Semantic Kernel
Semantic Kernel is an open-source Microsoft SDK for building AI applications that integrate language models with application code, plugins, and related capabilities.
Generative AI in Action
SK is an AI application framework that provides abstractions for integrating language models with prompts, functions, and external services.
- How it is used
- Supports PDF question answering by connecting model services, embeddings, and vector-backed memory.
- How deep it goes
- Shows service registration and memory setup, alongside database population; does not explain model internals.
- Examples
- Uses Python listings that extract PDFs, populate a vector database, and configure Semantic Kernel with Chroma.
- What you can do after
- Apply it via Semantic Kernel to assemble the demonstrated PDF Q&A service and memory configuration.
AI Agents in Action
Semantic Kernel is an open-source Microsoft software development kit for building AI applications that orchestrate language-model capabilities, plugins, and related services.
- How it is used
- Organizes agent actions by connecting language-model services, semantic functions, native functions, and plugins.
- How deep it goes
- Demonstrates installation, service registration, semantic-function execution, plugin composition, and GPT-interface construction, while focusing on actions rather than memory or planning.
- Examples
- Uses OpenAI or Azure OpenAI chat completion, TMDB API integration, temperature conversion, weather, creative writing, meal planning, and news plugins.
- What you can do after
- Build agent actions by installing SK, registering language-model services, creating functions, combining plugins, and exposing APIs through chat interfaces.
Building Distributed Applications that Work
Semantic Kernel is a collection of .NET libraries that provides interfaces for integrating large language models with application code.
- How it is used
- Integrates LLM functionality into .NET applications, including a distributed Aspire application.
- How deep it goes
- Defines Semantic Kernel’s role and capabilities at a high level without detailing implementation steps.
- Examples
- Uses a locally hosted product-question assistant and streamed chat responses as application examples.
- What you can do after
- Build .NET AI features that interact with LLMs, including chat experiences that stream responses.
Read how Building Distributed Applications that Work explains Semantic Kernel →
Related concepts
- large language modeluses · A large language model is a machine-learning model, typically neural, trained on extensive text data to process and generate language.
- Ollamauses · Ollama is software for downloading, running, and serving language models locally.
- pluginuses · A plugin is an optional software component that extends or modifies a host application’s capabilities through an extension or integration mechanism.
- client librarykind of · A client library is reusable software that provides programmatic interfaces for an application to interact with a service, platform, or system.
- native functionconfigured by · A native function is a conventionally programmed function that can be invoked by an orchestration framework or language-model application.
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