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.yml pulls the freshest :latest images (or your own locally built latest tags). Use docker-compose.yml when 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 :latest tags for aishifu/ai-shifu-api and aishifu/ai-shifu-cook-web. Use this when you want the freshest container build (either from Docker Hub or after running your own docker 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//**/*.json and 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-US and zh-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 by Authorization: Bearer;{token})
• VOLCENGINE_TTS_CLUSTER_ID (Cluster, default: volcano_tts)

In Shifu settings, select the provider name volcengine_http and choose a voice/model.

Collapse