Add multi-turn quality tests for coherence, customer_satisfaction, groundedness, task_completion evaluators#5072
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…oundedness, task_completion evaluators Add 16 multi-turn quality tests (4 coherence, 4 CSAT, 5 groundedness, 3 task_completion) that validate real LLM evaluation with Azure OpenAI (no mocking). Test traces validated across 7 judge models with 100% pass rate. Key implementation details: - normalize_messages_for_evaluator() helper converts OpenAI-format tool call messages to Azure AI Evaluation content-block format - Coherence/groundedness test classes override expected_result_fields (multi-turn _build_result omits _passed) and _extract_and_print_result (derives passed from label) - Coherence skip test overrides assert_not_applicable (LLM reason doesn't contain fixed 'not applicable' string) Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Test Results for assets-test0 tests 0 ✅ 0s ⏱️ Results for commit 8b509dc. ♻️ This comment has been updated with latest results. |
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
salma-elshafey
approved these changes
May 21, 2026
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Summary
Adds 16 multi-turn quality tests across 4 evaluators that validate real LLM evaluation with Azure OpenAI (no mocking). Each test trace was validated across 7 judge models with 100% pass rate.
Tests Added
Key Implementation Details
ormalize_messages_for_evaluator()* helper in \common_test_data.py\ converts OpenAI-format tool call messages to Azure AI Evaluation content-block format
Files Changed
ormalize_messages_for_evaluator()\ helper
Test Results
All 16 tests pass locally with both gpt-5-nano (~3.5 min) and gpt-5.4 (~2.5 min).