- 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>
282 lines
10 KiB
Python
282 lines
10 KiB
Python
"""FastAPI smoke tests for the Phase 0 endpoints.
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These tests bypass real LLM/Toolbox initialization by injecting a mocked
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``AppContext`` via FastAPI's ``dependency_overrides``. They cover:
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- /health 200 / 503
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- /info shape
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- /flush confirmation token enforcement
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- /process input validation (JSON / multipart / unsupported)
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- /process happy path with a fake workflow (no real LLM call)
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A separate file (`test_api_real_workflow.py`) exercises the real OntoCast
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pipeline behind a marker that is skipped unless an LLM key is configured.
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"""
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from __future__ import annotations
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import importlib
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import json
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import sys
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from contextlib import asynccontextmanager
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from pathlib import Path
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from types import SimpleNamespace
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from typing import Any
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from unittest.mock import AsyncMock, MagicMock
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import pytest
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from fastapi.testclient import TestClient
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REPO_ROOT = Path(__file__).resolve().parents[2]
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if str(REPO_ROOT) not in sys.path:
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sys.path.insert(0, str(REPO_ROOT))
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VENDORED_ONTOCAST = REPO_ROOT / "vendored" / "ontocast"
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if str(VENDORED_ONTOCAST) not in sys.path:
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sys.path.insert(0, str(VENDORED_ONTOCAST))
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main_module = importlib.import_module("ont_platform.api.main")
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deps_module = importlib.import_module("ont_platform.api.deps")
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from ontocast.onto.enum import RenderMode # noqa: E402
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# ─── Mocked AppContext factory ────────────────────────────────────────────
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def _make_mock_context(workflow_chunks: list[dict[str, Any]]) -> SimpleNamespace:
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"""Build an AppContext stand-in.
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``workflow_chunks`` is the sequence of state snapshots that the mock
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workflow.astream will yield. The last chunk becomes the response state.
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"""
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# Triple store and LLM are MagicMocks because /health/info just inspect them.
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triple_store = MagicMock()
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triple_store.clean = AsyncMock()
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tools = SimpleNamespace(
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llm=MagicMock(),
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llm_provider="openai",
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triple_store_manager=triple_store,
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update_dataset=AsyncMock(),
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)
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async def fake_astream(initial_state, *, stream_mode, config): # noqa: ARG001
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for chunk in workflow_chunks:
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yield chunk
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workflow = SimpleNamespace(astream=fake_astream)
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server_config = SimpleNamespace(
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render_mode=RenderMode.ONTOLOGY_AND_FACTS,
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max_visits_per_node=1,
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ontology_max_triples=1000,
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)
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settings = SimpleNamespace(
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phase=0,
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storage_backend="filesystem",
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working_directory=REPO_ROOT / "data" / "working",
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)
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return SimpleNamespace(
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settings=settings,
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tools=tools,
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workflow=workflow,
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server_config=server_config,
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recursion_limit=100,
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)
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@asynccontextmanager
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async def _noop_lifespan(app): # noqa: ARG001
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yield
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def _client_with_context(ctx: SimpleNamespace) -> TestClient:
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"""Return a TestClient whose ``get_app_context`` returns the given ctx.
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Bypassing the lifespan keeps tests fast and deterministic. We use
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``raise_server_exceptions=True`` (the default) so unexpected errors
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surface in test output.
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"""
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app = main_module.create_app()
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app.router.lifespan_context = _noop_lifespan
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app.dependency_overrides[deps_module.get_app_context] = lambda: ctx
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return TestClient(app, raise_server_exceptions=True, backend="asyncio")
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# ─── /health ──────────────────────────────────────────────────────────────
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def test_health_healthy() -> None:
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ctx = _make_mock_context([])
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with _client_with_context(ctx) as client:
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response = client.get("/health")
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assert response.status_code == 200
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body = response.json()
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assert body["status"] == "healthy"
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assert body["llm_provider"] == "openai"
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assert body["phase"] == 0
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assert body["storage_backend"] == "filesystem"
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def test_health_unhealthy_when_llm_missing() -> None:
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ctx = _make_mock_context([])
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ctx.tools.llm = None
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with _client_with_context(ctx) as client:
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response = client.get("/health")
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assert response.status_code == 503
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assert response.json()["status"] == "unhealthy"
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# ─── /info ────────────────────────────────────────────────────────────────
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def test_info_shape() -> None:
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ctx = _make_mock_context([])
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with _client_with_context(ctx) as client:
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response = client.get("/info")
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assert response.status_code == 200
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body = response.json()
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for key in ("name", "platform_version", "capabilities", "input_types", "output_types"):
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assert key in body, f"missing key: {key}"
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assert "text-to-triples" in body["capabilities"]
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# ─── /flush ───────────────────────────────────────────────────────────────
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def test_flush_requires_confirmation_token() -> None:
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ctx = _make_mock_context([])
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with _client_with_context(ctx) as client:
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response = client.post("/flush")
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assert response.status_code == 400
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assert "confirm" in response.json()["detail"].lower()
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def test_flush_with_correct_confirmation_succeeds() -> None:
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ctx = _make_mock_context([])
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with _client_with_context(ctx) as client:
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response = client.post(
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"/flush",
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params={"confirm": "YES-I-WANT-TO-DELETE-EVERYTHING"},
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)
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assert response.status_code == 200
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ctx.tools.triple_store_manager.clean.assert_awaited_once_with(dataset=None)
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# ─── /process — input validation ──────────────────────────────────────────
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def test_process_rejects_unsupported_content_type() -> None:
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ctx = _make_mock_context([])
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with _client_with_context(ctx) as client:
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response = client.post(
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"/process",
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content="raw text",
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headers={"Content-Type": "text/plain"},
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)
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assert response.status_code == 415
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def test_process_rejects_empty_json_body() -> None:
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ctx = _make_mock_context([])
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with _client_with_context(ctx) as client:
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response = client.post(
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"/process",
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content=b"",
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headers={"Content-Type": "application/json"},
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)
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assert response.status_code == 400
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# ─── /process — happy path with fake workflow ─────────────────────────────
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def _fake_workflow_state() -> dict[str, Any]:
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"""Build a minimal workflow state that /process can serialize."""
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onto_graph = MagicMock()
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onto_graph.serialize = MagicMock(return_value="@prefix ex: <urn:ex#> .")
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facts_graph = MagicMock()
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facts_graph.serialize = MagicMock(return_value="@prefix ex: <urn:ex#> . ex:a ex:b ex:c .")
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budget_tracker = MagicMock()
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budget_tracker.model_dump = MagicMock(
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return_value={
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"chars_sent": 42,
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"chars_received": 24,
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"calls_count": 3,
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"ontology_triples_generated": 5,
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"facts_triples_generated": 7,
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"ontology_operations_count": 1,
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"facts_operations_count": 2,
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}
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)
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current_ontology = MagicMock()
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current_ontology.graph = onto_graph
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return {
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"status": "success",
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"content_units": [MagicMock(), MagicMock()],
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"parallel_facts_units": [MagicMock(), MagicMock()],
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"render_mode": RenderMode.ONTOLOGY_AND_FACTS,
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"current_ontology": current_ontology,
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"aggregated_facts": facts_graph,
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"budget_tracker": budget_tracker,
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}
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def test_process_json_envelope_returns_ontology_and_facts(
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sample_json_path: Path,
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) -> None:
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workflow_state = _fake_workflow_state()
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ctx = _make_mock_context([workflow_state])
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payload = json.loads(sample_json_path.read_text(encoding="utf-8"))
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with _client_with_context(ctx) as client:
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response = client.post(
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"/process",
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content=json.dumps(payload),
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headers={"Content-Type": "application/json"},
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)
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assert response.status_code == 200, response.text
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body = response.json()
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assert body["status"] == "success"
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assert body["data"]["ontology"].startswith("@prefix")
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assert body["data"]["facts"].startswith("@prefix")
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assert body["metadata"]["chunks_processed"] == 2
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# Budget tracker must surface, otherwise Acceptance Gate 0 fails.
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assert body["metadata"]["budget"]["calls_count"] == 3
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assert body["metadata"]["budget"]["facts_triples_generated"] == 7
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def test_process_multipart_upload(sample_json_path: Path) -> None:
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workflow_state = _fake_workflow_state()
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ctx = _make_mock_context([workflow_state])
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with _client_with_context(ctx) as client:
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with sample_json_path.open("rb") as fh:
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response = client.post(
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"/process",
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files={"file": (sample_json_path.name, fh, "application/octet-stream")},
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)
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assert response.status_code == 200, response.text
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body = response.json()
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assert body["status"] == "success"
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assert body["data"]["ontology"]
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def test_process_workflow_exception_returns_500() -> None:
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"""If the workflow raises, /process must return 500 with error details."""
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async def boom(initial_state, *, stream_mode, config): # noqa: ARG001
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if False: # pragma: no cover — makes this a generator function
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yield {}
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raise RuntimeError("simulated failure")
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ctx = _make_mock_context([])
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ctx.workflow = SimpleNamespace(astream=boom)
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with _client_with_context(ctx) as client:
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response = client.post(
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"/process",
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content=json.dumps({"text": "anything"}),
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headers={"Content-Type": "application/json"},
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)
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assert response.status_code == 500
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body = response.json()
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assert body["status"] == "error"
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assert "simulated failure" in body["error"]
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