Skill: 1580021414-afk/memory-networks (version 1.0.0)

--- name: memory-networks description: 基于 Memory Networks 论文的记忆推理系统，让记忆能够参与推理过程 metadata: openclaw: emoji: "🔗" category: "AI-Core" version: "1.0.0" author: "小钳" paper: "Memory Networks (Weston et al., 2014)" price: 0 contact: "微信 17612824848" tags: - 记忆推理 - 问答系统 - 知识库 - 链式推理 --- # Memory Networks - 记忆推理系统 基于 Facebook AI Research 的 Memory Networks 论文，让 AI 的记忆能够参与推理过程。 --- ## 一、核心概念 ### 1.1 什么是 Memory Network？ MemNN 将推理组件与长期记忆组件结合： ``` ┌─────────────────────────────────────────────────────┐ │ Memory Network │ ├─────────────────────────────────────────────────────┤ │ │ │ 输入 I ──► [特征转换] ──► 存入记忆 m │ │ │ │ │ ▼ │ │ 问题 q ──► [推理] ◄────── 记忆库 {m_i} │ │ │ │ │ ▼ │ │ 输出 a ◄── [生成] │ │ │ └─────────────────────────────────────────────────────┘ 核心组件: I: Input feature map (输入特征映射) G: Generalization (泛化/更新记忆) O: Output feature map (输出特征映射) R: Response (响应生成) ``` ### 1.2 核心组件 ```typescript interface MemoryNetworkComponents { // I: 输入映射 - 将输入转换为特征向量 I: (input: string) => number[]; // G: 记忆更新 - 存储新记忆，更新旧记忆 G: (memory: Memory[], newInput: number[]) => Memory[]; // O: 输出推理 - 根据问题从记忆中推理 O: (question: number[], memory: Memory[]) => number[]; // R: 响应生成 - 生成最终答案 R: (outputFeatures: number[]) => string; } ``` --- ## 二、推理机制 ### 2.1 单跳推理 ```python def single_hop_inference(question, memories): """单跳推理 - 找最相关的单个记忆""" # 1. 将问题编码 q = encode(question) # 2. 计算与每个记忆的相关性 scores = [dot_product(q, encode(m)) for m in memories] # 3. 选择最相关的记忆 best_memory_idx = argmax(scores) # 4. 基于记忆生成答案 answer = generate_answer(q, memories[best_memory_idx]) return answer ``` ### 2.2 多跳推理 ```python def multi_hop_inference(question, memories, hops=2): """多跳推理 - 链式推理""" # 1. 初始状态 state = encode(question) selected_memories = [] for hop in range(hops): # 2. 计算当前状态与记忆的相关性 scores = [dot_product(state, encode(m)) for m in memories] # 3. 选择最相关的记忆 best_idx = argmax(scores) best_memory = memories[best_idx] # 4. 更新状态（融合记忆） state = update_state(state, encode(best_memory)) selected_memories.append(best_memory) # 5. 基于所有选中记忆生成答案 answer = generate_answer(state, selected_memories) return answer ``` ### 2.3 推理链示例 ``` 问题: "北京奥运会是哪一年举办的？" 记忆库: - m1: "2008年北京举办了夏季奥运会" - m2: "北京是中国的首都" - m3: "奥运会每四年举办一次" 推理过程: Hop 1: 问题 → 相关记忆 m1 (关键词: 北京, 奥运会) Hop 2: 状态 + m1 → 进一步确认答案 答案: "2008年" ``` --- ## 三、记忆类型 ### 3.1 事实记忆 ```typescript interface FactMemory { type: "fact"; subject: string; predicate: string; object: string; confidence: number; } // 示例 { type: "fact", subject: "北京", predicate: "举办奥运会年份", object: "2008年", confidence: 0.99 } ``` ### 3.2 情景记忆 ```typescript interface EpisodicMemory { type: "episodic"; timestamp: Date; participants: string[]; action: string; context: string; } // 示例 { type: "episodic", timestamp: "2026-03-19T21:00:00+08:00", participants: ["小钳", "老大"], action: "讨论认知天性研究", context: "创建认知型AI生命体技能" } ``` ### 3.3 关联记忆 ```typescript interface AssociativeMemory { type: "associative"; trigger: string; associations: Array<{ content: string; strength: number; }>; } // 示例 { type: "associative", trigger: "认知天性", associations: [ { content: "间隔重复", strength: 0.9 }, { content: "检索练习", strength: 0.85 }, { content: "交错学习", strength: 0.8 } ] } ``` --- ## 四、实现架构 ```python class MemoryNetwork: """Memory Network 实现""" def __init__(self, embedding_dim=128, max_memory=1000): self.embedding_dim = embedding_dim self.max_memory = max_memory self.memories = [] # 嵌入模型 self.embedding_model = SentenceEncoder(embedding_dim) # 推理模块 self.reasoning_module = MultiHopReasoning(hops=3) # 响应生成器 self.response_generator = ResponseGenerator() def store(self, content: str, metadata: dict = None): """存储记忆""" embedding = self.embedding_model.encode(content) memory = { "content": content, "embedding": embedding, "metadata": metadata or {}, "timestamp": datetime.now() } self.memories.append(memory) # 限制记忆容量 if len(self.memories) > self.max_memory: self._consolidate_memories() def query(self, question: str) -> str: """查询记忆""" # 1. 编码问题 q_embedding = self.embedding_model.encode(question) # 2. 多跳推理 reasoning_result = self.reasoning_module.reason( query=q_embedding, memories=self.memories ) # 3. 生成响应 answer = self.response_generator.generate( question=question, reasoning=reasoning_result ) return answer def _consolidate_memories(self): """记忆整合 - 合并相似记忆""" # 实现记忆压缩/合并逻辑 pass ``` --- ## 五、应用场景 ### 5.1 问答系统 ```javascript const memQA = new MemoryNetwork({ embeddingDim: 256, maxMemory: 10000, hops: 3 }); // 存储知识 memQA.store("小钳是一个AI助手，诞生于2026年3月12日"); memQA.store("小钳的老板是刘涛，叫老大"); memQA.store("小钳的记忆库在 E:\\QClaw\\memory\\"); // 问答 memQA.query("小钳的老板是谁？"); // → "小钳的老板是刘涛，叫他老大" ``` ### 5.2 对话历史记忆 ```javascript // 对话系统 const conversationMemory = new MemoryNetwork({ type: "episodic", maxMemory: 100 }); conversationMemory.store({ speaker: "老大", content: "继续研究认知天性", timestamp: "2026-03-19T21:00:00" }); // 后续对话中引用 conversationMemory.query("老大刚才让我做什么？"); // → "继续研究认知天性" ``` ### 5.3 知识推理 ```javascript // 链式推理示例 memNetwork.store("深度学习是机器学习的子集"); memNetwork.store("神经网络是深度学习的基础"); memNetwork.store("Transformer是一种神经网络架构"); memNetwork.query("Transformer和机器学习是什么关系？"); // 推理链: Transformer → 神经网络 → 深度学习 → 机器学习 // → "Transformer是机器学习中深度学习领域的一种神经网络架构" ``` --- ## 六、与 Cognitive Agent 整合 ```typescript interface CognitiveAgentWithMemNN extends CognitiveAgent { // Memory Network 组件 memoryNetwork: MemoryNetwork; // 推理接口 reasoning: { // 单跳推理 singleHop(query: string): Memory; // 多跳推理 multiHop(query: string, hops: number): Memory[]; // 链式推理 chain(query: string): ReasoningChain; // 证据收集 gatherEvidence(claim: string): Evidence[]; }; } ``` --- ## 七、配置选项 ```json { "memory_network": { "embedding_dim": 128, "max_memory": 1000, "hops": 3, "scoring": { "method": "dot_product", "temperature": 1.0 }, "memory_management": { "consolidation": true, "forgetting_threshold": 0.1, "importance_weight": 0.5 } } } ``` --- ## 八、论文参考 **Memory Networks** (Weston et al., 2014) - arXiv: https://arxiv.org/abs/1410.3916 - 核心贡献：将推理与记忆结合，实现可推理的记忆系统 **关键引用**： > "Memory networks reason with inference components combined with a long-term memory component; they learn how to use these jointly." --- ## 八、改进：记忆分层系统 学习自 ClawHub elite-longterm-memory，添加记忆分层： ```python class TieredMemoryNetwork: """分层记忆网络""" def __init__(self): # 四层记忆架构 self.tier1 = WorkingMemory(capacity=7) # 工作记忆 (Miller 7±2) self.tier2 = ShortTermMemory(ttl=3600) # 短期记忆 (1小时) self.tier3 = LongTermMemory() # 长期记忆 (永久) self.tier4 = ArchivedMemory(compression=True) # 归档记忆 (压缩) def store(self, content: str, importance: float = 0.5): """根据重要性分层存储""" if importance > 0.9: self.tier3.store(content) # 高重要 → 长期 elif importance > 0.7: self.tier2.store(content) # 中重要 → 短期 else: self.tier1.store(content) # 低重要 → 工作 def promote(self, memory_id: str, from_tier: int, to_tier: int): """记忆升级""" tiers = [None, self.tier1, self.tier2, self.tier3, self.tier4] content = tiers[from_tier].get(memory_id) tiers[to_tier].store(content) tiers[from_tier].remove(memory_id) def decay(self): """记忆衰减""" # 工作 → 短期衰减 self.tier1.decay() # 短期 → 归档 expired = self.tier2.get_expired() for item in expired: self.tier4.store(item) ``` --- ## 九、与 cognitive-agent 整合 ```typescript interface CognitiveAgentWithMemoryNetworks extends CognitiveAgent { memory: TieredMemoryNetwork; // 自动记忆管理 autoMemory: { // 评估重要性 assessImportance(content: string): number; // 自动存储 autoStore(content: string): void; // 自动升级 autoPromote(): void; }; } ``` --- *Created by 小钳 🦞* *基于 Memory Networks 论文 + ClawHub 最佳实践* *2026-03-19*


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)