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 providerIntegrationsLLMsAnyscaleAnyscaleAnyscale is a fully-managed Ray platform, on which you can build, deploy, and manage scalable AI and Python applicationsThis example goes over how to use LangChain to interact with Anyscale service. It will send the requests to Anyscale Service endpoint, which is concatenate ANYSCALE_SERVICE_URL and ANYSCALE_SERVICE_ROUTE, with a token defined in ANYSCALE_SERVICE_TOKENimport osos.environ["ANYSCALE_SERVICE_URL"] = ANYSCALE_SERVICE_URLos.environ["ANYSCALE_SERVICE_ROUTE"] = ANYSCALE_SERVICE_ROUTEos.environ["ANYSCALE_SERVICE_TOKEN"] = ANYSCALE_SERVICE_TOKENfrom langchain.llms import Anyscalefrom langchain import PromptTemplate, LLMChaintemplate = """Question: {question}Answer: Let's think step by step."""prompt = PromptTemplate(template=template, input_variables=["question"])llm = Anyscale()llm_chain