hgzero/design/backend/sequence/inner/ai-회의록자동작성.puml

@startuml
!theme mono

title AI Service 내부 시퀀스 - 회의록자동작성 (실시간 추천 포함)

participant "TranscriptController" as Controller
participant "TranscriptService" as Service
participant "SuggestionService" as SuggestService
participant "LLMClient" as LLM
participant "VectorService" as Vector
participant "TranscriptRepository" as Repo
database "Azure OpenAI" as OpenAI <<external>>
database "Vector DB" as VectorDB <<external>>
database "PostgreSQL" as DB <<external>>

== TranscriptReady 이벤트 수신 ==

note over Controller
Azure Event Hubs로부터
TranscriptReady 이벤트 수신
end note

Controller -> Service: processTranscript(meetingId, transcriptText)
activate Service

Service -> Service: 회의 맥락 정보 조회 준비

== 병렬 처리: 맥락 정보 수집 ==

par 회의 정보 조회
    Service -> Repo: getMeetingContext(meetingId)
    activate Repo
    Repo -> DB: SELECT meeting_info
    activate DB
    DB --> Repo: 회의 정보 반환
    deactivate DB
    Repo --> Service: 회의 맥락 정보
    deactivate Repo
else 이전 내용 조회
    Service -> Repo: getPreviousTranscripts(meetingId)
    activate Repo
    Repo -> DB: SELECT previous_content
    activate DB
    DB --> Repo: 이전 회의록
    deactivate DB
    Repo --> Service: 이전 내용
    deactivate Repo
end

Service -> Service: 프롬프트 생성
note right
시스템 프롬프트 생성
- 역할 정의
- 변환 규칙 적용
end note

== LLM 기반 회의록 작성 ==

Service -> LLM: generateMinutes(prompt, context)
activate LLM

LLM -> OpenAI: POST /chat/completions
activate OpenAI
note right
model: gpt-4o
temperature: 0.3
max_tokens: 2000
end note

OpenAI -> OpenAI: 텍스트 분석 및 정리

OpenAI --> LLM: 정리된 회의록 내용
deactivate OpenAI

LLM --> Service: 회의록 초안
deactivate LLM

== 회의록 저장 ==

Service -> Service: 회의록 데이터 구조화

Service -> Repo: saveTranscriptDraft(meetingId, content)
activate Repo

Repo -> DB: INSERT INTO ai_transcripts
activate DB
note right
저장 데이터:
- meeting_id
- content (JSON)
- status: DRAFT
end note

DB --> Repo: 저장 완료
deactivate DB

Repo --> Service: transcriptId
deactivate Repo

== 벡터 임베딩 생성 ==

Service -> Vector: createEmbedding(transcriptId, content)
activate Vector

Vector -> OpenAI: POST /embeddings
activate OpenAI
note right
model: text-embedding-3-large
end note

OpenAI --> Vector: 임베딩 벡터
deactivate OpenAI

Vector -> VectorDB: INSERT embedding
activate VectorDB

VectorDB --> Vector: 저장 완료
deactivate VectorDB

Vector --> Service: 임베딩 생성 완료
deactivate Vector

== 실시간 추천 병렬 처리 ==

Service -> SuggestService: generateRealtimeSuggestions(meetingId, transcriptText)
activate SuggestService

par 논의사항 제안 생성
    SuggestService -> LLM: suggestDiscussionTopics(meetingId, transcript)
    activate LLM

    LLM -> OpenAI: POST /chat/completions
    activate OpenAI
    note right
      프롬프트:
      - 현재 대화 맥락 분석
      - 빠진 논의 항목 찾기
      - 추가하면 좋을 주제 제안

      응답 형식:
      {
        "suggestions": [
          {
            "topic": "논의 주제",
            "reason": "제안 이유",
            "priority": "HIGH|MEDIUM|LOW"
          }
        ]
      }
    end note

    OpenAI --> LLM: 논의사항 제안 목록
    deactivate OpenAI

    LLM --> SuggestService: discussionSuggestions
    deactivate LLM

    SuggestService -> Repo: saveSuggestions(meetingId, "DISCUSSION", suggestions)
    activate Repo
    Repo -> DB: INSERT INTO ai_suggestions
    activate DB
    DB --> Repo: 저장 완료
    deactivate DB
    deactivate Repo

else 결정사항 제안 생성
    SuggestService -> LLM: suggestDecisions(meetingId, transcript)
    activate LLM

    LLM -> OpenAI: POST /chat/completions
    activate OpenAI
    note right
      프롬프트:
      - "~하기로 함" 패턴 감지
      - "~로 결정" 패턴 감지
      - 결정 내용 구조화

      응답 형식:
      {
        "decisions": [
          {
            "content": "결정 내용",
            "category": "기술|일정|리소스",
            "confidence": 0.0-1.0
          }
        ]
      }
    end note

    OpenAI --> LLM: 결정사항 제안 목록
    deactivate OpenAI

    LLM --> SuggestService: decisionSuggestions
    deactivate LLM

    SuggestService -> Repo: saveSuggestions(meetingId, "DECISION", suggestions)
    activate Repo
    Repo -> DB: INSERT INTO ai_suggestions
    activate DB
    DB --> Repo: 저장 완료
    deactivate DB
    deactivate Repo

end

SuggestService --> Service: 모든 추천사항 생성 완료
deactivate SuggestService

== 이벤트 발행 ==

Service -> Controller: 회의록 및 추천사항 생성 완료 응답
deactivate Service

Controller -> Controller: TranscriptSummaryCreated 발행
note right
이벤트 데이터:
- meetingId
- transcriptId
- content
- suggestions:
  * discussionTopics: []
  * decisions: []
end note

note over Controller, DB
처리 시간:
- 맥락 조회: 100-200ms
- LLM 회의록 생성: 3-5초
- 저장: 100-200ms
- 벡터화: 500ms-1초
- 실시간 추천 병렬 처리: 4-6초
  * 논의사항 제안: 2-3초
  * 결정사항 제안: 2-3초
총: 약 8-13초
end note

@enduml