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f1eec47b9b
CHANGELOG-Eintrag fuer den Cloud-Modell-Browser (0.3.6); qwen/qwen3.5-35b-a3b in EVAL_VERIFIED (Tool-Calling-Test bestanden). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
97 lines
3.3 KiB
Python
97 lines
3.3 KiB
Python
"""Modell-Auswahlmenü (Phase 1: Cloud/OpenRouter).
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Liefert die benutzbaren Chat-Modelle aus dem OpenRouter-Katalog mit Tool-Fähigkeit,
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⬆/⬇-Preisen pro Mio. Token und einer geschätzten „$/Antwort" (aus dem live
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gemessenen Turn-Token-Profil). Katalog wird gecacht. Lokale Modelle: Phase 2.
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Siehe Ideen/MODELL_MENUE_KONZEPT.md.
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"""
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import time
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import httpx
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from app.runtime_config import runtime_settings
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_CATALOG_URL = "https://openrouter.ai/api/v1/models"
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_TTL = 3600.0 # Katalog ändert sich selten
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_cache: dict = {"t": 0.0, "data": None}
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# Im eval/tool_calling als web-suchtauglich bestätigt (✅). Klein halten/pflegen.
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EVAL_VERIFIED = {
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"mistralai/mistral-small-3.2-24b-instruct",
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"qwen/qwen3.5-35b-a3b",
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}
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# Annahmen, bis echte Turn-Messdaten vorliegen.
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_DEFAULT_PROMPT_TOKENS = 700
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_DEFAULT_COMPLETION_TOKENS = 130
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def invalidate_cache() -> None:
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_cache["data"] = None
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def _f(x) -> float:
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try:
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return float(x)
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except (TypeError, ValueError):
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return 0.0
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async def _fetch_catalog(api_key: str) -> list:
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now = time.monotonic()
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if _cache["data"] is not None and now - _cache["t"] < _TTL:
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return _cache["data"]
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headers = {"Authorization": f"Bearer {api_key}"} if api_key else {}
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async with httpx.AsyncClient(timeout=30.0) as client:
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resp = await client.get(_CATALOG_URL, headers=headers)
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resp.raise_for_status()
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data = resp.json().get("data", [])
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_cache["data"] = data
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_cache["t"] = now
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return data
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async def build_model_menu(store=None, cfg=None) -> dict:
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cfg = cfg or runtime_settings
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try:
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catalog = await _fetch_catalog(cfg.openrouter_api_key)
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except Exception as exc: # noqa: BLE001 — graceful, UI zeigt Fehler
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return {"available": False, "error": str(exc), "models": [], "profile": {}}
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prof = store.get_turn_profile() if store is not None else {}
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avg_p = prof.get("avg_prompt") or _DEFAULT_PROMPT_TOKENS
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avg_c = prof.get("avg_completion") or _DEFAULT_COMPLETION_TOKENS
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models = []
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for m in catalog:
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modality = (m.get("architecture") or {}).get("modality") or ""
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if not modality.endswith("->text"): # nur Chat-/Completion-Modelle
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continue
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pricing = m.get("pricing") or {}
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p_in = _f(pricing.get("prompt")) # $/Token
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p_out = _f(pricing.get("completion"))
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mid = m.get("id")
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models.append({
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"id": mid,
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"name": m.get("name") or mid,
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"context_length": m.get("context_length"),
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"price_in_per_mtok": round(p_in * 1_000_000, 4),
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"price_out_per_mtok": round(p_out * 1_000_000, 4),
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"tool_supported": "tools" in (m.get("supported_parameters") or []),
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"eval_verified": mid in EVAL_VERIFIED,
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"est_cost_per_answer": round(avg_p * p_in + avg_c * p_out, 6),
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})
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# Sortierung: eval-bestätigt zuerst, dann tool-fähig, dann alphabetisch.
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models.sort(key=lambda x: (not x["eval_verified"], not x["tool_supported"], x["name"].lower()))
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return {
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"available": True,
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"models": models,
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"profile": {
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"avg_prompt": round(avg_p),
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"avg_completion": round(avg_c),
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"measured": bool(prof.get("samples")),
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},
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"current": cfg.openrouter_llm_model,
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}
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