`langchain_google_community.vertex_ai_search`.VertexAISearchRetriever¶

Note

VertexAISearchRetriever 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.

class langchain_google_community.vertex_ai_search.VertexAISearchRetriever[source]¶

Bases: BaseRetriever, _BaseVertexAISearchRetriever

Google Vertex AI Search retriever.

For a detailed explanation of the Vertex AI Search concepts and configuration parameters, refer to the product documentation. https://cloud.google.com/generative-ai-app-builder/docs/enterprise-search-introduction

Initializes private fields.

param boost_spec: Optional[Dict[Any, Any]] = 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 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 data_store_id: str [Required]¶: Vertex AI Search data store ID.

param engine_data_type: int = 0¶

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

Constraints

minimum = 0
maximum = 2

param filter: Optional[str] = None¶: Filter expression.

param get_extractive_answers: bool = False¶: If True return Extractive Answers, otherwise return Extractive Segments or Snippets.

param location_id: str = 'global'¶: Vertex AI Search data store location.

param max_documents: int = 5¶

The maximum number of documents to return.

Constraints

minimum = 1
maximum = 100

param max_extractive_answer_count: int = 1¶

The maximum number of extractive answers returned in each search result. At most 5 answers will be returned for each SearchResult.

Constraints

minimum = 1
maximum = 5

param max_extractive_segment_count: int = 1¶

The maximum number of extractive segments returned in each search result. Currently one segment will be returned for each SearchResult.

Constraints

minimum = 1
maximum = 1

param metadata: Optional[Dict[str, Any]] = None¶: Optional metadata associated with the retriever. Defaults to None. This metadata will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a retriever with its use case.

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

minimum = 0
maximum = 2

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

minimum = 0
maximum = 2

param tags: Optional[List[str]] = None¶: Optional list of tags associated with the retriever. Defaults to None. These tags will be associated with each call to this retriever, and passed as arguments to the handlers defined in callbacks. You can use these to eg identify a specific instance of a retriever with its use case.

async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶

Default implementation runs ainvoke in parallel using asyncio.gather.

The default implementation of batch works well for IO bound runnables.

Subclasses should override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.

Parameters

inputs (List[Input]) – A list of inputs to the Runnable.
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) – 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 (Optional[Any]) – 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: Optional[Union[RunnableConfig, Sequence[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → AsyncIterator[Tuple[int, Union[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 (Optional[Union[RunnableConfig, Sequence[RunnableConfig]]]) – 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 (Optional[Any]) – 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, Union[Output, Exception]]]

async aget_relevant_documents(query: str, *, callbacks: Callbacks = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, **kwargs: Any) → List[Document]¶

Deprecated since version langchain-core==0.1.46: Use ainvoke instead.

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, config: Optional[RunnableConfig] = None, **kwargs: Any) → List[Document]¶

Asynchronously invoke the retriever to get relevant documents.

Main entry point for asynchronous retriever invocations.

Parameters

input (str) – The query string.
config (Optional[RunnableConfig]) – Configuration for the retriever. Defaults to None.
kwargs (Any) – Additional arguments to pass to the retriever.

Returns

List of relevant documents.

Return type

List[Document]

Examples:

await retriever.ainvoke("query")

as_tool(args_schema: Optional[Type[BaseModel]] = None, *, name: Optional[str] = None, description: Optional[str] = None, arg_types: Optional[Dict[str, Type]] = None) → BaseTool¶

Beta

This API is in beta and may change in the future.

Create a BaseTool from a Runnable.

as_tool will instantiate a BaseTool with a name, description, and args_schema from a Runnable. Where possible, schemas are inferred from runnable.get_input_schema. Alternatively (e.g., if the Runnable takes a dict as input and the specific dict keys are not typed), the schema can be specified directly with args_schema. You can also pass arg_types to just specify the required arguments and their types.

Parameters

args_schema (Optional[Type[BaseModel]]) – The schema for the tool. Defaults to None.
name (Optional[str]) – The name of the tool. Defaults to None.
description (Optional[str]) – The description of the tool. Defaults to None.
arg_types (Optional[Dict[str, Type]]) – A dictionary of argument names to types. Defaults to None.

Returns

A BaseTool instance.

Return type

BaseTool

Typed dict input:

from typing import List
from typing_extensions import TypedDict
from langchain_core.runnables import RunnableLambda

class Args(TypedDict):
    a: int
    b: List[int]

def f(x: Args) -> str:
    return str(x["a"] * max(x["b"]))

runnable = RunnableLambda(f)
as_tool = runnable.as_tool()
as_tool.invoke({"a": 3, "b": [1, 2]})

dict input, specifying schema via args_schema:

from typing import Any, Dict, List
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.runnables import RunnableLambda

def f(x: Dict[str, Any]) -> str:
    return str(x["a"] * max(x["b"]))

class FSchema(BaseModel):
    """Apply a function to an integer and list of integers."""

    a: int = Field(..., description="Integer")
    b: List[int] = Field(..., description="List of ints")

runnable = RunnableLambda(f)
as_tool = runnable.as_tool(FSchema)
as_tool.invoke({"a": 3, "b": [1, 2]})

dict input, specifying schema via arg_types:

from typing import Any, Dict, List
from langchain_core.runnables import RunnableLambda

def f(x: Dict[str, Any]) -> str:
    return str(x["a"] * max(x["b"]))

runnable = RunnableLambda(f)
as_tool = runnable.as_tool(arg_types={"a": int, "b": List[int]})
as_tool.invoke({"a": 3, "b": [1, 2]})

String input:

from langchain_core.runnables import RunnableLambda

def f(x: str) -> str:
    return x + "a"

def g(x: str) -> str:
    return x + "z"

runnable = RunnableLambda(f) | g
as_tool = runnable.as_tool()
as_tool.invoke("b")

New in version 0.2.14.

async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶

Default implementation of astream, which calls ainvoke. Subclasses should override this method if they support streaming output.

Parameters

input (Input) – The input to the Runnable.
config (Optional[RunnableConfig]) – The config to use for the Runnable. Defaults to None.
kwargs (Optional[Any]) – Additional keyword arguments to pass to the Runnable.

Yields

The output of the Runnable.

Return type

AsyncIterator[Output]

astream_events(input: Any, config: Optional[RunnableConfig] = None, *, version: Literal['v1', 'v2'], include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Any) → AsyncIterator[Union[StandardStreamEvent, CustomStreamEvent]]¶

Beta

This API is in beta and may change in the future.

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 evens 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 (Optional[RunnableConfig]) – 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 (Optional[Sequence[str]]) – Only include events from runnables with matching names.
include_types (Optional[Sequence[str]]) – Only include events from runnables with matching types.
include_tags (Optional[Sequence[str]]) – Only include events from runnables with matching tags.
exclude_names (Optional[Sequence[str]]) – Exclude events from runnables with matching names.
exclude_types (Optional[Sequence[str]]) – Exclude events from runnables with matching types.
exclude_tags (Optional[Sequence[str]]) – 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[Union[StandardStreamEvent, CustomStreamEvent]]

batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶

Default implementation runs invoke in parallel using a thread pool executor.

The default implementation of batch works well for IO bound runnables.

Subclasses should override this method if they can batch more efficiently; e.g., if the underlying Runnable uses an API which supports a batch mode.

Parameters

inputs (List[Input]) –
config (Optional[Union[RunnableConfig, List[RunnableConfig]]]) –
return_exceptions (bool) –
kwargs (Optional[Any]) –

Return type

List[Output]

batch_as_completed(inputs: Sequence[Input], config: Optional[Union[RunnableConfig, Sequence[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → Iterator[Tuple[int, Union[Output, Exception]]]¶

Run invoke in parallel on a list of inputs, yielding results as they complete.

Parameters

inputs (Sequence[Input]) –
config (Optional[Union[RunnableConfig, Sequence[RunnableConfig]]]) –
return_exceptions (bool) –
kwargs (Optional[Any]) –

Return type

Iterator[Tuple[int, Union[Output, Exception]]]

configurable_alternatives(which: ConfigurableField, *, default_key: str = 'default', prefix_keys: bool = False, **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶

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 (Union[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[Input, Output]

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: Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) → RunnableSerializable[Input, Output]¶

Configure particular Runnable fields at runtime.

Parameters: **kwargs (Union[ConfigurableField, ConfigurableFieldSingleOption, ConfigurableFieldMultiOption]) – A dictionary of ConfigurableField instances to configure.
Returns: A new Runnable with the fields configured.
Return type: RunnableSerializable[Input, Output]

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: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, **kwargs: Any) → List[Document]¶

Deprecated since version langchain-core==0.1.46: Use invoke instead.

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, config: Optional[RunnableConfig] = None, **kwargs: Any) → List[Document]¶

Invoke the retriever to get relevant documents.

Main entry point for synchronous retriever invocations.

Parameters

input (str) – The query string.
config (Optional[RunnableConfig]) – Configuration for the retriever. Defaults to None.
kwargs (Any) – Additional arguments to pass to the retriever.

Returns

List of relevant documents.

Return type

List[Document]

Examples:

retriever.invoke("query")

stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶

Default implementation of stream, which calls invoke. Subclasses should override this method if they support streaming output.

Parameters

input (Input) – The input to the Runnable.
config (Optional[RunnableConfig]) – The config to use for the Runnable. Defaults to None.
kwargs (Optional[Any]) – Additional keyword arguments to pass to the Runnable.

Yields

The output of the Runnable.

Return type

Iterator[Output]

to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶

Serialize the Runnable to JSON.

Returns: A JSON-serializable representation of the Runnable.
Return type: Union[SerializedConstructor, SerializedNotImplemented]

property client_options: ClientOptions¶

langchain_google_community.vertex_ai_search.VertexAISearchRetriever¶

`langchain_google_community.vertex_ai_search`.VertexAISearchRetriever¶