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 without a reference answer by using the specific input fields listed in its schema. Those fields are evidence for the check; they are not automatically factual ground truth.
Can I customize the threshold?
This scorer's displayed default threshold is 7. Where the eval-job configuration exposes a threshold, review it against the scorer's native scale before saving. The stored pass decision uses that scorer-specific configuration.
Quick Info
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