feat: Add Memory system with Weaviate integration and MCP tools

MEMORY SYSTEM ARCHITECTURE:
- Weaviate-based memory storage (Thought, Message, Conversation collections)
- GPU embeddings with BAAI/bge-m3 (1024-dim, RTX 4070)
- 9 MCP tools for Claude Desktop integration

CORE MODULES (memory/):
- core/embedding_service.py: GPU embedder singleton with PyTorch
- schemas/memory_schemas.py: Weaviate schema definitions
- mcp/thought_tools.py: add_thought, search_thoughts, get_thought
- mcp/message_tools.py: add_message, get_messages, search_messages
- mcp/conversation_tools.py: get_conversation, search_conversations, list_conversations

FLASK TEMPLATES:
- conversation_view.html: Display single conversation with messages
- conversations.html: List all conversations with search
- memories.html: Browse and search thoughts

FEATURES:
- Semantic search across thoughts, messages, conversations
- Privacy levels (private, shared, public)
- Thought types (reflection, question, intuition, observation)
- Conversation categories with filtering
- Message ordering and role-based display

DATA (as of 2026-01-08):
- 102 Thoughts
- 377 Messages
- 12 Conversations

DOCUMENTATION:
- memory/README_MCP_TOOLS.md: Complete API reference and usage examples

All MCP tools tested and validated (see test_memory_mcp_tools.py in archive).

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

memory/core/__init__.py

@@ -0,0 +1,31 @@
+"""
+Memory Core Module - GPU Embedding Service and Utilities.
+
+This module provides core functionality for the unified RAG system:
+    - GPU-accelerated embeddings (RTX 4070 + PyTorch CUDA)
+    - Singleton embedding service
+    - Weaviate connection utilities
+
+Usage:
+    from memory.core import get_embedder, embed_text
+
+    # Get singleton embedder
+    embedder = get_embedder()
+
+    # Embed text
+    embedding = embed_text("Hello world")
+"""
+
+from memory.core.embedding_service import (
+    GPUEmbeddingService,
+    get_embedder,
+    embed_text,
+    embed_texts,
+)
+
+__all__ = [
+    "GPUEmbeddingService",
+    "get_embedder",
+    "embed_text",
+    "embed_texts",
+]