langchain: Add ruff rules B (#31908)

See https://docs.astral.sh/ruff/rules/#flake8-bugbear-b

Co-authored-by: Mason Daugherty <mason@langchain.dev>

libs/langchain/langchain/__init__.py

@@ -29,10 +29,12 @@ def _warn_on_import(name: str, replacement: Optional[str] = None) -> None:
         warnings.warn(
             f"Importing {name} from langchain root module is no longer supported. "
             f"Please use {replacement} instead.",
+            stacklevel=3,
         )
     else:
         warnings.warn(
             f"Importing {name} from langchain root module is no longer supported.",
+            stacklevel=3,
         )
 
 

libs/langchain/langchain/agents/agent.py

@@ -1366,7 +1366,7 @@ class AgentExecutor(Chain):
                     "again, pass `handle_parsing_errors=True` to the AgentExecutor. "
                     f"This is the error: {e!s}"
                 )
-                raise ValueError(msg)
+                raise ValueError(msg) from e
             text = str(e)
             if isinstance(self.handle_parsing_errors, bool):
                 if e.send_to_llm:
@@ -1380,7 +1380,7 @@ class AgentExecutor(Chain):
                 observation = self.handle_parsing_errors(e)
             else:
                 msg = "Got unexpected type of `handle_parsing_errors`"
-                raise ValueError(msg)
+                raise ValueError(msg) from e
             output = AgentAction("_Exception", observation, text)
             if run_manager:
                 run_manager.on_agent_action(output, color="green")
@@ -1505,7 +1505,7 @@ class AgentExecutor(Chain):
                     "again, pass `handle_parsing_errors=True` to the AgentExecutor. "
                     f"This is the error: {e!s}"
                 )
-                raise ValueError(msg)
+                raise ValueError(msg) from e
             text = str(e)
             if isinstance(self.handle_parsing_errors, bool):
                 if e.send_to_llm:
@@ -1519,7 +1519,7 @@ class AgentExecutor(Chain):
                 observation = self.handle_parsing_errors(e)
             else:
                 msg = "Got unexpected type of `handle_parsing_errors`"
-                raise ValueError(msg)
+                raise ValueError(msg) from e
             output = AgentAction("_Exception", observation, text)
             tool_run_kwargs = self._action_agent.tool_run_logging_kwargs()
             observation = await ExceptionTool().arun(

libs/langchain/langchain/agents/agent_toolkits/vectorstore/toolkit.py

@@ -36,9 +36,9 @@ class VectorStoreToolkit(BaseToolkit):
                 VectorStoreQATool,
                 VectorStoreQAWithSourcesTool,
             )
-        except ImportError:
+        except ImportError as e:
             msg = "You need to install langchain-community to use this toolkit."
-            raise ImportError(msg)
+            raise ImportError(msg) from e
         description = VectorStoreQATool.get_description(
             self.vectorstore_info.name,
             self.vectorstore_info.description,
@@ -79,9 +79,9 @@ class VectorStoreRouterToolkit(BaseToolkit):
             from langchain_community.tools.vectorstore.tool import (
                 VectorStoreQATool,
             )
-        except ImportError:
+        except ImportError as e:
             msg = "You need to install langchain-community to use this toolkit."
-            raise ImportError(msg)
+            raise ImportError(msg) from e
         for vectorstore_info in self.vectorstores:
             description = VectorStoreQATool.get_description(
                 vectorstore_info.name,

libs/langchain/langchain/agents/openai_functions_agent/base.py

@@ -32,6 +32,8 @@ from langchain.agents.output_parsers.openai_functions import (
     OpenAIFunctionsAgentOutputParser,
 )
 
+_NOT_SET = object()
+
 
 @deprecated("0.1.0", alternative="create_openai_functions_agent", removal="1.0")
 class OpenAIFunctionsAgent(BaseSingleActionAgent):
@@ -213,9 +215,7 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
     @classmethod
     def create_prompt(
         cls,
-        system_message: Optional[SystemMessage] = SystemMessage(
+        system_message: Optional[SystemMessage] = _NOT_SET,  # type: ignore[assignment]
-            content="You are a helpful AI assistant.",
-        ),
         extra_prompt_messages: Optional[list[BaseMessagePromptTemplate]] = None,
     ) -> ChatPromptTemplate:
         """Create prompt for this agent.
@@ -230,8 +230,13 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
             A prompt template to pass into this agent.
         """
         _prompts = extra_prompt_messages or []
+        system_message_ = (
+            system_message
+            if system_message is not _NOT_SET
+            else SystemMessage(content="You are a helpful AI assistant.")
+        )
         messages: list[Union[BaseMessagePromptTemplate, BaseMessage]]
-        messages = [system_message] if system_message else []
+        messages = [system_message_] if system_message_ else []
 
         messages.extend(
             [
@@ -249,9 +254,7 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
         tools: Sequence[BaseTool],
         callback_manager: Optional[BaseCallbackManager] = None,
         extra_prompt_messages: Optional[list[BaseMessagePromptTemplate]] = None,
-        system_message: Optional[SystemMessage] = SystemMessage(
+        system_message: Optional[SystemMessage] = _NOT_SET,  # type: ignore[assignment]
-            content="You are a helpful AI assistant.",
-        ),
         **kwargs: Any,
     ) -> BaseSingleActionAgent:
         """Construct an agent from an LLM and tools.
@@ -265,9 +268,14 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
                 Defaults to a default system message.
             kwargs: Additional parameters to pass to the agent.
         """
+        system_message_ = (
+            system_message
+            if system_message is not _NOT_SET
+            else SystemMessage(content="You are a helpful AI assistant.")
+        )
         prompt = cls.create_prompt(
             extra_prompt_messages=extra_prompt_messages,
-            system_message=system_message,
+            system_message=system_message_,
         )
         return cls(  # type: ignore[call-arg]
             llm=llm,

libs/langchain/langchain/agents/openai_functions_multi_agent/base.py

@@ -46,21 +46,21 @@ def _parse_ai_message(message: BaseMessage) -> Union[list[AgentAction], AgentFin
     if function_call:
         try:
             arguments = json.loads(function_call["arguments"], strict=False)
-        except JSONDecodeError:
+        except JSONDecodeError as e:
             msg = (
                 f"Could not parse tool input: {function_call} because "
                 f"the `arguments` is not valid JSON."
             )
-            raise OutputParserException(msg)
+            raise OutputParserException(msg) from e
 
         try:
             tools = arguments["actions"]
-        except (TypeError, KeyError):
+        except (TypeError, KeyError) as e:
             msg = (
                 f"Could not parse tool input: {function_call} because "
                 f"the `arguments` JSON does not contain `actions` key."
             )
-            raise OutputParserException(msg)
+            raise OutputParserException(msg) from e
 
         final_tools: list[AgentAction] = []
         for tool_schema in tools:
@@ -100,6 +100,9 @@ def _parse_ai_message(message: BaseMessage) -> Union[list[AgentAction], AgentFin
     )
 
 
+_NOT_SET = object()
+
+
 @deprecated("0.1.0", alternative="create_openai_tools_agent", removal="1.0")
 class OpenAIMultiFunctionsAgent(BaseMultiActionAgent):
     """Agent driven by OpenAIs function powered API.
@@ -263,9 +266,7 @@ class OpenAIMultiFunctionsAgent(BaseMultiActionAgent):
     @classmethod
     def create_prompt(
         cls,
-        system_message: Optional[SystemMessage] = SystemMessage(
+        system_message: Optional[SystemMessage] = _NOT_SET,  # type: ignore[assignment]
-            content="You are a helpful AI assistant.",
-        ),
         extra_prompt_messages: Optional[list[BaseMessagePromptTemplate]] = None,
     ) -> BasePromptTemplate:
         """Create prompt for this agent.
@@ -280,8 +281,13 @@ class OpenAIMultiFunctionsAgent(BaseMultiActionAgent):
             A prompt template to pass into this agent.
         """
         _prompts = extra_prompt_messages or []
+        system_message_ = (
+            system_message
+            if system_message is not _NOT_SET
+            else SystemMessage(content="You are a helpful AI assistant.")
+        )
         messages: list[Union[BaseMessagePromptTemplate, BaseMessage]]
-        messages = [system_message] if system_message else []
+        messages = [system_message_] if system_message_ else []
 
         messages.extend(
             [
@@ -299,9 +305,7 @@ class OpenAIMultiFunctionsAgent(BaseMultiActionAgent):
         tools: Sequence[BaseTool],
         callback_manager: Optional[BaseCallbackManager] = None,
         extra_prompt_messages: Optional[list[BaseMessagePromptTemplate]] = None,
-        system_message: Optional[SystemMessage] = SystemMessage(
+        system_message: Optional[SystemMessage] = _NOT_SET,  # type: ignore[assignment]
-            content="You are a helpful AI assistant.",
-        ),
         **kwargs: Any,
     ) -> BaseMultiActionAgent:
         """Construct an agent from an LLM and tools.
@@ -315,9 +319,14 @@ class OpenAIMultiFunctionsAgent(BaseMultiActionAgent):
                 Default is a default system message.
             kwargs: Additional arguments.
         """
+        system_message_ = (
+            system_message
+            if system_message is not _NOT_SET
+            else SystemMessage(content="You are a helpful AI assistant.")
+        )
         prompt = cls.create_prompt(
             extra_prompt_messages=extra_prompt_messages,
-            system_message=system_message,
+            system_message=system_message_,
         )
         return cls(  # type: ignore[call-arg]
             llm=llm,

libs/langchain/langchain/agents/output_parsers/openai_functions.py

@@ -47,12 +47,12 @@ class OpenAIFunctionsAgentOutputParser(AgentOutputParser):
                 else:
                     # otherwise it returns a json object
                     _tool_input = json.loads(function_call["arguments"], strict=False)
-            except JSONDecodeError:
+            except JSONDecodeError as e:
                 msg = (
                     f"Could not parse tool input: {function_call} because "
                     f"the `arguments` is not valid JSON."
                 )
-                raise OutputParserException(msg)
+                raise OutputParserException(msg) from e
 
             # HACK HACK HACK:
             # The code that encodes tool input into Open AI uses a special variable

libs/langchain/langchain/agents/output_parsers/react_json_single_input.py

@@ -72,10 +72,10 @@ class ReActJsonSingleInputOutputParser(AgentOutputParser):
                 text,
             )
 
-        except Exception:
+        except Exception as e:
             if not includes_answer:
                 msg = f"Could not parse LLM output: {text}"
-                raise OutputParserException(msg)
+                raise OutputParserException(msg) from e
             output = text.split(FINAL_ANSWER_ACTION)[-1].strip()
             return AgentFinish({"output": output}, text)
 

libs/langchain/langchain/agents/output_parsers/tools.py

@@ -46,12 +46,12 @@ def parse_ai_message_to_tool_action(
                 tool_calls.append(
                     ToolCall(name=function_name, args=args, id=tool_call["id"]),
                 )
-            except JSONDecodeError:
+            except JSONDecodeError as e:
                 msg = (
                     f"Could not parse tool input: {function} because "
                     f"the `arguments` is not valid JSON."
                 )
-                raise OutputParserException(msg)
+                raise OutputParserException(msg) from e
     for tool_call in tool_calls:
         # HACK HACK HACK:
         # The code that encodes tool input into Open AI uses a special variable

libs/langchain/langchain/callbacks/streamlit/__init__.py

@@ -70,12 +70,12 @@ def StreamlitCallbackHandler(
             from langchain_community.callbacks.streamlit.streamlit_callback_handler import (  # noqa: E501
                 StreamlitCallbackHandler as _InternalStreamlitCallbackHandler,
             )
-        except ImportError:
+        except ImportError as e:
             msg = (
                 "To use the StreamlitCallbackHandler, please install "
                 "langchain-community with `pip install langchain-community`."
             )
-            raise ImportError(msg)
+            raise ImportError(msg) from e
 
         return _InternalStreamlitCallbackHandler(
             parent_container,

libs/langchain/langchain/chains/base.py

@@ -255,6 +255,7 @@ class Chain(RunnableSerializable[dict[str, Any], dict[str, Any]], ABC):
             warnings.warn(
                 "callback_manager is deprecated. Please use callbacks instead.",
                 DeprecationWarning,
+                stacklevel=4,
             )
             values["callbacks"] = values.pop("callback_manager", None)
         return values

libs/langchain/langchain/chains/conversational_retrieval/base.py

@@ -504,6 +504,7 @@ class ChatVectorDBChain(BaseConversationalRetrievalChain):
         warnings.warn(
             "`ChatVectorDBChain` is deprecated - "
             "please use `from langchain.chains import ConversationalRetrievalChain`",
+            stacklevel=4,
         )
         return values
 

libs/langchain/langchain/chains/flare/base.py

@@ -202,7 +202,7 @@ class FlareChain(Chain):
 
         response = ""
 
-        for i in range(self.max_iter):
+        for _i in range(self.max_iter):
             _run_manager.on_text(
                 f"Current Response: {response}",
                 color="blue",
@@ -261,13 +261,13 @@ class FlareChain(Chain):
         """
         try:
             from langchain_openai import ChatOpenAI
-        except ImportError:
+        except ImportError as e:
             msg = (
                 "OpenAI is required for FlareChain. "
                 "Please install langchain-openai."
                 "pip install langchain-openai"
             )
-            raise ImportError(msg)
+            raise ImportError(msg) from e
         llm = ChatOpenAI(
             max_completion_tokens=max_generation_len,
             logprobs=True,

libs/langchain/langchain/chains/llm.py

@@ -350,6 +350,7 @@ class LLMChain(Chain):
         warnings.warn(
             "The predict_and_parse method is deprecated, "
             "instead pass an output parser directly to LLMChain.",
+            stacklevel=2,
         )
         result = self.predict(callbacks=callbacks, **kwargs)
         if self.prompt.output_parser is not None:
@@ -365,6 +366,7 @@ class LLMChain(Chain):
         warnings.warn(
             "The apredict_and_parse method is deprecated, "
             "instead pass an output parser directly to LLMChain.",
+            stacklevel=2,
         )
         result = await self.apredict(callbacks=callbacks, **kwargs)
         if self.prompt.output_parser is not None:
@@ -380,6 +382,7 @@ class LLMChain(Chain):
         warnings.warn(
             "The apply_and_parse method is deprecated, "
             "instead pass an output parser directly to LLMChain.",
+            stacklevel=2,
         )
         result = self.apply(input_list, callbacks=callbacks)
         return self._parse_generation(result)
@@ -404,6 +407,7 @@ class LLMChain(Chain):
         warnings.warn(
             "The aapply_and_parse method is deprecated, "
             "instead pass an output parser directly to LLMChain.",
+            stacklevel=2,
         )
         result = await self.aapply(input_list, callbacks=callbacks)
         return self._parse_generation(result)

libs/langchain/langchain/chains/llm_checker/base.py

@@ -112,6 +112,7 @@ class LLMCheckerChain(Chain):
                 "Directly instantiating an LLMCheckerChain with an llm is deprecated. "
                 "Please instantiate with question_to_checked_assertions_chain "
                 "or using the from_llm class method.",
+                stacklevel=5,
             )
             if (
                 "question_to_checked_assertions_chain" not in values

libs/langchain/langchain/chains/llm_math/base.py

@@ -166,17 +166,18 @@ class LLMMathChain(Chain):
     def raise_deprecation(cls, values: dict) -> Any:
         try:
             import numexpr  # noqa: F401
-        except ImportError:
+        except ImportError as e:
             msg = (
                 "LLMMathChain requires the numexpr package. "
                 "Please install it with `pip install numexpr`."
             )
-            raise ImportError(msg)
+            raise ImportError(msg) from e
         if "llm" in values:
             warnings.warn(
                 "Directly instantiating an LLMMathChain with an llm is deprecated. "
                 "Please instantiate with llm_chain argument or using the from_llm "
                 "class method.",
+                stacklevel=5,
             )
             if "llm_chain" not in values and values["llm"] is not None:
                 prompt = values.get("prompt", PROMPT)
@@ -216,7 +217,7 @@ class LLMMathChain(Chain):
                 f'LLMMathChain._evaluate("{expression}") raised error: {e}.'
                 " Please try again with a valid numerical expression"
             )
-            raise ValueError(msg)
+            raise ValueError(msg) from e
 
         # Remove any leading and trailing brackets from the output
         return re.sub(r"^\[|\]$", "", output)

libs/langchain/langchain/chains/llm_summarization_checker/base.py

@@ -118,6 +118,7 @@ class LLMSummarizationCheckerChain(Chain):
                 "Directly instantiating an LLMSummarizationCheckerChain with an llm is "
                 "deprecated. Please instantiate with"
                 " sequential_chain argument or using the from_llm class method.",
+                stacklevel=5,
             )
             if "sequential_chain" not in values and values["llm"] is not None:
                 values["sequential_chain"] = _load_sequential_chain(

libs/langchain/langchain/chains/loading.py

@@ -566,13 +566,13 @@ def _load_graph_cypher_chain(config: dict, **kwargs: Any) -> GraphCypherQAChain:
 
     try:
         from langchain_community.chains.graph_qa.cypher import GraphCypherQAChain
-    except ImportError:
+    except ImportError as e:
         msg = (
             "To use this GraphCypherQAChain functionality you must install the "
             "langchain_community package. "
             "You can install it with `pip install langchain_community`"
         )
-        raise ImportError(msg)
+        raise ImportError(msg) from e
     return GraphCypherQAChain(
         graph=graph,
         cypher_generation_chain=cypher_generation_chain,
@@ -614,13 +614,13 @@ def _load_api_chain(config: dict, **kwargs: Any) -> APIChain:
 def _load_llm_requests_chain(config: dict, **kwargs: Any) -> LLMRequestsChain:
     try:
         from langchain.chains.llm_requests import LLMRequestsChain
-    except ImportError:
+    except ImportError as e:
         msg = (
             "To use this LLMRequestsChain functionality you must install the "
             "langchain package. "
             "You can install it with `pip install langchain`"
         )
-        raise ImportError(msg)
+        raise ImportError(msg) from e
 
     if "llm_chain" in config:
         llm_chain_config = config.pop("llm_chain")

libs/langchain/langchain/chains/moderation.py

@@ -72,12 +72,12 @@ class OpenAIModerationChain(Chain):
                 values["client"] = openai.OpenAI(api_key=openai_api_key)
                 values["async_client"] = openai.AsyncOpenAI(api_key=openai_api_key)
 
-        except ImportError:
+        except ImportError as e:
             msg = (
                 "Could not import openai python package. "
                 "Please install it with `pip install openai`."
             )
-            raise ImportError(msg)
+            raise ImportError(msg) from e
         return values
 
     @property

libs/langchain/langchain/chains/natbot/base.py

@@ -74,6 +74,7 @@ class NatBotChain(Chain):
                 "Directly instantiating an NatBotChain with an llm is deprecated. "
                 "Please instantiate with llm_chain argument or using the from_llm "
                 "class method.",
+                stacklevel=5,
             )
             if "llm_chain" not in values and values["llm"] is not None:
                 values["llm_chain"] = PROMPT | values["llm"] | StrOutputParser()

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

@@ -63,12 +63,12 @@ class Crawler:
     def __init__(self) -> None:
         try:
             from playwright.sync_api import sync_playwright
-        except ImportError:
+        except ImportError as e:
             msg = (
                 "Could not import playwright python package. "
                 "Please install it with `pip install playwright`."
             )
-            raise ImportError(msg)
+            raise ImportError(msg) from e
         self.browser: Browser = (
             sync_playwright().start().chromium.launch(headless=False)
         )

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

@@ -94,12 +94,12 @@ def openapi_spec_to_openai_fn(
     """
     try:
         from langchain_community.tools import APIOperation
-    except ImportError:
+    except ImportError as e:
         msg = (
             "Could not import langchain_community.tools. "
             "Please install it with `pip install langchain-community`."
         )
-        raise ImportError(msg)
+        raise ImportError(msg) from e
 
     if not spec.paths:
         return [], lambda: None

libs/langchain/langchain/chains/qa_with_sources/vector_db.py

@@ -77,6 +77,7 @@ class VectorDBQAWithSourcesChain(BaseQAWithSourcesChain):
         warnings.warn(
             "`VectorDBQAWithSourcesChain` is deprecated - "
             "please use `from langchain.chains import RetrievalQAWithSourcesChain`",
+            stacklevel=5,
         )
         return values
 

libs/langchain/langchain/chains/query_constructor/base.py

@@ -64,7 +64,7 @@ class StructuredQueryOutputParser(BaseOutputParser[StructuredQuery]):
             )
         except Exception as e:
             msg = f"Parsing text\n{text}\n raised following error:\n{e}"
-            raise OutputParserException(msg)
+            raise OutputParserException(msg) from e
 
     @classmethod
     def from_components(

libs/langchain/langchain/chains/query_constructor/parser.py

@@ -162,6 +162,7 @@ class QueryTransformer(Transformer):
             warnings.warn(
                 "Dates are expected to be provided in ISO 8601 date format "
                 "(YYYY-MM-DD).",
+                stacklevel=3,
             )
         return {"date": item, "type": "date"}
 
@@ -173,9 +174,9 @@ class QueryTransformer(Transformer):
         except ValueError:
             try:
                 datetime.datetime.strptime(item, "%Y-%m-%dT%H:%M:%S")
-            except ValueError:
+            except ValueError as e:
                 msg = "Datetime values are expected to be in ISO 8601 format."
-                raise ValueError(msg)
+                raise ValueError(msg) from e
         return {"datetime": item, "type": "datetime"}
 
     def string(self, item: Any) -> str:

libs/langchain/langchain/chains/retrieval_qa/base.py

@@ -3,7 +3,6 @@
 from __future__ import annotations
 
 import inspect
-import warnings
 from abc import abstractmethod
 from typing import Any, Optional
 
@@ -320,15 +319,6 @@ class VectorDBQA(BaseRetrievalQA):
     search_kwargs: dict[str, Any] = Field(default_factory=dict)
     """Extra search args."""
 
-    @model_validator(mode="before")
-    @classmethod
-    def raise_deprecation(cls, values: dict) -> Any:
-        warnings.warn(
-            "`VectorDBQA` is deprecated - "
-            "please use `from langchain.chains import RetrievalQA`",
-        )
-        return values
-
     @model_validator(mode="before")
     @classmethod
     def validate_search_type(cls, values: dict) -> Any:

libs/langchain/langchain/chains/router/llm_router.py

@@ -193,4 +193,4 @@ class RouterOutputParser(BaseOutputParser[dict[str, str]]):
             return parsed
         except Exception as e:
             msg = f"Parsing text\n{text}\n raised following error:\n{e}"
-            raise OutputParserException(msg)
+            raise OutputParserException(msg) from e

libs/langchain/langchain/chains/sequential.py

@@ -102,7 +102,7 @@ class SequentialChain(Chain):
     ) -> dict[str, str]:
         known_values = inputs.copy()
         _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
-        for i, chain in enumerate(self.chains):
+        for _i, chain in enumerate(self.chains):
             callbacks = _run_manager.get_child()
             outputs = chain(known_values, return_only_outputs=True, callbacks=callbacks)
             known_values.update(outputs)
@@ -116,7 +116,7 @@ class SequentialChain(Chain):
         known_values = inputs.copy()
         _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
         callbacks = _run_manager.get_child()
-        for i, chain in enumerate(self.chains):
+        for _i, chain in enumerate(self.chains):
             outputs = await chain.acall(
                 known_values,
                 return_only_outputs=True,

libs/langchain/langchain/chat_models/__init__.py

@@ -36,6 +36,7 @@ def __getattr__(name: str) -> None:
             "Please import from langchain-community instead:\n\n"
             f"`from langchain_community.chat_models import {name}`.\n\n"
             "To install langchain-community run `pip install -U langchain-community`.",
+            stacklevel=2,
             category=LangChainDeprecationWarning,
         )
 

libs/langchain/langchain/chat_models/base.py

@@ -316,6 +316,7 @@ def init_chat_model(
             f"{config_prefix=} has been set but no fields are configurable. Set "
             f"`configurable_fields=(...)` to specify the model params that are "
             f"configurable.",
+            stacklevel=2,
         )
 
     if not configurable_fields:

libs/langchain/langchain/evaluation/embedding_distance/base.py

@@ -62,12 +62,12 @@ def _embedding_factory() -> Embeddings:
             from langchain_community.embeddings.openai import (  # type: ignore[no-redef]
                 OpenAIEmbeddings,
             )
-        except ImportError:
+        except ImportError as e:
             msg = (
                 "Could not import OpenAIEmbeddings. Please install the "
                 "OpenAIEmbeddings package using `pip install langchain-openai`."
             )
-            raise ImportError(msg)
+            raise ImportError(msg) from e
     return OpenAIEmbeddings()
 
 
@@ -139,14 +139,14 @@ class _EmbeddingDistanceChainMixin(Chain):
         if isinstance(embeddings, tuple(types_)):
             try:
                 import tiktoken  # noqa: F401
-            except ImportError:
+            except ImportError as e:
                 msg = (
                     "The tiktoken library is required to use the default "
                     "OpenAI embeddings with embedding distance evaluators."
                     " Please either manually select a different Embeddings object"
                     " or install tiktoken using `pip install tiktoken`."
                 )
-                raise ImportError(msg)
+                raise ImportError(msg) from e
         return values
 
     model_config = ConfigDict(
@@ -202,13 +202,13 @@ class _EmbeddingDistanceChainMixin(Chain):
         """
         try:
             from langchain_community.utils.math import cosine_similarity
-        except ImportError:
+        except ImportError as e:
             msg = (
                 "The cosine_similarity function is required to compute cosine distance."
                 " Please install the langchain-community package using"
                 " `pip install langchain-community`."
             )
-            raise ImportError(msg)
+            raise ImportError(msg) from e
         return 1.0 - cosine_similarity(a, b)
 
     @staticmethod

libs/langchain/langchain/evaluation/loading.py

@@ -61,12 +61,12 @@ def load_dataset(uri: str) -> list[dict]:
     """
     try:
         from datasets import load_dataset
-    except ImportError:
+    except ImportError as e:
         msg = (
             "load_dataset requires the `datasets` package."
             " Please install with `pip install datasets`"
         )
-        raise ImportError(msg)
+        raise ImportError(msg) from e
 
     dataset = load_dataset(f"LangChainDatasets/{uri}")
     return list(dataset["train"])
@@ -142,7 +142,7 @@ def load_evaluator(
                     from langchain_community.chat_models.openai import (  # type: ignore[no-redef]
                         ChatOpenAI,
                     )
-                except ImportError:
+                except ImportError as e:
                     msg = (
                         "Could not import langchain_openai or fallback onto "
                         "langchain_community. Please install langchain_openai "
@@ -150,7 +150,7 @@ def load_evaluator(
                         "It's recommended to install langchain_openai AND "
                         "specify a language model explicitly."
                     )
-                    raise ImportError(msg)
+                    raise ImportError(msg) from e
 
             llm = llm or ChatOpenAI(model="gpt-4", seed=42, temperature=0)
         except Exception as e:

libs/langchain/langchain/evaluation/parsing/json_distance.py

@@ -48,14 +48,14 @@ class JsonEditDistanceEvaluator(StringEvaluator):
         else:
             try:
                 from rapidfuzz import distance as rfd
-            except ImportError:
+            except ImportError as e:
                 msg = (
                     "The default string_distance operator for the "
                     " JsonEditDistanceEvaluator requires installation of "
                     "the rapidfuzz package. "
                     "Please install it with `pip install rapidfuzz`."
                 )
-                raise ImportError(msg)
+                raise ImportError(msg) from e
             self._string_distance = rfd.DamerauLevenshtein.normalized_distance
         if canonicalize is not None:
             self._canonicalize = canonicalize

libs/langchain/langchain/evaluation/parsing/json_schema.py

@@ -45,12 +45,12 @@ class JsonSchemaEvaluator(StringEvaluator):
         super().__init__()
         try:
             import jsonschema  # noqa: F401
-        except ImportError:
+        except ImportError as e:
             msg = (
                 "The JsonSchemaEvaluator requires the jsonschema package."
                 " Please install it with `pip install jsonschema`."
             )
-            raise ImportError(msg)
+            raise ImportError(msg) from e
 
     @property
     def requires_input(self) -> bool:
@@ -70,9 +70,9 @@ class JsonSchemaEvaluator(StringEvaluator):
     def _parse_json(self, node: Any) -> Union[dict, list, None, float, bool, int, str]:
         if isinstance(node, str):
             return parse_json_markdown(node)
-        if hasattr(node, "schema") and callable(getattr(node, "schema")):
+        if hasattr(node, "schema") and callable(node.schema):
             # Pydantic model
-            return getattr(node, "schema")()
+            return node.schema()
         return node
 
     def _validate(self, prediction: Any, schema: Any) -> dict:

libs/langchain/langchain/evaluation/schema.py

@@ -124,12 +124,12 @@ class _EvalArgsMixin:
             msg = f"{self.__class__.__name__} requires an input string."
             raise ValueError(msg)
         if input_ is not None and not self.requires_input:
-            warn(self._skip_input_warning)
+            warn(self._skip_input_warning, stacklevel=3)
         if self.requires_reference and reference is None:
             msg = f"{self.__class__.__name__} requires a reference string."
             raise ValueError(msg)
         if reference is not None and not self.requires_reference:
-            warn(self._skip_reference_warning)
+            warn(self._skip_reference_warning, stacklevel=3)
 
 
 class StringEvaluator(_EvalArgsMixin, ABC):

libs/langchain/langchain/evaluation/string_distance/base.py

@@ -29,12 +29,12 @@ def _load_rapidfuzz() -> Any:
     """
     try:
         import rapidfuzz
-    except ImportError:
+    except ImportError as e:
         msg = (
             "Please install the rapidfuzz library to use the FuzzyMatchStringEvaluator."
             "Please install it with `pip install rapidfuzz`."
         )
-        raise ImportError(msg)
+        raise ImportError(msg) from e
     return rapidfuzz.distance
 
 

libs/langchain/langchain/indexes/vectorstore.py

@@ -176,13 +176,14 @@ def _get_in_memory_vectorstore() -> type[VectorStore]:
 
     try:
         from langchain_community.vectorstores.inmemory import InMemoryVectorStore
-    except ImportError:
+    except ImportError as e:
         msg = "Please install langchain-community to use the InMemoryVectorStore."
-        raise ImportError(msg)
+        raise ImportError(msg) from e
     warnings.warn(
         "Using InMemoryVectorStore as the default vectorstore."
         "This memory store won't persist data. You should explicitly"
         "specify a vectorstore when using VectorstoreIndexCreator",
+        stacklevel=3,
     )
     return InMemoryVectorStore
 

libs/langchain/langchain/llms/__init__.py

@@ -552,6 +552,7 @@ def __getattr__(name: str) -> Any:
             "Please import from langchain-community instead:\n\n"
             f"`from langchain_community.llms import {name}`.\n\n"
             "To install langchain-community run `pip install -U langchain-community`.",
+            stacklevel=2,
             category=LangChainDeprecationWarning,
         )
 

libs/langchain/langchain/memory/chat_memory.py

@@ -58,6 +58,7 @@ class BaseChatMemory(BaseMemory, ABC):
                     f"'{self.__class__.__name__}' got multiple output keys:"
                     f" {outputs.keys()}. The default 'output' key is being used."
                     f" If this is not desired, please manually set 'output_key'.",
+                    stacklevel=3,
                 )
             else:
                 msg = (

libs/langchain/langchain/memory/combined.py

@@ -42,6 +42,7 @@ class CombinedMemory(BaseMemory):
                     "When using CombinedMemory, "
                     "input keys should be so the input is known. "
                     f" Was not set on {val}",
+                    stacklevel=5,
                 )
         return value
 

libs/langchain/langchain/memory/entity.py

@@ -115,12 +115,12 @@ class UpstashRedisEntityStore(BaseEntityStore):
     ):
         try:
             from upstash_redis import Redis
-        except ImportError:
+        except ImportError as e:
             msg = (
                 "Could not import upstash_redis python package. "
                 "Please install it with `pip install upstash_redis`."
             )
-            raise ImportError(msg)
+            raise ImportError(msg) from e
 
         super().__init__(*args, **kwargs)
 
@@ -211,23 +211,23 @@ class RedisEntityStore(BaseEntityStore):
     ):
         try:
             import redis
-        except ImportError:
+        except ImportError as e:
             msg = (
                 "Could not import redis python package. "
                 "Please install it with `pip install redis`."
             )
-            raise ImportError(msg)
+            raise ImportError(msg) from e
 
         super().__init__(*args, **kwargs)
 
         try:
             from langchain_community.utilities.redis import get_client
-        except ImportError:
+        except ImportError as e:
             msg = (
                 "Could not import langchain_community.utilities.redis.get_client. "
                 "Please install it with `pip install langchain-community`."
             )
-            raise ImportError(msg)
+            raise ImportError(msg) from e
 
         try:
             self.redis_client = get_client(redis_url=url, decode_responses=True)
@@ -311,12 +311,12 @@ class SQLiteEntityStore(BaseEntityStore):
         super().__init__(*args, **kwargs)
         try:
             import sqlite3
-        except ImportError:
+        except ImportError as e:
             msg = (
                 "Could not import sqlite3 python package. "
                 "Please install it with `pip install sqlite3`."
             )
-            raise ImportError(msg)
+            raise ImportError(msg) from e
 
         # Basic validation to prevent obviously malicious table/session names
         if not table_name.isidentifier() or not session_id.isidentifier():

libs/langchain/langchain/output_parsers/enum.py

@@ -26,12 +26,12 @@ class EnumOutputParser(BaseOutputParser[Enum]):
     def parse(self, response: str) -> Enum:
         try:
             return self.enum(response.strip())
-        except ValueError:
+        except ValueError as e:
             msg = (
                 f"Response '{response}' is not one of the "
                 f"expected values: {self._valid_values}"
             )
-            raise OutputParserException(msg)
+            raise OutputParserException(msg) from e
 
     def get_format_instructions(self) -> str:
         return f"Select one of the following options: {', '.join(self._valid_values)}"

libs/langchain/langchain/output_parsers/pandas_dataframe.py

@@ -137,17 +137,17 @@ class PandasDataFrameOutputParser(BaseOutputParser[dict[str, Any]]):
                         self.dataframe[request_params],
                         request_type,
                     )()
-        except (AttributeError, IndexError, KeyError):
+        except (AttributeError, IndexError, KeyError) as e:
             if request_type not in {"column", "row"}:
                 msg = f"Unsupported request type '{request_type}'. \
                         Please check the format instructions."
-                raise OutputParserException(msg)
+                raise OutputParserException(msg) from e
             msg = f"""Requested index {
                 request_params
                 if stripped_request_params is None
                 else stripped_request_params
             } is out of bounds."""
-            raise OutputParserException(msg)
+            raise OutputParserException(msg) from e
 
         return result
 

libs/langchain/langchain/retrievers/document_compressors/cohere_rerank.py

@@ -43,12 +43,12 @@ class CohereRerank(BaseDocumentCompressor):
         if not values.get("client"):
             try:
                 import cohere
-            except ImportError:
+            except ImportError as e:
                 msg = (
                     "Could not import cohere python package. "
                     "Please install it with `pip install cohere`."
                 )
-                raise ImportError(msg)
+                raise ImportError(msg) from e
             cohere_api_key = get_from_dict_or_env(
                 values,
                 "cohere_api_key",
@@ -92,7 +92,7 @@ class CohereRerank(BaseDocumentCompressor):
             max_chunks_per_doc=max_chunks_per_doc,
         )
         if hasattr(results, "results"):
-            results = getattr(results, "results")
+            results = results.results
         return [
             {"index": res.index, "relevance_score": res.relevance_score}
             for res in results

libs/langchain/langchain/retrievers/document_compressors/embeddings_filter.py

@@ -11,12 +11,12 @@ from pydantic import ConfigDict, Field
 def _get_similarity_function() -> Callable:
     try:
         from langchain_community.utils.math import cosine_similarity
-    except ImportError:
+    except ImportError as e:
         msg = (
             "To use please install langchain-community "
             "with `pip install langchain-community`."
         )
-        raise ImportError(msg)
+        raise ImportError(msg) from e
     return cosine_similarity
 
 
@@ -62,12 +62,12 @@ class EmbeddingsFilter(BaseDocumentCompressor):
                 _get_embeddings_from_stateful_docs,
                 get_stateful_documents,
             )
-        except ImportError:
+        except ImportError as e:
             msg = (
                 "To use please install langchain-community "
                 "with `pip install langchain-community`."
             )
-            raise ImportError(msg)
+            raise ImportError(msg) from e
 
         try:
             import numpy as np
@@ -105,12 +105,12 @@ class EmbeddingsFilter(BaseDocumentCompressor):
                 _aget_embeddings_from_stateful_docs,
                 get_stateful_documents,
             )
-        except ImportError:
+        except ImportError as e:
             msg = (
                 "To use please install langchain-community "
                 "with `pip install langchain-community`."
             )
-            raise ImportError(msg)
+            raise ImportError(msg) from e
 
         try:
             import numpy as np

libs/langchain/langchain/retrievers/merger_retriever.py

@@ -80,7 +80,7 @@ class MergerRetriever(BaseRetriever):
         merged_documents = []
         max_docs = max(map(len, retriever_docs), default=0)
         for i in range(max_docs):
-            for retriever, doc in zip(self.retrievers, retriever_docs):
+            for _retriever, doc in zip(self.retrievers, retriever_docs):
                 if i < len(doc):
                     merged_documents.append(doc[i])
 
@@ -116,7 +116,7 @@ class MergerRetriever(BaseRetriever):
         merged_documents = []
         max_docs = max(map(len, retriever_docs), default=0)
         for i in range(max_docs):
-            for retriever, doc in zip(self.retrievers, retriever_docs):
+            for _retriever, doc in zip(self.retrievers, retriever_docs):
                 if i < len(doc):
                     merged_documents.append(doc[i])
 

libs/langchain/langchain/retrievers/self_query/base.py

@@ -27,12 +27,12 @@ def _get_builtin_translator(vectorstore: VectorStore) -> Visitor:
     """Get the translator class corresponding to the vector store class."""
     try:
         import langchain_community  # noqa: F401
-    except ImportError:
+    except ImportError as e:
         msg = (
             "The langchain-community package must be installed to use this feature."
             " Please install it using `pip install langchain-community`."
         )
-        raise ImportError(msg)
+        raise ImportError(msg) from e
 
     from langchain_community.query_constructors.astradb import AstraDBTranslator
     from langchain_community.query_constructors.chroma import ChromaTranslator

libs/langchain/langchain/smith/evaluation/runner_utils.py

@@ -160,9 +160,9 @@ class EvalError(dict):
     def __getattr__(self, name: str) -> Any:
         try:
             return self[name]
-        except KeyError:
+        except KeyError as e:
             msg = f"'EvalError' object has no attribute '{name}'"
-            raise AttributeError(msg)
+            raise AttributeError(msg) from e
 
 
 def _wrap_in_chain_factory(
@@ -350,7 +350,7 @@ def _validate_example_inputs_for_language_model(
         except InputFormatError:
             try:
                 _get_messages(first_example.inputs or {})
-            except InputFormatError:
+            except InputFormatError as e:
                 msg = (
                     "Example inputs do not match language model input format. "
                     "Expected a dictionary with messages or a single prompt."
@@ -359,7 +359,7 @@ def _validate_example_inputs_for_language_model(
                     " to convert the example.inputs to a compatible format"
                     " for the llm or chat model you wish to evaluate."
                 )
-                raise InputFormatError(msg)
+                raise InputFormatError(msg) from e
 
 
 def _validate_example_inputs_for_chain(
@@ -1041,7 +1041,7 @@ run_on_dataset(
             f"Test project {project_name} already exists. Please use a different name:"
             f"\n\n{example_msg}"
         )
-        raise ValueError(msg)
+        raise ValueError(msg) from e
     comparison_url = dataset.url + f"/compare?selectedSessions={project.id}"
     print(  # noqa: T201
         f"View the evaluation results for project '{project_name}'"

libs/langchain/langchain/tools/__init__.py

@@ -79,6 +79,7 @@ def __getattr__(name: str) -> Any:
             "To install langchain-community run "
             "`pip install -U langchain-community`.",
             category=LangChainDeprecationWarning,
+            stacklevel=2,
         )
 
     return getattr(tools, name)

libs/langchain/pyproject.toml

@@ -146,6 +146,7 @@ ignore-words-list = "momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogy
 select = [
     "A",      # flake8-builtins
     "ASYNC",  # flake8-async
+    "B",      # flake8-bugbear
     "C4",     # flake8-comprehensions
     "COM",    # flake8-commas
     "D",      # pydocstyle

libs/langchain/tests/mock_servers/robot/server.py

@@ -1,7 +1,7 @@
 """A mock Robot server."""
 
 from enum import Enum
-from typing import Any, Optional, Union
+from typing import Annotated, Any, Optional, Union
 from uuid import uuid4
 
 import uvicorn
@@ -127,7 +127,9 @@ async def goto(x: int, y: int, z: int, cautiousness: Cautiousness) -> dict[str,
 
 @app.get("/get_state", description="Get the robot's state")
 async def get_state(
-    fields: list[StateItems] = Query(..., description="List of state items to return"),
+    fields: Annotated[
+        list[StateItems], Query(..., description="List of state items to return")
+    ],
 ) -> dict[str, Any]:
     state = {}
     for field in fields:

libs/langchain/tests/unit_tests/agents/test_agent.py

@@ -441,7 +441,10 @@ def test_agent_invalid_tool() -> None:
     )
 
     resp = agent("when was langchain made")
-    resp["intermediate_steps"][0][1] == "Foo is not a valid tool, try one of [Search]."
+    assert (
+        resp["intermediate_steps"][0][1]
+        == "Foo is not a valid tool, try one of [Search]."
+    )
 
 
 async def test_runnable_agent() -> None:

libs/langchain/tests/unit_tests/agents/test_agent_async.py

@@ -359,4 +359,7 @@ async def test_agent_invalid_tool() -> None:
     )
 
     resp = await agent.acall("when was langchain made")
-    resp["intermediate_steps"][0][1] == "Foo is not a valid tool, try one of [Search]."
+    assert (
+        resp["intermediate_steps"][0][1]
+        == "Foo is not a valid tool, try one of [Search]."
+    )

libs/langchain/tests/unit_tests/output_parsers/test_structured_parser.py

@@ -26,7 +26,8 @@ def test_parse() -> None:
     except OutputParserException:
         pass  # Test passes if OutputParserException is raised
     else:
-        assert False, f"Expected OutputParserException, but got {parser.parse(text)}"
+        msg = f"Expected OutputParserException, but got {parser.parse(text)}"
+        raise AssertionError(msg)
 
 
 def test_output_type() -> None:

libs/langchain/tests/unit_tests/output_parsers/test_yaml_parser.py

@@ -93,7 +93,8 @@ def test_yaml_output_parser_fail() -> None:
         print("parse_result:", e)  # noqa: T201
         assert "Failed to parse TestModel from completion" in str(e)
     else:
-        assert False, "Expected OutputParserException"
+        msg = "Expected OutputParserException"
+        raise AssertionError(msg)
 
 
 def test_yaml_output_parser_output_type() -> None:

libs/langchain/tests/unit_tests/smith/evaluation/test_runner_utils.py

@@ -65,7 +65,6 @@ _INVALID_PROMPTS = (
     _VALID_MESSAGES,
 )
 def test__get_messages_valid(inputs: dict[str, Any]) -> None:
-    {"messages": []}
     _get_messages(inputs)
 
 

libs/langchain/uv.lock

@@ -2734,7 +2734,7 @@ dev = [
     { name = "jupyter", specifier = ">=1.0.0,<2.0.0" },
     { name = "setuptools", specifier = ">=67.6.1,<68.0.0" },
 ]
-lint = [{ name = "ruff", specifier = ">=0.11.2,<0.12.0" }]
+lint = [{ name = "ruff", specifier = ">=0.12.2,<0.13" }]
 test = [
     { name = "blockbuster", specifier = "~=1.5.18" },
     { name = "freezegun", specifier = ">=1.2.2,<2.0.0" },
@@ -2900,7 +2900,7 @@ requires-dist = [
 [package.metadata.requires-dev]
 codespell = [{ name = "codespell", specifier = ">=2.2.0,<3.0.0" }]
 dev = [{ name = "langchain-core", editable = "../core" }]
-lint = [{ name = "ruff", specifier = ">=0.5,<1.0" }]
+lint = [{ name = "ruff", specifier = ">=0.12.2,<0.13" }]
 test = [
     { name = "freezegun", specifier = ">=1.2.2,<2.0.0" },
     { name = "langchain-core", editable = "../core" },