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Model Context Protocol: Architecting Semantic Memory Integration

April 24, 2025
6 min read

Model Context Protocol: Architecting Semantic Memory Integration

Executive Summary

The Model Context Protocol (MCP) addresses the critical need for large language models (LLMs) to access and utilize external knowledge effectively. By providing a standardized interface for semantic memory integration, MCP enables LLMs to dynamically retrieve and inject relevant information, enhancing their reasoning, accuracy, and adaptability. This protocol encompasses data structures, communication protocols, and indexing strategies optimized for low-latency retrieval and scalable memory management. The goal is to decouple the LLM's core parameters from its knowledge base, allowing for continuous updates and expansion of knowledge without retraining the entire model.

Technical Architecture

MCP's architecture revolves around providing a clear separation between the LLM and its external knowledge store. This separation allows for independent scaling and maintenance of each component. The core components are the Context Manager, Semantic Memory Index, and Data Adapters.

Core Components

  • Context Manager: The central orchestrator of MCP. It receives requests from the LLM, determines the relevant context, queries the Semantic Memory Index, and formats the retrieved information for injection back into the LLM. The Context Manager is responsible for request routing, caching, and load balancing across multiple Semantic Memory Index instances.
  • Semantic Memory Index: This component houses the indexed representation of the external knowledge. It utilizes vector embeddings, graph databases, or a combination thereof to enable efficient similarity searches. It receives queries from the Context Manager and returns relevant knowledge snippets.
  • Data Adapters: These components are responsible for ingesting and transforming data from various sources into a standardized format suitable for the Semantic Memory Index. They handle data cleaning, entity recognition, relationship extraction, and embedding generation.

Data Structures

The key data structures used in MCP include:

  • Context Request: A standardized format for LLMs to request context. It includes the query text, desired context length, and optional filters.
    interface ContextRequest {
    query: string;
    contextLength: number;
    filters?: { [key: string]: any };
}
  • Context Response: The format for returning retrieved context to the LLM. It includes the relevant knowledge snippets and metadata.
    interface ContextResponse {
    context: string;
    metadata: { [key: string]: any }[];
}
  • Knowledge Snippet: Represents a single unit of knowledge stored in the Semantic Memory Index. It includes the text content, vector embedding, and metadata.
    class KnowledgeSnippet:
    def __init__(self, text: str, embedding: list[float], metadata: dict):
        self.text = text
        self.embedding = embedding
        self.metadata = metadata

Implementation Specifications

The communication between the LLM, Context Manager, and Semantic Memory Index is typically implemented using gRPC or REST APIs. The Semantic Memory Index can be implemented using various technologies, including:

  • Vector Databases: ChromaDB, Pinecone, Weaviate
  • Graph Databases: Neo4j, JanusGraph
  • Hybrid Approaches: Combining vector databases with graph databases for richer semantic understanding.

The choice of technology depends on the specific requirements of the application, such as the size of the knowledge base, the complexity of the relationships between entities, and the desired latency.

Implementation Details

Let's delve into the implementation details, showcasing code snippets in TypeScript and Python.

Context Manager Implementation (TypeScript)

import { ContextRequest, ContextResponse } from './data-structures';
import { SemanticMemoryIndex } from './semantic-memory-index';

class ContextManager {
    private memoryIndex: SemanticMemoryIndex;
    private cache: Map<string, ContextResponse>;

    constructor(memoryIndex: SemanticMemoryIndex) {
        this.memoryIndex = memoryIndex;
        this.cache = new Map();
    }

    async getContext(request: ContextRequest): Promise<ContextResponse> {
        const cacheKey = JSON.stringify(request);
        if (this.cache.has(cacheKey)) {
            return this.cache.get(cacheKey)!;
        }

        const relevantSnippets = await this.memoryIndex.query(request.query, request.contextLength, request.filters);
        const context = relevantSnippets.map(snippet => snippet.text).join('\n');
        const metadata = relevantSnippets.map(snippet => snippet.metadata);

        const response: ContextResponse = {
            context: context,
            metadata: metadata
        };

        this.cache.set(cacheKey, response);
        return response;
    }
}

This TypeScript code demonstrates a simple Context Manager implementation. It utilizes a SemanticMemoryIndex (which we'll define later) to retrieve relevant knowledge snippets based on the ContextRequest. It also includes a basic caching mechanism to improve performance for frequently requested contexts.

Semantic Memory Index Implementation (Python)

Here's a Python example utilizing ChromaDB as the vector database for the Semantic Memory Index:

import chromadb
from chromadb.utils import embedding_functions
from typing import List, Dict

class SemanticMemoryIndex:
    def __init__(self, collection_name: str, embedding_function = "default"):
        self.client = chromadb.PersistentClient(path="chroma_db") # Or use chromadb.Client() for in-memory
        if embedding_function == "default":
            self.embedding_function = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="all-MiniLM-L6-v2")
        else:
            self.embedding_function = embedding_function

        try:
            self.collection = self.client.get_collection(name=collection_name, embedding_function=self.embedding_function)
        except ValueError:
            self.collection = self.client.create_collection(name=collection_name, embedding_function=self.embedding_function)


    def add(self, documents: List[str], metadatas: List[Dict], ids: List[str]):
        self.collection.add(
            documents=documents,
            metadatas=metadatas,
            ids=ids
        )

    def query(self, query_text: str, n_results: int = 5, filters: Dict = None) -> List[Dict]:
        results = self.collection.query(
            query_texts=[query_text],
            n_results=n_results,
            where=filters
        )
        # Structure the output for MCP compatibility.  ChromaDB's output is a bit different.
        snippets = []
        for i in range(len(results['documents'][0])):
            snippets.append({
                'text': results['documents'][0][i],
                'metadata': results['metadatas'][0][i]
            })
        return snippets

This Python code utilizes ChromaDB to store and retrieve knowledge snippets. The add method adds new knowledge to the index, while the query method retrieves the most relevant snippets based on a query text and optional filters. The embedding_function parameter allows for customization of the embedding model used to generate vector representations of the knowledge snippets.

Data Adapter Implementation (Python)

This Python example demonstrates a simple Data Adapter for ingesting data from a text file:

import json
from typing import List, Dict

class TextFileDataAdapter:
    def __init__(self, file_path: str):
        self.file_path = file_path

    def load_data(self) -> List[Dict]:
        data = []
        with open(self.file_path, 'r') as f:
            for line in f:
                try:
                    record = json.loads(line.strip())
                    data.append(record)
                except json.JSONDecodeError:
                    print(f"Skipping invalid JSON line: {line.strip()}")
        return data

    def transform_data(self, data: List[Dict]) -> List[Dict]:
        # Example transformation: Extract text and metadata fields
        transformed_data = []
        for record in data:
            try:
                text = record['text']
                metadata = record.get('metadata', {}) # Use .get() to handle missing metadata
                transformed_data.append({
                    'text': text,
                    'metadata': metadata
                })
            except KeyError as e:
                print(f"Skipping record due to missing key: {e}")
        return transformed_data

    def ingest_data(self, memory_index):
        raw_data = self.load_data()
        transformed_data = self.transform_data(raw_data)
        documents = [item['text'] for item in transformed_data]
        metadatas = [item['metadata'] for item in transformed_data]
        ids = [str(i) for i in range(len(documents))] # Simple ID generation
        memory_index.add(documents, metadatas, ids)

# Example Usage:
# adapter = TextFileDataAdapter("knowledge_data.jsonl") #JSON Lines format
# memory_index = SemanticMemoryIndex("my_knowledge_collection")
# adapter.ingest_data(memory_index)

This adapter reads data from a JSON Lines file (knowledge_data.jsonl), extracts the text and metadata fields, and ingests them into the SemanticMemoryIndex. Error handling is included to gracefully handle invalid JSON or missing keys. The ingest_data function handles the actual loading, transforming, and adding of data to the memory_index.

Key Technical Decisions

  • Vector Database Choice: The choice of ChromaDB was driven by its ease of use, open-source nature, and suitability for prototyping. For production environments, other vector databases like Pinecone or Weaviate might be more appropriate due to their scalability and performance characteristics.
  • Embedding Model: The all-MiniLM-L6-v2 model was chosen for its balance of accuracy and speed. Other embedding models, such as OpenAI's embeddings ...
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