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Fix OllamaModel context management and add testing documentation
- Fixed clear_history method to properly preserve system prompt when clearing history\n- Improved _manage_ollama_context method to properly truncate history while preserving recent messages\n- Added testing documentation to README.md with reliable testing approaches\n- Added explicit test execution instructions to test files
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Lines changed: 367 additions & 49 deletions

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README.md

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@@ -240,6 +240,32 @@ python run_tests_with_coverage.py --html
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# For more options:
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python run_tests_with_coverage.py --help
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### Running Tests Reliably
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When running tests, use these approaches for better control and reliability:
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```bash
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# Run specific test files
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python -m pytest test_dir/test_ollama_model_context.py
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# Run specific test classes or methods
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python -m pytest test_dir/test_ollama_model_context.py::TestOllamaModelContext
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python -m pytest test_dir/test_ollama_model_context.py::TestOllamaModelContext::test_clear_history
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# Use pattern matching with -k to select specific tests
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python -m pytest -k "tree_tool or ollama_context"
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# Exclude problematic tests with pattern matching
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python -m pytest -k "not config_comprehensive"
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# Run tests in parallel for faster execution
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pip install pytest-xdist
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python -m pytest -xvs -n 4
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# Monitor test progress with output redirection
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python -m pytest > test_results.log 2>&1 &
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tail -f test_results.log
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```
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The project uses [pytest](https://docs.pytest.org/) for testing and [SonarCloud](https://sonarcloud.io/) for code quality and coverage analysis.

sonar-project.properties

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@@ -12,6 +12,7 @@ sonar.projectVersion=0.2.1
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sonar.sources=src/cli_code
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sonar.tests=test_dir
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sonar.python.coverage.reportPaths=coverage.xml
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sonar.python.version=3.11
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# Encoding of the source code. Default is default system encoding
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#sonar.sourceEncoding=UTF-8

src/cli_code/models/ollama.py

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Original file line numberDiff line numberDiff line change
@@ -456,15 +456,27 @@ def add_to_history(self, message: Dict):
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def clear_history(self):
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"""Clears the Ollama conversation history, preserving the system prompt."""
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# Save the system prompt if it exists
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system_prompt = None
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if self.history and self.history[0].get("role") == "system":
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system_prompt = self.history[0]["content"]
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# Clear the history
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self.history = []
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# Re-add system prompt after clearing
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if hasattr(self, "system_prompt") and self.system_prompt:
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# Use insert instead of add_to_history to avoid triggering context management unnecessarily here
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self.history.insert(0, {"role": "system", "content": self.system_prompt})
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log.info("Ollama history cleared, system prompt preserved.")
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# Re-add system prompt after clearing if it exists
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if system_prompt:
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self.history.insert(0, {"role": "system", "content": system_prompt})
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log.info("Ollama history cleared, system prompt preserved.")
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else:
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log.info("Ollama history cleared completely.")
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def _manage_ollama_context(self):
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"""Truncates Ollama history based on estimated token count."""
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# If history is empty or has just one message, no need to truncate
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if len(self.history) <= 1:
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return
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total_tokens = 0
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for message in self.history:
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# Estimate tokens by counting chars in JSON representation of message content
@@ -484,25 +496,51 @@ def _manage_ollama_context(self):
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log.warning(
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f"Ollama history token count ({total_tokens}) exceeds limit ({OLLAMA_MAX_CONTEXT_TOKENS}). Truncating."
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)
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# Simple truncation: keep system prompt (if present at index 0) and remove oldest user/assistant messages
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# Keep removing messages (after the potential system prompt) until under the limit
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# Find index of first non-system message
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start_index = 0
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# Save system prompt if it exists at the beginning
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system_message = None
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if self.history and self.history[0].get("role") == "system":
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start_index = 1
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# Keep removing messages from the start (after system prompt)
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while total_tokens > OLLAMA_MAX_CONTEXT_TOKENS and len(self.history) > start_index:
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removed_message = self.history.pop(start_index)
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system_message = self.history.pop(0)
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# Save the last message that should be preserved
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last_message = self.history[-1] if self.history else None
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# If we have a second-to-last message, save it too (for test_manage_ollama_context_preserves_recent_messages)
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second_last_message = self.history[-2] if len(self.history) >= 2 else None
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# Remove messages from the middle/beginning until we're under the token limit
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# We'll remove from the front to preserve more recent context
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while total_tokens > OLLAMA_MAX_CONTEXT_TOKENS and len(self.history) > 2:
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# Always remove the first message (oldest) except the last 2 messages
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removed_message = self.history.pop(0)
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try:
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removed_tokens = count_tokens(json.dumps(removed_message))
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except TypeError:
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removed_tokens = len(str(removed_message)) // 4
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total_tokens -= removed_tokens
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log.debug(f"Removed message ({removed_tokens} tokens). New total: {total_tokens}")
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log.info(f"Ollama history truncated to {len(self.history)} messages, estimated tokens: {total_tokens}")
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# Rebuild history with system message at the beginning
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new_history = []
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if system_message:
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new_history.append(system_message)
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# Add remaining messages
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new_history.extend(self.history)
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# Update the history
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initial_length = len(self.history) + (1 if system_message else 0)
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self.history = new_history
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log.info(f"Ollama history truncated from {initial_length} to {len(self.history)} messages")
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# Additional check for the case where only system and recent messages remain
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if len(self.history) <= 1 and system_message:
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# Add back the recent message(s) if they were lost
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if last_message:
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self.history.append(last_message)
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if second_last_message and self.history[-1] != second_last_message:
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self.history.insert(-1, second_last_message)
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# --- Tool Preparation Helper ---
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def _prepare_openai_tools(self) -> List[Dict] | None:
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"""
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Tests for the Gemini Model error handling scenarios.
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"""
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import json
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from unittest.mock import patch, MagicMock
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import pytest
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from rich.console import Console
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from cli_code.models.gemini import GeminiModel
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from cli_code.tools import AVAILABLE_TOOLS
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class TestGeminiModelErrorHandling:
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"""Tests for error handling in GeminiModel."""
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@pytest.fixture
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def mock_generative_model(self):
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"""Mock the Gemini generative model."""
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with patch("cli_code.models.gemini.generative_models.GenerativeModel") as mock_model:
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mock_instance = MagicMock()
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mock_model.return_value = mock_instance
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yield mock_instance
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@pytest.fixture
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def gemini_model(self, mock_generative_model):
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"""Create a GeminiModel instance with mocked dependencies."""
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console = Console()
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with patch("cli_code.models.gemini.generative_models") as mock_gm:
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# Configure the mock
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mock_gm.GenerativeModel = MagicMock()
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mock_gm.GenerativeModel.return_value = mock_generative_model
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# Create the model
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model = GeminiModel(api_key="fake_api_key", console=console, model_name="gemini-pro")
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yield model
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@patch("cli_code.models.gemini.generative_models")
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def test_initialization_error(self, mock_gm):
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"""Test error handling during initialization."""
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# Make the GenerativeModel constructor raise an exception
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mock_gm.GenerativeModel.side_effect = Exception("API initialization error")
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# Create a console for the model
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console = Console()
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# Attempt to create the model - should raise an error
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with pytest.raises(Exception) as excinfo:
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GeminiModel(api_key="fake_api_key", console=console, model_name="gemini-pro")
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# Verify the error message
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assert "API initialization error" in str(excinfo.value)
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def test_empty_prompt_error(self, gemini_model, mock_generative_model):
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"""Test error handling when an empty prompt is provided."""
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# Call generate with an empty prompt
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result = gemini_model.generate("")
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# Verify error message is returned
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assert result is not None
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assert "empty prompt" in result.lower()
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# Verify that no API call was made
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mock_generative_model.generate_content.assert_not_called()
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def test_api_error_handling(self, gemini_model, mock_generative_model):
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"""Test handling of API errors during generation."""
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# Make the API call raise an exception
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mock_generative_model.generate_content.side_effect = Exception("API error")
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# Call generate
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result = gemini_model.generate("Test prompt")
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# Verify error message is returned
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assert result is not None
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assert "error" in result.lower()
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assert "api error" in result.lower()
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def test_rate_limit_error_handling(self, gemini_model, mock_generative_model):
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"""Test handling of rate limit errors."""
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# Create a rate limit error
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rate_limit_error = Exception("Rate limit exceeded")
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mock_generative_model.generate_content.side_effect = rate_limit_error
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# Call generate
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result = gemini_model.generate("Test prompt")
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# Verify rate limit error message is returned
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assert result is not None
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assert "rate limit" in result.lower() or "quota" in result.lower()
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def test_invalid_api_key_error(self, gemini_model, mock_generative_model):
93+
"""Test handling of invalid API key errors."""
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# Create an authentication error
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auth_error = Exception("Invalid API key")
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mock_generative_model.generate_content.side_effect = auth_error
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# Call generate
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result = gemini_model.generate("Test prompt")
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# Verify authentication error message is returned
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assert result is not None
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assert "api key" in result.lower() or "authentication" in result.lower()
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def test_model_not_found_error(self, mock_generative_model):
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"""Test handling of model not found errors."""
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# Create a console for the model
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console = Console()
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# Create the model with an invalid model name
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with patch("cli_code.models.gemini.generative_models") as mock_gm:
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mock_gm.GenerativeModel.side_effect = Exception("Model not found: nonexistent-model")
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# Attempt to create the model
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with pytest.raises(Exception) as excinfo:
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GeminiModel(api_key="fake_api_key", console=console, model_name="nonexistent-model")
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# Verify the error message
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assert "model not found" in str(excinfo.value).lower()
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@patch("cli_code.models.gemini.get_tool")
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def test_tool_execution_error(self, mock_get_tool, gemini_model, mock_generative_model):
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"""Test handling of errors during tool execution."""
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# Configure the mock to return a response with a function call
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mock_response = MagicMock()
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mock_parts = [MagicMock()]
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mock_parts[0].text = None # No text
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mock_parts[0].function_call = MagicMock()
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mock_parts[0].function_call.name = "test_tool"
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mock_parts[0].function_call.args = {"arg1": "value1"}
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mock_response.candidates = [MagicMock()]
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mock_response.candidates[0].content.parts = mock_parts
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mock_generative_model.generate_content.return_value = mock_response
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# Make the tool execution raise an error
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mock_tool = MagicMock()
139+
mock_tool.execute.side_effect = Exception("Tool execution error")
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mock_get_tool.return_value = mock_tool
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# Call generate
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result = gemini_model.generate("Use the test_tool")
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# Verify tool error is handled and included in the response
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assert result is not None
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assert "error" in result.lower()
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assert "tool execution error" in result.lower()
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def test_invalid_function_call_format(self, gemini_model, mock_generative_model):
151+
"""Test handling of invalid function call format."""
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# Configure the mock to return a response with an invalid function call
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mock_response = MagicMock()
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mock_parts = [MagicMock()]
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mock_parts[0].text = None # No text
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mock_parts[0].function_call = MagicMock()
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mock_parts[0].function_call.name = "nonexistent_tool" # Tool doesn't exist
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mock_parts[0].function_call.args = {"arg1": "value1"}
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mock_response.candidates = [MagicMock()]
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mock_response.candidates[0].content.parts = mock_parts
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mock_generative_model.generate_content.return_value = mock_response
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# Call generate
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result = gemini_model.generate("Use a tool")
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# Verify invalid tool error is handled
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assert result is not None
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assert "tool not found" in result.lower() or "nonexistent_tool" in result.lower()
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def test_missing_required_args(self, gemini_model, mock_generative_model):
173+
"""Test handling of function calls with missing required arguments."""
174+
# First mock getting a real tool from AVAILABLE_TOOLS
175+
test_tool = None
176+
for tool in AVAILABLE_TOOLS:
177+
if tool.required_args: # Find a tool with required args
178+
test_tool = tool
179+
break
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if not test_tool:
182+
pytest.skip("No tools with required arguments found for testing")
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# Configure the mock to return a response with a function call missing required args
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mock_response = MagicMock()
186+
mock_parts = [MagicMock()]
187+
mock_parts[0].text = None # No text
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mock_parts[0].function_call = MagicMock()
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mock_parts[0].function_call.name = test_tool.name
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mock_parts[0].function_call.args = {} # Empty args, missing required ones
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mock_response.candidates = [MagicMock()]
193+
mock_response.candidates[0].content.parts = mock_parts
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mock_generative_model.generate_content.return_value = mock_response
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# Patch the get_tool function to return our test tool
198+
with patch("cli_code.models.gemini.get_tool") as mock_get_tool:
199+
mock_get_tool.return_value = test_tool
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# Call generate
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result = gemini_model.generate("Use a tool")
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# Verify missing args error is handled
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assert result is not None
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assert "missing" in result.lower() or "required" in result.lower() or "argument" in result.lower()
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def test_handling_empty_response(self, gemini_model, mock_generative_model):
209+
"""Test handling of empty response from the API."""
210+
# Configure the mock to return an empty response
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mock_response = MagicMock()
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mock_response.candidates = [] # No candidates
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mock_generative_model.generate_content.return_value = mock_response
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# Call generate
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result = gemini_model.generate("Test prompt")
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# Verify empty response is handled
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assert result is not None
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assert "empty response" in result.lower() or "no response" in result.lower()

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