InkPulse

The repository is for InkPulse, a visual analytics system that supports the identification, search, and analysis of key interactions in human-AI co-writing.

The introduction video can be found here

Progress.mp4

Table of Contents

• InkPulse
  • Table of Contents
  • Getting Started
  • Project Structure
  • Data Structure
    • Detailed Data Specifications
      • Events (Individual User Actions)
      • Event Blocks (Grouped Actions)
      • Session Info (Session-Level Metadata)
  • How to import your own dataset

Getting Started

If running for the first time, ensure you have Node.js v18 or newer installed. You can check your version by running:

node -v

Install all dependencies by using:

npm install

Then, run the development server:

npm run dev

Open http://localhost:5173 with your browser to see the result.

Project Structure

.
├── .github/
│   └── workflows/        # github action files for app building and delopyment
│ 
├── src/
│   ├── components/       # Svelte UI components (charts, dialogs, panels)
│   ├── lib/              # Shared utilities/helpers
│   ├── routes/           # SvelteKit routes (pages + endpoints)
│   ├── workers/          # Web workers for background tasks
│   ├── app.d.ts          # App-level TypeScript declarations
│   └── app.html          # HTML template
│ 
├── static/
│   ├── backend/          # Python backend for data processing
│   ├── dataset/          # Processed datasets used by the app
│   └── patterns/         # User-saved patterns from visual exploration
│ 
├── package.json
└── README.md

Data Structure

InkPulse organizes writing session data across three hierarchical levels of abstraction: Events → Event Blocks → Session Info.

• Events (Individual User Actions): the finest level of granularity captures each individual user action (e.g., insertion, deletion, accept AI suggestion) during a writing session. Each session is stored as a separate JSON file named [session_id].json, located at static/dataset/[dataset_name]/json folder.
• Event Blocks (Grouped Actions): To facilitate analysis, individual events are grouped into event blocks. By default, an event block contains all consecutive actions a user performs while actively writing, ending when the user either requests AI suggestions or accepts an AI insertion. Each session is stored as [session_id].json within static/dataset/[dataset_name]/segment_results folder.
• Session Info (Session-Level Metadata): this file (static/dataset/[dataset_name]/session.json) contains high-level metadata (e.g., topic, writing ID, AI model) for writing sessions. This JSON files contains all the writing sessions from a specific dataset, with each JSON object corresponds to one writing session.

Detailed Data Specifications

Below is the structure and examples for the three levels.

Events (Individual User Actions)

Location: static/dataset/[dataset_name]/json/[session_id].json Each file is a writing session with the following structure

Schema:

type Event {
  // Type of action
  name: "suggestion-open" | "text-insert" | "text-delete" | string;       
  text?: string;         // Text content involved in the action (if applicable)
  eventSource: "user" | "api"; // Source of the action
  event_time: string;    // Timestamp, e.g., "YYYY-MM-DD hh:mm:ss"
  progress: number;      // Document-level progress (0–1)
  pos: number;           // Character position in the document
}

type SessionEvents {
  init_text: string[];   // Initial text representing the topic
  init_time: string[];   // Timestamp(s) when the initial text was presented
  text: string[];        // Full text content (after applying actions)
  events: Event[];       // List of all actions in the session
}

Example: /static/dataset/creative/json/016...84f.json

Event Blocks (Grouped Actions)

Location: static/dataset/[dataset_name]/segment_results/[session_id].json

Schema:

  type EventBlock = {
    start_progress: number; // Document progress at segment start (0–1)
    end_progress: number;   // Document progress at segment end (0–1)
    start_time: number;     // Start time in seconds since session start
    end_time: number;       // End time in seconds since session start
    actions: number[];      // List of action IDs in this block

    // Other user-defined attributes (e.g., scores, text length)
    [key: string]: number | string | boolean | number[] | string[] | null;
  };

  type EventBlocksFile = EventBlock[];

Additional, user-defined attributes (e.g., scores, text length) can be added as needed.

Example: /static/dataset/creative/segment_results/016...84f.json

Session Info (Session-Level Metadata)

Location: static/dataset/[dataset_name]/session.json

High-level metadata for all writing sessions. Each JSON object represents one complete session. Only session_id is required; all other fields are user-defined based on analysis needs.

Schema:

type SessionInfo = {
  session_id: string; // Required: unique session identifier

  // Optional / user-defined fields:
  writer_id?: string; // Unique writer identifier
  topic?: string;     // Writing prompt/topic

  // Other user-defined attributes
  [key: string]: string | number | boolean | null | undefined;
};

type SessionInfoFile = SessionInfo[];

Example: /static/dataset/creative/session.json

How to import your own dataset

Data Preprocessing: You can use static/backend/index.ipynb to preprocess the data, a Google olab version is available here. You need to use [dataset_name].zip instead of folder [dataset_name].

This script takes in two files (i) data/session.jsonl, which saves the complete writing action logs as the format specified in the CoAuther Dataset Schema, and (ii) data.csv, which specific the session level data at least session_id and prompt_code. Sample can be checked in static/import_dataset/creative.csv. The outputed folder [dataset_name] will contain all the files as described in Data Structure.

Loading Data into InkPulse:

• Method One: Running InkPulse Locally. Fork this repo and run InkPulse locally following the Getting Started instructions. Place the folder generated from last step within static/dataset and register your dataset ([dataset_name]) at static/dataset/dataset_name.json. You can then start your visual exploration.
• Method Two: Upload Directly to the Website. Direct upload support is currently under development. Stay tuned for updates!

Use the following code to convert your dataset into local database. NOTE: only folder that in static/dataset will be detected.

npx tsx scripts/import-groups.ts

Or, you can upload a .zip file on the website.