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121 lines
3.8 KiB
Python
121 lines
3.8 KiB
Python
# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
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# To run this file, you need to configure the PPIO API key
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# You can obtain your API key from PPIO platform: https://ppinfra.com/settings/key-management?utm_source=github_owl
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# Set it as PPIO_API_KEY="your-api-key" in your .env file or add it to your environment variables
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import sys
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from dotenv import load_dotenv
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from camel.models import ModelFactory
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from camel.toolkits import (
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ExcelToolkit,
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SearchToolkit,
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FileWriteToolkit,
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CodeExecutionToolkit,
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)
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from camel.types import ModelPlatformType, ModelType
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from camel.societies import RolePlaying
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from camel.logger import set_log_level
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from owl.utils import run_society
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import pathlib
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set_log_level(level="DEBUG")
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base_dir = pathlib.Path(__file__).parent.parent
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env_path = base_dir / "owl" / ".env"
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load_dotenv(dotenv_path=str(env_path))
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def construct_society(question: str) -> RolePlaying:
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r"""Construct a society of agents based on the given question.
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Args:
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question (str): The task or question to be addressed by the society.
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Returns:
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RolePlaying: A configured society of agents ready to address the question.
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"""
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# Create models for different components
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models = {
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"user": ModelFactory.create(
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model_platform=ModelPlatformType.PPIO,
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model_type=ModelType.PPIO_DEEPSEEK_V3_COMMUNITY,
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model_config_dict={"temperature": 0},
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),
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"assistant": ModelFactory.create(
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model_platform=ModelPlatformType.PPIO,
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model_type=ModelType.PPIO_DEEPSEEK_V3_COMMUNITY,
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model_config_dict={"temperature": 0},
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),
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}
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# Configure toolkits
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tools = [
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*CodeExecutionToolkit(sandbox="subprocess", verbose=True).get_tools(),
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SearchToolkit().search_duckduckgo,
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SearchToolkit().search_wiki,
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SearchToolkit().search_baidu,
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*ExcelToolkit().get_tools(),
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*FileWriteToolkit(output_dir="./").get_tools(),
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]
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# Configure agent roles and parameters
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user_agent_kwargs = {"model": models["user"]}
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assistant_agent_kwargs = {"model": models["assistant"], "tools": tools}
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# Configure task parameters
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task_kwargs = {
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"task_prompt": question,
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"with_task_specify": False,
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}
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# Create and return the society
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society = RolePlaying(
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**task_kwargs,
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user_role_name="user",
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user_agent_kwargs=user_agent_kwargs,
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assistant_role_name="assistant",
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assistant_agent_kwargs=assistant_agent_kwargs,
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output_language="Chinese",
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)
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return society
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def main():
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r"""Main function to run the OWL system with an example question."""
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# Example research question
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default_task = "使用百度整理2023年1月1日到2023年12月31日，中国股市的涨跌情况。"
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# Override default task if command line argument is provided
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task = sys.argv[1] if len(sys.argv) > 1 else default_task
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# Construct and run the society
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society = construct_society(task)
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answer, chat_history, token_count = run_society(society)
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# Output the result
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print(f"\033[94mAnswer: {answer}\033[0m")
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if __name__ == "__main__":
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main()
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