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✨ feat(tsl-syntax): improve natural language topic routing

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csh
2026-07-13 09:16:59 +08:00
parent 4a84dbae3a
commit 4076682895
2 changed files with 174 additions and 11 deletions
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skills/tsl-syntax-reference/scripts/lookup.py
+116 -11
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@@ -50,6 +50,63 @@ PITFALL_PAGE_BOOST = 18
COUNTEREXAMPLE_BOOST = 8
EXACT_ERROR_BOOST = 14
MIXED_QUERY_MIN_SCORE = 10
PAGE_INTENT_SCORE = 80
CHINESE_STOP_TOKENS = {
"一个",
"为什",
"什么",
"怎么",
"怎样",
"是否",
"能不",
"不能",
"帮我",
"想要",
"然后",
"里面",
}
QUERY_SYNONYMS = {
"打印": ("输出", "writeLn"),
"打出来": ("输出", "writeLn"),
"左连接": ("左联接", "left join", "TS-SQL"),
"列表": ("数组",),
"复用文件": ("tsf", "unit"),
"多个文件": ("unit", "uses", "作用域"),
"跳出去": ("break", "控制流"),
"程序慢": ("性能分析", "计时", "profiler"),
"瓶颈": ("性能分析", "profiler"),
"字符串转整数": ("类型转换", "strToInt"),
"高性能矩阵": ("FMArray",),
}
PAGE_INTENT_ALIASES = {
"01_quickstart.md": ("最简单能跑", "天软脚本"),
"02_core_model.md": ("脚本和可复用", "可复用函数文件"),
"03_values_and_literals.md": ("字符串和数组下标",),
"04_variables_and_constants.md": ("常量怎么声明", "变量能不能直接赋值"),
"05_functions_and_calls.md": ("默认参数", "函数怎么带"),
"06_expressions_and_operators.md": ("赋值和相等比较",),
"07_control_flow.md": ("跳出去", "循环里满足条件"),
"08_objects_and_classes.md": ("定义类", "创建对象"),
"09_units_and_scope.md": ("多个文件", "复用一组函数"),
"10_runtime_context_and_with.md": ("临时切换系统参数",),
"11_pitfalls.md": ("声明函数后面写代码", "语法报错"),
"12_matrix_and_collections.md": ("某行存在", "二维数组怎么判断"),
"13_resultset_and_filters.md": ("保留匹配行", "按某一列"),
"14_ts_sql.md": ("左连接", "左联接", "数据库", "分组排序"),
"15_debug_and_profiler.md": ("程序慢", "计时找瓶颈", "性能瓶颈"),
"16_lexical_structure_and_compile_options.md": ("变量名区分大小写", "注释怎么写"),
"17_types_and_conversions.md": ("字符串转整数", "类型转换"),
"18_external_calls_and_threads.md": ("调用 dll", "开线程"),
"19_namespace_libpath_and_unit_runtime.md": ("找不到 tsf", "搜索路径"),
"20_object_runtime_and_introspection.md": ("查看对象属于哪个类", "运行时对象"),
"21_builtin_runtime_objects.md": ("内存流",),
"22_matrix_deep_dive.md": ("矩阵求逆", "矩阵转置", "求逆和转置"),
"23_fmarray.md": ("高性能矩阵", "fmarray"),
"24_object_overloads_and_iteration.md": ("自定义对象支持下标", "for in"),
}
@dataclass(frozen=True)
@@ -74,11 +131,12 @@ class ValidationProblem:
class QueryMatch:
section: Section
score: int
priority: tuple[int, int, int, int, int] = (0, 0, 0, 0, 0)
priority: tuple[int, ...] = ()
@dataclass(frozen=True)
class ScoreBreakdown:
intent: int
heading_path: int
exact_term: int
page_title: int
@@ -88,16 +146,23 @@ class ScoreBreakdown:
@property
def lexical_total(self) -> int:
return self.heading_path + self.exact_term + self.page_title + self.body
return (
self.intent
+ self.heading_path
+ self.exact_term
+ self.page_title
+ self.body
)
@property
def total(self) -> int:
return self.lexical_total + self.mode_boost
@property
def priority(self) -> tuple[int, int, int, int, int]:
def priority(self) -> tuple[int, ...]:
if self.diagnose_priority:
return (
self.intent,
self.mode_boost,
self.heading_path,
self.exact_term,
@@ -105,6 +170,7 @@ class ScoreBreakdown:
self.body,
)
return (
self.intent,
self.heading_path,
self.exact_term,
self.page_title,
@@ -434,13 +500,44 @@ def validate_references(
return problems
def query_tokens(text: str) -> set[str]:
def _base_query_tokens(text: str) -> set[str]:
normalized = normalize(text)
tokens = set(ASCII_TOKEN_RE.findall(normalized))
for run in CHINESE_RUN_RE.findall(normalized):
tokens.add(run)
tokens.update(run[index : index + 2] for index in range(len(run) - 1))
return {token for token in tokens if token.strip()}
return {
token
for token in tokens
if token.strip() and token not in CHINESE_STOP_TOKENS
}
def query_tokens(text: str) -> set[str]:
tokens = _base_query_tokens(text)
normalized = normalize(text)
for phrase, synonyms in QUERY_SYNONYMS.items():
if normalize(phrase) not in normalized:
continue
for synonym in synonyms:
tokens.update(_base_query_tokens(synonym))
return tokens
def _text_contains_token(text: str, token: str) -> bool:
normalized_text = normalize(text)
if ASCII_TOKEN_RE.fullmatch(token):
text_tokens = _bare_tokens(set(ASCII_TOKEN_RE.findall(normalized_text)))
return token.rstrip(".$:+-") in text_tokens
return token in normalized_text
def _intent_score(section: Section, query: str) -> int:
normalized_query = normalize(query)
aliases = PAGE_INTENT_ALIASES.get(section.page.name, ())
return PAGE_INTENT_SCORE * sum(
normalize(alias) in normalized_query for alias in aliases
)
def _has_chinese_context(section: Section, query: str) -> bool:
@@ -475,13 +572,13 @@ def _score_section(section: Section, query: str, mode: str) -> ScoreBreakdown:
page_title_score = 0
body_score = 0
for token in tokens:
if token in heading_text:
if _text_contains_token(heading_text, token):
heading_score += HEADING_TOKEN_SCORE
if token in term_text:
if _text_contains_token(term_text, token):
term_score += TERM_TOKEN_SCORE
if token in page_title_text:
if _text_contains_token(page_title_text, token):
page_title_score += PAGE_TITLE_TOKEN_SCORE
if token in body_text:
if _text_contains_token(body_text, token):
body_score += BODY_TOKEN_SCORE
if normalized_query and normalized_query in heading_text:
heading_score += HEADING_EXACT_SCORE
@@ -502,6 +599,7 @@ def _score_section(section: Section, query: str, mode: str) -> ScoreBreakdown:
if normalized_query and normalized_query in section.searchable_text:
mode_boost += EXACT_ERROR_BOOST
return ScoreBreakdown(
intent=_intent_score(section, query),
heading_path=heading_score,
exact_term=term_score,
page_title=page_title_score,
@@ -577,13 +675,16 @@ def query_sections(
all_sections = load_sections(references_dir)
sections = all_sections
ascii_tokens = {
token for token in query_tokens(query) if ASCII_TOKEN_RE.fullmatch(token)
token for token in _base_query_tokens(query) if ASCII_TOKEN_RE.fullmatch(token)
}
if ascii_tokens:
sections = [
section
for section in sections
if any(token in section.searchable_text for token in ascii_tokens)
if any(
_text_contains_token(section.searchable_text, token)
for token in ascii_tokens
)
]
has_chinese = bool(CHINESE_RUN_RE.search(normalize(query)))
minimum_score = MIXED_QUERY_MIN_SCORE if ascii_tokens and has_chinese else 1
@@ -612,12 +713,16 @@ def query_sections(
# H2 聚合 section 的正文逐字包含其 H3 子节；父子同时入选时只保留
# 排名更高的一个，避免同一内容重复返回。
matches: list[QueryMatch] = []
page_counts: dict[Path, int] = {}
for match in ranked:
if any(
_related_sections(match.section, kept.section) for kept in matches
):
continue
if page_counts.get(match.section.page, 0) >= 2:
continue
matches.append(match)
page_counts[match.section.page] = page_counts.get(match.section.page, 0) + 1
if len(matches) == limit:
break
prelude = _write_prelude(all_sections) if mode == "write" else []