Python SDK

# Install with pip
pip install tenzro

# Install with async support
pip install tenzro[async]

# Install with all optional dependencies
pip install tenzro[all]

# Install from source
pip install git+https://github.com/tenzro/python-sdk.git

import tenzro
from tenzro import Tenzro

# Initialize the client
client = Tenzro(api_key="sk_your_key_here")

# Generate text with AI
response = client.cortex.generate(
    prompt="Write a hello world function in Python",
    model="gpt-4o",
    max_tokens=1000
)

print(response.content)
print(f"Tokens used: {response.tokens_used}")
print(f"Cost: ${response.cost_estimate}")

import asyncio
from tenzro import AsyncTenzro

async def main():
    # Initialize async client
    async_client = AsyncTenzro(api_key="sk_your_key_here")
    
    try:
        # Generate text asynchronously
        response = await async_client.cortex.generate(
            prompt="Explain quantum computing",
            model="claude-3.5-sonnet",
            max_tokens=1000
        )
        
        print(response.content)
        
        # Multiple concurrent requests
        tasks = [
            async_client.cortex.generate(
                prompt=f"Explain {topic}",
                model="gpt-4o"
            )
            for topic in ["AI", "blockchain", "cloud computing"]
        ]
        
        results = await asyncio.gather(*tasks)
        for i, result in enumerate(results):
            print(f"Topic {i+1}: {result.content[:100]}...")
            
    finally:
        await async_client.close()

# Run the async function
asyncio.run(main())

import pandas as pd
from tenzro import Tenzro

client = Tenzro()

# Load dataset
df = pd.read_csv("sales_data.csv")

# Generate insights with AI
data_summary = df.describe().to_string()

response = client.cortex.generate(
    prompt=f"""
    Analyze this sales dataset and provide insights:
    
    {data_summary}
    
    Provide 3 key insights and recommendations for improvement.
    """,
    model="claude-3.5-sonnet",
    max_tokens=1000
)

print("AI Analysis:")
print(response.content)

# Process each row with AI
insights = []
for index, row in df.head().iterrows():
    insight = client.cortex.generate(
        prompt=f"Analyze this sales record: {row.to_dict()}",
        model="gpt-4o",
        max_tokens=200
    )
    insights.append(insight.content)

# Add insights back to dataframe
df_sample = df.head().copy()
df_sample['ai_insights'] = insights

print(df_sample[['product', 'sales', 'ai_insights']])