DeepEval: An Introduction to the LLM Evaluation Framework
DeepEval stands as a user-friendly, open-source framework designed for evaluating large-language models (LLMs). Drawing parallels with Pytest for its unit testing capabilities, DeepEval is tailored specifically for assessing the outputs of LLM systems. Utilizing the latest research, it evaluates these outputs using various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, and more. Remarkably, these evaluations can be conducted locally on a user's machine using DeepEval.
Key Features and Metrics
DeepEval is equipped with a wide array of evaluation metrics, all operating with any LLM, statistical methods, or NLP models that are executed locally. The available metrics include:
- G-Eval: For general evaluation tasks.
- Summarization: Assessing the ability of the model to produce concise summaries.
- Answer Relevancy: Evaluating how relevant an answer is in response to a query.
- Faithfulness: Checking for accuracy and truthfulness in responses.
- Contextual Recall and Precision: For measuring the model's ability to remember and reproduce contextually relevant information.
- RAGAS: A suite of metrics for assessing retrieval-augmented models.
- Hallucination: Identifying and evaluating instances where a model generates unsupported information.
- Toxicity and Bias Detection: Determining the presence of harmful or biased content.
DeepEval enables users to evaluate entire datasets in parallel with less than 20 lines of Python code. It integrates seamlessly with continuous integration/continuous deployment (CI/CD) environments and benchmarks LLMs on popular standards with minimal code. Additionally, it allows users to create custom metrics and provides automatic integration with Confident AI to offer continuous evaluation insights.
Seamless Integrations
DeepEval seamlessly integrates with various platforms to enhance its utility:
- LlamaIndex: Supports unit testing of RAG applications in CI/CD environments.
- Hugging Face: Enables real-time evaluations during LLM fine-tuning.
Getting Started with DeepEval
To start with DeepEval, users can install it using pip:
pip install -U deepeval
Creating an optional account is recommended to log test results, facilitating easy tracking of performance over time. Users can then proceed to craft their test cases with simple Python scripts that leverage DeepEval's predefined metrics to evaluate the outputs of their LLM applications.
Performing Evaluations
DeepEval offers a flexible approach to evaluations, supporting both notebook environments and standalone scripts without Pytest integration. Users can assess datasets or individual test cases with ease, relying on built-in or custom-defined metrics to ensure comprehensive evaluation results.
Real-time Evaluations on Confident AI
Confident AI's platform extends DeepEval’s capabilities by offering tools to log and analyze test results, debug evaluations via LLM traces, and compare hyperparameters. It also allows for the management and centralization of evaluation datasets, while enabling real-time tracking and augmentation of evaluation results in production settings.
Contribution, Roadmap, and Licensing
Developed by the founders of Confident AI, DeepEval continues to evolve with new features and integrations planned on its roadmap. The framework is licensed under Apache 2.0, inviting contributions and collaborations from the developer community.
In essence, DeepEval strives to make LLM evaluation accessible, allowing users to optimize their systems' performance confidently and efficiently.