#!/usr/bin/env python3
"""
Automated import using the working memory system
This script processes the parsed conversations and imports them efficiently
"""
import json
import time
import sys
from pathlib import Path
def load_parsed_conversations():
"""Load the parsed conversations"""
conversations_file = Path("parsed_conversations.json")
if not conversations_file.exists():
print("ā parsed_conversations.json not found")
print("š” Run: python3 prepare_import.py first")
return None
with open(conversations_file, 'r', encoding='utf-8') as f:
conversations = json.load(f)
return conversations
def generate_import_commands(conversations, start_index=3):
"""Generate the import commands for remaining conversations"""
output_file = Path("import_commands.txt")
with open(output_file, 'w', encoding='utf-8') as f:
f.write(f"# Import commands for conversations {start_index+1}-{len(conversations)}\n")
f.write(f"# Copy and paste these into Claude one at a time\n\n")
for i in range(start_index, len(conversations)):
conv = conversations[i]
# Escape quotes and format for command
title = conv['title'].replace('"', '\\"')
date = conv['date']
# Truncate content if too long (limit for command)
content = conv['content']
if len(content) > 8000: # Reasonable limit for command length
content = content[:8000] + "\n\n[Content truncated for import]"
# Escape content
content = content.replace('"', '\\"').replace('\n', '\\n')
f.write(f"add_conversation(\"{content}\", \"{title}\", \"{date}\")\n\n")
# Add progress marker every 10 conversations
if (i + 1) % 10 == 0:
f.write(f"# Progress: {i+1}/{len(conversations)} conversations\n\n")
print(f"š Generated import commands in {output_file}")
return output_file
def create_batch_files(conversations, start_index=3, batch_size=20):
"""Create smaller batch files for easier processing"""
batch_dir = Path("import_batches")
batch_dir.mkdir(exist_ok=True)
total_remaining = len(conversations) - start_index
num_batches = (total_remaining + batch_size - 1) // batch_size
print(f"š¦ Creating {num_batches} batch files ({batch_size} conversations each)")
for batch_num in range(num_batches):
batch_start = start_index + (batch_num * batch_size)
batch_end = min(batch_start + batch_size, len(conversations))
batch_file = batch_dir / f"batch_{batch_num+1:02d}.json"
batch_conversations = conversations[batch_start:batch_end]
with open(batch_file, 'w', encoding='utf-8') as f:
json.dump(batch_conversations, f, indent=2, ensure_ascii=False)
print(f" š {batch_file}: conversations {batch_start+1}-{batch_end}")
print(f"\nš” Import batches created in {batch_dir}/")
print(f"š You can process these batches one at a time")
def main():
print("š Automated Import Setup")
print("========================")
# Load conversations
conversations = load_parsed_conversations()
if not conversations:
return
print(f"š¦ Loaded {len(conversations)} conversations")
print(f"ā
Already imported: 3 conversations")
print(f"ā³ Remaining: {len(conversations) - 3} conversations")
# Create batch files for easier processing
create_batch_files(conversations, start_index=3, batch_size=20)
print(f"\nš Next steps:")
print(f" 1. Process batches one at a time using import_batches/")
print(f" 2. Or continue importing manually conversation by conversation")
print(f" 3. Each batch contains ~20 conversations for manageable import")
print(f"\nš” Progress tracking:")
print(f" ā¢ Completed: 3/{len(conversations)} ({3/len(conversations)*100:.1f}%)")
print(f" ā¢ Remaining: {len(conversations)-3} conversations")
if __name__ == "__main__":
main()
