bibr 0.5.1

This patch release fixes six pipeline reliability issues affecting cancellation, OCR cache quality, request overrides, mixed input batches, and model cleanup.

Fixes

• Reclaim OCR and local LLM servers that finish starting after cancellation, including repeated interrupts and shutdown racing with startup.
• Keep local sentence segmentation serialized until inference finishes, preventing cancellation from releasing the model lock or unloading the model too early.
• Preserve OCR page-failure counts and warnings in cached results, and apply the current minimum success rate to cache hits.
• Allow served requests to enable reference parsing and Crossref enrichment when the deployment defaults reference parsing to off.
• Let native documents finish when layout initialization fails for PDFs in a mixed batch, including streaming runs.
• Release resident layout and segmentation models and front-matter classifier cache references when closing a pipeline.

Upgrade notes

Existing OCR cache entries are rebuilt once because older entries lack the completion evidence needed for quality checks. Cancellation waits for an active blocking operation to finish so its resources can be reclaimed safely.

python -m pip install --upgrade bibr==0.5.1

Full changelog