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Two namespaces that work together. embeddings turns text into vectors you can compare. rerank takes a query and a list of candidates and reorders them by relevance.

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Embedding text

Vectors come back in input order, so index i of the result matches index i of the input. Batch rather than looping: one call with fifty strings is far cheaper than fifty calls.

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Options

Leave normalize on unless you have a reason. With it off, similarity scores are not comparable across texts of different lengths.

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Reranking

Reranking is a different model from embedding. An embedding model encodes each text once and compares vectors, which is fast and approximate. A reranker reads the query and the candidate together, which is slower and much more accurate. The usual shape is both: retrieve widely with embeddings, then rerank the top handful.

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Where this fits

Most people reach for these through RAG, which does retrieval, reranking, and generation in one session. Use the namespaces directly when you want the pieces separately: semantic search with no generation, deduplication, clustering, or classification by nearest neighbour.

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Models

Embedding and reranking models are small, often tens of megabytes rather than gigabytes. That makes them cheap to keep resident alongside a language model.