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# ========= 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()
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