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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, | |
| })); | |
| } | |