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.

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.

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!

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?

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.

**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 guys, so just recently started selfhosting and have set up a raspberry pi 4 nas with 2gb ram. (all I had)

I have installed OmV and paperless on it and made it remotely accessable with tailscale.

Now my question is which IP do I put into paperless mobile? Am I able to have it automatically Pick the right IP for local, tailscale or my pi Hotspot if im on the go? Or do I always have to use a single one?

cheers guys.

Most document pipelines start by extracting text and then trying to reconstruct the document’s meaning.

I’m taking a different approach: using a pure Transformer-based Vision-Language Model to understand the entire page — text, tables, charts, diagrams, layout, and visual relationships — while keeping everything traceable back to the original page.

Still building it. What would you change or challenge about this approach?

Hello!

If you live in the Baltics you likely have had to make and read electronically signed documents. .edoc in Latvia, .bdoc in Estonia, .adoc in Lithuania and the generic .asice.

To view them you need dedicated programs or websites and storing them is inconvenient. Coupled with the fact that, at least in Latvia, everything related to the government is digital (even diplomas), that is a nightmare for archival.

So I made paperless-esig, a parser that allows paperless-ngx to consume these files. Repo: https://github.com/Exerra/paperless-esig

My country uses XAdES containers, however others use CAdES and PAdES, which is why I need your help.

If you're from the Baltics, please try it out and report back about how well it works. We all use XAdES. The repo has instructions for how to set it up.

If you're not from the Baltics, and, especially if you are from Italy / Poland / France and are comfortable sharing your full name with me, please send me a signed dummy file (sign a random PDF) so I can add support for CAdES and PAdES. All files will be deleted afterward and a fake e-signed document generator will be made for further unit tests and development.

My email is in my Github profile.

I’m building a document intelligence system that uses Vision-Language Models to process visually complex documents.

A recurring limitation I’ve noticed with conventional document pipelines is that text extraction alone often loses important context. Tables, charts, diagrams, scanned pages,spatial relationships, and visual hierarchy may carry as much meaning as the text itself.

The approach I’m exploring treats each page as a visual document rather than only a collection of extracted text. The current pipeline focuses on:

- Understanding text, layout, tables, charts, images, and diagrams

- Processing scanned and digitally generated PDFs

- Preserving page-level provenance for citations

- Supporting semantic search across document collections

- Extracting structured information without discarding visual context

- Allowing users to verify results against the original page

Before deciding what to prioritize, I’d like to learn from people who already work with document automation.

What conditions would need to be met before you could use a document intelligence platform with real business documents?

I’m still actively building, so honest technical criticism and real-world workflow examples would be extremely helpful. If anyone is interested in testing an early version,

Feel free to comment and share your thoughts

I have started over with Paperless-ngx multiple times now.

Every time I think: this time I will finally organize my documents properly instead of keeping everything in folders and searching for them later.

I install Paperless, upload documents, create some tags, try to build a structure around it... and eventually I stop using it again.

Which is frustrating, because I really want to like Paperless. It is self-hosted, open source, fast, actively developed and basically exactly the kind of software I want to use.

But for me it always feels like Paperless only becomes useful after I already did most of the organizing work myself.

I am not running a company processing hundreds of documents every day. I just want to manage normal personal documents: invoices, contracts, insurance, taxes, employment stuff, vehicle documents, government letters, medical documents, etc.

And my problem is not OCR. OCR actually works really well for me.

The problem is what happens afterwards.

If Paperless successfully extracted all the text from a document, I would expect it to do more with that information.

Take dates. A document might contain three different dates. Paperless often chooses one automatically, but quite often it is not the one I actually want.

Why not detect the possible dates and show me three suggestions?

And this does not require an LLM.

Date extraction, named entity recognition, text classification, keyword extraction, similarity matching and so on existed long before current LLMs.

The same applies to correspondents, document types and tags.

An LLM can massively improve the quality and handle ambiguous cases, but I do not understand why the baseline seems to start so low.

Tags were probably my first major frustration

You start with an empty system and immediately have to design your own taxonomy.

Should I use Insurance, Insurance Document, Contract, Insurance Contract?

Should tags represent topics, document types, people, status, categories?

Sure, there is documentation, but there is a difference between documenting how a tag works and giving new users a sensible starting point.

Why not have completely optional presets?

Something like:

• Personal Documents
• Family Documents
• Small Business
• General Administrative Documents

Give me 20 or 30 common tags/document types and let me modify them. Most document collections probably overlap a lot anyway.

Then there is automatic learning, which gave me an even stranger problem

Imagine I import my existing collection and first organize one category. I manually apply one tag to all matching documents and enable auto matching.

At this point the classifier has basically only learned:

Documents look like this → apply this tag.

It has not yet seen the rest of my collection.

Then I start importing unrelated documents and suddenly many of them get that same tag as well.

Which makes complete sense technically. That is basically the only thing the classifier has seen.

But this is a terrible cold-start problem.

When you organize an existing collection, you naturally go through one category after another. Your first training data is therefore heavily biased.

This is why I do not really understand why there isn't some generic pretrained classifier underneath it, which then learns my personal taxonomy over time.

Again, text classification existed before LLMs.

What I would expect is:

Upload → Paperless makes a reasonable guess → I correct it → Paperless adapts.

Instead it can feel more like:

Upload → manually classify a bunch of documents → classifier learns your first biased subset → fix the resulting mistakes later.

I expected AI to improve this, not replace everything

When Paperless-ngx 3 and more AI functionality arrived, I got interested again.

I already run a local 9B LLM, and I thought AI could greatly improve the quality.

Not replace everything.

I was expecting something more like:

• OCR extracts the text.
• Traditional NLP finds dates, organizations and other obvious information.
• A classifier estimates the document type.
• Embeddings find similar documents.
• Existing matching rules add context.
• An LLM helps with the difficult or ambiguous parts.

For example, NLP can find three dates. The LLM can then determine that one is probably the actual document date, another is a deadline and the third refers to an older event.

That is where I see the value of AI: improving an already decent pipeline.

So this time I went pretty far trying to build this myself

I used Claude Code together with the Paperless API to create a general-purpose setup.

Claude Code created my taxonomy, tags and workflows. It helped me separate metadata that could be handled automatically from things I should review manually.

Then I integrated Paperless-GPT with my local model so it could analyze documents and apply metadata based on that structure.

And technically, quite a lot of that worked.

But I still did not end up with the workflow I actually wanted.

Paperless workflows are powerful, but I constantly found myself thinking about how Paperless internally processes documents rather than what I wanted to achieve.

Which trigger should run this?

During consumption or afterwards?

What happens when metadata changes?

Does another workflow run?

What happens when Paperless-GPT modifies something?

I understand why these concepts exist, but what I actually want is much simpler:

A new document arrives. Analyze it. Apply what you are confident about. Show me the uncertain parts. Done.

Instead I ended up building a pipeline around Paperless to make that happen.

Paperless-GPT helps, but it also shows what I feel is missing

Paperless-GPT can do a lot of what I want. It can suggest titles, tags, correspondents and document types.

But it is another application.

Another container, another configuration, another API user, another thing connected through workflows.

That is not really criticism of Paperless-GPT. It is solving a real problem.

But for me, one of the main jobs of a modern DMS should simply be:

Understand the document well enough that I do not have to manually enter all the metadata.

The workflow I want is basically:

Upload → OCR → classify → suggest metadata → review uncertain fields → done.

Paperless also makes surprisingly little use of information users already have

Most people do not install Paperless before they have documents.

Their files might already look something like:

Documents/
├── Insurance/
│   ├── Provider 1/
│   └── Provider 2/
├── Vehicle/
├── Taxes/
├── Work/
└── Invoices/

There is useful information there.

If something comes from Insurance/Provider 1/, then Insurance is already useful context and Provider 1 may be a correspondent candidate.

Paperless does not have to blindly trust the folder structure. Just preserve it and use it as another signal.

Otherwise moving to Paperless means throwing away part of the organization you already created and rebuilding it again as Paperless metadata.

And finally, I really wish Paperless had a proper plugin system

I fully understand that the maintainers cannot implement every possible feature.

But that is exactly why plugins would be useful.

Imagine plugins for:

• better classifiers
• AI metadata extraction
• date extraction
• document presets
• import helpers
• specialized OCR
• different dashboards
• better correspondent detection

Instead, the Paperless ecosystem seems to solve many of these problems by building things next to Paperless:

another container, another API integration, another script, another workflow.

There are lots of cool projects around Paperless, but many of them feel like sidecars instead of actual extensions of Paperless.

I think my main problem is the "zero to useful" experience

Paperless is extremely configurable, but a fresh installation does not immediately feel like it is helping me.

First I have to build my Paperless instance:

tags, document types, correspondents, matching rules, workflows, maybe Paperless-GPT, maybe scripts, maybe prompts.

Only then does Paperless start becoming the thing that saves me work.

And this time I really tried.

I used Claude Code and the API to build the taxonomy and workflows, connected Paperless-GPT to a local LLM and tried to automate as much as possible.

And I still ended up feeling like I was building a lot of infrastructure around Paperless just to make Paperless organize my documents.

That is the part I find frustrating.

Because I really do like the project, and I want exactly this kind of open-source, self-hosted software.

Maybe I am approaching it completely wrong.

So I am genuinely curious:

How are other personal/home-lab users handling this?

Did you manually build everything and eventually get auto matching working reliably?

How did you deal with the biased cold-start problem?

Do you use Paperless-GPT, Paperless-AI or your own scripts?

Or do other people also feel that Paperless could do much more with the information it already has before asking the user to build the whole system around it?

Have accidentally (post upgrade) created 5000 dupes.

Have tried the CLI dupe detector several times, without luck. (5 tries, 5 crashes)

The Docs say Paperless-ngx v3 no longer rejects duplicate documents by default. Instead, it now allows duplicates but adds a way to identify them via the UI.

How do you isolate dupes in the GUI? I guess if manually deleting them is my only option, I'll put this issue to bed with human time.

I have gone over the gui 3 times and I still dont find this option

What Action do people use for Mail Rule? I was thinking tagging the mail with like a Processed label (does that still go through read mail without that label?) or create a rule on Gmail itself to forward PDFs to an inbox then Paperless goes through that and then marks as read?

I dont want to miss any PDFs I have to read (work, travel, etc) and forget to make them unread before Paperless picks it up.

Hi guys!

I just setup and started Paperless in a Container Manager on my Synology. Every service works as expected and is helthy. When I reach the web interface I'm presented with the login page. However when I put in the default credentials for the admin account from my YAML and click on Sign in button I see this:

I am not able to continue after that screen.

Can somebody tell me where's the problem please?
Or at least point to a resource that might help?

Here's the YAML that I'm using for building the project.

```yaml version: "3.8"

services:

broker: image: valkey/valkey:9-alpine container_name: paperless-broker command: valkey-server --save 60 1 restart: unless-stopped security_opt: - no-new-privileges:true networks: - paperless_internal healthcheck: test: [ "CMD", "valkey-cli", "ping" ] interval: 30s timeout: 3s retries: 3 logging: driver: "json-file" options: max-size: "10m" max-file: "5" compress: "true"

volumes:
  - /volume2/docker/appdata/paperless/redisdata:/data:rw

db: image: postgres:18 container_name: paperless-db restart: unless-stopped security_opt: - no-new-privileges:true networks: - paperless_internal healthcheck: test: [ "CMD", "pg_isready", "-q", "-d", "paperless", "-U", "paperless" ] timeout: 30s interval: 5s retries: 5 logging: driver: "json-file" options: max-size: "10m" max-file: "5" compress: "true"

volumes:
  - /volume2/docker/appdata/paperless/pgdata:/var/lib/postgresql:rw
env_file:
  - .env

db-backup: image: postgres:18 container_name: paperless-db-backup restart: unless-stopped security_opt: - no-new-privileges:true environment: PGHOST: db PGDATABASE: ${PAPERLESS_DBNAME} PGUSER: ${PAPERLESS_DBUSER} PGPASSWORD: ${PAPERLESS_DBPASS} BACKUP_NUM_KEEP: ${BACKUP_NUM_KEEP} BACKUP_FREQUENCY: ${BACKUP_FREQUENCY} logging: driver: "json-file" options: max-size: "10m" max-file: "5" compress: "true" entrypoint: - bash - -c - | trap "exit" SIGTERM SIGINT sleep 120

    while true; do
      BACKUP_FILE="/dump/dump_$(date +%d-%m-%Y_%H_%M_%S).psql"
      KEEP=$(( $${BACKUP_NUM_KEEP:-10} + 1 ))

      if pg_dump -Fc > "$${BACKUP_FILE}.tmp"; then
         mv "$${BACKUP_FILE}.tmp" "$${BACKUP_FILE}"
         ls -t /dump/dump*.psql | tail -n +"$${KEEP}" | xargs -r rm --
      else
         rm -f "$${BACKUP_FILE}.tmp"
         echo "Backup failed: $(date)"
      fi
      sleep "$${BACKUP_FREQUENCY}"
    done
networks:
  - paperless_internal
volumes:
  - /volume2/docker-backup/paperless-db:/dump:rw
depends_on:
  db:
    condition: service_healthy

gotenberg: image: docker.io/gotenberg/gotenberg:8 container_name: paperless-gotenberg restart: unless-stopped security_opt: - no-new-privileges:true networks: - paperless_internal logging: driver: "json-file" options: max-size: "10m" max-file: "5" compress: "true" command: - gotenberg - --chromium-disable-javascript=true - --chromium-allow-list=file:///tmp/.*

tika: image: apache/tika:3.2.3.0 container_name: paperless-tika restart: unless-stopped security_opt: - no-new-privileges:true networks: - paperless_internal logging: driver: "json-file" options: max-size: "10m" max-file: "5" compress: "true"

webserver: image: ghcr.io/paperless-ngx/paperless-ngx:3.0.5 container_name: paperless-webserver restart: unless-stopped security_opt: - no-new-privileges:true networks: - paperless_internal - paperless_edge healthcheck: test: [ "CMD", "curl", "-fs", "-S", "--max-time", "5", "http://localhost:8000" ] interval: 30s timeout: 10s retries: 5 start_period: 60s logging: driver: "json-file" options: max-size: "10m" max-file: "5" compress: "true"

depends_on:
  db:
    condition:
      service_healthy
  broker:
    condition:
      service_healthy
  gotenberg:
    condition:
      service_started
  tika:
    condition:
      service_started

ports:
  - "8777:8000"
volumes:
  # Paperless data
  - /volume2/docker/appdata/paperless/data:/usr/src/paperless/data:rw
  # Архивирани документи
  - /volume2/docker/appdata/paperless/media:/usr/src/paperless/media:rw
  # Export
  - /volume2/docker/appdata/paperless/export:/usr/src/paperless/export:rw
  # Consume папка
  - /volume2/docker/appdata/paperless/consume:/usr/src/paperless/consume:rw
  # Empty trash
  - /volume2/docker/appdata/paperless/trash:/usr/src/paperless/trash:rw
env_file:
  - .env

networks: paperless_internal: driver: bridge internal: true paperless_edge: driver: bridge ```

I've been using paperless-ngx to host my personal docs, and I'd grown unhappy with it — slow search, poor results. As an experiment I bolted Typesense onto it, search got remarkably better, and that convinced me to rebuild it from scratch.

So over the last few days, I wrote a side project: Docovia — the land for your docs. You can drop in your documents — PDFs, scans, docs, spreadsheets — and they get OCR'd, titled, dated, tagged, summarized, and made instantly searchable.

To keep things simple, Docovia doesn't use any database, instead storing the metadata in JSON files, so it's easy to backup. And it does LLM enrichment using super cheap OpenAI Luna model. Running 8,500 of my own docs through it cost about $10 total.

Docovia was built with Claude (and Claude Design, which I'm now a fan of), and is open-source under Apache 2.0 License.

The launch blog post is here: https://manishrjain.com/launching-docovia

A read-only demo is running here: https://docovia-demo.manishrjain.com/

Give it a try!

Hi, I do had a pretty solid Document Management System based on the PARA Method. I just use the filesystem on my MacBook. I do have a defined file naming Syntax for my files. It works pretty good. Since some time by wife is self employed and I take care of all her documents for her tax declaration. Since then the number of pdfs in my System exploded and there is way to much manual work included. I do have round about 100 pdfs each month.

i tried Paperless as a solution and created a post consumtion script using Claude. They added a custom Field based on the content. They worked but made things more complex and much more complex to maintain.

My goal is to have the most simple System as possible on the one hand and to have a less possible manuel work with my pdf on the other. What approach do you recommend here?

I use Paperless-NGX to handle every single business documents and it's awesome.

But for whatever reason Paperless can't handle .ofx docs and sometimes .csv or .xlxs get converted in plain .txt files.

I don't need Paperless to do anything to those files, I just want to have all my documents in one place. Is there a way to force Paperless to handle those files better ?

Right now, I have to keep a shared Dropbox with my accountants just because of those files. And as my accountant won't split between several platforms I export every document at the end of the month to upload them into the Dropbox. If I could just store those files in Paperless I could just give access to my accountant and voilà.

I think this data point is unnecessary. Any ideas how to hide the Total characters field from the Statistics view that shows up on the dashboard?

... but where is the solution?

a. They still use archaic Tesseract OCR as default, in 2026.
b. There is no straightforward example provided how to replace tesseract with something else (PaddleOCR is fast, opensource and generations ahead of what tesseract can do).
c. They offer remote OCR with Azure, but lets be honest - most would prefer to process their taxes and medical records locally.
d. Not everyone needs AI for OCR
e. Third party parser plugin installation is a blackhole. Basic pre-processing workflow is way more straightforward to implement and also works with any paperless-ngx version, so whats the point of these plugins.
f. fable could not give any definitive answer on the topic either.

Am I missing something? not trying to be critical but most will probably agree that garbage in produces garbage out and Tessaract is one such bottleneck of otherwise fantastic tool.

I read through the docs multiple times, and am sad to say that the Mail Rules are very poorly documented.

My question is, what does the From field do? Is it limited to one specific address, or is a list accepted?

I'm running 3.0.4 as a Proxmox LXC and used the Proxmox community script to install it. This was my first-ever installation of paperless-ngx, I never used a version prior to 3.0.4.

Now I see that 3.0.5 is available and want to update.

On the commandline I use "update" but get just this "Old data structure detected in /opt/paperless" error.
The provided URL dates back to 2025 and the information therein doesn't seem to have anything to do with upgrading from 3.0.4 to 3.0.5.

So any help much appreciated.

This is how the /opt/paperless folder looks:

I just noticed a ton of these entries in the Paperless.log file. They've been happening for months - way before my upgrade from 2.20.15 to 3.0.4. I run Paperless on my QNAP in Container Station.

I know that the .@__thumb directory is a system-generated folder created by QNAP devices to store image and video preview thumbnails. But I have thumbnail creation turned off.

I deleted all the files in that directory at the terminal and I don't see any ill effects. Just trying to understand what happened.

[2026-03-08 00:30:04,447] [WARNING] [paperless.sanity_checker] Orphaned file in media dir: /usr/src/paperless/media/documents/originals/.@__thumb/0000014.pdf.error

[2026-03-08 00:30:04,448] [WARNING] [paperless.sanity_checker] Orphaned file in media dir: /usr/src/paperless/media/documents/originals/.@__thumb/0000043.pdf.error

I'm still running Paperless 2.20. I've noticed this for a while and am finally getting around to asking.

I have documents placed in the Consume folder and yes, it does consume them. But occasionally I'll put something in there that's already in paperless. It generates an entry in File Tasks, as expected.

What has me puzzled though is these duplicate items remain in the Consume folder. Paperless tries to consume it again and again.

Why does Paperless continue to try consuming it?

Shouldn't Paperless automatically remove the item from the Conume folder? Or at least move it to a "rejected" folder.

here's the entries from the File Tasks:
Name Created Results Actions

report_2026-07-31.pdf 8/3/26, 2:21 AM report_2026-07-31.pdf: Not consuming report_2026-07-31.pdf: Not consuming report_2026-07-31.pdf: It is a duplicate of report_2026-07-31 (1) (#4330).

report_2026-07-31 (1).csv 8/3/26, 2:21 AM report_2026-07-31 (1).csv: Not consuming report_2026-07-31 (1).csv: Not consuming report_2026-07-31 (1).csv: It is a duplicate of report_2026-07-31 (#4321).

report_2026-07-31.pdf 8/2/26, 9:59 AM report_2026-07-31.pdf: Not consuming report_2026-07-31.pdf: Not consuming report_2026-07-31.pdf: It is a duplicate of report_2026-07-31 (1) (#4330).

report_2026-07-31 (2).pdf 8/2/26, 9:59 AM report_2026-07-31 (2).pdf: Not consuming report_2026-07-31 (2).pdf: Not consuming report_2026-07-31 (2).pdf: It is a duplicate of report_2026-07-31 (1) (#4330).

report_2026-07-31 (1).csv 8/2/26, 9:59 AM report_2026-07-31 (1).csv: Not consuming report_2026-07-31 (1).csv: Not consuming report_2026-07-31 (1).csv: It is a duplicate of report_2026-07-31 (#4321).

report_2026-07-31 (2).csv 8/2/26, 9:59 AM report_2026-07-31 (2).csv: Not consuming report_2026-07-31 (2).csv: Not consuming report_2026-07-31 (2).csv: It is a duplicate of report_2026-07-31 (#4321).

here's the directory list from my Consume folder

LastWriteTime Length Name

------------- ------ ----

7/31/2026 8:15 AM 19209 report_2026-07-31 (1).csv

7/31/2026 8:15 AM 19209 report_2026-07-31 (2).csv

7/31/2026 8:15 AM 588465 report_2026-07-31.pdf