Expand description
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§
- Accelerator
Parity - Result of the load-time vector parity check an accelerator passed; see
Embedder::accelerator_parity. - Embedder
- Embedder
Config - Nearest
- Vector
Store
Enums§
- Embed
Error - Execution
Provider - Where inference runs.
- Vector
Error
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 - EMBEDDINGGEMM
A2_ MODEL_ ID - EmbeddingGemma 2 text-only Q4 export (b64, accuracy level 4). Selected when
the model directory’s top-level
config.jsonmodel_typeisembedding_gemma2. - EMBEDDINGGEMMA_
MODEL_ ID