core: Add ruff rules TRY (tryceratops) (#29388)

TRY004 ("use TypeError rather than ValueError") existing errors are
marked as ignore to preserve backward compatibility.
LMK if you prefer to fix some of them.

Co-authored-by: Erick Friis <[email protected]>

libs/core/langchain_core/callbacks/manager.py

@@ -127,7 +127,7 @@ def trace_as_chain_group(
     except Exception as e:
         if not group_cm.ended:
             run_manager.on_chain_error(e)
-        raise e
+        raise
     else:
         if not group_cm.ended:
             run_manager.on_chain_end({})
@@ -207,7 +207,7 @@ async def atrace_as_chain_group(
     except Exception as e:
         if not group_cm.ended:
             await run_manager.on_chain_error(e)
-        raise e
+        raise
     else:
         if not group_cm.ended:
             await run_manager.on_chain_end({})
@@ -289,7 +289,7 @@ def handle_event(
                     f" {repr(e)}"
                 )
                 if handler.raise_error:
-                    raise e
+                    raise
     finally:
         if coros:
             try:
@@ -388,7 +388,7 @@ async def _ahandle_event_for_handler(
             f"Error in {handler.__class__.__name__}.{event_name} callback: {repr(e)}"
         )
         if handler.raise_error:
-            raise e
+            raise
 
 
 async def ahandle_event(

libs/core/langchain_core/language_models/chat_models.py

@@ -268,7 +268,7 @@ def _convert_input(self, input: LanguageModelInput) -> PromptValue:
                 f"Invalid input type {type(input)}. "
                 "Must be a PromptValue, str, or list of BaseMessages."
             )
-            raise ValueError(msg)
+            raise ValueError(msg)  # noqa: TRY004
 
     def invoke(
         self,
@@ -407,19 +407,21 @@ def stream(
                         generation = chunk
                     else:
                         generation += chunk
-                if generation is None:
-                    msg = "No generation chunks were returned"
-                    raise ValueError(msg)
             except BaseException as e:
                 run_manager.on_llm_error(
                     e,
                     response=LLMResult(
                         generations=[[generation]] if generation else []
                     ),
                 )
-                raise e
-            else:
-                run_manager.on_llm_end(LLMResult(generations=[[generation]]))
+                raise
+
+            if generation is None:
+                err = ValueError("No generation chunks were returned")
+                run_manager.on_llm_error(err, response=LLMResult(generations=[]))
+                raise err
+
+            run_manager.on_llm_end(LLMResult(generations=[[generation]]))
 
     async def astream(
         self,
@@ -485,19 +487,21 @@ async def astream(
                     generation = chunk
                 else:
                     generation += chunk
-            if generation is None:
-                msg = "No generation chunks were returned"
-                raise ValueError(msg)
         except BaseException as e:
             await run_manager.on_llm_error(
                 e,
                 response=LLMResult(generations=[[generation]] if generation else []),
             )
-            raise e
-        else:
-            await run_manager.on_llm_end(
-                LLMResult(generations=[[generation]]),
-            )
+            raise
+
+        if generation is None:
+            err = ValueError("No generation chunks were returned")
+            await run_manager.on_llm_error(err, response=LLMResult(generations=[]))
+            raise err
+
+        await run_manager.on_llm_end(
+            LLMResult(generations=[[generation]]),
+        )
 
     # --- Custom methods ---
 
@@ -641,7 +645,7 @@ def generate(
             except BaseException as e:
                 if run_managers:
                     run_managers[i].on_llm_error(e, response=LLMResult(generations=[]))
-                raise e
+                raise
         flattened_outputs = [
             LLMResult(generations=[res.generations], llm_output=res.llm_output)  # type: ignore[list-item]
             for res in results
@@ -1022,7 +1026,7 @@ def __call__(
             return generation.message
         else:
             msg = "Unexpected generation type"
-            raise ValueError(msg)
+            raise ValueError(msg)  # noqa: TRY004
 
     async def _call_async(
         self,
@@ -1039,7 +1043,7 @@ async def _call_async(
             return generation.message
         else:
             msg = "Unexpected generation type"
-            raise ValueError(msg)
+            raise ValueError(msg)  # noqa: TRY004
 
     @deprecated("0.1.7", alternative="invoke", removal="1.0")
     def call_as_llm(
@@ -1057,7 +1061,7 @@ def predict(
             return result.content
         else:
             msg = "Cannot use predict when output is not a string."
-            raise ValueError(msg)
+            raise ValueError(msg)  # noqa: TRY004
 
     @deprecated("0.1.7", alternative="invoke", removal="1.0")
     def predict_messages(
@@ -1082,7 +1086,7 @@ async def apredict(
             return result.content
         else:
             msg = "Cannot use predict when output is not a string."
-            raise ValueError(msg)
+            raise ValueError(msg)  # noqa: TRY004
 
     @deprecated("0.1.7", alternative="ainvoke", removal="1.0")
     async def apredict_messages(

libs/core/langchain_core/language_models/fake_chat_models.py

@@ -242,7 +242,7 @@ def _stream(
                 f"Expected generate to return a ChatResult, "
                 f"but got {type(chat_result)} instead."
             )
-            raise ValueError(msg)
+            raise ValueError(msg)  # noqa: TRY004
 
         message = chat_result.generations[0].message
 
@@ -251,7 +251,7 @@ def _stream(
                 f"Expected invoke to return an AIMessage, "
                 f"but got {type(message)} instead."
             )
-            raise ValueError(msg)
+            raise ValueError(msg)  # noqa: TRY004
 
         content = message.content
 

libs/core/langchain_core/language_models/llms.py

@@ -337,7 +337,7 @@ def _convert_input(self, input: LanguageModelInput) -> PromptValue:
                 f"Invalid input type {type(input)}. "
                 "Must be a PromptValue, str, or list of BaseMessages."
             )
-            raise ValueError(msg)
+            raise ValueError(msg)  # noqa: TRY004
 
     def _get_ls_params(
         self,
@@ -448,7 +448,7 @@ def batch(
                 if return_exceptions:
                     return cast(list[str], [e for _ in inputs])
                 else:
-                    raise e
+                    raise
         else:
             batches = [
                 inputs[i : i + max_concurrency]
@@ -494,7 +494,7 @@ async def abatch(
                 if return_exceptions:
                     return cast(list[str], [e for _ in inputs])
                 else:
-                    raise e
+                    raise
         else:
             batches = [
                 inputs[i : i + max_concurrency]
@@ -562,19 +562,21 @@ def stream(
                         generation = chunk
                     else:
                         generation += chunk
-                if generation is None:
-                    msg = "No generation chunks were returned"
-                    raise ValueError(msg)
             except BaseException as e:
                 run_manager.on_llm_error(
                     e,
                     response=LLMResult(
                         generations=[[generation]] if generation else []
                     ),
                 )
-                raise e
-            else:
-                run_manager.on_llm_end(LLMResult(generations=[[generation]]))
+                raise
+
+            if generation is None:
+                err = ValueError("No generation chunks were returned")
+                run_manager.on_llm_error(err, response=LLMResult(generations=[]))
+                raise err
+
+            run_manager.on_llm_end(LLMResult(generations=[[generation]]))
 
     async def astream(
         self,
@@ -632,17 +634,19 @@ async def astream(
                     generation = chunk
                 else:
                     generation += chunk
-            if generation is None:
-                msg = "No generation chunks were returned"
-                raise ValueError(msg)
         except BaseException as e:
             await run_manager.on_llm_error(
                 e,
                 response=LLMResult(generations=[[generation]] if generation else []),
             )
-            raise e
-        else:
-            await run_manager.on_llm_end(LLMResult(generations=[[generation]]))
+            raise
+
+        if generation is None:
+            err = ValueError("No generation chunks were returned")
+            await run_manager.on_llm_error(err, response=LLMResult(generations=[]))
+            raise err
+
+        await run_manager.on_llm_end(LLMResult(generations=[[generation]]))
 
     # --- Custom methods ---
 
@@ -790,7 +794,7 @@ def _generate_helper(
         except BaseException as e:
             for run_manager in run_managers:
                 run_manager.on_llm_error(e, response=LLMResult(generations=[]))
-            raise e
+            raise
         flattened_outputs = output.flatten()
         for manager, flattened_output in zip(run_managers, flattened_outputs):
             manager.on_llm_end(flattened_output)
@@ -850,7 +854,7 @@ def generate(
                 "Argument 'prompts' is expected to be of type List[str], received"
                 f" argument of type {type(prompts)}."
             )
-            raise ValueError(msg)
+            raise ValueError(msg)  # noqa: TRY004
         # Create callback managers
         if isinstance(metadata, list):
             metadata = [
@@ -1036,7 +1040,7 @@ async def _agenerate_helper(
                     for run_manager in run_managers
                 ]
             )
-            raise e
+            raise
         flattened_outputs = output.flatten()
         await asyncio.gather(
             *[
@@ -1289,7 +1293,7 @@ def __call__(
                 f"{type(prompt)}. If you want to run the LLM on multiple prompts, use "
                 "`generate` instead."
             )
-            raise ValueError(msg)
+            raise ValueError(msg)  # noqa: TRY004
         return (
             self.generate(
                 [prompt],

libs/core/langchain_core/messages/ai.py

@@ -363,6 +363,17 @@ def init_tool_calls(self) -> Self:
             return self
         tool_calls = []
         invalid_tool_calls = []
+
+        def add_chunk_to_invalid_tool_calls(chunk: ToolCallChunk) -> None:
+            invalid_tool_calls.append(
+                create_invalid_tool_call(
+                    name=chunk["name"],
+                    args=chunk["args"],
+                    id=chunk["id"],
+                    error=None,
+                )
+            )
+
         for chunk in self.tool_call_chunks:
             try:
                 args_ = parse_partial_json(chunk["args"]) if chunk["args"] != "" else {}  # type: ignore[arg-type]
@@ -375,17 +386,9 @@ def init_tool_calls(self) -> Self:
                         )
                     )
                 else:
-                    msg = "Malformed args."
-                    raise ValueError(msg)
+                    add_chunk_to_invalid_tool_calls(chunk)
             except Exception:
-                invalid_tool_calls.append(
-                    create_invalid_tool_call(
-                        name=chunk["name"],
-                        args=chunk["args"],
-                        id=chunk["id"],
-                        error=None,
-                    )
-                )
+                add_chunk_to_invalid_tool_calls(chunk)
         self.tool_calls = tool_calls
         self.invalid_tool_calls = invalid_tool_calls
         return self

libs/core/langchain_core/messages/utils.py

@@ -124,7 +124,7 @@ def get_buffer_string(
             role = m.role
         else:
             msg = f"Got unsupported message type: {m}"
-            raise ValueError(msg)
+            raise ValueError(msg)  # noqa: TRY004
         message = f"{role}: {m.content}"
         if isinstance(m, AIMessage) and "function_call" in m.additional_kwargs:
             message += f"{m.additional_kwargs['function_call']}"
@@ -1400,7 +1400,7 @@ def _get_message_openai_role(message: BaseMessage) -> str:
         return message.role
     else:
         msg = f"Unknown BaseMessage type {message.__class__}."
-        raise ValueError(msg)
+        raise ValueError(msg)  # noqa: TRY004
 
 
 def _convert_to_openai_tool_calls(tool_calls: list[ToolCall]) -> list[dict]:

libs/core/langchain_core/output_parsers/openai_tools.py

@@ -282,19 +282,20 @@ def parse_result(self, result: list[Generation], *, partial: bool = False) -> An
         name_dict = {tool.__name__: tool for tool in self.tools}
         pydantic_objects = []
         for res in json_results:
+            if not isinstance(res["args"], dict):
+                if partial:
+                    continue
+                msg = (
+                    f"Tool arguments must be specified as a dict, received: "
+                    f"{res['args']}"
+                )
+                raise ValueError(msg)
             try:
-                if not isinstance(res["args"], dict):
-                    msg = (
-                        f"Tool arguments must be specified as a dict, received: "
-                        f"{res['args']}"
-                    )
-                    raise ValueError(msg)
                 pydantic_objects.append(name_dict[res["type"]](**res["args"]))
-            except (ValidationError, ValueError) as e:
+            except (ValidationError, ValueError):
                 if partial:
                     continue
-                else:
-                    raise e
+                raise
         if self.first_tool_only:
             return pydantic_objects[0] if pydantic_objects else None
         else:

libs/core/langchain_core/output_parsers/pydantic.py

@@ -66,10 +66,10 @@ def parse_result(
         try:
             json_object = super().parse_result(result)
             return self._parse_obj(json_object)
-        except OutputParserException as e:
+        except OutputParserException:
             if partial:
                 return None
-            raise e
+            raise
 
     def parse(self, text: str) -> TBaseModel:
         """Parse the output of an LLM call to a pydantic object.