Phase 0.7 — Acceptance Gate 자동화 + LM Studio 통합 + OntoCast 버그 수정

- platform/ → ont_platform/ rename
  Python 내장 platform 모듈과 이름 충돌. numpy/scipy가 platform.machine() 호출 시
  우리 패키지를 가져와 AttributeError. ont_platform으로 변경하고 pyproject.toml,
  ont_platform/**, tests/** import 경로 모두 업데이트.

- ont_platform/config.py: lenient LLM builder 추가
  LM Studio/vLLM 등 OpenAI-호환 로컬 서버가 임의 모델 식별자(예: deepseek-r1-distill-
  qwen-7b)를 쓸 수 있도록 OntoCast의 OpenAIModel enum validation을 Pydantic
  model_construct로 우회. ToolConfig() 생성 시 충돌을 막기 위해 LLM_MODEL_NAME을
  잠시 비웠다가 lenient 인스턴스로 교체.

- ont_platform/api/deps.py: ToolBox 초기화를 asyncio.to_thread로 격리
  LLMTool.create()가 내부에서 asyncio.run()을 부르는데 lifespan/테스트가 이미
  async 컨텍스트라 이중 loop 충돌. 별도 스레드에서 sync 생성자 실행.

- 테스트 인프라 정비
  * tests/integration/test_api_smoke.py: TestClient 구버전 starlette 호환을 위해
    lifespan='off' 대신 app.router.lifespan_context = noop 패턴 적용.
  * tests/unit/test_convert_document.py, test_select_ontology.py: ontocast.agent
    __init__.py가 re-export한 함수가 서브모듈을 가리는 문제로 sys.modules에서
    실제 모듈 객체 직접 추출.
  * tests/e2e/conftest.py: .env 자동 로드 + provider별 skip 조건 (Ollama는
    LLM_API_KEY 불필요).
  * tests/e2e/test_phase0_full_pipeline.py: provider별 키 분기,
    HDBSCAN 클러스터링이 동작하도록 fixture 페이로드 16문장으로 확장.

- vendored OntoCast 버그 수정 3건 (VENDORED_MODIFICATIONS.md 기록):
  * agent/render_ontology.py: render_ontology_fresh()의 .format() 호출에 누락된
    ontology_prefix 인자 추가 (Bootstrap 단계에서 KeyError: 'ontology_prefix').
  * stategraph/node_factories.py: render_ontology/render_facts 노드의
    state.model_copy(deep=True)로 budget_tracker가 deep-copy되어 root state의
    BudgetTracker가 영원히 0인 채로 남던 버그 수정. 원본 인스턴스 공유로 변경.

- 문서 갱신
  README.md (Phase 0.7 부분완료 + ont_platform 폴더 이름),
  docs/phases/PHASE0_ACCEPTANCE_GATE.md (검증 이력 + Ollama/LM Studio 옵션),
  .env.example (LM Studio/Ollama/OpenAI 세 옵션 명시).

검증
- unit + integration 26/26 통과.
- e2e (LM Studio + Qwen3-8B / DeepSeek-R1-Distill-Qwen-7B): 워크플로우 끝까지
  실행 + 5번 LLM 호출 + LangGraph 전 노드 traceable 확인. 7-8B 로컬 모델은
  strict structured output(Turtle RDF in JSON) 한계로 ontology/facts TTL 자동
  생성 부분 성공. 클라우드 LLM 환경에서 재검증 필요.

Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>

ontology_platform/tests/e2e/conftest.py

@@ -1,12 +1,17 @@
 """E2E test configuration.
 
-These tests are SKIPPED by default. To run them set ``LLM_API_KEY`` and
-``LLM_PROVIDER`` (and any model overrides) in the environment, then run:
+These tests are SKIPPED by default unless a valid LLM provider is configured.
+The repo's `.env` file is auto-loaded so the same settings used by the app
+also drive the test run.
 
+Provider별 통과 조건:
+- openai  : `LLM_API_KEY` 필요 (LM Studio 등 OpenAI-호환 로컬 서버는 더미 키도 OK)
+- ollama  : 별도 키 불필요 (로컬 데몬만 동작하면 됨)
+
+To run:
     pytest tests/e2e -m e2e
 
-They exercise the full OntoCast workflow end-to-end — LLM calls included —
-and so they cost real money. Keep them out of CI default runs.
+LLM 호출이 실제로 일어남. OpenAI 클라우드는 비용 발생, 로컬 LLM은 무료.
 """
 
 from __future__ import annotations
@@ -22,18 +27,47 @@ for p in (REPO_ROOT, REPO_ROOT / "vendored" / "ontocast"):
     if str(p) not in sys.path:
         sys.path.insert(0, str(p))
 
+# Auto-load .env so e2e tests pick up the same LLM settings as the app.
+_env_path = REPO_ROOT / ".env"
+if _env_path.exists():
+    try:
+        from dotenv import load_dotenv
+
+        load_dotenv(_env_path, override=False)
+    except ImportError:
+        # python-dotenv가 없으면 직접 간단 파싱.
+        for line in _env_path.read_text(encoding="utf-8").splitlines():
+            line = line.strip()
+            if not line or line.startswith("#") or "=" not in line:
+                continue
+            key, _, value = line.partition("=")
+            os.environ.setdefault(key.strip(), value.strip())
+
+
+def _llm_configured() -> bool:
+    """Provider별로 e2e 실행 가능 여부 판단."""
+    provider = (os.environ.get("LLM_PROVIDER") or "openai").lower()
+    if provider == "ollama":
+        # Ollama는 별도 API 키 불필요.
+        return True
+    # openai 또는 OpenAI-호환 로컬 서버 — 더미 키라도 들어 있어야 OK.
+    return bool(os.environ.get("LLM_API_KEY"))
+
 
 def pytest_collection_modifyitems(
     config: pytest.Config, items: list[pytest.Item]
 ) -> None:
-    """Skip all e2e tests when LLM_API_KEY is not configured."""
-    if os.environ.get("LLM_API_KEY"):
+    """Skip e2e tests unless the LLM provider is properly configured."""
+    if _llm_configured():
         return
+    provider = (os.environ.get("LLM_PROVIDER") or "openai").lower()
     skip_marker = pytest.mark.skip(
-        reason="E2E tests require LLM_API_KEY in environment."
+        reason=(
+            f"E2E tests require LLM provider config. "
+            f"Provider={provider!r}, LLM_API_KEY={'set' if os.environ.get('LLM_API_KEY') else 'missing'}."
+        )
     )
     for item in items:
-        # Only apply to tests in this directory tree.
         if "tests/e2e" in str(item.fspath).replace("\\", "/"):
             item.add_marker(skip_marker)
 

ontology_platform/tests/e2e/test_phase0_full_pipeline.py

@@ -26,9 +26,9 @@ REPO_ROOT = Path(__file__).resolve().parents[2]
 if str(REPO_ROOT) not in sys.path:
     sys.path.insert(0, str(REPO_ROOT))
 
-main_module = importlib.import_module("platform.api.main")
-deps_module = importlib.import_module("platform.api.deps")
-platform_config = importlib.import_module("platform.config")
+main_module = importlib.import_module("ont_platform.api.main")
+deps_module = importlib.import_module("ont_platform.api.deps")
+platform_config = importlib.import_module("ont_platform.config")
 
 
 @pytest.mark.e2e
@@ -40,7 +40,12 @@ async def test_full_pipeline_writes_ontology_and_facts(
     # leak between runs.
     monkeypatch.setenv("ONTOCAST_WORKING_DIRECTORY", str(tmp_path / "work"))
     # Honor whatever LLM provider the operator configured.
-    assert os.environ.get("LLM_API_KEY"), "LLM_API_KEY must be set for e2e"
+    # OpenAI는 API 키 필수, Ollama는 로컬 데몬만 떠 있으면 됨.
+    provider = os.environ.get("LLM_PROVIDER", "openai").lower()
+    if provider == "openai":
+        assert os.environ.get("LLM_API_KEY"), (
+            "LLM_API_KEY must be set for e2e with OpenAI provider"
+        )
 
     # Force a fresh AppContext so the new working_directory wins.
     deps_module.reset_app_context_for_testing()
@@ -49,11 +54,28 @@ async def test_full_pipeline_writes_ontology_and_facts(
 
     app = main_module.create_app()
 
-    # Tiny but ontology-rich payload.
+    # OntoCast SemanticChunker가 HDBSCAN + UMAP을 쓰므로 최소 문장 수가
+    # 필요하다. 2문장짜리 toy 입력은 "k must be ≤ training points"로 실패한다.
+    # Acceptance Gate #1은 "단일 PDF/JSON → TTL 생성"이지 문장 수와 무관하므로,
+    # ontology-rich한 짧은 단락을 충분한 문장 수로 늘려준다.
     payload = {
         "text": (
-            "Alice works at Acme Corporation in Berlin. "
-            "Acme Corporation manufactures bicycles."
+            "Alice Carter works at Acme Corporation in Berlin. "
+            "Acme Corporation is a manufacturing company founded in 1992. "
+            "Acme manufactures bicycles and electric scooters. "
+            "Bob Lee is the chief engineer at Acme Corporation. "
+            "He reports to Alice Carter, who heads the engineering division. "
+            "Acme's main factory is located in Berlin, Germany. "
+            "The company also operates a research center in Munich. "
+            "Carol Schmidt leads research at the Munich center. "
+            "She previously worked at Globex Industries in Hamburg. "
+            "Globex Industries is a competitor in the bicycle market. "
+            "Acme exports bicycles to France, Italy, and Spain. "
+            "The product line includes road bikes, mountain bikes, and city bikes. "
+            "Alice Carter graduated from the Technical University of Berlin. "
+            "Bob Lee holds a doctorate in mechanical engineering. "
+            "Acme employs around 450 people across its three sites. "
+            "The company reported annual revenue of 120 million euros last year."
         ),
         "ontology_user_instruction": "Focus on person-organization-location relations.",
         "facts_user_instruction": "Extract employment and manufacturing facts.",