GoogleLensQueryRun#
- class langchain_community.tools.google_lens.tool.GoogleLensQueryRun[source]#
Bases:
BaseTool
Tool that queries the Google Lens API.
Initialize the tool.
Note
GoogleLensQueryRun implements the standard
Runnable Interface
. πThe
Runnable Interface
has additional methods that are available on runnables, such aswith_config
,with_types
,with_retry
,assign
,bind
,get_graph
, and more.- param api_wrapper: GoogleLensAPIWrapper [Required]#
- param args_schema: Annotated[ArgsSchema | None, SkipValidation()] = None#
Pydantic model class to validate and parse the toolβs input arguments.
Args schema should be either:
A subclass of pydantic.BaseModel.
A subclass of pydantic.v1.BaseModel if accessing v1 namespace in pydantic 2
a JSON schema dict
The tool schema.
- param callback_manager: BaseCallbackManager | None = None#
Deprecated since version 0.1.7: Use
callbacks()
instead. It will be removed in pydantic==1.0.Callback manager to add to the run trace.
- param callbacks: Callbacks = None#
Callbacks to be called during tool execution.
- param description: str = 'A wrapper around Google Lens Search. Useful for when you need to get information relatedto an image from Google LensInput should be a url to an image.'#
Used to tell the model how/when/why to use the tool.
You can provide few-shot examples as a part of the description.
- param handle_tool_error: bool | str | Callable[[ToolException], str] | None = False#
Handle the content of the ToolException thrown.
- param handle_validation_error: bool | str | Callable[[ValidationError | ValidationErrorV1], str] | None = False#
Handle the content of the ValidationError thrown.
- param metadata: dict[str, Any] | None = None#
Optional metadata associated with the tool. Defaults to None. This metadata will be associated with each call to this tool, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a tool with its use case.
- param response_format: Literal['content', 'content_and_artifact'] = 'content'#
The tool response format. Defaults to βcontentβ.
If βcontentβ then the output of the tool is interpreted as the contents of a ToolMessage. If βcontent_and_artifactβ then the output is expected to be a two-tuple corresponding to the (content, artifact) of a ToolMessage.
- param return_direct: bool = False#
Whether to return the toolβs output directly.
Setting this to True means that after the tool is called, the AgentExecutor will stop looping.
- param tags: list[str] | None = None#
Optional list of tags associated with the tool. Defaults to None. These tags will be associated with each call to this tool, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a tool with its use case.
- param verbose: bool = False#
Whether to log the toolβs progress.
- __call__(
- tool_input: str,
- callbacks: list[BaseCallbackHandler] | BaseCallbackManager | None = None,
Deprecated since version 0.1.47: Use
invoke()
instead. It will not be removed until langchain-core==1.0.Make tool callable (deprecated).
- Parameters:
tool_input (str) β The input to the tool.
callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None) β Callbacks to use during execution.
- Returns:
The toolβs output.
- Return type:
str
- async abatch(
- inputs: list[Input],
- config: RunnableConfig | list[RunnableConfig] | None = None,
- *,
- return_exceptions: bool = False,
- **kwargs: Any | None,
Default implementation runs
ainvoke
in parallel usingasyncio.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'tags'
,'metadata'
for tracing purposes,'max_concurrency'
for controlling how much work to do in parallel, and other keys. Please refer to theRunnableConfig
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,
Run
ainvoke
in parallel on a list of inputs.Yields 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'tags'
,'metadata'
for tracing purposes,'max_concurrency'
for controlling how much work to do in parallel, and other keys. Please refer to theRunnableConfig
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
.
- 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 | dict | ToolCall,
- config: RunnableConfig | None = None,
- **kwargs: Any,
Default implementation of
ainvoke
, callsinvoke
from a thread.The default implementation allows usage of async code even if the
Runnable
did not implement a native async version ofinvoke
.Subclasses should override this method if they can run asynchronously.
- Parameters:
input (str | dict | ToolCall)
config (RunnableConfig | None)
kwargs (Any)
- Return type:
Any
- async arun(
- tool_input: str | dict,
- verbose: bool | None = None,
- start_color: str | None = 'green',
- color: str | None = 'green',
- callbacks: Callbacks = None,
- *,
- tags: list[str] | None = None,
- metadata: dict[str, Any] | None = None,
- run_name: str | None = None,
- run_id: uuid.UUID | None = None,
- config: RunnableConfig | None = None,
- tool_call_id: str | None = None,
- **kwargs: Any,
Run the tool asynchronously.
- Parameters:
tool_input (Union[str, dict]) β The input to the tool.
verbose (Optional[bool]) β Whether to log the toolβs progress. Defaults to None.
start_color (Optional[str]) β The color to use when starting the tool. Defaults to βgreenβ.
color (Optional[str]) β The color to use when ending the tool. Defaults to βgreenβ.
callbacks (Callbacks) β Callbacks to be called during tool execution. Defaults to None.
tags (Optional[list[str]]) β Optional list of tags associated with the tool. Defaults to None.
metadata (Optional[dict[str, Any]]) β Optional metadata associated with the tool. Defaults to None.
run_name (Optional[str]) β The name of the run. Defaults to None.
run_id (Optional[uuid.UUID]) β The id of the run. Defaults to None.
config (Optional[RunnableConfig]) β The configuration for the tool. Defaults to None.
tool_call_id (Optional[str]) β The id of the tool call. Defaults to None.
kwargs (Any) β Keyword arguments to be passed to tool callbacks
- Returns:
The output of the tool.
- Raises:
ToolException β If an error occurs during tool execution.
- Return type:
Any
- async astream(
- input: Input,
- config: RunnableConfig | None = None,
- **kwargs: Any | None,
Default implementation of
astream
, which callsainvoke
.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'] = '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,
Generate a stream of events.
Use to create an iterator over
StreamEvents
that provide real-time information about the progress of theRunnable
, includingStreamEvents
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 theRunnable
that generated the event.run_id
: str - randomly generated ID associated with the given execution of theRunnable
that emitted the event. A childRunnable
that gets invoked as part of the execution of a parentRunnable
is assigned its own unique ID.parent_ids
: list[str] - The IDs of the parent runnables that generated the event. The rootRunnable
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 theRunnable
that generated the event.metadata
: Optional[dict[str, Any]] - The metadata of theRunnable
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.
event
name
chunk
input
output
on_chat_model_start
[model name]
{"messages": [[SystemMessage, HumanMessage]]}
on_chat_model_stream
[model name]
AIMessageChunk(content="hello")
on_chat_model_end
[model name]
{"messages": [[SystemMessage, HumanMessage]]}
AIMessageChunk(content="hello world")
on_llm_start
[model name]
{'input': 'hello'}
on_llm_stream
[model name]
``βHelloβ ``
on_llm_end
[model name]
'Hello human!'
on_chain_start
format_docs
on_chain_stream
format_docs
'hello world!, goodbye world!'
on_chain_end
format_docs
[Document(...)]
'hello world!, goodbye world!'
on_tool_start
some_tool
{"x": 1, "y": "2"}
on_tool_end
some_tool
{"x": 1, "y": "2"}
on_retriever_start
[retriever name]
{"query": "hello"}
on_retriever_end
[retriever name]
{"query": "hello"}
[Document(...), ..]
on_prompt_start
[template_name]
{"question": "hello"}
on_prompt_end
[template_name]
{"question": "hello"}
ChatPromptValue(messages: [SystemMessage, ...])
In addition to the standard events, users can also dispatch custom events (see example below).
Custom events will be only be surfaced with in the v2 version of the API!
A custom event has following format:
Attribute
Type
Description
name
str
A user defined name for the event.
data
Any
The data associated with the event. This can be anything, though we suggest making it JSON serializable.
Here are declarations associated with the standard events shown above:
format_docs
: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 (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 toastream_log
as this implementation ofastream_events
is built on top ofastream_log
.
- Yields:
An async stream of
StreamEvents
.- Raises:
NotImplementedError β If the version is not
'v1'
or'v2'
.- Return type:
AsyncIterator[StreamEvent]
- batch(
- inputs: list[Input],
- config: RunnableConfig | list[RunnableConfig] | None = None,
- *,
- return_exceptions: bool = False,
- **kwargs: Any | None,
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,
Run
invoke
in parallel on a list of inputs.Yields 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,
Bind arguments to a
Runnable
, returning a newRunnable
.Useful when a
Runnable
in a chain requires an argument that is not in the output of the previousRunnable
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_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'
- configurable_alternatives(
- which: ConfigurableField,
- *,
- default_key: str = 'default',
- prefix_keys: bool = False,
- **kwargs: Runnable[Input, Output] | Callable[[], Runnable[Input, Output]],
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
'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 returnRunnable
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-3-7-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 )
- configurable_fields( ) 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 )
- invoke(
- input: str | dict | ToolCall,
- config: RunnableConfig | None = None,
- **kwargs: Any,
Transform a single input into an output.
- Parameters:
input (str | dict | ToolCall) 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 theRunnableConfig
for more details. Defaults to None.kwargs (Any)
- Returns:
The output of the
Runnable
.- Return type:
Any
- run(
- tool_input: str | dict[str, Any],
- verbose: bool | None = None,
- start_color: str | None = 'green',
- color: str | None = 'green',
- callbacks: Callbacks = None,
- *,
- tags: list[str] | None = None,
- metadata: dict[str, Any] | None = None,
- run_name: str | None = None,
- run_id: uuid.UUID | None = None,
- config: RunnableConfig | None = None,
- tool_call_id: str | None = None,
- **kwargs: Any,
Run the tool.
- Parameters:
tool_input (Union[str, dict[str, Any]]) β The input to the tool.
verbose (Optional[bool]) β Whether to log the toolβs progress. Defaults to None.
start_color (Optional[str]) β The color to use when starting the tool. Defaults to βgreenβ.
color (Optional[str]) β The color to use when ending the tool. Defaults to βgreenβ.
callbacks (Callbacks) β Callbacks to be called during tool execution. Defaults to None.
tags (Optional[list[str]]) β Optional list of tags associated with the tool. Defaults to None.
metadata (Optional[dict[str, Any]]) β Optional metadata associated with the tool. Defaults to None.
run_name (Optional[str]) β The name of the run. Defaults to None.
run_id (Optional[uuid.UUID]) β The id of the run. Defaults to None.
config (Optional[RunnableConfig]) β The configuration for the tool. Defaults to None.
tool_call_id (Optional[str]) β The id of the tool call. Defaults to None.
kwargs (Any) β Keyword arguments to be passed to tool callbacks (event handler)
- Returns:
The output of the tool.
- Raises:
ToolException β If an error occurs during tool execution.
- Return type:
Any
- stream(
- input: Input,
- config: RunnableConfig | None = None,
- **kwargs: Any | None,
Default implementation of
stream
, which callsinvoke
.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,
Bind async lifecycle listeners to a
Runnable
, returning a newRunnable
.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 Called asynchronously before the
Runnable
starts running, with theRun
object. Defaults to None.on_end (Optional[AsyncListener]) β Called asynchronously after the
Runnable
finishes running, with theRun
object. Defaults to None.on_error (Optional[AsyncListener]) β Called asynchronously if the
Runnable
throws an error, with theRun
object. Defaults to None.
- Returns:
A new
Runnable
with the listeners bound.- Return type:
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
- with_config(
- config: RunnableConfig | None = None,
- **kwargs: Any,
Bind config to a
Runnable
, returning a newRunnable
.- 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 newRunnable
.The new
Runnable
will try the originalRunnable
, 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 originalRunnable
, 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 originalRunnable
, 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,
Bind lifecycle listeners to a
Runnable
, returning a newRunnable
.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, with theRun
object. Defaults to None.on_end (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) β Called after the
Runnable
finishes running, with theRun
object. Defaults to None.on_error (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) β Called if the
Runnable
throws an error, with theRun
object. 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, exponential_jitter_params: Optional[ExponentialJitterParams] = None, 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.
exponential_jitter_params (Optional[ExponentialJitterParams]) β Parameters for
tenacity.wait_exponential_jitter
. Namely:initial
,max
,exp_base
, andjitter
(all float values).
- 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)
- with_types(
- *,
- input_type: type[Input] | None = None,
- output_type: type[Output] | None = None,
Bind input and output types to a
Runnable
, returning a newRunnable
.- 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]
- property args: dict#
Get the toolβs input arguments schema.
- Returns:
Dictionary containing the toolβs argument properties.
- property is_single_input: bool#
Check if the tool accepts only a single input argument.
- Returns:
True if the tool has only one input argument, False otherwise.
- property tool_call_schema: type[BaseModel] | dict[str, Any]#
Get the schema for tool calls, excluding injected arguments.
- Returns:
The schema that should be used for tool calls from language models.
Examples using GoogleLensQueryRun