fix(keyword_extraction): accept Chinese commas/semicolons/newlines as keyword delimiters (#14540)

## What
Widen the keyword delimiter in `rag/svr/task_executor.py`:
both `build_chunks` (LLM `keyword_extraction` cache parsing) and
`run_dataflow` (chunk-level `keywords` ingestion) now split on
`, ， ; ； 、 \r \n` instead of only ASCII comma.

## Why
`rag/prompts/keyword_prompt.md` instructs the LLM:

> The keywords are delimited by ENGLISH COMMA.

In practice, Chinese-leaning models (Qwen / Tongyi-Qianwen, GLM,
etc.) frequently ignore this instruction when the source content is
Chinese and emit Chinese commas (`，`) instead. Result:
`cached.split(",")` sees the full LLM output as a *single* keyword.

Repro: `auto_keywords>=4` + Chinese docs + `qwen-plus@Tongyi-Qianwen`.
We observed entries in `important_kwd` like
`"功能介绍，配置说明，参数详解，问题排查"` — one bucket instead of four.

## Impact
- Silent data-quality bug; no exception thrown.
- BM25 `important_kwd^30` boost effectively stops firing — the
  indexed term is the whole list, never matches user query tokens.
- Any downstream aggregating `important_kwd` (tagging, analytics,
  candidate-keyword review UIs) sees garbage.

## Compatibility
- Pure widening of the splitter; ASCII-comma-only outputs continue
  to work identically.
- No schema / API change.

## Test plan
Manually verified against `qwen-plus@Tongyi-Qianwen` with
`auto_keywords=10` on Chinese .txt files:

- Before: `important_kwd` contains one element per chunk that is the
  full LLM string with `，`-separated phrases inside.
- After: `important_kwd` contains N elements, one per phrase, as the
  LLM intended.

rag/svr/task_executor.py

@@ -385,7 +385,7 @@ async def build_chunks(task, progress_callback):
                     cached = await keyword_extraction(chat_mdl, d["content_with_weight"], topn)
                 set_llm_cache(chat_mdl.llm_name, d["content_with_weight"], cached, "keywords", {"topn": topn})
             if cached:
-                d["important_kwd"] = cached.split(",")
+                d["important_kwd"] = [k for k in re.split(r"[,，;；、\r\n]+", cached) if k.strip()]
                 d["important_tks"] = rag_tokenizer.tokenize(" ".join(d["important_kwd"]))
             return
 
@@ -775,7 +775,7 @@ async def run_dataflow(task: dict):
             del ck["questions"]
         if "keywords" in ck:
             if "important_tks" not in ck:
-                ck["important_kwd"] = ck["keywords"].split(",")
+                ck["important_kwd"] = [k for k in re.split(r"[,，;；、\r\n]+", ck["keywords"]) if k.strip()]
                 ck["important_tks"] = rag_tokenizer.tokenize(str(ck["keywords"]))
             del ck["keywords"]
         if "summary" in ck: