Spaces:
Running
Running
File size: 5,086 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 | from typing import List
import asyncio
from sqlalchemy.ext.asyncio import AsyncSession
from app.db.models import ScamEvent, Entity, EventEntity
from app.schemas.response import AnalyzeResponse
from app.db.session import AsyncSessionLocal
from sqlalchemy import select, update
import uuid
from datetime import datetime
from app.services.scam_graph import scam_graph
from app.services.simulation_engine import simulation_engine
class MemoryService:
async def save_event(self, response: AnalyzeResponse, metadata: dict, embedding: list = None):
async with AsyncSessionLocal() as session:
# 1. Save Event to Postgres
event = ScamEvent(
id=uuid.UUID(response.id),
text=response.text,
source=response.source,
risk_score=response.risk_score,
decision=response.decision,
event_metadata={
"reasons": response.reasons,
"intent": response.intent.dict(),
"entities": response.entities.dict(),
"live_intel": {
"evidence": response.evidence,
"claimed_brand": response.claimed_brand,
"official_verify": response.official_verify.dict() if response.official_verify else None,
"next_steps": response.next_steps,
"verdict_synthesis": response.verdict_synthesis,
"breadcrumbs": response.breadcrumbs,
"similarity": response.similarity
},
**metadata
},
embedding=embedding
)
session.add(event)
# 2. Add to Scam Journey Graph (Depth)
graph_entities = {
"phones": response.entities.phones,
"upi_ids": response.entities.upi_ids,
"links": response.entities.domains
}
# Normalize intent signals for graph
signals = {
"urgency": int(response.intent.urgency * 10),
"impersonation": int(response.intent.impersonation * 10),
"payment": int(response.intent.payment * 10)
}
scam_graph.add_event(response.source, graph_entities, signals)
# 3. Optimization: Autonomous Security Drill
if response.risk_score > 85:
drill_scenario = f"The user has received this suspicious intake: '{response.text[:200]}...'. Simulation: What is the most likely social engineering escalation the scammer will try next?"
# Trigger background simulation
asyncio.create_task(simulation_engine.run_simulation(drill_scenario, {"source": response.source, "risk": response.risk_score}))
# 4. Process Entities for relational mapping
for phone in response.entities.phones:
await self._upsert_entity(session, "phone", phone, event.id)
for upi in response.entities.upi_ids:
await self._upsert_entity(session, "upi", upi, event.id)
for domain in response.entities.domains:
await self._upsert_entity(session, "domain", domain, event.id)
await session.commit()
async def _upsert_entity(self, session: AsyncSession, e_type: str, value: str, event_id: uuid.UUID):
stmt = select(Entity).where(Entity.type == e_type, Entity.value == value)
result = await session.execute(stmt)
entity = result.scalar_one_or_none()
if not entity:
entity = Entity(type=e_type, value=value)
session.add(entity)
await session.flush()
else:
entity.last_seen = datetime.utcnow()
# Link to event
link = EventEntity(event_id=event_id, entity_id=entity.id)
session.add(link)
async def check_overlap(self, phones: List[str], upis: List[str], domains: List[str]) -> List[dict]:
"""Detect if these entities have appeared in previous reports."""
async with AsyncSessionLocal() as session:
overlaps = []
all_vals = [("phone", p) for p in phones] + [("upi", u) for u in upis] + [("domain", d) for d in domains]
for e_type, val in all_vals:
stmt = select(Entity).where(Entity.type == e_type, Entity.value == val)
result = await session.execute(stmt)
entity = result.scalar_one_or_none()
if entity:
overlaps.append({
"source": "Janus Memory Graph",
"signal": "infrastructure_overlap",
"value": val,
"severity": "high",
"explanation": f"This '{val}' has been encountered in previous reports. Recidivism detected."
})
return overlaps
memory_service = MemoryService()
|