alvis
7f173f88d3
- ADR-0003: modular monolith for Phase 0 with documented extraction triggers - ADR-0004: Auth.js + OIDC-shaped boundary; dedicated provider when mobile ships - ADR-0005: protobuf for events, OpenAPI for HTTP, schema-registry CI gate - New architecture docs: data-model, metrics (magic proxies), privacy (Phase-0 feature) - Prime directives updated: privacy-as-feature, modular-by-package-deployable-by-stage - Roadmap revised: Apple OAuth deferred to M1; web push in M1; k3s intermediate; tip-kind-aware UI - PLAN updated: Phase-0 deletion endpoint, metrics baseline, compose profiles, import-boundary lint - License decision in README (ARR with OSS plan in Phase 5)
67 lines
4.0 KiB
Markdown
67 lines
4.0 KiB
Markdown
# Architecture overview
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## Guiding constraints
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- The **recommendation decision** is the hot path. Every architectural choice should shorten the distance between a new signal and a better tip.
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- Modularity lives in **code boundaries**. Deploy topology follows pressure, not anticipation (ADR-0003).
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- Python for ML, TypeScript for applications. Shared contracts regenerated from a single source of truth: OpenAPI for HTTP, protobuf for events (ADR-0005).
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- Privacy is a Phase-0 feature, not a Phase-5 compliance project (see `privacy.md`).
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## Modules
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| Module | Language | Responsibility | Owns data | Phase-0 process |
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|---|---|---|---|---|
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| `gateway` | TS | BFF for web/mobile; auth-check; fan-out | — | Node monolith |
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| `auth` | TS | OAuth (Google; Apple in M1), sessions, JWT | identities, sessions | Node monolith |
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| `profile` | TS | user profile, preferences, consents | profiles | Node monolith |
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| `integrations` | TS | third-party connectors, token vault, signal fetch | credentials, cursors | Node monolith |
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| `events` | TS | event-bus abstraction + durable log (M1) | signal store | Node monolith (in-proc emitter) |
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| `recommender` | TS | orchestration: candidates → policy → tip; feedback sink | tip history | Node monolith |
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| `notifier` | TS | push/email delivery, quiet hours, dedupe | delivery log | Node monolith (web push in M1) |
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| `ml/serving` | Python | online scoring for policies/models | — (stateless) | **separate process** |
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| `ml/pipelines` | Python | batch feature + training pipelines | feature store, models | separate (from M4) |
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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.
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## Data boundaries
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Each service owns its schema; no cross-service DB access. When `recommender` needs profile data, it calls `profile` (read model), not its DB.
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## Event flow
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```
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connector (integrations) ──emit──▶ events ──▶ feature pipelines (ml)
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│
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└──▶ recommender (context assembly)
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```
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User reactions (done / snooze / dismiss) are events too. They close the loop as rewards for bandit/RL policies.
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## Why these choices
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- **Modular monolith + Python ML** in Phase 0 to ship the walking skeleton fast without foreclosing decomposition (ADR-0003).
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- **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.
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- **Postgres** for OLTP; per-module schemas in dev; separate databases once modules extract.
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- **FastAPI + Pydantic** for ML serving — fast, typed, swappable runtime (ONNX, Triton) behind it.
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- **Protobuf** for event schemas with a schema registry (ADR-0005) — train/serve parity depends on this.
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- **OpenAPI** for HTTP; TS client auto-generated; Python pydantic hand-written while consumers are few.
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- **Feast** for feature store when we get there; homegrown adapter until then (Phase 1 seam).
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- **MLflow** for model registry; artifacts in MinIO/S3.
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- **Auth.js** embedded behind an OIDC-shaped boundary (ADR-0004). Swap to a standalone OIDC provider when mobile ships.
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- **k3s** as the first step beyond docker-compose — no "compose → full k8s" cliff.
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## Decision flow for a new tip
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```
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client ─► gateway ─► recommender
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│
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├─► candidates: integrations.fetchCandidates(user) + advice.library
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├─► context: FeatureAssembler(user, request)
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├─► policy: PolicyRegistry.get(policyName).pick(candidates, context)
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├─► shadows: run shadow policies in parallel, log their picks
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└─► persist: TipInstance{context_snapshot, policy, tip}
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◄─ tip
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```
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Feedback travels back the same path: `POST /feedback → events.emit(feedback.reaction)` → pipelines consume → bandit/model updated on next retrain.
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