Spaces:
Running
Running
File size: 6,997 Bytes
24f95f0 | 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 | """
Intelligent Cache Manager for MiroOrg v2.
Caches answers for generic queries to avoid redundant pipeline runs.
Only learns from specific/domain queries to keep memory clean.
"""
import json
import os
import time
import hashlib
from pathlib import Path
from typing import Dict, List, Any, Optional
from app.services.query_classifier import QueryType
CACHE_DIR = Path(__file__).parent.parent / "data" / "cache"
CACHE_DIR.mkdir(parents=True, exist_ok=True)
# TTL in hours
GENERIC_TTL_HOURS = 720 # 30 days
SPECIFIC_TTL_HOURS = 168 # 7 days
HYBRID_TTL_HOURS = 336 # 14 days
def _query_hash(query: str) -> str:
"""Generate a stable hash for a query string."""
return hashlib.md5(query.lower().strip().encode()).hexdigest()
class CacheEntry:
"""Single cache entry with TTL."""
def __init__(
self,
query: str,
answer: str,
query_type: QueryType,
domain: str,
ttl_hours: int,
model_insights: List[str] = None,
metadata: Dict = None,
):
self.query = query
self.answer = answer
self.query_type = query_type.value
self.domain = domain
self.created_at = time.time()
self.ttl_seconds = ttl_hours * 3600
self.hit_count = 0
self.model_insights = model_insights or []
self.metadata = metadata or {}
def is_expired(self) -> bool:
return (time.time() - self.created_at) > self.ttl_seconds
def to_dict(self) -> Dict:
return {
"query": self.query,
"answer": self.answer,
"query_type": self.query_type,
"domain": self.domain,
"created_at": self.created_at,
"ttl_seconds": self.ttl_seconds,
"hit_count": self.hit_count,
"model_insights": self.model_insights,
"metadata": self.metadata,
}
@classmethod
def from_dict(cls, data: Dict) -> "CacheEntry":
entry = cls(
query=data["query"],
answer=data["answer"],
query_type=QueryType(data["query_type"]),
domain=data["domain"],
ttl_hours=data["ttl_seconds"] // 3600,
model_insights=data.get("model_insights", []),
metadata=data.get("metadata", {}),
)
entry.created_at = data["created_at"]
entry.hit_count = data.get("hit_count", 0)
return entry
class IntelligentCacheManager:
"""Manages intelligent caching with TTL and hit tracking."""
def __init__(self, cache_dir: Path = CACHE_DIR):
self.cache_dir = cache_dir
self.cache_dir.mkdir(parents=True, exist_ok=True)
def _get_cache_path(self, query_hash: str) -> Path:
return self.cache_dir / f"{query_hash}.json"
def get(self, query: str) -> Optional[Dict]:
"""
Get cached answer for a query.
Returns:
Dict with answer, cached=True, cache_age_hours if found
None if not found or expired
"""
h = _query_hash(query)
path = self._get_cache_path(h)
if not path.exists():
return None
try:
with open(path) as f:
data = json.load(f)
entry = CacheEntry.from_dict(data)
if entry.is_expired():
path.unlink(missing_ok=True)
return None
# Increment hit count
entry.hit_count += 1
with open(path, "w") as f:
json.dump(entry.to_dict(), f, indent=2)
cache_age_hours = (time.time() - entry.created_at) / 3600
return {
"answer": entry.answer,
"cached": True,
"cache_age_hours": cache_age_hours,
"hit_count": entry.hit_count,
"query_type": entry.query_type,
"domain": entry.domain,
"model_insights": entry.model_insights,
}
except Exception:
return None
def put(
self,
query: str,
answer: str,
query_type: QueryType,
domain: str,
ttl_hours: int = None,
model_insights: List[str] = None,
metadata: Dict = None,
) -> None:
"""Store an answer in the cache."""
if ttl_hours is None:
if query_type == QueryType.GENERIC:
ttl_hours = GENERIC_TTL_HOURS
elif query_type == QueryType.SPECIFIC:
ttl_hours = SPECIFIC_TTL_HOURS
else:
ttl_hours = HYBRID_TTL_HOURS
entry = CacheEntry(
query=query,
answer=answer,
query_type=query_type,
domain=domain,
ttl_hours=ttl_hours,
model_insights=model_insights or [],
metadata=metadata or {},
)
h = _query_hash(query)
path = self._get_cache_path(h)
with open(path, "w") as f:
json.dump(entry.to_dict(), f, indent=2)
def cleanup_expired(self) -> int:
"""Remove expired cache entries. Returns count removed."""
count = 0
for path in self.cache_dir.glob("*.json"):
try:
with open(path) as f:
data = json.load(f)
entry = CacheEntry.from_dict(data)
if entry.is_expired():
path.unlink(missing_ok=True)
count += 1
except Exception:
path.unlink(missing_ok=True)
count += 1
return count
def get_stats(self) -> Dict:
"""Get cache statistics."""
total = 0
generic = 0
specific = 0
hybrid = 0
expired = 0
total_hits = 0
for path in self.cache_dir.glob("*.json"):
try:
with open(path) as f:
data = json.load(f)
entry = CacheEntry.from_dict(data)
total += 1
total_hits += entry.hit_count
if entry.is_expired():
expired += 1
elif entry.query_type == "generic":
generic += 1
elif entry.query_type == "specific":
specific += 1
else:
hybrid += 1
except Exception:
pass
# Calculate cache size
cache_size_bytes = sum(
p.stat().st_size for p in self.cache_dir.glob("*.json") if p.exists()
)
cache_size_mb = cache_size_bytes / (1024 * 1024)
return {
"total_entries": total,
"generic_entries": generic,
"specific_entries": specific,
"hybrid_entries": hybrid,
"expired_entries": expired,
"total_hits": total_hits,
"cache_size_mb": round(cache_size_mb, 2),
"avg_hits_per_entry": round(total_hits / total, 1) if total > 0 else 0,
}
|