feat: 模型推理异步加载与对话链路修复，同步基线

模型推理全异步化改造：
- 计算节点 InferenceSession 改为后台线程异步加载模型，load 立即返回，
  加载期间事件循环保持响应（/inference/status 与 /health 不阻塞）
- 后端模型加载改为异步派发 + 轮询对账器（reconcile_inference_loads），
  任务状态由 starting 自动推进到 ready/error，解决多节点启动超时
  （timeout of 120000ms exceeded）
- 推理删除/卸载改为任务感知 + 短超时，删除先删记录再 best-effort 卸载，
  不再被不可达节点阻塞；同节点新模型替换旧任务标记失效
- 流式对话透传 task_id/node_id 路由到真正加载模型的算力节点，
  useStreamChat 解析 SSE 错误帧以干净文案展示
- 对话历史按任务 id 本地持久化，退出重进可恢复；移除页脚提示文本
- 新增后端推理异步加载与计算节点异步状态机单元测试

Co-Authored-By: Claude <noreply@anthropic.com>

backend/app/db/platform_store.py

@@ -208,8 +208,12 @@ def parse_training_metric_line(line: str) -> dict[str, float] | None:
     if "loss" not in line and "learning_rate" not in line:
         return None
     result: dict[str, float] = {}
+    number_pattern = r"([-+]?(?:\d+(?:\.\d*)?|\.\d+)(?:[eE][-+]?\d+)?)"
+    step_match = re.search(rf"(?:^|[\s,{{])['\"]?step['\"]?\s*(?:=|:)\s*{number_pattern}", line, re.I)
+    if step_match:
+        result["step"] = float(step_match.group(1))
     for key in ["loss", "grad_norm", "learning_rate", "epoch"]:
-        match = re.search(rf"['\"]?{key}['\"]?\s*:\s*([-+]?\d+(?:\.\d+)?(?:[eE][-+]?\d+)?)", line)
+        match = re.search(rf"['\"]?{key}['\"]?\s*(?:=|:)\s*{number_pattern}", line, re.I)
         if match:
             result[key] = float(match.group(1))
     return result or None
@@ -452,6 +456,15 @@ class PlatformStore:
             )
             self._ensure_columns(conn, "gpus", {"last_seen_at": "TEXT"})
             self._ensure_columns(conn, "fine_tune_tasks", {"compute_job_id": "TEXT"})
+            self._ensure_columns(
+                conn,
+                "trained_models",
+                {
+                    "artifact_dir": "TEXT",
+                    "compute_node_id": "TEXT",
+                    "compute_node_name": "TEXT",
+                },
+            )
             self._ensure_columns(
                 conn,
                 "resource_replicas",
@@ -589,6 +602,15 @@ class PlatformStore:
         name = task.get("output_model_name") or f"{task['name']}-lora"
         exists = conn.execute("SELECT id FROM trained_models WHERE name=?", (name,)).fetchone()
         if exists:
+            conn.execute(
+                """
+                UPDATE trained_models
+                SET compute_node_id=COALESCE(compute_node_id, ?),
+                    compute_node_name=COALESCE(compute_node_name, ?)
+                WHERE id=?
+                """,
+                (task.get("compute_node_id"), task.get("compute_node_code") or task.get("compute_node_name"), exists["id"]),
+            )
             return
         model = conn.execute("SELECT path FROM models WHERE id=?", (task.get("base_model"),)).fetchone()
         output_dir = task.get("output_dir") or f"/data/yg-ft/outputs/{task['name']}"
@@ -596,8 +618,8 @@ class PlatformStore:
         conn.execute(
             """
             INSERT INTO trained_models
-            (id, name, train_methods, base_model_path, create_time, merged, merging, merged_path, artifact_dir)
-            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
+            (id, name, train_methods, base_model_path, create_time, merged, merging, merged_path, artifact_dir, compute_node_id, compute_node_name)
+            VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
             """,
             (
                 trained_model_id,
@@ -609,6 +631,8 @@ class PlatformStore:
                 0,
                 output_dir,
                 output_dir,
+                task.get("compute_node_id"),
+                task.get("compute_node_code") or task.get("compute_node_name"),
             ),
         )
         # Use real artifact data from compute node when available
@@ -875,7 +899,7 @@ class PlatformStore:
                 (
                     new_id("metric"),
                     task_id,
-                    line_number,
+                    int(metric.get("step") or line_number),
                     metric.get("epoch"),
                     metric.get("loss"),
                     metric.get("grad_norm"),
@@ -1342,15 +1366,25 @@ class PlatformStore:
         self.refresh_runtime_state()
         with self.connect() as conn:
             rows = conn.execute("SELECT * FROM trained_models ORDER BY create_time DESC").fetchall()
-            return [
-                {
+            items = []
+            for row in rows:
+                item = {
                     **dict(row),
                     "train_methods": json_loads(row["train_methods"], []),
                     "merged": bool(row["merged"]),
                     "merging": bool(row["merging"]),
                 }
-                for row in rows
-            ]
+                if not item.get("compute_node_id"):
+                    task_rows = conn.execute("SELECT payload FROM fine_tune_tasks ORDER BY create_time DESC").fetchall()
+                    for task in task_rows:
+                        task_payload = json_loads(task["payload"], {})
+                        output_name = task_payload.get("output_model_name") or f"{task_payload.get('name')}-lora"
+                        if output_name == item["name"]:
+                            item["compute_node_id"] = task_payload.get("compute_node_id")
+                            item["compute_node_name"] = task_payload.get("compute_node_code") or task_payload.get("compute_node_name")
+                            break
+                items.append(item)
+            return items
 
     def delete_trained_model(self, model_id: str) -> None:
         with self.connect() as conn:

backend/app/db/sql/001_platform_runtime.sql

@@ -33,7 +33,10 @@ CREATE TABLE IF NOT EXISTS trained_models (
     create_time TEXT NOT NULL,
     merged INTEGER NOT NULL DEFAULT 0,
     merging INTEGER NOT NULL DEFAULT 0,
-    merged_path TEXT
+    merged_path TEXT,
+    artifact_dir TEXT,
+    compute_node_id TEXT,
+    compute_node_name TEXT
 );
 
 CREATE TABLE IF NOT EXISTS model_lineage (