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"""
Advanced Long-term Memory Architecture for Persistent Agent Memory
Implements cross-session memory persistence with intelligent retrieval
"""
import sqlite3
import json
import hashlib
import numpy as np
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass, asdict
import logging
from pathlib import Path
logger = logging.getLogger(__name__)
@dataclass
class MemoryRecord:
"""Individual memory record with metadata"""
id: str
content: str
memory_type: str # episodic, semantic, procedural, strategic
timestamp: datetime
importance: float
access_count: int
last_accessed: datetime
embedding: Optional[List[float]] = None
tags: List[str] = None
agent_id: str = ""
session_id: str = ""
def __post_init__(self):
if self.tags is None:
self.tags = []
class LongTermMemoryManager:
"""Advanced persistent memory system with cross-session capabilities"""
def __init__(self, db_path: str = "data/cognitive/long_term_memory.db"):
"""Initialize long-term memory system"""
self.db_path = Path(db_path)
self.db_path.parent.mkdir(parents=True, exist_ok=True)
self._init_database()
self._memory_cache = {}
self._embeddings_model = None
def _init_database(self):
"""Initialize database schemas"""
with sqlite3.connect(self.db_path) as conn:
conn.execute("""
CREATE TABLE IF NOT EXISTS long_term_memory (
id TEXT PRIMARY KEY,
content TEXT NOT NULL,
memory_type TEXT NOT NULL,
timestamp TEXT NOT NULL,
importance REAL NOT NULL,
access_count INTEGER DEFAULT 0,
last_accessed TEXT NOT NULL,
embedding TEXT,
tags TEXT,
agent_id TEXT,
session_id TEXT,
created_at TEXT DEFAULT CURRENT_TIMESTAMP,
updated_at TEXT DEFAULT CURRENT_TIMESTAMP
)
""")
conn.execute("""
CREATE TABLE IF NOT EXISTS memory_associations (
id TEXT PRIMARY KEY,
memory_id_1 TEXT,
memory_id_2 TEXT,
association_type TEXT,
strength REAL,
created_at TEXT DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (memory_id_1) REFERENCES long_term_memory(id),
FOREIGN KEY (memory_id_2) REFERENCES long_term_memory(id)
)
""")
conn.execute("""
CREATE TABLE IF NOT EXISTS memory_consolidation_log (
id INTEGER PRIMARY KEY AUTOINCREMENT,
consolidation_type TEXT,
memories_processed INTEGER,
patterns_discovered INTEGER,
timestamp TEXT DEFAULT CURRENT_TIMESTAMP,
details TEXT
)
""")
# Create indices for performance
conn.execute("CREATE INDEX IF NOT EXISTS idx_memory_type ON long_term_memory(memory_type)")
conn.execute("CREATE INDEX IF NOT EXISTS idx_agent_id ON long_term_memory(agent_id)")
conn.execute("CREATE INDEX IF NOT EXISTS idx_importance ON long_term_memory(importance)")
conn.execute("CREATE INDEX IF NOT EXISTS idx_timestamp ON long_term_memory(timestamp)")
def store_memory(self, content: str, memory_type: str,
importance: float = 0.5, agent_id: str = "",
session_id: str = "", tags: List[str] = None) -> str:
"""Store a new memory with intelligent categorization"""
try:
memory_id = hashlib.sha256(f"{content}{memory_type}{datetime.now().isoformat()}".encode()).hexdigest()
record = MemoryRecord(
id=memory_id,
content=content,
memory_type=memory_type,
timestamp=datetime.now(),
importance=importance,
access_count=0,
last_accessed=datetime.now(),
tags=tags or [],
agent_id=agent_id,
session_id=session_id
)
# Generate embedding for semantic search
if self._embeddings_model:
record.embedding = self._generate_embedding(content)
with sqlite3.connect(self.db_path) as conn:
conn.execute("""
INSERT INTO long_term_memory (
id, content, memory_type, timestamp, importance,
access_count, last_accessed, embedding, tags, agent_id, session_id
) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
""", (
record.id, record.content, record.memory_type,
record.timestamp.isoformat(), record.importance,
record.access_count, record.last_accessed.isoformat(),
json.dumps(record.embedding) if record.embedding else None,
json.dumps(record.tags), record.agent_id, record.session_id
))
logger.info(f"Stored long-term memory: {memory_id[:8]}... ({memory_type})")
return memory_id
except Exception as e:
logger.error(f"Error storing memory: {e}")
return ""
def retrieve_memories(self, query: str = "", memory_type: str = "",
agent_id: str = "", limit: int = 10,
importance_threshold: float = 0.0) -> List[MemoryRecord]:
"""Retrieve memories with intelligent filtering and ranking"""
try:
with sqlite3.connect(self.db_path) as conn:
conditions = []
params = []
if query:
conditions.append("content LIKE ?")
params.append(f"%{query}%")
if memory_type:
conditions.append("memory_type = ?")
params.append(memory_type)
if agent_id:
conditions.append("agent_id = ?")
params.append(agent_id)
if importance_threshold > 0:
conditions.append("importance >= ?")
params.append(importance_threshold)
where_clause = " AND ".join(conditions) if conditions else "1=1"
cursor = conn.execute(f"""
SELECT * FROM long_term_memory
WHERE {where_clause}
ORDER BY importance DESC, access_count DESC, timestamp DESC
LIMIT ?
""", params + [limit])
memories = []
for row in cursor.fetchall():
memory = MemoryRecord(
id=row[0],
content=row[1],
memory_type=row[2],
timestamp=datetime.fromisoformat(row[3]),
importance=row[4],
access_count=row[5],
last_accessed=datetime.fromisoformat(row[6]),
embedding=json.loads(row[7]) if row[7] else None,
tags=json.loads(row[8]) if row[8] else [],
agent_id=row[9] or "",
session_id=row[10] or ""
)
memories.append(memory)
# Update access statistics
self._update_access_stats(memory.id)
logger.info(f"Retrieved {len(memories)} memories for query: {query[:50]}...")
return memories
except Exception as e:
logger.error(f"Error retrieving memories: {e}")
return []
def consolidate_memories(self) -> Dict[str, int]:
"""Advanced memory consolidation with pattern discovery"""
try:
stats = {
'memories_processed': 0,
'patterns_discovered': 0,
'associations_created': 0,
'memories_merged': 0
}
with sqlite3.connect(self.db_path) as conn:
# Get all memories for consolidation
cursor = conn.execute("""
SELECT * FROM long_term_memory
ORDER BY timestamp DESC
""")
memories = cursor.fetchall()
stats['memories_processed'] = len(memories)
# Pattern discovery through content similarity
for i, memory1 in enumerate(memories):
for j, memory2 in enumerate(memories[i+1:], i+1):
similarity = self._calculate_semantic_similarity(
memory1[1], memory2[1]
)
if similarity > 0.8: # High similarity threshold
self._create_memory_association(
memory1[0], memory2[0], "semantic_similarity", similarity
)
stats['associations_created'] += 1
stats['patterns_discovered'] += 1
# Temporal pattern detection
self._detect_temporal_patterns(memories)
# Log consolidation results
conn.execute("""
INSERT INTO memory_consolidation_log (
consolidation_type, memories_processed,
patterns_discovered, details
) VALUES (?, ?, ?, ?)
""", (
"full_consolidation", stats['memories_processed'],
stats['patterns_discovered'], json.dumps(stats)
))
logger.info(f"Memory consolidation complete: {stats}")
return stats
except Exception as e:
logger.error(f"Error during memory consolidation: {e}")
return {'error': str(e)}
def get_cross_session_context(self, agent_id: str, limit: int = 20) -> List[MemoryRecord]:
"""Retrieve cross-session context for agent continuity"""
try:
with sqlite3.connect(self.db_path) as conn:
cursor = conn.execute("""
SELECT * FROM long_term_memory
WHERE agent_id = ?
ORDER BY importance DESC, last_accessed DESC, timestamp DESC
LIMIT ?
""", (agent_id, limit))
memories = []
for row in cursor.fetchall():
memory = MemoryRecord(
id=row[0],
content=row[1],
memory_type=row[2],
timestamp=datetime.fromisoformat(row[3]),
importance=row[4],
access_count=row[5],
last_accessed=datetime.fromisoformat(row[6]),
embedding=json.loads(row[7]) if row[7] else None,
tags=json.loads(row[8]) if row[8] else [],
agent_id=row[9] or "",
session_id=row[10] or ""
)
memories.append(memory)
logger.info(f"Retrieved {len(memories)} cross-session memories for agent {agent_id}")
return memories
except Exception as e:
logger.error(f"Error retrieving cross-session context: {e}")
return []
def _update_access_stats(self, memory_id: str):
"""Update memory access statistics"""
try:
with sqlite3.connect(self.db_path) as conn:
conn.execute("""
UPDATE long_term_memory
SET access_count = access_count + 1,
last_accessed = ?,
updated_at = CURRENT_TIMESTAMP
WHERE id = ?
""", (datetime.now().isoformat(), memory_id))
except Exception as e:
logger.error(f"Error updating access stats: {e}")
def _generate_embedding(self, content: str) -> List[float]:
"""Generate embeddings for semantic search (placeholder)"""
# In production, use a proper embedding model
# For now, return a simple hash-based vector
hash_val = hash(content)
return [float((hash_val >> i) & 1) for i in range(128)]
def _calculate_semantic_similarity(self, text1: str, text2: str) -> float:
"""Calculate semantic similarity between texts"""
# Simple word overlap similarity (replace with proper embeddings)
words1 = set(text1.lower().split())
words2 = set(text2.lower().split())
if not words1 or not words2:
return 0.0
intersection = len(words1 & words2)
union = len(words1 | words2)
return intersection / union if union > 0 else 0.0
def _create_memory_association(self, memory_id_1: str, memory_id_2: str,
association_type: str, strength: float):
"""Create association between memories"""
try:
association_id = hashlib.sha256(
f"{memory_id_1}{memory_id_2}{association_type}".encode()
).hexdigest()
with sqlite3.connect(self.db_path) as conn:
conn.execute("""
INSERT OR REPLACE INTO memory_associations (
id, memory_id_1, memory_id_2, association_type, strength
) VALUES (?, ?, ?, ?, ?)
""", (association_id, memory_id_1, memory_id_2, association_type, strength))
except Exception as e:
logger.error(f"Error creating memory association: {e}")
def _detect_temporal_patterns(self, memories: List[Tuple]):
"""Detect temporal patterns in memory sequences"""
# Group memories by agent and detect sequences
agent_memories = {}
for memory in memories:
agent_id = memory[9] or "unknown"
if agent_id not in agent_memories:
agent_memories[agent_id] = []
agent_memories[agent_id].append(memory)
# Analyze patterns within each agent's memory timeline
for agent_id, agent_mem_list in agent_memories.items():
# Sort by timestamp
agent_mem_list.sort(key=lambda x: x[3]) # timestamp is at index 3
# Detect recurring patterns or sequences
# This is a simplified pattern detection
for i in range(len(agent_mem_list) - 2):
# Look for sequences of similar operations
mem1, mem2, mem3 = agent_mem_list[i:i+3]
# Check for similar memory types in sequence
if mem1[2] == mem2[2] == mem3[2]: # same memory_type
self._create_memory_association(
mem1[0], mem3[0], "temporal_sequence", 0.7
)
def get_memory_statistics(self) -> Dict[str, Any]:
"""Get comprehensive memory system statistics"""
try:
with sqlite3.connect(self.db_path) as conn:
stats = {}
# Basic counts
cursor = conn.execute("SELECT COUNT(*) FROM long_term_memory")
stats['total_memories'] = cursor.fetchone()[0]
# Memory type distribution
cursor = conn.execute("""
SELECT memory_type, COUNT(*)
FROM long_term_memory
GROUP BY memory_type
""")
stats['memory_types'] = dict(cursor.fetchall())
# Agent distribution
cursor = conn.execute("""
SELECT agent_id, COUNT(*)
FROM long_term_memory
WHERE agent_id != ''
GROUP BY agent_id
""")
stats['agent_distribution'] = dict(cursor.fetchall())
# Importance distribution
cursor = conn.execute("""
SELECT
CASE
WHEN importance >= 0.8 THEN 'high'
WHEN importance >= 0.5 THEN 'medium'
ELSE 'low'
END as importance_level,
COUNT(*)
FROM long_term_memory
GROUP BY importance_level
""")
stats['importance_distribution'] = dict(cursor.fetchall())
# Association statistics
cursor = conn.execute("SELECT COUNT(*) FROM memory_associations")
stats['total_associations'] = cursor.fetchone()[0]
return stats
except Exception as e:
logger.error(f"Error getting memory statistics: {e}")
return {'error': str(e)}
# Export the main class
__all__ = ['LongTermMemoryManager', 'MemoryRecord']
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