138 lines
6.0 KiB
Python
138 lines
6.0 KiB
Python
"""工作流提示词同步、运行时上下文与模型决策回归。"""
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from datetime import datetime, timezone
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import json
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from pathlib import Path
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import pytest
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from src.agent.collect import CollectReplyError, run_collect
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from src.agent.llm import FakeLLMService
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from src.agent.prompts import COLLECTION, load_prompt, render_prompt
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WORKFLOW = Path(__file__).resolve().parents[2] / "workflow/20261027/事故信息采集2025118.json"
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NODES = {
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"collection": "事故信息收集",
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"single_photo": "单车拍照",
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"dual_photo": "双车拍照",
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"single_photo_addon": "单车拍照#6",
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"dual_photo_addon": "双车拍照#7",
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"single_confirm": "单车信息确认",
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"dual_confirm": "双车信息确认",
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"extraction": "文本内容提取事故信息",
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}
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_INPUT_KEYS = {"extraction": "description"}
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@pytest.mark.parametrize(("prompt", "node_name"), NODES.items())
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def test_archived_prompt_matches_workflow(prompt, node_name):
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workflow = json.loads(WORKFLOW.read_text(encoding="utf-8-sig"))
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node = next(n for n in workflow["nodes"] if n["name"] == node_name)
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input_key = _INPUT_KEYS.get(prompt, "systemPrompt")
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original = next(i["value"] for i in node["inputs"] if i["key"] == input_key)
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archived = load_prompt(prompt).split("--- 原文开始 ---", 1)[1].split("--- 原文结束 ---", 1)[0]
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assert archived.strip() == original.strip()
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def test_render_replaces_time_and_person_variables():
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# 20261022：collection/extraction 提示词本体不含变量（动态上下文走
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# user 消息的 <turn_context>）；拍照/确认提示词尾部仍渲染人员变量。
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rendered = render_prompt(COLLECTION, {})
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assert "{{$" not in rendered
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rendered = render_prompt("dual_confirm", {
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"person_fields": {"hphm1": "测试A", "hphm2": "测试B", "sfzmwh1": "1234"}
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})
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assert "测试A" in rendered and "测试B" in rendered
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assert "1234" in rendered
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assert "{{$" not in rendered
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assert "未提供" in rendered
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def test_user_message_carries_turn_context():
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from src.agent.collect import _build_user_message
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from datetime import datetime, timezone
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now = datetime(2026, 10, 22, 8, 0, tzinfo=timezone.utc)
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form = {"vehicle_count": 0, "has_injury": False, "is_on_scene": False}
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message = _build_user_message({"input_text": "两辆", "accident_fields": form}, now=now)
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assert "<turn_context>" in message
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form_line = next(line for line in message.splitlines() if line.startswith("当前表单："))
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assert json.loads(form_line.removeprefix("当前表单：")) == form
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unfilled_line = next(line for line in message.splitlines() if line.startswith("表单未填写字段："))
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assert json.loads(unfilled_line.removeprefix("表单未填写字段：")) == [
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"accident_cause", "has_non_motor_vehicle_hit", "accident_time",
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]
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empty_message = _build_user_message({"input_text": "两辆"}, now=now)
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assert "表单未填写字段" not in empty_message
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assert "form：" not in message
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assert "form_unfilled_fields：" not in message
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assert "本轮参考时间：2026-10-22 08:00:00 +0000" in message
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assert "<user_input>" in message
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assert "两辆" in message
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def test_workflow_collection_receives_form_in_user_context():
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workflow = json.loads(WORKFLOW.read_text(encoding="utf-8-sig"))
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node = next(n for n in workflow["nodes"] if n["name"] == "事故信息收集")
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message = next(i["value"] for i in node["inputs"] if i["key"] == "userChatInput")
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context = message.split("</turn_context>", 1)[0]
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assert "当前表单：{{$VARIABLE_NODE_ID.bNJkeXot$}}" in context
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@pytest.mark.asyncio
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async def test_short_answer_does_not_override_model_routing():
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# “在”可能是明确回答；运行时不得按字数一律拒绝分流。
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llm = FakeLLMService(lambda s, u: "<state>2000</state>信息已齐，请拍摄车辆前方。")
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result = await run_collect({
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"stage_code": "1002", "input_text": "在", "clarify_count": 0,
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"dialogue": [{"role": "assistant", "content": "您还在事故现场吗？"}],
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}, llm)
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assert result["stage_code"] == "2000"
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assert result["clarify_count"] == 0
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assert len(llm.calls) == 2 # 对话 + 提取
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@pytest.mark.asyncio
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async def test_format_and_stage_errors_share_one_repair_budget():
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replies = iter(["缺少前缀", "<state>9999</state>非法阶段"])
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llm = FakeLLMService(lambda s, u: next(replies))
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with pytest.raises(CollectReplyError):
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await run_collect({"stage_code": "1002", "input_text": "追尾了"}, llm)
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assert len(llm.calls) == 2
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@pytest.mark.asyncio
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async def test_collect_sends_history_as_messages():
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history = [
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{"role": "user", "content": "没有人受伤"},
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{"role": "assistant", "content": "<state>1002</state>请问有几辆车？"},
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]
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llm = FakeLLMService(lambda s, u: "<state>1002</state>请问您还在现场吗？")
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await run_collect({
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"stage_code": "1002", "input_text": "两辆", "dialogue": history,
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}, llm)
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assert llm.calls[0]["history"] == history
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# 20261022 格式：<turn_context> + <user_input>
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assert "<turn_context>" in llm.calls[0]["user_message"]
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assert "<user_input>\n两辆\n</user_input>" in llm.calls[0]["user_message"]
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assert "没有人受伤" not in llm.calls[0]["user_message"]
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@pytest.mark.asyncio
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async def test_openai_adapter_preserves_message_roles():
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from types import SimpleNamespace
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from unittest.mock import AsyncMock
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from src.agent.llm import OpenAILLMService
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from langchain_core.messages import AIMessage
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create = AsyncMock(return_value=AIMessage(content="<state>1002</state>收到。"))
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llm = OpenAILLMService(model="test", api_key="test", max_retries=0)
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llm._client = SimpleNamespace(ainvoke=create)
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history = [{"role": "assistant", "content": "之前的问题"}]
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await llm.complete("系统提示词", "本轮输入", history=history)
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assert create.call_args.args[0] == [
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{"role": "system", "content": "系统提示词"},
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{"role": "assistant", "content": "之前的问题"},
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{"role": "user", "content": "本轮输入"},
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]
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