Update tools/quantum_tool.py

tools/quantum_tool.py

@@ -0,0 +1,37 @@
+from langchain.tools import BaseTool
+from typing import Type, List, Dict, Any
+from pydantic import BaseModel, Field
+from quantum.optimizer import optimize_treatment
+from services.logger import app_logger
+from services.metrics import log_tool_usage
+
+class QuantumOptimizerInput(BaseModel):
+    patient_data: Dict[str, Any] = Field(description="Dictionary of relevant patient characteristics (e.g., {'age': 55, 'gender': 'male'}).")
+    current_treatments: List[str] = Field(description="List of current medications or therapies (e.g., ['Aspirin 81mg', 'Metformin 500mg']).")
+    conditions: List[str] = Field(description="List of diagnosed conditions (e.g., ['Type 2 Diabetes', 'Hypertension']).")
+
+class QuantumTreatmentOptimizerTool(BaseTool):
+    name: str = "quantum_treatment_optimizer"
+    description: str = (
+        "A specialized tool that uses quantum-inspired algorithms to suggest optimized treatment plans. "
+        "Provide patient data, current treatments, and diagnosed conditions. "
+        "Use this when seeking novel therapeutic strategies or to optimize complex polypharmacy."
+    )
+    args_schema: Type[BaseModel] = QuantumOptimizerInput
+
+    def _run(self, patient_data: Dict[str, Any], current_treatments: List[str], conditions: List[str]) -> str:
+        app_logger.info(f"Quantum Optimizer Tool called with: {patient_data}, {current_treatments}, {conditions}")
+        log_tool_usage(self.name)
+        try:
+            result = optimize_treatment(patient_data, current_treatments, conditions)
+            # Format result for LLM
+            # Example: "Optimized suggestions: ..., Confidence: ..., Summary: ..."
+            # You might want to pretty-print the dict or convert to a string summary
+            return f"Quantum Optimizer Results: {result}" 
+        except Exception as e:
+            app_logger.error(f"Error in QuantumTreatmentOptimizerTool: {e}")
+            return f"Error during quantum optimization: {str(e)}"
+
+    async def _arun(self, patient_data: Dict[str, Any], current_treatments: List[str], conditions: List[str]) -> str:
+        # For simplicity, using sync version for now
+        return self._run(patient_data, current_treatments, conditions)