Merge pull request #43 from studio-recoding/feat/memory

[feat] generate emoji that represents today's schedule

app/database/chroma_db.py

@@ -57,7 +57,7 @@ async def add_db_data(schedule_data: AddScheduleDTO):
 
 # 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

app/dto/db_dto.py

@@ -17,3 +17,8 @@ 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

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/report_prompt.py

@@ -0,0 +1,17 @@
+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:
+        """

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)
 

app/routers/report.py

@@ -0,0 +1,55 @@
+import configparser
+import os
+
+from dotenv import load_dotenv
+from fastapi import APIRouter, Depends, status, HTTPException
+from langchain_community.chat_models import ChatOpenAI
+from langchain_core.prompts import PromptTemplate
+
+from app.dto.db_dto import ReportMemoryEmojiRequestDTO
+from app.dto.openai_dto import ChatResponse
+from app.prompt import report_prompt
+import app.database.chroma_db as vectordb
+
+router = APIRouter(
+    prefix="/report",
+    tags=["report"]
+)
+
+# description: load env variables from .env file
+load_dotenv()
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+# description: load config variables from openai_config.ini file
+CONFIG_FILE_PATH = "app/prompt/openai_config.ini"
+config = configparser.ConfigParser()
+config.read(CONFIG_FILE_PATH)
+
+@router.post("/memory_emoji", status_code=status.HTTP_200_OK)
+async def get_memory_emoji(user_data: ReportMemoryEmojiRequestDTO) -> ChatResponse:
+    try:
+        # 모델
+        config_report_emoji = config['NESS_RECOMMENDATION']
+
+        chat_model = ChatOpenAI(temperature=config_report_emoji['TEMPERATURE'],  # 창의성 (0.0 ~ 2.0)
+                                max_tokens=config_report_emoji['MAX_TOKENS'],  # 최대 토큰수
+                                model_name=config_report_emoji['MODEL_NAME'],  # 모델명
+                                openai_api_key=OPENAI_API_KEY  # API 키
+                                )
+
+        # vectordb에서 유저의 정보를 가져온다.
+        schedule = await vectordb.db_daily_schedule(user_data)
+
+        print(schedule)
+
+        # 템플릿
+        memory_emoji_template = report_prompt.Template.memory_emoji_template
+
+        prompt = PromptTemplate.from_template(memory_emoji_template)
+        result = chat_model.predict(prompt.format(output_language="Korean", schedule=schedule))
+        print(result)
+        return ChatResponse(ness=result)
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+