paperless-gpt
Use LLMs and LLM Vision (OCR) to handle paperless-ngx - Document Digitalization powered by AI
Stars: 419
paperless-gpt is a tool designed to generate accurate and meaningful document titles and tags for paperless-ngx using Large Language Models (LLMs). It supports multiple LLM providers, including OpenAI and Ollama. With paperless-gpt, you can streamline your document management by automatically suggesting appropriate titles and tags based on the content of your scanned documents. The tool offers features like multiple LLM support, customizable prompts, easy integration with paperless-ngx, user-friendly interface for reviewing and applying suggestions, dockerized deployment, automatic document processing, and an experimental OCR feature.
README:
paperless-gpt seamlessly pairs with paperless-ngx to generate AI-powered document titles and tags, saving you hours of manual sorting. While other tools may offer AI chat features, paperless-gpt stands out by supercharging OCR with LLMs—ensuring high accuracy, even with tricky scans. If you’re craving next-level text extraction and effortless document organization, this is your solution.
https://github.com/user-attachments/assets/bd5d38b9-9309-40b9-93ca-918dfa4f3fd4
-
LLM-Enhanced OCR
Harness Large Language Models (OpenAI or Ollama) for better-than-traditional OCR—turn messy or low-quality scans into context-aware, high-fidelity text. -
Automatic Title & Tag Generation
No more guesswork. Let the AI do the naming and categorizing. You can easily review suggestions and refine them if needed. -
Automatic Correspondent Generation
Automatically identify and generate correspondents from your documents, making it easier to track and organize your communications. -
Extensive Customization
- Prompt Templates: Tweak your AI prompts to reflect your domain, style, or preference.
- Tagging: Decide how documents get tagged—manually, automatically, or via OCR-based flows.
-
Simple Docker Deployment
A few environment variables, and you’re off! Compose it alongside paperless-ngx with minimal fuss. -
Unified Web UI
- Manual Review: Approve or tweak AI’s suggestions.
- Auto Processing: Focus only on edge cases while the rest is sorted for you.
-
Opt-In LLM-based OCR
If you opt in, your images get read by a Vision LLM, pushing boundaries beyond standard OCR tools.
- Key Highlights
- Getting Started
- Configuration
- OCR using AI
- Usage
- Contributing
- License
- Star History
- Disclaimer
- Docker installed.
- A running instance of paperless-ngx.
- Access to an LLM provider:
-
OpenAI: An API key with models like
gpt-4o
orgpt-3.5-turbo
. -
Ollama: A running Ollama server with models like
llama2
.
-
OpenAI: An API key with models like
Here’s an example docker-compose.yml
to spin up paperless-gpt alongside paperless-ngx:
version: "3.7"
services:
paperless-ngx:
image: ghcr.io/paperless-ngx/paperless-ngx:latest
# ... (your existing paperless-ngx config)
paperless-gpt:
image: icereed/paperless-gpt:latest
environment:
PAPERLESS_BASE_URL: 'http://paperless-ngx:8000'
PAPERLESS_API_TOKEN: 'your_paperless_api_token'
PAPERLESS_PUBLIC_URL: 'http://paperless.mydomain.com' # Optional
MANUAL_TAG: 'paperless-gpt' # Optional, default: paperless-gpt
AUTO_TAG: 'paperless-gpt-auto' # Optional, default: paperless-gpt-auto
LLM_PROVIDER: 'openai' # or 'ollama'
LLM_MODEL: 'gpt-4o' # or 'llama2'
OPENAI_API_KEY: 'your_openai_api_key'
# Optional - OPENAI_BASE_URL: 'https://litellm.yourinstallationof.it.com/v1'
LLM_LANGUAGE: 'English' # Optional, default: English
OLLAMA_HOST: 'http://host.docker.internal:11434' # If using Ollama
VISION_LLM_PROVIDER: 'ollama' # (for OCR) - openai or ollama
VISION_LLM_MODEL: 'minicpm-v' # (for OCR) - minicpm-v (ollama example), gpt-4o (for openai), etc.
AUTO_OCR_TAG: 'paperless-gpt-ocr-auto' # Optional, default: paperless-gpt-ocr-auto
OCR_LIMIT_PAGES: '5' # Optional, default: 5. Set to 0 for no limit.
LOG_LEVEL: 'info' # Optional: debug, warn, error
volumes:
- ./prompts:/app/prompts # Mount the prompts directory
ports:
- "8080:8080"
depends_on:
- paperless-ngx
Pro Tip: Replace placeholders with real values and read the logs if something looks off.
-
Clone the Repository
git clone https://github.com/icereed/paperless-gpt.git cd paperless-gpt
-
Create a
prompts
Directorymkdir prompts
-
Build the Docker Image
docker build -t paperless-gpt .
-
Run the Container
docker run -d \ -e PAPERLESS_BASE_URL='http://your_paperless_ngx_url' \ -e PAPERLESS_API_TOKEN='your_paperless_api_token' \ -e LLM_PROVIDER='openai' \ -e LLM_MODEL='gpt-4o' \ -e OPENAI_API_KEY='your_openai_api_key' \ -e LLM_LANGUAGE='English' \ -e VISION_LLM_PROVIDER='ollama' \ -e VISION_LLM_MODEL='minicpm-v' \ -e LOG_LEVEL='info' \ -v $(pwd)/prompts:/app/prompts \ -p 8080:8080 \ paperless-gpt
Note: When using Ollama, ensure that the Ollama server is running and accessible from the paperless-gpt container.
Variable | Description | Required |
---|---|---|
PAPERLESS_BASE_URL |
URL of your paperless-ngx instance (e.g. http://paperless-ngx:8000 ). |
Yes |
PAPERLESS_API_TOKEN |
API token for paperless-ngx. Generate one in paperless-ngx admin. | Yes |
PAPERLESS_PUBLIC_URL |
Public URL for Paperless (if different from PAPERLESS_BASE_URL ). |
No |
MANUAL_TAG |
Tag for manual processing. Default: paperless-gpt . |
No |
AUTO_TAG |
Tag for auto processing. Default: paperless-gpt-auto . |
No |
LLM_PROVIDER |
AI backend (openai or ollama ). |
Yes |
LLM_MODEL |
AI model name, e.g. gpt-4o , gpt-3.5-turbo , llama2 . |
Yes |
OPENAI_API_KEY |
OpenAI API key (required if using OpenAI). | Cond. |
OPENAI_BASE_URL |
OpenAI base URL (optional, if using a custom OpenAI compatible service like LiteLLM). | No |
LLM_LANGUAGE |
Likely language for documents (e.g. English ). Default: English . |
No |
OLLAMA_HOST |
Ollama server URL (e.g. http://host.docker.internal:11434 ). |
No |
VISION_LLM_PROVIDER |
AI backend for OCR (openai or ollama ). |
No |
VISION_LLM_MODEL |
Model name for OCR (e.g. minicpm-v ). |
No |
AUTO_OCR_TAG |
Tag for automatically processing docs with OCR. Default: paperless-gpt-ocr-auto . |
No |
LOG_LEVEL |
Application log level (info , debug , warn , error ). Default: info . |
No |
LISTEN_INTERFACE |
Network interface to listen on. Default: :8080 . |
No |
WEBUI_PATH |
Path for static content. Default: ./web-app/dist . |
No |
AUTO_GENERATE_TITLE |
Generate titles automatically if paperless-gpt-auto is used. Default: true . |
No |
AUTO_GENERATE_TAGS |
Generate tags automatically if paperless-gpt-auto is used. Default: true . |
No |
AUTO_GENERATE_CORRESPONDENTS |
Generate correspondents automatically if paperless-gpt-auto is used. Default: true . |
No |
OCR_LIMIT_PAGES |
Limit the number of pages for OCR. Set to 0 for no limit. Default: 5 . |
No |
CORRESPONDENT_BLACK_LIST |
A comma-separated list of names to exclude from the correspondents suggestions. Example: John Doe, Jane Smith . |
paperless-gpt’s flexible prompt templates let you shape how AI responds:
-
title_prompt.tmpl
: For document titles. -
tag_prompt.tmpl
: For tagging logic. -
ocr_prompt.tmpl
: For LLM OCR.
Mount them into your container via:
volumes:
- ./prompts:/app/prompts
Then tweak at will—paperless-gpt reloads them automatically on startup!
-
Tag Documents
- Add
paperless-gpt
or your custom tag to the docs you want to AI-ify.
- Add
-
Visit Web UI
- Go to
http://localhost:8080
(or your host) in your browser.
- Go to
-
Generate & Apply Suggestions
- Click “Generate Suggestions” to see AI-proposed titles/tags/correspondents.
- Approve, edit, or discard. Hit “Apply” to finalize in paperless-ngx.
-
Try LLM-Based OCR (Experimental)
- If you enabled
VISION_LLM_PROVIDER
andVISION_LLM_MODEL
, let AI-based OCR read your scanned PDFs. - Tag those documents with
paperless-gpt-ocr-auto
(or your customAUTO_OCR_TAG
).
- If you enabled
Tip: The entire pipeline can be fully automated if you prefer minimal manual intervention.
Click to expand the vanilla OCR vs. AI-powered OCR comparison
Image:
Vanilla Paperless-ngx OCR:
La Grande Recre
Gentre Gommercial 1'Esplanade
1349 LOLNAIN LA NEWWE
TA BERBOGAAL Tel =. 010 45,96 12
Ticket 1440112 03/11/2006 a 13597:
4007176614518. DINOS. TYRAMNESA
TOTAET.T.LES
ReslE par Lask-Euron
Rencu en Cash Euro
V.14.6 -Hotgese = VALERTE
TICKET A-GONGERVER PORR TONT. EEHANGE
HERET ET A BIENTOT
LLM-Powered OCR (OpenAI gpt-4o):
La Grande Récré
Centre Commercial l'Esplanade
1348 LOUVAIN LA NEUVE
TVA 860826401 Tel : 010 45 95 12
Ticket 14421 le 03/11/2006 à 15:27:18
4007176614518 DINOS TYRANNOSA 14.90
TOTAL T.T.C. 14.90
Réglé par Cash Euro 50.00
Rendu en Cash Euro 35.10
V.14.6 Hôtesse : VALERIE
TICKET A CONSERVER POUR TOUT ECHANGE
MERCI ET A BIENTOT
Image:
Vanilla Paperless-ngx OCR:
Invoice Number: 1-996-84199
Fed: Invoica Date: Sep01, 2014
Accaunt Number: 1334-8037-4
Page: 1012
Fod£x Tax ID 71.0427007
IRISINC
SHARON ANDERSON
4731 W ATLANTIC AVE STE BI
DELRAY BEACH FL 33445-3897 ’ a
Invoice Questions?
Bing, ‚Account Shipping Address: Contact FedEx Reı
ISINC
4731 W ATLANTIC AVE Phone: (800) 622-1147 M-F 7-6 (CST)
DELRAY BEACH FL 33445-3897 US Fax: (800) 548-3020
Internet: www.fedex.com
Invoice Summary Sep 01, 2014
FodEx Ground Services
Other Charges 11.00
Total Charges 11.00 Da £
>
polo) Fz// /G
TOTAL THIS INVOICE .... usps 11.00 P 2/1 f
‘The only charges accrued for this period is the Weekly Service Charge.
The Fedix Ground aceounts teferencedin his involce have been transteired and assigned 10, are owned by,andare payable to FedEx Express:
To onsurs propor credit, plasa raturn this portion wirh your payment 10 FodEx
‚Please do not staple or fold. Ploase make your chack payablı to FedEx.
[TI For change ol address, hc har and camphat lrm or never ide
Remittance Advice
Your payment is due by Sep 16, 2004
Number Number Dus
1334803719968 41993200000110071
AT 01 0391292 468448196 A**aDGT
IRISINC Illallun elalalssollallansdHilalellund
SHARON ANDERSON
4731 W ATLANTIC AVE STEBI FedEx
DELRAY BEACH FL 334453897 PO. Box 94516
PALATINE IL 60094-4515
LLM-Powered OCR (OpenAI gpt-4o):
FedEx. Invoice Number: 1-996-84199
Invoice Date: Sep 01, 2014
Account Number: 1334-8037-4
Page: 1 of 2
FedEx Tax ID: 71-0427007
I R I S INC
SHARON ANDERSON
4731 W ATLANTIC AVE STE B1
DELRAY BEACH FL 33445-3897
Invoice Questions?
Billing Account Shipping Address: Contact FedEx Revenue Services
I R I S INC Phone: (800) 622-1147 M-F 7-6 (CST)
4731 W ATLANTIC AVE Fax: (800) 548-3020
DELRAY BEACH FL 33445-3897 US Internet: www.fedex.com
Invoice Summary Sep 01, 2014
FedEx Ground Services
Other Charges 11.00
Total Charges .......................................................... USD $ 11.00
TOTAL THIS INVOICE .............................................. USD $ 11.00
The only charges accrued for this period is the Weekly Service Charge.
RECEIVED
SEP _ 8 REC'D
BY: _
posted 9/21/14
The FedEx Ground accounts referenced in this invoice have been transferred and assigned to, are owned by, and are payable to FedEx Express.
To ensure proper credit, please return this portion with your payment to FedEx.
Please do not staple or fold. Please make your check payable to FedEx.
❑ For change of address, check here and complete form on reverse side.
Remittance Advice
Your payment is due by Sep 16, 2004
Invoice
Number
1-996-84199
Account
Number
1334-8037-4
Amount
Due
USD $ 11.00
133480371996841993200000110071
AT 01 031292 468448196 A**3DGT
I R I S INC
SHARON ANDERSON
4731 W ATLANTIC AVE STE B1
DELRAY BEACH FL 33445-3897
FedEx
P.O. Box 94515
Why Does It Matter?
- Traditional OCR often jumbles text from complex or low-quality scans.
- Large Language Models interpret context and correct likely errors, producing results that are more precise and readable.
- You can integrate these cleaned-up texts into your paperless-ngx pipeline for better tagging, searching, and archiving.
- Vanilla OCR typically uses classical methods or Tesseract-like engines to extract text, which can result in garbled outputs for complex fonts or poor-quality scans.
- LLM-Powered OCR uses your chosen AI backend—OpenAI or Ollama—to interpret the image’s text in a more context-aware manner. This leads to fewer errors and more coherent text.
Pull requests and issues are welcome!
- Fork the repo
- Create a branch (
feature/my-awesome-update
) - Commit changes (
git commit -m "Improve X"
) - Open a PR
Check out our contributing guidelines for details.
paperless-gpt is licensed under the MIT License. Feel free to adapt and share!
This project is not officially affiliated with paperless-ngx. Use at your own risk.
paperless-gpt: The LLM-based companion your doc management has been waiting for. Enjoy effortless, intelligent document titles, tags, and next-level OCR.
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Daily Chinese ancient poetry and AI-generated images powered by Bing DALL-E-3. GitHub Action triggers the process automatically. Poetry is provided by Today's Poem API. The website is built with Astro.
exif-photo-blog
EXIF Photo Blog is a full-stack photo blog application built with Next.js, Vercel, and Postgres. It features built-in authentication, photo upload with EXIF extraction, photo organization by tag, infinite scroll, light/dark mode, automatic OG image generation, a CMD-K menu with photo search, experimental support for AI-generated descriptions, and support for Fujifilm simulations. The application is easy to deploy to Vercel with just a few clicks and can be customized with a variety of environment variables.
SillyTavern
SillyTavern is a user interface you can install on your computer (and Android phones) that allows you to interact with text generation AIs and chat/roleplay with characters you or the community create. SillyTavern is a fork of TavernAI 1.2.8 which is under more active development and has added many major features. At this point, they can be thought of as completely independent programs.
Twitter-Insight-LLM
This project enables you to fetch liked tweets from Twitter (using Selenium), save it to JSON and Excel files, and perform initial data analysis and image captions. This is part of the initial steps for a larger personal project involving Large Language Models (LLMs).
AISuperDomain
Aila Desktop Application is a powerful tool that integrates multiple leading AI models into a single desktop application. It allows users to interact with various AI models simultaneously, providing diverse responses and insights to their inquiries. With its user-friendly interface and customizable features, Aila empowers users to engage with AI seamlessly and efficiently. Whether you're a researcher, student, or professional, Aila can enhance your AI interactions and streamline your workflow.
ChatGPT-On-CS
This project is an intelligent dialogue customer service tool based on a large model, which supports access to platforms such as WeChat, Qianniu, Bilibili, Douyin Enterprise, Douyin, Doudian, Weibo chat, Xiaohongshu professional account operation, Xiaohongshu, Zhihu, etc. You can choose GPT3.5/GPT4.0/ Lazy Treasure Box (more platforms will be supported in the future), which can process text, voice and pictures, and access external resources such as operating systems and the Internet through plug-ins, and support enterprise AI applications customized based on their own knowledge base.
obs-localvocal
LocalVocal is a live-streaming AI assistant plugin for OBS that allows you to transcribe audio speech into text and perform various language processing functions on the text using AI / LLMs (Large Language Models). It's privacy-first, with all data staying on your machine, and requires no GPU, cloud costs, network, or downtime.