text: Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAleph AlphaAzureOpenAIBedrock EmbeddingsClarifaiCohereDashScopeDeepInfraElasticsearchEmbaasFake EmbeddingsGoogle Cloud Platform Vertex AI PaLMGPT4AllHugging Face HubInstructEmbeddingsJinaLlama-cppLocalAIMiniMaxModelScopeMosaicML embeddingsNLP CloudOpenAISageMaker Endpoint EmbeddingsSelf Hosted EmbeddingsSentence Transformers EmbeddingsSpacy EmbeddingTensorflowHubAgent toolkitsToolsVector storesGrouped by providerIntegrationsText embedding modelsSageMaker Endpoint EmbeddingsSageMaker Endpoint EmbeddingsLet's load the SageMaker Endpoints Embeddings class. The class can be used if you host, e.g. your own Hugging Face model on SageMaker.For instructions on how to do this, please see here. Note: In order to handle batched requests, you will need to adjust the return line in the predict_fn() function within the custom inference.py script:Change fromreturn {"vectors": sentence_embeddings[0].tolist()}to:return {"vectors": sentence_embeddings.tolist()}.pip3 install langchain boto3from typing import Dict, Listfrom langchain.embeddings import SagemakerEndpointEmbeddingsfrom langchain.embeddings.sagemaker_endpoint import EmbeddingsContentHandlerimport jsonclass ContentHandler(EmbeddingsContentHandler):    content_type = "application/json"    accepts = "application/json"    def transform_input(self, inputs: list[str], model_kwargs: Dict) -> bytes:        input_str = json.dumps({"inputs": inputs, **model_kwargs})        return