feat: Redis caching + async queue for LLM scaling (v1.6.0)

- Add async Redis client singleton (redis_client.py) for caching and arq pool
- Add arq job functions (jobs.py) for background LLM processing
- Cache ask/explain LLM responses with TTL (1h ask, 24h explain)
- Add async mode to /api/ask: enqueue job, return job_id, poll /api/jobs/{id}
- Add GET /api/jobs/{job_id} endpoint for job status polling
- Add arq worker service to docker-compose (dev + prod)
- Switch from Redis to Valkey (BSD fork) in Docker Compose
- Add REDIS_URL config setting
- Add tests for cache hit, async mode, and job status

.env.example

@@ -50,6 +50,11 @@ LLM_MAX_EVENTS=200
 LLM_TIMEOUT_SECONDS=30
 LLM_API_VERSION=
 
+# Valkey (caching + async job queue for LLM calls)
+# In Docker Compose, this is set automatically to redis://redis:6379/0
+# For local dev, start Valkey with: docker run -d -p 6379:6379 valkey/valkey:8-alpine
+REDIS_URL=redis://localhost:6379/0
+
 # Optional: privacy / access control
 # Hide entire services from users without PRIVACY_SERVICE_ROLES
 # PRIVACY_SERVICES=Exchange,Teams

ROADMAP.md

@@ -65,9 +65,10 @@ Goal: add AI-powered analysis and external tool integration.
 - [x] AI feature flag (`AI_FEATURES_ENABLED`) to gate LLM-dependent features
 - [x] Natural language query endpoint (`/api/ask`) with intent extraction and smart sampling
 - [x] MCP (Model Context Protocol) server for Claude Desktop / Cursor integration
+- [x] Valkey caching for LLM responses and frequent queries
+- [x] Async queue (arq) for LLM requests to prevent timeout/cost explosions at scale
 - [ ] Advanced analytics dashboard (trending operations, anomaly detection)
-- [ ] Redis caching for LLM responses and frequent queries
-- [ ] Async queue for LLM requests to prevent timeout/cost explosions at scale
 
 ## Completed in this PR
-All Phase 5 items marked done were implemented in v1.3.0.
+All Phase 5 items marked done were implemented in v1.3.0–v1.5.0.
+Redis caching + async queue implemented in v1.6.0, switched to Valkey.

VERSION

@@ -1 +1 @@
-1.5.0
+1.6.0

backend/config.py

@@ -57,6 +57,9 @@ class Settings(BaseSettings):
     PRIVACY_SENSITIVE_OPERATIONS: str = ""  # comma-separated, e.g. "MailItemsAccessed,Search-Mailbox,Send"
     PRIVACY_SERVICE_ROLES: str = ""  # comma-separated, e.g. "SecurityAdministrator,ComplianceAdministrator"
 
+    # Redis (caching + async job queue)
+    REDIS_URL: str = "redis://localhost:6379/0"
+
 
 _settings = Settings()
 
@@ -95,3 +98,5 @@ LLM_API_VERSION = _settings.LLM_API_VERSION
 PRIVACY_SERVICES = {s.strip() for s in _settings.PRIVACY_SERVICES.split(",") if s.strip()}
 PRIVACY_SENSITIVE_OPERATIONS = {o.strip() for o in _settings.PRIVACY_SENSITIVE_OPERATIONS.split(",") if o.strip()}
 PRIVACY_SERVICE_ROLES = {r.strip() for r in _settings.PRIVACY_SERVICE_ROLES.split(",") if r.strip()}
+
+REDIS_URL = _settings.REDIS_URL

backend/jobs.py

@@ -0,0 +1,113 @@
+"""arq job functions for async LLM processing."""
+
+import hashlib
+import json
+
+import structlog
+from arq.connections import RedisSettings
+from config import REDIS_URL
+
+logger = structlog.get_logger("aoc.jobs")
+
+# ---------------------------------------------------------------------------
+# Cache helpers
+# ---------------------------------------------------------------------------
+
+CACHE_TTL_ASK = 3600  # 1 hour
+CACHE_TTL_EXPLAIN = 86400  # 24 hours
+
+
+def _ask_cache_key(question: str, filters: dict, events: list) -> str:
+    payload = json.dumps({"q": question, "f": filters, "e": [e.get("id") for e in events]}, sort_keys=True)
+    return f"aoc:cache:ask:{hashlib.md5(payload.encode()).hexdigest()}"
+
+
+def _explain_cache_key(event_id: str) -> str:
+    return f"aoc:cache:explain:{event_id}"
+
+
+async def get_cached_ask(redis, question: str, filters: dict, events: list) -> dict | None:
+    key = _ask_cache_key(question, filters, events)
+    raw = await redis.get(key)
+    if raw:
+        return json.loads(raw)
+    return None
+
+
+async def set_cached_ask(redis, question: str, filters: dict, events: list, result: dict):
+    key = _ask_cache_key(question, filters, events)
+    await redis.setex(key, CACHE_TTL_ASK, json.dumps(result, default=str))
+
+
+async def get_cached_explain(redis, event_id: str) -> dict | None:
+    key = _explain_cache_key(event_id)
+    raw = await redis.get(key)
+    if raw:
+        return json.loads(raw)
+    return None
+
+
+async def set_cached_explain(redis, event_id: str, result: dict):
+    key = _explain_cache_key(event_id)
+    await redis.setex(key, CACHE_TTL_EXPLAIN, json.dumps(result, default=str))
+
+
+# ---------------------------------------------------------------------------
+# arq job functions
+# ---------------------------------------------------------------------------
+
+async def process_ask_question(ctx, question: str, filters: dict, events: list, total: int, excluded_services: list | None):
+    """Background job: call LLM for /api/ask and cache result."""
+    from routes.ask import _call_llm
+
+    redis = ctx["redis"]
+    try:
+        answer = await _call_llm(question, events, total=total, excluded_services=excluded_services)
+        result = {"status": "completed", "answer": answer, "llm_used": True, "llm_error": None}
+    except Exception as exc:
+        logger.warning("Async ask LLM failed", error=str(exc))
+        result = {"status": "failed", "answer": "", "llm_used": False, "llm_error": str(exc)}
+
+    await set_cached_ask(redis, question, filters, events, result)
+    return result
+
+
+async def process_explain_event(ctx, event_id: str, event: dict, related: list):
+    """Background job: call LLM for /api/events/{id}/explain and cache result."""
+    from routes.ask import _explain_event
+
+    redis = ctx["redis"]
+    try:
+        explanation = await _explain_event(event, related)
+        result = {"status": "completed", "explanation": explanation, "llm_used": True, "llm_error": None}
+    except Exception as exc:
+        logger.warning("Async explain LLM failed", error=str(exc))
+        result = {"status": "failed", "explanation": "", "llm_used": False, "llm_error": str(exc)}
+
+    await set_cached_explain(redis, event_id, result)
+    return result
+
+
+# ---------------------------------------------------------------------------
+# arq worker configuration
+# ---------------------------------------------------------------------------
+
+async def startup(ctx):
+    from redis.asyncio import Redis
+
+    ctx["redis"] = Redis.from_url(REDIS_URL, decode_responses=True)
+
+
+async def shutdown(ctx):
+    await ctx["redis"].close()
+
+
+class WorkerSettings:
+    functions = [process_ask_question, process_explain_event]
+    redis_settings = RedisSettings.from_dsn(REDIS_URL)
+    on_startup = startup
+    on_shutdown = shutdown
+    max_jobs = 10
+    job_timeout = 120
+    keep_result = 3600
+    keep_result_forever = False

backend/main.py

@@ -19,6 +19,7 @@ from routes.events import router as events_router
 from routes.fetch import router as fetch_router
 from routes.fetch import run_fetch
 from routes.health import router as health_router
+from routes.jobs import router as jobs_router
 from routes.rules import router as rules_router
 from routes.saved_searches import router as saved_searches_router
 from routes.webhooks import router as webhooks_router
@@ -122,6 +123,7 @@ if AI_FEATURES_ENABLED:
     app.mount("/mcp", mcp_asgi)
 app.include_router(saved_searches_router, prefix="/api")
 app.include_router(rules_router, prefix="/api")
+app.include_router(jobs_router, prefix="/api")
 
 
 @app.get("/health")
@@ -176,3 +178,6 @@ async def stop_periodic_fetch():
         task.cancel()
         with suppress(Exception):
             await task
+    from redis_client import close_redis_connections
+
+    await close_redis_connections()

backend/models/api.py

@@ -82,6 +82,7 @@ class AskRequest(BaseModel):
     end: str | None = None
     include_tags: list[str] | None = None
     exclude_tags: list[str] | None = None
+    async_mode: bool = False  # enqueue async job instead of waiting
 
 
 class AskEventRef(BaseModel):
@@ -101,3 +102,4 @@ class AskResponse(BaseModel):
     query_info: dict
     llm_used: bool
     llm_error: str | None = None
+    job_id: str | None = None

backend/redis_client.py

@@ -0,0 +1,36 @@
+"""Async Redis client singleton for caching and job queue."""
+
+import redis.asyncio as aioredis
+from arq import create_pool
+from arq.connections import ArqRedis, RedisSettings
+from config import REDIS_URL
+
+_arq_pool: ArqRedis | None = None
+_plain_redis: aioredis.Redis | None = None
+
+
+async def get_arq_pool() -> ArqRedis:
+    """Return a shared arq pool (ArqRedis extends redis.asyncio.Redis)."""
+    global _arq_pool
+    if _arq_pool is None:
+        _arq_pool = await create_pool(RedisSettings.from_dsn(REDIS_URL))
+    return _arq_pool
+
+
+async def get_redis() -> aioredis.Redis:
+    """Return a shared plain async Redis client."""
+    global _plain_redis
+    if _plain_redis is None:
+        _plain_redis = aioredis.from_url(REDIS_URL, decode_responses=True)
+    return _plain_redis
+
+
+async def close_redis_connections():
+    """Close all Redis connections (call on shutdown)."""
+    global _arq_pool, _plain_redis
+    if _arq_pool:
+        await _arq_pool.close()
+        _arq_pool = None
+    if _plain_redis:
+        await _plain_redis.close()
+        _plain_redis = None

backend/requirements.txt

@@ -14,3 +14,5 @@ prometheus-client
 httpx
 gunicorn
 mcp
+redis
+arq

backend/routes/ask.py

@@ -18,7 +18,9 @@ from config import (
 )
 from database import events_collection
 from fastapi import APIRouter, Depends, HTTPException
+from jobs import get_cached_ask, get_cached_explain, set_cached_ask, set_cached_explain
 from models.api import AskRequest, AskResponse
+from redis_client import get_arq_pool
 
 router = APIRouter(dependencies=[Depends(require_auth)])
 logger = structlog.get_logger("aoc.ask")
@@ -640,14 +642,23 @@ async def explain_event(event_id: str, user: dict = Depends(require_auth)):
             "llm_error": "LLM_API_KEY not configured",
         }
 
+    # Check cache first
+    redis = await get_arq_pool()
+    cached = await get_cached_explain(redis, event_id)
+    if cached:
+        cached["related_count"] = len(related)
+        return cached
+
     try:
         explanation = await _explain_event(event, related)
-        return {
+        result = {
             "explanation": explanation,
             "llm_used": True,
             "llm_error": None,
             "related_count": len(related),
         }
+        await set_cached_explain(redis, event_id, result)
+        return result
     except Exception as exc:
         logger.warning("Event explanation failed", error=str(exc))
         return {
@@ -746,19 +757,70 @@ async def ask_question(body: AskRequest, user: dict = Depends(require_auth)):
             llm_error="LLM not used — no events found." if not LLM_API_KEY else None,
         )
 
-    # Try LLM summarisation
+    # Try LLM summarisation (with caching + optional async)
     answer = ""
     llm_used = False
     llm_error = None
-    if not LLM_API_KEY:
+    job_id = None
-        llm_error = "LLM_API_KEY is not configured. Set it in your .env to enable AI narrative summarisation."
+
+    filters_snapshot = {
+        "services": body.services,
+        "actor": body.actor,
+        "operation": body.operation,
+        "result": body.result,
+        "start": body.start,
+        "end": body.end,
+        "include_tags": body.include_tags,
+        "exclude_tags": body.exclude_tags,
+    }
+
+    if LLM_API_KEY:
+        redis = await get_arq_pool()
+        cached = await get_cached_ask(redis, question, filters_snapshot, events)
+        if cached:
+            answer = cached.get("answer", "")
+            llm_used = cached.get("llm_used", False)
+            llm_error = cached.get("llm_error")
+        elif body.async_mode:
+            pool = await get_arq_pool()
+            job = await pool.enqueue_job(
+                "process_ask_question",
+                question,
+                filters_snapshot,
+                events,
+                total,
+                excluded_services,
+            )
+            job_id = job.job_id if job else None
+            return AskResponse(
+                answer="Your question is being processed. Poll /api/jobs/{job_id} for the result.",
+                events=[_to_event_ref(e) for e in events],
+                query_info={
+                    "entity": entity,
+                    "start": start,
+                    "end": end,
+                    "event_count": len(events),
+                    "total_matched": total,
+                    "services_queried": query_services,
+                    "excluded_services": excluded_services,
+                    "mongo_query": json.dumps(query, default=str),
+                },
+                llm_used=False,
+                llm_error=None,
+                job_id=job_id,
+            )
+        else:
+            try:
+                answer = await _call_llm(question, events, total=total, excluded_services=excluded_services)
+                llm_used = True
+                await set_cached_ask(redis, question, filters_snapshot, events, {
+                    "answer": answer, "llm_used": True, "llm_error": None,
+                })
+            except Exception as exc:
+                llm_error = f"LLM call failed: {exc}"
+                logger.warning("LLM call failed, falling back to structured summary", error=str(exc))
     else:
-        try:
+        llm_error = "LLM_API_KEY is not configured. Set it in your .env to enable AI narrative summarisation."
-            answer = await _call_llm(question, events, total=total, excluded_services=excluded_services)
-            llm_used = True
-        except Exception as exc:
-            llm_error = f"LLM call failed: {exc}"
-            logger.warning("LLM call failed, falling back to structured summary", error=str(exc))
 
     # Fallback: structured summary if LLM unavailable or failed
     if not answer:
@@ -797,4 +859,5 @@ async def ask_question(body: AskRequest, user: dict = Depends(require_auth)):
         },
         llm_used=llm_used,
         llm_error=llm_error,
+        job_id=job_id,
     )

backend/routes/jobs.py

@@ -0,0 +1,43 @@
+"""Job status endpoints for async LLM operations."""
+
+from arq.jobs import Job, JobStatus
+from auth import require_auth
+from fastapi import APIRouter, Depends, HTTPException
+from pydantic import BaseModel
+from redis_client import get_redis
+
+router = APIRouter(dependencies=[Depends(require_auth)])
+
+
+class JobStatusResponse(BaseModel):
+    job_id: str
+    status: str  # queued, in_progress, complete, not_found, deferred
+    result: dict | None = None
+    error: str | None = None
+
+
+@router.get("/jobs/{job_id}", response_model=JobStatusResponse)
+async def get_job_status(job_id: str, user: dict = Depends(require_auth)):
+    """Poll for the result of an async LLM job."""
+    redis = await get_redis()
+    job = Job(job_id, redis)
+    status = await job.status()
+
+    if status == JobStatus.not_found:
+        raise HTTPException(status_code=404, detail="Job not found")
+
+    result = None
+    error = None
+    if status == JobStatus.complete:
+        try:
+            result_data = await job.result(timeout=0)
+            result = result_data if isinstance(result_data, dict) else {"data": str(result_data)}
+        except Exception as exc:
+            error = str(exc)
+
+    return JobStatusResponse(
+        job_id=job_id,
+        status=status.value,
+        result=result,
+        error=error,
+    )

backend/tests/conftest.py

@@ -49,6 +49,20 @@ def client(mock_events_collection, mock_watermarks_collection, monkeypatch):
     monkeypatch.setattr("rules.rules_collection", audit_db["alert_rules"])
     monkeypatch.setattr("routes.rules.rules_collection", audit_db["alert_rules"])
 
+    # Mock Redis so tests don't require a running Redis server
+    class FakeRedis:
+        async def get(self, key):
+            return None
+        async def setex(self, key, ttl, value):
+            pass
+
+    async def fake_get_arq_pool():
+        return FakeRedis()
+
+    monkeypatch.setattr("redis_client.get_arq_pool", fake_get_arq_pool)
+    monkeypatch.setattr("routes.ask.get_arq_pool", fake_get_arq_pool)
+    monkeypatch.setattr("routes.jobs.get_redis", fake_get_arq_pool)
+
     from main import app
 
     return TestClient(app)

backend/tests/test_api.py

@@ -89,6 +89,17 @@ def test_explain_event_with_llm_mock(client, mock_events_collection, monkeypatch
 
     monkeypatch.setattr("routes.ask._explain_event", fake_explain)
 
+    class FakeRedis:
+        async def get(self, key):
+            return None
+        async def setex(self, key, ttl, value):
+            pass
+
+    async def fake_get_arq_pool():
+        return FakeRedis()
+
+    monkeypatch.setattr("routes.ask.get_arq_pool", fake_get_arq_pool)
+
     mock_events_collection.insert_one(
         {
             "id": "evt-explain2",

backend/tests/test_ask.py

@@ -350,3 +350,124 @@ class TestAskEndpoint:
         data = response.json()
         assert data["query_info"]["event_count"] == 1
         assert data["events"][0]["id"] == "evt-bob"
+
+
+class TestAskCaching:
+    def test_ask_cache_hit_returns_cached_answer(self, client, mock_events_collection, monkeypatch):
+        """If the answer is cached, the LLM should not be called."""
+        now = datetime.now(UTC)
+        mock_events_collection.insert_one(
+            {
+                "id": "evt-cache",
+                "timestamp": now.isoformat(),
+                "service": "Directory",
+                "operation": "Add user",
+                "result": "success",
+                "actor_display": "Alice",
+                "target_displays": ["USER-001"],
+                "display_summary": "summary",
+                "raw_text": "raw",
+            }
+        )
+
+        llm_called = False
+
+        async def fake_llm(question, events, total=None, excluded_services=None):
+            nonlocal llm_called
+            llm_called = True
+            return "This should NOT appear."
+
+        monkeypatch.setattr("routes.ask.LLM_API_KEY", "fake-key")
+        monkeypatch.setattr("routes.ask._call_llm", fake_llm)
+
+        # Pre-populate cache with a specific answer
+        class CachingFakeRedis:
+            def __init__(self):
+                self.store = {}
+
+            async def get(self, key):
+                return self.store.get(key)
+
+            async def setex(self, key, ttl, value):
+                self.store[key] = value
+
+        redis = CachingFakeRedis()
+        # Seed cache with the exact filters the endpoint will generate
+        import asyncio
+        from jobs import set_cached_ask
+        filters_snapshot = {
+            "services": None,
+            "actor": None,
+            "operation": None,
+            "result": None,
+            "start": None,
+            "end": None,
+            "include_tags": None,
+            "exclude_tags": None,
+        }
+        asyncio.run(set_cached_ask(redis, "What happened to USER-001?", filters_snapshot, [{"id": "evt-cache"}], {"answer": "Cached answer!", "llm_used": True, "llm_error": None}))
+
+        async def fake_get_arq_pool():
+            return redis
+
+        monkeypatch.setattr("routes.ask.get_arq_pool", fake_get_arq_pool)
+
+        response = client.post("/api/ask", json={"question": "What happened to USER-001?"})
+        assert response.status_code == 200
+        data = response.json()
+        assert data["answer"] == "Cached answer!"
+        assert data["llm_used"] is True
+        assert llm_called is False
+
+    def test_ask_async_mode_returns_job_id(self, client, mock_events_collection, monkeypatch):
+        """Async mode should return immediately with a job_id."""
+        now = datetime.now(UTC)
+        mock_events_collection.insert_one(
+            {
+                "id": "evt-async",
+                "timestamp": now.isoformat(),
+                "service": "Directory",
+                "operation": "Add user",
+                "result": "success",
+                "actor_display": "Alice",
+                "target_displays": ["USER-001"],
+                "display_summary": "summary",
+                "raw_text": "raw",
+            }
+        )
+
+        monkeypatch.setattr("routes.ask.LLM_API_KEY", "fake-key")
+
+        # Mock arq pool to capture enqueue_job call
+        class FakeArqPool:
+            def __init__(self):
+                self.enqueued = []
+
+            async def get(self, key):
+                return None
+
+            async def setex(self, key, ttl, value):
+                pass
+
+            async def enqueue_job(self, func, *args, **kwargs):
+                from unittest.mock import MagicMock
+                job = MagicMock()
+                job.job_id = "job-12345"
+                self.enqueued.append((func, args, kwargs))
+                return job
+
+        pool = FakeArqPool()
+
+        async def fake_get_arq_pool():
+            return pool
+
+        monkeypatch.setattr("routes.ask.get_arq_pool", fake_get_arq_pool)
+
+        response = client.post("/api/ask", json={"question": "What happened to USER-001?", "async_mode": True})
+        assert response.status_code == 200
+        data = response.json()
+        assert data["job_id"] == "job-12345"
+        assert data["llm_used"] is False
+        assert "being processed" in data["answer"]
+        assert len(pool.enqueued) == 1
+        assert pool.enqueued[0][0] == "process_ask_question"

docker-compose.prod.yml

@@ -1,4 +1,19 @@
 services:
+  redis:
+    image: valkey/valkey:8-alpine
+    container_name: aoc-redis
+    restart: always
+    volumes:
+      - redis_data:/data
+    networks:
+      - aoc-internal
+    healthcheck:
+      test: ["CMD", "redis-cli", "ping"]
+      interval: 10s
+      timeout: 3s
+      retries: 5
+      start_period: 5s
+
   mongo:
     image: mongo:7
     container_name: aoc-mongo
@@ -27,9 +42,12 @@ services:
       - .env
     environment:
       MONGO_URI: mongodb://${MONGO_ROOT_USERNAME}:${MONGO_ROOT_PASSWORD}@mongo:27017/
+      REDIS_URL: redis://redis:6379/0
     depends_on:
       mongo:
         condition: service_healthy
+      redis:
+        condition: service_healthy
     networks:
       - aoc-internal
     healthcheck:
@@ -39,6 +57,24 @@ services:
       retries: 3
       start_period: 10s
 
+  worker:
+    image: git.cqre.net/cqrenet/aoc-backend:${AOC_VERSION:-latest}
+    container_name: aoc-worker
+    restart: always
+    env_file:
+      - .env
+    environment:
+      MONGO_URI: mongodb://${MONGO_ROOT_USERNAME}:${MONGO_ROOT_PASSWORD}@mongo:27017/
+      REDIS_URL: redis://redis:6379/0
+    command: ["arq", "jobs.WorkerSettings"]
+    depends_on:
+      redis:
+        condition: service_healthy
+      mongo:
+        condition: service_healthy
+    networks:
+      - aoc-internal
+
   nginx:
     image: nginx:alpine
     container_name: aoc-nginx
@@ -58,6 +94,7 @@ services:
 
 volumes:
   mongo_data:
+  redis_data:
 
 networks:
   aoc-internal:

docker-compose.yml

@@ -1,4 +1,13 @@
 services:
+  redis:
+    image: valkey/valkey:8-alpine
+    container_name: aoc-redis
+    restart: always
+    ports:
+      - "6379:6379"
+    volumes:
+      - redis_data:/data
+
   mongo:
     image: mongo:7
     container_name: aoc-mongo
@@ -21,10 +30,27 @@ services:
       - .env
     environment:
       MONGO_URI: mongodb://${MONGO_ROOT_USERNAME}:${MONGO_ROOT_PASSWORD}@mongo:${MONGO_PORT}/
+      REDIS_URL: redis://redis:6379/0
     depends_on:
       - mongo
+      - redis
     ports:
       - "8000:8000"
 
+  worker:
+    build: ./backend
+    container_name: aoc-worker
+    restart: always
+    env_file:
+      - .env
+    environment:
+      MONGO_URI: mongodb://${MONGO_ROOT_USERNAME}:${MONGO_ROOT_PASSWORD}@mongo:${MONGO_PORT}/
+      REDIS_URL: redis://redis:6379/0
+    command: ["arq", "jobs.WorkerSettings"]
+    depends_on:
+      - redis
+      - mongo
+
 volumes:
   mongo_data:
+  redis_data: