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/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()