core: Add ruff rules RET (#29384)

See https://docs.astral.sh/ruff/rules/#flake8-return-ret
All auto-fixes

libs/core/langchain_core/_api/deprecation.py

@@ -466,7 +466,6 @@ def warn_deprecated(
                 f"{removal}"
             )
             raise NotImplementedError(msg)
-        else:
         removal = f"in {removal}"
 
     if not message:

libs/core/langchain_core/agents.py

@@ -185,7 +185,6 @@ def _convert_agent_action_to_messages(
     """
     if isinstance(agent_action, AgentActionMessageLog):
         return agent_action.message_log
-    else:
     return [AIMessage(content=agent_action.log)]
 
 
@@ -205,7 +204,6 @@ def _convert_agent_observation_to_messages(
     """
     if isinstance(agent_action, AgentActionMessageLog):
         return [_create_function_message(agent_action, observation)]
-    else:
     content = observation
     if not isinstance(observation, str):
         try:

libs/core/langchain_core/beta/runnables/context.py

@@ -59,9 +59,8 @@ def _key_from_id(id_: str) -> str:
     wout_prefix = id_.split(CONTEXT_CONFIG_PREFIX, maxsplit=1)[1]
     if wout_prefix.endswith(CONTEXT_CONFIG_SUFFIX_GET):
         return wout_prefix[: -len(CONTEXT_CONFIG_SUFFIX_GET)]
-    elif wout_prefix.endswith(CONTEXT_CONFIG_SUFFIX_SET):
+    if wout_prefix.endswith(CONTEXT_CONFIG_SUFFIX_SET):
         return wout_prefix[: -len(CONTEXT_CONFIG_SUFFIX_SET)]
-    else:
     msg = f"Invalid context config id {id_}"
     raise ValueError(msg)
 
@@ -197,7 +196,6 @@ class ContextGet(RunnableSerializable):
         configurable = config.get("configurable", {})
         if isinstance(self.key, list):
             return {key: configurable[id_]() for key, id_ in zip(self.key, self.ids)}
-        else:
         return configurable[self.ids[0]]()
 
     @override
@@ -209,7 +207,6 @@ class ContextGet(RunnableSerializable):
         if isinstance(self.key, list):
             values = await asyncio.gather(*(configurable[id_]() for id_ in self.ids))
             return dict(zip(self.key, values))
-        else:
         return await configurable[self.ids[0]]()
 
 
@@ -447,5 +444,4 @@ class PrefixContext:
 def _print_keys(keys: Union[str, Sequence[str]]) -> str:
     if isinstance(keys, str):
         return f"'{keys}'"
-    else:
     return ", ".join(f"'{k}'" for k in keys)

libs/core/langchain_core/document_loaders/langsmith.py

@@ -128,7 +128,6 @@ class LangSmithLoader(BaseLoader):
 def _stringify(x: Union[str, dict]) -> str:
     if isinstance(x, str):
         return x
-    else:
     try:
         return json.dumps(x, indent=2)
     except Exception:

libs/core/langchain_core/documents/base.py

@@ -54,7 +54,6 @@ class BaseMedia(Serializable):
         """
         if id_value is not None:
             return str(id_value)
-        else:
         return id_value
 
 
@@ -159,11 +158,10 @@ class Blob(BaseMedia):
         """Read data as a string."""
         if self.data is None and self.path:
             return Path(self.path).read_text(encoding=self.encoding)
-        elif isinstance(self.data, bytes):
+        if isinstance(self.data, bytes):
             return self.data.decode(self.encoding)
-        elif isinstance(self.data, str):
+        if isinstance(self.data, str):
             return self.data
-        else:
         msg = f"Unable to get string for blob {self}"
         raise ValueError(msg)
 
@@ -171,11 +169,10 @@ class Blob(BaseMedia):
         """Read data as bytes."""
         if isinstance(self.data, bytes):
             return self.data
-        elif isinstance(self.data, str):
+        if isinstance(self.data, str):
             return self.data.encode(self.encoding)
-        elif self.data is None and self.path:
+        if self.data is None and self.path:
             return Path(self.path).read_bytes()
-        else:
         msg = f"Unable to get bytes for blob {self}"
         raise ValueError(msg)
 
@@ -316,5 +313,4 @@ class Document(BaseMedia):
         # a more general solution of formatting content directly inside the prompts.
         if self.metadata:
             return f"page_content='{self.page_content}' metadata={self.metadata}"
-        else:
         return f"page_content='{self.page_content}'"

libs/core/langchain_core/example_selectors/length_based.py

@@ -79,7 +79,6 @@ class LengthBasedExampleSelector(BaseExampleSelector, BaseModel):
             new_length = remaining_length - self.example_text_lengths[i]
             if new_length < 0:
                 break
-            else:
             examples.append(self.examples[i])
             remaining_length = new_length
             i += 1

libs/core/langchain_core/example_selectors/semantic_similarity.py

@@ -54,7 +54,6 @@ class _VectorStoreExampleSelector(BaseExampleSelector, BaseModel, ABC):
     ) -> str:
         if input_keys:
             return " ".join(sorted_values({key: example[key] for key in input_keys}))
-        else:
         return " ".join(sorted_values(example))
 
     def _documents_to_examples(self, documents: list[Document]) -> list[dict]:

libs/core/langchain_core/indexing/api.py

@@ -152,11 +152,10 @@ def _get_source_id_assigner(
     """Get the source id from the document."""
     if source_id_key is None:
         return lambda doc: None
-    elif isinstance(source_id_key, str):
+    if isinstance(source_id_key, str):
         return lambda doc: doc.metadata[source_id_key]
-    elif callable(source_id_key):
+    if callable(source_id_key):
         return source_id_key
-    else:
     msg = (
         f"source_id_key should be either None, a string or a callable. "
         f"Got {source_id_key} of type {type(source_id_key)}."

libs/core/langchain_core/language_models/base.py

@@ -143,7 +143,6 @@ class BaseLanguageModel(
         """
         if verbose is None:
             return _get_verbosity()
-        else:
         return verbose
 
     @property
@@ -351,7 +350,6 @@ class BaseLanguageModel(
         """
         if self.custom_get_token_ids is not None:
             return self.custom_get_token_ids(text)
-        else:
         return _get_token_ids_default_method(text)
 
     def get_num_tokens(self, text: str) -> int:

libs/core/langchain_core/language_models/chat_models.py

@@ -284,11 +284,10 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
     def _convert_input(self, input: LanguageModelInput) -> PromptValue:
         if isinstance(input, PromptValue):
             return input
-        elif isinstance(input, str):
+        if isinstance(input, str):
             return StringPromptValue(text=input)
-        elif isinstance(input, Sequence):
+        if isinstance(input, Sequence):
             return ChatPromptValue(messages=convert_to_messages(input))
-        else:
         msg = (
             f"Invalid input type {type(input)}. "
             "Must be a PromptValue, str, or list of BaseMessages."
@@ -610,7 +609,6 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
             _cleanup_llm_representation(serialized_repr, 1)
             llm_string = json.dumps(serialized_repr, sort_keys=True)
             return llm_string + "---" + param_string
-        else:
         params = self._get_invocation_params(stop=stop, **kwargs)
         params = {**params, **kwargs}
         return str(sorted(params.items()))
@@ -1107,7 +1105,6 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
         ).generations[0][0]
         if isinstance(generation, ChatGeneration):
             return generation.message
-        else:
         msg = "Unexpected generation type"
         raise ValueError(msg)  # noqa: TRY004
 
@@ -1124,7 +1121,6 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
         generation = result.generations[0][0]
         if isinstance(generation, ChatGeneration):
             return generation.message
-        else:
         msg = "Unexpected generation type"
         raise ValueError(msg)  # noqa: TRY004
 
@@ -1167,7 +1163,6 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
         result = self([HumanMessage(content=text)], stop=_stop, **kwargs)
         if isinstance(result.content, str):
             return result.content
-        else:
         msg = "Cannot use predict when output is not a string."
         raise ValueError(msg)  # noqa: TRY004
 
@@ -1194,7 +1189,6 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
         )
         if isinstance(result.content, str):
             return result.content
-        else:
         msg = "Cannot use predict when output is not a string."
         raise ValueError(msg)  # noqa: TRY004
 
@@ -1391,7 +1385,6 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
                 [parser_none], exception_key="parsing_error"
             )
             return RunnableMap(raw=llm) | parser_with_fallback
-        else:
         return llm | output_parser
 
 

libs/core/langchain_core/language_models/llms.py

@@ -251,8 +251,7 @@ def update_cache(
         prompt = prompts[missing_prompt_idxs[i]]
         if llm_cache is not None:
             llm_cache.update(prompt, llm_string, result)
-    llm_output = new_results.llm_output
+    return new_results.llm_output
-    return llm_output
 
 
 async def aupdate_cache(
@@ -285,8 +284,7 @@ async def aupdate_cache(
         prompt = prompts[missing_prompt_idxs[i]]
         if llm_cache:
             await llm_cache.aupdate(prompt, llm_string, result)
-    llm_output = new_results.llm_output
+    return new_results.llm_output
-    return llm_output
 
 
 class BaseLLM(BaseLanguageModel[str], ABC):
@@ -330,11 +328,10 @@ class BaseLLM(BaseLanguageModel[str], ABC):
     def _convert_input(self, input: LanguageModelInput) -> PromptValue:
         if isinstance(input, PromptValue):
             return input
-        elif isinstance(input, str):
+        if isinstance(input, str):
             return StringPromptValue(text=input)
-        elif isinstance(input, Sequence):
+        if isinstance(input, Sequence):
             return ChatPromptValue(messages=convert_to_messages(input))
-        else:
         msg = (
             f"Invalid input type {type(input)}. "
             "Must be a PromptValue, str, or list of BaseMessages."
@@ -452,7 +449,6 @@ class BaseLLM(BaseLanguageModel[str], ABC):
             except Exception as e:
                 if return_exceptions:
                     return cast("list[str]", [e for _ in inputs])
-                else:
                 raise
         else:
             batches = [
@@ -499,7 +495,6 @@ class BaseLLM(BaseLanguageModel[str], ABC):
             except Exception as e:
                 if return_exceptions:
                     return cast("list[str]", [e for _ in inputs])
-                else:
                 raise
         else:
             batches = [
@@ -973,14 +968,13 @@ class BaseLLM(BaseLanguageModel[str], ABC):
                     callback_managers, prompts, run_name_list, run_ids_list
                 )
             ]
-            output = self._generate_helper(
+            return self._generate_helper(
                 prompts,
                 stop,
                 run_managers,
                 new_arg_supported=bool(new_arg_supported),
                 **kwargs,
             )
-            return output
         if len(missing_prompts) > 0:
             run_managers = [
                 callback_managers[idx].on_llm_start(
@@ -1232,14 +1226,13 @@ class BaseLLM(BaseLanguageModel[str], ABC):
                 ]
             )
             run_managers = [r[0] for r in run_managers]  # type: ignore[misc]
-            output = await self._agenerate_helper(
+            return await self._agenerate_helper(
                 prompts,
                 stop,
                 run_managers,  # type: ignore[arg-type]
                 new_arg_supported=bool(new_arg_supported),
                 **kwargs,  # type: ignore[arg-type]
             )
-            return output
         if len(missing_prompts) > 0:
             run_managers = await asyncio.gather(
                 *[

libs/core/langchain_core/load/dump.py

@@ -19,7 +19,6 @@ def default(obj: Any) -> Any:
     """
     if isinstance(obj, Serializable):
         return obj.to_json()
-    else:
     return to_json_not_implemented(obj)
 
 
@@ -36,7 +35,6 @@ def _dump_pydantic_models(obj: Any) -> Any:
         obj_copy = obj.model_copy(deep=True)
         obj_copy.message.additional_kwargs["parsed"] = parsed.model_dump()
         return obj_copy
-    else:
     return obj
 
 
@@ -64,13 +62,11 @@ def dumps(obj: Any, *, pretty: bool = False, **kwargs: Any) -> str:
         if pretty:
             indent = kwargs.pop("indent", 2)
             return json.dumps(obj, default=default, indent=indent, **kwargs)
-        else:
         return json.dumps(obj, default=default, **kwargs)
     except TypeError:
         if pretty:
             indent = kwargs.pop("indent", 2)
             return json.dumps(to_json_not_implemented(obj), indent=indent, **kwargs)
-        else:
         return json.dumps(to_json_not_implemented(obj), **kwargs)
 
 

libs/core/langchain_core/load/load.py

@@ -98,7 +98,6 @@ class Reviver:
             [key] = value["id"]
             if key in self.secrets_map:
                 return self.secrets_map[key]
-            else:
             if self.secrets_from_env and key in os.environ and os.environ[key]:
                 return os.environ[key]
             return None
@@ -130,7 +129,7 @@ class Reviver:
                 msg = f"Invalid namespace: {value}"
                 raise ValueError(msg)
             # Has explicit import path.
-            elif mapping_key in self.import_mappings:
+            if mapping_key in self.import_mappings:
                 import_path = self.import_mappings[mapping_key]
                 # Split into module and name
                 import_dir, name = import_path[:-1], import_path[-1]

libs/core/langchain_core/messages/ai.py

@@ -372,7 +372,7 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
     def __add__(self, other: Any) -> BaseMessageChunk:  # type: ignore
         if isinstance(other, AIMessageChunk):
             return add_ai_message_chunks(self, other)
-        elif isinstance(other, (list, tuple)) and all(
+        if isinstance(other, (list, tuple)) and all(
             isinstance(o, AIMessageChunk) for o in other
         ):
             return add_ai_message_chunks(self, *other)

libs/core/langchain_core/messages/base.py

@@ -65,7 +65,6 @@ class BaseMessage(Serializable):
         """Coerce the id field to a string."""
         if id_value is not None:
             return str(id_value)
-        else:
         return id_value
 
     def __init__(
@@ -225,7 +224,7 @@ class BaseMessageChunk(BaseMessage):
                     self.response_metadata, other.response_metadata
                 ),
             )
-        elif isinstance(other, list) and all(
+        if isinstance(other, list) and all(
             isinstance(o, BaseMessageChunk) for o in other
         ):
             content = merge_content(self.content, *(o.content for o in other))
@@ -241,7 +240,6 @@ class BaseMessageChunk(BaseMessage):
                 additional_kwargs=additional_kwargs,
                 response_metadata=response_metadata,
             )
-        else:
         msg = (
             'unsupported operand type(s) for +: "'
             f"{self.__class__.__name__}"

libs/core/langchain_core/messages/chat.py

@@ -53,7 +53,7 @@ class ChatMessageChunk(ChatMessage, BaseMessageChunk):
                 ),
                 id=self.id,
             )
-        elif isinstance(other, BaseMessageChunk):
+        if isinstance(other, BaseMessageChunk):
             return self.__class__(
                 role=self.role,
                 content=merge_content(self.content, other.content),
@@ -65,5 +65,4 @@ class ChatMessageChunk(ChatMessage, BaseMessageChunk):
                 ),
                 id=self.id,
             )
-        else:
         return super().__add__(other)

libs/core/langchain_core/messages/tool.py

@@ -320,7 +320,6 @@ def default_tool_parser(
     for raw_tool_call in raw_tool_calls:
         if "function" not in raw_tool_call:
             continue
-        else:
         function_name = raw_tool_call["function"]["name"]
         try:
             function_args = json.loads(raw_tool_call["function"]["arguments"])

libs/core/langchain_core/messages/utils.py

@@ -51,9 +51,8 @@ def _get_type(v: Any) -> str:
     """Get the type associated with the object for serialization purposes."""
     if isinstance(v, dict) and "type" in v:
         return v["type"]
-    elif hasattr(v, "type"):
+    if hasattr(v, "type"):
         return v.type
-    else:
     msg = (
         f"Expected either a dictionary with a 'type' key or an object "
         f"with a 'type' attribute. Instead got type {type(v)}."
@@ -138,31 +137,30 @@ def _message_from_dict(message: dict) -> BaseMessage:
     _type = message["type"]
     if _type == "human":
         return HumanMessage(**message["data"])
-    elif _type == "ai":
+    if _type == "ai":
         return AIMessage(**message["data"])
-    elif _type == "system":
+    if _type == "system":
         return SystemMessage(**message["data"])
-    elif _type == "chat":
+    if _type == "chat":
         return ChatMessage(**message["data"])
-    elif _type == "function":
+    if _type == "function":
         return FunctionMessage(**message["data"])
-    elif _type == "tool":
+    if _type == "tool":
         return ToolMessage(**message["data"])
-    elif _type == "remove":
+    if _type == "remove":
         return RemoveMessage(**message["data"])
-    elif _type == "AIMessageChunk":
+    if _type == "AIMessageChunk":
         return AIMessageChunk(**message["data"])
-    elif _type == "HumanMessageChunk":
+    if _type == "HumanMessageChunk":
         return HumanMessageChunk(**message["data"])
-    elif _type == "FunctionMessageChunk":
+    if _type == "FunctionMessageChunk":
         return FunctionMessageChunk(**message["data"])
-    elif _type == "ToolMessageChunk":
+    if _type == "ToolMessageChunk":
         return ToolMessageChunk(**message["data"])
-    elif _type == "SystemMessageChunk":
+    if _type == "SystemMessageChunk":
         return SystemMessageChunk(**message["data"])
-    elif _type == "ChatMessageChunk":
+    if _type == "ChatMessageChunk":
         return ChatMessageChunk(**message["data"])
-    else:
     msg = f"Got unexpected message type: {_type}"
     raise ValueError(msg)
 
@@ -387,7 +385,6 @@ def _runnable_support(func: Callable) -> Callable:
 
         if messages is not None:
             return func(messages, **kwargs)
-        else:
         return RunnableLambda(partial(func, **kwargs), name=func.__name__)
 
     wrapped.__doc__ = func.__doc__
@@ -472,8 +469,6 @@ def filter_messages(
             or (exclude_ids and msg.id in exclude_ids)
         ):
             continue
-        else:
-            pass
 
         if exclude_tool_calls is True and (
             (isinstance(msg, AIMessage) and msg.tool_calls)
@@ -926,7 +921,7 @@ def trim_messages(
             partial_strategy="first" if allow_partial else None,
             end_on=end_on,
         )
-    elif strategy == "last":
+    if strategy == "last":
         return _last_max_tokens(
             messages,
             max_tokens=max_tokens,
@@ -937,7 +932,6 @@ def trim_messages(
             end_on=end_on,
             text_splitter=text_splitter_fn,
         )
-    else:
     msg = f"Unrecognized {strategy=}. Supported strategies are 'last' and 'first'."
     raise ValueError(msg)
 
@@ -1269,7 +1263,6 @@ def convert_to_openai_messages(
 
     if is_single:
         return oai_messages[0]
-    else:
     return oai_messages
 
 
@@ -1347,7 +1340,7 @@ def _first_max_tokens(
                     if isinstance(block, str):
                         text = block
                         break
-                    elif isinstance(block, dict) and block.get("type") == "text":
+                    if isinstance(block, dict) and block.get("type") == "text":
                         text = block.get("text")
                         break
 
@@ -1517,17 +1510,16 @@ def _bytes_to_b64_str(bytes_: bytes) -> str:
 def _get_message_openai_role(message: BaseMessage) -> str:
     if isinstance(message, AIMessage):
         return "assistant"
-    elif isinstance(message, HumanMessage):
+    if isinstance(message, HumanMessage):
         return "user"
-    elif isinstance(message, ToolMessage):
+    if isinstance(message, ToolMessage):
         return "tool"
-    elif isinstance(message, SystemMessage):
+    if isinstance(message, SystemMessage):
         return message.additional_kwargs.get("__openai_role__", "system")
-    elif isinstance(message, FunctionMessage):
+    if isinstance(message, FunctionMessage):
         return "function"
-    elif isinstance(message, ChatMessage):
+    if isinstance(message, ChatMessage):
         return message.role
-    else:
     msg = f"Unknown BaseMessage type {message.__class__}."
     raise ValueError(msg)  # noqa: TRY004
 

libs/core/langchain_core/output_parsers/base.py

@@ -97,7 +97,6 @@ class BaseGenerationOutputParser(
                 config,
                 run_type="parser",
             )
-        else:
         return self._call_with_config(
             lambda inner_input: self.parse_result([Generation(text=inner_input)]),
             input,
@@ -121,7 +120,6 @@ class BaseGenerationOutputParser(
                 config,
                 run_type="parser",
             )
-        else:
         return await self._acall_with_config(
             lambda inner_input: self.aparse_result([Generation(text=inner_input)]),
             input,
@@ -203,7 +201,6 @@ class BaseOutputParser(
                 config,
                 run_type="parser",
             )
-        else:
         return self._call_with_config(
             lambda inner_input: self.parse_result([Generation(text=inner_input)]),
             input,
@@ -227,7 +224,6 @@ class BaseOutputParser(
                 config,
                 run_type="parser",
             )
-        else:
         return await self._acall_with_config(
             lambda inner_input: self.aparse_result([Generation(text=inner_input)]),
             input,

libs/core/langchain_core/output_parsers/json.py

@@ -53,8 +53,9 @@ class JsonOutputParser(BaseCumulativeTransformOutputParser[Any]):
     def _get_schema(self, pydantic_object: type[TBaseModel]) -> dict[str, Any]:
         if issubclass(pydantic_object, pydantic.BaseModel):
             return pydantic_object.model_json_schema()
-        elif issubclass(pydantic_object, pydantic.v1.BaseModel):
+        if issubclass(pydantic_object, pydantic.v1.BaseModel):
             return pydantic_object.schema()
+        return None
 
     def parse_result(self, result: list[Generation], *, partial: bool = False) -> Any:
         """Parse the result of an LLM call to a JSON object.
@@ -106,7 +107,6 @@ class JsonOutputParser(BaseCumulativeTransformOutputParser[Any]):
         """
         if self.pydantic_object is None:
             return "Return a JSON object."
-        else:
         # Copy schema to avoid altering original Pydantic schema.
         schema = dict(self._get_schema(self.pydantic_object).items())
 

libs/core/langchain_core/output_parsers/openai_functions.py

@@ -99,7 +99,6 @@ class JsonOutputFunctionsParser(BaseCumulativeTransformOutputParser[Any]):
         except KeyError as exc:
             if partial:
                 return None
-            else:
             msg = f"Could not parse function call: {exc}"
             raise OutputParserException(msg) from exc
         try:
@@ -109,7 +108,6 @@ class JsonOutputFunctionsParser(BaseCumulativeTransformOutputParser[Any]):
                         return parse_partial_json(
                             function_call["arguments"], strict=self.strict
                         )
-                    else:
                     return {
                         **function_call,
                         "arguments": parse_partial_json(

libs/core/langchain_core/output_parsers/openai_tools.py

@@ -241,9 +241,8 @@ class JsonOutputKeyToolsParser(JsonOutputToolsParser):
             )
             if self.return_id:
                 return single_result
-            elif single_result:
+            if single_result:
                 return single_result["args"]
-            else:
             return None
         parsed_result = [res for res in parsed_result if res["type"] == self.key_name]
         if not self.return_id:
@@ -300,5 +299,4 @@ class PydanticToolsParser(JsonOutputToolsParser):
                 raise
         if self.first_tool_only:
             return pydantic_objects[0] if pydantic_objects else None
-        else:
         return pydantic_objects

libs/core/langchain_core/output_parsers/pydantic.py

@@ -28,9 +28,8 @@ class PydanticOutputParser(JsonOutputParser, Generic[TBaseModel]):
             try:
                 if issubclass(self.pydantic_object, pydantic.BaseModel):
                     return self.pydantic_object.model_validate(obj)
-                elif issubclass(self.pydantic_object, pydantic.v1.BaseModel):
+                if issubclass(self.pydantic_object, pydantic.v1.BaseModel):
                     return self.pydantic_object.parse_obj(obj)
-                else:
                 msg = f"Unsupported model version for PydanticOutputParser: \
                             {self.pydantic_object.__class__}"
                 raise OutputParserException(msg)

libs/core/langchain_core/output_parsers/xml.py

@@ -282,5 +282,4 @@ def nested_element(path: list[str], elem: ET.Element) -> Any:
     """
     if len(path) == 0:
         return AddableDict({elem.tag: elem.text})
-    else:
     return AddableDict({path[0]: [nested_element(path[1:], elem)]})

libs/core/langchain_core/outputs/chat_generation.py

@@ -60,13 +60,11 @@ class ChatGeneration(Generation):
                     if isinstance(block, str):
                         text = block
                         break
-                    elif isinstance(block, dict) and "text" in block:
+                    if isinstance(block, dict) and "text" in block:
                         text = block["text"]
                         break
             else:
                 pass
-            else:
-                pass
             self.text = text
         except (KeyError, AttributeError) as e:
             msg = "Error while initializing ChatGeneration"
@@ -104,7 +102,7 @@ class ChatGenerationChunk(ChatGeneration):
                 message=self.message + other.message,
                 generation_info=generation_info or None,
             )
-        elif isinstance(other, list) and all(
+        if isinstance(other, list) and all(
             isinstance(x, ChatGenerationChunk) for x in other
         ):
             generation_info = merge_dicts(
@@ -115,8 +113,5 @@ class ChatGenerationChunk(ChatGeneration):
                 message=self.message + [chunk.message for chunk in other],
                 generation_info=generation_info or None,
             )
-        else:
+        msg = f"unsupported operand type(s) for +: '{type(self)}' and '{type(other)}'"
-            msg = (
-                f"unsupported operand type(s) for +: '{type(self)}' and '{type(other)}'"
-            )
         raise TypeError(msg)

libs/core/langchain_core/outputs/generation.py

@@ -64,8 +64,5 @@ class GenerationChunk(Generation):
                 text=self.text + other.text,
                 generation_info=generation_info or None,
             )
-        else:
+        msg = f"unsupported operand type(s) for +: '{type(self)}' and '{type(other)}'"
-            msg = (
-                f"unsupported operand type(s) for +: '{type(self)}' and '{type(other)}'"
-            )
         raise TypeError(msg)

libs/core/langchain_core/prompts/chat.py

@@ -513,7 +513,7 @@ class _StringImageMessagePromptTemplate(BaseMessagePromptTemplate):
                 partial_variables=partial_variables,
             )
             return cls(prompt=prompt, **kwargs)
-        elif isinstance(template, list):
+        if isinstance(template, list):
             if (partial_variables is not None) and len(partial_variables) > 0:
                 msg = "Partial variables are not supported for list of templates."
                 raise ValueError(msg)
@@ -571,7 +571,6 @@ class _StringImageMessagePromptTemplate(BaseMessagePromptTemplate):
                     msg = f"Invalid template: {tmpl}"
                     raise ValueError(msg)
             return cls(prompt=prompt, **kwargs)
-        else:
         msg = f"Invalid template: {template}"
         raise ValueError(msg)  # noqa: TRY004
 
@@ -625,8 +624,7 @@ class _StringImageMessagePromptTemplate(BaseMessagePromptTemplate):
             List of input variable names.
         """
         prompts = self.prompt if isinstance(self.prompt, list) else [self.prompt]
-        input_variables = [iv for prompt in prompts for iv in prompt.input_variables]
+        return [iv for prompt in prompts for iv in prompt.input_variables]
-        return input_variables
 
     def format(self, **kwargs: Any) -> BaseMessage:
         """Format the prompt template.
@@ -642,7 +640,6 @@ class _StringImageMessagePromptTemplate(BaseMessagePromptTemplate):
             return self._msg_class(
                 content=text, additional_kwargs=self.additional_kwargs
             )
-        else:
         content: list = []
         for prompt in self.prompt:
             inputs = {var: kwargs[var] for var in prompt.input_variables}
@@ -670,7 +667,6 @@ class _StringImageMessagePromptTemplate(BaseMessagePromptTemplate):
             return self._msg_class(
                 content=text, additional_kwargs=self.additional_kwargs
             )
-        else:
         content: list = []
         for prompt in self.prompt:
             inputs = {var: kwargs[var] for var in prompt.input_variables}
@@ -1034,23 +1030,22 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
             return ChatPromptTemplate(messages=self.messages + other.messages).partial(
                 **partials
             )  # type: ignore[call-arg]
-        elif isinstance(
+        if isinstance(
             other, (BaseMessagePromptTemplate, BaseMessage, BaseChatPromptTemplate)
         ):
             return ChatPromptTemplate(messages=self.messages + [other]).partial(
                 **partials
             )  # type: ignore[call-arg]
-        elif isinstance(other, (list, tuple)):
+        if isinstance(other, (list, tuple)):
             _other = ChatPromptTemplate.from_messages(other)
             return ChatPromptTemplate(messages=self.messages + _other.messages).partial(
                 **partials
             )  # type: ignore[call-arg]
-        elif isinstance(other, str):
+        if isinstance(other, str):
             prompt = HumanMessagePromptTemplate.from_template(other)
             return ChatPromptTemplate(messages=self.messages + [prompt]).partial(
                 **partials
             )  # type: ignore[call-arg]
-        else:
         msg = f"Unsupported operand type for +: {type(other)}"
         raise NotImplementedError(msg)
 
@@ -1322,7 +1317,6 @@ class ChatPromptTemplate(BaseChatPromptTemplate):
             start, stop, step = index.indices(len(self.messages))
             messages = self.messages[start:stop:step]
             return ChatPromptTemplate.from_messages(messages)
-        else:
         return self.messages[index]
 
     def __len__(self) -> int:

libs/core/langchain_core/prompts/few_shot.py

@@ -88,9 +88,8 @@ class _FewShotPromptTemplateMixin(BaseModel):
         """
         if self.examples is not None:
             return self.examples
-        elif self.example_selector is not None:
+        if self.example_selector is not None:
             return self.example_selector.select_examples(kwargs)
-        else:
         msg = "One of 'examples' and 'example_selector' should be provided"
         raise ValueError(msg)
 
@@ -108,9 +107,8 @@ class _FewShotPromptTemplateMixin(BaseModel):
         """
         if self.examples is not None:
             return self.examples
-        elif self.example_selector is not None:
+        if self.example_selector is not None:
             return await self.example_selector.aselect_examples(kwargs)
-        else:
         msg = "One of 'examples' and 'example_selector' should be provided"
         raise ValueError(msg)
 
@@ -394,12 +392,11 @@ class FewShotChatMessagePromptTemplate(
             {k: e[k] for k in self.example_prompt.input_variables} for e in examples
         ]
         # Format the examples.
-        messages = [
+        return [
             message
             for example in examples
             for message in self.example_prompt.format_messages(**example)
         ]
-        return messages
 
     async def aformat_messages(self, **kwargs: Any) -> list[BaseMessage]:
         """Async format kwargs into a list of messages.
@@ -416,12 +413,11 @@ class FewShotChatMessagePromptTemplate(
             {k: e[k] for k in self.example_prompt.input_variables} for e in examples
         ]
         # Format the examples.
-        messages = [
+        return [
             message
             for example in examples
             for message in await self.example_prompt.aformat_messages(**example)
         ]
-        return messages
 
     def format(self, **kwargs: Any) -> str:
         """Format the prompt with inputs generating a string.

libs/core/langchain_core/prompts/few_shot_with_templates.py

@@ -97,17 +97,15 @@ class FewShotPromptWithTemplates(StringPromptTemplate):
     def _get_examples(self, **kwargs: Any) -> list[dict]:
         if self.examples is not None:
             return self.examples
-        elif self.example_selector is not None:
+        if self.example_selector is not None:
             return self.example_selector.select_examples(kwargs)
-        else:
         raise ValueError
 
     async def _aget_examples(self, **kwargs: Any) -> list[dict]:
         if self.examples is not None:
             return self.examples
-        elif self.example_selector is not None:
+        if self.example_selector is not None:
             return await self.example_selector.aselect_examples(kwargs)
-        else:
         raise ValueError
 
     def format(self, **kwargs: Any) -> str:

libs/core/langchain_core/prompts/image.py

@@ -110,10 +110,9 @@ class ImagePromptTemplate(BasePromptTemplate[ImageURL]):
         if not url:
             msg = "Must provide url."
             raise ValueError(msg)
-        elif not isinstance(url, str):
+        if not isinstance(url, str):
             msg = "url must be a string."
-            raise ValueError(msg)
+            raise ValueError(msg)  # noqa: TRY004
-        else:
         output: ImageURL = {"url": url}
         if detail:
             # Don't check literal values here: let the API check them

libs/core/langchain_core/prompts/prompt.py

@@ -154,7 +154,6 @@ class PromptTemplate(StringPromptTemplate):
                 if k in partial_variables:
                     msg = "Cannot have same variable partialed twice."
                     raise ValueError(msg)
-                else:
                 partial_variables[k] = v
             return PromptTemplate(
                 template=template,
@@ -163,10 +162,9 @@ class PromptTemplate(StringPromptTemplate):
                 template_format="f-string",
                 validate_template=validate_template,
             )
-        elif isinstance(other, str):
+        if isinstance(other, str):
             prompt = PromptTemplate.from_template(other)
             return self + prompt
-        else:
         msg = f"Unsupported operand type for +: {type(other)}"
         raise NotImplementedError(msg)
 

libs/core/langchain_core/prompts/string.py

@@ -100,8 +100,7 @@ def _get_jinja2_variables_from_template(template: str) -> set[str]:
     # noqa for insecure warning elsewhere
     env = Environment()  # noqa: S701
     ast = env.parse(template)
-    variables = meta.find_undeclared_variables(ast)
+    return meta.find_undeclared_variables(ast)
-    return variables
 
 
 def mustache_formatter(template: str, /, **kwargs: Any) -> str:

libs/core/langchain_core/prompts/structured.py

@@ -166,6 +166,5 @@ class StructuredPrompt(ChatPromptTemplate):
                 *others[1:],
                 name=name,
             )
-        else:
         msg = "Structured prompts need to be piped to a language model."
         raise NotImplementedError(msg)

libs/core/langchain_core/retrievers.py

@@ -208,8 +208,7 @@ class BaseRetriever(RunnableSerializable[RetrieverInput, RetrieverOutput], ABC):
             default_retriever_name = default_retriever_name[:-9]
         default_retriever_name = default_retriever_name.lower()
 
-        ls_params = LangSmithRetrieverParams(ls_retriever_name=default_retriever_name)
+        return LangSmithRetrieverParams(ls_retriever_name=default_retriever_name)
-        return ls_params
 
     def invoke(
         self, input: str, config: Optional[RunnableConfig] = None, **kwargs: Any

libs/core/langchain_core/runnables/base.py

@@ -269,9 +269,7 @@ class Runnable(Generic[Input, Output], ABC):
         if suffix:
             if name_[0].isupper():
                 return name_ + suffix.title()
-            else:
             return name_ + "_" + suffix.lower()
-        else:
         return name_
 
     @property
@@ -513,10 +511,9 @@ class Runnable(Generic[Input, Output], ABC):
                 if field_name in [i for i in include if i != "configurable"]
             },
         }
-        model = create_model_v2(  # type: ignore[call-overload]
+        return create_model_v2(  # type: ignore[call-overload]
             self.get_name("Config"), field_definitions=all_fields
         )
-        return model
 
     def get_config_jsonschema(
         self, *, include: Optional[Sequence[str]] = None
@@ -2051,7 +2048,6 @@ class Runnable(Generic[Input, Output], ABC):
                 run_manager.on_chain_error(e)
             if return_exceptions:
                 return cast("list[Output]", [e for _ in input])
-            else:
             raise
         else:
             first_exception: Optional[Exception] = None
@@ -2063,7 +2059,6 @@ class Runnable(Generic[Input, Output], ABC):
                     run_manager.on_chain_end(out)
             if return_exceptions or first_exception is None:
                 return cast("list[Output]", output)
-            else:
             raise first_exception
 
     async def _abatch_with_config(
@@ -2130,7 +2125,6 @@ class Runnable(Generic[Input, Output], ABC):
             )
             if return_exceptions:
                 return cast("list[Output]", [e for _ in input])
-            else:
             raise
         else:
             first_exception: Optional[Exception] = None
@@ -2144,7 +2138,6 @@ class Runnable(Generic[Input, Output], ABC):
             await asyncio.gather(*coros)
             if return_exceptions or first_exception is None:
                 return cast("list[Output]", output)
-            else:
             raise first_exception
 
     def _transform_stream_with_config(
@@ -2615,7 +2608,7 @@ def _seq_input_schema(
     first = steps[0]
     if len(steps) == 1:
         return first.get_input_schema(config)
-    elif isinstance(first, RunnableAssign):
+    if isinstance(first, RunnableAssign):
         next_input_schema = _seq_input_schema(steps[1:], config)
         if not issubclass(next_input_schema, RootModel):
             # it's a dict as expected
@@ -2641,7 +2634,7 @@ def _seq_output_schema(
     last = steps[-1]
     if len(steps) == 1:
         return last.get_input_schema(config)
-    elif isinstance(last, RunnableAssign):
+    if isinstance(last, RunnableAssign):
         mapper_output_schema = last.mapper.get_output_schema(config)
         prev_output_schema = _seq_output_schema(steps[:-1], config)
         if not issubclass(prev_output_schema, RootModel):
@@ -2672,7 +2665,6 @@ def _seq_output_schema(
                         if k in last.keys
                     },
                 )
-            else:
             field = prev_output_schema.model_fields[last.keys]
             return create_model_v2(  # type: ignore[call-overload]
                 "RunnableSequenceOutput", root=(field.annotation, field.default)
@@ -2988,7 +2980,6 @@ class RunnableSequence(RunnableSerializable[Input, Output]):
                 other.last,
                 name=self.name or other.name,
             )
-        else:
         return RunnableSequence(
             self.first,
             *self.middle,
@@ -3017,7 +3008,6 @@ class RunnableSequence(RunnableSerializable[Input, Output]):
                 self.last,
                 name=other.name or self.name,
             )
-        else:
         return RunnableSequence(
             coerce_to_runnable(other),
             self.first,
@@ -3224,7 +3214,6 @@ class RunnableSequence(RunnableSerializable[Input, Output]):
                 rm.on_chain_error(e)
             if return_exceptions:
                 return cast("list[Output]", [e for _ in inputs])
-            else:
             raise
         else:
             first_exception: Optional[Exception] = None
@@ -3236,7 +3225,6 @@ class RunnableSequence(RunnableSerializable[Input, Output]):
                     run_manager.on_chain_end(out)
             if return_exceptions or first_exception is None:
                 return cast("list[Output]", inputs)
-            else:
             raise first_exception
 
     @override
@@ -3357,7 +3345,6 @@ class RunnableSequence(RunnableSerializable[Input, Output]):
             await asyncio.gather(*(rm.on_chain_error(e) for rm in run_managers))
             if return_exceptions:
                 return cast("list[Output]", [e for _ in inputs])
-            else:
             raise
         else:
             first_exception: Optional[Exception] = None
@@ -3371,7 +3358,6 @@ class RunnableSequence(RunnableSerializable[Input, Output]):
             await asyncio.gather(*coros)
             if return_exceptions or first_exception is None:
                 return cast("list[Output]", inputs)
-            else:
             raise first_exception
 
     def _transform(
@@ -3826,7 +3812,6 @@ class RunnableParallel(RunnableSerializable[Input, dict[str, Any]]):
                     return await asyncio.create_task(  # type: ignore
                         step.ainvoke(input, child_config), context=context
                     )
-                else:
                 return await asyncio.create_task(step.ainvoke(input, child_config))
 
         # gather results from all steps
@@ -4141,9 +4126,8 @@ class RunnableGenerator(Runnable[Input, Output]):
             first_param = next(iter(params.values()), None)
             if first_param and first_param.annotation != inspect.Parameter.empty:
                 return getattr(first_param.annotation, "__args__", (Any,))[0]
-            else:
-                return Any
         except ValueError:
+            pass
         return Any
 
     @override
@@ -4220,11 +4204,9 @@ class RunnableGenerator(Runnable[Input, Output]):
         if isinstance(other, RunnableGenerator):
             if hasattr(self, "_transform") and hasattr(other, "_transform"):
                 return self._transform == other._transform
-            elif hasattr(self, "_atransform") and hasattr(other, "_atransform"):
+            if hasattr(self, "_atransform") and hasattr(other, "_atransform"):
                 return self._atransform == other._atransform
-            else:
             return False
-        else:
         return False
 
     @override
@@ -4443,9 +4425,8 @@ class RunnableLambda(Runnable[Input, Output]):
             first_param = next(iter(params.values()), None)
             if first_param and first_param.annotation != inspect.Parameter.empty:
                 return first_param.annotation
-            else:
-                return Any
         except ValueError:
+            pass
         return Any
 
     @override
@@ -4472,7 +4453,6 @@ class RunnableLambda(Runnable[Input, Output]):
                 fields = {item[1:-1]: (Any, ...) for item in items}
                 # It's a dict, lol
                 return create_model_v2(self.get_name("Input"), field_definitions=fields)
-            else:
             module = getattr(func, "__module__", None)
             return create_model_v2(
                 self.get_name("Input"),
@@ -4513,9 +4493,8 @@ class RunnableLambda(Runnable[Input, Output]):
                 ):
                     return getattr(sig.return_annotation, "__args__", (Any,))[0]
                 return sig.return_annotation
-            else:
-                return Any
         except ValueError:
+            pass
         return Any
 
     @override
@@ -4607,11 +4586,9 @@ class RunnableLambda(Runnable[Input, Output]):
         if isinstance(other, RunnableLambda):
             if hasattr(self, "func") and hasattr(other, "func"):
                 return self.func == other.func
-            elif hasattr(self, "afunc") and hasattr(other, "afunc"):
+            if hasattr(self, "afunc") and hasattr(other, "afunc"):
                 return self.afunc == other.afunc
-            else:
             return False
-        else:
         return False
 
     def __repr__(self) -> str:
@@ -4806,11 +4783,7 @@ class RunnableLambda(Runnable[Input, Output]):
                 self._config(config, self.func),
                 **kwargs,
             )
-        else:
+        msg = "Cannot invoke a coroutine function synchronously.Use `ainvoke` instead."
-            msg = (
-                "Cannot invoke a coroutine function synchronously."
-                "Use `ainvoke` instead."
-            )
         raise TypeError(msg)
 
     @override
@@ -5886,7 +5859,7 @@ class RunnableBinding(RunnableBindingBase[Input, Output]):
                     )
 
                 return wrapper
-            elif config_param.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD:
+            if config_param.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD:
                 idx = list(inspect.signature(attr).parameters).index("config")
 
                 @wraps(attr)
@@ -5895,12 +5868,9 @@ class RunnableBinding(RunnableBindingBase[Input, Output]):
                         argsl = list(args)
                         argsl[idx] = merge_configs(self.config, argsl[idx])
                         return attr(*argsl, **kwargs)
-                    else:
                     return attr(
                         *args,
-                            config=merge_configs(
+                        config=merge_configs(self.config, kwargs.pop("config", None)),
-                                self.config, kwargs.pop("config", None)
-                            ),
                         **kwargs,
                     )
 
@@ -5957,13 +5927,12 @@ def coerce_to_runnable(thing: RunnableLike) -> Runnable[Input, Output]:
     """
     if isinstance(thing, Runnable):
         return thing
-    elif is_async_generator(thing) or inspect.isgeneratorfunction(thing):
+    if is_async_generator(thing) or inspect.isgeneratorfunction(thing):
         return RunnableGenerator(thing)
-    elif callable(thing):
+    if callable(thing):
         return RunnableLambda(cast("Callable[[Input], Output]", thing))
-    elif isinstance(thing, dict):
+    if isinstance(thing, dict):
         return cast("Runnable[Input, Output]", RunnableParallel(thing))
-    else:
     msg = (
         f"Expected a Runnable, callable or dict."
         f"Instead got an unsupported type: {type(thing)}"

libs/core/langchain_core/runnables/configurable.py

@@ -314,7 +314,6 @@ class DynamicRunnable(RunnableSerializable[Input, Output]):
 
             return wrapper
 
-        else:
         return attr
 
 
@@ -462,7 +461,6 @@ class RunnableConfigurableFields(DynamicRunnable[Input, Output]):
                 self.default.__class__(**{**init_params, **configurable}),
                 config,
             )
-        else:
         return (self.default, config)
 
 
@@ -638,13 +636,11 @@ class RunnableConfigurableAlternatives(DynamicRunnable[Input, Output]):
         # return the chosen alternative
         if which == self.default_key:
             return (self.default, config)
-        elif which in self.alternatives:
+        if which in self.alternatives:
             alt = self.alternatives[which]
             if isinstance(alt, Runnable):
                 return (alt, config)
-            else:
             return (alt(), config)
-        else:
         msg = f"Unknown alternative: {which}"
         raise ValueError(msg)
 
@@ -714,7 +710,6 @@ def make_options_spec(
             default=spec.default,
             is_shared=spec.is_shared,
         )
-    else:
     return ConfigurableFieldSpec(
         id=spec.id,
         name=spec.name,

libs/core/langchain_core/runnables/fallbacks.py

@@ -661,7 +661,6 @@ def _is_runnable_type(type_: Any) -> bool:
     origin = getattr(type_, "__origin__", None)
     if inspect.isclass(origin):
         return issubclass(origin, Runnable)
-    elif origin is typing.Union:
+    if origin is typing.Union:
         return all(_is_runnable_type(t) for t in type_.__args__)
-    else:
     return False

libs/core/langchain_core/runnables/graph.py

@@ -195,10 +195,7 @@ def node_data_str(id: str, data: Union[type[BaseModel], RunnableType]) -> str:
 
     if not is_uuid(id):
         return id
-    elif isinstance(data, Runnable):
+    data_str = data.get_name() if isinstance(data, Runnable) else data.__name__
-        data_str = data.get_name()
-    else:
-        data_str = data.__name__
     return data_str if not data_str.startswith("Runnable") else data_str[8:]
 
 
@@ -449,7 +446,6 @@ class Graph:
             label = unique_labels[node_id]
             if is_uuid(node_id):
                 return label
-            else:
             return node_id
 
         return Graph(

libs/core/langchain_core/runnables/graph_mermaid.py

@@ -407,7 +407,6 @@ def _render_mermaid_using_api(
             Path(output_file_path).write_bytes(response.content)
 
         return img_bytes
-    else:
     msg = (
         f"Failed to render the graph using the Mermaid.INK API. "
         f"Status code: {response.status_code}."

libs/core/langchain_core/runnables/history.py

@@ -398,8 +398,7 @@ class RunnableWithMessageHistory(RunnableBindingBase):
     @property
     @override
     def OutputType(self) -> type[Output]:
-        output_type = self._history_chain.OutputType
+        return self._history_chain.OutputType
-        return output_type
 
     def get_output_schema(
         self, config: Optional[RunnableConfig] = None
@@ -460,10 +459,10 @@ class RunnableWithMessageHistory(RunnableBindingBase):
 
             return [HumanMessage(content=input_val)]
         # If value is a single message, convert to a list
-        elif isinstance(input_val, BaseMessage):
+        if isinstance(input_val, BaseMessage):
             return [input_val]
         # If value is a list or tuple...
-        elif isinstance(input_val, (list, tuple)):
+        if isinstance(input_val, (list, tuple)):
             # Handle empty case
             if len(input_val) == 0:
                 return list(input_val)
@@ -475,7 +474,6 @@ class RunnableWithMessageHistory(RunnableBindingBase):
                     raise ValueError(msg)
                 return input_val[0]
             return list(input_val)
-        else:
         msg = (
             f"Expected str, BaseMessage, List[BaseMessage], or Tuple[BaseMessage]. "
             f"Got {input_val}."
@@ -507,11 +505,10 @@ class RunnableWithMessageHistory(RunnableBindingBase):
 
             return [AIMessage(content=output_val)]
         # If value is a single message, convert to a list
-        elif isinstance(output_val, BaseMessage):
+        if isinstance(output_val, BaseMessage):
             return [output_val]
-        elif isinstance(output_val, (list, tuple)):
+        if isinstance(output_val, (list, tuple)):
             return list(output_val)
-        else:
         msg = (
             f"Expected str, BaseMessage, List[BaseMessage], or Tuple[BaseMessage]. "
             f"Got {output_val}."

libs/core/langchain_core/runnables/passthrough.py

@@ -459,7 +459,7 @@ class RunnableAssign(RunnableSerializable[dict[str, Any], dict[str, Any]]):
             return create_model_v2(  # type: ignore[call-overload]
                 "RunnableAssignOutput", field_definitions=fields
             )
-        elif not issubclass(map_output_schema, RootModel):
+        if not issubclass(map_output_schema, RootModel):
             # ie. only map output is a dict
             # ie. input type is either unknown or inferred incorrectly
             return map_output_schema
@@ -741,11 +741,9 @@ class RunnablePick(RunnableSerializable[dict[str, Any], dict[str, Any]]):
 
         if isinstance(self.keys, str):
             return input.get(self.keys)
-        else:
         picked = {k: input.get(k) for k in self.keys if k in input}
         if picked:
             return AddableDict(picked)
-            else:
         return None
 
     def _invoke(

libs/core/langchain_core/runnables/utils.py

@@ -440,7 +440,6 @@ def get_function_nonlocals(func: Callable) -> list[Any]:
                     for part in kk.split(".")[1:]:
                         if vv is None:
                             break
-                        else:
                         try:
                             vv = getattr(vv, part)
                         except AttributeError:

libs/core/langchain_core/tools/base.py

@@ -501,7 +501,6 @@ class ChildTool(BaseTool):
             if isinstance(self.args_schema, dict):
                 return super().get_input_schema(config)
             return self.args_schema
-        else:
         return create_schema_from_function(self.name, self._run)
 
     @override
@@ -550,11 +549,10 @@ class ChildTool(BaseTool):
                 else:
                     input_args.parse_obj({key_: tool_input})
             return tool_input
-        else:
         if input_args is not None:
             if isinstance(input_args, dict):
                 return tool_input
-                elif issubclass(input_args, BaseModel):
+            if issubclass(input_args, BaseModel):
                 for k, v in get_all_basemodel_annotations(input_args).items():
                     if (
                         _is_injected_arg_type(v, injected_type=InjectedToolCallId)
@@ -592,14 +590,11 @@ class ChildTool(BaseTool):
                 result_dict = result.dict()
             else:
                 msg = (
-                        "args_schema must be a Pydantic BaseModel, "
+                    f"args_schema must be a Pydantic BaseModel, got {self.args_schema}"
-                        f"got {self.args_schema}"
                 )
                 raise NotImplementedError(msg)
             return {
-                    k: getattr(result, k)
+                k: getattr(result, k) for k, v in result_dict.items() if k in tool_input
-                    for k, v in result_dict.items()
-                    if k in tool_input
             }
         return tool_input
 
@@ -659,14 +654,13 @@ class ChildTool(BaseTool):
         # pass as a positional argument.
         if isinstance(tool_input, str):
             return (tool_input,), {}
-        elif isinstance(tool_input, dict):
+        if isinstance(tool_input, dict):
             # Make a shallow copy of the input to allow downstream code
             # to modify the root level of the input without affecting the
             # original input.
             # This is used by the tool to inject run time information like
             # the callback manager.
             return (), tool_input.copy()
-        else:
         # This code path is not expected to be reachable.
         msg = f"Invalid tool input type: {type(tool_input)}"
         raise TypeError(msg)
@@ -1012,9 +1006,8 @@ def _is_message_content_block(obj: Any) -> bool:
     """Check for OpenAI or Anthropic format tool message content blocks."""
     if isinstance(obj, str):
         return True
-    elif isinstance(obj, dict):
+    if isinstance(obj, dict):
         return obj.get("type", None) in ("text", "image_url", "image", "json")
-    else:
     return False
 
 
@@ -1153,17 +1146,15 @@ def _replace_type_vars(
     if isinstance(type_, TypeVar):
         if type_ in generic_map:
             return generic_map[type_]
-        elif default_to_bound:
+        if default_to_bound:
             return type_.__bound__ or Any
-        else:
         return type_
-    elif (origin := get_origin(type_)) and (args := get_args(type_)):
+    if (origin := get_origin(type_)) and (args := get_args(type_)):
         new_args = tuple(
             _replace_type_vars(arg, generic_map, default_to_bound=default_to_bound)
             for arg in args
         )
         return _py_38_safe_origin(origin)[new_args]  # type: ignore[index]
-    else:
     return type_
 
 

libs/core/langchain_core/tools/convert.py

@@ -310,14 +310,14 @@ def tool(
             msg = "Name must be a string for tool constructor"
             raise ValueError(msg)
         return _create_tool_factory(name_or_callable)(runnable)
-    elif name_or_callable is not None:
+    if name_or_callable is not None:
         if callable(name_or_callable) and hasattr(name_or_callable, "__name__"):
             # Used as a decorator without parameters
             # @tool
             # def my_tool():
             #    pass
             return _create_tool_factory(name_or_callable.__name__)(name_or_callable)
-        elif isinstance(name_or_callable, str):
+        if isinstance(name_or_callable, str):
             # Used with a new name for the tool
             # @tool("search")
             # def my_tool():
@@ -329,13 +329,12 @@ def tool(
             # def my_tool():
             #    pass
             return _create_tool_factory(name_or_callable)
-        else:
         msg = (
             f"The first argument must be a string or a callable with a __name__ "
             f"for tool decorator. Got {type(name_or_callable)}"
         )
         raise ValueError(msg)
-    else:
+
     # Tool is used as a decorator with parameters specified
     # @tool(parse_docstring=True)
     # def my_tool():
@@ -408,7 +407,6 @@ def convert_runnable_to_tool(
             coroutine=runnable.ainvoke,
             description=description,
         )
-    else:
 
     async def ainvoke_wrapper(
         callbacks: Optional[Callbacks] = None, **kwargs: Any

libs/core/langchain_core/tracers/base.py

@@ -183,11 +183,10 @@ class BaseTracer(_TracerCore, BaseCallbackHandler, ABC):
         Returns:
             The run.
         """
-        llm_run = self._llm_run_with_retry_event(
+        return self._llm_run_with_retry_event(
             retry_state=retry_state,
             run_id=run_id,
         )
-        return llm_run
 
     def on_llm_end(self, response: LLMResult, *, run_id: UUID, **kwargs: Any) -> Run:
         """End a trace for an LLM run.

libs/core/langchain_core/tracers/core.py

@@ -335,11 +335,10 @@ class _TracerCore(ABC):
         """Get the inputs for a chain run."""
         if self._schema_format in ("original", "original+chat"):
             return inputs if isinstance(inputs, dict) else {"input": inputs}
-        elif self._schema_format == "streaming_events":
+        if self._schema_format == "streaming_events":
             return {
                 "input": inputs,
             }
-        else:
         msg = f"Invalid format: {self._schema_format}"
         raise ValueError(msg)
 
@@ -347,11 +346,10 @@ class _TracerCore(ABC):
         """Get the outputs for a chain run."""
         if self._schema_format in ("original", "original+chat"):
             return outputs if isinstance(outputs, dict) else {"output": outputs}
-        elif self._schema_format == "streaming_events":
+        if self._schema_format == "streaming_events":
             return {
                 "output": outputs,
             }
-        else:
         msg = f"Invalid format: {self._schema_format}"
         raise ValueError(msg)
 

libs/core/langchain_core/tracers/event_stream.py

@@ -78,7 +78,7 @@ def _assign_name(name: Optional[str], serialized: Optional[dict[str, Any]]) -> s
     if serialized is not None:
         if "name" in serialized:
             return serialized["name"]
-        elif "id" in serialized:
+        if "id" in serialized:
             return serialized["id"][-1]
     return "Unnamed"
 

libs/core/langchain_core/tracers/stdout.py

@@ -91,13 +91,12 @@ class FunctionCallbackHandler(BaseTracer):
             A string with the breadcrumbs of the run.
         """
         parents = self.get_parents(run)[::-1]
-        string = " > ".join(
+        return " > ".join(
             f"{parent.run_type}:{parent.name}"
             if i != len(parents) - 1
             else f"{parent.run_type}:{parent.name}"
             for i, parent in enumerate(parents + [run])
         )
-        return string
 
     # logging methods
     def _on_chain_start(self, run: Run) -> None:

libs/core/langchain_core/utils/_merge.py

@@ -85,7 +85,7 @@ def merge_lists(left: Optional[list], *others: Optional[list]) -> Optional[list]
     for other in others:
         if other is None:
             continue
-        elif merged is None:
+        if merged is None:
             merged = other.copy()
         else:
             for e in other:
@@ -131,21 +131,20 @@ def merge_obj(left: Any, right: Any) -> Any:
     """
     if left is None or right is None:
         return left if left is not None else right
-    elif type(left) is not type(right):
+    if type(left) is not type(right):
         msg = (
             f"left and right are of different types. Left type:  {type(left)}. Right "
             f"type: {type(right)}."
         )
         raise TypeError(msg)
-    elif isinstance(left, str):
+    if isinstance(left, str):
         return left + right
-    elif isinstance(left, dict):
+    if isinstance(left, dict):
         return merge_dicts(left, right)
-    elif isinstance(left, list):
+    if isinstance(left, list):
         return merge_lists(left, right)
-    elif left == right:
+    if left == right:
         return left
-    else:
     msg = (
         f"Unable to merge {left=} and {right=}. Both must be of type str, dict, or "
         f"list, or else be two equal objects."

libs/core/langchain_core/utils/env.py

@@ -72,9 +72,8 @@ def get_from_env(key: str, env_key: str, default: Optional[str] = None) -> str:
     """
     if env_key in os.environ and os.environ[env_key]:
         return os.environ[env_key]
-    elif default is not None:
+    if default is not None:
         return default
-    else:
     msg = (
         f"Did not find {key}, please add an environment variable"
         f" `{env_key}` which contains it, or pass"

libs/core/langchain_core/utils/function_calling.py

@@ -266,9 +266,9 @@ def _convert_any_typed_dicts_to_pydantic(
 
     if type_ in visited:
         return visited[type_]
-    elif depth >= _MAX_TYPED_DICT_RECURSION:
+    if depth >= _MAX_TYPED_DICT_RECURSION:
         return type_
-    elif is_typeddict(type_):
+    if is_typeddict(type_):
         typed_dict = type_
         docstring = inspect.getdoc(typed_dict)
         annotations_ = typed_dict.__annotations__
@@ -292,7 +292,7 @@ def _convert_any_typed_dicts_to_pydantic(
                         f"type {type(field_desc)}."
                     )
                     raise ValueError(msg)
-                elif arg_desc := arg_descriptions.get(arg):
+                if arg_desc := arg_descriptions.get(arg):
                     field_kwargs["description"] = arg_desc
                 else:
                     pass
@@ -309,14 +309,13 @@ def _convert_any_typed_dicts_to_pydantic(
         model.__doc__ = description
         visited[typed_dict] = model
         return model
-    elif (origin := get_origin(type_)) and (type_args := get_args(type_)):
+    if (origin := get_origin(type_)) and (type_args := get_args(type_)):
         subscriptable_origin = _py_38_safe_origin(origin)
         type_args = tuple(
             _convert_any_typed_dicts_to_pydantic(arg, depth=depth + 1, visited=visited)
             for arg in type_args  # type: ignore[index]
         )
         return subscriptable_origin[type_args]  # type: ignore[index]
-    else:
     return type_
 
 
@@ -337,17 +336,15 @@ def _format_tool_to_openai_function(tool: BaseTool) -> FunctionDescription:
             return _convert_json_schema_to_openai_function(
                 tool.tool_call_schema, name=tool.name, description=tool.description
             )
-        elif issubclass(tool.tool_call_schema, (BaseModel, BaseModelV1)):
+        if issubclass(tool.tool_call_schema, (BaseModel, BaseModelV1)):
             return _convert_pydantic_to_openai_function(
                 tool.tool_call_schema, name=tool.name, description=tool.description
             )
-        else:
         error_msg = (
             f"Unsupported tool call schema: {tool.tool_call_schema}. "
             "Tool call schema must be a JSON schema dict or a Pydantic model."
         )
         raise ValueError(error_msg)
-    else:
     return {
         "name": tool.name,
         "description": tool.description,
@@ -730,7 +727,7 @@ def _parse_google_docstring(
             if block.startswith("Args:"):
                 args_block = block
                 break
-            elif block.startswith(("Returns:", "Example:")):
+            if block.startswith(("Returns:", "Example:")):
                 # Don't break in case Args come after
                 past_descriptors = True
             elif not past_descriptors:

libs/core/langchain_core/utils/input.py

@@ -26,8 +26,7 @@ def get_color_mapping(
     colors = list(_TEXT_COLOR_MAPPING.keys())
     if excluded_colors is not None:
         colors = [c for c in colors if c not in excluded_colors]
-    color_mapping = {item: colors[i % len(colors)] for i, item in enumerate(items)}
+    return {item: colors[i % len(colors)] for i, item in enumerate(items)}
-    return color_mapping
 
 
 def get_colored_text(text: str, color: str) -> str:

libs/core/langchain_core/utils/json.py

@@ -30,15 +30,13 @@ def _custom_parser(multiline_string: str) -> str:
     if isinstance(multiline_string, (bytes, bytearray)):
         multiline_string = multiline_string.decode()
 
-    multiline_string = re.sub(
+    return re.sub(
         r'("action_input"\:\s*")(.*?)(")',
         _replace_new_line,
         multiline_string,
         flags=re.DOTALL,
     )
 
-    return multiline_string
-
 
 # Adapted from https://github.com/KillianLucas/open-interpreter/blob/5b6080fae1f8c68938a1e4fa8667e3744084ee21/interpreter/utils/parse_partial_json.py
 # MIT License

libs/core/langchain_core/utils/json_schema.py

@@ -60,12 +60,11 @@ def _dereference_refs_helper(
             else:
                 obj_out[k] = v
         return obj_out
-    elif isinstance(obj, list):
+    if isinstance(obj, list):
         return [
             _dereference_refs_helper(el, full_schema, skip_keys, processed_refs)
             for el in obj
         ]
-    else:
     return obj
 
 

libs/core/langchain_core/utils/mustache.py

@@ -84,7 +84,6 @@ def l_sa_check(template: str, literal: str, is_standalone: bool) -> bool:
         # Then the next tag could be a standalone
         # Otherwise it can't be
         return padding.isspace() or padding == ""
-    else:
     return False
 
 
@@ -107,7 +106,6 @@ def r_sa_check(template: str, tag_type: str, is_standalone: bool) -> bool:
         return on_newline[0].isspace() or not on_newline[0]
 
     # If we're a tag can't be a standalone
-    else:
     return False
 
 

libs/core/langchain_core/utils/pydantic.py

@@ -89,7 +89,7 @@ def is_pydantic_v1_subclass(cls: type) -> bool:
     """Check if the installed Pydantic version is 1.x-like."""
     if PYDANTIC_MAJOR_VERSION == 1:
         return True
-    elif PYDANTIC_MAJOR_VERSION == 2:
+    if PYDANTIC_MAJOR_VERSION == 2:
         from pydantic.v1 import BaseModel as BaseModelV1
 
         if issubclass(cls, BaseModelV1):
@@ -335,7 +335,7 @@ def _create_subset_model(
             descriptions=descriptions,
             fn_description=fn_description,
         )
-    elif PYDANTIC_MAJOR_VERSION == 2:
+    if PYDANTIC_MAJOR_VERSION == 2:
         from pydantic.v1 import BaseModel as BaseModelV1
 
         if issubclass(model, BaseModelV1):
@@ -346,7 +346,6 @@ def _create_subset_model(
                 descriptions=descriptions,
                 fn_description=fn_description,
             )
-        else:
         return _create_subset_model_v2(
             name,
             model,
@@ -354,7 +353,6 @@ def _create_subset_model(
             descriptions=descriptions,
             fn_description=fn_description,
         )
-    else:
     msg = f"Unsupported pydantic version: {PYDANTIC_MAJOR_VERSION}"
     raise NotImplementedError(msg)
 
@@ -387,9 +385,8 @@ if PYDANTIC_MAJOR_VERSION == 2:
         if hasattr(model, "model_fields"):
             return model.model_fields  # type: ignore
 
-        elif hasattr(model, "__fields__"):
+        if hasattr(model, "__fields__"):
             return model.__fields__  # type: ignore
-        else:
         msg = f"Expected a Pydantic model. Got {type(model)}"
         raise TypeError(msg)
 

libs/core/langchain_core/utils/strings.py

@@ -14,11 +14,10 @@ def stringify_value(val: Any) -> str:
     """
     if isinstance(val, str):
         return val
-    elif isinstance(val, dict):
+    if isinstance(val, dict):
         return "\n" + stringify_dict(val)
-    elif isinstance(val, list):
+    if isinstance(val, list):
         return "\n".join(stringify_value(v) for v in val)
-    else:
     return str(val)
 
 

libs/core/langchain_core/utils/utils.py

@@ -392,10 +392,8 @@ def from_env(
 
         if isinstance(default, (str, type(None))):
             return default
-        else:
         if error_message:
             raise ValueError(error_message)
-            else:
         msg = (
             f"Did not find {key}, please add an environment variable"
             f" `{key}` which contains it, or pass"
@@ -454,12 +452,10 @@ def secret_from_env(
             return SecretStr(os.environ[key])
         if isinstance(default, str):
             return SecretStr(default)
-        elif default is None:
+        if default is None:
             return None
-        else:
         if error_message:
             raise ValueError(error_message)
-            else:
         msg = (
             f"Did not find {key}, please add an environment variable"
             f" `{key}` which contains it, or pass"

libs/core/langchain_core/vectorstores/base.py

@@ -340,14 +340,13 @@ class VectorStore(ABC):
         """
         if search_type == "similarity":
             return self.similarity_search(query, **kwargs)
-        elif search_type == "similarity_score_threshold":
+        if search_type == "similarity_score_threshold":
             docs_and_similarities = self.similarity_search_with_relevance_scores(
                 query, **kwargs
             )
             return [doc for doc, _ in docs_and_similarities]
-        elif search_type == "mmr":
+        if search_type == "mmr":
             return self.max_marginal_relevance_search(query, **kwargs)
-        else:
         msg = (
             f"search_type of {search_type} not allowed. Expected "
             "search_type to be 'similarity', 'similarity_score_threshold'"
@@ -375,14 +374,13 @@ class VectorStore(ABC):
         """
         if search_type == "similarity":
             return await self.asimilarity_search(query, **kwargs)
-        elif search_type == "similarity_score_threshold":
+        if search_type == "similarity_score_threshold":
             docs_and_similarities = await self.asimilarity_search_with_relevance_scores(
                 query, **kwargs
             )
             return [doc for doc, _ in docs_and_similarities]
-        elif search_type == "mmr":
+        if search_type == "mmr":
             return await self.amax_marginal_relevance_search(query, **kwargs)
-        else:
         msg = (
             f"search_type of {search_type} not allowed. Expected "
             "search_type to be 'similarity', 'similarity_score_threshold' or 'mmr'."

libs/core/langchain_core/vectorstores/in_memory.py

@@ -431,24 +431,22 @@ class InMemoryVectorStore(VectorStore):
         **kwargs: Any,
     ) -> list[tuple[Document, float]]:
         embedding = self.embedding.embed_query(query)
-        docs = self.similarity_search_with_score_by_vector(
+        return self.similarity_search_with_score_by_vector(
             embedding,
             k,
             **kwargs,
         )
-        return docs
 
     @override
     async def asimilarity_search_with_score(
         self, query: str, k: int = 4, **kwargs: Any
     ) -> list[tuple[Document, float]]:
         embedding = await self.embedding.aembed_query(query)
-        docs = self.similarity_search_with_score_by_vector(
+        return self.similarity_search_with_score_by_vector(
             embedding,
             k,
             **kwargs,
         )
-        return docs
 
     @override
     def similarity_search_by_vector(

libs/core/pyproject.toml

@@ -103,7 +103,6 @@ ignore = [
     "PGH",
     "PLR",
     "PYI",
-    "RET",
     "RUF",
     "SLF",
     "TD",

libs/core/tests/unit_tests/example_selectors/test_length_based_example_selector.py

@@ -17,12 +17,11 @@ EXAMPLES = [
 def selector() -> LengthBasedExampleSelector:
     """Get length based selector to use in tests."""
     prompts = PromptTemplate(input_variables=["question"], template="{question}")
-    selector = LengthBasedExampleSelector(
+    return LengthBasedExampleSelector(
         examples=EXAMPLES,
         example_prompt=prompts,
         max_length=30,
     )
-    return selector
 
 
 def test_selector_valid(selector: LengthBasedExampleSelector) -> None:

libs/core/tests/unit_tests/prompts/test_structured.py

@@ -18,7 +18,6 @@ def _fake_runnable(
 ) -> Union[BaseModel, dict]:
     if isclass(schema) and is_basemodel_subclass(schema):
         return schema(name="yo", value=value)
-    else:
     params = cast("dict", schema)["parameters"]
     return {k: 1 if k != "value" else value for k, v in params.items()}
 

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

@@ -219,7 +219,6 @@ def test_graph_sequence_map(snapshot: SnapshotAssertion) -> None:
     def conditional_str_parser(input: str) -> Runnable:
         if input == "a":
             return str_parser
-        else:
         return xml_parser
 
     sequence: Runnable = (

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

@@ -2954,9 +2954,8 @@ def test_higher_order_lambda_runnable(
     def router(input: dict[str, Any]) -> Runnable:
         if input["key"] == "math":
             return itemgetter("input") | math_chain
-        elif input["key"] == "english":
+        if input["key"] == "english":
             return itemgetter("input") | english_chain
-        else:
         msg = f"Unknown key: {input['key']}"
         raise ValueError(msg)
 
@@ -3011,9 +3010,8 @@ async def test_higher_order_lambda_runnable_async(mocker: MockerFixture) -> None
     def router(input: dict[str, Any]) -> Runnable:
         if input["key"] == "math":
             return itemgetter("input") | math_chain
-        elif input["key"] == "english":
+        if input["key"] == "english":
             return itemgetter("input") | english_chain
-        else:
         msg = f"Unknown key: {input['key']}"
         raise ValueError(msg)
 
@@ -3034,9 +3032,8 @@ async def test_higher_order_lambda_runnable_async(mocker: MockerFixture) -> None
     async def arouter(input: dict[str, Any]) -> Runnable:
         if input["key"] == "math":
             return itemgetter("input") | math_chain
-        elif input["key"] == "english":
+        if input["key"] == "english":
             return itemgetter("input") | english_chain
-        else:
         msg = f"Unknown key: {input['key']}"
         raise ValueError(msg)
 
@@ -3858,7 +3855,6 @@ def test_recursive_lambda() -> None:
     def _simple_recursion(x: int) -> Union[int, Runnable]:
         if x < 10:
             return RunnableLambda(lambda *args: _simple_recursion(x + 1))
-        else:
         return x
 
     runnable = RunnableLambda(_simple_recursion)
@@ -3873,10 +3869,9 @@ def test_retrying(mocker: MockerFixture) -> None:
         if x == 1:
             msg = "x is 1"
             raise ValueError(msg)
-        elif x == 2:
+        if x == 2:
             msg = "x is 2"
             raise RuntimeError(msg)
-        else:
         return x
 
     _lambda_mock = mocker.Mock(side_effect=_lambda)
@@ -3938,10 +3933,9 @@ async def test_async_retrying(mocker: MockerFixture) -> None:
         if x == 1:
             msg = "x is 1"
             raise ValueError(msg)
-        elif x == 2:
+        if x == 2:
             msg = "x is 2"
             raise RuntimeError(msg)
-        else:
         return x
 
     _lambda_mock = mocker.Mock(side_effect=_lambda)

libs/core/tests/unit_tests/runnables/test_runnable_events_v1.py

@@ -545,7 +545,6 @@ async def test_astream_events_from_model() -> None:
     def i_dont_stream(input: Any, config: RunnableConfig) -> Any:
         if sys.version_info >= (3, 11):
             return model.invoke(input)
-        else:
         return model.invoke(input, config)
 
     events = await _collect_events(i_dont_stream.astream_events("hello", version="v1"))
@@ -670,7 +669,6 @@ async def test_astream_events_from_model() -> None:
     async def ai_dont_stream(input: Any, config: RunnableConfig) -> Any:
         if sys.version_info >= (3, 11):
             return await model.ainvoke(input)
-        else:
         return await model.ainvoke(input, config)
 
     events = await _collect_events(ai_dont_stream.astream_events("hello", version="v1"))

libs/core/tests/unit_tests/runnables/test_runnable_events_v2.py

@@ -615,7 +615,6 @@ async def test_astream_with_model_in_chain() -> None:
     def i_dont_stream(input: Any, config: RunnableConfig) -> Any:
         if sys.version_info >= (3, 11):
             return model.invoke(input)
-        else:
         return model.invoke(input, config)
 
     events = await _collect_events(i_dont_stream.astream_events("hello", version="v2"))
@@ -724,7 +723,6 @@ async def test_astream_with_model_in_chain() -> None:
     async def ai_dont_stream(input: Any, config: RunnableConfig) -> Any:
         if sys.version_info >= (3, 11):
             return await model.ainvoke(input)
-        else:
         return await model.ainvoke(input, config)
 
     events = await _collect_events(ai_dont_stream.astream_events("hello", version="v2"))

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

@@ -334,7 +334,6 @@ class TestRunnableSequenceParallelTraceNesting:
                     parent_id_map[n] = matching_post.get("parent_run_id")
                 i += len(name)
                 continue
-            else:
             assert posts[i]["name"] == name
             dotted_order = posts[i]["dotted_order"]
             if prev_dotted_order is not None and not str(

libs/core/tests/unit_tests/test_tools.py

@@ -80,7 +80,6 @@ def _get_tool_call_json_schema(tool: BaseTool) -> dict:
 
     if hasattr(tool_schema, "model_json_schema"):
         return tool_schema.model_json_schema()
-    else:
     return tool_schema.schema()
 
 

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

@@ -599,8 +599,7 @@ def test_tracer_nested_runs_on_error() -> None:
 
 def _get_mock_client() -> Client:
     mock_session = MagicMock()
-    client = Client(session=mock_session, api_key="test")
+    return Client(session=mock_session, api_key="test")
-    return client
 
 
 def test_traceable_to_tracing() -> None: