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.

timeout#

Timeout for API calls in seconds.

Type:

Optional[int]

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 timeout: int | None = None#

param title_field: str | None = None#

param top_n: int = 10#

async acompress_documents(

documents: Sequence[Document],

query: str,

callbacks: Callbacks | None = None,

) → Sequence[Document]#

Async compress retrieved documents given the query context.

Parameters:

• documents (Sequence[Document]) – The retrieved documents.

• query (str) – The query context.

• callbacks (Optional[Callbacks]) – Optional callbacks to run during compression.

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]