Struct NonRankingEmbedder 

pub struct NonRankingEmbedder;

Zero-vector embedder: stores and retrieves facts, ranks nothing.

Every text embeds to the same all-zero vector, so cosine similarity is always 1.0 (or undefined) and recall degenerates to insertion order. It exists so the capability-shaped constructors — MemorySqlite::open, MemoryQdrant::connect, MemoryPgvector::connect — can hand back a working store with no embedding model present.

It is not a semantic substrate. Two adopters shipped production-shaped vector tiers on it and measured substring-grade recall before noticing. The safeguards, all of which the old DummyEmbedder name lacked:

• the name says what it does,
• the first Embedder::embed call logs a tracing::warn! once, and
• Embedder::is_ranking answers false, so a store built on it reports klieo_core::memory::RecallSemantics::NonRanking and a pipeline that asserts semantic recall at startup fails there instead of in a measurement.

Trait Implementations

impl Embedder for NonRankingEmbedder

fn dimension(&self) -> usize

Embedding dimensionality. Must be constant for a given Embedder instance — long-term memory backends reject vectors of the wrong length at runtime.

fn embed<'life0, 'life1, 'async_trait>( &'life0 self, texts: &'life1 [String], ) -> Pin<Box<dyn Future<Output = Result<Vec<Vec<f32>>, MemoryError>> + Send + 'async_trait>>

where 'life0: 'async_trait, 'life1: 'async_trait, NonRankingEmbedder: 'async_trait,

Compute one embedding per input text.

fn is_ranking(&self) -> bool

Whether distinct inputs produce distinct directions, i.e. whether a vector store wired with this embedder actually ranks by relevance.