duffyduckandClaude Opus 4.8
2d2c182a0c
Worker sendeten worker_hello nur EINMAL beim Connect. Startet das Diagnostic oder die Bridge NACH der Box neu, verpassen sie das hello (RVS spielt es nicht nach) und sehen nur noch worker_ping → die Box taucht nicht in der Compute- Flotte auf. Der Satellit ist genau deshalb zuverlaessig sichtbar: er wiederholt sat_hello periodisch. - alle vier Worker (f5tts/whisper/voxtral/llm-adapter): worker_hello wird jetzt zusaetzlich alle ~30s (jeder 3. Ping-Zyklus) wiederholt → ein neu gestartetes Diagnostic/Bridge lernt die Box innerhalb von 30s, ohne Box-Neustart. - diagnostic/server.js: Voice-Reconcile (f5tts) laeuft nur beim ERSTEN/erneuten Auftauchen der Box, nicht bei jedem 30s-hello-Resend (sonst Push/Pull-Dauerlauf). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
470 lines
19 KiB
Python
470 lines
19 KiB
Python
"""
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ARIA Local-LLM-Adapter (AI-Box) — Plan B, Phase B0.
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Bruecke zwischen RVS und dem lokalen llama.cpp-Server. Spiegelt das Muster der
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whisper-bridge: verbindet sich per WebSocket mit dem RVS (Token-Room, TLS mit
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ws-Fallback, Reconnect-Backoff), lauscht auf `llm_request` und ruft den lokalen
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llama.cpp-`/v1/chat/completions`-Endpoint (OpenAI-kompatibel), antwortet mit
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`llm_response` (korreliert per requestId).
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Topologie: AI-Box steht zuhause, ARIA im RZ — die Kommunikation laeuft ueber
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den RVS (wie TTS/STT), keine IPs zu pflegen. Nur URL + Token.
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Env:
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RVS_HOST, RVS_PORT, RVS_TLS, RVS_TLS_FALLBACK, RVS_TOKEN (wie f5tts/whisper)
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LLAMA_URL Default http://llama:8081 (llama.cpp im selben Compose-Netz)
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LLM_MODEL optionaler Modell-Name fuer llama (llama.cpp ignoriert ihn
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meist, dient nur der Transparenz im Log)
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LLM_TIMEOUT_SEC Default 60
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Bewusst NICHT-streamend in B0 (volle llm_response). Token-Streaming (llm_partial)
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kommt in B2 zusammen mit TTS-on-first-sentence.
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"""
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from __future__ import annotations
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import asyncio
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import json
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import logging
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import os
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import time
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import httpx
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import websockets
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
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)
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logger = logging.getLogger("llm-adapter")
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RVS_HOST = os.getenv("RVS_HOST", "").strip()
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RVS_PORT = os.getenv("RVS_PORT", "443").strip()
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RVS_TLS = os.getenv("RVS_TLS", "true").lower() == "true"
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RVS_TLS_FALLBACK = os.getenv("RVS_TLS_FALLBACK", "true").lower() == "true"
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RVS_TOKEN = os.getenv("RVS_TOKEN", "").strip()
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LLAMA_URL = os.getenv("LLAMA_URL", "http://llama:8081").rstrip("/")
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LLM_MODEL = os.getenv("LLM_MODEL", "qwen3-8b")
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LLM_TIMEOUT_SEC = float(os.getenv("LLM_TIMEOUT_SEC", "60"))
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# ── Compute-Fleet: Worker-Identitaet & Registrierung ──────────────
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# Meldet sich bei der aria-bridge (worker_hello) + periodischer worker_ping.
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NODE_NAME = os.getenv("NODE_NAME", "node").strip() or "node"
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GPU_IDS = os.getenv("NVIDIA_VISIBLE_DEVICES", "").strip()
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WORKER_SERVICE = "llm"
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INSTANCE_ID = f"{WORKER_SERVICE}@{NODE_NAME}"
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WORKER_PING_INTERVAL_S = int(os.getenv("WORKER_PING_INTERVAL_S", "10"))
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_inflight = 0 # laufende llm_requests (busy-Report im ping)
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# Qwen3 hat Thinking-Mode default AN — dann verbraet es Tokens in einem
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# <think>-Block und liefert (bei kleinem max_tokens) leeren/abgeschnittenen
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# content, ausserdem 3x langsamer. ARIAs schnelles Tier will KEIN Grübeln
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# (grübeln = harter Turn = Claude). Wir schalten Thinking daher per
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# chat_template_kwargs ab (Qwen3-Template versteht enable_thinking=false;
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# andere Templates ignorieren das kwarg). Bei einem Modell, das darauf
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# empfindlich reagiert: LLM_DISABLE_THINKING=false setzen.
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LLM_DISABLE_THINKING = os.getenv("LLM_DISABLE_THINKING", "true").lower() == "true"
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# ── Modell-Verwaltung (Stage D): Adapter besitzt llama-swaps Config ──
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# llama-swap liest die GENERIERTE Config (beschreibbar, im /models-Bind). Wir
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# erzeugen sie aus dem Basis-Template (kuratierte Defaults) + der persistenten
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# Box-Registry (per Diagnostic hinzugefuegte Modelle). So werden neue Modelle
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# ohne Image-Rebuild waehlbar.
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import yaml # pyyaml
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BASE_CONFIG_PATH = os.getenv("LLAMA_BASE_CONFIG", "/llamaswap/config.yaml")
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GEN_CONFIG_PATH = os.getenv("LLAMA_GEN_CONFIG", "/models/llama-swap.config.yaml")
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REGISTRY_PATH = os.getenv("LLM_REGISTRY", "/models/aria_models.json")
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# ── Auslastungs-Monitor (Stage E) ──────────────────────────
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import node_stats
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STATS_PATH = os.getenv("STATS_PATH", "/models/aria_stats.json")
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_total_tokens = 0 # kumulativ, fuer den Token-Graph
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_stats = node_stats.NodeStats(INSTANCE_ID, NODE_NAME, STATS_PATH,
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token_getter=lambda: _total_tokens, logger=logger)
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def _load_registry() -> list:
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try:
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with open(REGISTRY_PATH) as f:
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data = json.load(f)
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return data if isinstance(data, list) else []
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except Exception:
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return []
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def _save_registry(reg: list) -> None:
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try:
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tmp = REGISTRY_PATH + ".tmp"
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with open(tmp, "w") as f:
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json.dump(reg, f, indent=2)
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os.replace(tmp, REGISTRY_PATH)
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except Exception as e:
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logger.warning("Registry speichern fehlgeschlagen: %s", e)
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def _generate_config() -> int:
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"""Schreibt die llama-swap-Config aus Basis-Template + Registry. Gibt die
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Anzahl Modelle zurueck. Idempotent, bei jeder Aenderung + beim Start."""
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base = {}
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try:
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with open(BASE_CONFIG_PATH) as f:
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base = yaml.safe_load(f) or {}
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except Exception as e:
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logger.warning("Basis-Template %s nicht lesbar (%s)", BASE_CONFIG_PATH, e)
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models = dict(base.get("models") or {})
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for e in _load_registry():
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key = (e.get("key") or "").strip()
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repo = (e.get("hfRepo") or "").strip()
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if not key or not repo:
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continue
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quant = (e.get("quant") or "Q4_K_M").strip()
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ctx = int(e.get("ctx") or 8192)
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ngl = int(e.get("ngl") or 99)
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models[key] = {
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"cmd": (f"llama-server --port ${{PORT}} --host 127.0.0.1\n"
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f"-hf {repo}:{quant}\n-ngl {ngl} -c {ctx} --jinja"),
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"ttl": 3600,
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}
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out = dict(base)
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out["models"] = models
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try:
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os.makedirs(os.path.dirname(GEN_CONFIG_PATH), exist_ok=True)
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tmp = GEN_CONFIG_PATH + ".tmp"
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with open(tmp, "w") as f:
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yaml.safe_dump(out, f, sort_keys=False, default_flow_style=False)
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os.replace(tmp, GEN_CONFIG_PATH)
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logger.info("llama-swap-Config generiert: %d Modelle → %s", len(models), GEN_CONFIG_PATH)
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except Exception as e:
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logger.error("Config schreiben fehlgeschlagen: %s", e)
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return len(models)
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async def _reload_llama() -> None:
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"""Stoesst llama-swap-Reload an. Viele Builds watchen die Config-Datei ohnehin;
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zusaetzlich versuchen wir bekannte Reload-Endpunkte (Fehler ignoriert)."""
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for path in ("/api/config/reload", "/reload"):
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try:
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async with httpx.AsyncClient(timeout=10) as c:
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r = await c.post(f"{LLAMA_URL}{path}")
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if r.status_code < 400:
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logger.info("llama-swap reload via %s", path)
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return
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except Exception:
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pass
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logger.info("llama-swap reload: kein Endpoint — verlasse mich auf File-Watch")
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async def _send(ws, mtype: str, payload: dict) -> None:
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try:
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await ws.send(json.dumps({
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"type": mtype,
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"payload": payload,
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"timestamp": int(time.time() * 1000),
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}))
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except Exception as e:
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logger.warning("Send fehlgeschlagen (%s): %s", mtype, e)
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async def _call_llama(messages: list, *, max_tokens: int, temperature: float,
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stop, tools=None, model=None) -> dict:
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"""Ruft llama.cpp/llama-swap /v1/chat/completions (OpenAI-Format). Gibt
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{ok, content, tool_calls, error} zurueck — wirft nie.
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model: welches Modell llama-swap laden soll (B0.5). Kommt aus dem Request
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(Brain -> local_llm.json). Faellt auf LLM_MODEL (env) zurueck.
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tools: optionale OpenAI-Tool-Definitionen (B1b). Qwen3 (--jinja) kann
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natives Tool-Calling und liefert dann message.tool_calls."""
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body = {
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"model": model or LLM_MODEL,
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"messages": messages,
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"max_tokens": max_tokens,
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"temperature": temperature,
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"stream": False,
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}
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if stop:
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body["stop"] = stop
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if tools:
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body["tools"] = tools
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body["tool_choice"] = "auto"
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if LLM_DISABLE_THINKING:
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# llama.cpp (--jinja) reicht chat_template_kwargs an die Chat-Vorlage
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# weiter. Qwen3 unterdrueckt damit den <think>-Block.
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body["chat_template_kwargs"] = {"enable_thinking": False}
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try:
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async with httpx.AsyncClient(timeout=LLM_TIMEOUT_SEC) as client:
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r = await client.post(f"{LLAMA_URL}/v1/chat/completions", json=body)
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r.raise_for_status()
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data = r.json()
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msg = (data.get("choices") or [{}])[0].get("message", {}) or {}
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usage = data.get("usage") or {}
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try:
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global _total_tokens
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_total_tokens += int(usage.get("total_tokens") or 0)
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except Exception:
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pass
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return {
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"ok": True,
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"content": msg.get("content") or "",
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"tool_calls": msg.get("tool_calls") or None,
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"usage": usage,
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}
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except Exception as e:
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logger.warning("llama.cpp-Call fehlgeschlagen: %s", e)
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return {"ok": False, "content": "", "error": str(e)[:300]}
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# B0.5-2: Lade-Status ans Diagnostic (service_status, service="llm"). Wir kennen
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# den Download-Fortschritt nicht (llama-swap gibt ihn nicht her), aber wir melden
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# den Zustand bei Modellwechsel: loading -> ready/error. _last_model = aktuell
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# geladenes; _ready_models = in dieser Session schon einmal bereit gewesene
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# (fuer den "frisch geladen"-Hinweis 🎉 bei langem Erst-Load).
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_last_model = None
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_ready_models: set = set()
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async def _emit_llm_status(ws, state: str, model: str, **extra) -> None:
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await _send(ws, "service_status",
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{"service": "llm", "state": state, "model": model, **extra})
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async def _fetch_available_models() -> list:
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"""Fragt llama-swap ab, welche Modelle diese Box fahren kann (GET /v1/models,
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OpenAI-kompatibel → {data:[{id},...]}). Das sind die config.yaml-Keys.
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Defensiv: bei Fehler Fallback auf [LLM_MODEL]."""
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try:
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async with httpx.AsyncClient(timeout=10) as client:
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r = await client.get(f"{LLAMA_URL}/v1/models")
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r.raise_for_status()
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data = r.json()
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ids = [m.get("id") for m in (data.get("data") or []) if m.get("id")]
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return ids or [LLM_MODEL]
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except Exception as e:
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logger.warning("llama-swap /v1/models nicht abfragbar (%s) — Fallback [%s]", e, LLM_MODEL)
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return [LLM_MODEL]
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async def _announce(ws) -> None:
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"""Sendet ein frisches worker_hello mit der aktuellen Modell-Liste (nach
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Provision/Remove aufrufen, damit Bridge+Diagnostic das neue Modell lernen)."""
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models = await _fetch_available_models()
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await _send(ws, "worker_hello", {
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"instanceId": INSTANCE_ID, "service": WORKER_SERVICE,
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"node": NODE_NAME, "gpus": GPU_IDS, "model": LLM_MODEL,
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"models": models, # welche Modelle diese Box fahren kann (llama-swap-Keys)
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})
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logger.info("worker_hello: models=%s", models)
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async def _worker_register(ws) -> None:
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"""Meldet diesen Worker bei der aria-bridge an (worker_hello) und haelt die
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Flotten-Registry per periodischem worker_ping (mit busy-Status) frisch."""
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try:
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await _announce(ws)
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n = 0
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while True:
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await asyncio.sleep(WORKER_PING_INTERVAL_S)
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n += 1
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await _send(ws, "worker_ping",
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{"instanceId": INSTANCE_ID, "busy": _inflight > 0})
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if n % 3 == 0: # ~30s worker_hello wiederholen (wie der Satellit) →
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await _announce(ws) # auch neu gestartetes Diagnostic/Bridge lernt uns
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except asyncio.CancelledError:
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raise
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except Exception:
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return # Socket tot → still beenden; _run reconnectet + startet neu
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async def _handle_llm_request(ws, payload: dict) -> None:
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global _last_model, _inflight
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_inflight += 1
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try:
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await _do_llm_request(ws, payload)
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finally:
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_inflight -= 1
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async def _do_llm_request(ws, payload: dict) -> None:
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global _last_model
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req_id = payload.get("requestId", "")
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messages = payload.get("messages") or []
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if not isinstance(messages, list) or not messages:
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await _send(ws, "llm_response", {
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"requestId": req_id, "ok": False, "error": "leere/ungueltige messages",
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})
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return
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max_tokens = int(payload.get("max_tokens", 512) or 512)
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temperature = float(payload.get("temperature", 0.7) or 0.7)
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stop = payload.get("stop")
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tools = payload.get("tools") or None
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model = (payload.get("model") or "").strip() or None
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eff_model = model or LLM_MODEL
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# Modellwechsel (oder erster Request) → llama-swap laedt/swappt: Status melden.
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switching = eff_model != _last_model
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if switching:
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await _emit_llm_status(ws, "loading", eff_model)
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t0 = time.time()
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res = await _call_llama(messages, max_tokens=max_tokens,
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temperature=temperature, stop=stop, tools=tools,
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model=model)
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dt = time.time() - t0
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if switching:
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if res.get("ok"):
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fresh = (eff_model not in _ready_models) and dt > 25
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_ready_models.add(eff_model)
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_last_model = eff_model
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await _emit_llm_status(ws, "ready", eff_model,
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loadSeconds=round(dt, 1), freshlyDownloaded=fresh)
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else:
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# bei Fehler _last_model NICHT setzen → naechster Versuch meldet erneut loading
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await _emit_llm_status(ws, "error", eff_model,
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error=(res.get("error") or "")[:120])
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tc = res.get("tool_calls")
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logger.info("llm_request id=%s model=%s -> ok=%s %.2fs content_len=%d tool_calls=%d",
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(req_id[:8] if req_id else "?"), model or LLM_MODEL, res.get("ok"), dt,
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len(res.get("content") or ""), len(tc) if tc else 0)
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await _send(ws, "llm_response", {
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"requestId": req_id,
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"ok": res.get("ok", False),
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"content": res.get("content", ""),
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"tool_calls": tc,
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"error": res.get("error"),
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"model": model or LLM_MODEL,
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"elapsedMs": int(dt * 1000),
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})
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async def _handle_provision(ws, payload: dict) -> None:
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"""Fuegt ein Modell hinzu: Registry+Config schreiben, reload, dann Warmup
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(zieht das GGUF via -hf beim ersten Load). Meldet die neue Modell-Liste."""
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key = (payload.get("key") or "").strip()
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repo = (payload.get("hfRepo") or "").strip()
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if not key or not repo:
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await _send(ws, "llm_provision_result",
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{"instanceId": INSTANCE_ID, "key": key, "ok": False, "error": "key/hfRepo fehlt"})
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return
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entry = {
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"key": key, "hfRepo": repo,
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"quant": (payload.get("quant") or "Q4_K_M").strip(),
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"ctx": int(payload.get("ctx") or 8192),
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"ngl": int(payload.get("ngl") or 99),
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}
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reg = [e for e in _load_registry() if e.get("key") != key]
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reg.append(entry)
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_save_registry(reg)
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_generate_config()
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await _reload_llama()
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await _announce(ws) # Bridge/Diagnostic lernen das neue Modell
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# Warmup: Mini-Request → llama-swap laedt/zieht das Modell (Fortschritt via
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# service_status loading→ready, freshlyDownloaded).
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await _emit_llm_status(ws, "loading", key)
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t0 = time.time()
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res = await _call_llama([{"role": "user", "content": "hi"}],
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max_tokens=1, temperature=0.0, stop=None, model=key)
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dt = time.time() - t0
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if res.get("ok"):
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_ready_models.add(key)
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await _emit_llm_status(ws, "ready", key, loadSeconds=round(dt, 1),
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freshlyDownloaded=dt > 25)
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else:
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await _emit_llm_status(ws, "error", key, error=(res.get("error") or "")[:160])
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await _send(ws, "llm_provision_result",
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{"instanceId": INSTANCE_ID, "key": key, "ok": res.get("ok", False),
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"error": res.get("error"), "elapsedMs": int(dt * 1000)})
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logger.info("provision %s (%s) → ok=%s %.1fs", key, repo, res.get("ok"), dt)
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async def _handle_remove(ws, payload: dict) -> None:
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"""Entfernt ein Modell aus Registry+Config (GGUF bleibt im Cache)."""
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key = (payload.get("key") or "").strip()
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if not key:
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return
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reg = [e for e in _load_registry() if e.get("key") != key]
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_save_registry(reg)
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_generate_config()
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await _reload_llama()
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await _announce(ws)
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await _send(ws, "llm_provision_result",
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{"instanceId": INSTANCE_ID, "key": key, "ok": True, "removed": True})
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logger.info("removed model %s", key)
|
|
|
|
|
|
async def _run() -> None:
|
|
if not RVS_HOST:
|
|
logger.error("RVS_HOST nicht gesetzt — Abbruch")
|
|
return
|
|
if not RVS_TOKEN:
|
|
logger.error("RVS_TOKEN nicht gesetzt — Abbruch")
|
|
return
|
|
|
|
# llama-swap-Config aus Basis-Template + Registry erzeugen, BEVOR llama-swap
|
|
# sie braucht (llama-swap restart: unless-stopped faengt die Erst-Boot-
|
|
# Reihenfolge ab, falls es kurz vor uns startet).
|
|
_generate_config()
|
|
|
|
# Auslastungs-Sampler (GPU + Tokens) laeuft unabhaengig vom RVS.
|
|
asyncio.create_task(_stats.run_sampler())
|
|
|
|
use_tls = RVS_TLS
|
|
retry_s = 2
|
|
tls_fallback_tried = False
|
|
|
|
while True:
|
|
scheme = "wss" if use_tls else "ws"
|
|
url = f"{scheme}://{RVS_HOST}:{RVS_PORT}/ws?token={RVS_TOKEN}"
|
|
masked = url.replace(RVS_TOKEN, "***") if RVS_TOKEN else url
|
|
try:
|
|
logger.info("Verbinde zu RVS: %s (llama=%s)", masked, LLAMA_URL)
|
|
async with websockets.connect(
|
|
url, ping_interval=20, ping_timeout=10, max_size=16 * 1024 * 1024
|
|
) as ws:
|
|
logger.info("RVS verbunden — llm-adapter online")
|
|
retry_s = 2
|
|
tls_fallback_tried = False
|
|
ping_task = asyncio.create_task(_worker_register(ws))
|
|
async for raw in ws:
|
|
try:
|
|
msg = json.loads(raw)
|
|
except Exception:
|
|
continue
|
|
mtype = msg.get("type")
|
|
payload = msg.get("payload", {}) or {}
|
|
# Redundanz-Routing: gezielt an eine andere Instanz adressiert
|
|
# → ignorieren. Ohne targetInstance → wie bisher (jeder nimmt).
|
|
tgt = payload.get("targetInstance")
|
|
if tgt and tgt != INSTANCE_ID:
|
|
continue
|
|
# Auslastungs-Monitor (node_stats_*) abfangen.
|
|
if await _stats.handle(ws, mtype, payload, _send):
|
|
continue
|
|
if mtype not in ("llm_request", "llm_provision_model", "llm_remove_model"):
|
|
continue
|
|
if mtype == "llm_provision_model":
|
|
asyncio.create_task(_handle_provision(ws, payload))
|
|
elif mtype == "llm_remove_model":
|
|
asyncio.create_task(_handle_remove(ws, payload))
|
|
else:
|
|
# Jede Anfrage nebenlaeufig — llama.cpp serialisiert intern,
|
|
# aber wir blockieren so nicht den Empfang weiterer Messages.
|
|
asyncio.create_task(_handle_llm_request(ws, payload))
|
|
except Exception as e:
|
|
logger.warning("RVS-Verbindung verloren/fehlgeschlagen: %s", e)
|
|
try:
|
|
ping_task.cancel()
|
|
except NameError:
|
|
pass
|
|
if use_tls and RVS_TLS_FALLBACK and not tls_fallback_tried:
|
|
tls_fallback_tried = True
|
|
use_tls = False
|
|
logger.info("TLS fehlgeschlagen — Fallback auf ws://")
|
|
continue
|
|
await asyncio.sleep(min(retry_s, 30))
|
|
retry_s = min(retry_s * 2, 30)
|
|
use_tls = RVS_TLS
|
|
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(_run())
|