# 3) Load the model
        try:
            if isinstance(model, str):
                self.model = nlp.load(model)
            elif isinstance(model, NLUPipeline):
                self.model = model
            else:
                self.model = nlp.to_nlu_pipe(model)
        except Exception as exc:
            raise Exception("Failure loading model") from exc
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
[docs]    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Compute doc embeddings using a JohnSnowLabs transformer model.
        Args:
            texts: The list of texts to embed.
        Returns:
            List of embeddings, one for each text.
        """
        df = self.model.predict(texts, output_level="document")
        emb_col = None
        for c in df.columns:
            if "embedding" in c:
                emb_col = c
        return [vec.tolist() for vec in df[emb_col].tolist()]
[docs]    def embed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a JohnSnowLabs transformer model.
        Args:
            text: The text to embed.
        Returns:
            Embeddings for the text.
        """
        return self.embed_documents([text])[0]