refactor(agent): inject deps via context_schema, LLM-driven photo/confirm nodes

- 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
2026-10-21 15:11:16 +08:00
parent a5407e03b2
commit 6d0a2169c8
12 changed files with 794 additions and 187 deletions
@@ -0,0 +1,101 @@
+"""20261020 工作流的提示词移植与模型决策回归。"""
+
+from datetime import datetime, timezone
+import json
+from pathlib import Path
+
+import pytest
+
+from src.agent.collect import CollectReplyError, run_collect
+from src.agent.llm import FakeLLMService
+from src.agent.prompts import COLLECTION, load_prompt, render_prompt
+
+WORKFLOW = Path(__file__).resolve().parents[2] / "workflow/20261020/事故信息采集2025118.json"
+NODES = {
+    "collection": "事故信息收集",
+    "single_photo": "单车拍照",
+    "dual_photo": "双车拍照",
+    "single_photo_addon": "单车拍照#6",
+    "dual_photo_addon": "双车拍照#7",
+    "single_confirm": "单车信息确认",
+    "dual_confirm": "双车信息确认",
+}
+
+
+@pytest.mark.parametrize(("prompt", "node_name"), NODES.items())
+def test_archived_prompt_matches_workflow(prompt, node_name):
+    workflow = json.loads(WORKFLOW.read_text(encoding="utf-8-sig"))
+    node = next(n for n in workflow["nodes"] if n["name"] == node_name)
+    original = next(i["value"] for i in node["inputs"] if i["key"] == "systemPrompt")
+    archived = load_prompt(prompt).split("--- 原文开始 ---", 1)[1].split("--- 原文结束 ---", 1)[0]
+    assert archived.strip() == original.strip()
+
+
+def test_render_replaces_time_and_person_variables():
+    now = datetime(2026, 10, 20, 12, 0, tzinfo=timezone.utc)
+    rendered = render_prompt(COLLECTION, {}, now=now)
+    assert "2026-10-20 12:00:00 +0000" in rendered
+    rendered = render_prompt("dual_confirm", {
+        "person_fields": {"hphm1": "测试A", "hphm2": "测试B", "sfzmwh1": "1234"}
+    })
+    assert "测试A" in rendered and "测试B" in rendered
+    assert "1234" in rendered
+    assert "{{$" not in rendered
+    assert "未提供" in rendered
+
+
+@pytest.mark.asyncio
+async def test_short_answer_does_not_override_model_routing():
+    # “在”可能是明确回答；运行时不得按字数一律拒绝分流。
+    llm = FakeLLMService(lambda s, u: "<state>2000</state>信息已齐，请拍摄车辆前方。")
+    result = await run_collect({
+        "stage_code": "1002", "input_text": "在", "clarify_count": 0,
+        "dialogue": [{"role": "assistant", "content": "您还在事故现场吗？"}],
+    }, llm)
+    assert result["stage_code"] == "2000"
+    assert result["clarify_count"] == 0
+    assert len(llm.calls) == 1
+
+
+@pytest.mark.asyncio
+async def test_format_and_stage_errors_share_one_repair_budget():
+    replies = iter(["缺少前缀", "<state>9999</state>非法阶段"])
+    llm = FakeLLMService(lambda s, u: next(replies))
+    with pytest.raises(CollectReplyError):
+        await run_collect({"stage_code": "1002", "input_text": "追尾了"}, llm)
+    assert len(llm.calls) == 2
+
+
+@pytest.mark.asyncio
+async def test_collect_sends_history_as_messages():
+    history = [
+        {"role": "user", "content": "没有人受伤"},
+        {"role": "assistant", "content": "<state>1002</state>请问有几辆车？"},
+    ]
+    llm = FakeLLMService(lambda s, u: "<state>1002</state>请问您还在现场吗？")
+    await run_collect({
+        "stage_code": "1002", "input_text": "两辆", "dialogue": history,
+    }, llm)
+    assert llm.calls[0]["history"] == history
+    assert llm.calls[0]["user_message"] == "两辆"
+    assert "没有人受伤" not in llm.calls[0]["user_message"]
+
+
+@pytest.mark.asyncio
+async def test_openai_adapter_preserves_message_roles():
+    from types import SimpleNamespace
+    from unittest.mock import AsyncMock
+    from src.agent.llm import OpenAILLMService
+
+    create = AsyncMock(return_value=SimpleNamespace(choices=[
+        SimpleNamespace(message=SimpleNamespace(content="<state>1002</state>收到。"))
+    ]))
+    llm = OpenAILLMService(model="test", api_key="test", max_retries=0)
+    llm._client = SimpleNamespace(chat=SimpleNamespace(completions=SimpleNamespace(create=create)))
+    history = [{"role": "assistant", "content": "之前的问题"}]
+    await llm.complete("系统提示词", "本轮输入", history=history)
+    assert create.call_args.kwargs["messages"] == [
+        {"role": "system", "content": "系统提示词"},
+        {"role": "assistant", "content": "之前的问题"},
+        {"role": "user", "content": "本轮输入"},
+    ]