pyindus 0.1.0

Python package for Indus Chat API by Sarvam AI

PyIndus

A Python package for interacting with Indus, a ChatGPT alternative by Sarvam AI.

Installation

pip install pyindus

Or with uv:

uv add pyindus

Quick Start

IndusClient acts as a fully-featured, seamless SDK. It automatically saves, loads, and refreshes sessions for you.

1. Initial Login

Run this once to authenticate. The client will automatically save your session to indus_session.json by default.

from pyindus import IndusClient

# Login with phone number
client = IndusClient()
client.login("+91XXXXXXXXXX")

# Enter the OTP received via SMS
client.verify_otp("123456")

# The session is now authenticated and saved automatically!

2. Immediate Re-use (Like an SDK)

Run this anywhere else in your project. Because the session was saved, the client automatically loads it on __init__. If the token expires, the client will dynamically refresh it in the background.

from pyindus import IndusClient

# Automatically loads the previous session from 'indus_session.json'
client = IndusClient()

# Chat directly! No need to login again.
response = client.chat("What is quantum computing?")
print(response.answer)

Integration Guide: Custom Paths

If you're building a web app or managing multiple users, you can specify individual session files.

from pyindus import IndusClient

# Supply a unique path for the user's session
def handle_user_request(user_id, message):
    session_path = f"sessions/user_{user_id}.json"
    
    # Auto-loads and manages session in this specific file
    with IndusClient(session_file=session_path) as client:
        return client.chat(message)

Advanced Usage

Working with Specific Models

Indus supports different "Task Graphs" (models like Sarvam Think, Bulbul, etc.). By default, IndusClient selects the first available chat model automatically.

from pyindus import IndusClient

with IndusClient() as client:
    # List available models
    models = client.get_models()
    for model in models:
        print(f"{model.name}: {model.description}")

    # Use a specific model
    response = client.chat("Explain gravity", task_graph_uid=models[-1].uid)
    print(response.answer)

License

MIT