From a43c530fb852d1b071ced01e9e1db1c3b39a07e2 Mon Sep 17 00:00:00 2001 From: ThearchyHelios Date: Sun, 4 Oct 2026 11:02:06 +0800 Subject: [PATCH 1/5] =?UTF-8?q?=E7=BB=93=E6=9E=84=E5=8C=96=E6=8A=BD?= =?UTF-8?q?=E5=8F=96=E8=A2=AB=E6=88=AA=E6=96=AD=E6=97=B6=E5=8A=A0=E5=A4=A7?= =?UTF-8?q?=E9=A2=9D=E5=BA=A6=E9=87=8D=E8=AF=95=EF=BC=8C=E4=BB=8D=E6=88=AA?= =?UTF-8?q?=E6=96=AD=E5=B0=B1=E7=85=A7=E5=AE=9E=E8=AF=B4=E6=98=AF=E9=A2=9D?= =?UTF-8?q?=E5=BA=A6=E5=9C=A8=E6=8E=A8=E7=90=86=E9=98=B6=E6=AE=B5=E7=94=A8?= =?UTF-8?q?=E5=AE=8C=E4=BA=86?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 一次真实运行里 4 个开了按出处核对字段的 Agent 抽取全部失败：抽取关掉思考、额度 4096，可经 OpenAI 兼容接口接入的推理模型关不掉思考，额度在推理阶段就用完了（openai 抛 LengthFinishReasonError）；日志却叫人「稍后重试、去设置测试该模型」，两样都不解决问题。 - 额度默认至少 16384，节点配了更大的用节点的 - 认出输出被截断：openai 的 length limit 类错误、finish_reason=length、Anthropic 的 stop_reason=max_tokens；被截断时额度翻倍（最多 32768）重试一次，每次调用各记一行，被截断那次的用量照样记账 - 服务方嫌额度太大（400，报错里有 max_tokens 一类字样）时退回 8192、4096，不能因为额度提高反而抽不成 - 两次都截断：新日志 code agent_field_truncated（带 fields、max_tokens），原话说清额度在推理阶段用完、结构化结论没能写出来，下一步是换非推理模型或调大最大输出 token；字段照旧记空值、不以 0 代替。非截断类的失败照旧是 agent_field_unverified - 时间线给 agent_field_truncated 专门的标题、副标题和下一步，入口落到该 Agent 的最大输出 token；check-decode 补断言 --- backend/app/engine/nodes/llm.py | 149 +++++++++++++--- backend/tests/test_extract_truncation.py | 208 +++++++++++++++++++++++ frontend/src/run/decode.ts | 16 ++ scripts/check-decode.mjs | 41 +++++ 4 files changed, 392 insertions(+), 22 deletions(-) create mode 100644 backend/tests/test_extract_truncation.py diff --git a/backend/app/engine/nodes/llm.py b/backend/app/engine/nodes/llm.py index 5d0c51e4..6e2bd28e 100644 --- a/backend/app/engine/nodes/llm.py +++ b/backend/app/engine/nodes/llm.py @@ -541,25 +541,66 @@ async def run_agent(state: GraphState, ctx: NodeContext) -> dict[str, Any]: async def extract_step(payload: list[BaseMessage], schema: dict[str, Any]) -> dict[str, Any]: # cite_fields 的结构化抽取：进 checkpoint，节点重放时不再调用、不再计费。失败也在这里收住、 # 作为结果返回——agent 的结论还在，字段记空值、发警告，而不是把整个节点赔进去。 - # thinking 关掉：结构化输出靠强制工具调用，和思考模式不兼容，抽取也用不着思考 - spec = model_spec.model_copy(update={ - "thinking": "off", "max_tokens": max(int(ctx.cfg("max_tokens") or 0), 4096)}) - started = time.perf_counter() - ctx.emit(EventType.LLM_START, model=model_id, structured=True, purpose="cite_fields", - message_count=len(payload)) - try: - async with SessionLocal() as session: - extractor, _ = await get_chat_model(session, spec) - got = await extractor.with_structured_output(schema, include_raw=True).ainvoke(payload) - except Exception as e: # noqa: BLE001 + # thinking 关掉：结构化输出靠强制工具调用，和思考模式不兼容，抽取也用不着思考。但经 OpenAI 兼容 + # 接口接入的推理模型关不掉思考，推理照样算在额度里：额度见 EXTRACT_MAX_TOKENS，被截断时加大重试一次 + budget = max(int(ctx.cfg("max_tokens") or 0), EXTRACT_MAX_TOKENS) + spent = {"input_tokens": 0, "output_tokens": 0, "cost_usd": 0.0, "calls": 0} + rejected: int | None = None # 服务方嫌太大、拒收的额度：之后重试不再超过它 + retried = False + + def count(usage: dict[str, Any]) -> None: + for key in ("input_tokens", "output_tokens", "calls"): + spent[key] += int(usage.get(key) or 0) + spent["cost_usd"] = round(spent["cost_usd"] + float(usage.get("cost_usd") or 0), 6) + + while True: + spec = model_spec.model_copy(update={"thinking": "off", "max_tokens": budget}) + started = time.perf_counter() + ctx.emit(EventType.LLM_START, model=model_id, structured=True, purpose="cite_fields", + message_count=len(payload), max_tokens=budget) + failure: Exception | None = None + raw: BaseMessage | None = None + parsed: Any = None + try: + async with SessionLocal() as session: + extractor, _ = await get_chat_model(session, spec) + got = await extractor.with_structured_output(schema, include_raw=True).ainvoke(payload) + raw, parsed = _split_structured(got) + usage = _usage_of(raw if raw is not None else AIMessage(content=""), model_id) + except Exception as e: # noqa: BLE001 + failure, usage = e, _error_usage(e, model_id) + count(usage) + cut = truncated_error(failure) if failure is not None else (parsed is None and truncated_reply(raw)) + elapsed = int((time.perf_counter() - started) * 1000) + if failure is not None and not cut and _budget_rejected(failure) \ + and (lower := next((b for b in _EXTRACT_FALLBACKS if b < budget), None)) is not None: + # 服务方的输出上限比默认额度小（DeepSeek chat 系只收 8K）：退一档再来，别因为额度提高反而抽不成 + ctx.emit(EventType.LLM_END, agent=ctx.node.title, model=model_id, purpose="cite_fields", + duration_ms=elapsed, **usage, + error=f"模型接入不接受 {budget} token 的输出额度，已改用 {lower} token 重试") + rejected, budget = budget, lower + continue + bigger = min(budget * 2, EXTRACT_RETRY_CAP) + if cut and not retried and bigger > budget and (rejected is None or bigger < rejected): + ctx.emit(EventType.LLM_END, agent=ctx.node.title, model=model_id, purpose="cite_fields", + duration_ms=elapsed, **usage, + error=f"输出被截断：{budget} token 的输出额度用完了，结构化结论没有写完，" + f"已把额度加大到 {bigger} token 重试一次") + retried, budget = True, bigger + continue + if cut: + ctx.emit(EventType.LLM_END, agent=ctx.node.title, model=model_id, purpose="cite_fields", + duration_ms=elapsed, **usage, + error=f"输出被截断：{budget} token 的输出额度用完了，结构化结论没有写完") + return {"error": TRUNCATED_EXTRACT.format(budget=budget), "truncated": True, + "max_tokens": budget, **spent} + if failure is not None: + ctx.emit(EventType.LLM_END, agent=ctx.node.title, model=model_id, purpose="cite_fields", + duration_ms=elapsed, **usage, error=describe_exception(failure)) + return {"error": f"结构化抽取失败：{explain_model_error(failure, model_id)}", **spent} ctx.emit(EventType.LLM_END, agent=ctx.node.title, model=model_id, purpose="cite_fields", - duration_ms=int((time.perf_counter() - started) * 1000), error=describe_exception(e)) - return {"error": f"结构化抽取失败：{explain_model_error(e, model_id)}"} - raw, parsed = _split_structured(got) - usage = _usage_of(raw if raw is not None else AIMessage(content=""), model_id) - ctx.emit(EventType.LLM_END, agent=ctx.node.title, model=model_id, purpose="cite_fields", - duration_ms=int((time.perf_counter() - started) * 1000), **usage) - return {"parsed": parsed, **usage} + duration_ms=elapsed, **usage) + return {"parsed": parsed, **spent} final_text = "" #: 模型把工具调用写成了文字：纠正过一次了没有 @@ -778,8 +819,10 @@ async def run_agent(state: GraphState, ctx: NodeContext) -> dict[str, Any]: total_usage[key] += int(extracted.get(key) or 0) total_usage["cost_usd"] = round(total_usage["cost_usd"] + float(extracted.get("cost_usd") or 0), 6) cited = verify_cited_fields(extracted, fields, queries) - for code, message, names in _field_warnings(*cited, extracted.get("error")): - await once(ctx, EventType.LOG, level="warn", code=code, message=message, fields=names) + truncated = extracted.get("truncated") is True + for code, message, names in _field_warnings(*cited, extracted.get("error"), truncated=truncated): + extra = {"max_tokens": extracted.get("max_tokens")} if code == "agent_field_truncated" else {} + await once(ctx, EventType.LOG, level="warn", code=code, message=message, fields=names, **extra) total_usage["total_tokens"] = total_usage["input_tokens"] + total_usage["output_tokens"] result = { @@ -941,6 +984,60 @@ def cited_schema(schema: dict[str, Any]) -> dict[str, Any]: return {"title": "cited_fields", **rewrite(schema)} +#: cite_fields 结构化抽取的输出额度。thinking: off 只对 Anthropic 有用：经 OpenAI 兼容接口接入的推理模型 +#: （DeepSeek 系等）关不掉思考，推理也算在这个额度里。以前是 4096：2026-10 一次运行 4 个 Agent 的抽取全部在 +#: 推理阶段就用完了额度（completion_tokens=4096），一个字段都没写出来。节点配了更大的「最大输出 token」就用节点的 +EXTRACT_MAX_TOKENS = 16384 +#: 被截断时额度翻倍重试一次，最多到这里 +EXTRACT_RETRY_CAP = 32768 +#: 服务方嫌额度太大、拒收时依次退到这几档（DeepSeek chat 系只收 8K）。拒收发生在请求刚发出时，不产生用量 +_EXTRACT_FALLBACKS = (8192, 4096) +#: 服务方嫌额度太大时报错里会出现的字样（和 api/copilot.py 改图时退回小额度是同一个判断） +_BUDGET_WORDS = ("max_tokens", "max_completion_tokens", "max_output_tokens", "maximum context length") +#: 截断类报错的原话：openai 的 LengthFinishReasonError（结构化输出解析时 finish_reason=length） +_LENGTH_WORDS = ("length limit was reached", "finish_reason=length", "finish_reason='length'") +#: 两次都被截断时的原因（给人看）。重试、去设置页测试模型都不解决额度问题，不能照通用的模型报错那样说 +TRUNCATED_EXTRACT = ("结构化抽取没有完成：模型的输出额度在推理阶段就用完了（最后一次的额度为 {budget} token），" + "结构化结论没能写出来") +#: agent_field_truncated 的下一步（接在「N 个字段都记为空值」后面） +TRUNCATED_NEXT = ("可以为该 Agent 换用非推理模型（抽取沿用 Agent 的模型），或者调大该 Agent 的" + "「最大输出 token」后重新运行") + + +def truncated_error(error: BaseException | None) -> bool: + """这个异常是不是在说「输出被截断」：openai 的 LengthFinishReasonError，或者 langchain、网关转述的同一件事。 + + 服务方嫌额度太大的 400（报错里也有 max_tokens）不算：那是请求被拒，模型一个字都没写。 + """ + if error is None: + return False + if type(error).__name__ == "LengthFinishReasonError": + return True + text = str(error).lower() + return any(word in text for word in _LENGTH_WORDS) + + +def truncated_reply(raw: Any) -> bool: + """这条回复是不是因为额度用完停下的：OpenAI 系是 finish_reason=length，Anthropic 是 stop_reason=max_tokens。""" + meta = getattr(raw, "response_metadata", None) or {} + reason = meta.get("finish_reason") or meta.get("stop_reason") + return reason in ("length", "max_tokens") + + +def _budget_rejected(error: BaseException) -> bool: + status = getattr(error, "status_code", None) or getattr(getattr(error, "response", None), "status_code", None) + return status in (400, 422) and any(word in str(error).lower() for word in _BUDGET_WORDS) + + +def _error_usage(error: BaseException, model_id: str) -> dict[str, Any]: + """失败的那次调用花掉的用量。截断时 openai 的异常带着 completion.usage：额度是真花出去了，要记账。""" + usage = getattr(getattr(error, "completion", None), "usage", None) + inp = int(getattr(usage, "prompt_tokens", 0) or 0) + out = int(getattr(usage, "completion_tokens", 0) or 0) + return {"input_tokens": inp, "output_tokens": out, "total_tokens": inp + out, + "cost_usd": round(catalog.estimate_cost(model_id, inp, out), 6), "calls": int(bool(inp or out))} + + EXTRACT_SYSTEM = ( "你是数据抽取员：把智能体查到的数据按给定结构填好，每个字段都写出处。\n" "- value 是字段的值；from 写它在哪次查询的哪一格：call 是【证据 Qn】里的编号，row 是行号（从 0 数），" @@ -1184,9 +1281,12 @@ def _shown(value: Any) -> str: def _field_warnings(data: dict[str, Any], evidence: dict[str, Any], - failed: str | None) -> list[tuple[str, str, list[str]]]: + failed: str | None, *, truncated: bool = False) -> list[tuple[str, str, list[str]]]: """核对结果里要让人看见的：(code, message, 字段)。mismatch 和 unresolved 各一条；missing 是模型 - 照实说没查到，值已经是空的，不另发警告。""" + 照实说没查到，值已经是空的，不另发警告。 + + 抽取两次都被截断（truncated）时 code 是 agent_field_truncated：原因是输出额度在推理阶段就用完了， + 下一步是换非推理模型或调大最大输出 token，不是「稍后重试」——和别的抽取失败分开说。""" out: list[tuple[str, str, list[str]]] = [] mismatched = [(p, e) for p, e in evidence.items() if e.get("status") == "mismatch"] if mismatched: @@ -1196,6 +1296,11 @@ def _field_warnings(data: dict[str, Any], evidence: dict[str, Any], f"有 {len(mismatched)} 个字段与查询快照不一致，已按快照取值：{detail}", [p for p, _ in mismatched])) unresolved = [(p, e) for p, e in evidence.items() if e.get("status") == "unresolved"] if unresolved: + if failed and truncated: + out.append(("agent_field_truncated", + f"{failed}。{len(unresolved)} 个字段都记为空值（未以 0 代替）。{TRUNCATED_NEXT}", + [p for p, _ in unresolved])) + return out if failed: message = f"{failed}。{len(unresolved)} 个字段都记为空值（未以 0 代替）" else: diff --git a/backend/tests/test_extract_truncation.py b/backend/tests/test_extract_truncation.py new file mode 100644 index 00000000..89ee80ee --- /dev/null +++ b/backend/tests/test_extract_truncation.py @@ -0,0 +1,208 @@ +"""cite_fields 的结构化抽取：输出被截断时加大额度重试，仍截断就照实说是额度在推理阶段用完了。 + +2026-10 一次运行里 4 个开了 cite_fields 的 Agent 抽取全部失败：抽取用 thinking: off、额度 4096，而经 OpenAI 兼容接口 +接入的推理模型关不掉思考，4096 在推理阶段就用完了，openai 抛 LengthFinishReasonError（「Could not parse response +content as the length limit was reached」）。日志却写「请稍后重试；如持续失败，请到设置测试该模型」——重试、测试都没用。 + +- 额度默认至少 16384，节点配了更大的用节点的 +- 认出「输出被截断」：openai 的 length limit 类错误、finish_reason=length、Anthropic 的 stop_reason=max_tokens； + 被截断时额度翻倍（最多 32768）重试一次 +- 仍然截断：日志 code agent_field_truncated，说清是额度在推理阶段用完了，给出能做的下一步；字段记空值，不以 0 代替 +- 服务方嫌额度太大（400，报错里有 max_tokens 一类字样）：退回小一档的额度重来，不能因为额度提高反而抽不成 +- 非截断类的失败照旧（agent_field_unverified） + +夹具全部合成：门店、会员这类虚构的表。 +""" +from __future__ import annotations + +import json + +import pytest +from langchain_core.messages import AIMessage +from openai import LengthFinishReasonError +from openai.types import CompletionUsage +from openai.types.chat import ChatCompletion + +from app.engine.nodes import llm as llm_mod +from app.engine.runner import run_manager +from tests.test_agent_cited_fields import ( # noqa: F401 - 夹具按名字引入 + EXTRACTED, + FINAL, + SCHEMA, + SQLS, + TOOL, + Script, + engine_up, + events, + graph, + logs, + output_of, + shop, + wait, +) + + +def length_error(prompt: int = 12000, completion: int = 4096) -> LengthFinishReasonError: + """openai 的结构化输出在 finish_reason=length 时抛的那个错（和真实运行里的原话一样）。""" + usage = CompletionUsage(completion_tokens=completion, prompt_tokens=prompt, total_tokens=prompt + completion) + return LengthFinishReasonError(completion=ChatCompletion.model_construct( + id="x", choices=[], created=0, model="m", object="chat.completion", usage=usage)) + + +class BudgetRejected(Exception): + """服务方嫌输出额度太大时的 400（DeepSeek chat 系只收 8K）。""" + + status_code = 400 + + +class Extraction: + """记下每次抽取调用的额度（thinking 关掉的那次 get_chat_model），并按额度决定这次怎么回。 + + outcome(budget) 返回 None 表示照常回 EXTRACTED，抛异常就是这次调用失败。 + """ + + def __init__(self, monkeypatch, outcome): + from app.providers import mock_model + + self.budgets: list[int | None] = [] + real = llm_mod.get_chat_model + + async def spy(session, spec): + if spec.thinking == "off": + self.budgets.append(spec.max_tokens) + return await real(session, spec) + + monkeypatch.setattr(llm_mod, "get_chat_model", spy) + base = Script(monkeypatch) + loop = mock_model.MockChatModel._decide + + def decide(model, messages): + if model.response_format: + outcome(self.budgets[-1]) + return AIMessage(content=json.dumps(EXTRACTED, ensure_ascii=False)) + return loop(model, messages) + + monkeypatch.setattr(mock_model.MockChatModel, "_decide", decide) + self.script = base + + +# -------------------------------------------------------------------------- +# 认出「输出被截断」 +# -------------------------------------------------------------------------- + + +def test_length_errors_are_recognised(): + assert llm_mod.truncated_error(length_error()) + assert llm_mod.truncated_error(RuntimeError("Could not parse response content as the length limit was reached")) + assert not llm_mod.truncated_error(RuntimeError("网关超时")) + assert not llm_mod.truncated_error(BudgetRejected("max_tokens is too large: 16384")) + + +def test_cut_off_replies_are_recognised_for_both_families(): + assert llm_mod.truncated_reply(AIMessage(content="", response_metadata={"finish_reason": "length"})) + assert llm_mod.truncated_reply(AIMessage(content="", response_metadata={"stop_reason": "max_tokens"})) + assert not llm_mod.truncated_reply(AIMessage(content="", response_metadata={"finish_reason": "stop"})) + assert not llm_mod.truncated_reply(None) + + +# -------------------------------------------------------------------------- +# 额度与重试 +# -------------------------------------------------------------------------- + + +async def test_the_default_budget_leaves_room_for_reasoning(monkeypatch): + spy = Extraction(monkeypatch, lambda budget: None) + row = await wait((await run_manager.start(graph=graph(card=False), input_payload={})).id) + assert row.status == "succeeded", row.error + assert spy.budgets == [16384], "推理模型的思考也算在额度里：默认至少 16384" + + +async def test_a_larger_node_budget_is_used(monkeypatch): + spy = Extraction(monkeypatch, lambda budget: None) + row = await wait((await run_manager.start(graph=graph(card=False, max_tokens=24000), input_payload={})).id) + assert row.status == "succeeded", row.error + assert spy.budgets == [24000] + + +async def test_a_cut_off_extraction_is_retried_with_a_larger_budget(monkeypatch): + def outcome(budget): + if budget < 32768: + raise length_error(completion=budget) + + spy = Extraction(monkeypatch, outcome) + row = await wait((await run_manager.start(graph=graph(card=False), input_payload={})).id) + assert row.status == "succeeded", row.error + assert spy.budgets == [16384, 32768], "截断后翻倍重试一次" + bot = await output_of(row.id, "bot") + assert bot["data"]["gmv"] == 600.5 and bot["data_evidence"]["gmv"]["status"] == "verified" + evts = await events(row.id) + assert not logs(evts, "agent_field_truncated") + # EXTRACTED 里 region 指向本节点没有的 Q9：只有它核对不了，别的字段都照常核对 + assert [w["fields"] for w in logs(evts, "agent_field_unverified")] == [["region"]] + # 两次调用各记一行：第一次写明为什么重来 + ends = [e.data for e in evts if e.type == "llm.end" and e.data.get("purpose") == "cite_fields"] + assert len(ends) == 2 and "32768" in ends[0]["error"] and not ends[1].get("error") + # 被截断那次的用量照样记账（花出去的 token 是真的） + assert ends[0]["output_tokens"] == 16384 + + +async def test_cut_off_twice_is_reported_as_truncation_with_fields_empty(monkeypatch): + def outcome(budget): + raise length_error(completion=budget) + + spy = Extraction(monkeypatch, outcome) + row = await wait((await run_manager.start(graph=graph(card=False), input_payload={})).id) + assert row.status == "succeeded", row.error + assert spy.budgets == [16384, 32768], "只重试一次" + bot = await output_of(row.id, "bot") + assert bot["data"] == {k: None for k in SCHEMA["properties"]}, "记空值，不以 0 代替" + assert all(e["status"] == "unresolved" for e in bot["data_evidence"].values()) + evts = await events(row.id) + assert not logs(evts, "agent_field_unverified") + [warn] = logs(evts, "agent_field_truncated") + assert warn["level"] == "warn" and warn["fields"] == list(SCHEMA["properties"]) + message = warn["message"] + assert "推理阶段就用完了" in message and "结构化结论没能写出来" in message + assert "32768" in message and "非推理模型" in message and "最大输出 token" in message + assert "请稍后重试" not in message and "测试该模型" not in message, "重试、测试模型都不解决额度问题" + assert "未以 0 代替" in message + assert warn.get("max_tokens") == 32768 + + +async def test_a_node_budget_at_the_cap_is_not_retried(monkeypatch): + def outcome(budget): + raise length_error(completion=budget) + + spy = Extraction(monkeypatch, outcome) + row = await wait((await run_manager.start(graph=graph(card=False, max_tokens=40000), input_payload={})).id) + assert row.status == "succeeded", row.error + assert spy.budgets == [40000], "额度已经超过重试上限，再调一次也一样" + [warn] = logs(await events(row.id), "agent_field_truncated") + assert "40000" in warn["message"] + + +async def test_a_budget_the_service_rejects_falls_back(monkeypatch): + def outcome(budget): + if budget > 8192: + raise BudgetRejected(f"Invalid max_tokens value, the valid range of max_tokens is [1, 8192]: {budget}") + + spy = Extraction(monkeypatch, outcome) + row = await wait((await run_manager.start(graph=graph(card=False), input_payload={})).id) + assert row.status == "succeeded", row.error + assert spy.budgets == [16384, 8192] + bot = await output_of(row.id, "bot") + assert bot["data_evidence"]["gmv"]["status"] == "verified" + + +async def test_other_failures_are_reported_as_before(monkeypatch): + def outcome(budget): + raise RuntimeError("网关超时") + + spy = Extraction(monkeypatch, outcome) + row = await wait((await run_manager.start(graph=graph(card=False), input_payload={})).id) + assert row.status == "succeeded", row.error + assert spy.budgets == [16384], "不是截断就不重试" + evts = await events(row.id) + assert not logs(evts, "agent_field_truncated") + [warn] = logs(evts, "agent_field_unverified") + assert "网关超时" in warn["message"] and "未以 0 代替" in warn["message"] diff --git a/frontend/src/run/decode.ts b/frontend/src/run/decode.ts index cd668956..f82b4844 100644 --- a/frontend/src/run/decode.ts +++ b/frontend/src/run/decode.ts @@ -784,6 +784,22 @@ function explainLog(code: string | undefined, message: string, d: Record 0 ? d.max_tokens : undefined + return { + title: `模型的输出额度在推理阶段用完，${n ? `${n} 个` : ''}字段记为空值（未以 0 代替）`, + sub: `按出处抽取字段时输出被截断${budget ? `（已加大额度重试，最后一次为 ${formatNumber(budget)} token）` : ''}，` + + '结构化结论没能写出来', + next: '这些字段以空值传给下游，口径卡按缺少输入处理，报告不得据此断言没有数据。请为该 Agent 换用非推理模型' + + '（抽取沿用 Agent 的模型），或调大它的「最大输出 token」后重新运行', + fix: 'canvas', + fixField: 'max_tokens', + } + } case 'metric_incomplete': { // 结构化字段（metrics.py）：metric_name、reason（「基于被截断的查询结果计算（只取回了前 1000 行），结果不完整」）。 // 老运行的日志没有这两个字段：只给不点名的标题，原话留在展开区，不从中文里解析 diff --git a/scripts/check-decode.mjs b/scripts/check-decode.mjs index c3ef2f87..5045adb1 100644 --- a/scripts/check-decode.mjs +++ b/scripts/check-decode.mjs @@ -1481,6 +1481,47 @@ await section('agent 字段按出处核对（可点击证据第二期）：抽 check('「抽取结果格式有误（不是对象）」：同样说「失败，N 个字段记为空值」，原因放副标题，不说「出处不存在」', notObject?.title === '按出处抽取字段失败，2 个字段记为空值（未以 0 代替）' && notObject?.sub === '抽取结果格式有误（不是对象）' && !notObject?.next?.includes('出处在查询结果中不存在'), `${notObject?.title} / ${notObject?.sub} / ${notObject?.next}`) + // 两次抽取都被截断（2026-10 一次运行：推理模型关不掉思考，4096 的额度在推理阶段就用完了）：单独的 code， + // 说清是额度问题、给出能做的下一步，不说「稍后重试」「去设置测试该模型」（llm.py TRUNCATED_EXTRACT、TRUNCATED_NEXT） + const TRUNCATED = '结构化抽取没有完成：模型的输出额度在推理阶段就用完了（最后一次的额度为 32768 token），结构化结论没能写出来。' + + '5 个字段都记为空值（未以 0 代替）。可以为该 Agent 换用非推理模型（抽取沿用 Agent 的模型），或者调大该 Agent 的「最大输出 token」后重新运行' + check('后端发截断警告的说法还是夹具里这一种', llmPy.includes('"agent_field_truncated"') + && llmPy.includes('结构化抽取没有完成：模型的输出额度在推理阶段就用完了（最后一次的额度为 {budget} token），"') + && llmPy.includes('"结构化结论没能写出来"') && llmPy.includes('可以为该 Agent 换用非推理模型（抽取沿用 Agent 的模型），或者调大该 Agent 的"') + && llmPy.includes('已把额度加大到 {bigger} token 重试一次'), '改了 llm.py 的说法就同步改 decode.ts 的 explainLog 和这里的夹具') + const FIELDS5 = ['members', 'stores', 'coupons', 'redeemed', 'rate'] + const cut = flatten(mod.decodeRun([ + E(1, 'node.started', 'fetch', { node_type: 'agent', label: '会员专项' }), + E(2, 'llm.start', 'fetch', { model: 'm1', structured: true, purpose: 'cite_fields', message_count: 5, max_tokens: 16384 }), + E(3, 'llm.end', 'fetch', { agent: '会员专项', model: 'm1', purpose: 'cite_fields', duration_ms: 40000, output_tokens: 16384, + error: '输出被截断：16384 token 的输出额度用完了，结构化结论没有写完，已把额度加大到 32768 token 重试一次' }), + E(4, 'llm.start', 'fetch', { model: 'm1', structured: true, purpose: 'cite_fields', message_count: 5, max_tokens: 32768 }), + E(5, 'llm.end', 'fetch', { agent: '会员专项', model: 'm1', purpose: 'cite_fields', duration_ms: 80000, output_tokens: 32768, + error: '输出被截断：32768 token 的输出额度用完了，结构化结论没有写完' }), + E(6, 'log', 'fetch', { level: 'warn', code: 'agent_field_truncated', message: TRUNCATED, fields: FIELDS5, max_tokens: 32768 }), + E(7, 'node.finished', 'fetch', { duration_ms: 125000 }), + ])) + const tr = cut.find((s) => s.code === 'agent_field_truncated') + check('agent_field_truncated：标题说额度在推理阶段用完、几个字段记为空值', tr?.title === '模型的输出额度在推理阶段用完，5 个字段记为空值（未以 0 代替）', tr?.title) + check('……副标题说抽取时输出被截断、重试到的额度（读 max_tokens 字段）', + tr?.sub === '按出处抽取字段时输出被截断（已加大额度重试，最后一次为 32,768 token），结构化结论没能写出来', tr?.sub) + check('……下一步：换非推理模型或调大最大输出 token；不说稍后重试，不叫人去测试模型', + !!tr?.next?.includes('非推理模型') && !!tr?.next?.includes('「最大输出 token」') && !tr?.next?.includes('稍后重试') + && !tr?.next?.includes('测试') && !!tr?.next?.includes('不得据此断言没有数据'), tr?.next) + check('……入口打开该 Agent 的设置、落到最大输出 token；是提醒（warn），原话留在展开区', + tr?.fix === 'canvas' && tr?.fixField === 'max_tokens' && tr?.level === 'warn' && tr?.detail === TRUNCATED, + `${tr?.fix} ${tr?.fixField} ${tr?.level}`) + const tries = cut.filter((s) => s.kind === 'llm') + check('……两次抽取各成一行，第一次的展开区写明已加大额度重试', tries.length === 2 + && !!tries[0].detail?.includes('已把额度加大到 32768 token 重试一次') && !!tries[1].detail?.includes('结构化结论没有写完'), + tries.map((s) => s.detail).join(' | ')) + check('……节点本身照样成功', cut.find((s) => s.kind === 'node')?.status === 'done') + const bareCut = flatten(mod.decodeRun([E(1, 'node.started', 'x', { node_type: 'agent' }), + E(2, 'log', 'x', { level: 'warn', code: 'agent_field_truncated', message: '结构化抽取没有完成。3 个字段都记为空值（未以 0 代替）' })])) + .find((s) => s.code === 'agent_field_truncated') + check('……缺 fields、max_tokens 字段时字段数从原话里认，不出 undefined', + bareCut?.title === '模型的输出额度在推理阶段用完，3 个字段记为空值（未以 0 代替）' + && bareCut?.sub === '按出处抽取字段时输出被截断，结构化结论没能写出来', `${bareCut?.title} / ${bareCut?.sub}`) const node = mod.decodeRun([E(1, 'node.started', 'fetch', { node_type: 'agent' }), E(2, 'node.finished', 'fetch', { duration_ms: 1 })]) check('对照：普通 agent 节点不多出任何行', flatten(node).filter((s) => s.kind !== 'node').length === 0) const odd = flatten(mod.decodeRun([E(1, 'node.started', 'x', { node_type: 'agent' }), From bf98ce16b8d0b8e486426f14a27cc474960e8905 Mon Sep 17 00:00:00 2001 From: ThearchyHelios Date: Sun, 4 Oct 2026 11:14:41 +0800 Subject: [PATCH 2/5] =?UTF-8?q?=E8=BF=90=E8=A1=8C=E5=A4=8D=E6=A0=B8?= =?UTF-8?q?=E6=8A=8A=20agent=5Ffield=5Ftruncated=20=E7=AE=97=E4=BD=9C?= =?UTF-8?q?=E7=BC=BA=E5=8F=A3=EF=BC=88degraded=EF=BC=89=EF=BC=8C=E5=92=8C?= =?UTF-8?q?=20agent=5Ffield=5Funverified=20=E5=90=8C=E4=B8=80=E6=A1=A3?= =?UTF-8?q?=EF=BC=9A=E5=AD=97=E6=AE=B5=E9=83=BD=E8=AE=B0=E7=A9=BA=E5=80=BC?= =?UTF-8?q?=E3=80=81=E7=AD=94=E6=A1=88=E8=83=BD=E7=94=A8=EF=BC=8C=E7=BC=BA?= =?UTF-8?q?=E5=8F=A3=E6=98=AF=E8=BE=93=E5=87=BA=E9=A2=9D=E5=BA=A6=E5=9C=A8?= =?UTF-8?q?=E6=8E=A8=E7=90=86=E9=98=B6=E6=AE=B5=E7=94=A8=E5=AE=8C=E4=BA=86?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- backend/app/engine/review.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/backend/app/engine/review.py b/backend/app/engine/review.py index 1f671611..594ff9c2 100644 --- a/backend/app/engine/review.py +++ b/backend/app/engine/review.py @@ -95,6 +95,8 @@ WARN_CODES: dict[str, tuple[str, str]] = { # （记成空值，不兜底成 0）。答案能用，缺口要说清是哪一种 "agent_field_mismatch": ("agent_field_mismatch", DEGRADED), "agent_field_unverified": ("agent_field_unverified", DEGRADED), + # 结构化抽取两次都被截断（输出额度在推理阶段就用完了）：字段都记空值，答案能用，缺口要说清是额度问题 + "agent_field_truncated": ("agent_field_truncated", DEGRADED), # 结论句裁判（claims: judge）：触顶、没跑成的句子记为未裁判，出具不能判完整——是缺口，要说明 "judge_limit": ("judge_limit", DEGRADED), "judge_failed": ("judge_failed", DEGRADED), From 4e876ff9d816f2f4f9a9569036c82eb3e3e921f0 Mon Sep 17 00:00:00 2001 From: ThearchyHelios Date: Sun, 4 Oct 2026 11:17:13 +0800 Subject: [PATCH 3/5] =?UTF-8?q?=E5=86=99=E4=BD=9C=E8=80=85=E7=9A=84?= =?UTF-8?q?=E5=87=BA=E5=A4=84=E6=B8=85=E5=8D=95=E6=8C=89=E5=86=99=E4=BD=9C?= =?UTF-8?q?=E6=A8=A1=E5=9E=8B=E7=9A=84=E7=AA=97=E5=8F=A3=E5=AE=9A=E7=AF=87?= =?UTF-8?q?=E5=B9=85=EF=BC=8C=E6=94=BE=E4=B8=8D=E4=B8=8B=E6=97=B6=E5=88=86?= =?UTF-8?q?=E4=BC=98=E5=85=88=E7=BA=A7=E3=80=81=E6=8C=89=E6=9D=A5=E6=BA=90?= =?UTF-8?q?=E5=9D=87=E8=A1=A1=EF=BC=8C=E7=9C=81=E7=95=A5=E4=BA=86=E4=BB=80?= =?UTF-8?q?=E4=B9=88=E7=85=A7=E5=AE=9E=E8=AF=B4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 一次真实运行里证据台账有上百次查询（4 个 Agent 查询次数悬殊），清单写死 12000 字，十万字以上的目录被截到一万两千字以内，只有前二十来次查询进了写作者的视野；查询按编号排，前面的 Agent 占满了预算，有的 Agent 一次都没露面。 - 预算：写作模型上下文窗口的 40%（字数按 1 字约 1 token 保守算），窗口先看模型接入里给这个模型登记的 context，再看模型目录；拿不到按 128K（和 Agent 护栏同一个保守值），即 51200 字；下限 12000、上限 160000 - 第一优先：各 Agent 经按出处核对交来的结构化字段（换成报告目录里的全局编号，值按单元格引用的渲染规则写，给出可照抄的引用标记）、口径卡指标和运行输入，一律列出；结构化结论为空的来源写明不代表没有数据 - 查询按产出它的节点分组、轮流展开：字段引用到的查询先展开，再给每次查询列前 3 行，有余量再补到 20 行；表和字段、知识库检索最多占预算的四分之一 - 没展开的查询列出编号和一行摘要（表、行数）；约定只能在句末写 [[see:Qn]] 作依据，不猜行列、不凭摘要写数，数字照样逐字核对 - 清单开头明说目录有省略、各来源各省略了几次；报告节点发 report_catalog_trimmed 警告（带 queries、omitted、nodes），时间线给它专门的标题和下一步，运行复核算作缺口 - 放得下时清单和以前一字不差（多出的只有结构化字段那一节） --- backend/app/engine/evidence.py | 333 +++++++++++++++++-- backend/app/engine/nodes/report.py | 74 ++++- backend/app/engine/review.py | 2 + backend/tests/fixtures/evidence/store_run.py | 106 ++++++ backend/tests/test_report_catalog_budget.py | 209 ++++++++++++ frontend/src/run/decode.ts | 16 + scripts/check-copy.mjs | 5 +- scripts/check-decode.mjs | 20 ++ 8 files changed, 734 insertions(+), 31 deletions(-) create mode 100644 backend/tests/fixtures/evidence/store_run.py create mode 100644 backend/tests/test_report_catalog_budget.py diff --git a/backend/app/engine/evidence.py b/backend/app/engine/evidence.py index 62ebaf79..02a62c8b 100644 --- a/backend/app/engine/evidence.py +++ b/backend/app/engine/evidence.py @@ -47,7 +47,9 @@ import json import math import re import unicodedata +from collections import Counter from collections.abc import Callable +from dataclasses import dataclass, field from decimal import ROUND_HALF_UP, Context, Decimal from typing import Any, Literal, TypedDict @@ -57,6 +59,7 @@ from app.data.names import FILLERS, name_key from app.data.provenance_types import DOC_PROVENANCE from app.data.tabular import UNSHAPED_NOTE from app.engine.expressions import CellError, cell_value, column_kind, locate_cell +from app.engine.guards import FALLBACK_CONTEXT from app.engine.issuance import _tolerance, extract_numbers, number_allowance from app.engine.labels import option_label @@ -3094,10 +3097,12 @@ def _sql_check_note(artifact: Any, snaps: _Snapshots) -> str | None: "引用时须如实说明，不要当作确定的结论）") -def _query_prompt(entry: dict[str, Any], snaps: _Snapshots, cells_allowed: bool) -> list[str]: +def _query_prompt(entry: dict[str, Any], snaps: _Snapshots, cells_allowed: bool, + shown_rows: int = _PROMPT_ROWS) -> list[str]: """一次查询在写作目录里的样子：编号、来源、列，以及能引用的行（行号 + 渲染后的值）。 - 值按 [[v:]] 的渲染规则写出来：写作者在这里看到的，就是引用之后报告里显示的字。 + 值按 [[v:]] 的渲染规则写出来：写作者在这里看到的，就是引用之后报告里显示的字。shown_rows 是列出几行： + 目录放不下时先给每次查询列前几行（_PROMPT_COMPACT_ROWS），再补到 _PROMPT_ROWS。 """ alias = entry["alias"] cols = entry.get("columns") or [] @@ -3115,7 +3120,7 @@ def _query_prompt(entry: dict[str, Any], snaps: _Snapshots, cells_allowed: bool) columns = [str(c) for c in snapshot.get("columns") or []][:_PROMPT_COLS] rows = snapshot["rows"] example = None - for r, record in enumerate(rows[:_PROMPT_ROWS]): + for r, record in enumerate(rows[:shown_rows]): cells = [] for c in columns: try: @@ -3130,22 +3135,78 @@ def _query_prompt(entry: dict[str, Any], snaps: _Snapshots, cells_allowed: bool) lines.append(f" r{r}：" + "，".join(cells)) if not rows: lines.append(" （0 行，没有可以引用的格）") - elif len(rows) > _PROMPT_ROWS: - lines.append(f" …共 {len(rows)} 行，这里只列出前 {_PROMPT_ROWS} 行；后面的行也能按行号引用") + elif len(rows) > shown_rows: + lines.append(f" …共 {len(rows)} 行，这里只列出前 {shown_rows} 行；后面的行也能按行号引用") if rows and columns: lines.append(f" 例：[[v:{alias}.r0.{example or columns[0]}]]") return lines -def catalog_prompt(catalog: dict[str, Any], *, budget: int = 12000, cells_allowed: bool = True, - loader: Loader | None = None) -> str: - """证据目录的文字版，放进写作者的 prompt。超过预算就截断，并照实说截断了。 +#: 写作目录的字数预算（catalog_budget）按写作模型的上下文窗口定，不再写死 12000： +#: - 占窗口的 40%。其余留给写作规则和指令（约 4 千字）、重写轮次（每轮把上一稿和违规清单追加进对话，最多 3 轮）、 +#: 推理模型的思考和报告正文（输出额度）。字数按 1 字 ≈ 1 token 算：中文一个字约 0.6–1 个 token，目录里的数字、 +#: 列名、标点每个字更少，按 1:1 算偏保守，实际占用低于 40% +#: - 拿不到窗口时按 128K（和 Agent 护栏的 FALLBACK_CONTEXT 同一个保守值），即 51200 字。原来的 12000 字只放得下 +#: 20 多次查询：2026-10 一次上百次查询的运行，十万字以上的目录被截到一万两千字以内，写作者只看到前二十来次 +#: - 下限 12000：窗口很小（32K 以下）的本地模型维持原来的篇幅；上限 160000：百万窗口的模型每写一稿、每重写一次 +#: 都要重发整份目录，花费随之上涨，而 160000 字已经放得下那次运行的查询全部展开 +CATALOG_WINDOW_SHARE = 0.4 +CATALOG_MIN_CHARS = 12_000 +CATALOG_MAX_CHARS = 160_000 +#: 目录放不下时，先给每次查询列几行 +_PROMPT_COMPACT_ROWS = 3 +#: 放不下时表和字段、知识库检索两节合起来最多占预算的几分之一（剩下的给查询） +_PROMPT_TAIL_SHARE = 4 +_PROMPT_CUT = "\n…（目录太长，后面的省略了）" - 查询结果会列出能引用的行（要读快照）；cells_allowed=False 时只列编号，说明只能当依据。 - 沙箱代码节点不列：它的产出不能引用。 + +def catalog_budget(window: int | None) -> int: + """写作目录的字数预算：写作模型上下文窗口（token）的 40%，夹在上下限之间；拿不到窗口按 FALLBACK_CONTEXT。""" + size = window if isinstance(window, int) and not isinstance(window, bool) and window > 0 else FALLBACK_CONTEXT + return max(CATALOG_MIN_CHARS, min(CATALOG_MAX_CHARS, int(size * CATALOG_WINDOW_SHARE))) + + +DEFAULT_CATALOG_BUDGET = catalog_budget(None) + +#: 目录有省略时，开头跟在省略情况后面的约定（给模型看）。和数字核对一致：没展开的查询只能当依据，数照样逐字核对 +OMITTED_RULES = ( + "没展开的查询照样在证据台账里，只是这里没有列出它的数据：\n" + "- 不要据此断言「没有查到」「不存在」「缺失」「没有数据」，只能写「本报告未覆盖」，并说明依据不足；\n" + "- 要用没展开的查询，只能在句末写 [[see:Qn]] 作依据（Qn 换成它的编号）；不要写 [[v:]]、[[table:]] 去猜它的行和列，" + "也不要凭摘要写任何数字——数字照样逐字核对，没有出处的数会被打回重写。结构化结论里写好的引用标记可以照抄。" +) +#: 结构化结论为空的来源后面跟的那句（给模型看） +EMPTY_SOURCE_PROMPT = ("结构化结论为空（字段都记为空值）。这不代表没有数据：不得据此断言没有查到、不存在或缺失，" + "只能写本报告未覆盖") + + +@dataclass +class WriterCatalog: + """给写作者的出处清单（writer_catalog）：文字，以及省略了什么。 + + - trimmed：放不下、有省略；nodes 是各来源的查询数 [{node_id, title, total, full, compact, omitted}]， + full 列出前 20 行、compact 只列前几行、omitted 只列编号和摘要；omitted 是只列摘要的查询编号 + - empty_sources：结构化结论为空的来源（Agent 的名字） + 清单有省略、或有来源的结构化结论为空时，报告节点加写作规则（ABSENCE_RULE）；有省略时组装文档另查缺失断言 """ - snaps = _Snapshots(loader) - lines = ["可引用的证据（写数字只能用下面的引用标记，系统会换成真实数值）："] + + text: str + budget: int + queries: int = 0 + trimmed: bool = False + nodes: list[dict[str, Any]] = field(default_factory=list) + omitted: list[str] = field(default_factory=list) + empty_sources: list[str] = field(default_factory=list) + + @property + def guarded(self) -> bool: + """写作者看不全证据：缺失断言没有依据。""" + return self.trimmed or bool(self.empty_sources) + + +def _metric_prompt(catalog: dict[str, Any]) -> list[str]: + """口径卡指标和运行输入在写作目录里的样子。""" + lines: list[str] = [] groups: dict[tuple[str, str, str], list[dict[str, Any]]] = {} for entry in catalog.values(): if entry.get("kind") == "metric": @@ -3172,23 +3233,249 @@ def catalog_prompt(catalog: dict[str, Any], *, budget: int = 12000, cells_allowe if inputs: lines.append("\n运行输入：") lines.extend(f"- [[{e['alias']}]] = {e.get('rendered')}" for e in inputs) + return lines + + +def _dig_path(data: Any, path: str) -> Any: + for key in path.split("."): + data = data.get(key) if isinstance(data, dict) else None + return data + + +def _field_prompt(path: str, value: Any, ev: dict[str, Any], aliases: dict[str, str], snaps: _Snapshots, + cells_allowed: bool) -> str: + """一个结构化字段在写作目录里的那一行：值（按 [[v:]] 的渲染规则）和引用它的标记。""" + status = ev.get("status") + if status == "missing": + return f"- {path}：抽取时没有找到这个值（记为空，不代表库里没有）" + if status not in ("verified", "mismatch"): + return f"- {path}：出处核对不了（记为空）" + locator = ev.get("locator") if isinstance(ev.get("locator"), dict) else {} + alias = aliases.get(str(ev.get("artifact") or "")) + if alias is None: + return f"- {path}：所依据的查询不在这份目录里，不能引用" + if not cells_allowed: + return f"- {path}：出自 {alias}，只能在句末写 [[see:{alias}]] 作依据" + if isinstance(locator.get("rows"), list) and len(locator["rows"]) == 2: + first, last = locator["rows"] + named = locator.get("columns") if isinstance(locator.get("columns"), dict) else {"": locator.get("column")} + cols = ",".join(dict.fromkeys(str(c) for c in named.values() if c)) + many = f"（共 {last - first + 1} 行，整表最多 {TABLE_MAX_ROWS} 行，要分成几张表）" \ + if last - first + 1 > TABLE_MAX_ROWS else "" + return (f"- {path}：数组，出自 {alias} 第 {first}–{last} 行，可以单独一行写 " + f"[[table:{alias} cols={cols} rows={first}-{last}]]{many}") + row, column = locator.get("row"), locator.get("column") + shown = MISSING + snapshot, why = snaps.get(alias, ev.get("artifact")) + try: + if why: + raise CellError(why) + raw, column = locate_cell(snapshot, row, column) + shown = render_cell(raw, kind=column_kind(snapshot, column)) + except (CellError, RenderError): + try: + shown = render_cell(value) + except RenderError: + pass + return f"- {path} = {shown}：[[v:{alias}.r{row}.{column}]]" + + +def _fields_prompt(sources: list[dict[str, Any]], catalog: dict[str, Any], snaps: _Snapshots, + cells_allowed: bool) -> tuple[list[str], list[str], list[str]]: + """各 Agent 经按出处核对交来的结构化字段：(写作目录里的几行, 结构化结论为空的来源, 字段引用到的查询编号)。 + + 字段的出处记的是 Agent 自己的编号（它的第几次查询），这里换成报告目录里的全局编号：按快照工件认，同一个节点的 + 优先（工件内容寻址，两次一模一样的查询共用一份快照，指哪一次都是同一格）。 + """ + mine: dict[tuple[str, str], str] = {} + anyone: dict[str, str] = {} + for e in catalog.values(): + if e.get("kind") == "query": + mine.setdefault((str(e.get("node_id") or ""), str(e.get("artifact") or "")), e["alias"]) + anyone.setdefault(str(e.get("artifact") or ""), e["alias"]) + lines: list[str] = [] + empty: list[str] = [] + cited: list[str] = [] + for source in sources: + evidence = source.get("evidence") + if not isinstance(evidence, dict) or not evidence: + continue + node_id = str(source.get("node_id") or "") + title = str(source.get("title") or node_id) + data = source.get("data") if isinstance(source.get("data"), dict) else {} + if all(_dig_path(data, path) is None for path in evidence): + empty.append(title) + lines.append(f"「{title}」：{EMPTY_SOURCE_PROMPT}") + continue + aliases = {artifact: mine.get((node_id, artifact)) or alias for artifact, alias in anyone.items()} + lines.append(f"「{title}」：") + for path, ev in evidence.items(): + ev = ev if isinstance(ev, dict) else {} + lines.append(_field_prompt(path, _dig_path(data, path), ev, aliases, snaps, cells_allowed)) + if ev.get("status") in ("verified", "mismatch") and (alias := aliases.get(str(ev.get("artifact") or ""))): + cited.append(alias) + if not lines: + return [], [], [] + return (["\n各 Agent 交来的结构化结论（系统已逐格核对到查询快照；要写这些数，用后面的引用标记）：", *lines], + empty, list(dict.fromkeys(cited))) + + +def _query_brief(entry: dict[str, Any], tables: list[str]) -> str: + """没展开的查询在目录里的一行摘要：编号、查的表（没有表结构时写工具名）、行数。""" + rows = entry.get("rows") + what = ("、".join(tables[:3]) + (f" 等 {len(tables)} 张表" if len(tables) > 3 else "")) if tables \ + else str(entry.get("tool") or entry.get("source") or "查询") + size = f"{rows} 行" if isinstance(rows, int) else "行数不明" + return f" - {entry['alias']}：{what}，{size}" + ("（结果已截断）" if entry.get("truncated") else "") + + +def _cut_lines(lines: list[str], limit: int, note: str) -> list[str]: + """从头取到放得下 limit 个字为止，后面换成一句 note。""" + out: list[str] = [] + used = len(note) + 1 + for line in lines: + if used + len(line) + 1 > limit: + return [*out, note] + out.append(line) + used += len(line) + 1 + return out + + +def writer_catalog(catalog: dict[str, Any], *, budget: int = DEFAULT_CATALOG_BUDGET, cells_allowed: bool = True, + loader: Loader | None = None, fields: list[dict[str, Any]] | None = None, + titles: dict[str, str] | None = None) -> WriterCatalog: + """证据目录的文字版，放进写作者的 prompt，连同省略了什么（WriterCatalog）。 + + 查询结果会列出能引用的行（要读快照）；cells_allowed=False 时只列编号，说明只能当依据。 + 沙箱代码节点不列：它的产出不能引用。fields 是上游 Agent 经按出处核对交来的结构化字段 + [{node_id, title, data, evidence}]，titles 是节点在画布上的名字（分组标题用）。 + + 放得下就整份给（没有结构化字段时和以前一字不差）。放不下时按优先级、按来源均衡（_trimmed_catalog）： + 口径卡指标、运行输入、结构化字段一律列出；查询按产出它的节点分组、轮流展开；没展开的列编号和摘要； + 开头照实说目录有省略、各来源省略了几次。 + """ + snaps = _Snapshots(loader) + head = ["可引用的证据（写数字只能用下面的引用标记，系统会换成真实数值）："] + field_lines, empty, cited = _fields_prompt(fields or [], catalog, snaps, cells_allowed) + first = [*_metric_prompt(catalog), *field_lines] queries = [e for e in catalog.values() if e.get("kind") == "query"] - if queries: - lines.append(f"\n{CELL_RULES}" if cells_allowed else - "\n查询结果（这次出具不允许直接引用单元格，只能写在 [[see:Q1]] 这样的依据里）：") - for e in queries: - lines.extend(_query_prompt(e, snaps, cells_allowed)) + rules = ([f"\n{CELL_RULES}" if cells_allowed else + "\n查询结果（这次出具不允许直接引用单元格，只能写在 [[see:Q1]] 这样的依据里）："] if queries else []) + tail: list[str] = [] if (index := snaps.entities(catalog)).active: - lines.extend(_entity_prompt(catalog, partial=index.partial, snaps=snaps)) + tail.extend(_entity_prompt(catalog, partial=index.partial, snaps=snaps)) retrievals = [e for e in catalog.values() if e.get("kind") == "retrieval"] if retrievals: - lines.append(f"\n{QUOTE_RULES}") + tail.append(f"\n{QUOTE_RULES}") for e in retrievals: - lines.extend(_retrieval_prompt(e, snaps)) - text = "\n".join(lines) + tail.extend(_retrieval_prompt(e, snaps)) + full = {e["alias"]: _query_prompt(e, snaps, cells_allowed) for e in queries} + text = "\n".join([*head, *first, *rules, *(line for e in queries for line in full[e["alias"]]), *tail]) + if len(text) <= budget: + return WriterCatalog(text=text, budget=budget, queries=len(queries), empty_sources=empty) + return _trimmed_catalog(catalog, head, first, rules, queries, full, tail, budget=budget, snaps=snaps, + cells_allowed=cells_allowed, titles=titles or {}, empty=empty, pinned=cited) + + +def _trimmed_catalog(catalog: dict[str, Any], head: list[str], first: list[str], rules: list[str], + queries: list[dict[str, Any]], full: dict[str, list[str]], tail: list[str], *, budget: int, + snaps: _Snapshots, cells_allowed: bool, titles: dict[str, str], empty: list[str], + pinned: list[str]) -> WriterCatalog: + """放不下时的目录。以前从头截断：查询按编号排，前面的节点占满预算，后面的节点一次都不露面。 + + 1. 口径卡指标、运行输入、结构化字段一律列出（第一优先） + 2. 表和字段、知识库检索两节合起来最多占预算的 1/_PROMPT_TAIL_SHARE + 3. 每次查询先只占一行摘要；结构化字段引用到的查询先展开（pinned），再按产出它的节点分组、轮流展开： + 第一轮每次列前 _PROMPT_COMPACT_ROWS 行，第二轮补到 _PROMPT_ROWS 行，放不下为止 + 4. 开头写明目录有省略、各来源省略了几次，以及没展开的查询怎么用（OMITTED_RULES） + 最后整份量一次：超了就把最后展开的退回去；指标和字段本身就超（极少见）才从尾部截断。 + """ + if tail and len("\n".join(tail)) > budget // _PROMPT_TAIL_SHARE: + tail = _cut_lines(tail, budget // _PROMPT_TAIL_SHARE, "…（表和字段、知识库检索太长，后面的省略了）") + tables: dict[str, list[str]] = {} + for e in catalog.values(): + if e.get("kind") == "table": + for alias in e.get("queries") or []: + tables.setdefault(alias, []).append(str(e.get("name"))) + groups: dict[str, list[dict[str, Any]]] = {} + for e in queries: + groups.setdefault(str(e.get("node_id") or ""), []).append(e) + compact = {e["alias"]: _query_prompt(e, snaps, cells_allowed, _PROMPT_COMPACT_ROWS) for e in queries} + brief = {e["alias"]: [_query_brief(e, tables.get(e["alias"], []))] for e in queries} + shapes = {"brief": brief, "compact": compact, "full": full} + tier = {e["alias"]: "brief" for e in queries} + + def rows() -> list[dict[str, Any]]: + out = [] + for nid, entries in groups.items(): + got = Counter(tier[e["alias"]] for e in entries) + out.append({"node_id": nid, "title": titles.get(nid) or nid or "其他", "total": len(entries), + "full": got["full"], "compact": got["compact"], "omitted": got["brief"]}) + return out + + def assemble() -> str: + stats = rows() + per = "、".join(f"{r['title']} {r['total']} 次（展开 {r['full'] + r['compact']} 次，省略 {r['omitted']} 次）" + for r in stats) + n = {k: sum(r[k] for r in stats) for k in ("full", "compact", "omitted")} + notice = (f"注意：证据太多，这份目录有省略（篇幅上限约 {budget} 字）。共 {len(queries)} 次查询：" + f"{n['full']} 次列出前 {_PROMPT_ROWS} 行，{n['compact']} 次只列前 {_PROMPT_COMPACT_ROWS} 行，" + f"{n['omitted']} 次只列编号和摘要。各来源：{per}。") + body = [*head, notice, OMITTED_RULES, *first, *rules] + for r, entries in zip(stats, groups.values()): + body.append(f"\n来自「{r['title']}」的查询（共 {r['total']} 次，展开 {r['full'] + r['compact']} 次）：") + for e in entries: + if tier[e["alias"]] != "brief": + body.extend(shapes[tier[e["alias"]]][e["alias"]]) + if r["omitted"]: + body.append(f" 没展开（{r['omitted']} 次，只列编号和摘要）：") + body.extend(brief[e["alias"]][0] for e in entries if tier[e["alias"]] == "brief") + return "\n".join([*body, *tail]) + + def size(lines: list[str]) -> int: + return sum(len(line) + 1 for line in lines) + + # 省略情况那句里的数字会变长几位：留一点余量，最后再整份量一次 + remaining = budget - len(assemble()) - 8 * (len(groups) + 3) + upgraded: list[tuple[str, str]] = [] + for alias in pinned: + if tier.get(alias) == "brief" and (delta := size(compact[alias]) - size(brief[alias])) <= remaining: + tier[alias] = "compact" + remaining -= delta + upgraded.append((alias, "brief")) + for target, source in (("compact", "brief"), ("full", "compact")): + moved = True + while moved: + moved = False + for entries in groups.values(): + for e in entries: + alias = e["alias"] + if tier[alias] != source: + continue + delta = size(shapes[target][alias]) - size(shapes[source][alias]) + if delta <= remaining: + tier[alias] = target + remaining -= delta + upgraded.append((alias, source)) + moved = True + break + text = assemble() + while len(text) > budget and upgraded: + alias, source = upgraded.pop() + tier[alias] = source + text = assemble() if len(text) > budget: - text = text[:budget].rsplit("\n", 1)[0] + "\n…（目录太长，后面的省略了）" - return text + text = text[:budget - len(_PROMPT_CUT)].rsplit("\n", 1)[0] + _PROMPT_CUT + return WriterCatalog(text=text, budget=budget, queries=len(queries), trimmed=True, nodes=rows(), + omitted=[e["alias"] for e in queries if tier[e["alias"]] == "brief"], empty_sources=empty) + + +def catalog_prompt(catalog: dict[str, Any], *, budget: int = DEFAULT_CATALOG_BUDGET, cells_allowed: bool = True, + loader: Loader | None = None, fields: list[dict[str, Any]] | None = None, + titles: dict[str, str] | None = None) -> str: + """证据目录的文字版（writer_catalog 的文字）。超过预算时分优先级、按来源均衡，并照实说省略了什么。""" + return writer_catalog(catalog, budget=budget, cells_allowed=cells_allowed, loader=loader, fields=fields, + titles=titles).text def unshaped_table(entry: dict[str, Any], fetch: Callable[[Any], Any]) -> bool: diff --git a/backend/app/engine/nodes/report.py b/backend/app/engine/nodes/report.py index 1fcd50c4..a7d7cabc 100644 --- a/backend/app/engine/nodes/report.py +++ b/backend/app/engine/nodes/report.py @@ -50,11 +50,13 @@ from app.engine.evidence import ( MARKER_RULES, SEE_AND_CODE_RULES, StreamRenderer, + WriterCatalog, build_catalog, - catalog_prompt, + catalog_budget, compose_doc, describe_violations, ledger_enabled, + writer_catalog, ) from app.engine.nodes.llm import _invoke_streaming, _model_spec, _report_call from app.engine.schema import ( @@ -70,7 +72,8 @@ from app.engine.schema import ( ) from app.engine.state import GraphState, message_text, thinking_text from app.engine.toolcalls import leaked_markup, markup_warning -from app.providers.factory import ProviderNotConfigured, get_chat_model +from app.providers import catalog as model_catalog +from app.providers.factory import ModelSpec, ProviderNotConfigured, get_chat_model, resolve_provider ON_VIOLATION = ("fail", "flag") NUMBERS = ("strict", "off") @@ -140,6 +143,57 @@ def report_catalog(state: GraphState, spec: GraphSpec, node: GraphNode) -> dict[ return catalog +def report_fields(state: GraphState, spec: GraphSpec, node: GraphNode) -> list[dict[str, Any]]: + """上游 Agent 经按出处核对交来的结构化字段：[{node_id, title, data, evidence}]，按画布上的顺序。 + + 范围和 report_catalog 收查询的范围一致：祖先节点，写了 evidence_from 就只收那几个。只进写作目录（第一优先， + 放不下也列出），不进目录本身：出口复核按目录重建，字段的引用落到的是查询单元格，和以前一样核对。 + """ + sources = _setting(spec, node, "evidence_from", "ancestors") + if isinstance(sources, str) and sources != "ancestors": + sources = [sources] + keep = {str(s) for s in sources} if isinstance(sources, list) else None + allowed = _ancestors(spec, node.id) + outputs = state.get("nodes") or {} + found: list[dict[str, Any]] = [] + for other in spec.nodes: + result = outputs.get(other.id) + if other.id not in allowed or (keep is not None and other.id not in keep) or not isinstance(result, dict): + continue + if isinstance(result.get("data_evidence"), dict) and result["data_evidence"]: + found.append({"node_id": other.id, "title": other.title or other.id, "data": result.get("data"), + "evidence": result["data_evidence"]}) + return found + + +async def model_window(session: Any, spec: ModelSpec, model_id: str) -> int | None: + """写作模型的上下文窗口（token）：模型接入里给这个模型登记的 context 优先，其次模型目录 + （providers/catalog.py）；都没有返回 None，由 catalog_budget 取保守值。""" + try: + provider = await resolve_provider(session, spec.provider, spec.model or model_id) + except ProviderNotConfigured: + provider = None + for entry in (provider.models or []) if provider is not None else []: + if isinstance(entry, dict) and entry.get("id") == model_id: + raw = entry.get("context") + try: + value = int(raw) if not isinstance(raw, bool) and raw not in (None, "") else 0 + except (TypeError, ValueError): + value = 0 + if value > 0: + return value + return model_catalog.find_context(model_id) + + +def _trimmed_message(writer: WriterCatalog) -> str: + """写作目录有省略时的那条警告（给人看）：各来源省略了几次，写作模型看不到哪些明细。""" + per = "、".join(f"{r['title']}省略 {r['omitted']} 次" for r in writer.nodes if r["omitted"]) + shown = writer.queries - len(writer.omitted) + return (f"证据目录超出篇幅（约 {writer.budget} 字，按写作模型的上下文窗口确定）：{writer.queries} 次查询中 " + f"{shown} 次列出了数据，{len(writer.omitted)} 次只列编号和摘要（{per}）。写作模型看不到这些查询的明细，" + "报告不得据此断言没有数据") + + def report_entities(spec: GraphSpec, node: GraphNode) -> str: """报告节点的实体层：link（默认）/ off。出口复核调 verify_doc 时传 entities=(这个值 == "link")。 @@ -351,8 +405,7 @@ def _repair_request(violations: list[dict[str, Any]]) -> str: ) -def _messages(ctx: NodeContext, state: GraphState, catalog: dict[str, Any], cells: bool = True, - claims: str = "off") -> list[BaseMessage]: +def _messages(ctx: NodeContext, state: GraphState, writer: WriterCatalog, claims: str = "off") -> list[BaseMessage]: system = ctx.render_str(ctx.cfg("system", ""), state) instructions = ctx.render_str(ctx.cfg("instructions", ""), state).strip() \ or "根据下面的证据写一份简洁的报告，先总后分。" @@ -363,7 +416,7 @@ def _messages(ctx: NodeContext, state: GraphState, catalog: dict[str, Any], cell return [ SystemMessage(content="\n\n".join(p for p in (system, ROLE) if p)), HumanMessage(content=( - f"{instructions}\n\n{catalog_prompt(catalog, cells_allowed=cells)}\n\n{rules}\n\n" + f"{instructions}\n\n{writer.text}\n\n{rules}\n\n" "只输出报告正文（Markdown），不要写撰写说明。" )), ] @@ -392,6 +445,8 @@ async def run_report(state: GraphState, ctx: NodeContext) -> dict[str, Any]: model, model_id = await get_chat_model(session, _model_spec(ctx)) except ProviderNotConfigured as e: raise NodeError(ctx.node.id, not_configured(e)) from e + # 写作目录的篇幅按这个模型的上下文窗口定（evidence.catalog_budget） + window = await model_window(session, _model_spec(ctx), model_id) spec = ctx.run.spec catalog = report_catalog(state, spec, ctx.node) @@ -408,7 +463,14 @@ async def run_report(state: GraphState, ctx: NodeContext) -> dict[str, Any]: if warning := _no_evidence(catalog, cells): ctx.emit(EventType.LOG, level="warn", code="report_no_evidence", message=warning) - messages = _messages(ctx, state, catalog, cells, claims) + writer = writer_catalog(catalog, budget=catalog_budget(window), cells_allowed=cells, + fields=report_fields(state, spec, ctx.node), + titles={n.id: n.title or n.id for n in spec.nodes}) + if writer.trimmed: + ctx.emit(EventType.LOG, level="warn", code="report_catalog_trimmed", message=_trimmed_message(writer), + budget=writer.budget, window=window, queries=writer.queries, omitted=len(writer.omitted), + nodes=[{k: r[k] for k in ("node_id", "title", "total", "omitted")} for r in writer.nodes]) + messages = _messages(ctx, state, writer, claims) started = time.perf_counter() spent: list[dict[str, Any]] = [] nudged = False diff --git a/backend/app/engine/review.py b/backend/app/engine/review.py index 594ff9c2..98e0d36d 100644 --- a/backend/app/engine/review.py +++ b/backend/app/engine/review.py @@ -89,6 +89,8 @@ WARN_CODES: dict[str, tuple[str, str]] = { "report_repair": ("report_repair", DEGRADED), # 报告撰写节点的上游没有可引用的口径卡指标：报告里的数都没有出处 "report_no_evidence": ("report_no_evidence", DEGRADED), + # 证据目录超出篇幅，有的查询只列了编号和摘要：写作模型没看全证据，报告的覆盖面有缺口 + "report_catalog_trimmed": ("report_catalog_trimmed", DEGRADED), # 口径卡的指标集、报告文档没能落进工件库：答案照常，只是点不开证据 "evidence_store_failed": ("evidence_store_failed", DEGRADED), # agent 的结构化字段：模型报的值和查询快照对不上（已按快照取值），或者出处核对不了 diff --git a/backend/tests/fixtures/evidence/store_run.py b/backend/tests/fixtures/evidence/store_run.py new file mode 100644 index 00000000..138eb477 --- /dev/null +++ b/backend/tests/fixtures/evidence/store_run.py @@ -0,0 +1,106 @@ +"""合成的「4 个专项 Agent 并行查库 → 报告」场景：查询多、分布不均，每个 Agent 都交了按出处核对的字段。 + +形状仿一次真实运行（4 个 Agent 并行、查询次数悬殊，合计上百次），次数和内容全部虚构：门店、会员、卡券、活动。 +不连库、不调模型：台账条目、查询快照、表结构快照、Agent 的 data / data_evidence 都在内存里拼好，快照经 loader 取。 + +台账按节点跑完的先后排（state["evidence"] 是各节点结束时追加的），所以全局编号 Q1… 前 26 个都是会员专项的。 +""" +from __future__ import annotations + +from typing import Any + +from app.engine.evidence import cell_eid, make_eid + +#: (节点 id, 画布上的名字, 查询次数, 查的表) +AGENTS: list[tuple[str, str, int, str]] = [ + ("agent_member", "会员专项", 26, "member_daily"), + ("agent_store", "门店专项", 17, "store_daily"), + ("agent_coupon", "卡券核销", 79, "coupon_redemption"), + ("agent_campaign", "活动内容", 42, "campaign_view"), +] +COLUMNS = ["store_name", "biz_time", "cnt", "amount"] +TYPES = {"store_name": "text", "biz_time": "datetime", "cnt": "number", "amount": "number"} +ROWS = 30 +#: 结构化抽取失败（两次都被截断）的那个 Agent：字段全是空值 +EMPTY_AGENT = "agent_coupon" +FIELDS = ["total_cnt", "top_store", "latest_time", "top_stores", "churn_rate"] + + +def artifact_of(node_id: str, i: int) -> str: + return f"{node_id}-q{i:03d}".ljust(64, "0") + + +def snapshot(node_id: str, i: int, rows: int = ROWS) -> dict[str, Any]: + """第 i 次查询的快照：每行一个门店，时间是 ISO 写法（快照里存的就是这样）。""" + seed = sum(map(ord, node_id)) + i * 7 + return { + "columns": list(COLUMNS), + "column_types": dict(TYPES), + "rows": [[f"门店{(seed + r) % 90 + 1:02d}", f"2026-06-{r % 28 + 1:02d}T08:{(seed + r) % 60:02d}:39", + (seed * 13 + r * 17) % 900 + 10, f"{(seed * 31 + r * 7) % 9000 + 100}.50"] for r in range(rows)], + "truncated": False, + } + + +def schema_snapshot(table: str) -> dict[str, Any]: + return {"source": "shop", "tables": {table: {"columns": [{"name": c, "type": TYPES[c]} for c in COLUMNS]}}} + + +def build() -> tuple[list[dict[str, Any]], dict[str, Any], dict[str, Any], dict[str, str]]: + """(台账, 快照 {工件 id: 内容}, state["nodes"], {节点 id: 名字})。""" + ledger: list[dict[str, Any]] = [] + store: dict[str, Any] = {} + nodes: dict[str, Any] = {} + titles: dict[str, str] = {} + for node_id, title, count, table in AGENTS: + titles[node_id] = title + schema_id = f"schema-{table}".ljust(64, "0") + store[schema_id] = schema_snapshot(table) + ledger.append({"kind": "schema", "node_id": node_id, "exec": 1, "artifact": schema_id, "source": "shop", + "tables": {table: list(COLUMNS)}}) + for i in range(1, count + 1): + aid = artifact_of(node_id, i) + store[aid] = snapshot(node_id, i) + ledger.append({"kind": "query", "node_id": node_id, "exec": 1, "artifact": aid, + "via": f"tool-{aid}"[:64], "call_id": f"call_{i}", "tool": "db_query__shop", "source": "shop", + "columns": list(COLUMNS), "rows": ROWS, "truncated": False, + "schema_artifact": schema_id, "tables": [table]}) + nodes[node_id] = agent_output(node_id, store) + return ledger, store, nodes, titles + + +def agent_output(node_id: str, store: dict[str, Any]) -> dict[str, Any]: + """Agent 交出的结构化字段：本节点的 Q3 那一格、Q5 的前 5 行；EMPTY_AGENT 抽取失败，字段全空。""" + if node_id == EMPTY_AGENT: + reason = "结构化抽取没有完成：模型的输出额度在推理阶段就用完了（最后一次的额度为 32768 token），结构化结论没能写出来" + return {"text": "查到了大量核销和兑换记录。", "data": {f: None for f in FIELDS}, + "data_evidence": {f: {"status": "unresolved", "reason": reason} for f in FIELDS}} + q3, q5 = artifact_of(node_id, 3), artifact_of(node_id, 5) + first = store[q3]["rows"][0] + + def cell(column: str, index: int) -> dict[str, Any]: + return {"ref": f"Q3.r0.{column}", "call": "Q3", "artifact": q3, "via": f"tool-{q3}"[:64], + "locator": {"row": 0, "column": column}, "eid": cell_eid(q3, 0, column), "status": "verified"} + + rows_locator = {"rows": [0, 4], "column": "store_name"} + return { + "text": "结论见字段。", + "data": {"total_cnt": first[2], "top_store": first[0], "latest_time": first[1], + "top_stores": [r[0] for r in store[q5]["rows"][:5]], "churn_rate": None}, + "data_evidence": { + "total_cnt": cell("cnt", 2), "top_store": cell("store_name", 0), "latest_time": cell("biz_time", 1), + "top_stores": {"ref": "Q5.r0-4.store_name", "call": "Q5", "artifact": q5, "via": f"tool-{q5}"[:64], + "locator": rows_locator, "eid": make_eid("rows", q5, rows_locator), "status": "verified"}, + "churn_rate": {"ref": None, "status": "missing"}, + }, + } + + +def global_alias(node_id: str, local: int) -> str: + """本节点第 local 次查询在报告目录里的全局编号（台账按节点先后排）。""" + offset = 0 + for nid, _, count, _ in AGENTS: + if nid == node_id: + return f"Q{offset + local}" + offset += count + raise KeyError(node_id) diff --git a/backend/tests/test_report_catalog_budget.py b/backend/tests/test_report_catalog_budget.py new file mode 100644 index 00000000..3ea3da91 --- /dev/null +++ b/backend/tests/test_report_catalog_budget.py @@ -0,0 +1,209 @@ +"""写作者的出处清单：预算按写作模型的上下文窗口定；超出预算时分优先级、按来源均衡，省略了什么照实说。 + +2026-10 一次运行：证据台账里上百次查询（4 个 Agent 查询次数悬殊），出处清单写死 12000 字，十万字以上的目录 +被截到一万两千字以内，只有前二十来次查询进了写作者的视野，后面只有一句「目录太长，后面的省略了」。查询按编号排，前面的 +Agent 占满了预算，有的 Agent 一次都没露面；写作者于是写出「本轮没有查到任何核销结果」，可那个 Agent 查到了上百条。 + +- 预算：写作模型上下文窗口的 40%（字数按 1 字 ≈ 1 token 保守算），拿不到窗口按 128K；下限 12000、上限 160000 +- 第一优先：各 Agent 经按出处核对交来的结构化字段、口径卡指标，一律列出 +- 查询按产出它的节点分组、轮流取：先给每次查询前几行，再补到 20 行；每个节点都有代表 +- 没展开的查询列出编号和一行摘要（表、行数）；约定只能写 [[see:Qn]] 作依据，数字照样逐字核对 +- 清单开头明说「目录有省略」，各节点各省略了多少条 + +夹具全部合成（tests/fixtures/evidence/store_run.py）。 +""" +from __future__ import annotations + +import re + +import pytest + +import app.db.models # noqa: F401 - 建表（conftest 的 create_all）前要先登记模型 +from app.engine.evidence import ( + CATALOG_MAX_CHARS, + CATALOG_MIN_CHARS, + DEFAULT_CATALOG_BUDGET, + build_catalog, + catalog_budget, + catalog_prompt, + writer_catalog, +) +from tests.fixtures.evidence import store_run + +LEDGER, STORE, NODES, TITLES = store_run.build() + + +def load(artifact): + return STORE.get(artifact) + + +@pytest.fixture +def catalog(): + return build_catalog(nodes=NODES, ledger=LEDGER) + + +def sources(): + return [{"node_id": nid, "title": TITLES[nid], "data": NODES[nid]["data"], + "evidence": NODES[nid]["data_evidence"]} for nid, *_ in store_run.AGENTS] + + +def written(catalog, budget=DEFAULT_CATALOG_BUDGET, **kw): + return writer_catalog(catalog, budget=budget, loader=load, fields=sources(), titles=TITLES, **kw) + + +def group_of(text: str, title: str) -> str: + """清单里「来自「<名字>」的查询」那一段（到下一个来源或下一节为止）。""" + start = text.index(f"来自「{title}」的查询") + rest = text[start + 1:] + ends = [m.start() for m in re.finditer(r"\n(?:来自「|\n)", rest)] + return text[start:start + 1 + (ends[0] if ends else len(rest))] + + +# -------------------------------------------------------------------------- +# 预算 +# -------------------------------------------------------------------------- + + +def test_budget_follows_the_model_window(): + assert DEFAULT_CATALOG_BUDGET == catalog_budget(None) == 51_200, "拿不到窗口按 128K 的 40%" + assert catalog_budget(128_000) == 51_200 + assert catalog_budget(200_000) == 80_000 + assert catalog_budget(32_000) == 12_800 + assert catalog_budget(8_000) == CATALOG_MIN_CHARS == 12_000, "很小的窗口维持原来的篇幅" + assert catalog_budget(1_000_000) == CATALOG_MAX_CHARS == 160_000, "百万窗口也有上限：每写一稿都要重发整份目录" + assert catalog_budget(0) == catalog_budget(-5) == DEFAULT_CATALOG_BUDGET + + +def test_the_default_is_far_above_the_old_cut(): + assert DEFAULT_CATALOG_BUDGET >= 4 * 12_000 + + +# -------------------------------------------------------------------------- +# 放得下：照旧 +# -------------------------------------------------------------------------- + + +def test_a_catalog_that_fits_is_not_trimmed(): + small = build_catalog(nodes={}, ledger=[e for e in LEDGER if e.get("node_id") == "agent_store"]) + got = writer_catalog(small, loader=load) + assert not got.trimmed and not got.omitted + assert "目录有省略" not in got.text + assert got.text == catalog_prompt(small, loader=load), "没有结构化字段时和 catalog_prompt 一字不差" + assert "Q17：" in got.text and " r19：" in got.text + + +# -------------------------------------------------------------------------- +# 放不下：分优先级、按来源均衡（那次运行同样形状的场景） +# -------------------------------------------------------------------------- + + +@pytest.mark.parametrize("budget", [DEFAULT_CATALOG_BUDGET, 12_000]) +def test_every_agent_stays_in_view_within_the_budget(catalog, budget): + got = written(catalog, budget) + text = got.text + assert got.trimmed and len(text) <= budget, len(text) + for nid, title, count, table in store_run.AGENTS: + group = group_of(text, title) + assert f"共 {count} 次" in group + assert re.search(r"\n {2}r0：", group), f"「{title}」一次查询都没展开" + # 每次查询都在清单里：展开的有行，没展开的有编号和摘要 + total = sum(c for *_, c, _ in store_run.AGENTS) + for n in range(1, total + 1): + assert re.search(rf"(?:^|\n)\s*- Q{n}：", text), f"Q{n} 不见了" + + +def test_omitted_queries_are_listed_with_a_summary(catalog): + got = written(catalog) + assert got.omitted, "这个场景放不下全部展开" + for alias in got.omitted: + node = catalog[alias]["node_id"] + table = next(t for nid, _, _, t in store_run.AGENTS if nid == node) + assert re.search(rf"\n {{2}}- {alias}：{table}，30 行", got.text), alias + # 没展开的查询没有行 + first = got.omitted[0] + block = got.text[got.text.index(f"- {first}："):].split("\n", 2) + assert not block[1].lstrip().startswith("r0：") + + +def test_the_head_says_what_was_left_out_per_source(catalog): + got = written(catalog) + head = got.text[:800] + assert "目录有省略" in head + for row in got.nodes: + assert f"{row['title']} {row['total']} 次" in head and f"省略 {row['omitted']} 次" in head, row + assert sum(r["omitted"] for r in got.nodes) == len(got.omitted) + assert [r["node_id"] for r in got.nodes] == [nid for nid, *_ in store_run.AGENTS] + + +def test_how_to_use_an_omitted_query_is_spelled_out(catalog): + text = written(catalog).text + assert "[[see:Qn]]" in text and "本报告未覆盖" in text + assert "不要凭摘要写" in text and "数字照样逐字核对" in text + for phrase in ("没有查到", "不存在", "缺失", "没有数据"): + assert phrase in text + + +def test_structured_fields_come_first_and_point_at_global_cells(catalog): + got = written(catalog, 12_000) + text = got.text + fields_at = text.index("各 Agent 交来的结构化结论") + assert fields_at < text.index("来自「"), "结构化结论排在查询前面" + member_q3 = store_run.global_alias("agent_member", 3) + store_q3 = store_run.global_alias("agent_store", 3) + campaign_q5 = store_run.global_alias("agent_campaign", 5) + assert f"[[v:{member_q3}.r0.cnt]]" in text and f"[[v:{store_q3}.r0.store_name]]" in text + assert f"[[table:{campaign_q5} cols=store_name rows=0-4]]" in text + assert "churn_rate：抽取时没有找到这个值" in text + # 抽取失败的那个 Agent：结构化结论为空，不能据此断言没有数据 + block = text[text.index("「卡券核销」：", fields_at):] + assert block.startswith("「卡券核销」：结构化结论为空") and "本报告未覆盖" in block.split("\n", 1)[0] + assert got.empty_sources == ["卡券核销"] + + +def test_queries_the_fields_cite_are_expanded_first(catalog): + """结构化字段引用到的那几次查询先展开：写作者照抄字段的引用标记时，看得到那几行。""" + got = written(catalog, 12_000) + for nid in ("agent_member", "agent_store", "agent_campaign"): + for local in (3, 5): + alias = store_run.global_alias(nid, local) + assert alias not in got.omitted, alias + assert re.search(rf"\n- {alias}：.*\n {{2}}r0：", got.text), alias + + +def test_a_field_value_reads_like_the_cell_it_cites(catalog): + """字段后面写的值和引用之后报告里显示的一样（[[v:]] 的渲染规则）。""" + text = written(catalog).text + q3 = STORE[store_run.artifact_of("agent_member", 3)] + assert f"total_cnt = {q3['rows'][0][2]:,}：[[v:Q3.r0.cnt]]" in text + + +# -------------------------------------------------------------------------- +# 报告节点：窗口从哪来 +# -------------------------------------------------------------------------- + + +async def test_the_window_comes_from_the_provider_entry_then_the_catalog(): + """模型接入里给这个模型登记的 context 优先，其次模型目录；都没有返回 None（预算按保守值）。""" + from sqlalchemy import delete + + from app.db.base import SessionLocal + from app.db.models import Provider + from app.engine.nodes.report import model_window + from app.providers.factory import ModelSpec + + async with SessionLocal() as session: + row = Provider(name="fa-window-test", kind="mock", enabled=True, default_model="mock-wide", + models=[{"id": "mock-wide", "context": 64_000}, {"id": "mock-text", "context": "96000"}, + {"id": "mock-bare"}]) + session.add(row) + await session.commit() + try: + assert await model_window(session, ModelSpec(model="mock-wide"), "mock-wide") == 64_000 + assert await model_window(session, ModelSpec(model="mock-text"), "mock-text") == 96_000 + # 接入里没登记窗口：按模型目录（deepseek-chat 登记了 128K）；目录里也没有就是 None + assert await model_window(session, ModelSpec(model="deepseek-chat"), "deepseek-chat") == 128_000 + assert await model_window(session, ModelSpec(model="mock-bare"), "mock-bare") is None + assert catalog_budget(await model_window(session, ModelSpec(model="mock-wide"), "mock-wide")) == 25_600 + finally: + await session.execute(delete(Provider).where(Provider.id == row.id)) + await session.commit() diff --git a/frontend/src/run/decode.ts b/frontend/src/run/decode.ts index f82b4844..f9bab20c 100644 --- a/frontend/src/run/decode.ts +++ b/frontend/src/run/decode.ts @@ -756,6 +756,22 @@ function explainLog(code: string | undefined, message: string, d: Record typeof r?.omitted === 'number' && r.omitted > 0 && str(r?.title)) + .map((r) => `${r.title} 省略 ${formatNumber(r.omitted)}/${formatNumber(r.total)} 次`) + return { + title: omitted != null + ? `证据目录超出篇幅，${total != null ? `${formatNumber(total)} 次查询中 ` : ''}${formatNumber(omitted)} 次只列了编号和摘要` + : '证据目录超出篇幅，部分查询只列了编号和摘要', + sub: per.length ? per.join('、') : undefined, + next: '写作模型按来源均衡地看到了一部分查询的明细，被省略的查询仍在证据台账里，报告不得据此断言没有数据。' + + '需要完整明细时，请换用上下文窗口更大的写作模型，或让 Agent 在 SQL 中先汇总再交出结果', + } + } case 'agent_field_mismatch': { // 「有 1 个字段和查询快照对不上，已按快照取值：order_cnt 模型报 1240，快照是 1234」（llm.py _field_warnings） const n = message.match(/有\s*(\d+)\s*个字段/)?.[1] diff --git a/scripts/check-copy.mjs b/scripts/check-copy.mjs index 2376d6f1..e023b249 100644 --- a/scripts/check-copy.mjs +++ b/scripts/check-copy.mjs @@ -230,12 +230,13 @@ const MODEL_ONLY = { 'NODE_REFERENCE', 'ROLE', 'MARKUP_NUDGE', 'JUDGE_RULES', 'JUDGE_ROLE', 'JUDGE_RULE', 'REVIEW_SYSTEM', 'MARKER_RULES', 'GATE_SYSTEM', 'TOOL_MARKUP_NUDGE', '_UNKNOWN_ENTITY_FOR_MODEL', 'NODE_FIELD_FIX', '_FIX_TAIL', '_STREAM_PROTOCOL', '_RULES_HEAD', '_NO_LOWERING', '_ASK_HUMAN', '_UPGRADE_TASK', 'CELL_RULES', 'ENTITY_RULES', - 'EXTRACT_SYSTEM', 'GRAPH_SCHEMA', 'REVIEW_SCHEMA', '_CN_N', + 'EXTRACT_SYSTEM', 'GRAPH_SCHEMA', 'REVIEW_SCHEMA', '_CN_N', 'OMITTED_RULES', 'ABSENCE_RULE', ]), // 拼提示词的函数：生成 / 修正 / 升级请求、抽取消息、交给抽取模型的输出结构； // 助手的数据源上下文（api/copilot_context.py 的各段渲染、挑表请求，copilot.py 里挑表用的需求） funcs: new Set(['_user_message', '_repair_request', '_publish_fix_request', '_upgrade_assist_request', - '_extract_messages', '_from_rows', 'catalog_prompt', + '_extract_messages', '_from_rows', 'catalog_prompt', 'writer_catalog', '_trimmed_catalog', '_metric_prompt', + '_fields_prompt', '_field_prompt', '_query_brief', '_pick_messages', '_pick_need', '_publish_fix_need', '_upgrade_need', '_detail_block', '_selected_block', '_join_lines', '_route_line', '_source_head']), // 第二个参数是交回模型改写的指令（第一个是给人看的那句）：engine/evidence.py 的 _Reason(text, fix)、fail(text, fix) diff --git a/scripts/check-decode.mjs b/scripts/check-decode.mjs index 5045adb1..f11e795a 100644 --- a/scripts/check-decode.mjs +++ b/scripts/check-decode.mjs @@ -1300,6 +1300,26 @@ await section('报告核对 report.checked、口径卡的台账（可点击证 `${flagged?.status} ${flagged?.level}`) const bare = flatten(mod.decodeRun([E(1, 'report.checked', null, {})])).find((s) => s.code === 'report_checked') check('载荷缺字段也不崩，只说核对了报告', bare?.title === '核对报告' && !bare.artifact, bare?.title) + + // 写作目录超出篇幅（2026-10 一次运行：上百次查询只有前二十来次进了写作者的视野，界面上一个字都没说）： + // 现在按来源均衡地展开，没展开的列编号和摘要，并发一条警告。界面读结构化字段（queries、omitted、nodes） + check('后端发目录有省略的警告，带着结构化字段', reportPy.includes('code="report_catalog_trimmed"') + && reportPy.includes('queries=writer.queries, omitted=len(writer.omitted)') && reportPy.includes('"node_id", "title", "total", "omitted"'), + '改了 report.py 的字段就同步改 decode.ts 的 explainLog 和这里的夹具') + const TRIMMED = '证据目录超出篇幅（约 51200 字，按写作模型的上下文窗口确定）：164 次查询中 90 次列出了数据，74 次只列编号和摘要' + + '（卡券核销省略 52 次、活动内容省略 22 次）。写作模型看不到这些查询的明细，报告不得据此断言没有数据' + const trimmed = flatten(mod.decodeRun([E(1, 'node.started', 'w', { node_type: 'report', label: '写报告' }), + E(2, 'log', 'w', { level: 'warn', code: 'report_catalog_trimmed', message: TRIMMED, budget: 51200, window: null, queries: 164, + omitted: 74, nodes: [{ node_id: 'a', title: '会员专项', total: 26, omitted: 0 }, { node_id: 'b', title: '卡券核销', total: 79, omitted: 52 }, + { node_id: 'c', title: '活动内容', total: 42, omitted: 22 }] })])).find((s) => s.code === 'report_catalog_trimmed') + check('report_catalog_trimmed：标题说几次查询只列了编号和摘要', trimmed?.title === '证据目录超出篇幅，164 次查询中 74 次只列了编号和摘要', trimmed?.title) + check('……副标题按来源列省略了几次（没省略的来源不列）', trimmed?.sub === '卡券核销 省略 52/79 次、活动内容 省略 22/42 次', trimmed?.sub) + check('……下一步说被省略的仍在台账里、不得据此断言没有数据，以及怎么看全', !!trimmed?.next?.includes('不得据此断言没有数据') + && !!trimmed?.next?.includes('上下文窗口更大') && trimmed?.level === 'warn' && trimmed?.detail === TRIMMED, trimmed?.next) + const trimmedBare = flatten(mod.decodeRun([E(1, 'log', 'w', { level: 'warn', code: 'report_catalog_trimmed', message: '目录有省略' })])) + .find((s) => s.code === 'report_catalog_trimmed') + check('……缺字段时标题照样说人话，不出 undefined', trimmedBare?.title === '证据目录超出篇幅，部分查询只列了编号和摘要' + && trimmedBare?.sub === undefined, `${trimmedBare?.title} / ${trimmedBare?.sub}`) }) await section('工具门控 tool.gated（工具信任三档）', async () => { From 799c80b97e4ffd6e32f7c035ba6f61a73b1148d1 Mon Sep 17 00:00:00 2001 From: ThearchyHelios Date: Sun, 4 Oct 2026 11:28:23 +0800 Subject: [PATCH 4/5] =?UTF-8?q?=E6=B8=85=E5=8D=95=E6=9C=89=E7=9C=81?= =?UTF-8?q?=E7=95=A5=E6=97=B6=E4=B8=8D=E8=AE=B8=E6=96=AD=E8=A8=80=E3=80=8C?= =?UTF-8?q?=E6=B2=A1=E6=9C=89=E6=95=B0=E6=8D=AE=E3=80=8D=EF=BC=9A=E5=86=99?= =?UTF-8?q?=E4=BD=9C=E8=A7=84=E5=88=99=E5=8A=A0=E4=B8=80=E6=9D=A1=EF=BC=8C?= =?UTF-8?q?=E6=A0=B8=E5=AF=B9=E8=AE=A4=E5=87=BA=E7=BC=BA=E5=A4=B1=E6=96=AD?= =?UTF-8?q?=E8=A8=80=E4=BA=A4=E5=9B=9E=E9=87=8D=E5=86=99=E4=B8=80=E6=AC=A1?= =?UTF-8?q?=EF=BC=8C=E4=BB=8D=E6=9C=89=E7=9A=84=E5=8F=AA=E6=A0=87=E6=B3=A8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 一次真实运行里写作者只看到上百次查询里的前二十来次，却写出「某类数据缺失、本轮没有查到任何结果」这样的断言，可那个 Agent 实际查到了上百条记录。 - 写作规则：清单有省略、或有来源的结构化结论为空时，不得断言没有查到、不存在、缺失、没有数据，只能写「本报告未覆盖」并说明依据不足 - 组装文档：清单有省略时按一组明确的说法认出缺失断言（没有查到、未查询到、查不到、没有任何记录、数据为空、数据缺失、不存在相关记录、暂无数据等；「不存在明显差异」「不代表没有数据」这类不算，代码块不查），记新违规 absence_claim，带 span、所在片段和原句 - 和句中的 [[see:]] 一样归入 REWRITE_ONLY：交回重写一次，重写后仍有的只标注、不判失败；只剩这几类时不再按最多重写次数反复重写。仍有时报告节点发 report_absence_claim（带 count、texts），运行复核算作缺口 - 证据面板的违规清单标「缺失断言」并写明为什么依据不足，时间线给 report_absence_claim 专门的一行，文案在 terms.ts；check-decode、check-evidence 补断言 - 没展开的查询的摘要在没有表结构快照时从查询快照里的 SQL 认表名 - 回归测试：合成的门店、会员、卡券、活动库真跑「4 个 Agent 并行查库 → 报告」（查询次数虚构、分布悬殊），覆盖抽取截断重试、两次截断记空值、清单均衡和缺失断言的重写 --- backend/app/engine/evidence.py | 74 +++- backend/app/engine/nodes/report.py | 22 +- backend/app/engine/review.py | 2 + backend/tests/test_report_absence_claim.py | 82 +++++ .../tests/test_report_evidence_regression.py | 316 ++++++++++++++++++ frontend/src/lib/terms.ts | 13 + frontend/src/run/EvidencePanel.tsx | 12 +- frontend/src/run/decode.ts | 12 +- scripts/check-decode.mjs | 20 ++ scripts/check-evidence.mjs | 29 +- 10 files changed, 568 insertions(+), 14 deletions(-) create mode 100644 backend/tests/test_report_absence_claim.py create mode 100644 backend/tests/test_report_evidence_regression.py diff --git a/backend/app/engine/evidence.py b/backend/app/engine/evidence.py index 02a62c8b..ff830cfb 100644 --- a/backend/app/engine/evidence.py +++ b/backend/app/engine/evidence.py @@ -2474,11 +2474,13 @@ def compose_doc( cells_allowed: bool = True, loader: Loader | None = None, entities: bool = True, + absence: bool = False, ) -> dict[str, Any]: """模型写的原文（带标记）→ 报告文档：渲染后的 markdown、块、句、片段、统计、违规。 entities=False（报告节点 entities: off）：表名、字段名整层不管——不链接、不核对反引号， [[t:]] / [[c:]] 解析不了，原因是 ENTITIES_OFF_REASON。 + absence=True（写作目录有省略）：另查缺失断言（absence_claims），和句中的 [[see:]] 一样只在组装时查。 stats / violations 由 verify_doc 算出，和出口契约复核用的是同一个函数。 整表标记先展开成每格一个 [[v:]] 的 Markdown 表，之后和模型自己写的表格走同一条路； @@ -2634,8 +2636,10 @@ def compose_doc( "markdown": markdown, "source": source, "catalog": kept, "blocks": out_blocks, } checked = verify_doc(doc, catalog, allow_numbers=allow_numbers, cells_allowed=cells_allowed, loader=snaps) - # 句中的 [[see:]] 只在组装时查（交回写作者重写）：出口复核看的是数字和出处，句子通不通不影响出具 - doc["stats"], doc["violations"] = checked["stats"], checked["violations"] + inline_see(source) + # 句中的 [[see:]]、目录有省略时的缺失断言只在组装时查（交回写作者重写）：出口复核看的是数字和出处， + # 句子通不通、结论有没有越过证据不影响出具 + doc["stats"], doc["violations"] = checked["stats"], \ + checked["violations"] + inline_see(source) + (absence_claims(doc) if absence else []) return doc @@ -3035,6 +3039,58 @@ def inline_see(source: str) -> list[dict[str, Any]]: return [_violation("inline_see", INLINE_SEE_MESSAGE, text=m.group(0)) for m in _INLINE_SEE.finditer(source or "")] +#: 写作目录有省略、或者有来源的结构化结论为空时，拼在写作规则后面（给模型看） +ABSENCE_RULE = ( + "这次你看不到全部证据（证据目录有省略，或者有来源的结构化结论为空）：不得断言「没有查到」「未查到」「不存在」" + "「缺失」「没有数据」「无数据」这类结论。某个方向的证据没有展开或为空时，只能写「本报告未覆盖……」，并说明依据不足" + "（比如「目录有省略，相关查询未展开」），句末可以挂 [[see:Qn]] 指向相关查询。" +) + +#: 缺失断言：断言「没有数据」的几种说法。只认拿得准的，不泛化：「不存在明显差异」「缺少口径说明」这类说的是看到的 +#: 数据里的情况，不算；「不存在」「为空」「缺失」只认跟在数据、记录、结果后面（或者前面）的 +_ABSENCE = re.compile( + r"(?:没有|没|未|未能|没能)(?:查询|查)到|查不到|查无" + r"|(?:没有|暂无|并无)(?:任何)?(?:相关的?)?(?:数据|记录|结果)" + r"|无(?:任何)?(?:相关的?)?(?:数据|记录)(?![库源集表权字项列])" + r"|不存在(?:任何)?(?:相关的?)?(?:数据|记录|结果)|(?:数据|记录|结果)(?:均|都|全部)?(?:不存在|为空|缺失|空缺)" +) +#: 前面是这些词的不算：「不代表没有数据」「并非查不到」说的恰恰是不能下这个结论 +_ABSENCE_NEGATED = re.compile(r"(?:不代表|并不代表|不等于|不意味着|并非|不是|不能说明|不能断言|不得断言|不应断言)[^。！？\n]{0,6}$") +ABSENCE_MESSAGE = _Reason( + "证据目录有省略时写了缺失断言：写作模型没有看到全部证据，「没有查到」「不存在」「缺失」这类结论依据不足", + "证据目录有省略（没展开的查询只列了编号和摘要），这一句却断言了没有数据。看不到的证据不能当成不存在：" + "改成「本报告未覆盖……」，并说明依据不足（目录有省略、相关查询未展开），不要写「没有查到」「不存在」「缺失」「没有数据」", +) + + +def absence_claims(doc: dict[str, Any]) -> list[dict[str, Any]]: + """正文里的缺失断言（_ABSENCE），每处一条违规 absence_claim。只在写作目录有省略时由报告节点要求查（compose_doc 的 + absence）：目录全给了的时候，「本周没有退款记录」可能就是看了数据说的。 + + 在渲染后的正文里找（标记已经换成了字），代码块不查。带着 span、所在片段和句子：证据面板能定位、能显示原句。 + """ + markdown = str(doc.get("markdown") or "") + found: list[dict[str, Any]] = [] + for block in doc.get("blocks") or []: + if block.get("type") == "code": + continue + for unit in block.get("units") or []: + span = unit.get("span") + if not (isinstance(span, list) and len(span) == 2): + continue + sentence = markdown[span[0]:span[1]] + for m in _ABSENCE.finditer(sentence): + if _ABSENCE_NEGATED.search(sentence[:m.start()]): + continue + at = span[0] + m.start() + seg = next((g for g in unit.get("segments") or [] if g.get("kind") != "structural" + and isinstance(g.get("span"), list) and g["span"][0] <= at < g["span"][1]), None) + found.append(_violation("absence_claim", ABSENCE_MESSAGE, text=m.group(0), + span=[at, span[0] + m.end()], context=sentence.strip()[:120], + segment=seg.get("id") if seg else None, unit=unit.get("id"))) + return found + + #: 引用查询结果的写法。只跟着目录里的查询出现（catalog_prompt）：写作规则本身保持一期那份，升级前 #: 发起的运行跑到报告节点时提示一字不差；只有口径卡的目录也不该教写作者去引用不存在的 Q1 CELL_RULES = ( @@ -3320,8 +3376,16 @@ def _fields_prompt(sources: list[dict[str, Any]], catalog: dict[str, Any], snaps empty, list(dict.fromkeys(cited))) -def _query_brief(entry: dict[str, Any], tables: list[str]) -> str: - """没展开的查询在目录里的一行摘要：编号、查的表（没有表结构时写工具名）、行数。""" +def _query_brief(entry: dict[str, Any], tables: list[str], snaps: _Snapshots) -> str: + """没展开的查询在目录里的一行摘要：编号、查的表、行数。 + + 表先看目录里的表条目（带表结构快照的查询才有），没有就从查询快照里存的 SQL 认（sql_tables）， + 再没有写工具名。 + """ + if not tables: + snapshot, why = snaps.get(entry["alias"], entry.get("artifact")) + if not why and isinstance(snapshot.get("sql"), str): + tables = sql_tables(snapshot["sql"]) rows = entry.get("rows") what = ("、".join(tables[:3]) + (f" 等 {len(tables)} 张表" if len(tables) > 3 else "")) if tables \ else str(entry.get("tool") or entry.get("source") or "查询") @@ -3401,7 +3465,7 @@ def _trimmed_catalog(catalog: dict[str, Any], head: list[str], first: list[str], for e in queries: groups.setdefault(str(e.get("node_id") or ""), []).append(e) compact = {e["alias"]: _query_prompt(e, snaps, cells_allowed, _PROMPT_COMPACT_ROWS) for e in queries} - brief = {e["alias"]: [_query_brief(e, tables.get(e["alias"], []))] for e in queries} + brief = {e["alias"]: [_query_brief(e, tables.get(e["alias"], []), snaps)] for e in queries} shapes = {"brief": brief, "compact": compact, "full": full} tier = {e["alias"]: "brief" for e in queries} diff --git a/backend/app/engine/nodes/report.py b/backend/app/engine/nodes/report.py index a7d7cabc..c9bedf60 100644 --- a/backend/app/engine/nodes/report.py +++ b/backend/app/engine/nodes/report.py @@ -44,6 +44,7 @@ from app.engine import judge as judging from app.engine.context import NodeContext, NodeError from app.engine.labels import field_label, option_label from app.engine.evidence import ( + ABSENCE_RULE, CELLS_REASON, CLAIMS_RULE, ENTITY_KINDS, @@ -84,8 +85,10 @@ CLAIMS = CLAIMS_VALUES #: 只标注、不让写作者重写也不判失败的违规：可疑实体（受管的正式出具按缺口降档）、表结构快照不全时 #: 核对不了的名字（出口只标注） SOFT = frozenset({"unknown_entity", "unverified_entity"}) -#: 交回写作者重写、重写不好也不判失败的违规：句中的 [[see:]]（渲染后留下断句，数字和出处都没问题） -REWRITE_ONLY = frozenset({"inline_see"}) +#: 交回写作者重写一次、重写不好也不判失败的违规：句中的 [[see:]]（渲染后留下断句，数字和出处都没问题）； +#: 写作目录有省略时的缺失断言（「没有查到」「数据缺失」：写作者没看全证据，结论依据不足，但数字和出处都核对过）。 +#: 只剩这几类时不按 max_repairs 反复重写：重写一次没改掉，再写也多半一样，留在核对结果里标注 +REWRITE_ONLY = frozenset({"inline_see", "absence_claim"}) #: 重写一次就是多一次整篇的模型调用，写不对的模型多给几次也大多写不对 MAX_REPAIRS = 3 @@ -410,6 +413,9 @@ def _messages(ctx: NodeContext, state: GraphState, writer: WriterCatalog, claims instructions = ctx.render_str(ctx.cfg("instructions", ""), state).strip() \ or "根据下面的证据写一份简洁的报告，先总后分。" rules = f"{MARKER_RULES}\n{SEE_AND_CODE_RULES}" + if writer.guarded: + # 写作者看不全证据（目录有省略、有来源的结构化结论为空）：「没有查到」这类结论没有依据 + rules = f"{rules}\n{ABSENCE_RULE}" if claims in ("require_citation", CLAIMS_JUDGE): # 裁判只看写作者挂的依据：要判，就更得每句都挂上 rules = f"{rules}\n{CLAIMS_RULE}" + (f"\n{judging.JUDGE_RULE}" if claims == CLAIMS_JUDGE else "") @@ -500,12 +506,13 @@ async def run_report(state: GraphState, ctx: NodeContext) -> dict[str, Any]: def compose(raw: str) -> dict[str, Any]: return compose_doc(raw, catalog, node_id=ctx.node.id, run_id=ctx.run.run_id, allow_numbers=allow, - cells_allowed=cells, entities=entities) + cells_allowed=cells, entities=entities, absence=writer.trimmed) response, raw = await draft(messages) doc = compose(raw) repairs = 0 - while (blocking := _blocking(doc["violations"], numbers)) and repairs < max_repairs: + while (blocking := _blocking(doc["violations"], numbers)) and repairs < max_repairs \ + and not (repairs and not _fatal(blocking)): repairs += 1 ctx.emit(EventType.LOG, level="warn", code="report_repair", message=f"报告有 {len(blocking)} 处未通过核对（{_named(blocking)}），" @@ -528,6 +535,13 @@ async def run_report(state: GraphState, ctx: NodeContext) -> dict[str, Any]: draft=draft, compose=compose, numbers=numbers, blocking=blocking) spent.extend(usages) + if absent := [v for v in doc["violations"] if v.get("code") == "absence_claim"]: + # 重写之后还在（或者没让重写）：报告照常产出，这几处标注出来，时间线上单独说清为什么依据不足 + texts = list(dict.fromkeys(str(v.get("text") or "") for v in absent)) + tried = f"已要求模型重写 {repairs} 次仍未改掉" if repairs else "未要求重写" + ctx.emit(EventType.LOG, level="warn", code="report_absence_claim", count=len(absent), texts=texts, + message=f"报告在证据目录有省略时写了 {len(absent)} 处缺失断言（{_named(absent)}），{tried}：" + "被省略的查询仍在证据台账里，这类结论依据不足，应写作本报告未覆盖。报告照常产出，这几处已标注") doc_artifact = await _store(doc, ctx) ctx.emit(EventType.REPORT_CHECKED, doc_artifact=doc_artifact, ok=not doc["violations"], stats=doc["stats"], violations=doc["violations"][:20], repairs=repairs, diff --git a/backend/app/engine/review.py b/backend/app/engine/review.py index 98e0d36d..65c0b752 100644 --- a/backend/app/engine/review.py +++ b/backend/app/engine/review.py @@ -91,6 +91,8 @@ WARN_CODES: dict[str, tuple[str, str]] = { "report_no_evidence": ("report_no_evidence", DEGRADED), # 证据目录超出篇幅，有的查询只列了编号和摘要：写作模型没看全证据，报告的覆盖面有缺口 "report_catalog_trimmed": ("report_catalog_trimmed", DEGRADED), + # 目录有省略时写了「没有查到」这类缺失断言、重写后仍在：报告照常产出并标注，这几句结论依据不足 + "report_absence_claim": ("report_absence_claim", DEGRADED), # 口径卡的指标集、报告文档没能落进工件库：答案照常，只是点不开证据 "evidence_store_failed": ("evidence_store_failed", DEGRADED), # agent 的结构化字段：模型报的值和查询快照对不上（已按快照取值），或者出处核对不了 diff --git a/backend/tests/test_report_absence_claim.py b/backend/tests/test_report_absence_claim.py new file mode 100644 index 00000000..cdd3c7f1 --- /dev/null +++ b/backend/tests/test_report_absence_claim.py @@ -0,0 +1,82 @@ +"""缺失断言（absence_claim）：写作目录有省略时，「没有查到」「不存在」「缺失」「没有数据」这类结论依据不足。 + +2026-10 一次运行，写作者只看到上百次查询里的前二十来次，写出「某类数据缺失：本轮没有查到任何结果」这样的断言， +可那个 Agent 实际查到了上百条记录。 + +- 写作规则：清单有省略、或有来源的结构化结论为空时，不得断言没有数据，只能写「本报告未覆盖」并说明依据不足 +- 组装文档：清单有省略时按一组明确的说法认出缺失断言，记 absence_claim，交回重写一次；重写后仍有的只标注、不判失败 + （report.REWRITE_ONLY，和句中的 [[see:]] 同一种处理） +- 只认拿得准的说法：「不存在明显差异」「本报告未覆盖」「不代表没有数据」都不算 +""" +from __future__ import annotations + +import pytest + +from app.engine.evidence import ABSENCE_RULE, build_catalog, compose_doc +from app.engine.nodes.report import REWRITE_ONLY + + +@pytest.fixture +def catalog(): + card = {"kind": "metric_set", "caliber": "周报口径", "caliber_version": "v1", + "metrics": [{"id": "gmv", "name": "销售额", "value": 45678.5, "unit": "元", "format": "thousands"}]} + return build_catalog(nodes={"card": card}, ledger=[{"kind": "metric_set", "node_id": "card", "artifact": "c" * 64}]) + + +def claims(doc): + return [v for v in doc["violations"] if v["code"] == "absence_claim"] + + +def test_the_rule_tells_the_writer_what_to_write_instead(): + for phrase in ("没有查到", "不存在", "缺失", "没有数据", "本报告未覆盖", "依据不足"): + assert phrase in ABSENCE_RULE + assert "absence_claim" in REWRITE_ONLY and "inline_see" in REWRITE_ONLY + + +@pytest.mark.parametrize("text, found", [ + ("卡券数据缺失：本轮没有查到核销、兑换、退券的任何结果。", ["数据缺失", "没有查到"]), + ("卡券核销没有任何记录。", ["没有任何记录"]), + ("本周门店客流数据为空。", ["数据为空"]), + ("活动内容无数据。", ["无数据"]), + ("未查询到会员流失的情况。", ["未查询到"]), + ("库里不存在相关记录。", ["不存在相关记录"]), + ("兑换记录均不存在。", ["记录均不存在"]), + ("暂无数据，查不到核销明细。", ["暂无数据", "查不到"]), + ("| 方向 | 结论 |\n| --- | --- |\n| 卡券 | 没有查到 |", ["没有查到"]), +]) +def test_absence_claims_are_recognised(catalog, text, found): + doc = compose_doc(text, catalog, absence=True) + got = claims(doc) + assert [v["text"] for v in got] == found, doc["violations"] + for v in got: + assert doc["markdown"][v["span"][0]:v["span"][1]] == v["text"], "span 指着正文里的那几个字" + assert v.get("unit") and v.get("context") + assert "依据不足" in v["message"] and "本报告未覆盖" in v["for_model"] + + +@pytest.mark.parametrize("text", [ + "本报告未覆盖卡券核销的明细：证据目录有省略，相关查询未展开，依据不足。", + "两家门店的客单价不存在明显差异。", + "目录里没展开的查询不代表没有数据。", + "这并非没有查到，而是没有展开。", + "本周无数据库变更，无数据源切换。", + "本周销售额 [[m:gmv]]，缺少口径说明的部分已注明。", + "```sql\nSELECT * FROM t -- 没有查到\n```", +]) +def test_other_wording_is_left_alone(catalog, text): + assert not claims(compose_doc(text, catalog, absence=True)) + + +def test_only_checked_when_the_catalog_was_trimmed(catalog): + text = "卡券核销数据缺失：没有查到任何记录。" + assert not claims(compose_doc(text, catalog)) + assert claims(compose_doc(text, catalog, absence=True)) + + +def test_the_claim_can_be_located_in_the_document(catalog): + doc = compose_doc("## 卡券\n\n本周销售额 [[m:gmv]]。核销数据缺失。", catalog, absence=True) + [v] = claims(doc) + segments = {s["id"]: s for b in doc["blocks"] for u in b["units"] for s in u["segments"]} + seg = segments[v["segment"]] + assert seg["kind"] == "text" and seg["span"][0] <= v["span"][0] < seg["span"][1] + assert v["context"] == "核销数据缺失。" diff --git a/backend/tests/test_report_evidence_regression.py b/backend/tests/test_report_evidence_regression.py new file mode 100644 index 00000000..0d7eb648 --- /dev/null +++ b/backend/tests/test_report_evidence_regression.py @@ -0,0 +1,316 @@ +"""回归：「4 个专项 Agent 并行查库 → 报告」整条跑一遍，复现 2026-10 那次运行的形状。 + +那次运行状态是 succeeded，报告却不可靠：4 个 Agent 的结构化抽取全部被截断（推理模型的额度在推理阶段就用完了）； +写作者的出处清单写死 12000 字，上百次查询只有前二十来次露了面；写作者于是在清单被截断的情况下写了「数据缺失： +本轮没有查到任何核销结果」，可那个 Agent 查到了上百条。 + +这里用合成的门店、会员、卡券、活动库（SQLite）真跑：4 个 Agent 分别查 26、17、79、42 次（虚构的次数，一次并行发完），都配了 +output_schema 和按出处核对字段；写作模型、抽取模型都是脚本。验证： + +- 抽取被截断后加大额度重试成功；两次都截断的记 agent_field_truncated，字段为空值 +- 出处清单在预算内，每个 Agent 都有代表，被省略的查询有编号和摘要，开头说明各来源省略了几次 +- 清单有省略时的缺失断言被认出来（absence_claim）、交回重写；重写后仍有的只标注、不判失败 +""" +from __future__ import annotations + +import asyncio +import json +import re +import sqlite3 + +import pytest +from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage +from openai import LengthFinishReasonError +from openai.types import CompletionUsage +from openai.types.chat import ChatCompletion +from sqlalchemy import select + +from app.core import artifact_store +from app.data.engine import engines +from app.db.base import SessionLocal +from app.db.models import DataSource, Run, RunEvent +from app.engine.evidence import ABSENCE_RULE, DEFAULT_CATALOG_BUDGET, MARKER_RULES +from app.engine.nodes import llm as llm_mod +from app.engine.nodes import report as report_mod +from app.engine.runner import run_manager +from tests.fixtures.sources import drop_source + +SOURCE = "fa_store" +TOOL = f"db_query__{SOURCE}" +#: (节点 id, 画布上的名字, 记号, 查询次数, 表, 列) +AGENTS = [ + ("agent_member", "会员专项", "member", 26, "member_daily", ["store_name", "biz_date", "new_members", "active_members"]), + ("agent_store", "门店专项", "store", 17, "store_daily", ["store_name", "biz_date", "visits", "sales"]), + ("agent_coupon", "卡券核销", "coupon", 79, "coupon_redemption", ["store_name", "redeemed_at", "coupon_type", "amount"]), + ("agent_campaign", "活动内容", "campaign", 42, "campaign_view", ["campaign", "biz_date", "views", "clicks"]), +] +SCHEMA = {"type": "object", "properties": { + "first_store": {"type": "string", "description": "第一次查询第一行的门店或活动"}, + "first_value": {"type": "number", "description": "第一次查询第一行的第三列"}, + "churn_rate": {"type": "number", "description": "流失率（要计算，填 null）"}, +}} +ROWS_PER_QUERY = 5 + +ABSENT = ("## 概览\n\n各专项都已完成查询。[[see:Q1]]\n\n" + "## 卡券核销\n\n卡券数据缺失：本轮没有查到核销、兑换的任何结果。") +COVERED = ("## 概览\n\n各专项都已完成查询。[[see:Q1]]\n\n" + "## 卡券核销\n\n本报告未覆盖卡券核销的明细：证据目录有省略，相关查询未展开，依据不足。[[see:Q1]]") + + +def length_error(completion: int) -> LengthFinishReasonError: + usage = CompletionUsage(completion_tokens=completion, prompt_tokens=14000, total_tokens=14000 + completion) + return LengthFinishReasonError(completion=ChatCompletion.model_construct( + id="x", choices=[], created=0, model="m", object="chat.completion", usage=usage)) + + +@pytest.fixture(autouse=True) +async def engine_up(): + await run_manager.setup() + yield + await run_manager.shutdown() + + +@pytest.fixture +async def store_db(tmp_path): + """合成库：每张表 120 行，第 i 次查询取 OFFSET i 的 5 行，次次结果不同。""" + path = tmp_path / "store.db" + db = sqlite3.connect(path) + for _, _, _, _, table, cols in AGENTS: + db.execute(f"CREATE TABLE {table} ({cols[0]} TEXT, {cols[1]} TEXT, {cols[2]} {'TEXT' if table == 'coupon_redemption' else 'INTEGER'}, {cols[3]} REAL)") + rows = [] + for r in range(120): + when = f"2026-06-{r % 28 + 1:02d}" + (f"T08:{r % 60:02d}:39" if table == "coupon_redemption" else "") + third = f"券型{r % 4 + 1}" if table == "coupon_redemption" else (r * 7) % 300 + 5 + rows.append((f"门店{r % 30 + 1:02d}", when, third, round((r * 13) % 900 + 0.5, 1))) + db.executemany(f"INSERT INTO {table} VALUES (?, ?, ?, ?)", rows) + db.commit() + db.close() + async with SessionLocal() as session: + row = DataSource(name=SOURCE, kind="sqlite", database=str(path), readonly=True, enabled=True) + session.add(row) + await session.commit() + source_id = row.id + await engines.invalidate(source_id) + yield path + await drop_source(source_id) + + +def sql_of(table: str, cols: list[str], i: int) -> str: + return f"SELECT {', '.join(cols)} FROM {table} ORDER BY rowid LIMIT {ROWS_PER_QUERY} OFFSET {i}" + + +def node(nid, ntype, label, **config): + return {"id": nid, "type": ntype, "position": {"x": 0, "y": 0}, "data": {"label": label, "config": config}} + + +def graph(counts: dict[str, int] | None = None, **report): + nodes = [node("start", "input", "开始")] + for nid, title, key, count, table, cols in AGENTS: + nodes.append(node(nid, "agent", title, prompt=f"【{key}】按{title}的口径查数，每次查一段", tools=[TOOL], + approval="never", parallel_tools=True, output_schema=SCHEMA, cite_fields=True, + assign_to=f"{key}_findings")) + nodes.append(node("write", "report", "专项报告", instructions="汇总各专项的发现", **report)) + nodes.append(node("out", "output", "成果", fields=[{"name": "报告", "value": "{{ nodes.write.text }}"}])) + edges = [{"source": "start", "target": a[0]} for a in AGENTS] + [{"source": a[0], "target": "write"} for a in AGENTS] + edges.append({"source": "write", "target": "out"}) + return {"nodes": nodes, "edges": edges} + + +class World: + """脚本化的模型：Agent 一步并行发完自己的查询再给结论；抽取按 outcome 决定；写作者依次回 drafts。""" + + def __init__(self, monkeypatch, db_path, *, counts: dict[str, int], drafts: list[str], outcome): + from app.providers import mock_model + + self.counts = counts + self.drafts = drafts + self.writer: list[list] = [] + self.extract_calls: dict[str, list[int | None]] = {a[2]: [] for a in AGENTS} + self.budgets: list[int | None] = [] + db = sqlite3.connect(db_path) + self.first_rows = {key: db.execute(sql_of(table, cols, 1)).fetchone() for _, _, key, _, table, cols in AGENTS} + db.close() + real = llm_mod.get_chat_model + + async def spy(session, spec): + if spec.thinking == "off": + self.budgets.append(spec.max_tokens) + return await real(session, spec) + + monkeypatch.setattr(llm_mod, "get_chat_model", spy) + world = self + + def text(messages) -> str: + return "\n".join(m.content if isinstance(m.content, str) else json.dumps(m.content, ensure_ascii=False) + for m in messages) + + def who(messages) -> str: + found = re.search(r"【(member|store|coupon|campaign)】", text(messages)) + assert found, "认不出是哪个 Agent" + return found.group(1) + + def decide(model, messages): + if model.response_format: + key = who(messages) + world.extract_calls[key].append(world.budgets[-1]) + outcome(key, len(world.extract_calls[key]), world.budgets[-1]) + return AIMessage(content=json.dumps(world.extracted(key), ensure_ascii=False)) + if any(isinstance(m, SystemMessage) and "报告撰写人" in str(m.content) for m in messages): + world.writer.append(list(messages)) + return AIMessage(content=world.drafts[min(len(world.writer), len(world.drafts)) - 1]) + key = who(messages) + if model.tools and not any(isinstance(m, ToolMessage) for m in messages): + _, _, _, _, table, cols = next(a for a in AGENTS if a[2] == key) + return AIMessage(content="分段查询。", tool_calls=[ + {"name": TOOL, "args": {"sql": sql_of(table, cols, i)}, "id": f"{key}_{i}"} + for i in range(1, world.counts[key] + 1)]) + return AIMessage(content=f"【{key}】查完了，结论见各次查询。") + + monkeypatch.setattr(mock_model.MockChatModel, "_decide", decide) + + def extracted(self, key: str) -> dict: + first = self.first_rows[key] + _, _, _, _, _, cols = next(a for a in AGENTS if a[2] == key) + return {"first_store": {"value": first[0], "from": {"call": "Q1", "row": 0, "column": cols[0]}}, + "first_value": {"value": first[3], "from": {"call": "Q1", "row": 0, "column": cols[3]}}, + "churn_rate": {"value": None, "from": None}} + + +async def finish(graph_json) -> Run: + run = await run_manager.start(graph=graph_json, input_payload={}) + for _ in range(1200): + await asyncio.sleep(0.05) + async with SessionLocal() as session: + row = await session.get(Run, run.id) + if row.status in ("succeeded", "failed") and row.manifest_seq is not None: + return row + raise AssertionError(f"run {run.id} 没跑完：{row.status}") + + +async def events(run_id: str, etype: str | None = None) -> list[RunEvent]: + async with SessionLocal() as session: + q = select(RunEvent).where(RunEvent.run_id == run_id) + if etype: + q = q.where(RunEvent.type == etype) + return list((await session.execute(q.order_by(RunEvent.seq))).scalars()) + + +def logs(evts, code): + return [e.data for e in evts if e.type == "log" and e.data.get("code") == code] + + +async def output_of(run_id: str, node_id: str) -> dict: + [finished] = [e for e in await events(run_id, "node.finished") if e.node_id == node_id] + return artifact_store.load(finished.data["artifact"]) + + +def catalog_text(messages) -> str: + """写作者第一次收到的提示里出处清单那一段（从「可引用的证据」到写作规则之前）。""" + prompt = next(m.content for m in messages if isinstance(m, HumanMessage)) + return prompt[prompt.index("可引用的证据"):prompt.index(MARKER_RULES)].rstrip() + + +# -------------------------------------------------------------------------- +# 和那次运行同样的形状：上百次查询、分布不均 +# -------------------------------------------------------------------------- + + +async def test_the_reported_run_shape_end_to_end(monkeypatch, store_db): + counts = {key: count for _, _, key, count, _, _ in AGENTS} + + def outcome(key, attempt, budget): + if key == "coupon": + raise length_error(budget) # 两次都截断 + if key == "member" and attempt == 1: + raise length_error(budget) # 截断一次，加大额度后成功 + + world = World(monkeypatch, store_db, counts=counts, drafts=[ABSENT, COVERED], outcome=outcome) + row = await finish(graph()) + assert row.status == "succeeded", row.error + evts = await events(row.id) + + # 1. 抽取：会员专项截断后重试成功；卡券核销两次都截断，字段为空值 + assert world.extract_calls["member"] == [16384, 32768] + assert world.extract_calls["coupon"] == [16384, 32768] + assert world.extract_calls["store"] == world.extract_calls["campaign"] == [16384] + member = await output_of(row.id, "agent_member") + assert member["data"]["first_store"] == world.first_rows["member"][0] + assert member["data_evidence"]["first_store"]["status"] == "verified" + coupon = await output_of(row.id, "agent_coupon") + assert coupon["data"] == {"first_store": None, "first_value": None, "churn_rate": None} + [cut] = logs(evts, "agent_field_truncated") + assert cut["fields"] == ["first_store", "first_value", "churn_rate"] and "推理阶段就用完了" in cut["message"] + assert not logs(evts, "agent_field_unverified") + + # 2. 出处清单：预算内，每个 Agent 都有代表，被省略的有编号和摘要，开头说明各来源省略了几次 + first = world.writer[0] + text = catalog_text(first) + assert len(text) <= DEFAULT_CATALOG_BUDGET, len(text) + head = text[:900] + assert "目录有省略" in head + for _, title, _, count, table, _ in AGENTS: + assert f"{title} {count} 次" in head + group = text[text.index(f"来自「{title}」的查询"):] + group = group[:group.index("\n\n", 1)] if "\n\n" in group[1:] else group + assert f"共 {count} 次" in group and re.search(r"\n {2}r0：", group), f"「{title}」没有展开的查询" + briefs = re.findall(r"\n {2}- (Q\d+)：(\w+)，5 行", text) + assert briefs, "没有列出被省略的查询" + assert {t for _, t in briefs} <= {a[4] for a in AGENTS} + assert all(f"Q{n}：" in text for n in range(1, 165)), "每次查询都要在清单里（展开的或摘要）" + # 结构化结论排在查询前面；抽取失败的卡券核销写明结构化结论为空、不代表没有数据 + assert text.index("各 Agent 交来的结构化结论") < text.index("来自「") + assert "「卡券核销」：结构化结论为空" in text + # 写作规则里有缺失断言那一条 + assert ABSENCE_RULE in next(m.content for m in first if isinstance(m, HumanMessage)) + [trimmed] = logs(evts, "report_catalog_trimmed") + assert trimmed["queries"] == 164 and trimmed["budget"] == DEFAULT_CATALOG_BUDGET + assert [n["title"] for n in sorted(trimmed["nodes"], key=lambda n: n["title"])] == \ + sorted(a[1] for a in AGENTS) + assert trimmed["omitted"] == sum(n["omitted"] for n in trimmed["nodes"]) > 0 + + # 3. 缺失断言：认出来、交回重写一次；改成「本报告未覆盖」后通过 + assert len(world.writer) == 2 + request = str(world.writer[1][-1].content) + assert "本报告未覆盖" in request and "没有查到" in request + [repair] = logs(evts, "report_repair") + assert "数据缺失" in repair["message"] or "没有查到" in repair["message"] + [checked] = [e.data for e in evts if e.type == "report.checked"] + assert not [v for v in checked["violations"] if v["code"] == "absence_claim"] + assert not logs(evts, "report_absence_claim") + + +# -------------------------------------------------------------------------- +# 重写后仍然写缺失断言：只标注、不判失败 +# -------------------------------------------------------------------------- + +SMALL = {"member": 6, "store": 4, "coupon": 12, "campaign": 8} + + +async def test_an_absence_claim_that_survives_the_rewrite_is_flagged_not_failed(monkeypatch, store_db): + monkeypatch.setattr(report_mod, "catalog_budget", lambda window: 4000) + world = World(monkeypatch, store_db, counts=SMALL, drafts=[ABSENT], outcome=lambda *a: None) + row = await finish(graph(on_violation="fail", max_repairs=3)) + assert row.status == "succeeded", row.error + assert len(world.writer) == 2, "缺失断言只交回重写一次，不按最多重写次数反复重写" + evts = await events(row.id) + [checked] = [e.data for e in evts if e.type == "report.checked"] + assert checked["failed"] is False + claims = [v for v in checked["violations"] if v["code"] == "absence_claim"] + assert {v["text"] for v in claims} == {"数据缺失", "没有查到"} + assert all(v.get("segment") and v.get("context") for v in claims), "带着片段和原句：证据面板能定位" + [flag] = logs(evts, "report_absence_claim") + assert flag["level"] == "warn" and flag["count"] == 2 and flag["texts"] == ["数据缺失", "没有查到"] + assert "本报告未覆盖" in flag["message"] or "依据不足" in flag["message"] + + +async def test_without_omissions_an_absence_statement_is_left_alone(monkeypatch, store_db): + """目录全给了：「没有查到」可能就是看了数据说的，不查。""" + counts = {"member": 1, "store": 1, "coupon": 1, "campaign": 1} + world = World(monkeypatch, store_db, counts=counts, drafts=[ABSENT], outcome=lambda *a: None) + row = await finish(graph()) + assert row.status == "succeeded", row.error + assert len(world.writer) == 1 + evts = await events(row.id) + assert not logs(evts, "report_catalog_trimmed") and not logs(evts, "report_absence_claim") + prompt = next(m.content for m in world.writer[0] if isinstance(m, HumanMessage)) + assert ABSENCE_RULE not in prompt and "目录有省略" not in prompt diff --git a/frontend/src/lib/terms.ts b/frontend/src/lib/terms.ts index 37388a16..b47b1eec 100644 --- a/frontend/src/lib/terms.ts +++ b/frontend/src/lib/terms.ts @@ -203,6 +203,19 @@ export function evidenceTally(cited: number, total: number, other = 0): string { return parts.join(' · ') } +/** + * 缺失断言：证据目录有省略时，报告写了「没有查到」「不存在」「缺失」这类结论（违规 absence_claim，重写后仍有时 + * 报告节点另发日志 report_absence_claim）。证据面板的违规清单和运行时间线共用这一套说法 + */ +export const ABSENCE_CLAIM_TEXT = { + label: '缺失断言', + hint: '写作模型没有看到全部证据：被省略的查询仍在证据台账里，看不到不等于不存在，这类结论应写作「本报告未覆盖」。' + + '报告照常产出，这一处只标注、不判失败', + title: (n?: number) => `报告写了${n ? ` ${formatNumber(n)} 处` : ''}缺失断言，已标注（证据目录有省略）`, + next: '被省略的查询仍在证据台账里，可在证据面板中查看。请在报告撰写节点的指令中要求未展开的方向写作「本报告未覆盖」，' + + '或换用上下文窗口更大的写作模型后重新运行', +} + /** 证据面板和证据条的文案 */ export const EVIDENCE_TEXT = { allCited: (n: number) => `${formatNumber(n)} 个数字都有出处`, diff --git a/frontend/src/run/EvidencePanel.tsx b/frontend/src/run/EvidencePanel.tsx index 00a35156..b4b9dd14 100644 --- a/frontend/src/run/EvidencePanel.tsx +++ b/frontend/src/run/EvidencePanel.tsx @@ -18,7 +18,7 @@ import { } from '../lib/evidence' import { humanizeError } from '../lib/errors' import { formatDateTime, formatNumber, NONE, shortId } from '../lib/format' -import { EVIDENCE_TEXT, JUDGE_TEXT, MERGE_TEXT, NODE_TYPE_LABEL, RUN_SQL_CHECK_TEXT, SQL_CHECK_TEXT, nodeTypeLabel } from '../lib/terms' +import { ABSENCE_CLAIM_TEXT, EVIDENCE_TEXT, JUDGE_TEXT, MERGE_TEXT, NODE_TYPE_LABEL, RUN_SQL_CHECK_TEXT, SQL_CHECK_TEXT, nodeTypeLabel } from '../lib/terms' import { useCatalog } from '../store/catalog' import { askKey, segmentKey, useEvidence, useExplore, useVerdicts } from '../store/evidence' import type { @@ -1552,6 +1552,7 @@ const CODE_LABEL: Record = { unresolved_ref: '引用无法解析', unknown_entity: '疑似不存在的名称', unverified_entity: '无法核实的名称', + absence_claim: ABSENCE_CLAIM_TEXT.label, } /** 违规清单：报告撰写节点核对出来的全部问题，画得出线的能定位，画不出的说清在哪 */ @@ -1563,7 +1564,9 @@ function ViolationList({ doc, onLocate }: { doc: EvidenceDocData; onLocate: (seg

{EVIDENCE_TEXT.violationsHint}

    {list.map((v, i) => { - const seg = locatable(doc, v) + // 缺失断言说的是一整句话，正文里普通文字不是可聚焦的片段：不给「定位」，原句写在下面 + const sentence = v.code === 'absence_claim' + const seg = sentence ? null : locatable(doc, v) return (
  • @@ -1576,8 +1579,11 @@ function ViolationList({ doc, onLocate }: { doc: EvidenceDocData; onLocate: (seg )} {v.message &&

    {v.message}

    } + {v.code === 'absence_claim' && ( +

    {ABSENCE_CLAIM_TEXT.hint}

    + )} {v.context &&

    「{v.context.trim()}」

    } - {!seg && ( + {!seg && !sentence && (

    {v.segment ? EVIDENCE_TEXT.structural : EVIDENCE_TEXT.noSegment}

    diff --git a/frontend/src/run/decode.ts b/frontend/src/run/decode.ts index f9bab20c..6ab8bfc7 100644 --- a/frontend/src/run/decode.ts +++ b/frontend/src/run/decode.ts @@ -2,7 +2,7 @@ import type { CatalogDriftTable, RunEvent, SqlCheckItem, TeamMember, TeamRound, import type { NodeState, RunPhase } from './trace' import { formatDuration, formatNumber } from '../lib/format' import { - CATALOG_DRIFT_TEXT, CATALOG_PATCH_TEXT, COPILOT_CONTEXT_TEXT, JUDGE_TEXT, MERGE_TEXT, RUN_SQL_CHECK_TEXT, SCHEMA_PARTIAL_TEXT, SQL_CHECK_TEXT, TYPE_LABEL, + ABSENCE_CLAIM_TEXT, CATALOG_DRIFT_TEXT, CATALOG_PATCH_TEXT, COPILOT_CONTEXT_TEXT, JUDGE_TEXT, MERGE_TEXT, RUN_SQL_CHECK_TEXT, SCHEMA_PARTIAL_TEXT, SQL_CHECK_TEXT, TYPE_LABEL, claimTally, evidenceTally, issuanceLabel, nodeTypeLabel, } from '../lib/terms' @@ -756,6 +756,16 @@ function explainLog(code: string | undefined, message: string, d: Record 0 ? d.count : undefined + const texts = strs(d.texts) + return { + title: ABSENCE_CLAIM_TEXT.title(n), + sub: texts.length ? texts.map((t) => `「${t}」`).join('、') : undefined, + next: ABSENCE_CLAIM_TEXT.next, + } + } case 'report_catalog_trimmed': { // 写作目录超出篇幅（report.py _trimmed_message）。读结构化字段（queries、omitted、nodes），不从原话里认 const total = typeof d.queries === 'number' ? d.queries : undefined diff --git a/scripts/check-decode.mjs b/scripts/check-decode.mjs index f11e795a..ca9ad388 100644 --- a/scripts/check-decode.mjs +++ b/scripts/check-decode.mjs @@ -1320,6 +1320,26 @@ await section('报告核对 report.checked、口径卡的台账（可点击证 .find((s) => s.code === 'report_catalog_trimmed') check('……缺字段时标题照样说人话，不出 undefined', trimmedBare?.title === '证据目录超出篇幅，部分查询只列了编号和摘要' && trimmedBare?.sub === undefined, `${trimmedBare?.title} / ${trimmedBare?.sub}`) + + // 目录有省略时报告写了缺失断言（「没有查到」「数据缺失」），交回重写一次仍在：只标注、不判失败，时间线单独一行说清 + check('后端发缺失断言的警告，带着 count、texts', reportPy.includes('code="report_absence_claim"') + && reportPy.includes('count=len(absent), texts=texts'), '改了 report.py 的字段就同步改 decode.ts 的 explainLog 和这里的夹具') + const ABSENT = '报告在证据目录有省略时写了 2 处缺失断言（「数据缺失」、「没有查到」），已要求模型重写 1 次仍未改掉：' + + '被省略的查询仍在证据台账里，这类结论依据不足，应写作本报告未覆盖。报告照常产出，这几处已标注' + const absentRun = flatten(mod.decodeRun([E(1, 'node.started', 'w', { node_type: 'report', label: '写报告' }), + E(2, 'log', 'w', { level: 'warn', code: 'report_absence_claim', message: ABSENT, count: 2, texts: ['数据缺失', '没有查到'] }), + E(3, 'report.checked', 'w', { doc_artifact: 'abc', repairs: 1, ok: false, on_violation: 'fail', failed: false, + stats: { numbers: 0, numbers_cited: 0, uncited_numbers: 0, unresolved: 0 }, + violations: [{ code: 'absence_claim', text: '数据缺失', message: '证据目录有省略时写了缺失断言：写作模型没有看到全部证据，「没有查到」「不存在」「缺失」这类结论依据不足' }] }), + ])) + const ab = absentRun.find((s) => s.code === 'report_absence_claim') + check('report_absence_claim：标题说几处缺失断言、已标注、为什么（目录有省略）', ab?.title === '报告写了 2 处缺失断言，已标注（证据目录有省略）', ab?.title) + check('……副标题点名那几处说法', ab?.sub === '「数据缺失」、「没有查到」', ab?.sub) + check('……下一步说被省略的仍在台账里、应写「本报告未覆盖」；是提醒（warn），原话留在展开区', + !!ab?.next?.includes('本报告未覆盖') && !!ab?.next?.includes('证据台账') && ab?.level === 'warn' && ab?.detail === ABSENT, ab?.next) + const rcAbsent = absentRun.find((s) => s.code === 'report_checked') + check('……核对那一行照常完成（不判失败），违规清单里写着这条的说明', rcAbsent?.status === 'done' + && !!rcAbsent?.detail?.includes('缺失断言') && !!rcAbsent?.detail?.includes('依据不足'), `${rcAbsent?.status} ${rcAbsent?.detail}`) }) await section('工具门控 tool.gated（工具信任三档）', async () => { diff --git a/scripts/check-evidence.mjs b/scripts/check-evidence.mjs index 5126e1c0..3da07423 100644 --- a/scripts/check-evidence.mjs +++ b/scripts/check-evidence.mjs @@ -1094,7 +1094,34 @@ await section('sqlcheck', 'SQL 检查（数据目录阶段 4B）：查询步骤 await bad.ctx.close() }) -await section('q-narrow', '查询步骤在 360px：栏内展开、底部抽屉都不横向滚动；减少动效', async () => { +await section('absence', '缺失断言：证据目录有省略时写了「没有查到」，违规清单写明是什么、为什么依据不足，带着原句', async () => { + // 违规的形状：backend/app/engine/evidence.py 的 absence_claims（code、message、text、span、segment、unit、context） + const MESSAGE = '证据目录有省略时写了缺失断言：写作模型没有看到全部证据，「没有查到」「不存在」「缺失」这类结论依据不足' + const r = await probeQ((id, body) => body, { + patchQuery: (f) => { + const units = f.doc.blocks.flatMap((b) => b.units ?? []) + const unit = units.find((u) => (u.segments ?? []).some((s) => s.kind === 'text' && s.text.trim().length > 2)) + const seg = unit.segments.find((s) => s.kind === 'text' && s.text.trim().length > 2) + f.doc.violations = [...(f.doc.violations ?? []), { code: 'absence_claim', message: MESSAGE, text: '没有查到', + span: [seg.span[0], seg.span[0] + 1], segment: seg.id, unit: unit.id, context: '核销数据缺失：本轮没有查到任何结果。' }] + return f + }, + }) + await openQ(r.page, '1,288') + await r.page.locator('[data-evidence-panel] [data-ev-open-violations]').click() + await r.page.waitForSelector('[data-ev-violations]') + const item = r.page.locator('[data-ev-violation="absence_claim"]') + const text = await item.innerText().catch(() => '') + check('违规清单里有这一条，标「缺失断言」，不是笼统的「文档核对未通过」', text.includes('缺失断言') && !text.includes('文档核对未通过'), text) + check('……写明为什么依据不足、该怎么写（文案在 terms.ts）', text.includes(MESSAGE) + && (await item.locator('[data-ev-absence-hint]').innerText().catch(() => '')).includes('本报告未覆盖'), text.replace(/\s+/g, ' ')) + check('……带着原句；说的是一整句，不给「定位」，也不说「正文里没有对应文本」', text.includes('本轮没有查到任何结果') + && await item.getByRole('button', { name: '定位' }).count() === 0 && !text.includes('没有对应文本'), text.replace(/\s+/g, ' ')) + check('没有运行时报错', r.errors.length === 0, r.errors.join(' | ')) + await r.ctx.close() +}) + +await section('q-narrow','查询步骤在 360px：栏内展开、底部抽屉都不横向滚动；减少动效', async () => { const overflow = () => page.evaluate((sel) => { const el = document.querySelector(sel) return { sw: el.scrollWidth, cw: el.clientWidth } From 470f8b6641d91a637e4c1274413b3ec84b84e4f3 Mon Sep 17 00:00:00 2001 From: ThearchyHelios Date: Sun, 4 Oct 2026 11:33:09 +0800 Subject: [PATCH 5/5] =?UTF-8?q?=E6=8A=A5=E5=91=8A=E9=87=8C=E5=8D=95?= =?UTF-8?q?=E5=85=83=E6=A0=BC=E7=9A=84=20ISO=20=E6=97=A5=E6=9C=9F=E6=97=B6?= =?UTF-8?q?=E9=97=B4=E6=98=BE=E7=A4=BA=E6=88=90=E3=80=8C2026-06-30=2008:21?= =?UTF-8?q?:39=E3=80=8D=EF=BC=8C=E5=8F=AA=E6=94=B9=E6=98=BE=E7=A4=BA?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 报告表格里的时间以前照快照原样显示成 2026-06-30T08:21:39，读起来费劲。[[v:]] 和 [[table:]] 渲染出来的 ISO 日期时间改成用空格隔开（小数秒、时区照留），写作目录和流式输出同一套写法。快照里的值、引用记下的 value、eid、证据下钻都不变。 复核按文档生成时的规则：老文档存的是带 T 的字，拿新规则重新渲染会对不上、被当成文档被改过。所以显示规则也记版本：新文档里真有日期时间被改写时在顶层记 cell_display: 2（参与内容哈希），没有被改写的文档和以前一字不差；没有这个字段的按 1 版（原样）复核，记了认不出的值报 bad_schema。出口复核、期 4 的金样（test_provenance_unchanged）都照旧通过；裁判摘录是给模型看的，不改 --- backend/app/engine/evidence.py | 60 +++++++-- backend/tests/test_report_datetime_display.py | 118 ++++++++++++++++++ 2 files changed, 165 insertions(+), 13 deletions(-) create mode 100644 backend/tests/test_report_datetime_display.py diff --git a/backend/app/engine/evidence.py b/backend/app/engine/evidence.py index ff830cfb..e7fa5237 100644 --- a/backend/app/engine/evidence.py +++ b/backend/app/engine/evidence.py @@ -72,6 +72,15 @@ DOC_SCHEMA = "agentlab.report/1" #: 和记下的状态对不上，会被当成文档被改过（state_mismatch）。 ENTITY_SYNTAX = 2 ENTITY_SYNTAXES = frozenset({1, 2}) +#: 单元格显示规则的版本（文档顶层 cell_display）： +#: - 1：照快照里的原样显示，ISO 日期时间带 T（2026-06-30T08:21:39）。没有这个字段的老文档按它复核 +#: - 2：ISO 日期时间显示成「2026-06-30 08:21:39」（小数秒、时区照留）。只改显示：快照里的值、引用记下的 value、 +#: eid、证据下钻都不变 +#: 新组装的文档里真有日期时间被改写时才记 cell_display: 2（参与内容哈希），没有的文档和以前一字不差。复核必须按 +#: 文档生成时的那一版：老文档存的是带 T 的字，拿新规则重新渲染对不上，会被当成文档被改过（render_mismatch） +CELL_DISPLAY = 2 +CELL_DISPLAYS = frozenset({1, 2}) +_ISO_DATETIME = re.compile(r"^(\d{4}-\d{2}-\d{2})T(\d{2}:\d{2}(?::\d{2}(?:\.\d+)?)?(?:Z|[+-]\d{2}:?\d{2})?)$") # -------------------------------------------------------------------------- @@ -1273,7 +1282,8 @@ class _Snapshots: compose / verify / 流式渲染里：出口复核是另一次调用，照样重新取、重新验。 """ - def __init__(self, loader: Loader | None = None, *, entities: bool = True, syntax: int | None = None) -> None: + def __init__(self, loader: Loader | None = None, *, entities: bool = True, syntax: int | None = None, + display: int | None = None) -> None: self.loader = loader or load_artifact self.memo: dict[str, tuple[Any, str | None]] = {} self._index: tuple[dict[str, Any], _EntityIndex] | None = None @@ -1281,6 +1291,17 @@ class _Snapshots: self.entities_on = entities #: 这一次解析按哪一版实体语法：组装、流式渲染用当前版，复核按文档记下的版本（verify_doc） self.syntax = ENTITY_SYNTAX if syntax is None else syntax + #: 单元格按哪一版显示规则渲染（CELL_DISPLAY）：组装、流式渲染用当前版，复核按文档记下的版本 + self.display = CELL_DISPLAY if display is None else display + #: 这一次解析里有没有哪一格因为显示规则改了写法：组装时据此决定文档记不记 cell_display + self.reformatted = False + + def shown(self, raw: Any) -> Any: + """按显示规则要显示的原值：2 版起 ISO 日期时间的 T 换成空格。没改就是原值本身。""" + if self.display >= 2 and isinstance(raw, str) and (m := _ISO_DATETIME.match(raw.strip())): + self.reformatted = True + return f"{m.group(1)} {m.group(2)}" + return raw def entities(self, catalog: dict[str, Any]) -> _EntityIndex: if self._index is None or self._index[0] is not catalog: @@ -1289,11 +1310,12 @@ class _Snapshots: self._index = (catalog, _EntityIndex(shown, syntax=self.syntax)) return self._index[1] - def with_syntax(self, syntax: int) -> "_Snapshots": - """换一版实体语法的同一次解析。取过的快照共用：快照内容和语法版本无关，不必再取、再验一遍。""" - if syntax == self.syntax: + def with_syntax(self, syntax: int, display: int | None = None) -> "_Snapshots": + """换一版实体语法（和单元格显示规则）的同一次解析。取过的快照共用：快照内容和版本无关，不必再取、再验一遍。""" + display = self.display if display is None else display + if syntax == self.syntax and display == self.display: return self - twin = _Snapshots(self.loader, entities=self.entities_on, syntax=syntax) + twin = _Snapshots(self.loader, entities=self.entities_on, syntax=syntax, display=display) twin.memo = self.memo return twin @@ -1339,11 +1361,11 @@ class _Snapshots: def _snapshots(loader: Loader | _Snapshots | None, *, entities: bool = True, - syntax: int | None = None) -> _Snapshots: - """loader 已经是 _Snapshots 时沿用它（entities 不看）；给了 syntax 就换成那一版。""" + syntax: int | None = None, display: int | None = None) -> _Snapshots: + """loader 已经是 _Snapshots 时沿用它（entities 不看）；给了 syntax、display 就换成那一版。""" if isinstance(loader, _Snapshots): - return loader if syntax is None else loader.with_syntax(syntax) - return _Snapshots(loader, entities=entities, syntax=syntax) + return loader if syntax is None and display is None else loader.with_syntax(syntax or loader.syntax, display) + return _Snapshots(loader, entities=entities, syntax=syntax, display=display) _CELL_REF = re.compile(r"^(?P[QK]\d+)\.r(?P\d+)\.(?P.+)$") @@ -1393,7 +1415,7 @@ def _resolve_cell(marker: dict[str, Any], catalog: dict[str, Any], snaps: _Snaps try: raw, column = locate_cell(snapshot, parsed["row"], parsed["column"]) kind = column_kind(snapshot, column) - rendered = render_cell(raw, conv, kind=kind) + rendered = render_cell(snaps.shown(raw), conv, kind=kind) except CellError as e: return _unresolved(marker, alias, "cell", f"{alias} {e}") except RenderError as e: @@ -2630,8 +2652,10 @@ def compose_doc( # entity_syntax：这份文档按哪一版实体语法组装，复核照它走（verify_doc）。在文档顶层，参与内容哈希 # provenance：期 4 的文档标记（P4-SPEC 1.4）。带它的文档才有查询步骤的提示字段、推断来源、裁判摘录的新行； # 没有它的（期 4 之前组装的）一律按原样展示。写在顶层、参与内容哈希，事后补不上：补了哈希就变，封存链就断 + # cell_display：有单元格因为显示规则改了写法（ISO 日期时间）才记，复核照它渲染；没有的文档和以前一字不差 doc: dict[str, Any] = { "schema": DOC_SCHEMA, "entity_syntax": snaps.syntax, "provenance": DOC_PROVENANCE, + **({"cell_display": snaps.display} if snaps.reformatted and snaps.display != 1 else {}), "run_id": run_id, "node_id": node_id, "markdown": markdown, "source": source, "catalog": kept, "blocks": out_blocks, } @@ -2785,7 +2809,8 @@ def verify_doc( 有知识库检索时才有（它们才可能不是 0）：升级前的运行统计的键和以前一模一样。 """ entity_syntax = doc_entity_syntax(doc) - snaps = _snapshots(loader, entities=entities, syntax=entity_syntax or ENTITY_SYNTAX) + display = doc_cell_display(doc) + snaps = _snapshots(loader, entities=entities, syntax=entity_syntax or ENTITY_SYNTAX, display=display or CELL_DISPLAY) index = snaps.entities(catalog) violations: list[dict[str, Any]] = [] uncited: list[dict[str, Any]] = [] @@ -2803,6 +2828,8 @@ def verify_doc( if entity_syntax is None: violations.append(_violation("bad_schema", f"报告文档记录的表名、字段名规则版本「{doc.get('entity_syntax')}」无法识别")) + if display is None: + violations.append(_violation("bad_schema", f"报告文档记录的单元格显示规则版本「{doc.get('cell_display')}」无法识别")) markdown = str(doc.get("markdown") or "") masks: list[tuple[int, int]] = [] # 引用渲染出来的字 @@ -2949,6 +2976,13 @@ def verify_doc( return {"ok": not violations, "violations": violations, "stats": stats, "uncited": uncited} +def doc_cell_display(doc: Any) -> int | None: + """文档按哪一版单元格显示规则组装：没有 cell_display 字段的是 1 版（老文档、没有日期时间被改写的文档）， + 记了认不出的值返回 None。""" + raw = doc.get("cell_display", 1) if isinstance(doc, dict) else 1 + return raw if type(raw) is int and raw in CELL_DISPLAYS else None + + def doc_entity_syntax(doc: Any) -> int | None: """文档按哪一版实体语法组装：没有 entity_syntax 字段的是老文档（1 版），记了认不出的值返回 None。""" raw = doc.get("entity_syntax", 1) if isinstance(doc, dict) else 1 @@ -3182,7 +3216,7 @@ def _query_prompt(entry: dict[str, Any], snaps: _Snapshots, cells_allowed: bool, try: raw, _ = locate_cell(snapshot, r, c) kind = column_kind(snapshot, c) - shown, numeric = render_cell(raw, kind=kind), is_number(cell_value(raw, kind)) + shown, numeric = render_cell(snaps.shown(raw), kind=kind), is_number(cell_value(raw, kind)) except (CellError, RenderError): shown, numeric = MISSING, False cells.append(f"{c}={shown}") @@ -3327,7 +3361,7 @@ def _field_prompt(path: str, value: Any, ev: dict[str, Any], aliases: dict[str, if why: raise CellError(why) raw, column = locate_cell(snapshot, row, column) - shown = render_cell(raw, kind=column_kind(snapshot, column)) + shown = render_cell(snaps.shown(raw), kind=column_kind(snapshot, column)) except (CellError, RenderError): try: shown = render_cell(value) diff --git a/backend/tests/test_report_datetime_display.py b/backend/tests/test_report_datetime_display.py new file mode 100644 index 00000000..946e6eda --- /dev/null +++ b/backend/tests/test_report_datetime_display.py @@ -0,0 +1,118 @@ +"""报告里单元格的 ISO 日期时间显示成「2026-06-30 08:21:39」，不再是「2026-06-30T08:21:39」。 + +只改显示：快照里的值、引用记下的 value、eid、证据下钻都不变。复核要按文档生成时的规则：老文档里存的是带 T 的字， +拿新规则重新渲染会对不上，被当成文档被改过（render_mismatch）——所以新文档在顶层记 cell_display: 2（只在真有日期 +时间被改写时记，没有的文档和以前一字不差），没有这个字段的老文档按 1 版（原样）复核。期 4 的金样 +（test_provenance_unchanged）照旧逐字不变。 + +夹具合成：门店、卡券。 +""" +from __future__ import annotations + +import pytest + +from app.core.artifact_store import canonical_json +from app.engine import evidence as ev +from app.engine.evidence import ( + CELL_DISPLAY, + StreamRenderer, + build_catalog, + cell_eid, + compose_doc, + doc_cell_display, + iter_segments, + verify_doc, + writer_catalog, +) + +ART = "d" * 64 +SNAP = {"columns": ["store_name", "redeemed_at", "biz_date", "amount", "note"], + "column_types": {"store_name": "text", "redeemed_at": "datetime", "biz_date": "date", "amount": "number", + "note": "text"}, + "rows": [["门店01", "2026-06-30T08:21:39", "2026-06-30", 120, "T 开头的备注"], + ["门店02", "2026-06-29T23:05:00.250000+08:00", "2026-06-29", 80, "2026-06-29T10:00:00Z"]], + "truncated": False} +STORE = {ART: SNAP} + + +def load(artifact): + return STORE.get(artifact) + + +@pytest.fixture +def catalog(): + ledger = [{"kind": "query", "node_id": "fetch", "exec": 1, "artifact": ART, "tool": "db_query__shop", + "columns": SNAP["columns"], "rows": 2, "truncated": False}] + return build_catalog(nodes={}, ledger=ledger) + + +def cells(doc): + return [s for s in iter_segments(doc) if s.get("ref", "").startswith("v:")] + + +def test_iso_datetimes_read_with_a_space(catalog): + doc = compose_doc("最近一笔核销在 [[v:Q1.r0.redeemed_at]]，门店 [[v:Q1.r0.store_name]]。[[see:Q1]]", catalog, loader=load) + [when, store] = cells(doc) + assert when["text"] == "2026-06-30 08:21:39" and "2026-06-30 08:21:39" in doc["markdown"] + # 只改显示：引用记下的值、证据标识照旧 + assert when["cite"]["value"] == "2026-06-30T08:21:39" + assert when["cite"]["eid"] == cell_eid(ART, 0, "redeemed_at") + assert store["text"] == "门店01" + assert doc["cell_display"] == CELL_DISPLAY == 2 and doc_cell_display(doc) == 2 + assert '"cell_display":2' in canonical_json(doc), "在文档顶层，参与内容哈希" + assert verify_doc(doc, catalog, loader=load)["ok"] + + +def test_fractions_and_offsets_are_kept_and_dates_are_untouched(catalog): + doc = compose_doc("时间 [[v:Q1.r1.redeemed_at]]，日期 [[v:Q1.r1.biz_date]]，备注 [[v:Q1.r1.note]]。", catalog, + loader=load) + assert [s["text"] for s in cells(doc)] == ["2026-06-29 23:05:00.250000+08:00", "2026-06-29", "2026-06-29 10:00:00Z"] + assert verify_doc(doc, catalog, loader=load)["ok"] + + +def test_tables_read_the_same_way(catalog): + doc = compose_doc("[[table:Q1 cols=store_name,redeemed_at]]", catalog, loader=load) + assert "| 门店01 | 2026-06-30 08:21:39 |" in doc["markdown"] + assert "T08:21:39" not in doc["markdown"] + assert verify_doc(doc, catalog, loader=load)["ok"] + + +def test_documents_without_datetimes_are_unchanged(catalog): + doc = compose_doc("门店 [[v:Q1.r0.store_name]]，金额 [[v:Q1.r0.amount]]。", catalog, loader=load) + assert "cell_display" not in doc, "没有被改写的日期时间：文档和以前一字不差" + assert doc_cell_display(doc) == 1 + + +def test_old_documents_still_verify_as_they_were(catalog, monkeypatch): + """改动前组装的文档：存的是带 T 的字、没有 cell_display。按 1 版复核，不报 render_mismatch。""" + monkeypatch.setattr(ev, "CELL_DISPLAY", 1) + old = compose_doc("最近一笔核销在 [[v:Q1.r0.redeemed_at]]。", catalog, loader=load) + monkeypatch.undo() + assert "cell_display" not in old and "2026-06-30T08:21:39" in old["markdown"] + checked = verify_doc(old, catalog, loader=load) + assert checked["ok"], checked["violations"] + + +def test_a_document_edited_back_is_caught(catalog): + doc = compose_doc("最近一笔核销在 [[v:Q1.r0.redeemed_at]]。", catalog, loader=load) + [seg] = cells(doc) + start = seg["span"][0] + doc["markdown"] = doc["markdown"].replace("2026-06-30 08:21:39", "2026-06-30T08:21:39") + seg["text"] = "2026-06-30T08:21:39" + seg["span"] = [start, start + len(seg["text"])] + codes = {v["code"] for v in verify_doc(doc, catalog, loader=load)["violations"]} + assert "render_mismatch" in codes + + +def test_an_unknown_display_version_is_reported(catalog): + doc = compose_doc("最近一笔核销在 [[v:Q1.r0.redeemed_at]]。", catalog, loader=load) + doc["cell_display"] = 9 + assert "bad_schema" in {v["code"] for v in verify_doc(doc, catalog, loader=load)["violations"]} + + +def test_the_writer_and_the_stream_see_what_the_report_will_show(catalog): + text = writer_catalog(catalog, loader=load).text + assert "redeemed_at=2026-06-30 08:21:39" in text and "T08:21:39" not in text + stream = StreamRenderer(catalog, loader=load) + shown = stream.feed("最近一笔在 [[v:Q1.r0.redeemed_at]]。") + stream.flush() + assert "2026-06-30 08:21:39" in shown