95556ba9ea
* WIP Refactor resolving mode_names, should be done once at MCP call boundary Pass around model context instead Consensus tool allows one to get a consensus from multiple models, optionally assigning one a 'for' or 'against' stance to find nuanced responses. * Deduplication of model resolution, model_context should be available before reaching deeper parts of the code Improved abstraction when building conversations Throw programmer errors early * Guardrails Support for `model:option` format at MCP boundary so future tools can use additional options if needed instead of handling this only for consensus Model name now supports an optional ":option" for future use * Simplified async flow * Improved model for request to support natural language Simplified async flow * Improved model for request to support natural language Simplified async flow * Fix consensus tool async/sync patterns to match codebase standards CRITICAL FIXES: - Converted _get_consensus_responses from async to sync (matches other tools) - Converted store_conversation_turn from async to sync (add_turn is synchronous) - Removed unnecessary asyncio imports and sleep calls - Fixed ClosedResourceError in MCP protocol during long consensus operations PATTERN ALIGNMENT: - Consensus tool now follows same sync patterns as all other tools - Only execute() and prepare_prompt() are async (base class requirement) - All internal operations are synchronous like analyze, chat, debug, etc. TESTING: - MCP simulation test now passes: consensus_stance ✅ - Two-model consensus works correctly in ~35 seconds - Unknown stance handling defaults to neutral with warnings - All 9 unit tests pass (100% success rate) The consensus tool async patterns were anomalous in the codebase. This fix aligns it with the established synchronous patterns used by all other tools while maintaining full functionality. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com> * Fixed call order and added new test * Cleanup dead comments Docs for the new tool Improved tests --------- Co-authored-by: Claude <noreply@anthropic.com>
506 lines
23 KiB
Python
506 lines
23 KiB
Python
"""
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Test per-tool model default selection functionality
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"""
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import json
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from unittest.mock import MagicMock, patch
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import pytest
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from providers.registry import ModelProviderRegistry, ProviderType
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from tools.analyze import AnalyzeTool
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from tools.base import BaseTool
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from tools.chat import ChatTool
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from tools.codereview import CodeReviewTool
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from tools.debug import DebugIssueTool
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from tools.models import ToolModelCategory
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from tools.precommit import Precommit
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from tools.thinkdeep import ThinkDeepTool
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class TestToolModelCategories:
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"""Test that each tool returns the correct model category."""
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def test_thinkdeep_category(self):
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tool = ThinkDeepTool()
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assert tool.get_model_category() == ToolModelCategory.EXTENDED_REASONING
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def test_debug_category(self):
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tool = DebugIssueTool()
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assert tool.get_model_category() == ToolModelCategory.EXTENDED_REASONING
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def test_analyze_category(self):
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tool = AnalyzeTool()
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assert tool.get_model_category() == ToolModelCategory.EXTENDED_REASONING
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def test_precommit_category(self):
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tool = Precommit()
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assert tool.get_model_category() == ToolModelCategory.EXTENDED_REASONING
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def test_chat_category(self):
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tool = ChatTool()
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assert tool.get_model_category() == ToolModelCategory.FAST_RESPONSE
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def test_codereview_category(self):
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tool = CodeReviewTool()
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assert tool.get_model_category() == ToolModelCategory.BALANCED
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def test_base_tool_default_category(self):
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# Test that BaseTool defaults to BALANCED
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class TestTool(BaseTool):
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def get_name(self):
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return "test"
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def get_description(self):
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return "test"
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def get_input_schema(self):
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return {}
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def get_system_prompt(self):
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return "test"
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def get_request_model(self):
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return MagicMock
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async def prepare_prompt(self, request):
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return "test"
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tool = TestTool()
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assert tool.get_model_category() == ToolModelCategory.BALANCED
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class TestModelSelection:
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"""Test model selection based on tool categories."""
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def test_extended_reasoning_with_openai(self):
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"""Test EXTENDED_REASONING prefers o3 when OpenAI is available."""
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with patch.object(ModelProviderRegistry, "get_available_models") as mock_get_available:
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# Mock OpenAI models available
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mock_get_available.return_value = {
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"o3": ProviderType.OPENAI,
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"o3-mini": ProviderType.OPENAI,
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"o4-mini": ProviderType.OPENAI,
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}
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model = ModelProviderRegistry.get_preferred_fallback_model(ToolModelCategory.EXTENDED_REASONING)
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assert model == "o3"
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def test_extended_reasoning_with_gemini_only(self):
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"""Test EXTENDED_REASONING prefers pro when only Gemini is available."""
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with patch.object(ModelProviderRegistry, "get_available_models") as mock_get_available:
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# Mock only Gemini models available
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mock_get_available.return_value = {
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"gemini-2.5-pro-preview-06-05": ProviderType.GOOGLE,
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"gemini-2.5-flash-preview-05-20": ProviderType.GOOGLE,
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}
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model = ModelProviderRegistry.get_preferred_fallback_model(ToolModelCategory.EXTENDED_REASONING)
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# Should find the pro model for extended reasoning
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assert "pro" in model or model == "gemini-2.5-pro-preview-06-05"
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def test_fast_response_with_openai(self):
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"""Test FAST_RESPONSE prefers o4-mini when OpenAI is available."""
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with patch.object(ModelProviderRegistry, "get_available_models") as mock_get_available:
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# Mock OpenAI models available
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mock_get_available.return_value = {
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"o3": ProviderType.OPENAI,
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"o3-mini": ProviderType.OPENAI,
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"o4-mini": ProviderType.OPENAI,
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}
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model = ModelProviderRegistry.get_preferred_fallback_model(ToolModelCategory.FAST_RESPONSE)
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assert model == "o4-mini"
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def test_fast_response_with_gemini_only(self):
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"""Test FAST_RESPONSE prefers flash when only Gemini is available."""
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with patch.object(ModelProviderRegistry, "get_available_models") as mock_get_available:
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# Mock only Gemini models available
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mock_get_available.return_value = {
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"gemini-2.5-pro-preview-06-05": ProviderType.GOOGLE,
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"gemini-2.5-flash-preview-05-20": ProviderType.GOOGLE,
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}
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model = ModelProviderRegistry.get_preferred_fallback_model(ToolModelCategory.FAST_RESPONSE)
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# Should find the flash model for fast response
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assert "flash" in model or model == "gemini-2.5-flash-preview-05-20"
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def test_balanced_category_fallback(self):
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"""Test BALANCED category uses existing logic."""
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with patch.object(ModelProviderRegistry, "get_available_models") as mock_get_available:
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# Mock OpenAI models available
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mock_get_available.return_value = {
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"o3": ProviderType.OPENAI,
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"o3-mini": ProviderType.OPENAI,
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"o4-mini": ProviderType.OPENAI,
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}
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model = ModelProviderRegistry.get_preferred_fallback_model(ToolModelCategory.BALANCED)
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assert model == "o4-mini" # Balanced prefers o4-mini when OpenAI available
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def test_no_category_uses_balanced_logic(self):
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"""Test that no category specified uses balanced logic."""
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with patch.object(ModelProviderRegistry, "get_available_models") as mock_get_available:
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# Mock only Gemini models available
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mock_get_available.return_value = {
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"gemini-2.5-pro-preview-06-05": ProviderType.GOOGLE,
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"gemini-2.5-flash-preview-05-20": ProviderType.GOOGLE,
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}
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model = ModelProviderRegistry.get_preferred_fallback_model()
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# Should pick a reasonable default, preferring flash for balanced use
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assert "flash" in model or model == "gemini-2.5-flash-preview-05-20"
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class TestFlexibleModelSelection:
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"""Test that model selection handles various naming scenarios."""
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def test_fallback_handles_mixed_model_names(self):
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"""Test that fallback selection works with mix of full names and shorthands."""
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# Test with mix of full names and shorthands
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test_cases = [
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# Case 1: Mix of OpenAI shorthands and full names
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{
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"available": {"o3": ProviderType.OPENAI, "o4-mini": ProviderType.OPENAI},
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"category": ToolModelCategory.EXTENDED_REASONING,
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"expected": "o3",
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},
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# Case 2: Mix of Gemini shorthands and full names
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{
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"available": {
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"gemini-2.5-flash-preview-05-20": ProviderType.GOOGLE,
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"gemini-2.5-pro-preview-06-05": ProviderType.GOOGLE,
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},
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"category": ToolModelCategory.FAST_RESPONSE,
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"expected_contains": "flash",
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},
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# Case 3: Only shorthands available
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{
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"available": {"o4-mini": ProviderType.OPENAI, "o3-mini": ProviderType.OPENAI},
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"category": ToolModelCategory.FAST_RESPONSE,
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"expected": "o4-mini",
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},
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]
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for case in test_cases:
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with patch.object(ModelProviderRegistry, "get_available_models") as mock_get_available:
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mock_get_available.return_value = case["available"]
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model = ModelProviderRegistry.get_preferred_fallback_model(case["category"])
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if "expected" in case:
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assert model == case["expected"], f"Failed for case: {case}"
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elif "expected_contains" in case:
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assert (
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case["expected_contains"] in model
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), f"Expected '{case['expected_contains']}' in '{model}' for case: {case}"
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class TestCustomProviderFallback:
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"""Test fallback to custom/openrouter providers."""
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@patch.object(ModelProviderRegistry, "_find_extended_thinking_model")
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def test_extended_reasoning_custom_fallback(self, mock_find_thinking):
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"""Test EXTENDED_REASONING falls back to custom thinking model."""
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with patch.object(ModelProviderRegistry, "get_available_models") as mock_get_available:
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# No native models available, but OpenRouter is available
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mock_get_available.return_value = {"openrouter-model": ProviderType.OPENROUTER}
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mock_find_thinking.return_value = "custom/thinking-model"
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model = ModelProviderRegistry.get_preferred_fallback_model(ToolModelCategory.EXTENDED_REASONING)
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assert model == "custom/thinking-model"
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mock_find_thinking.assert_called_once()
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@patch.object(ModelProviderRegistry, "_find_extended_thinking_model")
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def test_extended_reasoning_final_fallback(self, mock_find_thinking):
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"""Test EXTENDED_REASONING falls back to pro when no custom found."""
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with patch.object(ModelProviderRegistry, "get_provider") as mock_get_provider:
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# No providers available
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mock_get_provider.return_value = None
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mock_find_thinking.return_value = None
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model = ModelProviderRegistry.get_preferred_fallback_model(ToolModelCategory.EXTENDED_REASONING)
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assert model == "gemini-2.5-pro-preview-06-05"
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class TestAutoModeErrorMessages:
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"""Test that auto mode error messages include suggested models."""
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@pytest.mark.asyncio
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async def test_thinkdeep_auto_error_message(self):
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"""Test ThinkDeep tool suggests appropriate model in auto mode."""
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with patch("config.IS_AUTO_MODE", True):
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with patch("config.DEFAULT_MODEL", "auto"):
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with patch.object(ModelProviderRegistry, "get_available_models") as mock_get_available:
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# Mock only Gemini models available
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mock_get_available.return_value = {
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"gemini-2.5-pro-preview-06-05": ProviderType.GOOGLE,
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"gemini-2.5-flash-preview-05-20": ProviderType.GOOGLE,
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}
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tool = ThinkDeepTool()
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result = await tool.execute({"prompt": "test", "model": "auto"})
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assert len(result) == 1
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assert "Model parameter is required in auto mode" in result[0].text
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# Should suggest a model suitable for extended reasoning (either full name or with 'pro')
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response_text = result[0].text
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assert "gemini-2.5-pro-preview-06-05" in response_text or "pro" in response_text
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assert "(category: extended_reasoning)" in response_text
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@pytest.mark.asyncio
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async def test_chat_auto_error_message(self):
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"""Test Chat tool suggests appropriate model in auto mode."""
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with patch("config.IS_AUTO_MODE", True):
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with patch("config.DEFAULT_MODEL", "auto"):
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with patch.object(ModelProviderRegistry, "get_available_models") as mock_get_available:
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# Mock OpenAI models available
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mock_get_available.return_value = {
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"o3": ProviderType.OPENAI,
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"o3-mini": ProviderType.OPENAI,
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"o4-mini": ProviderType.OPENAI,
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}
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tool = ChatTool()
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result = await tool.execute({"prompt": "test", "model": "auto"})
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assert len(result) == 1
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assert "Model parameter is required in auto mode" in result[0].text
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# Should suggest a model suitable for fast response
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response_text = result[0].text
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assert "o4-mini" in response_text or "o3-mini" in response_text or "mini" in response_text
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assert "(category: fast_response)" in response_text
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class TestFileContentPreparation:
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"""Test that file content preparation uses tool-specific model for capacity."""
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@patch("tools.base.read_files")
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@patch("tools.base.logger")
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def test_auto_mode_uses_tool_category(self, mock_logger, mock_read_files):
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"""Test that auto mode uses tool-specific model for capacity estimation."""
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mock_read_files.return_value = "file content"
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with patch.object(ModelProviderRegistry, "get_provider") as mock_get_provider:
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# Mock provider with capabilities
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mock_provider = MagicMock()
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mock_provider.get_capabilities.return_value = MagicMock(context_window=1_000_000)
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mock_get_provider.side_effect = lambda ptype: mock_provider if ptype == ProviderType.GOOGLE else None
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# Create a tool and test file content preparation
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tool = ThinkDeepTool()
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tool._current_model_name = "auto"
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# Set up model context to simulate normal execution flow
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from utils.model_context import ModelContext
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tool._model_context = ModelContext("gemini-2.5-pro-preview-06-05")
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# Call the method
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content, processed_files = tool._prepare_file_content_for_prompt(["/test/file.py"], None, "test")
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# Check that it logged the correct message about using model context
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debug_calls = [call for call in mock_logger.debug.call_args_list if "Using model context" in str(call)]
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assert len(debug_calls) > 0
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debug_message = str(debug_calls[0])
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# Should mention the model being used
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assert "gemini-2.5-pro-preview-06-05" in debug_message
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# Should mention file tokens (not content tokens)
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assert "file tokens" in debug_message
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class TestProviderHelperMethods:
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"""Test the helper methods for finding models from custom/openrouter."""
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def test_find_extended_thinking_model_custom(self):
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"""Test finding thinking model from custom provider."""
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with patch.object(ModelProviderRegistry, "get_provider") as mock_get_provider:
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from providers.custom import CustomProvider
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# Mock custom provider with thinking model
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mock_custom = MagicMock(spec=CustomProvider)
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mock_custom.model_registry = {
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"model1": {"supports_extended_thinking": False},
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"model2": {"supports_extended_thinking": True},
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"model3": {"supports_extended_thinking": False},
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}
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mock_get_provider.side_effect = lambda ptype: mock_custom if ptype == ProviderType.CUSTOM else None
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model = ModelProviderRegistry._find_extended_thinking_model()
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assert model == "model2"
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def test_find_extended_thinking_model_openrouter(self):
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"""Test finding thinking model from openrouter."""
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with patch.object(ModelProviderRegistry, "get_provider") as mock_get_provider:
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# Mock openrouter provider
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mock_openrouter = MagicMock()
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mock_openrouter.validate_model_name.side_effect = lambda m: m == "anthropic/claude-3.5-sonnet"
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mock_get_provider.side_effect = lambda ptype: mock_openrouter if ptype == ProviderType.OPENROUTER else None
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model = ModelProviderRegistry._find_extended_thinking_model()
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assert model == "anthropic/claude-3.5-sonnet"
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def test_find_extended_thinking_model_none_found(self):
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"""Test when no thinking model is found."""
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with patch.object(ModelProviderRegistry, "get_provider") as mock_get_provider:
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# No providers available
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mock_get_provider.return_value = None
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model = ModelProviderRegistry._find_extended_thinking_model()
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assert model is None
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class TestEffectiveAutoMode:
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"""Test the is_effective_auto_mode method."""
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def test_explicit_auto_mode(self):
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"""Test when DEFAULT_MODEL is explicitly 'auto'."""
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with patch("config.DEFAULT_MODEL", "auto"):
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with patch("config.IS_AUTO_MODE", True):
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tool = ChatTool()
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assert tool.is_effective_auto_mode() is True
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def test_unavailable_model_triggers_auto_mode(self):
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"""Test when DEFAULT_MODEL is set but not available."""
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with patch("config.DEFAULT_MODEL", "o3"):
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with patch("config.IS_AUTO_MODE", False):
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with patch.object(ModelProviderRegistry, "get_provider_for_model") as mock_get_provider:
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mock_get_provider.return_value = None # Model not available
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tool = ChatTool()
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assert tool.is_effective_auto_mode() is True
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def test_available_model_no_auto_mode(self):
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"""Test when DEFAULT_MODEL is set and available."""
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with patch("config.DEFAULT_MODEL", "pro"):
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with patch("config.IS_AUTO_MODE", False):
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with patch.object(ModelProviderRegistry, "get_provider_for_model") as mock_get_provider:
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mock_get_provider.return_value = MagicMock() # Model is available
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tool = ChatTool()
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assert tool.is_effective_auto_mode() is False
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class TestRuntimeModelSelection:
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"""Test runtime model selection behavior."""
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@pytest.mark.asyncio
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async def test_explicit_auto_in_request(self):
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"""Test when Claude explicitly passes model='auto'."""
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with patch("config.DEFAULT_MODEL", "pro"): # DEFAULT_MODEL is a real model
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with patch("config.IS_AUTO_MODE", False): # Not in auto mode
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tool = ThinkDeepTool()
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result = await tool.execute({"prompt": "test", "model": "auto"})
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# Should require model selection even though DEFAULT_MODEL is valid
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assert len(result) == 1
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assert "Model parameter is required in auto mode" in result[0].text
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@pytest.mark.asyncio
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async def test_unavailable_model_in_request(self):
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"""Test when Claude passes an unavailable model."""
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with patch("config.DEFAULT_MODEL", "pro"):
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with patch("config.IS_AUTO_MODE", False):
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with patch.object(ModelProviderRegistry, "get_provider_for_model") as mock_get_provider:
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# Model is not available
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mock_get_provider.return_value = None
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tool = ChatTool()
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result = await tool.execute({"prompt": "test", "model": "gpt-5-turbo"})
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# Should require model selection
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assert len(result) == 1
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# When a specific model is requested but not available, error message is different
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assert "gpt-5-turbo" in result[0].text
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assert "is not available" in result[0].text
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assert "(category: fast_response)" in result[0].text
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class TestSchemaGeneration:
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"""Test schema generation with different configurations."""
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def test_schema_with_explicit_auto_mode(self):
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"""Test schema when DEFAULT_MODEL='auto'."""
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with patch("config.DEFAULT_MODEL", "auto"):
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with patch("config.IS_AUTO_MODE", True):
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tool = ChatTool()
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schema = tool.get_input_schema()
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# Model should be required
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assert "model" in schema["required"]
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def test_schema_with_unavailable_default_model(self):
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"""Test schema when DEFAULT_MODEL is set but unavailable."""
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with patch("config.DEFAULT_MODEL", "o3"):
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with patch("config.IS_AUTO_MODE", False):
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|
with patch.object(ModelProviderRegistry, "get_provider_for_model") as mock_get_provider:
|
|
mock_get_provider.return_value = None # Model not available
|
|
|
|
tool = AnalyzeTool()
|
|
schema = tool.get_input_schema()
|
|
|
|
# Model should be required due to unavailable DEFAULT_MODEL
|
|
assert "model" in schema["required"]
|
|
|
|
def test_schema_with_available_default_model(self):
|
|
"""Test schema when DEFAULT_MODEL is available."""
|
|
with patch("config.DEFAULT_MODEL", "pro"):
|
|
with patch("config.IS_AUTO_MODE", False):
|
|
with patch.object(ModelProviderRegistry, "get_provider_for_model") as mock_get_provider:
|
|
mock_get_provider.return_value = MagicMock() # Model is available
|
|
|
|
tool = ThinkDeepTool()
|
|
schema = tool.get_input_schema()
|
|
|
|
# Model should NOT be required
|
|
assert "model" not in schema["required"]
|
|
|
|
|
|
class TestUnavailableModelFallback:
|
|
"""Test fallback behavior when DEFAULT_MODEL is not available."""
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_unavailable_default_model_fallback(self):
|
|
"""Test that unavailable DEFAULT_MODEL triggers auto mode behavior."""
|
|
with patch("config.DEFAULT_MODEL", "o3"): # Set DEFAULT_MODEL to a specific model
|
|
with patch("config.IS_AUTO_MODE", False): # Not in auto mode
|
|
with patch.object(ModelProviderRegistry, "get_provider_for_model") as mock_get_provider:
|
|
# Model is not available (no provider)
|
|
mock_get_provider.return_value = None
|
|
|
|
tool = ThinkDeepTool()
|
|
result = await tool.execute({"prompt": "test"}) # No model specified
|
|
|
|
# Should get auto mode error since model is unavailable
|
|
assert len(result) == 1
|
|
# When DEFAULT_MODEL is unavailable, the error message indicates the model is not available
|
|
assert "o3" in result[0].text
|
|
assert "is not available" in result[0].text
|
|
# The suggested model depends on which providers are available
|
|
# Just check that it suggests a model for the extended_reasoning category
|
|
assert "(category: extended_reasoning)" in result[0].text
|
|
|
|
@pytest.mark.asyncio
|
|
async def test_available_default_model_no_fallback(self):
|
|
"""Test that available DEFAULT_MODEL works normally."""
|
|
with patch("config.DEFAULT_MODEL", "pro"):
|
|
with patch("config.IS_AUTO_MODE", False):
|
|
with patch.object(ModelProviderRegistry, "get_provider_for_model") as mock_get_provider:
|
|
# Model is available
|
|
mock_provider = MagicMock()
|
|
mock_provider.generate_content.return_value = MagicMock(content="Test response", metadata={})
|
|
mock_get_provider.return_value = mock_provider
|
|
|
|
# Mock the provider lookup in BaseTool.get_model_provider
|
|
with patch.object(BaseTool, "get_model_provider") as mock_get_model_provider:
|
|
mock_get_model_provider.return_value = mock_provider
|
|
|
|
tool = ChatTool()
|
|
result = await tool.execute({"prompt": "test"}) # No model specified
|
|
|
|
# Should work normally, not require model parameter
|
|
assert len(result) == 1
|
|
output = json.loads(result[0].text)
|
|
assert output["status"] == "success"
|
|
assert "Test response" in output["content"]
|