Semantic Kernel and Microsoft.Extensions.AI: Better Together, Part 2

Table of Contents:

1. Getting Started with Microsoft.Extensions.AI and Semantic Kernel

   • Using Kernel Builder
   • Using a Chat Client directly with Azure OpenAI
   • Using Dependency Injection
   • Converting Between IChatCompletionService and IChatClient

2. Basic Embedding Generation

   • Working with Azure OpenAI Embeddings
   • Function Calling Integration
     • Creating and Using Kernel Functions
     • Working with KernelFunction Directly
   • Content Type Conversions
     • Using with Microsoft.Extensions.AI Types
   • Multiple Chat Providers

3. Conclusion

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1. Getting Started with Microsoft.Extensions.AI and Semantic Kernel:

• Using Kernel Builder: Exploring how to use Kernel Builder with Semantic Kernel.
• Using a Chat Client directly with Azure OpenAI: Utilizing a Chat Client with Azure OpenAI.
• Using Dependency Injection: Implementing Dependency Injection with Semantic Kernel.
• Converting Between IChatCompletionService and IChatClient: Seamless conversion between interfaces.

2. Basic Embedding Generation:

• Working with Azure OpenAI Embeddings: Generating embeddings with Azure OpenAI.
• Function Calling Integration:
  • Creating and Using Kernel Functions: Integration of function calling with Semantic Kernel.
  • Working with KernelFunction Directly: Direct usage of KernelFunction.
• Content Type Conversions:
  • Using with Microsoft.Extensions.AI Types: Conversion between content types.
• Multiple Chat Providers: Handling multiple chat providers.

3. Conclusion:

The integration between Microsoft.Extensions.AI and Semantic Kernel provides a powerful foundation for building AI applications. By leveraging both technologies, you gain flexibility, productivity, interoperability, and scalability. The examples provided demonstrate practical patterns that can be applied in various AI applications. Explore and experiment with these examples in your projects for enhanced AI capabilities.