Eric Wang
6d0a2169c8
- graph.py: LangGraph-native DI - StateGraph(context_schema=GraphDeps), nodes take (state, runtime: Runtime[GraphDeps]); build_graph() decoupled from LLM; deleted scaffold _event_reply/_fake_llm_propose/stub nodes - photo.py: model proposes stage+body via per-mode prompts (direct/addon); validator enforces clientMode exits; deterministic event forcing removed - confirm.py: single runtime for single/dual confirm, extraction call isolated per person_index - llm.py: workflow_fake_llm() (offline workflow-faithful responder, history-aware) for smoke/tests - backend: LangGraphBackend(graph, llm) passes context per call - tests: invoke helpers inject GraphDeps; illegal-proposal test asserts error + state untouched via second well-behaved fake Verified: 143 passed; smoke turns 1/2/1 with original welcome text
102 lines
4.1 KiB
Python
102 lines
4.1 KiB
Python
"""20261020 工作流的提示词移植与模型决策回归。"""
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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/20261020/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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}
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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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original = next(i["value"] for i in node["inputs"] if i["key"] == "systemPrompt")
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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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now = datetime(2026, 10, 20, 12, 0, tzinfo=timezone.utc)
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rendered = render_prompt(COLLECTION, {}, now=now)
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assert "2026-10-20 12:00:00 +0000" 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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@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) == 1
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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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assert llm.calls[0]["user_message"] == "两辆"
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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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create = AsyncMock(return_value=SimpleNamespace(choices=[
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SimpleNamespace(message=SimpleNamespace(content="<state>1002</state>收到。"))
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]))
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llm = OpenAILLMService(model="test", api_key="test", max_retries=0)
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llm._client = SimpleNamespace(chat=SimpleNamespace(completions=SimpleNamespace(create=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.kwargs["messages"] == [
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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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