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gcgj-dify-1.7.0/api/core/app/apps
QuantumGhost 2c1ab4879f
refactor(api): Separate SegmentType for Integer/Float to Enable Pydantic Serialization (#22025)
refactor(api): Separate SegmentType for Integer/Float to Enable Pydantic Serialization (#22025)

This PR addresses serialization issues in the VariablePool model by separating the `value_type` tags for `IntegerSegment`/`FloatSegment` and `IntegerVariable`/`FloatVariable`. Previously, both Integer and Float types shared the same `SegmentType.NUMBER` tag, causing conflicts during serialization.

Key changes:
- Introduce distinct `value_type` tags for Integer and Float segments/variables
- Add `VariableUnion` and `SegmentUnion` types for proper type discrimination
- Leverage Pydantic's discriminated union feature for seamless serialization/deserialization
- Enable accurate serialization of data structures containing these types

Closes #22024.
7 months ago
..
advanced_chat refactor(api): Separate SegmentType for Integer/Float to Enable Pydantic Serialization (#22025) 7 months ago
agent_chat chore: Simplify code logic (#21496) 7 months ago
chat chore: Simplify code logic (#21496) 7 months ago
common fix(api): files not returned in the answer node (#21807) 7 months ago
completion chore: Simplify code logic (#21496) 7 months ago
workflow refactor(api): Separate SegmentType for Integer/Float to Enable Pydantic Serialization (#22025) 7 months ago
README.md fix: some typos using typos (#11374) 1 year ago
__init__.py FEAT: NEW WORKFLOW ENGINE (#3160) 2 years ago
base_app_generate_response_converter.py Introduce Plugins (#13836) 12 months ago
base_app_generator.py fix(api): Fix resetting sys var causing internal server error (#21604) 7 months ago
base_app_queue_manager.py Introduce Plugins (#13836) 12 months ago
base_app_runner.py 🐛 Fix(Gemini LLM): Support Gemini 0.2.x plugin on agent app (#20794) 8 months ago
message_based_app_generator.py chore: Simplify code logic (#21496) 7 months ago
message_based_app_queue_manager.py chore: apply pep8-naming rules for naming convention (#8261) 1 year ago
workflow_app_runner.py refactor(api): Separate SegmentType for Integer/Float to Enable Pydantic Serialization (#22025) 7 months ago

README.md

Guidelines for Database Connection Management in App Runner and Task Pipeline

Due to the presence of tasks in App Runner that require long execution times, such as LLM generation and external requests, Flask-Sqlalchemy's strategy for database connection pooling is to allocate one connection (transaction) per request. This approach keeps a connection occupied even during non-DB tasks, leading to the inability to acquire new connections during high concurrency requests due to multiple long-running tasks.

Therefore, the database operations in App Runner and Task Pipeline must ensure connections are closed immediately after use, and it's better to pass IDs rather than Model objects to avoid detach errors.

Examples:

  1. Creating a new record:

    app = App(id=1)
    db.session.add(app)
    db.session.commit()
    db.session.refresh(app)  # Retrieve table default values, like created_at, cached in the app object, won't affect after close
    
    # Handle non-long-running tasks or store the content of the App instance in memory (via variable assignment).
    
    db.session.close()
    
    return app.id
    
  2. Fetching a record from the table:

    app = db.session.query(App).filter(App.id == app_id).first()
    
    created_at = app.created_at
    
    db.session.close()
    
    # Handle tasks (include long-running).
    
    
  3. Updating a table field:

    app = db.session.query(App).filter(App.id == app_id).first()
    
    app.updated_at = time.utcnow()
    db.session.commit()
    db.session.close()
    
    return app_id