feat: workflow init

[shaohuzhang1]

feat: workflow init

[f2c-ci-robot]

Adding the "do-not-merge/release-note-label-needed" label because no release-note block was detected, please follow our release note process to remove it.

Instructions for interacting with me using PR comments are available here. If you have questions or suggestions related to my behavior, please file an issue against the kubernetes/test-infra repository.

[f2c-ci-robot]

[APPROVALNOTIFIER] This PR is NOT APPROVED

This pull-request has been approved by:

The full list of commands accepted by this bot can be found here.

Needs approval from an approver in each of these files:

• OWNERS

Approvers can indicate their approval by writing /approve in a comment
Approvers can cancel approval by writing /approve cancel in a comment

[shaohuzhang1]

This code appears to be a Python implementation of various object models related to workflows in MaxKB, which likely refers to an AI-driven knowledge base platform. Here’s a quick review and some general suggestions:

Code Issues and Corrections

Class Definitions

• Many class definitions are missing method body definitions. If these classes should have more methods defined here, make sure they do.

Comments and Docstrings

• The docstring comments lack descriptions for the parameters of many functions and classes. Adding comprehensive docstrings can improve readability and maintainability.

Variable Naming and Consistency

• Some variable names, such as _id, _type, properties, etc., might not be fully consistent across classes or use cases. Consider using more descriptive variable names where appropriate.

Type Annotations

• All arguments in function signatures are annotated properly with types from the typing module, which is good.

Method Implementations

• Several methods like get_node, get_up_edge_nodes, etc., assume that certain keys exist in dictionaries (like up_node_map). Add checks to handle null or undefined values if necessary to avoid KeyError.

Edge Nodes Management

• The way edge nodes are managed internally through mappings (next_node_map and up_node_map) could benefit from being refactored to ensure efficient lookups and additions/removals.

Error Handling

• Ensure there is proper error handling both within individual functions and at the level where objects interact. This will make the system robust against invalid inputs.

Optimization Suggestions

1. Efficient Data Structures:

   • Use more efficient data structures like sets when needed, especially if you frequently need unique items, as lists require searching each time.

2. Lazy Evaluation in Generators:

   • For generators, consider using asynchronous generators async def. Asynchronous programming can significantly improve performance for large datasets.

3. Thread Safety:

   • If this workflow component runs concurrently, ensure it is thread-safe. You may want to use locks or other concurrency control mechanisms depending on the environment.

4. Caching Mechanisms:

   • To handle repetitive requests efficiently, implement caching layers around certain computations.

5. Code Refactoring:

   • Break down complex logic into smaller, reusable modules wherever possible. Overly long functions can become difficult to understand and maintain.

6. Performance Profiling:

   • Regularly profile your application to identify bottlenecks. Focus optimizations on areas that show high CPU or memory usage but do not affect functionality.

7. Security Considerations:

   • Ensure that data handling and transmission processes respect security best practices to protect sensitive information.

These recommendations are general guidelines based on common issues identified during static analysis of similar systems. Depending on specific requirements and constraints, further customization and tuning might be needed.

[shaohuzhang1]

The code provided appears to be a template for an INode class that forms part of the MaxKB project. Below are some observations, potential issues, and optimization suggestions:

Observations

• The file contains documentation comments indicating it's part of the MaxKB project and includes information about authorship (@Author：虎虎) and date created (2025/5/7 16:41).
• The class follows a similar structure to other classes with methods like execute, _run, and an __init__ method.

Potential Issues

1. Abstract Method Overloading: The get_node_serializer method is marked as abstract but does not have any implementation defined outside of the base class. This means subclasses must implement this method.

2. Context Initialization Logic: The context initialization logic uses bitwise OR operators on lists for concatenation, which can lead to unexpected behavior (e.g., empty strings being appended). It should use proper list handling, such as using .extend() instead of .|= if used within a pipeline framework where |= might be intended.

3. Missing Return Value from execute: The execute method returns nothing, which could indicate a bug or intended lack thereof. If execute should return something meaningful, such as output data or exceptions, it needs to be explicitly defined.

4. Code Style:

   • Using multiple blank lines between sections might improve readability.
   • Consistent indentation should be maintained throughout the codebase.

Optimization Suggestions

1. Reduce Magic Numbers and Implied Parameters:

   • Instead of passing explicit parameters directly in the constructor, define default values that are typically inferred (such as no chunk or up_node_id_list). For example:

     def __init__(self,
                  node,
                  workflow_manage,
                  chunk=None,
                  up_node_id_list=(),
                  loop_index=None):
         """
         Initialize the INode instance
     
         Arguments:
             node (dict): Node configuration object
             workflow_manage (obj): Workflow management controller/object
             chunk (Chunk, optional): Data chunk associated with this execution; defaults to None
             up_node_id_list (tuple, optional): IDs of the upstream nodes; defaults to ()
             loop_index (int, optional): Index representing current loop iteration; defaults to None
         """
         ...

2. Use Class Variables Wisely:

   • Since supported_workflow_type_list and type appear to be static across instances, they could be declared at the class level to prevent repeated instantiation overhead.

3. Avoid Redundant Checks:

   • While checks within the is_valid method are useful, ensure they do not perform unnecessary computations that may slow down subsequent operations.

4. Consider Exception Handling:

   • Ensure that error handling is comprehensive enough to deal with various edge cases and unexpected failures during execution.

5. Implement Type Hinting:

   • Consider adding type hints to make the code more readable and maintainable.

By addressing these points, the code will become more robust, efficient, and easier to understand.

[shaohuzhang1]

The provided Python code looks mostly correct for a basic implementation of a workflow manager, but it contains a couple of potential issues and areas for improvement:

Potential Issues:

1. Import Statement: The import statement from builtins import function is not standard practice in Python. It's better to use import functools if you need functions from the functools module.

   from functools import partial

2. Function Annotations: The variable name function is shadowing the built-in type in Python. Consider renaming it to something like consumer_function.

3. Enum Usage: Ensure that Channel, Chunk, and WorkflowType are properly defined elsewhere in your project.

4. Method Implementations: The methods stream and invoke are left empty. You may need to implement these depending on your requirements.

5. Docstrings: While all method docstrings are present, they could be more detailed and informative about what each method does.

6. Variable Naming Conventions: Names like _channel, _workflow, etc., suggest private variables, which might affect how this class interacts with other parts of your application.

Optimization Suggestions:

1. Code Clarity: Make sure that the comments and docstrings clearly describe the purpose of each part of the code. This can make it easier for others (or yourself) to understand the logic.

2. Imports: Keep imports at the top of your file for clarity.

3. Error Handling: Add error handling around potentially risky operations, such as accessing list elements or checking for valid inputs.

4. Separation of Concerns: If your workflow involves more complex interactions, consider separating different functionalities into separate classes or modules.

Here's an updated version of your code with some of these suggestions applied:

# coding=utf-8
"""
@project: MaxKB
@author: 虎虎
@file：workflow_manage.py
@date：2025/5/9 10:30
@desc:
"""

from functools import partial
from enum import Enum
from typing import List, Dict

class Workflow:
    pass

class Channel(Workflow):
    pass

class Chunk:
    pass


class WorkflowType(Enum):
    # 应用
    APPLICATION = "APPLICATION"
    # 知识库
    KNOWLEDGE = "KNOWLEDGE"


class WorkflowManage:
    """Class responsible for managing workflows."""
    
    _channel = Channel()
    
    def __init__(self,
                 workflow: Workflow,
                 chunk_list: List[Chunk],
                 start_node: Chunk,
                 workflow_type: WorkflowType,
                 body: Dict,
                 consumer_function: callable):  # Rename 'function' to avoid shadowing the built-in type
        self._workflow = workflow
        self._chunk_list = chunk_list
        self._start_node = start_node
        self._workflow_type = workflow_type
        self._body = body
        self._consumer_function = consumer_function
        
    def stream(self):
        """
        Method to handle data streaming based on the current context.
        
        :return:
        """
        # Example data processing logic
        processed_data = [item * 2 for item in self._chunk_list]
        return processed_data
    
    def invoke(self):
        """
        Invokes the consumer function with the appropriate context.
        
        :return:
        """
        # Invoke the consumer function with the current state
        self._consumer_function(self._workflow, self._chunk_list, self._start_node, self._workflow_type, self._body)

This should provide a cleaner and more maintainable code structure while addressing some of the mentioned points. Make sure to test the updated code thoroughly to ensure it works as expected.