sm-chat

The sm-chat library provides a streamlined interface for demonstrating and integrating with the SuperMaker AI chat platform. This package allows developers to quickly explore and prototype interactions with AI-powered conversational agents.

Installation

You can install the sm-chat package using pip: bash pip install sm-chat

Basic Usage

The following examples illustrate common use cases for the sm-chat library. Please note that specific API keys or authentication details might be required for full functionality, depending on the SuperMaker AI chat platform's requirements. Consult the SuperMaker AI documentation for details.

Example 1: Initiating a Simple Conversation

This example demonstrates a basic exchange with the chat agent. python import sm_chat

Assuming you have a valid API key or authentication token

api_key = "YOUR_API_KEY" # Replace with your actual API key

chat_client = sm_chat.ChatClient(api_key=api_key)

response = chat_client.send_message("Hello, how are you today?") print(response)

Example 2: Handling User Input and Generating Responses

This example shows how to manage user input and receive responses from the chat agent in an interactive loop. python import sm_chat

api_key = "YOUR_API_KEY" # Replace with your actual API key

chat_client = sm_chat.ChatClient(api_key=api_key)

while True: user_input = input("You: ") if user_input.lower() == "exit": break response = chat_client.send_message(user_input) print("AI: ", response)

Example 3: Customizing the Chat Agent's Personality (If Supported)

If the SuperMaker AI chat platform supports personality customization, this example demonstrates how to potentially modify the agent's behavior. This is a placeholder; refer to the actual API documentation for specific parameters. python import sm_chat

api_key = "YOUR_API_KEY" # Replace with your actual API key

chat_client = sm_chat.ChatClient(api_key=api_key)

Placeholder for personality customization (check SuperMaker AI documentation)

customization_options = { "tone": "professional", "style": "concise" }

chat_client.configure(customization_options) # Assuming a configure method exists

response = chat_client.send_message("Explain the concept of artificial intelligence.") print(response)

Example 4: Utilizing Context for More Relevant Responses

This demonstrates how to maintain context across multiple messages to achieve a more coherent conversation. python import sm_chat

api_key = "YOUR_API_KEY" # Replace with your actual API key

chat_client = sm_chat.ChatClient(api_key=api_key)

First Message

response1 = chat_client.send_message("I am planning a trip to Paris.") print("AI: ", response1)

Second Message (with context)

response2 = chat_client.send_message("What are some must-see attractions?", conversation_id=chat_client.conversation_id) print("AI: ", response2)

Features

• Simplified API: Provides an easy-to-use interface for interacting with the SuperMaker AI chat platform.
• Message Handling: Facilitates sending messages to the chat agent and receiving responses.
• Context Management: (Potentially) Supports maintaining conversation context for more coherent interactions.
• Customization: (Potentially) Allows for customizing the chat agent's personality and behavior (depending on platform support).
• Error Handling: Includes basic error handling to manage potential issues during communication.

License

MIT

This project is a gateway to the sm-chat ecosystem. For advanced features and full capabilities, please visit: https://supermaker.ai/chat/