embeddings_list: List[List[float]] = []
        for prompt in input:
            embeddings = self._process_emb_response(prompt)
            embeddings_list.append(embeddings)
        return embeddings_list
[docs]    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Embed documents using a Ollama deployed embedding model.
        Args:
            texts: The list of texts to embed.
        Returns:
            List of embeddings, one for each text.
        """
        instruction_pairs = [f"{self.embed_instruction}{text}" for text in texts]
        embeddings = self._embed(instruction_pairs)
        return embeddings
[docs]    def embed_query(self, text: str) -> List[float]:
        """Embed a query using a Ollama deployed embedding model.
        Args:
            text: The text to embed.
        Returns:
            Embeddings for the text.
        """
        instruction_pair = f"{self.query_instruction}{text}"
        embedding = self._embed([instruction_pair])[0]
        return embedding