Smart Contract Discovery System adalah sistem pencarian Smart Contract berbasis semantik yang memungkinkan pengguna menemukan Smart Contract yang relevan menggunakan query dalam bahasa alami. Sistem ini memanfaatkan Semantic Enrichment dengan Large Language Models (LLM) untuk memberikan hasil pencarian yang lebih akurat dan kontekstual dibandingkan metode pencarian berbasis kata kunci tradisional.

• 🧠 Semantic Search: Pencarian berbasis makna menggunakan vector embeddings dan LLM
• 🔍 Natural Language Queries: Mendukung pencarian dengan bahasa alami
• 📊 Smart Contract Analysis: Analisis mendalam terhadap kode Smart Contract
• 🛡️ Security Insights: Deteksi potensi risiko keamanan
• 🎨 Modern UI: Interface yang user-friendly dengan Next.js dan Tailwind CSS
• ⚡ Real-time Search: Pencarian cepat dengan caching dan optimisasi

Sistem ini terdiri dari beberapa komponen utama:

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Frontend      │    │   Backend API    │    │   Data Sources  │
│   (Next.js)     │◄──►│   (FastAPI)      │◄──►│   - DgraphDB    │
│                 │    │                  │    │   - ChromaDB    │
│   - Search UI   │    │   - Retriever    │    │   - Ethereum    │
│   - Results     │    │   - Enrichment   │    │     Archive     │
│   - Details     │    │   - Vector DB    │    │                 │
└─────────────────┘    └──────────────────┘    └─────────────────┘

Frontend:

• Next.js 15 with App Router
• TypeScript
• Tailwind CSS + Radix UI
• React Hook Form

Backend:

• FastAPI (Python)
• LangChain for LLM integration
• OpenAI GPT models
• HuggingFace embeddings

Databases:

• DgraphDB (Graph database untuk Smart Contract data)
• ChromaDB (Vector database untuk semantic search)

Data Extraction:

• eth2dgraph (Rust) untuk ekstraksi data dari Ethereum
• Alchemy Archive Node sebagai data source

tugas-akhir/
├── impl/                          # Implementation code
│   ├── eth2dgraph/               # Rust-based Ethereum data extractor
│   │   ├── src/                  # Rust source code
│   │   ├── Cargo.toml           # Rust dependencies
│   │   └── docker-compose.yml   # DgraphDB setup
│   ├── system/                   # Python backend system
│   │   ├── src/
│   │   │   ├── api/             # FastAPI endpoints
│   │   │   ├── core/            # Core business logic
│   │   │   │   ├── data_access/ # Database clients
│   │   │   │   ├── data_processing/ # LLM enrichment
│   │   │   │   └── data_retrieval/ # Search & retrieval
│   │   │   └── utils/           # Utility functions
│   │   ├── config/              # Configuration files
│   │   ├── requirements.txt     # Python dependencies
│   │   └── openapi.yaml         # API documentation
│   └── web/                      # Next.js frontend
│       ├── app/                 # App router pages
│       ├── components/          # React components
│       ├── hooks/               # Custom React hooks
│       ├── lib/                 # Utility libraries
│       └── package.json         # Node dependencies
└── src/                          # LaTeX thesis documentation
    ├── thesis.tex               # Main thesis document
    ├── chapters/                # Thesis chapters
    └── config/                  # LaTeX configuration

• Python 3.8+
• Node.js 18+
• Rust 2021 edition
• Docker & Docker Compose
• OpenAI API Key

1. Clone the repository

   git clone https://github.com/username/tugas-akhir.git
   cd tugas-akhir

2. Setup DgraphDB (Data Storage)

   cd impl/eth2dgraph
   docker-compose up -d

3. Setup Backend System

   cd impl/system
   pip install -r requirements.txt
   
   # Configure environment variables
   cp .env.example .env
   # Edit .env file with your API keys

4. Setup Frontend

   cd impl/web
   npm install

1. Start the Backend API

   cd impl/system
   python -m uvicorn src.api.api:app --reload --host 0.0.0.0 --port 8000

2. Start the Frontend

   cd impl/web
   npm run dev

3. Access the Application

   • Frontend: http://localhost:3000
   • Backend API: http://localhost:8000
   • API Documentation: http://localhost:8000/docs
   • DgraphDB UI (Ratel): http://localhost:8001

Query: "ERC20 token with mint and burn functions"
Results: Smart contracts that implement ERC20 standard with minting/burning capabilities

Query: "DeFi lending protocol with liquidation mechanism and oracle integration"
Results: Detailed analysis of DeFi contracts with specific functionalities

curl -X POST "http://localhost:8000/search" \
     -H "Content-Type: application/json" \
     -d '{
       "query": "NFT marketplace with royalty system",
       "limit": 10,
       "data": true
     }'

1. Extraction: eth2dgraph extracts Smart Contract data from Ethereum Archive Node
2. Storage: Raw data stored in DgraphDB
3. Enrichment: LLM processes contracts for semantic understanding
4. Vectorization: Embeddings create vector representations
5. Search: ChromaDB enables semantic similarity search

The system includes comprehensive testing methodologies:

• Relevansi Hasil Pencarian (UK1): Accuracy measurement using labeled datasets
• Kualitas Semantik Data (UK2): Semantic expressiveness evaluation
• Kemiripan Hasil (UK3): Semantic similarity scoring
• Konsistensi Hasil (UK4): Jaccard Index for result consistency

This work is part of the final thesis:

"Pengembangan Smart Contract Discovery System Berbasis Konteks Semantik"
Kenneth Ezekiel Suprantoni (13521089)
Institut Teknologi Bandung, 2025

Kenneth Ezekiel Suprantoni

• Institution: Institut Teknologi Bandung
• Program: Teknik Informatika