feat(langchain): add ruff rules PL (#32079)

See https://docs.astral.sh/ruff/rules/#pylint-pl

libs/langchain/langchain/__init__.py

@@ -357,7 +357,7 @@ def __getattr__(name: str) -> Any:
 
         return ElasticVectorSearch
     # For backwards compatibility
-    if name == "SerpAPIChain" or name == "SerpAPIWrapper":
+    if name in {"SerpAPIChain", "SerpAPIWrapper"}:
         from langchain_community.utilities import SerpAPIWrapper
 
         _warn_on_import(

libs/langchain/langchain/agents/format_scratchpad/xml.py

@@ -40,13 +40,13 @@ def format_xml(
             # Escape XML tags in tool names and inputs using custom delimiters
             tool = _escape(action.tool)
             tool_input = _escape(str(action.tool_input))
-            observation = _escape(str(observation))
+            observation_ = _escape(str(observation))
         else:
             tool = action.tool
             tool_input = str(action.tool_input)
-            observation = str(observation)
+            observation_ = str(observation)
         log += (
             f"<tool>{tool}</tool><tool_input>{tool_input}"
-            f"</tool_input><observation>{observation}</observation>"
+            f"</tool_input><observation>{observation_}</observation>"
         )
     return log

libs/langchain/langchain/agents/openai_assistant/base.py

@@ -582,7 +582,7 @@ class OpenAIAssistantRunnable(RunnableSerializable[dict, OutputType]):
             major_version = int(openai.version.VERSION.split(".")[0])
             minor_version = int(openai.version.VERSION.split(".")[1])
             version_gte_1_14 = (major_version > 1) or (
-                major_version == 1 and minor_version >= 14
+                major_version == 1 and minor_version >= 14  # noqa: PLR2004
             )
 
             messages = self.client.beta.threads.messages.list(
@@ -739,7 +739,7 @@ class OpenAIAssistantRunnable(RunnableSerializable[dict, OutputType]):
             major_version = int(openai.version.VERSION.split(".")[0])
             minor_version = int(openai.version.VERSION.split(".")[1])
             version_gte_1_14 = (major_version > 1) or (
-                major_version == 1 and minor_version >= 14
+                major_version == 1 and minor_version >= 14  # noqa: PLR2004
             )
 
             messages = await self.async_client.beta.threads.messages.list(

libs/langchain/langchain/agents/react/base.py

@@ -24,6 +24,9 @@ if TYPE_CHECKING:
     from langchain_community.docstore.base import Docstore
 
 
+_LOOKUP_AND_SEARCH_TOOLS = {"Lookup", "Search"}
+
+
 @deprecated(
     "0.1.0",
     message=AGENT_DEPRECATION_WARNING,
@@ -52,11 +55,11 @@ class ReActDocstoreAgent(Agent):
     def _validate_tools(cls, tools: Sequence[BaseTool]) -> None:
         validate_tools_single_input(cls.__name__, tools)
         super()._validate_tools(tools)
-        if len(tools) != 2:
+        if len(tools) != len(_LOOKUP_AND_SEARCH_TOOLS):
             msg = f"Exactly two tools must be specified, but got {tools}"
             raise ValueError(msg)
         tool_names = {tool.name for tool in tools}
-        if tool_names != {"Lookup", "Search"}:
+        if tool_names != _LOOKUP_AND_SEARCH_TOOLS:
             msg = f"Tool names should be Lookup and Search, got {tool_names}"
             raise ValueError(msg)
 

libs/langchain/langchain/chains/api/base.py

@@ -47,8 +47,8 @@ def _check_in_allowed_domain(url: str, limit_to_domains: Sequence[str]) -> bool:
     scheme, domain = _extract_scheme_and_domain(url)
 
     for allowed_domain in limit_to_domains:
-        allowed_scheme, allowed_domain = _extract_scheme_and_domain(allowed_domain)
+        allowed_scheme, allowed_domain_ = _extract_scheme_and_domain(allowed_domain)
-        if scheme == allowed_scheme and domain == allowed_domain:
+        if scheme == allowed_scheme and domain == allowed_domain_:
             return True
     return False
 

libs/langchain/langchain/chains/combine_documents/map_reduce.py

@@ -193,8 +193,7 @@ class MapReduceDocumentsChain(BaseCombineDocumentsChain):
                     "multiple llm_chain input_variables"
                 )
                 raise ValueError(msg)
-        else:
+        elif values["document_variable_name"] not in llm_chain_variables:
-            if values["document_variable_name"] not in llm_chain_variables:
             msg = (
                 f"document_variable_name {values['document_variable_name']} was "
                 f"not found in llm_chain input_variables: {llm_chain_variables}"

libs/langchain/langchain/chains/combine_documents/map_rerank.py

@@ -161,8 +161,7 @@ class MapRerankDocumentsChain(BaseCombineDocumentsChain):
                     "multiple llm_chain input_variables"
                 )
                 raise ValueError(msg)
-        else:
+        elif values["document_variable_name"] not in llm_chain_variables:
-            if values["document_variable_name"] not in llm_chain_variables:
             msg = (
                 f"document_variable_name {values['document_variable_name']} was "
                 f"not found in llm_chain input_variables: {llm_chain_variables}"

libs/langchain/langchain/chains/combine_documents/reduce.py

@@ -325,10 +325,10 @@ class ReduceDocumentsChain(BaseCombineDocumentsChain):
                 _token_max,
                 **kwargs,
             )
-            result_docs = []
+            result_docs = [
-            for docs in new_result_doc_list:
+                collapse_docs(docs_, _collapse_docs_func, **kwargs)
-                new_doc = collapse_docs(docs, _collapse_docs_func, **kwargs)
+                for docs_ in new_result_doc_list
-                result_docs.append(new_doc)
+            ]
             num_tokens = length_func(result_docs, **kwargs)
             retries += 1
             if self.collapse_max_retries and retries == self.collapse_max_retries:
@@ -364,10 +364,10 @@ class ReduceDocumentsChain(BaseCombineDocumentsChain):
                 _token_max,
                 **kwargs,
             )
-            result_docs = []
+            result_docs = [
-            for docs in new_result_doc_list:
+                await acollapse_docs(docs_, _collapse_docs_func, **kwargs)
-                new_doc = await acollapse_docs(docs, _collapse_docs_func, **kwargs)
+                for docs_ in new_result_doc_list
-                result_docs.append(new_doc)
+            ]
             num_tokens = length_func(result_docs, **kwargs)
             retries += 1
             if self.collapse_max_retries and retries == self.collapse_max_retries:

libs/langchain/langchain/chains/combine_documents/refine.py

@@ -140,8 +140,7 @@ class RefineDocumentsChain(BaseCombineDocumentsChain):
                     "multiple llm_chain input_variables"
                 )
                 raise ValueError(msg)
-        else:
+        elif values["document_variable_name"] not in llm_chain_variables:
-            if values["document_variable_name"] not in llm_chain_variables:
             msg = (
                 f"document_variable_name {values['document_variable_name']} was "
                 f"not found in llm_chain input_variables: {llm_chain_variables}"

libs/langchain/langchain/chains/combine_documents/stuff.py

@@ -180,8 +180,7 @@ class StuffDocumentsChain(BaseCombineDocumentsChain):
                     "multiple llm_chain_variables"
                 )
                 raise ValueError(msg)
-        else:
+        elif values["document_variable_name"] not in llm_chain_variables:
-            if values["document_variable_name"] not in llm_chain_variables:
             msg = (
                 f"document_variable_name {values['document_variable_name']} was "
                 f"not found in llm_chain input_variables: {llm_chain_variables}"

libs/langchain/langchain/chains/natbot/crawler.py

@@ -322,7 +322,7 @@ class Crawler:
 
             if node_name == "#text" and ancestor_exception and ancestor_node:
                 text = strings[node_value[index]]
-                if text == "|" or text == "•":
+                if text in {"|", "•"}:
                     continue
                 ancestor_node.append({"type": "type", "value": text})
             else:
@@ -367,7 +367,7 @@ class Crawler:
                     element_node_value = strings[text_index]
 
             # remove redundant elements
-            if ancestor_exception and (node_name != "a" and node_name != "button"):
+            if ancestor_exception and (node_name not in {"a", "button"}):
                 continue
 
             elements_in_view_port.append(
@@ -423,10 +423,7 @@ class Crawler:
             # not very elegant, more like a placeholder
             if (
                 (converted_node_name != "button" or meta == "")
-                and converted_node_name != "link"
+                and converted_node_name not in {"link", "input", "img", "textarea"}
-                and converted_node_name != "input"
-                and converted_node_name != "img"
-                and converted_node_name != "textarea"
             ) and inner_text.strip() == "":
                 continue
 

libs/langchain/langchain/chains/openai_functions/openapi.py

@@ -51,8 +51,7 @@ def _format_url(url: str, path_params: dict) -> str:
                 sep = ","
                 new_val = ""
             new_val += sep.join(kv_strs)
-        else:
+        elif param[0] == ".":
-            if param[0] == ".":
             new_val = f".{val}"
         elif param[0] == ";":
             new_val = f";{clean_param}={val}"
@@ -224,7 +223,7 @@ class SimpleRequestChain(Chain):
         _text = f"Calling endpoint {_pretty_name} with arguments:\n" + _pretty_args
         _run_manager.on_text(_text)
         api_response: Response = self.request_method(name, args)
-        if api_response.status_code != 200:
+        if api_response.status_code != requests.codes.ok:
             response = (
                 f"{api_response.status_code}: {api_response.reason}"
                 f"\nFor {name} "

libs/langchain/langchain/embeddings/__init__.py

@@ -87,7 +87,7 @@ class HypotheticalDocumentEmbedder:
         )
         from langchain.chains.hyde.base import HypotheticalDocumentEmbedder as H
 
-        return H(*args, **kwargs)  # type: ignore[return-value]
+        return H(*args, **kwargs)  # type: ignore[return-value] # noqa: PLE0101
 
     @classmethod
     def from_llm(cls, *args: Any, **kwargs: Any) -> Any:

libs/langchain/langchain/embeddings/base.py

@@ -89,7 +89,6 @@ def _infer_model_and_provider(
     if provider is None and ":" in model:
         provider, model_name = _parse_model_string(model)
     else:
-        provider = provider
         model_name = model
 
     if not provider:

libs/langchain/langchain/embeddings/cache.py

@@ -89,7 +89,7 @@ _warned_about_sha1: bool = False
 
 def _warn_about_sha1_encoder() -> None:
     """Emit a one-time warning about SHA-1 collision weaknesses."""
-    global _warned_about_sha1
+    global _warned_about_sha1  # noqa: PLW0603
     if not _warned_about_sha1:
         warnings.warn(
             "Using default key encoder: SHA-1 is *not* collision-resistant. "

libs/langchain/langchain/evaluation/agents/trajectory_eval_chain.py

@@ -36,6 +36,8 @@ from langchain.evaluation.agents.trajectory_eval_prompt import (
 )
 from langchain.evaluation.schema import AgentTrajectoryEvaluator, LLMEvalChain
 
+_MAX_SCORE = 5
+
 
 class TrajectoryEval(TypedDict):
     """A named tuple containing the score and reasoning for a trajectory."""
@@ -86,10 +88,10 @@ class TrajectoryOutputParser(BaseOutputParser):
             raise OutputParserException(msg)
         score = int(_score.group(1))
         # If the score is not in the range 1-5, raise an exception.
-        if not 1 <= score <= 5:
+        if not 1 <= score <= _MAX_SCORE:
             msg = f"Score is not a digit in the range 1-5: {text}"
             raise OutputParserException(msg)
-        normalized_score = (score - 1) / 4
+        normalized_score = (score - 1) / (_MAX_SCORE - 1)
         return TrajectoryEval(score=normalized_score, reasoning=reasoning)
 
 

libs/langchain/langchain/globals.py

@@ -39,7 +39,7 @@ def set_verbose(
         # have migrated to using `set_verbose()` here.
         langchain.verbose = value
 
-    global _verbose
+    global _verbose  # noqa: PLW0603
     _verbose = value
 
 
@@ -69,7 +69,6 @@ def get_verbose() -> bool:
         # directing them to use `set_verbose()` when they import `langchain.verbose`.
         old_verbose = langchain.verbose
 
-    global _verbose
     return _verbose or old_verbose
 
 
@@ -94,7 +93,7 @@ def set_debug(
         # have migrated to using `set_debug()` here.
         langchain.debug = value
 
-    global _debug
+    global _debug  # noqa: PLW0603
     _debug = value
 
 
@@ -122,7 +121,6 @@ def get_debug() -> bool:
         # directing them to use `set_debug()` when they import `langchain.debug`.
         old_debug = langchain.debug
 
-    global _debug
     return _debug or old_debug
 
 
@@ -147,7 +145,7 @@ def set_llm_cache(value: Optional["BaseCache"]) -> None:
         # once all users have migrated to using `set_llm_cache()` here.
         langchain.llm_cache = value
 
-    global _llm_cache
+    global _llm_cache  # noqa: PLW0603
     _llm_cache = value
 
 
@@ -179,5 +177,4 @@ def get_llm_cache() -> "BaseCache":
         # to use `set_llm_cache()` when they import `langchain.llm_cache`.
         old_llm_cache = langchain.llm_cache
 
-    global _llm_cache
     return _llm_cache or old_llm_cache

libs/langchain/langchain/output_parsers/combining.py

@@ -5,6 +5,8 @@ from typing import Any
 from langchain_core.output_parsers import BaseOutputParser
 from langchain_core.utils import pre_init
 
+_MIN_PARSERS = 2
+
 
 class CombiningOutputParser(BaseOutputParser[dict[str, Any]]):
     """Combine multiple output parsers into one."""
@@ -19,7 +21,7 @@ class CombiningOutputParser(BaseOutputParser[dict[str, Any]]):
     def validate_parsers(cls, values: dict[str, Any]) -> dict[str, Any]:
         """Validate the parsers."""
         parsers = values["parsers"]
-        if len(parsers) < 2:
+        if len(parsers) < _MIN_PARSERS:
             msg = "Must have at least two parsers"
             raise ValueError(msg)
         for parser in parsers:

libs/langchain/langchain/output_parsers/pandas_dataframe.py

@@ -79,7 +79,7 @@ class PandasDataFrameOutputParser(BaseOutputParser[dict[str, Any]]):
     def parse(self, request: str) -> dict[str, Any]:
         stripped_request_params = None
         splitted_request = request.strip().split(":")
-        if len(splitted_request) != 2:
+        if len(splitted_request) != 2:  # noqa: PLR2004
             msg = f"Request '{request}' is not correctly formatted. \
                     Please refer to the format instructions."
             raise OutputParserException(msg)
@@ -127,8 +127,7 @@ class PandasDataFrameOutputParser(BaseOutputParser[dict[str, Any]]):
                         filtered_df[stripped_request_params],
                         request_type,
                     )()
-            else:
+            elif request_type == "column":
-                if request_type == "column":
                 result[request_params] = self.dataframe[request_params]
             elif request_type == "row":
                 result[request_params] = self.dataframe.iloc[int(request_params)]

libs/langchain/langchain/retrievers/document_compressors/chain_filter.py

@@ -70,8 +70,7 @@ class LLMChainFilter(BaseDocumentCompressor):
                 output = output_[self.llm_chain.output_key]
                 if self.llm_chain.prompt.output_parser is not None:
                     include_doc = self.llm_chain.prompt.output_parser.parse(output)
-            else:
+            elif isinstance(output_, bool):
-                if isinstance(output_, bool):
                 include_doc = output_
             if include_doc:
                 filtered_docs.append(doc)
@@ -101,8 +100,7 @@ class LLMChainFilter(BaseDocumentCompressor):
                 output = output_[self.llm_chain.output_key]
                 if self.llm_chain.prompt.output_parser is not None:
                     include_doc = self.llm_chain.prompt.output_parser.parse(output)
-            else:
+            elif isinstance(output_, bool):
-                if isinstance(output_, bool):
                 include_doc = output_
             if include_doc:
                 filtered_docs.append(doc)

libs/langchain/pyproject.toml

@@ -147,11 +147,9 @@ select = [
     "A",      # flake8-builtins
     "B",      # flake8-bugbear
     "ASYNC",  # flake8-async
-    "B",      # flake8-bugbear
     "C4",     # flake8-comprehensions
     "COM",    # flake8-commas
     "D",      # pydocstyle
-    "DOC",    # pydoclint
     "DTZ",    # flake8-datetimez
     "E",      # pycodestyle error
     "EM",     # flake8-errmsg
@@ -164,9 +162,10 @@ select = [
     "ICN",    # flake8-import-conventions
     "INT",    # flake8-gettext
     "ISC",    # isort-comprehensions
+    "PERF",   # flake8-perf
     "PGH",    # pygrep-hooks
     "PIE",    # flake8-pie
-    "PERF",   # flake8-perf
+    "PL",     # pylint
     "PT",     # flake8-pytest-style
     "PTH",    # flake8-use-pathlib
     "PYI",    # flake8-pyi
@@ -175,9 +174,9 @@ select = [
     "RSE",    # flake8-rst-docstrings
     "RUF",    # ruff
     "S",      # flake8-bandit
-    "SLF",    # flake8-self
     "SLOT",   # flake8-slots
     "SIM",    # flake8-simplify
+    "SLF",    # flake8-self
     "T10",    # flake8-debugger
     "T20",    # flake8-print
     "TID",    # flake8-tidy-imports
@@ -198,12 +197,14 @@ ignore = [
     "COM812",  # Messes with the formatter
     "ISC001",  # Messes with the formatter
     "PERF203", # Rarely useful
+    "PLR09",   # Too many something (args, statements, etc)
     "S112",    # Rarely useful
     "RUF012",  # Doesn't play well with Pydantic
     "SLF001",  # Private member access
     "UP007",   # pyupgrade: non-pep604-annotation-union
 
-    # TODO
+    # TODO rules
+    "PLC0415", # pylint: import-outside-top-level
     "TRY301",  # tryceratops: raise-within-try
 ]
 unfixable = ["B028"] # People should intentionally tune the stacklevel
@@ -220,6 +221,7 @@ pyupgrade.keep-runtime-typing = true
     "S101",    # Tests need assertions
     "S311",    # Standard pseudo-random generators are not suitable for cryptographic purposes
     "SLF001",  # Private member access in tests
+    "PLR2004", # Magic value comparisons
 ]
 "langchain/chains/constitutional_ai/principles.py" = [
     "E501", # Line too long

libs/langchain/tests/unit_tests/conftest.py

@@ -130,8 +130,7 @@ def pytest_collection_modifyitems(
                             pytest.mark.skip(reason=f"Requires pkg: `{pkg}`"),
                         )
                         break
-        else:
+        elif only_extended:
-            if only_extended:
             item.add_marker(
                 pytest.mark.skip(reason="Skipping not an extended test."),
             )

libs/langchain/tests/unit_tests/retrievers/test_multi_query.py

@@ -1,4 +1,4 @@
-import pytest as pytest
+import pytest
 from langchain_core.documents import Document
 
 from langchain.retrievers.multi_query import LineListOutputParser, _unique_documents

libs/langchain/tests/unit_tests/stubs.py

@@ -10,6 +10,8 @@ class AnyStr(str):
     def __eq__(self, other: object) -> bool:
         return isinstance(other, str)
 
+    __hash__ = str.__hash__
+
 
 # The code below creates version of pydantic models
 # that will work in unit tests with AnyStr as id field