langchain: Add ruff rules B (#31908)

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

Co-authored-by: Mason Daugherty <[email protected]>

Author: cbornet

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 @@ def _iter_next_step(
                     "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 @@ def _iter_next_step(
                 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 @@ async def _aiter_next_step(
                     "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 @@ async def _aiter_next_step(
                 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 @@ def get_tools(self) -> list[BaseTool]:
                 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 @@ def get_tools(self) -> list[BaseTool]:
             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 @@
     OpenAIFunctionsAgentOutputParser,
 )
 
+_NOT_SET = object()
+
 
 @deprecated("0.1.0", alternative="create_openai_functions_agent", removal="1.0")
 class OpenAIFunctionsAgent(BaseSingleActionAgent):
@@ -213,9 +215,7 @@ def return_stopped_response(
     @classmethod
     def create_prompt(
         cls,
-        system_message: Optional[SystemMessage] = SystemMessage(
-            content="You are a helpful AI assistant.",
-        ),
+        system_message: Optional[SystemMessage] = _NOT_SET,  # type: ignore[assignment]
         extra_prompt_messages: Optional[list[BaseMessagePromptTemplate]] = None,
     ) -> ChatPromptTemplate:
         """Create prompt for this agent.
@@ -230,8 +230,13 @@ def create_prompt(
             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 @@ def from_llm_and_tools(
         tools: Sequence[BaseTool],
         callback_manager: Optional[BaseCallbackManager] = None,
         extra_prompt_messages: Optional[list[BaseMessagePromptTemplate]] = None,
-        system_message: Optional[SystemMessage] = SystemMessage(
-            content="You are a helpful AI assistant.",
-        ),
+        system_message: Optional[SystemMessage] = _NOT_SET,  # type: ignore[assignment]
         **kwargs: Any,
     ) -> BaseSingleActionAgent:
         """Construct an agent from an LLM and tools.
@@ -265,9 +268,14 @@ def from_llm_and_tools(
                 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 @@ async def aplan(
     @classmethod
     def create_prompt(
         cls,
-        system_message: Optional[SystemMessage] = SystemMessage(
-            content="You are a helpful AI assistant.",
-        ),
+        system_message: Optional[SystemMessage] = _NOT_SET,  # type: ignore[assignment]
         extra_prompt_messages: Optional[list[BaseMessagePromptTemplate]] = None,
     ) -> BasePromptTemplate:
         """Create prompt for this agent.
@@ -280,8 +281,13 @@ def create_prompt(
             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 @@ def from_llm_and_tools(
         tools: Sequence[BaseTool],
         callback_manager: Optional[BaseCallbackManager] = None,
         extra_prompt_messages: Optional[list[BaseMessagePromptTemplate]] = None,
-        system_message: Optional[SystemMessage] = SystemMessage(
-            content="You are a helpful AI assistant.",
-        ),
+        system_message: Optional[SystemMessage] = _NOT_SET,  # type: ignore[assignment]
         **kwargs: Any,
     ) -> BaseMultiActionAgent:
         """Construct an agent from an LLM and tools.
@@ -315,9 +319,14 @@ def from_llm_and_tools(
                 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 @@ def _parse_ai_message(message: BaseMessage) -> Union[AgentAction, AgentFinish]:
                 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 @@ def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
                 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 @@ def raise_callback_manager_deprecation(cls, values: dict) -> Any:
             warnings.warn(
                 "callback_manager is deprecated. Please use callbacks instead.",
                 DeprecationWarning,
+                stacklevel=4,
             )
             values["callbacks"] = values.pop("callback_manager", None)
         return values