Pointer AI Chat Assistant

July 15, 2025 · View on GitHub

GitHub release (latest by date) GitHub all releases GitHub

中文版 | English

An AI chat application built with Electron + React + TypeScript, supporting multi-model conversations, intelligent crosstab data analysis, and knowledge organization management.

基于 Electron + React + TypeScript 开发的AI聊天应用，支持多模型对话、交叉数据分析和知识组织管理。

查看中文 README 介绍

示例：渐进式交互，生成小说设定

Example

Key Features

AI Conversation System

  • Support for multiple AI models (OpenAI GPT, Claude, DeepSeek, etc.)
  • Streaming conversation responses with reasoning process display
  • Message tree branch management with conversation version control
  • Hierarchical chat history organization with parallel tab workflow
  • Global content search with keyword highlighting
  • Global AI generation task management with task monitoring and cancellation
  • Global Q&A traceability mechanism to track generation relationships across pages

Unique Features

  • AI Crosstab Analysis: Automatically generate structured comparison analysis tables
  • AI Object Manager: Visual knowledge data structure management
  • Data Import/Export: Support for mainstream AI platform data migration (OpenAI ChatGPT / Deepseek Chat)

Knowledge Management

  • Folder hierarchical organization
  • Message bookmarking and tagging
  • Batch operations and drag-and-drop sorting
  • Data backup and recovery

Main Interface

Main Interface

Crosstab Analysis

Crosstab Analysis

Object Manager

Object Manager

Quick Start

Requirements

  • Node.js 18+
  • Windows 10+, macOS 10.15+, or Linux

Installation & Setup

# Install dependencies
pnpm install

# Development mode
pnpm dev

# Build application
pnpm build:win    # Windows
pnpm build:mac    # macOS
pnpm build:linux  # Linux

Basic Configuration

  1. Launch the application and go to settings
  2. Configure AI model parameters:
    • Configuration name
    • API endpoint
    • Access key
    • Model identifier
  3. Select default model and test connection

Core Features

Crosstab Analysis

Convert any topic into structured comparison analysis tables, suitable for:

  • Academic research literature comparison
  • Business decision solution evaluation
  • Educational material knowledge organization
  • Product feature competitive analysis

Workflow:

  1. Input analysis topic
  2. AI automatically generates table structure
  3. Fill intersection data
  4. Manual editing and optimization

Object Browser

Visualize complex data structures with support for:

  • Tree structure display
  • AI automatic node generation
  • Manual editing and organization
  • Structured data export

Chat Branch Management

  • Message tree structure
  • Independent conversations between branches
  • Historical version switching
  • Context inheritance

Technical Architecture

Core Technologies

  • Frontend: React 19 + TypeScript + Ant Design
  • Backend: Electron main process
  • Build: Vite + Electron Builder
  • Styling: CSS Modules + SCSS

Project Structure

src/
├── main/          # Electron main process
├── renderer/      # Renderer process
│   ├── components/  # React components
│   ├── store/      # State management
│   ├── services/   # Business logic
│   └── utils/      # Utility functions
└── preload/       # Preload scripts

Key Dependencies

  • react-markdown: Markdown rendering
  • mermaid: Chart drawing
  • katex: Mathematical formulas
  • html2canvas: Screenshot functionality
  • rehype-highlight: Code highlighting

Use Cases

Education & Research: Course design, knowledge organization, literature analysis
Business Analysis: Market research, competitive comparison, strategic planning
Content Creation: Topic planning, material organization, structured writing
Personal Learning: Note organization, knowledge comparison, review materials

Development & Contribution

Development Workflow

  1. Fork the project and create a feature branch
  2. Follow TypeScript and ESLint standards
  3. Submit code and create Pull Request

Code Standards

  • Use functional components and Hooks
  • Follow conventional commits format
  • Maintain type safety

Key Improvement Areas

  • Bug fixes
  • Generation prompt and context optimization
  • Performance optimization and user experience improvement

License

MIT License - See LICENSE file for details