Add OpenAI-compatible LLM client service (v0.19.0)

LlmService wraps the local OpenAI-compatible server (default http://192.168.1.70:1111/v1, no auth) with native fetch only — no new dependencies. listModels() -> data[].id; chat(messages, opts?) -> choices[0].message.content (non-2xx throws with status + body snippet); summarize(text) = chat() with a concise-summary system prompt. Env: LLM_API_BASE / LLM_MODEL (qwen3.8-27b) / LLM_TIMEOUT_MS (60000, AbortSignal.timeout). Plain feature module wired into app.module.ts; docs synced (AGENTS.md, README env list).
2026-09-25 18:07:21 +00:00
parent 55e59d4963
commit c5a2815c01
8 changed files with 200 additions and 2 deletions
@@ -28,3 +28,4 @@
 - MESHTASTIC.md — rules for Meshtastic communication (owner authority, language, response protocol); read before acting on mesh messages.
 - Hourly cron weather report job (`src/weather`): fetches Open-Meteo current conditions and broadcasts a one-line report to the mesh on the pinned send channel; env `WEATHER_CRON`/`WEATHER_LAT`/`WEATHER_LON`/`WEATHER_CITY`.
 - Channel-2 auto-responder (`src/commands`): event-driven — `CommandService.onRx()` is called by `MeshService` for every decoded rx text (right after the notify gate) and dispatches exact, case-sensitive `#command` matches on `COMMAND_CHANNEL` (env, default 2) through a single-registration-point registry (handlers registered in the constructor via `register(name, fn)`; handler gets `{ text, from?, id? }`, returns reply string or void = no reply). First command: `#ping` → replies `#pong!`. Replies go through `MeshService.respond(id, reply)`: auto-routed (rx → DM back to the original sender unless the original was a broadcast; tx → its destination), riding the original record's channel with `replyId = id` so the recipient app renders it as a threaded quote — `COMMAND_CHANNEL` is only the trigger/listen gate. Only when the trigger has no packet id does it fall back to legacy sendText routing on `COMMAND_CHANNEL`. A failing handler/respond only logs a warn. DI: provided by `CommandModule`'s factory (injects `MeshService`, attaches itself via `attachCommands()`) — no constructor-level cycle; `forwardRef` was tried and left one side null in NestJS 12's injector.
+- LLM client (`src/llm`): `LlmService` wraps the local OpenAI-compatible server (default `http://192.168.1.70:1111/v1`, no auth) with native fetch — plain feature module, no Mesh/Schedule dependency. Methods: `listModels()` → `data[].id`; `chat(messages, { maxTokens?, temperature? })` → `choices[0].message.content` (non-2xx throws with status + body snippet); `summarize(text)` = chat() with system prompt "Summarize the following text concisely." Env: `LLM_API_BASE`, `LLM_MODEL` (default `qwen3.8-27b`), `LLM_TIMEOUT_MS` (default 60000, via `AbortSignal.timeout`).