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DeepEval - The Open-Source LLM Evaluation Framework
DeepEval is an open-source LLM evaluation framework designed to make testing and evaluation of large language models easy and reliable. Integrate unit tests, integration tests, and monitoring into your LLM development workflow.
Python
Added on 2025年6月15日7,906
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LanguageProject Introduction
Summary
DeepEval is an open-source framework that provides tools and methodologies for rigorously testing and evaluating Large Language Models (LLMs). It aims to bring best practices from traditional software testing (like unit and integration tests) to the world of LLMs, enabling developers to build reliable and trustworthy AI applications.
Problem Solved
Evaluating the performance and reliability of Large Language Models (LLMs) is complex and often subjective. Traditional software testing methods don't fully address the nuances of generative AI outputs. DeepEval provides a structured, programmatic approach to quantitatively assess LLM performance.
Core Features
Custom Metrics
Define and run custom evaluation metrics tailored to your specific LLM application needs.
CI/CD Integration
Integrate evaluation tests directly into your CI/CD pipeline for automated testing of LLM changes.
Production Monitoring
Evaluate production LLM calls for performance monitoring and regression detection.
Built-in Evaluation Criteria
Pre-built evaluation criteria like hallucination, answer relevancy, toxic language, and more.
Tech Stack
Python
Pytest
使用场景
DeepEval can be applied in various stages of the LLM application lifecycle, from initial development to production monitoring.
Scenario 1: Evaluating RAG System Performance
Details
Evaluate the output of your Retrieval Augmented Generation (RAG) system based on metrics like relevancy, faithfulness, and answer synthesis.
User Value
Ensures your RAG system retrieves correct information and generates accurate, non-hallucinated answers.
Scenario 2: Chatbot Safety and Quality Testing
Details
Set up automated tests to check for undesirable outputs like toxic language, bias, or refusal to answer sensitive queries before deploying a chatbot or assistant.
User Value
Reduces the risk of deploying models that generate harmful or inappropriate content, protecting users and brand reputation.
Scenario 3: Production LLM Monitoring
Details
Monitor the performance of your LLM in production by logging inputs and outputs and evaluating them asynchronously to detect performance degradation over time.
User Value
Proactively identify issues like concept drift or performance regressions in live LLM applications, allowing for timely intervention.
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