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cd8bd0a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | /**
* Parses the Google Generative Language `v1beta/models` listing into discovery models.
*
* Each model's `supportedGenerationMethods` is mapped to OmniRoute endpoints:
* - generateContent / generateAnswer → "chat"
* - predict → "images" (Imagen image generation)
* - predictLongRunning → "video" (Veo video generation)
* - embedContent → "embeddings"
* - bidiGenerateContent → "audio" (Live real-time audio)
*
* Model-id heuristics refine the long-running bucket because Google exposes both
* Imagen and Veo via long-running methods on the same endpoint:
* - id contains "veo" → ensure "video"
* - id contains "imagen" → force "images" (never "video")
*
* 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",
predict: "images",
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();
// Google exposes Imagen (image) and Veo (video) via long-running methods; the
// method alone can't always distinguish them, so refine by model id.
if (lowerId.includes("veo")) {
endpoints.add("video");
}
if (lowerId.includes("imagen")) {
endpoints.delete("video");
endpoints.add("images");
}
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;
});
}
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