ed
ecd8e82f2f
Per the 4-criteria decision rule: BiasProfile fails C1 (only used by
tool_presets + tool_bias), fails C2 (no state machine), fails C3 (no
dedicated test file), borderline C4. MERGE into the existing
src/tool_bias.py which already has ToolBiasEngine.
This commit:
1. Adds BiasProfile class definition to src/tool_bias.py at the top
(after the dataclass + typing imports).
2. Removes BiasProfile from src/models.py.
3. Adds lazy re-export via the existing __getattr__ in src/models.py
(EAGER would deadlock: tool_presets needs BiasProfile + tool_bias
needs Tool/ToolPreset, and both want models re-exports).
4. Updates src/tool_presets.py to use the local-import pattern for
BiasProfile (in load_all_bias_profiles) + adds
'from __future__ import annotations' so the 'BiasProfile' type
annotation is a string. This breaks the cycle.
5. Updates src/tool_bias.py to import Tool + ToolPreset from
src.tool_presets directly (no longer through models) + adds
'from __future__ import annotations'.
Verification: VC8 (BiasProfile)
from src.tool_bias import BiasProfile # OK
from src.tool_presets import Tool, ToolPreset # OK
from src.models import Tool, ToolPreset, BiasProfile # OK (lazy)
Tool is Tool returns True
ToolPreset is ToolPreset returns True
BiasProfile is BiasProfile returns True
Tests verified (10/10 PASS):
tests/test_tool_preset_manager.py (4 tests)
tests/test_bias_models.py (3 tests)
tests/test_tool_bias.py (3 tests)
Cycle resolution:
models -> tool_presets (lazy via __getattr__)
tool_presets -> tool_bias (local import in function body, only at call time)
tool_bias -> tool_presets (eager; OK because tool_presets is fully
loaded by the time tool_bias's class
definitions need Tool/ToolPreset)
The eager load of tool_bias from tool_presets is what made the
'from __future__ import annotations' necessary in both files (for
Tool/ToolPreset string annotations in tool_bias method signatures).
94 lines
3.0 KiB
Python
94 lines
3.0 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Optional
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from src.tool_presets import Tool, ToolPreset
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from src.type_aliases import Metadata
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@dataclass
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class BiasProfile:
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name: str
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tool_weights: Dict[str, int] = field(default_factory=dict)
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category_multipliers: Dict[str, float] = field(default_factory=dict)
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def to_dict(self) -> Metadata:
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return {
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"name": self.name,
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"tool_weights": self.tool_weights,
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"category_multipliers": self.category_multipliers,
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}
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@classmethod
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def from_dict(cls, data: Metadata) -> "BiasProfile":
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return cls(
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name = data["name"],
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tool_weights = data.get("tool_weights", {}),
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category_multipliers = data.get("category_multipliers", {}),
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)
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class ToolBiasEngine:
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def apply_semantic_nudges(self, tool_definitions: List[Dict[str, Any]], preset: ToolPreset) -> List[Dict[str, Any]]:
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"""
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[C: tests/test_tool_bias.py:test_apply_semantic_nudges, tests/test_tool_bias.py:test_parameter_bias_nudging]
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"""
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weight_map = {
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5: "[HIGH PRIORITY] ",
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4: "[PREFERRED] ",
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2: "[NOT RECOMMENDED] ",
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1: "[LOW PRIORITY] "
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}
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preset_tools: Dict[str, Tool] = {}
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for cat_tools in preset.categories.values():
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for t in cat_tools:
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if isinstance(t, Tool):
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preset_tools[t.name] = t
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for defn in tool_definitions:
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name = defn.get("name")
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if name in preset_tools:
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tool = preset_tools[name]
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prefix = weight_map.get(tool.weight, "")
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if prefix:
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defn["description"] = prefix + defn.get("description", "")
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if tool.parameter_bias:
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params = defn.get("parameters") or defn.get("input_schema")
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if params and "properties" in params:
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props = params["properties"]
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for p_name, bias in tool.parameter_bias.items():
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if p_name in props:
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p_desc = props[p_name].get("description", "")
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props[p_name]["description"] = f"[{bias}] {p_desc}".strip()
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return tool_definitions
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def generate_tooling_strategy(self, preset: ToolPreset, global_bias: BiasProfile) -> str:
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"""
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[C: tests/test_tool_bias.py:test_generate_tooling_strategy]
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"""
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lines = ["### Tooling Strategy"]
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preferred = []
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low_priority = []
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for cat_tools in preset.categories.values():
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for t in cat_tools:
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if not isinstance(t, Tool): continue
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if t.weight >= 5: preferred.append(f"{t.name} [HIGH PRIORITY]")
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elif t.weight == 4: preferred.append(f"{t.name} [PREFERRED]")
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elif t.weight == 2: low_priority.append(f"{t.name} [NOT RECOMMENDED]")
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elif t.weight <= 1: low_priority.append(f"{t.name} [LOW PRIORITY]")
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if preferred: lines.append(f"Preferred tools: {', '.join(preferred)}.")
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if low_priority: lines.append(f"Low-priority tools: {', '.join(low_priority)}.")
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if global_bias.category_multipliers:
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lines.append("Category focus multipliers:")
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for cat, mult in global_bias.category_multipliers.items():
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lines.append(f"- {cat}: {mult}x")
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return "\n\n".join(lines)
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