| 1 | from __future__ import annotations |
| 2 | |
| 3 | from typing import Any |
| 4 | |
| 5 | import httpx |
| 6 | from helpers.api import ApiHandler, Request, Response |
| 7 | from helpers.providers import get_provider_config |
| 8 | import models |
| 9 | |
| 10 | # Model name substrings to exclude from chat dropdowns and LiteLLM fallback results. |
| 11 | _NON_CHAT_EXCLUDE = frozenset({ |
| 12 | "dall-e", |
| 13 | "gpt-image", |
| 14 | "image", |
| 15 | "tts", |
| 16 | "text-to-speech", |
| 17 | "whisper", |
| 18 | "audio", |
| 19 | "transcribe", |
| 20 | "transcription", |
| 21 | "speech", |
| 22 | "realtime", |
| 23 | "embedding", |
| 24 | "embed", |
| 25 | "moderation", |
| 26 | "omni-moderation", |
| 27 | "vision-preview", |
| 28 | }) |
| 29 | _LOCAL_PLACEHOLDER_KEYS = { |
| 30 | "lm_studio": {"lm-studio"}, |
| 31 | "llama_cpp": {"llama-cpp"}, |
| 32 | "omlx": {"omlx"}, |
| 33 | "vllm": {"vllm"}, |
| 34 | } |
| 35 | |
| 36 | |
| 37 | class ModelSearch(ApiHandler): |
| 38 | async def process(self, input: dict, request: Request) -> dict | Response: |
| 39 | provider = str(input.get("provider", "") or "").strip().lower() |
| 40 | model_type = str(input.get("model_type", "chat") or "chat").strip().lower() |
| 41 | query = str(input.get("query", "") or "").strip().lower() |
| 42 | user_api_base = str(input.get("api_base", "") or "").strip() |
| 43 | |
| 44 | if not provider: |
| 45 | return {"models": [], "provider": "", "source": "none", "error": ""} |
| 46 | |
| 47 | cfg = self._get_provider_cfg(model_type, provider) |
| 48 | ml = self._get_models_list(cfg) |
| 49 | |
| 50 | models_list, source, error = await self._fetch_models(provider, cfg, ml, user_api_base) |
| 51 | |
| 52 | if not models_list: |
| 53 | fallback = self._litellm_fallback(provider, cfg) |
| 54 | if fallback: |
| 55 | models_list = fallback |
| 56 | source = "litellm_registry" |
| 57 | elif not source: |
| 58 | source = "none" |
| 59 | |
| 60 | models_list = self._filter_models(models_list, model_type) |
| 61 | if query: |
| 62 | models_list = [name for name in models_list if query in name.lower()] |
| 63 | |
| 64 | return { |
| 65 | "models": sorted(set(models_list), key=str.lower), |
| 66 | "provider": provider, |
| 67 | "source": source, |
| 68 | "error": error, |
| 69 | } |
| 70 | |
| 71 | @staticmethod |
| 72 | def _get_provider_cfg(model_type: str, provider: str) -> dict: |
| 73 | """Get provider config, falling back to chat config for models_list.""" |
| 74 | cfg = get_provider_config(model_type, provider) or {} |
| 75 | if model_type != "chat" and not cfg.get("models_list"): |
| 76 | chat_cfg = get_provider_config("chat", provider) or {} |
| 77 | if chat_cfg.get("models_list"): |
| 78 | merged = dict(cfg) |
| 79 | merged["models_list"] = chat_cfg["models_list"] |
| 80 | return merged |
| 81 | return cfg |
| 82 | |
| 83 | @staticmethod |
| 84 | def _get_models_list(cfg: dict) -> dict: |
| 85 | """Extract models_list sub-config.""" |
| 86 | return cfg.get("models_list") or {} |
| 87 | |
| 88 | async def _fetch_models( |
| 89 | self, |
| 90 | provider: str, |
| 91 | cfg: dict, |
| 92 | ml: dict, |
| 93 | user_api_base: str = "", |
| 94 | ) -> tuple[list[str], str, str]: |
| 95 | api_key = models.get_api_key(provider) |
| 96 | kwargs = (cfg or {}).get("kwargs", {}) or {} |
| 97 | api_base = user_api_base or kwargs.get("api_base", "") or ml.get("default_base", "") |
| 98 | effective_ml = dict(ml or {}) |
| 99 | |
| 100 | # Ollama's native endpoint is /api/tags, but user-supplied /v1 bases usually |
| 101 | # mean the OpenAI-compatible /v1/models endpoint. |
| 102 | if provider == "ollama" and user_api_base.rstrip("/").endswith("/v1"): |
| 103 | effective_ml["endpoint_url"] = "/models" |
| 104 | effective_ml["format"] = "openai" |
| 105 | |
| 106 | url, fmt = self._resolve_url(effective_ml, api_base) |
| 107 | if not url: |
| 108 | return [], "none", "" |
| 109 | |
| 110 | headers = self._build_headers(provider, api_key, cfg) |
| 111 | params = dict(effective_ml.get("params", {}) or {}) |
| 112 | |
| 113 | # Google uses query-param auth for the public models list endpoint. |
| 114 | if provider == "google" and api_key and api_key != "None": |
| 115 | params.setdefault("key", api_key) |
| 116 | |
| 117 | urls: list[tuple[str, str]] = [(url, fmt)] |
| 118 | if provider == "ollama" and fmt == "ollama": |
| 119 | ps_url = self._ollama_ps_url(url) |
| 120 | if ps_url and ps_url != url: |
| 121 | urls.append((ps_url, "ollama")) |
| 122 | |
| 123 | combined: list[str] = [] |
| 124 | errors: list[str] = [] |
| 125 | |
| 126 | try: |
| 127 | async with httpx.AsyncClient(timeout=10.0) as client: |
| 128 | for candidate_url, candidate_fmt in urls: |
| 129 | resp = await client.get(candidate_url, headers=headers, params=params) |
| 130 | if resp.status_code == 200: |
| 131 | combined.extend(self._parse(resp.json(), candidate_fmt)) |
| 132 | else: |
| 133 | errors.append(f"{candidate_url}: HTTP {resp.status_code}") |
| 134 | except Exception as exc: |
| 135 | errors.append(str(exc)) |
| 136 | |
| 137 | if combined: |
| 138 | return combined, "provider_endpoint", "" |
| 139 | return [], "provider_endpoint", "; ".join(errors) |
| 140 | |
| 141 | @staticmethod |
| 142 | def _resolve_url(ml: dict, api_base: str) -> tuple[str | None, str]: |
| 143 | fmt = ml.get("format", "openai") |
| 144 | endpoint = str(ml.get("endpoint_url", "") or "") |
| 145 | default_base = str(ml.get("default_base", "") or "") |
| 146 | |
| 147 | if endpoint.startswith("http://") or endpoint.startswith("https://"): |
| 148 | return endpoint, fmt |
| 149 | |
| 150 | base = str(api_base or default_base or "").strip() |
| 151 | if not base: |
| 152 | return None, fmt |
| 153 | |
| 154 | endpoint = endpoint or "/models" |
| 155 | base = base.rstrip("/") |
| 156 | |
| 157 | if not endpoint.startswith("/"): |
| 158 | endpoint = "/" + endpoint |
| 159 | |
| 160 | # Avoid doubled /v1/v1 when users enter a base ending in /v1 and metadata |
| 161 | # also contains a versioned endpoint. |
| 162 | if base.endswith("/v1") and endpoint.startswith("/v1/"): |
| 163 | endpoint = endpoint[3:] |
| 164 | |
| 165 | return base + endpoint, fmt |
| 166 | |
| 167 | @staticmethod |
| 168 | def _ollama_ps_url(resolved_url: str) -> str: |
| 169 | """Return the Ollama running-model endpoint for a resolved native URL.""" |
| 170 | marker = "/api/" |
| 171 | if marker not in resolved_url: |
| 172 | return "" |
| 173 | return resolved_url.split(marker, 1)[0].rstrip("/") + "/api/ps" |
| 174 | |
| 175 | def _build_headers(self, provider: str, api_key: str, cfg: dict | None) -> dict[str, str]: |
| 176 | headers: dict[str, str] = {} |
| 177 | has_key = bool(api_key and api_key.strip() and api_key != "None") |
| 178 | |
| 179 | if provider == "anthropic": |
| 180 | if has_key: |
| 181 | headers["x-api-key"] = api_key |
| 182 | headers["anthropic-version"] = "2023-06-01" |
| 183 | elif provider == "google": |
| 184 | pass |
| 185 | elif provider == "azure": |
| 186 | if has_key: |
| 187 | headers["api-key"] = api_key |
| 188 | elif provider != "ollama": |
| 189 | if has_key and api_key not in _LOCAL_PLACEHOLDER_KEYS.get(provider, set()): |
| 190 | headers["Authorization"] = f"Bearer {api_key}" |
| 191 | |
| 192 | extra = (cfg or {}).get("kwargs", {}).get("extra_headers", {}) |
| 193 | if isinstance(extra, dict): |
| 194 | for key, value in extra.items(): |
| 195 | if isinstance(value, str): |
| 196 | headers[key] = value |
| 197 | |
| 198 | return headers |
| 199 | |
| 200 | def _litellm_fallback(self, provider: str, cfg: dict | None) -> list[str]: |
| 201 | try: |
| 202 | import litellm |
| 203 | |
| 204 | registry = getattr(litellm, "models_by_provider", None) |
| 205 | if not registry: |
| 206 | return [] |
| 207 | |
| 208 | litellm_provider = (cfg or {}).get("litellm_provider", provider) |
| 209 | raw_models = registry.get(litellm_provider, set()) or set() |
| 210 | if not raw_models: |
| 211 | return [] |
| 212 | |
| 213 | prefix = litellm_provider + "/" |
| 214 | result: list[str] = [] |
| 215 | for name in raw_models: |
| 216 | clean = str(name or "") |
| 217 | clean = clean[len(prefix):] if clean.startswith(prefix) else clean |
| 218 | if clean and not self._is_non_chat_model(clean): |
| 219 | result.append(clean) |
| 220 | return result |
| 221 | except Exception: |
| 222 | return [] |
| 223 | |
| 224 | def _parse(self, data: dict | list, fmt: str) -> list[str]: |
| 225 | if isinstance(data, list): |
| 226 | return self._parse_list(data) |
| 227 | |
| 228 | if not isinstance(data, dict): |
| 229 | return [] |
| 230 | |
| 231 | if fmt == "ollama": |
| 232 | return self._parse_models_array(data.get("models", []), "name") |
| 233 | |
| 234 | if fmt == "google": |
| 235 | result = [] |
| 236 | for item in data.get("models", []) or []: |
| 237 | if not isinstance(item, dict): |
| 238 | continue |
| 239 | name = str(item.get("name", "") or "") |
| 240 | if name.startswith("models/"): |
| 241 | name = name[7:] |
| 242 | if name: |
| 243 | result.append(name) |
| 244 | return result |
| 245 | |
| 246 | if "data" in data: |
| 247 | return self._parse_models_array(data.get("data", []), "id") |
| 248 | |
| 249 | if "models" in data: |
| 250 | return self._parse_models_array(data.get("models", []), "id") |
| 251 | |
| 252 | return [] |
| 253 | |
| 254 | @staticmethod |
| 255 | def _parse_models_array(items: Any, primary_key: str) -> list[str]: |
| 256 | if not isinstance(items, list): |
| 257 | return [] |
| 258 | result = [] |
| 259 | for item in items: |
| 260 | if isinstance(item, str): |
| 261 | result.append(item) |
| 262 | elif isinstance(item, dict): |
| 263 | value = item.get(primary_key) or item.get("id") or item.get("name") |
| 264 | if value: |
| 265 | result.append(str(value)) |
| 266 | return result |
| 267 | |
| 268 | def _parse_list(self, data: list) -> list[str]: |
| 269 | result = [] |
| 270 | for item in data: |
| 271 | if isinstance(item, str): |
| 272 | result.append(item) |
| 273 | elif isinstance(item, dict): |
| 274 | value = item.get("id") or item.get("name") |
| 275 | if value: |
| 276 | result.append(str(value)) |
| 277 | return result |
| 278 | |
| 279 | def _filter_models(self, model_names: list[str], model_type: str) -> list[str]: |
| 280 | cleaned = [] |
| 281 | for name in model_names or []: |
| 282 | value = str(name or "").strip() |
| 283 | if not value: |
| 284 | continue |
| 285 | if model_type == "chat" and self._is_non_chat_model(value): |
| 286 | continue |
| 287 | cleaned.append(value) |
| 288 | return cleaned |
| 289 | |
| 290 | @staticmethod |
| 291 | def _is_non_chat_model(name: str) -> bool: |
| 292 | low = name.lower() |
| 293 | return any(token in low for token in _NON_CHAT_EXCLUDE) |