import { dot } from "@xenova/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 function innerProduct(embeddingA: Embedding, embeddingB: Embedding) { return 1.0 - dot(embeddingA, embeddingB); } export async function findSimilarSentences( embeddingModel: EmbeddingBackendModel, query: string, sentences: string[], { topK = 5 }: { topK: number } ): Promise { const inputs = [ `${embeddingModel.preQuery}${query}`, ...sentences.map((sentence) => `${embeddingModel.prePassage}${sentence}`), ]; const embeddingEndpoint = await embeddingModel.getEndpoint(); const output = await embeddingEndpoint({ inputs }); const queryEmbedding: Embedding = output[0]; const sentencesEmbeddings: Embedding[] = output.slice(1, inputs.length - 1); const distancesFromQuery: { distance: number; index: number }[] = [...sentencesEmbeddings].map( (sentenceEmbedding: Embedding, index: number) => { return { distance: innerProduct(queryEmbedding, sentenceEmbedding), index: index, }; } ); distancesFromQuery.sort((a, b) => { return a.distance - b.distance; }); // Return the indexes of the closest topK sentences return distancesFromQuery.slice(0, topK).map((item) => item.index); }