Bases: Chain
Deprecated since version 0.1.17: Use , `prompt | llm`() instead. It will not be removed until langchain==1.0.
Chain to run queries against LLMs.
This class is deprecated. See below for an example implementation using LangChain runnables:
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_openai import OpenAI
prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(
input_variables=["adjective"], template=prompt_template
)
llm = OpenAI()
chain = prompt | llm | StrOutputParser()
chain.invoke("your adjective here")
Example
from langchain.chains import LLMChain
from langchain_community.llms import OpenAI
from langchain_core.prompts import PromptTemplate
prompt_template = "Tell me a {adjective} joke"
prompt = PromptTemplate(
input_variables=["adjective"], template=prompt_template
)
llm = LLMChain(llm=OpenAI(), prompt=prompt)
[DEPRECATED] Use callbacks instead.
Optional list of callback handlers (or callback manager). Defaults to None. Callback handlers are called throughout the lifecycle of a call to a chain, starting with on_chain_start, ending with on_chain_end or on_chain_error. Each custom chain can optionally call additional callback methods, see Callback docs for full details.
Language model to call.
Optional memory object. Defaults to None. Memory is a class that gets called at the start and at the end of every chain. At the start, memory loads variables and passes them along in the chain. At the end, it saves any returned variables. There are many different types of memory - please see memory docs for the full catalog.
Optional metadata associated with the chain. Defaults to None. This metadata will be associated with each call to this chain, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a chain with its use case.
Output parser to use. Defaults to one that takes the most likely string but does not change it otherwise.
Prompt object to use.
Whether to return only the final parsed result. Defaults to True. If false, will return a bunch of extra information about the generation.
Optional list of tags associated with the chain. Defaults to None. These tags will be associated with each call to this chain, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a chain with its use case.
Whether or not run in verbose mode. In verbose mode, some intermediate logs will be printed to the console. Defaults to the global verbose value, accessible via langchain.globals.get_verbose().
Create LLMChain from LLM and template.
llm (BaseLanguageModel)
template (str)
Deprecated since version 0.1.0: Use invoke() instead. It will not be removed until langchain==1.0.
Execute the chain.
inputs (dict[str, Any] | Any) β Dictionary of inputs, or single input if chain expects only one param. Should contain all inputs specified in Chain.input_keys except for inputs that will be set by the chainβs memory.
return_only_outputs (bool) β Whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False.
callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None) β Callbacks to use for this chain run. These will be called in addition to callbacks passed to the chain during construction, but only these runtime callbacks will propagate to calls to other objects.
tags (list[str] | None) β List of string tags to pass to all callbacks. These will be passed in addition to tags passed to the chain during construction, but only these runtime tags will propagate to calls to other objects.
metadata (dict[str, Any] | None) β Optional metadata associated with the chain. Defaults to None
include_run_info (bool) β Whether to include run info in the response. Defaults to False.
run_name (str | None)
Chain.output_keys.
dict[str, Any]
Utilize the LLM generate method for speed gains.
input_list (list[dict[str, Any]])
callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None)
list[dict[str, str]]
Call apply and then parse the results.
input_list (list[dict[str, Any]])
callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None)
Sequence[str | list[str] | dict[str, str]]
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.
inputs (list[Input]) β A list of inputs to the Runnable.
config (RunnableConfig | list[RunnableConfig] | None) β 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. Defaults to None.
return_exceptions (bool) β Whether to return exceptions instead of raising them. Defaults to False.
kwargs (Any | None) β Additional keyword arguments to pass to the Runnable.
A list of outputs from the Runnable.
list[Output]
Run ainvoke in parallel on a list of inputs.
Yields results as they complete.
inputs (Sequence[Input]) β A list of inputs to the Runnable.
config (RunnableConfig | Sequence[RunnableConfig] | None) β 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. Defaults to None.
return_exceptions (bool) β Whether to return exceptions instead of raising them. Defaults to False.
kwargs (Any | None) β Additional keyword arguments to pass to the Runnable.
A tuple of the index of the input and the output from the Runnable.
AsyncIterator[tuple[int, Output | Exception]]
Deprecated since version 0.1.0: Use ainvoke() instead. It will not be removed until langchain==1.0.
Asynchronously execute the chain.
inputs (dict[str, Any] | Any) β Dictionary of inputs, or single input if chain expects only one param. Should contain all inputs specified in Chain.input_keys except for inputs that will be set by the chainβs memory.
return_only_outputs (bool) β Whether to return only outputs in the response. If True, only new keys generated by this chain will be returned. If False, both input keys and new keys generated by this chain will be returned. Defaults to False.
callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None) β Callbacks to use for this chain run. These will be called in addition to callbacks passed to the chain during construction, but only these runtime callbacks will propagate to calls to other objects.
tags (list[str] | None) β List of string tags to pass to all callbacks. These will be passed in addition to tags passed to the chain during construction, but only these runtime tags will propagate to calls to other objects.
metadata (dict[str, Any] | None) β Optional metadata associated with the chain. Defaults to None
include_run_info (bool) β Whether to include run info in the response. Defaults to False.
run_name (str | None)
Chain.output_keys.
dict[str, Any]
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.
input (dict[str, Any])
config (RunnableConfig | None)
kwargs (Any)
dict[str, Any]
Utilize the LLM generate method for speed gains.
input_list (list[dict[str, Any]])
callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None)
list[dict[str, str]]
Call apply and then parse the results.
input_list (list[dict[str, Any]])
callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None)
Sequence[str | list[str] | dict[str, str]]
Call apredict and then parse the results.
callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None)
kwargs (Any)
str | list[str] | dict[str, str]
Prepare chain inputs, including adding inputs from memory.
inputs (dict[str, Any] | Any) β Dictionary of raw inputs, or single input if chain expects only one param. Should contain all inputs specified in Chain.input_keys except for inputs that will be set by the chainβs memory.
A dictionary of all inputs, including those added by the chainβs memory.
dict[str, str]
Validate and prepare chain outputs, and save info about this run to memory.
inputs (dict[str, str]) β Dictionary of chain inputs, including any inputs added by chain memory.
outputs (dict[str, str]) β Dictionary of initial chain outputs.
return_only_outputs (bool) β Whether to only return the chain outputs. If False, inputs are also added to the final outputs.
A dict of the final chain outputs.
dict[str, str]
Prepare prompts from inputs.
input_list (list[dict[str, Any]])
run_manager (AsyncCallbackManagerForChainRun | None)
tuple[list[PromptValue], list[str] | None]
Deprecated since version 0.1.0: Use ainvoke() instead. It will not be removed until langchain==1.0.
Convenience method for executing chain.
The main difference between this method and Chain.__call__ is that this method expects inputs to be passed directly in as positional arguments or keyword arguments, whereas Chain.__call__ expects a single input dictionary with all the inputs
*args (Any) β If the chain expects a single input, it can be passed in as the sole positional argument.
callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None) β Callbacks to use for this chain run. These will be called in addition to callbacks passed to the chain during construction, but only these runtime callbacks will propagate to calls to other objects.
tags (list[str] | None) β List of string tags to pass to all callbacks. These will be passed in addition to tags passed to the chain during construction, but only these runtime tags will propagate to calls to other objects.
**kwargs (Any) β If the chain expects multiple inputs, they can be passed in directly as keyword arguments.
metadata (dict[str, Any] | None)
**kwargs
The chain output.
Any
Example
# Suppose we have a single-input chain that takes a 'question' string:
await chain.arun("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
await chain.arun(question=question, context=context)
# -> "The temperature in Boise is..."
Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
input (Input) β The input to the Runnable.
config (RunnableConfig | None) β The config to use for the Runnable. Defaults to None.
kwargs (Any | None) β Additional keyword arguments to pass to the Runnable.
The output of the Runnable.
AsyncIterator[Output]
Generate a stream of events.
Use to create an iterator over StreamEvents that provide real-time information about the progress of the Runnable, including StreamEvents from intermediate results.
A StreamEvent is a dictionary with the following schema:
event: str - Event names are of the format: on_[runnable_type]_(start|stream|end).
name: str - The name of the Runnable that generated the event.
run_id: str - randomly generated ID associated with the given execution of the Runnable that emitted the event. A child Runnable that gets invoked as part of the execution of a parent Runnable is assigned its own unique ID.
parent_ids: list[str] - The IDs of the parent runnables that generated the event. The root Runnable will have an empty list. The order of the parent IDs is from the root to the immediate parent. Only available for v2 version of the API. The v1 version of the API will return an empty list.
tags: Optional[list[str]] - The tags of the Runnable that generated the event.
metadata: Optional[dict[str, Any]] - The metadata of the Runnable that generated the event.
data: dict[str, Any]
Below is a table that illustrates some events that might be emitted by various chains. Metadata fields have been omitted from the table for brevity. Chain definitions have been included after the table.
Note
This reference table is for the v2 version of the schema.
In addition to the standard events, users can also dispatch custom events (see example below).
Custom events will be only be surfaced with in the v2 version of the API!
A custom event has following format:
Here are declarations associated with the standard events shown above:
format_docs:
def format_docs(docs: list[Document]) -> str:
'''Format the docs.'''
return ", ".join([doc.page_content for doc in docs])
format_docs = RunnableLambda(format_docs)
some_tool:
@tool
def some_tool(x: int, y: str) -> dict:
'''Some_tool.'''
return {"x": x, "y": y}
prompt:
template = ChatPromptTemplate.from_messages(
[("system", "You are Cat Agent 007"), ("human", "{question}")]
).with_config({"run_name": "my_template", "tags": ["my_template"]})
Example:
from langchain_core.runnables import RunnableLambda
async def reverse(s: str) -> str:
return s[::-1]
chain = RunnableLambda(func=reverse)
events = [
event async for event in chain.astream_events("hello", version="v2")
]
# will produce the following events (run_id, and parent_ids
# has been omitted for brevity):
[
{
"data": {"input": "hello"},
"event": "on_chain_start",
"metadata": {},
"name": "reverse",
"tags": [],
},
{
"data": {"chunk": "olleh"},
"event": "on_chain_stream",
"metadata": {},
"name": "reverse",
"tags": [],
},
{
"data": {"output": "olleh"},
"event": "on_chain_end",
"metadata": {},
"name": "reverse",
"tags": [],
},
]
Example: Dispatch Custom Event
from langchain_core.callbacks.manager import (
adispatch_custom_event,
)
from langchain_core.runnables import RunnableLambda, RunnableConfig
import asyncio
async def slow_thing(some_input: str, config: RunnableConfig) -> str:
"""Do something that takes a long time."""
await asyncio.sleep(1) # Placeholder for some slow operation
await adispatch_custom_event(
"progress_event",
{"message": "Finished step 1 of 3"},
config=config # Must be included for python < 3.10
)
await asyncio.sleep(1) # Placeholder for some slow operation
await adispatch_custom_event(
"progress_event",
{"message": "Finished step 2 of 3"},
config=config # Must be included for python < 3.10
)
await asyncio.sleep(1) # Placeholder for some slow operation
return "Done"
slow_thing = RunnableLambda(slow_thing)
async for event in slow_thing.astream_events("some_input", version="v2"):
print(event)
input (Any) β The input to the Runnable.
config (Optional[RunnableConfig]) β The config to use for the Runnable.
version (Literal['v1', 'v2']) β The version of the schema to use either 'v2' or 'v1'. Users should use 'v2'. 'v1' is for backwards compatibility and will be deprecated in 0.4.0. No default will be assigned until the API is stabilized. custom events will only be surfaced in 'v2'.
include_names (Optional[Sequence[str]]) β Only include events from Runnables with matching names.
include_types (Optional[Sequence[str]]) β Only include events from Runnables with matching types.
include_tags (Optional[Sequence[str]]) β Only include events from Runnables with matching tags.
exclude_names (Optional[Sequence[str]]) β Exclude events from Runnables with matching names.
exclude_types (Optional[Sequence[str]]) β Exclude events from Runnables with matching types.
exclude_tags (Optional[Sequence[str]]) β Exclude events from Runnables with matching tags.
kwargs (Any) β Additional keyword arguments to pass to the Runnable. These will be passed to astream_log as this implementation of astream_events is built on top of astream_log.
An async stream of StreamEvents.
NotImplementedError β If the version is not 'v1' or 'v2'.
AsyncIterator[StreamEvent]
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.
inputs (list[Input])
config (RunnableConfig | list[RunnableConfig] | None)
return_exceptions (bool)
kwargs (Any | None)
list[Output]
Run invoke in parallel on a list of inputs.
Yields results as they complete.
inputs (Sequence[Input])
config (RunnableConfig | Sequence[RunnableConfig] | None)
return_exceptions (bool)
kwargs (Any | None)
Iterator[tuple[int, Output | Exception]]
Bind arguments to a Runnable, returning a new Runnable.
Useful when a Runnable in a chain requires an argument that is not in the output of the previous Runnable or included in the user input.
kwargs (Any) β The arguments to bind to the Runnable.
A new Runnable with the arguments bound.
Runnable[Input, Output]
Example:
from langchain_ollama import ChatOllama
from langchain_core.output_parsers import StrOutputParser
llm = ChatOllama(model='llama2')
# Without bind.
chain = (
llm
| StrOutputParser()
)
chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two three four five.'
# With bind.
chain = (
llm.bind(stop=["three"])
| StrOutputParser()
)
chain.invoke("Repeat quoted words exactly: 'One two three four five.'")
# Output is 'One two'
Configure alternatives for Runnables that can be set at runtime.
which (ConfigurableField) β The ConfigurableField instance that will be used to select the alternative.
default_key (str) β The default key to use if no alternative is selected. Defaults to 'default'.
prefix_keys (bool) β Whether to prefix the keys with the ConfigurableField id. Defaults to False.
**kwargs (Runnable[Input, Output] | Callable[[], Runnable[Input, Output]]) β A dictionary of keys to Runnable instances or callables that return Runnable instances.
A new Runnable with the alternatives configured.
from langchain_anthropic import ChatAnthropic
from langchain_core.runnables.utils import ConfigurableField
from langchain_openai import ChatOpenAI
model = ChatAnthropic(
model_name="claude-3-7-sonnet-20250219"
).configurable_alternatives(
ConfigurableField(id="llm"),
default_key="anthropic",
openai=ChatOpenAI()
)
# uses the default model ChatAnthropic
print(model.invoke("which organization created you?").content)
# uses ChatOpenAI
print(
model.with_config(
configurable={"llm": "openai"}
).invoke("which organization created you?").content
)
Configure particular Runnable fields at runtime.
**kwargs (ConfigurableField | ConfigurableFieldSingleOption | ConfigurableFieldMultiOption) β A dictionary of ConfigurableField instances to configure.
A new Runnable with the fields configured.
from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI
model = ChatOpenAI(max_tokens=20).configurable_fields(
max_tokens=ConfigurableField(
id="output_token_number",
name="Max tokens in the output",
description="The maximum number of tokens in the output",
)
)
# max_tokens = 20
print(
"max_tokens_20: ",
model.invoke("tell me something about chess").content
)
# max_tokens = 200
print("max_tokens_200: ", model.with_config(
configurable={"output_token_number": 200}
).invoke("tell me something about chess").content
)
Create outputs from response.
llm_result (LLMResult)
list[dict[str, Any]]
Transform a single input into an output.
input (dict[str, Any]) β The input to the Runnable.
config (RunnableConfig | None) β 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. Defaults to None.
kwargs (Any)
The output of the Runnable.
dict[str, Any]
Call predict and then parse the results.
callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None)
kwargs (Any)
str | list[str] | dict[str, Any]
Prepare chain inputs, including adding inputs from memory.
inputs (dict[str, Any] | Any) β Dictionary of raw inputs, or single input if chain expects only one param. Should contain all inputs specified in Chain.input_keys except for inputs that will be set by the chainβs memory.
A dictionary of all inputs, including those added by the chainβs memory.
dict[str, str]
Validate and prepare chain outputs, and save info about this run to memory.
inputs (dict[str, str]) β Dictionary of chain inputs, including any inputs added by chain memory.
outputs (dict[str, str]) β Dictionary of initial chain outputs.
return_only_outputs (bool) β Whether to only return the chain outputs. If False, inputs are also added to the final outputs.
A dict of the final chain outputs.
dict[str, str]
Prepare prompts from inputs.
input_list (list[dict[str, Any]])
run_manager (CallbackManagerForChainRun | None)
tuple[list[PromptValue], list[str] | None]
Deprecated since version 0.1.0: Use invoke() instead. It will not be removed until langchain==1.0.
Convenience method for executing chain.
The main difference between this method and Chain.__call__ is that this method expects inputs to be passed directly in as positional arguments or keyword arguments, whereas Chain.__call__ expects a single input dictionary with all the inputs
*args (Any) β If the chain expects a single input, it can be passed in as the sole positional argument.
callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None) β Callbacks to use for this chain run. These will be called in addition to callbacks passed to the chain during construction, but only these runtime callbacks will propagate to calls to other objects.
tags (list[str] | None) β List of string tags to pass to all callbacks. These will be passed in addition to tags passed to the chain during construction, but only these runtime tags will propagate to calls to other objects.
**kwargs (Any) β If the chain expects multiple inputs, they can be passed in directly as keyword arguments.
metadata (dict[str, Any] | None)
**kwargs
The chain output.
Any
Example
# Suppose we have a single-input chain that takes a 'question' string:
chain.run("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
chain.run(question=question, context=context)
# -> "The temperature in Boise is..."
Save the chain.
null.
file_path (Path | str) β Path to file to save the chain to.
None
Example
chain.save(file_path="path/chain.yaml")
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
input (Input) β The input to the Runnable.
config (RunnableConfig | None) β The config to use for the Runnable. Defaults to None.
kwargs (Any | None) β Additional keyword arguments to pass to the Runnable.
The output of the Runnable.
Iterator[Output]
Bind async lifecycle listeners to a Runnable, returning a new Runnable.
The Run object contains information about the run, including its id, type, input, output, error, start_time, end_time, and any tags or metadata added to the run.
on_start (Optional[AsyncListener]) β Called asynchronously before the Runnable starts running, with the Run object. Defaults to None.
on_end (Optional[AsyncListener]) β Called asynchronously after the Runnable finishes running, with the Run object. Defaults to None.
on_error (Optional[AsyncListener]) β Called asynchronously if the Runnable throws an error, with the Run object. Defaults to None.
A new Runnable with the listeners bound.
Runnable[Input, Output]
Example:
from langchain_core.runnables import RunnableLambda, Runnable
from datetime import datetime, timezone
import time
import asyncio
def format_t(timestamp: float) -> str:
return datetime.fromtimestamp(timestamp, tz=timezone.utc).isoformat()
async def test_runnable(time_to_sleep : int):
print(f"Runnable[{time_to_sleep}s]: starts at {format_t(time.time())}")
await asyncio.sleep(time_to_sleep)
print(f"Runnable[{time_to_sleep}s]: ends at {format_t(time.time())}")
async def fn_start(run_obj : Runnable):
print(f"on start callback starts at {format_t(time.time())}")
await asyncio.sleep(3)
print(f"on start callback ends at {format_t(time.time())}")
async def fn_end(run_obj : Runnable):
print(f"on end callback starts at {format_t(time.time())}")
await asyncio.sleep(2)
print(f"on end callback ends at {format_t(time.time())}")
runnable = RunnableLambda(test_runnable).with_alisteners(
on_start=fn_start,
on_end=fn_end
)
async def concurrent_runs():
await asyncio.gather(runnable.ainvoke(2), runnable.ainvoke(3))
asyncio.run(concurrent_runs())
Result:
on start callback starts at 2025-03-01T07:05:22.875378+00:00
on start callback starts at 2025-03-01T07:05:22.875495+00:00
on start callback ends at 2025-03-01T07:05:25.878862+00:00
on start callback ends at 2025-03-01T07:05:25.878947+00:00
Runnable[2s]: starts at 2025-03-01T07:05:25.879392+00:00
Runnable[3s]: starts at 2025-03-01T07:05:25.879804+00:00
Runnable[2s]: ends at 2025-03-01T07:05:27.881998+00:00
on end callback starts at 2025-03-01T07:05:27.882360+00:00
Runnable[3s]: ends at 2025-03-01T07:05:28.881737+00:00
on end callback starts at 2025-03-01T07:05:28.882428+00:00
on end callback ends at 2025-03-01T07:05:29.883893+00:00
on end callback ends at 2025-03-01T07:05:30.884831+00:00
Bind config to a Runnable, returning a new Runnable.
config (RunnableConfig | None) β The config to bind to the Runnable.
kwargs (Any) β Additional keyword arguments to pass to the Runnable.
A new Runnable with the config bound.
Runnable[Input, Output]
Add fallbacks to a Runnable, returning a new Runnable.
The new Runnable will try the original Runnable, and then each fallback in order, upon failures.
fallbacks (Sequence[Runnable[Input, Output]]) β A sequence of runnables to try if the original Runnable fails.
exceptions_to_handle (tuple[type[BaseException], ...]) β A tuple of exception types to handle. Defaults to (Exception,).
exception_key (Optional[str]) β If string is specified then handled exceptions will be passed to fallbacks as part of the input under the specified key. If None, exceptions will not be passed to fallbacks. If used, the base Runnable and its fallbacks must accept a dictionary as input. Defaults to None.
A new Runnable that will try the original Runnable, and then each fallback in order, upon failures.
RunnableWithFallbacksT[Input, Output]
Example
from typing import Iterator
from langchain_core.runnables import RunnableGenerator
def _generate_immediate_error(input: Iterator) -> Iterator[str]:
raise ValueError()
yield ""
def _generate(input: Iterator) -> Iterator[str]:
yield from "foo bar"
runnable = RunnableGenerator(_generate_immediate_error).with_fallbacks(
[RunnableGenerator(_generate)]
)
print(''.join(runnable.stream({}))) #foo bar
fallbacks (Sequence[Runnable[Input, Output]]) β A sequence of runnables to try if the original Runnable fails.
exceptions_to_handle (tuple[type[BaseException], ...]) β A tuple of exception types to handle.
exception_key (Optional[str]) β If string is specified then handled exceptions will be passed to fallbacks as part of the input under the specified key. If None, exceptions will not be passed to fallbacks. If used, the base Runnable and its fallbacks must accept a dictionary as input.
A new Runnable that will try the original Runnable, and then each fallback in order, upon failures.
RunnableWithFallbacksT[Input, Output]
Bind lifecycle listeners to a Runnable, returning a new Runnable.
The Run object contains information about the run, including its id, type, input, output, error, start_time, end_time, and any tags or metadata added to the run.
on_start (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) β Called before the Runnable starts running, with the Run object. Defaults to None.
on_end (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) β Called after the Runnable finishes running, with the Run object. Defaults to None.
on_error (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) β Called if the Runnable throws an error, with the Run object. Defaults to None.
A new Runnable with the listeners bound.
Runnable[Input, Output]
Example:
from langchain_core.runnables import RunnableLambda
from langchain_core.tracers.schemas import Run
import time
def test_runnable(time_to_sleep : int):
time.sleep(time_to_sleep)
def fn_start(run_obj: Run):
print("start_time:", run_obj.start_time)
def fn_end(run_obj: Run):
print("end_time:", run_obj.end_time)
chain = RunnableLambda(test_runnable).with_listeners(
on_start=fn_start,
on_end=fn_end
)
chain.invoke(2)
Create a new Runnable that retries the original Runnable on exceptions.
retry_if_exception_type (tuple[type[BaseException], ...]) β A tuple of exception types to retry on. Defaults to (Exception,).
wait_exponential_jitter (bool) β Whether to add jitter to the wait time between retries. Defaults to True.
stop_after_attempt (int) β The maximum number of attempts to make before giving up. Defaults to 3.
exponential_jitter_params (Optional[ExponentialJitterParams]) β Parameters for tenacity.wait_exponential_jitter. Namely: initial, max, exp_base, and jitter (all float values).
A new Runnable that retries the original Runnable on exceptions.
Runnable[Input, Output]
Example:
from langchain_core.runnables import RunnableLambda
count = 0
def _lambda(x: int) -> None:
global count
count = count + 1
if x == 1:
raise ValueError("x is 1")
else:
pass
runnable = RunnableLambda(_lambda)
try:
runnable.with_retry(
stop_after_attempt=2,
retry_if_exception_type=(ValueError,),
).invoke(1)
except ValueError:
pass
assert (count == 2)
Bind input and output types to a Runnable, returning a new Runnable.
input_type (type[Input] | None) β The input type to bind to the Runnable. Defaults to None.
output_type (type[Output] | None) β The output type to bind to the Runnable. Defaults to None.
A new Runnable with the types bound.
Runnable[Input, Output]
Examples using LLMChain
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