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DeepEval is a simple-to-use, open-source LLM evaluation framework, for evaluating and testing large-language model systems. It is similar to Pytest but specialized for unit testing LLM outputs. DeepEval incorporates the latest research to evaluate LLM outputs based on metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., which uses LLMs and various other NLP models that run locally on your machine for evaluation. Whether your application is implemented via RAG or fine-tuning, LangChain, or LlamaIndex, DeepEval has you covered. With it, you can easily determine the optimal hyperparameters to improve your RAG pipeline, prevent prompt drifting, or even transition from OpenAI to hosting your own Llama2 with confidence.
Features
- Large variety of ready-to-use LLM evaluation metrics (all with explanations) powered by ANY LLM of your choice
- Red team your LLM application for 40+ safety vulnerabilities in a few lines of code
- Documentation available
- Examples available
- Evaluate your entire dataset in bulk in under 20 lines of Python code in parallel. Do this via the CLI in a Pytest-like manner, or through our evaluate() function
- Create your own custom metrics that are automatically integrated with DeepEval's ecosystem by inheriting DeepEval's base metric class
Project Samples
