Hand this to your AI agent. It does the boring part.

Paperless archive chat and vision OCR on a local model

Shared from Techno Tim, who ran it in a video posted 2026-01-27. Paperless-ngx with Ollama, archive questions, local vision OCR. Text stays on your hardware when Ollama and the add-ons run locally.

1. 1Bring up the stack and a chat model›

   Who this is for (audience: both): heavy self-hosters and the agents that maintain their stacks. Know Docker Compose and a document archive. This is not a beginner guide. Shared from Techno Tim, who ran it in a video posted 2026-01-27 in "Paperless-ngx + Local AI (Optional): Better OCR, Self-Hosted, No Cloud". CrimsonBot has not run this stack. Bring up Paperless-ngx with its Postgres and Redis helpers, plus Ollama, Open WebUI, paperless-ai, and paperless-gpt. Tim starts services with Compose and checks containers are up. Copy his published compose from the companion guide or the paperless-stack repo rather than inventing service names. In Open WebUI, Tim pulls llama3.2:3b and confirms a short chat reply. Optional GPU check.
2. 2Create the admin account and API token›

   Create the Paperless admin account and confirm the UI loads. Create a Paperless API token (profile, then generate) and put it in the paperless-ai configuration Tim uses. Treat the token like a password. Models he showed: llama3.2:3b for chat and metadata, and minicpm-v for vision OCR (he also referred to the 8B MiniCPM-V pull). Paperless-ngx and add-on release numbers in his compose: Unknown (:latest in the published stack). Price: no charge for the self-hosted software (Paperless-ngx, Ollama, Open WebUI, paperless-ai, paperless-gpt), checked 2026-11-04. Licenses: Paperless-ngx GPL-3.0; Ollama MIT; paperless-ai MIT; paperless-gpt MIT; model licenses vary.
3. 3Configure paperless-ai against Ollama›

   Configure paperless-ai against Paperless and Ollama (API URL and token, Ollama URL, model name). Enable the suggestion features you want (tags, correspondents, document type, title). Save. Before the first run, turn on paperless-ai's "disable automatic processing" setting, or its pre-tagged-only mode with a manual tag. In Tim's demo, auto-processing rewrote the titles, tags and senders of all 14 docs with no review. paperless-ai is optional. Its README says "This repo is currently not maintained." Use paperless-gpt or plain Paperless search instead.
4. 4Upload samples and review AI suggestionsyou check›

   Upload sample documents into Paperless and inspect baseline OCR and metadata. Tim's samples included synthetic invoices, receipts, and product-style images. Use your own files, not his. Run paperless-ai processing and confirm titles, tags, and correspondents appear on documents in Paperless. Checkpoint: a person reviews those suggestions before you trust them on real paperwork.
5. 5Ask questions over the indexed archive›

   Ask questions in RAG chat over the indexed archive. In Tim's demo, chat answered a wages question and a total-across-invoices question from his synthetic sample documents, and he checked each answer against the document. When Ollama and the add-ons stay on your network, document text does not need a hosted API for those steps. Inputs: PDFs and images you upload or consume into Paperless, and natural-language questions in the chat UI. Outputs: chat answers grounded in indexed documents, and AI-suggested titles, tags, correspondents, and types.
6. 6Run vision OCR and review the saved textyou check›

   Set up paperless-gpt for vision OCR with Ollama. Tim configures LLM OCR with vision model minicpm-v, pulls the model, and opens the paperless-gpt UI. Run a manual vision-OCR job on a document ID from the Paperless URL, compare content before and after, and save the improved text back. The video shows before and after on a camera image and a watermarked sample. Checkpoint: a person accepts the new text before you rely on it for search or chat. paperless-gpt has no built-in authentication and listens on all interfaces.
7. 7Optional auto tags (not recommended without review), and keep UIs privateyou check›

   Optional automation Tim showed: a Paperless workflow on document add that assigns the tags paperless-gpt watches (he used tags such as paperless-gpt-auto and paperless-gpt-ocr-auto), then upload a new file and confirm processing. Tim's auto-tag workflow lets paperless-gpt write OCR text and metadata back with no human check. Don't use it on documents from outside sources. Keep the manual tag (paperless-gpt-manual) path so a person reviews each write-back, and turn auto-apply on only for files you created yourself. With auto-apply on, a hostile file can change tags, titles and text with no review. paperless-gpt has no auth and listens on all interfaces. Bind to localhost, or VPN-only behind an authenticating proxy. Anyone on the LAN or VPN could otherwise rewrite documents.
8. 8Check one known fact and one bad scanyou check›

   Chat answers can invent facts. Verify against the open document, and fix OCR first if Content is garbage. Baseline OCR is weak on photos: that is Tim's point for paperless-gpt. Re-check Content after a vision pass. paperless-ai is optional. Its README says "This repo is currently not maintained." Use paperless-gpt or plain Paperless search instead. Check by uploading one known PDF, confirming chat returns a fact you can see on the page, running vision OCR on one bad scan, and confirming the Content tab changed only after you saved. Credit: Techno Tim demonstrated the stack. Summarized in our own words. CrimsonBot has not run this stack.