core[patch]: Add B(bugbear) ruff rules (#25520)

Co-authored-by: Bagatur <baskaryan@gmail.com>

libs/core/langchain_core/_api/beta_decorator.py

@@ -270,7 +270,7 @@ def warn_beta(
             message += f" {addendum}"
 
     warning = LangChainBetaWarning(message)
-    warnings.warn(warning, category=LangChainBetaWarning, stacklevel=2)
+    warnings.warn(warning, category=LangChainBetaWarning, stacklevel=4)
 
 
 def surface_langchain_beta_warnings() -> None:

libs/core/langchain_core/_api/deprecation.py

@@ -444,7 +444,7 @@ def warn_deprecated(
         LangChainPendingDeprecationWarning if pending else LangChainDeprecationWarning
     )
     warning = warning_cls(message)
-    warnings.warn(warning, category=LangChainDeprecationWarning, stacklevel=2)
+    warnings.warn(warning, category=LangChainDeprecationWarning, stacklevel=4)
 
 
 def surface_langchain_deprecation_warnings() -> None:

libs/core/langchain_core/document_loaders/base.py

@@ -14,7 +14,7 @@ if TYPE_CHECKING:
 from langchain_core.documents.base import Blob
 
 
-class BaseLoader(ABC):
+class BaseLoader(ABC):  # noqa: B024
     """Interface for Document Loader.
 
     Implementations should implement the lazy-loading method using generators

libs/core/langchain_core/graph_vectorstores/base.py

@@ -80,12 +80,12 @@ def _texts_to_nodes(
     for text in texts:
         try:
             _metadata = next(metadatas_it).copy() if metadatas_it else {}
-        except StopIteration:
+        except StopIteration as e:
-            raise ValueError("texts iterable longer than metadatas")
+            raise ValueError("texts iterable longer than metadatas") from e
         try:
             _id = next(ids_it) if ids_it else None
-        except StopIteration:
+        except StopIteration as e:
-            raise ValueError("texts iterable longer than ids")
+            raise ValueError("texts iterable longer than ids") from e
 
         links = _metadata.pop(METADATA_LINKS_KEY, [])
         if not isinstance(links, list):

libs/core/langchain_core/indexing/api.py

@@ -91,7 +91,7 @@ class _HashedDocument(Document):
             raise ValueError(
                 f"Failed to hash metadata: {e}. "
                 f"Please use a dict that can be serialized using json."
-            )
+            ) from e
 
         values["content_hash"] = content_hash
         values["metadata_hash"] = metadata_hash

libs/core/langchain_core/language_models/base.py

@@ -64,12 +64,12 @@ def get_tokenizer() -> Any:
     """
     try:
         from transformers import GPT2TokenizerFast  # type: ignore[import]
-    except ImportError:
+    except ImportError as e:
         raise ImportError(
             "Could not import transformers python package. "
             "This is needed in order to calculate get_token_ids. "
             "Please install it with `pip install transformers`."
-        )
+        ) from e
     # create a GPT-2 tokenizer instance
     return GPT2TokenizerFast.from_pretrained("gpt2")
 

libs/core/langchain_core/language_models/chat_models.py

@@ -235,6 +235,7 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
             warnings.warn(
                 "callback_manager is deprecated. Please use callbacks instead.",
                 DeprecationWarning,
+                stacklevel=5,
             )
             values["callbacks"] = values.pop("callback_manager", None)
         return values

libs/core/langchain_core/language_models/fake.py

@@ -69,6 +69,10 @@ class FakeListLLM(LLM):
         return {"responses": self.responses}
 
 
+class FakeListLLMError(Exception):
+    """Fake error for testing purposes."""
+
+
 class FakeStreamingListLLM(FakeListLLM):
     """Fake streaming list LLM for testing purposes.
 
@@ -98,7 +102,7 @@ class FakeStreamingListLLM(FakeListLLM):
                 self.error_on_chunk_number is not None
                 and i_c == self.error_on_chunk_number
             ):
-                raise Exception("Fake error")
+                raise FakeListLLMError
             yield c
 
     async def astream(
@@ -118,5 +122,5 @@ class FakeStreamingListLLM(FakeListLLM):
                 self.error_on_chunk_number is not None
                 and i_c == self.error_on_chunk_number
             ):
-                raise Exception("Fake error")
+                raise FakeListLLMError
             yield c

libs/core/langchain_core/language_models/fake_chat_models.py

@@ -44,6 +44,10 @@ class FakeMessagesListChatModel(BaseChatModel):
         return "fake-messages-list-chat-model"
 
 
+class FakeListChatModelError(Exception):
+    pass
+
+
 class FakeListChatModel(SimpleChatModel):
     """Fake ChatModel for testing purposes."""
 
@@ -93,7 +97,7 @@ class FakeListChatModel(SimpleChatModel):
                 self.error_on_chunk_number is not None
                 and i_c == self.error_on_chunk_number
             ):
-                raise Exception("Fake error")
+                raise FakeListChatModelError
 
             yield ChatGenerationChunk(message=AIMessageChunk(content=c))
 
@@ -116,7 +120,7 @@ class FakeListChatModel(SimpleChatModel):
                 self.error_on_chunk_number is not None
                 and i_c == self.error_on_chunk_number
             ):
-                raise Exception("Fake error")
+                raise FakeListChatModelError
             yield ChatGenerationChunk(message=AIMessageChunk(content=c))
 
     @property

libs/core/langchain_core/language_models/llms.py

@@ -114,7 +114,6 @@ def create_base_retry_decorator(
                     _log_error_once(f"Error in on_retry: {e}")
             else:
                 run_manager.on_retry(retry_state)
-        return None
 
     min_seconds = 4
     max_seconds = 10
@@ -311,6 +310,7 @@ class BaseLLM(BaseLanguageModel[str], ABC):
             warnings.warn(
                 "callback_manager is deprecated. Please use callbacks instead.",
                 DeprecationWarning,
+                stacklevel=5,
             )
             values["callbacks"] = values.pop("callback_manager", None)
         return values

libs/core/langchain_core/messages/utils.py

@@ -290,10 +290,10 @@ def _convert_to_message(message: MessageLikeRepresentation) -> BaseMessage:
                 msg_type = msg_kwargs.pop("type")
             # None msg content is not allowed
             msg_content = msg_kwargs.pop("content") or ""
-        except KeyError:
+        except KeyError as e:
             raise ValueError(
                 f"Message dict must contain 'role' and 'content' keys, got {message}"
-            )
+            ) from e
         _message = _create_message_from_message_type(
             msg_type, msg_content, **msg_kwargs
         )
@@ -344,9 +344,7 @@ def _runnable_support(func: Callable) -> Callable:
         if messages is not None:
             return func(messages, **kwargs)
         else:
-            return RunnableLambda(
+            return RunnableLambda(partial(func, **kwargs), name=func.__name__)
-                partial(func, **kwargs), name=getattr(func, "__name__")
-            )
 
     wrapped.__doc__ = func.__doc__
     return wrapped
@@ -791,7 +789,7 @@ def trim_messages(
         raise ValueError
     messages = convert_to_messages(messages)
     if hasattr(token_counter, "get_num_tokens_from_messages"):
-        list_token_counter = getattr(token_counter, "get_num_tokens_from_messages")
+        list_token_counter = token_counter.get_num_tokens_from_messages
     elif callable(token_counter):
         if (
             list(inspect.signature(token_counter).parameters.values())[0].annotation

libs/core/langchain_core/output_parsers/openai_functions.py

@@ -42,7 +42,9 @@ class OutputFunctionsParser(BaseGenerationOutputParser[Any]):
         try:
             func_call = copy.deepcopy(message.additional_kwargs["function_call"])
         except KeyError as exc:
-            raise OutputParserException(f"Could not parse function call: {exc}")
+            raise OutputParserException(
+                f"Could not parse function call: {exc}"
+            ) from exc
 
         if self.args_only:
             return func_call["arguments"]
@@ -100,7 +102,9 @@ class JsonOutputFunctionsParser(BaseCumulativeTransformOutputParser[Any]):
             if partial:
                 return None
             else:
-                raise OutputParserException(f"Could not parse function call: {exc}")
+                raise OutputParserException(
+                    f"Could not parse function call: {exc}"
+                ) from exc
         try:
             if partial:
                 try:
@@ -126,7 +130,7 @@ class JsonOutputFunctionsParser(BaseCumulativeTransformOutputParser[Any]):
                     except (json.JSONDecodeError, TypeError) as exc:
                         raise OutputParserException(
                             f"Could not parse function call data: {exc}"
-                        )
+                        ) from exc
                 else:
                     try:
                         return {
@@ -138,7 +142,7 @@ class JsonOutputFunctionsParser(BaseCumulativeTransformOutputParser[Any]):
                     except (json.JSONDecodeError, TypeError) as exc:
                         raise OutputParserException(
                             f"Could not parse function call data: {exc}"
-                        )
+                        ) from exc
         except KeyError:
             return None
 

libs/core/langchain_core/output_parsers/openai_tools.py

@@ -55,7 +55,7 @@ def parse_tool_call(
                 f"Function {raw_tool_call['function']['name']} arguments:\n\n"
                 f"{raw_tool_call['function']['arguments']}\n\nare not valid JSON. "
                 f"Received JSONDecodeError {e}"
-            )
+            ) from e
     parsed = {
         "name": raw_tool_call["function"]["name"] or "",
         "args": function_args or {},

libs/core/langchain_core/output_parsers/pydantic.py

@@ -32,12 +32,12 @@ class PydanticOutputParser(JsonOutputParser, Generic[TBaseModel]):
                             {self.pydantic_object.__class__}"
                     )
             except (pydantic.ValidationError, pydantic.v1.ValidationError) as e:
-                raise self._parser_exception(e, obj)
+                raise self._parser_exception(e, obj) from e
         else:  # pydantic v1
             try:
                 return self.pydantic_object.parse_obj(obj)
             except pydantic.ValidationError as e:
-                raise self._parser_exception(e, obj)
+                raise self._parser_exception(e, obj) from e
 
     def _parser_exception(
         self, e: Exception, json_object: dict

libs/core/langchain_core/output_parsers/xml.py

@@ -46,12 +46,12 @@ class _StreamingParser:
         if parser == "defusedxml":
             try:
                 from defusedxml import ElementTree as DET  # type: ignore
-            except ImportError:
+            except ImportError as e:
                 raise ImportError(
                     "defusedxml is not installed. "
                     "Please install it to use the defusedxml parser."
                     "You can install it with `pip install defusedxml` "
-                )
+                ) from e
             _parser = DET.DefusedXMLParser(target=TreeBuilder())
         else:
             _parser = None
@@ -189,13 +189,13 @@ class XMLOutputParser(BaseTransformOutputParser):
         if self.parser == "defusedxml":
             try:
                 from defusedxml import ElementTree as DET  # type: ignore
-            except ImportError:
+            except ImportError as e:
                 raise ImportError(
                     "defusedxml is not installed. "
                     "Please install it to use the defusedxml parser."
                     "You can install it with `pip install defusedxml`"
                     "See https://github.com/tiran/defusedxml for more details"
-                )
+                ) from e
             _ET = DET  # Use the defusedxml parser
         else:
             _ET = ET  # Use the standard library parser

libs/core/langchain_core/prompts/prompt.py

@@ -235,7 +235,9 @@ class PromptTemplate(StringPromptTemplate):
             template = f.read()
         if input_variables:
             warnings.warn(
-                "`input_variables' is deprecated and ignored.", DeprecationWarning
+                "`input_variables' is deprecated and ignored.",
+                DeprecationWarning,
+                stacklevel=2,
             )
         return cls.from_template(template=template, **kwargs)
 

libs/core/langchain_core/prompts/string.py

@@ -40,14 +40,14 @@ def jinja2_formatter(template: str, **kwargs: Any) -> str:
     """
     try:
         from jinja2.sandbox import SandboxedEnvironment
-    except ImportError:
+    except ImportError as e:
         raise ImportError(
             "jinja2 not installed, which is needed to use the jinja2_formatter. "
             "Please install it with `pip install jinja2`."
             "Please be cautious when using jinja2 templates. "
             "Do not expand jinja2 templates using unverified or user-controlled "
             "inputs as that can result in arbitrary Python code execution."
-        )
+        ) from e
 
     # This uses a sandboxed environment to prevent arbitrary code execution.
     # Jinja2 uses an opt-out rather than opt-in approach for sand-boxing.
@@ -81,17 +81,17 @@ def validate_jinja2(template: str, input_variables: List[str]) -> None:
         warning_message += f"Extra variables: {extra_variables}"
 
     if warning_message:
-        warnings.warn(warning_message.strip())
+        warnings.warn(warning_message.strip(), stacklevel=7)
 
 
 def _get_jinja2_variables_from_template(template: str) -> Set[str]:
     try:
         from jinja2 import Environment, meta
-    except ImportError:
+    except ImportError as e:
         raise ImportError(
             "jinja2 not installed, which is needed to use the jinja2_formatter. "
             "Please install it with `pip install jinja2`."
-        )
+        ) from e
     env = Environment()
     ast = env.parse(template)
     variables = meta.find_undeclared_variables(ast)

libs/core/langchain_core/retrievers.py

@@ -155,6 +155,7 @@ class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
                 "Retrievers must implement abstract `_get_relevant_documents` method"
                 " instead of `get_relevant_documents`",
                 DeprecationWarning,
+                stacklevel=4,
             )
             swap = cls.get_relevant_documents
             cls.get_relevant_documents = (  # type: ignore[assignment]
@@ -169,6 +170,7 @@ class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
                 "Retrievers must implement abstract `_aget_relevant_documents` method"
                 " instead of `aget_relevant_documents`",
                 DeprecationWarning,
+                stacklevel=4,
             )
             aswap = cls.aget_relevant_documents
             cls.aget_relevant_documents = (  # type: ignore[assignment]

libs/core/langchain_core/runnables/base.py

@@ -3915,7 +3915,7 @@ class RunnableGenerator(Runnable[Input, Output]):
 
     @property
     def InputType(self) -> Any:
-        func = getattr(self, "_transform", None) or getattr(self, "_atransform")
+        func = getattr(self, "_transform", None) or self._atransform
         try:
             params = inspect.signature(func).parameters
             first_param = next(iter(params.values()), None)
@@ -3928,7 +3928,7 @@ class RunnableGenerator(Runnable[Input, Output]):
 
     @property
     def OutputType(self) -> Any:
-        func = getattr(self, "_transform", None) or getattr(self, "_atransform")
+        func = getattr(self, "_transform", None) or self._atransform
         try:
             sig = inspect.signature(func)
             return (
@@ -4152,7 +4152,7 @@ class RunnableLambda(Runnable[Input, Output]):
     @property
     def InputType(self) -> Any:
         """The type of the input to this Runnable."""
-        func = getattr(self, "func", None) or getattr(self, "afunc")
+        func = getattr(self, "func", None) or self.afunc
         try:
             params = inspect.signature(func).parameters
             first_param = next(iter(params.values()), None)
@@ -4174,7 +4174,7 @@ class RunnableLambda(Runnable[Input, Output]):
         Returns:
             The input schema for this Runnable.
         """
-        func = getattr(self, "func", None) or getattr(self, "afunc")
+        func = getattr(self, "func", None) or self.afunc
 
         if isinstance(func, itemgetter):
             # This is terrible, but afaict it's not possible to access _items
@@ -4212,7 +4212,7 @@ class RunnableLambda(Runnable[Input, Output]):
         Returns:
             The type of the output of this Runnable.
         """
-        func = getattr(self, "func", None) or getattr(self, "afunc")
+        func = getattr(self, "func", None) or self.afunc
         try:
             sig = inspect.signature(func)
             if sig.return_annotation != inspect.Signature.empty:

libs/core/langchain_core/runnables/config.py

@@ -236,6 +236,7 @@ def get_config_list(
         warnings.warn(
             "Provided run_id be used only for the first element of the batch.",
             category=RuntimeWarning,
+            stacklevel=3,
         )
         subsequent = cast(
             RunnableConfig, {k: v for k, v in config.items() if k != "run_id"}

libs/core/langchain_core/runnables/graph.py

@@ -537,7 +537,7 @@ class Graph:
         *,
         with_styles: bool = True,
         curve_style: CurveStyle = CurveStyle.LINEAR,
-        node_colors: NodeStyles = NodeStyles(),
+        node_colors: Optional[NodeStyles] = None,
         wrap_label_n_words: int = 9,
     ) -> str:
         """Draw the graph as a Mermaid syntax string.
@@ -573,7 +573,7 @@ class Graph:
         self,
         *,
         curve_style: CurveStyle = CurveStyle.LINEAR,
-        node_colors: NodeStyles = NodeStyles(),
+        node_colors: Optional[NodeStyles] = None,
         wrap_label_n_words: int = 9,
         output_file_path: Optional[str] = None,
         draw_method: MermaidDrawMethod = MermaidDrawMethod.API,

libs/core/langchain_core/runnables/graph_mermaid.py

@@ -20,7 +20,7 @@ def draw_mermaid(
     last_node: Optional[str] = None,
     with_styles: bool = True,
     curve_style: CurveStyle = CurveStyle.LINEAR,
-    node_styles: NodeStyles = NodeStyles(),
+    node_styles: Optional[NodeStyles] = None,
     wrap_label_n_words: int = 9,
 ) -> str:
     """Draws a Mermaid graph using the provided graph data.
@@ -153,7 +153,7 @@ def draw_mermaid(
 
     # Add custom styles for nodes
     if with_styles:
-        mermaid_graph += _generate_mermaid_graph_styles(node_styles)
+        mermaid_graph += _generate_mermaid_graph_styles(node_styles or NodeStyles())
     return mermaid_graph
 
 

libs/core/langchain_core/runnables/retry.py

@@ -218,6 +218,8 @@ class RunnableRetry(RunnableBindingBase[Input, Output]):
         def pending(iterable: List[U]) -> List[U]:
             return [item for idx, item in enumerate(iterable) if idx not in results_map]
 
+        not_set: List[Output] = []
+        result = not_set
         try:
             for attempt in self._sync_retrying():
                 with attempt:
@@ -247,9 +249,7 @@ class RunnableRetry(RunnableBindingBase[Input, Output]):
                 ):
                     attempt.retry_state.set_result(result)
         except RetryError as e:
-            try:
+            if result is not_set:
-                result
-            except UnboundLocalError:
                 result = cast(List[Output], [e] * len(inputs))
 
         outputs: List[Union[Output, Exception]] = []
@@ -284,6 +284,8 @@ class RunnableRetry(RunnableBindingBase[Input, Output]):
         def pending(iterable: List[U]) -> List[U]:
             return [item for idx, item in enumerate(iterable) if idx not in results_map]
 
+        not_set: List[Output] = []
+        result = not_set
         try:
             async for attempt in self._async_retrying():
                 with attempt:
@@ -313,9 +315,7 @@ class RunnableRetry(RunnableBindingBase[Input, Output]):
                 ):
                     attempt.retry_state.set_result(result)
         except RetryError as e:
-            try:
+            if result is not_set:
-                result
-            except UnboundLocalError:
                 result = cast(List[Output], [e] * len(inputs))
 
         outputs: List[Union[Output, Exception]] = []

libs/core/langchain_core/runnables/utils.py

@@ -748,7 +748,7 @@ def is_async_generator(
     """
     return (
         inspect.isasyncgenfunction(func)
-        or hasattr(func, "__call__")
+        or hasattr(func, "__call__")  # noqa: B004
         and inspect.isasyncgenfunction(func.__call__)
     )
 
@@ -767,6 +767,6 @@ def is_async_callable(
     """
     return (
         asyncio.iscoroutinefunction(func)
-        or hasattr(func, "__call__")
+        or hasattr(func, "__call__")  # noqa: B004
         and asyncio.iscoroutinefunction(func.__call__)
     )

libs/core/langchain_core/tools/base.py

@@ -443,6 +443,7 @@ class ChildTool(BaseTool):
             warnings.warn(
                 "callback_manager is deprecated. Please use callbacks instead.",
                 DeprecationWarning,
+                stacklevel=6,
             )
             values["callbacks"] = values.pop("callback_manager", None)
         return values

libs/core/langchain_core/tools/convert.py

@@ -244,7 +244,7 @@ def _get_schema_from_runnable_and_arg_types(
                 "Tool input must be str or dict. If dict, dict arguments must be "
                 "typed. Either annotate types (e.g., with TypedDict) or pass "
                 f"arg_types into `.as_tool` to specify. {str(e)}"
-            )
+            ) from e
     fields = {key: (key_type, Field(...)) for key, key_type in arg_types.items()}
     return create_model(name, **fields)  # type: ignore
 

libs/core/langchain_core/tracers/core.py

@@ -516,15 +516,19 @@ class _TracerCore(ABC):
 
     def _end_trace(self, run: Run) -> Union[None, Coroutine[Any, Any, None]]:
         """End a trace for a run."""
+        return None
 
     def _on_run_create(self, run: Run) -> Union[None, Coroutine[Any, Any, None]]:
         """Process a run upon creation."""
+        return None
 
     def _on_run_update(self, run: Run) -> Union[None, Coroutine[Any, Any, None]]:
         """Process a run upon update."""
+        return None
 
     def _on_llm_start(self, run: Run) -> Union[None, Coroutine[Any, Any, None]]:
         """Process the LLM Run upon start."""
+        return None
 
     def _on_llm_new_token(
         self,
@@ -533,39 +537,52 @@ class _TracerCore(ABC):
         chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]],
     ) -> Union[None, Coroutine[Any, Any, None]]:
         """Process new LLM token."""
+        return None
 
     def _on_llm_end(self, run: Run) -> Union[None, Coroutine[Any, Any, None]]:
         """Process the LLM Run."""
+        return None
 
     def _on_llm_error(self, run: Run) -> Union[None, Coroutine[Any, Any, None]]:
         """Process the LLM Run upon error."""
+        return None
 
     def _on_chain_start(self, run: Run) -> Union[None, Coroutine[Any, Any, None]]:
         """Process the Chain Run upon start."""
+        return None
 
     def _on_chain_end(self, run: Run) -> Union[None, Coroutine[Any, Any, None]]:
         """Process the Chain Run."""
+        return None
 
     def _on_chain_error(self, run: Run) -> Union[None, Coroutine[Any, Any, None]]:
         """Process the Chain Run upon error."""
+        return None
 
     def _on_tool_start(self, run: Run) -> Union[None, Coroutine[Any, Any, None]]:
         """Process the Tool Run upon start."""
+        return None
 
     def _on_tool_end(self, run: Run) -> Union[None, Coroutine[Any, Any, None]]:
         """Process the Tool Run."""
+        return None
 
     def _on_tool_error(self, run: Run) -> Union[None, Coroutine[Any, Any, None]]:
         """Process the Tool Run upon error."""
+        return None
 
     def _on_chat_model_start(self, run: Run) -> Union[None, Coroutine[Any, Any, None]]:
         """Process the Chat Model Run upon start."""
+        return None
 
     def _on_retriever_start(self, run: Run) -> Union[None, Coroutine[Any, Any, None]]:
         """Process the Retriever Run upon start."""
+        return None
 
     def _on_retriever_end(self, run: Run) -> Union[None, Coroutine[Any, Any, None]]:
         """Process the Retriever Run."""
+        return None
 
     def _on_retriever_error(self, run: Run) -> Union[None, Coroutine[Any, Any, None]]:
         """Process the Retriever Run upon error."""
+        return None

libs/core/langchain_core/tracers/evaluation.py

@@ -144,11 +144,7 @@ class EvaluatorCallbackHandler(BaseTracer):
         example_id = str(run.reference_example_id)
         with self.lock:
             for res in eval_results:
-                run_id = (
+                run_id = str(getattr(res, "target_run_id", run.id))
-                    str(getattr(res, "target_run_id"))
-                    if hasattr(res, "target_run_id")
-                    else str(run.id)
-                )
                 self.logged_eval_results.setdefault((run_id, example_id), []).append(
                     res
                 )
@@ -179,11 +175,9 @@ class EvaluatorCallbackHandler(BaseTracer):
             source_info_: Dict[str, Any] = {}
             if res.evaluator_info:
                 source_info_ = {**res.evaluator_info, **source_info_}
-            run_id_ = (
+            run_id_ = getattr(res, "target_run_id", None)
-                getattr(res, "target_run_id")
+            if run_id_ is None:
-                if hasattr(res, "target_run_id") and res.target_run_id is not None
+                run_id_ = run.id
-                else run.id
-            )
             self.client.create_feedback(
                 run_id_,
                 res.key,

libs/core/langchain_core/tracers/schemas.py

@@ -22,6 +22,7 @@ def RunTypeEnum() -> Type[RunTypeEnumDep]:
         "RunTypeEnum is deprecated. Please directly use a string instead"
         " (e.g. 'llm', 'chain', 'tool').",
         DeprecationWarning,
+        stacklevel=2,
     )
     return RunTypeEnumDep
 

libs/core/langchain_core/utils/aiter.py

@@ -62,8 +62,8 @@ def py_anext(
         __anext__ = cast(
             Callable[[AsyncIterator[T]], Awaitable[T]], type(iterator).__anext__
         )
-    except AttributeError:
+    except AttributeError as e:
-        raise TypeError(f"{iterator!r} is not an async iterator")
+        raise TypeError(f"{iterator!r} is not an async iterator") from e
 
     if default is _no_default:
         return __anext__(iterator)

libs/core/langchain_core/utils/json.py

@@ -182,7 +182,7 @@ def parse_and_check_json_markdown(text: str, expected_keys: List[str]) -> dict:
     try:
         json_obj = parse_json_markdown(text)
     except json.JSONDecodeError as e:
-        raise OutputParserException(f"Got invalid JSON object. Error: {e}")
+        raise OutputParserException(f"Got invalid JSON object. Error: {e}") from e
     for key in expected_keys:
         if key not in json_obj:
             raise OutputParserException(

libs/core/langchain_core/utils/loading.py

@@ -21,7 +21,9 @@ def try_load_from_hub(
 ) -> Any:
     warnings.warn(
         "Loading from the deprecated github-based Hub is no longer supported. "
-        "Please use the new LangChain Hub at https://smith.langchain.com/hub instead."
+        "Please use the new LangChain Hub at https://smith.langchain.com/hub instead.",
+        DeprecationWarning,
+        stacklevel=2,
     )
     # return None, which indicates that we shouldn't load from old hub
     # and might just be a filepath for e.g. load_chain

libs/core/langchain_core/utils/mustache.py

@@ -3,13 +3,17 @@ Adapted from https://github.com/noahmorrison/chevron
 MIT License
 """
 
+from __future__ import annotations
+
 import logging
+from types import MappingProxyType
 from typing import (
     Any,
     Dict,
     Iterator,
     List,
     Literal,
+    Mapping,
     Optional,
     Sequence,
     Tuple,
@@ -22,7 +26,7 @@ from typing_extensions import TypeAlias
 logger = logging.getLogger(__name__)
 
 
-Scopes: TypeAlias = List[Union[Literal[False, 0], Dict[str, Any]]]
+Scopes: TypeAlias = List[Union[Literal[False, 0], Mapping[str, Any]]]
 
 
 # Globals
@@ -152,8 +156,8 @@ def parse_tag(template: str, l_del: str, r_del: str) -> Tuple[Tuple[str, str], s
     # Get the tag
     try:
         tag, template = template.split(r_del, 1)
-    except ValueError:
+    except ValueError as e:
-        raise ChevronError("unclosed tag " f"at line {_CURRENT_LINE}")
+        raise ChevronError("unclosed tag " f"at line {_CURRENT_LINE}") from e
 
     # Find the type meaning of the first character
     tag_type = tag_types.get(tag[0], "variable")
@@ -279,12 +283,12 @@ def tokenize(
             # is the same as us
             try:
                 last_section = open_sections.pop()
-            except IndexError:
+            except IndexError as e:
                 raise ChevronError(
                     f'Trying to close tag "{tag_key}"\n'
                     "Looks like it was not opened.\n"
                     f"line {_CURRENT_LINE + 1}"
-                )
+                ) from e
             if tag_key != last_section:
                 # Otherwise we need to complain
                 raise ChevronError(
@@ -411,7 +415,7 @@ def _get_key(
     return ""
 
 
-def _get_partial(name: str, partials_dict: Dict[str, str]) -> str:
+def _get_partial(name: str, partials_dict: Mapping[str, str]) -> str:
     """Load a partial"""
     try:
         # Maybe the partial is in the dictionary
@@ -425,11 +429,13 @@ def _get_partial(name: str, partials_dict: Dict[str, str]) -> str:
 #
 g_token_cache: Dict[str, List[Tuple[str, str]]] = {}
 
+EMPTY_DICT: MappingProxyType[str, str] = MappingProxyType({})
+
 
 def render(
     template: Union[str, List[Tuple[str, str]]] = "",
-    data: Dict[str, Any] = {},
+    data: Mapping[str, Any] = EMPTY_DICT,
-    partials_dict: Dict[str, str] = {},
+    partials_dict: Mapping[str, str] = EMPTY_DICT,
     padding: str = "",
     def_ldel: str = "{{",
     def_rdel: str = "}}",

libs/core/langchain_core/utils/utils.py

@@ -131,12 +131,12 @@ def guard_import(
     """
     try:
         module = importlib.import_module(module_name, package)
-    except (ImportError, ModuleNotFoundError):
+    except (ImportError, ModuleNotFoundError) as e:
         pip_name = pip_name or module_name.split(".")[0].replace("_", "-")
         raise ImportError(
             f"Could not import {module_name} python package. "
             f"Please install it with `pip install {pip_name}`."
-        )
+        ) from e
     return module
 
 
@@ -235,7 +235,8 @@ def build_extra_kwargs(
             warnings.warn(
                 f"""WARNING! {field_name} is not default parameter.
                 {field_name} was transferred to model_kwargs.
-                Please confirm that {field_name} is what you intended."""
+                Please confirm that {field_name} is what you intended.""",
+                stacklevel=7,
             )
             extra_kwargs[field_name] = values.pop(field_name)
 

libs/core/langchain_core/vectorstores/base.py

@@ -558,7 +558,8 @@ class VectorStore(ABC):
         ):
             warnings.warn(
                 "Relevance scores must be between"
-                f" 0 and 1, got {docs_and_similarities}"
+                f" 0 and 1, got {docs_and_similarities}",
+                stacklevel=2,
             )
 
         if score_threshold is not None:
@@ -568,7 +569,7 @@ class VectorStore(ABC):
                 if similarity >= score_threshold
             ]
             if len(docs_and_similarities) == 0:
-                warnings.warn(
+                logger.warning(
                     "No relevant docs were retrieved using the relevance score"
                     f" threshold {score_threshold}"
                 )
@@ -605,7 +606,8 @@ class VectorStore(ABC):
         ):
             warnings.warn(
                 "Relevance scores must be between"
-                f" 0 and 1, got {docs_and_similarities}"
+                f" 0 and 1, got {docs_and_similarities}",
+                stacklevel=2,
             )
 
         if score_threshold is not None:
@@ -615,7 +617,7 @@ class VectorStore(ABC):
                 if similarity >= score_threshold
             ]
             if len(docs_and_similarities) == 0:
-                warnings.warn(
+                logger.warning(
                     "No relevant docs were retrieved using the relevance score"
                     f" threshold {score_threshold}"
                 )

libs/core/langchain_core/vectorstores/in_memory.py

@@ -435,11 +435,11 @@ class InMemoryVectorStore(VectorStore):
 
         try:
             import numpy as np
-        except ImportError:
+        except ImportError as e:
             raise ImportError(
                 "numpy must be installed to use max_marginal_relevance_search "
                 "pip install numpy"
-            )
+            ) from e
 
         mmr_chosen_indices = maximal_marginal_relevance(
             np.array(embedding, dtype=np.float32),

libs/core/langchain_core/vectorstores/utils.py

@@ -34,11 +34,11 @@ def _cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
     """
     try:
         import numpy as np
-    except ImportError:
+    except ImportError as e:
         raise ImportError(
             "cosine_similarity requires numpy to be installed. "
             "Please install numpy with `pip install numpy`."
-        )
+        ) from e
 
     if len(X) == 0 or len(Y) == 0:
         return np.array([])
@@ -93,11 +93,11 @@ def maximal_marginal_relevance(
     """
     try:
         import numpy as np
-    except ImportError:
+    except ImportError as e:
         raise ImportError(
             "maximal_marginal_relevance requires numpy to be installed. "
             "Please install numpy with `pip install numpy`."
-        )
+        ) from e
 
     if min(k, len(embedding_list)) <= 0:
         return []

libs/core/pyproject.toml

@@ -41,7 +41,7 @@ python = ">=3.12.4"
 [tool.poetry.extras]
 
 [tool.ruff.lint]
-select = [ "E", "F", "I", "T201", "UP",]
+select = [ "B", "E", "F", "I", "T201", "UP",]
 ignore = [ "UP006", "UP007",]
 
 [tool.coverage.run]

libs/core/tests/unit_tests/language_models/chat_models/test_base.py

@@ -7,6 +7,7 @@ import pytest
 
 from langchain_core.callbacks import CallbackManagerForLLMRun
 from langchain_core.language_models import BaseChatModel, FakeListChatModel
+from langchain_core.language_models.fake_chat_models import FakeListChatModelError
 from langchain_core.messages import (
     AIMessage,
     AIMessageChunk,
@@ -110,7 +111,7 @@ async def test_stream_error_callback() -> None:
             responses=[message],
             error_on_chunk_number=i,
         )
-        with pytest.raises(Exception):
+        with pytest.raises(FakeListChatModelError):
             cb_async = FakeAsyncCallbackHandler()
             async for _ in llm.astream("Dummy message", callbacks=[cb_async]):
                 pass

libs/core/tests/unit_tests/language_models/llms/test_base.py

@@ -7,6 +7,7 @@ from langchain_core.callbacks import (
     CallbackManagerForLLMRun,
 )
 from langchain_core.language_models import BaseLLM, FakeListLLM, FakeStreamingListLLM
+from langchain_core.language_models.fake import FakeListLLMError
 from langchain_core.outputs import Generation, GenerationChunk, LLMResult
 from langchain_core.tracers.context import collect_runs
 from tests.unit_tests.fake.callbacks import (
@@ -108,7 +109,7 @@ async def test_stream_error_callback() -> None:
             responses=[message],
             error_on_chunk_number=i,
         )
-        with pytest.raises(Exception):
+        with pytest.raises(FakeListLLMError):
             cb_async = FakeAsyncCallbackHandler()
             async for _ in llm.astream("Dummy message", callbacks=[cb_async]):
                 pass

libs/core/tests/unit_tests/output_parsers/test_json.py

@@ -3,6 +3,7 @@ from typing import Any, AsyncIterator, Iterator, Tuple
 
 import pytest
 
+from langchain_core.exceptions import OutputParserException
 from langchain_core.output_parsers.json import (
     SimpleJsonOutputParser,
 )
@@ -531,7 +532,7 @@ async def test_partial_text_json_output_parser_diff_async() -> None:
 
 def test_raises_error() -> None:
     parser = SimpleJsonOutputParser()
-    with pytest.raises(Exception):
+    with pytest.raises(OutputParserException):
         parser.invoke("hi")
 
 

libs/core/tests/unit_tests/output_parsers/test_pydantic_parser.py

@@ -164,13 +164,9 @@ def test_pydantic_output_parser_fail() -> None:
         pydantic_object=TestModel
     )
 
-    try:
+    with pytest.raises(OutputParserException) as e:
         pydantic_parser.parse(DEF_RESULT_FAIL)
-    except OutputParserException as e:
-        print("parse_result:", e)  # noqa: T201
         assert "Failed to parse TestModel from completion" in str(e)
-    else:
-        assert False, "Expected OutputParserException"
 
 
 def test_pydantic_output_parser_type_inference() -> None:

libs/core/tests/unit_tests/pydantic_utils.py

@@ -28,7 +28,7 @@ def _replace_all_of_with_ref(schema: Any) -> None:
                 del schema["default"]
         else:
             # Recursively process nested schemas
-            for key, value in schema.items():
+            for value in schema.values():
                 if isinstance(value, (dict, list)):
                     _replace_all_of_with_ref(value)
     elif isinstance(schema, list):
@@ -47,7 +47,7 @@ def _remove_bad_none_defaults(schema: Any) -> None:
     See difference between Optional and NotRequired types in python.
     """
     if isinstance(schema, dict):
-        for key, value in schema.items():
+        for value in schema.values():
             if isinstance(value, dict):
                 if "default" in value and value["default"] is None:
                     any_of = value.get("anyOf", [])

libs/core/tests/unit_tests/runnables/test_fallbacks.py

@@ -307,7 +307,7 @@ async def test_fallbacks_astream() -> None:
         runnable = RunnableGenerator(_agenerate_delayed_error).with_fallbacks(
             [RunnableGenerator(_agenerate)]
         )
-        async for c in runnable.astream({}):
+        async for _ in runnable.astream({}):
             pass
 
 
@@ -373,7 +373,7 @@ def test_fallbacks_getattr() -> None:
     assert llm_with_fallbacks.foo == 3
 
     with pytest.raises(AttributeError):
-        llm_with_fallbacks.bar
+        assert llm_with_fallbacks.bar == 4
 
 
 def test_fallbacks_getattr_runnable_output() -> None:

libs/core/tests/unit_tests/runnables/test_runnable.py

@@ -1516,7 +1516,7 @@ async def test_default_method_implementations(mocker: MockerFixture) -> None:
     ) == [5, 7]
 
     assert len(spy.call_args_list) == 2
-    for i, call in enumerate(spy.call_args_list):
+    for call in spy.call_args_list:
         call_arg = call.args[0]
 
         if call_arg == "hello":
@@ -1533,7 +1533,7 @@ async def test_default_method_implementations(mocker: MockerFixture) -> None:
     assert fake.batch(["hello", "wooorld"], dict(tags=["a-tag"])) == [5, 7]
     assert len(spy.call_args_list) == 2
     assert set(call.args[0] for call in spy.call_args_list) == {"hello", "wooorld"}
-    for i, call in enumerate(spy.call_args_list):
+    for call in spy.call_args_list:
         assert call.args[1].get("tags") == ["a-tag"]
         assert call.args[1].get("metadata") == {}
     spy.reset_mock()
@@ -5205,7 +5205,7 @@ def test_invoke_stream_passthrough_assign_trace() -> None:
     assert tracer.runs[0].child_runs[0].name == "RunnableParallel<urls>"
 
     tracer = FakeTracer()
-    for item in chain.stream({"example": [1, 2, 3]}, dict(callbacks=[tracer])):
+    for _ in chain.stream({"example": [1, 2, 3]}, dict(callbacks=[tracer])):
         pass
 
     assert tracer.runs[0].name == "RunnableAssign<urls>"
@@ -5225,7 +5225,7 @@ async def test_ainvoke_astream_passthrough_assign_trace() -> None:
     assert tracer.runs[0].child_runs[0].name == "RunnableParallel<urls>"
 
     tracer = FakeTracer()
-    async for item in chain.astream({"example": [1, 2, 3]}, dict(callbacks=[tracer])):
+    async for _ in chain.astream({"example": [1, 2, 3]}, dict(callbacks=[tracer])):
         pass
 
     assert tracer.runs[0].name == "RunnableAssign<urls>"

libs/core/tests/unit_tests/tracers/test_langchain.py

@@ -140,7 +140,7 @@ class LangChainProjectNameTest(unittest.TestCase):
                     projects = []
 
                     def mock_create_run(**kwargs: Any) -> Any:
-                        projects.append(kwargs.get("project_name"))
+                        projects.append(kwargs.get("project_name"))  # noqa: B023
                         return unittest.mock.MagicMock()
 
                     client.create_run = mock_create_run
@@ -151,6 +151,4 @@ class LangChainProjectNameTest(unittest.TestCase):
                         run_id=UUID("9d878ab3-e5ca-4218-aef6-44cbdc90160a"),
                     )
                     tracer.wait_for_futures()
-                    assert (
+                    assert projects == [case.expected_project_name]
-                        len(projects) == 1 and projects[0] == case.expected_project_name
-                    )