feat: pipeline 执行时显性输出当前阶段与 AI 大模型信息

- run_pipeline 每步执行前打印阶段标题(序号/中文名/步骤名/日期)
- llm/report/embedding 步骤打印 AI 大模型供应商与模型名
  (provider/model 按 configs/llm_models.yaml 场景配置解析,缺 key 也可展示)

scheduler/pipeline.py

@@ -12,6 +12,7 @@
 from __future__ import annotations
 
 import json
+import os
 import subprocess
 import time
 from dataclasses import dataclass, field
@@ -23,6 +24,27 @@ from loguru import logger
 # 断点状态文件(按日期隔离,记录每步骤结果)
 DEFAULT_STATE_PATH = Path("data/pipeline/state.json")
 
+# 步骤名 → 中文阶段名(显性输出用)
+_STAGE_LABELS: dict[str, str] = {
+    "crawler": "M1 新闻抓取",
+    "xwlb": "M1 新闻联播抓取",
+    "extractor": "M2 正文提取",
+    "dedup": "M3 新闻去重",
+    "llm": "M4 LLM 事件抽取",
+    "embedding": "M5 向量化嵌入",
+    "qdrant": "M6 Qdrant 入库",
+    "report": "日报生成",
+    "cninfo_crawl": "cninfo 公告抓取",
+    "cninfo_extract": "cninfo 正文提取",
+    "cninfo_pdf": "cninfo PDF 补充",
+}
+
+# 使用对话大模型的步骤 → 对应 configs/llm_models.yaml 场景名
+_AI_LLM_SCENES: dict[str, str] = {
+    "llm": "event_extraction",
+    "report": "daily_report",
+}
+
 # 步骤超时(秒)
 STEP_TIMEOUTS: dict[str, int] = {
     "crawler": 900,     # M1 抓取(含 Playwright 浏览器,13 源约 8-12 min)
@@ -122,6 +144,73 @@ def _resume_start_index(
     return len(names)
 
 
+def _llm_scene_desc(scene: str) -> str | None:
+    """解析某 LLM 场景的 provider/model(仅展示,不校验 API key)。"""
+    try:
+        from configs.loader import load_scene_config
+
+        def _env(key: str) -> str | None:
+            v = os.environ.get(key)
+            return v.strip() if v else None
+
+        sc = load_scene_config(scene)
+        p = (sc.get("provider") or _env("LLM_PROVIDER") or "deepseek").lower()
+        if p in ("qwen", "dashscope"):
+            p = "qwen"
+        model = sc.get("model")
+        if not model:
+            if p == "qwen":
+                model = _env("QWEN_MODEL") or _env("LLM_MODEL")
+            else:
+                model = _env("DEEPSEEK_MODEL") or _env("LLM_MODEL")
+        if not model:
+            return None
+        return f"provider={p}, model={model}"
+    except Exception as e:  # noqa: BLE001 - 配置缺失时降级展示
+        logger.debug("AI 描述解析失败(场景 {}): {}", scene, e)
+        return None
+
+
+def _embedding_desc() -> str | None:
+    """解析 embedding 场景的 provider/model(不实例化模型,避免加载本地权重)。"""
+    try:
+        from configs.loader import load_scene_config
+        from embedding import resolve_provider_type
+
+        sc = load_scene_config("embedding")
+        pt = resolve_provider_type().value  # dashscope | local-bge
+        model = sc.get("model")
+        if not model:
+            if pt == "dashscope":
+                model = os.environ.get("DASHSCOPE_EMBEDDING_MODEL") or "text-embedding-v3"
+            else:
+                model = os.environ.get("LOCAL_EMBEDDING_MODEL") or "BAAI/bge-m3"
+        return f"provider={pt}, model={model}"
+    except Exception as e:  # noqa: BLE001
+        logger.debug("embedding 描述解析失败: {}", e)
+        return None
+
+
+def _log_stage_header(name: str, date_str: str, index: int, total: int) -> None:
+    """显性输出当前阶段(中文名 + 步骤名 + 序号 + 日期)。"""
+    label = _STAGE_LABELS.get(name, name)
+    logger.info("")
+    logger.info("═" * 56)
+    logger.info("阶段 {}/{}: {} [{}] 日期 {}", index, total, label, name, date_str)
+    logger.info("═" * 56)
+
+
+def _log_ai_info(name: str) -> None:
+    """本阶段用到 AI 大模型时,显性告知供应商与模型名。"""
+    if name in _AI_LLM_SCENES:
+        scene = _AI_LLM_SCENES[name]
+        desc = _llm_scene_desc(scene)
+        logger.info("🤖 本阶段使用 AI 大模型: {}", desc or "未配置(可能跳过或降级)")
+    elif name == "embedding":
+        desc = _embedding_desc()
+        logger.info("🤖 本阶段使用 AI 嵌入模型: {}", desc or "未配置(可能降级)")
+
+
 def run_step(name: str, date_str: str) -> StepResult:
     """执行单个 pipeline 步骤。
 
@@ -249,7 +338,10 @@ def run_pipeline(
             f" (跳过已成功 {names[:start_idx]})" if start_idx > 0 else "",
         )
 
-    for name in names[start_idx:]:
+    for i, name in enumerate(names[start_idx:], start=start_idx + 1):
+        # 显性输出当前阶段 + AI 大模型信息
+        _log_stage_header(name, date_str, i, len(names))
+        _log_ai_info(name)
         sr = run_step(name, date_str)
         result.steps.append(sr)
         # 记录断点状态(无论成败,便于下次 resume)