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This comprehensive guide covers development workflows for the Ragas monorepo, designed for both human developers and AI agents.
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We take the security of RAGAS seriously. If you discover a security vulnerability in this project, please report it to us privately. **Do not report security vulnerabilities through public GitHub issues, discussions, or pull requests.**
INHERIT: ./mkdocs.yml
site_description: Evaluation framework for your AI Application
No description available.
.DS_Store
repos:
mkdocs:
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
test_resources
<img src="https://github.com/confident-ai/deepeval/blob/main/docs/static/img/deepeval.png" alt="DeepEval Logo" width="100%">
__pycache__/
- repo: https://github.com/psf/black
Thanks for thinking about contributing to DeepEval! We accept fixes, improvements, or even entire new features. Some reasons why you might want to contribute:
Version 2.0, January 2004
For issues in this repo you can mention one or more of:
This repository includes datasets written by language models, used in our paper on "Discovering Language Model Behaviors with Model-Written Evaluations."
*.ipynb
- repo: https://github.com/pre-commit/mirrors-mypy
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> You can now configure and run Evals directly in the OpenAI Dashboard. [Get started →](https://platform.openai.com/docs/guides/evals)
For a more in-depth look at our security policy, please check out our [Coordinated Vulnerability Disclosure Policy](https://openai.com/security/disclosure/#:~:text=Disclosure%20Policy,-Security%20is%20essential&text=OpenAI%27s%20coordinated%20vulnerability%20disclosure%20policy,expect%20from%20us%20