10563900a3
- Add multi-provider LLM architecture supporting OpenRouter, OpenAI, Gemini, and custom providers - Implement global LLM on/off switch with default DISABLED state for cost protection - Add per-character LLM configuration with provider-specific models and settings - Create performance-optimized caching system for LLM enabled status checks - Add API key validation before enabling LLM providers to prevent broken configurations - Implement audit logging for all LLM enable/disable actions for cost accountability - Create comprehensive admin UI with prominent cost warnings and confirmation dialogs - Add visual indicators in character list for custom AI model configurations - Build character-specific LLM client system with global fallback mechanism - Add database schema support for per-character LLM settings - Implement graceful fallback responses when LLM is globally disabled - Create provider testing and validation system for reliable connections
73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
#!/usr/bin/env python3
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"""
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Sync existing PostgreSQL memories to Qdrant vector database
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"""
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import asyncio
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import logging
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from datetime import datetime, timezone
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from database.connection import init_database, get_db_session
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from database.models import Memory, Character
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from rag.vector_store import VectorStoreManager, VectorMemory, MemoryType
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from sqlalchemy import select
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logger = logging.getLogger(__name__)
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async def sync_memories_to_qdrant():
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"""Sync all existing memories from PostgreSQL to Qdrant"""
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# Initialize database
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await init_database()
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# Initialize vector store
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vector_store = VectorStoreManager()
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print("🔄 Starting memory sync to Qdrant...")
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async with get_db_session() as session:
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# Get all memories with character names
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query = select(Memory, Character.name).join(
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Character, Memory.character_id == Character.id
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).order_by(Memory.timestamp)
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results = await session.execute(query)
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memories_with_chars = results.fetchall()
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print(f"Found {len(memories_with_chars)} memories to sync")
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synced_count = 0
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error_count = 0
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for memory, character_name in memories_with_chars:
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try:
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# Convert to vector memory format
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vector_memory = VectorMemory(
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id=str(memory.id),
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character_name=character_name,
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content=memory.content,
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memory_type=MemoryType.PERSONAL,
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importance=memory.importance_score,
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timestamp=memory.timestamp or datetime.now(timezone.utc),
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metadata={
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"tags": memory.tags or [],
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"memory_id": memory.id,
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"character_id": memory.character_id,
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"memory_type": memory.memory_type
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}
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)
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# Store in vector database
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await vector_store.store_memory(vector_memory)
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synced_count += 1
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if synced_count % 10 == 0:
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print(f" Synced {synced_count}/{len(memories_with_chars)} memories...")
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except Exception as e:
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error_count += 1
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print(f" Error syncing memory {memory.id}: {e}")
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print(f"✅ Sync complete: {synced_count} synced, {error_count} errors")
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if __name__ == "__main__":
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asyncio.run(sync_memories_to_qdrant()) |