langchain: Add ruff rule RET (#31875)

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

---------

Co-authored-by: Mason Daugherty <github@mdrxy.com>

libs/langchain/langchain/__init__.py

@@ -48,25 +48,25 @@ def __getattr__(name: str) -> Any:
         _warn_on_import(name, replacement="langchain.agents.MRKLChain")
 
         return MRKLChain
-    elif name == "ReActChain":
+    if name == "ReActChain":
         from langchain.agents import ReActChain
 
         _warn_on_import(name, replacement="langchain.agents.ReActChain")
 
         return ReActChain
-    elif name == "SelfAskWithSearchChain":
+    if name == "SelfAskWithSearchChain":
         from langchain.agents import SelfAskWithSearchChain
 
         _warn_on_import(name, replacement="langchain.agents.SelfAskWithSearchChain")
 
         return SelfAskWithSearchChain
-    elif name == "ConversationChain":
+    if name == "ConversationChain":
         from langchain.chains import ConversationChain
 
         _warn_on_import(name, replacement="langchain.chains.ConversationChain")
 
         return ConversationChain
-    elif name == "LLMBashChain":
+    if name == "LLMBashChain":
         msg = (
             "This module has been moved to langchain-experimental. "
             "For more details: "
@@ -77,97 +77,97 @@ def __getattr__(name: str) -> Any:
         )
         raise ImportError(msg)
 
-    elif name == "LLMChain":
+    if name == "LLMChain":
         from langchain.chains import LLMChain
 
         _warn_on_import(name, replacement="langchain.chains.LLMChain")
 
         return LLMChain
-    elif name == "LLMCheckerChain":
+    if name == "LLMCheckerChain":
         from langchain.chains import LLMCheckerChain
 
         _warn_on_import(name, replacement="langchain.chains.LLMCheckerChain")
 
         return LLMCheckerChain
-    elif name == "LLMMathChain":
+    if name == "LLMMathChain":
         from langchain.chains import LLMMathChain
 
         _warn_on_import(name, replacement="langchain.chains.LLMMathChain")
 
         return LLMMathChain
-    elif name == "QAWithSourcesChain":
+    if name == "QAWithSourcesChain":
         from langchain.chains import QAWithSourcesChain
 
         _warn_on_import(name, replacement="langchain.chains.QAWithSourcesChain")
 
         return QAWithSourcesChain
-    elif name == "VectorDBQA":
+    if name == "VectorDBQA":
         from langchain.chains import VectorDBQA
 
         _warn_on_import(name, replacement="langchain.chains.VectorDBQA")
 
         return VectorDBQA
-    elif name == "VectorDBQAWithSourcesChain":
+    if name == "VectorDBQAWithSourcesChain":
         from langchain.chains import VectorDBQAWithSourcesChain
 
         _warn_on_import(name, replacement="langchain.chains.VectorDBQAWithSourcesChain")
 
         return VectorDBQAWithSourcesChain
-    elif name == "InMemoryDocstore":
+    if name == "InMemoryDocstore":
         from langchain_community.docstore import InMemoryDocstore
 
         _warn_on_import(name, replacement="langchain.docstore.InMemoryDocstore")
 
         return InMemoryDocstore
-    elif name == "Wikipedia":
+    if name == "Wikipedia":
         from langchain_community.docstore import Wikipedia
 
         _warn_on_import(name, replacement="langchain.docstore.Wikipedia")
 
         return Wikipedia
-    elif name == "Anthropic":
+    if name == "Anthropic":
         from langchain_community.llms import Anthropic
 
         _warn_on_import(name, replacement="langchain_community.llms.Anthropic")
 
         return Anthropic
-    elif name == "Banana":
+    if name == "Banana":
         from langchain_community.llms import Banana
 
         _warn_on_import(name, replacement="langchain_community.llms.Banana")
 
         return Banana
-    elif name == "CerebriumAI":
+    if name == "CerebriumAI":
         from langchain_community.llms import CerebriumAI
 
         _warn_on_import(name, replacement="langchain_community.llms.CerebriumAI")
 
         return CerebriumAI
-    elif name == "Cohere":
+    if name == "Cohere":
         from langchain_community.llms import Cohere
 
         _warn_on_import(name, replacement="langchain_community.llms.Cohere")
 
         return Cohere
-    elif name == "ForefrontAI":
+    if name == "ForefrontAI":
         from langchain_community.llms import ForefrontAI
 
         _warn_on_import(name, replacement="langchain_community.llms.ForefrontAI")
 
         return ForefrontAI
-    elif name == "GooseAI":
+    if name == "GooseAI":
         from langchain_community.llms import GooseAI
 
         _warn_on_import(name, replacement="langchain_community.llms.GooseAI")
 
         return GooseAI
-    elif name == "HuggingFaceHub":
+    if name == "HuggingFaceHub":
         from langchain_community.llms import HuggingFaceHub
 
         _warn_on_import(name, replacement="langchain_community.llms.HuggingFaceHub")
 
         return HuggingFaceHub
-    elif name == "HuggingFaceTextGenInference":
+    if name == "HuggingFaceTextGenInference":
         from langchain_community.llms import HuggingFaceTextGenInference
 
         _warn_on_import(
@@ -175,55 +175,55 @@ def __getattr__(name: str) -> Any:
         )
 
         return HuggingFaceTextGenInference
-    elif name == "LlamaCpp":
+    if name == "LlamaCpp":
         from langchain_community.llms import LlamaCpp
 
         _warn_on_import(name, replacement="langchain_community.llms.LlamaCpp")
 
         return LlamaCpp
-    elif name == "Modal":
+    if name == "Modal":
         from langchain_community.llms import Modal
 
         _warn_on_import(name, replacement="langchain_community.llms.Modal")
 
         return Modal
-    elif name == "OpenAI":
+    if name == "OpenAI":
         from langchain_community.llms import OpenAI
 
         _warn_on_import(name, replacement="langchain_community.llms.OpenAI")
 
         return OpenAI
-    elif name == "Petals":
+    if name == "Petals":
         from langchain_community.llms import Petals
 
         _warn_on_import(name, replacement="langchain_community.llms.Petals")
 
         return Petals
-    elif name == "PipelineAI":
+    if name == "PipelineAI":
         from langchain_community.llms import PipelineAI
 
         _warn_on_import(name, replacement="langchain_community.llms.PipelineAI")
 
         return PipelineAI
-    elif name == "SagemakerEndpoint":
+    if name == "SagemakerEndpoint":
         from langchain_community.llms import SagemakerEndpoint
 
         _warn_on_import(name, replacement="langchain_community.llms.SagemakerEndpoint")
 
         return SagemakerEndpoint
-    elif name == "StochasticAI":
+    if name == "StochasticAI":
         from langchain_community.llms import StochasticAI
 
         _warn_on_import(name, replacement="langchain_community.llms.StochasticAI")
 
         return StochasticAI
-    elif name == "Writer":
+    if name == "Writer":
         from langchain_community.llms import Writer
 
         _warn_on_import(name, replacement="langchain_community.llms.Writer")
 
         return Writer
-    elif name == "HuggingFacePipeline":
+    if name == "HuggingFacePipeline":
         from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
 
         _warn_on_import(
@@ -232,7 +232,7 @@ def __getattr__(name: str) -> Any:
         )
 
         return HuggingFacePipeline
-    elif name == "FewShotPromptTemplate":
+    if name == "FewShotPromptTemplate":
         from langchain_core.prompts import FewShotPromptTemplate
 
         _warn_on_import(
@@ -240,7 +240,7 @@ def __getattr__(name: str) -> Any:
         )
 
         return FewShotPromptTemplate
-    elif name == "Prompt":
+    if name == "Prompt":
         from langchain_core.prompts import PromptTemplate
 
         _warn_on_import(name, replacement="langchain_core.prompts.PromptTemplate")
@@ -248,19 +248,19 @@ def __getattr__(name: str) -> Any:
         # it's renamed as prompt template anyways
         # this is just for backwards compat
         return PromptTemplate
-    elif name == "PromptTemplate":
+    if name == "PromptTemplate":
         from langchain_core.prompts import PromptTemplate
 
         _warn_on_import(name, replacement="langchain_core.prompts.PromptTemplate")
 
         return PromptTemplate
-    elif name == "BasePromptTemplate":
+    if name == "BasePromptTemplate":
         from langchain_core.prompts import BasePromptTemplate
 
         _warn_on_import(name, replacement="langchain_core.prompts.BasePromptTemplate")
 
         return BasePromptTemplate
-    elif name == "ArxivAPIWrapper":
+    if name == "ArxivAPIWrapper":
         from langchain_community.utilities import ArxivAPIWrapper
 
         _warn_on_import(
@@ -268,7 +268,7 @@ def __getattr__(name: str) -> Any:
         )
 
         return ArxivAPIWrapper
-    elif name == "GoldenQueryAPIWrapper":
+    if name == "GoldenQueryAPIWrapper":
         from langchain_community.utilities import GoldenQueryAPIWrapper
 
         _warn_on_import(
@@ -276,7 +276,7 @@ def __getattr__(name: str) -> Any:
         )
 
         return GoldenQueryAPIWrapper
-    elif name == "GoogleSearchAPIWrapper":
+    if name == "GoogleSearchAPIWrapper":
         from langchain_community.utilities import GoogleSearchAPIWrapper
 
         _warn_on_import(
@@ -284,7 +284,7 @@ def __getattr__(name: str) -> Any:
         )
 
         return GoogleSearchAPIWrapper
-    elif name == "GoogleSerperAPIWrapper":
+    if name == "GoogleSerperAPIWrapper":
         from langchain_community.utilities import GoogleSerperAPIWrapper
 
         _warn_on_import(
@@ -292,7 +292,7 @@ def __getattr__(name: str) -> Any:
         )
 
         return GoogleSerperAPIWrapper
-    elif name == "PowerBIDataset":
+    if name == "PowerBIDataset":
         from langchain_community.utilities import PowerBIDataset
 
         _warn_on_import(
@@ -300,7 +300,7 @@ def __getattr__(name: str) -> Any:
         )
 
         return PowerBIDataset
-    elif name == "SearxSearchWrapper":
+    if name == "SearxSearchWrapper":
         from langchain_community.utilities import SearxSearchWrapper
 
         _warn_on_import(
@@ -308,7 +308,7 @@ def __getattr__(name: str) -> Any:
         )
 
         return SearxSearchWrapper
-    elif name == "WikipediaAPIWrapper":
+    if name == "WikipediaAPIWrapper":
         from langchain_community.utilities import WikipediaAPIWrapper
 
         _warn_on_import(
@@ -316,7 +316,7 @@ def __getattr__(name: str) -> Any:
         )
 
         return WikipediaAPIWrapper
-    elif name == "WolframAlphaAPIWrapper":
+    if name == "WolframAlphaAPIWrapper":
         from langchain_community.utilities import WolframAlphaAPIWrapper
 
         _warn_on_import(
@@ -324,19 +324,19 @@ def __getattr__(name: str) -> Any:
         )
 
         return WolframAlphaAPIWrapper
-    elif name == "SQLDatabase":
+    if name == "SQLDatabase":
         from langchain_community.utilities import SQLDatabase
 
         _warn_on_import(name, replacement="langchain_community.utilities.SQLDatabase")
 
         return SQLDatabase
-    elif name == "FAISS":
+    if name == "FAISS":
         from langchain_community.vectorstores import FAISS
 
         _warn_on_import(name, replacement="langchain_community.vectorstores.FAISS")
 
         return FAISS
-    elif name == "ElasticVectorSearch":
+    if name == "ElasticVectorSearch":
         from langchain_community.vectorstores import ElasticVectorSearch
 
         _warn_on_import(
@@ -345,7 +345,7 @@ def __getattr__(name: str) -> Any:
 
         return ElasticVectorSearch
     # For backwards compatibility
-    elif name == "SerpAPIChain" or name == "SerpAPIWrapper":
+    if name == "SerpAPIChain" or name == "SerpAPIWrapper":
         from langchain_community.utilities import SerpAPIWrapper
 
         _warn_on_import(
@@ -353,7 +353,7 @@ def __getattr__(name: str) -> Any:
         )
 
         return SerpAPIWrapper
-    elif name == "verbose":
+    if name == "verbose":
         from langchain.globals import _verbose
 
         _warn_on_import(
@@ -364,7 +364,7 @@ def __getattr__(name: str) -> Any:
         )
 
         return _verbose
-    elif name == "debug":
+    if name == "debug":
         from langchain.globals import _debug
 
         _warn_on_import(
@@ -375,7 +375,7 @@ def __getattr__(name: str) -> Any:
         )
 
         return _debug
-    elif name == "llm_cache":
+    if name == "llm_cache":
         from langchain.globals import _llm_cache
 
         _warn_on_import(
@@ -386,7 +386,6 @@ def __getattr__(name: str) -> Any:
         )
 
         return _llm_cache
-    else:
     msg = f"Could not find: {name}"
     raise AttributeError(msg)
 

libs/langchain/langchain/agents/agent.py

@@ -137,7 +137,6 @@ class BaseSingleActionAgent(BaseModel):
             return AgentFinish(
                 {"output": "Agent stopped due to iteration limit or time limit."}, ""
             )
-        else:
         msg = f"Got unsupported early_stopping_method `{early_stopping_method}`"
         raise ValueError(msg)
 
@@ -308,7 +307,6 @@ class BaseMultiActionAgent(BaseModel):
         if early_stopping_method == "force":
             # `force` just returns a constant string
             return AgentFinish({"output": "Agent stopped due to max iterations."}, "")
-        else:
         msg = f"Got unsupported early_stopping_method `{early_stopping_method}`"
         raise ValueError(msg)
 
@@ -815,8 +813,7 @@ class Agent(BaseSingleActionAgent):
         """
         full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
         full_output = await self.llm_chain.apredict(callbacks=callbacks, **full_inputs)
-        agent_output = await self.output_parser.aparse(full_output)
+        return await self.output_parser.aparse(full_output)
-        return agent_output
 
     def get_full_inputs(
         self, intermediate_steps: list[tuple[AgentAction, str]], **kwargs: Any
@@ -833,8 +830,7 @@ class Agent(BaseSingleActionAgent):
         """
         thoughts = self._construct_scratchpad(intermediate_steps)
         new_inputs = {"agent_scratchpad": thoughts, "stop": self._stop}
-        full_inputs = {**kwargs, **new_inputs}
+        return {**kwargs, **new_inputs}
-        return full_inputs
 
     @property
     def input_keys(self) -> list[str]:
@@ -970,7 +966,7 @@ class Agent(BaseSingleActionAgent):
             return AgentFinish(
                 {"output": "Agent stopped due to iteration limit or time limit."}, ""
             )
-        elif early_stopping_method == "generate":
+        if early_stopping_method == "generate":
             # Generate does one final forward pass
             thoughts = ""
             for action, observation in intermediate_steps:
@@ -990,11 +986,9 @@ class Agent(BaseSingleActionAgent):
             if isinstance(parsed_output, AgentFinish):
                 # If we can extract, we send the correct stuff
                 return parsed_output
-            else:
             # If we can extract, but the tool is not the final tool,
             # we just return the full output
             return AgentFinish({"output": full_output}, full_output)
-        else:
         msg = (
             "early_stopping_method should be one of `force` or `generate`, "
             f"got {early_stopping_method}"
@@ -1179,7 +1173,6 @@ class AgentExecutor(Chain):
         """
         if isinstance(self.agent, Runnable):
             return cast(RunnableAgentType, self.agent)
-        else:
         return self.agent
 
     def save(self, file_path: Union[Path, str]) -> None:
@@ -1249,7 +1242,6 @@ class AgentExecutor(Chain):
         """
         if self.return_intermediate_steps:
             return self._action_agent.return_values + ["intermediate_steps"]
-        else:
         return self._action_agent.return_values
 
     def lookup_tool(self, name: str) -> BaseTool:
@@ -1304,10 +1296,7 @@ class AgentExecutor(Chain):
                 msg = "Expected a single AgentFinish output, but got multiple values."
                 raise ValueError(msg)
             return values[-1]
-        else:
+        return [(a.action, a.observation) for a in values if isinstance(a, AgentStep)]
-            return [
-                (a.action, a.observation) for a in values if isinstance(a, AgentStep)
-            ]
 
     def _take_next_step(
         self,
@@ -1727,9 +1716,8 @@ class AgentExecutor(Chain):
             and self.trim_intermediate_steps > 0
         ):
             return intermediate_steps[-self.trim_intermediate_steps :]
-        elif callable(self.trim_intermediate_steps):
+        if callable(self.trim_intermediate_steps):
             return self.trim_intermediate_steps(intermediate_steps)
-        else:
         return intermediate_steps
 
     @override

libs/langchain/langchain/agents/chat/base.py

@@ -61,7 +61,6 @@ class ChatAgent(Agent):
                 f"(but I haven't seen any of it! I only see what "
                 f"you return as final answer):\n{agent_scratchpad}"
             )
-        else:
         return agent_scratchpad
 
     @classmethod

libs/langchain/langchain/agents/conversational_chat/output_parser.py

@@ -39,11 +39,9 @@ class ConvoOutputParser(AgentOutputParser):
                 # If the action indicates a final answer, return an AgentFinish
                 if action == "Final Answer":
                     return AgentFinish({"output": action_input}, text)
-                else:
                 # Otherwise, return an AgentAction with the specified action and
                 # input
                 return AgentAction(action, action_input, text)
-            else:
             # If the necessary keys aren't present in the response, raise an
             # exception
             msg = f"Missing 'action' or 'action_input' in LLM output: {text}"

libs/langchain/langchain/agents/format_scratchpad/openai_functions.py

@@ -23,7 +23,6 @@ def _convert_agent_action_to_messages(
         return list(agent_action.message_log) + [
             _create_function_message(agent_action, observation)
         ]
-    else:
     return [AIMessage(content=agent_action.log)]
 
 

libs/langchain/langchain/agents/json_chat/base.py

@@ -182,7 +182,7 @@ def create_json_chat_agent(
     else:
         llm_to_use = llm
 
-    agent = (
+    return (
         RunnablePassthrough.assign(
             agent_scratchpad=lambda x: format_log_to_messages(
                 x["intermediate_steps"], template_tool_response=template_tool_response
@@ -192,4 +192,3 @@ def create_json_chat_agent(
         | llm_to_use
         | JSONAgentOutputParser()
     )
-    return agent

libs/langchain/langchain/agents/mrkl/output_parser.py

@@ -55,7 +55,6 @@ class MRKLOutputParser(AgentOutputParser):
                 return AgentFinish(
                     {"output": text[start_index:end_index].strip()}, text[:end_index]
                 )
-            else:
             msg = f"{FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE}: {text}"
             raise OutputParserException(msg)
 
@@ -69,7 +68,7 @@ class MRKLOutputParser(AgentOutputParser):
 
             return AgentAction(action, tool_input, text)
 
-        elif includes_answer:
+        if includes_answer:
             return AgentFinish(
                 {"output": text.split(FINAL_ANSWER_ACTION)[-1].strip()}, text
             )
@@ -82,7 +81,7 @@ class MRKLOutputParser(AgentOutputParser):
                 llm_output=text,
                 send_to_llm=True,
             )
-        elif not re.search(
+        if not re.search(
             r"[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)", text, re.DOTALL
         ):
             msg = f"Could not parse LLM output: `{text}`"
@@ -92,7 +91,6 @@ class MRKLOutputParser(AgentOutputParser):
                 llm_output=text,
                 send_to_llm=True,
             )
-        else:
         msg = f"Could not parse LLM output: `{text}`"
         raise OutputParserException(msg)
 

libs/langchain/langchain/agents/openai_assistant/base.py

@@ -128,7 +128,6 @@ def _get_assistants_tool(
     """
     if _is_assistants_builtin_tool(tool):
         return tool  # type: ignore[return-value]
-    else:
     return convert_to_openai_tool(tool)
 
 
@@ -510,12 +509,11 @@ class OpenAIAssistantRunnable(RunnableSerializable[dict, OutputType]):
             for action, output in intermediate_steps
             if action.tool_call_id in required_tool_call_ids
         ]
-        submit_tool_outputs = {
+        return {
             "tool_outputs": tool_outputs,
             "run_id": last_action.run_id,
             "thread_id": last_action.thread_id,
         }
-        return submit_tool_outputs
 
     def _create_run(self, input_dict: dict) -> Any:
         params = {
@@ -558,12 +556,11 @@ class OpenAIAssistantRunnable(RunnableSerializable[dict, OutputType]):
                 "run_metadata",
             )
         }
-        run = self.client.beta.threads.create_and_run(
+        return self.client.beta.threads.create_and_run(
             assistant_id=self.assistant_id,
             thread=thread,
             **params,
         )
-        return run
 
     def _get_response(self, run: Any) -> Any:
         # TODO: Pagination
@@ -612,7 +609,7 @@ class OpenAIAssistantRunnable(RunnableSerializable[dict, OutputType]):
                 run_id=run.id,
                 thread_id=run.thread_id,
             )
-        elif run.status == "requires_action":
+        if run.status == "requires_action":
             if not self.as_agent:
                 return run.required_action.submit_tool_outputs.tool_calls
             actions = []
@@ -639,7 +636,6 @@ class OpenAIAssistantRunnable(RunnableSerializable[dict, OutputType]):
                     )
                 )
             return actions
-        else:
         run_info = json.dumps(run.dict(), indent=2)
         msg = f"Unexpected run status: {run.status}. Full run info:\n\n{run_info})"
         raise ValueError(msg)
@@ -668,12 +664,11 @@ class OpenAIAssistantRunnable(RunnableSerializable[dict, OutputType]):
             for action, output in intermediate_steps
             if action.tool_call_id in required_tool_call_ids
         ]
-        submit_tool_outputs = {
+        return {
             "tool_outputs": tool_outputs,
             "run_id": last_action.run_id,
             "thread_id": last_action.thread_id,
         }
-        return submit_tool_outputs
 
     async def _acreate_run(self, input_dict: dict) -> Any:
         params = {
@@ -716,12 +711,11 @@ class OpenAIAssistantRunnable(RunnableSerializable[dict, OutputType]):
                 "run_metadata",
             )
         }
-        run = await self.async_client.beta.threads.create_and_run(
+        return await self.async_client.beta.threads.create_and_run(
             assistant_id=self.assistant_id,
             thread=thread,
             **params,
         )
-        return run
 
     async def _aget_response(self, run: Any) -> Any:
         # TODO: Pagination
@@ -766,7 +760,7 @@ class OpenAIAssistantRunnable(RunnableSerializable[dict, OutputType]):
                 run_id=run.id,
                 thread_id=run.thread_id,
             )
-        elif run.status == "requires_action":
+        if run.status == "requires_action":
             if not self.as_agent:
                 return run.required_action.submit_tool_outputs.tool_calls
             actions = []
@@ -793,7 +787,6 @@ class OpenAIAssistantRunnable(RunnableSerializable[dict, OutputType]):
                     )
                 )
             return actions
-        else:
         run_info = json.dumps(run.dict(), indent=2)
         msg = f"Unexpected run status: {run.status}. Full run info:\n\n{run_info})"
         raise ValueError(msg)

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

@@ -132,8 +132,7 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
                 messages,
                 callbacks=callbacks,
             )
-        agent_decision = self.output_parser._parse_ai_message(predicted_message)
+        return self.output_parser._parse_ai_message(predicted_message)
-        return agent_decision
 
     async def aplan(
         self,
@@ -164,8 +163,7 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
         predicted_message = await self.llm.apredict_messages(
             messages, functions=self.functions, callbacks=callbacks
         )
-        agent_decision = self.output_parser._parse_ai_message(predicted_message)
+        return self.output_parser._parse_ai_message(predicted_message)
-        return agent_decision
 
     def return_stopped_response(
         self,
@@ -192,17 +190,15 @@ class OpenAIFunctionsAgent(BaseSingleActionAgent):
             return AgentFinish(
                 {"output": "Agent stopped due to iteration limit or time limit."}, ""
             )
-        elif early_stopping_method == "generate":
+        if early_stopping_method == "generate":
             # Generate does one final forward pass
             agent_decision = self.plan(
                 intermediate_steps, with_functions=False, **kwargs
             )
             if isinstance(agent_decision, AgentFinish):
                 return agent_decision
-            else:
             msg = f"got AgentAction with no functions provided: {agent_decision}"
             raise ValueError(msg)
-        else:
         msg = (
             "early_stopping_method should be one of `force` or `generate`, "
             f"got {early_stopping_method}"
@@ -358,7 +354,7 @@ def create_openai_functions_agent(
         )
         raise ValueError(msg)
     llm_with_tools = llm.bind(functions=[convert_to_openai_function(t) for t in tools])
-    agent = (
+    return (
         RunnablePassthrough.assign(
             agent_scratchpad=lambda x: format_to_openai_function_messages(
                 x["intermediate_steps"]
@@ -368,4 +364,3 @@ def create_openai_functions_agent(
         | llm_with_tools
         | OpenAIFunctionsAgentOutputParser()
     )
-    return agent

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

@@ -224,8 +224,7 @@ class OpenAIMultiFunctionsAgent(BaseMultiActionAgent):
         predicted_message = self.llm.predict_messages(
             messages, functions=self.functions, callbacks=callbacks
         )
-        agent_decision = _parse_ai_message(predicted_message)
+        return _parse_ai_message(predicted_message)
-        return agent_decision
 
     async def aplan(
         self,
@@ -254,8 +253,7 @@ class OpenAIMultiFunctionsAgent(BaseMultiActionAgent):
         predicted_message = await self.llm.apredict_messages(
             messages, functions=self.functions, callbacks=callbacks
         )
-        agent_decision = _parse_ai_message(predicted_message)
+        return _parse_ai_message(predicted_message)
-        return agent_decision
 
     @classmethod
     def create_prompt(

libs/langchain/langchain/agents/openai_tools/base.py

@@ -96,7 +96,7 @@ def create_openai_tools_agent(
         tools=[convert_to_openai_tool(tool, strict=strict) for tool in tools]
     )
 
-    agent = (
+    return (
         RunnablePassthrough.assign(
             agent_scratchpad=lambda x: format_to_openai_tool_messages(
                 x["intermediate_steps"]
@@ -106,4 +106,3 @@ def create_openai_tools_agent(
         | llm_with_tools
         | OpenAIToolsAgentOutputParser()
     )
-    return agent

libs/langchain/langchain/agents/output_parsers/json.py

@@ -49,7 +49,6 @@ class JSONAgentOutputParser(AgentOutputParser):
                 response = response[0]
             if response["action"] == "Final Answer":
                 return AgentFinish({"output": response["action_input"]}, text)
-            else:
             action_input = response.get("action_input", {})
             if action_input is None:
                 action_input = {}

libs/langchain/langchain/agents/output_parsers/react_single_input.py

@@ -65,7 +65,7 @@ class ReActSingleInputOutputParser(AgentOutputParser):
 
             return AgentAction(action, tool_input, text)
 
-        elif includes_answer:
+        if includes_answer:
             return AgentFinish(
                 {"output": text.split(FINAL_ANSWER_ACTION)[-1].strip()}, text
             )
@@ -78,7 +78,7 @@ class ReActSingleInputOutputParser(AgentOutputParser):
                 llm_output=text,
                 send_to_llm=True,
             )
-        elif not re.search(
+        if not re.search(
             r"[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)", text, re.DOTALL
         ):
             msg = f"Could not parse LLM output: `{text}`"
@@ -88,7 +88,6 @@ class ReActSingleInputOutputParser(AgentOutputParser):
                 llm_output=text,
                 send_to_llm=True,
             )
-        else:
         msg = f"Could not parse LLM output: `{text}`"
         raise OutputParserException(msg)
 

libs/langchain/langchain/agents/output_parsers/xml.py

@@ -36,12 +36,11 @@ class XMLAgentOutputParser(AgentOutputParser):
             if "</tool_input>" in _tool_input:
                 _tool_input = _tool_input.split("</tool_input>")[0]
             return AgentAction(tool=_tool, tool_input=_tool_input, log=text)
-        elif "<final_answer>" in text:
+        if "<final_answer>" in text:
             _, answer = text.split("<final_answer>")
             if "</final_answer>" in answer:
                 answer = answer.split("</final_answer>")[0]
             return AgentFinish(return_values={"output": answer}, log=text)
-        else:
         raise ValueError
 
     def get_format_instructions(self) -> str:

libs/langchain/langchain/agents/react/agent.py

@@ -134,7 +134,7 @@ def create_react_agent(
     else:
         llm_with_stop = llm
     output_parser = output_parser or ReActSingleInputOutputParser()
-    agent = (
+    return (
         RunnablePassthrough.assign(
             agent_scratchpad=lambda x: format_log_to_str(x["intermediate_steps"]),
         )
@@ -142,4 +142,3 @@ def create_react_agent(
         | llm_with_stop
         | output_parser
     )
-    return agent

libs/langchain/langchain/agents/react/base.py

@@ -96,7 +96,6 @@ class DocstoreExplorer:
         if isinstance(result, Document):
             self.document = result
             return self._summary
-        else:
         self.document = None
         return result
 
@@ -113,9 +112,8 @@ class DocstoreExplorer:
         lookups = [p for p in self._paragraphs if self.lookup_str in p.lower()]
         if len(lookups) == 0:
             return "No Results"
-        elif self.lookup_index >= len(lookups):
+        if self.lookup_index >= len(lookups):
             return "No More Results"
-        else:
         result_prefix = f"(Result {self.lookup_index + 1}/{len(lookups)})"
         return f"{result_prefix} {lookups[self.lookup_index]}"
 

libs/langchain/langchain/agents/react/output_parser.py

@@ -26,7 +26,6 @@ class ReActOutputParser(AgentOutputParser):
         action, action_input = re_matches.group(1), re_matches.group(2)
         if action == "Finish":
             return AgentFinish({"output": action_input}, text)
-        else:
         return AgentAction(action, action_input, text)
 
     @property

libs/langchain/langchain/agents/self_ask_with_search/base.py

@@ -195,7 +195,7 @@ def create_self_ask_with_search_agent(
         raise ValueError(msg)
 
     llm_with_stop = llm.bind(stop=["\nIntermediate answer:"])
-    agent = (
+    return (
         RunnablePassthrough.assign(
             agent_scratchpad=lambda x: format_log_to_str(
                 x["intermediate_steps"],
@@ -209,4 +209,3 @@ def create_self_ask_with_search_agent(
         | llm_with_stop
         | SelfAskOutputParser()
     )
-    return agent

libs/langchain/langchain/agents/structured_chat/base.py

@@ -62,7 +62,6 @@ class StructuredChatAgent(Agent):
                 f"(but I haven't seen any of it! I only see what "
                 f"you return as final answer):\n{agent_scratchpad}"
             )
-        else:
         return agent_scratchpad
 
     @classmethod
@@ -292,7 +291,7 @@ def create_structured_chat_agent(
     else:
         llm_with_stop = llm
 
-    agent = (
+    return (
         RunnablePassthrough.assign(
             agent_scratchpad=lambda x: format_log_to_str(x["intermediate_steps"]),
         )
@@ -300,4 +299,3 @@ def create_structured_chat_agent(
         | llm_with_stop
         | JSONAgentOutputParser()
     )
-    return agent

libs/langchain/langchain/agents/structured_chat/output_parser.py

@@ -42,11 +42,9 @@ class StructuredChatOutputParser(AgentOutputParser):
                     response = response[0]
                 if response["action"] == "Final Answer":
                     return AgentFinish({"output": response["action_input"]}, text)
-                else:
                 return AgentAction(
                     response["action"], response.get("action_input", {}), text
                 )
-            else:
             return AgentFinish({"output": text}, text)
         except Exception as e:
             msg = f"Could not parse LLM output: {text}"
@@ -93,9 +91,8 @@ class StructuredChatOutputParserWithRetries(AgentOutputParser):
                 llm=llm, parser=base_parser
             )
             return cls(output_fixing_parser=output_fixing_parser)
-        elif base_parser is not None:
+        if base_parser is not None:
             return cls(base_parser=base_parser)
-        else:
         return cls()
 
     @property

libs/langchain/langchain/agents/tool_calling_agent/base.py

@@ -100,7 +100,7 @@ def create_tool_calling_agent(
         )
     llm_with_tools = llm.bind_tools(tools)
 
-    agent = (
+    return (
         RunnablePassthrough.assign(
             agent_scratchpad=lambda x: message_formatter(x["intermediate_steps"])
         )
@@ -108,4 +108,3 @@ def create_tool_calling_agent(
         | llm_with_tools
         | ToolsAgentOutputParser()
     )
-    return agent

libs/langchain/langchain/agents/xml/base.py

@@ -221,7 +221,7 @@ def create_xml_agent(
     else:
         llm_with_stop = llm
 
-    agent = (
+    return (
         RunnablePassthrough.assign(
             agent_scratchpad=lambda x: format_xml(x["intermediate_steps"]),
         )
@@ -229,4 +229,3 @@ def create_xml_agent(
         | llm_with_stop
         | XMLAgentOutputParser()
     )
-    return agent

libs/langchain/langchain/callbacks/streaming_aiter_final_only.py

@@ -24,7 +24,6 @@ class AsyncFinalIteratorCallbackHandler(AsyncIteratorCallbackHandler):
     def check_if_answer_reached(self) -> bool:
         if self.strip_tokens:
             return self.last_tokens_stripped == self.answer_prefix_tokens_stripped
-        else:
         return self.last_tokens == self.answer_prefix_tokens
 
     def __init__(

libs/langchain/langchain/callbacks/streaming_stdout_final_only.py

@@ -25,7 +25,6 @@ class FinalStreamingStdOutCallbackHandler(StreamingStdOutCallbackHandler):
     def check_if_answer_reached(self) -> bool:
         if self.strip_tokens:
             return self.last_tokens_stripped == self.answer_prefix_tokens_stripped
-        else:
         return self.last_tokens == self.answer_prefix_tokens
 
     def __init__(

libs/langchain/langchain/chains/base.py

@@ -261,7 +261,6 @@ class Chain(RunnableSerializable[dict[str, Any], dict[str, Any]], ABC):
         """
         if verbose is None:
             return _get_verbosity()
-        else:
         return verbose
 
     @property
@@ -474,7 +473,6 @@ class Chain(RunnableSerializable[dict[str, Any], dict[str, Any]], ABC):
             self.memory.save_context(inputs, outputs)
         if return_only_outputs:
             return outputs
-        else:
         return {**inputs, **outputs}
 
     async def aprep_outputs(
@@ -500,7 +498,6 @@ class Chain(RunnableSerializable[dict[str, Any], dict[str, Any]], ABC):
             await self.memory.asave_context(inputs, outputs)
         if return_only_outputs:
             return outputs
-        else:
         return {**inputs, **outputs}
 
     def prep_inputs(self, inputs: Union[dict[str, Any], Any]) -> dict[str, str]:
@@ -628,7 +625,6 @@ class Chain(RunnableSerializable[dict[str, Any], dict[str, Any]], ABC):
                 " but none were provided."
             )
             raise ValueError(msg)
-        else:
         msg = (
             f"`run` supported with either positional arguments or keyword arguments"
             f" but not both. Got args: {args} and kwargs: {kwargs}."
@@ -687,7 +683,7 @@ class Chain(RunnableSerializable[dict[str, Any], dict[str, Any]], ABC):
                 f"one output key. Got {self.output_keys}."
             )
             raise ValueError(msg)
-        elif args and not kwargs:
+        if args and not kwargs:
             if len(args) != 1:
                 msg = "`run` supports only one positional argument."
                 raise ValueError(msg)

libs/langchain/langchain/chains/combine_documents/map_reduce.py

@@ -208,9 +208,7 @@ class MapReduceDocumentsChain(BaseCombineDocumentsChain):
         if isinstance(self.reduce_documents_chain, ReduceDocumentsChain):
             if self.reduce_documents_chain.collapse_documents_chain:
                 return self.reduce_documents_chain.collapse_documents_chain
-            else:
             return self.reduce_documents_chain.combine_documents_chain
-        else:
         msg = (
             f"`reduce_documents_chain` is of type "
             f"{type(self.reduce_documents_chain)} so it does not have "
@@ -223,7 +221,6 @@ class MapReduceDocumentsChain(BaseCombineDocumentsChain):
         """Kept for backward compatibility."""
         if isinstance(self.reduce_documents_chain, ReduceDocumentsChain):
             return self.reduce_documents_chain.combine_documents_chain
-        else:
         msg = (
             f"`reduce_documents_chain` is of type "
             f"{type(self.reduce_documents_chain)} so it does not have "

libs/langchain/langchain/chains/combine_documents/reduce.py

@@ -225,7 +225,6 @@ class ReduceDocumentsChain(BaseCombineDocumentsChain):
     def _collapse_chain(self) -> BaseCombineDocumentsChain:
         if self.collapse_documents_chain is not None:
             return self.collapse_documents_chain
-        else:
         return self.combine_documents_chain
 
     def combine_docs(

libs/langchain/langchain/chains/combine_documents/refine.py

@@ -222,8 +222,7 @@ class RefineDocumentsChain(BaseCombineDocumentsChain):
         base_inputs: dict = {
             self.document_variable_name: self.document_prompt.format(**document_info)
         }
-        inputs = {**base_inputs, **kwargs}
+        return {**base_inputs, **kwargs}
-        return inputs
 
     @property
     def _chain_type(self) -> str:

libs/langchain/langchain/chains/constitutional_ai/base.py

@@ -201,7 +201,6 @@ class ConstitutionalChain(Chain):
     ) -> list[ConstitutionalPrinciple]:
         if names is None:
             return list(PRINCIPLES.values())
-        else:
         return [PRINCIPLES[name] for name in names]
 
     @classmethod

libs/langchain/langchain/chains/elasticsearch_database/base.py

@@ -80,7 +80,6 @@ class ElasticsearchDatabaseChain(Chain):
         """
         if not self.return_intermediate_steps:
             return [self.output_key]
-        else:
         return [self.output_key, INTERMEDIATE_STEPS_KEY]
 
     def _list_indices(self) -> list[str]:

libs/langchain/langchain/chains/hyde/base.py

@@ -47,7 +47,6 @@ class HypotheticalDocumentEmbedder(Chain, Embeddings):
         """Output keys for Hyde's LLM chain."""
         if isinstance(self.llm_chain, LLMChain):
             return self.llm_chain.output_keys
-        else:
         return ["text"]
 
     def embed_documents(self, texts: list[str]) -> list[list[float]]:

libs/langchain/langchain/chains/llm.py

@@ -116,7 +116,6 @@ class LLMChain(Chain):
         """
         if self.return_final_only:
             return [self.output_key]
-        else:
         return [self.output_key, "full_generation"]
 
     def _call(
@@ -142,7 +141,6 @@ class LLMChain(Chain):
                 callbacks=callbacks,
                 **self.llm_kwargs,
             )
-        else:
         results = self.llm.bind(stop=stop, **self.llm_kwargs).batch(
             cast(list, prompts), {"callbacks": callbacks}
         )
@@ -169,7 +167,6 @@ class LLMChain(Chain):
                 callbacks=callbacks,
                 **self.llm_kwargs,
             )
-        else:
         results = await self.llm.bind(stop=stop, **self.llm_kwargs).abatch(
             cast(list, prompts), {"callbacks": callbacks}
         )
@@ -344,7 +341,6 @@ class LLMChain(Chain):
         result = self.predict(callbacks=callbacks, **kwargs)
         if self.prompt.output_parser is not None:
             return self.prompt.output_parser.parse(result)
-        else:
         return result
 
     async def apredict_and_parse(
@@ -358,7 +354,6 @@ class LLMChain(Chain):
         result = await self.apredict(callbacks=callbacks, **kwargs)
         if self.prompt.output_parser is not None:
             return self.prompt.output_parser.parse(result)
-        else:
         return result
 
     def apply_and_parse(
@@ -380,7 +375,6 @@ class LLMChain(Chain):
                 self.prompt.output_parser.parse(res[self.output_key])
                 for res in generation
             ]
-        else:
         return generation
 
     async def aapply_and_parse(
@@ -411,13 +405,12 @@ class LLMChain(Chain):
 def _get_language_model(llm_like: Runnable) -> BaseLanguageModel:
     if isinstance(llm_like, BaseLanguageModel):
         return llm_like
-    elif isinstance(llm_like, RunnableBinding):
+    if isinstance(llm_like, RunnableBinding):
         return _get_language_model(llm_like.bound)
-    elif isinstance(llm_like, RunnableWithFallbacks):
+    if isinstance(llm_like, RunnableWithFallbacks):
         return _get_language_model(llm_like.runnable)
-    elif isinstance(llm_like, (RunnableBranch, DynamicRunnable)):
+    if isinstance(llm_like, (RunnableBranch, DynamicRunnable)):
         return _get_language_model(llm_like.default)
-    else:
     msg = (
         f"Unable to extract BaseLanguageModel from llm_like object of type "
         f"{type(llm_like)}"

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

@@ -55,13 +55,12 @@ def _load_question_to_checked_assertions_chain(
         check_assertions_chain,
         revised_answer_chain,
     ]
-    question_to_checked_assertions_chain = SequentialChain(
+    return SequentialChain(
         chains=chains,  # type: ignore[arg-type]
         input_variables=["question"],
         output_variables=["revised_statement"],
         verbose=True,
     )
-    return question_to_checked_assertions_chain
 
 
 @deprecated(

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

@@ -32,7 +32,7 @@ def _load_sequential_chain(
     are_all_true_prompt: PromptTemplate,
     verbose: bool = False,
 ) -> SequentialChain:
-    chain = SequentialChain(
+    return SequentialChain(
         chains=[
             LLMChain(
                 llm=llm,
@@ -63,7 +63,6 @@ def _load_sequential_chain(
         output_variables=["all_true", "revised_summary"],
         verbose=verbose,
     )
-    return chain
 
 
 @deprecated(

libs/langchain/langchain/chains/loading.py

@@ -311,7 +311,6 @@ def _load_llm_math_chain(config: dict, **kwargs: Any) -> LLMMathChain:
         prompt = load_prompt(config.pop("prompt_path"))
     if llm_chain:
         return LLMMathChain(llm_chain=llm_chain, prompt=prompt, **config)  # type: ignore[arg-type]
-    else:
     return LLMMathChain(llm=llm, prompt=prompt, **config)
 
 
@@ -609,7 +608,6 @@ def _load_llm_requests_chain(config: dict, **kwargs: Any) -> LLMRequestsChain:
         return LLMRequestsChain(
             llm_chain=llm_chain, requests_wrapper=requests_wrapper, **config
         )
-    else:
     return LLMRequestsChain(llm_chain=llm_chain, **config)
 
 

libs/langchain/langchain/chains/moderation.py

@@ -100,7 +100,6 @@ class OpenAIModerationChain(Chain):
             error_str = "Text was found that violates OpenAI's content policy."
             if self.error:
                 raise ValueError(error_str)
-            else:
             return error_str
         return text
 

libs/langchain/langchain/chains/openai_functions/base.py

@@ -132,7 +132,7 @@ def create_openai_fn_chain(
     }
     if len(openai_functions) == 1 and enforce_single_function_usage:
         llm_kwargs["function_call"] = {"name": openai_functions[0]["name"]}
-    llm_chain = LLMChain(
+    return LLMChain(
         llm=llm,
         prompt=prompt,
         output_parser=output_parser,
@@ -140,7 +140,6 @@ def create_openai_fn_chain(
         output_key=output_key,
         **kwargs,
     )
-    return llm_chain
 
 
 @deprecated(

libs/langchain/langchain/chains/openai_functions/citation_fuzzy_match.py

@@ -149,10 +149,9 @@ def create_citation_fuzzy_match_chain(llm: BaseLanguageModel) -> LLMChain:
     ]
     prompt = ChatPromptTemplate(messages=messages)  # type: ignore[arg-type]
 
-    chain = LLMChain(
+    return LLMChain(
         llm=llm,
         prompt=prompt,
         llm_kwargs=llm_kwargs,
         output_parser=output_parser,
     )
-    return chain

libs/langchain/langchain/chains/openai_functions/extraction.py

@@ -103,7 +103,7 @@ def create_extraction_chain(
     extraction_prompt = prompt or ChatPromptTemplate.from_template(_EXTRACTION_TEMPLATE)
     output_parser = JsonKeyOutputFunctionsParser(key_name="info")
     llm_kwargs = get_llm_kwargs(function)
-    chain = LLMChain(
+    return LLMChain(
         llm=llm,
         prompt=extraction_prompt,
         llm_kwargs=llm_kwargs,
@@ -111,7 +111,6 @@ def create_extraction_chain(
         tags=tags,
         verbose=verbose,
     )
-    return chain
 
 
 @deprecated(
@@ -187,11 +186,10 @@ def create_extraction_chain_pydantic(
         pydantic_schema=PydanticSchema, attr_name="info"
     )
     llm_kwargs = get_llm_kwargs(function)
-    chain = LLMChain(
+    return LLMChain(
         llm=llm,
         prompt=extraction_prompt,
         llm_kwargs=llm_kwargs,
         output_parser=output_parser,
         verbose=verbose,
     )
-    return chain

libs/langchain/langchain/chains/openai_functions/qa_with_structure.py

@@ -100,14 +100,13 @@ def create_qa_with_structure_chain(
     ]
     prompt = prompt or ChatPromptTemplate(messages=messages)  # type: ignore[arg-type]
 
-    chain = LLMChain(
+    return LLMChain(
         llm=llm,
         prompt=prompt,
         llm_kwargs=llm_kwargs,
         output_parser=_output_parser,
         verbose=verbose,
     )
-    return chain
 
 
 @deprecated(

libs/langchain/langchain/chains/openai_functions/tagging.py

@@ -91,14 +91,13 @@ def create_tagging_chain(
     prompt = prompt or ChatPromptTemplate.from_template(_TAGGING_TEMPLATE)
     output_parser = JsonOutputFunctionsParser()
     llm_kwargs = get_llm_kwargs(function)
-    chain = LLMChain(
+    return LLMChain(
         llm=llm,
         prompt=prompt,
         llm_kwargs=llm_kwargs,
         output_parser=output_parser,
         **kwargs,
     )
-    return chain
 
 
 @deprecated(
@@ -164,11 +163,10 @@ def create_tagging_chain_pydantic(
     prompt = prompt or ChatPromptTemplate.from_template(_TAGGING_TEMPLATE)
     output_parser = PydanticOutputFunctionsParser(pydantic_schema=pydantic_schema)
     llm_kwargs = get_llm_kwargs(function)
-    chain = LLMChain(
+    return LLMChain(
         llm=llm,
         prompt=prompt,
         llm_kwargs=llm_kwargs,
         output_parser=output_parser,
         **kwargs,
     )
-    return chain

libs/langchain/langchain/chains/openai_tools/extraction.py

@@ -76,5 +76,4 @@ def create_extraction_chain_pydantic(
     functions = [convert_pydantic_to_openai_function(p) for p in pydantic_schemas]
     tools = [{"type": "function", "function": d} for d in functions]
     model = llm.bind(tools=tools)
-    chain = prompt | model | PydanticToolsParser(tools=pydantic_schemas)
+    return prompt | model | PydanticToolsParser(tools=pydantic_schemas)
-    return chain

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

@@ -91,13 +91,12 @@ class StructuredQueryOutputParser(BaseOutputParser[StructuredQuery]):
                 filter_directive = cast(
                     Optional[FilterDirective], get_parser().parse(raw_filter)
                 )
-                fixed = fix_filter_directive(
+                return fix_filter_directive(
                     filter_directive,
                     allowed_comparators=allowed_comparators,
                     allowed_operators=allowed_operators,
                     allowed_attributes=allowed_attributes,
                 )
-                return fixed
 
         else:
             ast_parse = get_parser(
@@ -131,13 +130,13 @@ def fix_filter_directive(
     ) or not filter:
         return filter
 
-    elif isinstance(filter, Comparison):
+    if isinstance(filter, Comparison):
         if allowed_comparators and filter.comparator not in allowed_comparators:
             return None
         if allowed_attributes and filter.attribute not in allowed_attributes:
             return None
         return filter
-    elif isinstance(filter, Operation):
+    if isinstance(filter, Operation):
         if allowed_operators and filter.operator not in allowed_operators:
             return None
         args = [
@@ -155,14 +154,12 @@ def fix_filter_directive(
         ]
         if not args:
             return None
-        elif len(args) == 1 and filter.operator in (Operator.AND, Operator.OR):
+        if len(args) == 1 and filter.operator in (Operator.AND, Operator.OR):
             return args[0]
-        else:
         return Operation(
             operator=filter.operator,
             arguments=args,
         )
-    else:
     return filter
 
 

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

@@ -101,9 +101,8 @@ class QueryTransformer(Transformer):
                 )
                 raise ValueError(msg)
             return Comparison(comparator=func, attribute=args[0], value=args[1])
-        elif len(args) == 1 and func in (Operator.AND, Operator.OR):
+        if len(args) == 1 and func in (Operator.AND, Operator.OR):
             return args[0]
-        else:
         return Operation(operator=func, arguments=args)
 
     def _match_func_name(self, func_name: str) -> Union[Operator, Comparator]:
@@ -118,7 +117,7 @@ class QueryTransformer(Transformer):
                 )
                 raise ValueError(msg)
             return Comparator(func_name)
-        elif func_name in set(Operator):
+        if func_name in set(Operator):
             if (
                 self.allowed_operators is not None
                 and func_name not in self.allowed_operators
@@ -129,7 +128,6 @@ class QueryTransformer(Transformer):
                 )
                 raise ValueError(msg)
             return Operator(func_name)
-        else:
         msg = (
             f"Received unrecognized function {func_name}. Valid functions are "
             f"{list(Operator) + list(Comparator)}"

libs/langchain/langchain/chains/retrieval.py

@@ -60,10 +60,8 @@ def create_retrieval_chain(
     else:
         retrieval_docs = (lambda x: x["input"]) | retriever
 
-    retrieval_chain = (
+    return (
         RunnablePassthrough.assign(
             context=retrieval_docs.with_config(run_name="retrieve_documents"),
         ).assign(answer=combine_docs_chain)
     ).with_config(run_name="retrieval_chain")
-
-    return retrieval_chain

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

@@ -157,7 +157,6 @@ class BaseRetrievalQA(Chain):
 
         if self.return_source_documents:
             return {self.output_key: answer, "source_documents": docs}
-        else:
         return {self.output_key: answer}
 
     @abstractmethod
@@ -200,7 +199,6 @@ class BaseRetrievalQA(Chain):
 
         if self.return_source_documents:
             return {self.output_key: answer, "source_documents": docs}
-        else:
         return {self.output_key: answer}
 
 

libs/langchain/langchain/chains/router/base.py

@@ -97,13 +97,12 @@ class MultiRouteChain(Chain):
         )
         if not route.destination:
             return self.default_chain(route.next_inputs, callbacks=callbacks)
-        elif route.destination in self.destination_chains:
+        if route.destination in self.destination_chains:
             return self.destination_chains[route.destination](
                 route.next_inputs, callbacks=callbacks
             )
-        elif self.silent_errors:
+        if self.silent_errors:
             return self.default_chain(route.next_inputs, callbacks=callbacks)
-        else:
         msg = f"Received invalid destination chain name '{route.destination}'"
         raise ValueError(msg)
 
@@ -123,14 +122,13 @@ class MultiRouteChain(Chain):
             return await self.default_chain.acall(
                 route.next_inputs, callbacks=callbacks
             )
-        elif route.destination in self.destination_chains:
+        if route.destination in self.destination_chains:
             return await self.destination_chains[route.destination].acall(
                 route.next_inputs, callbacks=callbacks
             )
-        elif self.silent_errors:
+        if self.silent_errors:
             return await self.default_chain.acall(
                 route.next_inputs, callbacks=callbacks
             )
-        else:
         msg = f"Received invalid destination chain name '{route.destination}'"
         raise ValueError(msg)

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

@@ -136,11 +136,10 @@ class LLMRouterChain(RouterChain):
         callbacks = _run_manager.get_child()
 
         prediction = self.llm_chain.predict(callbacks=callbacks, **inputs)
-        output = cast(
+        return cast(
             dict[str, Any],
             self.llm_chain.prompt.output_parser.parse(prediction),
         )
-        return output
 
     async def _acall(
         self,
@@ -149,11 +148,10 @@ class LLMRouterChain(RouterChain):
     ) -> dict[str, Any]:
         _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
         callbacks = _run_manager.get_child()
-        output = cast(
+        return cast(
             dict[str, Any],
             await self.llm_chain.apredict_and_parse(callbacks=callbacks, **inputs),
         )
-        return output
 
     @classmethod
     def from_llm(

libs/langchain/langchain/chains/sql_database/query.py

@@ -141,7 +141,6 @@ def create_sql_query_chain(
                 f"{db.dialect}"
             )
             raise ValueError(msg)
-        else:
         table_info_kwargs["get_col_comments"] = True
 
     inputs = {

libs/langchain/langchain/chains/structured_output/base.py

@@ -143,7 +143,6 @@ def create_openai_fn_runnable(
     output_parser = output_parser or get_openai_output_parser(functions)
     if prompt:
         return prompt | llm.bind(**llm_kwargs_) | output_parser
-    else:
     return llm.bind(**llm_kwargs_) | output_parser
 
 
@@ -413,7 +412,7 @@ def create_structured_output_runnable(
             first_tool_only=return_single,
         )
 
-    elif mode == "openai-functions":
+    if mode == "openai-functions":
         return _create_openai_functions_structured_output_runnable(
             output_schema,
             llm,
@@ -422,7 +421,7 @@ def create_structured_output_runnable(
             enforce_single_function_usage=force_function_usage,
             **kwargs,  # llm-specific kwargs
         )
-    elif mode == "openai-json":
+    if mode == "openai-json":
         if force_function_usage:
             msg = (
                 "enforce_single_function_usage is not supported for mode='openai-json'."
@@ -431,7 +430,6 @@ def create_structured_output_runnable(
         return _create_openai_json_runnable(
             output_schema, llm, prompt=prompt, output_parser=output_parser, **kwargs
         )
-    else:
     msg = (
         f"Invalid mode {mode}. Expected one of 'openai-tools', 'openai-functions', "
         f"'openai-json'."
@@ -460,7 +458,6 @@ def _create_openai_tools_runnable(
     )
     if prompt:
         return prompt | llm.bind(**llm_kwargs) | output_parser
-    else:
     return llm.bind(**llm_kwargs) | output_parser
 
 
@@ -535,7 +532,6 @@ def _create_openai_json_runnable(
             prompt = prompt.partial(output_schema=json.dumps(schema_as_dict, indent=2))
 
         return prompt | llm | output_parser
-    else:
     return llm | output_parser
 
 

libs/langchain/langchain/chains/transform.py

@@ -77,7 +77,6 @@ class TransformChain(Chain):
     ) -> dict[str, Any]:
         if self.atransform_cb is not None:
             return await self.atransform_cb(inputs)
-        else:
         self._log_once(
             "TransformChain's atransform is not provided, falling"
             " back to synchronous transform"

libs/langchain/langchain/chat_models/base.py

@@ -322,7 +322,6 @@ def init_chat_model(
         return _init_chat_model_helper(
             cast(str, model), model_provider=model_provider, **kwargs
         )
-    else:
     if model:
         kwargs["model"] = model
     if model_provider:
@@ -343,42 +342,42 @@ def _init_chat_model_helper(
         from langchain_openai import ChatOpenAI
 
         return ChatOpenAI(model=model, **kwargs)
-    elif model_provider == "anthropic":
+    if model_provider == "anthropic":
         _check_pkg("langchain_anthropic")
         from langchain_anthropic import ChatAnthropic
 
         return ChatAnthropic(model=model, **kwargs)  # type: ignore[call-arg,unused-ignore]
-    elif model_provider == "azure_openai":
+    if model_provider == "azure_openai":
         _check_pkg("langchain_openai")
         from langchain_openai import AzureChatOpenAI
 
         return AzureChatOpenAI(model=model, **kwargs)
-    elif model_provider == "azure_ai":
+    if model_provider == "azure_ai":
         _check_pkg("langchain_azure_ai")
         from langchain_azure_ai.chat_models import AzureAIChatCompletionsModel
 
         return AzureAIChatCompletionsModel(model=model, **kwargs)
-    elif model_provider == "cohere":
+    if model_provider == "cohere":
         _check_pkg("langchain_cohere")
         from langchain_cohere import ChatCohere
 
         return ChatCohere(model=model, **kwargs)
-    elif model_provider == "google_vertexai":
+    if model_provider == "google_vertexai":
         _check_pkg("langchain_google_vertexai")
         from langchain_google_vertexai import ChatVertexAI
 
         return ChatVertexAI(model=model, **kwargs)
-    elif model_provider == "google_genai":
+    if model_provider == "google_genai":
         _check_pkg("langchain_google_genai")
         from langchain_google_genai import ChatGoogleGenerativeAI
 
         return ChatGoogleGenerativeAI(model=model, **kwargs)
-    elif model_provider == "fireworks":
+    if model_provider == "fireworks":
         _check_pkg("langchain_fireworks")
         from langchain_fireworks import ChatFireworks
 
         return ChatFireworks(model=model, **kwargs)
-    elif model_provider == "ollama":
+    if model_provider == "ollama":
         try:
             _check_pkg("langchain_ollama")
             from langchain_ollama import ChatOllama
@@ -393,72 +392,70 @@ def _init_chat_model_helper(
                 _check_pkg("langchain_ollama")
 
         return ChatOllama(model=model, **kwargs)
-    elif model_provider == "together":
+    if model_provider == "together":
         _check_pkg("langchain_together")
         from langchain_together import ChatTogether
 
         return ChatTogether(model=model, **kwargs)
-    elif model_provider == "mistralai":
+    if model_provider == "mistralai":
         _check_pkg("langchain_mistralai")
         from langchain_mistralai import ChatMistralAI
 
         return ChatMistralAI(model=model, **kwargs)  # type: ignore[call-arg,unused-ignore]
-    elif model_provider == "huggingface":
+    if model_provider == "huggingface":
         _check_pkg("langchain_huggingface")
         from langchain_huggingface import ChatHuggingFace
 
         return ChatHuggingFace(model_id=model, **kwargs)
-    elif model_provider == "groq":
+    if model_provider == "groq":
         _check_pkg("langchain_groq")
         from langchain_groq import ChatGroq
 
         return ChatGroq(model=model, **kwargs)
-    elif model_provider == "bedrock":
+    if model_provider == "bedrock":
         _check_pkg("langchain_aws")
         from langchain_aws import ChatBedrock
 
         # TODO: update to use model= once ChatBedrock supports
         return ChatBedrock(model_id=model, **kwargs)
-    elif model_provider == "bedrock_converse":
+    if model_provider == "bedrock_converse":
         _check_pkg("langchain_aws")
         from langchain_aws import ChatBedrockConverse
 
         return ChatBedrockConverse(model=model, **kwargs)
-    elif model_provider == "google_anthropic_vertex":
+    if model_provider == "google_anthropic_vertex":
         _check_pkg("langchain_google_vertexai")
         from langchain_google_vertexai.model_garden import ChatAnthropicVertex
 
         return ChatAnthropicVertex(model=model, **kwargs)
-    elif model_provider == "deepseek":
+    if model_provider == "deepseek":
         _check_pkg("langchain_deepseek", pkg_kebab="langchain-deepseek")
         from langchain_deepseek import ChatDeepSeek
 
         return ChatDeepSeek(model=model, **kwargs)
-    elif model_provider == "nvidia":
+    if model_provider == "nvidia":
         _check_pkg("langchain_nvidia_ai_endpoints")
         from langchain_nvidia_ai_endpoints import ChatNVIDIA
 
         return ChatNVIDIA(model=model, **kwargs)
-    elif model_provider == "ibm":
+    if model_provider == "ibm":
         _check_pkg("langchain_ibm")
         from langchain_ibm import ChatWatsonx
 
         return ChatWatsonx(model_id=model, **kwargs)
-    elif model_provider == "xai":
+    if model_provider == "xai":
         _check_pkg("langchain_xai")
         from langchain_xai import ChatXAI
 
         return ChatXAI(model=model, **kwargs)
-    elif model_provider == "perplexity":
+    if model_provider == "perplexity":
         _check_pkg("langchain_perplexity")
         from langchain_perplexity import ChatPerplexity
 
         return ChatPerplexity(model=model, **kwargs)
-    else:
     supported = ", ".join(_SUPPORTED_PROVIDERS)
     msg = (
-            f"Unsupported {model_provider=}.\n\nSupported model providers are: "
+        f"Unsupported {model_provider=}.\n\nSupported model providers are: {supported}"
-            f"{supported}"
     )
     raise ValueError(msg)
 
@@ -490,25 +487,24 @@ _SUPPORTED_PROVIDERS = {
 def _attempt_infer_model_provider(model_name: str) -> Optional[str]:
     if any(model_name.startswith(pre) for pre in ("gpt-3", "gpt-4", "o1", "o3")):
         return "openai"
-    elif model_name.startswith("claude"):
+    if model_name.startswith("claude"):
         return "anthropic"
-    elif model_name.startswith("command"):
+    if model_name.startswith("command"):
         return "cohere"
-    elif model_name.startswith("accounts/fireworks"):
+    if model_name.startswith("accounts/fireworks"):
         return "fireworks"
-    elif model_name.startswith("gemini"):
+    if model_name.startswith("gemini"):
         return "google_vertexai"
-    elif model_name.startswith("amazon."):
+    if model_name.startswith("amazon."):
         return "bedrock"
-    elif model_name.startswith("mistral"):
+    if model_name.startswith("mistral"):
         return "mistralai"
-    elif model_name.startswith("deepseek"):
+    if model_name.startswith("deepseek"):
         return "deepseek"
-    elif model_name.startswith("grok"):
+    if model_name.startswith("grok"):
         return "xai"
-    elif model_name.startswith("sonar"):
+    if model_name.startswith("sonar"):
         return "perplexity"
-    else:
     return None
 
 
@@ -595,9 +591,8 @@ class _ConfigurableModel(Runnable[LanguageModelInput, Any]):
                 )
 
             return queue
-        elif self._default_config and (model := self._model()) and hasattr(model, name):
+        if self._default_config and (model := self._model()) and hasattr(model, name):
             return getattr(model, name)
-        else:
         msg = f"{name} is not a BaseChatModel attribute"
         if self._default_config:
             msg += " and is not implemented on the default model"
@@ -728,7 +723,6 @@ class _ConfigurableModel(Runnable[LanguageModelInput, Any]):
             )
         # If multiple configs default to Runnable.batch which uses executor to invoke
         # in parallel.
-        else:
         return super().batch(
             inputs, config=config, return_exceptions=return_exceptions, **kwargs
         )
@@ -751,7 +745,6 @@ class _ConfigurableModel(Runnable[LanguageModelInput, Any]):
             )
         # If multiple configs default to Runnable.batch which uses executor to invoke
         # in parallel.
-        else:
         return await super().abatch(
             inputs, config=config, return_exceptions=return_exceptions, **kwargs
         )

libs/langchain/langchain/embeddings/base.py

@@ -192,35 +192,34 @@ def init_embeddings(
         from langchain_openai import OpenAIEmbeddings
 
         return OpenAIEmbeddings(model=model_name, **kwargs)
-    elif provider == "azure_openai":
+    if provider == "azure_openai":
         from langchain_openai import AzureOpenAIEmbeddings
 
         return AzureOpenAIEmbeddings(model=model_name, **kwargs)
-    elif provider == "google_vertexai":
+    if provider == "google_vertexai":
         from langchain_google_vertexai import VertexAIEmbeddings
 
         return VertexAIEmbeddings(model=model_name, **kwargs)
-    elif provider == "bedrock":
+    if provider == "bedrock":
         from langchain_aws import BedrockEmbeddings
 
         return BedrockEmbeddings(model_id=model_name, **kwargs)
-    elif provider == "cohere":
+    if provider == "cohere":
         from langchain_cohere import CohereEmbeddings
 
         return CohereEmbeddings(model=model_name, **kwargs)
-    elif provider == "mistralai":
+    if provider == "mistralai":
         from langchain_mistralai import MistralAIEmbeddings
 
         return MistralAIEmbeddings(model=model_name, **kwargs)
-    elif provider == "huggingface":
+    if provider == "huggingface":
         from langchain_huggingface import HuggingFaceEmbeddings
 
         return HuggingFaceEmbeddings(model_name=model_name, **kwargs)
-    elif provider == "ollama":
+    if provider == "ollama":
         from langchain_ollama import OllamaEmbeddings
 
         return OllamaEmbeddings(model=model_name, **kwargs)
-    else:
     msg = (
         f"Provider '{provider}' is not supported.\n"
         f"Supported providers and their required packages:\n"

libs/langchain/langchain/evaluation/comparison/eval_chain.py

@@ -70,7 +70,7 @@ def resolve_pairwise_criteria(
             Criteria.DEPTH,
         ]
         return {k.value: _SUPPORTED_CRITERIA[k] for k in _default_criteria}
-    elif isinstance(criteria, Criteria):
+    if isinstance(criteria, Criteria):
         criteria_ = {criteria.value: _SUPPORTED_CRITERIA[criteria]}
     elif isinstance(criteria, str):
         if criteria in _SUPPORTED_CRITERIA:

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

@@ -186,7 +186,6 @@ class _EmbeddingDistanceChainMixin(Chain):
         }
         if metric in metrics:
             return metrics[metric]
-        else:
         msg = f"Invalid metric: {metric}"
         raise ValueError(msg)
 

libs/langchain/langchain/evaluation/loading.py

@@ -162,7 +162,6 @@ def load_evaluator(
             )
             raise ValueError(msg) from e
         return evaluator_cls.from_llm(llm=llm, **kwargs)
-    else:
     return evaluator_cls(**kwargs)
 
 

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

@@ -70,7 +70,7 @@ class JsonSchemaEvaluator(StringEvaluator):
     def _parse_json(self, node: Any) -> Union[dict, list, None, float, bool, int, str]:
         if isinstance(node, str):
             return parse_json_markdown(node)
-        elif hasattr(node, "schema") and callable(getattr(node, "schema")):
+        if hasattr(node, "schema") and callable(getattr(node, "schema")):
             # Pydantic model
             return getattr(node, "schema")()
         return node

libs/langchain/langchain/evaluation/qa/eval_chain.py

@@ -24,7 +24,7 @@ def _get_score(text: str) -> Optional[tuple[str, int]]:
     if match:
         if match.group(1).upper() == "CORRECT":
             return "CORRECT", 1
-        elif match.group(1).upper() == "INCORRECT":
+        if match.group(1).upper() == "INCORRECT":
             return "INCORRECT", 0
     try:
         first_word = (
@@ -32,7 +32,7 @@ def _get_score(text: str) -> Optional[tuple[str, int]]:
         )
         if first_word.upper() == "CORRECT":
             return "CORRECT", 1
-        elif first_word.upper() == "INCORRECT":
+        if first_word.upper() == "INCORRECT":
             return "INCORRECT", 0
         last_word = (
             text.strip()
@@ -41,7 +41,7 @@ def _get_score(text: str) -> Optional[tuple[str, int]]:
         )
         if last_word.upper() == "CORRECT":
             return "CORRECT", 1
-        elif last_word.upper() == "INCORRECT":
+        if last_word.upper() == "INCORRECT":
             return "INCORRECT", 0
     except IndexError:
         pass

libs/langchain/langchain/evaluation/schema.py

@@ -123,12 +123,12 @@ class _EvalArgsMixin:
         if self.requires_input and input is None:
             msg = f"{self.__class__.__name__} requires an input string."
             raise ValueError(msg)
-        elif input is not None and not self.requires_input:
+        if input is not None and not self.requires_input:
             warn(self._skip_input_warning)
         if self.requires_reference and reference is None:
             msg = f"{self.__class__.__name__} requires a reference string."
             raise ValueError(msg)
-        elif reference is not None and not self.requires_reference:
+        if reference is not None and not self.requires_reference:
             warn(self._skip_reference_warning)
 
 

libs/langchain/langchain/evaluation/scoring/eval_chain.py

@@ -70,7 +70,7 @@ def resolve_criteria(
             Criteria.DEPTH,
         ]
         return {k.value: _SUPPORTED_CRITERIA[k] for k in _default_criteria}
-    elif isinstance(criteria, Criteria):
+    if isinstance(criteria, Criteria):
         criteria_ = {criteria.value: _SUPPORTED_CRITERIA[criteria]}
     elif isinstance(criteria, str):
         if criteria in _SUPPORTED_CRITERIA:

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

@@ -138,7 +138,6 @@ class _RapidFuzzChainMixin(Chain):
         module = module_map[distance]
         if normalize_score:
             return module.normalized_distance
-        else:
         return module.distance
 
     @property

libs/langchain/langchain/hub.py

@@ -21,7 +21,6 @@ def _get_client(
         ls_client = LangSmithClient(api_url, api_key=api_key)
         if hasattr(ls_client, "push_prompt") and hasattr(ls_client, "pull_prompt"):
             return ls_client
-        else:
         from langchainhub import Client as LangChainHubClient
 
         return LangChainHubClient(api_url, api_key=api_key)
@@ -82,14 +81,13 @@ def push(
 
     # Then it's langchainhub
     manifest_json = dumps(object)
-    message = client.push(
+    return client.push(
         repo_full_name,
         manifest_json,
         parent_commit_hash=parent_commit_hash,
         new_repo_is_public=new_repo_is_public,
         new_repo_description=new_repo_description,
     )
-    return message
 
 
 def pull(
@@ -113,8 +111,7 @@ def pull(
 
     # Then it's langsmith
     if hasattr(client, "pull_prompt"):
-        response = client.pull_prompt(owner_repo_commit, include_model=include_model)
+        return client.pull_prompt(owner_repo_commit, include_model=include_model)
-        return response
 
     # Then it's langchainhub
     if hasattr(client, "pull_repo"):

libs/langchain/langchain/llms/__init__.py

@@ -561,7 +561,6 @@ def __getattr__(name: str) -> Any:
             k: v() for k, v in get_type_to_cls_dict().items()
         }
         return type_to_cls_dict
-    else:
     return getattr(llms, name)
 
 

libs/langchain/langchain/memory/entity.py

@@ -154,6 +154,7 @@ class UpstashRedisEntityStore(BaseEntityStore):
         logger.debug(
             f"Redis MEM set '{self.full_key_prefix}:{key}': '{value}' EX {self.ttl}"
         )
+        return None
 
     def delete(self, key: str) -> None:
         self.redis_client.delete(f"{self.full_key_prefix}:{key}")
@@ -255,6 +256,7 @@ class RedisEntityStore(BaseEntityStore):
         logger.debug(
             f"REDIS MEM set '{self.full_key_prefix}:{key}': '{value}' EX {self.ttl}"
         )
+        return None
 
     def delete(self, key: str) -> None:
         self.redis_client.delete(f"{self.full_key_prefix}:{key}")
@@ -362,6 +364,7 @@ class SQLiteEntityStore(BaseEntityStore):
             f'"{self.full_table_name}" (key, value) VALUES (?, ?)'
         )
         self._execute_query(query, (key, value))
+        return None
 
     def delete(self, key: str) -> None:
         """Deletes a key-value pair, safely quoting the table name."""

libs/langchain/langchain/output_parsers/boolean.py

@@ -34,7 +34,7 @@ class BooleanOutputParser(BaseOutputParser[bool]):
                 )
                 raise ValueError(msg)
             return True
-        elif self.false_val.upper() in truthy:
+        if self.false_val.upper() in truthy:
             if self.true_val.upper() in truthy:
                 msg = (
                     f"Ambiguous response. Both {self.true_val} and {self.false_val} "

libs/langchain/langchain/output_parsers/fix.py

@@ -71,7 +71,6 @@ class OutputFixingParser(BaseOutputParser[T]):
             except OutputParserException as e:
                 if retries == self.max_retries:
                     raise e
-                else:
                 retries += 1
                 if self.legacy and hasattr(self.retry_chain, "run"):
                     completion = self.retry_chain.run(
@@ -109,7 +108,6 @@ class OutputFixingParser(BaseOutputParser[T]):
             except OutputParserException as e:
                 if retries == self.max_retries:
                     raise e
-                else:
                 retries += 1
                 if self.legacy and hasattr(self.retry_chain, "arun"):
                     completion = await self.retry_chain.arun(

libs/langchain/langchain/output_parsers/pandas_dataframe.py

@@ -64,7 +64,7 @@ class PandasDataFrameOutputParser(BaseOutputParser[dict[str, Any]]):
             msg = f"Invalid array format in '{original_request_params}'. \
                     Please check the format instructions."
             raise OutputParserException(msg)
-        elif (
+        if (
             isinstance(parsed_array[0], int)
             and parsed_array[-1] > self.dataframe.index.max()
         ):

libs/langchain/langchain/output_parsers/regex.py

@@ -30,11 +30,9 @@ class RegexParser(BaseOutputParser[dict[str, str]]):
         match = re.search(self.regex, text)
         if match:
             return {key: match.group(i + 1) for i, key in enumerate(self.output_keys)}
-        else:
         if self.default_output_key is None:
             msg = f"Could not parse output: {text}"
             raise ValueError(msg)
-            else:
         return {
             key: text if key == self.default_output_key else ""
             for key in self.output_keys

libs/langchain/langchain/output_parsers/regex_dict.py

@@ -33,14 +33,11 @@ class RegexDictParser(BaseOutputParser[dict[str, str]]):
                         {expected_format} on text {text}"
                 )
                 raise ValueError(msg)
-            elif len(matches) > 1:
+            if len(matches) > 1:
                 msg = f"Multiple matches found for output key: {output_key} with \
                         expected format {expected_format} on text {text}"
                 raise ValueError(msg)
-            elif (
+            if self.no_update_value is not None and matches[0] == self.no_update_value:
-                self.no_update_value is not None and matches[0] == self.no_update_value
-            ):
                 continue
-            else:
             result[output_key] = matches[0]
         return result

libs/langchain/langchain/output_parsers/retry.py

@@ -107,7 +107,6 @@ class RetryOutputParser(BaseOutputParser[T]):
             except OutputParserException as e:
                 if retries == self.max_retries:
                     raise e
-                else:
                 retries += 1
                 if self.legacy and hasattr(self.retry_chain, "run"):
                     completion = self.retry_chain.run(
@@ -143,7 +142,6 @@ class RetryOutputParser(BaseOutputParser[T]):
             except OutputParserException as e:
                 if retries == self.max_retries:
                     raise e
-                else:
                 retries += 1
                 if self.legacy and hasattr(self.retry_chain, "arun"):
                     completion = await self.retry_chain.arun(
@@ -234,7 +232,6 @@ class RetryWithErrorOutputParser(BaseOutputParser[T]):
             except OutputParserException as e:
                 if retries == self.max_retries:
                     raise e
-                else:
                 retries += 1
                 if self.legacy and hasattr(self.retry_chain, "run"):
                     completion = self.retry_chain.run(
@@ -263,7 +260,6 @@ class RetryWithErrorOutputParser(BaseOutputParser[T]):
             except OutputParserException as e:
                 if retries == self.max_retries:
                     raise e
-                else:
                 retries += 1
                 if self.legacy and hasattr(self.retry_chain, "arun"):
                     completion = await self.retry_chain.arun(

libs/langchain/langchain/output_parsers/structured.py

@@ -89,7 +89,6 @@ class StructuredOutputParser(BaseOutputParser[dict[str, Any]]):
         )
         if only_json:
             return STRUCTURED_FORMAT_SIMPLE_INSTRUCTIONS.format(format=schema_str)
-        else:
         return STRUCTURED_FORMAT_INSTRUCTIONS.format(format=schema_str)
 
     def parse(self, text: str) -> dict[str, Any]:

libs/langchain/langchain/output_parsers/yaml.py

@@ -33,7 +33,6 @@ class YamlOutputParser(BaseOutputParser[T]):
             json_object = yaml.safe_load(yaml_str)
             if hasattr(self.pydantic_object, "model_validate"):
                 return self.pydantic_object.model_validate(json_object)
-            else:
             return self.pydantic_object.parse_obj(json_object)
 
         except (yaml.YAMLError, ValidationError) as e:

libs/langchain/langchain/retrievers/contextual_compression.py

@@ -45,7 +45,6 @@ class ContextualCompressionRetriever(BaseRetriever):
                 docs, query, callbacks=run_manager.get_child()
             )
             return list(compressed_docs)
-        else:
         return []
 
     async def _aget_relevant_documents(
@@ -71,5 +70,4 @@ class ContextualCompressionRetriever(BaseRetriever):
                 docs, query, callbacks=run_manager.get_child()
             )
             return list(compressed_docs)
-        else:
         return []

libs/langchain/langchain/retrievers/ensemble.py

@@ -174,9 +174,7 @@ class EnsembleRetriever(BaseRetriever):
         """
 
         # Get fused result of the retrievers.
-        fused_documents = self.rank_fusion(query, run_manager)
+        return self.rank_fusion(query, run_manager)
-
-        return fused_documents
 
     async def _aget_relevant_documents(
         self,
@@ -195,9 +193,7 @@ class EnsembleRetriever(BaseRetriever):
         """
 
         # Get fused result of the retrievers.
-        fused_documents = await self.arank_fusion(query, run_manager)
+        return await self.arank_fusion(query, run_manager)
-
-        return fused_documents
 
     def rank_fusion(
         self,
@@ -236,9 +232,7 @@ class EnsembleRetriever(BaseRetriever):
             ]
 
         # apply rank fusion
-        fused_documents = self.weighted_reciprocal_rank(retriever_docs)
+        return self.weighted_reciprocal_rank(retriever_docs)
-
-        return fused_documents
 
     async def arank_fusion(
         self,
@@ -280,9 +274,7 @@ class EnsembleRetriever(BaseRetriever):
             ]
 
         # apply rank fusion
-        fused_documents = self.weighted_reciprocal_rank(retriever_docs)
+        return self.weighted_reciprocal_rank(retriever_docs)
-
-        return fused_documents
 
     def weighted_reciprocal_rank(
         self, doc_lists: list[list[Document]]
@@ -318,7 +310,7 @@ class EnsembleRetriever(BaseRetriever):
 
         # Docs are deduplicated by their contents then sorted by their scores
         all_docs = chain.from_iterable(doc_lists)
-        sorted_docs = sorted(
+        return sorted(
             unique_by_key(
                 all_docs,
                 lambda doc: (
@@ -332,4 +324,3 @@ class EnsembleRetriever(BaseRetriever):
                 doc.page_content if self.id_key is None else doc.metadata[self.id_key]
             ],
         )
-        return sorted_docs

libs/langchain/langchain/retrievers/merger_retriever.py

@@ -31,9 +31,7 @@ class MergerRetriever(BaseRetriever):
         """
 
         # Merge the results of the retrievers.
-        merged_documents = self.merge_documents(query, run_manager)
+        return self.merge_documents(query, run_manager)
-
-        return merged_documents
 
     async def _aget_relevant_documents(
         self,
@@ -52,9 +50,7 @@ class MergerRetriever(BaseRetriever):
         """
 
         # Merge the results of the retrievers.
-        merged_documents = await self.amerge_documents(query, run_manager)
+        return await self.amerge_documents(query, run_manager)
-
-        return merged_documents
 
     def merge_documents(
         self, query: str, run_manager: CallbackManagerForRetrieverRun

libs/langchain/langchain/retrievers/re_phraser.py

@@ -74,10 +74,9 @@ class RePhraseQueryRetriever(BaseRetriever):
             query, {"callbacks": run_manager.get_child()}
         )
         logger.info(f"Re-phrased question: {re_phrased_question}")
-        docs = self.retriever.invoke(
+        return self.retriever.invoke(
             re_phrased_question, config={"callbacks": run_manager.get_child()}
         )
-        return docs
 
     async def _aget_relevant_documents(
         self,

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

@@ -119,18 +119,17 @@ def _get_builtin_translator(vectorstore: VectorStore) -> Visitor:
     }
     if isinstance(vectorstore, DatabricksVectorSearch):
         return DatabricksVectorSearchTranslator()
-    elif isinstance(vectorstore, MyScale):
+    if isinstance(vectorstore, MyScale):
         return MyScaleTranslator(metadata_key=vectorstore.metadata_column)
-    elif isinstance(vectorstore, Redis):
+    if isinstance(vectorstore, Redis):
         return RedisTranslator.from_vectorstore(vectorstore)
-    elif isinstance(vectorstore, TencentVectorDB):
+    if isinstance(vectorstore, TencentVectorDB):
         fields = [
             field.name for field in (vectorstore.meta_fields or []) if field.index
         ]
         return TencentVectorDBTranslator(fields)
-    elif vectorstore.__class__ in BUILTIN_TRANSLATORS:
+    if vectorstore.__class__ in BUILTIN_TRANSLATORS:
         return BUILTIN_TRANSLATORS[vectorstore.__class__]()
-    else:
     try:
         from langchain_astradb.vectorstores import AstraDBVectorStore
     except ImportError:
@@ -289,14 +288,12 @@ class SelfQueryRetriever(BaseRetriever):
     def _get_docs_with_query(
         self, query: str, search_kwargs: dict[str, Any]
     ) -> list[Document]:
-        docs = self.vectorstore.search(query, self.search_type, **search_kwargs)
+        return self.vectorstore.search(query, self.search_type, **search_kwargs)
-        return docs
 
     async def _aget_docs_with_query(
         self, query: str, search_kwargs: dict[str, Any]
     ) -> list[Document]:
-        docs = await self.vectorstore.asearch(query, self.search_type, **search_kwargs)
+        return await self.vectorstore.asearch(query, self.search_type, **search_kwargs)
-        return docs
 
     def _get_relevant_documents(
         self, query: str, *, run_manager: CallbackManagerForRetrieverRun
@@ -315,8 +312,7 @@ class SelfQueryRetriever(BaseRetriever):
         if self.verbose:
             logger.info(f"Generated Query: {structured_query}")
         new_query, search_kwargs = self._prepare_query(query, structured_query)
-        docs = self._get_docs_with_query(new_query, search_kwargs)
+        return self._get_docs_with_query(new_query, search_kwargs)
-        return docs
 
     async def _aget_relevant_documents(
         self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
@@ -335,8 +331,7 @@ class SelfQueryRetriever(BaseRetriever):
         if self.verbose:
             logger.info(f"Generated Query: {structured_query}")
         new_query, search_kwargs = self._prepare_query(query, structured_query)
-        docs = await self._aget_docs_with_query(new_query, search_kwargs)
+        return await self._aget_docs_with_query(new_query, search_kwargs)
-        return docs
 
     @classmethod
     def from_llm(

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

@@ -191,13 +191,13 @@ def _wrap_in_chain_factory(
             )
             raise ValueError(msg)
         return lambda: chain
-    elif isinstance(llm_or_chain_factory, BaseLanguageModel):
+    if isinstance(llm_or_chain_factory, BaseLanguageModel):
         return llm_or_chain_factory
-    elif isinstance(llm_or_chain_factory, Runnable):
+    if isinstance(llm_or_chain_factory, Runnable):
         # Memory may exist here, but it's not elegant to check all those cases.
         lcf = llm_or_chain_factory
         return lambda: lcf
-    elif callable(llm_or_chain_factory):
+    if callable(llm_or_chain_factory):
         if is_traceable_function(llm_or_chain_factory):
             runnable_ = as_runnable(cast(Callable, llm_or_chain_factory))
             return lambda: runnable_
@@ -215,13 +215,12 @@ def _wrap_in_chain_factory(
             # It's not uncommon to do an LLM constructor instead of raw LLM,
             # so we'll unpack it for the user.
             return _model
-        elif is_traceable_function(cast(Callable, _model)):
+        if is_traceable_function(cast(Callable, _model)):
             runnable_ = as_runnable(cast(Callable, _model))
             return lambda: runnable_
-        elif not isinstance(_model, Runnable):
+        if not isinstance(_model, Runnable):
             # This is unlikely to happen - a constructor for a model function
             return lambda: RunnableLambda(constructor)
-        else:
         # Typical correct case
         return constructor
     return llm_or_chain_factory
@@ -272,7 +271,6 @@ def _get_prompt(inputs: dict[str, Any]) -> str:
         raise InputFormatError(msg)
     if len(prompts) == 1:
         return prompts[0]
-    else:
     msg = f"LLM Run expects single prompt input. Got {len(prompts)} prompts."
     raise InputFormatError(msg)
 
@@ -321,7 +319,6 @@ def _get_messages(inputs: dict[str, Any]) -> dict:
             )
             raise InputFormatError(msg)
         return input_copy
-    else:
     msg = (
         f"Chat Run expects single List[dict] or List[List[dict]] 'messages'"
         f" input. Got {inputs}"
@@ -707,7 +704,6 @@ async def _arun_llm(
                     callbacks=callbacks, tags=tags or [], metadata=metadata or {}
                 ),
             )
-        else:
         msg = (
             "Input mapper returned invalid format"
             f" {prompt_or_messages}"
@@ -715,7 +711,6 @@ async def _arun_llm(
         )
         raise InputFormatError(msg)
 
-    else:
     try:
         prompt = _get_prompt(inputs)
         llm_output: Union[str, BaseMessage] = await llm.ainvoke(

libs/langchain/langchain/smith/evaluation/string_run_evaluator.py

@@ -28,7 +28,6 @@ def _get_messages_from_run_dict(messages: list[dict]) -> list[BaseMessage]:
     first_message = messages[0]
     if "lc" in first_message:
         return [load(dumpd(message)) for message in messages]
-    else:
     return messages_from_dict(messages)
 
 
@@ -106,14 +105,12 @@ class LLMStringRunMapper(StringRunMapper):
         if run.run_type != "llm":
             msg = "LLM RunMapper only supports LLM runs."
             raise ValueError(msg)
-        elif not run.outputs:
+        if not run.outputs:
             if run.error:
                 msg = f"Cannot evaluate errored LLM run {run.id}: {run.error}"
                 raise ValueError(msg)
-            else:
             msg = f"Run {run.id} has no outputs. Cannot evaluate this run."
             raise ValueError(msg)
-        else:
         try:
             inputs = self.serialize_inputs(run.inputs)
         except Exception as e:
@@ -142,9 +139,8 @@ class ChainStringRunMapper(StringRunMapper):
     def _get_key(self, source: dict, key: Optional[str], which: str) -> str:
         if key is not None:
             return source[key]
-        elif len(source) == 1:
+        if len(source) == 1:
             return next(iter(source.values()))
-        else:
         msg = (
             f"Could not map run {which} with multiple keys: "
             f"{source}\nPlease manually specify a {which}_key"
@@ -168,7 +164,7 @@ class ChainStringRunMapper(StringRunMapper):
                 f" '{self.input_key}'."
             )
             raise ValueError(msg)
-        elif self.prediction_key is not None and self.prediction_key not in run.outputs:
+        if self.prediction_key is not None and self.prediction_key not in run.outputs:
             available_keys = ", ".join(run.outputs.keys())
             msg = (
                 f"Run with ID {run.id} doesn't have the expected prediction key"
@@ -178,7 +174,6 @@ class ChainStringRunMapper(StringRunMapper):
             )
             raise ValueError(msg)
 
-        else:
         input_ = self._get_key(run.inputs, self.input_key, "input")
         prediction = self._get_key(run.outputs, self.prediction_key, "prediction")
         return {
@@ -224,7 +219,6 @@ class StringExampleMapper(Serializable):
                     " specify a reference_key."
                 )
                 raise ValueError(msg)
-            else:
             output = list(example.outputs.values())[0]
         elif self.reference_key not in example.outputs:
             msg = (

libs/langchain/langchain/tools/__init__.py

@@ -65,9 +65,8 @@ def _import_python_tool_PythonREPLTool() -> Any:
 def __getattr__(name: str) -> Any:
     if name == "PythonAstREPLTool":
         return _import_python_tool_PythonAstREPLTool()
-    elif name == "PythonREPLTool":
+    if name == "PythonREPLTool":
         return _import_python_tool_PythonREPLTool()
-    else:
     from langchain_community import tools
 
     # If not in interactive env, raise warning.

libs/langchain/pyproject.toml

@@ -143,7 +143,7 @@ ignore-regex = ".*(Stati Uniti|Tense=Pres).*"
 ignore-words-list = "momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogyny,unsecure,damon,crate,aadd,symbl,precesses,accademia,nin"
 
 [tool.ruff.lint]
-select = ["A", "C4", "E", "F", "I", "EM", "PGH003", "PIE", "SIM", "T201", "D", "UP", "S", "W"]
+select = ["A", "C4", "D", "E", "EM", "F", "I", "PGH003", "PIE", "RET", "S", "SIM", "T201", "UP", "W"]
 pydocstyle.convention = "google"
 pyupgrade.keep-runtime-typing = true
 

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

@@ -139,7 +139,6 @@ async def get_state(
 async def ask_for_passphrase(said_please: bool) -> dict[str, Any]:
     if said_please:
         return {"passphrase": f"The passphrase is {PASS_PHRASE}"}
-    else:
     return {"passphrase": "I won't share the passphrase without saying 'please'."}
 
 
@@ -153,7 +152,6 @@ async def recycle(password: SecretPassPhrase) -> dict[str, Any]:
     if password.pw == PASS_PHRASE:
         _ROBOT_STATE["destruct"] = True
         return {"status": "Self-destruct initiated", "state": _ROBOT_STATE}
-    else:
     _ROBOT_STATE["destruct"] = False
     raise HTTPException(
         status_code=400,

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

@@ -100,14 +100,13 @@ def _get_agent(**kwargs: Any) -> AgentExecutor:
         ),
     ]
 
-    agent = initialize_agent(
+    return initialize_agent(
         tools,
         fake_llm,
         agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
         verbose=True,
         **kwargs,
     )
-    return agent
 
 
 def test_agent_bad_action() -> None:

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

@@ -71,14 +71,13 @@ def _get_agent(**kwargs: Any) -> AgentExecutor:
         ),
     ]
 
-    agent = initialize_agent(
+    return initialize_agent(
         tools,
         fake_llm,
         agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
         verbose=True,
         **kwargs,
     )
-    return agent
 
 
 async def test_agent_bad_action() -> None:

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

@@ -11,7 +11,6 @@ def get_action_and_input(text: str) -> tuple[str, str]:
     output = output_parser.parse(text)
     if isinstance(output, AgentAction):
         return output.tool, str(output.tool_input)
-    else:
     return "Final Answer", output.return_values["output"]
 
 

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

@@ -16,7 +16,6 @@ def get_action_and_input(text: str) -> tuple[str, str]:
     output = MRKLOutputParser().parse(text)
     if isinstance(output, AgentAction):
         return output.tool, str(output.tool_input)
-    else:
     return "Final Answer", output.return_values["output"]
 
 

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

@@ -21,9 +21,8 @@ def get_action_and_input(text: str) -> tuple[str, str]:
     output = output_parser.parse(text)
     if isinstance(output, AgentAction):
         return output.tool, str(output.tool_input)
-    elif isinstance(output, AgentFinish):
+    if isinstance(output, AgentFinish):
         return output.return_values["output"], output.log
-    else:
     msg = "Unexpected output type"
     raise ValueError(msg)
 

libs/langchain/tests/unit_tests/chains/test_base.py

@@ -58,7 +58,6 @@ class FakeChain(Chain):
     ) -> dict[str, str]:
         if self.be_correct:
             return {"bar": "baz"}
-        else:
         return {"baz": "bar"}
 
 

libs/langchain/tests/unit_tests/evaluation/agents/test_eval_chain.py

@@ -54,7 +54,6 @@ class _FakeTrajectoryChatModel(FakeChatModel):
             response = self.queries[list(self.queries.keys())[self.response_index]]
             self.response_index = self.response_index + 1
             return response
-        else:
         prompt = messages[0].content
         return self.queries[prompt]
 

libs/langchain/tests/unit_tests/llms/fake_llm.py

@@ -45,7 +45,6 @@ class FakeLLM(LLM):
             return self.queries[prompt]
         if stop is None:
             return "foo"
-        else:
         return "bar"
 
     @property

libs/langchain/tests/unit_tests/retrievers/sequential_retriever.py

@@ -14,7 +14,6 @@ class SequentialRetriever(BaseRetriever):
     ) -> list[Document]:
         if self.response_index >= len(self.sequential_responses):
             return []
-        else:
         self.response_index += 1
         return self.sequential_responses[self.response_index - 1]