RetrieveResultItem#

class langchain_community.retrievers.kendra.RetrieveResultItem[source]#

Bases: ResultItem

Retrieve API result item.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

param Content: str | None [Required]#: The content of the item.

param DocumentAttributes: List[DocumentAttribute] | None = []#: The document attributes.

param DocumentId: str | None [Required]#: The document ID.

param DocumentTitle: str | None [Required]#: The document title.

param DocumentURI: str | None [Required]#: The document URI.

param Id: str | None [Required]#: The ID of the relevant result item.

param ScoreAttributes: dict | None [Required]#: The kendra score confidence

get_additional_metadata() → dict#

Document additional metadata dict. This returns any extra metadata except these:

result_id

document_id

source

title

excerpt

document_attributes

Return type:: dict

get_document_attributes_dict() → Dict[str, str | int | List[str] | None]#

Document attributes dict.

Return type:: Dict[str, str | int | List[str] | None]

get_excerpt() → str[source]#

Document excerpt or passage original content as retrieved by Kendra.

Return type:: str

get_score_attribute() → str#

Document Score Confidence

Return type:: str

get_title() → str[source]#

Document title.

Return type:: str

to_doc(page_content_formatter: ~typing.Callable[[~langchain_community.retrievers.kendra.ResultItem], str] = <function combined_text>) → Document#

Converts this item to a Document.

Parameters:: page_content_formatter (Callable[[ResultItem], str])
Return type:: Document