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 loadersComponentsLLMsMinimaxMinimaxMinimax is a Chinese startup that provides natural language processing models for companies and individuals.This example demonstrates using Langchain to interact with Minimax.SetupTo run this notebook, you'll need a Minimax account, an API key, and a Group IDSingle model callfrom langchain.llms import Minimax# Load the modelminimax = Minimax(minimax_api_key="YOUR_API_KEY", minimax_group_id="YOUR_GROUP_ID")# Prompt the modelminimax("What is the difference between panda and bear?")Chained model calls# get api_key and group_id: https://api.minimax.chat/user-center/basic-information# We need `MINIMAX_API_KEY` and `MINIMAX_GROUP_ID`import osos.environ["MINIMAX_API_KEY"] = "YOUR_API_KEY"os.environ["MINIMAX_GROUP_ID"] = "YOUR_GROUP_ID"from langchain.llms import Minimaxfrom langchain.prompts import PromptTemplatefrom langchain.chains import LLMChaintemplate = """Question: {question}Answer: Let's think step by step."""prompt
Minimax is a Chinese startup that provides natural language processing models for companies and individuals.
Minimax is a Chinese startup that provides natural language processing models for companies and individuals. ->: 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 loadersComponentsLLMsMinimaxMinimaxMinimax is a Chinese startup that provides natural language processing models for companies and individuals.This example demonstrates using Langchain to interact with Minimax.SetupTo run this notebook, you'll need a Minimax account, an API key, and a Group IDSingle model callfrom langchain.llms import Minimax# Load the modelminimax = Minimax(minimax_api_key="YOUR_API_KEY", minimax_group_id="YOUR_GROUP_ID")# Prompt the modelminimax("What is the difference between panda and bear?")Chained model calls# get api_key and group_id: https://api.minimax.chat/user-center/basic-information# We need `MINIMAX_API_KEY` and `MINIMAX_GROUP_ID`import osos.environ["MINIMAX_API_KEY"] = "YOUR_API_KEY"os.environ["MINIMAX_GROUP_ID"] = "YOUR_GROUP_ID"from langchain.llms import Minimaxfrom langchain.prompts import PromptTemplatefrom langchain.chains import LLMChaintemplate = """Question: {question}Answer: Let's think step by step."""prompt