# server — sync and the tutor Two endpoints, both stateless. The client owns its database and its transcript; this process owns a Postgres table and an API key. **The app does not need this to work.** With no server configured it runs entirely offline against its own SQLite and a local stand-in tutor. Adding a server turns on two things: the real 선생님, and syncing between devices. ``` POST /api/tutor {system, history, message} → SSE token stream GET /api/sync ?cursor=N → rows newer than the cursor POST /api/sync {rows} → upsert, last-write-wins GET /health → no auth, for the healthcheck ``` Everything under `/api` requires `Authorization: Bearer $HANKAN_TOKEN`. ### CORS — required for the phone The Android build talks to the Pi **cross-origin**: a Capacitor webview serves the app from its own origin (`http://localhost`), not from your domain. So the browser sends a preflight `OPTIONS` first, with no `Authorization` header — it is not permitted to attach one. Auth therefore has to run *after* CORS, or the preflight is answered 401 and the real request is never made. It surfaces as an opaque "Failed to fetch", which sends you looking at the network rather than at the middleware order. Any origin is allowed by default. That is not a hole: the gate is a bearer token rather than a cookie, so a hostile page gains nothing from being allowed to send a request it cannot authenticate. Set `HANKAN_ALLOWED_ORIGINS` to a comma-separated list to narrow it. ## Setting it up on the Pi ### 1. A database in the Postgres you already run ```sh docker exec -it psql -U postgres <<'SQL' CREATE ROLE hankan LOGIN PASSWORD 'pick-something-long'; CREATE DATABASE hankan OWNER hankan; SQL ``` The schema applies itself on boot — `server/sql/001-schema.sql` is idempotent, so there is no migration step to run by hand. ### 2. Configure ```sh cd server cp .env.example .env $EDITOR .env # DATABASE_URL, HANKAN_TOKEN, ANTHROPIC_API_KEY docker network ls # find the network your Postgres and Caddy share ``` `compose.yaml` declares that network `external`, so it attaches to your existing stack rather than starting a second Postgres. Nothing is published to the host: Caddy reaches the container by name on the shared network. ### 3. Caddy ```caddyfile hankan.example.com { # Compression must not touch the tutor stream — see below. encode zstd gzip { match { not path /api/tutor* } } handle /api/tutor* { reverse_proxy hankan:8787 { flush_interval -1 transport http { read_timeout 0 write_timeout 0 } } } handle { reverse_proxy hankan:8787 } } ``` **Compression is what usually breaks SSE, not buffering.** `encode` delays the header flush until body bytes arrive and holds already-flushed events inside an unfinished compression frame, so the stream looks like it hangs. Excluding the tutor route is the important line; `flush_interval -1` is belt-and-braces (Caddy already auto-flushes `text/event-stream`) and the zero timeouts stop a long turn being cut off mid-lesson. Do not set `response_buffers` on this route. The server also sends `Cache-Control: no-cache, no-transform` and a `: ping` heartbeat every 15s, which defeat most intermediary caching and idle timeouts. ### 4. Connect the app In the app: 오늘 → 서버 → the URL and the token. It syncs on connect, when the tab regains focus, and every five minutes. ## How sync works Row-level, last-write-wins on `updated_at`, cursor-based on a server-assigned `change_seq`. One user, so the loser of a conflict is at worst one SRS grade. Rows are stored generically — primary key as text, body as JSONB — because the server never reads inside a row. It stores and orders them; the client interprets them. That keeps the two schemas from having to move in lockstep. Three things are load-bearing: - **`change_seq` advances on every update, via a trigger.** A row edited after a client last pulled would otherwise sit below that client's cursor and never be delivered. Putting it in a trigger means no write path can forget. - **The pull cursor advances only as rows are applied**, never from the push response. The server's newest `change_seq` includes rows this device has not seen; adopting it would skip them permanently, and nothing would ever ask for that range again. - **Deletes travel as tombstones.** A deleted row leaves nothing to compare timestamps against, so without one the other device pushes its still-live copy back and the row silently returns. ### What never syncs `meta` holds the learner's preferences *and* bookkeeping that describes one install, so an allowlist decides what may leave the device (`shared/sync-protocol.mjs`). `dict.loadedBands` is the dangerous one: replicating it would tell a phone that had loaded bands 0–2 that it holds every row the desktop has, and the word rail would then fail to find words it believes are present. `server.token`, `sync.*` and `schema_version` are excluded for related reasons. ### The bug this schema is shaped around The original artifact stamped a fresh device's empty defaults as newer than the server's real history, and clobbered it. Here seeded and defaulted rows carry `updated_at = 0`, so they can never be dirty and can never win a conflict. It is not avoided, it is unrepresentable — `test/sync/roundtrip.test.ts` asserts it against a real Postgres. ## The tutor `POST /api/tutor` takes the assembled system prompt, the transcript the client owns, and the new message; it holds nothing between requests, so a dropped connection costs one turn rather than the conversation. The system prompt is passed as a cached block. It is ~12k characters of gate and is byte-identical for as long as the learner stays in one unit — many turns — so every turn after the first reads the prefix at a fraction of the input price. This is the single biggest cost lever in the design. ### Choosing a model `HANKAN_TUTOR_BACKEND` picks one of three: | | | |---|---| | `anthropic` (default) | The Claude API. `ANTHROPIC_API_KEY`. | | `openai` | Anything speaking OpenAI's `/chat/completions`: LM Studio, Ollama, llama.cpp, vLLM, LiteLLM, OpenRouter, OpenAI. | | `echo` | No model at all. Reflects the request back, for proving a deployment. | For `openai`, set `HANKAN_OPENAI_BASE_URL` and `HANKAN_OPENAI_MODEL`; `HANKAN_OPENAI_API_KEY` is only needed by hosted endpoints. **From inside the container, `localhost` is the container** — a model server on the Pi itself is `http://host.docker.internal:1234/v1`, which `compose.yaml` maps for you. Verified against LM Studio serving `openai/gpt-oss-20b`: a turn streams end to end from the app, through this server, to the model and back. **What the OpenAI path costs you: prompt caching.** The Anthropic backend marks the ~12k-character gate as a cached prefix, so every turn after the first reads it at a fraction of the input price. There is no portable equivalent, so against a *paid hosted* endpoint the whole system prompt is re-billed every turn — the single biggest cost lever in the design, gone. Against a local model it costs nothing, and the shape still pays. llama.cpp and LM Studio reuse their KV cache for an unchanged prefix, and the system prompt is byte-identical for as long as the learner stays in one unit. Measured on a 6,948-character prompt against gpt-oss-20b: **first token 1,563ms cold, 324ms with the prefix already warm.** Which is why the system prompt goes first and stays put in this backend too. A reasoning model served locally often emits chain-of-thought inline in `content` rather than in a separate field. `` blocks are stripped from the stream, including when the tags arrive split across chunks — otherwise they land in the lesson transcript and the block parser reads them as prose. `backends/` holds the seam. `anthropic.ts` is the Claude API and is the default. `openai.ts` is written against `fetch` rather than the `openai` package: the Anthropic SDK alone is 14MB in the image, this backend uses one endpoint with no tools and no retries, and local servers are the ones most likely to deviate from an SDK's expectations. `echo.ts` needs no API key and reflects the request back, chunk by chunk — set `HANKAN_TUTOR_BACKEND=echo` to prove a deployment (container, proxy, token, CORS, SSE through Caddy) from the phone before a key is involved and before anything is billed. Five things that can each break on their own, none of which involve Anthropic. `agent-sdk.ts` documents the subscription-billed path PORT.md originally specified and why it is not implemented — chiefly that its prompt accepts only user-role messages, so the transcript would have to be flattened into one turn. ## Running it locally ```sh docker run -d --name hankan-pg-test \ -e POSTGRES_PASSWORD=test -e POSTGRES_DB=hankan -p 55432:5432 postgres:16-alpine DATABASE_URL=postgres://postgres:test@localhost:55432/hankan \ HANKAN_TOKEN=test-token PORT=8788 HANKAN_TEST_MODE=1 \ node --experimental-strip-types server/src/main.ts # from the repo root, in another shell HANKAN_TEST_SERVER=http://localhost:8788 npm test ``` `HANKAN_TEST_MODE=1` adds `POST /api/test/reset`, which wipes the user's rows so each test starts clean. It exists only when that variable is set, so it cannot be reached on the Pi even if the token leaks. **Never set it in production.** The tutor's own tests run against a mock backend and need no API key.