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Desktop App Local AI Integration Guide
Integrate a local LLM into a desktop application using Electron or Tauri with a streaming Ollama backend.
The Prompt
# Desktop App Local AI Integration Guide You are a full-stack engineer specializing in local AI desktop apps. Design the integration of a local LLM into the specified desktop application. ## App Context - Framework: [Electron / Tauri / .NET WinForms / PyQt / Other] - App purpose: [DESCRIBE THE DESKTOP APP] - AI feature to add: [FEATURE, e.g., "inline text suggestions", "document summarizer sidebar", "chat assistant panel"] - Target OS: [Windows / macOS / Linux / Cross-platform] - Local model to use: [MODEL NAME via Ollama or direct llama.cpp] ## Integration Design ### Architecture Diagram Describe the component layers: UI layer → IPC/bridge → local inference server → model. Include which process hosts each component. ### Ollama API Integration Code snippet showing how to call the local Ollama REST API with streaming enabled from [FRAMEWORK]. Include error handling for model-not-loaded and timeout cases. ### Streaming UI How to display streaming token output in the UI in real time. Provide a code skeleton for the frontend rendering logic. ### Model Lifecycle Management How to check if Ollama is running on launch, start it automatically if needed, and handle model loading/unloading. ### Packaging & Distribution How to bundle the app so end users do not need to install Ollama separately — or, if bundling is impractical, the required installer steps. ### Privacy Guarantee How to verify and communicate to users that no data leaves the local machine.
📝 Fill in the blanks
Replace these placeholders with your own content:
[Electron / Tauri / .NET WinForms / PyQt / Other]
[DESCRIBE THE DESKTOP APP]
[FEATURE, e.g., "inline text suggestions", "document summarizer sidebar", "chat assistant panel"]
[Windows / macOS / Linux / Cross-platform]
[MODEL NAME via Ollama or direct llama.cpp]
[FRAMEWORK]
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