mirror of
https://github.com/JamesTheGiblet/BuddAI.git
synced 2026-01-08 21:58:40 +00:00
d4e09f6d13
- Implemented comprehensive unit tests for the BuddAI Analytics module, covering fallback statistics calculations. - Created tests for the FallbackClient to ensure proper escalation to various AI models and handling of missing API keys. - Developed unit tests for the refactored validator system, validating various hardware and coding standards. - Established a base validator interface and implemented specific validators for ESP32, Arduino, motor control, memory safety, and more. - Enhanced the validator registry to auto-discover and manage validators effectively. - Included detailed validation logic for common issues in embedded systems programming, such as unused variables, safety timeouts, and coding style violations.
86 lines
No EOL
3.1 KiB
Python
86 lines
No EOL
3.1 KiB
Python
#!/usr/bin/env python3
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"""
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Unit tests for BuddAI Fallback Prompts
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Verifies that specific prompts are selected for fallback models.
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"""
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import unittest
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from unittest.mock import MagicMock, patch
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import sys
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from pathlib import Path
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# Setup path
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REPO_ROOT = Path(__file__).parent.parent
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if str(REPO_ROOT) not in sys.path:
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sys.path.insert(0, str(REPO_ROOT))
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from buddai_executive import BuddAI
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class TestFallbackPrompts(unittest.TestCase):
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@patch('buddai_executive.OllamaClient')
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@patch('buddai_executive.StorageManager')
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@patch('buddai_executive.RepoManager')
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def setUp(self, MockRepo, MockStorage, MockOllama):
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# Suppress prints
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with patch('builtins.print'):
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self.ai = BuddAI(user_id="test_prompts", server_mode=True)
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# Mock dependencies
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self.ai.llm = MockOllama()
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self.ai.storage = MockStorage()
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self.ai.confidence_scorer = MagicMock()
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self.ai.personality_manager = MagicMock()
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self.ai.validator = MagicMock()
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self.ai.hardware_profile = MagicMock()
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self.ai.shadow_engine = MagicMock()
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self.ai.shadow_engine.get_all_suggestions.return_value = []
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# Mock FallbackClient
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self.ai.fallback_client = MagicMock()
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self.ai.fallback_client.is_available.return_value = True
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# Setup default mocks
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self.ai.validator.validate.return_value = (True, [])
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self.ai.hardware_profile.detect_hardware.return_value = "ESP32"
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self.ai.hardware_profile.apply_hardware_rules.return_value = "mocked_code_response"
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self.ai.extract_code = MagicMock(return_value=["void setup() {}"])
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def test_specific_prompts_used(self):
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"""Test that configured prompts are used for each model"""
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# Configure Personality
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self.ai.personality_manager.get_value.side_effect = lambda key, default=None: {
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"enabled": True,
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"confidence_threshold": 80,
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"fallback_models": ["claude", "gpt4"],
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"prompts": {
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"claude": "Claude Prompt: {context}",
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"gpt4": "GPT4 Prompt: {context}"
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}
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} if key == "ai_fallback" else default
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# Low confidence
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self.ai.confidence_scorer.calculate_confidence.return_value = 50
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self.ai.confidence_scorer.should_escalate.return_value = True
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# Mock LLM
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self.ai.llm.query.return_value = "Code: ```cpp\nvoid setup() {}\n```"
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# Mock escalate to return the expected prompt string based on model
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def escalate_side_effect(model, prompt, *args, **kwargs):
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if model == "claude":
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return "Claude Prompt: fix the motor"
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if model == "gpt4":
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return "GPT4 Prompt: fix the motor"
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return "Fallback"
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self.ai.fallback_client.escalate.side_effect = escalate_side_effect
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# Run
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user_msg = "fix the motor"
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response = self.ai.chat(user_msg)
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# Verify
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self.assertIn("Claude Prompt: fix the motor", response)
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self.assertIn("GPT4 Prompt: fix the motor", response)
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if __name__ == '__main__':
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unittest.main() |