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

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.