AI-Powered Architecture Diagram Generation

Automatically generate Mermaid architecture diagrams from your Terraform state files using AI analysis.

Quick Start

curl -X POST https://api.usenabla.com/v1/diagram \
  -H "X-Customer-Key: your_api_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Production Architecture",
    "statefile_path": "s3://terraform-states/prod/terraform.tfstate"
  }'

Python Implementation

Basic Diagram Generation

import requests
import base64
from typing import Dict, Optional

class DiagramGenerator:
    def __init__(self, api_key: str, base_url: str = "https://api.usenabla.com"):
        self.api_key = api_key
        self.base_url = base_url

    def generate_from_file(self,
                          statefile_path: str,
                          name: str,
                          model: Optional[str] = None) -> Dict:
        """Generate architecture diagram from local Terraform state."""

        with open(statefile_path, 'rb') as f:
            state_content = base64.b64encode(f.read()).decode('utf-8')

        payload = {
            "name": name,
            "statefile_content": state_content
        }

        if model:
            payload["model"] = model

        response = requests.post(
            f"{self.base_url}/v1/diagram",
            headers={
                "X-Customer-Key": self.api_key,
                "Content-Type": "application/json"
            },
            json=payload
        )
        response.raise_for_status()

        return response.json()

    def generate_from_remote(self,
                            remote_path: str,
                            name: str,
                            model: Optional[str] = None) -> Dict:
        """Generate architecture diagram from remote Terraform state."""

        payload = {
            "name": name,
            "statefile_path": remote_path
        }

        if model:
            payload["model"] = model

        response = requests.post(
            f"{self.base_url}/v1/diagram",
            headers={
                "X-Customer-Key": self.api_key,
                "Content-Type": "application/json"
            },
            json=payload
        )
        response.raise_for_status()

        return response.json()

    def save_diagram(self, result: Dict, output_path: str = "architecture.mmd"):
        """Save Mermaid diagram to file."""

        with open(output_path, 'w') as f:
            f.write(result["mermaid_content"])

        print(f"Diagram saved to {output_path}")
        print(f"Nodes: {result['metadata']['node_count']}")
        print(f"Edges: {result['metadata']['edge_count']}")

        return output_path

    def render_to_svg(self, mermaid_file: str, output_svg: str = "architecture.svg"):
        """Render Mermaid diagram to SVG using mmdc CLI."""
        import subprocess

        try:
            subprocess.run([
                "mmdc",
                "-i", mermaid_file,
                "-o", output_svg,
                "-b", "transparent"
            ], check=True)
            print(f"SVG rendered to {output_svg}")
        except FileNotFoundError:
            print("Error: mermaid-cli not installed. Run: npm install -g @mermaid-js/mermaid-cli")
        except subprocess.CalledProcessError as e:
            print(f"Error rendering diagram: {e}")

# Usage
generator = DiagramGenerator(api_key="your_api_key")

result = generator.generate_from_file(
    statefile_path="./terraform.tfstate",
    name="AWS Production Infrastructure"
)

# Save and render
mmd_file = generator.save_diagram(result)
generator.render_to_svg(mmd_file)

Advanced Usage with Custom AI Models

class AdvancedDiagramGenerator(DiagramGenerator):

    def generate_with_custom_model(self,
                                   statefile_path: str,
                                   name: str,
                                   model: str = "@cf/openai/gpt-oss-120b",
                                   api_key: Optional[str] = None) -> Dict:
        """
        Generate diagram using a custom AI model.

        Supported models:
        - @cf/openai/gpt-oss-120b (default)
        - @cf/meta/llama-3.1-70b-instruct
        - @cf/anthropic/claude-3-5-sonnet
        """

        with open(statefile_path, 'rb') as f:
            state_content = base64.b64encode(f.read()).decode('utf-8')

        payload = {
            "name": name,
            "statefile_content": state_content,
            "model": model
        }

        if api_key:
            payload["api_key"] = api_key

        response = requests.post(
            f"{self.base_url}/v1/diagram",
            headers={
                "X-Customer-Key": self.api_key,
                "Content-Type": "application/json"
            },
            json=payload
        )
        response.raise_for_status()

        return response.json()

    def generate_multiple_views(self, statefile_path: str, name: str) -> Dict[str, Dict]:
        """
        Generate multiple diagram views from the same infrastructure.

        Creates:
        - Network topology view
        - Security groups view
        - Data flow view
        - Service dependency view
        """

        views = {}

        # Use different AI prompting for different views
        # This would be handled server-side based on naming conventions

        views["network"] = self.generate_from_file(
            statefile_path,
            f"{name} - Network Topology"
        )

        views["security"] = self.generate_from_file(
            statefile_path,
            f"{name} - Security Architecture"
        )

        views["services"] = self.generate_from_file(
            statefile_path,
            f"{name} - Service Dependencies"
        )

        return views

    def compare_infrastructure_versions(self,
                                       old_state: str,
                                       new_state: str,
                                       name: str) -> Dict:
        """Generate diagrams showing infrastructure changes."""

        old_diagram = self.generate_from_file(old_state, f"{name} - Before")
        new_diagram = self.generate_from_file(new_state, f"{name} - After")

        return {
            "before": old_diagram,
            "after": new_diagram,
            "changes": {
                "nodes_added": new_diagram["metadata"]["node_count"] -
                              old_diagram["metadata"]["node_count"],
                "edges_added": new_diagram["metadata"]["edge_count"] -
                              old_diagram["metadata"]["edge_count"]
            }
        }

# Usage
generator = AdvancedDiagramGenerator(api_key="your_api_key")

# Generate with custom model
result = generator.generate_with_custom_model(
    statefile_path="./terraform.tfstate",
    name="Production Infrastructure",
    model="@cf/meta/llama-3.1-70b-instruct"
)

# Generate multiple views
views = generator.generate_multiple_views(
    statefile_path="./terraform.tfstate",
    name="Production Infrastructure"
)

for view_name, view_data in views.items():
    generator.save_diagram(view_data, f"architecture_{view_name}.mmd")

Diagram Customization

Post-Processing Diagrams

def customize_diagram(mermaid_content: str,
                     theme: str = "default",
                     direction: str = "TB") -> str:
    """
    Customize generated Mermaid diagram.

    Args:
        mermaid_content: Original Mermaid diagram
        theme: Mermaid theme (default, dark, forest, neutral)
        direction: Graph direction (TB, BT, LR, RL)
    """

    lines = mermaid_content.split('\n')

    # Update graph direction
    if lines[0].startswith('graph '):
        lines[0] = f'graph {direction}'

    # Add theme configuration
    theme_config = f"""
%%{{init: {{'theme':'{theme}'}}}}%%
"""

    # Insert theme after first line
    lines.insert(1, theme_config)

    return '\n'.join(lines)

def add_security_annotations(mermaid_content: str,
                            security_groups: Dict) -> str:
    """Add security group annotations to diagram."""

    lines = mermaid_content.split('\n')
    annotations = []

    for sg_name, sg_rules in security_groups.items():
        # Add visual indicators for security boundaries
        if 'public' in sg_name.lower():
            annotations.append(f'    {sg_name}:::publicClass')
        elif 'private' in sg_name.lower():
            annotations.append(f'    {sg_name}:::privateClass')

    # Add class definitions
    class_defs = """
    classDef publicClass fill:#ff6b6b,stroke:#c92a2a
    classDef privateClass fill:#51cf66,stroke:#2f9e44
"""

    lines.extend(annotations)
    lines.append(class_defs)

    return '\n'.join(lines)

# Usage
result = generator.generate_from_file(
    statefile_path="./terraform.tfstate",
    name="Production"
)

# Customize diagram
customized = customize_diagram(
    result["mermaid_content"],
    theme="dark",
    direction="LR"
)

# Add security annotations
security_groups = {
    "public_web_sg": {"ingress": ["80", "443"]},
    "private_app_sg": {"ingress": ["8080"]},
    "private_db_sg": {"ingress": ["5432"]}
}

annotated = add_security_annotations(customized, security_groups)

# Save
with open("architecture_customized.mmd", 'w') as f:
    f.write(annotated)

Documentation Generation

Automated Architecture Documentation

def generate_architecture_docs(statefile_path: str,
                              api_key: str,
                              output_dir: str = "./docs"):
    """
    Generate complete architecture documentation with diagrams.
    """
    import os
    import json
    from datetime import datetime

    os.makedirs(output_dir, exist_ok=True)

    # Generate diagram
    generator = DiagramGenerator(api_key=api_key)
    result = generator.generate_from_file(
        statefile_path=statefile_path,
        name="Infrastructure Architecture"
    )

    # Save Mermaid diagram
    mmd_path = os.path.join(output_dir, "architecture.mmd")
    with open(mmd_path, 'w') as f:
        f.write(result["mermaid_content"])

    # Generate markdown documentation
    doc = f"""# Infrastructure Architecture Documentation

**Generated**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
**Source**: {statefile_path}

## Overview

This document provides an architectural overview of the infrastructure defined in Terraform.

### Statistics

- **Nodes**: {result['metadata']['node_count']}
- **Connections**: {result['metadata']['edge_count']}
- **Title**: {result['metadata']['title']}

## Architecture Diagram

```mermaid
{result['mermaid_content']}

Component Details

Resources

The infrastructure consists of managed resources.

Network Topology

The architecture shows connections between components.

Generated Files

architecture.mmd - Mermaid diagram source
architecture.svg - Rendered diagram (generate with mmdc -i architecture.mmd -o architecture.svg)
metadata.json - Diagram metadata

Generated by Nabla Evidence Engine """

Save documentation

doc_path = os.path.join(output_dir, “ARCHITECTURE.md”) with open(doc_path, ‘w’) as f: f.write(doc)

Save metadata

metadata_path = os.path.join(output_dir, “metadata.json”) with open(metadata_path, ‘w’) as f: json.dump(result[‘metadata’], f, indent=2) print(f”Documentation generated in /”) return output_dir

Usage

generate_architecture_docs( statefile_path=”./terraform.tfstate”, api_key=“your_api_key”, output_dir=”./architecture_docs” )

## CI/CD Integration

### GitHub Actions - Auto-Generate Diagrams

```yaml
name: Update Architecture Diagrams

on:
  push:
    branches: [main]
    paths:
      - 'terraform/**'
  workflow_dispatch:

jobs:
  generate-diagrams:
    runs-on: ubuntu-latest

    steps:
      - uses: actions/checkout@v3

      - name: Setup Node.js
        uses: actions/setup-node@v3
        with:
          node-version: '18'

      - name: Install Mermaid CLI
        run: npm install -g @mermaid-js/mermaid-cli

      - name: Setup Terraform
        uses: hashicorp/setup-terraform@v2

      - name: Generate Terraform State
        working-directory: ./terraform
        env:
          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
          AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
        run: |
          terraform init
          terraform plan -out=plan.tfplan
          terraform show -json plan.tfplan > terraform.tfstate.json

      - name: Generate Architecture Diagram
        env:
          NABLA_API_KEY: ${{ secrets.NABLA_API_KEY }}
        run: |
          STATE_CONTENT=$(cat terraform/terraform.tfstate.json | base64 | tr -d '\n')

          RESPONSE=$(curl -X POST https://api.usenabla.com/v1/diagram \
            -H "X-Customer-Key: $NABLA_API_KEY" \
            -H "Content-Type: application/json" \
            -d "{
              \"name\": \"Production Infrastructure\",
              \"statefile_content\": \"$STATE_CONTENT\"
            }")

          echo "$RESPONSE" | jq -r '.mermaid_content' > docs/architecture.mmd
          echo "$RESPONSE" | jq '.metadata' > docs/architecture-metadata.json

      - name: Render Diagram to SVG
        run: |
          mmdc -i docs/architecture.mmd \
               -o docs/architecture.svg \
               -b transparent \
               -t dark

      - name: Render Diagram to PNG
        run: |
          mmdc -i docs/architecture.mmd \
               -o docs/architecture.png \
               -b white \
               -w 2000

      - name: Commit Updated Diagrams
        run: |
          git config user.name "github-actions[bot]"
          git config user.email "github-actions[bot]@users.noreply.github.com"
          git add docs/architecture.*
          git diff --quiet && git diff --staged --quiet || \
            git commit -m "docs: update architecture diagrams [skip ci]"
          git push

Interactive Diagram Viewer

<!DOCTYPE html>
<html>
<head>
    <title>Infrastructure Architecture</title>
    <script type="module">
        import mermaid from 'https://cdn.jsdelivr.net/npm/mermaid@10/dist/mermaid.esm.min.mjs';
        mermaid.initialize({
            startOnLoad: true,
            theme: 'dark',
            securityLevel: 'loose'
        });
    </script>
    <style>
        body {
            font-family: Arial, sans-serif;
            margin: 0;
            padding: 20px;
            background: #1e1e1e;
            color: #fff;
        }
        .container {
            max-width: 1200px;
            margin: 0 auto;
        }
        .controls {
            margin-bottom: 20px;
        }
        button {
            padding: 10px 20px;
            margin-right: 10px;
            background: #007acc;
            color: white;
            border: none;
            border-radius: 4px;
            cursor: pointer;
        }
        button:hover {
            background: #005a9e;
        }
        .mermaid {
            background: white;
            padding: 20px;
            border-radius: 8px;
        }
    </style>
</head>
<body>
    <div class="container">
        <h1>Infrastructure Architecture</h1>

        <div class="controls">
            <button onclick="refreshDiagram()">Refresh</button>
            <button onclick="downloadSVG()">Download SVG</button>
            <button onclick="changeTheme()">Toggle Theme</button>
        </div>

        <div class="mermaid" id="diagram">
            <!-- Diagram will be loaded here -->
        </div>
    </div>

    <script>
        async function loadDiagram() {
            const response = await fetch('architecture.mmd');
            const mermaidCode = await response.text();
            document.getElementById('diagram').textContent = mermaidCode;
            mermaid.init(undefined, document.querySelector('.mermaid'));
        }

        function downloadSVG() {
            const svg = document.querySelector('.mermaid svg');
            const blob = new Blob([svg.outerHTML], {type: 'image/svg+xml'});
            const url = URL.createObjectURL(blob);
            const a = document.createElement('a');
            a.href = url;
            a.download = 'architecture.svg';
            a.click();
        }

        function changeTheme() {
            // Toggle between themes
            const currentTheme = mermaid.initialize().theme;
            const newTheme = currentTheme === 'dark' ? 'default' : 'dark';
            mermaid.initialize({ theme: newTheme });
            location.reload();
        }

        function refreshDiagram() {
            location.reload();
        }

        loadDiagram();
    </script>
</body>
</html>

Best Practices

Version Control: Store generated diagrams in git to track infrastructure evolution
Automation: Auto-generate diagrams on every infrastructure change
Multiple Views: Create different diagram perspectives (network, security, services)
Documentation: Include diagrams in architecture documentation
CI/CD Gates: Use diagrams for infrastructure review in PRs

Error Handling

def generate_with_retry(generator: DiagramGenerator,
                       statefile_path: str,
                       max_retries: int = 3) -> Dict:
    """Generate diagram with retry logic."""

    for attempt in range(max_retries):
        try:
            return generator.generate_from_file(
                statefile_path=statefile_path,
                name="Infrastructure"
            )
        except requests.exceptions.HTTPError as e:
            if e.response.status_code == 422:
                print(f"Invalid state file: {e.response.json()}")
                return None
            elif attempt < max_retries - 1:
                print(f"Attempt {attempt + 1} failed, retrying...")
                import time
                time.sleep(2 ** attempt)
            else:
                raise

Getting started

Usage Examples

Architecture Boundary Diagrams

AI-Powered Architecture Diagram Generation

Quick Start

Python Implementation

Basic Diagram Generation

Advanced Usage with Custom AI Models

Diagram Customization

Post-Processing Diagrams

Documentation Generation

Automated Architecture Documentation

Component Details

Resources

Network Topology

Generated Files

Save documentation

Save metadata

Usage

Interactive Diagram Viewer

Best Practices

Error Handling

Getting started

Usage Examples

​AI-Powered Architecture Diagram Generation

​Quick Start

​Python Implementation

​Basic Diagram Generation

​Advanced Usage with Custom AI Models

​Diagram Customization

​Post-Processing Diagrams

​Documentation Generation

​Automated Architecture Documentation

​Component Details

​Resources

​Network Topology

​Generated Files

​Save documentation

​Save metadata

​Usage

​Interactive Diagram Viewer

​Best Practices

​Error Handling

AI-Powered Architecture Diagram Generation

Quick Start

Python Implementation

Basic Diagram Generation

Advanced Usage with Custom AI Models

Diagram Customization

Post-Processing Diagrams

Documentation Generation

Automated Architecture Documentation

Component Details

Resources

Network Topology

Generated Files

Save documentation

Save metadata

Usage

Interactive Diagram Viewer

Best Practices

Error Handling