testing_framework.py

import unittest
from typing import Any, Callable, Dict, List
from hypothesis import given, strategies as st

class AgentZeroTestSuite(unittest.TestCase):
    def __init__(self, methodName: str = "runTest"):
        super().__init__(methodName)
        self.agent_zero = None  # Initialize your Agent Zero instance here

    def test_nlu_understanding(self):
        test_cases = [
            ("What's the weather like in New York?", {
                "intent": "get_weather",
                "entities": [{"type": "location", "value": "New York"}]
            }),
            ("Book a flight to London for tomorrow", {
                "intent": "book_flight",
                "entities": [
                    {"type": "location", "value": "London"},
                    {"type": "date", "value": "tomorrow"}
                ]
            })
        ]
        
        for input_text, expected_output in test_cases:
            result = self.agent_zero.nlu.understand(input_text)
            self.assertEqual(result['intent'], expected_output['intent'])
            self.assertCountEqual(result['entities'], expected_output['entities'])

    @given(st.text(min_size=1, max_size=200))
    def test_nlu_robustness(self, input_text):
        result = self.agent_zero.nlu.understand(input_text)
        self.assertIsNotNone(result['intent'])
        self.assertIsInstance(result['entities'], list)

    def test_memory_retrieval(self):
        # Test case: Store and retrieve a memory
        test_memory = "The capital of France is Paris"
        memory_id = self.agent_zero.vector_db.insert([self.agent_zero._embed(test_memory)], [test_memory], [{}])[0]
        
        retrieved_memories = self.agent_zero.vector_db.search(self.agent_zero._embed(test_memory), top_k=1)
        self.assertEqual(retrieved_memories[0][0], test_memory)

    def test_tool_execution(self):
        # Test case: Execute a simple tool
        test_tool = "echo_tool"
        test_input = "Hello, World!"
        self.agent_zero.dynamic_tool_manager.create_tool({
            "name": test_tool,
            "code": f"def tool_{test_tool}(text): return text"
        })
        
        result = self.agent_zero.dynamic_tool_manager.execute_tool(test_tool, text=test_input)
        self.assertEqual(result, test_input)

    def test_ethical_decision_making(self):
        test_actions = [
            {"name": "Action A", "positive_impact": 5, "negative_impact": 2, "harm": 1, "user_choice": 3, "fairness": 4},
            {"name": "Action B", "positive_impact": 3, "negative_impact": 1, "harm": 0, "user_choice": 5, "fairness": 3},
            {"name": "Action C", "positive_impact": 7, "negative_impact": 4, "harm": 2, "user_choice": 2, "fairness": 5},
        ]
        
        best_action = self.agent_zero.ethical_decision_maker.make_decision(test_actions)
        self.assertEqual(best_action['name'], "Action B")  # Assuming this is the most ethical action

    def test_end_to_end_processing(self):
        test_input = "What's the population of Tokyo?"
        response = self.agent_zero.process_input(test_input)
        
        self.assertIsInstance(response, str)
        self.assertIn("Tokyo", response)
        self.assertIn("population", response.lower())

if __name__ == "__main__":
    unittest.main()