woosailibrary 1.1.0

AI Output Token Optimizer - Reduce OpenAI API costs by up to 88%

🚀 WoosAI Library

Reduce your OpenAI API costs by up to 88%!

WoosAI Library is a powerful Python library that optimizes OpenAI API calls through intelligent input compression, output optimization, advanced caching, and real-time statistics tracking.

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✨ Features

🎯 Core Features

• Input Optimization - Compress user inputs without losing meaning
• Output Optimization - Get concise, relevant responses
• Advanced Caching - LRU cache with pattern-based deletion
• Usage Statistics - Track costs, tokens, and savings in real-time
• Auto License - Free license auto-generated on first use

💰 Cost Savings

• Up to 88% cost reduction on OpenAI API calls
• Real-time tracking of cost savings
• Cache system eliminates repeated API calls

📊 Statistics & Monitoring

• Daily, monthly, and total usage statistics
• Token usage tracking
• Cost comparison (with/without WoosAI)
• Cache hit rate monitoring

💾 Advanced Caching

• LRU Eviction - Automatic removal of least-used entries
• TTL Support - Auto-expire old cache entries
• Pattern Deletion - Remove cache by regex pattern
• Auto Cleanup - Periodic automatic maintenance

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🚀 Quick Start

Installation

pip install woosailibrary

Basic Usage

import os
from woosailibrary import WoosAI

# Set your OpenAI API key
os.environ['OPENAI_API_KEY'] = 'your-openai-api-key'

# Initialize WoosAI (auto-generates free license on first use)
client = WoosAI()

# Make optimized API call
response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Explain AI in simple terms"}]
)

print(response.choices[0].message.content)

With Caching

# Enable caching for repeated queries
client = WoosAI(
    cache=True,              # Enable caching
    cache_ttl=24,            # Cache expires after 24 hours
    max_cache_size=1000,     # Store up to 1000 entries
    auto_cleanup_interval=100 # Auto cleanup every 100 operations
)

# First call - hits OpenAI API
response1 = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "What is AI?"}]
)

# Second call - returns from cache (FREE!)
response2 = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "What is AI?"}]
)

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📊 Statistics & Monitoring

View Statistics

# Display all statistics
client.display_stats()

# Get specific stats
today = client.get_today_stats()
monthly = client.get_monthly_stats()
total = client.get_total_stats()

print(f"Today's savings: {today['cost_saved']}")
print(f"Monthly savings: {monthly['cost_saved']}")
print(f"Total savings: {total['cost']['saved']}")

Example Output:

============================================================
📊 WoosAI Usage Statistics
============================================================

📅 Today (2025-10-23):
  Requests: 15
  Cost Saved: $2.50
  Tokens Saved: 3,500

📆 This Month (2025-10):
  Requests: 450
  Cost Saved: $75.00
  Projected Monthly: $225.00

🎯 Total (All Time):
  Requests: 1,200
  Cost Saved: $210.00
  Savings: 88.0%

============================================================

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💾 Cache Management

View Cache Statistics

# Display cache statistics
client.display_cache_stats()

# Get cache info
cache_info = client.get_cache_info()
print(f"Cache size: {cache_info['size_usage']}")

Example Output:

============================================================
💾 Advanced Cache Statistics
============================================================

📊 Performance:
  Cache Hits: 450
  Cache Misses: 150
  Hit Rate: 75.0%
  LRU Evictions: 50

💰 Savings:
  Cost Saved (from cache): $22.50

📦 Storage:
  Cached Entries: 850/1000 (85.0%)
  Active: 800
  Expired: 50

============================================================

Cache Management

# Clear expired cache entries
client.clear_expired_cache()

# Clear cache by pattern (e.g., weather-related queries)
client.clear_cache_by_pattern("weather|날씨")

# Clear cache older than 7 days
client.clear_old_cache(days=7)

# Clear all cache
client.clear_cache()

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🎯 Use Cases

1. FAQ Chatbot

client = WoosAI(cache=True, cache_ttl=168)  # 1 week cache

# Same questions = FREE responses!
for question in faq_questions:
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": question}]
    )

2. Customer Support Bot

client = WoosAI(cache=True, max_cache_size=5000)

response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "How do I reset my password?"}]
)

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📖 API Reference

WoosAI Client

client = WoosAI(
    api_key=None,              # OpenAI API key
    license_key=None,          # WoosAI license
    cache=False,               # Enable caching
    cache_ttl=24,              # Cache TTL in hours
    max_cache_size=1000,       # Maximum cache entries
    auto_cleanup_interval=100  # Auto cleanup frequency
)

Statistics Methods

client.get_today_stats()      # Get today's statistics
client.get_monthly_stats()    # Get monthly statistics
client.get_total_stats()      # Get total statistics
client.display_stats()        # Display statistics

Cache Methods

client.get_cache_info()              # Get cache info
client.display_cache_stats()         # Display cache stats
client.clear_cache()                 # Clear all cache
client.clear_cache_by_pattern(regex) # Clear by pattern
client.clear_expired_cache()         # Clear expired
client.clear_old_cache(days=7)       # Clear old entries

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🔧 Configuration

Configuration Files

WoosAI stores data in:

• Windows: C:\Users\<username>\.woosai\
• Linux/Mac: ~/.woosai/

Files:

• config.json - License information
• stats.json - Usage statistics
• cache/responses.json - Cached responses

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🔗 Links

• Website: https://woos-ai.com
• PyPI: https://pypi.org/project/woosailibrary/
• Support: [e-mail]

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📄 License

MIT License

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Made with ❤️ by WoosAI Team