CLI Tool¶

RapidAI includes a powerful CLI tool for scaffolding projects, running development servers, and deploying to cloud platforms.

Installation¶

The CLI is installed automatically with RapidAI:

pip install rapidai

Verify installation:

rapidai --version

Quick Start¶

Create a new chatbot project:

rapidai new my-chatbot
cd my-chatbot
pip install -r requirements.txt
rapidai dev

Your app is now running at http://localhost:8000!

Commands¶

`rapidai new`¶

Create a new RapidAI project from a template.

rapidai new <project-name> [OPTIONS]

Arguments:

project-name - Name of the project to create

Options:

-t, --template - Template to use (chatbot, rag, agent, api) [default: chatbot]
-d, --directory - Directory to create project in [default: .]

Examples:

# Create a chatbot project
rapidai new my-bot

# Create a RAG project
rapidai new doc-qa --template rag

# Create in a specific directory
rapidai new my-api --template api --directory ~/projects

Templates:

chatbot - Simple chatbot with conversation memory
rag - RAG application with document upload and Q&A
agent - AI agent with analysis and generation endpoints
api - REST API with authentication and CORS

`rapidai dev`¶

Run the development server with hot reload.

rapidai dev [OPTIONS]

Options:

-p, --port - Port to run on [default: 8000]
-h, --host - Host to bind to [default: 127.0.0.1]
--reload/--no-reload - Enable/disable auto-reload [default: reload]
-a, --app - Application module path [default: app:app]

Examples:

# Run on default port 8000
rapidai dev

# Run on custom port
rapidai dev --port 3000

# Disable hot reload
rapidai dev --no-reload

# Custom app module
rapidai dev --app myapp:application

Features:

Auto-reload on file changes
Colored console output
Clear error messages
Powered by Uvicorn

`rapidai deploy`¶

Deploy your application to a cloud platform.

rapidai deploy <platform> [OPTIONS]

Arguments:

platform - Cloud platform (fly, heroku, vercel, aws)

Options:

-n, --app-name - Application name for deployment
-r, --region - Region to deploy to

Examples:

# Deploy to Fly.io
rapidai deploy fly

# Deploy to Heroku with app name
rapidai deploy heroku --app-name my-app

# Deploy to Vercel
rapidai deploy vercel

# Deploy to AWS (shows instructions)
rapidai deploy aws

Supported Platforms:

Fly.io - Automatic deployment with generated fly.toml
Heroku - Git-based deployment with Procfile
Vercel - Serverless deployment with vercel.json
AWS - Manual deployment instructions

`rapidai test`¶

Run tests for your application.

rapidai test [OPTIONS]

Options:

--coverage/--no-coverage - Run with coverage [default: coverage]
-v, --verbose - Verbose output

Examples:

# Run tests with coverage
rapidai test

# Run without coverage
rapidai test --no-coverage

# Verbose output
rapidai test --verbose

Requirements:

pytest must be installed
Tests should be in tests/ directory

`rapidai docs`¶

Generate and serve project documentation.

rapidai docs [OPTIONS]

Options:

--serve/--build - Serve locally or build for production [default: serve]
-p, --port - Port to serve docs on [default: 8001]

Examples:

# Serve docs locally
rapidai docs

# Build for production
rapidai docs --build

# Serve on custom port
rapidai docs --port 3000

Requirements:

mkdocs and mkdocs-material must be installed
Docs should be in docs/ directory

Project Templates¶

Chatbot Template¶

A simple chatbot with conversation memory.

Features:

Conversation memory per user
Clear history endpoint
Environment-based configuration
Claude or GPT support

Files created:

my-chatbot/
├── app.py              # Main application
├── .env                # Environment variables
├── requirements.txt    # Dependencies
├── README.md          # Project documentation
└── tests/             # Test directory

Endpoints:

POST /chat - Chat with the bot
POST /clear - Clear conversation history

RAG Template¶

RAG application with document upload and Q&A.

Features:

Upload PDFs, DOCX, TXT, HTML, Markdown
Semantic search with embeddings
Context-aware answers
ChromaDB vector storage

Files created:

my-rag/
├── app.py              # Main application
├── .env                # Environment variables
├── requirements.txt    # Dependencies (includes RAG extras)
├── README.md          # Project documentation
├── tests/             # Test directory
└── docs/              # Document storage

Endpoints:

POST /upload - Upload documents
POST /ask - Ask questions
POST /search - Search documents

Agent Template¶

AI agent with analysis and content generation.

Features:

Text analysis with caching
Content generation
Interactive agent chat
Configurable styles

Files created:

my-agent/
├── app.py              # Main application
├── .env                # Environment variables
├── requirements.txt    # Dependencies
├── README.md          # Project documentation
└── tests/             # Test directory

Endpoints:

POST /analyze - Analyze text
POST /generate - Generate content
POST /chat - Interactive chat

API Template¶

REST API with authentication and CORS.

Features:

API key authentication
CORS enabled
Health check endpoint
Clean REST design

Files created:

my-api/
├── app.py              # Main application
├── .env                # Environment variables
├── requirements.txt    # Dependencies
├── README.md          # Project documentation
└── tests/             # Test directory

Endpoints:

GET / - API information
POST /complete - Complete prompts
POST /chat - Chat endpoint
GET /health - Health check

Deployment Guide¶

Fly.io Deployment¶

Install flyctl:

curl -L https://fly.io/install.sh | sh

Login:

flyctl auth login

Deploy:

rapidai deploy fly

What happens:

Generates fly.toml configuration
Creates or updates Fly.io app
Deploys using Paketo buildpacks
Configures health checks
Provides deployment URL

Environment variables:

Set secrets with:

flyctl secrets set ANTHROPIC_API_KEY=your-key

Heroku Deployment¶

Install Heroku CLI:

# macOS
brew install heroku/brew/heroku

# Or download from https://devcenter.heroku.com/articles/heroku-cli

Login:

heroku login

Deploy:

rapidai deploy heroku --app-name my-app

Push code:

git remote add heroku https://git.heroku.com/my-app.git
git push heroku main

What happens:

Generates Procfile
Creates Heroku app
Sets Python buildpack
Provides deployment instructions

Environment variables:

heroku config:set ANTHROPIC_API_KEY=your-key -a my-app

Vercel Deployment¶

Install Vercel CLI:

npm install -g vercel

Login:

vercel login

Deploy:

rapidai deploy vercel

What happens:

Generates vercel.json configuration
Deploys as serverless function
Provides deployment URL

Environment variables:

Set in Vercel dashboard or:

vercel env add ANTHROPIC_API_KEY

AWS Deployment¶

AWS deployment is manual. Use:

rapidai deploy aws

This provides instructions for:

AWS Lambda + API Gateway (serverless)
AWS ECS/Fargate (containers)
AWS Elastic Beanstalk (platform)

Development Workflow¶

1. Create Project¶

rapidai new my-project --template chatbot
cd my-project

2. Install Dependencies¶

pip install -r requirements.txt

3. Configure Environment¶

Edit .env:

ANTHROPIC_API_KEY=your-api-key-here
DEBUG=true

4. Run Development Server¶

rapidai dev

5. Test¶

rapidai test

6. Deploy¶

rapidai deploy fly

Best Practices¶

Project Structure¶

my-project/
├── app.py              # Main application
├── .env                # Environment variables (gitignored)
├── .env.example        # Example environment variables
├── requirements.txt    # Dependencies
├── README.md          # Documentation
├── tests/             # Tests
│   ├── __init__.py
│   └── test_app.py
├── prompts/           # Prompt templates (optional)
└── docs/              # Documents for RAG (optional)

Environment Variables¶

Always use .env for secrets:

# .env
ANTHROPIC_API_KEY=sk-ant-...
OPENAI_API_KEY=sk-...
DEBUG=true

Add .env.example for documentation:

# .env.example
ANTHROPIC_API_KEY=your-api-key-here
DEBUG=true

Testing¶

Create tests in tests/:

# tests/test_app.py
import pytest
from rapidai.testing import TestClient

def test_chat():
    from app import app
    client = TestClient(app)
    response = client.post("/chat", json={"user_id": "test", "message": "hi"})
    assert response.status_code == 200

Run tests:

rapidai test

Documentation¶

Keep README.md updated:

# My Project

## Setup

1. Install dependencies
2. Set environment variables
3. Run the dev server

## Endpoints

- POST /chat - Description
- GET /health - Description

Troubleshooting¶

Port Already in Use¶

# Use a different port
rapidai dev --port 3000

Module Not Found¶

# Make sure you're in the project directory
cd my-project

# Specify the module path
rapidai dev --app app:app

Deployment Fails¶

# Check requirements.txt
cat requirements.txt

# Ensure all files are committed
git status
git add .
git commit -m "Deploy"

Missing Dependencies¶

# Install all dependencies
pip install -r requirements.txt

# For development
pip install rapidai[dev]

Next Steps¶

See API Reference for complete CLI API documentation
Check Configuration for environment variables
Learn about Deployment best practices

CLI Tool¶

Installation¶

Quick Start¶

Commands¶

rapidai new¶

rapidai dev¶

rapidai deploy¶

rapidai test¶

rapidai docs¶

Project Templates¶

Chatbot Template¶

RAG Template¶

Agent Template¶

API Template¶

Deployment Guide¶

Fly.io Deployment¶

Heroku Deployment¶

Vercel Deployment¶

AWS Deployment¶

Development Workflow¶

1. Create Project¶

2. Install Dependencies¶

3. Configure Environment¶

4. Run Development Server¶

5. Test¶

6. Deploy¶

Best Practices¶

Project Structure¶

Environment Variables¶

Testing¶

Documentation¶

Troubleshooting¶

Port Already in Use¶

Module Not Found¶

Deployment Fails¶

Missing Dependencies¶

Next Steps¶

`rapidai new`¶

`rapidai dev`¶

`rapidai deploy`¶

`rapidai test`¶

`rapidai docs`¶