File size: 10,486 Bytes
b30f068 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 | """
Memory management system for storing and retrieving learned patterns.
"""
import json
import time
from typing import Dict, Any, List, Optional
from pathlib import Path
from dataclasses import dataclass, asdict
from src.utils.logging_config import logger
@dataclass
class MemoryEntry:
"""Single memory entry storing learned patterns."""
id: str
timestamp: float
query_pattern: Dict[str, Any]
result_pattern: Dict[str, Any]
quality_score: float
improvements: List[str]
context: Dict[str, Any]
class MemoryManager:
"""Manages persistent memory for the agentic AI system."""
def __init__(self, memory_file: str = "memory/system_memory.json"):
"""Initialize memory manager."""
self.memory_file = Path(memory_file)
self.memory_file.parent.mkdir(parents=True, exist_ok=True)
self.memory_entries: List[MemoryEntry] = []
self.max_entries = 1000
# Load existing memory
self._load_memory()
logger.info(f"Memory manager initialized with {len(self.memory_entries)} entries")
def store_memory(
self,
query: str,
results: Dict[str, Any],
quality_score: float,
improvements: List[str],
context: Dict[str, Any] = None
) -> str:
"""Store a new memory entry."""
entry_id = f"mem_{int(time.time())}_{len(self.memory_entries)}"
entry = MemoryEntry(
id=entry_id,
timestamp=time.time(),
query_pattern=self._extract_query_pattern(query),
result_pattern=self._extract_result_pattern(results),
quality_score=quality_score,
improvements=improvements,
context=context or {}
)
self.memory_entries.append(entry)
# Maintain memory size limit
if len(self.memory_entries) > self.max_entries:
self.memory_entries = self.memory_entries[-self.max_entries:]
# Save to disk
self._save_memory()
logger.debug(f"Stored memory entry: {entry_id}")
return entry_id
def retrieve_similar_memories(
self,
query: str,
limit: int = 5,
min_similarity: float = 0.3
) -> List[MemoryEntry]:
"""Retrieve memories similar to the given query."""
query_pattern = self._extract_query_pattern(query)
similar_memories = []
for entry in self.memory_entries:
similarity = self._calculate_similarity(query_pattern, entry.query_pattern)
if similarity >= min_similarity:
similar_memories.append((entry, similarity))
# Sort by similarity (descending) and return top results
similar_memories.sort(key=lambda x: x[1], reverse=True)
return [entry for entry, _ in similar_memories[:limit]]
def get_successful_patterns(self, min_quality: float = 0.7) -> List[MemoryEntry]:
"""Get memories with high quality scores."""
return [
entry for entry in self.memory_entries
if entry.quality_score >= min_quality
]
def get_failure_patterns(self, max_quality: float = 0.5) -> List[MemoryEntry]:
"""Get memories with low quality scores for learning from failures."""
return [
entry for entry in self.memory_entries
if entry.quality_score <= max_quality
]
def get_improvement_suggestions(self, query: str) -> List[str]:
"""Get improvement suggestions based on similar past experiences."""
similar_memories = self.retrieve_similar_memories(query)
all_improvements = []
for memory in similar_memories:
all_improvements.extend(memory.improvements)
# Count frequency and return most common suggestions
improvement_counts = {}
for improvement in all_improvements:
improvement_counts[improvement] = improvement_counts.get(improvement, 0) + 1
# Sort by frequency
sorted_improvements = sorted(
improvement_counts.items(),
key=lambda x: x[1],
reverse=True
)
return [improvement for improvement, _ in sorted_improvements[:5]]
def get_memory_stats(self) -> Dict[str, Any]:
"""Get statistics about stored memories."""
if not self.memory_entries:
return {'total_entries': 0}
quality_scores = [entry.quality_score for entry in self.memory_entries]
return {
'total_entries': len(self.memory_entries),
'avg_quality': sum(quality_scores) / len(quality_scores),
'high_quality_count': len([s for s in quality_scores if s >= 0.7]),
'low_quality_count': len([s for s in quality_scores if s <= 0.5]),
'oldest_entry': min(entry.timestamp for entry in self.memory_entries),
'newest_entry': max(entry.timestamp for entry in self.memory_entries)
}
def clear_old_memories(self, days_old: int = 30) -> int:
"""Clear memories older than specified days."""
cutoff_time = time.time() - (days_old * 24 * 60 * 60)
old_count = len(self.memory_entries)
self.memory_entries = [
entry for entry in self.memory_entries
if entry.timestamp > cutoff_time
]
removed_count = old_count - len(self.memory_entries)
if removed_count > 0:
self._save_memory()
logger.info(f"Cleared {removed_count} old memory entries")
return removed_count
def _extract_query_pattern(self, query: str) -> Dict[str, Any]:
"""Extract pattern features from a query."""
words = query.lower().split()
return {
'length': len(query),
'word_count': len(words),
'keywords': words,
'has_api_terms': any(term in query.lower() for term in ['api', 'endpoint', 'service']),
'has_data_terms': any(term in query.lower() for term in ['data', 'dataset', 'information']),
'has_action_terms': any(term in query.lower() for term in ['get', 'find', 'search', 'create', 'update']),
'complexity_score': len([w for w in words if len(w) > 6]) / len(words) if words else 0
}
def _extract_result_pattern(self, results: Dict[str, Any]) -> Dict[str, Any]:
"""Extract pattern features from results."""
api_matches = results.get('api_matches', {})
api_results = results.get('results', {})
return {
'match_count': api_matches.get('count', 0),
'executed_count': api_results.get('executed_count', 0),
'successful_count': api_results.get('successful_calls', 0),
'success_rate': (
api_results.get('successful_calls', 0) / api_results.get('executed_count', 1)
if api_results.get('executed_count', 0) > 0 else 0
),
'has_errors': any(
not r.get('success', True)
for r in api_results.get('data', [])
)
}
def _calculate_similarity(self, pattern1: Dict[str, Any], pattern2: Dict[str, Any]) -> float:
"""Calculate similarity between two query patterns."""
similarity_factors = []
# Keyword overlap
keywords1 = set(pattern1.get('keywords', []))
keywords2 = set(pattern2.get('keywords', []))
if keywords1 and keywords2:
overlap = len(keywords1.intersection(keywords2))
union = len(keywords1.union(keywords2))
keyword_similarity = overlap / union if union > 0 else 0
similarity_factors.append(keyword_similarity * 0.4)
# Length similarity
len1 = pattern1.get('length', 0)
len2 = pattern2.get('length', 0)
if len1 > 0 and len2 > 0:
length_similarity = 1 - abs(len1 - len2) / max(len1, len2)
similarity_factors.append(length_similarity * 0.2)
# Feature similarity (boolean features)
features = ['has_api_terms', 'has_data_terms', 'has_action_terms']
feature_matches = sum(
1 for feature in features
if pattern1.get(feature, False) == pattern2.get(feature, False)
)
feature_similarity = feature_matches / len(features)
similarity_factors.append(feature_similarity * 0.3)
# Complexity similarity
comp1 = pattern1.get('complexity_score', 0)
comp2 = pattern2.get('complexity_score', 0)
complexity_similarity = 1 - abs(comp1 - comp2)
similarity_factors.append(complexity_similarity * 0.1)
return sum(similarity_factors) if similarity_factors else 0.0
def _load_memory(self) -> None:
"""Load memory from disk."""
if not self.memory_file.exists():
return
try:
with open(self.memory_file, 'r') as f:
data = json.load(f)
self.memory_entries = [
MemoryEntry(**entry_data)
for entry_data in data.get('entries', [])
]
logger.info(f"Loaded {len(self.memory_entries)} memory entries from disk")
except Exception as e:
logger.error(f"Failed to load memory from disk: {e}")
self.memory_entries = []
def _save_memory(self) -> None:
"""Save memory to disk."""
try:
data = {
'version': '1.0',
'timestamp': time.time(),
'entries': [asdict(entry) for entry in self.memory_entries]
}
with open(self.memory_file, 'w') as f:
json.dump(data, f, indent=2)
logger.debug(f"Saved {len(self.memory_entries)} memory entries to disk")
except Exception as e:
logger.error(f"Failed to save memory to disk: {e}")
|