71 lines
3.7 KiB
Plaintext
71 lines
3.7 KiB
Plaintext
# Copy to .env and fill in: cp .env-template .env
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# script/server loads .env automatically; with Docker pass it via `docker run --env-file .env`
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# (so keep values unquoted). .env is git-ignored and excluded from the image; never commit it.
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# --- Server ---------------------------------------------------------------
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# PORT=3000
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# --- Gitea OAuth login ------------------------------------------------------
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# Register an OAuth2 application in Gitea (Settings > Applications) as a confidential
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# client, with the redirect URI <BASE_URL>/auth/callback
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GITEA_URL=https://gitea.example.com
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GITEA_CLIENT_ID=
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GITEA_CLIENT_SECRET=
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# Public URL of this app, as the browser reaches it. Use https in production: that also
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# marks the session cookie Secure.
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BASE_URL=http://localhost:3000
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# Signs and encrypts session cookies. Generate with: openssl rand -hex 32
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# Unset = a random key per start, so everyone is logged out on every restart.
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SESSION_SECRET=
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# --- Admin API --------------------------------------------------------------
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# Comma-separated Gitea usernames allowed to use the admin API (POST /nrepl).
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# Empty = nobody can.
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ADMIN_USERS=
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# --- Todo database --------------------------------------------------------
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# SQLite file for the todo group; the directory is created on first use. In Docker, mount a
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# volume here or the todos vanish with the container.
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# TODO_DB_PATH=data/todo.db
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# --- MCP OAuth database -----------------------------------------------------
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# SQLite file for the MCP server's OAuth state: registered clients, and the tokens (hashed)
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# of every client a user has approved. In Docker, mount a volume here too, or every MCP client
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# has to be approved again after a restart. BASE_URL (above) must be the address MCP clients use.
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# OAUTH_DB_PATH=data/oauth.db
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# --- Chatbot (LM Studio) ------------------------------------------------------
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# The chat widget in the UI (the green square, bottom left) talks to an LM Studio server through
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# the backend (POST /api/chat, logged-in users only). Point it at your instance: LM Studio's
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# server address, no /v1 (start its server, and enable "Serve on Local Network" for a remote one).
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# Unset LMSTUDIO_URL = the chatbot answers "not configured".
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# LMSTUDIO_URL=http://192.168.1.20:1234
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# Model id to ask for; empty = whichever model LM Studio has loaded.
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# LMSTUDIO_MODEL=
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# Only if the server requires an API key.
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# LMSTUDIO_API_KEY=
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# Seconds the LLM server may stay silent (before it starts answering, or between two pieces of the
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# reply) before giving up (default 120).
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# LMSTUDIO_TIMEOUT_SECONDS=120
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# Every chatbot turn is summarised in the server's log. To also keep the model's full reasoning and
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# replies, and the messages it was sent (for finding out why it loops), append a JSON trace to this
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# file. It holds what users typed, so keep it private.
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# CHAT_TRACE_FILE=data/chat-trace.jsonl
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# --- WebMCP origin trial ------------------------------------------------------
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# Chrome's WebMCP (the UI's browser tools) is in an origin trial. Register BASE_URL's origin at
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# https://developer.chrome.com/origintrials and paste the token here: the server then sends it
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# as an `Origin-Trial` header on the page, and Chrome turns WebMCP on for everyone who opens it.
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# The token is only good for that exact origin (scheme, host and port), and expires with the trial.
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# Empty = no header (in Chrome you can still enable chrome://flags/#enable-webmcp-testing).
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WEBMCP_ORIGIN_TRIAL_TOKEN=
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# --- nREPL debug server (POST /nrepl) -----------------------------------------
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# nREPL has no authentication and evaluates arbitrary code, so it listens on loopback
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# only by default. Changing NREPL_BIND to 0.0.0.0 exposes it to the network: only do that
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# behind a firewall or an SSH tunnel.
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# NREPL_PORT=7888
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# NREPL_BIND=127.0.0.1
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