# /home/user/app/tools/quantum_treatment_optimizer_tool.py from langchain_core.tools import BaseTool # Updated import path from typing import Type, List, Dict, Any, Optional # Optional for potentially missing fields in result from pydantic import BaseModel, Field # For input schema validation # Assuming your actual optimizer function is in this path # If it's in a different location, adjust the import. try: from quantum.optimizer import optimize_treatment except ImportError: # Provide a mock function if the actual optimizer is not available # This allows the rest of the app to run for UI/agent testing. app_logger.warning("Actual 'quantum.optimizer.optimize_treatment' not found. Using mock function for QuantumTreatmentOptimizerTool.") def optimize_treatment(patient_data: Dict[str, Any], current_treatments: List[str], conditions: List[str]) -> Dict[str, Any]: # Mock implementation for demonstration and testing mock_suggestions = [ f"Consider adjusting {current_treatments[0] if current_treatments else 'current treatment'} based on {conditions[0] if conditions else 'primary condition'}.", "Explore adding a complementary therapy Y.", "Monitor key biomarker Z closely." ] return { "simulated_optimization_id": "QO-Sim-12345", "suggested_actions": mock_suggestions, "primary_focus_condition": conditions[0] if conditions else "N/A", "confidence_level_simulated": 0.75, "summary_notes": "This simulated plan aims to address the primary condition while managing current treatments. Further clinical evaluation is essential." } from services.logger import app_logger # Your application logger from services.metrics import log_tool_usage # Your metrics logger class QuantumOptimizerInput(BaseModel): """Input schema for the QuantumTreatmentOptimizerTool.""" patient_data: Dict[str, Any] = Field( description=( "A dictionary containing relevant patient characteristics. " "Examples: {'age': 55, 'gender': 'Male', 'relevant_labs': {'creatinine': 1.2, 'hbA1c': 7.5}, " "'allergies': ['penicillin']}. This should be populated from the overall patient context." ) ) current_treatments: List[str] = Field( description="A list of current medications or therapies the patient is on (e.g., ['Aspirin 81mg', 'Metformin 500mg OD'])." ) conditions: List[str] = Field( description="A list of primary diagnosed conditions or symptoms to be addressed (e.g., ['Type 2 Diabetes', 'Hypertension', 'Chronic Back Pain'])." ) # Optional: Add other specific parameters your optimizer might need # optimization_goal: Optional[str] = Field(default=None, description="Specific goal for the optimization, e.g., 'minimize side effects', 'maximize efficacy for condition X'.") class QuantumTreatmentOptimizerTool(BaseTool): name: str = "quantum_treatment_optimizer" description: str = ( "A specialized (simulated) tool that uses advanced algorithms to suggest optimized or alternative treatment plans " "based on provided patient data, current treatments, and diagnosed conditions. " "Use this when seeking novel therapeutic strategies, needing to optimize complex polypharmacy, " "or exploring options for patients with multiple comorbidities. " "You MUST provide detailed 'patient_data', 'current_treatments', and 'conditions'." ) args_schema: Type[BaseModel] = QuantumOptimizerInput # return_direct: bool = False # Usually False, so the agent can process the tool's output def _format_results_for_llm(self, optimization_output: Dict[str, Any]) -> str: """ Formats the structured output from optimize_treatment into a natural language string that the LLM can easily understand and use in its response to the user. """ if not optimization_output or not isinstance(optimization_output, dict): return "The optimizer did not return a structured result." summary_lines = ["Quantum Treatment Optimizer Suggestions:"] if "suggested_actions" in optimization_output and optimization_output["suggested_actions"]: summary_lines.append(" Key Suggested Actions:") for action in optimization_output["suggested_actions"]: summary_lines.append(f" - {action}") if "primary_focus_condition" in optimization_output: summary_lines.append(f" Primary Focus: Addressing {optimization_output['primary_focus_condition']}.") if "confidence_level_simulated" in optimization_output: summary_lines.append(f" Simulated Confidence Level: {optimization_output['confidence_level_simulated']:.0%}") # Format as percentage if "summary_notes" in optimization_output: summary_lines.append(f" Summary Notes: {optimization_output['summary_notes']}") if "simulated_optimization_id" in optimization_output: summary_lines.append(f" (Simulated Optimization ID: {optimization_output['simulated_optimization_id']})") if len(summary_lines) == 1: # Only the initial title return f"The optimizer processed the request but provided no specific actionable suggestions. Raw data: {str(optimization_output)}" return "\n".join(summary_lines) def _run(self, patient_data: Dict[str, Any], current_treatments: List[str], conditions: List[str], **kwargs: Any) -> str: """ Executes the quantum treatment optimization. The arguments (patient_data, current_treatments, conditions) are automatically populated by LangChain from the 'action_input' dictionary provided by the LLM, based on the `args_schema` (QuantumOptimizerInput). """ # Any additional kwargs passed by the LLM in action_input that are not in the primary schema # will be available in `kwargs` if your BaseTool is set up to accept them or if you handle them. # For Pydantic validated args_schema, only defined fields are passed directly as named args. app_logger.info( f"Quantum Optimizer Tool called. Patient Data Keys: {list(patient_data.keys())}, " f"Treatments: {current_treatments}, Conditions: {conditions}" ) log_tool_usage(self.name, {"conditions_count": len(conditions), "treatments_count": len(current_treatments)}) # Basic validation (Pydantic handles schema, but you can add business logic checks) if not patient_data or not conditions: missing_info = [] if not patient_data: missing_info.append("'patient_data'") if not conditions: missing_info.append("'conditions'") return f"Error: Insufficient information provided for optimization. Missing: {', '.join(missing_info)}. Please provide comprehensive details." try: # Call your actual optimization logic optimization_output: Dict[str, Any] = optimize_treatment( patient_data=patient_data, current_treatments=current_treatments, conditions=conditions ) app_logger.info(f"Quantum optimizer raw output: {str(optimization_output)[:500]}...") # Log snippet # Format the potentially complex result into a string for the LLM formatted_result = self._format_results_for_llm(optimization_output) app_logger.info(f"Quantum optimizer formatted result for LLM: {formatted_result}") return formatted_result except ImportError as ie: # In case the mock was not used and import still fails app_logger.error(f"ImportError in QuantumTreatmentOptimizerTool (quantum.optimizer likely missing): {ie}", exc_info=True) return "Error: The core optimization module is currently unavailable." except Exception as e: app_logger.error(f"Unexpected error during quantum optimization process: {e}", exc_info=True) return f"Error encountered during the optimization process: {str(e)}. Please ensure input data is correctly formatted." async def _arun(self, patient_data: Dict[str, Any], current_treatments: List[str], conditions: List[str], **kwargs: Any) -> str: """ Asynchronous execution of the quantum treatment optimization. For truly async behavior, `optimize_treatment` should be an async function, or this method should run the sync `optimize_treatment` in a thread pool. """ app_logger.info( f"Quantum Optimizer Tool (async) called. Patient Data Keys: {list(patient_data.keys())}, " f"Treatments: {current_treatments}, Conditions: {conditions}" ) # For now, for simplicity with Streamlit, we can call the synchronous version. # If optimize_treatment is blocking, consider `asyncio.to_thread` for true async execution. # import asyncio # return await asyncio.to_thread(self._run, patient_data, current_treatments, conditions, **kwargs) return self._run(patient_data, current_treatments, conditions, **kwargs)