Model Context Protocol: Biomimetic Memory Systems for AI

Executive Summary

The Model Context Protocol (MCP) is a novel approach to managing and utilizing contextual information within artificial intelligence models. Inspired by the hierarchical and associative nature of human memory, MCP provides a structured framework for representing, storing, and retrieving relevant context, enabling AI systems to exhibit more nuanced and adaptive behavior. This protocol addresses the limitations of traditional context management techniques by offering a scalable and efficient mechanism for handling complex and dynamic contextual landscapes. MCP leverages biomimetic principles to mimic the organization and retrieval mechanisms of biological memory systems, resulting in improved performance and contextual awareness.

Technical Architecture

The technical architecture of MCP is built upon several core components, each designed to emulate aspects of human memory. These components work in concert to provide a robust and flexible context management system.

Core Components

Context Encoder: This component is responsible for transforming raw input data into a structured contextual representation. It leverages techniques like embedding models and feature extraction to capture relevant semantic information.
Hierarchical Memory: This is the central storage component, organized as a hierarchical structure. At the lowest level are episodic memories, representing specific events or observations. These are aggregated into semantic memories at higher levels, capturing general knowledge and relationships.
Association Engine: This component facilitates the retrieval of relevant context based on the current input and the state of the hierarchical memory. It uses associative retrieval mechanisms to identify and prioritize the most pertinent information.
Context Decoder: This component transforms the retrieved context back into a usable format for the AI model. It may involve techniques like attention mechanisms or context injection to integrate the retrieved information into the model's processing pipeline.
Context Updater: After the model processes the information, the context updater determines what new information should be stored in the memory. It updates the hierarchical memory by adding new episodic memories, adjusting the strengths of existing semantic memories, and creating new associations between memories.

Data Structures

MCP utilizes several key data structures to represent and manage contextual information.

Episodic Memory: Represents a specific event or observation. It typically includes:
- Timestamp: The time at which the event occurred.
- Content: The raw data associated with the event.
- Metadata: Additional information about the event, such as source, location, or associated tags.
- Embedding: A vector representation of the event's content, used for associative retrieval.
Semantic Memory: Represents general knowledge or relationships. It typically includes:
- Concept: The abstract concept represented by the memory.
- Attributes: Properties or characteristics associated with the concept.
- Relationships: Connections to other semantic memories, representing relationships between concepts.
- Weight: A measure of the memory's importance or relevance.
- Embedding: A vector representation of the concept, used for associative retrieval.
Association Matrix: Represents the relationships between memories. It is a matrix where each element represents the strength of the association between two memories. This matrix is used to efficiently retrieve related memories during context retrieval.

Implementation Specifications

The implementation of MCP involves several key technical considerations.

Data Serialization: Efficiently serializing and deserializing the data structures is crucial for performance. Protocols like Protocol Buffers or Apache Thrift can be used for this purpose.
Memory Allocation: Managing memory allocation is important to prevent memory leaks and ensure efficient resource utilization. Techniques like memory pooling or garbage collection can be used.
Concurrency: MCP must be designed to handle concurrent requests from multiple AI models. This requires careful synchronization and locking mechanisms to prevent data corruption.
Scalability: The architecture must be scalable to handle large amounts of contextual data. This may involve techniques like distributed memory storage or sharding.

Implementation Details

This section provides detailed code examples in Python and TypeScript to illustrate the implementation of MCP.

Python Implementation

import numpy as np
from typing import Dict, Any, List

class EpisodicMemory:
    def __init__(self, timestamp: float, content: str, metadata: Dict[str, Any], embedding: np.ndarray):
        self.timestamp = timestamp
        self.content = content
        self.metadata = metadata
        self.embedding = embedding

class SemanticMemory:
    def __init__(self, concept: str, attributes: Dict[str, Any], relationships: List[str], weight: float, embedding: np.ndarray):
        self.concept = concept
        self.attributes = attributes
        self.relationships = relationships
        self.weight = weight
        self.embedding = embedding

class HierarchicalMemory:
    def __init__(self):
        self.episodic_memories: List[EpisodicMemory] = []
        self.semantic_memories: List[SemanticMemory] = []
        self.association_matrix: np.ndarray = np.zeros((0, 0))

    def add_episodic_memory(self, memory: EpisodicMemory):
        self.episodic_memories.append(memory)
        self._update_association_matrix()

    def add_semantic_memory(self, memory: SemanticMemory):
        self.semantic_memories.append(memory)
        self._update_association_matrix()

    def _update_association_matrix(self):
        num_memories = len(self.episodic_memories) + len(self.semantic_memories)
        self.association_matrix = np.zeros((num_memories, num_memories))
        # TODO: Implement association logic based on memory content and relationships

    def retrieve_context(self, query_embedding: np.ndarray, top_k: int = 5) -> List[Any]:
        """Retrieves the top_k most relevant memories based on the query embedding."""
        all_memories = self.episodic_memories + self.semantic_memories
        if not all_memories:
            return []

        embeddings = np.array([memory.embedding for memory in all_memories])
        similarities = np.dot(embeddings, query_embedding)
        indices = np.argsort(similarities)[::-1][:top_k]  # Get top_k indices

        return [all_memories[i] for i in indices]

# Example Usage
if __name__ == '__main__':
    # Create episodic memory
    episodic_memory = EpisodicMemory(
        timestamp=1678886400.0,
        content="The cat sat on the mat.",
        metadata={"location": "living room"},
        embedding=np.array([0.1, 0.2, 0.3])
    )

    # Create semantic memory
    semantic_memory = SemanticMemory(
        concept="Cat",
        attributes={"type": "mammal", "color": "various"},
        relationships=["animal", "pet"],
        weight=0.8,
        embedding=np.array([0.4, 0.5, 0.6])
    )

    # Create hierarchical memory
    hierarchical_memory = HierarchicalMemory()
    hierarchical_memory.add_episodic_memory(episodic_memory)
    hierarchical_memory.add_semantic_memory(semantic_memory)

    # Retrieve context
    query_embedding = np.array([0.3, 0.4, 0.5])
    context = hierarchical_memory.retrieve_context(query_embedding)

    print(f"Retrieved context: {context}")

TypeScript Implementation

interface EpisodicMemory {
    timestamp: number;
    content: string;
    metadata: { [key: string]: any };
    embedding: number[];
}

interface SemanticMemory {
    concept: string;
    attributes: { [key: string]: any };
    relationships: string[];
    weight: number;
    embedding: number[];
}

class HierarchicalMemory {
    episodicMemories: EpisodicMemory[] = [];
    semanticMemories: SemanticMemory[] = [];
    associationMatrix: number[][] = [];

    addEpisodicMemory(memory: EpisodicMemory) {
        this.episodicMemories.push(memory);
        this.updateAssociationMatrix();
    }

    addSemanticMemory(memory: SemanticMemory) {
        this.semanticMemories.push(memory);
        this.updateAssociationMatrix();
    }

    private updateAssociationMatrix() {
        const numMemories = this.episodicMemories.length + this.semanticMemories.length;
        this.associationMatrix = Array(numMemories).fill(null).map(() => Array(numMemories).fill(0));
        // TODO: Implement association logic based on memory content and relationships
    }

    retrieveContext(queryEmbedding: number[], topK: number = 5): (EpisodicMemory | SemanticMemory)[] {
        const allMemories = [...this.episodicMemories, ...this.semanticMemories];
        if (allMemories.length === 0) {
            return [];
        }

        const similarities = allMemories.map(memory => this.calculateSimilarity(memory.embedding, queryEmbedding));
        const indices = similarities.map((similarity, index) => ({ similarity, index }))
            .sort((a, b) => b.similarity - a.similarity)
            .slice(0, topK)
            .map(item => item.index);

        return indices.map(i => allMemories[i]);
    }

    private calculateSimilarity(embedding1: number[], embedding2: number[]): number {
        let dotProduct = 0;
        let magnitude1 = 0;
        let magnitude2 = 0;

        for (let i = 0; i < embedding1.length; i++) {
            dotProduct += embedding1[i] * embedding2[i];
            magnitude1 += embedding1[i] * embedding1[i];
            magnitude2 += embedding2[i] * embedding2[i];
        }

        magnitude1 = Math.sqrt(magnitude1);
        magnitude2 = Math.sqrt(magnitude2);

        if (magnitude1 === 0 || magnitude2 === 0) {
            return 0;
        }

        return dotProduct / (magnitude1 * magnitude2);
    }
}

// Example Usage
const episodicMemory: EpisodicMemory = {
    timesta...