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feat(text-splitters): ruff fixes and rules (#32502)

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Mason Daugherty 2025-08-11 13:28:22 -04:00 committed by GitHub
parent 27b6b53f20
commit 457ce9c4b0
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12 changed files with 161 additions and 69 deletions
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24
libs/text-splitters/langchain_text_splitters/base.py
View File

@ -3,11 +3,10 @@ from __future__ import annotations
import copy import copy
import logging import logging
from abc import ABC, abstractmethod from abc import ABC, abstractmethod
from collections.abc import Collection, Iterable, Sequence
from collections.abc import Set as AbstractSet
from dataclasses import dataclass from dataclasses import dataclass
from enum import Enum from enum import Enum
from typing import ( from typing import (
TYPE_CHECKING,
Any, Any,
Callable, Callable,
Literal, Literal,
@ -17,6 +16,11 @@ from typing import (
) )
from langchain_core.documents import BaseDocumentTransformer, Document from langchain_core.documents import BaseDocumentTransformer, Document
from typing_extensions import Self
if TYPE_CHECKING:
from collections.abc import Collection, Iterable, Sequence
from collections.abc import Set as AbstractSet
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@ -163,33 +167,33 @@ class TextSplitter(BaseDocumentTransformer, ABC):
def _huggingface_tokenizer_length(text: str) -> int: def _huggingface_tokenizer_length(text: str) -> int:
return len(tokenizer.tokenize(text)) return len(tokenizer.tokenize(text))
except ImportError: except ImportError as err:
msg = ( msg = (
"Could not import transformers python package. " "Could not import transformers python package. "
"Please install it with `pip install transformers`." "Please install it with `pip install transformers`."
) )
raise ValueError(msg) raise ValueError(msg) from err
return cls(length_function=_huggingface_tokenizer_length, **kwargs) return cls(length_function=_huggingface_tokenizer_length, **kwargs)
@classmethod @classmethod
def from_tiktoken_encoder( def from_tiktoken_encoder(
cls: type[TS], cls,
encoding_name: str = "gpt2", encoding_name: str = "gpt2",
model_name: Optional[str] = None, model_name: Optional[str] = None,
allowed_special: Union[Literal["all"], AbstractSet[str]] = set(), allowed_special: Union[Literal["all"], AbstractSet[str]] = set(),
disallowed_special: Union[Literal["all"], Collection[str]] = "all", disallowed_special: Union[Literal["all"], Collection[str]] = "all",
**kwargs: Any, **kwargs: Any,
) -> TS: ) -> Self:
"""Text splitter that uses tiktoken encoder to count length.""" """Text splitter that uses tiktoken encoder to count length."""
try: try:
import tiktoken import tiktoken
except ImportError: except ImportError as err:
msg = ( msg = (
"Could not import tiktoken python package. " "Could not import tiktoken python package. "
"This is needed in order to calculate max_tokens_for_prompt. " "This is needed in order to calculate max_tokens_for_prompt. "
"Please install it with `pip install tiktoken`." "Please install it with `pip install tiktoken`."
) )
raise ImportError(msg) raise ImportError(msg) from err
if model_name is not None: if model_name is not None:
enc = tiktoken.encoding_for_model(model_name) enc = tiktoken.encoding_for_model(model_name)
@ -238,13 +242,13 @@ class TokenTextSplitter(TextSplitter):
super().__init__(**kwargs) super().__init__(**kwargs)
try: try:
import tiktoken import tiktoken
except ImportError: except ImportError as err:
msg = ( msg = (
"Could not import tiktoken python package. " "Could not import tiktoken python package. "
"This is needed in order to for TokenTextSplitter. " "This is needed in order to for TokenTextSplitter. "
"Please install it with `pip install tiktoken`." "Please install it with `pip install tiktoken`."
) )
raise ImportError(msg) raise ImportError(msg) from err
if model_name is not None: if model_name is not None:
enc = tiktoken.encoding_for_model(model_name) enc = tiktoken.encoding_for_model(model_name)

4
libs/text-splitters/langchain_text_splitters/character.py
View File

@ -39,8 +39,8 @@ class CharacterTextSplitter(TextSplitter):
) )
# 4. Decide merge separator: # 4. Decide merge separator:
# - if keep_separator or lookaround → don’t re-insert # - if keep_separator or lookaround -> don't re-insert
# - else → re-insert literal separator # - else -> re-insert literal separator
merge_sep = "" merge_sep = ""
if not (self._keep_separator or is_lookaround): if not (self._keep_separator or is_lookaround):
merge_sep = self._separator merge_sep = self._separator

34
libs/text-splitters/langchain_text_splitters/html.py
View File

@ -3,9 +3,9 @@ from __future__ import annotations
import copy import copy
import pathlib import pathlib
import re import re
from collections.abc import Iterable, Sequence
from io import StringIO from io import StringIO
from typing import ( from typing import (
TYPE_CHECKING,
Any, Any,
Callable, Callable,
Literal, Literal,
@ -21,6 +21,11 @@ from langchain_core.documents import BaseDocumentTransformer, Document
from langchain_text_splitters.character import RecursiveCharacterTextSplitter from langchain_text_splitters.character import RecursiveCharacterTextSplitter
if TYPE_CHECKING:
from collections.abc import Iterable, Sequence
from bs4.element import PageElement
class ElementType(TypedDict): class ElementType(TypedDict):
"""Element type as typed dict.""" """Element type as typed dict."""
@ -159,7 +164,11 @@ class HTMLHeaderTextSplitter:
requests.RequestException: If the HTTP request fails. requests.RequestException: If the HTTP request fails.
""" """
kwargs.setdefault("timeout", timeout) kwargs.setdefault("timeout", timeout)
response = requests.get(url, **kwargs) response = requests.get(
url,
timeout=kwargs.get("timeout", timeout),
**{k: v for k, v in kwargs.items() if k != "timeout"},
)
response.raise_for_status() response.raise_for_status()
return self.split_text(response.text) return self.split_text(response.text)
@ -229,9 +238,9 @@ class HTMLHeaderTextSplitter:
children = list(node.children) children = list(node.children)
from bs4.element import Tag from bs4.element import Tag
for child in reversed(children): stack.extend(
if isinstance(child, Tag): child for child in reversed(children) if isinstance(child, Tag)
stack.append(child) )
tag = getattr(node, "name", None) tag = getattr(node, "name", None)
if not tag: if not tag:
@ -382,7 +391,6 @@ class HTMLSectionSplitter:
""" """
try: try:
from bs4 import BeautifulSoup from bs4 import BeautifulSoup
from bs4.element import PageElement
except ImportError as e: except ImportError as e:
msg = "Unable to import BeautifulSoup/PageElement, \ msg = "Unable to import BeautifulSoup/PageElement, \
please install with `pip install \ please install with `pip install \
@ -396,7 +404,7 @@ class HTMLSectionSplitter:
headers = soup.find_all(["body", *headers]) # type: ignore[assignment] headers = soup.find_all(["body", *headers]) # type: ignore[assignment]
for i, header in enumerate(headers): for i, header in enumerate(headers):
header_element = cast(PageElement, header) header_element = cast("PageElement", header)
if i == 0: if i == 0:
current_header = "#TITLE#" current_header = "#TITLE#"
current_header_tag = "h1" current_header_tag = "h1"
@ -477,7 +485,7 @@ class HTMLSectionSplitter:
return [ return [
Document( Document(
cast(str, section["content"]), cast("str", section["content"]),
metadata={ metadata={
self.headers_to_split_on[str(section["tag_name"])]: section[ self.headers_to_split_on[str(section["tag_name"])]: section[
"header" "header"
@ -587,19 +595,19 @@ class HTMLSemanticPreservingSplitter(BaseDocumentTransformer):
denylist_tags: Optional[list[str]] = None, denylist_tags: Optional[list[str]] = None,
preserve_parent_metadata: bool = False, preserve_parent_metadata: bool = False,
keep_separator: Union[bool, Literal["start", "end"]] = True, keep_separator: Union[bool, Literal["start", "end"]] = True,
): ) -> None:
"""Initialize splitter.""" """Initialize splitter."""
try: try:
from bs4 import BeautifulSoup, Tag from bs4 import BeautifulSoup, Tag
self._BeautifulSoup = BeautifulSoup self._BeautifulSoup = BeautifulSoup
self._Tag = Tag self._Tag = Tag
except ImportError: except ImportError as err:
msg = ( msg = (
"Could not import BeautifulSoup. " "Could not import BeautifulSoup. "
"Please install it with 'pip install bs4'." "Please install it with 'pip install bs4'."
) )
raise ImportError(msg) raise ImportError(msg) from err
self._headers_to_split_on = sorted(headers_to_split_on) self._headers_to_split_on = sorted(headers_to_split_on)
self._max_chunk_size = max_chunk_size self._max_chunk_size = max_chunk_size
@ -647,11 +655,11 @@ class HTMLSemanticPreservingSplitter(BaseDocumentTransformer):
nltk.download("stopwords") nltk.download("stopwords")
self._stopwords = set(nltk.corpus.stopwords.words(self._stopword_lang)) self._stopwords = set(nltk.corpus.stopwords.words(self._stopword_lang))
except ImportError: except ImportError as err:
msg = ( msg = (
"Could not import nltk. Please install it with 'pip install nltk'." "Could not import nltk. Please install it with 'pip install nltk'."
) )
raise ImportError(msg) raise ImportError(msg) from err
def split_text(self, text: str) -> list[Document]: def split_text(self, text: str) -> list[Document]:
"""Splits the provided HTML text into smaller chunks based on the configuration. """Splits the provided HTML text into smaller chunks based on the configuration.

2
libs/text-splitters/langchain_text_splitters/json.py
View File

@ -23,7 +23,7 @@ class RecursiveJsonSplitter:
def __init__( def __init__(
self, max_chunk_size: int = 2000, min_chunk_size: Optional[int] = None self, max_chunk_size: int = 2000, min_chunk_size: Optional[int] = None
): ) -> None:
"""Initialize the chunk size configuration for text processing. """Initialize the chunk size configuration for text processing.
This constructor sets up the maximum and minimum chunk sizes, ensuring that This constructor sets up the maximum and minimum chunk sizes, ensuring that

4
libs/text-splitters/langchain_text_splitters/konlpy.py
View File

@ -21,12 +21,12 @@ class KonlpyTextSplitter(TextSplitter):
self._separator = separator self._separator = separator
try: try:
import konlpy import konlpy
except ImportError: except ImportError as err:
msg = """ msg = """
Konlpy is not installed, please install it with Konlpy is not installed, please install it with
`pip install konlpy` `pip install konlpy`
""" """
raise ImportError(msg) raise ImportError(msg) from err
self.kkma = konlpy.tag.Kkma() self.kkma = konlpy.tag.Kkma()
def split_text(self, text: str) -> list[str]: def split_text(self, text: str) -> list[str]:

4
libs/text-splitters/langchain_text_splitters/markdown.py
View File

@ -26,7 +26,7 @@ class MarkdownHeaderTextSplitter:
headers_to_split_on: list[tuple[str, str]], headers_to_split_on: list[tuple[str, str]],
return_each_line: bool = False, # noqa: FBT001,FBT002 return_each_line: bool = False, # noqa: FBT001,FBT002
strip_headers: bool = True, # noqa: FBT001,FBT002 strip_headers: bool = True, # noqa: FBT001,FBT002
): ) -> None:
"""Create a new MarkdownHeaderTextSplitter. """Create a new MarkdownHeaderTextSplitter.
Args: Args:
@ -281,7 +281,7 @@ class ExperimentalMarkdownSyntaxTextSplitter:
headers_to_split_on: Union[list[tuple[str, str]], None] = None, headers_to_split_on: Union[list[tuple[str, str]], None] = None,
return_each_line: bool = False, # noqa: FBT001,FBT002 return_each_line: bool = False, # noqa: FBT001,FBT002
strip_headers: bool = True, # noqa: FBT001,FBT002 strip_headers: bool = True, # noqa: FBT001,FBT002
): ) -> None:
"""Initialize the text splitter with header splitting and formatting options. """Initialize the text splitter with header splitting and formatting options.
This constructor sets up the required configuration for splitting text into This constructor sets up the required configuration for splitting text into

4
libs/text-splitters/langchain_text_splitters/nltk.py
View File

@ -31,9 +31,9 @@ class NLTKTextSplitter(TextSplitter):
self._tokenizer = nltk.tokenize._get_punkt_tokenizer(self._language) self._tokenizer = nltk.tokenize._get_punkt_tokenizer(self._language)
else: else:
self._tokenizer = nltk.tokenize.sent_tokenize self._tokenizer = nltk.tokenize.sent_tokenize
except ImportError: except ImportError as err:
msg = "NLTK is not installed, please install it with `pip install nltk`." msg = "NLTK is not installed, please install it with `pip install nltk`."
raise ImportError(msg) raise ImportError(msg) from err
def split_text(self, text: str) -> list[str]: def split_text(self, text: str) -> list[str]:
"""Split incoming text and return chunks.""" """Split incoming text and return chunks."""

4
libs/text-splitters/langchain_text_splitters/sentence_transformers.py
View File

@ -20,13 +20,13 @@ class SentenceTransformersTokenTextSplitter(TextSplitter):
try: try:
from sentence_transformers import SentenceTransformer from sentence_transformers import SentenceTransformer
except ImportError: except ImportError as err:
msg = ( msg = (
"Could not import sentence_transformers python package. " "Could not import sentence_transformers python package. "
"This is needed in order to for SentenceTransformersTokenTextSplitter. " "This is needed in order to for SentenceTransformersTokenTextSplitter. "
"Please install it with `pip install sentence-transformers`." "Please install it with `pip install sentence-transformers`."
) )
raise ImportError(msg) raise ImportError(msg) from err
self.model_name = model_name self.model_name = model_name
self._model = SentenceTransformer(self.model_name) self._model = SentenceTransformer(self.model_name)

8
libs/text-splitters/langchain_text_splitters/spacy.py
View File

@ -45,11 +45,13 @@ def _make_spacy_pipeline_for_splitting(
) -> Any: # avoid importing spacy ) -> Any: # avoid importing spacy
try: try:
import spacy import spacy
except ImportError: except ImportError as err:
msg = "Spacy is not installed, please install it with `pip install spacy`." msg = "Spacy is not installed, please install it with `pip install spacy`."
raise ImportError(msg) raise ImportError(msg) from err
if pipeline == "sentencizer": if pipeline == "sentencizer":
sentencizer: Any = spacy.lang.en.English() from spacy.lang.en import English
sentencizer: Any = English()
sentencizer.add_pipe("sentencizer") sentencizer.add_pipe("sentencizer")
else: else:
sentencizer = spacy.load(pipeline, exclude=["ner", "tagger"]) sentencizer = spacy.load(pipeline, exclude=["ner", "tagger"])

74
libs/text-splitters/pyproject.toml
View File

@ -6,7 +6,10 @@ build-backend = "pdm.backend"
authors = [] authors = []
license = { text = "MIT" } license = { text = "MIT" }
requires-python = ">=3.9" requires-python = ">=3.9"
dependencies = ["langchain-core<1.0.0,>=0.3.72"] dependencies = [
"langchain-core<1.0.0,>=0.3.72",
"pip>=25.2",
]
name = "langchain-text-splitters" name = "langchain-text-splitters"
version = "0.3.9" version = "0.3.9"
description = "LangChain text splitting utilities" description = "LangChain text splitting utilities"
@ -18,7 +21,7 @@ readme = "README.md"
repository = "https://github.com/langchain-ai/langchain" repository = "https://github.com/langchain-ai/langchain"
[dependency-groups] [dependency-groups]
lint = ["ruff<0.13,>=0.12.2", "langchain-core"] lint = ["ruff<0.13,>=0.12.8", "langchain-core"]
typing = [ typing = [
"mypy<2.0,>=1.15", "mypy<2.0,>=1.15",
"lxml-stubs<1.0.0,>=0.5.1", "lxml-stubs<1.0.0,>=0.5.1",
@ -42,6 +45,7 @@ test_integration = [
"nltk<4.0.0,>=3.9.1", "nltk<4.0.0,>=3.9.1",
"transformers<5.0.0,>=4.51.3", "transformers<5.0.0,>=4.51.3",
"sentence-transformers>=3.0.1", "sentence-transformers>=3.0.1",
"tiktoken<1.0.0,>=0.8.0",
] ]
[tool.uv.sources] [tool.uv.sources]
@ -61,10 +65,72 @@ ignore_missing_imports = "True"
target-version = "py39" target-version = "py39"
[tool.ruff.lint] [tool.ruff.lint]
select = ["E", "F", "FBT", "I", "UP", "PGH003", "T201", "D"] select = [
ignore = ["D100",] "A", # flake8-builtins
"B", # flake8-bugbear
"ASYNC", # flake8-async
"C4", # flake8-comprehensions
"COM", # flake8-commas
"D", # pydocstyle
"E", # pycodestyle error
"EM", # flake8-errmsg
"F", # pyflakes
"FA", # flake8-future-annotations
"FBT", # flake8-boolean-trap
"FLY", # flake8-flynt
"I", # isort
"ICN", # flake8-import-conventions
"INT", # flake8-gettext
"ISC", # isort-comprehensions
"PGH", # pygrep-hooks
"PIE", # flake8-pie
"PERF", # flake8-perf
"PYI", # flake8-pyi
"Q", # flake8-quotes
"RET", # flake8-return
"RSE", # flake8-rst-docstrings
"RUF", # ruff
"S", # flake8-bandit
"SLF", # flake8-self
"SLOT", # flake8-slots
"SIM", # flake8-simplify
"T10", # flake8-debugger
"T20", # flake8-print
"TID", # flake8-tidy-imports
"UP", # pyupgrade
"W", # pycodestyle warning
"YTT", # flake8-2020
]
ignore = [
"D100", # pydocstyle: Missing docstring in public module
"D101", # pydocstyle: Missing docstring in public class
"D102", # pydocstyle: Missing docstring in public method
"D103", # pydocstyle: Missing docstring in public function
"D104", # pydocstyle: Missing docstring in public package
"D105", # pydocstyle: Missing docstring in magic method
"D107", # pydocstyle: Missing docstring in __init__
"D203", # Messes with the formatter
"D213", # pydocstyle: Multi-line docstring summary should start at the second line (incompatible with D212)
"D407", # pydocstyle: Missing-dashed-underline-after-section
"COM812", # Messes with the formatter
"ISC001", # Messes with the formatter
"PERF203", # Rarely useful
"S112", # Rarely useful
"RUF012", # Doesn't play well with Pydantic
"SLF001", # Private member access
"UP007", # pyupgrade: non-pep604-annotation-union
"UP045", # pyupgrade: non-pep604-annotation-optional
]
unfixable = ["B028"] # People should intentionally tune the stacklevel
pyupgrade.keep-runtime-typing = true pyupgrade.keep-runtime-typing = true
[tool.ruff.lint.extend-per-file-ignores]
"tests/**/*.py" = [
"S101", # Tests need assertions
"S311", # Standard pseudo-random generators are not suitable for cryptographic purposes
]
[tool.coverage.run] [tool.coverage.run]
omit = ["tests/*"] omit = ["tests/*"]

7
libs/text-splitters/scripts/check_imports.py
View File

@ -1,7 +1,6 @@
import random
import string
import sys import sys
import traceback import traceback
import uuid
from importlib.machinery import SourceFileLoader from importlib.machinery import SourceFileLoader
if __name__ == "__main__": if __name__ == "__main__":
@ -9,9 +8,7 @@ if __name__ == "__main__":
has_failure = False has_failure = False
for file in files: for file in files:
try: try:
module_name = "".join( module_name = f"test_module_{uuid.uuid4().hex[:20]}"
random.choice(string.ascii_letters) for _ in range(20)
)
SourceFileLoader(module_name, file).load_module() SourceFileLoader(module_name, file).load_module()
except Exception: except Exception:
has_failure = True has_failure = True

61
libs/text-splitters/uv.lock
View File

@ -1,5 +1,5 @@
version = 1 version = 1
revision = 2 revision = 3
requires-python = ">=3.9" requires-python = ">=3.9"
resolution-markers = [ resolution-markers = [
"python_full_version >= '3.13'", "python_full_version >= '3.13'",
@ -1090,7 +1090,7 @@ wheels = [
[[package]] [[package]]
name = "langchain-core" name = "langchain-core"
version = "0.3.72" version = "0.3.74"
source = { editable = "../core" } source = { editable = "../core" }
dependencies = [ dependencies = [
{ name = "jsonpatch" }, { name = "jsonpatch" },
@ -1152,6 +1152,7 @@ version = "0.3.9"
source = { editable = "." } source = { editable = "." }
dependencies = [ dependencies = [
{ name = "langchain-core" }, { name = "langchain-core" },
{ name = "pip" },
] ]
[package.dev-dependencies] [package.dev-dependencies]
@ -1178,6 +1179,7 @@ test-integration = [
{ name = "sentence-transformers" }, { name = "sentence-transformers" },
{ name = "spacy" }, { name = "spacy" },
{ name = "thinc" }, { name = "thinc" },
{ name = "tiktoken" },
{ name = "transformers" }, { name = "transformers" },
] ]
typing = [ typing = [
@ -1188,7 +1190,10 @@ typing = [
] ]
[package.metadata] [package.metadata]
requires-dist = [{ name = "langchain-core", editable = "../core" }] requires-dist = [
{ name = "langchain-core", editable = "../core" },
{ name = "pip", specifier = ">=25.2" },
]
[package.metadata.requires-dev] [package.metadata.requires-dev]
dev = [ dev = [
@ -1197,7 +1202,7 @@ dev = [
] ]
lint = [ lint = [
{ name = "langchain-core", editable = "../core" }, { name = "langchain-core", editable = "../core" },
{ name = "ruff", specifier = ">=0.12.2,<0.13" }, { name = "ruff", specifier = ">=0.12.8,<0.13" },
] ]
test = [ test = [
{ name = "freezegun", specifier = ">=1.2.2,<2.0.0" }, { name = "freezegun", specifier = ">=1.2.2,<2.0.0" },
@ -1214,6 +1219,7 @@ test-integration = [
{ name = "sentence-transformers", specifier = ">=3.0.1" }, { name = "sentence-transformers", specifier = ">=3.0.1" },
{ name = "spacy", specifier = ">=3.8.7,<4.0.0" }, { name = "spacy", specifier = ">=3.8.7,<4.0.0" },
{ name = "thinc", specifier = ">=8.3.6,<9.0.0" }, { name = "thinc", specifier = ">=8.3.6,<9.0.0" },
{ name = "tiktoken", specifier = ">=0.8.0,<1.0.0" },
{ name = "transformers", specifier = ">=4.51.3,<5.0.0" }, { name = "transformers", specifier = ">=4.51.3,<5.0.0" },
] ]
typing = [ typing = [
@ -2133,6 +2139,15 @@ wheels = [
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] ]
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name = "pip"
version = "25.2"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/20/16/650289cd3f43d5a2fadfd98c68bd1e1e7f2550a1a5326768cddfbcedb2c5/pip-25.2.tar.gz", hash = "sha256:578283f006390f85bb6282dffb876454593d637f5d1be494b5202ce4877e71f2", size = 1840021, upload-time = "2025-07-30T21:50:15.401Z" }
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