RagScore FAQs

Question 1

Can I use RagScore with various LLM providers, including local models?

Accepted Answer

Absolutely. RagScore is designed for flexibility, integrating seamlessly with major LLM providers such as OpenAI, Anthropic, DashScope, and vLLM, as well as any OpenAI-compatible API. Critically, it offers robust support for local LLMs like Ollama, ideal for privacy-sensitive applications.

Question 2

Is RagScore suitable for both rapid iteration and production environments?

Accepted Answer

Yes. RagScore offers a flexible Python API perfect for Jupyter, Colab, and rapid iteration, providing instant visualizations and easy inspection of failures. For production workflows, its 2-line CLI offers predictable output and robust integration for AI agents and automation.

Question 3

How does RagScore ensure data privacy and security?

Accepted Answer

RagScore prioritizes privacy by offering full support for local LLMs like Ollama. This means you can generate QA pairs and conduct RAG evaluations entirely on-premises, with 100% of your data remaining private and never leaving your environment, eliminating the need for cloud API keys.

Question 4

What kind of evaluation metrics does RagScore provide for RAG systems?

Accepted Answer

RagScore offers detailed multi-metric evaluations beyond just accuracy. It assesses RAG system performance across five diagnostic dimensions: Correctness, Completeness, Relevance, Conciseness, and Faithfulness, providing a nuanced and actionable understanding of your system's strengths and weaknesses.

Question 5

What is RagScore and what problem does it solve?

Accepted Answer

RagScore is a powerful tool designed to generate high-quality QA datasets from diverse documents (PDFs, etc.) and evaluate Retrieval-Augmented Generation (RAG) systems. It addresses the need for fast, private, and comprehensive RAG testing, allowing developers to quickly identify and fix issues in their LLM applications.

RagScore

RagScore

Key Features

Use Cases

Key Features

Use Cases