examples/run_deepseek_zh.py

# ========= 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. =========


# To run this file, you need to configure the DeepSeek API key
# You can obtain your API key from DeepSeek platform: https://platform.deepseek.com/api_keys
# Set it as DEEPSEEK_API_KEY="your-api-key" in your .env file or add it to your environment variables

import sys
from dotenv import load_dotenv

from camel.models import ModelFactory
from camel.toolkits import (
    ExcelToolkit,
    SearchToolkit,
    FileWriteToolkit,
    CodeExecutionToolkit,
)
from camel.types import ModelPlatformType, ModelType
from camel.societies import RolePlaying
from camel.logger import set_log_level

from owl.utils import run_society

import pathlib

set_log_level(level="DEBUG")

base_dir = pathlib.Path(__file__).parent.parent
env_path = base_dir / "owl" / ".env"
load_dotenv(dotenv_path=str(env_path))


def construct_society(question: str) -> RolePlaying:
    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:
        RolePlaying: A configured society of agents ready to address the question.
    """

    # Create models for different components
    models = {
        "user": ModelFactory.create(
            model_platform=ModelPlatformType.DEEPSEEK,
            model_type=ModelType.DEEPSEEK_CHAT,
            model_config_dict={"temperature": 0},
        ),
        "assistant": ModelFactory.create(
            model_platform=ModelPlatformType.DEEPSEEK,
            model_type=ModelType.DEEPSEEK_CHAT,
            model_config_dict={"temperature": 0},
        ),
    }

    # Configure toolkits
    tools = [
        *CodeExecutionToolkit(sandbox="subprocess", verbose=True).get_tools(),
        SearchToolkit().search_duckduckgo,
        SearchToolkit().search_wiki,
        SearchToolkit().search_baidu,
        *ExcelToolkit().get_tools(),
        *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 = RolePlaying(
        **task_kwargs,
        user_role_name="user",
        user_agent_kwargs=user_agent_kwargs,
        assistant_role_name="assistant",
        assistant_agent_kwargs=assistant_agent_kwargs,
        output_language="Chinese",
    )

    return society


def main():
    r"""Main function to run the OWL system with an example question."""
    # Example research question
    default_task = "搜索OWL项目最近的新闻并生成一篇报告，最后保存到本地。"

    # Override default task if command line argument is provided
    task = sys.argv[1] if len(sys.argv) > 1 else default_task

    # Construct and run the society
    society = construct_society(task)

    answer, chat_history, token_count = run_society(society)

    # Output the result
    print(f"\033[94mAnswer: {answer}\033[0m")


if __name__ == "__main__":
    main()