DEV Community

TSOTSI1
TSOTSI1

Posted on

TECH

Hackathon Project Presentation

Slide 1: Title Slide

  • Title: Hackathon Project Presentation Integrating Semantic Kernel with Azure AI
  • Subtitle: Leveraging Singleton Injection for Efficient Plugin Management
  • Additional Info:
    • Hackathon Name / Event Date
    • Team Name / Project Name

Slide 2: Agenda

  • Introduction & Project Overview
  • Semantic Kernel & Azure AI Overview
  • Architecture & Dependency Injection
  • Implementation Highlights
  • Demo & Results
  • Challenges, Future Work & Q&A

Slide 3: Project Overview

  • Objective:
    • Develop an innovative solution integrating Semantic Kernel and Azure AI.
  • Key Technologies:
    • Semantic Kernel: For semantic processing and AI integration
    • Azure AI: To enhance scalability and performance
  • Hackathon Goals:
    • Demonstrate rapid prototyping and advanced integration techniques
  • Unique Selling Points:
    • Dynamic plugin management using DI (AddSingleton, AddScoped, AddTransient)

Slide 4: Semantic Kernel Overview

  • What is Semantic Kernel?
    • A framework to integrate semantic capabilities into your application.
  • Core Features:
    • Language understanding
    • Knowledge integration
    • Native plugin support for extended functionality
  • Documentation Reference:

Slide 5: Azure AI Integration

  • Overview of Azure AI:
    • A suite of AI services enhancing cognitive capabilities.
  • Key Benefits:
    • Scalability
    • Robust performance
    • Seamless integration with Semantic Kernel
  • Impact on Project:
    • Improved accuracy in semantic processing
    • Enhanced overall user experience

Slide 6: Dependency Injection & Plugin Management

  • Singleton Injection:
    • Utilizes AddSingleton to initialize and reuse a single instance of Semantic Kernel.
  • Other DI Methods:
    • AddScoped: Manages per-request instances (e.g., specific plugins)
    • AddTransient: Creates a new instance per usage
  • Why This Matters:
    • Ensures optimal resource management and application performance.
  • Reference Example:

Slide 7: Architecture Diagram

  • Diagram Elements:
    • Semantic Kernel Initialization: Registered via AddSingleton
    • Plugin Registration: Managed using AddScoped and AddTransient
    • Integration Flow:
    • User requests → Application services → Semantic Kernel → Azure AI responses
  • Explanation:
    • Visual walkthrough of how components interact and support dynamic plugin behavior.

Slide 8: Code Implementation Highlights

  • Service Configuration:

    • Code snippet showing DI setup: 
    // Register Semantic Kernel as a Singleton
services.AddSingleton<SemanticKernel>(sp => new SemanticKernel());

// Register plugins with different lifetimes
services.AddScoped<IPlugin, ScopedPlugin>();
services.AddTransient<IPlugin, TransientPlugin>();

  • Key Points:

    • Centralized initialization of Semantic Kernel
    • Flexible plugin injection for customized behavior
    • Optimized performance via proper lifecycle management

Slide 9: Demo & Results

  • Live Demo / Screenshots:
    • Display the application in action, highlighting semantic processing and Azure AI responses.
  • Performance Metrics:
    • Response time improvements
    • Enhanced scalability and resource management
  • User Impact:
    • Improved accuracy and dynamic functionality through native plugins

Slide 10: Challenges and Learnings

  • Technical Challenges:
    • Integrating multiple DI lifecycles
    • Managing stateful vs. stateless components
  • Resolutions:
    • Leveraged DI best practices (Singleton, Scoped, Transient)
    • Iterative testing and real-time adjustments during development
  • Key Learnings:
    • Importance of clear architectural design
    • Balancing performance with flexibility in plugin management

Slide 11: Future Work and Enhancements

  • Potential Improvements:
    • Integrate additional Azure AI services
    • Expand plugin functionalities with more native capabilities
  • Scalability Considerations:
    • Optimize DI configurations for larger deployments
  • Long-Term Vision:
    • Build a robust, extensible AI integration platform leveraging Semantic Kernel

Slide 12: Conclusion

  • Summary:
    • Successfully integrated Semantic Kernel with Azure AI using advanced DI techniques.
  • Project Impact:
    • Demonstrated a scalable, efficient approach for managing AI-powered functionalities.
  • Final Thoughts:
    • Emphasis on continuous learning and innovation for future enhancements.

Slide 13: Q&A

  • Discussion:
    • Invite questions and open the floor for discussion.
  • Contact Information:
    • Provide details for follow-up queries.

Top comments (1)

Collapse
 
john_william_2219ea1d68f7 profile image
John William •

Tech is the driving force behind how we live, work, and connect — it's constantly shifting, opening doors to new possibilities and challenges. Staying updated with technology doesn't always mean following the crowd, but being open to learning can possibly give you an edge. Trwho Tech might offer perspectives or resources that could help you explore what’s emerging and what’s worth paying attention to. While nothing is guaranteed, engaging with the right platform may lead to smarter choices.