ExtensionsAI 0.1.0 generation

Lodestar.Extensions.AI 0.1.0. This page is frozen at that release. Read the current documentation for what main says now. A link to a decision or a migration page follows main, and leaves the archive.

Embedding generation — Lodestar.Extensions.AI

One type, OnnxEmbeddingGenerator: it presents the ONNX embedding path as Microsoft.Extensions.AI's IEmbeddingGenerator<string, Embedding<float>>, so a consumer written against that abstraction — a Semantic Kernel pipeline, a Microsoft.Extensions.AI chain, anything that takes the interface — can hold Lodestar embeddings without knowing Lodestar.

This package adds no arithmetic. Every vector it returns comes from OnnxTextEmbedder.EmbedBatch, unchanged, so what it owes is the interface contract and not a numerical one. That is why it carries no oracle corpus of its own: its suite asserts that its output is identical to the embedder's, and the embedder's own frozen corpora say what those numbers are.

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 arrangement Lodestar.Onnx makes, and for the same reason.

Where this sits

Lodestar.Extensions.AI is the second member of the satellite tier, after Lodestar.Onnx. It exists because a core package carries no external dependency and an external dependency earns its own package, named for it — decisions/0076. A caller who wants embeddings and not the Microsoft.Extensions.AI abstractions takes Lodestar.Onnx alone and restores nothing extra.

Types

Type	What it is
OnnxEmbeddingGenerator	Presents an OnnxTextEmbedder as an IEmbeddingGenerator<string, Embedding<float>>.

See also

• ONNX inference — the package this one adapts.
• Semantic search with embeddings — the chain it sits in.
• Python → C# equivalence.