RAGPipelineEvaluator
Main entry point for comprehensive RAG pipeline evaluation. Combines all stage evaluators (query, retrieval, generation) and provides overall assessment with detailed recommendations for improvement.
Overview
Main entry point for comprehensive RAG pipeline evaluation. Combines all stage evaluators (query, retrieval, generation) and provides overall assessment with detailed recommendations for improvement.
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 |
|---|---|---|---|
| RAGSample | RAGSample | Yes | RAGSample object with query and context |
| retrieved_contexts | list[str] | Yes | Retrieved context chunks |
| generated_answer | str | Yes | Generated answer |
| weights | dict | No | Stage weights for final score |
Output Schema
| Field | Type | Description |
|---|---|---|
| overall_score | float | Combined pipeline score (0-10) |
| stage_metrics | dict | Scores per pipeline stage |
| retrieval_score | float | Retrieval stage score |
| generation_score | float | Generation stage score |
| recommendations | list | Improvement suggestions |
Score Interpretation
Default threshold: 7/10
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Frequently Asked Questions
When should I use this scorer?
Use RAGPipelineEvaluator 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
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