Merge pull request #81 from studio-recoding/refactor/chat-completion

feat: chat 분류에 completion 추가
studio-recoding · Jul 17, 2024 · 39b261e · 39b261e
2 parents 5187ed7 + aa6b9ea
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diff --git a/app/prompt/openai_prompt.py b/app/prompt/openai_prompt.py
@@ -60,43 +60,46 @@ class Template:
     """
 
     case_classify_template = """
-                    Task: User Chat Classification
-                    You are a case classifier integrated in scheduler application.
-                    Please analyze User Chat according to the following criteria and return the appropriate case number (1, 2, 3, 4).
-                    {chat_type}
-                
-                    - Case 1: \
-                    The question is a general information request, advice, or simple conversation, and does not require accessing the user's schedule database.
-                    - Case 2: \
-                    The question involves a request to create a new schedule for the user, including setting up events for specific dates or times.
-                    - Case 3: \
-                    The question requires accessing or searching through the user's previous schedule information. This might involve past schedules, preferences, or other relevant details.
-                    - Case 4: \
-                    The question involves a request to delete an event or events from the user's schedule, necessitating identification and removal of specific entries from the database.
-                    
-                    After analyzing the content of the question, return the most suitable case number.
-                    YOU MUST ANSWER ONLY WITH NUMBER (1, 2, 3, 4). OTHER WORDS ARE PROHIBITED. IT IS VERY IMPORTANT TO RETURN ONLY THE NUMBERS. NO YAPPING!
-                    
-                    Task: Analyze the content of the question and return the most suitable case number.
-                    Example 1:
-                    User Chat: "What's the weather like tomorrow?"
-                    Answer: 1
-                    
-                    Example 2:
-                    User Chat: "I have a meeting with Dr. Lee next Monday at 10 AM."
-                    Answer: 2
-                    
-                    Example 3:
-                    User Chat: "Did I have any appointments on the last Friday?"
-                    Answer: 3
-
-                    Example 4:
-                    User Chat: "Please delete my appointment with Dr. Smith next Tuesday."
-                    Answer: 4
-                    
-                    Example 5:
-                    User Chat: {question}
-                    Answer:
+            Task: User Chat and Context Classification
+            You are a case classifier integrated into a scheduler application.
+            Please analyze User Chat along with its context from previous interactions according to the following criteria and return the appropriate case number (1, 2, 3, 4).
+            {chat_type}
+            
+            - Case 1: 
+            The question is a general information request, advice, or simple conversation, and does not require accessing the user's schedule database.
+            
+            - Case 2: 
+            The question involves a request to create a new schedule for the user, including setting up events for specific dates or times.
+            
+            - Case 3: 
+            The question requires accessing or searching through the user's previous schedule information or any related conversation context. This might involve past schedules, preferences, or other relevant details from earlier chats.
+            
+            - Case 4: 
+            The question involves a request to delete an event or events from the user's schedule, necessitating identification and removal of specific entries from the database, potentially including information about past conversations regarding the event.
+            
+            After analyzing the content of the question and its context, return the most suitable case number.
+            YOU MUST ANSWER ONLY WITH NUMBER (1, 2, 3, 4). OTHER WORDS ARE PROHIBITED. IT IS VERY IMPORTANT TO RETURN ONLY THE NUMBERS. NO YAPPING!
+            
+            Task: Analyze the content of the question along with the chat history and return the most suitable case number.
+            Example 1:
+            User Chat: "What's the weather like tomorrow?"
+            Context: None
+            Answer: 1
+            
+            Example 2:
+            User Chat: "I have a meeting with Dr. Lee next Monday at 10 AM."
+            Context: User previously asked for a free slot on Monday.
+            Answer: 2
+            
+            Example 3:
+            User Chat: "Did I have any appointments on the last Friday?"
+            Context: User often asks about weekly appointments to reflect on time management.
+            Answer: 3
+            
+            Example 4:
+            User Chat: "Please delete my appointment with Dr. Smith next Tuesday."
+            Context: User mentioned dissatisfaction with this appointment in a previous chat.
+            Answer: 4
                     """
 
     chat_type_stt_template = """

diff --git a/app/routers/chat.py b/app/routers/chat.py
@@ -41,12 +41,14 @@ async def get_langchain_case(data: PromptRequest) -> ChatCaseResponse:
                             model_name=config_chat['MODEL_NAME'],  # 모델명
                             openai_api_key=OPENAI_API_KEY  # API 키
                             )
+
+    # 이전 대화 내역 가져오기
+    previous_conversations = fetch_previous_conversations(data.member_id)
+    print(previous_conversations)
+
     question = data.prompt # 유저로부터 받은 채팅의 내용
     chat_type = data.chatType # 위스퍼 사용 여부 [STT, USER]
 
-    # description: give NESS's instruction as for case analysis
-    my_template = openai_prompt.Template.case_classify_template
-
     # chat type에 따라 적합한 프롬프트를 삽입
     if chat_type == "STT":
         chat_type_prompt = openai_prompt.Template.chat_type_stt_template
@@ -55,8 +57,27 @@ async def get_langchain_case(data: PromptRequest) -> ChatCaseResponse:
     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))
+    # description: give NESS's instruction as for case analysis
+    case_classify_template = openai_prompt.Template.case_classify_template
+    # prompt = PromptTemplate.from_template(my_template)
+
+    # system, human, ai
+    chat_prompt = ChatPromptTemplate.from_messages(
+        previous_conversations + [
+            ("system", case_classify_template),
+            ("human", "User Chat: {question}\nAnswer: ")
+        ]
+    )
+    # case = chat_model.predict(prompt.format(question=question, chat_type=chat_type_prompt))
+    # 프롬프트와 모델을 chaining
+    chain = chat_prompt | chat_model
+
+    response = chain.invoke({
+        "chat_type": chat_type_prompt,
+        "question": question
+    })
+
+    case = response.content
 
     # 각 케이스에도 chat type에 따라 적합한 프롬프트 삽입 필요
     print(case)