124 lines
3.9 KiB
Markdown
124 lines
3.9 KiB
Markdown
# ML-integrasjon i OSVauco
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**Fase:** ML-1 implementert | ML-2 og ML-3 planlagt
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**Sist oppdatert:** 2026-05-26
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---
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## Hvorfor ML i OPAX?
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OPAX er et multi-agent system. ML-laget gjør agentene sterkere på tre måter:
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1. **Parallell kjøring** (ML-1): Dask DAG lar agenter kjøre samtidig i stedet for sekvensielt.
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2. **Adaptiv intelligens** (ML-2): Agentene lærer fra faktisk bruk — heavy-mode aktiveres basert på historikk, ikke hardkodede regler.
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3. **Strategisk innsikt** (ML-3): XGBoost feature importance avslører hvilke agenter og signaler som faktisk driver vellykkede utfall.
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---
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## Fase ML-1: Grunnlag (implementert)
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### Arkitektur
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```
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main.py → build_agent_dag([agent_a, agent_b], payload)
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│
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Dask task graph
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│
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┌──────────┴──────────┐
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▼ ▼
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run_agent_task(a) run_agent_task(b) ← parallelt
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│ │
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└──────────┬──────────┘
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▼
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execute_dag(tasks, scheduler="threads")
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│
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▼
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[result_a, result_b] → AgentStateStore.push()
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│
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telemetry.log_agent_call()
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```
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### Koble ML-laget inn i main.py
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```python
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# Eksempel: erstatt sekvensielle kall med parallell DAG
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from ml import build_agent_dag, execute_dag, get_store, log_agent_call
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import time
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@app.post("/run")
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def run_agent(req: RunRequest):
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store = get_store()
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payload = {"message": req.message, "user_id": req.user_id,
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"session_id": req.session_id, "mode": req.mode}
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# Velg agenter basert på mode
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if req.mode == "heavy":
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agent_fns = [billing_agent_heavy, rag_agent, multi_agent]
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else:
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agent_fns = [billing_agent, rag_agent]
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start = time.monotonic()
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tasks = build_agent_dag(agent_fns, payload)
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results = execute_dag(tasks, scheduler="threads")
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total_dur = time.monotonic() - start
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# Push resultater til state store
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for r in results:
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if r["success"]:
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store.push(r["agent"], "last_result", r["result"])
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store.push(r["agent"], "last_duration_s", r["duration_s"])
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# Logg DAG-kjøring
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from ml.telemetry import log_dag_execution
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log_dag_execution(dag_id=req.session_id, agent_results=results,
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total_duration_s=total_dur)
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# Returner kombinert svar
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return {"response": [r["result"] for r in results if r["success"]]}
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```
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---
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## Fase ML-2: Adaptiv intelligens (planlagt)
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| Komponent | Fil | Repo |
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| Inkrementell feedback-loop | `ml/feedback_loop.py` | `vauco-gemini-tui-bridge` |
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| ASHA hyperparametertuning | `ml/hypertuner.py` | `vauco-bootstrap` |
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| Heavy-mode predictor | `ml/heavy_predictor.py` | `OSVauco` |
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**Krav:** `dask-ml>=2024.4.0`, `ray[tune]>=2.10.0` (vauco-bootstrap only)
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---
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## Fase ML-3: Strategisk innsikt (planlagt)
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| Komponent | Fil | Repo |
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| XGBoost feature importance | `ml/agent_importance.py` | `OSVauco` |
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| Population-Based Training | `experiments/pbt.py` | `deep-dream` |
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**Krav:** `xgboost>=2.0.0` (legg til requirements.txt i Fase ML-3)
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---
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## Telemetri-ruting
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| Miljø | `TELEMETRY_DIR` | Arkivering |
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| Lokal dev | `/tmp/vauco_telemetry/` | Manuell |
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| Cloud Run | `/tmp/vauco_telemetry/` | `10-cost-guard.sh` cron → GCS |
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| Prod skala | `gs://propane-will-491900-m5-telemetry/` | Auto via GCS sink |
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---
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## Cross-repo referanser
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| Repo | ML-relatert fil | Fase |
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| `OSVauco` | `ml/`, `AGENTS.md`, `docs/ml-integration.md` | ML-1 |
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| `vauco-gemini-tui-bridge` | `ml/feedback_loop.py` (planlagt) | ML-2 |
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| `vauco-bootstrap` | `ml/hypertuner.py` (planlagt) | ML-2 |
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| `vauco-os` | Konsumerer beste konfig fra state store | ML-2 |
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| `deep-dream` | `experiments/pbt.py` (planlagt) | ML-3 |
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