Files
Maven/AGENTS.md
T
kami da60c14399 chore: docker, config, delivery sinks, dialogue, and agent docs
- Dockerfile: multi-stage build with CGO_ENABLED=0, embedder model copy,
  non-root user, healthcheck, and /data volume.
- docker-compose.yml: mavend + mavweb services with shared volume, health
  checks, and restart policy.
- .gitignore: ignore models/llm/*.gguf, deploy/telegram.env, tmp artifacts.
- deploy/mavend.json: add LLM, phraser, voice sections (embedder, model
  paths, wake sensitivity). Add telegram token env-var expansion.
- deploy/telegram.env.example: template for telegram bot token.
- internal/config/config.go: add LLM config struct, voice config struct
  (embedder, llama, wake sensitivity), telegram token loading.
- telegramsink: add chat intent delivery support alongside existing types.
- voicesink: skip empty payloads in delivery.
- dialogue/session: add chat intent to anaphora resolution, test coverage.
- AGENTS.md: update with LLM embedder, LFM model download/configure steps,
  new UI conventions.
- REARCH.md: architecture research document.
- cmd/mavend/main.go: wire LLM config, phraser, embedder, telegram config,
  WebAuthn, IPC event/routine handlers, and reactive notes.
2026-07-10 15:49:27 +04:00

4.3 KiB

Maven — Agent Context

Vikunja

This repo maps to Maven (project ID: 2) in Vikunja. Feature work, bugs, deployment tasks all go here. MCP endpoint: http://localhost:9100/mcp (or http://192.168.1.104:9100/mcp from workpc)

Rendering / previewing the web UI locally

To see mavweb pages with real data without touching the production stack:

R=/tmp/mvn-preview; mkdir -p $R
go build -o $R/mavend ./cmd/mavend/ && go build -o $R/mavweb ./cmd/mavweb/
cat > $R/mavend.json <<EOF
{ "db_path": "$R/maven.db", "socket_path": "$R/mavend.sock",
  "state_dir": "$R", "tick_interval": "10s" }
EOF
$R/mavend -config $R/mavend.json &
$R/mavweb -addr 127.0.0.1:9299 -core $R/mavend.sock &
  • No models/voice/phraser config needed — the phraser stub covers it; mavend runs fine bare. mavweb serves /, /dash, /history, /trace, /notifications, /tools.
  • Socket path must be short — unix sockets cap at ~108 chars; a deep tmp dir fails with bind: invalid argument.
  • Seed data through ipc.Client (internal package — the seeder must live inside the module, e.g. a throwaway cmd/seedtmp/main.go, deleted after): WriteFact, RecordNudge+ResolveNudge, CreateReminder, ProposeTool.
  • /trace is empty until the first tick fires (wait one tick_interval).
  • Screenshots: chromium --headless --disable-gpu --screenshot=out.png --window-size=1280,900 --hide-scrollbars --virtual-time-budget=2000 http://127.0.0.1:9299/dash (use --window-size=430,900 for the phone/PWA view). Always pass --virtual-time-budget — without it the screenshot can snap mid-layout and silently drop elements (the PWA lang toggle "disappeared" this way).

Embedder model for intent routing

The router uses a multilingual sentence embedder to classify intents and recall notes. Without it, the floor HashEmbedder is used — deterministic but weak (Russian recall rarely clears the confidence gate, many commands fall to "clarify").

Download the embedder (ONNX, ~90 MB):

make download-embedder

This fetches paraphrase-multilingual-MiniLM-L12-v2 (384-dim, 12-layer, supports 50+ languages including Russian) to models/embedder/.

Also need ONNX Runtime (libonnxruntime.so):

curl -sL "https://github.com/microsoft/onnxruntime/releases/download/v1.15.1/onnxruntime-linux-x64-1.15.1.tgz" | tar xz
sudo cp onnxruntime-linux-x64-1.15.1/lib/libonnxruntime.so* /usr/local/lib/

Configure in deploy/mavend.json:

"voice": {
  "embedder": {
    "model_path": "models/embedder/model_quantized.onnx",
    "tokenizer_path": "models/embedder/tokenizer.json",
    "lib_path": "/usr/local/lib/libonnxruntime.so"
  }
}

Without the embedder block, the daemon uses HashEmbedder (works, but weak on Russian recall — you may see many "clarify" responses).

LFM model for router + phraser

The daemon uses a single resident LFM (sub-1B) for both routing (intent classification + slot extraction) and phrasing (nudges, reminders, reactive replies). Without it, the StubPhraser + HashEmbedder classifier are used — deterministic but stiff (canned confirmations, weak Russian recall).

Download the model (GGUF, ~780 MB):

make download-llm

Or manually:

curl -sL "https://huggingface.co/lfm/LFM2.5-1.2B-Instruct-GGUF/resolve/main/LFM2.5-1.2B-Instruct-Q4_K_M.gguf" \
  -o models/llm/LFM2.5-1.2B-Instruct-Q4_K_M.gguf

Configure in deploy/mavend.json — the phraser block points at this model and the daemon spawns llama-server as a subprocess. The router and replier use the same llama-server via the shared internal/llm client.

Telegram tokens are read from deploy/telegram.env (gitignored), expanded via ${VAR} in the JSON config.

Routing is now LFM-first with classifier fallback. The LLM router runs after stage-0 (exact-match grammar) and before the classifier cascade. On any error or parse failure, the classifier handles the utterance — the turn never breaks on the model.

Web UI conventions

  • All server-rendered pages share cmd/mavweb/static/ui.css (served at /ui.css) and the nav template partial (navHTML in cmd/mavweb/main.go, invoked as {{template "nav" "<active-page>"}}). New pages must link both — no per-page inline <style> beyond true one-offs.
  • Wrap every table in <div class=scroll> so wide data pans on a phone instead of breaking the layout.