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import type { EmbeddingBackendModel } from "$lib/server/embeddingModels"; |
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import { getSentenceSimilarity } from "$lib/server/sentenceSimilarity"; |
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export async function getCombinedSentenceSimilarity( |
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embeddingModel: EmbeddingBackendModel, |
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query: string, |
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sentences: string[] |
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): ReturnType<typeof getSentenceSimilarity> { |
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const combinedSentences = sentences.reduce<{ text: string; indices: number[] }[]>( |
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(acc, sentence, idx) => { |
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const lastSentence = acc[acc.length - 1]; |
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if (!lastSentence) return [{ text: sentence, indices: [idx] }]; |
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if (lastSentence.text.length + sentence.length < embeddingModel.chunkCharLength) { |
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lastSentence.text += ` ${sentence}`; |
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lastSentence.indices.push(idx); |
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return acc; |
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} |
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return [...acc, { text: sentence, indices: [idx] }]; |
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}, |
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[] |
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); |
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const embeddings = await getSentenceSimilarity( |
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embeddingModel, |
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query, |
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combinedSentences.map(({ text }) => text) |
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); |
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return embeddings.flatMap((embedding, idx) => { |
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const { indices } = combinedSentences[idx]; |
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return indices.map((i) => ({ ...embedding, idx: i })); |
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}); |
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} |
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