988d33bd5f
On a reasoning model the reasoning is spent from the same completion budget
and it goes first, so a small ceiling truncates the actual reply away. It
fails silently and looks exactly like a model that cannot follow the system
prompt, which is the expensive way to debug it.
Measured on gpt-oss-20b against the real ~3.5k-token system prompt, same
prompt and same model, only the ceiling changing:
1400 an empty string, 0 bytes, after 27s
2048 a truncated half-Korean fragment, none of the required blocks
8000 99% English prose, no romanization, 0 batchim violations in the
task lines, all three blocks, 11s
The old comment justified 2048 by worrying about 4k-context local models.
That reasoning was wrong: the system prompt alone is ~3.5k tokens, so such
a model cannot run this app at all and there was nothing to protect.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
40 lines
1.7 KiB
Plaintext
40 lines
1.7 KiB
Plaintext
# Copy to .env and fill in. Never commit .env.
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# The `hankan` database in your existing Postgres. `postgres` here is the
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# service name on the shared docker network, not a hostname on the Pi.
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DATABASE_URL=postgres://hankan:CHANGE_ME@postgres:5432/hankan
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# Shared secret between the app and this server. Generate one:
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# openssl rand -base64 32
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HANKAN_TOKEN=CHANGE_ME
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# Which model serves the tutor: anthropic (default) | openai | echo.
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# Omit the whole tutor config and sync still works — the app falls back to
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# its local stand-in tutor.
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HANKAN_TUTOR_BACKEND=anthropic
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# For HANKAN_TUTOR_BACKEND=anthropic.
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ANTHROPIC_API_KEY=
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# For HANKAN_TUTOR_BACKEND=openai — anything speaking /chat/completions:
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# LM Studio, Ollama, llama.cpp, vLLM, LiteLLM, OpenRouter, OpenAI.
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# From a container, localhost is the container: use the host's LAN address
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# or host.docker.internal, not 127.0.0.1.
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# LM Studio http://<host>:1234/v1 model = the id shown in its UI
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# Ollama http://<host>:11434/v1 model = e.g. qwen2.5:14b
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# llama.cpp http://<host>:8080/v1
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# OpenRouter https://openrouter.ai/api/v1
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HANKAN_OPENAI_BASE_URL=http://host.docker.internal:1234/v1
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HANKAN_OPENAI_MODEL=local-model
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# Local servers ignore this; hosted ones require it. Leave blank for local.
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HANKAN_OPENAI_API_KEY=
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# Completion ceiling. Do not lower this much: on a reasoning model the
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# reasoning comes out of the same budget and goes first, so a small value
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# truncates the actual lesson away. gpt-oss-20b returns an empty string at
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# 1400 and a broken half-Korean fragment at 2048.
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HANKAN_OPENAI_MAX_TOKENS=8192
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# The docker network your existing Postgres and Caddy are on.
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# docker network ls
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HANKAN_NETWORK=caddy_default
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