Documentation
Complete guide to using Icestar.ai platform and understanding our technology stack.
Quick Start
Get up and running with Icestar.ai in minutes. Follow these simple steps to integrate our AI services into your application.
# Install the Icestar.ai SDK
pip install icestar-ai
# Initialize the client
from icestar import Client
client = Client(api_key="your-api-key")
# Make your first AI request
response = client.generate_text("Hello, world!")
print(response.text)
Technology Stack
Icestar.ai is built using cutting-edge technologies to ensure scalability, reliability, and performance. Here's our comprehensive tech stack:
Backend Infrastructure
Laravel 11
Modern PHP framework for robust web applications with elegant syntax and powerful features.
Laravel Sail
Lightweight command-line interface for interacting with Laravel's default Docker development environment.
Django
High-level Python web framework for rapid development and clean, pragmatic design.
AI & Machine Learning
LangChain
Framework for developing applications powered by language models with composability and flexibility.
Pandas
Powerful data manipulation and analysis library for Python with data structures and operations.
TensorFlow
End-to-end open source platform for machine learning with comprehensive tools and libraries.
PyTorch
Deep learning framework that accelerates the path from research prototyping to production deployment.
Scikit-learn
Machine learning library for Python with simple and efficient tools for data mining and analysis.
NumPy
Fundamental package for scientific computing with Python, providing array objects and routines.
Database & Storage
MySQL
Relational database management system for storing structured data with high performance.
Redis
In-memory data structure store used as database, cache, and message broker.
Elasticsearch
Distributed search and analytics engine for real-time data processing and search.
Frontend & UI
React
JavaScript library for building user interfaces with component-based architecture.
Tailwind CSS
Utility-first CSS framework for rapidly building custom user interfaces.
Vite
Next generation frontend tooling with fast hot module replacement and build optimization.
API Reference
Authentication
All API requests require authentication using your API key. Include it in the Authorization header.
curl -H "Authorization: Bearer your-api-key" \
-H "Content-Type: application/json" \
https://api.icestar.ai/v1/generate
Text Generation
Generate human-like text using our advanced language models.
POST /v1/generate
{
"prompt": "Explain quantum computing",
"max_tokens": 150,
"temperature": 0.7
}
Code Examples
Python Example
import requests
def generate_text(prompt, api_key):
url = "https://api.icestar.ai/v1/generate"
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
data = {
"prompt": prompt,
"max_tokens": 100
}
response = requests.post(url, headers=headers, json=data)
return response.json()
# Usage
result = generate_text("Hello, world!", "your-api-key")
print(result["text"])
JavaScript Example
async function generateText(prompt, apiKey) {
const response = await fetch('https://api.icestar.ai/v1/generate', {
method: 'POST',
headers: {
'Authorization': `Bearer ${apiKey}`,
'Content-Type': 'application/json'
},
body: JSON.stringify({
prompt: prompt,
max_tokens: 100
})
});
const data = await response.json();
return data.text;
}
// Usage
generateText("Hello, world!", "your-api-key")
.then(text => console.log(text));
Deployment
Docker Deployment
Deploy Icestar.ai using Docker for easy containerization and scaling.
# Dockerfile
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["python", "manage.py", "runserver", "0.0.0.0:8000"]
Environment Variables
# .env
DATABASE_URL=mysql://user:password@localhost:3306/icestar
REDIS_URL=redis://localhost:6379
OPENAI_API_KEY=your-openai-key
SECRET_KEY=your-secret-key