fix(zhipu): 精确化 <None> 正则并原地更新 result_chain

根据 sourcery-ai review 意见进行以下修复：

1. _GLM_NULL_TOKEN_RE 移除 re.IGNORECASE：GLM 只输出精确的
   <None>（大写 N），去掉不必要的大小写不敏感标志使正则更精确，
   避免误匹配非 GLM 控制 token 的合法内容。

2. _parse_openai_completion 改用 completion_text setter 原地
   更新文本，不再重建整个 result_chain，从而保留 tool call
   等非文本组件，防止丢弃基础解析器生成的更丰富结构。

3. 同步更新测试：<none>/<NONE> 不再被清除，断言改为保留原文。

Author: NayukiChiba

astrbot/core/provider/sources/zhipu_source.py

@@ -7,7 +7,6 @@
 from openai.types.chat import ChatCompletion
 
 from astrbot.core.agent.tool import ToolSet
-from astrbot.core.message.message_event_result import MessageChain
 
 from ..entities import LLMResponse
 from ..register import register_provider_adapter
@@ -20,9 +19,11 @@
     re.IGNORECASE,
 )
 
-# GLM's "null response" signal — the model outputs <None> (sometimes prefixed with
-# whitespace/newlines) to indicate it has nothing to say.
-_GLM_NULL_TOKEN_RE = re.compile(r"<None>", re.IGNORECASE)
+# GLM's "null response" signal — the model outputs exactly <None> (capital N, like Python's
+# None literal) to indicate it has nothing to say.  We intentionally do NOT use re.IGNORECASE
+# here: GLM always emits <None> with a capital N, and a case-insensitive match could
+# accidentally remove unrelated HTML/XML-like content that merely starts with "none".
+_GLM_NULL_TOKEN_RE = re.compile(r"<None>")
 
 
 @register_provider_adapter("zhipu_chat_completion", "智谱 Chat Completion 提供商适配器")
@@ -70,11 +71,11 @@ async def _parse_openai_completion(
         llm_response = await super()._parse_openai_completion(completion, tools)
 
         # Apply GLM special token cleaning to the assembled completion text.
+        # Use the completion_text setter so that non-Plain components (e.g. tool calls)
+        # in the chain are preserved; only the Plain text segments are updated in-place.
         if llm_response.completion_text:
             cleaned = self._clean_glm_special_tokens(llm_response.completion_text)
             if cleaned != llm_response.completion_text:
-                llm_response.result_chain = (
-                    MessageChain().message(cleaned) if cleaned else MessageChain()
-                )
+                llm_response.completion_text = cleaned
 
         return llm_response

tests/test_zhipu_source.py

@@ -65,10 +65,12 @@ def test_null_token_surrounded_by_whitespace(self):
         assert ProviderZhipu._clean_glm_special_tokens("  <None>  ") == ""
 
     def test_null_token_case_insensitive_lower(self):
-        assert ProviderZhipu._clean_glm_special_tokens("<none>") == ""
+        # Without re.IGNORECASE, lowercase <none> is not a GLM token and must be preserved.
+        assert ProviderZhipu._clean_glm_special_tokens("<none>") == "<none>"
 
     def test_null_token_case_insensitive_upper(self):
-        assert ProviderZhipu._clean_glm_special_tokens("<NONE>") == ""
+        # Without re.IGNORECASE, uppercase <NONE> is not a GLM token and must be preserved.
+        assert ProviderZhipu._clean_glm_special_tokens("<NONE>") == "<NONE>"
 
     def test_null_token_in_middle_of_text(self):
         result = ProviderZhipu._clean_glm_special_tokens("hello <None> world")