6b85c7ef68
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>
51 lines
2.3 KiB
Markdown
51 lines
2.3 KiB
Markdown
---
|
||
okf: "0.1"
|
||
type: service
|
||
visibility: private
|
||
status: active
|
||
updated: 2026-07-30
|
||
links:
|
||
- ../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).
|