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swarms/swarms/agents/reasoning_duo.py

110 lines
3.8 KiB

from typing import List
from loguru import logger
from swarms.prompts.reasoning_prompt import REASONING_PROMPT
from swarms.structs.agent import Agent
from swarms.utils.output_types import OutputType
from swarms.structs.conversation import Conversation
from swarms.utils.history_output_formatter import (
history_output_formatter,
)
class ReasoningDuo:
"""
ReasoningDuo is a class that encapsulates the functionality of two agents: a reasoning agent and a main agent.
Attributes:
model_name (str): The name of the model used for the reasoning agent.
description (str): A description of the reasoning agent.
model_names (list[str]): A list of model names for the agents.
system_prompt (str): The system prompt for the main agent.
reasoning_agent (Agent): An instance of the Agent class for reasoning tasks.
main_agent (Agent): An instance of the Agent class for main tasks.
"""
def __init__(
self,
agent_name: str = "reasoning-agent-01",
agent_description: str = "A highly intelligent and thoughtful AI designed to provide accurate and well-reasoned answers to the user's questions.",
model_name: str = "gpt-4o-mini",
description: str = "A highly intelligent and thoughtful AI designed to provide accurate and well-reasoned answers to the user's questions.",
model_names: list[str] = ["gpt-4o-mini", "gpt-4o"],
system_prompt: str = "You are a helpful assistant that can answer questions and help with tasks.",
output_type: OutputType = "dict",
):
self.agent_name = agent_name
self.agent_description = agent_description
self.model_name = model_name
self.description = description
self.output_type = output_type
self.reasoning_agent = Agent(
agent_name="Your",
description="A highly intelligent and thoughtful AI designed to provide accurate and well-reasoned answers to the user's questions.",
system_prompt=REASONING_PROMPT,
max_loops=1,
model_name=model_names[0],
dynamic_temperature_enabled=True,
)
self.main_agent = Agent(
agent_name=self.agent_name,
description=self.agent_description,
system_prompt=system_prompt,
max_loops=1,
model_name=model_names[1],
dynamic_temperature_enabled=True,
)
self.conversation = Conversation()
def run(self, task: str):
"""
Executes the reasoning and main agents on the provided task.
Args:
task (str): The task to be processed by the agents.
Returns:
str: The output from the main agent after processing the task.
"""
logger.info(f"Running task: {task}")
self.conversation.add(role="user", content=task)
output_reasoner = self.reasoning_agent.run(task)
self.conversation.add(
role=self.reasoning_agent.agent_name,
content=output_reasoner,
)
prompt = f"Task: {task} \n\n Your thoughts: {output_reasoner}"
output_main = self.main_agent.run(prompt)
self.conversation.add(
role=self.main_agent.agent_name, content=output_main
)
return history_output_formatter(
self.conversation, self.output_type
)
def batched_run(self, tasks: List[str]):
"""
Executes the run method for a list of tasks.
Args:
tasks (list[str]): A list of tasks to be processed.
Returns:
list: A list of outputs from the main agent for each task.
"""
outputs = []
for task in tasks:
logger.info(f"Processing task: {task}")
outputs.append(self.run(task))
return outputs