wehub-resource-sync
90 lines
2.3 KiB
TypeScript
90 lines
2.3 KiB
TypeScript
// fetch llm model data from `https://models.dev/api.json`
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// and output to `src/server/utils/model_prices_and_context_window_v2.json`
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import 'dotenv/config';
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import fs from 'fs-extra';
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import path from 'path';
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import { fileURLToPath } from 'url';
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import { z } from 'zod';
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const __filename = fileURLToPath(import.meta.url);
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const __dirname = path.dirname(__filename);
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const SOURCE_URL = 'https://models.dev/api.json';
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const TARGET_PATH = path.resolve(
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__dirname,
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'../src/server/utils/model_prices_and_context_window_v2.json'
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);
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const modelSchema = z
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.object({
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id: z.string(),
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name: z.string(),
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cost: z
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.object({
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input: z.number().optional(),
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output: z.number().optional(),
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})
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.passthrough()
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.optional(),
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limit: z
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.object({
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context: z.number().optional(),
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output: z.number().optional(),
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})
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.passthrough()
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.optional(),
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})
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.passthrough();
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const providerSchema = z
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.object({
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name: z.string(),
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models: z.record(z.string(), modelSchema),
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})
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.passthrough();
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const llmModelDataSchema = z.record(z.string(), providerSchema);
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export type LLMModelData = z.infer<typeof llmModelDataSchema>;
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export type LLMProvider = z.infer<typeof providerSchema>;
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export type LLMModel = z.infer<typeof modelSchema>;
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async function fetchLLMModelDataV2() {
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try {
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console.log(`Fetching LLM model data from: ${SOURCE_URL}`);
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const response = await fetch(SOURCE_URL);
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if (!response.ok) {
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throw new Error(
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`Failed to fetch data: ${response.status} ${response.statusText}`
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);
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}
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const raw = await response.json();
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llmModelDataSchema.parse(raw);
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const targetDir = path.dirname(TARGET_PATH);
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await fs.ensureDir(targetDir);
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await fs.writeJSON(TARGET_PATH, raw, { spaces: 2 });
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console.log(`LLM model data v2 has been written to: ${TARGET_PATH}`);
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console.log(`Total providers: ${Object.keys(raw).length}`);
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let totalModels = 0;
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Object.values(raw).forEach((provider: any) => {
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if (provider.models) {
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totalModels += Object.keys(provider.models).length;
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}
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});
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console.log(`Total models: ${totalModels}`);
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} catch (err) {
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console.error('Error fetching or writing LLM model data v2:', err);
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process.exit(1);
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}
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}
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fetchLLMModelDataV2();
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