"""Runnable that retries a Runnable if it fails.""" from typing import ( TYPE_CHECKING, Any, Optional, TypeVar, Union, cast, ) from tenacity import ( AsyncRetrying, RetryCallState, RetryError, Retrying, retry_if_exception_type, stop_after_attempt, wait_exponential_jitter, ) from typing_extensions import TypedDict, override from langchain_core.runnables.base import RunnableBindingBase from langchain_core.runnables.config import RunnableConfig, patch_config from langchain_core.runnables.utils import Input, Output if TYPE_CHECKING: from langchain_core.callbacks.manager import ( AsyncCallbackManagerForChainRun, CallbackManagerForChainRun, ) T = TypeVar("T", CallbackManagerForChainRun, AsyncCallbackManagerForChainRun) U = TypeVar("U") class ExponentialJitterParams(TypedDict, total=False): """Parameters for ``tenacity.wait_exponential_jitter``.""" initial: float """Initial wait.""" max: float """Maximum wait.""" exp_base: float """Base for exponential backoff.""" jitter: float """Random additional wait sampled from random.uniform(0, jitter).""" class RunnableRetry(RunnableBindingBase[Input, Output]): """Retry a Runnable if it fails. RunnableRetry can be used to add retry logic to any object that subclasses the base Runnable. Such retries are especially useful for network calls that may fail due to transient errors. The RunnableRetry is implemented as a RunnableBinding. The easiest way to use it is through the `.with_retry()` method on all Runnables. Example: Here's an example that uses a RunnableLambda to raise an exception .. code-block:: python import time def foo(input) -> None: '''Fake function that raises an exception.''' raise ValueError(f"Invoking foo failed. At time {time.time()}") runnable = RunnableLambda(foo) runnable_with_retries = runnable.with_retry( retry_if_exception_type=(ValueError,), # Retry only on ValueError wait_exponential_jitter=True, # Add jitter to the exponential backoff stop_after_attempt=2, # Try twice exponential_jitter_params={"initial": 2}, # if desired, customize backoff ) # The method invocation above is equivalent to the longer form below: runnable_with_retries = RunnableRetry( bound=runnable, retry_exception_types=(ValueError,), max_attempt_number=2, wait_exponential_jitter=True, exponential_jitter_params={"initial": 2}, ) This logic can be used to retry any Runnable, including a chain of Runnables, but in general it's best practice to keep the scope of the retry as small as possible. For example, if you have a chain of Runnables, you should only retry the Runnable that is likely to fail, not the entire chain. Example: .. code-block:: python from langchain_core.chat_models import ChatOpenAI from langchain_core.prompts import PromptTemplate template = PromptTemplate.from_template("tell me a joke about {topic}.") model = ChatOpenAI(temperature=0.5) # Good chain = template | model.with_retry() # Bad chain = template | model retryable_chain = chain.with_retry() """ # noqa: E501 retry_exception_types: tuple[type[BaseException], ...] = (Exception,) """The exception types to retry on. By default all exceptions are retried. In general you should only retry on exceptions that are likely to be transient, such as network errors. Good exceptions to retry are all server errors (5xx) and selected client errors (4xx) such as 429 Too Many Requests. """ wait_exponential_jitter: bool = True """Whether to add jitter to the exponential backoff.""" exponential_jitter_params: Optional[ExponentialJitterParams] = None """Parameters for ``tenacity.wait_exponential_jitter``. Namely: ``initial``, ``max``, ``exp_base``, and ``jitter`` (all float values). """ max_attempt_number: int = 3 """The maximum number of attempts to retry the Runnable.""" @property def _kwargs_retrying(self) -> dict[str, Any]: kwargs: dict[str, Any] = {} if self.max_attempt_number: kwargs["stop"] = stop_after_attempt(self.max_attempt_number) if self.wait_exponential_jitter: kwargs["wait"] = wait_exponential_jitter( **(self.exponential_jitter_params or {}) ) if self.retry_exception_types: kwargs["retry"] = retry_if_exception_type(self.retry_exception_types) return kwargs def _sync_retrying(self, **kwargs: Any) -> Retrying: return Retrying(**self._kwargs_retrying, **kwargs) def _async_retrying(self, **kwargs: Any) -> AsyncRetrying: return AsyncRetrying(**self._kwargs_retrying, **kwargs) @staticmethod def _patch_config( config: RunnableConfig, run_manager: "T", retry_state: RetryCallState, ) -> RunnableConfig: attempt = retry_state.attempt_number tag = f"retry:attempt:{attempt}" if attempt > 1 else None return patch_config(config, callbacks=run_manager.get_child(tag)) def _patch_config_list( self, config: list[RunnableConfig], run_manager: list["T"], retry_state: RetryCallState, ) -> list[RunnableConfig]: return [ self._patch_config(c, rm, retry_state) for c, rm in zip(config, run_manager) ] def _invoke( self, input_: Input, run_manager: "CallbackManagerForChainRun", config: RunnableConfig, **kwargs: Any, ) -> Output: for attempt in self._sync_retrying(reraise=True): with attempt: result = super().invoke( input_, self._patch_config(config, run_manager, attempt.retry_state), **kwargs, ) if attempt.retry_state.outcome and not attempt.retry_state.outcome.failed: attempt.retry_state.set_result(result) return result @override def invoke( self, input: Input, config: Optional[RunnableConfig] = None, **kwargs: Any ) -> Output: return self._call_with_config(self._invoke, input, config, **kwargs) async def _ainvoke( self, input_: Input, run_manager: "AsyncCallbackManagerForChainRun", config: RunnableConfig, **kwargs: Any, ) -> Output: async for attempt in self._async_retrying(reraise=True): with attempt: result = await super().ainvoke( input_, self._patch_config(config, run_manager, attempt.retry_state), **kwargs, ) if attempt.retry_state.outcome and not attempt.retry_state.outcome.failed: attempt.retry_state.set_result(result) return result @override async def ainvoke( self, input: Input, config: Optional[RunnableConfig] = None, **kwargs: Any ) -> Output: return await self._acall_with_config(self._ainvoke, input, config, **kwargs) def _batch( self, inputs: list[Input], run_manager: list["CallbackManagerForChainRun"], config: list[RunnableConfig], **kwargs: Any, ) -> list[Union[Output, Exception]]: results_map: dict[int, Output] = {} def pending(iterable: list[U]) -> list[U]: return [item for idx, item in enumerate(iterable) if idx not in results_map] not_set: list[Output] = [] result = not_set try: for attempt in self._sync_retrying(): with attempt: # Get the results of the inputs that have not succeeded yet. result = super().batch( pending(inputs), self._patch_config_list( pending(config), pending(run_manager), attempt.retry_state ), return_exceptions=True, **kwargs, ) # Register the results of the inputs that have succeeded. first_exception = None for i, r in enumerate(result): if isinstance(r, Exception): if not first_exception: first_exception = r continue results_map[i] = r # If any exception occurred, raise it, to retry the failed ones if first_exception: raise first_exception if ( attempt.retry_state.outcome and not attempt.retry_state.outcome.failed ): attempt.retry_state.set_result(result) except RetryError as e: if result is not_set: result = cast("list[Output]", [e] * len(inputs)) outputs: list[Union[Output, Exception]] = [] for idx in range(len(inputs)): if idx in results_map: outputs.append(results_map[idx]) else: outputs.append(result.pop(0)) return outputs @override def batch( self, inputs: list[Input], config: Optional[Union[RunnableConfig, list[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Any, ) -> list[Output]: return self._batch_with_config( self._batch, inputs, config, return_exceptions=return_exceptions, **kwargs ) async def _abatch( self, inputs: list[Input], run_manager: list["AsyncCallbackManagerForChainRun"], config: list[RunnableConfig], **kwargs: Any, ) -> list[Union[Output, Exception]]: results_map: dict[int, Output] = {} def pending(iterable: list[U]) -> list[U]: return [item for idx, item in enumerate(iterable) if idx not in results_map] not_set: list[Output] = [] result = not_set try: async for attempt in self._async_retrying(): with attempt: # Get the results of the inputs that have not succeeded yet. result = await super().abatch( pending(inputs), self._patch_config_list( pending(config), pending(run_manager), attempt.retry_state ), return_exceptions=True, **kwargs, ) # Register the results of the inputs that have succeeded. first_exception = None for i, r in enumerate(result): if isinstance(r, Exception): if not first_exception: first_exception = r continue results_map[i] = r # If any exception occurred, raise it, to retry the failed ones if first_exception: raise first_exception if ( attempt.retry_state.outcome and not attempt.retry_state.outcome.failed ): attempt.retry_state.set_result(result) except RetryError as e: if result is not_set: result = cast("list[Output]", [e] * len(inputs)) outputs: list[Union[Output, Exception]] = [] for idx in range(len(inputs)): if idx in results_map: outputs.append(results_map[idx]) else: outputs.append(result.pop(0)) return outputs @override async def abatch( self, inputs: list[Input], config: Optional[Union[RunnableConfig, list[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Any, ) -> list[Output]: return await self._abatch_with_config( self._abatch, inputs, config, return_exceptions=return_exceptions, **kwargs ) # stream() and transform() are not retried because retrying a stream # is not very intuitive.