44 lines
2.5 KiB
Bash
44 lines
2.5 KiB
Bash
# ============================================================
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# SmallClaw – environment variables
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# Copy this file to .env and customise it.
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# docker-compose.yml reads these automatically.
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# ============================================================
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# ── Active provider ─────────────────────────────────────────
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# One of: ollama | lm_studio | llama_cpp | openai | openai_codex
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SMALLCLAW_PROVIDER=ollama
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# ── Port ────────────────────────────────────────────────────
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# Port the SmallClaw gateway will be accessible on the HOST
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# The app internally always runs on 18789 inside the container.
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# Change HOST_PORT to map it to a different port on your machine.
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HOST_PORT=18789
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# ── Ollama (default) ────────────────────────────────────────
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# Model to auto-pull on first run (only used when PROVIDER=ollama)
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SMALLCLAW_DEFAULT_MODEL=qwen3:4b
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# Ollama endpoint (leave as-is when using the bundled ollama container)
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# Change to http://host.docker.internal:11434 to use Ollama on your host machine
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OLLAMA_HOST=http://ollama:11434
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# ── LM Studio ───────────────────────────────────────────────
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# LM Studio runs on the HOST, not inside Docker.
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# Use host.docker.internal to reach it from inside the container.
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LM_STUDIO_ENDPOINT=http://host.docker.internal:1234
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# LM_STUDIO_API_KEY= # optional – only if you enabled auth in LM Studio
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# LM_STUDIO_MODEL= # e.g. mistral-nemo-instruct-2407
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# ── llama.cpp ───────────────────────────────────────────────
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# llama.cpp server also runs on the HOST.
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LLAMA_CPP_ENDPOINT=http://host.docker.internal:8080
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# LLAMA_CPP_MODEL= # e.g. Meta-Llama-3-8B-Instruct.Q4_K_M.gguf
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# ── OpenAI (API key) ────────────────────────────────────────
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OPENAI_API_KEY=
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# OPENAI_MODEL=gpt-4o
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# ── OpenAI Codex (OAuth / ChatGPT Plus) ─────────────────────
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# No key needed – SmallClaw handles the OAuth flow.
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# Mount your .smallclaw dir (see docker-compose.yml) so tokens persist.
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# CODEX_MODEL=gpt-5.3-codex
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