feat(bias): implement data models and storage for tool weighting and bias profiles

tests/test_bias_models.py

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+import pytest
+from src.models import Tool, ToolPreset, BiasProfile
+
+def test_tool_model():
+ tool = Tool(name="read_file", weight=5, parameter_bias={"path": "preferred"})
+ data = tool.to_dict()
+ assert data["name"] == "read_file"
+ assert data["weight"] == 5
+ assert data["parameter_bias"]["path"] == "preferred"
+ 
+ tool2 = Tool.from_dict(data)
+ assert tool2.name == "read_file"
+ assert tool2.weight == 5
+ assert tool2.parameter_bias["path"] == "preferred"
+
+def test_tool_preset_extension():
+ # Verify that ToolPreset correctly parses and serializes Tool objects
+ tool_data = {"name": "read_file", "weight": 4, "parameter_bias": {"path": "high"}}
+ raw_data = {"categories": {"General": [tool_data]}}
+ 
+ # Test parsing via from_dict
+ preset = ToolPreset.from_dict("test", raw_data)
+ assert isinstance(preset.categories["General"][0], Tool)
+ assert preset.categories["General"][0].weight == 4
+ 
+ # Test serialization
+ data = preset.to_dict()
+ assert data["categories"]["General"][0]["weight"] == 4
+ assert data["categories"]["General"][0]["name"] == "read_file"
+
+def test_bias_profile_model():
+ profile = BiasProfile(
+  name="Execution-Focused",
+  tool_weights={"run_powershell": 5},
+  category_multipliers={"Surgical": 1.5}
+ )
+ data = profile.to_dict()
+ assert data["tool_weights"]["run_powershell"] == 5
+ assert data["category_multipliers"]["Surgical"] == 1.5
+ 
+ # BiasProfile.from_dict expects 'name' inside the dict as well if coming from load_all_bias_profiles
+ data["name"] = "Execution-Focused"
+ profile2 = BiasProfile.from_dict(data)
+ assert profile2.name == "Execution-Focused"
+ assert profile2.tool_weights["run_powershell"] == 5
+ assert profile2.category_multipliers["Surgical"] == 1.5