# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. ========= # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. ========= from dotenv import load_dotenv from camel.models import ModelFactory from camel.toolkits import ( SearchToolkit, WebToolkit, FileWriteToolkit, ) from camel.types import ModelPlatformType, ModelType from camel.logger import set_log_level from utils import OwlRolePlaying, run_society load_dotenv() set_log_level(level="DEBUG") def construct_society(question: str) -> OwlRolePlaying: r"""Construct a society of agents based on the given question. Args: question (str): The task or question to be addressed by the society. Returns: OwlRolePlaying: A configured society of agents ready to address the question. """ # Create models for different components models = { "user": ModelFactory.create( model_platform=ModelPlatformType.OPENAI, model_type=ModelType.GPT_4O, model_config_dict={"temperature": 0}, ), "assistant": ModelFactory.create( model_platform=ModelPlatformType.OPENAI, model_type=ModelType.GPT_4O, model_config_dict={"temperature": 0}, ), "web": ModelFactory.create( model_platform=ModelPlatformType.OPENAI, model_type=ModelType.GPT_4O, model_config_dict={"temperature": 0}, ), "planning": ModelFactory.create( model_platform=ModelPlatformType.OPENAI, model_type=ModelType.GPT_4O, model_config_dict={"temperature": 0}, ), } # Configure toolkits tools = [ *WebToolkit( headless=False, # Set to True for headless mode (e.g., on remote servers) web_agent_model=models["web"], planning_agent_model=models["planning"], ).get_tools(), SearchToolkit().search_duckduckgo, SearchToolkit().search_wiki, *FileWriteToolkit(output_dir="./").get_tools(), ] # Configure agent roles and parameters user_agent_kwargs = {"model": models["user"]} assistant_agent_kwargs = {"model": models["assistant"], "tools": tools} # Configure task parameters task_kwargs = { "task_prompt": question, "with_task_specify": False, } # Create and return the society society = OwlRolePlaying( **task_kwargs, user_role_name="user", user_agent_kwargs=user_agent_kwargs, assistant_role_name="assistant", assistant_agent_kwargs=assistant_agent_kwargs, ) return society def main(): r"""Main function to run the OWL system with an example question.""" # Example research question question = "Navigate to Amazon.com and identify one product that is attractive to coders. Please provide me with the product name and price. No need to verify your answer." # Construct and run the society society = construct_society(question) answer, chat_history, token_count = run_society(society) # Output the result print(f"\033[94mAnswer: {answer}\033[0m") if __name__ == "__main__": main()