Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleArceeAzure MLAzure OpenAIBaidu QianfanBananaBasetenBeamBedrockBittensorCerebriumAIChatGLMClarifaiCohereC TransformersCTranslate2DatabricksDeepInfraDeepSparseEden AIFireworksForefrontAIGCP Vertex AIGooseAIGPT4AllGradientHugging Face HubHugging Face Local PipelinesHuggingface TextGen InferenceJavelin AI Gateway TutorialJSONFormerKoboldAI APILlama.cppLLM Caching integrationsManifestMinimaxModalMosaicMLNLP CloudOctoAIOllamaOpaquePromptsOpenAIOpenLLMOpenLMPetalsPipelineAIPredibasePrediction GuardPromptLayer OpenAIRELLMReplicateRunhouseSageMakerEndpointStochasticAINebula (Symbl.ai)TextGenTitan TakeoffTogether AITongyi QwenvLLMWriterXorbits Inference (Xinference)YandexGPTChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsLLMsAnyscaleAnyscaleAnyscale 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 Endpoint. import osos.environ["ANYSCALE_API_BASE"] = ANYSCALE_API_BASEos.environ["ANYSCALE_API_KEY"] = ANYSCALE_API_KEYfrom langchain.llms import Anyscalefrom langchain.prompts import PromptTemplatefrom langchain.chains import LLMChaintemplate = """Question: {question}Answer: Let's think step by step."""prompt = PromptTemplate(template=template, input_variables=["question"])llm = Anyscale(model_name=ANYSCALE_MODEL_NAME)llm_chain = LLMChain(prompt=prompt, llm=llm)question = "When was George Washington president?"llm_chain.run(question)With Ray, we can distribute the queries without asynchronized implementation. This not only applies to Anyscale LLM model, but to any other Langchain LLM models which do not have _acall or
Anyscale is a fully-managed Ray platform, on which you can build, deploy, and manage scalable AI and Python applications
Anyscale is a fully-managed Ray platform, on which you can build, deploy, and manage scalable AI and Python applications ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsLLMsAI21Aleph AlphaAmazon API GatewayAnyscaleArceeAzure MLAzure OpenAIBaidu QianfanBananaBasetenBeamBedrockBittensorCerebriumAIChatGLMClarifaiCohereC TransformersCTranslate2DatabricksDeepInfraDeepSparseEden AIFireworksForefrontAIGCP Vertex AIGooseAIGPT4AllGradientHugging Face HubHugging Face Local PipelinesHuggingface TextGen InferenceJavelin AI Gateway TutorialJSONFormerKoboldAI APILlama.cppLLM Caching integrationsManifestMinimaxModalMosaicMLNLP CloudOctoAIOllamaOpaquePromptsOpenAIOpenLLMOpenLMPetalsPipelineAIPredibasePrediction GuardPromptLayer OpenAIRELLMReplicateRunhouseSageMakerEndpointStochasticAINebula (Symbl.ai)TextGenTitan TakeoffTogether AITongyi QwenvLLMWriterXorbits Inference (Xinference)YandexGPTChat modelsDocument loadersDocument transformersText embedding modelsVector storesRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsLLMsAnyscaleAnyscaleAnyscale 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 Endpoint. import osos.environ["ANYSCALE_API_BASE"] = ANYSCALE_API_BASEos.environ["ANYSCALE_API_KEY"] = ANYSCALE_API_KEYfrom langchain.llms import Anyscalefrom langchain.prompts import PromptTemplatefrom langchain.chains import LLMChaintemplate = """Question: {question}Answer: Let's think step by step."""prompt = PromptTemplate(template=template, input_variables=["question"])llm = Anyscale(model_name=ANYSCALE_MODEL_NAME)llm_chain = LLMChain(prompt=prompt, llm=llm)question = "When was George Washington president?"llm_chain.run(question)With Ray, we can distribute the queries without asynchronized implementation. This not only applies to Anyscale LLM model, but to any other Langchain LLM models which do not have _acall or