Struct FastEmbedProvider 

pub struct FastEmbedProvider { /* private fields */ }

FastEmbed embedding provider

Generates embeddings locally using ONNX runtime. Models are downloaded on first use and cached in ~/.fastembed_cache/

Implementations

impl FastEmbedProvider

pub fn new() -> Result<Self>

Create a new FastEmbed provider with the default model (AllMiniLM)

pub fn with_model(model: FastEmbedModel) -> Result<Self>

Create a new FastEmbed provider with a specific model

pub fn from_model_name(name: &str) -> Result<Self>

Create from a model name string

Trait Implementations

impl Default for FastEmbedProvider

fn default() -> Self

Returns the “default value” for a type.

impl EmbeddingProvider for FastEmbedProvider

fn embed_documents<'life0, 'async_trait>( &'life0 self, texts: Vec<String>, ) -> Pin<Box<dyn Future<Output = Result<Vec<Vec<f32>>>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait,

Generate embeddings for multiple documents

fn dimensions(&self) -> usize

Get the embedding dimension size

fn model_name(&self) -> &str

Get the model name/identifier

fn provider_name(&self) -> &str

Get the provider name (e.g., “fastembed”, “openai”, “ollama”)

fn max_batch_size(&self) -> usize

Get the maximum batch size for embed_documents

fn health_check<'life0, 'async_trait>( &'life0 self, ) -> Pin<Box<dyn Future<Output = Result<bool>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait,

Check if the provider is available (API key set, server running, etc.)

fn embed_query<'life0, 'life1, 'async_trait>( &'life0 self, text: &'life1 str, ) -> Pin<Box<dyn Future<Output = Result<Vec<f32>>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait,

Generate embedding for a single query

fn embed_documents_batched<'life0, 'async_trait>( &'life0 self, texts: Vec<String>, ) -> Pin<Box<dyn Future<Output = Result<Vec<Vec<f32>>>> + Send + 'async_trait>>

where Self: 'async_trait, 'life0: 'async_trait,

Embed documents in batches, respecting max_batch_size