""" OmniCoreX Planning Module Implementation of high-level planning components central to OmniCoreX’s ultra high-tech AI. This module generates multi-step reasoning plans, manages dynamic tool invocations, and orchestrates execution flows with adaptive and context-aware strategies. Features: - Hierarchical multi-step plan generation based on input goals and context. - Dynamic selection and invocation of external or internal tools. - Real-time plan adaptation and interruption handling. - Support for conditional branching and iterative reasoning. - Integration hooks for memory and knowledge modules. """ from typing import Any, Dict, List, Optional, Callable import logging logger = logging.getLogger(__name__) logging.basicConfig(level=logging.DEBUG) class PlanStep: """ Represents a single actionable step in a reasoning plan. """ def __init__(self, step_type: str, content: Any = None, tool_name: Optional[str] = None, tool_params: Optional[Dict[str, Any]] = None, conditions: Optional[Dict[str, Any]] = None): """ Initializes a PlanStep. Args: step_type: Type of action ('tool', 'respond', 'compute', 'wait', etc.). content: Core content or instruction associated with the step. tool_name: Name of the tool to invoke, if applicable. tool_params: Parameters for the tool invocation. conditions: Optional dict specifying conditions for execution or branching. """ self.step_type = step_type self.content = content self.tool_name = tool_name self.tool_params = tool_params or {} self.conditions = conditions or {} def __repr__(self): return (f"PlanStep(type={self.step_type}, content={self.content}, " f"tool={self.tool_name}, params={self.tool_params}, conditions={self.conditions})") class Planner: """ Core planner for OmniCoreX responsible for generating and managing multi-step reasoning plans. """ def __init__(self, tool_registry: Optional[Dict[str, Callable[..., Any]]] = None, memory_provider: Optional[Callable[[str], Any]] = None): """ Initializes the Planner. Args: tool_registry: Dict mapping tool names to callable implementations. memory_provider: Callable to access memory/context data by key or query. """ self.tool_registry = tool_registry or {} self.memory_provider = memory_provider self.current_plan: List[PlanStep] = [] self.current_step_index = 0 def generate_plan(self, goal: str, context: Optional[str] = None) -> List[PlanStep]: """ Generates a multi-step reasoning plan to achieve the specified goal. Args: goal: The high-level objective or query. context: Optional context information or memory. Returns: List of PlanStep objects defining the detailed plan. """ logger.debug(f"Generating plan for goal: {goal} with context: {context}") # Placeholder plan generation that can be replaced with advanced AI methods plan = [] # Step 1: Analyze Goal plan.append(PlanStep(step_type="compute", content=f"Analyze input goal: {goal}")) # Step 2: Look up context if available if context: plan.append(PlanStep(step_type="compute", content=f"Integrate contextual information.")) # Step 3: Decide tool usage dynamically based on keywords (demo logic) if "search" in goal.lower(): plan.append(PlanStep(step_type="tool", tool_name="web_search", tool_params={"query": goal})) elif "calculate" in goal.lower() or "compute" in goal.lower(): plan.append(PlanStep(step_type="tool", tool_name="calculator", tool_params={"expression": goal})) else: plan.append(PlanStep(step_type="respond", content=f"Preliminary response: Processing {goal}")) # Step 4: Finalize response plan.append(PlanStep(step_type="respond", content="Present final response to user.")) logger.debug(f"Generated plan: {plan}") self.current_plan = plan self.current_step_index = 0 return plan def get_next_step(self) -> Optional[PlanStep]: """ Gets the next step in the current plan, or None if plan is complete. Returns: The next PlanStep or None. """ if self.current_step_index >= len(self.current_plan): logger.debug("Plan complete, no further steps.") return None step = self.current_plan[self.current_step_index] self.current_step_index += 1 logger.debug(f"Advancing to next plan step: {step}") return step def execute_step(self, step: PlanStep) -> Any: """ Executes the provided plan step, invoking tools or performing computations. Args: step: The PlanStep to execute. Returns: The result of the step's execution. """ logger.debug(f"Executing plan step: {step}") if step.step_type == "tool": # Invoke registered tool tool_func = self.tool_registry.get(step.tool_name) if tool_func: try: result = tool_func(**step.tool_params) logger.debug(f"Tool '{step.tool_name}' returned: {result}") return result except Exception as e: logger.error(f"Error executing tool '{step.tool_name}': {e}") return {"error": str(e)} else: error_msg = f"Tool '{step.tool_name}' not found." logger.error(error_msg) return {"error": error_msg} elif step.step_type == "compute": # Placeholder compute action: simulate or integrate with reasoning engine logger.debug(f"Computing: {step.content}") return f"Computed: {step.content}" elif step.step_type == "respond": # Prepare response action content logger.debug(f"Responding with content: {step.content}") return step.content elif step.step_type == "wait": # Wait or pause action duration = step.tool_params.get("duration", 1) logger.debug(f"Waiting for {duration} seconds.") import time time.sleep(duration) return f"Waited {duration} seconds" else: logger.warning(f"Unknown step_type '{step.step_type}'. No action performed.") return None def interrupt_and_replan(self, new_context: str, new_goal: Optional[str] = None) -> List[PlanStep]: """ Interrupts current plan and generates a new plan based on updated context or goal. Args: new_context: New contextual data influencing replanning. new_goal: Optional new goal to re-plan for. Returns: New list of PlanStep objects forming the refreshed plan. """ logger.info(f"Interrupting current plan for replanning with new context: {new_context} and goal: {new_goal}") goal = new_goal if new_goal else "Replan goal" return self.generate_plan(goal=goal, context=new_context) # Example Tool Implementations (to be registered) def web_search(query: str) -> Dict[str, Any]: # Simulate a mock web search tool logger.info(f"Web search tool searching for query: {query}") return {"results": [f"Result 1 for {query}", f"Result 2 for {query}"]} def calculator(expression: str) -> Dict[str, Any]: # Simulate a calculator tool evaluating simple math expression logger.info(f"Calculator tool evaluating: {expression}") try: result = eval(expression, {"__builtins__": {}}) return {"result": result} except Exception as e: return {"error": str(e)} if __name__ == "__main__": # Minimal demonstration of planning and execution planner = Planner(tool_registry={ "web_search": web_search, "calculator": calculator }) goal = "Search latest AI research papers" context = "User interested in machine learning breakthroughs." plan = planner.generate_plan(goal, context) for step in plan: output = planner.execute_step(step) print(f"Step result: {output}") # Demonstrate interruption and replanning new_context = "Urgent request: calculate 2+2" new_plan = planner.interrupt_and_replan(new_context, new_goal="Calculate 2 + 2") print("\nNew plan after interruption:") for step in new_plan: output = planner.execute_step(step) print(f"Step result: {output}")