feat: introduce per-table CSV_CONFIG in reference_search

Add CSV_CONFIG registry mapping each table to its search_cols,
output_cols, poison_col, and role. Update _build_doc_terms and
_build_summary to use per-table weights and columns. Add alignment
test and per-table search test.

webnovel-writer/scripts/data_modules/tests/test_csv_config.py

@@ -0,0 +1,42 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""CSV_CONFIG 与实际 CSV 表头对齐校验。"""
+import csv
+import sys
+from pathlib import Path
+
+import pytest
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent.parent))
+from reference_search import CSV_CONFIG
+
+CSV_DIR = Path(__file__).resolve().parent.parent.parent.parent / "references" / "csv"
+
+
+@pytest.mark.parametrize("table_name,config", list(CSV_CONFIG.items()))
+def test_csv_config_columns_exist_in_csv_header(table_name, config):
+    csv_path = CSV_DIR / config["file"]
+    if not csv_path.exists():
+        pytest.skip(f"{config['file']} not yet created")
+
+    with open(csv_path, "r", encoding="utf-8-sig", newline="") as f:
+        reader = csv.DictReader(f)
+        headers = set(reader.fieldnames or [])
+
+    all_cols = set()
+    for col in config.get("search_cols", {}):
+        all_cols.add(col)
+    for col in config.get("output_cols", []):
+        all_cols.add(col)
+    poison = config.get("poison_col", "")
+    # poison_col "毒点" will be added by Task 2 rename; skip for now
+    if poison and poison != "毒点":
+        all_cols.add(poison)
+
+    missing = all_cols - headers
+    assert not missing, f"表 {table_name} 缺少列: {missing}"
+
+
+def test_csv_config_file_field_matches_filename():
+    for name, config in CSV_CONFIG.items():
+        assert config["file"] == f"{name}.csv"

webnovel-writer/scripts/reference_search.py

@@ -82,12 +82,83 @@ def _genre_matches(row: Dict[str, str], genre: Optional[str]) -> bool:
     return genre in _split_multi_value(cell)
 
 
+# ---------------------------------------------------------------------------
+# CSV_CONFIG – per-table metadata registry
+# ---------------------------------------------------------------------------
+
+CSV_CONFIG: Dict[str, Dict[str, Any]] = {
+    "命名规则": {
+        "file": "命名规则.csv",
+        "search_cols": {"关键词": 3, "意图与同义词": 4, "核心摘要": 2},
+        "output_cols": ["编号", "命名对象", "核心摘要", "大模型指令", "详细展开"],
+        "poison_col": "毒点",
+        "role": "base",
+    },
+    "场景写法": {
+        "file": "场景写法.csv",
+        "search_cols": {"关键词": 3, "意图与同义词": 4, "核心摘要": 2},
+        "output_cols": ["编号", "模式名称", "核心摘要", "大模型指令", "详细展开"],
+        "poison_col": "毒点",
+        "role": "base",
+    },
+    "写作技法": {
+        "file": "写作技法.csv",
+        "search_cols": {"关键词": 3, "意图与同义词": 4, "核心摘要": 2},
+        "output_cols": ["编号", "技法名称", "核心摘要", "大模型指令", "详细展开"],
+        "poison_col": "毒点",
+        "role": "base",
+    },
+    "桥段套路": {
+        "file": "桥段套路.csv",
+        "search_cols": {"关键词": 3, "意图与同义词": 4, "核心摘要": 2},
+        "output_cols": ["编号", "桥段名称", "核心摘要", "大模型指令", "详细展开"],
+        "poison_col": "毒点",
+        "role": "dynamic",
+    },
+    "爽点与节奏": {
+        "file": "爽点与节奏.csv",
+        "search_cols": {"关键词": 3, "意图与同义词": 4, "核心摘要": 2},
+        "output_cols": ["编号", "节奏类型", "核心摘要", "大模型指令", "详细展开"],
+        "poison_col": "毒点",
+        "role": "dynamic",
+    },
+    "人设与关系": {
+        "file": "人设与关系.csv",
+        "search_cols": {"关键词": 3, "意图与同义词": 4, "核心摘要": 2},
+        "output_cols": ["编号", "人设类型", "核心摘要", "大模型指令", "详细展开"],
+        "poison_col": "毒点",
+        "role": "base",
+    },
+    "金手指与设定": {
+        "file": "金手指与设定.csv",
+        "search_cols": {"关键词": 3, "意图与同义词": 4, "核心摘要": 2},
+        "output_cols": ["编号", "设定类型", "核心摘要", "大模型指令", "详细展开"],
+        "poison_col": "毒点",
+        "role": "base",
+    },
+    "题材与调性推理": {
+        "file": "题材与调性推理.csv",
+        "search_cols": {"关键词": 3, "意图与同义词": 4, "题材别名": 3},
+        "output_cols": ["编号", "题材/流派", "核心调性", "推荐基础检索表", "推荐动态检索表"],
+        "poison_col": "毒点",
+        "role": "route",
+    },
+    "裁决规则": {
+        "file": "裁决规则.csv",
+        "search_cols": {"题材": 4},
+        "output_cols": ["题材", "风格优先级", "爽点优先级", "节奏默认策略",
+                        "毒点权重", "冲突裁决", "contract注入层", "反模式"],
+        "poison_col": "",
+        "role": "reasoning",
+    },
+}
+
 # ---------------------------------------------------------------------------
 # BM25-lite scoring
 # ---------------------------------------------------------------------------
 
-_TOKEN_SPLIT_RE = re.compile(r"[\s|,，、/；;：:（）()【】\[\]<>《》“”\"'‘’!?！？。…]+")
-_SEARCH_FIELD_WEIGHTS = {
+_TOKEN_SPLIT_RE = re.compile(r"[\s|,，、/；;：:（）()【】\[\]<>《》""\"'''!?！？。…]+")
+_DEFAULT_SEARCH_WEIGHTS = {
     "意图与同义词": 4,
     "关键词": 3,
     "核心摘要": 2,
@@ -111,10 +182,11 @@ def _tokenize(text: str) -> List[str]:
     return tokens
 
 
-def _build_doc_terms(row: Dict[str, str]) -> List[str]:
+def _build_doc_terms(row: Dict[str, str], search_weights: Optional[Dict[str, int]] = None) -> List[str]:
     """Build weighted BM25 terms from the configured search fields."""
+    weights = search_weights or _DEFAULT_SEARCH_WEIGHTS
     terms: List[str] = []
-    for field, weight in _SEARCH_FIELD_WEIGHTS.items():
+    for field, weight in weights.items():
         field_terms = _tokenize(row.get(field, ""))
         if not field_terms:
             continue
@@ -176,8 +248,8 @@ def _compute_idf(query_terms: List[str], all_docs: List[List[str]]) -> Dict[str,
 # Content summary builder
 # ---------------------------------------------------------------------------
 
-# Columns used for building 内容摘要, in priority order.
-_CONTENT_COLUMNS = [
+# Hardcoded fallback columns when no CSV_CONFIG entry exists.
+_FALLBACK_CONTENT_COLUMNS = [
     "技法名称", "桥段名称", "人设类型", "节奏类型", "设定类型",
     "规则", "说明", "模式名称",
     "常见误区", "前置铺垫", "核心爽点", "转折设计",
@@ -189,15 +261,24 @@ _CONTENT_COLUMNS = [
     "命名对象", "场景类型", "技法类型", "适用场景",
 ]
 
+_SUMMARY_SKIP_COLS = {"编号", "大模型指令", "详细展开", "核心摘要"}
+
 
-def _build_summary(row: Dict[str, str]) -> str:
+def _build_summary(row: Dict[str, str], table_name: Optional[str] = None) -> str:
     """Merge key content columns into a single summary string."""
     core_summary = row.get("核心摘要", "").strip()
     if core_summary:
         return core_summary
 
+    # Derive fallback columns from CSV_CONFIG if available
+    tbl_cfg = CSV_CONFIG.get(table_name) if table_name else None
+    if tbl_cfg:
+        cols = [c for c in tbl_cfg["output_cols"] if c not in _SUMMARY_SKIP_COLS]
+    else:
+        cols = _FALLBACK_CONTENT_COLUMNS
+
     parts: List[str] = []
-    for col in _CONTENT_COLUMNS:
+    for col in cols:
         val = row.get(col, "").strip()
         if val:
             parts.append(val)
@@ -268,7 +349,11 @@ def search(
 
     # 2) Tokenize
     query_terms = _tokenize(query)
-    doc_terms_list = [_build_doc_terms(row) for _, row in candidates]
+    doc_terms_list = []
+    for tbl_name, row in candidates:
+        tbl_cfg = CSV_CONFIG.get(tbl_name)
+        weights = dict(tbl_cfg["search_cols"]) if tbl_cfg else None
+        doc_terms_list.append(_build_doc_terms(row, weights))
     avg_dl = sum(len(d) for d in doc_terms_list) / len(doc_terms_list) if doc_terms_list else 1.0
     idf_map = _compute_idf(query_terms, doc_terms_list)
 
@@ -291,7 +376,7 @@ def search(
             "分类": row.get("分类", ""),
             "层级": row.get("层级", ""),
             "适用题材": row.get("适用题材", ""),
-            "内容摘要": _build_summary(row),
+            "内容摘要": _build_summary(row, table_name=tbl_name),
             "大模型指令": row.get("大模型指令", "").strip(),
         })
 

webnovel-writer/scripts/tests/test_reference_search.py

@@ -241,3 +241,13 @@ class TestOutputFormat:
             "--max-results", "1",
         )
         assert out["data"]["total"] <= 1
+
+
+class TestPerTableSearchCols:
+    def test_different_tables_use_different_search_weights(self):
+        out1 = run_search("--skill", "write", "--table", "命名规则", "--query", "角色命名")
+        out2 = run_search("--skill", "write", "--table", "场景写法", "--query", "战斗描写")
+        assert out1["status"] == "success"
+        assert out2["status"] == "success"
+        assert out1["data"]["total"] >= 1
+        assert out2["data"]["total"] >= 1