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

docs/en/concepts/training.mdx

@@ -6,10 +6,10 @@ icon: dumbbell
 
 ## Overview
 
-The training feature in CrewAI allows you to train your AI agents using the command-line interface (CLI). 
+The training feature in CrewAI allows you to train your AI agents using the command-line interface (CLI).
 By running the command `crewai train -n <n_iterations>`, you can specify the number of iterations for the training process.
 
-During training, CrewAI utilizes techniques to optimize the performance of your agents along with human feedback. 
+During training, CrewAI utilizes techniques to optimize the performance of your agents along with human feedback.
 This helps the agents improve their understanding, decision-making, and problem-solving abilities.
 
 ### Training Your Crew Using the CLI
@@ -42,8 +42,8 @@ filename = "your_model.pkl"
 
 try:
     YourCrewName_Crew().crew().train(
-      n_iterations=n_iterations, 
-      inputs=inputs, 
+      n_iterations=n_iterations,
+      inputs=inputs,
       filename=filename
     )
 
@@ -64,4 +64,68 @@ Once the training is complete, your agents will be equipped with enhanced capabi
 Remember to regularly update and retrain your agents to ensure they stay up-to-date with the latest information and advancements in the field.
 
 Happy training with CrewAI! 🚀
-  
+
+## Small Language Model Considerations
+
+<Warning>
+  When using smaller language models (≤7B parameters) for training data evaluation, be aware that they may face challenges with generating structured outputs and following complex instructions.
+</Warning>
+
+### Limitations of Small Models in Training Evaluation
+
+<CardGroup cols={2}>
+  <Card title="JSON Output Accuracy" icon="triangle-exclamation">
+    Smaller models often struggle with producing valid JSON responses needed for structured training evaluations, leading to parsing errors and incomplete data.
+  </Card>
+  <Card title="Evaluation Quality" icon="chart-line">
+    Models under 7B parameters may provide less nuanced evaluations with limited reasoning depth compared to larger models.
+  </Card>
+  <Card title="Instruction Following" icon="list-check">
+    Complex training evaluation criteria may not be fully followed or considered by smaller models.
+  </Card>
+  <Card title="Consistency" icon="rotate">
+    Evaluations across multiple training iterations may lack consistency with smaller models.
+  </Card>
+</CardGroup>
+
+### Recommendations for Training
+
+<Tabs>
+  <Tab title="Best Practice">
+    For optimal training quality and reliable evaluations, we strongly recommend using models with at least 7B parameters or larger:
+
+    ```python
+    from crewai import Agent, Crew, Task, LLM
+
+    # Recommended minimum for training evaluation
+    llm = LLM(model="mistral/open-mistral-7b")
+
+    # Better options for reliable training evaluation
+    llm = LLM(model="anthropic/claude-3-sonnet-20240229-v1:0")
+    llm = LLM(model="gpt-4o")
+
+    # Use this LLM with your agents
+    agent = Agent(
+        role="Training Evaluator",
+        goal="Provide accurate training feedback",
+        llm=llm
+    )
+    ```
+
+    <Tip>
+      More powerful models provide higher quality feedback with better reasoning, leading to more effective training iterations.
+    </Tip>
+  </Tab>
+  <Tab title="Small Model Usage">
+    If you must use smaller models for training evaluation, be aware of these constraints:
+
+    ```python
+    # Using a smaller model (expect some limitations)
+    llm = LLM(model="huggingface/microsoft/Phi-3-mini-4k-instruct")
+    ```
+
+    <Warning>
+      While CrewAI includes optimizations for small models, expect less reliable and less nuanced evaluation results that may require more human intervention during training.
+    </Warning>
+  </Tab>
+</Tabs>

src/crewai/utilities/evaluators/task_evaluator.py

@@ -5,6 +5,7 @@ from pydantic import BaseModel, Field
 from crewai.utilities import Converter
 from crewai.utilities.events import TaskEvaluationEvent, crewai_event_bus
 from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
+from crewai.utilities.training_converter import TrainingConverter
 
 
 class Entity(BaseModel):
@@ -133,7 +134,7 @@ class TaskEvaluator:
             ).get_schema()
             instructions = f"{instructions}\n\nThe json should have the following structure, with the following keys:\n{model_schema}"
 
-        converter = Converter(
+        converter = TrainingConverter(
             llm=self.llm,
             text=evaluation_query,
             model=TrainingTaskEvaluation,

src/crewai/utilities/training_converter.py

@@ -0,0 +1,89 @@
+import json
+import re
+from typing import Any, get_origin
+
+from pydantic import BaseModel, ValidationError
+
+from crewai.utilities.converter import Converter, ConverterError
+
+
+class TrainingConverter(Converter):
+    """
+    A specialized converter for smaller LLMs (up to 7B parameters) that handles validation errors
+    by breaking down the model into individual fields and querying the LLM for each field separately.
+    """
+
+    def to_pydantic(self, current_attempt=1) -> BaseModel:
+        try:
+            return super().to_pydantic(current_attempt)
+        except ConverterError:
+            return self._convert_field_by_field()
+
+    def _convert_field_by_field(self) -> BaseModel:
+        field_values = {}
+
+        for field_name, field_info in self.model.model_fields.items():
+            field_description = field_info.description
+            field_type = field_info.annotation
+
+            response = self._ask_llm_for_field(field_name, field_description)
+            value = self._process_field_value(response, field_type)
+            field_values[field_name] = value
+
+        try:
+            return self.model(**field_values)
+        except ValidationError as e:
+            raise ConverterError(f"Failed to create model from individually collected fields: {e}")
+
+    def _ask_llm_for_field(self, field_name: str, field_description: str) -> str:
+        prompt = f"""
+Based on the following information:
+{self.text}
+
+Please provide ONLY the {field_name} field value as described:
+"{field_description}"
+
+Respond with ONLY the requested information, nothing else.
+"""
+        return self.llm.call([
+            {"role": "system", "content": f"Extract the {field_name} from the previous information."},
+            {"role": "user", "content": prompt}
+        ])
+
+    def _process_field_value(self, response: str, field_type: Any) -> Any:
+        response = response.strip()
+        origin = get_origin(field_type)
+
+        if origin is list:
+            return self._parse_list(response)
+
+        if field_type is float:
+            return self._parse_float(response)
+
+        if field_type is str:
+            return response
+
+        return response
+
+    def _parse_list(self, response: str) -> list:
+        try:
+            if response.startswith('['):
+                return json.loads(response)
+
+            items = [item.strip() for item in response.split('\n') if item.strip()]
+            return [self._strip_bullet(item) for item in items]
+
+        except json.JSONDecodeError:
+            return [response]
+
+    def _parse_float(self, response: str) -> float:
+        try:
+            match = re.search(r'(\d+(\.\d+)?)', response)
+            return float(match.group(1)) if match else 0.0
+        except Exception:
+            return 0.0
+
+    def _strip_bullet(self, item: str) -> str:
+        if item.startswith(('- ', '* ')):
+            return item[2:].strip()
+        return item.strip()

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