TNSAAI Python Client

A powerful, OpenAI-compatible Python SDK for TNSA NGen3 Pro and Lite Models.

Installation

pip install tnsaai-client

Quick Start

from tnsaai import TNSA

# Initialize the client
client = TNSA(api_key="your-api-key", base_url="https://api.tnsaai.com")

# Create a chat completion
response = client.chat.create(
    model="NGen3.9-Pro",
    messages=[{"role": "user", "content": "Hello!"}]
)

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

Streaming

stream = client.chat.create(
    model="NGen3.9-Lite",
    messages=[{"role": "user", "content": "Tell me a story"}],
    stream=True
)

for chunk in stream:
    if chunk.content:
        print(chunk.content, end="")

Async Usage

import asyncio
from tnsaai import AsyncTNSA

async def main():
    async with AsyncTNSA(api_key="your-api-key") as client:
        response = await client.chat.create(
            model="NGen3.9-Pro",
            messages=[{"role": "user", "content": "Hello!"}]
        )
        print(response.choices[0].message.content)

asyncio.run(main())

Available Models

• NGen3.9-Pro - High-performance model for complex tasks
• NGen3.9-Lite - Fast, efficient model for general use
• Farmvaidya-Bot - Agricultural domain-specific model

Configuration

Set your API key as an environment variable:

export TNSA_API_KEY="your-api-key"
export TNSA_BASE_URL="https://api.tnsaai.com"

Or pass it directly:

client = TNSA(
    api_key="your-api-key",
    base_url="https://api.tnsaai.com"
)

Features

• ✅ OpenAI-compatible API
• ✅ Synchronous and asynchronous clients
• ✅ Streaming responses
• ✅ Comprehensive error handling
• ✅ Automatic retries with exponential backoff
• ✅ Usage tracking and cost estimation
• ✅ Conversation management
• ✅ Type hints and IDE support

Support

• Email: info@tnsaai.com
• Documentation: https://docs.tnsaai.com
• Issues: https://github.com/tnsaai/tnsaai-python/issues