PydanticToolsParser#
- class langchain_core.output_parsers.openai_tools.PydanticToolsParser[source]#
Bases:
JsonOutputToolsParser
Parse tools from OpenAI response.
Note
PydanticToolsParser implements the standard
Runnable Interface
. πThe
Runnable Interface
has additional methods that are available on runnables, such aswith_types
,with_retry
,assign
,bind
,get_graph
, and more.- param diff: bool = False#
In streaming mode, whether to yield diffs between the previous and current parsed output, or just the current parsed output.
- param first_tool_only: bool = False#
Whether to return only the first tool call.
If False, the result will be a list of tool calls, or an empty list if no tool calls are found.
If true, and multiple tool calls are found, only the first one will be returned, and the other tool calls will be ignored. If no tool calls are found, None will be returned.
- param return_id: bool = False#
Whether to return the tool call id.
- param strict: bool = False#
Whether to allow non-JSON-compliant strings.
See: https://docs.python.org/3/library/json.html#encoders-and-decoders
Useful when the parsed output may include unicode characters or new lines.
- param tools: Annotated[list[type[BaseModel]], SkipValidation()] [Required]#
The tools to parse.
- async abatch(inputs: list[Input], config: RunnableConfig | list[RunnableConfig] | None = None, *, return_exceptions: bool = False, **kwargs: Any | None) list[Output] #
Default implementation runs ainvoke in parallel using asyncio.gather.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.
- Parameters:
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 βtagsβ, βmetadataβ for tracing purposes, βmax_concurrencyβ 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.
- Returns:
A list of outputs from the Runnable.
- Return type:
list[Output]
- async abatch_as_completed(inputs: Sequence[Input], config: RunnableConfig | Sequence[RunnableConfig] | None = None, *, return_exceptions: bool = False, **kwargs: Any | None) AsyncIterator[tuple[int, Output | Exception]] #
Run ainvoke in parallel on a list of inputs, yielding results as they complete.
- Parameters:
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 βtagsβ, βmetadataβ for tracing purposes, βmax_concurrencyβ for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details. Defaults to None. 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.
- Yields:
A tuple of the index of the input and the output from the Runnable.
- Return type:
AsyncIterator[tuple[int, Output | Exception]]
- async ainvoke(input: str | BaseMessage, config: RunnableConfig | None = None, **kwargs: Any | None) T #
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.
- Parameters:
input (str | BaseMessage)
config (RunnableConfig | None)
kwargs (Any | None)
- Return type:
T
- async aparse(text: str) T #
Async parse a single string model output into some structure.
- Parameters:
text (str) β String output of a language model.
- Returns:
Structured output.
- Return type:
T
- async aparse_result(result: list[Generation], *, partial: bool = False) T #
Async parse a list of candidate model Generations into a specific format.
- The return value is parsed from only the first Generation in the result, which
is assumed to be the highest-likelihood Generation.
- Parameters:
result (list[Generation]) β A list of Generations to be parsed. The Generations are assumed to be different candidate outputs for a single model input.
partial (bool) β Whether to parse the output as a partial result. This is useful for parsers that can parse partial results. Default is False.
- Returns:
Structured output.
- Return type:
T
- async astream(input: Input, config: RunnableConfig | None = None, **kwargs: Any | None) AsyncIterator[Output] #
Default implementation of astream, which calls ainvoke. Subclasses should override this method if they support streaming output.
- Parameters:
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.
- Yields:
The output of the Runnable.
- Return type:
AsyncIterator[Output]
- async astream_events(input: Any, config: RunnableConfig | None = None, *, version: Literal['v1', 'v2'], include_names: Sequence[str] | None = None, include_types: Sequence[str] | None = None, include_tags: Sequence[str] | None = None, exclude_names: Sequence[str] | None = None, exclude_types: Sequence[str] | None = None, exclude_tags: Sequence[str] | None = None, **kwargs: Any) AsyncIterator[StandardStreamEvent | CustomStreamEvent] #
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 theformat: 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 ofthe 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 thatgenerated 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 generatedthe event.
metadata
: Optional[Dict[str, Any]] - The metadata of the Runnablethat 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.
ATTENTION This reference table is for the V2 version of the schema.
event
name
chunk
input
output
on_chat_model_start
[model name]
{βmessagesβ: [[SystemMessage, HumanMessage]]}
on_chat_model_stream
[model name]
AIMessageChunk(content=βhelloβ)
on_chat_model_end
[model name]
{βmessagesβ: [[SystemMessage, HumanMessage]]}
AIMessageChunk(content=βhello worldβ)
on_llm_start
[model name]
{βinputβ: βhelloβ}
on_llm_stream
[model name]
βHelloβ
on_llm_end
[model name]
βHello human!β
on_chain_start
format_docs
on_chain_stream
format_docs
βhello world!, goodbye world!β
on_chain_end
format_docs
[Document(β¦)]
βhello world!, goodbye world!β
on_tool_start
some_tool
{βxβ: 1, βyβ: β2β}
on_tool_end
some_tool
{βxβ: 1, βyβ: β2β}
on_retriever_start
[retriever name]
{βqueryβ: βhelloβ}
on_retriever_end
[retriever name]
{βqueryβ: βhelloβ}
[Document(β¦), ..]
on_prompt_start
[template_name]
{βquestionβ: βhelloβ}
on_prompt_end
[template_name]
{βquestionβ: βhelloβ}
ChatPromptValue(messages: [SystemMessage, β¦])
In addition to the standard events, users can also dispatch custom events (see example below).
Custom events will be only be surfaced with in the v2 version of the API!
A custom event has following format:
Attribute
Type
Description
name
str
A user defined name for the event.
data
Any
The data associated with the event. This can be anything, though we suggest making it JSON serializable.
Here are declarations associated with the standard events shown above:
format_docs:
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)
- Parameters:
input (Any) β The input to the Runnable.
config (RunnableConfig | None) 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 (Sequence[str] | None) β Only include events from runnables with matching names.
include_types (Sequence[str] | None) β Only include events from runnables with matching types.
include_tags (Sequence[str] | None) β Only include events from runnables with matching tags.
exclude_names (Sequence[str] | None) β Exclude events from runnables with matching names.
exclude_types (Sequence[str] | None) β Exclude events from runnables with matching types.
exclude_tags (Sequence[str] | None) 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.
- Yields:
An async stream of StreamEvents.
- Raises:
NotImplementedError β If the version is not v1 or v2.
- Return type:
AsyncIterator[StandardStreamEvent | CustomStreamEvent]
- batch(inputs: list[Input], config: RunnableConfig | list[RunnableConfig] | None = None, *, return_exceptions: bool = False, **kwargs: Any | None) list[Output] #
Default implementation runs invoke in parallel using a thread pool executor.
The default implementation of batch works well for IO bound runnables.
Subclasses should override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.
- Parameters:
inputs (list[Input])
config (RunnableConfig | list[RunnableConfig] | None)
return_exceptions (bool)
kwargs (Any | None)
- Return type:
list[Output]
- batch_as_completed(inputs: Sequence[Input], config: RunnableConfig | Sequence[RunnableConfig] | None = None, *, return_exceptions: bool = False, **kwargs: Any | None) Iterator[tuple[int, Output | Exception]] #
Run invoke in parallel on a list of inputs, yielding results as they complete.
- Parameters:
inputs (Sequence[Input])
config (RunnableConfig | Sequence[RunnableConfig] | None)
return_exceptions (bool)
kwargs (Any | None)
- Return type:
Iterator[tuple[int, Output | Exception]]
- bind(**kwargs: Any) Runnable[Input, Output] #
Bind arguments to a Runnable, returning a new Runnable.
Useful when a Runnable in a chain requires an argument that is not in the output of the previous Runnable or included in the user input.
- Parameters:
kwargs (Any) β The arguments to bind to the Runnable.
- Returns:
A new Runnable with the arguments bound.
- Return type:
Runnable[Input, Output]
Example:
from langchain_community.chat_models 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'
- configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Runnable[Input, Output] | Callable[[], Runnable[Input, Output]]) RunnableSerializable #
Configure alternatives for Runnables that can be set at runtime.
- Parameters:
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 βdefaultβ.
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.
- Returns:
A new Runnable with the alternatives configured.
- Return type:
from langchain_anthropic import ChatAnthropic from langchain_core.runnables.utils import ConfigurableField from langchain_openai import ChatOpenAI model = ChatAnthropic( model_name="claude-3-sonnet-20240229" ).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 )
- configurable_fields(**kwargs: ConfigurableField | ConfigurableFieldSingleOption | ConfigurableFieldMultiOption) RunnableSerializable #
Configure particular Runnable fields at runtime.
- Parameters:
**kwargs (ConfigurableField | ConfigurableFieldSingleOption | ConfigurableFieldMultiOption) β A dictionary of ConfigurableField instances to configure.
- Returns:
A new Runnable with the fields configured.
- Return type:
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 )
- get_format_instructions() str #
Instructions on how the LLM output should be formatted.
- Return type:
str
- invoke(input: str | BaseMessage, config: RunnableConfig | None = None, **kwargs: Any) T #
Transform a single input into an output. Override to implement.
- Parameters:
input (str | BaseMessage) β The input to the Runnable.
config (RunnableConfig | None) β A config to use when invoking the Runnable. The config supports standard keys like βtagsβ, βmetadataβ for tracing purposes, βmax_concurrencyβ for controlling how much work to do in parallel, and other keys. Please refer to the RunnableConfig for more details.
kwargs (Any)
- Returns:
The output of the Runnable.
- Return type:
T
- parse(text: str) Any #
Parse the output of an LLM call to a list of tool calls.
- Parameters:
text (str) β The output of the LLM call.
- Returns:
The parsed tool calls.
- Return type:
Any
- parse_result(result: list[Generation], *, partial: bool = False) Any [source]#
Parse the result of an LLM call to a list of Pydantic objects.
- Parameters:
result (list[Generation]) β The result of the LLM call.
partial (bool) β Whether to parse partial JSON. If True, the output will be a JSON object containing all the keys that have been returned so far. If False, the output will be the full JSON object. Default is False.
- Returns:
The parsed Pydantic objects.
- Raises:
OutputParserException β If the output is not valid JSON.
- Return type:
Any
- parse_with_prompt(completion: str, prompt: PromptValue) Any #
Parse the output of an LLM call with the input prompt for context.
The prompt is largely provided in the event the OutputParser wants to retry or fix the output in some way, and needs information from the prompt to do so.
- Parameters:
completion (str) β String output of a language model.
prompt (PromptValue) β Input PromptValue.
- Returns:
Structured output.
- Return type:
Any
- stream(input: Input, config: RunnableConfig | None = None, **kwargs: Any | None) Iterator[Output] #
Default implementation of stream, which calls invoke. Subclasses should override this method if they support streaming output.
- Parameters:
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.
- Yields:
The output of the Runnable.
- Return type:
Iterator[Output]
- with_alisteners(*, on_start: AsyncListener | None = None, on_end: AsyncListener | None = None, on_error: AsyncListener | None = None) Runnable[Input, Output] #
Bind asynchronous lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Asynchronously called before the Runnable starts running. on_end: Asynchronously called after the Runnable finishes running. on_error: Asynchronously called if the Runnable throws an error.
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.
- Parameters:
on_start (Optional[AsyncListener]) β Asynchronously called before the Runnable starts running. Defaults to None.
on_end (Optional[AsyncListener]) β Asynchronously called after the Runnable finishes running. Defaults to None.
on_error (Optional[AsyncListener]) β Asynchronously called if the Runnable throws an error. Defaults to None.
- Returns:
A new Runnable with the listeners bound.
- Return type:
Runnable[Input, Output]
Example:
from langchain_core.runnables import RunnableLambda import time 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 2024-05-16T14:20:29.637053+00:00 on start callback starts at 2024-05-16T14:20:29.637150+00:00 on start callback ends at 2024-05-16T14:20:32.638305+00:00 on start callback ends at 2024-05-16T14:20:32.638383+00:00 Runnable[3s]: starts at 2024-05-16T14:20:32.638849+00:00 Runnable[5s]: starts at 2024-05-16T14:20:32.638999+00:00 Runnable[3s]: ends at 2024-05-16T14:20:35.640016+00:00 on end callback starts at 2024-05-16T14:20:35.640534+00:00 Runnable[5s]: ends at 2024-05-16T14:20:37.640169+00:00 on end callback starts at 2024-05-16T14:20:37.640574+00:00 on end callback ends at 2024-05-16T14:20:37.640654+00:00 on end callback ends at 2024-05-16T14:20:39.641751+00:00
- with_config(config: RunnableConfig | None = None, **kwargs: Any) Runnable[Input, Output] #
Bind config to a Runnable, returning a new Runnable.
- Parameters:
config (RunnableConfig | None) β The config to bind to the Runnable.
kwargs (Any) β Additional keyword arguments to pass to the Runnable.
- Returns:
A new Runnable with the config bound.
- Return type:
Runnable[Input, Output]
- with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: tuple[type[BaseException], ...] = (<class 'Exception'>,), exception_key: Optional[str] = None) RunnableWithFallbacksT[Input, Output] #
Add fallbacks to a Runnable, returning a new Runnable.
The new Runnable will try the original Runnable, and then each fallback in order, upon failures.
- Parameters:
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.
- Returns:
A new Runnable that will try the original Runnable, and then each fallback in order, upon failures.
- Return type:
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
- Parameters:
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.
- Returns:
A new Runnable that will try the original Runnable, and then each fallback in order, upon failures.
- Return type:
RunnableWithFallbacksT[Input, Output]
- with_listeners(*, on_start: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None = None, on_end: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None = None, on_error: Callable[[Run], None] | Callable[[Run, RunnableConfig], None] | None = None) Runnable[Input, Output] #
Bind lifecycle listeners to a Runnable, returning a new Runnable.
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.
- Parameters:
on_start (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) β Called before the Runnable starts running. Defaults to None.
on_end (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) β Called after the Runnable finishes running. Defaults to None.
on_error (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) β Called if the Runnable throws an error. Defaults to None.
- Returns:
A new Runnable with the listeners bound.
- Return type:
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)
- with_retry(*, retry_if_exception_type: tuple[type[BaseException], ...] = (<class '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.
- Parameters:
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.
- Returns:
A new Runnable that retries the original Runnable on exceptions.
- Return type:
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)
- Parameters:
retry_if_exception_type (tuple[type[BaseException], ...]) β A tuple of exception types to retry on
wait_exponential_jitter (bool) β Whether to add jitter to the wait time between retries
stop_after_attempt (int) β The maximum number of attempts to make before giving up
- Returns:
A new Runnable that retries the original Runnable on exceptions.
- Return type:
Runnable[Input, Output]
- with_types(*, input_type: type[Input] | None = None, output_type: type[Output] | None = None) Runnable[Input, Output] #
Bind input and output types to a Runnable, returning a new Runnable.
- Parameters:
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.
- Returns:
A new Runnable with the types bound.
- Return type:
Runnable[Input, Output]
Examples using PydanticToolsParser