Bump the minimum `langchain-core` dependency to `>=1.2.21` across all 14
partner packages in the monorepo. Aligns partner lower bounds with the
latest core release so consumers pick up recent fixes (notably the
`ModelProfile` schema drift fix from core 1.2.21).
CI lint jobs use `uv run --all-groups` for all tools, but ruff doesn't
need dependency resolution — only mypy does. By splitting into
`UV_RUN_LINT` (ruff) and `UV_RUN_TYPE` (mypy), the CI-facing targets run
ruff with `--group lint` only, giving fast-fail feedback before mypy
triggers the full environment sync.
For packages where source code only conditionally imports heavy deps
(text-splitters, huggingface), `lint_package` also overrides
`UV_RUN_TYPE` to `--group lint --group typing`, skipping the ~3.5GB
`test_integration` download entirely. `lint_tests` keeps `--all-groups`
since test code legitimately imports those deps.
Additionally, `lint_imports.sh` was inconsistently wired — most packages
had the script but weren't calling it.
## Changes
**Makefile optimization**
- Introduce `UV_RUN_LINT` and `UV_RUN_TYPE` Make variables, both
defaulting to `uv run --all-groups`. For `lint_package` and
`lint_tests`, `UV_RUN_LINT` is overridden to `uv run --group lint` so
ruff runs instantly without syncing heavy deps
- For `text-splitters` and `huggingface`, override `UV_RUN_TYPE` on
`lint_package` to `uv run --group lint --group typing` — mypy runs
without downloading torch, CUDA, spacy, etc.
**mypy config for lean groups**
- Add `transformers` and `transformers.*` to `ignore_missing_imports` in
`text-splitters` pyproject.toml (conditional `try/except` import, same
treatment as existing `konlpy`/`nltk` entries)
- Add `torch`, `torch.*`, `langchain_community`, `langchain_community.*`
to `ignore_missing_imports` in `huggingface` pyproject.toml
- Add dual `# type: ignore[unreachable, unused-ignore]` in
`text-splitters/base.py` to handle the `PreTrainedTokenizerBase`
isinstance check that behaves differently depending on whether
transformers is installed
**lint_imports.sh consistency**
- Add `./scripts/lint_imports.sh` to the lint recipe in every package
that wasn't calling it (standard-tests, model-profiles, all 15
partners), and create the script for the two packages missing it
entirely (`model-profiles`, `openrouter`)
- Update all `lint_imports.sh` scripts to allow `from langchain.agents`
and `from langchain.tools` imports (legitimate v1 middleware
dependencies used by `langchain-anthropic` and `langchain-openai`)
Bump `transformers` and `sentence-transformers` lower bounds in
`langchain-huggingface` to resolve a dependency conflict with
`huggingface-hub` 1.x. The existing constraints allowed
`huggingface-hub>=0.33.4,<2.0.0` (so hub 1.x is valid), but
`transformers` 4.x requires `huggingface-hub<1.0` — causing the
pre-release CI job to fail when `uv pip install --force-reinstall`
resolved hub to 1.5.0 while leaving `transformers` at 4.56.2.
Breaking change for users on transformers 4.x or
sentence-transformers<5.2.0 who install langchain-huggingface[full].
- 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
**Description:**
`langchain_huggingface` has a very large installation size of around 600
MB (on a Mac with Python 3.11). This is due to its dependency on
`sentence-transformers`, which in turn depends on `torch`, which is 320
MB all by itself. Similarly, the depedency on `transformers` adds
another set of heavy dependencies. With those dependencies removed, the
installation of `langchain_huggingface` only takes up ~26 MB. This is
only 5 % of the full installation!
These libraries are not necessary to use `langchain_huggingface`'s API
wrapper classes, only for local inferences/embeddings. All import
statements for those two libraries already have import guards in place
(try/catch with a helpful "please install x" message).
This PR therefore moves those two libraries to an optional dependency
group `full`. So a `pip install langchain_huggingface` will only install
the lightweight version, and a `pip install
"langchain_huggingface[full]"` will install all dependencies.
I know this may break existing code, because `sentence-transformers` and
`transformers` are now no longer installed by default. Given that users
will see helpful error messages when that happens, and the major impact
of this small change, I hope that you will still consider this PR.
**Dependencies:** No new dependencies, but new optional grouping.
Hi there, I'm Célina from 🤗,
This PR introduces support for Hugging Face's serverless Inference
Providers (documentation
[here](https://huggingface.co/docs/inference-providers/index)), allowing
users to specify different providers for chat completion and text
generation tasks.
This PR also removes the usage of `InferenceClient.post()` method in
`HuggingFaceEndpoint`, in favor of the task-specific `text_generation`
method. `InferenceClient.post()` is deprecated and will be removed in
`huggingface_hub v0.31.0`.
---
## Changes made
- bumped the minimum required version of the `huggingface-hub` package
to ensure compatibility with the latest API usage.
- added a `provider` field to `HuggingFaceEndpoint`, enabling users to
select the inference provider (e.g., 'cerebras', 'together',
'fireworks-ai'). Defaults to `hf-inference` (HF Inference API).
- replaced the deprecated `InferenceClient.post()` call in
`HuggingFaceEndpoint` with the task-specific `text_generation` method
for future-proofing, `post()` will be removed in huggingface-hub
v0.31.0.
- updated the `ChatHuggingFace` component:
- added async and streaming support.
- added support for tool calling.
- exposed underlying chat completion parameters for more granular
control.
- Added integration tests for `ChatHuggingFace` and updated the
corresponding unit tests.
✅ All changes are backward compatible.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
Follow up to https://github.com/langchain-ai/langsmith-sdk/pull/1696,
I've bumped the `langsmith` version where applicable in `uv.lock`.
Type checking problems here because deps have been updated in
`pyproject.toml` and `uv lock` hasn't been run - we should enforce that
in the future - goes with the other dependabot todos :).
Hi there, This is a complementary PR to #30733.
This PR introduces support for Hugging Face's serverless Inference
Providers (documentation
[here](https://huggingface.co/docs/inference-providers/index)), allowing
users to specify different providers
This PR also removes the usage of `InferenceClient.post()` method in
`HuggingFaceEndpointEmbeddings`, in favor of the task-specific
`feature_extraction` method. `InferenceClient.post()` is deprecated and
will be removed in `huggingface_hub` v0.31.0.
## Changes made
- bumped the minimum required version of the `huggingface_hub` package
to ensure compatibility with the latest API usage.
- added a provider field to `HuggingFaceEndpointEmbeddings`, enabling
users to select the inference provider.
- replaced the deprecated `InferenceClient.post()` call in
`HuggingFaceEndpointEmbeddings` with the task-specific
`feature_extraction` method for future-proofing, `post()` will be
removed in `huggingface-hub` v0.31.0.
✅ All changes are backward compatible.
---------
Co-authored-by: Lucain <lucainp@gmail.com>
Co-authored-by: ccurme <chester.curme@gmail.com>
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
