FileMind

Getting Started

Get FileMind up and running in under ten minutes. This guide covers installation, the first-run setup wizard, and your first library scan. It describes FileMind 0.4.1-beta.

System Requirements

MinimumRecommended
OSWindows 10 or laterWindows 11
RAM8 GB16 GB (for local AI models)
Disk~880 MB download, ~2.5 GB installedPlus ~2.5 GB for the built-in AI model, or 2–10 GB for an Ollama model
GPUNot requiredAn NVIDIA GPU speeds up local AI models considerably

The search and OCR models are bundled with the installer, so scanning, indexing and search all work with no internet connection. Choosing a language model is a required step in setup — Ask needs one, and rename proposals fall back to it. If you choose the built-in model it is downloaded at that point; the Ollama and LM Studio options use a model already on your machine, and the cloud options need only a key.

Installation

  1. Download FileMind-Setup-0.4.1-beta.exe from the download page
  2. Run the installer. The beta is not code-signed yet, so Windows shows a blue Windows protected your PC screen. Click More info, then Run anyway. The download page explains the prompt and publishes the SHA-256 checksum so you can verify the file first.
  3. FileMind appears in your Start menu and launches when the installer finishes

FileMind currently ships for Windows only. A macOS build is planned — every license already covers both platforms, so there's nothing further to buy when it ships. Beta builds update themselves: when a newer beta is published, FileMind downloads it in the background and installs it on the next restart.

First Launch

On first launch FileMind opens the five-step Setup Wizard: a welcome screen, your library location and folders, a language model, the first scan, and a short tour. Choosing the built-in model is the quickest route — one click, no account, no separate install — but Ollama, LM Studio, and cloud API keys are all supported.

The FileMind setup wizard welcome screen, step 1 of 5
The wizard runs once. You can re-run it later from Settings → Setup.

Your First Scan

The wizard runs the first scan for you as soon as you have chosen a model. To scan more folders later:

  1. On the Dashboard, click Add Directory and choose a folder — FileMind scans recursively through subfolders
  2. Click Step 1: Scan & Extract Metadata
  3. Watch progress in Recent Jobs. You can keep using the app — the stat cards update as papers are indexed
  4. Click Step 2: Generate Rename Proposals, then review them in the Rename Queue
The setup wizard scanning a library folder, showing per-file progress
The first scan runs inside the wizard. Continue in background lets you carry on while it finishes.
The FileMind dashboard after a scan: scanned directories, stat cards for PDFs, metadata, proposals and errors, the three pipeline buttons, and recent jobs
The dashboard after scanning 14 papers and generating proposals. The AI Ready badge means the language model is connected.

What Happens During a Scan

For each PDF, FileMind:

  1. Extracts text — native extraction for digital PDFs, in reading order, with running headers and footers removed
  2. Scores text quality — pages with too little or too garbled text are routed to OCR
  3. Runs OCR when needed — PaddleOCR is bundled and runs locally, so scanned papers become searchable too. Files are marked Clean text, Partial OCR, OCR only, or Failed
  4. Detects identifiers — looks for a DOI or arXiv ID in the first pages
  5. Looks up metadata — arXiv's API for arXiv papers, Crossref for DOIs and titles. Both are free and unmetered, which is why they run by default; OpenAlex is off by default because its anonymous daily budget runs out on a first scan
  6. Parses heuristically — extracts title, authors, and year from the document layout when no lookup matches
  7. Chunks and embeds the text — for exact, semantic, and hybrid search, and for the related-papers list on every paper

Rename proposals are a separate step so you decide when they are generated. That step composes a filename from the validated metadata and may fall back to the language model only when the deterministic methods fail. See Rename Workflow.

Next Steps