feat: whisper 사용 가능하도록 프롬프트 작성 완료

app/prompt/openai_prompt.py

@@ -82,11 +82,13 @@ class Template:
     """
     case1_template = """
             {persona}
-            YOU MUST USE {output_language} TO RESPOND TO THE USER INPUT.
+            {chat_type}
+            YOU MUST USE {output_language} TO RESPOND TO THE USER INPUT. Current time is {current_time}. Respond to the user considering the current time.
             User input: {question}
             """
     case2_template = """
             {persona}
+            {chat_type}
             The user's input contains information about a new event they want to add to their schedule. You have two tasks to perform:
 
             1. Respond kindly to the user's input. YOU MUST USE {output_language} TO RESPOND TO THE USER INPUT.
@@ -118,6 +120,8 @@ class Template:
 
     case3_template = """
     {persona}
+    {chat_type}
+    Current time is {current_time}. Respond to the user considering the current time.
     When responding to user inputs, it's crucial to adapt your responses to the specified output language, maintaining a consistent and accessible communication style. YOU MUST USE {output_language} TO RESPOND TO THE USER INPUT. Your responses should not only be accurate but also display empathy and understanding of the user's needs.
     You are equipped with a state-of-the-art RAG (Retrieval-Augmented Generation) technique, enabling you to dynamically pull relevant schedule information from a comprehensive database tailored to the user's specific inquiries. This technique enhances your ability to provide precise, context-aware responses by leveraging real-time data retrieval combined with advanced natural language understanding.
     

app/routers/chat.py

@@ -46,27 +46,26 @@ async def get_langchain_case(data: PromptRequest) -> ChatCaseResponse:
 
     # chat type에 따라 적합한 프롬프트를 삽입
     if chat_type == "STT":
-        prompt = PromptTemplate.from_template(my_template)
         chat_type_prompt = openai_prompt.Template.chat_type_stt_template
-        case = chat_model.predict(prompt.format(question=question, chat_type=chat_type_prompt))
     elif chat_type == "USER":
-        prompt = PromptTemplate.from_template(my_template)
         chat_type_prompt = openai_prompt.Template.chat_type_user_template
-        case = chat_model.predict(prompt.format(question=question, chat_type=chat_type_prompt))
     else:
         raise HTTPException(status_code=500, detail="WRONG CHAT TYPE")
 
+    prompt = PromptTemplate.from_template(my_template)
+    case = chat_model.predict(prompt.format(question=question, chat_type=chat_type_prompt))
+
     # 각 케이스에도 chat type에 따라 적합한 프롬프트 삽입 필요
     print(case)
     case = int(case)
     if case == 1:
-        response = await get_langchain_normal(data)
+        response = await get_langchain_normal(data, chat_type_prompt)
 
     elif case == 2:
-        response = await get_langchain_schedule(data)
+        response = await get_langchain_schedule(data, chat_type_prompt)
 
     elif case == 3:
-        response = await get_langchain_rag(data)
+        response = await get_langchain_rag(data, chat_type_prompt)
 
     else:
         response = "좀 더 명확한 요구가 필요해요. 다시 한 번 얘기해주실 수 있나요?"
@@ -77,7 +76,7 @@ async def get_langchain_case(data: PromptRequest) -> ChatCaseResponse:
 
 # case 1 : normal
 #@router.post("/case/normal") # 테스트용 엔드포인트
-async def get_langchain_normal(data: PromptRequest): # case 1 : normal
+async def get_langchain_normal(data: PromptRequest, chat_type_prompt): # case 1 : normal
     print("running case 1")
     # description: use langchain
     config_normal = config['NESS_NORMAL']
@@ -95,13 +94,14 @@ async def get_langchain_normal(data: PromptRequest): # case 1 : normal
     my_template = openai_prompt.Template.case1_template
 
     prompt = PromptTemplate.from_template(my_template)
-    response = chat_model.predict(prompt.format(persona=user_persona_prompt, output_language="Korean", question=question))
+    current_time = datetime.now()
+    response = chat_model.predict(prompt.format(persona=user_persona_prompt, output_language="Korean", question=question, current_time=current_time, chat_type=chat_type_prompt))
     print(response)
     return response
 
 # case 2 : 일정 생성
 #@router.post("/case/make_schedule") # 테스트용 엔드포인트
-async def get_langchain_schedule(data: PromptRequest):
+async def get_langchain_schedule(data: PromptRequest, chat_type_prompt):
     print("running case 2")
     # description: use langchain
     config_normal = config['NESS_NORMAL']
@@ -118,13 +118,13 @@ async def get_langchain_schedule(data: PromptRequest):
 
     prompt = PromptTemplate.from_template(case2_template)
     current_time = datetime.now()
-    response = chat_model.predict(prompt.format(persona=user_persona_prompt, output_language="Korean", question=question, current_time=current_time))
+    response = chat_model.predict(prompt.format(persona=user_persona_prompt, output_language="Korean", question=question, current_time=current_time, chat_type=chat_type_prompt))
     print(response)
     return response
 
 # case 3 : rag
 #@router.post("/case/rag") # 테스트용 엔드포인트
-async def get_langchain_rag(data: PromptRequest):
+async def get_langchain_rag(data: PromptRequest, chat_type_prompt):
     print("running case 3")
     # description: use langchain
     config_normal = config['NESS_NORMAL']
@@ -145,6 +145,7 @@ async def get_langchain_rag(data: PromptRequest):
     case3_template = openai_prompt.Template.case3_template
 
     prompt = PromptTemplate.from_template(case3_template)
-    response = chat_model.predict(prompt.format(persona=user_persona_prompt, output_language="Korean", question=question, schedule=schedule))
+    current_time = datetime.now()
+    response = chat_model.predict(prompt.format(persona=user_persona_prompt, output_language="Korean", question=question, schedule=schedule, current_time=current_time, chat_type=chat_type_prompt))
     print(response)
     return response