254 lines
8.7 KiB
Python
254 lines
8.7 KiB
Python
from __future__ import annotations
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import os
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from types import SimpleNamespace
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import pytest
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import pytest_asyncio
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from fastapi import FastAPI
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from httpx import ASGITransport, AsyncClient
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from sqlalchemy import JSON
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from sqlalchemy.ext.asyncio import async_sessionmaker, create_async_engine
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os.environ["DATABASE_URL"] = "postgresql+asyncpg://tester:tester@localhost:5432/pmflow_test"
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from app.api.ai_steward_api import routes_mounted_ai
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from app.core.database import get_db
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from app.dependencies.auth import get_current_user
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from app.dependencies.tenant import get_tenant_id
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from app.models.base import Base
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from app.models.mounted_ai_runtime import (
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MountedAgentTrace,
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MountedConversationRun,
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MountedDraftWorkspace,
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MountedToolCapabilityPolicy,
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MountedTopicWorkspace,
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)
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from app.models.training_pipeline import TrainingDataset, TrainingEvaluation, TrainingJob
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def _use_sqlite_json_types() -> None:
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for table in (
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MountedTopicWorkspace.__table__,
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MountedDraftWorkspace.__table__,
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MountedConversationRun.__table__,
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MountedAgentTrace.__table__,
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MountedToolCapabilityPolicy.__table__,
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TrainingDataset.__table__,
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TrainingJob.__table__,
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TrainingEvaluation.__table__,
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):
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for column in table.columns:
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if column.name in {
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"facts_json",
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"goals_json",
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"constraints_json",
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"options_json",
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"open_questions",
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"pending_actions",
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"bound_entities",
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"metadata_json",
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"requirement_card",
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"draft_payload",
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"required_confirmations",
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"context_layers_hit",
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"tool_calls",
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"execution_outcome",
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"input_summary",
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"output_summary",
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"tool_trace",
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"source_scope",
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"tenant_scope",
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"policy_metadata",
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"selection_rules",
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"channel_scope",
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"metrics_json",
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"evaluation_summary",
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}:
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column.type = JSON()
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@pytest_asyncio.fixture
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async def db_session():
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engine = create_async_engine("sqlite+aiosqlite:///:memory:")
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_use_sqlite_json_types()
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async with engine.begin() as conn:
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await conn.run_sync(
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lambda sync_conn: Base.metadata.create_all(
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sync_conn,
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tables=[
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MountedTopicWorkspace.__table__,
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MountedDraftWorkspace.__table__,
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MountedConversationRun.__table__,
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MountedAgentTrace.__table__,
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MountedToolCapabilityPolicy.__table__,
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TrainingDataset.__table__,
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TrainingJob.__table__,
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TrainingEvaluation.__table__,
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],
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)
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)
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maker = async_sessionmaker(engine, expire_on_commit=False)
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async with maker() as session:
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yield session
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await engine.dispose()
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def _build_app(db_session):
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app = FastAPI()
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app.include_router(routes_mounted_ai.router)
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async def _fake_get_db():
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yield db_session
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app.dependency_overrides[get_db] = _fake_get_db
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app.dependency_overrides[get_tenant_id] = lambda: 1
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app.dependency_overrides[get_current_user] = lambda: SimpleNamespace(
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id=9001,
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tenant_id=1,
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role="ADMIN",
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name="管理员",
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username="admin",
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)
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return app
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@pytest.mark.asyncio
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async def test_mounted_ai_admin_routes_return_runtime_views(db_session) -> None:
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topic = MountedTopicWorkspace(
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tenant_id=1,
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topic_key="feishu:chat_1:topic_1",
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source_system="feishu",
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channel_ref="chat_1",
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thread_key="thread_1",
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mode="plan",
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status="active",
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open_questions=["负责人是谁"],
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readiness_score=0.4,
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)
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db_session.add(topic)
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await db_session.flush()
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draft = MountedDraftWorkspace(
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tenant_id=1,
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topic_workspace_id=topic.id,
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state="collecting",
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draft_version=2,
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requirement_card={"goals": ["做挂载 AI"]},
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)
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run = MountedConversationRun(
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tenant_id=1,
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trace_id="mounted-run-1",
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topic_workspace_id=topic.id,
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source_system="feishu",
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source_message_id="om_123",
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mode_before="plan",
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mode_after="clarify",
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active_agent="Clarifier",
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status="completed",
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trigger_reason="missing_owner",
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context_layers_hit={"thread": True, "topic": True},
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final_reply="还差负责人信息。",
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)
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db_session.add_all([draft, run])
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await db_session.flush()
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trace = MountedAgentTrace(
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tenant_id=1,
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conversation_run_id=run.id,
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agent_name="Clarifier",
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status="completed",
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latency_ms=128,
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input_summary={"mode": "plan"},
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output_summary={"question": "这件事打算谁来负责？"},
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)
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policy = MountedToolCapabilityPolicy(
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tenant_id=1,
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tool_name="project.create",
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risk_level="high",
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confirm_required=True,
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auto_execute_allowed=False,
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)
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eval_dataset = TrainingDataset(
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tenant_id=1,
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dataset_type="eval",
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version="teacher-frozen-v1",
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status="ready",
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sample_count=18,
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selection_rules={"suite_kind": "teacher_eval_frozen_set"},
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)
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db_session.add_all([trace, policy, eval_dataset])
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await db_session.flush()
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eval_job = TrainingJob(
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tenant_id=1,
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dataset_id=eval_dataset.id,
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job_type="sft",
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base_model_family="gpt-5.4",
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target_slot="high",
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status="queued",
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provider_backend="openai",
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)
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db_session.add(eval_job)
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await db_session.flush()
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db_session.add(
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TrainingEvaluation(
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tenant_id=1,
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training_job_id=eval_job.id,
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evaluation_type="offline_judge",
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status="completed",
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score=91.5,
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evaluation_summary={"pass": True},
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)
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)
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await db_session.commit()
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app = _build_app(db_session)
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transport = ASGITransport(app=app)
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async with AsyncClient(transport=transport, base_url="http://test") as client:
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sources_resp = await client.get("/ai-steward/mounted-ai/sources")
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assert sources_resp.status_code == 200
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assert sources_resp.json()["data"]["items"][0]["source_system"] == "feishu"
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assert sources_resp.json()["data"]["items"][0]["topic_count"] == 1
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topics_resp = await client.get("/ai-steward/mounted-ai/topics")
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assert topics_resp.status_code == 200
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assert topics_resp.json()["data"]["items"][0]["topic_key"] == "feishu:chat_1:topic_1"
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assert topics_resp.json()["data"]["items"][0]["draft_state"] == "collecting"
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runs_resp = await client.get("/ai-steward/mounted-ai/runs")
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assert runs_resp.status_code == 200
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assert runs_resp.json()["data"]["items"][0]["active_agent"] == "Clarifier"
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detail_resp = await client.get(f"/ai-steward/mounted-ai/runs/{run.id}")
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assert detail_resp.status_code == 200
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assert detail_resp.json()["data"]["agent_traces"][0]["agent_name"] == "Clarifier"
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cutover_resp = await client.get("/ai-steward/mounted-ai/cutover-status")
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assert cutover_resp.status_code == 200
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assert cutover_resp.json()["data"]["mounted_runtime_ready"] is True
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assert cutover_resp.json()["data"]["tool_policy_count"] == 1
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assert cutover_resp.json()["data"]["legacy_cutover_state"] == "retired"
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assert cutover_resp.json()["data"]["retired_item_count"] >= 4
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assert cutover_resp.json()["data"]["active_item_count"] == 0
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assert {
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item["key"]: item["status"]
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for item in cutover_resp.json()["data"]["legacy_cutover_items"]
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} == {
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"ai_pmo_ingress_consultant_capture": "retired",
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"feishu_mention_consultant_reply": "retired",
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"signal_capture_legacy_entry": "retired",
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"legacy_feishu_queue_followup": "retired",
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}
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eval_suites_resp = await client.get("/ai-steward/mounted-ai/eval-suites")
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assert eval_suites_resp.status_code == 200
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assert eval_suites_resp.json()["data"]["items"][0]["suite_kind"] == "teacher_eval_frozen_set"
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assert eval_suites_resp.json()["data"]["items"][0]["latest_score"] == 91.5
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eval_suite_detail_resp = await client.get(f"/ai-steward/mounted-ai/eval-suites/{eval_dataset.id}")
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assert eval_suite_detail_resp.status_code == 200
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assert eval_suite_detail_resp.json()["data"]["item"]["sample_count"] == 18
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assert eval_suite_detail_resp.json()["data"]["evaluations"][0]["evaluation_type"] == "offline_judge"
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