Local AI Assistant with MAUI, Semantic Kernel, Ollama & LiteDB

Most AI apps depend on cloud APIs, bringing cost, latency, privacy, and connectivity concerns. This guide takes the opposite path, building a cross-platform assistant that runs entirely on-device — perfect for enterprise, field work, and privacy-sensitive scenarios.

What you’ll learn

• Why each piece of the stack matters: .NET MAUI (UI), Semantic Kernel (AI orchestration), Ollama (local LLM runtime), and LiteDB (embedded vector database)
• Installing Ollama and pulling a lightweight local model like Phi-3, Llama 3, Mistral, or Gemma
• Implementing Retrieval-Augmented Generation (RAG) fully on-device by storing document chunks, embeddings, and metadata in LiteDB
• Structuring prompt pipelines, context memory, and function orchestration with Semantic Kernel
• Adding a floating chat UI with Shaunebu.MAUI.FloatingChatButton
• The concrete benefits: zero cloud cost, full data privacy, offline support, and fast local retrieval

A must-read if you want private, offline AI baked directly into a cross-platform app.