devshakti-ai 0.1.2

Python SDK for Shakti LLM API - OpenAI compatible

DevShakti AI SDK

Python SDK for Shakti AI - An OpenAI-compatible LLM API.

🚀 Installation

pip install devshakti-ai

📚 Documentation

Full documentation, interactive playground, and API reference available at:

🔗 https://shakti-one.vercel.app

⚡ Quick Start

from shakti import ChatShakti
import json

# Initialize client
client = ChatShakti(api_key="sk-your-api-key")

# Simple completion
response = client.chat.completions.create(
    messages=[{"role": "user", "content": "Hello"}]
)
print(response['choices'][0]['message']['content'])

🎯 Streaming Example

from shakti import ChatShakti
import json

client = ChatShakti(api_key="sk-your-api-key")

# Streaming response
for chunk in client.chat.completions.create(
    messages=[{"role": "user", "content": "Tell me a story"}],
    stream=True
):
    chunk_dict = json.loads(chunk)
    content = chunk_dict['choices'][0]['delta'].get('content', '')
    if content:
        print(content, end='', flush=True)

🎨 Custom Configuration

from shakti import ChatShakti

# Custom base URL and parameters
client = ChatShakti(
    api_key="sk-your-api-key",
    base_url="https://devshakti.serveo.net"  # Optional
)

response = client.chat.completions.create(
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"}
    ],
    model="shakti-01-chat",
    temperature=0.7,
    max_tokens=1024,
    stream=False
)

✨ Features

• ✅ OpenAI-compatible API - Easy migration from OpenAI
• ✅ Streaming support - Real-time token streaming
• ✅ Simple Python interface - Clean, intuitive SDK
• ✅ Free tier available - Get started at no cost
• ✅ Fast responses - 2-3 second average latency
• ✅ Rate limiting - 60 requests/min, 150k tokens/min

🔧 API Endpoints

• Base URL: https://devshakti.serveo.net
• Chat Completions: /v1/chat/completions
• Models: /v1/models
• Health Check: /health

📝 Basic Usage

Simple Request

response = client.chat.completions.create(
    messages=[{"role": "user", "content": "What is Python?"}]
)

With System Prompt

response = client.chat.completions.create(
    messages=[
        {"role": "system", "content": "You are a coding expert."},
        {"role": "user", "content": "Explain async/await"}
    ]
)

Streaming Tokens

for chunk in client.chat.completions.create(
    messages=[{"role": "user", "content": "Write a poem"}],
    stream=True
):
    # Process each token as it arrives
    chunk_dict = json.loads(chunk)
    content = chunk_dict['choices'][0]['delta'].get('content', '')
    if content:
        print(content, end='', flush=True)

🔑 Get Your API Key

Visit https://shakti-one.vercel.app to:

• 🔐 Generate your free API key
• 🎮 Try the interactive playground
• 📖 Read full documentation
• 💻 See more code examples

📊 Rate Limits

• Requests: 60 per minute
• Tokens: 150,000 per minute
• Concurrent: 10 requests

🛠️ Requirements

• Python 3.7+
• requests library (installed automatically)

🤝 Support

• Documentation: https://shakti-one.vercel.app
• GitHub Issues: https://github.com/shivnathtathe/shakti-sdk/issues
• Email: [e-mail]

📄 License

MIT License - See LICENSE file for details.

🌟 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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Made with ❤️ by Shivnath Tathe

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