"/pipeline"
            "/feature-extraction"
            f"/{self.model_name}"
        )
    @property
    def _headers(self) -> dict:
        return {"Authorization": f"Bearer {self.api_key}"}
[docs]    def embed_documents(self, texts: List[str]) -> List[List[float]]:
        """Get the embeddings for a list of texts.
        Args:
            texts (Documents): A list of texts to get embeddings for.
        Returns:
            Embedded texts as List[List[float]], where each inner List[float]
                corresponds to a single input text.
        Example:
            .. code-block:: python
                from langchain.embeddings import HuggingFaceInferenceAPIEmbeddings
                hf_embeddings = HuggingFaceInferenceAPIEmbeddings(
                    api_key="your_api_key",
                    model_name="sentence-transformers/all-MiniLM-l6-v2"
                )
                texts = ["Hello, world!", "How are you?"]
                hf_embeddings.embed_documents(texts)
        """
        response = requests.post(
            self._api_url,
            headers=self._headers,
            json={
                "inputs": texts,
                "options": {"wait_for_model": True, "use_cache": True},
            },
        )
        return response.json()
[docs]    def embed_query(self, text: str) -> List[float]:
        """Compute query embeddings using a HuggingFace transformer model.
        Args:
            text: The text to embed.
        Returns:
            Embeddings for the text.
        """
        return self.embed_documents([text])[0]