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0d3f7cc | 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 | """Long-term memory implementation using Qdrant with Redis/local fallback."""
from __future__ import annotations
import json
import logging
from datetime import UTC, datetime
from pathlib import Path
from typing import Any
from hermes.config.settings import get_settings
logger = logging.getLogger(__name__)
_embedding_model: Any = None
def _get_embedding_model() -> Any:
"""Get or create singleton embedding model."""
global _embedding_model
if _embedding_model is None:
try:
from sentence_transformers import SentenceTransformer
settings = get_settings()
_embedding_model = SentenceTransformer(settings.model.embedding_model)
except Exception:
_embedding_model = False # Sentinel: tried and failed
return _embedding_model if _embedding_model is not False else None
class LongTermMemory:
"""Long-term memory using vector database with fallback chain: Qdrant → Redis → JSON file."""
def __init__(self) -> None:
self.settings = get_settings()
self._client: Any = None
self._redis: Any = None
self._collection = self.settings.database.qdrant_collection
self._local_store: list[dict[str, Any]] = []
self._json_path = Path("data/long_term_memory.json")
self._load_json()
def _load_json(self) -> None:
"""Load persisted memories from JSON file."""
try:
if self._json_path.exists():
with open(self._json_path, encoding="utf-8") as f:
self._local_store = json.load(f)
except Exception as e:
logger.warning(f"Could not load memory JSON: {e}")
def _save_json(self) -> None:
"""Persist memories to JSON file."""
try:
self._json_path.parent.mkdir(parents=True, exist_ok=True)
with open(self._json_path, "w", encoding="utf-8") as f:
json.dump(self._local_store, f, indent=2, default=str)
except Exception as e:
logger.warning(f"Could not save memory JSON: {e}")
async def initialize(self) -> None:
"""Initialize the long-term memory with fallback chain."""
# Try Qdrant first
try:
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams
self._client = QdrantClient(url=self.settings.database.qdrant_url)
collections = self._client.get_collections().collections
collection_names = [c.name for c in collections]
if self._collection not in collection_names:
self._client.create_collection(
collection_name=self._collection,
vectors_config=VectorParams(
size=self.settings.model.embedding_dimension,
distance=Distance.COSINE,
),
)
logger.info(f"Created Qdrant collection: {self._collection}")
return
except Exception as e:
logger.warning(f"Qdrant unavailable: {e}")
self._client = None
# Try Redis as fallback
try:
import redis.asyncio as aioredis
self._redis = aioredis.from_url(
self.settings.database.redis_url,
decode_responses=True,
)
await self._redis.ping()
logger.info("Using Redis for long-term memory")
return
except Exception as e:
logger.warning(f"Redis unavailable: {e}")
self._redis = None
# Final fallback: JSON file
logger.info("Using JSON file for long-term memory")
async def store(
self,
key: str,
value: Any,
category: str = "general",
metadata: dict[str, Any] | None = None,
) -> None:
"""Store a memory entry."""
entry = {
"key": key,
"value": value,
"category": category,
"metadata": metadata or {},
"timestamp": datetime.now(UTC).isoformat(),
}
self._local_store.append(entry)
if self._client:
try:
from qdrant_client.models import PointStruct
embedding = await self._generate_embedding(str(value))
point = PointStruct(
id=len(self._local_store),
vector=embedding,
payload=entry,
)
self._client.upsert(collection_name=self._collection, points=[point])
return
except Exception as e:
logger.error(f"Qdrant store failed: {e}")
if self._redis:
try:
await self._redis.hset(
f"memory:{category}",
key,
json.dumps(entry, default=str),
)
return
except Exception as e:
logger.error(f"Redis store failed: {e}")
self._save_json()
async def retrieve(
self,
query: str | None = None,
key: str | None = None,
category: str | None = None,
limit: int = 10,
) -> list[dict[str, Any]]:
"""Retrieve memory entries."""
if key:
for entry in reversed(self._local_store):
if entry.get("key") == key:
return [entry]
return []
if self._client and query:
try:
embedding = await self._generate_embedding(query)
results = self._client.search(
collection_name=self._collection,
query_vector=embedding,
limit=limit,
)
return [r.payload for r in results if r.payload]
except Exception as e:
logger.error(f"Qdrant search failed: {e}")
if self._redis and query:
try:
pattern = f"memory:{category or '*'}"
keys = await self._redis.keys(pattern)
results = []
for redis_key in keys:
data = await self._redis.hgetall(redis_key)
for _, val in data.items():
entry = json.loads(val)
if query.lower() in str(entry.get("value", "")).lower():
results.append(entry)
return results[-limit:]
except Exception as e:
logger.error(f"Redis search failed: {e}")
results = self._local_store
if category:
results = [e for e in results if e.get("category") == category]
if query:
results = [e for e in results if query.lower() in str(e.get("value", "")).lower()]
return results[-limit:]
async def delete(self, key: str) -> bool:
"""Delete a memory entry."""
self._local_store = [e for e in self._local_store if e.get("key") != key]
self._save_json()
return True
async def list_all(self, category: str | None = None) -> list[dict[str, Any]]:
"""List all memory entries."""
if category:
return [e for e in self._local_store if e.get("category") == category]
return self._local_store.copy()
async def _generate_embedding(self, text: str) -> list[float]:
"""Generate embedding for text using singleton model."""
model = _get_embedding_model()
if model is not None:
try:
embedding = model.encode(text)
return embedding.tolist()
except Exception as e:
logger.warning(f"Embedding generation failed: {e}")
import hashlib
hash_val = hashlib.md5(text.encode()).hexdigest()
return [float(int(hash_val[i : i + 2], 16)) / 255.0 for i in range(0, 32, 2)]
async def get_stats(self) -> dict[str, Any]:
"""Get memory statistics."""
categories: dict[str, int] = {}
for entry in self._local_store:
cat = entry.get("category", "unknown")
categories[cat] = categories.get(cat, 0) + 1
return {
"total_entries": len(self._local_store),
"categories": categories,
"qdrant_connected": self._client is not None,
"redis_connected": self._redis is not None,
"backend": "qdrant" if self._client else ("redis" if self._redis else "json"),
}
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