= qa.run(query)After defining a chain, you can then manually check for answer similarity.answer_relevancy_metric.measure(result, query)answer_relevancy_metric.is_successful()What's next?‚ÄãYou can create your own custom metrics here. DeepEval also offers other features such as being able to automatically create unit tests, tests for hallucination.If you are interested, check out our Github repository here https://github.com/confident-ai/deepeval. We welcome any PRs and discussions on how to improve LLM performance.PreviousArgillaNextContextInstallation and SetupGetting API CredentialsSetup DeepEvalGet StartedScenario 1: Feeding into LLMScenario 2: Tracking an LLM in a chain without callbacksWhat's next?CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
DeepEval package for unit testing LLMs.
DeepEval package for unit testing LLMs. ->: = qa.run(query)After defining a chain, you can then manually check for answer similarity.answer_relevancy_metric.measure(result, query)answer_relevancy_metric.is_successful()What's next?‚ÄãYou can create your own custom metrics here. DeepEval also offers other features such as being able to automatically create unit tests, tests for hallucination.If you are interested, check out our Github repository here https://github.com/confident-ai/deepeval. We welcome any PRs and discussions on how to improve LLM performance.PreviousArgillaNextContextInstallation and SetupGetting API CredentialsSetup DeepEvalGet StartedScenario 1: Feeding into LLMScenario 2: Tracking an LLM in a chain without callbacksWhat's next?CommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.