---
license: apache-2.0
language:
- en
base_model:
- mradermacher/oh-dcft-v3.1-claude-3-5-sonnet-20241022-GGUF
- openai/whisper-large-v3-turbo

pipeline_tag: memory-management
inference_api: true

title: Adaptive Memory Architecture (AMA)
description: >
  A biomimetic, multi-tier memory management system designed to revolutionize 
  how AI systems process, store, and retrieve information. Featuring dynamic 
  semantic embedding, intelligent relationship tracking, and adaptive memory compression.

key_features:
- Multi-tier memory management
- Semantic embedding integration
- Dynamic relationship inference
- Intelligent memory compression
- Contextually aware information processing

technical_details:
  memory_tiers:
    - volatile_short_term:
        capacity: 10 items
        characteristics: 
          - High-speed access
          - Recent interactions
          - Cache-like implementation
    - persistent_long_term:
        capacity: unlimited
        characteristics:
          - Important concept storage
          - Hierarchical knowledge representation
    - context_working_memory:
        capacity: 5 items
        characteristics:
          - Current conversation state
          - Active task parameters

performance_metrics:
  retrieval_speed: O(log n)
  semantic_similarity_calculation: cosine distance
  memory_compression_ratio: adaptive

research_potential:
  - Neuromorphic memory modeling
  - Adaptive learning systems
  - Cognitive architecture development

ethical_considerations:
  - Transparent memory tracking
  - Configurable confidence scoring
  - Relationship type inference

usage:
  - python
  - memory_manager = MemoryManager()
  - memory_manager.insert("AI ethics are crucial")
  - results = memory_manager.retrieve("ethical AI")
---
class AdvancedMemoryManager {
    constructor(config = {}) {
        // Configurable embedding models
        this.embeddingModels = {
            default: new SemanticEmbedding(),
            multilingual: new MultilingualEmbedding(),
            specialized: {
                text: new TextSpecificEmbedding(),
                numerical: new NumericalEmbedding()
            }
        };

        // Adaptive pruning configuration
        this.pruningConfig = {
            strategies: [
                'temporal_decay',
                'importance_score',
                'relationship_density'
            ],
            thresholds: {
                maxMemorySize: config.maxMemorySize || 10000,
                compressionTrigger: config.compressionTrigger || 0.8
            }
        };

        // Advanced indexing for efficient retrieval
        this.semanticIndex = new ApproximateNearestNeighborIndex();
    }

    async selectOptimalEmbeddingModel(content) {
        // Dynamically select most appropriate embedding model
        if (this.isMultilingualContent(content)) {
            return this.embeddingModels.multilingual;
        }
        if (this.isNumericalContent(content)) {
            return this.embeddingModels.specialized.numerical;
        }
        return this.embeddingModels.default;
    }

    async insert(content, options = {}) {
        const embeddingModel = await this.selectOptimalEmbeddingModel(content);
        const memoryItem = new MemoryItem(content, {
            ...options,
            embeddingModel
        });

        // Advanced indexing and relationship tracking
        this.semanticIndex.add(memoryItem);
        this.trackRelationships(memoryItem);

        return memoryItem;
    }

    async intelligentRetrieve(query, options = {}) {
        const {
            maxResults = 10,
            similarityThreshold = 0.7,
            includeRelated = true
        } = options;

        // Semantic and relationship-aware retrieval
        const semanticResults = this.semanticIndex.search(query, {
            maxResults,
            threshold: similarityThreshold
        });

        if (includeRelated) {
            return this.expandWithRelatedMemories(semanticResults);
        }

        return semanticResults;
    }

    async performMemoryCompression() {
        const compressionCandidates = this.identifyCompressionCandidates();
        const compressedMemories = compressionCandidates.map(this.compressMemory);
        
        return {
            originalCount: compressionCandidates.length,
            compressedCount: compressedMemories.length,
            compressionRatio: compressedMemories.length / compressionCandidates.length
        };
    }
}
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 AdvancedMemoryManager {
    constructor(config = {}) {
        // Configurable embedding models
        this.embeddingModels = {
            default: new SemanticEmbedding(),
            multilingual: new MultilingualEmbedding(),
            specialized: {
                text: new TextSpecificEmbedding(),
                numerical: new NumericalEmbedding()
            }
        };

        // Adaptive pruning configuration
        this.pruningConfig = {
            strategies: [
                'temporal_decay',
                'importance_score',
                'relationship_density'
            ],
            thresholds: {
                maxMemorySize: config.maxMemorySize || 10000,
                compressionTrigger: config.compressionTrigger || 0.8
            }
        };

        // Advanced indexing for efficient retrieval
        this.semanticIndex = new ApproximateNearestNeighborIndex();
    }

    async selectOptimalEmbeddingModel(content) {
        // Dynamically select most appropriate embedding model
        if (this.isMultilingualContent(content)) {
            return this.embeddingModels.multilingual;
        }
        if (this.isNumericalContent(content)) {
            return this.embeddingModels.specialized.numerical;
        }
        return this.embeddingModels.default;
    }

    async insert(content, options = {}) {
        const embeddingModel = await this.selectOptimalEmbeddingModel(content);
        const memoryItem = new MemoryItem(content, {
            ...options,
            embeddingModel
        });

        // Advanced indexing and relationship tracking
        this.semanticIndex.add(memoryItem);
        this.trackRelationships(memoryItem);

        return memoryItem;
    }

    async intelligentRetrieve(query, options = {}) {
        const {
            maxResults = 10,
            similarityThreshold = 0.7,
            includeRelated = true
        } = options;

        // Semantic and relationship-aware retrieval
        const semanticResults = this.semanticIndex.search(query, {
            maxResults,
            threshold: similarityThreshold
        });

        if (includeRelated) {
            return this.expandWithRelatedMemories(semanticResults);
        }

        return semanticResults;
    }

    async performMemoryCompression() {
        const compressionCandidates = this.identifyCompressionCandidates();
        const compressedMemories = compressionCandidates.map(this.compressMemory);
        
        return {
            originalCount: compressionCandidates.length,
            compressedCount: compressedMemories.length,
            compressionRatio: compressedMemories.length / compressionCandidates.length
        };
    }
}
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);
    }
}
const crypto = require('crypto');

class MemoryItem {
    constructor(content, options = {}) {
        this.id = crypto.randomUUID();
        this.content = content;
        this.type = options.type || 'text';
        this.isFactual = options.isFactual || 0.5;
        this.confidence = options.confidence || 0.5;
        
        this.timestamp = Date.now();
        this.accessCount = 0;
        this.importance = 5;
        
        this.embedding = null;
        this.related = new Map();
        this.tags = new Set();
    }

    addRelationship(memoryItem, weight = 1.0) {
        this.related.set(memoryItem.id, {
            memory: memoryItem,
            weight: weight,
            type: this.determineRelationshipType(memoryItem)
        });
    }

    determineRelationshipType(memoryItem) {
        // Basic relationship type inference
        const content1 = this.content.toLowerCase();
        const content2 = memoryItem.content.toLowerCase();
        
        const sharedWords = content1.split(' ')
            .filter(word => content2.includes(word));
        
        const similarityRatio = sharedWords.length / 
            Math.max(content1.split(' ').length, content2.split(' ').length);
        
        if (similarityRatio > 0.5) return 'VERY_CLOSE';
        if (similarityRatio > 0.2) return 'RELATED';
        return 'DISTANT';
    }

    incrementAccess() {
        this.accessCount++;
        this.updateImportance();
    }

    updateImportance() {
        // Dynamic importance calculation
        this.importance = Math.min(
            10, 
            5 + Math.log(this.accessCount + 1)
        );
    }
}

module.exports = MemoryItem;
class MemoryTier {
    constructor(name, options = {}) {
        this.name = name;
        this.items = new Map();
        this.maxCapacity = options.maxCapacity || Infinity;
        this.pruneStrategy = options.pruneStrategy || 'LRU';
    }

    insert(memoryItem) {
        if (this.items.size >= this.maxCapacity) {
            this.prune();
        }
        this.items.set(memoryItem.id, memoryItem);
    }

    prune() {
        switch(this.pruneStrategy) {
            case 'LRU':
                const oldestItem = Array.from(this.items.values())
                    .sort((a, b) => a.timestamp - b.timestamp)[0];
                this.items.delete(oldestItem.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;
        }
    }

    retrieve(query) {
        return Array.from(this.items.values())
            .filter(item => item.content.includes(query));
    }
}

module.exports = MemoryTier;
const MemoryItem = require('./memory-item');
const MemoryTier = require('./memory-tier');
const SemanticEmbedding = require('./semantic-embedding');

class MemoryManager {
    constructor(config = {}) {
        this.embeddingService = new SemanticEmbedding();
        
        this.tiers = {
            volatileShortTerm: new MemoryTier('Volatile Short-Term', { 
                maxCapacity: config.shortTermCapacity || 10 
            }),
            persistentLongTerm: new MemoryTier('Persistent Long-Term'),
            contextWorkingMemory: new MemoryTier('Context/Working Memory', { 
                maxCapacity: config.workingMemoryCapacity || 5 
            })
        };

        this.allMemories = new Map();
    }

    async insert(content, options = {}) {
        const memoryItem = new MemoryItem(content, options);
        
        // Insert into all tiers
        Object.values(this.tiers).forEach(tier => {
            tier.insert(memoryItem);
        });

        this.allMemories.set(memoryItem.id, memoryItem);
        return memoryItem;
    }

    async retrieve(query) {
        // Aggregate results from all tiers
        const results = Object.values(this.tiers)
            .flatMap(tier => tier.retrieve(query));
        
        // Deduplicate and sort by importance
        return [...new Set(results)]
            .sort((a, b) => b.importance - a.importance);
    }

    async findSemanticallySimilar(memoryItem, threshold = 0.7) {
        const similar = [];
        
        for (let [, memory] of this.allMemories) {
            if (memory.id !== memoryItem.id) {
                const similarity = this.calculateSemanticSimilarity(
                    memory.content, 
                    memoryItem.content
                );
                
                if (similarity >= threshold) {
                    similar.push({ memory, similarity });
                }
            }
        }
        
        return similar.sort((a, b) => b.similarity - a.similarity);
    }

    calculateSemanticSimilarity(content1, content2) {
        // Simple similarity calculation
        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);
    }
}

module.exports = MemoryManager;
class SemanticEmbedding {
    constructor() {
        this.embeddingCache = new Map();
    }

    async generateEmbedding(text) {
        // Check cache first
        if (this.embeddingCache.has(text)) {
            return this.embeddingCache.get(text);
        }

        // Simple embedding generation
        const tokens = text.toLowerCase().split(/\s+/);
        const embedding = tokens.map(token => this.simpleTokenEmbedding(token));
        
        // Cache the embedding
        this.embeddingCache.set(text, embedding);
        
        return embedding;
    }

    simpleTokenEmbedding(token) {
        // Very basic embedding - just a numerical representation
        return token.split('').map(char => char.charCodeAt(0));
    }

    calculateSemanticSimilarity(embedding1, embedding2) {
        // Cosine similarity approximation
        const dotProduct = embedding1.reduce(
            (sum, val, i) => sum + val * (embedding2[i] || 0), 
            0
        );
        
        const magnitude1 = Math.sqrt(
            embedding1.reduce((sum, val) => sum + val * val, 0)
        );
        
        const magnitude2 = Math.sqrt(
            embedding2.reduce((sum, val) => sum + val * val, 0)
        );

        return dotProduct / (magnitude1 * magnitude2);
    }
}