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Getting Started with Code-to-Documentation AI Agent

Last Updated: 2/12/2026

Getting Started with Code-to-Documentation AI Agent

Overview

The Code-to-Documentation AI Agent is an intelligent system designed to automatically generate and maintain documentation from source code. This guide will help you get up and running quickly.

What is Code-to-Doc?

Code-to-Doc is an AI-powered agent system that:

  • Automatically extracts function docstrings and code documentation
  • Generates comprehensive documentation using RAGFlow integration
  • Maintains up-to-date docs as your codebase evolves
  • Provides intelligent context through retrieval-augmented generation (RAG)

Architecture Overview

The system consists of several key components:

Core Components

  1. AI Agent Layer - Orchestrates documentation generation workflows
  2. RAGFlow Integration - Provides context-aware documentation generation
  3. Code Parser - Extracts docstrings and code structure
  4. Documentation Generator - Creates formatted documentation output

Prerequisites

Before you begin, ensure you have:

  • Access to the code repository
  • Python 3.8+ installed
  • API credentials for the AI service
  • RAGFlow integration configured
  • Basic understanding of your codebase structure

Quick Start

Step 1: Installation

# Clone the repository
git clone <repository-url>
cd code-to-doc-demo
 
# Install dependencies
pip install -r requirements.txt

Step 2: Configuration

Create a configuration file with your settings:

# config.yaml
ai_agent:
  model: "claude-3"
  api_key: "your-api-key"
 
ragflow:
  endpoint: "https://ragflow-instance.com"
  api_key: "your-ragflow-key"
 
source:
  code_paths:
    - "./src"
    - "./lib"

Step 3: Run Your First Documentation Generation

# Generate documentation for your codebase
python generate_docs.py --config config.yaml --output ./docs

Key Concepts

Function Docstrings

The system extracts docstrings from your code and uses them as the foundation for documentation. Ensure your code follows standard docstring conventions:

def example_function(param1: str, param2: int) -> bool:
    """
    Brief description of the function.
    
    Args:
        param1: Description of param1
        param2: Description of param2
    
    Returns:
        Description of return value
    """
    pass

RAGFlow Integration

RAGFlow enhances documentation generation by:

  • Providing relevant context from existing documentation
  • Maintaining consistency across documentation
  • Suggesting improvements based on best practices

AI Agent Workflow

The agent follows this workflow:

  1. Scan - Identifies code files and structures
  2. Extract - Pulls docstrings and metadata
  3. Enhance - Uses RAGFlow to add context
  4. Generate - Creates formatted documentation
  5. Validate - Checks for completeness and accuracy

Common Use Cases

Generate API Documentation

python generate_docs.py --type api --output ./api-docs

Update Existing Documentation

python generate_docs.py --mode update --input ./docs --output ./docs

Generate Documentation for Specific Modules

python generate_docs.py --modules auth,database,api --output ./docs

Best Practices

  1. Keep docstrings up-to-date - The quality of generated docs depends on your source docstrings
  2. Use consistent formatting - Follow your team’s docstring conventions
  3. Review generated docs - Always review AI-generated content before publishing
  4. Iterate and improve - Use feedback to refine the documentation generation process
  5. Integrate with CI/CD - Automate documentation generation in your pipeline

Troubleshooting

Documentation Generation Fails

  • Check your API credentials are valid
  • Ensure code paths in config are correct
  • Verify Python version compatibility

Missing or Incomplete Documentation

  • Review source code docstrings
  • Check RAGFlow connection
  • Increase context window in configuration

Performance Issues

  • Limit scope to specific modules
  • Use incremental generation mode
  • Optimize RAGFlow query parameters

Next Steps

Now that you’re set up, explore these advanced topics:

Support & Resources

  • Documentation Space: Check the code-to-doc-demo space for detailed documentation
  • Issues: Report bugs and request features in the repository
  • Team Chat: Join the engineering channel for questions

Contributing

Interested in improving the system? Check out:

  • Contribution guidelines in the repository
  • Open issues and feature requests
  • Development setup documentation

Questions? Reach out to the engineering team or check the code-to-doc-demo documentation space for more details.

Escalation ProceduresTeam Contacts