This also uses a fixed embedding which will not be compatible with all machine learning projects that people will want to use a vector database for. The chosen embedding only supports text so making an image search for example wouldn't be possible to do.
Most vector databases are using some local or external provider to get the embeddings and then using some storage engine to store and retreive the embeddings. Whether it's pgvector leaning on postgresql, or chromadb on sqlite, or pinecone originally being on rocksdb (I believe they've now built their own engine).
This is no different, and is still in it's infancy, so one presumes they might add support for other methods of getting the embeddings much as chromadb has.
retrieve != query
this project is extremely simplistic in regards to its vector search tech. pgvector is an open source implementation of an _index_ (multiple algos actually), this uses Cloudflare's completely proprietary index with a single call.