# Architecture overview

## Guiding constraints

- The **recommendation decision** is the hot path. Every architectural choice should shorten the distance between a new signal and a better tip.
- Modularity lives in **code boundaries**. Deploy topology follows pressure, not anticipation (ADR-0003).
- Python for ML, TypeScript for applications. Shared contracts regenerated from a single source of truth: OpenAPI for HTTP, protobuf for events (ADR-0005).
- Privacy is a Phase-0 feature, not a Phase-5 compliance project (see `privacy.md`).

## Modules

| Module | Language | Responsibility | Owns data | Phase-0 process |
|---|---|---|---|---|
| `gateway` | TS | BFF for web/mobile; auth-check; fan-out | — | Node monolith |
| `auth` | TS | OAuth (Google; Apple in M1), sessions, JWT | identities, sessions | Node monolith |
| `profile` | TS | user profile, preferences, consents | profiles | Node monolith |
| `integrations` | TS | third-party connectors, token vault, signal fetch | credentials, cursors | Node monolith |
| `events` | TS | event-bus abstraction + durable log | signal store | Node monolith (in-proc emitter, bridges to NATS JetStream when `NATS_URL` set) |
| `recommender` | TS | orchestration: candidates → policy → tip; feedback sink | tip history | Node monolith |
| `notifier` | TS | push/email delivery, quiet hours, dedupe | delivery log | Node monolith (web push in M1) |
| `ml/serving` | Python | online scoring for policies/models | — (stateless) | **separate process** |
| `ml/pipelines` | Python | batch feature + training pipelines | feature store, models | separate (from M4) |

Extraction from the monolith is triggered by language boundary, scaling hotspot, SLA divergence, team ownership, or regulatory isolation (ADR-0003). `ml/serving` is pre-extracted on language grounds.

## Data boundaries

Each service owns its schema; no cross-service DB access. When `recommender` needs profile data, it calls `profile` (read model), not its DB.

## Event flow

```
connector (integrations) ──emit──▶ events ──▶ feature pipelines (ml)
                                     │
                                     └──▶ recommender (context assembly)
```

User reactions (done / snooze / dismiss) are events too. They close the loop as rewards for bandit/RL policies.

## Why these choices

- **Modular monolith + Python ML** in Phase 0 to ship the walking skeleton fast without foreclosing decomposition (ADR-0003).
- **NATS JetStream** over Kafka for Phase 1: lighter, single-binary, fits the "one VM" deployment. Swap to Kafka in Phase 4 if fan-out justifies it.
- **Postgres** for OLTP; per-module schemas in dev; separate databases once modules extract.
- **FastAPI + Pydantic** for ML serving — fast, typed, swappable runtime (ONNX, Triton) behind it.
- **Protobuf** for event schemas with a schema registry (ADR-0005) — train/serve parity depends on this.
- **OpenAPI** for HTTP; TS client auto-generated; Python pydantic hand-written while consumers are few.
- **Feast** for feature store when we get there; homegrown adapter until then (Phase 1 seam).
- **MLflow** for model registry and experiment tracking; deployed at `o.alogins.net/mlflow`.
- **Auth.js** embedded behind an OIDC-shaped boundary (ADR-0004). Swap to a standalone OIDC provider when mobile ships.
- **k3s** as the first step beyond docker-compose — no "compose → full k8s" cliff.

## AI stack

All LLM inference routes through **LiteLLM** (`llm.alogins.net`) backed by **Ollama** (local, `localhost:11434`). This means:
- Model aliases (`tip-generator`, `embedder`, `judge`) decouple code from model names.
- Swapping qwen2.5 → llama3.2 = one-line config change in LiteLLM, zero code change in oO.
- Cloud fallback (Anthropic) is opt-in and gated behind `ANTHROPIC_API_KEY` — used only in offline simulation.

**OpenWebUI** (`ai.alogins.net`) is the human-facing interface for prompt iteration and model testing during development.

## Decision flow for a new tip (Phase 2 target)

```
client ─► gateway ─► recommender (TS)
                          │
                          ▼
                     ml/serving (Python)
                          │
                          ├─► context:    ml/features/context.py
                          │               (tasks + reactions + time patterns → prompt)
                          │
                          ├─► generate:   LiteLLM → Ollama
                          │               → N TipCandidates {content, kind, model, prompt_version}
                          │
                          ├─► score:      bandit policy scores each candidate
                          │
                          ├─► shadows:    shadow policies log picks without serving
                          │
                          └─► persist:    tip_scores {candidate, policy, features, latency}
                          ◄─  best TipCandidate
```

**Phase 1 (shipped M1):** candidates come from Todoist task list, no LLM. The bandit scores tasks directly.

**Phase 2 (shipped M2):** LLM candidates are generated in parallel with Todoist fetch. Both pools are merged, scored by the bandit, and the winner served. `tip_scores` tracks `prompt_version`, `llm_model`, and `tip_kind` for every row.

Feedback: `POST /feedback → events.emit(reaction)` → online bandit update + `prompt_version` tracked for A/B analysis.