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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. =========
# To run this file, you need to configure the Qwen API key
# You can obtain your API key from Bailian platform: bailian.console.aliyun.com
# Set it as QWEN_API_KEY="your-api-key" in your .env file or add it to your environment variables
from dotenv import load_dotenv
from camel.models import ModelFactory
from camel.toolkits import WebToolkit, SearchToolkit, FileWriteToolkit
from camel.types import ModelPlatformType, ModelType
from utils import OwlRolePlaying, run_society
from camel.logger import set_log_level
set_log_level(level="DEBUG")
load_dotenv()
def construct_society(question: str) -> OwlRolePlaying:
r"""Construct the society based on the question."""
user_role_name = "user"
assistant_role_name = "assistant"
user_model = ModelFactory.create(
model_platform=ModelPlatformType.QWEN,
model_type=ModelType.QWEN_MAX,
model_config_dict={"temperature": 0},
)
assistant_model = ModelFactory.create(
model_platform=ModelPlatformType.QWEN,
model_type=ModelType.QWEN_MAX,
model_config_dict={"temperature": 0},
)
planning_model = ModelFactory.create(
model_platform=ModelPlatformType.QWEN,
model_type=ModelType.QWEN_MAX,
model_config_dict={"temperature": 0},
)
web_model = ModelFactory.create(
model_platform=ModelPlatformType.QWEN,
model_type=ModelType.QWEN_VL_MAX,
model_config_dict={"temperature": 0},
)
tools_list = [
*WebToolkit(
headless=False,
web_agent_model=web_model,
planning_agent_model=planning_model,
output_language="Chinese",
).get_tools(),
SearchToolkit().search_duckduckgo,
*FileWriteToolkit(output_dir="./").get_tools(),
]
user_role_name = "user"
user_agent_kwargs = dict(model=user_model)
assistant_role_name = "assistant"
assistant_agent_kwargs = dict(model=assistant_model, tools=tools_list)
task_kwargs = {
"task_prompt": question,
"with_task_specify": False,
}
society = OwlRolePlaying(
**task_kwargs,
user_role_name=user_role_name,
user_agent_kwargs=user_agent_kwargs,
assistant_role_name=assistant_role_name,
assistant_agent_kwargs=assistant_agent_kwargs,
output_language="Chinese",
)
return society
# Example case
question = "浏览亚马逊并找出一款对程序员有吸引力的产品。请提供产品名称和价格"
society = construct_society(question)
answer, chat_history, token_count = run_society(society)
print(f"\033[94mAnswer: {answer}\033[0m")
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