Write Once, Teach Personally

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• 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.

• 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.

• Writing AI agent for rapid script generation and maintenance
• Knowledge base
• Speech input and output

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.

For source code installation, please refer to the Installation Manual

Make sure your machine has installed Docker and Docker Compose.

git clone https://github.com/ai-shifu/ai-shifu.git
cd ai-shifu/docker

# Use Docker-ready defaults (matches bundled MySQL/Redis services)
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.

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: Defaults to docker Redis service
# - 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

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.

• 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).

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

• Shared translations live in src/i18n/<locale>/**/*.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; the pseudo-locale qps-ploc is available for validation but hidden from the UI.