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

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)