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 providerIntegrationsLLMsoctoaiOn this pageoctoaiOctoAI Compute Serviceâ€‹This example goes over how to use LangChain to interact with OctoAI LLM endpointsEnvironment setupâ€‹To run our example app, there are four simple steps to take:Clone the MPT-7B demo template to your OctoAI account by visiting https://octoai.cloud/templates/mpt-7b-demo then clicking "Clone Template." If you want to use a different LLM model, you can also containerize the model and make a custom OctoAI endpoint yourself, by following Build a Container from Python and Create a Custom Endpoint from a ContainerPaste your Endpoint URL in the code cell belowGet an API Token from your OctoAI account page.Paste your API key in in the code cell belowimport