Nonlinearworkflow

Class Name: NonlinearWorkflow

This class represents a Directed Acyclic Graph (DAG) workflow used to store tasks and their dependencies in a workflow. The structures can validate, execute and store the order of tasks present in the workflow. It has the following attributes and methods:

Attributes:

• tasks (dict): A dictionary mapping task names to Task objects.
• edges (dict): A dictionary mapping task names to a list of dependencies.
• stopping_token (str): The token which denotes the end condition for the workflow execution. Default: <DONE>

Methods:

1. __init__(self, stopping_token: str = "<DONE>"): The initialization method that sets up the NonlinearWorkflow object with an optional stopping token. This token marks the end of the workflow.

2. Args:

   • stopping_token (str): The token to denote the end condition for the workflow execution.

3. add(task: Task, *dependencies: str): Adds a task to the workflow along with its dependencies. This method is used to add a new task to the workflow with an optional list of dependency tasks.

4. Args:
   • task (Task): The task to be added.
   • dependencies (varargs): Variable number of dependency task names.

5. Returns: None

6. run(): This method runs the workflow by executing tasks in topological order. It runs the tasks according to the sequence of dependencies.

7. Raises:
   • Exception: If a circular dependency is detected.
8. Returns: None

Examples:

Usage Example 1:

from swarms.models import OpenAIChat
from swarms.structs import NonlinearWorkflow, Task

# Initialize the OpenAIChat model
llm = OpenAIChat(openai_api_key="")
# Create a new Task
task = Task(llm, "What's the weather in Miami")
# Initialize the NonlinearWorkflow
workflow = NonlinearWorkflow()
# Add task to the workflow
workflow.add(task)
# Execute the workflow
workflow.run()

Usage Example 2:

from swarms.models import OpenAIChat
from swarms.structs import NonlinearWorkflow, Task

# Initialize the OpenAIChat model
llm = OpenAIChat(openai_api_key="")
# Create new Tasks
task1 = Task(llm, "What's the weather in Miami")
task2 = Task(llm, "Book a flight to New York")
task3 = Task(llm, "Find a hotel in Paris")
# Initialize the NonlinearWorkflow
workflow = NonlinearWorkflow()
# Add tasks to the workflow with dependencies
workflow.add(task1, task2.name)
workflow.add(task2, task3.name)
workflow.add(task3, "OpenAIChat Initialization")
# Execute the workflow
workflow.run()

Usage Example 3:

from swarms.models import OpenAIChat
from swarms.structs import NonlinearWorkflow, Task

# Initialize the OpenAIChat model
llm = OpenAIChat(openai_api_key="")
# Create new Tasks
task1 = Task(llm, "What's the weather in Miami")
task2 = Task(llm, "Book a flight to New York")
task3 = Task(llm, "Find a hotel in Paris")
# Initialize the NonlinearWorkflow
workflow = NonlinearWorkflow()
# Add tasks to the workflow with dependencies
workflow.add(task1)
workflow.add(task2, task1.name)
workflow.add(task3, task1.name, task2.name)
# Execute the workflow
workflow.run()

These examples illustrate the three main types of usage for the NonlinearWorkflow class and how it can be used to represent a directed acyclic graph (DAG) workflow with tasks and their dependencies.

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The explanatory documentation details the architectural aspects, methods, attributes, examples, and usage patterns for the NonlinearWorkflow class. By following the module and function definition structure, the documentation provides clear and comprehensive descriptions of the class and its functionalities.