Struct HashEmbedder 

pub struct HashEmbedder { /* private fields */ }

Zero-dependency hash-based embedder.

Produces deterministic embeddings using FNV-1a hashing. Not semantic — captures lexical overlap only — but always available and extremely fast.

Examples

use frankensearch_embed::HashEmbedder;

let embedder = HashEmbedder::default_384();
let vec = embedder.embed_sync("hello world");
assert_eq!(vec.len(), 384);

Implementations

impl HashEmbedder

pub fn new(dimension: usize, algorithm: HashAlgorithm) -> Self

Create a hash embedder with the given dimension and algorithm.

Panics

Panics if dimension is zero.

pub fn default_384() -> Self

Default FNV-modular embedder with 384 dimensions.

pub fn default_256() -> Self

Default FNV-modular embedder with 256 dimensions (fast-tier compatibility).

pub fn jl_384(seed: u64) -> Self

JL-projection embedder with 384 dimensions and the given seed.

pub fn embed_sync(&self, text: &str) -> Vec<f32>

Synchronous embedding (no async overhead needed for ~0.07ms).

Trait Implementations

impl Clone for HashEmbedder

fn clone(&self) -> HashEmbedder

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for HashEmbedder

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

Formats the value using the given formatter.

impl Embedder for HashEmbedder

fn embed<'a>(&'a self, _cx: &'a Cx, text: &'a str) -> SearchFuture<'a, Vec<f32>>

Embed a single text string into a vector of f32 floats.

fn embed_batch<'a>( &'a self, _cx: &'a Cx, texts: &'a [&'a str], ) -> SearchFuture<'a, Vec<Vec<f32>>>

Embed a batch of text strings.

fn dimension(&self) -> usize

The dimensionality of embedding vectors produced by this model.

fn id(&self) -> &str

A unique, stable identifier for this embedder.

fn model_name(&self) -> &str

Human-readable model name.

fn is_semantic(&self) -> bool

Whether this embedder produces semantically meaningful vectors.

fn category(&self) -> ModelCategory

The speed/quality category of this embedder.

fn is_ready(&self) -> bool

Whether this embedder is loaded and operational.

fn tier(&self) -> ModelTier

Default progressive tier assignment.

fn supports_mrl(&self) -> bool

Whether this model supports Matryoshka Representation Learning (dimension truncation for faster search with controlled quality loss).

fn truncate_embedding( &self, embedding: &[f32], target_dim: usize, ) -> Result<Vec<f32>, SearchError>

Truncate and re-normalize embedding to target_dim.