🧩 Memory Vector Store

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A lightweight memory-based vector store with persistent storage support for both Node.js and browser environments. Efficiently store, retrieve, and search vector embeddings with minimal dependencies.

Features

• 🪶 Lightweight: Minimal dependencies and small footprint
• 🔄 Cross-platform: Works in both Node.js and browser environments
• 💾 Persistent storage: Automatic saving to localStorage (browser) or file system (Node.js)
• 🔍 Similarity search: Built-in cosine similarity for vector searching
• 🧩 Framework agnostic: Works with any embedding model or framework
• ⚡ Performance optimized: Caching and debounced saving for better performance

Installation

npm install memory-vector-store
# or
yarn add memory-vector-store
# or
pnpm add memory-vector-store

Basic Usage

import { memoryVectorStore } from 'memory-vector-store';

// Define your vector parser function
const vectorParser = (text) => {
  // Example: convert text to vector
  return [1, 2, 3]; // return a vector representation
};

// Create a vector store
const store = memoryVectorStore(vectorParser);

Using with OpenAI

import OpenAI from 'openai';
// For Node.js
import { memoryVectorStore } from 'memory-vector-store';
// For Browser: import { browserMemoryVectorStore } from 'memory-vector-store/browser';

const openai = new OpenAI({
  apiKey: 'YOUR_OPENAI_API_KEY',
});

const vectorParser = async (data) => {
  const response = await openai.embeddings.create({
    model: 'text-embedding-ada-002',
    input: data,
  });

  return response.data[0].embedding;
};

const vectorStore = memoryVectorStore(vectorParser, { autoSave: false });

const dataList = [
  'Adidas Soccer Cleats',
  'Nike Sports Jacket',
  'Adidas Training Shorts',
  'Nike Basketball Sneakers',
  'Adidas Running Shoes',
  'Nike Casual T-Shirt',
  'Adidas Casual Hoodie',
  'Nike Sports Bag',
  'Adidas Leggings',
];

for (const data of dataList) {
  await vectorStore.add(data);
}

const result = await vectorStore.similaritySearch('foot', 2);

console.log(result.map((v) => v.data));
// [ 'Adidas Running Shoes', 'Nike Basketball Sneakers' ]

Using with Vercel AI SDK and Ollama

import { embed } from 'ai';
import { ollama } from 'ollama-ai-provider';
// For Node.js
import { memoryVectorStore } from 'memory-vector-store';
// For Browser: import { memoryVectorStore } from 'memory-vector-store/browser';

const vectorParser = async (data) => {
  const result = await embed({
    model: ollama.embedding('nomic-embed-text'),
    value: data,
  });
  return result.embedding;
};

const vectorStore = memoryVectorStore(vectorParser, { autoSave: false });

const dataList = [
  'Adidas Soccer Cleats',
  'Nike Sports Jacket',
  'Adidas Training Shorts',
  'Nike Basketball Sneakers',
  'Adidas Running Shoes',
  'Nike Casual T-Shirt',
  'Adidas Casual Hoodie',
  'Nike Sports Bag',
  'Adidas Leggings',
];

for (const data of dataList) {
  await vectorStore.add(data);
}

const result = await vectorStore.similaritySearch('foot', 2);

console.log(result.map((v) => v.data));
// [ 'Adidas Running Shoes', 'Nike Basketball Sneakers' ]

API

memoryVectorStore(vectorParser, options?)

Creates a new vector store instance for Node.js environment.

browserMemoryVectorStore(vectorParser, options?) (from 'memory-vector-store/browser')

Creates a new vector store instance for browser environment.

Parameters:

• vectorParser: Function that converts text to a vector representation
• options: (Optional) Configuration options

Options:

• autoSave: (Default: true) Automatically save changes to storage
• debug: (Default: false) Enable debug logging
• maxFileSizeMB: Maximum storage size in MB (Browser: 0.1-3MB, Node: 1-1000MB)
• storagePath: Storage path/key (Default browser: 'memory-vector-store', Node: '{cwd}/node_modules/mvsl/data.json')

Store Methods

• add(data: string): Add data to the vector store
• similaritySearch(query: string, k?: number, filter?: Function): Search for similar items
• remove(data: string): Remove a specific item
• clear(): Remove all items
• getAll(): Get all stored items
• count(): Get the number of stored items
• save(): Manually save the store

Limitations and Recommendations

• Size Constraints:
  • Browser version is limited to 3MB due to localStorage constraints
  • Node.js version defaults to 500MB maximum, which may not be suitable for very large datasets
• No Distributed Support: Does not support distributed or multi-user scenarios
• Basic Vector Search: Uses simple cosine similarity - may not offer specialized optimizations of dedicated vector databases

Storage

The library automatically uses the appropriate storage mechanism:

• Browser: Uses localStorage with a default limit of 3MB
• Node.js: Uses file system storage with a default limit of 500MB