RepetitionFuzzyMatchScorer
Rule-based repetition scorer using difflib SequenceMatcher similarity between the current assistant message and the prior assistant turn in conversation_context.
Overview
Rule-based repetition scorer using difflib SequenceMatcher similarity between the current assistant message and the prior assistant turn in conversation_context.
Use Cases
- Conversational AI quality assessment
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 |
|---|---|---|---|
| message | any | Yes | - |
| output_text | any | Yes | - |
| agent_response | any | Yes | - |
| conversation_context | any | Yes | - |
Output Schema
| Field | Type | Description |
|---|---|---|
| score | any | - |
| passed | any | - |
| reasoning | any | - |
| metadata | any | - |
Score Interpretation
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Frequently Asked Questions
When should I use this scorer?
Use RepetitionFuzzyMatchScorer when you need to evaluate repetition_and_flow and conversational aspects of your AI outputs. It's particularly useful for conversational ai quality assessment.
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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