"""
Konstruct Agent Memory Layer.

Two-layer conversational memory system:

1. Short-term (Redis sliding window):
   - Stores the last N messages verbatim
   - Zero latency — Redis is always available
   - Provides immediate in-session context continuity
   - See: short_term.py

2. Long-term (pgvector HNSW similarity search):
   - Stores all messages as semantic embeddings
   - Retrieves top-K semantically relevant past exchanges
   - Provides cross-session recall (user preferences, past issues, etc.)
   - Embedding model: all-MiniLM-L6-v2 (384 dimensions)
   - See: long_term.py

Memory scoping: All operations are keyed by (tenant_id, agent_id, user_id).
This ensures complete isolation — no cross-tenant, cross-agent, or cross-user
contamination is possible.
"""
