Hankan/server

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

docker exec -it <your-postgres-container> 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

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

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.

Give a reasoning model room. Its reasoning is spent from the same max_tokens budget and goes first, so too small a value truncates the lesson away entirely — and it fails silently, looking exactly like a model that cannot follow instructions. Measured on gpt-oss-20b with the real ~3.5k-token system prompt: an empty string at 1,400, a broken half-Korean fragment with none of the required blocks at 2,048, and a correct English lesson with all three at 8,000. HANKAN_OPENAI_MAX_TOKENS defaults to 8192 for that reason.

A reasoning model served locally often emits chain-of-thought inline in content rather than in a separate field. <think> 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

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.