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import { TEXT_EMBEDDING_MODELS } from "$env/static/private";
import { z } from "zod";
import { sum } from "$lib/utils/sum";
import {
embeddingEndpoints,
embeddingEndpointSchema,
type EmbeddingEndpoint,
} from "$lib/server/embeddingEndpoints/embeddingEndpoints";
import { embeddingEndpointTransformersJS } from "$lib/server/embeddingEndpoints/transformersjs/embeddingEndpoints";
import JSON5 from "json5";
const modelConfig = z.object({
/** Used as an identifier in DB */
id: z.string().optional(),
/** Used to link to the model page, and for inference */
name: z.string().min(1),
displayName: z.string().min(1).optional(),
description: z.string().min(1).optional(),
websiteUrl: z.string().url().optional(),
modelUrl: z.string().url().optional(),
endpoints: z.array(embeddingEndpointSchema).nonempty(),
chunkCharLength: z.number().positive(),
maxBatchSize: z.number().positive().optional(),
preQuery: z.string().default(""),
prePassage: z.string().default(""),
});
// Default embedding model for backward compatibility
const rawEmbeddingModelJSON =
TEXT_EMBEDDING_MODELS ||
`[
{
"name": "Xenova/gte-small",
"chunkCharLength": 512,
"endpoints": [
{ "type": "transformersjs" }
]
}
]`;
const embeddingModelsRaw = z.array(modelConfig).parse(JSON5.parse(rawEmbeddingModelJSON));
const processEmbeddingModel = async (m: z.infer<typeof modelConfig>) => ({
...m,
id: m.id || m.name,
});
const addEndpoint = (m: Awaited<ReturnType<typeof processEmbeddingModel>>) => ({
...m,
getEndpoint: async (): Promise<EmbeddingEndpoint> => {
if (!m.endpoints) {
return embeddingEndpointTransformersJS({
type: "transformersjs",
weight: 1,
model: m,
});
}
const totalWeight = sum(m.endpoints.map((e) => e.weight));
let random = Math.random() * totalWeight;
for (const endpoint of m.endpoints) {
if (random < endpoint.weight) {
const args = { ...endpoint, model: m };
switch (args.type) {
case "tei":
return embeddingEndpoints.tei(args);
case "transformersjs":
return embeddingEndpoints.transformersjs(args);
case "openai":
return embeddingEndpoints.openai(args);
case "hfapi":
return embeddingEndpoints.hfapi(args);
default:
throw new Error(`Unknown endpoint type: ${args}`);
}
}
random -= endpoint.weight;
}
throw new Error(`Failed to select embedding endpoint`);
},
});
export const embeddingModels = await Promise.all(
embeddingModelsRaw.map((e) => processEmbeddingModel(e).then(addEndpoint))
);
export const defaultEmbeddingModel = embeddingModels[0];
const validateEmbeddingModel = (_models: EmbeddingBackendModel[], key: "id" | "name") => {
return z.enum([_models[0][key], ..._models.slice(1).map((m) => m[key])]);
};
export const validateEmbeddingModelById = (_models: EmbeddingBackendModel[]) => {
return validateEmbeddingModel(_models, "id");
};
export const validateEmbeddingModelByName = (_models: EmbeddingBackendModel[]) => {
return validateEmbeddingModel(_models, "name");
};
export type EmbeddingBackendModel = typeof defaultEmbeddingModel;
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