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# ────────────────────────────── memo/persistent.py ──────────────────────────────
"""
Persistent Memory System
MongoDB-based persistent memory storage with semantic search capabilities.
"""
import os
import uuid
from typing import List, Dict, Any, Optional, Tuple
from datetime import datetime, timezone
from utils.logger import get_logger
from utils.rag.embeddings import EmbeddingClient
logger = get_logger("PERSISTENT_MEMORY", __name__)
class PersistentMemory:
"""MongoDB-based persistent memory system with semantic search"""
def __init__(self, mongo_uri: str, db_name: str, embedder: EmbeddingClient):
self.mongo_uri = mongo_uri
self.db_name = db_name
self.embedder = embedder
# MongoDB connection
try:
from pymongo import MongoClient
self.client = MongoClient(mongo_uri)
self.db = self.client[db_name]
self.memories = self.db["memories"]
# Create indexes for efficient querying
self.memories.create_index([("user_id", 1), ("memory_type", 1)])
self.memories.create_index([("user_id", 1), ("created_at", -1)])
self.memories.create_index([("user_id", 1), ("project_id", 1)])
logger.info(f"[PERSISTENT_MEMORY] Connected to MongoDB: {db_name}")
except Exception as e:
logger.error(f"[PERSISTENT_MEMORY] Failed to connect to MongoDB: {e}")
raise
def add_memory(self, user_id: str, content: str, memory_type: str,
project_id: str = None, importance: str = "medium",
tags: List[str] = None, metadata: Dict[str, Any] = None) -> str:
"""Add a memory entry to the persistent system"""
try:
# Generate embedding for semantic search
embedding = self.embedder.embed([content])[0] if content else None
# Create summary
summary = content[:200] + "..." if len(content) > 200 else content
memory_entry = {
"id": str(uuid.uuid4()),
"user_id": user_id,
"project_id": project_id,
"memory_type": memory_type,
"content": content,
"summary": summary,
"importance": importance,
"tags": tags or [],
"created_at": datetime.now(timezone.utc),
"updated_at": datetime.now(timezone.utc),
"last_accessed": datetime.now(timezone.utc),
"access_count": 0,
"embedding": embedding,
"metadata": metadata or {}
}
# Store in MongoDB
self.memories.insert_one(memory_entry)
logger.info(f"[PERSISTENT_MEMORY] Added {memory_type} memory for user {user_id}")
return memory_entry["id"]
except Exception as e:
logger.error(f"[PERSISTENT_MEMORY] Failed to add memory: {e}")
raise
def get_memories(self, user_id: str, memory_type: str = None,
project_id: str = None, limit: int = 50) -> List[Dict[str, Any]]:
"""Get memories for a user with optional filtering"""
try:
query = {"user_id": user_id}
if memory_type:
query["memory_type"] = memory_type
if project_id:
query["project_id"] = project_id
cursor = self.memories.find(query).sort("created_at", -1).limit(limit)
return list(cursor)
except Exception as e:
logger.error(f"[PERSISTENT_MEMORY] Failed to get memories: {e}")
return []
def search_memories(self, user_id: str, query: str, memory_types: List[str] = None,
project_id: str = None, limit: int = 10) -> List[Tuple[Dict[str, Any], float]]:
"""Search memories using semantic similarity"""
try:
# Generate query embedding
query_embedding = self.embedder.embed([query])[0]
# Build MongoDB query
mongo_query = {
"user_id": user_id,
"embedding": {"$exists": True}
}
if memory_types:
mongo_query["memory_type"] = {"$in": memory_types}
if project_id:
mongo_query["project_id"] = project_id
# Get all matching memories
cursor = self.memories.find(mongo_query)
# Calculate similarities
results = []
for doc in cursor:
try:
if doc.get("embedding"):
# Calculate cosine similarity
similarity = self._cosine_similarity(query_embedding, doc["embedding"])
results.append((doc, similarity))
except Exception as e:
logger.warning(f"[PERSISTENT_MEMORY] Failed to process memory for search: {e}")
continue
# Sort by similarity and return top results
results.sort(key=lambda x: x[1], reverse=True)
return results[:limit]
except Exception as e:
logger.error(f"[PERSISTENT_MEMORY] Failed to search memories: {e}")
return []
def _cosine_similarity(self, a: List[float], b: List[float]) -> float:
"""Calculate cosine similarity between two vectors"""
try:
import numpy as np
from memo.context import cosine_similarity
a_np = np.array(a)
b_np = np.array(b)
return cosine_similarity(a_np, b_np)
except Exception:
return 0.0
def clear_user_memories(self, user_id: str) -> int:
"""Clear all memories for a user"""
try:
result = self.memories.delete_many({"user_id": user_id})
logger.info(f"[PERSISTENT_MEMORY] Cleared {result.deleted_count} memories for user {user_id}")
return result.deleted_count
except Exception as e:
logger.error(f"[PERSISTENT_MEMORY] Failed to clear user memories: {e}")
return 0
def get_memory_stats(self, user_id: str) -> Dict[str, Any]:
"""Get memory statistics for a user"""
try:
stats = {
"total_memories": self.memories.count_documents({"user_id": user_id}),
"by_type": {},
"recent_activity": 0
}
# Count by memory type
pipeline = [
{"$match": {"user_id": user_id}},
{"$group": {"_id": "$memory_type", "count": {"$sum": 1}}}
]
for result in self.memories.aggregate(pipeline):
stats["by_type"][result["_id"]] = result["count"]
# Recent activity (last 7 days)
from datetime import timedelta
week_ago = datetime.now(timezone.utc) - timedelta(days=7)
stats["recent_activity"] = self.memories.count_documents({
"user_id": user_id,
"created_at": {"$gte": week_ago}
})
return stats
except Exception as e:
logger.error(f"[PERSISTENT_MEMORY] Failed to get memory stats: {e}")
return {}
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