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Friday-Subconscious / backend /scripts /flush_learning_queue.py

Paritosh Upadhyay

Neural Core Maturation: Self-Evolution Initiated

64cc56e 6 months ago

2.1 kB

"""
Project Friday — Master Learning Queue Flush
Bridges existing ConversationMemory patterns into the LearningTarget queue for high-velocity maturation.
"""
import sys
import os
from pathlib import Path
import logging

# Add backend to path
sys.path.append(os.path.join(os.getcwd(), "backend"))

from app.core import database
from app.models.entities import ConversationMemory
from app.services import holocron

logging.basicConfig(level=logging.INFO, format="%(asctime)s | FLUSH | %(message)s")
logger = logging.getLogger("friday.flush")

def flush_memory_to_queue():
    """Converts 310,000+ memory patterns into formal learning targets."""
    logger.info("Initializing Master Queue Sync...")
    
    with database.SessionLocal() as db:
        # 1. Fetch distinct topics from memory that aren't already targets
        logger.info("Scanning for unqueued mandates in memory store...")
        
        # In this context, 'entities' holds the unique reference ID like MANAGEMENT_MASTER_X
        # We'll use the memory ID ranges or categories
        
        # Map categories
        for category, target_type in [("ManagementSkill", "management"), ("PersonalityExample", "personality")]:
            logger.info(f"Processing category: {category} -> {target_type}")
            
            # Use chunks to avoid memory issues
            batch_size = 5000
            offset = 0
            
            while True:
                mem_items = db.query(ConversationMemory.entities)\
                              .filter_by(topic=category)\
                              .offset(offset).limit(batch_size).all()
                
                if not mem_items: break
                
                topics = [m.entities for m in mem_items]
                holocron.add_learning_targets(topics, category=target_type)
                
                offset += batch_size
                logger.info(f"Synchronized {offset} patterns for {target_type}...")
                
    logger.info("✓ Master Learning Queue Flush Complete.")

if __name__ == "__main__":
    flush_memory_to_queue()

	"""
	Project Friday — Master Learning Queue Flush
	Bridges existing ConversationMemory patterns into the LearningTarget queue for high-velocity maturation.
	"""
	import sys
	import os
	from pathlib import Path
	import logging

	# Add backend to path
	sys.path.append(os.path.join(os.getcwd(), "backend"))

	from app.core import database
	from app.models.entities import ConversationMemory
	from app.services import holocron

	logging.basicConfig(level=logging.INFO, format="%(asctime)s \| FLUSH \| %(message)s")
	logger = logging.getLogger("friday.flush")

	def flush_memory_to_queue():
	"""Converts 310,000+ memory patterns into formal learning targets."""
	logger.info("Initializing Master Queue Sync...")

	with database.SessionLocal() as db:
	# 1. Fetch distinct topics from memory that aren't already targets
	logger.info("Scanning for unqueued mandates in memory store...")

	# In this context, 'entities' holds the unique reference ID like MANAGEMENT_MASTER_X
	# We'll use the memory ID ranges or categories

	# Map categories
	for category, target_type in [("ManagementSkill", "management"), ("PersonalityExample", "personality")]:
	logger.info(f"Processing category: {category} -> {target_type}")

	# Use chunks to avoid memory issues
	batch_size = 5000
	offset = 0

	while True:
	mem_items = db.query(ConversationMemory.entities)\
	.filter_by(topic=category)\
	.offset(offset).limit(batch_size).all()

	if not mem_items: break

	topics = [m.entities for m in mem_items]
	holocron.add_learning_targets(topics, category=target_type)

	offset += batch_size
	logger.info(f"Synchronized {offset} patterns for {target_type}...")

	logger.info("✓ Master Learning Queue Flush Complete.")

	if __name__ == "__main__":
	flush_memory_to_queue()