Embeddings onnx

Development build. This page describes main, not a released package. The latest published Lodestar.Embeddings is 0.4.0 — read its documentation.

ONNX inference — Lodestar.Embeddings

One type, OnnxTextEmbedder: it runs a sentence-transformer model and gives you a vector per text. It is the only place in Lodestar where a model file is required, and the only place ONNX Runtime is referenced — that dependency is deliberately confined to this namespace so the rest of the package has none.

Why every example here is unexecuted

Weights are never committed to this repository. A running example would need a model of tens of megabytes, and decisions/0003 rules that out; tools/fetch_*.py pulls vocabularies against a pinned SHA-256 when they are needed, and weights are not among them.

So the fences on these pages compile against the packed package and are marked docs-run: skip, which is what that marker is for. The same exclusion is declared in the packaging sample, where OnnxTextEmbedder is one of its two documented exclusions.

Where the vectors come from, and where they go

This namespace produces vectors. Turning text into the token ids it wants is Lodestar.Embeddings.Tokenization; reducing a sequence of vectors to one is Lodestar.Embeddings.Pooling; searching a set of them is Lodestar.Embeddings.Search. This page is the middle step of four, and the only one that needs a file from outside. The other three are named without links here because their pages do not exist yet — they arrive with #226, #233 and #231, and a link written ahead of its target is a link that is broken until then.

Types

Type	What it is
OnnxTextEmbedder	Runs an ONNX sentence-transformer and returns vectors.

See also

• Semantic search with embeddings — the guide, end to end.
• Python → C# equivalence.