┌─────────────────────────────────────────────────────────┐
│                   Client Applications                     │
│        Web UI | API Clients | Dashboard | DeepWiki       │
└────────────────────┬────────────────────────────────────┘
                     │ HTTP/WebSocket/SSE
┌────────────────────▼────────────────────────────────────┐
│            NEURAL MCP SERVER (Ports 8000/8765)          │
│                FastAPI Gateway & Router                  │
├──────────────────────────────────────────────────────────┤
│              Iterative Processing Core                   │
│   Phase 1 → Phase 2 → Phase 3 → Phase 4 → Output       │
├──────────────────────────────────────────────────────────┤
│                 Component Systems                        │
│  Memory System | RAG System | Code Analyzer | LLM       │
├──────────────────────────────────────────────────────────┤
│              Infrastructure Layer                        │
│   Ollama AI | ChromaDB | SQLite | File System          │
└──────────────────────────────────────────────────────────┘

# 1. Clone the repository
git clone https://github.com/your-org/NeuralMCPServer.git
cd NeuralMCPServer

# 2. Run the automated setup
setup.bat

# 3. Start the server
start_neural.bat

# 1. Clone the repository
git clone https://github.com/your-org/NeuralMCPServer.git
cd NeuralMCPServer

# 2. Run the automated setup
chmod +x setup.sh
./setup.sh

# 3. Start the server
./start_neural.sh

# 1. Clone the repository
git clone https://github.com/your-org/NeuralMCPServer.git
cd NeuralMCPServer

# 2. Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# 3. Install dependencies
pip install --upgrade pip setuptools wheel
pip install -r requirements.txt

# 4. Initialize data directories and databases
python setup_data.py

# 5. Download AI models
python download_models.py

# 6. Install Ollama (if not already installed)
# Windows: Download from https://ollama.ai
# Linux/Mac:
curl -fsSL https://ollama.ai/install.sh | sh

# 7. Configure the system (optional)
cp configs/config.example.json configs/config.json
# Edit config.json with your settings

# 8. Start the server
python mcp_server.py

# 9. Access the dashboard
# Open browser to http://localhost:8000

# For production deployment with GPU support
pip install torch==2.1.2+cu118 torchvision==0.16.2+cu118 -f https://download.pytorch.org/whl/torch_stable.html

# For air-gapped environments
pip download -r requirements.txt -d ./offline_packages
pip install --no-index --find-links ./offline_packages -r requirements.txt

# Using Docker (recommended for production)
docker-compose up -d

from neural_system import NeuralMCPServer

server = NeuralMCPServer()
analysis = await server.analyze_repository("/path/to/codebase")
# Returns comprehensive code metrics, security issues, and insights

documentation = await server.generate_documentation(
    project_path="/path/to/project",
    format="markdown",
    include_diagrams=True
)

security_report = await server.security_scan(
    target="/path/to/code",
    compliance_standards=["OWASP", "CWE", "GDPR"]
)

knowledge_base = await server.extract_knowledge(
    sources=["/docs", "/wikis", "/code"],
    index_name="corporate_knowledge"
)

curl -X POST http://localhost:8000/analyze_repository \
  -H "Content-Type: application/json" \
  -d '{
    "path": "/path/to/repository",
    "deep_analysis": true,
    "include_security": true
  }'

const ws = new WebSocket('ws://localhost:8000/ws');
ws.onmessage = (event) => {
    const data = JSON.parse(event.data);
    console.log('Real-time update:', data);
};

D:\NeuralMCPServer\
├── core/                      # Core neural components
│   ├── enhanced_memory_system.py
│   ├── mcp_rag_system.py
│   ├── mcp_code_analyzer.py
│   └── llm_interface_ollama.py
├── configs/                   # Configuration files
│   ├── config.json           # Main configuration
│   └── rag_config.json       # RAG parameters
├── data/                      # Data storage
│   ├── vector_db/            # ChromaDB storage
│   ├── documents/            # Document library
│   └── analysis_cache/       # Processing cache
├── architecture/              # Architecture docs
│   └── NEURAL_ARCHITECTURE.html
├── mcp_server.py             # Main server
├── mcp_server_visual.py      # Visual dashboard
└── requirements.txt          # Dependencies

python mcp_server.py

gunicorn -w 4 -k uvicorn.workers.UvicornWorker mcp_server:app

FROM python:3.11-slim
WORKDIR /app
COPY . .
RUN pip install -r requirements.txt
CMD ["uvicorn", "mcp_server:app", "--host", "0.0.0.0", "--port", "8000"]

apiVersion: apps/v1
kind: Deployment
metadata:
  name: neural-system
spec:
  replicas: 3
  selector:
    matchLabels:
      app: neural-system
  template:
    metadata:
      labels:
        app: neural-system
    spec:
      containers:
      - name: neural-mcp
        image: neural-system:latest
        ports:
        - containerPort: 8000

from neural_client import NeuralClient

client = NeuralClient("http://localhost:8000")
result = await client.analyze_code("def hello(): return 'world'")

import { NeuralClient } from '@neural/client';

const client = new NeuralClient({
  baseURL: 'http://localhost:8000',
  apiKey: 'your-api-key'
});

const analysis = await client.analyzeRepository('/path/to/repo');

# Batch processing
curl -X POST http://localhost:8000/mcp/batch \
  -H "Content-Type: application/json" \
  -d @batch_request.json

{
  "model": {
    "provider": "ollama",
    "name": "llama3.2",
    "temperature": 0.7,
    "max_tokens": 32000,
    "gpu_layers": 35
  },
  "performance": {
    "worker_processes": 4,
    "max_concurrent_requests": 100,
    "cache_ttl_seconds": 300
  },
  "security": {
    "enable_auth": true,
    "rate_limit_per_minute": 60,
    "allowed_origins": ["https://your-domain.com"]
  }
}

# Run unit tests
pytest tests/unit

# Run integration tests
pytest tests/integration

# Run with coverage
pytest --cov=neural_system --cov-report=html

# Run security tests
bandit -r neural_system/
semgrep --config=auto .

# Install development dependencies
pip install -r requirements-dev.txt

# Run pre-commit hooks
pre-commit install

# Run tests before committing
pytest && flake8 && mypy .