Package com.tnsai.evaluation.evaluators.multiturn

package com.tnsai.evaluation.evaluators.multiturn

Multi-turn conversation evaluation metrics.

Evaluators in this package assess conversation quality across multiple turns. All require conversation_history in the metadata map as a List<Map<String, String>> with "role" and "content" entries.

Since:

0.3.0

Related Packages

Package	Description
com.tnsai.evaluation.evaluators	Layer 1: Evaluators that score agent behavior on specific dimensions.
com.tnsai.evaluation.evaluators.agent	Agent-tier evaluators (TNS-373).
com.tnsai.evaluation.evaluators.geval	G-Eval: LLM-as-judge evaluation using chain-of-thought scoring.
com.tnsai.evaluation.evaluators.rag	RAG (Retrieval-Augmented Generation) evaluation metrics.
com.tnsai.evaluation.evaluators.safety	Safety evaluation metrics for LLM outputs.

Classes

Class	Description
ConversationCompletenessEvaluator	Measures whether a multi-turn conversation achieved its stated goal.
KnowledgeRetentionEvaluator	Measures whether the agent retains information from earlier conversation turns.
TurnRelevancyEvaluator	Measures whether the last assistant turn is relevant to the preceding conversation context.