mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-07-06 09:21:16 -03:00
feat(ui): redesign AI Provider settings with provider presets and model catalog
- Replace hardcoded provider list with PROVIDER_PRESETS (OpenAI, Ollama, DeepSeek, Groq, OpenRouter, OpenCode Go, Custom) - Load model lists from models.dev/api.json catalog at startup - Add Combobox vanilla JS component for model/base-URL selection - Fetch local Ollama models via live API instead of catalog - Hide API key values from frontend (boolean-only llm_api_key_set) - Add i18n translations for all 9+ locales - Update snapshot tests for new response fields
This commit is contained in:
Will Miao
@@ -19,11 +19,165 @@ from .errors import LLMNotConfiguredError, LLMRateLimitError, LLMResponseError
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logger = logging.getLogger(__name__)
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# Default API base URLs per provider
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# ---------------------------------------------------------------------------
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# Model catalog sourced from opencode's maintained model registry.
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# maps provider_id -> list of model IDs.
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# ---------------------------------------------------------------------------
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_MODEL_CATALOG_URL = "https://models.dev/api.json"
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# In-memory cache: maps provider slug -> list of model ID strings.
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_catalog_cache: Optional[Dict[str, List[str]]] = None
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_CATALOG_TIMEOUT = aiohttp.ClientTimeout(total=30)
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async def _load_model_catalog() -> Dict[str, List[str]]:
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"""Fetch and parse the model catalog, returning ``{provider_id: [model_id, ...]}``.
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The JSON at ``_MODEL_CATALOG_URL`` is a dict keyed by provider slug; each
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value has a ``models`` sub-dict keyed by model ID. Only the model IDs are
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kept. The result is cached in memory after the first successful fetch.
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Subsequent calls return the cached data immediately.
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"""
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global _catalog_cache
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if _catalog_cache is not None:
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return _catalog_cache
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try:
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async with aiohttp.ClientSession(timeout=_CATALOG_TIMEOUT) as session:
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async with session.get(_MODEL_CATALOG_URL) as resp:
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if resp.status != 200:
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logger.warning("Model catalog returned HTTP %s", resp.status)
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return _catalog_cache or {}
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data = await resp.json()
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except (aiohttp.ClientError, asyncio.TimeoutError, json.JSONDecodeError) as exc:
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logger.warning("Failed to fetch model catalog: %s", exc)
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return _catalog_cache or {}
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if not isinstance(data, dict):
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logger.warning("Model catalog is not a dict, got %s", type(data).__name__)
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return _catalog_cache or {}
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result: Dict[str, List[str]] = {}
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for provider_id, provider_info in data.items():
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if not isinstance(provider_info, dict):
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continue
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models_dict = provider_info.get("models")
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if not isinstance(models_dict, dict):
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continue
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model_ids = [str(mid) for mid in models_dict.keys() if isinstance(mid, str)]
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if model_ids:
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result[provider_id] = model_ids
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_catalog_cache = result
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logger.info(
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"Loaded model catalog: %d providers, %d total models",
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len(result),
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sum(len(m) for m in result.values()),
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)
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return result
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# Short timeout for Ollama's local API
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_OLLAMA_API_TIMEOUT = aiohttp.ClientTimeout(total=8)
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async def fetch_ollama_models(api_base: str) -> List[str]:
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"""Fetch locally available models from a running Ollama instance.
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Uses Ollama's OpenAI-compatible ``GET {api_base}/models`` endpoint.
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Returns an empty list if Ollama is not reachable (not running).
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"""
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url = f"{api_base.rstrip('/')}/models"
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try:
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async with aiohttp.ClientSession(timeout=_OLLAMA_API_TIMEOUT) as session:
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async with session.get(url) as resp:
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if resp.status != 200:
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logger.debug("Ollama API returned HTTP %s from %s", resp.status, api_base)
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return []
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data = await resp.json()
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except (aiohttp.ClientError, asyncio.TimeoutError, json.JSONDecodeError) as exc:
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logger.debug("Ollama not reachable at %s: %s", api_base, exc)
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return []
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raw = data.get("data") if isinstance(data, dict) else None
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if not isinstance(raw, list):
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return []
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return [
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str(entry["id"]) for entry in raw
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if isinstance(entry, dict) and isinstance(entry.get("id"), str)
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]
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async def get_provider_model_ids(provider_id: str) -> List[str]:
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"""Return the list of known model IDs for *provider_id* from the catalog.
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The catalog is loaded on first call and cached thereafter. If the
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provider is not found an empty list is returned (never raises).
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"""
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catalog = await _load_model_catalog()
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return catalog.get(provider_id, [])
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async def get_all_provider_models(
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provider_ids: List[str],
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) -> Dict[str, List[str]]:
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"""Return model lists for a subset of providers in one call.
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Loads the catalog (cached) and returns only the requested providers.
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Handy for embedding lightweight data into the template context.
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"""
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catalog = await _load_model_catalog()
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return {
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pid: catalog.get(pid, [])
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for pid in provider_ids
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}
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# Provider preset definitions.
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# Each entry contains display metadata and defaults for the UI.
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# The key is the internal provider id stored in ``llm_provider``.
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# Models are NOT listed here — they come from the opencode model catalog at
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# runtime (see :func:`get_provider_model_ids`).
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PROVIDER_PRESETS: Dict[str, Dict[str, Any]] = {
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"openai": {
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"name": "OpenAI",
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"api_base": "https://api.openai.com/v1",
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"requires_key": True,
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},
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"ollama": {
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"name": "Ollama (local)",
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"api_base": "http://localhost:11434/v1",
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"requires_key": False,
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},
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"deepseek": {
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"name": "DeepSeek",
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"api_base": "https://api.deepseek.com/v1",
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"requires_key": True,
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},
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"groq": {
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"name": "Groq",
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"api_base": "https://api.groq.com/openai/v1",
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"requires_key": True,
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},
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"openrouter": {
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"name": "OpenRouter",
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"api_base": "https://openrouter.ai/api/v1",
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"requires_key": True,
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},
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"opencode-go": {
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"name": "OpenCode Go",
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"api_base": "https://opencode.ai/zen/go/v1",
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"requires_key": True,
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},
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# "custom" is handled specially (no preset api_base, requires user input)
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}
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# Legacy lookup derived from PROVIDER_PRESETS for backward compat.
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_PROVIDER_DEFAULTS: Dict[str, str] = {
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"openai": "https://api.openai.com/v1",
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"ollama": "http://localhost:11434/v1",
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# "custom" requires an explicit llm_api_base from the user
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pid: info["api_base"]
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for pid, info in PROVIDER_PRESETS.items()
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if info.get("api_base")
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}
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# Request timeout for LLM calls (seconds)
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@@ -57,6 +211,10 @@ class LLMService:
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from .settings_manager import get_settings_manager
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cls._instance = cls(get_settings_manager())
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# Start preloading the model catalog in the background so
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# the settings UI never blocks on it. The catalog is
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# cached after the first fetch (see _load_model_catalog).
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asyncio.create_task(_load_model_catalog())
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return cls._instance
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@classmethod
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@@ -79,20 +237,31 @@ class LLMService:
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"model": self._settings.get("llm_model", ""),
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}
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@staticmethod
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def _provider_requires_key(provider: str) -> bool:
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"""Return ``False`` when the given provider id does not need an API key."""
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preset = PROVIDER_PRESETS.get(provider, {})
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return bool(preset.get("requires_key", True))
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def is_configured(self) -> bool:
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"""Return ``True`` when the LLM provider is minimally configured.
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A provider is considered configured when ``llm_model`` is set and
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(for non-Ollama) an API key is configured.
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an API key is configured for providers that require one (e.g.
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Ollama does not).
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"""
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cfg = self._get_config()
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has_model = bool(cfg["model"])
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has_key = bool(cfg["api_key"]) or cfg["provider"] == "ollama"
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has_key = bool(cfg["api_key"]) or not self._provider_requires_key(cfg["provider"])
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return has_model and has_key
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def _resolve_api_base(self, provider: str, api_base: str) -> str:
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"""Resolve the API base URL for the given provider."""
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"""Resolve the API base URL for the given provider.
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If ``api_base`` is explicitly set (non-empty), it takes priority.
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Otherwise the default from :data:`PROVIDER_PRESETS` is used.
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"""
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if api_base:
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return api_base.rstrip("/")
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@@ -115,12 +284,13 @@ class LLMService:
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cfg = self._get_config()
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has_model = bool(cfg["model"])
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has_key = bool(cfg["api_key"]) or cfg["provider"] == "ollama"
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needs_key = self._provider_requires_key(cfg["provider"])
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has_key = bool(cfg["api_key"]) or not needs_key
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if not (has_model and has_key):
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parts = []
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if not has_model:
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parts.append("No LLM model specified")
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if not has_key and cfg["provider"] != "ollama":
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if not has_key and needs_key:
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parts.append("No LLM API key configured")
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detail = "; ".join(parts) if parts else "LLM provider is not configured"
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raise LLMNotConfiguredError(
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