[feat] 데모용 코드 완성

app/database/chroma_db.py

@@ -10,7 +10,7 @@
 import os
 import datetime
 from dotenv import load_dotenv
-from app.dto.db_dto import AddScheduleDTO, RecommendationMainRequestDTO
+from app.dto.db_dto import AddScheduleDTO, RecommendationMainRequestDTO, ReportTagsRequestDTO
 
 load_dotenv()
 CHROMA_DB_IP_ADDRESS = os.getenv("CHROMA_DB_IP_ADDRESS")
@@ -48,28 +48,66 @@ async def search_db_query(query):
 # 스프링 백엔드로부터 chroma DB에 저장할 데이터를 받아 DB에 추가한다.
 async def add_db_data(schedule_data: AddScheduleDTO):
     schedule_date = schedule_data.schedule_datetime_start.split("T")[0]
+    year = int(schedule_date.split("-")[0])
+    month = int(schedule_date.split("-")[1])
+    date = int(schedule_date.split("-")[2])
     schedules.add(
         documents=[schedule_data.data],
         ids=[str(schedule_data.schedule_id)],
-        metadatas=[{"date": schedule_date, "datetime_start": schedule_data.schedule_datetime_start, "datetime_end": schedule_data.schedule_datetime_end, "member": schedule_data.member_id, "category": schedule_data.category, "location": schedule_data.location, "person": schedule_data.person}]
+        metadatas=[{"year": year, "month": month, "date": date, "datetime_start": schedule_data.schedule_datetime_start, "datetime_end": schedule_data.schedule_datetime_end, "member": schedule_data.member_id, "category": schedule_data.category, "location": schedule_data.location, "person": schedule_data.person}]
     )
     return True
 
 # 메인페이지 한 줄 추천 기능에 사용하는 함수
 # 유저의 id, 해당 날짜로 필터링
-async def db_recommendation_main(user_data: RecommendationMainRequestDTO):
+async def db_daily_schedule(user_data: RecommendationMainRequestDTO):
     member = user_data.member_id
     schedule_datetime_start = user_data.schedule_datetime_start
     schedule_datetime_end = user_data.schedule_datetime_end
     schedule_date = schedule_datetime_start.split("T")[0]
+    year = int(schedule_date.split("-")[0])
+    month = int(schedule_date.split("-")[1])
+    date = int(schedule_date.split("-")[2])
     persona = user_data.user_persona or "hard working"
     results = schedules.query(
         query_texts=[persona],
         n_results=5,
         where={"$and":
                [
                    {"member": {"$eq": int(member)}},
-                   {"date": {"$eq": schedule_date}}
+                   {"year": {"$eq": year}},
+                   {"month": {"$eq": month}},
+                   {"date": {"$eq": date}}
+               ]
+        }
+        # where_document={"$contains":"search_string"}  # optional filter
+    )
+    return results['documents']
+
+# 태그 생성용 스케쥴 반환 - 카테고리에 따라
+async def db_monthly_tag_schedule(user_data: ReportTagsRequestDTO):
+    member = user_data.member_id
+    schedule_datetime_start = user_data.schedule_datetime_start
+    schedule_datetime_end = user_data.schedule_datetime_end
+    schedule_date = schedule_datetime_start.split("T")[0]
+    year = int(schedule_date.split("-")[0])
+    month = int(schedule_date.split("-")[1])
+    date = int(schedule_date.split("-")[2])
+    persona = user_data.user_persona or "hard working"
+    results = schedules.query(
+        query_texts=[persona],
+        n_results=15,
+        where={"$and":
+               [
+                   {"member": {"$eq": int(member)}},
+                   {"year": {"$eq": year}},
+                   {"month": {"$eq": month - 1}}
+                   # {"$or":
+                   #  [{"$and":
+                   #        [{"month": {"$eq": month-1}}, {"date": {"$gte": 10}}]},
+                   #   {"$and":
+                   #        [{"month": {"$eq": month}}, {"date": {"$lt": 10}}]}
+                   #  ]}
                ]
         }
         # where_document={"$contains":"search_string"}  # optional filter

app/dto/db_dto.py

@@ -7,7 +7,7 @@ class AddScheduleDTO(BaseModel):
     schedule_datetime_end: str
     schedule_id: int
     member_id: int
-    category: str
+    category: int
     location: str
     person: str
 
@@ -17,3 +17,15 @@ class RecommendationMainRequestDTO(BaseModel):
     schedule_datetime_start: str
     schedule_datetime_end: str
 
+class ReportMemoryEmojiRequestDTO(BaseModel):
+    member_id: int
+    user_persona: str
+    schedule_datetime_start: str
+    schedule_datetime_end: str
+
+class ReportTagsRequestDTO(BaseModel):
+    member_id: int
+    user_persona: str
+    schedule_datetime_start: str
+    schedule_datetime_end: str
+

app/dto/openai_dto.py

@@ -1,5 +1,5 @@
 from pydantic import BaseModel
-
+from typing import List
 
 class PromptRequest(BaseModel):
     prompt: str
@@ -9,4 +9,11 @@ class ChatResponse(BaseModel):
 
 class ChatCaseResponse(BaseModel):
     ness: str
-    case: int
+    case: int
+
+class TagDescription(BaseModel):
+    tag: str
+    desc: str
+
+class TagsResponse(BaseModel):
+    tagList: List[TagDescription]

app/main.py

@@ -32,6 +32,9 @@
 from app.routers import recommendation
 app.include_router(recommendation.router)
 
+from app.routers import report
+app.include_router(report.router)
+
 # description: prevent CORS error
 origins = [
     "*",

app/prompt/openai_config.ini

@@ -1,14 +1,19 @@
 [NESS_NORMAL]
-TEMPERATURE     = 0
+TEMPERATURE     = 0.5
 MAX_TOKENS      = 2048
-MODEL_NAME      = gpt-3.5-turbo-1106
+MODEL_NAME      = gpt-4
 
 [NESS_CASE]
 TEMPERATURE     = 0
 MAX_TOKENS      = 2048
 MODEL_NAME      = gpt-4
 
 [NESS_RECOMMENDATION]
-TEMPERATURE     = 0
+TEMPERATURE     = 1
+MAX_TOKENS      = 2048
+MODEL_NAME      = gpt-4
+
+[NESS_TAGS]
+TEMPERATURE     = 0.5
 MAX_TOKENS      = 2048
-MODEL_NAME      = gpt-3.5-turbo-1106
+MODEL_NAME      = gpt-4

app/prompt/openai_prompt.py

@@ -2,12 +2,14 @@ class Template:
     recommendation_template = """
                     You are an AI assistant designed to recommend daily activities based on a user's schedule. You will receive a day's worth of the user's schedule information. Your task is to understand that schedule and, based on it, recommend an activity for the user to perform that day. There are a few rules you must follow in your recommendations:
                     1. YOU MUST USE {output_language} TO RESPOND TO THE USER INPUT.
-                    2. Ensure your recommendation is encouraging, so the user doesn't feel compelled.
+                    2. Ensure your recommendation is encouraging and delicate, so the user doesn't feel compelled.
                     3. The recommendation must be concise, limited to one sentence without any additional commentary.
+                    4. The recommendation must not exceed 20 characters in the {output_language}.
+                    5. The final punctuation of the recommendation must be exclusively an exclamation point or a question mark. 
 
                     Example:
                     User schedule:  [Practice guitar, Calculate accuracy, Study backend development, Run AI models in the lab, Study NEST.JS]
-                    AI Recommendation: "Your day is filled with learning and research. how about taking a short walk in between studies?"
+                    AI Recommendation: "공부 사이에 짧게 산책 어때요?"
 
                     User schedule: {schedule}
                     AI Recommendation:
@@ -48,36 +50,39 @@ class Template:
                     Answer:
                     """
     case1_template = """
-    You are a friendly assistant who helps users manage their schedules. Respond kindly to the user's input. YOU MUST USE {output_language} TO RESPOND TO THE USER INPUT.
-    User input: {question}
-    """
+            You are a friendly assistant, NESS. NESS helps users manage their schedules. Respond kindly to the user's input. YOU MUST USE {output_language} TO RESPOND TO THE USER INPUT.
+            User input: {question}
+            """
     case2_template = """
-        You are a friendly assistant who helps users manage their schedules. The user's input contains information about a new event they want to add to their schedule. You have two tasks to perform:
+            You are a friendly assistant who helps users manage their schedules. 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.
-        2. Organize the event the user wants to add into a json format for saving in a database. The returned json will have keys for info, location, person, and date.
-        - info: Summarizes what the user wants to do. This value must always be present.
-        - location: If the user's event information includes a place, save that place as the value.
-        - person: If the user's event mentions a person they want to include, save that person as the value.
-        - date: If the user's event information includes a specific date and time, save that date and time in datetime format.
-        Separate the outputs for tasks 1 and 2 with a special token <separate>.
+            1. Respond kindly to the user's input. YOU MUST USE {output_language} TO RESPOND TO THE USER INPUT.
+            2. Organize the event the user wants to add into a json format for saving in a database. The returned json will have keys for info, location, person, and date.
+            - info: Summarizes what the user wants to do. This value must always be present.
+            - location: If the user's event information includes a place, save that place as the value.
+            - person: If the user's event mentions a person they want to include, save that person as the value.
+            - date: If the user's event information includes a specific date and time, save that date and time in datetime format. Dates should be organized based on the current time at the user's location. Current time is {current_time}.
+            Separate the outputs for tasks 1 and 2 with a special token <separate>.
 
-        Example for one-shot learning:
+            Example for one-shot learning:
 
-        User input: "I have a meeting with Dr. Smith at her office on March 3rd at 10am."
+            User input: "I have a meeting with Dr. Smith at her office on March 3rd at 10am."
 
-        Response to user:
-        "I've added your meeting with Dr. Smith at her office on March 3rd at 10am to your schedule. Is there anything else you'd like to add or modify?"
-        <separate>
-        {{
-            "info": "meeting with Dr. Smith",
-            "location": "Dr. Smith's office",
-            "person": "Dr. Smith",
-            "date": "2023-03-03T10:00:00"
-        }}
+            Response to user:
+            "I've added your meeting with Dr. Smith at her office on March 3rd at 10am to your schedule. Is there anything else you'd like to add or modify?"
+            <separate>
+            {{
+                "info": "meeting with Dr. Smith",
+                "location": "Dr. Smith's office",
+                "person": "Dr. Smith",
+                "date": "2023-03-03T10:00:00"
+            }}
+
+            User input: {question}
+
+            Response to user:
+            """
 
-        User input: {question}
-        """
     case3_template = """
 
     You are an advanced, friendly assistant dedicated to helping users efficiently manage their schedules and navigate their day-to-day tasks with ease. Your primary role is to interact with users in a supportive and courteous manner, ensuring they feel valued and assisted at every step.

app/prompt/report_prompt.py

@@ -0,0 +1,36 @@
+class Template:
+    memory_emoji_template = """
+        You are an AI assistant designed to return an emoji that represents a user's day based on their schedule. You will receive information about the user's daily schedule. Your task is to analyze this schedule and select a single emoji that encapsulates the day's activities. There are a few guidelines you must adhere to in your selection:
+
+        YOU MUST RESPOND WITH A SINGLE EMOJI without any additional commentary.
+        The emoji must reflect the overall theme or mood of the day's activities.
+        Your recommendation should be intuitive, making it easy for the user to see the connection between the schedule and the chosen emoji.
+        Example:
+        User schedule: [Practice guitar, Calculate accuracy, Study backend development, Run AI models in the lab, Study NEST.JS]
+        AI Recommendation: 🎓
+
+        User schedule: [Morning jog, Office work, Lunch with friends, Evening yoga]
+        AI Recommendation: ☀️
+
+        User schedule: {schedule}
+        AI Recommendation:
+        """
+
+    report_tags_template = """
+            You are an AI assistant tasked with analyzing a user's schedule over the span of a month. From this detailed schedule, you will distill three keywords that best encapsulate the user's activities, interests, or achievements throughout the month. Additionally, you will provide a brief explanation for each keyword to illustrate why it was chosen, making the output more informative and engaging. Here are the rules for your analysis:
+
+            YOU MUST USE {output_language} TO RESPOND TO THE INPUT.
+            YOU MUST PROVIDE THREE KEYWORDS in your response, each accompanied by a concise explanation.
+            The keywords must capture the essence of the user's monthly activities, highlighting aspects that are both rewarding and enjoyable.
+            Your selections should be creative and personalized, aiming to reflect the user's unique experiences over the month.
+            Each explanation of the keywords must not exceed 50 characters in the {output_language}.
+            Example:
+            User's monthly schedule: [Attended a programming bootcamp, Completed a marathon, Read three novels, Volunteered at the local food bank, Started a blog about sustainability]
+            AI Recommendation: "공부 매니아: 이번 달엔 공부를 정말 많이 하셨군요!, 환경 지킴이: 지속 가능한 생활에 대한 열정이 느껴져요., 자기 계발 홀릭: 성장을 위해 노력하는 모습이 멋져요."
+
+            User's monthly schedule: [Took photography classes, Explored three new hiking trails, Organized a neighborhood clean-up, Experimented with vegan recipes]
+            AI Recommendation: "모험가: 새로운 활동에 많이 도전하셨네요!, 미식가: 레시피 실험을 했다니 멋져요., 도파민 중독자: 즐거운 일을 많이 만드시네요!"
+
+            User's monthly schedule: {schedule}
+            AI Recommendation:
+            """

app/routers/chat.py

@@ -6,6 +6,7 @@
 from fastapi import APIRouter, HTTPException, status
 from langchain_community.chat_models import ChatOpenAI
 from langchain_core.prompts import PromptTemplate
+from datetime import datetime
 
 from app.dto.openai_dto import PromptRequest, ChatResponse, ChatCaseResponse
 from app.prompt import openai_prompt
@@ -30,7 +31,7 @@
 async def get_langchain_case(data: PromptRequest) -> ChatCaseResponse:
     # description: use langchain
 
-    config_chat = config['NESS_CHAT']
+    config_chat = config['NESS_CASE']
 
     chat_model = ChatOpenAI(temperature=config_chat['TEMPERATURE'],  # 창의성 (0.0 ~ 2.0)
                             max_tokens=config_chat['MAX_TOKENS'],  # 최대 토큰수
@@ -57,9 +58,11 @@ async def get_langchain_case(data: PromptRequest) -> ChatCaseResponse:
         response = await get_langchain_rag(data)
 
     else:
-        print("wrong case classification")
-        # 적절한 HTTP 상태 코드와 함께 오류 메시지를 반환하거나, 다른 처리를 할 수 있습니다.
-        raise HTTPException(status_code=400, detail="Wrong case classification")
+        # print("wrong case classification")
+        # # 적절한 HTTP 상태 코드와 함께 오류 메시지를 반환하거나, 다른 처리를 할 수 있습니다.
+        # raise HTTPException(status_code=400, detail="Wrong case classification")
+        response = "좀 더 명확한 요구가 필요해요. 다시 한 번 얘기해주실 수 있나요?"
+        case = "Exception"
 
     return ChatCaseResponse(ness=response, case=case)
 
@@ -104,7 +107,8 @@ async def get_langchain_schedule(data: PromptRequest):
     case2_template = openai_prompt.Template.case2_template
 
     prompt = PromptTemplate.from_template(case2_template)
-    response = chat_model.predict(prompt.format(output_language="Korean", question=question))
+    current_time = datetime.now()
+    response = chat_model.predict(prompt.format(output_language="Korean", question=question, current_time=current_time))
     print(response)
     return response
 

app/routers/recommendation.py

@@ -38,7 +38,7 @@ async def get_recommendation(user_data: RecommendationMainRequestDTO) -> ChatRes
                                 )
 
         # vectordb에서 유저의 정보를 가져온다.
-        schedule = await vectordb.db_recommendation_main(user_data)
+        schedule = await vectordb.db_daily_schedule(user_data)
 
         print(schedule)