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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}")
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