Crate rightkit_embed 

Local embeddings and a persistent cosine vector store.

Feature boundaries: inference (ONNX text embedder) and vector (SQLite-backed cosine store). Both are default-on; a vector-only build does not pull ort or tokenizers.

Structs

AcceleratorParity

Result of the load-time vector parity check an accelerator passed; see Embedder::accelerator_parity.

CoreMlUnits

The compute units of each Core ML function (embed_32 .. embed_512).

CoreMlUnitsParseError

An unreadable compute-unit or unit-map specification.

Embedder

EmbedderConfig

LoadTimings

Where an E40 load spent its time; see Embedder::load_timings. Phases are wall-clock durations of the work as Embedder::load ran it; they nest (session_creates happen inside the probe and the parity check) and do not add up to total exactly.

Nearest

SessionCreate

One graph build during an E40 load.

VectorStore

Enums

CoreMlComputeUnit

Which Core ML compute units run one function (MLComputeUnits).

EmbedError

ExecutionProvider

Where inference runs.

ParityCacheOutcome

What the load-time parity check did with the opt-in verdict cache (EmbedderConfig::with_parity_cache_dir).

VectorError

Constants

ACCELERATOR_PARITY_MIN_COSINE

An accelerator is admitted only if every parity prompt’s native-dimension vector has at least this cosine similarity to the CPU provider’s vector for the same prompt. Two correct runs of the same Q4 graph agree at about 0.999; a DirectML run of EmbeddingGemma 2 Q4 measured 0.58-0.72.

DEFAULT_MAX_BATCH

Default rows per ONNX call for embed_batch / embed_query_batch.

DEFAULT_VECTOR_DIM

EMBEDDINGGEMMA2_COREML_MODEL_ID

EmbeddingGemma 2 Core ML build (macOS 15+): EmbeddingGemma2Text.mlpackage (one fixed-shape function per length 32, 64, 128, 256, 512) and a host-side bf16 token table, run through the Core ML framework directly (an .mlpackage cannot be loaded through ONNX Runtime). Selected when the model directory holds that layout. Same weights, different numerics from the Q4 and E40 models, so a different pipeline id.

EMBEDDINGGEMMA2_E40_MODEL_ID

EmbeddingGemma 2 E40 export: fp16, five fixed-length graphs and a host-side token table (embed_tokens.f16.npy), run on DirectML (Windows) or CPU. Selected when the model directory holds that layout. Same weights as the Q4 model, different numerics, so a different pipeline id.

EMBEDDINGGEMMA2_MODEL_ID

EmbeddingGemma 2 text-only Q4 export (b64, accuracy level 4). Selected when the model directory’s top-level config.json model_type is embedding_gemma2.

EMBEDDINGGEMMA_MODEL_ID

NEURAL_ENGINE_MAX_TOKENS

Longest graph (in tokens) the default map sends to the Neural Engine; longer graphs use the GPU.

Functions

cosine_similarity

truncate_and_normalize