feat(llm): 结构化 API 错误信息提取，429/401/403/500 等错误中文提示，前端实时日志友好展示

web/core/step_impl/step2_merge_redundancy.py

@@ -61,8 +61,8 @@ def run_step2(dict_data: dict, llm: LLMClient | None = None,
 
     # 3. 高频字段
     if log:
-        log("INFO", "[3/3] 计算高频字段（出现 ≥10 张表）...", step="2")
-    redundancy = _find_redundancy(by_table)
+        log("INFO", "[3/3] 计算高频字段（出现 ≥10 张表，LLM 分类）...", step="2")
+    redundancy = _find_redundancy(by_table, llm, log)
     if log:
         log("INFO", f"  · 高频字段 {len(redundancy)} 个", step="2")
 
@@ -195,19 +195,97 @@ def _find_replacement(table: str, all_tables) -> str | None:
     return None
 
 
-def _find_redundancy(by_table: dict) -> list[dict]:
+def _find_redundancy(by_table: dict, llm, log: Callable | None) -> list[dict]:
+    """高频字段分析：Counter 预筛 → LLM 分类（必跑）。
+
+    Counter 找出出现 ≥10 张表的字段（纯规则、毫秒级）。
+    然后对每个候选调 LLM 分类：
+      - common_base       通用基础字段，可保留
+      - common_business   业务上合理共享，可保留
+      - suspicious        命名相同但含义可能不一致，需核对
+      - true_redundancy   真冗余，建议合并 / 抽字典
+
+    LLM 必跑：若调用失败（网络/解析）会让该字段标记 llm_failed，不影响其他字段；
+    若整批不可用由上层（orchestrator）视为关键失败。
+    """
+    # 1. Counter 预筛
     counter: Counter = Counter()
-    for cols in by_table.values():
+    type_by_field: dict[str, set[str]] = {}
+    tables_by_field: dict[str, set[str]] = {}
+    for tname, cols in by_table.items():
         for c in cols:
-            counter[c["column_name"]] += 1
-    logger.debug(f"字段频次统计: 共 {len(counter)} 个不同字段名")
-    return [
+            fname = c["column_name"]
+            counter[fname] += 1
+            type_by_field.setdefault(fname, set()).add(c.get("data_type", "") or "")
+            tables_by_field.setdefault(fname, set()).add(tname)
+
+    candidates = [(f, n) for f, n in counter.most_common()
+                  if n >= 10 and f not in COMMON_FIELDS]
+    logger.debug(f"字段频次统计: 共 {len(counter)} 个不同字段名, "
+                 f"候选高频字段 {len(candidates)} 个")
+    if not candidates:
+        return []
+
+    # 2. 构造 LLM 输入
+    llm_inputs = [
         {
             "field": f,
             "table_count": n,
-            "risk": "高频出现，需评估是否为业务必要字段" if n >= 30 else "中频出现",
-            "suggestion": "建议评审是否需要统一到公共字典表" if n >= 30 else "建议评审",
+            "sample_tables": sorted(tables_by_field[f])[:5],
+            "sample_types": sorted(type_by_field[f])[:3],
         }
-        for f, n in counter.most_common()
-        if n >= 10 and f not in COMMON_FIELDS
+        for f, n in candidates
     ]
+    BATCH = 15
+    annotated: list[dict | None] = []
+    total_batches = (len(llm_inputs) + BATCH - 1) // BATCH
+    for batch_idx in range(0, len(llm_inputs), BATCH):
+        batch = llm_inputs[batch_idx:batch_idx + BATCH]
+        idx = batch_idx // BATCH + 1
+        if log:
+            log("INFO",
+                f"  · LLM 分类批次 [{idx}/{total_batches}] "
+                f"({len(batch)} 个字段)",
+                step="2")
+        try:
+            results = llm.classify_redundant_fields_batch(batch)
+        except Exception as e:
+            if log:
+                log("ERROR",
+                    f"  · LLM 分类批次 [{idx}/{total_batches}] 整体失败: {e}（任务将终止）",
+                    step="2")
+            logger.exception("Step 2 LLM 分类批次失败")
+            raise  # required step → 抛给上层
+        annotated.extend(results)
+
+    # 3. 汇总：每个候选都给出最终记录（LLM 成功的带 classification / reasoning / recommendation；
+    #    LLM 失败的标 llm_failed，仍保留 field + table_count 便于定位）
+    out: list[dict] = []
+    for cand, ann in zip(candidates, annotated):
+        f, n = cand
+        if ann:
+            out.append({
+                "field": f,
+                "table_count": n,
+                "classification": ann["classification"],
+                "reasoning": ann["reasoning"],
+                "recommendation": ann["recommendation"],
+                "source": "llm",
+            })
+        else:
+            out.append({
+                "field": f,
+                "table_count": n,
+                "classification": "unknown",
+                "reasoning": "LLM 解析失败",
+                "recommendation": "需人工核对",
+                "source": "llm_failed",
+            })
+    if log:
+        cls_count = {}
+        for r in out:
+            cls_count[r["classification"]] = cls_count.get(r["classification"], 0) + 1
+        summary = ", ".join(f"{k}={v}" for k, v in cls_count.items())
+        if log:
+            log("INFO", f"  · LLM 分类结果: {summary}", step="2")
+    return out
\ No newline at end of file

web/core/step_impl/step5_missing_comments.py

-"""Step 5: 缺失注释字段检查 + LLM 推测
+"""Step 5: 缺失注释字段检查 + LLM 推测（必跑 LLM）
 
-优先用 LLM 推测无注释字段的语义（替换原硬编码 COMMENT_HINTS）；
-LLM 不可用时降级为本地规则。
+字段注释的语义判断本质上需要 LLM：
+- 拼音首字母缩写（如 xzqhbm）、业务缩写、英文组合 → 必须由 LLM 翻译
+- 硬编码字典兜底已删除（缺 LLM = 任务失败，无需 fallback）
 
 产出：
-  - summary: 总数 + 推测命中率
+  - summary: 总数 + LLM 命中率
   - by_table: 每表缺失注释数量
-  - predicted_comments: 推测出的注释（含置信度）
-  - unpredictable_sample: 未推测到的样本
+  - predicted_comments: LLM 推测出的注释（含置信度、来源 = "llm"）
+  - unpredicted_sample: LLM 单条失败的样本（标 llm_failed）
 """
 
 from __future__ import annotations
@@ -21,31 +22,6 @@ from ..llm import LLMClient
 logger = logging.getLogger(__name__)
 
 
-# 兜底规则（LLM 不可用时使用）
-FALLBACK_HINTS = {
-    "id": "主键ID",
-    "create_time": "创建时间",
-    "update_time": "更新时间",
-    "create_by": "创建人",
-    "update_by": "更新人",
-    "remark": "备注",
-    "del_flag": "删除标记",
-    "tenant_id": "租户ID",
-    "dept_id": "部门ID",
-    "project_id": "项目ID",
-    "project_name": "项目名称",
-    "project_code": "项目编号",
-    "site_id": "工地ID",
-    "site_name": "工地名称",
-    "status": "状态",
-    "sort_order": "排序",
-    "start_time": "开始时间",
-    "end_time": "结束时间",
-    "type": "类型",
-    "code": "编码",
-}
-
-
 def run_step5(dict_data: dict, llm: LLMClient | None = None,
               log: Callable | None = None) -> dict:
     columns = dict_data.get("data_dictionary", [])
@@ -57,19 +33,16 @@ def run_step5(dict_data: dict, llm: LLMClient | None = None,
     if log:
         log("INFO", f"待检查字段总数: {len(columns)}", step="5")
 
+    # 1. 收集所有无注释字段，按 (table_name, table_comment) 分组
     missing = []
     by_table: dict[str, int] = defaultdict(int)
-    predicted = []
-    unpredictable = []
+    grouped: dict[tuple[str, str], list[dict]] = defaultdict(list)
 
-    # 先用规则快速批匹配
     for r in columns:
         comment = (r.get("column_comment") or "").strip()
         if comment:
             continue
         by_table[r["table_name"]] += 1
-
-        fallback = FALLBACK_HINTS.get(r["column_name"])
         entry = {
             "table_name": r["table_name"],
             "table_comment": r.get("table_comment", ""),
@@ -81,73 +54,77 @@ def run_step5(dict_data: dict, llm: LLMClient | None = None,
             "confidence": "low",
             "reason": "",
         }
-
-        if fallback:
-            entry["predicted"] = fallback
-            entry["confidence"] = "high"
-            entry["reason"] = "字段名匹配内置规则"
-            predicted.append(entry)
-        else:
-            unpredictable.append(entry)
         missing.append(entry)
+        grouped[(r["table_name"], r.get("table_comment", ""))].append(entry)
 
     if log:
         log("INFO",
             f"  · 缺失注释字段: {len(missing)} 个 (覆盖 {len(by_table)} 张表)",
             step="5")
-        log("INFO",
-            f"  · 内置规则命中: {len(predicted)}, 需 LLM 推测: {len(unpredictable)}",
-            step="5")
 
-    # 用 LLM 处理 unpredictable（如果可用）
-    if llm and llm.available and unpredictable:
+    # 2. 按表分批调用 LLM（必跑；单批失败会让任务终止）
+    if not missing:
+        if log:
+            log("INFO", "  · 无缺失注释字段，跳过 LLM", step="5")
+        return _empty_result(by_table)
+
+    BATCH = 15
+    predicted = []
+    unpredicted = []
+    llm_called = 0
+    for (tname, tcomment), fields in grouped.items():
+        # 把同表的字段切片成 BATCH 大小
+        chunks = [fields[i:i + BATCH] for i in range(0, len(fields), BATCH)]
+        for chunk_idx, chunk in enumerate(chunks, 1):
+            llm_called += 1
             if log:
-            log("INFO", f"[LLM] 调用 LLM 推测 {len(unpredictable)} 个无规则命中的字段注释", step="5")
-        llm_predicted = []
-        still_unknown = []
-        for idx, entry in enumerate(unpredictable, 1):
+                log("INFO",
+                    f"  · LLM 推测 [{llm_called}] {tname} "
+                    f"({chunk_idx}/{len(chunks)} 批, {len(chunk)} 个字段)",
+                    step="5")
             try:
-                r = llm.predict_field_comment(
-                    table_name=entry["table_name"],
-                    table_comment=entry["table_comment"],
-                    column_name=entry["column_name"],
-                    data_type=entry["data_type"],
+                results = llm.predict_field_comments_batch(
+                    table_name=tname,
+                    table_comment=tcomment,
+                    fields=[{"column_name": e["column_name"],
+                             "data_type": e["data_type"]} for e in chunk],
                 )
+            except Exception as e:
+                # 单批失败 → 让任务终止（required step）
+                if log:
+                    log("ERROR",
+                        f"  · LLM 推测失败（{tname} 第 {chunk_idx} 批）: {e}（任务将终止）",
+                        step="5")
+                logger.exception("Step 5 LLM 推测批次失败")
+                raise
+
+            # 把 LLM 结果写回 entry
+            for entry, r in zip(chunk, results):
                 if r and r.get("comment"):
                     entry["predicted"] = r["comment"]
                     entry["confidence"] = r.get("confidence", "low")
                     entry["reason"] = "LLM 推测"
-                    llm_predicted.append(entry)
-                    # 从 predicted 列表的视角也算推测成功
+                    entry["source"] = "llm"
                     predicted.append(entry)
-                    if log and idx % 5 == 0:
-                        log("DEBUG",
-                            f"  · LLM 推测进度 {idx}/{len(unpredictable)} "
-                            f"(已成功 {len(llm_predicted)})",
-                            step="5")
                 else:
-                    still_unknown.append(entry)
-            except Exception as e:
-                if log:
-                    log("WARN",
-                        f"LLM 推测失败 ({entry['table_name']}.{entry['column_name']}): {e}",
-                        step="5")
-                still_unknown.append(entry)
-        if log:
-            log("INFO", f"[LLM] 推测成功 {len(llm_predicted)} / {len(unpredictable)}", step="5")
-        unpredictable = still_unknown
+                    entry["reason"] = "LLM 解析失败"
+                    entry["source"] = "llm_failed"
+                    unpredicted.append(entry)
 
-    # 按表聚合
+    # 3. 按表聚合
     by_table_list = sorted(
         [{"table_name": t, "missing_count": c} for t, c in by_table.items()],
         key=lambda x: x["missing_count"], reverse=True
     )[:20]
 
     if log:
+        log("INFO",
+            f"  · LLM 命中 {len(predicted)}/{len(missing)}, "
+            f"LLM 解析失败 {len(unpredicted)}",
+            step="5")
         log("INFO",
             f"缺失注释 {len(missing)} 个字段，覆盖 {len(by_table)} 张表；"
-            f"推测成功 {len(predicted)}（规则 {sum(1 for p in predicted if p.get('reason') == '字段名匹配内置规则')}, "
-            f"LLM {sum(1 for p in predicted if p.get('reason') == 'LLM 推测')}）",
+            f"LLM 推测成功 {len(predicted)}",
             step="5")
 
     return {
@@ -155,11 +132,27 @@ def run_step5(dict_data: dict, llm: LLMClient | None = None,
             "total_missing_comments": len(missing),
             "tables_affected": len(by_table),
             "predicted_count": len(predicted),
-            "predicted_by_rules": sum(1 for p in predicted if p.get("reason") == "字段名匹配内置规则"),
-            "predicted_by_llm": sum(1 for p in predicted if p.get("reason") == "LLM 推测"),
-            "unpredicted_count": len(unpredictable),
+            "predicted_by_llm": len(predicted),
+            "unpredicted_count": len(unpredicted),
+            "llm_calls": llm_called,
         },
         "by_table": by_table_list,
         "predicted_comments": predicted[:50],
-        "unpredictable_sample": unpredictable[:50],
+        "unpredictable_sample": unpredicted[:50],
+    }
+
+
+def _empty_result(by_table: dict) -> dict:
+    return {
+        "summary": {
+            "total_missing_comments": 0,
+            "tables_affected": len(by_table),
+            "predicted_count": 0,
+            "predicted_by_llm": 0,
+            "unpredicted_count": 0,
+            "llm_calls": 0,
+        },
+        "by_table": [],
+        "predicted_comments": [],
+        "unpredictable_sample": [],
     }
\ No newline at end of file