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Cost Katana Python - Quick Start

Get started in 3 simple steps!

Step 1: Install

pip install costkatana

Package Name: costkatana (Python/PyPI) vs cost-katana (JavaScript/NPM)

Step 2: Get API Key

Visit costkatana.com/settings and copy your API key (starts with dak_)

Step 3: Use It

Zero Config Mode

import cost_katana as ck

# Just works if you have API key in environment
response = ck.ai('gpt-4', 'Hello, world!')
print(response.text)
print(f"Cost: ${response.cost}")

Or Configure Once

import cost_katana as ck

# Configure once at the start
ck.configure(api_key='dak_your_key_here')

# Then use anywhere
response = ck.ai('gpt-4', 'Explain quantum computing')
print(response.text)

Common Patterns

Chat Conversation

import cost_katana as ck

chat = ck.chat('gpt-4')
chat.send('Hello!')
chat.send('What can you help with?')
chat.send('Tell me a joke')

print(f"Total cost: ${chat.total_cost}")

Model Comparison

import cost_katana as ck

models = ['gpt-4', 'gpt-3.5-turbo', 'claude-3-haiku']
prompt = 'Explain machine learning'

for model in models:
    response = ck.ai(model, prompt)
    print(f"{model}: ${response.cost:.4f}")

Enable Optimization

import cost_katana as ck

# Cortex: 40-75% cost reduction
response = ck.ai('gpt-4', 'Write a comprehensive guide',
                 cortex=True)

print(f"Optimized: {response.optimized}")

Smart Caching

import cost_katana as ck

# First call - costs money
r1 = ck.ai('gpt-4', 'What is 2+2?', cache=True)

# Second call - free from cache
r2 = ck.ai('gpt-4', 'What is 2+2?', cache=True)
print(r2.cached)  # True

Environment Setup

Option 1: Environment Variable

export COST_KATANA_API_KEY="dak_your_key_here"

Then in Python:

import cost_katana as ck

# Auto-detects from environment
response = ck.ai('gpt-4', 'Hello')

Option 2: Config File

Create config.json:

{
  "api_key": "dak_your_key_here",
  "default_model": "gpt-3.5-turbo",
  "enable_cache": true,
  "enable_cortex": true
}

Then:

import cost_katana as ck

ck.configure(config_file='config.json')

Option 3: Direct Configuration

import cost_katana as ck

ck.configure(
    api_key='dak_your_key_here',
    cortex=True,
    cache=True
)

Real-World Examples

FastAPI Integration

from fastapi import FastAPI
import cost_katana as ck

app = FastAPI()

@app.post("/api/chat")
async def chat(request: dict):
    response = ck.ai('gpt-4', request['prompt'])
    return {
        'text': response.text,
        'cost': response.cost
    }

Flask Integration

from flask import Flask, request, jsonify
import cost_katana as ck

app = Flask(__name__)

@app.route('/api/chat', methods=['POST'])
def chat():
    prompt = request.json['prompt']
    response = ck.ai('gpt-4', prompt, cache=True)
    return jsonify({
        'text': response.text,
        'cost': response.cost
    })

Django Integration

from django.http import JsonResponse
import cost_katana as ck

def chat_view(request):
    prompt = request.POST.get('prompt')
    response = ck.ai('gpt-4', prompt)
    return JsonResponse({
        'text': response.text,
        'cost': response.cost
    })

Tips

Save Money

# Use cheaper models for simple tasks
ck.ai('gpt-3.5-turbo', 'Simple question')    # 10x cheaper

# Enable optimization for long content
ck.ai('gpt-4', 'Long article', cortex=True)  # 40-75% savings

# Cache repeated queries
ck.ai('gpt-4', 'FAQ answer', cache=True)     # Free on repeats

Better Responses

# Control creativity
ck.ai('gpt-4', 'Story', temperature=1.5)     # More creative

# Limit response length
ck.ai('gpt-4', 'Explain AI', max_tokens=100) # Short answer

# Use system prompts
ck.ai('gpt-4', 'Question', 
      system_message='You are an expert')

Command Line Interface

The Python package includes a CLI command:

# After installing costkatana
pip install costkatana

# Python CLI command
costkatana chat
costkatana ask "What is Python?"

Or use the NPM global CLI (more features):

# Install NPM CLI globally
npm install -g cost-katana-cli

# JavaScript CLI command
cost-katana chat
cost-katana ask "What is Python?"
cost-katana compare "Test" --models gpt-4,claude-3-sonnet

Supported Models

Provider Models Best For
OpenAI gpt-4, gpt-3.5-turbo, gpt-4o General purpose, function calling
Anthropic claude-3-sonnet, claude-3-haiku Analysis, coding, safety
Google gemini-pro, gemini-flash Creative tasks, multimodal
AWS nova-pro, nova-lite Enterprise, cost optimization

Need Help?

You're ready to use AI in Python! 🚀

import cost_katana as ck
response = ck.ai('gpt-4', 'Hello!')