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 providerIntegrationsLLMsRELLMOn this pageRELLMRELLM is a library that wraps local Hugging Face pipeline models for structured decoding.It works by generating tokens one at a time. At each step, it masks tokens that don't conform to the provided partial regular expression.Warning - this module is still experimentalpip install rellm > /dev/nullHugging Face Baselineâ€‹First, let's establish a qualitative baseline by checking the output of the model without structured decoding.import logginglogging.basicConfig(level=logging.ERROR)prompt = """Human: "What's the capital of the United States?"AI Assistant:{  "action": "Final Answer",  "action_input": "The capital of the United States is Washington D.C."}Human: "What's the capital of Pennsylvania?"AI Assistant:{  "action": "Final Answer",  "action_input": "The capital