AI & LLM Integration

Pushduck documentation provides AI-friendly endpoints that make it easy for large language models (LLMs) and automated tools to access and process our documentation content.

Available Endpoints

📄 Complete Documentation Export

Access all documentation content in a single, structured format:

GET /llms.txt

This endpoint returns all documentation pages in a clean, AI-readable format with:

• Page titles and URLs
• Descriptions and metadata
• Full content with proper formatting
• Structured sections and hierarchies

Example Usage:

curl https://your-domain.com/llms.txt

📑 Individual Page Access

Access any documentation page's raw content by appending .mdx to its URL:

GET /docs/{page-path}.mdx

Examples:

• /docs/quick-start.mdx - Quick start guide content
• /docs/api/hooks/use-upload.mdx - Hook documentation
• /docs/guides/setup/aws-s3.mdx - AWS S3 setup guide

Use Cases

🤖 AI Assistant Integration

• Train custom AI models on our documentation
• Create chatbots that can answer questions about Pushduck
• Build intelligent documentation search systems

🔧 Development Tools

• Generate code examples and snippets
• Create automated documentation tests
• Build CLI tools that reference our docs

📊 Content Analysis

• Analyze documentation completeness
• Track content changes over time
• Generate documentation metrics

Content Format

The LLM endpoints return content in a structured format:

# Page Title
URL: /docs/page-path

Page description here

# Section Headers
Content with proper markdown formatting...

## Subsections
- Lists and bullet points
- Code blocks with syntax highlighting
- Tables and structured data

Technical Details

• Caching: Content is cached for optimal performance
• Processing: Uses Remark pipeline with MDX and GFM support
• Format: Clean markdown with frontmatter removed
• Encoding: UTF-8 text format
• CORS: Enabled for cross-origin requests

Rate Limiting

These endpoints are designed for programmatic access and don't have aggressive rate limiting. However, please be respectful:

• Cache responses when possible
• Avoid excessive automated requests
• Use appropriate user agents for your tools

Examples

Python Script

import requests

# Get all documentation
response = requests.get('https://your-domain.com/llms.txt')
docs_content = response.text

# Get specific page
page_response = requests.get('https://your-domain.com/docs/quick-start.mdx')
page_content = page_response.text

Node.js/JavaScript

// Fetch all documentation
const allDocs = await fetch("/llms.txt").then((r) => r.text());

// Fetch specific page
const quickStart = await fetch("/docs/quick-start.mdx").then((r) => r.text());

cURL

# Download all docs to file
curl -o pushduck-docs.txt https://your-domain.com/llms.txt

# Get specific page content
curl https://your-domain.com/docs/api/hooks/use-upload.mdx

Integration with Popular AI Tools

OpenAI GPT

Use the /llms.txt endpoint to provide context about Pushduck in your GPT conversations.

Claude/Anthropic

Feed documentation content to Claude for detailed analysis and code generation.

Local LLMs

Download content for training or fine-tuning local language models.

These AI-friendly endpoints make it easy to integrate Pushduck documentation into your development workflow and AI-powered tools!