# Clonar
git clone https://github.com/amaralphp/mcp-rag-server.git
cd mcp-rag-server

# Configurar
cp .env.example .env

# Iniciar (Qdrant + Server)
docker compose up -d

# Acessar
# API:     http://localhost:8000
# Swagger: http://localhost:8000/docs
# Qdrant:  http://localhost:6333/dashboard

# Clonar
git clone https://github.com/amaralphp/mcp-rag-server.git
cd mcp-rag-server

# Ambiente virtual
python -m venv venv
source venv/bin/activate  # Linux/Mac
.\venv\Scripts\Activate.ps1  # Windows

# Dependencias
pip install -e .

# Qdrant (Docker separado)
docker run -d --name qdrant -p 6333:6333 -p 6334:6334 qdrant/qdrant

# Iniciar servidor MCP
python -m src.mcp_server

# OU API REST
uvicorn src.api.main:app --reload --port 8000

# Ingerir documento
result = await ingest(
    content="Texto do documento...",
    source="arquivo.md",
    chunk_size=500
)

# Buscar contexto
result = await query(
    query="Qual e a pergunta?",
    top_k=5
)

mcp-rag ingest --file documento.md
mcp-rag query "minha pergunta"
mcp-rag list
mcp-rag delete <doc_id>
mcp-rag health

# Ingerir
curl -X POST http://localhost:8000/ingest \
  -H "Content-Type: application/json" \
  -d '{"content": "Texto...", "source": "doc.md"}'

# Buscar
curl -X POST http://localhost:8000/query \
  -H "Content-Type: application/json" \
  -d '{"query": "minha pergunta", "top_k": 5}'

mcp-rag-server/
├── src/
│   ├── mcp_server.py
│   ├── cli.py
│   ├── tools/
│   ├── chunking/
│   ├── embeddings/
│   ├── vectorstore/
│   └── api/
│       ├── main.py
│       ├── routes/
│       └── schemas.py
├── tests/
├── docker-compose.yml
├── Dockerfile
├── requirements.txt
├── pyproject.toml
├── .env.example
├── .github/workflows/ci.yml
├── LICENSE
└── README.md

pytest -v
pytest --cov=src --cov-report=html

flowchart LR
    A[Push/PR] --> B[Ruff Lint]
    B --> C[MyPy]
    C --> D[Pytest]
    D --> E[Docker Build]
    E --> F{Branch main?}
    F -->|Sim| G[Push Image]
    F -->|Nao| H[Pronto]