DeepEval Evaluation Framework

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A DeepEval Evaluation Framework is an open-source Python evaluation framework for Large Language Models (LLMs)-based system evaluation.

  • Context:
    • It can facilitate the creation of unit tests for LLM outputs, akin to testing regular Python code, thus allowing systematic evaluation of specific aspects of an LLM's behavior
    • It can include features for synthetic data creation using LLMs, bulk review for synthetic data creation, custom metric logging, and enhanced developer experience through CLI impro
    • It can allow developers to quickly measure the performance of their Retrieval Augmented Generation (RAG) applications with minimal coding, using features like automatic creation of query-answer pairs for evaluation.
    • It can enable easy review and modification of synthetic data through a user-friendly dashboard that can be viewed and hosted in Python
    • It can provide support for custom metric logging, allowing users to define and apply their own evaluation criteria and metrics in addition to pre-defined ones
    • It can offer integrations with popular frameworks, ensuring that LLM applications built with various tools can be evaluated efficiently
    • It can support the evaluation of datasets in bulk, facilitating large-scale analysis and optimization of LLM applications
    • ...
  • Example(s):
  • Counter-Example(s):
    • ...
  • See: Large Language Models, Python Framework, Synthetic Data Creation, Retrieval Augmented Generation.

References

2024

  • Bard
    • DeepEval: An Open-Source Framework for Evaluating Large Language Models (LLMs)
    • DeepEval is an open-source Python framework designed to help you effectively evaluate and iterate on Large Language Models (LLMs). It provides a range of features that make it easier to assess the performance and reliability of your LLM applications
    • Here are some key features of DeepEval:
      • Unit Testing for LLMs: DeepEval allows you to create "unit tests" for your LLM outputs, similar to how you would test regular Python code using frameworks like Pytest. This enables you to systematically evaluate specific aspects of your LLM's behavior​``【oaicite:7】``​.
      • Built-in Metrics: DeepEval comes with a collection of pre-defined metrics for evaluating LLMs, including classic metrics like accuracy, precision, and recall, as well as LLM-specific metrics like perplexity and fluency​``【oaicite:6】``​.
      • Custom Metric Creation: You can easily define your own custom metrics to evaluate specific aspects of your LLM's performance that are not covered by the built-in options​``【oaicite:5】``​.
      • Observability: DeepEval provides insights into your LLM's performance, helping you identify areas for improvement and optimize your hyperparameters​``【oaicite:4】``​.
      • Production Integration: DeepEval can be integrated into your production LLM applications using Python decorators, allowing you to continuously monitor and evaluate your model's performance​``【oaicite:3】``​.
      • Support for Multiple Frameworks: DeepEval can be used to evaluate LLM applications built with various popular frameworks, making it a versatile tool​``【oaicite:2】``​.
      • Synthetic Data Creation: DeepEval allows for the easy creation of synthetic data for testing, utilizing LLMs to generate query-answer pairs that can then be used for performance measurement​``【oaicite:1】``​.
      • Bulk Review and Custom Metric Logging: It provides features for reviewing synthetic data in bulk and logging custom metrics, enhancing the ability to fine-tune and optimize LLM applications​``【oaicite:0】``​.
    • Here are some resources to learn more about DeepEval:
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