USAGE.md

Usage

Note: Generating or updating documents requires the dependent environment of the API service.

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Using Environment Variables

OpenAI Example

export DOCAPI_MODEL=openai:gpt-4o-mini

export OPENAI_API_KEY=your_api_key

# Generate documentation for Flask
docapi generate server.py

# Generate documentation for Django
# docapi generate manage.py

# Update documentation for Flask
docapi update server.py

# Update documentation for Django
# docapi update manage.py

# Launch the web server to display the documentation
docapi serve

Azure OpenAI Example

export DOCAPI_MODEL=azure-openai:gpt-4o-mini

export AZURE_OPENAI_API_KEY=your_api_key

export AZURE_OPENAI_ENDPOINT=your_endpoint

export OPENAI_API_VERSION=api_version

# Generate documentation with a custom template
docapi generate server.py --template <template_path>

# Update documentation with a custom template
docapi update server.py --template <template_path>

# Launch the web service on a custom IP and port
docapi serve docs --ip 0.0.0.0 --port 9000

XAI Example

export DOCAPI_MODEL=xai:grok-beta

export XAI_API_KEY=your_api_key

# Generate documentation
docapi generate manage.py

# Update documentation
docapi update manage.py

# Launch the web service
docapi serve

Open-Source Models Example

export DOCAPI_MODEL=open-source:model_name

export OPENAI_API_KEY=your_api_key

export OPENAI_API_BASE=api_base_url

# Generate documentation
docapi generate server.py

# Update documentation
docapi update server.py

# Launch the web service
docapi serve

Baidu Qianfan Example

export DOCAPI_MODEL=baidu:ERNIE-4.0-Turbo-8K

export QIANFAN_ACCESS_KEY=your_access_key

export QIANFAN_SECRET_KEY=your_secret_key

# Generate documentation
docapi generate server.py

# Update documentation
docapi update server.py

# Launch the web service
docapi serve

Tongyi Qianwen Example

export DOCAPI_MODEL=aliyun:qwen-turbo

export DASHSCOPE_API_KEY=your_api_key

# Generate documentation with parallel processing
docapi generate manage.py --workers 6

# Update documentation with parallel processing
docapi update manage.py --workers 6

# Launch the web service
docapi serve

Zhipu AI Example

export DOCAPI_MODEL=zhipu:glm-4-flash

export ZHIPUAI_API_KEY=your_api_key

# Generate documentation
docapi generate server.py

# Update documentation
docapi update server.py

# Launch the web service
docapi serve

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Using Environment Variable Configuration Files

To use a configuration file, create and edit a .env file:

DOCAPI_MODEL = openai:gpt-4o-mini

OPENAI_API_KEY = your_api_key

DASHSCOPE_API_KEY = your_api_key

Run commands with the .env file:

# Generate documentation
docapi generate server.py --env .env

# Update documentation
docapi update server.py --env .env

# Launch the web service
docapi serve

Example with a specific model and custom settings:

# Generate documentation
docapi generate server.py docs --env .env --model aliyun:qwen-turbo

# Update documentation
docapi update server.py docs --env .env --model aliyun:qwen-turbo

# Launch the web service on a specific IP and port
docapi serve docs --ip 0.0.0.0 --port 9000

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Using Code Directly

import os
from docapi import DocAPI

# Configure API key
os.environ['OPENAI_API_KEY'] = "your_api_key"

# Initialize DocAPI with a specific model
docapi = DocAPI.build(lang="zh", model="openai:gpt-4o-mini")

# Generate documentation
docapi.generate("flask_project/server.py", "docs")

# Update documentation (uncomment to use)
# docapi.update("flask_project/server.py", "docs")

# Serve documentation locally
# docapi.serve("docs", ip="127.0.0.1", port=8080)