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from camel.models import ModelFactory
from camel.toolkits import *
from camel.types import ModelPlatformType, ModelType
from camel.configs import ChatGPTConfig
from typing import List, Dict
from dotenv import load_dotenv
from retry import retry
from loguru import logger
from utils import OwlRolePlaying, process_tools, run_society
import os
load_dotenv()
def construct_society(question: str) -> OwlRolePlaying:
r"""Construct the society based on the question."""
user_role_name = "user"
assistant_role_name = "assistant"
user_model = ModelFactory.create(
model_platform=ModelPlatformType.OPENAI,
model_type=ModelType.GPT_4O,
model_config_dict=ChatGPTConfig(temperature=0, top_p=1).as_dict(), # [Optional] the config for model
)
assistant_model = ModelFactory.create(
model_platform=ModelPlatformType.OPENAI,
model_type=ModelType.GPT_4O,
model_config_dict=ChatGPTConfig(temperature=0, top_p=1).as_dict(), # [Optional] the config for model
)
user_tools = []
assistant_tools = [
"WebToolkit",
'DocumentProcessingToolkit',
'VideoAnalysisToolkit',
'CodeExecutionToolkit',
'ImageAnalysisToolkit',
'AudioAnalysisToolkit',
"SearchToolkit",
"ExcelToolkit",
]
user_role_name = 'user'
user_agent_kwargs = {
'model': user_model,
'tools': process_tools(user_tools),
}
assistant_role_name = 'assistant'
assistant_agent_kwargs = {
'model': assistant_model,
'tools': process_tools(assistant_tools),
}
task_kwargs = {
'task_prompt': question,
'with_task_specify': False,
}
society = OwlRolePlaying(
**task_kwargs,
user_role_name=user_role_name,
user_agent_kwargs=user_agent_kwargs,
assistant_role_name=assistant_role_name,
assistant_agent_kwargs=assistant_agent_kwargs,
)
return society
# Example case
question = "What was the volume in m^3 of the fish bag that was calculated in the University of Leicester paper `Can Hiccup Supply Enough Fish to Maintain a Dragon’s Diet?` "
society = construct_society(question)
answer, chat_history, token_count = run_society(society)
logger.success(f"Answer: {answer}")
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