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Basic Information

Notate is a cross-platform desktop chat application designed to provide seamless AI interactions for end users and developers. It centralizes access to multiple AI providers and local inference backends, enabling conversations with models from OpenAI, Anthropic, Google, XAI, OpenRouter, and DeepSeek as well as locally hosted models via llamacpp, transformers, or ollama. The app includes document question-and-answer workflows through RAG integration with ChromaDB and tools for ingesting files or URLs into FileCollections. It is packaged for Windows, macOS, and Linux installers and offers developer API access, configurable model endpoints and settings, experimental reasoning features, and a local-only mode for privacy-sensitive use. The repository contains frontend Electron project structure, build and distribution scripts, development run commands, system requirements, and user-facing documentation and screenshots.

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App Details

Features
Notate provides multi-model support across cloud providers and local inference backends, local deployment options using llamacpp, transformers, and ollama, and built-in RAG workflows with ChromaDB for document Q&A. It offers flexible configuration of API endpoints and model settings, developer API access, experimental reasoning tools, and a privacy-oriented local-only mode. The application includes FileCollections and collection ingestion tools to import content from files or URLs, platform-specific installers and packaging targets for .exe, .dmg, AppImage and .deb, and a developer-focused build and run workflow for macOS, Windows, and Linux. The README documents system and external requirements, recommended hardware for local inference, screenshots demonstrating the chat UI and settings, and community support via Discord.
Use Cases
Notate helps users and developers interact with multiple AI models from a single desktop client while supporting local model inference for privacy and offline workflows. The RAG and ChromaDB integration enable extracting answers from ingested documents, making it useful for knowledge work, research assistance, and document-centric Q&A. Flexible model configuration and developer API access let technical users integrate custom endpoints and tailor model behavior. Packaged installers and cross-platform build scripts simplify deployment for end users, while FileCollections and ingestion tools streamline adding source material. The local-only mode and explicit system requirements make it possible for privacy-conscious deployments and local experimentation with large models when appropriate hardware is available.

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