VertexAIRank#

class langchain_google_community.vertex_rank.VertexAIRank[source]#

Bases: BaseDocumentCompressor

Initializes the Vertex AI Ranker with configurable parameters.

Inherits from BaseDocumentCompressor for document processing and validation features, respectively.

project_id#

Google Cloud project ID

Type:

str

location_id#

Location ID for the ranking service.

Type:

str

ranking_config#

Required. The name of the rank service config, such as default_config. It is set to default_config by default if unspecified.

Type:

str

model#

The identifier of the model to use. It is one of:

  • semantic-ranker-512@latest: Semantic ranking model with maximum input token size 512.

It is set to semantic-ranker-512@latest by default if unspecified.

Type:

str

top_n#

The number of results to return. If this is unset or no bigger than zero, returns all results.

Type:

int

ignore_record_details_in_response#

If true, the response will contain only record ID and score. By default, it is false, the response will contain record details.

Type:

bool

id_field#

Specifies a unique document metadata field

Type:

Optional[str]

to use as an id.
title_field#

Specifies the document metadata field

Type:

Optional[str]

to use as title.
credentials#

Google Cloud credentials object.

Type:

Optional[Credentials]

credentials_path#

Path to the Google Cloud service

Type:

Optional[str]

account credentials file.

Constructor for VertexAIRanker, allowing for specification of ranking configuration and initialization of Google Cloud services.

The parameters accepted are the same as the attributes listed above.

param client: Any = None#
param credentials: Credentials | None = None#
param credentials_path: str | None = None#
param id_field: str | None = None#
param ignore_record_details_in_response: bool = False#
param location_id: str = 'global'#
param model: str = 'semantic-ranker-512@latest'#
param project_id: str = None#
param ranking_config: str = 'default_config'#
param title_field: str | None = None#
param top_n: int = 10#
async acompress_documents(documents: Sequence[Document], query: str, callbacks: list[BaseCallbackHandler] | BaseCallbackManager | None = None) → Sequence[Document]#

Async compress retrieved documents given the query context.

Parameters:
Returns:

The compressed documents.

Return type:

Sequence[Document]

compress_documents(documents: Sequence[Document], query: str, callbacks: list[BaseCallbackHandler] | BaseCallbackManager | None = None) → Sequence[Document][source]#

Compresses documents using Vertex AI’s rerank API.

Parameters:

  • documents (Sequence[Document]) – List of Document instances to compress.

  • query (str) – Query string to use for compressing the documents.

  • callbacks (list[BaseCallbackHandler] | BaseCallbackManager | None) – Callbacks to execute during compression (not used here).

Returns:

A list of Document instances, compressed.

Return type:

Sequence[Document]