73 lines
1.9 KiB
Python
73 lines
1.9 KiB
Python
from backend.ai.llm import hugging_llm
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from langchain_core.prompts import ChatPromptTemplate
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from backend.prompts.all_prompt import SUMMARY_SYSTEM_PROMPT
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from backend.repository.models import CallHistory
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from backend.schemas.summary_schema import CallSummary, CallCreate
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from sqlalchemy.orm import Session
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from backend.schemas.summary_schema import CallRequest
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# LLM transcript 요약 시키기
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def summary_call(transcript: str):
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summary_prompt = ChatPromptTemplate.from_messages(
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[
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("system", SUMMARY_SYSTEM_PROMPT),
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("human", "상담내용\n{transcript}"),
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]
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)
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structured_llm = hugging_llm.with_structured_output(CallSummary)
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summary_chain = summary_prompt | structured_llm
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result = summary_chain.invoke({"transcript" : transcript})
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return result
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def save_call_history(db, data):
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"""상담 저장"""
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history = CallHistory(
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customer_id = data.customer_id,
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transcript = data.transcript,
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summary = data.summary,
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category = data.category,
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sentiment = data.sentiment,
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customer_issue = data.customer_issue,
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resolution = data.resolution
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)
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db.add(history)
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db.commit()
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db.refresh(history)
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return history
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def evaluate_call():
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"""상담 평가"""
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pass
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def save_call_evaluation():
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"""상담 평가 내용 저장"""
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pass
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def create_call_history(req: CallRequest, db:Session):
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# 요약
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# CallSummary
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summary = summary_call(req.transcript)
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print("summary",summary)
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# 데이터베이스 저장용 객체
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call_data = CallCreate(
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customer_id = req.customer_id,
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transcript = req.transcript,
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summary = summary.summary,
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category = summary.category,
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sentiment = summary.sentiment,
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customer_issue = summary.customer_issue,
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resolution = summary.resolution
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)
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return save_call_history(db = db, data = call_data)
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