diff --git a/PMLLarchitecture.js b/PMLLarchitecture.js new file mode 100644 index 0000000..2734096 --- /dev/null +++ b/PMLLarchitecture.js @@ -0,0 +1,921 @@ +// Simplified Memory Item +class MemoryItem { + constructor(content, type = "text", isFactual = 0.5) { + this.content = content; + this.type = type; // e.g., "text", "numerical", "reference" + this.isFactual = isFactual; // Factual ↔ Imaginative gradient (0.0-1.0) + this.timestamp = Date.now(); + this.accessCount = 0; + this.importance = 5; // Default importance + this.related = []; // For graph-like relationships + } + + addRelationship(memoryItem) { + this.related.push(memoryItem); + } + + increaseAccessCount() { + this.accessCount++; + } +} + +// Simplified Memory Tier +class MemoryTier { + constructor(name, maxCapacity = Infinity) { + this.name = name; + this.items = []; + this.maxCapacity = maxCapacity; + } + + insert(memoryItem) { + if (this.items.length >= this.maxCapacity) { + this.prune(); + } + this.items.push(memoryItem); + } + + retrieve(query) { + // Basic linear search for demonstration + return this.items.filter((item) => item.content.includes(query)); + } + + prune(){ + if(this.name === "Volatile Short-Term"){ + this.items.shift(); + } else { + this.items.sort((a, b) => a.importance - b.importance); + this.items.shift(); + } + } +} + +// Memory Manager +class MemoryManager { + constructor() { + this.volatileShortTerm = new MemoryTier("Volatile Short-Term", 5); // Limited capacity + this.persistentLongTerm = new MemoryTier("Persistent Long-Term"); // Unlimited capacity + this.contextWorkingMemory = new MemoryTier("Context/Working Memory", 3); // Small capacity + + this.allMemories = []; + } + + insert(memoryItem) { + this.allMemories.push(memoryItem) + // For this example, we insert into all tiers + this.volatileShortTerm.insert(memoryItem); + this.persistentLongTerm.insert(memoryItem); + this.contextWorkingMemory.insert(memoryItem); + } + + retrieve(query) { + // Simple retrieval from each tier, then combine + const results = [ + ...this.volatileShortTerm.retrieve(query), + ...this.persistentLongTerm.retrieve(query), + ...this.contextWorkingMemory.retrieve(query), + ]; + + results.forEach(result => result.increaseAccessCount()) + return results; + } + + promoteMemories(){ + // Simple promotion for demonstration + this.allMemories.forEach(memory => { + if (memory.accessCount >= 3) { + memory.importance += 2; + } + if(memory.importance >= 8){ + this.persistentLongTerm.insert(memory); + } + }); + } +} + +// Example Usage +const memoryManager = new MemoryManager(); + +// Create and insert memory items +const memory1 = new MemoryItem("The sky is blue.", "text", 0.9); +const memory2 = new MemoryItem("Cats can fly.", "text", 0.1); +const memory3 = new MemoryItem("The Earth revolves around the Sun.", "text", 1.0); +const memory4 = new MemoryItem("The capital of France is Paris", "text", 0.95); +const memory5 = new MemoryItem("Dogs are a domesticated descendant of wolves.", "text", 0.8); +const memory6 = new MemoryItem("Water is essential for life.", "text", 0.9); +const memory7 = new MemoryItem("The Eiffel Tower is in Rome", "text", 0.1); +const memory8 = new MemoryItem("Humans are mammals.", "text", 0.9); + +memory1.addRelationship(memory3); // Create some relationships +memory3.addRelationship(memory1); + +memoryManager.insert(memory1); +memoryManager.insert(memory2); +memoryManager.insert(memory3); +memoryManager.insert(memory4); +memoryManager.insert(memory5); +memoryManager.insert(memory6); +memoryManager.insert(memory7); +memoryManager.insert(memory8); + +// Retrieve memory items +let results = memoryManager.retrieve("sky"); +console.log("Retrieval results for 'sky':", results.map((r) => r.content)); + +results = memoryManager.retrieve("Paris"); +console.log("Retrieval results for 'Paris':", results.map((r) => r.content)); + +//Promote memories +memoryManager.promoteMemories(); + +results = memoryManager.retrieve("sky"); +console.log("Retrieval results for 'sky':", results.map((r) => r.content)); + +// ============================================================================= +// Code from the previous file +// ============================================================================= +const architectureComponents = { + // Primary memory tiers + memoryTiers: [ + { + name: "Volatile Short-Term", + characteristics: [ + "High access speed", + "Recent interactions", + "Cache-like implementation", + "Vector embeddings for semantic lookup", + "Graph structure for relationship modeling" + ], + implementation: "In-memory cache with vector embeddings" + }, + { + name: "Persistent Long-Term", + characteristics: [ + "Slower access but higher capacity", + "Important concepts and knowledge", + "Hierarchical tree structure", + "Compressed/summarized information" + ], + implementation: "Tree hierarchy gradient storage system" + }, + { + name: "Context/Working Memory", + characteristics: [ + "Current conversation state", + "Active task parameters", + "High-priority information" + ], + implementation: "Small, fast-access key-value store" + } + ], + + // Classification system + memoryClassification: { + gradients: [ + { + dimension: "Factual ↔ Imaginative", + description: "Distinguishes between verified facts and constructed/creative content", + implementation: "Continuous gradient with confidence scores (0.0-1.0)" + }, + { + dimension: "Temporal Relevance", + description: "How relevant the memory is to the current context based on time", + implementation: "Decay function with half-life parameters" + }, + { + dimension: "Access Frequency", + description: "How often the memory has been retrieved", + implementation: "Counter with normalization" + }, + { + dimension: "Importance", + description: "Manually or automatically flagged critical information", + implementation: "Priority score (0-10)" + } + ], + tags: [ + "Source attribution", + "Topic categories", + "User-specific markers", + "Confidence levels" + ] + }, + + // Memory operations + operations: { + insertion: { + description: "Adding new memories to the system", + complexity: "O(log n) for tree insertion, O(1) for cache", + challenges: "Real-time embedding generation, proper initial classification" + }, + retrieval: { + description: "Finding relevant memories for a given query", + complexity: "O(log n) for ANN search + O(d) for graph traversal", + approaches: [ + "Vector similarity search", + "Graph traversal for related concepts", + "Multi-hop reasoning paths", + "Hybrid keyword + semantic search" + ] + }, + promotion: { + description: "Moving memories between tiers based on importance", + triggers: [ + "Access frequency thresholds", + "Explicit importance flags", + "Relationship density in graph", + "Age with continuous decay functions" + ] + }, + pruning: { + description: "Removing or compressing less important memories", + approaches: [ + "Graph-based importance scoring", + "LRU (Least Recently Used) policies", + "Summarization for compression", + "Merging similar/related memories" + ] + } + }, + + // Data structure implementations + dataStructures: { + graphBased: { + nodes: "Memory items with vector embeddings", + edges: "Semantic relationships with weights", + algorithms: [ + "PageRank variants for importance", + "Shortest path for reasoning chains", + "Community detection for related concept clusters" + ] + }, + vectorStores: { + implementation: "ANN (Approximate Nearest Neighbor) indexes", + options: [ + "HNSW (Hierarchical Navigable Small World)", + "FAISS library implementation", + "Optimized cosine similarity search" + ] + }, + cacheImplementation: { + structure: "Two-tier with mimeograph rollout mechanism", + operations: "Fast access recent memory with graduated persistence" + } + }, + + // Special features + specialFeatures: { + dreamVsAwake: { + description: "Classification between factual (awake) and imaginative (dream) memories", + implementation: "Boolean flag with continuous confidence score", + usage: "Contextually appropriate memory retrieval based on task type" + }, + consistencyManagement: { + description: "Detecting and resolving contradictions in memory", + approaches: [ + "Logical contradiction detection", + "Confidence-based resolution", + "Explicit versioning of conflicting information", + "Source prioritization" + ] + }, + multiModalSupport: { + description: "Support for different types of memory beyond text", + types: [ + "Text representations", + "Structured data (JSON, tables)", + "Numerical data", + "References to external resources" + ] + } + } + }; + + // Strengths analysis + const strengths = [ + "Three-tier architecture balances performance and capacity needs", + "Graph-based vector storage enables sophisticated relationship modeling", + "Factual/imaginative gradient supports both knowledge and creative tasks", + "Dynamic memory promotion and pruning mimics human memory processes", + "Hierarchical structure allows for efficient scaling", + "Classification system enables context-appropriate retrieval policies" + ]; + + // Potential challenges + const challenges = [ + "Computational overhead of real-time graph maintenance", + "Balancing response time with memory depth", + "Ensuring factual consistency across long interaction chains", + "Preventing memory poisoning with incorrect information", + "Handling context switches without losing important information", + "Determining optimal decay rates for different types of information", + "Storage efficiency for large-scale deployment" + ]; + + // Overall architecture assessment + console.log("MEMORY ARCHITECTURE ANALYSIS"); + console.log("==========================="); + console.log("\nCore Structure:"); + console.log("- Primary tiers: " + architectureComponents.memoryTiers.map(t => t.name).join(", ")); + console.log("- Classification dimensions: " + architectureComponents.memoryClassification.gradients.map(g => g.dimension).join(", ")); + + console.log("\nKey Strengths:"); + strengths.forEach(s => console.log("✓ " + s)); + + console.log("\nPotential Challenges:"); + challenges.forEach(c => console.log("! " + c)); + + console.log("\nImplementation Complexity:"); + console.log("- Insertion: " + architectureComponents.operations.insertion.complexity); + console.log("- Retrieval: " + architectureComponents.operations.retrieval.complexity); + console.log("- Storage: Varies by tier (O(1) for cache, O(n) for persistent storage)"); + + console.log("\nUnique Features:"); + console.log("- Dream vs. Awake Memory Classification"); + console.log("- Mimeograph Rollout Mechanism"); + console.log("- Graph-Based Vectorization"); + console.log("- Multi-Dimensional Memory Classification"); + +const natural = require('natural'); +const tf = require('@tensorflow/tfjs-node'); +const { Word2Vec } = require('word2vec'); + +class SemanticEmbedding { + constructor() { + this.model = null; + this.vectorSize = 100; + } + + async initialize() { + // Placeholder for more advanced embedding initialization + this.model = await tf.loadLayersModel('path/to/embedding/model'); + } + + async generateEmbedding(text) { + // Generate semantic vector representation + const tokens = natural.tokenize(text.toLowerCase()); + const embedding = await this.model.predict(tokens); + return embedding; + } + + calculateSemanticSimilarity(embedding1, embedding2) { + // Cosine similarity calculation + return tf.losses.cosineDistance(embedding1, embedding2); + } +} + +class MemoryItem { + constructor(content, { + type = "text", + isFactual = 0.5, + source = null, + confidence = 0.5 + } = {}) { + this.id = crypto.randomUUID(); // Unique identifier + this.content = content; + this.type = type; + this.isFactual = isFactual; + this.confidence = confidence; + this.source = source; + + this.timestamp = Date.now(); + this.accessCount = 0; + this.importance = 5; + + this.embedding = null; + this.related = new Map(); // Enhanced relationship tracking + this.tags = new Set(); + } + + async computeEmbedding(embeddingService) { + this.embedding = await embeddingService.generateEmbedding(this.content); + } + + addRelationship(memoryItem, weight = 1.0) { + this.related.set(memoryItem.id, { + memory: memoryItem, + weight: weight, + type: this.determineRelationshipType(memoryItem) + }); + } + + determineRelationshipType(memoryItem) { + // Semantic relationship type inference + const semanticDistance = this.calculateSemanticDistance(memoryItem); + if (semanticDistance < 0.2) return 'VERY_CLOSE'; + if (semanticDistance < 0.5) return 'RELATED'; + return 'DISTANT'; + } + + calculateSemanticDistance(memoryItem) { + // Placeholder for semantic distance calculation + return Math.random(); // Replace with actual embedding comparison + } + + incrementAccess() { + this.accessCount++; + this.updateImportance(); + } + + updateImportance() { + // Dynamic importance calculation + this.importance = Math.min( + 10, + 5 + Math.log(this.accessCount + 1) + ); + } +} + +class MemoryTier { + constructor(name, { + maxCapacity = Infinity, + pruneStrategy = 'LRU' + } = {}) { + this.name = name; + this.items = new Map(); // Use Map for efficient lookups + this.maxCapacity = maxCapacity; + this.pruneStrategy = pruneStrategy; + } + + insert(memoryItem) { + if (this.items.size >= this.maxCapacity) { + this.prune(); + } + this.items.set(memoryItem.id, memoryItem); + } + + prune() { + switch(this.pruneStrategy) { + case 'LRU': + const lruItem = Array.from(this.items.values()) + .sort((a, b) => a.timestamp - b.timestamp)[0]; + this.items.delete(lruItem.id); + break; + case 'LEAST_IMPORTANT': + const leastImportant = Array.from(this.items.values()) + .sort((a, b) => a.importance - b.importance)[0]; + this.items.delete(leastImportant.id); + break; + } + } + + async retrieve(query, embeddingService, topK = 5) { + const queryEmbedding = await embeddingService.generateEmbedding(query); + + const scoredResults = Array.from(this.items.values()) + .map(item => ({ + memory: item, + similarity: embeddingService.calculateSemanticSimilarity( + item.embedding, + queryEmbedding + ) + })) + .sort((a, b) => b.similarity - a.similarity) + .slice(0, topK); + + return scoredResults.map(r => r.memory); + } +} + +class MemoryManager { + constructor() { + this.embeddingService = new SemanticEmbedding(); + + this.volatileShortTerm = new MemoryTier("Volatile Short-Term", { + maxCapacity: 10, + pruneStrategy: 'LRU' + }); + + this.persistentLongTerm = new MemoryTier("Persistent Long-Term"); + this.contextWorkingMemory = new MemoryTier("Context/Working Memory", { + maxCapacity: 5 + }); + + this.allMemories = new Map(); + } + + async initialize() { + await this.embeddingService.initialize(); + } + + async insert(content, options = {}) { + const memoryItem = new MemoryItem(content, options); + await memoryItem.computeEmbedding(this.embeddingService); + + // Insert into all appropriate tiers + this.volatileShortTerm.insert(memoryItem); + this.persistentLongTerm.insert(memoryItem); + this.contextWorkingMemory.insert(memoryItem); + + this.allMemories.set(memoryItem.id, memoryItem); + return memoryItem; + } + + async retrieve(query, tier = null) { + if (tier) { + return tier.retrieve(query, this.embeddingService); + } + + // Parallel retrieval across tiers + const results = await Promise.all([ + this.volatileShortTerm.retrieve(query, this.embeddingService), + this.persistentLongTerm.retrieve(query, this.embeddingService), + this.contextWorkingMemory.retrieve(query, this.embeddingService) + ]); + + // Flatten and deduplicate results + return [...new Set(results.flat())]; + } + + async findSemanticallySimilar(memoryItem, threshold = 0.7) { + const similar = []; + for (let [, memory] of this.allMemories) { + if (memory.id !== memoryItem.id) { + const similarity = this.embeddingService.calculateSemanticSimilarity( + memory.embedding, + memoryItem.embedding + ); + if (similarity >= threshold) { + similar.push({ memory, similarity }); + } + } + } + return similar.sort((a, b) => b.similarity - a.similarity); + } +} + +// Example Usage +async function demonstrateMemorySystem() { + const memoryManager = new MemoryManager(); + await memoryManager.initialize(); + + // Insert memories + const aiEthicsMem = await memoryManager.insert( + "AI should be developed with strong ethical considerations", + { + type: "concept", + isFactual: 0.9, + confidence: 0.8 + } + ); + + const aiResearchMem = await memoryManager.insert( + "Machine learning research is advancing rapidly", + { + type: "research", + isFactual: 0.95 + } + ); + + // Create relationships + aiEthicsMem.addRelationship(aiResearchMem); + + // Retrieve memories + const retrievedMemories = await memoryManager.retrieve("AI ethics"); + console.log("Retrieved Memories:", retrievedMemories); + + // Find semantically similar memories + const similarMemories = await memoryManager.findSemanticallySimilar(aiEthicsMem); + console.log("Similar Memories:", similarMemories); +} + +demonstrateMemorySystem(); + +module.exports = { MemoryManager, MemoryItem, MemoryTier }; + +class MemoryTracer { + constructor() { + this.generationLog = new Map(); // Track memory generation lineage + this.redundancyMap = new Map(); // Track potential redundant memories + this.compressionMetrics = { + totalMemories: 0, + uniqueMemories: 0, + redundancyRate: 0, + compressionPotential: 0 + }; + } + + trackGeneration(memoryItem, parentMemories = []) { + // Create a generation trace + const generationEntry = { + id: memoryItem.id, + timestamp: Date.now(), + content: memoryItem.content, + parents: parentMemories.map(m => m.id), + lineage: [ + ...parentMemories.flatMap(p => + this.generationLog.get(p.id)?.lineage || [] + ), + memoryItem.id + ] + }; + + this.generationLog.set(memoryItem.id, generationEntry); + this.updateRedundancyMetrics(memoryItem); + } + + updateRedundancyMetrics(memoryItem) { + // Semantic similarity check for redundancy + const similarityThreshold = 0.9; + let redundancyCount = 0; + + for (let [, existingMemory] of this.redundancyMap) { + const similarity = this.calculateSemanticSimilarity( + existingMemory.content, + memoryItem.content + ); + + if (similarity >= similarityThreshold) { + redundancyCount++; + this.redundancyMap.set(memoryItem.id, { + memory: memoryItem, + similarTo: existingMemory.id, + similarity: similarity + }); + } + } + + // Update compression metrics + this.compressionMetrics.totalMemories++; + this.compressionMetrics.redundancyRate = + (redundancyCount / this.compressionMetrics.totalMemories); + this.compressionMetrics.compressionPotential = + this.calculateCompressionPotential(); + } + + calculateSemanticSimilarity(content1, content2) { + // Placeholder for semantic similarity calculation + // In a real implementation, use embedding-based similarity + const words1 = new Set(content1.toLowerCase().split(/\s+/)); + const words2 = new Set(content2.toLowerCase().split(/\s+/)); + + const intersection = new Set( + [...words1].filter(x => words2.has(x)) + ); + + return intersection.size / Math.max(words1.size, words2.size); + } + + calculateCompressionPotential() { + // Advanced compression potential calculation + const { totalMemories, redundancyRate } = this.compressionMetrics; + + // Exponential decay of compression potential + return Math.min(1, Math.exp(-redundancyRate) * + (1 - 1 / (1 + totalMemories))); + } + + compressMemories(memoryManager) { + const compressibleMemories = []; + + // Identify memories for potential compression + for (let [id, redundancyEntry] of this.redundancyMap) { + if (redundancyEntry.similarity >= 0.9) { + compressibleMemories.push({ + id: id, + similarTo: redundancyEntry.similarTo, + similarity: redundancyEntry.similarity + }); + } + } + + // Compression strategy + const compressionStrategy = (memories) => { + // Group similar memories + const memoryGroups = new Map(); + + memories.forEach(memoryInfo => { + const groupKey = memoryInfo.similarTo; + if (!memoryGroups.has(groupKey)) { + memoryGroups.set(groupKey, []); + } + memoryGroups.get(groupKey).push(memoryInfo); + }); + + // Merge similar memory groups + const mergedMemories = []; + for (let [baseId, group] of memoryGroups) { + const baseMemory = memoryManager.allMemories.get(baseId); + + // Create a compressed representation + const compressedContent = this.createCompressedContent( + group.map(g => + memoryManager.allMemories.get(g.id).content + ) + ); + + // Create a new compressed memory item + const compressedMemory = new MemoryItem(compressedContent, { + type: baseMemory.type, + isFactual: baseMemory.isFactual, + confidence: Math.max(...group.map(g => + memoryManager.allMemories.get(g.id).confidence + )) + }); + + mergedMemories.push(compressedMemory); + } + + return mergedMemories; + }; + + // Execute compression + const compressedMemories = compressionStrategy(compressibleMemories); + + // Update memory manager + compressedMemories.forEach(memory => { + memoryManager.insert(memory); + }); + + // Log compression results + console.log('Memory Compression Report:', { + totalCompressed: compressibleMemories.length, + compressionPotential: this.compressionMetrics.compressionPotential + }); + + return compressedMemories; + } + + createCompressedContent(contents) { + // Intelligently combine similar memory contents + const uniqueWords = new Set( + contents.flatMap(content => + content.toLowerCase().split(/\s+/) + ) + ); + + // Create a concise summary + return Array.from(uniqueWords).slice(0, 20).join(' '); + } +} + +// Modify MemoryManager to incorporate tracing +class MemoryManager { + constructor() { + // ... existing constructor code ... + this.memoryTracer = new MemoryTracer(); + } + + async insert(content, options = {}, parentMemories = []) { + const memoryItem = new MemoryItem(content, options); + + // Compute embedding and trace generation + await memoryItem.computeEmbedding(this.embeddingService); + this.memoryTracer.trackGeneration(memoryItem, parentMemories); + + // ... existing insertion code ... + + return memoryItem; + } + + performMemoryCompression() { + return this.memoryTracer.compressMemories(this); + } +} + +class MemoryTracer { + constructor() { + this.generationLog = new Map(); + this.redundancyThreshold = 0.85; // Semantic similarity threshold + this.compressionMetrics = { + totalMemories: 0, + redundantMemories: 0, + compressionRatio: 0, + lastCompression: null + }; + } + + trackGeneration(memoryItem, sourceOperation) { + if (!this.generationLog.has(memoryItem.id)) { + this.generationLog.set(memoryItem.id, { + timestamp: Date.now(), + sourceOperation, + content: memoryItem.content, + metadata: { + type: memoryItem.type, + isFactual: memoryItem.isFactual, + confidence: memoryItem.confidence + }, + accessHistory: [], + redundancyScore: 0 + }); + this.compressionMetrics.totalMemories++; + } + } + + recordAccess(memoryItem) { + const logEntry = this.generationLog.get(memoryItem.id); + if (logEntry) { + logEntry.accessHistory.push({ + timestamp: Date.now(), + accessCount: memoryItem.accessCount + }); + } + } + + async detectRedundancy(memoryManager, memoryItem) { + const similarMemories = await memoryManager.findSemanticallySimilar( + memoryItem, + this.redundancyThreshold + ); + + if (similarMemories.length > 1) { + const logEntry = this.generationLog.get(memoryItem.id); + if (logEntry) { + logEntry.redundancyScore = similarMemories.length; + this.compressionMetrics.redundantMemories++; + } + return similarMemories; + } + return []; + } + + compressMemories(memoryManager) { + const compressibleMemories = Array.from(memoryManager.allMemories.values()) + .filter(memory => { + const logEntry = this.generationLog.get(memory.id); + return logEntry && + (logEntry.redundancyScore > 1 || + memory.accessCount === 0 || + (Date.now() - memory.timestamp > 30 * 24 * 60 * 60 * 1000)); // 30 days + }); + + // Compression strategy: Keep most representative memory, remove others + compressibleMemories.forEach(memory => { + if (memory.accessCount === 0) { + memoryManager.removeMemory(memory); + } + }); + + this.updateCompressionMetrics(compressibleMemories.length); + } + + updateCompressionMetrics(compressedCount) { + this.compressionMetrics.compressionRatio = + compressedCount / this.compressionMetrics.totalMemories; + this.compressionMetrics.lastCompression = Date.now(); + } + + generateComprehensiveReport() { + return { + totalMemories: this.compressionMetrics.totalMemories, + redundantMemories: this.compressionMetrics.redundantMemories, + compressionRatio: this.compressionMetrics.compressionRatio, + lastCompression: this.compressionMetrics.lastCompression + }; + } +} + +// Modify MemoryManager to integrate tracing +class MemoryManager { + constructor() { + // ... existing constructor code + this.memoryTracer = new MemoryTracer(); + } + + async insert(content, options = {}) { + const memoryItem = new MemoryItem(content, options); + await memoryItem.computeEmbedding(this.embeddingService); + + // Track memory generation + this.memoryTracer.trackGeneration(memoryItem, 'insert'); + + // Detect potential redundancies + const redundancies = await this.memoryTracer.detectRedundancy(this, memoryItem); + + // Insert into tiers + this.volatileShortTerm.insert(memoryItem); + this.persistentLongTerm.insert(memoryItem); + this.contextWorkingMemory.insert(memoryItem); + + this.allMemories.set(memoryItem.id, memoryItem); + return memoryItem; + } + + async retrieve(query, tier = null) { + const results = await super.retrieve(query, tier); + + // Track access for each retrieved memory + results.forEach(memory => { + this.memoryTracer.recordAccess(memory); + }); + + return results; + } + + performMemoryCompression() { + this.memoryTracer.compressMemories(this); + } + + removeMemory(memory) { + this.allMemories.delete(memory.id); + this.volatileShortTerm.items.delete(memory.id); + this.persistentLongTerm.items.delete(memory.id); + this.contextWorkingMemory.items.delete(memory.id); + } + + getMemoryCompressionReport() { + return this.memoryTracer.generateComprehensiveReport(); + } +} + +I know this redudant, but I just liked both responses. I also don't think you ever have said any of my insight was brilliant until just now so let's get this brilliancy fully figured out yes?