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Important
This application is a research prototype. It may not run stably and extracted results can be inaccurate. Always check outputs for validity before using them in practice.
A web application that turns unstructured medical/lab documents into structured JSON using LLMs. Upload PDFs, images, or spreadsheets — extract data with configurable schemas and prompts, then evaluate results against ground truth.
Works with any OpenAI-compatible API: use official services (OpenAI, Mistral OCR) for convenience, or run everything fully local with self-hosted models (DeepSeek-OCR-2 via KatDocExtract, vision LLMs like Gemma 4 via vLLM) for sensitive environments.
📖 Full documentation → katherlab.github.io/llmaixweb Installation, a per-page user guide, an operator handbook, and the developer reference.
- Upload & organize — PDF, DOC/DOCX, images, CSV/XLSX, TXT files with column selection and previews.
- Preprocessing & OCR — four extraction engines to choose from (see OCR engines).
- Visual schema editor — tree-based JSON schema editor with nested objects, arrays, all JSON types, import/export, and validation.
- LLM trials — run extraction trials across different prompts, schemas, and models. Temperature control, token tracking, batch execution. Works with any OpenAI-compatible endpoint.
- Evaluation — upload ground truth CSVs, compare field-by-field, compute per-field and overall accuracy metrics.
- Privacy-first — run fully local or with self-hosted providers. No forced external calls.
- Admin dashboard — user management (invitations, roles), provider configuration, Celery monitoring.
- Authentication & SSO — email/password login with account lockout, refresh tokens, and optional OpenID Connect single sign-on (Google, Keycloak, Azure AD, …) with just-in-time user provisioning.
Tech stack: Vue 3 + Vite + TailwindCSS (frontend), FastAPI (backend), SQLAlchemy, Celery, Pydantic for configuration.
The full end-to-end workflow — from raw files to a scored extraction — using the eight fictitious lung-embolism reports that ship with the repo. Walk through it step by step in the quickstart.
1. Upload & preprocess |
2. Review documents |
3. Run a trial |
4. Inspect extractions |
5. Map ground truth |
6. Evaluate |
git clone https://github.com/KatherLab/llmaixweb
cd llmaixweb
cp .env.example .env
# Edit .env — at minimum SECRET_KEY (OPENAI_API_* are optional)
docker compose up -dOpen http://localhost:5173 and create an admin account on first visit.
For OCR engines, compose overlays, environment variables, and self-hosted setups, see the Installation guide.
| Guide | Link |
|---|---|
| Quickstart walkthrough — CSV of 8 reports → evaluated results | docs |
| User guide — a page per tab (Files → Preprocessing → Documents → Schemas → Trials → Evaluation) | docs |
| Administration — users, SSO, settings, task monitoring | docs |
| Operations — deployment, configuration, upgrading, backups, troubleshooting | docs |
| Security & governance — threat model, data flow, retention, DPIA | docs |
| Development — contributing, developer guide, architecture | docs |
The long-form guides also live in-repo: USAGE.md, DEPLOY.md, DEVELOPER.md, and AGENTS.md.
- Keep PHI strictly local unless you explicitly configure a remote provider.
- Prefer self-hosted, OpenAI-compatible endpoints for clinical data.
- Review your .env secrets and never commit them.
- Report vulnerabilities privately — see SECURITY.md.
Not a certified medical device. The deploying institution is the data controller; see the security & governance docs.
AGPL-3.0 — see LICENSE. Third-party components are listed in THIRD_PARTY_NOTICES.md.