Toolkits in SmartGraph provide a powerful way to extend the capabilities of AI assistants by giving them access to external data sources or functionalities. This guide will walk you through creating custom toolkits and using them with the CompletionComponent.

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Understanding Toolkits

In SmartGraph, a toolkit is a collection of functions that can be used by an AI assistant to perform specific tasks. Toolkits are based on the abstract Toolkit class and must implement certain methods and properties.

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The Base Toolkit Class

from abc import ABC, abstractmethod
from typing import Any, Dict, List

class Toolkit(ABC):
    @property
    @abstractmethod
    def name(self) -> str:
        """Return the name of the toolkit."""
        pass

    @property
    @abstractmethod
    def description(self) -> str:
        """Return a description of the toolkit."""
        pass

    @property
    @abstractmethod
    def functions(self) -> Dict[str, Any]:
        """Return a dictionary of functions provided by the toolkit."""
        pass

    @property
    def schemas(self) -> List[Dict[str, Any]]:
        """Return a list of function schemas for the toolkit."""
        return [
            {
                "type": "function",
                "function": {
                    "name": name,
                    "description": func.__doc__,
                    "parameters": getattr(func, "schema", {}),
                },
            }
            for name, func in self.functions.items()
        ]

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Creating a Custom Toolkit

Let’s walk through creating a custom toolkit using the WeatherToolkit as an example: 
from typing import Any, Dict, List
from .base_toolkit import Toolkit

class WeatherToolkit(Toolkit):
    def __init__(self, api_key: str):
        self._api_key = api_key

    @property
    def name(self) -> str:
        return "weather_toolkit"

    @property
    def description(self) -> str:
        return "A toolkit for fetching weather information"

    @property
    def functions(self) -> Dict[str, Any]:
        return {"get_temperature": self.get_temperature}

    @property
    def schemas(self) -> List[Dict[str, Any]]:
        return [
            {
                "type": "function",
                "function": {
                    "name": "get_temperature",
                    "description": "Get the current temperature for a specific location",
                    "parameters": {
                        "type": "object",
                        "properties": {
                            "location": {
                                "type": "string",
                                "description": "The city and country, e.g., 'London, UK'",
                            }
                        },
                        "required": ["location"],
                    },
                },
            }
        ]

    async def get_temperature(self, location: str) -> Dict[str, Any]:
        # Simulated API call
        # In a real implementation, you would make an API call to a weather service here
        return {"temperature": 22.5, "unit": "Celsius", "location": location}
Key points in creating a custom toolkit:
  1. Inherit from the Toolkit base class.
  2. Implement the required properties: name, description, functions, and schemas.
  3. Define the functions that the toolkit will provide (e.g., get_temperature).
  4. Specify the schema for each function, including parameters and their types.

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Using a Toolkit with CompletionComponent

Once you have created a custom toolkit, you can use it with a CompletionComponent to enhance your AI assistant’s capabilities. Here’s an example: 
import asyncio
import os
from dotenv import load_dotenv
from smartgraph.components import CompletionComponent
from smartgraph.logging import SmartGraphLogger
from smartgraph.tools.weather_toolkit import WeatherToolkit

# Load environment variables and set up logging
load_dotenv()
logger = SmartGraphLogger.get_logger()
logger.set_level("INFO")

async def process_with_llm(input_message: str) -> dict:
    # Create a WeatherToolkit
    weather_toolkit = WeatherToolkit(api_key=os.getenv("OPENWEATHER_API_KEY"))

    # Create a CompletionComponent with the WeatherToolkit
    completion = CompletionComponent(
        "AI_Completion",
        model="gpt-3.5-turbo",
        temperature=0.7,
        api_key=os.getenv("OPENAI_API_KEY"),
        toolkits=[weather_toolkit],
    )

    input_data = {"message": input_message}
    logger.info(f"Processing input: {input_message}")

    try:
        result = await completion.process(input_data)
        if "error" in result:
            logger.error(f"Error during processing: {result['error']}")
            return {"error": result["error"]}
        else:
            logger.info(f"Received: {result}")
            return {"response": result.get("ai_response", "No response")}
    except Exception as e:
        logger.error(f"Unexpected error during processing: {str(e)}", exc_info=True)
        return {"error": str(e)}

async def main():
    input_message = "What's the current temperature in London?"
    result = await process_with_llm(input_message)

    if "error" in result:
        print(f"Error: {result['error']}")
    else:
        print(f"AI Response: {result['response']}")

if __name__ == "__main__":
    asyncio.run(main())
In this example:
  1. We create an instance of the WeatherToolkit.
  2. We pass the toolkit to the CompletionComponent when initializing it.
  3. The AI assistant can now use the functions provided by the WeatherToolkit to answer weather-related questions.

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Best Practices for Creating and Using Toolkits

  1. Modularity: Design toolkits to be modular and focused on specific functionalities.
  2. Error Handling: Implement robust error handling in toolkit functions.
  3. Documentation: Provide clear descriptions for toolkit functions and their parameters.
  4. API Key Management: Use environment variables for sensitive information like API keys.
  5. Asynchronous Design: Make toolkit functions asynchronous when dealing with I/O operations.
  6. Testing: Create unit tests for your toolkit functions to ensure reliability.
  7. Versioning: Consider versioning your toolkits if you plan to make breaking changes.

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Conclusion

Toolkits in SmartGraph provide a powerful mechanism to extend the capabilities of AI assistants. By creating custom toolkits, you can give your AI access to external data sources, APIs, or custom functionality, making it more versatile and capable of handling a wide range of tasks. Remember to design your toolkits with modularity, error handling, and clear documentation in mind. With well-designed toolkits, you can create AI assistants that can perform complex tasks and integrate seamlessly with external systems and data sources.