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Model Context Protocol: Enhancing AI with Dynamic Memory Management

April 14, 2025
6 min read

Model Context Protocol: Enhancing AI with Dynamic Memory Management

Executive Summary

The Model Context Protocol (MCP) provides a structured methodology for AI models to access and utilize relevant contextual information. It aims to overcome the limitations of fixed-context windows in large language models (LLMs) and other AI systems by enabling dynamic adjustment of the context based on the current task and available resources. This involves creating efficient data structures for context storage, defining standardized interfaces for context retrieval, and implementing adaptive algorithms for managing the context window size. MCP facilitates more informed decision-making, improved performance, and greater flexibility in diverse application domains.

Technical Architecture

The architecture of MCP comprises several key components that work together to provide dynamic context management. These components include the Context Store, Context Retriever, Context Manager, and the Model Interface.

Core Components

  • Context Store: This component is responsible for storing and organizing contextual information. It can be implemented using various data structures, such as key-value stores, graph databases, or vector databases, depending on the specific requirements of the application.
  • Context Retriever: The Context Retriever provides an interface for querying the Context Store and retrieving relevant contextual information based on a given query. It typically employs techniques such as semantic search, keyword matching, or graph traversal to identify the most relevant context.
  • Context Manager: This component manages the overall context window, including dynamically adjusting its size and prioritizing which information to include based on factors such as relevance, recency, and resource constraints. It implements algorithms for adaptive context window sizing and context pruning.
  • Model Interface: The Model Interface provides a standardized way for AI models to access and utilize the contextual information provided by the MCP. It defines the format of the context and the methods for passing it to the model.

Data Structures

The choice of data structure for the Context Store depends on the nature of the contextual information and the performance requirements of the application. Some common data structures include:

  • Key-Value Stores: Suitable for storing simple contextual information, where each piece of context is associated with a unique key. Examples include Redis and Memcached.
  • Graph Databases: Ideal for representing complex relationships between different pieces of contextual information. Examples include Neo4j and JanusGraph.
  • Vector Databases: Designed for storing and querying high-dimensional vector embeddings, which can be used to represent the semantic meaning of contextual information. Examples include Pinecone and Weaviate.
  • Relational Databases: Can be used if structured data with complex relationships is necessary.

Implementation Specifications

The implementation of MCP involves defining clear interfaces and protocols for communication between the different components. This includes:

  • Context Store API: Defines the methods for storing, retrieving, and updating contextual information.
  • Context Retriever API: Defines the methods for querying the Context Store and retrieving relevant context.
  • Context Manager API: Defines the methods for managing the context window, including setting the window size and prioritizing context.
  • Model Interface API: Defines the format of the context and the methods for passing it to the model.

Implementation Details

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

TypeScript Implementation

// Context Store Interface
interface ContextStore {
  store(key: string, value: any): Promise<void>;
  retrieve(key: string): Promise<any>;
  delete(key: string): Promise<void>;
}

// In-memory Context Store Implementation
class InMemoryContextStore implements ContextStore {
  private store: { [key: string]: any } = {};

  async store(key: string, value: any): Promise<void> {
    this.store[key] = value;
  }

  async retrieve(key: string): Promise<any> {
    return this.store[key];
  }

  async delete(key: string): Promise<void> {
    delete this.store[key];
  }
}

// Context Retriever Interface
interface ContextRetriever {
  retrieveContext(query: string): Promise<any>;
}

// Simple Keyword-based Context Retriever
class KeywordContextRetriever implements ContextRetriever {
  private contextStore: ContextStore;

  constructor(contextStore: ContextStore) {
    this.contextStore = contextStore;
  }

  async retrieveContext(query: string): Promise<any> {
    const keywords = query.split(" "); // Simple keyword extraction
    let relevantContext: any[] = [];

    for (const key in (this.contextStore as InMemoryContextStore).store) {
      if (keywords.some(keyword => key.includes(keyword))) {
        relevantContext.push((this.contextStore as InMemoryContextStore).store[key]);
      }
    }

    return relevantContext;
  }
}

// Context Manager Interface
interface ContextManager {
  setContextWindowSize(size: number): void;
  pruneContext(): void;
}

// Simple Context Manager with fixed window size
class SimpleContextManager implements ContextManager {
  private contextWindowSize: number;

  constructor(initialSize: number) {
    this.contextWindowSize = initialSize;
  }

  setContextWindowSize(size: number): void {
    this.contextWindowSize = size;
  }

  pruneContext(): void {
    // In a more complex implementation, this would remove less relevant context
    console.log("Context pruning not implemented in this simple example.");
  }
}

// Example Usage
async function main() {
  const contextStore = new InMemoryContextStore();
  await contextStore.store("user_profile", { name: "Alice", age: 30 });
  await contextStore.store("product_catalog", { products: ["Laptop", "Mouse", "Keyboard"] });

  const contextRetriever = new KeywordContextRetriever(contextStore);
  const relevantContext = await contextRetriever.retrieveContext("user Alice product");

  console.log("Relevant Context:", relevantContext);

  const contextManager = new SimpleContextManager(10);
  contextManager.setContextWindowSize(20);
  contextManager.pruneContext();
}

main();

Python Implementation

from abc import ABC, abstractmethod

# Context Store Interface
class ContextStore(ABC):
    @abstractmethod
    async def store(self, key: str, value: any):
        pass

    @abstractmethod
    async def retrieve(self, key: str):
        pass

    @abstractmethod
    async def delete(self, key: str):
        pass

# In-memory Context Store Implementation
class InMemoryContextStore(ContextStore):
    def __init__(self):
        self.store = {}

    async def store(self, key: str, value: any):
        self.store[key] = value

    async def retrieve(self, key: str):
        return self.store.get(key)

    async def delete(self, key: str):
        if key in self.store:
            del self.store[key]

# Context Retriever Interface
class ContextRetriever(ABC):
    @abstractmethod
    async def retrieve_context(self, query: str):
        pass

# Simple Keyword-based Context Retriever
class KeywordContextRetriever(ContextRetriever):
    def __init__(self, context_store: ContextStore):
        self.context_store = context_store

    async def retrieve_context(self, query: str):
        keywords = query.split()  # Simple keyword extraction
        relevant_context = []

        for key, value in self.context_store.store.items():
            if any(keyword in key for keyword in keywords):
                relevant_context.append(value)

        return relevant_context

# Context Manager Interface
class ContextManager(ABC):
    @abstractmethod
    def set_context_window_size(self, size: int):
        pass

    @abstractmethod
    def prune_context(self):
        pass

# Simple Context Manager with fixed window size
class SimpleContextManager(ContextManager):
    def __init__(self, initial_size: int):
        self.context_window_size = initial_size

    def set_context_window_size(self, size: int):
        self.context_window_size = size

    def prune_context(self):
        # In a more complex implementation, this would remove less relevant context
        print("Context pruning not implemented in this simple example.")

# Example Usage
async def main():
    context_store = InMemoryContextStore()
    await context_store.store("user_profile", {"name": "Alice", "age": 30})
    await context_store.store("product_catalog", {"products": ["Laptop", "Mouse", "Keyboard"]})

    context_retriever = KeywordContextRetriever(context_store)
    relevant_context = await context_retriever.retrieve_context("user Alice product")

    print("Relevant Context:", relevant_context)

    context_manager = SimpleContextManager(10)
    context_manager.set_context_window_size(20)
    context_manager.prune_context()

import asyncio
asyncio.run(main())

Data Structures and Algorithms

  • Context Scoring: A crucial algorithm is context scoring, which assigns a relevance score to each piece of contextual information. This score can be based on factors such as keyword frequency, semantic similarity, and recency.
    def score_context(query: str, context: str) -> float:
    """
    Calculates a relevance score between a query and a context string.
    Uses a simple keyword frequency approach.
    """
    query_keywords = query.split()
    context_words = context.split()
    common_keywords = set(query_keywords) & set(context_words)
    return len(common_keywords) / len(context_words) if len(context_words) > 0 else 0.0
  • Adaptive Context Window Sizing: This algorithm dynamically adjusts the size of the context window based on the current task and available resources. One approach is to use a feedback loop that monitors the model's performance and adjusts the window size accordingly.
    class AdaptiveC...
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