[Feat & Fix & Comment] 오답분석 기능 답변 고도화 및 연관 개념 답변, 서버 로그 추가

[Feat & Fix & Comment] 오답분석 기능 답변 고도화 및 연관 개념 답변, 서버 로그 추가

app/main.py

@@ -12,6 +12,8 @@
 import os
 from openai import OpenAI
 from pymilvus import connections, utility, FieldSchema, CollectionSchema, DataType, Collection
+import time
+from datetime import datetime
 
 # 로깅 추가
 logging.basicConfig(level=logging.INFO)
@@ -20,10 +22,10 @@
 app = FastAPI()
 
 #  클라이언트 생성
-s3_client = boto3.client( "s3",
-                          region_name="ap-northeast-2")
+s3_client = boto3.client("s3",
+                          region_name="ap-northeast-2",)
 ssm_client = boto3.client('ssm',
-                          region_name='ap-northeast-2')
+                          region_name="ap-northeast-2",)
 
 # s3 버킷 연결
 try:
@@ -150,7 +152,7 @@ async def upload_directly(upload_file: UploadFile = File(...)):
 
 
 @app.get("/analysis/whole")
-async def analysis(problem_url = None):
+async def analysis(problem_url=None):
     """ Curriculum-based Chat Completion API with CLOVA OCR & ChatGPT  """
     await connect_milvus()  # milvus 서버 연결
 
@@ -159,11 +161,15 @@ async def analysis(problem_url = None):
     else:
         problem_text = await ocr(problem_url)
 
-    retrieving_result = await retrieve(problem_text)
+    retrieving_result, subjects, units, concepts = await retrieve(problem_text)
     question = await augment(retrieving_result, problem_text)
     answer = await generate(question)
 
-    return JSONResponse(content={"message": "Problem Analysis Finished Successfully", "answer": answer})
+    return JSONResponse(content={"message": "Problem Analysis Finished Successfully",
+                                 "subject": list(set(subjects)),
+                                 "unit": list(set(units)),
+                                 "key_concept": list(set(concepts)),
+                                 "answer": answer})
 
 
 @app.get("/analysis/ocr")
@@ -174,12 +180,13 @@ async def ocr(problem_url: str):
     import time
     import json
 
-    logger.info("Analyzing problem from this image URL: %s", problem_url)
     try:
+        dt3 = datetime.fromtimestamp(time.time())
         s3_key = parse_s3_url(problem_url)
         img_bytes = download_image_from_s3(s3_key)  # download from S3
         extension = s3_key.split(".")[-1]
-        logger.info("Completed Download & Sending Requests... '%s'", s3_key)
+        dt4 = datetime.fromtimestamp(time.time())
+        logger.info(f"{dt3}~{dt4}: 이미지 다운로드 완료")
 
         clova_api_url = ssm_client.get_parameter(
             Name='/ono/fastapi/CLOVA_API_URL',
@@ -211,18 +218,18 @@ async def ocr(problem_url: str):
         files = [
             ('file', image_file)
         ]
-        logger.info("Processing OCR & Receiving Responses...")
 
+        dt3 = datetime.fromtimestamp(time.time())
         ocr_response = requests.request("POST", clova_api_url, headers=headers, data=payload, files=files).text
         ocr_response_json = json.loads(ocr_response)
-        logger.info("***** Finished OCR Successfully *****")
+        dt4 = datetime.fromtimestamp(time.time())
+        logger.info(f"{dt3}~{dt4}: 이미지 OCR 완료")
 
         infer_texts = []
         for image in ocr_response_json["images"]:
             for field in image["fields"]:
                 infer_texts.append(field["inferText"])
         result = ' '.join(infer_texts)
-
         return result
 
     except Exception as pe:
@@ -256,14 +263,14 @@ async def upload_curriculum_txt(upload_file: UploadFile = File(...)):
 
 # Mivlus DB 연결
 SERVER = os.getenv('SERVER')
-logger.info(f"* log >> 환경변수 SERVER: {SERVER}로 받아왔습니다.")
+logger.info(f"* log >> 환경변수를 SERVER({SERVER})로 받아왔습니다.")
 
 MILVUS_HOST = ssm_client.get_parameter(
     Name=f'/ono/{SERVER}/fastapi/MILVUS_HOST_NAME',
     WithDecryption=False
 )['Parameter']['Value']
 MILVUS_PORT = 19530
-COLLECTION_NAME = 'Math2015Curriculum'
+COLLECTION_NAME = 'Curriculum2015'
 DIMENSION = 1536
 INDEX_TYPE = "IVF_FLAT"
 
@@ -272,13 +279,16 @@ async def upload_curriculum_txt(upload_file: UploadFile = File(...)):
 async def connect_milvus():
     try:
         # Milvus 서버 연결
-        connections.connect(alias="default", host=MILVUS_HOST, port=MILVUS_PORT)
-        logger.info(f"* log >> Milvus Server is connected to {MILVUS_HOST}:{MILVUS_PORT}")
+        dt1 = str(datetime.fromtimestamp(time.time()))
+        connections.connect(alias="default", host=MILVUS_HOST, port=MILVUS_PORT)  # server 용
+        # connections.connect(host="127.0.0.1", port=19530, db="default")  # localhost 용
+        dt2 = str(datetime.fromtimestamp(time.time()))
+        logger.info(f"{dt1} ~ {dt2}: Milvus 서버 {MILVUS_HOST}:{MILVUS_PORT}에 연결 완료")
 
         # 컬렉션의 스키마 출력
         if utility.has_collection(COLLECTION_NAME):
             collection = Collection(COLLECTION_NAME)
-            logger.info("* 존재하는 Collection Schema:")
+            logger.info(f"* 존재하는 Collection {COLLECTION_NAME} Schema:")
             for field in collection.schema.fields:
                 logger.info(f"    - Field Name: {field.name}, Data Type #: {field.dtype}")
 
@@ -297,18 +307,16 @@ async def create_milvus():
         FieldSchema(name='id', dtype=DataType.INT64, is_primary=True, auto_id=True),
         FieldSchema(name='content', dtype=DataType.VARCHAR, max_length=65535),
         FieldSchema(name='content_embedding', dtype=DataType.FLOAT_VECTOR, dim=DIMENSION),
+        FieldSchema(name='subject_name', dtype=DataType.VARCHAR, max_length=100),  # Meta Data1
+        FieldSchema(name='unit_name', dtype=DataType.VARCHAR, max_length=100),  # Meta Data2
+        FieldSchema(name='main_concept', dtype=DataType.VARCHAR, max_length=100),  # Meta Data3
     ]
-    schema = CollectionSchema(fields=fields, description='Math2015Curriculum embedding collection')
+    schema = CollectionSchema(fields=fields, description='2015 Korean High School Curriculum Collection')
     collection = Collection(name=COLLECTION_NAME, schema=schema)
-    logger.info(f"* log >> Collection [{COLLECTION_NAME}] is created.")
+    logger.info(f"* log >> New Collection [{COLLECTION_NAME}] is created.")
 
     # 인덱스 생성
-    # 스칼라 인덱스
-    collection.create_index(
-        field_name="id"
-    )
-    # 벡터 인덱스
-    index_params = {
+    index_params = {  # 벡터 인덱스
         'index_type': INDEX_TYPE,
         'metric_type': 'COSINE',
         'params': {
@@ -342,15 +350,15 @@ def get_embedding(client, text_list):
 
 
 @app.get("/milvus/insert")
-async def insert_curriculum_embeddings():
+async def insert_curriculum_embeddings(subject: str):
     """ s3에서 교과과정을 읽고 임베딩하여 Milvus에 삽입 """
     # Milvus 연결
     await connect_milvus()
     collection = Collection(COLLECTION_NAME)
 
     # S3 내 커리큘럼 데이터 로드
-    texts = []
-    prefix = 'curriculum/science2015/'  # 경로
+    texts, subject_names, unit_names, main_concepts = [], [], [], []
+    prefix = f'curriculum/{subject}2015/'  # 경로
     try:
         # 버킷에서 파일 목록 가져오기
         s3_curriculum_response = s3_client.list_objects_v2(Bucket=BUCKET_NAME, Prefix=prefix)
@@ -360,20 +368,34 @@ async def insert_curriculum_embeddings():
                 # S3 객체 가져오기
                 obj = s3_client.get_object(Bucket=BUCKET_NAME, Key=s3_key)
                 # 텍스트 읽기
-                text = obj['Body'].read().decode('utf-8')
+                data = obj['Body'].read().decode('utf-8')
+                lines = data.splitlines()
+                # 메타 데이터 추출
+                meatdata_lines = [line.strip('#').strip() for line in lines[:3]]
+                subject_name = meatdata_lines[0]
+                subject_names.append(subject_name)
+                unit_name = meatdata_lines[1]
+                unit_names.append(unit_name)
+                main_concept = meatdata_lines[2]
+                main_concepts.append(main_concept)
+                # 교과과정 내용 추출
+                text = '\n'.join(lines[3:]).strip()
                 texts.append(text)
         logger.info(f"* log >> read {len(texts)} texts from S3")
     except Exception as e:
         logger.error(f"Error reading curriculum from S3: {e}")
 
-    # 데이터 임베딩
+    # 교과과정 내용 임베딩
     content_embeddings = get_embedding(openai_client, texts)
     logger.info(f"* log >> embedding 완료. dimension: {DIMENSION}")
 
     # 데이터 삽입
     data = [
         texts,  # content 필드
-        content_embeddings  # content_embedding 필드
+        content_embeddings,  # content_embedding 필드
+        subject_names,  # subject_name 필드
+        unit_names,  # unit_name 필드
+        main_concepts  # main_concept 필드
     ]
     collection.insert(data)
 
@@ -390,66 +412,80 @@ async def retrieve(problem_text: str):
 
         # 검색 테스트
         query = problem_text
+        dt5 = str(datetime.fromtimestamp(time.time()))
         query_embeddings = [get_embedding(openai_client, [query])]
         if not query_embeddings or query_embeddings[0] is None:
             raise ValueError("Embedding generation failed")
-        logger.info(f"* log >> Query embedding 완료")
+        dt6 = str(datetime.fromtimestamp(time.time()))
+        logger.info(f"{dt5} ~ {dt6}: 쿼리 임베딩 완료")
 
         search_params = {
             'metric_type': 'COSINE',
             'params': {
                 'probe': 20
             },
         }
+        dt5 = str(datetime.fromtimestamp(time.time()))
         results = collection.search(
             data=query_embeddings[0],
             anns_field='content_embedding',
             param=search_params,
             limit=3,
             expr=None,
-            output_fields=['content']
+            output_fields=['content', 'subject_name', 'unit_name', 'main_concept']
         )
+        dt6 = str(datetime.fromtimestamp(time.time()))
         context = ' '.join([result.entity.get('content') for result in results[0]])
-        logger.info(f"* log >> context found")
-
-        # 결과 확인
-        '''logger.info(f"* log >> 쿼리 결과")
+        subjects_list = [result.entity.get('subject_name') for result in results[0]]
+        unit_list = [result.entity.get('unit_name') for result in results[0]]
+        main_concept_list = [result.entity.get('main_concept') for result in results[0]]
+        logger.info(f"{dt5} ~ {dt6}: 검색 완료")
+        logs = ""
         for result in results[0]:
-            logger.info("\n-------------------------------------------------------------------")
-            logger.info(f"Score : {result.distance}, \nText : \n{result.entity.get('content')}")'''
-
-        return context
+            logs += ("\n"+f"Score : {result.distance}, \
+            \nInfo: {result.entity.get('subject_name')}\
+             > {result.entity.get('unit_name')}\
+            > {result.entity.get('main_concept')}, \
+            \nText : {result.entity.get('content')}"+"\n\n")
+        logger.info(f"* log >> 검색 결과: {logs}")
+
+        return context, subjects_list, unit_list, main_concept_list
     except Exception as e:
         logger.error(f"Error in search: {e}")
         raise HTTPException(status_code=500, detail=str(e))
 
 @app.get("/analysis/augmentation")
 async def augment(curriculum_context, query):
-    prompt = "교과과정에 기반하여 이 문제에 필요한 개념을 말해줘. 응답은 자연어처럼 제공해줘. \n"
-    context = curriculum_context
-    passage = query
+    prompt = "너는 공책이야. 내가 준 교육과정 중에 아래 문제와 관련된 교육과정을 골라서 고등학생 한 명에게\
+    이 문제를 왜 틀리거나 헷갈릴 수 있으며, 어떤 개념과 사고과정 등이 필요해 보이는지를 \
+    이 문제의 의도와 핵심을 짚어가되 너무 길지 않게 정리해서 고등학생에게 보여줘.\n"
+    context = f"교과과정은 이렇고 {curriculum_context}"
+    passage = f"문제는 이러해 {query}."
     augmented_query = prompt + context + passage
     return augmented_query
 
 @app.get("/analysis/generation")
 async def generate(question):
     def get_chatgpt_response(client, question, model="gpt-4o-mini"):
         try:
+            dt7 = str(datetime.fromtimestamp(time.time()))
             gpt_response = client.chat.completions.create(
                 model=model,
                 messages=[
                     {"role": "user",
                      "content": question
                      }
                 ],
-                temperature=0.5
+                temperature=0.7
             )
+            dt8 = str(datetime.fromtimestamp(time.time()))
+            logger.info(f"{dt7} ~ {dt8}: LLM 응답 완료")
             return gpt_response.choices[0].message.content
         except Exception as e:
             logger.info(f"Error during GPT querying: {e}")
             return None
 
     chatgpt_response = get_chatgpt_response(openai_client, question)
-    logger.info(f"* log >> ChatGPT Response: {chatgpt_response}")
+    logger.info(f"* log >> 응답 결과 \n {chatgpt_response}")
     return chatgpt_response