iomgaa
9ca9035190
保真 TRM4 算法 #11: json_repair 兜底、submit_answer 终止、 pluggy hook 生命周期、无效工具不计步。 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
242 lines
8.2 KiB
Python
242 lines
8.2 KiB
Python
"""core/agent/loop.py 单元测试。
|
||
|
||
算法保真 #11 — AgentLoop 推理循环引擎。
|
||
9 个测试覆盖: 终止、预算、无效工具、解析错误、JSON 修复、
|
||
thinking 捕获、token 累加、call_id 透传、pluggy hook。
|
||
"""
|
||
|
||
from __future__ import annotations
|
||
|
||
import json
|
||
from typing import TYPE_CHECKING, Any
|
||
from unittest.mock import AsyncMock
|
||
|
||
import pytest
|
||
|
||
from core.agent.loop import AgentLoop
|
||
from core.agent.protocols import hookimpl
|
||
from core.types import LLMResponse
|
||
|
||
if TYPE_CHECKING:
|
||
from core.agent.types import LoopResult, Step
|
||
|
||
|
||
# ── 测试基础设施 ──────────────────────────────────────────────
|
||
|
||
|
||
class _StubDispatcher:
|
||
"""测试用工具调度器。"""
|
||
|
||
async def dispatch(
|
||
self, tool_name: str, args: dict[str, Any], *, context: dict[str, Any]
|
||
) -> str:
|
||
if tool_name == "submit_answer":
|
||
return "答案已提交"
|
||
if tool_name == "search_tree":
|
||
return "搜索结果: 找到节点 L2-3"
|
||
raise ValueError(f"未知工具: {tool_name}")
|
||
|
||
|
||
def _make_response(content: str, thinking: str = "") -> LLMResponse:
|
||
"""构造测试用 LLMResponse。"""
|
||
return LLMResponse(
|
||
content=content,
|
||
thinking=thinking,
|
||
model="test-model",
|
||
provider="test",
|
||
prompt_tokens=10,
|
||
completion_tokens=10,
|
||
latency_ms=100,
|
||
ttft_ms=50.0,
|
||
max_inter_token_ms=10.0,
|
||
cache_hit=False,
|
||
call_id="test-call-id",
|
||
)
|
||
|
||
|
||
def _submit_json(answer: str = "42") -> str:
|
||
"""构造 submit_answer 的 JSON 响应。"""
|
||
return json.dumps(
|
||
{
|
||
"reflect": {"observation": "找到答案"},
|
||
"plan": {"next_step": "提交"},
|
||
"action": {"tool": "submit_answer", "args": {"answer": answer}},
|
||
}
|
||
)
|
||
|
||
|
||
def _search_json() -> str:
|
||
"""构造 search_tree 的 JSON 响应。"""
|
||
return json.dumps(
|
||
{
|
||
"reflect": {"observation": "需要搜索"},
|
||
"plan": {"next_step": "搜索"},
|
||
"action": {"tool": "search_tree", "args": {"query": "test"}},
|
||
}
|
||
)
|
||
|
||
|
||
def _invalid_tool_json() -> str:
|
||
"""构造无效工具的 JSON 响应。"""
|
||
return json.dumps(
|
||
{
|
||
"reflect": {},
|
||
"plan": {},
|
||
"action": {"tool": "unknown_tool", "args": {}},
|
||
}
|
||
)
|
||
|
||
|
||
# ── 测试用例 ──────────────────────────────────────────────────
|
||
|
||
|
||
class TestAgentLoop:
|
||
"""AgentLoop 推理循环引擎测试。"""
|
||
|
||
@pytest.mark.asyncio
|
||
async def test_submit_answer_terminates_loop(self) -> None:
|
||
"""submit_answer 终止循环 → finished, result=args, steps_used=1。"""
|
||
llm = AsyncMock()
|
||
llm.chat.return_value = _make_response(_submit_json())
|
||
|
||
loop = AgentLoop(llm=llm, max_steps=10)
|
||
result = await loop.run("system", "user", _StubDispatcher())
|
||
|
||
assert result.stop_reason == "finished"
|
||
assert result.result == {"answer": "42"}
|
||
assert result.steps_used == 1
|
||
assert len(result.steps) == 1
|
||
|
||
@pytest.mark.asyncio
|
||
async def test_budget_exceeded(self) -> None:
|
||
"""max_steps=3 用完 → budget_exceeded, steps_used=3。"""
|
||
llm = AsyncMock()
|
||
llm.chat.return_value = _make_response(_search_json())
|
||
|
||
loop = AgentLoop(llm=llm, max_steps=3)
|
||
result = await loop.run("system", "user", _StubDispatcher())
|
||
|
||
assert result.stop_reason == "budget_exceeded"
|
||
assert result.steps_used == 3
|
||
|
||
@pytest.mark.asyncio
|
||
async def test_invalid_tool_not_counted_as_step(self) -> None:
|
||
"""无效工具（ValueError）不计步 → steps_used=1。"""
|
||
llm = AsyncMock()
|
||
llm.chat.side_effect = [
|
||
_make_response(_invalid_tool_json()),
|
||
_make_response(_submit_json()),
|
||
]
|
||
|
||
loop = AgentLoop(llm=llm, max_steps=10)
|
||
result = await loop.run("system", "user", _StubDispatcher())
|
||
|
||
assert result.stop_reason == "finished"
|
||
assert result.steps_used == 1
|
||
|
||
@pytest.mark.asyncio
|
||
async def test_parse_error_after_max_retries(self) -> None:
|
||
"""非 JSON 内容连续失败 → parse_error, steps_used=0。"""
|
||
llm = AsyncMock()
|
||
llm.chat.return_value = _make_response("这不是JSON内容")
|
||
|
||
loop = AgentLoop(llm=llm, max_steps=10, max_retries=3)
|
||
result = await loop.run("system", "user", _StubDispatcher())
|
||
|
||
assert result.stop_reason == "parse_error"
|
||
assert result.steps_used == 0
|
||
|
||
@pytest.mark.asyncio
|
||
async def test_json_repair_handles_malformed(self) -> None:
|
||
"""轻微 JSON 缺陷（缺少闭合花括号）被 json_repair 修复。"""
|
||
malformed = (
|
||
'{"reflect": {}, "plan": {}, '
|
||
'"action": {"tool": "submit_answer", "args": {"answer": "42"}}'
|
||
)
|
||
llm = AsyncMock()
|
||
llm.chat.return_value = _make_response(malformed)
|
||
|
||
loop = AgentLoop(llm=llm, max_steps=10)
|
||
result = await loop.run("system", "user", _StubDispatcher())
|
||
|
||
assert result.stop_reason == "finished"
|
||
assert result.result == {"answer": "42"}
|
||
|
||
@pytest.mark.asyncio
|
||
async def test_thinking_content_captured_in_step(self) -> None:
|
||
"""LLMResponse.thinking → Step.thought。"""
|
||
llm = AsyncMock()
|
||
llm.chat.return_value = _make_response(_submit_json(), thinking="深度思考过程")
|
||
|
||
loop = AgentLoop(llm=llm, max_steps=10)
|
||
result = await loop.run("system", "user", _StubDispatcher())
|
||
|
||
assert result.steps[0].thought == "深度思考过程"
|
||
|
||
@pytest.mark.asyncio
|
||
async def test_token_usage_accumulated(self) -> None:
|
||
"""多步 token 累加: 3 次调用 × 10 tokens = 30。"""
|
||
llm = AsyncMock()
|
||
llm.chat.side_effect = [
|
||
_make_response(_search_json()),
|
||
_make_response(_search_json()),
|
||
_make_response(_submit_json()),
|
||
]
|
||
|
||
loop = AgentLoop(llm=llm, max_steps=10)
|
||
result = await loop.run("system", "user", _StubDispatcher())
|
||
|
||
assert result.token_usage["prompt_tokens"] == 30
|
||
assert result.token_usage["completion_tokens"] == 30
|
||
|
||
@pytest.mark.asyncio
|
||
async def test_call_id_propagated_to_step(self) -> None:
|
||
"""LLMResponse.call_id → Step.call_id。"""
|
||
llm = AsyncMock()
|
||
llm.chat.return_value = _make_response(_submit_json())
|
||
|
||
loop = AgentLoop(llm=llm, max_steps=10)
|
||
result = await loop.run("system", "user", _StubDispatcher())
|
||
|
||
assert result.steps[0].call_id == "test-call-id"
|
||
|
||
@pytest.mark.asyncio
|
||
async def test_pluggy_hooks_called(self) -> None:
|
||
"""TrackingPlugin 验证 before_step/after_tool/after_step/on_finish 全部触发。"""
|
||
|
||
class TrackingPlugin:
|
||
"""记录 hook 调用事件的测试插件。"""
|
||
|
||
def __init__(self) -> None:
|
||
self.events: list[str] = []
|
||
|
||
@hookimpl
|
||
async def before_step(self, iteration: int, messages: list[dict[str, Any]]) -> None:
|
||
self.events.append(f"before_step:{iteration}")
|
||
|
||
@hookimpl
|
||
async def after_tool(self, iteration: int, step: Step) -> str | None:
|
||
self.events.append(f"after_tool:{iteration}")
|
||
return None
|
||
|
||
@hookimpl
|
||
async def after_step(self, iteration: int, messages: list[dict[str, Any]]) -> None:
|
||
self.events.append(f"after_step:{iteration}")
|
||
|
||
@hookimpl
|
||
async def on_finish(self, result: LoopResult) -> None:
|
||
self.events.append(f"on_finish:{result.stop_reason}")
|
||
|
||
tracker = TrackingPlugin()
|
||
llm = AsyncMock()
|
||
llm.chat.return_value = _make_response(_submit_json())
|
||
|
||
loop = AgentLoop(llm=llm, max_steps=10)
|
||
result = await loop.run("system", "user", _StubDispatcher(), plugins=[tracker])
|
||
|
||
assert result.stop_reason == "finished"
|
||
assert "before_step:0" in tracker.events
|
||
assert "after_tool:0" in tracker.events
|
||
assert "after_step:0" in tracker.events
|
||
assert "on_finish:finished" in tracker.events
|