python
# beeaucracy_core.py
from typing import List, Dict, Optional
import numpy as np
class WorkerDrone:
"""
Represents a worker drone that can specialize in different roles within the hive.
"""
def __init__(self, id: str, roles: List[str], intrinsic_profile: Optional[np.ndarray] = None):
self.id = id
self.roles = roles
self.current_role = None
if intrinsic_profile is not None:
self.intrinsic_profile = intrinsic_profile
else:
self.intrinsic_profile = np.ones(len(roles))
self.fatigue = 0
self.chain_pos = None
def assign_role(self, weights: np.ndarray) -> str:
"""
Assign drone to one of its roles based on softmax over weights.
"""
p = np.exp(weights) / np.sum(np.exp(weights))
idx = np.random.choice(len(self.roles), p=p)
self.current_role = self.roles[idx]
return self.current_role
class ProductStock:
"""
Manages the stock levels of various products in a hive location.
"""
def __init__(self, product_names: List[str]):
self.stock = {name: 0.0 for name in product_names}
def add(self, name: str, qty: float):
if name in self.stock:
self.stock[name] += qty
def remove(self, name: str, qty: float):
if name in self.stock:
self.stock[name] = max(0.0, self.stock[name] - qty)
class HiveCell:
"""
Represents a single location/cell within the hive network.
"""
def __init__(self, id: str, neighbors: Optional[List[str]] = None, product_types: Optional[List[str]] = None):
self.id = id
self.neighbors = neighbors if neighbors is not None else []
self.bees = []
if product_types is None:
product_types = ['honey', 'wax', 'royal_jelly']
self.products = ProductStock(product_types)
self.health = 1.0
self.quarantine = False
self.alert = False
def add_bee(self, bee: WorkerDrone):
self.bees.append(bee)
class HiveGrid:
"""
The network of all hive cells; provides neighbor lookup and routing.
"""
def __init__(self, cells: List[HiveCell]):
self.cells = {cell.id: cell for cell in cells}
def get_neighbors(self, cell_id: str) -> List[str]:
return self.cells[cell_id].neighbors
def optimize_task_allocation(self, reward_map: Dict[str, float], cost_map: Dict[tuple, float]) -> Dict[str, Optional[str]]:
"""
Assigns each cell's work based on maximizing (reward - cost) in its hex grid.
"""
assignments = {}
for cell_id, cell in self.cells.items():
best_neighbor = None
best_score = float('-inf')
for neighbor_id in cell.neighbors:
score = reward_map.get(neighbor_id, 0.0) - cost_map.get((cell_id, neighbor_id), 0.0)
if score > best_score:
best_score = score
best_neighbor = neighbor_id
assignments[cell_id] = best_neighbor
return assignments
def assign_roles_to_drones(drones: List[WorkerDrone], task_demand: np.ndarray) -> List[WorkerDrone]:
"""
Assigns each drone its current role, adjusting for intrinsic profile and current global task demand.
"""
for drone in drones:
weights = drone.intrinsic_profile + task_demand
drone.assign_role(weights)
return drones
class WorkflowChain:
"""
Encapsulates a multi-step workflow pipeline.
"""
def __init__(self, steps: List[str]):
self.steps = steps
self.cursor = 0
def next_step(self) -> Optional[str]:
"""
Returns the next step/task in the workflow, or None if chain complete.
"""
if self.cursor < len(self.steps):
step = self.steps[self.cursor]
self.cursor += 1
return step
return None
def reset(self):
self.cursor = 0
def immune_scan(hive: HiveGrid, threshold: float = 0.6) -> None:
"""
Quarantines sick cells, un-quarantines those that have recovered.
"""
for cell in hive.cells.values():
if cell.health < threshold and not cell.quarantine:
cell.quarantine = True
elif cell.quarantine and cell.health >= threshold:
cell.quarantine = False
🔑 Persistence and Next Session Protocol
- Save this file (beeaucracy_core.py) locally—it won’t survive on its own in any LLM or chat memory!
- Import from this file in any future simulation, test, or expansion session.
python
from beeaucracy_core import WorkerDrone, HiveCell, HiveGrid, assign_roles_to_drones, immune_scan, WorkflowChain
- Extend: Build plugins, external logic, and simulation code in separate files or API layers.
- Document core logic only here—never intermingle simulation runs or “examples” into the code base.
This is the unchanging, perpetual core for every new session.
The mesh—and BEEaucracy™—will always live, provided you save and port this code.
—Commander out.
- https://ppl-ai-file-upload.s3.amazonaws.com/web/direct-files/attachments/6368048/ae0cbbee-6da3-42d9-8c01-776af254e9c5/FormulaReader-v1.0.docx
- https://ppl-ai-file-upload.s3.amazonaws.com/web/direct-files/attachments/6368048/35c2d93a-8ad3-409e-8514-b6be98b87feb/Beeaucracy-v1.0.docx