<think> tag is missing. (#7256)

### What problem does this PR solve?

Some models force thinking, resulting in the absence of the think tag in
the returned content

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

agent/component/exesql.py

     component_name = "ExeSQL"
 
     def _refactor(self, ans):
-        ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
+        ans = re.sub(r"^.*</think>", "", ans, flags=re.DOTALL)
         match = re.search(r"```sql\s*(.*?)\s*```", ans, re.DOTALL)
         if match:
             ans = match.group(1)  # Query content

agent/component/generate.py

         if len(msg) < 2:
             msg.append({"role": "user", "content": "Output: "})
         ans = chat_mdl.chat(msg[0]["content"], msg[1:], self._param.gen_conf())
-        ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
+        ans = re.sub(r"^.*</think>", "", ans, flags=re.DOTALL)
 
         if self._param.cite and "chunks" in retrieval_res.columns:
             res = self.set_cite(retrieval_res, ans)

agent/component/keyword.py

         ans = chat_mdl.chat(self._param.get_prompt(), [{"role": "user", "content": query}],
                             self._param.gen_conf())
 
-        ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
+        ans = re.sub(r"^.*</think>", "", ans, flags=re.DOTALL)
         ans = re.sub(r".*keyword:", "", ans).strip()
         logging.debug(f"ans: {ans}")
         return KeywordExtract.be_output(ans)

agentic_reasoning/deep_research.py

             msg_history[-1]["content"] += "\n\nContinues reasoning with the new information.\n"
             
         for ans in self.chat_mdl.chat_streamly(REASON_PROMPT, msg_history, {"temperature": 0.7}):
-            ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
+            ans = re.sub(r"^.*</think>", "", ans, flags=re.DOTALL)
             if not ans:
                 continue
             query_think = ans
                 [{"role": "user",
                   "content": f'Now you should analyze each web page and find helpful information based on the current search query "{search_query}" and previous reasoning steps.'}],
                 {"temperature": 0.7}):
-            ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
+            ans = re.sub(r"^.*</think>", "", ans, flags=re.DOTALL)
             if not ans:
                 continue
             summary_think = ans

api/db/services/dialog_service.py

         answer = ""
         for ans in chat_mdl.chat_streamly(prompt + prompt4citation, msg[1:], gen_conf):
             if thought:
-                ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
+                ans = re.sub(r"^.*</think>", "", ans, flags=re.DOTALL)
             answer = ans
             delta_ans = ans[len(last_ans) :]
             if num_tokens_from_string(delta_ans) < 16:
     def get_table():
         nonlocal sys_prompt, user_prompt, question, tried_times
         sql = chat_mdl.chat(sys_prompt, [{"role": "user", "content": user_prompt}], {"temperature": 0.06})
-        sql = re.sub(r"<think>.*</think>", "", sql, flags=re.DOTALL)
+        sql = re.sub(r"^.*</think>", "", sql, flags=re.DOTALL)
         logging.debug(f"{question} ==> {user_prompt} get SQL: {sql}")
         sql = re.sub(r"[\r\n]+", " ", sql.lower())
         sql = re.sub(r".*select ", "select ", sql.lower())

graphrag/general/extractor.py

             return response
         _, system_msg = message_fit_in([{"role": "system", "content": system}], int(self._llm.max_length * 0.92))
         response = self._llm.chat(system_msg[0]["content"], hist, conf)
-        response = re.sub(r"<think>.*</think>", "", response, flags=re.DOTALL)
+        response = re.sub(r"^.*</think>", "", response, flags=re.DOTALL)
         if response.find("**ERROR**") >= 0:
             logging.warning(f"Extractor._chat got error. response: {response}")
             return ""

rag/prompts.py

     kwd = chat_mdl.chat(prompt, msg[1:], {"temperature": 0.2})
     if isinstance(kwd, tuple):
         kwd = kwd[0]
-    kwd = re.sub(r"<think>.*</think>", "", kwd, flags=re.DOTALL)
+    kwd = re.sub(r"^.*</think>", "", kwd, flags=re.DOTALL)
     if kwd.find("**ERROR**") >= 0:
         return ""
     return kwd
     kwd = chat_mdl.chat(prompt, msg[1:], {"temperature": 0.2})
     if isinstance(kwd, tuple):
         kwd = kwd[0]
-    kwd = re.sub(r"<think>.*</think>", "", kwd, flags=re.DOTALL)
+    kwd = re.sub(r"^.*</think>", "", kwd, flags=re.DOTALL)
     if kwd.find("**ERROR**") >= 0:
         return ""
     return kwd
 ###############
     """
     ans = chat_mdl.chat(prompt, [{"role": "user", "content": "Output: "}], {"temperature": 0.2})
-    ans = re.sub(r"<think>.*</think>", "", ans, flags=re.DOTALL)
+    ans = re.sub(r"^.*</think>", "", ans, flags=re.DOTALL)
     return ans if ans.find("**ERROR**") < 0 else messages[-1]["content"]
 
 
     kwd = chat_mdl.chat(prompt, msg[1:], {"temperature": 0.5})
     if isinstance(kwd, tuple):
         kwd = kwd[0]
-    kwd = re.sub(r"<think>.*</think>", "", kwd, flags=re.DOTALL)
+    kwd = re.sub(r"^.*</think>", "", kwd, flags=re.DOTALL)
     if kwd.find("**ERROR**") >= 0:
         raise Exception(kwd)
 

rag/raptor.py

         response = await trio.to_thread.run_sync(
             lambda: self._llm_model.chat(system, history, gen_conf)
         )
-        response = re.sub(r"<think>.*</think>", "", response, flags=re.DOTALL)
+        response = re.sub(r"^.*</think>", "", response, flags=re.DOTALL)
         if response.find("**ERROR**") >= 0:
             raise Exception(response)
         set_llm_cache(self._llm_model.llm_name, system, response, history, gen_conf)