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import { dot } from "@huggingface/transformers";
import type { EmbeddingBackendModel } from "$lib/server/embeddingModels";
import type { Embedding } from "$lib/server/embeddingEndpoints/embeddingEndpoints";
// see here: https://github.com/nmslib/hnswlib/blob/359b2ba87358224963986f709e593d799064ace6/README.md?plain=1#L34
export function innerProduct(embeddingA: Embedding, embeddingB: Embedding) {
return 1.0 - dot(embeddingA, embeddingB);
}
export async function getSentenceSimilarity(
embeddingModel: EmbeddingBackendModel,
query: string,
sentences: string[]
): Promise<{ distance: number; embedding: Embedding; idx: number }[]> {
const inputs = [
`${embeddingModel.preQuery}${query}`,
...sentences.map((sentence) => `${embeddingModel.prePassage}${sentence}`),
];
const embeddingEndpoint = await embeddingModel.getEndpoint();
const output = await embeddingEndpoint({ inputs }).catch((err) => {
throw Error("Failed to generate embeddings for sentence similarity", { cause: err });
});
const queryEmbedding: Embedding = output[0];
const sentencesEmbeddings: Embedding[] = output.slice(1);
return sentencesEmbeddings.map((sentenceEmbedding, idx) => ({
distance: innerProduct(queryEmbedding, sentenceEmbedding),
embedding: sentenceEmbedding,
idx,
}));
}
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