v3.8.14: Bug fix for model downloading in environments without pip on PATH

• Fix spacy download failing in environments where pip is not on PATH but is available as a Python module (e.g., some virtual environments and containers)

v3.8.13: Pin confection to new version

The v3.8.12 release didn't update the confection pin, which meant that if you did an upgrade-install models wouldn't load.

v3.8.12

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.

v3.8.11: Add Windows ARM wheels

Add wheels for Python 3.11, 3.12, 3.13 and 3.14 for Windows ARM. Windows ARM wheels for Python 3.10 and earlier are not available in numpy, so aren't provided.

v3.8.10: Fix missing Python 3.14 wheels

release-v3.8.10

Windows arm needs to be disabled at the ci level, so remove this skip…

v3.8.9: Support Python 3.14

Add wheels for Python 3.14

v3.8.8: Fix deprecation warnings, update requirements, drop 3.9

• Fix deprecation warnings from click imports
• Update requirements, including switch to typer-slim to reduce dependency footprint
• Drop support for Python 3.9 (end-of-life)

Other dependencies in spaCy's tree have also been updated to widen the numpy compatibility pin, which should reduce installation problems for some users.

v3.8.7: Python 3.13 support, Cython 3, centralize registry entries

In order to support Python 3.13, spaCy is now compiled with Cython 3. This brings a change to the way types are handled at runtime (Cython 3 uses the from __future__ import annotations semantics, which stores types as strings at runtime. This difference caused problems for components registered within Cython files, as we rely on building Pydantic models from factory function signatures to do validation.

To support Python 3.13 we therefore create a new module, spacy.pipeline.factories, which contains the factory function implementations. __getattr__ import shims have been added to the previous locations of these functions to prevent backwards incompatibilities.

As well as moving the factories, the new implementation avoids import-time side-effects, by moving the actual calls to the decorator inside a function, which is executed once when the Language class is initialised.

A matching change has been made to the catalogue registry decorators. A new module spacy.registrations has been created that performs all the catalogue registrations. Moving these registrations away from the functions prevents these decorators from running at import time. This change was not necessary for the Python 3.13 support, but it means we no longer rely on any import-time side-effects, which will allow us to improve spaCy's import time and therefore CLI execution time. The change also makes maintenance easier as it's easier to find the implementations of different registry functions (this may help library users as well).

v3.8.6: Restore wheels, remove Python 3.13 compatibility

Restores support for wheels for ARM platforms, while correctly noting compatibility range.

v3.8.3: Improve memory zone stability

Fix bug in memory zones when non-transient strings were added to the StringStore inside a memory zone. This caused a bug in the morphological analyser that caused string not found errors when applied during a memory zone.