Dify provides plugin extension functionality that allows developers to extend platform capabilities through plugins. Core concepts of the plugin system include:
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Plugin Types
- Tool Plugins: Provide additional tools and features
- Model Plugins: Integrate new LLM models
- Data Source Plugins: Connect to external data sources
- Output Plugins: Customize output formats and channels
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Plugin Development
- Develop plugins using Python
- Follow Dify plugin specifications
- Implement necessary interfaces and methods
- Provide plugin configuration interface
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Plugin Management
- Plugin installation and uninstallation
- Plugin enablement and disablement
- Plugin version management
- Plugin dependency management
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Common Plugin Use Cases
- Search Tools: Google Search, Bing Search
- Data Processing: Excel processing, PDF parsing
- External APIs: Call third-party services
- Message Push: Slack, DingTalk, WeChat Work
Plugin development example (Python):
pythonfrom typing import Any, Dict from dify_plugin import Tool class MyCustomTool(Tool): def get_runtime_parameters(self) -> Dict[str, Any]: return { "name": "my_tool", "description": "My custom tool", "parameters": { "input": { "type": "string", "description": "Input parameter" } } } def invoke(self, parameters: Dict[str, Any]) -> Dict[str, Any]: input_data = parameters.get("input", "") # Processing logic result = f"Processed: {input_data}" return {"result": result}
Best practices:
- Plugins should have clear documentation and examples
- Handle errors and exceptions properly
- Provide reasonable default configurations
- Consider performance and resource consumption
Candidates should understand the basic concepts of Dify's plugin system and how to develop and use plugins to extend Dify's functionality.