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Moss embeds text on-device with built-in models (moss-minilm, moss-mediumlm). If you already generate embeddings elsewhere - a proprietary model, a hosted embedding API, or a shared pipeline across services - use model_id="custom" to supply your own vectors. Moss indexes and searches them; it does not load a local model.

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How it works

  • At index time, every document must carry its own embedding. With model_id="custom", Moss does not embed for you. (If you omit model_id and every document has an embedding, Moss infers "custom" automatically; mixed documents are rejected.)
  • At query time, you must pass the query vector via QueryOptions.embedding, because there is no local model to embed the query text.
  • All vectors must share the same dimensionality.
  • Sessions support custom embeddings too: open the session with model_id="custom", set each document’s embedding, and pass a query embedding (QueryOptions.embedding) on every query.
With model_id="custom", adding a document without .embedding, or querying without QueryOptions.embedding, raises a ValueError.

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Implementation

Runnable, per-language examples (cloud index and session) live in the SDK guides:

Local embeddings

Use the built-in on-device models.

Hybrid search

Blend semantic and keyword scoring.