Add unit tests for LLM dialogue with configurable chat history

- Introduce `TestLLMDialogue` for testing LLM conversational behavior.
- Implement multi-turn dialogue tests with logical Chinese and English prompts.
- Mock configurable history management using `RunnableWithMessageHistory`.

tests/test_llm_dialogue.py

@@ -0,0 +1,112 @@
+import unittest
+
+from langchain.chains.conversation.memory import ConversationBufferWindowMemory
+from langchain_community.chat_models.llamacpp import ChatLlamaCpp
+from langchain_core.chat_history import InMemoryChatMessageHistory
+from langchain_core.messages import SystemMessage
+from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder
+from langchain_core.runnables.history import RunnableWithMessageHistory
+
+from config import paths
+
+CHINESE_SYSTEM_PROMPT = (
+    "你是善于模拟真实的思考过程的AI助手。"
+    "回答时，必须首先生成一个不超过5个字的简短句子，"
+    "比如：\"让我想一下\"、\"在我看来\"、\"稍等我理一理\"、\"不错的问题\"、\"稍等片刻\"等，然后再进行正式回答，"
+    "保持中文口语化表达，禁用emoji和系统相关描述，确保衔接词与内容存在合理逻辑关联。"
+    "/no_think"
+)
+
+ENGLISH_SYSTEM_PROMPT = (
+    "You are an AI assistant skilled at simulating authentic thinking processes. "
+    "When responding, you must first generate a brief phrase of no more than 5 words, "
+    "such as: 'Let me think', 'I see', 'Let me process this', 'Good question', 'One moment', etc., then proceed with your formal response. "
+    "Maintain natural conversational English expression, avoid emojis and system-related descriptions, and ensure logical coherence between transitional phrases and content."
+    "/no_think"
+)
+
+
+class TestLLMDialogue(unittest.TestCase):
+
+    def setUp(self):
+        model_params = {
+            'n_ctx': 32768,
+            'temperature': 0.7,
+            'top_p': 0.9,
+            'top_k': 20,
+            'model_kwargs': {
+                'mini_p': 0,
+                'presence_penalty': 1.5
+            },
+            'verbose': False
+        }
+        self.history_store = {}
+
+        model_path = paths.LLM_MODELS_PATH / 'qwen' / 'Qwen3-8B-Q6_K.gguf'
+        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 | langchain_instance
+        self.chain_with_history = RunnableWithMessageHistory(chain, self.get_session_history,
+                                                             history_messages_key='history')
+        self.warmup()
+
+        # 连续对话测试问题集
+        self.user_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？"
+        ]
+
+    def get_session_history(self, session_id: str) -> InMemoryChatMessageHistory:
+        if session_id not in self.history_store:
+            message_history = InMemoryChatMessageHistory()
+            self.history_store[session_id] = message_history
+            return self.history_store[session_id]
+
+        memory = ConversationBufferWindowMemory(
+            chat_memory=self.history_store[session_id],
+            k=3,
+            return_messages=True,
+        )
+        assert len(memory.memory_variables) == 1
+        key = memory.memory_variables[0]
+        messages = memory.load_memory_variables({})[key]
+        self.history_store[session_id] = InMemoryChatMessageHistory(messages=messages)
+        return self.history_store[session_id]
+
+    def warmup(self):
+        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):
+            pass
+
+    def test_dialogue(self):
+        session_id = 'test_dialogue'
+        for user_question in self.user_questions:
+            print('User question:', user_question)
+            config = {"configurable": {"session_id": session_id}}
+            print(f'LLM answer: ', end='')
+            for chunk in self.chain_with_history.stream(input={'input': user_question}, config=config):
+                print(chunk.content, end='')
+            print()
+            print('-' * 80)
+            print()