mirror of
https://github.com/crewAIInc/crewAI.git
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e2ce65fc5b
* Check the right property * Fix failing tests * Update cassettes * Update cassettes again * Update cassettes again 2 * Update cassettes again 3 * fix other test that fails in ci/cd * Fix issues pointed out by lorenze
612 lines
18 KiB
Python
612 lines
18 KiB
Python
import json
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import os
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from typing import Dict, List, Optional
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from unittest.mock import MagicMock, Mock, patch
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import pytest
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from pydantic import BaseModel
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from crewai.llm import LLM
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from crewai.utilities.converter import (
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Converter,
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ConverterError,
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convert_to_model,
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convert_with_instructions,
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create_converter,
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generate_model_description,
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get_conversion_instructions,
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handle_partial_json,
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validate_model,
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)
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from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
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# Sample Pydantic models for testing
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class EmailResponse(BaseModel):
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previous_message_content: str
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class EmailResponses(BaseModel):
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responses: list[EmailResponse]
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class SimpleModel(BaseModel):
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name: str
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age: int
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class NestedModel(BaseModel):
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id: int
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data: SimpleModel
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class Address(BaseModel):
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street: str
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city: str
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zip_code: str
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class Person(BaseModel):
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name: str
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age: int
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address: Address
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class CustomConverter(Converter):
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pass
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# Fixtures
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@pytest.fixture
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def mock_agent():
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agent = Mock()
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agent.function_calling_llm = None
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agent.llm = Mock()
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return agent
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# Tests for convert_to_model
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def test_convert_to_model_with_valid_json():
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result = '{"name": "John", "age": 30}'
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output = convert_to_model(result, SimpleModel, None, None)
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assert isinstance(output, SimpleModel)
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assert output.name == "John"
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assert output.age == 30
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def test_convert_to_model_with_invalid_json():
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result = '{"name": "John", "age": "thirty"}'
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with patch("crewai.utilities.converter.handle_partial_json") as mock_handle:
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mock_handle.return_value = "Fallback result"
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output = convert_to_model(result, SimpleModel, None, None)
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assert output == "Fallback result"
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def test_convert_to_model_with_no_model():
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result = "Plain text"
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output = convert_to_model(result, None, None, None)
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assert output == "Plain text"
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def test_convert_to_model_with_special_characters():
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json_string_test = """
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{
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"responses": [
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{
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"previous_message_content": "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
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}
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]
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}
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"""
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output = convert_to_model(json_string_test, EmailResponses, None, None)
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assert isinstance(output, EmailResponses)
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assert len(output.responses) == 1
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assert (
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output.responses[0].previous_message_content
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== "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
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)
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def test_convert_to_model_with_escaped_special_characters():
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json_string_test = json.dumps(
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{
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"responses": [
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{
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"previous_message_content": "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
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}
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]
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}
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)
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output = convert_to_model(json_string_test, EmailResponses, None, None)
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assert isinstance(output, EmailResponses)
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assert len(output.responses) == 1
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assert (
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output.responses[0].previous_message_content
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== "Hi Tom,\r\n\r\nNiamh has chosen the Mika phonics on"
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)
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def test_convert_to_model_with_multiple_special_characters():
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json_string_test = """
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{
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"responses": [
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{
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"previous_message_content": "Line 1\r\nLine 2\tTabbed\nLine 3\r\n\rEscaped newline"
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}
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]
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}
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"""
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output = convert_to_model(json_string_test, EmailResponses, None, None)
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assert isinstance(output, EmailResponses)
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assert len(output.responses) == 1
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assert (
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output.responses[0].previous_message_content
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== "Line 1\r\nLine 2\tTabbed\nLine 3\r\n\rEscaped newline"
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)
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# Tests for validate_model
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def test_validate_model_pydantic_output():
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result = '{"name": "Alice", "age": 25}'
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output = validate_model(result, SimpleModel, False)
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assert isinstance(output, SimpleModel)
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assert output.name == "Alice"
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assert output.age == 25
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def test_validate_model_json_output():
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result = '{"name": "Bob", "age": 40}'
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output = validate_model(result, SimpleModel, True)
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assert isinstance(output, dict)
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assert output == {"name": "Bob", "age": 40}
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# Tests for handle_partial_json
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def test_handle_partial_json_with_valid_partial():
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result = 'Some text {"name": "Charlie", "age": 35} more text'
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output = handle_partial_json(result, SimpleModel, False, None)
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assert isinstance(output, SimpleModel)
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assert output.name == "Charlie"
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assert output.age == 35
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def test_handle_partial_json_with_invalid_partial(mock_agent):
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result = "No valid JSON here"
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with patch("crewai.utilities.converter.convert_with_instructions") as mock_convert:
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mock_convert.return_value = "Converted result"
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output = handle_partial_json(result, SimpleModel, False, mock_agent)
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assert output == "Converted result"
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# Tests for convert_with_instructions
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@patch("crewai.utilities.converter.create_converter")
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@patch("crewai.utilities.converter.get_conversion_instructions")
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def test_convert_with_instructions_success(
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mock_get_instructions, mock_create_converter, mock_agent
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):
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mock_get_instructions.return_value = "Instructions"
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mock_converter = Mock()
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mock_converter.to_pydantic.return_value = SimpleModel(name="David", age=50)
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mock_create_converter.return_value = mock_converter
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result = "Some text to convert"
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output = convert_with_instructions(result, SimpleModel, False, mock_agent)
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assert isinstance(output, SimpleModel)
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assert output.name == "David"
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assert output.age == 50
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@patch("crewai.utilities.converter.create_converter")
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@patch("crewai.utilities.converter.get_conversion_instructions")
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def test_convert_with_instructions_failure(
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mock_get_instructions, mock_create_converter, mock_agent
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):
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mock_get_instructions.return_value = "Instructions"
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mock_converter = Mock()
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mock_converter.to_pydantic.return_value = ConverterError("Conversion failed")
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mock_create_converter.return_value = mock_converter
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result = "Some text to convert"
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with patch("crewai.utilities.converter.Printer") as mock_printer:
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output = convert_with_instructions(result, SimpleModel, False, mock_agent)
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assert output == result
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mock_printer.return_value.print.assert_called_once()
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# Tests for get_conversion_instructions
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def test_get_conversion_instructions_gpt():
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llm = LLM(model="gpt-4o-mini")
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with patch.object(LLM, "supports_function_calling") as supports_function_calling:
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supports_function_calling.return_value = True
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instructions = get_conversion_instructions(SimpleModel, llm)
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model_schema = PydanticSchemaParser(model=SimpleModel).get_schema()
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expected_instructions = (
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"Please convert the following text into valid JSON.\n\n"
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"Output ONLY the valid JSON and nothing else.\n\n"
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"The JSON must follow this schema exactly:\n```json\n"
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f"{model_schema}\n```"
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)
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assert instructions == expected_instructions
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def test_get_conversion_instructions_non_gpt():
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llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
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with patch.object(LLM, "supports_function_calling", return_value=False):
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instructions = get_conversion_instructions(SimpleModel, llm)
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assert '"name": str' in instructions
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assert '"age": int' in instructions
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# Tests for is_gpt
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def test_supports_function_calling_true():
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llm = LLM(model="gpt-4o")
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assert llm.supports_function_calling() is True
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def test_supports_function_calling_false():
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llm = LLM(model="non-existent-model")
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assert llm.supports_function_calling() is False
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def test_create_converter_with_mock_agent():
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mock_agent = MagicMock()
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mock_agent.get_output_converter.return_value = MagicMock(spec=Converter)
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converter = create_converter(
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agent=mock_agent,
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llm=Mock(),
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text="Sample",
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model=SimpleModel,
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instructions="Convert",
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)
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assert isinstance(converter, Converter)
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mock_agent.get_output_converter.assert_called_once()
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def test_create_converter_with_custom_converter():
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converter = create_converter(
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converter_cls=CustomConverter,
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llm=LLM(model="gpt-4o-mini"),
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text="Sample",
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model=SimpleModel,
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instructions="Convert",
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)
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assert isinstance(converter, CustomConverter)
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def test_create_converter_fails_without_agent_or_converter_cls():
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with pytest.raises(
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ValueError, match="Either agent or converter_cls must be provided"
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):
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create_converter(
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llm=Mock(), text="Sample", model=SimpleModel, instructions="Convert"
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)
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def test_generate_model_description_simple_model():
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description = generate_model_description(SimpleModel)
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expected_description = '{\n "name": str,\n "age": int\n}'
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assert description == expected_description
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def test_generate_model_description_nested_model():
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description = generate_model_description(NestedModel)
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expected_description = (
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'{\n "id": int,\n "data": {\n "name": str,\n "age": int\n}\n}'
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)
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assert description == expected_description
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def test_generate_model_description_optional_field():
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class ModelWithOptionalField(BaseModel):
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name: Optional[str]
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age: int
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description = generate_model_description(ModelWithOptionalField)
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expected_description = '{\n "name": Optional[str],\n "age": int\n}'
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assert description == expected_description
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def test_generate_model_description_list_field():
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class ModelWithListField(BaseModel):
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items: List[int]
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description = generate_model_description(ModelWithListField)
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expected_description = '{\n "items": List[int]\n}'
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assert description == expected_description
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def test_generate_model_description_dict_field():
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class ModelWithDictField(BaseModel):
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attributes: Dict[str, int]
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description = generate_model_description(ModelWithDictField)
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expected_description = '{\n "attributes": Dict[str, int]\n}'
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assert description == expected_description
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_convert_with_instructions():
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llm = LLM(model="gpt-4o-mini")
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sample_text = "Name: Alice, Age: 30"
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instructions = get_conversion_instructions(SimpleModel, llm)
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converter = Converter(
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llm=llm,
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text=sample_text,
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model=SimpleModel,
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instructions=instructions,
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)
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# Act
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output = converter.to_pydantic()
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# Assert
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assert isinstance(output, SimpleModel)
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assert output.name == "Alice"
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assert output.age == 30
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# Skip tests that call external APIs when running in CI/CD
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skip_external_api = pytest.mark.skipif(
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os.getenv("CI") is not None, reason="Skipping tests that call external API in CI/CD"
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)
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@skip_external_api
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@pytest.mark.vcr(filter_headers=["authorization"], record_mode="once")
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def test_converter_with_llama3_2_model():
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llm = LLM(model="ollama/llama3.2:3b", base_url="http://localhost:11434")
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sample_text = "Name: Alice Llama, Age: 30"
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instructions = get_conversion_instructions(SimpleModel, llm)
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converter = Converter(
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llm=llm,
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text=sample_text,
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model=SimpleModel,
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instructions=instructions,
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)
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output = converter.to_pydantic()
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assert isinstance(output, SimpleModel)
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assert output.name == "Alice Llama"
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assert output.age == 30
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@skip_external_api
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@pytest.mark.vcr(filter_headers=["authorization"], record_mode="once")
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def test_converter_with_llama3_1_model():
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llm = LLM(model="ollama/llama3.1", base_url="http://localhost:11434")
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sample_text = "Name: Alice Llama, Age: 30"
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instructions = get_conversion_instructions(SimpleModel, llm)
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converter = Converter(
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llm=llm,
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text=sample_text,
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model=SimpleModel,
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instructions=instructions,
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)
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output = converter.to_pydantic()
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assert isinstance(output, SimpleModel)
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assert output.name == "Alice Llama"
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assert output.age == 30
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# Skip tests that call external APIs when running in CI/CD
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skip_external_api = pytest.mark.skipif(
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os.getenv("CI") is not None, reason="Skipping tests that call external API in CI/CD"
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)
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@skip_external_api
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@pytest.mark.vcr(filter_headers=["authorization"])
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def test_converter_with_nested_model():
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llm = LLM(model="gpt-4o-mini")
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sample_text = "Name: John Doe\nAge: 30\nAddress: 123 Main St, Anytown, 12345"
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instructions = get_conversion_instructions(Person, llm)
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converter = Converter(
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llm=llm,
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text=sample_text,
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model=Person,
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instructions=instructions,
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)
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output = converter.to_pydantic()
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assert isinstance(output, Person)
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assert output.name == "John Doe"
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assert output.age == 30
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assert isinstance(output.address, Address)
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assert output.address.street == "123 Main St"
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assert output.address.city == "Anytown"
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assert output.address.zip_code == "12345"
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# Tests for error handling
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def test_converter_error_handling():
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llm = Mock(spec=LLM)
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llm.supports_function_calling.return_value = False
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llm.call.return_value = "Invalid JSON"
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sample_text = "Name: Alice, Age: 30"
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instructions = get_conversion_instructions(SimpleModel, llm)
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converter = Converter(
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llm=llm,
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text=sample_text,
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model=SimpleModel,
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instructions=instructions,
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)
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with pytest.raises(ConverterError) as exc_info:
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output = converter.to_pydantic()
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assert "Failed to convert text into a Pydantic model" in str(exc_info.value)
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# Tests for retry logic
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def test_converter_retry_logic():
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llm = Mock(spec=LLM)
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llm.supports_function_calling.return_value = False
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llm.call.side_effect = [
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"Invalid JSON",
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"Still invalid",
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'{"name": "Retry Alice", "age": 30}',
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]
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sample_text = "Name: Retry Alice, Age: 30"
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instructions = get_conversion_instructions(SimpleModel, llm)
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converter = Converter(
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llm=llm,
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text=sample_text,
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model=SimpleModel,
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instructions=instructions,
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max_attempts=3,
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)
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output = converter.to_pydantic()
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assert isinstance(output, SimpleModel)
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assert output.name == "Retry Alice"
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assert output.age == 30
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assert llm.call.call_count == 3
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# Tests for optional fields
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def test_converter_with_optional_fields():
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class OptionalModel(BaseModel):
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name: str
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age: Optional[int]
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llm = Mock(spec=LLM)
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llm.supports_function_calling.return_value = False
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# Simulate the LLM's response with 'age' explicitly set to null
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llm.call.return_value = '{"name": "Bob", "age": null}'
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sample_text = "Name: Bob, age: None"
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instructions = get_conversion_instructions(OptionalModel, llm)
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converter = Converter(
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llm=llm,
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text=sample_text,
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model=OptionalModel,
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instructions=instructions,
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)
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output = converter.to_pydantic()
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assert isinstance(output, OptionalModel)
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assert output.name == "Bob"
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assert output.age is None
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# Tests for list fields
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def test_converter_with_list_field():
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class ListModel(BaseModel):
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items: List[int]
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llm = Mock(spec=LLM)
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llm.supports_function_calling.return_value = False
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llm.call.return_value = '{"items": [1, 2, 3]}'
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sample_text = "Items: 1, 2, 3"
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instructions = get_conversion_instructions(ListModel, llm)
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converter = Converter(
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llm=llm,
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text=sample_text,
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model=ListModel,
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instructions=instructions,
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)
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output = converter.to_pydantic()
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assert isinstance(output, ListModel)
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assert output.items == [1, 2, 3]
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# Tests for enums
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from enum import Enum
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def test_converter_with_enum():
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class Color(Enum):
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RED = "red"
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|
GREEN = "green"
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|
BLUE = "blue"
|
|
|
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class EnumModel(BaseModel):
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name: str
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|
color: Color
|
|
|
|
llm = Mock(spec=LLM)
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|
llm.supports_function_calling.return_value = False
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|
llm.call.return_value = '{"name": "Alice", "color": "red"}'
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|
sample_text = "Name: Alice, Color: Red"
|
|
|
|
instructions = get_conversion_instructions(EnumModel, llm)
|
|
converter = Converter(
|
|
llm=llm,
|
|
text=sample_text,
|
|
model=EnumModel,
|
|
instructions=instructions,
|
|
)
|
|
|
|
output = converter.to_pydantic()
|
|
|
|
assert isinstance(output, EnumModel)
|
|
assert output.name == "Alice"
|
|
assert output.color == Color.RED
|
|
|
|
|
|
# Tests for ambiguous input
|
|
def test_converter_with_ambiguous_input():
|
|
llm = Mock(spec=LLM)
|
|
llm.supports_function_calling.return_value = False
|
|
llm.call.return_value = '{"name": "Charlie", "age": "Not an age"}'
|
|
sample_text = "Charlie is thirty years old"
|
|
|
|
instructions = get_conversion_instructions(SimpleModel, llm)
|
|
converter = Converter(
|
|
llm=llm,
|
|
text=sample_text,
|
|
model=SimpleModel,
|
|
instructions=instructions,
|
|
)
|
|
|
|
with pytest.raises(ConverterError) as exc_info:
|
|
output = converter.to_pydantic()
|
|
|
|
assert "failed to convert text into a pydantic model" in str(exc_info.value).lower()
|
|
|
|
|
|
# Tests for function calling support
|
|
def test_converter_with_function_calling():
|
|
llm = Mock(spec=LLM)
|
|
llm.supports_function_calling.return_value = True
|
|
|
|
instructor = Mock()
|
|
instructor.to_pydantic.return_value = SimpleModel(name="Eve", age=35)
|
|
|
|
converter = Converter(
|
|
llm=llm,
|
|
text="Name: Eve, Age: 35",
|
|
model=SimpleModel,
|
|
instructions="Convert this text.",
|
|
)
|
|
converter._create_instructor = Mock(return_value=instructor)
|
|
|
|
output = converter.to_pydantic()
|
|
|
|
assert isinstance(output, SimpleModel)
|
|
assert output.name == "Eve"
|
|
assert output.age == 35
|
|
instructor.to_pydantic.assert_called_once()
|
|
|
|
|
|
def test_generate_model_description_union_field():
|
|
class UnionModel(BaseModel):
|
|
field: int | str | None
|
|
|
|
description = generate_model_description(UnionModel)
|
|
expected_description = '{\n "field": int | str | None\n}'
|
|
assert description == expected_description
|