spacy <=3.8.14 imports click without declaring it (#13971), and modern
typer no longer depends on click, so the pre-upgrade install fails on
import without it.
- Download wheels from the run's own artifacts: the release created by
build_wheels is a draft, and drafts can't be looked up by tag, so the
release-downloader step always 404'd
- Drop the checkout: the source tree shadowed the installed wheel when
running python from the workspace root (No module named spacy.symbols)
- Strip the release-v prefix from the tag before using it as a pip
version specifier
numpy >=2.3 only publishes manylinux_2_28 wheels, so pip inside the
manylinux2014 build container falls back to compiling numpy from
source, which fails (container GCC is too old for numpy's meson
build). Bump the x86_64 and aarch64 build images to manylinux_2_28,
matching numpy's own baseline.
mypy 1.5.x crashes with an internal error on numpy>=2.3 type stubs,
which killed the mypy step on Python 3.12+. Bump to mypy 1.20.x and
fix the type errors the newer mypy reports.
Also sync the confection pin in requirements.txt with setup.cfg
(>=1.3.2), which was the single test failure on Python 3.10/3.11.
_get_pip_install_cmd() uses shutil.which("pip") to check for pip, then
returns [sys.executable, "-m", "pip", "install"] which invokes pip as a
module. The binary check fails in venvs where ensurepip creates pip3 but
not pip (common on Debian/Ubuntu), even though python -m pip works.
Replace shutil.which("pip") with importlib.util.find_spec("pip"), which
tests the actual precondition: whether pip is importable in the current
interpreter. The uv branch is unchanged since uv is a standalone binary.
Fixes#13946
After wheels are built:
- smoke_test: install from wheel, download en_core_web_sm, verify entities
- upgrade_test: install previous spacy, download model, upgrade from wheel,
verify model still loads and produces entities
Use confection v1.3 and Thinc v8.3.13, which implement custom validation logic in place of Pydantic, allowing us to properly adopt Pydantic v2 and provide full Python 3.14 support.
Our dependency tree used Pydantic v1 in unusual ways, and relied on behaviours that Pydantic v2 reformed. In the time since Pydantic v2 was released there were a few attempts to migrate over to it, but the task has been complicated by the fact that the confection library has a fairly tangled implementation and I had reduced availability for open-source work in 2024 and 2025.
Specifically, our library confection provides the extensible configuration system we use in spaCy and Thinc. The config system allows you to refer to values that will be supplied by arbitrary functions, that e.g. define some neural network model or its sublayers. The functionality in confection is complicated because we aggressively prioritised user experience in the specification, even if it required increased implementation complexity.
Confection's original implementation built a dynamic Pydantic v1 schema for function-supplied values ("promises"). We validate the schema before calling any promises, and then validate the schema again after calling all the promises and substituting in their values. The variable-interpolation system adds further difficulties to the implementation, and we have to do it all subclassing the Python built-in configparser, which ties us to implementation choices I'd do differently if I had a clean slate.
Here's one summary of Pydantic v1-specific behaviours that the migration to v2 particularly difficult for us. This particular summary was produced during a session with Claude Code Opus 4.6, so nuances of it might be wrong. The full history of attempts at doing this spans over different refactors separated by a few months at a time, so I don't have a full record of all the things that I struggled with. It's possible some details of this summary are incorrect though.
The core problem we kept hitting: Pydantic v2 compiles validation schemas upfront and has much stricter immutability. The whole session has been a series of workarounds for this:
```
1. Schema mutation — v1 let you mutate __fields__ in place; v2 needs model_rebuild() which loses forward ref namespaces, or create_model subclasses which don't propagate to parent schemas.
2. model_dump vs dict — v2 converts dataclasses to dicts, breaking resolved objects. Needed a custom _model_to_dict helper.
3. model_construct drops extras — v2 silently drops fields with extra="forbid", needed manual workarounds.
4. Strict coercion — v2 coerces ndarray to List[Floats1d] via iteration, needed strict=True.
5. Forward refs — Every schema with TYPE_CHECKING imports needs model_rebuild() with the right namespace, and that breaks when confection re-rebuilds later.
In order to adjust for behavioural differences like this, I'd refactored confection to build the different versions of the schema in multiple passes, instead of building all the representations together as we'd been doing. However this refactor itself had problems, further complicating the migration.
```
~I've now bitten the bullet and rolled back the refactor I'd been attempting of confection, and instead replaced the Pydantic validation with custom logic. This allows Confection to remove Pydantic as a dependency entirely.~ Update: Actually I went back and got the refactor working. All much nicer now.
I've taken some lengths to explain this because migrating off a dependency after breaking changes can be a sensitive topic. I want to stress that the changes Pydantic made from v1 to v2 are very good, and I greatly appreciate them as a user of FastAPI in our services. It would be very bad for the ecosystem if Pydantic pinned themselves to exactly matching the behaviours they had in v1 just to avoid breaking support for the sort of thing we'd been doing. Instead users who were relying on those behaviours like us should just find some way to adapt --- either vendor the v1 version we need, or change our behaviours, or implement an alternative. I would have liked to do this sooner but we've ultimately gone with the third option.
- Update expected error counts in test_pattern_validation.py for pydantic v2
(v2 reports errors for all union members, increasing counts for OP and
nested pattern validation)
- Fix AttributeRulerPatternType to include List[MatcherPatternType] in
the union (v2 is strict about nested list-of-list-of-dict types that
v1 accepted laxly)
- Replace pydantic.v1 compat imports with direct v2 imports
- Replace class Config with model_config = ConfigDict(...)
- Replace @validator with @field_validator
- Replace ConstrainedStr with constr()
- Replace min_items with min_length, allow_population_by_field_name
with populate_by_name
- Add model_rebuild() calls in __init__.py for forward ref resolution
- Update test error type assertions for v2
Replace all pydantic.v1 compat imports with direct pydantic v2 imports.
Migrate schemas to v2 API: ConfigDict instead of inner Config class,
field_validator instead of validator, RootModel instead of __root__,
model_dump() instead of dict(), model_validate() instead of parse_obj(),
Annotated[str, StringConstraints()] instead of ConstrainedStr,
min_length instead of min_items, populate_by_name instead of
allow_population_by_field_name.
Wrap yield in try/finally in StringStore.memory_zone and
Vocab.memory_zone so transient state is always cleaned up,
even when an exception propagates through the context manager.
Matthew Honnibal