fix: delete knowledge pipeline but pipeline and workflow don't delete

[JohnJyong]

Important

1. Make sure you have read our contribution guidelines
2. Ensure there is an associated issue and you have been assigned to it
3. Use the correct syntax to link this PR: Fixes #<issue number>.

Summary

fix：delete knowledge pipeline but pipeline and workflow don't delete

Screenshots

Before	After
...	...

Checklist

• This change requires a documentation update, included: Dify Document
• I understand that this PR may be closed in case there was no previous discussion or issues. (This doesn't apply to typos!)
• I've added a test for each change that was introduced, and I tried as much as possible to make a single atomic change.
• I've updated the documentation accordingly.
• I ran dev/reformat(backend) and cd web && npx lint-staged(frontend) to appease the lint gods

[gemini-code-assist]

Summary of Changes

Hello @JohnJyong, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request resolves an issue where deleting a knowledge pipeline did not correctly remove its corresponding pipeline and workflow entries from the system. The changes introduce a mechanism to cascade these deletions, ensuring that all related components are cleaned up when a dataset is removed. This improves data integrity and system hygiene by preventing stale or orphaned records.

Highlights

• Pipeline and Workflow Deletion: Implemented logic to ensure that when a knowledge pipeline (dataset) is deleted, its associated pipeline and workflow records are also removed from the database. This prevents orphaned entries and maintains data consistency.
• Event Handler Update: The clean_when_dataset_deleted event handler was updated to pass the pipeline_id to the dataset cleanup task, enabling the new deletion functionality.
• Comprehensive Unit Tests: A new, extensive unit test file (test_clean_dataset_task.py) was added to cover various scenarios for the dataset cleanup task, including specific tests for pipeline and workflow deletion, error handling, and attachment cleanup.

Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point by creating a comment using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands on the current page.

Feature	Command	Description
Code Review	/gemini review	Performs a code review for the current pull request in its current state.
Pull Request Summary	/gemini summary	Provides a summary of the current pull request in its current state.
Comment	@gemini-code-assist	Responds in comments when explicitly tagged, both in pull request comments and review comments.
Help	/gemini help	Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Footnotes

1. Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩

[gemini-code-assist]

Code Review

This pull request fixes an issue where deleting a knowledge pipeline did not delete the associated pipeline and workflow records. The changes involve passing the pipeline_id to the cleanup task and adding logic to delete the Pipeline and Workflow entities. A comprehensive test suite has also been added for the cleanup task.

My review identifies a critical logical issue in the new deletion logic. The code attempts to find a Workflow by comparing its app_id with the pipeline_id, which is likely incorrect and could lead to not deleting the workflow or deleting the wrong one. I've provided a suggestion to fix this by first fetching the pipeline to get its associated app_id and then using that to correctly identify and delete the workflow before deleting the pipeline itself.

[wylswz]

lgtm