"""Base classes and utilities for `Runnable`s."""

from __future__ import annotations

import asyncio
import collections
import contextlib
import functools
import inspect
import threading
from abc import ABC, abstractmethod
from collections.abc import (
    AsyncGenerator,
    AsyncIterator,
    Awaitable,
    Callable,
    Coroutine,
    Iterator,
    Mapping,
    Sequence,
)
from concurrent.futures import FIRST_COMPLETED, wait
from functools import wraps
from itertools import tee
from operator import itemgetter
from types import GenericAlias
from typing import (
    TYPE_CHECKING,
    Any,
    Generic,
    Literal,
    Protocol,
    TypeVar,
    cast,
    get_args,
    get_type_hints,
    overload,
)

from pydantic import BaseModel, ConfigDict, Field, RootModel
from typing_extensions import override

from langchain_core._api import beta_decorator
from langchain_core.callbacks.manager import AsyncCallbackManager, CallbackManager
from langchain_core.load.serializable import (
    Serializable,
    SerializedConstructor,
    SerializedNotImplemented,
)
from langchain_core.runnables.config import (
    RunnableConfig,
    acall_func_with_variable_args,
    call_func_with_variable_args,
    ensure_config,
    get_async_callback_manager_for_config,
    get_callback_manager_for_config,
    get_config_list,
    get_executor_for_config,
    merge_configs,
    patch_config,
    run_in_executor,
    set_config_context,
)
from langchain_core.runnables.utils import (
    AddableDict,
    AnyConfigurableField,
    ConfigurableField,
    ConfigurableFieldSpec,
    Input,
    Output,
    accepts_config,
    accepts_run_manager,
    coro_with_context,
    gated_coro,
    gather_with_concurrency,
    get_function_first_arg_dict_keys,
    get_function_nonlocals,
    get_lambda_source,
    get_unique_config_specs,
    indent_lines_after_first,
    is_async_callable,
    is_async_generator,
)
from langchain_core.tracers._streaming import _StreamingCallbackHandler
from langchain_core.tracers.event_stream import (
    _astream_events_implementation_v1,
    _astream_events_implementation_v2,
)
from langchain_core.tracers.log_stream import (
    LogStreamCallbackHandler,
    _astream_log_implementation,
)
from langchain_core.tracers.root_listeners import (
    AsyncRootListenersTracer,
    RootListenersTracer,
)
from langchain_core.utils.aiter import aclosing, atee, py_anext
from langchain_core.utils.iter import safetee
from langchain_core.utils.pydantic import create_model_v2

if TYPE_CHECKING:
    from langchain_core.callbacks.manager import (
        AsyncCallbackManagerForChainRun,
        CallbackManagerForChainRun,
    )
    from langchain_core.prompts.base import BasePromptTemplate
    from langchain_core.runnables.fallbacks import (
        RunnableWithFallbacks as RunnableWithFallbacksT,
    )
    from langchain_core.runnables.graph import Graph
    from langchain_core.runnables.retry import ExponentialJitterParams
    from langchain_core.runnables.schema import StreamEvent
    from langchain_core.tools import BaseTool
    from langchain_core.tracers.log_stream import RunLog, RunLogPatch
    from langchain_core.tracers.root_listeners import AsyncListener
    from langchain_core.tracers.schemas import Run


Other = TypeVar("Other")

_RUNNABLE_GENERIC_NUM_ARGS = 2  # Input and Output


class Runnable(ABC, Generic[Input, Output]):
    """A unit of work that can be invoked, batched, streamed, transformed and composed.

    Key Methods
    ===========

    - **`invoke`/`ainvoke`**: Transforms a single input into an output.
    - **`batch`/`abatch`**: Efficiently transforms multiple inputs into outputs.
    - **`stream`/`astream`**: Streams output from a single input as it's produced.
    - **`astream_log`**: Streams output and selected intermediate results from an
        input.

    Built-in optimizations:

    - **Batch**: By default, batch runs invoke() in parallel using a thread pool
        executor. Override to optimize batching.

    - **Async**: Methods with `'a'` suffix are asynchronous. By default, they execute
        the sync counterpart using asyncio's thread pool.
        Override for native async.

    All methods accept an optional config argument, which can be used to configure
    execution, add tags and metadata for tracing and debugging etc.

    Runnables expose schematic information about their input, output and config via
    the `input_schema` property, the `output_schema` property and `config_schema`
    method.

    Composition
    ===========

    Runnable objects can be composed together to create chains in a declarative way.

    Any chain constructed this way will automatically have sync, async, batch, and
    streaming support.

    The main composition primitives are `RunnableSequence` and `RunnableParallel`.

    **`RunnableSequence`** invokes a series of runnables sequentially, with
    one Runnable's output serving as the next's input. Construct using
    the `|` operator or by passing a list of runnables to `RunnableSequence`.

    **`RunnableParallel`** invokes runnables concurrently, providing the same input
    to each. Construct it using a dict literal within a sequence or by passing a
    dict to `RunnableParallel`.


    For example,

    ```python
    from langchain_core.runnables import RunnableLambda

    # A RunnableSequence constructed using the `|` operator
    sequence = RunnableLambda(lambda x: x + 1) | RunnableLambda(lambda x: x * 2)
    sequence.invoke(1)  # 4
    sequence.batch([1, 2, 3])  # [4, 6, 8]


    # A sequence that contains a RunnableParallel constructed using a dict literal
    sequence = RunnableLambda(lambda x: x + 1) | {
        "mul_2": RunnableLambda(lambda x: x * 2),
        "mul_5": RunnableLambda(lambda x: x * 5),
    }
    sequence.invoke(1)  # {'mul_2': 4, 'mul_5': 10}
    ```

    Standard Methods
    ================

    All `Runnable`s expose additional methods that can be used to modify their
    behavior (e.g., add a retry policy, add lifecycle listeners, make them
    configurable, etc.).

    These methods will work on any `Runnable`, including `Runnable` chains
    constructed by composing other `Runnable`s.
    See the individual methods for details.

    For example,

    ```python
    from langchain_core.runnables import RunnableLambda

    import random

    def add_one(x: int) -> int:
        return x + 1


    def buggy_double(y: int) -> int:
        \"\"\"Buggy code that will fail 70% of the time\"\"\"
        if random.random() > 0.3:
            print('This code failed, and will probably be retried!')  # noqa: T201
            raise ValueError('Triggered buggy code')
        return y * 2

    sequence = (
        RunnableLambda(add_one) |
        RunnableLambda(buggy_double).with_retry( # Retry on failure
            stop_after_attempt=10,
            wait_exponential_jitter=False
        )
    )

    print(sequence.input_schema.model_json_schema()) # Show inferred input schema
    print(sequence.output_schema.model_json_schema()) # Show inferred output schema
    print(sequence.invoke(2)) # invoke the sequence (note the retry above!!)
    ```

    Debugging and tracing
    =====================

    As the chains get longer, it can be useful to be able to see intermediate results
    to debug and trace the chain.

    You can set the global debug flag to True to enable debug output for all chains:

    ```python
    from langchain_core.globals import set_debug

    set_debug(True)
    ```

    Alternatively, you can pass existing or custom callbacks to any given chain:

    ```python
    from langchain_core.tracers import ConsoleCallbackHandler

    chain.invoke(..., config={"callbacks": [ConsoleCallbackHandler()]})
    ```

    For a UI (and much more) checkout [LangSmith](https://docs.langchain.com/langsmith/home).

    """

    name: str | None
    """The name of the `Runnable`. Used for debugging and tracing."""

    def get_name(self, suffix: str | None = None, *, name: str | None = None) -> str:
        """Get the name of the `Runnable`.

        Args:
            suffix: An optional suffix to append to the name.
            name: An optional name to use instead of the `Runnable`'s name.

        Returns:
            The name of the `Runnable`.
        """
        if name:
            name_ = name
        elif hasattr(self, "name") and self.name:
            name_ = self.name
        else:
            # Here we handle a case where the runnable subclass is also a pydantic
            # model.
            cls = self.__class__
            # Then it's a pydantic sub-class, and we have to check
            # whether it's a generic, and if so recover the original name.
            if (
                hasattr(
                    cls,
                    "__pydantic_generic_metadata__",
                )
                and "origin" in cls.__pydantic_generic_metadata__
                and cls.__pydantic_generic_metadata__["origin"] is not None
            ):
                name_ = cls.__pydantic_generic_metadata__["origin"].__name__
            else:
                name_ = cls.__name__

        if suffix:
            if name_[0].isupper():
                return name_ + suffix.title()
            return name_ + "_" + suffix.lower()
        return name_

    @property
    def InputType(self) -> type[Input]:  # noqa: N802
        """Input type.

        The type of input this `Runnable` accepts specified as a type annotation.

        Raises:
            TypeError: If the input type cannot be inferred.
        """
        # First loop through all parent classes and if any of them is
        # a Pydantic model, we will pick up the generic parameterization
        # from that model via the __pydantic_generic_metadata__ attribute.
        for base in self.__class__.mro():
            if hasattr(base, "__pydantic_generic_metadata__"):
                metadata = base.__pydantic_generic_metadata__
                if (
                    "args" in metadata
                    and len(metadata["args"]) == _RUNNABLE_GENERIC_NUM_ARGS
                ):
                    return metadata["args"][0]

        # If we didn't find a Pydantic model in the parent classes,
        # then loop through __orig_bases__. This corresponds to
        # Runnables that are not pydantic models.
        for cls in self.__class__.__orig_bases__:  # type: ignore[attr-defined]
            type_args = get_args(cls)
            if type_args and len(type_args) == _RUNNABLE_GENERIC_NUM_ARGS:
                return type_args[0]

        msg = (
            f"Runnable {self.get_name()} doesn't have an inferable InputType. "
            "Override the InputType property to specify the input type."
        )
        raise TypeError(msg)

    @property
    def OutputType(self) -> type[Output]:  # noqa: N802
        """Output Type.

        The type of output this `Runnable` produces specified as a type annotation.

        Raises:
            TypeError: If the output type cannot be inferred.
        """
        # First loop through bases -- this will help generic
        # any pydantic models.
        for base in self.__class__.mro():
            if hasattr(base, "__pydantic_generic_metadata__"):
                metadata = base.__pydantic_generic_metadata__
                if (
                    "args" in metadata
                    and len(metadata["args"]) == _RUNNABLE_GENERIC_NUM_ARGS
                ):
                    return metadata["args"][1]

        for cls in self.__class__.__orig_bases__:  # type: ignore[attr-defined]
            type_args = get_args(cls)
            if type_args and len(type_args) == _RUNNABLE_GENERIC_NUM_ARGS:
                return type_args[1]

        msg = (
            f"Runnable {self.get_name()} doesn't have an inferable OutputType. "
            "Override the OutputType property to specify the output type."
        )
        raise TypeError(msg)

    @property
    def input_schema(self) -> type[BaseModel]:
        """The type of input this `Runnable` accepts specified as a Pydantic model."""
        return self.get_input_schema()

    def get_input_schema(
        self,
        config: RunnableConfig | None = None,  # noqa: ARG002
    ) -> type[BaseModel]:
        """Get a Pydantic model that can be used to validate input to the `Runnable`.

        `Runnable` objects that leverage the `configurable_fields` and
        `configurable_alternatives` methods will have a dynamic input schema that
        depends on which configuration the `Runnable` is invoked with.

        This method allows to get an input schema for a specific configuration.

        Args:
            config: A config to use when generating the schema.

        Returns:
            A Pydantic model that can be used to validate input.
        """
        root_type = self.InputType

        if (
            inspect.isclass(root_type)
            and not isinstance(root_type, GenericAlias)
            and issubclass(root_type, BaseModel)
        ):
            return root_type

        return create_model_v2(
            self.get_name("Input"),
            root=root_type,
            # create model needs access to appropriate type annotations to be
            # able to construct the Pydantic model.
            # When we create the model, we pass information about the namespace
            # where the model is being created, so the type annotations can
            # be resolved correctly as well.
            # self.__class__.__module__ handles the case when the Runnable is
            # being sub-classed in a different module.
            module_name=self.__class__.__module__,
        )

    def get_input_jsonschema(
        self, config: RunnableConfig | None = None
    ) -> dict[str, Any]:
        """Get a JSON schema that represents the input to the `Runnable`.

        Args:
            config: A config to use when generating the schema.

        Returns:
            A JSON schema that represents the input to the `Runnable`.

        Example:
            ```python
            from langchain_core.runnables import RunnableLambda


            def add_one(x: int) -> int:
                return x + 1


            runnable = RunnableLambda(add_one)

            print(runnable.get_input_jsonschema())
            ```

        !!! version-added "Added in `langchain-core` 0.3.0"

        """
        return self.get_input_schema(config).model_json_schema()

    @property
    def output_schema(self) -> type[BaseModel]:
        """Output schema.

        The type of output this `Runnable` produces specified as a Pydantic model.
        """
        return self.get_output_schema()

    def get_output_schema(
        self,
        config: RunnableConfig | None = None,  # noqa: ARG002
    ) -> type[BaseModel]:
        """Get a Pydantic model that can be used to validate output to the `Runnable`.

        `Runnable` objects that leverage the `configurable_fields` and
        `configurable_alternatives` methods will have a dynamic output schema that
        depends on which configuration the `Runnable` is invoked with.

        This method allows to get an output schema for a specific configuration.

        Args:
            config: A config to use when generating the schema.

        Returns:
            A Pydantic model that can be used to validate output.
        """
        root_type = self.OutputType

        if (
            inspect.isclass(root_type)
            and not isinstance(root_type, GenericAlias)
            and issubclass(root_type, BaseModel)
        ):
            return root_type

        return create_model_v2(
            self.get_name("Output"),
            root=root_type,
            # create model needs access to appropriate type annotations to be
            # able to construct the Pydantic model.
            # When we create the model, we pass information about the namespace
            # where the model is being created, so the type annotations can
            # be resolved correctly as well.
            # self.__class__.__module__ handles the case when the Runnable is
            # being sub-classed in a different module.
            module_name=self.__class__.__module__,
        )

    def get_output_jsonschema(
        self, config: RunnableConfig | None = None
    ) -> dict[str, Any]:
        """Get a JSON schema that represents the output of the `Runnable`.

        Args:
            config: A config to use when generating the schema.

        Returns:
            A JSON schema that represents the output of the `Runnable`.

        Example:
            ```python
            from langchain_core.runnables import RunnableLambda


            def add_one(x: int) -> int:
                return x + 1


            runnable = RunnableLambda(add_one)

            print(runnable.get_output_jsonschema())
            ```

        !!! version-added "Added in `langchain-core` 0.3.0"

        """
        return self.get_output_schema(config).model_json_schema()

    @property
    def config_specs(self) -> list[ConfigurableFieldSpec]:
        """List configurable fields for this `Runnable`."""
        return []

    def config_schema(self, *, include: Sequence[str] | None = None) -> type[BaseModel]:
        """The type of config this `Runnable` accepts specified as a Pydantic model.

        To mark a field as configurable, see the `configurable_fields`
        and `configurable_alternatives` methods.

        Args:
            include: A list of fields to include in the config schema.

        Returns:
            A Pydantic model that can be used to validate config.

        """
        include = include or []
        config_specs = self.config_specs
        configurable = (
            create_model_v2(
                "Configurable",
                field_definitions={
                    spec.id: (
                        spec.annotation,
                        Field(
                            spec.default, title=spec.name, description=spec.description
                        ),
                    )
                    for spec in config_specs
                },
            )
            if config_specs
            else None
        )

        # Many need to create a typed dict instead to implement NotRequired!
        all_fields = {
            **({"configurable": (configurable, None)} if configurable else {}),
            **{
                field_name: (field_type, None)
                for field_name, field_type in get_type_hints(RunnableConfig).items()
                if field_name in [i for i in include if i != "configurable"]
            },
        }
        return create_model_v2(self.get_name("Config"), field_definitions=all_fields)

    def get_config_jsonschema(
        self, *, include: Sequence[str] | None = None
    ) -> dict[str, Any]:
        """Get a JSON schema that represents the config of the `Runnable`.

        Args:
            include: A list of fields to include in the config schema.

        Returns:
            A JSON schema that represents the config of the `Runnable`.

        !!! version-added "Added in `langchain-core` 0.3.0"

        """
        return self.config_schema(include=include).model_json_schema()

    def get_graph(self, config: RunnableConfig | None = None) -> Graph:
        """Return a graph representation of this `Runnable`."""
        # Import locally to prevent circular import
        from langchain_core.runnables.graph import Graph  # noqa: PLC0415

        graph = Graph()
        try:
            input_node = graph.add_node(self.get_input_schema(config))
        except TypeError:
            input_node = graph.add_node(create_model_v2(self.get_name("Input")))
        runnable_node = graph.add_node(
            self, metadata=config.get("metadata") if config else None
        )
        try:
            output_node = graph.add_node(self.get_output_schema(config))
        except TypeError:
            output_node = graph.add_node(create_model_v2(self.get_name("Output")))
        graph.add_edge(input_node, runnable_node)
        graph.add_edge(runnable_node, output_node)
        return graph

    def get_prompts(
        self, config: RunnableConfig | None = None
    ) -> list[BasePromptTemplate]:
        """Return a list of prompts used by this `Runnable`."""
        # Import locally to prevent circular import
        from langchain_core.prompts.base import BasePromptTemplate  # noqa: PLC0415

        return [
            node.data
            for node in self.get_graph(config=config).nodes.values()
            if isinstance(node.data, BasePromptTemplate)
        ]

    def __or__(
        self,
        other: Runnable[Any, Other]
        | Callable[[Iterator[Any]], Iterator[Other]]
        | Callable[[AsyncIterator[Any]], AsyncIterator[Other]]
        | Callable[[Any], Other]
        | Mapping[str, Runnable[Any, Other] | Callable[[Any], Other] | Any],
    ) -> RunnableSerializable[Input, Other]:
        """Runnable "or" operator.

        Compose this `Runnable` with another object to create a
        `RunnableSequence`.

        Args:
            other: Another `Runnable` or a `Runnable`-like object.

        Returns:
            A new `Runnable`.
        """
        return RunnableSequence(self, coerce_to_runnable(other))

    def __ror__(
        self,
        other: Runnable[Other, Any]
        | Callable[[Iterator[Other]], Iterator[Any]]
        | Callable[[AsyncIterator[Other]], AsyncIterator[Any]]
        | Callable[[Other], Any]
        | Mapping[str, Runnable[Other, Any] | Callable[[Other], Any] | Any],
    ) -> RunnableSerializable[Other, Output]:
        """Runnable "reverse-or" operator.

        Compose this `Runnable` with another object to create a
        `RunnableSequence`.

        Args:
            other: Another `Runnable` or a `Runnable`-like object.

        Returns:
            A new `Runnable`.
        """
        return RunnableSequence(coerce_to_runnable(other), self)

    def pipe(
        self,
        *others: Runnable[Any, Other] | Callable[[Any], Other],
        name: str | None = None,
    ) -> RunnableSerializable[Input, Other]:
        """Pipe `Runnable` objects.

        Compose this `Runnable` with `Runnable`-like objects to make a
        `RunnableSequence`.

        Equivalent to `RunnableSequence(self, *others)` or `self | others[0] | ...`

        Example:
            ```python
            from langchain_core.runnables import RunnableLambda


            def add_one(x: int) -> int:
                return x + 1


            def mul_two(x: int) -> int:
                return x * 2


            runnable_1 = RunnableLambda(add_one)
            runnable_2 = RunnableLambda(mul_two)
            sequence = runnable_1.pipe(runnable_2)
            # Or equivalently:
            # sequence = runnable_1 | runnable_2
            # sequence = RunnableSequence(first=runnable_1, last=runnable_2)
            sequence.invoke(1)
            await sequence.ainvoke(1)
            # -> 4

            sequence.batch([1, 2, 3])
            await sequence.abatch([1, 2, 3])
            # -> [4, 6, 8]
            ```

        Args:
            *others: Other `Runnable` or `Runnable`-like objects to compose
            name: An optional name for the resulting `RunnableSequence`.

        Returns:
            A new `Runnable`.
        """
        return RunnableSequence(self, *others, name=name)

    def pick(self, keys: str | list[str]) -> RunnableSerializable[Any, Any]:
        """Pick keys from the output `dict` of this `Runnable`.

        Pick a single key:

        ```python
        import json

        from langchain_core.runnables import RunnableLambda, RunnableMap

        as_str = RunnableLambda(str)
        as_json = RunnableLambda(json.loads)
        chain = RunnableMap(str=as_str, json=as_json)

        chain.invoke("[1, 2, 3]")
        # -> {"str": "[1, 2, 3]", "json": [1, 2, 3]}

        json_only_chain = chain.pick("json")
        json_only_chain.invoke("[1, 2, 3]")
        # -> [1, 2, 3]
        ```

        Pick a list of keys:

        ```python
        from typing import Any

        import json

        from langchain_core.runnables import RunnableLambda, RunnableMap

        as_str = RunnableLambda(str)
        as_json = RunnableLambda(json.loads)


        def as_bytes(x: Any) -> bytes:
            return bytes(x, "utf-8")


        chain = RunnableMap(str=as_str, json=as_json, bytes=RunnableLambda(as_bytes))

        chain.invoke("[1, 2, 3]")
        # -> {"str": "[1, 2, 3]", "json": [1, 2, 3], "bytes": b"[1, 2, 3]"}

        json_and_bytes_chain = chain.pick(["json", "bytes"])
        json_and_bytes_chain.invoke("[1, 2, 3]")
        # -> {"json": [1, 2, 3], "bytes": b"[1, 2, 3]"}
        ```

        Args:
            keys: A key or list of keys to pick from the output dict.

        Returns:
            a new `Runnable`.

        """
        # Import locally to prevent circular import
        from langchain_core.runnables.passthrough import RunnablePick  # noqa: PLC0415

        return self | RunnablePick(keys)

    def assign(
        self,
        **kwargs: Runnable[dict[str, Any], Any]
        | Callable[[dict[str, Any]], Any]
        | Mapping[str, Runnable[dict[str, Any], Any] | Callable[[dict[str, Any]], Any]],
    ) -> RunnableSerializable[Any, Any]:
        """Assigns new fields to the `dict` output of this `Runnable`.

        ```python
        from langchain_core.language_models.fake import FakeStreamingListLLM
        from langchain_core.output_parsers import StrOutputParser
        from langchain_core.prompts import SystemMessagePromptTemplate
        from langchain_core.runnables import Runnable
        from operator import itemgetter

        prompt = (
            SystemMessagePromptTemplate.from_template("You are a nice assistant.")
            + "{question}"
        )
        model = FakeStreamingListLLM(responses=["foo-lish"])

        chain: Runnable = prompt | model | {"str": StrOutputParser()}

        chain_with_assign = chain.assign(hello=itemgetter("str") | model)

        print(chain_with_assign.input_schema.model_json_schema())
        # {'title': 'PromptInput', 'type': 'object', 'properties':
        {'question': {'title': 'Question', 'type': 'string'}}}
        print(chain_with_assign.output_schema.model_json_schema())
        # {'title': 'RunnableSequenceOutput', 'type': 'object', 'properties':
        {'str': {'title': 'Str',
        'type': 'string'}, 'hello': {'title': 'Hello', 'type': 'string'}}}
        ```

        Args:
            **kwargs: A mapping of keys to `Runnable` or `Runnable`-like objects
                that will be invoked with the entire output dict of this `Runnable`.

        Returns:
            A new `Runnable`.

        """
        # Import locally to prevent circular import
        from langchain_core.runnables.passthrough import RunnableAssign  # noqa: PLC0415

        return self | RunnableAssign(RunnableParallel[dict[str, Any]](kwargs))

    """ --- Public API --- """

    @abstractmethod
    def invoke(
        self,
        input: Input,
        config: RunnableConfig | None = None,
        **kwargs: Any,
    ) -> Output:
        """Transform a single input into an output.

        Args:
            input: The input to the `Runnable`.
            config: A config to use when invoking the `Runnable`.

                The config supports standard keys like `'tags'`, `'metadata'` for
                tracing purposes, `'max_concurrency'` for controlling how much work to
                do in parallel, and other keys.

                Please refer to `RunnableConfig` for more details.

        Returns:
            The output of the `Runnable`.
        """

    async def ainvoke(
        self,
        input: Input,
        config: RunnableConfig | None = None,
        **kwargs: Any,
    ) -> Output:
        """Transform a single input into an output.

        Args:
            input: The input to the `Runnable`.
            config: A config to use when invoking the `Runnable`.

                The config supports standard keys like `'tags'`, `'metadata'` for
                tracing purposes, `'max_concurrency'` for controlling how much work to
                do in parallel, and other keys.

                Please refer to `RunnableConfig` for more details.

        Returns:
            The output of the `Runnable`.
        """
        return await run_in_executor(config, self.invoke, input, config, **kwargs)

    def batch(
        self,
        inputs: list[Input],
        config: RunnableConfig | list[RunnableConfig] | None = None,
        *,
        return_exceptions: bool = False,
        **kwargs: Any | None,
    ) -> list[Output]:
        """Default implementation runs invoke in parallel using a thread pool executor.

        The default implementation of batch works well for IO bound runnables.

        Subclasses must override this method if they can batch more efficiently;
        e.g., if the underlying `Runnable` uses an API which supports a batch mode.

        Args:
            inputs: A list of inputs to the `Runnable`.
            config: A config to use when invoking the `Runnable`. The config supports
                standard keys like `'tags'`, `'metadata'` for
                tracing purposes, `'max_concurrency'` for controlling how much work
                to do in parallel, and other keys.

                Please refer to `RunnableConfig` for more details.
            return_exceptions: Whether to return exceptions instead of raising them.
            **kwargs: Additional keyword arguments to pass to the `Runnable`.

        Returns:
            A list of outputs from the `Runnable`.
        """
        if not inputs:
            return []

        configs = get_config_list(config, len(inputs))

        def invoke(input_: Input, config: RunnableConfig) -> Output | Exception:
            if return_exceptions:
                try:
                    return self.invoke(input_, config, **kwargs)
                except Exception as e:
                    return e
            else:
                return self.invoke(input_, config, **kwargs)

        # If there's only one input, don't bother with the executor
        if len(inputs) == 1:
            return cast("list[Output]", [invoke(inputs[0], configs[0])])

        with get_executor_for_config(configs[0]) as executor:
            return cast("list[Output]", list(executor.map(invoke, inputs, configs)))

    @overload
    def batch_as_completed(
        self,
        inputs: Sequence[Input],
        config: RunnableConfig | Sequence[RunnableConfig] | None = None,
        *,
        return_exceptions: Literal[False] = False,
        **kwargs: Any,
    ) -> Iterator[tuple[int, Output]]: ...

    @overload
    def batch_as_completed(
        self,
        inputs: Sequence[Input],
        config: RunnableConfig | Sequence[RunnableConfig] | None = None,
        *,
        return_exceptions: Literal[True],
        **kwargs: Any,
    ) -> Iterator[tuple[int, Output | Exception]]: ...

    def batch_as_completed(
        self,
        inputs: Sequence[Input],
        config: RunnableConfig | Sequence[RunnableConfig] | None = None,
        *,
        return_exceptions: bool = False,
        **kwargs: Any | None,
    ) -> Iterator[tuple[int, Output | Exception]]:
        """Run `invoke` in parallel on a list of inputs.

        Yields results as they complete.

        Args:
            inputs: A list of inputs to the `Runnable`.
            config: A config to use when invoking the `Runnable`.

                The config supports standard keys like `'tags'`, `'metadata'` for
                tracing purposes, `'max_concurrency'` for controlling how much work to
                do in parallel, and other keys.

                Please refer to `RunnableConfig` for more details.
            return_exceptions: Whether to return exceptions instead of raising them.
            **kwargs: Additional keyword arguments to pass to the `Runnable`.

        Yields:
            Tuples of the index of the input and the output from the `Runnable`.

        """
        if not inputs:
            return

        configs = get_config_list(config, len(inputs))

        def invoke(
            i: int, input_: Input, config: RunnableConfig
        ) -> tuple[int, Output | Exception]:
            if return_exceptions:
                try:
                    out: Output | Exception = self.invoke(input_, config, **kwargs)
                except Exception as e:
                    out = e
            else:
                out = self.invoke(input_, config, **kwargs)

            return (i, out)

        if len(inputs) == 1:
            yield invoke(0, inputs[0], configs[0])
            return

        with get_executor_for_config(configs[0]) as executor:
            futures = {
                executor.submit(invoke, i, input_, config)
                for i, (input_, config) in enumerate(zip(inputs, configs, strict=False))
            }

            try:
                while futures:
                    done, futures = wait(futures, return_when=FIRST_COMPLETED)
                    while done:
                        yield done.pop().result()
            finally:
                for future in futures:
                    future.cancel()

    async def abatch(
        self,
        inputs: list[Input],
        config: RunnableConfig | list[RunnableConfig] | None = None,
        *,
        return_exceptions: bool = False,
        **kwargs: Any | None,
    ) -> list[Output]:
        """Default implementation runs `ainvoke` in parallel using `asyncio.gather`.

        The default implementation of `batch` works well for IO bound runnables.

        Subclasses must override this method if they can batch more efficiently;
        e.g., if the underlying `Runnable` uses an API which supports a batch mode.

        Args:
            inputs: A list of inputs to the `Runnable`.
            config: A config to use when invoking the `Runnable`.

                The config supports standard keys like `'tags'`, `'metadata'` for
                tracing purposes, `'max_concurrency'` for controlling how much work to
                do in parallel, and other keys.

                Please refer to `RunnableConfig` for more details.
            return_exceptions: Whether to return exceptions instead of raising them.
            **kwargs: Additional keyword arguments to pass to the `Runnable`.

        Returns:
            A list of outputs from the `Runnable`.

        """
        if not inputs:
            return []

        configs = get_config_list(config, len(inputs))

        async def ainvoke(value: Input, config: RunnableConfig) -> Output | Exception:
            if return_exceptions:
                try:
                    return await self.ainvoke(value, config, **kwargs)
                except Exception as e:
                    return e
            else:
                return await self.ainvoke(value, config, **kwargs)

        coros = map(ainvoke, inputs, configs)
        return await gather_with_concurrency(configs[0].get("max_concurrency"), *coros)

    @overload
    def abatch_as_completed(
        self,
        inputs: Sequence[Input],
        config: RunnableConfig | Sequence[RunnableConfig] | None = None,
        *,
        return_exceptions: Literal[False] = False,
        **kwargs: Any | None,
    ) -> AsyncIterator[tuple[int, Output]]: ...

    @overload
    def abatch_as_completed(
        self,
        inputs: Sequence[Input],
        config: RunnableConfig | Sequence[RunnableConfig] | None = None,
        *,
        return_exceptions: Literal[True],
        **kwargs: Any | None,
    ) -> AsyncIterator[tuple[int, Output | Exception]]: ...

    async def abatch_as_completed(
        self,
        inputs: Sequence[Input],
        config: RunnableConfig | Sequence[RunnableConfig] | None = None,
        *,
        return_exceptions: bool = False,
        **kwargs: Any | None,
    ) -> AsyncIterator[tuple[int, Output | Exception]]:
        """Run `ainvoke` in parallel on a list of inputs.

        Yields results as they complete.

        Args:
            inputs: A list of inputs to the `Runnable`.
            config: A config to use when invoking the `Runnable`.

                The config supports standard keys like `'tags'`, `'metadata'` for
                tracing purposes, `'max_concurrency'` for controlling how much work to
                do in parallel, and other keys.

                Please refer to `RunnableConfig` for more details.
            return_exceptions: Whether to return exceptions instead of raising them.
            **kwargs: Additional keyword arguments to pass to the `Runnable`.

        Yields:
            A tuple of the index of the input and the output from the `Runnable`.

        """
        if not inputs:
            return

        configs = get_config_list(config, len(inputs))
        # Get max_concurrency from first config, defaulting to None (unlimited)
        max_concurrency = configs[0].get("max_concurrency") if configs else None
        semaphore = asyncio.Semaphore(max_concurrency) if max_concurrency else None

        async def ainvoke_task(
            i: int, input_: Input, config: RunnableConfig
        ) -> tuple[int, Output | Exception]:
            if return_exceptions:
                try:
                    out: Output | Exception = await self.ainvoke(
                        input_, config, **kwargs
                    )
                except Exception as e:
                    out = e
            else:
                out = await self.ainvoke(input_, config, **kwargs)
            return (i, out)

        coros = [
            gated_coro(semaphore, ainvoke_task(i, input_, config))
            if semaphore
            else ainvoke_task(i, input_, config)
            for i, (input_, config) in enumerate(zip(inputs, configs, strict=False))
        ]

        for coro in asyncio.as_completed(coros):
            yield await coro

    def stream(
        self,
        input: Input,
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> Iterator[Output]:
        """Default implementation of `stream`, which calls `invoke`.

        Subclasses must override this method if they support streaming output.

        Args:
            input: The input to the `Runnable`.
            config: The config to use for the `Runnable`.
            **kwargs: Additional keyword arguments to pass to the `Runnable`.

        Yields:
            The output of the `Runnable`.

        """
        yield self.invoke(input, config, **kwargs)

    async def astream(
        self,
        input: Input,
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> AsyncIterator[Output]:
        """Default implementation of `astream`, which calls `ainvoke`.

        Subclasses must override this method if they support streaming output.

        Args:
            input: The input to the `Runnable`.
            config: The config to use for the `Runnable`.
            **kwargs: Additional keyword arguments to pass to the `Runnable`.

        Yields:
            The output of the `Runnable`.

        """
        yield await self.ainvoke(input, config, **kwargs)

    @overload
    def astream_log(
        self,
        input: Any,
        config: RunnableConfig | None = None,
        *,
        diff: Literal[True] = True,
        with_streamed_output_list: bool = True,
        include_names: Sequence[str] | None = None,
        include_types: Sequence[str] | None = None,
        include_tags: Sequence[str] | None = None,
        exclude_names: Sequence[str] | None = None,
        exclude_types: Sequence[str] | None = None,
        exclude_tags: Sequence[str] | None = None,
        **kwargs: Any,
    ) -> AsyncIterator[RunLogPatch]: ...

    @overload
    def astream_log(
        self,
        input: Any,
        config: RunnableConfig | None = None,
        *,
        diff: Literal[False],
        with_streamed_output_list: bool = True,
        include_names: Sequence[str] | None = None,
        include_types: Sequence[str] | None = None,
        include_tags: Sequence[str] | None = None,
        exclude_names: Sequence[str] | None = None,
        exclude_types: Sequence[str] | None = None,
        exclude_tags: Sequence[str] | None = None,
        **kwargs: Any,
    ) -> AsyncIterator[RunLog]: ...

    async def astream_log(
        self,
        input: Any,
        config: RunnableConfig | None = None,
        *,
        diff: bool = True,
        with_streamed_output_list: bool = True,
        include_names: Sequence[str] | None = None,
        include_types: Sequence[str] | None = None,
        include_tags: Sequence[str] | None = None,
        exclude_names: Sequence[str] | None = None,
        exclude_types: Sequence[str] | None = None,
        exclude_tags: Sequence[str] | None = None,
        **kwargs: Any,
    ) -> AsyncIterator[RunLogPatch] | AsyncIterator[RunLog]:
        """Stream all output from a `Runnable`, as reported to the callback system.

        This includes all inner runs of LLMs, Retrievers, Tools, etc.

        Output is streamed as Log objects, which include a list of
        Jsonpatch ops that describe how the state of the run has changed in each
        step, and the final state of the run.

        The Jsonpatch ops can be applied in order to construct state.

        Args:
            input: The input to the `Runnable`.
            config: The config to use for the `Runnable`.
            diff: Whether to yield diffs between each step or the current state.
            with_streamed_output_list: Whether to yield the `streamed_output` list.
            include_names: Only include logs with these names.
            include_types: Only include logs with these types.
            include_tags: Only include logs with these tags.
            exclude_names: Exclude logs with these names.
            exclude_types: Exclude logs with these types.
            exclude_tags: Exclude logs with these tags.
            **kwargs: Additional keyword arguments to pass to the `Runnable`.

        Yields:
            A `RunLogPatch` or `RunLog` object.

        """
        stream = LogStreamCallbackHandler(
            auto_close=False,
            include_names=include_names,
            include_types=include_types,
            include_tags=include_tags,
            exclude_names=exclude_names,
            exclude_types=exclude_types,
            exclude_tags=exclude_tags,
            _schema_format="original",
        )

        # Mypy isn't resolving the overloads here
        # Likely an issue b/c `self` is being passed through
        # and it's can't map it to Runnable[Input,Output]?
        async for item in _astream_log_implementation(  # type: ignore[call-overload]
            self,
            input,
            config,
            diff=diff,
            stream=stream,
            with_streamed_output_list=with_streamed_output_list,
            **kwargs,
        ):
            yield item

    async def astream_events(
        self,
        input: Any,
        config: RunnableConfig | None = None,
        *,
        version: Literal["v1", "v2"] = "v2",
        include_names: Sequence[str] | None = None,
        include_types: Sequence[str] | None = None,
        include_tags: Sequence[str] | None = None,
        exclude_names: Sequence[str] | None = None,
        exclude_types: Sequence[str] | None = None,
        exclude_tags: Sequence[str] | None = None,
        **kwargs: Any,
    ) -> AsyncIterator[StreamEvent]:
        """Generate a stream of events.

        Use to create an iterator over `StreamEvent` that provide real-time information
        about the progress of the `Runnable`, including `StreamEvent` from intermediate
        results.

        A `StreamEvent` is a dictionary with the following schema:

        - `event`: Event names are of the format:
            `on_[runnable_type]_(start|stream|end)`.
        - `name`: The name of the `Runnable` that generated the event.
        - `run_id`: Randomly generated ID associated with the given execution of the
            `Runnable` that emitted the event. A child `Runnable` that gets invoked as
            part of the execution of a parent `Runnable` is assigned its own unique ID.
        - `parent_ids`: The IDs of the parent runnables that generated the event. The
            root `Runnable` will have an empty list. The order of the parent IDs is from
            the root to the immediate parent. Only available for v2 version of the API.
            The v1 version of the API will return an empty list.
        - `tags`: The tags of the `Runnable` that generated the event.
        - `metadata`: The metadata of the `Runnable` that generated the event.
        - `data`: The data associated with the event. The contents of this field
            depend on the type of event. See the table below for more details.

        Below is a table that illustrates some events that might be emitted by various
        chains. Metadata fields have been omitted from the table for brevity.
        Chain definitions have been included after the table.

        !!! note
            This reference table is for the v2 version of the schema.

        | event                  | name                 | chunk                               | input                                             | output                                              |
        | ---------------------- | -------------------- | ----------------------------------- | ------------------------------------------------- | --------------------------------------------------- |
        | `on_chat_model_start`  | `'[model name]'`     |                                     | `{"messages": [[SystemMessage, HumanMessage]]}`   |                                                     |
        | `on_chat_model_stream` | `'[model name]'`     | `AIMessageChunk(content="hello")`   |                                                   |                                                     |
        | `on_chat_model_end`    | `'[model name]'`     |                                     | `{"messages": [[SystemMessage, HumanMessage]]}`   | `AIMessageChunk(content="hello world")`             |
        | `on_llm_start`         | `'[model name]'`     |                                     | `{'input': 'hello'}`                              |                                                     |
        | `on_llm_stream`        | `'[model name]'`     | `'Hello' `                          |                                                   |                                                     |
        | `on_llm_end`           | `'[model name]'`     |                                     | `'Hello human!'`                                  |                                                     |
        | `on_chain_start`       | `'format_docs'`      |                                     |                                                   |                                                     |
        | `on_chain_stream`      | `'format_docs'`      | `'hello world!, goodbye world!'`    |                                                   |                                                     |
        | `on_chain_end`         | `'format_docs'`      |                                     | `[Document(...)]`                                 | `'hello world!, goodbye world!'`                    |
        | `on_tool_start`        | `'some_tool'`        |                                     | `{"x": 1, "y": "2"}`                              |                                                     |
        | `on_tool_end`          | `'some_tool'`        |                                     |                                                   | `{"x": 1, "y": "2"}`                                |
        | `on_retriever_start`   | `'[retriever name]'` |                                     | `{"query": "hello"}`                              |                                                     |
        | `on_retriever_end`     | `'[retriever name]'` |                                     | `{"query": "hello"}`                              | `[Document(...), ..]`                               |
        | `on_prompt_start`      | `'[template_name]'`  |                                     | `{"question": "hello"}`                           |                                                     |
        | `on_prompt_end`        | `'[template_name]'`  |                                     | `{"question": "hello"}`                           | `ChatPromptValue(messages: [SystemMessage, ...])`   |

        In addition to the standard events, users can also dispatch custom events (see example below).

        Custom events will be only be surfaced with in the v2 version of the API!

        A custom event has following format:

        | Attribute   | Type   | Description                                                                                               |
        | ----------- | ------ | --------------------------------------------------------------------------------------------------------- |
        | `name`      | `str`  | A user defined name for the event.                                                                        |
        | `data`      | `Any`  | The data associated with the event. This can be anything, though we suggest making it JSON serializable.  |

        Here are declarations associated with the standard events shown above:

        `format_docs`:

        ```python
        def format_docs(docs: list[Document]) -> str:
            '''Format the docs.'''
            return ", ".join([doc.page_content for doc in docs])


        format_docs = RunnableLambda(format_docs)
        ```

        `some_tool`:

        ```python
        @tool
        def some_tool(x: int, y: str) -> dict:
            '''Some_tool.'''
            return {"x": x, "y": y}
        ```

        `prompt`:

        ```python
        template = ChatPromptTemplate.from_messages(
            [
                ("system", "You are Cat Agent 007"),
                ("human", "{question}"),
            ]
        ).with_config({"run_name": "my_template", "tags": ["my_template"]})
        ```

        For instance:

        ```python
        from langchain_core.runnables import RunnableLambda


        async def reverse(s: str) -> str:
            return s[::-1]


        chain = RunnableLambda(func=reverse)

        events = [event async for event in chain.astream_events("hello", version="v2")]

        # Will produce the following events
        # (run_id, and parent_ids has been omitted for brevity):
        [
            {
                "data": {"input": "hello"},
                "event": "on_chain_start",
                "metadata": {},
                "name": "reverse",
                "tags": [],
            },
            {
                "data": {"chunk": "olleh"},
                "event": "on_chain_stream",
                "metadata": {},
                "name": "reverse",
                "tags": [],
            },
            {
                "data": {"output": "olleh"},
                "event": "on_chain_end",
                "metadata": {},
                "name": "reverse",
                "tags": [],
            },
        ]
        ```

        ```python title="Example: Dispatch Custom Event"
        from langchain_core.callbacks.manager import (
            adispatch_custom_event,
        )
        from langchain_core.runnables import RunnableLambda, RunnableConfig
        import asyncio


        async def slow_thing(some_input: str, config: RunnableConfig) -> str:
            \"\"\"Do something that takes a long time.\"\"\"
            await asyncio.sleep(1) # Placeholder for some slow operation
            await adispatch_custom_event(
                "progress_event",
                {"message": "Finished step 1 of 3"},
                config=config # Must be included for python < 3.10
            )
            await asyncio.sleep(1) # Placeholder for some slow operation
            await adispatch_custom_event(
                "progress_event",
                {"message": "Finished step 2 of 3"},
                config=config # Must be included for python < 3.10
            )
            await asyncio.sleep(1) # Placeholder for some slow operation
            return "Done"

        slow_thing = RunnableLambda(slow_thing)

        async for event in slow_thing.astream_events("some_input", version="v2"):
            print(event)
        ```

        Args:
            input: The input to the `Runnable`.
            config: The config to use for the `Runnable`.
            version: The version of the schema to use either `'v2'` or `'v1'`.
                Users should use `'v2'`.
                `'v1'` is for backwards compatibility and will be deprecated
                in `0.4.0`.
                No default will be assigned until the API is stabilized.
                custom events will only be surfaced in `'v2'`.
            include_names: Only include events from `Runnable` objects with matching names.
            include_types: Only include events from `Runnable` objects with matching types.
            include_tags: Only include events from `Runnable` objects with matching tags.
            exclude_names: Exclude events from `Runnable` objects with matching names.
            exclude_types: Exclude events from `Runnable` objects with matching types.
            exclude_tags: Exclude events from `Runnable` objects with matching tags.
            **kwargs: Additional keyword arguments to pass to the `Runnable`.
                These will be passed to `astream_log` as this implementation
                of `astream_events` is built on top of `astream_log`.

        Yields:
            An async stream of `StreamEvent`.

        Raises:
            NotImplementedError: If the version is not `'v1'` or `'v2'`.

        """  # noqa: E501
        if version == "v2":
            event_stream = _astream_events_implementation_v2(
                self,
                input,
                config=config,
                include_names=include_names,
                include_types=include_types,
                include_tags=include_tags,
                exclude_names=exclude_names,
                exclude_types=exclude_types,
                exclude_tags=exclude_tags,
                **kwargs,
            )
        elif version == "v1":
            # First implementation, built on top of astream_log API
            # This implementation will be deprecated as of 0.2.0
            event_stream = _astream_events_implementation_v1(
                self,
                input,
                config=config,
                include_names=include_names,
                include_types=include_types,
                include_tags=include_tags,
                exclude_names=exclude_names,
                exclude_types=exclude_types,
                exclude_tags=exclude_tags,
                **kwargs,
            )
        else:
            msg = 'Only versions "v1" and "v2" of the schema is currently supported.'
            raise NotImplementedError(msg)

        async with aclosing(event_stream):
            async for event in event_stream:
                yield event

    def transform(
        self,
        input: Iterator[Input],
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> Iterator[Output]:
        """Transform inputs to outputs.

        Default implementation of transform, which buffers input and calls `astream`.

        Subclasses must override this method if they can start producing output while
        input is still being generated.

        Args:
            input: An iterator of inputs to the `Runnable`.
            config: The config to use for the `Runnable`.
            **kwargs: Additional keyword arguments to pass to the `Runnable`.

        Yields:
            The output of the `Runnable`.

        """
        final: Input
        got_first_val = False

        for ichunk in input:
            # The default implementation of transform is to buffer input and
            # then call stream.
            # It'll attempt to gather all input into a single chunk using
            # the `+` operator.
            # If the input is not addable, then we'll assume that we can
            # only operate on the last chunk,
            # and we'll iterate until we get to the last chunk.
            if not got_first_val:
                final = ichunk
                got_first_val = True
            else:
                try:
                    final = final + ichunk  # type: ignore[operator]
                except TypeError:
                    final = ichunk

        if got_first_val:
            yield from self.stream(final, config, **kwargs)

    async def atransform(
        self,
        input: AsyncIterator[Input],
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> AsyncIterator[Output]:
        """Transform inputs to outputs.

        Default implementation of atransform, which buffers input and calls `astream`.

        Subclasses must override this method if they can start producing output while
        input is still being generated.

        Args:
            input: An async iterator of inputs to the `Runnable`.
            config: The config to use for the `Runnable`.
            **kwargs: Additional keyword arguments to pass to the `Runnable`.

        Yields:
            The output of the `Runnable`.

        """
        final: Input
        got_first_val = False

        async for ichunk in input:
            # The default implementation of transform is to buffer input and
            # then call stream.
            # It'll attempt to gather all input into a single chunk using
            # the `+` operator.
            # If the input is not addable, then we'll assume that we can
            # only operate on the last chunk,
            # and we'll iterate until we get to the last chunk.
            if not got_first_val:
                final = ichunk
                got_first_val = True
            else:
                try:
                    final = final + ichunk  # type: ignore[operator]
                except TypeError:
                    final = ichunk

        if got_first_val:
            async for output in self.astream(final, config, **kwargs):
                yield output

    def bind(self, **kwargs: Any) -> Runnable[Input, Output]:
        """Bind arguments to a `Runnable`, returning a new `Runnable`.

        Useful when a `Runnable` in a chain requires an argument that is not
        in the output of the previous `Runnable` or included in the user input.

        Args:
            **kwargs: The arguments to bind to the `Runnable`.

        Returns:
            A new `Runnable` with the arguments bound.

        Example:
            ```python
            from langchain_ollama import ChatOllama
            from langchain_core.output_parsers import StrOutputParser

            model = ChatOllama(model="llama3.1")

            # Without bind
            chain = model | StrOutputParser()

            chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
            # Output is 'One two three four five.'

            # With bind
            chain = model.bind(stop=["three"]) | StrOutputParser()

            chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
            # Output is 'One two'
            ```
        """
        return RunnableBinding(bound=self, kwargs=kwargs, config={})

    def with_config(
        self,
        config: RunnableConfig | None = None,
        # Sadly Unpack is not well-supported by mypy so this will have to be untyped
        **kwargs: Any,
    ) -> Runnable[Input, Output]:
        """Bind config to a `Runnable`, returning a new `Runnable`.

        Args:
            config: The config to bind to the `Runnable`.
            **kwargs: Additional keyword arguments to pass to the `Runnable`.

        Returns:
            A new `Runnable` with the config bound.

        """
        return RunnableBinding(
            bound=self,
            config=cast(
                "RunnableConfig",
                {**(config or {}), **kwargs},
            ),
            kwargs={},
        )

    def with_listeners(
        self,
        *,
        on_start: Callable[[Run], None]
        | Callable[[Run, RunnableConfig], None]
        | None = None,
        on_end: Callable[[Run], None]
        | Callable[[Run, RunnableConfig], None]
        | None = None,
        on_error: Callable[[Run], None]
        | Callable[[Run, RunnableConfig], None]
        | None = None,
    ) -> Runnable[Input, Output]:
        """Bind lifecycle listeners to a `Runnable`, returning a new `Runnable`.

        The Run object contains information about the run, including its `id`,
        `type`, `input`, `output`, `error`, `start_time`, `end_time`, and
        any tags or metadata added to the run.

        Args:
            on_start: Called before the `Runnable` starts running, with the `Run`
                object.
            on_end: Called after the `Runnable` finishes running, with the `Run`
                object.
            on_error: Called if the `Runnable` throws an error, with the `Run`
                object.

        Returns:
            A new `Runnable` with the listeners bound.

        Example:
            ```python
            from langchain_core.runnables import RunnableLambda
            from langchain_core.tracers.schemas import Run

            import time


            def test_runnable(time_to_sleep: int):
                time.sleep(time_to_sleep)


            def fn_start(run_obj: Run):
                print("start_time:", run_obj.start_time)


            def fn_end(run_obj: Run):
                print("end_time:", run_obj.end_time)


            chain = RunnableLambda(test_runnable).with_listeners(
                on_start=fn_start, on_end=fn_end
            )
            chain.invoke(2)
            ```
        """
        return RunnableBinding(
            bound=self,
            config_factories=[
                lambda config: {
                    "callbacks": [
                        RootListenersTracer(
                            config=config,
                            on_start=on_start,
                            on_end=on_end,
                            on_error=on_error,
                        )
                    ],
                }
            ],
        )

    def with_alisteners(
        self,
        *,
        on_start: AsyncListener | None = None,
        on_end: AsyncListener | None = None,
        on_error: AsyncListener | None = None,
    ) -> Runnable[Input, Output]:
        """Bind async lifecycle listeners to a `Runnable`.

        Returns a new `Runnable`.

        The Run object contains information about the run, including its `id`,
        `type`, `input`, `output`, `error`, `start_time`, `end_time`, and
        any tags or metadata added to the run.

        Args:
            on_start: Called asynchronously before the `Runnable` starts running,
                with the `Run` object.
            on_end: Called asynchronously after the `Runnable` finishes running,
                with the `Run` object.
            on_error: Called asynchronously if the `Runnable` throws an error,
                with the `Run` object.

        Returns:
            A new `Runnable` with the listeners bound.

        Example:
            ```python
            from langchain_core.runnables import RunnableLambda, Runnable
            from datetime import datetime, timezone
            import time
            import asyncio


            def format_t(timestamp: float) -> str:
                return datetime.fromtimestamp(timestamp, tz=timezone.utc).isoformat()


            async def test_runnable(time_to_sleep: int):
                print(f"Runnable[{time_to_sleep}s]: starts at {format_t(time.time())}")
                await asyncio.sleep(time_to_sleep)
                print(f"Runnable[{time_to_sleep}s]: ends at {format_t(time.time())}")


            async def fn_start(run_obj: Runnable):
                print(f"on start callback starts at {format_t(time.time())}")
                await asyncio.sleep(3)
                print(f"on start callback ends at {format_t(time.time())}")


            async def fn_end(run_obj: Runnable):
                print(f"on end callback starts at {format_t(time.time())}")
                await asyncio.sleep(2)
                print(f"on end callback ends at {format_t(time.time())}")


            runnable = RunnableLambda(test_runnable).with_alisteners(
                on_start=fn_start, on_end=fn_end
            )


            async def concurrent_runs():
                await asyncio.gather(runnable.ainvoke(2), runnable.ainvoke(3))


            asyncio.run(concurrent_runs())
            # Result:
            # on start callback starts at 2025-03-01T07:05:22.875378+00:00
            # on start callback starts at 2025-03-01T07:05:22.875495+00:00
            # on start callback ends at 2025-03-01T07:05:25.878862+00:00
            # on start callback ends at 2025-03-01T07:05:25.878947+00:00
            # Runnable[2s]: starts at 2025-03-01T07:05:25.879392+00:00
            # Runnable[3s]: starts at 2025-03-01T07:05:25.879804+00:00
            # Runnable[2s]: ends at 2025-03-01T07:05:27.881998+00:00
            # on end callback starts at 2025-03-01T07:05:27.882360+00:00
            # Runnable[3s]: ends at 2025-03-01T07:05:28.881737+00:00
            # on end callback starts at 2025-03-01T07:05:28.882428+00:00
            # on end callback ends at 2025-03-01T07:05:29.883893+00:00
            # on end callback ends at 2025-03-01T07:05:30.884831+00:00
            ```
        """
        return RunnableBinding(
            bound=self,
            config_factories=[
                lambda config: {
                    "callbacks": [
                        AsyncRootListenersTracer(
                            config=config,
                            on_start=on_start,
                            on_end=on_end,
                            on_error=on_error,
                        )
                    ],
                }
            ],
        )

    def with_types(
        self,
        *,
        input_type: type[Input] | None = None,
        output_type: type[Output] | None = None,
    ) -> Runnable[Input, Output]:
        """Bind input and output types to a `Runnable`, returning a new `Runnable`.

        Args:
            input_type: The input type to bind to the `Runnable`.
            output_type: The output type to bind to the `Runnable`.

        Returns:
            A new `Runnable` with the types bound.
        """
        return RunnableBinding(
            bound=self,
            custom_input_type=input_type,
            custom_output_type=output_type,
            kwargs={},
        )

    def with_retry(
        self,
        *,
        retry_if_exception_type: tuple[type[BaseException], ...] = (Exception,),
        wait_exponential_jitter: bool = True,
        exponential_jitter_params: ExponentialJitterParams | None = None,
        stop_after_attempt: int = 3,
    ) -> Runnable[Input, Output]:
        """Create a new `Runnable` that retries the original `Runnable` on exceptions.

        Args:
            retry_if_exception_type: A tuple of exception types to retry on.
            wait_exponential_jitter: Whether to add jitter to the wait
                time between retries.
            stop_after_attempt: The maximum number of attempts to make before
                giving up.
            exponential_jitter_params: Parameters for
                `tenacity.wait_exponential_jitter`. Namely: `initial`, `max`,
                `exp_base`, and `jitter` (all `float` values).

        Returns:
            A new `Runnable` that retries the original `Runnable` on exceptions.

        Example:
            ```python
            from langchain_core.runnables import RunnableLambda

            count = 0


            def _lambda(x: int) -> None:
                global count
                count = count + 1
                if x == 1:
                    raise ValueError("x is 1")
                else:
                    pass


            runnable = RunnableLambda(_lambda)
            try:
                runnable.with_retry(
                    stop_after_attempt=2,
                    retry_if_exception_type=(ValueError,),
                ).invoke(1)
            except ValueError:
                pass

            assert count == 2
            ```
        """
        # Import locally to prevent circular import
        from langchain_core.runnables.retry import RunnableRetry  # noqa: PLC0415

        return RunnableRetry(
            bound=self,
            kwargs={},
            config={},
            retry_exception_types=retry_if_exception_type,
            wait_exponential_jitter=wait_exponential_jitter,
            max_attempt_number=stop_after_attempt,
            exponential_jitter_params=exponential_jitter_params,
        )

    def map(self) -> Runnable[list[Input], list[Output]]:
        """Return a new `Runnable` that maps a list of inputs to a list of outputs.

        Calls `invoke` with each input.

        Returns:
            A new `Runnable` that maps a list of inputs to a list of outputs.

        Example:
            ```python
            from langchain_core.runnables import RunnableLambda


            def _lambda(x: int) -> int:
                return x + 1


            runnable = RunnableLambda(_lambda)
            print(runnable.map().invoke([1, 2, 3]))  # [2, 3, 4]
            ```
        """
        return RunnableEach(bound=self)

    def with_fallbacks(
        self,
        fallbacks: Sequence[Runnable[Input, Output]],
        *,
        exceptions_to_handle: tuple[type[BaseException], ...] = (Exception,),
        exception_key: str | None = None,
    ) -> RunnableWithFallbacksT[Input, Output]:
        """Add fallbacks to a `Runnable`, returning a new `Runnable`.

        The new `Runnable` will try the original `Runnable`, and then each fallback
        in order, upon failures.

        Args:
            fallbacks: A sequence of runnables to try if the original `Runnable`
                fails.
            exceptions_to_handle: A tuple of exception types to handle.
            exception_key: If `string` is specified then handled exceptions will be
                passed to fallbacks as part of the input under the specified key.

                If `None`, exceptions will not be passed to fallbacks.

                If used, the base `Runnable` and its fallbacks must accept a
                dictionary as input.

        Returns:
            A new `Runnable` that will try the original `Runnable`, and then each
                Fallback in order, upon failures.

        Example:
            ```python
            from typing import Iterator

            from langchain_core.runnables import RunnableGenerator


            def _generate_immediate_error(input: Iterator) -> Iterator[str]:
                raise ValueError()
                yield ""


            def _generate(input: Iterator) -> Iterator[str]:
                yield from "foo bar"


            runnable = RunnableGenerator(_generate_immediate_error).with_fallbacks(
                [RunnableGenerator(_generate)]
            )
            print("".join(runnable.stream({})))  # foo bar
            ```

        Args:
            fallbacks: A sequence of runnables to try if the original `Runnable`
                fails.
            exceptions_to_handle: A tuple of exception types to handle.
            exception_key: If `string` is specified then handled exceptions will be
                passed to fallbacks as part of the input under the specified key.

                If `None`, exceptions will not be passed to fallbacks.

                If used, the base `Runnable` and its fallbacks must accept a
                dictionary as input.

        Returns:
            A new `Runnable` that will try the original `Runnable`, and then each
                Fallback in order, upon failures.
        """
        # Import locally to prevent circular import
        from langchain_core.runnables.fallbacks import (  # noqa: PLC0415
            RunnableWithFallbacks,
        )

        return RunnableWithFallbacks(
            runnable=self,
            fallbacks=fallbacks,
            exceptions_to_handle=exceptions_to_handle,
            exception_key=exception_key,
        )

    """ --- Helper methods for Subclasses --- """

    def _call_with_config(
        self,
        func: Callable[[Input], Output]
        | Callable[[Input, CallbackManagerForChainRun], Output]
        | Callable[[Input, CallbackManagerForChainRun, RunnableConfig], Output],
        input_: Input,
        config: RunnableConfig | None,
        run_type: str | None = None,
        serialized: dict[str, Any] | None = None,
        **kwargs: Any | None,
    ) -> Output:
        """Call with config.

        Helper method to transform an `Input` value to an `Output` value,
        with callbacks.

        Use this method to implement `invoke` in subclasses.

        """
        config = ensure_config(config)
        callback_manager = get_callback_manager_for_config(config)
        run_manager = callback_manager.on_chain_start(
            serialized,
            input_,
            run_type=run_type,
            name=config.get("run_name") or self.get_name(),
            run_id=config.pop("run_id", None),
        )
        try:
            child_config = patch_config(config, callbacks=run_manager.get_child())
            with set_config_context(child_config) as context:
                output = cast(
                    "Output",
                    context.run(
                        call_func_with_variable_args,  # type: ignore[arg-type]
                        func,
                        input_,
                        config,
                        run_manager,
                        **kwargs,
                    ),
                )
        except BaseException as e:
            run_manager.on_chain_error(e)
            raise
        else:
            run_manager.on_chain_end(output)
            return output

    async def _acall_with_config(
        self,
        func: Callable[[Input], Awaitable[Output]]
        | Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]]
        | Callable[
            [Input, AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]
        ],
        input_: Input,
        config: RunnableConfig | None,
        run_type: str | None = None,
        serialized: dict[str, Any] | None = None,
        **kwargs: Any | None,
    ) -> Output:
        """Async call with config.

        Helper method to transform an `Input` value to an `Output` value,
        with callbacks.

        Use this method to implement `ainvoke` in subclasses.
        """
        config = ensure_config(config)
        callback_manager = get_async_callback_manager_for_config(config)
        run_manager = await callback_manager.on_chain_start(
            serialized,
            input_,
            run_type=run_type,
            name=config.get("run_name") or self.get_name(),
            run_id=config.pop("run_id", None),
        )
        try:
            child_config = patch_config(config, callbacks=run_manager.get_child())
            with set_config_context(child_config) as context:
                coro = acall_func_with_variable_args(
                    func, input_, config, run_manager, **kwargs
                )
                output: Output = await coro_with_context(coro, context)
        except BaseException as e:
            await run_manager.on_chain_error(e)
            raise
        else:
            await run_manager.on_chain_end(output)
            return output

    def _batch_with_config(
        self,
        func: Callable[[list[Input]], list[Exception | Output]]
        | Callable[
            [list[Input], list[CallbackManagerForChainRun]], list[Exception | Output]
        ]
        | Callable[
            [list[Input], list[CallbackManagerForChainRun], list[RunnableConfig]],
            list[Exception | Output],
        ],
        inputs: list[Input],
        config: RunnableConfig | list[RunnableConfig] | None = None,
        *,
        return_exceptions: bool = False,
        run_type: str | None = None,
        **kwargs: Any | None,
    ) -> list[Output]:
        """Transform a list of inputs to a list of outputs, with callbacks.

        Helper method to transform an `Input` value to an `Output` value,
        with callbacks. Use this method to implement `invoke` in subclasses.

        """
        if not inputs:
            return []

        configs = get_config_list(config, len(inputs))
        callback_managers = [get_callback_manager_for_config(c) for c in configs]
        run_managers = [
            callback_manager.on_chain_start(
                None,
                input_,
                run_type=run_type,
                name=config.get("run_name") or self.get_name(),
                run_id=config.pop("run_id", None),
            )
            for callback_manager, input_, config in zip(
                callback_managers, inputs, configs, strict=False
            )
        ]
        try:
            if accepts_config(func):
                kwargs["config"] = [
                    patch_config(c, callbacks=rm.get_child())
                    for c, rm in zip(configs, run_managers, strict=False)
                ]
            if accepts_run_manager(func):
                kwargs["run_manager"] = run_managers
            output = func(inputs, **kwargs)  # type: ignore[call-arg]
        except BaseException as e:
            for run_manager in run_managers:
                run_manager.on_chain_error(e)
            if return_exceptions:
                return cast("list[Output]", [e for _ in inputs])
            raise
        else:
            first_exception: Exception | None = None
            for run_manager, out in zip(run_managers, output, strict=False):
                if isinstance(out, Exception):
                    first_exception = first_exception or out
                    run_manager.on_chain_error(out)
                else:
                    run_manager.on_chain_end(out)
            if return_exceptions or first_exception is None:
                return cast("list[Output]", output)
            raise first_exception

    async def _abatch_with_config(
        self,
        func: Callable[[list[Input]], Awaitable[list[Exception | Output]]]
        | Callable[
            [list[Input], list[AsyncCallbackManagerForChainRun]],
            Awaitable[list[Exception | Output]],
        ]
        | Callable[
            [list[Input], list[AsyncCallbackManagerForChainRun], list[RunnableConfig]],
            Awaitable[list[Exception | Output]],
        ],
        inputs: list[Input],
        config: RunnableConfig | list[RunnableConfig] | None = None,
        *,
        return_exceptions: bool = False,
        run_type: str | None = None,
        **kwargs: Any | None,
    ) -> list[Output]:
        """Transform a list of inputs to a list of outputs, with callbacks.

        Helper method to transform an `Input` value to an `Output` value,
        with callbacks.

        Use this method to implement `invoke` in subclasses.

        """
        if not inputs:
            return []

        configs = get_config_list(config, len(inputs))
        callback_managers = [get_async_callback_manager_for_config(c) for c in configs]
        run_managers: list[AsyncCallbackManagerForChainRun] = await asyncio.gather(
            *(
                callback_manager.on_chain_start(
                    None,
                    input_,
                    run_type=run_type,
                    name=config.get("run_name") or self.get_name(),
                    run_id=config.pop("run_id", None),
                )
                for callback_manager, input_, config in zip(
                    callback_managers, inputs, configs, strict=False
                )
            )
        )
        try:
            if accepts_config(func):
                kwargs["config"] = [
                    patch_config(c, callbacks=rm.get_child())
                    for c, rm in zip(configs, run_managers, strict=False)
                ]
            if accepts_run_manager(func):
                kwargs["run_manager"] = run_managers
            output = await func(inputs, **kwargs)  # type: ignore[call-arg]
        except BaseException as e:
            await asyncio.gather(
                *(run_manager.on_chain_error(e) for run_manager in run_managers)
            )
            if return_exceptions:
                return cast("list[Output]", [e for _ in inputs])
            raise
        else:
            first_exception: Exception | None = None
            coros: list[Awaitable[None]] = []
            for run_manager, out in zip(run_managers, output, strict=False):
                if isinstance(out, Exception):
                    first_exception = first_exception or out
                    coros.append(run_manager.on_chain_error(out))
                else:
                    coros.append(run_manager.on_chain_end(out))
            await asyncio.gather(*coros)
            if return_exceptions or first_exception is None:
                return cast("list[Output]", output)
            raise first_exception

    def _transform_stream_with_config(
        self,
        inputs: Iterator[Input],
        transformer: Callable[[Iterator[Input]], Iterator[Output]]
        | Callable[[Iterator[Input], CallbackManagerForChainRun], Iterator[Output]]
        | Callable[
            [Iterator[Input], CallbackManagerForChainRun, RunnableConfig],
            Iterator[Output],
        ],
        config: RunnableConfig | None,
        run_type: str | None = None,
        **kwargs: Any | None,
    ) -> Iterator[Output]:
        """Transform a stream with config.

        Helper method to transform an `Iterator` of `Input` values into an
        `Iterator` of `Output` values, with callbacks.

        Use this to implement `stream` or `transform` in `Runnable` subclasses.

        """
        # tee the input so we can iterate over it twice
        input_for_tracing, input_for_transform = tee(inputs, 2)
        # Start the input iterator to ensure the input Runnable starts before this one
        final_input: Input | None = next(input_for_tracing, None)
        final_input_supported = True
        final_output: Output | None = None
        final_output_supported = True

        config = ensure_config(config)
        callback_manager = get_callback_manager_for_config(config)
        run_manager = callback_manager.on_chain_start(
            None,
            {"input": ""},
            run_type=run_type,
            name=config.get("run_name") or self.get_name(),
            run_id=config.pop("run_id", None),
        )
        try:
            child_config = patch_config(config, callbacks=run_manager.get_child())
            if accepts_config(transformer):
                kwargs["config"] = child_config
            if accepts_run_manager(transformer):
                kwargs["run_manager"] = run_manager
            with set_config_context(child_config) as context:
                iterator = context.run(transformer, input_for_transform, **kwargs)  # type: ignore[arg-type]
                if stream_handler := next(
                    (
                        cast("_StreamingCallbackHandler", h)
                        for h in run_manager.handlers
                        # instance check OK here, it's a mixin
                        if isinstance(h, _StreamingCallbackHandler)
                    ),
                    None,
                ):
                    # populates streamed_output in astream_log() output if needed
                    iterator = stream_handler.tap_output_iter(
                        run_manager.run_id, iterator
                    )
                try:
                    while True:
                        chunk: Output = context.run(next, iterator)
                        yield chunk
                        if final_output_supported:
                            if final_output is None:
                                final_output = chunk
                            else:
                                try:
                                    final_output = final_output + chunk  # type: ignore[operator]
                                except TypeError:
                                    final_output = chunk
                                    final_output_supported = False
                        else:
                            final_output = chunk
                except (StopIteration, GeneratorExit):
                    pass
                for ichunk in input_for_tracing:
                    if final_input_supported:
                        if final_input is None:
                            final_input = ichunk
                        else:
                            try:
                                final_input = final_input + ichunk  # type: ignore[operator]
                            except TypeError:
                                final_input = ichunk
                                final_input_supported = False
                    else:
                        final_input = ichunk
        except BaseException as e:
            run_manager.on_chain_error(e, inputs=final_input)
            raise
        else:
            run_manager.on_chain_end(final_output, inputs=final_input)

    async def _atransform_stream_with_config(
        self,
        inputs: AsyncIterator[Input],
        transformer: Callable[[AsyncIterator[Input]], AsyncIterator[Output]]
        | Callable[
            [AsyncIterator[Input], AsyncCallbackManagerForChainRun],
            AsyncIterator[Output],
        ]
        | Callable[
            [AsyncIterator[Input], AsyncCallbackManagerForChainRun, RunnableConfig],
            AsyncIterator[Output],
        ],
        config: RunnableConfig | None,
        run_type: str | None = None,
        **kwargs: Any | None,
    ) -> AsyncIterator[Output]:
        """Transform a stream with config.

        Helper method to transform an Async `Iterator` of `Input` values into an
        Async `Iterator` of `Output` values, with callbacks.

        Use this to implement `astream` or `atransform` in `Runnable` subclasses.

        """
        # tee the input so we can iterate over it twice
        input_for_tracing, input_for_transform = atee(inputs, 2)
        # Start the input iterator to ensure the input Runnable starts before this one
        final_input: Input | None = await py_anext(input_for_tracing, None)
        final_input_supported = True
        final_output: Output | None = None
        final_output_supported = True

        config = ensure_config(config)
        callback_manager = get_async_callback_manager_for_config(config)
        run_manager = await callback_manager.on_chain_start(
            None,
            {"input": ""},
            run_type=run_type,
            name=config.get("run_name") or self.get_name(),
            run_id=config.pop("run_id", None),
        )
        try:
            child_config = patch_config(config, callbacks=run_manager.get_child())
            if accepts_config(transformer):
                kwargs["config"] = child_config
            if accepts_run_manager(transformer):
                kwargs["run_manager"] = run_manager
            with set_config_context(child_config) as context:
                iterator_ = context.run(transformer, input_for_transform, **kwargs)  # type: ignore[arg-type]

                if stream_handler := next(
                    (
                        cast("_StreamingCallbackHandler", h)
                        for h in run_manager.handlers
                        # instance check OK here, it's a mixin
                        if isinstance(h, _StreamingCallbackHandler)
                    ),
                    None,
                ):
                    # populates streamed_output in astream_log() output if needed
                    iterator = stream_handler.tap_output_aiter(
                        run_manager.run_id, iterator_
                    )
                else:
                    iterator = iterator_
                try:
                    while True:
                        chunk = await coro_with_context(py_anext(iterator), context)
                        yield chunk
                        if final_output_supported:
                            if final_output is None:
                                final_output = chunk
                            else:
                                try:
                                    final_output = final_output + chunk
                                except TypeError:
                                    final_output = chunk
                                    final_output_supported = False
                        else:
                            final_output = chunk
                except StopAsyncIteration:
                    pass
                async for ichunk in input_for_tracing:
                    if final_input_supported:
                        if final_input is None:
                            final_input = ichunk
                        else:
                            try:
                                final_input = final_input + ichunk  # type: ignore[operator]
                            except TypeError:
                                final_input = ichunk
                                final_input_supported = False
                    else:
                        final_input = ichunk
        except BaseException as e:
            await run_manager.on_chain_error(e, inputs=final_input)
            raise
        else:
            await run_manager.on_chain_end(final_output, inputs=final_input)
        finally:
            if iterator_ is not None and hasattr(iterator_, "aclose"):
                await iterator_.aclose()

    @beta_decorator.beta(message="This API is in beta and may change in the future.")
    def as_tool(
        self,
        args_schema: type[BaseModel] | None = None,
        *,
        name: str | None = None,
        description: str | None = None,
        arg_types: dict[str, type] | None = None,
    ) -> BaseTool:
        """Create a `BaseTool` from a `Runnable`.

        `as_tool` will instantiate a `BaseTool` with a name, description, and
        `args_schema` from a `Runnable`. Where possible, schemas are inferred
        from `runnable.get_input_schema`.

        Alternatively (e.g., if the `Runnable` takes a dict as input and the specific
        `dict` keys are not typed), the schema can be specified directly with
        `args_schema`.

        You can also pass `arg_types` to just specify the required arguments and their
        types.

        Args:
            args_schema: The schema for the tool.
            name: The name of the tool.
            description: The description of the tool.
            arg_types: A dictionary of argument names to types.

        Returns:
            A `BaseTool` instance.

        Typed dict input:

        ```python
        from typing_extensions import TypedDict
        from langchain_core.runnables import RunnableLambda


        class Args(TypedDict):
            a: int
            b: list[int]


        def f(x: Args) -> str:
            return str(x["a"] * max(x["b"]))


        runnable = RunnableLambda(f)
        as_tool = runnable.as_tool()
        as_tool.invoke({"a": 3, "b": [1, 2]})
        ```

        `dict` input, specifying schema via `args_schema`:

        ```python
        from typing import Any
        from pydantic import BaseModel, Field
        from langchain_core.runnables import RunnableLambda

        def f(x: dict[str, Any]) -> str:
            return str(x["a"] * max(x["b"]))

        class FSchema(BaseModel):
            \"\"\"Apply a function to an integer and list of integers.\"\"\"

            a: int = Field(..., description="Integer")
            b: list[int] = Field(..., description="List of ints")

        runnable = RunnableLambda(f)
        as_tool = runnable.as_tool(FSchema)
        as_tool.invoke({"a": 3, "b": [1, 2]})
        ```

        `dict` input, specifying schema via `arg_types`:

        ```python
        from typing import Any
        from langchain_core.runnables import RunnableLambda


        def f(x: dict[str, Any]) -> str:
            return str(x["a"] * max(x["b"]))


        runnable = RunnableLambda(f)
        as_tool = runnable.as_tool(arg_types={"a": int, "b": list[int]})
        as_tool.invoke({"a": 3, "b": [1, 2]})
        ```

        `str` input:

        ```python
        from langchain_core.runnables import RunnableLambda


        def f(x: str) -> str:
            return x + "a"


        def g(x: str) -> str:
            return x + "z"


        runnable = RunnableLambda(f) | g
        as_tool = runnable.as_tool()
        as_tool.invoke("b")
        ```
        """
        # Avoid circular import
        from langchain_core.tools import convert_runnable_to_tool  # noqa: PLC0415

        return convert_runnable_to_tool(
            self,
            args_schema=args_schema,
            name=name,
            description=description,
            arg_types=arg_types,
        )


class RunnableSerializable(Serializable, Runnable[Input, Output]):
    """Runnable that can be serialized to JSON."""

    name: str | None = None

    model_config = ConfigDict(
        # Suppress warnings from pydantic protected namespaces
        # (e.g., `model_`)
        protected_namespaces=(),
    )

    @override
    def to_json(self) -> SerializedConstructor | SerializedNotImplemented:
        """Serialize the `Runnable` to JSON.

        Returns:
            A JSON-serializable representation of the `Runnable`.

        """
        dumped = super().to_json()
        with contextlib.suppress(Exception):
            dumped["name"] = self.get_name()
        return dumped

    def configurable_fields(
        self, **kwargs: AnyConfigurableField
    ) -> RunnableSerializable[Input, Output]:
        """Configure particular `Runnable` fields at runtime.

        Args:
            **kwargs: A dictionary of `ConfigurableField` instances to configure.

        Raises:
            ValueError: If a configuration key is not found in the `Runnable`.

        Returns:
            A new `Runnable` with the fields configured.

        ```python
        from langchain_core.runnables import ConfigurableField
        from langchain_openai import ChatOpenAI

        model = ChatOpenAI(max_tokens=20).configurable_fields(
            max_tokens=ConfigurableField(
                id="output_token_number",
                name="Max tokens in the output",
                description="The maximum number of tokens in the output",
            )
        )

        # max_tokens = 20
        print("max_tokens_20: ", model.invoke("tell me something about chess").content)

        # max_tokens = 200
        print(
            "max_tokens_200: ",
            model.with_config(configurable={"output_token_number": 200})
            .invoke("tell me something about chess")
            .content,
        )
        ```
        """
        # Import locally to prevent circular import
        from langchain_core.runnables.configurable import (  # noqa: PLC0415
            RunnableConfigurableFields,
        )

        model_fields = type(self).model_fields
        for key in kwargs:
            if key not in model_fields:
                msg = (
                    f"Configuration key {key} not found in {self}: "
                    f"available keys are {model_fields.keys()}"
                )
                raise ValueError(msg)

        return RunnableConfigurableFields(default=self, fields=kwargs)

    def configurable_alternatives(
        self,
        which: ConfigurableField,
        *,
        default_key: str = "default",
        prefix_keys: bool = False,
        **kwargs: Runnable[Input, Output] | Callable[[], Runnable[Input, Output]],
    ) -> RunnableSerializable[Input, Output]:
        """Configure alternatives for `Runnable` objects that can be set at runtime.

        Args:
            which: The `ConfigurableField` instance that will be used to select the
                alternative.
            default_key: The default key to use if no alternative is selected.
            prefix_keys: Whether to prefix the keys with the `ConfigurableField` id.
            **kwargs: A dictionary of keys to `Runnable` instances or callables that
                return `Runnable` instances.

        Returns:
            A new `Runnable` with the alternatives configured.

        ```python
        from langchain_anthropic import ChatAnthropic
        from langchain_core.runnables.utils import ConfigurableField
        from langchain_openai import ChatOpenAI

        model = ChatAnthropic(
            model_name="claude-sonnet-4-5-20250929"
        ).configurable_alternatives(
            ConfigurableField(id="llm"),
            default_key="anthropic",
            openai=ChatOpenAI(),
        )

        # uses the default model ChatAnthropic
        print(model.invoke("which organization created you?").content)

        # uses ChatOpenAI
        print(
            model.with_config(configurable={"llm": "openai"})
            .invoke("which organization created you?")
            .content
        )
        ```
        """
        # Import locally to prevent circular import
        from langchain_core.runnables.configurable import (  # noqa: PLC0415
            RunnableConfigurableAlternatives,
        )

        return RunnableConfigurableAlternatives(
            which=which,
            default=self,
            alternatives=kwargs,
            default_key=default_key,
            prefix_keys=prefix_keys,
        )


def _seq_input_schema(
    steps: list[Runnable[Any, Any]], config: RunnableConfig | None
) -> type[BaseModel]:
    # Import locally to prevent circular import
    from langchain_core.runnables.passthrough import (  # noqa: PLC0415
        RunnableAssign,
        RunnablePick,
    )

    first = steps[0]
    if len(steps) == 1:
        return first.get_input_schema(config)
    if isinstance(first, RunnableAssign):
        next_input_schema = _seq_input_schema(steps[1:], config)
        if not issubclass(next_input_schema, RootModel):
            # it's a dict as expected
            return create_model_v2(
                "RunnableSequenceInput",
                field_definitions={
                    k: (v.annotation, v.default)
                    for k, v in next_input_schema.model_fields.items()
                    if k not in first.mapper.steps__
                },
            )
    elif isinstance(first, RunnablePick):
        return _seq_input_schema(steps[1:], config)

    return first.get_input_schema(config)


def _seq_output_schema(
    steps: list[Runnable[Any, Any]], config: RunnableConfig | None
) -> type[BaseModel]:
    # Import locally to prevent circular import
    from langchain_core.runnables.passthrough import (  # noqa: PLC0415
        RunnableAssign,
        RunnablePick,
    )

    last = steps[-1]
    if len(steps) == 1:
        return last.get_input_schema(config)
    if isinstance(last, RunnableAssign):
        mapper_output_schema = last.mapper.get_output_schema(config)
        prev_output_schema = _seq_output_schema(steps[:-1], config)
        if not issubclass(prev_output_schema, RootModel):
            # it's a dict as expected
            return create_model_v2(
                "RunnableSequenceOutput",
                field_definitions={
                    **{
                        k: (v.annotation, v.default)
                        for k, v in prev_output_schema.model_fields.items()
                    },
                    **{
                        k: (v.annotation, v.default)
                        for k, v in mapper_output_schema.model_fields.items()
                    },
                },
            )
    elif isinstance(last, RunnablePick):
        prev_output_schema = _seq_output_schema(steps[:-1], config)
        if not issubclass(prev_output_schema, RootModel):
            # it's a dict as expected
            if isinstance(last.keys, list):
                return create_model_v2(
                    "RunnableSequenceOutput",
                    field_definitions={
                        k: (v.annotation, v.default)
                        for k, v in prev_output_schema.model_fields.items()
                        if k in last.keys
                    },
                )
            field = prev_output_schema.model_fields[last.keys]
            return create_model_v2(
                "RunnableSequenceOutput", root=(field.annotation, field.default)
            )

    return last.get_output_schema(config)


_RUNNABLE_SEQUENCE_MIN_STEPS = 2


class RunnableSequence(RunnableSerializable[Input, Output]):
    """Sequence of `Runnable` objects, where the output of one is the input of the next.

    **`RunnableSequence`** is the most important composition operator in LangChain
    as it is used in virtually every chain.

    A `RunnableSequence` can be instantiated directly or more commonly by using the
    `|` operator where either the left or right operands (or both) must be a
    `Runnable`.

    Any `RunnableSequence` automatically supports sync, async, batch.

    The default implementations of `batch` and `abatch` utilize threadpools and
    asyncio gather and will be faster than naive invocation of `invoke` or `ainvoke`
    for IO bound `Runnable`s.

    Batching is implemented by invoking the batch method on each component of the
    `RunnableSequence` in order.

    A `RunnableSequence` preserves the streaming properties of its components, so if
    all components of the sequence implement a `transform` method -- which
    is the method that implements the logic to map a streaming input to a streaming
    output -- then the sequence will be able to stream input to output!

    If any component of the sequence does not implement transform then the
    streaming will only begin after this component is run. If there are
    multiple blocking components, streaming begins after the last one.

    !!! note
        `RunnableLambdas` do not support `transform` by default! So if you need to
        use a `RunnableLambdas` be careful about where you place them in a
        `RunnableSequence` (if you need to use the `stream`/`astream` methods).

        If you need arbitrary logic and need streaming, you can subclass
        Runnable, and implement `transform` for whatever logic you need.

    Here is a simple example that uses simple functions to illustrate the use of
    `RunnableSequence`:

        ```python
        from langchain_core.runnables import RunnableLambda


        def add_one(x: int) -> int:
            return x + 1


        def mul_two(x: int) -> int:
            return x * 2


        runnable_1 = RunnableLambda(add_one)
        runnable_2 = RunnableLambda(mul_two)
        sequence = runnable_1 | runnable_2
        # Or equivalently:
        # sequence = RunnableSequence(first=runnable_1, last=runnable_2)
        sequence.invoke(1)
        await sequence.ainvoke(1)

        sequence.batch([1, 2, 3])
        await sequence.abatch([1, 2, 3])
        ```

    Here's an example that uses streams JSON output generated by an LLM:

        ```python
        from langchain_core.output_parsers.json import SimpleJsonOutputParser
        from langchain_openai import ChatOpenAI

        prompt = PromptTemplate.from_template(
            "In JSON format, give me a list of {topic} and their "
            "corresponding names in French, Spanish and in a "
            "Cat Language."
        )

        model = ChatOpenAI()
        chain = prompt | model | SimpleJsonOutputParser()

        async for chunk in chain.astream({"topic": "colors"}):
            print("-")  # noqa: T201
            print(chunk, sep="", flush=True)  # noqa: T201
        ```
    """

    # The steps are broken into first, middle and last, solely for type checking
    # purposes. It allows specifying the `Input` on the first type, the `Output` of
    # the last type.
    first: Runnable[Input, Any]
    """The first `Runnable` in the sequence."""
    middle: list[Runnable[Any, Any]] = Field(default_factory=list)
    """The middle `Runnable` in the sequence."""
    last: Runnable[Any, Output]
    """The last `Runnable` in the sequence."""

    def __init__(
        self,
        *steps: RunnableLike,
        name: str | None = None,
        first: Runnable[Any, Any] | None = None,
        middle: list[Runnable[Any, Any]] | None = None,
        last: Runnable[Any, Any] | None = None,
    ) -> None:
        """Create a new `RunnableSequence`.

        Args:
            steps: The steps to include in the sequence.
            name: The name of the `Runnable`.
            first: The first `Runnable` in the sequence.
            middle: The middle `Runnable` objects in the sequence.
            last: The last `Runnable` in the sequence.

        Raises:
            ValueError: If the sequence has less than 2 steps.
        """
        steps_flat: list[Runnable] = []
        if not steps and first is not None and last is not None:
            steps_flat = [first] + (middle or []) + [last]
        for step in steps:
            if isinstance(step, RunnableSequence):
                steps_flat.extend(step.steps)
            else:
                steps_flat.append(coerce_to_runnable(step))
        if len(steps_flat) < _RUNNABLE_SEQUENCE_MIN_STEPS:
            msg = (
                f"RunnableSequence must have at least {_RUNNABLE_SEQUENCE_MIN_STEPS} "
                f"steps, got {len(steps_flat)}"
            )
            raise ValueError(msg)
        super().__init__(
            first=steps_flat[0],
            middle=list(steps_flat[1:-1]),
            last=steps_flat[-1],
            name=name,
        )

    @classmethod
    @override
    def get_lc_namespace(cls) -> list[str]:
        """Get the namespace of the LangChain object.

        Returns:
            `["langchain", "schema", "runnable"]`
        """
        return ["langchain", "schema", "runnable"]

    @property
    def steps(self) -> list[Runnable[Any, Any]]:
        """All the `Runnable`s that make up the sequence in order.

        Returns:
            A list of `Runnable`s.
        """
        return [self.first, *self.middle, self.last]

    @classmethod
    @override
    def is_lc_serializable(cls) -> bool:
        """Return `True` as this class is serializable."""
        return True

    model_config = ConfigDict(
        arbitrary_types_allowed=True,
    )

    @property
    @override
    def InputType(self) -> type[Input]:
        """The type of the input to the `Runnable`."""
        return self.first.InputType

    @property
    @override
    def OutputType(self) -> type[Output]:
        """The type of the output of the `Runnable`."""
        return self.last.OutputType

    @override
    def get_input_schema(self, config: RunnableConfig | None = None) -> type[BaseModel]:
        """Get the input schema of the `Runnable`.

        Args:
            config: The config to use.

        Returns:
            The input schema of the `Runnable`.

        """
        return _seq_input_schema(self.steps, config)

    @override
    def get_output_schema(
        self, config: RunnableConfig | None = None
    ) -> type[BaseModel]:
        """Get the output schema of the `Runnable`.

        Args:
            config: The config to use.

        Returns:
            The output schema of the `Runnable`.

        """
        return _seq_output_schema(self.steps, config)

    @property
    @override
    def config_specs(self) -> list[ConfigurableFieldSpec]:
        """Get the config specs of the `Runnable`.

        Returns:
            The config specs of the `Runnable`.

        """
        # Import locally to prevent circular import
        return get_unique_config_specs(
            [spec for step in self.steps for spec in step.config_specs]
        )

    @override
    def get_graph(self, config: RunnableConfig | None = None) -> Graph:
        """Get the graph representation of the `Runnable`.

        Args:
            config: The config to use.

        Returns:
            The graph representation of the `Runnable`.

        Raises:
            ValueError: If a `Runnable` has no first or last node.

        """
        # Import locally to prevent circular import
        from langchain_core.runnables.graph import Graph  # noqa: PLC0415

        graph = Graph()
        for step in self.steps:
            current_last_node = graph.last_node()
            step_graph = step.get_graph(config)
            if step is not self.first:
                step_graph.trim_first_node()
            if step is not self.last:
                step_graph.trim_last_node()
            step_first_node, _ = graph.extend(step_graph)
            if not step_first_node:
                msg = f"Runnable {step} has no first node"
                raise ValueError(msg)
            if current_last_node:
                graph.add_edge(current_last_node, step_first_node)

        return graph

    @override
    def __repr__(self) -> str:
        return "\n| ".join(
            repr(s) if i == 0 else indent_lines_after_first(repr(s), "| ")
            for i, s in enumerate(self.steps)
        )

    @override
    def __or__(
        self,
        other: Runnable[Any, Other]
        | Callable[[Iterator[Any]], Iterator[Other]]
        | Callable[[AsyncIterator[Any]], AsyncIterator[Other]]
        | Callable[[Any], Other]
        | Mapping[str, Runnable[Any, Other] | Callable[[Any], Other] | Any],
    ) -> RunnableSerializable[Input, Other]:
        if isinstance(other, RunnableSequence):
            return RunnableSequence(
                self.first,
                *self.middle,
                self.last,
                other.first,
                *other.middle,
                other.last,
                name=self.name or other.name,
            )
        return RunnableSequence(
            self.first,
            *self.middle,
            self.last,
            coerce_to_runnable(other),
            name=self.name,
        )

    @override
    def __ror__(
        self,
        other: Runnable[Other, Any]
        | Callable[[Iterator[Other]], Iterator[Any]]
        | Callable[[AsyncIterator[Other]], AsyncIterator[Any]]
        | Callable[[Other], Any]
        | Mapping[str, Runnable[Other, Any] | Callable[[Other], Any] | Any],
    ) -> RunnableSerializable[Other, Output]:
        if isinstance(other, RunnableSequence):
            return RunnableSequence(
                other.first,
                *other.middle,
                other.last,
                self.first,
                *self.middle,
                self.last,
                name=other.name or self.name,
            )
        return RunnableSequence(
            coerce_to_runnable(other),
            self.first,
            *self.middle,
            self.last,
            name=self.name,
        )

    @override
    def invoke(
        self, input: Input, config: RunnableConfig | None = None, **kwargs: Any
    ) -> Output:
        # setup callbacks and context
        config = ensure_config(config)
        callback_manager = get_callback_manager_for_config(config)
        # start the root run
        run_manager = callback_manager.on_chain_start(
            None,
            input,
            name=config.get("run_name") or self.get_name(),
            run_id=config.pop("run_id", None),
        )
        input_ = input

        # invoke all steps in sequence
        try:
            for i, step in enumerate(self.steps):
                # mark each step as a child run
                config = patch_config(
                    config, callbacks=run_manager.get_child(f"seq:step:{i + 1}")
                )
                with set_config_context(config) as context:
                    if i == 0:
                        input_ = context.run(step.invoke, input_, config, **kwargs)
                    else:
                        input_ = context.run(step.invoke, input_, config)
        # finish the root run
        except BaseException as e:
            run_manager.on_chain_error(e)
            raise
        else:
            run_manager.on_chain_end(input_)
            return cast("Output", input_)

    @override
    async def ainvoke(
        self,
        input: Input,
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> Output:
        # setup callbacks and context
        config = ensure_config(config)
        callback_manager = get_async_callback_manager_for_config(config)
        # start the root run
        run_manager = await callback_manager.on_chain_start(
            None,
            input,
            name=config.get("run_name") or self.get_name(),
            run_id=config.pop("run_id", None),
        )
        input_ = input

        # invoke all steps in sequence
        try:
            for i, step in enumerate(self.steps):
                # mark each step as a child run
                config = patch_config(
                    config, callbacks=run_manager.get_child(f"seq:step:{i + 1}")
                )
                with set_config_context(config) as context:
                    if i == 0:
                        part = functools.partial(step.ainvoke, input_, config, **kwargs)
                    else:
                        part = functools.partial(step.ainvoke, input_, config)
                    input_ = await coro_with_context(part(), context, create_task=True)
            # finish the root run
        except BaseException as e:
            await run_manager.on_chain_error(e)
            raise
        else:
            await run_manager.on_chain_end(input_)
            return cast("Output", input_)

    @override
    def batch(
        self,
        inputs: list[Input],
        config: RunnableConfig | list[RunnableConfig] | None = None,
        *,
        return_exceptions: bool = False,
        **kwargs: Any | None,
    ) -> list[Output]:
        if not inputs:
            return []

        # setup callbacks and context
        configs = get_config_list(config, len(inputs))
        callback_managers = [
            CallbackManager.configure(
                inheritable_callbacks=config.get("callbacks"),
                local_callbacks=None,
                verbose=False,
                inheritable_tags=config.get("tags"),
                local_tags=None,
                inheritable_metadata=config.get("metadata"),
                local_metadata=None,
            )
            for config in configs
        ]
        # start the root runs, one per input
        run_managers = [
            cm.on_chain_start(
                None,
                input_,
                name=config.get("run_name") or self.get_name(),
                run_id=config.pop("run_id", None),
            )
            for cm, input_, config in zip(
                callback_managers, inputs, configs, strict=False
            )
        ]

        # invoke
        try:
            if return_exceptions:
                # Track which inputs (by index) failed so far
                # If an input has failed it will be present in this map,
                # and the value will be the exception that was raised.
                failed_inputs_map: dict[int, Exception] = {}
                for stepidx, step in enumerate(self.steps):
                    # Assemble the original indexes of the remaining inputs
                    # (i.e. the ones that haven't failed yet)
                    remaining_idxs = [
                        i for i in range(len(configs)) if i not in failed_inputs_map
                    ]
                    # Invoke the step on the remaining inputs
                    inputs = step.batch(
                        [
                            inp
                            for i, inp in zip(remaining_idxs, inputs, strict=False)
                            if i not in failed_inputs_map
                        ],
                        [
                            # each step a child run of the corresponding root run
                            patch_config(
                                config,
                                callbacks=rm.get_child(f"seq:step:{stepidx + 1}"),
                            )
                            for i, (rm, config) in enumerate(
                                zip(run_managers, configs, strict=False)
                            )
                            if i not in failed_inputs_map
                        ],
                        return_exceptions=return_exceptions,
                        **(kwargs if stepidx == 0 else {}),
                    )
                    # If an input failed, add it to the map
                    failed_inputs_map.update(
                        {
                            i: inp
                            for i, inp in zip(remaining_idxs, inputs, strict=False)
                            if isinstance(inp, Exception)
                        }
                    )
                    inputs = [inp for inp in inputs if not isinstance(inp, Exception)]
                    # If all inputs have failed, stop processing
                    if len(failed_inputs_map) == len(configs):
                        break

                # Reassemble the outputs, inserting Exceptions for failed inputs
                inputs_copy = inputs.copy()
                inputs = []
                for i in range(len(configs)):
                    if i in failed_inputs_map:
                        inputs.append(cast("Input", failed_inputs_map[i]))
                    else:
                        inputs.append(inputs_copy.pop(0))
            else:
                for i, step in enumerate(self.steps):
                    inputs = step.batch(
                        inputs,
                        [
                            # each step a child run of the corresponding root run
                            patch_config(
                                config, callbacks=rm.get_child(f"seq:step:{i + 1}")
                            )
                            for rm, config in zip(run_managers, configs, strict=False)
                        ],
                        return_exceptions=return_exceptions,
                        **(kwargs if i == 0 else {}),
                    )

        # finish the root runs
        except BaseException as e:
            for rm in run_managers:
                rm.on_chain_error(e)
            if return_exceptions:
                return cast("list[Output]", [e for _ in inputs])
            raise
        else:
            first_exception: Exception | None = None
            for run_manager, out in zip(run_managers, inputs, strict=False):
                if isinstance(out, Exception):
                    first_exception = first_exception or out
                    run_manager.on_chain_error(out)
                else:
                    run_manager.on_chain_end(out)
            if return_exceptions or first_exception is None:
                return cast("list[Output]", inputs)
            raise first_exception

    @override
    async def abatch(
        self,
        inputs: list[Input],
        config: RunnableConfig | list[RunnableConfig] | None = None,
        *,
        return_exceptions: bool = False,
        **kwargs: Any | None,
    ) -> list[Output]:
        if not inputs:
            return []

        # setup callbacks and context
        configs = get_config_list(config, len(inputs))
        callback_managers = [
            AsyncCallbackManager.configure(
                inheritable_callbacks=config.get("callbacks"),
                local_callbacks=None,
                verbose=False,
                inheritable_tags=config.get("tags"),
                local_tags=None,
                inheritable_metadata=config.get("metadata"),
                local_metadata=None,
            )
            for config in configs
        ]
        # start the root runs, one per input
        run_managers: list[AsyncCallbackManagerForChainRun] = await asyncio.gather(
            *(
                cm.on_chain_start(
                    None,
                    input_,
                    name=config.get("run_name") or self.get_name(),
                    run_id=config.pop("run_id", None),
                )
                for cm, input_, config in zip(
                    callback_managers, inputs, configs, strict=False
                )
            )
        )

        # invoke .batch() on each step
        # this uses batching optimizations in Runnable subclasses, like LLM
        try:
            if return_exceptions:
                # Track which inputs (by index) failed so far
                # If an input has failed it will be present in this map,
                # and the value will be the exception that was raised.
                failed_inputs_map: dict[int, Exception] = {}
                for stepidx, step in enumerate(self.steps):
                    # Assemble the original indexes of the remaining inputs
                    # (i.e. the ones that haven't failed yet)
                    remaining_idxs = [
                        i for i in range(len(configs)) if i not in failed_inputs_map
                    ]
                    # Invoke the step on the remaining inputs
                    inputs = await step.abatch(
                        [
                            inp
                            for i, inp in zip(remaining_idxs, inputs, strict=False)
                            if i not in failed_inputs_map
                        ],
                        [
                            # each step a child run of the corresponding root run
                            patch_config(
                                config,
                                callbacks=rm.get_child(f"seq:step:{stepidx + 1}"),
                            )
                            for i, (rm, config) in enumerate(
                                zip(run_managers, configs, strict=False)
                            )
                            if i not in failed_inputs_map
                        ],
                        return_exceptions=return_exceptions,
                        **(kwargs if stepidx == 0 else {}),
                    )
                    # If an input failed, add it to the map
                    failed_inputs_map.update(
                        {
                            i: inp
                            for i, inp in zip(remaining_idxs, inputs, strict=False)
                            if isinstance(inp, Exception)
                        }
                    )
                    inputs = [inp for inp in inputs if not isinstance(inp, Exception)]
                    # If all inputs have failed, stop processing
                    if len(failed_inputs_map) == len(configs):
                        break

                # Reassemble the outputs, inserting Exceptions for failed inputs
                inputs_copy = inputs.copy()
                inputs = []
                for i in range(len(configs)):
                    if i in failed_inputs_map:
                        inputs.append(cast("Input", failed_inputs_map[i]))
                    else:
                        inputs.append(inputs_copy.pop(0))
            else:
                for i, step in enumerate(self.steps):
                    inputs = await step.abatch(
                        inputs,
                        [
                            # each step a child run of the corresponding root run
                            patch_config(
                                config, callbacks=rm.get_child(f"seq:step:{i + 1}")
                            )
                            for rm, config in zip(run_managers, configs, strict=False)
                        ],
                        return_exceptions=return_exceptions,
                        **(kwargs if i == 0 else {}),
                    )
        # finish the root runs
        except BaseException as e:
            await asyncio.gather(*(rm.on_chain_error(e) for rm in run_managers))
            if return_exceptions:
                return cast("list[Output]", [e for _ in inputs])
            raise
        else:
            first_exception: Exception | None = None
            coros: list[Awaitable[None]] = []
            for run_manager, out in zip(run_managers, inputs, strict=False):
                if isinstance(out, Exception):
                    first_exception = first_exception or out
                    coros.append(run_manager.on_chain_error(out))
                else:
                    coros.append(run_manager.on_chain_end(out))
            await asyncio.gather(*coros)
            if return_exceptions or first_exception is None:
                return cast("list[Output]", inputs)
            raise first_exception

    def _transform(
        self,
        inputs: Iterator[Input],
        run_manager: CallbackManagerForChainRun,
        config: RunnableConfig,
        **kwargs: Any,
    ) -> Iterator[Output]:
        steps = [self.first, *self.middle, self.last]
        # transform the input stream of each step with the next
        # steps that don't natively support transforming an input stream will
        # buffer input in memory until all available, and then start emitting output
        final_pipeline = cast("Iterator[Output]", inputs)
        for idx, step in enumerate(steps):
            config = patch_config(
                config, callbacks=run_manager.get_child(f"seq:step:{idx + 1}")
            )
            if idx == 0:
                final_pipeline = step.transform(final_pipeline, config, **kwargs)
            else:
                final_pipeline = step.transform(final_pipeline, config)

        yield from final_pipeline

    async def _atransform(
        self,
        inputs: AsyncIterator[Input],
        run_manager: AsyncCallbackManagerForChainRun,
        config: RunnableConfig,
        **kwargs: Any,
    ) -> AsyncIterator[Output]:
        steps = [self.first, *self.middle, self.last]
        # stream the last steps
        # transform the input stream of each step with the next
        # steps that don't natively support transforming an input stream will
        # buffer input in memory until all available, and then start emitting output
        final_pipeline = cast("AsyncIterator[Output]", inputs)
        for idx, step in enumerate(steps):
            config = patch_config(
                config,
                callbacks=run_manager.get_child(f"seq:step:{idx + 1}"),
            )
            if idx == 0:
                final_pipeline = step.atransform(final_pipeline, config, **kwargs)
            else:
                final_pipeline = step.atransform(final_pipeline, config)
        async for output in final_pipeline:
            yield output

    @override
    def transform(
        self,
        input: Iterator[Input],
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> Iterator[Output]:
        yield from self._transform_stream_with_config(
            input,
            self._transform,
            patch_config(config, run_name=(config or {}).get("run_name") or self.name),
            **kwargs,
        )

    @override
    def stream(
        self,
        input: Input,
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> Iterator[Output]:
        yield from self.transform(iter([input]), config, **kwargs)

    @override
    async def atransform(
        self,
        input: AsyncIterator[Input],
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> AsyncIterator[Output]:
        async for chunk in self._atransform_stream_with_config(
            input,
            self._atransform,
            patch_config(config, run_name=(config or {}).get("run_name") or self.name),
            **kwargs,
        ):
            yield chunk

    @override
    async def astream(
        self,
        input: Input,
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> AsyncIterator[Output]:
        async def input_aiter() -> AsyncIterator[Input]:
            yield input

        async for chunk in self.atransform(input_aiter(), config, **kwargs):
            yield chunk


class RunnableParallel(RunnableSerializable[Input, dict[str, Any]]):
    """Runnable that runs a mapping of `Runnable`s in parallel.

    Returns a mapping of their outputs.

    `RunnableParallel` is one of the two main composition primitives,
    alongside `RunnableSequence`. It invokes `Runnable`s concurrently, providing the
    same input to each.

    A `RunnableParallel` can be instantiated directly or by using a dict literal
    within a sequence.

    Here is a simple example that uses functions to illustrate the use of
    `RunnableParallel`:

        ```python
        from langchain_core.runnables import RunnableLambda


        def add_one(x: int) -> int:
            return x + 1


        def mul_two(x: int) -> int:
            return x * 2


        def mul_three(x: int) -> int:
            return x * 3


        runnable_1 = RunnableLambda(add_one)
        runnable_2 = RunnableLambda(mul_two)
        runnable_3 = RunnableLambda(mul_three)

        sequence = runnable_1 | {  # this dict is coerced to a RunnableParallel
            "mul_two": runnable_2,
            "mul_three": runnable_3,
        }
        # Or equivalently:
        # sequence = runnable_1 | RunnableParallel(
        #     {"mul_two": runnable_2, "mul_three": runnable_3}
        # )
        # Also equivalently:
        # sequence = runnable_1 | RunnableParallel(
        #     mul_two=runnable_2,
        #     mul_three=runnable_3,
        # )

        sequence.invoke(1)
        await sequence.ainvoke(1)

        sequence.batch([1, 2, 3])
        await sequence.abatch([1, 2, 3])
        ```

    `RunnableParallel` makes it easy to run `Runnable`s in parallel. In the below
    example, we simultaneously stream output from two different `Runnable` objects:

        ```python
        from langchain_core.prompts import ChatPromptTemplate
        from langchain_core.runnables import RunnableParallel
        from langchain_openai import ChatOpenAI

        model = ChatOpenAI()
        joke_chain = (
            ChatPromptTemplate.from_template("tell me a joke about {topic}") | model
        )
        poem_chain = (
            ChatPromptTemplate.from_template("write a 2-line poem about {topic}")
            | model
        )

        runnable = RunnableParallel(joke=joke_chain, poem=poem_chain)

        # Display stream
        output = {key: "" for key, _ in runnable.output_schema()}
        for chunk in runnable.stream({"topic": "bear"}):
            for key in chunk:
                output[key] = output[key] + chunk[key].content
            print(output)  # noqa: T201
        ```
    """

    steps__: Mapping[str, Runnable[Input, Any]]

    def __init__(
        self,
        steps__: Mapping[
            str,
            Runnable[Input, Any]
            | Callable[[Input], Any]
            | Mapping[str, Runnable[Input, Any] | Callable[[Input], Any]],
        ]
        | None = None,
        **kwargs: Runnable[Input, Any]
        | Callable[[Input], Any]
        | Mapping[str, Runnable[Input, Any] | Callable[[Input], Any]],
    ) -> None:
        """Create a `RunnableParallel`.

        Args:
            steps__: The steps to include.
            **kwargs: Additional steps to include.

        """
        merged = {**steps__} if steps__ is not None else {}
        merged.update(kwargs)
        super().__init__(
            steps__={key: coerce_to_runnable(r) for key, r in merged.items()}
        )

    @classmethod
    @override
    def is_lc_serializable(cls) -> bool:
        """Return `True` as this class is serializable."""
        return True

    @classmethod
    @override
    def get_lc_namespace(cls) -> list[str]:
        """Get the namespace of the LangChain object.

        Returns:
            `["langchain", "schema", "runnable"]`
        """
        return ["langchain", "schema", "runnable"]

    model_config = ConfigDict(
        arbitrary_types_allowed=True,
    )

    @override
    def get_name(self, suffix: str | None = None, *, name: str | None = None) -> str:
        """Get the name of the `Runnable`.

        Args:
            suffix: The suffix to use.
            name: The name to use.

        Returns:
            The name of the `Runnable`.

        """
        name = name or self.name or f"RunnableParallel<{','.join(self.steps__.keys())}>"
        return super().get_name(suffix, name=name)

    @property
    @override
    def InputType(self) -> Any:
        """The type of the input to the `Runnable`."""
        for step in self.steps__.values():
            if step.InputType:
                return step.InputType

        return Any

    @override
    def get_input_schema(self, config: RunnableConfig | None = None) -> type[BaseModel]:
        """Get the input schema of the `Runnable`.

        Args:
            config: The config to use.

        Returns:
            The input schema of the `Runnable`.

        """
        if all(
            s.get_input_schema(config).model_json_schema().get("type", "object")
            == "object"
            for s in self.steps__.values()
        ):
            for step in self.steps__.values():
                fields = step.get_input_schema(config).model_fields
                root_field = fields.get("root")
                if root_field is not None and root_field.annotation != Any:
                    return super().get_input_schema(config)

            # This is correct, but pydantic typings/mypy don't think so.
            return create_model_v2(
                self.get_name("Input"),
                field_definitions={
                    k: (v.annotation, v.default)
                    for step in self.steps__.values()
                    for k, v in step.get_input_schema(config).model_fields.items()
                    if k != "__root__"
                },
            )

        return super().get_input_schema(config)

    @override
    def get_output_schema(
        self, config: RunnableConfig | None = None
    ) -> type[BaseModel]:
        """Get the output schema of the `Runnable`.

        Args:
            config: The config to use.

        Returns:
            The output schema of the `Runnable`.

        """
        fields = {k: (v.OutputType, ...) for k, v in self.steps__.items()}
        return create_model_v2(self.get_name("Output"), field_definitions=fields)

    @property
    @override
    def config_specs(self) -> list[ConfigurableFieldSpec]:
        """Get the config specs of the `Runnable`.

        Returns:
            The config specs of the `Runnable`.

        """
        return get_unique_config_specs(
            spec for step in self.steps__.values() for spec in step.config_specs
        )

    @override
    def get_graph(self, config: RunnableConfig | None = None) -> Graph:
        """Get the graph representation of the `Runnable`.

        Args:
            config: The config to use.

        Returns:
            The graph representation of the `Runnable`.

        Raises:
            ValueError: If a `Runnable` has no first or last node.

        """
        # Import locally to prevent circular import
        from langchain_core.runnables.graph import Graph  # noqa: PLC0415

        graph = Graph()
        input_node = graph.add_node(self.get_input_schema(config))
        output_node = graph.add_node(self.get_output_schema(config))
        for step in self.steps__.values():
            step_graph = step.get_graph()
            step_graph.trim_first_node()
            step_graph.trim_last_node()
            if not step_graph:
                graph.add_edge(input_node, output_node)
            else:
                step_first_node, step_last_node = graph.extend(step_graph)
                if not step_first_node:
                    msg = f"Runnable {step} has no first node"
                    raise ValueError(msg)
                if not step_last_node:
                    msg = f"Runnable {step} has no last node"
                    raise ValueError(msg)
                graph.add_edge(input_node, step_first_node)
                graph.add_edge(step_last_node, output_node)

        return graph

    @override
    def __repr__(self) -> str:
        map_for_repr = ",\n  ".join(
            f"{k}: {indent_lines_after_first(repr(v), '  ' + k + ': ')}"
            for k, v in self.steps__.items()
        )
        return "{\n  " + map_for_repr + "\n}"

    @override
    def invoke(
        self, input: Input, config: RunnableConfig | None = None, **kwargs: Any
    ) -> dict[str, Any]:
        # setup callbacks
        config = ensure_config(config)
        callback_manager = CallbackManager.configure(
            inheritable_callbacks=config.get("callbacks"),
            local_callbacks=None,
            verbose=False,
            inheritable_tags=config.get("tags"),
            local_tags=None,
            inheritable_metadata=config.get("metadata"),
            local_metadata=None,
        )
        # start the root run
        run_manager = callback_manager.on_chain_start(
            None,
            input,
            name=config.get("run_name") or self.get_name(),
            run_id=config.pop("run_id", None),
        )

        def _invoke_step(
            step: Runnable[Input, Any], input_: Input, config: RunnableConfig, key: str
        ) -> Any:
            child_config = patch_config(
                config,
                # mark each step as a child run
                callbacks=run_manager.get_child(f"map:key:{key}"),
            )
            with set_config_context(child_config) as context:
                return context.run(
                    step.invoke,
                    input_,
                    child_config,
                )

        # gather results from all steps
        try:
            # copy to avoid issues from the caller mutating the steps during invoke()
            steps = dict(self.steps__)

            with get_executor_for_config(config) as executor:
                futures = [
                    executor.submit(_invoke_step, step, input, config, key)
                    for key, step in steps.items()
                ]
                output = {
                    key: future.result()
                    for key, future in zip(steps, futures, strict=False)
                }
        # finish the root run
        except BaseException as e:
            run_manager.on_chain_error(e)
            raise
        else:
            run_manager.on_chain_end(output)
            return output

    @override
    async def ainvoke(
        self,
        input: Input,
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> dict[str, Any]:
        # setup callbacks
        config = ensure_config(config)
        callback_manager = get_async_callback_manager_for_config(config)
        # start the root run
        run_manager = await callback_manager.on_chain_start(
            None,
            input,
            name=config.get("run_name") or self.get_name(),
            run_id=config.pop("run_id", None),
        )

        async def _ainvoke_step(
            step: Runnable[Input, Any], input_: Input, config: RunnableConfig, key: str
        ) -> Any:
            child_config = patch_config(
                config,
                callbacks=run_manager.get_child(f"map:key:{key}"),
            )
            with set_config_context(child_config) as context:
                return await coro_with_context(
                    step.ainvoke(input_, child_config), context, create_task=True
                )

        # gather results from all steps
        try:
            # copy to avoid issues from the caller mutating the steps during invoke()
            steps = dict(self.steps__)
            results = await asyncio.gather(
                *(
                    _ainvoke_step(
                        step,
                        input,
                        # mark each step as a child run
                        config,
                        key,
                    )
                    for key, step in steps.items()
                )
            )
            output = dict(zip(steps, results, strict=False))
        # finish the root run
        except BaseException as e:
            await run_manager.on_chain_error(e)
            raise
        else:
            await run_manager.on_chain_end(output)
            return output

    def _transform(
        self,
        inputs: Iterator[Input],
        run_manager: CallbackManagerForChainRun,
        config: RunnableConfig,
    ) -> Iterator[AddableDict]:
        # Shallow copy steps to ignore mutations while in progress
        steps = dict(self.steps__)
        # Each step gets a copy of the input iterator,
        # which is consumed in parallel in a separate thread.
        input_copies = list(safetee(inputs, len(steps), lock=threading.Lock()))
        with get_executor_for_config(config) as executor:
            # Create the transform() generator for each step
            named_generators = [
                (
                    name,
                    step.transform(
                        input_copies.pop(),
                        patch_config(
                            config, callbacks=run_manager.get_child(f"map:key:{name}")
                        ),
                    ),
                )
                for name, step in steps.items()
            ]
            # Start the first iteration of each generator
            futures = {
                executor.submit(next, generator): (step_name, generator)
                for step_name, generator in named_generators
            }
            # Yield chunks from each as they become available,
            # and start the next iteration of that generator that yielded it.
            # When all generators are exhausted, stop.
            while futures:
                completed_futures, _ = wait(futures, return_when=FIRST_COMPLETED)
                for future in completed_futures:
                    (step_name, generator) = futures.pop(future)
                    try:
                        chunk = AddableDict({step_name: future.result()})
                        yield chunk
                        futures[executor.submit(next, generator)] = (
                            step_name,
                            generator,
                        )
                    except StopIteration:
                        pass

    @override
    def transform(
        self,
        input: Iterator[Input],
        config: RunnableConfig | None = None,
        **kwargs: Any,
    ) -> Iterator[dict[str, Any]]:
        yield from self._transform_stream_with_config(
            input, self._transform, config, **kwargs
        )

    @override
    def stream(
        self,
        input: Input,
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> Iterator[dict[str, Any]]:
        yield from self.transform(iter([input]), config)

    async def _atransform(
        self,
        inputs: AsyncIterator[Input],
        run_manager: AsyncCallbackManagerForChainRun,
        config: RunnableConfig,
    ) -> AsyncIterator[AddableDict]:
        # Shallow copy steps to ignore mutations while in progress
        steps = dict(self.steps__)
        # Each step gets a copy of the input iterator,
        # which is consumed in parallel in a separate thread.
        input_copies = list(atee(inputs, len(steps), lock=asyncio.Lock()))
        # Create the transform() generator for each step
        named_generators = [
            (
                name,
                step.atransform(
                    input_copies.pop(),
                    patch_config(
                        config, callbacks=run_manager.get_child(f"map:key:{name}")
                    ),
                ),
            )
            for name, step in steps.items()
        ]

        # Wrap in a coroutine to satisfy linter
        async def get_next_chunk(generator: AsyncIterator) -> Output | None:
            return await py_anext(generator)

        # Start the first iteration of each generator
        tasks = {
            asyncio.create_task(get_next_chunk(generator)): (step_name, generator)
            for step_name, generator in named_generators
        }
        # Yield chunks from each as they become available,
        # and start the next iteration of the generator that yielded it.
        # When all generators are exhausted, stop.
        while tasks:
            completed_tasks, _ = await asyncio.wait(
                tasks, return_when=asyncio.FIRST_COMPLETED
            )
            for task in completed_tasks:
                (step_name, generator) = tasks.pop(task)
                try:
                    chunk = AddableDict({step_name: task.result()})
                    yield chunk
                    new_task = asyncio.create_task(get_next_chunk(generator))
                    tasks[new_task] = (step_name, generator)
                except StopAsyncIteration:
                    pass

    @override
    async def atransform(
        self,
        input: AsyncIterator[Input],
        config: RunnableConfig | None = None,
        **kwargs: Any,
    ) -> AsyncIterator[dict[str, Any]]:
        async for chunk in self._atransform_stream_with_config(
            input, self._atransform, config, **kwargs
        ):
            yield chunk

    @override
    async def astream(
        self,
        input: Input,
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> AsyncIterator[dict[str, Any]]:
        async def input_aiter() -> AsyncIterator[Input]:
            yield input

        async for chunk in self.atransform(input_aiter(), config):
            yield chunk


# We support both names
RunnableMap = RunnableParallel


class RunnableGenerator(Runnable[Input, Output]):
    """`Runnable` that runs a generator function.

    `RunnableGenerator`s can be instantiated directly or by using a generator within
    a sequence.

    `RunnableGenerator`s can be used to implement custom behavior, such as custom
    output parsers, while preserving streaming capabilities. Given a generator function
    with a signature `Iterator[A] -> Iterator[B]`, wrapping it in a
    `RunnableGenerator` allows it to emit output chunks as soon as they are streamed
    in from the previous step.

    !!! note
        If a generator function has a `signature A -> Iterator[B]`, such that it
        requires its input from the previous step to be completed before emitting chunks
        (e.g., most LLMs need the entire prompt available to start generating), it can
        instead be wrapped in a `RunnableLambda`.

    Here is an example to show the basic mechanics of a `RunnableGenerator`:

        ```python
        from typing import Any, AsyncIterator, Iterator

        from langchain_core.runnables import RunnableGenerator


        def gen(input: Iterator[Any]) -> Iterator[str]:
            for token in ["Have", " a", " nice", " day"]:
                yield token


        runnable = RunnableGenerator(gen)
        runnable.invoke(None)  # "Have a nice day"
        list(runnable.stream(None))  # ["Have", " a", " nice", " day"]
        runnable.batch([None, None])  # ["Have a nice day", "Have a nice day"]


        # Async version:
        async def agen(input: AsyncIterator[Any]) -> AsyncIterator[str]:
            for token in ["Have", " a", " nice", " day"]:
                yield token


        runnable = RunnableGenerator(agen)
        await runnable.ainvoke(None)  # "Have a nice day"
        [p async for p in runnable.astream(None)]  # ["Have", " a", " nice", " day"]
        ```

    `RunnableGenerator` makes it easy to implement custom behavior within a streaming
    context. Below we show an example:

        ```python
        from langchain_core.prompts import ChatPromptTemplate
        from langchain_core.runnables import RunnableGenerator, RunnableLambda
        from langchain_openai import ChatOpenAI
        from langchain_core.output_parsers import StrOutputParser


        model = ChatOpenAI()
        chant_chain = (
            ChatPromptTemplate.from_template("Give me a 3 word chant about {topic}")
            | model
            | StrOutputParser()
        )


        def character_generator(input: Iterator[str]) -> Iterator[str]:
            for token in input:
                if "," in token or "." in token:
                    yield "👏" + token
                else:
                    yield token


        runnable = chant_chain | character_generator
        assert type(runnable.last) is RunnableGenerator
        "".join(runnable.stream({"topic": "waste"}))  # Reduce👏, Reuse👏, Recycle👏.


        # Note that RunnableLambda can be used to delay streaming of one step in a
        # sequence until the previous step is finished:
        def reverse_generator(input: str) -> Iterator[str]:
            # Yield characters of input in reverse order.
            for character in input[::-1]:
                yield character


        runnable = chant_chain | RunnableLambda(reverse_generator)
        "".join(runnable.stream({"topic": "waste"}))  # ".elcycer ,esuer ,ecudeR"
        ```
    """

    def __init__(
        self,
        transform: Callable[[Iterator[Input]], Iterator[Output]]
        | Callable[[AsyncIterator[Input]], AsyncIterator[Output]],
        atransform: Callable[[AsyncIterator[Input]], AsyncIterator[Output]]
        | None = None,
        *,
        name: str | None = None,
    ) -> None:
        """Initialize a `RunnableGenerator`.

        Args:
            transform: The transform function.
            atransform: The async transform function.
            name: The name of the `Runnable`.

        Raises:
            TypeError: If the transform is not a generator function.

        """
        if atransform is not None:
            self._atransform = atransform
            func_for_name: Callable = atransform

        if is_async_generator(transform):
            self._atransform = transform
            func_for_name = transform
        elif inspect.isgeneratorfunction(transform):
            self._transform = transform
            func_for_name = transform
        else:
            msg = (
                "Expected a generator function type for `transform`."
                f"Instead got an unsupported type: {type(transform)}"
            )
            raise TypeError(msg)

        try:
            self.name = name or func_for_name.__name__
        except AttributeError:
            self.name = "RunnableGenerator"

    @property
    @override
    def InputType(self) -> Any:
        func = getattr(self, "_transform", None) or self._atransform
        try:
            params = inspect.signature(func).parameters
            first_param = next(iter(params.values()), None)
            if first_param and first_param.annotation != inspect.Parameter.empty:
                return getattr(first_param.annotation, "__args__", (Any,))[0]
        except ValueError:
            pass
        return Any

    @override
    def get_input_schema(self, config: RunnableConfig | None = None) -> type[BaseModel]:
        # Override the default implementation.
        # For a runnable generator, we need to bring to provide the
        # module of the underlying function when creating the model.
        root_type = self.InputType

        func = getattr(self, "_transform", None) or self._atransform
        module = getattr(func, "__module__", None)

        if (
            inspect.isclass(root_type)
            and not isinstance(root_type, GenericAlias)
            and issubclass(root_type, BaseModel)
        ):
            return root_type

        return create_model_v2(
            self.get_name("Input"),
            root=root_type,
            # To create the schema, we need to provide the module
            # where the underlying function is defined.
            # This allows pydantic to resolve type annotations appropriately.
            module_name=module,
        )

    @property
    @override
    def OutputType(self) -> Any:
        func = getattr(self, "_transform", None) or self._atransform
        try:
            sig = inspect.signature(func)
            return (
                getattr(sig.return_annotation, "__args__", (Any,))[0]
                if sig.return_annotation != inspect.Signature.empty
                else Any
            )
        except ValueError:
            return Any

    @override
    def get_output_schema(
        self, config: RunnableConfig | None = None
    ) -> type[BaseModel]:
        # Override the default implementation.
        # For a runnable generator, we need to bring to provide the
        # module of the underlying function when creating the model.
        root_type = self.OutputType
        func = getattr(self, "_transform", None) or self._atransform
        module = getattr(func, "__module__", None)

        if (
            inspect.isclass(root_type)
            and not isinstance(root_type, GenericAlias)
            and issubclass(root_type, BaseModel)
        ):
            return root_type

        return create_model_v2(
            self.get_name("Output"),
            root=root_type,
            # To create the schema, we need to provide the module
            # where the underlying function is defined.
            # This allows pydantic to resolve type annotations appropriately.
            module_name=module,
        )

    @override
    def __eq__(self, other: object) -> bool:
        if isinstance(other, RunnableGenerator):
            if hasattr(self, "_transform") and hasattr(other, "_transform"):
                return self._transform == other._transform
            if hasattr(self, "_atransform") and hasattr(other, "_atransform"):
                return self._atransform == other._atransform
            return False
        return False

    __hash__ = None  # type: ignore[assignment]

    @override
    def __repr__(self) -> str:
        return f"RunnableGenerator({self.name})"

    @override
    def transform(
        self,
        input: Iterator[Input],
        config: RunnableConfig | None = None,
        **kwargs: Any,
    ) -> Iterator[Output]:
        if not hasattr(self, "_transform"):
            msg = f"{self!r} only supports async methods."
            raise NotImplementedError(msg)
        return self._transform_stream_with_config(
            input,
            self._transform,  # type: ignore[arg-type]
            config,
            **kwargs,
        )

    @override
    def stream(
        self,
        input: Input,
        config: RunnableConfig | None = None,
        **kwargs: Any,
    ) -> Iterator[Output]:
        return self.transform(iter([input]), config, **kwargs)

    @override
    def invoke(
        self, input: Input, config: RunnableConfig | None = None, **kwargs: Any
    ) -> Output:
        final: Output | None = None
        for output in self.stream(input, config, **kwargs):
            final = output if final is None else final + output  # type: ignore[operator]
        return cast("Output", final)

    @override
    def atransform(
        self,
        input: AsyncIterator[Input],
        config: RunnableConfig | None = None,
        **kwargs: Any,
    ) -> AsyncIterator[Output]:
        if not hasattr(self, "_atransform"):
            msg = f"{self!r} only supports sync methods."
            raise NotImplementedError(msg)

        return self._atransform_stream_with_config(
            input, self._atransform, config, **kwargs
        )

    @override
    def astream(
        self,
        input: Input,
        config: RunnableConfig | None = None,
        **kwargs: Any,
    ) -> AsyncIterator[Output]:
        async def input_aiter() -> AsyncIterator[Input]:
            yield input

        return self.atransform(input_aiter(), config, **kwargs)

    @override
    async def ainvoke(
        self, input: Input, config: RunnableConfig | None = None, **kwargs: Any
    ) -> Output:
        final: Output | None = None
        async for output in self.astream(input, config, **kwargs):
            final = output if final is None else final + output  # type: ignore[operator]
        return cast("Output", final)


class RunnableLambda(Runnable[Input, Output]):
    """`RunnableLambda` converts a python callable into a `Runnable`.

    Wrapping a callable in a `RunnableLambda` makes the callable usable
    within either a sync or async context.

    `RunnableLambda` can be composed as any other `Runnable` and provides
    seamless integration with LangChain tracing.

    `RunnableLambda` is best suited for code that does not need to support
    streaming. If you need to support streaming (i.e., be able to operate
    on chunks of inputs and yield chunks of outputs), use `RunnableGenerator`
    instead.

    Note that if a `RunnableLambda` returns an instance of `Runnable`, that
    instance is invoked (or streamed) during execution.

    Examples:
        ```python
        # This is a RunnableLambda
        from langchain_core.runnables import RunnableLambda


        def add_one(x: int) -> int:
            return x + 1


        runnable = RunnableLambda(add_one)

        runnable.invoke(1)  # returns 2
        runnable.batch([1, 2, 3])  # returns [2, 3, 4]

        # Async is supported by default by delegating to the sync implementation
        await runnable.ainvoke(1)  # returns 2
        await runnable.abatch([1, 2, 3])  # returns [2, 3, 4]


        # Alternatively, can provide both synd and sync implementations
        async def add_one_async(x: int) -> int:
            return x + 1


        runnable = RunnableLambda(add_one, afunc=add_one_async)
        runnable.invoke(1)  # Uses add_one
        await runnable.ainvoke(1)  # Uses add_one_async
        ```
    """

    def __init__(
        self,
        func: Callable[[Input], Iterator[Output]]
        | Callable[[Input], Runnable[Input, Output]]
        | Callable[[Input], Output]
        | Callable[[Input, RunnableConfig], Output]
        | Callable[[Input, CallbackManagerForChainRun], Output]
        | Callable[[Input, CallbackManagerForChainRun, RunnableConfig], Output]
        | Callable[[Input], Awaitable[Output]]
        | Callable[[Input], AsyncIterator[Output]]
        | Callable[[Input, RunnableConfig], Awaitable[Output]]
        | Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]]
        | Callable[
            [Input, AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]
        ],
        afunc: Callable[[Input], Awaitable[Output]]
        | Callable[[Input], AsyncIterator[Output]]
        | Callable[[Input, RunnableConfig], Awaitable[Output]]
        | Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]]
        | Callable[
            [Input, AsyncCallbackManagerForChainRun, RunnableConfig], Awaitable[Output]
        ]
        | None = None,
        name: str | None = None,
    ) -> None:
        """Create a `RunnableLambda` from a callable, and async callable or both.

        Accepts both sync and async variants to allow providing efficient
        implementations for sync and async execution.

        Args:
            func: Either sync or async callable
            afunc: An async callable that takes an input and returns an output.

            name: The name of the `Runnable`.

        Raises:
            TypeError: If the `func` is not a callable type.
            TypeError: If both `func` and `afunc` are provided.

        """
        if afunc is not None:
            self.afunc = afunc
            func_for_name: Callable = afunc

        if is_async_callable(func) or is_async_generator(func):
            if afunc is not None:
                msg = (
                    "Func was provided as a coroutine function, but afunc was "
                    "also provided. If providing both, func should be a regular "
                    "function to avoid ambiguity."
                )
                raise TypeError(msg)
            self.afunc = func
            func_for_name = func
        elif callable(func):
            self.func = cast("Callable[[Input], Output]", func)
            func_for_name = func
        else:
            msg = (
                "Expected a callable type for `func`."
                f"Instead got an unsupported type: {type(func)}"
            )
            raise TypeError(msg)

        try:
            if name is not None:
                self.name = name
            elif func_for_name.__name__ != "<lambda>":
                self.name = func_for_name.__name__
        except AttributeError:
            pass

        self._repr: str | None = None

    @property
    @override
    def InputType(self) -> Any:
        """The type of the input to this `Runnable`."""
        func = getattr(self, "func", None) or self.afunc
        try:
            params = inspect.signature(func).parameters
            first_param = next(iter(params.values()), None)
            if first_param and first_param.annotation != inspect.Parameter.empty:
                return first_param.annotation
        except ValueError:
            pass
        return Any

    @override
    def get_input_schema(self, config: RunnableConfig | None = None) -> type[BaseModel]:
        """The Pydantic schema for the input to this `Runnable`.

        Args:
            config: The config to use.

        Returns:
            The input schema for this `Runnable`.

        """
        func = getattr(self, "func", None) or self.afunc

        if isinstance(func, itemgetter):
            # This is terrible, but afaict it's not possible to access _items
            # on itemgetter objects, so we have to parse the repr
            items = str(func).replace("operator.itemgetter(", "")[:-1].split(", ")
            if all(
                item[0] == "'" and item[-1] == "'" and item != "''" for item in items
            ):
                fields = {item[1:-1]: (Any, ...) for item in items}
                # It's a dict, lol
                return create_model_v2(self.get_name("Input"), field_definitions=fields)
            module = getattr(func, "__module__", None)
            return create_model_v2(
                self.get_name("Input"),
                root=list[Any],
                # To create the schema, we need to provide the module
                # where the underlying function is defined.
                # This allows pydantic to resolve type annotations appropriately.
                module_name=module,
            )

        if self.InputType != Any:
            return super().get_input_schema(config)

        if dict_keys := get_function_first_arg_dict_keys(func):
            return create_model_v2(
                self.get_name("Input"),
                field_definitions=dict.fromkeys(dict_keys, (Any, ...)),
            )

        return super().get_input_schema(config)

    @property
    @override
    def OutputType(self) -> Any:
        """The type of the output of this `Runnable` as a type annotation.

        Returns:
            The type of the output of this `Runnable`.

        """
        func = getattr(self, "func", None) or self.afunc
        try:
            sig = inspect.signature(func)
            if sig.return_annotation != inspect.Signature.empty:
                # unwrap iterator types
                if getattr(sig.return_annotation, "__origin__", None) in {
                    collections.abc.Iterator,
                    collections.abc.AsyncIterator,
                }:
                    return getattr(sig.return_annotation, "__args__", (Any,))[0]
                return sig.return_annotation
        except ValueError:
            pass
        return Any

    @override
    def get_output_schema(
        self, config: RunnableConfig | None = None
    ) -> type[BaseModel]:
        # Override the default implementation.
        # For a runnable lambda, we need to bring to provide the
        # module of the underlying function when creating the model.
        root_type = self.OutputType
        func = getattr(self, "func", None) or self.afunc
        module = getattr(func, "__module__", None)

        if (
            inspect.isclass(root_type)
            and not isinstance(root_type, GenericAlias)
            and issubclass(root_type, BaseModel)
        ):
            return root_type

        return create_model_v2(
            self.get_name("Output"),
            root=root_type,
            # To create the schema, we need to provide the module
            # where the underlying function is defined.
            # This allows pydantic to resolve type annotations appropriately.
            module_name=module,
        )

    @functools.cached_property
    def deps(self) -> list[Runnable]:
        """The dependencies of this `Runnable`.

        Returns:
            The dependencies of this `Runnable`. If the function has nonlocal
            variables that are `Runnable`s, they are considered dependencies.

        """
        if hasattr(self, "func"):
            objects = get_function_nonlocals(self.func)
        elif hasattr(self, "afunc"):
            objects = get_function_nonlocals(self.afunc)
        else:
            objects = []

        deps: list[Runnable] = []
        for obj in objects:
            if isinstance(obj, Runnable):
                deps.append(obj)
            elif isinstance(getattr(obj, "__self__", None), Runnable):
                deps.append(obj.__self__)
        return deps

    @property
    @override
    def config_specs(self) -> list[ConfigurableFieldSpec]:
        return get_unique_config_specs(
            spec for dep in self.deps for spec in dep.config_specs
        )

    @override
    def get_graph(self, config: RunnableConfig | None = None) -> Graph:
        if deps := self.deps:
            # Import locally to prevent circular import
            from langchain_core.runnables.graph import Graph  # noqa: PLC0415

            graph = Graph()
            input_node = graph.add_node(self.get_input_schema(config))
            output_node = graph.add_node(self.get_output_schema(config))
            for dep in deps:
                dep_graph = dep.get_graph()
                dep_graph.trim_first_node()
                dep_graph.trim_last_node()
                if not dep_graph:
                    graph.add_edge(input_node, output_node)
                else:
                    dep_first_node, dep_last_node = graph.extend(dep_graph)
                    if not dep_first_node:
                        msg = f"Runnable {dep} has no first node"
                        raise ValueError(msg)
                    if not dep_last_node:
                        msg = f"Runnable {dep} has no last node"
                        raise ValueError(msg)
                    graph.add_edge(input_node, dep_first_node)
                    graph.add_edge(dep_last_node, output_node)
        else:
            graph = super().get_graph(config)

        return graph

    @override
    def __eq__(self, other: object) -> bool:
        if isinstance(other, RunnableLambda):
            if hasattr(self, "func") and hasattr(other, "func"):
                return self.func == other.func
            if hasattr(self, "afunc") and hasattr(other, "afunc"):
                return self.afunc == other.afunc
            return False
        return False

    __hash__ = None  # type: ignore[assignment]

    def __repr__(self) -> str:
        """Return a string representation of this `Runnable`."""
        if self._repr is None:
            if hasattr(self, "func") and isinstance(self.func, itemgetter):
                self._repr = f"RunnableLambda({str(self.func)[len('operator.') :]})"
            elif hasattr(self, "func"):
                self._repr = f"RunnableLambda({get_lambda_source(self.func) or '...'})"
            elif hasattr(self, "afunc"):
                self._repr = (
                    f"RunnableLambda(afunc={get_lambda_source(self.afunc) or '...'})"
                )
            else:
                self._repr = "RunnableLambda(...)"
        return self._repr

    def _invoke(
        self,
        input_: Input,
        run_manager: CallbackManagerForChainRun,
        config: RunnableConfig,
        **kwargs: Any,
    ) -> Output:
        if inspect.isgeneratorfunction(self.func):
            output: Output | None = None
            for chunk in call_func_with_variable_args(
                cast("Callable[[Input], Iterator[Output]]", self.func),
                input_,
                config,
                run_manager,
                **kwargs,
            ):
                if output is None:
                    output = chunk
                else:
                    try:
                        output = output + chunk  # type: ignore[operator]
                    except TypeError:
                        output = chunk
        else:
            output = call_func_with_variable_args(
                self.func, input_, config, run_manager, **kwargs
            )
        # If the output is a Runnable, invoke it
        if isinstance(output, Runnable):
            recursion_limit = config["recursion_limit"]
            if recursion_limit <= 0:
                msg = (
                    f"Recursion limit reached when invoking {self} with input {input_}."
                )
                raise RecursionError(msg)
            output = output.invoke(
                input_,
                patch_config(
                    config,
                    callbacks=run_manager.get_child(),
                    recursion_limit=recursion_limit - 1,
                ),
            )
        return cast("Output", output)

    async def _ainvoke(
        self,
        value: Input,
        run_manager: AsyncCallbackManagerForChainRun,
        config: RunnableConfig,
        **kwargs: Any,
    ) -> Output:
        if hasattr(self, "afunc"):
            afunc = self.afunc
        else:
            if inspect.isgeneratorfunction(self.func):

                def func(
                    value: Input,
                    run_manager: AsyncCallbackManagerForChainRun,
                    config: RunnableConfig,
                    **kwargs: Any,
                ) -> Output:
                    output: Output | None = None
                    for chunk in call_func_with_variable_args(
                        cast("Callable[[Input], Iterator[Output]]", self.func),
                        value,
                        config,
                        run_manager.get_sync(),
                        **kwargs,
                    ):
                        if output is None:
                            output = chunk
                        else:
                            try:
                                output = output + chunk  # type: ignore[operator]
                            except TypeError:
                                output = chunk
                    return cast("Output", output)

            else:

                def func(
                    value: Input,
                    run_manager: AsyncCallbackManagerForChainRun,
                    config: RunnableConfig,
                    **kwargs: Any,
                ) -> Output:
                    return call_func_with_variable_args(
                        self.func, value, config, run_manager.get_sync(), **kwargs
                    )

            @wraps(func)
            async def f(*args: Any, **kwargs: Any) -> Any:
                return await run_in_executor(config, func, *args, **kwargs)

            afunc = f

        if is_async_generator(afunc):
            output: Output | None = None
            async with aclosing(
                cast(
                    "AsyncGenerator[Any, Any]",
                    acall_func_with_variable_args(
                        cast("Callable", afunc),
                        value,
                        config,
                        run_manager,
                        **kwargs,
                    ),
                )
            ) as stream:
                async for chunk in cast(
                    "AsyncIterator[Output]",
                    stream,
                ):
                    if output is None:
                        output = chunk
                    else:
                        try:
                            output = output + chunk  # type: ignore[operator]
                        except TypeError:
                            output = chunk
        else:
            output = await acall_func_with_variable_args(
                cast("Callable", afunc), value, config, run_manager, **kwargs
            )
        # If the output is a Runnable, invoke it
        if isinstance(output, Runnable):
            recursion_limit = config["recursion_limit"]
            if recursion_limit <= 0:
                msg = (
                    f"Recursion limit reached when invoking {self} with input {value}."
                )
                raise RecursionError(msg)
            output = await output.ainvoke(
                value,
                patch_config(
                    config,
                    callbacks=run_manager.get_child(),
                    recursion_limit=recursion_limit - 1,
                ),
            )
        return cast("Output", output)

    @override
    def invoke(
        self,
        input: Input,
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> Output:
        """Invoke this `Runnable` synchronously.

        Args:
            input: The input to this `Runnable`.
            config: The config to use.
            **kwargs: Additional keyword arguments.

        Returns:
            The output of this `Runnable`.

        Raises:
            TypeError: If the `Runnable` is a coroutine function.

        """
        if hasattr(self, "func"):
            return self._call_with_config(
                self._invoke,
                input,
                ensure_config(config),
                **kwargs,
            )
        msg = "Cannot invoke a coroutine function synchronously.Use `ainvoke` instead."
        raise TypeError(msg)

    @override
    async def ainvoke(
        self,
        input: Input,
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> Output:
        """Invoke this `Runnable` asynchronously.

        Args:
            input: The input to this `Runnable`.
            config: The config to use.
            **kwargs: Additional keyword arguments.

        Returns:
            The output of this `Runnable`.

        """
        return await self._acall_with_config(
            self._ainvoke,
            input,
            ensure_config(config),
            **kwargs,
        )

    def _transform(
        self,
        chunks: Iterator[Input],
        run_manager: CallbackManagerForChainRun,
        config: RunnableConfig,
        **kwargs: Any,
    ) -> Iterator[Output]:
        final: Input
        got_first_val = False
        for ichunk in chunks:
            # By definitions, RunnableLambdas consume all input before emitting output.
            # If the input is not addable, then we'll assume that we can
            # only operate on the last chunk.
            # So we'll iterate until we get to the last chunk!
            if not got_first_val:
                final = ichunk
                got_first_val = True
            else:
                try:
                    final = final + ichunk  # type: ignore[operator]
                except TypeError:
                    final = ichunk

        if inspect.isgeneratorfunction(self.func):
            output: Output | None = None
            for chunk in call_func_with_variable_args(
                self.func, final, config, run_manager, **kwargs
            ):
                yield chunk
                if output is None:
                    output = chunk
                else:
                    try:
                        output = output + chunk
                    except TypeError:
                        output = chunk
        else:
            output = call_func_with_variable_args(
                self.func, final, config, run_manager, **kwargs
            )

        # If the output is a Runnable, use its stream output
        if isinstance(output, Runnable):
            recursion_limit = config["recursion_limit"]
            if recursion_limit <= 0:
                msg = (
                    f"Recursion limit reached when invoking {self} with input {final}."
                )
                raise RecursionError(msg)
            for chunk in output.stream(
                final,
                patch_config(
                    config,
                    callbacks=run_manager.get_child(),
                    recursion_limit=recursion_limit - 1,
                ),
            ):
                yield chunk
        elif not inspect.isgeneratorfunction(self.func):
            # Otherwise, just yield it
            yield cast("Output", output)

    @override
    def transform(
        self,
        input: Iterator[Input],
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> Iterator[Output]:
        if hasattr(self, "func"):
            yield from self._transform_stream_with_config(
                input,
                self._transform,
                ensure_config(config),
                **kwargs,
            )
        else:
            msg = (
                "Cannot stream a coroutine function synchronously."
                "Use `astream` instead."
            )
            raise TypeError(msg)

    @override
    def stream(
        self,
        input: Input,
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> Iterator[Output]:
        return self.transform(iter([input]), config, **kwargs)

    async def _atransform(
        self,
        chunks: AsyncIterator[Input],
        run_manager: AsyncCallbackManagerForChainRun,
        config: RunnableConfig,
        **kwargs: Any,
    ) -> AsyncIterator[Output]:
        final: Input
        got_first_val = False
        async for ichunk in chunks:
            # By definitions, RunnableLambdas consume all input before emitting output.
            # If the input is not addable, then we'll assume that we can
            # only operate on the last chunk.
            # So we'll iterate until we get to the last chunk!
            if not got_first_val:
                final = ichunk
                got_first_val = True
            else:
                try:
                    final = final + ichunk  # type: ignore[operator]
                except TypeError:
                    final = ichunk

        if hasattr(self, "afunc"):
            afunc = self.afunc
        else:
            if inspect.isgeneratorfunction(self.func):
                msg = (
                    "Cannot stream from a generator function asynchronously."
                    "Use .stream() instead."
                )
                raise TypeError(msg)

            def func(
                input_: Input,
                run_manager: AsyncCallbackManagerForChainRun,
                config: RunnableConfig,
                **kwargs: Any,
            ) -> Output:
                return call_func_with_variable_args(
                    self.func, input_, config, run_manager.get_sync(), **kwargs
                )

            @wraps(func)
            async def f(*args: Any, **kwargs: Any) -> Any:
                return await run_in_executor(config, func, *args, **kwargs)

            afunc = f

        if is_async_generator(afunc):
            output: Output | None = None
            async for chunk in cast(
                "AsyncIterator[Output]",
                acall_func_with_variable_args(
                    cast("Callable", afunc),
                    final,
                    config,
                    run_manager,
                    **kwargs,
                ),
            ):
                yield chunk
                if output is None:
                    output = chunk
                else:
                    try:
                        output = output + chunk  # type: ignore[operator]
                    except TypeError:
                        output = chunk
        else:
            output = await acall_func_with_variable_args(
                cast("Callable", afunc),
                final,
                config,
                run_manager,
                **kwargs,
            )

        # If the output is a Runnable, use its astream output
        if isinstance(output, Runnable):
            recursion_limit = config["recursion_limit"]
            if recursion_limit <= 0:
                msg = (
                    f"Recursion limit reached when invoking {self} with input {final}."
                )
                raise RecursionError(msg)
            async for chunk in output.astream(
                final,
                patch_config(
                    config,
                    callbacks=run_manager.get_child(),
                    recursion_limit=recursion_limit - 1,
                ),
            ):
                yield chunk
        elif not is_async_generator(afunc):
            # Otherwise, just yield it
            yield cast("Output", output)

    @override
    async def atransform(
        self,
        input: AsyncIterator[Input],
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> AsyncIterator[Output]:
        async for output in self._atransform_stream_with_config(
            input,
            self._atransform,
            ensure_config(config),
            **kwargs,
        ):
            yield output

    @override
    async def astream(
        self,
        input: Input,
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> AsyncIterator[Output]:
        async def input_aiter() -> AsyncIterator[Input]:
            yield input

        async for chunk in self.atransform(input_aiter(), config, **kwargs):
            yield chunk


class RunnableEachBase(RunnableSerializable[list[Input], list[Output]]):
    """RunnableEachBase class.

    `Runnable` that calls another `Runnable` for each element of the input sequence.

    Use only if creating a new `RunnableEach` subclass with different `__init__`
    args.

    See documentation for `RunnableEach` for more details.

    """

    bound: Runnable[Input, Output]

    model_config = ConfigDict(
        arbitrary_types_allowed=True,
    )

    @property
    @override
    def InputType(self) -> Any:
        return list[self.bound.InputType]  # type: ignore[name-defined]

    @override
    def get_input_schema(self, config: RunnableConfig | None = None) -> type[BaseModel]:
        return create_model_v2(
            self.get_name("Input"),
            root=(
                list[self.bound.get_input_schema(config)],  # type: ignore[misc]
                None,
            ),
            # create model needs access to appropriate type annotations to be
            # able to construct the Pydantic model.
            # When we create the model, we pass information about the namespace
            # where the model is being created, so the type annotations can
            # be resolved correctly as well.
            # self.__class__.__module__ handles the case when the Runnable is
            # being sub-classed in a different module.
            module_name=self.__class__.__module__,
        )

    @property
    @override
    def OutputType(self) -> type[list[Output]]:
        return list[self.bound.OutputType]  # type: ignore[name-defined]

    @override
    def get_output_schema(
        self, config: RunnableConfig | None = None
    ) -> type[BaseModel]:
        schema = self.bound.get_output_schema(config)
        return create_model_v2(
            self.get_name("Output"),
            root=list[schema],  # type: ignore[valid-type]
            # create model needs access to appropriate type annotations to be
            # able to construct the Pydantic model.
            # When we create the model, we pass information about the namespace
            # where the model is being created, so the type annotations can
            # be resolved correctly as well.
            # self.__class__.__module__ handles the case when the Runnable is
            # being sub-classed in a different module.
            module_name=self.__class__.__module__,
        )

    @property
    @override
    def config_specs(self) -> list[ConfigurableFieldSpec]:
        return self.bound.config_specs

    @override
    def get_graph(self, config: RunnableConfig | None = None) -> Graph:
        return self.bound.get_graph(config)

    @classmethod
    @override
    def is_lc_serializable(cls) -> bool:
        """Return `True` as this class is serializable."""
        return True

    @classmethod
    @override
    def get_lc_namespace(cls) -> list[str]:
        """Get the namespace of the LangChain object.

        Returns:
            `["langchain", "schema", "runnable"]`
        """
        return ["langchain", "schema", "runnable"]

    def _invoke(
        self,
        inputs: list[Input],
        run_manager: CallbackManagerForChainRun,
        config: RunnableConfig,
        **kwargs: Any,
    ) -> list[Output]:
        configs = [
            patch_config(config, callbacks=run_manager.get_child()) for _ in inputs
        ]
        return self.bound.batch(inputs, configs, **kwargs)

    @override
    def invoke(
        self, input: list[Input], config: RunnableConfig | None = None, **kwargs: Any
    ) -> list[Output]:
        return self._call_with_config(self._invoke, input, config, **kwargs)

    async def _ainvoke(
        self,
        inputs: list[Input],
        run_manager: AsyncCallbackManagerForChainRun,
        config: RunnableConfig,
        **kwargs: Any,
    ) -> list[Output]:
        configs = [
            patch_config(config, callbacks=run_manager.get_child()) for _ in inputs
        ]
        return await self.bound.abatch(inputs, configs, **kwargs)

    @override
    async def ainvoke(
        self, input: list[Input], config: RunnableConfig | None = None, **kwargs: Any
    ) -> list[Output]:
        return await self._acall_with_config(self._ainvoke, input, config, **kwargs)

    @override
    async def astream_events(
        self,
        input: Input,
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> AsyncIterator[StreamEvent]:
        def _error_stream_event(message: str) -> StreamEvent:
            raise NotImplementedError(message)

        for _ in range(1):
            yield _error_stream_event(
                "RunnableEach does not support astream_events yet."
            )


class RunnableEach(RunnableEachBase[Input, Output]):
    """RunnableEach class.

    `Runnable` that calls another `Runnable` for each element of the input sequence.

    It allows you to call multiple inputs with the bounded `Runnable`.

    `RunnableEach` makes it easy to run multiple inputs for the `Runnable`.
    In the below example, we associate and run three inputs
    with a `Runnable`:

        ```python
        from langchain_core.runnables.base import RunnableEach
        from langchain_openai import ChatOpenAI
        from langchain_core.prompts import ChatPromptTemplate
        from langchain_core.output_parsers import StrOutputParser
        prompt = ChatPromptTemplate.from_template("Tell me a short joke about
        {topic}")
        model = ChatOpenAI()
        output_parser = StrOutputParser()
        runnable = prompt | model | output_parser
        runnable_each = RunnableEach(bound=runnable)
        output = runnable_each.invoke([{'topic':'Computer Science'},
                                    {'topic':'Art'},
                                    {'topic':'Biology'}])
        print(output)  # noqa: T201

        ```
    """

    @override
    def get_name(self, suffix: str | None = None, *, name: str | None = None) -> str:
        name = name or self.name or f"RunnableEach<{self.bound.get_name()}>"
        return super().get_name(suffix, name=name)

    @override
    def bind(self, **kwargs: Any) -> RunnableEach[Input, Output]:
        return RunnableEach(bound=self.bound.bind(**kwargs))

    @override
    def with_config(
        self, config: RunnableConfig | None = None, **kwargs: Any
    ) -> RunnableEach[Input, Output]:
        return RunnableEach(bound=self.bound.with_config(config, **kwargs))

    @override
    def with_listeners(
        self,
        *,
        on_start: Callable[[Run], None]
        | Callable[[Run, RunnableConfig], None]
        | None = None,
        on_end: Callable[[Run], None]
        | Callable[[Run, RunnableConfig], None]
        | None = None,
        on_error: Callable[[Run], None]
        | Callable[[Run, RunnableConfig], None]
        | None = None,
    ) -> RunnableEach[Input, Output]:
        """Bind lifecycle listeners to a `Runnable`, returning a new `Runnable`.

        The `Run` object contains information about the run, including its `id`,
        `type`, `input`, `output`, `error`, `start_time`, `end_time`, and
        any tags or metadata added to the run.

        Args:
            on_start: Called before the `Runnable` starts running, with the `Run`
                object.
            on_end: Called after the `Runnable` finishes running, with the `Run`
                object.
            on_error: Called if the `Runnable` throws an error, with the `Run`
                object.

        Returns:
            A new `Runnable` with the listeners bound.

        """
        return RunnableEach(
            bound=self.bound.with_listeners(
                on_start=on_start, on_end=on_end, on_error=on_error
            )
        )

    def with_alisteners(
        self,
        *,
        on_start: AsyncListener | None = None,
        on_end: AsyncListener | None = None,
        on_error: AsyncListener | None = None,
    ) -> RunnableEach[Input, Output]:
        """Bind async lifecycle listeners to a `Runnable`.

        Returns a new `Runnable`.

        The `Run` object contains information about the run, including its `id`,
        `type`, `input`, `output`, `error`, `start_time`, `end_time`, and
        any tags or metadata added to the run.

        Args:
            on_start: Called asynchronously before the `Runnable` starts running,
                with the `Run` object.
            on_end: Called asynchronously after the `Runnable` finishes running,
                with the `Run` object.
            on_error: Called asynchronously if the `Runnable` throws an error,
                with the `Run` object.

        Returns:
            A new `Runnable` with the listeners bound.

        """
        return RunnableEach(
            bound=self.bound.with_alisteners(
                on_start=on_start, on_end=on_end, on_error=on_error
            )
        )


class RunnableBindingBase(RunnableSerializable[Input, Output]):  # type: ignore[no-redef]
    """`Runnable` that delegates calls to another `Runnable` with a set of `**kwargs`.

    Use only if creating a new `RunnableBinding` subclass with different `__init__`
    args.

    See documentation for `RunnableBinding` for more details.

    """

    bound: Runnable[Input, Output]
    """The underlying `Runnable` that this `Runnable` delegates to."""

    kwargs: Mapping[str, Any] = Field(default_factory=dict)
    """kwargs to pass to the underlying `Runnable` when running.

    For example, when the `Runnable` binding is invoked the underlying
    `Runnable` will be invoked with the same input but with these additional
    kwargs.

    """

    config: RunnableConfig = Field(default_factory=RunnableConfig)
    """The config to bind to the underlying `Runnable`."""

    config_factories: list[Callable[[RunnableConfig], RunnableConfig]] = Field(
        default_factory=list
    )
    """The config factories to bind to the underlying `Runnable`."""

    # Union[Type[Input], BaseModel] + things like list[str]
    custom_input_type: Any | None = None
    """Override the input type of the underlying `Runnable` with a custom type.

    The type can be a Pydantic model, or a type annotation (e.g., `list[str]`).
    """
    # Union[Type[Output], BaseModel] + things like list[str]
    custom_output_type: Any | None = None
    """Override the output type of the underlying `Runnable` with a custom type.

    The type can be a Pydantic model, or a type annotation (e.g., `list[str]`).
    """

    model_config = ConfigDict(
        arbitrary_types_allowed=True,
    )

    def __init__(
        self,
        *,
        bound: Runnable[Input, Output],
        kwargs: Mapping[str, Any] | None = None,
        config: RunnableConfig | None = None,
        config_factories: list[Callable[[RunnableConfig], RunnableConfig]]
        | None = None,
        custom_input_type: type[Input] | BaseModel | None = None,
        custom_output_type: type[Output] | BaseModel | None = None,
        **other_kwargs: Any,
    ) -> None:
        """Create a `RunnableBinding` from a `Runnable` and kwargs.

        Args:
            bound: The underlying `Runnable` that this `Runnable` delegates calls
                to.
            kwargs: optional kwargs to pass to the underlying `Runnable`, when running
                the underlying `Runnable` (e.g., via `invoke`, `batch`,
                `transform`, or `stream` or async variants)

            config: optional config to bind to the underlying `Runnable`.

            config_factories: optional list of config factories to apply to the
                config before binding to the underlying `Runnable`.

            custom_input_type: Specify to override the input type of the underlying
                `Runnable` with a custom type.
            custom_output_type: Specify to override the output type of the underlying
                `Runnable` with a custom type.
            **other_kwargs: Unpacked into the base class.
        """
        super().__init__(
            bound=bound,
            kwargs=kwargs or {},
            config=config or {},
            config_factories=config_factories or [],
            custom_input_type=custom_input_type,
            custom_output_type=custom_output_type,
            **other_kwargs,
        )
        # if we don't explicitly set config to the TypedDict here,
        # the pydantic init above will strip out any of the "extra"
        # fields even though total=False on the typed dict.
        self.config = config or {}

    @override
    def get_name(self, suffix: str | None = None, *, name: str | None = None) -> str:
        return self.bound.get_name(suffix, name=name)

    @property
    @override
    def InputType(self) -> type[Input]:
        return (
            cast("type[Input]", self.custom_input_type)
            if self.custom_input_type is not None
            else self.bound.InputType
        )

    @property
    @override
    def OutputType(self) -> type[Output]:
        return (
            cast("type[Output]", self.custom_output_type)
            if self.custom_output_type is not None
            else self.bound.OutputType
        )

    @override
    def get_input_schema(self, config: RunnableConfig | None = None) -> type[BaseModel]:
        if self.custom_input_type is not None:
            return super().get_input_schema(config)
        return self.bound.get_input_schema(merge_configs(self.config, config))

    @override
    def get_output_schema(
        self, config: RunnableConfig | None = None
    ) -> type[BaseModel]:
        if self.custom_output_type is not None:
            return super().get_output_schema(config)
        return self.bound.get_output_schema(merge_configs(self.config, config))

    @property
    @override
    def config_specs(self) -> list[ConfigurableFieldSpec]:
        return self.bound.config_specs

    @override
    def get_graph(self, config: RunnableConfig | None = None) -> Graph:
        return self.bound.get_graph(self._merge_configs(config))

    @classmethod
    @override
    def is_lc_serializable(cls) -> bool:
        """Return `True` as this class is serializable."""
        return True

    @classmethod
    @override
    def get_lc_namespace(cls) -> list[str]:
        """Get the namespace of the LangChain object.

        Returns:
            `["langchain", "schema", "runnable"]`
        """
        return ["langchain", "schema", "runnable"]

    def _merge_configs(self, *configs: RunnableConfig | None) -> RunnableConfig:
        config = merge_configs(self.config, *configs)
        return merge_configs(config, *(f(config) for f in self.config_factories))

    @override
    def invoke(
        self,
        input: Input,
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> Output:
        return self.bound.invoke(
            input,
            self._merge_configs(config),
            **{**self.kwargs, **kwargs},
        )

    @override
    async def ainvoke(
        self,
        input: Input,
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> Output:
        return await self.bound.ainvoke(
            input,
            self._merge_configs(config),
            **{**self.kwargs, **kwargs},
        )

    @override
    def batch(
        self,
        inputs: list[Input],
        config: RunnableConfig | list[RunnableConfig] | None = None,
        *,
        return_exceptions: bool = False,
        **kwargs: Any | None,
    ) -> list[Output]:
        if isinstance(config, list):
            configs = cast(
                "list[RunnableConfig]",
                [self._merge_configs(conf) for conf in config],
            )
        else:
            configs = [self._merge_configs(config) for _ in range(len(inputs))]
        return self.bound.batch(
            inputs,
            configs,
            return_exceptions=return_exceptions,
            **{**self.kwargs, **kwargs},
        )

    @override
    async def abatch(
        self,
        inputs: list[Input],
        config: RunnableConfig | list[RunnableConfig] | None = None,
        *,
        return_exceptions: bool = False,
        **kwargs: Any | None,
    ) -> list[Output]:
        if isinstance(config, list):
            configs = cast(
                "list[RunnableConfig]",
                [self._merge_configs(conf) for conf in config],
            )
        else:
            configs = [self._merge_configs(config) for _ in range(len(inputs))]
        return await self.bound.abatch(
            inputs,
            configs,
            return_exceptions=return_exceptions,
            **{**self.kwargs, **kwargs},
        )

    @overload
    def batch_as_completed(
        self,
        inputs: Sequence[Input],
        config: RunnableConfig | Sequence[RunnableConfig] | None = None,
        *,
        return_exceptions: Literal[False] = False,
        **kwargs: Any,
    ) -> Iterator[tuple[int, Output]]: ...

    @overload
    def batch_as_completed(
        self,
        inputs: Sequence[Input],
        config: RunnableConfig | Sequence[RunnableConfig] | None = None,
        *,
        return_exceptions: Literal[True],
        **kwargs: Any,
    ) -> Iterator[tuple[int, Output | Exception]]: ...

    @override
    def batch_as_completed(
        self,
        inputs: Sequence[Input],
        config: RunnableConfig | Sequence[RunnableConfig] | None = None,
        *,
        return_exceptions: bool = False,
        **kwargs: Any | None,
    ) -> Iterator[tuple[int, Output | Exception]]:
        if isinstance(config, Sequence):
            configs = cast(
                "list[RunnableConfig]",
                [self._merge_configs(conf) for conf in config],
            )
        else:
            configs = [self._merge_configs(config) for _ in range(len(inputs))]
        # lol mypy
        if return_exceptions:
            yield from self.bound.batch_as_completed(
                inputs,
                configs,
                return_exceptions=return_exceptions,
                **{**self.kwargs, **kwargs},
            )
        else:
            yield from self.bound.batch_as_completed(
                inputs,
                configs,
                return_exceptions=return_exceptions,
                **{**self.kwargs, **kwargs},
            )

    @overload
    def abatch_as_completed(
        self,
        inputs: Sequence[Input],
        config: RunnableConfig | Sequence[RunnableConfig] | None = None,
        *,
        return_exceptions: Literal[False] = False,
        **kwargs: Any | None,
    ) -> AsyncIterator[tuple[int, Output]]: ...

    @overload
    def abatch_as_completed(
        self,
        inputs: Sequence[Input],
        config: RunnableConfig | Sequence[RunnableConfig] | None = None,
        *,
        return_exceptions: Literal[True],
        **kwargs: Any | None,
    ) -> AsyncIterator[tuple[int, Output | Exception]]: ...

    @override
    async def abatch_as_completed(
        self,
        inputs: Sequence[Input],
        config: RunnableConfig | Sequence[RunnableConfig] | None = None,
        *,
        return_exceptions: bool = False,
        **kwargs: Any | None,
    ) -> AsyncIterator[tuple[int, Output | Exception]]:
        if isinstance(config, Sequence):
            configs = cast(
                "list[RunnableConfig]",
                [self._merge_configs(conf) for conf in config],
            )
        else:
            configs = [self._merge_configs(config) for _ in range(len(inputs))]
        if return_exceptions:
            async for item in self.bound.abatch_as_completed(
                inputs,
                configs,
                return_exceptions=return_exceptions,
                **{**self.kwargs, **kwargs},
            ):
                yield item
        else:
            async for item in self.bound.abatch_as_completed(
                inputs,
                configs,
                return_exceptions=return_exceptions,
                **{**self.kwargs, **kwargs},
            ):
                yield item

    @override
    def stream(
        self,
        input: Input,
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> Iterator[Output]:
        yield from self.bound.stream(
            input,
            self._merge_configs(config),
            **{**self.kwargs, **kwargs},
        )

    @override
    async def astream(
        self,
        input: Input,
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> AsyncIterator[Output]:
        async for item in self.bound.astream(
            input,
            self._merge_configs(config),
            **{**self.kwargs, **kwargs},
        ):
            yield item

    @override
    async def astream_events(
        self,
        input: Input,
        config: RunnableConfig | None = None,
        **kwargs: Any | None,
    ) -> AsyncIterator[StreamEvent]:
        async for item in self.bound.astream_events(
            input, self._merge_configs(config), **{**self.kwargs, **kwargs}
        ):
            yield item

    @override
    def transform(
        self,
        input: Iterator[Input],
        config: RunnableConfig | None = None,
        **kwargs: Any,
    ) -> Iterator[Output]:
        yield from self.bound.transform(
            input,
            self._merge_configs(config),
            **{**self.kwargs, **kwargs},
        )

    @override
    async def atransform(
        self,
        input: AsyncIterator[Input],
        config: RunnableConfig | None = None,
        **kwargs: Any,
    ) -> AsyncIterator[Output]:
        async for item in self.bound.atransform(
            input,
            self._merge_configs(config),
            **{**self.kwargs, **kwargs},
        ):
            yield item


class RunnableBinding(RunnableBindingBase[Input, Output]):  # type: ignore[no-redef]
    """Wrap a `Runnable` with additional functionality.

    A `RunnableBinding` can be thought of as a "runnable decorator" that
    preserves the essential features of `Runnable`; i.e., batching, streaming,
    and async support, while adding additional functionality.

    Any class that inherits from `Runnable` can be bound to a `RunnableBinding`.
    Runnables expose a standard set of methods for creating `RunnableBindings`
    or sub-classes of `RunnableBindings` (e.g., `RunnableRetry`,
    `RunnableWithFallbacks`) that add additional functionality.

    These methods include:

    - `bind`: Bind kwargs to pass to the underlying `Runnable` when running it.
    - `with_config`: Bind config to pass to the underlying `Runnable` when running
        it.
    - `with_listeners`:  Bind lifecycle listeners to the underlying `Runnable`.
    - `with_types`: Override the input and output types of the underlying
        `Runnable`.
    - `with_retry`: Bind a retry policy to the underlying `Runnable`.
    - `with_fallbacks`: Bind a fallback policy to the underlying `Runnable`.

    Example:
    `bind`: Bind kwargs to pass to the underlying `Runnable` when running it.

        ```python
        # Create a Runnable binding that invokes the chat model with the
        # additional kwarg `stop=['-']` when running it.
        from langchain_openai import ChatOpenAI

        model = ChatOpenAI()
        model.invoke('Say "Parrot-MAGIC"', stop=["-"])  # Should return `Parrot`
        # Using it the easy way via `bind` method which returns a new
        # RunnableBinding
        runnable_binding = model.bind(stop=["-"])
        runnable_binding.invoke('Say "Parrot-MAGIC"')  # Should return `Parrot`
        ```
        Can also be done by instantiating a `RunnableBinding` directly (not
        recommended):

        ```python
        from langchain_core.runnables import RunnableBinding

        runnable_binding = RunnableBinding(
            bound=model,
            kwargs={"stop": ["-"]},  # <-- Note the additional kwargs
        )
        runnable_binding.invoke('Say "Parrot-MAGIC"')  # Should return `Parrot`
        ```
    """

    @override
    def bind(self, **kwargs: Any) -> Runnable[Input, Output]:
        """Bind additional kwargs to a `Runnable`, returning a new `Runnable`.

        Args:
            **kwargs: The kwargs to bind to the `Runnable`.

        Returns:
            A new `Runnable` with the same type and config as the original,
            but with the additional kwargs bound.

        """
        return self.__class__(
            bound=self.bound,
            config=self.config,
            config_factories=self.config_factories,
            kwargs={**self.kwargs, **kwargs},
            custom_input_type=self.custom_input_type,
            custom_output_type=self.custom_output_type,
        )

    @override
    def with_config(
        self,
        config: RunnableConfig | None = None,
        # Sadly Unpack is not well supported by mypy so this will have to be untyped
        **kwargs: Any,
    ) -> Runnable[Input, Output]:
        return self.__class__(
            bound=self.bound,
            kwargs=self.kwargs,
            config=cast("RunnableConfig", {**self.config, **(config or {}), **kwargs}),
            config_factories=self.config_factories,
            custom_input_type=self.custom_input_type,
            custom_output_type=self.custom_output_type,
        )

    @override
    def with_listeners(
        self,
        *,
        on_start: Callable[[Run], None]
        | Callable[[Run, RunnableConfig], None]
        | None = None,
        on_end: Callable[[Run], None]
        | Callable[[Run, RunnableConfig], None]
        | None = None,
        on_error: Callable[[Run], None]
        | Callable[[Run, RunnableConfig], None]
        | None = None,
    ) -> Runnable[Input, Output]:
        """Bind lifecycle listeners to a `Runnable`, returning a new `Runnable`.

        The `Run` object contains information about the run, including its `id`,
        `type`, `input`, `output`, `error`, `start_time`, `end_time`, and
        any tags or metadata added to the run.

        Args:
            on_start: Called before the `Runnable` starts running, with the `Run`
                object.
            on_end: Called after the `Runnable` finishes running, with the `Run`
                object.
            on_error: Called if the `Runnable` throws an error, with the `Run`
                object.

        Returns:
            A new `Runnable` with the listeners bound.
        """

        def listener_config_factory(config: RunnableConfig) -> RunnableConfig:
            return {
                "callbacks": [
                    RootListenersTracer(
                        config=config,
                        on_start=on_start,
                        on_end=on_end,
                        on_error=on_error,
                    )
                ],
            }

        return self.__class__(
            bound=self.bound,
            kwargs=self.kwargs,
            config=self.config,
            config_factories=[listener_config_factory, *self.config_factories],
            custom_input_type=self.custom_input_type,
            custom_output_type=self.custom_output_type,
        )

    @override
    def with_types(
        self,
        input_type: type[Input] | BaseModel | None = None,
        output_type: type[Output] | BaseModel | None = None,
    ) -> Runnable[Input, Output]:
        return self.__class__(
            bound=self.bound,
            kwargs=self.kwargs,
            config=self.config,
            config_factories=self.config_factories,
            custom_input_type=(
                input_type if input_type is not None else self.custom_input_type
            ),
            custom_output_type=(
                output_type if output_type is not None else self.custom_output_type
            ),
        )

    @override
    def with_retry(self, **kwargs: Any) -> Runnable[Input, Output]:
        return self.__class__(
            bound=self.bound.with_retry(**kwargs),
            kwargs=self.kwargs,
            config=self.config,
            config_factories=self.config_factories,
        )

    @override
    def __getattr__(self, name: str) -> Any:  # type: ignore[misc]
        attr = getattr(self.bound, name)

        if callable(attr) and (
            config_param := inspect.signature(attr).parameters.get("config")
        ):
            if config_param.kind == inspect.Parameter.KEYWORD_ONLY:

                @wraps(attr)
                def wrapper(*args: Any, **kwargs: Any) -> Any:
                    return attr(
                        *args,
                        config=merge_configs(self.config, kwargs.pop("config", None)),
                        **kwargs,
                    )

                return wrapper
            if config_param.kind == inspect.Parameter.POSITIONAL_OR_KEYWORD:
                idx = list(inspect.signature(attr).parameters).index("config")

                @wraps(attr)
                def wrapper(*args: Any, **kwargs: Any) -> Any:
                    if len(args) >= idx + 1:
                        argsl = list(args)
                        argsl[idx] = merge_configs(self.config, argsl[idx])
                        return attr(*argsl, **kwargs)
                    return attr(
                        *args,
                        config=merge_configs(self.config, kwargs.pop("config", None)),
                        **kwargs,
                    )

                return wrapper

        return attr


class _RunnableCallableSync(Protocol[Input, Output]):
    def __call__(self, _in: Input, /, *, config: RunnableConfig) -> Output: ...


class _RunnableCallableAsync(Protocol[Input, Output]):
    def __call__(
        self, _in: Input, /, *, config: RunnableConfig
    ) -> Awaitable[Output]: ...


class _RunnableCallableIterator(Protocol[Input, Output]):
    def __call__(
        self, _in: Iterator[Input], /, *, config: RunnableConfig
    ) -> Iterator[Output]: ...


class _RunnableCallableAsyncIterator(Protocol[Input, Output]):
    def __call__(
        self, _in: AsyncIterator[Input], /, *, config: RunnableConfig
    ) -> AsyncIterator[Output]: ...


RunnableLike = (
    Runnable[Input, Output]
    | Callable[[Input], Output]
    | Callable[[Input], Awaitable[Output]]
    | Callable[[Iterator[Input]], Iterator[Output]]
    | Callable[[AsyncIterator[Input]], AsyncIterator[Output]]
    | _RunnableCallableSync[Input, Output]
    | _RunnableCallableAsync[Input, Output]
    | _RunnableCallableIterator[Input, Output]
    | _RunnableCallableAsyncIterator[Input, Output]
    | Mapping[str, Any]
)


def coerce_to_runnable(thing: RunnableLike) -> Runnable[Input, Output]:
    """Coerce a `Runnable`-like object into a `Runnable`.

    Args:
        thing: A `Runnable`-like object.

    Returns:
        A `Runnable`.

    Raises:
        TypeError: If the object is not `Runnable`-like.
    """
    if isinstance(thing, Runnable):
        return thing
    if is_async_generator(thing) or inspect.isgeneratorfunction(thing):
        return RunnableGenerator(thing)
    if callable(thing):
        return RunnableLambda(cast("Callable[[Input], Output]", thing))
    if isinstance(thing, dict):
        return cast("Runnable[Input, Output]", RunnableParallel(thing))
    msg = (
        f"Expected a Runnable, callable or dict."
        f"Instead got an unsupported type: {type(thing)}"
    )
    raise TypeError(msg)


@overload
def chain(
    func: Callable[[Input], Coroutine[Any, Any, Output]],
) -> Runnable[Input, Output]: ...


@overload
def chain(
    func: Callable[[Input], Iterator[Output]],
) -> Runnable[Input, Output]: ...


@overload
def chain(
    func: Callable[[Input], AsyncIterator[Output]],
) -> Runnable[Input, Output]: ...


@overload
def chain(
    func: Callable[[Input], Output],
) -> Runnable[Input, Output]: ...


def chain(
    func: Callable[[Input], Output]
    | Callable[[Input], Iterator[Output]]
    | Callable[[Input], Coroutine[Any, Any, Output]]
    | Callable[[Input], AsyncIterator[Output]],
) -> Runnable[Input, Output]:
    """Decorate a function to make it a `Runnable`.

    Sets the name of the `Runnable` to the name of the function.
    Any runnables called by the function will be traced as dependencies.

    Args:
        func: A `Callable`.

    Returns:
        A `Runnable`.

    Example:
        ```python
        from langchain_core.runnables import chain
        from langchain_core.prompts import PromptTemplate
        from langchain_openai import OpenAI


        @chain
        def my_func(fields):
            prompt = PromptTemplate("Hello, {name}!")
            model = OpenAI()
            formatted = prompt.invoke(**fields)

            for chunk in model.stream(formatted):
                yield chunk
        ```
    """
    return RunnableLambda(func)
