2024-12-17

沒什麼組織、一些倏忽即逝有趣或不有趣的想法

• Aider use case sharing

  • Use the doc from Aider to build slides from Aider
    • e.g. generate a slides elaborate on what is aider, its main features, saperate and illustrate the commands we could use in different usage group in marp
    • Aider in IDE
      • // XXX AI!, // XXX AI?
    • Commit/ Shell Integration
    • It event can change color theme
  • Use Aider to understand Airflow's codebase
    • /architect what is the main components of airflow, show the class diagram in mermaidjs with their responsibility and interaction relationship
    • /architect show the sequence diagram of how OpenAITriggerBatchOperator works in mermaidjs
    • /architect show the sequence diagram of how deferrable operator works in mermaidjs
  • Use Aider to add unit tests
    • e.g. generate unit tests for the functionality of XXX with the coding style of YYY
  • Use Aider to consolidate the ideas, let Aider be your real second brain to help you think, recall and organize your thoughts
    • e.g. generate the slides for the design of pollm according to the concepts about why we need llm translation, Aider, and the pollm design (with added all the random-thoughts notes)
  • Use Aider to do inline translation for po files
    • # 將這句翻譯成中文 AI!

• How TaskFlow API works in Airflow

  • Overview provided by Aider:

    1. Decorator Creation Phase

    @task
    def my_function():
        pass
    

    • TaskDecoratorCollection.__call__ routes to python_task
    • task_decorator_factory creates a _TaskDecorator instance
    • Wraps the original function with task configuration
    2. Task Definition Phase

    result = my_function()
    

    • Creates _PythonDecoratedOperator instance
    • Sets up task parameters and configuration
    • Returns XComArg for dependency management
    3. Execution Phase

    # When DAG runs
    ti = TaskInstance(task)
    ti.execute(context)
    

    • Executes the wrapped Python function
    • Handles result pushing to XCom
    • Manages task state and dependencies

    Key Components:

    • TaskDecoratorCollection: Entry point for @task decorator
    • _TaskDecorator: Handles task creation and configuration
    • _PythonDecoratedOperator: Executes the actual Python function
    • XComArg: Manages task dependencies and results

    The architecture allows for:

    • Clean separation between definition and execution
    • Flexible task configuration
    • Integration with Airflow's DAG system
    • XCom-based data passing
    • Task mapping and dynamic task generation

    Class Diagram:

    Sequence Diagram: