David Blanc Brioir
5a732e885f
Added two export scripts to backup memory collections: 1. export_conversations.py: - Exports all Conversation + Message objects to markdown - Includes conversation metadata (category, timestamps, participants) - Formats messages chronologically with role indicators - Generated: docs/conversations.md (12 conversations, 377 messages) 2. export_thoughts.py: - Exports all Thought objects to markdown - Groups by thought_type with summary statistics - Includes metadata (trigger, emotional_state, concepts, privacy) - Generated: docs/thoughts.md (104 thoughts across 8 types) Both scripts use UTF-8 encoding for markdown output with emoji formatting for better readability. Exports stored in docs/ for versioned backup of memory collections. Stats: - Conversations: 12 (5 testing, 7 general) - Messages: 377 total - Thoughts: 104 (28 reflection, 36 synthesis, 27 test) - Privacy: 100% private Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
141 lines
4.4 KiB
Python
141 lines
4.4 KiB
Python
#!/usr/bin/env python3
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"""
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Export thoughts from Weaviate to Markdown file.
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Exports all thoughts to docs/thoughts.md
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"""
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import weaviate
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from datetime import datetime
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from pathlib import Path
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def export_thoughts_to_md(output_file: str = "docs/thoughts.md"):
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"""Export all thoughts to markdown file."""
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# Connect to Weaviate
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client = weaviate.connect_to_local()
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try:
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# Get collection
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thought_collection = client.collections.get("Thought")
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# Fetch all thoughts (sorted by timestamp)
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thoughts_response = thought_collection.query.fetch_objects(
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limit=1000
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)
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thoughts = thoughts_response.objects
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print(f"Found {len(thoughts)} thoughts")
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# Sort by timestamp (most recent first)
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thoughts = sorted(
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thoughts,
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key=lambda t: t.properties.get("timestamp", datetime.min),
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reverse=True
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)
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# Build markdown content
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lines = []
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lines.append("# Thoughts Export")
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lines.append(f"\n**Exported**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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lines.append(f"\n**Total thoughts**: {len(thoughts)}")
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lines.append("\n---\n")
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# Group by type
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thoughts_by_type = {}
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for thought in thoughts:
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thought_type = thought.properties.get("thought_type", "unknown")
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if thought_type not in thoughts_by_type:
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thoughts_by_type[thought_type] = []
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thoughts_by_type[thought_type].append(thought)
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# Write summary by type
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lines.append("## Summary by Type\n")
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for thought_type in sorted(thoughts_by_type.keys()):
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count = len(thoughts_by_type[thought_type])
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lines.append(f"- **{thought_type}**: {count}")
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lines.append("\n---\n")
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# Process each thought
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for idx, thought in enumerate(thoughts, 1):
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props = thought.properties
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content = props.get("content", "No content")
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thought_type = props.get("thought_type", "unknown")
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timestamp = props.get("timestamp")
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trigger = props.get("trigger", "")
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emotional_state = props.get("emotional_state", "")
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concepts = props.get("concepts", [])
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privacy_level = props.get("privacy_level", "private")
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context = props.get("context", "")
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# Format timestamp
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timestamp_str = timestamp.strftime('%Y-%m-%d %H:%M:%S') if timestamp else "N/A"
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# Type emoji
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type_emoji = {
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"reflexion": "💭",
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"question": "❓",
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"intuition": "💡",
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"observation": "👁️",
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"conclusion": "✅",
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"hypothesis": "🤔",
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"discovery": "🔍"
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}.get(thought_type, "📝")
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# Privacy emoji
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privacy_emoji = {
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"private": "🔒",
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"shared": "👥",
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"public": "🌐"
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}.get(privacy_level, "❓")
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# Write thought entry
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lines.append(f"## {type_emoji} Thought {idx}: {thought_type.upper()}")
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lines.append(f"\n**Timestamp**: {timestamp_str}")
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lines.append(f"**Privacy**: {privacy_emoji} {privacy_level}")
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if trigger:
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lines.append(f"**Trigger**: {trigger}")
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if emotional_state:
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lines.append(f"**Emotional state**: {emotional_state}")
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if concepts:
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lines.append(f"**Concepts**: {', '.join(concepts)}")
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lines.append(f"\n### Content\n\n{content}\n")
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if context:
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lines.append(f"**Context**: {context}\n")
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lines.append("\n---\n")
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# Write to file
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output_path = Path(output_file)
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output_path.parent.mkdir(parents=True, exist_ok=True)
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with open(output_path, 'w', encoding='utf-8') as f:
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f.write('\n'.join(lines))
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print(f"\n[OK] Exported {len(thoughts)} thoughts to {output_file}")
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# Stats
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print(f" Types: {dict((k, len(v)) for k, v in thoughts_by_type.items())}")
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privacy_stats = {}
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for t in thoughts:
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privacy = t.properties.get("privacy_level", "unknown")
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privacy_stats[privacy] = privacy_stats.get(privacy, 0) + 1
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print(f" Privacy: {dict(privacy_stats)}")
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finally:
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client.close()
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if __name__ == "__main__":
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export_thoughts_to_md()
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