core: Add ruff rules RET

libs/core/langchain_core/language_models/llms.py

@@ -249,8 +249,7 @@ def update_cache(
         prompt = prompts[missing_prompt_idxs[i]]
         if llm_cache is not None:
             llm_cache.update(prompt, llm_string, result)
-    llm_output = new_results.llm_output
-    return llm_output
+    return new_results.llm_output
 
 
 async def aupdate_cache(
@@ -283,8 +282,7 @@ async def aupdate_cache(
         prompt = prompts[missing_prompt_idxs[i]]
         if llm_cache:
             await llm_cache.aupdate(prompt, llm_string, result)
-    llm_output = new_results.llm_output
-    return llm_output
+    return new_results.llm_output
 
 
 class BaseLLM(BaseLanguageModel[str], ABC):
@@ -953,10 +951,9 @@ def generate(
                     callback_managers, prompts, run_name_list, run_ids_list
                 )
             ]
-            output = self._generate_helper(
+            return self._generate_helper(
                 prompts, stop, run_managers, bool(new_arg_supported), **kwargs
             )
-            return output
         if len(missing_prompts) > 0:
             run_managers = [
                 callback_managers[idx].on_llm_start(
@@ -1201,14 +1198,13 @@ async def agenerate(
                 ]
             )
             run_managers = [r[0] for r in run_managers]  # type: ignore[misc]
-            output = await self._agenerate_helper(
+            return await self._agenerate_helper(
                 prompts,
                 stop,
                 run_managers,  # type: ignore[arg-type]
                 bool(new_arg_supported),
                 **kwargs,  # type: ignore[arg-type]
             )
-            return output
         if len(missing_prompts) > 0:
             run_managers = await asyncio.gather(
                 *[

libs/core/langchain_core/output_parsers/json.py

@@ -53,6 +53,7 @@ def _get_schema(self, pydantic_object: type[TBaseModel]) -> dict[str, Any]:
             return pydantic_object.model_json_schema()
         if issubclass(pydantic_object, pydantic.v1.BaseModel):
             return pydantic_object.schema()
+        return None
 
     def parse_result(self, result: list[Generation], *, partial: bool = False) -> Any:
         """Parse the result of an LLM call to a JSON object.

libs/core/langchain_core/prompts/chat.py

@@ -629,8 +629,7 @@ def input_variables(self) -> list[str]:
             List of input variable names.
         """
         prompts = self.prompt if isinstance(self.prompt, list) else [self.prompt]
-        input_variables = [iv for prompt in prompts for iv in prompt.input_variables]
-        return input_variables
+        return [iv for prompt in prompts for iv in prompt.input_variables]
 
     def format(self, **kwargs: Any) -> BaseMessage:
         """Format the prompt template.

libs/core/langchain_core/prompts/few_shot.py

@@ -389,12 +389,11 @@ def format_messages(self, **kwargs: Any) -> list[BaseMessage]:
             {k: e[k] for k in self.example_prompt.input_variables} for e in examples
         ]
         # Format the examples.
-        messages = [
+        return [
             message
             for example in examples
             for message in self.example_prompt.format_messages(**example)
         ]
-        return messages
 
     async def aformat_messages(self, **kwargs: Any) -> list[BaseMessage]:
         """Async format kwargs into a list of messages.
@@ -411,12 +410,11 @@ async def aformat_messages(self, **kwargs: Any) -> list[BaseMessage]:
             {k: e[k] for k in self.example_prompt.input_variables} for e in examples
         ]
         # Format the examples.
-        messages = [
+        return [
             message
             for example in examples
             for message in await self.example_prompt.aformat_messages(**example)
         ]
-        return messages
 
     def format(self, **kwargs: Any) -> str:
         """Format the prompt with inputs generating a string.

libs/core/langchain_core/prompts/string.py

@@ -99,8 +99,7 @@ def _get_jinja2_variables_from_template(template: str) -> set[str]:
     # noqa for insecure warning elsewhere
     env = Environment()  # noqa: S701
     ast = env.parse(template)
-    variables = meta.find_undeclared_variables(ast)
-    return variables
+    return meta.find_undeclared_variables(ast)
 
 
 def mustache_formatter(template: str, /, **kwargs: Any) -> str:

libs/core/langchain_core/retrievers.py

@@ -206,8 +206,7 @@ def _get_ls_params(self, **kwargs: Any) -> LangSmithRetrieverParams:
             default_retriever_name = default_retriever_name[:-9]
         default_retriever_name = default_retriever_name.lower()
 
-        ls_params = LangSmithRetrieverParams(ls_retriever_name=default_retriever_name)
-        return ls_params
+        return LangSmithRetrieverParams(ls_retriever_name=default_retriever_name)
 
     def invoke(
         self, input: str, config: Optional[RunnableConfig] = None, **kwargs: Any

libs/core/langchain_core/runnables/base.py

@@ -509,10 +509,9 @@ def config_schema(
                 if field_name in [i for i in include if i != "configurable"]
             },
         }
-        model = create_model_v2(  # type: ignore[call-overload]
+        return create_model_v2(  # type: ignore[call-overload]
             self.get_name("Config"), field_definitions=all_fields
         )
-        return model
 
     def get_config_jsonschema(
         self, *, include: Optional[Sequence[str]] = None

libs/core/langchain_core/runnables/history.py

@@ -399,8 +399,7 @@ def get_input_schema(
     @property
     @override
     def OutputType(self) -> type[Output]:
-        output_type = self._history_chain.OutputType
-        return output_type
+        return self._history_chain.OutputType
 
     def get_output_schema(
         self, config: Optional[RunnableConfig] = None

libs/core/langchain_core/tracers/base.py

@@ -180,11 +180,10 @@ def on_retry(
         Returns:
             The run.
         """
-        llm_run = self._llm_run_with_retry_event(
+        return self._llm_run_with_retry_event(
             retry_state=retry_state,
             run_id=run_id,
         )
-        return llm_run
 
     def on_llm_end(self, response: LLMResult, *, run_id: UUID, **kwargs: Any) -> Run:
         """End a trace for an LLM run.

libs/core/langchain_core/tracers/stdout.py

@@ -84,13 +84,12 @@ def get_breadcrumbs(self, run: Run) -> str:
             A string with the breadcrumbs of the run.
         """
         parents = self.get_parents(run)[::-1]
-        string = " > ".join(
+        return " > ".join(
             f"{parent.run_type}:{parent.name}"
             if i != len(parents) - 1
             else f"{parent.run_type}:{parent.name}"
             for i, parent in enumerate(parents + [run])
         )
-        return string
 
     # logging methods
     def _on_chain_start(self, run: Run) -> None:

libs/core/langchain_core/utils/input.py

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

libs/core/langchain_core/utils/json.py

@@ -26,15 +26,13 @@ def _custom_parser(multiline_string: str) -> str:
     if isinstance(multiline_string, (bytes, bytearray)):
         multiline_string = multiline_string.decode()
 
-    multiline_string = re.sub(
+    return re.sub(
         r'("action_input"\:\s*")(.*?)(")',
         _replace_new_line,
         multiline_string,
         flags=re.DOTALL,
     )
 
-    return multiline_string
-
 
 # Adapted from https://github.com/KillianLucas/open-interpreter/blob/5b6080fae1f8c68938a1e4fa8667e3744084ee21/interpreter/utils/parse_partial_json.py
 # MIT License

libs/core/langchain_core/vectorstores/in_memory.py

@@ -381,23 +381,21 @@ def similarity_search_with_score(
         **kwargs: Any,
     ) -> list[tuple[Document, float]]:
         embedding = self.embedding.embed_query(query)
-        docs = self.similarity_search_with_score_by_vector(
+        return self.similarity_search_with_score_by_vector(
             embedding,
             k,
             **kwargs,
         )
-        return docs
 
     async def asimilarity_search_with_score(
         self, query: str, k: int = 4, **kwargs: Any
     ) -> list[tuple[Document, float]]:
         embedding = await self.embedding.aembed_query(query)
-        docs = self.similarity_search_with_score_by_vector(
+        return self.similarity_search_with_score_by_vector(
             embedding,
             k,
             **kwargs,
         )
-        return docs
 
     def similarity_search_by_vector(
         self,

libs/core/langchain_core/vectorstores/utils.py

@@ -57,8 +57,7 @@ def _cosine_similarity(x: Matrix, y: Matrix) -> np.ndarray:
 
         x = np.array(x, dtype=np.float32)
         y = np.array(y, dtype=np.float32)
-        z = 1 - np.array(simd.cdist(x, y, metric="cosine"))
-        return z
+        return 1 - np.array(simd.cdist(x, y, metric="cosine"))
     except ImportError:
         logger.debug(
             "Unable to import simsimd, defaulting to NumPy implementation. If you want "

libs/core/pyproject.toml

@@ -44,7 +44,7 @@ python = ">=3.12.4"
 [tool.poetry.extras]
 
 [tool.ruff.lint]
-select = [ "ASYNC", "B", "C4", "COM", "DJ", "E", "EM", "EXE", "F", "FLY", "FURB", "I", "ICN", "INT", "LOG", "N", "NPY", "PD", "PIE", "Q", "RSE", "S", "SIM", "SLOT", "T10", "T201", "TID", "UP", "W", "YTT",]
+select = [ "ASYNC", "B", "C4", "COM", "DJ", "E", "EM", "EXE", "F", "FLY", "FURB", "I", "ICN", "INT", "LOG", "N", "NPY", "PD", "PIE", "Q", "RET", "RSE", "S", "SIM", "SLOT", "T10", "T201", "TID", "UP", "W", "YTT",]
 ignore = [ "COM812", "UP007", "S110", "S112",]
 
 [tool.coverage.run]

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

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

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

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