kami
d0afd9d4f6
Stock Qwen3-1.7B, not the CPT'd one — that training is still running. It won on both fixtures we have, measured tonight on an otherwise idle box: routing, 77 RU cases, intent-only: 67.5% vs 59.7% for Qwen3.5-0.8B talk fixture, 27 cases: 20/27 vs 11-17/27 It also beat Qwen3.5-2B, which is 20% larger, on every routing column. Two other things came with it: n_ctx goes 2048 -> 4096. This is a Thinking variant, so reasoning tokens need the room, and 4096 is the context every score above was measured at. Shipping 2048 would ship something nobody measured. The doc now says not to bother with sub-500M models, because I checked and they are not close. LFM2.5-350M routes at 5.2% — worse than guessing among 7 intents — and answers "столица Франции?" with "Сторзит", which is not a word. The 230M replies to Russian in Spanish. Their published IFEval and BFCL numbers are good and they are all English. Note the routing gain needs the LLM router actually wired on to show up. It is still nil, so this commit buys the phrasing improvement today and the routing improvement when that lands. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CGeSZxh1DCtRxmFVSYVGvJ
124 lines
6.6 KiB
Markdown
124 lines
6.6 KiB
Markdown
# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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Maven is a self-hosted, privacy-first voice assistant (Russian + English). Go daemons
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talking over unix sockets; one resident small model for routing + phrasing; whisper.cpp STT, piper TTS.
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Deploy target is a Ryzen laptop (homesrv) with Vulkan offload to the Vega iGPU (`n_gpu_layers: 99`,
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compose passes `/dev/dri` + the render gid) — the resident model stays ≤1.7B either way.
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**Resident model:** currently **Qwen3-1.7B** (`UD-Q4_K_XL`), stock — not yet the CPT'd one.
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It replaced Qwen3.5-0.8B on 2026-07-31 because it measured better on both fixtures we have:
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67.5% vs 59.7% intent-only on the 77-case RU routing fixture, and 20/27 vs 11-17/27 on the
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talk fixture. See `MODEL-BAKEOFF-31-07-2026.md`. It is a Thinking variant, so `n_ctx` is 4096
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— reasoning tokens need the room, and 4096 is what the scores above were measured at.
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The **target** is still the locally CPT'd **Qwen3-1.7B** (Vikunja #122, training in flight).
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Stock already speaks good Russian; what it gets wrong is the persona — it writes `я рад`,
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masculine, where Maven needs `рада`. That is what the CPT is for.
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**Do not bother with sub-500M models.** LFM2.5-230M and 350M were measured on 2026-07-31 and
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both are unusable in Russian: the 350M routes at 5.2% (worse than guessing) and answers
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"столица Франции?" with the invented non-word "Сторзит"; the 230M replies to Russian in
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Spanish. Their strong published IFEval/BFCL numbers are English-only. Model files live in
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`/mnt/hdd1/llms`, bind-mounted to `/opt/maven/models/llm` — which **shadows** the repo's
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`models/llm/`, so the LFM2.5 gguf sitting there is not loaded by anything. Swapping the resident
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model is a one-line change to `phraser.model_path` in `deploy/mavend.json`.
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See `REARCH.md` for the target architecture, `DESIGN.md` for the folded design spec, and
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`AGENTS.md` for local-preview + model-download recipes.
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## Build & test
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CGO daemons (`mavend`, `mavsttd`, `mavttsd`, `mavenclient`) need the vendored toolchain
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and libs wired through the Makefile — **do not** call `go build` on them bare, use `make`:
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```sh
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make build # all 8 binaries
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make build-web # single daemon (pure-Go ones: web/waked/poll/caldav build without CGO)
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make test # go test -race across ./internal/... ./cmd/... with CGO env set
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```
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Run a single test (must carry the CGO env for packages that touch STT/TTS/voice):
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```sh
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CGO_CFLAGS="-I$(pwd)/deps/include -I$(pwd)/deps/whisper.cpp/ggml/include" \
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CGO_LDFLAGS="-L$(pwd)/deps/lib -Wl,-rpath,$(pwd)/deps/lib" \
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LD_LIBRARY_PATH="$(pwd)/deps/lib" \
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deps/go/go/bin/go test -run TestName ./internal/router/
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```
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Pure-Go packages (`router`, `memory`, `mavweb`, …) run under a plain `go test ./pkg/`.
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## The daemons (`cmd/`)
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| Binary | Role |
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| `mavend` | **Core.** Router, phraser, memory, reminders, digestion tick. Owns the DB + IPC socket. |
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| `mavweb` | HTTP UI + PWA (`/dash`, `/history`, `/trace`, `/notifications`, `/tools`); WebAuthn auth. Connects to mavend's socket. |
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| `mavsttd` | Speech-to-text (whisper.cpp, CGO). |
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| `mavttsd` | Text-to-speech (piper subprocess). |
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| `mavwaked` | Wake-word / VAD gate. |
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| `mavenclient` | Voice loop client (mic → stt → core → tts). |
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| `mavpoll` | Telegram long-poll reach. |
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| `mavcaldav` | CalDAV calendar sync. |
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Daemons are wired socket-to-socket, not linked. `internal/ipc` is the client/server wire
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protocol; the config in `deploy/mavend.json` (with `${VAR}` env expansion from gitignored
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`deploy/telegram.env`) sets socket paths, model paths, and the phraser/embedder blocks.
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## Routing — read this before touching the router
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`internal/router/` has TWO layered engines and the committed default is an **interim
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stopgap, not the intended design** (see memory `routing-architecture-target`):
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- **Target (REARCH.md):** LLM-as-router. One resident Qwen3-1.7B (`llmrouter.go`) emits
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GBNF-constrained structured JSON, and the SAME model phrases replies. Embedder is demoted
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from a routing gate to a RAG hint.
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- **Current stopgap:** `llmrouter` is wired `nil` (around `voice.go`), so the
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`classifier.go` + `embedder.go` nearest-neighbour cascade actually runs. It routes by
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similarity to frozen seed phrases — the known cause of weak RU query handling.
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Cascade order: `stage0.go` exact-match fast-path → LLM router (when non-nil) → classifier
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fallback. Any LLM error falls through to the classifier so a turn never breaks on the model.
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## LLM output contract
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All phrasing paths emit `{"response":"...","mood":"..."}` (parsed in `replier_llm.go` and
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`internal/phraser/llmphraser.go`), with fallback to plain text and the legacy
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`{"body","summary"}`. Mood is a fixed enum. Router prompt is a separate contract:
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`[{"intent":<enum>, key?, value?, text?, verb?}, ...]`, 7 intents (`fact, reminder,
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note, query, act, chat, system`). `llm/check_prompt_parity.py` in the training
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workspace enforces that the Go and relabelling prompts remain identical.
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## Non-goals (hard constraints)
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Not a nag, not autonomous. Maven's persona is **feminine** — Russian
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self-reference must use feminine forms (the user is male; see memory `maven-persona-gender`).
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**"Never phones home" is DEPRECATED** (owner's call, 2026-07-31). It used to be a hard
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constraint and it is not one any more: a 0.8B — and a 1.7B — does not know enough to answer
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world questions, so she needs to read external sources. What replaces it:
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- **No telemetry, no cloud model, no third-party account.** That part never changes. Nothing
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about Maven is reported to anyone, and inference stays on the box.
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- **Local sources first.** Kiwix ZIMs on homesrv (Wikipedia, ifixit) before anything on the
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network. Reading beats recalling for a small model, and a local read costs nothing.
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- **External search is allowed and off unless configured**, like the weather and telegram
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capabilities.
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- **His notes and facts are never search input.** Looking up why the sky is blue and sending
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his stored personal notes to an upstream engine are different acts. Only the utterance goes
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out, never the persona block, history, or matched notes.
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## Web UI conventions
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Server-rendered pages share `cmd/mavweb/static/ui.css` (served at `/ui.css`) and the `nav`
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partial (`navHTML` in `cmd/mavweb/main.go`, `{{template "nav" "<active-page>"}}`). No
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per-page `<style>` beyond true one-offs. Wrap every table in `<div class=scroll>` so wide
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data pans on a phone. Local preview + headless screenshot recipe is in `AGENTS.md`.
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## Vikunja
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This repo is project **Maven** (ID 2) in Vikunja. MCP: `http://localhost:9100/mcp` (or
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`http://192.168.1.104:9100/mcp` from workpc). Feature/bug/deploy tasks go there.
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