try:
            t = res.json()
            embeddings = t["embeddings"]
        except requests.exceptions.JSONDecodeError as e:
            raise ValueError(
                f"Error raised by inference API: {e}.\nResponse: {res.text}"
            )
        return embeddings
[docs]    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Embed documents using a Deep Infra deployed embedding model.
        Args:
            texts: The list of texts to embed.
        Returns:
            List of embeddings, one for each text.
        """
        instruction_pairs = [f"{self.query_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 Deep Infra 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
By Harrison Chase
    
      © Copyright 2023, Harrison Chase.
      
  Last updated on Jun 16, 2023.
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/embeddings/deepinfra.html