LlamaChat SDK

A Python SDK for interacting with Llama language models through a chat interface. This SDK provides a simple way to integrate Llama's powerful language models into your applications, with support for function calling and various model sizes.

Features

• 🤖 Easy integration with Llama language models
• 🔄 Support for multiple model sizes (1B, 3B, 8B, and 70B parameters)
• 🛠️ Function registration and calling capabilities
• 🐞 Debug mode for troubleshooting
• 💬 Conversation history management
• 🔌 Simple API interface

Installation

pip install llamachat-sdk

Quick Start

from run import LlamaChat, LlamaModel

# Initialize the chat client
chat = LlamaChat(
    api_key="your_api_key_here",
    model=LlamaModel.LLAMA_8B,
    debug=False
)

# Send a message and get a response
response = chat.chat("Hello, how are you?")
print(response)

Available Models

The SDK supports the following Llama models:

• LLAMA_1B: 1 billion parameter model
• LLAMA_3B: 3 billion parameter model
• LLAMA_8B: 8 billion parameter model (default)
• LLAMA_70B: 70 billion parameter model

Function Registration

You can register custom functions that the AI can call:

def get_weather(city: str) -> str:
    return f"The weather in {city} is sunny!"

# Register a single function
chat.register_function(
    func=get_weather,
    description="Get the current weather for a given city"
)

# Register multiple functions
functions = [
    {
        "function": get_weather,
        "description": "Get the current weather for a given city"
    },
    # Add more functions as needed
]
chat.register_functions(functions)

Advanced Usage

Debug Mode

Enable debug mode to log API responses:

chat = LlamaChat(
    api_key="your_api_key_here",
    model=LlamaModel.LLAMA_8B,
    debug=True
)

Custom Model Selection

Use a custom model string if needed:

chat = LlamaChat(
    api_key="your_api_key_here",
    model="custom-model-identifier"
)

Function Calling Example

# Register a function
def calculate_sum(a: int, b: int) -> int:
    return a + b

chat.register_function(
    calculate_sum,
    "Calculate the sum of two numbers"
)

# The AI can now use this function
response = chat.chat("What is 5 plus 3?")
# The AI might respond with a function call to calculate_sum(5, 3)

API Reference

LlamaChat Class

class LlamaChat:
    def __init__(
        self,
        api_key: str,
        model: Union[LlamaModel, str] = LlamaModel.LLAMA_8B,
        debug: bool = False
    )

Parameters:

• api_key (str): Your API authentication key
• model (Union[LlamaModel, str]): The Llama model to use
• debug (bool): Enable debug logging

Methods:

• chat(message: str) -> str: Send a message and get a response
• register_function(func: Callable, description: str): Register a single function
• register_functions(functions: List[Dict[str, Any]]): Register multiple functions

Error Handling

The SDK includes built-in error handling for:

• API connection issues
• Invalid function calls
• Response parsing errors

Development

The source code is available in the run.py file. To contribute:

1. Fork the repository
2. Create a feature branch
3. Submit a pull request

License

MIT License

Support

For support, please open an issue in the GitHub repository or contact the maintainers.