BIBRA

A metadata extraction and verification tool that integrates multiple methods for extracting, verifying, and reconciling metadata.

Features

• Metadata Extraction: Multiple methods including LLM prompting, fine-tuned models, traditional NLP, and machine learning
• Verification & Benchmarking: Tools for verifying quality against gold standard/ground truth datasets
• External Integration: Authority control and vocabulary reconciliation with external systems
• Web UI: Interactive interface for metadata processing
• REST API: Backend microservice for integration with cataloging tools and data enrichment processes

Install via PyPI

Install the latest release of the bibra package from PyPI into a fresh Python virtualenv:

pip install bibra

Use via Docker

We publish Docker images on Quay.io with the name natlibfi/bibra. To run a shell within the image, use the command

docker run -it --rm quay.io/natlibfi/bibra bash

If you don't specify the command to run, the image will start a Uvicorn server for the REST API and Web UI on port 8000:

docker run -it --rm quay.io/natlibfi/bibra

Getting started

See the wiki for documentation on setting up and configuring BIBRA and LLM services.

Development install

Install development dependencies:

uv sync

Alternatively, install as a global CLI tool (in editable mode) so prefixing CLI commands with uv run is not needed:

uv tool install -e .

Install web UI dependencies:

npm install

Pre-commit hook

Automating the Ruff linter and formatter checks on git commits can be enabled by installing the pre-commit hook:

uv run pre-commit install

Skipping the Ruff checks when committing can be done by adding the --no-verify option to the git commit command.

Usage

See the available CLI commands:

uv run bibra

Start up the server:

uv run uvicorn bibra.main:app

Testing

Python Tests

Run the Python test suite with:

uv run pytest

Cypress E2E Tests

Run the Cypress end-to-end tests:

Run Cypress in interactive mode (opens Cypress GUI):

npx cypress open

Run Cypress headless

npm run cy:run

Use of AI Tools

This project uses AI‑powered development tools, including the Zoo Code VSCode extension, to support the development process. AI assistance may be used for tasks such as:

• generating and refactoring code and tests
• drafting documentation
• exploring ideas and potential solutions

All LLM‑generated content is manually reviewed and approved before being included in the project and the use of AI is disclosed via the pull request template. We indicate AI use, how much human effort went into the work and especially into verifying the result of AI using the AI Traffic Lights Protocol by Nila Löber. AI:ORANGE is the minimum level required for merging pull requests.