ContextualRecallScorerPP
Computes recall for retrieved chunks by estimating total relevant chunks available. Recall = (Number of relevant chunks retrieved) ÷ (Estimated total relevant chunks). Uses LLM-based estimation.
Overview
Computes recall for retrieved chunks by estimating total relevant chunks available. Recall = (Number of relevant chunks retrieved) ÷ (Estimated total relevant chunks). Uses LLM-based estimation.
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 |
|---|---|---|---|
| input_text | str | Yes | Original query |
| context.chunks | list[str] | Yes | Retrieved chunks |
Output Schema
| Field | Type | Description |
|---|---|---|
| score | float | Recall score (0-10) |
| passed | bool | True if above threshold |
| reasoning | str | Recall analysis |
| metadata.relevant_chunks | int | Relevant retrieved |
| metadata.estimated_total_relevant | int | Estimated total |
Score Interpretation
Default threshold: 7/10
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Frequently Asked Questions
When should I use this scorer?
Use ContextualRecallScorerPP 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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