Source code for langchain_core.runnables.passthrough

"""Implementation of the RunnablePassthrough."""

from __future__ import annotations

import asyncio
import inspect
import threading
from typing import (
    TYPE_CHECKING,
    Any,
    AsyncIterator,
    Awaitable,
    Callable,
    Dict,
    Iterator,
    List,
    Mapping,
    Optional,
    Type,
    Union,
    cast,
)

from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables.base import (
    Other,
    Runnable,
    RunnableParallel,
    RunnableSerializable,
)
from langchain_core.runnables.config import (
    RunnableConfig,
    acall_func_with_variable_args,
    call_func_with_variable_args,
    ensure_config,
    get_executor_for_config,
    patch_config,
)
from langchain_core.runnables.graph import Graph
from langchain_core.runnables.utils import (
    AddableDict,
    ConfigurableFieldSpec,
    create_model,
)
from langchain_core.utils.aiter import atee, py_anext
from langchain_core.utils.iter import safetee

if TYPE_CHECKING:
    from langchain_core.callbacks.manager import (
        AsyncCallbackManagerForChainRun,
        CallbackManagerForChainRun,
    )


[docs]def identity(x: Other) -> Other: """Identity function""" return x
[docs]async def aidentity(x: Other) -> Other: """Async identity function""" return x
[docs]class RunnablePassthrough(RunnableSerializable[Other, Other]): """Runnable to passthrough inputs unchanged or with additional keys. This runnable behaves almost like the identity function, except that it can be configured to add additional keys to the output, if the input is a dict. The examples below demonstrate this Runnable works using a few simple chains. The chains rely on simple lambdas to make the examples easy to execute and experiment with. Examples: .. code-block:: python from langchain_core.runnables import ( RunnableLambda, RunnableParallel, RunnablePassthrough, ) runnable = RunnableParallel( origin=RunnablePassthrough(), modified=lambda x: x+1 ) runnable.invoke(1) # {'origin': 1, 'modified': 2} def fake_llm(prompt: str) -> str: # Fake LLM for the example return "completion" chain = RunnableLambda(fake_llm) | { 'original': RunnablePassthrough(), # Original LLM output 'parsed': lambda text: text[::-1] # Parsing logic } chain.invoke('hello') # {'original': 'completion', 'parsed': 'noitelpmoc'} In some cases, it may be useful to pass the input through while adding some keys to the output. In this case, you can use the `assign` method: .. code-block:: python from langchain_core.runnables import RunnablePassthrough def fake_llm(prompt: str) -> str: # Fake LLM for the example return "completion" runnable = { 'llm1': fake_llm, 'llm2': fake_llm, } | RunnablePassthrough.assign( total_chars=lambda inputs: len(inputs['llm1'] + inputs['llm2']) ) runnable.invoke('hello') # {'llm1': 'completion', 'llm2': 'completion', 'total_chars': 20} """ input_type: Optional[Type[Other]] = None func: Optional[ Union[Callable[[Other], None], Callable[[Other, RunnableConfig], None]] ] = None afunc: Optional[ Union[ Callable[[Other], Awaitable[None]], Callable[[Other, RunnableConfig], Awaitable[None]], ] ] = None def __repr_args__(self) -> Any: # Without this repr(self) raises a RecursionError # See https://github.com/pydantic/pydantic/issues/7327 return [] def __init__( self, func: Optional[ Union[ Union[Callable[[Other], None], Callable[[Other, RunnableConfig], None]], Union[ Callable[[Other], Awaitable[None]], Callable[[Other, RunnableConfig], Awaitable[None]], ], ] ] = None, afunc: Optional[ Union[ Callable[[Other], Awaitable[None]], Callable[[Other, RunnableConfig], Awaitable[None]], ] ] = None, *, input_type: Optional[Type[Other]] = None, **kwargs: Any, ) -> None: if inspect.iscoroutinefunction(func): afunc = func func = None super().__init__(func=func, afunc=afunc, input_type=input_type, **kwargs) # type: ignore[call-arg]
[docs] @classmethod def is_lc_serializable(cls) -> bool: return True
[docs] @classmethod def get_lc_namespace(cls) -> List[str]: """Get the namespace of the langchain object.""" return ["langchain", "schema", "runnable"]
@property def InputType(self) -> Any: return self.input_type or Any @property def OutputType(self) -> Any: return self.input_type or Any
[docs] @classmethod def assign( cls, **kwargs: Union[ Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any], Mapping[ str, Union[Runnable[Dict[str, Any], Any], Callable[[Dict[str, Any]], Any]], ], ], ) -> "RunnableAssign": """Merge the Dict input with the output produced by the mapping argument. Args: mapping: A mapping from keys to runnables or callables. Returns: A runnable that merges the Dict input with the output produced by the mapping argument. """ return RunnableAssign(RunnableParallel(kwargs))
[docs] def invoke( self, input: Other, config: Optional[RunnableConfig] = None, **kwargs: Any ) -> Other: if self.func is not None: call_func_with_variable_args( self.func, input, ensure_config(config), **kwargs ) return self._call_with_config(identity, input, config)
[docs] async def ainvoke( self, input: Other, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any], ) -> Other: if self.afunc is not None: await acall_func_with_variable_args( self.afunc, input, ensure_config(config), **kwargs ) elif self.func is not None: call_func_with_variable_args( self.func, input, ensure_config(config), **kwargs ) return await self._acall_with_config(aidentity, input, config)
[docs] def transform( self, input: Iterator[Other], config: Optional[RunnableConfig] = None, **kwargs: Any, ) -> Iterator[Other]: if self.func is None: for chunk in self._transform_stream_with_config(input, identity, config): yield chunk else: final: Other got_first_chunk = False for chunk in self._transform_stream_with_config(input, identity, config): yield chunk if not got_first_chunk: final = chunk got_first_chunk = True else: try: final = final + chunk # type: ignore[operator] except TypeError: final = chunk if got_first_chunk: call_func_with_variable_args( self.func, final, ensure_config(config), **kwargs )
[docs] async def atransform( self, input: AsyncIterator[Other], config: Optional[RunnableConfig] = None, **kwargs: Any, ) -> AsyncIterator[Other]: if self.afunc is None and self.func is None: async for chunk in self._atransform_stream_with_config( input, identity, config ): yield chunk else: got_first_chunk = False async for chunk in self._atransform_stream_with_config( input, identity, config ): yield chunk # By definitions, a function will operate on the aggregated # input. So we'll aggregate the input until we get to the last # chunk. # If the input is not addable, then we'll assume that we can # only operate on the last chunk. if not got_first_chunk: final = chunk got_first_chunk = True else: try: final = final + chunk # type: ignore[operator] except TypeError: final = chunk if got_first_chunk: config = ensure_config(config) if self.afunc is not None: await acall_func_with_variable_args( self.afunc, final, config, **kwargs ) elif self.func is not None: call_func_with_variable_args(self.func, final, config, **kwargs)
[docs] def stream( self, input: Other, config: Optional[RunnableConfig] = None, **kwargs: Any, ) -> Iterator[Other]: return self.transform(iter([input]), config, **kwargs)
[docs] async def astream( self, input: Other, config: Optional[RunnableConfig] = None, **kwargs: Any, ) -> AsyncIterator[Other]: async def input_aiter() -> AsyncIterator[Other]: yield input async for chunk in self.atransform(input_aiter(), config, **kwargs): yield chunk
_graph_passthrough: RunnablePassthrough = RunnablePassthrough()
[docs]class RunnableAssign(RunnableSerializable[Dict[str, Any], Dict[str, Any]]): """Runnable that assigns key-value pairs to Dict[str, Any] inputs. The `RunnableAssign` class takes input dictionaries and, through a `RunnableParallel` instance, applies transformations, then combines these with the original data, introducing new key-value pairs based on the mapper's logic. Examples: .. code-block:: python # This is a RunnableAssign from typing import Dict from langchain_core.runnables.passthrough import ( RunnableAssign, RunnableParallel, ) from langchain_core.runnables.base import RunnableLambda def add_ten(x: Dict[str, int]) -> Dict[str, int]: return {"added": x["input"] + 10} mapper = RunnableParallel( {"add_step": RunnableLambda(add_ten),} ) runnable_assign = RunnableAssign(mapper) # Synchronous example runnable_assign.invoke({"input": 5}) # returns {'input': 5, 'add_step': {'added': 15}} # Asynchronous example await runnable_assign.ainvoke({"input": 5}) # returns {'input': 5, 'add_step': {'added': 15}} """ mapper: RunnableParallel[Dict[str, Any]] def __init__(self, mapper: RunnableParallel[Dict[str, Any]], **kwargs: Any) -> None: super().__init__(mapper=mapper, **kwargs) # type: ignore[call-arg]
[docs] @classmethod def is_lc_serializable(cls) -> bool: return True
[docs] @classmethod def get_lc_namespace(cls) -> List[str]: """Get the namespace of the langchain object.""" return ["langchain", "schema", "runnable"]
[docs] def get_name( self, suffix: Optional[str] = None, *, name: Optional[str] = None ) -> str: name = ( name or self.name or f"RunnableAssign<{','.join(self.mapper.steps__.keys())}>" ) return super().get_name(suffix, name=name)
[docs] def get_input_schema( self, config: Optional[RunnableConfig] = None ) -> Type[BaseModel]: map_input_schema = self.mapper.get_input_schema(config) if not map_input_schema.__custom_root_type__: # ie. it's a dict return map_input_schema return super().get_input_schema(config)
[docs] def get_output_schema( self, config: Optional[RunnableConfig] = None ) -> Type[BaseModel]: map_input_schema = self.mapper.get_input_schema(config) map_output_schema = self.mapper.get_output_schema(config) if ( not map_input_schema.__custom_root_type__ and not map_output_schema.__custom_root_type__ ): # ie. both are dicts return create_model( # type: ignore[call-overload] "RunnableAssignOutput", **{ k: (v.type_, v.default) for s in (map_input_schema, map_output_schema) for k, v in s.__fields__.items() }, ) elif not map_output_schema.__custom_root_type__: # ie. only map output is a dict # ie. input type is either unknown or inferred incorrectly return map_output_schema return super().get_output_schema(config)
@property def config_specs(self) -> List[ConfigurableFieldSpec]: return self.mapper.config_specs
[docs] def get_graph(self, config: RunnableConfig | None = None) -> Graph: # get graph from mapper graph = self.mapper.get_graph(config) # add passthrough node and edges input_node = graph.first_node() output_node = graph.last_node() if input_node is not None and output_node is not None: passthrough_node = graph.add_node(_graph_passthrough) graph.add_edge(input_node, passthrough_node) graph.add_edge(passthrough_node, output_node) return graph
def _invoke( self, input: Dict[str, Any], run_manager: CallbackManagerForChainRun, config: RunnableConfig, **kwargs: Any, ) -> Dict[str, Any]: assert isinstance( input, dict ), "The input to RunnablePassthrough.assign() must be a dict." return { **input, **self.mapper.invoke( input, patch_config(config, callbacks=run_manager.get_child()), **kwargs, ), }
[docs] def invoke( self, input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any, ) -> Dict[str, Any]: return self._call_with_config(self._invoke, input, config, **kwargs)
async def _ainvoke( self, input: Dict[str, Any], run_manager: AsyncCallbackManagerForChainRun, config: RunnableConfig, **kwargs: Any, ) -> Dict[str, Any]: assert isinstance( input, dict ), "The input to RunnablePassthrough.assign() must be a dict." return { **input, **await self.mapper.ainvoke( input, patch_config(config, callbacks=run_manager.get_child()), **kwargs, ), }
[docs] async def ainvoke( self, input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any, ) -> Dict[str, Any]: return await self._acall_with_config(self._ainvoke, input, config, **kwargs)
def _transform( self, input: Iterator[Dict[str, Any]], run_manager: CallbackManagerForChainRun, config: RunnableConfig, **kwargs: Any, ) -> Iterator[Dict[str, Any]]: # collect mapper keys mapper_keys = set(self.mapper.steps__.keys()) # create two streams, one for the map and one for the passthrough for_passthrough, for_map = safetee(input, 2, lock=threading.Lock()) # create map output stream map_output = self.mapper.transform( for_map, patch_config( config, callbacks=run_manager.get_child(), ), **kwargs, ) # get executor to start map output stream in background with get_executor_for_config(config) as executor: # start map output stream first_map_chunk_future = executor.submit( next, map_output, # type: ignore None, ) # consume passthrough stream for chunk in for_passthrough: assert isinstance( chunk, dict ), "The input to RunnablePassthrough.assign() must be a dict." # remove mapper keys from passthrough chunk, to be overwritten by map filtered = AddableDict( {k: v for k, v in chunk.items() if k not in mapper_keys} ) if filtered: yield filtered # yield map output yield cast(Dict[str, Any], first_map_chunk_future.result()) for chunk in map_output: yield chunk
[docs] def transform( self, input: Iterator[Dict[str, Any]], config: Optional[RunnableConfig] = None, **kwargs: Any | None, ) -> Iterator[Dict[str, Any]]: yield from self._transform_stream_with_config( input, self._transform, config, **kwargs )
async def _atransform( self, input: AsyncIterator[Dict[str, Any]], run_manager: AsyncCallbackManagerForChainRun, config: RunnableConfig, **kwargs: Any, ) -> AsyncIterator[Dict[str, Any]]: # collect mapper keys mapper_keys = set(self.mapper.steps__.keys()) # create two streams, one for the map and one for the passthrough for_passthrough, for_map = atee(input, 2, lock=asyncio.Lock()) # create map output stream map_output = self.mapper.atransform( for_map, patch_config( config, callbacks=run_manager.get_child(), ), **kwargs, ) # start map output stream first_map_chunk_task: asyncio.Task = asyncio.create_task( py_anext(map_output, None), # type: ignore[arg-type] ) # consume passthrough stream async for chunk in for_passthrough: assert isinstance( chunk, dict ), "The input to RunnablePassthrough.assign() must be a dict." # remove mapper keys from passthrough chunk, to be overwritten by map output filtered = AddableDict( {k: v for k, v in chunk.items() if k not in mapper_keys} ) if filtered: yield filtered # yield map output yield await first_map_chunk_task async for chunk in map_output: yield chunk
[docs] async def atransform( self, input: AsyncIterator[Dict[str, Any]], config: Optional[RunnableConfig] = None, **kwargs: Any, ) -> AsyncIterator[Dict[str, Any]]: async for chunk in self._atransform_stream_with_config( input, self._atransform, config, **kwargs ): yield chunk
[docs] def stream( self, input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any, ) -> Iterator[Dict[str, Any]]: return self.transform(iter([input]), config, **kwargs)
[docs] async def astream( self, input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any, ) -> AsyncIterator[Dict[str, Any]]: async def input_aiter() -> AsyncIterator[Dict[str, Any]]: yield input async for chunk in self.atransform(input_aiter(), config, **kwargs): yield chunk
[docs]class RunnablePick(RunnableSerializable[Dict[str, Any], Dict[str, Any]]): """Runnable that picks keys from Dict[str, Any] inputs. RunnablePick class represents a runnable that selectively picks keys from a dictionary input. It allows you to specify one or more keys to extract from the input dictionary. It returns a new dictionary containing only the selected keys. Example : .. code-block:: python from langchain_core.runnables.passthrough import RunnablePick input_data = { 'name': 'John', 'age': 30, 'city': 'New York', 'country': 'USA' } runnable = RunnablePick(keys=['name', 'age']) output_data = runnable.invoke(input_data) print(output_data) # Output: {'name': 'John', 'age': 30} """ keys: Union[str, List[str]] def __init__(self, keys: Union[str, List[str]], **kwargs: Any) -> None: super().__init__(keys=keys, **kwargs) # type: ignore[call-arg]
[docs] @classmethod def is_lc_serializable(cls) -> bool: return True
[docs] @classmethod def get_lc_namespace(cls) -> List[str]: """Get the namespace of the langchain object.""" return ["langchain", "schema", "runnable"]
[docs] def get_name( self, suffix: Optional[str] = None, *, name: Optional[str] = None ) -> str: name = ( name or self.name or f"RunnablePick<{','.join([self.keys] if isinstance(self.keys, str) else self.keys)}>" # noqa: E501 ) return super().get_name(suffix, name=name)
def _pick(self, input: Dict[str, Any]) -> Any: assert isinstance( input, dict ), "The input to RunnablePassthrough.assign() must be a dict." if isinstance(self.keys, str): return input.get(self.keys) else: picked = {k: input.get(k) for k in self.keys if k in input} if picked: return AddableDict(picked) else: return None def _invoke( self, input: Dict[str, Any], ) -> Dict[str, Any]: return self._pick(input)
[docs] def invoke( self, input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any, ) -> Dict[str, Any]: return self._call_with_config(self._invoke, input, config, **kwargs)
async def _ainvoke( self, input: Dict[str, Any], ) -> Dict[str, Any]: return self._pick(input)
[docs] async def ainvoke( self, input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any, ) -> Dict[str, Any]: return await self._acall_with_config(self._ainvoke, input, config, **kwargs)
def _transform( self, input: Iterator[Dict[str, Any]], ) -> Iterator[Dict[str, Any]]: for chunk in input: picked = self._pick(chunk) if picked is not None: yield picked
[docs] def transform( self, input: Iterator[Dict[str, Any]], config: Optional[RunnableConfig] = None, **kwargs: Any, ) -> Iterator[Dict[str, Any]]: yield from self._transform_stream_with_config( input, self._transform, config, **kwargs )
async def _atransform( self, input: AsyncIterator[Dict[str, Any]], ) -> AsyncIterator[Dict[str, Any]]: async for chunk in input: picked = self._pick(chunk) if picked is not None: yield picked
[docs] async def atransform( self, input: AsyncIterator[Dict[str, Any]], config: Optional[RunnableConfig] = None, **kwargs: Any, ) -> AsyncIterator[Dict[str, Any]]: async for chunk in self._atransform_stream_with_config( input, self._atransform, config, **kwargs ): yield chunk
[docs] def stream( self, input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any, ) -> Iterator[Dict[str, Any]]: return self.transform(iter([input]), config, **kwargs)
[docs] async def astream( self, input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any, ) -> AsyncIterator[Dict[str, Any]]: async def input_aiter() -> AsyncIterator[Dict[str, Any]]: yield input async for chunk in self.atransform(input_aiter(), config, **kwargs): yield chunk