Struct txtai::embeddings::Embeddings

pub struct Embeddings { /* private fields */ }

Embeddings definition

Implementations

impl Embeddings

Embeddings implementation

pub fn new() -> Embeddings

Creates an Embeddings instance.

pub fn with_url(url: &str) -> Embeddings

Creates an Embeddings instance.

Arguments

• url - API url

pub fn with_url_token(url: &str, token: &str) -> Embeddings

Creates an Embeddings instance.

Arguments

• url - API url
• token - API token

pub async fn query( &self, query: &str, limit: i32, weights: Option<f32>, index: Option<&str> ) -> APIResponse

Runs an Embeddings search. Returns Response. This method allows callers to customize the serialization of the response.

Arguments

• query - query text
• limit - maximum results
• weights - hybrid score weights, if applicable
• index - index name, if applicable

pub async fn search( &self, query: &str, limit: i32, weights: Option<f32>, index: Option<&str> ) -> SearchResults

Finds documents in the embeddings model most similar to the input query. Returns a list of {id: value, score: value} sorted by highest score, where id is the document id in the embeddings model.

Arguments

• query - query text
• limit - maximum results
• weights - hybrid score weights, if applicable
• index - index name, if applicable

pub async fn batchsearch( &self, queries: &Vec<&str>, limit: i32, weights: Option<f32>, index: Option<&str> ) -> SearchResultsBatch

Finds documents in the embeddings model most similar to the input queries. Returns a list of {id: value, score: value} sorted by highest score per query, where id is the document id in the embeddings model.

Arguments

• queries - queries text
• limit - maximum results
• weights - hybrid score weights, if applicable
• index - index name, if applicable

pub async fn add<T: Serialize>(&self, documents: &Vec<T>) -> APIResponse

Adds a batch of documents for indexing.

Arguments

• documents - list of {id: value, text: value}

pub async fn index(&self) -> APIResponse

Builds an embeddings index for previously batched documents.

pub async fn upsert(&self) -> APIResponse

Runs an embeddings upsert operation for previously batched documents.

pub async fn delete(&self, ids: &Vec<&str>) -> Ids

Deletes from an embeddings index. Returns list of ids deleted.

Arguments

• ids - list of ids to delete

pub async fn reindex( &self, config: HashMap<&str, &str>, function: Option<&str> ) -> APIResponse

Recreates this embeddings index using config. This method only works if document content storage is enabled.

pub async fn count(&self) -> Count

Total number of elements in this embeddings index.

pub async fn similarity(&self, query: &str, texts: &Vec<&str>) -> IndexResults

Computes the similarity between query and list of text. Returns a list of {id: value, score: value} sorted by highest score, where id is the index in texts.

Arguments

• query - query text
• texts - list of text

pub async fn batchsimilarity( &self, queries: &Vec<&str>, texts: &Vec<&str> ) -> IndexResultsBatch

Computes the similarity between list of queries and list of text. Returns a list of {id: value, score: value} sorted by highest score per query, where id is the index in texts.

Arguments

• queries - queries text
• texts - list of text

pub async fn transform(&self, text: &str) -> Embedding

Transforms text into an embeddings array.

Arguments

• text - input text

pub async fn batchtransform(&self, texts: &str) -> EmbeddingBatch

Transforms list of text into embeddings arrays.

Arguments

• texts - lists of text