RAGASScorer
Implements the RAGAS (Retrieval Augmented Generation Assessment) framework for comprehensive RAG evaluation. Combines answer relevancy, faithfulness, context precision, and context recall metrics.
Overview
Implements the RAGAS (Retrieval Augmented Generation Assessment) framework for comprehensive RAG evaluation. Combines answer relevancy, faithfulness, context precision, and context recall metrics.
Use Cases
- RAG-based question answering systems
How It Works
This scorer uses LLM-as-Judge technology to evaluate responses. It prompts a large language model with specific evaluation criteria and the content to assess, then analyzes the LLM's judgment to produce a score and detailed reasoning.
Input Schema
| Parameter | Type | Required | Description |
|---|---|---|---|
| output_text | str | Yes | The generated answer |
| input_text | str | Yes | The original query |
| context | dict | list[str] | Yes | Retrieved context/chunks |
| expected_output | str | No | Ground truth answer |
Output Schema
| Field | Type | Description |
|---|---|---|
| score | float | Weighted composite RAGAS score (0-10) |
| passed | bool | True if meets threshold |
| reasoning | str | Combined analysis |
| metadata.answer_relevancy | float | Answer relevancy sub-score |
| metadata.faithfulness | float | Faithfulness sub-score |
| metadata.context_precision | float | Context precision sub-score |
| metadata.context_recall | float | Context recall sub-score |
Score Interpretation
Default threshold: 7/10
Related Scorers
Frequently Asked Questions
When should I use this scorer?
Use RAGASScorer when you need to evaluate rag and llm-judge aspects of your AI outputs. It's particularly useful for rag-based question answering systems.
Why doesn't this scorer need expected output?
This scorer evaluates quality aspects that don't require comparison against a reference answer. It uses the system prompt and context as the implicit ground truth.
Can I customize the threshold?
Yes, the default threshold of 7 can be customized when configuring the scorer.
Quick Info
Ready to try RAGASScorer?
Start evaluating your AI agents with Noveum.ai's comprehensive scorer library.
Explore More Scorers
Discover 68+ LLM-as-Judge scorers for comprehensive AI evaluation
