🌲 LearnWithAI

AI-Assisted Deep Learning Platform powered by FastAPI + LangChain + SQLite/MySQL

LearnWithAI helps you manage learning domains through a horizontal knowledge tree, engage in deep learning conversations with an AI tutor, and persist everything locally.

Key Features:

• Knowledge Tree — Horizontally expanding tree structure with infinite drill-down
• AI Tutor — Each node binds an independent conversation; AI acts as a learning mentor
• Rich Notes — Quill-based rich text editor with RAG semantic search across notes
• Plan Mode — AI recursively explores a domain and auto-generates a structured learning path
• Skill Templates — Reusable prompt templates for different learning scenarios
• Multi-LLM — Supports OpenAI, Anthropic Claude, and Ollama local models
• Chat History — All conversations persist to database, survives refresh
• Admin Dashboard — Token usage statistics, user and domain overview
• Responsive — Desktop and mobile (PWA-like) interfaces

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Prerequisites

• Python 3.10+
• pip (Python package manager)

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Quick Start

1. Install Dependencies

cd LearnWithAI
pip install -r requirements.txt

Depending on your LLM backend, you may need an additional package:

# For OpenAI
pip install langchain-openai

# For Anthropic Claude
pip install langchain-anthropic

# For Ollama (local inference)
pip install langchain-ollama

2. Configure LLM

Set environment variables (copy .env.example or set directly):

# Option A: Direct environment variables
export LLM_PROVIDER=openai           # openai / anthropic / ollama
export LLM_MODEL=gpt-4o-mini
export LLM_API_KEY=sk-your-key-here
# export LLM_API_BASE=https://your-proxy-url/v1   # Optional: custom API base URL

# Option B: Use .env file
cp .env.example .env
# Edit .env with your settings

LLM Backend Examples:

Provider	Variables
OpenAI	LLM_PROVIDER=openai LLM_MODEL=gpt-4o-mini LLM_API_KEY=sk-xxx
Anthropic	LLM_PROVIDER=anthropic LLM_MODEL=claude-sonnet-4-20250514 LLM_API_KEY=sk-ant-xxx
Ollama	LLM_PROVIDER=ollama LLM_MODEL=llama3.2 (no API key needed)

3. Start the Server

python main.py

Expected output:

  🌲 LearnWithAI 开发模式
  📡 127.0.0.1:7860
  🤖 openai / gpt-4o-mini
  💾 SQLite: /path/to/LearnWithAI/data/learn.db

Open http://127.0.0.1:7860 in your browser.

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Configuration Reference

All settings are read from environment variables (or .env file at project root).

Variable	Default	Description
DATABASE_URL	""	MySQL connection string, e.g. mysql+pymysql://user:pass@host/db. Leave empty for SQLite.
LLM_PROVIDER	openai	LLM provider: openai / anthropic / ollama
LLM_MODEL	gpt-4o-mini	Model name
LLM_API_KEY	""	API key
LLM_API_BASE	""	Custom API base URL (for proxies / relays)
LLM_TEMPERATURE	0.7	Generation randomness
JWT_SECRET	learnwithai-dev-secret-change-in-prod	JWT signing secret
HOST	127.0.0.1	Listening address
PORT	7860	Listening port
ADMIN_USERNAME	admin	Admin username
ENV	development	Set to production to disable hot-reload and use 4 workers

Security: In production, always set JWT_SECRET to a strong random string.

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Production Deployment

1. System Dependencies (Linux)

# Ubuntu / Debian
sudo apt update
sudo apt install -y python3 python3-venv python3-pip git

# CentOS / RHEL / Fedora
sudo yum install -y python3 python3-pip git

2. Clone and Setup

git clone https://github.com/your-org/LearnWithAI.git
cd LearnWithAI
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

3. Configure

cp .env.example .env
# Edit .env with your production settings
vim .env

Key configuration for production:

LLM_API_KEY=sk-xxx
LLM_API_BASE=https://your-proxy-url/v1   # if using a relay
HOST=0.0.0.0
PORT=7860
ENV=production
JWT_SECRET=your-strong-random-secret
ADMIN_USERNAME=admin
DATABASE_URL=                             # leave empty for SQLite, or use MySQL

4. Run with systemd (Recommended)

Create a service file:

sudo vim /etc/systemd/system/learnwithai.service

[Unit]
Description=LearnWithAI - AI Learning Platform
After=network.target

[Service]
Type=simple
User=your-user
WorkingDirectory=/home/your-user/LearnWithAI
ExecStart=/home/your-user/LearnWithAI/venv/bin/python main.py
Restart=always
RestartSec=5
Environment=ENV=production

[Install]
WantedBy=multi-user.target

Enable and start:

sudo systemctl daemon-reload
sudo systemctl start learnwithai
sudo systemctl enable learnwithai    # auto-start on boot
sudo systemctl status learnwithai

View logs:

sudo journalctl -u learnwithai -f

5. Nginx Reverse Proxy (Optional)

sudo apt install -y nginx    # Ubuntu/Debian
sudo yum install -y nginx    # CentOS/RHEL

Create config:

sudo vim /etc/nginx/sites-available/learnwithai

server {
    listen 80;
    server_name your-domain.com;

    client_max_body_size 10m;

    location / {
        proxy_pass http://127.0.0.1:7860;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
        proxy_set_header X-Forwarded-Proto $scheme;
    }
}

Enable and restart:

sudo ln -s /etc/nginx/sites-available/learnwithai /etc/nginx/sites-enabled/
sudo nginx -t
sudo systemctl restart nginx

6. SSL with Let's Encrypt (Optional)

sudo apt install -y certbot python3-certbot-nginx
sudo certbot --nginx -d your-domain.com

7. Run Directly without systemd

cd /path/to/LearnWithAI
source venv/bin/activate
nohup python main.py > app.log 2>&1 &
tail -f app.log

8. Management Commands

sudo systemctl status learnwithai      # check status
sudo systemctl restart learnwithai     # restart
sudo systemctl stop learnwithai        # stop
sudo journalctl -u learnwithai -n 50   # recent logs
sudo journalctl -u learnwithai -f      # follow logs

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Data Management

Export Database

# Export to stdout
python cli/dump.py

# Export to file
python cli/dump.py -o backup.sql

# Export MySQL database
DATABASE_URL=mysql+pymysql://user:pass@host/dbname python cli/dump.py -o backup.sql

Import Database

python cli/import_data.py -i backup.sql

# Import to MySQL target
DATABASE_URL=mysql+pymysql://user:pass@host/dbname python cli/import_data.py -i backup.sql

Note: The target database should have empty tables (existing data may cause primary key conflicts).

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Tech Stack

• Backend: FastAPI + Uvicorn
• AI: LangChain v1.x Agent + SummarizationMiddleware (OpenAI / Anthropic / Ollama)
• Database: SQLite (default) / MySQL (optional) + SQLAlchemy
• Frontend: Vanilla HTML/CSS/JS + D3.js v7 + Quill.js
• Auth: JWT (python-jose) + SHA-256 password hashing
• RAG: LangChain text splitters + numpy cosine similarity

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License

MIT