ci: CI 를 실제로 돌게 만들고 그 사이 쌓인 위반 정리

.github/workflows/ci.yml

@@ -0,0 +1,130 @@
+name: CI
+
+# 기본 브랜치는 dev 다(origin/HEAD -> origin/dev). 이전 lint.yml 은 트리거가
+# main/develop 이라 한 번도 실행되지 않았고, 그 사이 dev 에 eslint 에러와
+# 의존성 취약점이 그대로 쌓였다. 트리거를 실제 브랜치에 맞추고 레이어별
+# 테스트까지 함께 돌린다.
+on:
+  pull_request:
+    branches: [dev, main]
+  push:
+    branches: [dev, main]
+
+# 같은 브랜치에 연속 push 하면 이전 실행은 취소한다(러너 점유 방지).
+concurrency:
+  group: ci-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  frontend:
+    name: Frontend (lint · types · test · build)
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-node@v4
+        with:
+          node-version-file: frontend/.nvmrc
+          cache: npm
+          cache-dependency-path: frontend/package-lock.json
+
+      - name: Install
+        working-directory: ./frontend
+        run: npm ci
+
+      - name: Lint
+        working-directory: ./frontend
+        run: npm run lint
+
+      - name: Type check
+        working-directory: ./frontend
+        run: npm run type-check
+
+      - name: Test
+        working-directory: ./frontend
+        run: npm run test
+
+      # 타입체크가 통과해도 번들 단계에서 깨지는 경우(정적 자산·플러그인)를 잡는다.
+      - name: Build
+        working-directory: ./frontend
+        run: npm run build
+
+  backend:
+    name: Backend (Core, Spring)
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-java@v4
+        with:
+          distribution: temurin
+          java-version: '21'
+          cache: gradle
+
+      # checkstyle 은 build.gradle 에 플러그인이 없어 태스크 자체가 없다.
+      # 도입하는 PR 에서 이 잡에 단계를 추가한다.
+      - name: Test
+        working-directory: ./backend
+        run: ./gradlew test --no-daemon
+
+      - name: Upload test report
+        if: failure()
+        uses: actions/upload-artifact@v4
+        with:
+          name: backend-test-report
+          path: backend/build/reports/tests/test
+
+  ai:
+    name: AI (FastAPI/LangChain)
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.13'
+
+      - uses: astral-sh/setup-uv@v5
+        with:
+          enable-cache: true
+
+      - name: Install
+        working-directory: ./ai
+        run: uv sync --extra dev
+
+      - name: Lint (flake8)
+        working-directory: ./ai
+        run: uv run flake8 .
+
+      - name: Format check (black)
+        working-directory: ./ai
+        run: uv run black --check .
+
+      - name: Test
+        working-directory: ./ai
+        run: uv run pytest -q
+
+  realtime:
+    name: RealTime (Go)
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      # Go 코드는 realtime/ 에 있다. 이전 워크플로는 ./backend 에서 실행해
+      # go.mod 가 없어 조용히 통과했고, realtime 은 검증된 적이 없다.
+      - uses: actions/setup-go@v5
+        with:
+          go-version-file: realtime/go.mod
+          cache-dependency-path: realtime/go.sum
+
+      - name: Build
+        working-directory: ./realtime
+        run: go build ./...
+
+      - name: Vet
+        working-directory: ./realtime
+        run: go vet ./...
+
+      - name: Test
+        working-directory: ./realtime
+        run: go test ./...

ai/src/ai_server/api/voice_stream.py

@@ -86,7 +86,11 @@ async def pump_transcripts() -> None:
             await asyncio.wait_for(pump, timeout=10.0)
         except asyncio.TimeoutError:
             pump.cancel()
-            log.warn("voice_stream.pump.timeout", session_id=session_id, message_id=message_id)
+            log.warn(
+                "voice_stream.pump.timeout",
+                session_id=session_id,
+                message_id=message_id,
+            )
         result = await session.result()
         await session.close()
 

ai/src/ai_server/messaging/consumers/tts_consumer.py

@@ -225,9 +225,7 @@ async def _synth(sentence: str) -> TtsResult:
             ext = self._EXT_BY_CONTENT_TYPE.get(
                 res.content_type.split(";")[0].strip(), "mp3"
             )
-            seg_key = (
-                f"interview/tts/{req.session_id}/{req.message_id}/seg-{seq}.{ext}"
-            )
+            seg_key = f"interview/tts/{req.session_id}/{req.message_id}/seg-{seq}.{ext}"
             try:
                 await self._storage.put_bytes(
                     seg_key, res.audio_bytes, content_type=res.content_type

ai/src/ai_server/model/messages/tts.py

@@ -7,22 +7,24 @@
 
 class GenerateTtsRequest(BaseModel):
     """Core 가 질문 메시지 commit 후 발행. AI 가 text 를 TTS 합성."""
+
     model_config = camel_config()
 
     session_id: int
-    message_id: int                      # interview_messages.id (INTERVIEWER 질문)
+    message_id: int  # interview_messages.id (INTERVIEWER 질문)
     text: str
     mode: Literal["PERSONALITY", "TECHNICAL", "INTEGRATED"]
     job_category: Literal["FRONTEND", "BACKEND", "INFRA", "DBA"]
 
 
 class TtsCallbackPayload(BaseModel):
     """AI → Core. 합성 결과(S3 키) 또는 실패. Core TtsCallbackPayload 와 호환."""
+
     model_config = camel_config()
 
     session_id: int
     message_id: int
     status: Literal["SUCCEEDED", "FAILED"]
-    audio_key: str | None = None         # camelCase 직렬화 → audioKey
+    audio_key: str | None = None  # camelCase 직렬화 → audioKey
     duration_sec: float | None = None
     error_code: str | None = None

ai/src/ai_server/model/messages/voice.py

@@ -9,10 +9,11 @@
 
 class AnalyzeVoiceRequest(BaseModel):
     """Core 가 음성 답변 업로드 commit 후 발행."""
+
     model_config = camel_config()
 
     session_id: int
-    message_id: int                         # interview_messages.id (placeholder, STT 후 content 채움)
+    message_id: int  # interview_messages.id (placeholder, STT 후 content 채움)
     parent_question_message_id: int | None = None
     audio_s3_key: str
     content_type: str
@@ -23,6 +24,7 @@ class AnalyzeVoiceRequest(BaseModel):
 
 class VoiceCallbackPayload(BaseModel):
     """AI → Core. STT 결과 + 음성 지표."""
+
     model_config = camel_config()
 
     session_id: int
@@ -32,4 +34,4 @@ class VoiceCallbackPayload(BaseModel):
     silence_duration_sec: float | None = None
     filler_word_counts: dict[str, int] = Field(default_factory=dict)
     pronunciation_accuracy: float | None = None
-    error_code: str | None = None           # 실패 시 코드 (Core 가 message FAILED 마킹)
+    error_code: str | None = None  # 실패 시 코드 (Core 가 message FAILED 마킹)

ai/src/ai_server/observability/llm_logging_callback.py

@@ -22,16 +22,22 @@ class CoreAiLogCallback(AsyncCallbackHandler):
     metadata 에서 토큰 수를 추출한다.
     """
 
-    def __init__(self, *, core_client: CoreClient, request_type: str, default_model: str) -> None:
+    def __init__(
+        self, *, core_client: CoreClient, request_type: str, default_model: str
+    ) -> None:
         self._core = core_client
         self._request_type = request_type
         self._default_model = default_model
         self._start_at: dict[UUID, float] = {}
 
-    async def on_llm_start(self, serialized, prompts, *, run_id: UUID, **kwargs: Any) -> None:
+    async def on_llm_start(
+        self, serialized, prompts, *, run_id: UUID, **kwargs: Any
+    ) -> None:
         self._start_at[run_id] = time.perf_counter()
 
-    async def on_llm_end(self, response: LLMResult, *, run_id: UUID, **kwargs: Any) -> None:
+    async def on_llm_end(
+        self, response: LLMResult, *, run_id: UUID, **kwargs: Any
+    ) -> None:
         latency_ms = self._latency_ms(run_id)
         in_tok, out_tok, model = _extract_usage(response, self._default_model)
         await self._fire(
@@ -44,7 +50,9 @@ async def on_llm_end(self, response: LLMResult, *, run_id: UUID, **kwargs: Any)
             error_message=None,
         )
 
-    async def on_llm_error(self, error: BaseException, *, run_id: UUID, **kwargs: Any) -> None:
+    async def on_llm_error(
+        self, error: BaseException, *, run_id: UUID, **kwargs: Any
+    ) -> None:
         latency_ms = self._latency_ms(run_id)
         await self._fire(
             request_type=self._request_type,
@@ -69,10 +77,13 @@ async def _do() -> None:
                 await self._core.record_ai_log(**kwargs)
             except Exception as exc:
                 log.warn("core.ai_log.background_failed", error=str(exc), **kwargs)
+
         asyncio.create_task(_do())
 
 
-def _extract_usage(response: LLMResult, default_model: str) -> tuple[int | None, int | None, str]:
+def _extract_usage(
+    response: LLMResult, default_model: str
+) -> tuple[int | None, int | None, str]:
     in_tok = out_tok = None
     model = default_model
     try:
@@ -90,8 +101,16 @@ def _extract_usage(response: LLMResult, default_model: str) -> tuple[int | None,
                     info = getattr(gen, "generation_info", None) or {}
                     usage = info.get("usage_metadata") or info.get("token_usage") or {}
                     if isinstance(usage, dict):
-                        in_tok = in_tok or usage.get("input_tokens") or usage.get("prompt_tokens")
-                        out_tok = out_tok or usage.get("output_tokens") or usage.get("completion_tokens")
+                        in_tok = (
+                            in_tok
+                            or usage.get("input_tokens")
+                            or usage.get("prompt_tokens")
+                        )
+                        out_tok = (
+                            out_tok
+                            or usage.get("output_tokens")
+                            or usage.get("completion_tokens")
+                        )
     except Exception as exc:
         log.debug("ai_log.usage_extract_failed", error=str(exc))
     return in_tok, out_tok, model