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homelab-codex-ws/kb/services/ollama-piha.md
oskar 6b85c7ef68 feat(kb): SPLIT service+runbook — 10 serwisow -> 20 dokumentow 
Wzorzec mechaniczny: sekcje deploy/verify/install/testy wycinane do
kb/runbooks/<serwis>-*.md, reszta zostaje dokumentem type: service.
Wzajemne `links` w obie strony. Tresc sekcji nietknieta — przenoszone
doslownie, dodany wylacznie naglowek H1 nowego runbooka.

kb-query, paperless-worker, planner-agent, ha-diag-agent, ollama-piha,
narty27, home-assistant, ha-mcp, job-gmail-header-backfill, job-mail-body-ingest.

Weryfikacja: dla kazdego pliku multizbior niepustych linii
(main + runbook) == oryginal z HEAD. Zero zgubionych, zero dodanych.

Recon szacowal 13 splitow service+runbook; faktycznie 2-typowych jest 10,
pozostale 5 (paperless, nextcloud, gokapi, fleet-prometheus, deploy-runner)
sa 3-typowe i ida osobno jako splity wielotypowe.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-04 16:58:46 +02:00

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0.1 service private active 2026-07-30
../runbooks/ollama-piha-deploy.md

ollama-piha

Local CPU Ollama on PIHA, serving exactly one purpose: the fallback embed backend for kb-query while SOLARIA (the GPU node, ~16 h/day powered off) sleeps. Model: bge-m3 — the same model as SOLARIA's Ollama, because query embeddings must live in the same vector space as the pgvector index (document_chunk.embedding VECTOR(1024)); a different/smaller model is not an option (module 5 phase 4 plan §2 decision 2).

Expected latency: bge-m3 embeds in ~207 ms on SOLARIA's GPU vs ~790 ms on x86 CPU; on the Pi 5 expect single seconds per query (plus model load, since the model is never resident — see below). Slower but alive beats fast but dead.

Design constraints

  • OLLAMA_KEEP_ALIVE=0 (pinned in compose): PIHA is the RAM-bound 8 GB box shared with Home Assistant. The model is unloaded immediately after every call — a transient ~1.5–2 GB spike per embed, ~100 MB idle daemon, never a resident cost.
  • mem_limit: 2560m (host override, hosts/piha/runtime/ollama-piha/): hard cgroup ceiling, plan §2 D2 starting value. The cgroup OOM killer restarts this container instead of the host OOM killer picking a victim (which could be Home Assistant). Confirm/trim after live calibration.
  • Bind: 127.0.0.1 + LAN_BIND_IP (192.168.31.5) only — kb-query calls it over the host LAN interface (same pattern as kb-query → kb-postgres:5433). Never 0.0.0.0, never a Tailscale bind, no public ingress.
  • Storage: Docker named volume ollama_piha_models (NVMe data-root), not a bind mount — the ollama image runs as in-container root and would break PIHA's uid pattern (host oskar=1004, containers uid 1000, setgid group pi) if it wrote to a shared bind directory.

Relation to kb-query

kb-query's router (services/kb-query/app/embed_router.py) health-checks SOLARIA with a ~30 s cache and only sends embeds here while SOLARIA is down. kb-query verifies at first use that this backend actually serves bge-m3 (/api/tags) and refuses to embed against a mismatched model. Configuration: EMBED_FALLBACK_URL=http://192.168.31.5:11434 in services/kb-query/.env. See services/kb-query/README.md for the fallback verification plan (tests A/B/C).