Embeddings

Reviewed by Proof · Updated

Definition

An embedding is a list of numbers that represents the meaning of a piece of text, so that similar texts get similar numbers.

Key takeaways

  • An embedding model turns text into a vector, often hundreds or thousands of numbers long.
  • Texts with similar meaning end up close together, even if they use different words.
  • Embeddings power semantic search, clustering, recommendations and the retrieval step of RAG.
  • Closeness is usually measured with cosine similarity.
  • luv13 doesn't serve an embeddings endpoint right now. POST /v1/embeddings returned 404 on 2026-09-30.

How they're used

  1. Run each document chunk through an embedding model and store the vectors.
  2. When a query comes in, embed it with the same model.
  3. Find the stored vectors closest to the query vector.
  4. Use those chunks, for example as context in a chat prompt.

Vectors from different embedding models aren't compatible. Embed your documents and your queries with the same model.

Cosine similarity

Cosine similarity compares the direction of two vectors. It ranges from -1 to 1, and higher means more alike. In practice you compare scores against each other, not against a fixed cutoff, because the typical range depends on the model.

Using embeddings with luv13

Because luv13 doesn't offer embeddings, you'd create them with a separate embedding model or service, then send the matching text to a luv13 chat model. See Retrieval-Augmented Generation for the full pattern and Listing Models for what luv13 serves today.

You can check the current status yourself. A 404 means the endpoint isn't there:

curl -i https://api.luv13.ai/v1/embeddings \
  -H "Authorization: Bearer $LUV13_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "luv13/glm-5.3-flash", "input": "hello"}'