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swarms/swarms/structs/base_workflow.py

322 lines
10 KiB

import json
from typing import Any, Dict, List, Optional
from termcolor import colored
from swarms.structs.base import BaseStructure
from swarms.structs.task import Task
class BaseWorkflow(BaseStructure):
"""
Base class for workflows.
Attributes:
task_pool (list): A list to store tasks.
Methods:
add(task: Task = None, tasks: List[Task] = None, *args, **kwargs):
Adds a task or a list of tasks to the task pool.
run():
Abstract method to run the workflow.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.task_pool = []
def add(
self,
task: Task = None,
tasks: List[Task] = None,
*args,
**kwargs,
):
"""
Adds a task or a list of tasks to the task pool.
Args:
task (Task, optional): A single task to add. Defaults to None.
tasks (List[Task], optional): A list of tasks to add. Defaults to None.
Raises:
ValueError: If neither task nor tasks are provided.
"""
if task:
self.task_pool.append(task)
elif tasks:
self.task_pool.extend(tasks)
else:
raise ValueError(
"You must provide a task or a list of tasks"
)
def run(self):
"""
Abstract method to run the workflow.
"""
raise NotImplementedError("You must implement this method")
def __sequential_loop(self):
"""
Abstract method for the sequential loop.
"""
# raise NotImplementedError("You must implement this method")
pass
def __log(self, message: str):
"""
Logs a message if verbose mode is enabled.
Args:
message (str): The message to log.
"""
if self.verbose:
print(message)
def __str__(self):
return f"Workflow with {len(self.task_pool)} tasks"
def __repr__(self):
return f"Workflow with {len(self.task_pool)} tasks"
def reset(self) -> None:
"""Resets the workflow by clearing the results of each task."""
try:
for task in self.tasks:
task.result = None
except Exception as error:
print(
colored(f"Error resetting workflow: {error}", "red"),
)
def get_task_results(self) -> Dict[str, Any]:
"""
Returns the results of each task in the workflow.
Returns:
Dict[str, Any]: The results of each task in the workflow
"""
try:
return {
task.description: task.result for task in self.tasks
}
except Exception as error:
print(
colored(
f"Error getting task results: {error}", "red"
),
)
def remove_task(self, task: str) -> None:
"""Remove tasks from sequential workflow"""
try:
self.tasks = [
task
for task in self.tasks
if task.description != task
]
except Exception as error:
print(
colored(
f"Error removing task from workflow: {error}",
"red",
),
)
def update_task(self, task: str, **updates) -> None:
"""
Updates the arguments of a task in the workflow.
Args:
task (str): The description of the task to update.
**updates: The updates to apply to the task.
Raises:
ValueError: If the task is not found in the workflow.
Examples:
>>> from swarms.models import OpenAIChat
>>> from swarms.structs import SequentialWorkflow
>>> llm = OpenAIChat(openai_api_key="")
>>> workflow = SequentialWorkflow(max_loops=1)
>>> workflow.add("What's the weather in miami", llm)
>>> workflow.add("Create a report on these metrics", llm)
>>> workflow.update_task("What's the weather in miami", max_tokens=1000)
>>> workflow.tasks[0].kwargs
{'max_tokens': 1000}
"""
try:
for task in self.tasks:
if task.description == task:
task.kwargs.update(updates)
break
else:
raise ValueError(
f"Task {task} not found in workflow."
)
except Exception as error:
print(
colored(
f"Error updating task in workflow: {error}", "red"
),
)
def delete_task(self, task: str) -> None:
"""
Delete a task from the workflow.
Args:
task (str): The description of the task to delete.
Raises:
ValueError: If the task is not found in the workflow.
Examples:
>>> from swarms.models import OpenAIChat
>>> from swarms.structs import SequentialWorkflow
>>> llm = OpenAIChat(openai_api_key="")
>>> workflow = SequentialWorkflow(max_loops=1)
>>> workflow.add("What's the weather in miami", llm)
>>> workflow.add("Create a report on these metrics", llm)
>>> workflow.delete_task("What's the weather in miami")
>>> workflow.tasks
[Task(description='Create a report on these metrics', agent=Agent(llm=OpenAIChat(openai_api_key=''), max_loops=1, dashboard=False), args=[], kwargs={}, result=None, history=[])]
"""
try:
for task in self.tasks:
if task.description == task:
self.tasks.remove(task)
break
else:
raise ValueError(
f"Task {task} not found in workflow."
)
except Exception as error:
print(
colored(
f"Error deleting task from workflow: {error}",
"red",
),
)
def save_workflow_state(
self,
filepath: Optional[str] = "sequential_workflow_state.json",
**kwargs,
) -> None:
"""
Saves the workflow state to a json file.
Args:
filepath (str): The path to save the workflow state to.
Examples:
>>> from swarms.models import OpenAIChat
>>> from swarms.structs import SequentialWorkflow
>>> llm = OpenAIChat(openai_api_key="")
>>> workflow = SequentialWorkflow(max_loops=1)
>>> workflow.add("What's the weather in miami", llm)
>>> workflow.add("Create a report on these metrics", llm)
>>> workflow.save_workflow_state("sequential_workflow_state.json")
"""
try:
filepath = filepath or self.saved_state_filepath
with open(filepath, "w") as f:
# Saving the state as a json for simplicuty
state = {
"tasks": [
{
"description": task.description,
"args": task.args,
"kwargs": task.kwargs,
"result": task.result,
"history": task.history,
}
for task in self.tasks
],
"max_loops": self.max_loops,
}
json.dump(state, f, indent=4)
except Exception as error:
print(
colored(
f"Error saving workflow state: {error}",
"red",
)
)
def add_objective_to_workflow(self, task: str, **kwargs) -> None:
"""Adds an objective to the workflow."""
try:
print(
colored(
"""
Adding Objective to Workflow...""",
"green",
attrs=["bold", "underline"],
)
)
task = Task(
description=task,
agent=kwargs["agent"],
args=list(kwargs["args"]),
kwargs=kwargs["kwargs"],
)
self.tasks.append(task)
except Exception as error:
print(
colored(
f"Error adding objective to workflow: {error}",
"red",
)
)
def load_workflow_state(
self, filepath: str = None, **kwargs
) -> None:
"""
Loads the workflow state from a json file and restores the workflow state.
Args:
filepath (str): The path to load the workflow state from.
Examples:
>>> from swarms.models import OpenAIChat
>>> from swarms.structs import SequentialWorkflow
>>> llm = OpenAIChat(openai_api_key="")
>>> workflow = SequentialWorkflow(max_loops=1)
>>> workflow.add("What's the weather in miami", llm)
>>> workflow.add("Create a report on these metrics", llm)
>>> workflow.save_workflow_state("sequential_workflow_state.json")
>>> workflow.load_workflow_state("sequential_workflow_state.json")
"""
try:
filepath = filepath or self.restore_state_filepath
with open(filepath, "r") as f:
state = json.load(f)
self.max_loops = state["max_loops"]
self.tasks = []
for task_state in state["tasks"]:
task = Task(
description=task_state["description"],
agent=task_state["agent"],
args=task_state["args"],
kwargs=task_state["kwargs"],
result=task_state["result"],
history=task_state["history"],
)
self.tasks.append(task)
except Exception as error:
print(
colored(
f"Error loading workflow state: {error}",
"red",
)
)