Knowledge Management

Knowledge management involves systematically capturing, organizing, and managing knowledge assets such as documentation, diagrams, and workflows. With the rise of Infrastructure as Code (IaC) and other code-driven methodologies, managing knowledge assets as code has become essential for modern development practices.

Introduction

What is Knowledge Management?

Knowledge management refers to the practice of creating, sharing, using, and managing knowledge and information within an organization. It includes documents, diagrams, workflows, and other assets critical for collaboration and operational excellence.

Why Manage Knowledge as Code?

1. Version Control:
   • Track changes over time using tools like Git.
2. Collaboration:
   • Enhance teamwork by integrating documentation into development workflows.
3. Automation:
   • Generate and deploy knowledge assets automatically using pipelines.
4. Reproducibility:
   • Ensure consistency across environments by embedding knowledge in the codebase.

Key Components of Knowledge Management

Documentation as Code

• Use version-controlled text formats (e.g., Markdown) for documentation.
• Deploy documentation sites automatically using static site generators.

Diagrams as Code

• Generate reproducible diagrams using text-based formats and programmatic tools.
• Replace static images with tools like Mermaid, PlantUML, and Python libraries.

Automation and Reproducibility

• Integrate knowledge assets into CI/CD workflows.
• Automatically update documentation and diagrams with code changes.

Diagram: Knowledge Management Workflow

graph TD
    Code -->|Changes| Documentation
    Code -->|Changes| Diagrams
    Documentation -->|Deploy| StaticSite
    Diagrams -->|Render| Visuals
    StaticSite -->|Feedback| Team
    Visuals -->|Feedback| Team

Hold "Alt" / "Option" to enable pan & zoom

Core Areas of Knowledge Management

1. Documentation as Code:

   • Manage user guides, API references, and workflows in Markdown.
   • Use Git for version control and static site generators for publishing.

2. Diagrams as Code:

   • Create sequence diagrams, architecture diagrams, and workflows programmatically.
   • Use tools like Mermaid for Markdown-integrated diagrams or Python for dynamic, data-driven visuals.

3. Automation:

   • Incorporate documentation and diagram generation into CI/CD pipelines.
   • Ensure knowledge assets are always up to date.

4. Library of Examples:

   • Pre-built templates and examples for common diagrams and workflows.

Tools Overview

Documentation Tools

• Markdown: Lightweight text-based documentation.
• Static Site Generators: Tools like MkDocs, Docusaurus, or Hugo.

Diagram Tools

• Mermaid: Embed diagrams in Markdown using a text-based syntax.
• PlantUML: Create advanced UML diagrams with text descriptions.
• Python Libraries: Generate diagrams programmatically with libraries like Matplotlib and Graphviz.

What is Documentation as Code?

Documentation as Code involves creating and managing documentation in a text-based format (e.g., Markdown) stored in version control systems like Git. It integrates seamlessly into development workflows, ensuring that documentation evolves alongside the code.

Key Benefits

1. Version Control:
   • Track changes to documentation over time using Git.
2. Collaboration:
   • Encourage contributions from all team members through pull requests.
3. Automation:
   • Automatically build and deploy documentation sites using CI/CD pipelines.
4. Single Source of Truth:
   • Ensure documentation is always aligned with the latest code changes.

Documentation Tools

Markdown

• Description:
  • A lightweight markup language ideal for creating structured, human-readable documentation.
• Use Cases:
  • README files, API references, and quick guides.

Static Site Generators

• Convert Markdown files into interactive documentation sites.

Popular Tools

1. MkDocs:

   • Simple and fast static site generator for project documentation.
   • Ideal for teams needing minimal setup.
   • Extensions like MkDocs Material enhance usability and aesthetics.

2. Docusaurus:

   • Focused on documentation websites for open-source and enterprise projects.
   • Built-in React support for advanced customization.

3. Hugo:

   • Flexible and performant static site generator with wide theming options.

Git Integration

• Workflow:
  1. Store documentation alongside code in the same repository.
  2. Use branching and pull requests to review updates.
  3. Deploy changes automatically through CI/CD pipelines.

Best Practices

Align Documentation with Development

• Write or update documentation as part of the development process.
• Example: Include API changes in the same pull request as the code changes.

Automate Documentation Builds

• Use CI/CD pipelines to generate and deploy documentation automatically.

Example Pipeline

name: Build Documentation

on:
  push:
    branches:
      - main

jobs:
  build:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout code
      uses: actions/checkout@v2

    - name: Setup Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x'

    - name: Install MkDocs
      run: pip install mkdocs mkdocs-material

    - name: Build Documentation
      run: mkdocs build

    - name: Deploy to GitHub Pages
      uses: peaceiris/actions-gh-pages@v3
      with:
        publish_dir: ./site

Standardize Formats and Styles

• Use templates to ensure consistency across documentation.
• Tools like Prettier can format Markdown files automatically.

Real-World Examples

1. API Documentation:
   • Store API references in Markdown and render them into a searchable site using MkDocs or Docusaurus.
2. Team Knowledge Base:
   • Use a static site generator to create an internal knowledge base, integrating diagrams via Mermaid.

Diagram: Documentation Workflow

graph TD
    WriteMarkdown --> VersionControl
    VersionControl --> BuildStaticSite
    BuildStaticSite --> DeploySite
    DeploySite --> DocumentationSite

Hold "Alt" / "Option" to enable pan & zoom

What is Diagrams as Code?

Diagrams as Code involves creating diagrams using text-based or programmatic tools that integrate into the development workflow. This approach eliminates static, manually created images, enabling teams to update, review, and automate diagrams alongside the codebase.

Key Benefits

1. Version Control:
   • Manage diagrams in Git for traceability and collaboration.
2. Reproducibility:
   • Ensure diagrams are generated consistently from the same source.
3. Automation:
   • Automatically generate and update diagrams in CI/CD pipelines.
4. Dynamic Content:
   • Programmatically generate diagrams from live data or configurations.

Tools for Diagrams as Code

Mermaid

• Description:
  • A text-based diagramming tool integrated with Markdown and static site generators.
• Diagram Types:
  • Flowcharts, sequence diagrams, Gantt charts, entity-relationship diagrams, and more.

Example: Sequence Diagram

sequenceDiagram
    participant User
    participant API
    participant Database

    User->>API: Send Request
    API->>Database: Query Data
    Database-->>API: Return Data
    API-->>User: Send Response

Hold "Alt" / "Option" to enable pan & zoom

PlantUML

• Description:
  • A versatile tool for creating UML diagrams using plain text.
• Diagram Types:
  • Class diagrams, use case diagrams, component diagrams, and activity diagrams.

Example: Class Diagram

@startuml
class User {
    + string Name
    + int Age
    + GetDetails()
}

class API {
    + SendRequest()
    + ReceiveResponse()
}

User --> API
@enduml

Python Libraries

• Description:
  • Programmatically generate diagrams and visualizations from data or configurations.
• Libraries:
  • Matplotlib: For charts and graphs.
  • Graphviz: For hierarchical and flow diagrams.
  • PyGraphviz: For complex graph structures.

Example: Graphviz Workflow

from graphviz import Digraph

dot = Digraph()
dot.node('A', 'Start')
dot.node('B', 'Process')
dot.node('C', 'End')

dot.edges(['AB', 'BC'])

dot.render('workflow', format='png', cleanup=True)

Diagrams Library (Python)

• Description:
  • A Python library specifically for creating cloud architecture diagrams.
• Supported Platforms:
  • AWS, Azure, GCP, Kubernetes, and on-premises solutions.

Example: Cloud Architecture

from diagrams import Diagram
from diagrams.aws.compute import EC2
from diagrams.aws.database import RDS
from diagrams.aws.network import ELB

with Diagram("Simple Architecture", show=False):
    ELB("Load Balancer") >> EC2("Web Server") >> RDS("Database")

Best Practices

Choose the Right Tool

• Mermaid:
  • Best for quick, embedded diagrams in Markdown.
• PlantUML:
  • Ideal for detailed UML diagrams.
• Python Libraries:
  • Use for dynamic, data-driven diagrams.

Automate Diagram Generation

• Use CI/CD pipelines to generate and update diagrams programmatically.

Example Pipeline for Mermaid Diagrams

name: Generate Diagrams

on:
  push:
    branches:
      - main

jobs:
  build:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout code
      uses: actions/checkout@v2

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

    - name: Generate Diagrams
      run: mmdc -i diagram.mmd -o diagram.png

Version Control for Diagrams

• Store source files (e.g., .mmd, .puml) in Git alongside the code.
• Use branching and pull requests to review changes.

Diagram: Diagrams as Code Workflow

graph TD
    WriteDiagramCode --> VersionControl
    VersionControl --> GenerateDiagram
    GenerateDiagram --> ReviewDiagram
    ReviewDiagram --> PublishDiagram
    PublishDiagram --> Documentation

Hold "Alt" / "Option" to enable pan & zoom

Common Use Cases

1. Architecture Diagrams:
   • Use Mermaid or Python to represent microservices, cloud deployments, or system workflows.
2. Sequence Diagrams:
   • Model interactions between components with Mermaid or PlantUML.
3. Cloud Infrastructure:
   • Use the Diagrams Python library for cloud architecture diagrams.

Why Automate Knowledge Management?

1. Consistency:
   • Automated pipelines ensure documentation and diagrams are always aligned with the latest code changes.
2. Efficiency:
   • Eliminate manual effort in generating and updating knowledge assets.
3. Scalability:
   • Handle large and complex projects with minimal overhead.
4. Reproducibility:
   • Generate consistent outputs across environments.

Tools for Automation

Static Site Generators

• MkDocs:
  • Build and deploy documentation sites from Markdown files.
  • Automation: Automatically trigger a rebuild when changes are pushed.
• Docusaurus:
  • Ideal for interactive documentation with React-based customization.

Diagram Generation Tools

• Mermaid CLI:
  • Command-line tool for generating diagrams from .mmd files.
• PlantUML CLI:
  • Generate UML diagrams from .puml source files.
• Python Libraries:
  • Automate diagram creation for data-driven workflows using Graphviz or the Diagrams library.

CI/CD Integration

• GitHub Actions:
  • Automate build, test, and deployment workflows for knowledge assets.
• Azure DevOps Pipelines:
  • Create reusable pipelines for documentation and diagrams.
• GitLab CI/CD:
  • Define pipelines to build and publish knowledge artifacts.

Example Workflows

Automating Documentation Builds

Pipeline for MkDocs

name: Build and Deploy Documentation

on:
  push:
    branches:
      - main

jobs:
  build:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout code
      uses: actions/checkout@v2

    - name: Setup Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x'

    - name: Install MkDocs
      run: pip install mkdocs mkdocs-material

    - name: Build Documentation
      run: mkdocs build

    - name: Deploy to GitHub Pages
      uses: peaceiris/actions-gh-pages@v3
      with:
        publish_dir: ./site

Automating Diagram Generation

Pipeline for Mermaid Diagrams

name: Generate Mermaid Diagrams

on:
  push:
    branches:
      - main

jobs:
  generate:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout code
      uses: actions/checkout@v2

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

    - name: Generate Diagrams
      run: mmdc -i architecture.mmd -o architecture.png

Pipeline for Python-Based Diagrams

name: Generate Python Diagrams

on:
  push:
    branches:
      - main

jobs:
  generate:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout code
      uses: actions/checkout@v2

    - name: Setup Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x'

    - name: Install Dependencies
      run: pip install diagrams

    - name: Generate Diagrams
      run: python generate_diagrams.py

Integration Strategies

Documentation and Code in One Repository

• Store Markdown files and diagram sources alongside the codebase.
• Use Git branching and pull requests to review changes to knowledge assets.

Automated Deployment

• Use pipelines to:
  • Rebuild documentation sites when Markdown files are updated.
  • Regenerate diagrams when .mmd or .puml files are modified.
  • Deploy the outputs to static hosting platforms (e.g., GitHub Pages, Azure Static Web Apps).

Monitoring and Validation

• Include steps in pipelines to validate knowledge assets:
  • Lint Markdown files for formatting errors.
  • Check diagram source files for syntax issues.

Diagram: Automation Workflow

graph TD
    CodeChange --> TriggerPipeline
    TriggerPipeline --> GenerateDocumentation
    TriggerPipeline --> GenerateDiagrams
    GenerateDocumentation --> DeploySite
    GenerateDiagrams --> PublishVisuals
    DeploySite --> KnowledgePortal
    PublishVisuals --> KnowledgePortal

Hold "Alt" / "Option" to enable pan & zoom

Best Practices for Automation

1. Reusable Pipelines:
   • Create modular pipelines that can be reused across projects.
2. Continuous Updates:
   • Automate builds and deployments on every push to the main branch.
3. Visibility:
   • Host generated assets (e.g., documentation, diagrams) on accessible platforms for team-wide visibility.
4. Version Control:
   • Commit generated artifacts or their source files to version control for traceability.

Best Practices for Knowledge Management

Treat Knowledge as Code

• Store all knowledge assets, including documentation and diagrams, in version control.
• Use branching strategies and pull requests for reviewing updates to knowledge assets.

Automate Whenever Possible

• Use CI/CD pipelines to automate:
  • Documentation builds and deployments.
  • Diagram generation and publishing.

Standardize Formats and Tools

• Choose a consistent set of tools and formats to ensure uniformity across projects:
  • Documentation: Markdown + MkDocs.
  • Diagrams: Mermaid, PlantUML, or Python libraries.

Integrate with Development Workflows

• Keep documentation and diagrams close to the codebase to ensure updates occur alongside code changes.

Prioritize Reproducibility

• Ensure diagrams and documentation can be generated reliably from their source files.
• Avoid manually created or updated assets to reduce errors.

Templates Library

Documentation Templates

README.md Template

# Project Name

## Description
Briefly describe the purpose of the project.

## Features
- Feature 1
- Feature 2

## Getting Started
Instructions for setting up and running the project.

## Contributing
Guidelines for contributing to the project.

## License
Details about the project license.

2. MkDocs Configuration

site_name: Knowledge Management Docs
theme:
  name: material
nav:
  - Home: index.md
  - Diagrams: diagrams.md

Diagram Templates

Mermaid Diagram: CI/CD Workflow

graph TD
    CodeCommit --> PipelineTrigger
    PipelineTrigger --> Build
    Build --> Test
    Test --> Deploy

Hold "Alt" / "Option" to enable pan & zoom

PlantUML: Class Diagram

@startuml
class Project {
    + string Name
    + string Description
    + void Start()
}
@enduml

Python Diagram: Cloud Architecture

from diagrams import Diagram
from diagrams.aws.compute import EC2
from diagrams.aws.database import RDS
from diagrams.aws.network import ELB

with Diagram("Web Service", show=False):
    ELB("Load Balancer") >> EC2("Application Server") >> RDS("Database")

CI/CD Pipelines

Documentation Deployment Pipeline

name: Deploy Documentation

on:
  push:
    branches:
      - main

jobs:
  deploy:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout code
      uses: actions/checkout@v2

    - name: Setup Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x'

    - name: Install MkDocs
      run: pip install mkdocs mkdocs-material

    - name: Build and Deploy
      run: |
        mkdocs build
        mkdocs gh-deploy

Real-World Applications

1. Internal Knowledge Portals:
   • Use MkDocs and Mermaid to create interactive documentation for team processes.
2. Architecture Visualization:
   • Automate cloud infrastructure diagrams using Python and the Diagrams library.
3. Versioned Documentation:
   • Host multiple versions of documentation for APIs or services, aligned with release branches.

Conclusion

Managing knowledge as code ensures reproducibility, automation, and alignment with modern development practices. By leveraging tools like MkDocs, Mermaid, and Python libraries, teams can create dynamic and scalable knowledge assets integrated into their workflows.

References

• MkDocs Documentation
• Mermaid Documentation
• PlantUML Documentation
• Diagrams Library