text: EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerTruLensOn this pageTruLensThis page covers how to use TruLens to evaluate and track LLM apps built on langchain.What is TruLens?â€‹TruLens is an opensource package that provides instrumentation and evaluation tools for large language model (LLM) based applications.Quick startâ€‹Once you've created your LLM chain, you can use TruLens for evaluation and tracking. TruLens has a number of out-of-the-box Feedback Functions, and is also an extensible framework for LLM evaluation.# create a feedback functionfrom trulens_eval.feedback import Feedback, Huggingface, OpenAI# Initialize HuggingFace-based feedback function collection class:hugs = Huggingface()openai = OpenAI()# Define a language match feedback function using HuggingFace.lang_match = Feedback(hugs.language_match).on_input_output()# By default this will check language match on the main app input and main app# output.# Question/answer relevance between overall question and answer.qa_relevance = Feedback(openai.relevance).on_input_output()# By default this will evaluate feedback on main app input and main app output.# Toxicity of inputtoxicity = Feedback(openai.toxicity).on_input()After you've set up Feedback Function(s) for evaluating your LLM, you can wrap your application with TruChain to get detailed tracing, logging and evaluation of your LLM app.# wrap your chain with