Struct KnowledgeBaseVectorSearchConfiguration 

#[non_exhaustive]
pub struct KnowledgeBaseVectorSearchConfiguration {
    pub number_of_results: Option<i32>,
    pub override_search_type: Option<SearchType>,
    pub filter: Option<RetrievalFilter>,
    pub implicit_filter_configuration: Option<ImplicitFilterConfiguration>,
    pub reranking_configuration: Option<VectorSearchRerankingConfiguration>,
}

The configuration details for returning the results from the knowledge base vector search.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

number_of_results: Option<i32>

The number of text chunks to retrieve; the number of results to return.

override_search_type: Option<SearchType>

By default, Amazon Bedrock decides a search strategy for you. If you're using an Amazon OpenSearch Serverless vector store that contains a filterable text field, you can specify whether to query the knowledge base with a HYBRID search using both vector embeddings and raw text, or SEMANTIC search using only vector embeddings. For other vector store configurations, only SEMANTIC search is available.

filter: Option<RetrievalFilter>

Specifies the filters to use on the metadata fields in the knowledge base data sources before returning results.

implicit_filter_configuration: Option<ImplicitFilterConfiguration>

Configuration for implicit filtering in Knowledge Base vector searches. This allows the system to automatically apply filters based on the query context without requiring explicit filter expressions.

reranking_configuration: Option<VectorSearchRerankingConfiguration>

Configuration for reranking search results in Knowledge Base vector searches. Reranking improves search relevance by reordering initial vector search results using more sophisticated relevance models.

Implementations

impl KnowledgeBaseVectorSearchConfiguration

pub fn number_of_results(&self) -> Option<i32>

The number of text chunks to retrieve; the number of results to return.

pub fn override_search_type(&self) -> Option<&SearchType>

By default, Amazon Bedrock decides a search strategy for you. If you're using an Amazon OpenSearch Serverless vector store that contains a filterable text field, you can specify whether to query the knowledge base with a HYBRID search using both vector embeddings and raw text, or SEMANTIC search using only vector embeddings. For other vector store configurations, only SEMANTIC search is available.

pub fn filter(&self) -> Option<&RetrievalFilter>

Specifies the filters to use on the metadata fields in the knowledge base data sources before returning results.

pub fn implicit_filter_configuration( &self, ) -> Option<&ImplicitFilterConfiguration>

Configuration for implicit filtering in Knowledge Base vector searches. This allows the system to automatically apply filters based on the query context without requiring explicit filter expressions.

pub fn reranking_configuration( &self, ) -> Option<&VectorSearchRerankingConfiguration>

Configuration for reranking search results in Knowledge Base vector searches. Reranking improves search relevance by reordering initial vector search results using more sophisticated relevance models.

impl KnowledgeBaseVectorSearchConfiguration

pub fn builder() -> KnowledgeBaseVectorSearchConfigurationBuilder

Creates a new builder-style object to manufacture KnowledgeBaseVectorSearchConfiguration.

Trait Implementations

impl Clone for KnowledgeBaseVectorSearchConfiguration

fn clone(&self) -> KnowledgeBaseVectorSearchConfiguration

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for KnowledgeBaseVectorSearchConfiguration

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq for KnowledgeBaseVectorSearchConfiguration

fn eq(&self, other: &KnowledgeBaseVectorSearchConfiguration) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for KnowledgeBaseVectorSearchConfiguration