mirror of https://github.com/hwchase17/langchain
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
3955 lines
140 KiB
Python
3955 lines
140 KiB
Python
from __future__ import annotations
|
|
|
|
import asyncio
|
|
import collections
|
|
import inspect
|
|
import threading
|
|
from abc import ABC, abstractmethod
|
|
from concurrent.futures import FIRST_COMPLETED, wait
|
|
from contextvars import copy_context
|
|
from copy import deepcopy
|
|
from functools import wraps
|
|
from itertools import groupby, tee
|
|
from operator import itemgetter
|
|
from typing import (
|
|
TYPE_CHECKING,
|
|
Any,
|
|
AsyncIterator,
|
|
Awaitable,
|
|
Callable,
|
|
Coroutine,
|
|
Dict,
|
|
Generic,
|
|
Iterator,
|
|
List,
|
|
Mapping,
|
|
Optional,
|
|
Sequence,
|
|
Set,
|
|
Tuple,
|
|
Type,
|
|
TypeVar,
|
|
Union,
|
|
cast,
|
|
overload,
|
|
)
|
|
|
|
from typing_extensions import Literal, get_args
|
|
|
|
from langchain_core.load.dump import dumpd, dumps
|
|
from langchain_core.load.serializable import Serializable
|
|
from langchain_core.pydantic_v1 import BaseConfig, BaseModel, Field, create_model
|
|
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,
|
|
var_child_runnable_config,
|
|
)
|
|
from langchain_core.runnables.graph import Graph
|
|
from langchain_core.runnables.utils import (
|
|
AddableDict,
|
|
AnyConfigurableField,
|
|
ConfigurableField,
|
|
ConfigurableFieldSpec,
|
|
Input,
|
|
Output,
|
|
accepts_config,
|
|
accepts_context,
|
|
accepts_run_manager,
|
|
gather_with_concurrency,
|
|
get_function_first_arg_dict_keys,
|
|
get_function_nonlocals,
|
|
get_lambda_source,
|
|
get_unique_config_specs,
|
|
indent_lines_after_first,
|
|
)
|
|
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,
|
|
)
|
|
from langchain_core.prompts.base import BasePromptTemplate
|
|
from langchain_core.runnables.fallbacks import (
|
|
RunnableWithFallbacks as RunnableWithFallbacksT,
|
|
)
|
|
from langchain_core.tracers.log_stream import RunLog, RunLogPatch
|
|
from langchain_core.tracers.root_listeners import Listener
|
|
|
|
|
|
Other = TypeVar("Other")
|
|
|
|
|
|
class _SchemaConfig(BaseConfig):
|
|
arbitrary_types_allowed = True
|
|
|
|
|
|
class Runnable(Generic[Input, Output], ABC):
|
|
"""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.
|
|
|
|
LCEL and Composition
|
|
====================
|
|
|
|
The LangChain Expression Language (LCEL) is a declarative way to compose Runnables
|
|
into chains. 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,
|
|
|
|
.. code-block:: 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 Runnables 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 Runnables. See the individual methods for details.
|
|
|
|
For example,
|
|
|
|
.. code-block:: 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!')
|
|
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.schema()) # Show inferred input schema
|
|
print(sequence.output_schema.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:
|
|
|
|
.. code-block:: python
|
|
|
|
from langchain_core.globals import set_debug
|
|
set_debug(True)
|
|
|
|
Alternatively, you can pass existing or custom callbacks to any given chain:
|
|
|
|
.. code-block:: python
|
|
|
|
from langchain_core.tracers import ConsoleCallbackHandler
|
|
|
|
chain.invoke(
|
|
...,
|
|
config={'callbacks': [ConsoleCallbackHandler()]}
|
|
)
|
|
|
|
For a UI (and much more) checkout LangSmith: https://docs.smith.langchain.com/
|
|
"""
|
|
|
|
name: Optional[str] = None
|
|
"""The name of the runnable. Used for debugging and tracing."""
|
|
|
|
def get_name(
|
|
self, suffix: Optional[str] = None, *, name: Optional[str] = None
|
|
) -> str:
|
|
"""Get the name of the runnable."""
|
|
name = name or self.name or self.__class__.__name__
|
|
if suffix:
|
|
if name[0].isupper():
|
|
return name + suffix.title()
|
|
else:
|
|
return name + "_" + suffix.lower()
|
|
else:
|
|
return name
|
|
|
|
@property
|
|
def InputType(self) -> Type[Input]:
|
|
"""The type of input this runnable accepts specified as a type annotation."""
|
|
for cls in self.__class__.__orig_bases__: # type: ignore[attr-defined]
|
|
type_args = get_args(cls)
|
|
if type_args and len(type_args) == 2:
|
|
return type_args[0]
|
|
|
|
raise TypeError(
|
|
f"Runnable {self.get_name()} doesn't have an inferable InputType. "
|
|
"Override the InputType property to specify the input type."
|
|
)
|
|
|
|
@property
|
|
def OutputType(self) -> Type[Output]:
|
|
"""The type of output this runnable produces specified as a type annotation."""
|
|
for cls in self.__class__.__orig_bases__: # type: ignore[attr-defined]
|
|
type_args = get_args(cls)
|
|
if type_args and len(type_args) == 2:
|
|
return type_args[1]
|
|
|
|
raise TypeError(
|
|
f"Runnable {self.get_name()} doesn't have an inferable OutputType. "
|
|
"Override the OutputType property to specify the output type."
|
|
)
|
|
|
|
@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: Optional[RunnableConfig] = None
|
|
) -> Type[BaseModel]:
|
|
"""Get a pydantic model that can be used to validate input to the runnable.
|
|
|
|
Runnables 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 issubclass(root_type, BaseModel):
|
|
return root_type
|
|
|
|
return create_model(
|
|
self.get_name("Input"),
|
|
__root__=(root_type, None),
|
|
__config__=_SchemaConfig,
|
|
)
|
|
|
|
@property
|
|
def output_schema(self) -> Type[BaseModel]:
|
|
"""The type of output this runnable produces specified as a pydantic model."""
|
|
return self.get_output_schema()
|
|
|
|
def get_output_schema(
|
|
self, config: Optional[RunnableConfig] = None
|
|
) -> Type[BaseModel]:
|
|
"""Get a pydantic model that can be used to validate output to the runnable.
|
|
|
|
Runnables 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 issubclass(root_type, BaseModel):
|
|
return root_type
|
|
|
|
return create_model(
|
|
self.get_name("Output"),
|
|
__root__=(root_type, None),
|
|
__config__=_SchemaConfig,
|
|
)
|
|
|
|
@property
|
|
def config_specs(self) -> List[ConfigurableFieldSpec]:
|
|
"""List configurable fields for this runnable."""
|
|
return []
|
|
|
|
def config_schema(
|
|
self, *, include: Optional[Sequence[str]] = 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( # type: ignore[call-overload]
|
|
"Configurable",
|
|
**{
|
|
spec.id: (
|
|
spec.annotation,
|
|
Field(
|
|
spec.default, title=spec.name, description=spec.description
|
|
),
|
|
)
|
|
for spec in config_specs
|
|
},
|
|
__config__=_SchemaConfig,
|
|
)
|
|
if config_specs
|
|
else None
|
|
)
|
|
|
|
return create_model( # type: ignore[call-overload]
|
|
self.get_name("Config"),
|
|
__config__=_SchemaConfig,
|
|
**({"configurable": (configurable, None)} if configurable else {}),
|
|
**{
|
|
field_name: (field_type, None)
|
|
for field_name, field_type in RunnableConfig.__annotations__.items()
|
|
if field_name in [i for i in include if i != "configurable"]
|
|
},
|
|
)
|
|
|
|
def get_graph(self, config: Optional[RunnableConfig] = None) -> Graph:
|
|
"""Return a graph representation of this runnable."""
|
|
from langchain_core.runnables.graph import Graph
|
|
|
|
graph = Graph()
|
|
input_node = graph.add_node(self.get_input_schema(config))
|
|
runnable_node = graph.add_node(self)
|
|
output_node = graph.add_node(self.get_output_schema(config))
|
|
graph.add_edge(input_node, runnable_node)
|
|
graph.add_edge(runnable_node, output_node)
|
|
return graph
|
|
|
|
def get_prompts(
|
|
self, config: Optional[RunnableConfig] = None
|
|
) -> List[BasePromptTemplate]:
|
|
from langchain_core.prompts.base import BasePromptTemplate
|
|
|
|
prompts = []
|
|
for _, node in self.get_graph(config=config).nodes.items():
|
|
if isinstance(node.data, BasePromptTemplate):
|
|
prompts.append(node.data)
|
|
return prompts
|
|
|
|
def __or__(
|
|
self,
|
|
other: Union[
|
|
Runnable[Any, Other],
|
|
Callable[[Any], Other],
|
|
Callable[[Iterator[Any]], Iterator[Other]],
|
|
Mapping[str, Union[Runnable[Any, Other], Callable[[Any], Other], Any]],
|
|
],
|
|
) -> RunnableSerializable[Input, Other]:
|
|
"""Compose this runnable with another object to create a RunnableSequence."""
|
|
return RunnableSequence(self, coerce_to_runnable(other))
|
|
|
|
def __ror__(
|
|
self,
|
|
other: Union[
|
|
Runnable[Other, Any],
|
|
Callable[[Other], Any],
|
|
Callable[[Iterator[Other]], Iterator[Any]],
|
|
Mapping[str, Union[Runnable[Other, Any], Callable[[Other], Any], Any]],
|
|
],
|
|
) -> RunnableSerializable[Other, Output]:
|
|
"""Compose this runnable with another object to create a RunnableSequence."""
|
|
return RunnableSequence(coerce_to_runnable(other), self)
|
|
|
|
def pipe(
|
|
self,
|
|
*others: Union[Runnable[Any, Other], Callable[[Any], Other]],
|
|
name: Optional[str] = None,
|
|
) -> RunnableSerializable[Input, Other]:
|
|
"""Compose this runnable with another object to create a RunnableSequence."""
|
|
return RunnableSequence(self, *others, name=name)
|
|
|
|
def pick(self, keys: Union[str, List[str]]) -> RunnableSerializable[Any, Any]:
|
|
"""Pick keys from the dict output of this runnable.
|
|
Returns a new runnable."""
|
|
from langchain_core.runnables.passthrough import RunnablePick
|
|
|
|
return self | RunnablePick(keys)
|
|
|
|
def assign(
|
|
self,
|
|
**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]],
|
|
],
|
|
],
|
|
) -> RunnableSerializable[Any, Any]:
|
|
"""Assigns new fields to the dict output of this runnable.
|
|
Returns a new runnable."""
|
|
from langchain_core.runnables.passthrough import RunnableAssign
|
|
|
|
return self | RunnableAssign(RunnableParallel(kwargs))
|
|
|
|
""" --- Public API --- """
|
|
|
|
@abstractmethod
|
|
def invoke(self, input: Input, config: Optional[RunnableConfig] = None) -> Output:
|
|
"""Transform a single input into an output. Override to implement.
|
|
|
|
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 the RunnableConfig
|
|
for more details.
|
|
|
|
Returns:
|
|
The output of the runnable.
|
|
"""
|
|
|
|
async def ainvoke(
|
|
self, input: Input, config: Optional[RunnableConfig] = None, **kwargs: Any
|
|
) -> Output:
|
|
"""Default implementation of ainvoke, calls invoke from a thread.
|
|
|
|
The default implementation allows usage of async code even if
|
|
the runnable did not implement a native async version of invoke.
|
|
|
|
Subclasses should override this method if they can run asynchronously.
|
|
"""
|
|
return await run_in_executor(config, self.invoke, input, config, **kwargs)
|
|
|
|
def batch(
|
|
self,
|
|
inputs: List[Input],
|
|
config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None,
|
|
*,
|
|
return_exceptions: bool = False,
|
|
**kwargs: Optional[Any],
|
|
) -> 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 should override this method if they can batch more efficiently;
|
|
e.g., if the underlying runnable uses an API which supports a batch mode.
|
|
"""
|
|
if not inputs:
|
|
return []
|
|
|
|
configs = get_config_list(config, len(inputs))
|
|
|
|
def invoke(input: Input, config: RunnableConfig) -> Union[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)))
|
|
|
|
async def abatch(
|
|
self,
|
|
inputs: List[Input],
|
|
config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None,
|
|
*,
|
|
return_exceptions: bool = False,
|
|
**kwargs: Optional[Any],
|
|
) -> List[Output]:
|
|
"""Default implementation runs ainvoke in parallel using asyncio.gather.
|
|
|
|
The default implementation of batch works well for IO bound runnables.
|
|
|
|
Subclasses should override this method if they can batch more efficiently;
|
|
e.g., if the underlying runnable uses an API which supports a batch mode.
|
|
"""
|
|
if not inputs:
|
|
return []
|
|
|
|
configs = get_config_list(config, len(inputs))
|
|
|
|
async def ainvoke(
|
|
input: Input, config: RunnableConfig
|
|
) -> Union[Output, Exception]:
|
|
if return_exceptions:
|
|
try:
|
|
return await self.ainvoke(input, config, **kwargs)
|
|
except Exception as e:
|
|
return e
|
|
else:
|
|
return await self.ainvoke(input, config, **kwargs)
|
|
|
|
coros = map(ainvoke, inputs, configs)
|
|
return await gather_with_concurrency(configs[0].get("max_concurrency"), *coros)
|
|
|
|
def stream(
|
|
self,
|
|
input: Input,
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> Iterator[Output]:
|
|
"""
|
|
Default implementation of stream, which calls invoke.
|
|
Subclasses should override this method if they support streaming output.
|
|
"""
|
|
yield self.invoke(input, config, **kwargs)
|
|
|
|
async def astream(
|
|
self,
|
|
input: Input,
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> AsyncIterator[Output]:
|
|
"""
|
|
Default implementation of astream, which calls ainvoke.
|
|
Subclasses should override this method if they support streaming output.
|
|
"""
|
|
yield await self.ainvoke(input, config, **kwargs)
|
|
|
|
@overload
|
|
def astream_log(
|
|
self,
|
|
input: Any,
|
|
config: Optional[RunnableConfig] = None,
|
|
*,
|
|
diff: Literal[True] = True,
|
|
with_streamed_output_list: bool = True,
|
|
include_names: Optional[Sequence[str]] = None,
|
|
include_types: Optional[Sequence[str]] = None,
|
|
include_tags: Optional[Sequence[str]] = None,
|
|
exclude_names: Optional[Sequence[str]] = None,
|
|
exclude_types: Optional[Sequence[str]] = None,
|
|
exclude_tags: Optional[Sequence[str]] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> AsyncIterator[RunLogPatch]:
|
|
...
|
|
|
|
@overload
|
|
def astream_log(
|
|
self,
|
|
input: Any,
|
|
config: Optional[RunnableConfig] = None,
|
|
*,
|
|
diff: Literal[False],
|
|
with_streamed_output_list: bool = True,
|
|
include_names: Optional[Sequence[str]] = None,
|
|
include_types: Optional[Sequence[str]] = None,
|
|
include_tags: Optional[Sequence[str]] = None,
|
|
exclude_names: Optional[Sequence[str]] = None,
|
|
exclude_types: Optional[Sequence[str]] = None,
|
|
exclude_tags: Optional[Sequence[str]] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> AsyncIterator[RunLog]:
|
|
...
|
|
|
|
async def astream_log(
|
|
self,
|
|
input: Any,
|
|
config: Optional[RunnableConfig] = None,
|
|
*,
|
|
diff: bool = True,
|
|
with_streamed_output_list: bool = True,
|
|
include_names: Optional[Sequence[str]] = None,
|
|
include_types: Optional[Sequence[str]] = None,
|
|
include_tags: Optional[Sequence[str]] = None,
|
|
exclude_names: Optional[Sequence[str]] = None,
|
|
exclude_types: Optional[Sequence[str]] = None,
|
|
exclude_tags: Optional[Sequence[str]] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> Union[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.
|
|
"""
|
|
import jsonpatch # type: ignore[import]
|
|
|
|
from langchain_core.callbacks.base import BaseCallbackManager
|
|
from langchain_core.tracers.log_stream import (
|
|
LogStreamCallbackHandler,
|
|
RunLog,
|
|
RunLogPatch,
|
|
)
|
|
|
|
# Create a stream handler that will emit Log objects
|
|
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,
|
|
)
|
|
|
|
# Assign the stream handler to the config
|
|
config = ensure_config(config)
|
|
callbacks = config.get("callbacks")
|
|
if callbacks is None:
|
|
config["callbacks"] = [stream]
|
|
elif isinstance(callbacks, list):
|
|
config["callbacks"] = callbacks + [stream]
|
|
elif isinstance(callbacks, BaseCallbackManager):
|
|
callbacks = callbacks.copy()
|
|
callbacks.add_handler(stream, inherit=True)
|
|
config["callbacks"] = callbacks
|
|
else:
|
|
raise ValueError(
|
|
f"Unexpected type for callbacks: {callbacks}."
|
|
"Expected None, list or AsyncCallbackManager."
|
|
)
|
|
|
|
# Call the runnable in streaming mode,
|
|
# add each chunk to the output stream
|
|
async def consume_astream() -> None:
|
|
try:
|
|
prev_final_output: Optional[Output] = None
|
|
final_output: Optional[Output] = None
|
|
|
|
async for chunk in self.astream(input, config, **kwargs):
|
|
prev_final_output = final_output
|
|
if final_output is None:
|
|
final_output = chunk
|
|
else:
|
|
try:
|
|
final_output = final_output + chunk # type: ignore
|
|
except TypeError:
|
|
final_output = chunk
|
|
patches: List[Dict[str, Any]] = []
|
|
if with_streamed_output_list:
|
|
patches.append(
|
|
{
|
|
"op": "add",
|
|
"path": "/streamed_output/-",
|
|
# chunk cannot be shared between
|
|
# streamed_output and final_output
|
|
# otherwise jsonpatch.apply will
|
|
# modify both
|
|
"value": deepcopy(chunk),
|
|
}
|
|
)
|
|
for op in jsonpatch.JsonPatch.from_diff(
|
|
prev_final_output, final_output, dumps=dumps
|
|
):
|
|
patches.append({**op, "path": f"/final_output{op['path']}"})
|
|
await stream.send_stream.send(RunLogPatch(*patches))
|
|
finally:
|
|
await stream.send_stream.aclose()
|
|
|
|
# Start the runnable in a task, so we can start consuming output
|
|
task = asyncio.create_task(consume_astream())
|
|
|
|
try:
|
|
# Yield each chunk from the output stream
|
|
if diff:
|
|
async for log in stream:
|
|
yield log
|
|
else:
|
|
state = RunLog(state=None) # type: ignore[arg-type]
|
|
async for log in stream:
|
|
state = state + log
|
|
yield state
|
|
finally:
|
|
# Wait for the runnable to finish, if not cancelled (eg. by break)
|
|
try:
|
|
await task
|
|
except asyncio.CancelledError:
|
|
pass
|
|
|
|
def transform(
|
|
self,
|
|
input: Iterator[Input],
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> Iterator[Output]:
|
|
"""
|
|
Default implementation of transform, which buffers input and then calls stream.
|
|
Subclasses should override this method if they can start producing output while
|
|
input is still being generated.
|
|
"""
|
|
final: Input
|
|
got_first_val = False
|
|
|
|
for chunk in input:
|
|
if not got_first_val:
|
|
final = chunk
|
|
got_first_val = True
|
|
else:
|
|
# Make a best effort to gather, for any type that supports `+`
|
|
# This method should throw an error if gathering fails.
|
|
final = final + chunk # type: ignore[operator]
|
|
|
|
if got_first_val:
|
|
yield from self.stream(final, config, **kwargs)
|
|
|
|
async def atransform(
|
|
self,
|
|
input: AsyncIterator[Input],
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> AsyncIterator[Output]:
|
|
"""
|
|
Default implementation of atransform, which buffers input and calls astream.
|
|
Subclasses should override this method if they can start producing output while
|
|
input is still being generated.
|
|
"""
|
|
final: Input
|
|
got_first_val = False
|
|
|
|
async for chunk in input:
|
|
if not got_first_val:
|
|
final = chunk
|
|
got_first_val = True
|
|
else:
|
|
# Make a best effort to gather, for any type that supports `+`
|
|
# This method should throw an error if gathering fails.
|
|
final = final + chunk # type: ignore[operator]
|
|
|
|
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.
|
|
"""
|
|
return RunnableBinding(bound=self, kwargs=kwargs, config={})
|
|
|
|
def with_config(
|
|
self,
|
|
config: Optional[RunnableConfig] = 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.
|
|
"""
|
|
return RunnableBinding(
|
|
bound=self,
|
|
config=cast(
|
|
RunnableConfig,
|
|
{**(config or {}), **kwargs},
|
|
), # type: ignore[misc]
|
|
kwargs={},
|
|
)
|
|
|
|
def with_listeners(
|
|
self,
|
|
*,
|
|
on_start: Optional[Listener] = None,
|
|
on_end: Optional[Listener] = None,
|
|
on_error: Optional[Listener] = None,
|
|
) -> Runnable[Input, Output]:
|
|
"""
|
|
Bind lifecycle listeners to a Runnable, returning a new Runnable.
|
|
|
|
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.
|
|
|
|
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.
|
|
"""
|
|
from langchain_core.tracers.root_listeners import RootListenersTracer
|
|
|
|
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_types(
|
|
self,
|
|
*,
|
|
input_type: Optional[Type[Input]] = None,
|
|
output_type: Optional[Type[Output]] = None,
|
|
) -> Runnable[Input, Output]:
|
|
"""
|
|
Bind input and output types to a Runnable, returning a new Runnable.
|
|
"""
|
|
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,
|
|
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
|
|
|
|
Returns:
|
|
A new Runnable that retries the original runnable on exceptions.
|
|
"""
|
|
from langchain_core.runnables.retry import RunnableRetry
|
|
|
|
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,
|
|
)
|
|
|
|
def map(self) -> Runnable[List[Input], List[Output]]:
|
|
"""
|
|
Return a new Runnable that maps a list of inputs to a list of outputs,
|
|
by calling invoke() with each input.
|
|
"""
|
|
return RunnableEach(bound=self)
|
|
|
|
def with_fallbacks(
|
|
self,
|
|
fallbacks: Sequence[Runnable[Input, Output]],
|
|
*,
|
|
exceptions_to_handle: Tuple[Type[BaseException], ...] = (Exception,),
|
|
) -> RunnableWithFallbacksT[Input, Output]:
|
|
"""Add fallbacks to a runnable, returning a new Runnable.
|
|
|
|
Args:
|
|
fallbacks: A sequence of runnables to try if the original runnable fails.
|
|
exceptions_to_handle: A tuple of exception types to handle.
|
|
|
|
Returns:
|
|
A new Runnable that will try the original runnable, and then each
|
|
fallback in order, upon failures.
|
|
"""
|
|
from langchain_core.runnables.fallbacks import RunnableWithFallbacks
|
|
|
|
return RunnableWithFallbacks(
|
|
runnable=self,
|
|
fallbacks=fallbacks,
|
|
exceptions_to_handle=exceptions_to_handle,
|
|
)
|
|
|
|
""" --- Helper methods for Subclasses --- """
|
|
|
|
def _call_with_config(
|
|
self,
|
|
func: Union[
|
|
Callable[[Input], Output],
|
|
Callable[[Input, CallbackManagerForChainRun], Output],
|
|
Callable[[Input, CallbackManagerForChainRun, RunnableConfig], Output],
|
|
],
|
|
input: Input,
|
|
config: Optional[RunnableConfig],
|
|
run_type: Optional[str] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> Output:
|
|
"""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(
|
|
dumpd(self),
|
|
input,
|
|
run_type=run_type,
|
|
name=config.get("run_name") or self.get_name(),
|
|
)
|
|
try:
|
|
child_config = patch_config(config, callbacks=run_manager.get_child())
|
|
context = copy_context()
|
|
context.run(var_child_runnable_config.set, child_config)
|
|
output = cast(
|
|
Output,
|
|
context.run(
|
|
call_func_with_variable_args,
|
|
func, # type: ignore[arg-type]
|
|
input, # type: ignore[arg-type]
|
|
config,
|
|
run_manager,
|
|
**kwargs,
|
|
),
|
|
)
|
|
except BaseException as e:
|
|
run_manager.on_chain_error(e)
|
|
raise
|
|
else:
|
|
run_manager.on_chain_end(dumpd(output))
|
|
return output
|
|
|
|
async def _acall_with_config(
|
|
self,
|
|
func: Union[
|
|
Callable[[Input], Awaitable[Output]],
|
|
Callable[[Input, AsyncCallbackManagerForChainRun], Awaitable[Output]],
|
|
Callable[
|
|
[Input, AsyncCallbackManagerForChainRun, RunnableConfig],
|
|
Awaitable[Output],
|
|
],
|
|
],
|
|
input: Input,
|
|
config: Optional[RunnableConfig],
|
|
run_type: Optional[str] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> Output:
|
|
"""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(
|
|
dumpd(self),
|
|
input,
|
|
run_type=run_type,
|
|
name=config.get("run_name") or self.get_name(),
|
|
)
|
|
try:
|
|
child_config = patch_config(config, callbacks=run_manager.get_child())
|
|
context = copy_context()
|
|
context.run(var_child_runnable_config.set, child_config)
|
|
coro = acall_func_with_variable_args(
|
|
func, input, config, run_manager, **kwargs
|
|
)
|
|
if accepts_context(asyncio.create_task):
|
|
output: Output = await asyncio.create_task(coro, context=context) # type: ignore
|
|
else:
|
|
output = await coro
|
|
except BaseException as e:
|
|
await run_manager.on_chain_error(e)
|
|
raise
|
|
else:
|
|
await run_manager.on_chain_end(dumpd(output))
|
|
return output
|
|
|
|
def _batch_with_config(
|
|
self,
|
|
func: Union[
|
|
Callable[[List[Input]], List[Union[Exception, Output]]],
|
|
Callable[
|
|
[List[Input], List[CallbackManagerForChainRun]],
|
|
List[Union[Exception, Output]],
|
|
],
|
|
Callable[
|
|
[List[Input], List[CallbackManagerForChainRun], List[RunnableConfig]],
|
|
List[Union[Exception, Output]],
|
|
],
|
|
],
|
|
input: List[Input],
|
|
config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None,
|
|
*,
|
|
return_exceptions: bool = False,
|
|
run_type: Optional[str] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> List[Output]:
|
|
"""Helper method to transform an Input value to an Output value,
|
|
with callbacks. Use this method to implement invoke() in subclasses."""
|
|
if not input:
|
|
return []
|
|
|
|
configs = get_config_list(config, len(input))
|
|
callback_managers = [get_callback_manager_for_config(c) for c in configs]
|
|
run_managers = [
|
|
callback_manager.on_chain_start(
|
|
dumpd(self),
|
|
input,
|
|
run_type=run_type,
|
|
name=config.get("run_name") or self.get_name(),
|
|
)
|
|
for callback_manager, input, config in zip(
|
|
callback_managers, input, configs
|
|
)
|
|
]
|
|
try:
|
|
if accepts_config(func):
|
|
kwargs["config"] = [
|
|
patch_config(c, callbacks=rm.get_child())
|
|
for c, rm in zip(configs, run_managers)
|
|
]
|
|
if accepts_run_manager(func):
|
|
kwargs["run_manager"] = run_managers
|
|
output = func(input, **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 input])
|
|
else:
|
|
raise
|
|
else:
|
|
first_exception: Optional[Exception] = None
|
|
for run_manager, out in zip(run_managers, output):
|
|
if isinstance(out, Exception):
|
|
first_exception = first_exception or out
|
|
run_manager.on_chain_error(out)
|
|
else:
|
|
run_manager.on_chain_end(dumpd(out))
|
|
if return_exceptions or first_exception is None:
|
|
return cast(List[Output], output)
|
|
else:
|
|
raise first_exception
|
|
|
|
async def _abatch_with_config(
|
|
self,
|
|
func: Union[
|
|
Callable[[List[Input]], Awaitable[List[Union[Exception, Output]]]],
|
|
Callable[
|
|
[List[Input], List[AsyncCallbackManagerForChainRun]],
|
|
Awaitable[List[Union[Exception, Output]]],
|
|
],
|
|
Callable[
|
|
[
|
|
List[Input],
|
|
List[AsyncCallbackManagerForChainRun],
|
|
List[RunnableConfig],
|
|
],
|
|
Awaitable[List[Union[Exception, Output]]],
|
|
],
|
|
],
|
|
input: List[Input],
|
|
config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None,
|
|
*,
|
|
return_exceptions: bool = False,
|
|
run_type: Optional[str] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> List[Output]:
|
|
"""Helper method to transform an Input value to an Output value,
|
|
with callbacks. Use this method to implement invoke() in subclasses."""
|
|
if not input:
|
|
return []
|
|
|
|
configs = get_config_list(config, len(input))
|
|
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(
|
|
dumpd(self),
|
|
input,
|
|
run_type=run_type,
|
|
name=config.get("run_name") or self.get_name(),
|
|
)
|
|
for callback_manager, input, config in zip(
|
|
callback_managers, input, configs
|
|
)
|
|
)
|
|
)
|
|
try:
|
|
if accepts_config(func):
|
|
kwargs["config"] = [
|
|
patch_config(c, callbacks=rm.get_child())
|
|
for c, rm in zip(configs, run_managers)
|
|
]
|
|
if accepts_run_manager(func):
|
|
kwargs["run_manager"] = run_managers
|
|
output = await func(input, **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 input])
|
|
else:
|
|
raise
|
|
else:
|
|
first_exception: Optional[Exception] = None
|
|
coros: List[Awaitable[None]] = []
|
|
for run_manager, out in zip(run_managers, output):
|
|
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(dumpd(out)))
|
|
await asyncio.gather(*coros)
|
|
if return_exceptions or first_exception is None:
|
|
return cast(List[Output], output)
|
|
else:
|
|
raise first_exception
|
|
|
|
def _transform_stream_with_config(
|
|
self,
|
|
input: Iterator[Input],
|
|
transformer: Union[
|
|
Callable[[Iterator[Input]], Iterator[Output]],
|
|
Callable[[Iterator[Input], CallbackManagerForChainRun], Iterator[Output]],
|
|
Callable[
|
|
[
|
|
Iterator[Input],
|
|
CallbackManagerForChainRun,
|
|
RunnableConfig,
|
|
],
|
|
Iterator[Output],
|
|
],
|
|
],
|
|
config: Optional[RunnableConfig],
|
|
run_type: Optional[str] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> Iterator[Output]:
|
|
"""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(input, 2)
|
|
# Start the input iterator to ensure the input runnable starts before this one
|
|
final_input: Optional[Input] = next(input_for_tracing, None)
|
|
final_input_supported = True
|
|
final_output: Optional[Output] = None
|
|
final_output_supported = True
|
|
|
|
config = ensure_config(config)
|
|
callback_manager = get_callback_manager_for_config(config)
|
|
run_manager = callback_manager.on_chain_start(
|
|
dumpd(self),
|
|
{"input": ""},
|
|
run_type=run_type,
|
|
name=config.get("run_name") or self.get_name(),
|
|
)
|
|
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
|
|
context = copy_context()
|
|
context.run(var_child_runnable_config.set, child_config)
|
|
iterator = context.run(transformer, input_for_transform, **kwargs) # type: ignore[arg-type]
|
|
try:
|
|
while True:
|
|
chunk: Output = context.run(next, iterator) # type: ignore
|
|
yield chunk
|
|
if final_output_supported:
|
|
if final_output is None:
|
|
final_output = chunk
|
|
else:
|
|
try:
|
|
final_output = final_output + chunk # type: ignore
|
|
except TypeError:
|
|
final_output = None
|
|
final_output_supported = False
|
|
except StopIteration:
|
|
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
|
|
except TypeError:
|
|
final_input = None
|
|
final_input_supported = False
|
|
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,
|
|
input: AsyncIterator[Input],
|
|
transformer: Union[
|
|
Callable[[AsyncIterator[Input]], AsyncIterator[Output]],
|
|
Callable[
|
|
[AsyncIterator[Input], AsyncCallbackManagerForChainRun],
|
|
AsyncIterator[Output],
|
|
],
|
|
Callable[
|
|
[
|
|
AsyncIterator[Input],
|
|
AsyncCallbackManagerForChainRun,
|
|
RunnableConfig,
|
|
],
|
|
AsyncIterator[Output],
|
|
],
|
|
],
|
|
config: Optional[RunnableConfig],
|
|
run_type: Optional[str] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> AsyncIterator[Output]:
|
|
"""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(input, 2)
|
|
# Start the input iterator to ensure the input runnable starts before this one
|
|
final_input: Optional[Input] = await py_anext(input_for_tracing, None)
|
|
final_input_supported = True
|
|
final_output: Optional[Output] = 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(
|
|
dumpd(self),
|
|
{"input": ""},
|
|
run_type=run_type,
|
|
name=config.get("run_name") or self.get_name(),
|
|
)
|
|
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
|
|
context = copy_context()
|
|
context.run(var_child_runnable_config.set, child_config)
|
|
iterator = context.run(transformer, input_for_transform, **kwargs) # type: ignore[arg-type]
|
|
try:
|
|
while True:
|
|
if accepts_context(asyncio.create_task):
|
|
chunk: Output = await asyncio.create_task( # type: ignore[call-arg]
|
|
py_anext(iterator), # type: ignore[arg-type]
|
|
context=context,
|
|
)
|
|
else:
|
|
chunk = cast(Output, await py_anext(iterator))
|
|
yield chunk
|
|
if final_output_supported:
|
|
if final_output is None:
|
|
final_output = chunk
|
|
else:
|
|
try:
|
|
final_output = final_output + chunk # type: ignore
|
|
except TypeError:
|
|
final_output = None
|
|
final_output_supported = False
|
|
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 = None
|
|
final_input_supported = False
|
|
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)
|
|
|
|
|
|
class RunnableSerializable(Serializable, Runnable[Input, Output]):
|
|
"""A Runnable that can be serialized to JSON."""
|
|
|
|
name: Optional[str] = None
|
|
"""The name of the runnable. Used for debugging and tracing."""
|
|
|
|
def configurable_fields(
|
|
self, **kwargs: AnyConfigurableField
|
|
) -> RunnableSerializable[Input, Output]:
|
|
from langchain_core.runnables.configurable import RunnableConfigurableFields
|
|
|
|
for key in kwargs:
|
|
if key not in self.__fields__:
|
|
raise ValueError(
|
|
f"Configuration key {key} not found in {self}: "
|
|
"available keys are {self.__fields__.keys()}"
|
|
)
|
|
|
|
return RunnableConfigurableFields(default=self, fields=kwargs)
|
|
|
|
def configurable_alternatives(
|
|
self,
|
|
which: ConfigurableField,
|
|
*,
|
|
default_key: str = "default",
|
|
prefix_keys: bool = False,
|
|
**kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]],
|
|
) -> RunnableSerializable[Input, Output]:
|
|
from langchain_core.runnables.configurable import (
|
|
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: Optional[RunnableConfig]
|
|
) -> Type[BaseModel]:
|
|
from langchain_core.runnables.passthrough import RunnableAssign, RunnablePick
|
|
|
|
first = steps[0]
|
|
if len(steps) == 1:
|
|
return first.get_input_schema(config)
|
|
elif isinstance(first, RunnableAssign):
|
|
next_input_schema = _seq_input_schema(steps[1:], config)
|
|
if not next_input_schema.__custom_root_type__:
|
|
# it's a dict as expected
|
|
return create_model( # type: ignore[call-overload]
|
|
"RunnableSequenceInput",
|
|
**{
|
|
k: (v.annotation, v.default)
|
|
for k, v in next_input_schema.__fields__.items()
|
|
if k not in first.mapper.steps
|
|
},
|
|
__config__=_SchemaConfig,
|
|
)
|
|
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: Optional[RunnableConfig]
|
|
) -> Type[BaseModel]:
|
|
from langchain_core.runnables.passthrough import RunnableAssign, RunnablePick
|
|
|
|
last = steps[-1]
|
|
if len(steps) == 1:
|
|
return last.get_input_schema(config)
|
|
elif isinstance(last, RunnableAssign):
|
|
mapper_output_schema = last.mapper.get_output_schema(config)
|
|
prev_output_schema = _seq_output_schema(steps[:-1], config)
|
|
if not prev_output_schema.__custom_root_type__:
|
|
# it's a dict as expected
|
|
return create_model( # type: ignore[call-overload]
|
|
"RunnableSequenceOutput",
|
|
**{
|
|
**{
|
|
k: (v.annotation, v.default)
|
|
for k, v in prev_output_schema.__fields__.items()
|
|
},
|
|
**{
|
|
k: (v.annotation, v.default)
|
|
for k, v in mapper_output_schema.__fields__.items()
|
|
},
|
|
},
|
|
__config__=_SchemaConfig,
|
|
)
|
|
elif isinstance(last, RunnablePick):
|
|
prev_output_schema = _seq_output_schema(steps[:-1], config)
|
|
if not prev_output_schema.__custom_root_type__:
|
|
# it's a dict as expected
|
|
if isinstance(last.keys, list):
|
|
return create_model( # type: ignore[call-overload]
|
|
"RunnableSequenceOutput",
|
|
**{
|
|
k: (v.annotation, v.default)
|
|
for k, v in prev_output_schema.__fields__.items()
|
|
if k in last.keys
|
|
},
|
|
__config__=_SchemaConfig,
|
|
)
|
|
else:
|
|
field = prev_output_schema.__fields__[last.keys]
|
|
return create_model( # type: ignore[call-overload]
|
|
"RunnableSequenceOutput",
|
|
__root__=(field.annotation, field.default),
|
|
__config__=_SchemaConfig,
|
|
)
|
|
|
|
return last.get_output_schema(config)
|
|
|
|
|
|
class RunnableSequence(RunnableSerializable[Input, Output]):
|
|
"""A sequence of runnables, where the output of each 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 runnables.
|
|
|
|
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.
|
|
|
|
Please 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:
|
|
|
|
.. code-block:: 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 runnable.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:
|
|
|
|
.. code-block:: python
|
|
|
|
from langchain_core.output_parsers.json import SimpleJsonOutputParser
|
|
from langchain.chat_models.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('-')
|
|
print(chunk, sep='', flush=True)
|
|
"""
|
|
|
|
# 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 runnables in the sequence."""
|
|
last: Runnable[Any, Output]
|
|
"""The last runnable in the sequence."""
|
|
|
|
def __init__(
|
|
self,
|
|
*steps: RunnableLike,
|
|
name: Optional[str] = None,
|
|
first: Optional[Runnable[Any, Any]] = None,
|
|
middle: Optional[List[Runnable[Any, Any]]] = None,
|
|
last: Optional[Runnable[Any, Any]] = None,
|
|
) -> None:
|
|
"""Create a new RunnableSequence.
|
|
|
|
Args:
|
|
steps: The steps to include in the sequence.
|
|
"""
|
|
steps_flat: List[Runnable] = []
|
|
if not steps:
|
|
if 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) < 2:
|
|
raise ValueError(
|
|
f"RunnableSequence must have at least 2 steps, got {len(steps_flat)}"
|
|
)
|
|
super().__init__(
|
|
first=steps_flat[0],
|
|
middle=list(steps_flat[1:-1]),
|
|
last=steps_flat[-1],
|
|
name=name,
|
|
)
|
|
|
|
@classmethod
|
|
def get_lc_namespace(cls) -> List[str]:
|
|
"""Get the namespace of the langchain object."""
|
|
return ["langchain", "schema", "runnable"]
|
|
|
|
@property
|
|
def steps(self) -> List[Runnable[Any, Any]]:
|
|
"""All the runnables that make up the sequence in order."""
|
|
return [self.first] + self.middle + [self.last]
|
|
|
|
@classmethod
|
|
def is_lc_serializable(cls) -> bool:
|
|
return True
|
|
|
|
class Config:
|
|
arbitrary_types_allowed = True
|
|
|
|
@property
|
|
def InputType(self) -> Type[Input]:
|
|
return self.first.InputType
|
|
|
|
@property
|
|
def OutputType(self) -> Type[Output]:
|
|
return self.last.OutputType
|
|
|
|
def get_input_schema(
|
|
self, config: Optional[RunnableConfig] = None
|
|
) -> Type[BaseModel]:
|
|
return _seq_input_schema(self.steps, config)
|
|
|
|
def get_output_schema(
|
|
self, config: Optional[RunnableConfig] = None
|
|
) -> Type[BaseModel]:
|
|
return _seq_output_schema(self.steps, config)
|
|
|
|
@property
|
|
def config_specs(self) -> List[ConfigurableFieldSpec]:
|
|
from langchain_core.beta.runnables.context import (
|
|
CONTEXT_CONFIG_PREFIX,
|
|
_key_from_id,
|
|
)
|
|
|
|
# get all specs
|
|
all_specs = [
|
|
(spec, idx)
|
|
for idx, step in enumerate(self.steps)
|
|
for spec in step.config_specs
|
|
]
|
|
# calculate context dependencies
|
|
specs_by_pos = groupby(
|
|
[tup for tup in all_specs if tup[0].id.startswith(CONTEXT_CONFIG_PREFIX)],
|
|
lambda x: x[1],
|
|
)
|
|
next_deps: Set[str] = set()
|
|
deps_by_pos: Dict[int, Set[str]] = {}
|
|
for pos, specs in specs_by_pos:
|
|
deps_by_pos[pos] = next_deps
|
|
next_deps = next_deps | {spec[0].id for spec in specs}
|
|
# assign context dependencies
|
|
for pos, (spec, idx) in enumerate(all_specs):
|
|
if spec.id.startswith(CONTEXT_CONFIG_PREFIX):
|
|
all_specs[pos] = (
|
|
ConfigurableFieldSpec(
|
|
id=spec.id,
|
|
annotation=spec.annotation,
|
|
name=spec.name,
|
|
default=spec.default,
|
|
description=spec.description,
|
|
is_shared=spec.is_shared,
|
|
dependencies=[
|
|
d
|
|
for d in deps_by_pos[idx]
|
|
if _key_from_id(d) != _key_from_id(spec.id)
|
|
]
|
|
+ (spec.dependencies or []),
|
|
),
|
|
idx,
|
|
)
|
|
|
|
return get_unique_config_specs(spec for spec, _ in all_specs)
|
|
|
|
def get_graph(self, config: Optional[RunnableConfig] = None) -> Graph:
|
|
from langchain_core.runnables.graph import Graph
|
|
|
|
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()
|
|
graph.extend(step_graph)
|
|
step_first_node = step_graph.first_node()
|
|
if not step_first_node:
|
|
raise ValueError(f"Runnable {step} has no first node")
|
|
if current_last_node:
|
|
graph.add_edge(current_last_node, step_first_node)
|
|
|
|
return graph
|
|
|
|
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)
|
|
)
|
|
|
|
def __or__(
|
|
self,
|
|
other: Union[
|
|
Runnable[Any, Other],
|
|
Callable[[Any], Other],
|
|
Callable[[Iterator[Any]], Iterator[Other]],
|
|
Mapping[str, Union[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,
|
|
)
|
|
else:
|
|
return RunnableSequence(
|
|
self.first,
|
|
*self.middle,
|
|
self.last,
|
|
coerce_to_runnable(other),
|
|
name=self.name,
|
|
)
|
|
|
|
def __ror__(
|
|
self,
|
|
other: Union[
|
|
Runnable[Other, Any],
|
|
Callable[[Other], Any],
|
|
Callable[[Iterator[Other]], Iterator[Any]],
|
|
Mapping[str, Union[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,
|
|
)
|
|
else:
|
|
return RunnableSequence(
|
|
coerce_to_runnable(other),
|
|
self.first,
|
|
*self.middle,
|
|
self.last,
|
|
name=self.name,
|
|
)
|
|
|
|
def invoke(self, input: Input, config: Optional[RunnableConfig] = None) -> Output:
|
|
from langchain_core.beta.runnables.context import config_with_context
|
|
|
|
# setup callbacks and context
|
|
config = config_with_context(ensure_config(config), self.steps)
|
|
callback_manager = get_callback_manager_for_config(config)
|
|
# start the root run
|
|
run_manager = callback_manager.on_chain_start(
|
|
dumpd(self), input, name=config.get("run_name") or self.get_name()
|
|
)
|
|
|
|
# invoke all steps in sequence
|
|
try:
|
|
for i, step in enumerate(self.steps):
|
|
input = step.invoke(
|
|
input,
|
|
# mark each step as a child run
|
|
patch_config(
|
|
config, callbacks=run_manager.get_child(f"seq:step:{i+1}")
|
|
),
|
|
)
|
|
# 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)
|
|
|
|
async def ainvoke(
|
|
self,
|
|
input: Input,
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> Output:
|
|
from langchain_core.beta.runnables.context import aconfig_with_context
|
|
|
|
# setup callbacks and context
|
|
config = aconfig_with_context(ensure_config(config), self.steps)
|
|
callback_manager = get_async_callback_manager_for_config(config)
|
|
# start the root run
|
|
run_manager = await callback_manager.on_chain_start(
|
|
dumpd(self), input, name=config.get("run_name") or self.get_name()
|
|
)
|
|
|
|
# invoke all steps in sequence
|
|
try:
|
|
for i, step in enumerate(self.steps):
|
|
input = await step.ainvoke(
|
|
input,
|
|
# mark each step as a child run
|
|
patch_config(
|
|
config, callbacks=run_manager.get_child(f"seq:step:{i+1}")
|
|
),
|
|
)
|
|
# 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)
|
|
|
|
def batch(
|
|
self,
|
|
inputs: List[Input],
|
|
config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None,
|
|
*,
|
|
return_exceptions: bool = False,
|
|
**kwargs: Optional[Any],
|
|
) -> List[Output]:
|
|
from langchain_core.beta.runnables.context import config_with_context
|
|
from langchain_core.callbacks.manager import CallbackManager
|
|
|
|
if not inputs:
|
|
return []
|
|
|
|
# setup callbacks and context
|
|
configs = [
|
|
config_with_context(c, self.steps)
|
|
for c in 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(
|
|
dumpd(self),
|
|
input,
|
|
name=config.get("run_name") or self.get_name(),
|
|
)
|
|
for cm, input, config in zip(callback_managers, inputs, configs)
|
|
]
|
|
|
|
# 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)
|
|
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))
|
|
if i not in failed_inputs_map
|
|
],
|
|
return_exceptions=return_exceptions,
|
|
**kwargs,
|
|
)
|
|
# If an input failed, add it to the map
|
|
for i, inp in zip(remaining_idxs, inputs):
|
|
if isinstance(inp, Exception):
|
|
failed_inputs_map[i] = inp
|
|
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)
|
|
],
|
|
)
|
|
|
|
# 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])
|
|
else:
|
|
raise
|
|
else:
|
|
first_exception: Optional[Exception] = None
|
|
for run_manager, out in zip(run_managers, inputs):
|
|
if isinstance(out, Exception):
|
|
first_exception = first_exception or out
|
|
run_manager.on_chain_error(out)
|
|
else:
|
|
run_manager.on_chain_end(dumpd(out))
|
|
if return_exceptions or first_exception is None:
|
|
return cast(List[Output], inputs)
|
|
else:
|
|
raise first_exception
|
|
|
|
async def abatch(
|
|
self,
|
|
inputs: List[Input],
|
|
config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None,
|
|
*,
|
|
return_exceptions: bool = False,
|
|
**kwargs: Optional[Any],
|
|
) -> List[Output]:
|
|
from langchain_core.beta.runnables.context import aconfig_with_context
|
|
from langchain_core.callbacks.manager import AsyncCallbackManager
|
|
|
|
if not inputs:
|
|
return []
|
|
|
|
# setup callbacks and context
|
|
configs = [
|
|
aconfig_with_context(c, self.steps)
|
|
for c in 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(
|
|
dumpd(self),
|
|
input,
|
|
name=config.get("run_name") or self.get_name(),
|
|
)
|
|
for cm, input, config in zip(callback_managers, inputs, configs)
|
|
)
|
|
)
|
|
|
|
# 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)
|
|
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))
|
|
if i not in failed_inputs_map
|
|
],
|
|
return_exceptions=return_exceptions,
|
|
**kwargs,
|
|
)
|
|
# If an input failed, add it to the map
|
|
for i, inp in zip(remaining_idxs, inputs):
|
|
if isinstance(inp, Exception):
|
|
failed_inputs_map[i] = inp
|
|
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)
|
|
],
|
|
)
|
|
# 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])
|
|
else:
|
|
raise
|
|
else:
|
|
first_exception: Optional[Exception] = None
|
|
coros: List[Awaitable[None]] = []
|
|
for run_manager, out in zip(run_managers, inputs):
|
|
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(dumpd(out)))
|
|
await asyncio.gather(*coros)
|
|
if return_exceptions or first_exception is None:
|
|
return cast(List[Output], inputs)
|
|
else:
|
|
raise first_exception
|
|
|
|
def _transform(
|
|
self,
|
|
input: Iterator[Input],
|
|
run_manager: CallbackManagerForChainRun,
|
|
config: RunnableConfig,
|
|
) -> Iterator[Output]:
|
|
from langchain_core.beta.runnables.context import config_with_context
|
|
|
|
steps = [self.first] + self.middle + [self.last]
|
|
config = config_with_context(config, self.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(Iterator[Output], input)
|
|
for step in steps:
|
|
final_pipeline = step.transform(
|
|
final_pipeline,
|
|
patch_config(
|
|
config,
|
|
callbacks=run_manager.get_child(f"seq:step:{steps.index(step)+1}"),
|
|
),
|
|
)
|
|
|
|
for output in final_pipeline:
|
|
yield output
|
|
|
|
async def _atransform(
|
|
self,
|
|
input: AsyncIterator[Input],
|
|
run_manager: AsyncCallbackManagerForChainRun,
|
|
config: RunnableConfig,
|
|
) -> AsyncIterator[Output]:
|
|
from langchain_core.beta.runnables.context import aconfig_with_context
|
|
|
|
steps = [self.first] + self.middle + [self.last]
|
|
config = aconfig_with_context(config, self.steps)
|
|
|
|
# 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], input)
|
|
for step in steps:
|
|
final_pipeline = step.atransform(
|
|
final_pipeline,
|
|
patch_config(
|
|
config,
|
|
callbacks=run_manager.get_child(f"seq:step:{steps.index(step)+1}"),
|
|
),
|
|
)
|
|
async for output in final_pipeline:
|
|
yield output
|
|
|
|
def transform(
|
|
self,
|
|
input: Iterator[Input],
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> 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,
|
|
)
|
|
|
|
def stream(
|
|
self,
|
|
input: Input,
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> Iterator[Output]:
|
|
yield from self.transform(iter([input]), config, **kwargs)
|
|
|
|
async def atransform(
|
|
self,
|
|
input: AsyncIterator[Input],
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> 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
|
|
|
|
async def astream(
|
|
self,
|
|
input: Input,
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> 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]]):
|
|
"""
|
|
A runnable that runs a mapping of runnables in parallel,
|
|
and returns a mapping of their outputs.
|
|
"""
|
|
|
|
steps: Mapping[str, Runnable[Input, Any]]
|
|
|
|
def __init__(
|
|
self,
|
|
__steps: Optional[
|
|
Mapping[
|
|
str,
|
|
Union[
|
|
Runnable[Input, Any],
|
|
Callable[[Input], Any],
|
|
Mapping[str, Union[Runnable[Input, Any], Callable[[Input], Any]]],
|
|
],
|
|
]
|
|
] = None,
|
|
**kwargs: Union[
|
|
Runnable[Input, Any],
|
|
Callable[[Input], Any],
|
|
Mapping[str, Union[Runnable[Input, Any], Callable[[Input], Any]]],
|
|
],
|
|
) -> None:
|
|
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
|
|
def is_lc_serializable(cls) -> bool:
|
|
return True
|
|
|
|
@classmethod
|
|
def get_lc_namespace(cls) -> List[str]:
|
|
"""Get the namespace of the langchain object."""
|
|
return ["langchain", "schema", "runnable"]
|
|
|
|
class Config:
|
|
arbitrary_types_allowed = True
|
|
|
|
def get_name(
|
|
self, suffix: Optional[str] = None, *, name: Optional[str] = None
|
|
) -> str:
|
|
name = name or self.name or f"RunnableParallel<{','.join(self.steps.keys())}>"
|
|
return super().get_name(suffix, name=name)
|
|
|
|
@property
|
|
def InputType(self) -> Any:
|
|
for step in self.steps.values():
|
|
if step.InputType:
|
|
return step.InputType
|
|
|
|
return Any
|
|
|
|
def get_input_schema(
|
|
self, config: Optional[RunnableConfig] = None
|
|
) -> Type[BaseModel]:
|
|
if all(
|
|
s.get_input_schema(config).schema().get("type", "object") == "object"
|
|
for s in self.steps.values()
|
|
):
|
|
# This is correct, but pydantic typings/mypy don't think so.
|
|
return create_model( # type: ignore[call-overload]
|
|
self.get_name("Input"),
|
|
**{
|
|
k: (v.annotation, v.default)
|
|
for step in self.steps.values()
|
|
for k, v in step.get_input_schema(config).__fields__.items()
|
|
if k != "__root__"
|
|
},
|
|
__config__=_SchemaConfig,
|
|
)
|
|
|
|
return super().get_input_schema(config)
|
|
|
|
def get_output_schema(
|
|
self, config: Optional[RunnableConfig] = None
|
|
) -> Type[BaseModel]:
|
|
# This is correct, but pydantic typings/mypy don't think so.
|
|
return create_model( # type: ignore[call-overload]
|
|
self.get_name("Output"),
|
|
**{k: (v.OutputType, None) for k, v in self.steps.items()},
|
|
__config__=_SchemaConfig,
|
|
)
|
|
|
|
@property
|
|
def config_specs(self) -> List[ConfigurableFieldSpec]:
|
|
return get_unique_config_specs(
|
|
spec for step in self.steps.values() for spec in step.config_specs
|
|
)
|
|
|
|
def get_graph(self, config: Optional[RunnableConfig] = None) -> Graph:
|
|
from langchain_core.runnables.graph import Graph
|
|
|
|
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:
|
|
graph.extend(step_graph)
|
|
step_first_node = step_graph.first_node()
|
|
if not step_first_node:
|
|
raise ValueError(f"Runnable {step} has no first node")
|
|
step_last_node = step_graph.last_node()
|
|
if not step_last_node:
|
|
raise ValueError(f"Runnable {step} has no last node")
|
|
graph.add_edge(input_node, step_first_node)
|
|
graph.add_edge(step_last_node, output_node)
|
|
|
|
return graph
|
|
|
|
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}"
|
|
|
|
def invoke(
|
|
self, input: Input, config: Optional[RunnableConfig] = None
|
|
) -> Dict[str, Any]:
|
|
from langchain_core.callbacks.manager import CallbackManager
|
|
|
|
# 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(
|
|
dumpd(self), input, name=config.get("run_name") or self.get_name()
|
|
)
|
|
|
|
# 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(
|
|
step.invoke,
|
|
input,
|
|
# mark each step as a child run
|
|
patch_config(
|
|
config,
|
|
callbacks=run_manager.get_child(f"map:key:{key}"),
|
|
),
|
|
)
|
|
for key, step in steps.items()
|
|
]
|
|
output = {key: future.result() for key, future in zip(steps, futures)}
|
|
# finish the root run
|
|
except BaseException as e:
|
|
run_manager.on_chain_error(e)
|
|
raise
|
|
else:
|
|
run_manager.on_chain_end(output)
|
|
return output
|
|
|
|
async def ainvoke(
|
|
self,
|
|
input: Input,
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> 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(
|
|
dumpd(self), input, name=config.get("run_name") or self.get_name()
|
|
)
|
|
|
|
# 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(
|
|
*(
|
|
step.ainvoke(
|
|
input,
|
|
# mark each step as a child run
|
|
patch_config(
|
|
config, callbacks=run_manager.get_child(f"map:key:{key}")
|
|
),
|
|
)
|
|
for key, step in steps.items()
|
|
)
|
|
)
|
|
output = {key: value for key, value in zip(steps, results)}
|
|
# 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,
|
|
input: 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(input, 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
|
|
|
|
def transform(
|
|
self,
|
|
input: Iterator[Input],
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Any,
|
|
) -> Iterator[Dict[str, Any]]:
|
|
yield from self._transform_stream_with_config(
|
|
input, self._transform, config, **kwargs
|
|
)
|
|
|
|
def stream(
|
|
self,
|
|
input: Input,
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> Iterator[Dict[str, Any]]:
|
|
yield from self.transform(iter([input]), config)
|
|
|
|
async def _atransform(
|
|
self,
|
|
input: 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(input, 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) -> Optional[Output]:
|
|
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
|
|
|
|
async def atransform(
|
|
self,
|
|
input: AsyncIterator[Input],
|
|
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
|
|
|
|
async def astream(
|
|
self,
|
|
input: Input,
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> 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]):
|
|
"""
|
|
A runnable that runs a generator function.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
transform: Union[
|
|
Callable[[Iterator[Input]], Iterator[Output]],
|
|
Callable[[AsyncIterator[Input]], AsyncIterator[Output]],
|
|
],
|
|
atransform: Optional[
|
|
Callable[[AsyncIterator[Input]], AsyncIterator[Output]]
|
|
] = None,
|
|
) -> None:
|
|
if atransform is not None:
|
|
self._atransform = atransform
|
|
func_for_name: Callable = atransform
|
|
|
|
if inspect.isasyncgenfunction(transform):
|
|
self._atransform = transform
|
|
func_for_name = transform
|
|
elif inspect.isgeneratorfunction(transform):
|
|
self._transform = transform
|
|
func_for_name = transform
|
|
else:
|
|
raise TypeError(
|
|
"Expected a generator function type for `transform`."
|
|
f"Instead got an unsupported type: {type(transform)}"
|
|
)
|
|
|
|
try:
|
|
self.name = func_for_name.__name__
|
|
except AttributeError:
|
|
pass
|
|
|
|
@property
|
|
def InputType(self) -> Any:
|
|
func = getattr(self, "_transform", None) or getattr(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]
|
|
else:
|
|
return Any
|
|
except ValueError:
|
|
return Any
|
|
|
|
@property
|
|
def OutputType(self) -> Any:
|
|
func = getattr(self, "_transform", None) or getattr(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
|
|
|
|
def __eq__(self, other: Any) -> bool:
|
|
if isinstance(other, RunnableGenerator):
|
|
if hasattr(self, "_transform") and hasattr(other, "_transform"):
|
|
return self._transform == other._transform
|
|
elif hasattr(self, "_atransform") and hasattr(other, "_atransform"):
|
|
return self._atransform == other._atransform
|
|
else:
|
|
return False
|
|
else:
|
|
return False
|
|
|
|
def __repr__(self) -> str:
|
|
if hasattr(self, "_transform"):
|
|
return f"RunnableGenerator({self._transform.__name__})"
|
|
elif hasattr(self, "_atransform"):
|
|
return f"RunnableGenerator({self._atransform.__name__})"
|
|
else:
|
|
return "RunnableGenerator(...)"
|
|
|
|
def transform(
|
|
self,
|
|
input: Iterator[Input],
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Any,
|
|
) -> Iterator[Output]:
|
|
return self._transform_stream_with_config(
|
|
input, self._transform, config, **kwargs
|
|
)
|
|
|
|
def stream(
|
|
self,
|
|
input: Input,
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Any,
|
|
) -> Iterator[Output]:
|
|
return self.transform(iter([input]), config, **kwargs)
|
|
|
|
def invoke(
|
|
self, input: Input, config: Optional[RunnableConfig] = None, **kwargs: Any
|
|
) -> Output:
|
|
final = None
|
|
for output in self.stream(input, config, **kwargs):
|
|
if final is None:
|
|
final = output
|
|
else:
|
|
final = final + output
|
|
return cast(Output, final)
|
|
|
|
def atransform(
|
|
self,
|
|
input: AsyncIterator[Input],
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Any,
|
|
) -> AsyncIterator[Output]:
|
|
if not hasattr(self, "_atransform"):
|
|
raise NotImplementedError("This runnable does not support async methods.")
|
|
|
|
return self._atransform_stream_with_config(
|
|
input, self._atransform, config, **kwargs
|
|
)
|
|
|
|
def astream(
|
|
self,
|
|
input: Input,
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Any,
|
|
) -> AsyncIterator[Output]:
|
|
async def input_aiter() -> AsyncIterator[Input]:
|
|
yield input
|
|
|
|
return self.atransform(input_aiter(), config, **kwargs)
|
|
|
|
async def ainvoke(
|
|
self, input: Input, config: Optional[RunnableConfig] = None, **kwargs: Any
|
|
) -> Output:
|
|
final = None
|
|
async for output in self.astream(input, config, **kwargs):
|
|
if final is None:
|
|
final = output
|
|
else:
|
|
final = final + output
|
|
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.
|
|
|
|
Examples:
|
|
|
|
.. code-block:: 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: Union[
|
|
Union[
|
|
Callable[[Input], Output],
|
|
Callable[[Input], Iterator[Output]],
|
|
Callable[[Input, RunnableConfig], Output],
|
|
Callable[[Input, CallbackManagerForChainRun], Output],
|
|
Callable[[Input, CallbackManagerForChainRun, RunnableConfig], Output],
|
|
],
|
|
Union[
|
|
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: Optional[
|
|
Union[
|
|
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:
|
|
"""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.
|
|
"""
|
|
if afunc is not None:
|
|
self.afunc = afunc
|
|
func_for_name: Callable = afunc
|
|
|
|
if inspect.iscoroutinefunction(func) or inspect.isasyncgenfunction(func):
|
|
if afunc is not None:
|
|
raise TypeError(
|
|
"Func was provided as a coroutine function, but afunc was "
|
|
"also provided. If providing both, func should be a regular "
|
|
"function to avoid ambiguity."
|
|
)
|
|
self.afunc = func
|
|
func_for_name = func
|
|
elif callable(func):
|
|
self.func = cast(Callable[[Input], Output], func)
|
|
func_for_name = func
|
|
else:
|
|
raise TypeError(
|
|
"Expected a callable type for `func`."
|
|
f"Instead got an unsupported type: {type(func)}"
|
|
)
|
|
|
|
try:
|
|
if func_for_name.__name__ != "<lambda>":
|
|
self.name = func_for_name.__name__
|
|
except AttributeError:
|
|
pass
|
|
|
|
@property
|
|
def InputType(self) -> Any:
|
|
"""The type of the input to this runnable."""
|
|
func = getattr(self, "func", None) or getattr(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
|
|
else:
|
|
return Any
|
|
except ValueError:
|
|
return Any
|
|
|
|
def get_input_schema(
|
|
self, config: Optional[RunnableConfig] = None
|
|
) -> Type[BaseModel]:
|
|
"""The pydantic schema for the input to this runnable."""
|
|
func = getattr(self, "func", None) or getattr(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 len(item) > 2 for item in items
|
|
):
|
|
# It's a dict, lol
|
|
return create_model(
|
|
self.get_name("Input"),
|
|
**{item[1:-1]: (Any, None) for item in items}, # type: ignore
|
|
__config__=_SchemaConfig,
|
|
)
|
|
else:
|
|
return create_model(
|
|
self.get_name("Input"),
|
|
__root__=(List[Any], None),
|
|
__config__=_SchemaConfig,
|
|
)
|
|
|
|
if self.InputType != Any:
|
|
return super().get_input_schema(config)
|
|
|
|
if dict_keys := get_function_first_arg_dict_keys(func):
|
|
return create_model(
|
|
self.get_name("Input"),
|
|
**{key: (Any, None) for key in dict_keys}, # type: ignore
|
|
__config__=_SchemaConfig,
|
|
)
|
|
|
|
return super().get_input_schema(config)
|
|
|
|
@property
|
|
def OutputType(self) -> Any:
|
|
"""The type of the output of this runnable as a type annotation."""
|
|
func = getattr(self, "func", None) or getattr(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
|
|
else:
|
|
return Any
|
|
except ValueError:
|
|
return Any
|
|
|
|
@property
|
|
def deps(self) -> List[Runnable]:
|
|
"""The dependencies of this runnable."""
|
|
if hasattr(self, "func"):
|
|
objects = get_function_nonlocals(self.func)
|
|
elif hasattr(self, "afunc"):
|
|
objects = get_function_nonlocals(self.afunc)
|
|
else:
|
|
objects = []
|
|
|
|
return [obj for obj in objects if isinstance(obj, Runnable)]
|
|
|
|
@property
|
|
def config_specs(self) -> List[ConfigurableFieldSpec]:
|
|
return get_unique_config_specs(
|
|
spec for dep in self.deps for spec in dep.config_specs
|
|
)
|
|
|
|
def get_graph(self, config: RunnableConfig | None = None) -> Graph:
|
|
if deps := self.deps:
|
|
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:
|
|
graph.extend(dep_graph)
|
|
dep_first_node = dep_graph.first_node()
|
|
if not dep_first_node:
|
|
raise ValueError(f"Runnable {dep} has no first node")
|
|
dep_last_node = dep_graph.last_node()
|
|
if not dep_last_node:
|
|
raise ValueError(f"Runnable {dep} has no last node")
|
|
graph.add_edge(input_node, dep_first_node)
|
|
graph.add_edge(dep_last_node, output_node)
|
|
else:
|
|
graph = super().get_graph(config)
|
|
|
|
return graph
|
|
|
|
def __eq__(self, other: Any) -> bool:
|
|
if isinstance(other, RunnableLambda):
|
|
if hasattr(self, "func") and hasattr(other, "func"):
|
|
return self.func == other.func
|
|
elif hasattr(self, "afunc") and hasattr(other, "afunc"):
|
|
return self.afunc == other.afunc
|
|
else:
|
|
return False
|
|
else:
|
|
return False
|
|
|
|
def __repr__(self) -> str:
|
|
"""A string representation of this runnable."""
|
|
if hasattr(self, "func") and isinstance(self.func, itemgetter):
|
|
return f"RunnableLambda({str(self.func)[len('operator.'):]})"
|
|
elif hasattr(self, "func"):
|
|
return f"RunnableLambda({get_lambda_source(self.func) or '...'})"
|
|
elif hasattr(self, "afunc"):
|
|
return f"RunnableLambda(afunc={get_lambda_source(self.afunc) or '...'})"
|
|
else:
|
|
return "RunnableLambda(...)"
|
|
|
|
def _invoke(
|
|
self,
|
|
input: Input,
|
|
run_manager: CallbackManagerForChainRun,
|
|
config: RunnableConfig,
|
|
**kwargs: Any,
|
|
) -> Output:
|
|
if inspect.isgeneratorfunction(self.func):
|
|
output: Optional[Output] = 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:
|
|
raise RecursionError(
|
|
f"Recursion limit reached when invoking {self} with input {input}."
|
|
)
|
|
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,
|
|
input: Input,
|
|
run_manager: AsyncCallbackManagerForChainRun,
|
|
config: RunnableConfig,
|
|
**kwargs: Any,
|
|
) -> Output:
|
|
if hasattr(self, "afunc"):
|
|
afunc = self.afunc
|
|
else:
|
|
if inspect.isgeneratorfunction(self.func):
|
|
|
|
def func(
|
|
input: Input,
|
|
run_manager: AsyncCallbackManagerForChainRun,
|
|
config: RunnableConfig,
|
|
) -> Output:
|
|
output: Optional[Output] = None
|
|
for chunk in call_func_with_variable_args(
|
|
cast(Callable[[Input], Iterator[Output]], self.func),
|
|
input,
|
|
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(
|
|
input: Input,
|
|
run_manager: AsyncCallbackManagerForChainRun,
|
|
config: RunnableConfig,
|
|
) -> Output:
|
|
return call_func_with_variable_args(
|
|
self.func, input, config, run_manager.get_sync(), **kwargs
|
|
)
|
|
|
|
@wraps(func)
|
|
async def f(*args, **kwargs): # type: ignore[no-untyped-def]
|
|
return await run_in_executor(config, func, *args, **kwargs)
|
|
|
|
afunc = f
|
|
|
|
if inspect.isasyncgenfunction(afunc):
|
|
output: Optional[Output] = None
|
|
async for chunk in cast(
|
|
AsyncIterator[Output],
|
|
acall_func_with_variable_args(
|
|
cast(Callable, afunc),
|
|
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 = await acall_func_with_variable_args(
|
|
cast(Callable, afunc), 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:
|
|
raise RecursionError(
|
|
f"Recursion limit reached when invoking {self} with input {input}."
|
|
)
|
|
output = await output.ainvoke(
|
|
input,
|
|
patch_config(
|
|
config,
|
|
callbacks=run_manager.get_child(),
|
|
recursion_limit=recursion_limit - 1,
|
|
),
|
|
)
|
|
return cast(Output, output)
|
|
|
|
def _config(
|
|
self, config: Optional[RunnableConfig], callable: Callable[..., Any]
|
|
) -> RunnableConfig:
|
|
config = ensure_config(config)
|
|
|
|
if config.get("run_name") is None:
|
|
try:
|
|
run_name = callable.__name__
|
|
except AttributeError:
|
|
run_name = None
|
|
if run_name is not None:
|
|
return patch_config(config, run_name=run_name)
|
|
|
|
return config
|
|
|
|
def invoke(
|
|
self,
|
|
input: Input,
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> Output:
|
|
"""Invoke this runnable synchronously."""
|
|
if hasattr(self, "func"):
|
|
return self._call_with_config(
|
|
self._invoke,
|
|
input,
|
|
self._config(config, self.func),
|
|
**kwargs,
|
|
)
|
|
else:
|
|
raise TypeError(
|
|
"Cannot invoke a coroutine function synchronously."
|
|
"Use `ainvoke` instead."
|
|
)
|
|
|
|
async def ainvoke(
|
|
self,
|
|
input: Input,
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> Output:
|
|
"""Invoke this runnable asynchronously."""
|
|
the_func = self.afunc if hasattr(self, "afunc") else self.func
|
|
return await self._acall_with_config(
|
|
self._ainvoke,
|
|
input,
|
|
self._config(config, the_func),
|
|
**kwargs,
|
|
)
|
|
|
|
def _transform(
|
|
self,
|
|
input: Iterator[Input],
|
|
run_manager: CallbackManagerForChainRun,
|
|
config: RunnableConfig,
|
|
**kwargs: Any,
|
|
) -> Iterator[Output]:
|
|
final: Optional[Input] = None
|
|
for ichunk in input:
|
|
if final is None:
|
|
final = ichunk
|
|
else:
|
|
try:
|
|
final = final + ichunk # type: ignore[operator]
|
|
except TypeError:
|
|
final = ichunk
|
|
|
|
if inspect.isgeneratorfunction(self.func):
|
|
output: Optional[Output] = None
|
|
for chunk in call_func_with_variable_args(
|
|
self.func, cast(Input, 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, cast(Input, 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:
|
|
raise RecursionError(
|
|
f"Recursion limit reached when invoking "
|
|
f"{self} with input {final}."
|
|
)
|
|
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)
|
|
|
|
def transform(
|
|
self,
|
|
input: Iterator[Input],
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> Iterator[Output]:
|
|
if hasattr(self, "func"):
|
|
for output in self._transform_stream_with_config(
|
|
input,
|
|
self._transform,
|
|
self._config(config, self.func),
|
|
**kwargs,
|
|
):
|
|
yield output
|
|
else:
|
|
raise TypeError(
|
|
"Cannot stream a coroutine function synchronously."
|
|
"Use `astream` instead."
|
|
)
|
|
|
|
def stream(
|
|
self,
|
|
input: Input,
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> Iterator[Output]:
|
|
return self.transform(iter([input]), config, **kwargs)
|
|
|
|
async def _atransform(
|
|
self,
|
|
input: AsyncIterator[Input],
|
|
run_manager: AsyncCallbackManagerForChainRun,
|
|
config: RunnableConfig,
|
|
**kwargs: Any,
|
|
) -> AsyncIterator[Output]:
|
|
final: Optional[Input] = None
|
|
async for ichunk in input:
|
|
if final is None:
|
|
final = ichunk
|
|
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):
|
|
raise TypeError(
|
|
"Cannot stream from a generator function asynchronously."
|
|
"Use .stream() instead."
|
|
)
|
|
|
|
def func(
|
|
input: Input,
|
|
run_manager: AsyncCallbackManagerForChainRun,
|
|
config: RunnableConfig,
|
|
) -> Output:
|
|
return call_func_with_variable_args(
|
|
self.func, input, config, run_manager.get_sync(), **kwargs
|
|
)
|
|
|
|
@wraps(func)
|
|
async def f(*args, **kwargs): # type: ignore[no-untyped-def]
|
|
return await run_in_executor(config, func, *args, **kwargs)
|
|
|
|
afunc = f
|
|
|
|
if inspect.isasyncgenfunction(afunc):
|
|
output: Optional[Output] = None
|
|
async for chunk in cast(
|
|
AsyncIterator[Output],
|
|
acall_func_with_variable_args(
|
|
cast(Callable, afunc),
|
|
cast(Input, 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), cast(Input, 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:
|
|
raise RecursionError(
|
|
f"Recursion limit reached when invoking "
|
|
f"{self} with input {final}."
|
|
)
|
|
async for chunk in output.astream(
|
|
final,
|
|
patch_config(
|
|
config,
|
|
callbacks=run_manager.get_child(),
|
|
recursion_limit=recursion_limit - 1,
|
|
),
|
|
):
|
|
yield chunk
|
|
elif not inspect.isasyncgenfunction(afunc):
|
|
# Otherwise, just yield it
|
|
yield cast(Output, output)
|
|
|
|
async def atransform(
|
|
self,
|
|
input: AsyncIterator[Input],
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> AsyncIterator[Output]:
|
|
async for output in self._atransform_stream_with_config(
|
|
input,
|
|
self._atransform,
|
|
self._config(config, self.afunc if hasattr(self, "afunc") else self.func),
|
|
**kwargs,
|
|
):
|
|
yield output
|
|
|
|
async def astream(
|
|
self,
|
|
input: Input,
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> 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]]):
|
|
"""
|
|
A runnable that delegates calls to another runnable
|
|
with each element of the input sequence.
|
|
|
|
Use only if creating a new RunnableEach subclass with different __init__ args.
|
|
"""
|
|
|
|
bound: Runnable[Input, Output]
|
|
|
|
class Config:
|
|
arbitrary_types_allowed = True
|
|
|
|
@property
|
|
def InputType(self) -> Any:
|
|
return List[self.bound.InputType] # type: ignore[name-defined]
|
|
|
|
def get_input_schema(
|
|
self, config: Optional[RunnableConfig] = None
|
|
) -> Type[BaseModel]:
|
|
return create_model(
|
|
self.get_name("Input"),
|
|
__root__=(
|
|
List[self.bound.get_input_schema(config)], # type: ignore
|
|
None,
|
|
),
|
|
__config__=_SchemaConfig,
|
|
)
|
|
|
|
@property
|
|
def OutputType(self) -> Type[List[Output]]:
|
|
return List[self.bound.OutputType] # type: ignore[name-defined]
|
|
|
|
def get_output_schema(
|
|
self, config: Optional[RunnableConfig] = None
|
|
) -> Type[BaseModel]:
|
|
schema = self.bound.get_output_schema(config)
|
|
return create_model(
|
|
self.get_name("Output"),
|
|
__root__=(
|
|
List[schema], # type: ignore
|
|
None,
|
|
),
|
|
__config__=_SchemaConfig,
|
|
)
|
|
|
|
@property
|
|
def config_specs(self) -> List[ConfigurableFieldSpec]:
|
|
return self.bound.config_specs
|
|
|
|
def get_graph(self, config: Optional[RunnableConfig] = None) -> Graph:
|
|
return self.bound.get_graph(config)
|
|
|
|
@classmethod
|
|
def is_lc_serializable(cls) -> bool:
|
|
return True
|
|
|
|
@classmethod
|
|
def get_lc_namespace(cls) -> List[str]:
|
|
"""Get the namespace of the langchain object."""
|
|
return ["langchain", "schema", "runnable"]
|
|
|
|
def _invoke(
|
|
self,
|
|
inputs: List[Input],
|
|
run_manager: CallbackManagerForChainRun,
|
|
config: RunnableConfig,
|
|
**kwargs: Any,
|
|
) -> List[Output]:
|
|
return self.bound.batch(
|
|
inputs, patch_config(config, callbacks=run_manager.get_child()), **kwargs
|
|
)
|
|
|
|
def invoke(
|
|
self, input: List[Input], config: Optional[RunnableConfig] = 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]:
|
|
return await self.bound.abatch(
|
|
inputs, patch_config(config, callbacks=run_manager.get_child()), **kwargs
|
|
)
|
|
|
|
async def ainvoke(
|
|
self, input: List[Input], config: Optional[RunnableConfig] = None, **kwargs: Any
|
|
) -> List[Output]:
|
|
return await self._acall_with_config(self._ainvoke, input, config, **kwargs)
|
|
|
|
|
|
class RunnableEach(RunnableEachBase[Input, Output]):
|
|
"""
|
|
A runnable that delegates calls to another runnable
|
|
with each element of the input sequence.
|
|
"""
|
|
|
|
@classmethod
|
|
def get_lc_namespace(cls) -> List[str]:
|
|
"""Get the namespace of the langchain object."""
|
|
return ["langchain", "schema", "runnable"]
|
|
|
|
def get_name(
|
|
self, suffix: Optional[str] = None, *, name: Optional[str] = None
|
|
) -> str:
|
|
name = name or self.name or f"RunnableEach<{self.bound.get_name()}>"
|
|
return super().get_name(suffix, name=name)
|
|
|
|
def bind(self, **kwargs: Any) -> RunnableEach[Input, Output]:
|
|
return RunnableEach(bound=self.bound.bind(**kwargs))
|
|
|
|
def with_config(
|
|
self, config: Optional[RunnableConfig] = None, **kwargs: Any
|
|
) -> RunnableEach[Input, Output]:
|
|
return RunnableEach(bound=self.bound.with_config(config, **kwargs))
|
|
|
|
def with_listeners(
|
|
self,
|
|
*,
|
|
on_start: Optional[Listener] = None,
|
|
on_end: Optional[Listener] = None,
|
|
on_error: Optional[Listener] = None,
|
|
) -> RunnableEach[Input, Output]:
|
|
"""
|
|
Bind lifecycle listeners to a Runnable, returning a new Runnable.
|
|
|
|
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.
|
|
|
|
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.
|
|
"""
|
|
return RunnableEach(
|
|
bound=self.bound.with_listeners(
|
|
on_start=on_start, on_end=on_end, on_error=on_error
|
|
)
|
|
)
|
|
|
|
|
|
class RunnableBindingBase(RunnableSerializable[Input, Output]):
|
|
"""A 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=dict)
|
|
"""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: Optional[Any] = 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: Optional[Any] = 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]`).
|
|
"""
|
|
|
|
class Config:
|
|
arbitrary_types_allowed = True
|
|
|
|
def __init__(
|
|
self,
|
|
*,
|
|
bound: Runnable[Input, Output],
|
|
kwargs: Optional[Mapping[str, Any]] = None,
|
|
config: Optional[RunnableConfig] = None,
|
|
config_factories: Optional[
|
|
List[Callable[[RunnableConfig], RunnableConfig]]
|
|
] = None,
|
|
custom_input_type: Optional[Union[Type[Input], BaseModel]] = None,
|
|
custom_output_type: Optional[Union[Type[Output], BaseModel]] = 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: config_factories:
|
|
config_factories: optional list of config factories to apply to the
|
|
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.
|
|
"""
|
|
config = config or {}
|
|
# config_specs contains the list of valid `configurable` keys
|
|
if configurable := config.get("configurable", None):
|
|
allowed_keys = set(s.id for s in bound.config_specs)
|
|
for key in configurable:
|
|
if key not in allowed_keys:
|
|
raise ValueError(
|
|
f"Configurable key '{key}' not found in runnable with"
|
|
f" config keys: {allowed_keys}"
|
|
)
|
|
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,
|
|
)
|
|
|
|
def get_name(
|
|
self, suffix: Optional[str] = None, *, name: Optional[str] = None
|
|
) -> str:
|
|
return self.bound.get_name(suffix, name=name)
|
|
|
|
@property
|
|
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
|
|
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
|
|
)
|
|
|
|
def get_input_schema(
|
|
self, config: Optional[RunnableConfig] = 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))
|
|
|
|
def get_output_schema(
|
|
self, config: Optional[RunnableConfig] = 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
|
|
def config_specs(self) -> List[ConfigurableFieldSpec]:
|
|
return self.bound.config_specs
|
|
|
|
def get_graph(self, config: Optional[RunnableConfig] = None) -> Graph:
|
|
return self.bound.get_graph(config)
|
|
|
|
@classmethod
|
|
def is_lc_serializable(cls) -> bool:
|
|
return True
|
|
|
|
@classmethod
|
|
def get_lc_namespace(cls) -> List[str]:
|
|
"""Get the namespace of the langchain object."""
|
|
return ["langchain", "schema", "runnable"]
|
|
|
|
def _merge_configs(self, *configs: Optional[RunnableConfig]) -> RunnableConfig:
|
|
config = merge_configs(self.config, *configs)
|
|
return merge_configs(config, *(f(config) for f in self.config_factories))
|
|
|
|
def invoke(
|
|
self,
|
|
input: Input,
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> Output:
|
|
return self.bound.invoke(
|
|
input,
|
|
self._merge_configs(config),
|
|
**{**self.kwargs, **kwargs},
|
|
)
|
|
|
|
async def ainvoke(
|
|
self,
|
|
input: Input,
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> Output:
|
|
return await self.bound.ainvoke(
|
|
input,
|
|
self._merge_configs(config),
|
|
**{**self.kwargs, **kwargs},
|
|
)
|
|
|
|
def batch(
|
|
self,
|
|
inputs: List[Input],
|
|
config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None,
|
|
*,
|
|
return_exceptions: bool = False,
|
|
**kwargs: Optional[Any],
|
|
) -> 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},
|
|
)
|
|
|
|
async def abatch(
|
|
self,
|
|
inputs: List[Input],
|
|
config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None,
|
|
*,
|
|
return_exceptions: bool = False,
|
|
**kwargs: Optional[Any],
|
|
) -> 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},
|
|
)
|
|
|
|
def stream(
|
|
self,
|
|
input: Input,
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> Iterator[Output]:
|
|
yield from self.bound.stream(
|
|
input,
|
|
self._merge_configs(config),
|
|
**{**self.kwargs, **kwargs},
|
|
)
|
|
|
|
async def astream(
|
|
self,
|
|
input: Input,
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Optional[Any],
|
|
) -> AsyncIterator[Output]:
|
|
async for item in self.bound.astream(
|
|
input,
|
|
self._merge_configs(config),
|
|
**{**self.kwargs, **kwargs},
|
|
):
|
|
yield item
|
|
|
|
def transform(
|
|
self,
|
|
input: Iterator[Input],
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Any,
|
|
) -> Iterator[Output]:
|
|
yield from self.bound.transform(
|
|
input,
|
|
self._merge_configs(config),
|
|
**{**self.kwargs, **kwargs},
|
|
)
|
|
|
|
async def atransform(
|
|
self,
|
|
input: AsyncIterator[Input],
|
|
config: Optional[RunnableConfig] = None,
|
|
**kwargs: Any,
|
|
) -> AsyncIterator[Output]:
|
|
async for item in self.bound.atransform(
|
|
input,
|
|
self._merge_configs(config),
|
|
**{**self.kwargs, **kwargs},
|
|
):
|
|
yield item
|
|
|
|
|
|
RunnableBindingBase.update_forward_refs(RunnableConfig=RunnableConfig)
|
|
|
|
|
|
class RunnableBinding(RunnableBindingBase[Input, Output]):
|
|
"""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.
|
|
|
|
.. code-block:: python
|
|
|
|
# Create a runnable binding that invokes the ChatModel with the
|
|
# additional kwarg `stop=['-']` when running it.
|
|
from langchain_community.chat_models 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):
|
|
|
|
.. code-block:: 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`
|
|
"""
|
|
|
|
@classmethod
|
|
def get_lc_namespace(cls) -> List[str]:
|
|
"""Get the namespace of the langchain object."""
|
|
return ["langchain", "schema", "runnable"]
|
|
|
|
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,
|
|
kwargs={**self.kwargs, **kwargs},
|
|
custom_input_type=self.custom_input_type,
|
|
custom_output_type=self.custom_output_type,
|
|
)
|
|
|
|
def with_config(
|
|
self,
|
|
config: Optional[RunnableConfig] = 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}),
|
|
custom_input_type=self.custom_input_type,
|
|
custom_output_type=self.custom_output_type,
|
|
)
|
|
|
|
def with_listeners(
|
|
self,
|
|
*,
|
|
on_start: Optional[Listener] = None,
|
|
on_end: Optional[Listener] = None,
|
|
on_error: Optional[Listener] = None,
|
|
) -> Runnable[Input, Output]:
|
|
"""Bind lifecycle listeners to a Runnable, returning a new Runnable.
|
|
|
|
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:
|
|
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.
|
|
"""
|
|
from langchain_core.tracers.root_listeners import RootListenersTracer
|
|
|
|
return self.__class__(
|
|
bound=self.bound,
|
|
kwargs=self.kwargs,
|
|
config=self.config,
|
|
config_factories=[
|
|
lambda config: {
|
|
"callbacks": [
|
|
RootListenersTracer(
|
|
config=config,
|
|
on_start=on_start,
|
|
on_end=on_end,
|
|
on_error=on_error,
|
|
)
|
|
],
|
|
}
|
|
],
|
|
custom_input_type=self.custom_input_type,
|
|
custom_output_type=self.custom_output_type,
|
|
)
|
|
|
|
def with_types(
|
|
self,
|
|
input_type: Optional[Union[Type[Input], BaseModel]] = None,
|
|
output_type: Optional[Union[Type[Output], BaseModel]] = None,
|
|
) -> Runnable[Input, Output]:
|
|
return self.__class__(
|
|
bound=self.bound,
|
|
kwargs=self.kwargs,
|
|
config=self.config,
|
|
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,
|
|
)
|
|
|
|
def with_retry(self, **kwargs: Any) -> Runnable[Input, Output]:
|
|
return self.__class__(
|
|
bound=self.bound.with_retry(**kwargs),
|
|
kwargs=self.kwargs,
|
|
config=self.config,
|
|
)
|
|
|
|
|
|
RunnableLike = Union[
|
|
Runnable[Input, Output],
|
|
Callable[[Input], Output],
|
|
Callable[[Input], Awaitable[Output]],
|
|
Callable[[Iterator[Input]], Iterator[Output]],
|
|
Callable[[AsyncIterator[Input]], AsyncIterator[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.
|
|
"""
|
|
if isinstance(thing, Runnable):
|
|
return thing
|
|
elif inspect.isasyncgenfunction(thing) or inspect.isgeneratorfunction(thing):
|
|
return RunnableGenerator(thing)
|
|
elif callable(thing):
|
|
return RunnableLambda(cast(Callable[[Input], Output], thing))
|
|
elif isinstance(thing, dict):
|
|
return cast(Runnable[Input, Output], RunnableParallel(thing))
|
|
else:
|
|
raise TypeError(
|
|
f"Expected a Runnable, callable or dict."
|
|
f"Instead got an unsupported type: {type(thing)}"
|
|
)
|
|
|
|
|
|
@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: Union[
|
|
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:
|
|
|
|
.. code-block:: python
|
|
|
|
from langchain_core.runnables import chain
|
|
from langchain_core.prompts import PromptTemplate
|
|
from langchain.llms import OpenAI
|
|
|
|
@chain
|
|
def my_func(fields):
|
|
prompt = PromptTemplate("Hello, {name}!")
|
|
llm = OpenAI()
|
|
formatted = prompt.invoke(**fields)
|
|
|
|
for chunk in llm.stream(formatted):
|
|
yield chunk
|
|
"""
|
|
return RunnableLambda(func)
|