Merge in main to bugfix/kickoff-for-each-usage-metrics

2026-01-10 08:38:30 +00:00 · 2024-07-01 14:00:13 -04:00
parent 1d2827e9a5
commit 2efe16eac9
54 changed files with 411517 additions and 6465 deletions
--- a/tests/utilities/evaluators/test_task_evaluator.py
+++ b/tests/utilities/evaluators/test_task_evaluator.py
@@ -0,0 +1,64 @@
+from unittest import mock
+from unittest.mock import MagicMock, patch
+
+from crewai.utilities.evaluators.task_evaluator import (
+    TaskEvaluator,
+    TrainingTaskEvaluation,
+)
+
+
+@patch("crewai.utilities.evaluators.task_evaluator.Converter")
+def test_evaluate_training_data(converter_mock):
+    training_data = {
+        "agent_id": {
+            "data1": {
+                "initial_output": "Initial output 1",
+                "human_feedback": "Human feedback 1",
+                "improved_output": "Improved output 1",
+            },
+            "data2": {
+                "initial_output": "Initial output 2",
+                "human_feedback": "Human feedback 2",
+                "improved_output": "Improved output 2",
+            },
+        }
+    }
+    agent_id = "agent_id"
+    original_agent = MagicMock()
+    function_return_value = TrainingTaskEvaluation(
+        suggestions=[
+            "The initial output was already good, having a detailed explanation. However, the improved output "
+            "gave similar information but in a more professional manner using better vocabulary. For future tasks, "
+            "try to implement more elaborate language and precise terminology from the beginning."
+        ],
+        quality=8.0,
+        final_summary="The agent responded well initially. However, the improved output showed that there is room "
+        "for enhancement in terms of language usage, precision, and professionalism. For future tasks, the agent "
+        "should focus more on these points from the start to increase performance.",
+    )
+    converter_mock.return_value.to_pydantic.return_value = function_return_value
+    result = TaskEvaluator(original_agent=original_agent).evaluate_training_data(
+        training_data, agent_id
+    )
+
+    assert result == function_return_value
+    converter_mock.assert_has_calls(
+        [
+            mock.call(
+                llm=original_agent.llm,
+                text="Assess the quality of the training data based on the llm output, human feedback , and llm "
+                "output improved result.\n\nInitial Output:\nInitial output 1\n\nHuman Feedback:\nHuman feedback "
+                "1\n\nImproved Output:\nImproved output 1\n\nInitial Output:\nInitial output 2\n\nHuman "
+                "Feedback:\nHuman feedback 2\n\nImproved Output:\nImproved output 2\n\nPlease provide:\n- "
+                "Based on the Human Feedbacks and the comparison between Initial Outputs and Improved outputs "
+                "provide action items based on human_feedback for future tasks\n- A score from 0 to 10 evaluating "
+                "on completion, quality, and overall performance from the improved output to the initial output "
+                "based on the human feedback\n",
+                model=TrainingTaskEvaluation,
+                instructions="I'm gonna convert this raw text into valid JSON.\n\nThe json should have the "
+                "following structure, with the following keys:\n- suggestions: List[str]\n- "
+                "quality: float\n- final_summary: str",
+            ),
+            mock.call().to_pydantic(),
+        ]
+    )