Files
OmniRoute/src/lib/providerModels/geminiModelsParser.ts
backryun df90591415 feat(providers): refresh curated model catalogs and retire Imagen 4 (#10537)
* feat(providers): refresh Gemini Flash catalogs and pricing

* fix(providers): refresh gemini-web Flash catalog

* chore(providers): eliminate Gemini 3.5/3.6 Flash models

* feat(providers): refresh Perplexity Web model mappings

* feat(providers): refresh PromptQL and Notion catalogs

* feat(providers): refresh KIE TinyCMS and Conol catalogs

* feat(providers): refresh OpenCode Zen catalog

* chore(providers): finish Gemini Flash cleanup

* chore(providers): retire Google Imagen 4
2026-08-18 12:27:46 -03:00

82 lines
2.9 KiB
TypeScript

/**
* Parses the Google Generative Language `v1beta/models` listing into discovery models.
*
* Each model's `supportedGenerationMethods` is mapped to OmniRoute endpoints:
* - generateContent / generateAnswer → "chat"
* - predictLongRunning → "video" (Veo video generation)
* - embedContent → "embeddings"
* - bidiGenerateContent → "audio" (Live real-time audio)
*
* Model-id heuristics ensure Veo models remain in the video bucket.
*
* Note: `gemini-*-image` models (e.g. gemini-3-pro-image) generate images via the
* regular `generateContent` path, so they stay "chat" (image output is a chat
* modality) and are intentionally NOT reclassified as "images".
*
* This is shared by the `gemini` discovery config and the `vertex` /
* `vertex-partner` (incl. Vertex AI Express key) discovery branches, so every
* model the account can access — chat, image, video, audio and embeddings —
* surfaces dynamically instead of being limited to the small static registry.
*/
const METHOD_TO_ENDPOINT: Record<string, string> = {
generateContent: "chat",
embedContent: "embeddings",
predictLongRunning: "video",
bidiGenerateContent: "audio",
generateAnswer: "chat",
};
const IGNORED_METHODS = new Set([
"countTokens",
"countTextTokens",
"createCachedContent",
"batchGenerateContent",
"asyncBatchEmbedContent",
]);
export interface GeminiDiscoveryModel {
id: string;
name: string;
supportedEndpoints: string[];
inputTokenLimit?: number;
outputTokenLimit?: number;
description?: string;
supportsThinking?: boolean;
[key: string]: unknown;
}
export function parseGeminiModelsList(data: any): GeminiDiscoveryModel[] {
return (data?.models || []).map((m: Record<string, unknown>) => {
const methods: string[] = Array.isArray(m.supportedGenerationMethods)
? (m.supportedGenerationMethods as string[])
: [];
const endpoints = new Set<string>(
methods
.filter((method) => !IGNORED_METHODS.has(method))
.map((method) => METHOD_TO_ENDPOINT[method] || "chat")
);
const id = ((m.name as string) || (m.id as string) || "").replace(/^models\//, "");
const lowerId = id.toLowerCase();
// Keep Veo models in the video bucket even when the method list is incomplete.
if (lowerId.includes("veo")) {
endpoints.add("video");
}
if (endpoints.size === 0) endpoints.add("chat");
return {
...m,
id,
name: (m.displayName as string) || id,
supportedEndpoints: [...endpoints],
...(typeof m.inputTokenLimit === "number" ? { inputTokenLimit: m.inputTokenLimit } : {}),
...(typeof m.outputTokenLimit === "number" ? { outputTokenLimit: m.outputTokenLimit } : {}),
...(typeof m.description === "string" ? { description: m.description } : {}),
...(m.thinking === true ? { supportsThinking: true } : {}),
} as GeminiDiscoveryModel;
});
}