adpa 0.1.6

Advanced Data Processing and Analytics Framework

ADPA Framework

Overview

ADPA (Advanced Data Processing and Analytics) is a comprehensive framework for data processing, analytics, and machine learning tasks. It provides a robust foundation for building scalable, secure, and maintainable data applications.

🚀 Quick Start

pip install adpa

from adpa.text2sql import Text2SQLConverter

# Initialize the converter
converter = Text2SQLConverter()

# Convert natural language to SQL
query = "Find all users who joined after 2024"
sql = converter.convert(query)
print(sql)

📚 Read the Quick Start Guide

✨ Features

Core Components

• Text2SQL Engine: Convert natural language to SQL with schema validation
• Agent System: Autonomous agents for complex data processing tasks
• LLM Integration: Support for multiple LLM providers (OpenAI, Anthropic, Azure)
• Database Operations: Unified interface for database interactions
• Security Layer: Built-in security features and input validation

Advanced Features

• Monitoring: Real-time performance and resource monitoring
• Caching: Intelligent caching system for improved performance
• Scaling: Horizontal scaling capabilities for large workloads
• API Integration: Ready-to-use API interfaces
• UI Components: Modern web interface components

🛠️ Installation

Basic Installation

pip install adpa

With Optional Dependencies

# With all features
pip install "adpa[all]"

# With specific features
pip install "adpa[llm,monitoring]"

📖 Documentation

• Official Documentation
• API Reference
• Examples
• Contributing Guide

🌟 Examples

Text to SQL Conversion

from adpa.text2sql import Text2SQLConverter
from adpa.database import DatabaseManager

# Initialize components
converter = Text2SQLConverter()
db = DatabaseManager()

# Convert and execute query
query = "Show me sales trends for last month"
sql = converter.convert(query)
results = db.execute(sql)

Agent System Usage

from adpa.agents import AgentSystem
from adpa.agents.types import Task

# Initialize agent system
agent_system = AgentSystem()

# Create and execute task
task = Task(
    description="Analyze user behavior patterns",
    data={"timeframe": "last_week"}
)
result = agent_system.execute_task(task)

Monitoring Integration

from adpa.monitoring import Monitor

# Initialize monitoring
monitor = Monitor()

# Track operations
with monitor.track("data_processing"):
    # Your code here
    pass

# Get metrics
metrics = monitor.get_metrics()

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

# Clone repository
git clone https://github.com/achimdehnert/adpa.git
cd adpa

# Install development dependencies
pip install poetry
poetry install --with dev,test,docs

# Run tests
poetry run pytest
poetry run robot -d results tests/robot/tests/

📊 Project Status

• Latest Release: v1.5.0
• Python Versions: 3.11, 3.12
• Development Status: Beta
• License: MIT

🔗 Links

• GitHub Repository
• PyPI Package
• Documentation
• Issue Tracker
• Discussions

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.