Create run_qwen_mini_zh.py

owl/run_qwen_mini_zh.py

@@ -0,0 +1,96 @@
+from dotenv import load_dotenv
+load_dotenv()
+
+from camel.models import ModelFactory
+from camel.toolkits import WebToolkit,SearchToolkit,FunctionTool
+from camel.types import ModelPlatformType,ModelType
+
+from loguru import logger
+
+from utils import OwlRolePlaying, run_society
+import os
+
+
+model_scope_api_key = os.getenv("MODELSCOPE_API_KEY")
+
+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="qwen-max",
+             model_config_dict={"temperature": 0},
+         )
+
+    assistant_model = ModelFactory.create(
+             model_platform=ModelPlatformType.QWEN,
+             model_type="qwen-max",
+             model_config_dict={"temperature": 0},
+         )
+
+    search_model = ModelFactory.create(
+             model_platform=ModelPlatformType.QWEN,
+             model_type="qwen-max",
+             model_config_dict={"temperature": 0},
+         )
+
+    planning_model = ModelFactory.create(
+             model_platform=ModelPlatformType.QWEN,
+             model_type="qwen-max",
+             model_config_dict={"temperature": 0},
+         )
+
+    web_model = ModelFactory.create(
+             model_platform=ModelPlatformType.QWEN,
+             model_type="qwen-vl-plus-latest",
+             model_config_dict={"temperature": 0},
+         )
+
+    tools_list = [
+        *WebToolkit(
+            headless=False, 
+            web_agent_model=web_model, 
+            planning_agent_model=planning_model,
+            output_language='中文'
+        ).get_tools(),
+        FunctionTool(SearchToolkit(model=search_model).search_duckduckgo),
+    ]
+
+    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,
+        'output_language': '中文',
+    }
+
+    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,
+    )
+    
+    return society
+
+
+# Example case
+question = "打开小红书上浏览推荐栏目下的前三个笔记内容，不要登陆，之后给我一个总结报告"
+
+society = construct_society(question)
+answer, chat_history, token_count = run_society(society)
+
+logger.success(f"Answer: {answer}")
+
+
+
+
+