Struct Embedder 

pub struct Embedder { /* private fields */ }

Implementations

impl Embedder

pub fn load(config: EmbedderConfig) -> Result<Self, EmbedError>

pub fn model_id(&self) -> &str

pub fn native_dim(&self) -> usize

pub fn dimension(&self) -> usize

pub fn provider_used(&self) -> &str

Provider serving inference: "cpu", "directml" or "coreml".

pub fn fallback_reason(&self) -> Option<&str>

Why an accelerator was replaced by CPU, when it was.

pub fn pipeline_id(&self) -> String

Stable fingerprint of everything that changes the produced vectors, so callers can detect when stored vectors need re-embedding. Fed by:

• the pipeline schema version of this crate’s embedding math;
• model id and native dimension;
• SHA-256 of model_q4.onnx, model_q4.onnx_data, tokenizer.json and config.json (absent file recorded as absent);
• the document and query prompt templates;
• pooling and normalization descriptions;
• Matryoshka truncate dimension and max token length;
• whether Gemma 2 modality sentinels are scrubbed.

Not fed by provider, thread count or batch size: those change only floating-point noise (see max_batch tolerance in the README), not the pipeline. Computed once on first call (hashes the model files) and cached; format rkpipe1-<32 hex>.

pub fn embed(&self, text: &str) -> Result<Vec<f32>, EmbedError>

pub fn embed_query(&self, text: &str) -> Result<Vec<f32>, EmbedError>

pub fn embed_batch<'a, I>(&self, texts: I) -> Result<Vec<Vec<f32>>, EmbedError>

where I: IntoIterator<Item = &'a str>,

Embeds documents in input order. Texts are tokenized together, ordered by length so chunks pad little, run max_batch rows per ONNX call with the attention mask honoured, then returned in input order.

pub fn embed_query_batch<'a, I>( &self, texts: I, ) -> Result<Vec<Vec<f32>>, EmbedError>

where I: IntoIterator<Item = &'a str>,

Query-prompt counterpart of Self::embed_batch.