
ai-shifu
LLM-powered AI guide that leads and drives intelligent conversations
Stars: 183

AI-Shifu is an AI-led chat flow tool powered by LLM that provides an interactive and immersive experience for users. It allows users to follow a preset chat flow while being able to ask questions and affect the conversation. The tool can make personalized outputs based on user identity, interests, and preferences, making users feel like they are receiving one-on-one service. It is suitable for education, storytelling, product guides, surveys, and game NPC scenarios.
README:
AI-Shifu serves as a teacher, broadcaster, storyteller, and guide... As an AI-driven narrator, AI-Shifu can present any textual content to each user in a fully personalized way, creating an unprecedented reading experience.
- Personalized Output: Fully personalized content output based on user's identity, background, interests and preferences, comparable to one-on-one human interaction.
- Rich Media: Supports multiple content formats including Markdown, HTML, Mermaid diagrams, as well as embedded images and videos.
- Content Safety: Master scripts control all output to reduce hallucinations and ensure content quality.
- Q & A: Users can ask questions at any time to get contextually relevant intelligent answers.
- Interactive: Ask questions to users at any time, driving subsequent processes based on user responses.
- Script Editor: Easily edit scripts and preview effects.
- [ ] Overall refactoring
- [ ] 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
# For minimal setup (only required variables):
cp .env.example.minimal .env
# Or for full configuration options:
cp .env.example.full .env
# Edit .env and configure the required variables:
# - SQLALCHEMY_DATABASE_URI: Database connection
# - SECRET_KEY: JWT signing key (generate with: python -c "import secrets; print(secrets.token_urlsafe(32))")
# - UNIVERSAL_VERIFICATION_CODE: Test verification code
# - At least one LLM API key (OPENAI_API_KEY, ERNIE_API_KEY, etc.)
docker compose up -d
git clone https://github.com/ai-shifu/ai-shifu.git
cd ai-shifu/docker
# Choose configuration template:
cp .env.example.minimal .env # For minimal setup
# OR
cp .env.example.full .env # For full configuration
# Configure the required variables in .env file
# See .env.example.minimal for required variables
# See .env.example.full for all available options
./dev_in_docker.sh
After Docker starts:
- Open
http://localhost:8080
in your browser to access the user interface - Open
http://localhost:8081
in your browser to access the script editor - Use any phone number for login; the default universal verification code is 1024 (for demo/testing only — change or disable in production)
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