VertexAISearchSummaryTool#

class langchain_google_community.vertex_ai_search.VertexAISearchSummaryTool[source]#

Bases: BaseTool, VertexAISearchRetriever

Class that exposes a tool to interface with an App in Vertex Search and Conversation and get the summary of the documents retrieved.

Initialize the tool.

Note

VertexAISearchSummaryTool implements the standard Runnable Interface. 🏃

The Runnable Interface has additional methods that are available on runnables, such as with_types, with_retry, assign, bind, get_graph, and more.

param args_schema: Annotated[TypeBaseModel | None, SkipValidation()] = None#

Pydantic model class to validate and parse the tool’s input arguments.

Args schema should be either:

  • A subclass of pydantic.BaseModel.

or - A subclass of pydantic.v1.BaseModel if accessing v1 namespace in pydantic 2

The tool schema.

param boost_spec: Dict[Any, Any] | None = None#

BoostSpec for boosting search results. A protobuf should be provided.

https://cloud.google.com/generative-ai-app-builder/docs/boost-search-results https://cloud.google.com/generative-ai-app-builder/docs/reference/rest/v1beta/BoostSpec

param callback_manager: BaseCallbackManager | None = None#

Deprecated since version 0.1.7: Use callbacks() instead. It will be removed in None==1.0.

Callback manager to add to the run trace.

param callbacks: Callbacks = None#

Callbacks to be called during tool execution.

param canonical_filter: str | None = None#

Canonical filter expression.

param credentials: Any = None#

The default custom credentials (google.auth.credentials.Credentials) to use when making API calls. If not provided, credentials will be ascertained from the environment.

param custom_embedding: Embeddings | None = None#

Custom embedding model for the retriever. (Bring your own embedding) It needs to match the embedding model that was used to embed docs in the datastore. It needs to be a langchain embedding VertexAIEmbeddings(project=”{PROJECT}”) If you provide an embedding model, you also need to provide a ranking_expression and a custom_embedding_field_path. https://cloud.google.com/generative-ai-app-builder/docs/bring-embeddings

param custom_embedding_field_path: str | None = None#

The field path for the custom embedding used in the Vertex AI datastore schema.

param custom_embedding_ratio: float | None = 0.0#

Controls the ranking of results. Value should be between 0 and 1. It will generate the ranking_expression in the following manner: “{custom_embedding_ratio} * dotProduct({custom_embedding_field_path}) + {1 - custom_embedding_ratio} * relevance_score”

param data_store_id: str [Required]#

Vertex AI Search data store ID.

param description: str [Required]#

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 engine_data_type: int = 0#

Defines the Vertex AI Search data type 0 - Unstructured data 1 - Structured data 2 - Website data

Constraints:

  • ge = 0

  • le = 2

param filter: str | None = None#

Filter expression.

param get_extractive_answers: bool = False#

If True return Extractive Answers, otherwise return Extractive Segments or Snippets.

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 location_id: str = 'global'#

Vertex AI Search data store location.

param max_documents: int = 5#

The maximum number of documents to return.

Constraints:

  • ge = 1

  • le = 100

param max_extractive_answer_count: int = 1#

The maximum number of extractive answers to return per search result.

Constraints:

  • ge = 1

  • le = 5

param max_extractive_segment_count: int = 1#

The maximum number of extractive segments to return per search result.

Constraints:

  • ge = 1

  • le = 10

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 num_next_segments: int = 1#

Specifies the number of text segments following the matched segment to return. This provides context after the relevant text. Value must be between 1 and 3.

Constraints:

  • ge = 1

  • le = 3

param num_previous_segments: int = 1#

Specifies the number of text segments preceding the matched segment to return. This provides context before the relevant text. Value must be between 1 and 3.

Constraints:

  • ge = 1

  • le = 3

param order_by: str | None = None#

Comma-separated list of fields to order by.

param project_id: str [Required]#

Google Cloud Project ID.

param query_expansion_condition: int = 1#

Specification to determine under which conditions query expansion should occur. 0 - Unspecified query expansion condition. In this case, server behavior defaults

to disabled

1 - Disabled query expansion. Only the exact search query is used, even if

SearchResponse.total_size is zero.

2 - Automatic query expansion built by the Search API.

Constraints:

  • ge = 0

  • le = 2

param response_format: Literal['content', 'content_and_artifact'] = 'content'#

The tool response format. Defaults to ‘content’.

If “content” then the output of the tool is interpreted as the contents of a ToolMessage. If “content_and_artifact” 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 tool’s output directly.

Setting this to True means that after the tool is called, the AgentExecutor will stop looping.

param return_extractive_segment_score: bool = False#

If set to True, the relevance score for each extractive segment will be included in the search results. This can be useful for ranking or filtering segments.

param serving_config_id: str = 'default_config'#

Vertex AI Search serving config ID.

param spell_correction_mode: int = 2#

Specification to determine under which conditions query expansion should occur. 0 - Unspecified spell correction mode. In this case, server behavior defaults

to auto.

1 - Suggestion only. Search API will try to find a spell suggestion if there is any

and put in the SearchResponse.corrected_query. The spell suggestion will not be used as the search query.

2 - Automatic spell correction built by the Search API.

Search will be based on the corrected query if found.

Constraints:

  • ge = 0

  • le = 2

param summary_include_citations: bool = True#

Whether to include citations in the summary

param summary_prompt: str | None = None#

Prompt for the summarization agent

param summary_result_count: int = 3#

Number of documents to include in the summary

param summary_spec_kwargs: Dict[str, Any] [Optional]#

Additional kwargs for SearchRequest.ContentSearchSpec.SummarySpec

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 tool’s progress.

__call__(tool_input: str, callbacks: list[BaseCallbackHandler] | BaseCallbackManager | None = None) → str#

Deprecated since version langchain-core==0.1.47: Use invoke() instead. It will not be removed until langchain-core==1.0.

Make tool callable.

Parameters:
Return type:

str

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]) – A list of inputs to the Runnable.

  • config (RunnableConfig | list[RunnableConfig] | None) – 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) – Whether to return exceptions instead of raising them. Defaults to False.

  • kwargs (Any | None) – 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]) – A list of inputs to the Runnable.

  • config (RunnableConfig | Sequence[RunnableConfig] | None) – 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. Defaults to None.

  • return_exceptions (bool) – Whether to return exceptions instead of raising them. Defaults to False.

  • kwargs (Any | None) – 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 aget_relevant_documents(query: str, *, callbacks: Callbacks = None, tags: list[str] | None = None, metadata: dict[str, Any] | None = None, run_name: str | None = None, **kwargs: Any) → list[Document]#

Deprecated since version langchain-core==0.1.46: Use ainvoke() instead. It will not be removed until langchain-core==1.0.

Asynchronously get documents relevant to a query.

Users should favor using .ainvoke or .abatch rather than aget_relevant_documents directly.

Parameters:

  • query (str) – string to find relevant documents for.

  • callbacks (Callbacks) – Callback manager or list of callbacks.

  • tags (Optional[list[str]]) – Optional list of tags associated with the retriever. These tags will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. Defaults to None.

  • metadata (Optional[dict[str, Any]]) – Optional metadata associated with the retriever. This metadata will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. Defaults to None.

  • run_name (Optional[str]) – Optional name for the run. Defaults to None.

  • kwargs (Any) – Additional arguments to pass to the retriever.

Returns:

List of relevant documents.

Return type:

list[Document]

async ainvoke(input: str | dict | ToolCall, config: RunnableConfig | None = None, **kwargs: Any) → 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.

Parameters:
Return type:

Any

async arun(tool_input: str | dict, verbose: bool | None = None, start_color: str | None = 'green', color: str | None = 'green', callbacks: list[BaseCallbackHandler] | BaseCallbackManager | None = None, *, tags: list[str] | None = None, metadata: dict[str, Any] | None = None, run_name: str | None = None, run_id: UUID | None = None, config: RunnableConfig | None = None, tool_call_id: str | None = None, **kwargs: Any) → Any#

Run the tool asynchronously.

Parameters:

  • tool_input (str | dict) – The input to the tool.

  • verbose (bool | None) – Whether to log the tool’s progress. Defaults to None.

  • start_color (str | None) – The color to use when starting the tool. Defaults to ‘green’.

  • color (str | None) – The color to use when ending the tool. Defaults to ‘green’.

  • callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None) – Callbacks to be called during tool execution. Defaults to None.

  • tags (list[str] | None) – Optional list of tags associated with the tool. Defaults to None.

  • metadata (dict[str, Any] | None) – Optional metadata associated with the tool. Defaults to None.

  • run_name (str | None) – The name of the run. Defaults to None.

  • run_id (UUID | None) – The id of the run. Defaults to None.

  • config (RunnableConfig | None) – The configuration for the tool. Defaults to None.

  • tool_call_id (str | None) – The id of the tool call. Defaults to None.

  • kwargs (Any) – Keyword arguments to be passed to tool callbacks

Returns:

The output of the tool.

Raises:

ToolException – If an error occurs during tool execution.

Return type:

Any

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) – The input to the Runnable.

  • config (RunnableConfig | None) – The config to use for the Runnable. Defaults to None.

  • kwargs (Any | None) – 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 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.

ATTENTION 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]

‘Hello’

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) – The input to the Runnable.

  • config (RunnableConfig | None) – The config to use for the Runnable.

  • version (Literal['v1', 'v2']) – 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) – Only include events from runnables with matching names.

  • include_types (Sequence[str] | None) – Only include events from runnables with matching types.

  • include_tags (Sequence[str] | None) – Only include events from runnables with matching tags.

  • exclude_names (Sequence[str] | None) – Exclude events from runnables with matching names.

  • exclude_types (Sequence[str] | None) – Exclude events from runnables with matching types.

  • exclude_tags (Sequence[str] | None) – Exclude events from runnables with matching tags.

  • kwargs (Any) – 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 – 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:
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:
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) – 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) – The ConfigurableField instance that will be used to select the alternative.

  • default_key (str) – The default key to use if no alternative is selected. Defaults to “default”.

  • prefix_keys (bool) – Whether to prefix the keys with the ConfigurableField id. Defaults to False.

  • **kwargs (Runnable[Input, Output] | Callable[[], Runnable[Input, Output]]) – A dictionary of keys to Runnable instances or callables that return Runnable instances.

Returns:

A new Runnable with the alternatives configured.

Return type:

RunnableSerializable

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) – A dictionary of ConfigurableField instances to configure.

Returns:

A new Runnable with the fields configured.

Return type:

RunnableSerializable

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_relevant_documents(query: str, *, callbacks: Callbacks = None, tags: list[str] | None = None, metadata: dict[str, Any] | None = None, run_name: str | None = None, **kwargs: Any) → list[Document]#

Deprecated since version langchain-core==0.1.46: Use invoke() instead. It will not be removed until langchain-core==1.0.

Retrieve documents relevant to a query.

Users should favor using .invoke or .batch rather than get_relevant_documents directly.

Parameters:

  • query (str) – string to find relevant documents for.

  • callbacks (Callbacks) – Callback manager or list of callbacks. Defaults to None.

  • tags (Optional[list[str]]) – Optional list of tags associated with the retriever. These tags will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. Defaults to None.

  • metadata (Optional[dict[str, Any]]) – Optional metadata associated with the retriever. This metadata will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. Defaults to None.

  • run_name (Optional[str]) – Optional name for the run. Defaults to None.

  • kwargs (Any) – Additional arguments to pass to the retriever.

Returns:

List of relevant documents.

Return type:

list[Document]

invoke(input: str | dict | ToolCall, config: RunnableConfig | None = None, **kwargs: Any) → Any#

Transform a single input into an output. Override to implement.

Parameters:

  • input (str | dict | ToolCall) – The input to the Runnable.

  • config (RunnableConfig | None) – 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.

  • 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: list[BaseCallbackHandler] | BaseCallbackManager | None = None, *, tags: list[str] | None = None, metadata: dict[str, Any] | None = None, run_name: str | None = None, run_id: UUID | None = None, config: RunnableConfig | None = None, tool_call_id: str | None = None, **kwargs: Any) → Any#

Run the tool.

Parameters:

  • tool_input (str | dict[str, Any]) – The input to the tool.

  • verbose (bool | None) – Whether to log the tool’s progress. Defaults to None.

  • start_color (str | None) – The color to use when starting the tool. Defaults to ‘green’.

  • color (str | None) – The color to use when ending the tool. Defaults to ‘green’.

  • callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None) – Callbacks to be called during tool execution. Defaults to None.

  • tags (list[str] | None) – Optional list of tags associated with the tool. Defaults to None.

  • metadata (dict[str, Any] | None) – Optional metadata associated with the tool. Defaults to None.

  • run_name (str | None) – The name of the run. Defaults to None.

  • run_id (UUID | None) – The id of the run. Defaults to None.

  • config (RunnableConfig | None) – The configuration for the tool. Defaults to None.

  • tool_call_id (str | None) – The id of the tool call. Defaults to None.

  • kwargs (Any) – Keyword arguments to be passed to tool callbacks

Returns:

The output of the tool.

Raises:

ToolException – If an error occurs during tool execution.

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) – The input to the Runnable.

  • config (RunnableConfig | None) – The config to use for the Runnable. Defaults to None.

  • kwargs (Any | None) – 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]) – Asynchronously called before the Runnable starts running. Defaults to None.

  • on_end (Optional[AsyncListener]) – Asynchronously called after the Runnable finishes running. Defaults to None.

  • on_error (Optional[AsyncListener]) – 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) – The config to bind to the Runnable.

  • kwargs (Any) – 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]]) – A sequence of runnables to try if the original Runnable fails.

  • exceptions_to_handle (tuple[type[BaseException], ...]) – A tuple of exception types to handle. Defaults to (Exception,).

  • exception_key (Optional[str]) – 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]]) – A sequence of runnables to try if the original Runnable fails.

  • exceptions_to_handle (tuple[type[BaseException], ...]) – A tuple of exception types to handle.

  • exception_key (Optional[str]) – 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]]]) – Called before the Runnable starts running. Defaults to None.

  • on_end (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) – Called after the Runnable finishes running. Defaults to None.

  • on_error (Optional[Union[Callable[[Run], None], Callable[[Run, RunnableConfig], None]]]) – 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], ...]) – A tuple of exception types to retry on. Defaults to (Exception,).

  • wait_exponential_jitter (bool) – Whether to add jitter to the wait time between retries. Defaults to True.

  • stop_after_attempt (int) – 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], ...]) – A tuple of exception types to retry on

  • wait_exponential_jitter (bool) – Whether to add jitter to the wait time between retries

  • stop_after_attempt (int) – 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) – The input type to bind to the Runnable. Defaults to None.

  • output_type (type[Output] | None) – 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#
property client_options: ClientOptions#
property is_single_input: bool#

Whether the tool only accepts a single input.

property tool_call_schema: type[BaseModel]#