Vector Vault 0.1.1

Vector Vault 0.1.1

A local-first desktop app that reads your documents, files them where they belong, and lets you find anything — documents and photos — by describing it in plain English.

Everything runs on your machine. The language model, the embeddings, your files: nothing is uploaded anywhere.

Fixed since 0.1.0

• The app has a proper icon everywhere — title bar, taskbar, Start Menu, and the new desktop shortcut the installer now creates.
• The folder-selection dialog now opens in front of the window instead of behind it.
• The Library and Images icons in the side rail actually render (invalid icon names in 0.1.0).
• The welcome buttons launch their feature directly — folder picker opens, photo search activates — instead of typing example text into the chat.
• The welcome screen clears once a conversation starts.
• Installing 0.1.1 over 0.1.0 upgrades in place; your library and settings are kept.

What it does

• 📂 Drop a folder into the chat — it reads every file, builds a knowledge base, and proposes where each one belongs, with a reason. Nothing moves until you click Apply, and every move can be undone.
• 💬 Ask your documents anything — answers cite the exact file and page. If your library doesn't cover it, it says so instead of making something up.
• 🖼️ Find photos by describing them — "a man lifting a baby", "sunset on a beach". No tags, no filenames; it looks at the pictures.
• 🗂️ Works while you're not looking — lives in the system tray, watches your Downloads folder, and files new documents automatically. Closing the window doesn't stop it; quit from the tray icon.

Install

1. Download VectorVault-0.1.1-win64.msi below and run it.
2. Windows SmartScreen will warn you — the installer is not code-signed yet. Click More info → Run anyway.
3. On first launch, a setup screen detects your GPU and RAM, recommends a language model that actually fits your hardware, installs Ollama, and downloads the model with a progress bar. Internet is needed for this one step; after that the app is fully offline.

Requirements: Windows 10/11 (64-bit). ~1.2 GB for the app plus 1–9 GB for the language model depending on what your hardware supports. A GPU with ≥4 GB VRAM is recommended but not required.

Good to know

• The academic classifier ships with a sample B.Tech CSE curriculum. Edit curriculum.yaml (see the README) to teach it your own subjects — it rebuilds its knowledge base automatically.
• Scanned/photographed PDFs are detected and routed to the Review queue, but OCR isn't built yet — that's the next milestone.
• The AI never executes commands against your files. It proposes a plan; a logged, reversible engine applies it. Classification thresholds were calibrated by measurement, not guesswork.

Under the hood

Ollama (qwen2.5, hardware-matched) · bge-small embeddings · CLIP image search · SQLite + write-ahead move log · 114 tests

SHA256 (VectorVault-0.1.1-win64.msi):
919BA63DB415A2BF2485CB215E2ED1E0D1ABA63B3BDF686764D90170F1BD4398

Vector Vault 0.1.0

Vector Vault 0.1.0

A local-first desktop app that reads your documents, files them where they belong, and lets you find anything — documents and photos — by describing it in plain English.

Everything runs on your machine. The language model, the embeddings, your files: nothing is uploaded anywhere.

What it does

• 📂 Drop a folder into the chat — it reads every file, builds a knowledge base, and proposes where each one belongs, with a reason. Nothing moves until you click Apply, and every move can be undone.
• 💬 Ask your documents anything — answers cite the exact file and page. If your library doesn't cover it, it says so instead of making something up.
• 🖼️ Find photos by describing them — "a man lifting a baby", "sunset on a beach". No tags, no filenames; it looks at the pictures.
• 🗂️ Works while you're not looking — lives in the system tray, watches your Downloads folder, and files new documents automatically. Closing the window doesn't stop it; quit from the tray icon.

Install

1. Download VectorVault-0.1.0-win64.msi below and run it.
2. Windows SmartScreen will warn you — the installer is not code-signed yet. Click More info → Run anyway.
3. On first launch, a setup screen detects your GPU and RAM, recommends a language model that actually fits your hardware, installs Ollama, and downloads the model with a progress bar. Internet is needed for this one step; after that the app is fully offline.

Requirements: Windows 10/11 (64-bit). ~1.2 GB for the app plus 1–9 GB for the language model depending on what your hardware supports. A GPU with ≥4 GB VRAM is recommended but not required.

Good to know

• The academic classifier ships with a sample B.Tech CSE curriculum. Edit curriculum.yaml (see the README) to teach it your own subjects — it rebuilds its knowledge base automatically.
• Scanned/photographed PDFs are detected and routed to the Review queue, but OCR isn't built yet — that's the next milestone.
• The AI never executes commands against your files. It proposes a plan; a logged, reversible engine applies it. Classification thresholds were calibrated by measurement, not guesswork.

Under the hood

Ollama (qwen2.5, hardware-matched) · bge-small embeddings · CLIP image search · SQLite + write-ahead move log · 114 tests

SHA256 (VectorVault-0.1.0-win64.msi):
A80626AC2896215F422F06087F49540CA2430B849FDF78B198F0B1B311B0D97C