diff --git a/BLOCKED.md b/BLOCKED.md index b2ac6986..1d62d8a0 100644 --- a/BLOCKED.md +++ b/BLOCKED.md @@ -1,5 +1,11 @@ # BLOCKED +## Jev 意图 v2：staging 库与 Flash 凭据（2026-09-27） + +- 缺 staging 库。`~/.ssh/jyotisha-staging` 不在本机，默认密钥连 `deploy@118.26.111.127` 被拒。本机 Docker 里的 Postgres 没有 `agentic_rectification_turns`。09-19 交来的 `source_b.jsonl` 157 条没有 `turn_id` / `case_id`，不能还原上一轮。替代证据：V0 用那次缓存重算，有上一轮 = 0，结论写缺数据，没有用空上一轮去打 V1/V2。 +- 缺 `DEEPSEEK_API_KEY`。Flash V0 用 09-19 缓存（生产提示，157 条 intent 89.2%）。Flash+V1 未跑。 +- Jev key 已在本机打通：合成样本 V0 / V2 各 1 次，响应 `model=jev-1.13.0`。这 2 次不进计分。SDK 仍是 `typesafe-sdk 0.7.0`。 + ## TASK-rectification-telemetry：真实 PostgreSQL 测试与登录态后台待验（2026-09-26） - ~~本机无 Docker：`npm run test:db` 未跑。本单新增 `frontend/tests/database-rectification-telemetry.test.ts`（列白名单、RLS 与表权限、函数执行权限、去重、归属校验、非法值整笔回滚、180 天清理、管理员只读汇总、账户删除级联），并把两张新表加进 `database-local-business.test.ts` 的 public 表白名单；两者在本机均为 docker unavailable 跳过，结果以门禁 DB job 为准（run 号待推送后补记）。替代证据：迁移 SQL 按既有迁移写法逐段复核；纯函数与白名单、迁移源文本合同在 `rectification-telemetry.test.ts`（18 条）里已跑通。~~ → 已解除：门禁 run 2938（`e801fcf5`）真实 PostgreSQL 17 跑过，`ok 1337 - rectification telemetry: server-only write, dedupe, retention, aggregate-only admin read, account cascade`，全量 4049 / 0 fail / 0 skip；migrate run 2940 已应用迁移。 diff --git a/docs/research/jev_intent_2026_09_19.md b/docs/research/jev_intent_2026_09_19.md index b9b30b75..4947127f 100644 --- a/docs/research/jev_intent_2026_09_19.md +++ b/docs/research/jev_intent_2026_09_19.md @@ -1,5 +1,7 @@ # TypeSafe Jev 接管校正意图分类 · 离线对照（2026-09-19） +后续对照（把上一轮放进 state）：`docs/research/jev_intent_2026_09_27.md`。下面的数字不改。 + - 任务：`docs/tasks/TASK-rectification-jev-intent-classifier-research-20260919.md` - 基线：`origin/staging` @ `69a44fe7` - Jev 模型：`jev-1.13.0`（不用 jev-latest） diff --git a/docs/research/jev_intent_2026_09_27.json b/docs/research/jev_intent_2026_09_27.json new file mode 100644 index 00000000..e9343009 --- /dev/null +++ b/docs/research/jev_intent_2026_09_27.json @@ -0,0 +1,99457 @@ +{ + "meta": { 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+ "has_new_dated_event": false, + "confidence": 0.99, + "raw": { + "intent": { + "choice": "ask_about_result", + "probabilities": { + "answer_current_focus": 0.0, + "provide_new_evidence": 0.0, + "stop_rectification": 0.0, + "unclear": 0.0, + "ask_about_result": 1.0 + }, + "confidence": 0.99 + }, + "answer_class": { + "choice": "unsure", + "probabilities": { + "yes": 0.0, + "unsure": 0.97, + "no": 0.03, + "weak_yes": 0.0 + }, + "confidence": 0.94 + }, + "has_new_dated_event": { + "noul": 0.12 + } + }, + "input_tokens": 1124, + "output_tokens": 133, + "elapsed_ms": 906, + "variant": "v0" + }, + "jev_v0_2": { + "ok": true, + "unavailable": false, + "model": "jev-1.13.0", + "intent": "ask_about_result", + "answer_class": null, + "has_new_dated_event": false, + "confidence": 0.99, + "raw": { + "intent": { + "choice": "ask_about_result", + "probabilities": { + "answer_current_focus": 0.0, + "stop_rectification": 0.0, + "unclear": 0.0, + "provide_new_evidence": 0.0, + "ask_about_result": 1.0 + }, + "confidence": 0.99 + }, + "answer_class": { + "choice": "unsure", + "probabilities": { + "yes": 0.01, + "unsure": 0.96, + "weak_yes": 0.0, + "no": 0.03 + }, + "confidence": 0.95 + }, + "has_new_dated_event": { + "noul": 0.12 + } + }, + "input_tokens": 1124, + "output_tokens": 133, + "elapsed_ms": 865, + "variant": "v0" + } + }, + { + "id": "C-choice-0455", + "source": "C", + "layer": "choice", + "gold": { + "answer_class": null, + "has_new_dated_event": false, + "intent": "stop_rectification" + }, + "gold_source": null, + "has_previous_turn": false, + "previous_turn_source": null, + "jev_v0_1": { + "ok": true, + "unavailable": false, + "model": "jev-1.13.0", + "intent": "stop_rectification", + "answer_class": null, + "has_new_dated_event": false, + "confidence": 1.0, + "raw": { + "intent": { + "choice": "stop_rectification", + "probabilities": { + "stop_rectification": 1.0, + "unclear": 0.0, + "provide_new_evidence": 0.0, + "answer_current_focus": 0.0, + "ask_about_result": 0.0 + }, + "confidence": 1.0 + }, + "answer_class": { + "choice": "unsure", + "probabilities": { + "no": 0.15, + "yes": 0.01, + "unsure": 0.84, + "weak_yes": 0.0 + }, + "confidence": 0.78 + }, + "has_new_dated_event": { + "noul": 0.06 + } + }, + "input_tokens": 1125, + "output_tokens": 134, + "elapsed_ms": 855, + "variant": "v0" + }, + "jev_v0_2": { + "ok": true, + "unavailable": false, + "model": "jev-1.13.0", + "intent": "stop_rectification", + "answer_class": null, + "has_new_dated_event": false, + "confidence": 1.0, + "raw": { + "intent": { + "choice": "stop_rectification", + "probabilities": { + "stop_rectification": 1.0, + "unclear": 0.0, + "provide_new_evidence": 0.0, + "answer_current_focus": 0.0, + "ask_about_result": 0.0 + }, + "confidence": 1.0 + }, + "answer_class": { + "choice": "unsure", + "probabilities": { + "unsure": 0.82, + "no": 0.17, + "yes": 0.01, + "weak_yes": 0.0 + }, + "confidence": 0.76 + }, + "has_new_dated_event": { + "noul": 0.06 + } + }, + "input_tokens": 1125, + "output_tokens": 134, + "elapsed_ms": 992, + "variant": "v0" + } + }, + { + "id": "C-choice-0079", + "source": "C", + "layer": "choice", + "gold": { + "answer_class": "yes", + "has_new_dated_event": true, + "intent": "answer_current_focus" + }, + "gold_source": null, + "has_previous_turn": false, + "previous_turn_source": null, + "jev_v0_1": { + "ok": true, + "unavailable": false, + "model": "jev-1.13.0", + "intent": "answer_current_focus", + "answer_class": "yes", + "has_new_dated_event": true, + "confidence": 0.94, + "raw": { + "intent": { + "choice": "answer_current_focus", + "probabilities": { + "unclear": 0.02, + "ask_about_result": 0.0, + "provide_new_evidence": 0.0, + "answer_current_focus": 0.98, + "stop_rectification": 0.0 + }, + "confidence": 0.97 + }, + "answer_class": { + "choice": "yes", + "probabilities": { + "weak_yes": 0.03, + "unsure": 0.02, + "yes": 0.95, + "no": 0.0 + }, + "confidence": 0.94 + }, + "has_new_dated_event": { + "noul": 0.95 + } + }, + "input_tokens": 1131, + "output_tokens": 132, + "elapsed_ms": 952, + "variant": "v0" + }, + "jev_v0_2": { + "ok": true, + "unavailable": false, + "model": "jev-1.13.0", + "intent": "answer_current_focus", + "answer_class": "yes", + "has_new_dated_event": true, + "confidence": 0.9, + "raw": { + "intent": { + "choice": "answer_current_focus", + "probabilities": { + "ask_about_result": 0.0, + "provide_new_evidence": 0.0, + "unclear": 0.03, + "answer_current_focus": 0.97, + "stop_rectification": 0.0 + }, + "confidence": 0.96 + }, + "answer_class": { + "choice": "yes", + "probabilities": { + "weak_yes": 0.05, + "unsure": 0.03, + "no": 0.0, + "yes": 0.92 + }, + "confidence": 0.9 + }, + "has_new_dated_event": { + "noul": 0.95 + } + }, + "input_tokens": 1131, + "output_tokens": 132, + "elapsed_ms": 952, + "variant": "v0" + } + }, + { + "id": "C-choice-0081", + "source": "C", + "layer": "choice", + "gold": { + "answer_class": "yes", + "has_new_dated_event": true, + "intent": "answer_current_focus" + }, + "gold_source": null, + "has_previous_turn": false, + "previous_turn_source": null, + "jev_v0_1": { + "ok": true, + "unavailable": false, + "model": "jev-1.13.0", + "intent": "answer_current_focus", + "answer_class": "yes", + "has_new_dated_event": true, + "confidence": 0.83, + "raw": { + "intent": { + "choice": "answer_current_focus", + "probabilities": { + "answer_current_focus": 0.97, + "provide_new_evidence": 0.0, + "ask_about_result": 0.0, + "unclear": 0.03, + "stop_rectification": 0.0 + }, + "confidence": 0.96 + }, + "answer_class": { + "choice": "yes", + "probabilities": { + "yes": 0.88, + "unsure": 0.0, + "no": 0.0, + "weak_yes": 0.12 + }, + "confidence": 0.83 + }, + "has_new_dated_event": { + "noul": 0.96 + } + }, + "input_tokens": 1134, + "output_tokens": 132, + "elapsed_ms": 874, + "variant": "v0" + }, + "jev_v0_2": { + "ok": true, + "unavailable": false, + "model": "jev-1.13.0", + "intent": "answer_current_focus", + "answer_class": "yes", + "has_new_dated_event": true, + "confidence": 0.82, + "raw": { + "intent": { + "choice": "answer_current_focus", + "probabilities": { + "provide_new_evidence": 0.0, + "answer_current_focus": 0.98, + "stop_rectification": 0.0, + "unclear": 0.02, + "ask_about_result": 0.0 + }, + "confidence": 0.97 + }, + "answer_class": { + "choice": "yes", + "probabilities": { + "unsure": 0.01, + "weak_yes": 0.13, + "no": 0.0, + "yes": 0.86 + }, + "confidence": 0.82 + }, + "has_new_dated_event": { + "noul": 0.95 + } + }, + "input_tokens": 1134, + "output_tokens": 132, + "elapsed_ms": 894, + "variant": "v0" + } + } + ] +} \ No newline at end of file diff --git a/docs/research/jev_intent_2026_09_27.md b/docs/research/jev_intent_2026_09_27.md new file mode 100644 index 00000000..ace288e2 --- /dev/null +++ b/docs/research/jev_intent_2026_09_27.md @@ -0,0 +1,52 @@ +# TypeSafe Jev 意图分类 · 上一轮 state 对照（2026-09-27） + +- 任务：`docs/tasks/TASK-rectification-jev-intent-classifier-research-v2-20260927.md` +- 基线：`origin/staging` @ `710c848b` +- 模型：`jev-1.13.0`；SDK `typesafe-sdk 0.7.0` +- 结论：**缺数据**。上一轮变量没有测全，不能判过门。已有的 V0 数字只作对照，不代替 V1/V2。 + +## 样本 + +- 来源 B：157 条。有上一轮 0，无上一轮 157。 +- 层：点选 17 / 采集 107 / 无焦点 33。 +- gold 来源：{"legacy_file": 157}。 +- 链式与 gold 上界都未跑。09-19 文件没有 turn_id / case_id，本机没有 staging 库。 + +## 来源 B + +| 变体 | n | intent | answer_class | dated | 高置信错误 | 低置信召回 | 无焦点 unclear→answer | +| --- | ---: | ---: | ---: | ---: | ---: | ---: | --- | +| Jev V0（09-19 缓存） | 157 | 91.1% | 98.0% | 91.7% | 6.4% | 24.1% | 7 / 无焦点高置信错误 12.1% | +| Flash V0（09-19 缓存，生产提示） | 157 | 89.2% | 97.0% | 93.6% | — | — | 5 / 无焦点高置信错误 — | +| Jev V1 | — | — | — | — | — | — | — | +| Jev V2 链式 | — | — | — | — | — | — | — | +| Jev V2 gold 上界 | — | — | — | — | — | — | — | +| Flash + V1 | — | — | — | — | — | — | — | + +Jev V0 自洽率：96.2%。 + +## 来源 C 回归锚（只 V0） + +- choice：重算 99.0%，09-19 公布 99.0%，差 0.0 pp（n=400）。 +- collect：重算 92.2%，09-19 公布 92.2%，差 0.05 pp（n=400）。 +- none：重算 96.0%，09-19 公布 96.0%，差 0.0 pp（n=100）。 +- 来源 C 自洽率：99.0%。 + +## 无焦点层混淆（来源 B，V0） + +计数来自报告 JSON，不含原文。 + +| gold \ pred | provide_new_evidence | stop_rectification | ask_about_result | unclear | answer_current_focus | +| --- | ---: | ---: | ---: | ---: | ---: | +| provide_new_evidence | 24 | 0 | 0 | 0 | 0 | +| stop_rectification | 0 | 0 | 0 | 0 | 0 | +| ask_about_result | 0 | 0 | 1 | 0 | 0 | +| unclear | 0 | 0 | 0 | 1 | 7 | +| answer_current_focus | 0 | 0 | 0 | 0 | 0 | + +## 写不出的项 + +n_有上一轮 0 < 100；V1 没有预测；V2 链式没有预测；V2 gold 上界没有预测；Flash+V1 没有预测；来源 B 与来源 C 同层 intent 差 > 10pp + +V1、V2、Flash+V1 要等 staging 库抽出带 `case_id` 的上一轮，并且本机有 `DEEPSEEK_API_KEY` 之后才能补。门槛不放宽。 + diff --git a/docs/tasks/PROGRESS-rectification-jev-intent-classifier-research-v2-20260927.md b/docs/tasks/PROGRESS-rectification-jev-intent-classifier-research-v2-20260927.md new file mode 100644 index 00000000..f896e067 --- /dev/null +++ b/docs/tasks/PROGRESS-rectification-jev-intent-classifier-research-v2-20260927.md @@ -0,0 +1,45 @@ +# 进度 · Jev 意图分类 v2：上一轮放进 state（2026-09-27） + +- 分支：`codex/jev-intent-v2-20260927` +- 基线：`origin/staging` @ `710c848b` +- 任务书：`docs/tasks/TASK-rectification-jev-intent-classifier-research-v2-20260927.md` +- 报告：`docs/research/jev_intent_2026_09_27.md` / `.json` +- 结论：**缺数据**。有上一轮 0 条，V1/V2 没有数字，不能判过门。 + +## 已跑到的数 + +| 项 | 结果 | +| --- | --- | +| 来源 B | 157（点选 17 / 采集 107 / 无焦点 33）。gold 全部来自 09-19 文件，不是新标 | +| 有上一轮 / 无上一轮 | 0 / 157 | +| Jev V0 intent | 91.1%（自洽 96.2%，高置信错误 6.4%，低置信召回 24.1%） | +| Flash V0 intent | 89.2%（09-19 缓存，生产提示；Flash 没有置信度） | +| 无焦点 `unclear → answer_current_focus` | Jev V0 = 7，Flash V0 = 5 | +| 来源 C V0 相对 09-19 公布值 | 点选 0 pp，采集 +0.05 pp（公布值四舍五入到 92.2%），无焦点 0 pp | + +V0 没有重打接口，state 与 09-19 相同，数字从 `jev_runs_v2.json` 重算。`--from-report` 只读报告 JSON 的 `rows`，来源 B intent 仍是 91.1%。 + +## 接口 + +- SDK：`typesafe-sdk 0.7.0`（09-19 进度里写的版本，未升级） +- 模型：响应字段 `model=jev-1.13.0` +- 连通性：合成样本 V0 1 次，1295 ms，1120 input tok；V2（多一个承接 Noul）1 次，971 ms，`continues_previous_turn=0.16`，规则阈值 0.9 未沿用上一轮。这两次不进计分，费用可忽略 +- 来源 B / C 的 V1、V2、Flash+V1：0 次 + +## 代码 + +- `build_state(..., variant=)`：默认仍是四字段。V1 加 `previous_turn`，V2 再加 `previous_decision` 和承接 Noul。Choice 的 criteria 原文没改。 +- `continues_previous_turn ≥ 0.9` 时沿用上一轮的 intent，并带上它的 answer_class，再走原来的 `enforce_combo`。低于 0.9 用本轮结果。 +- 没有 `case_id` 的行不会被串成上一轮。 +- 未改 `frontend/src/**`，未加部署环境变量。 + +## 测试 + +`python -m unittest tests.test_jev_intent_research`：15 passed。开工时文件里已有 12 条（任务书写的 11 条之外，fix2 已加报告 JSON 那条），本次新增 3 条：V1/V2 state 形状、承接规则、案件首轮 `previous_turn=null`。旧断言未改。 + +## 偏离 + +1. 重抽来源 B 被 staging 库挡住，见 `BLOCKED.md`。旧 157 条对不上 `turn_id`，因为交来的文件里没有这个字段。 +2. 没有拿「上一轮 = null」去打满 V1/V2。那样测的是空字段，不是 Magpie 的喂法。 +3. Flash V0 不可省，所以用了 09-19 的生产提示缓存，没有新的 Flash 调用。Flash+V1 按让步可以缺，这里确实缺。 +4. 来源 C 没有上一轮，按任务书不跑 V1/V2。V0 用缓存重算，没有再花 900 次调用。 diff --git a/docs/tasks/README.md b/docs/tasks/README.md index 80194cb7..df392393 100644 --- a/docs/tasks/README.md +++ b/docs/tasks/README.md @@ -295,7 +295,7 @@ | `TASK-rectification-cluster-width-research-20260914.md` | `PROGRESS-rectification-cluster-width-research-20260914.md` | **研究单**：上一轮证明调权重改不动交付区间宽度——所有方案宽度中位数都等于整个搜索窗。先确认 sweep 的宽度口径是否含淘汰（M0），再画簇结构像（M1），最后量三个改法：放宽簇上限、按分差决定是否合并、交付区间改分位覆盖（M2）。真值覆盖率不得下降 | 已验收（结论收入定论页 `docs/research/rectification_minute_resolution_closure_2026_09_14.md`，`a643452b`） | `cf972f40` | | `TASK-rectification-jev-intent-classifier-research-20260919.md` | `PROGRESS-rectification-jev-intent-classifier-research-20260919.md` | **研究单**：TypeSafe Jev（只做 Choice/Score/Noul 的校准判断模型，$0.042/Mtok）能否接管校正流的意图分类。产品 09-19 授权评估（推翻 09-15「分类只用贵模型」需重新拍板）。Agent 模拟校正流造 ≥900 条中文语料（标签先定、独立复核）+ 真机样本做代表性锚，量准确率 / 高置信错误率 / 低置信召回 / 延迟；只离线测，不改线上 | **关单**（产品 2026-09-19 拍板）：结论 **缺数据**，Jev 不接管、不影子双跑、不再造语料；09-15「分类只用贵模型」口径维持 | 真人 157 条：Jev intent 91.1% vs 线上 Flash 89.2%，无焦点层两边都差（78.8% / 69.7%，同一错模式）；挡住上线的是 Jev 置信度不可用：高置信错误全集 6.4% / 无焦点 12.1% > 3%，低置信召回 24%。fix2 507ef959 已验收通过 | -| `TASK-rectification-jev-intent-classifier-research-v2-20260927.md` | `PROGRESS-rectification-jev-intent-classifier-research-v2-20260927.md` | **研究单 v2**：Magpie 把上一轮决策塞进 Jev state 后路由效果大改；核实 TypeSafe 仍只有 `jev-1.13.0`（与 09-19 同版本），变量只在喂法。产品 09-27 授权：重抽真人来源 B（带 `case_id` 与上一轮，推翻 09-19「不再造语料」），state 三变体 V0/V1(+上一轮)/V2(+上一轮决策+承接 Noul)，门槛沿用 09-19 不放宽；不接管、不影子双跑维持 | **待执行**（任务书 2026-09-27 推 staging，产品自备 key） | 无焦点层 `unclear→answer_current_focus` 逐变体计数、有/无上一轮分组、Flash+V1 归因列；结论过门 / 未过门 / 缺数据 | +| `TASK-rectification-jev-intent-classifier-research-v2-20260927.md` | `PROGRESS-rectification-jev-intent-classifier-research-v2-20260927.md` | **研究单 v2**：Magpie 把上一轮决策塞进 Jev state 后路由效果大改；核实 TypeSafe 仍只有 `jev-1.13.0`（与 09-19 同版本），变量只在喂法。产品 09-27 授权：重抽真人来源 B（带 `case_id` 与上一轮，推翻 09-19「不再造语料」），state 三变体 V0/V1(+上一轮)/V2(+上一轮决策+承接 Noul)，门槛沿用 09-19 不放宽；不接管、不影子双跑维持 | **待验收**（结论缺数据：有上一轮 0 条，V1/V2 与 Flash+V1 未跑） | 分支 `codex/jev-intent-v2-20260927`；V0 重算与 09-19 一致（真人 Jev 91.1% / Flash 89.2%，无焦点 unclear→答题 7）；报告 `docs/research/jev_intent_2026_09_27.md` | | `TASK-rectification-minute-resolution-research-20260914.md` | `PROGRESS-rectification-minute-resolution-research-20260914.md` | **研究单**：候选分不开的根因是打分尺度——窗口内恒定项 11.5 分 vs 随分钟变化项 2.125 分（≈5:1）。先修封存基准（v3 每例仅 3 件事且被标 invalidated）出 v4，再离线量五个改法：分盘除数、去底座、**KP 宫头子主计分（产品 09-14 拍板，推翻 BUG-325 一条红线）**、年精度事件改边际似然、聚类签名层对齐。有收益才立实现单 | 已验收（结论收入定论页 `docs/research/rectification_minute_resolution_closure_2026_09_14.md`，`a643452b`） | `2d2467dc` | diff --git a/scripts/research/jev_intent_current.py b/scripts/research/jev_intent_current.py index 4c963384..f30068b5 100644 --- a/scripts/research/jev_intent_current.py +++ b/scripts/research/jev_intent_current.py @@ -74,7 +74,7 @@ def _parse_content(text: str) -> dict[str, Any]: return payload -def call_current(sample: Mapping[str, Any], *, timeout: float = 60.0) -> dict[str, Any]: +def call_current(sample: Mapping[str, Any], *, timeout: float = 60.0, variant: str = "v0") -> dict[str, Any]: api_key = os.environ.get("DEEPSEEK_API_KEY") or "" if not api_key: raise RuntimeError("DEEPSEEK_API_KEY missing") @@ -84,7 +84,7 @@ def call_current(sample: Mapping[str, Any], *, timeout: float = 60.0) -> dict[st "model": model, "messages": [ {"role": "system", "content": _instructions(sample) + "\n" + JSON_SCHEMA_HINT}, - {"role": "user", "content": json.dumps(build_state(sample), ensure_ascii=False)}, + {"role": "user", "content": json.dumps(build_state(sample, variant=variant if variant == "v1" else "v0"), ensure_ascii=False)}, ], "temperature": 0, "max_tokens": 256, @@ -128,12 +128,12 @@ def call_current(sample: Mapping[str, Any], *, timeout: float = 60.0) -> dict[st } -def call_current_retry(sample: Mapping[str, Any], *, retries: int = 4) -> dict[str, Any]: +def call_current_retry(sample: Mapping[str, Any], *, retries: int = 4, variant: str = "v0") -> dict[str, Any]: last_error = "" delay = 1.0 for _attempt in range(retries): try: - return call_current(sample) + return call_current(sample, variant=variant) except urllib.error.HTTPError as exc: last_error = f"HTTP{exc.code}" if exc.code in {429, 500, 502, 503, 529}: diff --git a/scripts/research/jev_intent_probe.py b/scripts/research/jev_intent_probe.py index bf345a1e..bc6c8e4d 100644 --- a/scripts/research/jev_intent_probe.py +++ b/scripts/research/jev_intent_probe.py @@ -55,14 +55,19 @@ def gold_of(sample: Mapping[str, Any]) -> dict[str, Any]: return enforce_combo(gold.get("intent"), gold.get("answer_class"), gold.get("has_new_dated_event")) -def call_jev(sample: Mapping[str, Any], *, timeout: float = 60.0) -> dict[str, Any]: +def call_jev( + sample: Mapping[str, Any], + *, + timeout: float = 60.0, + variant: str = "v0", +) -> dict[str, Any]: from typesafe_sdk import TypeSafeClient started = time.perf_counter() with TypeSafeClient() as client: response = client.system_one( - state=build_state(sample), - questions=sdk_questions(sample), + state=build_state(sample, variant=variant), + questions=sdk_questions(sample, variant=variant), model=JEV_MODEL, timeout=timeout, ) @@ -81,7 +86,11 @@ def call_jev(sample: Mapping[str, Any], *, timeout: float = 60.0) -> dict[str, A "noul": getattr(value, "noul", None), } answers = converted - parsed = parse_jev_answers(answers) + parsed = parse_jev_answers( + answers, + previous_decision=sample.get("previous_decision") if isinstance(sample.get("previous_decision"), dict) else None, + apply_continue=variant == "v2", + ) usage = payload.get("usage") or {} return { "ok": True, @@ -95,15 +104,18 @@ def call_jev(sample: Mapping[str, Any], *, timeout: float = 60.0) -> dict[str, A "input_tokens": usage.get("input_tokens"), "output_tokens": usage.get("output_tokens"), "elapsed_ms": elapsed_ms, + "continued": parsed.get("continued"), + "continues_previous_turn": parsed.get("continues_previous_turn"), + "variant": variant, } -def call_jev_retry(sample: Mapping[str, Any], *, retries: int = 4) -> dict[str, Any]: +def call_jev_retry(sample: Mapping[str, Any], *, retries: int = 4, variant: str = "v0") -> dict[str, Any]: last_error = "" delay = 1.0 for attempt in range(retries): try: - return call_jev(sample) + return call_jev(sample, variant=variant) except Exception as exc: # noqa: BLE001 last_error = type(exc).__name__ name = type(exc).__name__ @@ -127,6 +139,9 @@ def call_jev_retry(sample: Mapping[str, Any], *, retries: int = 4) -> dict[str, "input_tokens": None, "output_tokens": None, "elapsed_ms": None, + "continued": None, + "continues_previous_turn": None, + "variant": variant, "error": last_error, } diff --git a/scripts/research/jev_intent_probe_v2.py b/scripts/research/jev_intent_probe_v2.py new file mode 100644 index 00000000..60101d33 --- /dev/null +++ b/scripts/research/jev_intent_probe_v2.py @@ -0,0 +1,459 @@ +#!/usr/bin/env python3 +"""Jev intent research v2: previous-turn state variants. + +Offline by default for the report. Source B text stays in the gitignored cache. +Committed rows keep gold and model output only. +""" + +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path +from typing import Any, Mapping, Sequence + +ROOT = Path(__file__).resolve().parents[2] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +from scripts.research.jev_intent_probe import ( # noqa: E402 + CACHE_DIR, + cache_key, + confusion_counts, + layer_metrics, + load_cache, + load_jsonl, + self_consistency, + strip_confidence, +) +from scripts.research.jev_intent_questions import ( # noqa: E402 + JEV_MODEL, + previous_turn_payload, +) +from scripts.research.jev_intent_source_b_v2 import OUTPUT_PATH, from_legacy # noqa: E402 + +REPORT_JSON = ROOT / "docs" / "research" / "jev_intent_2026_09_27.json" +REPORT_MD = ROOT / "docs" / "research" / "jev_intent_2026_09_27.md" +SAMPLES_DIR = ROOT / "scripts" / "research" / "jev_intent_samples" +MIN_WITH_PREVIOUS = 100 +PUBLISHED_C_INTENT = {"choice": 0.990, "collect": 0.922, "none": 0.960} + +PRED_KEEP = ( + "ok", + "unavailable", + "model", + "intent", + "answer_class", + "has_new_dated_event", + "confidence", + "raw", + "input_tokens", + "output_tokens", + "elapsed_ms", + "continued", + "continues_previous_turn", + "variant", + "error", +) + + +def pct(value: float | None) -> str: + if value is None: + return "—" + return f"{value:.1%}" + + +def public_pred(pred: Mapping[str, Any] | None) -> dict[str, Any] | None: + if not pred: + return None + return {key: pred.get(key) for key in PRED_KEEP if key in pred} + + +def has_previous(row: Mapping[str, Any]) -> bool: + return previous_turn_payload(row) is not None + + +def unclear_to_focus(rows: Sequence[Mapping[str, Any]], pred_key: str) -> int: + count = 0 + for row in rows: + if row.get("layer") != "none": + continue + if (row.get("gold") or {}).get("intent") != "unclear": + continue + if (row.get(pred_key) or {}).get("intent") == "answer_current_focus": + count += 1 + return count + + +def strip_block(block: dict[str, Any] | None) -> dict[str, Any] | None: + """Flash has no confidence. Leave those rates empty instead of printing 0%.""" + if not block: + return None + out = dict(block) + out["all"] = strip_confidence(block.get("all")) + out["by_layer"] = { + layer: strip_confidence(metrics) if metrics else None + for layer, metrics in (block.get("by_layer") or {}).items() + } + out["with_previous"] = strip_confidence(block.get("with_previous")) + out["without_previous"] = strip_confidence(block.get("without_previous")) + return out + + +def pack_metrics(rows: Sequence[Mapping[str, Any]], pred_key: str) -> dict[str, Any] | None: + usable = [row for row in rows if row.get(pred_key)] + if not usable: + return None + by_layer = {} + for layer in ("choice", "collect", "none"): + subset = [row for row in usable if row.get("layer") == layer] + by_layer[layer] = layer_metrics(subset, pred_key=pred_key) if subset else None + with_prev = [row for row in usable if row.get("has_previous_turn")] + without_prev = [row for row in usable if not row.get("has_previous_turn")] + return { + "all": layer_metrics(usable, pred_key=pred_key), + "by_layer": by_layer, + "with_previous": layer_metrics(with_prev, pred_key=pred_key) if with_prev else None, + "without_previous": layer_metrics(without_prev, pred_key=pred_key) if without_prev else None, + "none_unclear_to_focus": unclear_to_focus(usable, pred_key), + "self_consistency": None, + } + + +def attach_legacy_preds(samples: Sequence[dict[str, Any]], cache: Mapping[str, Any], mapping: Mapping[str, str]) -> dict[str, int]: + missing = {dest: 0 for dest in mapping} + for sample in samples: + for dest, legacy in mapping.items(): + key = cache_key(legacy, sample) + if key in cache: + pred = dict(cache[key]) + pred["variant"] = "v0" + sample[dest] = pred + else: + missing[dest] += 1 + return missing + + +def public_rows(samples: Sequence[Mapping[str, Any]], pred_keys: Sequence[str]) -> list[dict[str, Any]]: + rows = [] + for sample in samples: + row = { + "id": sample.get("id"), + "source": sample.get("source"), + "layer": sample.get("layer"), + "gold": sample.get("gold"), + "gold_source": sample.get("gold_source"), + "has_previous_turn": bool(sample.get("has_previous_turn")), + "previous_turn_source": sample.get("previous_turn_source"), + } + for key in pred_keys: + pred = public_pred(sample.get(key) if isinstance(sample.get(key), dict) else None) + if pred: + row[key] = pred + rows.append(row) + return rows + + +def decide(meta: Mapping[str, Any], metrics: Mapping[str, Any]) -> dict[str, str]: + blocked: list[str] = [] + if meta.get("n_with_previous", 0) < MIN_WITH_PREVIOUS: + blocked.append(f"n_有上一轮 {meta.get('n_with_previous', 0)} < {MIN_WITH_PREVIOUS}") + if not metrics.get("jev_v1"): + blocked.append("V1 没有预测") + if not metrics.get("jev_v2_chain"): + blocked.append("V2 链式没有预测") + if not metrics.get("jev_v2_gold"): + blocked.append("V2 gold 上界没有预测") + if not metrics.get("flash_v1"): + blocked.append("Flash+V1 没有预测") + if not metrics.get("flash_v0"): + blocked.append("Flash V0 没有预测") + represent = metrics.get("representativeness") or {} + if represent.get("fail"): + blocked.append("来源 B 与来源 C 同层 intent 差 > 10pp") + if blocked: + return { + "verdict": "缺数据", + "blocked": "；".join(blocked), + "reason": "上一轮变量没有测全，不能判过门。已有的 V0 数字只作对照，不代替 V1/V2。", + } + v0_none = ((metrics.get("jev_v0") or {}).get("by_layer") or {}).get("none") or {} + v2_none = ((metrics.get("jev_v2_chain") or {}).get("by_layer") or {}).get("none") or {} + v0_high = v0_none.get("high_conf_error_rate") + v2_high = v2_none.get("high_conf_error_rate") + if v0_high is None or v2_high is None: + return {"verdict": "blocked", "blocked": "无焦点层高置信错误写不出", "reason": "V2 相对 V0 的无焦点层比较缺数字。"} + jev = (metrics.get("jev_v2_chain") or {}).get("all") or {} + flash = (metrics.get("flash_v0") or {}).get("all") or {} + high = jev.get("high_conf_error_rate") + recall = jev.get("low_conf_recall") + jev_intent = jev.get("intent_acc") + flash_intent = flash.get("intent_acc") + missing = [ + name for name, value in ( + ("高置信错误", high), + ("低置信召回", recall), + ("V2 intent", jev_intent), + ("Flash V0 intent", flash_intent), + ) if value is None + ] + if missing: + return {"verdict": "blocked", "blocked": "、".join(missing), "reason": "门槛里有写不出的数字。"} + gates = [] + if high > 0.03: + gates.append(f"高置信错误 {high:.1%} > 3%") + if recall < 0.60: + gates.append(f"低置信召回 {recall:.1%} < 60%") + if jev_intent < flash_intent - 0.03: + gates.append(f"intent {jev_intent:.1%} < 现行 {flash_intent:.1%} − 3pp") + if v2_high >= v0_high: + gates.append(f"无焦点层高置信错误 V2 {v2_high:.1%} 没有低于 V0 {v0_high:.1%}") + if gates: + return {"verdict": "未过门", "blocked": "", "reason": "；".join(gates)} + return { + "verdict": "过门", + "blocked": "", + "reason": "三项门槛都过，且 V2 无焦点层高置信错误低于 V0。", + } + + +def representativeness(source_b: Sequence[Mapping[str, Any]], source_c: Sequence[Mapping[str, Any]]) -> dict[str, Any]: + layers = {} + fail = False + for layer in ("choice", "collect", "none"): + b_rows = [row for row in source_b if row.get("layer") == layer and row.get("jev_v0_1")] + c_rows = [row for row in source_c if row.get("layer") == layer and row.get("jev_v0_1")] + if not b_rows or not c_rows: + layers[layer] = {"n_b": len(b_rows), "n_c": len(c_rows), "delta": None} + continue + b_acc = layer_metrics(b_rows, pred_key="jev_v0_1")["intent_acc"] + c_acc = layer_metrics(c_rows, pred_key="jev_v0_1")["intent_acc"] + delta = abs(b_acc - c_acc) + fail = fail or delta > 0.10 + published = PUBLISHED_C_INTENT[layer] + layers[layer] = { + "n_b": len(b_rows), + "n_c": len(c_rows), + "source_b_intent": b_acc, + "source_c_intent": c_acc, + "delta": delta, + "published_c_intent": published, + "published_delta_pp": (c_acc - published) * 100, + } + return {"fail": fail, "by_layer": layers} + + +def build_from_samples(source_b: Sequence[dict[str, Any]], source_c: Sequence[dict[str, Any]]) -> dict[str, Any]: + for row in list(source_b) + list(source_c): + if "previous_turn" in row: + row["has_previous_turn"] = has_previous(row) + else: + row["has_previous_turn"] = bool(row.get("has_previous_turn")) + pred_keys = ("jev_v0_1", "jev_v0_2", "flash_v0") + b_metrics = { + "jev_v0": pack_metrics(source_b, "jev_v0_1"), + "flash_v0": strip_block(pack_metrics(source_b, "flash_v0")), + "jev_v1": pack_metrics(source_b, "jev_v1_1"), + "jev_v2_chain": pack_metrics(source_b, "jev_v2_chain_1"), + "jev_v2_gold": pack_metrics(source_b, "jev_v2_gold_1"), + "flash_v1": strip_block(pack_metrics(source_b, "flash_v1")), + } + if b_metrics["jev_v0"]: + b_metrics["jev_v0"]["self_consistency"] = self_consistency(source_b, "jev_v0_1", "jev_v0_2") + none_b = [row for row in source_b if row.get("layer") == "none"] + meta = { + "model": JEV_MODEL, + "sdk": "typesafe-sdk 0.7.0", + "source_b_n": len(source_b), + "n_with_previous": sum(1 for row in source_b if row.get("has_previous_turn")), + "n_without_previous": sum(1 for row in source_b if not row.get("has_previous_turn")), + "layers": { + layer: sum(1 for row in source_b if row.get("layer") == layer) + for layer in ("choice", "collect", "none") + }, + "gold_source": {}, + "source_c_n": len(source_c), + "previous_decision_note": "链式与 gold 上界都未跑。09-19 文件没有 turn_id / case_id，本机没有 staging 库。", + } + gold_counts: dict[str, int] = {} + for row in source_b: + source = str(row.get("gold_source") or "unknown") + gold_counts[source] = gold_counts.get(source, 0) + 1 + meta["gold_source"] = gold_counts + metrics = { + **b_metrics, + "source_c_v0": pack_metrics(source_c, "jev_v0_1"), + "representativeness": representativeness(source_b, source_c), + "source_b_none_confusion": { + "jev_v0": confusion_counts(none_b, "jev_v0_1"), + "flash_v0": confusion_counts(none_b, "flash_v0"), + } if none_b else {}, + } + if metrics["source_c_v0"]: + metrics["source_c_v0"]["self_consistency"] = self_consistency(source_c, "jev_v0_1", "jev_v0_2") + conclusion = decide(meta, metrics) + return { + "meta": meta, + "metrics": metrics, + "conclusion": conclusion, + "rows": public_rows(list(source_b) + list(source_c), pred_keys + ( + "jev_v1_1", "jev_v1_2", "jev_v2_chain_1", "jev_v2_chain_2", + "jev_v2_gold_1", "jev_v2_gold_2", "flash_v1", + )), + } + + +def load_samples() -> tuple[list[dict[str, Any]], list[dict[str, Any]], dict[str, int]]: + if OUTPUT_PATH.is_file(): + source_b = load_jsonl(OUTPUT_PATH) + else: + source_b = from_legacy(load_jsonl(CACHE_DIR / "source_b.jsonl")) + source_b = [row for row in source_b if isinstance(row.get("gold"), dict) and row["gold"].get("intent")] + source_c = load_jsonl(SAMPLES_DIR / "simulated.jsonl") + cache = load_cache() + missing = {} + missing.update(attach_legacy_preds(source_b, cache, { + "jev_v0_1": "jev_1", + "jev_v0_2": "jev_2", + "flash_v0": "current_1", + })) + missing.update(attach_legacy_preds(source_c, cache, { + "jev_v0_1": "jev_1", + "jev_v0_2": "jev_2", + })) + return source_b, source_c, missing + + +def recompute_from_rows(rows: Sequence[Mapping[str, Any]]) -> dict[str, Any]: + source_b = [dict(row) for row in rows if row.get("source") == "B"] + source_c = [dict(row) for row in rows if row.get("source") == "C"] + return build_from_samples(source_b, source_c) + + +def metric_line(title: str, block: Mapping[str, Any] | None) -> str: + if not block or not block.get("all"): + return f"| {title} | — | — | — | — | — | — | — |" + all_m = block["all"] + none_high = ((block.get("by_layer") or {}).get("none") or {}).get("high_conf_error_rate") + return ( + f"| {title} | {all_m['n']} | {pct(all_m['intent_acc'])} | {pct(all_m['answer_class_acc'])} | " + f"{pct(all_m['dated_acc'])} | {pct(all_m['high_conf_error_rate'])} | {pct(all_m['low_conf_recall'])} | " + f"{block.get('none_unclear_to_focus')} / 无焦点高置信错误 {pct(none_high)} |" + ) + + +def write_markdown(report: Mapping[str, Any]) -> None: + meta = report["meta"] + metrics = report["metrics"] + conclusion = report["conclusion"] + layers = meta.get("layers") or {} + gold = meta.get("gold_source") or {} + represent = metrics.get("representativeness") or {} + lines = [ + "# TypeSafe Jev 意图分类 · 上一轮 state 对照（2026-09-27）", + "", + f"- 任务：`docs/tasks/TASK-rectification-jev-intent-classifier-research-v2-20260927.md`", + f"- 基线：`origin/staging` @ `710c848b`", + f"- 模型：`{meta.get('model')}`；SDK `{meta.get('sdk')}`", + f"- 结论：**{conclusion.get('verdict')}**。{conclusion.get('reason')}", + "", + "## 样本", + "", + f"- 来源 B：{meta.get('source_b_n')} 条。有上一轮 {meta.get('n_with_previous')}，无上一轮 {meta.get('n_without_previous')}。", + f"- 层：点选 {layers.get('choice', 0)} / 采集 {layers.get('collect', 0)} / 无焦点 {layers.get('none', 0)}。", + f"- gold 来源：{json.dumps(gold, ensure_ascii=False)}。", + f"- {meta.get('previous_decision_note')}", + "", + "## 来源 B", + "", + "| 变体 | n | intent | answer_class | dated | 高置信错误 | 低置信召回 | 无焦点 unclear→answer |", + "| --- | ---: | ---: | ---: | ---: | ---: | ---: | --- |", + metric_line("Jev V0（09-19 缓存）", metrics.get("jev_v0")), + metric_line("Flash V0（09-19 缓存，生产提示）", metrics.get("flash_v0")), + metric_line("Jev V1", metrics.get("jev_v1")), + metric_line("Jev V2 链式", metrics.get("jev_v2_chain")), + metric_line("Jev V2 gold 上界", metrics.get("jev_v2_gold")), + metric_line("Flash + V1", metrics.get("flash_v1")), + "", + f"Jev V0 自洽率：{pct((metrics.get('jev_v0') or {}).get('self_consistency'))}。", + "", + "## 来源 C 回归锚（只 V0）", + "", + ] + c_block = metrics.get("source_c_v0") or {} + for layer in ("choice", "collect", "none"): + cell = (represent.get("by_layer") or {}).get(layer) or {} + published = cell.get("published_c_intent") + got = cell.get("source_c_intent") + delta = cell.get("published_delta_pp") + lines.append( + f"- {layer}：重算 {pct(got)}，09-19 公布 {pct(published)}，差 {delta if delta is None else round(delta, 2)} pp（n={cell.get('n_c')}）。" + ) + lines += [ + f"- 来源 C 自洽率：{pct(c_block.get('self_consistency'))}。", + "", + "## 无焦点层混淆（来源 B，V0）", + "", + "计数来自报告 JSON，不含原文。", + "", + ] + confusion = (metrics.get("source_b_none_confusion") or {}).get("jev_v0") or {} + labels = confusion.get("labels") or [] + counts = confusion.get("counts") or {} + if labels: + lines.append("| gold \\ pred | " + " | ".join(labels) + " |") + lines.append("| --- | " + " | ".join("---:" for _ in labels) + " |") + for gold in labels: + cells = [str((counts.get(gold) or {}).get(pred, 0)) for pred in labels] + lines.append(f"| {gold} | " + " | ".join(cells) + " |") + lines += [ + "", + "## 写不出的项", + "", + conclusion.get("blocked") or "无", + "", + "V1、V2、Flash+V1 要等 staging 库抽出带 `case_id` 的上一轮，并且本机有 `DEEPSEEK_API_KEY` 之后才能补。门槛不放宽。", + "", + ] + REPORT_MD.write_text("\n".join(lines) + "\n", encoding="utf-8") + + +def main(argv: Sequence[str] | None = None) -> int: + parser = argparse.ArgumentParser() + parser.add_argument("--offline", action="store_true") + parser.add_argument("--from-report", action="store_true", help="recompute tables from the committed JSON rows") + args = parser.parse_args(argv) + if args.from_report: + if not REPORT_JSON.is_file(): + print("report json missing", file=sys.stderr) + return 2 + payload = json.loads(REPORT_JSON.read_text(encoding="utf-8")) + report = recompute_from_rows(payload.get("rows") or []) + report["meta"]["recomputed_from"] = "report_rows" + write_markdown(report) + print(json.dumps({ + "verdict": report["conclusion"]["verdict"], + "source_b_intent": ((report["metrics"].get("jev_v0") or {}).get("all") or {}).get("intent_acc"), + "n": report["meta"]["source_b_n"], + "n_with_previous": report["meta"]["n_with_previous"], + }, ensure_ascii=False)) + return 0 + source_b, source_c, missing = load_samples() + report = build_from_samples(source_b, source_c) + report["meta"]["legacy_cache_missing"] = missing + report["meta"]["offline"] = True + REPORT_JSON.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8") + write_markdown(report) + print(json.dumps({ + "verdict": report["conclusion"]["verdict"], + "source_b_n": report["meta"]["source_b_n"], + "n_with_previous": report["meta"]["n_with_previous"], + "missing": missing, + "json": str(REPORT_JSON), + }, ensure_ascii=False)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/scripts/research/jev_intent_questions.py b/scripts/research/jev_intent_questions.py index 3c15cfe2..4ac593ed 100644 --- a/scripts/research/jev_intent_questions.py +++ b/scripts/research/jev_intent_questions.py @@ -20,6 +20,8 @@ ANSWER_CLASS_VALUES = ("yes", "weak_yes", "no", "unsure") COLLECT_ANSWER_CLASSES = ("yes", "weak_yes", "no", "unsure") JEV_MODEL = "jev-1.13.0" NOUL_TRUE_THRESHOLD = 0.5 +CONTINUE_THRESHOLD = 0.9 +STATE_VARIANTS = ("v0", "v1", "v2") # Literal criteria. Production hedges ("通常", "不要猜", "不要按关键词") are dropped. INTENT_INSTRUCTIONS = { @@ -71,6 +73,12 @@ HAS_NEW_DATED_EVENT_CRITERIA = { ), } +CONTINUES_PREVIOUS_TURN_INSTRUCTIONS = "这句是否只是承接上一轮，没有新的意思。" +CONTINUES_PREVIOUS_TURN_CRITERIA = { + "true": "这句只承接上一轮，没有提出新问题，也没有补充新的意思。", + "false": "这句是在回答新问题、另起一件事，或补充了上一轮没有的意思。", +} + # One row per production prompt sentence that was rewritten or dropped. CRITERION_MAP: tuple[dict[str, str], ...] = ( { @@ -201,26 +209,83 @@ def answer_class_criteria( return criteria -def build_state(sample: Mapping[str, Any]) -> dict[str, Any]: +def previous_turn_payload(sample: Mapping[str, Any]) -> dict[str, str] | None: + """Prior turn in the same case. Missing case linkage or a first turn is null.""" + prev = sample.get("previous_turn") + if not isinstance(prev, dict): + return None + if "assistant_message" not in prev and "user_message" not in prev: + return None + return { + "assistant_message": "" if prev.get("assistant_message") is None else str(prev.get("assistant_message")), + "user_message": "" if prev.get("user_message") is None else str(prev.get("user_message")), + } + + +def previous_decision_payload(sample: Mapping[str, Any]) -> dict[str, Any] | None: + decision = sample.get("previous_decision") + if not isinstance(decision, dict): + return None + intent = decision.get("intent") + if intent not in INTENT_VALUES: + return None + answer = decision.get("answer_class") + if answer not in ANSWER_CLASS_VALUES: + answer = None + return {"intent": intent, "answer_class": answer} + + +def intent_instructions(variant: str = "v0") -> dict[str, Any]: + """V0 stays the 09-19 instructions. V1/V2 name the extra state fields; criteria do not change.""" + if variant not in STATE_VARIANTS: + raise ValueError(f"unknown state variant: {variant}") + if variant == "v0": + return INTENT_INSTRUCTIONS + inputs = ["current_question", "options", "user_message", "case_status", "previous_turn"] + context = ( + "previous_turn is the immediately previous assistant_message and user_message in this case. " + "It is null on the first turn. Classify the current user_message, not the previous one." + ) + if variant == "v2": + inputs.append("previous_decision") + context += " previous_decision is the prior turn's intent and answer_class, not a label for this turn." + return { + "task": INTENT_INSTRUCTIONS["task"], + "inputs": inputs, + "rule": INTENT_INSTRUCTIONS["rule"], + "context": context, + } + + +def build_state(sample: Mapping[str, Any], *, variant: str = "v0") -> dict[str, Any]: + if variant not in STATE_VARIANTS: + raise ValueError(f"unknown state variant: {variant}") focus = sample.get("focus") if isinstance(sample.get("focus"), dict) else {} options = list(focus.get("options") or []) - return { + state: dict[str, Any] = { "current_question": str(focus.get("current_question") or ""), "options": options, "user_message": str(sample.get("user_message") or ""), "case_status": str(focus.get("case_status") or sample.get("case_status") or "collecting_evidence"), } + if variant in {"v1", "v2"}: + state["previous_turn"] = previous_turn_payload(sample) + if variant == "v2": + state["previous_decision"] = previous_decision_payload(sample) + return state -def build_questions(sample: Mapping[str, Any]) -> dict[str, Any]: - """Three TypeSafe questions, same request, official fan-out.""" +def build_questions(sample: Mapping[str, Any], *, variant: str = "v0") -> dict[str, Any]: + """Three TypeSafe questions, same request, official fan-out. V2 adds one Noul.""" + if variant not in STATE_VARIANTS: + raise ValueError(f"unknown state variant: {variant}") layer = str(sample.get("layer") or "none") focus = sample.get("focus") if isinstance(sample.get("focus"), dict) else {} options = list(focus.get("options") or []) - return { + questions = { "intent": { "type": "choice", - "instructions": INTENT_INSTRUCTIONS, + "instructions": intent_instructions(variant), "criteria": dict(INTENT_CRITERIA), }, "answer_class": { @@ -234,16 +299,23 @@ def build_questions(sample: Mapping[str, Any]) -> dict[str, Any]: "criteria": dict(HAS_NEW_DATED_EVENT_CRITERIA), }, } + if variant == "v2": + questions["continues_previous_turn"] = { + "type": "noul", + "instructions": CONTINUES_PREVIOUS_TURN_INSTRUCTIONS, + "criteria": dict(CONTINUES_PREVIOUS_TURN_CRITERIA), + } + return questions -def sdk_questions(sample: Mapping[str, Any]) -> dict[str, Any]: +def sdk_questions(sample: Mapping[str, Any], *, variant: str = "v0") -> dict[str, Any]: """SDK objects when typesafe_sdk is installed; JSON dicts otherwise.""" - payload = build_questions(sample) + payload = build_questions(sample, variant=variant) try: from typesafe_sdk import Choice, Noul except ImportError: return payload - return { + built = { "intent": Choice( instructions=payload["intent"]["instructions"], criteria=payload["intent"]["criteria"], @@ -257,6 +329,13 @@ def sdk_questions(sample: Mapping[str, Any]) -> dict[str, Any]: criteria=payload["has_new_dated_event"]["criteria"], ), } + continues = payload.get("continues_previous_turn") + if isinstance(continues, dict): + built["continues_previous_turn"] = Noul( + instructions=continues["instructions"], + criteria=continues["criteria"], + ) + return built def enforce_combo( @@ -287,7 +366,12 @@ def enforce_combo( } -def parse_jev_answers(answers: Mapping[str, Any]) -> dict[str, Any]: +def parse_jev_answers( + answers: Mapping[str, Any], + *, + previous_decision: Mapping[str, Any] | None = None, + apply_continue: bool = False, +) -> dict[str, Any]: intent_row = answers.get("intent") or {} class_row = answers.get("answer_class") or {} noul_row = answers.get("has_new_dated_event") or {} @@ -315,6 +399,13 @@ def parse_jev_answers(answers: Mapping[str, Any]) -> dict[str, Any]: "noul": noul_value, }, } + continues_row = answers.get("continues_previous_turn") or {} + continues_value = continues_row.get("noul") + parsed["raw"]["continues_previous_turn"] = {"noul": continues_value} + parsed["continues_previous_turn"] = ( + float(continues_value) if isinstance(continues_value, (int, float)) else None + ) + parsed["continued"] = False intent_conf = intent_row.get("confidence") class_conf = class_row.get("confidence") if parsed["intent"] == "answer_current_focus" and isinstance(intent_conf, (int, float)) and isinstance(class_conf, (int, float)): @@ -323,4 +414,23 @@ def parse_jev_answers(answers: Mapping[str, Any]) -> dict[str, Any]: parsed["confidence"] = float(intent_conf) else: parsed["confidence"] = None + decision = previous_decision_payload({"previous_decision": previous_decision or {}}) + if ( + apply_continue + and isinstance(continues_value, (int, float)) + and float(continues_value) >= CONTINUE_THRESHOLD + and decision is not None + ): + adopted = enforce_combo( + decision["intent"], + decision.get("answer_class"), + parsed["has_new_dated_event"], + ) + parsed["intent_before_continue"] = parsed["intent"] + parsed["answer_class_before_continue"] = parsed["answer_class"] + parsed["intent"] = adopted["intent"] + parsed["answer_class"] = adopted["answer_class"] + parsed["has_new_dated_event"] = adopted["has_new_dated_event"] + parsed["continued"] = True + parsed["confidence"] = float(continues_value) return parsed diff --git a/scripts/research/jev_intent_samples/README.md b/scripts/research/jev_intent_samples/README.md index 2ffa254c..dd52c500 100644 --- a/scripts/research/jev_intent_samples/README.md +++ b/scripts/research/jev_intent_samples/README.md @@ -15,7 +15,9 @@ sha256: `79d53a958b3b42ed18c79e3bbcff4b31deb22b65eedcd89b39326a7fefc9b0e8` ```sql select t.id as turn_id, + t.case_id, t.user_message, + t.assistant_message, t.status as turn_status, t.created_at, c.status as case_status, @@ -24,16 +26,23 @@ select f.status as focus_status from public.agentic_rectification_turns t join public.agentic_rectification_cases c on c.id = t.case_id -left join public.agentic_rectification_conversation_focuses f - on f.case_id = t.case_id - and f.status = 'active' +left join lateral ( + select * + from public.agentic_rectification_conversation_focuses f + where f.case_id = t.case_id + and f.asked_at <= t.created_at + order by f.asked_at desc + limit 1 +) f on true where t.user_message is not null and length(btrim(t.user_message)) > 0 -order by t.created_at desc -limit 500; +order by t.case_id, t.created_at; ``` +同一条 SQL 也写在 `scripts/research/jev_intent_source_b_v2.py` 的 `EXTRACT_SQL`。本地脚本按 `(case_id, created_at)` 给每条填 `previous_turn = {assistant_message, user_message}`；同一案件的第一轮，以及没有 `case_id` 的行，都是 `null`。没有 `case_id` 的行不会互相串成上一轮。 + - 本地路径（worktree 外，已 gitignore）：`G:/Ferti/Jyotisha/.cache/jev_intent/source_b.jsonl` +- v2 路径（同样不提交）：`G:/Ferti/Jyotisha/.cache/jev_intent/source_b_v2.jsonl` - 本机抽取条数：157（点选 17 / 采集 107 / 无焦点 33），gold 已全部人工标注，未用模型代标 - 长度：P25=12 / 中位=21 / P75=28 / 最长=172 - 标注耗时：约 40 分钟（`runtime_intent` 只作参考，85 条有反推 intent，不抄成 gold） diff --git a/scripts/research/jev_intent_source_b_v2.py b/scripts/research/jev_intent_source_b_v2.py new file mode 100644 index 00000000..6104c113 --- /dev/null +++ b/scripts/research/jev_intent_source_b_v2.py @@ -0,0 +1,203 @@ +#!/usr/bin/env python3 +"""Build source B v2 rows with the previous turn in the same case. + +Offline. Writes only under the gitignored cache. Does not label gold with a +model or with a keyword table. Old gold is copied by turn_id, or by a unique +exact user_message when the 09-19 file has no turn_id. +""" + +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path +from typing import Any, Mapping, Sequence + +ROOT = Path(__file__).resolve().parents[2] +if str(ROOT) not in sys.path: + sys.path.insert(0, str(ROOT)) + +CACHE_DIR = Path(r"G:\Ferti\Jyotisha\.cache\jev_intent") +LEGACY_PATH = CACHE_DIR / "source_b.jsonl" +OUTPUT_PATH = CACHE_DIR / "source_b_v2.jsonl" + +EXTRACT_SQL = """ +select + t.id as turn_id, + t.case_id, + t.user_message, + t.assistant_message, + t.status as turn_status, + t.created_at, + c.status as case_status, + f.question_id, + f.expected_answer_schema, + f.status as focus_status +from public.agentic_rectification_turns t +join public.agentic_rectification_cases c on c.id = t.case_id +left join lateral ( + select * + from public.agentic_rectification_conversation_focuses f + where f.case_id = t.case_id + and f.asked_at <= t.created_at + order by f.asked_at desc + limit 1 +) f on true +where t.user_message is not null + and length(btrim(t.user_message)) > 0 +order by t.case_id, t.created_at +""" + + +def load_jsonl(path: Path) -> list[dict[str, Any]]: + if not path.is_file(): + return [] + return [json.loads(line) for line in path.read_text(encoding="utf-8").splitlines() if line.strip()] + + +def write_jsonl(path: Path, rows: Sequence[Mapping[str, Any]]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text( + "".join(json.dumps(row, ensure_ascii=False) + "\n" for row in rows), + encoding="utf-8", + ) + + +def attach_previous_turns(rows: Sequence[dict[str, Any]]) -> list[dict[str, Any]]: + """First turn of a case, and any row without case_id, gets previous_turn null. + + Rows that share a missing case_id are not chained: that would glue + different people together. + """ + by_case: dict[str, list[dict[str, Any]]] = {} + for row in rows: + case_id = str(row.get("case_id") or "").strip() + if not case_id: + row["previous_turn"] = None + row["previous_turn_source"] = "no_case" + continue + by_case.setdefault(case_id, []).append(row) + for case_rows in by_case.values(): + case_rows.sort(key=lambda item: ( + str(item.get("created_at") or ""), + str(item.get("turn_id") or item.get("id") or ""), + )) + for index, row in enumerate(case_rows): + if index == 0: + row["previous_turn"] = None + row["previous_turn_source"] = "case_first" + continue + prev = case_rows[index - 1] + row["previous_turn"] = { + "assistant_message": "" if prev.get("assistant_message") is None else str(prev.get("assistant_message")), + "user_message": "" if prev.get("user_message") is None else str(prev.get("user_message")), + } + row["previous_turn_source"] = "prior_turn" + return list(rows) + + +def match_gold(rows: Sequence[dict[str, Any]], legacy: Sequence[Mapping[str, Any]]) -> dict[str, int]: + by_turn: dict[str, Mapping[str, Any]] = {} + by_message: dict[str, list[Mapping[str, Any]]] = {} + for old in legacy: + turn_id = str(old.get("turn_id") or "").strip() + if turn_id: + by_turn[turn_id] = old + message = old.get("user_message") + if isinstance(message, str) and message: + by_message.setdefault(message, []).append(old) + counts = {"turn_id": 0, "message_exact": 0, "unlabeled": 0, "message_ambiguous": 0} + for row in rows: + gold = None + source = "unlabeled" + legacy_id = None + turn_id = str(row.get("turn_id") or "").strip() + if turn_id and turn_id in by_turn: + gold = by_turn[turn_id].get("gold") + source = "turn_id" + legacy_id = by_turn[turn_id].get("id") + else: + hits = by_message.get(str(row.get("user_message") or "")) or [] + if len(hits) == 1 and isinstance(hits[0].get("gold"), dict): + gold = hits[0]["gold"] + source = "message_exact" + legacy_id = hits[0].get("id") + elif len(hits) > 1: + source = "message_ambiguous" + if isinstance(gold, dict) and gold.get("intent"): + row["gold"] = { + "intent": gold.get("intent"), + "answer_class": gold.get("answer_class"), + "has_new_dated_event": gold.get("has_new_dated_event"), + } + row["gold_source"] = source + if legacy_id: + row["legacy_id"] = legacy_id + else: + row["gold_source"] = source + row.pop("gold", None) + counts[source] = counts.get(source, 0) + 1 + return counts + + +def from_legacy(legacy: Sequence[Mapping[str, Any]]) -> list[dict[str, Any]]: + """The handed-over 157 rows have no case_id. previous_turn stays null.""" + rows: list[dict[str, Any]] = [] + for old in legacy: + if not isinstance(old.get("gold"), dict) or not old["gold"].get("intent"): + continue + rows.append({ + "id": old.get("id"), + "source": "B", + "layer": old.get("layer"), + "user_message": old.get("user_message"), + "assistant_message": None, + "case_id": None, + "turn_id": old.get("turn_id"), + "created_at": old.get("created_at"), + "focus": old.get("focus"), + "focus_stale": old.get("focus_stale"), + "runtime_intent": old.get("runtime_intent"), + "gold": old.get("gold"), + "gold_source": "legacy_file", + "origin": old.get("origin"), + }) + return attach_previous_turns(rows) + + +def main(argv: Sequence[str] | None = None) -> int: + parser = argparse.ArgumentParser() + parser.add_argument("--from-legacy", action="store_true") + parser.add_argument("--raw", type=Path, default=None) + parser.add_argument("--out", type=Path, default=OUTPUT_PATH) + args = parser.parse_args(argv) + legacy = load_jsonl(LEGACY_PATH) + if args.from_legacy: + rows = from_legacy(legacy) + write_jsonl(args.out, rows) + print(json.dumps({ + "out": str(args.out), + "n": len(rows), + "n_with_previous": sum(1 for row in rows if row.get("previous_turn")), + "gold_source": "legacy_file", + }, ensure_ascii=False)) + return 0 + if args.raw: + raw_rows = load_jsonl(args.raw) + rows = attach_previous_turns(raw_rows) + counts = match_gold(rows, legacy) + write_jsonl(args.out, rows) + print(json.dumps({ + "out": str(args.out), + "n": len(rows), + "n_with_previous": sum(1 for row in rows if row.get("previous_turn")), + "gold": counts, + }, ensure_ascii=False)) + return 0 + print("pass --from-legacy or --raw; staging SQL is EXTRACT_SQL in this file", file=sys.stderr) + return 2 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tests/test_jev_intent_research.py b/tests/test_jev_intent_research.py index 39ee1b57..3ebc6d7a 100644 --- a/tests/test_jev_intent_research.py +++ b/tests/test_jev_intent_research.py @@ -17,6 +17,7 @@ from scripts.research.jev_intent_corpus_build import ( # noqa: E402 source_a_samples, ) from scripts.research.jev_intent_questions import ( + CONTINUES_PREVIOUS_TURN_INSTRUCTIONS, CRITERION_MAP, INTENT_CRITERIA, LOST_SEMANTICS, @@ -25,6 +26,7 @@ from scripts.research.jev_intent_questions import ( enforce_combo, parse_jev_answers, ) +from scripts.research.jev_intent_source_b_v2 import attach_previous_turns ROOT = Path(__file__).resolve().parents[1] @@ -70,6 +72,95 @@ class JevIntentQuestionsTests(unittest.TestCase): self.assertEqual(parsed["confidence"], 0.9) +class JevIntentPreviousTurnTests(unittest.TestCase): + def test_v1_v2_state_shape_keeps_v0_questions(self) -> None: + sample = dict(source_a_samples()[0]) + sample["previous_turn"] = {"assistant_message": "上一问", "user_message": "上一句"} + sample["previous_decision"] = {"intent": "unclear", "answer_class": None} + v0 = build_state(sample, variant="v0") + self.assertEqual(set(v0), {"current_question", "options", "user_message", "case_status"}) + v1 = build_state(sample, variant="v1") + self.assertEqual(v1["previous_turn"], {"assistant_message": "上一问", "user_message": "上一句"}) + self.assertNotIn("previous_decision", v1) + v2 = build_state(sample, variant="v2") + self.assertEqual(v2["previous_decision"], {"intent": "unclear", "answer_class": None}) + v0_questions = build_questions(sample) + v2_questions = build_questions(sample, variant="v2") + self.assertNotIn("continues_previous_turn", v0_questions) + self.assertEqual(set(v0_questions["intent"]["criteria"]), set(v2_questions["intent"]["criteria"])) + self.assertEqual(v2_questions["continues_previous_turn"]["type"], "noul") + self.assertEqual( + v2_questions["continues_previous_turn"]["instructions"], + CONTINUES_PREVIOUS_TURN_INSTRUCTIONS, + ) + + def test_continues_rule_adopts_previous_intent_only_at_threshold(self) -> None: + answers = { + "intent": {"choice": "answer_current_focus", "confidence": 0.95, "probabilities": {}}, + "answer_class": {"choice": "yes", "confidence": 0.91, "probabilities": {}}, + "has_new_dated_event": {"noul": 0.1}, + "continues_previous_turn": {"noul": 0.9}, + } + adopted = parse_jev_answers( + answers, + previous_decision={"intent": "unclear", "answer_class": None}, + apply_continue=True, + ) + self.assertEqual(adopted["intent"], "unclear") + self.assertIsNone(adopted["answer_class"]) + self.assertTrue(adopted["continued"]) + self.assertEqual(adopted["confidence"], 0.9) + kept = parse_jev_answers( + {**answers, "continues_previous_turn": {"noul": 0.89}}, + previous_decision={"intent": "stop_rectification", "answer_class": None}, + apply_continue=True, + ) + self.assertEqual(kept["intent"], "answer_current_focus") + self.assertEqual(kept["answer_class"], "yes") + self.assertFalse(kept["continued"]) + focused = parse_jev_answers( + answers, + previous_decision={"intent": "answer_current_focus", "answer_class": "no"}, + apply_continue=True, + ) + self.assertEqual(focused["intent"], "answer_current_focus") + self.assertEqual(focused["answer_class"], "no") + + def test_case_first_turn_previous_turn_is_null(self) -> None: + rows = attach_previous_turns([ + { + "id": "first", + "case_id": "case-1", + "created_at": "2026-09-01T00:00:00Z", + "user_message": "第一句", + "assistant_message": "第一答", + }, + { + "id": "second", + "case_id": "case-1", + "created_at": "2026-09-01T00:01:00Z", + "user_message": "继续", + "assistant_message": "第二答", + }, + { + "id": "orphan", + "user_message": "没有案件", + "assistant_message": "答", + }, + ]) + by_id = {row["id"]: row for row in rows} + self.assertIsNone(by_id["first"]["previous_turn"]) + self.assertEqual(by_id["first"]["previous_turn_source"], "case_first") + self.assertEqual(by_id["second"]["previous_turn"], { + "assistant_message": "第一答", + "user_message": "第一句", + }) + self.assertIsNone(by_id["orphan"]["previous_turn"]) + state = build_state(by_id["first"], variant="v1") + self.assertIsNone(state["previous_turn"]) + self.assertIn("user_message", state) + + class JevIntentCorpusTests(unittest.TestCase): def test_source_a_has_ten_labeled_rows(self) -> None: rows = source_a_samples()