text: Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleAzure OpenAIAzureML Online EndpointBananaBasetenBeamBedrockCerebriumAIChatGLMClarifaiCohereC TransformersDatabricksDeepInfraForefrontAIGoogle Cloud Platform Vertex AI PaLMGooseAIGPT4AllHugging Face HubHugging Face Local PipelinesHuggingface TextGen InferenceJSONFormerKoboldAI APILlama-cppCaching integrationsManifestModalMosaicMLNLP CloudoctoaiOpenAIOpenLLMOpenLMPetalsPipelineAIPredibasePrediction GuardPromptLayer OpenAIRELLMReplicateRunhouseSageMakerEndpointStochasticAITextGenTongyi QwenWriterMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesGrouped by providerIntegrationsLLMsSageMakerEndpointOn this pageSageMakerEndpointAmazon SageMaker is a system that can build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows.This notebooks goes over how to use an LLM hosted on a SageMaker endpoint.pip3 install langchain boto3Set upâ€‹You have to set up following required parameters of the SagemakerEndpoint call:endpoint_name: The name of the endpoint from the deployed Sagemaker model.
  Must be unique within an AWS Region.credentials_profile_name: The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which
  has either access keys or role information specified.
  If not specified, the default credential profile or, if on an EC2 instance,
  credentials from IMDS will be used.