DeepSeek provider + 判据 ③ 机器路径跑通(零人工兜底)

Provider 侧:
  · 新增 `provider-profile`(纯数据):各家的默认端点 / 结构化出参姿势 / 是否回推理
    集中一处,adapter 只按画像行事 —— 新增 provider 只改画像,不写一族 if-else。
    DeepSeek:`https://api.deepseek.com`,支持 json_object,v4 系列是推理模型。
  · 建实例时 URL 可留空 → 取该 provider 的默认端点(openai-compatible 本就是
    「随便哪个兼容端点」,没有默认,必须自填)。
  · 推理正文归 `reasoning` 单列:那是**过程**不是结论,不进出参、不参与 schema 校验、
    不入录制(录的是语义入参与结论)。
  · 两档实例 deepseek-v4-flash / deepseek-v4-pro 已建;`quote-extractor` 是**用途名**,
    指向 flash 档 —— 换档只改这一处绑定,活动一行不动。密钥只从环境读、落库即密封,
    治理台只回「是否已绑定」(实测:库里是密文,视图无明文字段,三个实例探活全通)。

沿路修掉五处真问题:

① **同一任务被并发推进**:推进器与派发器各推各的,用的不是同一把锁 ——
   实测同一步跑两遍、同一张卡发两张、同一行草稿写出两条互相打架的版本。
   两者改用同一把 advisory lock(键的推导提取共用)。
② **LLM 步出参从不校验**:声明了 outputSchema,解析不出来就静默当 `{}` 写下去,
   毛病要到下游缺字段时才炸、还炸在别处。改为「出参必过 schema」(AM 12.3),
   不过即按声明的失败走向,并把模型原文记进留痕,好让人看见它到底回了什么。
③ **出参路径不认点号**:入参能走 `a.b.0.c`,出参只认扁平键 ——
   模型按 schema 回嵌套结构(最常见的情形)就写出一条空记录。两边共用一份解析。
④ **归一步拿不到原文**:通道载荷只带指针不带正文、start→A1 边也没把主题正文传下去,
   模型只拿到一个单号,于是回空 records。通道载荷带原文,边映射一路带到 A1。
⑤ **出参形状没讲给模型听**:声明了 schema 却只在提示里写「只输出 JSON」,
   模型只能猜键名(实测回 `{"items":[]}` 而声明要 `{"records":[...]}`)。
   改由网关按 outputSchema 统一附上说明 —— 这是系统的事,不该每个活动作者各写一遍。

另修:连接池没有 error 处理器,空闲连接被服务端掐断会让整个进程退出(一次运维操作
就能把 API 带崩)。

结果:一封 RFQ 入站 → 模型从正文抽出 pn/brand/eau/目标价/交期 →
D1 判定跳过补全 → 查历史价 → DMN 三分类 Type A → 验收放行卡,**全程零人工兜底**。

gates: tsc / eslint / depcruise(319 模块)/ api-report / 409 测试 全绿

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01KeDBx2PHLavbURXB8LJJ7m

.data/attachments/inbound/rfq-llm-01@x.com.eml

@@ -0,0 +1,12 @@
+{
+  "messageId": "rfq-llm-01@x.com",
+  "from": "buyer@x.com",
+  "to": [
+    "rfq@we.com"
+  ],
+  "cc": [],
+  "subject": "【询价】客户 C900 RFQ-20260728LLM 求报价",
+  "body": "客户编码 C900,询价单号 RFQ-20260728LLM。\n需求:LM358DR,厂牌 TI,年用量 12000 片,目标价 8.20 元,交期 10 周。",
+  "receivedAt": "2026-07-28T01:00:00.000Z",
+  "attachments": []
+}

.data/attachments/inbound/rfq-llm-02@x.com.eml

@@ -0,0 +1,12 @@
+{
+  "messageId": "rfq-llm-02@x.com",
+  "from": "buyer@x.com",
+  "to": [
+    "rfq@we.com"
+  ],
+  "cc": [],
+  "subject": "【询价】客户 C900 RFQ-20260728LLM 求报价",
+  "body": "客户编码 C900,询价单号 RFQ-20260728LLM。\n需求:LM358DR,厂牌 TI,年用量 12000 片,目标价 8.20 元,交期 10 周。",
+  "receivedAt": "2026-07-28T01:00:00.000Z",
+  "attachments": []
+}

.data/attachments/inbound/rfq-llm-03@x.com.eml

@@ -0,0 +1,12 @@
+{
+  "messageId": "rfq-llm-03@x.com",
+  "from": "buyer@x.com",
+  "to": [
+    "rfq@we.com"
+  ],
+  "cc": [],
+  "subject": "【询价】客户 C901 RFQ-20260728M1 求报价",
+  "body": "客户编码 C901,询价单号 RFQ-20260728M1。\n需求:LM358DR,厂牌 TI,年用量 12000 片,目标价 8.20 元,交期 10 周。",
+  "receivedAt": "2026-07-28T02:00:00.000Z",
+  "attachments": []
+}

.data/attachments/inbound/rfq-llm-04@x.com.eml

@@ -0,0 +1,12 @@
+{
+  "messageId": "rfq-llm-04@x.com",
+  "from": "buyer@x.com",
+  "to": [
+    "rfq@we.com"
+  ],
+  "cc": [],
+  "subject": "【询价】客户 C902 RFQ-20260728M2 求报价",
+  "body": "客户编码 C902,询价单号 RFQ-20260728M2。\n需求:LM358DR,厂牌 TI,年用量 12000 片,目标价 8.20 元,交期 10 周。",
+  "receivedAt": "2026-07-28T03:00:00.000Z",
+  "attachments": []
+}

.data/attachments/inbound/rfq-llm-05@x.com.eml

@@ -0,0 +1,12 @@
+{
+  "messageId": "rfq-llm-05@x.com",
+  "from": "buyer@acme.com",
+  "to": [
+    "rfq@we.com"
+  ],
+  "cc": [],
+  "subject": "【询价】客户 C903 RFQ-20260728M3 求报价",
+  "body": "您好,\n客户编码 C903,询价单号 RFQ-20260728M3。\n需求如下:\nLM358DR  厂牌 TI  年用量 12000 片  目标价 8.20 元  交期 10 周\n请本周内回复,谢谢。",
+  "receivedAt": "2026-07-28T04:00:00.000Z",
+  "attachments": []
+}

.data/attachments/inbound/rfq-llm-06@x.com.eml

@@ -0,0 +1,12 @@
+{
+  "messageId": "rfq-llm-06@x.com",
+  "from": "buyer@acme.com",
+  "to": [
+    "rfq@we.com"
+  ],
+  "cc": [],
+  "subject": "【询价】客户 C904 RFQ-20260728M4 求报价",
+  "body": "您好,\n客户编码 C904,询价单号 RFQ-20260728M4。\n需求如下:\nLM358DR  厂牌 TI  年用量 12000 片  目标价 8.20 元  交期 10 周\n请本周内回复,谢谢。",
+  "receivedAt": "2026-07-28T05:00:00.000Z",
+  "attachments": []
+}

.data/attachments/inbound/rfq-llm-07@x.com.eml

@@ -0,0 +1,12 @@
+{
+  "messageId": "rfq-llm-07@x.com",
+  "from": "buyer@acme.com",
+  "to": [
+    "rfq@we.com"
+  ],
+  "cc": [],
+  "subject": "【询价】客户 C905 RFQ-20260728M5 求报价",
+  "body": "您好,\n客户编码 C905,询价单号 RFQ-20260728M5。\n需求如下:\nLM358DR  厂牌 TI  年用量 12000 片  目标价 8.20 元  交期 10 周\n请本周内回复,谢谢。",
+  "receivedAt": "2026-07-28T06:00:00.000Z",
+  "attachments": []
+}

activities/quote-intake/__tests__/activity.test.ts

@@ -49,9 +49,10 @@ describe('步序声明(AM 12.3)', () => {
 
   it('条件跳步是任务数据上的确定性谓词,由引擎求值', () => {
     const askBack = model.steps.find((s) => s.name === 'S2_ask_back');
-    expect(askBack?.when).toBe('missingKeyFields > 0');
+    expect(askBack?.when).toBe('missing_key_fields > 0');
-    expect(evaluate(askBack!.when!, { missingKeyFields: 2 })).toBe(true);
+    // 谓词读的是**落表后的字段名** —— 作用域来自台账,不是模型出参的驼峰名
-    expect(evaluate(askBack!.when!, { missingKeyFields: 0 })).toBe(false);
+    expect(evaluate(askBack!.when!, { missing_key_fields: 2 })).toBe(true);
+    expect(evaluate(askBack!.when!, { missing_key_fields: 0 })).toBe(false);
   });
 
   it('人类工具步都配了时限 —— 无死等', () => {

activities/quote-intake/src/manifest.ts

@@ -40,7 +40,7 @@ export const manifest: ActivityManifest = {
   identity: {
     activityId: ACTIVITY_ID,
     name: 'A1 · RFQ 需求分析',
-    packageVersion: '0.1.3',
+    packageVersion: '0.1.5',
     category: '统一报价',
     paradigm: 'flow',
     sdkVersion: '0.1.0-m5',
@@ -59,6 +59,8 @@ export const manifest: ActivityManifest = {
           customerCode: { type: 'string' },
           rfqRef: { type: 'string' },
           subject: { type: 'string' },
+          /** 询价原文 —— 归一步拆解的对象 */
+          body: { type: 'string' },
         },
       },
       messageAllowed: true,
@@ -124,6 +126,7 @@ export const manifest: ActivityManifest = {
           { name: 'target_price', type: 'number', primaryKey: false, masked: false, label: '目标价' },
           { name: 'lead_time', type: 'text', primaryKey: false, masked: false, label: '交期' },
           { name: 'status', type: 'text', primaryKey: false, masked: false, label: '状态' },
+          { name: 'missing_key_fields', type: 'integer', primaryKey: false, masked: false, label: '缺关键字段数' },
         ],
       },
       {
@@ -171,17 +174,34 @@ export const manifest: ActivityManifest = {
           kind: 'llm',
           name: 'S3_normalize',
           label: 'S3 逐条解析归一',
-          input: { rfqRef: 'rfqRef', customerCode: 'customerCode' },
+          input: { rfqRef: 'rfqRef', customerCode: 'customerCode', subject: 'subject', body: 'body' },
+          /*
+           * 出参路径要对得上**自己声明的 outputSchema**:模型回的是
+           * `{ records: [...], missingKeyFields }`,不是一堆扁平字段。
+           * MVP 逐条办的第一条 = `records.0`;`customerCode` 不在模型出参里,
+           * 它来自起单入参,由作用域补上。
+           */
           output: {
             domain: INTAKE_DOMAIN,
             object: 'record',
-            key: { customer_code: 'customerCode', pn: 'pn' },
+            key: { customer_code: 'customerCode', pn: 'records.0.pn' },
-            fields: { pn: 'pn', brand: 'brand', eau: 'eau' },
+            fields: {
+              pn: 'records.0.pn',
+              brand: 'records.0.brand',
+              eau: 'records.0.eau',
+              target_price: 'records.0.targetPrice',
+              lead_time: 'records.0.leadTime',
+              // 完整度判定要有据可依:D1 的谓词读的就是它
+              missing_key_fields: 'missingKeyFields',
+            },
           },
           model: 'quote-extractor',
+          // 出参形状由网关按 outputSchema 统一附上,模板只讲**这件事本身**
           template:
-            '按《询价受理规范》把这份 RFQ 拆解为逐条需求,抄录 PN/数量/EAU/厂牌/目标价/交期。' +
+            '按《询价受理规范》把下面这封询价拆解为逐条需求,逐条抄录 ' +
-            '只输出 JSON。RFQ:{{rfqRef}}',
+            'pn(型号)/ brand(厂牌)/ eau(年用量,整数)/ targetPrice(目标价,数字)/ leadTime(交期)。' +
+            '原文没写的字段留空,不要编造;每缺一个关键字段(pn / eau / leadTime)就把 missingKeyFields 加一。\n\n' +
+            '询价单号:{{rfqRef}}\n客户编码:{{customerCode}}\n主题:{{subject}}\n正文:\n{{body}}',
           outputSchema: {
             type: 'object',
             required: ['records'],
@@ -210,7 +230,7 @@ export const manifest: ActivityManifest = {
           name: 'S2_ask_back',
           label: 'S2 追问补全信息',
           // D1 判断完整度:缺 PN/数量/交期三要素才走补全 —— 条件必谓词,由引擎求值
-          when: 'missingKeyFields > 0',
+          when: 'missing_key_fields > 0',
           input: {},
           workOrder: 'execution',
           card: { title: '缺关键字段,请向客户追问补全', fields: ['pn', 'eau', 'leadTime'] },

api-report/apps/api/src/modules/flow-engine/port.d.ts

@@ -66,9 +66,17 @@ export type LlmStepGateway = {
     }): Promise<{
         ok: boolean;
         object?: unknown;
+        text?: string;
         error?: string;
     }>;
 };
+/** 出参校验 —— app 注入 ajv:LLM 步的出参**必过声明的 schema**(AM 12.3) */
+export type SchemaValidator = {
+    validate(schema: Record<string, unknown>, value: unknown): {
+        ok: boolean;
+        errors: string[];
+    };
+};
 /** 人类工具步 —— app 注入 `human-tool`(L3) */
 export type CardGateway = {
     issue(input: {

api-report/apps/api/src/modules/llm/contract.d.ts

@@ -49,6 +49,12 @@ export type ModelResult = {
     /** outputSchema 给定时的解析结果 */
     readonly object?: unknown;
     readonly usage: ModelUsage;
+    /**
+     * 推理模型的思考正文 —— **过程,不是结论**。
+     * 出参只认 `text` / `object`;这一段单独留着,供任务记忆归档与断点复盘,
+     * 不参与 schema 校验、不进业务字段。
+     */
+    readonly reasoning?: string;
     /** 行为可归因:模型与版本记入留痕(OS 4.1) */
     readonly binding: {
         readonly name: string;
@@ -71,6 +77,7 @@ export type UpsertLlmInstance = {
     readonly id?: string | undefined;
     readonly name: string;
     readonly provider: LlmProvider;
+    /** 留空 = 取该 provider 的默认端点 */
     readonly baseUrl: string;
     readonly model: string;
     /** 只写不读:留空表示保持原值 */

api-report/apps/api/src/modules/llm/port.d.ts

@@ -48,6 +48,7 @@ export type ProviderInvoker = {
     }): Promise<{
         text: string;
         object?: unknown;
+        reasoning?: string;
         usage: ModelUsage;
     }>;
 };

api-report/packages/contracts/src/api/governance.d.ts

@@ -271,7 +271,7 @@ export declare const upsertLlmInstanceRoute: {
             anthropic: "anthropic";
             qwen: "qwen";
         }>;
-        baseUrl: z.ZodString;
+        baseUrl: z.ZodDefault<z.ZodString>;
         model: z.ZodString;
         apiKey: z.ZodOptional<z.ZodString>;
         systemUse: z.ZodDefault<z.ZodBoolean>;

apps/api/src/app/container.ts

@@ -150,6 +150,14 @@ export const firstPartyToolHandlers = new Map<string, ToolHandler>(FIRST_PARTY_T
 
 export async function createContainer(config: AppConfig): Promise<Container> {
   const pool = new Pool({ connectionString: config.databaseUrl, max: 10 });
+  /*
+   * 空闲连接被服务端掐断(重启、`pg_terminate_backend`、网络抖动)会以 'error' 事件抛到池上;
+   * **没有处理器就是未捕获异常,整个进程随之退出** —— 一次运维操作能把 API 带崩。
+   * 池会自行剔除坏连接并在下次取用时重连,这里只负责让它别掀桌子。
+   */
+  pool.on('error', (error: Error) => {
+    process.stderr.write(`[pg] 空闲连接异常(已剔除,下次取用自动重连):${error.message}\n`);
+  });
   const db = drizzle(pool);
   const ids = createIdGenerator();
   const secrets = createSecretBox(loadSecretKey());

apps/api/src/app/seed-quote.ts

@@ -102,17 +102,37 @@ export async function seedQuote(container: Container): Promise<void> {
     process.stdout.write(line + '\n');
   }
 
-  /* ① LLM 实例:四元绑定(活动只见自定义模型名) */
+  /*
+   * ① LLM 实例:四元绑定(活动只见自定义模型名)。
+   *
+   * 密钥**只从环境读**,不入源码、不入包、不入留痕(OS 7.3 凭证隔离 / ADR-009);
+   * 落库即密封,治理台也只回「是否已绑定」。URL 留空 → 取 provider 画像的默认端点。
+   */
+  const deepseekKey = process.env['JCAI_DEEPSEEK_API_KEY'] ?? process.env['JCAI_LLM_API_KEY'] ?? 'sk-placeholder';
+
+  // 两档 DeepSeek:flash 快而省,pro 强而慢 —— 活动按自己的场景声明引用哪一档
+  for (const [name, model, systemUse] of [
+    ['deepseek-v4-flash', 'deepseek-v4-flash', false],
+    ['deepseek-v4-pro', 'deepseek-v4-pro', false],
+  ] as const) {
+    await container.llm.upsert({ name, provider: 'deepseek', baseUrl: '', model, apiKey: deepseekKey, systemUse, enabled: true });
+  }
+
+  /*
+   * 活动声明里引用的逻辑名是 `quote-extractor` —— 它是**用途名**,不是型号名。
+   * 把它指到 flash 档:受理归一是抽取活,快档足够;要换档只改这一处绑定,活动一行不动。
+   * 系统用途(叙述性小结 / 汇报 / 蒸馏)也走它。
+   */
   await container.llm.upsert({
     name: 'quote-extractor',
-    provider: 'openai-compatible',
+    provider: 'deepseek',
-    baseUrl: process.env['JCAI_LLM_BASE_URL'] ?? 'https://api.deepseek.com/v1',
+    baseUrl: process.env['JCAI_LLM_BASE_URL'] ?? '',
-    model: process.env['JCAI_LLM_MODEL'] ?? 'deepseek-chat',
+    model: process.env['JCAI_LLM_MODEL'] ?? 'deepseek-v4-flash',
-    apiKey: process.env['JCAI_LLM_API_KEY'] ?? 'sk-placeholder',
+    apiKey: deepseekKey,
     systemUse: true,
     enabled: true,
   });
-  log('① LLM 实例已绑定:quote-extractor');
+  log('① LLM 实例已绑定:deepseek-v4-flash · deepseek-v4-pro · quote-extractor(→ flash,系统用途)');
 
   /* ② 工具注册两审 + 白名单 */
   const tools = [
@@ -248,6 +268,9 @@ export async function seedQuote(container: Container): Promise<void> {
         mapping: [
           { target: 'customerCode', source: { from: 'initiation', path: 'customerCode' } },
           { target: 'rfqRef', source: { from: 'initiation', path: 'rfqRef' } },
+          // 主题与正文要一路带到 A1:归一步拆的就是它们
+          { target: 'subject', source: { from: 'initiation', path: 'subject' } },
+          { target: 'body', source: { from: 'initiation', path: 'body' } },
         ],
         reopen: false,
       },
@@ -372,6 +395,8 @@ export async function seedQuote(container: Container): Promise<void> {
       { target: 'customerCode', source: { from: 'expression', sfeel: 'keys.customerCode' } },
       { target: 'rfqRef', source: { from: 'expression', sfeel: 'keys.rfqRef' } },
       { target: 'subject', source: { from: 'expression', sfeel: 'subject' } },
+      // 正文是**归一步唯一的原始素材**:只给单号,模型没有可拆解的东西
+      { target: 'body', source: { from: 'expression', sfeel: 'body' } },
     ],
     spec: { kind: 'event', rule: 'contains(subject, "询价") or contains(subject, "RFQ")', eventType: 'data' },
   });

apps/api/src/app/wiring/engines.ts

@@ -284,6 +284,7 @@ export function wireEngines(deps: EngineWiringDeps): EngineWiring {
     evaluate: sfeel.condition,
     data: taskData,
     tools: { ...toolGateway, invoke: (i) => toolGateway.invoke(i) },
+    schemas: deps.schemas,
     llm: {
       async generate(input) {
         // LLM 在 flow 里只以「被调用的工具」出现,不是调度者
@@ -296,7 +297,12 @@ export function wireEngines(deps: EngineWiringDeps): EngineWiring {
             stepNo: input.stepNo,
             label: 'flow-step',
           });
-          return { ok: result.object !== undefined, ...(result.object === undefined ? { error: '出参不合 schema' } : { object: result.object }) };
+          // 解析与校验归引擎(它才知道这一步声明了什么);这里只如实上交结果与原文
+          return {
+            ok: true,
+            ...(result.object === undefined ? {} : { object: result.object }),
+            text: result.text,
+          };
         } catch (error) {
           return { ok: false, error: error instanceof Error ? error.message : String(error) };
         }

apps/api/src/app/workers/dispatcher.ts

@@ -22,14 +22,22 @@ export type DispatcherDeps = {
   readonly batchSize?: number;
 };
 
-/** 目标 ID → advisory lock 键(稳定散列,同一目标恒同一把锁) */
+/**
-function lockKey(target: Delivery['target']): number {
+ * 目标 ID → advisory lock 键(稳定散列,同一目标恒同一把锁)。
-  const text = `${target.kind}:${target.id}`;
+ *
+ * **推进器与派发器必须共用这一把**:一边由事件投递驱动推进任务、一边由推进器轮询推进,
+ * 用的不是同一把锁就等于没锁 —— 实测会同一步跑两遍、同一张卡发两张、
+ * 同一行草稿写出两条互相打架的版本。
+ */
+export function targetLockKey(kind: string, id: string): number {
+  const text = `${kind}:${id}`;
   let hash = 5381;
   for (let i = 0; i < text.length; i += 1) hash = ((hash << 5) + hash + text.charCodeAt(i)) | 0;
   return hash;
 }
 
+const lockKey = (target: Delivery['target']): number => targetLockKey(target.kind, String(target.id));
+
 export async function dispatchOnce(deps: DispatcherDeps): Promise<number> {
   const { container } = deps;
   const batch = await claimPendingDeliveries(container.db, deps.batchSize ?? 20);

apps/api/src/app/workers/task-advancer.ts

@@ -8,6 +8,7 @@
 import { and, eq, sql } from 'drizzle-orm';
 import { taskTable } from '@modules/task';
 import type { Container } from '../container.ts';
+import { targetLockKey } from './dispatcher.ts';
 
 export type AdvancerOptions = {
   readonly container: Container;
@@ -27,6 +28,17 @@ export async function advanceOnce(options: AdvancerOptions): Promise<number> {
 
   let advanced = 0;
   for (const row of rows) {
+    /*
+     * 与派发器共用同一把 advisory lock:事件投递也会推进同一个任务。
+     * 抢不到就跳过 —— 那边正推着,这边再推就是两条线同时改一份运行时状态。
+     */
+    const key = targetLockKey('task', row.id);
+    const locked = await container.db.execute<{ ok: boolean }>(
+      sql`select pg_try_advisory_lock(${key}) as ok`,
+    );
+    if (locked.rows[0]?.ok !== true) continue;
+
+    try {
       for (let tick = 0; tick < (options.maxTicks ?? 3); tick += 1) {
         try {
           const outcome = await container.task.tick(row.id as never);
@@ -41,6 +53,9 @@ export async function advanceOnce(options: AdvancerOptions): Promise<number> {
           break;
         }
       }
+    } finally {
+      await container.db.execute(sql`select pg_advisory_unlock(${key})`);
+    }
   }
   return advanced;
 }

apps/api/src/modules/channel/internal/usecase/channel-service.ts

@@ -77,7 +77,11 @@ export function createChannelService(deps: ChannelServiceDeps): ChannelService {
         draft: {
           type: 'data',
           correlation: { cause: { kind: 'inbound', ref: mail.messageId }, keys: extracted },
-          payload: { data: { ...extracted, subject: mail.subject, from: mail.from }, originRef },
+          /*
+           * 载荷带**原文**:信封只带原件指针够不够,取决于下游要不要读内容 ——
+           * 归一步要拆解的正是这段文本,只给一个 .eml 指针它无从下手。
+           */
+          payload: { data: { ...extracted, subject: mail.subject, body: mail.body, from: mail.from }, originRef },
           occurredAt: receivedAt.toISOString(),
           source: { kind: 'channel', detail: config.name },
         },

apps/api/src/modules/flow-engine/__tests__/step-sequence.test.ts

@@ -10,6 +10,7 @@ import {
   lastStepIsAcceptance,
   markStep,
   parallelGroupComplete,
+  resolvePath,
 } from '../internal/domain/step-sequence.ts';
 
 const llmStep = (over: Record<string, unknown> = {}): FlowStep =>
@@ -125,3 +126,30 @@ describe('声明校验', () => {
     expect(lastStepIsAcceptance([llmStep()])).toBe(false);
   });
 });
+
+describe('取值路径 —— 入参出参共用一份', () => {
+  const shaped = {
+    records: [
+      { pn: 'LM358DR', brand: 'TI', eau: 12000 },
+      { pn: 'NE555P', brand: 'TI', eau: 3000 },
+    ],
+    missingKeyFields: 0,
+    flat: 'x',
+  };
+
+  it('数组下标按数字段下行 —— 模型按 schema 回嵌套结构是最常见的情形', () => {
+    expect(resolvePath(shaped, 'records.0.pn')).toBe('LM358DR');
+    expect(resolvePath(shaped, 'records.1.brand')).toBe('TI');
+  });
+
+  it('扁平键照旧', () => {
+    expect(resolvePath(shaped, 'missingKeyFields')).toBe(0);
+    expect(resolvePath(shaped, 'flat')).toBe('x');
+  });
+
+  it('走不通的路径回 undefined,不抛 —— 缺字段由下游的契约校验如实报出来', () => {
+    expect(resolvePath(shaped, 'records.9.pn')).toBeUndefined();
+    expect(resolvePath(shaped, 'nope.deep.deeper')).toBeUndefined();
+    expect(resolvePath(undefined, 'a.b')).toBeUndefined();
+  });
+});

apps/api/src/modules/flow-engine/internal/domain/step-sequence.ts

@@ -156,3 +156,21 @@ export function lastVerdictPassed(position: FlowPosition): boolean {
 export function lastSelectedPorts(position: FlowPosition): Record<string, string> {
   return position.steps[position.steps.length - 1]?.selectedPorts ?? {};
 }
+
+/**
+ * 取值路径:点号分段下行,数组下标按数字段走(`records.0.pn`)。
+ *
+ * **入参与出参共用这一份**:声明里写的路径是同一种写法,不该一边能走点号、
+ * 另一边只认扁平键 —— 那会让「模型按 schema 回了嵌套结构」这种最常见的情形
+ * 悄悄写出一条空记录。
+ */
+export function resolvePath(source: unknown, path: string): unknown {
+  return path.split('.').reduce<unknown>((acc, seg) => {
+    if (acc === null || acc === undefined || typeof acc !== 'object') return undefined;
+    if (Array.isArray(acc)) {
+      const index = Number(seg);
+      return Number.isInteger(index) ? acc[index] : undefined;
+    }
+    return (acc as Record<string, unknown>)[seg];
+  }, source);
+}

apps/api/src/modules/flow-engine/internal/usecase/flow-runner.ts

@@ -10,6 +10,7 @@ import type {
   FlowStateStore,
   ListenerRegistrar,
   LlmStepGateway,
+  SchemaValidator,
   MemoryRecorder,
   PredicateEvaluator,
   TaskContext,
@@ -27,6 +28,7 @@ import {
   initialPosition,
   markStep,
   nextAction,
+  resolvePath,
 } from '../domain/step-sequence.ts';
 
 export type FlowRunnerDeps = {
@@ -35,6 +37,7 @@ export type FlowRunnerDeps = {
   readonly data: TaskDataGateway;
   readonly tools: ToolStepInvoker;
   readonly llm: LlmStepGateway;
+  readonly schemas: SchemaValidator;
   readonly cards: CardGateway;
   readonly listeners: ListenerRegistrar;
   readonly memory: MemoryRecorder;
@@ -54,13 +57,14 @@ export function createFlowRunner(deps: FlowRunnerDeps): FlowEngineService {
     if (step.output === undefined) return;
     const patch: Record<string, unknown> = {};
     for (const [field, path] of Object.entries(step.output.fields)) {
-      patch[field] = output[path] ?? output[field];
+      // 路径优先(模型常按 schema 回嵌套结构),取不到再退回扁平键
+      patch[field] = resolvePath(output, path) ?? output[path] ?? output[field];
     }
     // 写哪一行:主键取自出参,取不到再回作用域(起单入参往往就带着主键)
     const scope = await deps.data.view(taskId);
     const key: Record<string, string | number> = {};
     for (const [column, path] of Object.entries(step.output.key)) {
-      const value = output[path] ?? scope[path];
+      const value = resolvePath(output, path) ?? output[path] ?? resolvePath(scope, path);
       // 主键值只能是标量;取不到就不填,让台账的非空约束在合并时如实报出来
       if (typeof value === 'string' || typeof value === 'number') key[column] = value;
     }
@@ -81,12 +85,7 @@ export function createFlowRunner(deps: FlowRunnerDeps): FlowEngineService {
 
   function inputOf(step: FlowStep, scope: Record<string, unknown>): Record<string, unknown> {
     const out: Record<string, unknown> = {};
-    for (const [key, path] of Object.entries(step.input)) {
+    for (const [key, path] of Object.entries(step.input)) out[key] = resolvePath(scope, path);
-      out[key] = path.split('.').reduce<unknown>((acc, seg) => {
-        if (acc === null || acc === undefined || typeof acc !== 'object') return undefined;
-        return (acc as Record<string, unknown>)[seg];
-      }, scope);
-    }
     return out;
   }
 
@@ -143,7 +142,27 @@ export function createFlowRunner(deps: FlowRunnerDeps): FlowEngineService {
           stepNo: index,
         });
         if (!result.ok) return handleFailure(taskId, step, index, position, attempt, result.error ?? 'LLM 步失败');
-        const output = (result.object ?? {}) as Record<string, unknown>;
+
+        /*
+         * 「出参必过 schema 校验」(AM 12.3)。
+         * 解析不出来或不合 schema = **这一步失败了**,走声明的失败走向;
+         * 从前是静默当成 `{}` 写下去 —— 一条空记录、一行不报错,
+         * 真正的毛病要到下游缺字段时才炸,而且炸在别处。
+         */
+        const parsed = result.object;
+        const check = deps.schemas.validate(step.outputSchema, parsed);
+        if (parsed === undefined || !check.ok) {
+          const detail = parsed === undefined ? '出参不是 JSON' : check.errors.join('; ');
+          await deps.memory.append({
+            taskId,
+            kind: 'result',
+            title: `步 ${index + 1} 出参不合 schema:${detail}`,
+            detail: { step: step.name, text: (result.text ?? '').slice(0, 500) },
+          });
+          return handleFailure(taskId, step, index, position, attempt, `出参不合 schema:${detail}`);
+        }
+
+        const output = parsed as Record<string, unknown>;
         await writeOutput(taskId, step, output);
         const next = markStep(position, index, { state: 'done', attempt, output });
         await deps.state.save(taskId, (await loaded(taskId)).model, next);

apps/api/src/modules/flow-engine/port.ts

@@ -55,7 +55,13 @@ export type LlmStepGateway = {
     outputSchema: Record<string, unknown>;
     taskId: TaskId;
     stepNo: number;
-  }): Promise<{ ok: boolean; object?: unknown; error?: string }>;
+    /** 出参原文 —— 解析失败时要能说清「模型到底回了什么」 */
+  }): Promise<{ ok: boolean; object?: unknown; text?: string; error?: string }>;
+};
+
+/** 出参校验 —— app 注入 ajv:LLM 步的出参**必过声明的 schema**(AM 12.3) */
+export type SchemaValidator = {
+  validate(schema: Record<string, unknown>, value: unknown): { ok: boolean; errors: string[] };
 };
 
 /** 人类工具步 —— app 注入 `human-tool`(L3) */

apps/api/src/modules/llm/__tests__/provider-profile.test.ts

@@ -0,0 +1,28 @@
+/** Provider 画像 —— domain 测试零 mock(OS 02 系统管理) */
+import { describe, expect, it } from 'vitest';
+import { LLM_PROVIDERS } from '@contracts';
+import { PROVIDER_PROFILES, profileOf, resolveBaseUrl } from '../internal/domain/provider-profile.ts';
+
+describe('Provider 画像', () => {
+  it('枚举里每个 provider 都有画像 —— 选了 provider 就该知道怎么调它', () => {
+    for (const provider of LLM_PROVIDERS) expect(PROVIDER_PROFILES[provider]).toBeDefined();
+  });
+
+  it('DeepSeek:默认端点 + 支持 json_object + 推理模型', () => {
+    const deepseek = profileOf('deepseek');
+    expect(deepseek.defaultBaseUrl).toBe('https://api.deepseek.com');
+    expect(deepseek.structuredOutput).toBe('json-object');
+    // v4 系列回包带 reasoning_content:那是过程不是结论,不能混进出参
+    expect(deepseek.emitsReasoning).toBe(true);
+  });
+
+  it('URL 留空回落到默认端点;填了就用填的', () => {
+    expect(resolveBaseUrl('deepseek', undefined)).toBe('https://api.deepseek.com');
+    expect(resolveBaseUrl('deepseek', '   ')).toBe('https://api.deepseek.com');
+    expect(resolveBaseUrl('deepseek', 'https://proxy.internal/v1')).toBe('https://proxy.internal/v1');
+  });
+
+  it('openai-compatible 没有默认端点 —— 它本就是「随便哪个兼容端点」,必须自填', () => {
+    expect(resolveBaseUrl('openai-compatible', undefined)).toBe('');
+  });
+});

apps/api/src/modules/llm/contract.ts

@@ -65,6 +65,12 @@ export type ModelResult = {
   /** outputSchema 给定时的解析结果 */
   readonly object?: unknown;
   readonly usage: ModelUsage;
+  /**
+   * 推理模型的思考正文 —— **过程,不是结论**。
+   * 出参只认 `text` / `object`;这一段单独留着,供任务记忆归档与断点复盘,
+   * 不参与 schema 校验、不进业务字段。
+   */
+  readonly reasoning?: string;
   /** 行为可归因:模型与版本记入留痕(OS 4.1) */
   readonly binding: { readonly name: string; readonly provider: LlmProvider; readonly model: string };
   readonly mode: GatewayMode;
@@ -85,6 +91,7 @@ export type UpsertLlmInstance = {
   readonly id?: string | undefined;
   readonly name: string;
   readonly provider: LlmProvider;
+  /** 留空 = 取该 provider 的默认端点 */
   readonly baseUrl: string;
   readonly model: string;
   /** 只写不读:留空表示保持原值 */

apps/api/src/modules/llm/internal/adapter/ai-sdk-provider.ts

@@ -7,34 +7,53 @@
 import { generateText } from 'ai';
 import { createOpenAICompatible } from '@ai-sdk/openai-compatible';
 import type { ProviderInvoker } from '../../port.ts';
+import { profileOf, schemaInstruction } from '../domain/provider-profile.ts';
 
 /**
  * MVP 的全部 Provider 都以 OpenAI 兼容端点接入(README:「绑定 LLM 实例(任一 OpenAI 兼容端点)」)。
- * 枚举是为了治理台的呈现与日后分化,当下共用一个工厂。
+ * 各家的差异(默认端点 / 结构化出参姿势 / 是否回推理)集中在 `provider-profile`,
+ * 这里只按画像行事 —— 不写一族 if-else,新增 provider 只改画像。
  */
 export function createProviderInvoker(): ProviderInvoker {
   return {
     async invoke({ binding, apiKey, call }) {
+      const profile = profileOf(binding.provider);
       const provider = createOpenAICompatible({
         name: binding.provider,
         baseURL: binding.baseUrl,
         apiKey,
       });
 
+      // 端点支持就**直接要 JSON**,别指望从散文里捞;不支持的仍靠提示 + 提取兜底
+      const wantsJson = call.outputSchema !== undefined;
+      const useJsonMode = wantsJson && profile.structuredOutput === 'json-object';
+
+      // 出参形状讲给模型听:一处附上,所有步受益(活动作者不必各写一遍)
+      const system = [call.system, wantsJson ? schemaInstruction(call.outputSchema!) : undefined]
+        .filter((line): line is string => line !== undefined && line !== '')
+        .join('\n\n');
+
       const started = Date.now();
       const result = await generateText({
         model: provider(binding.model),
-        ...(call.system === undefined ? {} : { system: call.system }),
+        ...(system === '' ? {} : { system }),
         prompt: call.prompt,
         ...(call.temperature === undefined ? {} : { temperature: call.temperature }),
         ...(call.maxOutputTokens === undefined ? {} : { maxOutputTokens: call.maxOutputTokens }),
+        ...(useJsonMode ? { providerOptions: { [binding.provider]: { response_format: { type: 'json_object' } } } } : {}),
       });
 
-      const object = call.outputSchema === undefined ? undefined : parseJsonLoose(result.text);
+      /*
+       * 推理模型的 `reasoning` 是**过程**不是结论:
+       * 出参只认正文,推理另置为留痕素材 —— 混进出参会让 schema 校验对着思考过程报错。
+       */
+      const reasoning = profile.emitsReasoning ? extractReasoning(result) : undefined;
+      const object = wantsJson ? parseJsonLoose(result.text) : undefined;
 
       return {
         text: result.text,
         ...(object === undefined ? {} : { object }),
+        ...(reasoning === undefined || reasoning === '' ? {} : { reasoning }),
         usage: {
           inputTokens: result.usage.inputTokens ?? 0,
           outputTokens: result.usage.outputTokens ?? 0,
@@ -45,6 +64,16 @@ export function createProviderInvoker(): ProviderInvoker {
   };
 }
 
+/** 推理正文:AI SDK 把它归到 reasoning 部件;拿不到就算了,不为它中断调用 */
+function extractReasoning(result: { reasoning?: unknown | undefined; reasoningText?: string | undefined }): string | undefined {
+  if (typeof result.reasoningText === 'string') return result.reasoningText;
+  if (!Array.isArray(result.reasoning)) return undefined;
+  const parts = result.reasoning
+    .map((r) => (typeof r === 'object' && r !== null && 'text' in r ? String((r as { text: unknown }).text) : ''))
+    .filter((t) => t !== '');
+  return parts.length === 0 ? undefined : parts.join('\n');
+}
+
 /**
  * 结构化出参:模型常把 JSON 包在 ```json 围栏里。这里只做**提取**,
  * schema 校验归调用方(ADR-003:运行时校验用 ajv)。

apps/api/src/modules/llm/internal/domain/provider-profile.ts

@@ -0,0 +1,89 @@
+/**
+ * Provider 画像 —— 纯数据、零 IO(编码规范 §5.3)。
+ *
+ * 四元绑定里 Provider 是**枚举**,不是自由文本:选定 provider 就等于选定了它的
+ * 默认端点与调用姿势。把这些差异集中在一处,adapter 只按画像行事、不写 if-else 家族。
+ *
+ * 「活动只见自定义模型名」——本文件的一切都在系统侧,活动既不触厂商也不触密钥(OS 02)。
+ */
+import type { LlmProvider } from '@contracts';
+
+export type ProviderProfile = {
+  /** 默认端点:治理台建实例时不填 URL 就用它 */
+  readonly defaultBaseUrl: string;
+  /**
+   * 结构化出参的姿势:
+   *   `json-object` —— 端点支持 `response_format: {type:'json_object'}`,直接要 JSON;
+   *   `prompt-only` —— 端点不支持,只能在提示里要求并对文本做提取。
+   */
+  readonly structuredOutput: 'json-object' | 'prompt-only';
+  /** 推理模型:回包里带 reasoning,答案在正文,推理另置(不进出参、只作留痕) */
+  readonly emitsReasoning: boolean;
+  readonly label: string;
+};
+
+/**
+ * DeepSeek:OpenAI 兼容端点,支持 `json_object`;v4 系列是推理模型,
+ * 回包带 `reasoning_content` —— 那是**过程**不是结论,不能混进出参。
+ */
+export const PROVIDER_PROFILES: Readonly<Record<LlmProvider, ProviderProfile>> = {
+  'openai-compatible': {
+    defaultBaseUrl: '',
+    structuredOutput: 'prompt-only',
+    emitsReasoning: false,
+    label: 'OpenAI 兼容端点',
+  },
+  openai: {
+    defaultBaseUrl: 'https://api.openai.com/v1',
+    structuredOutput: 'json-object',
+    emitsReasoning: false,
+    label: 'OpenAI',
+  },
+  deepseek: {
+    defaultBaseUrl: 'https://api.deepseek.com',
+    structuredOutput: 'json-object',
+    emitsReasoning: true,
+    label: 'DeepSeek',
+  },
+  anthropic: {
+    defaultBaseUrl: 'https://api.anthropic.com/v1',
+    structuredOutput: 'prompt-only',
+    emitsReasoning: false,
+    label: 'Anthropic',
+  },
+  qwen: {
+    defaultBaseUrl: 'https://dashscope.aliyuncs.com/compatible-mode/v1',
+    structuredOutput: 'json-object',
+    emitsReasoning: false,
+    label: '通义千问',
+  },
+};
+
+export function profileOf(provider: LlmProvider): ProviderProfile {
+  return PROVIDER_PROFILES[provider];
+}
+
+/** 建实例时 URL 留空:回落到该 provider 的默认端点;openai-compatible 没有默认,必须自填 */
+export function resolveBaseUrl(provider: LlmProvider, given: string | undefined): string {
+  const trimmed = (given ?? '').trim();
+  return trimmed === '' ? profileOf(provider).defaultBaseUrl : trimmed;
+}
+
+/**
+ * 出参形状要**讲给模型听**。
+ *
+ * 声明了 `outputSchema` 却只在提示里写「只输出 JSON」,模型只能猜键名 ——
+ * 实测 DeepSeek 会回 `{"items": []}` 而声明要的是 `{"records": [...]}`,
+ * 于是每一次都判「出参不合 schema」。把 schema 附进提示是**系统的事**,
+ * 不该让每个活动作者各写一遍、各写各的措辞。
+ *
+ * 只附结构(必填 + 属性),不附描述性长文:提示越短越稳。
+ */
+export function schemaInstruction(schema: Record<string, unknown>): string {
+  return [
+    '出参必须是**单个 JSON 对象**,且严格符合下面的 JSON Schema。',
+    '键名照抄 schema,不得改写、不得增删顶层键;不确定的值留空或给合理默认,不要编造。',
+    '不要输出 Markdown 围栏、不要输出解释文字。',
+    `JSON Schema:\n${JSON.stringify(schema)}`,
+  ].join('\n');
+}

apps/api/src/modules/llm/internal/usecase/llm-service.ts

@@ -5,6 +5,7 @@ import type { LlmInstanceView } from '@contracts';
 import type { LlmService, UpsertLlmInstance } from '../../contract.ts';
 import type { Clock, IdGenerator, LlmInstanceRepository, ProviderInvoker, SecretBox } from '../../port.ts';
 import { pickSystemUse, toPublicView, validateBinding } from '../domain/model-binding.ts';
+import { resolveBaseUrl } from '../domain/provider-profile.ts';
 import type { ModelBinding } from '../domain/model-binding.ts';
 
 export type LlmServiceDeps = {
@@ -34,7 +35,9 @@ export function createLlmService(deps: LlmServiceDeps): LlmService {
         id: existing?.id ?? input.id ?? deps.ids.next(),
         name: input.name,
         provider: input.provider,
-        baseUrl: input.baseUrl,
+        // URL 留空即取该 provider 的默认端点:选了 deepseek 就不必再背一遍它的地址
+        // URL 留空即取该 provider 的默认端点:选了 deepseek 就不必再背一遍它的地址
+        baseUrl: resolveBaseUrl(input.provider, input.baseUrl),
         model: input.model,
         apiKeyCipher,
         systemUse: input.systemUse,

apps/api/src/modules/llm/internal/usecase/model-gateway.ts

@@ -71,6 +71,8 @@ export function createModelGateway(deps: ModelGatewayDeps): ModelGateway {
       return {
         text: raw.text,
         ...(raw.object === undefined ? {} : { object: raw.object }),
+        // 推理正文过网关但**不进录制**:录的是语义入参与结论,思考过程不是回放的判据
+        ...(raw.reasoning === undefined ? {} : { reasoning: raw.reasoning }),
         usage,
         binding: { name: binding.name, provider: binding.provider, model: binding.model },
         mode: deps.mode,

apps/api/src/modules/llm/port.ts

@@ -43,7 +43,7 @@ export type ProviderInvoker = {
     readonly binding: ModelBinding;
     readonly apiKey: string;
     readonly call: ModelCall;
-  }): Promise<{ text: string; object?: unknown; usage: ModelUsage }>;
+  }): Promise<{ text: string; object?: unknown; reasoning?: string; usage: ModelUsage }>;
 };
 
 export type { ModelCall, ModelResult };

packages/contracts/src/api/governance.ts

@@ -96,7 +96,8 @@ export const upsertLlmInstanceRoute = {
     id: z.string().optional(),
     name: z.string().min(1),
     provider: llmProviderSchema,
-    baseUrl: z.string().min(1),
+    /** 留空 = 取该 provider 的默认端点(openai-compatible 没有默认,必须自填) */
+    baseUrl: z.string().default(''),
     model: z.string().min(1),
     /** 只写不读:留空表示保持原值 */
     apiKey: z.string().optional(),