Module onnx 

ONNX Runtime local embedding provider.

Provides local embedding inference using ONNX Runtime, eliminating the need for an external API. Supports sentence-transformer models such as all-MiniLM-L6-v2 exported to ONNX format.

Feature gating

When compiled without the onnx feature the module provides a stub that validates the model path but returns Error::Embedding from embed() and embed_batch().

When compiled with the onnx feature the module loads the ONNX session and a HuggingFace tokenizer, then performs real local inference with mean-pooling and L2 normalisation.

[features]
onnx = ["dep:ort", "dep:tokenizers", "dep:ndarray"]

[dependencies]
ort = { version = "2", optional = true }
tokenizers = { version = "0.21", optional = true, default-features = false }
ndarray = { version = "0.16", optional = true }

Example (stub)

use mnemo_core::embedding::onnx::OnnxEmbedding;
use mnemo_core::embedding::EmbeddingProvider;

// Will succeed only if the path exists on disk.
let provider = OnnxEmbedding::new("/models/all-MiniLM-L6-v2.onnx", 384)
    .expect("model path must exist");

assert_eq!(provider.dimensions(), 384);
assert_eq!(provider.model_path(), "/models/all-MiniLM-L6-v2.onnx");

Structs

OnnxEmbedding

ONNX-based local embedding provider.