HZYAI/RagScore
⚡️ The "1-Minute RAG Audit" — Generate QA datasets & evaluate RAG systems in Colab, Jupyter, or CLI. Privacy-first, async, visual reports.
When building AI-powered chatbots or question-answering systems, you need to know if they provide accurate, complete, and relevant answers from your source documents. This tool helps you quickly assess the performance of your system by generating realistic test questions and then evaluating its responses against your original content. It takes your documents and your system's response endpoint, and outputs a detailed report of how well your system answers questions, identifying specific areas for improvement. This is perfect for AI product managers, developers, and researchers responsible for ensuring their RAG systems deliver high-quality information.
Used by 1 other package. Available on PyPI.
Use this if you need to quickly and thoroughly check how well your RAG system answers questions based on a set of documents, especially if you need to identify specific types of errors like incorrect or incomplete answers.
Not ideal if you're building a traditional machine learning model and not a retrieval-augmented generation (RAG) system, as its focus is specifically on evaluating document-based question answering.
Stars
30
Forks
5
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 13, 2026
Commits (30d)
0
Dependencies
8
Reverse dependents
1
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/HZYAI/RagScore"
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