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vector-embedb
vector-embedb/src/index.js

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Readme

EmbeDB

Vector based image and text database

How it works

EmbeDB uses a vector based approach to store data. This means that the data is stored as a vector of numbers, called an embedding. This allows for fast retrieval of similar data.

What can you do with it?

Similar image search
Long term memory
Web searching
Much more!

Installation

EmbeDB requires Node.js and Python 3 to be installed.

Install the package using npm:

npm install embedb

Then, create a .env file in the root directory of your project and add the API keys for the models you want to use.

HUGGINGFACE_API_KEY=<your api key>
OPENAI_API_KEY=<your api key>

Usage

First, require the module and create a new instance of the database.

Memory(model<string, default='huggingface'>)

const Memory = require('embedb');

const memory = new Memory();

Inserting data

To memorize text, use the memorize method.

async Memory.memorize({
    key<string>,
    value<string>,
    model<string, default='huggingface'>
})

await memory.memorize({
    key: 'What is my name?',
    value: 'EmbeDB',
});

To memorize an image, you must pass in the image path and use an image model such as resnet50.

await memory.memorize({
    key: 'Matrix meme',
    value: './matrixMeme.png',
    model: 'resnet50',
});

To memorize multiple items, use the memorizeAll method.

async Memory.memorizeAll([
    {
        key<string>,
        value<string>,
    },
    {
        key<string>,
        value<string>,
    },
], model<string, default='huggingface'>)

await memory.memorizeAll([
  {
    key: "What is my name?",
    value: "EmbeDB",
  },
  {
    key: "Who is the president of the United States in 2023?"
    value: "Joe Biden",
  },
]);

Retrieving data

To retrieve the first most similar memory item, use the recall method.

async Memory.recall(key<string>, n<number> model<string, default='huggingface'>) -> MemoryItem{
    key<string>,
    value<string>,
    similarity<number>,
    prune<function>
}

const data = await memory.recall("What's my name?");

To retrieve the first n most similar memory items, use the recall method with the second parameter as n

const name = await memory.recall("What's my name?", 2);
/*
{
    key: "What is my name?",
    value: "EmbeDB",
    similarity: 0.9999999999999999,
    prune: [Function: prune]
},
{
    key: "What is your name?",
    value: "User",
    similarity: 0.3664122137402344,
    prune: [Function: prune]
}
*/

Deleting data

To delete a memory item, use the prune method on a returned memory item from recall.

MemoryItem.prune()

const name = await memory.recall("What's my name?");

await name.prune();

Loading saved data

Memory.load(memoryData<object>)

To load saved data, use the load method.

const fs = require('fs');
await memory.load(JSON.parse(await fs.promises.readFile('./memory.json')));

JSPM

vector-embedb