New embedding models support queries, so you don’t need to hallucinate a document before finding the nearest neighbor. Curious how it compares to this approach since you’d get to skip the LLM altogether.
What does it mean that an "embedding model supports queries"? An embedding model maps text to embedding vectors. You can always perform queries with such embedding vectors against a stored set of embeddings.
