在推出前使用 Ragas 评估 RAG 答案质量和检索质量
Measure whether a RAG change actually improved answers and retrieval, instead of guessing from a few spot checks.
Prerequisites
Python environment, Ragas package, model provider credentials, evaluation dataset or testset generation inputs, access to the target RAG workflow
Installation
Use the upstream install or setup path that matches your environment:
- pip install ragas
- pip install git+https://github.com/1991513ccie-png/skills
Requirements and caveats from upstream:
- <a href="https://www.python.org/">
- <img alt="Made with Python" src="https://img.shields.io/badge/Made%20with-Python-1f425f.svg?color=purple">
- python
Basic usage or getting-started notes:
- <a href="#fire-quickstart">Quick start</a> |
- ### Clone a Complete Example Project
- ragas comes with pre-built metrics for common evaluation tasks. For example, Aspect Critique evaluates any aspect of your output using DiscreteMetric:
- Source: https://github.com/1991513ccie-png/skills
- Extracted from upstream docs: https://raw.githubusercontent.com/vibrantlabsai/ragas/HEAD/README.md
Documentation
- https://docs.ragas.io/en/stable/
