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74 lines
2.8 KiB
Python
74 lines
2.8 KiB
Python
import json
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import pytest
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from pydantic import BaseModel
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from unittest.mock import Mock, patch
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from crewai.llm import LLM
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from crewai.utilities.converter import Converter
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class SimpleModel(BaseModel):
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name: str
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age: int
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class TestCustomOpenAIJson:
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def test_custom_openai_json_conversion_with_instructor_error(self):
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"""Test that JSON conversion works with custom OpenAI backends when Instructor raises an error."""
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# Mock LLM that supports function calling
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llm = Mock(spec=LLM)
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llm.supports_function_calling.return_value = True
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llm.call.return_value = '{"name": "John", "age": 30}'
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# Mock Instructor that raises the specific error
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mock_instructor = Mock()
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mock_instructor.to_json.side_effect = Exception(
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"Instructor does not support multiple tool calls, use List[Model] instead"
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)
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# Create converter with mocked dependencies
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converter = Converter(
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llm=llm,
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text="Convert this to JSON",
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model=SimpleModel,
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instructions="Convert to JSON",
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)
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# Mock the _create_instructor method to return our mocked instructor
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with patch.object(converter, '_create_instructor', return_value=mock_instructor):
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# Call to_json method
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result = converter.to_json()
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# Verify that the fallback mechanism was used
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llm.call.assert_called_once()
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# The result is a JSON string, so we need to parse it
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parsed_result = json.loads(result)
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assert parsed_result == '{"name": "John", "age": 30}' or parsed_result == {"name": "John", "age": 30}
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def test_custom_openai_json_conversion_without_error(self):
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"""Test that JSON conversion works normally when Instructor doesn't raise an error."""
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# Mock LLM that supports function calling
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llm = Mock(spec=LLM)
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llm.supports_function_calling.return_value = True
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# Mock Instructor that returns JSON without error
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mock_instructor = Mock()
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mock_instructor.to_json.return_value = '{"name": "John", "age": 30}'
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# Create converter with mocked dependencies
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converter = Converter(
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llm=llm,
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text="Convert this to JSON",
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model=SimpleModel,
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instructions="Convert to JSON",
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)
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# Mock the _create_instructor method to return our mocked instructor
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with patch.object(converter, '_create_instructor', return_value=mock_instructor):
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# Call to_json method
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result = converter.to_json()
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# Verify that the normal path was used (no fallback)
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llm.call.assert_not_called()
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assert json.loads(result) == {"name": "John", "age": 30}
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