📄 Paperless-AI

March 31, 2026 · View on GitHub

⚠️ IMPORTANT NOTICE:

This repo is currently not maintained. I appreciate all the efforts from the community pushing PRs and creating issues. I'm currently rewriting the entire codebase with a more stable, up-to-date architecture. But I'm limited to evenings, and doing support here while also working on the rewrite just isn't feasible for me. With the upcoming official AI integration in Paperless-ngx itself, I'm also not sure if I'll complete the rewrite or continue maintaining this repo at all.

Paperless-AI is an AI-powered extension for Paperless-ngx that brings automatic document classification, smart tagging, and semantic search using OpenAI-compatible APIs and Ollama.

It enables fully automated document workflows, contextual chat, and powerful customization — all via an intuitive web interface.

💡 Just ask:
“When did I sign my rental agreement?”
“What was the amount of the last electricity bill?”
“Which documents mention my health insurance?”

Powered by Retrieval-Augmented Generation (RAG), you can now search semantically across your full archive and get precise, natural language answers.

✨ Features

🔄 Automated Document Processing

Detects new documents in Paperless-ngx automatically
Analyzes content using OpenAI API, Ollama, and other compatible backends
Assigns title, tags, document type, and correspondent
Built-in support for:
- Ollama (Mistral, Llama, Phi-3, Gemma-2)
- OpenAI
- DeepSeek.ai
- OpenRouter.ai
- Perplexity.ai
- Together.ai
- LiteLLM
- VLLM
- Fastchat
- Gemini (Google)
- ...and more!

🧠 RAG-Based AI Chat

Natural language document search and Q&A
Understands full document context (not just keywords)
Semantic memory powered by your own data
Fast, intelligent, privacy-friendly document queries

⚙️ Manual Processing

Web interface for manual AI tagging
Useful when reviewing sensitive documents
Accessible via /manual

🧩 Smart Tagging & Rules

Define rules to limit which documents are processed
Disable prompts and apply tags automatically
Set custom output tags for tracked classification

🚀 Installation

⚠️ First-time install: Restart the container after completing setup (API keys, preferences) to build RAG index.
🔁 Not required for updates.

📘 Installation Wiki

🐳 Docker Support

Health monitoring and auto-restart
Persistent volumes and graceful shutdown
Works out of the box with minimal setup

🔧 Local Development

# Install dependencies
npm install

# Start development/test mode
npm run test

🧭 Roadmap Highlights

✅ Multi-AI model support
✅ Multilingual document analysis
✅ Tag rules and filters
✅ Integrated document chat with RAG
✅ Responsive web interface

🤝 Contributing

We welcome PRs and contributions!

# Fork, clone, then:
git checkout -b feature/YourFeature
# After changes:
git commit -m "Add YourFeature"
git push origin feature/YourFeature

Then open a Pull Request via GitHub.