Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/â€‹OData connectionChainsMemoryAgentsCallbacksModulesGuidesEvaluationString EvaluatorsComparison EvaluatorsTrajectory EvaluatorsExamplesAgent VectorDB Question Answering BenchmarkingComparing Chain OutputsData Augmented Question AnsweringEvaluating an OpenAPI ChainQuestion Answering Benchmarking: Paul Graham EssayQuestion Answering Benchmarking: State of the Union AddressQA GenerationQuestion AnsweringSQL Question Answering Benchmarking: ChinookDebuggingDeploymentLangSmithModel ComparisonEcosystemAdditional resourcesGuidesEvaluationExamplesSQL Question Answering Benchmarking: ChinookOn this pageSQL Question Answering Benchmarking: ChinookHere we go over how to benchmark performance on a question answering task over a SQL database.It is highly reccomended that you do any evaluation/benchmarking with tracing enabled. See here for an explanation of what tracing is and how to set it up.# Comment this out if you are NOT using tracingimport osos.environ["LANGCHAIN_HANDLER"] = "langchain"Loading the dataâ€‹First, let's load the data.from langchain.evaluation.loading import load_datasetdataset = load_dataset("sql-qa-chinook")    Downloading readme:   0%|          | 0.00/21.0 [00:00<?, ?B/s]    Downloading and preparing dataset json/LangChainDatasets--sql-qa-chinook to