feat: add advisor recommendation review flow

app/service/agent/implementations/advisor.py

@@ -15,11 +15,18 @@ class AdvisorAgent(FundQueryDemoAgent):
         allowed_roles=("customer", "advisor", "operator", "admin"),
         allowed_portals=("api",),
         allowed_tools=(
-            "query_fund_quote", "query_investment_goal", "analyze_portfolio",
+            "query_fund_quote",
+            "query_investment_goal",
+            "analyze_portfolio",
             "generate_asset_allocation",
+            "recommend_products",
         ),
         supported_intents=(
-            "fund_quote", "investment_goal", "portfolio_analysis", "asset_allocation",
+            "fund_quote",
+            "investment_goal",
+            "portfolio_analysis",
+            "asset_allocation",
+            "product_recommend",
         ),
     )
 
@@ -50,6 +57,14 @@ class AdvisorAgent(FundQueryDemoAgent):
                 "generate_asset_allocation", {}, intent="asset_allocation", context=context
             )
             return CoreResult(text=self._describe_allocation(output))
+        if (
+            self._classified_intent is not None
+            and self._classified_intent.intent == "product_recommend"
+        ):
+            output = await self.call_tool(
+                "recommend_products", {"limit": 3}, intent="product_recommend", context=context
+            )
+            return CoreResult(text=self._describe_recommendation(output))
         return await super().handle(request, context)
 
     @staticmethod
@@ -98,13 +113,33 @@ class AdvisorAgent(FundQueryDemoAgent):
             return "当前没有足够的场内基金数据生成资产配置。"
         parts = [
             f"{item.get('label', item.get('asset_class'))} {item.get('target_pct')}%"
-            for item in allocation if isinstance(item, dict)
+            for item in allocation
+            if isinstance(item, dict)
         ]
         optimization = result.get("optimization")
         dynamic = isinstance(optimization, dict) and bool(optimization.get("dynamic"))
         mode = "动态历史因子优化" if dynamic else "静态配置（历史数据覆盖不足）"
         return (
-            f"资产配置分析完成（{mode}）：" + "，".join(parts)
+            f"资产配置分析完成（{mode}）："
+            + "，".join(parts)
             + "。该结果综合考虑收益目标、最大回撤、流动性和投资期限，"
             "仅供分析参考，不构成交易指令。"
         )
+
+    @staticmethod
+    def _describe_recommendation(result: object) -> str:
+        if not isinstance(result, dict):
+            return "产品推荐暂不可用，请稍后重试。"
+        if result.get("status") == "profile_required":
+            return "当前缺少有效风险画像，暂不能推荐产品。"
+        if result.get("status") == "investment_goal_required":
+            return "当前没有已确认的投资目标，暂不能推荐产品。"
+        products = result.get("products")
+        if not isinstance(products, list) or not products:
+            return "当前没有通过适当性和证据校验的场内基金产品。"
+        names = [
+            f"{item.get('product_code')} {item.get('product_name')}"
+            for item in products
+            if isinstance(item, dict)
+        ]
+        return "推荐分析结果：" + "、".join(names) + "。方案须经审核发布，不构成交易指令。"