feat(system):优化后端

1.新增后端测试 2.增加了后端的加密 3.增加了i18n（国际化）
2026-05-31 15:39:34 +00:00
parent affe460180
commit 99520c69d7
118 changed files with 8174 additions and 1491 deletions
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+"""Workflow graph 引擎本身（节点跳转 / 失败处理 / 派发 / HITL）的单元测试。
+
+不经过 ConsciousnessNode 入口，也不依赖 ray runtime —— 直接调用
+``run_workflow_graph(workflow_data, trace_id, deps=...)``，注入一套全部用
+``AsyncMock``/lambda 实现的 ``WorkflowDeps``。验证 graph 驱动确实把状态机跑通。
+"""
+
+from __future__ import annotations
+
+from typing import Any, Dict, List, Tuple
+from unittest.mock import AsyncMock
+
+import pytest
+
+from kilostar.core.work.workflow.workflow_engine import (
+    WorkflowDeps,
+    WorkflowGraphState,
+    run_workflow_graph,
+)
+from kilostar.core.work.workflow.model import WorkflowStatus
+
+
+def _make_deps(
+    *,
+    skill_outputs: List[Tuple[str, bool]] | None = None,
+    consciousness_outputs: List[Tuple[str, bool]] | None = None,
+    received_replies: List[str] | None = None,
+) -> tuple[WorkflowDeps, Dict[str, List[Any]]]:
+    """构造一对 ``(deps, sink)``：sink 收集所有 IO 调用，方便断言。
+
+    ``skill_outputs`` / ``consciousness_outputs`` 是按顺序消费的 ``(text, success)``
+    队列，run_skill / run_consciousness 每被调一次取一个。``received_replies``
+    用于 HumanApproval 节点的 get_received。
+    """
+    sink: Dict[str, List[Any]] = {
+        "upsert": [],
+        "status": [],
+        "pending": [],
+        "received_calls": [],
+        "skill_calls": [],
+        "consciousness_calls": [],
+    }
+
+    skill_queue = list(skill_outputs or [])
+    consc_queue = list(consciousness_outputs or [])
+    reply_queue = list(received_replies or [])
+
+    upsert = AsyncMock(side_effect=lambda *a, **kw: sink["upsert"].append((a, kw)))
+    status = AsyncMock(side_effect=lambda tid, st: sink["status"].append((tid, st)))
+    pending = AsyncMock(side_effect=lambda tid, msg: sink["pending"].append((tid, msg)))
+
+    async def _get_received(tid: str) -> str:
+        sink["received_calls"].append(tid)
+        return reply_queue.pop(0) if reply_queue else ""
+
+    async def _run_skill(step, state):
+        sink["skill_calls"].append((step, state.current_step_index))
+        if not skill_queue:
+            return "(no fixture)", True
+        return skill_queue.pop(0)
+
+    async def _run_consciousness(step, state):
+        sink["consciousness_calls"].append((step, state.current_step_index))
+        if not consc_queue:
+            return "(no fixture)", True
+        return consc_queue.pop(0)
+
+    return (
+        WorkflowDeps(
+            upsert_workflow_context=upsert,
+            update_workflow_status=status,
+            put_pending=pending,
+            get_received=_get_received,
+            run_skill=_run_skill,
+            run_consciousness=_run_consciousness,
+        ),
+        sink,
+    )
+
+
+# ─── 基本路径 ─────────────────────────────────────────────────────────────
+
+
+@pytest.mark.asyncio
+async def test_skill_individual_path_completes_linear_steps():
+    """两步顺序工作流 (skill_individual)：成功 → 成功，应推进到 COMPLETED。"""
+    deps, sink = _make_deps(skill_outputs=[("ok1", True), ("ok2", True)])
+    workflow_data = {
+        "work_link": [
+            {"step": 1, "name": "s1", "action": "do1", "outputs": "o1",
+             "node": "skill_individual", "agent_id": "a1"},
+            {"step": 2, "name": "s2", "action": "do2", "outputs": "o2",
+             "node": "skill_individual", "agent_id": "a2"},
+        ]
+    }
+    final = await run_workflow_graph(workflow_data, "trace-ok", deps=deps)
+    assert final == WorkflowStatus.COMPLETED.value
+    # run_skill 被调了两次，consciousness 没被调
+    assert len(sink["skill_calls"]) == 2
+    assert len(sink["consciousness_calls"]) == 0
+
+
+@pytest.mark.asyncio
+async def test_consciousness_node_path_dispatches_to_consciousness():
+    """node=consciousness_node 的 step 应被派给 run_consciousness。"""
+    deps, sink = _make_deps(
+        consciousness_outputs=[("ok-from-consciousness", True)]
+    )
+    workflow_data = {
+        "work_link": [
+            {"step": 1, "name": "s1", "action": "summarize",
+             "node": "consciousness_node"},
+        ]
+    }
+    final = await run_workflow_graph(workflow_data, "trace-c", deps=deps)
+    assert final == WorkflowStatus.COMPLETED.value
+    assert len(sink["consciousness_calls"]) == 1
+    assert len(sink["skill_calls"]) == 0
+
+
+@pytest.mark.asyncio
+async def test_mixed_path_dispatches_correctly():
+    """混合 step：第一步 skill_individual，第二步 consciousness_node。"""
+    deps, sink = _make_deps(
+        skill_outputs=[("o1", True)],
+        consciousness_outputs=[("o2", True)],
+    )
+    workflow_data = {
+        "work_link": [
+            {"step": 1, "name": "s1", "action": "do",
+             "node": "skill_individual", "agent_id": "a1"},
+            {"step": 2, "name": "s2", "action": "review",
+             "node": "consciousness_node"},
+        ]
+    }
+    final = await run_workflow_graph(workflow_data, "trace-mix", deps=deps)
+    assert final == WorkflowStatus.COMPLETED.value
+    assert len(sink["skill_calls"]) == 1
+    assert len(sink["consciousness_calls"]) == 1
+
+
+@pytest.mark.asyncio
+async def test_unknown_node_type_falls_to_failed():
+    """未识别的 node 类型应直接收尾 FAILED，不静默跑成功。"""
+    deps, sink = _make_deps()
+    workflow_data = {
+        "work_link": [
+            {"step": 1, "name": "s1", "action": "do",
+             "node": "phantom_node"},
+        ]
+    }
+    final = await run_workflow_graph(workflow_data, "trace-unknown", deps=deps)
+    assert final == WorkflowStatus.FAILED.value
+    assert len(sink["skill_calls"]) == 0
+    assert len(sink["consciousness_calls"]) == 0
+
+
+# ─── logic_gate 跳转语义 ──────────────────────────────────────────────────
+
+
+@pytest.mark.asyncio
+async def test_if_pass_exit_short_circuits():
+    """``logic_gate.if_pass=exit`` 应在该步成功后立即收尾。"""
+    deps, sink = _make_deps(skill_outputs=[("ok", True)])
+    workflow_data = {
+        "work_link": [
+            {"step": 1, "name": "s1", "action": "do", "outputs": "o",
+             "node": "skill_individual", "agent_id": "a1",
+             "logic_gate": {"if_pass": "exit", "if_fail": "jump_to_step_1"}},
+            {"step": 2, "name": "s2", "action": "skipped",
+             "node": "skill_individual", "agent_id": "a2"},
+        ]
+    }
+    final = await run_workflow_graph(workflow_data, "trace-exit", deps=deps)
+    assert final == WorkflowStatus.COMPLETED.value
+    # 第二步不应被派发
+    assert len(sink["skill_calls"]) == 1
+
+
+@pytest.mark.asyncio
+async def test_failure_without_jump_marks_failed():
+    """run_skill 报失败且 if_fail 不指向跳转 → FAILED。"""
+    deps, sink = _make_deps(skill_outputs=[("boom", False)])
+    workflow_data = {
+        "work_link": [
+            {"step": 1, "name": "s1", "action": "do",
+             "node": "skill_individual", "agent_id": "a1"},
+        ]
+    }
+    final = await run_workflow_graph(workflow_data, "trace-fail", deps=deps)
+    assert final == WorkflowStatus.FAILED.value
+
+
+@pytest.mark.asyncio
+async def test_failure_jumps_back_then_succeeds():
+    """步 2 失败但 if_fail=jump_to_step_1，回到步 1 重跑后继续到步 2 成功。"""
+    deps, sink = _make_deps(
+        skill_outputs=[
+            ("o1-first", True),    # step 1 第一次成功
+            ("boom", False),        # step 2 第一次失败 → 跳回 step 1
+            ("o1-retry", True),     # step 1 重跑
+            ("o2-final", True),     # step 2 第二次成功
+        ]
+    )
+    workflow_data = {
+        "work_link": [
+            {"step": 1, "name": "s1", "action": "do",
+             "node": "skill_individual", "agent_id": "a1"},
+            {"step": 2, "name": "s2", "action": "do",
+             "node": "skill_individual", "agent_id": "a2",
+             "logic_gate": {"if_fail": "jump_to_step_1", "if_pass": "continue"}},
+        ]
+    }
+    final = await run_workflow_graph(workflow_data, "trace-jump", deps=deps)
+    assert final == WorkflowStatus.COMPLETED.value
+    # skill 应被调 4 次
+    assert len(sink["skill_calls"]) == 4
+
+
+# ─── HITL ────────────────────────────────────────────────────────────────
+
+
+@pytest.mark.asyncio
+async def test_human_approval_approve_continues():
+    """require_approval=True 时进入 HumanApproval；用户回 approve 后继续执行。"""
+    deps, sink = _make_deps(
+        skill_outputs=[("ok", True)],
+        received_replies=["approve"],
+    )
+    workflow_data = {
+        "work_link": [
+            {"step": 1, "name": "danger", "action": "rm -rf /",
+             "node": "skill_individual", "agent_id": "a1",
+             "require_approval": True},
+        ]
+    }
+    final = await run_workflow_graph(workflow_data, "trace-hitl-ok", deps=deps)
+    assert final == WorkflowStatus.COMPLETED.value
+    # get_received 被调用过一次
+    assert sink["received_calls"] == ["trace-hitl-ok"]
+    # 应该有一条"需要人工审批"的 SSE
+    msgs = [m for _, m in sink["pending"]]
+    assert any("需要人工审批" in m for m in msgs)
+    # skill 才被实际派发
+    assert len(sink["skill_calls"]) == 1
+
+
+@pytest.mark.asyncio
+async def test_human_approval_reject_aborts():
+    """用户回 reject → 不执行 step，整个工作流落到 FAILED。"""
+    deps, sink = _make_deps(
+        skill_outputs=[("ok", True)],
+        received_replies=["reject"],
+    )
+    workflow_data = {
+        "work_link": [
+            {"step": 1, "name": "danger", "action": "do",
+             "node": "skill_individual", "agent_id": "a1",
+             "require_approval": True},
+        ]
+    }
+    final = await run_workflow_graph(workflow_data, "trace-hitl-no", deps=deps)
+    assert final == WorkflowStatus.FAILED.value
+    # skill 不应被派发
+    assert len(sink["skill_calls"]) == 0
+
+
+# ─── 边界 ─────────────────────────────────────────────────────────────────
+
+
+@pytest.mark.asyncio
+async def test_empty_work_link_completes_immediately():
+    """空 work_link：Initialize → Dispatch 直接判定越界 → Finalize(COMPLETED)。"""
+    deps, sink = _make_deps()
+    final = await run_workflow_graph({"work_link": []}, "trace-empty", deps=deps)
+    assert final == WorkflowStatus.COMPLETED.value
+    msgs = [m for _, m in sink["pending"]]
+    assert not any("执行步骤" in m for m in msgs)
+    assert any("工作流执行完成" in m for m in msgs)
+
+
+def test_workflow_graph_state_defaults():
+    """State 默认值确保各字段类型契约稳定。"""
+    state = WorkflowGraphState(trace_id="t")
+    assert state.blackboard == {}
+    assert state.work_link == []
+    assert state.current_step_index == 0
+    assert state.final_status == WorkflowStatus.RUNNING.value
+    assert state.logs == []
+    assert state.original_command == ""