Write Once, Teach Personally English | 简体中文 AI-Shifu is designed for creators, instructors, and training/education teams, offering a scalable one-on-one teaching agent. Provide your expertise and teaching intent once, AI-Shifu…
Write Once, Teach Personally
English | [简体中文](README_ZH-CN.md)
AI-Shifu is designed for creators, instructors, and training/education teams, offering a scalable one-on-one teaching agent. Provide your expertise and teaching intent once, AI-Shifu will expand it into complete, personalized learning experiences. It adapts in real time to each learner’s profile with tailored explanations, interactive probing, assessments, and a full feedback loop—amplifying both your efficiency and the learner’s experience.
Developed by the AI-Shifu Team and the Research Center of Intelligent Software Engineering at Harbin Institute of Technology.
Core Capabilities
- Personalized explanation engine — Generates learning paths and tone based on learner background, goals, and level.
- Interactive Q&A & probing — Decomposes questions, asks clarifiers, and suggests next actions during sessions.
- Rapid course assembly — Author with high-level frameworks and intent; AI-Shifu elaborates into lessons, activities, and assessments.
- Reduced production & delivery overhead — Minimizes repetitive prep and support; every learner gets a dedicated “AI tutor.”
- Multi-channel integration — Embeddable in websites, course platforms, and enterprise training portals.
Use Cases
- Course creators — Hand a single lesson framework to AI-Shifu; learners receive personalized explanations and real-time interaction.
- Enterprise training — Input training content once; employees get role- and background-specific learning paths.
- Educators — Provide a syllabus to generate personalized coaching content plus a Q&A assistant.
Roadmap
- [ ] Writing AI agent for rapid script generation and maintenance
- [ ] Knowledge base
- [ ] Speech input and output
Using AI-Shifu
Platform
AI-Shifu.com is an education platform powered by AI-Shifu. You can try it and learn the AI-guided courses developed by human experts.
Self-hosting
> For source code installation, please refer to the [Installation Manual](INSTALL_MANUAL.md)
Make sure your machine has installed Docker and Docker Compose.
Quick Start (Docker, zero config)
bash
git clone https://github.com/ai-shifu/ai-shifu.git
cd ai-shifu/docker
# Use Docker-ready defaults (matches bundled MySQL service; Redis is optional)
cp .env.example.full .env
# Only required change: edit .env and set at least one LLM API key
# (e.g., OPENAI_API_KEY=sk-..., ERNIE_API_KEY=..., etc.)
# Start all services
docker compose -f docker-compose.latest.yml up -d
Notes
- First verified user is automatically promoted to Admin and Creator; the bundled demo course is assigned to this user.
- Default universal verification code for demos is 1024 (change via
UNIVERSAL_VERIFICATION_CODE). docker-compose.latest.ymlpulls the freshest:latestimages (or your own locally builtlatesttags). Usedocker-compose.ymlwhen you need pinned release tags for reproducible environments.
Using Docker Hub image (customize)
bash
git clone https://github.com/ai-shifu/ai-shifu.git
cd ai-shifu/docker
# Copy the full template (contains defaults for Docker usage)
cp .env.example.full .env
# Edit .env and customize as needed (only mandatory change is an LLM key):
# - OPENAI_API_KEY / ERNIE_API_KEY / GLM_API_KEY / ...
# - SQLALCHEMY_DATABASE_URI: Defaults to docker MySQL service
# - REDIS_HOST: Optional; set to enable Redis caching/locks (leave empty to disable)
# - SECRET_KEY: Defaults to a demo value; change for production (generate with: python -c "import secrets; print(secrets.token_urlsafe(32))")
# - UNIVERSAL_VERIFICATION_CODE: Test verification code (remove/empty in production)
# - Any other optional integrations
docker compose -f docker-compose.latest.yml up -d # Use -f docker-compose.yml for pinned versions
Development mode (dev_in_docker.sh)
bash
git clone https://github.com/ai-shifu/ai-shifu.git
cd ai-shifu/docker
cp .env.example.full .env
# Edit .env and set your preferred LLM API key(s)
./dev_in_docker.sh
dev_in_docker.sh builds the backend and frontend images from your local source tree and then launches docker-compose.dev.yml (hot reload + bind mounts). Use it whenever you need to iterate on code without managing Python/Node runtimes locally.
Compose files
docker-compose.latest.yml: tracks the:latesttags foraishifu/ai-shifu-apiandaishifu/ai-shifu-cook-web. Use this when you want the freshest container build (either from Docker Hub or after running your owndocker build ... -t aishifu/...:latest).docker-compose.yml: pins each image to a specific release tag for reproducible deployments (recommended for staging/prod mirrors or CI).
Access
After Docker starts:
1. Open http://localhost:8080 in your browser to access Cook Web (learner interface and authoring console)
2. Use any phone number for login; the default universal verification code is 1024 (for demo/testing only — change or disable in production)
3. The first verified user becomes Admin and Creator
Internationalization (i18n)
- Shared translations live in
src/i18n//**/*.jsonand are consumed by both Backend and Cook Web. - See the consolidated guide for conventions, scripts, and CI checks:
docs/i18n.md. - Frontend language list only exposes
en-USandzh-CN.
Text-to-Speech (TTS)
AI-Shifu supports multiple TTS providers. To enable Volcengine HTTP v1/tts, set:
VOLCENGINE_TTS_APP_KEY(AppID)VOLCENGINE_TTS_ACCESS_KEY(Token used byAuthorization: Bearer;{token})VOLCENGINE_TTS_CLUSTER_ID(Cluster, default:volcano_tts)
volcengine_http and choose a voice/model.Members
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