Introduction | 简体中文 ｜はじめに

About The

ts-fsrs is a versatile package based on TypeScript that supports ES modules, CommonJS, and UMD. It implements the Free Spaced Repetition Scheduler (FSRS) algorithm, enabling developers to integrate FSRS into their flashcard applications to enhance the user learning experience.

The workflow for TS-FSRS can be referenced from the following resources:

• google driver: ts-fsrs-workflow.drawio (You may provide commentary)
• github: ts-fsrs-workflow.drawio

Usage

The [email protected] package requires Node.js version 16.0.0 or higher. Starting with [email protected], the minimum required Node.js version is 18.0.0. From version 3.5.6 onwards, ts-fsrs supports CommonJS, ESM, and UMD module systems.

npm install ts-fsrs
yarn install ts-fsrs
pnpm install ts-fsrs
bun install ts-fsrs

Example

import {createEmptyCard, formatDate, fsrs, generatorParameters, Rating, Grades} from 'ts-fsrs';

const params = generatorParameters({ enable_fuzz: true, enable_short_term: false });
const f = fsrs(params);
const card = createEmptyCard(new Date('2022-2-1 10:00:00'));// createEmptyCard();
const now = new Date('2022-2-2 10:00:00');// new Date();
const scheduling_cards = f.repeat(card, now);

// console.log(scheduling_cards);
for (const item of scheduling_cards) {
    // grades = [Rating.Again, Rating.Hard, Rating.Good, Rating.Easy]
    const grade = item.log.rating
    const { log, card } = item;
    console.group(`${Rating[grade]}`);
    console.table({
        [`card_${Rating[grade]}`]: {
            ...card,
            due: formatDate(card.due),
            last_review: formatDate(card.last_review as Date),
        },
    });
    console.table({
        [`log_${Rating[grade]}`]: {
            ...log,
            review: formatDate(log.review),
        },
    });
    console.groupEnd();
    console.log('----------------------------------------------------------------');
}

More refer:

• Docs - Github Pages
• Example.html - Github Pages
• Browser (ts-fsrs package using CDN)
• ts-fsrs-demo - Next.js+Prisma
• spaced - Next.js+Drizzle+tRPC

Basic Use

1. Initialization:

To begin, create an empty card instance and set the current date(default: current time from system):

import { Card, createEmptyCard } from "ts-fsrs";
let card: Card = createEmptyCard();
// createEmptyCard(new Date('2022-2-1 10:00:00'));
// createEmptyCard(new Date(Date.UTC(2023, 9, 18, 14, 32, 3, 370)));
// createEmptyCard(new Date('2023-09-18T14:32:03.370Z'));

2. Parameter Configuration:

The library allows for customization of SRS parameters. Use generatorParameters to produce the final set of parameters for the SRS algorithm. Here's an example setting a maximum interval:

import { Card, createEmptyCard, generatorParameters, FSRSParameters } from "ts-fsrs";
let card: Card = createEmptyCard();
const params: FSRSParameters = generatorParameters({ maximum_interval: 1000 });

3. Scheduling with FSRS:

The core functionality lies in the fsrs function. When invoked, it returns a collection of cards scheduled based on different potential user ratings:

import {
  Card,
  createEmptyCard,
  generatorParameters,
  FSRSParameters,
  FSRS,
  RecordLog,
} from "ts-fsrs";

let card: Card = createEmptyCard();
const f: FSRS = new FSRS(); // or const f: FSRS = fsrs(params);
let scheduling_cards: RecordLog = f.repeat(card, new Date());
// if you want to specify the grade, you can use the following code: (ts-fsrs >=4.0.0)
// let scheduling_card: RecordLog = f.next(card, new Date(), Rating.Good);

4. Retrieving Scheduled Cards:

Once you have the scheduling_cards object, you can retrieve cards based on user ratings. For instance, to access the card scheduled for a 'Good' rating:

const good: RecordLogItem = scheduling_cards[Rating.Good];
const newCard: Card = good.card;

Get the new state of card for each rating:

scheduling_cards[Rating.Again].card
scheduling_cards[Rating.Again].log

scheduling_cards[Rating.Hard].card
scheduling_cards[Rating.Hard].log

scheduling_cards[Rating.Good].card
scheduling_cards[Rating.Good].log

scheduling_cards[Rating.Easy].card
scheduling_cards[Rating.Easy].log

5. Understanding Card Attributes:

Each Card object consists of various attributes that determine its status, scheduling, and other metrics:

type Card = {
  due: Date;           // Date when the card is next due for review
  stability: number;   // A measure of how well the information is retained
  difficulty: number;  // Reflects the inherent difficulty of the card content
  elapsed_days: number; // Days since the card was last reviewed
  scheduled_days: number; // The interval at which the card is next scheduled
  reps: number;          // Total number of times the card has been reviewed
  lapses: number;        // Times the card was forgotten or remembered incorrectly
  state: State;          // The current state of the card (New, Learning, Review, Relearning)
  last_review?: Date;    // The most recent review date, if applicable
};

6. Understanding Log Attributes:

Each ReviewLog object contains various attributes that determine the review record information associated with the card, used for analysis, undoing the review, and optimization (WIP).

type ReviewLog = {
    rating: Rating; // Rating of the review (Again, Hard, Good, Easy)
    state: State; // State of the review (New, Learning, Review, Relearning)
    due: Date;  // Date of the last scheduling
    stability: number; // Stability of the card before the review
    difficulty: number; // Difficulty of the card before the review
    elapsed_days: number; // Number of days elapsed since the last review
    last_elapsed_days: number; // Number of days between the last two reviews
    scheduled_days: number; // Number of days until the next review
    review: Date; // Date of the review
}