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feat: add cli mode (#337)
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examples/run_cli.py

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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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BrowserToolkit,
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VideoAnalysisToolkit,
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ImageAnalysisToolkit,
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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, DocumentProcessingToolkit
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import pathlib
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# Set the log level to DEBUG for detailed debugging information
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set_log_level(level="DEBUG")
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# Get the parent directory of the current file and construct the path to the .env file
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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 get_user_input(prompt):
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# Get user input and strip leading/trailing whitespace
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return input(prompt).strip()
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def get_construct_params() -> dict[str, any]:
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# Welcome message
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print("Welcome to owl! Have fun!")
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# Select model platform type
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model_platforms = ModelPlatformType
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print("Please select the model platform type:")
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for i, platform in enumerate(model_platforms, 1):
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print(f"{i}. {platform}")
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model_platform_choice = int(
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get_user_input("Please enter the model platform number:")
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)
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selected_model_platform = list(model_platforms)[model_platform_choice - 1]
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print(f"The model platform you selected is: {selected_model_platform}")
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# Select model type
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models = ModelType
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print("Please select the model type:")
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for i, model in enumerate(models, 1):
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print(f"{i}. {model}")
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model_choice = int(get_user_input("Please enter the model number:"))
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selected_model = list(models)[model_choice - 1]
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print(f"The model you selected is: {selected_model}")
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# Select language
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languages = ["English", "Chinese"]
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print("Please select the language:")
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for i, lang in enumerate(languages, 1):
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print(f"{i}. {lang}")
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language_choice = int(get_user_input("Please enter the language number:"))
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selected_language = languages[language_choice - 1]
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print(f"The language you selected is: {selected_language}")
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# Enter the question
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question = get_user_input("Please enter your question:")
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print(f"Your question is: {question}")
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return {
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"language": selected_language,
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"model_type": selected_model,
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"model_platform": selected_model_platform,
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"question": question,
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}
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def construct_society() -> RolePlaying:
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# Get user input parameters
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params = get_construct_params()
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question = params["question"]
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selected_model_type = params["model_type"]
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selected_model_platform = params["model_platform"]
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selected_language = params["language"]
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# Create model instances for different roles
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models = {
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"user": ModelFactory.create(
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model_platform=selected_model_platform,
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model_type=selected_model_type,
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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=selected_model_platform,
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model_type=selected_model_type,
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model_config_dict={"temperature": 0},
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),
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"web": ModelFactory.create(
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model_platform=selected_model_platform,
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model_type=selected_model_type,
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model_config_dict={"temperature": 0},
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),
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"planning": ModelFactory.create(
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model_platform=selected_model_platform,
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model_type=selected_model_type,
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model_config_dict={"temperature": 0},
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),
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"video": ModelFactory.create(
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model_platform=selected_model_platform,
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model_type=selected_model_type,
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model_config_dict={"temperature": 0},
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),
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"image": ModelFactory.create(
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model_platform=selected_model_platform,
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model_type=selected_model_type,
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model_config_dict={"temperature": 0},
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),
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"document": ModelFactory.create(
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model_platform=selected_model_platform,
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model_type=selected_model_type,
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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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*BrowserToolkit(
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headless=False,
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web_agent_model=models["web"],
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planning_agent_model=models["planning"],
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output_language="Chinese",
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).get_tools(),
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*VideoAnalysisToolkit(model=models["video"]).get_tools(),
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*CodeExecutionToolkit(sandbox="subprocess", verbose=True).get_tools(),
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*ImageAnalysisToolkit(model=models["image"]).get_tools(),
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SearchToolkit().search_duckduckgo,
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SearchToolkit().search_google,
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SearchToolkit().search_wiki,
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SearchToolkit().search_baidu,
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SearchToolkit().search_bing,
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*ExcelToolkit().get_tools(),
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*DocumentProcessingToolkit(model=models["document"]).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=selected_language,
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)
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return society
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def main():
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# Construct the society
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society = construct_society()
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# Run the society and get the answer, chat history, and token count
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answer, chat_history, token_count = run_society(society)
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# Print the answer
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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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