Switching models when running locally is fairly easy - as long as you have them downloaded you can switch them in and out with a just a config setting - cant quite remember, but you may need to rebuild the vectorstore when switching though.

LangChain has the embeddings for major providers:

  def build_vectorstore(docs):
    """
    Create vectorstore from documents using configured embedding model.
    """
    # Choose embedding model
    if cfg.EMBED_MODEL.lower() == "openai":
        embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
    elif cfg.EMBED_MODEL.lower() == "huggingface":
        from langchain_community.embeddings import HuggingFaceEmbeddings
        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
    elif cfg.EMBED_MODEL.lower() == "nomic-embed-text":
        from langchain_ollama import OllamaEmbeddings
        embeddings = OllamaEmbeddings(model=cfg.EMBED_MODEL)