Struct SearchEngineBuilder 

pub struct SearchEngineBuilder<K, D = u32, T = DefaultTokenizer> { /* private fields */ }

A consuming builder for SearchEngine. K is the type of the document id, D is the type of the token embedder and T is the type of the tokenizer.

Implementations

impl<K, D, T> SearchEngineBuilder<K, D, T>

where K: Hash + Eq + Clone, D: TokenEmbedder, D::EmbeddingSpace: Eq + Hash + Clone, T: Tokenizer + Sync,

pub fn with_avgdl(avgdl: f32) -> SearchEngineBuilder<K, D, T>

where T: Default,

Constructs a new SearchEngineBuilder with the given average document length. Use this if you know the average document length in advance. If you don’t, but you have your full corpus ahead of time, use with_documents or with_corpus instead.

If you have neither the full corpus nor a sample of it, you can configure the embedder to disregard document length by setting b to 0.0. In this case, it doesn’t matter what value you pass to with_avgdl.

The average document length is the average number of tokens in a document from your corpus; if you need access to this value, you can construct an Embedder and call avgdl on it.

pub fn with_tokenizer_and_documents( tokenizer: T, documents: impl IntoIterator<Item = impl Into<Document<K>>>, ) -> SearchEngineBuilder<K, D, T>

Constructs a new SearchEngineBuilder with the given documents. The search engine will fit to the given documents, using the given tokenizer. When you call build, the builder will pre-populate the search engine with the given documents, and pass on the tokenizer.

pub fn with_tokenizer_and_corpus( tokenizer: T, corpus: impl IntoIterator<Item = impl Into<String>>, ) -> SearchEngineBuilder<u32, D, T>

Constructs a new SearchEngineBuilder with the corpus. The search engine will fit to the given corpus, using the given tokenizer. When you call build, the builder will pre-populate the search engine with the given corpus, and pass on the tokenizer. This function will automatically generate u32 ids for each entry in your corpus.

pub fn tokenizer(self, tokenizer: T) -> Self

Sets the tokenizer of the embedder.

pub fn k1(self, k1: f32) -> Self

Sets the k1 parameter of the embedder.

pub fn b(self, b: f32) -> Self

Sets the b parameter of the embedder.

pub fn avgdl(self, avgdl: f32) -> Self

Overrides the average document length of the embedder.

pub fn build(self) -> SearchEngine<K, D, T>

Builds the search engine.

impl<K, D> SearchEngineBuilder<K, D, DefaultTokenizer>

where K: Hash + Eq + Clone, D: TokenEmbedder, D::EmbeddingSpace: Eq + Hash + Clone,

pub fn with_documents( language_mode: impl Into<LanguageMode>, documents: impl IntoIterator<Item = impl Into<Document<K>>>, ) -> Self

Constructs a new SearchEngineBuilder with the given documents. The search engine will fit to the given documents, using the default tokenizer configured with the given language mode. When you call build, the builder will pre-populate the search engine with the given documents, and pass on the tokenizer.

pub fn with_corpus( language_mode: impl Into<LanguageMode>, corpus: impl IntoIterator<Item = impl Into<String>>, ) -> SearchEngineBuilder<u32, D, DefaultTokenizer>

Constructs a new SearchEngineBuilder with the corpus. The search engine will fit to the given corpus, using the default tokenizer configured with the given language mode. When you call build, the builder will pre-populate the search engine with the given corpus and pass on the tokenizer. This function will automatically generate u32 ids for each entry in your corpus.

pub fn language_mode(self, language_mode: impl Into<LanguageMode>) -> Self

Sets the tokenizer to the default tokenizer with the given language mode.