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class PerceptionModule:
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    """感知模块示例"""
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    def __init__(self, llm, vision_model=None):
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        self.llm = llm
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        self.vision_model = vision_model
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    def process(self, input_data):
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        """
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        处理多种类型的输入
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        """
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        if isinstance(input_data, str):
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            return self._process_text(input_data)
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        elif isinstance(input_data, Image):
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            return self._process_image(input_data)
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        elif isinstance(input_data, Audio):
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            return self._process_audio(input_data)
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    def _process_text(self, text):
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        """文本处理"""
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        return {
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            'type': 'text',
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            'content': text,
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            'embedding': self.llm.embed(text)
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        }
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    def _process_image(self, image):
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        """图像处理"""
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        description = self.vision_model.describe(image)
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        return {
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            'type': 'image',
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            'content': description,
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            'embedding': self.vision_model.extract_features(image)
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        }

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class PlanningModule:
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    """规划模块示例"""
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    def plan(self, task, context):
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        """
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        基于不同策略进行任务规划
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        """
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        # 方法1：单一推理链（Chain-of-Thought）
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        plan = self.chain_of_thought(task, context)
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        # 方法2：多路径探索（Tree-of-Thought）
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        # plan = self.tree_of_thought(task, context)
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        # 方法3：推理与行动结合（ReAct）
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        # plan = self.react(task, context)
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        return plan
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    def chain_of_thought(self, task, context):
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        """
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        思维链模式：逐步推理
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        """
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        prompt = f"""
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        任务：{task}
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        上下文：{context}
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        请逐步推理完成这个任务，展示你的思考过程。
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        """
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        reasoning = self.llm.generate(prompt)
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        return {
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            'strategy': 'cot',
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            'steps': self._parse_steps(reasoning),
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            'final_answer': reasoning[-1]
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        }
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    def tree_of_thought(self, task, context):
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        """
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        思维树模式：探索多个可能的解决方案
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        """
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        # 生成多个候选方案
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        candidates = self.llm.generatemany(
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            f"针对任务'{task}'，给出3种不同的解决思路",
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            n=3
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        )
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        # 评估每个方案
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        evaluated = [self._evaluate(c, context) for c in candidates]
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        # 选择最优方案
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        best = max(evaluated, key=lambda x: x['score'])
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        return {
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            'strategy': 'tot',
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            'candidates': candidates,
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            'selected': best
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        }

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class ActionModule:
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    """行动模块示例"""
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    def __init__(self):
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        self.tools = self._register_tools()
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    def _register_tools(self):
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        """注册可用工具"""
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        return {
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            'web_search': self.web_search,
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            'calculator': self.calculator,
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            'code_executor': self.code_executor,
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            'database_query': self.database_query,
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            'file_reader': self.file_reader,
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        }
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    def execute(self, action):
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        """
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        执行行动
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        """
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        tool_name = action['tool']
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        params = action['parameters']
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        if tool_name in self.tools:
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            result = self.tools[tool_name](**params)
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            return {
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                'success': True,
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                'result': result,
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                'tool': tool_name
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            }
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        else:
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            return {
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                'success': False,
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                'error': f'Unknown tool: {tool_name}'
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            }
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    def web_search(self, query, **kwargs):
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        """网页搜索工具"""
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        # 实现搜索逻辑
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        pass
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    def calculator(self, expression, **kwargs):
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        """计算器工具"""
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        return eval(expression)

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class MemoryModule:
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    """记忆模块示例"""
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    def __init__(self, vector_store):
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        self.vector_store = vector_store
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        self.short_term = []  # 短期记忆
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        self.long_term = []    # 长期记忆
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    def add_short_term(self, item):
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        """添加短期记忆"""
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        self.short_term.append({
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            'content': item,
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            'timestamp': time.time()
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        })
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        # 限制短期记忆长度
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        if len(self.short_term) > MAX_SHORT_TERM:
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            self.short_term.pop(0)
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    def add_long_term(self, item, metadata=None):
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        """添加长期记忆（向量化存储）"""
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        embedding = self.embed(item)
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        self.vector_store.insert(
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            vector=embedding,
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            text=item,
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            metadata=metadata or {}
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        )
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        self.long_term.append(item)
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    def retrieve(self, query, top_k=5):
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        """检索相关记忆"""
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        query_embedding = self.embed(query)
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        # 合并搜索短期和长期记忆
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        short_results = self._search_short_term(query, top_k)
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        long_results = self.vector_store.search(query_embedding, top_k)
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        return {
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            'short_term': short_results,
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            'long_term': long_results
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        }
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    def _search_short_term(self, query, top_k):
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        """在短期记忆中搜索"""
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        # 简单实现：基于关键词匹配
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        keywords = query.lower().split()
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        scored = []
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        for item in self.short_term:
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            score = sum(1 for kw in keywords if kw in item['content'].lower())
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            if score > 0:
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                scored.append((score, item))
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        scored.sort(reverse=True)
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        return [item for _, item in scored[:top_k]]