[개발일기] Rails 8로 AI 에이전트 리뷰 시스템 만들면서 삽질한 이야기

# Before
SYSTEM_PROMPT = "당신은 편집 보조자입니다."

# After
SYSTEM_PROMPT = "당신은 함께 원고를 검토하는 동료 작가입니다."

# 새 마이그레이션 없이 기존 JSONB 활용
TRAIT_KEYS = %w[abilities current_goal weakness personality].freeze

TRAIT_KEYS.each do |key|
  define_method(key) { properties&.dig(key) }
  define_method(:"#{key}=") { |val| self.properties = (properties || {}).merge(key => val) }
end

def traits_present?
  TRAIT_KEYS.any? { |k| properties&.dig(k).present? }
end

# 핵심 에이전트 (점수에 반영)
AGENTS = {
  commercial: Agents::CommercialAgent,
  storytelling: Agents::StorytellingAgent,
  consistency: Agents::ConsistencyAgent,
  fact_check: Agents::FactCheckAgent
}.freeze

# 보조 에이전트 (실패해도 핵심 결과에 영향 없음)
SUPPLEMENTARY_AGENTS = {
  plot_thread_detection: Agents::PlotThreadDetectionAgent
}.freeze

def run_supplementary_agents(bible, agent_metadata)
  SUPPLEMENTARY_AGENTS.to_h do |key, klass|
    # ... 실행 로직
    if outcome.success?
      [key, outcome.value!]
    else
      [key, nil]  # nil이지 FAILED_RESULT가 아님
    end
  rescue StandardError => e
    [key, nil]  # 예외도 조용히 처리
  end
end

class PlotThreadResponseContract < Dry::Validation::Contract
  json do
    required(:detected_threads).array(:hash) do
      required(:type).filled(:string)
      required(:source_entity).filled(:string)
      required(:target_entity).filled(:string)
      required(:confidence).filled(:float, gteq?: 0.0, lteq?: 1.0)
      required(:thread_scope).filled(:string, included_in?: %w[short_term mid_term long_term])
    end
    optional(:resolution_candidates).array(:hash)
    optional(:density_warning).maybe(:string)
  end
end

def evaluate(content, context: {})
  raw = call_llm(content, context)
  parsed = JSON.parse(raw)
  result = contract.call(parsed)

  if result.success?
    Success(result.to_h)
  else
    Failure(errors: result.errors.to_h)
  end
end

# 특정 시점까지의 관계만 조회
edges = LoreEdge
  .where(source_node_id: node_ids)
  .where("valid_from_chapter <= ?", chapter_position)
  .where("valid_until_chapter IS NULL OR valid_until_chapter >= ?", chapter_position)

EDGE_TYPES = %w[relationship foreshadowing resolution cause_effect].freeze

# 한국어 라벨
EDGE_TYPE_LABELS = {
  "relationship" => "관계",
  "foreshadowing" => "복선",
  "resolution" => "회수",
  "cause_effect" => "인과"
}.freeze

def importance_score(node)
  base = node.lore_edges.count * 12                    # 관계 많을수록 중요
  base += category_bonus(node.category)                  # 캐릭터 > 사건 > 장소
  base += recency_bonus(node, @chapter_position)         # 최근 등장 보너스
  base
end

def word_count_color_class(count)
  case count
  when 0...4500    then "error"      # 빨강 — 너무 짧음
  when 4500...5000 then "warning"    # 주황 — 조금 더
  when 5000...5500 then "success"    # 초록 — 적정
  when 5500...6000 then "info"       # 남색 — 충분
  else                  "overflow"   # 노랑 — 길 수 있음
  end
end

// Stimulus 컨트롤러
export default class extends Controller {
  static targets = ["step", "sidebar", "progress"]

  connect() {
    this.currentStep = parseInt(localStorage.getItem("guide_step") || "0")
    this.maxVisited = parseInt(localStorage.getItem("guide_max") || "0")
    this.showStep(this.currentStep)
  }

  next() {
    if (this.currentStep < this.totalSteps - 1) {
      this.currentStep++
      this.maxVisited = Math.max(this.maxVisited, this.currentStep)
      this.persist()
      this.showStep(this.currentStep)
    }
  }

  persist() {
    localStorage.setItem("guide_step", this.currentStep)
    localStorage.setItem("guide_max", this.maxVisited)
  }
}

# Job 레벨 — 첫 번째 안전장치
class ExtractLoreJob < ApplicationJob
  def perform(chapter)
    # 1. 핵심 추출 (실패하면 전체 실패)
    extraction = LoreExtractionService.new(chapter).extract!

    # 2. 특성 추출 (실패해도 핵심 추출은 보존)
    begin
      traits = TraitExtractionService.new(chapter, chapter.project).extract
      extraction.payload["trait_updates"] = traits if traits.any?
      extraction.save!
    rescue => e
      Rails.logger.error("[TraitExtraction] #{e.message}")
      # 조용히 넘어감 — 핵심 추출은 이미 완료
    end
  end
end

# Service 레벨 — 두 번째 안전장치
class TraitExtractionService
  def extract
    return [] if @characters.empty?
    # ... AI 호출
  rescue StandardError => e
    Rails.logger.error("[TraitExtractionService] #{e.message}")
    []  # 빈 배열 반환, 절대 예외를 던지지 않음
  end
end

def apply_trait_updates!
  trait_updates.each do |update|
    next if update["confidence"].to_f < 0.5  # 확신도 낮으면 스킵
    # ...
  end
end