luxumbra-ai-learn

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AI知识库智能学习小程序

An AI-powered WeChat mini app for topic-based quiz generation, knowledge-base learning, and intelligent study reports.

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Table of Contents

• 简体中文
  • 项目简介
  • 核心特性
  • 技术栈
  • 项目结构
  • 快速开始
  • 环境变量
  • 测试
  • 部署
• English
  • Overview
  • Features
  • Tech Stack
  • Project Structure
  • Quick Start
  • Environment Variables
  • Testing
  • Deployment

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简体中文

项目简介

AI知识库智能学习小程序是一个面向微信生态的智能学习产品。用户可以输入任意学习主题，或上传私有知识文档，系统会结合大模型、联网搜索与知识库检索能力，自动生成题目、提供讲解，并输出学习复盘报告，形成完整的学习闭环。

核心特性

• AI 自动出题：基于用户输入主题生成学习题目
• 联网搜索增强：结合 Tavily 获取最新知识上下文
• 知识库出题：支持 PDF、Word、Markdown、TXT 文档上传与 RAG 检索
• 即时讲解反馈：答题后立即返回答案与解析
• AI 学习复盘：生成掌握度、薄弱点与学习建议
• 题目配图能力：支持 AI 生成配图并存储到腾讯云 COS
• 微信用户体系：支持登录、历史记录与报告回看
• 云端部署：支持 Docker 容器化部署到微信云托管

技术栈

模块	技术
小程序前端	Taro 4、React 18、TypeScript、Sass
后端服务	Python 3.11、FastAPI、Pydantic v2、Uvicorn
AI 编排	LangChain、LangGraph
模型与检索	DeepSeek、阿里云百炼、Tavily
向量数据库	Chroma
数据存储	MySQL、腾讯云 COS
鉴权	微信 jscode2session、JWT
测试	pytest、pytest-asyncio
部署	Docker、微信云托管

项目结构

.
├── backend/        # FastAPI 后端服务
├── frontend/       # Taro 微信小程序前端
├── docs/           # 项目文档（本地保留）
├── openspec/       # 规格文档（本地保留）
└── prototypes/     # 原型文件（本地保留）

快速开始

1. 启动后端

cd backend
python -m venv venv
venv\Scripts\Activate.ps1
pip install -r requirements.txt
copy .env.example .env
python -m uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

后端启动后可访问：

• http://localhost:8000/docs
• http://localhost:8000/api/v1/health

2. 启动前端

cd frontend
npm install
npm run dev:weapp

然后使用微信开发者工具打开 frontend/dist。

环境变量

后端运行依赖 .env 配置，请基于 backend/.env.example 创建本地环境变量文件。

cd backend
copy .env.example .env

生产环境请通过部署平台环境变量注入真实配置，不要提交敏感信息到仓库。

测试

cd backend
pytest

部署

项目已提供微信云托管可用的容器配置：

• backend/Dockerfile
• backend/.dockerignore

后端可以直接通过 Docker 容器方式部署到微信云托管或其他兼容平台。

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English

Overview

AI Knowledge Base Smart Learning Mini App is an intelligent learning product built for the WeChat ecosystem. Users can enter any learning topic or upload private knowledge documents, and the system uses LLMs, web search, and knowledge-base retrieval to generate quizzes, explanations, and learning reports in a complete study workflow.

Features

• AI-powered quiz generation based on user topics
• Web search enhancement with Tavily for up-to-date context
• Knowledge-base quiz generation with PDF, Word, Markdown, and TXT uploads
• Instant answer checking and explanations
• AI learning report with mastery analysis and suggestions
• AI-generated question illustrations with Tencent COS storage
• WeChat user system with login, history, and report review
• Docker-based deployment for WeChat Cloud Run

Tech Stack

Layer	Technology
Mini App Frontend	Taro 4, React 18, TypeScript, Sass
Backend	Python 3.11, FastAPI, Pydantic v2, Uvicorn
AI Orchestration	LangChain, LangGraph
Models & Retrieval	DeepSeek, Alibaba Bailian, Tavily
Vector Store	Chroma
Storage	MySQL, Tencent COS
Auth	WeChat jscode2session, JWT
Testing	pytest, pytest-asyncio
Deployment	Docker, WeChat Cloud Run

Project Structure

.
├── backend/        # FastAPI backend
├── frontend/       # Taro-based WeChat mini app frontend
├── docs/           # project docs (kept locally)
├── openspec/       # spec files (kept locally)
└── prototypes/     # prototype files (kept locally)

Quick Start

1. Start the backend

cd backend
python -m venv venv
venv\Scripts\Activate.ps1
pip install -r requirements.txt
copy .env.example .env
python -m uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

Available endpoints after startup:

• http://localhost:8000/docs
• http://localhost:8000/api/v1/health

2. Start the frontend

cd frontend
npm install
npm run dev:weapp

Then open frontend/dist in WeChat DevTools.

Environment Variables

The backend relies on .env configuration. Create a local environment file from backend/.env.example.

cd backend
copy .env.example .env

For production, inject real secrets through your deployment platform instead of committing them into the repository.

Testing

cd backend
pytest

Deployment

This project already includes container files for WeChat Cloud Run:

• backend/Dockerfile
• backend/.dockerignore

The backend can be deployed as a Docker container to WeChat Cloud Run or other compatible platforms.