PDfiles

do you have lots of pdfs that you want to search through visually? do you have an nvidia gpu? if yes, this is for you!

Features

search through pdfs by text description (not OCR)

search.mp4

search for all photos similar to a particular photo

similar.mp4

reverse image search — upload a photo to find similar pages

can use OPT files to speed up indexing

can export your index files to backup or share with others

• on the web gui with admin mode enabled, or via cli

Quick Start

1. Install Docker

2. Clone the repo:

   git clone https://github.com/pdfiles/pdfiles.git
   cd pdfiles

3. Configure:

   cp .env.example .env
   # Edit .env — set DATA_PATH to your documents folder
   

4. Start:

   ./pdfiles.sh up

   On Windows: pdfiles.bat up

5. Open http://localhost

To stop: ./pdfiles.sh down (or pdfiles.bat down on Windows)

First startup downloads the search model (~4 GB) and takes 2-3 minutes.

Usage

Command	Description
./pdfiles.sh up	Start services
./pdfiles.sh update	Pull latest images and restart
./pdfiles.sh down	Stop services
./pdfiles.sh logs	View logs
./pdfiles.sh status	Health check
./pdfiles.sh backup	Backup index to sqlite
./pdfiles.sh restore DIR	Restore from a backup
./pdfiles.sh --help	All commands

A note on updates

Qdrant data is stored in a docker volume that will persist down and up, reboots, etc. Updates should not affect this, which you can do with pdfiles update, but if you have spent a long time indexing files, it is always safest to pdfiles backup first.

You do not need to reindex files to update

Requirements

• Docker and Docker Compose
• NVIDIA GPU (12+ GB VRAM) for indexing, or a prebuilt search index for CPU-only mode:

  docker compose -f docker-compose.cpu.yml up -d

How it works

pdfiles scans through all of the pdfs that you mount in DATA_PATH and saves each page's essence in the form of vectors. when you type something in search, these words get turned into vectors; and then the two sets of vectors get compared. the resulting files are an ordered list of images that are closest to what you search. this is what enables "find similar photos" as well.

Architecture

flowchart TD
    PDF[PDF Pages] --> Bouncer --> Indexer
    Indexer --> ColQwen2.5 --> Qdrant[(Qdrant)]

    Query[Text / Image / Similar] --> ColQwen2.5
    Qdrant -->|MaxSim| Results[Ranked Results]

Loading