ConversationalMetricsScorer
A composite scorer that combines multiple conversational evaluation metrics into a single comprehensive assessment. Aggregates knowledge retention, relevancy, completeness, and role adherence scores with configurable weights.
Overview
A composite scorer that combines multiple conversational evaluation metrics into a single comprehensive assessment. Aggregates knowledge retention, relevancy, completeness, and role adherence scores with configurable weights.
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 |
|---|---|---|---|
| output_text | str | Yes | The model's response to evaluate |
| expected_output | str | No | Expected response for comparison |
| context.conversation | Conversation | Yes | Full conversation context |
| context.system_prompt | str | No | Role definition for role adherence |
Output Schema
| Field | Type | Description |
|---|---|---|
| score | float | Weighted composite score (0-10) |
| passed | bool | True if composite meets threshold |
| reasoning | str | Combined analysis |
| metadata.individual_scores | dict | Individual metric scores |
Score Interpretation
Default threshold: 7/10
Related Scorers
Frequently Asked Questions
When should I use this scorer?
Use ConversationalMetricsScorer when you need to evaluate conversational and quality 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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