Embeddings as an I/O schema are just text-in, a list of numbers out. There are very few embedding models which require enough preprocessing to warrant an abstraction. (A soft example is the new nomic-embed-text-v1, which requires adding prefix annotations: https://huggingface.co/nomic-ai/nomic-embed-text-v1 )
This submission only about I/O schema: the embeddings themselves are dependent on the model, and since OpenAI's models are closed source no one can reproduce them.
No direct embedding model can be cross-compatable. (exception: constrastive learning models like CLIP)
Probably not, embedding vectors aren't conpatible across different embedding models, and other tools presenting OAI-compatible APIs don't use OAI-compatible embedding models (e.g., oobabooga lets you configure different embeddings models, but none of them produce compatible vectors to the OAI ones.)
