multiple levels for richer context retrieval.`, }, { heading: "4. Graph Reasoning Paths", body: `Traces explicit entityβ†’relationβ†’entity chains: - Scott Derrickson β†’ BORN_IN β†’ Denver, CO β†’ LOCATED_IN β†’ United States - Ed Wood β†’ BORN_IN β†’ Poughkeepsie, NY β†’ LOCATED_IN β†’ United States These paths are included in the LLM prompt as structured evidence, making answers verifiable and explainable.`, }, { heading: "5. Real-Time Cost Tracking", body: `Measures per-query economics: - Input/output tokens counted per provider's tokenization - USD cost calculated using current provider pricing - Latency measured end-to-end (graph + LLM) - Interactive projections: "What would 100K queries/month cost?"`, }, ], }, { id: "deployment", title: "Deployment", icon: "🐳", content: [ { heading: "Docker Deployment", code: `# Build the image docker build -t graphrag . # Run with environment variables docker run -d \\ -p 3000:3000 \\ -p 8000:8000 \\ -e TIGERGRAPH_HOST=https://your-instance.i.tgcloud.io \\ -e ANTHROPIC_API_KEY=sk-ant-... \\ graphrag`, }, { heading: "TigerGraph Cloud Setup", body: `1. Create a free account at tgcloud.io 2. Create a new cluster (free tier: 50MB storage) 3. Install the GraphRAG schema: - Go to GraphStudio - Import the schema from graphrag/setup_tigergraph.py 4. Set TIGERGRAPH_HOST, TIGERGRAPH_GRAPH, TIGERGRAPH_SECRET in .env`, }, { heading: "Running Locally", code: `# Backend (Python) cd graphrag-inference-hackathon pip install -r requirements.txt python -m graphrag.main # Frontend (Next.js) cd web npm install npm run dev # Open http://localhost:3000`, }, ], }, ]; export function DocsContent() { const [activeSection, setActiveSection] = useState("quickstart"); const section = SECTIONS.find(s => s.id === activeSection) || SECTIONS[0]; return (
{/* Sidebar */} {/* Mobile Tabs */}
{SECTIONS.map((s) => ( ))}
{/* Content */}
{section.icon}

{section.title}

{section.content.map((block, i) => (

{block.heading}

{block.body && (
{block.body.split(/(\*\*[^*]+\*\*)/).map((part, j) => { if (part.startsWith("**") && part.endsWith("**")) { return {part.slice(2, -2)}; } return {part}; })}
)} {block.code && ( 
                    {block.code}
)}
))}
); }