callcenter 프로젝트 완료

- 상담 기록 분석 및 요약
- 상담 기록 db 저장
- 상담 평가
- 상담 평가 db 저장

project/CALLCENTER_APP/backend/services/assistant_service.py

@@ -0,0 +1,39 @@
+from backend.ai.llm import hugging_llm
+from langchain_core.prompts import ChatPromptTemplate
+from backend.prompts.all_prompt import SUMMARY_SYSTEM_PROMPT, CALL_ASSISTANT_PROMPT
+from backend.repository.models import CallHistory
+from backend.schemas.summary_schema import CallSummary, CallCreate
+from sqlalchemy.orm import Session
+from backend.schemas.assistant_schema import AssistantRequest
+from langchain_chroma import Chroma
+from backend.ai.embedding import watson_embedding
+from langchain_core.output_parsers import StrOutputParser
+
+# 질의 응답
+def answer_assistant_question(customer_id:int, question:str, db:Session):
+    # 1 단계 : 벡터 DB에서 질의
+    # 벡터db 불러오기
+    vectorstore = Chroma(embedding_function=watson_embedding, persist_directory="./vectordb")
+    # as_retriever()
+    retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
+    # invoke() = docs => page_content join
+    docs = retriever.invoke(question)
+    sim_context = "\n\n".join(doc.page_content for doc in docs)
+    # 2 단계 : DB 검색
+    # 고객이 이전에 질문한 내역을 추출
+    if customer_id:
+        histories = db.query(CallHistory).filter(CallHistory.customer_id == customer_id).order_by(CallHistory.created_at.desc()).limit(5).all()
+
+        # 문제, 해결 컬럼만 문자열로 추출
+        customer_text ="\n".join([f"""
+        문제: {h.customer_issue}\n
+        해결: {h.resolution}
+""" for h in histories])
+
+    # 1, 2 단계 => LLM => 답변 생성
+    prompt = ChatPromptTemplate.from_template(CALL_ASSISTANT_PROMPT)
+    chain = prompt | hugging_llm | StrOutputParser()
+    result = chain.invoke({"sim_context": sim_context, "customer_text": customer_text, "question": question})
+
+    return {"answer" : result}
+    # return AssistantRequest(answer=result)

project/CALLCENTER_APP/backend/services/call_service.py

@@ -1,8 +1,8 @@
 from backend.ai.llm import hugging_llm
 from langchain_core.prompts import ChatPromptTemplate
-from backend.prompts.all_prompt import SUMMARY_SYSTEM_PROMPT
-from backend.repository.models import CallHistory
-from backend.schemas.summary_schema import CallSummary, CallCreate
+from backend.prompts.all_prompt import SUMMARY_SYSTEM_PROMPT, CALL_EVALUATION_PROMPT
+from backend.repository.models import CallHistory, CallEvaluation
+from backend.schemas.summary_schema import CallSummary, CallCreate, CallEvaluationResponse
 from sqlalchemy.orm import Session
 
 from backend.schemas.summary_schema import CallRequest
@@ -41,15 +41,51 @@ def save_call_history(db, data):
 
     return history
 
-def evaluate_call():
+def evaluate_call(transcript:str):
     """상담  평가"""
+    structured_llm = hugging_llm.with_structured_output(CallEvaluationResponse)
+    prompt = ChatPromptTemplate.from_template(CALL_EVALUATION_PROMPT)
 
-    pass
+    chain = prompt | structured_llm
+    return chain.invoke({"transcript":transcript})
 
-def save_call_evaluation():
+def calculate_score(evaluation):
+    score = 0
+
+    if evaluation.identity_verification:
+        score += 25
+
+    if evaluation.empathy:
+        score += 25
+
+    if evaluation.issue_resolution:
+        score += 25
+
+    if evaluation.survey_guidance:
+        score += 25
+
+    return score
+
+def save_call_evaluation(db:Session, call_id:int, evaluation:CallEvaluationResponse):
     """상담 평가 내용 저장"""
+    score = calculate_score(evaluation)
+
+    entity = CallEvaluation(
+        call_id = call_id,
+        identity_verification = evaluation.identity_verification,
+        identity_verification_reason = evaluation.identity_verification_reason,
+        empathy = evaluation.empathy,
+        empathy_reason = evaluation.empathy_reason,
+        issue_resolution = evaluation.issue_resolution,
+        issue_resolution_reason = evaluation.issue_resolution_reason,
+        survey_guidance = evaluation.survey_guidance,
+        survey_guidance_reason = evaluation.survey_guidance_reason,
+        score = score,
+    )
+    db.add(entity)
+    db.commit()
+    db.refresh(entity)
 
-    pass
 
 def create_call_history(req: CallRequest, db:Session):
     # 요약
@@ -66,7 +102,19 @@ def create_call_history(req: CallRequest, db:Session):
         customer_issue = summary.customer_issue,
         resolution = summary.resolution
     )
+    history = save_call_history(db = db, data = call_data)
 
-    return save_call_history(db = db, data = call_data)
-
+    # 상담 평가
+    evaluation = evaluate_call(req.transcript)
+    # 평가 저장
+    save_call_evaluation(db = db, call_id=history.call_id, evaluation=evaluation)
 
+    return CallSummary(
+        summary=summary.summary,
+        keywords=summary.keywords,
+        category=summary.category,
+        sentiment=summary.sentiment,
+        action_items=summary.action_items,
+        customer_issue=summary.customer_issue,
+        resolution=summary.resolution,
+    )

project/CALLCENTER_APP/backend/services/evalu_service.py

@@ -0,0 +1,23 @@
+from sqlalchemy.orm import Session
+from backend.repository.models import CallEvaluation
+from backend.schemas.evaluation_schema import EvaluationResponse
+
+
+# 질의 응답
+def get_call_evaluation(call_id:int, db:Session):
+    # DB 검색 - call_id와 일치하는 정보 추출
+    if call_id:
+        evaluation = db.query(CallEvaluation).filter(CallEvaluation.call_id == call_id).first()
+
+    return EvaluationResponse(
+        identity_verification=evaluation.identity_verification,
+        identity_verification_reason=evaluation.identity_verification_reason,
+        empathy=evaluation.empathy,
+        empathy_reason=evaluation.empathy_reason,
+        issue_resolution=evaluation.issue_resolution,
+        issue_resolution_reason=evaluation.issue_resolution_reason,
+        survey_guidance=evaluation.survey_guidance,
+        survey_guidance_reason=evaluation.survey_guidance_reason,
+        score=evaluation.score,
+        created_at=evaluation.created_at,
+    )