text: = json.dumps({"inputs": inputs, **model_kwargs})        return input_str.encode("utf-8")    def transform_output(self, output: bytes) -> List[List[float]]:        response_json = json.loads(output.read().decode("utf-8"))        return response_json["vectors"]content_handler = ContentHandler()embeddings = SagemakerEndpointEmbeddings(    # endpoint_name="endpoint-name",    # credentials_profile_name="credentials-profile-name",    endpoint_name="huggingface-pytorch-inference-2023-03-21-16-14-03-834",    region_name="us-east-1",    content_handler=content_handler,)query_result = embeddings.embed_query("foo")doc_results = embeddings.embed_documents(["foo"])doc_resultsPreviousOpenAINextSelf Hosted EmbeddingsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.