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DeepInsight Learning Agent 🚀

An AI-powered academic assistant built with React, TypeScript, and Google Gemini. It helps users truly understand complex content through logical breakdown, heuristic questioning, and Feynman testing.

✨ Key Features

  • 🧠 Logic Decomposition: Automatically identifies core arguments and evidentiary structures in complex texts.
  • ❓ Heuristic Questioning: Generates deep-thinking questions using Chain-of-Thought (CoT) to stimulate critical analysis.
  • 🧩 Knowledge Synthesis: Summarizes content into modular, digestible insights.
  • 🎓 Feynman Test Generation: Creates targeted assessment quizzes to verify true knowledge internalization.
  • 🎨 Modern UI: Beautifully crafted interface with smooth animations and responsive design.

🧠 Agent Architecture & Workflow

The DeepInsight Learning Agent follows a structured multi-stage reasoning process to ensure deep internalization of knowledge.

graph TD
    A[Raw Text Input] --> B{AI Context Analysis}
    B --> C[Logic Decomposition]
    B --> D[Heuristic Questioning]
    B --> E[Feynman Quiz Generation]
    C --> F[Logic Map Visualization]
    D --> G[Deep Thinking Prompts]
    E --> H[Self-Assessment Quiz]
    F & G & H --> I[Knowledge Internalization]
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🖥️ Terminal Execution Example

When running the ingestion process, the agent provides detailed feedback on its reasoning steps:

🚀 [AGENT START] Initiating Deep-Learning Workflow...
📊 [INPUT] Document received. Length: 5420 characters.
🔍 [PROCESS] Step 1: Analyzing document structure and semantic clusters...
   ↳ [Thought] Scanning for key concepts and logical transitions...
✅ [SUCCESS] Semantic structure parsed.
🧠 [PROCESS] Step 2: Generating high-order reasoning questions...
   ↳ [Thought] Identifying logical gaps in the text...
✅ [SUCCESS] 2 deep-thinking questions generated.
🌐 [PROCESS] Step 3: Executing web-based knowledge retrieval...
   ↳ [Tool Call] Searching: 'How does non-locality challenge the principle of local realism?'
   ↳ [Status] Connecting to DuckDuckGo Search API...
✅ [SUCCESS] External knowledge integrated into context.
📝 [PROCESS] Step 4: Synthesizing Feynman-style learning package...
   ↳ [Thought] Mapping extracted knowledge to pedagogical patterns...
✅ [SUCCESS] Learning package finalized.
🌈 [WORKFLOW COMPLETE] All tasks finished successfully.

🛠️ Tech Stack

  • Frontend: React 19, TypeScript, Vite
  • Styling: Tailwind CSS
  • Animation: Motion (formerly Framer Motion)
  • AI Engine: Google Gemini API
  • Icons: Lucide React

🚀 Getting Started

1. Prerequisites

  • Node.js 18+
  • A Google Gemini API Key

2. Installation

# Clone the repository
git clone https://github.com/yourusername/deepinsight-learning-agent.git

# Navigate to project directory
cd deepinsight-learning-agent

# Install dependencies
npm install

3. Configuration

Create a .env file in the root directory (never commit this to GitHub):

VITE_GEMINI_API_KEY=your_gemini_api_key_here

4. Run the App

npm run dev

📄 License

Distributed under the Apache-2.0 License. See LICENSE for more information.


Built with ❤️ for lifelong learners.

About

An intelligent multi-agent workflow designed for deep reading and knowledge internalization using the Feynman technique.一个基于费曼学习法设计的、用于深度阅读与知识内化的智能多智能体工作流。

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