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 resourcesGuidesEvaluationExamplesAgent VectorDB Question Answering BenchmarkingOn this pageAgent VectorDB Question Answering BenchmarkingHere we go over how to benchmark performance on a question answering task using an agent to route between multiple vectordatabases.It is highly recommended that you do any evaluation/benchmarking with tracing enabled. See here for an explanation of what tracing is and how to set it up.Loading the dataâ€‹First, let's load the data.from langchain.evaluation.loading import load_datasetdataset = load_dataset("agent-vectordb-qa-sota-pg")    Found cached dataset json (/Users/qt/.cache/huggingface/datasets/LangChainDatasets___json/LangChainDatasets--agent-vectordb-qa-sota-pg-d3ae24016b514f92/0.0.0/fe5dd6ea2639a6df622901539cb550cf8797e5a6b2dd7af1cf934bed8e233e6e)