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 providerIntegrationsLLMsStochasticAIStochasticAIStochastic Acceleration Platform aims to simplify the life cycle of a Deep Learning model. From uploading and versioning the model, through training, compression and acceleration to putting it into production.This example goes over how to use LangChain to interact with StochasticAI models.You have to get the API_KEY and the API_URL here.from getpass import getpassSTOCHASTICAI_API_KEY = getpass()     Â·Â·Â·Â·Â·Â·Â·Â·import osos.environ["STOCHASTICAI_API_KEY"] = STOCHASTICAI_API_KEYYOUR_API_URL = getpass()     Â·Â·Â·Â·Â·Â·Â·Â·from langchain.llms import StochasticAIfrom langchain import PromptTemplate,