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)