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import type { EmbeddingBackendModel } from "$lib/server/embeddingModels";
import { getSentenceSimilarity } from "$lib/server/sentenceSimilarity";
/**
* Combines sentences together to reach the maximum character limit of the embedding model
* Improves performance considerably when using CPU embedding
*/
export async function getCombinedSentenceSimilarity(
embeddingModel: EmbeddingBackendModel,
query: string,
sentences: string[]
): ReturnType<typeof getSentenceSimilarity> {
const combinedSentences = sentences.reduce<{ text: string; indices: number[] }[]>(
(acc, sentence, idx) => {
const lastSentence = acc[acc.length - 1];
if (!lastSentence) return [{ text: sentence, indices: [idx] }];
if (lastSentence.text.length + sentence.length < embeddingModel.chunkCharLength) {
lastSentence.text += ` ${sentence}`;
lastSentence.indices.push(idx);
return acc;
}
return [...acc, { text: sentence, indices: [idx] }];
},
[]
);
const embeddings = await getSentenceSimilarity(
embeddingModel,
query,
combinedSentences.map(({ text }) => text)
);
return embeddings.flatMap((embedding, idx) => {
const { indices } = combinedSentences[idx];
return indices.map((i) => ({ ...embedding, idx: i }));
});
}
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