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97 lines
2.7 KiB
Python
97 lines
2.7 KiB
Python
from dotenv import load_dotenv
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load_dotenv()
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from camel.models import ModelFactory
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from camel.toolkits import (
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WebToolkit,
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DocumentProcessingToolkit,
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VideoAnalysisToolkit,
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AudioAnalysisToolkit,
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CodeExecutionToolkit,
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ImageAnalysisToolkit,
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SearchToolkit,
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ExcelToolkit,
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FunctionTool
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)
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from camel.types import ModelPlatformType, ModelType
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from camel.configs import ChatGPTConfig
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from typing import List, Dict
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from retry import retry
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from loguru import logger
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from utils import OwlRolePlaying, run_society
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import os
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def construct_society(question: str) -> OwlRolePlaying:
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r"""Construct the society based on the question."""
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user_role_name = "user"
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assistant_role_name = "assistant"
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user_model = ModelFactory.create(
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model_platform=ModelPlatformType.OPENAI,
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model_type=ModelType.GPT_4O,
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model_config_dict=ChatGPTConfig(temperature=0, top_p=1).as_dict(), # [Optional] the config for model
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)
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assistant_model = ModelFactory.create(
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model_platform=ModelPlatformType.OPENAI,
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model_type=ModelType.GPT_4O,
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model_config_dict=ChatGPTConfig(temperature=0, top_p=1).as_dict(), # [Optional] the config for model
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)
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tools_list = [
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*WebToolkit(
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headless=False,
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web_agent_model=assistant_model,
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planning_agent_model=assistant_model
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).get_tools(),
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# *DocumentProcessingToolkit().get_tools(),
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# *VideoAnalysisToolkit(model=assistant_model).get_tools(), # This requires OpenAI Key
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# *AudioAnalysisToolkit().get_tools(), # This requires OpenAI Key
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# *CodeExecutionToolkit().get_tools(),
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# *ImageAnalysisToolkit(model=assistant_model).get_tools(),
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FunctionTool(SearchToolkit(model=assistant_model).search_duckduckgo),
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# *ExcelToolkit().get_tools()
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]
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user_role_name = 'user'
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user_agent_kwargs = dict(model=user_model)
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assistant_role_name = 'assistant'
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assistant_agent_kwargs = dict(model=assistant_model,
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tools=tools_list)
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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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society = OwlRolePlaying(
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**task_kwargs,
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user_role_name=user_role_name,
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user_agent_kwargs=user_agent_kwargs,
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assistant_role_name=assistant_role_name,
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assistant_agent_kwargs=assistant_agent_kwargs,
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)
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return society
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# Example case
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question = "What was the volume in m^3 of the fish bag that was calculated in the University of Leicester paper `Can Hiccup Supply Enough Fish to Maintain a Dragon’s Diet?` "
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society = construct_society(question)
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answer, chat_history, token_count = run_society(society)
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logger.success(f"Answer: {answer}")
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