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* fix(ai-bundles): lock the numpy-1.x ABI closure so the OCR bundle can't strand scipy The OCR bundle installs paddleocr[doc-parser] 3.4, whose dependency closure drags numpy 1.26.4 up to 2.5.1 and pulls scipy/scikit-learn/pandas wheels built against the numpy 2.x ABI. build-bundle.sh re-pinned only numpy (basePackages), so those numpy-2.x wheels stayed behind; the by-dir-name site-packages diff then shipped them, and once merged onto the numpy==1.26.4 base they raise "numpy.dtype size changed" on import. Because the dispatcher pre-imports every ML library at startup and disables all AI after 5 crashes in 60s, one stranded scipy takes down every AI tool, not just OCR (observed on a CPU host: remove-background worked before the OCR bundle and broke after). All-7 installs escaped it through last-writer-wins ordering; a subset install did not, which is why it surfaced only intermittently. Fix: add a manifest "constraints" list (numpy, scipy, scikit-learn, scikit-image, pandas pinned to numpy-1.x-ABI versions) and apply it via PIP_CONSTRAINT to every bundle pip install, so no bundle can pull a numpy-2.x wheel. paddleocr 3.4.1 still resolves cleanly under the lock and the pinned stack imports without ABI error on numpy 1.26.4 (validated on py3.12). Also import scipy/sklearn in the OCR path of verify-bundle.sh so CI catches this class in isolation, and add a manifest regression test. Note: the published bundles must be rebuilt and republished (ai-bundles.yml) for this to reach already-installed bases. Claude-Session: https://claude.ai/code/session_01UvVCMNUBrgpghk8gye5gav * chore(ai-bundles): sync OCR manifest sha256 to the rebuilt numpy-1.x bundles Rebuilt the OCR bundle for both arches with the numpy-1.x-ABI constraints from this PR and republished the tars to deepsafe/feature-bundles/v2.0.0, then updated the baked manifest sha256 and sizes so installs verify against the fixed archives: amd64-gpu 5.93 GB sha 2a00a3184f6a635f1fa9ae2a6517ad740a11f9e5ff58c098d2fd369a2bb1e16b arm64-cpu 1.98 GB sha 6868c264069dcb74c6675c0b1f58dc1c9f60d9aa4459725e3dbde07a99a6a09a Both tars ship scipy 1.12.0 / scikit-learn 1.4.2 / pandas 2.2.2 (numpy-1.x-ABI) and zero numpy-2.x wheels, verified by listing the archive contents. Stopgap note: these tars were built against the ghcr.io latest base (the 2.0.0 image is not published to GHCR), so they are not byte-identical to what the CI build will produce. When ai-bundles.yml rebuilds at the 2.0.0 release, it will mint fresh sha256 values and this manifest must be re-synced to them. Claude-Session: https://claude.ai/code/session_01UvVCMNUBrgpghk8gye5gav