[chore] test cicd

.github/workflows/cicd.yml

@@ -77,7 +77,7 @@ jobs:
 
 
       - name: Remove old Docker image
-        run: docker rmi -f $(docker images -aq) || true 
+        run: docker rmi -f $(docker images -aq) || true
 
       - name: Docker run
         run: |

app/main.py

@@ -3,21 +3,7 @@
 from dotenv import load_dotenv
 
 # BACKEND
-from fastapi import FastAPI
 from fastapi import FastAPI, HTTPException
-import asyncio
-
-# VECTOR DB Module
-import app.database.chroma_db as vectordb
-
-# AI
-from langchain.chat_models import ChatOpenAI
-from langchain.prompts import PromptTemplate
-
-# DTO
-from app.dto import openai_dto
-from app.prompt import openai_prompt
-from app.dto.db_dto import AddScheduleDTO
 
 # ETC
 import os

app/routers/chat.py

@@ -43,7 +43,7 @@ async def get_langchain_case(data: openai_dto.PromptRequest):
     my_template = openai_prompt.Template.case_classify_template
 
     prompt = PromptTemplate.from_template(my_template)
-    case = chat_model.predict(prompt.format(question=question))
+    case = await chat_model.predict(prompt.format(question=question))
 
     print(case)
     case = int(case)
@@ -73,14 +73,12 @@ async def get_langchain_normal(data: openai_dto.PromptRequest): # case 1 : norma
                             openai_api_key=OPENAI_API_KEY  # API 키
                             )
     question = data.prompt
-    # 기존 모델 문제 생김
-    # chat_model = LangchainOpenAI(openai_api_key=OPENAI_API_KEY)
 
     # description: give NESS's ideal instruction as template
     my_template = openai_prompt.Template.case1_template
 
     prompt = PromptTemplate.from_template(my_template)
-    response = chat_model.predict(prompt.format(output_language="Korean", question=question))
+    response = await chat_model.predict(prompt.format(output_language="Korean", question=question))
     print(response)
     return response
 
@@ -98,7 +96,7 @@ async def get_langchain_schedule(data: openai_dto.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))
+    response = await chat_model.predict(prompt.format(output_language="Korean", question=question))
     print(response)
     return response
 
@@ -124,6 +122,6 @@ async def get_langchain_rag(data: openai_dto.PromptRequest):
     # 여기서는 chat_model.predict가 비동기 함수인지, 동기 함수인지에 따라 처리가 달라질 수 있습니다.
     # 만약 비동기 함수라면 await를 사용해야 합니다. 아래 코드는 동기 함수를 가정하고 작성되었습니다.
     # 비동기 함수라면, 예: response = await chat_model.predict(...) 형태로 수정해야 합니다.
-    response = chat_model.predict(prompt.format(output_language="Korean", question=question, schedule=schedule))
+    response = await chat_model.predict(prompt.format(output_language="Korean", question=question, schedule=schedule))
     print(response)
     return response

app/routers/recommendation.py

@@ -23,7 +23,7 @@
 config.read(CONFIG_FILE_PATH)
 
 @router.get("/main")
-def get_recommendation():
+async def get_recommendation():
 
     # 모델
     chat_model = ChatOpenAI(temperature=0,  # 창의성 (0.0 ~ 2.0)
@@ -37,4 +37,4 @@ def get_recommendation():
     recommendation_template = openai_prompt.Template.recommendation_template
 
     prompt = PromptTemplate.from_template(recommendation_template)
-    return chat_model.predict(prompt.format())
+    return await chat_model.predict(prompt.format())

requirements.txt

@@ -1,10 +1,9 @@
 chromadb==0.4.22
 fastapi==0.109.0
 uvicorn==0.26.0
-langchain==0.1.1
 langchain-community==0.0.13
 openai==1.8.0
 python-dotenv==1.0.0
 starlette==0.35.1
 pydantic==2.5.3
-sentence-transformers==2.5.1
+sentence-transformers==2.5.1