refactor(tree): extract _embed_l2_subtree to reduce embed_all complexity

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

app/tree/index.py

@@ -481,6 +481,27 @@ class TreeIndex:
             if l1.embedding is None:
                 l1.embedding = embed_fn(l1.summary)[0].astype(np.float32)
             for l2 in l1.children:
+                self._embed_l2_subtree(l2, embed_fn)
+        self.metadata.embed_model = model_name
+        self.metadata.embed_dim = embed_dim
+        logger.info(
+            "embed_all 完成",
+            model=model_name,
+            embed_dim=embed_dim,
+        )
+
+    def _embed_l2_subtree(
+        self,
+        l2: L2Node,
+        embed_fn: Callable[[str | list[str]], np.ndarray],
+    ) -> None:
+        """对单个 L2 节点及其 L3 子节点执行 embedding（仅处理 embedding 为 None 的节点）。
+
+        参数:
+            l2: 待 embed 的 L2 节点。
+            embed_fn: EmbeddingModel.embed 方法，接受 str 或 List[str]，
+                      返回 [N, D] ndarray。
+        """
         if l2.embedding is None:
             l2.embedding = embed_fn(l2.description)[0].astype(np.float32)
         # L3 批量 embed
@@ -490,13 +511,6 @@ class TreeIndex:
             embs = embed_fn(texts).astype(np.float32)  # [N, D]
             for l3, emb in zip(need_embed, embs, strict=True):
                 l3.embedding = emb
-        self.metadata.embed_model = model_name
-        self.metadata.embed_dim = embed_dim
-        logger.info(
-            "embed_all 完成",
-            model=model_name,
-            embed_dim=embed_dim,
-        )
 
     # ------------------------------------------------------------------ #
     # 嵌入矩阵提取