Merge pull request #34 from studio-recoding/feat/cicd

[fix] 의존성 수정

app/database/chroma_db.py

@@ -3,6 +3,9 @@
 from chromadb.utils import embedding_functions
 from chromadb.config import Settings
 
+# FAST API
+from fastapi import Depends
+
 # ETC
 import os
 import datetime
@@ -11,12 +14,6 @@
 
 load_dotenv()
 CHROMA_DB_IP_ADDRESS = os.getenv("CHROMA_DB_IP_ADDRESS")
-# # 로컬에 ChromaDB가 저장된 경우
-# DIR = os.path.dirname(os.path.abspath(__file__))
-# DB_PATH = os.path.join(DIR, 'data')
-# # 로컬 디스크에서 ChromaDB 실행: allow_reset으로 데이터베이스 초기화, anonymized_telemetry를 False로 설정하여 텔레메트리 수집 비활성화
-# chroma_client = chromadb.PersistentClient(path=DB_PATH,
-#                                           settings=Settings(allow_reset=True, anonymized_telemetry=False))
 
 # description: 원격 EC2 인스턴스에서 ChromaDB에 연결
 chroma_client = chromadb.HttpClient(host=CHROMA_DB_IP_ADDRESS, port=8000)
@@ -55,4 +52,7 @@ async def add_db_data(schedule_data: AddScheduleDTO):
         ids=[str(schedule_data.schedule_id)],
         metadatas=[{"datetime": schedule_data.schedule_datetime, "member": schedule_data.member_id, "category": schedule_data.category}]
     )
-    return True
+    return True
+
+def get_chroma_client():
+    return chroma_client

app/dto/openai_dto.py

@@ -3,3 +3,6 @@
 
 class PromptRequest(BaseModel):
     prompt: str
+
+class ChatResponse(BaseModel):
+    ness: str

app/main.py

@@ -3,7 +3,7 @@
 from dotenv import load_dotenv
 
 # BACKEND
-from fastapi import FastAPI, HTTPException
+from fastapi import FastAPI, Depends
 
 # ETC
 import os

app/routers/chat.py

@@ -3,11 +3,11 @@
 import os
 
 from dotenv import load_dotenv
-from fastapi import APIRouter, HTTPException
+from fastapi import APIRouter, HTTPException, status
 from langchain_community.chat_models import ChatOpenAI
 from langchain_core.prompts import PromptTemplate
 
-from app.dto import openai_dto
+from app.dto.openai_dto import PromptRequest, ChatResponse
 from app.prompt import openai_prompt
 
 import app.database.chroma_db as vectordb
@@ -26,8 +26,8 @@
 config = configparser.ConfigParser()
 config.read(CONFIG_FILE_PATH)
 
-@router.post("/case")
-async def get_langchain_case(data: openai_dto.PromptRequest):
+@router.post("/case", status_code=status.HTTP_200_OK, response_model=ChatResponse)
+async def get_langchain_case(data: PromptRequest) -> ChatResponse:
     # description: use langchain
 
     config_normal = config['NESS_NORMAL']
@@ -64,7 +64,7 @@ async def get_langchain_case(data: openai_dto.PromptRequest):
 
 # case 1 : normal
 #@router.post("/case/normal") # 테스트용 엔드포인트
-async def get_langchain_normal(data: openai_dto.PromptRequest): # case 1 : normal
+async def get_langchain_normal(data: PromptRequest) -> ChatResponse: # case 1 : normal
     print("running case 1")
     # description: use langchain
     chat_model = ChatOpenAI(temperature=0,  # 창의성 (0.0 ~ 2.0)
@@ -82,11 +82,11 @@ async def get_langchain_normal(data: openai_dto.PromptRequest): # case 1 : norma
     prompt = PromptTemplate.from_template(my_template)
     response = chat_model.predict(prompt.format(output_language="Korean", question=question))
     print(response)
-    return response
+    return ChatResponse(ness=response)
 
 # case 2 : 일정 생성
 #@router.post("/case/make_schedule") # 테스트용 엔드포인트
-async def get_langchain_schedule(data: openai_dto.PromptRequest):
+async def get_langchain_schedule(data: PromptRequest) -> ChatResponse:
     print("running case 2")
     # description: use langchain
     chat_model = ChatOpenAI(temperature=0,  # 창의성 (0.0 ~ 2.0)
@@ -100,11 +100,11 @@ async def get_langchain_schedule(data: openai_dto.PromptRequest):
     prompt = PromptTemplate.from_template(case2_template)
     response = chat_model.predict(prompt.format(output_language="Korean", question=question))
     print(response)
-    return response
+    return ChatResponse(ness=response)
 
 # case 3 : rag
 #@router.post("/case/rag") # 테스트용 엔드포인트
-async def get_langchain_rag(data: openai_dto.PromptRequest):
+async def get_langchain_rag(data: PromptRequest) -> ChatResponse:
     print("running case 3")
     # description: use langchain
     chat_model = ChatOpenAI(temperature=0,  # 창의성 (0.0 ~ 2.0)
@@ -126,4 +126,4 @@ async def get_langchain_rag(data: openai_dto.PromptRequest):
     # 비동기 함수라면, 예: response = await chat_model.predict(...) 형태로 수정해야 합니다.
     response = chat_model.predict(prompt.format(output_language="Korean", question=question, schedule=schedule))
     print(response)
-    return response
+    return ChatResponse(ness=response)

app/routers/chromadb.py

@@ -2,9 +2,10 @@
 import os
 
 from dotenv import load_dotenv
-from fastapi import APIRouter, HTTPException
+from fastapi import APIRouter, HTTPException, Depends
 
 from app.dto.db_dto import AddScheduleDTO
+from app.database.chroma_db import add_db_data, get_chroma_client
 
 router = APIRouter(
     prefix="/chromadb",
@@ -22,11 +23,10 @@
 
 
 @router.post("/add_schedule")
-async def add_schedule_endpoint(schedule_data: AddScheduleDTO, vectordb=None):
+async def add_schedule_endpoint(schedule_data: AddScheduleDTO, chroma_client=Depends(get_chroma_client)):
     try:
-        # vectordb.add_db_data 함수를 비동기적으로 호출합니다.
-        await vectordb.add_db_data(schedule_data)
+        # 직접 `add_db_data` 함수를 비동기적으로 호출합니다.
+        await add_db_data(schedule_data)
         return {"message": "Schedule added successfully"}
     except Exception as e:
-        # 에러 처리: 에러가 발생하면 HTTP 500 응답을 반환합니다.
-        raise HTTPException(status_code=500, detail=str(e))
+        raise HTTPException(status_code=500, detail=str(e))