- Removes Codespell from deps, docs, and `Makefile`s
- Python version requirements in all `pyproject.toml` files now use the
`~=` (compatible release) specifier
- All dependency groups and main dependencies now use explicit lower and
upper bounds, reducing potential for breaking changes
## Summary
This PR fixes several bugs and improves the example code in
`BaseChatMessageHistory` docstring that would prevent it from working
correctly.
### Bugs Fixed
- **Critical bug**: Fixed `json.dump(messages, f)` →
`json.dump(serialized, f)` - was using wrong variable
- **NameError**: Fixed bare variable references to use
`self.storage_path` and `self.session_id`
- **Missing imports**: Added required imports (`json`, `os`, message
converters) to make example runnable
### Improvements
- Added missing type hints following project standards (`messages() ->
list[BaseMessage]`, `clear() -> None`)
- Added robust error handling with `FileNotFoundError` exception
handling
- Added directory creation with `os.makedirs(exist_ok=True)` to prevent
path errors
- Improved performance: `json.load(f)` instead of `json.loads(f.read())`
- Added explicit UTF-8 encoding to all file operations
- Updated stores.py to use modern union syntax (`int | None` vs
`Optional[int]`)
### Test Plan
- [x] Code passes linting (`ruff check`)
- [x] Example code now has all required imports and proper syntax
- [x] Fixed variable references prevent runtime errors
- [x] Follows project's type annotation standards
The example code in the docstring is now fully functional and follows
LangChain's coding standards.
---------
Co-authored-by: sadiqkhzn <sadiqkhzn@users.noreply.github.com>
Release core 0.3.63
Small update just to expand the list of well known tools. This is
necessary while the logic lives in langchain-core.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
The first in a sequence of PRs focusing on improving performance in
core. We're starting with reducing import times for common structures,
hence the benchmarks here.
The benchmark looks a little bit complicated - we have to use a process
so that we don't suffer from Python's import caching system. I tried
doing manual modification of `sys.modules` between runs, but that's
pretty tricky / hacky to get right, hence the subprocess approach.
Motivated by extremely slow baseline for common imports (we're talking
2-5 seconds):
<img width="633" alt="Screenshot 2025-04-09 at 12 48 12 PM"
src="https://github.com/user-attachments/assets/994616fe-1798-404d-bcbe-48ad0eb8a9a0"
/>
Also added a `make benchmark` command to make local runs easy :).
Currently using walltimes so that we can track total time despite using
a manual proces.
Generally, this PR is CI performance focused + aims to clean up some
dependencies at the same time.
1. Unpins upper bounds for `numpy` in all `pyproject.toml` files where
`numpy` is specified
2. Requires `numpy >= 2.1.0` for Python 3.13 and `numpy > v1.26.0` for
Python 3.12, plus a `numpy` min version bump for `chroma`
3. Speeds up CI by minutes - linting on Python 3.13, installing `numpy <
2.1.0` was taking [~3
minutes](https://github.com/langchain-ai/langchain/actions/runs/14316342925/job/40123305868?pr=30713),
now the entire env setup takes a few seconds
4. Deleted the `numpy` test dependency from partners where that was not
used, specifically `huggingface`, `voyageai`, `xai`, and `nomic`.
It's a bit unfortunate that `langchain-community` depends on `numpy`, we
might want to try to fix that in the future...
Closes https://github.com/langchain-ai/langchain/issues/26026
Fixes https://github.com/langchain-ai/langchain/issues/30555
Release notes: https://pydantic.dev/articles/pydantic-v2-11-release
Covered here:
- We no longer access `model_fields` on class instances (that is now
deprecated);
- Update schema normalization for Pydantic version testing to reflect
changes to generated JSON schema (addition of `"additionalProperties":
True` for dict types with value Any or object).
## Considerations:
### Changes to JSON schema generation
#### Tool-calling / structured outputs
This may impact tool-calling + structured outputs for some providers,
but schema generation only changes if you have parameters of the form
`dict`, `dict[str, Any]`, `dict[str, object]`, etc. If dict parameters
are typed my understanding is there are no changes.
For OpenAI for example, untyped dicts work for structured outputs with
default settings before and after updating Pydantic, and error both
before/after if `strict=True`.
### Use of `model_fields`
There is one spot where we previously accessed `super(cls,
self).model_fields`, where `cls` is an object in the MRO. This was done
for the purpose of tracking aliases in secrets. I've updated this to
always be `type(self).model_fields`-- see comment in-line for detail.
---------
Co-authored-by: Sydney Runkle <54324534+sydney-runkle@users.noreply.github.com>
