graph-dependency-analyzer 0.2.0

Knowledge Graph for multi-language repository dependency analysis — Java, TypeScript, Python, Groovy, SQL/Oracle

Graph Project - Dependency Analysis System

📍 Project Location

Path: C:\DevWorkspace\Graph Project\

🎯 Purpose

Sistema de análise de dependências de código com Clean Architecture para repositórios Java.

Este é um novo projeto independente que implementa:

• 🔍 Análise AST com Tree-sitter para extração de estruturas de código
• 🔗 Detecção de dependências internas (código-para-código) e externas (APIs, DBs, bibliotecas)
• 📊 Armazenamento de grafo de dependências no Neo4j para análise de impacto
• 🧠 Geração de embeddings (OpenAI) e armazenamento no Qdrant para busca semântica
• 🚀 Processamento paralelo de múltiplos repositórios

🏗️ Architecture

Clean Architecture com 4 camadas:

src/
├── domain/              # 💎 Entities, Value Objects, Interfaces
│   ├── entities/
│   ├── value_objects/
│   ├── repositories/
│   └── services/
├── application/         # ⚙️ Use Cases, DTOs, Services
│   ├── use_cases/
│   ├── dtos/
│   └── services/
├── infrastructure/      # 🔧 Adaptadores (Qdrant, Neo4j, OpenAI, Tree-sitter)
│   ├── parsing/
│   ├── persistence/
│   └── external_apis/
└── presentation/        # 🌐 Controllers, Routers FastAPI
    └── api/
        └── rest/

🛠️ Technology Stack

• Language: Python 3.11+
• Framework: FastAPI
• AST Parsing: Tree-sitter + tree-sitter-java
• Graph DB: Neo4j 5.15+
• Vector DB: Qdrant 1.13+
• Embeddings: OpenAI text-embedding-3-small (1536 dims)
• Build Tools: Maven/Gradle parsing

🎓 Phase 1 Scope

• ✅ Language Support: Java only
• ✅ Analysis: Static (deterministic) - NO AI/LLM
• ✅ Dependencies: Internal + External (APIs, DBs, libraries)
• ✅ Storage: Dual (Neo4j graph + Qdrant vectors)

🚀 Future Phases

• 🔮 Phase 2: Multi-language (Python, JS/TS, Go, C#)
• 🔮 Phase 3: Advanced features (code quality, architectural patterns, complexity metrics)

📚 Documentation

• Specifications: .sdd/specs/clean-architecture-batch-indexing/
  • requirements.md - Functional requirements
  • design.md - Technical design
  • tasks.md - Implementation tasks
  • research.md - Research findings

🚀 Quick Start

Ver guia completo: QUICKSTART.md

# 1. Configurar .env
copy .env.example .env
# Editar .env com OPENAI_API_KEY

# 2. Subir Docker
docker-compose up -d

# 3. Instalar dependências
pip install -e .

# 4. Iniciar API
uvicorn src.presentation.api.rest.main:app --reload --port 8000

# 5. Testar
# Abrir http://localhost:8000/docs

🧪 Testing

# Run all tests
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=src --cov-report=html

📋 Status

Current Phase: Setup & Implementation
Task Progress: See tasks.md for detailed progress

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Created: 2026-06-30
Specification: clean-architecture-batch-indexing