# paperless-gpt [![License](https://img.shields.io/github/license/icereed/paperless-gpt)](LICENSE) [![Discord Banner](https://img.shields.io/badge/Join%20us%20on-Discord-blue?logo=discord)](https://discord.gg/fJQppDH2J7) [![Docker Pulls](https://img.shields.io/docker/pulls/icereed/paperless-gpt)](https://hub.docker.com/r/icereed/paperless-gpt) [![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg)](CODE_OF_CONDUCT.md) ![Screenshot](./paperless-gpt-screenshot.png) **paperless-gpt** seamlessly pairs with [paperless-ngx][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 --- ## Key Highlights 1. **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. 2. **Use specialized AI OCR services** - **LLM OCR**: Use OpenAI or Ollama to extract text from images. - **Google Document AI**: Leverage Google's powerful Document AI for OCR tasks. - **Azure Document Intelligence**: Use Microsoft's enterprise OCR solution. 3. **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. 4. **Supports DeepSeek reasoning models in Ollama** Greatly enhance accuracy by using a reasoning model like `deepseek-r1:8b`. The perfect tradeoff between privacy and performance! Of course, if you got enough GPUs or NPUs, a bigger model will enhance the experience. 5. **Automatic Correspondent Generation** Automatically identify and generate correspondents from your documents, making it easier to track and organize your communications. 6. **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. 7. **Simple Docker Deployment** A few environment variables, and you're off! Compose it alongside paperless-ngx with minimal fuss. 8. **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. --- ## Table of Contents - [Key Highlights](#key-highlights) - [Getting Started](#getting-started) - [Prerequisites](#prerequisites) - [Installation](#installation) - [Docker Compose](#docker-compose) - [Manual Setup](#manual-setup) - [OCR Providers](#ocr-providers) - [LLM-based OCR](#1-llm-based-ocr-default) - [Azure Document Intelligence](#2-azure-document-intelligence) - [Google Document AI](#3-google-document-ai) - [Comparing OCR Providers](#comparing-ocr-providers) - [Choosing the Right Provider](#choosing-the-right-provider) - [Configuration](#configuration) - [Environment Variables](#environment-variables) - [Custom Prompt Templates](#custom-prompt-templates) - [Prompt Templates Directory](#prompt-templates-directory) - [Mounting the Prompts Directory](#mounting-the-prompts-directory) - [Editing the Prompt Templates](#editing-the-prompt-templates) - [Template Syntax and Variables](#template-syntax-and-variables) - [OCR using AI](#llm-based-ocr-compare-for-yourself) - [Usage](#usage) - [Contributing](#contributing) - [License](#license) - [Star History](#star-history) - [Disclaimer](#disclaimer) --- ## Getting Started ### Prerequisites - [Docker][docker-install] installed. - A running instance of [paperless-ngx][paperless-ngx]. - Access to an LLM provider: - **OpenAI**: An API key with models like `gpt-4o` or `gpt-3.5-turbo`. - **Ollama**: A running Ollama server with models like `deepseek-r1:8b`. ### Installation #### Docker Compose Here's an example `docker-compose.yml` to spin up **paperless-gpt** alongside paperless-ngx: ```yaml 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 'deepseek-r1:8b' # Optional, but recommended for Ollama TOKEN_LIMIT: 1000 OPENAI_API_KEY: "your_openai_api_key" # Optional - OPENAI_BASE_URL: 'https://litellm.yourinstallationof.it.com/v1' LLM_LANGUAGE: "English" # Optional, default: English # OCR Configuration - Choose one: # Option 1: LLM-based OCR OCR_PROVIDER: "llm" # Default OCR provider VISION_LLM_PROVIDER: "ollama" # openai or ollama VISION_LLM_MODEL: "minicpm-v" # minicpm-v (ollama) or gpt-4v (openai) OLLAMA_HOST: "http://host.docker.internal:11434" # If using Ollama # Option 2: Google Document AI # OCR_PROVIDER: 'google_docai' # Use Google Document AI # GOOGLE_PROJECT_ID: 'your-project' # Your GCP project ID # GOOGLE_LOCATION: 'us' # Document AI region # GOOGLE_PROCESSOR_ID: 'processor-id' # Your processor ID # GOOGLE_APPLICATION_CREDENTIALS: '/app/credentials.json' # Path to service account key # Option 3: Azure Document Intelligence # OCR_PROVIDER: 'azure' # Use Azure Document Intelligence # AZURE_DOCAI_ENDPOINT: 'your-endpoint' # Your Azure endpoint URL # AZURE_DOCAI_KEY: 'your-key' # Your Azure API key # AZURE_DOCAI_MODEL_ID: 'prebuilt-read' # Optional, defaults to prebuilt-read # AZURE_DOCAI_TIMEOUT_SECONDS: '120' # Optional, defaults to 120 seconds 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 # For Google Document AI: - ${HOME}/.config/gcloud/application_default_credentials.json:/app/credentials.json ports: - "8080:8080" depends_on: - paperless-ngx ``` **Pro Tip**: Replace placeholders with real values and read the logs if something looks off. #### Manual Setup 1. **Clone the Repository** ```bash git clone https://github.com/icereed/paperless-gpt.git cd paperless-gpt ``` 2. **Create a `prompts` Directory** ```bash mkdir prompts ``` 3. **Build the Docker Image** ```bash docker build -t paperless-gpt . ``` 4. **Run the Container** ```bash 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 ``` --- ## OCR Providers paperless-gpt supports three different OCR providers, each with unique strengths and capabilities: ### 1. LLM-based OCR (Default) - **Key Features**: - Uses vision-capable LLMs like GPT-4V or MiniCPM-V - High accuracy with complex layouts and difficult scans - Context-aware text recognition - Self-correcting capabilities for OCR errors - **Best For**: - Complex or unusual document layouts - Poor quality scans - Documents with mixed languages - **Configuration**: ```yaml OCR_PROVIDER: "llm" VISION_LLM_PROVIDER: "openai" # or "ollama" VISION_LLM_MODEL: "gpt-4v" # or "minicpm-v" ``` ### 2. Azure Document Intelligence - **Key Features**: - Enterprise-grade OCR solution - Prebuilt models for common document types - Layout preservation and table detection - Fast processing speeds - **Best For**: - Business documents and forms - High-volume processing - Documents requiring layout analysis - **Configuration**: ```yaml OCR_PROVIDER: "azure" AZURE_DOCAI_ENDPOINT: "https://your-endpoint.cognitiveservices.azure.com/" AZURE_DOCAI_KEY: "your-key" AZURE_DOCAI_MODEL_ID: "prebuilt-read" # optional AZURE_DOCAI_TIMEOUT_SECONDS: "120" # optional ``` ### 3. Google Document AI - **Key Features**: - Specialized document processors - Strong form field detection - Multi-language support - High accuracy on structured documents - **Best For**: - Forms and structured documents - Documents with tables - Multi-language documents - **Configuration**: ```yaml OCR_PROVIDER: "google_docai" GOOGLE_PROJECT_ID: "your-project" GOOGLE_LOCATION: "us" GOOGLE_PROCESSOR_ID: "processor-id" ``` ## Configuration ### Environment Variables # **Note:** When using Ollama, ensure that the Ollama server is running and accessible from the paperless-gpt container. | Variable | Description | Required | Default | | -------------------------------- | ---------------------------------------------------------------------------------------------------------------- | -------- | ---------------------- | | `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. | No | paperless-gpt | | `AUTO_TAG` | Tag for auto processing. | No | paperless-gpt-auto | | `LLM_PROVIDER` | AI backend (`openai` or `ollama`). | Yes | | | `LLM_MODEL` | AI model name, e.g. `gpt-4o`, `gpt-3.5-turbo`, `deepseek-r1:8b`. | 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`). | No | English | | `OLLAMA_HOST` | Ollama server URL (e.g. `http://host.docker.internal:11434`). | No | | | `OCR_PROVIDER` | OCR provider to use (`llm`, `azure`, or `google_docai`). | No | llm | | `VISION_LLM_PROVIDER` | AI backend for LLM OCR (`openai` or `ollama`). Required if OCR_PROVIDER is `llm`. | Cond. | | | `VISION_LLM_MODEL` | Model name for LLM OCR (e.g. `minicpm-v`). Required if OCR_PROVIDER is `llm`. | Cond. | | | `AZURE_DOCAI_ENDPOINT` | Azure Document Intelligence endpoint. Required if OCR_PROVIDER is `azure`. | Cond. | | | `AZURE_DOCAI_KEY` | Azure Document Intelligence API key. Required if OCR_PROVIDER is `azure`. | Cond. | | | `AZURE_DOCAI_MODEL_ID` | Azure Document Intelligence model ID. Optional if using `azure` provider. | No | prebuilt-read | | `AZURE_DOCAI_TIMEOUT_SECONDS` | Azure Document Intelligence timeout in seconds. | No | 120 | | `GOOGLE_PROJECT_ID` | Google Cloud project ID. Required if OCR_PROVIDER is `google_docai`. | Cond. | | | `GOOGLE_LOCATION` | Google Cloud region (e.g. `us`, `eu`). Required if OCR_PROVIDER is `google_docai`. | Cond. | | | `GOOGLE_PROCESSOR_ID` | Document AI processor ID. Required if OCR_PROVIDER is `google_docai`. | Cond. | | | `GOOGLE_APPLICATION_CREDENTIALS` | Path to the mounted Google service account key. Required if OCR_PROVIDER is `google_docai`. | Cond. | | | `AUTO_OCR_TAG` | Tag for automatically processing docs with OCR. | No | paperless-gpt-ocr-auto | | `LOG_LEVEL` | Application log level (`info`, `debug`, `warn`, `error`). | No | info | | `LISTEN_INTERFACE` | Network interface to listen on. | No | 8080 | | `AUTO_GENERATE_TITLE` | Generate titles automatically if `paperless-gpt-auto` is used. | No | true | | `AUTO_GENERATE_TAGS` | Generate tags automatically if `paperless-gpt-auto` is used. | No | true | | `AUTO_GENERATE_CORRESPONDENTS` | Generate correspondents automatically if `paperless-gpt-auto` is used. | No | true | | `OCR_LIMIT_PAGES` | Limit the number of pages for OCR. Set to `0` for no limit. | No | 5 | | `TOKEN_LIMIT` | Maximum tokens allowed for prompts/content. Set to `0` to disable limit. Useful for smaller LLMs. | No | | | `CORRESPONDENT_BLACK_LIST` | A comma-separated list of names to exclude from the correspondents suggestions. Example: `John Doe, Jane Smith`. | No | | ### Custom Prompt Templates paperless-gpt's flexible **prompt templates** let you shape how AI responds: 1. **`title_prompt.tmpl`**: For document titles. 2. **`tag_prompt.tmpl`**: For tagging logic. 3. **`ocr_prompt.tmpl`**: For LLM OCR. 4. **`correspondent_prompt.tmpl`**: For correspondent identification. Mount them into your container via: ```yaml volumes: - ./prompts:/app/prompts ``` Then tweak at will—**paperless-gpt** reloads them automatically on startup! #### Template Variables Each template has access to specific variables: **title_prompt.tmpl**: - `{{.Language}}` - Target language (e.g., "English") - `{{.Content}}` - Document content text - `{{.Title}}` - Original document title **tag_prompt.tmpl**: - `{{.Language}}` - Target language - `{{.AvailableTags}}` - List of existing tags in paperless-ngx - `{{.OriginalTags}}` - Document's current tags - `{{.Title}}` - Document title - `{{.Content}}` - Document content text **ocr_prompt.tmpl**: - `{{.Language}}` - Target language **correspondent_prompt.tmpl**: - `{{.Language}}` - Target language - `{{.AvailableCorrespondents}}` - List of existing correspondents - `{{.BlackList}}` - List of blacklisted correspondent names - `{{.Title}}` - Document title - `{{.Content}}` - Document content text The templates use Go's text/template syntax. paperless-gpt automatically reloads template changes on startup. --- ## Usage 1. **Tag Documents** - Add `paperless-gpt` tag to documents for manual processing - Add `paperless-gpt-auto` for automatic processing - Add `paperless-gpt-ocr-auto` for automatic OCR processing 2. **Visit Web UI** - Go to `http://localhost:8080` (or your host) in your browser - Review documents tagged for processing 3. **Generate & Apply Suggestions** - Click "Generate Suggestions" to see AI-proposed titles/tags/correspondents - Review and approve or edit suggestions - Click "Apply" to save changes to paperless-ngx 4. **OCR Processing** - Tag documents with appropriate OCR tag to process them - Monitor progress in the Web UI - Review results and apply changes --- ## LLM-Based OCR: Compare for Yourself
Click to expand the vanilla OCR vs. AI-powered OCR comparison ### Example 1 **Image**: ![Image](demo/ocr-example1.jpg) **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 ``` --- ### Example 2 **Image**: ![Image](demo/ocr-example2.jpg) **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. ### How It Works - **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. --- ## Troubleshooting ### Working with Local LLMs When using local LLMs (like those through Ollama), you might need to adjust certain settings to optimize performance: #### Token Management - Use `TOKEN_LIMIT` environment variable to control the maximum number of tokens sent to the LLM - Smaller models might truncate content unexpectedly if given too much text - Start with a conservative limit (e.g., 1000 tokens) and adjust based on your model's capabilities - Set to `0` to disable the limit (use with caution) Example configuration for smaller models: ```yaml environment: TOKEN_LIMIT: "2000" # Adjust based on your model's context window LLM_PROVIDER: "ollama" LLM_MODEL: "deepseek-r1:8b" # Or other local model ``` Common issues and solutions: - If you see truncated or incomplete responses, try lowering the `TOKEN_LIMIT` - If processing is too limited, gradually increase the limit while monitoring performance - For models with larger context windows, you can increase the limit or disable it entirely ## Contributing **Pull requests** and **issues** are welcome! 1. Fork the repo 2. Create a branch (`feature/my-awesome-update`) 3. Commit changes (`git commit -m "Improve X"`) 4. Open a PR Check out our [contributing guidelines](CONTRIBUTING.md) for details. --- ## License paperless-gpt is licensed under the [MIT License](LICENSE). Feel free to adapt and share! --- ## Star History [![Star History Chart](https://api.star-history.com/svg?repos=icereed/paperless-gpt&type=Date)](https://star-history.com/#icereed/paperless-gpt&Date) --- ## Disclaimer This project is **not** officially affiliated with [paperless-ngx][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. [paperless-ngx]: https://github.com/paperless-ngx/paperless-ngx [docker-install]: https://docs.docker.com/get-docker/