Knovum 0.0.2: Building an Open-Source Local-First AI Canvas with Tauri 2, Vue 3, and Rust

说明与用途

Knovum 0.0.2 — 基于 Tauri 2、Vue 3、Rust、SQLite 和 LanceDB 的开源本地 AI 工作区，提供无限二维画布、45 MB 空闲内存和本地优先存储。

问题

Existing desktop AI wrappers suffer from a linear chat interface that hampers deep research synthesis and consume 700 MB–1 GB of RAM before loading model weights.

结果

Knovum achieves a local‑first AI workspace with sub‑second startup, 45 MB idle RAM, and a 2D canvas that supports side‑by‑side document comparison, while maintaining efficient vector search and low memory usage.

工作流

• 1. Use Tauri 2 to orchestrate a native WebView with a compiled Rust backend for sub‑second cold boot and 45 MB idle RAM.
• 2. Build a Vue 3 frontend with Konva for an infinite 2D canvas and JSON Canvas specification for workspace persistence.
• 3. Store notes, boards, and relations in SQLite with strict migrations.
• 4. Run an in‑process LanceDB for fast vector similarity search without external daemons.
• 5. Route local LLM calls through Ollama API and embed Whisper for on‑device transcription.
• 6. Implement differential space indexing to rebuild only gaps during updates.
• 7. Enforce streaming conversation boundaries and mutation‑aware citation integrity.
• 8. Harden Windows desktop with opaque WebView2 bootstrapper and safe async window reveal.

证据

Existing desktop frontends repeatedly hit two frustrating walls: The 1D Chat Trap and The Electron Memory Tax.

来源

Knovum cold‑boots in sub‑second time and idles around 45 MB of RAM.

来源

Every visual workspace is stored directly on your local filesystem using the open JSON Canvas (.canvas) specification.

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