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/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