77 lines
3.4 KiB
Markdown
77 lines
3.4 KiB
Markdown
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# ollama-piha
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Local CPU Ollama on **PIHA**, serving exactly one purpose: the **fallback embed
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backend** for `kb-query` while SOLARIA (the GPU node, ~16 h/day powered off)
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sleeps. Model: `bge-m3` — the **same** model as SOLARIA's Ollama, because query
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embeddings must live in the same vector space as the pgvector index
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(`document_chunk.embedding VECTOR(1024)`); a different/smaller model is not an
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option (module 5 phase 4 plan §2 decision 2).
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Expected latency: bge-m3 embeds in ~207 ms on SOLARIA's GPU vs ~790 ms on x86
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CPU; on the Pi 5 expect single seconds per query (plus model load, since the
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model is never resident — see below). Slower but alive beats fast but dead.
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## Design constraints
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- **`OLLAMA_KEEP_ALIVE=0`** (pinned in compose): PIHA is the RAM-bound 8 GB box
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shared with Home Assistant. The model is unloaded immediately after every
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call — a transient ~1.5–2 GB spike per embed, ~100 MB idle daemon, never a
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resident cost.
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- **`mem_limit: 2560m`** (host override, `hosts/piha/runtime/ollama-piha/`):
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hard cgroup ceiling, plan §2 D2 starting value. The cgroup OOM killer
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restarts this container instead of the host OOM killer picking a victim
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(which could be Home Assistant). Confirm/trim after live calibration.
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- **Bind**: `127.0.0.1` + `LAN_BIND_IP` (192.168.31.5) only — kb-query calls it
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over the host LAN interface (same pattern as kb-query → kb-postgres:5433).
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Never `0.0.0.0`, never a Tailscale bind, no public ingress.
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- **Storage**: Docker named volume `ollama_piha_models` (NVMe data-root), not a
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bind mount — the ollama image runs as in-container root and would break
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PIHA's uid pattern (host oskar=1004, containers uid 1000, setgid group pi)
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if it wrote to a shared bind directory.
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## Deploy (PIHA, master, after merge)
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```bash
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cd ~/homelab-codex-ws && git pull
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cp services/ollama-piha/env.example services/ollama-piha/.env # LAN_BIND_IP
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docker compose -f services/ollama-piha/docker-compose.yml \
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-f hosts/piha/runtime/ollama-piha/docker-compose.override.yml \
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--env-file services/ollama-piha/.env up -d
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```
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**Then pull the model — this does NOT happen automatically:**
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```bash
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docker exec ollama-piha ollama pull bge-m3
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```
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Verify:
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```bash
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services/ollama-piha/healthcheck.sh # checks container + API + bge-m3 present
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time curl -s http://127.0.0.1:11434/api/embeddings \
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-d '{"model":"bge-m3","prompt":"test kalibracyjny"}' | head -c 80
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```
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(`deploy-node.sh` on PIHA also picks this service up from
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`hosts/piha/services.yaml` once `.env` exists — the `ollama pull bge-m3` step
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stays manual either way.)
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## Calibration (plan §5 step 4 — gate, not formality)
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Before trusting the fallback under load, on live PIHA at a normal (not
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night-quiet) hour: run a few embeds as above while watching
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`docker stats ollama-piha`, note peak RAM and wall time. Verdict per plan §5
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step 5: keep as default fallback / tune `mem_limit` / fall back to explicit
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503 degradation.
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## Relation to kb-query
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kb-query's router (`services/kb-query/app/embed_router.py`) health-checks
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SOLARIA with a ~30 s cache and only sends embeds here while SOLARIA is down.
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kb-query verifies at first use that this backend actually serves `bge-m3`
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(`/api/tags`) and refuses to embed against a mismatched model. Configuration:
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`EMBED_FALLBACK_URL=http://192.168.31.5:11434` in `services/kb-query/.env`.
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See `services/kb-query/README.md` for the fallback verification plan (tests
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A/B/C).
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