add function to qwen

camel/models/_utils.py

@@ -0,0 +1,52 @@
+# ========= 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. =========
+import textwrap
+from typing import Optional, Type
+
+from pydantic import BaseModel
+
+
+def get_prompt_with_response_format(
+    response_format: Optional[Type[BaseModel]],
+    user_message: str,
+) -> str:
+    """
+    This function generates a prompt based on the provided Pydantic model and
+    user message.
+
+    Args:
+        response_format (Optional[Type[BaseModel]]): The Pydantic model class.
+        user_message (str): The user message to be used in the prompt.
+
+    Returns:
+        str: A prompt string for the LLM.
+    """
+    if response_format is None:
+        return user_message
+
+    json_schema = response_format.model_json_schema()
+    updated_prompt = textwrap.dedent(
+        f"""\
+        Given the user message, please generate a JSON response adhering 
+        to the following JSON schema:
+        {json_schema}
+        Make sure the JSON response is valid and matches the EXACT structure 
+        defined in the schema. Your result should only be a valid json 
+        object, without any other text or comments.
+        
+        Following is the original user message:
+        {user_message}
+        """
+    )
+    return updated_prompt

camel/models/anthropic_model.py

@@ -169,9 +169,7 @@ async def _arun(
         )
 
         # format response to openai format
-        response = self._convert_response_from_anthropic_to_openai(response)
-
-        return response
+        return self._convert_response_from_anthropic_to_openai(response)
 
     def check_model_config(self):
         r"""Check whether the model configuration is valid for anthropic

camel/models/qwen_model.py

@@ -21,6 +21,7 @@
 from camel.configs import QWEN_API_PARAMS, QwenConfig
 from camel.messages import OpenAIMessage
 from camel.models import BaseModelBackend
+from camel.models._utils import get_prompt_with_response_format
 from camel.types import (
     ChatCompletion,
     ChatCompletionChunk,
@@ -148,25 +149,21 @@ def _prepare_request(
         tools: Optional[List[Dict[str, Any]]],
     ) -> Dict[str, Any]:
         request_config = self.model_config_dict.copy()
+        user_message = messages[-1]
 
-        if tools:
-            request_config["tools"] = tools
-
-        if response_format is None:
-            return request_config
+        if not isinstance(user_message["content"], str):
+            raise ValueError("Only text messages are supported")
 
-        # get all keys of the response_format
-        response_format_keys = response_format.model_fields.keys()
-        additional_prompt = (
-            "The response should be in JSON format with the following keys: "
-            f"{', '.join(response_format_keys)}."
-        )
-        user_message = messages[-1]
-        user_message["content"] = (
-            f"{user_message['content']}\n{additional_prompt}"
+        user_message["content"] = get_prompt_with_response_format(
+            response_format, user_message["content"]
         )
+        if tools:
+            request_config["tools"] = tools
+        elif response_format:
+            # Improve stability with native response format support
+            # This config will be unstable if being used with tools
+            request_config["response_format"] = {"type": "json_object"}
 
-        request_config["response_format"] = {"type": "json_object"}
         return request_config
 
     @property

camel/utils/__init__.py

@@ -22,7 +22,6 @@
     download_github_subdirectory,
     download_tasks,
     func_string_to_callable,
-    generate_prompt_for_structured_output,
     get_first_int,
     get_prompt_template_key_words,
     get_pydantic_major_version,
@@ -81,5 +80,4 @@
     "handle_http_error",
     "get_pydantic_model",
     "download_github_subdirectory",
-    "generate_prompt_for_structured_output",
 ]

camel/utils/commons.py

@@ -692,39 +692,3 @@ def download_github_subdirectory(
             download_github_subdirectory(
                 repo, f'{subdir}/{file["name"]}', file_path, branch
             )
-
-
-def generate_prompt_for_structured_output(
-    response_format: Optional[Type[BaseModel]],
-    user_message: str,
-) -> str:
-    """
-    This function generates a prompt based on the provided Pydantic model and
-    user message.
-
-    Args:
-        response_format (Type[BaseModel]): The Pydantic model class.
-        user_message (str): The user message to be used in the prompt.
-
-    Returns:
-        str: A prompt string for the LLM.
-    """
-    if response_format is None:
-        return user_message
-
-    json_schema = response_format.model_json_schema()
-    sys_prompt = (
-        "Given the user message, please generate a JSON response adhering "
-        "to the following JSON schema:\n"
-        f"{json_schema}\n"
-        "Make sure the JSON response is valid and matches the EXACT structure "
-        "defined in the schema. Your result should only be a valid json "
-        "object, without any other text or comments.\n"
-    )
-    user_prompt = f"User message: {user_message}\n"
-
-    final_prompt = f"""
-    {sys_prompt}
-    {user_prompt}
-    """
-    return final_prompt

examples/simple_agent.py

@@ -1,8 +1,20 @@
+# ========= 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. =========
 from pydantic import BaseModel
 
 from camel.agents import ChatAgent
 from camel.models import ModelFactory
-from camel.toolkits import WeatherToolkit
 from camel.types import ModelPlatformType, ModelType
 
 model = ModelFactory.create(
@@ -12,14 +24,14 @@
 
 
 class ResponseFormat(BaseModel):
-    weather: str
-    time: str
+    celsius: str
+    fahrenheit: str
 
 
-agent = ChatAgent(model=model, tools=[WeatherToolkit().get_weather_data])
+agent = ChatAgent(model=model)
 
 resp = agent.step(
-    "What's the current weather in New York?",
+    "At what tempreature does water boil?",
     response_format=ResponseFormat,
 )
 print(resp.msg.content)