update wendong

examples/run_qwen_zh.py

@@ -0,0 +1,148 @@
+# ========= 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 (
+    CodeExecutionToolkit,
+    ExcelToolkit,
+    ImageAnalysisToolkit,
+    SearchToolkit,
+    VideoAnalysisToolkit,
+    BrowserToolkit,
+    FileWriteToolkit,
+)
+from camel.types import ModelPlatformType, ModelType
+from camel.societies import RolePlaying
+
+from owl.utils import run_society, DocumentProcessingToolkit
+
+from camel.logger import set_log_level
+
+set_log_level(level="DEBUG")
+
+load_dotenv()
+
+
+def construct_society(question: str) -> RolePlaying:
+    """
+    Construct a society of agents based on the given question.
+
+    Args:
+        question (str): The task or question to be addressed by the society.
+
+    Returns:
+        RolePlaying: A configured society of agents ready to address the question.
+    """
+
+    # Create models for different components
+    models = {
+        "user": ModelFactory.create(
+            model_platform=ModelPlatformType.QWEN,
+            model_type=ModelType.QWEN_MAX,
+            model_config_dict={"temperature": 0},
+        ),
+        "assistant": ModelFactory.create(
+            model_platform=ModelPlatformType.QWEN,
+            model_type=ModelType.QWEN_MAX,
+            model_config_dict={"temperature": 0},
+        ),
+        "web": ModelFactory.create(
+            model_platform=ModelPlatformType.QWEN,
+            model_type=ModelType.QWEN_VL_MAX,
+            model_config_dict={"temperature": 0},
+        ),
+        "planning": ModelFactory.create(
+            model_platform=ModelPlatformType.QWEN,
+            model_type=ModelType.QWEN_MAX,
+            model_config_dict={"temperature": 0},
+        ),
+        "video": ModelFactory.create(
+            model_platform=ModelPlatformType.QWEN,
+            model_type=ModelType.QWEN_VL_MAX,
+            model_config_dict={"temperature": 0},
+        ),
+        "image": ModelFactory.create(
+            model_platform=ModelPlatformType.QWEN,
+            model_type=ModelType.QWEN_VL_MAX,
+            model_config_dict={"temperature": 0},
+        ),
+        "document": ModelFactory.create(
+            model_platform=ModelPlatformType.QWEN,
+            model_type=ModelType.QWEN_VL_MAX,
+            model_config_dict={"temperature": 0},
+        ),
+    }
+
+    # Configure toolkits
+    tools = [
+        *BrowserToolkit(
+            headless=False,  # Set to True for headless mode (e.g., on remote servers)
+            web_agent_model=models["web"],
+            planning_agent_model=models["planning"],
+            output_language="Chinese",
+        ).get_tools(),
+        *VideoAnalysisToolkit(model=models["video"]).get_tools(),
+        *CodeExecutionToolkit(sandbox="subprocess", verbose=True).get_tools(),
+        *ImageAnalysisToolkit(model=models["image"]).get_tools(),
+        SearchToolkit().search_duckduckgo,
+        SearchToolkit().search_google,  # Comment this out if you don't have google search
+        SearchToolkit().search_wiki,
+        *ExcelToolkit().get_tools(),
+        *DocumentProcessingToolkit(model=models["document"]).get_tools(),
+        *FileWriteToolkit(output_dir="./").get_tools(),
+    ]
+
+    # Configure agent roles and parameters
+    user_agent_kwargs = {"model": models["user"]}
+    assistant_agent_kwargs = {"model": models["assistant"], "tools": tools}
+
+    # Configure task parameters
+    task_kwargs = {
+        "task_prompt": question,
+        "with_task_specify": False,
+    }
+
+    # Create and return the society
+    society = RolePlaying(
+        **task_kwargs,
+        user_role_name="user",
+        user_agent_kwargs=user_agent_kwargs,
+        assistant_role_name="assistant",
+        assistant_agent_kwargs=assistant_agent_kwargs,
+        output_language="Chinese",
+    )
+
+    return society
+
+
+def main():
+    r"""Main function to run the OWL system with an example question."""
+    # Example research question
+    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"
+
+    # Construct and run the society
+    society = construct_society(question)
+    answer, chat_history, token_count = run_society(society)
+
+    # Output the result
+    print(f"\033[94mAnswer: {answer}\033[0m")
+
+
+if __name__ == "__main__":
+    main()