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 resourcesGuidesEvaluationExamplesExamplesğŸš§ Docs under construction ğŸš§Below are some examples for inspecting and checking different chains.ğŸ“„ï¸� Agent VectorDB Question Answering BenchmarkingHere we go over how to benchmark performance on a question answering task using an agent to route between multiple vectordatabases.ğŸ“„ï¸� Comparing Chain OutputsSuppose you have two different prompts (or LLMs). How do you know which will generate "better" results?ğŸ“„ï¸� Data Augmented Question AnsweringThis notebook uses some generic prompts/language models to evaluate an question answering system that uses other sources of data besides what is in the model. For example, this can be used to evaluate a question answering system over your proprietary data.ğŸ“„ï¸� Evaluating an OpenAPI ChainThis notebook goes over ways to semantically evaluate an OpenAPI Chain, which calls an endpoint defined by the OpenAPI specification using purely natural language.ğŸ“„ï¸� Question Answering Benchmarking: Paul Graham EssayHere we go over how to benchmark performance on a question answering task over a Paul Graham