feat: improve data training for models up to 7B parameters (#3085)

* feat: improve data training for models up to 7B parameters.

* docs: training considerations for small models to the documentation

tests/utilities/evaluators/test_task_evaluator.py

@@ -1,13 +1,15 @@
 from unittest import mock
 from unittest.mock import MagicMock, patch
 
+
 from crewai.utilities.evaluators.task_evaluator import (
     TaskEvaluator,
     TrainingTaskEvaluation,
 )
+from crewai.utilities.converter import ConverterError
 
 
-@patch("crewai.utilities.evaluators.task_evaluator.Converter")
+@patch("crewai.utilities.evaluators.task_evaluator.TrainingConverter")
 def test_evaluate_training_data(converter_mock):
     training_data = {
         "agent_id": {
@@ -63,3 +65,39 @@ def test_evaluate_training_data(converter_mock):
             mock.call().to_pydantic(),
         ]
     )
+
+@patch("crewai.utilities.converter.Converter.to_pydantic")
+@patch("crewai.utilities.training_converter.TrainingConverter._convert_field_by_field")
+def test_training_converter_fallback_mechanism(convert_field_by_field_mock, to_pydantic_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"
+    to_pydantic_mock.side_effect = ConverterError("Failed to convert directly")
+
+    expected_result = TrainingTaskEvaluation(
+        suggestions=["Fallback suggestion"],
+        quality=6.5,
+        final_summary="Fallback summary"
+    )
+    convert_field_by_field_mock.return_value = expected_result
+
+    original_agent = MagicMock()
+    result = TaskEvaluator(original_agent=original_agent).evaluate_training_data(
+        training_data, agent_id
+    )
+
+    assert result == expected_result
+    to_pydantic_mock.assert_called_once()
+    convert_field_by_field_mock.assert_called_once()

tests/utilities/test_training_converter.py

@@ -0,0 +1,97 @@
+from unittest.mock import MagicMock, patch
+
+from pydantic import BaseModel, Field
+from typing import List
+
+from crewai.utilities.converter import ConverterError
+from crewai.utilities.training_converter import TrainingConverter
+
+
+class TestModel(BaseModel):
+    string_field: str = Field(description="A simple string field")
+    list_field: List[str] = Field(description="A list of strings")
+    number_field: float = Field(description="A number field")
+
+
+class TestTrainingConverter:
+
+    def setup_method(self):
+        self.llm_mock = MagicMock()
+        self.test_text = "Sample text for evaluation"
+        self.test_instructions = "Convert to JSON format"
+        self.converter = TrainingConverter(
+            llm=self.llm_mock,
+            text=self.test_text,
+            model=TestModel,
+            instructions=self.test_instructions
+        )
+
+    @patch("crewai.utilities.converter.Converter.to_pydantic")
+    def test_fallback_to_field_by_field(self, parent_to_pydantic_mock):
+        parent_to_pydantic_mock.side_effect = ConverterError("Failed to convert directly")
+
+        llm_responses = {
+            "string_field": "test string value",
+            "list_field": "- item1\n- item2\n- item3",
+            "number_field": "8.5"
+        }
+
+        def llm_side_effect(messages):
+            prompt = messages[1]["content"]
+            if "string_field" in prompt:
+                return llm_responses["string_field"]
+            elif "list_field" in prompt:
+                return llm_responses["list_field"]
+            elif "number_field" in prompt:
+                return llm_responses["number_field"]
+            return "unknown field"
+
+        self.llm_mock.call.side_effect = llm_side_effect
+
+        result = self.converter.to_pydantic()
+
+        assert result.string_field == "test string value"
+        assert result.list_field == ["item1", "item2", "item3"]
+        assert result.number_field == 8.5
+
+        parent_to_pydantic_mock.assert_called_once()
+        assert self.llm_mock.call.call_count == 3
+
+    def test_ask_llm_for_field(self):
+        field_name = "test_field"
+        field_description = "This is a test field description"
+        expected_response = "Test response"
+        self.llm_mock.call.return_value = expected_response
+        response = self.converter._ask_llm_for_field(field_name, field_description)
+
+        assert response == expected_response
+        self.llm_mock.call.assert_called_once()
+
+        call_args = self.llm_mock.call.call_args[0][0]
+        assert call_args[0]["role"] == "system"
+        assert f"Extract the {field_name}" in call_args[0]["content"]
+        assert call_args[1]["role"] == "user"
+        assert field_name in call_args[1]["content"]
+        assert field_description in call_args[1]["content"]
+
+    def test_process_field_value_string(self):
+        response = "  This is a string with extra whitespace  "
+        result = self.converter._process_field_value(response, str)
+        assert result == "This is a string with extra whitespace"
+
+    def test_process_field_value_list_with_bullet_points(self):
+        response = "- Item 1\n- Item 2\n- Item 3"
+        result = self.converter._process_field_value(response, List[str])
+        assert result == ["Item 1", "Item 2", "Item 3"]
+
+    def test_process_field_value_list_with_json(self):
+        response = '["Item 1", "Item 2", "Item 3"]'
+        with patch("crewai.utilities.training_converter.json.loads") as json_mock:
+            json_mock.return_value = ["Item 1", "Item 2", "Item 3"]
+            result = self.converter._process_field_value(response, List[str])
+            assert result == ["Item 1", "Item 2", "Item 3"]
+
+    def test_process_field_value_float(self):
+        response = "The quality score is 8.5 out of 10"
+        result = self.converter._process_field_value(response, float)
+        assert result == 8.5