File size: 4,650 Bytes
483b7d0 | 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 | from __future__ import annotations
import time
from collections import OrderedDict
from dataclasses import dataclass, field
from typing import TYPE_CHECKING
import numpy as np
from secrag.core.config import Settings, get_settings
from secrag.core.logging import get_logger
from secrag.core.types import QueryResponse
from secrag.observability.tracing import span
from secrag.retrieval.embedder import Embedder
if TYPE_CHECKING:
from numpy.typing import NDArray
log = get_logger(__name__)
@dataclass(slots=True)
class CacheEntry:
question: str
vector: NDArray[np.float32]
response: QueryResponse
created_at: float = field(default_factory=time.time)
def is_expired(self, ttl_s: int) -> bool:
return ttl_s > 0 and (time.time() - self.created_at) > ttl_s
@dataclass(slots=True)
class CacheStats:
hits: int = 0
misses: int = 0
evictions: int = 0
expirations: int = 0
@property
def lookups(self) -> int:
return self.hits + self.misses
@property
def hit_rate(self) -> float:
return self.hits / self.lookups if self.lookups else 0.0
def to_dict(self) -> dict[str, float | int]:
return {
"hits": self.hits,
"misses": self.misses,
"evictions": self.evictions,
"expirations": self.expirations,
"lookups": self.lookups,
"hit_rate": round(self.hit_rate, 4),
}
class SemanticCache:
def __init__(self, settings: Settings | None = None, embedder: Embedder | None = None) -> None:
self.settings = settings or get_settings()
self.embedder = embedder or Embedder(self.settings)
self.stats = CacheStats()
self._partitions: dict[str, OrderedDict[str, CacheEntry]] = {}
@property
def enabled(self) -> bool:
return self.settings.cache_enabled
@property
def size(self) -> int:
return sum(len(p) for p in self._partitions.values())
def clear(self) -> None:
self._partitions.clear()
self.stats = CacheStats()
def get(self, question: str, partition: str = "") -> QueryResponse | None:
if not self.enabled:
return None
bucket = self._partitions.get(partition)
if not bucket:
self.stats.misses += 1
return None
with span("cache_lookup", partition=partition or "default"):
vector = self.embedder.embed_query(question)
best_key, best_score = None, 0.0
for key, entry in list(bucket.items()):
if entry.is_expired(self.settings.cache_ttl_s):
del bucket[key]
self.stats.expirations += 1
continue
score = float(np.dot(vector, entry.vector))
if score > best_score:
best_key, best_score = key, score
if best_key is not None and best_score >= self.settings.cache_similarity_threshold:
entry = bucket[best_key]
bucket.move_to_end(best_key)
self.stats.hits += 1
log.info(
"cache_hit",
similarity=round(best_score, 4),
original=entry.question[:60],
)
hit = entry.response.model_copy(deep=True)
hit.cached = True
return hit
self.stats.misses += 1
return None
def put(self, question: str, response: QueryResponse, partition: str = "") -> None:
if not self.enabled:
return
bucket = self._partitions.setdefault(partition, OrderedDict())
vector = self.embedder.embed_query(question)
stored = response.model_copy(deep=True)
stored.cached = False
bucket[question] = CacheEntry(question=question, vector=vector, response=stored)
bucket.move_to_end(question)
while len(bucket) > self.settings.cache_max_entries:
bucket.popitem(last=False)
self.stats.evictions += 1
def snapshot(self) -> dict[str, object]:
return {
"enabled": self.enabled,
"size": self.size,
"partitions": len(self._partitions),
"threshold": self.settings.cache_similarity_threshold,
**self.stats.to_dict(),
}
def partition_key(tickers: list[str], fiscal_years: list[int], sections: list[str]) -> str:
return "|".join(
(
",".join(sorted(t.upper() for t in tickers)),
",".join(str(y) for y in sorted(fiscal_years)),
",".join(sorted(sections)),
)
)
|