feat: link ai quality actions to changes

project-docs/V2-ai-quality-improvement-linked-changes-2026-06-23.md

@@ -0,0 +1,67 @@
+# V2 AI 质量改进项关联真实变更记录 - 2026-06-23
+
+## 背景
+
+上一轮已经完成 Prompt 发布/回滚闭环。本轮继续推进“AI 质量问题归因 -> 改进动作 -> 真实配置变更 -> 效果回看”的闭环，避免质量改进项只停留在文字状态。
+
+## 本轮完成内容
+
+1. 改进项增加真实关联字段
+   - `linked_prompt_template_id`
+   - `linked_prompt_template_version`
+   - `linked_knowledge_base_id`
+   - `linked_import_batch_id`
+
+2. AI Platform 自动推断关联对象
+   - Prompt 维度改进项会尝试关联对应 Prompt 模板和版本。
+   - 知识库维度改进项会关联知识库 ID 和该知识库最新导入批次。
+   - 创建接口仍支持前端或 SDK 显式传入关联字段。
+
+3. 持久化兼容
+   - SQLAlchemy 模型增加对应字段。
+   - 自动兼容迁移会给已有 `ai_quality_improvement_actions` 表补列。
+   - 内存仓库与 SQL 仓库均支持创建、更新、列表、看板返回关联字段。
+
+4. OpenAPI 与 SDK 同步
+   - `AiQualityImprovementActionCreate`、`AiQualityImprovementActionUpdate`、`AiQualityImprovementAction` 同步新增关联字段。
+   - Python SDK 支持创建和更新改进项时传入关联字段。
+
+5. 管理后台质量运营页增强
+   - 页面加载 Prompt 模板和知识库导入批次候选。
+   - 从 Prompt 归因生成改进项时，自动关联 Prompt 模板 ID 和版本。
+   - 从知识库归因生成改进项时，自动关联知识库 ID 和最新导入批次。
+   - 改进项卡片展示“关联变更”，并提供跳转 Prompt 中心入口。
+   - 新增 `/api/ai-platform/knowledge-import-batches` 前端代理。
+
+## 验证结果
+
+- `python -m pytest yuqei-ai-platform/services/ai-platform-api/tests -q`
+  - 26 passed
+- `python -m pytest yuqei-ai-sdk-python/tests -q`
+  - 2 passed
+- `npm run web:typecheck`
+  - passed
+- `npm run web:build`
+  - passed
+- OpenAPI YAML 解析
+  - passed
+
+## 当前闭环价值
+
+现在质量运营路径变成：
+
+1. 质量看板发现某个模型、Prompt、知识库或标签异常集中。
+2. 管理员从归因卡片生成改进项。
+3. 改进项自动挂上相关 Prompt 版本或知识库导入批次。
+4. 管理员去 Prompt 中心发布新版本，或补充知识库导入批次。
+5. 后续 AI 调用继续进入质量看板，改进项显示处理后异常率、质量分、待处理标签变化。
+
+这让“调整 Prompt”“补知识库”开始具备可追溯的配置变更记录。
+
+## 下一步建议
+
+下一步可以进入“改进动作执行页”：
+
+- 对 `tune_prompt` 改进项，提供直接创建 Prompt 草稿的入口。
+- 对 `improve_knowledge` 改进项，提供上传/导入知识文档入口。
+- 改进项完成时自动要求填写“实际变更对象”，防止误标完成。

yuqei-ai-platform/packages/ai-openapi/openapi.yaml

@@ -1666,6 +1666,22 @@ components:
         root_cause_hint:
           type: string
           nullable: true
+        linked_prompt_template_id:
+          type: string
+          nullable: true
+          maxLength: 64
+        linked_prompt_template_version:
+          type: string
+          nullable: true
+          maxLength: 64
+        linked_knowledge_base_id:
+          type: string
+          nullable: true
+          maxLength: 64
+        linked_import_batch_id:
+          type: string
+          nullable: true
+          maxLength: 64
         evaluation_days:
           type: integer
           minimum: 1
@@ -1690,6 +1706,22 @@ components:
         resolution_note:
           type: string
           nullable: true
+        linked_prompt_template_id:
+          type: string
+          nullable: true
+          maxLength: 64
+        linked_prompt_template_version:
+          type: string
+          nullable: true
+          maxLength: 64
+        linked_knowledge_base_id:
+          type: string
+          nullable: true
+          maxLength: 64
+        linked_import_batch_id:
+          type: string
+          nullable: true
+          maxLength: 64
     AiQualityImprovementAction:
       allOf:
         - $ref: "#/components/schemas/AiQualityImprovementActionCreate"

yuqei-ai-platform/services/ai-platform-api/src/yuqei_ai_platform_api/models.py

@@ -172,6 +172,10 @@ class AiQualityImprovementActionModel(Base):
     source_label_codes_json: Mapped[str] = mapped_column(Text, default="[]")
     source_run_ids_json: Mapped[str] = mapped_column(Text, default="[]")
     root_cause_hint: Mapped[str | None] = mapped_column(Text, nullable=True)
+    linked_prompt_template_id: Mapped[str | None] = mapped_column(String(64), nullable=True)
+    linked_prompt_template_version: Mapped[str | None] = mapped_column(String(64), nullable=True)
+    linked_knowledge_base_id: Mapped[str | None] = mapped_column(String(64), nullable=True)
+    linked_import_batch_id: Mapped[str | None] = mapped_column(String(64), nullable=True)
     evaluation_days: Mapped[int] = mapped_column(Integer, default=14)
     baseline_total_runs: Mapped[int] = mapped_column(Integer, default=0)
     baseline_affected_runs: Mapped[int] = mapped_column(Integer, default=0)

yuqei-ai-platform/services/ai-platform-api/src/yuqei_ai_platform_api/repository.py

@@ -335,6 +335,10 @@ class AiQualityImprovementActionCreate(BaseModel):
     source_label_codes: list[str] = Field(default_factory=list)
     source_run_ids: list[str] = Field(default_factory=list)
     root_cause_hint: str | None = None
+    linked_prompt_template_id: str | None = Field(default=None, max_length=64)
+    linked_prompt_template_version: str | None = Field(default=None, max_length=64)
+    linked_knowledge_base_id: str | None = Field(default=None, max_length=64)
+    linked_import_batch_id: str | None = Field(default=None, max_length=64)
     evaluation_days: int = Field(default=14, ge=1, le=90)
 
 
@@ -343,6 +347,10 @@ class AiQualityImprovementActionUpdate(BaseModel):
     owner: str | None = Field(default=None, max_length=128)
     priority: str | None = Field(default=None, pattern="^(low|medium|high|critical)$")
     resolution_note: str | None = None
+    linked_prompt_template_id: str | None = Field(default=None, max_length=64)
+    linked_prompt_template_version: str | None = Field(default=None, max_length=64)
+    linked_knowledge_base_id: str | None = Field(default=None, max_length=64)
+    linked_import_batch_id: str | None = Field(default=None, max_length=64)
 
 
 class AiQualityImprovementAction(AiQualityImprovementActionCreate):
@@ -866,6 +874,11 @@ class InMemoryAiPlatformRepository:
         now = datetime.now(UTC)
         with self._lock:
             baseline = _calculate_improvement_action_stats(payload, self.ai_run_audits)
+            payload = _with_inferred_action_links(
+                payload,
+                prompt_templates=self.prompt_templates.values(),
+                knowledge_import_batches=self.knowledge_import_batches.values(),
+            )
             action = AiQualityImprovementAction(
                 id=f"qa-action-{uuid4().hex[:12]}",
                 title=payload.title or _default_improvement_action_title(payload),
@@ -906,6 +919,14 @@ class InMemoryAiPlatformRepository:
                 update["priority"] = payload.priority
             if payload.resolution_note is not None:
                 update["resolution_note"] = payload.resolution_note
+            if payload.linked_prompt_template_id is not None:
+                update["linked_prompt_template_id"] = payload.linked_prompt_template_id
+            if payload.linked_prompt_template_version is not None:
+                update["linked_prompt_template_version"] = payload.linked_prompt_template_version
+            if payload.linked_knowledge_base_id is not None:
+                update["linked_knowledge_base_id"] = payload.linked_knowledge_base_id
+            if payload.linked_import_batch_id is not None:
+                update["linked_import_batch_id"] = payload.linked_import_batch_id
             updated = action.model_copy(update=update)
             self.quality_improvement_actions[action_id] = updated
             return _materialize_improvement_action(updated, self.ai_run_audits)
@@ -956,15 +977,15 @@ class SqlAlchemyAiPlatformRepository:
         self._vector_search_backend = vector_search_backend
         if auto_create:
             Base.metadata.create_all(self._engine)
-            self._ensure_prompt_template_columns()
+            self._ensure_compat_columns()
 
-    def _ensure_prompt_template_columns(self) -> None:
+    def _ensure_compat_columns(self) -> None:
         with self._engine.begin() as connection:
             dialect = self._engine.dialect.name
-            existing_columns: set[str]
+            prompt_columns: set[str]
             if dialect == "sqlite":
                 rows = connection.execute(text("PRAGMA table_info(prompt_templates)")).mappings().all()
-                existing_columns = {str(row["name"]) for row in rows}
+                prompt_columns = {str(row["name"]) for row in rows}
             else:
                 rows = connection.execute(
                     text(
@@ -972,15 +993,38 @@ class SqlAlchemyAiPlatformRepository:
                         "where table_name = 'prompt_templates'"
                     )
                 ).mappings().all()
-                existing_columns = {str(row["column_name"]) for row in rows}
-            if "status" not in existing_columns:
+                prompt_columns = {str(row["column_name"]) for row in rows}
+            if "status" not in prompt_columns:
                 connection.execute(text("ALTER TABLE prompt_templates ADD COLUMN status VARCHAR(32) DEFAULT 'draft'"))
-            if "rollback_from_version" not in existing_columns:
+            if "rollback_from_version" not in prompt_columns:
                 connection.execute(text("ALTER TABLE prompt_templates ADD COLUMN rollback_from_version VARCHAR(32)"))
-            if "published_at" not in existing_columns:
+            if "published_at" not in prompt_columns:
                 column_type = "TIMESTAMP" if dialect == "sqlite" else "TIMESTAMP WITH TIME ZONE"
                 connection.execute(text(f"ALTER TABLE prompt_templates ADD COLUMN published_at {column_type}"))
 
+            if dialect == "sqlite":
+                rows = connection.execute(text("PRAGMA table_info(ai_quality_improvement_actions)")).mappings().all()
+                action_columns = {str(row["name"]) for row in rows}
+            else:
+                rows = connection.execute(
+                    text(
+                        "select column_name from information_schema.columns "
+                        "where table_name = 'ai_quality_improvement_actions'"
+                    )
+                ).mappings().all()
+                action_columns = {str(row["column_name"]) for row in rows}
+            action_column_specs = {
+                "linked_prompt_template_id": "VARCHAR(64)",
+                "linked_prompt_template_version": "VARCHAR(64)",
+                "linked_knowledge_base_id": "VARCHAR(64)",
+                "linked_import_batch_id": "VARCHAR(64)",
+            }
+            for column_name, column_type in action_column_specs.items():
+                if column_name not in action_columns:
+                    connection.execute(
+                        text(f"ALTER TABLE ai_quality_improvement_actions ADD COLUMN {column_name} {column_type}")
+                    )
+
     def upsert_provider_config(self, payload: ProviderConfigCreate) -> ProviderConfig:
         now = datetime.now(UTC)
         with self._session_factory.begin() as session:
@@ -1589,6 +1633,17 @@ class SqlAlchemyAiPlatformRepository:
             ).all()
             runs = [_ai_run_audit_from_model(row) for row in audit_rows]
             baseline = _calculate_improvement_action_stats(payload, runs)
+            payload = _with_inferred_action_links(
+                payload,
+                prompt_templates=[
+                    _prompt_template_from_model(row)
+                    for row in session.scalars(select(PromptTemplateModel)).all()
+                ],
+                knowledge_import_batches=[
+                    _knowledge_import_batch_from_model(row)
+                    for row in session.scalars(select(KnowledgeImportBatchModel)).all()
+                ],
+            )
             row = AiQualityImprovementActionModel(
                 id=f"qa-action-{uuid4().hex[:12]}",
                 dimension=payload.dimension,
@@ -1603,6 +1658,10 @@ class SqlAlchemyAiPlatformRepository:
                 source_label_codes_json=_encode_json_list(payload.source_label_codes),
                 source_run_ids_json=_encode_json_list(payload.source_run_ids),
                 root_cause_hint=payload.root_cause_hint,
+                linked_prompt_template_id=payload.linked_prompt_template_id,
+                linked_prompt_template_version=payload.linked_prompt_template_version,
+                linked_knowledge_base_id=payload.linked_knowledge_base_id,
+                linked_import_batch_id=payload.linked_import_batch_id,
                 evaluation_days=payload.evaluation_days,
                 baseline_total_runs=baseline["total_runs"],
                 baseline_affected_runs=baseline["affected_runs"],
@@ -1632,6 +1691,14 @@ class SqlAlchemyAiPlatformRepository:
                 row.priority = payload.priority
             if payload.resolution_note is not None:
                 row.resolution_note = payload.resolution_note
+            if payload.linked_prompt_template_id is not None:
+                row.linked_prompt_template_id = payload.linked_prompt_template_id
+            if payload.linked_prompt_template_version is not None:
+                row.linked_prompt_template_version = payload.linked_prompt_template_version
+            if payload.linked_knowledge_base_id is not None:
+                row.linked_knowledge_base_id = payload.linked_knowledge_base_id
+            if payload.linked_import_batch_id is not None:
+                row.linked_import_batch_id = payload.linked_import_batch_id
             row.updated_at = now
             row.resolved_at = now if payload.status in {"resolved", "false_positive", "closed"} else None
 
@@ -1926,6 +1993,10 @@ def _quality_improvement_action_from_model(row: AiQualityImprovementActionModel)
         source_label_codes=[str(item) for item in _decode_json_list(row.source_label_codes_json)],
         source_run_ids=[str(item) for item in _decode_json_list(row.source_run_ids_json)],
         root_cause_hint=row.root_cause_hint,
+        linked_prompt_template_id=row.linked_prompt_template_id,
+        linked_prompt_template_version=row.linked_prompt_template_version,
+        linked_knowledge_base_id=row.linked_knowledge_base_id,
+        linked_import_batch_id=row.linked_import_batch_id,
         evaluation_days=row.evaluation_days,
         baseline_total_runs=row.baseline_total_runs,
         baseline_affected_runs=row.baseline_affected_runs,
@@ -1938,6 +2009,61 @@ def _quality_improvement_action_from_model(row: AiQualityImprovementActionModel)
     )
 
 
+def _with_inferred_action_links(
+    payload: AiQualityImprovementActionCreate,
+    *,
+    prompt_templates: Iterable[PromptTemplate],
+    knowledge_import_batches: Iterable[KnowledgeImportBatch],
+) -> AiQualityImprovementActionCreate:
+    update: dict[str, str] = {}
+    if payload.dimension == "prompt_template" and not payload.linked_prompt_template_id:
+        prompt = _find_prompt_template_link(payload.key, prompt_templates)
+        if prompt is not None:
+            update["linked_prompt_template_id"] = prompt.id
+            update["linked_prompt_template_version"] = prompt.version
+    if payload.dimension == "knowledge_base" or payload.linked_knowledge_base_id:
+        if payload.dimension == "knowledge_base" and not payload.linked_knowledge_base_id and payload.key != "no_knowledge_source":
+            update["linked_knowledge_base_id"] = payload.key
+        knowledge_base_id = payload.linked_knowledge_base_id or update.get("linked_knowledge_base_id")
+        if knowledge_base_id and not payload.linked_import_batch_id:
+            batch = _find_latest_import_batch_link(knowledge_base_id, knowledge_import_batches)
+            if batch is not None:
+                update["linked_import_batch_id"] = batch.id
+    return payload.model_copy(update=update) if update else payload
+
+
+def _find_prompt_template_link(
+    key: str,
+    prompt_templates: Iterable[PromptTemplate],
+) -> PromptTemplate | None:
+    candidates = [
+        template
+        for template in prompt_templates
+        if template.id == key or template.name == key or f"{template.name}:{template.version}" == key
+    ]
+    if not candidates:
+        return None
+    return sorted(
+        candidates,
+        key=lambda item: item.published_at or item.updated_at,
+        reverse=True,
+    )[0]
+
+
+def _find_latest_import_batch_link(
+    knowledge_base_id: str,
+    batches: Iterable[KnowledgeImportBatch],
+) -> KnowledgeImportBatch | None:
+    candidates = [
+        batch
+        for batch in batches
+        if batch.knowledge_base_id == knowledge_base_id
+    ]
+    if not candidates:
+        return None
+    return sorted(candidates, key=lambda item: item.updated_at, reverse=True)[0]
+
+
 def _ensure_utc(value: datetime) -> datetime:
     if value.tzinfo is None:
         return value.replace(tzinfo=UTC)

yuqei-ai-platform/services/ai-platform-api/tests/test_platform_data.py

@@ -326,6 +326,53 @@ def test_quality_attribution_can_create_and_resolve_improvement_action() -> None
     assert dashboard_action["effect_summary"]
 
 
+def test_quality_improvement_action_links_prompt_and_knowledge_changes() -> None:
+    repository = InMemoryAiPlatformRepository()
+    repository.seed_defaults()
+    prompt = repository.get_prompt_template("legal_qa.default")
+    assert prompt is not None
+    batch = repository.create_knowledge_import_batch(
+        KnowledgeImportBatchCreate(
+            knowledge_base_id="laws-cn",
+            source_name="civil-code-refresh",
+            source_type="law_import",
+        )
+    )
+
+    prompt_action = repository.create_quality_improvement_action(
+        AiQualityImprovementActionCreate(
+            dimension="prompt_template",
+            key=prompt.id,
+            label="legal_qa.default",
+            action_type="tune_prompt",
+            evaluation_days=7,
+        )
+    )
+    assert prompt_action.linked_prompt_template_id == prompt.id
+    assert prompt_action.linked_prompt_template_version == prompt.version
+
+    knowledge_action = repository.create_quality_improvement_action(
+        AiQualityImprovementActionCreate(
+            dimension="knowledge_base",
+            key="laws-cn",
+            label="laws-cn",
+            action_type="improve_knowledge",
+            evaluation_days=7,
+        )
+    )
+    assert knowledge_action.linked_knowledge_base_id == "laws-cn"
+    assert knowledge_action.linked_import_batch_id == batch.id
+
+    updated = repository.update_quality_improvement_action(
+        knowledge_action.id,
+        AiQualityImprovementActionUpdate(
+            status="in_progress",
+            linked_import_batch_id="kb-batch-manual",
+        ),
+    )
+    assert updated.linked_import_batch_id == "kb-batch-manual"
+
+
 def test_knowledge_document_can_be_added_and_searched() -> None:
     client = make_client()
 

yuqei-ai-sdk-python/src/yuqei_sdk/ai_platform.py

@@ -345,6 +345,10 @@ class AiQualityImprovementActionCreate(BaseModel):
     source_label_codes: list[str] = Field(default_factory=list)
     source_run_ids: list[str] = Field(default_factory=list)
     root_cause_hint: str | None = None
+    linked_prompt_template_id: str | None = None
+    linked_prompt_template_version: str | None = None
+    linked_knowledge_base_id: str | None = None
+    linked_import_batch_id: str | None = None
     evaluation_days: int = 14
 
 
@@ -353,6 +357,10 @@ class AiQualityImprovementActionUpdate(BaseModel):
     owner: str | None = Field(default=None, max_length=128)
     priority: str | None = None
     resolution_note: str | None = None
+    linked_prompt_template_id: str | None = None
+    linked_prompt_template_version: str | None = None
+    linked_knowledge_base_id: str | None = None
+    linked_import_batch_id: str | None = None
 
 
 class AiQualityImprovementAction(AiQualityImprovementActionCreate):

yuqei-ai-sdk-python/tests/test_ai_platform.py

@@ -115,6 +115,10 @@ def test_ai_platform_client_covers_admin_data_endpoints() -> None:
             "source_label_codes": ["source_limited"],
             "source_run_ids": ["run-1"],
             "root_cause_hint": "Model quality issues are concentrated.",
+            "linked_prompt_template_id": "prompt-1",
+            "linked_prompt_template_version": "v2",
+            "linked_knowledge_base_id": "laws-cn",
+            "linked_import_batch_id": "kb-batch-1",
             "evaluation_days": 7,
             "baseline_total_runs": 1,
             "baseline_affected_runs": 1,
@@ -621,6 +625,7 @@ def test_ai_platform_client_covers_admin_data_endpoints() -> None:
             assert payload["dimension"] == "model"
             assert payload["action_type"] == "tune_model"
             assert payload["source_label_codes"] == ["source_limited"]
+            assert payload["linked_prompt_template_id"] == "prompt-1"
             return httpx.Response(200, json=quality_action_payload())
         if request.url.path == "/api/v1/audit/quality-improvement-actions":
             query = parse_qs(request.url.query.decode())
@@ -636,6 +641,7 @@ def test_ai_platform_client_covers_admin_data_endpoints() -> None:
             assert request.method == "PATCH"
             assert payload["status"] == "resolved"
             assert payload["resolution_note"] == "Provider config was tuned."
+            assert payload["linked_import_batch_id"] == "kb-batch-2"
             return httpx.Response(
                 200,
                 json=quality_action_payload(
@@ -802,10 +808,13 @@ def test_ai_platform_client_covers_admin_data_endpoints() -> None:
             priority="high",
             source_label_codes=["source_limited"],
             source_run_ids=["run-1"],
+            linked_prompt_template_id="prompt-1",
             evaluation_days=7,
         )
     )
     assert created_action.status == "open"
+    assert created_action.linked_prompt_template_id == "prompt-1"
+    assert created_action.linked_import_batch_id == "kb-batch-1"
     assert created_action.baseline_affected_runs == 1
     assert created_action.effect_status == "unchanged"
     listed_actions = client.list_quality_improvement_actions(
@@ -823,6 +832,7 @@ def test_ai_platform_client_covers_admin_data_endpoints() -> None:
         AiQualityImprovementActionUpdate(
             status="resolved",
             resolution_note="Provider config was tuned.",
+            linked_import_batch_id="kb-batch-2",
         ),
     )
     assert updated_action.status == "resolved"

yuqei-contract-platform/apps/contract-web/src/app/admin/ai-quality-dashboard/page.tsx

@@ -84,6 +84,10 @@ type QualityImprovementAction = {
   source_label_codes: string[];
   source_run_ids: string[];
   root_cause_hint?: string | null;
+  linked_prompt_template_id?: string | null;
+  linked_prompt_template_version?: string | null;
+  linked_knowledge_base_id?: string | null;
+  linked_import_batch_id?: string | null;
   evaluation_days: number;
   baseline_total_runs: number;
   baseline_affected_runs: number;
@@ -112,6 +116,24 @@ type QualityDashboard = {
   improvement_actions?: QualityImprovementAction[];
 };
 
+type PromptTemplateOption = {
+  id: string;
+  name: string;
+  version: string;
+  status: string;
+  published_at?: string | null;
+  updated_at: string;
+};
+
+type KnowledgeImportBatchOption = {
+  id: string;
+  knowledge_base_id: string;
+  source_name: string;
+  source_type: string;
+  status: string;
+  updated_at: string;
+};
+
 type LoadState =
   | { status: "loading" }
   | { status: "ready"; dashboard: QualityDashboard }
@@ -181,6 +203,8 @@ export default function AiQualityDashboardPage() {
   const [days, setDays] = useState(14);
   const [actionMessage, setActionMessage] = useState<string | null>(null);
   const [actionBusyKey, setActionBusyKey] = useState<string | null>(null);
+  const [promptOptions, setPromptOptions] = useState<PromptTemplateOption[]>([]);
+  const [importBatchOptions, setImportBatchOptions] = useState<KnowledgeImportBatchOption[]>([]);
   const [filters, setFilters] = useState<ActionFilters>({
     status: "",
     owner: "",
@@ -209,8 +233,24 @@ export default function AiQualityDashboardPage() {
     }
   }
 
+  async function loadLinkOptions() {
+    const [promptResponse, batchResponse] = await Promise.allSettled([
+      fetch("/api/ai-platform/prompt-templates", { cache: "no-store" }),
+      fetch("/api/ai-platform/knowledge-import-batches?limit=50", { cache: "no-store" })
+    ]);
+    if (promptResponse.status === "fulfilled" && promptResponse.value.ok) {
+      const payload = await promptResponse.value.json().catch(() => []);
+      setPromptOptions(Array.isArray(payload) ? payload : []);
+    }
+    if (batchResponse.status === "fulfilled" && batchResponse.value.ok) {
+      const payload = await batchResponse.value.json().catch(() => []);
+      setImportBatchOptions(Array.isArray(payload) ? payload : []);
+    }
+  }
+
   useEffect(() => {
     void loadDashboard(days);
+    void loadLinkOptions();
     // eslint-disable-next-line react-hooks/exhaustive-deps
   }, []);
 
@@ -249,6 +289,7 @@ export default function AiQualityDashboardPage() {
           priority: inferPriority(item),
           source_label_codes: item.top_label_codes,
           root_cause_hint: item.root_cause_hint,
+          ...inferActionLinkPayload(item, promptOptions, importBatchOptions),
           evaluation_days: days
         })
       });
@@ -257,6 +298,7 @@ export default function AiQualityDashboardPage() {
         throw new Error(payload?.message ?? `HTTP ${response.status}`);
       }
       setActionMessage("已生成改进项，可以在下方队列继续处理。");
+      await loadLinkOptions();
       await loadDashboard(days);
     } catch (error) {
       setActionMessage(error instanceof Error ? error.message : "生成改进项失败");
@@ -600,8 +642,14 @@ function QualityActionItem({
         <span>变化 {formatSignedPercent(action.affected_rate_delta)}</span>
       </div>
       {action.resolution_note && <span className="item-meta">处理说明：{action.resolution_note}</span>}
+      <LinkedChangeSummary action={action} />
       {action.effect_summary && <span className="item-meta">效果复盘：{action.effect_summary}</span>}
       <div className="action-row">
+        {action.linked_prompt_template_id && (
+          <Link className="button button-soft" href="/admin/ai-prompts">
+            查看 Prompt
+          </Link>
+        )}
         {action.status === "open" && (
           <button className="button button-soft" disabled={busy} onClick={() => void onUpdate(action, "in_progress")} type="button">
             <PlayCircle size={16} />
@@ -625,6 +673,20 @@ function QualityActionItem({
   );
 }
 
+function LinkedChangeSummary({ action }: { action: QualityImprovementAction }) {
+  const links = [
+    action.linked_prompt_template_id
+      ? `Prompt ${action.linked_prompt_template_version || action.linked_prompt_template_id}`
+      : null,
+    action.linked_knowledge_base_id ? `知识库 ${action.linked_knowledge_base_id}` : null,
+    action.linked_import_batch_id ? `导入批次 ${action.linked_import_batch_id}` : null
+  ].filter(Boolean);
+  if (links.length === 0) {
+    return null;
+  }
+  return <span className="item-meta">关联变更：{links.join(" · ")}</span>;
+}
+
 function filterImprovementActions(actions: QualityImprovementAction[], filters: ActionFilters) {
   return actions.filter((action) => (
     (!filters.status || action.status === filters.status)
@@ -665,6 +727,54 @@ function inferPriority(item: QualityAttributionItem) {
   return "medium";
 }
 
+function inferActionLinkPayload(
+  item: QualityAttributionItem,
+  promptOptions: PromptTemplateOption[],
+  importBatchOptions: KnowledgeImportBatchOption[]
+) {
+  if (item.dimension === "prompt_template") {
+    const prompt = findPromptOption(item.key, promptOptions);
+    return {
+      linked_prompt_template_id: prompt?.id ?? item.key,
+      linked_prompt_template_version: prompt?.version
+    };
+  }
+  if (item.dimension === "knowledge_base" && item.key !== "no_knowledge_source") {
+    const batch = findLatestImportBatchOption(item.key, importBatchOptions);
+    return {
+      linked_knowledge_base_id: item.key,
+      linked_import_batch_id: batch?.id
+    };
+  }
+  if (item.dimension === "label_code" && item.key.includes("source")) {
+    const batch = findLatestImportBatchOption("default", importBatchOptions);
+    return {
+      linked_knowledge_base_id: "default",
+      linked_import_batch_id: batch?.id
+    };
+  }
+  return {};
+}
+
+function findPromptOption(key: string, options: PromptTemplateOption[]) {
+  const matches = options.filter((option) => (
+    option.id === key
+    || option.name === key
+    || `${option.name}:${option.version}` === key
+  ));
+  return matches.sort((left, right) => {
+    const leftTime = new Date(left.published_at || left.updated_at).getTime();
+    const rightTime = new Date(right.published_at || right.updated_at).getTime();
+    return rightTime - leftTime;
+  })[0];
+}
+
+function findLatestImportBatchOption(knowledgeBaseId: string, options: KnowledgeImportBatchOption[]) {
+  return options
+    .filter((option) => option.knowledge_base_id === knowledgeBaseId)
+    .sort((left, right) => new Date(right.updated_at).getTime() - new Date(left.updated_at).getTime())[0];
+}
+
 function defaultResolutionNote(status: string) {
   if (status === "resolved") {
     return "已处理，等待后续 AI 调用数据验证异常率是否下降。";

yuqei-contract-platform/apps/contract-web/src/app/api/ai-platform/knowledge-import-batches/route.ts

@@ -0,0 +1,38 @@
+import { NextResponse } from "next/server";
+
+const aiPlatformBaseUrl =
+  process.env.AI_PLATFORM_API_BASE_URL ??
+  process.env.NEXT_PUBLIC_AI_PLATFORM_API_BASE_URL ??
+  "http://localhost:8101";
+
+export async function GET(request: Request) {
+  const requestUrl = new URL(request.url);
+  const targetUrl = new URL("/api/v1/knowledge-import-batches", aiPlatformBaseUrl);
+  requestUrl.searchParams.forEach((value, key) => {
+    targetUrl.searchParams.set(key, value);
+  });
+
+  try {
+    const response = await fetch(targetUrl, { cache: "no-store" });
+    const payload: unknown = await response.json().catch(() => null);
+    if (!response.ok) {
+      return NextResponse.json(
+        {
+          message: "AI Platform knowledge import batch service returned an error.",
+          status: response.status,
+          payload
+        },
+        { status: response.status }
+      );
+    }
+    return NextResponse.json(payload);
+  } catch (error) {
+    return NextResponse.json(
+      {
+        message: error instanceof Error ? error.message : "AI Platform knowledge import batch service is unavailable.",
+        baseUrl: aiPlatformBaseUrl
+      },
+      { status: 502 }
+    );
+  }
+}