AI Feedback Analysis with the Facade Pattern

Hi devs,

In AI development, keeping our code clean and manageable can sometimes feel like a battle, especially as we integrate more complex processes. That’s where the Facade Pattern comes in handy. This design pattern helps us simplify the interface for complex systems, making the code easier to work with and far less intimidating.

With Microsoft's Semantic Kernel, we can harness AI capabilities, such as sentiment analysis and summarization, without having to dive deep into each component every time. Here’s how we can use the Facade Pattern with Semantic Kernel in C# to streamline these tasks and keep our code readable.

Why Use the Facade Pattern with AI?

Think about a typical AI feedback system: it might involve multiple steps like preprocessing text, analyzing sentiment, and summarizing content. Each of these could require a separate model or API call. With the Facade Pattern, we can wrap these into a single, high-level interface, so our main application only calls on one method rather than managing each individual piece.

Code Example: AI Feedback Analysis in C# with Semantic Kernel

Here’s a practical example where we build a facade to handle feedback analysis, using Semantic Kernel to power AI tasks like sentiment analysis and text summarization.

First, make sure you have Microsoft.SemanticKernel installed.

dotnet add package Microsoft.SemanticKernel

Then, let’s set up the components in C#.

using System;
using Microsoft.SemanticKernel;
using System.Threading.Tasks;

namespace AIFacadeWithSemanticKernel
{
    // Step 1: Define subsystems using Semantic Kernel
    public class TextPreprocessor
    {
        public string PreprocessText(string text)
        {
            Console.WriteLine("Preprocessing text...");
            // Simplified preprocessing, more advanced options could go here
            return text.ToLower();
        }
    }

    public class SentimentAnalyzer
    {
        private readonly Kernel _kernel;

        public SentimentAnalyzer(Kernel kernel)
        {
            _kernel = kernel;
        }

        public async Task<string> AnalyzeSentiment(string text)
        {
            Console.WriteLine("Analyzing sentiment...");
            var sentimentPrompt = "Analyze the sentiment of the following text: " + text;
            var sentiment = await _kernel.RunAsync(sentimentPrompt);
            return sentiment;
        }
    }

    public class SummaryGenerator
    {
        private readonly Kernel _kernel;

        public SummaryGenerator(Kernel kernel)
        {
            _kernel = kernel;
        }

        public async Task<string> GenerateSummary(string text)
        {
            Console.WriteLine("Generating summary...");
            var summaryPrompt = "Summarize the following feedback: " + text;
            var summary = await _kernel.RunAsync(summaryPrompt);
            return summary;
        }
    }

    // Step 2: Define the Facade
    public class FeedbackAnalysisFacade
    {
        private readonly TextPreprocessor _preprocessor;
        private readonly SentimentAnalyzer _sentimentAnalyzer;
        private readonly SummaryGenerator _summaryGenerator;

        public FeedbackAnalysisFacade(Kernel kernel)
        {
            _preprocessor = new TextPreprocessor();
            _sentimentAnalyzer = new SentimentAnalyzer(kernel);
            _summaryGenerator = new SummaryGenerator(kernel);
        }

        public async Task AnalyzeFeedback(string feedback)
        {
            var preprocessedText = _preprocessor.PreprocessText(feedback);
            var sentiment = await _sentimentAnalyzer.AnalyzeSentiment(preprocessedText);
            var summary = await _summaryGenerator.GenerateSummary(preprocessedText);

            Console.WriteLine($"Sentiment: {sentiment}");
            Console.WriteLine($"Summary: {summary}");
        }
    }

    // Step 3: Main Program to Run the Facade
    class Program
    {
        static async Task Main(string[] args)
        {
            var kernel = new Kernel(); // Set up Semantic Kernel with necessary configurations
            var feedback = "I absolutely love the customer service experience!";
            var facade = new FeedbackAnalysisFacade(kernel);

            Console.WriteLine("Analyzing Feedback:");
            await facade.AnalyzeFeedback(feedback);
        }
    }
}

What This Does

TextPreprocessor: Handles basic preprocessing (this could be expanded as needed).
SentimentAnalyzer: Uses Semantic Kernel to determine sentiment.
SummaryGenerator: Summarizes the feedback using Semantic Kernel.

By wrapping these components into the FeedbackAnalysisFacade, the main program only needs to call a single method, AnalyzeFeedback, keeping our code clean and organized.

Why It Works

The Facade Pattern helps us maintain scalability and readability. As the AI model evolves, or if we want to add new steps, we only need to adjust within the facade. This is particularly useful in AI projects, where workflows can get complex quickly.

Final Thoughts

Using the Facade Pattern with AI simplifies things. Whether you’re analyzing customer feedback, running complex models, or just getting started with Semantic Kernel, this pattern keeps things tidy, making your code easier to understand and extend. Give it a try, and see how it changes the way you organize complex AI operations!

Blog

AI Feedback Analysis with the Facade Pattern

Daniel Azevedo

Why Use the Facade Pattern with AI?

Code Example: AI Feedback Analysis in C# with Semantic Kernel

What This Does

Why It Works

Final Thoughts

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