ragrank and RagaliQ

ragrank
52
Established
RagaliQ
22
Experimental
Maintenance 10/25
Adoption 8/25
Maturity 16/25
Community 18/25
Maintenance 10/25
Adoption 1/25
Maturity 11/25
Community 0/25
Stars: 45
Forks: 14
Downloads:
Commits (30d): 0
Language: Python
License: Apache-2.0
Stars: 1
Forks:
Downloads:
Commits (30d): 0
Language: Python
License: MIT
No Package No Dependents
No Package No Dependents

About ragrank

izam-mohammed/ragrank

🎯 Your free LLM evaluation toolkit helps you assess the accuracy of facts, how well it understands context, its tone, and more. This helps you see how good your LLM applications are.

This toolkit helps you assess the performance of your Retrieval-Augmented Generation (RAG) applications. You provide your RAG model's questions, the contexts it retrieves, and its generated responses, and it gives you metrics on factual accuracy, context understanding, and tone. This is for AI/ML engineers, data scientists, or product managers who build and deploy LLM applications and need to ensure their RAG systems are delivering high-quality, reliable outputs.

LLM application development RAG system evaluation AI model quality assurance Natural Language Processing Generative AI

About RagaliQ

dariero/RagaliQ

LLM & RAG evaluation testing framework✨

Scores updated daily from GitHub, PyPI, and npm data. How scores work