blockdoc 1.0.0

A simple, powerful standard for structured content that works beautifully with LLMs, humans, and modern editors

BlockDoc

A simple, powerful standard for structured content that works beautifully with LLMs, humans, and modern editors.

Why blockdoc?

blockdoc provides a lightweight, flexible format for structured content that is:

• LLM-friendly: Optimized for AI generation and targeted modifications
• Simple: Flat structure with semantic IDs and minimal nesting
• Extensible: Core block types with room for custom extensions
• Framework-agnostic: Works with any frontend or backend technology
• Database-ready: Easy to store and query in SQL or NoSQL databases

Core Concepts

BlockDoc is based on a block-based architecture where content is organized into discrete, individually addressable blocks. Each block has:

• A semantic ID (like 'intro', 'section-1')
• A block type ('text', 'heading', 'image', 'code')
• Content (in Markdown for text-based blocks)
• Optional metadata

This architecture enables:

• Targeted updates to specific sections
• Better organization of content
• Easy integration with LLMs
• Flexible rendering in different formats

Core Block Types

1. Text - Standard paragraphs with Markdown support
2. Heading - Section headers with configurable levels
3. Image - Pictures with src, alt text, and optional caption
4. Code - Code blocks with syntax highlighting
5. List - Ordered or unordered lists
6. Quote - Blockquote content
7. Embed - Embedded content (videos, social media posts)
8. Divider - Horizontal rule/separator

Design Principles

1. Simplicity: Minimal structure with only necessary properties
2. LLM-Friendly: Optimized for AI content generation and modification
3. Human-Editable: Clear, readable format for direct editing
4. Database-Ready: Easily stored in SQL or NoSQL databases
5. Extensible: Core types with support for custom block types
6. Semantic: Meaningful IDs for blocks rather than auto-generated IDs
7. Portable: Framework-agnostic with multiple render targets

{
  "article": {
    "title": "Getting Started with blockdoc",
    "blocks": [
      {
        "id": "intro",
        "type": "text",
        "content": "blockdoc makes structured content **simple**."
      },
      {
        "id": "first-steps",
        "type": "heading",
        "level": 2,
        "content": "First Steps"
      },
      {
        "id": "step-one",
        "type": "text",
        "content": "Install blockdoc using npm: `npm install blockdoc`"
      }
    ]
  }
}

Installation

Install BlockDoc from PyPI:

pip install blockdoc

Usage

from blockdoc import BlockDocDocument, Block

# Create a new document
doc = BlockDocDocument({
    "title": "My First BlockDoc Post",
})

# Add blocks using factory methods
doc.add_block(Block.text("intro", "Welcome to my first post!"))
doc.add_block(Block.heading("section-1", 2, "Getting Started"))
doc.add_block(Block.text("content-1", "This is **formatted** content with [links](https://example.com)."))

# Render to HTML
html = doc.render_to_html()
print(html)

# Render to Markdown
markdown = doc.render_to_markdown()
print(markdown)

# Export to JSON
json_str = doc.to_json()
print(json_str)

Working with LLMs

BlockDoc shines when generating or modifying content with LLMs:

from blockdoc import BlockDocDocument
import openai

# Update a specific section using an LLM
async def update_section(document, block_id, prompt):
    block = document.get_block(block_id)
    
    response = await openai.chat.completions.create(
        model="gpt-4",
        messages=[
            {
                "role": "system",
                "content": f"Update the following content to {prompt}. Return only the updated content."
            },
            {
                "role": "user",
                "content": block["content"],
            },
        ],
    )
    
    document.update_block(block_id, {
        "content": response.choices[0].message.content,
    })
    
    return document

Documentation

• Full Specification
• API Reference
• Tutorials
  • Getting Started
  • Block Types
  • LLM Integration
• Examples
  • Simple Blog
  • React Demo
  • LLM Integration

Development

Installation

# Clone the repository
git clone https://github.com/berrydev-ai/blockdoc-python.git
cd blockdoc-python

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

# Install development dependencies
pip install -e ".[dev]"

Testing

BlockDoc uses pytest for testing. To run the tests:

# Run tests
pytest

# Run tests with coverage report
pytest --cov=blockdoc

# Run a specific test file
pytest tests/core/test_block.py

Contributing

We welcome contributions! See CONTRIBUTING.md for details on how to contribute, including our testing guidelines.

License

MIT