Merge pull request #150 from AnguseZhang/dev/zhouh

feat: unify quotation marks and implement dynamic language detection

Author: Rasic2

agents/matmaster_agent/callback.py

@@ -11,8 +11,8 @@
 from google.genai.types import FunctionCall, Part
 
 from agents.matmaster_agent.constant import FRONTEND_STATE_KEY
-from agents.matmaster_agent.model import TransferCheck
-from agents.matmaster_agent.prompt import get_transfer_check_prompt
+from agents.matmaster_agent.model import TransferCheck, UserContent
+from agents.matmaster_agent.prompt import get_transfer_check_prompt, get_user_content_lang
 from agents.matmaster_agent.utils.llm_response_utils import has_function_call
 
 logger = logging.getLogger(__name__)
@@ -25,7 +25,6 @@ async def matmaster_prepare_state(callback_context: CallbackContext) -> Optional
 
     callback_context.state[FRONTEND_STATE_KEY] = callback_context.state.get(FRONTEND_STATE_KEY, {})
     callback_context.state[FRONTEND_STATE_KEY]['biz'] = callback_context.state[FRONTEND_STATE_KEY].get('biz', {})
-    callback_context.state['target_language'] = callback_context.state[FRONTEND_STATE_KEY].get('target_language', 'zh')
     callback_context.state['long_running_ids'] = callback_context.state.get('long_running_ids', [])
     callback_context.state['long_running_jobs'] = callback_context.state.get('long_running_jobs', {})
     callback_context.state['long_running_jobs_count'] = callback_context.state.get('long_running_jobs_count', 0)
@@ -39,6 +38,15 @@ async def matmaster_prepare_state(callback_context: CallbackContext) -> Optional
     callback_context.state['invocation_id_with_tool_call'] = callback_context.state.get('invocation_id_with_tool_call',
                                                                                         None)
 
+    user_content = callback_context.user_content.parts[0].text
+    prompt = get_user_content_lang().format(user_content=user_content)
+    response = litellm.completion(model='azure/gpt-4o', messages=[{'role': 'user', 'content': prompt}],
+                                  response_format=UserContent)
+    result: dict = json.loads(response.choices[0].message.content)
+    logger.info(f"[matmaster_prepare_state] user_content = {result}")
+    language = str(result.get('language', 'zh'))
+    callback_context.state['target_language'] = language
+
 
 # after_model_callback
 async def matmaster_check_transfer(callback_context: CallbackContext, llm_response: LlmResponse) -> Optional[

agents/matmaster_agent/model.py

@@ -54,3 +54,7 @@ class DFlowJobInfo(BaseModel):
 class TransferCheck(BaseModel):
     is_transfer: bool
     target_agent: str
+
+
+class UserContent(BaseModel):
+    language: str

agents/matmaster_agent/prompt.py

@@ -783,3 +783,17 @@ def get_params_check_info_prompt():
 
 Generate an appropriate confirmation message based on the provided function information and the user's language.
 """
+
+
+def get_user_content_lang():
+    return """
+You are a professional assistant responsible for analysing language of user_content.
+
+User Content:
+{user_content}
+
+Provide your analysis in the following JSON format:
+{{
+    "language": <string>
+}}
+"""