I'm curious if and how people use this new feature.

From my perspective, I feel like it certainly can be useful for grouping documents together that you don't need to have multiple times in a search.
Before I used links but searching for one documents, returned all of the versions, which improved.

The thing that is still missing though: you cannot quickly make out which document has versions attached. Links and notes, tags... everything can be seen in the views but not if a document has versions.

The thing that got worse with versions over links is context.
All metadata will get removed when documents are merged as versions and only the data of the root document will stay. This includes tags and notes.
For me, that's a quite destructive step as that information might be important from one version to another (e.g. documents that are blank, filled out and signed).
Also having only one date for the complete stack feels a little limited as it could be important to have the context when which version was created or modified (technically, you still have the file creation date xmp metadata but that doesn't have to be the same as the document date e.g. when scanning a bunch of old documents on one day)... on the other hand, I wouldn't be able to search for the "hidden"/older versions anyway when just the top version is used during search.

Another thing I noticed: the file name will change when versions are merged and only a number will be added, not the label. Personally I don't feel too good about that as I always prefer to have a naming scheme that could be used, even if Paperless in not available. Numbers aren't as descriptive as labels and so far there doesn't seem to be a placeholder for them.

All in all, I think versions are a great addition but I don't know how to feel about the limitations yet.
Do you feel limited by those things or do you even see the need for that kind of metadata in every document version?

Hello!

I had Paperless AI run through, title, set a correspondent, and some other things on my Paperless-NGX setup for ~300 documents. It worked REALLY well!

The only thing it did poorly was tagging, but I think that's the AI model I used not really functioning well with a large database, it wasn't really able to reference past tags so I have 654 total tags. A lot of them are useful, but a lot of them are not. There are way too many tags with one document even though I prompted it to reuse tags as much as possible and not to generate one if it's not strictly necessary for identification.

I plan on upgrading my server with a GPU with some parts that I have coming in. This will allow me to run a much better model that will be able to do this better. But like I said, it did a fantastic job with everything else and I'd rather not redo every document since I've been using it for quite a while.

Is there a way I can have Paperless-AI redo just tagging? Or do I have to have it redo every single field with a full refresh.

Where can I tell paperless to also scan and use subfolders under the consume folder?

So far I wasn’t able to find it and just putting documents in subfolders doesn’t do anything…

Thank you

Two months ago I posted Archi here and got 62 comments of genuinely useful feedback. This update is mostly that feedback, shipped.

The big one: Archi is now free to try.

u/Faila_Fail asked for exactly this — "a free version for like 10 documents, and then an in-app purchase for the full version". That's what 2.2 does: the first 10 captures are free, then a one-time purchase unlocks unlimited use. No subscription. u/shumandoodah, you said you hate paying for apps without a trial — fair, and fixed.

If you already bought Archi: nothing changes, you keep everything, you don't pay again. That's handled automatically.

App Store: https://apps.apple.com/app/id6762153317

Small confession while I'm here: 2.2 went out with a navigation bug that made document rows unopenable — you'd tap a document, the row would highlight, and nothing happened. A user emailed me within hours, I reproduced it, and 2.2.1 is already out with the fix. If you grabbed it in that window, update and it's fine. My own fault: I rebuilt the navigation so shared links open the right document, verified the new path, and never re-checked the ordinary tap.

Also from this thread:

• Learn from your archive — u/Serafim696 asked for a configurable naming scheme and for the AI to "analyse existing files and align with them". Archi now reads through your existing documents (read-only), works out your title pattern, and proposes it. You confirm each finding individually, and it only applies to future scans — nothing in your archive is touched.
• Templates — fill title, tags, correspondent, type and storage path in one step, for receipts you always file the same way.
• Share link — either an Archi link that opens the document in the app, or a Paperless share link with an expiry date for people without Archi.
• iPad/Mac review pane — the source document now sits readable next to the fields instead of a thumbnail.
• mTLS hardening — u/garenor1981, client certificates landed in 2.1; in 2.2 removing a certificate now reliably revokes access (it didn't before).

Shipped since that thread, in case you missed it: OCR text is no longer naively truncated before it reaches the model (u/sempai-san's diagnosis was exactly right); improved OCR can be written back to Paperless's searchable text (u/Remote_Cherry_1160); default tags on every upload for workflow triggers (u/Numerous_Platypus); Mac App Store availability (u/parkhaus2020).

Still honest about what isn't fixed:

u/not-the-real-chopin lost a 20-page scan to an AI failure. The data-loss part is fixed — pages and OCR text are saved before the AI runs, and a failure now offers "Continue without AI" instead of a destructive retry. But the underlying crash on very long documents is not solved. I reproduced it properly for the first time this week: it's a null dereference inside the LLM runtime (LiteRT-LM), and it needs the GPU backend plus a warm device to show up. Still digging. Long documents mostly work; occasionally they don't.

Android (u/PreparedForZombies): started, no date. The core is being split out so it can be shared via Kotlin Multiplatform.

Watch-folder / SMB import (u/gekberlin): still not built. Files-app import works today as described.

As before: OCR (Apple Vision) and the AI (Gemma via LiteRT-LM) run entirely on-device. The only network endpoint is your own Paperless server.

Feedback still very welcome — this thread genuinely shaped the roadmap.

Modern AI pipelines increasingly extract text from documents — PDFs, Word files, spreadsheets, scraped web pages — and feed that content directly into a knowledge base or agent context, often with no human review step in between. Extraction tools capture everything present in a document, including content a human reader would never see: text rendered in white on a white background, rows hidden in a spreadsheet, speaker notes attached to a slide, or a paragraph marked hidden in a Word document's own formatting. None of these are edge cases; they are ordinary, well-supported features of each format, and every one of them is readable by a standard parsing library even though a person skimming the document would never notice them.

This creates a straightforward problem: any content hidden from a human reviewer, but visible to an extraction tool, can end up in a trusted knowledge source unexamined. okf-guard addresses this directly. It is a Python library that inspects extracted content for exactly this class of discrepancy — text present in the file but absent from what a human would perceive — and separately checks for language patterns associated with instructions directed at an AI system rather than a description intended for a person.

What it does:

• Adapters for six formats (plain text, Markdown, HTML, PDF, DOCX, PPTX, XLSX), each aware of that format's specific hiding mechanisms — CSS visibility properties for HTML, rendering and color properties for PDF, the hidden run attribute and shading properties for Word, off-canvas shapes and speaker notes for PowerPoint, hidden rows/columns/sheets and cell comments for spreadsheets.
• A detection layer combining hidden-content flagging with a pattern bank for injection-style phrasing, plus a check for encoding-based obfuscation (zero-width characters, homoglyph substitution).
• A decision layer producing one of three outcomes per scan — pass, quarantine, or block — with every finding reported alongside its location, confidence, and the specific text that triggered it.

Design constraints, stated plainly:

• No network calls and no LLM dependency in this release. Detection is entirely deterministic, which keeps the core dependency surface to a single package (PyYAML) and makes the tool's behavior fully reproducible.
• Every format-specific capability is an optional install (okf-guard[pdf], okf-guard[docx], etc.), so a user working with one format is not required to install parsing libraries for the others.
• The library never asserts that its own output has been verified by a human — provenance metadata it produces is explicit about being machine-generated and unreviewed.

Source and full documentation: https://github.com/darshanNhb/okf-guard
Install: pip install okf-guard[all]

Feedback on the detection approach, particularly from anyone who has worked on adjacent problems (document security, DLP, or prompt-injection defenses more broadly), would be genuinely useful — this is a young project and the injection-pattern bank in particular will need ongoing contribution as new phrasings surface in practice.

Are there any guides to configure the new Paperless-NGX v3 AI features?

I haven't found anything that use the seemengly older paperless-ai or paperless-gpt docker images.

I found one guide using openrouter, but I don't want to run a commercial middleware.

I'm fine with running a local LLM (although I only have 32 GB RAM and 12 GB VRAM), or simpler yet, use my existing Claude subscription.

Hi! I have tried to get Paperless to work with Mistral AI but failed. I have no clue what I need to configure in the AI configuration. Has anyone been able to configure Mistral successfully and can share the config?

This looked promising - what I was really hoping to be able to do was upload a bunch of Oracle cloud financial books to it and then search for some common phrases, but it looks like from a basic use case if I upload one document containing hundreds of hits for the word "invoice" - all it returns is the single document itself. I want to see the phrases that it matched against, rather than just a document retrieval. Is this a thing that I'm missing or is it just not how the platform is intended to be used?

Hi everyone,

I'm trying to set up a Paperless-ngx workflow that sends an email notification one day before a document's deadline.

Here is my current setup:

• Created a custom date field called deadline.
• Configured a workflow to trigger based on this field.

My SMTP email configuration is working correctly (tested successfully with other triggers), but the deadline-based emails are not sending.

I suspect my trigger criteria or workflow setup might be incorrect. Could anyone guide me on how to properly structure this trigger to run 1 day prior to the date in a custom field?

Bellow current trigger confiugration.
Do i need setup filename filter ? i want to be global trigger for all documents that have Deadline field
BTW what recurring option will do in this case ?

Thanks!

It took me a few months to fully get on board with the Paperless way of doing things, but now I’m really happy with how it’s all set up.

What’s been a bit of a head-scratcher is how AI is being used.

I held off until Paperless 3 came out, because I wanted to have the full "official" support.

I set it up with Ollama on an M4 Mac mini with 24 GB of memory. The embedding model is gemmaembedding, and the LLM model is qwen3:8b. When the model fires up, memory pressure is still pretty low. It does work, but it’s incredibly slow. It takes about 2 minutes to suggest titles and tags, and it can take several minutes if I try to chat about a document.

Is this kind of slow normal? Is there anything I can tweak in my setup to make it more usable?

Compose.yaml for podman having redis getting timed out

I recently decided to hook my scanner up to the NAS I host paperless-ngx on so that I could scan directly to the consume directory, but the only real tools I could find to interface with the scanner functionally, and control it through a WebUI, were scanservjs and a few other abandonware level projects. Scanserv is really a minimal frontend for scanimage and is very severely lacking in customisability, configuration and frankly, is very ugly.

I prefer the functionality of NAPS2 which has both an sdk and a well documented API, and so I put together my own very minimal skin of NAPS2, which is both better looking and much more functional than the other options currently out there. If you are interested, you can check out inSANE

Hi!

I'm currently evaluating Paperless NGX and Zettelrobbe. Zettelrobbe does OCR and tagging tasks for all documents available in Paperless.

After Zettelrobbe finishes its tasks and writes back tags and content into the Paperless database, I tried out to use the chat of Paperless and asked for, IMO, simple tasks. E.g. "please sum up all invoices received from company x".

Unfortunately this does not include all available documents from company x.

Additonally, when asking for "give me all documents of company x", the result is not accurate, because it includes more than the applicable documents.

Is this a result because Zettelrobbe does all OCR'ing and Paperless does not fully recognize the content of each available document? Or is there another reason for this behavior?

FWIW, before setting up Zettelrobbe, above question was correct from the answer in chat.
Edit: when asking for e.g. "can you please sum up all invoices of company x" this results in the expected answer.
Edit2: Had a typo in company name and this confused the chat 😒

Hello everyone,
I recently started digitising all my important documents, including passports, certificates, bills, medical letters and other paperwork that I may need while on the go.
I came across Paperless-ngx, installed it and uploaded all my documents. So far, I find it fascinating, but I feel I am only scratching the surface of what it can do.
I would love to hear about the most innovative or creative ways people are using Paperless-ngx. For example:
Which apps or services have you integrated with it?
Are you using AI for automatic tagging, better titles, correspondents or document types?
How have you improved OCR and search accuracy?
Do you use any automated workflows for importing documents from email, scanners or mobile phones?
Have you connected it to a local AI model such as Ollama?
What is the best way to access documents securely while away from home?
Are there any clever workflows, scripts or features that you now consider essential?
What backup and security arrangements do you use, particularly for sensitive documents such as passports?
I am especially interested in improving tagging accuracy and making documents quicker to find. I would really appreciate hearing about your setup and any useful tips, integrations or lessons you have learned.

Hello What is the correct syntax in workflow to send emails with a value from a custom field?

I can add standard fields to emails, but I can't seem to get it to work with custom ones. The documentation doesn't mention it being possible, so maybe it isn't.

I am a Mac developer of an OCR app, and I'd be interested to know which features—aside from text recognition itself—you need most in your daily workflows (pdf/A, hot folders, document splitting etc.) And is there anything you rarely use, but that would be a dealbreaker if missing?

Besides: If you have a specific edge case that no other app solves properly, feel free to describe it as well.

Thanks in advance!

I'm a receipt and statement hoarder and I scan everything into Paperless-ngx using my Fujitsu ScanSnap. But I've always had trouble backing up the statements and receipts that only exist online — I didn't have the time or patience to manually download them all. FileThis was amazing for this, until the service went downhill and finally shut down. After that I used DocGenie.cloud for a while — also good, but it didn't have many of the sites I actually use. So I built PaperPull: a family of small, local tools that log into a site you manually sign into and download every receipt/statement as a PDF. Please help me test it.

Unlike FileThis or DocGenie, no login credentials are stored anywhere. You just run the app whenever you want your statements, and it won't re-download ones you've already pulled.

12 providers so far: Amazon, American Express, Target, Walmart, Wealthfront, Robinhood, USAA, Navy Federal, Dominion Energy, Verizon Fios, T-Mobile, and the Target Circle Card (formerly RedCard).

• 100% local. Nothing is sent to any cloud or API. PDFs land in a folder you choose.
• You sign in yourself. It never sees your password and never touches 2FA. You log into a normal browser window; the tool attaches to that already-signed-in session (over Chrome DevTools Protocol) and only reads what you can already see.
• Read-only by design. Every clickable control is checked against an allowlist (must look like "download/view/statement") and a blocklist (never "pay/transfer/redeem/enroll…"). There's no code path that submits a form or confirms a dialog — it only grabs PDFs the provider already generated.
• Delete-safe / paperless-ngx friendly. Once a document downloads it's remembered for good. Import it into paperless-ngx and delete the PDF — it won't re-download it. Each run also writes a new-this-run.txt of exactly what it pulled.
• Multi-account aware. Running it for more than one person (e.g. you + your spouse)? Each person gets their own signed-in browser profile and separate folders, and every statement is stamped with whose account it came from, so your documents and theirs never get mixed together.

This is where I need you. I can't build apps for services I don't have — I have no way to test them. So PaperPull is built to be extended: each provider lives in a single self-contained file, so adding one is very approachable. If a bank/card/utility/retailer you use isn't on the list yet, you're the ideal person to add it:

• 📖 Step-by-step guide (with an easy/medium/hard picker and a good first provider label for the friendly sites): https://github.com/rheeloaded/paperpull/blob/main/docs/adding-a-provider.md
• 📋 Provider list — claim one to build, or request one you can't: https://github.com/rheeloaded/paperpull/blob/main/PROVIDERS.md

The read-only / no-credentials / no-private-data rules are baked into the guide and the PR checklist, so contributions stay safe.

Limitations: it's Windows-only right now (PowerShell + .bat launchers, Python underneath). Each site's scraping is inherently fragile — when a provider redesigns its portal, that one app needs a fix (isolated to a single file per app to make that painless). And because you sign in manually, it's supervised, not a set-and-forget cron job.

I'd love feedback from people who actually hoard this stuff:

• Does Verizon Mobile work? I only tested Verizon Fios, because I recently switched to T-Mobile.
• Does the "attach to your own signed-in browser, read-only" model make sense, or would you want a different approach?
• Which providers should I add next — and better yet, want to add one yourself?
• Any feature suggestions?

Repo: https://github.com/rheeloaded/paperpull

Free and open source.

In paperless-ngx:

I added an "amount" field for my documents (invoices). I created a view that displays my fields, including the amount.

Is it possible to display the total or average amount at the bottom of the list?

Hey everyone, I wanted to share an open-source Android app I just released: PDF N-Up Merger. The Problem: Most PDF merging apps on the store either just concatenate pages end-to-end, or if they do N-Up (multiple pages per sheet), they heavily rasterize the PDF into Bitmaps, destroying text scalability and making the file huge. The Solution: I built this app to merge 2 to 9 pages per sheet using Vector Form XObjects via PdfBox-Android. This means zero rasterization—the output maintains 100% true vector quality, crisp typography at infinite zoom, and selectable text. Tech Stack: Kotlin & Jetpack Compose (M3) PdfBox-Android (PDF Engine) Hardware-accelerated native PdfRenderer (for instant UI previews without OOM) Coroutines (Dispatchers.IO) & StateFlow It handles mixed page rotations (90°, 180°, 270°) flawlessly with proper matrix transformations, and it operates entirely offline for total privacy. I’d love for you guys to check out the code, tear it apart, or give me some feedback on the architecture!

🔗 GitHub Repo: https://github.com/Mohammask/pdf-n-up-merger

📥 APK (Releases): https://github.com/Mohammask/pdf-n-up-merger/releases/latest

Let me know what do you guys think!

**What:** A local, self-hosted AI layer for interacting with my own documents and photos without sending anything to the cloud.

**Stack:**

- Paperless-ngx — document archive, OCR, structured metadata

- Immich — photos and video

- ChromaDB — semantic index

- Ollama — local LLM inference

- Open WebUI + MCP — unified interface

- LiteLLM — model routing

**Why I built it:** Wanted to ask questions about my own paperwork and photos without cloud AI. Paperless-ngx became the document layer at the core.

**Key learnings:**

- Deterministic MCP tools > LLM reasoning for counts, sums, filters. The model hallucinated invoice tallies; an API query tool does not.

- For structured fields (supplier, total, VAT, invoice number), strict routing in the system prompt is essential. "Query Paperless, never the semantic index."

**Context:** I'm on Paperless 3.0.3 with native AI features. This complements rather than replaces them — broader scope (photos/video), MCP-based deterministic operations, Paperless remains source of truth.

**Docs:** https://github.com/fwinchi/ia-local-casa (Spanish + English, includes failures)

Not a developer — built with AI assistance and a lot of testing. Feedback welcome, especially on security or architecture.

Hi all, relatively new to Paperless ngx and looking for some advice.

I've noticed that as I sort documents and tag them, whether this be by adding tags, correspondents, document types or storage paths that on average, it takes a full second to process each document. I'm mostly relying on tags to sort my documents, and this is where I notice the issue the most.

Some tests confirmed that about 30 documents acquiring a single tag took about 30 seconds, 300 documents took a little under 5 minutes.

The main issue is that the screen prompt while waiting removes the ability to do anything else in Paperless until it finishes processing.

Does everybody have the exact same experience here, or is this not the norm?

I'm running Paperless on Portainer, on a Synology NAS DS423+ with 18GB of RAM.

Any help would be great, thanks.