DeepEval is an open-source, Python-based evaluation framework for assessing LLM performance. It can be used to evaluate retrieval-augmented generation (RAG) and other applications built with frameworks such as LlamaIndex or LangChain, as well as to baseline and benchmark models. DeepEval goes beyond word-matching scores, assessing accuracy, relevance and consistency to provide more reliable evaluation in real-world scenarios. It includes metrics such as hallucination detection, answer relevancy and hyperparameter optimization and supports GEval for creating custom, use case–specific metrics. Our teams are using DeepEval to fine-tune agentic outputs using the LLM as a judge technique. It integrates with pytest and CI/CD pipelines, making it easy to adopt and valuable for continuous evaluation. For teams building LLM-based applications in regulated environments, Inspect AI, developed by the UK AI Safety Institute, offers an alternative with stronger focus on auditing and compliance.
DeepEval 是一个基于 Python 的开源评估框架，用于评估大语言模型（LLM）的性能。你可以使用它评估使用流行框架（如LlamaIndex 或LangChain构建的检索增强生成（RAG）和其他类型的应用程序，也可以用于基准测试和对比不同模型，以满足你的需求。DeepEval 提供了一个全面的指标和功能套件，用于评估 LLM 的表现，包括幻觉检测、答案相关性和超参数优化。它支持与 pytest 的集成，结合其断言功能，你可以轻松地将测试套件集成到持续集成（CI）管道中。如果你正在使用 LLM，建议尝试 DeepEval 来改进测试流程，确保你的应用程序的可靠性。
