Refactor `test_llm_dialogue.py` for improved pipeline creation and streamlined testing

- Replace manual chain setup with `create_langchain_pipeline` for cleaner initialization.
- Simplify warmup logic and add optional pipeline parameter.
- Enhance test logging with structured outputs and detailed round information.

tests/test_llm_dialogue.py

@@ -44,18 +44,12 @@ class TestLLMDialogue(unittest.TestCase):
         model_path = paths.LLM_MODELS_PATH / 'qwen' / 'Qwen3-8B-Q6_K.gguf'
         self.langchain_instance = ChatLlamaCpp(model_path=model_path.as_posix(), **model_params)
 
-        system_message = SystemMessage(content=CHINESE_SYSTEM_PROMPT)
-        human_message = HumanMessagePromptTemplate.from_template("{input}")
-        prompt = ChatPromptTemplate(messages=[
-            system_message,
-            MessagesPlaceholder(variable_name="history"),
-            human_message
-        ])
-
-        chain = prompt | self.langchain_instance
-        self.chain_with_history = RunnableWithMessageHistory(chain, self.get_session_history,
-                                                             history_messages_key='history')
-        self.warmup()
+        pipeline = create_langchain_pipeline(
+            self.langchain_instance,
+            self._get_prompt_by_language('zh'),
+            self.get_session_history
+        )
+        self.warmup(pipeline)
 
         self.test_datasets = [
             {
@@ -64,16 +58,12 @@ class TestLLMDialogue(unittest.TestCase):
                 'questions': [
                     # 第1轮：开放性话题引入
                     "最近人工智能技术发展很快，你觉得AI对我们日常生活带来了哪些改变？",
-
                     # 第2轮：基于前一个回答的深入探讨
                     "你刚才提到的这些改变中，哪一个你认为是最重要的？为什么？",
-
                     # 第3轮：转向具体场景和个人观点
                     "如果让你选择一个AI应用来帮助解决教育领域的问题，你会选择什么？具体怎么实现？",
-
                     # 第4轮：挑战性问题，测试逻辑思维
                     "但是也有人担心AI在教育中会让学生过度依赖技术，失去独立思考能力。你怎么看待这个担忧？",
-
                     # 第5轮：总结性问题，测试整合能力
                     "综合我们刚才讨论的内容，你认为在AI快速发展的时代，普通人应该如何适应和准备？"
                 ]
@@ -161,10 +151,10 @@ class TestLLMDialogue(unittest.TestCase):
         self.history_store[session_id] = InMemoryChatMessageHistory(messages=messages)
         return self.history_store[session_id]
 
-    def warmup(self):
+    def warmup(self, pipeline: RunnableWithMessageHistory = None):
         session_id = 'warmup'
         config = {"configurable": {"session_id": session_id}}
-        for chunk in self.chain_with_history.stream(input={'input': 'This is a warmup step.'}, config=config):
+        for chunk in pipeline.stream(input={'input': 'This is a warmup step.'}, config=config):
             pass
 
     def _get_prompt_by_language(self, language: str) -> str:
@@ -177,15 +167,31 @@ class TestLLMDialogue(unittest.TestCase):
     def test_dialogue(self):
         for test_dataset in self.test_datasets:
             session_id = test_dataset.get('session_id')
-            print(f'Test dataset: {session_id}')
-            print('=' * 80)
-            for question in test_dataset.get('questions'):
-                print('Test question:', question)
-                config = {"configurable": {"session_id": session_id}}
-                prompt = self._get_prompt_by_language(test_dataset.get('language'))
-                pipeline = create_langchain_pipeline(self.langchain_instance, prompt, self.get_session_history)
-                print(f'LLM answer: ', end='')
+            language = test_dataset.get('language')
+            questions = test_dataset.get('questions')
+
+            # --- Test Suite Header ---
+            print("\n" + "=" * 80)
+            print(f"  🧪 EXECUTING TEST SUITE: {session_id} ({language.upper()})")
+            print("=" * 80)
+
+            # --- Langchain Pipeline Setup ---
+            prompt = self._get_prompt_by_language(language)
+            pipeline = create_langchain_pipeline(self.langchain_instance, prompt, self.get_session_history)
+            config = {"configurable": {"session_id": session_id}}
+
+            for i, question in enumerate(questions):
+                round_num = i + 1
+                print(f"\n🗣️  Round {round_num}")
+                print(f"  👤 User: {question}")
+                print(f"  🤖 AI:   ", end='')
+
+                # --- Stream and print LLM response ---
                 for chunk in pipeline.stream(input={'input': question}, config=config):
-                    print(chunk.content, end='')
-                print()
-                print('-' * 80)
+                    print(chunk.content, end='', flush=True)
+
+                print("\n" + "-" * 80)
+
+            # --- Test Suite Footer ---
+            print(f"\n✅ TEST SUITE COMPLETED: {session_id}")
+            print("=" * 80 + "\n")